Part 6: Convolutional Neural Networks in hls4ml#
In this notebook you will learn how to train a pruned and quantized convolutional neural network (CNN) and deploy it using hls4ml. For this exercise, we will use the Street View House Numbers (SVHN) Dataset (http://ufldl.stanford.edu/housenumbers/).
The SVHN dataset consists of real-world images of house numbers extracted from Google Street View images. The format is similar to that of the MNIST dataset, but is a much more challenging real-world problem, as illustrated by the examples shown below.
All the images are in RGB format and have been cropped to 32x32 pixels. Unlike MNIST, more than one digit can be present in the same image and in these cases, the center digit is used to assign a label to the image. Each image can belong to one of 10 classes, corresponding to digits 0 through 9.
The SVHN dataset consists of 73,257 images for training (and 531,131 extra samples that are easier to classify and can be used as additional training data) and 26,032 images for testing.
Start with the neccessary imports#
import matplotlib.pyplot as plt
import numpy as np
import time
import tensorflow.compat.v2 as tf
import tensorflow_datasets as tfds
2024-09-05 18:37:44.590022: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: SSE4.1 SSE4.2 AVX AVX2 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/runner/miniconda3/envs/hls4ml-tutorial/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
from .autonotebook import tqdm as notebook_tqdm
Fetch the SVHN dataset using Tensorflow Dataset#
In this part we will fetch the trainining, validation and test dataset using Tensorflow Datasets (https://www.tensorflow.org/datasets). We will not use the ‘extra’ training in order to save time, but you could fetch it by adding split='train[:90%]+extra'. We will use the first 90% of the training data for training and the last 10% for validation.
ds_train, info = tfds.load('svhn_cropped', split='train[:90%]', with_info=True, as_supervised=True)
ds_test = tfds.load('svhn_cropped', split='test', shuffle_files=True, as_supervised=True)
ds_val = tfds.load('svhn_cropped', split='train[-10%:]', shuffle_files=True, as_supervised=True)
assert isinstance(ds_train, tf.data.Dataset)
train_size = int(info.splits['train'].num_examples)
input_shape = info.features['image'].shape
n_classes = info.features['label'].num_classes
print('Training on {} samples of input shape {}, belonging to {} classes'.format(train_size, input_shape, n_classes))
fig = tfds.show_examples(ds_train, info)
2024-09-05 18:37:46.494478: W tensorflow/core/platform/cloud/google_auth_provider.cc:184] All attempts to get a Google authentication bearer token failed, returning an empty token. Retrieving token from files failed with "NOT_FOUND: Could not locate the credentials file.". Retrieving token from GCE failed with "FAILED_PRECONDITION: Error executing an HTTP request: libcurl code 6 meaning 'Could not resolve hostname', error details: Could not resolve host: metadata".
Downloading and preparing dataset Unknown size (download: Unknown size, generated: Unknown size, total: Unknown size) to /home/runner/tensorflow_datasets/svhn_cropped/3.0.0...
Dl Completed...: 0 url [00:00, ? url/s]
Dl Size...: 0 MiB [00:00, ? MiB/s]
Dl Completed...: 0%| | 0/1 [00:00<?, ? url/s]
Dl Size...: 0 MiB [00:00, ? MiB/s]
Dl Completed...: 0%| | 0/2 [00:00<?, ? url/s]
Dl Size...: 0 MiB [00:00, ? MiB/s]
Dl Completed...: 0%| | 0/3 [00:00<?, ? url/s]
Dl Size...: 0 MiB [00:00, ? MiB/s]
Dl Completed...: 0%| | 0/3 [00:00<?, ? url/s]
Dl Size...: 0%| | 0/61 [00:00<?, ? MiB/s]
Dl Completed...: 0%| | 0/3 [00:00<?, ? url/s]
Dl Size...: 0%| | 0/1328 [00:00<?, ? MiB/s]
Dl Completed...: 0%| | 0/3 [00:00<?, ? url/s]
Dl Size...: 0%| | 0/1501 [00:00<?, ? MiB/s]
Dl Size...: 0%| | 1/1501 [00:01<42:21, 1.69s/ MiB]
Dl Completed...: 0%| | 0/3 [00:01<?, ? url/s]
Dl Size...: 0%| | 1/1501 [00:01<42:21, 1.69s/ MiB]
Dl Completed...: 0%| | 0/3 [00:01<?, ? url/s]
Dl Size...: 0%| | 2/1501 [00:01<42:19, 1.69s/ MiB]
Dl Size...: 0%| | 3/1501 [00:01<11:55, 2.09 MiB/s]
Dl Completed...: 0%| | 0/3 [00:01<?, ? url/s]
Dl Size...: 0%| | 3/1501 [00:01<11:55, 2.09 MiB/s]
Dl Completed...: 0%| | 0/3 [00:02<?, ? url/s]
Dl Size...: 0%| | 4/1501 [00:02<11:55, 2.09 MiB/s]
Dl Size...: 0%| | 5/1501 [00:02<09:24, 2.65 MiB/s]
Dl Completed...: 0%| | 0/3 [00:02<?, ? url/s]
Dl Size...: 0%| | 5/1501 [00:02<09:24, 2.65 MiB/s]
Dl Completed...: 0%| | 0/3 [00:02<?, ? url/s]
Dl Size...: 0%| | 6/1501 [00:02<09:23, 2.65 MiB/s]
Dl Size...: 0%| | 7/1501 [00:02<06:41, 3.72 MiB/s]
Dl Completed...: 0%| | 0/3 [00:02<?, ? url/s]
Dl Size...: 0%| | 7/1501 [00:02<06:41, 3.72 MiB/s]
Dl Size...: 1%| | 8/1501 [00:02<05:43, 4.35 MiB/s]
Dl Completed...: 0%| | 0/3 [00:02<?, ? url/s]
Dl Size...: 1%| | 8/1501 [00:02<05:43, 4.35 MiB/s]
Dl Completed...: 0%| | 0/3 [00:02<?, ? url/s]
Dl Size...: 1%| | 9/1501 [00:02<05:43, 4.35 MiB/s]
Dl Size...: 1%| | 10/1501 [00:02<04:05, 6.07 MiB/s]
Dl Completed...: 0%| | 0/3 [00:02<?, ? url/s]
Dl Size...: 1%| | 10/1501 [00:02<04:05, 6.07 MiB/s]
Dl Completed...: 0%| | 0/3 [00:02<?, ? url/s]
Dl Size...: 1%| | 11/1501 [00:02<04:05, 6.07 MiB/s]
Dl Size...: 1%| | 12/1501 [00:03<03:22, 7.34 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 1%| | 12/1501 [00:03<03:22, 7.34 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 1%| | 13/1501 [00:03<03:22, 7.34 MiB/s]
Dl Size...: 1%| | 14/1501 [00:03<03:03, 8.11 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 1%| | 14/1501 [00:03<03:03, 8.11 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 1%| | 15/1501 [00:03<03:03, 8.11 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 1%| | 16/1501 [00:03<03:03, 8.11 MiB/s]
Dl Size...: 1%| | 17/1501 [00:03<02:21, 10.47 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 1%| | 17/1501 [00:03<02:21, 10.47 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 1%| | 18/1501 [00:03<02:21, 10.47 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 1%|▏ | 19/1501 [00:03<02:21, 10.47 MiB/s]
Dl Size...: 1%|▏ | 20/1501 [00:03<01:54, 12.98 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 1%|▏ | 20/1501 [00:03<01:54, 12.98 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 1%|▏ | 21/1501 [00:03<01:54, 12.98 MiB/s]
Dl Size...: 1%|▏ | 22/1501 [00:03<01:45, 14.00 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 1%|▏ | 22/1501 [00:03<01:45, 14.00 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 2%|▏ | 23/1501 [00:03<01:45, 14.00 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 2%|▏ | 24/1501 [00:03<01:45, 14.00 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 2%|▏ | 25/1501 [00:03<01:45, 14.00 MiB/s]
Dl Size...: 2%|▏ | 26/1501 [00:03<01:17, 19.14 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 2%|▏ | 26/1501 [00:03<01:17, 19.14 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 2%|▏ | 27/1501 [00:03<01:17, 19.14 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 2%|▏ | 28/1501 [00:03<01:16, 19.14 MiB/s]
Dl Size...: 2%|▏ | 29/1501 [00:03<01:15, 19.39 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 2%|▏ | 29/1501 [00:03<01:15, 19.39 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 2%|▏ | 30/1501 [00:03<01:15, 19.39 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 2%|▏ | 31/1501 [00:03<01:15, 19.39 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 2%|▏ | 32/1501 [00:03<01:15, 19.39 MiB/s]
Dl Size...: 2%|▏ | 33/1501 [00:04<01:08, 21.28 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 2%|▏ | 33/1501 [00:04<01:08, 21.28 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 2%|▏ | 34/1501 [00:04<01:08, 21.28 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 2%|▏ | 35/1501 [00:04<01:08, 21.28 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 2%|▏ | 36/1501 [00:04<01:08, 21.28 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 2%|▏ | 37/1501 [00:04<01:08, 21.28 MiB/s]
Dl Size...: 3%|▎ | 38/1501 [00:04<00:58, 24.82 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 3%|▎ | 38/1501 [00:04<00:58, 24.82 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 3%|▎ | 39/1501 [00:04<00:58, 24.82 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 3%|▎ | 40/1501 [00:04<00:58, 24.82 MiB/s]
Dl Size...: 3%|▎ | 41/1501 [00:04<00:56, 25.79 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 3%|▎ | 41/1501 [00:04<00:56, 25.79 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 3%|▎ | 42/1501 [00:04<00:56, 25.79 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 3%|▎ | 43/1501 [00:04<00:56, 25.79 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 3%|▎ | 44/1501 [00:04<00:56, 25.79 MiB/s]
Dl Size...: 3%|▎ | 45/1501 [00:04<00:52, 27.95 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 3%|▎ | 45/1501 [00:04<00:52, 27.95 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 3%|▎ | 46/1501 [00:04<00:52, 27.95 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 3%|▎ | 47/1501 [00:04<00:52, 27.95 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 3%|▎ | 48/1501 [00:04<00:51, 27.95 MiB/s]
Dl Size...: 3%|▎ | 49/1501 [00:04<00:47, 30.79 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 3%|▎ | 49/1501 [00:04<00:47, 30.79 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 3%|▎ | 50/1501 [00:04<00:47, 30.79 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 3%|▎ | 51/1501 [00:04<00:47, 30.79 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 3%|▎ | 52/1501 [00:04<00:47, 30.79 MiB/s]
Dl Size...: 4%|▎ | 53/1501 [00:04<00:44, 32.29 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 4%|▎ | 53/1501 [00:04<00:44, 32.29 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 4%|▎ | 54/1501 [00:04<00:44, 32.29 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 4%|▎ | 55/1501 [00:04<00:44, 32.29 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 4%|▎ | 56/1501 [00:04<00:44, 32.29 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 4%|▍ | 57/1501 [00:04<00:44, 32.29 MiB/s]
Dl Size...: 4%|▍ | 58/1501 [00:04<00:40, 35.44 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 4%|▍ | 58/1501 [00:04<00:40, 35.44 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 4%|▍ | 59/1501 [00:04<00:40, 35.44 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 4%|▍ | 60/1501 [00:04<00:40, 35.44 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 4%|▍ | 61/1501 [00:04<00:40, 35.44 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 4%|▍ | 62/1501 [00:04<00:40, 35.44 MiB/s]
Dl Size...: 4%|▍ | 63/1501 [00:04<00:39, 36.76 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 4%|▍ | 63/1501 [00:04<00:39, 36.76 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 4%|▍ | 64/1501 [00:04<00:39, 36.76 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 4%|▍ | 65/1501 [00:04<00:39, 36.76 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 4%|▍ | 66/1501 [00:04<00:39, 36.76 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 4%|▍ | 67/1501 [00:04<00:39, 36.76 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 5%|▍ | 68/1501 [00:04<00:38, 36.76 MiB/s]
Dl Size...: 5%|▍ | 69/1501 [00:04<00:34, 41.91 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 5%|▍ | 69/1501 [00:04<00:34, 41.91 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 5%|▍ | 70/1501 [00:05<00:34, 41.91 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 5%|▍ | 71/1501 [00:05<00:34, 41.91 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 5%|▍ | 72/1501 [00:05<00:34, 41.91 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 5%|▍ | 73/1501 [00:05<00:34, 41.91 MiB/s]
Dl Size...: 5%|▍ | 74/1501 [00:05<00:33, 42.14 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 5%|▍ | 74/1501 [00:05<00:33, 42.14 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 5%|▍ | 75/1501 [00:05<00:33, 42.14 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 5%|▌ | 76/1501 [00:05<00:33, 42.14 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 5%|▌ | 77/1501 [00:05<00:33, 42.14 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 5%|▌ | 78/1501 [00:05<00:33, 42.14 MiB/s]
Dl Size...: 5%|▌ | 79/1501 [00:05<00:32, 43.88 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 5%|▌ | 79/1501 [00:05<00:32, 43.88 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 5%|▌ | 80/1501 [00:05<00:32, 43.88 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 5%|▌ | 81/1501 [00:05<00:32, 43.88 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 5%|▌ | 82/1501 [00:05<00:32, 43.88 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 6%|▌ | 83/1501 [00:05<00:32, 43.88 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 6%|▌ | 84/1501 [00:05<00:32, 43.88 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 6%|▌ | 85/1501 [00:05<00:32, 43.88 MiB/s]
Dl Size...: 6%|▌ | 86/1501 [00:05<00:30, 46.51 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 6%|▌ | 86/1501 [00:05<00:30, 46.51 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 6%|▌ | 87/1501 [00:05<00:30, 46.51 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 6%|▌ | 88/1501 [00:05<00:30, 46.51 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 6%|▌ | 89/1501 [00:05<00:30, 46.51 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 6%|▌ | 90/1501 [00:05<00:30, 46.51 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 6%|▌ | 91/1501 [00:05<00:30, 46.51 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 6%|▌ | 92/1501 [00:05<00:30, 46.51 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 6%|▌ | 93/1501 [00:05<00:30, 46.51 MiB/s]
Dl Size...: 6%|▋ | 94/1501 [00:05<00:25, 54.79 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 6%|▋ | 94/1501 [00:05<00:25, 54.79 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 6%|▋ | 95/1501 [00:05<00:25, 54.79 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 6%|▋ | 96/1501 [00:05<00:25, 54.79 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 6%|▋ | 97/1501 [00:05<00:25, 54.79 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 7%|▋ | 98/1501 [00:05<00:25, 54.79 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 7%|▋ | 99/1501 [00:05<00:25, 54.79 MiB/s]
Dl Size...: 7%|▋ | 100/1501 [00:05<00:25, 54.80 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 7%|▋ | 100/1501 [00:05<00:25, 54.80 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 7%|▋ | 101/1501 [00:05<00:25, 54.80 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 7%|▋ | 102/1501 [00:05<00:25, 54.80 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 7%|▋ | 103/1501 [00:05<00:25, 54.80 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 7%|▋ | 104/1501 [00:05<00:25, 54.80 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 7%|▋ | 105/1501 [00:05<00:25, 54.80 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 7%|▋ | 106/1501 [00:05<00:25, 54.80 MiB/s]
Dl Size...: 7%|▋ | 107/1501 [00:05<00:23, 58.11 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 7%|▋ | 107/1501 [00:05<00:23, 58.11 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 7%|▋ | 108/1501 [00:05<00:23, 58.11 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 7%|▋ | 109/1501 [00:05<00:23, 58.11 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 7%|▋ | 110/1501 [00:05<00:23, 58.11 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 7%|▋ | 111/1501 [00:05<00:23, 58.11 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 7%|▋ | 112/1501 [00:05<00:23, 58.11 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 8%|▊ | 113/1501 [00:05<00:23, 58.11 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 8%|▊ | 114/1501 [00:05<00:23, 58.11 MiB/s]
Dl Size...: 8%|▊ | 115/1501 [00:05<00:21, 63.37 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 8%|▊ | 115/1501 [00:05<00:21, 63.37 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 8%|▊ | 116/1501 [00:05<00:21, 63.37 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 8%|▊ | 117/1501 [00:05<00:21, 63.37 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 8%|▊ | 118/1501 [00:05<00:21, 63.37 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 8%|▊ | 119/1501 [00:05<00:21, 63.37 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 8%|▊ | 120/1501 [00:05<00:21, 63.37 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 8%|▊ | 121/1501 [00:05<00:21, 63.37 MiB/s]
Dl Size...: 8%|▊ | 122/1501 [00:05<00:21, 64.93 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 8%|▊ | 122/1501 [00:05<00:21, 64.93 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 8%|▊ | 123/1501 [00:05<00:21, 64.93 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 8%|▊ | 124/1501 [00:05<00:21, 64.93 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 8%|▊ | 125/1501 [00:05<00:21, 64.93 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 8%|▊ | 126/1501 [00:05<00:21, 64.93 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 8%|▊ | 127/1501 [00:05<00:21, 64.93 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 9%|▊ | 128/1501 [00:05<00:21, 64.93 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 9%|▊ | 129/1501 [00:05<00:21, 64.93 MiB/s]
Dl Size...: 9%|▊ | 130/1501 [00:05<00:20, 68.52 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 9%|▊ | 130/1501 [00:05<00:20, 68.52 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 9%|▊ | 131/1501 [00:05<00:19, 68.52 MiB/s]
Dl Completed...: 0%| | 0/3 [00:06<?, ? url/s]
Dl Size...: 9%|▉ | 132/1501 [00:06<00:19, 68.52 MiB/s]
Dl Completed...: 0%| | 0/3 [00:06<?, ? url/s]
Dl Size...: 9%|▉ | 133/1501 [00:06<00:19, 68.52 MiB/s]
Dl Completed...: 0%| | 0/3 [00:06<?, ? url/s]
Dl Size...: 9%|▉ | 134/1501 [00:06<00:19, 68.52 MiB/s]
Dl Completed...: 0%| | 0/3 [00:06<?, ? url/s]
Dl Size...: 9%|▉ | 135/1501 [00:06<00:19, 68.52 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:06<00:12, 6.04s/ url]
Dl Completed...: 33%|███▎ | 1/3 [00:06<00:12, 6.04s/ url]
Dl Size...: 9%|▉ | 135/1501 [00:06<00:19, 68.52 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:06<00:12, 6.04s/ url]
Dl Size...: 9%|▉ | 136/1501 [00:06<00:19, 68.52 MiB/s]
Dl Size...: 9%|▉ | 137/1501 [00:06<00:19, 68.72 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:06<00:12, 6.04s/ url]
Dl Size...: 9%|▉ | 137/1501 [00:06<00:19, 68.72 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:06<00:12, 6.04s/ url]
Dl Size...: 9%|▉ | 138/1501 [00:06<00:19, 68.72 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:06<00:12, 6.04s/ url]
Dl Size...: 9%|▉ | 139/1501 [00:06<00:19, 68.72 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:06<00:12, 6.04s/ url]
Dl Size...: 9%|▉ | 140/1501 [00:06<00:19, 68.72 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:06<00:12, 6.04s/ url]
Dl Size...: 9%|▉ | 141/1501 [00:06<00:19, 68.72 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:06<00:12, 6.04s/ url]
Dl Size...: 9%|▉ | 142/1501 [00:06<00:19, 68.72 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:06<00:12, 6.04s/ url]
Dl Size...: 10%|▉ | 143/1501 [00:06<00:19, 68.72 MiB/s]
Dl Size...: 10%|▉ | 144/1501 [00:06<00:23, 57.17 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:06<00:12, 6.04s/ url]
Dl Size...: 10%|▉ | 144/1501 [00:06<00:23, 57.17 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:06<00:12, 6.04s/ url]
Dl Size...: 10%|▉ | 145/1501 [00:06<00:23, 57.17 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:06<00:12, 6.04s/ url]
Dl Size...: 10%|▉ | 146/1501 [00:06<00:23, 57.17 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:06<00:12, 6.04s/ url]
Dl Size...: 10%|▉ | 147/1501 [00:06<00:23, 57.17 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:06<00:12, 6.04s/ url]
Dl Size...: 10%|▉ | 148/1501 [00:06<00:23, 57.17 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:06<00:12, 6.04s/ url]
Dl Size...: 10%|▉ | 149/1501 [00:06<00:23, 57.17 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:06<00:12, 6.04s/ url]
Dl Size...: 10%|▉ | 150/1501 [00:06<00:23, 57.17 MiB/s]
Dl Size...: 10%|█ | 151/1501 [00:06<00:26, 51.79 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:06<00:12, 6.04s/ url]
Dl Size...: 10%|█ | 151/1501 [00:06<00:26, 51.79 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:06<00:12, 6.04s/ url]
Dl Size...: 10%|█ | 152/1501 [00:06<00:26, 51.79 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:06<00:12, 6.04s/ url]
Dl Size...: 10%|█ | 153/1501 [00:06<00:26, 51.79 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:06<00:12, 6.04s/ url]
Dl Size...: 10%|█ | 154/1501 [00:06<00:26, 51.79 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:06<00:12, 6.04s/ url]
Dl Size...: 10%|█ | 155/1501 [00:06<00:25, 51.79 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:06<00:12, 6.04s/ url]
Dl Size...: 10%|█ | 156/1501 [00:06<00:25, 51.79 MiB/s]
Dl Size...: 10%|█ | 157/1501 [00:06<00:28, 47.65 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:06<00:12, 6.04s/ url]
Dl Size...: 10%|█ | 157/1501 [00:06<00:28, 47.65 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:06<00:12, 6.04s/ url]
Dl Size...: 11%|█ | 158/1501 [00:06<00:28, 47.65 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:06<00:12, 6.04s/ url]
Dl Size...: 11%|█ | 159/1501 [00:06<00:28, 47.65 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:06<00:12, 6.04s/ url]
Dl Size...: 11%|█ | 160/1501 [00:06<00:28, 47.65 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:06<00:12, 6.04s/ url]
Dl Size...: 11%|█ | 161/1501 [00:06<00:28, 47.65 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:06<00:12, 6.04s/ url]
Dl Size...: 11%|█ | 162/1501 [00:06<00:28, 47.65 MiB/s]
Dl Size...: 11%|█ | 163/1501 [00:06<00:30, 44.49 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:06<00:12, 6.04s/ url]
Dl Size...: 11%|█ | 163/1501 [00:06<00:30, 44.49 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:06<00:12, 6.04s/ url]
Dl Size...: 11%|█ | 164/1501 [00:06<00:30, 44.49 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:06<00:12, 6.04s/ url]
Dl Size...: 11%|█ | 165/1501 [00:06<00:30, 44.49 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:06<00:12, 6.04s/ url]
Dl Size...: 11%|█ | 166/1501 [00:06<00:30, 44.49 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:06<00:12, 6.04s/ url]
Dl Size...: 11%|█ | 167/1501 [00:06<00:29, 44.49 MiB/s]
Dl Size...: 11%|█ | 168/1501 [00:06<00:29, 45.59 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:06<00:12, 6.04s/ url]
Dl Size...: 11%|█ | 168/1501 [00:06<00:29, 45.59 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:06<00:12, 6.04s/ url]
Dl Size...: 11%|█▏ | 169/1501 [00:06<00:29, 45.59 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:06<00:12, 6.04s/ url]
Dl Size...: 11%|█▏ | 170/1501 [00:06<00:29, 45.59 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:06<00:12, 6.04s/ url]
Dl Size...: 11%|█▏ | 171/1501 [00:06<00:29, 45.59 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:06<00:12, 6.04s/ url]
Dl Size...: 11%|█▏ | 172/1501 [00:06<00:29, 45.59 MiB/s]
Dl Size...: 12%|█▏ | 173/1501 [00:06<00:30, 43.03 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:06<00:12, 6.04s/ url]
Dl Size...: 12%|█▏ | 173/1501 [00:06<00:30, 43.03 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:06<00:12, 6.04s/ url]
Dl Size...: 12%|█▏ | 174/1501 [00:06<00:30, 43.03 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:06<00:12, 6.04s/ url]
Dl Size...: 12%|█▏ | 175/1501 [00:06<00:30, 43.03 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:07<00:12, 6.04s/ url]
Dl Size...: 12%|█▏ | 176/1501 [00:07<00:30, 43.03 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:07<00:12, 6.04s/ url]
Dl Size...: 12%|█▏ | 177/1501 [00:07<00:30, 43.03 MiB/s]
Dl Size...: 12%|█▏ | 178/1501 [00:07<00:30, 43.99 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:07<00:12, 6.04s/ url]
Dl Size...: 12%|█▏ | 178/1501 [00:07<00:30, 43.99 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:07<00:12, 6.04s/ url]
Dl Size...: 12%|█▏ | 179/1501 [00:07<00:30, 43.99 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:07<00:12, 6.04s/ url]
Dl Size...: 12%|█▏ | 180/1501 [00:07<00:30, 43.99 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:07<00:12, 6.04s/ url]
Dl Size...: 12%|█▏ | 181/1501 [00:07<00:30, 43.99 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:07<00:12, 6.04s/ url]
Dl Size...: 12%|█▏ | 182/1501 [00:07<00:29, 43.99 MiB/s]
Dl Size...: 12%|█▏ | 183/1501 [00:07<00:30, 43.04 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:07<00:12, 6.04s/ url]
Dl Size...: 12%|█▏ | 183/1501 [00:07<00:30, 43.04 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:07<00:12, 6.04s/ url]
Dl Size...: 12%|█▏ | 184/1501 [00:07<00:30, 43.04 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:07<00:12, 6.04s/ url]
Dl Size...: 12%|█▏ | 185/1501 [00:07<00:30, 43.04 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:07<00:12, 6.04s/ url]
Dl Size...: 12%|█▏ | 186/1501 [00:07<00:30, 43.04 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:07<00:12, 6.04s/ url]
Dl Size...: 12%|█▏ | 187/1501 [00:07<00:30, 43.04 MiB/s]
Dl Size...: 13%|█▎ | 188/1501 [00:07<00:31, 41.54 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:07<00:12, 6.04s/ url]
Dl Size...: 13%|█▎ | 188/1501 [00:07<00:31, 41.54 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:07<00:12, 6.04s/ url]
Dl Size...: 13%|█▎ | 189/1501 [00:07<00:31, 41.54 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:07<00:12, 6.04s/ url]
Dl Size...: 13%|█▎ | 190/1501 [00:07<00:31, 41.54 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:07<00:12, 6.04s/ url]
Dl Size...: 13%|█▎ | 191/1501 [00:07<00:31, 41.54 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:07<00:12, 6.04s/ url]
Dl Size...: 13%|█▎ | 192/1501 [00:07<00:31, 41.54 MiB/s]
Dl Size...: 13%|█▎ | 193/1501 [00:07<00:31, 40.99 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:07<00:12, 6.04s/ url]
Dl Size...: 13%|█▎ | 193/1501 [00:07<00:31, 40.99 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:07<00:12, 6.04s/ url]
Dl Size...: 13%|█▎ | 194/1501 [00:07<00:31, 40.99 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:07<00:12, 6.04s/ url]
Dl Size...: 13%|█▎ | 195/1501 [00:07<00:31, 40.99 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:07<00:12, 6.04s/ url]
Dl Size...: 13%|█▎ | 196/1501 [00:07<00:31, 40.99 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:07<00:12, 6.04s/ url]
Dl Size...: 13%|█▎ | 197/1501 [00:07<00:31, 40.99 MiB/s]
Dl Size...: 13%|█▎ | 198/1501 [00:07<00:30, 43.11 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:07<00:12, 6.04s/ url]
Dl Size...: 13%|█▎ | 198/1501 [00:07<00:30, 43.11 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:07<00:12, 6.04s/ url]
Dl Size...: 13%|█▎ | 199/1501 [00:07<00:30, 43.11 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:07<00:12, 6.04s/ url]
Dl Size...: 13%|█▎ | 200/1501 [00:07<00:30, 43.11 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:07<00:12, 6.04s/ url]
Dl Size...: 13%|█▎ | 201/1501 [00:07<00:30, 43.11 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:07<00:12, 6.04s/ url]
Dl Size...: 13%|█▎ | 202/1501 [00:07<00:30, 43.11 MiB/s]
Dl Size...: 14%|█▎ | 203/1501 [00:07<00:31, 41.34 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:07<00:12, 6.04s/ url]
Dl Size...: 14%|█▎ | 203/1501 [00:07<00:31, 41.34 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:07<00:12, 6.04s/ url]
Dl Size...: 14%|█▎ | 204/1501 [00:07<00:31, 41.34 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:07<00:12, 6.04s/ url]
Dl Size...: 14%|█▎ | 205/1501 [00:07<00:31, 41.34 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:07<00:12, 6.04s/ url]
Dl Size...: 14%|█▎ | 206/1501 [00:07<00:31, 41.34 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:07<00:12, 6.04s/ url]
Dl Size...: 14%|█▍ | 207/1501 [00:07<00:31, 41.34 MiB/s]
Dl Size...: 14%|█▍ | 208/1501 [00:07<00:30, 43.09 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:07<00:12, 6.04s/ url]
Dl Size...: 14%|█▍ | 208/1501 [00:07<00:30, 43.09 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:07<00:12, 6.04s/ url]
Dl Size...: 14%|█▍ | 209/1501 [00:07<00:29, 43.09 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:07<00:12, 6.04s/ url]
Dl Size...: 14%|█▍ | 210/1501 [00:07<00:29, 43.09 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:07<00:12, 6.04s/ url]
Dl Size...: 14%|█▍ | 211/1501 [00:07<00:29, 43.09 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:07<00:12, 6.04s/ url]
Dl Size...: 14%|█▍ | 212/1501 [00:07<00:29, 43.09 MiB/s]
Dl Size...: 14%|█▍ | 213/1501 [00:07<00:30, 42.09 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:07<00:12, 6.04s/ url]
Dl Size...: 14%|█▍ | 213/1501 [00:07<00:30, 42.09 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:07<00:12, 6.04s/ url]
Dl Size...: 14%|█▍ | 214/1501 [00:07<00:30, 42.09 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:07<00:12, 6.04s/ url]
Dl Size...: 14%|█▍ | 215/1501 [00:07<00:30, 42.09 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:07<00:12, 6.04s/ url]
Dl Size...: 14%|█▍ | 216/1501 [00:07<00:30, 42.09 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:08<00:12, 6.04s/ url]
Dl Size...: 14%|█▍ | 217/1501 [00:08<00:30, 42.09 MiB/s]
Dl Size...: 15%|█▍ | 218/1501 [00:08<00:31, 40.52 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:08<00:12, 6.04s/ url]
Dl Size...: 15%|█▍ | 218/1501 [00:08<00:31, 40.52 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:08<00:12, 6.04s/ url]
Dl Size...: 15%|█▍ | 219/1501 [00:08<00:31, 40.52 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:08<00:12, 6.04s/ url]
Dl Size...: 15%|█▍ | 220/1501 [00:08<00:31, 40.52 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:08<00:12, 6.04s/ url]
Dl Size...: 15%|█▍ | 221/1501 [00:08<00:31, 40.52 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:08<00:12, 6.04s/ url]
Dl Size...: 15%|█▍ | 222/1501 [00:08<00:31, 40.52 MiB/s]
Dl Size...: 15%|█▍ | 223/1501 [00:08<00:30, 41.37 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:08<00:12, 6.04s/ url]
Dl Size...: 15%|█▍ | 223/1501 [00:08<00:30, 41.37 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:08<00:12, 6.04s/ url]
Dl Size...: 15%|█▍ | 224/1501 [00:08<00:30, 41.37 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:08<00:12, 6.04s/ url]
Dl Size...: 15%|█▍ | 225/1501 [00:08<00:30, 41.37 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:08<00:12, 6.04s/ url]
Dl Size...: 15%|█▌ | 226/1501 [00:08<00:30, 41.37 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:08<00:12, 6.04s/ url]
Dl Size...: 15%|█▌ | 227/1501 [00:08<00:30, 41.37 MiB/s]
Dl Size...: 15%|█▌ | 228/1501 [00:08<00:30, 42.16 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:08<00:12, 6.04s/ url]
Dl Size...: 15%|█▌ | 228/1501 [00:08<00:30, 42.16 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:08<00:12, 6.04s/ url]
Dl Size...: 15%|█▌ | 229/1501 [00:08<00:30, 42.16 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:08<00:12, 6.04s/ url]
Dl Size...: 15%|█▌ | 230/1501 [00:08<00:30, 42.16 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:08<00:12, 6.04s/ url]
Dl Size...: 15%|█▌ | 231/1501 [00:08<00:30, 42.16 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:08<00:12, 6.04s/ url]
Dl Size...: 15%|█▌ | 232/1501 [00:08<00:30, 42.16 MiB/s]
Dl Size...: 16%|█▌ | 233/1501 [00:08<00:30, 42.07 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:08<00:12, 6.04s/ url]
Dl Size...: 16%|█▌ | 233/1501 [00:08<00:30, 42.07 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:08<00:12, 6.04s/ url]
Dl Size...: 16%|█▌ | 234/1501 [00:08<00:30, 42.07 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:08<00:12, 6.04s/ url]
Dl Size...: 16%|█▌ | 235/1501 [00:08<00:30, 42.07 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:08<00:12, 6.04s/ url]
Dl Size...: 16%|█▌ | 236/1501 [00:08<00:30, 42.07 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:08<00:12, 6.04s/ url]
Dl Size...: 16%|█▌ | 237/1501 [00:08<00:30, 42.07 MiB/s]
Dl Size...: 16%|█▌ | 238/1501 [00:08<00:29, 42.16 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:08<00:12, 6.04s/ url]
Dl Size...: 16%|█▌ | 238/1501 [00:08<00:29, 42.16 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:08<00:12, 6.04s/ url]
Dl Size...: 16%|█▌ | 239/1501 [00:08<00:29, 42.16 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:08<00:12, 6.04s/ url]
Dl Size...: 16%|█▌ | 240/1501 [00:08<00:29, 42.16 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:08<00:12, 6.04s/ url]
Dl Size...: 16%|█▌ | 241/1501 [00:08<00:29, 42.16 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:08<00:12, 6.04s/ url]
Dl Size...: 16%|█▌ | 242/1501 [00:08<00:29, 42.16 MiB/s]
Dl Size...: 16%|█▌ | 243/1501 [00:08<00:30, 40.82 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:08<00:12, 6.04s/ url]
Dl Size...: 16%|█▌ | 243/1501 [00:08<00:30, 40.82 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:08<00:12, 6.04s/ url]
Dl Size...: 16%|█▋ | 244/1501 [00:08<00:30, 40.82 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:08<00:12, 6.04s/ url]
Dl Size...: 16%|█▋ | 245/1501 [00:08<00:30, 40.82 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:08<00:12, 6.04s/ url]
Dl Size...: 16%|█▋ | 246/1501 [00:08<00:30, 40.82 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:08<00:12, 6.04s/ url]
Dl Size...: 16%|█▋ | 247/1501 [00:08<00:30, 40.82 MiB/s]
Dl Size...: 17%|█▋ | 248/1501 [00:08<00:30, 41.10 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:08<00:12, 6.04s/ url]
Dl Size...: 17%|█▋ | 248/1501 [00:08<00:30, 41.10 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:08<00:12, 6.04s/ url]
Dl Size...: 17%|█▋ | 249/1501 [00:08<00:30, 41.10 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:08<00:12, 6.04s/ url]
Dl Size...: 17%|█▋ | 250/1501 [00:08<00:30, 41.10 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:08<00:12, 6.04s/ url]
Dl Size...: 17%|█▋ | 251/1501 [00:08<00:30, 41.10 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:08<00:12, 6.04s/ url]
Dl Size...: 17%|█▋ | 252/1501 [00:08<00:30, 41.10 MiB/s]
Dl Size...: 17%|█▋ | 253/1501 [00:08<00:30, 41.07 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:08<00:12, 6.04s/ url]
Dl Size...: 17%|█▋ | 253/1501 [00:08<00:30, 41.07 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:08<00:12, 6.04s/ url]
Dl Size...: 17%|█▋ | 254/1501 [00:08<00:30, 41.07 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:08<00:12, 6.04s/ url]
Dl Size...: 17%|█▋ | 255/1501 [00:08<00:30, 41.07 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:08<00:12, 6.04s/ url]
Dl Size...: 17%|█▋ | 256/1501 [00:08<00:30, 41.07 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:08<00:12, 6.04s/ url]
Dl Size...: 17%|█▋ | 257/1501 [00:08<00:30, 41.07 MiB/s]
Dl Size...: 17%|█▋ | 258/1501 [00:08<00:29, 42.86 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:08<00:12, 6.04s/ url]
Dl Size...: 17%|█▋ | 258/1501 [00:08<00:29, 42.86 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:09<00:12, 6.04s/ url]
Dl Size...: 17%|█▋ | 259/1501 [00:09<00:28, 42.86 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:09<00:12, 6.04s/ url]
Dl Size...: 17%|█▋ | 260/1501 [00:09<00:28, 42.86 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:09<00:12, 6.04s/ url]
Dl Size...: 17%|█▋ | 261/1501 [00:09<00:28, 42.86 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:09<00:12, 6.04s/ url]
Dl Size...: 17%|█▋ | 262/1501 [00:09<00:28, 42.86 MiB/s]
Dl Size...: 18%|█▊ | 263/1501 [00:09<00:28, 43.98 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:09<00:12, 6.04s/ url]
Dl Size...: 18%|█▊ | 263/1501 [00:09<00:28, 43.98 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:09<00:12, 6.04s/ url]
Dl Size...: 18%|█▊ | 264/1501 [00:09<00:28, 43.98 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:09<00:12, 6.04s/ url]
Dl Size...: 18%|█▊ | 265/1501 [00:09<00:28, 43.98 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:09<00:12, 6.04s/ url]
Dl Size...: 18%|█▊ | 266/1501 [00:09<00:28, 43.98 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:09<00:12, 6.04s/ url]
Dl Size...: 18%|█▊ | 267/1501 [00:09<00:28, 43.98 MiB/s]
Dl Size...: 18%|█▊ | 268/1501 [00:09<00:30, 40.26 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:09<00:12, 6.04s/ url]
Dl Size...: 18%|█▊ | 268/1501 [00:09<00:30, 40.26 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:09<00:12, 6.04s/ url]
Dl Size...: 18%|█▊ | 269/1501 [00:09<00:30, 40.26 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:09<00:12, 6.04s/ url]
Dl Size...: 18%|█▊ | 270/1501 [00:09<00:30, 40.26 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:09<00:12, 6.04s/ url]
Dl Size...: 18%|█▊ | 271/1501 [00:09<00:30, 40.26 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:09<00:12, 6.04s/ url]
Dl Size...: 18%|█▊ | 272/1501 [00:09<00:30, 40.26 MiB/s]
Dl Size...: 18%|█▊ | 273/1501 [00:09<00:28, 42.47 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:09<00:12, 6.04s/ url]
Dl Size...: 18%|█▊ | 273/1501 [00:09<00:28, 42.47 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:09<00:12, 6.04s/ url]
Dl Size...: 18%|█▊ | 274/1501 [00:09<00:28, 42.47 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:09<00:12, 6.04s/ url]
Dl Size...: 18%|█▊ | 275/1501 [00:09<00:28, 42.47 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:09<00:12, 6.04s/ url]
Dl Size...: 18%|█▊ | 276/1501 [00:09<00:28, 42.47 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:09<00:12, 6.04s/ url]
Dl Size...: 18%|█▊ | 277/1501 [00:09<00:28, 42.47 MiB/s]
Dl Size...: 19%|█▊ | 278/1501 [00:09<00:29, 41.20 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:09<00:12, 6.04s/ url]
Dl Size...: 19%|█▊ | 278/1501 [00:09<00:29, 41.20 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:09<00:12, 6.04s/ url]
Dl Size...: 19%|█▊ | 279/1501 [00:09<00:29, 41.20 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:09<00:12, 6.04s/ url]
Dl Size...: 19%|█▊ | 280/1501 [00:09<00:29, 41.20 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:09<00:12, 6.04s/ url]
Dl Size...: 19%|█▊ | 281/1501 [00:09<00:29, 41.20 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:09<00:12, 6.04s/ url]
Dl Size...: 19%|█▉ | 282/1501 [00:09<00:29, 41.20 MiB/s]
Dl Size...: 19%|█▉ | 283/1501 [00:09<00:28, 42.99 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:09<00:12, 6.04s/ url]
Dl Size...: 19%|█▉ | 283/1501 [00:09<00:28, 42.99 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:09<00:12, 6.04s/ url]
Dl Size...: 19%|█▉ | 284/1501 [00:09<00:28, 42.99 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:09<00:12, 6.04s/ url]
Dl Size...: 19%|█▉ | 285/1501 [00:09<00:28, 42.99 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:09<00:12, 6.04s/ url]
Dl Size...: 19%|█▉ | 286/1501 [00:09<00:28, 42.99 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:09<00:12, 6.04s/ url]
Dl Size...: 19%|█▉ | 287/1501 [00:09<00:28, 42.99 MiB/s]
Dl Size...: 19%|█▉ | 288/1501 [00:09<00:28, 43.23 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:09<00:12, 6.04s/ url]
Dl Size...: 19%|█▉ | 288/1501 [00:09<00:28, 43.23 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:09<00:12, 6.04s/ url]
Dl Size...: 19%|█▉ | 289/1501 [00:09<00:28, 43.23 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:09<00:12, 6.04s/ url]
Dl Size...: 19%|█▉ | 290/1501 [00:09<00:28, 43.23 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:09<00:12, 6.04s/ url]
Dl Size...: 19%|█▉ | 291/1501 [00:09<00:27, 43.23 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:09<00:12, 6.04s/ url]
Dl Size...: 19%|█▉ | 292/1501 [00:09<00:27, 43.23 MiB/s]
Dl Size...: 20%|█▉ | 293/1501 [00:09<00:30, 39.71 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:09<00:12, 6.04s/ url]
Dl Size...: 20%|█▉ | 293/1501 [00:09<00:30, 39.71 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:09<00:12, 6.04s/ url]
Dl Size...: 20%|█▉ | 294/1501 [00:09<00:30, 39.71 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:09<00:12, 6.04s/ url]
Dl Size...: 20%|█▉ | 295/1501 [00:09<00:30, 39.71 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:09<00:12, 6.04s/ url]
Dl Size...: 20%|█▉ | 296/1501 [00:09<00:30, 39.71 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:09<00:12, 6.04s/ url]
Dl Size...: 20%|█▉ | 297/1501 [00:09<00:30, 39.71 MiB/s]
Dl Size...: 20%|█▉ | 298/1501 [00:09<00:28, 42.00 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:09<00:12, 6.04s/ url]
Dl Size...: 20%|█▉ | 298/1501 [00:09<00:28, 42.00 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:09<00:12, 6.04s/ url]
Dl Size...: 20%|█▉ | 299/1501 [00:09<00:28, 42.00 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:09<00:12, 6.04s/ url]
Dl Size...: 20%|█▉ | 300/1501 [00:09<00:28, 42.00 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:10<00:12, 6.04s/ url]
Dl Size...: 20%|██ | 301/1501 [00:10<00:28, 42.00 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:10<00:12, 6.04s/ url]
Dl Size...: 20%|██ | 302/1501 [00:10<00:28, 42.00 MiB/s]
Dl Size...: 20%|██ | 303/1501 [00:10<00:28, 42.04 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:10<00:12, 6.04s/ url]
Dl Size...: 20%|██ | 303/1501 [00:10<00:28, 42.04 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:10<00:12, 6.04s/ url]
Dl Size...: 20%|██ | 304/1501 [00:10<00:28, 42.04 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:10<00:12, 6.04s/ url]
Dl Size...: 20%|██ | 305/1501 [00:10<00:28, 42.04 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:10<00:12, 6.04s/ url]
Dl Size...: 20%|██ | 306/1501 [00:10<00:28, 42.04 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:10<00:12, 6.04s/ url]
Dl Size...: 20%|██ | 307/1501 [00:10<00:28, 42.04 MiB/s]
Dl Size...: 21%|██ | 308/1501 [00:10<00:27, 42.92 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:10<00:12, 6.04s/ url]
Dl Size...: 21%|██ | 308/1501 [00:10<00:27, 42.92 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:10<00:12, 6.04s/ url]
Dl Size...: 21%|██ | 309/1501 [00:10<00:27, 42.92 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:10<00:12, 6.04s/ url]
Dl Size...: 21%|██ | 310/1501 [00:10<00:27, 42.92 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:10<00:12, 6.04s/ url]
Dl Size...: 21%|██ | 311/1501 [00:10<00:27, 42.92 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:10<00:12, 6.04s/ url]
Dl Size...: 21%|██ | 312/1501 [00:10<00:27, 42.92 MiB/s]
Dl Size...: 21%|██ | 313/1501 [00:10<00:26, 44.70 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:10<00:12, 6.04s/ url]
Dl Size...: 21%|██ | 313/1501 [00:10<00:26, 44.70 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:10<00:12, 6.04s/ url]
Dl Size...: 21%|██ | 314/1501 [00:10<00:26, 44.70 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:10<00:12, 6.04s/ url]
Dl Size...: 21%|██ | 315/1501 [00:10<00:26, 44.70 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:10<00:12, 6.04s/ url]
Dl Size...: 21%|██ | 316/1501 [00:10<00:26, 44.70 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:10<00:12, 6.04s/ url]
Dl Size...: 21%|██ | 317/1501 [00:10<00:26, 44.70 MiB/s]
Dl Size...: 21%|██ | 318/1501 [00:10<00:29, 40.27 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:10<00:12, 6.04s/ url]
Dl Size...: 21%|██ | 318/1501 [00:10<00:29, 40.27 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:10<00:12, 6.04s/ url]
Dl Size...: 21%|██▏ | 319/1501 [00:10<00:29, 40.27 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:10<00:12, 6.04s/ url]
Dl Size...: 21%|██▏ | 320/1501 [00:10<00:29, 40.27 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:10<00:12, 6.04s/ url]
Dl Size...: 21%|██▏ | 321/1501 [00:10<00:29, 40.27 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:10<00:12, 6.04s/ url]
Dl Size...: 21%|██▏ | 322/1501 [00:10<00:29, 40.27 MiB/s]
Dl Size...: 22%|██▏ | 323/1501 [00:10<00:29, 40.49 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:10<00:12, 6.04s/ url]
Dl Size...: 22%|██▏ | 323/1501 [00:10<00:29, 40.49 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:10<00:12, 6.04s/ url]
Dl Size...: 22%|██▏ | 324/1501 [00:10<00:29, 40.49 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:10<00:12, 6.04s/ url]
Dl Size...: 22%|██▏ | 325/1501 [00:10<00:29, 40.49 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:10<00:12, 6.04s/ url]
Dl Size...: 22%|██▏ | 326/1501 [00:10<00:29, 40.49 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:10<00:12, 6.04s/ url]
Dl Size...: 22%|██▏ | 327/1501 [00:10<00:28, 40.49 MiB/s]
Dl Size...: 22%|██▏ | 328/1501 [00:10<00:28, 40.87 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:10<00:12, 6.04s/ url]
Dl Size...: 22%|██▏ | 328/1501 [00:10<00:28, 40.87 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:10<00:12, 6.04s/ url]
Dl Size...: 22%|██▏ | 329/1501 [00:10<00:28, 40.87 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:10<00:12, 6.04s/ url]
Dl Size...: 22%|██▏ | 330/1501 [00:10<00:28, 40.87 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:10<00:12, 6.04s/ url]
Dl Size...: 22%|██▏ | 331/1501 [00:10<00:28, 40.87 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:10<00:12, 6.04s/ url]
Dl Size...: 22%|██▏ | 332/1501 [00:10<00:28, 40.87 MiB/s]
Dl Size...: 22%|██▏ | 333/1501 [00:10<00:28, 41.19 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:10<00:12, 6.04s/ url]
Dl Size...: 22%|██▏ | 333/1501 [00:10<00:28, 41.19 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:10<00:12, 6.04s/ url]
Dl Size...: 22%|██▏ | 334/1501 [00:10<00:28, 41.19 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:10<00:12, 6.04s/ url]
Dl Size...: 22%|██▏ | 335/1501 [00:10<00:28, 41.19 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:10<00:12, 6.04s/ url]
Dl Size...: 22%|██▏ | 336/1501 [00:10<00:28, 41.19 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:10<00:12, 6.04s/ url]
Dl Size...: 22%|██▏ | 337/1501 [00:10<00:28, 41.19 MiB/s]
Dl Size...: 23%|██▎ | 338/1501 [00:10<00:27, 42.40 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:10<00:12, 6.04s/ url]
Dl Size...: 23%|██▎ | 338/1501 [00:10<00:27, 42.40 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:10<00:12, 6.04s/ url]
Dl Size...: 23%|██▎ | 339/1501 [00:10<00:27, 42.40 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:10<00:12, 6.04s/ url]
Dl Size...: 23%|██▎ | 340/1501 [00:10<00:27, 42.40 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:10<00:12, 6.04s/ url]
Dl Size...: 23%|██▎ | 341/1501 [00:10<00:27, 42.40 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:11<00:12, 6.04s/ url]
Dl Size...: 23%|██▎ | 342/1501 [00:11<00:27, 42.40 MiB/s]
Dl Size...: 23%|██▎ | 343/1501 [00:11<00:26, 42.90 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:11<00:12, 6.04s/ url]
Dl Size...: 23%|██▎ | 343/1501 [00:11<00:26, 42.90 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:11<00:12, 6.04s/ url]
Dl Size...: 23%|██▎ | 344/1501 [00:11<00:26, 42.90 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:11<00:12, 6.04s/ url]
Dl Size...: 23%|██▎ | 345/1501 [00:11<00:26, 42.90 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:11<00:12, 6.04s/ url]
Dl Size...: 23%|██▎ | 346/1501 [00:11<00:26, 42.90 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:11<00:12, 6.04s/ url]
Dl Size...: 23%|██▎ | 347/1501 [00:11<00:26, 42.90 MiB/s]
Dl Size...: 23%|██▎ | 348/1501 [00:11<00:28, 40.15 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:11<00:12, 6.04s/ url]
Dl Size...: 23%|██▎ | 348/1501 [00:11<00:28, 40.15 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:11<00:12, 6.04s/ url]
Dl Size...: 23%|██▎ | 349/1501 [00:11<00:28, 40.15 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:11<00:12, 6.04s/ url]
Dl Size...: 23%|██▎ | 350/1501 [00:11<00:28, 40.15 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:11<00:12, 6.04s/ url]
Dl Size...: 23%|██▎ | 351/1501 [00:11<00:28, 40.15 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:11<00:12, 6.04s/ url]
Dl Size...: 23%|██▎ | 352/1501 [00:11<00:28, 40.15 MiB/s]
Dl Size...: 24%|██▎ | 353/1501 [00:11<00:28, 40.99 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:11<00:12, 6.04s/ url]
Dl Size...: 24%|██▎ | 353/1501 [00:11<00:28, 40.99 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:11<00:12, 6.04s/ url]
Dl Size...: 24%|██▎ | 354/1501 [00:11<00:27, 40.99 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:11<00:12, 6.04s/ url]
Dl Size...: 24%|██▎ | 355/1501 [00:11<00:27, 40.99 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:11<00:12, 6.04s/ url]
Dl Size...: 24%|██▎ | 356/1501 [00:11<00:27, 40.99 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:11<00:12, 6.04s/ url]
Dl Size...: 24%|██▍ | 357/1501 [00:11<00:27, 40.99 MiB/s]
Dl Size...: 24%|██▍ | 358/1501 [00:11<00:30, 37.09 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:11<00:12, 6.04s/ url]
Dl Size...: 24%|██▍ | 358/1501 [00:11<00:30, 37.09 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:11<00:12, 6.04s/ url]
Dl Size...: 24%|██▍ | 359/1501 [00:11<00:30, 37.09 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:11<00:12, 6.04s/ url]
Dl Size...: 24%|██▍ | 360/1501 [00:11<00:30, 37.09 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:11<00:12, 6.04s/ url]
Dl Size...: 24%|██▍ | 361/1501 [00:11<00:30, 37.09 MiB/s]
Dl Size...: 24%|██▍ | 362/1501 [00:11<00:31, 36.42 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:11<00:12, 6.04s/ url]
Dl Size...: 24%|██▍ | 362/1501 [00:11<00:31, 36.42 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:11<00:12, 6.04s/ url]
Dl Size...: 24%|██▍ | 363/1501 [00:11<00:31, 36.42 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:11<00:12, 6.04s/ url]
Dl Size...: 24%|██▍ | 364/1501 [00:11<00:31, 36.42 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:11<00:12, 6.04s/ url]
Dl Size...: 24%|██▍ | 365/1501 [00:11<00:31, 36.42 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:11<00:12, 6.04s/ url]
Dl Size...: 24%|██▍ | 366/1501 [00:11<00:31, 36.42 MiB/s]
Dl Size...: 24%|██▍ | 367/1501 [00:11<00:32, 34.73 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:11<00:12, 6.04s/ url]
Dl Size...: 24%|██▍ | 367/1501 [00:11<00:32, 34.73 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:11<00:12, 6.04s/ url]
Dl Size...: 25%|██▍ | 368/1501 [00:11<00:32, 34.73 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:11<00:12, 6.04s/ url]
Dl Size...: 25%|██▍ | 369/1501 [00:11<00:32, 34.73 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:11<00:12, 6.04s/ url]
Dl Size...: 25%|██▍ | 370/1501 [00:11<00:32, 34.73 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:11<00:12, 6.04s/ url]
Dl Size...: 25%|██▍ | 371/1501 [00:11<00:32, 34.73 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:11<00:12, 6.04s/ url]
Dl Size...: 25%|██▍ | 372/1501 [00:11<00:32, 34.73 MiB/s]
Dl Size...: 25%|██▍ | 373/1501 [00:11<00:30, 36.63 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:11<00:12, 6.04s/ url]
Dl Size...: 25%|██▍ | 373/1501 [00:11<00:30, 36.63 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:11<00:12, 6.04s/ url]
Dl Size...: 25%|██▍ | 374/1501 [00:11<00:30, 36.63 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:11<00:12, 6.04s/ url]
Dl Size...: 25%|██▍ | 375/1501 [00:11<00:30, 36.63 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:11<00:12, 6.04s/ url]
Dl Size...: 25%|██▌ | 376/1501 [00:11<00:30, 36.63 MiB/s]
Dl Size...: 25%|██▌ | 377/1501 [00:11<00:30, 36.63 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:11<00:12, 6.04s/ url]
Dl Size...: 25%|██▌ | 377/1501 [00:11<00:30, 36.63 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:12<00:12, 6.04s/ url]
Dl Size...: 25%|██▌ | 378/1501 [00:12<00:30, 36.63 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:12<00:12, 6.04s/ url]
Dl Size...: 25%|██▌ | 379/1501 [00:12<00:30, 36.63 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:12<00:12, 6.04s/ url]
Dl Size...: 25%|██▌ | 380/1501 [00:12<00:30, 36.63 MiB/s]
Dl Size...: 25%|██▌ | 381/1501 [00:12<00:33, 33.45 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:12<00:12, 6.04s/ url]
Dl Size...: 25%|██▌ | 381/1501 [00:12<00:33, 33.45 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:12<00:12, 6.04s/ url]
Dl Size...: 25%|██▌ | 382/1501 [00:12<00:33, 33.45 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:12<00:12, 6.04s/ url]
Dl Size...: 26%|██▌ | 383/1501 [00:12<00:33, 33.45 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:12<00:12, 6.04s/ url]
Dl Size...: 26%|██▌ | 384/1501 [00:12<00:33, 33.45 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:12<00:12, 6.04s/ url]
Dl Size...: 26%|██▌ | 385/1501 [00:12<00:33, 33.45 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:12<00:12, 6.04s/ url]
Dl Size...: 26%|██▌ | 386/1501 [00:12<00:33, 33.45 MiB/s]
Dl Size...: 26%|██▌ | 387/1501 [00:12<00:32, 34.32 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:12<00:12, 6.04s/ url]
Dl Size...: 26%|██▌ | 387/1501 [00:12<00:32, 34.32 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:12<00:12, 6.04s/ url]
Dl Size...: 26%|██▌ | 388/1501 [00:12<00:32, 34.32 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:12<00:12, 6.04s/ url]
Dl Size...: 26%|██▌ | 389/1501 [00:12<00:32, 34.32 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:12<00:12, 6.04s/ url]
Dl Size...: 26%|██▌ | 390/1501 [00:12<00:32, 34.32 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:12<00:12, 6.04s/ url]
Dl Size...: 26%|██▌ | 391/1501 [00:12<00:32, 34.32 MiB/s]
Dl Size...: 26%|██▌ | 392/1501 [00:12<00:32, 34.02 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:12<00:12, 6.04s/ url]
Dl Size...: 26%|██▌ | 392/1501 [00:12<00:32, 34.02 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:12<00:12, 6.04s/ url]
Dl Size...: 26%|██▌ | 393/1501 [00:12<00:32, 34.02 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:12<00:12, 6.04s/ url]
Dl Size...: 26%|██▌ | 394/1501 [00:12<00:32, 34.02 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:12<00:12, 6.04s/ url]
Dl Size...: 26%|██▋ | 395/1501 [00:12<00:32, 34.02 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:12<00:12, 6.04s/ url]
Dl Size...: 26%|██▋ | 396/1501 [00:12<00:32, 34.02 MiB/s]
Dl Size...: 26%|██▋ | 397/1501 [00:12<00:29, 37.33 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:12<00:12, 6.04s/ url]
Dl Size...: 26%|██▋ | 397/1501 [00:12<00:29, 37.33 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:12<00:12, 6.04s/ url]
Dl Size...: 27%|██▋ | 398/1501 [00:12<00:29, 37.33 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:12<00:12, 6.04s/ url]
Dl Size...: 27%|██▋ | 399/1501 [00:12<00:29, 37.33 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:12<00:12, 6.04s/ url]
Dl Size...: 27%|██▋ | 400/1501 [00:12<00:29, 37.33 MiB/s]
Dl Size...: 27%|██▋ | 401/1501 [00:12<00:31, 34.63 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:12<00:12, 6.04s/ url]
Dl Size...: 27%|██▋ | 401/1501 [00:12<00:31, 34.63 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:12<00:12, 6.04s/ url]
Dl Size...: 27%|██▋ | 402/1501 [00:12<00:31, 34.63 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:12<00:12, 6.04s/ url]
Dl Size...: 27%|██▋ | 403/1501 [00:12<00:31, 34.63 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:12<00:12, 6.04s/ url]
Dl Size...: 27%|██▋ | 404/1501 [00:12<00:31, 34.63 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:12<00:12, 6.04s/ url]
Dl Size...: 27%|██▋ | 405/1501 [00:12<00:31, 34.63 MiB/s]
Dl Size...: 27%|██▋ | 406/1501 [00:12<00:28, 37.93 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:12<00:12, 6.04s/ url]
Dl Size...: 27%|██▋ | 406/1501 [00:12<00:28, 37.93 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:12<00:12, 6.04s/ url]
Dl Size...: 27%|██▋ | 407/1501 [00:12<00:28, 37.93 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:12<00:12, 6.04s/ url]
Dl Size...: 27%|██▋ | 408/1501 [00:12<00:28, 37.93 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:12<00:12, 6.04s/ url]
Dl Size...: 27%|██▋ | 409/1501 [00:12<00:28, 37.93 MiB/s]
Dl Size...: 27%|██▋ | 410/1501 [00:12<00:30, 35.54 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:12<00:12, 6.04s/ url]
Dl Size...: 27%|██▋ | 410/1501 [00:12<00:30, 35.54 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:12<00:12, 6.04s/ url]
Dl Size...: 27%|██▋ | 411/1501 [00:12<00:30, 35.54 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:12<00:12, 6.04s/ url]
Dl Size...: 27%|██▋ | 412/1501 [00:12<00:30, 35.54 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:13<00:12, 6.04s/ url]
Dl Size...: 28%|██▊ | 413/1501 [00:13<00:30, 35.54 MiB/s]
Dl Size...: 28%|██▊ | 414/1501 [00:13<00:30, 35.93 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:13<00:12, 6.04s/ url]
Dl Size...: 28%|██▊ | 414/1501 [00:13<00:30, 35.93 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:13<00:12, 6.04s/ url]
Dl Size...: 28%|██▊ | 415/1501 [00:13<00:30, 35.93 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:13<00:12, 6.04s/ url]
Dl Size...: 28%|██▊ | 416/1501 [00:13<00:30, 35.93 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:13<00:12, 6.04s/ url]
Dl Size...: 28%|██▊ | 417/1501 [00:13<00:30, 35.93 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:13<00:12, 6.04s/ url]
Dl Size...: 28%|██▊ | 418/1501 [00:13<00:30, 35.93 MiB/s]
Dl Size...: 28%|██▊ | 419/1501 [00:13<00:31, 34.73 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:13<00:12, 6.04s/ url]
Dl Size...: 28%|██▊ | 419/1501 [00:13<00:31, 34.73 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:13<00:12, 6.04s/ url]
Dl Size...: 28%|██▊ | 420/1501 [00:13<00:31, 34.73 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:13<00:12, 6.04s/ url]
Dl Size...: 28%|██▊ | 421/1501 [00:13<00:31, 34.73 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:13<00:12, 6.04s/ url]
Dl Size...: 28%|██▊ | 422/1501 [00:13<00:31, 34.73 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:13<00:12, 6.04s/ url]
Dl Size...: 28%|██▊ | 423/1501 [00:13<00:31, 34.73 MiB/s]
Dl Size...: 28%|██▊ | 424/1501 [00:13<00:28, 38.33 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:13<00:12, 6.04s/ url]
Dl Size...: 28%|██▊ | 424/1501 [00:13<00:28, 38.33 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:13<00:12, 6.04s/ url]
Dl Size...: 28%|██▊ | 425/1501 [00:13<00:28, 38.33 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:13<00:12, 6.04s/ url]
Dl Size...: 28%|██▊ | 426/1501 [00:13<00:28, 38.33 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:13<00:12, 6.04s/ url]
Dl Size...: 28%|██▊ | 427/1501 [00:13<00:28, 38.33 MiB/s]
Dl Size...: 29%|██▊ | 428/1501 [00:13<00:30, 35.76 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:13<00:12, 6.04s/ url]
Dl Size...: 29%|██▊ | 428/1501 [00:13<00:30, 35.76 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:13<00:12, 6.04s/ url]
Dl Size...: 29%|██▊ | 429/1501 [00:13<00:29, 35.76 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:13<00:12, 6.04s/ url]
Dl Size...: 29%|██▊ | 430/1501 [00:13<00:29, 35.76 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:13<00:12, 6.04s/ url]
Dl Size...: 29%|██▊ | 431/1501 [00:13<00:29, 35.76 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:13<00:12, 6.04s/ url]
Dl Size...: 29%|██▉ | 432/1501 [00:13<00:29, 35.76 MiB/s]
Dl Size...: 29%|██▉ | 433/1501 [00:13<00:27, 39.03 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:13<00:12, 6.04s/ url]
Dl Size...: 29%|██▉ | 433/1501 [00:13<00:27, 39.03 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:13<00:12, 6.04s/ url]
Dl Size...: 29%|██▉ | 434/1501 [00:13<00:27, 39.03 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:13<00:12, 6.04s/ url]
Dl Size...: 29%|██▉ | 435/1501 [00:13<00:27, 39.03 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:13<00:12, 6.04s/ url]
Dl Size...: 29%|██▉ | 436/1501 [00:13<00:27, 39.03 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:13<00:12, 6.04s/ url]
Dl Size...: 29%|██▉ | 437/1501 [00:13<00:27, 39.03 MiB/s]
Dl Size...: 29%|██▉ | 438/1501 [00:13<00:28, 37.58 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:13<00:12, 6.04s/ url]
Dl Size...: 29%|██▉ | 438/1501 [00:13<00:28, 37.58 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:13<00:12, 6.04s/ url]
Dl Size...: 29%|██▉ | 439/1501 [00:13<00:28, 37.58 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:13<00:12, 6.04s/ url]
Dl Size...: 29%|██▉ | 440/1501 [00:13<00:28, 37.58 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:13<00:12, 6.04s/ url]
Dl Size...: 29%|██▉ | 441/1501 [00:13<00:28, 37.58 MiB/s]
Dl Size...: 29%|██▉ | 442/1501 [00:13<00:27, 38.06 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:13<00:12, 6.04s/ url]
Dl Size...: 29%|██▉ | 442/1501 [00:13<00:27, 38.06 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:13<00:12, 6.04s/ url]
Dl Size...: 30%|██▉ | 443/1501 [00:13<00:27, 38.06 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:13<00:12, 6.04s/ url]
Dl Size...: 30%|██▉ | 444/1501 [00:13<00:27, 38.06 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:13<00:12, 6.04s/ url]
Dl Size...: 30%|██▉ | 445/1501 [00:13<00:27, 38.06 MiB/s]
Dl Size...: 30%|██▉ | 446/1501 [00:13<00:32, 32.01 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:13<00:12, 6.04s/ url]
Dl Size...: 30%|██▉ | 446/1501 [00:13<00:32, 32.01 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:13<00:12, 6.04s/ url]
Dl Size...: 30%|██▉ | 447/1501 [00:13<00:32, 32.01 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:13<00:12, 6.04s/ url]
Dl Size...: 30%|██▉ | 448/1501 [00:13<00:32, 32.01 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:13<00:12, 6.04s/ url]
Dl Size...: 30%|██▉ | 449/1501 [00:13<00:32, 32.01 MiB/s]
Dl Size...: 30%|██▉ | 450/1501 [00:14<00:31, 33.70 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:14<00:12, 6.04s/ url]
Dl Size...: 30%|██▉ | 450/1501 [00:14<00:31, 33.70 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:14<00:12, 6.04s/ url]
Dl Size...: 30%|███ | 451/1501 [00:14<00:31, 33.70 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:14<00:12, 6.04s/ url]
Dl Size...: 30%|███ | 452/1501 [00:14<00:31, 33.70 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:14<00:12, 6.04s/ url]
Dl Size...: 30%|███ | 453/1501 [00:14<00:31, 33.70 MiB/s]
Dl Size...: 30%|███ | 454/1501 [00:14<00:35, 29.53 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:14<00:12, 6.04s/ url]
Dl Size...: 30%|███ | 454/1501 [00:14<00:35, 29.53 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:14<00:12, 6.04s/ url]
Dl Size...: 30%|███ | 455/1501 [00:14<00:35, 29.53 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:14<00:12, 6.04s/ url]
Dl Size...: 30%|███ | 456/1501 [00:14<00:35, 29.53 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:14<00:12, 6.04s/ url]
Dl Size...: 30%|███ | 457/1501 [00:14<00:35, 29.53 MiB/s]
Dl Size...: 31%|███ | 458/1501 [00:14<00:37, 27.61 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:14<00:12, 6.04s/ url]
Dl Size...: 31%|███ | 458/1501 [00:14<00:37, 27.61 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:14<00:12, 6.04s/ url]
Dl Size...: 31%|███ | 459/1501 [00:14<00:37, 27.61 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:14<00:12, 6.04s/ url]
Dl Size...: 31%|███ | 460/1501 [00:14<00:37, 27.61 MiB/s]
Dl Size...: 31%|███ | 461/1501 [00:14<00:40, 25.53 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:14<00:12, 6.04s/ url]
Dl Size...: 31%|███ | 461/1501 [00:14<00:40, 25.53 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:14<00:12, 6.04s/ url]
Dl Size...: 31%|███ | 462/1501 [00:14<00:40, 25.53 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:14<00:12, 6.04s/ url]
Dl Size...: 31%|███ | 463/1501 [00:14<00:40, 25.53 MiB/s]
Dl Size...: 31%|███ | 464/1501 [00:14<00:52, 19.73 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:14<00:12, 6.04s/ url]
Dl Size...: 31%|███ | 464/1501 [00:14<00:52, 19.73 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:14<00:12, 6.04s/ url]
Dl Size...: 31%|███ | 465/1501 [00:14<00:52, 19.73 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:14<00:12, 6.04s/ url]
Dl Size...: 31%|███ | 466/1501 [00:14<00:52, 19.73 MiB/s]
Dl Size...: 31%|███ | 467/1501 [00:14<00:53, 19.17 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:14<00:12, 6.04s/ url]
Dl Size...: 31%|███ | 467/1501 [00:14<00:53, 19.17 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:15<00:12, 6.04s/ url]
Dl Size...: 31%|███ | 468/1501 [00:15<00:53, 19.17 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:15<00:12, 6.04s/ url]
Dl Size...: 31%|███ | 469/1501 [00:15<00:53, 19.17 MiB/s]
Dl Size...: 31%|███▏ | 470/1501 [00:15<00:56, 18.41 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:15<00:12, 6.04s/ url]
Dl Size...: 31%|███▏ | 470/1501 [00:15<00:56, 18.41 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:15<00:12, 6.04s/ url]
Dl Size...: 31%|███▏ | 471/1501 [00:15<00:55, 18.41 MiB/s]
Dl Size...: 31%|███▏ | 472/1501 [00:15<01:01, 16.72 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:15<00:12, 6.04s/ url]
Dl Size...: 31%|███▏ | 472/1501 [00:15<01:01, 16.72 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:15<00:12, 6.04s/ url]
Dl Size...: 32%|███▏ | 473/1501 [00:15<01:01, 16.72 MiB/s]
Dl Size...: 32%|███▏ | 474/1501 [00:15<01:01, 16.74 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:15<00:12, 6.04s/ url]
Dl Size...: 32%|███▏ | 474/1501 [00:15<01:01, 16.74 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:15<00:12, 6.04s/ url]
Dl Size...: 32%|███▏ | 475/1501 [00:15<01:01, 16.74 MiB/s]
Dl Size...: 32%|███▏ | 476/1501 [00:15<01:02, 16.44 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:15<00:12, 6.04s/ url]
Dl Size...: 32%|███▏ | 476/1501 [00:15<01:02, 16.44 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:15<00:12, 6.04s/ url]
Dl Size...: 32%|███▏ | 477/1501 [00:15<01:02, 16.44 MiB/s]
Dl Size...: 32%|███▏ | 478/1501 [00:15<01:04, 15.98 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:15<00:12, 6.04s/ url]
Dl Size...: 32%|███▏ | 478/1501 [00:15<01:04, 15.98 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:15<00:12, 6.04s/ url]
Dl Size...: 32%|███▏ | 479/1501 [00:15<01:03, 15.98 MiB/s]
Dl Size...: 32%|███▏ | 480/1501 [00:15<01:03, 16.14 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:15<00:12, 6.04s/ url]
Dl Size...: 32%|███▏ | 480/1501 [00:15<01:03, 16.14 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:15<00:12, 6.04s/ url]
Dl Size...: 32%|███▏ | 481/1501 [00:15<01:03, 16.14 MiB/s]
Dl Size...: 32%|███▏ | 482/1501 [00:15<01:04, 15.87 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:15<00:12, 6.04s/ url]
Dl Size...: 32%|███▏ | 482/1501 [00:15<01:04, 15.87 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:16<00:12, 6.04s/ url]
Dl Size...: 32%|███▏ | 483/1501 [00:16<01:04, 15.87 MiB/s]
Dl Size...: 32%|███▏ | 484/1501 [00:16<01:00, 16.68 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:16<00:12, 6.04s/ url]
Dl Size...: 32%|███▏ | 484/1501 [00:16<01:00, 16.68 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:16<00:12, 6.04s/ url]
Dl Size...: 32%|███▏ | 485/1501 [00:16<01:00, 16.68 MiB/s]
Dl Size...: 32%|███▏ | 486/1501 [00:16<01:04, 15.74 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:16<00:12, 6.04s/ url]
Dl Size...: 32%|███▏ | 486/1501 [00:16<01:04, 15.74 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:16<00:12, 6.04s/ url]
Dl Size...: 32%|███▏ | 487/1501 [00:16<01:04, 15.74 MiB/s]
Dl Size...: 33%|███▎ | 488/1501 [00:16<01:01, 16.57 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:16<00:12, 6.04s/ url]
Dl Size...: 33%|███▎ | 488/1501 [00:16<01:01, 16.57 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:16<00:12, 6.04s/ url]
Dl Size...: 33%|███▎ | 489/1501 [00:16<01:01, 16.57 MiB/s]
Dl Size...: 33%|███▎ | 490/1501 [00:16<01:06, 15.31 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:16<00:12, 6.04s/ url]
Dl Size...: 33%|███▎ | 490/1501 [00:16<01:06, 15.31 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:16<00:12, 6.04s/ url]
Dl Size...: 33%|███▎ | 491/1501 [00:16<01:05, 15.31 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:16<00:12, 6.04s/ url]
Dl Size...: 33%|███▎ | 492/1501 [00:16<01:05, 15.31 MiB/s]
Dl Size...: 33%|███▎ | 493/1501 [00:16<01:02, 16.01 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:16<00:12, 6.04s/ url]
Dl Size...: 33%|███▎ | 493/1501 [00:16<01:02, 16.01 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:16<00:12, 6.04s/ url]
Dl Size...: 33%|███▎ | 494/1501 [00:16<01:02, 16.01 MiB/s]
Dl Size...: 33%|███▎ | 495/1501 [00:16<00:59, 16.80 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:16<00:12, 6.04s/ url]
Dl Size...: 33%|███▎ | 495/1501 [00:16<00:59, 16.80 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:16<00:12, 6.04s/ url]
Dl Size...: 33%|███▎ | 496/1501 [00:16<00:59, 16.80 MiB/s]
Dl Size...: 33%|███▎ | 497/1501 [00:16<01:03, 15.90 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:16<00:12, 6.04s/ url]
Dl Size...: 33%|███▎ | 497/1501 [00:16<01:03, 15.90 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:16<00:12, 6.04s/ url]
Dl Size...: 33%|███▎ | 498/1501 [00:16<01:03, 15.90 MiB/s]
Dl Size...: 33%|███▎ | 499/1501 [00:16<01:00, 16.53 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:16<00:12, 6.04s/ url]
Dl Size...: 33%|███▎ | 499/1501 [00:16<01:00, 16.53 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:17<00:12, 6.04s/ url]
Dl Size...: 33%|███▎ | 500/1501 [00:17<01:00, 16.53 MiB/s]
Dl Size...: 33%|███▎ | 501/1501 [00:17<01:03, 15.79 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:17<00:12, 6.04s/ url]
Dl Size...: 33%|███▎ | 501/1501 [00:17<01:03, 15.79 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:17<00:12, 6.04s/ url]
Dl Size...: 33%|███▎ | 502/1501 [00:17<01:03, 15.79 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:17<00:12, 6.04s/ url]
Dl Size...: 34%|███▎ | 503/1501 [00:17<01:03, 15.79 MiB/s]
Dl Size...: 34%|███▎ | 504/1501 [00:17<00:59, 16.65 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:17<00:12, 6.04s/ url]
Dl Size...: 34%|███▎ | 504/1501 [00:17<00:59, 16.65 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:17<00:12, 6.04s/ url]
Dl Size...: 34%|███▎ | 505/1501 [00:17<00:59, 16.65 MiB/s]
Dl Size...: 34%|███▎ | 506/1501 [00:17<01:05, 15.16 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:17<00:12, 6.04s/ url]
Dl Size...: 34%|███▎ | 506/1501 [00:17<01:05, 15.16 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:17<00:12, 6.04s/ url]
Dl Size...: 34%|███▍ | 507/1501 [00:17<01:05, 15.16 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:17<00:12, 6.04s/ url]
Dl Size...: 34%|███▍ | 508/1501 [00:17<01:05, 15.16 MiB/s]
Dl Size...: 34%|███▍ | 509/1501 [00:17<01:00, 16.29 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:17<00:12, 6.04s/ url]
Dl Size...: 34%|███▍ | 509/1501 [00:17<01:00, 16.29 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:17<00:12, 6.04s/ url]
Dl Size...: 34%|███▍ | 510/1501 [00:17<01:00, 16.29 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:17<00:12, 6.04s/ url]
Dl Size...: 34%|███▍ | 511/1501 [00:17<01:00, 16.29 MiB/s]
Dl Size...: 34%|███▍ | 512/1501 [00:17<00:57, 17.05 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:17<00:12, 6.04s/ url]
Dl Size...: 34%|███▍ | 512/1501 [00:17<00:57, 17.05 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:17<00:12, 6.04s/ url]
Dl Size...: 34%|███▍ | 513/1501 [00:17<00:57, 17.05 MiB/s]
Dl Size...: 34%|███▍ | 514/1501 [00:17<00:57, 17.13 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:17<00:12, 6.04s/ url]
Dl Size...: 34%|███▍ | 514/1501 [00:17<00:57, 17.13 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:17<00:12, 6.04s/ url]
Dl Size...: 34%|███▍ | 515/1501 [00:17<00:57, 17.13 MiB/s]
Dl Size...: 34%|███▍ | 516/1501 [00:17<00:58, 16.96 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:17<00:12, 6.04s/ url]
Dl Size...: 34%|███▍ | 516/1501 [00:17<00:58, 16.96 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:18<00:12, 6.04s/ url]
Dl Size...: 34%|███▍ | 517/1501 [00:18<00:58, 16.96 MiB/s]
Dl Size...: 35%|███▍ | 518/1501 [00:18<00:58, 16.88 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:18<00:12, 6.04s/ url]
Dl Size...: 35%|███▍ | 518/1501 [00:18<00:58, 16.88 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:18<00:12, 6.04s/ url]
Dl Size...: 35%|███▍ | 519/1501 [00:18<00:58, 16.88 MiB/s]
Dl Size...: 35%|███▍ | 520/1501 [00:18<00:57, 16.99 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:18<00:12, 6.04s/ url]
Dl Size...: 35%|███▍ | 520/1501 [00:18<00:57, 16.99 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:18<00:12, 6.04s/ url]
Dl Size...: 35%|███▍ | 521/1501 [00:18<00:57, 16.99 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:18<00:12, 6.04s/ url]
Dl Size...: 35%|███▍ | 522/1501 [00:18<00:57, 16.99 MiB/s]
Dl Size...: 35%|███▍ | 523/1501 [00:18<00:54, 17.96 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:18<00:12, 6.04s/ url]
Dl Size...: 35%|███▍ | 523/1501 [00:18<00:54, 17.96 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:18<00:12, 6.04s/ url]
Dl Size...: 35%|███▍ | 524/1501 [00:18<00:54, 17.96 MiB/s]
Dl Size...: 35%|███▍ | 525/1501 [00:18<01:00, 16.17 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:18<00:12, 6.04s/ url]
Dl Size...: 35%|███▍ | 525/1501 [00:18<01:00, 16.17 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:18<00:12, 6.04s/ url]
Dl Size...: 35%|███▌ | 526/1501 [00:18<01:00, 16.17 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:18<00:12, 6.04s/ url]
Dl Size...: 35%|███▌ | 527/1501 [00:18<01:00, 16.17 MiB/s]
Dl Size...: 35%|███▌ | 528/1501 [00:18<00:56, 17.12 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:18<00:12, 6.04s/ url]
Dl Size...: 35%|███▌ | 528/1501 [00:18<00:56, 17.12 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:18<00:12, 6.04s/ url]
Dl Size...: 35%|███▌ | 529/1501 [00:18<00:56, 17.12 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:18<00:12, 6.04s/ url]
Dl Size...: 35%|███▌ | 530/1501 [00:18<00:56, 17.12 MiB/s]
Dl Size...: 35%|███▌ | 531/1501 [00:18<00:53, 17.97 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:18<00:12, 6.04s/ url]
Dl Size...: 35%|███▌ | 531/1501 [00:18<00:53, 17.97 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:18<00:12, 6.04s/ url]
Dl Size...: 35%|███▌ | 532/1501 [00:18<00:53, 17.97 MiB/s]
Dl Size...: 36%|███▌ | 533/1501 [00:18<00:56, 17.19 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:18<00:12, 6.04s/ url]
Dl Size...: 36%|███▌ | 533/1501 [00:18<00:56, 17.19 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:18<00:12, 6.04s/ url]
Dl Size...: 36%|███▌ | 534/1501 [00:18<00:56, 17.19 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:19<00:12, 6.04s/ url]
Dl Size...: 36%|███▌ | 535/1501 [00:19<00:56, 17.19 MiB/s]
Dl Size...: 36%|███▌ | 536/1501 [00:19<00:55, 17.29 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:19<00:12, 6.04s/ url]
Dl Size...: 36%|███▌ | 536/1501 [00:19<00:55, 17.29 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:19<00:12, 6.04s/ url]
Dl Size...: 36%|███▌ | 537/1501 [00:19<00:55, 17.29 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:19<00:12, 6.04s/ url]
Dl Size...: 36%|███▌ | 538/1501 [00:19<00:55, 17.29 MiB/s]
Dl Size...: 36%|███▌ | 539/1501 [00:19<00:54, 17.74 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:19<00:12, 6.04s/ url]
Dl Size...: 36%|███▌ | 539/1501 [00:19<00:54, 17.74 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:19<00:12, 6.04s/ url]
Dl Size...: 36%|███▌ | 540/1501 [00:19<00:54, 17.74 MiB/s]
Dl Size...: 36%|███▌ | 541/1501 [00:19<00:57, 16.64 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:19<00:12, 6.04s/ url]
Dl Size...: 36%|███▌ | 541/1501 [00:19<00:57, 16.64 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:19<00:12, 6.04s/ url]
Dl Size...: 36%|███▌ | 542/1501 [00:19<00:57, 16.64 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:19<00:12, 6.04s/ url]
Dl Size...: 36%|███▌ | 543/1501 [00:19<00:57, 16.64 MiB/s]
Dl Size...: 36%|███▌ | 544/1501 [00:19<00:56, 16.80 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:19<00:12, 6.04s/ url]
Dl Size...: 36%|███▌ | 544/1501 [00:19<00:56, 16.80 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:19<00:12, 6.04s/ url]
Dl Size...: 36%|███▋ | 545/1501 [00:19<00:56, 16.80 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:19<00:12, 6.04s/ url]
Dl Size...: 36%|███▋ | 546/1501 [00:19<00:56, 16.80 MiB/s]
Dl Size...: 36%|███▋ | 547/1501 [00:19<00:53, 17.85 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:19<00:12, 6.04s/ url]
Dl Size...: 36%|███▋ | 547/1501 [00:19<00:53, 17.85 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:19<00:12, 6.04s/ url]
Dl Size...: 37%|███▋ | 548/1501 [00:19<00:53, 17.85 MiB/s]
Dl Size...: 37%|███▋ | 549/1501 [00:19<00:53, 17.80 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:19<00:12, 6.04s/ url]
Dl Size...: 37%|███▋ | 549/1501 [00:19<00:53, 17.80 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:19<00:12, 6.04s/ url]
Dl Size...: 37%|███▋ | 550/1501 [00:19<00:53, 17.80 MiB/s]
Dl Size...: 37%|███▋ | 551/1501 [00:20<00:54, 17.37 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:20<00:12, 6.04s/ url]
Dl Size...: 37%|███▋ | 551/1501 [00:20<00:54, 17.37 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:20<00:12, 6.04s/ url]
Dl Size...: 37%|███▋ | 552/1501 [00:20<00:54, 17.37 MiB/s]
Dl Size...: 37%|███▋ | 553/1501 [00:20<00:54, 17.46 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:20<00:12, 6.04s/ url]
Dl Size...: 37%|███▋ | 553/1501 [00:20<00:54, 17.46 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:20<00:12, 6.04s/ url]
Dl Size...: 37%|███▋ | 554/1501 [00:20<00:54, 17.46 MiB/s]
Dl Size...: 37%|███▋ | 555/1501 [00:20<00:55, 16.96 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:20<00:12, 6.04s/ url]
Dl Size...: 37%|███▋ | 555/1501 [00:20<00:55, 16.96 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:20<00:12, 6.04s/ url]
Dl Size...: 37%|███▋ | 556/1501 [00:20<00:55, 16.96 MiB/s]
Dl Size...: 37%|███▋ | 557/1501 [00:20<00:53, 17.67 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:20<00:12, 6.04s/ url]
Dl Size...: 37%|███▋ | 557/1501 [00:20<00:53, 17.67 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:20<00:12, 6.04s/ url]
Dl Size...: 37%|███▋ | 558/1501 [00:20<00:53, 17.67 MiB/s]
Dl Size...: 37%|███▋ | 559/1501 [00:20<00:56, 16.59 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:20<00:12, 6.04s/ url]
Dl Size...: 37%|███▋ | 559/1501 [00:20<00:56, 16.59 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:20<00:12, 6.04s/ url]
Dl Size...: 37%|███▋ | 560/1501 [00:20<00:56, 16.59 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:20<00:12, 6.04s/ url]
Dl Size...: 37%|███▋ | 561/1501 [00:20<00:56, 16.59 MiB/s]
Dl Size...: 37%|███▋ | 562/1501 [00:20<00:54, 17.35 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:20<00:12, 6.04s/ url]
Dl Size...: 37%|███▋ | 562/1501 [00:20<00:54, 17.35 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:20<00:12, 6.04s/ url]
Dl Size...: 38%|███▊ | 563/1501 [00:20<00:54, 17.35 MiB/s]
Dl Size...: 38%|███▊ | 564/1501 [00:20<00:57, 16.26 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:20<00:12, 6.04s/ url]
Dl Size...: 38%|███▊ | 564/1501 [00:20<00:57, 16.26 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:20<00:12, 6.04s/ url]
Dl Size...: 38%|███▊ | 565/1501 [00:20<00:57, 16.26 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:20<00:12, 6.04s/ url]
Dl Size...: 38%|███▊ | 566/1501 [00:20<00:57, 16.26 MiB/s]
Dl Size...: 38%|███▊ | 567/1501 [00:20<00:56, 16.57 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:20<00:12, 6.04s/ url]
Dl Size...: 38%|███▊ | 567/1501 [00:20<00:56, 16.57 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:21<00:12, 6.04s/ url]
Dl Size...: 38%|███▊ | 568/1501 [00:21<00:56, 16.57 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:21<00:12, 6.04s/ url]
Dl Size...: 38%|███▊ | 569/1501 [00:21<00:56, 16.57 MiB/s]
Dl Size...: 38%|███▊ | 570/1501 [00:21<00:52, 17.57 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:21<00:12, 6.04s/ url]
Dl Size...: 38%|███▊ | 570/1501 [00:21<00:52, 17.57 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:21<00:12, 6.04s/ url]
Dl Size...: 38%|███▊ | 571/1501 [00:21<00:52, 17.57 MiB/s]
Dl Size...: 38%|███▊ | 572/1501 [00:21<00:51, 17.90 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:21<00:12, 6.04s/ url]
Dl Size...: 38%|███▊ | 572/1501 [00:21<00:51, 17.90 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:21<00:12, 6.04s/ url]
Dl Size...: 38%|███▊ | 573/1501 [00:21<00:51, 17.90 MiB/s]
Dl Size...: 38%|███▊ | 574/1501 [00:21<00:53, 17.31 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:21<00:12, 6.04s/ url]
Dl Size...: 38%|███▊ | 574/1501 [00:21<00:53, 17.31 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:21<00:12, 6.04s/ url]
Dl Size...: 38%|███▊ | 575/1501 [00:21<00:53, 17.31 MiB/s]
Dl Size...: 38%|███▊ | 576/1501 [00:21<00:52, 17.60 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:21<00:12, 6.04s/ url]
Dl Size...: 38%|███▊ | 576/1501 [00:21<00:52, 17.60 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:21<00:12, 6.04s/ url]
Dl Size...: 38%|███▊ | 577/1501 [00:21<00:52, 17.60 MiB/s]
Dl Size...: 39%|███▊ | 578/1501 [00:21<00:52, 17.70 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:21<00:12, 6.04s/ url]
Dl Size...: 39%|███▊ | 578/1501 [00:21<00:52, 17.70 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:21<00:12, 6.04s/ url]
Dl Size...: 39%|███▊ | 579/1501 [00:21<00:52, 17.70 MiB/s]
Dl Size...: 39%|███▊ | 580/1501 [00:21<00:52, 17.56 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:21<00:12, 6.04s/ url]
Dl Size...: 39%|███▊ | 580/1501 [00:21<00:52, 17.56 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:21<00:12, 6.04s/ url]
Dl Size...: 39%|███▊ | 581/1501 [00:21<00:52, 17.56 MiB/s]
Dl Size...: 39%|███▉ | 582/1501 [00:21<00:52, 17.35 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:21<00:12, 6.04s/ url]
Dl Size...: 39%|███▉ | 582/1501 [00:21<00:52, 17.35 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:21<00:12, 6.04s/ url]
Dl Size...: 39%|███▉ | 583/1501 [00:21<00:52, 17.35 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:21<00:12, 6.04s/ url]
Dl Size...: 39%|███▉ | 584/1501 [00:21<00:52, 17.35 MiB/s]
Dl Size...: 39%|███▉ | 585/1501 [00:21<00:50, 18.28 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:21<00:12, 6.04s/ url]
Dl Size...: 39%|███▉ | 585/1501 [00:21<00:50, 18.28 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:22<00:12, 6.04s/ url]
Dl Size...: 39%|███▉ | 586/1501 [00:22<00:50, 18.28 MiB/s]
Dl Size...: 39%|███▉ | 587/1501 [00:22<00:56, 16.28 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:22<00:12, 6.04s/ url]
Dl Size...: 39%|███▉ | 587/1501 [00:22<00:56, 16.28 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:22<00:12, 6.04s/ url]
Dl Size...: 39%|███▉ | 588/1501 [00:22<00:56, 16.28 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:22<00:12, 6.04s/ url]
Dl Size...: 39%|███▉ | 589/1501 [00:22<00:56, 16.28 MiB/s]
Dl Size...: 39%|███▉ | 590/1501 [00:22<00:53, 17.09 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:22<00:12, 6.04s/ url]
Dl Size...: 39%|███▉ | 590/1501 [00:22<00:53, 17.09 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:22<00:12, 6.04s/ url]
Dl Size...: 39%|███▉ | 591/1501 [00:22<00:53, 17.09 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:22<00:12, 6.04s/ url]
Dl Size...: 39%|███▉ | 592/1501 [00:22<00:53, 17.09 MiB/s]
Dl Size...: 40%|███▉ | 593/1501 [00:22<00:50, 17.84 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:22<00:12, 6.04s/ url]
Dl Size...: 40%|███▉ | 593/1501 [00:22<00:50, 17.84 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:22<00:12, 6.04s/ url]
Dl Size...: 40%|███▉ | 594/1501 [00:22<00:50, 17.84 MiB/s]
Dl Size...: 40%|███▉ | 595/1501 [00:22<00:51, 17.44 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:22<00:12, 6.04s/ url]
Dl Size...: 40%|███▉ | 595/1501 [00:22<00:51, 17.44 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:22<00:12, 6.04s/ url]
Dl Size...: 40%|███▉ | 596/1501 [00:22<00:51, 17.44 MiB/s]
Dl Size...: 40%|███▉ | 597/1501 [00:22<00:51, 17.67 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:22<00:12, 6.04s/ url]
Dl Size...: 40%|███▉ | 597/1501 [00:22<00:51, 17.67 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:22<00:12, 6.04s/ url]
Dl Size...: 40%|███▉ | 598/1501 [00:22<00:51, 17.67 MiB/s]
Dl Size...: 40%|███▉ | 599/1501 [00:22<00:50, 17.84 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:22<00:12, 6.04s/ url]
Dl Size...: 40%|███▉ | 599/1501 [00:22<00:50, 17.84 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:22<00:12, 6.04s/ url]
Dl Size...: 40%|███▉ | 600/1501 [00:22<00:50, 17.84 MiB/s]
Dl Size...: 40%|████ | 601/1501 [00:22<00:52, 17.03 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:22<00:12, 6.04s/ url]
Dl Size...: 40%|████ | 601/1501 [00:22<00:52, 17.03 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:22<00:12, 6.04s/ url]
Dl Size...: 40%|████ | 602/1501 [00:22<00:52, 17.03 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:22<00:12, 6.04s/ url]
Dl Size...: 40%|████ | 603/1501 [00:22<00:52, 17.03 MiB/s]
Dl Size...: 40%|████ | 604/1501 [00:23<00:51, 17.42 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:23<00:12, 6.04s/ url]
Dl Size...: 40%|████ | 604/1501 [00:23<00:51, 17.42 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:23<00:12, 6.04s/ url]
Dl Size...: 40%|████ | 605/1501 [00:23<00:51, 17.42 MiB/s]
Dl Size...: 40%|████ | 606/1501 [00:23<00:52, 17.06 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:23<00:12, 6.04s/ url]
Dl Size...: 40%|████ | 606/1501 [00:23<00:52, 17.06 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:23<00:12, 6.04s/ url]
Dl Size...: 40%|████ | 607/1501 [00:23<00:52, 17.06 MiB/s]
Dl Size...: 41%|████ | 608/1501 [00:23<00:52, 17.08 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:23<00:12, 6.04s/ url]
Dl Size...: 41%|████ | 608/1501 [00:23<00:52, 17.08 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:23<00:12, 6.04s/ url]
Dl Size...: 41%|████ | 609/1501 [00:23<00:52, 17.08 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:23<00:12, 6.04s/ url]
Dl Size...: 41%|████ | 610/1501 [00:23<00:52, 17.08 MiB/s]
Dl Size...: 41%|████ | 611/1501 [00:23<00:50, 17.70 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:23<00:12, 6.04s/ url]
Dl Size...: 41%|████ | 611/1501 [00:23<00:50, 17.70 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:23<00:12, 6.04s/ url]
Dl Size...: 41%|████ | 612/1501 [00:23<00:50, 17.70 MiB/s]
Dl Size...: 41%|████ | 613/1501 [00:23<00:49, 18.11 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:23<00:12, 6.04s/ url]
Dl Size...: 41%|████ | 613/1501 [00:23<00:49, 18.11 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:23<00:12, 6.04s/ url]
Dl Size...: 41%|████ | 614/1501 [00:23<00:48, 18.11 MiB/s]
Dl Size...: 41%|████ | 615/1501 [00:23<00:52, 17.03 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:23<00:12, 6.04s/ url]
Dl Size...: 41%|████ | 615/1501 [00:23<00:52, 17.03 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:23<00:12, 6.04s/ url]
Dl Size...: 41%|████ | 616/1501 [00:23<00:51, 17.03 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:23<00:12, 6.04s/ url]
Dl Size...: 41%|████ | 617/1501 [00:23<00:51, 17.03 MiB/s]
Dl Size...: 41%|████ | 618/1501 [00:23<00:47, 18.48 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:23<00:12, 6.04s/ url]
Dl Size...: 41%|████ | 618/1501 [00:23<00:47, 18.48 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:23<00:12, 6.04s/ url]
Dl Size...: 41%|████ | 619/1501 [00:23<00:47, 18.48 MiB/s]
Dl Size...: 41%|████▏ | 620/1501 [00:23<00:50, 17.46 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:23<00:12, 6.04s/ url]
Dl Size...: 41%|████▏ | 620/1501 [00:23<00:50, 17.46 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:23<00:12, 6.04s/ url]
Dl Size...: 41%|████▏ | 621/1501 [00:23<00:50, 17.46 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:24<00:12, 6.04s/ url]
Dl Size...: 41%|████▏ | 622/1501 [00:24<00:50, 17.46 MiB/s]
Dl Size...: 42%|████▏ | 623/1501 [00:24<00:48, 17.98 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:24<00:12, 6.04s/ url]
Dl Size...: 42%|████▏ | 623/1501 [00:24<00:48, 17.98 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:24<00:12, 6.04s/ url]
Dl Size...: 42%|████▏ | 624/1501 [00:24<00:48, 17.98 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:24<00:12, 6.04s/ url]
Dl Size...: 42%|████▏ | 625/1501 [00:24<00:48, 17.98 MiB/s]
Dl Size...: 42%|████▏ | 626/1501 [00:24<00:47, 18.31 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:24<00:12, 6.04s/ url]
Dl Size...: 42%|████▏ | 626/1501 [00:24<00:47, 18.31 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:24<00:12, 6.04s/ url]
Dl Size...: 42%|████▏ | 627/1501 [00:24<00:47, 18.31 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:24<00:12, 6.04s/ url]
Dl Size...: 42%|████▏ | 628/1501 [00:24<00:47, 18.31 MiB/s]
Dl Size...: 42%|████▏ | 629/1501 [00:24<00:46, 18.73 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:24<00:12, 6.04s/ url]
Dl Size...: 42%|████▏ | 629/1501 [00:24<00:46, 18.73 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:24<00:12, 6.04s/ url]
Dl Size...: 42%|████▏ | 630/1501 [00:24<00:46, 18.73 MiB/s]
Dl Size...: 42%|████▏ | 631/1501 [00:24<00:47, 18.46 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:24<00:12, 6.04s/ url]
Dl Size...: 42%|████▏ | 631/1501 [00:24<00:47, 18.46 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:24<00:12, 6.04s/ url]
Dl Size...: 42%|████▏ | 632/1501 [00:24<00:47, 18.46 MiB/s]
Dl Size...: 42%|████▏ | 633/1501 [00:24<00:49, 17.70 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:24<00:12, 6.04s/ url]
Dl Size...: 42%|████▏ | 633/1501 [00:24<00:49, 17.70 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:24<00:12, 6.04s/ url]
Dl Size...: 42%|████▏ | 634/1501 [00:24<00:48, 17.70 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:24<00:12, 6.04s/ url]
Dl Size...: 42%|████▏ | 635/1501 [00:24<00:48, 17.70 MiB/s]
Dl Size...: 42%|████▏ | 636/1501 [00:24<00:47, 18.10 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:24<00:12, 6.04s/ url]
Dl Size...: 42%|████▏ | 636/1501 [00:24<00:47, 18.10 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:24<00:12, 6.04s/ url]
Dl Size...: 42%|████▏ | 637/1501 [00:24<00:47, 18.10 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:24<00:12, 6.04s/ url]
Dl Size...: 43%|████▎ | 638/1501 [00:24<00:47, 18.10 MiB/s]
Dl Size...: 43%|████▎ | 639/1501 [00:25<00:47, 18.34 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:25<00:12, 6.04s/ url]
Dl Size...: 43%|████▎ | 639/1501 [00:25<00:47, 18.34 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:25<00:12, 6.04s/ url]
Dl Size...: 43%|████▎ | 640/1501 [00:25<00:46, 18.34 MiB/s]
Dl Size...: 43%|████▎ | 641/1501 [00:25<00:47, 18.21 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:25<00:12, 6.04s/ url]
Dl Size...: 43%|████▎ | 641/1501 [00:25<00:47, 18.21 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:25<00:12, 6.04s/ url]
Dl Size...: 43%|████▎ | 642/1501 [00:25<00:47, 18.21 MiB/s]
Dl Size...: 43%|████▎ | 643/1501 [00:25<00:48, 17.82 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:25<00:12, 6.04s/ url]
Dl Size...: 43%|████▎ | 643/1501 [00:25<00:48, 17.82 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:25<00:12, 6.04s/ url]
Dl Size...: 43%|████▎ | 644/1501 [00:25<00:48, 17.82 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:25<00:12, 6.04s/ url]
Dl Size...: 43%|████▎ | 645/1501 [00:25<00:48, 17.82 MiB/s]
Dl Size...: 43%|████▎ | 646/1501 [00:25<00:46, 18.48 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:25<00:12, 6.04s/ url]
Dl Size...: 43%|████▎ | 646/1501 [00:25<00:46, 18.48 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:25<00:12, 6.04s/ url]
Dl Size...: 43%|████▎ | 647/1501 [00:25<00:46, 18.48 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:25<00:12, 6.04s/ url]
Dl Size...: 43%|████▎ | 648/1501 [00:25<00:46, 18.48 MiB/s]
Dl Size...: 43%|████▎ | 649/1501 [00:25<00:43, 19.58 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:25<00:12, 6.04s/ url]
Dl Size...: 43%|████▎ | 649/1501 [00:25<00:43, 19.58 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:25<00:12, 6.04s/ url]
Dl Size...: 43%|████▎ | 650/1501 [00:25<00:43, 19.58 MiB/s]
Dl Size...: 43%|████▎ | 651/1501 [00:25<00:43, 19.57 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:25<00:12, 6.04s/ url]
Dl Size...: 43%|████▎ | 651/1501 [00:25<00:43, 19.57 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:25<00:12, 6.04s/ url]
Dl Size...: 43%|████▎ | 652/1501 [00:25<00:43, 19.57 MiB/s]
Dl Size...: 44%|████▎ | 653/1501 [00:25<00:45, 18.74 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:25<00:12, 6.04s/ url]
Dl Size...: 44%|████▎ | 653/1501 [00:25<00:45, 18.74 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:25<00:12, 6.04s/ url]
Dl Size...: 44%|████▎ | 654/1501 [00:25<00:45, 18.74 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:25<00:12, 6.04s/ url]
Dl Size...: 44%|████▎ | 655/1501 [00:25<00:45, 18.74 MiB/s]
Dl Size...: 44%|████▎ | 656/1501 [00:25<00:44, 19.20 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:25<00:12, 6.04s/ url]
Dl Size...: 44%|████▎ | 656/1501 [00:25<00:44, 19.20 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:25<00:12, 6.04s/ url]
Dl Size...: 44%|████▍ | 657/1501 [00:25<00:43, 19.20 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:25<00:12, 6.04s/ url]
Dl Size...: 44%|████▍ | 658/1501 [00:25<00:43, 19.20 MiB/s]
Dl Size...: 44%|████▍ | 659/1501 [00:26<00:44, 19.08 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:26<00:12, 6.04s/ url]
Dl Size...: 44%|████▍ | 659/1501 [00:26<00:44, 19.08 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:26<00:12, 6.04s/ url]
Dl Size...: 44%|████▍ | 660/1501 [00:26<00:44, 19.08 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:26<00:12, 6.04s/ url]
Dl Size...: 44%|████▍ | 661/1501 [00:26<00:44, 19.08 MiB/s]
Dl Size...: 44%|████▍ | 662/1501 [00:26<00:44, 19.01 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:26<00:12, 6.04s/ url]
Dl Size...: 44%|████▍ | 662/1501 [00:26<00:44, 19.01 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:26<00:12, 6.04s/ url]
Dl Size...: 44%|████▍ | 663/1501 [00:26<00:44, 19.01 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:26<00:12, 6.04s/ url]
Dl Size...: 44%|████▍ | 664/1501 [00:26<00:44, 19.01 MiB/s]
Dl Size...: 44%|████▍ | 665/1501 [00:26<00:42, 19.56 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:26<00:12, 6.04s/ url]
Dl Size...: 44%|████▍ | 665/1501 [00:26<00:42, 19.56 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:26<00:12, 6.04s/ url]
Dl Size...: 44%|████▍ | 666/1501 [00:26<00:42, 19.56 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:26<00:12, 6.04s/ url]
Dl Size...: 44%|████▍ | 667/1501 [00:26<00:42, 19.56 MiB/s]
Dl Size...: 45%|████▍ | 668/1501 [00:26<00:40, 20.57 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:26<00:12, 6.04s/ url]
Dl Size...: 45%|████▍ | 668/1501 [00:26<00:40, 20.57 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:26<00:12, 6.04s/ url]
Dl Size...: 45%|████▍ | 669/1501 [00:26<00:40, 20.57 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:26<00:12, 6.04s/ url]
Dl Size...: 45%|████▍ | 670/1501 [00:26<00:40, 20.57 MiB/s]
Dl Size...: 45%|████▍ | 671/1501 [00:26<00:40, 20.67 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:26<00:12, 6.04s/ url]
Dl Size...: 45%|████▍ | 671/1501 [00:26<00:40, 20.67 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:26<00:12, 6.04s/ url]
Dl Size...: 45%|████▍ | 672/1501 [00:26<00:40, 20.67 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:26<00:12, 6.04s/ url]
Dl Size...: 45%|████▍ | 673/1501 [00:26<00:40, 20.67 MiB/s]
Dl Size...: 45%|████▍ | 674/1501 [00:26<00:40, 20.30 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:26<00:12, 6.04s/ url]
Dl Size...: 45%|████▍ | 674/1501 [00:26<00:40, 20.30 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:26<00:12, 6.04s/ url]
Dl Size...: 45%|████▍ | 675/1501 [00:26<00:40, 20.30 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:26<00:12, 6.04s/ url]
Dl Size...: 45%|████▌ | 676/1501 [00:26<00:40, 20.30 MiB/s]
Dl Size...: 45%|████▌ | 677/1501 [00:26<00:41, 19.87 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:26<00:12, 6.04s/ url]
Dl Size...: 45%|████▌ | 677/1501 [00:26<00:41, 19.87 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:26<00:12, 6.04s/ url]
Dl Size...: 45%|████▌ | 678/1501 [00:26<00:41, 19.87 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:27<00:12, 6.04s/ url]
Dl Size...: 45%|████▌ | 679/1501 [00:27<00:41, 19.87 MiB/s]
Dl Size...: 45%|████▌ | 680/1501 [00:27<00:40, 20.41 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:27<00:12, 6.04s/ url]
Dl Size...: 45%|████▌ | 680/1501 [00:27<00:40, 20.41 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:27<00:12, 6.04s/ url]
Dl Size...: 45%|████▌ | 681/1501 [00:27<00:40, 20.41 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:27<00:12, 6.04s/ url]
Dl Size...: 45%|████▌ | 682/1501 [00:27<00:40, 20.41 MiB/s]
Dl Size...: 46%|████▌ | 683/1501 [00:27<00:38, 21.33 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:27<00:12, 6.04s/ url]
Dl Size...: 46%|████▌ | 683/1501 [00:27<00:38, 21.33 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:27<00:12, 6.04s/ url]
Dl Size...: 46%|████▌ | 684/1501 [00:27<00:38, 21.33 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:27<00:12, 6.04s/ url]
Dl Size...: 46%|████▌ | 685/1501 [00:27<00:38, 21.33 MiB/s]
Dl Size...: 46%|████▌ | 686/1501 [00:27<00:38, 21.21 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:27<00:12, 6.04s/ url]
Dl Size...: 46%|████▌ | 686/1501 [00:27<00:38, 21.21 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:27<00:12, 6.04s/ url]
Dl Size...: 46%|████▌ | 687/1501 [00:27<00:38, 21.21 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:27<00:12, 6.04s/ url]
Dl Size...: 46%|████▌ | 688/1501 [00:27<00:38, 21.21 MiB/s]
Dl Size...: 46%|████▌ | 689/1501 [00:27<00:39, 20.68 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:27<00:12, 6.04s/ url]
Dl Size...: 46%|████▌ | 689/1501 [00:27<00:39, 20.68 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:27<00:12, 6.04s/ url]
Dl Size...: 46%|████▌ | 690/1501 [00:27<00:39, 20.68 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:27<00:12, 6.04s/ url]
Dl Size...: 46%|████▌ | 691/1501 [00:27<00:39, 20.68 MiB/s]
