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
2023-12-15 17:19:47.003087: 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)
2023-12-15 17:19:48.730509: 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 'Couldn't resolve host name', 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%| | 0/1267 [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<36:33, 1.46s/ MiB]
Dl Completed...: 0%| | 0/3 [00:01<?, ? url/s]
Dl Size...: 0%| | 1/1501 [00:01<36:33, 1.46s/ MiB]
Dl Completed...: 0%| | 0/3 [00:01<?, ? url/s]
Dl Size...: 0%| | 2/1501 [00:01<36:31, 1.46s/ MiB]
Dl Completed...: 0%| | 0/3 [00:01<?, ? url/s]
Dl Size...: 0%| | 3/1501 [00:01<36:30, 1.46s/ MiB]
Dl Size...: 0%| | 4/1501 [00:01<09:50, 2.53 MiB/s]
Dl Completed...: 0%| | 0/3 [00:01<?, ? url/s]
Dl Size...: 0%| | 4/1501 [00:01<09:50, 2.53 MiB/s]
Dl Completed...: 0%| | 0/3 [00:01<?, ? url/s]
Dl Size...: 0%| | 5/1501 [00:01<09:50, 2.53 MiB/s]
Dl Completed...: 0%| | 0/3 [00:01<?, ? url/s]
Dl Size...: 0%| | 6/1501 [00:01<09:49, 2.53 MiB/s]
Dl Size...: 0%| | 7/1501 [00:02<05:49, 4.28 MiB/s]
Dl Completed...: 0%| | 0/3 [00:02<?, ? url/s]
Dl Size...: 0%| | 7/1501 [00:02<05:49, 4.28 MiB/s]
Dl Completed...: 0%| | 0/3 [00:02<?, ? url/s]
Dl Size...: 1%| | 8/1501 [00:02<05:49, 4.28 MiB/s]
Dl Completed...: 0%| | 0/3 [00:02<?, ? url/s]
Dl Size...: 1%| | 9/1501 [00:02<05:48, 4.28 MiB/s]
Dl Size...: 1%| | 10/1501 [00:02<04:02, 6.14 MiB/s]
Dl Completed...: 0%| | 0/3 [00:02<?, ? url/s]
Dl Size...: 1%| | 10/1501 [00:02<04:02, 6.14 MiB/s]
Dl Completed...: 0%| | 0/3 [00:02<?, ? url/s]
Dl Size...: 1%| | 11/1501 [00:02<04:02, 6.14 MiB/s]
Dl Completed...: 0%| | 0/3 [00:02<?, ? url/s]
Dl Size...: 1%| | 12/1501 [00:02<04:02, 6.14 MiB/s]
Dl Size...: 1%| | 13/1501 [00:02<02:58, 8.32 MiB/s]
Dl Completed...: 0%| | 0/3 [00:02<?, ? url/s]
Dl Size...: 1%| | 13/1501 [00:02<02:58, 8.32 MiB/s]
Dl Completed...: 0%| | 0/3 [00:02<?, ? url/s]
Dl Size...: 1%| | 14/1501 [00:02<02:58, 8.32 MiB/s]
Dl Completed...: 0%| | 0/3 [00:02<?, ? url/s]
Dl Size...: 1%| | 15/1501 [00:02<02:58, 8.32 MiB/s]
Dl Size...: 1%| | 16/1501 [00:02<02:21, 10.50 MiB/s]
Dl Completed...: 0%| | 0/3 [00:02<?, ? url/s]
Dl Size...: 1%| | 16/1501 [00:02<02:21, 10.50 MiB/s]
Dl Completed...: 0%| | 0/3 [00:02<?, ? url/s]
Dl Size...: 1%| | 17/1501 [00:02<02:21, 10.50 MiB/s]
Dl Size...: 1%| | 18/1501 [00:02<02:09, 11.46 MiB/s]
Dl Completed...: 0%| | 0/3 [00:02<?, ? url/s]
Dl Size...: 1%| | 18/1501 [00:02<02:09, 11.46 MiB/s]
Dl Completed...: 0%| | 0/3 [00:02<?, ? url/s]
Dl Size...: 1%|▏ | 19/1501 [00:02<02:09, 11.46 MiB/s]
Dl Completed...: 0%| | 0/3 [00:02<?, ? url/s]
Dl Size...: 1%|▏ | 20/1501 [00:02<02:09, 11.46 MiB/s]
Dl Completed...: 0%| | 0/3 [00:02<?, ? url/s]
Dl Size...: 1%|▏ | 21/1501 [00:02<02:09, 11.46 MiB/s]
Dl Size...: 1%|▏ | 22/1501 [00:02<01:34, 15.58 MiB/s]
Dl Completed...: 0%| | 0/3 [00:02<?, ? url/s]
Dl Size...: 1%|▏ | 22/1501 [00:02<01:34, 15.58 MiB/s]
Dl Completed...: 0%| | 0/3 [00:02<?, ? url/s]
Dl Size...: 2%|▏ | 23/1501 [00:02<01:34, 15.58 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 2%|▏ | 24/1501 [00:03<01:34, 15.58 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 2%|▏ | 25/1501 [00:03<01:34, 15.58 MiB/s]
Dl Size...: 2%|▏ | 26/1501 [00:03<01:13, 19.97 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 2%|▏ | 26/1501 [00:03<01:13, 19.97 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 2%|▏ | 27/1501 [00:03<01:13, 19.97 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 2%|▏ | 28/1501 [00:03<01:13, 19.97 MiB/s]
Dl Size...: 2%|▏ | 29/1501 [00:03<01:10, 20.82 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 2%|▏ | 29/1501 [00:03<01:10, 20.82 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 2%|▏ | 30/1501 [00:03<01:10, 20.82 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 2%|▏ | 31/1501 [00:03<01:10, 20.82 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 2%|▏ | 32/1501 [00:03<01:10, 20.82 MiB/s]
Dl Size...: 2%|▏ | 33/1501 [00:03<01:02, 23.62 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 2%|▏ | 33/1501 [00:03<01:02, 23.62 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 2%|▏ | 34/1501 [00:03<01:02, 23.62 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 2%|▏ | 35/1501 [00:03<01:02, 23.62 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 2%|▏ | 36/1501 [00:03<01:02, 23.62 MiB/s]
Dl Size...: 2%|▏ | 37/1501 [00:03<00:54, 26.90 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 2%|▏ | 37/1501 [00:03<00:54, 26.90 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 3%|▎ | 38/1501 [00:03<00:54, 26.90 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 3%|▎ | 39/1501 [00:03<00:54, 26.90 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 3%|▎ | 40/1501 [00:03<00:54, 26.90 MiB/s]
Dl Size...: 3%|▎ | 41/1501 [00:03<00:49, 29.33 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 3%|▎ | 41/1501 [00:03<00:49, 29.33 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 3%|▎ | 42/1501 [00:03<00:49, 29.33 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 3%|▎ | 43/1501 [00:03<00:49, 29.33 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 3%|▎ | 44/1501 [00:03<00:49, 29.33 MiB/s]
Dl Size...: 3%|▎ | 45/1501 [00:03<00:45, 31.95 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 3%|▎ | 45/1501 [00:03<00:45, 31.95 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 3%|▎ | 46/1501 [00:03<00:45, 31.95 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 3%|▎ | 47/1501 [00:03<00:45, 31.95 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 3%|▎ | 48/1501 [00:03<00:45, 31.95 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 3%|▎ | 49/1501 [00:03<00:45, 31.95 MiB/s]