Dl Size...: 46%|████▌ | 692/1501 [00:27<00:38, 21.04 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:27<00:12, 6.04s/ url]
Dl Size...: 46%|████▌ | 692/1501 [00:27<00:38, 21.04 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:27<00:12, 6.04s/ url]
Dl Size...: 46%|████▌ | 693/1501 [00:27<00:38, 21.04 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:27<00:12, 6.04s/ url]
Dl Size...: 46%|████▌ | 694/1501 [00:27<00:38, 21.04 MiB/s]
Dl Size...: 46%|████▋ | 695/1501 [00:27<00:35, 22.42 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:27<00:12, 6.04s/ url]
Dl Size...: 46%|████▋ | 695/1501 [00:27<00:35, 22.42 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:27<00:12, 6.04s/ url]
Dl Size...: 46%|████▋ | 696/1501 [00:27<00:35, 22.42 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:27<00:12, 6.04s/ url]
Dl Size...: 46%|████▋ | 697/1501 [00:27<00:35, 22.42 MiB/s]
Dl Size...: 47%|████▋ | 698/1501 [00:27<00:36, 22.30 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:27<00:12, 6.04s/ url]
Dl Size...: 47%|████▋ | 698/1501 [00:27<00:36, 22.30 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:27<00:12, 6.04s/ url]
Dl Size...: 47%|████▋ | 699/1501 [00:27<00:35, 22.30 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:27<00:12, 6.04s/ url]
Dl Size...: 47%|████▋ | 700/1501 [00:27<00:35, 22.30 MiB/s]
Dl Size...: 47%|████▋ | 701/1501 [00:27<00:33, 23.62 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:27<00:12, 6.04s/ url]
Dl Size...: 47%|████▋ | 701/1501 [00:27<00:33, 23.62 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:28<00:12, 6.04s/ url]
Dl Size...: 47%|████▋ | 702/1501 [00:28<00:33, 23.62 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:28<00:12, 6.04s/ url]
Dl Size...: 47%|████▋ | 703/1501 [00:28<00:33, 23.62 MiB/s]
Dl Size...: 47%|████▋ | 704/1501 [00:28<00:33, 23.46 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:28<00:12, 6.04s/ url]
Dl Size...: 47%|████▋ | 704/1501 [00:28<00:33, 23.46 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:28<00:12, 6.04s/ url]
Dl Size...: 47%|████▋ | 705/1501 [00:28<00:33, 23.46 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:28<00:12, 6.04s/ url]
Dl Size...: 47%|████▋ | 706/1501 [00:28<00:33, 23.46 MiB/s]
Dl Size...: 47%|████▋ | 707/1501 [00:28<00:32, 24.52 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:28<00:12, 6.04s/ url]
Dl Size...: 47%|████▋ | 707/1501 [00:28<00:32, 24.52 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:28<00:12, 6.04s/ url]
Dl Size...: 47%|████▋ | 708/1501 [00:28<00:32, 24.52 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:28<00:12, 6.04s/ url]
Dl Size...: 47%|████▋ | 709/1501 [00:28<00:32, 24.52 MiB/s]
Dl Size...: 47%|████▋ | 710/1501 [00:28<00:34, 22.93 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:28<00:12, 6.04s/ url]
Dl Size...: 47%|████▋ | 710/1501 [00:28<00:34, 22.93 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:28<00:12, 6.04s/ url]
Dl Size...: 47%|████▋ | 711/1501 [00:28<00:34, 22.93 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:28<00:12, 6.04s/ url]
Dl Size...: 47%|████▋ | 712/1501 [00:28<00:34, 22.93 MiB/s]
Dl Size...: 48%|████▊ | 713/1501 [00:28<00:33, 23.69 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:28<00:12, 6.04s/ url]
Dl Size...: 48%|████▊ | 713/1501 [00:28<00:33, 23.69 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:28<00:12, 6.04s/ url]
Dl Size...: 48%|████▊ | 714/1501 [00:28<00:33, 23.69 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:28<00:12, 6.04s/ url]
Dl Size...: 48%|████▊ | 715/1501 [00:28<00:33, 23.69 MiB/s]
Dl Size...: 48%|████▊ | 716/1501 [00:28<00:33, 23.38 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:28<00:12, 6.04s/ url]
Dl Size...: 48%|████▊ | 716/1501 [00:28<00:33, 23.38 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:28<00:12, 6.04s/ url]
Dl Size...: 48%|████▊ | 717/1501 [00:28<00:33, 23.38 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:28<00:12, 6.04s/ url]
Dl Size...: 48%|████▊ | 718/1501 [00:28<00:33, 23.38 MiB/s]
Dl Size...: 48%|████▊ | 719/1501 [00:28<00:32, 23.90 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:28<00:12, 6.04s/ url]
Dl Size...: 48%|████▊ | 719/1501 [00:28<00:32, 23.90 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:28<00:12, 6.04s/ url]
Dl Size...: 48%|████▊ | 720/1501 [00:28<00:32, 23.90 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:28<00:12, 6.04s/ url]
Dl Size...: 48%|████▊ | 721/1501 [00:28<00:32, 23.90 MiB/s]
Dl Size...: 48%|████▊ | 722/1501 [00:28<00:32, 24.15 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:28<00:12, 6.04s/ url]
Dl Size...: 48%|████▊ | 722/1501 [00:28<00:32, 24.15 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:28<00:12, 6.04s/ url]
Dl Size...: 48%|████▊ | 723/1501 [00:28<00:32, 24.15 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:28<00:12, 6.04s/ url]
Dl Size...: 48%|████▊ | 724/1501 [00:28<00:32, 24.15 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:28<00:12, 6.04s/ url]
Dl Size...: 48%|████▊ | 725/1501 [00:28<00:32, 24.15 MiB/s]
Dl Size...: 48%|████▊ | 726/1501 [00:29<00:31, 24.69 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:29<00:12, 6.04s/ url]
Dl Size...: 48%|████▊ | 726/1501 [00:29<00:31, 24.69 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:29<00:12, 6.04s/ url]
Dl Size...: 48%|████▊ | 727/1501 [00:29<00:31, 24.69 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:29<00:12, 6.04s/ url]
Dl Size...: 49%|████▊ | 728/1501 [00:29<00:31, 24.69 MiB/s]
Dl Size...: 49%|████▊ | 729/1501 [00:29<00:30, 25.23 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:29<00:12, 6.04s/ url]
Dl Size...: 49%|████▊ | 729/1501 [00:29<00:30, 25.23 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:29<00:12, 6.04s/ url]
Dl Size...: 49%|████▊ | 730/1501 [00:29<00:30, 25.23 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:29<00:12, 6.04s/ url]
Dl Size...: 49%|████▊ | 731/1501 [00:29<00:30, 25.23 MiB/s]
Dl Size...: 49%|████▉ | 732/1501 [00:29<00:29, 25.94 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:29<00:12, 6.04s/ url]
Dl Size...: 49%|████▉ | 732/1501 [00:29<00:29, 25.94 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:29<00:12, 6.04s/ url]
Dl Size...: 49%|████▉ | 733/1501 [00:29<00:29, 25.94 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:29<00:12, 6.04s/ url]
Dl Size...: 49%|████▉ | 734/1501 [00:29<00:29, 25.94 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:29<00:12, 6.04s/ url]
Dl Size...: 49%|████▉ | 735/1501 [00:29<00:29, 25.94 MiB/s]
Dl Size...: 49%|████▉ | 736/1501 [00:29<00:29, 26.21 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:29<00:12, 6.04s/ url]
Dl Size...: 49%|████▉ | 736/1501 [00:29<00:29, 26.21 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:29<00:12, 6.04s/ url]
Dl Size...: 49%|████▉ | 737/1501 [00:29<00:29, 26.21 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:29<00:12, 6.04s/ url]
Dl Size...: 49%|████▉ | 738/1501 [00:29<00:29, 26.21 MiB/s]
Dl Size...: 49%|████▉ | 739/1501 [00:29<00:29, 25.91 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:29<00:12, 6.04s/ url]
Dl Size...: 49%|████▉ | 739/1501 [00:29<00:29, 25.91 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:29<00:12, 6.04s/ url]
Dl Size...: 49%|████▉ | 740/1501 [00:29<00:29, 25.91 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:29<00:12, 6.04s/ url]
Dl Size...: 49%|████▉ | 741/1501 [00:29<00:29, 25.91 MiB/s]
Dl Size...: 49%|████▉ | 742/1501 [00:29<00:28, 26.43 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:29<00:12, 6.04s/ url]
Dl Size...: 49%|████▉ | 742/1501 [00:29<00:28, 26.43 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:29<00:12, 6.04s/ url]
Dl Size...: 50%|████▉ | 743/1501 [00:29<00:28, 26.43 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:29<00:12, 6.04s/ url]
Dl Size...: 50%|████▉ | 744/1501 [00:29<00:28, 26.43 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:29<00:12, 6.04s/ url]
Dl Size...: 50%|████▉ | 745/1501 [00:29<00:28, 26.43 MiB/s]
Dl Size...: 50%|████▉ | 746/1501 [00:29<00:27, 27.61 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:29<00:12, 6.04s/ url]
Dl Size...: 50%|████▉ | 746/1501 [00:29<00:27, 27.61 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:29<00:12, 6.04s/ url]
Dl Size...: 50%|████▉ | 747/1501 [00:29<00:27, 27.61 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:29<00:12, 6.04s/ url]
Dl Size...: 50%|████▉ | 748/1501 [00:29<00:27, 27.61 MiB/s]
Dl Size...: 50%|████▉ | 749/1501 [00:29<00:28, 26.79 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:29<00:12, 6.04s/ url]
Dl Size...: 50%|████▉ | 749/1501 [00:29<00:28, 26.79 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:29<00:12, 6.04s/ url]
Dl Size...: 50%|████▉ | 750/1501 [00:29<00:28, 26.79 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:29<00:12, 6.04s/ url]
Dl Size...: 50%|█████ | 751/1501 [00:29<00:27, 26.79 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:29<00:12, 6.04s/ url]
Dl Size...: 50%|█████ | 752/1501 [00:29<00:27, 26.79 MiB/s]
Dl Size...: 50%|█████ | 753/1501 [00:29<00:25, 28.95 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:30<00:12, 6.04s/ url]
Dl Size...: 50%|█████ | 753/1501 [00:29<00:25, 28.95 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:30<00:12, 6.04s/ url]
Dl Size...: 50%|█████ | 754/1501 [00:30<00:25, 28.95 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:30<00:12, 6.04s/ url]
Dl Size...: 50%|█████ | 755/1501 [00:30<00:25, 28.95 MiB/s]
Dl Size...: 50%|█████ | 756/1501 [00:30<00:26, 28.19 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:30<00:12, 6.04s/ url]
Dl Size...: 50%|█████ | 756/1501 [00:30<00:26, 28.19 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:30<00:12, 6.04s/ url]
Dl Size...: 50%|█████ | 757/1501 [00:30<00:26, 28.19 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:30<00:12, 6.04s/ url]
Dl Size...: 50%|█████ | 758/1501 [00:30<00:26, 28.19 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:30<00:12, 6.04s/ url]
Dl Size...: 51%|█████ | 759/1501 [00:30<00:26, 28.19 MiB/s]
Dl Size...: 51%|█████ | 760/1501 [00:30<00:25, 29.33 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:30<00:12, 6.04s/ url]
Dl Size...: 51%|█████ | 760/1501 [00:30<00:25, 29.33 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:30<00:12, 6.04s/ url]
Dl Size...: 51%|█████ | 761/1501 [00:30<00:25, 29.33 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:30<00:12, 6.04s/ url]
Dl Size...: 51%|█████ | 762/1501 [00:30<00:25, 29.33 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:30<00:12, 6.04s/ url]
Dl Size...: 51%|█████ | 763/1501 [00:30<00:25, 29.33 MiB/s]
Dl Size...: 51%|█████ | 764/1501 [00:30<00:23, 31.65 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:30<00:12, 6.04s/ url]
Dl Size...: 51%|█████ | 764/1501 [00:30<00:23, 31.65 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:30<00:12, 6.04s/ url]
Dl Size...: 51%|█████ | 765/1501 [00:30<00:23, 31.65 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:30<00:12, 6.04s/ url]
Dl Size...: 51%|█████ | 766/1501 [00:30<00:23, 31.65 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:30<00:12, 6.04s/ url]
Dl Size...: 51%|█████ | 767/1501 [00:30<00:23, 31.65 MiB/s]
Dl Size...: 51%|█████ | 768/1501 [00:30<00:24, 29.75 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:30<00:12, 6.04s/ url]
Dl Size...: 51%|█████ | 768/1501 [00:30<00:24, 29.75 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:30<00:12, 6.04s/ url]
Dl Size...: 51%|█████ | 769/1501 [00:30<00:24, 29.75 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:30<00:12, 6.04s/ url]
Dl Size...: 51%|█████▏ | 770/1501 [00:30<00:24, 29.75 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:30<00:12, 6.04s/ url]
Dl Size...: 51%|█████▏ | 771/1501 [00:30<00:24, 29.75 MiB/s]
Dl Size...: 51%|█████▏ | 772/1501 [00:30<00:24, 30.04 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:30<00:12, 6.04s/ url]
Dl Size...: 51%|█████▏ | 772/1501 [00:30<00:24, 30.04 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:30<00:16, 16.96s/ url]
Dl Completed...: 67%|██████▋ | 2/3 [00:30<00:16, 16.96s/ url]
Dl Size...: 51%|█████▏ | 772/1501 [00:30<00:24, 30.04 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:30<00:16, 16.96s/ url]
Dl Size...: 51%|█████▏ | 773/1501 [00:30<00:24, 30.04 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:30<00:16, 16.96s/ url]
Dl Size...: 52%|█████▏ | 774/1501 [00:30<00:24, 30.04 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:30<00:16, 16.96s/ url]
Dl Size...: 52%|█████▏ | 775/1501 [00:30<00:24, 30.04 MiB/s]
Dl Size...: 52%|█████▏ | 776/1501 [00:30<00:28, 25.19 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:30<00:16, 16.96s/ url]
Dl Size...: 52%|█████▏ | 776/1501 [00:30<00:28, 25.19 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:30<00:16, 16.96s/ url]
Dl Size...: 52%|█████▏ | 777/1501 [00:30<00:28, 25.19 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:30<00:16, 16.96s/ url]
Dl Size...: 52%|█████▏ | 778/1501 [00:30<00:28, 25.19 MiB/s]
Dl Size...: 52%|█████▏ | 779/1501 [00:31<00:31, 23.18 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:31<00:16, 16.96s/ url]
Dl Size...: 52%|█████▏ | 779/1501 [00:31<00:31, 23.18 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:31<00:16, 16.96s/ url]
Dl Size...: 52%|█████▏ | 780/1501 [00:31<00:31, 23.18 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:31<00:16, 16.96s/ url]
Dl Size...: 52%|█████▏ | 781/1501 [00:31<00:31, 23.18 MiB/s]
Dl Size...: 52%|█████▏ | 782/1501 [00:31<00:32, 22.01 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:31<00:16, 16.96s/ url]
Dl Size...: 52%|█████▏ | 782/1501 [00:31<00:32, 22.01 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:31<00:16, 16.96s/ url]
Dl Size...: 52%|█████▏ | 783/1501 [00:31<00:32, 22.01 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:31<00:16, 16.96s/ url]
Dl Size...: 52%|█████▏ | 784/1501 [00:31<00:32, 22.01 MiB/s]
Dl Size...: 52%|█████▏ | 785/1501 [00:31<00:32, 21.81 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:31<00:16, 16.96s/ url]
Dl Size...: 52%|█████▏ | 785/1501 [00:31<00:32, 21.81 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:31<00:16, 16.96s/ url]
Dl Size...: 52%|█████▏ | 786/1501 [00:31<00:32, 21.81 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:31<00:16, 16.96s/ url]
Dl Size...: 52%|█████▏ | 787/1501 [00:31<00:32, 21.81 MiB/s]
Dl Size...: 52%|█████▏ | 788/1501 [00:31<00:38, 18.76 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:31<00:16, 16.96s/ url]
Dl Size...: 52%|█████▏ | 788/1501 [00:31<00:38, 18.76 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:31<00:16, 16.96s/ url]
Dl Size...: 53%|█████▎ | 789/1501 [00:31<00:37, 18.76 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:31<00:16, 16.96s/ url]
Dl Size...: 53%|█████▎ | 790/1501 [00:31<00:37, 18.76 MiB/s]
Dl Size...: 53%|█████▎ | 791/1501 [00:31<00:37, 19.16 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:31<00:16, 16.96s/ url]
Dl Size...: 53%|█████▎ | 791/1501 [00:31<00:37, 19.16 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:31<00:16, 16.96s/ url]
Dl Size...: 53%|█████▎ | 792/1501 [00:31<00:36, 19.16 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:31<00:16, 16.96s/ url]
Dl Size...: 53%|█████▎ | 793/1501 [00:31<00:36, 19.16 MiB/s]
Dl Size...: 53%|█████▎ | 794/1501 [00:31<00:37, 19.07 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:31<00:16, 16.96s/ url]
Dl Size...: 53%|█████▎ | 794/1501 [00:31<00:37, 19.07 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:31<00:16, 16.96s/ url]
Dl Size...: 53%|█████▎ | 795/1501 [00:31<00:37, 19.07 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:31<00:16, 16.96s/ url]
Dl Size...: 53%|█████▎ | 796/1501 [00:31<00:36, 19.07 MiB/s]
Dl Size...: 53%|█████▎ | 797/1501 [00:31<00:36, 19.05 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:31<00:16, 16.96s/ url]
Dl Size...: 53%|█████▎ | 797/1501 [00:31<00:36, 19.05 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:31<00:16, 16.96s/ url]
Dl Size...: 53%|█████▎ | 798/1501 [00:31<00:36, 19.05 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:32<00:16, 16.96s/ url]
Dl Size...: 53%|█████▎ | 799/1501 [00:32<00:36, 19.05 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:32<00:16, 16.96s/ url]
Dl Size...: 53%|█████▎ | 800/1501 [00:32<00:36, 19.05 MiB/s]
Dl Size...: 53%|█████▎ | 801/1501 [00:32<00:34, 20.46 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:32<00:16, 16.96s/ url]
Dl Size...: 53%|█████▎ | 801/1501 [00:32<00:34, 20.46 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:32<00:16, 16.96s/ url]
Dl Size...: 53%|█████▎ | 802/1501 [00:32<00:34, 20.46 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:32<00:16, 16.96s/ url]
Dl Size...: 53%|█████▎ | 803/1501 [00:32<00:34, 20.46 MiB/s]
Dl Size...: 54%|█████▎ | 804/1501 [00:32<00:34, 20.11 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:32<00:16, 16.96s/ url]
Dl Size...: 54%|█████▎ | 804/1501 [00:32<00:34, 20.11 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:32<00:16, 16.96s/ url]
Dl Size...: 54%|█████▎ | 805/1501 [00:32<00:34, 20.11 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:32<00:16, 16.96s/ url]
Dl Size...: 54%|█████▎ | 806/1501 [00:32<00:34, 20.11 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:32<00:16, 16.96s/ url]
Dl Size...: 54%|█████▍ | 807/1501 [00:32<00:34, 20.11 MiB/s]
Dl Size...: 54%|█████▍ | 808/1501 [00:32<00:32, 21.23 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:32<00:16, 16.96s/ url]
Dl Size...: 54%|█████▍ | 808/1501 [00:32<00:32, 21.23 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:32<00:16, 16.96s/ url]
Dl Size...: 54%|█████▍ | 809/1501 [00:32<00:32, 21.23 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:32<00:16, 16.96s/ url]
Dl Size...: 54%|█████▍ | 810/1501 [00:32<00:32, 21.23 MiB/s]
Dl Size...: 54%|█████▍ | 811/1501 [00:32<00:33, 20.78 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:32<00:16, 16.96s/ url]
Dl Size...: 54%|█████▍ | 811/1501 [00:32<00:33, 20.78 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:32<00:16, 16.96s/ url]
Dl Size...: 54%|█████▍ | 812/1501 [00:32<00:33, 20.78 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:32<00:16, 16.96s/ url]
Dl Size...: 54%|█████▍ | 813/1501 [00:32<00:33, 20.78 MiB/s]
Dl Size...: 54%|█████▍ | 814/1501 [00:32<00:30, 22.71 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:32<00:16, 16.96s/ url]
Dl Size...: 54%|█████▍ | 814/1501 [00:32<00:30, 22.71 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:32<00:16, 16.96s/ url]
Dl Size...: 54%|█████▍ | 815/1501 [00:32<00:30, 22.71 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:32<00:16, 16.96s/ url]
Dl Size...: 54%|█████▍ | 816/1501 [00:32<00:30, 22.71 MiB/s]
Dl Size...: 54%|█████▍ | 817/1501 [00:32<00:31, 21.84 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:32<00:16, 16.96s/ url]
Dl Size...: 54%|█████▍ | 817/1501 [00:32<00:31, 21.84 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:32<00:16, 16.96s/ url]
Dl Size...: 54%|█████▍ | 818/1501 [00:32<00:31, 21.84 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:32<00:16, 16.96s/ url]
Dl Size...: 55%|█████▍ | 819/1501 [00:32<00:31, 21.84 MiB/s]
Dl Size...: 55%|█████▍ | 820/1501 [00:33<00:31, 21.31 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:33<00:16, 16.96s/ url]
Dl Size...: 55%|█████▍ | 820/1501 [00:33<00:31, 21.31 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:33<00:16, 16.96s/ url]
Dl Size...: 55%|█████▍ | 821/1501 [00:33<00:31, 21.31 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:33<00:16, 16.96s/ url]
Dl Size...: 55%|█████▍ | 822/1501 [00:33<00:31, 21.31 MiB/s]
Dl Size...: 55%|█████▍ | 823/1501 [00:33<00:29, 23.14 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:33<00:16, 16.96s/ url]
Dl Size...: 55%|█████▍ | 823/1501 [00:33<00:29, 23.14 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:33<00:16, 16.96s/ url]
Dl Size...: 55%|█████▍ | 824/1501 [00:33<00:29, 23.14 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:33<00:16, 16.96s/ url]
Dl Size...: 55%|█████▍ | 825/1501 [00:33<00:29, 23.14 MiB/s]
Dl Size...: 55%|█████▌ | 826/1501 [00:33<00:30, 22.24 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:33<00:16, 16.96s/ url]
Dl Size...: 55%|█████▌ | 826/1501 [00:33<00:30, 22.24 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:33<00:16, 16.96s/ url]
Dl Size...: 55%|█████▌ | 827/1501 [00:33<00:30, 22.24 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:33<00:16, 16.96s/ url]
Dl Size...: 55%|█████▌ | 828/1501 [00:33<00:30, 22.24 MiB/s]
Dl Size...: 55%|█████▌ | 829/1501 [00:33<00:27, 24.02 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:33<00:16, 16.96s/ url]
Dl Size...: 55%|█████▌ | 829/1501 [00:33<00:27, 24.02 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:33<00:16, 16.96s/ url]
Dl Size...: 55%|█████▌ | 830/1501 [00:33<00:27, 24.02 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:33<00:16, 16.96s/ url]
Dl Size...: 55%|█████▌ | 831/1501 [00:33<00:27, 24.02 MiB/s]
Dl Size...: 55%|█████▌ | 832/1501 [00:33<00:29, 22.90 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:33<00:16, 16.96s/ url]
Dl Size...: 55%|█████▌ | 832/1501 [00:33<00:29, 22.90 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:33<00:16, 16.96s/ url]
Dl Size...: 55%|█████▌ | 833/1501 [00:33<00:29, 22.90 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:33<00:16, 16.96s/ url]
Dl Size...: 56%|█████▌ | 834/1501 [00:33<00:29, 22.90 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:33<00:16, 16.96s/ url]
Dl Size...: 56%|█████▌ | 835/1501 [00:33<00:29, 22.90 MiB/s]
Dl Size...: 56%|█████▌ | 836/1501 [00:33<00:28, 23.74 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:33<00:16, 16.96s/ url]
Dl Size...: 56%|█████▌ | 836/1501 [00:33<00:28, 23.74 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:33<00:16, 16.96s/ url]
Dl Size...: 56%|█████▌ | 837/1501 [00:33<00:27, 23.74 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:33<00:16, 16.96s/ url]
Dl Size...: 56%|█████▌ | 838/1501 [00:33<00:27, 23.74 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:33<00:16, 16.96s/ url]
Dl Size...: 56%|█████▌ | 839/1501 [00:33<00:27, 23.74 MiB/s]
Dl Size...: 56%|█████▌ | 840/1501 [00:33<00:27, 24.33 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:33<00:16, 16.96s/ url]
Dl Size...: 56%|█████▌ | 840/1501 [00:33<00:27, 24.33 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:33<00:16, 16.96s/ url]
Dl Size...: 56%|█████▌ | 841/1501 [00:33<00:27, 24.33 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:33<00:16, 16.96s/ url]
Dl Size...: 56%|█████▌ | 842/1501 [00:33<00:27, 24.33 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:33<00:16, 16.96s/ url]
Dl Size...: 56%|█████▌ | 843/1501 [00:33<00:27, 24.33 MiB/s]
Dl Size...: 56%|█████▌ | 844/1501 [00:33<00:26, 24.80 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:33<00:16, 16.96s/ url]
Dl Size...: 56%|█████▌ | 844/1501 [00:33<00:26, 24.80 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:34<00:16, 16.96s/ url]
Dl Size...: 56%|█████▋ | 845/1501 [00:34<00:26, 24.80 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:34<00:16, 16.96s/ url]
Dl Size...: 56%|█████▋ | 846/1501 [00:34<00:26, 24.80 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:34<00:16, 16.96s/ url]
Dl Size...: 56%|█████▋ | 847/1501 [00:34<00:26, 24.80 MiB/s]
Dl Size...: 56%|█████▋ | 848/1501 [00:34<00:24, 26.72 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:34<00:16, 16.96s/ url]
Dl Size...: 56%|█████▋ | 848/1501 [00:34<00:24, 26.72 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:34<00:16, 16.96s/ url]
Dl Size...: 57%|█████▋ | 849/1501 [00:34<00:24, 26.72 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:34<00:16, 16.96s/ url]
Dl Size...: 57%|█████▋ | 850/1501 [00:34<00:24, 26.72 MiB/s]
Dl Size...: 57%|█████▋ | 851/1501 [00:34<00:25, 25.52 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:34<00:16, 16.96s/ url]
Dl Size...: 57%|█████▋ | 851/1501 [00:34<00:25, 25.52 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:34<00:16, 16.96s/ url]
Dl Size...: 57%|█████▋ | 852/1501 [00:34<00:25, 25.52 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:34<00:16, 16.96s/ url]
Dl Size...: 57%|█████▋ | 853/1501 [00:34<00:25, 25.52 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:34<00:16, 16.96s/ url]
Dl Size...: 57%|█████▋ | 854/1501 [00:34<00:25, 25.52 MiB/s]
Dl Size...: 57%|█████▋ | 855/1501 [00:34<00:24, 26.04 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:34<00:16, 16.96s/ url]
Dl Size...: 57%|█████▋ | 855/1501 [00:34<00:24, 26.04 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:34<00:16, 16.96s/ url]
Dl Size...: 57%|█████▋ | 856/1501 [00:34<00:24, 26.04 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:34<00:16, 16.96s/ url]
Dl Size...: 57%|█████▋ | 857/1501 [00:34<00:24, 26.04 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:34<00:16, 16.96s/ url]
Dl Size...: 57%|█████▋ | 858/1501 [00:34<00:24, 26.04 MiB/s]
Dl Size...: 57%|█████▋ | 859/1501 [00:34<00:22, 28.51 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:34<00:16, 16.96s/ url]
Dl Size...: 57%|█████▋ | 859/1501 [00:34<00:22, 28.51 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:34<00:16, 16.96s/ url]
Dl Size...: 57%|█████▋ | 860/1501 [00:34<00:22, 28.51 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:34<00:16, 16.96s/ url]
Dl Size...: 57%|█████▋ | 861/1501 [00:34<00:22, 28.51 MiB/s]
Dl Size...: 57%|█████▋ | 862/1501 [00:34<00:24, 26.35 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:34<00:16, 16.96s/ url]
Dl Size...: 57%|█████▋ | 862/1501 [00:34<00:24, 26.35 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:34<00:16, 16.96s/ url]
Dl Size...: 57%|█████▋ | 863/1501 [00:34<00:24, 26.35 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:34<00:16, 16.96s/ url]
Dl Size...: 58%|█████▊ | 864/1501 [00:34<00:24, 26.35 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:34<00:16, 16.96s/ url]
Dl Size...: 58%|█████▊ | 865/1501 [00:34<00:24, 26.35 MiB/s]
Dl Size...: 58%|█████▊ | 866/1501 [00:34<00:23, 26.93 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:34<00:16, 16.96s/ url]
Dl Size...: 58%|█████▊ | 866/1501 [00:34<00:23, 26.93 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:34<00:16, 16.96s/ url]
Dl Size...: 58%|█████▊ | 867/1501 [00:34<00:23, 26.93 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:34<00:16, 16.96s/ url]
Dl Size...: 58%|█████▊ | 868/1501 [00:34<00:23, 26.93 MiB/s]
Dl Size...: 58%|█████▊ | 869/1501 [00:34<00:22, 27.54 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:34<00:16, 16.96s/ url]
Dl Size...: 58%|█████▊ | 869/1501 [00:34<00:22, 27.54 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:34<00:16, 16.96s/ url]
Dl Size...: 58%|█████▊ | 870/1501 [00:34<00:22, 27.54 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:34<00:16, 16.96s/ url]
Dl Size...: 58%|█████▊ | 871/1501 [00:34<00:22, 27.54 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:34<00:16, 16.96s/ url]
Dl Size...: 58%|█████▊ | 872/1501 [00:34<00:22, 27.54 MiB/s]
Dl Size...: 58%|█████▊ | 873/1501 [00:35<00:20, 29.92 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:35<00:16, 16.96s/ url]
Dl Size...: 58%|█████▊ | 873/1501 [00:35<00:20, 29.92 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:35<00:16, 16.96s/ url]
Dl Size...: 58%|█████▊ | 874/1501 [00:35<00:20, 29.92 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:35<00:16, 16.96s/ url]
Dl Size...: 58%|█████▊ | 875/1501 [00:35<00:20, 29.92 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:35<00:16, 16.96s/ url]
Dl Size...: 58%|█████▊ | 876/1501 [00:35<00:20, 29.92 MiB/s]
Dl Size...: 58%|█████▊ | 877/1501 [00:35<00:21, 29.37 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:35<00:16, 16.96s/ url]
Dl Size...: 58%|█████▊ | 877/1501 [00:35<00:21, 29.37 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:35<00:16, 16.96s/ url]
Dl Size...: 58%|█████▊ | 878/1501 [00:35<00:21, 29.37 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:35<00:16, 16.96s/ url]
Dl Size...: 59%|█████▊ | 879/1501 [00:35<00:21, 29.37 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:35<00:16, 16.96s/ url]
Dl Size...: 59%|█████▊ | 880/1501 [00:35<00:21, 29.37 MiB/s]
Dl Size...: 59%|█████▊ | 881/1501 [00:35<00:21, 28.88 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:35<00:16, 16.96s/ url]
Dl Size...: 59%|█████▊ | 881/1501 [00:35<00:21, 28.88 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:35<00:16, 16.96s/ url]
Dl Size...: 59%|█████▉ | 882/1501 [00:35<00:21, 28.88 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:35<00:16, 16.96s/ url]
Dl Size...: 59%|█████▉ | 883/1501 [00:35<00:21, 28.88 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:35<00:16, 16.96s/ url]
Dl Size...: 59%|█████▉ | 884/1501 [00:35<00:21, 28.88 MiB/s]
Dl Size...: 59%|█████▉ | 885/1501 [00:35<00:19, 31.26 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:35<00:16, 16.96s/ url]
Dl Size...: 59%|█████▉ | 885/1501 [00:35<00:19, 31.26 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:35<00:16, 16.96s/ url]
Dl Size...: 59%|█████▉ | 886/1501 [00:35<00:19, 31.26 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:35<00:16, 16.96s/ url]
Dl Size...: 59%|█████▉ | 887/1501 [00:35<00:19, 31.26 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:35<00:16, 16.96s/ url]
Dl Size...: 59%|█████▉ | 888/1501 [00:35<00:19, 31.26 MiB/s]
Dl Size...: 59%|█████▉ | 889/1501 [00:35<00:20, 29.84 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:35<00:16, 16.96s/ url]
Dl Size...: 59%|█████▉ | 889/1501 [00:35<00:20, 29.84 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:35<00:16, 16.96s/ url]
Dl Size...: 59%|█████▉ | 890/1501 [00:35<00:20, 29.84 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:35<00:16, 16.96s/ url]
Dl Size...: 59%|█████▉ | 891/1501 [00:35<00:20, 29.84 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:35<00:16, 16.96s/ url]
Dl Size...: 59%|█████▉ | 892/1501 [00:35<00:20, 29.84 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:35<00:16, 16.96s/ url]
Dl Size...: 59%|█████▉ | 893/1501 [00:35<00:20, 29.84 MiB/s]
Dl Size...: 60%|█████▉ | 894/1501 [00:35<00:19, 30.39 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:35<00:16, 16.96s/ url]
Dl Size...: 60%|█████▉ | 894/1501 [00:35<00:19, 30.39 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:35<00:16, 16.96s/ url]
Dl Size...: 60%|█████▉ | 895/1501 [00:35<00:19, 30.39 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:35<00:16, 16.96s/ url]
Dl Size...: 60%|█████▉ | 896/1501 [00:35<00:19, 30.39 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:35<00:16, 16.96s/ url]
Dl Size...: 60%|█████▉ | 897/1501 [00:35<00:19, 30.39 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:35<00:16, 16.96s/ url]
Dl Size...: 60%|█████▉ | 898/1501 [00:35<00:19, 30.39 MiB/s]
Dl Size...: 60%|█████▉ | 899/1501 [00:35<00:19, 31.24 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:35<00:16, 16.96s/ url]
Dl Size...: 60%|█████▉ | 899/1501 [00:35<00:19, 31.24 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:35<00:16, 16.96s/ url]
Dl Size...: 60%|█████▉ | 900/1501 [00:35<00:19, 31.24 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:35<00:16, 16.96s/ url]
Dl Size...: 60%|██████ | 901/1501 [00:35<00:19, 31.24 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:35<00:16, 16.96s/ url]
Dl Size...: 60%|██████ | 902/1501 [00:35<00:19, 31.24 MiB/s]
Dl Size...: 60%|██████ | 903/1501 [00:35<00:18, 32.75 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:35<00:16, 16.96s/ url]
Dl Size...: 60%|██████ | 903/1501 [00:35<00:18, 32.75 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:36<00:16, 16.96s/ url]
Dl Size...: 60%|██████ | 904/1501 [00:36<00:18, 32.75 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:36<00:16, 16.96s/ url]
Dl Size...: 60%|██████ | 905/1501 [00:36<00:18, 32.75 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:36<00:16, 16.96s/ url]
Dl Size...: 60%|██████ | 906/1501 [00:36<00:18, 32.75 MiB/s]
Dl Size...: 60%|██████ | 907/1501 [00:36<00:18, 32.25 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:36<00:16, 16.96s/ url]
Dl Size...: 60%|██████ | 907/1501 [00:36<00:18, 32.25 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:36<00:16, 16.96s/ url]
Dl Size...: 60%|██████ | 908/1501 [00:36<00:18, 32.25 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:36<00:16, 16.96s/ url]
Dl Size...: 61%|██████ | 909/1501 [00:36<00:18, 32.25 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:36<00:16, 16.96s/ url]
Dl Size...: 61%|██████ | 910/1501 [00:36<00:18, 32.25 MiB/s]
Dl Size...: 61%|██████ | 911/1501 [00:36<00:17, 33.09 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:36<00:16, 16.96s/ url]
Dl Size...: 61%|██████ | 911/1501 [00:36<00:17, 33.09 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:36<00:16, 16.96s/ url]
Dl Size...: 61%|██████ | 912/1501 [00:36<00:17, 33.09 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:36<00:16, 16.96s/ url]
Dl Size...: 61%|██████ | 913/1501 [00:36<00:17, 33.09 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:36<00:16, 16.96s/ url]
Dl Size...: 61%|██████ | 914/1501 [00:36<00:17, 33.09 MiB/s]
Dl Size...: 61%|██████ | 915/1501 [00:36<00:17, 33.01 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:36<00:16, 16.96s/ url]
Dl Size...: 61%|██████ | 915/1501 [00:36<00:17, 33.01 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:36<00:16, 16.96s/ url]
Dl Size...: 61%|██████ | 916/1501 [00:36<00:17, 33.01 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:36<00:16, 16.96s/ url]
Dl Size...: 61%|██████ | 917/1501 [00:36<00:17, 33.01 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:36<00:16, 16.96s/ url]
Dl Size...: 61%|██████ | 918/1501 [00:36<00:17, 33.01 MiB/s]
Dl Size...: 61%|██████ | 919/1501 [00:36<00:17, 33.30 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:36<00:16, 16.96s/ url]
Dl Size...: 61%|██████ | 919/1501 [00:36<00:17, 33.30 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:36<00:16, 16.96s/ url]
Dl Size...: 61%|██████▏ | 920/1501 [00:36<00:17, 33.30 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:36<00:16, 16.96s/ url]
Dl Size...: 61%|██████▏ | 921/1501 [00:36<00:17, 33.30 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:36<00:16, 16.96s/ url]
Dl Size...: 61%|██████▏ | 922/1501 [00:36<00:17, 33.30 MiB/s]
Dl Size...: 61%|██████▏ | 923/1501 [00:36<00:16, 35.03 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:36<00:16, 16.96s/ url]
Dl Size...: 61%|██████▏ | 923/1501 [00:36<00:16, 35.03 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:36<00:16, 16.96s/ url]
Dl Size...: 62%|██████▏ | 924/1501 [00:36<00:16, 35.03 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:36<00:16, 16.96s/ url]
Dl Size...: 62%|██████▏ | 925/1501 [00:36<00:16, 35.03 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:36<00:16, 16.96s/ url]
Dl Size...: 62%|██████▏ | 926/1501 [00:36<00:16, 35.03 MiB/s]
Dl Size...: 62%|██████▏ | 927/1501 [00:36<00:16, 34.53 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:36<00:16, 16.96s/ url]
Dl Size...: 62%|██████▏ | 927/1501 [00:36<00:16, 34.53 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:36<00:16, 16.96s/ url]
Dl Size...: 62%|██████▏ | 928/1501 [00:36<00:16, 34.53 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:36<00:16, 16.96s/ url]
Dl Size...: 62%|██████▏ | 929/1501 [00:36<00:16, 34.53 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:36<00:16, 16.96s/ url]
Dl Size...: 62%|██████▏ | 930/1501 [00:36<00:16, 34.53 MiB/s]
Dl Size...: 62%|██████▏ | 931/1501 [00:36<00:16, 34.89 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:36<00:16, 16.96s/ url]
Dl Size...: 62%|██████▏ | 931/1501 [00:36<00:16, 34.89 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:36<00:16, 16.96s/ url]
Dl Size...: 62%|██████▏ | 932/1501 [00:36<00:16, 34.89 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:36<00:16, 16.96s/ url]
Dl Size...: 62%|██████▏ | 933/1501 [00:36<00:16, 34.89 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:36<00:16, 16.96s/ url]
Dl Size...: 62%|██████▏ | 934/1501 [00:36<00:16, 34.89 MiB/s]
Dl Size...: 62%|██████▏ | 935/1501 [00:36<00:16, 34.58 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:36<00:16, 16.96s/ url]
Dl Size...: 62%|██████▏ | 935/1501 [00:36<00:16, 34.58 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:36<00:16, 16.96s/ url]
Dl Size...: 62%|██████▏ | 936/1501 [00:36<00:16, 34.58 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:36<00:16, 16.96s/ url]
Dl Size...: 62%|██████▏ | 937/1501 [00:36<00:16, 34.58 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:36<00:16, 16.96s/ url]
Dl Size...: 62%|██████▏ | 938/1501 [00:36<00:16, 34.58 MiB/s]
Dl Size...: 63%|██████▎ | 939/1501 [00:37<00:16, 34.29 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:37<00:16, 16.96s/ url]
Dl Size...: 63%|██████▎ | 939/1501 [00:37<00:16, 34.29 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:37<00:16, 16.96s/ url]
Dl Size...: 63%|██████▎ | 940/1501 [00:37<00:16, 34.29 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:37<00:16, 16.96s/ url]
Dl Size...: 63%|██████▎ | 941/1501 [00:37<00:16, 34.29 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:37<00:16, 16.96s/ url]
Dl Size...: 63%|██████▎ | 942/1501 [00:37<00:16, 34.29 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:37<00:16, 16.96s/ url]
Dl Size...: 63%|██████▎ | 943/1501 [00:37<00:16, 34.29 MiB/s]
Dl Size...: 63%|██████▎ | 944/1501 [00:37<00:15, 35.72 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:37<00:16, 16.96s/ url]
Dl Size...: 63%|██████▎ | 944/1501 [00:37<00:15, 35.72 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:37<00:16, 16.96s/ url]
Dl Size...: 63%|██████▎ | 945/1501 [00:37<00:15, 35.72 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:37<00:16, 16.96s/ url]
Dl Size...: 63%|██████▎ | 946/1501 [00:37<00:15, 35.72 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:37<00:16, 16.96s/ url]
Dl Size...: 63%|██████▎ | 947/1501 [00:37<00:15, 35.72 MiB/s]
Dl Size...: 63%|██████▎ | 948/1501 [00:37<00:15, 34.69 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:37<00:16, 16.96s/ url]
Dl Size...: 63%|██████▎ | 948/1501 [00:37<00:15, 34.69 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:37<00:16, 16.96s/ url]
Dl Size...: 63%|██████▎ | 949/1501 [00:37<00:15, 34.69 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:37<00:16, 16.96s/ url]
Dl Size...: 63%|██████▎ | 950/1501 [00:37<00:15, 34.69 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:37<00:16, 16.96s/ url]
Dl Size...: 63%|██████▎ | 951/1501 [00:37<00:15, 34.69 MiB/s]
Dl Size...: 63%|██████▎ | 952/1501 [00:37<00:15, 34.58 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:37<00:16, 16.96s/ url]
Dl Size...: 63%|██████▎ | 952/1501 [00:37<00:15, 34.58 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:37<00:16, 16.96s/ url]
Dl Size...: 63%|██████▎ | 953/1501 [00:37<00:15, 34.58 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:37<00:16, 16.96s/ url]
Dl Size...: 64%|██████▎ | 954/1501 [00:37<00:15, 34.58 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:37<00:16, 16.96s/ url]
Dl Size...: 64%|██████▎ | 955/1501 [00:37<00:15, 34.58 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:37<00:16, 16.96s/ url]
Dl Size...: 64%|██████▎ | 956/1501 [00:37<00:15, 34.58 MiB/s]
Dl Size...: 64%|██████▍ | 957/1501 [00:37<00:15, 35.74 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:37<00:16, 16.96s/ url]
Dl Size...: 64%|██████▍ | 957/1501 [00:37<00:15, 35.74 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:37<00:16, 16.96s/ url]
Dl Size...: 64%|██████▍ | 958/1501 [00:37<00:15, 35.74 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:37<00:16, 16.96s/ url]
Dl Size...: 64%|██████▍ | 959/1501 [00:37<00:15, 35.74 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:37<00:16, 16.96s/ url]
Dl Size...: 64%|██████▍ | 960/1501 [00:37<00:15, 35.74 MiB/s]
Dl Size...: 64%|██████▍ | 961/1501 [00:37<00:18, 29.40 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:37<00:16, 16.96s/ url]
Dl Size...: 64%|██████▍ | 961/1501 [00:37<00:18, 29.40 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:37<00:16, 16.96s/ url]
Dl Size...: 64%|██████▍ | 962/1501 [00:37<00:18, 29.40 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:37<00:16, 16.96s/ url]
Dl Size...: 64%|██████▍ | 963/1501 [00:37<00:18, 29.40 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:37<00:16, 16.96s/ url]
Dl Size...: 64%|██████▍ | 964/1501 [00:37<00:18, 29.40 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:37<00:16, 16.96s/ url]
Dl Size...: 64%|██████▍ | 965/1501 [00:37<00:18, 29.40 MiB/s]
Dl Size...: 64%|██████▍ | 966/1501 [00:37<00:18, 29.52 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:37<00:16, 16.96s/ url]
Dl Size...: 64%|██████▍ | 966/1501 [00:37<00:18, 29.52 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:37<00:16, 16.96s/ url]
Dl Size...: 64%|██████▍ | 967/1501 [00:37<00:18, 29.52 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:37<00:16, 16.96s/ url]
Dl Size...: 64%|██████▍ | 968/1501 [00:37<00:18, 29.52 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:37<00:16, 16.96s/ url]
Dl Size...: 65%|██████▍ | 969/1501 [00:37<00:18, 29.52 MiB/s]
Dl Size...: 65%|██████▍ | 970/1501 [00:38<00:18, 28.18 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:38<00:16, 16.96s/ url]
Dl Size...: 65%|██████▍ | 970/1501 [00:38<00:18, 28.18 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:38<00:16, 16.96s/ url]
Dl Size...: 65%|██████▍ | 971/1501 [00:38<00:18, 28.18 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:38<00:16, 16.96s/ url]
Dl Size...: 65%|██████▍ | 972/1501 [00:38<00:18, 28.18 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:38<00:16, 16.96s/ url]
Dl Size...: 65%|██████▍ | 973/1501 [00:38<00:18, 28.18 MiB/s]
Dl Size...: 65%|██████▍ | 974/1501 [00:38<00:19, 26.70 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:38<00:16, 16.96s/ url]
Dl Size...: 65%|██████▍ | 974/1501 [00:38<00:19, 26.70 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:38<00:16, 16.96s/ url]
Dl Size...: 65%|██████▍ | 975/1501 [00:38<00:19, 26.70 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:38<00:16, 16.96s/ url]
Dl Size...: 65%|██████▌ | 976/1501 [00:38<00:19, 26.70 MiB/s]
Dl Size...: 65%|██████▌ | 977/1501 [00:38<00:21, 24.30 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:38<00:16, 16.96s/ url]
Dl Size...: 65%|██████▌ | 977/1501 [00:38<00:21, 24.30 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:38<00:16, 16.96s/ url]
Dl Size...: 65%|██████▌ | 978/1501 [00:38<00:21, 24.30 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:38<00:16, 16.96s/ url]
Dl Size...: 65%|██████▌ | 979/1501 [00:38<00:21, 24.30 MiB/s]
Dl Size...: 65%|██████▌ | 980/1501 [00:38<00:23, 22.64 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:38<00:16, 16.96s/ url]
Dl Size...: 65%|██████▌ | 980/1501 [00:38<00:23, 22.64 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:38<00:16, 16.96s/ url]
Dl Size...: 65%|██████▌ | 981/1501 [00:38<00:22, 22.64 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:38<00:16, 16.96s/ url]
Dl Size...: 65%|██████▌ | 982/1501 [00:38<00:22, 22.64 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:38<00:16, 16.96s/ url]
Dl Size...: 65%|██████▌ | 983/1501 [00:38<00:22, 22.64 MiB/s]
Dl Size...: 66%|██████▌ | 984/1501 [00:38<00:22, 22.91 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:38<00:16, 16.96s/ url]
Dl Size...: 66%|██████▌ | 984/1501 [00:38<00:22, 22.91 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:38<00:16, 16.96s/ url]
Dl Size...: 66%|██████▌ | 985/1501 [00:38<00:22, 22.91 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:38<00:16, 16.96s/ url]
Dl Size...: 66%|██████▌ | 986/1501 [00:38<00:22, 22.91 MiB/s]
Dl Size...: 66%|██████▌ | 987/1501 [00:38<00:23, 21.70 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:38<00:16, 16.96s/ url]
Dl Size...: 66%|██████▌ | 987/1501 [00:38<00:23, 21.70 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:38<00:16, 16.96s/ url]
Dl Size...: 66%|██████▌ | 988/1501 [00:38<00:23, 21.70 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:38<00:16, 16.96s/ url]
Dl Size...: 66%|██████▌ | 989/1501 [00:38<00:23, 21.70 MiB/s]
Dl Size...: 66%|██████▌ | 990/1501 [00:39<00:24, 20.76 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:39<00:16, 16.96s/ url]
Dl Size...: 66%|██████▌ | 990/1501 [00:39<00:24, 20.76 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:39<00:16, 16.96s/ url]
Dl Size...: 66%|██████▌ | 991/1501 [00:39<00:24, 20.76 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:39<00:16, 16.96s/ url]
Dl Size...: 66%|██████▌ | 992/1501 [00:39<00:24, 20.76 MiB/s]
Dl Size...: 66%|██████▌ | 993/1501 [00:39<00:25, 19.73 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:39<00:16, 16.96s/ url]
Dl Size...: 66%|██████▌ | 993/1501 [00:39<00:25, 19.73 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:39<00:16, 16.96s/ url]
Dl Size...: 66%|██████▌ | 994/1501 [00:39<00:25, 19.73 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:39<00:16, 16.96s/ url]
Dl Size...: 66%|██████▋ | 995/1501 [00:39<00:25, 19.73 MiB/s]
Dl Size...: 66%|██████▋ | 996/1501 [00:39<00:25, 19.57 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:39<00:16, 16.96s/ url]
Dl Size...: 66%|██████▋ | 996/1501 [00:39<00:25, 19.57 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:39<00:16, 16.96s/ url]
Dl Size...: 66%|██████▋ | 997/1501 [00:39<00:25, 19.57 MiB/s]
Dl Size...: 66%|██████▋ | 998/1501 [00:39<00:28, 17.55 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:39<00:16, 16.96s/ url]
Dl Size...: 66%|██████▋ | 998/1501 [00:39<00:28, 17.55 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:39<00:16, 16.96s/ url]
Dl Size...: 67%|██████▋ | 999/1501 [00:39<00:28, 17.55 MiB/s]
Dl Size...: 67%|██████▋ | 1000/1501 [00:39<00:30, 16.33 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:39<00:16, 16.96s/ url]
Dl Size...: 67%|██████▋ | 1000/1501 [00:39<00:30, 16.33 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:39<00:16, 16.96s/ url]
Dl Size...: 67%|██████▋ | 1001/1501 [00:39<00:30, 16.33 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:39<00:16, 16.96s/ url]
Dl Size...: 67%|██████▋ | 1002/1501 [00:39<00:30, 16.33 MiB/s]
Dl Size...: 67%|██████▋ | 1003/1501 [00:39<00:30, 16.59 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:39<00:16, 16.96s/ url]
Dl Size...: 67%|██████▋ | 1003/1501 [00:39<00:30, 16.59 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:39<00:16, 16.96s/ url]
Dl Size...: 67%|██████▋ | 1004/1501 [00:39<00:29, 16.59 MiB/s]
Dl Size...: 67%|██████▋ | 1005/1501 [00:39<00:31, 15.62 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:39<00:16, 16.96s/ url]
Dl Size...: 67%|██████▋ | 1005/1501 [00:39<00:31, 15.62 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:40<00:16, 16.96s/ url]
Dl Size...: 67%|██████▋ | 1006/1501 [00:40<00:31, 15.62 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:40<00:16, 16.96s/ url]
Dl Size...: 67%|██████▋ | 1007/1501 [00:40<00:31, 15.62 MiB/s]
Dl Size...: 67%|██████▋ | 1008/1501 [00:40<00:30, 16.12 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:40<00:16, 16.96s/ url]
Dl Size...: 67%|██████▋ | 1008/1501 [00:40<00:30, 16.12 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:40<00:16, 16.96s/ url]
Dl Size...: 67%|██████▋ | 1009/1501 [00:40<00:30, 16.12 MiB/s]
Dl Size...: 67%|██████▋ | 1010/1501 [00:40<00:32, 15.21 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:40<00:16, 16.96s/ url]
Dl Size...: 67%|██████▋ | 1010/1501 [00:40<00:32, 15.21 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:40<00:16, 16.96s/ url]
Dl Size...: 67%|██████▋ | 1011/1501 [00:40<00:32, 15.21 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:40<00:16, 16.96s/ url]
Dl Size...: 67%|██████▋ | 1012/1501 [00:40<00:32, 15.21 MiB/s]
Dl Size...: 67%|██████▋ | 1013/1501 [00:40<00:30, 16.00 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:40<00:16, 16.96s/ url]
Dl Size...: 67%|██████▋ | 1013/1501 [00:40<00:30, 16.00 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:40<00:16, 16.96s/ url]
Dl Size...: 68%|██████▊ | 1014/1501 [00:40<00:30, 16.00 MiB/s]
Dl Size...: 68%|██████▊ | 1015/1501 [00:40<00:32, 15.15 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:40<00:16, 16.96s/ url]
Dl Size...: 68%|██████▊ | 1015/1501 [00:40<00:32, 15.15 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:40<00:16, 16.96s/ url]
Dl Size...: 68%|██████▊ | 1016/1501 [00:40<00:32, 15.15 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:40<00:16, 16.96s/ url]
Dl Size...: 68%|██████▊ | 1017/1501 [00:40<00:31, 15.15 MiB/s]
Dl Size...: 68%|██████▊ | 1018/1501 [00:40<00:30, 15.98 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:40<00:16, 16.96s/ url]
Dl Size...: 68%|██████▊ | 1018/1501 [00:40<00:30, 15.98 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:40<00:16, 16.96s/ url]
Dl Size...: 68%|██████▊ | 1019/1501 [00:40<00:30, 15.98 MiB/s]
Dl Size...: 68%|██████▊ | 1020/1501 [00:40<00:31, 15.20 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:40<00:16, 16.96s/ url]
Dl Size...: 68%|██████▊ | 1020/1501 [00:40<00:31, 15.20 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:41<00:16, 16.96s/ url]
Dl Size...: 68%|██████▊ | 1021/1501 [00:41<00:31, 15.20 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:41<00:16, 16.96s/ url]
Dl Size...: 68%|██████▊ | 1022/1501 [00:41<00:31, 15.20 MiB/s]
Dl Size...: 68%|██████▊ | 1023/1501 [00:41<00:29, 15.96 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:41<00:16, 16.96s/ url]
Dl Size...: 68%|██████▊ | 1023/1501 [00:41<00:29, 15.96 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:41<00:16, 16.96s/ url]
Dl Size...: 68%|██████▊ | 1024/1501 [00:41<00:29, 15.96 MiB/s]
Dl Size...: 68%|██████▊ | 1025/1501 [00:41<00:30, 15.42 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:41<00:16, 16.96s/ url]
Dl Size...: 68%|██████▊ | 1025/1501 [00:41<00:30, 15.42 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:41<00:16, 16.96s/ url]
Dl Size...: 68%|██████▊ | 1026/1501 [00:41<00:30, 15.42 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:41<00:16, 16.96s/ url]
Dl Size...: 68%|██████▊ | 1027/1501 [00:41<00:30, 15.42 MiB/s]
Dl Size...: 68%|██████▊ | 1028/1501 [00:41<00:29, 15.93 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:41<00:16, 16.96s/ url]
Dl Size...: 68%|██████▊ | 1028/1501 [00:41<00:29, 15.93 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:41<00:16, 16.96s/ url]
Dl Size...: 69%|██████▊ | 1029/1501 [00:41<00:29, 15.93 MiB/s]
Dl Size...: 69%|██████▊ | 1030/1501 [00:41<00:30, 15.68 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:41<00:16, 16.96s/ url]
Dl Size...: 69%|██████▊ | 1030/1501 [00:41<00:30, 15.68 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:41<00:16, 16.96s/ url]
Dl Size...: 69%|██████▊ | 1031/1501 [00:41<00:29, 15.68 MiB/s]
Dl Size...: 69%|██████▉ | 1032/1501 [00:41<00:28, 16.26 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:41<00:16, 16.96s/ url]
Dl Size...: 69%|██████▉ | 1032/1501 [00:41<00:28, 16.26 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:41<00:16, 16.96s/ url]
Dl Size...: 69%|██████▉ | 1033/1501 [00:41<00:28, 16.26 MiB/s]
Dl Size...: 69%|██████▉ | 1034/1501 [00:41<00:29, 15.88 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:41<00:16, 16.96s/ url]
Dl Size...: 69%|██████▉ | 1034/1501 [00:41<00:29, 15.88 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:41<00:16, 16.96s/ url]
Dl Size...: 69%|██████▉ | 1035/1501 [00:41<00:29, 15.88 MiB/s]
Dl Size...: 69%|██████▉ | 1036/1501 [00:41<00:28, 16.45 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:41<00:16, 16.96s/ url]
Dl Size...: 69%|██████▉ | 1036/1501 [00:41<00:28, 16.45 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:41<00:16, 16.96s/ url]
Dl Size...: 69%|██████▉ | 1037/1501 [00:41<00:28, 16.45 MiB/s]
Dl Size...: 69%|██████▉ | 1038/1501 [00:42<00:29, 15.95 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:42<00:16, 16.96s/ url]
Dl Size...: 69%|██████▉ | 1038/1501 [00:42<00:29, 15.95 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:42<00:16, 16.96s/ url]
Dl Size...: 69%|██████▉ | 1039/1501 [00:42<00:28, 15.95 MiB/s]
Dl Size...: 69%|██████▉ | 1040/1501 [00:42<00:27, 16.73 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:42<00:16, 16.96s/ url]
Dl Size...: 69%|██████▉ | 1040/1501 [00:42<00:27, 16.73 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:42<00:16, 16.96s/ url]
Dl Size...: 69%|██████▉ | 1041/1501 [00:42<00:27, 16.73 MiB/s]
Dl Size...: 69%|██████▉ | 1042/1501 [00:42<00:28, 16.08 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:42<00:16, 16.96s/ url]
Dl Size...: 69%|██████▉ | 1042/1501 [00:42<00:28, 16.08 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:42<00:16, 16.96s/ url]
Dl Size...: 69%|██████▉ | 1043/1501 [00:42<00:28, 16.08 MiB/s]
Dl Size...: 70%|██████▉ | 1044/1501 [00:42<00:27, 16.75 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:42<00:16, 16.96s/ url]
Dl Size...: 70%|██████▉ | 1044/1501 [00:42<00:27, 16.75 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:42<00:16, 16.96s/ url]
Dl Size...: 70%|██████▉ | 1045/1501 [00:42<00:27, 16.75 MiB/s]
Dl Size...: 70%|██████▉ | 1046/1501 [00:42<00:28, 16.18 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:42<00:16, 16.96s/ url]
Dl Size...: 70%|██████▉ | 1046/1501 [00:42<00:28, 16.18 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:42<00:16, 16.96s/ url]
Dl Size...: 70%|██████▉ | 1047/1501 [00:42<00:28, 16.18 MiB/s]
Dl Size...: 70%|██████▉ | 1048/1501 [00:42<00:26, 17.09 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:42<00:16, 16.96s/ url]
Dl Size...: 70%|██████▉ | 1048/1501 [00:42<00:26, 17.09 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:42<00:16, 16.96s/ url]
Dl Size...: 70%|██████▉ | 1049/1501 [00:42<00:26, 17.09 MiB/s]
Dl Size...: 70%|██████▉ | 1050/1501 [00:42<00:27, 16.19 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:42<00:16, 16.96s/ url]
Dl Size...: 70%|██████▉ | 1050/1501 [00:42<00:27, 16.19 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:42<00:16, 16.96s/ url]
Dl Size...: 70%|███████ | 1051/1501 [00:42<00:27, 16.19 MiB/s]
Dl Size...: 70%|███████ | 1052/1501 [00:42<00:26, 17.13 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:42<00:16, 16.96s/ url]
Dl Size...: 70%|███████ | 1052/1501 [00:42<00:26, 17.13 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:42<00:16, 16.96s/ url]
Dl Size...: 70%|███████ | 1053/1501 [00:42<00:26, 17.13 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:42<00:16, 16.96s/ url]
Dl Size...: 70%|███████ | 1054/1501 [00:42<00:26, 17.13 MiB/s]
Dl Size...: 70%|███████ | 1055/1501 [00:43<00:25, 17.20 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:43<00:16, 16.96s/ url]
Dl Size...: 70%|███████ | 1055/1501 [00:43<00:25, 17.20 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:43<00:16, 16.96s/ url]
Dl Size...: 70%|███████ | 1056/1501 [00:43<00:25, 17.20 MiB/s]
Dl Size...: 70%|███████ | 1057/1501 [00:43<00:26, 16.61 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:43<00:16, 16.96s/ url]
Dl Size...: 70%|███████ | 1057/1501 [00:43<00:26, 16.61 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:43<00:16, 16.96s/ url]
Dl Size...: 70%|███████ | 1058/1501 [00:43<00:26, 16.61 MiB/s]
Dl Size...: 71%|███████ | 1059/1501 [00:43<00:25, 17.09 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:43<00:16, 16.96s/ url]
Dl Size...: 71%|███████ | 1059/1501 [00:43<00:25, 17.09 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:43<00:16, 16.96s/ url]
Dl Size...: 71%|███████ | 1060/1501 [00:43<00:25, 17.09 MiB/s]
Dl Size...: 71%|███████ | 1061/1501 [00:43<00:26, 16.58 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:43<00:16, 16.96s/ url]
Dl Size...: 71%|███████ | 1061/1501 [00:43<00:26, 16.58 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:43<00:16, 16.96s/ url]
Dl Size...: 71%|███████ | 1062/1501 [00:43<00:26, 16.58 MiB/s]
Dl Size...: 71%|███████ | 1063/1501 [00:43<00:25, 17.37 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:43<00:16, 16.96s/ url]
Dl Size...: 71%|███████ | 1063/1501 [00:43<00:25, 17.37 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:43<00:16, 16.96s/ url]
Dl Size...: 71%|███████ | 1064/1501 [00:43<00:25, 17.37 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:43<00:16, 16.96s/ url]
Dl Size...: 71%|███████ | 1065/1501 [00:43<00:25, 17.37 MiB/s]
Dl Size...: 71%|███████ | 1066/1501 [00:43<00:25, 17.35 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:43<00:16, 16.96s/ url]
Dl Size...: 71%|███████ | 1066/1501 [00:43<00:25, 17.35 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:43<00:16, 16.96s/ url]
Dl Size...: 71%|███████ | 1067/1501 [00:43<00:25, 17.35 MiB/s]
Dl Size...: 71%|███████ | 1068/1501 [00:43<00:25, 16.74 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:43<00:16, 16.96s/ url]
Dl Size...: 71%|███████ | 1068/1501 [00:43<00:25, 16.74 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:43<00:16, 16.96s/ url]
Dl Size...: 71%|███████ | 1069/1501 [00:43<00:25, 16.74 MiB/s]
Dl Size...: 71%|███████▏ | 1070/1501 [00:43<00:25, 17.16 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:43<00:16, 16.96s/ url]
Dl Size...: 71%|███████▏ | 1070/1501 [00:43<00:25, 17.16 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:43<00:16, 16.96s/ url]
Dl Size...: 71%|███████▏ | 1071/1501 [00:43<00:25, 17.16 MiB/s]
Dl Size...: 71%|███████▏ | 1072/1501 [00:44<00:25, 16.78 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:44<00:16, 16.96s/ url]
Dl Size...: 71%|███████▏ | 1072/1501 [00:44<00:25, 16.78 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:44<00:16, 16.96s/ url]
Dl Size...: 71%|███████▏ | 1073/1501 [00:44<00:25, 16.78 MiB/s]
Dl Size...: 72%|███████▏ | 1074/1501 [00:44<00:24, 17.45 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:44<00:16, 16.96s/ url]
Dl Size...: 72%|███████▏ | 1074/1501 [00:44<00:24, 17.45 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:44<00:16, 16.96s/ url]
Dl Size...: 72%|███████▏ | 1075/1501 [00:44<00:24, 17.45 MiB/s]
Dl Size...: 72%|███████▏ | 1076/1501 [00:44<00:23, 18.05 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:44<00:16, 16.96s/ url]
Dl Size...: 72%|███████▏ | 1076/1501 [00:44<00:23, 18.05 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:44<00:16, 16.96s/ url]
Dl Size...: 72%|███████▏ | 1077/1501 [00:44<00:23, 18.05 MiB/s]
Dl Size...: 72%|███████▏ | 1078/1501 [00:44<00:24, 17.08 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:44<00:16, 16.96s/ url]
Dl Size...: 72%|███████▏ | 1078/1501 [00:44<00:24, 17.08 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:44<00:16, 16.96s/ url]
Dl Size...: 72%|███████▏ | 1079/1501 [00:44<00:24, 17.08 MiB/s]
Dl Size...: 72%|███████▏ | 1080/1501 [00:44<00:24, 17.53 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:44<00:16, 16.96s/ url]
Dl Size...: 72%|███████▏ | 1080/1501 [00:44<00:24, 17.53 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:44<00:16, 16.96s/ url]
Dl Size...: 72%|███████▏ | 1081/1501 [00:44<00:23, 17.53 MiB/s]
Dl Size...: 72%|███████▏ | 1082/1501 [00:44<00:24, 16.83 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:44<00:16, 16.96s/ url]
Dl Size...: 72%|███████▏ | 1082/1501 [00:44<00:24, 16.83 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:44<00:16, 16.96s/ url]
Dl Size...: 72%|███████▏ | 1083/1501 [00:44<00:24, 16.83 MiB/s]
Dl Size...: 72%|███████▏ | 1084/1501 [00:44<00:23, 17.41 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:44<00:16, 16.96s/ url]
Dl Size...: 72%|███████▏ | 1084/1501 [00:44<00:23, 17.41 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:44<00:16, 16.96s/ url]
Dl Size...: 72%|███████▏ | 1085/1501 [00:44<00:23, 17.41 MiB/s]
Dl Size...: 72%|███████▏ | 1086/1501 [00:44<00:24, 16.82 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:44<00:16, 16.96s/ url]
Dl Size...: 72%|███████▏ | 1086/1501 [00:44<00:24, 16.82 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:44<00:16, 16.96s/ url]
Dl Size...: 72%|███████▏ | 1087/1501 [00:44<00:24, 16.82 MiB/s]
Dl Size...: 72%|███████▏ | 1088/1501 [00:44<00:23, 17.30 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:44<00:16, 16.96s/ url]
Dl Size...: 72%|███████▏ | 1088/1501 [00:44<00:23, 17.30 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:45<00:16, 16.96s/ url]
Dl Size...: 73%|███████▎ | 1089/1501 [00:45<00:23, 17.30 MiB/s]
Dl Size...: 73%|███████▎ | 1090/1501 [00:45<00:24, 16.79 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:45<00:16, 16.96s/ url]
Dl Size...: 73%|███████▎ | 1090/1501 [00:45<00:24, 16.79 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:45<00:16, 16.96s/ url]
Dl Size...: 73%|███████▎ | 1091/1501 [00:45<00:24, 16.79 MiB/s]
Dl Size...: 73%|███████▎ | 1092/1501 [00:45<00:23, 17.33 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:45<00:16, 16.96s/ url]
Dl Size...: 73%|███████▎ | 1092/1501 [00:45<00:23, 17.33 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:45<00:16, 16.96s/ url]
Dl Size...: 73%|███████▎ | 1093/1501 [00:45<00:23, 17.33 MiB/s]
Dl Size...: 73%|███████▎ | 1094/1501 [00:45<00:22, 17.82 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:45<00:16, 16.96s/ url]
Dl Size...: 73%|███████▎ | 1094/1501 [00:45<00:22, 17.82 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:45<00:16, 16.96s/ url]
Dl Size...: 73%|███████▎ | 1095/1501 [00:45<00:22, 17.82 MiB/s]
Dl Size...: 73%|███████▎ | 1096/1501 [00:45<00:24, 16.84 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:45<00:16, 16.96s/ url]
Dl Size...: 73%|███████▎ | 1096/1501 [00:45<00:24, 16.84 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:45<00:16, 16.96s/ url]
Dl Size...: 73%|███████▎ | 1097/1501 [00:45<00:23, 16.84 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:45<00:16, 16.96s/ url]
Dl Size...: 73%|███████▎ | 1098/1501 [00:45<00:23, 16.84 MiB/s]
Dl Size...: 73%|███████▎ | 1099/1501 [00:45<00:23, 17.26 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:45<00:16, 16.96s/ url]
Dl Size...: 73%|███████▎ | 1099/1501 [00:45<00:23, 17.26 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:45<00:16, 16.96s/ url]
Dl Size...: 73%|███████▎ | 1100/1501 [00:45<00:23, 17.26 MiB/s]
Dl Size...: 73%|███████▎ | 1101/1501 [00:45<00:22, 17.80 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:45<00:16, 16.96s/ url]
Dl Size...: 73%|███████▎ | 1101/1501 [00:45<00:22, 17.80 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:45<00:16, 16.96s/ url]
Dl Size...: 73%|███████▎ | 1102/1501 [00:45<00:22, 17.80 MiB/s]
Dl Size...: 73%|███████▎ | 1103/1501 [00:45<00:23, 16.86 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:45<00:16, 16.96s/ url]
Dl Size...: 73%|███████▎ | 1103/1501 [00:45<00:23, 16.86 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:45<00:16, 16.96s/ url]
Dl Size...: 74%|███████▎ | 1104/1501 [00:45<00:23, 16.86 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:45<00:16, 16.96s/ url]
Dl Size...: 74%|███████▎ | 1105/1501 [00:45<00:23, 16.86 MiB/s]
Dl Size...: 74%|███████▎ | 1106/1501 [00:46<00:22, 17.32 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:46<00:16, 16.96s/ url]
Dl Size...: 74%|███████▎ | 1106/1501 [00:46<00:22, 17.32 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:46<00:16, 16.96s/ url]
Dl Size...: 74%|███████▍ | 1107/1501 [00:46<00:22, 17.32 MiB/s]
Dl Size...: 74%|███████▍ | 1108/1501 [00:46<00:22, 17.74 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:46<00:16, 16.96s/ url]
Dl Size...: 74%|███████▍ | 1108/1501 [00:46<00:22, 17.74 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:46<00:16, 16.96s/ url]
Dl Size...: 74%|███████▍ | 1109/1501 [00:46<00:22, 17.74 MiB/s]
Dl Size...: 74%|███████▍ | 1110/1501 [00:46<00:23, 16.84 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:46<00:16, 16.96s/ url]
Dl Size...: 74%|███████▍ | 1110/1501 [00:46<00:23, 16.84 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:46<00:16, 16.96s/ url]
Dl Size...: 74%|███████▍ | 1111/1501 [00:46<00:23, 16.84 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:46<00:16, 16.96s/ url]
Dl Size...: 74%|███████▍ | 1112/1501 [00:46<00:23, 16.84 MiB/s]
Dl Size...: 74%|███████▍ | 1113/1501 [00:46<00:22, 17.31 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:46<00:16, 16.96s/ url]
Dl Size...: 74%|███████▍ | 1113/1501 [00:46<00:22, 17.31 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:46<00:16, 16.96s/ url]
Dl Size...: 74%|███████▍ | 1114/1501 [00:46<00:22, 17.31 MiB/s]
Dl Size...: 74%|███████▍ | 1115/1501 [00:46<00:22, 16.95 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:46<00:16, 16.96s/ url]
Dl Size...: 74%|███████▍ | 1115/1501 [00:46<00:22, 16.95 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:46<00:16, 16.96s/ url]
Dl Size...: 74%|███████▍ | 1116/1501 [00:46<00:22, 16.95 MiB/s]
Dl Size...: 74%|███████▍ | 1117/1501 [00:46<00:22, 17.10 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:46<00:16, 16.96s/ url]
Dl Size...: 74%|███████▍ | 1117/1501 [00:46<00:22, 17.10 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:46<00:16, 16.96s/ url]
Dl Size...: 74%|███████▍ | 1118/1501 [00:46<00:22, 17.10 MiB/s]
Dl Size...: 75%|███████▍ | 1119/1501 [00:46<00:24, 15.81 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:46<00:16, 16.96s/ url]
Dl Size...: 75%|███████▍ | 1119/1501 [00:46<00:24, 15.81 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:46<00:16, 16.96s/ url]
Dl Size...: 75%|███████▍ | 1120/1501 [00:46<00:24, 15.81 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:46<00:16, 16.96s/ url]
Dl Size...: 75%|███████▍ | 1121/1501 [00:46<00:24, 15.81 MiB/s]
Dl Size...: 75%|███████▍ | 1122/1501 [00:47<00:23, 16.12 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:47<00:16, 16.96s/ url]
Dl Size...: 75%|███████▍ | 1122/1501 [00:47<00:23, 16.12 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:47<00:16, 16.96s/ url]
Dl Size...: 75%|███████▍ | 1123/1501 [00:47<00:23, 16.12 MiB/s]
Dl Size...: 75%|███████▍ | 1124/1501 [00:47<00:25, 14.98 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:47<00:16, 16.96s/ url]
Dl Size...: 75%|███████▍ | 1124/1501 [00:47<00:25, 14.98 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:47<00:16, 16.96s/ url]
Dl Size...: 75%|███████▍ | 1125/1501 [00:47<00:25, 14.98 MiB/s]
Dl Size...: 75%|███████▌ | 1126/1501 [00:47<00:26, 14.25 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:47<00:16, 16.96s/ url]
Dl Size...: 75%|███████▌ | 1126/1501 [00:47<00:26, 14.25 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:47<00:16, 16.96s/ url]
Dl Size...: 75%|███████▌ | 1127/1501 [00:47<00:26, 14.25 MiB/s]
Dl Size...: 75%|███████▌ | 1128/1501 [00:47<00:27, 13.77 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:47<00:16, 16.96s/ url]
Dl Size...: 75%|███████▌ | 1128/1501 [00:47<00:27, 13.77 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:47<00:16, 16.96s/ url]
Dl Size...: 75%|███████▌ | 1129/1501 [00:47<00:27, 13.77 MiB/s]
Dl Size...: 75%|███████▌ | 1130/1501 [00:47<00:27, 13.39 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:47<00:16, 16.96s/ url]
Dl Size...: 75%|███████▌ | 1130/1501 [00:47<00:27, 13.39 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:47<00:16, 16.96s/ url]
Dl Size...: 75%|███████▌ | 1131/1501 [00:47<00:27, 13.39 MiB/s]
Dl Size...: 75%|███████▌ | 1132/1501 [00:47<00:27, 13.40 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:47<00:16, 16.96s/ url]
Dl Size...: 75%|███████▌ | 1132/1501 [00:47<00:27, 13.40 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:47<00:16, 16.96s/ url]
Dl Size...: 75%|███████▌ | 1133/1501 [00:47<00:27, 13.40 MiB/s]
Dl Size...: 76%|███████▌ | 1134/1501 [00:47<00:27, 13.59 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:47<00:16, 16.96s/ url]
Dl Size...: 76%|███████▌ | 1134/1501 [00:47<00:27, 13.59 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:48<00:16, 16.96s/ url]
Dl Size...: 76%|███████▌ | 1135/1501 [00:48<00:26, 13.59 MiB/s]
Dl Size...: 76%|███████▌ | 1136/1501 [00:48<00:27, 13.27 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:48<00:16, 16.96s/ url]
Dl Size...: 76%|███████▌ | 1136/1501 [00:48<00:27, 13.27 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:48<00:16, 16.96s/ url]
Dl Size...: 76%|███████▌ | 1137/1501 [00:48<00:27, 13.27 MiB/s]
Dl Size...: 76%|███████▌ | 1138/1501 [00:48<00:25, 14.06 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:48<00:16, 16.96s/ url]
Dl Size...: 76%|███████▌ | 1138/1501 [00:48<00:25, 14.06 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:48<00:16, 16.96s/ url]
Dl Size...: 76%|███████▌ | 1139/1501 [00:48<00:25, 14.06 MiB/s]
Dl Size...: 76%|███████▌ | 1140/1501 [00:48<00:26, 13.73 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:48<00:16, 16.96s/ url]
Dl Size...: 76%|███████▌ | 1140/1501 [00:48<00:26, 13.73 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:48<00:16, 16.96s/ url]
Dl Size...: 76%|███████▌ | 1141/1501 [00:48<00:26, 13.73 MiB/s]
Dl Size...: 76%|███████▌ | 1142/1501 [00:48<00:25, 14.19 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:48<00:16, 16.96s/ url]
Dl Size...: 76%|███████▌ | 1142/1501 [00:48<00:25, 14.19 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:48<00:16, 16.96s/ url]
Dl Size...: 76%|███████▌ | 1143/1501 [00:48<00:25, 14.19 MiB/s]
Dl Size...: 76%|███████▌ | 1144/1501 [00:48<00:24, 14.76 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:48<00:16, 16.96s/ url]
Dl Size...: 76%|███████▌ | 1144/1501 [00:48<00:24, 14.76 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:48<00:16, 16.96s/ url]
Dl Size...: 76%|███████▋ | 1145/1501 [00:48<00:24, 14.76 MiB/s]
Dl Size...: 76%|███████▋ | 1146/1501 [00:48<00:24, 14.21 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:48<00:16, 16.96s/ url]
Dl Size...: 76%|███████▋ | 1146/1501 [00:48<00:24, 14.21 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:48<00:16, 16.96s/ url]
Dl Size...: 76%|███████▋ | 1147/1501 [00:48<00:24, 14.21 MiB/s]
Dl Size...: 76%|███████▋ | 1148/1501 [00:48<00:24, 14.33 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:48<00:16, 16.96s/ url]
Dl Size...: 76%|███████▋ | 1148/1501 [00:48<00:24, 14.33 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:48<00:16, 16.96s/ url]
Dl Size...: 77%|███████▋ | 1149/1501 [00:48<00:24, 14.33 MiB/s]
Dl Size...: 77%|███████▋ | 1150/1501 [00:49<00:24, 14.21 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:49<00:16, 16.96s/ url]
Dl Size...: 77%|███████▋ | 1150/1501 [00:49<00:24, 14.21 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:49<00:16, 16.96s/ url]
Dl Size...: 77%|███████▋ | 1151/1501 [00:49<00:24, 14.21 MiB/s]
Dl Size...: 77%|███████▋ | 1152/1501 [00:49<00:23, 14.81 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:49<00:16, 16.96s/ url]
Dl Size...: 77%|███████▋ | 1152/1501 [00:49<00:23, 14.81 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:49<00:16, 16.96s/ url]
Dl Size...: 77%|███████▋ | 1153/1501 [00:49<00:23, 14.81 MiB/s]
Dl Size...: 77%|███████▋ | 1154/1501 [00:49<00:23, 14.89 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:49<00:16, 16.96s/ url]
Dl Size...: 77%|███████▋ | 1154/1501 [00:49<00:23, 14.89 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:49<00:16, 16.96s/ url]
Dl Size...: 77%|███████▋ | 1155/1501 [00:49<00:23, 14.89 MiB/s]
Dl Size...: 77%|███████▋ | 1156/1501 [00:49<00:22, 15.50 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:49<00:16, 16.96s/ url]
Dl Size...: 77%|███████▋ | 1156/1501 [00:49<00:22, 15.50 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:49<00:16, 16.96s/ url]
Dl Size...: 77%|███████▋ | 1157/1501 [00:49<00:22, 15.50 MiB/s]
Dl Size...: 77%|███████▋ | 1158/1501 [00:49<00:23, 14.76 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:49<00:16, 16.96s/ url]
Dl Size...: 77%|███████▋ | 1158/1501 [00:49<00:23, 14.76 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:49<00:16, 16.96s/ url]
Dl Size...: 77%|███████▋ | 1159/1501 [00:49<00:23, 14.76 MiB/s]
Dl Size...: 77%|███████▋ | 1160/1501 [00:49<00:22, 14.90 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:49<00:16, 16.96s/ url]
Dl Size...: 77%|███████▋ | 1160/1501 [00:49<00:22, 14.90 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:49<00:16, 16.96s/ url]
Dl Size...: 77%|███████▋ | 1161/1501 [00:49<00:22, 14.90 MiB/s]
Dl Size...: 77%|███████▋ | 1162/1501 [00:49<00:21, 15.57 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:49<00:16, 16.96s/ url]
Dl Size...: 77%|███████▋ | 1162/1501 [00:49<00:21, 15.57 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:49<00:16, 16.96s/ url]
Dl Size...: 77%|███████▋ | 1163/1501 [00:49<00:21, 15.57 MiB/s]
Dl Size...: 78%|███████▊ | 1164/1501 [00:49<00:21, 15.49 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:49<00:16, 16.96s/ url]
Dl Size...: 78%|███████▊ | 1164/1501 [00:49<00:21, 15.49 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:50<00:16, 16.96s/ url]
Dl Size...: 78%|███████▊ | 1165/1501 [00:50<00:21, 15.49 MiB/s]
Dl Size...: 78%|███████▊ | 1166/1501 [00:50<00:20, 16.04 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:50<00:16, 16.96s/ url]
Dl Size...: 78%|███████▊ | 1166/1501 [00:50<00:20, 16.04 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:50<00:16, 16.96s/ url]
Dl Size...: 78%|███████▊ | 1167/1501 [00:50<00:20, 16.04 MiB/s]
Dl Size...: 78%|███████▊ | 1168/1501 [00:50<00:21, 15.34 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:50<00:16, 16.96s/ url]
Dl Size...: 78%|███████▊ | 1168/1501 [00:50<00:21, 15.34 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:50<00:16, 16.96s/ url]
Dl Size...: 78%|███████▊ | 1169/1501 [00:50<00:21, 15.34 MiB/s]
Dl Size...: 78%|███████▊ | 1170/1501 [00:50<00:20, 16.32 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:50<00:16, 16.96s/ url]
Dl Size...: 78%|███████▊ | 1170/1501 [00:50<00:20, 16.32 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:50<00:16, 16.96s/ url]
Dl Size...: 78%|███████▊ | 1171/1501 [00:50<00:20, 16.32 MiB/s]
Dl Size...: 78%|███████▊ | 1172/1501 [00:50<00:21, 15.50 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:50<00:16, 16.96s/ url]
Dl Size...: 78%|███████▊ | 1172/1501 [00:50<00:21, 15.50 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:50<00:16, 16.96s/ url]
Dl Size...: 78%|███████▊ | 1173/1501 [00:50<00:21, 15.50 MiB/s]
Dl Size...: 78%|███████▊ | 1174/1501 [00:50<00:21, 15.52 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:50<00:16, 16.96s/ url]
Dl Size...: 78%|███████▊ | 1174/1501 [00:50<00:21, 15.52 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:50<00:16, 16.96s/ url]
Dl Size...: 78%|███████▊ | 1175/1501 [00:50<00:21, 15.52 MiB/s]
Dl Size...: 78%|███████▊ | 1176/1501 [00:50<00:20, 15.96 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:50<00:16, 16.96s/ url]
Dl Size...: 78%|███████▊ | 1176/1501 [00:50<00:20, 15.96 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:50<00:16, 16.96s/ url]
Dl Size...: 78%|███████▊ | 1177/1501 [00:50<00:20, 15.96 MiB/s]
Dl Size...: 78%|███████▊ | 1178/1501 [00:50<00:20, 15.88 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:50<00:16, 16.96s/ url]
Dl Size...: 78%|███████▊ | 1178/1501 [00:50<00:20, 15.88 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:50<00:16, 16.96s/ url]
Dl Size...: 79%|███████▊ | 1179/1501 [00:50<00:20, 15.88 MiB/s]
Dl Size...: 79%|███████▊ | 1180/1501 [00:50<00:19, 16.37 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:50<00:16, 16.96s/ url]
Dl Size...: 79%|███████▊ | 1180/1501 [00:50<00:19, 16.37 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:50<00:16, 16.96s/ url]
Dl Size...: 79%|███████▊ | 1181/1501 [00:50<00:19, 16.37 MiB/s]
Dl Size...: 79%|███████▊ | 1182/1501 [00:51<00:19, 16.02 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:51<00:16, 16.96s/ url]
Dl Size...: 79%|███████▊ | 1182/1501 [00:51<00:19, 16.02 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:51<00:16, 16.96s/ url]
Dl Size...: 79%|███████▉ | 1183/1501 [00:51<00:19, 16.02 MiB/s]
Dl Size...: 79%|███████▉ | 1184/1501 [00:51<00:19, 16.64 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:51<00:16, 16.96s/ url]
Dl Size...: 79%|███████▉ | 1184/1501 [00:51<00:19, 16.64 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:51<00:16, 16.96s/ url]
Dl Size...: 79%|███████▉ | 1185/1501 [00:51<00:18, 16.64 MiB/s]
Dl Size...: 79%|███████▉ | 1186/1501 [00:51<00:19, 16.35 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:51<00:16, 16.96s/ url]
Dl Size...: 79%|███████▉ | 1186/1501 [00:51<00:19, 16.35 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:51<00:16, 16.96s/ url]
Dl Size...: 79%|███████▉ | 1187/1501 [00:51<00:19, 16.35 MiB/s]
Dl Size...: 79%|███████▉ | 1188/1501 [00:51<00:18, 16.72 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:51<00:16, 16.96s/ url]
Dl Size...: 79%|███████▉ | 1188/1501 [00:51<00:18, 16.72 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:51<00:16, 16.96s/ url]
Dl Size...: 79%|███████▉ | 1189/1501 [00:51<00:18, 16.72 MiB/s]
Dl Size...: 79%|███████▉ | 1190/1501 [00:51<00:19, 16.10 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:51<00:16, 16.96s/ url]
Dl Size...: 79%|███████▉ | 1190/1501 [00:51<00:19, 16.10 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:51<00:16, 16.96s/ url]
Dl Size...: 79%|███████▉ | 1191/1501 [00:51<00:19, 16.10 MiB/s]
Dl Size...: 79%|███████▉ | 1192/1501 [00:51<00:18, 16.87 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:51<00:16, 16.96s/ url]
Dl Size...: 79%|███████▉ | 1192/1501 [00:51<00:18, 16.87 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:51<00:16, 16.96s/ url]
Dl Size...: 79%|███████▉ | 1193/1501 [00:51<00:18, 16.87 MiB/s]
Dl Size...: 80%|███████▉ | 1194/1501 [00:51<00:18, 16.20 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:51<00:16, 16.96s/ url]
Dl Size...: 80%|███████▉ | 1194/1501 [00:51<00:18, 16.20 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:51<00:16, 16.96s/ url]
Dl Size...: 80%|███████▉ | 1195/1501 [00:51<00:18, 16.20 MiB/s]
Dl Size...: 80%|███████▉ | 1196/1501 [00:51<00:17, 16.95 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:51<00:16, 16.96s/ url]
Dl Size...: 80%|███████▉ | 1196/1501 [00:51<00:17, 16.95 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:51<00:16, 16.96s/ url]
Dl Size...: 80%|███████▉ | 1197/1501 [00:51<00:17, 16.95 MiB/s]
Dl Size...: 80%|███████▉ | 1198/1501 [00:52<00:17, 17.14 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:52<00:16, 16.96s/ url]
Dl Size...: 80%|███████▉ | 1198/1501 [00:52<00:17, 17.14 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:52<00:16, 16.96s/ url]
Dl Size...: 80%|███████▉ | 1199/1501 [00:52<00:17, 17.14 MiB/s]
Dl Size...: 80%|███████▉ | 1200/1501 [00:52<00:17, 17.09 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:52<00:16, 16.96s/ url]
Dl Size...: 80%|███████▉ | 1200/1501 [00:52<00:17, 17.09 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:52<00:16, 16.96s/ url]
Dl Size...: 80%|████████ | 1201/1501 [00:52<00:17, 17.09 MiB/s]
Dl Size...: 80%|████████ | 1202/1501 [00:52<00:17, 17.29 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:52<00:16, 16.96s/ url]
Dl Size...: 80%|████████ | 1202/1501 [00:52<00:17, 17.29 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:52<00:16, 16.96s/ url]
Dl Size...: 80%|████████ | 1203/1501 [00:52<00:17, 17.29 MiB/s]
Dl Size...: 80%|████████ | 1204/1501 [00:52<00:17, 17.03 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:52<00:16, 16.96s/ url]
Dl Size...: 80%|████████ | 1204/1501 [00:52<00:17, 17.03 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:52<00:16, 16.96s/ url]
Dl Size...: 80%|████████ | 1205/1501 [00:52<00:17, 17.03 MiB/s]
Dl Size...: 80%|████████ | 1206/1501 [00:52<00:17, 17.03 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:52<00:16, 16.96s/ url]
Dl Size...: 80%|████████ | 1206/1501 [00:52<00:17, 17.03 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:52<00:16, 16.96s/ url]
Dl Size...: 80%|████████ | 1207/1501 [00:52<00:17, 17.03 MiB/s]
Dl Size...: 80%|████████ | 1208/1501 [00:52<00:17, 16.85 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:52<00:16, 16.96s/ url]
Dl Size...: 80%|████████ | 1208/1501 [00:52<00:17, 16.85 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:52<00:16, 16.96s/ url]
Dl Size...: 81%|████████ | 1209/1501 [00:52<00:17, 16.85 MiB/s]
Dl Size...: 81%|████████ | 1210/1501 [00:52<00:16, 17.34 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:52<00:16, 16.96s/ url]
Dl Size...: 81%|████████ | 1210/1501 [00:52<00:16, 17.34 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:52<00:16, 16.96s/ url]
Dl Size...: 81%|████████ | 1211/1501 [00:52<00:16, 17.34 MiB/s]
Dl Size...: 81%|████████ | 1212/1501 [00:52<00:17, 16.53 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:52<00:16, 16.96s/ url]
Dl Size...: 81%|████████ | 1212/1501 [00:52<00:17, 16.53 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:52<00:16, 16.96s/ url]
Dl Size...: 81%|████████ | 1213/1501 [00:52<00:17, 16.53 MiB/s]
Dl Size...: 81%|████████ | 1214/1501 [00:52<00:16, 17.18 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:52<00:16, 16.96s/ url]
Dl Size...: 81%|████████ | 1214/1501 [00:52<00:16, 17.18 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:53<00:16, 16.96s/ url]
Dl Size...: 81%|████████ | 1215/1501 [00:53<00:16, 17.18 MiB/s]
Dl Size...: 81%|████████ | 1216/1501 [00:53<00:17, 16.55 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:53<00:16, 16.96s/ url]
Dl Size...: 81%|████████ | 1216/1501 [00:53<00:17, 16.55 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:53<00:16, 16.96s/ url]
Dl Size...: 81%|████████ | 1217/1501 [00:53<00:17, 16.55 MiB/s]
Dl Size...: 81%|████████ | 1218/1501 [00:53<00:16, 17.12 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:53<00:16, 16.96s/ url]
Dl Size...: 81%|████████ | 1218/1501 [00:53<00:16, 17.12 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:53<00:16, 16.96s/ url]
Dl Size...: 81%|████████ | 1219/1501 [00:53<00:16, 17.12 MiB/s]
Dl Size...: 81%|████████▏ | 1220/1501 [00:53<00:16, 17.34 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:53<00:16, 16.96s/ url]
Dl Size...: 81%|████████▏ | 1220/1501 [00:53<00:16, 17.34 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:53<00:16, 16.96s/ url]
Dl Size...: 81%|████████▏ | 1221/1501 [00:53<00:16, 17.34 MiB/s]
Dl Size...: 81%|████████▏ | 1222/1501 [00:53<00:15, 17.50 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:53<00:16, 16.96s/ url]
Dl Size...: 81%|████████▏ | 1222/1501 [00:53<00:15, 17.50 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:53<00:16, 16.96s/ url]
Dl Size...: 81%|████████▏ | 1223/1501 [00:53<00:15, 17.50 MiB/s]
Dl Size...: 82%|████████▏ | 1224/1501 [00:53<00:15, 17.45 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:53<00:16, 16.96s/ url]
Dl Size...: 82%|████████▏ | 1224/1501 [00:53<00:15, 17.45 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:53<00:16, 16.96s/ url]
Dl Size...: 82%|████████▏ | 1225/1501 [00:53<00:15, 17.45 MiB/s]
Dl Size...: 82%|████████▏ | 1226/1501 [00:53<00:16, 17.07 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:53<00:16, 16.96s/ url]
Dl Size...: 82%|████████▏ | 1226/1501 [00:53<00:16, 17.07 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:53<00:16, 16.96s/ url]
Dl Size...: 82%|████████▏ | 1227/1501 [00:53<00:16, 17.07 MiB/s]
Dl Size...: 82%|████████▏ | 1228/1501 [00:53<00:15, 17.35 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:53<00:16, 16.96s/ url]
Dl Size...: 82%|████████▏ | 1228/1501 [00:53<00:15, 17.35 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:53<00:16, 16.96s/ url]
Dl Size...: 82%|████████▏ | 1229/1501 [00:53<00:15, 17.35 MiB/s]
Dl Size...: 82%|████████▏ | 1230/1501 [00:53<00:16, 16.84 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:53<00:16, 16.96s/ url]
Dl Size...: 82%|████████▏ | 1230/1501 [00:53<00:16, 16.84 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:53<00:16, 16.96s/ url]
Dl Size...: 82%|████████▏ | 1231/1501 [00:53<00:16, 16.84 MiB/s]
Dl Size...: 82%|████████▏ | 1232/1501 [00:54<00:15, 17.29 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:54<00:16, 16.96s/ url]
Dl Size...: 82%|████████▏ | 1232/1501 [00:54<00:15, 17.29 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:54<00:16, 16.96s/ url]
Dl Size...: 82%|████████▏ | 1233/1501 [00:54<00:15, 17.29 MiB/s]
Dl Size...: 82%|████████▏ | 1234/1501 [00:54<00:15, 16.89 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:54<00:16, 16.96s/ url]
Dl Size...: 82%|████████▏ | 1234/1501 [00:54<00:15, 16.89 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:54<00:16, 16.96s/ url]
Dl Size...: 82%|████████▏ | 1235/1501 [00:54<00:15, 16.89 MiB/s]
Dl Size...: 82%|████████▏ | 1236/1501 [00:54<00:15, 17.27 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:54<00:16, 16.96s/ url]
Dl Size...: 82%|████████▏ | 1236/1501 [00:54<00:15, 17.27 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:54<00:16, 16.96s/ url]
Dl Size...: 82%|████████▏ | 1237/1501 [00:54<00:15, 17.27 MiB/s]
Dl Size...: 82%|████████▏ | 1238/1501 [00:54<00:14, 17.83 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:54<00:16, 16.96s/ url]
Dl Size...: 82%|████████▏ | 1238/1501 [00:54<00:14, 17.83 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:54<00:16, 16.96s/ url]
Dl Size...: 83%|████████▎ | 1239/1501 [00:54<00:14, 17.83 MiB/s]
Dl Size...: 83%|████████▎ | 1240/1501 [00:54<00:15, 17.38 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:54<00:16, 16.96s/ url]
Dl Size...: 83%|████████▎ | 1240/1501 [00:54<00:15, 17.38 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:54<00:16, 16.96s/ url]
Dl Size...: 83%|████████▎ | 1241/1501 [00:54<00:14, 17.38 MiB/s]
Dl Size...: 83%|████████▎ | 1242/1501 [00:54<00:14, 17.49 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:54<00:16, 16.96s/ url]
Dl Size...: 83%|████████▎ | 1242/1501 [00:54<00:14, 17.49 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:54<00:16, 16.96s/ url]
Dl Size...: 83%|████████▎ | 1243/1501 [00:54<00:14, 17.49 MiB/s]
Dl Size...: 83%|████████▎ | 1244/1501 [00:54<00:15, 16.84 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:54<00:16, 16.96s/ url]
Dl Size...: 83%|████████▎ | 1244/1501 [00:54<00:15, 16.84 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:54<00:16, 16.96s/ url]
Dl Size...: 83%|████████▎ | 1245/1501 [00:54<00:15, 16.84 MiB/s]
Dl Size...: 83%|████████▎ | 1246/1501 [00:54<00:14, 17.04 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:54<00:16, 16.96s/ url]
Dl Size...: 83%|████████▎ | 1246/1501 [00:54<00:14, 17.04 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:54<00:16, 16.96s/ url]
Dl Size...: 83%|████████▎ | 1247/1501 [00:54<00:14, 17.04 MiB/s]
Dl Size...: 83%|████████▎ | 1248/1501 [00:54<00:16, 15.43 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:54<00:16, 16.96s/ url]
Dl Size...: 83%|████████▎ | 1248/1501 [00:54<00:16, 15.43 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:55<00:16, 16.96s/ url]
Dl Size...: 83%|████████▎ | 1249/1501 [00:55<00:16, 15.43 MiB/s]
Dl Size...: 83%|████████▎ | 1250/1501 [00:55<00:17, 14.30 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:55<00:16, 16.96s/ url]
Dl Size...: 83%|████████▎ | 1250/1501 [00:55<00:17, 14.30 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:55<00:16, 16.96s/ url]
Dl Size...: 83%|████████▎ | 1251/1501 [00:55<00:17, 14.30 MiB/s]
Dl Size...: 83%|████████▎ | 1252/1501 [00:55<00:18, 13.71 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:55<00:16, 16.96s/ url]
Dl Size...: 83%|████████▎ | 1252/1501 [00:55<00:18, 13.71 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:55<00:16, 16.96s/ url]
Dl Size...: 83%|████████▎ | 1253/1501 [00:55<00:18, 13.71 MiB/s]
Dl Size...: 84%|████████▎ | 1254/1501 [00:55<00:18, 13.48 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:55<00:16, 16.96s/ url]
Dl Size...: 84%|████████▎ | 1254/1501 [00:55<00:18, 13.48 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:55<00:16, 16.96s/ url]
Dl Size...: 84%|████████▎ | 1255/1501 [00:55<00:18, 13.48 MiB/s]
Dl Size...: 84%|████████▎ | 1256/1501 [00:55<00:18, 13.17 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:55<00:16, 16.96s/ url]
Dl Size...: 84%|████████▎ | 1256/1501 [00:55<00:18, 13.17 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:55<00:16, 16.96s/ url]
Dl Size...: 84%|████████▎ | 1257/1501 [00:55<00:18, 13.17 MiB/s]
Dl Size...: 84%|████████▍ | 1258/1501 [00:55<00:18, 13.05 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:55<00:16, 16.96s/ url]
Dl Size...: 84%|████████▍ | 1258/1501 [00:55<00:18, 13.05 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:55<00:16, 16.96s/ url]
Dl Size...: 84%|████████▍ | 1259/1501 [00:55<00:18, 13.05 MiB/s]
Dl Size...: 84%|████████▍ | 1260/1501 [00:55<00:18, 12.89 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:55<00:16, 16.96s/ url]
Dl Size...: 84%|████████▍ | 1260/1501 [00:55<00:18, 12.89 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:56<00:16, 16.96s/ url]
Dl Size...: 84%|████████▍ | 1261/1501 [00:56<00:18, 12.89 MiB/s]
Dl Size...: 84%|████████▍ | 1262/1501 [00:56<00:18, 12.97 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:56<00:16, 16.96s/ url]
Dl Size...: 84%|████████▍ | 1262/1501 [00:56<00:18, 12.97 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:56<00:16, 16.96s/ url]
Dl Size...: 84%|████████▍ | 1263/1501 [00:56<00:18, 12.97 MiB/s]
Dl Size...: 84%|████████▍ | 1264/1501 [00:56<00:18, 12.95 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:56<00:16, 16.96s/ url]
Dl Size...: 84%|████████▍ | 1264/1501 [00:56<00:18, 12.95 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:56<00:16, 16.96s/ url]
Dl Size...: 84%|████████▍ | 1265/1501 [00:56<00:18, 12.95 MiB/s]
Dl Size...: 84%|████████▍ | 1266/1501 [00:56<00:18, 13.05 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:56<00:16, 16.96s/ url]
Dl Size...: 84%|████████▍ | 1266/1501 [00:56<00:18, 13.05 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:56<00:16, 16.96s/ url]
Dl Size...: 84%|████████▍ | 1267/1501 [00:56<00:17, 13.05 MiB/s]
Dl Size...: 84%|████████▍ | 1268/1501 [00:56<00:18, 12.91 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:56<00:16, 16.96s/ url]
Dl Size...: 84%|████████▍ | 1268/1501 [00:56<00:18, 12.91 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:56<00:16, 16.96s/ url]
Dl Size...: 85%|████████▍ | 1269/1501 [00:56<00:17, 12.91 MiB/s]
Dl Size...: 85%|████████▍ | 1270/1501 [00:56<00:17, 13.54 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:56<00:16, 16.96s/ url]
Dl Size...: 85%|████████▍ | 1270/1501 [00:56<00:17, 13.54 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:56<00:16, 16.96s/ url]
Dl Size...: 85%|████████▍ | 1271/1501 [00:56<00:16, 13.54 MiB/s]
Dl Size...: 85%|████████▍ | 1272/1501 [00:56<00:16, 13.73 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:56<00:16, 16.96s/ url]
Dl Size...: 85%|████████▍ | 1272/1501 [00:56<00:16, 13.73 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:56<00:16, 16.96s/ url]
Dl Size...: 85%|████████▍ | 1273/1501 [00:56<00:16, 13.73 MiB/s]
Dl Size...: 85%|████████▍ | 1274/1501 [00:56<00:16, 13.43 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:56<00:16, 16.96s/ url]
Dl Size...: 85%|████████▍ | 1274/1501 [00:56<00:16, 13.43 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:57<00:16, 16.96s/ url]
Dl Size...: 85%|████████▍ | 1275/1501 [00:57<00:16, 13.43 MiB/s]
Dl Size...: 85%|████████▌ | 1276/1501 [00:57<00:16, 13.48 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:57<00:16, 16.96s/ url]
Dl Size...: 85%|████████▌ | 1276/1501 [00:57<00:16, 13.48 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:57<00:16, 16.96s/ url]
Dl Size...: 85%|████████▌ | 1277/1501 [00:57<00:16, 13.48 MiB/s]
Dl Size...: 85%|████████▌ | 1278/1501 [00:57<00:16, 13.44 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:57<00:16, 16.96s/ url]
Dl Size...: 85%|████████▌ | 1278/1501 [00:57<00:16, 13.44 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:57<00:16, 16.96s/ url]
Dl Size...: 85%|████████▌ | 1279/1501 [00:57<00:16, 13.44 MiB/s]
Dl Size...: 85%|████████▌ | 1280/1501 [00:57<00:15, 14.00 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:57<00:16, 16.96s/ url]
Dl Size...: 85%|████████▌ | 1280/1501 [00:57<00:15, 14.00 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:57<00:16, 16.96s/ url]
Dl Size...: 85%|████████▌ | 1281/1501 [00:57<00:15, 14.00 MiB/s]
Dl Size...: 85%|████████▌ | 1282/1501 [00:57<00:15, 14.05 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:57<00:16, 16.96s/ url]
Dl Size...: 85%|████████▌ | 1282/1501 [00:57<00:15, 14.05 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:57<00:16, 16.96s/ url]
Dl Size...: 85%|████████▌ | 1283/1501 [00:57<00:15, 14.05 MiB/s]
Dl Size...: 86%|████████▌ | 1284/1501 [00:57<00:15, 13.88 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:57<00:16, 16.96s/ url]
Dl Size...: 86%|████████▌ | 1284/1501 [00:57<00:15, 13.88 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:57<00:16, 16.96s/ url]
Dl Size...: 86%|████████▌ | 1285/1501 [00:57<00:15, 13.88 MiB/s]
Dl Size...: 86%|████████▌ | 1286/1501 [00:57<00:15, 13.79 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:57<00:16, 16.96s/ url]
Dl Size...: 86%|████████▌ | 1286/1501 [00:57<00:15, 13.79 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:57<00:16, 16.96s/ url]
Dl Size...: 86%|████████▌ | 1287/1501 [00:57<00:15, 13.79 MiB/s]
Dl Size...: 86%|████████▌ | 1288/1501 [00:57<00:15, 13.86 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:57<00:16, 16.96s/ url]
Dl Size...: 86%|████████▌ | 1288/1501 [00:57<00:15, 13.86 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:58<00:16, 16.96s/ url]
Dl Size...: 86%|████████▌ | 1289/1501 [00:58<00:15, 13.86 MiB/s]
Dl Size...: 86%|████████▌ | 1290/1501 [00:58<00:14, 14.38 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:58<00:16, 16.96s/ url]
Dl Size...: 86%|████████▌ | 1290/1501 [00:58<00:14, 14.38 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:58<00:16, 16.96s/ url]
Dl Size...: 86%|████████▌ | 1291/1501 [00:58<00:14, 14.38 MiB/s]
Dl Size...: 86%|████████▌ | 1292/1501 [00:58<00:14, 14.05 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:58<00:16, 16.96s/ url]
Dl Size...: 86%|████████▌ | 1292/1501 [00:58<00:14, 14.05 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:58<00:16, 16.96s/ url]
Dl Size...: 86%|████████▌ | 1293/1501 [00:58<00:14, 14.05 MiB/s]
Dl Size...: 86%|████████▌ | 1294/1501 [00:58<00:14, 14.08 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:58<00:16, 16.96s/ url]
Dl Size...: 86%|████████▌ | 1294/1501 [00:58<00:14, 14.08 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:58<00:16, 16.96s/ url]
Dl Size...: 86%|████████▋ | 1295/1501 [00:58<00:14, 14.08 MiB/s]
Dl Size...: 86%|████████▋ | 1296/1501 [00:58<00:14, 14.06 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:58<00:16, 16.96s/ url]
Dl Size...: 86%|████████▋ | 1296/1501 [00:58<00:14, 14.06 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:58<00:16, 16.96s/ url]
Dl Size...: 86%|████████▋ | 1297/1501 [00:58<00:14, 14.06 MiB/s]
Dl Size...: 86%|████████▋ | 1298/1501 [00:58<00:13, 14.51 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:58<00:16, 16.96s/ url]
Dl Size...: 86%|████████▋ | 1298/1501 [00:58<00:13, 14.51 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:58<00:16, 16.96s/ url]
Dl Size...: 87%|████████▋ | 1299/1501 [00:58<00:13, 14.51 MiB/s]
Dl Size...: 87%|████████▋ | 1300/1501 [00:58<00:14, 14.28 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:58<00:16, 16.96s/ url]
Dl Size...: 87%|████████▋ | 1300/1501 [00:58<00:14, 14.28 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:58<00:16, 16.96s/ url]
Dl Size...: 87%|████████▋ | 1301/1501 [00:58<00:14, 14.28 MiB/s]
Dl Size...: 87%|████████▋ | 1302/1501 [00:58<00:14, 14.14 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:58<00:16, 16.96s/ url]
Dl Size...: 87%|████████▋ | 1302/1501 [00:58<00:14, 14.14 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:59<00:16, 16.96s/ url]
Dl Size...: 87%|████████▋ | 1303/1501 [00:59<00:14, 14.14 MiB/s]
Dl Size...: 87%|████████▋ | 1304/1501 [00:59<00:13, 14.52 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:59<00:16, 16.96s/ url]
Dl Size...: 87%|████████▋ | 1304/1501 [00:59<00:13, 14.52 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:59<00:16, 16.96s/ url]
Dl Size...: 87%|████████▋ | 1305/1501 [00:59<00:13, 14.52 MiB/s]
Dl Size...: 87%|████████▋ | 1306/1501 [00:59<00:13, 14.44 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:59<00:16, 16.96s/ url]
Dl Size...: 87%|████████▋ | 1306/1501 [00:59<00:13, 14.44 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:59<00:16, 16.96s/ url]
Dl Size...: 87%|████████▋ | 1307/1501 [00:59<00:13, 14.44 MiB/s]
Dl Size...: 87%|████████▋ | 1308/1501 [00:59<00:13, 14.34 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:59<00:16, 16.96s/ url]
Dl Size...: 87%|████████▋ | 1308/1501 [00:59<00:13, 14.34 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:59<00:16, 16.96s/ url]
Dl Size...: 87%|████████▋ | 1309/1501 [00:59<00:13, 14.34 MiB/s]
Dl Size...: 87%|████████▋ | 1310/1501 [00:59<00:13, 14.41 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:59<00:16, 16.96s/ url]
Dl Size...: 87%|████████▋ | 1310/1501 [00:59<00:13, 14.41 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:59<00:16, 16.96s/ url]
Dl Size...: 87%|████████▋ | 1311/1501 [00:59<00:13, 14.41 MiB/s]
Dl Size...: 87%|████████▋ | 1312/1501 [00:59<00:12, 14.80 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:59<00:16, 16.96s/ url]
Dl Size...: 87%|████████▋ | 1312/1501 [00:59<00:12, 14.80 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:59<00:16, 16.96s/ url]
Dl Size...: 87%|████████▋ | 1313/1501 [00:59<00:12, 14.80 MiB/s]
Dl Size...: 88%|████████▊ | 1314/1501 [00:59<00:12, 14.57 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:59<00:16, 16.96s/ url]
Dl Size...: 88%|████████▊ | 1314/1501 [00:59<00:12, 14.57 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:59<00:16, 16.96s/ url]