Dl Size...: 3%|▎ | 50/1501 [00:03<00:42, 34.42 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 3%|▎ | 50/1501 [00:03<00:42, 34.42 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 3%|▎ | 51/1501 [00:03<00:42, 34.42 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 3%|▎ | 52/1501 [00:03<00:42, 34.42 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 4%|▎ | 53/1501 [00:03<00:42, 34.42 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 4%|▎ | 54/1501 [00:03<00:42, 34.42 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 4%|▎ | 55/1501 [00:03<00:42, 34.42 MiB/s]
Dl Size...: 4%|▎ | 56/1501 [00:03<00:35, 41.00 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 4%|▎ | 56/1501 [00:03<00:35, 41.00 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 4%|▍ | 57/1501 [00:03<00:35, 41.00 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 4%|▍ | 58/1501 [00:03<00:35, 41.00 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 4%|▍ | 59/1501 [00:03<00:35, 41.00 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 4%|▍ | 60/1501 [00:03<00:35, 41.00 MiB/s]
Dl Size...: 4%|▍ | 61/1501 [00:03<00:35, 41.11 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 4%|▍ | 61/1501 [00:03<00:35, 41.11 MiB/s]
Dl Completed...: 0%| | 0/3 [00:03<?, ? url/s]
Dl Size...: 4%|▍ | 62/1501 [00:03<00:35, 41.11 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 4%|▍ | 63/1501 [00:04<00:34, 41.11 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 4%|▍ | 64/1501 [00:04<00:34, 41.11 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 4%|▍ | 65/1501 [00:04<00:34, 41.11 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 4%|▍ | 66/1501 [00:04<00:34, 41.11 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 4%|▍ | 67/1501 [00:04<00:34, 41.11 MiB/s]
Dl Size...: 5%|▍ | 68/1501 [00:04<00:31, 44.80 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 5%|▍ | 68/1501 [00:04<00:31, 44.80 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 5%|▍ | 69/1501 [00:04<00:31, 44.80 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 5%|▍ | 70/1501 [00:04<00:31, 44.80 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 5%|▍ | 71/1501 [00:04<00:31, 44.80 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 5%|▍ | 72/1501 [00:04<00:31, 44.80 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 5%|▍ | 73/1501 [00:04<00:31, 44.80 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 5%|▍ | 74/1501 [00:04<00:31, 44.80 MiB/s]
Dl Size...: 5%|▍ | 75/1501 [00:04<00:28, 50.82 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 5%|▍ | 75/1501 [00:04<00:28, 50.82 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 5%|▌ | 76/1501 [00:04<00:28, 50.82 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 5%|▌ | 77/1501 [00:04<00:28, 50.82 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 5%|▌ | 78/1501 [00:04<00:27, 50.82 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 5%|▌ | 79/1501 [00:04<00:27, 50.82 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 5%|▌ | 80/1501 [00:04<00:27, 50.82 MiB/s]
Dl Size...: 5%|▌ | 81/1501 [00:04<00:27, 50.76 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 5%|▌ | 81/1501 [00:04<00:27, 50.76 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 5%|▌ | 82/1501 [00:04<00:27, 50.76 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 6%|▌ | 83/1501 [00:04<00:27, 50.76 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 6%|▌ | 84/1501 [00:04<00:27, 50.76 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 6%|▌ | 85/1501 [00:04<00:27, 50.76 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 6%|▌ | 86/1501 [00:04<00:27, 50.76 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 6%|▌ | 87/1501 [00:04<00:27, 50.76 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 6%|▌ | 88/1501 [00:04<00:27, 50.76 MiB/s]
Dl Size...: 6%|▌ | 89/1501 [00:04<00:24, 57.32 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 6%|▌ | 89/1501 [00:04<00:24, 57.32 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 6%|▌ | 90/1501 [00:04<00:24, 57.32 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 6%|▌ | 91/1501 [00:04<00:24, 57.32 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 6%|▌ | 92/1501 [00:04<00:24, 57.32 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 6%|▌ | 93/1501 [00:04<00:24, 57.32 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 6%|▋ | 94/1501 [00:04<00:24, 57.32 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 6%|▋ | 95/1501 [00:04<00:24, 57.32 MiB/s]
Dl Size...: 6%|▋ | 96/1501 [00:04<00:23, 58.72 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 6%|▋ | 96/1501 [00:04<00:23, 58.72 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 6%|▋ | 97/1501 [00:04<00:23, 58.72 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 7%|▋ | 98/1501 [00:04<00:23, 58.72 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 7%|▋ | 99/1501 [00:04<00:23, 58.72 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 7%|▋ | 100/1501 [00:04<00:23, 58.72 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 7%|▋ | 101/1501 [00:04<00:23, 58.72 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 7%|▋ | 102/1501 [00:04<00:23, 58.72 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 7%|▋ | 103/1501 [00:04<00:23, 58.72 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 7%|▋ | 104/1501 [00:04<00:23, 58.72 MiB/s]
Dl Size...: 7%|▋ | 105/1501 [00:04<00:21, 65.75 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 7%|▋ | 105/1501 [00:04<00:21, 65.75 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 7%|▋ | 106/1501 [00:04<00:21, 65.75 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 7%|▋ | 107/1501 [00:04<00:21, 65.75 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 7%|▋ | 108/1501 [00:04<00:21, 65.75 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 7%|▋ | 109/1501 [00:04<00:21, 65.75 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 7%|▋ | 110/1501 [00:04<00:21, 65.75 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 7%|▋ | 111/1501 [00:04<00:21, 65.75 MiB/s]