Dl Size...: 88%|████████▊ | 1315/1501 [00:59<00:12, 14.57 MiB/s]
Dl Size...: 88%|████████▊ | 1316/1501 [00:59<00:12, 14.33 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:59<00:16, 16.96s/ url]
Dl Size...: 88%|████████▊ | 1316/1501 [00:59<00:12, 14.33 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:59<00:16, 16.96s/ url]
Dl Size...: 88%|████████▊ | 1317/1501 [00:59<00:12, 14.33 MiB/s]
Dl Size...: 88%|████████▊ | 1318/1501 [01:00<00:12, 14.68 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:00<00:16, 16.96s/ url]
Dl Size...: 88%|████████▊ | 1318/1501 [01:00<00:12, 14.68 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:00<00:16, 16.96s/ url]
Dl Size...: 88%|████████▊ | 1319/1501 [01:00<00:12, 14.68 MiB/s]
Dl Size...: 88%|████████▊ | 1320/1501 [01:00<00:12, 14.69 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:00<00:16, 16.96s/ url]
Dl Size...: 88%|████████▊ | 1320/1501 [01:00<00:12, 14.69 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:00<00:16, 16.96s/ url]
Dl Size...: 88%|████████▊ | 1321/1501 [01:00<00:12, 14.69 MiB/s]
Dl Size...: 88%|████████▊ | 1322/1501 [01:00<00:12, 14.27 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:00<00:16, 16.96s/ url]
Dl Size...: 88%|████████▊ | 1322/1501 [01:00<00:12, 14.27 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:00<00:16, 16.96s/ url]
Dl Size...: 88%|████████▊ | 1323/1501 [01:00<00:12, 14.27 MiB/s]
Dl Size...: 88%|████████▊ | 1324/1501 [01:00<00:12, 14.51 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:00<00:16, 16.96s/ url]
Dl Size...: 88%|████████▊ | 1324/1501 [01:00<00:12, 14.51 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:00<00:16, 16.96s/ url]
Dl Size...: 88%|████████▊ | 1325/1501 [01:00<00:12, 14.51 MiB/s]
Dl Size...: 88%|████████▊ | 1326/1501 [01:00<00:11, 14.80 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:00<00:16, 16.96s/ url]
Dl Size...: 88%|████████▊ | 1326/1501 [01:00<00:11, 14.80 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:00<00:16, 16.96s/ url]
Dl Size...: 88%|████████▊ | 1327/1501 [01:00<00:11, 14.80 MiB/s]
Dl Size...: 88%|████████▊ | 1328/1501 [01:00<00:11, 14.51 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:00<00:16, 16.96s/ url]
Dl Size...: 88%|████████▊ | 1328/1501 [01:00<00:11, 14.51 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:00<00:16, 16.96s/ url]
Dl Size...: 89%|████████▊ | 1329/1501 [01:00<00:11, 14.51 MiB/s]
Dl Size...: 89%|████████▊ | 1330/1501 [01:00<00:11, 14.38 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:00<00:16, 16.96s/ url]
Dl Size...: 89%|████████▊ | 1330/1501 [01:00<00:11, 14.38 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:00<00:16, 16.96s/ url]
Dl Size...: 89%|████████▊ | 1331/1501 [01:00<00:11, 14.38 MiB/s]
Dl Size...: 89%|████████▊ | 1332/1501 [01:01<00:11, 14.74 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:01<00:16, 16.96s/ url]
Dl Size...: 89%|████████▊ | 1332/1501 [01:01<00:11, 14.74 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:01<00:16, 16.96s/ url]
Dl Size...: 89%|████████▉ | 1333/1501 [01:01<00:11, 14.74 MiB/s]
Dl Size...: 89%|████████▉ | 1334/1501 [01:01<00:11, 14.59 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:01<00:16, 16.96s/ url]
Dl Size...: 89%|████████▉ | 1334/1501 [01:01<00:11, 14.59 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:01<00:16, 16.96s/ url]
Dl Size...: 89%|████████▉ | 1335/1501 [01:01<00:11, 14.59 MiB/s]
Dl Size...: 89%|████████▉ | 1336/1501 [01:01<00:11, 14.58 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:01<00:16, 16.96s/ url]
Dl Size...: 89%|████████▉ | 1336/1501 [01:01<00:11, 14.58 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:01<00:16, 16.96s/ url]
Dl Size...: 89%|████████▉ | 1337/1501 [01:01<00:11, 14.58 MiB/s]
Dl Size...: 89%|████████▉ | 1338/1501 [01:01<00:10, 14.86 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:01<00:16, 16.96s/ url]
Dl Size...: 89%|████████▉ | 1338/1501 [01:01<00:10, 14.86 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:01<00:16, 16.96s/ url]
Dl Size...: 89%|████████▉ | 1339/1501 [01:01<00:10, 14.86 MiB/s]
Dl Size...: 89%|████████▉ | 1340/1501 [01:01<00:11, 14.55 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:01<00:16, 16.96s/ url]
Dl Size...: 89%|████████▉ | 1340/1501 [01:01<00:11, 14.55 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:01<00:16, 16.96s/ url]
Dl Size...: 89%|████████▉ | 1341/1501 [01:01<00:10, 14.55 MiB/s]
Dl Size...: 89%|████████▉ | 1342/1501 [01:01<00:11, 14.45 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:01<00:16, 16.96s/ url]
Dl Size...: 89%|████████▉ | 1342/1501 [01:01<00:11, 14.45 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:01<00:16, 16.96s/ url]
Dl Size...: 89%|████████▉ | 1343/1501 [01:01<00:10, 14.45 MiB/s]
Dl Size...: 90%|████████▉ | 1344/1501 [01:01<00:10, 14.30 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:01<00:16, 16.96s/ url]
Dl Size...: 90%|████████▉ | 1344/1501 [01:01<00:10, 14.30 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:01<00:16, 16.96s/ url]
Dl Size...: 90%|████████▉ | 1345/1501 [01:01<00:10, 14.30 MiB/s]
Dl Size...: 90%|████████▉ | 1346/1501 [01:01<00:10, 14.55 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:01<00:16, 16.96s/ url]
Dl Size...: 90%|████████▉ | 1346/1501 [01:01<00:10, 14.55 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:02<00:16, 16.96s/ url]
Dl Size...: 90%|████████▉ | 1347/1501 [01:02<00:10, 14.55 MiB/s]
Dl Size...: 90%|████████▉ | 1348/1501 [01:02<00:10, 14.79 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:02<00:16, 16.96s/ url]
Dl Size...: 90%|████████▉ | 1348/1501 [01:02<00:10, 14.79 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:02<00:16, 16.96s/ url]
Dl Size...: 90%|████████▉ | 1349/1501 [01:02<00:10, 14.79 MiB/s]
Dl Size...: 90%|████████▉ | 1350/1501 [01:02<00:10, 14.50 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:02<00:16, 16.96s/ url]
Dl Size...: 90%|████████▉ | 1350/1501 [01:02<00:10, 14.50 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:02<00:16, 16.96s/ url]
Dl Size...: 90%|█████████ | 1351/1501 [01:02<00:10, 14.50 MiB/s]
Dl Size...: 90%|█████████ | 1352/1501 [01:02<00:10, 14.73 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:02<00:16, 16.96s/ url]
Dl Size...: 90%|█████████ | 1352/1501 [01:02<00:10, 14.73 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:02<00:16, 16.96s/ url]
Dl Size...: 90%|█████████ | 1353/1501 [01:02<00:10, 14.73 MiB/s]
Dl Size...: 90%|█████████ | 1354/1501 [01:02<00:10, 14.52 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:02<00:16, 16.96s/ url]
Dl Size...: 90%|█████████ | 1354/1501 [01:02<00:10, 14.52 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:02<00:16, 16.96s/ url]
Dl Size...: 90%|█████████ | 1355/1501 [01:02<00:10, 14.52 MiB/s]
Dl Size...: 90%|█████████ | 1356/1501 [01:02<00:09, 14.56 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:02<00:16, 16.96s/ url]
Dl Size...: 90%|█████████ | 1356/1501 [01:02<00:09, 14.56 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:02<00:16, 16.96s/ url]
Dl Size...: 90%|█████████ | 1357/1501 [01:02<00:09, 14.56 MiB/s]
Dl Size...: 90%|█████████ | 1358/1501 [01:02<00:09, 14.64 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:02<00:16, 16.96s/ url]
Dl Size...: 90%|█████████ | 1358/1501 [01:02<00:09, 14.64 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:02<00:16, 16.96s/ url]
Dl Size...: 91%|█████████ | 1359/1501 [01:02<00:09, 14.64 MiB/s]
Dl Size...: 91%|█████████ | 1360/1501 [01:02<00:09, 14.72 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:02<00:16, 16.96s/ url]
Dl Size...: 91%|█████████ | 1360/1501 [01:02<00:09, 14.72 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:03<00:16, 16.96s/ url]
Dl Size...: 91%|█████████ | 1361/1501 [01:03<00:09, 14.72 MiB/s]
Dl Size...: 91%|█████████ | 1362/1501 [01:03<00:09, 14.58 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:03<00:16, 16.96s/ url]
Dl Size...: 91%|█████████ | 1362/1501 [01:03<00:09, 14.58 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:03<00:16, 16.96s/ url]
Dl Size...: 91%|█████████ | 1363/1501 [01:03<00:09, 14.58 MiB/s]
Dl Size...: 91%|█████████ | 1364/1501 [01:03<00:09, 14.40 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:03<00:16, 16.96s/ url]
Dl Size...: 91%|█████████ | 1364/1501 [01:03<00:09, 14.40 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:03<00:16, 16.96s/ url]
Dl Size...: 91%|█████████ | 1365/1501 [01:03<00:09, 14.40 MiB/s]
Dl Size...: 91%|█████████ | 1366/1501 [01:03<00:09, 14.68 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:03<00:16, 16.96s/ url]
Dl Size...: 91%|█████████ | 1366/1501 [01:03<00:09, 14.68 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:03<00:16, 16.96s/ url]
Dl Size...: 91%|█████████ | 1367/1501 [01:03<00:09, 14.68 MiB/s]
Dl Size...: 91%|█████████ | 1368/1501 [01:03<00:09, 14.49 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:03<00:16, 16.96s/ url]
Dl Size...: 91%|█████████ | 1368/1501 [01:03<00:09, 14.49 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:03<00:16, 16.96s/ url]
Dl Size...: 91%|█████████ | 1369/1501 [01:03<00:09, 14.49 MiB/s]
Dl Size...: 91%|█████████▏| 1370/1501 [01:03<00:09, 14.48 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:03<00:16, 16.96s/ url]
Dl Size...: 91%|█████████▏| 1370/1501 [01:03<00:09, 14.48 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:03<00:16, 16.96s/ url]
Dl Size...: 91%|█████████▏| 1371/1501 [01:03<00:08, 14.48 MiB/s]
Dl Size...: 91%|█████████▏| 1372/1501 [01:03<00:08, 14.66 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:03<00:16, 16.96s/ url]
Dl Size...: 91%|█████████▏| 1372/1501 [01:03<00:08, 14.66 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:03<00:16, 16.96s/ url]
Dl Size...: 91%|█████████▏| 1373/1501 [01:03<00:08, 14.66 MiB/s]
Dl Size...: 92%|█████████▏| 1374/1501 [01:03<00:08, 14.72 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:03<00:16, 16.96s/ url]
Dl Size...: 92%|█████████▏| 1374/1501 [01:03<00:08, 14.72 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:03<00:16, 16.96s/ url]
Dl Size...: 92%|█████████▏| 1375/1501 [01:03<00:08, 14.72 MiB/s]
Dl Size...: 92%|█████████▏| 1376/1501 [01:04<00:08, 14.54 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:04<00:16, 16.96s/ url]
Dl Size...: 92%|█████████▏| 1376/1501 [01:04<00:08, 14.54 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:04<00:16, 16.96s/ url]
Dl Size...: 92%|█████████▏| 1377/1501 [01:04<00:08, 14.54 MiB/s]
Dl Size...: 92%|█████████▏| 1378/1501 [01:04<00:08, 14.78 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:04<00:16, 16.96s/ url]
Dl Size...: 92%|█████████▏| 1378/1501 [01:04<00:08, 14.78 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:04<00:16, 16.96s/ url]
Dl Size...: 92%|█████████▏| 1379/1501 [01:04<00:08, 14.78 MiB/s]
Dl Size...: 92%|█████████▏| 1380/1501 [01:04<00:08, 14.80 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:04<00:16, 16.96s/ url]
Dl Size...: 92%|█████████▏| 1380/1501 [01:04<00:08, 14.80 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:04<00:16, 16.96s/ url]
Dl Size...: 92%|█████████▏| 1381/1501 [01:04<00:08, 14.80 MiB/s]
Dl Size...: 92%|█████████▏| 1382/1501 [01:04<00:08, 14.65 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:04<00:16, 16.96s/ url]
Dl Size...: 92%|█████████▏| 1382/1501 [01:04<00:08, 14.65 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:04<00:16, 16.96s/ url]
Dl Size...: 92%|█████████▏| 1383/1501 [01:04<00:08, 14.65 MiB/s]
Dl Size...: 92%|█████████▏| 1384/1501 [01:04<00:08, 14.51 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:04<00:16, 16.96s/ url]
Dl Size...: 92%|█████████▏| 1384/1501 [01:04<00:08, 14.51 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:04<00:16, 16.96s/ url]
Dl Size...: 92%|█████████▏| 1385/1501 [01:04<00:07, 14.51 MiB/s]
Dl Size...: 92%|█████████▏| 1386/1501 [01:04<00:07, 14.87 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:04<00:16, 16.96s/ url]
Dl Size...: 92%|█████████▏| 1386/1501 [01:04<00:07, 14.87 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:04<00:16, 16.96s/ url]
Dl Size...: 92%|█████████▏| 1387/1501 [01:04<00:07, 14.87 MiB/s]
Dl Size...: 92%|█████████▏| 1388/1501 [01:04<00:07, 14.52 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:04<00:16, 16.96s/ url]
Dl Size...: 92%|█████████▏| 1388/1501 [01:04<00:07, 14.52 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:04<00:16, 16.96s/ url]
Dl Size...: 93%|█████████▎| 1389/1501 [01:04<00:07, 14.52 MiB/s]
Dl Size...: 93%|█████████▎| 1390/1501 [01:04<00:07, 14.68 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:04<00:16, 16.96s/ url]
Dl Size...: 93%|█████████▎| 1390/1501 [01:04<00:07, 14.68 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:05<00:16, 16.96s/ url]
Dl Size...: 93%|█████████▎| 1391/1501 [01:05<00:07, 14.68 MiB/s]
Dl Size...: 93%|█████████▎| 1392/1501 [01:05<00:07, 14.98 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:05<00:16, 16.96s/ url]
Dl Size...: 93%|█████████▎| 1392/1501 [01:05<00:07, 14.98 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:05<00:16, 16.96s/ url]
Dl Size...: 93%|█████████▎| 1393/1501 [01:05<00:07, 14.98 MiB/s]
Dl Size...: 93%|█████████▎| 1394/1501 [01:05<00:07, 14.61 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:05<00:16, 16.96s/ url]
Dl Size...: 93%|█████████▎| 1394/1501 [01:05<00:07, 14.61 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:05<00:16, 16.96s/ url]
Dl Size...: 93%|█████████▎| 1395/1501 [01:05<00:07, 14.61 MiB/s]
Dl Size...: 93%|█████████▎| 1396/1501 [01:05<00:07, 14.52 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:05<00:16, 16.96s/ url]
Dl Size...: 93%|█████████▎| 1396/1501 [01:05<00:07, 14.52 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:05<00:16, 16.96s/ url]
Dl Size...: 93%|█████████▎| 1397/1501 [01:05<00:07, 14.52 MiB/s]
Dl Size...: 93%|█████████▎| 1398/1501 [01:05<00:06, 15.22 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:05<00:16, 16.96s/ url]
Dl Size...: 93%|█████████▎| 1398/1501 [01:05<00:06, 15.22 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:05<00:16, 16.96s/ url]
Dl Size...: 93%|█████████▎| 1399/1501 [01:05<00:06, 15.22 MiB/s]
Dl Size...: 93%|█████████▎| 1400/1501 [01:05<00:06, 14.69 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:05<00:16, 16.96s/ url]
Dl Size...: 93%|█████████▎| 1400/1501 [01:05<00:06, 14.69 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:05<00:16, 16.96s/ url]
Dl Size...: 93%|█████████▎| 1401/1501 [01:05<00:06, 14.69 MiB/s]
Dl Size...: 93%|█████████▎| 1402/1501 [01:05<00:06, 14.55 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:05<00:16, 16.96s/ url]
Dl Size...: 93%|█████████▎| 1402/1501 [01:05<00:06, 14.55 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:05<00:16, 16.96s/ url]
Dl Size...: 93%|█████████▎| 1403/1501 [01:05<00:06, 14.55 MiB/s]
Dl Size...: 94%|█████████▎| 1404/1501 [01:05<00:06, 15.37 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:05<00:16, 16.96s/ url]
Dl Size...: 94%|█████████▎| 1404/1501 [01:05<00:06, 15.37 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:05<00:16, 16.96s/ url]
Dl Size...: 94%|█████████▎| 1405/1501 [01:05<00:06, 15.37 MiB/s]
Dl Size...: 94%|█████████▎| 1406/1501 [01:06<00:06, 14.92 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:06<00:16, 16.96s/ url]
Dl Size...: 94%|█████████▎| 1406/1501 [01:06<00:06, 14.92 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:06<00:16, 16.96s/ url]
Dl Size...: 94%|█████████▎| 1407/1501 [01:06<00:06, 14.92 MiB/s]
Dl Size...: 94%|█████████▍| 1408/1501 [01:06<00:06, 15.06 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:06<00:16, 16.96s/ url]
Dl Size...: 94%|█████████▍| 1408/1501 [01:06<00:06, 15.06 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:06<00:16, 16.96s/ url]
Dl Size...: 94%|█████████▍| 1409/1501 [01:06<00:06, 15.06 MiB/s]
Dl Size...: 94%|█████████▍| 1410/1501 [01:06<00:05, 15.38 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:06<00:16, 16.96s/ url]
Dl Size...: 94%|█████████▍| 1410/1501 [01:06<00:05, 15.38 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:06<00:16, 16.96s/ url]
Dl Size...: 94%|█████████▍| 1411/1501 [01:06<00:05, 15.38 MiB/s]
Dl Size...: 94%|█████████▍| 1412/1501 [01:06<00:05, 14.90 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:06<00:16, 16.96s/ url]
Dl Size...: 94%|█████████▍| 1412/1501 [01:06<00:05, 14.90 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:06<00:16, 16.96s/ url]
Dl Size...: 94%|█████████▍| 1413/1501 [01:06<00:05, 14.90 MiB/s]
Dl Size...: 94%|█████████▍| 1414/1501 [01:06<00:05, 14.93 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:06<00:16, 16.96s/ url]
Dl Size...: 94%|█████████▍| 1414/1501 [01:06<00:05, 14.93 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:06<00:16, 16.96s/ url]
Dl Size...: 94%|█████████▍| 1415/1501 [01:06<00:05, 14.93 MiB/s]
Dl Size...: 94%|█████████▍| 1416/1501 [01:06<00:05, 15.32 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:06<00:16, 16.96s/ url]
Dl Size...: 94%|█████████▍| 1416/1501 [01:06<00:05, 15.32 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:06<00:16, 16.96s/ url]
Dl Size...: 94%|█████████▍| 1417/1501 [01:06<00:05, 15.32 MiB/s]
Dl Size...: 94%|█████████▍| 1418/1501 [01:06<00:05, 14.96 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:06<00:16, 16.96s/ url]
Dl Size...: 94%|█████████▍| 1418/1501 [01:06<00:05, 14.96 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:06<00:16, 16.96s/ url]
Dl Size...: 95%|█████████▍| 1419/1501 [01:06<00:05, 14.96 MiB/s]
Dl Size...: 95%|█████████▍| 1420/1501 [01:06<00:05, 14.99 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:06<00:16, 16.96s/ url]
Dl Size...: 95%|█████████▍| 1420/1501 [01:06<00:05, 14.99 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:07<00:16, 16.96s/ url]
Dl Size...: 95%|█████████▍| 1421/1501 [01:07<00:05, 14.99 MiB/s]
Dl Size...: 95%|█████████▍| 1422/1501 [01:07<00:05, 15.28 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:07<00:16, 16.96s/ url]
Dl Size...: 95%|█████████▍| 1422/1501 [01:07<00:05, 15.28 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:07<00:16, 16.96s/ url]
Dl Size...: 95%|█████████▍| 1423/1501 [01:07<00:05, 15.28 MiB/s]
Dl Size...: 95%|█████████▍| 1424/1501 [01:07<00:05, 15.29 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:07<00:16, 16.96s/ url]
Dl Size...: 95%|█████████▍| 1424/1501 [01:07<00:05, 15.29 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:07<00:16, 16.96s/ url]
Dl Size...: 95%|█████████▍| 1425/1501 [01:07<00:04, 15.29 MiB/s]
Dl Size...: 95%|█████████▌| 1426/1501 [01:07<00:04, 15.75 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:07<00:16, 16.96s/ url]
Dl Size...: 95%|█████████▌| 1426/1501 [01:07<00:04, 15.75 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:07<00:16, 16.96s/ url]
Dl Size...: 95%|█████████▌| 1427/1501 [01:07<00:04, 15.75 MiB/s]
Dl Size...: 95%|█████████▌| 1428/1501 [01:07<00:04, 15.34 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:07<00:16, 16.96s/ url]
Dl Size...: 95%|█████████▌| 1428/1501 [01:07<00:04, 15.34 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:07<00:16, 16.96s/ url]
Dl Size...: 95%|█████████▌| 1429/1501 [01:07<00:04, 15.34 MiB/s]
Dl Size...: 95%|█████████▌| 1430/1501 [01:07<00:04, 14.49 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:07<00:16, 16.96s/ url]
Dl Size...: 95%|█████████▌| 1430/1501 [01:07<00:04, 14.49 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:07<00:16, 16.96s/ url]
Dl Size...: 95%|█████████▌| 1431/1501 [01:07<00:04, 14.49 MiB/s]
Dl Size...: 95%|█████████▌| 1432/1501 [01:07<00:05, 12.97 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:07<00:16, 16.96s/ url]
Dl Size...: 95%|█████████▌| 1432/1501 [01:07<00:05, 12.97 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:07<00:16, 16.96s/ url]
Dl Size...: 95%|█████████▌| 1433/1501 [01:07<00:05, 12.97 MiB/s]
Dl Size...: 96%|█████████▌| 1434/1501 [01:08<00:05, 11.75 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:08<00:16, 16.96s/ url]
Dl Size...: 96%|█████████▌| 1434/1501 [01:08<00:05, 11.75 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:08<00:16, 16.96s/ url]
Dl Size...: 96%|█████████▌| 1435/1501 [01:08<00:05, 11.75 MiB/s]
Dl Size...: 96%|█████████▌| 1436/1501 [01:08<00:05, 11.36 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:08<00:16, 16.96s/ url]
Dl Size...: 96%|█████████▌| 1436/1501 [01:08<00:05, 11.36 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:08<00:16, 16.96s/ url]
Dl Size...: 96%|█████████▌| 1437/1501 [01:08<00:05, 11.36 MiB/s]
Dl Size...: 96%|█████████▌| 1438/1501 [01:08<00:05, 10.97 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:08<00:16, 16.96s/ url]
Dl Size...: 96%|█████████▌| 1438/1501 [01:08<00:05, 10.97 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:08<00:16, 16.96s/ url]
Dl Size...: 96%|█████████▌| 1439/1501 [01:08<00:05, 10.97 MiB/s]
Dl Size...: 96%|█████████▌| 1440/1501 [01:08<00:05, 11.00 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:08<00:16, 16.96s/ url]
Dl Size...: 96%|█████████▌| 1440/1501 [01:08<00:05, 11.00 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:08<00:16, 16.96s/ url]
Dl Size...: 96%|█████████▌| 1441/1501 [01:08<00:05, 11.00 MiB/s]
Dl Size...: 96%|█████████▌| 1442/1501 [01:08<00:05, 10.96 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:08<00:16, 16.96s/ url]
Dl Size...: 96%|█████████▌| 1442/1501 [01:08<00:05, 10.96 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:08<00:16, 16.96s/ url]
Dl Size...: 96%|█████████▌| 1443/1501 [01:08<00:05, 10.96 MiB/s]
Dl Size...: 96%|█████████▌| 1444/1501 [01:08<00:05, 10.84 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:08<00:16, 16.96s/ url]
Dl Size...: 96%|█████████▌| 1444/1501 [01:08<00:05, 10.84 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:09<00:16, 16.96s/ url]
Dl Size...: 96%|█████████▋| 1445/1501 [01:09<00:05, 10.84 MiB/s]
Dl Size...: 96%|█████████▋| 1446/1501 [01:09<00:05, 10.78 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:09<00:16, 16.96s/ url]
Dl Size...: 96%|█████████▋| 1446/1501 [01:09<00:05, 10.78 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:09<00:16, 16.96s/ url]
Dl Size...: 96%|█████████▋| 1447/1501 [01:09<00:05, 10.78 MiB/s]
Dl Size...: 96%|█████████▋| 1448/1501 [01:09<00:05, 9.67 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:09<00:16, 16.96s/ url]
Dl Size...: 96%|█████████▋| 1448/1501 [01:09<00:05, 9.67 MiB/s]
Dl Size...: 97%|█████████▋| 1449/1501 [01:09<00:05, 9.19 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:09<00:16, 16.96s/ url]
Dl Size...: 97%|█████████▋| 1449/1501 [01:09<00:05, 9.19 MiB/s]
Dl Size...: 97%|█████████▋| 1450/1501 [01:09<00:05, 9.09 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:09<00:16, 16.96s/ url]
Dl Size...: 97%|█████████▋| 1450/1501 [01:09<00:05, 9.09 MiB/s]
Dl Size...: 97%|█████████▋| 1451/1501 [01:09<00:05, 8.75 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:09<00:16, 16.96s/ url]
Dl Size...: 97%|█████████▋| 1451/1501 [01:09<00:05, 8.75 MiB/s]
Dl Size...: 97%|█████████▋| 1452/1501 [01:09<00:05, 8.69 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:09<00:16, 16.96s/ url]
Dl Size...: 97%|█████████▋| 1452/1501 [01:09<00:05, 8.69 MiB/s]
Dl Size...: 97%|█████████▋| 1453/1501 [01:10<00:05, 8.46 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:10<00:16, 16.96s/ url]
Dl Size...: 97%|█████████▋| 1453/1501 [01:10<00:05, 8.46 MiB/s]
Dl Size...: 97%|█████████▋| 1454/1501 [01:10<00:05, 8.55 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:10<00:16, 16.96s/ url]
Dl Size...: 97%|█████████▋| 1454/1501 [01:10<00:05, 8.55 MiB/s]
Dl Size...: 97%|█████████▋| 1455/1501 [01:10<00:05, 8.44 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:10<00:16, 16.96s/ url]
Dl Size...: 97%|█████████▋| 1455/1501 [01:10<00:05, 8.44 MiB/s]
Dl Size...: 97%|█████████▋| 1456/1501 [01:10<00:05, 8.64 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:10<00:16, 16.96s/ url]
Dl Size...: 97%|█████████▋| 1456/1501 [01:10<00:05, 8.64 MiB/s]
Dl Size...: 97%|█████████▋| 1457/1501 [01:10<00:05, 8.23 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:10<00:16, 16.96s/ url]
Dl Size...: 97%|█████████▋| 1457/1501 [01:10<00:05, 8.23 MiB/s]
Dl Size...: 97%|█████████▋| 1458/1501 [01:10<00:05, 8.58 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:10<00:16, 16.96s/ url]
Dl Size...: 97%|█████████▋| 1458/1501 [01:10<00:05, 8.58 MiB/s]
Dl Size...: 97%|█████████▋| 1459/1501 [01:10<00:04, 8.65 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:10<00:16, 16.96s/ url]
Dl Size...: 97%|█████████▋| 1459/1501 [01:10<00:04, 8.65 MiB/s]
Dl Size...: 97%|█████████▋| 1460/1501 [01:10<00:04, 8.79 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:10<00:16, 16.96s/ url]
Dl Size...: 97%|█████████▋| 1460/1501 [01:10<00:04, 8.79 MiB/s]
Dl Size...: 97%|█████████▋| 1461/1501 [01:10<00:04, 8.79 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:10<00:16, 16.96s/ url]
Dl Size...: 97%|█████████▋| 1461/1501 [01:10<00:04, 8.79 MiB/s]
Dl Size...: 97%|█████████▋| 1462/1501 [01:11<00:04, 8.56 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:11<00:16, 16.96s/ url]
Dl Size...: 97%|█████████▋| 1462/1501 [01:11<00:04, 8.56 MiB/s]
Dl Size...: 97%|█████████▋| 1463/1501 [01:11<00:04, 8.66 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:11<00:16, 16.96s/ url]
Dl Size...: 97%|█████████▋| 1463/1501 [01:11<00:04, 8.66 MiB/s]
Dl Size...: 98%|█████████▊| 1464/1501 [01:11<00:04, 8.43 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:11<00:16, 16.96s/ url]
Dl Size...: 98%|█████████▊| 1464/1501 [01:11<00:04, 8.43 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:11<00:16, 16.96s/ url]
Dl Size...: 98%|█████████▊| 1465/1501 [01:11<00:04, 8.43 MiB/s]
Dl Size...: 98%|█████████▊| 1466/1501 [01:11<00:03, 9.06 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:11<00:16, 16.96s/ url]
Dl Size...: 98%|█████████▊| 1466/1501 [01:11<00:03, 9.06 MiB/s]
Dl Size...: 98%|█████████▊| 1467/1501 [01:11<00:03, 8.80 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:11<00:16, 16.96s/ url]
Dl Size...: 98%|█████████▊| 1467/1501 [01:11<00:03, 8.80 MiB/s]
Dl Size...: 98%|█████████▊| 1468/1501 [01:11<00:03, 9.07 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:11<00:16, 16.96s/ url]
Dl Size...: 98%|█████████▊| 1468/1501 [01:11<00:03, 9.07 MiB/s]
Dl Size...: 98%|█████████▊| 1469/1501 [01:11<00:03, 8.98 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:11<00:16, 16.96s/ url]
Dl Size...: 98%|█████████▊| 1469/1501 [01:11<00:03, 8.98 MiB/s]
Dl Size...: 98%|█████████▊| 1470/1501 [01:11<00:03, 9.03 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:11<00:16, 16.96s/ url]
Dl Size...: 98%|█████████▊| 1470/1501 [01:11<00:03, 9.03 MiB/s]
Dl Size...: 98%|█████████▊| 1471/1501 [01:12<00:03, 9.05 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:12<00:16, 16.96s/ url]
Dl Size...: 98%|█████████▊| 1471/1501 [01:12<00:03, 9.05 MiB/s]
Dl Size...: 98%|█████████▊| 1472/1501 [01:12<00:03, 8.56 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:12<00:16, 16.96s/ url]
Dl Size...: 98%|█████████▊| 1472/1501 [01:12<00:03, 8.56 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:12<00:16, 16.96s/ url]
Dl Size...: 98%|█████████▊| 1473/1501 [01:12<00:03, 8.56 MiB/s]
Dl Size...: 98%|█████████▊| 1474/1501 [01:12<00:02, 9.30 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:12<00:16, 16.96s/ url]
Dl Size...: 98%|█████████▊| 1474/1501 [01:12<00:02, 9.30 MiB/s]
Dl Size...: 98%|█████████▊| 1475/1501 [01:12<00:02, 8.82 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:12<00:16, 16.96s/ url]
Dl Size...: 98%|█████████▊| 1475/1501 [01:12<00:02, 8.82 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:12<00:16, 16.96s/ url]
Dl Size...: 98%|█████████▊| 1476/1501 [01:12<00:02, 8.82 MiB/s]
Dl Size...: 98%|█████████▊| 1477/1501 [01:12<00:02, 9.40 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:12<00:16, 16.96s/ url]
Dl Size...: 98%|█████████▊| 1477/1501 [01:12<00:02, 9.40 MiB/s]
Dl Size...: 98%|█████████▊| 1478/1501 [01:12<00:02, 8.84 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:12<00:16, 16.96s/ url]
Dl Size...: 98%|█████████▊| 1478/1501 [01:12<00:02, 8.84 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:12<00:16, 16.96s/ url]
Dl Size...: 99%|█████████▊| 1479/1501 [01:12<00:02, 8.84 MiB/s]
Dl Size...: 99%|█████████▊| 1480/1501 [01:13<00:02, 9.31 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:13<00:16, 16.96s/ url]
Dl Size...: 99%|█████████▊| 1480/1501 [01:13<00:02, 9.31 MiB/s]
Dl Size...: 99%|█████████▊| 1481/1501 [01:13<00:02, 8.96 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:13<00:16, 16.96s/ url]
Dl Size...: 99%|█████████▊| 1481/1501 [01:13<00:02, 8.96 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:13<00:16, 16.96s/ url]
Dl Size...: 99%|█████████▊| 1482/1501 [01:13<00:02, 8.96 MiB/s]
Dl Size...: 99%|█████████▉| 1483/1501 [01:13<00:01, 9.37 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:13<00:16, 16.96s/ url]
Dl Size...: 99%|█████████▉| 1483/1501 [01:13<00:01, 9.37 MiB/s]
Dl Size...: 99%|█████████▉| 1484/1501 [01:13<00:01, 9.08 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:13<00:16, 16.96s/ url]
Dl Size...: 99%|█████████▉| 1484/1501 [01:13<00:01, 9.08 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:13<00:16, 16.96s/ url]
Dl Size...: 99%|█████████▉| 1485/1501 [01:13<00:01, 9.08 MiB/s]
Dl Size...: 99%|█████████▉| 1486/1501 [01:13<00:01, 9.51 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:13<00:16, 16.96s/ url]
Dl Size...: 99%|█████████▉| 1486/1501 [01:13<00:01, 9.51 MiB/s]
Dl Size...: 99%|█████████▉| 1487/1501 [01:13<00:01, 9.12 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:13<00:16, 16.96s/ url]
Dl Size...: 99%|█████████▉| 1487/1501 [01:13<00:01, 9.12 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:13<00:16, 16.96s/ url]
Dl Size...: 99%|█████████▉| 1488/1501 [01:13<00:01, 9.12 MiB/s]
Dl Size...: 99%|█████████▉| 1489/1501 [01:14<00:01, 9.53 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:14<00:16, 16.96s/ url]
Dl Size...: 99%|█████████▉| 1489/1501 [01:14<00:01, 9.53 MiB/s]
Dl Size...: 99%|█████████▉| 1490/1501 [01:14<00:01, 8.96 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:14<00:16, 16.96s/ url]
Dl Size...: 99%|█████████▉| 1490/1501 [01:14<00:01, 8.96 MiB/s]
Dl Size...: 99%|█████████▉| 1491/1501 [01:14<00:01, 8.87 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:14<00:16, 16.96s/ url]
Dl Size...: 99%|█████████▉| 1491/1501 [01:14<00:01, 8.87 MiB/s]
Dl Size...: 99%|█████████▉| 1492/1501 [01:14<00:01, 8.29 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:14<00:16, 16.96s/ url]
Dl Size...: 99%|█████████▉| 1492/1501 [01:14<00:01, 8.29 MiB/s]
Dl Size...: 99%|█████████▉| 1493/1501 [01:14<00:01, 7.74 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:14<00:16, 16.96s/ url]
Dl Size...: 99%|█████████▉| 1493/1501 [01:14<00:01, 7.74 MiB/s]
Dl Size...: 100%|█████████▉| 1494/1501 [01:14<00:00, 7.34 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:14<00:16, 16.96s/ url]
Dl Size...: 100%|█████████▉| 1494/1501 [01:14<00:00, 7.34 MiB/s]
Dl Size...: 100%|█████████▉| 1495/1501 [01:14<00:00, 7.05 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:14<00:16, 16.96s/ url]
Dl Size...: 100%|█████████▉| 1495/1501 [01:14<00:00, 7.05 MiB/s]
Dl Size...: 100%|█████████▉| 1496/1501 [01:15<00:00, 7.63 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:15<00:16, 16.96s/ url]
Dl Size...: 100%|█████████▉| 1496/1501 [01:15<00:00, 7.63 MiB/s]
Dl Size...: 100%|█████████▉| 1497/1501 [01:15<00:00, 7.41 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:15<00:16, 16.96s/ url]
Dl Size...: 100%|█████████▉| 1497/1501 [01:15<00:00, 7.41 MiB/s]
Dl Size...: 100%|█████████▉| 1498/1501 [01:15<00:00, 7.10 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:15<00:16, 16.96s/ url]
Dl Size...: 100%|█████████▉| 1498/1501 [01:15<00:00, 7.10 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:15<00:16, 16.96s/ url]
Dl Size...: 100%|█████████▉| 1499/1501 [01:15<00:00, 7.10 MiB/s]
Dl Size...: 100%|█████████▉| 1500/1501 [01:15<00:00, 7.55 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:15<00:16, 16.96s/ url]
Dl Size...: 100%|█████████▉| 1500/1501 [01:15<00:00, 7.55 MiB/s]
Dl Size...: 100%|██████████| 1501/1501 [01:15<00:00, 7.80 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:15<00:16, 16.96s/ url]
Dl Size...: 100%|██████████| 1501/1501 [01:15<00:00, 7.80 MiB/s]
Dl Completed...: 100%|██████████| 3/3 [01:15<00:00, 29.83s/ url]
Dl Completed...: 100%|██████████| 3/3 [01:15<00:00, 29.83s/ url]
Dl Size...: 100%|██████████| 1501/1501 [01:15<00:00, 7.80 MiB/s]
Dl Size...: 100%|██████████| 1501/1501 [01:15<00:00, 19.81 MiB/s]
Dl Completed...: 100%|██████████| 3/3 [01:15<00:00, 25.26s/ url]
Generating splits...: 0%| | 0/3 [00:00<?, ? splits/s]
Generating train examples...: 0 examples [00:00, ? examples/s]
2024-09-05 18:39:03.629872: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: SSE4.1 SSE4.2 AVX AVX2 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
Generating train examples...: 1 examples [00:01, 1.25s/ examples]
Generating train examples...: 314 examples [00:01, 322.66 examples/s]
Generating train examples...: 634 examples [00:01, 691.15 examples/s]
Generating train examples...: 951 examples [00:01, 1074.35 examples/s]
Generating train examples...: 1269 examples [00:01, 1454.65 examples/s]
Generating train examples...: 1589 examples [00:01, 1812.25 examples/s]
Generating train examples...: 1912 examples [00:01, 2133.62 examples/s]
Generating train examples...: 2230 examples [00:01, 2388.93 examples/s]
Generating train examples...: 2550 examples [00:02, 2598.35 examples/s]
Generating train examples...: 2873 examples [00:02, 2768.73 examples/s]
Generating train examples...: 3194 examples [00:02, 2891.48 examples/s]
Generating train examples...: 3515 examples [00:02, 2979.57 examples/s]
Generating train examples...: 3834 examples [00:02, 3039.58 examples/s]
Generating train examples...: 4153 examples [00:02, 3077.46 examples/s]
Generating train examples...: 4472 examples [00:02, 3093.91 examples/s]
Generating train examples...: 4789 examples [00:02, 3109.22 examples/s]
Generating train examples...: 5106 examples [00:02, 3125.82 examples/s]
Generating train examples...: 5423 examples [00:02, 3135.81 examples/s]
Generating train examples...: 5740 examples [00:03, 3134.07 examples/s]
Generating train examples...: 6056 examples [00:03, 3137.84 examples/s]
Generating train examples...: 6374 examples [00:03, 3147.79 examples/s]
Generating train examples...: 6692 examples [00:03, 3154.80 examples/s]
Generating train examples...: 7009 examples [00:03, 3157.52 examples/s]
Generating train examples...: 7326 examples [00:03, 3155.86 examples/s]
Generating train examples...: 7645 examples [00:03, 3165.17 examples/s]
Generating train examples...: 7964 examples [00:03, 3171.83 examples/s]
Generating train examples...: 8282 examples [00:03, 3172.01 examples/s]
Generating train examples...: 8600 examples [00:03, 3174.17 examples/s]
Generating train examples...: 8918 examples [00:04, 3164.69 examples/s]
Generating train examples...: 9238 examples [00:04, 3174.83 examples/s]
Generating train examples...: 9556 examples [00:04, 3174.47 examples/s]
Generating train examples...: 9874 examples [00:04, 3160.75 examples/s]
Generating train examples...: 10191 examples [00:04, 3162.71 examples/s]
Generating train examples...: 10508 examples [00:04, 3160.53 examples/s]
Generating train examples...: 10825 examples [00:04, 3158.50 examples/s]
Generating train examples...: 11146 examples [00:04, 3173.75 examples/s]
Generating train examples...: 11464 examples [00:04, 3173.02 examples/s]
Generating train examples...: 11782 examples [00:04, 3167.34 examples/s]
Generating train examples...: 12105 examples [00:05, 3184.68 examples/s]
Generating train examples...: 12424 examples [00:05, 3158.82 examples/s]
Generating train examples...: 12742 examples [00:05, 3162.26 examples/s]
Generating train examples...: 13060 examples [00:05, 3166.89 examples/s]
Generating train examples...: 13377 examples [00:05, 3160.75 examples/s]
Generating train examples...: 13697 examples [00:05, 3169.54 examples/s]
Generating train examples...: 14014 examples [00:05, 3160.19 examples/s]
Generating train examples...: 14331 examples [00:05, 3162.89 examples/s]
Generating train examples...: 14648 examples [00:05, 3158.77 examples/s]
Generating train examples...: 14965 examples [00:05, 3159.11 examples/s]
Generating train examples...: 15284 examples [00:06, 3167.85 examples/s]
Generating train examples...: 15602 examples [00:06, 3171.28 examples/s]
Generating train examples...: 15920 examples [00:06, 3169.57 examples/s]
Generating train examples...: 16238 examples [00:06, 3169.04 examples/s]
Generating train examples...: 16555 examples [00:06, 3153.58 examples/s]
Generating train examples...: 16871 examples [00:06, 3146.00 examples/s]
Generating train examples...: 17186 examples [00:06, 3142.91 examples/s]
Generating train examples...: 17504 examples [00:06, 3153.77 examples/s]
Generating train examples...: 17820 examples [00:06, 3152.73 examples/s]
Generating train examples...: 18136 examples [00:06, 3146.59 examples/s]
Generating train examples...: 18451 examples [00:07, 3147.11 examples/s]
Generating train examples...: 18766 examples [00:07, 3144.53 examples/s]
Generating train examples...: 19082 examples [00:07, 3148.50 examples/s]
Generating train examples...: 19401 examples [00:07, 3159.57 examples/s]
Generating train examples...: 19719 examples [00:07, 3163.03 examples/s]
Generating train examples...: 20036 examples [00:07, 3138.01 examples/s]
Generating train examples...: 20350 examples [00:07, 3116.29 examples/s]
Generating train examples...: 20662 examples [00:07, 3116.79 examples/s]
Generating train examples...: 20977 examples [00:07, 3124.03 examples/s]
Generating train examples...: 21294 examples [00:07, 3137.41 examples/s]
Generating train examples...: 21608 examples [00:08, 3128.56 examples/s]
Generating train examples...: 21921 examples [00:08, 3124.36 examples/s]
Generating train examples...: 22234 examples [00:08, 3102.05 examples/s]
Generating train examples...: 22545 examples [00:08, 3104.11 examples/s]
Generating train examples...: 22861 examples [00:08, 3119.34 examples/s]
Generating train examples...: 23173 examples [00:08, 3106.02 examples/s]
Generating train examples...: 23484 examples [00:08, 3104.63 examples/s]
Generating train examples...: 23798 examples [00:08, 3113.92 examples/s]
Generating train examples...: 24110 examples [00:08, 3111.46 examples/s]
Generating train examples...: 24423 examples [00:08, 3116.50 examples/s]
Generating train examples...: 24735 examples [00:09, 3111.52 examples/s]
Generating train examples...: 25047 examples [00:09, 3092.65 examples/s]
Generating train examples...: 25359 examples [00:09, 3100.35 examples/s]
Generating train examples...: 25672 examples [00:09, 3108.94 examples/s]
Generating train examples...: 25987 examples [00:09, 3121.16 examples/s]
Generating train examples...: 26302 examples [00:09, 3128.60 examples/s]
Generating train examples...: 26620 examples [00:09, 3142.46 examples/s]
Generating train examples...: 26936 examples [00:09, 3147.65 examples/s]
Generating train examples...: 27251 examples [00:09, 3142.23 examples/s]
Generating train examples...: 27573 examples [00:09, 3162.57 examples/s]
Generating train examples...: 27890 examples [00:10, 3161.00 examples/s]
Generating train examples...: 28207 examples [00:10, 3157.99 examples/s]
Generating train examples...: 28523 examples [00:10, 3151.97 examples/s]
Generating train examples...: 28839 examples [00:10, 3141.16 examples/s]
Generating train examples...: 29155 examples [00:10, 3145.64 examples/s]
Generating train examples...: 29476 examples [00:10, 3162.28 examples/s]
Generating train examples...: 29793 examples [00:10, 3161.96 examples/s]
Generating train examples...: 30110 examples [00:10, 3160.48 examples/s]
Generating train examples...: 30427 examples [00:10, 3148.09 examples/s]
Generating train examples...: 30744 examples [00:10, 3152.15 examples/s]
Generating train examples...: 31061 examples [00:11, 3157.43 examples/s]
Generating train examples...: 31377 examples [00:11, 3151.08 examples/s]
Generating train examples...: 31696 examples [00:11, 3160.05 examples/s]
Generating train examples...: 32017 examples [00:11, 3173.78 examples/s]
Generating train examples...: 32335 examples [00:11, 3169.88 examples/s]
Generating train examples...: 32652 examples [00:11, 3169.37 examples/s]
Generating train examples...: 32969 examples [00:11, 3161.22 examples/s]
Generating train examples...: 33286 examples [00:11, 3162.51 examples/s]
Generating train examples...: 33604 examples [00:11, 3165.30 examples/s]
Generating train examples...: 33921 examples [00:12, 3157.03 examples/s]
Generating train examples...: 34237 examples [00:12, 3137.52 examples/s]
Generating train examples...: 34551 examples [00:12, 3120.90 examples/s]
Generating train examples...: 34864 examples [00:12, 3111.53 examples/s]
Generating train examples...: 35179 examples [00:12, 3122.01 examples/s]
Generating train examples...: 35492 examples [00:12, 3119.85 examples/s]
Generating train examples...: 35808 examples [00:12, 3131.29 examples/s]
Generating train examples...: 36122 examples [00:12, 3109.62 examples/s]
Generating train examples...: 36434 examples [00:12, 3105.79 examples/s]
Generating train examples...: 36747 examples [00:12, 3110.39 examples/s]
Generating train examples...: 37059 examples [00:13, 3113.06 examples/s]
Generating train examples...: 37371 examples [00:13, 3107.99 examples/s]
Generating train examples...: 37684 examples [00:13, 3111.34 examples/s]
Generating train examples...: 37996 examples [00:13, 3106.97 examples/s]
Generating train examples...: 38310 examples [00:13, 3116.63 examples/s]
Generating train examples...: 38622 examples [00:13, 3114.58 examples/s]
Generating train examples...: 38934 examples [00:13, 3113.72 examples/s]
Generating train examples...: 39246 examples [00:13, 3099.90 examples/s]
Generating train examples...: 39557 examples [00:13, 3089.48 examples/s]
Generating train examples...: 39867 examples [00:13, 3090.37 examples/s]
Generating train examples...: 40181 examples [00:14, 3102.17 examples/s]
Generating train examples...: 40492 examples [00:14, 3097.34 examples/s]
Generating train examples...: 40806 examples [00:14, 3107.85 examples/s]
Generating train examples...: 41118 examples [00:14, 3109.63 examples/s]
Generating train examples...: 41429 examples [00:14, 3098.72 examples/s]
Generating train examples...: 41740 examples [00:14, 3099.75 examples/s]
Generating train examples...: 42051 examples [00:14, 3102.40 examples/s]
Generating train examples...: 42365 examples [00:14, 3111.09 examples/s]
Generating train examples...: 42678 examples [00:14, 3112.62 examples/s]
Generating train examples...: 42990 examples [00:14, 3113.13 examples/s]
Generating train examples...: 43303 examples [00:15, 3117.94 examples/s]
Generating train examples...: 43616 examples [00:15, 3118.53 examples/s]
Generating train examples...: 43928 examples [00:15, 3116.50 examples/s]
Generating train examples...: 44240 examples [00:15, 3105.63 examples/s]
Generating train examples...: 44555 examples [00:15, 3116.54 examples/s]
Generating train examples...: 44870 examples [00:15, 3123.88 examples/s]
Generating train examples...: 45183 examples [00:15, 3123.08 examples/s]
Generating train examples...: 45496 examples [00:15, 3117.08 examples/s]
Generating train examples...: 45814 examples [00:15, 3133.19 examples/s]
Generating train examples...: 46133 examples [00:15, 3149.36 examples/s]
Generating train examples...: 46453 examples [00:16, 3163.65 examples/s]
Generating train examples...: 46770 examples [00:16, 3148.47 examples/s]
Generating train examples...: 47087 examples [00:16, 3152.23 examples/s]
Generating train examples...: 47406 examples [00:16, 3161.16 examples/s]
Generating train examples...: 47723 examples [00:16, 3160.12 examples/s]
Generating train examples...: 48041 examples [00:16, 3162.86 examples/s]
Generating train examples...: 48358 examples [00:16, 3154.70 examples/s]
Generating train examples...: 48675 examples [00:16, 3158.19 examples/s]
Generating train examples...: 48991 examples [00:16, 3136.28 examples/s]
Generating train examples...: 49307 examples [00:16, 3141.67 examples/s]
Generating train examples...: 49623 examples [00:17, 3146.73 examples/s]
Generating train examples...: 49938 examples [00:17, 3142.98 examples/s]
Generating train examples...: 50253 examples [00:17, 3124.95 examples/s]
Generating train examples...: 50566 examples [00:17, 3124.18 examples/s]
Generating train examples...: 50881 examples [00:17, 3129.33 examples/s]
Generating train examples...: 51195 examples [00:17, 3131.66 examples/s]
Generating train examples...: 51509 examples [00:17, 3133.43 examples/s]
Generating train examples...: 51823 examples [00:17, 3133.05 examples/s]
Generating train examples...: 52141 examples [00:17, 3145.29 examples/s]
Generating train examples...: 52456 examples [00:17, 3144.23 examples/s]
Generating train examples...: 52771 examples [00:18, 3137.62 examples/s]
Generating train examples...: 53086 examples [00:18, 3139.20 examples/s]
Generating train examples...: 53400 examples [00:18, 3130.34 examples/s]
Generating train examples...: 53714 examples [00:18, 3130.58 examples/s]
Generating train examples...: 54028 examples [00:18, 3119.14 examples/s]
Generating train examples...: 54340 examples [00:18, 3078.34 examples/s]
Generating train examples...: 54648 examples [00:18, 3075.93 examples/s]
Generating train examples...: 54961 examples [00:18, 3090.01 examples/s]
Generating train examples...: 55271 examples [00:18, 3087.37 examples/s]
Generating train examples...: 55581 examples [00:18, 3090.75 examples/s]
Generating train examples...: 55891 examples [00:19, 3090.88 examples/s]
Generating train examples...: 56201 examples [00:19, 3090.74 examples/s]
Generating train examples...: 56511 examples [00:19, 3082.66 examples/s]
Generating train examples...: 56820 examples [00:19, 3073.04 examples/s]
Generating train examples...: 57135 examples [00:19, 3092.89 examples/s]
Generating train examples...: 57451 examples [00:19, 3111.91 examples/s]
Generating train examples...: 57763 examples [00:19, 3096.78 examples/s]
Generating train examples...: 58077 examples [00:19, 3106.32 examples/s]
Generating train examples...: 58388 examples [00:19, 3090.45 examples/s]
Generating train examples...: 58698 examples [00:19, 3077.67 examples/s]
Generating train examples...: 59010 examples [00:20, 3087.11 examples/s]
Generating train examples...: 59320 examples [00:20, 3089.23 examples/s]
Generating train examples...: 59629 examples [00:20, 3086.99 examples/s]
Generating train examples...: 59940 examples [00:20, 3092.66 examples/s]
Generating train examples...: 60250 examples [00:20, 3083.41 examples/s]
Generating train examples...: 60565 examples [00:20, 3101.01 examples/s]
Generating train examples...: 60876 examples [00:20, 3080.83 examples/s]
Generating train examples...: 61186 examples [00:20, 3085.54 examples/s]
Generating train examples...: 61498 examples [00:20, 3095.33 examples/s]
Generating train examples...: 61809 examples [00:20, 3098.99 examples/s]
Generating train examples...: 62119 examples [00:21, 3095.74 examples/s]
Generating train examples...: 62430 examples [00:21, 3097.59 examples/s]
Generating train examples...: 62747 examples [00:21, 3118.13 examples/s]
Generating train examples...: 63059 examples [00:21, 3108.32 examples/s]
Generating train examples...: 63370 examples [00:21, 3106.79 examples/s]
Generating train examples...: 63681 examples [00:21, 3099.16 examples/s]
Generating train examples...: 63993 examples [00:21, 3103.41 examples/s]
Generating train examples...: 64305 examples [00:21, 3107.39 examples/s]
Generating train examples...: 64617 examples [00:21, 3110.46 examples/s]
Generating train examples...: 64933 examples [00:21, 3122.54 examples/s]
Generating train examples...: 65246 examples [00:22, 3122.85 examples/s]
Generating train examples...: 65562 examples [00:22, 3132.73 examples/s]
Generating train examples...: 65877 examples [00:22, 3135.33 examples/s]
Generating train examples...: 66191 examples [00:22, 3136.30 examples/s]
Generating train examples...: 66505 examples [00:22, 3117.20 examples/s]
Generating train examples...: 66817 examples [00:22, 3099.86 examples/s]
Generating train examples...: 67128 examples [00:22, 3099.12 examples/s]
Generating train examples...: 67442 examples [00:22, 3108.70 examples/s]
Generating train examples...: 67753 examples [00:22, 3087.32 examples/s]
Generating train examples...: 68063 examples [00:22, 3088.30 examples/s]
Generating train examples...: 68380 examples [00:23, 3112.43 examples/s]
Generating train examples...: 68698 examples [00:23, 3131.82 examples/s]
Generating train examples...: 69020 examples [00:23, 3156.95 examples/s]
Generating train examples...: 69341 examples [00:23, 3172.67 examples/s]
Generating train examples...: 69667 examples [00:23, 3196.27 examples/s]
Generating train examples...: 69990 examples [00:23, 3205.83 examples/s]
Generating train examples...: 70311 examples [00:23, 3196.11 examples/s]
Generating train examples...: 70636 examples [00:23, 3210.08 examples/s]
Generating train examples...: 70958 examples [00:23, 3197.72 examples/s]
Generating train examples...: 71278 examples [00:23, 3196.83 examples/s]
Generating train examples...: 71598 examples [00:24, 3184.63 examples/s]
Generating train examples...: 71917 examples [00:24, 3161.75 examples/s]
Generating train examples...: 72234 examples [00:24, 3147.72 examples/s]
Generating train examples...: 72556 examples [00:24, 3167.15 examples/s]
Generating train examples...: 72873 examples [00:24, 3165.91 examples/s]
Generating train examples...: 73190 examples [00:24, 3151.25 examples/s]
Shuffling /home/runner/tensorflow_datasets/svhn_cropped/3.0.0.incompleteMZ04LU/svhn_cropped-train.tfrecord*...: 0%| | 0/73257 [00:00<?, ? examples/s]
Shuffling /home/runner/tensorflow_datasets/svhn_cropped/3.0.0.incompleteMZ04LU/svhn_cropped-train.tfrecord*...: 24%|██▍ | 17583/73257 [00:00<00:00, 175811.72 examples/s]
Shuffling /home/runner/tensorflow_datasets/svhn_cropped/3.0.0.incompleteMZ04LU/svhn_cropped-train.tfrecord*...: 58%|█████▊ | 42529/73257 [00:00<00:00, 219122.37 examples/s]
Shuffling /home/runner/tensorflow_datasets/svhn_cropped/3.0.0.incompleteMZ04LU/svhn_cropped-train.tfrecord*...: 92%|█████████▏| 67688/73257 [00:00<00:00, 233940.03 examples/s]
Generating splits...: 33%|███▎ | 1/3 [00:24<00:49, 24.95s/ splits]
Generating test examples...: 0 examples [00:00, ? examples/s]
Generating test examples...: 1 examples [00:00, 2.31 examples/s]
Generating test examples...: 315 examples [00:00, 780.31 examples/s]
Generating test examples...: 631 examples [00:00, 1401.85 examples/s]
Generating test examples...: 940 examples [00:00, 1859.42 examples/s]
Generating test examples...: 1249 examples [00:00, 2203.46 examples/s]
Generating test examples...: 1560 examples [00:00, 2462.10 examples/s]
Generating test examples...: 1869 examples [00:01, 2642.96 examples/s]
Generating test examples...: 2185 examples [00:01, 2792.50 examples/s]
Generating test examples...: 2497 examples [00:01, 2886.56 examples/s]
Generating test examples...: 2810 examples [00:01, 2957.09 examples/s]
Generating test examples...: 3120 examples [00:01, 2998.11 examples/s]
Generating test examples...: 3438 examples [00:01, 3051.23 examples/s]
Generating test examples...: 3754 examples [00:01, 3083.12 examples/s]
Generating test examples...: 4073 examples [00:01, 3114.74 examples/s]
Generating test examples...: 4391 examples [00:01, 3133.70 examples/s]
Generating test examples...: 4709 examples [00:01, 3146.85 examples/s]
Generating test examples...: 5027 examples [00:02, 3155.76 examples/s]
Generating test examples...: 5349 examples [00:02, 3173.01 examples/s]
Generating test examples...: 5668 examples [00:02, 3173.93 examples/s]
Generating test examples...: 5988 examples [00:02, 3178.93 examples/s]
Generating test examples...: 6307 examples [00:02, 3166.20 examples/s]
Generating test examples...: 6625 examples [00:02, 3168.51 examples/s]
Generating test examples...: 6943 examples [00:02, 3154.14 examples/s]
Generating test examples...: 7259 examples [00:02, 3147.62 examples/s]
Generating test examples...: 7577 examples [00:02, 3155.11 examples/s]
Generating test examples...: 7893 examples [00:02, 3135.67 examples/s]
Generating test examples...: 8207 examples [00:03, 3126.70 examples/s]
Generating test examples...: 8523 examples [00:03, 3133.33 examples/s]
Generating test examples...: 8837 examples [00:03, 3127.06 examples/s]
Generating test examples...: 9151 examples [00:03, 3130.27 examples/s]
Generating test examples...: 9465 examples [00:03, 3121.74 examples/s]
Generating test examples...: 9778 examples [00:03, 3106.55 examples/s]
Generating test examples...: 10089 examples [00:03, 3106.48 examples/s]
Generating test examples...: 10401 examples [00:03, 3109.96 examples/s]
Generating test examples...: 10715 examples [00:03, 3116.38 examples/s]
Generating test examples...: 11027 examples [00:03, 3104.82 examples/s]
Generating test examples...: 11339 examples [00:04, 3107.10 examples/s]
Generating test examples...: 11653 examples [00:04, 3115.03 examples/s]
Generating test examples...: 11965 examples [00:04, 3083.26 examples/s]
Generating test examples...: 12276 examples [00:04, 3090.36 examples/s]
Generating test examples...: 12587 examples [00:04, 3095.19 examples/s]
Generating test examples...: 12897 examples [00:04, 3091.15 examples/s]
Generating test examples...: 13207 examples [00:04, 3074.73 examples/s]
Generating test examples...: 13515 examples [00:04, 3074.05 examples/s]
Generating test examples...: 13828 examples [00:04, 3087.81 examples/s]
Generating test examples...: 14139 examples [00:04, 3094.36 examples/s]
Generating test examples...: 14449 examples [00:05, 3034.36 examples/s]
Generating test examples...: 14757 examples [00:05, 3047.58 examples/s]
Generating test examples...: 15070 examples [00:05, 3069.68 examples/s]
Generating test examples...: 15381 examples [00:05, 3079.11 examples/s]
Generating test examples...: 15690 examples [00:05, 3079.55 examples/s]
Generating test examples...: 15999 examples [00:05, 3071.50 examples/s]
Generating test examples...: 16307 examples [00:05, 3073.76 examples/s]
Generating test examples...: 16620 examples [00:05, 3088.84 examples/s]
Generating test examples...: 16935 examples [00:05, 3105.64 examples/s]
Generating test examples...: 17247 examples [00:05, 3109.06 examples/s]
Generating test examples...: 17558 examples [00:06, 3107.86 examples/s]
Generating test examples...: 17869 examples [00:06, 3103.90 examples/s]
Generating test examples...: 18183 examples [00:06, 3111.96 examples/s]
Generating test examples...: 18495 examples [00:06, 3099.71 examples/s]
Generating test examples...: 18805 examples [00:06, 3091.64 examples/s]
Generating test examples...: 19115 examples [00:06, 3086.63 examples/s]
Generating test examples...: 19424 examples [00:06, 3077.79 examples/s]
Generating test examples...: 19732 examples [00:06, 3077.30 examples/s]
Generating test examples...: 20041 examples [00:06, 3080.56 examples/s]
Generating test examples...: 20350 examples [00:06, 3063.38 examples/s]
Generating test examples...: 20661 examples [00:07, 3074.56 examples/s]
Generating test examples...: 20970 examples [00:07, 3078.32 examples/s]
Generating test examples...: 21278 examples [00:07, 3073.63 examples/s]
Generating test examples...: 21586 examples [00:07, 3067.74 examples/s]
Generating test examples...: 21893 examples [00:07, 3053.48 examples/s]
Generating test examples...: 22199 examples [00:07, 3037.32 examples/s]
Generating test examples...: 22508 examples [00:07, 3052.19 examples/s]
Generating test examples...: 22814 examples [00:07, 3049.78 examples/s]
Generating test examples...: 23119 examples [00:07, 3049.79 examples/s]
Generating test examples...: 23424 examples [00:07, 3047.62 examples/s]
Generating test examples...: 23729 examples [00:08, 3046.31 examples/s]
Generating test examples...: 24034 examples [00:08, 3038.82 examples/s]
Generating test examples...: 24339 examples [00:08, 3041.28 examples/s]
Generating test examples...: 24648 examples [00:08, 3055.10 examples/s]
Generating test examples...: 24954 examples [00:08, 3053.95 examples/s]
Generating test examples...: 25264 examples [00:08, 3066.37 examples/s]
Generating test examples...: 25573 examples [00:08, 3071.32 examples/s]
Generating test examples...: 25881 examples [00:08, 3073.24 examples/s]
Shuffling /home/runner/tensorflow_datasets/svhn_cropped/3.0.0.incompleteMZ04LU/svhn_cropped-test.tfrecord*...: 0%| | 0/26032 [00:00<?, ? examples/s]
Shuffling /home/runner/tensorflow_datasets/svhn_cropped/3.0.0.incompleteMZ04LU/svhn_cropped-test.tfrecord*...: 89%|████████▉ | 23201/26032 [00:00<00:00, 231995.84 examples/s]
Generating splits...: 67%|██████▋ | 2/3 [00:33<00:15, 15.55s/ splits]
Generating extra examples...: 0 examples [00:00, ? examples/s]
Generating extra examples...: 1 examples [00:08, 8.79s/ examples]
Generating extra examples...: 319 examples [00:08, 50.95 examples/s]
Generating extra examples...: 641 examples [00:08, 121.71 examples/s]
Generating extra examples...: 961 examples [00:09, 216.86 examples/s]
Generating extra examples...: 1292 examples [00:09, 347.63 examples/s]
Generating extra examples...: 1614 examples [00:09, 511.23 examples/s]
Generating extra examples...: 1940 examples [00:09, 718.05 examples/s]
Generating extra examples...: 2261 examples [00:09, 959.83 examples/s]
Generating extra examples...: 2586 examples [00:09, 1238.51 examples/s]
Generating extra examples...: 2912 examples [00:09, 1537.80 examples/s]
Generating extra examples...: 3236 examples [00:09, 1834.52 examples/s]
Generating extra examples...: 3558 examples [00:09, 2110.81 examples/s]
Generating extra examples...: 3881 examples [00:09, 2358.37 examples/s]
Generating extra examples...: 4204 examples [00:10, 2567.07 examples/s]
Generating extra examples...: 4526 examples [00:10, 2731.69 examples/s]
Generating extra examples...: 4848 examples [00:10, 2855.14 examples/s]
Generating extra examples...: 5169 examples [00:10, 2950.73 examples/s]
Generating extra examples...: 5490 examples [00:10, 3017.68 examples/s]
Generating extra examples...: 5811 examples [00:10, 3070.57 examples/s]
Generating extra examples...: 6132 examples [00:10, 3089.90 examples/s]
Generating extra examples...: 6453 examples [00:10, 3123.51 examples/s]
Generating extra examples...: 6779 examples [00:10, 3163.41 examples/s]
Generating extra examples...: 7100 examples [00:11, 3169.79 examples/s]
Generating extra examples...: 7421 examples [00:11, 3180.10 examples/s]
Generating extra examples...: 7742 examples [00:11, 3180.77 examples/s]
Generating extra examples...: 8062 examples [00:11, 3169.09 examples/s]
Generating extra examples...: 8381 examples [00:11, 3167.71 examples/s]
Generating extra examples...: 8700 examples [00:11, 3172.44 examples/s]
Generating extra examples...: 9018 examples [00:11, 3172.10 examples/s]
Generating extra examples...: 9339 examples [00:11, 3182.93 examples/s]
Generating extra examples...: 9659 examples [00:11, 3185.99 examples/s]
Generating extra examples...: 9983 examples [00:11, 3199.53 examples/s]
Generating extra examples...: 10304 examples [00:12, 3193.66 examples/s]
Generating extra examples...: 10624 examples [00:12, 3183.40 examples/s]
Generating extra examples...: 10943 examples [00:12, 3177.28 examples/s]
Generating extra examples...: 11261 examples [00:12, 3170.30 examples/s]
Generating extra examples...: 11583 examples [00:12, 3182.85 examples/s]
Generating extra examples...: 11903 examples [00:12, 3187.65 examples/s]
Generating extra examples...: 12222 examples [00:12, 3175.28 examples/s]
Generating extra examples...: 12540 examples [00:12, 3169.87 examples/s]
Generating extra examples...: 12858 examples [00:12, 3160.12 examples/s]
Generating extra examples...: 13175 examples [00:12, 3152.08 examples/s]
Generating extra examples...: 13491 examples [00:13, 3120.43 examples/s]
Generating extra examples...: 13805 examples [00:13, 3123.68 examples/s]
Generating extra examples...: 14118 examples [00:13, 3113.97 examples/s]
Generating extra examples...: 14435 examples [00:13, 3130.45 examples/s]
Generating extra examples...: 14752 examples [00:13, 3140.69 examples/s]
Generating extra examples...: 15069 examples [00:13, 3148.45 examples/s]
Generating extra examples...: 15384 examples [00:13, 3145.25 examples/s]
Generating extra examples...: 15701 examples [00:13, 3151.91 examples/s]
Generating extra examples...: 16021 examples [00:13, 3165.91 examples/s]
Generating extra examples...: 16340 examples [00:13, 3171.50 examples/s]
Generating extra examples...: 16658 examples [00:14, 3169.81 examples/s]
Generating extra examples...: 16979 examples [00:14, 3180.34 examples/s]
Generating extra examples...: 17298 examples [00:14, 3154.31 examples/s]
Generating extra examples...: 17618 examples [00:14, 3166.25 examples/s]
Generating extra examples...: 17936 examples [00:14, 3168.82 examples/s]
Generating extra examples...: 18260 examples [00:14, 3187.84 examples/s]
Generating extra examples...: 18584 examples [00:14, 3201.25 examples/s]
Generating extra examples...: 18907 examples [00:14, 3208.48 examples/s]
Generating extra examples...: 19229 examples [00:14, 3209.45 examples/s]
Generating extra examples...: 19551 examples [00:14, 3212.17 examples/s]
Generating extra examples...: 19873 examples [00:15, 3193.31 examples/s]
Generating extra examples...: 20193 examples [00:15, 3187.72 examples/s]
Generating extra examples...: 20512 examples [00:15, 3175.84 examples/s]
Generating extra examples...: 20830 examples [00:15, 3174.65 examples/s]
Generating extra examples...: 21148 examples [00:15, 3175.17 examples/s]
Generating extra examples...: 21467 examples [00:15, 3178.91 examples/s]
Generating extra examples...: 21785 examples [00:15, 3177.35 examples/s]
Generating extra examples...: 22109 examples [00:15, 3193.07 examples/s]
Generating extra examples...: 22430 examples [00:15, 3196.22 examples/s]
Generating extra examples...: 22750 examples [00:15, 3181.93 examples/s]
Generating extra examples...: 23072 examples [00:16, 3190.46 examples/s]
Generating extra examples...: 23394 examples [00:16, 3196.37 examples/s]
Generating extra examples...: 23715 examples [00:16, 3197.44 examples/s]
Generating extra examples...: 24035 examples [00:16, 3184.61 examples/s]
Generating extra examples...: 24357 examples [00:16, 3192.36 examples/s]
Generating extra examples...: 24681 examples [00:16, 3204.99 examples/s]
Generating extra examples...: 25006 examples [00:16, 3215.32 examples/s]
Generating extra examples...: 25328 examples [00:16, 3208.56 examples/s]
Generating extra examples...: 25650 examples [00:16, 3208.89 examples/s]
Generating extra examples...: 25971 examples [00:16, 3198.91 examples/s]
Generating extra examples...: 26291 examples [00:17, 3198.41 examples/s]
Generating extra examples...: 26611 examples [00:17, 3187.41 examples/s]
Generating extra examples...: 26930 examples [00:17, 3173.95 examples/s]
Generating extra examples...: 27248 examples [00:17, 3139.21 examples/s]
Generating extra examples...: 27566 examples [00:17, 3149.80 examples/s]
Generating extra examples...: 27883 examples [00:17, 3155.30 examples/s]
Generating extra examples...: 28203 examples [00:17, 3165.77 examples/s]
Generating extra examples...: 28520 examples [00:17, 3164.75 examples/s]
Generating extra examples...: 28839 examples [00:17, 3169.59 examples/s]
Generating extra examples...: 29156 examples [00:17, 3156.62 examples/s]
Generating extra examples...: 29472 examples [00:18, 3155.95 examples/s]
Generating extra examples...: 29794 examples [00:18, 3172.22 examples/s]
Generating extra examples...: 30112 examples [00:18, 3163.74 examples/s]
Generating extra examples...: 30429 examples [00:18, 3141.96 examples/s]
Generating extra examples...: 30744 examples [00:18, 3140.09 examples/s]
Generating extra examples...: 31060 examples [00:18, 3145.90 examples/s]
Generating extra examples...: 31375 examples [00:18, 3141.71 examples/s]
Generating extra examples...: 31692 examples [00:18, 3147.44 examples/s]
Generating extra examples...: 32007 examples [00:18, 3137.58 examples/s]
Generating extra examples...: 32326 examples [00:18, 3152.28 examples/s]
Generating extra examples...: 32642 examples [00:19, 3142.67 examples/s]
Generating extra examples...: 32957 examples [00:19, 3139.03 examples/s]
Generating extra examples...: 33275 examples [00:19, 3150.65 examples/s]
Generating extra examples...: 33594 examples [00:19, 3160.40 examples/s]
Generating extra examples...: 33917 examples [00:19, 3177.87 examples/s]
Generating extra examples...: 34235 examples [00:19, 3171.54 examples/s]
Generating extra examples...: 34553 examples [00:19, 3155.06 examples/s]
Generating extra examples...: 34874 examples [00:19, 3169.66 examples/s]
Generating extra examples...: 35191 examples [00:19, 3163.32 examples/s]
Generating extra examples...: 35510 examples [00:19, 3168.70 examples/s]
Generating extra examples...: 35827 examples [00:20, 3165.87 examples/s]
Generating extra examples...: 36144 examples [00:20, 3164.00 examples/s]
Generating extra examples...: 36462 examples [00:20, 3166.51 examples/s]
Generating extra examples...: 36779 examples [00:20, 3147.05 examples/s]
Generating extra examples...: 37104 examples [00:20, 3175.12 examples/s]
Generating extra examples...: 37431 examples [00:20, 3202.83 examples/s]
Generating extra examples...: 37757 examples [00:20, 3218.56 examples/s]
Generating extra examples...: 38083 examples [00:20, 3229.95 examples/s]
Generating extra examples...: 38411 examples [00:20, 3242.17 examples/s]
Generating extra examples...: 38739 examples [00:20, 3252.48 examples/s]
Generating extra examples...: 39067 examples [00:21, 3260.61 examples/s]
Generating extra examples...: 39394 examples [00:21, 3249.50 examples/s]
Generating extra examples...: 39719 examples [00:21, 3247.62 examples/s]
Generating extra examples...: 40044 examples [00:21, 3242.43 examples/s]
Generating extra examples...: 40369 examples [00:21, 3227.41 examples/s]
Generating extra examples...: 40692 examples [00:21, 3219.48 examples/s]
Generating extra examples...: 41014 examples [00:21, 3209.65 examples/s]
Generating extra examples...: 41338 examples [00:21, 3218.58 examples/s]
Generating extra examples...: 41660 examples [00:21, 3204.51 examples/s]
Generating extra examples...: 41981 examples [00:21, 3189.42 examples/s]
Generating extra examples...: 42300 examples [00:22, 3186.71 examples/s]
Generating extra examples...: 42627 examples [00:22, 3210.69 examples/s]
Generating extra examples...: 42952 examples [00:22, 3221.03 examples/s]
Generating extra examples...: 43275 examples [00:22, 3213.85 examples/s]
Generating extra examples...: 43601 examples [00:22, 3225.44 examples/s]
Generating extra examples...: 43930 examples [00:22, 3242.06 examples/s]
Generating extra examples...: 44255 examples [00:22, 3230.72 examples/s]
Generating extra examples...: 44579 examples [00:22, 3211.72 examples/s]
Generating extra examples...: 44901 examples [00:22, 3205.34 examples/s]
Generating extra examples...: 45228 examples [00:22, 3223.76 examples/s]
Generating extra examples...: 45551 examples [00:23, 3198.78 examples/s]
Generating extra examples...: 45871 examples [00:23, 3177.44 examples/s]
Generating extra examples...: 46189 examples [00:23, 3167.46 examples/s]
Generating extra examples...: 46508 examples [00:23, 3172.53 examples/s]
Generating extra examples...: 46826 examples [00:23, 3170.16 examples/s]
Generating extra examples...: 47144 examples [00:23, 3161.67 examples/s]
Generating extra examples...: 47463 examples [00:23, 3170.00 examples/s]
Generating extra examples...: 47781 examples [00:23, 3164.81 examples/s]
Generating extra examples...: 48098 examples [00:23, 3161.91 examples/s]
Generating extra examples...: 48417 examples [00:23, 3167.80 examples/s]
Generating extra examples...: 48736 examples [00:24, 3170.90 examples/s]
Generating extra examples...: 49054 examples [00:24, 3173.45 examples/s]
Generating extra examples...: 49372 examples [00:24, 3161.32 examples/s]
Generating extra examples...: 49689 examples [00:24, 3161.19 examples/s]
Generating extra examples...: 50006 examples [00:24, 3159.53 examples/s]
Generating extra examples...: 50322 examples [00:24, 3147.26 examples/s]
Generating extra examples...: 50637 examples [00:24, 3141.28 examples/s]
Generating extra examples...: 50952 examples [00:24, 3136.65 examples/s]
Generating extra examples...: 51266 examples [00:24, 3134.55 examples/s]
Generating extra examples...: 51580 examples [00:24, 3126.38 examples/s]
Generating extra examples...: 51899 examples [00:25, 3143.76 examples/s]
Generating extra examples...: 52214 examples [00:25, 3143.08 examples/s]
Generating extra examples...: 52530 examples [00:25, 3147.85 examples/s]
Generating extra examples...: 52845 examples [00:25, 3139.85 examples/s]
Generating extra examples...: 53163 examples [00:25, 3150.33 examples/s]
Generating extra examples...: 53479 examples [00:25, 3146.47 examples/s]
Generating extra examples...: 53795 examples [00:25, 3149.07 examples/s]
Generating extra examples...: 54110 examples [00:25, 3146.79 examples/s]
Generating extra examples...: 54425 examples [00:25, 3025.80 examples/s]
Generating extra examples...: 54741 examples [00:26, 3063.63 examples/s]
Generating extra examples...: 55057 examples [00:26, 3091.22 examples/s]
Generating extra examples...: 55374 examples [00:26, 3112.07 examples/s]
Generating extra examples...: 55688 examples [00:26, 3119.64 examples/s]
Generating extra examples...: 56001 examples [00:26, 3114.82 examples/s]
Generating extra examples...: 56317 examples [00:26, 3126.53 examples/s]
Generating extra examples...: 56638 examples [00:26, 3150.34 examples/s]
Generating extra examples...: 56956 examples [00:26, 3157.04 examples/s]
Generating extra examples...: 57273 examples [00:26, 3160.39 examples/s]
Generating extra examples...: 57596 examples [00:26, 3178.51 examples/s]
Generating extra examples...: 57914 examples [00:27, 3175.89 examples/s]
Generating extra examples...: 58232 examples [00:27, 3175.21 examples/s]
Generating extra examples...: 58550 examples [00:27, 3170.68 examples/s]
Generating extra examples...: 58873 examples [00:27, 3186.48 examples/s]
Generating extra examples...: 59194 examples [00:27, 3192.25 examples/s]
Generating extra examples...: 59514 examples [00:27, 3177.81 examples/s]
Generating extra examples...: 59833 examples [00:27, 3179.14 examples/s]
Generating extra examples...: 60151 examples [00:27, 3174.41 examples/s]
Generating extra examples...: 60469 examples [00:27, 3175.48 examples/s]
Generating extra examples...: 60787 examples [00:27, 3172.63 examples/s]
Generating extra examples...: 61107 examples [00:28, 3178.17 examples/s]
Generating extra examples...: 61426 examples [00:28, 3179.25 examples/s]
Generating extra examples...: 61744 examples [00:28, 3176.47 examples/s]
Generating extra examples...: 62065 examples [00:28, 3185.97 examples/s]
Generating extra examples...: 62384 examples [00:28, 3148.93 examples/s]
Generating extra examples...: 62707 examples [00:28, 3170.54 examples/s]
Generating extra examples...: 63027 examples [00:28, 3177.38 examples/s]
Generating extra examples...: 63346 examples [00:28, 3179.77 examples/s]
Generating extra examples...: 63665 examples [00:28, 3179.51 examples/s]
Generating extra examples...: 63983 examples [00:28, 3176.63 examples/s]
Generating extra examples...: 64301 examples [00:29, 3163.30 examples/s]
Generating extra examples...: 64618 examples [00:29, 3156.95 examples/s]
Generating extra examples...: 64934 examples [00:29, 3147.75 examples/s]
Generating extra examples...: 65249 examples [00:29, 3147.49 examples/s]
Generating extra examples...: 65564 examples [00:29, 3137.83 examples/s]
Generating extra examples...: 65878 examples [00:29, 3138.16 examples/s]
Generating extra examples...: 66192 examples [00:29, 3137.28 examples/s]
Generating extra examples...: 66506 examples [00:29, 3126.38 examples/s]
Generating extra examples...: 66823 examples [00:29, 3138.12 examples/s]
Generating extra examples...: 67137 examples [00:29, 3128.33 examples/s]
Generating extra examples...: 67451 examples [00:30, 3130.35 examples/s]
Generating extra examples...: 67765 examples [00:30, 3125.60 examples/s]
Generating extra examples...: 68079 examples [00:30, 3129.26 examples/s]
Generating extra examples...: 68394 examples [00:30, 3134.36 examples/s]
Generating extra examples...: 68708 examples [00:30, 3118.42 examples/s]
Generating extra examples...: 69029 examples [00:30, 3144.15 examples/s]
Generating extra examples...: 69347 examples [00:30, 3154.57 examples/s]
Generating extra examples...: 69663 examples [00:30, 3134.20 examples/s]
Generating extra examples...: 69984 examples [00:30, 3153.15 examples/s]
Generating extra examples...: 70307 examples [00:30, 3175.16 examples/s]
Generating extra examples...: 70626 examples [00:31, 3178.27 examples/s]
Generating extra examples...: 70944 examples [00:31, 3165.26 examples/s]
Generating extra examples...: 71262 examples [00:31, 3166.47 examples/s]
Generating extra examples...: 71579 examples [00:31, 3166.21 examples/s]
Generating extra examples...: 71899 examples [00:31, 3174.55 examples/s]
Generating extra examples...: 72218 examples [00:31, 3178.25 examples/s]
Generating extra examples...: 72536 examples [00:31, 3177.68 examples/s]
Generating extra examples...: 72859 examples [00:31, 3191.49 examples/s]
Generating extra examples...: 73179 examples [00:31, 3176.52 examples/s]
Generating extra examples...: 73497 examples [00:31, 3168.68 examples/s]
Generating extra examples...: 73814 examples [00:32, 3161.69 examples/s]
Generating extra examples...: 74131 examples [00:32, 3152.25 examples/s]
Generating extra examples...: 74447 examples [00:32, 3145.63 examples/s]
Generating extra examples...: 74764 examples [00:32, 3152.11 examples/s]
Generating extra examples...: 75080 examples [00:32, 3122.03 examples/s]
Generating extra examples...: 75397 examples [00:32, 3136.06 examples/s]
Generating extra examples...: 75715 examples [00:32, 3147.20 examples/s]
Generating extra examples...: 76030 examples [00:32, 3146.59 examples/s]
Generating extra examples...: 76345 examples [00:32, 3145.66 examples/s]
Generating extra examples...: 76660 examples [00:32, 3131.02 examples/s]
Generating extra examples...: 76974 examples [00:33, 3124.19 examples/s]
Generating extra examples...: 77288 examples [00:33, 3127.28 examples/s]
Generating extra examples...: 77601 examples [00:33, 3113.81 examples/s]
Generating extra examples...: 77913 examples [00:33, 3103.70 examples/s]
Generating extra examples...: 78224 examples [00:33, 3061.54 examples/s]
Generating extra examples...: 78531 examples [00:33, 3063.13 examples/s]
Generating extra examples...: 78845 examples [00:33, 3084.61 examples/s]
Generating extra examples...: 79156 examples [00:33, 3091.88 examples/s]
Generating extra examples...: 79466 examples [00:33, 3089.79 examples/s]
Generating extra examples...: 79776 examples [00:33, 3089.09 examples/s]
Generating extra examples...: 80089 examples [00:34, 3100.59 examples/s]
Generating extra examples...: 80403 examples [00:34, 3110.69 examples/s]
Generating extra examples...: 80715 examples [00:34, 3100.13 examples/s]
Generating extra examples...: 81027 examples [00:34, 3105.08 examples/s]
Generating extra examples...: 81338 examples [00:34, 3087.28 examples/s]
Generating extra examples...: 81647 examples [00:34, 3083.84 examples/s]
Generating extra examples...: 81956 examples [00:34, 3072.66 examples/s]
Generating extra examples...: 82264 examples [00:34, 3069.94 examples/s]
Generating extra examples...: 82572 examples [00:34, 3065.60 examples/s]
Generating extra examples...: 82879 examples [00:34, 3062.99 examples/s]
Generating extra examples...: 83186 examples [00:35, 3062.34 examples/s]
Generating extra examples...: 83496 examples [00:35, 3071.95 examples/s]
Generating extra examples...: 83806 examples [00:35, 3080.29 examples/s]
Generating extra examples...: 84115 examples [00:35, 3081.88 examples/s]
Generating extra examples...: 84424 examples [00:35, 3038.53 examples/s]
Generating extra examples...: 84736 examples [00:35, 3062.27 examples/s]
Generating extra examples...: 85048 examples [00:35, 3079.03 examples/s]
Generating extra examples...: 85357 examples [00:35, 3075.35 examples/s]
Generating extra examples...: 85668 examples [00:35, 3084.89 examples/s]
Generating extra examples...: 85978 examples [00:35, 3087.64 examples/s]
Generating extra examples...: 86287 examples [00:36, 3080.87 examples/s]
Generating extra examples...: 86596 examples [00:36, 3082.93 examples/s]
Generating extra examples...: 86905 examples [00:36, 3082.74 examples/s]
Generating extra examples...: 87214 examples [00:36, 3081.68 examples/s]
Generating extra examples...: 87523 examples [00:36, 3048.10 examples/s]
Generating extra examples...: 87835 examples [00:36, 3068.02 examples/s]
Generating extra examples...: 88144 examples [00:36, 3072.50 examples/s]
Generating extra examples...: 88453 examples [00:36, 3075.82 examples/s]
Generating extra examples...: 88761 examples [00:36, 3077.00 examples/s]
Generating extra examples...: 89070 examples [00:36, 3080.75 examples/s]
Generating extra examples...: 89379 examples [00:37, 3066.49 examples/s]
Generating extra examples...: 89694 examples [00:37, 3089.02 examples/s]
Generating extra examples...: 90006 examples [00:37, 3094.97 examples/s]
Generating extra examples...: 90325 examples [00:37, 3120.90 examples/s]
Generating extra examples...: 90638 examples [00:37, 3099.96 examples/s]
Generating extra examples...: 90954 examples [00:37, 3117.54 examples/s]
Generating extra examples...: 91269 examples [00:37, 3124.79 examples/s]
Generating extra examples...: 91585 examples [00:37, 3133.29 examples/s]
Generating extra examples...: 91899 examples [00:37, 3128.91 examples/s]
Generating extra examples...: 92212 examples [00:37, 3124.62 examples/s]
Generating extra examples...: 92525 examples [00:38, 3112.17 examples/s]
Generating extra examples...: 92838 examples [00:38, 3114.34 examples/s]
Generating extra examples...: 93152 examples [00:38, 3120.82 examples/s]
Generating extra examples...: 93471 examples [00:38, 3138.99 examples/s]
Generating extra examples...: 93787 examples [00:38, 3142.54 examples/s]
Generating extra examples...: 94102 examples [00:38, 3114.35 examples/s]
Generating extra examples...: 94415 examples [00:38, 3118.64 examples/s]
Generating extra examples...: 94728 examples [00:38, 3120.99 examples/s]
Generating extra examples...: 95044 examples [00:38, 3132.24 examples/s]
Generating extra examples...: 95358 examples [00:38, 3123.08 examples/s]
Generating extra examples...: 95674 examples [00:39, 3132.72 examples/s]
Generating extra examples...: 95988 examples [00:39, 3131.63 examples/s]
Generating extra examples...: 96305 examples [00:39, 3141.71 examples/s]
Generating extra examples...: 96620 examples [00:39, 3140.46 examples/s]
Generating extra examples...: 96935 examples [00:39, 3137.04 examples/s]
Generating extra examples...: 97249 examples [00:39, 3101.65 examples/s]
Generating extra examples...: 97568 examples [00:39, 3125.20 examples/s]
Generating extra examples...: 97881 examples [00:39, 3092.55 examples/s]
Generating extra examples...: 98191 examples [00:39, 3086.61 examples/s]
Generating extra examples...: 98500 examples [00:40, 3080.15 examples/s]
Generating extra examples...: 98811 examples [00:40, 3088.84 examples/s]
Generating extra examples...: 99123 examples [00:40, 3096.54 examples/s]
Generating extra examples...: 99434 examples [00:40, 3097.99 examples/s]
Generating extra examples...: 99746 examples [00:40, 3102.87 examples/s]
Generating extra examples...: 100057 examples [00:40, 3098.17 examples/s]
Generating extra examples...: 100367 examples [00:40, 3060.78 examples/s]
Generating extra examples...: 100678 examples [00:40, 3073.35 examples/s]
Generating extra examples...: 100986 examples [00:40, 3064.81 examples/s]
Generating extra examples...: 101298 examples [00:40, 3079.85 examples/s]
Generating extra examples...: 101609 examples [00:41, 3088.75 examples/s]
Generating extra examples...: 101918 examples [00:41, 3087.25 examples/s]
Generating extra examples...: 102229 examples [00:41, 3091.31 examples/s]
Generating extra examples...: 102539 examples [00:41, 3093.02 examples/s]
Generating extra examples...: 102849 examples [00:41, 3086.65 examples/s]
Generating extra examples...: 103158 examples [00:41, 3085.03 examples/s]
Generating extra examples...: 103467 examples [00:41, 3080.36 examples/s]
Generating extra examples...: 103776 examples [00:41, 3079.64 examples/s]
Generating extra examples...: 104085 examples [00:41, 3081.91 examples/s]
Generating extra examples...: 104396 examples [00:41, 3087.52 examples/s]
Generating extra examples...: 104710 examples [00:42, 3102.22 examples/s]
Generating extra examples...: 105029 examples [00:42, 3126.51 examples/s]
Generating extra examples...: 105343 examples [00:42, 3129.58 examples/s]
Generating extra examples...: 105657 examples [00:42, 3131.87 examples/s]
Generating extra examples...: 105971 examples [00:42, 3123.60 examples/s]
Generating extra examples...: 106286 examples [00:42, 3130.22 examples/s]
Generating extra examples...: 106600 examples [00:42, 3117.39 examples/s]
Generating extra examples...: 106921 examples [00:42, 3142.24 examples/s]
Generating extra examples...: 107237 examples [00:42, 3145.97 examples/s]
Generating extra examples...: 107552 examples [00:42, 3134.55 examples/s]
Generating extra examples...: 107866 examples [00:43, 3102.54 examples/s]
Generating extra examples...: 108177 examples [00:43, 3094.81 examples/s]
Generating extra examples...: 108490 examples [00:43, 3104.94 examples/s]
Generating extra examples...: 108807 examples [00:43, 3124.16 examples/s]
Generating extra examples...: 109120 examples [00:43, 3120.46 examples/s]
Generating extra examples...: 109435 examples [00:43, 3128.24 examples/s]
Generating extra examples...: 109748 examples [00:43, 3085.11 examples/s]
Generating extra examples...: 110064 examples [00:43, 3104.32 examples/s]
Generating extra examples...: 110378 examples [00:43, 3112.31 examples/s]
Generating extra examples...: 110695 examples [00:43, 3127.59 examples/s]
Generating extra examples...: 111008 examples [00:44, 3125.20 examples/s]
Generating extra examples...: 111321 examples [00:44, 3121.57 examples/s]
Generating extra examples...: 111634 examples [00:44, 3112.50 examples/s]
Generating extra examples...: 111946 examples [00:44, 3109.75 examples/s]
Generating extra examples...: 112257 examples [00:44, 3107.96 examples/s]
Generating extra examples...: 112568 examples [00:44, 3103.50 examples/s]
Generating extra examples...: 112879 examples [00:44, 3096.24 examples/s]
Generating extra examples...: 113191 examples [00:44, 3100.74 examples/s]
Generating extra examples...: 113502 examples [00:44, 3099.77 examples/s]
Generating extra examples...: 113812 examples [00:44, 3086.96 examples/s]
Generating extra examples...: 114121 examples [00:45, 3074.52 examples/s]
Generating extra examples...: 114430 examples [00:45, 3077.16 examples/s]
Generating extra examples...: 114738 examples [00:45, 3075.57 examples/s]
Generating extra examples...: 115046 examples [00:45, 3060.84 examples/s]
Generating extra examples...: 115358 examples [00:45, 3078.25 examples/s]
Generating extra examples...: 115667 examples [00:45, 3080.62 examples/s]
Generating extra examples...: 115976 examples [00:45, 3042.33 examples/s]
Generating extra examples...: 116285 examples [00:45, 3055.59 examples/s]
Generating extra examples...: 116594 examples [00:45, 3065.35 examples/s]
Generating extra examples...: 116901 examples [00:45, 3065.23 examples/s]
Generating extra examples...: 117209 examples [00:46, 3068.02 examples/s]
Generating extra examples...: 117516 examples [00:46, 3066.94 examples/s]
Generating extra examples...: 117826 examples [00:46, 3076.53 examples/s]
Generating extra examples...: 118134 examples [00:46, 3076.68 examples/s]
Generating extra examples...: 118444 examples [00:46, 3082.52 examples/s]
Generating extra examples...: 118753 examples [00:46, 3075.88 examples/s]
Generating extra examples...: 119061 examples [00:46, 3059.13 examples/s]
Generating extra examples...: 119370 examples [00:46, 3065.47 examples/s]
Generating extra examples...: 119682 examples [00:46, 3081.16 examples/s]
Generating extra examples...: 119997 examples [00:46, 3100.08 examples/s]
Generating extra examples...: 120312 examples [00:47, 3113.81 examples/s]
Generating extra examples...: 120630 examples [00:47, 3131.53 examples/s]
Generating extra examples...: 120947 examples [00:47, 3141.39 examples/s]
Generating extra examples...: 121263 examples [00:47, 3145.89 examples/s]
Generating extra examples...: 121581 examples [00:47, 3154.74 examples/s]
Generating extra examples...: 121900 examples [00:47, 3163.93 examples/s]
Generating extra examples...: 122217 examples [00:47, 3155.89 examples/s]
Generating extra examples...: 122538 examples [00:47, 3169.39 examples/s]
Generating extra examples...: 122859 examples [00:47, 3181.34 examples/s]
Generating extra examples...: 123178 examples [00:47, 3158.58 examples/s]
Generating extra examples...: 123494 examples [00:48, 3141.33 examples/s]
Generating extra examples...: 123815 examples [00:48, 3159.21 examples/s]
Generating extra examples...: 124134 examples [00:48, 3165.82 examples/s]
Generating extra examples...: 124451 examples [00:48, 3154.52 examples/s]
Generating extra examples...: 124767 examples [00:48, 3151.37 examples/s]
Generating extra examples...: 125086 examples [00:48, 3159.91 examples/s]
Generating extra examples...: 125406 examples [00:48, 3169.03 examples/s]
Generating extra examples...: 125723 examples [00:48, 3149.31 examples/s]
Generating extra examples...: 126043 examples [00:48, 3162.43 examples/s]
Generating extra examples...: 126363 examples [00:48, 3171.69 examples/s]
Generating extra examples...: 126683 examples [00:49, 3176.69 examples/s]
Generating extra examples...: 127006 examples [00:49, 3191.61 examples/s]
Generating extra examples...: 127326 examples [00:49, 3167.91 examples/s]
Generating extra examples...: 127643 examples [00:49, 3156.21 examples/s]
Generating extra examples...: 127959 examples [00:49, 3151.12 examples/s]
Generating extra examples...: 128275 examples [00:49, 3121.46 examples/s]
Generating extra examples...: 128588 examples [00:49, 3074.69 examples/s]
Generating extra examples...: 128898 examples [00:49, 3079.77 examples/s]
Generating extra examples...: 129207 examples [00:49, 3080.32 examples/s]
Generating extra examples...: 129519 examples [00:49, 3089.39 examples/s]
Generating extra examples...: 129836 examples [00:50, 3110.85 examples/s]
Generating extra examples...: 130149 examples [00:50, 3112.83 examples/s]
Generating extra examples...: 130462 examples [00:50, 3116.54 examples/s]
Generating extra examples...: 130774 examples [00:50, 3106.80 examples/s]
Generating extra examples...: 131085 examples [00:50, 3103.57 examples/s]
Generating extra examples...: 131396 examples [00:50, 3093.52 examples/s]
Generating extra examples...: 131706 examples [00:50, 3065.56 examples/s]
Generating extra examples...: 132014 examples [00:50, 3069.82 examples/s]
Generating extra examples...: 132323 examples [00:50, 3073.12 examples/s]
Generating extra examples...: 132633 examples [00:50, 3080.43 examples/s]
Generating extra examples...: 132942 examples [00:51, 3061.79 examples/s]
Generating extra examples...: 133249 examples [00:51, 3053.60 examples/s]
Generating extra examples...: 133557 examples [00:51, 3059.52 examples/s]
Generating extra examples...: 133866 examples [00:51, 3068.18 examples/s]
Generating extra examples...: 134177 examples [00:51, 3078.68 examples/s]
Generating extra examples...: 134489 examples [00:51, 3089.98 examples/s]
Generating extra examples...: 134802 examples [00:51, 3099.97 examples/s]
Generating extra examples...: 135114 examples [00:51, 3103.56 examples/s]
Generating extra examples...: 135426 examples [00:51, 3107.61 examples/s]
Generating extra examples...: 135737 examples [00:51, 3101.38 examples/s]
Generating extra examples...: 136049 examples [00:52, 3103.52 examples/s]
Generating extra examples...: 136364 examples [00:52, 3114.59 examples/s]
Generating extra examples...: 136679 examples [00:52, 3123.25 examples/s]
Generating extra examples...: 136993 examples [00:52, 3126.68 examples/s]
Generating extra examples...: 137306 examples [00:52, 3112.85 examples/s]
Generating extra examples...: 137618 examples [00:52, 3104.70 examples/s]
Generating extra examples...: 137929 examples [00:52, 3066.67 examples/s]
Generating extra examples...: 138236 examples [00:52, 3062.79 examples/s]
Generating extra examples...: 138548 examples [00:52, 3079.36 examples/s]
Generating extra examples...: 138856 examples [00:53, 3077.73 examples/s]
Generating extra examples...: 139164 examples [00:53, 3071.73 examples/s]
Generating extra examples...: 139474 examples [00:53, 3078.48 examples/s]
Generating extra examples...: 139786 examples [00:53, 3088.85 examples/s]
Generating extra examples...: 140096 examples [00:53, 3090.48 examples/s]
Generating extra examples...: 140409 examples [00:53, 3099.86 examples/s]
Generating extra examples...: 140719 examples [00:53, 3096.01 examples/s]
Generating extra examples...: 141029 examples [00:53, 3073.48 examples/s]
Generating extra examples...: 141342 examples [00:53, 3088.39 examples/s]
Generating extra examples...: 141655 examples [00:53, 3100.44 examples/s]
Generating extra examples...: 141966 examples [00:54, 3084.71 examples/s]
Generating extra examples...: 142275 examples [00:54, 3079.74 examples/s]
Generating extra examples...: 142583 examples [00:54, 3074.63 examples/s]
Generating extra examples...: 142894 examples [00:54, 3082.77 examples/s]
Generating extra examples...: 143207 examples [00:54, 3096.57 examples/s]
Generating extra examples...: 143523 examples [00:54, 3115.01 examples/s]
Generating extra examples...: 143835 examples [00:54, 3116.01 examples/s]
Generating extra examples...: 144147 examples [00:54, 3111.18 examples/s]
Generating extra examples...: 144462 examples [00:54, 3120.00 examples/s]
Generating extra examples...: 144778 examples [00:54, 3131.55 examples/s]
Generating extra examples...: 145092 examples [00:55, 3129.99 examples/s]
Generating extra examples...: 145406 examples [00:55, 3108.19 examples/s]
Generating extra examples...: 145722 examples [00:55, 3122.25 examples/s]
Generating extra examples...: 146035 examples [00:55, 3124.30 examples/s]
Generating extra examples...: 146348 examples [00:55, 3118.47 examples/s]
Generating extra examples...: 146660 examples [00:55, 3098.70 examples/s]
Generating extra examples...: 146970 examples [00:55, 3092.94 examples/s]
Generating extra examples...: 147284 examples [00:55, 3106.83 examples/s]
Generating extra examples...: 147595 examples [00:55, 3087.75 examples/s]
Generating extra examples...: 147904 examples [00:55, 3078.06 examples/s]
Generating extra examples...: 148218 examples [00:56, 3095.83 examples/s]
Generating extra examples...: 148529 examples [00:56, 3098.36 examples/s]
Generating extra examples...: 148840 examples [00:56, 3100.74 examples/s]
Generating extra examples...: 149156 examples [00:56, 3116.04 examples/s]
Generating extra examples...: 149469 examples [00:56, 3118.68 examples/s]
Generating extra examples...: 149783 examples [00:56, 3123.28 examples/s]
Generating extra examples...: 150097 examples [00:56, 3128.00 examples/s]
Generating extra examples...: 150412 examples [00:56, 3134.01 examples/s]
Generating extra examples...: 150726 examples [00:56, 3081.22 examples/s]
Generating extra examples...: 151037 examples [00:56, 3089.53 examples/s]
Generating extra examples...: 151347 examples [00:57, 3092.28 examples/s]
Generating extra examples...: 151657 examples [00:57, 3080.71 examples/s]
Generating extra examples...: 151966 examples [00:57, 3074.48 examples/s]
Generating extra examples...: 152277 examples [00:57, 3084.22 examples/s]
Generating extra examples...: 152587 examples [00:57, 3087.41 examples/s]
Generating extra examples...: 152896 examples [00:57, 3086.12 examples/s]
Generating extra examples...: 153205 examples [00:57, 3082.84 examples/s]
Generating extra examples...: 153516 examples [00:57, 3089.72 examples/s]
Generating extra examples...: 153825 examples [00:57, 3077.17 examples/s]
Generating extra examples...: 154135 examples [00:57, 3082.39 examples/s]
Generating extra examples...: 154446 examples [00:58, 3088.88 examples/s]
Generating extra examples...: 154755 examples [00:58, 3084.03 examples/s]
Generating extra examples...: 155067 examples [00:58, 3094.11 examples/s]
Generating extra examples...: 155378 examples [00:58, 3095.84 examples/s]
Generating extra examples...: 155688 examples [00:58, 3087.67 examples/s]
Generating extra examples...: 155997 examples [00:58, 3082.88 examples/s]
Generating extra examples...: 156306 examples [00:58, 3071.17 examples/s]
Generating extra examples...: 156618 examples [00:58, 3083.75 examples/s]
Generating extra examples...: 156927 examples [00:58, 3049.07 examples/s]
Generating extra examples...: 157235 examples [00:58, 3058.21 examples/s]
Generating extra examples...: 157541 examples [00:59, 3056.93 examples/s]
Generating extra examples...: 157849 examples [00:59, 3061.38 examples/s]
Generating extra examples...: 158156 examples [00:59, 3054.15 examples/s]
Generating extra examples...: 158465 examples [00:59, 3063.17 examples/s]
Generating extra examples...: 158772 examples [00:59, 3062.75 examples/s]
Generating extra examples...: 159085 examples [00:59, 3082.04 examples/s]
Generating extra examples...: 159394 examples [00:59, 3072.49 examples/s]
Generating extra examples...: 159702 examples [00:59, 3065.91 examples/s]
Generating extra examples...: 160009 examples [00:59, 3056.34 examples/s]
Generating extra examples...: 160317 examples [00:59, 3061.93 examples/s]
Generating extra examples...: 160626 examples [01:00, 3068.11 examples/s]
Generating extra examples...: 160936 examples [01:00, 3074.87 examples/s]
Generating extra examples...: 161244 examples [01:00, 3069.81 examples/s]
Generating extra examples...: 161551 examples [01:00, 3064.31 examples/s]
Generating extra examples...: 161859 examples [01:00, 3066.65 examples/s]
Generating extra examples...: 162168 examples [01:00, 3071.04 examples/s]
Generating extra examples...: 162478 examples [01:00, 3077.22 examples/s]
Generating extra examples...: 162786 examples [01:00, 3059.52 examples/s]
Generating extra examples...: 163092 examples [01:00, 3033.17 examples/s]
Generating extra examples...: 163404 examples [01:00, 3057.89 examples/s]
Generating extra examples...: 163710 examples [01:01, 3047.32 examples/s]
Generating extra examples...: 164017 examples [01:01, 3051.33 examples/s]
Generating extra examples...: 164333 examples [01:01, 3082.90 examples/s]
Generating extra examples...: 164645 examples [01:01, 3091.33 examples/s]
Generating extra examples...: 164962 examples [01:01, 3112.23 examples/s]
Generating extra examples...: 165279 examples [01:01, 3126.19 examples/s]
Generating extra examples...: 165597 examples [01:01, 3139.69 examples/s]
Generating extra examples...: 165915 examples [01:01, 3151.37 examples/s]
Generating extra examples...: 166231 examples [01:01, 3099.90 examples/s]
Generating extra examples...: 166542 examples [01:01, 3100.80 examples/s]
Generating extra examples...: 166856 examples [01:02, 3109.21 examples/s]
Generating extra examples...: 167168 examples [01:02, 3091.26 examples/s]
Generating extra examples...: 167478 examples [01:02, 3078.34 examples/s]
Generating extra examples...: 167793 examples [01:02, 3098.50 examples/s]
Generating extra examples...: 168107 examples [01:02, 3110.72 examples/s]
Generating extra examples...: 168420 examples [01:02, 3113.93 examples/s]
Generating extra examples...: 168732 examples [01:02, 3107.86 examples/s]
Generating extra examples...: 169043 examples [01:02, 3099.84 examples/s]
Generating extra examples...: 169354 examples [01:02, 3067.24 examples/s]
Generating extra examples...: 169669 examples [01:02, 3088.74 examples/s]
Generating extra examples...: 169989 examples [01:03, 3120.27 examples/s]
Generating extra examples...: 170302 examples [01:03, 3112.68 examples/s]
Generating extra examples...: 170618 examples [01:03, 3124.82 examples/s]
Generating extra examples...: 170931 examples [01:03, 3126.17 examples/s]
Generating extra examples...: 171244 examples [01:03, 3120.95 examples/s]
Generating extra examples...: 171557 examples [01:03, 3104.07 examples/s]
Generating extra examples...: 171870 examples [01:03, 3109.94 examples/s]
Generating extra examples...: 172182 examples [01:03, 3106.66 examples/s]
Generating extra examples...: 172493 examples [01:03, 3091.03 examples/s]
Generating extra examples...: 172803 examples [01:03, 3086.00 examples/s]
Generating extra examples...: 173112 examples [01:04, 3069.60 examples/s]
Generating extra examples...: 173422 examples [01:04, 3078.11 examples/s]
Generating extra examples...: 173731 examples [01:04, 3081.16 examples/s]
Generating extra examples...: 174041 examples [01:04, 3084.66 examples/s]
Generating extra examples...: 174350 examples [01:04, 3078.63 examples/s]
Generating extra examples...: 174658 examples [01:04, 3075.89 examples/s]
Generating extra examples...: 174966 examples [01:04, 3072.22 examples/s]
Generating extra examples...: 175274 examples [01:04, 3060.10 examples/s]
Generating extra examples...: 175581 examples [01:04, 3056.00 examples/s]
Generating extra examples...: 175889 examples [01:04, 3061.04 examples/s]
Generating extra examples...: 176196 examples [01:05, 3054.77 examples/s]
Generating extra examples...: 176506 examples [01:05, 3066.00 examples/s]
Generating extra examples...: 176813 examples [01:05, 3066.76 examples/s]
Generating extra examples...: 177122 examples [01:05, 3072.79 examples/s]
Generating extra examples...: 177430 examples [01:05, 3063.82 examples/s]
Generating extra examples...: 177737 examples [01:05, 3063.44 examples/s]
Generating extra examples...: 178047 examples [01:05, 3071.62 examples/s]
Generating extra examples...: 178356 examples [01:05, 3075.60 examples/s]
Generating extra examples...: 178664 examples [01:05, 3046.40 examples/s]
Generating extra examples...: 178972 examples [01:05, 3053.75 examples/s]
Generating extra examples...: 179278 examples [01:06, 3053.61 examples/s]
Generating extra examples...: 179588 examples [01:06, 3064.63 examples/s]
Generating extra examples...: 179900 examples [01:06, 3079.18 examples/s]
Generating extra examples...: 180212 examples [01:06, 3089.89 examples/s]
Generating extra examples...: 180528 examples [01:06, 3110.08 examples/s]
Generating extra examples...: 180847 examples [01:06, 3133.63 examples/s]
Generating extra examples...: 181165 examples [01:06, 3145.31 examples/s]
Generating extra examples...: 181480 examples [01:06, 3144.84 examples/s]
Generating extra examples...: 181795 examples [01:06, 3142.79 examples/s]
Generating extra examples...: 182113 examples [01:07, 3153.31 examples/s]
Generating extra examples...: 182429 examples [01:07, 3134.82 examples/s]
Generating extra examples...: 182743 examples [01:07, 3130.62 examples/s]
Generating extra examples...: 183057 examples [01:07, 3128.14 examples/s]
Generating extra examples...: 183370 examples [01:07, 3120.50 examples/s]
Generating extra examples...: 183683 examples [01:07, 3113.06 examples/s]
Generating extra examples...: 183995 examples [01:07, 3103.12 examples/s]
Generating extra examples...: 184306 examples [01:07, 3103.26 examples/s]
Generating extra examples...: 184618 examples [01:07, 3106.16 examples/s]
Generating extra examples...: 184929 examples [01:07, 3091.94 examples/s]
Generating extra examples...: 185239 examples [01:08, 3093.15 examples/s]
Generating extra examples...: 185550 examples [01:08, 3097.55 examples/s]
Generating extra examples...: 185860 examples [01:08, 3091.24 examples/s]
Generating extra examples...: 186175 examples [01:08, 3106.43 examples/s]
Generating extra examples...: 186488 examples [01:08, 3111.20 examples/s]
Generating extra examples...: 186800 examples [01:08, 3111.02 examples/s]
Generating extra examples...: 187112 examples [01:08, 3107.91 examples/s]
Generating extra examples...: 187423 examples [01:08, 3098.30 examples/s]
Generating extra examples...: 187733 examples [01:08, 3080.08 examples/s]
Generating extra examples...: 188042 examples [01:08, 3030.24 examples/s]
Generating extra examples...: 188348 examples [01:09, 3037.71 examples/s]
Generating extra examples...: 188654 examples [01:09, 3043.67 examples/s]
Generating extra examples...: 188961 examples [01:09, 3050.02 examples/s]
Generating extra examples...: 189267 examples [01:09, 3039.39 examples/s]
Generating extra examples...: 189573 examples [01:09, 3044.56 examples/s]
Generating extra examples...: 189879 examples [01:09, 3047.58 examples/s]
Generating extra examples...: 190185 examples [01:09, 3048.31 examples/s]
Generating extra examples...: 190490 examples [01:09, 3043.15 examples/s]
Generating extra examples...: 190795 examples [01:09, 3034.82 examples/s]
Generating extra examples...: 191099 examples [01:09, 3035.39 examples/s]
Generating extra examples...: 191410 examples [01:10, 3055.39 examples/s]
Generating extra examples...: 191716 examples [01:10, 3056.18 examples/s]
Generating extra examples...: 192022 examples [01:10, 3056.97 examples/s]
Generating extra examples...: 192328 examples [01:10, 3053.15 examples/s]
Generating extra examples...: 192634 examples [01:10, 3052.23 examples/s]
Generating extra examples...: 192941 examples [01:10, 3055.26 examples/s]
Generating extra examples...: 193247 examples [01:10, 3050.12 examples/s]
Generating extra examples...: 193553 examples [01:10, 3048.06 examples/s]
Generating extra examples...: 193858 examples [01:10, 3047.60 examples/s]
Generating extra examples...: 194163 examples [01:10, 3021.29 examples/s]
Generating extra examples...: 194466 examples [01:11, 3023.79 examples/s]
Generating extra examples...: 194775 examples [01:11, 3041.02 examples/s]
Generating extra examples...: 195081 examples [01:11, 3045.69 examples/s]
Generating extra examples...: 195389 examples [01:11, 3054.92 examples/s]
Generating extra examples...: 195695 examples [01:11, 3052.23 examples/s]
Generating extra examples...: 196006 examples [01:11, 3066.18 examples/s]
Generating extra examples...: 196314 examples [01:11, 3067.55 examples/s]
Generating extra examples...: 196627 examples [01:11, 3083.65 examples/s]
Generating extra examples...: 196936 examples [01:11, 3075.20 examples/s]
Generating extra examples...: 197244 examples [01:11, 3070.01 examples/s]
Generating extra examples...: 197552 examples [01:12, 3066.98 examples/s]
Generating extra examples...: 197863 examples [01:12, 3078.94 examples/s]
Generating extra examples...: 198172 examples [01:12, 3081.24 examples/s]
Generating extra examples...: 198481 examples [01:12, 3072.46 examples/s]
Generating extra examples...: 198790 examples [01:12, 3076.12 examples/s]
Generating extra examples...: 199100 examples [01:12, 3079.87 examples/s]
Generating extra examples...: 199408 examples [01:12, 3076.68 examples/s]
Generating extra examples...: 199716 examples [01:12, 3075.78 examples/s]
Generating extra examples...: 200026 examples [01:12, 3082.72 examples/s]
Generating extra examples...: 200335 examples [01:12, 3078.01 examples/s]
Generating extra examples...: 200643 examples [01:13, 3066.31 examples/s]
Generating extra examples...: 200950 examples [01:13, 3062.37 examples/s]
Generating extra examples...: 201257 examples [01:13, 3063.06 examples/s]
Generating extra examples...: 201566 examples [01:13, 3069.62 examples/s]
Generating extra examples...: 201873 examples [01:13, 3060.83 examples/s]
Generating extra examples...: 202180 examples [01:13, 3056.76 examples/s]
Generating extra examples...: 202490 examples [01:13, 3068.12 examples/s]
Generating extra examples...: 202797 examples [01:13, 3065.15 examples/s]
Generating extra examples...: 203104 examples [01:13, 3064.53 examples/s]
Generating extra examples...: 203416 examples [01:13, 3078.08 examples/s]
Generating extra examples...: 203724 examples [01:14, 3068.93 examples/s]
Generating extra examples...: 204037 examples [01:14, 3085.12 examples/s]
Generating extra examples...: 204346 examples [01:14, 3082.54 examples/s]
Generating extra examples...: 204655 examples [01:14, 3080.03 examples/s]
Generating extra examples...: 204964 examples [01:14, 3079.57 examples/s]
Generating extra examples...: 205273 examples [01:14, 3081.98 examples/s]
Generating extra examples...: 205582 examples [01:14, 3079.13 examples/s]
Generating extra examples...: 205891 examples [01:14, 3081.15 examples/s]
Generating extra examples...: 206200 examples [01:14, 3070.17 examples/s]
Generating extra examples...: 206508 examples [01:14, 3060.39 examples/s]
Generating extra examples...: 206815 examples [01:15, 3035.36 examples/s]
Generating extra examples...: 207125 examples [01:15, 3052.26 examples/s]
Generating extra examples...: 207435 examples [01:15, 3064.94 examples/s]
Generating extra examples...: 207742 examples [01:15, 3065.57 examples/s]
Generating extra examples...: 208052 examples [01:15, 3075.23 examples/s]
Generating extra examples...: 208360 examples [01:15, 3071.49 examples/s]
Generating extra examples...: 208668 examples [01:15, 3069.75 examples/s]
Generating extra examples...: 208975 examples [01:15, 3067.86 examples/s]
Generating extra examples...: 209284 examples [01:15, 3073.84 examples/s]
Generating extra examples...: 209592 examples [01:15, 3073.51 examples/s]
Generating extra examples...: 209900 examples [01:16, 3041.93 examples/s]
Generating extra examples...: 210207 examples [01:16, 3049.18 examples/s]
Generating extra examples...: 210515 examples [01:16, 3056.03 examples/s]
Generating extra examples...: 210824 examples [01:16, 3064.00 examples/s]
Generating extra examples...: 211133 examples [01:16, 3071.47 examples/s]
Generating extra examples...: 211442 examples [01:16, 3075.91 examples/s]
Generating extra examples...: 211750 examples [01:16, 3065.18 examples/s]
Generating extra examples...: 212059 examples [01:16, 3071.77 examples/s]
Generating extra examples...: 212369 examples [01:16, 3078.53 examples/s]
Generating extra examples...: 212680 examples [01:16, 3085.41 examples/s]
Generating extra examples...: 212989 examples [01:17, 3080.72 examples/s]
Generating extra examples...: 213298 examples [01:17, 3079.05 examples/s]
Generating extra examples...: 213609 examples [01:17, 3087.25 examples/s]
Generating extra examples...: 213918 examples [01:17, 3080.51 examples/s]
Generating extra examples...: 214227 examples [01:17, 3081.57 examples/s]
Generating extra examples...: 214536 examples [01:17, 3080.83 examples/s]
Generating extra examples...: 214845 examples [01:17, 3075.98 examples/s]
Generating extra examples...: 215153 examples [01:17, 3074.23 examples/s]
Generating extra examples...: 215465 examples [01:17, 3086.11 examples/s]
Generating extra examples...: 215777 examples [01:17, 3093.72 examples/s]
Generating extra examples...: 216087 examples [01:18, 3071.47 examples/s]
Generating extra examples...: 216396 examples [01:18, 3076.69 examples/s]
Generating extra examples...: 216704 examples [01:18, 3076.02 examples/s]
Generating extra examples...: 217013 examples [01:18, 3079.99 examples/s]
Generating extra examples...: 217323 examples [01:18, 3083.38 examples/s]
Generating extra examples...: 217634 examples [01:18, 3089.30 examples/s]
Generating extra examples...: 217945 examples [01:18, 3094.29 examples/s]
Generating extra examples...: 218255 examples [01:18, 3089.95 examples/s]
Generating extra examples...: 218568 examples [01:18, 3098.37 examples/s]
Generating extra examples...: 218880 examples [01:18, 3103.99 examples/s]
Generating extra examples...: 219191 examples [01:19, 3034.45 examples/s]
Generating extra examples...: 219495 examples [01:19, 3017.02 examples/s]
Generating extra examples...: 219802 examples [01:19, 3031.11 examples/s]
Generating extra examples...: 220109 examples [01:19, 3039.73 examples/s]
Generating extra examples...: 220418 examples [01:19, 3052.76 examples/s]
Generating extra examples...: 220725 examples [01:19, 3056.28 examples/s]
Generating extra examples...: 221031 examples [01:19, 3055.83 examples/s]
Generating extra examples...: 221337 examples [01:19, 3051.20 examples/s]
Generating extra examples...: 221648 examples [01:19, 3064.87 examples/s]
Generating extra examples...: 221956 examples [01:19, 3068.56 examples/s]
Generating extra examples...: 222265 examples [01:20, 3073.09 examples/s]
Generating extra examples...: 222574 examples [01:20, 3076.64 examples/s]
Generating extra examples...: 222882 examples [01:20, 3067.30 examples/s]
Generating extra examples...: 223189 examples [01:20, 3057.06 examples/s]
Generating extra examples...: 223498 examples [01:20, 3063.75 examples/s]
Generating extra examples...: 223807 examples [01:20, 3069.42 examples/s]
Generating extra examples...: 224118 examples [01:20, 3081.06 examples/s]
Generating extra examples...: 224427 examples [01:20, 3081.00 examples/s]
Generating extra examples...: 224736 examples [01:20, 3078.88 examples/s]
Generating extra examples...: 225044 examples [01:20, 3075.77 examples/s]
Generating extra examples...: 225352 examples [01:21, 3042.00 examples/s]
Generating extra examples...: 225660 examples [01:21, 3052.28 examples/s]
Generating extra examples...: 225966 examples [01:21, 3041.34 examples/s]
Generating extra examples...: 226271 examples [01:21, 3043.87 examples/s]
Generating extra examples...: 226582 examples [01:21, 3061.34 examples/s]
Generating extra examples...: 226894 examples [01:21, 3077.72 examples/s]
Generating extra examples...: 227206 examples [01:21, 3088.32 examples/s]
Generating extra examples...: 227518 examples [01:21, 3096.87 examples/s]
Generating extra examples...: 227831 examples [01:21, 3104.10 examples/s]
Generating extra examples...: 228142 examples [01:21, 3097.18 examples/s]
Generating extra examples...: 228452 examples [01:22, 3049.70 examples/s]
Generating extra examples...: 228767 examples [01:22, 3079.10 examples/s]
Generating extra examples...: 229078 examples [01:22, 3088.18 examples/s]
Generating extra examples...: 229390 examples [01:22, 3097.00 examples/s]
Generating extra examples...: 229700 examples [01:22, 3071.37 examples/s]
Generating extra examples...: 230008 examples [01:22, 3067.59 examples/s]
Generating extra examples...: 230319 examples [01:22, 3078.05 examples/s]
Generating extra examples...: 230627 examples [01:22, 3069.73 examples/s]
Generating extra examples...: 230938 examples [01:22, 3081.21 examples/s]
Generating extra examples...: 231248 examples [01:23, 3083.24 examples/s]
Generating extra examples...: 231557 examples [01:23, 3049.35 examples/s]
Generating extra examples...: 231863 examples [01:23, 3047.88 examples/s]
Generating extra examples...: 232171 examples [01:23, 3055.67 examples/s]
Generating extra examples...: 232482 examples [01:23, 3070.33 examples/s]
Generating extra examples...: 232790 examples [01:23, 3068.91 examples/s]
Generating extra examples...: 233100 examples [01:23, 3077.06 examples/s]
Generating extra examples...: 233411 examples [01:23, 3085.50 examples/s]
Generating extra examples...: 233722 examples [01:23, 3091.94 examples/s]
Generating extra examples...: 234033 examples [01:23, 3095.82 examples/s]
Generating extra examples...: 234344 examples [01:24, 3098.93 examples/s]
Generating extra examples...: 234654 examples [01:24, 3050.13 examples/s]
Generating extra examples...: 234964 examples [01:24, 3062.96 examples/s]
Generating extra examples...: 235271 examples [01:24, 3064.20 examples/s]
Generating extra examples...: 235578 examples [01:24, 3065.06 examples/s]
Generating extra examples...: 235886 examples [01:24, 3067.05 examples/s]
Generating extra examples...: 236193 examples [01:24, 3058.54 examples/s]
Generating extra examples...: 236500 examples [01:24, 3058.82 examples/s]
Generating extra examples...: 236808 examples [01:24, 3062.29 examples/s]
Generating extra examples...: 237115 examples [01:24, 3060.43 examples/s]
Generating extra examples...: 237422 examples [01:25, 3054.62 examples/s]
Generating extra examples...: 237728 examples [01:25, 3052.86 examples/s]
Generating extra examples...: 238034 examples [01:25, 3047.65 examples/s]
Generating extra examples...: 238339 examples [01:25, 3048.19 examples/s]
Generating extra examples...: 238649 examples [01:25, 3062.46 examples/s]
Generating extra examples...: 238956 examples [01:25, 3059.15 examples/s]
Generating extra examples...: 239265 examples [01:25, 3066.49 examples/s]
Generating extra examples...: 239575 examples [01:25, 3075.66 examples/s]
Generating extra examples...: 239883 examples [01:25, 3028.92 examples/s]
Generating extra examples...: 240187 examples [01:25, 2981.29 examples/s]
Generating extra examples...: 240494 examples [01:26, 3007.08 examples/s]
Generating extra examples...: 240804 examples [01:26, 3032.42 examples/s]
Generating extra examples...: 241108 examples [01:26, 3009.01 examples/s]
Generating extra examples...: 241416 examples [01:26, 3029.31 examples/s]
Generating extra examples...: 241724 examples [01:26, 3042.00 examples/s]
Generating extra examples...: 242029 examples [01:26, 3040.82 examples/s]
Generating extra examples...: 242335 examples [01:26, 3046.01 examples/s]
Generating extra examples...: 242646 examples [01:26, 3062.45 examples/s]
Generating extra examples...: 242958 examples [01:26, 3076.77 examples/s]
Generating extra examples...: 243269 examples [01:26, 3083.53 examples/s]
Generating extra examples...: 243578 examples [01:27, 3081.18 examples/s]
Generating extra examples...: 243887 examples [01:27, 3076.19 examples/s]
Generating extra examples...: 244195 examples [01:27, 3023.15 examples/s]
Generating extra examples...: 244502 examples [01:27, 3035.61 examples/s]
Generating extra examples...: 244809 examples [01:27, 3045.01 examples/s]
Generating extra examples...: 245116 examples [01:27, 3050.64 examples/s]
Generating extra examples...: 245422 examples [01:27, 3050.14 examples/s]
Generating extra examples...: 245733 examples [01:27, 3066.13 examples/s]
Generating extra examples...: 246041 examples [01:27, 3068.78 examples/s]
Generating extra examples...: 246352 examples [01:27, 3080.97 examples/s]
Generating extra examples...: 246661 examples [01:28, 3078.26 examples/s]
Generating extra examples...: 246969 examples [01:28, 3071.53 examples/s]
Generating extra examples...: 247277 examples [01:28, 3068.94 examples/s]
Generating extra examples...: 247585 examples [01:28, 3071.31 examples/s]
Generating extra examples...: 247895 examples [01:28, 3078.75 examples/s]
Generating extra examples...: 248203 examples [01:28, 3074.16 examples/s]
Generating extra examples...: 248517 examples [01:28, 3092.11 examples/s]
Generating extra examples...: 248827 examples [01:28, 3087.34 examples/s]
Generating extra examples...: 249136 examples [01:28, 3073.80 examples/s]
Generating extra examples...: 249445 examples [01:28, 3077.59 examples/s]
Generating extra examples...: 249753 examples [01:29, 3070.36 examples/s]
Generating extra examples...: 250064 examples [01:29, 3081.24 examples/s]
Generating extra examples...: 250373 examples [01:29, 3060.59 examples/s]
Generating extra examples...: 250686 examples [01:29, 3079.67 examples/s]
Generating extra examples...: 250995 examples [01:29, 3067.63 examples/s]
Generating extra examples...: 251302 examples [01:29, 3051.75 examples/s]
Generating extra examples...: 251608 examples [01:29, 3051.16 examples/s]
Generating extra examples...: 251918 examples [01:29, 3065.03 examples/s]
Generating extra examples...: 252227 examples [01:29, 3069.70 examples/s]
Generating extra examples...: 252535 examples [01:29, 3071.30 examples/s]
Generating extra examples...: 252845 examples [01:30, 3077.39 examples/s]
Generating extra examples...: 253153 examples [01:30, 3067.96 examples/s]
Generating extra examples...: 253460 examples [01:30, 2997.10 examples/s]
Generating extra examples...: 253767 examples [01:30, 3015.97 examples/s]
Generating extra examples...: 254076 examples [01:30, 3037.31 examples/s]
Generating extra examples...: 254382 examples [01:30, 3044.00 examples/s]
Generating extra examples...: 254689 examples [01:30, 3051.09 examples/s]
Generating extra examples...: 254997 examples [01:30, 3059.17 examples/s]
Generating extra examples...: 255307 examples [01:30, 3070.71 examples/s]
Generating extra examples...: 255615 examples [01:30, 3068.43 examples/s]
Generating extra examples...: 255923 examples [01:31, 3071.43 examples/s]
Generating extra examples...: 256237 examples [01:31, 3091.36 examples/s]
Generating extra examples...: 256547 examples [01:31, 3030.93 examples/s]
Generating extra examples...: 256855 examples [01:31, 3044.03 examples/s]
Generating extra examples...: 257162 examples [01:31, 3051.01 examples/s]
Generating extra examples...: 257470 examples [01:31, 3057.85 examples/s]
Generating extra examples...: 257779 examples [01:31, 3066.26 examples/s]
Generating extra examples...: 258086 examples [01:31, 3064.70 examples/s]
Generating extra examples...: 258393 examples [01:31, 3066.10 examples/s]
Generating extra examples...: 258700 examples [01:31, 3063.26 examples/s]
Generating extra examples...: 259008 examples [01:32, 3067.38 examples/s]
Generating extra examples...: 259315 examples [01:32, 3061.58 examples/s]
Generating extra examples...: 259622 examples [01:32, 3031.06 examples/s]
Generating extra examples...: 259927 examples [01:32, 3033.96 examples/s]
Generating extra examples...: 260234 examples [01:32, 3043.42 examples/s]
Generating extra examples...: 260541 examples [01:32, 3049.05 examples/s]
Generating extra examples...: 260849 examples [01:32, 3057.16 examples/s]
Generating extra examples...: 261156 examples [01:32, 3058.40 examples/s]
Generating extra examples...: 261463 examples [01:32, 3061.49 examples/s]
Generating extra examples...: 261770 examples [01:32, 3063.88 examples/s]
Generating extra examples...: 262077 examples [01:33, 3061.82 examples/s]
Generating extra examples...: 262384 examples [01:33, 3053.15 examples/s]
Generating extra examples...: 262690 examples [01:33, 3041.51 examples/s]
Generating extra examples...: 263003 examples [01:33, 3065.85 examples/s]
Generating extra examples...: 263311 examples [01:33, 3067.81 examples/s]
Generating extra examples...: 263618 examples [01:33, 3066.90 examples/s]
Generating extra examples...: 263926 examples [01:33, 3068.23 examples/s]
Generating extra examples...: 264233 examples [01:33, 3065.22 examples/s]
Generating extra examples...: 264540 examples [01:33, 3001.90 examples/s]
Generating extra examples...: 264846 examples [01:33, 3016.49 examples/s]
Generating extra examples...: 265152 examples [01:34, 3027.95 examples/s]
Generating extra examples...: 265460 examples [01:34, 3040.17 examples/s]
Generating extra examples...: 265765 examples [01:34, 3015.27 examples/s]
Generating extra examples...: 266072 examples [01:34, 3029.12 examples/s]
Generating extra examples...: 266376 examples [01:34, 3030.41 examples/s]
Generating extra examples...: 266685 examples [01:34, 3047.34 examples/s]
Generating extra examples...: 266990 examples [01:34, 3046.34 examples/s]
Generating extra examples...: 267296 examples [01:34, 3049.37 examples/s]
Generating extra examples...: 267605 examples [01:34, 3059.60 examples/s]
Generating extra examples...: 267911 examples [01:35, 3056.41 examples/s]
Generating extra examples...: 268217 examples [01:35, 3047.41 examples/s]
Generating extra examples...: 268522 examples [01:35, 3043.80 examples/s]
Generating extra examples...: 268827 examples [01:35, 3010.40 examples/s]
Generating extra examples...: 269136 examples [01:35, 3031.75 examples/s]
Generating extra examples...: 269445 examples [01:35, 3048.30 examples/s]
Generating extra examples...: 269751 examples [01:35, 3051.30 examples/s]
Generating extra examples...: 270057 examples [01:35, 3047.47 examples/s]
Generating extra examples...: 270365 examples [01:35, 3054.31 examples/s]
Generating extra examples...: 270673 examples [01:35, 3061.52 examples/s]
Generating extra examples...: 270981 examples [01:36, 3066.26 examples/s]
Generating extra examples...: 271288 examples [01:36, 3051.31 examples/s]
Generating extra examples...: 271599 examples [01:36, 3066.53 examples/s]
Generating extra examples...: 271907 examples [01:36, 3068.89 examples/s]
Generating extra examples...: 272214 examples [01:36, 3065.65 examples/s]
Generating extra examples...: 272522 examples [01:36, 3068.12 examples/s]
Generating extra examples...: 272830 examples [01:36, 3069.80 examples/s]
Generating extra examples...: 273138 examples [01:36, 3069.92 examples/s]
Generating extra examples...: 273445 examples [01:36, 3065.54 examples/s]
Generating extra examples...: 273754 examples [01:36, 3071.14 examples/s]
Generating extra examples...: 274062 examples [01:37, 3051.99 examples/s]
Generating extra examples...: 274368 examples [01:37, 3045.25 examples/s]
Generating extra examples...: 274673 examples [01:37, 3023.08 examples/s]
Generating extra examples...: 274978 examples [01:37, 3029.96 examples/s]
Generating extra examples...: 275287 examples [01:37, 3047.09 examples/s]
Generating extra examples...: 275593 examples [01:37, 3048.28 examples/s]
Generating extra examples...: 275898 examples [01:37, 3044.99 examples/s]
Generating extra examples...: 276205 examples [01:37, 3052.02 examples/s]
Generating extra examples...: 276514 examples [01:37, 3062.87 examples/s]
Generating extra examples...: 276824 examples [01:37, 3071.97 examples/s]
Generating extra examples...: 277134 examples [01:38, 3077.52 examples/s]
Generating extra examples...: 277442 examples [01:38, 3065.08 examples/s]
Generating extra examples...: 277749 examples [01:38, 3059.64 examples/s]
Generating extra examples...: 278055 examples [01:38, 3051.81 examples/s]
Generating extra examples...: 278361 examples [01:38, 3041.12 examples/s]
Generating extra examples...: 278666 examples [01:38, 3042.65 examples/s]
Generating extra examples...: 278971 examples [01:38, 3039.77 examples/s]
Generating extra examples...: 279277 examples [01:38, 3045.14 examples/s]
Generating extra examples...: 279588 examples [01:38, 3061.86 examples/s]
Generating extra examples...: 279895 examples [01:38, 3060.39 examples/s]
Generating extra examples...: 280203 examples [01:39, 3063.87 examples/s]
Generating extra examples...: 280511 examples [01:39, 3066.62 examples/s]
Generating extra examples...: 280818 examples [01:39, 3066.66 examples/s]
Generating extra examples...: 281128 examples [01:39, 3074.56 examples/s]
Generating extra examples...: 281436 examples [01:39, 3068.18 examples/s]
Generating extra examples...: 281743 examples [01:39, 3068.21 examples/s]
Generating extra examples...: 282053 examples [01:39, 3075.60 examples/s]
Generating extra examples...: 282361 examples [01:39, 3075.40 examples/s]
Generating extra examples...: 282669 examples [01:39, 3073.66 examples/s]
Generating extra examples...: 282980 examples [01:39, 3083.98 examples/s]
Generating extra examples...: 283289 examples [01:40, 3072.47 examples/s]
Generating extra examples...: 283597 examples [01:40, 3072.94 examples/s]
Generating extra examples...: 283906 examples [01:40, 3076.32 examples/s]
Generating extra examples...: 284214 examples [01:40, 3075.95 examples/s]
Generating extra examples...: 284522 examples [01:40, 3075.04 examples/s]
Generating extra examples...: 284830 examples [01:40, 3063.80 examples/s]
Generating extra examples...: 285137 examples [01:40, 3059.27 examples/s]
Generating extra examples...: 285445 examples [01:40, 3064.52 examples/s]
Generating extra examples...: 285752 examples [01:40, 3064.80 examples/s]
Generating extra examples...: 286059 examples [01:40, 3053.46 examples/s]
Generating extra examples...: 286369 examples [01:41, 3066.54 examples/s]
Generating extra examples...: 286677 examples [01:41, 3069.19 examples/s]
Generating extra examples...: 286984 examples [01:41, 3059.68 examples/s]
Generating extra examples...: 287290 examples [01:41, 3040.66 examples/s]
Generating extra examples...: 287595 examples [01:41, 3025.06 examples/s]
Generating extra examples...: 287900 examples [01:41, 3030.47 examples/s]
Generating extra examples...: 288207 examples [01:41, 3041.90 examples/s]
Generating extra examples...: 288515 examples [01:41, 3052.18 examples/s]
Generating extra examples...: 288821 examples [01:41, 3050.62 examples/s]
Generating extra examples...: 289127 examples [01:41, 3040.44 examples/s]
Generating extra examples...: 289433 examples [01:42, 3045.08 examples/s]
Generating extra examples...: 289745 examples [01:42, 3067.31 examples/s]
Generating extra examples...: 290057 examples [01:42, 3080.74 examples/s]
Generating extra examples...: 290366 examples [01:42, 3080.31 examples/s]
Generating extra examples...: 290675 examples [01:42, 3062.63 examples/s]
Generating extra examples...: 290982 examples [01:42, 3053.00 examples/s]
Generating extra examples...: 291288 examples [01:42, 3049.51 examples/s]
Generating extra examples...: 291597 examples [01:42, 3060.91 examples/s]
Generating extra examples...: 291904 examples [01:42, 3049.55 examples/s]
Generating extra examples...: 292209 examples [01:42, 3033.65 examples/s]
Generating extra examples...: 292514 examples [01:43, 3036.31 examples/s]
Generating extra examples...: 292818 examples [01:43, 3036.26 examples/s]
Generating extra examples...: 293122 examples [01:43, 3013.53 examples/s]
Generating extra examples...: 293429 examples [01:43, 3029.01 examples/s]
Generating extra examples...: 293732 examples [01:43, 3001.70 examples/s]
Generating extra examples...: 294039 examples [01:43, 3021.86 examples/s]
Generating extra examples...: 294344 examples [01:43, 3026.85 examples/s]
Generating extra examples...: 294648 examples [01:43, 3029.49 examples/s]
Generating extra examples...: 294957 examples [01:43, 3046.31 examples/s]
Generating extra examples...: 295264 examples [01:43, 3052.89 examples/s]
Generating extra examples...: 295570 examples [01:44, 3054.90 examples/s]
Generating extra examples...: 295876 examples [01:44, 3045.13 examples/s]
Generating extra examples...: 296181 examples [01:44, 3044.52 examples/s]
Generating extra examples...: 296487 examples [01:44, 3048.97 examples/s]
Generating extra examples...: 296792 examples [01:44, 2998.79 examples/s]
Generating extra examples...: 297094 examples [01:44, 3004.52 examples/s]
Generating extra examples...: 297400 examples [01:44, 3018.54 examples/s]
Generating extra examples...: 297708 examples [01:44, 3033.62 examples/s]
Generating extra examples...: 298018 examples [01:44, 3052.84 examples/s]
Generating extra examples...: 298327 examples [01:44, 3063.64 examples/s]
Generating extra examples...: 298634 examples [01:45, 3046.99 examples/s]
Generating extra examples...: 298940 examples [01:45, 3049.03 examples/s]
Generating extra examples...: 299248 examples [01:45, 3055.54 examples/s]
Generating extra examples...: 299554 examples [01:45, 3055.02 examples/s]
Generating extra examples...: 299860 examples [01:45, 3055.62 examples/s]
Generating extra examples...: 300166 examples [01:45, 3046.33 examples/s]
Generating extra examples...: 300475 examples [01:45, 3056.77 examples/s]
Generating extra examples...: 300782 examples [01:45, 3058.41 examples/s]
Generating extra examples...: 301088 examples [01:45, 3057.02 examples/s]
Generating extra examples...: 301394 examples [01:45, 3044.27 examples/s]
Generating extra examples...: 301699 examples [01:46, 3043.21 examples/s]
Generating extra examples...: 302004 examples [01:46, 3042.68 examples/s]
Generating extra examples...: 302309 examples [01:46, 3039.72 examples/s]
Generating extra examples...: 302615 examples [01:46, 3044.42 examples/s]
Generating extra examples...: 302920 examples [01:46, 3032.08 examples/s]
Generating extra examples...: 303225 examples [01:46, 3036.55 examples/s]
Generating extra examples...: 303529 examples [01:46, 3035.43 examples/s]
Generating extra examples...: 303836 examples [01:46, 3044.35 examples/s]
Generating extra examples...: 304144 examples [01:46, 3052.43 examples/s]
Generating extra examples...: 304450 examples [01:46, 3053.78 examples/s]
Generating extra examples...: 304756 examples [01:47, 3055.32 examples/s]
Generating extra examples...: 305065 examples [01:47, 3065.19 examples/s]
Generating extra examples...: 305372 examples [01:47, 3064.40 examples/s]
Generating extra examples...: 305684 examples [01:47, 3078.36 examples/s]
Generating extra examples...: 305992 examples [01:47, 3068.49 examples/s]
Generating extra examples...: 306299 examples [01:47, 3059.46 examples/s]
Generating extra examples...: 306608 examples [01:47, 3067.61 examples/s]
Generating extra examples...: 306915 examples [01:47, 3067.13 examples/s]
Generating extra examples...: 307226 examples [01:47, 3077.52 examples/s]
Generating extra examples...: 307536 examples [01:47, 3083.18 examples/s]
Generating extra examples...: 307845 examples [01:48, 3077.02 examples/s]
Generating extra examples...: 308153 examples [01:48, 3072.56 examples/s]
Generating extra examples...: 308461 examples [01:48, 3067.36 examples/s]
Generating extra examples...: 308768 examples [01:48, 3066.82 examples/s]
Generating extra examples...: 309075 examples [01:48, 3053.16 examples/s]
Generating extra examples...: 309386 examples [01:48, 3069.70 examples/s]
Generating extra examples...: 309693 examples [01:48, 3065.53 examples/s]
Generating extra examples...: 310003 examples [01:48, 3074.33 examples/s]
Generating extra examples...: 310311 examples [01:48, 3075.13 examples/s]
Generating extra examples...: 310620 examples [01:48, 3077.54 examples/s]
Generating extra examples...: 310931 examples [01:49, 3084.14 examples/s]
Generating extra examples...: 311243 examples [01:49, 3092.42 examples/s]
Generating extra examples...: 311553 examples [01:49, 3089.20 examples/s]
Generating extra examples...: 311863 examples [01:49, 3090.32 examples/s]
Generating extra examples...: 312173 examples [01:49, 3017.71 examples/s]
Generating extra examples...: 312481 examples [01:49, 3035.78 examples/s]
Generating extra examples...: 312788 examples [01:49, 3045.59 examples/s]
Generating extra examples...: 313093 examples [01:49, 3044.27 examples/s]
Generating extra examples...: 313402 examples [01:49, 3057.54 examples/s]
Generating extra examples...: 313710 examples [01:49, 3063.12 examples/s]
Generating extra examples...: 314018 examples [01:50, 3065.52 examples/s]
Generating extra examples...: 314326 examples [01:50, 3068.39 examples/s]
Generating extra examples...: 314633 examples [01:50, 3065.01 examples/s]
Generating extra examples...: 314943 examples [01:50, 3073.61 examples/s]
Generating extra examples...: 315251 examples [01:50, 3071.99 examples/s]
Generating extra examples...: 315559 examples [01:50, 3053.58 examples/s]
Generating extra examples...: 315865 examples [01:50, 3050.09 examples/s]
Generating extra examples...: 316176 examples [01:50, 3066.76 examples/s]
Generating extra examples...: 316483 examples [01:50, 3062.91 examples/s]
Generating extra examples...: 316790 examples [01:51, 3060.32 examples/s]
Generating extra examples...: 317098 examples [01:51, 3063.79 examples/s]
Generating extra examples...: 317406 examples [01:51, 3066.98 examples/s]
Generating extra examples...: 317713 examples [01:51, 3052.24 examples/s]
Generating extra examples...: 318024 examples [01:51, 3068.75 examples/s]
Generating extra examples...: 318332 examples [01:51, 3071.99 examples/s]
Generating extra examples...: 318640 examples [01:51, 3062.31 examples/s]
Generating extra examples...: 318949 examples [01:51, 3068.60 examples/s]
Generating extra examples...: 319257 examples [01:51, 3069.25 examples/s]
Generating extra examples...: 319567 examples [01:51, 3075.42 examples/s]
Generating extra examples...: 319875 examples [01:52, 3075.98 examples/s]
Generating extra examples...: 320184 examples [01:52, 3077.59 examples/s]
Generating extra examples...: 320492 examples [01:52, 3074.49 examples/s]
Generating extra examples...: 320800 examples [01:52, 3073.87 examples/s]
Generating extra examples...: 321109 examples [01:52, 3075.92 examples/s]
Generating extra examples...: 321417 examples [01:52, 3071.41 examples/s]
Generating extra examples...: 321725 examples [01:52, 3060.65 examples/s]
Generating extra examples...: 322035 examples [01:52, 3069.29 examples/s]
Generating extra examples...: 322347 examples [01:52, 3082.91 examples/s]
Generating extra examples...: 322658 examples [01:52, 3088.56 examples/s]
Generating extra examples...: 322969 examples [01:53, 3094.25 examples/s]
Generating extra examples...: 323280 examples [01:53, 3097.55 examples/s]
Generating extra examples...: 323590 examples [01:53, 3089.68 examples/s]
Generating extra examples...: 323900 examples [01:53, 3089.48 examples/s]
Generating extra examples...: 324209 examples [01:53, 3087.23 examples/s]
Generating extra examples...: 324521 examples [01:53, 3095.93 examples/s]
Generating extra examples...: 324831 examples [01:53, 3083.32 examples/s]
Generating extra examples...: 325140 examples [01:53, 3082.41 examples/s]
Generating extra examples...: 325449 examples [01:53, 3079.00 examples/s]
Generating extra examples...: 325765 examples [01:53, 3100.41 examples/s]
Generating extra examples...: 326076 examples [01:54, 3096.89 examples/s]
Generating extra examples...: 326386 examples [01:54, 3087.29 examples/s]
Generating extra examples...: 326698 examples [01:54, 3096.97 examples/s]
Generating extra examples...: 327008 examples [01:54, 3089.36 examples/s]
Generating extra examples...: 327317 examples [01:54, 3079.44 examples/s]
Generating extra examples...: 327627 examples [01:54, 3085.00 examples/s]
Generating extra examples...: 327936 examples [01:54, 3043.93 examples/s]
Generating extra examples...: 328243 examples [01:54, 3051.45 examples/s]
Generating extra examples...: 328549 examples [01:54, 3052.07 examples/s]
Generating extra examples...: 328858 examples [01:54, 3061.83 examples/s]
Generating extra examples...: 329167 examples [01:55, 3068.67 examples/s]
Generating extra examples...: 329474 examples [01:55, 3056.03 examples/s]
Generating extra examples...: 329780 examples [01:55, 3048.52 examples/s]
Generating extra examples...: 330085 examples [01:55, 3041.63 examples/s]
Generating extra examples...: 330394 examples [01:55, 3053.48 examples/s]
Generating extra examples...: 330700 examples [01:55, 3051.91 examples/s]
Generating extra examples...: 331006 examples [01:55, 3034.62 examples/s]
Generating extra examples...: 331312 examples [01:55, 3039.67 examples/s]
Generating extra examples...: 331616 examples [01:55, 3038.71 examples/s]
Generating extra examples...: 331923 examples [01:55, 3046.97 examples/s]
Generating extra examples...: 332228 examples [01:56, 3043.81 examples/s]
Generating extra examples...: 332533 examples [01:56, 2985.15 examples/s]
Generating extra examples...: 332843 examples [01:56, 3018.48 examples/s]
Generating extra examples...: 333148 examples [01:56, 3024.72 examples/s]
Generating extra examples...: 333456 examples [01:56, 3038.75 examples/s]
Generating extra examples...: 333761 examples [01:56, 3033.92 examples/s]
Generating extra examples...: 334065 examples [01:56, 3009.47 examples/s]
Generating extra examples...: 334375 examples [01:56, 3035.09 examples/s]
Generating extra examples...: 334681 examples [01:56, 3041.80 examples/s]
Generating extra examples...: 334986 examples [01:56, 2957.10 examples/s]
Generating extra examples...: 335294 examples [01:57, 2990.70 examples/s]
Generating extra examples...: 335600 examples [01:57, 3008.28 examples/s]
Generating extra examples...: 335908 examples [01:57, 3027.95 examples/s]
Generating extra examples...: 336217 examples [01:57, 3046.23 examples/s]
Generating extra examples...: 336522 examples [01:57, 3038.62 examples/s]
Generating extra examples...: 336830 examples [01:57, 3049.85 examples/s]
Generating extra examples...: 337136 examples [01:57, 3034.82 examples/s]
Generating extra examples...: 337444 examples [01:57, 3046.76 examples/s]
Generating extra examples...: 337750 examples [01:57, 3048.13 examples/s]
Generating extra examples...: 338055 examples [01:57, 3043.28 examples/s]
Generating extra examples...: 338362 examples [01:58, 3051.15 examples/s]
Generating extra examples...: 338669 examples [01:58, 3055.09 examples/s]
Generating extra examples...: 338977 examples [01:58, 3059.87 examples/s]
Generating extra examples...: 339284 examples [01:58, 3062.68 examples/s]
Generating extra examples...: 339591 examples [01:58, 3060.41 examples/s]
Generating extra examples...: 339900 examples [01:58, 3068.48 examples/s]
Generating extra examples...: 340207 examples [01:58, 3064.19 examples/s]
Generating extra examples...: 340514 examples [01:58, 3064.37 examples/s]
Generating extra examples...: 340822 examples [01:58, 3067.27 examples/s]
Generating extra examples...: 341129 examples [01:58, 3061.03 examples/s]
Generating extra examples...: 341436 examples [01:59, 3061.74 examples/s]
Generating extra examples...: 341745 examples [01:59, 3069.45 examples/s]
Generating extra examples...: 342055 examples [01:59, 3078.20 examples/s]
Generating extra examples...: 342363 examples [01:59, 3062.61 examples/s]
Generating extra examples...: 342670 examples [01:59, 3064.32 examples/s]
Generating extra examples...: 342980 examples [01:59, 3074.74 examples/s]
Generating extra examples...: 343288 examples [01:59, 3075.93 examples/s]
Generating extra examples...: 343599 examples [01:59, 3084.50 examples/s]
Generating extra examples...: 343909 examples [01:59, 3089.01 examples/s]
Generating extra examples...: 344218 examples [01:59, 3082.53 examples/s]
Generating extra examples...: 344527 examples [02:00, 3069.45 examples/s]
Generating extra examples...: 344834 examples [02:00, 3065.40 examples/s]
Generating extra examples...: 345145 examples [02:00, 3077.55 examples/s]
Generating extra examples...: 345453 examples [02:00, 3069.00 examples/s]
Generating extra examples...: 345763 examples [02:00, 3075.01 examples/s]
Generating extra examples...: 346071 examples [02:00, 3074.22 examples/s]
Generating extra examples...: 346379 examples [02:00, 3067.40 examples/s]
Generating extra examples...: 346687 examples [02:00, 3070.79 examples/s]
Generating extra examples...: 346995 examples [02:00, 3068.99 examples/s]
Generating extra examples...: 347307 examples [02:00, 3083.11 examples/s]
Generating extra examples...: 347616 examples [02:01, 3076.30 examples/s]
Generating extra examples...: 347924 examples [02:01, 3058.32 examples/s]
Generating extra examples...: 348231 examples [02:01, 3060.20 examples/s]
Generating extra examples...: 348538 examples [02:01, 3037.21 examples/s]
Generating extra examples...: 348842 examples [02:01, 3036.04 examples/s]
Generating extra examples...: 349149 examples [02:01, 3045.04 examples/s]
Generating extra examples...: 349457 examples [02:01, 3053.68 examples/s]
Generating extra examples...: 349763 examples [02:01, 3045.98 examples/s]
Generating extra examples...: 350068 examples [02:01, 3040.44 examples/s]
Generating extra examples...: 350373 examples [02:01, 3043.13 examples/s]
Generating extra examples...: 350678 examples [02:02, 3043.65 examples/s]
Generating extra examples...: 350983 examples [02:02, 3042.56 examples/s]
Generating extra examples...: 351293 examples [02:02, 3058.85 examples/s]
Generating extra examples...: 351602 examples [02:02, 3067.86 examples/s]
Generating extra examples...: 351913 examples [02:02, 3080.32 examples/s]
Generating extra examples...: 352222 examples [02:02, 3081.71 examples/s]
Generating extra examples...: 352531 examples [02:02, 3074.62 examples/s]
Generating extra examples...: 352839 examples [02:02, 3029.27 examples/s]
Generating extra examples...: 353143 examples [02:02, 3029.59 examples/s]
Generating extra examples...: 353451 examples [02:02, 3042.02 examples/s]
Generating extra examples...: 353756 examples [02:03, 3044.24 examples/s]
Generating extra examples...: 354065 examples [02:03, 3054.70 examples/s]
Generating extra examples...: 354374 examples [02:03, 3062.39 examples/s]
Generating extra examples...: 354681 examples [02:03, 3061.38 examples/s]
Generating extra examples...: 354988 examples [02:03, 3050.39 examples/s]
Generating extra examples...: 355296 examples [02:03, 3057.69 examples/s]
Generating extra examples...: 355603 examples [02:03, 3059.67 examples/s]
Generating extra examples...: 355909 examples [02:03, 3057.12 examples/s]
Generating extra examples...: 356215 examples [02:03, 3049.72 examples/s]
Generating extra examples...: 356520 examples [02:03, 3044.36 examples/s]
Generating extra examples...: 356826 examples [02:04, 3046.78 examples/s]
Generating extra examples...: 357136 examples [02:04, 3060.93 examples/s]
Generating extra examples...: 357446 examples [02:04, 3070.60 examples/s]
Generating extra examples...: 357758 examples [02:04, 3084.81 examples/s]
Generating extra examples...: 358073 examples [02:04, 3102.55 examples/s]
Generating extra examples...: 358389 examples [02:04, 3117.93 examples/s]
Generating extra examples...: 358702 examples [02:04, 3119.35 examples/s]
Generating extra examples...: 359014 examples [02:04, 3052.53 examples/s]
Generating extra examples...: 359323 examples [02:04, 3062.45 examples/s]
Generating extra examples...: 359637 examples [02:04, 3083.39 examples/s]
Generating extra examples...: 359950 examples [02:05, 3095.99 examples/s]
Generating extra examples...: 360260 examples [02:05, 3094.58 examples/s]
Generating extra examples...: 360570 examples [02:05, 3085.20 examples/s]
Generating extra examples...: 360882 examples [02:05, 3094.62 examples/s]
Generating extra examples...: 361192 examples [02:05, 3094.81 examples/s]
Generating extra examples...: 361502 examples [02:05, 3095.69 examples/s]
Generating extra examples...: 361812 examples [02:05, 3088.19 examples/s]
Generating extra examples...: 362121 examples [02:05, 3076.20 examples/s]
Generating extra examples...: 362429 examples [02:05, 3073.14 examples/s]
Generating extra examples...: 362740 examples [02:06, 3080.94 examples/s]
Generating extra examples...: 363053 examples [02:06, 3093.99 examples/s]
Generating extra examples...: 363364 examples [02:06, 3098.07 examples/s]
Generating extra examples...: 363674 examples [02:06, 3095.46 examples/s]
Generating extra examples...: 363984 examples [02:06, 3096.57 examples/s]
Generating extra examples...: 364294 examples [02:06, 3094.32 examples/s]
Generating extra examples...: 364604 examples [02:06, 3078.81 examples/s]
Generating extra examples...: 364916 examples [02:06, 3089.61 examples/s]
Generating extra examples...: 365225 examples [02:06, 3087.02 examples/s]
Generating extra examples...: 365534 examples [02:06, 3084.57 examples/s]
Generating extra examples...: 365843 examples [02:07, 3064.61 examples/s]
Generating extra examples...: 366155 examples [02:07, 3080.89 examples/s]
Generating extra examples...: 366464 examples [02:07, 3082.02 examples/s]
Generating extra examples...: 366773 examples [02:07, 3075.85 examples/s]
Generating extra examples...: 367081 examples [02:07, 3068.54 examples/s]
Generating extra examples...: 367388 examples [02:07, 3054.24 examples/s]
Generating extra examples...: 367694 examples [02:07, 3046.98 examples/s]
Generating extra examples...: 367999 examples [02:07, 3045.04 examples/s]
Generating extra examples...: 368304 examples [02:07, 2975.50 examples/s]
Generating extra examples...: 368609 examples [02:07, 2995.23 examples/s]
Generating extra examples...: 368918 examples [02:08, 3021.07 examples/s]
Generating extra examples...: 369227 examples [02:08, 3040.52 examples/s]
Generating extra examples...: 369532 examples [02:08, 3039.79 examples/s]
Generating extra examples...: 369837 examples [02:08, 3036.28 examples/s]
Generating extra examples...: 370142 examples [02:08, 3039.01 examples/s]
Generating extra examples...: 370446 examples [02:08, 3038.84 examples/s]
Generating extra examples...: 370750 examples [02:08, 3036.80 examples/s]
Generating extra examples...: 371055 examples [02:08, 3038.20 examples/s]
Generating extra examples...: 371360 examples [02:08, 3040.93 examples/s]
Generating extra examples...: 371666 examples [02:08, 3043.77 examples/s]
Generating extra examples...: 371971 examples [02:09, 3043.29 examples/s]
Generating extra examples...: 372276 examples [02:09, 3041.99 examples/s]
Generating extra examples...: 372581 examples [02:09, 3038.38 examples/s]
Generating extra examples...: 372885 examples [02:09, 3035.99 examples/s]
Generating extra examples...: 373195 examples [02:09, 3051.75 examples/s]
Generating extra examples...: 373502 examples [02:09, 3054.57 examples/s]
Generating extra examples...: 373808 examples [02:09, 3050.41 examples/s]
Generating extra examples...: 374117 examples [02:09, 3061.22 examples/s]
Generating extra examples...: 374424 examples [02:09, 3057.97 examples/s]
Generating extra examples...: 374730 examples [02:09, 3054.90 examples/s]
Generating extra examples...: 375039 examples [02:10, 3063.08 examples/s]
Generating extra examples...: 375352 examples [02:10, 3081.13 examples/s]
Generating extra examples...: 375661 examples [02:10, 3069.78 examples/s]
Generating extra examples...: 375970 examples [02:10, 3073.63 examples/s]
Generating extra examples...: 376278 examples [02:10, 3071.08 examples/s]
Generating extra examples...: 376586 examples [02:10, 3069.84 examples/s]
Generating extra examples...: 376893 examples [02:10, 3065.01 examples/s]
Generating extra examples...: 377201 examples [02:10, 3069.20 examples/s]
Generating extra examples...: 377508 examples [02:10, 3002.28 examples/s]
Generating extra examples...: 377815 examples [02:10, 3020.61 examples/s]
Generating extra examples...: 378118 examples [02:11, 3022.57 examples/s]
Generating extra examples...: 378421 examples [02:11, 3017.02 examples/s]
Generating extra examples...: 378727 examples [02:11, 3027.56 examples/s]
Generating extra examples...: 379032 examples [02:11, 3033.02 examples/s]
Generating extra examples...: 379336 examples [02:11, 3027.34 examples/s]
Generating extra examples...: 379639 examples [02:11, 3024.45 examples/s]
Generating extra examples...: 379942 examples [02:11, 3022.06 examples/s]
Generating extra examples...: 380250 examples [02:11, 3037.88 examples/s]
Generating extra examples...: 380560 examples [02:11, 3056.17 examples/s]
Generating extra examples...: 380866 examples [02:11, 3047.05 examples/s]
Generating extra examples...: 381173 examples [02:12, 3051.16 examples/s]
Generating extra examples...: 381482 examples [02:12, 3060.49 examples/s]
Generating extra examples...: 381789 examples [02:12, 3060.25 examples/s]
Generating extra examples...: 382099 examples [02:12, 3071.21 examples/s]
Generating extra examples...: 382412 examples [02:12, 3087.44 examples/s]
Generating extra examples...: 382726 examples [02:12, 3100.74 examples/s]
Generating extra examples...: 383037 examples [02:12, 3103.36 examples/s]
Generating extra examples...: 383350 examples [02:12, 3109.22 examples/s]
Generating extra examples...: 383663 examples [02:12, 3114.58 examples/s]
Generating extra examples...: 383975 examples [02:12, 3097.59 examples/s]
Generating extra examples...: 384291 examples [02:13, 3113.81 examples/s]
Generating extra examples...: 384606 examples [02:13, 3123.35 examples/s]
Generating extra examples...: 384919 examples [02:13, 3124.20 examples/s]
Generating extra examples...: 385232 examples [02:13, 3114.57 examples/s]
Generating extra examples...: 385544 examples [02:13, 3104.63 examples/s]
Generating extra examples...: 385857 examples [02:13, 3109.98 examples/s]
Generating extra examples...: 386169 examples [02:13, 3098.84 examples/s]
Generating extra examples...: 386479 examples [02:13, 3089.67 examples/s]
Generating extra examples...: 386788 examples [02:13, 3082.49 examples/s]
Generating extra examples...: 387097 examples [02:13, 3071.78 examples/s]
Generating extra examples...: 387405 examples [02:14, 3069.20 examples/s]
Generating extra examples...: 387717 examples [02:14, 3083.63 examples/s]
Generating extra examples...: 388031 examples [02:14, 3098.65 examples/s]
Generating extra examples...: 388344 examples [02:14, 3105.24 examples/s]
Generating extra examples...: 388657 examples [02:14, 3109.45 examples/s]
Generating extra examples...: 388968 examples [02:14, 3106.39 examples/s]
Generating extra examples...: 389279 examples [02:14, 3104.94 examples/s]
Generating extra examples...: 389590 examples [02:14, 3080.98 examples/s]
Generating extra examples...: 389899 examples [02:14, 3073.23 examples/s]
Generating extra examples...: 390207 examples [02:14, 2979.78 examples/s]
Generating extra examples...: 390510 examples [02:15, 2993.63 examples/s]
Generating extra examples...: 390817 examples [02:15, 3013.43 examples/s]
Generating extra examples...: 391127 examples [02:15, 3038.67 examples/s]
Generating extra examples...: 391438 examples [02:15, 3057.99 examples/s]
Generating extra examples...: 391746 examples [02:15, 3062.89 examples/s]
Generating extra examples...: 392053 examples [02:15, 3061.36 examples/s]
Generating extra examples...: 392360 examples [02:15, 3053.77 examples/s]
Generating extra examples...: 392666 examples [02:15, 3050.03 examples/s]
Generating extra examples...: 392972 examples [02:15, 3052.82 examples/s]
Generating extra examples...: 393278 examples [02:15, 3030.40 examples/s]
Generating extra examples...: 393582 examples [02:16, 3028.99 examples/s]
Generating extra examples...: 393887 examples [02:16, 3033.84 examples/s]
Generating extra examples...: 394192 examples [02:16, 3037.26 examples/s]
Generating extra examples...: 394497 examples [02:16, 3040.15 examples/s]
Generating extra examples...: 394802 examples [02:16, 3041.94 examples/s]
Generating extra examples...: 395113 examples [02:16, 3060.28 examples/s]
Generating extra examples...: 395420 examples [02:16, 3052.98 examples/s]
Generating extra examples...: 395731 examples [02:16, 3068.39 examples/s]
Generating extra examples...: 396040 examples [02:16, 3071.84 examples/s]
Generating extra examples...: 396348 examples [02:16, 3053.28 examples/s]
Generating extra examples...: 396654 examples [02:17, 3055.21 examples/s]
Generating extra examples...: 396962 examples [02:17, 3061.00 examples/s]
Generating extra examples...: 397270 examples [02:17, 3065.26 examples/s]
Generating extra examples...: 397577 examples [02:17, 3060.02 examples/s]
Generating extra examples...: 397884 examples [02:17, 3059.63 examples/s]
Generating extra examples...: 398191 examples [02:17, 3060.92 examples/s]
Generating extra examples...: 398499 examples [02:17, 3065.53 examples/s]
Generating extra examples...: 398808 examples [02:17, 3069.65 examples/s]
Generating extra examples...: 399116 examples [02:17, 3071.74 examples/s]
Generating extra examples...: 399424 examples [02:17, 3032.48 examples/s]
Generating extra examples...: 399732 examples [02:18, 3044.63 examples/s]
Generating extra examples...: 400040 examples [02:18, 3053.21 examples/s]
Generating extra examples...: 400350 examples [02:18, 3065.53 examples/s]
Generating extra examples...: 400657 examples [02:18, 3062.37 examples/s]
Generating extra examples...: 400964 examples [02:18, 3061.65 examples/s]
Generating extra examples...: 401271 examples [02:18, 3056.43 examples/s]
Generating extra examples...: 401579 examples [02:18, 3060.31 examples/s]
Generating extra examples...: 401887 examples [02:18, 3065.40 examples/s]
Generating extra examples...: 402197 examples [02:18, 3074.35 examples/s]
Generating extra examples...: 402507 examples [02:18, 3081.14 examples/s]
Generating extra examples...: 402817 examples [02:19, 3086.56 examples/s]
Generating extra examples...: 403128 examples [02:19, 3091.56 examples/s]
Generating extra examples...: 403438 examples [02:19, 3088.99 examples/s]
Generating extra examples...: 403747 examples [02:19, 3050.90 examples/s]
Generating extra examples...: 404053 examples [02:19, 3052.14 examples/s]
Generating extra examples...: 404361 examples [02:19, 3058.48 examples/s]
Generating extra examples...: 404667 examples [02:19, 3057.85 examples/s]
Generating extra examples...: 404973 examples [02:19, 3056.09 examples/s]
Generating extra examples...: 405282 examples [02:19, 3065.86 examples/s]
Generating extra examples...: 405589 examples [02:20, 3064.78 examples/s]
Generating extra examples...: 405896 examples [02:20, 3059.31 examples/s]
Generating extra examples...: 406202 examples [02:20, 3056.34 examples/s]
Generating extra examples...: 406508 examples [02:20, 3054.13 examples/s]
Generating extra examples...: 406814 examples [02:20, 3046.88 examples/s]
Generating extra examples...: 407123 examples [02:20, 3059.19 examples/s]
Generating extra examples...: 407433 examples [02:20, 3070.53 examples/s]
Generating extra examples...: 407747 examples [02:20, 3090.42 examples/s]
Generating extra examples...: 408060 examples [02:20, 3101.82 examples/s]
Generating extra examples...: 408377 examples [02:20, 3121.85 examples/s]
Generating extra examples...: 408690 examples [02:21, 3112.65 examples/s]
Generating extra examples...: 409006 examples [02:21, 3125.68 examples/s]
Generating extra examples...: 409321 examples [02:21, 3130.67 examples/s]
Generating extra examples...: 409635 examples [02:21, 3132.46 examples/s]
Generating extra examples...: 409950 examples [02:21, 3135.63 examples/s]
Generating extra examples...: 410267 examples [02:21, 3143.31 examples/s]
Generating extra examples...: 410582 examples [02:21, 3136.81 examples/s]
Generating extra examples...: 410896 examples [02:21, 3113.26 examples/s]
Generating extra examples...: 411211 examples [02:21, 3123.38 examples/s]
Generating extra examples...: 411527 examples [02:21, 3134.00 examples/s]
Generating extra examples...: 411841 examples [02:22, 3095.11 examples/s]
Generating extra examples...: 412152 examples [02:22, 3096.70 examples/s]
Generating extra examples...: 412467 examples [02:22, 3111.62 examples/s]
Generating extra examples...: 412787 examples [02:22, 3135.22 examples/s]
Generating extra examples...: 413104 examples [02:22, 3142.78 examples/s]
Generating extra examples...: 413421 examples [02:22, 3148.22 examples/s]
Generating extra examples...: 413739 examples [02:22, 3155.68 examples/s]
Generating extra examples...: 414057 examples [02:22, 3161.01 examples/s]
Generating extra examples...: 414374 examples [02:22, 3153.11 examples/s]
Generating extra examples...: 414690 examples [02:22, 3146.03 examples/s]
Generating extra examples...: 415005 examples [02:23, 3121.82 examples/s]
Generating extra examples...: 415318 examples [02:23, 3112.68 examples/s]
Generating extra examples...: 415630 examples [02:23, 3100.31 examples/s]
Generating extra examples...: 415941 examples [02:23, 3073.13 examples/s]
Generating extra examples...: 416249 examples [02:23, 3069.27 examples/s]
Generating extra examples...: 416556 examples [02:23, 3057.23 examples/s]
Generating extra examples...: 416868 examples [02:23, 3073.27 examples/s]
Generating extra examples...: 417177 examples [02:23, 3077.14 examples/s]
Generating extra examples...: 417485 examples [02:23, 3069.44 examples/s]
Generating extra examples...: 417792 examples [02:23, 3068.83 examples/s]
Generating extra examples...: 418101 examples [02:24, 3075.05 examples/s]
Generating extra examples...: 418409 examples [02:24, 3073.28 examples/s]
Generating extra examples...: 418725 examples [02:24, 3095.59 examples/s]
Generating extra examples...: 419039 examples [02:24, 3105.58 examples/s]
Generating extra examples...: 419350 examples [02:24, 3104.20 examples/s]
Generating extra examples...: 419661 examples [02:24, 3102.93 examples/s]
Generating extra examples...: 419975 examples [02:24, 3113.23 examples/s]
Generating extra examples...: 420288 examples [02:24, 3117.47 examples/s]
Generating extra examples...: 420601 examples [02:24, 3118.52 examples/s]
Generating extra examples...: 420913 examples [02:24, 3117.70 examples/s]
Generating extra examples...: 421225 examples [02:25, 3114.08 examples/s]
Generating extra examples...: 421537 examples [02:25, 3008.25 examples/s]
Generating extra examples...: 421845 examples [02:25, 3027.59 examples/s]
Generating extra examples...: 422155 examples [02:25, 3048.08 examples/s]
Generating extra examples...: 422461 examples [02:25, 3040.37 examples/s]
Generating extra examples...: 422774 examples [02:25, 3065.70 examples/s]
Generating extra examples...: 423081 examples [02:25, 3063.65 examples/s]
Generating extra examples...: 423388 examples [02:25, 3055.81 examples/s]
Generating extra examples...: 423694 examples [02:25, 3056.89 examples/s]
Generating extra examples...: 424007 examples [02:25, 3077.67 examples/s]
Generating extra examples...: 424315 examples [02:26, 3077.51 examples/s]
Generating extra examples...: 424625 examples [02:26, 3083.89 examples/s]
Generating extra examples...: 424938 examples [02:26, 3097.02 examples/s]
Generating extra examples...: 425248 examples [02:26, 3091.48 examples/s]
Generating extra examples...: 425561 examples [02:26, 3101.05 examples/s]
Generating extra examples...: 425872 examples [02:26, 3095.68 examples/s]
Generating extra examples...: 426182 examples [02:26, 3093.21 examples/s]
Generating extra examples...: 426492 examples [02:26, 3084.54 examples/s]
Generating extra examples...: 426804 examples [02:26, 3093.31 examples/s]
Generating extra examples...: 427114 examples [02:26, 3094.81 examples/s]
Generating extra examples...: 427424 examples [02:27, 3089.96 examples/s]
Generating extra examples...: 427734 examples [02:27, 3041.50 examples/s]
Generating extra examples...: 428041 examples [02:27, 3049.19 examples/s]
Generating extra examples...: 428348 examples [02:27, 3052.30 examples/s]
Generating extra examples...: 428654 examples [02:27, 3054.00 examples/s]
Generating extra examples...: 428960 examples [02:27, 3052.59 examples/s]
Generating extra examples...: 429266 examples [02:27, 3036.41 examples/s]
Generating extra examples...: 429570 examples [02:27, 3029.34 examples/s]
Generating extra examples...: 429874 examples [02:27, 3030.30 examples/s]
Generating extra examples...: 430178 examples [02:27, 3032.91 examples/s]
Generating extra examples...: 430482 examples [02:28, 3033.01 examples/s]
Generating extra examples...: 430786 examples [02:28, 2978.00 examples/s]
Generating extra examples...: 431092 examples [02:28, 2999.24 examples/s]
Generating extra examples...: 431400 examples [02:28, 3020.30 examples/s]
Generating extra examples...: 431710 examples [02:28, 3041.46 examples/s]
Generating extra examples...: 432015 examples [02:28, 3027.95 examples/s]
Generating extra examples...: 432319 examples [02:28, 3029.72 examples/s]
Generating extra examples...: 432623 examples [02:28, 3032.43 examples/s]
Generating extra examples...: 432927 examples [02:28, 3026.34 examples/s]
Generating extra examples...: 433232 examples [02:28, 3032.75 examples/s]
Generating extra examples...: 433536 examples [02:29, 3029.72 examples/s]
Generating extra examples...: 433840 examples [02:29, 3031.23 examples/s]
Generating extra examples...: 434144 examples [02:29, 3031.78 examples/s]
Generating extra examples...: 434450 examples [02:29, 3039.53 examples/s]
Generating extra examples...: 434755 examples [02:29, 3042.00 examples/s]
Generating extra examples...: 435063 examples [02:29, 3051.81 examples/s]
Generating extra examples...: 435369 examples [02:29, 3039.38 examples/s]
Generating extra examples...: 435675 examples [02:29, 3045.05 examples/s]
Generating extra examples...: 435980 examples [02:29, 3045.22 examples/s]
Generating extra examples...: 436285 examples [02:29, 3033.58 examples/s]
Generating extra examples...: 436589 examples [02:30, 3028.35 examples/s]
Generating extra examples...: 436892 examples [02:30, 2944.79 examples/s]
Generating extra examples...: 437199 examples [02:30, 2980.99 examples/s]
Generating extra examples...: 437503 examples [02:30, 2997.84 examples/s]
Generating extra examples...: 437813 examples [02:30, 3027.81 examples/s]
Generating extra examples...: 438117 examples [02:30, 3021.49 examples/s]
Generating extra examples...: 438422 examples [02:30, 3027.57 examples/s]
Generating extra examples...: 438734 examples [02:30, 3052.62 examples/s]
Generating extra examples...: 439046 examples [02:30, 3072.65 examples/s]
Generating extra examples...: 439357 examples [02:30, 3082.89 examples/s]
Generating extra examples...: 439667 examples [02:31, 3087.06 examples/s]
Generating extra examples...: 439976 examples [02:31, 3069.12 examples/s]
Generating extra examples...: 440290 examples [02:31, 3089.45 examples/s]
Generating extra examples...: 440599 examples [02:31, 3079.93 examples/s]
Generating extra examples...: 440912 examples [02:31, 3092.81 examples/s]
Generating extra examples...: 441224 examples [02:31, 3100.54 examples/s]
Generating extra examples...: 441539 examples [02:31, 3112.77 examples/s]
Generating extra examples...: 441851 examples [02:31, 3094.89 examples/s]
Generating extra examples...: 442161 examples [02:31, 3069.21 examples/s]
Generating extra examples...: 442468 examples [02:31, 3067.71 examples/s]
Generating extra examples...: 442777 examples [02:32, 3071.46 examples/s]
Generating extra examples...: 443085 examples [02:32, 3058.79 examples/s]
Generating extra examples...: 443392 examples [02:32, 3060.03 examples/s]
Generating extra examples...: 443700 examples [02:32, 3064.75 examples/s]
Generating extra examples...: 444011 examples [02:32, 3077.90 examples/s]
Generating extra examples...: 444319 examples [02:32, 3068.65 examples/s]
Generating extra examples...: 444628 examples [02:32, 3073.58 examples/s]
Generating extra examples...: 444936 examples [02:32, 3075.22 examples/s]
Generating extra examples...: 445244 examples [02:32, 3070.87 examples/s]
Generating extra examples...: 445552 examples [02:33, 3068.77 examples/s]
Generating extra examples...: 445859 examples [02:33, 3054.86 examples/s]
Generating extra examples...: 446165 examples [02:33, 3041.32 examples/s]
Generating extra examples...: 446475 examples [02:33, 3056.94 examples/s]
Generating extra examples...: 446783 examples [02:33, 3062.83 examples/s]
Generating extra examples...: 447090 examples [02:33, 3055.75 examples/s]
Generating extra examples...: 447396 examples [02:33, 3047.17 examples/s]
Generating extra examples...: 447703 examples [02:33, 3053.45 examples/s]
Generating extra examples...: 448009 examples [02:33, 3054.17 examples/s]
Generating extra examples...: 448315 examples [02:33, 3052.77 examples/s]
Generating extra examples...: 448621 examples [02:34, 3054.37 examples/s]
Generating extra examples...: 448929 examples [02:34, 3059.62 examples/s]
Generating extra examples...: 449235 examples [02:34, 3037.99 examples/s]
Generating extra examples...: 449539 examples [02:34, 3017.32 examples/s]
Generating extra examples...: 449844 examples [02:34, 3025.52 examples/s]
Generating extra examples...: 450155 examples [02:34, 3047.88 examples/s]
Generating extra examples...: 450465 examples [02:34, 3061.78 examples/s]
Generating extra examples...: 450772 examples [02:34, 3056.33 examples/s]
Generating extra examples...: 451081 examples [02:34, 3066.35 examples/s]
Generating extra examples...: 451388 examples [02:34, 3060.36 examples/s]
Generating extra examples...: 451695 examples [02:35, 3059.00 examples/s]
Generating extra examples...: 452001 examples [02:35, 3052.91 examples/s]
Generating extra examples...: 452308 examples [02:35, 3055.34 examples/s]
Generating extra examples...: 452614 examples [02:35, 2958.86 examples/s]
Generating extra examples...: 452918 examples [02:35, 2980.95 examples/s]
Generating extra examples...: 453224 examples [02:35, 3003.12 examples/s]
Generating extra examples...: 453531 examples [02:35, 3020.14 examples/s]
Generating extra examples...: 453839 examples [02:35, 3035.84 examples/s]
Generating extra examples...: 454143 examples [02:35, 3036.94 examples/s]
Generating extra examples...: 454447 examples [02:35, 3032.83 examples/s]
Generating extra examples...: 454751 examples [02:36, 3031.71 examples/s]
Generating extra examples...: 455055 examples [02:36, 3030.76 examples/s]
Generating extra examples...: 455364 examples [02:36, 3047.37 examples/s]
Generating extra examples...: 455675 examples [02:36, 3063.68 examples/s]
Generating extra examples...: 455989 examples [02:36, 3085.08 examples/s]
Generating extra examples...: 456306 examples [02:36, 3109.72 examples/s]
Generating extra examples...: 456618 examples [02:36, 3111.99 examples/s]
Generating extra examples...: 456932 examples [02:36, 3118.21 examples/s]
Generating extra examples...: 457245 examples [02:36, 3118.98 examples/s]
Generating extra examples...: 457557 examples [02:36, 3119.10 examples/s]
Generating extra examples...: 457870 examples [02:37, 3120.25 examples/s]
Generating extra examples...: 458183 examples [02:37, 3116.68 examples/s]
Generating extra examples...: 458496 examples [02:37, 3117.15 examples/s]
Generating extra examples...: 458808 examples [02:37, 3031.56 examples/s]
Generating extra examples...: 459114 examples [02:37, 3039.43 examples/s]
Generating extra examples...: 459421 examples [02:37, 3047.62 examples/s]
Generating extra examples...: 459733 examples [02:37, 3068.00 examples/s]
Generating extra examples...: 460041 examples [02:37, 3070.34 examples/s]
Generating extra examples...: 460351 examples [02:37, 3076.77 examples/s]
Generating extra examples...: 460659 examples [02:37, 3069.84 examples/s]
Generating extra examples...: 460967 examples [02:38, 3069.92 examples/s]
Generating extra examples...: 461275 examples [02:38, 3069.10 examples/s]
Generating extra examples...: 461587 examples [02:38, 3081.83 examples/s]
Generating extra examples...: 461896 examples [02:38, 2987.46 examples/s]
Generating extra examples...: 462206 examples [02:38, 3018.88 examples/s]
Generating extra examples...: 462516 examples [02:38, 3039.93 examples/s]
Generating extra examples...: 462824 examples [02:38, 3050.75 examples/s]
Generating extra examples...: 463130 examples [02:38, 3036.86 examples/s]
Generating extra examples...: 463442 examples [02:38, 3059.87 examples/s]
Generating extra examples...: 463755 examples [02:38, 3077.70 examples/s]
Generating extra examples...: 464063 examples [02:39, 3073.97 examples/s]
Generating extra examples...: 464371 examples [02:39, 3072.61 examples/s]
Generating extra examples...: 464686 examples [02:39, 3093.95 examples/s]
Generating extra examples...: 464996 examples [02:39, 3067.12 examples/s]
Generating extra examples...: 465303 examples [02:39, 3045.41 examples/s]
Generating extra examples...: 465611 examples [02:39, 3054.23 examples/s]
Generating extra examples...: 465917 examples [02:39, 3047.70 examples/s]
Generating extra examples...: 466222 examples [02:39, 3044.82 examples/s]
Generating extra examples...: 466530 examples [02:39, 3054.23 examples/s]
Generating extra examples...: 466837 examples [02:39, 3056.26 examples/s]
Generating extra examples...: 467145 examples [02:40, 3063.26 examples/s]
Generating extra examples...: 467452 examples [02:40, 3056.26 examples/s]
Generating extra examples...: 467760 examples [02:40, 3061.59 examples/s]
Generating extra examples...: 468067 examples [02:40, 2965.24 examples/s]
Generating extra examples...: 468371 examples [02:40, 2985.59 examples/s]
Generating extra examples...: 468677 examples [02:40, 3005.37 examples/s]
Generating extra examples...: 468986 examples [02:40, 3029.75 examples/s]
Generating extra examples...: 469296 examples [02:40, 3050.24 examples/s]
Generating extra examples...: 469609 examples [02:40, 3072.37 examples/s]
Generating extra examples...: 469920 examples [02:40, 3081.21 examples/s]
Generating extra examples...: 470229 examples [02:41, 3081.80 examples/s]
Generating extra examples...: 470538 examples [02:41, 3061.36 examples/s]
Generating extra examples...: 470845 examples [02:41, 3060.68 examples/s]
Generating extra examples...: 471152 examples [02:41, 3005.20 examples/s]
Generating extra examples...: 471459 examples [02:41, 3022.56 examples/s]
Generating extra examples...: 471762 examples [02:41, 3023.89 examples/s]
Generating extra examples...: 472071 examples [02:41, 3042.56 examples/s]
Generating extra examples...: 472378 examples [02:41, 3049.43 examples/s]
Generating extra examples...: 472685 examples [02:41, 3053.46 examples/s]
Generating extra examples...: 472993 examples [02:41, 3060.42 examples/s]
Generating extra examples...: 473301 examples [02:42, 3064.17 examples/s]
Generating extra examples...: 473608 examples [02:42, 3057.86 examples/s]
Generating extra examples...: 473914 examples [02:42, 3052.55 examples/s]
Generating extra examples...: 474220 examples [02:42, 3036.39 examples/s]
Generating extra examples...: 474525 examples [02:42, 3038.69 examples/s]
Generating extra examples...: 474834 examples [02:42, 3053.98 examples/s]
Generating extra examples...: 475140 examples [02:42, 3050.45 examples/s]
Generating extra examples...: 475446 examples [02:42, 3051.63 examples/s]
Generating extra examples...: 475753 examples [02:42, 3055.46 examples/s]
Generating extra examples...: 476061 examples [02:42, 3060.40 examples/s]
Generating extra examples...: 476368 examples [02:43, 3062.97 examples/s]
Generating extra examples...: 476675 examples [02:43, 3062.44 examples/s]
Generating extra examples...: 476982 examples [02:43, 3062.31 examples/s]
Generating extra examples...: 477289 examples [02:43, 2971.20 examples/s]
Generating extra examples...: 477592 examples [02:43, 2986.94 examples/s]
Generating extra examples...: 477898 examples [02:43, 3005.90 examples/s]
Generating extra examples...: 478204 examples [02:43, 3020.89 examples/s]
Generating extra examples...: 478508 examples [02:43, 3026.16 examples/s]
Generating extra examples...: 478814 examples [02:43, 3035.17 examples/s]
Generating extra examples...: 479118 examples [02:44, 3031.68 examples/s]
Generating extra examples...: 479423 examples [02:44, 3034.13 examples/s]
Generating extra examples...: 479731 examples [02:44, 3045.98 examples/s]
Generating extra examples...: 480041 examples [02:44, 3060.19 examples/s]
Generating extra examples...: 480348 examples [02:44, 3062.53 examples/s]
Generating extra examples...: 480655 examples [02:44, 3059.37 examples/s]
Generating extra examples...: 480964 examples [02:44, 3065.76 examples/s]
Generating extra examples...: 481272 examples [02:44, 3068.47 examples/s]
Generating extra examples...: 481579 examples [02:44, 3056.25 examples/s]
Generating extra examples...: 481885 examples [02:44, 3053.23 examples/s]
Generating extra examples...: 482194 examples [02:45, 3061.56 examples/s]
Generating extra examples...: 482501 examples [02:45, 3050.21 examples/s]
Generating extra examples...: 482807 examples [02:45, 3052.23 examples/s]
Generating extra examples...: 483115 examples [02:45, 3060.13 examples/s]
Generating extra examples...: 483422 examples [02:45, 3062.29 examples/s]
Generating extra examples...: 483729 examples [02:45, 2969.78 examples/s]
Generating extra examples...: 484032 examples [02:45, 2985.62 examples/s]
Generating extra examples...: 484335 examples [02:45, 2996.98 examples/s]
Generating extra examples...: 484640 examples [02:45, 3011.25 examples/s]
Generating extra examples...: 484949 examples [02:45, 3032.17 examples/s]
Generating extra examples...: 485253 examples [02:46, 3026.89 examples/s]
Generating extra examples...: 485556 examples [02:46, 3017.85 examples/s]
Generating extra examples...: 485859 examples [02:46, 3019.08 examples/s]
Generating extra examples...: 486168 examples [02:46, 3037.31 examples/s]
Generating extra examples...: 486477 examples [02:46, 3050.35 examples/s]
Generating extra examples...: 486783 examples [02:46, 3042.99 examples/s]
Generating extra examples...: 487088 examples [02:46, 3037.45 examples/s]
Generating extra examples...: 487392 examples [02:46, 3037.96 examples/s]
Generating extra examples...: 487700 examples [02:46, 3049.79 examples/s]
Generating extra examples...: 488005 examples [02:46, 3047.17 examples/s]
Generating extra examples...: 488312 examples [02:47, 3052.04 examples/s]
Generating extra examples...: 488620 examples [02:47, 3057.98 examples/s]
Generating extra examples...: 488926 examples [02:47, 3051.39 examples/s]
Generating extra examples...: 489234 examples [02:47, 3057.38 examples/s]
Generating extra examples...: 489540 examples [02:47, 3051.44 examples/s]
Generating extra examples...: 489846 examples [02:47, 3014.21 examples/s]
Generating extra examples...: 490158 examples [02:47, 3045.03 examples/s]
Generating extra examples...: 490481 examples [02:47, 3098.50 examples/s]
Generating extra examples...: 490803 examples [02:47, 3133.97 examples/s]
Generating extra examples...: 491119 examples [02:47, 3139.61 examples/s]
Generating extra examples...: 491437 examples [02:48, 3150.16 examples/s]
Generating extra examples...: 491756 examples [02:48, 3160.18 examples/s]
Generating extra examples...: 492079 examples [02:48, 3179.70 examples/s]
Generating extra examples...: 492398 examples [02:48, 3180.98 examples/s]
Generating extra examples...: 492724 examples [02:48, 3203.07 examples/s]
Generating extra examples...: 493045 examples [02:48, 3174.97 examples/s]
Generating extra examples...: 493363 examples [02:48, 3154.70 examples/s]
Generating extra examples...: 493679 examples [02:48, 3122.15 examples/s]
Generating extra examples...: 493992 examples [02:48, 3111.34 examples/s]
Generating extra examples...: 494306 examples [02:48, 3116.83 examples/s]
Generating extra examples...: 494618 examples [02:49, 3091.50 examples/s]
Generating extra examples...: 494928 examples [02:49, 3084.26 examples/s]
Generating extra examples...: 495237 examples [02:49, 3082.98 examples/s]
Generating extra examples...: 495546 examples [02:49, 3083.49 examples/s]
Generating extra examples...: 495855 examples [02:49, 3084.48 examples/s]
Generating extra examples...: 496166 examples [02:49, 3091.24 examples/s]
Generating extra examples...: 496483 examples [02:49, 3113.98 examples/s]
Generating extra examples...: 496803 examples [02:49, 3138.42 examples/s]
Generating extra examples...: 497120 examples [02:49, 3146.44 examples/s]
Generating extra examples...: 497435 examples [02:49, 3135.39 examples/s]
Generating extra examples...: 497749 examples [02:50, 3108.32 examples/s]
Generating extra examples...: 498063 examples [02:50, 3116.29 examples/s]
Generating extra examples...: 498375 examples [02:50, 3094.78 examples/s]
Generating extra examples...: 498685 examples [02:50, 3092.44 examples/s]
Generating extra examples...: 498995 examples [02:50, 3078.34 examples/s]
Generating extra examples...: 499303 examples [02:50, 2967.14 examples/s]
Generating extra examples...: 499606 examples [02:50, 2984.54 examples/s]
Generating extra examples...: 499908 examples [02:50, 2994.61 examples/s]
Generating extra examples...: 500215 examples [02:50, 3014.15 examples/s]
Generating extra examples...: 500520 examples [02:50, 3022.60 examples/s]
Generating extra examples...: 500829 examples [02:51, 3040.48 examples/s]
Generating extra examples...: 501134 examples [02:51, 3036.14 examples/s]
Generating extra examples...: 501444 examples [02:51, 3054.87 examples/s]
Generating extra examples...: 501754 examples [02:51, 3066.15 examples/s]
Generating extra examples...: 502065 examples [02:51, 3077.98 examples/s]
Generating extra examples...: 502373 examples [02:51, 3061.24 examples/s]
Generating extra examples...: 502681 examples [02:51, 3063.84 examples/s]
Generating extra examples...: 502992 examples [02:51, 3074.16 examples/s]
Generating extra examples...: 503301 examples [02:51, 3078.35 examples/s]
Generating extra examples...: 503609 examples [02:51, 3075.18 examples/s]
Generating extra examples...: 503918 examples [02:52, 3078.33 examples/s]
Generating extra examples...: 504226 examples [02:52, 3073.70 examples/s]
Generating extra examples...: 504536 examples [02:52, 3078.69 examples/s]
Generating extra examples...: 504844 examples [02:52, 3075.69 examples/s]
Generating extra examples...: 505152 examples [02:52, 3066.89 examples/s]
Generating extra examples...: 505459 examples [02:52, 3005.89 examples/s]
Generating extra examples...: 505771 examples [02:52, 3036.23 examples/s]
Generating extra examples...: 506075 examples [02:52, 3033.61 examples/s]
Generating extra examples...: 506379 examples [02:52, 3035.08 examples/s]
Generating extra examples...: 506686 examples [02:52, 3042.96 examples/s]
Generating extra examples...: 506993 examples [02:53, 3050.42 examples/s]
Generating extra examples...: 507301 examples [02:53, 3056.82 examples/s]
Generating extra examples...: 507611 examples [02:53, 3068.63 examples/s]
Generating extra examples...: 507929 examples [02:53, 3100.08 examples/s]
Generating extra examples...: 508245 examples [02:53, 3117.30 examples/s]
Generating extra examples...: 508559 examples [02:53, 3122.17 examples/s]
Generating extra examples...: 508872 examples [02:53, 3114.02 examples/s]
Generating extra examples...: 509189 examples [02:53, 3129.72 examples/s]
Generating extra examples...: 509505 examples [02:53, 3137.10 examples/s]
Generating extra examples...: 509819 examples [02:53, 3134.02 examples/s]
Generating extra examples...: 510133 examples [02:54, 3110.79 examples/s]
Generating extra examples...: 510445 examples [02:54, 3104.52 examples/s]
Generating extra examples...: 510756 examples [02:54, 3103.03 examples/s]
Generating extra examples...: 511067 examples [02:54, 3089.46 examples/s]
Generating extra examples...: 511381 examples [02:54, 3102.10 examples/s]
Generating extra examples...: 511692 examples [02:54, 3087.07 examples/s]
Generating extra examples...: 512001 examples [02:54, 3080.52 examples/s]
Generating extra examples...: 512310 examples [02:54, 3076.25 examples/s]
Generating extra examples...: 512618 examples [02:54, 3074.40 examples/s]
Generating extra examples...: 512926 examples [02:55, 3072.05 examples/s]
Generating extra examples...: 513234 examples [02:55, 3047.28 examples/s]
Generating extra examples...: 513543 examples [02:55, 3059.07 examples/s]
Generating extra examples...: 513850 examples [02:55, 3060.12 examples/s]
Generating extra examples...: 514159 examples [02:55, 3065.74 examples/s]
Generating extra examples...: 514469 examples [02:55, 3073.37 examples/s]
Generating extra examples...: 514777 examples [02:55, 3068.23 examples/s]
Generating extra examples...: 515084 examples [02:55, 3065.74 examples/s]
Generating extra examples...: 515391 examples [02:55, 3064.13 examples/s]
Generating extra examples...: 515698 examples [02:55, 3060.24 examples/s]
Generating extra examples...: 516005 examples [02:56, 3061.53 examples/s]
Generating extra examples...: 516315 examples [02:56, 3070.58 examples/s]
Generating extra examples...: 516625 examples [02:56, 3079.07 examples/s]
Generating extra examples...: 516934 examples [02:56, 3080.63 examples/s]
Generating extra examples...: 517244 examples [02:56, 3084.98 examples/s]
Generating extra examples...: 517556 examples [02:56, 3093.73 examples/s]
Generating extra examples...: 517866 examples [02:56, 3076.82 examples/s]
Generating extra examples...: 518174 examples [02:56, 3059.08 examples/s]
Generating extra examples...: 518481 examples [02:56, 3061.43 examples/s]
Generating extra examples...: 518788 examples [02:56, 3040.64 examples/s]
Generating extra examples...: 519098 examples [02:57, 3057.20 examples/s]
Generating extra examples...: 519404 examples [02:57, 3042.61 examples/s]
Generating extra examples...: 519710 examples [02:57, 3046.93 examples/s]
Generating extra examples...: 520018 examples [02:57, 3056.56 examples/s]
Generating extra examples...: 520324 examples [02:57, 3053.01 examples/s]
Generating extra examples...: 520630 examples [02:57, 3048.37 examples/s]
Generating extra examples...: 520935 examples [02:57, 3038.49 examples/s]
Generating extra examples...: 521239 examples [02:57, 3019.94 examples/s]
Generating extra examples...: 521552 examples [02:57, 3050.98 examples/s]
Generating extra examples...: 521864 examples [02:57, 3070.02 examples/s]
Generating extra examples...: 522177 examples [02:58, 3085.38 examples/s]
Generating extra examples...: 522492 examples [02:58, 3104.07 examples/s]
Generating extra examples...: 522808 examples [02:58, 3118.82 examples/s]
Generating extra examples...: 523120 examples [02:58, 3118.63 examples/s]
Generating extra examples...: 523435 examples [02:58, 3126.02 examples/s]
Generating extra examples...: 523749 examples [02:58, 3128.49 examples/s]
Generating extra examples...: 524062 examples [02:58, 3115.03 examples/s]
Generating extra examples...: 524374 examples [02:58, 3034.47 examples/s]
Generating extra examples...: 524681 examples [02:58, 3041.93 examples/s]
Generating extra examples...: 524986 examples [02:58, 3041.16 examples/s]
Generating extra examples...: 525298 examples [02:59, 3062.18 examples/s]
Generating extra examples...: 525605 examples [02:59, 3062.70 examples/s]
Generating extra examples...: 525914 examples [02:59, 3068.12 examples/s]
Generating extra examples...: 526221 examples [02:59, 3064.04 examples/s]
Generating extra examples...: 526530 examples [02:59, 3068.74 examples/s]
Generating extra examples...: 526840 examples [02:59, 3077.81 examples/s]
Generating extra examples...: 527153 examples [02:59, 3090.63 examples/s]
Generating extra examples...: 527466 examples [02:59, 3101.31 examples/s]
Generating extra examples...: 527777 examples [02:59, 3103.74 examples/s]
Generating extra examples...: 528088 examples [02:59, 3092.13 examples/s]
Generating extra examples...: 528399 examples [03:00, 3095.12 examples/s]
Generating extra examples...: 528709 examples [03:00, 3090.46 examples/s]
Generating extra examples...: 529019 examples [03:00, 3080.55 examples/s]
Generating extra examples...: 529328 examples [03:00, 3076.33 examples/s]
Generating extra examples...: 529636 examples [03:00, 3058.18 examples/s]
Generating extra examples...: 529942 examples [03:00, 3052.49 examples/s]
Generating extra examples...: 530250 examples [03:00, 3057.80 examples/s]
Generating extra examples...: 530556 examples [03:00, 2948.93 examples/s]
Generating extra examples...: 530864 examples [03:00, 2985.55 examples/s]
Shuffling /home/runner/tensorflow_datasets/svhn_cropped/3.0.0.incompleteMZ04LU/svhn_cropped-extra.tfrecord*...: 0%| | 0/531131 [00:00<?, ? examples/s]
Shuffling /home/runner/tensorflow_datasets/svhn_cropped/3.0.0.incompleteMZ04LU/svhn_cropped-extra.tfrecord*...: 0%| | 1/531131 [00:00<72:55:03, 2.02 examples/s]
Shuffling /home/runner/tensorflow_datasets/svhn_cropped/3.0.0.incompleteMZ04LU/svhn_cropped-extra.tfrecord*...: 4%|▍ | 22591/531131 [00:00<00:10, 50654.52 examples/s]
Shuffling /home/runner/tensorflow_datasets/svhn_cropped/3.0.0.incompleteMZ04LU/svhn_cropped-extra.tfrecord*...: 8%|▊ | 45011/531131 [00:00<00:05, 92757.38 examples/s]
Shuffling /home/runner/tensorflow_datasets/svhn_cropped/3.0.0.incompleteMZ04LU/svhn_cropped-extra.tfrecord*...: 13%|█▎ | 67874/531131 [00:00<00:03, 127724.92 examples/s]
Shuffling /home/runner/tensorflow_datasets/svhn_cropped/3.0.0.incompleteMZ04LU/svhn_cropped-extra.tfrecord*...: 17%|█▋ | 90711/531131 [00:00<00:02, 154779.72 examples/s]
Shuffling /home/runner/tensorflow_datasets/svhn_cropped/3.0.0.incompleteMZ04LU/svhn_cropped-extra.tfrecord*...: 21%|██▏ | 113495/531131 [00:00<00:02, 175050.56 examples/s]
Shuffling /home/runner/tensorflow_datasets/svhn_cropped/3.0.0.incompleteMZ04LU/svhn_cropped-extra.tfrecord*...: 26%|██▌ | 136406/531131 [00:01<00:02, 190392.06 examples/s]
Shuffling /home/runner/tensorflow_datasets/svhn_cropped/3.0.0.incompleteMZ04LU/svhn_cropped-extra.tfrecord*...: 30%|███ | 159488/531131 [00:01<00:01, 202051.75 examples/s]
Shuffling /home/runner/tensorflow_datasets/svhn_cropped/3.0.0.incompleteMZ04LU/svhn_cropped-extra.tfrecord*...: 34%|███▍ | 182508/531131 [00:01<00:01, 210264.38 examples/s]
Shuffling /home/runner/tensorflow_datasets/svhn_cropped/3.0.0.incompleteMZ04LU/svhn_cropped-extra.tfrecord*...: 39%|███▊ | 205403/531131 [00:01<00:01, 215755.99 examples/s]
Shuffling /home/runner/tensorflow_datasets/svhn_cropped/3.0.0.incompleteMZ04LU/svhn_cropped-extra.tfrecord*...: 43%|████▎ | 228600/531131 [00:01<00:01, 220550.22 examples/s]
Shuffling /home/runner/tensorflow_datasets/svhn_cropped/3.0.0.incompleteMZ04LU/svhn_cropped-extra.tfrecord*...: 47%|████▋ | 252049/531131 [00:01<00:01, 224688.97 examples/s]
Shuffling /home/runner/tensorflow_datasets/svhn_cropped/3.0.0.incompleteMZ04LU/svhn_cropped-extra.tfrecord*...: 52%|█████▏ | 275587/531131 [00:01<00:01, 227871.19 examples/s]
Shuffling /home/runner/tensorflow_datasets/svhn_cropped/3.0.0.incompleteMZ04LU/svhn_cropped-extra.tfrecord*...: 56%|█████▋ | 298974/531131 [00:01<00:01, 229658.85 examples/s]
Shuffling /home/runner/tensorflow_datasets/svhn_cropped/3.0.0.incompleteMZ04LU/svhn_cropped-extra.tfrecord*...: 61%|██████ | 322205/531131 [00:01<00:00, 228668.24 examples/s]
Shuffling /home/runner/tensorflow_datasets/svhn_cropped/3.0.0.incompleteMZ04LU/svhn_cropped-extra.tfrecord*...: 65%|██████▌ | 345622/531131 [00:01<00:00, 230300.01 examples/s]
Shuffling /home/runner/tensorflow_datasets/svhn_cropped/3.0.0.incompleteMZ04LU/svhn_cropped-extra.tfrecord*...: 69%|██████▉ | 368975/531131 [00:02<00:00, 231259.68 examples/s]
Shuffling /home/runner/tensorflow_datasets/svhn_cropped/3.0.0.incompleteMZ04LU/svhn_cropped-extra.tfrecord*...: 74%|███████▍ | 392252/531131 [00:02<00:00, 231706.82 examples/s]
Shuffling /home/runner/tensorflow_datasets/svhn_cropped/3.0.0.incompleteMZ04LU/svhn_cropped-extra.tfrecord*...: 78%|███████▊ | 415488/531131 [00:02<00:00, 231347.66 examples/s]
Shuffling /home/runner/tensorflow_datasets/svhn_cropped/3.0.0.incompleteMZ04LU/svhn_cropped-extra.tfrecord*...: 83%|████████▎ | 438669/531131 [00:02<00:00, 231246.95 examples/s]
Shuffling /home/runner/tensorflow_datasets/svhn_cropped/3.0.0.incompleteMZ04LU/svhn_cropped-extra.tfrecord*...: 87%|████████▋ | 461826/531131 [00:02<00:00, 231168.26 examples/s]
Shuffling /home/runner/tensorflow_datasets/svhn_cropped/3.0.0.incompleteMZ04LU/svhn_cropped-extra.tfrecord*...: 91%|█████████▏| 485177/531131 [00:02<00:00, 231861.45 examples/s]
Shuffling /home/runner/tensorflow_datasets/svhn_cropped/3.0.0.incompleteMZ04LU/svhn_cropped-extra.tfrecord*...: 96%|█████████▌| 508496/531131 [00:02<00:00, 232253.16 examples/s]
Generating splits...: 100%|██████████| 3/3 [03:37<00:00, 92.40s/ splits]
Dataset svhn_cropped downloaded and prepared to /home/runner/tensorflow_datasets/svhn_cropped/3.0.0. Subsequent calls will reuse this data.
Training on 73257 samples of input shape (32, 32, 3), belonging to 10 classes
We’ll use TensorFlow Dataset to prepare our datasets. We’ll fetch the training dataset as tuples, and the test dataset as numpy arrays
def preprocess(image, label, nclasses=10):
image = tf.cast(image, tf.float32) / 255.0
label = tf.one_hot(tf.squeeze(label), nclasses)
return image, label
batch_size = 1024
train_data = ds_train.map(preprocess, n_classes) # Get dataset as image and one-hot encoded labels, divided by max RGB
train_data = train_data.shuffle(4096).batch(batch_size).prefetch(tf.data.experimental.AUTOTUNE)
for example in train_data.take(1):
break
print("X train batch shape = {}, Y train batch shape = {} ".format(example[0].shape, example[1].shape))
val_data = ds_val.map(preprocess, n_classes)
val_data = val_data.batch(batch_size)
val_data = val_data.prefetch(tf.data.experimental.AUTOTUNE)
# For testing, we get the full dataset in memory as it's rather small.
# We fetch it as numpy arrays to have access to labels and images separately
X_test, Y_test = tfds.as_numpy(tfds.load('svhn_cropped', split='test', batch_size=-1, as_supervised=True))
X_test, Y_test = preprocess(X_test, Y_test, nclasses=n_classes)
print("X test batch shape = {}, Y test batch shape = {} ".format(X_test.shape, Y_test.shape))
X train batch shape = (1024, 32, 32, 3), Y train batch shape = (1024, 10)
X test batch shape = (26032, 32, 32, 3), Y test batch shape = (26032, 10)
Defining the model#
We then need to define a model. For the lowest possible latency, each layer should have a maximum number of trainable parameters of 4096. This is due to fixed limits in the Vivado compiler, beyond which maximally unrolled (=parallel) compilation will fail. This will allow us to use strategy = 'latency' in the hls4ml part, rather than strategy = 'resource', in turn resulting in lower latency
from tensorflow.keras.layers import Input
from tensorflow.keras.layers import BatchNormalization
from tensorflow.keras.layers import Conv2D
from tensorflow.keras.regularizers import l1
from tensorflow.keras.layers import MaxPooling2D
from tensorflow.keras.layers import Activation
from tensorflow.keras.layers import Flatten
from tensorflow.keras.layers import Dense
from tensorflow.keras.models import Model
filters_per_conv_layer = [16, 16, 24]
neurons_per_dense_layer = [42, 64]
x = x_in = Input(input_shape)
for i, f in enumerate(filters_per_conv_layer):
print(('Adding convolutional block {} with N={} filters').format(i, f))
x = Conv2D(
int(f),
kernel_size=(3, 3),
strides=(1, 1),
kernel_initializer='lecun_uniform',
kernel_regularizer=l1(0.0001),
use_bias=False,
name='conv_{}'.format(i),
)(x)
x = BatchNormalization(name='bn_conv_{}'.format(i))(x)
x = Activation('relu', name='conv_act_%i' % i)(x)
x = MaxPooling2D(pool_size=(2, 2), name='pool_{}'.format(i))(x)
x = Flatten()(x)
for i, n in enumerate(neurons_per_dense_layer):
print(('Adding dense block {} with N={} neurons').format(i, n))
x = Dense(n, kernel_initializer='lecun_uniform', kernel_regularizer=l1(0.0001), name='dense_%i' % i, use_bias=False)(x)
x = BatchNormalization(name='bn_dense_{}'.format(i))(x)
x = Activation('relu', name='dense_act_%i' % i)(x)
x = Dense(int(n_classes), name='output_dense')(x)
x_out = Activation('softmax', name='output_softmax')(x)
model = Model(inputs=[x_in], outputs=[x_out], name='keras_baseline')
model.summary()
Adding convolutional block 0 with N=16 filters
Adding convolutional block 1 with N=16 filters
Adding convolutional block 2 with N=24 filters
Adding dense block 0 with N=42 neurons
Adding dense block 1 with N=64 neurons
Model: "keras_baseline"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_1 (InputLayer) [(None, 32, 32, 3)] 0
conv_0 (Conv2D) (None, 30, 30, 16) 432
bn_conv_0 (BatchNormalizati (None, 30, 30, 16) 64
on)
conv_act_0 (Activation) (None, 30, 30, 16) 0
pool_0 (MaxPooling2D) (None, 15, 15, 16) 0
conv_1 (Conv2D) (None, 13, 13, 16) 2304
bn_conv_1 (BatchNormalizati (None, 13, 13, 16) 64
on)
conv_act_1 (Activation) (None, 13, 13, 16) 0
pool_1 (MaxPooling2D) (None, 6, 6, 16) 0
conv_2 (Conv2D) (None, 4, 4, 24) 3456
bn_conv_2 (BatchNormalizati (None, 4, 4, 24) 96
on)
conv_act_2 (Activation) (None, 4, 4, 24) 0
pool_2 (MaxPooling2D) (None, 2, 2, 24) 0
flatten (Flatten) (None, 96) 0
dense_0 (Dense) (None, 42) 4032
bn_dense_0 (BatchNormalizat (None, 42) 168
ion)
dense_act_0 (Activation) (None, 42) 0
dense_1 (Dense) (None, 64) 2688
bn_dense_1 (BatchNormalizat (None, 64) 256
ion)
dense_act_1 (Activation) (None, 64) 0
output_dense (Dense) (None, 10) 650
output_softmax (Activation) (None, 10) 0
=================================================================
Total params: 14,210
Trainable params: 13,886
Non-trainable params: 324
_________________________________________________________________
Lets check if this model can be implemented completely unrolled (=parallel)
for layer in model.layers:
if layer.__class__.__name__ in ['Conv2D', 'Dense']:
w = layer.get_weights()[0]
layersize = np.prod(w.shape)
print("{}: {}".format(layer.name, layersize)) # 0 = weights, 1 = biases
if layersize > 4096: # assuming that shape[0] is batch, i.e., 'None'
print("Layer {} is too large ({}), are you sure you want to train?".format(layer.name, layersize))
conv_0: 432
conv_1: 2304
conv_2: 3456
dense_0: 4032
dense_1: 2688
output_dense: 640
Looks good! It’s below the Vivado-enforced unroll limit of 4096.
Prune dense and convolutional layers#
Since we’ve seen in the previous notebooks that pruning can be done at no accuracy cost, let’s prune the convolutional and dense layers to 50% sparsity, skipping the output layer
import tensorflow_model_optimization as tfmot
from tensorflow_model_optimization.sparsity import keras as sparsity
from tensorflow_model_optimization.python.core.sparsity.keras import pruning_callbacks
NSTEPS = int(train_size * 0.9) // batch_size # 90% train, 10% validation in 10-fold cross validation
print('Number of training steps per epoch is {}'.format(NSTEPS))
# Prune all convolutional and dense layers gradually from 0 to 50% sparsity every 2 epochs,
# ending by the 10th epoch
def pruneFunction(layer):
pruning_params = {
'pruning_schedule': sparsity.PolynomialDecay(
initial_sparsity=0.0, final_sparsity=0.50, begin_step=NSTEPS * 2, end_step=NSTEPS * 10, frequency=NSTEPS
)
}
if isinstance(layer, tf.keras.layers.Conv2D):
return tfmot.sparsity.keras.prune_low_magnitude(layer, **pruning_params)
if isinstance(layer, tf.keras.layers.Dense) and layer.name != 'output_dense':
return tfmot.sparsity.keras.prune_low_magnitude(layer, **pruning_params)
return layer
model_pruned = tf.keras.models.clone_model(model, clone_function=pruneFunction)
Number of training steps per epoch is 64
WARNING:tensorflow:From /home/runner/miniconda3/envs/hls4ml-tutorial/lib/python3.10/site-packages/tensorflow/python/autograph/pyct/static_analysis/liveness.py:83: Analyzer.lamba_check (from tensorflow.python.autograph.pyct.static_analysis.liveness) is deprecated and will be removed after 2023-09-23.
Instructions for updating:
Lambda fuctions will be no more assumed to be used in the statement where they are used, or at least in the same block. https://github.com/tensorflow/tensorflow/issues/56089
WARNING:tensorflow:From /home/runner/miniconda3/envs/hls4ml-tutorial/lib/python3.10/site-packages/tensorflow/python/autograph/pyct/static_analysis/liveness.py:83: Analyzer.lamba_check (from tensorflow.python.autograph.pyct.static_analysis.liveness) is deprecated and will be removed after 2023-09-23.
Instructions for updating:
Lambda fuctions will be no more assumed to be used in the statement where they are used, or at least in the same block. https://github.com/tensorflow/tensorflow/issues/56089
Train baseline#
We’re now ready to train the model! We defined the batch size and n epochs above. We won’t use callbacks that store the best weights only, since this might select a weight configuration that has not yet reached 50% sparsity.