Dl Size...: 7%|▋ | 112/1501 [00:04<00:21, 65.98 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 7%|▋ | 112/1501 [00:04<00:21, 65.98 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 8%|▊ | 113/1501 [00:04<00:21, 65.98 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 8%|▊ | 114/1501 [00:04<00:21, 65.98 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 8%|▊ | 115/1501 [00:04<00:21, 65.98 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 8%|▊ | 116/1501 [00:04<00:20, 65.98 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 8%|▊ | 117/1501 [00:04<00:20, 65.98 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 8%|▊ | 118/1501 [00:04<00:20, 65.98 MiB/s]
Dl Size...: 8%|▊ | 119/1501 [00:04<00:21, 63.82 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 8%|▊ | 119/1501 [00:04<00:21, 63.82 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 8%|▊ | 120/1501 [00:04<00:21, 63.82 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 8%|▊ | 121/1501 [00:04<00:21, 63.82 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 8%|▊ | 122/1501 [00:04<00:21, 63.82 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 8%|▊ | 123/1501 [00:04<00:21, 63.82 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 8%|▊ | 124/1501 [00:04<00:21, 63.82 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 8%|▊ | 125/1501 [00:04<00:21, 63.82 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 8%|▊ | 126/1501 [00:04<00:21, 63.82 MiB/s]
Dl Completed...: 0%| | 0/3 [00:04<?, ? url/s]
Dl Size...: 8%|▊ | 127/1501 [00:04<00:21, 63.82 MiB/s]
Dl Size...: 9%|▊ | 128/1501 [00:05<00:19, 69.39 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 9%|▊ | 128/1501 [00:05<00:19, 69.39 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 9%|▊ | 129/1501 [00:05<00:19, 69.39 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 9%|▊ | 130/1501 [00:05<00:19, 69.39 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 9%|▊ | 131/1501 [00:05<00:19, 69.39 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 9%|▉ | 132/1501 [00:05<00:19, 69.39 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 9%|▉ | 133/1501 [00:05<00:19, 69.39 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 9%|▉ | 134/1501 [00:05<00:19, 69.39 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 9%|▉ | 135/1501 [00:05<00:19, 69.39 MiB/s]
Dl Size...: 9%|▉ | 136/1501 [00:05<00:19, 71.60 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 9%|▉ | 136/1501 [00:05<00:19, 71.60 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 9%|▉ | 137/1501 [00:05<00:19, 71.60 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 9%|▉ | 138/1501 [00:05<00:19, 71.60 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 9%|▉ | 139/1501 [00:05<00:19, 71.60 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 9%|▉ | 140/1501 [00:05<00:19, 71.60 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 9%|▉ | 141/1501 [00:05<00:18, 71.60 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 9%|▉ | 142/1501 [00:05<00:18, 71.60 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 10%|▉ | 143/1501 [00:05<00:18, 71.60 MiB/s]
Dl Size...: 10%|▉ | 144/1501 [00:05<00:20, 65.82 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 10%|▉ | 144/1501 [00:05<00:20, 65.82 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 10%|▉ | 145/1501 [00:05<00:20, 65.82 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 10%|▉ | 146/1501 [00:05<00:20, 65.82 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 10%|▉ | 147/1501 [00:05<00:20, 65.82 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 10%|▉ | 148/1501 [00:05<00:20, 65.82 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 10%|▉ | 149/1501 [00:05<00:20, 65.82 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 10%|▉ | 150/1501 [00:05<00:20, 65.82 MiB/s]
Dl Size...: 10%|█ | 151/1501 [00:05<00:20, 65.39 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 10%|█ | 151/1501 [00:05<00:20, 65.39 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 10%|█ | 152/1501 [00:05<00:20, 65.39 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 10%|█ | 153/1501 [00:05<00:20, 65.39 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 10%|█ | 154/1501 [00:05<00:20, 65.39 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 10%|█ | 155/1501 [00:05<00:20, 65.39 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 10%|█ | 156/1501 [00:05<00:20, 65.39 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 10%|█ | 157/1501 [00:05<00:20, 65.39 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 11%|█ | 158/1501 [00:05<00:20, 65.39 MiB/s]
Dl Size...: 11%|█ | 159/1501 [00:05<00:19, 67.84 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 11%|█ | 159/1501 [00:05<00:19, 67.84 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 11%|█ | 160/1501 [00:05<00:19, 67.84 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 11%|█ | 161/1501 [00:05<00:19, 67.84 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 11%|█ | 162/1501 [00:05<00:19, 67.84 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 11%|█ | 163/1501 [00:05<00:19, 67.84 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 11%|█ | 164/1501 [00:05<00:19, 67.84 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 11%|█ | 165/1501 [00:05<00:19, 67.84 MiB/s]
Dl Size...: 11%|█ | 166/1501 [00:05<00:19, 67.22 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 11%|█ | 166/1501 [00:05<00:19, 67.22 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 11%|█ | 167/1501 [00:05<00:19, 67.22 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 11%|█ | 168/1501 [00:05<00:19, 67.22 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 11%|█▏ | 169/1501 [00:05<00:19, 67.22 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 11%|█▏ | 170/1501 [00:05<00:19, 67.22 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 11%|█▏ | 171/1501 [00:05<00:19, 67.22 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 11%|█▏ | 172/1501 [00:05<00:19, 67.22 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 12%|█▏ | 173/1501 [00:05<00:19, 67.22 MiB/s]
Dl Size...: 12%|█▏ | 174/1501 [00:05<00:18, 70.76 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 12%|█▏ | 174/1501 [00:05<00:18, 70.76 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 12%|█▏ | 175/1501 [00:05<00:18, 70.76 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 12%|█▏ | 176/1501 [00:05<00:18, 70.76 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 12%|█▏ | 177/1501 [00:05<00:18, 70.76 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 12%|█▏ | 178/1501 [00:05<00:18, 70.76 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 12%|█▏ | 179/1501 [00:05<00:18, 70.76 MiB/s]
Dl Completed...: 0%| | 0/3 [00:05<?, ? url/s]
Dl Size...: 12%|█▏ | 180/1501 [00:05<00:18, 70.76 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:05<00:11, 5.77s/ url]
Dl Completed...: 33%|███▎ | 1/3 [00:05<00:11, 5.77s/ url]
Dl Size...: 12%|█▏ | 180/1501 [00:05<00:18, 70.76 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:05<00:11, 5.77s/ url]
Dl Size...: 12%|█▏ | 181/1501 [00:05<00:18, 70.76 MiB/s]
Dl Size...: 12%|█▏ | 182/1501 [00:05<00:20, 65.08 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:05<00:11, 5.77s/ url]
Dl Size...: 12%|█▏ | 182/1501 [00:05<00:20, 65.08 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:05<00:11, 5.77s/ url]
Dl Size...: 12%|█▏ | 183/1501 [00:05<00:20, 65.08 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:05<00:11, 5.77s/ url]
Dl Size...: 12%|█▏ | 184/1501 [00:05<00:20, 65.08 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:05<00:11, 5.77s/ url]
Dl Size...: 12%|█▏ | 185/1501 [00:05<00:20, 65.08 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:05<00:11, 5.77s/ url]
Dl Size...: 12%|█▏ | 186/1501 [00:05<00:20, 65.08 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:05<00:11, 5.77s/ url]
Dl Size...: 12%|█▏ | 187/1501 [00:05<00:20, 65.08 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:05<00:11, 5.77s/ url]
Dl Size...: 13%|█▎ | 188/1501 [00:05<00:20, 65.08 MiB/s]
Dl Size...: 13%|█▎ | 189/1501 [00:05<00:23, 56.74 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:05<00:11, 5.77s/ url]
Dl Size...: 13%|█▎ | 189/1501 [00:05<00:23, 56.74 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:06<00:11, 5.77s/ url]
Dl Size...: 13%|█▎ | 190/1501 [00:06<00:23, 56.74 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:06<00:11, 5.77s/ url]
Dl Size...: 13%|█▎ | 191/1501 [00:06<00:23, 56.74 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:06<00:11, 5.77s/ url]
Dl Size...: 13%|█▎ | 192/1501 [00:06<00:23, 56.74 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:06<00:11, 5.77s/ url]
Dl Size...: 13%|█▎ | 193/1501 [00:06<00:23, 56.74 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:06<00:11, 5.77s/ url]
Dl Size...: 13%|█▎ | 194/1501 [00:06<00:23, 56.74 MiB/s]
Dl Size...: 13%|█▎ | 195/1501 [00:06<00:26, 48.61 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:06<00:11, 5.77s/ url]
Dl Size...: 13%|█▎ | 195/1501 [00:06<00:26, 48.61 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:06<00:11, 5.77s/ url]
Dl Size...: 13%|█▎ | 196/1501 [00:06<00:26, 48.61 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:06<00:11, 5.77s/ url]
Dl Size...: 13%|█▎ | 197/1501 [00:06<00:26, 48.61 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:06<00:11, 5.77s/ url]
Dl Size...: 13%|█▎ | 198/1501 [00:06<00:26, 48.61 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:06<00:11, 5.77s/ url]
Dl Size...: 13%|█▎ | 199/1501 [00:06<00:26, 48.61 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:06<00:11, 5.77s/ url]
Dl Size...: 13%|█▎ | 200/1501 [00:06<00:26, 48.61 MiB/s]
Dl Size...: 13%|█▎ | 201/1501 [00:06<00:28, 44.93 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:06<00:11, 5.77s/ url]
Dl Size...: 13%|█▎ | 201/1501 [00:06<00:28, 44.93 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:06<00:11, 5.77s/ url]
Dl Size...: 13%|█▎ | 202/1501 [00:06<00:28, 44.93 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:06<00:11, 5.77s/ url]
Dl Size...: 14%|█▎ | 203/1501 [00:06<00:28, 44.93 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:06<00:11, 5.77s/ url]
Dl Size...: 14%|█▎ | 204/1501 [00:06<00:28, 44.93 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:06<00:11, 5.77s/ url]
Dl Size...: 14%|█▎ | 205/1501 [00:06<00:28, 44.93 MiB/s]
Dl Size...: 14%|█▎ | 206/1501 [00:06<00:29, 44.54 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:06<00:11, 5.77s/ url]
Dl Size...: 14%|█▎ | 206/1501 [00:06<00:29, 44.54 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:06<00:11, 5.77s/ url]
Dl Size...: 14%|█▍ | 207/1501 [00:06<00:29, 44.54 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:06<00:11, 5.77s/ url]
Dl Size...: 14%|█▍ | 208/1501 [00:06<00:29, 44.54 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:06<00:11, 5.77s/ url]
Dl Size...: 14%|█▍ | 209/1501 [00:06<00:29, 44.54 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:06<00:11, 5.77s/ url]
Dl Size...: 14%|█▍ | 210/1501 [00:06<00:28, 44.54 MiB/s]
Dl Size...: 14%|█▍ | 211/1501 [00:06<00:30, 41.71 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:06<00:11, 5.77s/ url]