train = True # True if you want to retrain, false if you want to load a previsously trained model
n_epochs = 30
if train:
LOSS = tf.keras.losses.CategoricalCrossentropy()
OPTIMIZER = tf.keras.optimizers.Adam(learning_rate=3e-3, beta_1=0.9, beta_2=0.999, epsilon=1e-07, amsgrad=True)
model_pruned.compile(loss=LOSS, optimizer=OPTIMIZER, metrics=["accuracy"])
callbacks = [
tf.keras.callbacks.EarlyStopping(patience=10, verbose=1),
tf.keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=3, verbose=1),
pruning_callbacks.UpdatePruningStep(),
]
start = time.time()
model_pruned.fit(train_data, epochs=n_epochs, validation_data=val_data, callbacks=callbacks)
end = time.time()
print('It took {} minutes to train Keras model'.format((end - start) / 60.0))
model_pruned.save('pruned_cnn_model.h5')
else:
from qkeras.utils import _add_supported_quantized_objects
from tensorflow_model_optimization.python.core.sparsity.keras import pruning_wrapper
co = {}
_add_supported_quantized_objects(co)
co['PruneLowMagnitude'] = pruning_wrapper.PruneLowMagnitude
model_pruned = tf.keras.models.load_model('pruned_cnn_model.h5', custom_objects=co)
Epoch 1/30
1/65 [..............................] - ETA: 3:30 - loss: 2.9483 - accuracy: 0.0830
2/65 [..............................] - ETA: 16s - loss: 2.8612 - accuracy: 0.0840
3/65 [>.............................] - ETA: 16s - loss: 2.7905 - accuracy: 0.0892
4/65 [>.............................] - ETA: 16s - loss: 2.7489 - accuracy: 0.0916
5/65 [=>............................] - ETA: 15s - loss: 2.7072 - accuracy: 0.0959
6/65 [=>............................] - ETA: 15s - loss: 2.6668 - accuracy: 0.1040
7/65 [==>...........................] - ETA: 15s - loss: 2.6295 - accuracy: 0.1123
8/65 [==>...........................] - ETA: 14s - loss: 2.5970 - accuracy: 0.1244
9/65 [===>..........................] - ETA: 14s - loss: 2.5680 - accuracy: 0.1373
10/65 [===>..........................] - ETA: 14s - loss: 2.5357 - accuracy: 0.1486
11/65 [====>.........................] - ETA: 13s - loss: 2.5058 - accuracy: 0.1604
12/65 [====>.........................] - ETA: 13s - loss: 2.4774 - accuracy: 0.1712
13/65 [=====>........................] - ETA: 13s - loss: 2.4502 - accuracy: 0.1831
14/65 [=====>........................] - ETA: 13s - loss: 2.4237 - accuracy: 0.1931
15/65 [=====>........................] - ETA: 12s - loss: 2.4008 - accuracy: 0.2013
16/65 [======>.......................] - ETA: 12s - loss: 2.3788 - accuracy: 0.2089
17/65 [======>.......................] - ETA: 12s - loss: 2.3542 - accuracy: 0.2183
18/65 [=======>......................] - ETA: 12s - loss: 2.3312 - accuracy: 0.2274
19/65 [=======>......................] - ETA: 11s - loss: 2.3104 - accuracy: 0.2355
20/65 [========>.....................] - ETA: 11s - loss: 2.2913 - accuracy: 0.2436
21/65 [========>.....................] - ETA: 11s - loss: 2.2717 - accuracy: 0.2515
22/65 [=========>....................] - ETA: 11s - loss: 2.2500 - accuracy: 0.2595
23/65 [=========>....................] - ETA: 10s - loss: 2.2308 - accuracy: 0.2663
24/65 [==========>...................] - ETA: 10s - loss: 2.2122 - accuracy: 0.2731
25/65 [==========>...................] - ETA: 10s - loss: 2.1938 - accuracy: 0.2798
26/65 [===========>..................] - ETA: 9s - loss: 2.1758 - accuracy: 0.2868
27/65 [===========>..................] - ETA: 9s - loss: 2.1590 - accuracy: 0.2928
28/65 [===========>..................] - ETA: 9s - loss: 2.1415 - accuracy: 0.2991
29/65 [============>.................] - ETA: 9s - loss: 2.1239 - accuracy: 0.3064
30/65 [============>.................] - ETA: 8s - loss: 2.1063 - accuracy: 0.3138
31/65 [=============>................] - ETA: 8s - loss: 2.0883 - accuracy: 0.3213
32/65 [=============>................] - ETA: 8s - loss: 2.0730 - accuracy: 0.3277
33/65 [==============>...............] - ETA: 8s - loss: 2.0573 - accuracy: 0.3342
34/65 [==============>...............] - ETA: 7s - loss: 2.0428 - accuracy: 0.3402
35/65 [===============>..............] - ETA: 7s - loss: 2.0257 - accuracy: 0.3468
36/65 [===============>..............] - ETA: 7s - loss: 2.0111 - accuracy: 0.3521
37/65 [================>.............] - ETA: 7s - loss: 1.9958 - accuracy: 0.3583
38/65 [================>.............] - ETA: 6s - loss: 1.9794 - accuracy: 0.3648
39/65 [=================>............] - ETA: 6s - loss: 1.9634 - accuracy: 0.3712
40/65 [=================>............] - ETA: 6s - loss: 1.9489 - accuracy: 0.3765
41/65 [=================>............] - ETA: 6s - loss: 1.9343 - accuracy: 0.3824
42/65 [==================>...........] - ETA: 5s - loss: 1.9192 - accuracy: 0.3882
43/65 [==================>...........] - ETA: 5s - loss: 1.9049 - accuracy: 0.3936
44/65 [===================>..........] - ETA: 5s - loss: 1.8911 - accuracy: 0.3986
45/65 [===================>..........] - ETA: 5s - loss: 1.8773 - accuracy: 0.4041
46/65 [====================>.........] - ETA: 4s - loss: 1.8642 - accuracy: 0.4089
47/65 [====================>.........] - ETA: 4s - loss: 1.8506 - accuracy: 0.4144
48/65 [=====================>........] - ETA: 4s - loss: 1.8366 - accuracy: 0.4198
49/65 [=====================>........] - ETA: 4s - loss: 1.8225 - accuracy: 0.4250
50/65 [======================>.......] - ETA: 3s - loss: 1.8089 - accuracy: 0.4301
51/65 [======================>.......] - ETA: 3s - loss: 1.7964 - accuracy: 0.4348
52/65 [=======================>......] - ETA: 3s - loss: 1.7839 - accuracy: 0.4395
53/65 [=======================>......] - ETA: 3s - loss: 1.7716 - accuracy: 0.4441
54/65 [=======================>......] - ETA: 2s - loss: 1.7586 - accuracy: 0.4489
55/65 [========================>.....] - ETA: 2s - loss: 1.7462 - accuracy: 0.4532
56/65 [========================>.....] - ETA: 2s - loss: 1.7346 - accuracy: 0.4577
57/65 [=========================>....] - ETA: 2s - loss: 1.7226 - accuracy: 0.4625
58/65 [=========================>....] - ETA: 1s - loss: 1.7105 - accuracy: 0.4671
59/65 [==========================>...] - ETA: 1s - loss: 1.6998 - accuracy: 0.4709
60/65 [==========================>...] - ETA: 1s - loss: 1.6887 - accuracy: 0.4749
61/65 [===========================>..] - ETA: 1s - loss: 1.6775 - accuracy: 0.4791
62/65 [===========================>..] - ETA: 0s - loss: 1.6667 - accuracy: 0.4828
63/65 [============================>.] - ETA: 0s - loss: 1.6552 - accuracy: 0.4870
64/65 [============================>.] - ETA: 0s - loss: 1.6448 - accuracy: 0.4907
65/65 [==============================] - ETA: 0s - loss: 1.6408 - accuracy: 0.4923
65/65 [==============================] - 20s 261ms/step - loss: 1.6408 - accuracy: 0.4923 - val_loss: 2.2242 - val_accuracy: 0.2128 - lr: 0.0030
Epoch 2/30
1/65 [..............................] - ETA: 31s - loss: 0.9862 - accuracy: 0.7363
2/65 [..............................] - ETA: 15s - loss: 1.0174 - accuracy: 0.7266
3/65 [>.............................] - ETA: 15s - loss: 0.9867 - accuracy: 0.7363
4/65 [>.............................] - ETA: 15s - loss: 0.9699 - accuracy: 0.7378
5/65 [=>............................] - ETA: 15s - loss: 0.9535 - accuracy: 0.7406
6/65 [=>............................] - ETA: 14s - loss: 0.9480 - accuracy: 0.7415
7/65 [==>...........................] - ETA: 14s - loss: 0.9423 - accuracy: 0.7426
8/65 [==>...........................] - ETA: 14s - loss: 0.9380 - accuracy: 0.7445
9/65 [===>..........................] - ETA: 14s - loss: 0.9374 - accuracy: 0.7458
10/65 [===>..........................] - ETA: 13s - loss: 0.9364 - accuracy: 0.7477
11/65 [====>.........................] - ETA: 13s - loss: 0.9291 - accuracy: 0.7501
12/65 [====>.........................] - ETA: 13s - loss: 0.9228 - accuracy: 0.7507
13/65 [=====>........................] - ETA: 13s - loss: 0.9195 - accuracy: 0.7517
14/65 [=====>........................] - ETA: 12s - loss: 0.9119 - accuracy: 0.7538
15/65 [=====>........................] - ETA: 12s - loss: 0.9075 - accuracy: 0.7552
16/65 [======>.......................] - ETA: 12s - loss: 0.9039 - accuracy: 0.7562
17/65 [======>.......................] - ETA: 12s - loss: 0.9008 - accuracy: 0.7574
18/65 [=======>......................] - ETA: 11s - loss: 0.8986 - accuracy: 0.7584
19/65 [=======>......................] - ETA: 11s - loss: 0.8955 - accuracy: 0.7595
20/65 [========>.....................] - ETA: 11s - loss: 0.8945 - accuracy: 0.7597
21/65 [========>.....................] - ETA: 11s - loss: 0.8890 - accuracy: 0.7606
22/65 [=========>....................] - ETA: 10s - loss: 0.8876 - accuracy: 0.7606
23/65 [=========>....................] - ETA: 10s - loss: 0.8839 - accuracy: 0.7624
24/65 [==========>...................] - ETA: 10s - loss: 0.8818 - accuracy: 0.7635
25/65 [==========>...................] - ETA: 10s - loss: 0.8769 - accuracy: 0.7649
26/65 [===========>..................] - ETA: 9s - loss: 0.8730 - accuracy: 0.7660
27/65 [===========>..................] - ETA: 9s - loss: 0.8688 - accuracy: 0.7672
28/65 [===========>..................] - ETA: 9s - loss: 0.8684 - accuracy: 0.7669
29/65 [============>.................] - ETA: 9s - loss: 0.8658 - accuracy: 0.7676
30/65 [============>.................] - ETA: 8s - loss: 0.8604 - accuracy: 0.7696
31/65 [=============>................] - ETA: 8s - loss: 0.8589 - accuracy: 0.7700
32/65 [=============>................] - ETA: 8s - loss: 0.8581 - accuracy: 0.7708
33/65 [==============>...............] - ETA: 8s - loss: 0.8571 - accuracy: 0.7712
34/65 [==============>...............] - ETA: 7s - loss: 0.8547 - accuracy: 0.7720
35/65 [===============>..............] - ETA: 7s - loss: 0.8509 - accuracy: 0.7732
36/65 [===============>..............] - ETA: 7s - loss: 0.8476 - accuracy: 0.7742
37/65 [================>.............] - ETA: 7s - loss: 0.8449 - accuracy: 0.7753
38/65 [================>.............] - ETA: 6s - loss: 0.8428 - accuracy: 0.7761
39/65 [=================>............] - ETA: 6s - loss: 0.8400 - accuracy: 0.7770
40/65 [=================>............] - ETA: 6s - loss: 0.8371 - accuracy: 0.7779
41/65 [=================>............] - ETA: 6s - loss: 0.8347 - accuracy: 0.7789
42/65 [==================>...........] - ETA: 5s - loss: 0.8319 - accuracy: 0.7797
43/65 [==================>...........] - ETA: 5s - loss: 0.8288 - accuracy: 0.7806
44/65 [===================>..........] - ETA: 5s - loss: 0.8270 - accuracy: 0.7811
45/65 [===================>..........] - ETA: 5s - loss: 0.8250 - accuracy: 0.7817
46/65 [====================>.........] - ETA: 4s - loss: 0.8231 - accuracy: 0.7821
47/65 [====================>.........] - ETA: 4s - loss: 0.8205 - accuracy: 0.7827
48/65 [=====================>........] - ETA: 4s - loss: 0.8178 - accuracy: 0.7835
49/65 [=====================>........] - ETA: 4s - loss: 0.8147 - accuracy: 0.7849
50/65 [======================>.......] - ETA: 3s - loss: 0.8132 - accuracy: 0.7853
51/65 [======================>.......] - ETA: 3s - loss: 0.8107 - accuracy: 0.7861
52/65 [=======================>......] - ETA: 3s - loss: 0.8083 - accuracy: 0.7871
53/65 [=======================>......] - ETA: 3s - loss: 0.8053 - accuracy: 0.7879
54/65 [=======================>......] - ETA: 2s - loss: 0.8024 - accuracy: 0.7888
55/65 [========================>.....] - ETA: 2s - loss: 0.8005 - accuracy: 0.7892
56/65 [========================>.....] - ETA: 2s - loss: 0.7988 - accuracy: 0.7898
57/65 [=========================>....] - ETA: 2s - loss: 0.7974 - accuracy: 0.7903
58/65 [=========================>....] - ETA: 1s - loss: 0.7960 - accuracy: 0.7907
59/65 [==========================>...] - ETA: 1s - loss: 0.7932 - accuracy: 0.7917
60/65 [==========================>...] - ETA: 1s - loss: 0.7920 - accuracy: 0.7920
61/65 [===========================>..] - ETA: 1s - loss: 0.7902 - accuracy: 0.7925
62/65 [===========================>..] - ETA: 0s - loss: 0.7877 - accuracy: 0.7930
63/65 [============================>.] - ETA: 0s - loss: 0.7856 - accuracy: 0.7936
64/65 [============================>.] - ETA: 0s - loss: 0.7834 - accuracy: 0.7943
65/65 [==============================] - ETA: 0s - loss: 0.7833 - accuracy: 0.7943
65/65 [==============================] - 17s 260ms/step - loss: 0.7833 - accuracy: 0.7943 - val_loss: 1.4600 - val_accuracy: 0.5364 - lr: 0.0030
Epoch 3/30
1/65 [..............................] - ETA: 30s - loss: 0.6394 - accuracy: 0.8535
2/65 [..............................] - ETA: 17s - loss: 0.7019 - accuracy: 0.8257
3/65 [>.............................] - ETA: 16s - loss: 0.6744 - accuracy: 0.8281
4/65 [>.............................] - ETA: 16s - loss: 0.6754 - accuracy: 0.8264
5/65 [=>............................] - ETA: 15s - loss: 0.6759 - accuracy: 0.8270
6/65 [=>............................] - ETA: 15s - loss: 0.6661 - accuracy: 0.8302
7/65 [==>...........................] - ETA: 15s - loss: 0.6701 - accuracy: 0.8299
8/65 [==>...........................] - ETA: 14s - loss: 0.6666 - accuracy: 0.8303
9/65 [===>..........................] - ETA: 14s - loss: 0.6676 - accuracy: 0.8301
10/65 [===>..........................] - ETA: 14s - loss: 0.6659 - accuracy: 0.8307
11/65 [====>.........................] - ETA: 13s - loss: 0.6645 - accuracy: 0.8316
12/65 [====>.........................] - ETA: 13s - loss: 0.6611 - accuracy: 0.8323
13/65 [=====>........................] - ETA: 13s - loss: 0.6589 - accuracy: 0.8326
14/65 [=====>........................] - ETA: 13s - loss: 0.6583 - accuracy: 0.8322
15/65 [=====>........................] - ETA: 12s - loss: 0.6551 - accuracy: 0.8337
16/65 [======>.......................] - ETA: 12s - loss: 0.6520 - accuracy: 0.8345
17/65 [======>.......................] - ETA: 12s - loss: 0.6507 - accuracy: 0.8344
18/65 [=======>......................] - ETA: 12s - loss: 0.6505 - accuracy: 0.8337
19/65 [=======>......................] - ETA: 11s - loss: 0.6492 - accuracy: 0.8340
20/65 [========>.....................] - ETA: 11s - loss: 0.6483 - accuracy: 0.8346
21/65 [========>.....................] - ETA: 11s - loss: 0.6482 - accuracy: 0.8345
22/65 [=========>....................] - ETA: 10s - loss: 0.6445 - accuracy: 0.8355
23/65 [=========>....................] - ETA: 10s - loss: 0.6437 - accuracy: 0.8355
24/65 [==========>...................] - ETA: 10s - loss: 0.6439 - accuracy: 0.8358
25/65 [==========>...................] - ETA: 10s - loss: 0.6413 - accuracy: 0.8366
26/65 [===========>..................] - ETA: 9s - loss: 0.6419 - accuracy: 0.8367
27/65 [===========>..................] - ETA: 9s - loss: 0.6409 - accuracy: 0.8370
28/65 [===========>..................] - ETA: 9s - loss: 0.6416 - accuracy: 0.8369
29/65 [============>.................] - ETA: 9s - loss: 0.6419 - accuracy: 0.8370
30/65 [============>.................] - ETA: 8s - loss: 0.6410 - accuracy: 0.8374
31/65 [=============>................] - ETA: 8s - loss: 0.6400 - accuracy: 0.8373
32/65 [=============>................] - ETA: 8s - loss: 0.6396 - accuracy: 0.8376
33/65 [==============>...............] - ETA: 8s - loss: 0.6373 - accuracy: 0.8381
34/65 [==============>...............] - ETA: 7s - loss: 0.6372 - accuracy: 0.8380
35/65 [===============>..............] - ETA: 7s - loss: 0.6384 - accuracy: 0.8375
36/65 [===============>..............] - ETA: 7s - loss: 0.6386 - accuracy: 0.8377
37/65 [================>.............] - ETA: 7s - loss: 0.6373 - accuracy: 0.8382
38/65 [================>.............] - ETA: 6s - loss: 0.6362 - accuracy: 0.8385
39/65 [=================>............] - ETA: 6s - loss: 0.6359 - accuracy: 0.8383
40/65 [=================>............] - ETA: 6s - loss: 0.6337 - accuracy: 0.8390
41/65 [=================>............] - ETA: 6s - loss: 0.6339 - accuracy: 0.8387
42/65 [==================>...........] - ETA: 5s - loss: 0.6312 - accuracy: 0.8397
43/65 [==================>...........] - ETA: 5s - loss: 0.6305 - accuracy: 0.8399
44/65 [===================>..........] - ETA: 5s - loss: 0.6304 - accuracy: 0.8402
45/65 [===================>..........] - ETA: 5s - loss: 0.6311 - accuracy: 0.8401
46/65 [====================>.........] - ETA: 4s - loss: 0.6310 - accuracy: 0.8402
47/65 [====================>.........] - ETA: 4s - loss: 0.6291 - accuracy: 0.8409
48/65 [=====================>........] - ETA: 4s - loss: 0.6282 - accuracy: 0.8412
49/65 [=====================>........] - ETA: 4s - loss: 0.6275 - accuracy: 0.8417
50/65 [======================>.......] - ETA: 3s - loss: 0.6267 - accuracy: 0.8418
51/65 [======================>.......] - ETA: 3s - loss: 0.6252 - accuracy: 0.8422
52/65 [=======================>......] - ETA: 3s - loss: 0.6240 - accuracy: 0.8426
53/65 [=======================>......] - ETA: 3s - loss: 0.6230 - accuracy: 0.8429
54/65 [=======================>......] - ETA: 2s - loss: 0.6218 - accuracy: 0.8433
55/65 [========================>.....] - ETA: 2s - loss: 0.6206 - accuracy: 0.8436
56/65 [========================>.....] - ETA: 2s - loss: 0.6191 - accuracy: 0.8440
57/65 [=========================>....] - ETA: 2s - loss: 0.6182 - accuracy: 0.8443
58/65 [=========================>....] - ETA: 1s - loss: 0.6171 - accuracy: 0.8447
59/65 [==========================>...] - ETA: 1s - loss: 0.6163 - accuracy: 0.8450
60/65 [==========================>...] - ETA: 1s - loss: 0.6156 - accuracy: 0.8451
61/65 [===========================>..] - ETA: 1s - loss: 0.6153 - accuracy: 0.8453
62/65 [===========================>..] - ETA: 0s - loss: 0.6143 - accuracy: 0.8455
63/65 [============================>.] - ETA: 0s - loss: 0.6136 - accuracy: 0.8456
64/65 [============================>.] - ETA: 0s - loss: 0.6133 - accuracy: 0.8456
65/65 [==============================] - ETA: 0s - loss: 0.6137 - accuracy: 0.8454
65/65 [==============================] - 17s 258ms/step - loss: 0.6137 - accuracy: 0.8454 - val_loss: 1.1035 - val_accuracy: 0.6717 - lr: 0.0030
Epoch 4/30
1/65 [..............................] - ETA: 30s - loss: 0.6064 - accuracy: 0.8516
2/65 [..............................] - ETA: 16s - loss: 0.6136 - accuracy: 0.8525
3/65 [>.............................] - ETA: 15s - loss: 0.6061 - accuracy: 0.8551
4/65 [>.............................] - ETA: 15s - loss: 0.5929 - accuracy: 0.8567
5/65 [=>............................] - ETA: 15s - loss: 0.5840 - accuracy: 0.8596
6/65 [=>............................] - ETA: 15s - loss: 0.5782 - accuracy: 0.8577
7/65 [==>...........................] - ETA: 15s - loss: 0.5811 - accuracy: 0.8580
8/65 [==>...........................] - ETA: 14s - loss: 0.5828 - accuracy: 0.8563
9/65 [===>..........................] - ETA: 14s - loss: 0.5739 - accuracy: 0.8585
10/65 [===>..........................] - ETA: 14s - loss: 0.5784 - accuracy: 0.8575
11/65 [====>.........................] - ETA: 13s - loss: 0.5771 - accuracy: 0.8585
12/65 [====>.........................] - ETA: 13s - loss: 0.5735 - accuracy: 0.8597
13/65 [=====>........................] - ETA: 13s - loss: 0.5721 - accuracy: 0.8591
14/65 [=====>........................] - ETA: 13s - loss: 0.5724 - accuracy: 0.8594
15/65 [=====>........................] - ETA: 12s - loss: 0.5659 - accuracy: 0.8615
16/65 [======>.......................] - ETA: 12s - loss: 0.5633 - accuracy: 0.8626
17/65 [======>.......................] - ETA: 12s - loss: 0.5639 - accuracy: 0.8620
18/65 [=======>......................] - ETA: 12s - loss: 0.5603 - accuracy: 0.8633
19/65 [=======>......................] - ETA: 11s - loss: 0.5607 - accuracy: 0.8635
20/65 [========>.....................] - ETA: 11s - loss: 0.5635 - accuracy: 0.8630
21/65 [========>.....................] - ETA: 11s - loss: 0.5628 - accuracy: 0.8627
22/65 [=========>....................] - ETA: 11s - loss: 0.5616 - accuracy: 0.8629
23/65 [=========>....................] - ETA: 10s - loss: 0.5605 - accuracy: 0.8630
24/65 [==========>...................] - ETA: 10s - loss: 0.5582 - accuracy: 0.8639
25/65 [==========>...................] - ETA: 10s - loss: 0.5563 - accuracy: 0.8644
26/65 [===========>..................] - ETA: 10s - loss: 0.5559 - accuracy: 0.8647
27/65 [===========>..................] - ETA: 9s - loss: 0.5575 - accuracy: 0.8644
28/65 [===========>..................] - ETA: 9s - loss: 0.5576 - accuracy: 0.8641
29/65 [============>.................] - ETA: 9s - loss: 0.5576 - accuracy: 0.8638
30/65 [============>.................] - ETA: 9s - loss: 0.5574 - accuracy: 0.8636
31/65 [=============>................] - ETA: 8s - loss: 0.5575 - accuracy: 0.8635
32/65 [=============>................] - ETA: 8s - loss: 0.5586 - accuracy: 0.8633
33/65 [==============>...............] - ETA: 8s - loss: 0.5583 - accuracy: 0.8635
34/65 [==============>...............] - ETA: 8s - loss: 0.5590 - accuracy: 0.8633
35/65 [===============>..............] - ETA: 7s - loss: 0.5573 - accuracy: 0.8639
36/65 [===============>..............] - ETA: 7s - loss: 0.5576 - accuracy: 0.8637
37/65 [================>.............] - ETA: 7s - loss: 0.5561 - accuracy: 0.8640
38/65 [================>.............] - ETA: 6s - loss: 0.5570 - accuracy: 0.8635
39/65 [=================>............] - ETA: 6s - loss: 0.5555 - accuracy: 0.8636
40/65 [=================>............] - ETA: 6s - loss: 0.5555 - accuracy: 0.8635
41/65 [=================>............] - ETA: 6s - loss: 0.5553 - accuracy: 0.8635
42/65 [==================>...........] - ETA: 5s - loss: 0.5540 - accuracy: 0.8640
43/65 [==================>...........] - ETA: 5s - loss: 0.5532 - accuracy: 0.8643
44/65 [===================>..........] - ETA: 5s - loss: 0.5538 - accuracy: 0.8642
45/65 [===================>..........] - ETA: 5s - loss: 0.5529 - accuracy: 0.8643
46/65 [====================>.........] - ETA: 4s - loss: 0.5527 - accuracy: 0.8640
47/65 [====================>.........] - ETA: 4s - loss: 0.5523 - accuracy: 0.8641
48/65 [=====================>........] - ETA: 4s - loss: 0.5512 - accuracy: 0.8643
49/65 [=====================>........] - ETA: 4s - loss: 0.5507 - accuracy: 0.8644
50/65 [======================>.......] - ETA: 3s - loss: 0.5497 - accuracy: 0.8647
51/65 [======================>.......] - ETA: 3s - loss: 0.5485 - accuracy: 0.8653
52/65 [=======================>......] - ETA: 3s - loss: 0.5477 - accuracy: 0.8655
53/65 [=======================>......] - ETA: 3s - loss: 0.5477 - accuracy: 0.8654
54/65 [=======================>......] - ETA: 2s - loss: 0.5476 - accuracy: 0.8653
55/65 [========================>.....] - ETA: 2s - loss: 0.5473 - accuracy: 0.8654
56/65 [========================>.....] - ETA: 2s - loss: 0.5466 - accuracy: 0.8657
57/65 [=========================>....] - ETA: 2s - loss: 0.5446 - accuracy: 0.8662
58/65 [=========================>....] - ETA: 1s - loss: 0.5443 - accuracy: 0.8662
59/65 [==========================>...] - ETA: 1s - loss: 0.5440 - accuracy: 0.8661
60/65 [==========================>...] - ETA: 1s - loss: 0.5436 - accuracy: 0.8664
61/65 [===========================>..] - ETA: 1s - loss: 0.5444 - accuracy: 0.8661
62/65 [===========================>..] - ETA: 0s - loss: 0.5445 - accuracy: 0.8661
63/65 [============================>.] - ETA: 0s - loss: 0.5447 - accuracy: 0.8659
64/65 [============================>.] - ETA: 0s - loss: 0.5446 - accuracy: 0.8657
65/65 [==============================] - ETA: 0s - loss: 0.5445 - accuracy: 0.8658
65/65 [==============================] - 17s 262ms/step - loss: 0.5445 - accuracy: 0.8658 - val_loss: 1.0664 - val_accuracy: 0.6803 - lr: 0.0030
Epoch 5/30
1/65 [..............................] - ETA: 32s - loss: 0.5478 - accuracy: 0.8643
2/65 [..............................] - ETA: 16s - loss: 0.5593 - accuracy: 0.8594
3/65 [>.............................] - ETA: 15s - loss: 0.5364 - accuracy: 0.8656
4/65 [>.............................] - ETA: 15s - loss: 0.5490 - accuracy: 0.8657
5/65 [=>............................] - ETA: 15s - loss: 0.5512 - accuracy: 0.8668
6/65 [=>............................] - ETA: 15s - loss: 0.5470 - accuracy: 0.8657
7/65 [==>...........................] - ETA: 14s - loss: 0.5436 - accuracy: 0.8655
8/65 [==>...........................] - ETA: 14s - loss: 0.5381 - accuracy: 0.8668
9/65 [===>..........................] - ETA: 14s - loss: 0.5331 - accuracy: 0.8681
10/65 [===>..........................] - ETA: 13s - loss: 0.5332 - accuracy: 0.8673
11/65 [====>.........................] - ETA: 13s - loss: 0.5296 - accuracy: 0.8680
12/65 [====>.........................] - ETA: 13s - loss: 0.5246 - accuracy: 0.8689
13/65 [=====>........................] - ETA: 13s - loss: 0.5223 - accuracy: 0.8701
14/65 [=====>........................] - ETA: 12s - loss: 0.5224 - accuracy: 0.8704
15/65 [=====>........................] - ETA: 12s - loss: 0.5197 - accuracy: 0.8712
16/65 [======>.......................] - ETA: 12s - loss: 0.5201 - accuracy: 0.8708
17/65 [======>.......................] - ETA: 12s - loss: 0.5181 - accuracy: 0.8709
18/65 [=======>......................] - ETA: 11s - loss: 0.5177 - accuracy: 0.8718
19/65 [=======>......................] - ETA: 11s - loss: 0.5160 - accuracy: 0.8721
20/65 [========>.....................] - ETA: 11s - loss: 0.5136 - accuracy: 0.8729
21/65 [========>.....................] - ETA: 11s - loss: 0.5135 - accuracy: 0.8727
22/65 [=========>....................] - ETA: 10s - loss: 0.5122 - accuracy: 0.8730
23/65 [=========>....................] - ETA: 10s - loss: 0.5122 - accuracy: 0.8730
24/65 [==========>...................] - ETA: 10s - loss: 0.5135 - accuracy: 0.8724
25/65 [==========>...................] - ETA: 10s - loss: 0.5130 - accuracy: 0.8725
26/65 [===========>..................] - ETA: 9s - loss: 0.5118 - accuracy: 0.8729
27/65 [===========>..................] - ETA: 9s - loss: 0.5102 - accuracy: 0.8732
28/65 [===========>..................] - ETA: 9s - loss: 0.5091 - accuracy: 0.8737
29/65 [============>.................] - ETA: 9s - loss: 0.5095 - accuracy: 0.8734
30/65 [============>.................] - ETA: 8s - loss: 0.5104 - accuracy: 0.8735
31/65 [=============>................] - ETA: 8s - loss: 0.5095 - accuracy: 0.8740
32/65 [=============>................] - ETA: 8s - loss: 0.5097 - accuracy: 0.8741
33/65 [==============>...............] - ETA: 8s - loss: 0.5084 - accuracy: 0.8746
34/65 [==============>...............] - ETA: 7s - loss: 0.5080 - accuracy: 0.8747
35/65 [===============>..............] - ETA: 7s - loss: 0.5082 - accuracy: 0.8748
36/65 [===============>..............] - ETA: 7s - loss: 0.5077 - accuracy: 0.8751
37/65 [================>.............] - ETA: 7s - loss: 0.5083 - accuracy: 0.8751
38/65 [================>.............] - ETA: 6s - loss: 0.5085 - accuracy: 0.8749
39/65 [=================>............] - ETA: 6s - loss: 0.5066 - accuracy: 0.8755
40/65 [=================>............] - ETA: 6s - loss: 0.5074 - accuracy: 0.8754
41/65 [=================>............] - ETA: 6s - loss: 0.5060 - accuracy: 0.8757
42/65 [==================>...........] - ETA: 5s - loss: 0.5059 - accuracy: 0.8757
43/65 [==================>...........] - ETA: 5s - loss: 0.5059 - accuracy: 0.8756
44/65 [===================>..........] - ETA: 5s - loss: 0.5064 - accuracy: 0.8753
45/65 [===================>..........] - ETA: 5s - loss: 0.5054 - accuracy: 0.8755
46/65 [====================>.........] - ETA: 4s - loss: 0.5049 - accuracy: 0.8758
47/65 [====================>.........] - ETA: 4s - loss: 0.5048 - accuracy: 0.8757
48/65 [=====================>........] - ETA: 4s - loss: 0.5044 - accuracy: 0.8760
49/65 [=====================>........] - ETA: 4s - loss: 0.5044 - accuracy: 0.8761
50/65 [======================>.......] - ETA: 3s - loss: 0.5036 - accuracy: 0.8763
51/65 [======================>.......] - ETA: 3s - loss: 0.5034 - accuracy: 0.8763
52/65 [=======================>......] - ETA: 3s - loss: 0.5024 - accuracy: 0.8764
53/65 [=======================>......] - ETA: 3s - loss: 0.5013 - accuracy: 0.8767
54/65 [=======================>......] - ETA: 2s - loss: 0.5007 - accuracy: 0.8769
55/65 [========================>.....] - ETA: 2s - loss: 0.4999 - accuracy: 0.8770
56/65 [========================>.....] - ETA: 2s - loss: 0.4993 - accuracy: 0.8772
57/65 [=========================>....] - ETA: 2s - loss: 0.4987 - accuracy: 0.8772
58/65 [=========================>....] - ETA: 1s - loss: 0.4984 - accuracy: 0.8774
59/65 [==========================>...] - ETA: 1s - loss: 0.4979 - accuracy: 0.8775
60/65 [==========================>...] - ETA: 1s - loss: 0.4988 - accuracy: 0.8772
61/65 [===========================>..] - ETA: 1s - loss: 0.4994 - accuracy: 0.8769
62/65 [===========================>..] - ETA: 0s - loss: 0.5005 - accuracy: 0.8766
63/65 [============================>.] - ETA: 0s - loss: 0.4995 - accuracy: 0.8767
64/65 [============================>.] - ETA: 0s - loss: 0.4992 - accuracy: 0.8768
65/65 [==============================] - ETA: 0s - loss: 0.4987 - accuracy: 0.8769
65/65 [==============================] - 17s 258ms/step - loss: 0.4987 - accuracy: 0.8769 - val_loss: 0.7790 - val_accuracy: 0.7858 - lr: 0.0030
Epoch 6/30
1/65 [..............................] - ETA: 32s - loss: 0.4491 - accuracy: 0.8818
2/65 [..............................] - ETA: 16s - loss: 0.4387 - accuracy: 0.8892
3/65 [>.............................] - ETA: 15s - loss: 0.4536 - accuracy: 0.8864
4/65 [>.............................] - ETA: 15s - loss: 0.4665 - accuracy: 0.8853
5/65 [=>............................] - ETA: 15s - loss: 0.4674 - accuracy: 0.8857
6/65 [=>............................] - ETA: 14s - loss: 0.4837 - accuracy: 0.8805
7/65 [==>...........................] - ETA: 14s - loss: 0.4894 - accuracy: 0.8800
8/65 [==>...........................] - ETA: 14s - loss: 0.4882 - accuracy: 0.8804
9/65 [===>..........................] - ETA: 14s - loss: 0.4883 - accuracy: 0.8805
10/65 [===>..........................] - ETA: 13s - loss: 0.4846 - accuracy: 0.8821
11/65 [====>.........................] - ETA: 13s - loss: 0.4821 - accuracy: 0.8825
12/65 [====>.........................] - ETA: 13s - loss: 0.4798 - accuracy: 0.8840
13/65 [=====>........................] - ETA: 13s - loss: 0.4765 - accuracy: 0.8849
14/65 [=====>........................] - ETA: 12s - loss: 0.4779 - accuracy: 0.8848
15/65 [=====>........................] - ETA: 12s - loss: 0.4778 - accuracy: 0.8844
16/65 [======>.......................] - ETA: 12s - loss: 0.4766 - accuracy: 0.8845
17/65 [======>.......................] - ETA: 12s - loss: 0.4746 - accuracy: 0.8857
18/65 [=======>......................] - ETA: 11s - loss: 0.4745 - accuracy: 0.8849
19/65 [=======>......................] - ETA: 11s - loss: 0.4751 - accuracy: 0.8843
20/65 [========>.....................] - ETA: 11s - loss: 0.4757 - accuracy: 0.8837
21/65 [========>.....................] - ETA: 11s - loss: 0.4767 - accuracy: 0.8833
22/65 [=========>....................] - ETA: 10s - loss: 0.4766 - accuracy: 0.8835
23/65 [=========>....................] - ETA: 10s - loss: 0.4751 - accuracy: 0.8837
24/65 [==========>...................] - ETA: 10s - loss: 0.4742 - accuracy: 0.8841
25/65 [==========>...................] - ETA: 10s - loss: 0.4723 - accuracy: 0.8844
26/65 [===========>..................] - ETA: 9s - loss: 0.4709 - accuracy: 0.8847
27/65 [===========>..................] - ETA: 9s - loss: 0.4704 - accuracy: 0.8845
28/65 [===========>..................] - ETA: 9s - loss: 0.4698 - accuracy: 0.8844
29/65 [============>.................] - ETA: 9s - loss: 0.4706 - accuracy: 0.8843
30/65 [============>.................] - ETA: 8s - loss: 0.4710 - accuracy: 0.8841
31/65 [=============>................] - ETA: 8s - loss: 0.4720 - accuracy: 0.8842
32/65 [=============>................] - ETA: 8s - loss: 0.4724 - accuracy: 0.8841
33/65 [==============>...............] - ETA: 8s - loss: 0.4721 - accuracy: 0.8843
34/65 [==============>...............] - ETA: 7s - loss: 0.4715 - accuracy: 0.8848
35/65 [===============>..............] - ETA: 7s - loss: 0.4716 - accuracy: 0.8847
36/65 [===============>..............] - ETA: 7s - loss: 0.4711 - accuracy: 0.8850
37/65 [================>.............] - ETA: 7s - loss: 0.4706 - accuracy: 0.8849
38/65 [================>.............] - ETA: 6s - loss: 0.4707 - accuracy: 0.8848
39/65 [=================>............] - ETA: 6s - loss: 0.4710 - accuracy: 0.8847
40/65 [=================>............] - ETA: 6s - loss: 0.4706 - accuracy: 0.8847
41/65 [=================>............] - ETA: 6s - loss: 0.4705 - accuracy: 0.8847
42/65 [==================>...........] - ETA: 5s - loss: 0.4706 - accuracy: 0.8849
43/65 [==================>...........] - ETA: 5s - loss: 0.4702 - accuracy: 0.8847
44/65 [===================>..........] - ETA: 5s - loss: 0.4705 - accuracy: 0.8849
45/65 [===================>..........] - ETA: 5s - loss: 0.4705 - accuracy: 0.8850
46/65 [====================>.........] - ETA: 4s - loss: 0.4720 - accuracy: 0.8848
47/65 [====================>.........] - ETA: 4s - loss: 0.4713 - accuracy: 0.8850
48/65 [=====================>........] - ETA: 4s - loss: 0.4704 - accuracy: 0.8855
49/65 [=====================>........] - ETA: 4s - loss: 0.4700 - accuracy: 0.8856
50/65 [======================>.......] - ETA: 3s - loss: 0.4686 - accuracy: 0.8860
51/65 [======================>.......] - ETA: 3s - loss: 0.4679 - accuracy: 0.8858
52/65 [=======================>......] - ETA: 3s - loss: 0.4681 - accuracy: 0.8857
53/65 [=======================>......] - ETA: 3s - loss: 0.4674 - accuracy: 0.8857
54/65 [=======================>......] - ETA: 2s - loss: 0.4665 - accuracy: 0.8862
55/65 [========================>.....] - ETA: 2s - loss: 0.4658 - accuracy: 0.8862
56/65 [========================>.....] - ETA: 2s - loss: 0.4652 - accuracy: 0.8863
57/65 [=========================>....] - ETA: 2s - loss: 0.4646 - accuracy: 0.8864
58/65 [=========================>....] - ETA: 1s - loss: 0.4645 - accuracy: 0.8865
59/65 [==========================>...] - ETA: 1s - loss: 0.4678 - accuracy: 0.8852
60/65 [==========================>...] - ETA: 1s - loss: 0.4692 - accuracy: 0.8845
61/65 [===========================>..] - ETA: 0s - loss: 0.4708 - accuracy: 0.8837
62/65 [===========================>..] - ETA: 0s - loss: 0.4713 - accuracy: 0.8835
63/65 [============================>.] - ETA: 0s - loss: 0.4718 - accuracy: 0.8833
64/65 [============================>.] - ETA: 0s - loss: 0.4722 - accuracy: 0.8831
65/65 [==============================] - ETA: 0s - loss: 0.4716 - accuracy: 0.8833
65/65 [==============================] - 17s 257ms/step - loss: 0.4716 - accuracy: 0.8833 - val_loss: 0.9978 - val_accuracy: 0.7123 - lr: 0.0030
Epoch 7/30
1/65 [..............................] - ETA: 30s - loss: 0.5066 - accuracy: 0.8711
2/65 [..............................] - ETA: 15s - loss: 0.5174 - accuracy: 0.8735
3/65 [>.............................] - ETA: 15s - loss: 0.5049 - accuracy: 0.8743
4/65 [>.............................] - ETA: 15s - loss: 0.4998 - accuracy: 0.8755
5/65 [=>............................] - ETA: 15s - loss: 0.4960 - accuracy: 0.8766
6/65 [=>............................] - ETA: 14s - loss: 0.4952 - accuracy: 0.8740
7/65 [==>...........................] - ETA: 14s - loss: 0.4955 - accuracy: 0.8740
8/65 [==>...........................] - ETA: 14s - loss: 0.4928 - accuracy: 0.8752
9/65 [===>..........................] - ETA: 13s - loss: 0.4856 - accuracy: 0.8773
10/65 [===>..........................] - ETA: 13s - loss: 0.4903 - accuracy: 0.8756
11/65 [====>.........................] - ETA: 13s - loss: 0.4848 - accuracy: 0.8771
12/65 [====>.........................] - ETA: 13s - loss: 0.4778 - accuracy: 0.8792
13/65 [=====>........................] - ETA: 12s - loss: 0.4752 - accuracy: 0.8807
14/65 [=====>........................] - ETA: 12s - loss: 0.4756 - accuracy: 0.8810
15/65 [=====>........................] - ETA: 12s - loss: 0.4768 - accuracy: 0.8805
16/65 [======>.......................] - ETA: 12s - loss: 0.4757 - accuracy: 0.8810
17/65 [======>.......................] - ETA: 12s - loss: 0.4760 - accuracy: 0.8810
18/65 [=======>......................] - ETA: 11s - loss: 0.4753 - accuracy: 0.8807
19/65 [=======>......................] - ETA: 11s - loss: 0.4722 - accuracy: 0.8813
20/65 [========>.....................] - ETA: 11s - loss: 0.4734 - accuracy: 0.8812
21/65 [========>.....................] - ETA: 11s - loss: 0.4723 - accuracy: 0.8821
22/65 [=========>....................] - ETA: 10s - loss: 0.4720 - accuracy: 0.8820
23/65 [=========>....................] - ETA: 10s - loss: 0.4697 - accuracy: 0.8830
24/65 [==========>...................] - ETA: 10s - loss: 0.4689 - accuracy: 0.8832
25/65 [==========>...................] - ETA: 10s - loss: 0.4681 - accuracy: 0.8835
26/65 [===========>..................] - ETA: 9s - loss: 0.4675 - accuracy: 0.8833
27/65 [===========>..................] - ETA: 9s - loss: 0.4680 - accuracy: 0.8832
28/65 [===========>..................] - ETA: 9s - loss: 0.4655 - accuracy: 0.8839
29/65 [============>.................] - ETA: 9s - loss: 0.4662 - accuracy: 0.8837
30/65 [============>.................] - ETA: 8s - loss: 0.4676 - accuracy: 0.8830
31/65 [=============>................] - ETA: 8s - loss: 0.4662 - accuracy: 0.8839
32/65 [=============>................] - ETA: 8s - loss: 0.4660 - accuracy: 0.8839
33/65 [==============>...............] - ETA: 8s - loss: 0.4669 - accuracy: 0.8841
34/65 [==============>...............] - ETA: 7s - loss: 0.4653 - accuracy: 0.8844
35/65 [===============>..............] - ETA: 7s - loss: 0.4652 - accuracy: 0.8845
36/65 [===============>..............] - ETA: 7s - loss: 0.4655 - accuracy: 0.8843
37/65 [================>.............] - ETA: 7s - loss: 0.4651 - accuracy: 0.8844
38/65 [================>.............] - ETA: 6s - loss: 0.4644 - accuracy: 0.8849
39/65 [=================>............] - ETA: 6s - loss: 0.4636 - accuracy: 0.8850
40/65 [=================>............] - ETA: 6s - loss: 0.4642 - accuracy: 0.8850
41/65 [=================>............] - ETA: 6s - loss: 0.4634 - accuracy: 0.8852
42/65 [==================>...........] - ETA: 5s - loss: 0.4624 - accuracy: 0.8854
43/65 [==================>...........] - ETA: 5s - loss: 0.4617 - accuracy: 0.8857
44/65 [===================>..........] - ETA: 5s - loss: 0.4615 - accuracy: 0.8856
45/65 [===================>..........] - ETA: 5s - loss: 0.4609 - accuracy: 0.8859
46/65 [====================>.........] - ETA: 4s - loss: 0.4601 - accuracy: 0.8862
47/65 [====================>.........] - ETA: 4s - loss: 0.4597 - accuracy: 0.8864
48/65 [=====================>........] - ETA: 4s - loss: 0.4585 - accuracy: 0.8866
49/65 [=====================>........] - ETA: 4s - loss: 0.4579 - accuracy: 0.8870
50/65 [======================>.......] - ETA: 3s - loss: 0.4567 - accuracy: 0.8872
51/65 [======================>.......] - ETA: 3s - loss: 0.4569 - accuracy: 0.8870
52/65 [=======================>......] - ETA: 3s - loss: 0.4567 - accuracy: 0.8872
53/65 [=======================>......] - ETA: 3s - loss: 0.4561 - accuracy: 0.8872
54/65 [=======================>......] - ETA: 2s - loss: 0.4554 - accuracy: 0.8873
55/65 [========================>.....] - ETA: 2s - loss: 0.4547 - accuracy: 0.8874
56/65 [========================>.....] - ETA: 2s - loss: 0.4540 - accuracy: 0.8877
57/65 [=========================>....] - ETA: 2s - loss: 0.4535 - accuracy: 0.8879
58/65 [=========================>....] - ETA: 1s - loss: 0.4539 - accuracy: 0.8878
59/65 [==========================>...] - ETA: 1s - loss: 0.4536 - accuracy: 0.8879
60/65 [==========================>...] - ETA: 1s - loss: 0.4534 - accuracy: 0.8880
61/65 [===========================>..] - ETA: 1s - loss: 0.4533 - accuracy: 0.8881
62/65 [===========================>..] - ETA: 0s - loss: 0.4531 - accuracy: 0.8882
63/65 [============================>.] - ETA: 0s - loss: 0.4524 - accuracy: 0.8884
64/65 [============================>.] - ETA: 0s - loss: 0.4524 - accuracy: 0.8883
65/65 [==============================] - ETA: 0s - loss: 0.4526 - accuracy: 0.8883
65/65 [==============================] - 17s 258ms/step - loss: 0.4526 - accuracy: 0.8883 - val_loss: 0.5923 - val_accuracy: 0.8415 - lr: 0.0030
Epoch 8/30
1/65 [..............................] - ETA: 31s - loss: 0.4155 - accuracy: 0.8984
2/65 [..............................] - ETA: 17s - loss: 0.4508 - accuracy: 0.8911
3/65 [>.............................] - ETA: 16s - loss: 0.4445 - accuracy: 0.8939
4/65 [>.............................] - ETA: 16s - loss: 0.4485 - accuracy: 0.8916
5/65 [=>............................] - ETA: 15s - loss: 0.4508 - accuracy: 0.8906
6/65 [=>............................] - ETA: 15s - loss: 0.4478 - accuracy: 0.8931
7/65 [==>...........................] - ETA: 15s - loss: 0.4461 - accuracy: 0.8926
8/65 [==>...........................] - ETA: 14s - loss: 0.4463 - accuracy: 0.8923
9/65 [===>..........................] - ETA: 14s - loss: 0.4484 - accuracy: 0.8920
10/65 [===>..........................] - ETA: 14s - loss: 0.4483 - accuracy: 0.8926
11/65 [====>.........................] - ETA: 13s - loss: 0.4534 - accuracy: 0.8913
12/65 [====>.........................] - ETA: 13s - loss: 0.4491 - accuracy: 0.8924
13/65 [=====>........................] - ETA: 13s - loss: 0.4445 - accuracy: 0.8939
14/65 [=====>........................] - ETA: 12s - loss: 0.4434 - accuracy: 0.8947
15/65 [=====>........................] - ETA: 12s - loss: 0.4439 - accuracy: 0.8938
16/65 [======>.......................] - ETA: 12s - loss: 0.4443 - accuracy: 0.8934
17/65 [======>.......................] - ETA: 12s - loss: 0.4442 - accuracy: 0.8933
18/65 [=======>......................] - ETA: 11s - loss: 0.4409 - accuracy: 0.8944
19/65 [=======>......................] - ETA: 11s - loss: 0.4365 - accuracy: 0.8954
20/65 [========>.....................] - ETA: 11s - loss: 0.4352 - accuracy: 0.8956
21/65 [========>.....................] - ETA: 11s - loss: 0.4339 - accuracy: 0.8956
22/65 [=========>....................] - ETA: 10s - loss: 0.4329 - accuracy: 0.8956
23/65 [=========>....................] - ETA: 10s - loss: 0.4326 - accuracy: 0.8952
24/65 [==========>...................] - ETA: 10s - loss: 0.4334 - accuracy: 0.8950
25/65 [==========>...................] - ETA: 10s - loss: 0.4327 - accuracy: 0.8954
26/65 [===========>..................] - ETA: 9s - loss: 0.4348 - accuracy: 0.8944
27/65 [===========>..................] - ETA: 9s - loss: 0.4350 - accuracy: 0.8941
28/65 [===========>..................] - ETA: 9s - loss: 0.4362 - accuracy: 0.8937
29/65 [============>.................] - ETA: 9s - loss: 0.4354 - accuracy: 0.8940
30/65 [============>.................] - ETA: 8s - loss: 0.4345 - accuracy: 0.8945
31/65 [=============>................] - ETA: 8s - loss: 0.4354 - accuracy: 0.8943
32/65 [=============>................] - ETA: 8s - loss: 0.4355 - accuracy: 0.8939
33/65 [==============>...............] - ETA: 8s - loss: 0.4361 - accuracy: 0.8937
34/65 [==============>...............] - ETA: 7s - loss: 0.4358 - accuracy: 0.8934
35/65 [===============>..............] - ETA: 7s - loss: 0.4348 - accuracy: 0.8938
36/65 [===============>..............] - ETA: 7s - loss: 0.4344 - accuracy: 0.8938
37/65 [================>.............] - ETA: 7s - loss: 0.4351 - accuracy: 0.8937
38/65 [================>.............] - ETA: 6s - loss: 0.4351 - accuracy: 0.8938
39/65 [=================>............] - ETA: 6s - loss: 0.4348 - accuracy: 0.8940
40/65 [=================>............] - ETA: 6s - loss: 0.4346 - accuracy: 0.8939
41/65 [=================>............] - ETA: 6s - loss: 0.4338 - accuracy: 0.8940
42/65 [==================>...........] - ETA: 5s - loss: 0.4333 - accuracy: 0.8942
43/65 [==================>...........] - ETA: 5s - loss: 0.4323 - accuracy: 0.8943
44/65 [===================>..........] - ETA: 5s - loss: 0.4323 - accuracy: 0.8942
45/65 [===================>..........] - ETA: 5s - loss: 0.4317 - accuracy: 0.8944
46/65 [====================>.........] - ETA: 4s - loss: 0.4308 - accuracy: 0.8945
47/65 [====================>.........] - ETA: 4s - loss: 0.4310 - accuracy: 0.8944
48/65 [=====================>........] - ETA: 4s - loss: 0.4310 - accuracy: 0.8944
49/65 [=====================>........] - ETA: 4s - loss: 0.4305 - accuracy: 0.8945
50/65 [======================>.......] - ETA: 3s - loss: 0.4304 - accuracy: 0.8944
51/65 [======================>.......] - ETA: 3s - loss: 0.4303 - accuracy: 0.8945
52/65 [=======================>......] - ETA: 3s - loss: 0.4298 - accuracy: 0.8948
53/65 [=======================>......] - ETA: 3s - loss: 0.4288 - accuracy: 0.8950
54/65 [=======================>......] - ETA: 2s - loss: 0.4281 - accuracy: 0.8951
55/65 [========================>.....] - ETA: 2s - loss: 0.4280 - accuracy: 0.8953
56/65 [========================>.....] - ETA: 2s - loss: 0.4277 - accuracy: 0.8955
57/65 [=========================>....] - ETA: 2s - loss: 0.4270 - accuracy: 0.8957
58/65 [=========================>....] - ETA: 1s - loss: 0.4272 - accuracy: 0.8957
59/65 [==========================>...] - ETA: 1s - loss: 0.4264 - accuracy: 0.8957
60/65 [==========================>...] - ETA: 1s - loss: 0.4263 - accuracy: 0.8958
61/65 [===========================>..] - ETA: 1s - loss: 0.4263 - accuracy: 0.8957
62/65 [===========================>..] - ETA: 0s - loss: 0.4263 - accuracy: 0.8957
63/65 [============================>.] - ETA: 0s - loss: 0.4258 - accuracy: 0.8958
64/65 [============================>.] - ETA: 0s - loss: 0.4262 - accuracy: 0.8957
65/65 [==============================] - ETA: 0s - loss: 0.4262 - accuracy: 0.8956
65/65 [==============================] - 17s 258ms/step - loss: 0.4262 - accuracy: 0.8956 - val_loss: 0.5695 - val_accuracy: 0.8474 - lr: 0.0030
Epoch 9/30
1/65 [..............................] - ETA: 29s - loss: 0.4686 - accuracy: 0.8838
2/65 [..............................] - ETA: 15s - loss: 0.4427 - accuracy: 0.8960
3/65 [>.............................] - ETA: 15s - loss: 0.4326 - accuracy: 0.9001
4/65 [>.............................] - ETA: 15s - loss: 0.4315 - accuracy: 0.8994
5/65 [=>............................] - ETA: 15s - loss: 0.4354 - accuracy: 0.8969
6/65 [=>............................] - ETA: 14s - loss: 0.4378 - accuracy: 0.8962
7/65 [==>...........................] - ETA: 14s - loss: 0.4403 - accuracy: 0.8949
8/65 [==>...........................] - ETA: 14s - loss: 0.4430 - accuracy: 0.8937
9/65 [===>..........................] - ETA: 14s - loss: 0.4398 - accuracy: 0.8932
10/65 [===>..........................] - ETA: 13s - loss: 0.4371 - accuracy: 0.8938
11/65 [====>.........................] - ETA: 13s - loss: 0.4321 - accuracy: 0.8960
12/65 [====>.........................] - ETA: 13s - loss: 0.4288 - accuracy: 0.8975
13/65 [=====>........................] - ETA: 13s - loss: 0.4281 - accuracy: 0.8970
14/65 [=====>........................] - ETA: 13s - loss: 0.4289 - accuracy: 0.8964
15/65 [=====>........................] - ETA: 12s - loss: 0.4267 - accuracy: 0.8965
16/65 [======>.......................] - ETA: 12s - loss: 0.4234 - accuracy: 0.8970
17/65 [======>.......................] - ETA: 12s - loss: 0.4237 - accuracy: 0.8967
18/65 [=======>......................] - ETA: 11s - loss: 0.4249 - accuracy: 0.8962
19/65 [=======>......................] - ETA: 11s - loss: 0.4225 - accuracy: 0.8965
20/65 [========>.....................] - ETA: 11s - loss: 0.4208 - accuracy: 0.8969
21/65 [========>.....................] - ETA: 11s - loss: 0.4184 - accuracy: 0.8976
22/65 [=========>....................] - ETA: 10s - loss: 0.4177 - accuracy: 0.8981
23/65 [=========>....................] - ETA: 10s - loss: 0.4184 - accuracy: 0.8978
24/65 [==========>...................] - ETA: 10s - loss: 0.4170 - accuracy: 0.8979
25/65 [==========>...................] - ETA: 10s - loss: 0.4155 - accuracy: 0.8982
26/65 [===========>..................] - ETA: 9s - loss: 0.4158 - accuracy: 0.8985
27/65 [===========>..................] - ETA: 9s - loss: 0.4153 - accuracy: 0.8984
28/65 [===========>..................] - ETA: 9s - loss: 0.4140 - accuracy: 0.8989
29/65 [============>.................] - ETA: 9s - loss: 0.4149 - accuracy: 0.8985
30/65 [============>.................] - ETA: 8s - loss: 0.4156 - accuracy: 0.8982
31/65 [=============>................] - ETA: 8s - loss: 0.4144 - accuracy: 0.8984
32/65 [=============>................] - ETA: 8s - loss: 0.4158 - accuracy: 0.8979
33/65 [==============>...............] - ETA: 8s - loss: 0.4170 - accuracy: 0.8975
34/65 [==============>...............] - ETA: 7s - loss: 0.4173 - accuracy: 0.8977
35/65 [===============>..............] - ETA: 7s - loss: 0.4169 - accuracy: 0.8981
36/65 [===============>..............] - ETA: 7s - loss: 0.4170 - accuracy: 0.8980
37/65 [================>.............] - ETA: 7s - loss: 0.4167 - accuracy: 0.8983
38/65 [================>.............] - ETA: 6s - loss: 0.4175 - accuracy: 0.8980
39/65 [=================>............] - ETA: 6s - loss: 0.4160 - accuracy: 0.8984
40/65 [=================>............] - ETA: 6s - loss: 0.4165 - accuracy: 0.8981
41/65 [=================>............] - ETA: 6s - loss: 0.4165 - accuracy: 0.8981
42/65 [==================>...........] - ETA: 5s - loss: 0.4163 - accuracy: 0.8980
43/65 [==================>...........] - ETA: 5s - loss: 0.4162 - accuracy: 0.8982
44/65 [===================>..........] - ETA: 5s - loss: 0.4165 - accuracy: 0.8981
45/65 [===================>..........] - ETA: 5s - loss: 0.4163 - accuracy: 0.8982
46/65 [====================>.........] - ETA: 4s - loss: 0.4163 - accuracy: 0.8982
47/65 [====================>.........] - ETA: 4s - loss: 0.4170 - accuracy: 0.8981
48/65 [=====================>........] - ETA: 4s - loss: 0.4166 - accuracy: 0.8982
49/65 [=====================>........] - ETA: 4s - loss: 0.4162 - accuracy: 0.8984
50/65 [======================>.......] - ETA: 3s - loss: 0.4169 - accuracy: 0.8979
51/65 [======================>.......] - ETA: 3s - loss: 0.4159 - accuracy: 0.8982
52/65 [=======================>......] - ETA: 3s - loss: 0.4150 - accuracy: 0.8983
53/65 [=======================>......] - ETA: 3s - loss: 0.4145 - accuracy: 0.8984
54/65 [=======================>......] - ETA: 2s - loss: 0.4135 - accuracy: 0.8986
55/65 [========================>.....] - ETA: 2s - loss: 0.4128 - accuracy: 0.8988
56/65 [========================>.....] - ETA: 2s - loss: 0.4120 - accuracy: 0.8990
57/65 [=========================>....] - ETA: 2s - loss: 0.4119 - accuracy: 0.8990
58/65 [=========================>....] - ETA: 1s - loss: 0.4118 - accuracy: 0.8991
59/65 [==========================>...] - ETA: 1s - loss: 0.4110 - accuracy: 0.8993
60/65 [==========================>...] - ETA: 1s - loss: 0.4106 - accuracy: 0.8993
61/65 [===========================>..] - ETA: 1s - loss: 0.4115 - accuracy: 0.8992
62/65 [===========================>..] - ETA: 0s - loss: 0.4110 - accuracy: 0.8992
63/65 [============================>.] - ETA: 0s - loss: 0.4120 - accuracy: 0.8991
64/65 [============================>.] - ETA: 0s - loss: 0.4125 - accuracy: 0.8990
65/65 [==============================] - ETA: 0s - loss: 0.4121 - accuracy: 0.8990
65/65 [==============================] - 17s 259ms/step - loss: 0.4121 - accuracy: 0.8990 - val_loss: 0.5090 - val_accuracy: 0.8695 - lr: 0.0030
Epoch 10/30
1/65 [..............................] - ETA: 30s - loss: 0.4410 - accuracy: 0.8945
2/65 [..............................] - ETA: 15s - loss: 0.4220 - accuracy: 0.8960
3/65 [>.............................] - ETA: 15s - loss: 0.4097 - accuracy: 0.9036
4/65 [>.............................] - ETA: 15s - loss: 0.3950 - accuracy: 0.9077
5/65 [=>............................] - ETA: 15s - loss: 0.3979 - accuracy: 0.9062
6/65 [=>............................] - ETA: 15s - loss: 0.4039 - accuracy: 0.9033
7/65 [==>...........................] - ETA: 14s - loss: 0.4150 - accuracy: 0.8990
8/65 [==>...........................] - ETA: 14s - loss: 0.4111 - accuracy: 0.8993
9/65 [===>..........................] - ETA: 14s - loss: 0.4134 - accuracy: 0.8997
10/65 [===>..........................] - ETA: 13s - loss: 0.4122 - accuracy: 0.8998
11/65 [====>.........................] - ETA: 13s - loss: 0.4124 - accuracy: 0.8986
12/65 [====>.........................] - ETA: 13s - loss: 0.4098 - accuracy: 0.9004
13/65 [=====>........................] - ETA: 13s - loss: 0.4112 - accuracy: 0.9002
14/65 [=====>........................] - ETA: 12s - loss: 0.4118 - accuracy: 0.8993
15/65 [=====>........................] - ETA: 12s - loss: 0.4107 - accuracy: 0.9003
16/65 [======>.......................] - ETA: 12s - loss: 0.4119 - accuracy: 0.8996
17/65 [======>.......................] - ETA: 12s - loss: 0.4100 - accuracy: 0.9000
18/65 [=======>......................] - ETA: 11s - loss: 0.4097 - accuracy: 0.9002
19/65 [=======>......................] - ETA: 11s - loss: 0.4096 - accuracy: 0.9000
20/65 [========>.....................] - ETA: 11s - loss: 0.4080 - accuracy: 0.9001
21/65 [========>.....................] - ETA: 11s - loss: 0.4086 - accuracy: 0.8997
22/65 [=========>....................] - ETA: 10s - loss: 0.4076 - accuracy: 0.8999
23/65 [=========>....................] - ETA: 10s - loss: 0.4093 - accuracy: 0.8993
24/65 [==========>...................] - ETA: 10s - loss: 0.4095 - accuracy: 0.8992
25/65 [==========>...................] - ETA: 10s - loss: 0.4098 - accuracy: 0.8994
26/65 [===========>..................] - ETA: 9s - loss: 0.4088 - accuracy: 0.8997
27/65 [===========>..................] - ETA: 9s - loss: 0.4076 - accuracy: 0.8999
28/65 [===========>..................] - ETA: 9s - loss: 0.4074 - accuracy: 0.8998
29/65 [============>.................] - ETA: 9s - loss: 0.4071 - accuracy: 0.8998
30/65 [============>.................] - ETA: 8s - loss: 0.4085 - accuracy: 0.8992
31/65 [=============>................] - ETA: 8s - loss: 0.4085 - accuracy: 0.8994
32/65 [=============>................] - ETA: 8s - loss: 0.4079 - accuracy: 0.8999
33/65 [==============>...............] - ETA: 7s - loss: 0.4077 - accuracy: 0.8998
34/65 [==============>...............] - ETA: 7s - loss: 0.4073 - accuracy: 0.9001
35/65 [===============>..............] - ETA: 7s - loss: 0.4078 - accuracy: 0.9000
36/65 [===============>..............] - ETA: 7s - loss: 0.4076 - accuracy: 0.9002
37/65 [================>.............] - ETA: 6s - loss: 0.4088 - accuracy: 0.8999
38/65 [================>.............] - ETA: 6s - loss: 0.4094 - accuracy: 0.8997
39/65 [=================>............] - ETA: 6s - loss: 0.4087 - accuracy: 0.8999
40/65 [=================>............] - ETA: 6s - loss: 0.4073 - accuracy: 0.9003
41/65 [=================>............] - ETA: 6s - loss: 0.4073 - accuracy: 0.9004
42/65 [==================>...........] - ETA: 5s - loss: 0.4070 - accuracy: 0.9005
43/65 [==================>...........] - ETA: 5s - loss: 0.4074 - accuracy: 0.9005
44/65 [===================>..........] - ETA: 5s - loss: 0.4069 - accuracy: 0.9005
45/65 [===================>..........] - ETA: 5s - loss: 0.4062 - accuracy: 0.9006
46/65 [====================>.........] - ETA: 4s - loss: 0.4052 - accuracy: 0.9008
47/65 [====================>.........] - ETA: 4s - loss: 0.4052 - accuracy: 0.9008
48/65 [=====================>........] - ETA: 4s - loss: 0.4064 - accuracy: 0.9008
49/65 [=====================>........] - ETA: 4s - loss: 0.4060 - accuracy: 0.9009
50/65 [======================>.......] - ETA: 3s - loss: 0.4062 - accuracy: 0.9008
51/65 [======================>.......] - ETA: 3s - loss: 0.4062 - accuracy: 0.9006
52/65 [=======================>......] - ETA: 3s - loss: 0.4057 - accuracy: 0.9008
53/65 [=======================>......] - ETA: 3s - loss: 0.4053 - accuracy: 0.9007
54/65 [=======================>......] - ETA: 2s - loss: 0.4048 - accuracy: 0.9008
55/65 [========================>.....] - ETA: 2s - loss: 0.4038 - accuracy: 0.9010
56/65 [========================>.....] - ETA: 2s - loss: 0.4022 - accuracy: 0.9014
57/65 [=========================>....] - ETA: 2s - loss: 0.4022 - accuracy: 0.9014
58/65 [=========================>....] - ETA: 1s - loss: 0.4020 - accuracy: 0.9015
59/65 [==========================>...] - ETA: 1s - loss: 0.4028 - accuracy: 0.9012
60/65 [==========================>...] - ETA: 1s - loss: 0.4030 - accuracy: 0.9012
61/65 [===========================>..] - ETA: 1s - loss: 0.4031 - accuracy: 0.9011
62/65 [===========================>..] - ETA: 0s - loss: 0.4025 - accuracy: 0.9013
63/65 [============================>.] - ETA: 0s - loss: 0.4021 - accuracy: 0.9014
64/65 [============================>.] - ETA: 0s - loss: 0.4019 - accuracy: 0.9015
65/65 [==============================] - ETA: 0s - loss: 0.4019 - accuracy: 0.9015
65/65 [==============================] - 17s 258ms/step - loss: 0.4019 - accuracy: 0.9015 - val_loss: 0.5378 - val_accuracy: 0.8583 - lr: 0.0030
Epoch 11/30
1/65 [..............................] - ETA: 30s - loss: 0.4262 - accuracy: 0.8975
2/65 [..............................] - ETA: 16s - loss: 0.4228 - accuracy: 0.9033
3/65 [>.............................] - ETA: 16s - loss: 0.4044 - accuracy: 0.9076
4/65 [>.............................] - ETA: 16s - loss: 0.4025 - accuracy: 0.9055
5/65 [=>............................] - ETA: 15s - loss: 0.3975 - accuracy: 0.9053
6/65 [=>............................] - ETA: 15s - loss: 0.4028 - accuracy: 0.9027
7/65 [==>...........................] - ETA: 15s - loss: 0.4055 - accuracy: 0.9015
8/65 [==>...........................] - ETA: 15s - loss: 0.4050 - accuracy: 0.9023
9/65 [===>..........................] - ETA: 14s - loss: 0.4052 - accuracy: 0.9027
10/65 [===>..........................] - ETA: 14s - loss: 0.4105 - accuracy: 0.9011
11/65 [====>.........................] - ETA: 14s - loss: 0.4074 - accuracy: 0.9017
12/65 [====>.........................] - ETA: 13s - loss: 0.4077 - accuracy: 0.9019
13/65 [=====>........................] - ETA: 13s - loss: 0.4060 - accuracy: 0.9019
14/65 [=====>........................] - ETA: 13s - loss: 0.4058 - accuracy: 0.9018
15/65 [=====>........................] - ETA: 13s - loss: 0.4063 - accuracy: 0.9020
16/65 [======>.......................] - ETA: 12s - loss: 0.4051 - accuracy: 0.9018
17/65 [======>.......................] - ETA: 12s - loss: 0.4024 - accuracy: 0.9025
18/65 [=======>......................] - ETA: 12s - loss: 0.4020 - accuracy: 0.9020
19/65 [=======>......................] - ETA: 11s - loss: 0.4005 - accuracy: 0.9027
20/65 [========>.....................] - ETA: 11s - loss: 0.3996 - accuracy: 0.9034
21/65 [========>.....................] - ETA: 11s - loss: 0.3997 - accuracy: 0.9032
22/65 [=========>....................] - ETA: 11s - loss: 0.4003 - accuracy: 0.9026
23/65 [=========>....................] - ETA: 10s - loss: 0.3987 - accuracy: 0.9030
24/65 [==========>...................] - ETA: 10s - loss: 0.4000 - accuracy: 0.9027
25/65 [==========>...................] - ETA: 10s - loss: 0.4009 - accuracy: 0.9020
26/65 [===========>..................] - ETA: 10s - loss: 0.4012 - accuracy: 0.9021
27/65 [===========>..................] - ETA: 9s - loss: 0.4013 - accuracy: 0.9018
28/65 [===========>..................] - ETA: 9s - loss: 0.4006 - accuracy: 0.9019
29/65 [============>.................] - ETA: 9s - loss: 0.3993 - accuracy: 0.9022
30/65 [============>.................] - ETA: 8s - loss: 0.3995 - accuracy: 0.9021
31/65 [=============>................] - ETA: 8s - loss: 0.4003 - accuracy: 0.9020
32/65 [=============>................] - ETA: 8s - loss: 0.3992 - accuracy: 0.9025
33/65 [==============>...............] - ETA: 8s - loss: 0.3981 - accuracy: 0.9029
34/65 [==============>...............] - ETA: 7s - loss: 0.3988 - accuracy: 0.9025
35/65 [===============>..............] - ETA: 7s - loss: 0.4000 - accuracy: 0.9025
36/65 [===============>..............] - ETA: 7s - loss: 0.4004 - accuracy: 0.9023
37/65 [================>.............] - ETA: 7s - loss: 0.3995 - accuracy: 0.9026
38/65 [================>.............] - ETA: 6s - loss: 0.3995 - accuracy: 0.9025
39/65 [=================>............] - ETA: 6s - loss: 0.3988 - accuracy: 0.9027
40/65 [=================>............] - ETA: 6s - loss: 0.3986 - accuracy: 0.9027
41/65 [=================>............] - ETA: 6s - loss: 0.3988 - accuracy: 0.9027
42/65 [==================>...........] - ETA: 5s - loss: 0.3980 - accuracy: 0.9030
43/65 [==================>...........] - ETA: 5s - loss: 0.3982 - accuracy: 0.9029
44/65 [===================>..........] - ETA: 5s - loss: 0.3980 - accuracy: 0.9028
45/65 [===================>..........] - ETA: 5s - loss: 0.3993 - accuracy: 0.9023
46/65 [====================>.........] - ETA: 4s - loss: 0.3990 - accuracy: 0.9023
47/65 [====================>.........] - ETA: 4s - loss: 0.3990 - accuracy: 0.9026
48/65 [=====================>........] - ETA: 4s - loss: 0.3987 - accuracy: 0.9025
49/65 [=====================>........] - ETA: 4s - loss: 0.3984 - accuracy: 0.9026
50/65 [======================>.......] - ETA: 3s - loss: 0.3985 - accuracy: 0.9025
51/65 [======================>.......] - ETA: 3s - loss: 0.3979 - accuracy: 0.9026
52/65 [=======================>......] - ETA: 3s - loss: 0.3974 - accuracy: 0.9026
53/65 [=======================>......] - ETA: 3s - loss: 0.3970 - accuracy: 0.9027
54/65 [=======================>......] - ETA: 2s - loss: 0.3966 - accuracy: 0.9027
55/65 [========================>.....] - ETA: 2s - loss: 0.3970 - accuracy: 0.9025
56/65 [========================>.....] - ETA: 2s - loss: 0.3958 - accuracy: 0.9028
57/65 [=========================>....] - ETA: 2s - loss: 0.3962 - accuracy: 0.9026
58/65 [=========================>....] - ETA: 1s - loss: 0.3968 - accuracy: 0.9023
59/65 [==========================>...] - ETA: 1s - loss: 0.3956 - accuracy: 0.9025
60/65 [==========================>...] - ETA: 1s - loss: 0.3953 - accuracy: 0.9027
61/65 [===========================>..] - ETA: 1s - loss: 0.3947 - accuracy: 0.9028
62/65 [===========================>..] - ETA: 0s - loss: 0.3940 - accuracy: 0.9031
63/65 [============================>.] - ETA: 0s - loss: 0.3939 - accuracy: 0.9032
64/65 [============================>.] - ETA: 0s - loss: 0.3939 - accuracy: 0.9031
65/65 [==============================] - ETA: 0s - loss: 0.3938 - accuracy: 0.9032
65/65 [==============================] - 17s 259ms/step - loss: 0.3938 - accuracy: 0.9032 - val_loss: 0.5010 - val_accuracy: 0.8707 - lr: 0.0030
Epoch 12/30
1/65 [..............................] - ETA: 30s - loss: 0.3973 - accuracy: 0.8984
2/65 [..............................] - ETA: 16s - loss: 0.3953 - accuracy: 0.8989
3/65 [>.............................] - ETA: 16s - loss: 0.3927 - accuracy: 0.9007
4/65 [>.............................] - ETA: 15s - loss: 0.3936 - accuracy: 0.9043
5/65 [=>............................] - ETA: 15s - loss: 0.3942 - accuracy: 0.9043
6/65 [=>............................] - ETA: 15s - loss: 0.3940 - accuracy: 0.9048
7/65 [==>...........................] - ETA: 14s - loss: 0.3993 - accuracy: 0.9039
8/65 [==>...........................] - ETA: 14s - loss: 0.4000 - accuracy: 0.9037
9/65 [===>..........................] - ETA: 14s - loss: 0.4035 - accuracy: 0.9033
10/65 [===>..........................] - ETA: 13s - loss: 0.4022 - accuracy: 0.9040
11/65 [====>.........................] - ETA: 13s - loss: 0.3993 - accuracy: 0.9046
12/65 [====>.........................] - ETA: 13s - loss: 0.3946 - accuracy: 0.9048
13/65 [=====>........................] - ETA: 13s - loss: 0.3937 - accuracy: 0.9046
14/65 [=====>........................] - ETA: 13s - loss: 0.3936 - accuracy: 0.9044
15/65 [=====>........................] - ETA: 12s - loss: 0.3964 - accuracy: 0.9037
16/65 [======>.......................] - ETA: 12s - loss: 0.3969 - accuracy: 0.9037
17/65 [======>.......................] - ETA: 12s - loss: 0.3956 - accuracy: 0.9041
18/65 [=======>......................] - ETA: 12s - loss: 0.3943 - accuracy: 0.9039
19/65 [=======>......................] - ETA: 11s - loss: 0.3918 - accuracy: 0.9049
20/65 [========>.....................] - ETA: 11s - loss: 0.3954 - accuracy: 0.9036
21/65 [========>.....................] - ETA: 11s - loss: 0.3943 - accuracy: 0.9039
22/65 [=========>....................] - ETA: 11s - loss: 0.3932 - accuracy: 0.9039
23/65 [=========>....................] - ETA: 10s - loss: 0.3912 - accuracy: 0.9047
24/65 [==========>...................] - ETA: 10s - loss: 0.3913 - accuracy: 0.9049
25/65 [==========>...................] - ETA: 10s - loss: 0.3919 - accuracy: 0.9045
26/65 [===========>..................] - ETA: 10s - loss: 0.3916 - accuracy: 0.9043
27/65 [===========>..................] - ETA: 9s - loss: 0.3929 - accuracy: 0.9040
28/65 [===========>..................] - ETA: 9s - loss: 0.3925 - accuracy: 0.9043
29/65 [============>.................] - ETA: 9s - loss: 0.3909 - accuracy: 0.9047
30/65 [============>.................] - ETA: 9s - loss: 0.3897 - accuracy: 0.9050
31/65 [=============>................] - ETA: 8s - loss: 0.3905 - accuracy: 0.9046
32/65 [=============>................] - ETA: 8s - loss: 0.3898 - accuracy: 0.9047
33/65 [==============>...............] - ETA: 8s - loss: 0.3907 - accuracy: 0.9048
34/65 [==============>...............] - ETA: 7s - loss: 0.3917 - accuracy: 0.9048
35/65 [===============>..............] - ETA: 7s - loss: 0.3921 - accuracy: 0.9046
36/65 [===============>..............] - ETA: 7s - loss: 0.3924 - accuracy: 0.9046
37/65 [================>.............] - ETA: 7s - loss: 0.3921 - accuracy: 0.9043
38/65 [================>.............] - ETA: 6s - loss: 0.3926 - accuracy: 0.9040
39/65 [=================>............] - ETA: 6s - loss: 0.3924 - accuracy: 0.9040
40/65 [=================>............] - ETA: 6s - loss: 0.3930 - accuracy: 0.9037
41/65 [=================>............] - ETA: 6s - loss: 0.3941 - accuracy: 0.9036
42/65 [==================>...........] - ETA: 5s - loss: 0.3944 - accuracy: 0.9038
43/65 [==================>...........] - ETA: 5s - loss: 0.3940 - accuracy: 0.9040
44/65 [===================>..........] - ETA: 5s - loss: 0.3947 - accuracy: 0.9035
45/65 [===================>..........] - ETA: 5s - loss: 0.3933 - accuracy: 0.9039
46/65 [====================>.........] - ETA: 4s - loss: 0.3932 - accuracy: 0.9039
47/65 [====================>.........] - ETA: 4s - loss: 0.3942 - accuracy: 0.9036
48/65 [=====================>........] - ETA: 4s - loss: 0.3930 - accuracy: 0.9039
49/65 [=====================>........] - ETA: 4s - loss: 0.3924 - accuracy: 0.9041
50/65 [======================>.......] - ETA: 3s - loss: 0.3923 - accuracy: 0.9042
51/65 [======================>.......] - ETA: 3s - loss: 0.3919 - accuracy: 0.9041
52/65 [=======================>......] - ETA: 3s - loss: 0.3910 - accuracy: 0.9042
53/65 [=======================>......] - ETA: 3s - loss: 0.3906 - accuracy: 0.9045
54/65 [=======================>......] - ETA: 2s - loss: 0.3901 - accuracy: 0.9045
55/65 [========================>.....] - ETA: 2s - loss: 0.3893 - accuracy: 0.9050
56/65 [========================>.....] - ETA: 2s - loss: 0.3885 - accuracy: 0.9053
57/65 [=========================>....] - ETA: 2s - loss: 0.3887 - accuracy: 0.9052
58/65 [=========================>....] - ETA: 1s - loss: 0.3881 - accuracy: 0.9054
59/65 [==========================>...] - ETA: 1s - loss: 0.3877 - accuracy: 0.9055
60/65 [==========================>...] - ETA: 1s - loss: 0.3874 - accuracy: 0.9054
61/65 [===========================>..] - ETA: 1s - loss: 0.3873 - accuracy: 0.9056
62/65 [===========================>..] - ETA: 0s - loss: 0.3868 - accuracy: 0.9058
63/65 [============================>.] - ETA: 0s - loss: 0.3872 - accuracy: 0.9057
64/65 [============================>.] - ETA: 0s - loss: 0.3870 - accuracy: 0.9056
65/65 [==============================] - ETA: 0s - loss: 0.3873 - accuracy: 0.9055
65/65 [==============================] - 17s 264ms/step - loss: 0.3873 - accuracy: 0.9055 - val_loss: 0.4682 - val_accuracy: 0.8814 - lr: 0.0030
Epoch 13/30
1/65 [..............................] - ETA: 32s - loss: 0.3803 - accuracy: 0.9180
2/65 [..............................] - ETA: 16s - loss: 0.4002 - accuracy: 0.9116
3/65 [>.............................] - ETA: 16s - loss: 0.4092 - accuracy: 0.9059
4/65 [>.............................] - ETA: 16s - loss: 0.3989 - accuracy: 0.9070
5/65 [=>............................] - ETA: 15s - loss: 0.4000 - accuracy: 0.9059
6/65 [=>............................] - ETA: 15s - loss: 0.3948 - accuracy: 0.9059
7/65 [==>...........................] - ETA: 15s - loss: 0.3953 - accuracy: 0.9050
8/65 [==>...........................] - ETA: 14s - loss: 0.3980 - accuracy: 0.9041
9/65 [===>..........................] - ETA: 14s - loss: 0.3973 - accuracy: 0.9044
10/65 [===>..........................] - ETA: 14s - loss: 0.3891 - accuracy: 0.9070
11/65 [====>.........................] - ETA: 13s - loss: 0.3872 - accuracy: 0.9066
12/65 [====>.........................] - ETA: 13s - loss: 0.3848 - accuracy: 0.9076
13/65 [=====>........................] - ETA: 13s - loss: 0.3847 - accuracy: 0.9072
14/65 [=====>........................] - ETA: 13s - loss: 0.3830 - accuracy: 0.9083
15/65 [=====>........................] - ETA: 12s - loss: 0.3844 - accuracy: 0.9078
16/65 [======>.......................] - ETA: 12s - loss: 0.3817 - accuracy: 0.9088
17/65 [======>.......................] - ETA: 12s - loss: 0.3823 - accuracy: 0.9082
18/65 [=======>......................] - ETA: 12s - loss: 0.3831 - accuracy: 0.9081
19/65 [=======>......................] - ETA: 11s - loss: 0.3825 - accuracy: 0.9087
20/65 [========>.....................] - ETA: 11s - loss: 0.3818 - accuracy: 0.9084
21/65 [========>.....................] - ETA: 11s - loss: 0.3826 - accuracy: 0.9083
22/65 [=========>....................] - ETA: 10s - loss: 0.3843 - accuracy: 0.9080
23/65 [=========>....................] - ETA: 10s - loss: 0.3850 - accuracy: 0.9079
24/65 [==========>...................] - ETA: 10s - loss: 0.3850 - accuracy: 0.9079
25/65 [==========>...................] - ETA: 10s - loss: 0.3843 - accuracy: 0.9082
26/65 [===========>..................] - ETA: 9s - loss: 0.3837 - accuracy: 0.9082
27/65 [===========>..................] - ETA: 9s - loss: 0.3839 - accuracy: 0.9081
28/65 [===========>..................] - ETA: 9s - loss: 0.3828 - accuracy: 0.9084
29/65 [============>.................] - ETA: 9s - loss: 0.3845 - accuracy: 0.9079
30/65 [============>.................] - ETA: 8s - loss: 0.3844 - accuracy: 0.9079
31/65 [=============>................] - ETA: 8s - loss: 0.3847 - accuracy: 0.9076
32/65 [=============>................] - ETA: 8s - loss: 0.3839 - accuracy: 0.9075
33/65 [==============>...............] - ETA: 8s - loss: 0.3839 - accuracy: 0.9070
34/65 [==============>...............] - ETA: 7s - loss: 0.3846 - accuracy: 0.9069
35/65 [===============>..............] - ETA: 7s - loss: 0.3847 - accuracy: 0.9068
36/65 [===============>..............] - ETA: 7s - loss: 0.3864 - accuracy: 0.9062
37/65 [================>.............] - ETA: 7s - loss: 0.3866 - accuracy: 0.9060
38/65 [================>.............] - ETA: 6s - loss: 0.3860 - accuracy: 0.9060
39/65 [=================>............] - ETA: 6s - loss: 0.3845 - accuracy: 0.9062
40/65 [=================>............] - ETA: 6s - loss: 0.3851 - accuracy: 0.9060
41/65 [=================>............] - ETA: 6s - loss: 0.3850 - accuracy: 0.9059
42/65 [==================>...........] - ETA: 5s - loss: 0.3848 - accuracy: 0.9059
43/65 [==================>...........] - ETA: 5s - loss: 0.3849 - accuracy: 0.9060
44/65 [===================>..........] - ETA: 5s - loss: 0.3855 - accuracy: 0.9059
45/65 [===================>..........] - ETA: 5s - loss: 0.3857 - accuracy: 0.9061
46/65 [====================>.........] - ETA: 4s - loss: 0.3864 - accuracy: 0.9058
47/65 [====================>.........] - ETA: 4s - loss: 0.3858 - accuracy: 0.9059
48/65 [=====================>........] - ETA: 4s - loss: 0.3858 - accuracy: 0.9058
49/65 [=====================>........] - ETA: 4s - loss: 0.3853 - accuracy: 0.9059
50/65 [======================>.......] - ETA: 3s - loss: 0.3850 - accuracy: 0.9058
51/65 [======================>.......] - ETA: 3s - loss: 0.3853 - accuracy: 0.9057
52/65 [=======================>......] - ETA: 3s - loss: 0.3851 - accuracy: 0.9057
53/65 [=======================>......] - ETA: 3s - loss: 0.3846 - accuracy: 0.9057
54/65 [=======================>......] - ETA: 2s - loss: 0.3844 - accuracy: 0.9057
55/65 [========================>.....] - ETA: 2s - loss: 0.3834 - accuracy: 0.9061
56/65 [========================>.....] - ETA: 2s - loss: 0.3825 - accuracy: 0.9064
57/65 [=========================>....] - ETA: 2s - loss: 0.3829 - accuracy: 0.9061
58/65 [=========================>....] - ETA: 1s - loss: 0.3827 - accuracy: 0.9062
59/65 [==========================>...] - ETA: 1s - loss: 0.3823 - accuracy: 0.9064
60/65 [==========================>...] - ETA: 1s - loss: 0.3820 - accuracy: 0.9063
61/65 [===========================>..] - ETA: 1s - loss: 0.3821 - accuracy: 0.9063
62/65 [===========================>..] - ETA: 0s - loss: 0.3818 - accuracy: 0.9064
63/65 [============================>.] - ETA: 0s - loss: 0.3817 - accuracy: 0.9063
64/65 [============================>.] - ETA: 0s - loss: 0.3813 - accuracy: 0.9063
65/65 [==============================] - ETA: 0s - loss: 0.3813 - accuracy: 0.9063
65/65 [==============================] - 17s 259ms/step - loss: 0.3813 - accuracy: 0.9063 - val_loss: 0.4866 - val_accuracy: 0.8763 - lr: 0.0030
Epoch 14/30
1/65 [..............................] - ETA: 31s - loss: 0.3975 - accuracy: 0.9053
2/65 [..............................] - ETA: 16s - loss: 0.4054 - accuracy: 0.9009
3/65 [>.............................] - ETA: 16s - loss: 0.3934 - accuracy: 0.9030
4/65 [>.............................] - ETA: 15s - loss: 0.3918 - accuracy: 0.9031
5/65 [=>............................] - ETA: 15s - loss: 0.3948 - accuracy: 0.9018
6/65 [=>............................] - ETA: 15s - loss: 0.3840 - accuracy: 0.9049
7/65 [==>...........................] - ETA: 15s - loss: 0.3873 - accuracy: 0.9051
8/65 [==>...........................] - ETA: 14s - loss: 0.3922 - accuracy: 0.9032
9/65 [===>..........................] - ETA: 14s - loss: 0.3902 - accuracy: 0.9049
10/65 [===>..........................] - ETA: 14s - loss: 0.3859 - accuracy: 0.9061
11/65 [====>.........................] - ETA: 14s - loss: 0.3829 - accuracy: 0.9077
12/65 [====>.........................] - ETA: 13s - loss: 0.3791 - accuracy: 0.9084
13/65 [=====>........................] - ETA: 13s - loss: 0.3790 - accuracy: 0.9084
14/65 [=====>........................] - ETA: 13s - loss: 0.3792 - accuracy: 0.9088
15/65 [=====>........................] - ETA: 12s - loss: 0.3794 - accuracy: 0.9080
16/65 [======>.......................] - ETA: 12s - loss: 0.3801 - accuracy: 0.9082
17/65 [======>.......................] - ETA: 12s - loss: 0.3812 - accuracy: 0.9078
18/65 [=======>......................] - ETA: 12s - loss: 0.3797 - accuracy: 0.9083
19/65 [=======>......................] - ETA: 11s - loss: 0.3809 - accuracy: 0.9075
20/65 [========>.....................] - ETA: 11s - loss: 0.3812 - accuracy: 0.9077
21/65 [========>.....................] - ETA: 11s - loss: 0.3812 - accuracy: 0.9079
22/65 [=========>....................] - ETA: 11s - loss: 0.3822 - accuracy: 0.9077
23/65 [=========>....................] - ETA: 10s - loss: 0.3798 - accuracy: 0.9082
24/65 [==========>...................] - ETA: 10s - loss: 0.3797 - accuracy: 0.9082
25/65 [==========>...................] - ETA: 10s - loss: 0.3794 - accuracy: 0.9081
26/65 [===========>..................] - ETA: 10s - loss: 0.3800 - accuracy: 0.9076
27/65 [===========>..................] - ETA: 9s - loss: 0.3791 - accuracy: 0.9080
28/65 [===========>..................] - ETA: 9s - loss: 0.3785 - accuracy: 0.9081
29/65 [============>.................] - ETA: 9s - loss: 0.3790 - accuracy: 0.9080
30/65 [============>.................] - ETA: 9s - loss: 0.3791 - accuracy: 0.9078
31/65 [=============>................] - ETA: 8s - loss: 0.3789 - accuracy: 0.9076
32/65 [=============>................] - ETA: 8s - loss: 0.3804 - accuracy: 0.9073
33/65 [==============>...............] - ETA: 8s - loss: 0.3808 - accuracy: 0.9070
34/65 [==============>...............] - ETA: 7s - loss: 0.3810 - accuracy: 0.9069
35/65 [===============>..............] - ETA: 7s - loss: 0.3802 - accuracy: 0.9071
36/65 [===============>..............] - ETA: 7s - loss: 0.3820 - accuracy: 0.9065
37/65 [================>.............] - ETA: 7s - loss: 0.3819 - accuracy: 0.9066
38/65 [================>.............] - ETA: 6s - loss: 0.3818 - accuracy: 0.9063
39/65 [=================>............] - ETA: 6s - loss: 0.3811 - accuracy: 0.9064
40/65 [=================>............] - ETA: 6s - loss: 0.3824 - accuracy: 0.9062
41/65 [=================>............] - ETA: 6s - loss: 0.3820 - accuracy: 0.9063
42/65 [==================>...........] - ETA: 5s - loss: 0.3817 - accuracy: 0.9064
43/65 [==================>...........] - ETA: 5s - loss: 0.3823 - accuracy: 0.9064
44/65 [===================>..........] - ETA: 5s - loss: 0.3825 - accuracy: 0.9064
45/65 [===================>..........] - ETA: 5s - loss: 0.3825 - accuracy: 0.9066
46/65 [====================>.........] - ETA: 4s - loss: 0.3830 - accuracy: 0.9065
47/65 [====================>.........] - ETA: 4s - loss: 0.3823 - accuracy: 0.9067
48/65 [=====================>........] - ETA: 4s - loss: 0.3809 - accuracy: 0.9069
49/65 [=====================>........] - ETA: 4s - loss: 0.3807 - accuracy: 0.9070
50/65 [======================>.......] - ETA: 3s - loss: 0.3799 - accuracy: 0.9071
51/65 [======================>.......] - ETA: 3s - loss: 0.3791 - accuracy: 0.9073
52/65 [=======================>......] - ETA: 3s - loss: 0.3787 - accuracy: 0.9073
53/65 [=======================>......] - ETA: 3s - loss: 0.3776 - accuracy: 0.9076
54/65 [=======================>......] - ETA: 2s - loss: 0.3768 - accuracy: 0.9077
55/65 [========================>.....] - ETA: 2s - loss: 0.3765 - accuracy: 0.9078
56/65 [========================>.....] - ETA: 2s - loss: 0.3761 - accuracy: 0.9080
57/65 [=========================>....] - ETA: 2s - loss: 0.3760 - accuracy: 0.9080
58/65 [=========================>....] - ETA: 1s - loss: 0.3756 - accuracy: 0.9081
59/65 [==========================>...] - ETA: 1s - loss: 0.3757 - accuracy: 0.9081
60/65 [==========================>...] - ETA: 1s - loss: 0.3751 - accuracy: 0.9082
61/65 [===========================>..] - ETA: 1s - loss: 0.3754 - accuracy: 0.9081
62/65 [===========================>..] - ETA: 0s - loss: 0.3755 - accuracy: 0.9080
63/65 [============================>.] - ETA: 0s - loss: 0.3754 - accuracy: 0.9081
64/65 [============================>.] - ETA: 0s - loss: 0.3754 - accuracy: 0.9080
65/65 [==============================] - ETA: 0s - loss: 0.3752 - accuracy: 0.9081
65/65 [==============================] - 17s 263ms/step - loss: 0.3752 - accuracy: 0.9081 - val_loss: 0.4921 - val_accuracy: 0.8720 - lr: 0.0030
Epoch 15/30
1/65 [..............................] - ETA: 30s - loss: 0.3828 - accuracy: 0.9199
2/65 [..............................] - ETA: 16s - loss: 0.3880 - accuracy: 0.9106
3/65 [>.............................] - ETA: 16s - loss: 0.3879 - accuracy: 0.9076
4/65 [>.............................] - ETA: 15s - loss: 0.3816 - accuracy: 0.9099
5/65 [=>............................] - ETA: 15s - loss: 0.3894 - accuracy: 0.9086
6/65 [=>............................] - ETA: 15s - loss: 0.3903 - accuracy: 0.9080
7/65 [==>...........................] - ETA: 15s - loss: 0.3914 - accuracy: 0.9075
8/65 [==>...........................] - ETA: 14s - loss: 0.3882 - accuracy: 0.9078
9/65 [===>..........................] - ETA: 14s - loss: 0.3850 - accuracy: 0.9086
10/65 [===>..........................] - ETA: 14s - loss: 0.3829 - accuracy: 0.9083
11/65 [====>.........................] - ETA: 13s - loss: 0.3808 - accuracy: 0.9084
12/65 [====>.........................] - ETA: 13s - loss: 0.3818 - accuracy: 0.9084
13/65 [=====>........................] - ETA: 13s - loss: 0.3784 - accuracy: 0.9095
14/65 [=====>........................] - ETA: 12s - loss: 0.3733 - accuracy: 0.9106
15/65 [=====>........................] - ETA: 12s - loss: 0.3742 - accuracy: 0.9102
16/65 [======>.......................] - ETA: 12s - loss: 0.3742 - accuracy: 0.9100
17/65 [======>.......................] - ETA: 12s - loss: 0.3719 - accuracy: 0.9111
18/65 [=======>......................] - ETA: 11s - loss: 0.3710 - accuracy: 0.9113
19/65 [=======>......................] - ETA: 11s - loss: 0.3724 - accuracy: 0.9109
20/65 [========>.....................] - ETA: 11s - loss: 0.3706 - accuracy: 0.9112
21/65 [========>.....................] - ETA: 11s - loss: 0.3732 - accuracy: 0.9102
22/65 [=========>....................] - ETA: 10s - loss: 0.3734 - accuracy: 0.9103
23/65 [=========>....................] - ETA: 10s - loss: 0.3744 - accuracy: 0.9099
24/65 [==========>...................] - ETA: 10s - loss: 0.3744 - accuracy: 0.9102
25/65 [==========>...................] - ETA: 10s - loss: 0.3744 - accuracy: 0.9100
26/65 [===========>..................] - ETA: 9s - loss: 0.3764 - accuracy: 0.9096
27/65 [===========>..................] - ETA: 9s - loss: 0.3757 - accuracy: 0.9097
28/65 [===========>..................] - ETA: 9s - loss: 0.3759 - accuracy: 0.9093
29/65 [============>.................] - ETA: 9s - loss: 0.3764 - accuracy: 0.9092
30/65 [============>.................] - ETA: 8s - loss: 0.3788 - accuracy: 0.9082
31/65 [=============>................] - ETA: 8s - loss: 0.3772 - accuracy: 0.9085
32/65 [=============>................] - ETA: 8s - loss: 0.3768 - accuracy: 0.9088
33/65 [==============>...............] - ETA: 8s - loss: 0.3767 - accuracy: 0.9088
34/65 [==============>...............] - ETA: 7s - loss: 0.3765 - accuracy: 0.9089
35/65 [===============>..............] - ETA: 7s - loss: 0.3763 - accuracy: 0.9086
36/65 [===============>..............] - ETA: 7s - loss: 0.3777 - accuracy: 0.9080
37/65 [================>.............] - ETA: 7s - loss: 0.3780 - accuracy: 0.9082
38/65 [================>.............] - ETA: 6s - loss: 0.3769 - accuracy: 0.9084
39/65 [=================>............] - ETA: 6s - loss: 0.3768 - accuracy: 0.9082
40/65 [=================>............] - ETA: 6s - loss: 0.3775 - accuracy: 0.9077
41/65 [=================>............] - ETA: 6s - loss: 0.3770 - accuracy: 0.9082
42/65 [==================>...........] - ETA: 5s - loss: 0.3769 - accuracy: 0.9082
43/65 [==================>...........] - ETA: 5s - loss: 0.3773 - accuracy: 0.9082
44/65 [===================>..........] - ETA: 5s - loss: 0.3771 - accuracy: 0.9080
45/65 [===================>..........] - ETA: 5s - loss: 0.3767 - accuracy: 0.9080
46/65 [====================>.........] - ETA: 4s - loss: 0.3760 - accuracy: 0.9082
47/65 [====================>.........] - ETA: 4s - loss: 0.3763 - accuracy: 0.9079
48/65 [=====================>........] - ETA: 4s - loss: 0.3759 - accuracy: 0.9081
49/65 [=====================>........] - ETA: 4s - loss: 0.3751 - accuracy: 0.9083
50/65 [======================>.......] - ETA: 3s - loss: 0.3741 - accuracy: 0.9084
51/65 [======================>.......] - ETA: 3s - loss: 0.3733 - accuracy: 0.9086
52/65 [=======================>......] - ETA: 3s - loss: 0.3733 - accuracy: 0.9083
53/65 [=======================>......] - ETA: 3s - loss: 0.3734 - accuracy: 0.9083
54/65 [=======================>......] - ETA: 2s - loss: 0.3728 - accuracy: 0.9084
55/65 [========================>.....] - ETA: 2s - loss: 0.3724 - accuracy: 0.9085
56/65 [========================>.....] - ETA: 2s - loss: 0.3722 - accuracy: 0.9086
57/65 [=========================>....] - ETA: 2s - loss: 0.3719 - accuracy: 0.9088
58/65 [=========================>....] - ETA: 1s - loss: 0.3721 - accuracy: 0.9087
59/65 [==========================>...] - ETA: 1s - loss: 0.3719 - accuracy: 0.9088
60/65 [==========================>...] - ETA: 1s - loss: 0.3710 - accuracy: 0.9091
61/65 [===========================>..] - ETA: 1s - loss: 0.3705 - accuracy: 0.9091
62/65 [===========================>..] - ETA: 0s - loss: 0.3706 - accuracy: 0.9090
63/65 [============================>.] - ETA: 0s - loss: 0.3709 - accuracy: 0.9091
64/65 [============================>.] - ETA: 0s - loss: 0.3710 - accuracy: 0.9088
65/65 [==============================] - ETA: 0s - loss: 0.3709 - accuracy: 0.9088
Epoch 15: ReduceLROnPlateau reducing learning rate to 0.001500000013038516.
65/65 [==============================] - 17s 260ms/step - loss: 0.3709 - accuracy: 0.9088 - val_loss: 0.4841 - val_accuracy: 0.8788 - lr: 0.0030
Epoch 16/30
1/65 [..............................] - ETA: 31s - loss: 0.4125 - accuracy: 0.9004
2/65 [..............................] - ETA: 16s - loss: 0.4044 - accuracy: 0.9009
3/65 [>.............................] - ETA: 15s - loss: 0.3980 - accuracy: 0.9027
4/65 [>.............................] - ETA: 15s - loss: 0.3844 - accuracy: 0.9077
5/65 [=>............................] - ETA: 15s - loss: 0.3781 - accuracy: 0.9102
6/65 [=>............................] - ETA: 15s - loss: 0.3830 - accuracy: 0.9084
7/65 [==>...........................] - ETA: 15s - loss: 0.3770 - accuracy: 0.9096
8/65 [==>...........................] - ETA: 14s - loss: 0.3814 - accuracy: 0.9086
9/65 [===>..........................] - ETA: 14s - loss: 0.3789 - accuracy: 0.9087
10/65 [===>..........................] - ETA: 14s - loss: 0.3789 - accuracy: 0.9077
11/65 [====>.........................] - ETA: 14s - loss: 0.3737 - accuracy: 0.9092
12/65 [====>.........................] - ETA: 13s - loss: 0.3714 - accuracy: 0.9104
13/65 [=====>........................] - ETA: 13s - loss: 0.3716 - accuracy: 0.9103
14/65 [=====>........................] - ETA: 13s - loss: 0.3680 - accuracy: 0.9116
15/65 [=====>........................] - ETA: 12s - loss: 0.3685 - accuracy: 0.9113
16/65 [======>.......................] - ETA: 12s - loss: 0.3698 - accuracy: 0.9111
17/65 [======>.......................] - ETA: 12s - loss: 0.3698 - accuracy: 0.9112
18/65 [=======>......................] - ETA: 12s - loss: 0.3679 - accuracy: 0.9123
19/65 [=======>......................] - ETA: 11s - loss: 0.3681 - accuracy: 0.9121
20/65 [========>.....................] - ETA: 11s - loss: 0.3679 - accuracy: 0.9121
21/65 [========>.....................] - ETA: 11s - loss: 0.3680 - accuracy: 0.9118
22/65 [=========>....................] - ETA: 11s - loss: 0.3674 - accuracy: 0.9120
23/65 [=========>....................] - ETA: 10s - loss: 0.3667 - accuracy: 0.9120
24/65 [==========>...................] - ETA: 10s - loss: 0.3653 - accuracy: 0.9125
25/65 [==========>...................] - ETA: 10s - loss: 0.3643 - accuracy: 0.9128
26/65 [===========>..................] - ETA: 10s - loss: 0.3626 - accuracy: 0.9137
27/65 [===========>..................] - ETA: 9s - loss: 0.3623 - accuracy: 0.9137
28/65 [===========>..................] - ETA: 9s - loss: 0.3616 - accuracy: 0.9138
29/65 [============>.................] - ETA: 9s - loss: 0.3625 - accuracy: 0.9133
30/65 [============>.................] - ETA: 9s - loss: 0.3624 - accuracy: 0.9130
31/65 [=============>................] - ETA: 8s - loss: 0.3624 - accuracy: 0.9128
32/65 [=============>................] - ETA: 8s - loss: 0.3641 - accuracy: 0.9124
33/65 [==============>...............] - ETA: 8s - loss: 0.3651 - accuracy: 0.9121
34/65 [==============>...............] - ETA: 8s - loss: 0.3638 - accuracy: 0.9123
35/65 [===============>..............] - ETA: 7s - loss: 0.3633 - accuracy: 0.9126
36/65 [===============>..............] - ETA: 7s - loss: 0.3644 - accuracy: 0.9121
37/65 [================>.............] - ETA: 7s - loss: 0.3641 - accuracy: 0.9119
38/65 [================>.............] - ETA: 6s - loss: 0.3634 - accuracy: 0.9122
39/65 [=================>............] - ETA: 6s - loss: 0.3625 - accuracy: 0.9124
40/65 [=================>............] - ETA: 6s - loss: 0.3626 - accuracy: 0.9122
41/65 [=================>............] - ETA: 6s - loss: 0.3623 - accuracy: 0.9123
42/65 [==================>...........] - ETA: 5s - loss: 0.3622 - accuracy: 0.9126
43/65 [==================>...........] - ETA: 5s - loss: 0.3618 - accuracy: 0.9127
44/65 [===================>..........] - ETA: 5s - loss: 0.3613 - accuracy: 0.9130
45/65 [===================>..........] - ETA: 5s - loss: 0.3609 - accuracy: 0.9130
46/65 [====================>.........] - ETA: 4s - loss: 0.3615 - accuracy: 0.9129
47/65 [====================>.........] - ETA: 4s - loss: 0.3608 - accuracy: 0.9130
48/65 [=====================>........] - ETA: 4s - loss: 0.3616 - accuracy: 0.9127
49/65 [=====================>........] - ETA: 4s - loss: 0.3611 - accuracy: 0.9128
50/65 [======================>.......] - ETA: 3s - loss: 0.3608 - accuracy: 0.9128
51/65 [======================>.......] - ETA: 3s - loss: 0.3601 - accuracy: 0.9130
52/65 [=======================>......] - ETA: 3s - loss: 0.3597 - accuracy: 0.9130
53/65 [=======================>......] - ETA: 3s - loss: 0.3586 - accuracy: 0.9135
54/65 [=======================>......] - ETA: 2s - loss: 0.3582 - accuracy: 0.9135
55/65 [========================>.....] - ETA: 2s - loss: 0.3571 - accuracy: 0.9138
56/65 [========================>.....] - ETA: 2s - loss: 0.3565 - accuracy: 0.9140
57/65 [=========================>....] - ETA: 2s - loss: 0.3566 - accuracy: 0.9141
58/65 [=========================>....] - ETA: 1s - loss: 0.3558 - accuracy: 0.9143
59/65 [==========================>...] - ETA: 1s - loss: 0.3554 - accuracy: 0.9145
60/65 [==========================>...] - ETA: 1s - loss: 0.3554 - accuracy: 0.9145
61/65 [===========================>..] - ETA: 1s - loss: 0.3548 - accuracy: 0.9146
62/65 [===========================>..] - ETA: 0s - loss: 0.3544 - accuracy: 0.9147
63/65 [============================>.] - ETA: 0s - loss: 0.3541 - accuracy: 0.9148
64/65 [============================>.] - ETA: 0s - loss: 0.3539 - accuracy: 0.9149
65/65 [==============================] - ETA: 0s - loss: 0.3539 - accuracy: 0.9149
65/65 [==============================] - 17s 262ms/step - loss: 0.3539 - accuracy: 0.9149 - val_loss: 0.4557 - val_accuracy: 0.8849 - lr: 0.0015
Epoch 17/30
1/65 [..............................] - ETA: 30s - loss: 0.3552 - accuracy: 0.9111
2/65 [..............................] - ETA: 16s - loss: 0.3582 - accuracy: 0.9160
3/65 [>.............................] - ETA: 15s - loss: 0.3481 - accuracy: 0.9160
4/65 [>.............................] - ETA: 15s - loss: 0.3728 - accuracy: 0.9104
5/65 [=>............................] - ETA: 15s - loss: 0.3765 - accuracy: 0.9100
6/65 [=>............................] - ETA: 15s - loss: 0.3745 - accuracy: 0.9106
7/65 [==>...........................] - ETA: 14s - loss: 0.3677 - accuracy: 0.9129
8/65 [==>...........................] - ETA: 14s - loss: 0.3627 - accuracy: 0.9141
9/65 [===>..........................] - ETA: 14s - loss: 0.3613 - accuracy: 0.9138
10/65 [===>..........................] - ETA: 13s - loss: 0.3593 - accuracy: 0.9146
11/65 [====>.........................] - ETA: 13s - loss: 0.3578 - accuracy: 0.9148
12/65 [====>.........................] - ETA: 13s - loss: 0.3569 - accuracy: 0.9151
13/65 [=====>........................] - ETA: 13s - loss: 0.3535 - accuracy: 0.9161
14/65 [=====>........................] - ETA: 12s - loss: 0.3515 - accuracy: 0.9167
15/65 [=====>........................] - ETA: 12s - loss: 0.3519 - accuracy: 0.9169
16/65 [======>.......................] - ETA: 12s - loss: 0.3542 - accuracy: 0.9159
17/65 [======>.......................] - ETA: 12s - loss: 0.3539 - accuracy: 0.9158
18/65 [=======>......................] - ETA: 11s - loss: 0.3525 - accuracy: 0.9162
19/65 [=======>......................] - ETA: 11s - loss: 0.3514 - accuracy: 0.9170
20/65 [========>.....................] - ETA: 11s - loss: 0.3527 - accuracy: 0.9167
21/65 [========>.....................] - ETA: 11s - loss: 0.3534 - accuracy: 0.9162
22/65 [=========>....................] - ETA: 10s - loss: 0.3543 - accuracy: 0.9161
23/65 [=========>....................] - ETA: 10s - loss: 0.3547 - accuracy: 0.9156
24/65 [==========>...................] - ETA: 10s - loss: 0.3530 - accuracy: 0.9162
25/65 [==========>...................] - ETA: 10s - loss: 0.3535 - accuracy: 0.9160
26/65 [===========>..................] - ETA: 9s - loss: 0.3543 - accuracy: 0.9156
27/65 [===========>..................] - ETA: 9s - loss: 0.3530 - accuracy: 0.9157
28/65 [===========>..................] - ETA: 9s - loss: 0.3534 - accuracy: 0.9154
29/65 [============>.................] - ETA: 9s - loss: 0.3516 - accuracy: 0.9157
30/65 [============>.................] - ETA: 8s - loss: 0.3527 - accuracy: 0.9155
31/65 [=============>................] - ETA: 8s - loss: 0.3519 - accuracy: 0.9159
32/65 [=============>................] - ETA: 8s - loss: 0.3524 - accuracy: 0.9159
33/65 [==============>...............] - ETA: 8s - loss: 0.3505 - accuracy: 0.9165
34/65 [==============>...............] - ETA: 7s - loss: 0.3500 - accuracy: 0.9163
35/65 [===============>..............] - ETA: 7s - loss: 0.3525 - accuracy: 0.9156
36/65 [===============>..............] - ETA: 7s - loss: 0.3526 - accuracy: 0.9154
37/65 [================>.............] - ETA: 7s - loss: 0.3538 - accuracy: 0.9150
38/65 [================>.............] - ETA: 6s - loss: 0.3540 - accuracy: 0.9149
39/65 [=================>............] - ETA: 6s - loss: 0.3544 - accuracy: 0.9147
40/65 [=================>............] - ETA: 6s - loss: 0.3537 - accuracy: 0.9149
41/65 [=================>............] - ETA: 6s - loss: 0.3547 - accuracy: 0.9146
42/65 [==================>...........] - ETA: 5s - loss: 0.3540 - accuracy: 0.9148
43/65 [==================>...........] - ETA: 5s - loss: 0.3542 - accuracy: 0.9149
44/65 [===================>..........] - ETA: 5s - loss: 0.3550 - accuracy: 0.9146
45/65 [===================>..........] - ETA: 5s - loss: 0.3537 - accuracy: 0.9151
46/65 [====================>.........] - ETA: 4s - loss: 0.3539 - accuracy: 0.9149
47/65 [====================>.........] - ETA: 4s - loss: 0.3525 - accuracy: 0.9153
48/65 [=====================>........] - ETA: 4s - loss: 0.3515 - accuracy: 0.9155
49/65 [=====================>........] - ETA: 4s - loss: 0.3514 - accuracy: 0.9155
50/65 [======================>.......] - ETA: 3s - loss: 0.3518 - accuracy: 0.9152
51/65 [======================>.......] - ETA: 3s - loss: 0.3512 - accuracy: 0.9152
52/65 [=======================>......] - ETA: 3s - loss: 0.3508 - accuracy: 0.9152
53/65 [=======================>......] - ETA: 3s - loss: 0.3503 - accuracy: 0.9153
54/65 [=======================>......] - ETA: 2s - loss: 0.3497 - accuracy: 0.9155
55/65 [========================>.....] - ETA: 2s - loss: 0.3495 - accuracy: 0.9156
56/65 [========================>.....] - ETA: 2s - loss: 0.3493 - accuracy: 0.9156
57/65 [=========================>....] - ETA: 2s - loss: 0.3496 - accuracy: 0.9157
58/65 [=========================>....] - ETA: 1s - loss: 0.3502 - accuracy: 0.9155
59/65 [==========================>...] - ETA: 1s - loss: 0.3499 - accuracy: 0.9157
60/65 [==========================>...] - ETA: 1s - loss: 0.3498 - accuracy: 0.9156
61/65 [===========================>..] - ETA: 1s - loss: 0.3495 - accuracy: 0.9155
62/65 [===========================>..] - ETA: 0s - loss: 0.3495 - accuracy: 0.9155
63/65 [============================>.] - ETA: 0s - loss: 0.3487 - accuracy: 0.9157
64/65 [============================>.] - ETA: 0s - loss: 0.3487 - accuracy: 0.9158
65/65 [==============================] - ETA: 0s - loss: 0.3485 - accuracy: 0.9158
65/65 [==============================] - 17s 260ms/step - loss: 0.3485 - accuracy: 0.9158 - val_loss: 0.4501 - val_accuracy: 0.8859 - lr: 0.0015
Epoch 18/30
1/65 [..............................] - ETA: 32s - loss: 0.3246 - accuracy: 0.9209
2/65 [..............................] - ETA: 16s - loss: 0.3462 - accuracy: 0.9214
3/65 [>.............................] - ETA: 15s - loss: 0.3454 - accuracy: 0.9189
4/65 [>.............................] - ETA: 15s - loss: 0.3553 - accuracy: 0.9167
5/65 [=>............................] - ETA: 15s - loss: 0.3575 - accuracy: 0.9143
6/65 [=>............................] - ETA: 15s - loss: 0.3556 - accuracy: 0.9129
7/65 [==>...........................] - ETA: 14s - loss: 0.3598 - accuracy: 0.9120
8/65 [==>...........................] - ETA: 14s - loss: 0.3592 - accuracy: 0.9143
9/65 [===>..........................] - ETA: 14s - loss: 0.3607 - accuracy: 0.9145
10/65 [===>..........................] - ETA: 14s - loss: 0.3584 - accuracy: 0.9149
11/65 [====>.........................] - ETA: 13s - loss: 0.3572 - accuracy: 0.9145
12/65 [====>.........................] - ETA: 13s - loss: 0.3573 - accuracy: 0.9153
13/65 [=====>........................] - ETA: 13s - loss: 0.3551 - accuracy: 0.9162
14/65 [=====>........................] - ETA: 13s - loss: 0.3544 - accuracy: 0.9169
15/65 [=====>........................] - ETA: 12s - loss: 0.3512 - accuracy: 0.9173
16/65 [======>.......................] - ETA: 12s - loss: 0.3491 - accuracy: 0.9179
17/65 [======>.......................] - ETA: 12s - loss: 0.3487 - accuracy: 0.9179
18/65 [=======>......................] - ETA: 12s - loss: 0.3470 - accuracy: 0.9182
19/65 [=======>......................] - ETA: 11s - loss: 0.3466 - accuracy: 0.9184
20/65 [========>.....................] - ETA: 11s - loss: 0.3488 - accuracy: 0.9179
21/65 [========>.....................] - ETA: 11s - loss: 0.3503 - accuracy: 0.9178
22/65 [=========>....................] - ETA: 10s - loss: 0.3497 - accuracy: 0.9177
23/65 [=========>....................] - ETA: 10s - loss: 0.3480 - accuracy: 0.9179
24/65 [==========>...................] - ETA: 10s - loss: 0.3476 - accuracy: 0.9180
25/65 [==========>...................] - ETA: 10s - loss: 0.3486 - accuracy: 0.9179
26/65 [===========>..................] - ETA: 9s - loss: 0.3494 - accuracy: 0.9175
27/65 [===========>..................] - ETA: 9s - loss: 0.3485 - accuracy: 0.9177
28/65 [===========>..................] - ETA: 9s - loss: 0.3491 - accuracy: 0.9172
29/65 [============>.................] - ETA: 9s - loss: 0.3478 - accuracy: 0.9176
30/65 [============>.................] - ETA: 8s - loss: 0.3481 - accuracy: 0.9176
31/65 [=============>................] - ETA: 8s - loss: 0.3478 - accuracy: 0.9176
32/65 [=============>................] - ETA: 8s - loss: 0.3483 - accuracy: 0.9172
33/65 [==============>...............] - ETA: 8s - loss: 0.3500 - accuracy: 0.9168
34/65 [==============>...............] - ETA: 7s - loss: 0.3516 - accuracy: 0.9162
35/65 [===============>..............] - ETA: 7s - loss: 0.3499 - accuracy: 0.9167
36/65 [===============>..............] - ETA: 7s - loss: 0.3519 - accuracy: 0.9159
37/65 [================>.............] - ETA: 7s - loss: 0.3516 - accuracy: 0.9160
38/65 [================>.............] - ETA: 6s - loss: 0.3515 - accuracy: 0.9159
39/65 [=================>............] - ETA: 6s - loss: 0.3514 - accuracy: 0.9159
40/65 [=================>............] - ETA: 6s - loss: 0.3513 - accuracy: 0.9158
41/65 [=================>............] - ETA: 6s - loss: 0.3509 - accuracy: 0.9158
42/65 [==================>...........] - ETA: 5s - loss: 0.3516 - accuracy: 0.9158
43/65 [==================>...........] - ETA: 5s - loss: 0.3513 - accuracy: 0.9159
44/65 [===================>..........] - ETA: 5s - loss: 0.3514 - accuracy: 0.9158
45/65 [===================>..........] - ETA: 5s - loss: 0.3508 - accuracy: 0.9161
46/65 [====================>.........] - ETA: 4s - loss: 0.3516 - accuracy: 0.9158
47/65 [====================>.........] - ETA: 4s - loss: 0.3506 - accuracy: 0.9162
48/65 [=====================>........] - ETA: 4s - loss: 0.3505 - accuracy: 0.9164
49/65 [=====================>........] - ETA: 4s - loss: 0.3505 - accuracy: 0.9165
50/65 [======================>.......] - ETA: 3s - loss: 0.3495 - accuracy: 0.9168
51/65 [======================>.......] - ETA: 3s - loss: 0.3495 - accuracy: 0.9167
52/65 [=======================>......] - ETA: 3s - loss: 0.3491 - accuracy: 0.9166
53/65 [=======================>......] - ETA: 3s - loss: 0.3481 - accuracy: 0.9168
54/65 [=======================>......] - ETA: 2s - loss: 0.3485 - accuracy: 0.9166
55/65 [========================>.....] - ETA: 2s - loss: 0.3477 - accuracy: 0.9168
56/65 [========================>.....] - ETA: 2s - loss: 0.3474 - accuracy: 0.9169
57/65 [=========================>....] - ETA: 2s - loss: 0.3472 - accuracy: 0.9169
58/65 [=========================>....] - ETA: 1s - loss: 0.3472 - accuracy: 0.9171
59/65 [==========================>...] - ETA: 1s - loss: 0.3470 - accuracy: 0.9171
60/65 [==========================>...] - ETA: 1s - loss: 0.3465 - accuracy: 0.9173
61/65 [===========================>..] - ETA: 1s - loss: 0.3464 - accuracy: 0.9173
62/65 [===========================>..] - ETA: 0s - loss: 0.3454 - accuracy: 0.9175
63/65 [============================>.] - ETA: 0s - loss: 0.3452 - accuracy: 0.9174
64/65 [============================>.] - ETA: 0s - loss: 0.3450 - accuracy: 0.9175
65/65 [==============================] - ETA: 0s - loss: 0.3448 - accuracy: 0.9175
65/65 [==============================] - 17s 259ms/step - loss: 0.3448 - accuracy: 0.9175 - val_loss: 0.4492 - val_accuracy: 0.8841 - lr: 0.0015
Epoch 19/30
1/65 [..............................] - ETA: 30s - loss: 0.3375 - accuracy: 0.9092
2/65 [..............................] - ETA: 15s - loss: 0.3678 - accuracy: 0.9067
3/65 [>.............................] - ETA: 16s - loss: 0.3751 - accuracy: 0.9030
4/65 [>.............................] - ETA: 15s - loss: 0.3590 - accuracy: 0.9065
5/65 [=>............................] - ETA: 15s - loss: 0.3610 - accuracy: 0.9076
6/65 [=>............................] - ETA: 15s - loss: 0.3486 - accuracy: 0.9129
7/65 [==>...........................] - ETA: 15s - loss: 0.3473 - accuracy: 0.9135
8/65 [==>...........................] - ETA: 15s - loss: 0.3467 - accuracy: 0.9146
9/65 [===>..........................] - ETA: 14s - loss: 0.3472 - accuracy: 0.9143
10/65 [===>..........................] - ETA: 14s - loss: 0.3466 - accuracy: 0.9146
11/65 [====>.........................] - ETA: 14s - loss: 0.3455 - accuracy: 0.9146
12/65 [====>.........................] - ETA: 13s - loss: 0.3442 - accuracy: 0.9154
13/65 [=====>........................] - ETA: 13s - loss: 0.3470 - accuracy: 0.9148
14/65 [=====>........................] - ETA: 13s - loss: 0.3446 - accuracy: 0.9155
15/65 [=====>........................] - ETA: 13s - loss: 0.3446 - accuracy: 0.9153
16/65 [======>.......................] - ETA: 12s - loss: 0.3487 - accuracy: 0.9146
17/65 [======>.......................] - ETA: 12s - loss: 0.3482 - accuracy: 0.9145
18/65 [=======>......................] - ETA: 12s - loss: 0.3448 - accuracy: 0.9153
19/65 [=======>......................] - ETA: 11s - loss: 0.3448 - accuracy: 0.9157
20/65 [========>.....................] - ETA: 11s - loss: 0.3436 - accuracy: 0.9161
21/65 [========>.....................] - ETA: 11s - loss: 0.3443 - accuracy: 0.9162
22/65 [=========>....................] - ETA: 11s - loss: 0.3460 - accuracy: 0.9154
23/65 [=========>....................] - ETA: 10s - loss: 0.3448 - accuracy: 0.9158
24/65 [==========>...................] - ETA: 10s - loss: 0.3449 - accuracy: 0.9160
25/65 [==========>...................] - ETA: 10s - loss: 0.3460 - accuracy: 0.9161
26/65 [===========>..................] - ETA: 10s - loss: 0.3463 - accuracy: 0.9161
27/65 [===========>..................] - ETA: 9s - loss: 0.3452 - accuracy: 0.9164
28/65 [===========>..................] - ETA: 9s - loss: 0.3457 - accuracy: 0.9163
29/65 [============>.................] - ETA: 9s - loss: 0.3463 - accuracy: 0.9159
30/65 [============>.................] - ETA: 8s - loss: 0.3460 - accuracy: 0.9160
31/65 [=============>................] - ETA: 8s - loss: 0.3461 - accuracy: 0.9157
32/65 [=============>................] - ETA: 8s - loss: 0.3478 - accuracy: 0.9151
33/65 [==============>...............] - ETA: 8s - loss: 0.3473 - accuracy: 0.9153
34/65 [==============>...............] - ETA: 7s - loss: 0.3472 - accuracy: 0.9154
35/65 [===============>..............] - ETA: 7s - loss: 0.3479 - accuracy: 0.9154
36/65 [===============>..............] - ETA: 7s - loss: 0.3475 - accuracy: 0.9153
37/65 [================>.............] - ETA: 7s - loss: 0.3476 - accuracy: 0.9153
38/65 [================>.............] - ETA: 6s - loss: 0.3484 - accuracy: 0.9152
39/65 [=================>............] - ETA: 6s - loss: 0.3483 - accuracy: 0.9154
40/65 [=================>............] - ETA: 6s - loss: 0.3481 - accuracy: 0.9155
41/65 [=================>............] - ETA: 6s - loss: 0.3467 - accuracy: 0.9158
42/65 [==================>...........] - ETA: 5s - loss: 0.3470 - accuracy: 0.9155
43/65 [==================>...........] - ETA: 5s - loss: 0.3470 - accuracy: 0.9154
44/65 [===================>..........] - ETA: 5s - loss: 0.3471 - accuracy: 0.9152
45/65 [===================>..........] - ETA: 5s - loss: 0.3465 - accuracy: 0.9153
46/65 [====================>.........] - ETA: 4s - loss: 0.3463 - accuracy: 0.9155
47/65 [====================>.........] - ETA: 4s - loss: 0.3470 - accuracy: 0.9154
48/65 [=====================>........] - ETA: 4s - loss: 0.3460 - accuracy: 0.9156
49/65 [=====================>........] - ETA: 4s - loss: 0.3463 - accuracy: 0.9154
50/65 [======================>.......] - ETA: 3s - loss: 0.3457 - accuracy: 0.9156
51/65 [======================>.......] - ETA: 3s - loss: 0.3459 - accuracy: 0.9155
52/65 [=======================>......] - ETA: 3s - loss: 0.3458 - accuracy: 0.9155
53/65 [=======================>......] - ETA: 3s - loss: 0.3456 - accuracy: 0.9154
54/65 [=======================>......] - ETA: 2s - loss: 0.3453 - accuracy: 0.9155
55/65 [========================>.....] - ETA: 2s - loss: 0.3451 - accuracy: 0.9156
56/65 [========================>.....] - ETA: 2s - loss: 0.3446 - accuracy: 0.9158
57/65 [=========================>....] - ETA: 2s - loss: 0.3442 - accuracy: 0.9160
58/65 [=========================>....] - ETA: 1s - loss: 0.3437 - accuracy: 0.9163
59/65 [==========================>...] - ETA: 1s - loss: 0.3436 - accuracy: 0.9164
60/65 [==========================>...] - ETA: 1s - loss: 0.3430 - accuracy: 0.9166
61/65 [===========================>..] - ETA: 1s - loss: 0.3425 - accuracy: 0.9165
62/65 [===========================>..] - ETA: 0s - loss: 0.3430 - accuracy: 0.9164
63/65 [============================>.] - ETA: 0s - loss: 0.3422 - accuracy: 0.9167
64/65 [============================>.] - ETA: 0s - loss: 0.3420 - accuracy: 0.9167
65/65 [==============================] - ETA: 0s - loss: 0.3423 - accuracy: 0.9167
65/65 [==============================] - 17s 264ms/step - loss: 0.3423 - accuracy: 0.9167 - val_loss: 0.4452 - val_accuracy: 0.8849 - lr: 0.0015
Epoch 20/30
1/65 [..............................] - ETA: 31s - loss: 0.3592 - accuracy: 0.9102
2/65 [..............................] - ETA: 16s - loss: 0.3374 - accuracy: 0.9175
3/65 [>.............................] - ETA: 16s - loss: 0.3403 - accuracy: 0.9160
4/65 [>.............................] - ETA: 16s - loss: 0.3412 - accuracy: 0.9155
5/65 [=>............................] - ETA: 15s - loss: 0.3469 - accuracy: 0.9160
6/65 [=>............................] - ETA: 15s - loss: 0.3530 - accuracy: 0.9150
7/65 [==>...........................] - ETA: 15s - loss: 0.3496 - accuracy: 0.9153
8/65 [==>...........................] - ETA: 15s - loss: 0.3517 - accuracy: 0.9155
9/65 [===>..........................] - ETA: 14s - loss: 0.3493 - accuracy: 0.9166
10/65 [===>..........................] - ETA: 14s - loss: 0.3486 - accuracy: 0.9170
11/65 [====>.........................] - ETA: 14s - loss: 0.3457 - accuracy: 0.9186
12/65 [====>.........................] - ETA: 13s - loss: 0.3446 - accuracy: 0.9190
13/65 [=====>........................] - ETA: 13s - loss: 0.3457 - accuracy: 0.9190
14/65 [=====>........................] - ETA: 13s - loss: 0.3454 - accuracy: 0.9191
15/65 [=====>........................] - ETA: 12s - loss: 0.3439 - accuracy: 0.9196
16/65 [======>.......................] - ETA: 12s - loss: 0.3425 - accuracy: 0.9199
17/65 [======>.......................] - ETA: 12s - loss: 0.3406 - accuracy: 0.9197
18/65 [=======>......................] - ETA: 12s - loss: 0.3394 - accuracy: 0.9197
19/65 [=======>......................] - ETA: 11s - loss: 0.3398 - accuracy: 0.9196
20/65 [========>.....................] - ETA: 11s - loss: 0.3397 - accuracy: 0.9190
21/65 [========>.....................] - ETA: 11s - loss: 0.3404 - accuracy: 0.9192
22/65 [=========>....................] - ETA: 11s - loss: 0.3408 - accuracy: 0.9193
23/65 [=========>....................] - ETA: 10s - loss: 0.3419 - accuracy: 0.9189
24/65 [==========>...................] - ETA: 10s - loss: 0.3440 - accuracy: 0.9181
25/65 [==========>...................] - ETA: 10s - loss: 0.3422 - accuracy: 0.9182
26/65 [===========>..................] - ETA: 10s - loss: 0.3415 - accuracy: 0.9183
27/65 [===========>..................] - ETA: 9s - loss: 0.3422 - accuracy: 0.9180
28/65 [===========>..................] - ETA: 9s - loss: 0.3418 - accuracy: 0.9176
29/65 [============>.................] - ETA: 9s - loss: 0.3413 - accuracy: 0.9178
30/65 [============>.................] - ETA: 8s - loss: 0.3433 - accuracy: 0.9169
31/65 [=============>................] - ETA: 8s - loss: 0.3435 - accuracy: 0.9169
32/65 [=============>................] - ETA: 8s - loss: 0.3437 - accuracy: 0.9170
33/65 [==============>...............] - ETA: 8s - loss: 0.3426 - accuracy: 0.9173
34/65 [==============>...............] - ETA: 7s - loss: 0.3426 - accuracy: 0.9171
35/65 [===============>..............] - ETA: 7s - loss: 0.3437 - accuracy: 0.9167
36/65 [===============>..............] - ETA: 7s - loss: 0.3439 - accuracy: 0.9166
37/65 [================>.............] - ETA: 7s - loss: 0.3445 - accuracy: 0.9163
38/65 [================>.............] - ETA: 6s - loss: 0.3454 - accuracy: 0.9161
39/65 [=================>............] - ETA: 6s - loss: 0.3445 - accuracy: 0.9164
40/65 [=================>............] - ETA: 6s - loss: 0.3469 - accuracy: 0.9159
41/65 [=================>............] - ETA: 6s - loss: 0.3471 - accuracy: 0.9158
42/65 [==================>...........] - ETA: 5s - loss: 0.3463 - accuracy: 0.9159
43/65 [==================>...........] - ETA: 5s - loss: 0.3450 - accuracy: 0.9163
44/65 [===================>..........] - ETA: 5s - loss: 0.3442 - accuracy: 0.9162
45/65 [===================>..........] - ETA: 5s - loss: 0.3441 - accuracy: 0.9163
46/65 [====================>.........] - ETA: 4s - loss: 0.3453 - accuracy: 0.9158
47/65 [====================>.........] - ETA: 4s - loss: 0.3457 - accuracy: 0.9157
48/65 [=====================>........] - ETA: 4s - loss: 0.3457 - accuracy: 0.9157
49/65 [=====================>........] - ETA: 4s - loss: 0.3451 - accuracy: 0.9158
50/65 [======================>.......] - ETA: 3s - loss: 0.3444 - accuracy: 0.9158
51/65 [======================>.......] - ETA: 3s - loss: 0.3442 - accuracy: 0.9158
52/65 [=======================>......] - ETA: 3s - loss: 0.3430 - accuracy: 0.9163
53/65 [=======================>......] - ETA: 3s - loss: 0.3426 - accuracy: 0.9165
54/65 [=======================>......] - ETA: 2s - loss: 0.3423 - accuracy: 0.9166
55/65 [========================>.....] - ETA: 2s - loss: 0.3421 - accuracy: 0.9167
56/65 [========================>.....] - ETA: 2s - loss: 0.3414 - accuracy: 0.9167
57/65 [=========================>....] - ETA: 2s - loss: 0.3409 - accuracy: 0.9169
58/65 [=========================>....] - ETA: 1s - loss: 0.3410 - accuracy: 0.9170
59/65 [==========================>...] - ETA: 1s - loss: 0.3416 - accuracy: 0.9168
60/65 [==========================>...] - ETA: 1s - loss: 0.3414 - accuracy: 0.9167
61/65 [===========================>..] - ETA: 1s - loss: 0.3418 - accuracy: 0.9165
62/65 [===========================>..] - ETA: 0s - loss: 0.3412 - accuracy: 0.9167
63/65 [============================>.] - ETA: 0s - loss: 0.3405 - accuracy: 0.9169
64/65 [============================>.] - ETA: 0s - loss: 0.3400 - accuracy: 0.9171
65/65 [==============================] - ETA: 0s - loss: 0.3401 - accuracy: 0.9170
65/65 [==============================] - 17s 260ms/step - loss: 0.3401 - accuracy: 0.9170 - val_loss: 0.4535 - val_accuracy: 0.8833 - lr: 0.0015
Epoch 21/30
1/65 [..............................] - ETA: 31s - loss: 0.3349 - accuracy: 0.9189
2/65 [..............................] - ETA: 16s - loss: 0.3358 - accuracy: 0.9185
3/65 [>.............................] - ETA: 16s - loss: 0.3524 - accuracy: 0.9163
4/65 [>.............................] - ETA: 16s - loss: 0.3437 - accuracy: 0.9180
5/65 [=>............................] - ETA: 15s - loss: 0.3479 - accuracy: 0.9158
6/65 [=>............................] - ETA: 15s - loss: 0.3520 - accuracy: 0.9139
7/65 [==>...........................] - ETA: 15s - loss: 0.3494 - accuracy: 0.9136
8/65 [==>...........................] - ETA: 14s - loss: 0.3484 - accuracy: 0.9139
9/65 [===>..........................] - ETA: 14s - loss: 0.3454 - accuracy: 0.9158
10/65 [===>..........................] - ETA: 14s - loss: 0.3462 - accuracy: 0.9166
11/65 [====>.........................] - ETA: 13s - loss: 0.3443 - accuracy: 0.9168
12/65 [====>.........................] - ETA: 13s - loss: 0.3428 - accuracy: 0.9172
13/65 [=====>........................] - ETA: 13s - loss: 0.3429 - accuracy: 0.9175
14/65 [=====>........................] - ETA: 13s - loss: 0.3438 - accuracy: 0.9172
15/65 [=====>........................] - ETA: 12s - loss: 0.3430 - accuracy: 0.9173
16/65 [======>.......................] - ETA: 12s - loss: 0.3448 - accuracy: 0.9170
17/65 [======>.......................] - ETA: 12s - loss: 0.3446 - accuracy: 0.9170
18/65 [=======>......................] - ETA: 12s - loss: 0.3433 - accuracy: 0.9172
19/65 [=======>......................] - ETA: 11s - loss: 0.3441 - accuracy: 0.9170
20/65 [========>.....................] - ETA: 11s - loss: 0.3433 - accuracy: 0.9167
21/65 [========>.....................] - ETA: 11s - loss: 0.3438 - accuracy: 0.9167
22/65 [=========>....................] - ETA: 11s - loss: 0.3438 - accuracy: 0.9169
23/65 [=========>....................] - ETA: 10s - loss: 0.3432 - accuracy: 0.9173
24/65 [==========>...................] - ETA: 10s - loss: 0.3443 - accuracy: 0.9168
25/65 [==========>...................] - ETA: 10s - loss: 0.3442 - accuracy: 0.9169
26/65 [===========>..................] - ETA: 9s - loss: 0.3427 - accuracy: 0.9172
27/65 [===========>..................] - ETA: 9s - loss: 0.3422 - accuracy: 0.9171
28/65 [===========>..................] - ETA: 9s - loss: 0.3413 - accuracy: 0.9172
29/65 [============>.................] - ETA: 9s - loss: 0.3415 - accuracy: 0.9171
30/65 [============>.................] - ETA: 8s - loss: 0.3419 - accuracy: 0.9166
31/65 [=============>................] - ETA: 8s - loss: 0.3430 - accuracy: 0.9166
32/65 [=============>................] - ETA: 8s - loss: 0.3434 - accuracy: 0.9166
33/65 [==============>...............] - ETA: 8s - loss: 0.3441 - accuracy: 0.9162
34/65 [==============>...............] - ETA: 7s - loss: 0.3424 - accuracy: 0.9166
35/65 [===============>..............] - ETA: 7s - loss: 0.3430 - accuracy: 0.9165
36/65 [===============>..............] - ETA: 7s - loss: 0.3429 - accuracy: 0.9163
37/65 [================>.............] - ETA: 7s - loss: 0.3446 - accuracy: 0.9157
38/65 [================>.............] - ETA: 6s - loss: 0.3441 - accuracy: 0.9158
39/65 [=================>............] - ETA: 6s - loss: 0.3444 - accuracy: 0.9159
40/65 [=================>............] - ETA: 6s - loss: 0.3440 - accuracy: 0.9160
41/65 [=================>............] - ETA: 6s - loss: 0.3449 - accuracy: 0.9159
42/65 [==================>...........] - ETA: 5s - loss: 0.3442 - accuracy: 0.9161
43/65 [==================>...........] - ETA: 5s - loss: 0.3441 - accuracy: 0.9163
44/65 [===================>..........] - ETA: 5s - loss: 0.3439 - accuracy: 0.9162
45/65 [===================>..........] - ETA: 5s - loss: 0.3429 - accuracy: 0.9164
46/65 [====================>.........] - ETA: 4s - loss: 0.3432 - accuracy: 0.9163
47/65 [====================>.........] - ETA: 4s - loss: 0.3429 - accuracy: 0.9166
48/65 [=====================>........] - ETA: 4s - loss: 0.3427 - accuracy: 0.9167
49/65 [=====================>........] - ETA: 4s - loss: 0.3430 - accuracy: 0.9168
50/65 [======================>.......] - ETA: 3s - loss: 0.3428 - accuracy: 0.9169
51/65 [======================>.......] - ETA: 3s - loss: 0.3418 - accuracy: 0.9171
52/65 [=======================>......] - ETA: 3s - loss: 0.3416 - accuracy: 0.9170
53/65 [=======================>......] - ETA: 3s - loss: 0.3418 - accuracy: 0.9169
54/65 [=======================>......] - ETA: 2s - loss: 0.3412 - accuracy: 0.9171
55/65 [========================>.....] - ETA: 2s - loss: 0.3414 - accuracy: 0.9170
56/65 [========================>.....] - ETA: 2s - loss: 0.3404 - accuracy: 0.9172
57/65 [=========================>....] - ETA: 2s - loss: 0.3398 - accuracy: 0.9174
58/65 [=========================>....] - ETA: 1s - loss: 0.3401 - accuracy: 0.9173
59/65 [==========================>...] - ETA: 1s - loss: 0.3392 - accuracy: 0.9175
60/65 [==========================>...] - ETA: 1s - loss: 0.3389 - accuracy: 0.9176
61/65 [===========================>..] - ETA: 1s - loss: 0.3380 - accuracy: 0.9176
62/65 [===========================>..] - ETA: 0s - loss: 0.3383 - accuracy: 0.9177
63/65 [============================>.] - ETA: 0s - loss: 0.3380 - accuracy: 0.9179
64/65 [============================>.] - ETA: 0s - loss: 0.3381 - accuracy: 0.9178
65/65 [==============================] - ETA: 0s - loss: 0.3380 - accuracy: 0.9179
65/65 [==============================] - 17s 259ms/step - loss: 0.3380 - accuracy: 0.9179 - val_loss: 0.4673 - val_accuracy: 0.8804 - lr: 0.0015
Epoch 22/30
1/65 [..............................] - ETA: 30s - loss: 0.3355 - accuracy: 0.9238
2/65 [..............................] - ETA: 15s - loss: 0.3230 - accuracy: 0.9258
3/65 [>.............................] - ETA: 15s - loss: 0.3388 - accuracy: 0.9186
4/65 [>.............................] - ETA: 15s - loss: 0.3315 - accuracy: 0.9216
5/65 [=>............................] - ETA: 15s - loss: 0.3424 - accuracy: 0.9193
6/65 [=>............................] - ETA: 14s - loss: 0.3460 - accuracy: 0.9172
7/65 [==>...........................] - ETA: 14s - loss: 0.3418 - accuracy: 0.9181
8/65 [==>...........................] - ETA: 14s - loss: 0.3425 - accuracy: 0.9175
9/65 [===>..........................] - ETA: 14s - loss: 0.3445 - accuracy: 0.9180
10/65 [===>..........................] - ETA: 13s - loss: 0.3484 - accuracy: 0.9163
11/65 [====>.........................] - ETA: 13s - loss: 0.3481 - accuracy: 0.9157
12/65 [====>.........................] - ETA: 13s - loss: 0.3454 - accuracy: 0.9173
13/65 [=====>........................] - ETA: 13s - loss: 0.3444 - accuracy: 0.9174
14/65 [=====>........................] - ETA: 12s - loss: 0.3438 - accuracy: 0.9180
15/65 [=====>........................] - ETA: 12s - loss: 0.3437 - accuracy: 0.9181
16/65 [======>.......................] - ETA: 12s - loss: 0.3434 - accuracy: 0.9183
17/65 [======>.......................] - ETA: 12s - loss: 0.3418 - accuracy: 0.9187
18/65 [=======>......................] - ETA: 11s - loss: 0.3412 - accuracy: 0.9186
19/65 [=======>......................] - ETA: 11s - loss: 0.3400 - accuracy: 0.9193
20/65 [========>.....................] - ETA: 11s - loss: 0.3399 - accuracy: 0.9193
21/65 [========>.....................] - ETA: 11s - loss: 0.3392 - accuracy: 0.9191
22/65 [=========>....................] - ETA: 10s - loss: 0.3393 - accuracy: 0.9187
23/65 [=========>....................] - ETA: 10s - loss: 0.3384 - accuracy: 0.9188
24/65 [==========>...................] - ETA: 10s - loss: 0.3405 - accuracy: 0.9179
25/65 [==========>...................] - ETA: 10s - loss: 0.3396 - accuracy: 0.9180
26/65 [===========>..................] - ETA: 9s - loss: 0.3396 - accuracy: 0.9176
27/65 [===========>..................] - ETA: 9s - loss: 0.3389 - accuracy: 0.9178
28/65 [===========>..................] - ETA: 9s - loss: 0.3401 - accuracy: 0.9172
29/65 [============>.................] - ETA: 9s - loss: 0.3399 - accuracy: 0.9171
30/65 [============>.................] - ETA: 8s - loss: 0.3396 - accuracy: 0.9170
31/65 [=============>................] - ETA: 8s - loss: 0.3391 - accuracy: 0.9169
32/65 [=============>................] - ETA: 8s - loss: 0.3416 - accuracy: 0.9164
33/65 [==============>...............] - ETA: 8s - loss: 0.3410 - accuracy: 0.9163
34/65 [==============>...............] - ETA: 7s - loss: 0.3419 - accuracy: 0.9164
35/65 [===============>..............] - ETA: 7s - loss: 0.3419 - accuracy: 0.9164
36/65 [===============>..............] - ETA: 7s - loss: 0.3416 - accuracy: 0.9163
37/65 [================>.............] - ETA: 7s - loss: 0.3414 - accuracy: 0.9166
38/65 [================>.............] - ETA: 6s - loss: 0.3422 - accuracy: 0.9165
39/65 [=================>............] - ETA: 6s - loss: 0.3428 - accuracy: 0.9164
40/65 [=================>............] - ETA: 6s - loss: 0.3422 - accuracy: 0.9167
41/65 [=================>............] - ETA: 6s - loss: 0.3413 - accuracy: 0.9170
42/65 [==================>...........] - ETA: 5s - loss: 0.3406 - accuracy: 0.9172
43/65 [==================>...........] - ETA: 5s - loss: 0.3415 - accuracy: 0.9170
44/65 [===================>..........] - ETA: 5s - loss: 0.3412 - accuracy: 0.9172
45/65 [===================>..........] - ETA: 5s - loss: 0.3405 - accuracy: 0.9172
46/65 [====================>.........] - ETA: 4s - loss: 0.3402 - accuracy: 0.9173
47/65 [====================>.........] - ETA: 4s - loss: 0.3401 - accuracy: 0.9174
48/65 [=====================>........] - ETA: 4s - loss: 0.3400 - accuracy: 0.9174
49/65 [=====================>........] - ETA: 4s - loss: 0.3395 - accuracy: 0.9175
50/65 [======================>.......] - ETA: 3s - loss: 0.3391 - accuracy: 0.9176
51/65 [======================>.......] - ETA: 3s - loss: 0.3390 - accuracy: 0.9175
52/65 [=======================>......] - ETA: 3s - loss: 0.3380 - accuracy: 0.9177
53/65 [=======================>......] - ETA: 3s - loss: 0.3383 - accuracy: 0.9174
54/65 [=======================>......] - ETA: 2s - loss: 0.3380 - accuracy: 0.9175
55/65 [========================>.....] - ETA: 2s - loss: 0.3380 - accuracy: 0.9175
56/65 [========================>.....] - ETA: 2s - loss: 0.3376 - accuracy: 0.9177
57/65 [=========================>....] - ETA: 2s - loss: 0.3369 - accuracy: 0.9179
58/65 [=========================>....] - ETA: 1s - loss: 0.3363 - accuracy: 0.9181
59/65 [==========================>...] - ETA: 1s - loss: 0.3364 - accuracy: 0.9181
60/65 [==========================>...] - ETA: 1s - loss: 0.3370 - accuracy: 0.9181
61/65 [===========================>..] - ETA: 1s - loss: 0.3375 - accuracy: 0.9179
62/65 [===========================>..] - ETA: 0s - loss: 0.3371 - accuracy: 0.9179
63/65 [============================>.] - ETA: 0s - loss: 0.3366 - accuracy: 0.9181
64/65 [============================>.] - ETA: 0s - loss: 0.3366 - accuracy: 0.9182
65/65 [==============================] - ETA: 0s - loss: 0.3364 - accuracy: 0.9182
Epoch 22: ReduceLROnPlateau reducing learning rate to 0.000750000006519258.