Dl Size...: 14%|█▍ | 211/1501 [00:06<00:30, 41.71 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:06<00:11, 5.77s/ url]
Dl Size...: 14%|█▍ | 212/1501 [00:06<00:30, 41.71 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:06<00:11, 5.77s/ url]
Dl Size...: 14%|█▍ | 213/1501 [00:06<00:30, 41.71 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:06<00:11, 5.77s/ url]
Dl Size...: 14%|█▍ | 214/1501 [00:06<00:30, 41.71 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:06<00:11, 5.77s/ url]
Dl Size...: 14%|█▍ | 215/1501 [00:06<00:30, 41.71 MiB/s]
Dl Size...: 14%|█▍ | 216/1501 [00:06<00:31, 41.27 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:06<00:11, 5.77s/ url]
Dl Size...: 14%|█▍ | 216/1501 [00:06<00:31, 41.27 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:06<00:11, 5.77s/ url]
Dl Size...: 14%|█▍ | 217/1501 [00:06<00:31, 41.27 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:06<00:11, 5.77s/ url]
Dl Size...: 15%|█▍ | 218/1501 [00:06<00:31, 41.27 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:06<00:11, 5.77s/ url]
Dl Size...: 15%|█▍ | 219/1501 [00:06<00:31, 41.27 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:06<00:11, 5.77s/ url]
Dl Size...: 15%|█▍ | 220/1501 [00:06<00:31, 41.27 MiB/s]
Dl Size...: 15%|█▍ | 221/1501 [00:06<00:31, 40.21 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:06<00:11, 5.77s/ url]
Dl Size...: 15%|█▍ | 221/1501 [00:06<00:31, 40.21 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:06<00:11, 5.77s/ url]
Dl Size...: 15%|█▍ | 222/1501 [00:06<00:31, 40.21 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:06<00:11, 5.77s/ url]
Dl Size...: 15%|█▍ | 223/1501 [00:06<00:31, 40.21 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:06<00:11, 5.77s/ url]
Dl Size...: 15%|█▍ | 224/1501 [00:06<00:31, 40.21 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:06<00:11, 5.77s/ url]
Dl Size...: 15%|█▍ | 225/1501 [00:06<00:31, 40.21 MiB/s]
Dl Size...: 15%|█▌ | 226/1501 [00:06<00:30, 42.48 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:06<00:11, 5.77s/ url]
Dl Size...: 15%|█▌ | 226/1501 [00:06<00:30, 42.48 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:06<00:11, 5.77s/ url]
Dl Size...: 15%|█▌ | 227/1501 [00:06<00:29, 42.48 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:06<00:11, 5.77s/ url]
Dl Size...: 15%|█▌ | 228/1501 [00:06<00:29, 42.48 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:07<00:11, 5.77s/ url]
Dl Size...: 15%|█▌ | 229/1501 [00:07<00:29, 42.48 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:07<00:11, 5.77s/ url]
Dl Size...: 15%|█▌ | 230/1501 [00:07<00:29, 42.48 MiB/s]
Dl Size...: 15%|█▌ | 231/1501 [00:07<00:30, 41.66 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:07<00:11, 5.77s/ url]
Dl Size...: 15%|█▌ | 231/1501 [00:07<00:30, 41.66 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:07<00:11, 5.77s/ url]
Dl Size...: 15%|█▌ | 232/1501 [00:07<00:30, 41.66 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:07<00:11, 5.77s/ url]
Dl Size...: 16%|█▌ | 233/1501 [00:07<00:30, 41.66 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:07<00:11, 5.77s/ url]
Dl Size...: 16%|█▌ | 234/1501 [00:07<00:30, 41.66 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:07<00:11, 5.77s/ url]
Dl Size...: 16%|█▌ | 235/1501 [00:07<00:30, 41.66 MiB/s]
Dl Size...: 16%|█▌ | 236/1501 [00:07<00:31, 40.01 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:07<00:11, 5.77s/ url]
Dl Size...: 16%|█▌ | 236/1501 [00:07<00:31, 40.01 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:07<00:11, 5.77s/ url]
Dl Size...: 16%|█▌ | 237/1501 [00:07<00:31, 40.01 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:07<00:11, 5.77s/ url]
Dl Size...: 16%|█▌ | 238/1501 [00:07<00:31, 40.01 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:07<00:11, 5.77s/ url]
Dl Size...: 16%|█▌ | 239/1501 [00:07<00:31, 40.01 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:07<00:11, 5.77s/ url]
Dl Size...: 16%|█▌ | 240/1501 [00:07<00:31, 40.01 MiB/s]
Dl Size...: 16%|█▌ | 241/1501 [00:07<00:31, 39.89 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:07<00:11, 5.77s/ url]
Dl Size...: 16%|█▌ | 241/1501 [00:07<00:31, 39.89 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:07<00:11, 5.77s/ url]
Dl Size...: 16%|█▌ | 242/1501 [00:07<00:31, 39.89 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:07<00:11, 5.77s/ url]
Dl Size...: 16%|█▌ | 243/1501 [00:07<00:31, 39.89 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:07<00:11, 5.77s/ url]
Dl Size...: 16%|█▋ | 244/1501 [00:07<00:31, 39.89 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:07<00:11, 5.77s/ url]
Dl Size...: 16%|█▋ | 245/1501 [00:07<00:31, 39.89 MiB/s]
Dl Size...: 16%|█▋ | 246/1501 [00:07<00:32, 39.14 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:07<00:11, 5.77s/ url]
Dl Size...: 16%|█▋ | 246/1501 [00:07<00:32, 39.14 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:07<00:11, 5.77s/ url]
Dl Size...: 16%|█▋ | 247/1501 [00:07<00:32, 39.14 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:07<00:11, 5.77s/ url]
Dl Size...: 17%|█▋ | 248/1501 [00:07<00:32, 39.14 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:07<00:11, 5.77s/ url]
Dl Size...: 17%|█▋ | 249/1501 [00:07<00:31, 39.14 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:07<00:11, 5.77s/ url]
Dl Size...: 17%|█▋ | 250/1501 [00:07<00:31, 39.14 MiB/s]
Dl Size...: 17%|█▋ | 251/1501 [00:07<00:30, 41.32 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:07<00:11, 5.77s/ url]
Dl Size...: 17%|█▋ | 251/1501 [00:07<00:30, 41.32 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:07<00:11, 5.77s/ url]
Dl Size...: 17%|█▋ | 252/1501 [00:07<00:30, 41.32 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:07<00:11, 5.77s/ url]