65/65 [==============================] - 17s 260ms/step - loss: 0.3364 - accuracy: 0.9182 - val_loss: 0.4595 - val_accuracy: 0.8826 - lr: 0.0015
Epoch 23/30
1/65 [..............................] - ETA: 30s - loss: 0.3563 - accuracy: 0.9102
2/65 [..............................] - ETA: 15s - loss: 0.3487 - accuracy: 0.9165
3/65 [>.............................] - ETA: 16s - loss: 0.3409 - accuracy: 0.9176
4/65 [>.............................] - ETA: 15s - loss: 0.3377 - accuracy: 0.9199
5/65 [=>............................] - ETA: 15s - loss: 0.3403 - accuracy: 0.9203
6/65 [=>............................] - ETA: 15s - loss: 0.3403 - accuracy: 0.9199
7/65 [==>...........................] - ETA: 14s - loss: 0.3464 - accuracy: 0.9181
8/65 [==>...........................] - ETA: 14s - loss: 0.3395 - accuracy: 0.9202
9/65 [===>..........................] - ETA: 14s - loss: 0.3387 - accuracy: 0.9204
10/65 [===>..........................] - ETA: 13s - loss: 0.3358 - accuracy: 0.9202
11/65 [====>.........................] - ETA: 13s - loss: 0.3363 - accuracy: 0.9204
12/65 [====>.........................] - ETA: 13s - loss: 0.3384 - accuracy: 0.9198
13/65 [=====>........................] - ETA: 13s - loss: 0.3352 - accuracy: 0.9212
14/65 [=====>........................] - ETA: 12s - loss: 0.3361 - accuracy: 0.9209
15/65 [=====>........................] - ETA: 12s - loss: 0.3349 - accuracy: 0.9210
16/65 [======>.......................] - ETA: 12s - loss: 0.3353 - accuracy: 0.9210
17/65 [======>.......................] - ETA: 12s - loss: 0.3354 - accuracy: 0.9202
18/65 [=======>......................] - ETA: 11s - loss: 0.3357 - accuracy: 0.9201
19/65 [=======>......................] - ETA: 11s - loss: 0.3339 - accuracy: 0.9206
20/65 [========>.....................] - ETA: 11s - loss: 0.3350 - accuracy: 0.9197
21/65 [========>.....................] - ETA: 11s - loss: 0.3339 - accuracy: 0.9201
22/65 [=========>....................] - ETA: 10s - loss: 0.3341 - accuracy: 0.9200
23/65 [=========>....................] - ETA: 10s - loss: 0.3358 - accuracy: 0.9196
24/65 [==========>...................] - ETA: 10s - loss: 0.3347 - accuracy: 0.9201
25/65 [==========>...................] - ETA: 10s - loss: 0.3351 - accuracy: 0.9200
26/65 [===========>..................] - ETA: 9s - loss: 0.3341 - accuracy: 0.9201
27/65 [===========>..................] - ETA: 9s - loss: 0.3330 - accuracy: 0.9205
28/65 [===========>..................] - ETA: 9s - loss: 0.3327 - accuracy: 0.9203
29/65 [============>.................] - ETA: 9s - loss: 0.3332 - accuracy: 0.9200
30/65 [============>.................] - ETA: 8s - loss: 0.3327 - accuracy: 0.9203
31/65 [=============>................] - ETA: 8s - loss: 0.3319 - accuracy: 0.9206
32/65 [=============>................] - ETA: 8s - loss: 0.3329 - accuracy: 0.9207
33/65 [==============>...............] - ETA: 8s - loss: 0.3331 - accuracy: 0.9203
34/65 [==============>...............] - ETA: 7s - loss: 0.3330 - accuracy: 0.9201
35/65 [===============>..............] - ETA: 7s - loss: 0.3325 - accuracy: 0.9199
36/65 [===============>..............] - ETA: 7s - loss: 0.3333 - accuracy: 0.9198
37/65 [================>.............] - ETA: 7s - loss: 0.3340 - accuracy: 0.9194
38/65 [================>.............] - ETA: 6s - loss: 0.3344 - accuracy: 0.9195
39/65 [=================>............] - ETA: 6s - loss: 0.3339 - accuracy: 0.9195
40/65 [=================>............] - ETA: 6s - loss: 0.3333 - accuracy: 0.9197
41/65 [=================>............] - ETA: 6s - loss: 0.3335 - accuracy: 0.9195
42/65 [==================>...........] - ETA: 5s - loss: 0.3328 - accuracy: 0.9198
43/65 [==================>...........] - ETA: 5s - loss: 0.3331 - accuracy: 0.9198
44/65 [===================>..........] - ETA: 5s - loss: 0.3325 - accuracy: 0.9201
45/65 [===================>..........] - ETA: 5s - loss: 0.3330 - accuracy: 0.9199
46/65 [====================>.........] - ETA: 4s - loss: 0.3323 - accuracy: 0.9200
47/65 [====================>.........] - ETA: 4s - loss: 0.3315 - accuracy: 0.9202
48/65 [=====================>........] - ETA: 4s - loss: 0.3319 - accuracy: 0.9202
49/65 [=====================>........] - ETA: 4s - loss: 0.3315 - accuracy: 0.9203
50/65 [======================>.......] - ETA: 3s - loss: 0.3310 - accuracy: 0.9203
51/65 [======================>.......] - ETA: 3s - loss: 0.3299 - accuracy: 0.9206
52/65 [=======================>......] - ETA: 3s - loss: 0.3300 - accuracy: 0.9206
53/65 [=======================>......] - ETA: 3s - loss: 0.3293 - accuracy: 0.9206
54/65 [=======================>......] - ETA: 2s - loss: 0.3287 - accuracy: 0.9209
55/65 [========================>.....] - ETA: 2s - loss: 0.3287 - accuracy: 0.9209
56/65 [========================>.....] - ETA: 2s - loss: 0.3279 - accuracy: 0.9211
57/65 [=========================>....] - ETA: 2s - loss: 0.3273 - accuracy: 0.9212
58/65 [=========================>....] - ETA: 1s - loss: 0.3273 - accuracy: 0.9212
59/65 [==========================>...] - ETA: 1s - loss: 0.3267 - accuracy: 0.9215
60/65 [==========================>...] - ETA: 1s - loss: 0.3266 - accuracy: 0.9214
61/65 [===========================>..] - ETA: 1s - loss: 0.3261 - accuracy: 0.9217
62/65 [===========================>..] - ETA: 0s - loss: 0.3259 - accuracy: 0.9217
63/65 [============================>.] - ETA: 0s - loss: 0.3256 - accuracy: 0.9217
64/65 [============================>.] - ETA: 0s - loss: 0.3261 - accuracy: 0.9217
65/65 [==============================] - ETA: 0s - loss: 0.3261 - accuracy: 0.9217
65/65 [==============================] - 17s 259ms/step - loss: 0.3261 - accuracy: 0.9217 - val_loss: 0.4458 - val_accuracy: 0.8851 - lr: 7.5000e-04
Epoch 24/30
1/65 [..............................] - ETA: 30s - loss: 0.3380 - accuracy: 0.9209
2/65 [..............................] - ETA: 16s - loss: 0.3434 - accuracy: 0.9238
3/65 [>.............................] - ETA: 16s - loss: 0.3441 - accuracy: 0.9235
4/65 [>.............................] - ETA: 15s - loss: 0.3302 - accuracy: 0.9253
5/65 [=>............................] - ETA: 15s - loss: 0.3321 - accuracy: 0.9242
6/65 [=>............................] - ETA: 15s - loss: 0.3355 - accuracy: 0.9211
7/65 [==>...........................] - ETA: 15s - loss: 0.3337 - accuracy: 0.9196
8/65 [==>...........................] - ETA: 14s - loss: 0.3337 - accuracy: 0.9175
9/65 [===>..........................] - ETA: 14s - loss: 0.3336 - accuracy: 0.9184
10/65 [===>..........................] - ETA: 14s - loss: 0.3313 - accuracy: 0.9192
11/65 [====>.........................] - ETA: 13s - loss: 0.3285 - accuracy: 0.9205
12/65 [====>.........................] - ETA: 13s - loss: 0.3296 - accuracy: 0.9203
13/65 [=====>........................] - ETA: 13s - loss: 0.3310 - accuracy: 0.9200
14/65 [=====>........................] - ETA: 13s - loss: 0.3298 - accuracy: 0.9208
15/65 [=====>........................] - ETA: 12s - loss: 0.3326 - accuracy: 0.9200
16/65 [======>.......................] - ETA: 12s - loss: 0.3322 - accuracy: 0.9198
17/65 [======>.......................] - ETA: 12s - loss: 0.3323 - accuracy: 0.9198
18/65 [=======>......................] - ETA: 12s - loss: 0.3325 - accuracy: 0.9198
19/65 [=======>......................] - ETA: 11s - loss: 0.3314 - accuracy: 0.9202
20/65 [========>.....................] - ETA: 11s - loss: 0.3338 - accuracy: 0.9199
21/65 [========>.....................] - ETA: 11s - loss: 0.3320 - accuracy: 0.9204
22/65 [=========>....................] - ETA: 10s - loss: 0.3300 - accuracy: 0.9213
23/65 [=========>....................] - ETA: 10s - loss: 0.3295 - accuracy: 0.9211
24/65 [==========>...................] - ETA: 10s - loss: 0.3294 - accuracy: 0.9210
25/65 [==========>...................] - ETA: 10s - loss: 0.3288 - accuracy: 0.9214
26/65 [===========>..................] - ETA: 10s - loss: 0.3281 - accuracy: 0.9213
27/65 [===========>..................] - ETA: 9s - loss: 0.3277 - accuracy: 0.9212
28/65 [===========>..................] - ETA: 9s - loss: 0.3271 - accuracy: 0.9211
29/65 [============>.................] - ETA: 9s - loss: 0.3282 - accuracy: 0.9211
30/65 [============>.................] - ETA: 8s - loss: 0.3288 - accuracy: 0.9206
31/65 [=============>................] - ETA: 8s - loss: 0.3278 - accuracy: 0.9207
32/65 [=============>................] - ETA: 8s - loss: 0.3279 - accuracy: 0.9207
33/65 [==============>...............] - ETA: 8s - loss: 0.3283 - accuracy: 0.9207
34/65 [==============>...............] - ETA: 7s - loss: 0.3298 - accuracy: 0.9203
35/65 [===============>..............] - ETA: 7s - loss: 0.3289 - accuracy: 0.9204
36/65 [===============>..............] - ETA: 7s - loss: 0.3289 - accuracy: 0.9204
37/65 [================>.............] - ETA: 7s - loss: 0.3309 - accuracy: 0.9199
38/65 [================>.............] - ETA: 6s - loss: 0.3309 - accuracy: 0.9200
39/65 [=================>............] - ETA: 6s - loss: 0.3301 - accuracy: 0.9201
40/65 [=================>............] - ETA: 6s - loss: 0.3289 - accuracy: 0.9203
41/65 [=================>............] - ETA: 6s - loss: 0.3296 - accuracy: 0.9201
42/65 [==================>...........] - ETA: 5s - loss: 0.3289 - accuracy: 0.9202
43/65 [==================>...........] - ETA: 5s - loss: 0.3287 - accuracy: 0.9203
44/65 [===================>..........] - ETA: 5s - loss: 0.3293 - accuracy: 0.9202
45/65 [===================>..........] - ETA: 5s - loss: 0.3293 - accuracy: 0.9203
46/65 [====================>.........] - ETA: 4s - loss: 0.3281 - accuracy: 0.9207
47/65 [====================>.........] - ETA: 4s - loss: 0.3271 - accuracy: 0.9209
48/65 [=====================>........] - ETA: 4s - loss: 0.3276 - accuracy: 0.9207
49/65 [=====================>........] - ETA: 4s - loss: 0.3274 - accuracy: 0.9209
50/65 [======================>.......] - ETA: 3s - loss: 0.3262 - accuracy: 0.9212
51/65 [======================>.......] - ETA: 3s - loss: 0.3253 - accuracy: 0.9215
52/65 [=======================>......] - ETA: 3s - loss: 0.3253 - accuracy: 0.9215
53/65 [=======================>......] - ETA: 3s - loss: 0.3252 - accuracy: 0.9215
54/65 [=======================>......] - ETA: 2s - loss: 0.3262 - accuracy: 0.9211
55/65 [========================>.....] - ETA: 2s - loss: 0.3248 - accuracy: 0.9214
56/65 [========================>.....] - ETA: 2s - loss: 0.3245 - accuracy: 0.9216
57/65 [=========================>....] - ETA: 2s - loss: 0.3241 - accuracy: 0.9217
58/65 [=========================>....] - ETA: 1s - loss: 0.3243 - accuracy: 0.9219
59/65 [==========================>...] - ETA: 1s - loss: 0.3241 - accuracy: 0.9219
60/65 [==========================>...] - ETA: 1s - loss: 0.3238 - accuracy: 0.9220
61/65 [===========================>..] - ETA: 1s - loss: 0.3237 - accuracy: 0.9221
62/65 [===========================>..] - ETA: 0s - loss: 0.3236 - accuracy: 0.9220
63/65 [============================>.] - ETA: 0s - loss: 0.3234 - accuracy: 0.9221
64/65 [============================>.] - ETA: 0s - loss: 0.3229 - accuracy: 0.9222
65/65 [==============================] - ETA: 0s - loss: 0.3229 - accuracy: 0.9222
65/65 [==============================] - 17s 260ms/step - loss: 0.3229 - accuracy: 0.9222 - val_loss: 0.4334 - val_accuracy: 0.8893 - lr: 7.5000e-04
Epoch 25/30
1/65 [..............................] - ETA: 31s - loss: 0.3462 - accuracy: 0.9121
2/65 [..............................] - ETA: 15s - loss: 0.3289 - accuracy: 0.9204
3/65 [>.............................] - ETA: 15s - loss: 0.3281 - accuracy: 0.9202
4/65 [>.............................] - ETA: 15s - loss: 0.3251 - accuracy: 0.9211
5/65 [=>............................] - ETA: 15s - loss: 0.3321 - accuracy: 0.9213
6/65 [=>............................] - ETA: 14s - loss: 0.3357 - accuracy: 0.9193
7/65 [==>...........................] - ETA: 14s - loss: 0.3293 - accuracy: 0.9209
8/65 [==>...........................] - ETA: 14s - loss: 0.3291 - accuracy: 0.9213
9/65 [===>..........................] - ETA: 14s - loss: 0.3321 - accuracy: 0.9201
10/65 [===>..........................] - ETA: 13s - loss: 0.3285 - accuracy: 0.9210
11/65 [====>.........................] - ETA: 13s - loss: 0.3274 - accuracy: 0.9206
12/65 [====>.........................] - ETA: 13s - loss: 0.3276 - accuracy: 0.9204
13/65 [=====>........................] - ETA: 13s - loss: 0.3268 - accuracy: 0.9202
14/65 [=====>........................] - ETA: 12s - loss: 0.3279 - accuracy: 0.9207
15/65 [=====>........................] - ETA: 12s - loss: 0.3267 - accuracy: 0.9214
16/65 [======>.......................] - ETA: 12s - loss: 0.3250 - accuracy: 0.9216
17/65 [======>.......................] - ETA: 12s - loss: 0.3248 - accuracy: 0.9219
18/65 [=======>......................] - ETA: 12s - loss: 0.3234 - accuracy: 0.9223
19/65 [=======>......................] - ETA: 11s - loss: 0.3247 - accuracy: 0.9220
20/65 [========>.....................] - ETA: 11s - loss: 0.3227 - accuracy: 0.9226
21/65 [========>.....................] - ETA: 11s - loss: 0.3230 - accuracy: 0.9224
22/65 [=========>....................] - ETA: 11s - loss: 0.3231 - accuracy: 0.9221
23/65 [=========>....................] - ETA: 10s - loss: 0.3221 - accuracy: 0.9219
24/65 [==========>...................] - ETA: 10s - loss: 0.3226 - accuracy: 0.9220
25/65 [==========>...................] - ETA: 10s - loss: 0.3239 - accuracy: 0.9217
26/65 [===========>..................] - ETA: 9s - loss: 0.3237 - accuracy: 0.9215
27/65 [===========>..................] - ETA: 9s - loss: 0.3243 - accuracy: 0.9217
28/65 [===========>..................] - ETA: 9s - loss: 0.3242 - accuracy: 0.9217
29/65 [============>.................] - ETA: 9s - loss: 0.3239 - accuracy: 0.9216
30/65 [============>.................] - ETA: 8s - loss: 0.3249 - accuracy: 0.9212
31/65 [=============>................] - ETA: 8s - loss: 0.3258 - accuracy: 0.9209
32/65 [=============>................] - ETA: 8s - loss: 0.3252 - accuracy: 0.9210
33/65 [==============>...............] - ETA: 8s - loss: 0.3254 - accuracy: 0.9211
34/65 [==============>...............] - ETA: 7s - loss: 0.3254 - accuracy: 0.9212
35/65 [===============>..............] - ETA: 7s - loss: 0.3267 - accuracy: 0.9207
36/65 [===============>..............] - ETA: 7s - loss: 0.3267 - accuracy: 0.9208
37/65 [================>.............] - ETA: 7s - loss: 0.3266 - accuracy: 0.9207
38/65 [================>.............] - ETA: 6s - loss: 0.3261 - accuracy: 0.9209
39/65 [=================>............] - ETA: 6s - loss: 0.3252 - accuracy: 0.9212
40/65 [=================>............] - ETA: 6s - loss: 0.3251 - accuracy: 0.9209
41/65 [=================>............] - ETA: 6s - loss: 0.3257 - accuracy: 0.9210
42/65 [==================>...........] - ETA: 5s - loss: 0.3264 - accuracy: 0.9211
43/65 [==================>...........] - ETA: 5s - loss: 0.3265 - accuracy: 0.9211
44/65 [===================>..........] - ETA: 5s - loss: 0.3270 - accuracy: 0.9210
45/65 [===================>..........] - ETA: 5s - loss: 0.3266 - accuracy: 0.9212
46/65 [====================>.........] - ETA: 4s - loss: 0.3268 - accuracy: 0.9213
47/65 [====================>.........] - ETA: 4s - loss: 0.3268 - accuracy: 0.9213
48/65 [=====================>........] - ETA: 4s - loss: 0.3261 - accuracy: 0.9215
49/65 [=====================>........] - ETA: 4s - loss: 0.3255 - accuracy: 0.9215
50/65 [======================>.......] - ETA: 3s - loss: 0.3242 - accuracy: 0.9219
51/65 [======================>.......] - ETA: 3s - loss: 0.3240 - accuracy: 0.9220
52/65 [=======================>......] - ETA: 3s - loss: 0.3232 - accuracy: 0.9221
53/65 [=======================>......] - ETA: 3s - loss: 0.3229 - accuracy: 0.9221
54/65 [=======================>......] - ETA: 2s - loss: 0.3225 - accuracy: 0.9223
55/65 [========================>.....] - ETA: 2s - loss: 0.3220 - accuracy: 0.9225
56/65 [========================>.....] - ETA: 2s - loss: 0.3216 - accuracy: 0.9226
57/65 [=========================>....] - ETA: 2s - loss: 0.3218 - accuracy: 0.9226
58/65 [=========================>....] - ETA: 1s - loss: 0.3220 - accuracy: 0.9224
59/65 [==========================>...] - ETA: 1s - loss: 0.3212 - accuracy: 0.9226
60/65 [==========================>...] - ETA: 1s - loss: 0.3211 - accuracy: 0.9227
61/65 [===========================>..] - ETA: 1s - loss: 0.3208 - accuracy: 0.9228
62/65 [===========================>..] - ETA: 0s - loss: 0.3208 - accuracy: 0.9229
63/65 [============================>.] - ETA: 0s - loss: 0.3203 - accuracy: 0.9230
64/65 [============================>.] - ETA: 0s - loss: 0.3206 - accuracy: 0.9229
65/65 [==============================] - ETA: 0s - loss: 0.3204 - accuracy: 0.9230
65/65 [==============================] - 17s 259ms/step - loss: 0.3204 - accuracy: 0.9230 - val_loss: 0.4341 - val_accuracy: 0.8879 - lr: 7.5000e-04
Epoch 26/30
1/65 [..............................] - ETA: 30s - loss: 0.3552 - accuracy: 0.9082
2/65 [..............................] - ETA: 15s - loss: 0.3562 - accuracy: 0.9131
3/65 [>.............................] - ETA: 16s - loss: 0.3389 - accuracy: 0.9154
4/65 [>.............................] - ETA: 15s - loss: 0.3440 - accuracy: 0.9136
5/65 [=>............................] - ETA: 15s - loss: 0.3360 - accuracy: 0.9164
6/65 [=>............................] - ETA: 15s - loss: 0.3362 - accuracy: 0.9178
7/65 [==>...........................] - ETA: 15s - loss: 0.3360 - accuracy: 0.9178
8/65 [==>...........................] - ETA: 14s - loss: 0.3327 - accuracy: 0.9189
9/65 [===>..........................] - ETA: 14s - loss: 0.3297 - accuracy: 0.9201
10/65 [===>..........................] - ETA: 14s - loss: 0.3275 - accuracy: 0.9217
11/65 [====>.........................] - ETA: 14s - loss: 0.3252 - accuracy: 0.9226
12/65 [====>.........................] - ETA: 13s - loss: 0.3224 - accuracy: 0.9229
13/65 [=====>........................] - ETA: 13s - loss: 0.3218 - accuracy: 0.9220
14/65 [=====>........................] - ETA: 13s - loss: 0.3226 - accuracy: 0.9215
15/65 [=====>........................] - ETA: 13s - loss: 0.3214 - accuracy: 0.9215
16/65 [======>.......................] - ETA: 12s - loss: 0.3227 - accuracy: 0.9215
17/65 [======>.......................] - ETA: 12s - loss: 0.3257 - accuracy: 0.9206
18/65 [=======>......................] - ETA: 12s - loss: 0.3224 - accuracy: 0.9218
19/65 [=======>......................] - ETA: 11s - loss: 0.3218 - accuracy: 0.9224
20/65 [========>.....................] - ETA: 11s - loss: 0.3226 - accuracy: 0.9222
21/65 [========>.....................] - ETA: 11s - loss: 0.3217 - accuracy: 0.9226
22/65 [=========>....................] - ETA: 11s - loss: 0.3242 - accuracy: 0.9218
23/65 [=========>....................] - ETA: 10s - loss: 0.3235 - accuracy: 0.9217
24/65 [==========>...................] - ETA: 10s - loss: 0.3230 - accuracy: 0.9215
25/65 [==========>...................] - ETA: 10s - loss: 0.3236 - accuracy: 0.9213
26/65 [===========>..................] - ETA: 10s - loss: 0.3233 - accuracy: 0.9213
27/65 [===========>..................] - ETA: 9s - loss: 0.3224 - accuracy: 0.9219
28/65 [===========>..................] - ETA: 9s - loss: 0.3214 - accuracy: 0.9222
29/65 [============>.................] - ETA: 9s - loss: 0.3224 - accuracy: 0.9221
30/65 [============>.................] - ETA: 9s - loss: 0.3228 - accuracy: 0.9220
31/65 [=============>................] - ETA: 8s - loss: 0.3219 - accuracy: 0.9223
32/65 [=============>................] - ETA: 8s - loss: 0.3229 - accuracy: 0.9222
33/65 [==============>...............] - ETA: 8s - loss: 0.3235 - accuracy: 0.9221
34/65 [==============>...............] - ETA: 8s - loss: 0.3239 - accuracy: 0.9219
35/65 [===============>..............] - ETA: 7s - loss: 0.3251 - accuracy: 0.9214
36/65 [===============>..............] - ETA: 7s - loss: 0.3258 - accuracy: 0.9212
37/65 [================>.............] - ETA: 7s - loss: 0.3258 - accuracy: 0.9209
38/65 [================>.............] - ETA: 6s - loss: 0.3251 - accuracy: 0.9212
39/65 [=================>............] - ETA: 6s - loss: 0.3243 - accuracy: 0.9214
40/65 [=================>............] - ETA: 6s - loss: 0.3251 - accuracy: 0.9212
41/65 [=================>............] - ETA: 6s - loss: 0.3251 - accuracy: 0.9212
42/65 [==================>...........] - ETA: 5s - loss: 0.3245 - accuracy: 0.9215
43/65 [==================>...........] - ETA: 5s - loss: 0.3239 - accuracy: 0.9217
44/65 [===================>..........] - ETA: 5s - loss: 0.3239 - accuracy: 0.9217
45/65 [===================>..........] - ETA: 5s - loss: 0.3237 - accuracy: 0.9217
46/65 [====================>.........] - ETA: 4s - loss: 0.3239 - accuracy: 0.9215
47/65 [====================>.........] - ETA: 4s - loss: 0.3242 - accuracy: 0.9215
48/65 [=====================>........] - ETA: 4s - loss: 0.3235 - accuracy: 0.9219
49/65 [=====================>........] - ETA: 4s - loss: 0.3241 - accuracy: 0.9217
50/65 [======================>.......] - ETA: 3s - loss: 0.3234 - accuracy: 0.9218
51/65 [======================>.......] - ETA: 3s - loss: 0.3220 - accuracy: 0.9223
52/65 [=======================>......] - ETA: 3s - loss: 0.3225 - accuracy: 0.9222
53/65 [=======================>......] - ETA: 3s - loss: 0.3223 - accuracy: 0.9223
54/65 [=======================>......] - ETA: 2s - loss: 0.3219 - accuracy: 0.9223
55/65 [========================>.....] - ETA: 2s - loss: 0.3219 - accuracy: 0.9225
56/65 [========================>.....] - ETA: 2s - loss: 0.3212 - accuracy: 0.9225
57/65 [=========================>....] - ETA: 2s - loss: 0.3206 - accuracy: 0.9227
58/65 [=========================>....] - ETA: 1s - loss: 0.3203 - accuracy: 0.9228
59/65 [==========================>...] - ETA: 1s - loss: 0.3198 - accuracy: 0.9229
60/65 [==========================>...] - ETA: 1s - loss: 0.3197 - accuracy: 0.9228
61/65 [===========================>..] - ETA: 1s - loss: 0.3195 - accuracy: 0.9228
62/65 [===========================>..] - ETA: 0s - loss: 0.3197 - accuracy: 0.9229
63/65 [============================>.] - ETA: 0s - loss: 0.3202 - accuracy: 0.9229
64/65 [============================>.] - ETA: 0s - loss: 0.3196 - accuracy: 0.9230
65/65 [==============================] - ETA: 0s - loss: 0.3201 - accuracy: 0.9228
65/65 [==============================] - 17s 262ms/step - loss: 0.3201 - accuracy: 0.9228 - val_loss: 0.4342 - val_accuracy: 0.8890 - lr: 7.5000e-04
Epoch 27/30
1/65 [..............................] - ETA: 29s - loss: 0.3093 - accuracy: 0.9248
2/65 [..............................] - ETA: 15s - loss: 0.3270 - accuracy: 0.9219
3/65 [>.............................] - ETA: 15s - loss: 0.3246 - accuracy: 0.9229
4/65 [>.............................] - ETA: 16s - loss: 0.3302 - accuracy: 0.9197
5/65 [=>............................] - ETA: 15s - loss: 0.3351 - accuracy: 0.9201
6/65 [=>............................] - ETA: 15s - loss: 0.3316 - accuracy: 0.9211
7/65 [==>...........................] - ETA: 15s - loss: 0.3311 - accuracy: 0.9212
8/65 [==>...........................] - ETA: 14s - loss: 0.3230 - accuracy: 0.9233
9/65 [===>..........................] - ETA: 14s - loss: 0.3236 - accuracy: 0.9227
10/65 [===>..........................] - ETA: 14s - loss: 0.3267 - accuracy: 0.9218
11/65 [====>.........................] - ETA: 13s - loss: 0.3249 - accuracy: 0.9227
12/65 [====>.........................] - ETA: 13s - loss: 0.3211 - accuracy: 0.9236
13/65 [=====>........................] - ETA: 13s - loss: 0.3200 - accuracy: 0.9239
14/65 [=====>........................] - ETA: 13s - loss: 0.3212 - accuracy: 0.9240
15/65 [=====>........................] - ETA: 12s - loss: 0.3206 - accuracy: 0.9240
16/65 [======>.......................] - ETA: 12s - loss: 0.3215 - accuracy: 0.9232
17/65 [======>.......................] - ETA: 12s - loss: 0.3201 - accuracy: 0.9234
18/65 [=======>......................] - ETA: 12s - loss: 0.3193 - accuracy: 0.9236
19/65 [=======>......................] - ETA: 11s - loss: 0.3194 - accuracy: 0.9233
20/65 [========>.....................] - ETA: 11s - loss: 0.3187 - accuracy: 0.9238
21/65 [========>.....................] - ETA: 11s - loss: 0.3200 - accuracy: 0.9235
22/65 [=========>....................] - ETA: 10s - loss: 0.3206 - accuracy: 0.9235
23/65 [=========>....................] - ETA: 10s - loss: 0.3230 - accuracy: 0.9225
24/65 [==========>...................] - ETA: 10s - loss: 0.3224 - accuracy: 0.9229
25/65 [==========>...................] - ETA: 10s - loss: 0.3238 - accuracy: 0.9229
26/65 [===========>..................] - ETA: 9s - loss: 0.3226 - accuracy: 0.9231
27/65 [===========>..................] - ETA: 9s - loss: 0.3215 - accuracy: 0.9234
28/65 [===========>..................] - ETA: 9s - loss: 0.3220 - accuracy: 0.9232
29/65 [============>.................] - ETA: 9s - loss: 0.3226 - accuracy: 0.9231
30/65 [============>.................] - ETA: 8s - loss: 0.3232 - accuracy: 0.9228
31/65 [=============>................] - ETA: 8s - loss: 0.3237 - accuracy: 0.9227
32/65 [=============>................] - ETA: 8s - loss: 0.3250 - accuracy: 0.9223
33/65 [==============>...............] - ETA: 8s - loss: 0.3246 - accuracy: 0.9221
34/65 [==============>...............] - ETA: 7s - loss: 0.3243 - accuracy: 0.9223
35/65 [===============>..............] - ETA: 7s - loss: 0.3242 - accuracy: 0.9222
36/65 [===============>..............] - ETA: 7s - loss: 0.3250 - accuracy: 0.9220
37/65 [================>.............] - ETA: 7s - loss: 0.3237 - accuracy: 0.9223
38/65 [================>.............] - ETA: 6s - loss: 0.3254 - accuracy: 0.9219
39/65 [=================>............] - ETA: 6s - loss: 0.3260 - accuracy: 0.9214
40/65 [=================>............] - ETA: 6s - loss: 0.3257 - accuracy: 0.9217
41/65 [=================>............] - ETA: 6s - loss: 0.3263 - accuracy: 0.9217
42/65 [==================>...........] - ETA: 5s - loss: 0.3252 - accuracy: 0.9219
43/65 [==================>...........] - ETA: 5s - loss: 0.3251 - accuracy: 0.9221
44/65 [===================>..........] - ETA: 5s - loss: 0.3245 - accuracy: 0.9222
45/65 [===================>..........] - ETA: 5s - loss: 0.3233 - accuracy: 0.9226
46/65 [====================>.........] - ETA: 4s - loss: 0.3235 - accuracy: 0.9225
47/65 [====================>.........] - ETA: 4s - loss: 0.3231 - accuracy: 0.9225
48/65 [=====================>........] - ETA: 4s - loss: 0.3225 - accuracy: 0.9228
49/65 [=====================>........] - ETA: 4s - loss: 0.3221 - accuracy: 0.9230
50/65 [======================>.......] - ETA: 3s - loss: 0.3222 - accuracy: 0.9229
51/65 [======================>.......] - ETA: 3s - loss: 0.3217 - accuracy: 0.9229
52/65 [=======================>......] - ETA: 3s - loss: 0.3208 - accuracy: 0.9232
53/65 [=======================>......] - ETA: 3s - loss: 0.3207 - accuracy: 0.9231
54/65 [=======================>......] - ETA: 2s - loss: 0.3195 - accuracy: 0.9235
55/65 [========================>.....] - ETA: 2s - loss: 0.3190 - accuracy: 0.9237
56/65 [========================>.....] - ETA: 2s - loss: 0.3188 - accuracy: 0.9237
57/65 [=========================>....] - ETA: 2s - loss: 0.3195 - accuracy: 0.9235
58/65 [=========================>....] - ETA: 1s - loss: 0.3196 - accuracy: 0.9235
59/65 [==========================>...] - ETA: 1s - loss: 0.3193 - accuracy: 0.9233
60/65 [==========================>...] - ETA: 1s - loss: 0.3196 - accuracy: 0.9233
61/65 [===========================>..] - ETA: 1s - loss: 0.3190 - accuracy: 0.9234
62/65 [===========================>..] - ETA: 0s - loss: 0.3183 - accuracy: 0.9236
63/65 [============================>.] - ETA: 0s - loss: 0.3190 - accuracy: 0.9234
64/65 [============================>.] - ETA: 0s - loss: 0.3190 - accuracy: 0.9235
65/65 [==============================] - ETA: 0s - loss: 0.3192 - accuracy: 0.9235
65/65 [==============================] - 17s 259ms/step - loss: 0.3192 - accuracy: 0.9235 - val_loss: 0.4312 - val_accuracy: 0.8886 - lr: 7.5000e-04
Epoch 28/30
1/65 [..............................] - ETA: 30s - loss: 0.3259 - accuracy: 0.9258
2/65 [..............................] - ETA: 16s - loss: 0.3296 - accuracy: 0.9219
3/65 [>.............................] - ETA: 16s - loss: 0.3265 - accuracy: 0.9219
4/65 [>.............................] - ETA: 16s - loss: 0.3254 - accuracy: 0.9226
5/65 [=>............................] - ETA: 15s - loss: 0.3342 - accuracy: 0.9205
6/65 [=>............................] - ETA: 15s - loss: 0.3394 - accuracy: 0.9206
7/65 [==>...........................] - ETA: 15s - loss: 0.3365 - accuracy: 0.9203
8/65 [==>...........................] - ETA: 14s - loss: 0.3375 - accuracy: 0.9204
9/65 [===>..........................] - ETA: 14s - loss: 0.3349 - accuracy: 0.9207
10/65 [===>..........................] - ETA: 14s - loss: 0.3313 - accuracy: 0.9219
11/65 [====>.........................] - ETA: 14s - loss: 0.3257 - accuracy: 0.9234
12/65 [====>.........................] - ETA: 13s - loss: 0.3208 - accuracy: 0.9248
13/65 [=====>........................] - ETA: 13s - loss: 0.3223 - accuracy: 0.9241
14/65 [=====>........................] - ETA: 13s - loss: 0.3226 - accuracy: 0.9240
15/65 [=====>........................] - ETA: 12s - loss: 0.3225 - accuracy: 0.9236
16/65 [======>.......................] - ETA: 12s - loss: 0.3226 - accuracy: 0.9240
17/65 [======>.......................] - ETA: 12s - loss: 0.3180 - accuracy: 0.9255
18/65 [=======>......................] - ETA: 12s - loss: 0.3173 - accuracy: 0.9253
19/65 [=======>......................] - ETA: 11s - loss: 0.3196 - accuracy: 0.9242
20/65 [========>.....................] - ETA: 11s - loss: 0.3206 - accuracy: 0.9239
21/65 [========>.....................] - ETA: 11s - loss: 0.3193 - accuracy: 0.9244
22/65 [=========>....................] - ETA: 11s - loss: 0.3202 - accuracy: 0.9241
23/65 [=========>....................] - ETA: 10s - loss: 0.3184 - accuracy: 0.9240
24/65 [==========>...................] - ETA: 10s - loss: 0.3188 - accuracy: 0.9240
25/65 [==========>...................] - ETA: 10s - loss: 0.3212 - accuracy: 0.9240
26/65 [===========>..................] - ETA: 10s - loss: 0.3216 - accuracy: 0.9238
27/65 [===========>..................] - ETA: 9s - loss: 0.3211 - accuracy: 0.9236
28/65 [===========>..................] - ETA: 9s - loss: 0.3209 - accuracy: 0.9231
29/65 [============>.................] - ETA: 9s - loss: 0.3218 - accuracy: 0.9225
30/65 [============>.................] - ETA: 9s - loss: 0.3237 - accuracy: 0.9219
31/65 [=============>................] - ETA: 8s - loss: 0.3232 - accuracy: 0.9222
32/65 [=============>................] - ETA: 8s - loss: 0.3239 - accuracy: 0.9219
33/65 [==============>...............] - ETA: 8s - loss: 0.3228 - accuracy: 0.9219
34/65 [==============>...............] - ETA: 7s - loss: 0.3234 - accuracy: 0.9220
35/65 [===============>..............] - ETA: 7s - loss: 0.3243 - accuracy: 0.9216
36/65 [===============>..............] - ETA: 7s - loss: 0.3249 - accuracy: 0.9215
37/65 [================>.............] - ETA: 7s - loss: 0.3251 - accuracy: 0.9214
38/65 [================>.............] - ETA: 6s - loss: 0.3239 - accuracy: 0.9218
39/65 [=================>............] - ETA: 6s - loss: 0.3239 - accuracy: 0.9219
40/65 [=================>............] - ETA: 6s - loss: 0.3244 - accuracy: 0.9216
41/65 [=================>............] - ETA: 6s - loss: 0.3239 - accuracy: 0.9217
42/65 [==================>...........] - ETA: 5s - loss: 0.3232 - accuracy: 0.9219
43/65 [==================>...........] - ETA: 5s - loss: 0.3223 - accuracy: 0.9221
44/65 [===================>..........] - ETA: 5s - loss: 0.3217 - accuracy: 0.9223
45/65 [===================>..........] - ETA: 5s - loss: 0.3209 - accuracy: 0.9225
46/65 [====================>.........] - ETA: 4s - loss: 0.3223 - accuracy: 0.9222
47/65 [====================>.........] - ETA: 4s - loss: 0.3229 - accuracy: 0.9221
48/65 [=====================>........] - ETA: 4s - loss: 0.3226 - accuracy: 0.9220
49/65 [=====================>........] - ETA: 4s - loss: 0.3226 - accuracy: 0.9220
50/65 [======================>.......] - ETA: 3s - loss: 0.3219 - accuracy: 0.9221
51/65 [======================>.......] - ETA: 3s - loss: 0.3222 - accuracy: 0.9220
52/65 [=======================>......] - ETA: 3s - loss: 0.3214 - accuracy: 0.9222
53/65 [=======================>......] - ETA: 3s - loss: 0.3209 - accuracy: 0.9224
54/65 [=======================>......] - ETA: 2s - loss: 0.3200 - accuracy: 0.9226
55/65 [========================>.....] - ETA: 2s - loss: 0.3196 - accuracy: 0.9226
56/65 [========================>.....] - ETA: 2s - loss: 0.3197 - accuracy: 0.9226
57/65 [=========================>....] - ETA: 2s - loss: 0.3192 - accuracy: 0.9227
58/65 [=========================>....] - ETA: 1s - loss: 0.3193 - accuracy: 0.9226
59/65 [==========================>...] - ETA: 1s - loss: 0.3189 - accuracy: 0.9227
60/65 [==========================>...] - ETA: 1s - loss: 0.3184 - accuracy: 0.9228
61/65 [===========================>..] - ETA: 1s - loss: 0.3178 - accuracy: 0.9230
62/65 [===========================>..] - ETA: 0s - loss: 0.3174 - accuracy: 0.9230
63/65 [============================>.] - ETA: 0s - loss: 0.3175 - accuracy: 0.9231
64/65 [============================>.] - ETA: 0s - loss: 0.3181 - accuracy: 0.9229
65/65 [==============================] - ETA: 0s - loss: 0.3180 - accuracy: 0.9230
65/65 [==============================] - 17s 263ms/step - loss: 0.3180 - accuracy: 0.9230 - val_loss: 0.4337 - val_accuracy: 0.8875 - lr: 7.5000e-04
Epoch 29/30
1/65 [..............................] - ETA: 30s - loss: 0.3170 - accuracy: 0.9160
2/65 [..............................] - ETA: 15s - loss: 0.3272 - accuracy: 0.9170
3/65 [>.............................] - ETA: 15s - loss: 0.3320 - accuracy: 0.9180
4/65 [>.............................] - ETA: 15s - loss: 0.3288 - accuracy: 0.9194
5/65 [=>............................] - ETA: 15s - loss: 0.3277 - accuracy: 0.9187
6/65 [=>............................] - ETA: 14s - loss: 0.3307 - accuracy: 0.9172
7/65 [==>...........................] - ETA: 14s - loss: 0.3317 - accuracy: 0.9178
8/65 [==>...........................] - ETA: 14s - loss: 0.3277 - accuracy: 0.9193
9/65 [===>..........................] - ETA: 14s - loss: 0.3265 - accuracy: 0.9201
10/65 [===>..........................] - ETA: 13s - loss: 0.3257 - accuracy: 0.9208
11/65 [====>.........................] - ETA: 13s - loss: 0.3242 - accuracy: 0.9204
12/65 [====>.........................] - ETA: 13s - loss: 0.3228 - accuracy: 0.9203
13/65 [=====>........................] - ETA: 13s - loss: 0.3234 - accuracy: 0.9205
14/65 [=====>........................] - ETA: 13s - loss: 0.3213 - accuracy: 0.9222
15/65 [=====>........................] - ETA: 12s - loss: 0.3197 - accuracy: 0.9229
16/65 [======>.......................] - ETA: 12s - loss: 0.3186 - accuracy: 0.9230
17/65 [======>.......................] - ETA: 12s - loss: 0.3221 - accuracy: 0.9219
18/65 [=======>......................] - ETA: 11s - loss: 0.3235 - accuracy: 0.9216
19/65 [=======>......................] - ETA: 11s - loss: 0.3220 - accuracy: 0.9221
20/65 [========>.....................] - ETA: 11s - loss: 0.3215 - accuracy: 0.9220
21/65 [========>.....................] - ETA: 11s - loss: 0.3219 - accuracy: 0.9222
22/65 [=========>....................] - ETA: 10s - loss: 0.3206 - accuracy: 0.9231
23/65 [=========>....................] - ETA: 10s - loss: 0.3203 - accuracy: 0.9229
24/65 [==========>...................] - ETA: 10s - loss: 0.3194 - accuracy: 0.9232
25/65 [==========>...................] - ETA: 10s - loss: 0.3186 - accuracy: 0.9229
26/65 [===========>..................] - ETA: 9s - loss: 0.3189 - accuracy: 0.9230
27/65 [===========>..................] - ETA: 9s - loss: 0.3197 - accuracy: 0.9226
28/65 [===========>..................] - ETA: 9s - loss: 0.3188 - accuracy: 0.9227
29/65 [============>.................] - ETA: 9s - loss: 0.3196 - accuracy: 0.9224
30/65 [============>.................] - ETA: 8s - loss: 0.3196 - accuracy: 0.9225
31/65 [=============>................] - ETA: 8s - loss: 0.3200 - accuracy: 0.9219
32/65 [=============>................] - ETA: 8s - loss: 0.3208 - accuracy: 0.9219
33/65 [==============>...............] - ETA: 8s - loss: 0.3210 - accuracy: 0.9218
34/65 [==============>...............] - ETA: 7s - loss: 0.3212 - accuracy: 0.9214
35/65 [===============>..............] - ETA: 7s - loss: 0.3211 - accuracy: 0.9212
36/65 [===============>..............] - ETA: 7s - loss: 0.3214 - accuracy: 0.9213
37/65 [================>.............] - ETA: 7s - loss: 0.3224 - accuracy: 0.9210
38/65 [================>.............] - ETA: 6s - loss: 0.3221 - accuracy: 0.9212
39/65 [=================>............] - ETA: 6s - loss: 0.3215 - accuracy: 0.9213
40/65 [=================>............] - ETA: 6s - loss: 0.3213 - accuracy: 0.9214
41/65 [=================>............] - ETA: 6s - loss: 0.3210 - accuracy: 0.9214
42/65 [==================>...........] - ETA: 5s - loss: 0.3213 - accuracy: 0.9214
43/65 [==================>...........] - ETA: 5s - loss: 0.3212 - accuracy: 0.9216
44/65 [===================>..........] - ETA: 5s - loss: 0.3215 - accuracy: 0.9217
45/65 [===================>..........] - ETA: 5s - loss: 0.3213 - accuracy: 0.9216
46/65 [====================>.........] - ETA: 4s - loss: 0.3207 - accuracy: 0.9218
47/65 [====================>.........] - ETA: 4s - loss: 0.3202 - accuracy: 0.9221
48/65 [=====================>........] - ETA: 4s - loss: 0.3195 - accuracy: 0.9224
49/65 [=====================>........] - ETA: 4s - loss: 0.3200 - accuracy: 0.9223
50/65 [======================>.......] - ETA: 3s - loss: 0.3197 - accuracy: 0.9223
51/65 [======================>.......] - ETA: 3s - loss: 0.3196 - accuracy: 0.9224
52/65 [=======================>......] - ETA: 3s - loss: 0.3191 - accuracy: 0.9226
53/65 [=======================>......] - ETA: 3s - loss: 0.3190 - accuracy: 0.9225
54/65 [=======================>......] - ETA: 2s - loss: 0.3192 - accuracy: 0.9225
55/65 [========================>.....] - ETA: 2s - loss: 0.3183 - accuracy: 0.9228
56/65 [========================>.....] - ETA: 2s - loss: 0.3183 - accuracy: 0.9227
57/65 [=========================>....] - ETA: 2s - loss: 0.3176 - accuracy: 0.9231
58/65 [=========================>....] - ETA: 1s - loss: 0.3173 - accuracy: 0.9231
59/65 [==========================>...] - ETA: 1s - loss: 0.3167 - accuracy: 0.9233
60/65 [==========================>...] - ETA: 1s - loss: 0.3171 - accuracy: 0.9233
61/65 [===========================>..] - ETA: 1s - loss: 0.3169 - accuracy: 0.9234
62/65 [===========================>..] - ETA: 0s - loss: 0.3169 - accuracy: 0.9234
63/65 [============================>.] - ETA: 0s - loss: 0.3164 - accuracy: 0.9236
64/65 [============================>.] - ETA: 0s - loss: 0.3160 - accuracy: 0.9237
65/65 [==============================] - ETA: 0s - loss: 0.3158 - accuracy: 0.9237
65/65 [==============================] - 17s 262ms/step - loss: 0.3158 - accuracy: 0.9237 - val_loss: 0.4340 - val_accuracy: 0.8866 - lr: 7.5000e-04
Epoch 30/30
1/65 [..............................] - ETA: 30s - loss: 0.3303 - accuracy: 0.9170
2/65 [..............................] - ETA: 19s - loss: 0.3504 - accuracy: 0.9136
3/65 [>.............................] - ETA: 17s - loss: 0.3387 - accuracy: 0.9167
4/65 [>.............................] - ETA: 16s - loss: 0.3326 - accuracy: 0.9192
5/65 [=>............................] - ETA: 16s - loss: 0.3294 - accuracy: 0.9207
6/65 [=>............................] - ETA: 16s - loss: 0.3288 - accuracy: 0.9204
7/65 [==>...........................] - ETA: 15s - loss: 0.3333 - accuracy: 0.9187
8/65 [==>...........................] - ETA: 15s - loss: 0.3293 - accuracy: 0.9210
9/65 [===>..........................] - ETA: 14s - loss: 0.3229 - accuracy: 0.9223
10/65 [===>..........................] - ETA: 14s - loss: 0.3247 - accuracy: 0.9218
11/65 [====>.........................] - ETA: 14s - loss: 0.3226 - accuracy: 0.9219
12/65 [====>.........................] - ETA: 13s - loss: 0.3205 - accuracy: 0.9239
13/65 [=====>........................] - ETA: 13s - loss: 0.3201 - accuracy: 0.9241
14/65 [=====>........................] - ETA: 13s - loss: 0.3193 - accuracy: 0.9244
15/65 [=====>........................] - ETA: 12s - loss: 0.3202 - accuracy: 0.9237
16/65 [======>.......................] - ETA: 12s - loss: 0.3191 - accuracy: 0.9236
17/65 [======>.......................] - ETA: 12s - loss: 0.3194 - accuracy: 0.9234
18/65 [=======>......................] - ETA: 12s - loss: 0.3194 - accuracy: 0.9238
19/65 [=======>......................] - ETA: 11s - loss: 0.3188 - accuracy: 0.9242
20/65 [========>.....................] - ETA: 11s - loss: 0.3178 - accuracy: 0.9243
21/65 [========>.....................] - ETA: 11s - loss: 0.3174 - accuracy: 0.9246
22/65 [=========>....................] - ETA: 11s - loss: 0.3170 - accuracy: 0.9248
23/65 [=========>....................] - ETA: 10s - loss: 0.3170 - accuracy: 0.9249
24/65 [==========>...................] - ETA: 10s - loss: 0.3165 - accuracy: 0.9248
25/65 [==========>...................] - ETA: 10s - loss: 0.3187 - accuracy: 0.9240
26/65 [===========>..................] - ETA: 10s - loss: 0.3182 - accuracy: 0.9237
27/65 [===========>..................] - ETA: 9s - loss: 0.3190 - accuracy: 0.9234
28/65 [===========>..................] - ETA: 9s - loss: 0.3178 - accuracy: 0.9237
29/65 [============>.................] - ETA: 9s - loss: 0.3189 - accuracy: 0.9235
30/65 [============>.................] - ETA: 9s - loss: 0.3186 - accuracy: 0.9235
31/65 [=============>................] - ETA: 8s - loss: 0.3181 - accuracy: 0.9233
32/65 [=============>................] - ETA: 8s - loss: 0.3187 - accuracy: 0.9228
33/65 [==============>...............] - ETA: 8s - loss: 0.3179 - accuracy: 0.9230
34/65 [==============>...............] - ETA: 8s - loss: 0.3205 - accuracy: 0.9227
35/65 [===============>..............] - ETA: 7s - loss: 0.3206 - accuracy: 0.9225
36/65 [===============>..............] - ETA: 7s - loss: 0.3212 - accuracy: 0.9223
37/65 [================>.............] - ETA: 7s - loss: 0.3202 - accuracy: 0.9226
38/65 [================>.............] - ETA: 6s - loss: 0.3198 - accuracy: 0.9224
39/65 [=================>............] - ETA: 6s - loss: 0.3207 - accuracy: 0.9223
40/65 [=================>............] - ETA: 6s - loss: 0.3202 - accuracy: 0.9223
41/65 [=================>............] - ETA: 6s - loss: 0.3201 - accuracy: 0.9222
42/65 [==================>...........] - ETA: 5s - loss: 0.3199 - accuracy: 0.9224
43/65 [==================>...........] - ETA: 5s - loss: 0.3200 - accuracy: 0.9224
44/65 [===================>..........] - ETA: 5s - loss: 0.3197 - accuracy: 0.9225
45/65 [===================>..........] - ETA: 5s - loss: 0.3197 - accuracy: 0.9224
46/65 [====================>.........] - ETA: 4s - loss: 0.3194 - accuracy: 0.9225
47/65 [====================>.........] - ETA: 4s - loss: 0.3189 - accuracy: 0.9227
48/65 [=====================>........] - ETA: 4s - loss: 0.3190 - accuracy: 0.9229
49/65 [=====================>........] - ETA: 4s - loss: 0.3193 - accuracy: 0.9227
50/65 [======================>.......] - ETA: 3s - loss: 0.3194 - accuracy: 0.9226
51/65 [======================>.......] - ETA: 3s - loss: 0.3191 - accuracy: 0.9228
52/65 [=======================>......] - ETA: 3s - loss: 0.3192 - accuracy: 0.9226
53/65 [=======================>......] - ETA: 3s - loss: 0.3185 - accuracy: 0.9228
54/65 [=======================>......] - ETA: 2s - loss: 0.3178 - accuracy: 0.9229
55/65 [========================>.....] - ETA: 2s - loss: 0.3177 - accuracy: 0.9230
56/65 [========================>.....] - ETA: 2s - loss: 0.3175 - accuracy: 0.9231
57/65 [=========================>....] - ETA: 2s - loss: 0.3167 - accuracy: 0.9232
58/65 [=========================>....] - ETA: 1s - loss: 0.3168 - accuracy: 0.9232
59/65 [==========================>...] - ETA: 1s - loss: 0.3165 - accuracy: 0.9234
60/65 [==========================>...] - ETA: 1s - loss: 0.3163 - accuracy: 0.9235
61/65 [===========================>..] - ETA: 1s - loss: 0.3161 - accuracy: 0.9236
62/65 [===========================>..] - ETA: 0s - loss: 0.3158 - accuracy: 0.9238
63/65 [============================>.] - ETA: 0s - loss: 0.3160 - accuracy: 0.9236
64/65 [============================>.] - ETA: 0s - loss: 0.3152 - accuracy: 0.9239
65/65 [==============================] - ETA: 0s - loss: 0.3151 - accuracy: 0.9240
Epoch 30: ReduceLROnPlateau reducing learning rate to 0.000375000003259629.
65/65 [==============================] - 17s 262ms/step - loss: 0.3151 - accuracy: 0.9240 - val_loss: 0.4342 - val_accuracy: 0.8882 - lr: 7.5000e-04
It took 8.776950001716614 minutes to train Keras model
You’ll notice the accuracy is lower than that in the hls4ml CNN paper (https://arxiv.org/abs/2101.05108) despite the model being the same. The reson for this is that we didn’t use the extra training data in order to save time. If you want to futher optimize the network, increasing the training data is a good place to start. Enlarging the model architecture comes at a high latency/resource cost.
Quantization and the fused Conv2D+BatchNormalization layer in QKeras#
Let’s now create a pruned an quantized model using QKeras. For this, we will use a fused Convolutional and BatchNormalization (BN) layer from QKeras, which will further speed up the implementation when we implement the model using hls4ml.
There is currently no fused Dense+BatchNoralization layer available in QKeras, so we’ll use Keras BatchNormalization when BN follows a Dense layer for now. We’ll use the same precision everywhere, namely a bit width of 6 and 0 integer bits (this will be implemented as<6,1> in hls4ml, due to the missing sign-bit). For now, make sure to set use_bias=True in QConv2DBatchnorm to avoid problems during synthesis.
from qkeras import QActivation
from qkeras import QDense, QConv2DBatchnorm
x = x_in = Input(shape=input_shape)
for i, f in enumerate(filters_per_conv_layer):
print(('Adding fused QConv+BN block {} with N={} filters').format(i, f))
x = QConv2DBatchnorm(
int(f),
kernel_size=(3, 3),
strides=(1, 1),
kernel_quantizer="quantized_bits(6,0,alpha=1)",
bias_quantizer="quantized_bits(6,0,alpha=1)",
kernel_initializer='lecun_uniform',
kernel_regularizer=l1(0.0001),
use_bias=True,
name='fused_convbn_{}'.format(i),
)(x)
x = QActivation('quantized_relu(6)', name='conv_act_%i' % i)(x)
x = MaxPooling2D(pool_size=(2, 2), name='pool_{}'.format(i))(x)
x = Flatten()(x)
for i, n in enumerate(neurons_per_dense_layer):
print(('Adding QDense block {} with N={} neurons').format(i, n))
x = QDense(
n,
kernel_quantizer="quantized_bits(6,0,alpha=1)",
kernel_initializer='lecun_uniform',
kernel_regularizer=l1(0.0001),
name='dense_%i' % i,
use_bias=False,
)(x)
x = BatchNormalization(name='bn_dense_{}'.format(i))(x)
x = QActivation('quantized_relu(6)', name='dense_act_%i' % i)(x)
x = Dense(int(n_classes), name='output_dense')(x)
x_out = Activation('softmax', name='output_softmax')(x)
qmodel = Model(inputs=[x_in], outputs=[x_out], name='qkeras')
qmodel.summary()
Adding fused QConv+BN block 0 with N=16 filters
Adding fused QConv+BN block 1 with N=16 filters
Adding fused QConv+BN block 2 with N=24 filters
Adding QDense block 0 with N=42 neurons
Adding QDense block 1 with N=64 neurons
Model: "qkeras"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_2 (InputLayer) [(None, 32, 32, 3)] 0
fused_convbn_0 (QConv2DBatc (None, 30, 30, 16) 513
hnorm)
conv_act_0 (QActivation) (None, 30, 30, 16) 0
pool_0 (MaxPooling2D) (None, 15, 15, 16) 0
fused_convbn_1 (QConv2DBatc (None, 13, 13, 16) 2385
hnorm)
conv_act_1 (QActivation) (None, 13, 13, 16) 0
pool_1 (MaxPooling2D) (None, 6, 6, 16) 0
fused_convbn_2 (QConv2DBatc (None, 4, 4, 24) 3577
hnorm)
conv_act_2 (QActivation) (None, 4, 4, 24) 0
pool_2 (MaxPooling2D) (None, 2, 2, 24) 0
flatten_1 (Flatten) (None, 96) 0
dense_0 (QDense) (None, 42) 4032
bn_dense_0 (BatchNormalizat (None, 42) 168
ion)
dense_act_0 (QActivation) (None, 42) 0
dense_1 (QDense) (None, 64) 2688
bn_dense_1 (BatchNormalizat (None, 64) 256
ion)
dense_act_1 (QActivation) (None, 64) 0
output_dense (Dense) (None, 10) 650
output_softmax (Activation) (None, 10) 0
=================================================================
Total params: 14,269
Trainable params: 13,942
Non-trainable params: 327
_________________________________________________________________
# Print the quantized layers
from qkeras.autoqkeras.utils import print_qmodel_summary
print_qmodel_summary(qmodel)
fused_convbn_0 f=16 quantized_bits(6,0,0,alpha=1) quantized_bits(6,0,0,alpha=1)
conv_act_0 quantized_relu(6)
fused_convbn_1 f=16 quantized_bits(6,0,0,alpha=1) quantized_bits(6,0,0,alpha=1)
conv_act_1 quantized_relu(6)
fused_convbn_2 f=24 quantized_bits(6,0,0,alpha=1) quantized_bits(6,0,0,alpha=1)
conv_act_2 quantized_relu(6)
dense_0 u=42 quantized_bits(6,0,0,alpha=1)
bn_dense_0 is normal keras bn layer
dense_act_0 quantized_relu(6)
dense_1 u=64 quantized_bits(6,0,0,alpha=1)
bn_dense_1 is normal keras bn layer
dense_act_1 quantized_relu(6)
You see that a bias quantizer is defined, although we are not using a bias term for the layers. This is set automatically by QKeras. In addition, you’ll note that alpha='1'. This sets the weight scale per channel to 1 (no scaling). The default is alpha='auto_po2', which sets the weight scale per channel to be a power-of-2, such that an actual hardware implementation can be performed by just shifting the result of the convolutional/dense layer to the right or left by checking the sign of the scale and then taking the log2 of the scale.
Let’s now prune and train this model! If you want, you can also train the unpruned version, qmodel and see how the performance compares. We will stick to the pruned one here. Again, we do not use a model checkpoint which stores the best weights, in order to ensure the model is trained to the desired sparsity.
qmodel_pruned = tf.keras.models.clone_model(qmodel, clone_function=pruneFunction)
train = True
n_epochs = 30
if train:
LOSS = tf.keras.losses.CategoricalCrossentropy()
OPTIMIZER = tf.keras.optimizers.Adam(learning_rate=3e-3, beta_1=0.9, beta_2=0.999, epsilon=1e-07, amsgrad=True)
qmodel_pruned.compile(loss=LOSS, optimizer=OPTIMIZER, metrics=["accuracy"])
callbacks = [
tf.keras.callbacks.EarlyStopping(patience=10, verbose=1),
tf.keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=3, verbose=1),
pruning_callbacks.UpdatePruningStep(),
]
start = time.time()
history = qmodel_pruned.fit(train_data, epochs=n_epochs, validation_data=val_data, callbacks=callbacks, verbose=1)
end = time.time()
print('\n It took {} minutes to train!\n'.format((end - start) / 60.0))
qmodel_pruned.save('quantized_pruned_cnn_model.h5')
else:
from qkeras.utils import _add_supported_quantized_objects
from tensorflow_model_optimization.python.core.sparsity.keras import pruning_wrapper
co = {}
_add_supported_quantized_objects(co)
co['PruneLowMagnitude'] = pruning_wrapper.PruneLowMagnitude
qmodel_pruned = tf.keras.models.load_model('quantized_pruned_cnn_model.h5', custom_objects=co)
Epoch 1/30
1/65 [..............................] - ETA: 4:24 - loss: 2.6032 - accuracy: 0.0977
2/65 [..............................] - ETA: 29s - loss: 2.5689 - accuracy: 0.1104
3/65 [>.............................] - ETA: 29s - loss: 2.5614 - accuracy: 0.1071
4/65 [>.............................] - ETA: 28s - loss: 2.5482 - accuracy: 0.1067
5/65 [=>............................] - ETA: 28s - loss: 2.5354 - accuracy: 0.1094
6/65 [=>............................] - ETA: 27s - loss: 2.5187 - accuracy: 0.1143
7/65 [==>...........................] - ETA: 27s - loss: 2.5052 - accuracy: 0.1172
8/65 [==>...........................] - ETA: 26s - loss: 2.4926 - accuracy: 0.1239
9/65 [===>..........................] - ETA: 26s - loss: 2.4784 - accuracy: 0.1297
10/65 [===>..........................] - ETA: 25s - loss: 2.4670 - accuracy: 0.1343
11/65 [====>.........................] - ETA: 25s - loss: 2.4590 - accuracy: 0.1396
12/65 [====>.........................] - ETA: 24s - loss: 2.4529 - accuracy: 0.1423
13/65 [=====>........................] - ETA: 24s - loss: 2.4438 - accuracy: 0.1472
14/65 [=====>........................] - ETA: 23s - loss: 2.4365 - accuracy: 0.1515
15/65 [=====>........................] - ETA: 23s - loss: 2.4298 - accuracy: 0.1550
16/65 [======>.......................] - ETA: 23s - loss: 2.4213 - accuracy: 0.1602
17/65 [======>.......................] - ETA: 22s - loss: 2.4167 - accuracy: 0.1624
18/65 [=======>......................] - ETA: 22s - loss: 2.4105 - accuracy: 0.1661
19/65 [=======>......................] - ETA: 21s - loss: 2.4052 - accuracy: 0.1700
20/65 [========>.....................] - ETA: 21s - loss: 2.3985 - accuracy: 0.1738
21/65 [========>.....................] - ETA: 20s - loss: 2.3929 - accuracy: 0.1762
22/65 [=========>....................] - ETA: 20s - loss: 2.3867 - accuracy: 0.1788
23/65 [=========>....................] - ETA: 19s - loss: 2.3827 - accuracy: 0.1811
24/65 [==========>...................] - ETA: 19s - loss: 2.3777 - accuracy: 0.1828
25/65 [==========>...................] - ETA: 18s - loss: 2.3720 - accuracy: 0.1858
26/65 [===========>..................] - ETA: 18s - loss: 2.3680 - accuracy: 0.1875
27/65 [===========>..................] - ETA: 17s - loss: 2.3644 - accuracy: 0.1892
28/65 [===========>..................] - ETA: 17s - loss: 2.3586 - accuracy: 0.1923
29/65 [============>.................] - ETA: 16s - loss: 2.3540 - accuracy: 0.1943
30/65 [============>.................] - ETA: 16s - loss: 2.3491 - accuracy: 0.1967
31/65 [=============>................] - ETA: 15s - loss: 2.3446 - accuracy: 0.1991
32/65 [=============>................] - ETA: 15s - loss: 2.3411 - accuracy: 0.2009
33/65 [==============>...............] - ETA: 15s - loss: 2.3349 - accuracy: 0.2047
34/65 [==============>...............] - ETA: 14s - loss: 2.3307 - accuracy: 0.2071
35/65 [===============>..............] - ETA: 14s - loss: 2.3266 - accuracy: 0.2092
36/65 [===============>..............] - ETA: 13s - loss: 2.3219 - accuracy: 0.2116
37/65 [================>.............] - ETA: 13s - loss: 2.3173 - accuracy: 0.2143
38/65 [================>.............] - ETA: 12s - loss: 2.3134 - accuracy: 0.2161
39/65 [=================>............] - ETA: 12s - loss: 2.3089 - accuracy: 0.2182
40/65 [=================>............] - ETA: 11s - loss: 2.3050 - accuracy: 0.2198
41/65 [=================>............] - ETA: 11s - loss: 2.3003 - accuracy: 0.2225
42/65 [==================>...........] - ETA: 10s - loss: 2.2952 - accuracy: 0.2251
43/65 [==================>...........] - ETA: 10s - loss: 2.2908 - accuracy: 0.2271
44/65 [===================>..........] - ETA: 9s - loss: 2.2866 - accuracy: 0.2291
45/65 [===================>..........] - ETA: 9s - loss: 2.2814 - accuracy: 0.2317
46/65 [====================>.........] - ETA: 8s - loss: 2.2772 - accuracy: 0.2334
47/65 [====================>.........] - ETA: 8s - loss: 2.2714 - accuracy: 0.2362
48/65 [=====================>........] - ETA: 7s - loss: 2.2661 - accuracy: 0.2387
49/65 [=====================>........] - ETA: 7s - loss: 2.2611 - accuracy: 0.2411
50/65 [======================>.......] - ETA: 6s - loss: 2.2551 - accuracy: 0.2437
51/65 [======================>.......] - ETA: 6s - loss: 2.2498 - accuracy: 0.2458
52/65 [=======================>......] - ETA: 6s - loss: 2.2446 - accuracy: 0.2482
53/65 [=======================>......] - ETA: 5s - loss: 2.2391 - accuracy: 0.2503
54/65 [=======================>......] - ETA: 5s - loss: 2.2349 - accuracy: 0.2521
55/65 [========================>.....] - ETA: 4s - loss: 2.2296 - accuracy: 0.2541
56/65 [========================>.....] - ETA: 4s - loss: 2.2243 - accuracy: 0.2569
57/65 [=========================>....] - ETA: 3s - loss: 2.2193 - accuracy: 0.2590
58/65 [=========================>....] - ETA: 3s - loss: 2.2135 - accuracy: 0.2618
59/65 [==========================>...] - ETA: 2s - loss: 2.2086 - accuracy: 0.2639
60/65 [==========================>...] - ETA: 2s - loss: 2.2034 - accuracy: 0.2661
61/65 [===========================>..] - ETA: 1s - loss: 2.1986 - accuracy: 0.2685
62/65 [===========================>..] - ETA: 1s - loss: 2.1932 - accuracy: 0.2708
63/65 [============================>.] - ETA: 0s - loss: 2.1885 - accuracy: 0.2728
64/65 [============================>.] - ETA: 0s - loss: 2.1839 - accuracy: 0.2745
65/65 [==============================] - ETA: 0s - loss: 2.1817 - accuracy: 0.2755
65/65 [==============================] - 35s 482ms/step - loss: 2.1817 - accuracy: 0.2755 - val_loss: 2.2886 - val_accuracy: 0.2173 - lr: 0.0030
Epoch 2/30
1/65 [..............................] - ETA: 45s - loss: 1.8350 - accuracy: 0.4160
2/65 [..............................] - ETA: 29s - loss: 1.8172 - accuracy: 0.4224
3/65 [>.............................] - ETA: 29s - loss: 1.7976 - accuracy: 0.4284
4/65 [>.............................] - ETA: 28s - loss: 1.8138 - accuracy: 0.4250
5/65 [=>............................] - ETA: 27s - loss: 1.8177 - accuracy: 0.4238
6/65 [=>............................] - ETA: 27s - loss: 1.8175 - accuracy: 0.4230
7/65 [==>...........................] - ETA: 26s - loss: 1.8110 - accuracy: 0.4265
8/65 [==>...........................] - ETA: 26s - loss: 1.8007 - accuracy: 0.4297
9/65 [===>..........................] - ETA: 25s - loss: 1.7960 - accuracy: 0.4321
10/65 [===>..........................] - ETA: 25s - loss: 1.7898 - accuracy: 0.4339
11/65 [====>.........................] - ETA: 24s - loss: 1.7836 - accuracy: 0.4356
12/65 [====>.........................] - ETA: 24s - loss: 1.7766 - accuracy: 0.4387
13/65 [=====>........................] - ETA: 23s - loss: 1.7708 - accuracy: 0.4422
14/65 [=====>........................] - ETA: 23s - loss: 1.7655 - accuracy: 0.4444
15/65 [=====>........................] - ETA: 23s - loss: 1.7625 - accuracy: 0.4459
16/65 [======>.......................] - ETA: 22s - loss: 1.7533 - accuracy: 0.4511
17/65 [======>.......................] - ETA: 22s - loss: 1.7452 - accuracy: 0.4541
18/65 [=======>......................] - ETA: 21s - loss: 1.7382 - accuracy: 0.4575
19/65 [=======>......................] - ETA: 21s - loss: 1.7319 - accuracy: 0.4593
20/65 [========>.....................] - ETA: 20s - loss: 1.7232 - accuracy: 0.4627
21/65 [========>.....................] - ETA: 20s - loss: 1.7165 - accuracy: 0.4657
22/65 [=========>....................] - ETA: 19s - loss: 1.7080 - accuracy: 0.4688
23/65 [=========>....................] - ETA: 19s - loss: 1.6988 - accuracy: 0.4724
24/65 [==========>...................] - ETA: 18s - loss: 1.6898 - accuracy: 0.4760
25/65 [==========>...................] - ETA: 18s - loss: 1.6821 - accuracy: 0.4783
26/65 [===========>..................] - ETA: 17s - loss: 1.6758 - accuracy: 0.4801
27/65 [===========>..................] - ETA: 17s - loss: 1.6662 - accuracy: 0.4838
28/65 [===========>..................] - ETA: 17s - loss: 1.6578 - accuracy: 0.4872
29/65 [============>.................] - ETA: 16s - loss: 1.6524 - accuracy: 0.4888
30/65 [============>.................] - ETA: 16s - loss: 1.6453 - accuracy: 0.4908
31/65 [=============>................] - ETA: 15s - loss: 1.6375 - accuracy: 0.4934
32/65 [=============>................] - ETA: 15s - loss: 1.6331 - accuracy: 0.4951
33/65 [==============>...............] - ETA: 14s - loss: 1.6259 - accuracy: 0.4978
34/65 [==============>...............] - ETA: 14s - loss: 1.6204 - accuracy: 0.4998
35/65 [===============>..............] - ETA: 13s - loss: 1.6140 - accuracy: 0.5018
36/65 [===============>..............] - ETA: 13s - loss: 1.6087 - accuracy: 0.5037
37/65 [================>.............] - ETA: 12s - loss: 1.6017 - accuracy: 0.5063
38/65 [================>.............] - ETA: 12s - loss: 1.5954 - accuracy: 0.5084
39/65 [=================>............] - ETA: 11s - loss: 1.5881 - accuracy: 0.5110
40/65 [=================>............] - ETA: 11s - loss: 1.5820 - accuracy: 0.5135
41/65 [=================>............] - ETA: 10s - loss: 1.5768 - accuracy: 0.5158
42/65 [==================>...........] - ETA: 10s - loss: 1.5696 - accuracy: 0.5186
43/65 [==================>...........] - ETA: 10s - loss: 1.5629 - accuracy: 0.5211
44/65 [===================>..........] - ETA: 9s - loss: 1.5568 - accuracy: 0.5240
45/65 [===================>..........] - ETA: 9s - loss: 1.5501 - accuracy: 0.5262
46/65 [====================>.........] - ETA: 8s - loss: 1.5462 - accuracy: 0.5275
47/65 [====================>.........] - ETA: 8s - loss: 1.5400 - accuracy: 0.5299
48/65 [=====================>........] - ETA: 7s - loss: 1.5341 - accuracy: 0.5324
49/65 [=====================>........] - ETA: 7s - loss: 1.5270 - accuracy: 0.5348
50/65 [======================>.......] - ETA: 6s - loss: 1.5208 - accuracy: 0.5368
51/65 [======================>.......] - ETA: 6s - loss: 1.5146 - accuracy: 0.5390
52/65 [=======================>......] - ETA: 5s - loss: 1.5099 - accuracy: 0.5406
53/65 [=======================>......] - ETA: 5s - loss: 1.5037 - accuracy: 0.5426
54/65 [=======================>......] - ETA: 5s - loss: 1.4975 - accuracy: 0.5447
55/65 [========================>.....] - ETA: 4s - loss: 1.4921 - accuracy: 0.5464
56/65 [========================>.....] - ETA: 4s - loss: 1.4859 - accuracy: 0.5489
57/65 [=========================>....] - ETA: 3s - loss: 1.4800 - accuracy: 0.5508
58/65 [=========================>....] - ETA: 3s - loss: 1.4743 - accuracy: 0.5526
59/65 [==========================>...] - ETA: 2s - loss: 1.4687 - accuracy: 0.5546
60/65 [==========================>...] - ETA: 2s - loss: 1.4633 - accuracy: 0.5565
61/65 [===========================>..] - ETA: 1s - loss: 1.4574 - accuracy: 0.5582
62/65 [===========================>..] - ETA: 1s - loss: 1.4515 - accuracy: 0.5605
63/65 [============================>.] - ETA: 0s - loss: 1.4460 - accuracy: 0.5624
64/65 [============================>.] - ETA: 0s - loss: 1.4409 - accuracy: 0.5642
65/65 [==============================] - ETA: 0s - loss: 1.4384 - accuracy: 0.5651
65/65 [==============================] - 31s 468ms/step - loss: 1.4384 - accuracy: 0.5651 - val_loss: 2.2410 - val_accuracy: 0.2138 - lr: 0.0030
Epoch 3/30
1/65 [..............................] - ETA: 44s - loss: 1.1446 - accuracy: 0.6758
2/65 [..............................] - ETA: 27s - loss: 1.1486 - accuracy: 0.6680
3/65 [>.............................] - ETA: 28s - loss: 1.1324 - accuracy: 0.6764
4/65 [>.............................] - ETA: 28s - loss: 1.1061 - accuracy: 0.6858
5/65 [=>............................] - ETA: 27s - loss: 1.1085 - accuracy: 0.6871
6/65 [=>............................] - ETA: 27s - loss: 1.1003 - accuracy: 0.6891
7/65 [==>...........................] - ETA: 27s - loss: 1.0968 - accuracy: 0.6889
8/65 [==>...........................] - ETA: 26s - loss: 1.0818 - accuracy: 0.6952
9/65 [===>..........................] - ETA: 26s - loss: 1.0746 - accuracy: 0.6999
10/65 [===>..........................] - ETA: 25s - loss: 1.0670 - accuracy: 0.7030
11/65 [====>.........................] - ETA: 25s - loss: 1.0665 - accuracy: 0.7042
12/65 [====>.........................] - ETA: 24s - loss: 1.0610 - accuracy: 0.7056
13/65 [=====>........................] - ETA: 24s - loss: 1.0611 - accuracy: 0.7059
14/65 [=====>........................] - ETA: 23s - loss: 1.0546 - accuracy: 0.7084
15/65 [=====>........................] - ETA: 23s - loss: 1.0498 - accuracy: 0.7090
16/65 [======>.......................] - ETA: 22s - loss: 1.0473 - accuracy: 0.7100
17/65 [======>.......................] - ETA: 22s - loss: 1.0397 - accuracy: 0.7133
18/65 [=======>......................] - ETA: 21s - loss: 1.0339 - accuracy: 0.7152
19/65 [=======>......................] - ETA: 21s - loss: 1.0324 - accuracy: 0.7158
20/65 [========>.....................] - ETA: 21s - loss: 1.0297 - accuracy: 0.7158
21/65 [========>.....................] - ETA: 20s - loss: 1.0250 - accuracy: 0.7171
22/65 [=========>....................] - ETA: 20s - loss: 1.0224 - accuracy: 0.7176
23/65 [=========>....................] - ETA: 19s - loss: 1.0171 - accuracy: 0.7195
24/65 [==========>...................] - ETA: 19s - loss: 1.0155 - accuracy: 0.7201
25/65 [==========>...................] - ETA: 18s - loss: 1.0118 - accuracy: 0.7216
26/65 [===========>..................] - ETA: 18s - loss: 1.0091 - accuracy: 0.7220
27/65 [===========>..................] - ETA: 17s - loss: 1.0070 - accuracy: 0.7222
28/65 [===========>..................] - ETA: 17s - loss: 1.0027 - accuracy: 0.7230
29/65 [============>.................] - ETA: 16s - loss: 1.0001 - accuracy: 0.7237
30/65 [============>.................] - ETA: 16s - loss: 0.9996 - accuracy: 0.7237
31/65 [=============>................] - ETA: 15s - loss: 0.9973 - accuracy: 0.7242
32/65 [=============>................] - ETA: 15s - loss: 0.9954 - accuracy: 0.7249
33/65 [==============>...............] - ETA: 14s - loss: 0.9924 - accuracy: 0.7259
34/65 [==============>...............] - ETA: 14s - loss: 0.9901 - accuracy: 0.7266
35/65 [===============>..............] - ETA: 13s - loss: 0.9888 - accuracy: 0.7270
36/65 [===============>..............] - ETA: 13s - loss: 0.9881 - accuracy: 0.7273
37/65 [================>.............] - ETA: 12s - loss: 0.9881 - accuracy: 0.7274
38/65 [================>.............] - ETA: 12s - loss: 0.9846 - accuracy: 0.7284
39/65 [=================>............] - ETA: 11s - loss: 0.9818 - accuracy: 0.7292
40/65 [=================>............] - ETA: 11s - loss: 0.9810 - accuracy: 0.7291
41/65 [=================>............] - ETA: 11s - loss: 0.9792 - accuracy: 0.7295
42/65 [==================>...........] - ETA: 10s - loss: 0.9767 - accuracy: 0.7301
43/65 [==================>...........] - ETA: 10s - loss: 0.9742 - accuracy: 0.7310
44/65 [===================>..........] - ETA: 9s - loss: 0.9717 - accuracy: 0.7320
45/65 [===================>..........] - ETA: 9s - loss: 0.9691 - accuracy: 0.7329
46/65 [====================>.........] - ETA: 8s - loss: 0.9675 - accuracy: 0.7334
47/65 [====================>.........] - ETA: 8s - loss: 0.9661 - accuracy: 0.7335
48/65 [=====================>........] - ETA: 7s - loss: 0.9635 - accuracy: 0.7345
49/65 [=====================>........] - ETA: 7s - loss: 0.9604 - accuracy: 0.7353
50/65 [======================>.......] - ETA: 6s - loss: 0.9589 - accuracy: 0.7356
51/65 [======================>.......] - ETA: 6s - loss: 0.9568 - accuracy: 0.7362
52/65 [=======================>......] - ETA: 5s - loss: 0.9553 - accuracy: 0.7365
53/65 [=======================>......] - ETA: 5s - loss: 0.9525 - accuracy: 0.7376
54/65 [=======================>......] - ETA: 5s - loss: 0.9509 - accuracy: 0.7384
55/65 [========================>.....] - ETA: 4s - loss: 0.9490 - accuracy: 0.7389
56/65 [========================>.....] - ETA: 4s - loss: 0.9468 - accuracy: 0.7397
57/65 [=========================>....] - ETA: 3s - loss: 0.9449 - accuracy: 0.7404
58/65 [=========================>....] - ETA: 3s - loss: 0.9436 - accuracy: 0.7406
59/65 [==========================>...] - ETA: 2s - loss: 0.9410 - accuracy: 0.7417
60/65 [==========================>...] - ETA: 2s - loss: 0.9378 - accuracy: 0.7427
61/65 [===========================>..] - ETA: 1s - loss: 0.9365 - accuracy: 0.7433
62/65 [===========================>..] - ETA: 1s - loss: 0.9333 - accuracy: 0.7440
63/65 [============================>.] - ETA: 0s - loss: 0.9325 - accuracy: 0.7445
64/65 [============================>.] - ETA: 0s - loss: 0.9305 - accuracy: 0.7452
65/65 [==============================] - ETA: 0s - loss: 0.9296 - accuracy: 0.7454
65/65 [==============================] - 31s 467ms/step - loss: 0.9296 - accuracy: 0.7454 - val_loss: 1.5909 - val_accuracy: 0.5206 - lr: 0.0030
Epoch 4/30
1/65 [..............................] - ETA: 45s - loss: 0.7544 - accuracy: 0.8076
2/65 [..............................] - ETA: 30s - loss: 0.8006 - accuracy: 0.7891
3/65 [>.............................] - ETA: 29s - loss: 0.8074 - accuracy: 0.7894
4/65 [>.............................] - ETA: 29s - loss: 0.8154 - accuracy: 0.7891
5/65 [=>............................] - ETA: 28s - loss: 0.8209 - accuracy: 0.7844
6/65 [=>............................] - ETA: 28s - loss: 0.8128 - accuracy: 0.7863
7/65 [==>...........................] - ETA: 27s - loss: 0.8062 - accuracy: 0.7868
8/65 [==>...........................] - ETA: 27s - loss: 0.8066 - accuracy: 0.7865
9/65 [===>..........................] - ETA: 26s - loss: 0.8046 - accuracy: 0.7875
10/65 [===>..........................] - ETA: 26s - loss: 0.8034 - accuracy: 0.7885
11/65 [====>.........................] - ETA: 25s - loss: 0.7973 - accuracy: 0.7894
12/65 [====>.........................] - ETA: 25s - loss: 0.7957 - accuracy: 0.7893
13/65 [=====>........................] - ETA: 24s - loss: 0.7949 - accuracy: 0.7897
14/65 [=====>........................] - ETA: 23s - loss: 0.7970 - accuracy: 0.7891
15/65 [=====>........................] - ETA: 23s - loss: 0.7967 - accuracy: 0.7889
16/65 [======>.......................] - ETA: 22s - loss: 0.7955 - accuracy: 0.7893
17/65 [======>.......................] - ETA: 22s - loss: 0.7977 - accuracy: 0.7887
18/65 [=======>......................] - ETA: 21s - loss: 0.7955 - accuracy: 0.7891
19/65 [=======>......................] - ETA: 21s - loss: 0.7925 - accuracy: 0.7900
20/65 [========>.....................] - ETA: 20s - loss: 0.7909 - accuracy: 0.7904
21/65 [========>.....................] - ETA: 20s - loss: 0.7908 - accuracy: 0.7905
22/65 [=========>....................] - ETA: 19s - loss: 0.7884 - accuracy: 0.7908
23/65 [=========>....................] - ETA: 19s - loss: 0.7870 - accuracy: 0.7913
24/65 [==========>...................] - ETA: 19s - loss: 0.7863 - accuracy: 0.7916
25/65 [==========>...................] - ETA: 18s - loss: 0.7843 - accuracy: 0.7924
26/65 [===========>..................] - ETA: 18s - loss: 0.7837 - accuracy: 0.7923
27/65 [===========>..................] - ETA: 17s - loss: 0.7835 - accuracy: 0.7920
28/65 [===========>..................] - ETA: 17s - loss: 0.7828 - accuracy: 0.7920
29/65 [============>.................] - ETA: 16s - loss: 0.7803 - accuracy: 0.7926
30/65 [============>.................] - ETA: 16s - loss: 0.7796 - accuracy: 0.7928
31/65 [=============>................] - ETA: 15s - loss: 0.7799 - accuracy: 0.7925
32/65 [=============>................] - ETA: 15s - loss: 0.7791 - accuracy: 0.7926
33/65 [==============>...............] - ETA: 14s - loss: 0.7788 - accuracy: 0.7924
34/65 [==============>...............] - ETA: 14s - loss: 0.7798 - accuracy: 0.7920
35/65 [===============>..............] - ETA: 13s - loss: 0.7778 - accuracy: 0.7928
36/65 [===============>..............] - ETA: 13s - loss: 0.7752 - accuracy: 0.7936
37/65 [================>.............] - ETA: 12s - loss: 0.7749 - accuracy: 0.7937
38/65 [================>.............] - ETA: 12s - loss: 0.7752 - accuracy: 0.7935
39/65 [=================>............] - ETA: 11s - loss: 0.7733 - accuracy: 0.7940
40/65 [=================>............] - ETA: 11s - loss: 0.7727 - accuracy: 0.7939
41/65 [=================>............] - ETA: 11s - loss: 0.7723 - accuracy: 0.7939
42/65 [==================>...........] - ETA: 10s - loss: 0.7700 - accuracy: 0.7946
43/65 [==================>...........] - ETA: 10s - loss: 0.7697 - accuracy: 0.7948
44/65 [===================>..........] - ETA: 9s - loss: 0.7691 - accuracy: 0.7950
45/65 [===================>..........] - ETA: 9s - loss: 0.7682 - accuracy: 0.7952
46/65 [====================>.........] - ETA: 8s - loss: 0.7672 - accuracy: 0.7958
47/65 [====================>.........] - ETA: 8s - loss: 0.7667 - accuracy: 0.7962
48/65 [=====================>........] - ETA: 7s - loss: 0.7653 - accuracy: 0.7967
49/65 [=====================>........] - ETA: 7s - loss: 0.7650 - accuracy: 0.7968
50/65 [======================>.......] - ETA: 6s - loss: 0.7637 - accuracy: 0.7972
51/65 [======================>.......] - ETA: 6s - loss: 0.7636 - accuracy: 0.7972
52/65 [=======================>......] - ETA: 5s - loss: 0.7630 - accuracy: 0.7973
53/65 [=======================>......] - ETA: 5s - loss: 0.7625 - accuracy: 0.7976
54/65 [=======================>......] - ETA: 5s - loss: 0.7617 - accuracy: 0.7979
55/65 [========================>.....] - ETA: 4s - loss: 0.7602 - accuracy: 0.7984
56/65 [========================>.....] - ETA: 4s - loss: 0.7593 - accuracy: 0.7988
57/65 [=========================>....] - ETA: 3s - loss: 0.7582 - accuracy: 0.7989
58/65 [=========================>....] - ETA: 3s - loss: 0.7571 - accuracy: 0.7993
59/65 [==========================>...] - ETA: 2s - loss: 0.7559 - accuracy: 0.7997
60/65 [==========================>...] - ETA: 2s - loss: 0.7551 - accuracy: 0.7999
61/65 [===========================>..] - ETA: 1s - loss: 0.7545 - accuracy: 0.8001
62/65 [===========================>..] - ETA: 1s - loss: 0.7546 - accuracy: 0.8002
63/65 [============================>.] - ETA: 0s - loss: 0.7536 - accuracy: 0.8006
64/65 [============================>.] - ETA: 0s - loss: 0.7536 - accuracy: 0.8006
65/65 [==============================] - ETA: 0s - loss: 0.7537 - accuracy: 0.8005
65/65 [==============================] - 31s 470ms/step - loss: 0.7537 - accuracy: 0.8005 - val_loss: 1.4052 - val_accuracy: 0.5759 - lr: 0.0030
Epoch 5/30
1/65 [..............................] - ETA: 42s - loss: 0.7009 - accuracy: 0.8330
2/65 [..............................] - ETA: 28s - loss: 0.7183 - accuracy: 0.8208
3/65 [>.............................] - ETA: 28s - loss: 0.7256 - accuracy: 0.8135
4/65 [>.............................] - ETA: 27s - loss: 0.7347 - accuracy: 0.8096
5/65 [=>............................] - ETA: 27s - loss: 0.7271 - accuracy: 0.8098
6/65 [=>............................] - ETA: 26s - loss: 0.7303 - accuracy: 0.8065
7/65 [==>...........................] - ETA: 26s - loss: 0.7305 - accuracy: 0.8068
8/65 [==>...........................] - ETA: 26s - loss: 0.7326 - accuracy: 0.8069
9/65 [===>..........................] - ETA: 25s - loss: 0.7251 - accuracy: 0.8099
10/65 [===>..........................] - ETA: 25s - loss: 0.7241 - accuracy: 0.8114
11/65 [====>.........................] - ETA: 25s - loss: 0.7224 - accuracy: 0.8114
12/65 [====>.........................] - ETA: 24s - loss: 0.7249 - accuracy: 0.8102
13/65 [=====>........................] - ETA: 24s - loss: 0.7245 - accuracy: 0.8105
14/65 [=====>........................] - ETA: 23s - loss: 0.7199 - accuracy: 0.8112
15/65 [=====>........................] - ETA: 23s - loss: 0.7235 - accuracy: 0.8104
16/65 [======>.......................] - ETA: 22s - loss: 0.7250 - accuracy: 0.8100
17/65 [======>.......................] - ETA: 22s - loss: 0.7227 - accuracy: 0.8103
18/65 [=======>......................] - ETA: 21s - loss: 0.7214 - accuracy: 0.8107
19/65 [=======>......................] - ETA: 21s - loss: 0.7194 - accuracy: 0.8118
20/65 [========>.....................] - ETA: 20s - loss: 0.7175 - accuracy: 0.8125
21/65 [========>.....................] - ETA: 20s - loss: 0.7142 - accuracy: 0.8136
22/65 [=========>....................] - ETA: 19s - loss: 0.7125 - accuracy: 0.8143
23/65 [=========>....................] - ETA: 19s - loss: 0.7135 - accuracy: 0.8139
24/65 [==========>...................] - ETA: 18s - loss: 0.7137 - accuracy: 0.8138
25/65 [==========>...................] - ETA: 18s - loss: 0.7110 - accuracy: 0.8143
26/65 [===========>..................] - ETA: 17s - loss: 0.7107 - accuracy: 0.8142
27/65 [===========>..................] - ETA: 17s - loss: 0.7105 - accuracy: 0.8139
28/65 [===========>..................] - ETA: 17s - loss: 0.7090 - accuracy: 0.8149
29/65 [============>.................] - ETA: 16s - loss: 0.7071 - accuracy: 0.8153
30/65 [============>.................] - ETA: 16s - loss: 0.7068 - accuracy: 0.8151
31/65 [=============>................] - ETA: 15s - loss: 0.7057 - accuracy: 0.8151
32/65 [=============>................] - ETA: 15s - loss: 0.7046 - accuracy: 0.8152
33/65 [==============>...............] - ETA: 14s - loss: 0.7052 - accuracy: 0.8149
34/65 [==============>...............] - ETA: 14s - loss: 0.7056 - accuracy: 0.8146
35/65 [===============>..............] - ETA: 13s - loss: 0.7040 - accuracy: 0.8152
36/65 [===============>..............] - ETA: 13s - loss: 0.7048 - accuracy: 0.8149
37/65 [================>.............] - ETA: 12s - loss: 0.7053 - accuracy: 0.8147
38/65 [================>.............] - ETA: 12s - loss: 0.7043 - accuracy: 0.8149
39/65 [=================>............] - ETA: 11s - loss: 0.7038 - accuracy: 0.8151
40/65 [=================>............] - ETA: 11s - loss: 0.7041 - accuracy: 0.8148
41/65 [=================>............] - ETA: 10s - loss: 0.7022 - accuracy: 0.8154
42/65 [==================>...........] - ETA: 10s - loss: 0.7006 - accuracy: 0.8156
43/65 [==================>...........] - ETA: 10s - loss: 0.7004 - accuracy: 0.8159
44/65 [===================>..........] - ETA: 9s - loss: 0.6994 - accuracy: 0.8162
45/65 [===================>..........] - ETA: 9s - loss: 0.6991 - accuracy: 0.8164
46/65 [====================>.........] - ETA: 8s - loss: 0.6991 - accuracy: 0.8164
47/65 [====================>.........] - ETA: 8s - loss: 0.6978 - accuracy: 0.8169
48/65 [=====================>........] - ETA: 7s - loss: 0.6966 - accuracy: 0.8173
49/65 [=====================>........] - ETA: 7s - loss: 0.6956 - accuracy: 0.8175
50/65 [======================>.......] - ETA: 6s - loss: 0.6947 - accuracy: 0.8177
51/65 [======================>.......] - ETA: 6s - loss: 0.6943 - accuracy: 0.8178
52/65 [=======================>......] - ETA: 5s - loss: 0.6934 - accuracy: 0.8182
53/65 [=======================>......] - ETA: 5s - loss: 0.6920 - accuracy: 0.8189
54/65 [=======================>......] - ETA: 5s - loss: 0.6907 - accuracy: 0.8192
55/65 [========================>.....] - ETA: 4s - loss: 0.6895 - accuracy: 0.8195
56/65 [========================>.....] - ETA: 4s - loss: 0.6891 - accuracy: 0.8196
57/65 [=========================>....] - ETA: 3s - loss: 0.6881 - accuracy: 0.8199
58/65 [=========================>....] - ETA: 3s - loss: 0.6874 - accuracy: 0.8201
59/65 [==========================>...] - ETA: 2s - loss: 0.6858 - accuracy: 0.8206
60/65 [==========================>...] - ETA: 2s - loss: 0.6871 - accuracy: 0.8200
61/65 [===========================>..] - ETA: 1s - loss: 0.6894 - accuracy: 0.8193
62/65 [===========================>..] - ETA: 1s - loss: 0.6895 - accuracy: 0.8193
63/65 [============================>.] - ETA: 0s - loss: 0.6901 - accuracy: 0.8190
64/65 [============================>.] - ETA: 0s - loss: 0.6896 - accuracy: 0.8193
65/65 [==============================] - ETA: 0s - loss: 0.6893 - accuracy: 0.8193
65/65 [==============================] - 31s 470ms/step - loss: 0.6893 - accuracy: 0.8193 - val_loss: 1.2372 - val_accuracy: 0.6280 - lr: 0.0030
Epoch 6/30
1/65 [..............................] - ETA: 45s - loss: 0.7161 - accuracy: 0.8008
2/65 [..............................] - ETA: 27s - loss: 0.7126 - accuracy: 0.8149
3/65 [>.............................] - ETA: 27s - loss: 0.7324 - accuracy: 0.8044
4/65 [>.............................] - ETA: 27s - loss: 0.7197 - accuracy: 0.8103
5/65 [=>............................] - ETA: 27s - loss: 0.7201 - accuracy: 0.8104
6/65 [=>............................] - ETA: 26s - loss: 0.7113 - accuracy: 0.8127
7/65 [==>...........................] - ETA: 26s - loss: 0.7068 - accuracy: 0.8133
8/65 [==>...........................] - ETA: 25s - loss: 0.7066 - accuracy: 0.8130
9/65 [===>..........................] - ETA: 25s - loss: 0.6989 - accuracy: 0.8151
10/65 [===>..........................] - ETA: 25s - loss: 0.7014 - accuracy: 0.8142
11/65 [====>.........................] - ETA: 24s - loss: 0.7010 - accuracy: 0.8150
12/65 [====>.........................] - ETA: 24s - loss: 0.6967 - accuracy: 0.8159
13/65 [=====>........................] - ETA: 23s - loss: 0.7012 - accuracy: 0.8139
14/65 [=====>........................] - ETA: 23s - loss: 0.6976 - accuracy: 0.8154
15/65 [=====>........................] - ETA: 23s - loss: 0.6940 - accuracy: 0.8175
16/65 [======>.......................] - ETA: 22s - loss: 0.6930 - accuracy: 0.8174
17/65 [======>.......................] - ETA: 22s - loss: 0.6934 - accuracy: 0.8165
18/65 [=======>......................] - ETA: 21s - loss: 0.6943 - accuracy: 0.8165
19/65 [=======>......................] - ETA: 21s - loss: 0.6905 - accuracy: 0.8180
20/65 [========>.....................] - ETA: 20s - loss: 0.6900 - accuracy: 0.8180
21/65 [========>.....................] - ETA: 20s - loss: 0.6870 - accuracy: 0.8185
22/65 [=========>....................] - ETA: 19s - loss: 0.6852 - accuracy: 0.8187
23/65 [=========>....................] - ETA: 19s - loss: 0.6829 - accuracy: 0.8193
24/65 [==========>...................] - ETA: 18s - loss: 0.6806 - accuracy: 0.8199
25/65 [==========>...................] - ETA: 18s - loss: 0.6810 - accuracy: 0.8197
26/65 [===========>..................] - ETA: 17s - loss: 0.6806 - accuracy: 0.8199
27/65 [===========>..................] - ETA: 17s - loss: 0.6797 - accuracy: 0.8200
28/65 [===========>..................] - ETA: 17s - loss: 0.6784 - accuracy: 0.8207
29/65 [============>.................] - ETA: 16s - loss: 0.6777 - accuracy: 0.8204
30/65 [============>.................] - ETA: 16s - loss: 0.6776 - accuracy: 0.8201
31/65 [=============>................] - ETA: 15s - loss: 0.6761 - accuracy: 0.8206
32/65 [=============>................] - ETA: 15s - loss: 0.6776 - accuracy: 0.8201
33/65 [==============>...............] - ETA: 14s - loss: 0.6755 - accuracy: 0.8206
34/65 [==============>...............] - ETA: 14s - loss: 0.6751 - accuracy: 0.8204
35/65 [===============>..............] - ETA: 13s - loss: 0.6741 - accuracy: 0.8207
36/65 [===============>..............] - ETA: 13s - loss: 0.6738 - accuracy: 0.8210
37/65 [================>.............] - ETA: 12s - loss: 0.6734 - accuracy: 0.8213
38/65 [================>.............] - ETA: 12s - loss: 0.6728 - accuracy: 0.8213
39/65 [=================>............] - ETA: 11s - loss: 0.6717 - accuracy: 0.8217
40/65 [=================>............] - ETA: 11s - loss: 0.6721 - accuracy: 0.8219
41/65 [=================>............] - ETA: 10s - loss: 0.6711 - accuracy: 0.8223
42/65 [==================>...........] - ETA: 10s - loss: 0.6699 - accuracy: 0.8228
43/65 [==================>...........] - ETA: 10s - loss: 0.6686 - accuracy: 0.8231
44/65 [===================>..........] - ETA: 9s - loss: 0.6700 - accuracy: 0.8227
45/65 [===================>..........] - ETA: 9s - loss: 0.6693 - accuracy: 0.8228
46/65 [====================>.........] - ETA: 8s - loss: 0.6686 - accuracy: 0.8229
47/65 [====================>.........] - ETA: 8s - loss: 0.6684 - accuracy: 0.8230
48/65 [=====================>........] - ETA: 7s - loss: 0.6678 - accuracy: 0.8233
49/65 [=====================>........] - ETA: 7s - loss: 0.6673 - accuracy: 0.8235
50/65 [======================>.......] - ETA: 6s - loss: 0.6670 - accuracy: 0.8235
51/65 [======================>.......] - ETA: 6s - loss: 0.6666 - accuracy: 0.8237
52/65 [=======================>......] - ETA: 5s - loss: 0.6657 - accuracy: 0.8238
53/65 [=======================>......] - ETA: 5s - loss: 0.6654 - accuracy: 0.8239
54/65 [=======================>......] - ETA: 5s - loss: 0.6639 - accuracy: 0.8244
55/65 [========================>.....] - ETA: 4s - loss: 0.6629 - accuracy: 0.8245
56/65 [========================>.....] - ETA: 4s - loss: 0.6621 - accuracy: 0.8248
57/65 [=========================>....] - ETA: 3s - loss: 0.6614 - accuracy: 0.8251
58/65 [=========================>....] - ETA: 3s - loss: 0.6609 - accuracy: 0.8252
59/65 [==========================>...] - ETA: 2s - loss: 0.6612 - accuracy: 0.8252
60/65 [==========================>...] - ETA: 2s - loss: 0.6617 - accuracy: 0.8248
61/65 [===========================>..] - ETA: 1s - loss: 0.6613 - accuracy: 0.8248
62/65 [===========================>..] - ETA: 1s - loss: 0.6621 - accuracy: 0.8245
63/65 [============================>.] - ETA: 0s - loss: 0.6627 - accuracy: 0.8243
64/65 [============================>.] - ETA: 0s - loss: 0.6636 - accuracy: 0.8240
65/65 [==============================] - ETA: 0s - loss: 0.6639 - accuracy: 0.8238
65/65 [==============================] - 31s 467ms/step - loss: 0.6639 - accuracy: 0.8238 - val_loss: 1.1448 - val_accuracy: 0.6600 - lr: 0.0030
Epoch 7/30
1/65 [..............................] - ETA: 46s - loss: 0.7594 - accuracy: 0.7920
2/65 [..............................] - ETA: 29s - loss: 0.7351 - accuracy: 0.8008
3/65 [>.............................] - ETA: 29s - loss: 0.6985 - accuracy: 0.8092
4/65 [>.............................] - ETA: 28s - loss: 0.6963 - accuracy: 0.8120
5/65 [=>............................] - ETA: 27s - loss: 0.6949 - accuracy: 0.8123
6/65 [=>............................] - ETA: 26s - loss: 0.6947 - accuracy: 0.8128
7/65 [==>...........................] - ETA: 26s - loss: 0.6942 - accuracy: 0.8119
8/65 [==>...........................] - ETA: 25s - loss: 0.6911 - accuracy: 0.8146
9/65 [===>..........................] - ETA: 25s - loss: 0.6812 - accuracy: 0.8178
10/65 [===>..........................] - ETA: 25s - loss: 0.6800 - accuracy: 0.8186
11/65 [====>.........................] - ETA: 24s - loss: 0.6797 - accuracy: 0.8183
12/65 [====>.........................] - ETA: 24s - loss: 0.6716 - accuracy: 0.8206
13/65 [=====>........................] - ETA: 23s - loss: 0.6700 - accuracy: 0.8212
14/65 [=====>........................] - ETA: 23s - loss: 0.6717 - accuracy: 0.8205
15/65 [=====>........................] - ETA: 23s - loss: 0.6746 - accuracy: 0.8197
16/65 [======>.......................] - ETA: 22s - loss: 0.6722 - accuracy: 0.8199
17/65 [======>.......................] - ETA: 22s - loss: 0.6724 - accuracy: 0.8189
18/65 [=======>......................] - ETA: 21s - loss: 0.6705 - accuracy: 0.8191
19/65 [=======>......................] - ETA: 21s - loss: 0.6687 - accuracy: 0.8189
20/65 [========>.....................] - ETA: 20s - loss: 0.6700 - accuracy: 0.8191
21/65 [========>.....................] - ETA: 20s - loss: 0.6703 - accuracy: 0.8195
22/65 [=========>....................] - ETA: 19s - loss: 0.6675 - accuracy: 0.8203
23/65 [=========>....................] - ETA: 19s - loss: 0.6655 - accuracy: 0.8211
24/65 [==========>...................] - ETA: 18s - loss: 0.6662 - accuracy: 0.8211
25/65 [==========>...................] - ETA: 18s - loss: 0.6668 - accuracy: 0.8211
26/65 [===========>..................] - ETA: 18s - loss: 0.6679 - accuracy: 0.8207
27/65 [===========>..................] - ETA: 17s - loss: 0.6662 - accuracy: 0.8208
28/65 [===========>..................] - ETA: 17s - loss: 0.6660 - accuracy: 0.8205