Dl Size...: 17%|█▋ | 253/1501 [00:07<00:30, 41.32 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:07<00:11, 5.77s/ url]
Dl Size...: 17%|█▋ | 254/1501 [00:07<00:30, 41.32 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:07<00:11, 5.77s/ url]
Dl Size...: 17%|█▋ | 255/1501 [00:07<00:30, 41.32 MiB/s]
Dl Size...: 17%|█▋ | 256/1501 [00:07<00:30, 40.66 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:07<00:11, 5.77s/ url]
Dl Size...: 17%|█▋ | 256/1501 [00:07<00:30, 40.66 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:07<00:11, 5.77s/ url]
Dl Size...: 17%|█▋ | 257/1501 [00:07<00:30, 40.66 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:07<00:11, 5.77s/ url]
Dl Size...: 17%|█▋ | 258/1501 [00:07<00:30, 40.66 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:07<00:11, 5.77s/ url]
Dl Size...: 17%|█▋ | 259/1501 [00:07<00:30, 40.66 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:07<00:11, 5.77s/ url]
Dl Size...: 17%|█▋ | 260/1501 [00:07<00:30, 40.66 MiB/s]
Dl Size...: 17%|█▋ | 261/1501 [00:07<00:29, 42.40 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:07<00:11, 5.77s/ url]
Dl Size...: 17%|█▋ | 261/1501 [00:07<00:29, 42.40 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:07<00:11, 5.77s/ url]
Dl Size...: 17%|█▋ | 262/1501 [00:07<00:29, 42.40 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:07<00:11, 5.77s/ url]
Dl Size...: 18%|█▊ | 263/1501 [00:07<00:29, 42.40 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:07<00:11, 5.77s/ url]
Dl Size...: 18%|█▊ | 264/1501 [00:07<00:29, 42.40 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:07<00:11, 5.77s/ url]
Dl Size...: 18%|█▊ | 265/1501 [00:07<00:29, 42.40 MiB/s]
Dl Size...: 18%|█▊ | 266/1501 [00:07<00:30, 41.14 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:07<00:11, 5.77s/ url]
Dl Size...: 18%|█▊ | 266/1501 [00:07<00:30, 41.14 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:07<00:11, 5.77s/ url]
Dl Size...: 18%|█▊ | 267/1501 [00:07<00:29, 41.14 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:07<00:11, 5.77s/ url]
Dl Size...: 18%|█▊ | 268/1501 [00:07<00:29, 41.14 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:08<00:11, 5.77s/ url]
Dl Size...: 18%|█▊ | 269/1501 [00:08<00:29, 41.14 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:08<00:11, 5.77s/ url]
Dl Size...: 18%|█▊ | 270/1501 [00:08<00:29, 41.14 MiB/s]
Dl Size...: 18%|█▊ | 271/1501 [00:08<00:29, 41.06 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:08<00:11, 5.77s/ url]
Dl Size...: 18%|█▊ | 271/1501 [00:08<00:29, 41.06 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:08<00:11, 5.77s/ url]
Dl Size...: 18%|█▊ | 272/1501 [00:08<00:29, 41.06 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:08<00:11, 5.77s/ url]
Dl Size...: 18%|█▊ | 273/1501 [00:08<00:29, 41.06 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:08<00:11, 5.77s/ url]
Dl Size...: 18%|█▊ | 274/1501 [00:08<00:29, 41.06 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:08<00:11, 5.77s/ url]
Dl Size...: 18%|█▊ | 275/1501 [00:08<00:29, 41.06 MiB/s]
Dl Size...: 18%|█▊ | 276/1501 [00:08<00:30, 40.77 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:08<00:11, 5.77s/ url]
Dl Size...: 18%|█▊ | 276/1501 [00:08<00:30, 40.77 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:08<00:11, 5.77s/ url]
Dl Size...: 18%|█▊ | 277/1501 [00:08<00:30, 40.77 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:08<00:11, 5.77s/ url]
Dl Size...: 19%|█▊ | 278/1501 [00:08<00:29, 40.77 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:08<00:11, 5.77s/ url]
Dl Size...: 19%|█▊ | 279/1501 [00:08<00:29, 40.77 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:08<00:11, 5.77s/ url]
Dl Size...: 19%|█▊ | 280/1501 [00:08<00:29, 40.77 MiB/s]
Dl Size...: 19%|█▊ | 281/1501 [00:08<00:28, 42.14 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:08<00:11, 5.77s/ url]
Dl Size...: 19%|█▊ | 281/1501 [00:08<00:28, 42.14 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:08<00:11, 5.77s/ url]
Dl Size...: 19%|█▉ | 282/1501 [00:08<00:28, 42.14 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:08<00:11, 5.77s/ url]
Dl Size...: 19%|█▉ | 283/1501 [00:08<00:28, 42.14 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:08<00:11, 5.77s/ url]
Dl Size...: 19%|█▉ | 284/1501 [00:08<00:28, 42.14 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:08<00:11, 5.77s/ url]
Dl Size...: 19%|█▉ | 285/1501 [00:08<00:28, 42.14 MiB/s]
Dl Size...: 19%|█▉ | 286/1501 [00:08<00:28, 42.23 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:08<00:11, 5.77s/ url]
Dl Size...: 19%|█▉ | 286/1501 [00:08<00:28, 42.23 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:08<00:11, 5.77s/ url]
Dl Size...: 19%|█▉ | 287/1501 [00:08<00:28, 42.23 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:08<00:11, 5.77s/ url]
Dl Size...: 19%|█▉ | 288/1501 [00:08<00:28, 42.23 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:08<00:11, 5.77s/ url]
Dl Size...: 19%|█▉ | 289/1501 [00:08<00:28, 42.23 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:08<00:11, 5.77s/ url]
Dl Size...: 19%|█▉ | 290/1501 [00:08<00:28, 42.23 MiB/s]
Dl Size...: 19%|█▉ | 291/1501 [00:08<00:27, 43.46 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:08<00:11, 5.77s/ url]
Dl Size...: 19%|█▉ | 291/1501 [00:08<00:27, 43.46 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:08<00:11, 5.77s/ url]
Dl Size...: 19%|█▉ | 292/1501 [00:08<00:27, 43.46 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:08<00:11, 5.77s/ url]
Dl Size...: 20%|█▉ | 293/1501 [00:08<00:27, 43.46 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:08<00:11, 5.77s/ url]
Dl Size...: 20%|█▉ | 294/1501 [00:08<00:27, 43.46 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:08<00:11, 5.77s/ url]
Dl Size...: 20%|█▉ | 295/1501 [00:08<00:27, 43.46 MiB/s]
Dl Size...: 20%|█▉ | 296/1501 [00:08<00:27, 43.38 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:08<00:11, 5.77s/ url]
Dl Size...: 20%|█▉ | 296/1501 [00:08<00:27, 43.38 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:08<00:11, 5.77s/ url]
Dl Size...: 20%|█▉ | 297/1501 [00:08<00:27, 43.38 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:08<00:11, 5.77s/ url]
Dl Size...: 20%|█▉ | 298/1501 [00:08<00:27, 43.38 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:08<00:11, 5.77s/ url]
Dl Size...: 20%|█▉ | 299/1501 [00:08<00:27, 43.38 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:08<00:11, 5.77s/ url]
Dl Size...: 20%|█▉ | 300/1501 [00:08<00:27, 43.38 MiB/s]
Dl Size...: 20%|██ | 301/1501 [00:08<00:28, 42.66 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:08<00:11, 5.77s/ url]
Dl Size...: 20%|██ | 301/1501 [00:08<00:28, 42.66 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:08<00:11, 5.77s/ url]
Dl Size...: 20%|██ | 302/1501 [00:08<00:28, 42.66 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:08<00:11, 5.77s/ url]
Dl Size...: 20%|██ | 303/1501 [00:08<00:28, 42.66 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:08<00:11, 5.77s/ url]
Dl Size...: 20%|██ | 304/1501 [00:08<00:28, 42.66 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:08<00:11, 5.77s/ url]
Dl Size...: 20%|██ | 305/1501 [00:08<00:28, 42.66 MiB/s]
Dl Size...: 20%|██ | 306/1501 [00:08<00:28, 41.97 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:08<00:11, 5.77s/ url]
Dl Size...: 20%|██ | 306/1501 [00:08<00:28, 41.97 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:08<00:11, 5.77s/ url]
Dl Size...: 20%|██ | 307/1501 [00:08<00:28, 41.97 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:08<00:11, 5.77s/ url]
Dl Size...: 21%|██ | 308/1501 [00:08<00:28, 41.97 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:08<00:11, 5.77s/ url]
Dl Size...: 21%|██ | 309/1501 [00:08<00:28, 41.97 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:08<00:11, 5.77s/ url]
Dl Size...: 21%|██ | 310/1501 [00:08<00:28, 41.97 MiB/s]
Dl Size...: 21%|██ | 311/1501 [00:08<00:28, 42.10 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:08<00:11, 5.77s/ url]
Dl Size...: 21%|██ | 311/1501 [00:08<00:28, 42.10 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:08<00:11, 5.77s/ url]
Dl Size...: 21%|██ | 312/1501 [00:08<00:28, 42.10 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:09<00:11, 5.77s/ url]
Dl Size...: 21%|██ | 313/1501 [00:09<00:28, 42.10 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:09<00:11, 5.77s/ url]
Dl Size...: 21%|██ | 314/1501 [00:09<00:28, 42.10 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:09<00:11, 5.77s/ url]
Dl Size...: 21%|██ | 315/1501 [00:09<00:28, 42.10 MiB/s]
Dl Size...: 21%|██ | 316/1501 [00:09<00:26, 44.18 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:09<00:11, 5.77s/ url]
Dl Size...: 21%|██ | 316/1501 [00:09<00:26, 44.18 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:09<00:11, 5.77s/ url]
Dl Size...: 21%|██ | 317/1501 [00:09<00:26, 44.18 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:09<00:11, 5.77s/ url]
Dl Size...: 21%|██ | 318/1501 [00:09<00:26, 44.18 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:09<00:11, 5.77s/ url]
Dl Size...: 21%|██▏ | 319/1501 [00:09<00:26, 44.18 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:09<00:11, 5.77s/ url]
Dl Size...: 21%|██▏ | 320/1501 [00:09<00:26, 44.18 MiB/s]
Dl Size...: 21%|██▏ | 321/1501 [00:09<00:27, 43.32 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:09<00:11, 5.77s/ url]
Dl Size...: 21%|██▏ | 321/1501 [00:09<00:27, 43.32 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:09<00:11, 5.77s/ url]
Dl Size...: 21%|██▏ | 322/1501 [00:09<00:27, 43.32 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:09<00:11, 5.77s/ url]
Dl Size...: 22%|██▏ | 323/1501 [00:09<00:27, 43.32 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:09<00:11, 5.77s/ url]
Dl Size...: 22%|██▏ | 324/1501 [00:09<00:27, 43.32 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:09<00:11, 5.77s/ url]
Dl Size...: 22%|██▏ | 325/1501 [00:09<00:27, 43.32 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:09<00:11, 5.77s/ url]
Dl Size...: 22%|██▏ | 326/1501 [00:09<00:27, 43.32 MiB/s]
Dl Size...: 22%|██▏ | 327/1501 [00:09<00:27, 43.45 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:09<00:11, 5.77s/ url]
Dl Size...: 22%|██▏ | 327/1501 [00:09<00:27, 43.45 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:09<00:11, 5.77s/ url]
Dl Size...: 22%|██▏ | 328/1501 [00:09<00:26, 43.45 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:09<00:11, 5.77s/ url]
Dl Size...: 22%|██▏ | 329/1501 [00:09<00:26, 43.45 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:09<00:11, 5.77s/ url]
Dl Size...: 22%|██▏ | 330/1501 [00:09<00:26, 43.45 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:09<00:11, 5.77s/ url]
Dl Size...: 22%|██▏ | 331/1501 [00:09<00:26, 43.45 MiB/s]
Dl Size...: 22%|██▏ | 332/1501 [00:09<00:26, 43.90 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:09<00:11, 5.77s/ url]
Dl Size...: 22%|██▏ | 332/1501 [00:09<00:26, 43.90 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:09<00:11, 5.77s/ url]
Dl Size...: 22%|██▏ | 333/1501 [00:09<00:26, 43.90 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:09<00:11, 5.77s/ url]
Dl Size...: 22%|██▏ | 334/1501 [00:09<00:26, 43.90 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:09<00:11, 5.77s/ url]
Dl Size...: 22%|██▏ | 335/1501 [00:09<00:26, 43.90 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:09<00:11, 5.77s/ url]
Dl Size...: 22%|██▏ | 336/1501 [00:09<00:26, 43.90 MiB/s]