29/65 [============>.................] - ETA: 16s - loss: 0.6657 - accuracy: 0.8203
30/65 [============>.................] - ETA: 16s - loss: 0.6655 - accuracy: 0.8201
31/65 [=============>................] - ETA: 15s - loss: 0.6666 - accuracy: 0.8197
32/65 [=============>................] - ETA: 15s - loss: 0.6682 - accuracy: 0.8190
33/65 [==============>...............] - ETA: 14s - loss: 0.6674 - accuracy: 0.8190
34/65 [==============>...............] - ETA: 14s - loss: 0.6664 - accuracy: 0.8191
35/65 [===============>..............] - ETA: 13s - loss: 0.6656 - accuracy: 0.8194
36/65 [===============>..............] - ETA: 13s - loss: 0.6665 - accuracy: 0.8192
37/65 [================>.............] - ETA: 12s - loss: 0.6669 - accuracy: 0.8189
38/65 [================>.............] - ETA: 12s - loss: 0.6672 - accuracy: 0.8188
39/65 [=================>............] - ETA: 12s - loss: 0.6673 - accuracy: 0.8188
40/65 [=================>............] - ETA: 11s - loss: 0.6676 - accuracy: 0.8186
41/65 [=================>............] - ETA: 11s - loss: 0.6660 - accuracy: 0.8193
42/65 [==================>...........] - ETA: 10s - loss: 0.6669 - accuracy: 0.8190
43/65 [==================>...........] - ETA: 10s - loss: 0.6672 - accuracy: 0.8189
44/65 [===================>..........] - ETA: 9s - loss: 0.6662 - accuracy: 0.8191
45/65 [===================>..........] - ETA: 9s - loss: 0.6644 - accuracy: 0.8200
46/65 [====================>.........] - ETA: 8s - loss: 0.6640 - accuracy: 0.8202
47/65 [====================>.........] - ETA: 8s - loss: 0.6642 - accuracy: 0.8203
48/65 [=====================>........] - ETA: 7s - loss: 0.6638 - accuracy: 0.8203
49/65 [=====================>........] - ETA: 7s - loss: 0.6635 - accuracy: 0.8203
50/65 [======================>.......] - ETA: 6s - loss: 0.6619 - accuracy: 0.8208
51/65 [======================>.......] - ETA: 6s - loss: 0.6603 - accuracy: 0.8213
52/65 [=======================>......] - ETA: 6s - loss: 0.6597 - accuracy: 0.8214
53/65 [=======================>......] - ETA: 5s - loss: 0.6594 - accuracy: 0.8214
54/65 [=======================>......] - ETA: 5s - loss: 0.6577 - accuracy: 0.8219
55/65 [========================>.....] - ETA: 4s - loss: 0.6566 - accuracy: 0.8222
56/65 [========================>.....] - ETA: 4s - loss: 0.6557 - accuracy: 0.8225
57/65 [=========================>....] - ETA: 3s - loss: 0.6545 - accuracy: 0.8228
58/65 [=========================>....] - ETA: 3s - loss: 0.6539 - accuracy: 0.8231
59/65 [==========================>...] - ETA: 2s - loss: 0.6537 - accuracy: 0.8231
60/65 [==========================>...] - ETA: 2s - loss: 0.6534 - accuracy: 0.8233
61/65 [===========================>..] - ETA: 1s - loss: 0.6527 - accuracy: 0.8235
62/65 [===========================>..] - ETA: 1s - loss: 0.6523 - accuracy: 0.8235
63/65 [============================>.] - ETA: 0s - loss: 0.6513 - accuracy: 0.8237
64/65 [============================>.] - ETA: 0s - loss: 0.6511 - accuracy: 0.8237
65/65 [==============================] - ETA: 0s - loss: 0.6511 - accuracy: 0.8236
65/65 [==============================] - 31s 472ms/step - loss: 0.6511 - accuracy: 0.8236 - val_loss: 1.1266 - val_accuracy: 0.6545 - lr: 0.0030
Epoch 8/30
1/65 [..............................] - ETA: 44s - loss: 0.5746 - accuracy: 0.8447
2/65 [..............................] - ETA: 28s - loss: 0.6057 - accuracy: 0.8403
3/65 [>.............................] - ETA: 27s - loss: 0.6273 - accuracy: 0.8333
4/65 [>.............................] - ETA: 27s - loss: 0.6279 - accuracy: 0.8308
5/65 [=>............................] - ETA: 27s - loss: 0.6199 - accuracy: 0.8318
6/65 [=>............................] - ETA: 26s - loss: 0.6151 - accuracy: 0.8333
7/65 [==>...........................] - ETA: 26s - loss: 0.6135 - accuracy: 0.8324
8/65 [==>...........................] - ETA: 25s - loss: 0.6168 - accuracy: 0.8312
9/65 [===>..........................] - ETA: 25s - loss: 0.6138 - accuracy: 0.8326
10/65 [===>..........................] - ETA: 25s - loss: 0.6101 - accuracy: 0.8340
11/65 [====>.........................] - ETA: 24s - loss: 0.6060 - accuracy: 0.8352
12/65 [====>.........................] - ETA: 24s - loss: 0.6043 - accuracy: 0.8360
13/65 [=====>........................] - ETA: 23s - loss: 0.6033 - accuracy: 0.8368
14/65 [=====>........................] - ETA: 23s - loss: 0.6053 - accuracy: 0.8364
15/65 [=====>........................] - ETA: 23s - loss: 0.6046 - accuracy: 0.8374
16/65 [======>.......................] - ETA: 22s - loss: 0.6005 - accuracy: 0.8394
17/65 [======>.......................] - ETA: 22s - loss: 0.6008 - accuracy: 0.8392
18/65 [=======>......................] - ETA: 21s - loss: 0.6016 - accuracy: 0.8392
19/65 [=======>......................] - ETA: 21s - loss: 0.6016 - accuracy: 0.8397
20/65 [========>.....................] - ETA: 20s - loss: 0.6014 - accuracy: 0.8396
21/65 [========>.....................] - ETA: 20s - loss: 0.5992 - accuracy: 0.8405
22/65 [=========>....................] - ETA: 19s - loss: 0.6012 - accuracy: 0.8402
23/65 [=========>....................] - ETA: 19s - loss: 0.6006 - accuracy: 0.8408
24/65 [==========>...................] - ETA: 18s - loss: 0.5996 - accuracy: 0.8405
25/65 [==========>...................] - ETA: 18s - loss: 0.5989 - accuracy: 0.8410
26/65 [===========>..................] - ETA: 17s - loss: 0.5964 - accuracy: 0.8416
27/65 [===========>..................] - ETA: 17s - loss: 0.5952 - accuracy: 0.8418
28/65 [===========>..................] - ETA: 17s - loss: 0.5944 - accuracy: 0.8421
29/65 [============>.................] - ETA: 16s - loss: 0.5933 - accuracy: 0.8424
30/65 [============>.................] - ETA: 16s - loss: 0.5928 - accuracy: 0.8429
31/65 [=============>................] - ETA: 15s - loss: 0.5916 - accuracy: 0.8434
32/65 [=============>................] - ETA: 15s - loss: 0.5913 - accuracy: 0.8433
33/65 [==============>...............] - ETA: 14s - loss: 0.5937 - accuracy: 0.8424
34/65 [==============>...............] - ETA: 14s - loss: 0.5959 - accuracy: 0.8418
35/65 [===============>..............] - ETA: 13s - loss: 0.5964 - accuracy: 0.8420
36/65 [===============>..............] - ETA: 13s - loss: 0.5979 - accuracy: 0.8416
37/65 [================>.............] - ETA: 12s - loss: 0.5994 - accuracy: 0.8413
38/65 [================>.............] - ETA: 12s - loss: 0.5996 - accuracy: 0.8412
39/65 [=================>............] - ETA: 11s - loss: 0.5979 - accuracy: 0.8416
40/65 [=================>............] - ETA: 11s - loss: 0.5978 - accuracy: 0.8415
41/65 [=================>............] - ETA: 11s - loss: 0.5973 - accuracy: 0.8414
42/65 [==================>...........] - ETA: 10s - loss: 0.5984 - accuracy: 0.8413
43/65 [==================>...........] - ETA: 10s - loss: 0.5975 - accuracy: 0.8416
44/65 [===================>..........] - ETA: 9s - loss: 0.5987 - accuracy: 0.8410
45/65 [===================>..........] - ETA: 9s - loss: 0.5990 - accuracy: 0.8406
46/65 [====================>.........] - ETA: 8s - loss: 0.5992 - accuracy: 0.8402
47/65 [====================>.........] - ETA: 8s - loss: 0.5989 - accuracy: 0.8405
48/65 [=====================>........] - ETA: 7s - loss: 0.5990 - accuracy: 0.8405
49/65 [=====================>........] - ETA: 7s - loss: 0.5989 - accuracy: 0.8409
50/65 [======================>.......] - ETA: 6s - loss: 0.5978 - accuracy: 0.8410
51/65 [======================>.......] - ETA: 6s - loss: 0.5983 - accuracy: 0.8409
52/65 [=======================>......] - ETA: 5s - loss: 0.5978 - accuracy: 0.8412
53/65 [=======================>......] - ETA: 5s - loss: 0.5970 - accuracy: 0.8414
54/65 [=======================>......] - ETA: 5s - loss: 0.5979 - accuracy: 0.8411
55/65 [========================>.....] - ETA: 4s - loss: 0.5974 - accuracy: 0.8413
56/65 [========================>.....] - ETA: 4s - loss: 0.5981 - accuracy: 0.8411
57/65 [=========================>....] - ETA: 3s - loss: 0.5982 - accuracy: 0.8411
58/65 [=========================>....] - ETA: 3s - loss: 0.5985 - accuracy: 0.8409
59/65 [==========================>...] - ETA: 2s - loss: 0.5970 - accuracy: 0.8413
60/65 [==========================>...] - ETA: 2s - loss: 0.5964 - accuracy: 0.8416
61/65 [===========================>..] - ETA: 1s - loss: 0.5966 - accuracy: 0.8414
62/65 [===========================>..] - ETA: 1s - loss: 0.5968 - accuracy: 0.8416
63/65 [============================>.] - ETA: 0s - loss: 0.5964 - accuracy: 0.8416
64/65 [============================>.] - ETA: 0s - loss: 0.5961 - accuracy: 0.8415
65/65 [==============================] - ETA: 0s - loss: 0.5956 - accuracy: 0.8416
65/65 [==============================] - 31s 467ms/step - loss: 0.5956 - accuracy: 0.8416 - val_loss: 0.9876 - val_accuracy: 0.7189 - lr: 0.0030
Epoch 9/30
1/65 [..............................] - ETA: 44s - loss: 0.5799 - accuracy: 0.8486
2/65 [..............................] - ETA: 28s - loss: 0.5855 - accuracy: 0.8481
3/65 [>.............................] - ETA: 27s - loss: 0.5941 - accuracy: 0.8454
4/65 [>.............................] - ETA: 27s - loss: 0.5871 - accuracy: 0.8486
5/65 [=>............................] - ETA: 27s - loss: 0.5758 - accuracy: 0.8504
6/65 [=>............................] - ETA: 27s - loss: 0.5843 - accuracy: 0.8465
7/65 [==>...........................] - ETA: 26s - loss: 0.5847 - accuracy: 0.8460
8/65 [==>...........................] - ETA: 26s - loss: 0.5816 - accuracy: 0.8455
9/65 [===>..........................] - ETA: 25s - loss: 0.5802 - accuracy: 0.8461
10/65 [===>..........................] - ETA: 25s - loss: 0.5799 - accuracy: 0.8454
11/65 [====>.........................] - ETA: 24s - loss: 0.5807 - accuracy: 0.8454
12/65 [====>.........................] - ETA: 24s - loss: 0.5795 - accuracy: 0.8464
13/65 [=====>........................] - ETA: 24s - loss: 0.5821 - accuracy: 0.8466
14/65 [=====>........................] - ETA: 23s - loss: 0.5769 - accuracy: 0.8486
15/65 [=====>........................] - ETA: 22s - loss: 0.5790 - accuracy: 0.8484
16/65 [======>.......................] - ETA: 22s - loss: 0.5760 - accuracy: 0.8489
17/65 [======>.......................] - ETA: 21s - loss: 0.5775 - accuracy: 0.8485
18/65 [=======>......................] - ETA: 21s - loss: 0.5791 - accuracy: 0.8475
19/65 [=======>......................] - ETA: 20s - loss: 0.5795 - accuracy: 0.8471
20/65 [========>.....................] - ETA: 20s - loss: 0.5782 - accuracy: 0.8478
21/65 [========>.....................] - ETA: 20s - loss: 0.5768 - accuracy: 0.8480
22/65 [=========>....................] - ETA: 19s - loss: 0.5741 - accuracy: 0.8488
23/65 [=========>....................] - ETA: 19s - loss: 0.5736 - accuracy: 0.8493
24/65 [==========>...................] - ETA: 18s - loss: 0.5739 - accuracy: 0.8491
25/65 [==========>...................] - ETA: 18s - loss: 0.5744 - accuracy: 0.8482
26/65 [===========>..................] - ETA: 17s - loss: 0.5747 - accuracy: 0.8485
27/65 [===========>..................] - ETA: 17s - loss: 0.5732 - accuracy: 0.8488
28/65 [===========>..................] - ETA: 16s - loss: 0.5718 - accuracy: 0.8494
29/65 [============>.................] - ETA: 16s - loss: 0.5721 - accuracy: 0.8493
30/65 [============>.................] - ETA: 15s - loss: 0.5709 - accuracy: 0.8495
31/65 [=============>................] - ETA: 15s - loss: 0.5712 - accuracy: 0.8498
32/65 [=============>................] - ETA: 14s - loss: 0.5718 - accuracy: 0.8495
33/65 [==============>...............] - ETA: 14s - loss: 0.5719 - accuracy: 0.8492
34/65 [==============>...............] - ETA: 14s - loss: 0.5723 - accuracy: 0.8493
35/65 [===============>..............] - ETA: 13s - loss: 0.5719 - accuracy: 0.8492
36/65 [===============>..............] - ETA: 13s - loss: 0.5715 - accuracy: 0.8494
37/65 [================>.............] - ETA: 12s - loss: 0.5710 - accuracy: 0.8496
38/65 [================>.............] - ETA: 12s - loss: 0.5719 - accuracy: 0.8492
39/65 [=================>............] - ETA: 11s - loss: 0.5721 - accuracy: 0.8492
40/65 [=================>............] - ETA: 11s - loss: 0.5724 - accuracy: 0.8494
41/65 [=================>............] - ETA: 10s - loss: 0.5721 - accuracy: 0.8493
42/65 [==================>...........] - ETA: 10s - loss: 0.5724 - accuracy: 0.8492
43/65 [==================>...........] - ETA: 9s - loss: 0.5711 - accuracy: 0.8495
44/65 [===================>..........] - ETA: 9s - loss: 0.5701 - accuracy: 0.8497
45/65 [===================>..........] - ETA: 9s - loss: 0.5702 - accuracy: 0.8497
46/65 [====================>.........] - ETA: 8s - loss: 0.5700 - accuracy: 0.8499
47/65 [====================>.........] - ETA: 8s - loss: 0.5714 - accuracy: 0.8495
48/65 [=====================>........] - ETA: 7s - loss: 0.5715 - accuracy: 0.8496
49/65 [=====================>........] - ETA: 7s - loss: 0.5711 - accuracy: 0.8496
50/65 [======================>.......] - ETA: 6s - loss: 0.5707 - accuracy: 0.8495
51/65 [======================>.......] - ETA: 6s - loss: 0.5688 - accuracy: 0.8502
52/65 [=======================>......] - ETA: 5s - loss: 0.5687 - accuracy: 0.8501
53/65 [=======================>......] - ETA: 5s - loss: 0.5679 - accuracy: 0.8504
54/65 [=======================>......] - ETA: 5s - loss: 0.5683 - accuracy: 0.8501
55/65 [========================>.....] - ETA: 4s - loss: 0.5678 - accuracy: 0.8502
56/65 [========================>.....] - ETA: 4s - loss: 0.5669 - accuracy: 0.8504
57/65 [=========================>....] - ETA: 3s - loss: 0.5663 - accuracy: 0.8507
58/65 [=========================>....] - ETA: 3s - loss: 0.5660 - accuracy: 0.8509
59/65 [==========================>...] - ETA: 2s - loss: 0.5653 - accuracy: 0.8510
60/65 [==========================>...] - ETA: 2s - loss: 0.5652 - accuracy: 0.8512
61/65 [===========================>..] - ETA: 1s - loss: 0.5649 - accuracy: 0.8513
62/65 [===========================>..] - ETA: 1s - loss: 0.5655 - accuracy: 0.8511
63/65 [============================>.] - ETA: 0s - loss: 0.5659 - accuracy: 0.8509
64/65 [============================>.] - ETA: 0s - loss: 0.5653 - accuracy: 0.8511
65/65 [==============================] - ETA: 0s - loss: 0.5656 - accuracy: 0.8510
65/65 [==============================] - 31s 466ms/step - loss: 0.5656 - accuracy: 0.8510 - val_loss: 1.0347 - val_accuracy: 0.6930 - lr: 0.0030
Epoch 10/30
1/65 [..............................] - ETA: 43s - loss: 0.5624 - accuracy: 0.8613
2/65 [..............................] - ETA: 29s - loss: 0.5711 - accuracy: 0.8525
3/65 [>.............................] - ETA: 29s - loss: 0.5756 - accuracy: 0.8529
4/65 [>.............................] - ETA: 29s - loss: 0.5587 - accuracy: 0.8557
5/65 [=>............................] - ETA: 28s - loss: 0.5593 - accuracy: 0.8545
6/65 [=>............................] - ETA: 27s - loss: 0.5568 - accuracy: 0.8551
7/65 [==>...........................] - ETA: 27s - loss: 0.5604 - accuracy: 0.8537
8/65 [==>...........................] - ETA: 26s - loss: 0.5636 - accuracy: 0.8512
9/65 [===>..........................] - ETA: 25s - loss: 0.5560 - accuracy: 0.8530
10/65 [===>..........................] - ETA: 25s - loss: 0.5550 - accuracy: 0.8535
11/65 [====>.........................] - ETA: 24s - loss: 0.5533 - accuracy: 0.8542
12/65 [====>.........................] - ETA: 24s - loss: 0.5579 - accuracy: 0.8534
13/65 [=====>........................] - ETA: 23s - loss: 0.5555 - accuracy: 0.8536
14/65 [=====>........................] - ETA: 23s - loss: 0.5577 - accuracy: 0.8530
15/65 [=====>........................] - ETA: 22s - loss: 0.5562 - accuracy: 0.8533
16/65 [======>.......................] - ETA: 22s - loss: 0.5569 - accuracy: 0.8531
17/65 [======>.......................] - ETA: 22s - loss: 0.5573 - accuracy: 0.8527
18/65 [=======>......................] - ETA: 21s - loss: 0.5603 - accuracy: 0.8521
19/65 [=======>......................] - ETA: 21s - loss: 0.5601 - accuracy: 0.8517
20/65 [========>.....................] - ETA: 20s - loss: 0.5610 - accuracy: 0.8517
21/65 [========>.....................] - ETA: 20s - loss: 0.5612 - accuracy: 0.8507
22/65 [=========>....................] - ETA: 19s - loss: 0.5607 - accuracy: 0.8508
23/65 [=========>....................] - ETA: 19s - loss: 0.5603 - accuracy: 0.8504
24/65 [==========>...................] - ETA: 18s - loss: 0.5597 - accuracy: 0.8508
25/65 [==========>...................] - ETA: 18s - loss: 0.5597 - accuracy: 0.8505
26/65 [===========>..................] - ETA: 17s - loss: 0.5587 - accuracy: 0.8507
27/65 [===========>..................] - ETA: 17s - loss: 0.5592 - accuracy: 0.8508
28/65 [===========>..................] - ETA: 16s - loss: 0.5610 - accuracy: 0.8504
29/65 [============>.................] - ETA: 16s - loss: 0.5610 - accuracy: 0.8504
30/65 [============>.................] - ETA: 16s - loss: 0.5596 - accuracy: 0.8508
31/65 [=============>................] - ETA: 15s - loss: 0.5608 - accuracy: 0.8502
32/65 [=============>................] - ETA: 15s - loss: 0.5603 - accuracy: 0.8505
33/65 [==============>...............] - ETA: 14s - loss: 0.5622 - accuracy: 0.8501
34/65 [==============>...............] - ETA: 14s - loss: 0.5652 - accuracy: 0.8492
35/65 [===============>..............] - ETA: 13s - loss: 0.5665 - accuracy: 0.8489
36/65 [===============>..............] - ETA: 13s - loss: 0.5664 - accuracy: 0.8487
37/65 [================>.............] - ETA: 12s - loss: 0.5678 - accuracy: 0.8483
38/65 [================>.............] - ETA: 12s - loss: 0.5692 - accuracy: 0.8476
39/65 [=================>............] - ETA: 11s - loss: 0.5685 - accuracy: 0.8481
40/65 [=================>............] - ETA: 11s - loss: 0.5694 - accuracy: 0.8478
41/65 [=================>............] - ETA: 10s - loss: 0.5692 - accuracy: 0.8478
42/65 [==================>...........] - ETA: 10s - loss: 0.5687 - accuracy: 0.8478
43/65 [==================>...........] - ETA: 10s - loss: 0.5681 - accuracy: 0.8480
44/65 [===================>..........] - ETA: 9s - loss: 0.5685 - accuracy: 0.8478
45/65 [===================>..........] - ETA: 9s - loss: 0.5699 - accuracy: 0.8475
46/65 [====================>.........] - ETA: 8s - loss: 0.5700 - accuracy: 0.8476
47/65 [====================>.........] - ETA: 8s - loss: 0.5703 - accuracy: 0.8476
48/65 [=====================>........] - ETA: 7s - loss: 0.5718 - accuracy: 0.8473
49/65 [=====================>........] - ETA: 7s - loss: 0.5708 - accuracy: 0.8475
50/65 [======================>.......] - ETA: 6s - loss: 0.5703 - accuracy: 0.8474
51/65 [======================>.......] - ETA: 6s - loss: 0.5695 - accuracy: 0.8476
52/65 [=======================>......] - ETA: 5s - loss: 0.5690 - accuracy: 0.8478
53/65 [=======================>......] - ETA: 5s - loss: 0.5686 - accuracy: 0.8479
54/65 [=======================>......] - ETA: 5s - loss: 0.5677 - accuracy: 0.8481
55/65 [========================>.....] - ETA: 4s - loss: 0.5677 - accuracy: 0.8481
56/65 [========================>.....] - ETA: 4s - loss: 0.5673 - accuracy: 0.8482
57/65 [=========================>....] - ETA: 3s - loss: 0.5664 - accuracy: 0.8486
58/65 [=========================>....] - ETA: 3s - loss: 0.5662 - accuracy: 0.8487
59/65 [==========================>...] - ETA: 2s - loss: 0.5663 - accuracy: 0.8486
60/65 [==========================>...] - ETA: 2s - loss: 0.5668 - accuracy: 0.8486
61/65 [===========================>..] - ETA: 1s - loss: 0.5672 - accuracy: 0.8485
62/65 [===========================>..] - ETA: 1s - loss: 0.5669 - accuracy: 0.8485
63/65 [============================>.] - ETA: 0s - loss: 0.5663 - accuracy: 0.8488
64/65 [============================>.] - ETA: 0s - loss: 0.5666 - accuracy: 0.8486
65/65 [==============================] - ETA: 0s - loss: 0.5665 - accuracy: 0.8486
65/65 [==============================] - 31s 468ms/step - loss: 0.5665 - accuracy: 0.8486 - val_loss: 0.8361 - val_accuracy: 0.7548 - lr: 0.0030
Epoch 11/30
1/65 [..............................] - ETA: 43s - loss: 0.5768 - accuracy: 0.8486
2/65 [..............................] - ETA: 29s - loss: 0.5859 - accuracy: 0.8462
3/65 [>.............................] - ETA: 28s - loss: 0.5753 - accuracy: 0.8538
4/65 [>.............................] - ETA: 28s - loss: 0.5681 - accuracy: 0.8525
5/65 [=>............................] - ETA: 28s - loss: 0.5586 - accuracy: 0.8525
6/65 [=>............................] - ETA: 27s - loss: 0.5609 - accuracy: 0.8514
7/65 [==>...........................] - ETA: 27s - loss: 0.5597 - accuracy: 0.8516
8/65 [==>...........................] - ETA: 26s - loss: 0.5544 - accuracy: 0.8535
9/65 [===>..........................] - ETA: 26s - loss: 0.5556 - accuracy: 0.8532
10/65 [===>..........................] - ETA: 25s - loss: 0.5544 - accuracy: 0.8534
11/65 [====>.........................] - ETA: 25s - loss: 0.5555 - accuracy: 0.8523
12/65 [====>.........................] - ETA: 24s - loss: 0.5562 - accuracy: 0.8516
13/65 [=====>........................] - ETA: 24s - loss: 0.5599 - accuracy: 0.8507
14/65 [=====>........................] - ETA: 23s - loss: 0.5581 - accuracy: 0.8518
15/65 [=====>........................] - ETA: 23s - loss: 0.5599 - accuracy: 0.8511
16/65 [======>.......................] - ETA: 22s - loss: 0.5562 - accuracy: 0.8525
17/65 [======>.......................] - ETA: 22s - loss: 0.5561 - accuracy: 0.8524
18/65 [=======>......................] - ETA: 21s - loss: 0.5541 - accuracy: 0.8529
19/65 [=======>......................] - ETA: 21s - loss: 0.5563 - accuracy: 0.8522
20/65 [========>.....................] - ETA: 20s - loss: 0.5559 - accuracy: 0.8521
21/65 [========>.....................] - ETA: 20s - loss: 0.5549 - accuracy: 0.8522
22/65 [=========>....................] - ETA: 19s - loss: 0.5529 - accuracy: 0.8524
23/65 [=========>....................] - ETA: 19s - loss: 0.5526 - accuracy: 0.8524
24/65 [==========>...................] - ETA: 19s - loss: 0.5529 - accuracy: 0.8520
25/65 [==========>...................] - ETA: 18s - loss: 0.5527 - accuracy: 0.8518
26/65 [===========>..................] - ETA: 18s - loss: 0.5522 - accuracy: 0.8517
27/65 [===========>..................] - ETA: 17s - loss: 0.5513 - accuracy: 0.8521
28/65 [===========>..................] - ETA: 17s - loss: 0.5508 - accuracy: 0.8525
29/65 [============>.................] - ETA: 16s - loss: 0.5492 - accuracy: 0.8530
30/65 [============>.................] - ETA: 16s - loss: 0.5482 - accuracy: 0.8533
31/65 [=============>................] - ETA: 15s - loss: 0.5472 - accuracy: 0.8538
32/65 [=============>................] - ETA: 15s - loss: 0.5497 - accuracy: 0.8529
33/65 [==============>...............] - ETA: 14s - loss: 0.5496 - accuracy: 0.8530
34/65 [==============>...............] - ETA: 14s - loss: 0.5496 - accuracy: 0.8530
35/65 [===============>..............] - ETA: 13s - loss: 0.5500 - accuracy: 0.8527
36/65 [===============>..............] - ETA: 13s - loss: 0.5490 - accuracy: 0.8533
37/65 [================>.............] - ETA: 12s - loss: 0.5470 - accuracy: 0.8540
38/65 [================>.............] - ETA: 12s - loss: 0.5480 - accuracy: 0.8535
39/65 [=================>............] - ETA: 11s - loss: 0.5480 - accuracy: 0.8536
40/65 [=================>............] - ETA: 11s - loss: 0.5493 - accuracy: 0.8530
41/65 [=================>............] - ETA: 10s - loss: 0.5500 - accuracy: 0.8528
42/65 [==================>...........] - ETA: 10s - loss: 0.5500 - accuracy: 0.8527
43/65 [==================>...........] - ETA: 10s - loss: 0.5504 - accuracy: 0.8527
44/65 [===================>..........] - ETA: 9s - loss: 0.5511 - accuracy: 0.8525
45/65 [===================>..........] - ETA: 9s - loss: 0.5506 - accuracy: 0.8527
46/65 [====================>.........] - ETA: 8s - loss: 0.5511 - accuracy: 0.8525
47/65 [====================>.........] - ETA: 8s - loss: 0.5504 - accuracy: 0.8530
48/65 [=====================>........] - ETA: 7s - loss: 0.5503 - accuracy: 0.8531
49/65 [=====================>........] - ETA: 7s - loss: 0.5500 - accuracy: 0.8531
50/65 [======================>.......] - ETA: 6s - loss: 0.5496 - accuracy: 0.8531
51/65 [======================>.......] - ETA: 6s - loss: 0.5497 - accuracy: 0.8534
52/65 [=======================>......] - ETA: 5s - loss: 0.5492 - accuracy: 0.8535
53/65 [=======================>......] - ETA: 5s - loss: 0.5494 - accuracy: 0.8533
54/65 [=======================>......] - ETA: 5s - loss: 0.5484 - accuracy: 0.8535
55/65 [========================>.....] - ETA: 4s - loss: 0.5482 - accuracy: 0.8538
56/65 [========================>.....] - ETA: 4s - loss: 0.5475 - accuracy: 0.8541
57/65 [=========================>....] - ETA: 3s - loss: 0.5479 - accuracy: 0.8540
58/65 [=========================>....] - ETA: 3s - loss: 0.5474 - accuracy: 0.8542
59/65 [==========================>...] - ETA: 2s - loss: 0.5480 - accuracy: 0.8541
60/65 [==========================>...] - ETA: 2s - loss: 0.5482 - accuracy: 0.8541
61/65 [===========================>..] - ETA: 1s - loss: 0.5478 - accuracy: 0.8543
62/65 [===========================>..] - ETA: 1s - loss: 0.5476 - accuracy: 0.8544
63/65 [============================>.] - ETA: 0s - loss: 0.5474 - accuracy: 0.8545
64/65 [============================>.] - ETA: 0s - loss: 0.5463 - accuracy: 0.8548
65/65 [==============================] - ETA: 0s - loss: 0.5464 - accuracy: 0.8546
65/65 [==============================] - 31s 469ms/step - loss: 0.5464 - accuracy: 0.8546 - val_loss: 0.7943 - val_accuracy: 0.7673 - lr: 0.0030
Epoch 12/30
1/65 [..............................] - ETA: 45s - loss: 0.5417 - accuracy: 0.8438
2/65 [..............................] - ETA: 29s - loss: 0.5445 - accuracy: 0.8457
3/65 [>.............................] - ETA: 29s - loss: 0.5382 - accuracy: 0.8519
4/65 [>.............................] - ETA: 28s - loss: 0.5453 - accuracy: 0.8530
5/65 [=>............................] - ETA: 28s - loss: 0.5526 - accuracy: 0.8494
6/65 [=>............................] - ETA: 28s - loss: 0.5530 - accuracy: 0.8506
7/65 [==>...........................] - ETA: 27s - loss: 0.5599 - accuracy: 0.8496
8/65 [==>...........................] - ETA: 27s - loss: 0.5558 - accuracy: 0.8511
9/65 [===>..........................] - ETA: 26s - loss: 0.5564 - accuracy: 0.8510
10/65 [===>..........................] - ETA: 26s - loss: 0.5501 - accuracy: 0.8528
11/65 [====>.........................] - ETA: 25s - loss: 0.5539 - accuracy: 0.8524
12/65 [====>.........................] - ETA: 25s - loss: 0.5492 - accuracy: 0.8534
13/65 [=====>........................] - ETA: 24s - loss: 0.5457 - accuracy: 0.8552
14/65 [=====>........................] - ETA: 24s - loss: 0.5433 - accuracy: 0.8554
15/65 [=====>........................] - ETA: 23s - loss: 0.5428 - accuracy: 0.8557
16/65 [======>.......................] - ETA: 23s - loss: 0.5426 - accuracy: 0.8555
17/65 [======>.......................] - ETA: 22s - loss: 0.5437 - accuracy: 0.8555
18/65 [=======>......................] - ETA: 22s - loss: 0.5418 - accuracy: 0.8559
19/65 [=======>......................] - ETA: 21s - loss: 0.5418 - accuracy: 0.8563
20/65 [========>.....................] - ETA: 21s - loss: 0.5398 - accuracy: 0.8569
21/65 [========>.....................] - ETA: 20s - loss: 0.5362 - accuracy: 0.8582
22/65 [=========>....................] - ETA: 20s - loss: 0.5362 - accuracy: 0.8583
23/65 [=========>....................] - ETA: 19s - loss: 0.5341 - accuracy: 0.8587
24/65 [==========>...................] - ETA: 19s - loss: 0.5335 - accuracy: 0.8588
25/65 [==========>...................] - ETA: 18s - loss: 0.5328 - accuracy: 0.8590
26/65 [===========>..................] - ETA: 18s - loss: 0.5316 - accuracy: 0.8589
27/65 [===========>..................] - ETA: 17s - loss: 0.5310 - accuracy: 0.8593
28/65 [===========>..................] - ETA: 17s - loss: 0.5299 - accuracy: 0.8597
29/65 [============>.................] - ETA: 16s - loss: 0.5304 - accuracy: 0.8592
30/65 [============>.................] - ETA: 16s - loss: 0.5318 - accuracy: 0.8590
31/65 [=============>................] - ETA: 15s - loss: 0.5323 - accuracy: 0.8587
32/65 [=============>................] - ETA: 15s - loss: 0.5338 - accuracy: 0.8582
33/65 [==============>...............] - ETA: 14s - loss: 0.5346 - accuracy: 0.8580
34/65 [==============>...............] - ETA: 14s - loss: 0.5357 - accuracy: 0.8579
35/65 [===============>..............] - ETA: 13s - loss: 0.5348 - accuracy: 0.8583
36/65 [===============>..............] - ETA: 13s - loss: 0.5359 - accuracy: 0.8580
37/65 [================>.............] - ETA: 12s - loss: 0.5342 - accuracy: 0.8586
38/65 [================>.............] - ETA: 12s - loss: 0.5333 - accuracy: 0.8585
39/65 [=================>............] - ETA: 11s - loss: 0.5341 - accuracy: 0.8582
40/65 [=================>............] - ETA: 11s - loss: 0.5334 - accuracy: 0.8585
41/65 [=================>............] - ETA: 11s - loss: 0.5338 - accuracy: 0.8585
42/65 [==================>...........] - ETA: 10s - loss: 0.5338 - accuracy: 0.8585
43/65 [==================>...........] - ETA: 10s - loss: 0.5331 - accuracy: 0.8586
44/65 [===================>..........] - ETA: 9s - loss: 0.5324 - accuracy: 0.8587
45/65 [===================>..........] - ETA: 9s - loss: 0.5324 - accuracy: 0.8588
46/65 [====================>.........] - ETA: 8s - loss: 0.5332 - accuracy: 0.8584
47/65 [====================>.........] - ETA: 8s - loss: 0.5329 - accuracy: 0.8585
48/65 [=====================>........] - ETA: 7s - loss: 0.5329 - accuracy: 0.8585
49/65 [=====================>........] - ETA: 7s - loss: 0.5316 - accuracy: 0.8589
50/65 [======================>.......] - ETA: 6s - loss: 0.5317 - accuracy: 0.8588
51/65 [======================>.......] - ETA: 6s - loss: 0.5311 - accuracy: 0.8592
52/65 [=======================>......] - ETA: 5s - loss: 0.5313 - accuracy: 0.8593
53/65 [=======================>......] - ETA: 5s - loss: 0.5298 - accuracy: 0.8597
54/65 [=======================>......] - ETA: 5s - loss: 0.5292 - accuracy: 0.8601
55/65 [========================>.....] - ETA: 4s - loss: 0.5290 - accuracy: 0.8603
56/65 [========================>.....] - ETA: 4s - loss: 0.5288 - accuracy: 0.8605
57/65 [=========================>....] - ETA: 3s - loss: 0.5286 - accuracy: 0.8607
58/65 [=========================>....] - ETA: 3s - loss: 0.5280 - accuracy: 0.8610
59/65 [==========================>...] - ETA: 2s - loss: 0.5282 - accuracy: 0.8610
60/65 [==========================>...] - ETA: 2s - loss: 0.5283 - accuracy: 0.8610
61/65 [===========================>..] - ETA: 1s - loss: 0.5276 - accuracy: 0.8613
62/65 [===========================>..] - ETA: 1s - loss: 0.5262 - accuracy: 0.8618
63/65 [============================>.] - ETA: 0s - loss: 0.5264 - accuracy: 0.8617
64/65 [============================>.] - ETA: 0s - loss: 0.5266 - accuracy: 0.8616
65/65 [==============================] - ETA: 0s - loss: 0.5264 - accuracy: 0.8617
65/65 [==============================] - 31s 471ms/step - loss: 0.5264 - accuracy: 0.8617 - val_loss: 0.7808 - val_accuracy: 0.7714 - lr: 0.0030
Epoch 13/30
1/65 [..............................] - ETA: 43s - loss: 0.4988 - accuracy: 0.8633
2/65 [..............................] - ETA: 28s - loss: 0.5084 - accuracy: 0.8579
3/65 [>.............................] - ETA: 28s - loss: 0.5084 - accuracy: 0.8620
4/65 [>.............................] - ETA: 27s - loss: 0.5113 - accuracy: 0.8618
5/65 [=>............................] - ETA: 27s - loss: 0.5099 - accuracy: 0.8615
6/65 [=>............................] - ETA: 27s - loss: 0.5103 - accuracy: 0.8618
7/65 [==>...........................] - ETA: 26s - loss: 0.5130 - accuracy: 0.8616
8/65 [==>...........................] - ETA: 26s - loss: 0.5132 - accuracy: 0.8615
9/65 [===>..........................] - ETA: 25s - loss: 0.5122 - accuracy: 0.8631
10/65 [===>..........................] - ETA: 25s - loss: 0.5177 - accuracy: 0.8625
11/65 [====>.........................] - ETA: 24s - loss: 0.5181 - accuracy: 0.8622
12/65 [====>.........................] - ETA: 24s - loss: 0.5163 - accuracy: 0.8627
13/65 [=====>........................] - ETA: 23s - loss: 0.5160 - accuracy: 0.8631
14/65 [=====>........................] - ETA: 23s - loss: 0.5174 - accuracy: 0.8631
15/65 [=====>........................] - ETA: 22s - loss: 0.5172 - accuracy: 0.8629
16/65 [======>.......................] - ETA: 22s - loss: 0.5160 - accuracy: 0.8638
17/65 [======>.......................] - ETA: 21s - loss: 0.5156 - accuracy: 0.8637
18/65 [=======>......................] - ETA: 21s - loss: 0.5141 - accuracy: 0.8634
19/65 [=======>......................] - ETA: 21s - loss: 0.5141 - accuracy: 0.8632
20/65 [========>.....................] - ETA: 20s - loss: 0.5163 - accuracy: 0.8625
21/65 [========>.....................] - ETA: 20s - loss: 0.5153 - accuracy: 0.8624
22/65 [=========>....................] - ETA: 19s - loss: 0.5142 - accuracy: 0.8627
23/65 [=========>....................] - ETA: 19s - loss: 0.5153 - accuracy: 0.8629
24/65 [==========>...................] - ETA: 18s - loss: 0.5126 - accuracy: 0.8637
25/65 [==========>...................] - ETA: 18s - loss: 0.5144 - accuracy: 0.8634
26/65 [===========>..................] - ETA: 17s - loss: 0.5139 - accuracy: 0.8632
27/65 [===========>..................] - ETA: 17s - loss: 0.5120 - accuracy: 0.8637
28/65 [===========>..................] - ETA: 16s - loss: 0.5107 - accuracy: 0.8638
29/65 [============>.................] - ETA: 16s - loss: 0.5101 - accuracy: 0.8640
30/65 [============>.................] - ETA: 15s - loss: 0.5105 - accuracy: 0.8641
31/65 [=============>................] - ETA: 15s - loss: 0.5104 - accuracy: 0.8638
32/65 [=============>................] - ETA: 14s - loss: 0.5120 - accuracy: 0.8636
33/65 [==============>...............] - ETA: 14s - loss: 0.5125 - accuracy: 0.8637
34/65 [==============>...............] - ETA: 14s - loss: 0.5131 - accuracy: 0.8636
35/65 [===============>..............] - ETA: 13s - loss: 0.5152 - accuracy: 0.8631
36/65 [===============>..............] - ETA: 13s - loss: 0.5152 - accuracy: 0.8631
37/65 [================>.............] - ETA: 12s - loss: 0.5164 - accuracy: 0.8628
38/65 [================>.............] - ETA: 12s - loss: 0.5161 - accuracy: 0.8629
39/65 [=================>............] - ETA: 11s - loss: 0.5157 - accuracy: 0.8625
40/65 [=================>............] - ETA: 11s - loss: 0.5149 - accuracy: 0.8628
41/65 [=================>............] - ETA: 10s - loss: 0.5161 - accuracy: 0.8627
42/65 [==================>...........] - ETA: 10s - loss: 0.5150 - accuracy: 0.8635
43/65 [==================>...........] - ETA: 10s - loss: 0.5151 - accuracy: 0.8632
44/65 [===================>..........] - ETA: 9s - loss: 0.5153 - accuracy: 0.8632
45/65 [===================>..........] - ETA: 9s - loss: 0.5159 - accuracy: 0.8626
46/65 [====================>.........] - ETA: 8s - loss: 0.5162 - accuracy: 0.8624
47/65 [====================>.........] - ETA: 8s - loss: 0.5165 - accuracy: 0.8623
48/65 [=====================>........] - ETA: 7s - loss: 0.5158 - accuracy: 0.8624
49/65 [=====================>........] - ETA: 7s - loss: 0.5158 - accuracy: 0.8625
50/65 [======================>.......] - ETA: 6s - loss: 0.5152 - accuracy: 0.8629
51/65 [======================>.......] - ETA: 6s - loss: 0.5155 - accuracy: 0.8629
52/65 [=======================>......] - ETA: 5s - loss: 0.5154 - accuracy: 0.8630
53/65 [=======================>......] - ETA: 5s - loss: 0.5151 - accuracy: 0.8632
54/65 [=======================>......] - ETA: 5s - loss: 0.5147 - accuracy: 0.8632
55/65 [========================>.....] - ETA: 4s - loss: 0.5136 - accuracy: 0.8635
56/65 [========================>.....] - ETA: 4s - loss: 0.5128 - accuracy: 0.8638
57/65 [=========================>....] - ETA: 3s - loss: 0.5135 - accuracy: 0.8638
58/65 [=========================>....] - ETA: 3s - loss: 0.5136 - accuracy: 0.8638
59/65 [==========================>...] - ETA: 2s - loss: 0.5143 - accuracy: 0.8636
60/65 [==========================>...] - ETA: 2s - loss: 0.5140 - accuracy: 0.8637
61/65 [===========================>..] - ETA: 1s - loss: 0.5131 - accuracy: 0.8639
62/65 [===========================>..] - ETA: 1s - loss: 0.5129 - accuracy: 0.8639
63/65 [============================>.] - ETA: 0s - loss: 0.5123 - accuracy: 0.8641
64/65 [============================>.] - ETA: 0s - loss: 0.5121 - accuracy: 0.8642
65/65 [==============================] - ETA: 0s - loss: 0.5118 - accuracy: 0.8643
65/65 [==============================] - 31s 467ms/step - loss: 0.5118 - accuracy: 0.8643 - val_loss: 0.6206 - val_accuracy: 0.8242 - lr: 0.0030
Epoch 14/30
1/65 [..............................] - ETA: 44s - loss: 0.5438 - accuracy: 0.8438
2/65 [..............................] - ETA: 29s - loss: 0.5347 - accuracy: 0.8525
3/65 [>.............................] - ETA: 28s - loss: 0.5348 - accuracy: 0.8564
4/65 [>.............................] - ETA: 28s - loss: 0.5348 - accuracy: 0.8557
5/65 [=>............................] - ETA: 27s - loss: 0.5345 - accuracy: 0.8557
6/65 [=>............................] - ETA: 26s - loss: 0.5246 - accuracy: 0.8589
7/65 [==>...........................] - ETA: 26s - loss: 0.5208 - accuracy: 0.8604
8/65 [==>...........................] - ETA: 25s - loss: 0.5223 - accuracy: 0.8604
9/65 [===>..........................] - ETA: 25s - loss: 0.5152 - accuracy: 0.8627
10/65 [===>..........................] - ETA: 25s - loss: 0.5155 - accuracy: 0.8634
11/65 [====>.........................] - ETA: 24s - loss: 0.5109 - accuracy: 0.8647
12/65 [====>.........................] - ETA: 24s - loss: 0.5092 - accuracy: 0.8646
13/65 [=====>........................] - ETA: 24s - loss: 0.5077 - accuracy: 0.8651
14/65 [=====>........................] - ETA: 23s - loss: 0.5058 - accuracy: 0.8657
15/65 [=====>........................] - ETA: 23s - loss: 0.5066 - accuracy: 0.8658
16/65 [======>.......................] - ETA: 22s - loss: 0.5094 - accuracy: 0.8648
17/65 [======>.......................] - ETA: 22s - loss: 0.5093 - accuracy: 0.8652
18/65 [=======>......................] - ETA: 21s - loss: 0.5098 - accuracy: 0.8651
19/65 [=======>......................] - ETA: 21s - loss: 0.5089 - accuracy: 0.8652
20/65 [========>.....................] - ETA: 20s - loss: 0.5090 - accuracy: 0.8648
21/65 [========>.....................] - ETA: 20s - loss: 0.5106 - accuracy: 0.8639
22/65 [=========>....................] - ETA: 19s - loss: 0.5081 - accuracy: 0.8650
23/65 [=========>....................] - ETA: 19s - loss: 0.5065 - accuracy: 0.8653
24/65 [==========>...................] - ETA: 18s - loss: 0.5051 - accuracy: 0.8656
25/65 [==========>...................] - ETA: 18s - loss: 0.5035 - accuracy: 0.8664
26/65 [===========>..................] - ETA: 17s - loss: 0.5045 - accuracy: 0.8661
27/65 [===========>..................] - ETA: 17s - loss: 0.5026 - accuracy: 0.8668
28/65 [===========>..................] - ETA: 16s - loss: 0.5025 - accuracy: 0.8668
29/65 [============>.................] - ETA: 16s - loss: 0.5039 - accuracy: 0.8664
30/65 [============>.................] - ETA: 16s - loss: 0.5043 - accuracy: 0.8665
31/65 [=============>................] - ETA: 15s - loss: 0.5047 - accuracy: 0.8666
32/65 [=============>................] - ETA: 15s - loss: 0.5032 - accuracy: 0.8669
33/65 [==============>...............] - ETA: 14s - loss: 0.5042 - accuracy: 0.8667
34/65 [==============>...............] - ETA: 14s - loss: 0.5054 - accuracy: 0.8666
35/65 [===============>..............] - ETA: 13s - loss: 0.5051 - accuracy: 0.8668
36/65 [===============>..............] - ETA: 13s - loss: 0.5059 - accuracy: 0.8669
37/65 [================>.............] - ETA: 12s - loss: 0.5064 - accuracy: 0.8669
38/65 [================>.............] - ETA: 12s - loss: 0.5069 - accuracy: 0.8666
39/65 [=================>............] - ETA: 11s - loss: 0.5064 - accuracy: 0.8666
40/65 [=================>............] - ETA: 11s - loss: 0.5054 - accuracy: 0.8671
41/65 [=================>............] - ETA: 10s - loss: 0.5056 - accuracy: 0.8672
42/65 [==================>...........] - ETA: 10s - loss: 0.5040 - accuracy: 0.8675
43/65 [==================>...........] - ETA: 10s - loss: 0.5040 - accuracy: 0.8676
44/65 [===================>..........] - ETA: 9s - loss: 0.5046 - accuracy: 0.8674
45/65 [===================>..........] - ETA: 9s - loss: 0.5064 - accuracy: 0.8669
46/65 [====================>.........] - ETA: 8s - loss: 0.5069 - accuracy: 0.8668
47/65 [====================>.........] - ETA: 8s - loss: 0.5071 - accuracy: 0.8665
48/65 [=====================>........] - ETA: 7s - loss: 0.5063 - accuracy: 0.8668
49/65 [=====================>........] - ETA: 7s - loss: 0.5056 - accuracy: 0.8667
50/65 [======================>.......] - ETA: 6s - loss: 0.5055 - accuracy: 0.8667
51/65 [======================>.......] - ETA: 6s - loss: 0.5045 - accuracy: 0.8670
52/65 [=======================>......] - ETA: 5s - loss: 0.5046 - accuracy: 0.8670
53/65 [=======================>......] - ETA: 5s - loss: 0.5048 - accuracy: 0.8670
54/65 [=======================>......] - ETA: 5s - loss: 0.5051 - accuracy: 0.8667
55/65 [========================>.....] - ETA: 4s - loss: 0.5049 - accuracy: 0.8667
56/65 [========================>.....] - ETA: 4s - loss: 0.5053 - accuracy: 0.8667
57/65 [=========================>....] - ETA: 3s - loss: 0.5052 - accuracy: 0.8666
58/65 [=========================>....] - ETA: 3s - loss: 0.5041 - accuracy: 0.8670
59/65 [==========================>...] - ETA: 2s - loss: 0.5040 - accuracy: 0.8669
60/65 [==========================>...] - ETA: 2s - loss: 0.5040 - accuracy: 0.8669
61/65 [===========================>..] - ETA: 1s - loss: 0.5034 - accuracy: 0.8669
62/65 [===========================>..] - ETA: 1s - loss: 0.5032 - accuracy: 0.8670
63/65 [============================>.] - ETA: 0s - loss: 0.5035 - accuracy: 0.8671
64/65 [============================>.] - ETA: 0s - loss: 0.5038 - accuracy: 0.8670
65/65 [==============================] - ETA: 0s - loss: 0.5043 - accuracy: 0.8668
65/65 [==============================] - 31s 467ms/step - loss: 0.5043 - accuracy: 0.8668 - val_loss: 0.5875 - val_accuracy: 0.8347 - lr: 0.0030
Epoch 15/30
1/65 [..............................] - ETA: 44s - loss: 0.4902 - accuracy: 0.8711
2/65 [..............................] - ETA: 30s - loss: 0.5353 - accuracy: 0.8569
3/65 [>.............................] - ETA: 29s - loss: 0.5267 - accuracy: 0.8597
4/65 [>.............................] - ETA: 28s - loss: 0.5241 - accuracy: 0.8596
5/65 [=>............................] - ETA: 27s - loss: 0.5279 - accuracy: 0.8586
6/65 [=>............................] - ETA: 27s - loss: 0.5183 - accuracy: 0.8600
7/65 [==>...........................] - ETA: 26s - loss: 0.5138 - accuracy: 0.8624
8/65 [==>...........................] - ETA: 26s - loss: 0.5119 - accuracy: 0.8640
9/65 [===>..........................] - ETA: 25s - loss: 0.5191 - accuracy: 0.8621
10/65 [===>..........................] - ETA: 25s - loss: 0.5193 - accuracy: 0.8624
11/65 [====>.........................] - ETA: 24s - loss: 0.5214 - accuracy: 0.8609
12/65 [====>.........................] - ETA: 24s - loss: 0.5236 - accuracy: 0.8608
13/65 [=====>........................] - ETA: 23s - loss: 0.5211 - accuracy: 0.8613
14/65 [=====>........................] - ETA: 23s - loss: 0.5218 - accuracy: 0.8610
15/65 [=====>........................] - ETA: 23s - loss: 0.5216 - accuracy: 0.8617
16/65 [======>.......................] - ETA: 22s - loss: 0.5231 - accuracy: 0.8608
17/65 [======>.......................] - ETA: 22s - loss: 0.5223 - accuracy: 0.8608
18/65 [=======>......................] - ETA: 21s - loss: 0.5214 - accuracy: 0.8608
19/65 [=======>......................] - ETA: 21s - loss: 0.5201 - accuracy: 0.8604
20/65 [========>.....................] - ETA: 20s - loss: 0.5220 - accuracy: 0.8601
21/65 [========>.....................] - ETA: 20s - loss: 0.5235 - accuracy: 0.8593
22/65 [=========>....................] - ETA: 19s - loss: 0.5229 - accuracy: 0.8596
23/65 [=========>....................] - ETA: 19s - loss: 0.5233 - accuracy: 0.8596
24/65 [==========>...................] - ETA: 18s - loss: 0.5220 - accuracy: 0.8600
25/65 [==========>...................] - ETA: 18s - loss: 0.5200 - accuracy: 0.8608
26/65 [===========>..................] - ETA: 17s - loss: 0.5184 - accuracy: 0.8615
27/65 [===========>..................] - ETA: 17s - loss: 0.5188 - accuracy: 0.8614
28/65 [===========>..................] - ETA: 16s - loss: 0.5180 - accuracy: 0.8617
29/65 [============>.................] - ETA: 16s - loss: 0.5179 - accuracy: 0.8616
30/65 [============>.................] - ETA: 15s - loss: 0.5175 - accuracy: 0.8617
31/65 [=============>................] - ETA: 15s - loss: 0.5159 - accuracy: 0.8621
32/65 [=============>................] - ETA: 15s - loss: 0.5156 - accuracy: 0.8618
33/65 [==============>...............] - ETA: 14s - loss: 0.5145 - accuracy: 0.8621
34/65 [==============>...............] - ETA: 14s - loss: 0.5159 - accuracy: 0.8617
35/65 [===============>..............] - ETA: 13s - loss: 0.5170 - accuracy: 0.8616
36/65 [===============>..............] - ETA: 13s - loss: 0.5152 - accuracy: 0.8620
37/65 [================>.............] - ETA: 12s - loss: 0.5164 - accuracy: 0.8616
38/65 [================>.............] - ETA: 12s - loss: 0.5166 - accuracy: 0.8613
39/65 [=================>............] - ETA: 11s - loss: 0.5165 - accuracy: 0.8613
40/65 [=================>............] - ETA: 11s - loss: 0.5173 - accuracy: 0.8615
41/65 [=================>............] - ETA: 11s - loss: 0.5166 - accuracy: 0.8618
42/65 [==================>...........] - ETA: 10s - loss: 0.5168 - accuracy: 0.8617
43/65 [==================>...........] - ETA: 10s - loss: 0.5162 - accuracy: 0.8618
44/65 [===================>..........] - ETA: 9s - loss: 0.5163 - accuracy: 0.8617
45/65 [===================>..........] - ETA: 9s - loss: 0.5151 - accuracy: 0.8620
46/65 [====================>.........] - ETA: 8s - loss: 0.5142 - accuracy: 0.8620
47/65 [====================>.........] - ETA: 8s - loss: 0.5132 - accuracy: 0.8624
48/65 [=====================>........] - ETA: 7s - loss: 0.5131 - accuracy: 0.8622
49/65 [=====================>........] - ETA: 7s - loss: 0.5128 - accuracy: 0.8623
50/65 [======================>.......] - ETA: 6s - loss: 0.5123 - accuracy: 0.8625
51/65 [======================>.......] - ETA: 6s - loss: 0.5138 - accuracy: 0.8621
52/65 [=======================>......] - ETA: 5s - loss: 0.5128 - accuracy: 0.8627
53/65 [=======================>......] - ETA: 5s - loss: 0.5125 - accuracy: 0.8627
54/65 [=======================>......] - ETA: 5s - loss: 0.5124 - accuracy: 0.8628
55/65 [========================>.....] - ETA: 4s - loss: 0.5125 - accuracy: 0.8627
56/65 [========================>.....] - ETA: 4s - loss: 0.5126 - accuracy: 0.8627
57/65 [=========================>....] - ETA: 3s - loss: 0.5125 - accuracy: 0.8626
58/65 [=========================>....] - ETA: 3s - loss: 0.5123 - accuracy: 0.8627
59/65 [==========================>...] - ETA: 2s - loss: 0.5118 - accuracy: 0.8628
60/65 [==========================>...] - ETA: 2s - loss: 0.5112 - accuracy: 0.8630
61/65 [===========================>..] - ETA: 1s - loss: 0.5104 - accuracy: 0.8633
62/65 [===========================>..] - ETA: 1s - loss: 0.5106 - accuracy: 0.8632
63/65 [============================>.] - ETA: 0s - loss: 0.5104 - accuracy: 0.8633
64/65 [============================>.] - ETA: 0s - loss: 0.5108 - accuracy: 0.8633
65/65 [==============================] - ETA: 0s - loss: 0.5107 - accuracy: 0.8633
65/65 [==============================] - 31s 469ms/step - loss: 0.5107 - accuracy: 0.8633 - val_loss: 0.6556 - val_accuracy: 0.8137 - lr: 0.0030
Epoch 16/30
1/65 [..............................] - ETA: 45s - loss: 0.5072 - accuracy: 0.8672
2/65 [..............................] - ETA: 29s - loss: 0.4913 - accuracy: 0.8696
3/65 [>.............................] - ETA: 28s - loss: 0.4970 - accuracy: 0.8636
4/65 [>.............................] - ETA: 28s - loss: 0.5050 - accuracy: 0.8665
5/65 [=>............................] - ETA: 28s - loss: 0.5130 - accuracy: 0.8633
6/65 [=>............................] - ETA: 27s - loss: 0.5134 - accuracy: 0.8612
7/65 [==>...........................] - ETA: 27s - loss: 0.5170 - accuracy: 0.8591
8/65 [==>...........................] - ETA: 26s - loss: 0.5157 - accuracy: 0.8608
9/65 [===>..........................] - ETA: 26s - loss: 0.5160 - accuracy: 0.8612
10/65 [===>..........................] - ETA: 25s - loss: 0.5172 - accuracy: 0.8603
11/65 [====>.........................] - ETA: 25s - loss: 0.5163 - accuracy: 0.8604
12/65 [====>.........................] - ETA: 24s - loss: 0.5168 - accuracy: 0.8605
13/65 [=====>........................] - ETA: 24s - loss: 0.5149 - accuracy: 0.8616
14/65 [=====>........................] - ETA: 23s - loss: 0.5094 - accuracy: 0.8628
15/65 [=====>........................] - ETA: 23s - loss: 0.5106 - accuracy: 0.8628
16/65 [======>.......................] - ETA: 22s - loss: 0.5087 - accuracy: 0.8629
17/65 [======>.......................] - ETA: 22s - loss: 0.5105 - accuracy: 0.8624
18/65 [=======>......................] - ETA: 21s - loss: 0.5112 - accuracy: 0.8622
19/65 [=======>......................] - ETA: 21s - loss: 0.5102 - accuracy: 0.8622
20/65 [========>.....................] - ETA: 20s - loss: 0.5090 - accuracy: 0.8624
21/65 [========>.....................] - ETA: 20s - loss: 0.5076 - accuracy: 0.8625
22/65 [=========>....................] - ETA: 19s - loss: 0.5090 - accuracy: 0.8620
23/65 [=========>....................] - ETA: 19s - loss: 0.5084 - accuracy: 0.8624
24/65 [==========>...................] - ETA: 18s - loss: 0.5076 - accuracy: 0.8630
25/65 [==========>...................] - ETA: 18s - loss: 0.5065 - accuracy: 0.8629
26/65 [===========>..................] - ETA: 17s - loss: 0.5052 - accuracy: 0.8633
27/65 [===========>..................] - ETA: 17s - loss: 0.5045 - accuracy: 0.8632
28/65 [===========>..................] - ETA: 16s - loss: 0.5044 - accuracy: 0.8631
29/65 [============>.................] - ETA: 16s - loss: 0.5017 - accuracy: 0.8641
30/65 [============>.................] - ETA: 15s - loss: 0.5028 - accuracy: 0.8639
31/65 [=============>................] - ETA: 15s - loss: 0.5026 - accuracy: 0.8640
32/65 [=============>................] - ETA: 15s - loss: 0.5022 - accuracy: 0.8643
33/65 [==============>...............] - ETA: 14s - loss: 0.5017 - accuracy: 0.8646
34/65 [==============>...............] - ETA: 14s - loss: 0.5029 - accuracy: 0.8641
35/65 [===============>..............] - ETA: 13s - loss: 0.5027 - accuracy: 0.8643
36/65 [===============>..............] - ETA: 13s - loss: 0.5021 - accuracy: 0.8645
37/65 [================>.............] - ETA: 12s - loss: 0.5024 - accuracy: 0.8644
38/65 [================>.............] - ETA: 12s - loss: 0.5027 - accuracy: 0.8644
39/65 [=================>............] - ETA: 11s - loss: 0.5037 - accuracy: 0.8641
40/65 [=================>............] - ETA: 11s - loss: 0.5043 - accuracy: 0.8639
41/65 [=================>............] - ETA: 11s - loss: 0.5039 - accuracy: 0.8641
42/65 [==================>...........] - ETA: 10s - loss: 0.5024 - accuracy: 0.8646
43/65 [==================>...........] - ETA: 10s - loss: 0.5022 - accuracy: 0.8646
44/65 [===================>..........] - ETA: 9s - loss: 0.5014 - accuracy: 0.8650
45/65 [===================>..........] - ETA: 9s - loss: 0.5010 - accuracy: 0.8651
46/65 [====================>.........] - ETA: 8s - loss: 0.5005 - accuracy: 0.8649
47/65 [====================>.........] - ETA: 8s - loss: 0.5003 - accuracy: 0.8652
48/65 [=====================>........] - ETA: 7s - loss: 0.5000 - accuracy: 0.8653
49/65 [=====================>........] - ETA: 7s - loss: 0.5006 - accuracy: 0.8651
50/65 [======================>.......] - ETA: 6s - loss: 0.4999 - accuracy: 0.8653
51/65 [======================>.......] - ETA: 6s - loss: 0.5000 - accuracy: 0.8653
52/65 [=======================>......] - ETA: 5s - loss: 0.4991 - accuracy: 0.8656
53/65 [=======================>......] - ETA: 5s - loss: 0.4984 - accuracy: 0.8659
54/65 [=======================>......] - ETA: 5s - loss: 0.4985 - accuracy: 0.8658
55/65 [========================>.....] - ETA: 4s - loss: 0.4973 - accuracy: 0.8664
56/65 [========================>.....] - ETA: 4s - loss: 0.4978 - accuracy: 0.8661
57/65 [=========================>....] - ETA: 3s - loss: 0.4974 - accuracy: 0.8661
58/65 [=========================>....] - ETA: 3s - loss: 0.4972 - accuracy: 0.8662
59/65 [==========================>...] - ETA: 2s - loss: 0.4973 - accuracy: 0.8662
60/65 [==========================>...] - ETA: 2s - loss: 0.4970 - accuracy: 0.8663
61/65 [===========================>..] - ETA: 1s - loss: 0.4974 - accuracy: 0.8662
62/65 [===========================>..] - ETA: 1s - loss: 0.4977 - accuracy: 0.8661
63/65 [============================>.] - ETA: 0s - loss: 0.4982 - accuracy: 0.8660
64/65 [============================>.] - ETA: 0s - loss: 0.4980 - accuracy: 0.8660
65/65 [==============================] - ETA: 0s - loss: 0.4978 - accuracy: 0.8662
65/65 [==============================] - 31s 470ms/step - loss: 0.4978 - accuracy: 0.8662 - val_loss: 0.6546 - val_accuracy: 0.8190 - lr: 0.0030
Epoch 17/30
1/65 [..............................] - ETA: 45s - loss: 0.5554 - accuracy: 0.8535
2/65 [..............................] - ETA: 29s - loss: 0.5430 - accuracy: 0.8584
3/65 [>.............................] - ETA: 29s - loss: 0.5041 - accuracy: 0.8714
4/65 [>.............................] - ETA: 28s - loss: 0.5083 - accuracy: 0.8687
5/65 [=>............................] - ETA: 28s - loss: 0.5085 - accuracy: 0.8662
6/65 [=>............................] - ETA: 27s - loss: 0.5074 - accuracy: 0.8672
7/65 [==>...........................] - ETA: 27s - loss: 0.5060 - accuracy: 0.8672
8/65 [==>...........................] - ETA: 26s - loss: 0.5049 - accuracy: 0.8682
9/65 [===>..........................] - ETA: 26s - loss: 0.5074 - accuracy: 0.8679
10/65 [===>..........................] - ETA: 25s - loss: 0.5033 - accuracy: 0.8687
11/65 [====>.........................] - ETA: 25s - loss: 0.4984 - accuracy: 0.8693
12/65 [====>.........................] - ETA: 24s - loss: 0.4994 - accuracy: 0.8686
13/65 [=====>........................] - ETA: 24s - loss: 0.5004 - accuracy: 0.8680
14/65 [=====>........................] - ETA: 23s - loss: 0.4988 - accuracy: 0.8682
15/65 [=====>........................] - ETA: 23s - loss: 0.4995 - accuracy: 0.8683
16/65 [======>.......................] - ETA: 22s - loss: 0.4990 - accuracy: 0.8680
17/65 [======>.......................] - ETA: 22s - loss: 0.5010 - accuracy: 0.8678
18/65 [=======>......................] - ETA: 21s - loss: 0.5007 - accuracy: 0.8678
19/65 [=======>......................] - ETA: 21s - loss: 0.5004 - accuracy: 0.8678
20/65 [========>.....................] - ETA: 20s - loss: 0.4993 - accuracy: 0.8672
21/65 [========>.....................] - ETA: 20s - loss: 0.5007 - accuracy: 0.8668
22/65 [=========>....................] - ETA: 19s - loss: 0.4982 - accuracy: 0.8672
23/65 [=========>....................] - ETA: 19s - loss: 0.4974 - accuracy: 0.8670
24/65 [==========>...................] - ETA: 18s - loss: 0.4941 - accuracy: 0.8676
25/65 [==========>...................] - ETA: 18s - loss: 0.4935 - accuracy: 0.8688
26/65 [===========>..................] - ETA: 17s - loss: 0.4931 - accuracy: 0.8690
27/65 [===========>..................] - ETA: 17s - loss: 0.4950 - accuracy: 0.8685
28/65 [===========>..................] - ETA: 16s - loss: 0.4938 - accuracy: 0.8686
29/65 [============>.................] - ETA: 16s - loss: 0.4943 - accuracy: 0.8686
30/65 [============>.................] - ETA: 15s - loss: 0.4937 - accuracy: 0.8688
31/65 [=============>................] - ETA: 15s - loss: 0.4936 - accuracy: 0.8690
32/65 [=============>................] - ETA: 15s - loss: 0.4930 - accuracy: 0.8693
33/65 [==============>...............] - ETA: 14s - loss: 0.4947 - accuracy: 0.8689
34/65 [==============>...............] - ETA: 14s - loss: 0.4941 - accuracy: 0.8693
35/65 [===============>..............] - ETA: 13s - loss: 0.4938 - accuracy: 0.8690
36/65 [===============>..............] - ETA: 13s - loss: 0.4934 - accuracy: 0.8692
37/65 [================>.............] - ETA: 12s - loss: 0.4954 - accuracy: 0.8688
38/65 [================>.............] - ETA: 12s - loss: 0.4947 - accuracy: 0.8688
39/65 [=================>............] - ETA: 11s - loss: 0.4946 - accuracy: 0.8688
40/65 [=================>............] - ETA: 11s - loss: 0.4945 - accuracy: 0.8687
41/65 [=================>............] - ETA: 11s - loss: 0.4944 - accuracy: 0.8687
42/65 [==================>...........] - ETA: 10s - loss: 0.4940 - accuracy: 0.8690
43/65 [==================>...........] - ETA: 10s - loss: 0.4931 - accuracy: 0.8692
44/65 [===================>..........] - ETA: 9s - loss: 0.4930 - accuracy: 0.8694
45/65 [===================>..........] - ETA: 9s - loss: 0.4922 - accuracy: 0.8696
46/65 [====================>.........] - ETA: 8s - loss: 0.4915 - accuracy: 0.8697
47/65 [====================>.........] - ETA: 8s - loss: 0.4917 - accuracy: 0.8696
48/65 [=====================>........] - ETA: 7s - loss: 0.4915 - accuracy: 0.8697
49/65 [=====================>........] - ETA: 7s - loss: 0.4911 - accuracy: 0.8699
50/65 [======================>.......] - ETA: 6s - loss: 0.4924 - accuracy: 0.8695
51/65 [======================>.......] - ETA: 6s - loss: 0.4913 - accuracy: 0.8699
52/65 [=======================>......] - ETA: 5s - loss: 0.4895 - accuracy: 0.8705
53/65 [=======================>......] - ETA: 5s - loss: 0.4891 - accuracy: 0.8707
54/65 [=======================>......] - ETA: 5s - loss: 0.4888 - accuracy: 0.8709
55/65 [========================>.....] - ETA: 4s - loss: 0.4882 - accuracy: 0.8710
56/65 [========================>.....] - ETA: 4s - loss: 0.4884 - accuracy: 0.8709
57/65 [=========================>....] - ETA: 3s - loss: 0.4881 - accuracy: 0.8711
58/65 [=========================>....] - ETA: 3s - loss: 0.4883 - accuracy: 0.8711
59/65 [==========================>...] - ETA: 2s - loss: 0.4883 - accuracy: 0.8712
60/65 [==========================>...] - ETA: 2s - loss: 0.4869 - accuracy: 0.8715
61/65 [===========================>..] - ETA: 1s - loss: 0.4865 - accuracy: 0.8715
62/65 [===========================>..] - ETA: 1s - loss: 0.4859 - accuracy: 0.8718
63/65 [============================>.] - ETA: 0s - loss: 0.4864 - accuracy: 0.8716
64/65 [============================>.] - ETA: 0s - loss: 0.4872 - accuracy: 0.8711
65/65 [==============================] - ETA: 0s - loss: 0.4869 - accuracy: 0.8711
Epoch 17: ReduceLROnPlateau reducing learning rate to 0.001500000013038516.
65/65 [==============================] - 31s 471ms/step - loss: 0.4869 - accuracy: 0.8711 - val_loss: 0.6634 - val_accuracy: 0.8109 - lr: 0.0030
Epoch 18/30
1/65 [..............................] - ETA: 44s - loss: 0.4791 - accuracy: 0.8691
2/65 [..............................] - ETA: 29s - loss: 0.4969 - accuracy: 0.8687
3/65 [>.............................] - ETA: 29s - loss: 0.4887 - accuracy: 0.8717
4/65 [>.............................] - ETA: 28s - loss: 0.4837 - accuracy: 0.8728
5/65 [=>............................] - ETA: 28s - loss: 0.4888 - accuracy: 0.8729
6/65 [=>............................] - ETA: 27s - loss: 0.4818 - accuracy: 0.8765
7/65 [==>...........................] - ETA: 27s - loss: 0.4850 - accuracy: 0.8723
8/65 [==>...........................] - ETA: 26s - loss: 0.4821 - accuracy: 0.8734
9/65 [===>..........................] - ETA: 26s - loss: 0.4867 - accuracy: 0.8721
10/65 [===>..........................] - ETA: 25s - loss: 0.4903 - accuracy: 0.8710
11/65 [====>.........................] - ETA: 24s - loss: 0.4895 - accuracy: 0.8711
12/65 [====>.........................] - ETA: 24s - loss: 0.4941 - accuracy: 0.8711
13/65 [=====>........................] - ETA: 23s - loss: 0.4927 - accuracy: 0.8707
14/65 [=====>........................] - ETA: 23s - loss: 0.4901 - accuracy: 0.8715
15/65 [=====>........................] - ETA: 22s - loss: 0.4864 - accuracy: 0.8723
16/65 [======>.......................] - ETA: 22s - loss: 0.4862 - accuracy: 0.8726
17/65 [======>.......................] - ETA: 22s - loss: 0.4869 - accuracy: 0.8728
18/65 [=======>......................] - ETA: 21s - loss: 0.4838 - accuracy: 0.8741
19/65 [=======>......................] - ETA: 21s - loss: 0.4841 - accuracy: 0.8737
20/65 [========>.....................] - ETA: 20s - loss: 0.4857 - accuracy: 0.8731
21/65 [========>.....................] - ETA: 20s - loss: 0.4845 - accuracy: 0.8734
22/65 [=========>....................] - ETA: 19s - loss: 0.4838 - accuracy: 0.8736
23/65 [=========>....................] - ETA: 19s - loss: 0.4845 - accuracy: 0.8733
24/65 [==========>...................] - ETA: 18s - loss: 0.4814 - accuracy: 0.8742
25/65 [==========>...................] - ETA: 18s - loss: 0.4803 - accuracy: 0.8745
26/65 [===========>..................] - ETA: 17s - loss: 0.4799 - accuracy: 0.8745
27/65 [===========>..................] - ETA: 17s - loss: 0.4787 - accuracy: 0.8753
28/65 [===========>..................] - ETA: 16s - loss: 0.4786 - accuracy: 0.8753
29/65 [============>.................] - ETA: 16s - loss: 0.4790 - accuracy: 0.8752
30/65 [============>.................] - ETA: 15s - loss: 0.4785 - accuracy: 0.8752
31/65 [=============>................] - ETA: 15s - loss: 0.4785 - accuracy: 0.8750
32/65 [=============>................] - ETA: 14s - loss: 0.4774 - accuracy: 0.8752
33/65 [==============>...............] - ETA: 14s - loss: 0.4771 - accuracy: 0.8754
34/65 [==============>...............] - ETA: 14s - loss: 0.4777 - accuracy: 0.8751
35/65 [===============>..............] - ETA: 13s - loss: 0.4779 - accuracy: 0.8750
36/65 [===============>..............] - ETA: 13s - loss: 0.4778 - accuracy: 0.8752
37/65 [================>.............] - ETA: 12s - loss: 0.4781 - accuracy: 0.8754
38/65 [================>.............] - ETA: 12s - loss: 0.4780 - accuracy: 0.8754
39/65 [=================>............] - ETA: 11s - loss: 0.4778 - accuracy: 0.8754
40/65 [=================>............] - ETA: 11s - loss: 0.4771 - accuracy: 0.8758
41/65 [=================>............] - ETA: 10s - loss: 0.4755 - accuracy: 0.8764
42/65 [==================>...........] - ETA: 10s - loss: 0.4754 - accuracy: 0.8763
43/65 [==================>...........] - ETA: 10s - loss: 0.4762 - accuracy: 0.8760
44/65 [===================>..........] - ETA: 9s - loss: 0.4756 - accuracy: 0.8762
45/65 [===================>..........] - ETA: 9s - loss: 0.4754 - accuracy: 0.8762
46/65 [====================>.........] - ETA: 8s - loss: 0.4748 - accuracy: 0.8762
47/65 [====================>.........] - ETA: 8s - loss: 0.4748 - accuracy: 0.8762
48/65 [=====================>........] - ETA: 7s - loss: 0.4733 - accuracy: 0.8766
49/65 [=====================>........] - ETA: 7s - loss: 0.4727 - accuracy: 0.8768
50/65 [======================>.......] - ETA: 6s - loss: 0.4732 - accuracy: 0.8767
51/65 [======================>.......] - ETA: 6s - loss: 0.4734 - accuracy: 0.8764
52/65 [=======================>......] - ETA: 5s - loss: 0.4724 - accuracy: 0.8767
53/65 [=======================>......] - ETA: 5s - loss: 0.4715 - accuracy: 0.8768
54/65 [=======================>......] - ETA: 5s - loss: 0.4714 - accuracy: 0.8768
55/65 [========================>.....] - ETA: 4s - loss: 0.4709 - accuracy: 0.8769
56/65 [========================>.....] - ETA: 4s - loss: 0.4705 - accuracy: 0.8771
57/65 [=========================>....] - ETA: 3s - loss: 0.4709 - accuracy: 0.8768
58/65 [=========================>....] - ETA: 3s - loss: 0.4701 - accuracy: 0.8769
59/65 [==========================>...] - ETA: 2s - loss: 0.4698 - accuracy: 0.8770
60/65 [==========================>...] - ETA: 2s - loss: 0.4697 - accuracy: 0.8771
61/65 [===========================>..] - ETA: 1s - loss: 0.4698 - accuracy: 0.8770
62/65 [===========================>..] - ETA: 1s - loss: 0.4694 - accuracy: 0.8769
63/65 [============================>.] - ETA: 0s - loss: 0.4694 - accuracy: 0.8769
64/65 [============================>.] - ETA: 0s - loss: 0.4694 - accuracy: 0.8769
65/65 [==============================] - ETA: 0s - loss: 0.4691 - accuracy: 0.8770
65/65 [==============================] - 31s 466ms/step - loss: 0.4691 - accuracy: 0.8770 - val_loss: 0.6014 - val_accuracy: 0.8344 - lr: 0.0015
Epoch 19/30
1/65 [..............................] - ETA: 44s - loss: 0.5265 - accuracy: 0.8633
2/65 [..............................] - ETA: 30s - loss: 0.5059 - accuracy: 0.8652
3/65 [>.............................] - ETA: 29s - loss: 0.5059 - accuracy: 0.8662
4/65 [>.............................] - ETA: 28s - loss: 0.5018 - accuracy: 0.8699
5/65 [=>............................] - ETA: 28s - loss: 0.4864 - accuracy: 0.8748
6/65 [=>............................] - ETA: 27s - loss: 0.4883 - accuracy: 0.8734
7/65 [==>...........................] - ETA: 26s - loss: 0.4930 - accuracy: 0.8717
8/65 [==>...........................] - ETA: 26s - loss: 0.4887 - accuracy: 0.8728
9/65 [===>..........................] - ETA: 25s - loss: 0.4873 - accuracy: 0.8729
10/65 [===>..........................] - ETA: 25s - loss: 0.4841 - accuracy: 0.8735
11/65 [====>.........................] - ETA: 24s - loss: 0.4796 - accuracy: 0.8743
12/65 [====>.........................] - ETA: 24s - loss: 0.4778 - accuracy: 0.8743
13/65 [=====>........................] - ETA: 23s - loss: 0.4764 - accuracy: 0.8743
14/65 [=====>........................] - ETA: 23s - loss: 0.4757 - accuracy: 0.8744
15/65 [=====>........................] - ETA: 22s - loss: 0.4750 - accuracy: 0.8739
16/65 [======>.......................] - ETA: 22s - loss: 0.4760 - accuracy: 0.8738
17/65 [======>.......................] - ETA: 21s - loss: 0.4768 - accuracy: 0.8733
18/65 [=======>......................] - ETA: 21s - loss: 0.4740 - accuracy: 0.8747
19/65 [=======>......................] - ETA: 20s - loss: 0.4691 - accuracy: 0.8758
20/65 [========>.....................] - ETA: 20s - loss: 0.4690 - accuracy: 0.8756
21/65 [========>.....................] - ETA: 20s - loss: 0.4692 - accuracy: 0.8755
22/65 [=========>....................] - ETA: 19s - loss: 0.4682 - accuracy: 0.8757
23/65 [=========>....................] - ETA: 19s - loss: 0.4662 - accuracy: 0.8761
24/65 [==========>...................] - ETA: 18s - loss: 0.4661 - accuracy: 0.8764
25/65 [==========>...................] - ETA: 18s - loss: 0.4673 - accuracy: 0.8766
26/65 [===========>..................] - ETA: 17s - loss: 0.4659 - accuracy: 0.8768
27/65 [===========>..................] - ETA: 17s - loss: 0.4629 - accuracy: 0.8781
28/65 [===========>..................] - ETA: 16s - loss: 0.4635 - accuracy: 0.8782
29/65 [============>.................] - ETA: 16s - loss: 0.4646 - accuracy: 0.8779
30/65 [============>.................] - ETA: 15s - loss: 0.4644 - accuracy: 0.8782
31/65 [=============>................] - ETA: 15s - loss: 0.4641 - accuracy: 0.8783
32/65 [=============>................] - ETA: 15s - loss: 0.4649 - accuracy: 0.8783
33/65 [==============>...............] - ETA: 14s - loss: 0.4645 - accuracy: 0.8783
34/65 [==============>...............] - ETA: 14s - loss: 0.4647 - accuracy: 0.8781
35/65 [===============>..............] - ETA: 13s - loss: 0.4651 - accuracy: 0.8780
36/65 [===============>..............] - ETA: 13s - loss: 0.4659 - accuracy: 0.8780
37/65 [================>.............] - ETA: 12s - loss: 0.4653 - accuracy: 0.8782
38/65 [================>.............] - ETA: 12s - loss: 0.4649 - accuracy: 0.8780
39/65 [=================>............] - ETA: 11s - loss: 0.4640 - accuracy: 0.8780
40/65 [=================>............] - ETA: 11s - loss: 0.4644 - accuracy: 0.8783
41/65 [=================>............] - ETA: 10s - loss: 0.4642 - accuracy: 0.8783
42/65 [==================>...........] - ETA: 10s - loss: 0.4652 - accuracy: 0.8781
43/65 [==================>...........] - ETA: 10s - loss: 0.4642 - accuracy: 0.8784
44/65 [===================>..........] - ETA: 9s - loss: 0.4651 - accuracy: 0.8781
45/65 [===================>..........] - ETA: 9s - loss: 0.4650 - accuracy: 0.8780
46/65 [====================>.........] - ETA: 8s - loss: 0.4648 - accuracy: 0.8779
47/65 [====================>.........] - ETA: 8s - loss: 0.4651 - accuracy: 0.8778
48/65 [=====================>........] - ETA: 7s - loss: 0.4646 - accuracy: 0.8778
49/65 [=====================>........] - ETA: 7s - loss: 0.4641 - accuracy: 0.8780
50/65 [======================>.......] - ETA: 6s - loss: 0.4633 - accuracy: 0.8782
51/65 [======================>.......] - ETA: 6s - loss: 0.4619 - accuracy: 0.8786
52/65 [=======================>......] - ETA: 5s - loss: 0.4612 - accuracy: 0.8788
53/65 [=======================>......] - ETA: 5s - loss: 0.4611 - accuracy: 0.8787
54/65 [=======================>......] - ETA: 5s - loss: 0.4614 - accuracy: 0.8786
55/65 [========================>.....] - ETA: 4s - loss: 0.4615 - accuracy: 0.8785
56/65 [========================>.....] - ETA: 4s - loss: 0.4616 - accuracy: 0.8786
57/65 [=========================>....] - ETA: 3s - loss: 0.4607 - accuracy: 0.8789
58/65 [=========================>....] - ETA: 3s - loss: 0.4602 - accuracy: 0.8791
59/65 [==========================>...] - ETA: 2s - loss: 0.4602 - accuracy: 0.8791
60/65 [==========================>...] - ETA: 2s - loss: 0.4600 - accuracy: 0.8793
61/65 [===========================>..] - ETA: 1s - loss: 0.4599 - accuracy: 0.8792
62/65 [===========================>..] - ETA: 1s - loss: 0.4600 - accuracy: 0.8793
63/65 [============================>.] - ETA: 0s - loss: 0.4602 - accuracy: 0.8792
64/65 [============================>.] - ETA: 0s - loss: 0.4597 - accuracy: 0.8793
65/65 [==============================] - ETA: 0s - loss: 0.4596 - accuracy: 0.8793
65/65 [==============================] - 30s 465ms/step - loss: 0.4596 - accuracy: 0.8793 - val_loss: 0.5456 - val_accuracy: 0.8531 - lr: 0.0015
Epoch 20/30
1/65 [..............................] - ETA: 43s - loss: 0.4298 - accuracy: 0.8926
2/65 [..............................] - ETA: 27s - loss: 0.4361 - accuracy: 0.8892
3/65 [>.............................] - ETA: 27s - loss: 0.4573 - accuracy: 0.8812
4/65 [>.............................] - ETA: 27s - loss: 0.4588 - accuracy: 0.8823
5/65 [=>............................] - ETA: 27s - loss: 0.4599 - accuracy: 0.8809
6/65 [=>............................] - ETA: 27s - loss: 0.4603 - accuracy: 0.8797
7/65 [==>...........................] - ETA: 26s - loss: 0.4601 - accuracy: 0.8796
8/65 [==>...........................] - ETA: 26s - loss: 0.4638 - accuracy: 0.8785
9/65 [===>..........................] - ETA: 25s - loss: 0.4653 - accuracy: 0.8786
10/65 [===>..........................] - ETA: 25s - loss: 0.4605 - accuracy: 0.8804
11/65 [====>.........................] - ETA: 24s - loss: 0.4619 - accuracy: 0.8793
12/65 [====>.........................] - ETA: 24s - loss: 0.4594 - accuracy: 0.8800
13/65 [=====>........................] - ETA: 23s - loss: 0.4606 - accuracy: 0.8793
14/65 [=====>........................] - ETA: 23s - loss: 0.4598 - accuracy: 0.8793
15/65 [=====>........................] - ETA: 22s - loss: 0.4626 - accuracy: 0.8781
16/65 [======>.......................] - ETA: 22s - loss: 0.4638 - accuracy: 0.8775
17/65 [======>.......................] - ETA: 21s - loss: 0.4612 - accuracy: 0.8777
18/65 [=======>......................] - ETA: 21s - loss: 0.4601 - accuracy: 0.8779
19/65 [=======>......................] - ETA: 20s - loss: 0.4609 - accuracy: 0.8778
20/65 [========>.....................] - ETA: 20s - loss: 0.4598 - accuracy: 0.8781
21/65 [========>.....................] - ETA: 19s - loss: 0.4577 - accuracy: 0.8788
22/65 [=========>....................] - ETA: 19s - loss: 0.4580 - accuracy: 0.8782
23/65 [=========>....................] - ETA: 19s - loss: 0.4585 - accuracy: 0.8783
24/65 [==========>...................] - ETA: 18s - loss: 0.4580 - accuracy: 0.8784
25/65 [==========>...................] - ETA: 18s - loss: 0.4553 - accuracy: 0.8797
26/65 [===========>..................] - ETA: 17s - loss: 0.4548 - accuracy: 0.8798
27/65 [===========>..................] - ETA: 17s - loss: 0.4546 - accuracy: 0.8797
28/65 [===========>..................] - ETA: 16s - loss: 0.4545 - accuracy: 0.8799
29/65 [============>.................] - ETA: 16s - loss: 0.4566 - accuracy: 0.8792
30/65 [============>.................] - ETA: 15s - loss: 0.4574 - accuracy: 0.8790
31/65 [=============>................] - ETA: 15s - loss: 0.4582 - accuracy: 0.8787
32/65 [=============>................] - ETA: 15s - loss: 0.4569 - accuracy: 0.8788
33/65 [==============>...............] - ETA: 14s - loss: 0.4581 - accuracy: 0.8786
34/65 [==============>...............] - ETA: 14s - loss: 0.4583 - accuracy: 0.8786
35/65 [===============>..............] - ETA: 13s - loss: 0.4585 - accuracy: 0.8784
36/65 [===============>..............] - ETA: 13s - loss: 0.4575 - accuracy: 0.8790
37/65 [================>.............] - ETA: 12s - loss: 0.4583 - accuracy: 0.8786
38/65 [================>.............] - ETA: 12s - loss: 0.4575 - accuracy: 0.8789
39/65 [=================>............] - ETA: 11s - loss: 0.4581 - accuracy: 0.8788
40/65 [=================>............] - ETA: 11s - loss: 0.4589 - accuracy: 0.8790
41/65 [=================>............] - ETA: 10s - loss: 0.4584 - accuracy: 0.8791
42/65 [==================>...........] - ETA: 10s - loss: 0.4587 - accuracy: 0.8794
43/65 [==================>...........] - ETA: 10s - loss: 0.4586 - accuracy: 0.8794
44/65 [===================>..........] - ETA: 9s - loss: 0.4584 - accuracy: 0.8795
45/65 [===================>..........] - ETA: 9s - loss: 0.4586 - accuracy: 0.8796
46/65 [====================>.........] - ETA: 8s - loss: 0.4591 - accuracy: 0.8794
47/65 [====================>.........] - ETA: 8s - loss: 0.4588 - accuracy: 0.8795
48/65 [=====================>........] - ETA: 7s - loss: 0.4576 - accuracy: 0.8798
49/65 [=====================>........] - ETA: 7s - loss: 0.4580 - accuracy: 0.8798
50/65 [======================>.......] - ETA: 6s - loss: 0.4582 - accuracy: 0.8797
51/65 [======================>.......] - ETA: 6s - loss: 0.4577 - accuracy: 0.8799
52/65 [=======================>......] - ETA: 5s - loss: 0.4574 - accuracy: 0.8801
53/65 [=======================>......] - ETA: 5s - loss: 0.4563 - accuracy: 0.8805
54/65 [=======================>......] - ETA: 5s - loss: 0.4571 - accuracy: 0.8804
55/65 [========================>.....] - ETA: 4s - loss: 0.4573 - accuracy: 0.8803
56/65 [========================>.....] - ETA: 4s - loss: 0.4563 - accuracy: 0.8807
57/65 [=========================>....] - ETA: 3s - loss: 0.4562 - accuracy: 0.8807
58/65 [=========================>....] - ETA: 3s - loss: 0.4562 - accuracy: 0.8808
59/65 [==========================>...] - ETA: 2s - loss: 0.4558 - accuracy: 0.8808
60/65 [==========================>...] - ETA: 2s - loss: 0.4557 - accuracy: 0.8809
61/65 [===========================>..] - ETA: 1s - loss: 0.4562 - accuracy: 0.8807
62/65 [===========================>..] - ETA: 1s - loss: 0.4555 - accuracy: 0.8810
63/65 [============================>.] - ETA: 0s - loss: 0.4550 - accuracy: 0.8809
64/65 [============================>.] - ETA: 0s - loss: 0.4547 - accuracy: 0.8809
65/65 [==============================] - ETA: 0s - loss: 0.4549 - accuracy: 0.8808
65/65 [==============================] - 31s 466ms/step - loss: 0.4549 - accuracy: 0.8808 - val_loss: 0.6239 - val_accuracy: 0.8301 - lr: 0.0015
Epoch 21/30
1/65 [..............................] - ETA: 45s - loss: 0.5154 - accuracy: 0.8643
2/65 [..............................] - ETA: 30s - loss: 0.4695 - accuracy: 0.8730
3/65 [>.............................] - ETA: 29s - loss: 0.4614 - accuracy: 0.8779
4/65 [>.............................] - ETA: 29s - loss: 0.4650 - accuracy: 0.8779
5/65 [=>............................] - ETA: 28s - loss: 0.4653 - accuracy: 0.8785
6/65 [=>............................] - ETA: 27s - loss: 0.4715 - accuracy: 0.8766
7/65 [==>...........................] - ETA: 27s - loss: 0.4690 - accuracy: 0.8782
8/65 [==>...........................] - ETA: 26s - loss: 0.4647 - accuracy: 0.8787
9/65 [===>..........................] - ETA: 26s - loss: 0.4609 - accuracy: 0.8797
10/65 [===>..........................] - ETA: 25s - loss: 0.4588 - accuracy: 0.8809
11/65 [====>.........................] - ETA: 24s - loss: 0.4596 - accuracy: 0.8802
12/65 [====>.........................] - ETA: 24s - loss: 0.4580 - accuracy: 0.8813
13/65 [=====>........................] - ETA: 23s - loss: 0.4597 - accuracy: 0.8811
14/65 [=====>........................] - ETA: 23s - loss: 0.4578 - accuracy: 0.8815
15/65 [=====>........................] - ETA: 22s - loss: 0.4567 - accuracy: 0.8811
16/65 [======>.......................] - ETA: 22s - loss: 0.4573 - accuracy: 0.8805
17/65 [======>.......................] - ETA: 22s - loss: 0.4591 - accuracy: 0.8795
18/65 [=======>......................] - ETA: 21s - loss: 0.4588 - accuracy: 0.8793
19/65 [=======>......................] - ETA: 21s - loss: 0.4587 - accuracy: 0.8791
20/65 [========>.....................] - ETA: 20s - loss: 0.4574 - accuracy: 0.8796
21/65 [========>.....................] - ETA: 20s - loss: 0.4571 - accuracy: 0.8796
22/65 [=========>....................] - ETA: 19s - loss: 0.4578 - accuracy: 0.8790
23/65 [=========>....................] - ETA: 19s - loss: 0.4579 - accuracy: 0.8791
24/65 [==========>...................] - ETA: 18s - loss: 0.4562 - accuracy: 0.8796
25/65 [==========>...................] - ETA: 18s - loss: 0.4556 - accuracy: 0.8796
26/65 [===========>..................] - ETA: 17s - loss: 0.4556 - accuracy: 0.8797
27/65 [===========>..................] - ETA: 17s - loss: 0.4559 - accuracy: 0.8796
28/65 [===========>..................] - ETA: 16s - loss: 0.4559 - accuracy: 0.8796
29/65 [============>.................] - ETA: 16s - loss: 0.4547 - accuracy: 0.8799
30/65 [============>.................] - ETA: 16s - loss: 0.4545 - accuracy: 0.8798
31/65 [=============>................] - ETA: 15s - loss: 0.4546 - accuracy: 0.8799
32/65 [=============>................] - ETA: 15s - loss: 0.4553 - accuracy: 0.8795
33/65 [==============>...............] - ETA: 14s - loss: 0.4554 - accuracy: 0.8795
34/65 [==============>...............] - ETA: 14s - loss: 0.4548 - accuracy: 0.8797
35/65 [===============>..............] - ETA: 13s - loss: 0.4558 - accuracy: 0.8795
36/65 [===============>..............] - ETA: 13s - loss: 0.4554 - accuracy: 0.8796
37/65 [================>.............] - ETA: 12s - loss: 0.4562 - accuracy: 0.8791
38/65 [================>.............] - ETA: 12s - loss: 0.4562 - accuracy: 0.8792
39/65 [=================>............] - ETA: 11s - loss: 0.4547 - accuracy: 0.8796
40/65 [=================>............] - ETA: 11s - loss: 0.4539 - accuracy: 0.8796
41/65 [=================>............] - ETA: 11s - loss: 0.4524 - accuracy: 0.8804
42/65 [==================>...........] - ETA: 10s - loss: 0.4516 - accuracy: 0.8806
43/65 [==================>...........] - ETA: 10s - loss: 0.4521 - accuracy: 0.8805
44/65 [===================>..........] - ETA: 9s - loss: 0.4518 - accuracy: 0.8805
45/65 [===================>..........] - ETA: 9s - loss: 0.4526 - accuracy: 0.8803
46/65 [====================>.........] - ETA: 8s - loss: 0.4525 - accuracy: 0.8806
47/65 [====================>.........] - ETA: 8s - loss: 0.4527 - accuracy: 0.8806
48/65 [=====================>........] - ETA: 7s - loss: 0.4515 - accuracy: 0.8809
49/65 [=====================>........] - ETA: 7s - loss: 0.4517 - accuracy: 0.8809
50/65 [======================>.......] - ETA: 6s - loss: 0.4516 - accuracy: 0.8810
51/65 [======================>.......] - ETA: 6s - loss: 0.4508 - accuracy: 0.8813
52/65 [=======================>......] - ETA: 5s - loss: 0.4504 - accuracy: 0.8816
53/65 [=======================>......] - ETA: 5s - loss: 0.4503 - accuracy: 0.8816
54/65 [=======================>......] - ETA: 5s - loss: 0.4499 - accuracy: 0.8817
55/65 [========================>.....] - ETA: 4s - loss: 0.4502 - accuracy: 0.8815
56/65 [========================>.....] - ETA: 4s - loss: 0.4500 - accuracy: 0.8816
57/65 [=========================>....] - ETA: 3s - loss: 0.4495 - accuracy: 0.8817
58/65 [=========================>....] - ETA: 3s - loss: 0.4492 - accuracy: 0.8819
59/65 [==========================>...] - ETA: 2s - loss: 0.4498 - accuracy: 0.8819
60/65 [==========================>...] - ETA: 2s - loss: 0.4505 - accuracy: 0.8815
61/65 [===========================>..] - ETA: 1s - loss: 0.4498 - accuracy: 0.8817
62/65 [===========================>..] - ETA: 1s - loss: 0.4497 - accuracy: 0.8817
63/65 [============================>.] - ETA: 0s - loss: 0.4491 - accuracy: 0.8818
64/65 [============================>.] - ETA: 0s - loss: 0.4490 - accuracy: 0.8819
65/65 [==============================] - ETA: 0s - loss: 0.4495 - accuracy: 0.8816
65/65 [==============================] - 31s 470ms/step - loss: 0.4495 - accuracy: 0.8816 - val_loss: 0.5804 - val_accuracy: 0.8391 - lr: 0.0015
Epoch 22/30
1/65 [..............................] - ETA: 43s - loss: 0.4684 - accuracy: 0.8730
2/65 [..............................] - ETA: 28s - loss: 0.4965 - accuracy: 0.8726
3/65 [>.............................] - ETA: 27s - loss: 0.4776 - accuracy: 0.8760
4/65 [>.............................] - ETA: 27s - loss: 0.4814 - accuracy: 0.8735
5/65 [=>............................] - ETA: 27s - loss: 0.4714 - accuracy: 0.8764
6/65 [=>............................] - ETA: 26s - loss: 0.4735 - accuracy: 0.8742
7/65 [==>...........................] - ETA: 26s - loss: 0.4752 - accuracy: 0.8736
8/65 [==>...........................] - ETA: 25s - loss: 0.4779 - accuracy: 0.8730
9/65 [===>..........................] - ETA: 25s - loss: 0.4735 - accuracy: 0.8746
10/65 [===>..........................] - ETA: 24s - loss: 0.4685 - accuracy: 0.8745
11/65 [====>.........................] - ETA: 24s - loss: 0.4671 - accuracy: 0.8751
12/65 [====>.........................] - ETA: 24s - loss: 0.4615 - accuracy: 0.8770
13/65 [=====>........................] - ETA: 23s - loss: 0.4591 - accuracy: 0.8778
14/65 [=====>........................] - ETA: 23s - loss: 0.4582 - accuracy: 0.8785
15/65 [=====>........................] - ETA: 22s - loss: 0.4584 - accuracy: 0.8781
16/65 [======>.......................] - ETA: 22s - loss: 0.4607 - accuracy: 0.8779
17/65 [======>.......................] - ETA: 21s - loss: 0.4554 - accuracy: 0.8796
18/65 [=======>......................] - ETA: 21s - loss: 0.4530 - accuracy: 0.8799
19/65 [=======>......................] - ETA: 20s - loss: 0.4531 - accuracy: 0.8790
20/65 [========>.....................] - ETA: 20s - loss: 0.4513 - accuracy: 0.8793
21/65 [========>.....................] - ETA: 19s - loss: 0.4514 - accuracy: 0.8791
22/65 [=========>....................] - ETA: 19s - loss: 0.4520 - accuracy: 0.8789
23/65 [=========>....................] - ETA: 19s - loss: 0.4515 - accuracy: 0.8793
24/65 [==========>...................] - ETA: 18s - loss: 0.4521 - accuracy: 0.8793
25/65 [==========>...................] - ETA: 18s - loss: 0.4506 - accuracy: 0.8800
26/65 [===========>..................] - ETA: 17s - loss: 0.4493 - accuracy: 0.8806
27/65 [===========>..................] - ETA: 17s - loss: 0.4490 - accuracy: 0.8809
28/65 [===========>..................] - ETA: 16s - loss: 0.4498 - accuracy: 0.8806
29/65 [============>.................] - ETA: 16s - loss: 0.4507 - accuracy: 0.8804
30/65 [============>.................] - ETA: 15s - loss: 0.4502 - accuracy: 0.8808
31/65 [=============>................] - ETA: 15s - loss: 0.4492 - accuracy: 0.8809
32/65 [=============>................] - ETA: 15s - loss: 0.4495 - accuracy: 0.8806
33/65 [==============>...............] - ETA: 14s - loss: 0.4498 - accuracy: 0.8803
34/65 [==============>...............] - ETA: 14s - loss: 0.4493 - accuracy: 0.8806
35/65 [===============>..............] - ETA: 13s - loss: 0.4488 - accuracy: 0.8806
36/65 [===============>..............] - ETA: 13s - loss: 0.4495 - accuracy: 0.8806
37/65 [================>.............] - ETA: 12s - loss: 0.4487 - accuracy: 0.8810
38/65 [================>.............] - ETA: 12s - loss: 0.4480 - accuracy: 0.8813
39/65 [=================>............] - ETA: 11s - loss: 0.4476 - accuracy: 0.8816
40/65 [=================>............] - ETA: 11s - loss: 0.4478 - accuracy: 0.8817
41/65 [=================>............] - ETA: 10s - loss: 0.4476 - accuracy: 0.8817
42/65 [==================>...........] - ETA: 10s - loss: 0.4478 - accuracy: 0.8816
43/65 [==================>...........] - ETA: 10s - loss: 0.4477 - accuracy: 0.8819
44/65 [===================>..........] - ETA: 9s - loss: 0.4470 - accuracy: 0.8819
45/65 [===================>..........] - ETA: 9s - loss: 0.4459 - accuracy: 0.8822
46/65 [====================>.........] - ETA: 8s - loss: 0.4466 - accuracy: 0.8821
47/65 [====================>.........] - ETA: 8s - loss: 0.4461 - accuracy: 0.8823
48/65 [=====================>........] - ETA: 7s - loss: 0.4459 - accuracy: 0.8821
49/65 [=====================>........] - ETA: 7s - loss: 0.4458 - accuracy: 0.8822
50/65 [======================>.......] - ETA: 6s - loss: 0.4459 - accuracy: 0.8822
51/65 [======================>.......] - ETA: 6s - loss: 0.4453 - accuracy: 0.8823
52/65 [=======================>......] - ETA: 5s - loss: 0.4457 - accuracy: 0.8821
53/65 [=======================>......] - ETA: 5s - loss: 0.4459 - accuracy: 0.8821
54/65 [=======================>......] - ETA: 5s - loss: 0.4460 - accuracy: 0.8821
55/65 [========================>.....] - ETA: 4s - loss: 0.4461 - accuracy: 0.8819
56/65 [========================>.....] - ETA: 4s - loss: 0.4467 - accuracy: 0.8817
57/65 [=========================>....] - ETA: 3s - loss: 0.4454 - accuracy: 0.8823
58/65 [=========================>....] - ETA: 3s - loss: 0.4462 - accuracy: 0.8821
59/65 [==========================>...] - ETA: 2s - loss: 0.4457 - accuracy: 0.8821
60/65 [==========================>...] - ETA: 2s - loss: 0.4453 - accuracy: 0.8824
61/65 [===========================>..] - ETA: 1s - loss: 0.4452 - accuracy: 0.8824
62/65 [===========================>..] - ETA: 1s - loss: 0.4451 - accuracy: 0.8825
63/65 [============================>.] - ETA: 0s - loss: 0.4450 - accuracy: 0.8823
64/65 [============================>.] - ETA: 0s - loss: 0.4453 - accuracy: 0.8822
65/65 [==============================] - ETA: 0s - loss: 0.4455 - accuracy: 0.8823
Epoch 22: ReduceLROnPlateau reducing learning rate to 0.000750000006519258.
65/65 [==============================] - 31s 469ms/step - loss: 0.4455 - accuracy: 0.8823 - val_loss: 0.5684 - val_accuracy: 0.8483 - lr: 0.0015
Epoch 23/30
1/65 [..............................] - ETA: 43s - loss: 0.4860 - accuracy: 0.8711
2/65 [..............................] - ETA: 27s - loss: 0.5065 - accuracy: 0.8657
3/65 [>.............................] - ETA: 27s - loss: 0.4999 - accuracy: 0.8675
4/65 [>.............................] - ETA: 27s - loss: 0.4805 - accuracy: 0.8718
5/65 [=>............................] - ETA: 26s - loss: 0.4763 - accuracy: 0.8723
6/65 [=>............................] - ETA: 26s - loss: 0.4795 - accuracy: 0.8745
7/65 [==>...........................] - ETA: 26s - loss: 0.4772 - accuracy: 0.8743
8/65 [==>...........................] - ETA: 25s - loss: 0.4760 - accuracy: 0.8752
9/65 [===>..........................] - ETA: 25s - loss: 0.4713 - accuracy: 0.8770
10/65 [===>..........................] - ETA: 24s - loss: 0.4724 - accuracy: 0.8764
11/65 [====>.........................] - ETA: 24s - loss: 0.4659 - accuracy: 0.8780
12/65 [====>.........................] - ETA: 23s - loss: 0.4634 - accuracy: 0.8782
13/65 [=====>........................] - ETA: 23s - loss: 0.4646 - accuracy: 0.8779
14/65 [=====>........................] - ETA: 22s - loss: 0.4620 - accuracy: 0.8785
15/65 [=====>........................] - ETA: 22s - loss: 0.4600 - accuracy: 0.8786
16/65 [======>.......................] - ETA: 22s - loss: 0.4600 - accuracy: 0.8779
17/65 [======>.......................] - ETA: 21s - loss: 0.4582 - accuracy: 0.8788
18/65 [=======>......................] - ETA: 21s - loss: 0.4603 - accuracy: 0.8778
19/65 [=======>......................] - ETA: 20s - loss: 0.4602 - accuracy: 0.8784
20/65 [========>.....................] - ETA: 20s - loss: 0.4584 - accuracy: 0.8787
21/65 [========>.....................] - ETA: 19s - loss: 0.4560 - accuracy: 0.8791
22/65 [=========>....................] - ETA: 19s - loss: 0.4532 - accuracy: 0.8797
23/65 [=========>....................] - ETA: 18s - loss: 0.4531 - accuracy: 0.8796
24/65 [==========>...................] - ETA: 18s - loss: 0.4535 - accuracy: 0.8793
25/65 [==========>...................] - ETA: 17s - loss: 0.4527 - accuracy: 0.8794
26/65 [===========>..................] - ETA: 17s - loss: 0.4497 - accuracy: 0.8803
27/65 [===========>..................] - ETA: 17s - loss: 0.4482 - accuracy: 0.8808
28/65 [===========>..................] - ETA: 16s - loss: 0.4470 - accuracy: 0.8809
29/65 [============>.................] - ETA: 16s - loss: 0.4462 - accuracy: 0.8810
30/65 [============>.................] - ETA: 15s - loss: 0.4450 - accuracy: 0.8816
31/65 [=============>................] - ETA: 15s - loss: 0.4444 - accuracy: 0.8821
32/65 [=============>................] - ETA: 14s - loss: 0.4455 - accuracy: 0.8817
33/65 [==============>...............] - ETA: 14s - loss: 0.4453 - accuracy: 0.8820
34/65 [==============>...............] - ETA: 14s - loss: 0.4445 - accuracy: 0.8823
35/65 [===============>..............] - ETA: 13s - loss: 0.4441 - accuracy: 0.8824
36/65 [===============>..............] - ETA: 13s - loss: 0.4435 - accuracy: 0.8828
37/65 [================>.............] - ETA: 12s - loss: 0.4425 - accuracy: 0.8832
38/65 [================>.............] - ETA: 12s - loss: 0.4435 - accuracy: 0.8829
39/65 [=================>............] - ETA: 11s - loss: 0.4433 - accuracy: 0.8829
40/65 [=================>............] - ETA: 11s - loss: 0.4431 - accuracy: 0.8831
41/65 [=================>............] - ETA: 10s - loss: 0.4432 - accuracy: 0.8830
42/65 [==================>...........] - ETA: 10s - loss: 0.4424 - accuracy: 0.8830
43/65 [==================>...........] - ETA: 9s - loss: 0.4422 - accuracy: 0.8829
44/65 [===================>..........] - ETA: 9s - loss: 0.4416 - accuracy: 0.8833
45/65 [===================>..........] - ETA: 9s - loss: 0.4411 - accuracy: 0.8835
46/65 [====================>.........] - ETA: 8s - loss: 0.4411 - accuracy: 0.8835
47/65 [====================>.........] - ETA: 8s - loss: 0.4409 - accuracy: 0.8835
48/65 [=====================>........] - ETA: 7s - loss: 0.4405 - accuracy: 0.8837
49/65 [=====================>........] - ETA: 7s - loss: 0.4399 - accuracy: 0.8836
50/65 [======================>.......] - ETA: 6s - loss: 0.4402 - accuracy: 0.8836
51/65 [======================>.......] - ETA: 6s - loss: 0.4394 - accuracy: 0.8838
52/65 [=======================>......] - ETA: 5s - loss: 0.4387 - accuracy: 0.8839
53/65 [=======================>......] - ETA: 5s - loss: 0.4398 - accuracy: 0.8837
54/65 [=======================>......] - ETA: 5s - loss: 0.4395 - accuracy: 0.8837
55/65 [========================>.....] - ETA: 4s - loss: 0.4393 - accuracy: 0.8836
56/65 [========================>.....] - ETA: 4s - loss: 0.4404 - accuracy: 0.8833
57/65 [=========================>....] - ETA: 3s - loss: 0.4398 - accuracy: 0.8835
58/65 [=========================>....] - ETA: 3s - loss: 0.4400 - accuracy: 0.8835
59/65 [==========================>...] - ETA: 2s - loss: 0.4395 - accuracy: 0.8835
60/65 [==========================>...] - ETA: 2s - loss: 0.4396 - accuracy: 0.8834
61/65 [===========================>..] - ETA: 1s - loss: 0.4400 - accuracy: 0.8832
62/65 [===========================>..] - ETA: 1s - loss: 0.4396 - accuracy: 0.8833
63/65 [============================>.] - ETA: 0s - loss: 0.4396 - accuracy: 0.8833
64/65 [============================>.] - ETA: 0s - loss: 0.4393 - accuracy: 0.8833
65/65 [==============================] - ETA: 0s - loss: 0.4390 - accuracy: 0.8834
65/65 [==============================] - 31s 468ms/step - loss: 0.4390 - accuracy: 0.8834 - val_loss: 0.5921 - val_accuracy: 0.8369 - lr: 7.5000e-04
Epoch 24/30
1/65 [..............................] - ETA: 44s - loss: 0.4632 - accuracy: 0.8662
2/65 [..............................] - ETA: 29s - loss: 0.4638 - accuracy: 0.8774
3/65 [>.............................] - ETA: 29s - loss: 0.4711 - accuracy: 0.8747
4/65 [>.............................] - ETA: 28s - loss: 0.4692 - accuracy: 0.8770
5/65 [=>............................] - ETA: 28s - loss: 0.4616 - accuracy: 0.8803
6/65 [=>............................] - ETA: 27s - loss: 0.4536 - accuracy: 0.8830
7/65 [==>...........................] - ETA: 26s - loss: 0.4529 - accuracy: 0.8834
8/65 [==>...........................] - ETA: 26s - loss: 0.4485 - accuracy: 0.8843
9/65 [===>..........................] - ETA: 25s - loss: 0.4496 - accuracy: 0.8837
10/65 [===>..........................] - ETA: 25s - loss: 0.4518 - accuracy: 0.8823
11/65 [====>.........................] - ETA: 24s - loss: 0.4476 - accuracy: 0.8835
12/65 [====>.........................] - ETA: 24s - loss: 0.4479 - accuracy: 0.8828
13/65 [=====>........................] - ETA: 23s - loss: 0.4471 - accuracy: 0.8826
14/65 [=====>........................] - ETA: 23s - loss: 0.4473 - accuracy: 0.8821
15/65 [=====>........................] - ETA: 22s - loss: 0.4473 - accuracy: 0.8818
16/65 [======>.......................] - ETA: 22s - loss: 0.4457 - accuracy: 0.8821
17/65 [======>.......................] - ETA: 21s - loss: 0.4470 - accuracy: 0.8818
18/65 [=======>......................] - ETA: 21s - loss: 0.4454 - accuracy: 0.8817
19/65 [=======>......................] - ETA: 20s - loss: 0.4457 - accuracy: 0.8818
20/65 [========>.....................] - ETA: 20s - loss: 0.4458 - accuracy: 0.8817
21/65 [========>.....................] - ETA: 19s - loss: 0.4470 - accuracy: 0.8813
22/65 [=========>....................] - ETA: 19s - loss: 0.4453 - accuracy: 0.8821
23/65 [=========>....................] - ETA: 19s - loss: 0.4423 - accuracy: 0.8828
24/65 [==========>...................] - ETA: 18s - loss: 0.4418 - accuracy: 0.8835
25/65 [==========>...................] - ETA: 18s - loss: 0.4411 - accuracy: 0.8838
26/65 [===========>..................] - ETA: 17s - loss: 0.4407 - accuracy: 0.8841
27/65 [===========>..................] - ETA: 17s - loss: 0.4400 - accuracy: 0.8845
28/65 [===========>..................] - ETA: 16s - loss: 0.4396 - accuracy: 0.8845
29/65 [============>.................] - ETA: 16s - loss: 0.4407 - accuracy: 0.8841
30/65 [============>.................] - ETA: 15s - loss: 0.4393 - accuracy: 0.8848
31/65 [=============>................] - ETA: 15s - loss: 0.4375 - accuracy: 0.8854
32/65 [=============>................] - ETA: 15s - loss: 0.4380 - accuracy: 0.8852
33/65 [==============>...............] - ETA: 14s - loss: 0.4385 - accuracy: 0.8849
34/65 [==============>...............] - ETA: 14s - loss: 0.4382 - accuracy: 0.8849
35/65 [===============>..............] - ETA: 13s - loss: 0.4373 - accuracy: 0.8853
36/65 [===============>..............] - ETA: 13s - loss: 0.4374 - accuracy: 0.8853
37/65 [================>.............] - ETA: 12s - loss: 0.4387 - accuracy: 0.8852
38/65 [================>.............] - ETA: 12s - loss: 0.4385 - accuracy: 0.8850
39/65 [=================>............] - ETA: 11s - loss: 0.4385 - accuracy: 0.8851
40/65 [=================>............] - ETA: 11s - loss: 0.4382 - accuracy: 0.8852
41/65 [=================>............] - ETA: 11s - loss: 0.4383 - accuracy: 0.8851
42/65 [==================>...........] - ETA: 10s - loss: 0.4380 - accuracy: 0.8850
43/65 [==================>...........] - ETA: 10s - loss: 0.4368 - accuracy: 0.8854
44/65 [===================>..........] - ETA: 9s - loss: 0.4375 - accuracy: 0.8851
45/65 [===================>..........] - ETA: 9s - loss: 0.4384 - accuracy: 0.8850
46/65 [====================>.........] - ETA: 8s - loss: 0.4391 - accuracy: 0.8851
47/65 [====================>.........] - ETA: 8s - loss: 0.4381 - accuracy: 0.8853
48/65 [=====================>........] - ETA: 7s - loss: 0.4381 - accuracy: 0.8851
49/65 [=====================>........] - ETA: 7s - loss: 0.4381 - accuracy: 0.8852
50/65 [======================>.......] - ETA: 6s - loss: 0.4392 - accuracy: 0.8848
51/65 [======================>.......] - ETA: 6s - loss: 0.4381 - accuracy: 0.8852
52/65 [=======================>......] - ETA: 5s - loss: 0.4381 - accuracy: 0.8851
53/65 [=======================>......] - ETA: 5s - loss: 0.4373 - accuracy: 0.8852
54/65 [=======================>......] - ETA: 5s - loss: 0.4374 - accuracy: 0.8849
55/65 [========================>.....] - ETA: 4s - loss: 0.4372 - accuracy: 0.8849
56/65 [========================>.....] - ETA: 4s - loss: 0.4369 - accuracy: 0.8851
57/65 [=========================>....] - ETA: 3s - loss: 0.4370 - accuracy: 0.8852
58/65 [=========================>....] - ETA: 3s - loss: 0.4373 - accuracy: 0.8851
59/65 [==========================>...] - ETA: 2s - loss: 0.4375 - accuracy: 0.8849
60/65 [==========================>...] - ETA: 2s - loss: 0.4376 - accuracy: 0.8850
61/65 [===========================>..] - ETA: 1s - loss: 0.4381 - accuracy: 0.8848
62/65 [===========================>..] - ETA: 1s - loss: 0.4377 - accuracy: 0.8850
63/65 [============================>.] - ETA: 0s - loss: 0.4372 - accuracy: 0.8850
64/65 [============================>.] - ETA: 0s - loss: 0.4368 - accuracy: 0.8849
65/65 [==============================] - ETA: 0s - loss: 0.4373 - accuracy: 0.8848
65/65 [==============================] - 31s 472ms/step - loss: 0.4373 - accuracy: 0.8848 - val_loss: 0.5346 - val_accuracy: 0.8553 - lr: 7.5000e-04
Epoch 25/30
1/65 [..............................] - ETA: 43s - loss: 0.5430 - accuracy: 0.8613
2/65 [..............................] - ETA: 29s - loss: 0.5070 - accuracy: 0.8750
3/65 [>.............................] - ETA: 28s - loss: 0.4920 - accuracy: 0.8760
4/65 [>.............................] - ETA: 28s - loss: 0.4827 - accuracy: 0.8757
5/65 [=>............................] - ETA: 27s - loss: 0.4675 - accuracy: 0.8820
6/65 [=>............................] - ETA: 27s - loss: 0.4674 - accuracy: 0.8799
7/65 [==>...........................] - ETA: 26s - loss: 0.4656 - accuracy: 0.8797
8/65 [==>...........................] - ETA: 26s - loss: 0.4620 - accuracy: 0.8793
9/65 [===>..........................] - ETA: 25s - loss: 0.4559 - accuracy: 0.8814
10/65 [===>..........................] - ETA: 25s - loss: 0.4495 - accuracy: 0.8830
11/65 [====>.........................] - ETA: 24s - loss: 0.4519 - accuracy: 0.8820
12/65 [====>.........................] - ETA: 24s - loss: 0.4498 - accuracy: 0.8828
13/65 [=====>........................] - ETA: 23s - loss: 0.4507 - accuracy: 0.8825
14/65 [=====>........................] - ETA: 23s - loss: 0.4493 - accuracy: 0.8825
15/65 [=====>........................] - ETA: 22s - loss: 0.4507 - accuracy: 0.8822
16/65 [======>.......................] - ETA: 22s - loss: 0.4486 - accuracy: 0.8829
17/65 [======>.......................] - ETA: 21s - loss: 0.4491 - accuracy: 0.8828
18/65 [=======>......................] - ETA: 21s - loss: 0.4470 - accuracy: 0.8836
19/65 [=======>......................] - ETA: 20s - loss: 0.4469 - accuracy: 0.8835
20/65 [========>.....................] - ETA: 20s - loss: 0.4459 - accuracy: 0.8841
21/65 [========>.....................] - ETA: 20s - loss: 0.4448 - accuracy: 0.8845
22/65 [=========>....................] - ETA: 19s - loss: 0.4449 - accuracy: 0.8846
23/65 [=========>....................] - ETA: 19s - loss: 0.4446 - accuracy: 0.8847
24/65 [==========>...................] - ETA: 18s - loss: 0.4446 - accuracy: 0.8844
25/65 [==========>...................] - ETA: 18s - loss: 0.4437 - accuracy: 0.8846
26/65 [===========>..................] - ETA: 17s - loss: 0.4423 - accuracy: 0.8851
27/65 [===========>..................] - ETA: 17s - loss: 0.4418 - accuracy: 0.8851
28/65 [===========>..................] - ETA: 16s - loss: 0.4404 - accuracy: 0.8853
29/65 [============>.................] - ETA: 16s - loss: 0.4410 - accuracy: 0.8849
30/65 [============>.................] - ETA: 15s - loss: 0.4406 - accuracy: 0.8850
31/65 [=============>................] - ETA: 15s - loss: 0.4396 - accuracy: 0.8853
32/65 [=============>................] - ETA: 15s - loss: 0.4392 - accuracy: 0.8855
33/65 [==============>...............] - ETA: 14s - loss: 0.4396 - accuracy: 0.8855
34/65 [==============>...............] - ETA: 14s - loss: 0.4393 - accuracy: 0.8856
35/65 [===============>..............] - ETA: 13s - loss: 0.4386 - accuracy: 0.8857
36/65 [===============>..............] - ETA: 13s - loss: 0.4414 - accuracy: 0.8847
37/65 [================>.............] - ETA: 12s - loss: 0.4412 - accuracy: 0.8849
38/65 [================>.............] - ETA: 12s - loss: 0.4416 - accuracy: 0.8848
39/65 [=================>............] - ETA: 11s - loss: 0.4420 - accuracy: 0.8847
40/65 [=================>............] - ETA: 11s - loss: 0.4416 - accuracy: 0.8848
41/65 [=================>............] - ETA: 10s - loss: 0.4416 - accuracy: 0.8848
42/65 [==================>...........] - ETA: 10s - loss: 0.4425 - accuracy: 0.8847
43/65 [==================>...........] - ETA: 10s - loss: 0.4415 - accuracy: 0.8851
44/65 [===================>..........] - ETA: 9s - loss: 0.4413 - accuracy: 0.8850
45/65 [===================>..........] - ETA: 9s - loss: 0.4408 - accuracy: 0.8852
46/65 [====================>.........] - ETA: 8s - loss: 0.4412 - accuracy: 0.8850
47/65 [====================>.........] - ETA: 8s - loss: 0.4414 - accuracy: 0.8849
48/65 [=====================>........] - ETA: 7s - loss: 0.4412 - accuracy: 0.8850
49/65 [=====================>........] - ETA: 7s - loss: 0.4407 - accuracy: 0.8853
50/65 [======================>.......] - ETA: 6s - loss: 0.4401 - accuracy: 0.8855
51/65 [======================>.......] - ETA: 6s - loss: 0.4399 - accuracy: 0.8854
52/65 [=======================>......] - ETA: 5s - loss: 0.4396 - accuracy: 0.8854
53/65 [=======================>......] - ETA: 5s - loss: 0.4401 - accuracy: 0.8852
54/65 [=======================>......] - ETA: 5s - loss: 0.4406 - accuracy: 0.8851
55/65 [========================>.....] - ETA: 4s - loss: 0.4397 - accuracy: 0.8854
56/65 [========================>.....] - ETA: 4s - loss: 0.4383 - accuracy: 0.8859
57/65 [=========================>....] - ETA: 3s - loss: 0.4382 - accuracy: 0.8858
58/65 [=========================>....] - ETA: 3s - loss: 0.4382 - accuracy: 0.8859
59/65 [==========================>...] - ETA: 2s - loss: 0.4383 - accuracy: 0.8857
60/65 [==========================>...] - ETA: 2s - loss: 0.4391 - accuracy: 0.8854
61/65 [===========================>..] - ETA: 1s - loss: 0.4396 - accuracy: 0.8851
62/65 [===========================>..] - ETA: 1s - loss: 0.4402 - accuracy: 0.8848
63/65 [============================>.] - ETA: 0s - loss: 0.4400 - accuracy: 0.8848
64/65 [============================>.] - ETA: 0s - loss: 0.4404 - accuracy: 0.8848
65/65 [==============================] - ETA: 0s - loss: 0.4408 - accuracy: 0.8847
65/65 [==============================] - 31s 467ms/step - loss: 0.4408 - accuracy: 0.8847 - val_loss: 0.5814 - val_accuracy: 0.8432 - lr: 7.5000e-04
Epoch 26/30
1/65 [..............................] - ETA: 42s - loss: 0.4817 - accuracy: 0.8711
2/65 [..............................] - ETA: 29s - loss: 0.4944 - accuracy: 0.8726
3/65 [>.............................] - ETA: 28s - loss: 0.4844 - accuracy: 0.8753
4/65 [>.............................] - ETA: 28s - loss: 0.4676 - accuracy: 0.8816
5/65 [=>............................] - ETA: 28s - loss: 0.4624 - accuracy: 0.8834
6/65 [=>............................] - ETA: 27s - loss: 0.4583 - accuracy: 0.8830
7/65 [==>...........................] - ETA: 26s - loss: 0.4586 - accuracy: 0.8817
8/65 [==>...........................] - ETA: 26s - loss: 0.4606 - accuracy: 0.8809
9/65 [===>..........................] - ETA: 25s - loss: 0.4567 - accuracy: 0.8819
10/65 [===>..........................] - ETA: 25s - loss: 0.4554 - accuracy: 0.8822
11/65 [====>.........................] - ETA: 24s - loss: 0.4530 - accuracy: 0.8825
12/65 [====>.........................] - ETA: 24s - loss: 0.4519 - accuracy: 0.8828
13/65 [=====>........................] - ETA: 23s - loss: 0.4497 - accuracy: 0.8836
14/65 [=====>........................] - ETA: 23s - loss: 0.4524 - accuracy: 0.8827
15/65 [=====>........................] - ETA: 22s - loss: 0.4489 - accuracy: 0.8847
16/65 [======>.......................] - ETA: 22s - loss: 0.4475 - accuracy: 0.8844
17/65 [======>.......................] - ETA: 22s - loss: 0.4495 - accuracy: 0.8832
18/65 [=======>......................] - ETA: 21s - loss: 0.4473 - accuracy: 0.8838
19/65 [=======>......................] - ETA: 21s - loss: 0.4448 - accuracy: 0.8847
20/65 [========>.....................] - ETA: 20s - loss: 0.4433 - accuracy: 0.8852
21/65 [========>.....................] - ETA: 20s - loss: 0.4423 - accuracy: 0.8852
22/65 [=========>....................] - ETA: 19s - loss: 0.4422 - accuracy: 0.8852
23/65 [=========>....................] - ETA: 19s - loss: 0.4411 - accuracy: 0.8855
24/65 [==========>...................] - ETA: 18s - loss: 0.4410 - accuracy: 0.8854
25/65 [==========>...................] - ETA: 18s - loss: 0.4415 - accuracy: 0.8851
26/65 [===========>..................] - ETA: 17s - loss: 0.4423 - accuracy: 0.8844
27/65 [===========>..................] - ETA: 17s - loss: 0.4431 - accuracy: 0.8843
28/65 [===========>..................] - ETA: 17s - loss: 0.4424 - accuracy: 0.8846
29/65 [============>.................] - ETA: 16s - loss: 0.4436 - accuracy: 0.8844
30/65 [============>.................] - ETA: 16s - loss: 0.4425 - accuracy: 0.8850
31/65 [=============>................] - ETA: 15s - loss: 0.4430 - accuracy: 0.8850
32/65 [=============>................] - ETA: 15s - loss: 0.4417 - accuracy: 0.8854
33/65 [==============>...............] - ETA: 14s - loss: 0.4429 - accuracy: 0.8851
34/65 [==============>...............] - ETA: 14s - loss: 0.4434 - accuracy: 0.8850
35/65 [===============>..............] - ETA: 13s - loss: 0.4442 - accuracy: 0.8848
36/65 [===============>..............] - ETA: 13s - loss: 0.4459 - accuracy: 0.8844
37/65 [================>.............] - ETA: 12s - loss: 0.4461 - accuracy: 0.8841
38/65 [================>.............] - ETA: 12s - loss: 0.4446 - accuracy: 0.8846
39/65 [=================>............] - ETA: 11s - loss: 0.4442 - accuracy: 0.8849
40/65 [=================>............] - ETA: 11s - loss: 0.4444 - accuracy: 0.8847
41/65 [=================>............] - ETA: 11s - loss: 0.4443 - accuracy: 0.8849
42/65 [==================>...........] - ETA: 10s - loss: 0.4440 - accuracy: 0.8850
43/65 [==================>...........] - ETA: 10s - loss: 0.4451 - accuracy: 0.8844
44/65 [===================>..........] - ETA: 9s - loss: 0.4442 - accuracy: 0.8847
45/65 [===================>..........] - ETA: 9s - loss: 0.4435 - accuracy: 0.8849
46/65 [====================>.........] - ETA: 8s - loss: 0.4429 - accuracy: 0.8850
47/65 [====================>.........] - ETA: 8s - loss: 0.4427 - accuracy: 0.8849
48/65 [=====================>........] - ETA: 7s - loss: 0.4421 - accuracy: 0.8852
49/65 [=====================>........] - ETA: 7s - loss: 0.4415 - accuracy: 0.8855
50/65 [======================>.......] - ETA: 6s - loss: 0.4414 - accuracy: 0.8856
51/65 [======================>.......] - ETA: 6s - loss: 0.4408 - accuracy: 0.8856
52/65 [=======================>......] - ETA: 5s - loss: 0.4417 - accuracy: 0.8854
53/65 [=======================>......] - ETA: 5s - loss: 0.4412 - accuracy: 0.8854
54/65 [=======================>......] - ETA: 5s - loss: 0.4404 - accuracy: 0.8855
55/65 [========================>.....] - ETA: 4s - loss: 0.4403 - accuracy: 0.8855
56/65 [========================>.....] - ETA: 4s - loss: 0.4400 - accuracy: 0.8854
57/65 [=========================>....] - ETA: 3s - loss: 0.4399 - accuracy: 0.8853
58/65 [=========================>....] - ETA: 3s - loss: 0.4392 - accuracy: 0.8854
59/65 [==========================>...] - ETA: 2s - loss: 0.4394 - accuracy: 0.8852
60/65 [==========================>...] - ETA: 2s - loss: 0.4399 - accuracy: 0.8850
61/65 [===========================>..] - ETA: 1s - loss: 0.4404 - accuracy: 0.8847
62/65 [===========================>..] - ETA: 1s - loss: 0.4405 - accuracy: 0.8847
63/65 [============================>.] - ETA: 0s - loss: 0.4405 - accuracy: 0.8845
64/65 [============================>.] - ETA: 0s - loss: 0.4402 - accuracy: 0.8846
65/65 [==============================] - ETA: 0s - loss: 0.4398 - accuracy: 0.8847
65/65 [==============================] - 31s 470ms/step - loss: 0.4398 - accuracy: 0.8847 - val_loss: 0.5413 - val_accuracy: 0.8530 - lr: 7.5000e-04
Epoch 27/30
1/65 [..............................] - ETA: 45s - loss: 0.4714 - accuracy: 0.8760
2/65 [..............................] - ETA: 29s - loss: 0.4639 - accuracy: 0.8818
3/65 [>.............................] - ETA: 29s - loss: 0.4684 - accuracy: 0.8805
4/65 [>.............................] - ETA: 28s - loss: 0.4673 - accuracy: 0.8806
5/65 [=>............................] - ETA: 27s - loss: 0.4524 - accuracy: 0.8836
6/65 [=>............................] - ETA: 27s - loss: 0.4641 - accuracy: 0.8781
7/65 [==>...........................] - ETA: 26s - loss: 0.4602 - accuracy: 0.8800
8/65 [==>...........................] - ETA: 26s - loss: 0.4596 - accuracy: 0.8802
9/65 [===>..........................] - ETA: 25s - loss: 0.4577 - accuracy: 0.8812
10/65 [===>..........................] - ETA: 25s - loss: 0.4562 - accuracy: 0.8808
11/65 [====>.........................] - ETA: 24s - loss: 0.4537 - accuracy: 0.8817
12/65 [====>.........................] - ETA: 23s - loss: 0.4525 - accuracy: 0.8815
13/65 [=====>........................] - ETA: 23s - loss: 0.4531 - accuracy: 0.8815
14/65 [=====>........................] - ETA: 22s - loss: 0.4535 - accuracy: 0.8813
15/65 [=====>........................] - ETA: 22s - loss: 0.4508 - accuracy: 0.8820
16/65 [======>.......................] - ETA: 22s - loss: 0.4501 - accuracy: 0.8823
17/65 [======>.......................] - ETA: 21s - loss: 0.4462 - accuracy: 0.8832
18/65 [=======>......................] - ETA: 21s - loss: 0.4468 - accuracy: 0.8838
19/65 [=======>......................] - ETA: 21s - loss: 0.4475 - accuracy: 0.8836
20/65 [========>.....................] - ETA: 20s - loss: 0.4461 - accuracy: 0.8837
21/65 [========>.....................] - ETA: 20s - loss: 0.4450 - accuracy: 0.8839
22/65 [=========>....................] - ETA: 19s - loss: 0.4445 - accuracy: 0.8839
23/65 [=========>....................] - ETA: 19s - loss: 0.4425 - accuracy: 0.8846
24/65 [==========>...................] - ETA: 18s - loss: 0.4419 - accuracy: 0.8844
25/65 [==========>...................] - ETA: 18s - loss: 0.4410 - accuracy: 0.8844
26/65 [===========>..................] - ETA: 17s - loss: 0.4396 - accuracy: 0.8847
27/65 [===========>..................] - ETA: 17s - loss: 0.4391 - accuracy: 0.8844
28/65 [===========>..................] - ETA: 17s - loss: 0.4382 - accuracy: 0.8848
29/65 [============>.................] - ETA: 16s - loss: 0.4384 - accuracy: 0.8848
30/65 [============>.................] - ETA: 16s - loss: 0.4372 - accuracy: 0.8850
31/65 [=============>................] - ETA: 15s - loss: 0.4381 - accuracy: 0.8847
32/65 [=============>................] - ETA: 15s - loss: 0.4393 - accuracy: 0.8846
33/65 [==============>...............] - ETA: 14s - loss: 0.4391 - accuracy: 0.8849
34/65 [==============>...............] - ETA: 14s - loss: 0.4395 - accuracy: 0.8847
35/65 [===============>..............] - ETA: 13s - loss: 0.4401 - accuracy: 0.8846
36/65 [===============>..............] - ETA: 13s - loss: 0.4403 - accuracy: 0.8843
37/65 [================>.............] - ETA: 12s - loss: 0.4407 - accuracy: 0.8839
38/65 [================>.............] - ETA: 12s - loss: 0.4402 - accuracy: 0.8840
39/65 [=================>............] - ETA: 11s - loss: 0.4407 - accuracy: 0.8839
40/65 [=================>............] - ETA: 11s - loss: 0.4400 - accuracy: 0.8842
41/65 [=================>............] - ETA: 11s - loss: 0.4398 - accuracy: 0.8843
42/65 [==================>...........] - ETA: 10s - loss: 0.4391 - accuracy: 0.8844
43/65 [==================>...........] - ETA: 10s - loss: 0.4392 - accuracy: 0.8845
44/65 [===================>..........] - ETA: 9s - loss: 0.4394 - accuracy: 0.8843
45/65 [===================>..........] - ETA: 9s - loss: 0.4396 - accuracy: 0.8844
46/65 [====================>.........] - ETA: 8s - loss: 0.4398 - accuracy: 0.8843
47/65 [====================>.........] - ETA: 8s - loss: 0.4396 - accuracy: 0.8845
48/65 [=====================>........] - ETA: 7s - loss: 0.4388 - accuracy: 0.8849
49/65 [=====================>........] - ETA: 7s - loss: 0.4388 - accuracy: 0.8848
50/65 [======================>.......] - ETA: 6s - loss: 0.4383 - accuracy: 0.8849
51/65 [======================>.......] - ETA: 6s - loss: 0.4393 - accuracy: 0.8846
52/65 [=======================>......] - ETA: 5s - loss: 0.4396 - accuracy: 0.8846
53/65 [=======================>......] - ETA: 5s - loss: 0.4388 - accuracy: 0.8845
54/65 [=======================>......] - ETA: 5s - loss: 0.4397 - accuracy: 0.8845
55/65 [========================>.....] - ETA: 4s - loss: 0.4391 - accuracy: 0.8846
56/65 [========================>.....] - ETA: 4s - loss: 0.4389 - accuracy: 0.8847
57/65 [=========================>....] - ETA: 3s - loss: 0.4390 - accuracy: 0.8844
58/65 [=========================>....] - ETA: 3s - loss: 0.4390 - accuracy: 0.8843
59/65 [==========================>...] - ETA: 2s - loss: 0.4398 - accuracy: 0.8842
60/65 [==========================>...] - ETA: 2s - loss: 0.4396 - accuracy: 0.8842
61/65 [===========================>..] - ETA: 1s - loss: 0.4400 - accuracy: 0.8841
62/65 [===========================>..] - ETA: 1s - loss: 0.4411 - accuracy: 0.8837
63/65 [============================>.] - ETA: 0s - loss: 0.4407 - accuracy: 0.8839
64/65 [============================>.] - ETA: 0s - loss: 0.4408 - accuracy: 0.8840
65/65 [==============================] - ETA: 0s - loss: 0.4409 - accuracy: 0.8839
Epoch 27: ReduceLROnPlateau reducing learning rate to 0.000375000003259629.
65/65 [==============================] - 31s 470ms/step - loss: 0.4409 - accuracy: 0.8839 - val_loss: 0.5539 - val_accuracy: 0.8489 - lr: 7.5000e-04
Epoch 28/30
1/65 [..............................] - ETA: 43s - loss: 0.4766 - accuracy: 0.8809
2/65 [..............................] - ETA: 28s - loss: 0.4778 - accuracy: 0.8784
3/65 [>.............................] - ETA: 28s - loss: 0.4785 - accuracy: 0.8789
4/65 [>.............................] - ETA: 27s - loss: 0.4706 - accuracy: 0.8806
5/65 [=>............................] - ETA: 27s - loss: 0.4670 - accuracy: 0.8811
6/65 [=>............................] - ETA: 26s - loss: 0.4694 - accuracy: 0.8791
7/65 [==>...........................] - ETA: 25s - loss: 0.4679 - accuracy: 0.8799
8/65 [==>...........................] - ETA: 25s - loss: 0.4642 - accuracy: 0.8802
9/65 [===>..........................] - ETA: 24s - loss: 0.4639 - accuracy: 0.8811
10/65 [===>..........................] - ETA: 24s - loss: 0.4582 - accuracy: 0.8813
11/65 [====>.........................] - ETA: 24s - loss: 0.4574 - accuracy: 0.8821
12/65 [====>.........................] - ETA: 23s - loss: 0.4552 - accuracy: 0.8826
13/65 [=====>........................] - ETA: 23s - loss: 0.4562 - accuracy: 0.8819
14/65 [=====>........................] - ETA: 22s - loss: 0.4533 - accuracy: 0.8829
15/65 [=====>........................] - ETA: 22s - loss: 0.4532 - accuracy: 0.8830
16/65 [======>.......................] - ETA: 21s - loss: 0.4502 - accuracy: 0.8827
17/65 [======>.......................] - ETA: 21s - loss: 0.4514 - accuracy: 0.8820
18/65 [=======>......................] - ETA: 21s - loss: 0.4501 - accuracy: 0.8818
19/65 [=======>......................] - ETA: 20s - loss: 0.4485 - accuracy: 0.8822
20/65 [========>.....................] - ETA: 20s - loss: 0.4492 - accuracy: 0.8822
21/65 [========>.....................] - ETA: 19s - loss: 0.4513 - accuracy: 0.8811
22/65 [=========>....................] - ETA: 19s - loss: 0.4504 - accuracy: 0.8814
23/65 [=========>....................] - ETA: 18s - loss: 0.4497 - accuracy: 0.8818
24/65 [==========>...................] - ETA: 18s - loss: 0.4494 - accuracy: 0.8819
25/65 [==========>...................] - ETA: 18s - loss: 0.4505 - accuracy: 0.8814
26/65 [===========>..................] - ETA: 17s - loss: 0.4483 - accuracy: 0.8820
27/65 [===========>..................] - ETA: 17s - loss: 0.4479 - accuracy: 0.8820
28/65 [===========>..................] - ETA: 16s - loss: 0.4474 - accuracy: 0.8824
29/65 [============>.................] - ETA: 16s - loss: 0.4486 - accuracy: 0.8818
30/65 [============>.................] - ETA: 15s - loss: 0.4468 - accuracy: 0.8826
31/65 [=============>................] - ETA: 15s - loss: 0.4462 - accuracy: 0.8828
32/65 [=============>................] - ETA: 14s - loss: 0.4463 - accuracy: 0.8831
33/65 [==============>...............] - ETA: 14s - loss: 0.4465 - accuracy: 0.8828
34/65 [==============>...............] - ETA: 14s - loss: 0.4450 - accuracy: 0.8834
35/65 [===============>..............] - ETA: 13s - loss: 0.4454 - accuracy: 0.8832
36/65 [===============>..............] - ETA: 13s - loss: 0.4457 - accuracy: 0.8833
37/65 [================>.............] - ETA: 12s - loss: 0.4462 - accuracy: 0.8831
38/65 [================>.............] - ETA: 12s - loss: 0.4448 - accuracy: 0.8837
39/65 [=================>............] - ETA: 11s - loss: 0.4429 - accuracy: 0.8842
40/65 [=================>............] - ETA: 11s - loss: 0.4440 - accuracy: 0.8837
41/65 [=================>............] - ETA: 10s - loss: 0.4435 - accuracy: 0.8840
42/65 [==================>...........] - ETA: 10s - loss: 0.4434 - accuracy: 0.8842
43/65 [==================>...........] - ETA: 10s - loss: 0.4433 - accuracy: 0.8840
44/65 [===================>..........] - ETA: 9s - loss: 0.4426 - accuracy: 0.8843
45/65 [===================>..........] - ETA: 9s - loss: 0.4424 - accuracy: 0.8842
46/65 [====================>.........] - ETA: 8s - loss: 0.4425 - accuracy: 0.8842
47/65 [====================>.........] - ETA: 8s - loss: 0.4430 - accuracy: 0.8842
48/65 [=====================>........] - ETA: 7s - loss: 0.4418 - accuracy: 0.8846
49/65 [=====================>........] - ETA: 7s - loss: 0.4415 - accuracy: 0.8847
50/65 [======================>.......] - ETA: 6s - loss: 0.4415 - accuracy: 0.8847
51/65 [======================>.......] - ETA: 6s - loss: 0.4413 - accuracy: 0.8848
52/65 [=======================>......] - ETA: 5s - loss: 0.4402 - accuracy: 0.8850
53/65 [=======================>......] - ETA: 5s - loss: 0.4395 - accuracy: 0.8850
54/65 [=======================>......] - ETA: 5s - loss: 0.4395 - accuracy: 0.8851
55/65 [========================>.....] - ETA: 4s - loss: 0.4389 - accuracy: 0.8853
56/65 [========================>.....] - ETA: 4s - loss: 0.4390 - accuracy: 0.8850
57/65 [=========================>....] - ETA: 3s - loss: 0.4386 - accuracy: 0.8851
58/65 [=========================>....] - ETA: 3s - loss: 0.4390 - accuracy: 0.8849
59/65 [==========================>...] - ETA: 2s - loss: 0.4387 - accuracy: 0.8850
60/65 [==========================>...] - ETA: 2s - loss: 0.4383 - accuracy: 0.8854
61/65 [===========================>..] - ETA: 1s - loss: 0.4386 - accuracy: 0.8851
62/65 [===========================>..] - ETA: 1s - loss: 0.4378 - accuracy: 0.8853
63/65 [============================>.] - ETA: 0s - loss: 0.4377 - accuracy: 0.8853
64/65 [============================>.] - ETA: 0s - loss: 0.4383 - accuracy: 0.8852
65/65 [==============================] - ETA: 0s - loss: 0.4382 - accuracy: 0.8851
65/65 [==============================] - 31s 469ms/step - loss: 0.4382 - accuracy: 0.8851 - val_loss: 0.5310 - val_accuracy: 0.8589 - lr: 3.7500e-04
Epoch 29/30
1/65 [..............................] - ETA: 42s - loss: 0.4677 - accuracy: 0.8779
2/65 [..............................] - ETA: 28s - loss: 0.4546 - accuracy: 0.8877
3/65 [>.............................] - ETA: 27s - loss: 0.4559 - accuracy: 0.8848
4/65 [>.............................] - ETA: 27s - loss: 0.4688 - accuracy: 0.8813
5/65 [=>............................] - ETA: 26s - loss: 0.4692 - accuracy: 0.8795
6/65 [=>............................] - ETA: 26s - loss: 0.4625 - accuracy: 0.8805
7/65 [==>...........................] - ETA: 25s - loss: 0.4571 - accuracy: 0.8818
8/65 [==>...........................] - ETA: 25s - loss: 0.4514 - accuracy: 0.8831
9/65 [===>..........................] - ETA: 25s - loss: 0.4491 - accuracy: 0.8839
10/65 [===>..........................] - ETA: 24s - loss: 0.4479 - accuracy: 0.8841
11/65 [====>.........................] - ETA: 24s - loss: 0.4465 - accuracy: 0.8845
12/65 [====>.........................] - ETA: 24s - loss: 0.4415 - accuracy: 0.8856
13/65 [=====>........................] - ETA: 23s - loss: 0.4457 - accuracy: 0.8849
14/65 [=====>........................] - ETA: 23s - loss: 0.4427 - accuracy: 0.8860
15/65 [=====>........................] - ETA: 22s - loss: 0.4434 - accuracy: 0.8852
16/65 [======>.......................] - ETA: 22s - loss: 0.4445 - accuracy: 0.8846
17/65 [======>.......................] - ETA: 21s - loss: 0.4435 - accuracy: 0.8848
18/65 [=======>......................] - ETA: 21s - loss: 0.4417 - accuracy: 0.8858
19/65 [=======>......................] - ETA: 20s - loss: 0.4401 - accuracy: 0.8861
20/65 [========>.....................] - ETA: 20s - loss: 0.4377 - accuracy: 0.8867
21/65 [========>.....................] - ETA: 20s - loss: 0.4391 - accuracy: 0.8863
22/65 [=========>....................] - ETA: 19s - loss: 0.4377 - accuracy: 0.8864
23/65 [=========>....................] - ETA: 19s - loss: 0.4362 - accuracy: 0.8868
24/65 [==========>...................] - ETA: 18s - loss: 0.4357 - accuracy: 0.8866
25/65 [==========>...................] - ETA: 18s - loss: 0.4349 - accuracy: 0.8867
26/65 [===========>..................] - ETA: 17s - loss: 0.4375 - accuracy: 0.8862
27/65 [===========>..................] - ETA: 17s - loss: 0.4373 - accuracy: 0.8863
28/65 [===========>..................] - ETA: 16s - loss: 0.4363 - accuracy: 0.8868
29/65 [============>.................] - ETA: 16s - loss: 0.4365 - accuracy: 0.8863
30/65 [============>.................] - ETA: 16s - loss: 0.4370 - accuracy: 0.8860
31/65 [=============>................] - ETA: 15s - loss: 0.4376 - accuracy: 0.8858
32/65 [=============>................] - ETA: 15s - loss: 0.4398 - accuracy: 0.8854
33/65 [==============>...............] - ETA: 14s - loss: 0.4394 - accuracy: 0.8854
34/65 [==============>...............] - ETA: 14s - loss: 0.4386 - accuracy: 0.8855
35/65 [===============>..............] - ETA: 13s - loss: 0.4393 - accuracy: 0.8853
36/65 [===============>..............] - ETA: 13s - loss: 0.4382 - accuracy: 0.8858
37/65 [================>.............] - ETA: 12s - loss: 0.4400 - accuracy: 0.8851
38/65 [================>.............] - ETA: 12s - loss: 0.4408 - accuracy: 0.8849
39/65 [=================>............] - ETA: 11s - loss: 0.4408 - accuracy: 0.8849
40/65 [=================>............] - ETA: 11s - loss: 0.4403 - accuracy: 0.8849
41/65 [=================>............] - ETA: 11s - loss: 0.4393 - accuracy: 0.8851
42/65 [==================>...........] - ETA: 10s - loss: 0.4390 - accuracy: 0.8852
43/65 [==================>...........] - ETA: 10s - loss: 0.4386 - accuracy: 0.8852
44/65 [===================>..........] - ETA: 9s - loss: 0.4383 - accuracy: 0.8854
45/65 [===================>..........] - ETA: 9s - loss: 0.4374 - accuracy: 0.8854
46/65 [====================>.........] - ETA: 8s - loss: 0.4372 - accuracy: 0.8856
47/65 [====================>.........] - ETA: 8s - loss: 0.4379 - accuracy: 0.8855
48/65 [=====================>........] - ETA: 7s - loss: 0.4382 - accuracy: 0.8852
49/65 [=====================>........] - ETA: 7s - loss: 0.4375 - accuracy: 0.8856
50/65 [======================>.......] - ETA: 6s - loss: 0.4383 - accuracy: 0.8853
51/65 [======================>.......] - ETA: 6s - loss: 0.4382 - accuracy: 0.8855
52/65 [=======================>......] - ETA: 5s - loss: 0.4373 - accuracy: 0.8859
53/65 [=======================>......] - ETA: 5s - loss: 0.4378 - accuracy: 0.8857
54/65 [=======================>......] - ETA: 5s - loss: 0.4374 - accuracy: 0.8858
55/65 [========================>.....] - ETA: 4s - loss: 0.4370 - accuracy: 0.8860
56/65 [========================>.....] - ETA: 4s - loss: 0.4367 - accuracy: 0.8862
57/65 [=========================>....] - ETA: 3s - loss: 0.4365 - accuracy: 0.8863
58/65 [=========================>....] - ETA: 3s - loss: 0.4366 - accuracy: 0.8862
59/65 [==========================>...] - ETA: 2s - loss: 0.4358 - accuracy: 0.8864
60/65 [==========================>...] - ETA: 2s - loss: 0.4357 - accuracy: 0.8863
61/65 [===========================>..] - ETA: 1s - loss: 0.4356 - accuracy: 0.8865
62/65 [===========================>..] - ETA: 1s - loss: 0.4357 - accuracy: 0.8864
63/65 [============================>.] - ETA: 0s - loss: 0.4353 - accuracy: 0.8864
64/65 [============================>.] - ETA: 0s - loss: 0.4362 - accuracy: 0.8862
65/65 [==============================] - ETA: 0s - loss: 0.4363 - accuracy: 0.8862
65/65 [==============================] - 31s 470ms/step - loss: 0.4363 - accuracy: 0.8862 - val_loss: 0.5324 - val_accuracy: 0.8580 - lr: 3.7500e-04
Epoch 30/30
1/65 [..............................] - ETA: 42s - loss: 0.4640 - accuracy: 0.8760
2/65 [..............................] - ETA: 28s - loss: 0.4564 - accuracy: 0.8774
3/65 [>.............................] - ETA: 28s - loss: 0.4682 - accuracy: 0.8776
4/65 [>.............................] - ETA: 28s - loss: 0.4675 - accuracy: 0.8792
5/65 [=>............................] - ETA: 27s - loss: 0.4637 - accuracy: 0.8807
6/65 [=>............................] - ETA: 27s - loss: 0.4600 - accuracy: 0.8807
7/65 [==>...........................] - ETA: 26s - loss: 0.4560 - accuracy: 0.8811
8/65 [==>...........................] - ETA: 26s - loss: 0.4569 - accuracy: 0.8807
9/65 [===>..........................] - ETA: 25s - loss: 0.4584 - accuracy: 0.8803
10/65 [===>..........................] - ETA: 25s - loss: 0.4578 - accuracy: 0.8802
11/65 [====>.........................] - ETA: 24s - loss: 0.4544 - accuracy: 0.8814
12/65 [====>.........................] - ETA: 24s - loss: 0.4513 - accuracy: 0.8818
13/65 [=====>........................] - ETA: 23s - loss: 0.4466 - accuracy: 0.8830
14/65 [=====>........................] - ETA: 23s - loss: 0.4471 - accuracy: 0.8830
15/65 [=====>........................] - ETA: 23s - loss: 0.4471 - accuracy: 0.8823
16/65 [======>.......................] - ETA: 22s - loss: 0.4484 - accuracy: 0.8821
17/65 [======>.......................] - ETA: 22s - loss: 0.4490 - accuracy: 0.8818
18/65 [=======>......................] - ETA: 21s - loss: 0.4466 - accuracy: 0.8822
19/65 [=======>......................] - ETA: 21s - loss: 0.4442 - accuracy: 0.8828
20/65 [========>.....................] - ETA: 20s - loss: 0.4446 - accuracy: 0.8828
21/65 [========>.....................] - ETA: 20s - loss: 0.4429 - accuracy: 0.8827
22/65 [=========>....................] - ETA: 19s - loss: 0.4416 - accuracy: 0.8831
23/65 [=========>....................] - ETA: 19s - loss: 0.4422 - accuracy: 0.8829
24/65 [==========>...................] - ETA: 19s - loss: 0.4423 - accuracy: 0.8829
25/65 [==========>...................] - ETA: 18s - loss: 0.4434 - accuracy: 0.8829
26/65 [===========>..................] - ETA: 18s - loss: 0.4429 - accuracy: 0.8831
27/65 [===========>..................] - ETA: 17s - loss: 0.4428 - accuracy: 0.8829
28/65 [===========>..................] - ETA: 17s - loss: 0.4438 - accuracy: 0.8824
29/65 [============>.................] - ETA: 16s - loss: 0.4438 - accuracy: 0.8822
30/65 [============>.................] - ETA: 16s - loss: 0.4424 - accuracy: 0.8825
31/65 [=============>................] - ETA: 15s - loss: 0.4406 - accuracy: 0.8831
32/65 [=============>................] - ETA: 15s - loss: 0.4420 - accuracy: 0.8830
33/65 [==============>...............] - ETA: 14s - loss: 0.4414 - accuracy: 0.8829
34/65 [==============>...............] - ETA: 14s - loss: 0.4409 - accuracy: 0.8830
35/65 [===============>..............] - ETA: 14s - loss: 0.4403 - accuracy: 0.8836
36/65 [===============>..............] - ETA: 13s - loss: 0.4405 - accuracy: 0.8834
37/65 [================>.............] - ETA: 13s - loss: 0.4401 - accuracy: 0.8834
38/65 [================>.............] - ETA: 12s - loss: 0.4401 - accuracy: 0.8835
39/65 [=================>............] - ETA: 12s - loss: 0.4397 - accuracy: 0.8836
40/65 [=================>............] - ETA: 11s - loss: 0.4406 - accuracy: 0.8834
41/65 [=================>............] - ETA: 11s - loss: 0.4405 - accuracy: 0.8836
42/65 [==================>...........] - ETA: 10s - loss: 0.4398 - accuracy: 0.8837
43/65 [==================>...........] - ETA: 10s - loss: 0.4385 - accuracy: 0.8843
44/65 [===================>..........] - ETA: 9s - loss: 0.4386 - accuracy: 0.8845
45/65 [===================>..........] - ETA: 9s - loss: 0.4383 - accuracy: 0.8848
46/65 [====================>.........] - ETA: 8s - loss: 0.4382 - accuracy: 0.8848
47/65 [====================>.........] - ETA: 8s - loss: 0.4384 - accuracy: 0.8848
48/65 [=====================>........] - ETA: 7s - loss: 0.4381 - accuracy: 0.8849
49/65 [=====================>........] - ETA: 7s - loss: 0.4377 - accuracy: 0.8852
50/65 [======================>.......] - ETA: 6s - loss: 0.4380 - accuracy: 0.8852
51/65 [======================>.......] - ETA: 6s - loss: 0.4373 - accuracy: 0.8854
52/65 [=======================>......] - ETA: 6s - loss: 0.4376 - accuracy: 0.8851
53/65 [=======================>......] - ETA: 5s - loss: 0.4370 - accuracy: 0.8853
54/65 [=======================>......] - ETA: 5s - loss: 0.4372 - accuracy: 0.8851
55/65 [========================>.....] - ETA: 4s - loss: 0.4370 - accuracy: 0.8852
56/65 [========================>.....] - ETA: 4s - loss: 0.4368 - accuracy: 0.8853
57/65 [=========================>....] - ETA: 3s - loss: 0.4370 - accuracy: 0.8852
58/65 [=========================>....] - ETA: 3s - loss: 0.4372 - accuracy: 0.8850
59/65 [==========================>...] - ETA: 2s - loss: 0.4373 - accuracy: 0.8848
60/65 [==========================>...] - ETA: 2s - loss: 0.4377 - accuracy: 0.8847
61/65 [===========================>..] - ETA: 1s - loss: 0.4376 - accuracy: 0.8847
62/65 [===========================>..] - ETA: 1s - loss: 0.4364 - accuracy: 0.8850
63/65 [============================>.] - ETA: 0s - loss: 0.4363 - accuracy: 0.8849
64/65 [============================>.] - ETA: 0s - loss: 0.4363 - accuracy: 0.8849
65/65 [==============================] - ETA: 0s - loss: 0.4363 - accuracy: 0.8849
65/65 [==============================] - 31s 474ms/step - loss: 0.4363 - accuracy: 0.8849 - val_loss: 0.5115 - val_accuracy: 0.8651 - lr: 3.7500e-04
It took 15.584588424364727 minutes to train!
We note that training a model quantization aware, takes around twice as long as when not quantizing during training! The validation accuracy is very similar to that of the floating point model equivalent, despite containing significantly less information
Performance#
Let’s look at some ROC curves to compare the performance. Lets choose a few numbers so it doesn’t get confusing. Feel free to change the numbers in labels.
predict_baseline = model_pruned.predict(X_test)
test_score_baseline = model_pruned.evaluate(X_test, Y_test)
predict_qkeras = qmodel_pruned.predict(X_test)
test_score_qkeras = qmodel_pruned.evaluate(X_test, Y_test)
print('Keras accuracy = {} , QKeras 6-bit accuracy = {}'.format(test_score_baseline[1], test_score_qkeras[1]))
1/814 [..............................] - ETA: 1:49
19/814 [..............................] - ETA: 2s
37/814 [>.............................] - ETA: 2s
55/814 [=>............................] - ETA: 2s
73/814 [=>............................] - ETA: 2s
91/814 [==>...........................] - ETA: 2s
109/814 [===>..........................] - ETA: 2s
127/814 [===>..........................] - ETA: 1s
146/814 [====>.........................] - ETA: 1s
164/814 [=====>........................] - ETA: 1s
182/814 [=====>........................] - ETA: 1s
201/814 [======>.......................] - ETA: 1s
220/814 [=======>......................] - ETA: 1s
239/814 [=======>......................] - ETA: 1s
258/814 [========>.....................] - ETA: 1s
276/814 [=========>....................] - ETA: 1s
295/814 [=========>....................] - ETA: 1s
313/814 [==========>...................] - ETA: 1s
331/814 [===========>..................] - ETA: 1s
350/814 [===========>..................] - ETA: 1s
368/814 [============>.................] - ETA: 1s
387/814 [=============>................] - ETA: 1s
406/814 [=============>................] - ETA: 1s
424/814 [==============>...............] - ETA: 1s
443/814 [===============>..............] - ETA: 1s
462/814 [================>.............] - ETA: 0s
481/814 [================>.............] - ETA: 0s
499/814 [=================>............] - ETA: 0s
517/814 [==================>...........] - ETA: 0s
536/814 [==================>...........] - ETA: 0s
554/814 [===================>..........] - ETA: 0s
574/814 [====================>.........] - ETA: 0s
594/814 [====================>.........] - ETA: 0s
614/814 [=====================>........] - ETA: 0s
634/814 [======================>.......] - ETA: 0s
654/814 [=======================>......] - ETA: 0s
674/814 [=======================>......] - ETA: 0s
694/814 [========================>.....] - ETA: 0s
714/814 [=========================>....] - ETA: 0s
734/814 [==========================>...] - ETA: 0s
754/814 [==========================>...] - ETA: 0s
774/814 [===========================>..] - ETA: 0s
794/814 [============================>.] - ETA: 0s
814/814 [==============================] - ETA: 0s
814/814 [==============================] - 2s 3ms/step
1/814 [..............................] - ETA: 13s - loss: 0.2692 - accuracy: 0.9688
19/814 [..............................] - ETA: 2s - loss: 0.4755 - accuracy: 0.8865
37/814 [>.............................] - ETA: 2s - loss: 0.4671 - accuracy: 0.8860
54/814 [>.............................] - ETA: 2s - loss: 0.4683 - accuracy: 0.8877
72/814 [=>............................] - ETA: 2s - loss: 0.4524 - accuracy: 0.8867
89/814 [==>...........................] - ETA: 2s - loss: 0.4572 - accuracy: 0.8813
107/814 [==>...........................] - ETA: 2s - loss: 0.4574 - accuracy: 0.8817
126/814 [===>..........................] - ETA: 1s - loss: 0.4529 - accuracy: 0.8842
145/814 [====>.........................] - ETA: 1s - loss: 0.4641 - accuracy: 0.8797
164/814 [=====>........................] - ETA: 1s - loss: 0.4674 - accuracy: 0.8780
182/814 [=====>........................] - ETA: 1s - loss: 0.4601 - accuracy: 0.8807
200/814 [======>.......................] - ETA: 1s - loss: 0.4579 - accuracy: 0.8819
218/814 [=======>......................] - ETA: 1s - loss: 0.4559 - accuracy: 0.8817
236/814 [=======>......................] - ETA: 1s - loss: 0.4581 - accuracy: 0.8815
254/814 [========>.....................] - ETA: 1s - loss: 0.4577 - accuracy: 0.8816
272/814 [=========>....................] - ETA: 1s - loss: 0.4545 - accuracy: 0.8836
291/814 [=========>....................] - ETA: 1s - loss: 0.4545 - accuracy: 0.8839
310/814 [==========>...................] - ETA: 1s - loss: 0.4586 - accuracy: 0.8832
329/814 [===========>..................] - ETA: 1s - loss: 0.4597 - accuracy: 0.8835
348/814 [===========>..................] - ETA: 1s - loss: 0.4598 - accuracy: 0.8833
367/814 [============>.................] - ETA: 1s - loss: 0.4616 - accuracy: 0.8826
386/814 [=============>................] - ETA: 1s - loss: 0.4605 - accuracy: 0.8826
405/814 [=============>................] - ETA: 1s - loss: 0.4576 - accuracy: 0.8828
422/814 [==============>...............] - ETA: 1s - loss: 0.4560 - accuracy: 0.8831
441/814 [===============>..............] - ETA: 1s - loss: 0.4587 - accuracy: 0.8826
460/814 [===============>..............] - ETA: 0s - loss: 0.4569 - accuracy: 0.8832
479/814 [================>.............] - ETA: 0s - loss: 0.4580 - accuracy: 0.8834
498/814 [=================>............] - ETA: 0s - loss: 0.4601 - accuracy: 0.8830
517/814 [==================>...........] - ETA: 0s - loss: 0.4597 - accuracy: 0.8829
536/814 [==================>...........] - ETA: 0s - loss: 0.4602 - accuracy: 0.8829
553/814 [===================>..........] - ETA: 0s - loss: 0.4635 - accuracy: 0.8822
571/814 [====================>.........] - ETA: 0s - loss: 0.4645 - accuracy: 0.8824
590/814 [====================>.........] - ETA: 0s - loss: 0.4649 - accuracy: 0.8827
608/814 [=====================>........] - ETA: 0s - loss: 0.4656 - accuracy: 0.8823
625/814 [======================>.......] - ETA: 0s - loss: 0.4641 - accuracy: 0.8828
642/814 [======================>.......] - ETA: 0s - loss: 0.4634 - accuracy: 0.8828
659/814 [=======================>......] - ETA: 0s - loss: 0.4636 - accuracy: 0.8829
676/814 [=======================>......] - ETA: 0s - loss: 0.4648 - accuracy: 0.8827
693/814 [========================>.....] - ETA: 0s - loss: 0.4647 - accuracy: 0.8828
710/814 [=========================>....] - ETA: 0s - loss: 0.4648 - accuracy: 0.8826
728/814 [=========================>....] - ETA: 0s - loss: 0.4656 - accuracy: 0.8826
746/814 [==========================>...] - ETA: 0s - loss: 0.4642 - accuracy: 0.8831
764/814 [===========================>..] - ETA: 0s - loss: 0.4630 - accuracy: 0.8835
780/814 [===========================>..] - ETA: 0s - loss: 0.4621 - accuracy: 0.8835
798/814 [============================>.] - ETA: 0s - loss: 0.4626 - accuracy: 0.8835
814/814 [==============================] - 2s 3ms/step - loss: 0.4652 - accuracy: 0.8830
1/814 [..............................] - ETA: 5:23
11/814 [..............................] - ETA: 4s
22/814 [..............................] - ETA: 3s
32/814 [>.............................] - ETA: 3s
42/814 [>.............................] - ETA: 3s
52/814 [>.............................] - ETA: 3s
62/814 [=>............................] - ETA: 3s
72/814 [=>............................] - ETA: 3s
83/814 [==>...........................] - ETA: 3s
93/814 [==>...........................] - ETA: 3s
103/814 [==>...........................] - ETA: 3s
114/814 [===>..........................] - ETA: 3s
125/814 [===>..........................] - ETA: 3s
136/814 [====>.........................] - ETA: 3s
147/814 [====>.........................] - ETA: 3s
157/814 [====>.........................] - ETA: 3s
167/814 [=====>........................] - ETA: 3s
177/814 [=====>........................] - ETA: 3s
187/814 [=====>........................] - ETA: 3s
197/814 [======>.......................] - ETA: 3s
207/814 [======>.......................] - ETA: 3s
218/814 [=======>......................] - ETA: 3s
228/814 [=======>......................] - ETA: 2s
238/814 [=======>......................] - ETA: 2s
248/814 [========>.....................] - ETA: 2s
259/814 [========>.....................] - ETA: 2s
270/814 [========>.....................] - ETA: 2s
281/814 [=========>....................] - ETA: 2s
292/814 [=========>....................] - ETA: 2s
303/814 [==========>...................] - ETA: 2s
313/814 [==========>...................] - ETA: 2s
324/814 [==========>...................] - ETA: 2s
335/814 [===========>..................] - ETA: 2s
346/814 [===========>..................] - ETA: 2s
357/814 [============>.................] - ETA: 2s
368/814 [============>.................] - ETA: 2s
378/814 [============>.................] - ETA: 2s
389/814 [=============>................] - ETA: 2s
400/814 [=============>................] - ETA: 2s
411/814 [==============>...............] - ETA: 2s
421/814 [==============>...............] - ETA: 1s
431/814 [==============>...............] - ETA: 1s
442/814 [===============>..............] - ETA: 1s
453/814 [===============>..............] - ETA: 1s
463/814 [================>.............] - ETA: 1s
474/814 [================>.............] - ETA: 1s
484/814 [================>.............] - ETA: 1s
495/814 [=================>............] - ETA: 1s
506/814 [=================>............] - ETA: 1s
516/814 [==================>...........] - ETA: 1s
526/814 [==================>...........] - ETA: 1s
537/814 [==================>...........] - ETA: 1s
547/814 [===================>..........] - ETA: 1s
558/814 [===================>..........] - ETA: 1s
569/814 [===================>..........] - ETA: 1s
580/814 [====================>.........] - ETA: 1s
591/814 [====================>.........] - ETA: 1s
601/814 [=====================>........] - ETA: 1s
612/814 [=====================>........] - ETA: 1s
623/814 [=====================>........] - ETA: 0s
633/814 [======================>.......] - ETA: 0s
644/814 [======================>.......] - ETA: 0s
655/814 [=======================>......] - ETA: 0s
665/814 [=======================>......] - ETA: 0s
676/814 [=======================>......] - ETA: 0s
687/814 [========================>.....] - ETA: 0s
698/814 [========================>.....] - ETA: 0s
709/814 [=========================>....] - ETA: 0s
719/814 [=========================>....] - ETA: 0s
728/814 [=========================>....] - ETA: 0s
738/814 [==========================>...] - ETA: 0s
749/814 [==========================>...] - ETA: 0s
759/814 [==========================>...] - ETA: 0s
770/814 [===========================>..] - ETA: 0s
781/814 [===========================>..] - ETA: 0s
792/814 [============================>.] - ETA: 0s
802/814 [============================>.] - ETA: 0s
813/814 [============================>.] - ETA: 0s
814/814 [==============================] - 4s 5ms/step
1/814 [..............................] - ETA: 15s - loss: 0.4816 - accuracy: 0.8750
11/814 [..............................] - ETA: 4s - loss: 0.5048 - accuracy: 0.8665
21/814 [..............................] - ETA: 4s - loss: 0.5240 - accuracy: 0.8586
32/814 [>.............................] - ETA: 3s - loss: 0.5013 - accuracy: 0.8643
42/814 [>.............................] - ETA: 3s - loss: 0.5403 - accuracy: 0.8594
52/814 [>.............................] - ETA: 3s - loss: 0.5296 - accuracy: 0.8624
62/814 [=>............................] - ETA: 3s - loss: 0.5166 - accuracy: 0.8644
72/814 [=>............................] - ETA: 3s - loss: 0.5231 - accuracy: 0.8624
81/814 [=>............................] - ETA: 3s - loss: 0.5304 - accuracy: 0.8623
91/814 [==>...........................] - ETA: 3s - loss: 0.5414 - accuracy: 0.8592
101/814 [==>...........................] - ETA: 3s - loss: 0.5375 - accuracy: 0.8605
111/814 [===>..........................] - ETA: 3s - loss: 0.5445 - accuracy: 0.8573
121/814 [===>..........................] - ETA: 3s - loss: 0.5381 - accuracy: 0.8592
131/814 [===>..........................] - ETA: 3s - loss: 0.5324 - accuracy: 0.8578
141/814 [====>.........................] - ETA: 3s - loss: 0.5447 - accuracy: 0.8535
151/814 [====>.........................] - ETA: 3s - loss: 0.5445 - accuracy: 0.8524
161/814 [====>.........................] - ETA: 3s - loss: 0.5438 - accuracy: 0.8521
171/814 [=====>........................] - ETA: 3s - loss: 0.5399 - accuracy: 0.8534
182/814 [=====>........................] - ETA: 3s - loss: 0.5367 - accuracy: 0.8539
193/814 [======>.......................] - ETA: 3s - loss: 0.5310 - accuracy: 0.8548
204/814 [======>.......................] - ETA: 3s - loss: 0.5316 - accuracy: 0.8540
215/814 [======>.......................] - ETA: 3s - loss: 0.5291 - accuracy: 0.8547
226/814 [=======>......................] - ETA: 2s - loss: 0.5286 - accuracy: 0.8551
237/814 [=======>......................] - ETA: 2s - loss: 0.5293 - accuracy: 0.8555
248/814 [========>.....................] - ETA: 2s - loss: 0.5285 - accuracy: 0.8566
259/814 [========>.....................] - ETA: 2s - loss: 0.5264 - accuracy: 0.8570
270/814 [========>.....................] - ETA: 2s - loss: 0.5271 - accuracy: 0.8569
281/814 [=========>....................] - ETA: 2s - loss: 0.5260 - accuracy: 0.8574
292/814 [=========>....................] - ETA: 2s - loss: 0.5270 - accuracy: 0.8565
302/814 [==========>...................] - ETA: 2s - loss: 0.5268 - accuracy: 0.8568
313/814 [==========>...................] - ETA: 2s - loss: 0.5273 - accuracy: 0.8565
325/814 [==========>...................] - ETA: 2s - loss: 0.5292 - accuracy: 0.8564
336/814 [===========>..................] - ETA: 2s - loss: 0.5297 - accuracy: 0.8567
347/814 [===========>..................] - ETA: 2s - loss: 0.5276 - accuracy: 0.8576
358/814 [============>.................] - ETA: 2s - loss: 0.5276 - accuracy: 0.8577
369/814 [============>.................] - ETA: 2s - loss: 0.5279 - accuracy: 0.8576
380/814 [=============>................] - ETA: 2s - loss: 0.5291 - accuracy: 0.8573
391/814 [=============>................] - ETA: 2s - loss: 0.5288 - accuracy: 0.8576
402/814 [=============>................] - ETA: 2s - loss: 0.5272 - accuracy: 0.8577
413/814 [==============>...............] - ETA: 1s - loss: 0.5267 - accuracy: 0.8577
423/814 [==============>...............] - ETA: 1s - loss: 0.5251 - accuracy: 0.8580
434/814 [==============>...............] - ETA: 1s - loss: 0.5267 - accuracy: 0.8576
445/814 [===============>..............] - ETA: 1s - loss: 0.5275 - accuracy: 0.8576
456/814 [===============>..............] - ETA: 1s - loss: 0.5284 - accuracy: 0.8577
467/814 [================>.............] - ETA: 1s - loss: 0.5285 - accuracy: 0.8579
477/814 [================>.............] - ETA: 1s - loss: 0.5289 - accuracy: 0.8577
487/814 [================>.............] - ETA: 1s - loss: 0.5294 - accuracy: 0.8577
497/814 [=================>............] - ETA: 1s - loss: 0.5312 - accuracy: 0.8572
507/814 [=================>............] - ETA: 1s - loss: 0.5300 - accuracy: 0.8572
517/814 [==================>...........] - ETA: 1s - loss: 0.5315 - accuracy: 0.8568
528/814 [==================>...........] - ETA: 1s - loss: 0.5311 - accuracy: 0.8572
539/814 [==================>...........] - ETA: 1s - loss: 0.5319 - accuracy: 0.8573
550/814 [===================>..........] - ETA: 1s - loss: 0.5321 - accuracy: 0.8572
561/814 [===================>..........] - ETA: 1s - loss: 0.5355 - accuracy: 0.8557
572/814 [====================>.........] - ETA: 1s - loss: 0.5357 - accuracy: 0.8556
583/814 [====================>.........] - ETA: 1s - loss: 0.5369 - accuracy: 0.8554
594/814 [====================>.........] - ETA: 1s - loss: 0.5363 - accuracy: 0.8555
605/814 [=====================>........] - ETA: 1s - loss: 0.5363 - accuracy: 0.8555
616/814 [=====================>........] - ETA: 0s - loss: 0.5355 - accuracy: 0.8557
627/814 [======================>.......] - ETA: 0s - loss: 0.5366 - accuracy: 0.8552
638/814 [======================>.......] - ETA: 0s - loss: 0.5366 - accuracy: 0.8553
649/814 [======================>.......] - ETA: 0s - loss: 0.5376 - accuracy: 0.8554
660/814 [=======================>......] - ETA: 0s - loss: 0.5376 - accuracy: 0.8556
671/814 [=======================>......] - ETA: 0s - loss: 0.5373 - accuracy: 0.8557
682/814 [========================>.....] - ETA: 0s - loss: 0.5380 - accuracy: 0.8552
692/814 [========================>.....] - ETA: 0s - loss: 0.5382 - accuracy: 0.8551
702/814 [========================>.....] - ETA: 0s - loss: 0.5394 - accuracy: 0.8547
713/814 [=========================>....] - ETA: 0s - loss: 0.5393 - accuracy: 0.8550
723/814 [=========================>....] - ETA: 0s - loss: 0.5391 - accuracy: 0.8551
733/814 [==========================>...] - ETA: 0s - loss: 0.5412 - accuracy: 0.8546
743/814 [==========================>...] - ETA: 0s - loss: 0.5420 - accuracy: 0.8545
753/814 [==========================>...] - ETA: 0s - loss: 0.5414 - accuracy: 0.8547
763/814 [===========================>..] - ETA: 0s - loss: 0.5406 - accuracy: 0.8549
773/814 [===========================>..] - ETA: 0s - loss: 0.5404 - accuracy: 0.8550
783/814 [===========================>..] - ETA: 0s - loss: 0.5409 - accuracy: 0.8549
793/814 [============================>.] - ETA: 0s - loss: 0.5411 - accuracy: 0.8550
803/814 [============================>.] - ETA: 0s - loss: 0.5419 - accuracy: 0.8549
813/814 [============================>.] - ETA: 0s - loss: 0.5423 - accuracy: 0.8546
814/814 [==============================] - 4s 5ms/step - loss: 0.5421 - accuracy: 0.8546
Keras accuracy = 0.8829517364501953 , QKeras 6-bit accuracy = 0.8546404242515564
import matplotlib.pyplot as plt
import pandas as pd
from sklearn import metrics
labels = ['%i' % nr for nr in range(0, n_classes)] # If you want to look at all the labels
# labels = ['0','1','9'] # Look at only a few labels, here for digits 0, 1 and 9
print('Plotting ROC for labels {}'.format(labels))
df = pd.DataFrame()
df_q = pd.DataFrame()
fpr = {}
tpr = {}
auc1 = {}
fpr_q = {}
tpr_q = {}
auc1_q = {}
%matplotlib inline
colors = ['#67001f', '#b2182b', '#d6604d', '#f4a582', '#fddbc7', '#d1e5f0', '#92c5de', '#4393c3', '#2166ac', '#053061']
fig, ax = plt.subplots(figsize=(10, 10))
for i, label in enumerate(labels):
df[label] = Y_test[:, int(label)]
df[label + '_pred'] = predict_baseline[:, int(label)]
fpr[label], tpr[label], threshold = metrics.roc_curve(df[label], df[label + '_pred'])
auc1[label] = metrics.auc(fpr[label], tpr[label])
df_q[label] = Y_test[:, int(label)]
df_q[label + '_pred'] = predict_qkeras[:, int(label)]
fpr_q[label], tpr_q[label], threshold_q = metrics.roc_curve(df_q[label], df_q[label + '_pred'])
auc1_q[label] = metrics.auc(fpr_q[label], tpr_q[label])
plt.plot(
fpr[label],
tpr[label],
label=r'{}, AUC Keras = {:.1f}% AUC QKeras = {:.1f}%)'.format(label, auc1[label] * 100, auc1_q[label] * 100),
linewidth=1.5,
c=colors[i],
linestyle='solid',
)
plt.plot(fpr_q[label], tpr_q[label], linewidth=1.5, c=colors[i], linestyle='dotted')
plt.semilogx()
plt.ylabel("True Positive Rate")
plt.xlabel("False Positive Rate")
plt.xlim(0.01, 1.0)
plt.ylim(0.5, 1.1)
plt.legend(loc='lower right')
plt.figtext(
0.2,
0.83,
r'Accuracy Keras = {:.1f}% QKeras 8-bit = {:.1f}%'.format(test_score_baseline[1] * 100, test_score_qkeras[1] * 100),
wrap=True,
horizontalalignment='left',
verticalalignment='center',
)
from matplotlib.lines import Line2D
lines = [Line2D([0], [0], ls='-'), Line2D([0], [0], ls='--')]
from matplotlib.legend import Legend
leg = Legend(ax, lines, labels=['Keras', 'QKeras'], loc='lower right', frameon=False)
ax.add_artist(leg)
Plotting ROC for labels ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']
<matplotlib.legend.Legend at 0x7fc924e56470>
The difference in AUC between the fp32 Keras model and the 8-bit QKeras model, is small, as we have seen for the previous examples. You can find a bonus exercise below, Bonus: Automatic quantization, where we’ll use AutoQKeras to find the best heterogeneously quantized model, given a set of resource and accuracy constriants.
Check sparsity#
Let’s also check the per-layer sparsity:
def doWeights(model):
allWeightsByLayer = {}
for layer in model.layers:
if (layer._name).find("batch") != -1 or len(layer.get_weights()) < 1:
continue
weights = layer.weights[0].numpy().flatten()
allWeightsByLayer[layer._name] = weights
print('Layer {}: % of zeros = {}'.format(layer._name, np.sum(weights == 0) / np.size(weights)))
labelsW = []
histosW = []
for key in reversed(sorted(allWeightsByLayer.keys())):
labelsW.append(key)
histosW.append(allWeightsByLayer[key])
fig = plt.figure(figsize=(10, 10))
bins = np.linspace(-1.5, 1.5, 50)
plt.hist(histosW, bins, histtype='stepfilled', stacked=True, label=labelsW, edgecolor='black')
plt.legend(frameon=False, loc='upper left')
plt.ylabel('Number of Weights')
plt.xlabel('Weights')
plt.figtext(0.2, 0.38, model._name, wrap=True, horizontalalignment='left', verticalalignment='center')
doWeights(model_pruned)
doWeights(qmodel_pruned)
Layer prune_low_magnitude_conv_0: % of zeros = 0.5
Layer bn_conv_0: % of zeros = 0.0
Layer prune_low_magnitude_conv_1: % of zeros = 0.5
Layer bn_conv_1: % of zeros = 0.0
Layer prune_low_magnitude_conv_2: % of zeros = 0.5
Layer bn_conv_2: % of zeros = 0.0
Layer prune_low_magnitude_dense_0: % of zeros = 0.5
Layer bn_dense_0: % of zeros = 0.0
Layer prune_low_magnitude_dense_1: % of zeros = 0.5
Layer bn_dense_1: % of zeros = 0.0
Layer output_dense: % of zeros = 0.0
Layer prune_low_magnitude_fused_convbn_0: % of zeros = 0.5
Layer prune_low_magnitude_fused_convbn_1: % of zeros = 0.5
Layer prune_low_magnitude_fused_convbn_2: % of zeros = 0.5
Layer prune_low_magnitude_dense_0: % of zeros = 0.5
Layer bn_dense_0: % of zeros = 0.0
Layer prune_low_magnitude_dense_1: % of zeros = 0.5
Layer bn_dense_1: % of zeros = 0.0
Layer output_dense: % of zeros = 0.0
We see that 50% of the weights per layer are set to zero, as expected. Now, let’s synthesize the floating point Keras model and the QKeras quantized model!
CNNs in hls4ml#
In this part, we will take the two models we trained above (the floating-point 32 Keras model and the 6-bit QKeras model), and synthesize them with hls4ml. Although your models are probably already in memory, let’s load them from scratch. We need to pass the appropriate custom QKeras/pruning layers when loading, and remove the pruning parameters that were saved together with the model.
from tensorflow_model_optimization.sparsity.keras import strip_pruning
from tensorflow_model_optimization.python.core.sparsity.keras import pruning_wrapper
from qkeras.utils import _add_supported_quantized_objects
co = {}
_add_supported_quantized_objects(co)
co['PruneLowMagnitude'] = pruning_wrapper.PruneLowMagnitude
model = tf.keras.models.load_model('pruned_cnn_model.h5', custom_objects=co)
model = strip_pruning(model)
qmodel = tf.keras.models.load_model('quantized_pruned_cnn_model.h5', custom_objects=co)
qmodel = strip_pruning(qmodel)
Now, we need to define the hls4ml and Vivado configurations. Two things will change with respect to what was done in the previous exercises. First, we will use IOType= 'io_stream' in the Vivado configuration.
You must use IOType= 'io_stream' if attempting to synthesize a large convolutional neural network.
The CNN implementation in hls4ml is based on streams, which are synthesized in hardware as first in, first out (FIFO) buffers. Shift registers are used to keep track of the last <kernel height - 1> rows of input pixels, and maintains a shifting snapshot of the convolution kernel.
This is illustrated in the gif below. Here, the input image is at the top-left and the output image at the bottom left. The top right image shows the internal state of the shift registers and convolutional kernel. The red square indicates the current pixels contained within the convolutional kernel.
Lastly, we will use ['Strategy'] = 'Latency' for all the layers in the hls4ml configuration. If one layer would have >4096 elements, we sould set ['Strategy'] = 'Resource' for that layer, or increase the reuse factor by hand. You can find examples of how to do this below.
import hls4ml
import plotting
# First, the baseline model
hls_config = hls4ml.utils.config_from_keras_model(model, granularity='name')
# Set the precision and reuse factor for the full model
hls_config['Model']['Precision'] = 'ap_fixed<16,6>'
hls_config['Model']['ReuseFactor'] = 1
# Create an entry for each layer, here you can for instance change the strategy for a layer to 'resource'
# or increase the reuse factor individually for large layers.
# In this case, we designed the model to be small enough for a fully parallel implementation
# so we use the latency strategy and reuse factor of 1 for all layers.
for Layer in hls_config['LayerName'].keys():
hls_config['LayerName'][Layer]['Strategy'] = 'Latency'
hls_config['LayerName'][Layer]['ReuseFactor'] = 1
# If you want best numerical performance for high-accuray models, while the default latency strategy is faster but numerically more unstable
hls_config['LayerName']['output_softmax']['Strategy'] = 'Stable'
plotting.print_dict(hls_config)
cfg = hls4ml.converters.create_config(backend='Vivado')
cfg['IOType'] = 'io_stream' # Must set this if using CNNs!
cfg['HLSConfig'] = hls_config
cfg['KerasModel'] = model
cfg['OutputDir'] = 'pruned_cnn/'
cfg['XilinxPart'] = 'xcu250-figd2104-2L-e'
hls_model = hls4ml.converters.keras_to_hls(cfg)
hls_model.compile()
/home/runner/miniconda3/envs/hls4ml-tutorial/lib/python3.10/site-packages/hls4ml/converters/__init__.py:27: UserWarning: WARNING: Pytorch converter is not enabled!
warnings.warn("WARNING: Pytorch converter is not enabled!", stacklevel=1)
WARNING: Failed to import handlers from pooling.py: No module named 'torch'.
WARNING: Failed to import handlers from merge.py: No module named 'torch'.
WARNING: Failed to import handlers from convolution.py: No module named 'torch'.
WARNING: Failed to import handlers from core.py: No module named 'torch'.
WARNING: Failed to import handlers from reshape.py: No module named 'torch'.
Interpreting Model
Topology:
Layer name: input_1, layer type: InputLayer, input shapes: [[None, 32, 32, 3]], output shape: [None, 32, 32, 3]
Layer name: conv_0, layer type: Conv2D, input shapes: [[None, 32, 32, 3]], output shape: [None, 30, 30, 16]
Layer name: bn_conv_0, layer type: BatchNormalization, input shapes: [[None, 30, 30, 16]], output shape: [None, 30, 30, 16]
Layer name: conv_act_0, layer type: Activation, input shapes: [[None, 30, 30, 16]], output shape: [None, 30, 30, 16]
Layer name: pool_0, layer type: MaxPooling2D, input shapes: [[None, 30, 30, 16]], output shape: [None, 15, 15, 16]
Layer name: conv_1, layer type: Conv2D, input shapes: [[None, 15, 15, 16]], output shape: [None, 13, 13, 16]
Layer name: bn_conv_1, layer type: BatchNormalization, input shapes: [[None, 13, 13, 16]], output shape: [None, 13, 13, 16]
Layer name: conv_act_1, layer type: Activation, input shapes: [[None, 13, 13, 16]], output shape: [None, 13, 13, 16]
Layer name: pool_1, layer type: MaxPooling2D, input shapes: [[None, 13, 13, 16]], output shape: [None, 6, 6, 16]
Layer name: conv_2, layer type: Conv2D, input shapes: [[None, 6, 6, 16]], output shape: [None, 4, 4, 24]
Layer name: bn_conv_2, layer type: BatchNormalization, input shapes: [[None, 4, 4, 24]], output shape: [None, 4, 4, 24]
Layer name: conv_act_2, layer type: Activation, input shapes: [[None, 4, 4, 24]], output shape: [None, 4, 4, 24]
Layer name: pool_2, layer type: MaxPooling2D, input shapes: [[None, 4, 4, 24]], output shape: [None, 2, 2, 24]
Layer name: flatten, layer type: Reshape, input shapes: [[None, 2, 2, 24]], output shape: [None, 96]
Layer name: dense_0, layer type: Dense, input shapes: [[None, 96]], output shape: [None, 42]
Layer name: bn_dense_0, layer type: BatchNormalization, input shapes: [[None, 42]], output shape: [None, 42]
Layer name: dense_act_0, layer type: Activation, input shapes: [[None, 42]], output shape: [None, 42]
Layer name: dense_1, layer type: Dense, input shapes: [[None, 42]], output shape: [None, 64]
Layer name: bn_dense_1, layer type: BatchNormalization, input shapes: [[None, 64]], output shape: [None, 64]
Layer name: dense_act_1, layer type: Activation, input shapes: [[None, 64]], output shape: [None, 64]
Layer name: output_dense, layer type: Dense, input shapes: [[None, 64]], output shape: [None, 10]
Layer name: output_softmax, layer type: Softmax, input shapes: [[None, 10]], output shape: [None, 10]
Model
Precision: ap_fixed<16,6>
ReuseFactor: 1
Strategy: Latency
BramFactor: 1000000000
TraceOutput: False
LayerName
input_1
Trace: False
Precision
result: fixed<16,6>
Strategy: Latency
ReuseFactor: 1
conv_0
Trace: False
Precision
result: fixed<16,6>
weight: fixed<16,6>
bias: fixed<16,6>
Strategy: Latency
ReuseFactor: 1
conv_0_linear
Trace: False
Precision
result: fixed<16,6>
Strategy: Latency
ReuseFactor: 1
bn_conv_0
Trace: False
Precision
result: fixed<16,6>
scale: fixed<16,6>
bias: fixed<16,6>
Strategy: Latency
ReuseFactor: 1
conv_act_0
Trace: False
Precision
result: fixed<16,6>
Strategy: Latency
ReuseFactor: 1
pool_0
Trace: False
Precision
result: fixed<16,6>
Strategy: Latency
ReuseFactor: 1
conv_1
Trace: False
Precision
result: fixed<16,6>
weight: fixed<16,6>
bias: fixed<16,6>
Strategy: Latency
ReuseFactor: 1
conv_1_linear
Trace: False
Precision
result: fixed<16,6>
Strategy: Latency
ReuseFactor: 1
bn_conv_1
Trace: False
Precision
result: fixed<16,6>
scale: fixed<16,6>
bias: fixed<16,6>
Strategy: Latency
ReuseFactor: 1
conv_act_1
Trace: False
Precision
result: fixed<16,6>
Strategy: Latency
ReuseFactor: 1
pool_1
Trace: False
Precision
result: fixed<16,6>
Strategy: Latency
ReuseFactor: 1
conv_2
Trace: False
Precision
result: fixed<16,6>
weight: fixed<16,6>
bias: fixed<16,6>
Strategy: Latency
ReuseFactor: 1
conv_2_linear
Trace: False
Precision
result: fixed<16,6>
Strategy: Latency
ReuseFactor: 1
bn_conv_2
Trace: False
Precision
result: fixed<16,6>
scale: fixed<16,6>
bias: fixed<16,6>
Strategy: Latency
ReuseFactor: 1
conv_act_2
Trace: False
Precision
result: fixed<16,6>
Strategy: Latency
ReuseFactor: 1
pool_2
Trace: False
Precision
result: fixed<16,6>
Strategy: Latency
ReuseFactor: 1
flatten
Trace: False
Precision
result: fixed<16,6>
Strategy: Latency
ReuseFactor: 1
dense_0
Trace: False
Precision
result: fixed<16,6>
weight: fixed<16,6>
bias: fixed<16,6>
Strategy: Latency
ReuseFactor: 1
dense_0_linear
Trace: False
Precision
result: fixed<16,6>
Strategy: Latency
ReuseFactor: 1
bn_dense_0
Trace: False
Precision
result: fixed<16,6>
scale: fixed<16,6>
bias: fixed<16,6>
Strategy: Latency
ReuseFactor: 1
dense_act_0
Trace: False
Precision
result: fixed<16,6>
Strategy: Latency
ReuseFactor: 1
dense_1
Trace: False
Precision
result: fixed<16,6>
weight: fixed<16,6>
bias: fixed<16,6>
Strategy: Latency
ReuseFactor: 1
dense_1_linear
Trace: False
Precision
result: fixed<16,6>
Strategy: Latency
ReuseFactor: 1
bn_dense_1
Trace: False
Precision
result: fixed<16,6>
scale: fixed<16,6>
bias: fixed<16,6>
Strategy: Latency
ReuseFactor: 1
dense_act_1
Trace: False
Precision
result: fixed<16,6>
Strategy: Latency
ReuseFactor: 1
output_dense
Trace: False
Precision
result: fixed<16,6>
weight: fixed<16,6>
bias: fixed<16,6>
Strategy: Latency
ReuseFactor: 1
output_dense_linear
Trace: False
Precision
result: fixed<16,6>
Strategy: Latency
ReuseFactor: 1
output_softmax
Trace: False
Precision
result: fixed<16,6>
Strategy: Stable
ReuseFactor: 1
Interpreting Model
Topology:
Layer name: input_1, layer type: InputLayer, input shapes: [[None, 32, 32, 3]], output shape: [None, 32, 32, 3]
Layer name: conv_0, layer type: Conv2D, input shapes: [[None, 32, 32, 3]], output shape: [None, 30, 30, 16]
Layer name: bn_conv_0, layer type: BatchNormalization, input shapes: [[None, 30, 30, 16]], output shape: [None, 30, 30, 16]
Layer name: conv_act_0, layer type: Activation, input shapes: [[None, 30, 30, 16]], output shape: [None, 30, 30, 16]
Layer name: pool_0, layer type: MaxPooling2D, input shapes: [[None, 30, 30, 16]], output shape: [None, 15, 15, 16]
Layer name: conv_1, layer type: Conv2D, input shapes: [[None, 15, 15, 16]], output shape: [None, 13, 13, 16]
Layer name: bn_conv_1, layer type: BatchNormalization, input shapes: [[None, 13, 13, 16]], output shape: [None, 13, 13, 16]
Layer name: conv_act_1, layer type: Activation, input shapes: [[None, 13, 13, 16]], output shape: [None, 13, 13, 16]
Layer name: pool_1, layer type: MaxPooling2D, input shapes: [[None, 13, 13, 16]], output shape: [None, 6, 6, 16]
Layer name: conv_2, layer type: Conv2D, input shapes: [[None, 6, 6, 16]], output shape: [None, 4, 4, 24]
Layer name: bn_conv_2, layer type: BatchNormalization, input shapes: [[None, 4, 4, 24]], output shape: [None, 4, 4, 24]
Layer name: conv_act_2, layer type: Activation, input shapes: [[None, 4, 4, 24]], output shape: [None, 4, 4, 24]
Layer name: pool_2, layer type: MaxPooling2D, input shapes: [[None, 4, 4, 24]], output shape: [None, 2, 2, 24]
Layer name: flatten, layer type: Reshape, input shapes: [[None, 2, 2, 24]], output shape: [None, 96]
Layer name: dense_0, layer type: Dense, input shapes: [[None, 96]], output shape: [None, 42]
Layer name: bn_dense_0, layer type: BatchNormalization, input shapes: [[None, 42]], output shape: [None, 42]
Layer name: dense_act_0, layer type: Activation, input shapes: [[None, 42]], output shape: [None, 42]
Layer name: dense_1, layer type: Dense, input shapes: [[None, 42]], output shape: [None, 64]
Layer name: bn_dense_1, layer type: BatchNormalization, input shapes: [[None, 64]], output shape: [None, 64]
Layer name: dense_act_1, layer type: Activation, input shapes: [[None, 64]], output shape: [None, 64]
Layer name: output_dense, layer type: Dense, input shapes: [[None, 64]], output shape: [None, 10]
Layer name: output_softmax, layer type: Softmax, input shapes: [[None, 10]], output shape: [None, 10]
Creating HLS model
WARNING: Layer conv_0 requires "dataflow" pipeline style. Switching to "dataflow" pipeline style.
Writing HLS project
WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.
WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.
Done
Let’s get a nice overview over the various shapes and precisions used for each layer through hls4ml.utils.plot_model, as well as look at the weight profile using hls4ml.model.profiling.numerical. The weight profiling returns two plots: Before (top) and after (bottom) various optimizations applied to the HLS model before the final translation to HLS, for instance the fusing of Dense and BatchNormalization layers.
hls4ml.utils.plot_model(hls_model, show_shapes=True, show_precision=True, to_file=None)
hls4ml.model.profiling.numerical(model=model, hls_model=hls_model)
Interpreting Model
Topology:
Layer name: input_1, layer type: InputLayer, input shapes: [[None, 32, 32, 3]], output shape: [None, 32, 32, 3]
Layer name: conv_0, layer type: Conv2D, input shapes: [[None, 32, 32, 3]], output shape: [None, 30, 30, 16]
Layer name: bn_conv_0, layer type: BatchNormalization, input shapes: [[None, 30, 30, 16]], output shape: [None, 30, 30, 16]
Layer name: conv_act_0, layer type: Activation, input shapes: [[None, 30, 30, 16]], output shape: [None, 30, 30, 16]
Layer name: pool_0, layer type: MaxPooling2D, input shapes: [[None, 30, 30, 16]], output shape: [None, 15, 15, 16]
Layer name: conv_1, layer type: Conv2D, input shapes: [[None, 15, 15, 16]], output shape: [None, 13, 13, 16]
Layer name: bn_conv_1, layer type: BatchNormalization, input shapes: [[None, 13, 13, 16]], output shape: [None, 13, 13, 16]
Layer name: conv_act_1, layer type: Activation, input shapes: [[None, 13, 13, 16]], output shape: [None, 13, 13, 16]
Layer name: pool_1, layer type: MaxPooling2D, input shapes: [[None, 13, 13, 16]], output shape: [None, 6, 6, 16]
Layer name: conv_2, layer type: Conv2D, input shapes: [[None, 6, 6, 16]], output shape: [None, 4, 4, 24]
Layer name: bn_conv_2, layer type: BatchNormalization, input shapes: [[None, 4, 4, 24]], output shape: [None, 4, 4, 24]
Layer name: conv_act_2, layer type: Activation, input shapes: [[None, 4, 4, 24]], output shape: [None, 4, 4, 24]
Layer name: pool_2, layer type: MaxPooling2D, input shapes: [[None, 4, 4, 24]], output shape: [None, 2, 2, 24]
Layer name: flatten, layer type: Reshape, input shapes: [[None, 2, 2, 24]], output shape: [None, 96]
Layer name: dense_0, layer type: Dense, input shapes: [[None, 96]], output shape: [None, 42]
Layer name: bn_dense_0, layer type: BatchNormalization, input shapes: [[None, 42]], output shape: [None, 42]
Layer name: dense_act_0, layer type: Activation, input shapes: [[None, 42]], output shape: [None, 42]
Layer name: dense_1, layer type: Dense, input shapes: [[None, 42]], output shape: [None, 64]
Layer name: bn_dense_1, layer type: BatchNormalization, input shapes: [[None, 64]], output shape: [None, 64]
Layer name: dense_act_1, layer type: Activation, input shapes: [[None, 64]], output shape: [None, 64]
Layer name: output_dense, layer type: Dense, input shapes: [[None, 64]], output shape: [None, 10]
Layer name: output_softmax, layer type: Softmax, input shapes: [[None, 10]], output shape: [None, 10]
Creating HLS model
WARNING: Layer conv_0 requires "dataflow" pipeline style. Switching to "dataflow" pipeline style.
Profiling weights (before optimization)
Profiling weights (final / after optimization)
(<Figure size 640x480 with 1 Axes>,
<Figure size 640x480 with 1 Axes>,
None,
None)
The colored boxes are the distribution of the weights of the model, and the gray band illustrates the numerical range covered by the chosen fixed point precision. As we configured, this model uses a precision of ap_fixed<16,6> for all layers of the model. Let’s now build our QKeras model
# Then the QKeras model
hls_config_q = hls4ml.utils.config_from_keras_model(qmodel, granularity='name')
hls_config_q['Model']['ReuseFactor'] = 1
hls_config['Model']['Precision'] = 'ap_fixed<16,6>'
hls_config_q['LayerName']['output_softmax']['Strategy'] = 'Stable'
plotting.print_dict(hls_config_q)
cfg_q = hls4ml.converters.create_config(backend='Vivado')
cfg_q['IOType'] = 'io_stream' # Must set this if using CNNs!
cfg_q['HLSConfig'] = hls_config_q
cfg_q['KerasModel'] = qmodel
cfg_q['OutputDir'] = 'quantized_pruned_cnn/'
cfg_q['XilinxPart'] = 'xcu250-figd2104-2L-e'
hls_model_q = hls4ml.converters.keras_to_hls(cfg_q)
hls_model_q.compile()
Interpreting Model
Topology:
Layer name: input_2, layer type: InputLayer, input shapes: [[None, 32, 32, 3]], output shape: [None, 32, 32, 3]
Layer name: fused_convbn_0, layer type: QConv2DBatchnorm, input shapes: [[None, 32, 32, 3]], output shape: [None, 30, 30, 16]
Layer name: conv_act_0, layer type: Activation, input shapes: [[None, 30, 30, 16]], output shape: [None, 30, 30, 16]
Layer name: pool_0, layer type: MaxPooling2D, input shapes: [[None, 30, 30, 16]], output shape: [None, 15, 15, 16]
Layer name: fused_convbn_1, layer type: QConv2DBatchnorm, input shapes: [[None, 15, 15, 16]], output shape: [None, 13, 13, 16]
Layer name: conv_act_1, layer type: Activation, input shapes: [[None, 13, 13, 16]], output shape: [None, 13, 13, 16]
Layer name: pool_1, layer type: MaxPooling2D, input shapes: [[None, 13, 13, 16]], output shape: [None, 6, 6, 16]
Layer name: fused_convbn_2, layer type: QConv2DBatchnorm, input shapes: [[None, 6, 6, 16]], output shape: [None, 4, 4, 24]
Layer name: conv_act_2, layer type: Activation, input shapes: [[None, 4, 4, 24]], output shape: [None, 4, 4, 24]
Layer name: pool_2, layer type: MaxPooling2D, input shapes: [[None, 4, 4, 24]], output shape: [None, 2, 2, 24]
Layer name: flatten_1, layer type: Reshape, input shapes: [[None, 2, 2, 24]], output shape: [None, 96]
Layer name: dense_0, layer type: QDense, input shapes: [[None, 96]], output shape: [None, 42]
Layer name: bn_dense_0, layer type: BatchNormalization, input shapes: [[None, 42]], output shape: [None, 42]
Layer name: dense_act_0, layer type: Activation, input shapes: [[None, 42]], output shape: [None, 42]
Layer name: dense_1, layer type: QDense, input shapes: [[None, 42]], output shape: [None, 64]
Layer name: bn_dense_1, layer type: BatchNormalization, input shapes: [[None, 64]], output shape: [None, 64]
Layer name: dense_act_1, layer type: Activation, input shapes: [[None, 64]], output shape: [None, 64]
Layer name: output_dense, layer type: Dense, input shapes: [[None, 64]], output shape: [None, 10]
Layer name: output_softmax, layer type: Softmax, input shapes: [[None, 10]], output shape: [None, 10]
Model
Precision: fixed<16,6>
ReuseFactor: 1
Strategy: Latency
BramFactor: 1000000000
TraceOutput: False
LayerName
input_2
Trace: False
Precision
result: fixed<16,6>
fused_convbn_0
Trace: False
Precision
result: fixed<16,6>
weight: fixed<6,1>
bias: fixed<6,1>
fused_convbn_0_linear
Trace: False
Precision
result: fixed<16,6>
conv_act_0
Trace: False
Precision
result: ufixed<6,0,RND_CONV,SAT>
pool_0
Trace: False
Precision
result: fixed<16,6>
fused_convbn_1
Trace: False
Precision
result: fixed<16,6>
weight: fixed<6,1>
bias: fixed<6,1>
fused_convbn_1_linear
Trace: False
Precision
result: fixed<16,6>
conv_act_1
Trace: False
Precision
result: ufixed<6,0,RND_CONV,SAT>
pool_1
Trace: False
Precision
result: fixed<16,6>
fused_convbn_2
Trace: False
Precision
result: fixed<16,6>
weight: fixed<6,1>
bias: fixed<6,1>
fused_convbn_2_linear
Trace: False
Precision
result: fixed<16,6>
conv_act_2
Trace: False
Precision
result: ufixed<6,0,RND_CONV,SAT>
pool_2
Trace: False
Precision
result: fixed<16,6>
flatten_1
Trace: False
Precision
result: fixed<16,6>
dense_0
Trace: False
Precision
result: fixed<16,6>
weight: fixed<6,1>
bias: fixed<16,6>
dense_0_linear
Trace: False
Precision
result: fixed<16,6>
bn_dense_0
Trace: False
Precision
result: fixed<16,6>
scale: fixed<16,6>
bias: fixed<16,6>
dense_act_0
Trace: False
Precision
result: ufixed<6,0,RND_CONV,SAT>
dense_1
Trace: False
Precision
result: fixed<16,6>
weight: fixed<6,1>
bias: fixed<16,6>
dense_1_linear
Trace: False
Precision
result: fixed<16,6>
bn_dense_1
Trace: False
Precision
result: fixed<16,6>
scale: fixed<16,6>
bias: fixed<16,6>
dense_act_1
Trace: False
Precision
result: ufixed<6,0,RND_CONV,SAT>
output_dense
Trace: False
Precision
result: fixed<16,6>
weight: fixed<16,6>
bias: fixed<16,6>
output_dense_linear
Trace: False
Precision
result: fixed<16,6>
output_softmax
Trace: False
Precision
result: fixed<16,6>
Strategy: Stable
Interpreting Model
Topology:
Layer name: input_2, layer type: InputLayer, input shapes: [[None, 32, 32, 3]], output shape: [None, 32, 32, 3]
Layer name: fused_convbn_0, layer type: QConv2DBatchnorm, input shapes: [[None, 32, 32, 3]], output shape: [None, 30, 30, 16]
Layer name: conv_act_0, layer type: Activation, input shapes: [[None, 30, 30, 16]], output shape: [None, 30, 30, 16]
Layer name: pool_0, layer type: MaxPooling2D, input shapes: [[None, 30, 30, 16]], output shape: [None, 15, 15, 16]
Layer name: fused_convbn_1, layer type: QConv2DBatchnorm, input shapes: [[None, 15, 15, 16]], output shape: [None, 13, 13, 16]
Layer name: conv_act_1, layer type: Activation, input shapes: [[None, 13, 13, 16]], output shape: [None, 13, 13, 16]
Layer name: pool_1, layer type: MaxPooling2D, input shapes: [[None, 13, 13, 16]], output shape: [None, 6, 6, 16]
Layer name: fused_convbn_2, layer type: QConv2DBatchnorm, input shapes: [[None, 6, 6, 16]], output shape: [None, 4, 4, 24]
Layer name: conv_act_2, layer type: Activation, input shapes: [[None, 4, 4, 24]], output shape: [None, 4, 4, 24]
Layer name: pool_2, layer type: MaxPooling2D, input shapes: [[None, 4, 4, 24]], output shape: [None, 2, 2, 24]
Layer name: flatten_1, layer type: Reshape, input shapes: [[None, 2, 2, 24]], output shape: [None, 96]
Layer name: dense_0, layer type: QDense, input shapes: [[None, 96]], output shape: [None, 42]
Layer name: bn_dense_0, layer type: BatchNormalization, input shapes: [[None, 42]], output shape: [None, 42]
Layer name: dense_act_0, layer type: Activation, input shapes: [[None, 42]], output shape: [None, 42]
Layer name: dense_1, layer type: QDense, input shapes: [[None, 42]], output shape: [None, 64]
Layer name: bn_dense_1, layer type: BatchNormalization, input shapes: [[None, 64]], output shape: [None, 64]
Layer name: dense_act_1, layer type: Activation, input shapes: [[None, 64]], output shape: [None, 64]
Layer name: output_dense, layer type: Dense, input shapes: [[None, 64]], output shape: [None, 10]
Layer name: output_softmax, layer type: Softmax, input shapes: [[None, 10]], output shape: [None, 10]
Creating HLS model
WARNING: Layer fused_convbn_0 requires "dataflow" pipeline style. Switching to "dataflow" pipeline style.
Writing HLS project
WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.
WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.
Done
Let’s plot the model and profile the weights her too
hls4ml.model.profiling.numerical(model=qmodel, hls_model=hls_model_q)
hls4ml.utils.plot_model(hls_model_q, show_shapes=True, show_precision=True, to_file=None)
Interpreting Model
Topology:
Layer name: input_2, layer type: InputLayer, input shapes: [[None, 32, 32, 3]], output shape: [None, 32, 32, 3]
Layer name: fused_convbn_0, layer type: QConv2DBatchnorm, input shapes: [[None, 32, 32, 3]], output shape: [None, 30, 30, 16]
Layer name: conv_act_0, layer type: Activation, input shapes: [[None, 30, 30, 16]], output shape: [None, 30, 30, 16]
Layer name: pool_0, layer type: MaxPooling2D, input shapes: [[None, 30, 30, 16]], output shape: [None, 15, 15, 16]
Layer name: fused_convbn_1, layer type: QConv2DBatchnorm, input shapes: [[None, 15, 15, 16]], output shape: [None, 13, 13, 16]
Layer name: conv_act_1, layer type: Activation, input shapes: [[None, 13, 13, 16]], output shape: [None, 13, 13, 16]
Layer name: pool_1, layer type: MaxPooling2D, input shapes: [[None, 13, 13, 16]], output shape: [None, 6, 6, 16]
Layer name: fused_convbn_2, layer type: QConv2DBatchnorm, input shapes: [[None, 6, 6, 16]], output shape: [None, 4, 4, 24]
Layer name: conv_act_2, layer type: Activation, input shapes: [[None, 4, 4, 24]], output shape: [None, 4, 4, 24]
Layer name: pool_2, layer type: MaxPooling2D, input shapes: [[None, 4, 4, 24]], output shape: [None, 2, 2, 24]
Layer name: flatten_1, layer type: Reshape, input shapes: [[None, 2, 2, 24]], output shape: [None, 96]
Layer name: dense_0, layer type: QDense, input shapes: [[None, 96]], output shape: [None, 42]
Layer name: bn_dense_0, layer type: BatchNormalization, input shapes: [[None, 42]], output shape: [None, 42]
Layer name: dense_act_0, layer type: Activation, input shapes: [[None, 42]], output shape: [None, 42]
Layer name: dense_1, layer type: QDense, input shapes: [[None, 42]], output shape: [None, 64]
Layer name: bn_dense_1, layer type: BatchNormalization, input shapes: [[None, 64]], output shape: [None, 64]
Layer name: dense_act_1, layer type: Activation, input shapes: [[None, 64]], output shape: [None, 64]
Layer name: output_dense, layer type: Dense, input shapes: [[None, 64]], output shape: [None, 10]
Layer name: output_softmax, layer type: Softmax, input shapes: [[None, 10]], output shape: [None, 10]
Creating HLS model
WARNING: Layer fused_convbn_0 requires "dataflow" pipeline style. Switching to "dataflow" pipeline style.
Profiling weights (before optimization)
Weights for dense_0 are only zeros, ignoring.
Weights for dense_1 are only zeros, ignoring.
Profiling weights (final / after optimization)
Weights for dense_0 are only zeros, ignoring.
Weights for dense_1 are only zeros, ignoring.
For the 6-bit QKeras model, we see that different precisions are used for different layers.
Accuracy with bit-accurate emulation#
Let’s check that the hls4ml accuracy matches the original. This usually takes some time, so let’s do it over a reduced dataset
X_test_reduced = X_test[:3000]
Y_test_reduced = Y_test[:3000]
y_predict = model.predict(X_test_reduced)
y_predict_hls4ml = hls_model.predict(np.ascontiguousarray(X_test_reduced))
1/94 [..............................] - ETA: 7s
22/94 [======>.......................] - ETA: 0s
43/94 [============>.................] - ETA: 0s
64/94 [===================>..........] - ETA: 0s
83/94 [=========================>....] - ETA: 0s
94/94 [==============================] - 0s 3ms/step
y_predict_q = qmodel.predict(X_test_reduced)
y_predict_hls4ml_q = hls_model_q.predict(np.ascontiguousarray(X_test_reduced))
1/94 [..............................] - ETA: 31s
12/94 [==>...........................] - ETA: 0s
23/94 [======>.......................] - ETA: 0s
35/94 [==========>...................] - ETA: 0s
47/94 [==============>...............] - ETA: 0s
59/94 [=================>............] - ETA: 0s
71/94 [=====================>........] - ETA: 0s
83/94 [=========================>....] - ETA: 0s
94/94 [==============================] - 1s 4ms/step
import plotting
from sklearn.metrics import accuracy_score
def plotROC(Y, y_pred, y_pred_hls4ml, label="Model"):
accuracy_keras = float(accuracy_score(np.argmax(Y, axis=1), np.argmax(y_pred, axis=1)))
accuracy_hls4ml = float(accuracy_score(np.argmax(Y, axis=1), np.argmax(y_pred_hls4ml, axis=1)))
print("Accuracy Keras: {}".format(accuracy_keras))
print("Accuracy hls4ml: {}".format(accuracy_hls4ml))
fig, ax = plt.subplots(figsize=(9, 9))
_ = plotting.makeRoc(Y, y_pred, labels=['%i' % nr for nr in range(n_classes)])
plt.gca().set_prop_cycle(None) # reset the colors
_ = plotting.makeRoc(Y, y_pred_hls4ml, labels=['%i' % nr for nr in range(n_classes)], linestyle='--')
from matplotlib.lines import Line2D
lines = [Line2D([0], [0], ls='-'), Line2D([0], [0], ls='--')]
from matplotlib.legend import Legend
leg = Legend(ax, lines, labels=['Keras', 'hls4ml'], loc='lower right', frameon=False)
ax.add_artist(leg)
plt.figtext(0.2, 0.38, label, wrap=True, horizontalalignment='left', verticalalignment='center')
plt.ylim(0.01, 1.0)
plt.xlim(0.7, 1.0)
# Plot the pruned floating point model:
plotROC(Y_test_reduced, y_predict, y_predict_hls4ml, label="Keras")
# Plot the pruned and quantized QKeras model
plotROC(Y_test_reduced, y_predict_q, y_predict_hls4ml_q, label="QKeras")
Accuracy Keras: 0.8816666666666667
Accuracy hls4ml: 0.8816666666666667
Accuracy Keras: 0.8603333333333333
Accuracy hls4ml: 0.8586666666666667
Looks good! Let’s synthesize the models.
Logic synthesis#
This takes quite a while for CNN models, up to one hour for the models considered here. In the interest of time, we have therefore provided the neccessary reports for the models considered. You can also synthesize them yourself if you have time, and as usual follow the progress using tail -f pruned_cnn/vivado_hls.log and tail -f quantized_pruned_cnn/vivado_hls.log.
import os
os.environ['PATH'] = os.environ['XILINX_VIVADO'] + '/bin:' + os.environ['PATH']
synth = False # Only if you want to synthesize the models yourself (>1h per model) rather than look at the provided reports.
if synth:
hls_model.build(csim=False, synth=True, vsynth=True)
hls_model_q.build(csim=False, synth=True, vsynth=True)
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
Cell In[26], line 3
1 import os
----> 3 os.environ['PATH'] = os.environ['XILINX_VIVADO'] + '/bin:' + os.environ['PATH']
5 synth = False # Only if you want to synthesize the models yourself (>1h per model) rather than look at the provided reports.
6 if synth:
File ~/miniconda3/envs/hls4ml-tutorial/lib/python3.10/os.py:680, in _Environ.__getitem__(self, key)
677 value = self._data[self.encodekey(key)]
678 except KeyError:
679 # raise KeyError with the original key value
--> 680 raise KeyError(key) from None
681 return self.decodevalue(value)
KeyError: 'XILINX_VIVADO'
We extract the latency from the C synthesis, namely the report in <project_dir>/myproject_prj/solution1/syn/report/myproject_csynth.rpt. A more accurate latency estimate can be obtained from running cosim by passing hls_model.build(csim=False, synth=True, vsynth=True, cosim=True) ( = C/RTL cosimulation, synthesised HLS code is run on a simulator and tested on C test bench) but this takes a lot of time so we will skip it here.
The resource estimates are obtained from the Vivado logic synthesis, and can be extracted from the report in <project_dir>/vivado_synth.rpt. Let’s fetch the most relevant numbers:
def getReports(indir):
data_ = {}
report_vsynth = Path('{}/vivado_synth.rpt'.format(indir))
report_csynth = Path('{}/myproject_prj/solution1/syn/report/myproject_csynth.rpt'.format(indir))
if report_vsynth.is_file() and report_csynth.is_file():
print('Found valid vsynth and synth in {}! Fetching numbers'.format(indir))
# Get the resources from the logic synthesis report
with report_vsynth.open() as report:
lines = np.array(report.readlines())
data_['lut'] = int(lines[np.array(['CLB LUTs*' in line for line in lines])][0].split('|')[2])
data_['ff'] = int(lines[np.array(['CLB Registers' in line for line in lines])][0].split('|')[2])
data_['bram'] = float(lines[np.array(['Block RAM Tile' in line for line in lines])][0].split('|')[2])
data_['dsp'] = int(lines[np.array(['DSPs' in line for line in lines])][0].split('|')[2])
data_['lut_rel'] = float(lines[np.array(['CLB LUTs*' in line for line in lines])][0].split('|')[5])
data_['ff_rel'] = float(lines[np.array(['CLB Registers' in line for line in lines])][0].split('|')[5])
data_['bram_rel'] = float(lines[np.array(['Block RAM Tile' in line for line in lines])][0].split('|')[5])
data_['dsp_rel'] = float(lines[np.array(['DSPs' in line for line in lines])][0].split('|')[5])
with report_csynth.open() as report:
lines = np.array(report.readlines())
lat_line = lines[np.argwhere(np.array(['Latency (cycles)' in line for line in lines])).flatten()[0] + 3]
data_['latency_clks'] = int(lat_line.split('|')[2])
data_['latency_mus'] = float(lat_line.split('|')[2]) * 5.0 / 1000.0
data_['latency_ii'] = int(lat_line.split('|')[6])
return data_
from pathlib import Path
import pprint
data_pruned_ref = getReports('pruned_cnn')
data_quantized_pruned = getReports('quantized_pruned_cnn')
print("\n Resource usage and latency: Pruned")
pprint.pprint(data_pruned_ref)
print("\n Resource usage and latency: Pruned + quantized")
pprint.pprint(data_quantized_pruned)
We see that the latency is of around 5 microseconds for both the quantized and the unquantized model, but that the resources are signifcantly reduced using QKeras.
Congratulations! You have now reached the end of this notebook. If you have some spare time, you can have a look at the bonus exercise below, where you will learn how to perform a bayesian optimization over the QKeras quantizers in order to obtain an optimally heterogeneously quantized model.
Bonus exercise: Automatic quantization with AutoQKeras#
In this bonus exercise, you will learn how to find the optimal heterogeneously quantized model using AutoQKeras. For more details, you can look at the AutoQKeras notebook.
Let’s first check the estimated energy consumption of the QKeras 6-bit model using QTools. By setting for_reference=True you can print out the unquantized model energy consumption and compare the two. Note that this only works for QKeras layers.
filters_per_conv_layer = [16, 16, 24]
neurons_per_dense_layer = [42, 64]
x = x_in = Input(input_shape)
for i, f in enumerate(filters_per_conv_layer):
print(('Adding convolutional block {} with N={} filters').format(i, f))
x = Conv2D(
int(f),
kernel_size=(3, 3),
strides=(1, 1),
kernel_initializer='lecun_uniform',
kernel_regularizer=l1(0.0001),
use_bias=False,
name='conv_{}'.format(i),
)(x)
x = BatchNormalization(name='bn_conv_{}'.format(i))(x)
x = Activation('relu', name='conv_act_%i' % i)(x)
x = MaxPooling2D(pool_size=(2, 2), name='pool_{}'.format(i))(x)
x = Flatten()(x)
for i, n in enumerate(neurons_per_dense_layer):
print(('Adding dense block {} with N={} neurons').format(i, n))
x = Dense(n, kernel_initializer='lecun_uniform', kernel_regularizer=l1(0.0001), name='dense_%i' % i, use_bias=False)(x)
x = BatchNormalization(name='bn_dense_{}'.format(i))(x)
x = Activation('relu', name='dense_act_%i' % i)(x)
x = Dense(int(n_classes), name='output_dense')(x)
x_out = Activation('softmax', name='output_softmax')(x)
baseline_model = Model(inputs=[x_in], outputs=[x_out], name='keras_baseline')
LOSS = tf.keras.losses.CategoricalCrossentropy()
OPTIMIZER = tf.keras.optimizers.Adam(learning_rate=3e-3, beta_1=0.9, beta_2=0.999, epsilon=1e-07, amsgrad=True)
baseline_model.compile(loss=LOSS, optimizer=OPTIMIZER, metrics=["accuracy"])
from qkeras import print_qstats
# for automatic quantization
import pprint
from qkeras.autoqkeras import *
from qkeras import *
from qkeras.utils import model_quantize
from qkeras.qtools import run_qtools
from qkeras.qtools import settings as qtools_settings
from tensorflow_model_optimization.python.core.sparsity.keras import pruning_wrapper
from qkeras import quantized_bits
from qkeras import QDense, QActivation
q = run_qtools.QTools(
baseline_model,
process="horowitz",
source_quantizers=[quantized_bits(16, 5, 1)],
is_inference=True,
weights_path=None,
keras_quantizer="fp16",
keras_accumulator="fp16",
for_reference=False,
)
q.qtools_stats_print()
energy_dict = q.pe(
weights_on_memory="fixed", activations_on_memory="fixed", min_sram_size=8 * 16 * 1024 * 1024, rd_wr_on_io=False
)
# get stats of energy distribution in each layer
energy_profile = q.extract_energy_profile(qtools_settings.cfg.include_energy, energy_dict)
# extract sum of energy of each layer according to the rule specified in
# qtools_settings.cfg.include_energy
total_energy = q.extract_energy_sum(qtools_settings.cfg.include_energy, energy_dict)
pprint.pprint(energy_profile)
print()
print("Total energy: {:.6f} uJ".format(total_energy / 1000000.0))
Now, lets use AutoQKeras to find an optimally heterogeneously quantized model for us. For more details, check the AutoQKeras tutorial linked above. As baseline model, we’ll use the pruned floating point Keras model from above.
# These are the quantizers we'll test in the bayesian optimization
quantization_config = {
"kernel": {
"quantized_bits(2,0,1,alpha=1.0)": 2,
"quantized_bits(4,0,1,alpha=1.0)": 4,
"quantized_bits(6,0,1,alpha=1.0)": 6,
"quantized_bits(8,0,1,alpha=1.0)": 8,
},
"bias": {
"quantized_bits(2,0,1,alpha=1.0)": 2,
"quantized_bits(4,0,1,alpha=1.0)": 4,
"quantized_bits(6,0,1,alpha=1.0)": 6,
"quantized_bits(8,0,1,alpha=1.0)": 8,
},
"activation": {
"quantized_relu(3,1)": 3,
"quantized_relu(4,2)": 4,
"quantized_relu(8,2)": 8,
"quantized_relu(8,4)": 8,
"quantized_relu(16,6)": 16,
},
"linear": {
"quantized_bits(2,0,1,alpha=1.0)": 2,
"quantized_bits(4,0,1,alpha=1.0)": 4,
"quantized_bits(6,0,1,alpha=1.0)": 6,
"quantized_bits(8,0,1,alpha=1.0)": 8,
},
}
# These are the layer types we will quantize
limit = {
"Dense": [8, 8, 16],
"Conv2D": [8, 8, 16],
"Activation": [16],
}
# Use this if you want to minimize the model bit size
goal_bits = {
"type": "bits",
"params": {
"delta_p": 8.0, # We tolerate up to a +8% accuracy change
"delta_n": 8.0, # We tolerate down to a -8% accuracy change
"rate": 2.0, # We want a x2 times smaller model
"stress": 1.0, # Force the reference model size to be smaller by setting stress<1
"input_bits": 8,
"output_bits": 8,
"ref_bits": 8,
"config": {"default": ["parameters", "activations"]},
},
}
# Use this if you want to minimize the model energy consumption
goal_energy = {
"type": "energy",
"params": {
"delta_p": 8.0,
"delta_n": 8.0,
"rate": 2.0,
"stress": 1.0,
"process": "horowitz",
"parameters_on_memory": ["sram", "sram"],
"activations_on_memory": ["sram", "sram"],
"rd_wr_on_io": [False, False],
"min_sram_size": [0, 0],
"source_quantizers": ["fp32"],
"reference_internal": "int8",
"reference_accumulator": "int32",
},
}
run_config = {
"goal": goal_energy,
"quantization_config": quantization_config,
"learning_rate_optimizer": False,
"transfer_weights": False, # Randomely initialize weights
"mode": "bayesian", # This can be bayesian,random,hyperband
"seed": 42,
"limit": limit,
"tune_filters": "layer",
"tune_filters_exceptions": "^output",
"distribution_strategy": None,
"max_trials": 5, # Let's just do 5 trials for this demonstrator, ideally you should do as many as possible
}
from qkeras.autoqkeras import AutoQKeras
autoqk = AutoQKeras(baseline_model, output_dir="autoq_cnn", metrics=["acc"], custom_objects={}, **run_config)
autoqk.fit(train_data, validation_data=val_data, epochs=15)
aqmodel = autoqk.get_best_model()
print_qmodel_summary(aqmodel)
# Train for the full epochs
callbacks = [
tf.keras.callbacks.EarlyStopping(patience=10, verbose=1),
tf.keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=3, verbose=1),
]
start = time.time()
history = aqmodel.fit(train_data, epochs=n_epochs, validation_data=val_data, callbacks=callbacks, verbose=1)
end = time.time()
print('\n It took {} minutes to train!\n'.format((end - start) / 60.0))
# This model has some remnants from the optimization procedure attached to it, so let's define a new one
aqmodel.save_weights("autoqkeras_cnn_weights.h5")
layers = [l for l in aqmodel.layers]
x = layers[0].output
for i in range(1, len(layers)):
x = layers[i](x)
new_model = Model(inputs=[layers[0].input], outputs=[x])
LOSS = tf.keras.losses.CategoricalCrossentropy()
OPTIMIZER = tf.keras.optimizers.Adam(learning_rate=3e-3, beta_1=0.9, beta_2=0.999, epsilon=1e-07, amsgrad=True)
new_model.compile(loss=LOSS, optimizer=OPTIMIZER, metrics=["accuracy"])
new_model.summary()
new_model.load_weights("autoqkeras_cnn_weights.h5")
print_qmodel_summary(new_model)
Let’s check what the best heterogeneously quantized model looks like (keep in mind we only did a few trials, the optimization obviosuly didn’t have time to converge at the minimum but yo get the idea!)
hls_config_aq = hls4ml.utils.config_from_keras_model(new_model, granularity='name')
hls_config_aq['Model']['ReuseFactor'] = 1
hls_config_aq['Model']['Precision'] = 'ap_fixed<16,6>'
hls_config_aq['LayerName']['output_softmax']['Strategy'] = 'Stable'
plotting.print_dict(hls_config_aq)
cfg_aq = hls4ml.converters.create_config(backend='Vivado')
cfg_aq['IOType'] = 'io_stream' # Must set this if using CNNs!
cfg_aq['HLSConfig'] = hls_config_aq
cfg_aq['KerasModel'] = new_model
cfg_aq['OutputDir'] = 'autoqkeras_cnn/'
cfg_aq['XilinxPart'] = 'xcu250-figd2104-2L-e'
hls_model_aq = hls4ml.converters.keras_to_hls(cfg_aq)
hls_model_aq.compile()
y_predict_aq = aqmodel.predict(X_test_reduced)
y_predict_hls4ml_aq = hls_model_aq.predict(np.ascontiguousarray(X_test_reduced))
accuracy_keras = float(accuracy_score(np.argmax(Y_test_reduced, axis=1), np.argmax(y_predict_aq, axis=1)))
accuracy_hls4ml = float(accuracy_score(np.argmax(Y_test_reduced, axis=1), np.argmax(y_predict_hls4ml_aq, axis=1)))
print("Accuracy AutoQ Keras: {}".format(accuracy_keras))
print("Accuracy AutoQ hls4ml: {}".format(accuracy_hls4ml))
The accuracy is slightly lower for this heterogeneously quantized model. Due to some randomness in the optimization procedure, you’re going to have to synthesize this one yourself!
synth = True
if synth:
hls_model_aq.build(csim=False, synth=True, vsynth=True)
data_autoq = getReports('autoq_cnn')
print("\n Resource usage and latency: AutoQ")
pprint.pprint(data_autoq)