Dl Size...: 22%|██▏ | 337/1501 [00:09<00:26, 43.35 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:09<00:11, 5.77s/ url]
Dl Size...: 22%|██▏ | 337/1501 [00:09<00:26, 43.35 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:09<00:11, 5.77s/ url]
Dl Size...: 23%|██▎ | 338/1501 [00:09<00:26, 43.35 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:09<00:11, 5.77s/ url]
Dl Size...: 23%|██▎ | 339/1501 [00:09<00:26, 43.35 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:09<00:11, 5.77s/ url]
Dl Size...: 23%|██▎ | 340/1501 [00:09<00:26, 43.35 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:09<00:11, 5.77s/ url]
Dl Size...: 23%|██▎ | 341/1501 [00:09<00:26, 43.35 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:09<00:11, 5.77s/ url]
Dl Size...: 23%|██▎ | 342/1501 [00:09<00:26, 43.35 MiB/s]
Dl Size...: 23%|██▎ | 343/1501 [00:09<00:25, 45.07 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:09<00:11, 5.77s/ url]
Dl Size...: 23%|██▎ | 343/1501 [00:09<00:25, 45.07 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:09<00:11, 5.77s/ url]
Dl Size...: 23%|██▎ | 344/1501 [00:09<00:25, 45.07 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:09<00:11, 5.77s/ url]
Dl Size...: 23%|██▎ | 345/1501 [00:09<00:25, 45.07 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:09<00:11, 5.77s/ url]
Dl Size...: 23%|██▎ | 346/1501 [00:09<00:25, 45.07 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:09<00:11, 5.77s/ url]
Dl Size...: 23%|██▎ | 347/1501 [00:09<00:25, 45.07 MiB/s]
Dl Size...: 23%|██▎ | 348/1501 [00:09<00:26, 43.84 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:09<00:11, 5.77s/ url]
Dl Size...: 23%|██▎ | 348/1501 [00:09<00:26, 43.84 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:09<00:11, 5.77s/ url]
Dl Size...: 23%|██▎ | 349/1501 [00:09<00:26, 43.84 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:09<00:11, 5.77s/ url]
Dl Size...: 23%|██▎ | 350/1501 [00:09<00:26, 43.84 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:09<00:11, 5.77s/ url]
Dl Size...: 23%|██▎ | 351/1501 [00:09<00:26, 43.84 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:09<00:11, 5.77s/ url]
Dl Size...: 23%|██▎ | 352/1501 [00:09<00:26, 43.84 MiB/s]
Dl Size...: 24%|██▎ | 353/1501 [00:09<00:26, 43.35 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:09<00:11, 5.77s/ url]
Dl Size...: 24%|██▎ | 353/1501 [00:09<00:26, 43.35 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:09<00:11, 5.77s/ url]
Dl Size...: 24%|██▎ | 354/1501 [00:09<00:26, 43.35 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:09<00:11, 5.77s/ url]
Dl Size...: 24%|██▎ | 355/1501 [00:09<00:26, 43.35 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:10<00:11, 5.77s/ url]
Dl Size...: 24%|██▎ | 356/1501 [00:10<00:26, 43.35 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:10<00:11, 5.77s/ url]
Dl Size...: 24%|██▍ | 357/1501 [00:10<00:26, 43.35 MiB/s]
Dl Size...: 24%|██▍ | 358/1501 [00:10<00:25, 44.04 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:10<00:11, 5.77s/ url]
Dl Size...: 24%|██▍ | 358/1501 [00:10<00:25, 44.04 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:10<00:11, 5.77s/ url]
Dl Size...: 24%|██▍ | 359/1501 [00:10<00:25, 44.04 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:10<00:11, 5.77s/ url]
Dl Size...: 24%|██▍ | 360/1501 [00:10<00:25, 44.04 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:10<00:11, 5.77s/ url]
Dl Size...: 24%|██▍ | 361/1501 [00:10<00:25, 44.04 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:10<00:11, 5.77s/ url]
Dl Size...: 24%|██▍ | 362/1501 [00:10<00:25, 44.04 MiB/s]
Dl Size...: 24%|██▍ | 363/1501 [00:10<00:25, 44.71 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:10<00:11, 5.77s/ url]
Dl Size...: 24%|██▍ | 363/1501 [00:10<00:25, 44.71 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:10<00:11, 5.77s/ url]
Dl Size...: 24%|██▍ | 364/1501 [00:10<00:25, 44.71 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:10<00:11, 5.77s/ url]
Dl Size...: 24%|██▍ | 365/1501 [00:10<00:25, 44.71 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:10<00:11, 5.77s/ url]
Dl Size...: 24%|██▍ | 366/1501 [00:10<00:25, 44.71 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:10<00:11, 5.77s/ url]
Dl Size...: 24%|██▍ | 367/1501 [00:10<00:25, 44.71 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:10<00:11, 5.77s/ url]
Dl Size...: 25%|██▍ | 368/1501 [00:10<00:25, 44.71 MiB/s]
Dl Size...: 25%|██▍ | 369/1501 [00:10<00:25, 44.81 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:10<00:11, 5.77s/ url]
Dl Size...: 25%|██▍ | 369/1501 [00:10<00:25, 44.81 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:10<00:11, 5.77s/ url]
Dl Size...: 25%|██▍ | 370/1501 [00:10<00:25, 44.81 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:10<00:11, 5.77s/ url]
Dl Size...: 25%|██▍ | 371/1501 [00:10<00:25, 44.81 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:10<00:11, 5.77s/ url]
Dl Size...: 25%|██▍ | 372/1501 [00:10<00:25, 44.81 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:10<00:11, 5.77s/ url]
Dl Size...: 25%|██▍ | 373/1501 [00:10<00:25, 44.81 MiB/s]
Dl Size...: 25%|██▍ | 374/1501 [00:10<00:25, 44.88 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:10<00:11, 5.77s/ url]
Dl Size...: 25%|██▍ | 374/1501 [00:10<00:25, 44.88 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:10<00:11, 5.77s/ url]
Dl Size...: 25%|██▍ | 375/1501 [00:10<00:25, 44.88 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:10<00:11, 5.77s/ url]
Dl Size...: 25%|██▌ | 376/1501 [00:10<00:25, 44.88 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:10<00:11, 5.77s/ url]
Dl Size...: 25%|██▌ | 377/1501 [00:10<00:25, 44.88 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:10<00:11, 5.77s/ url]
Dl Size...: 25%|██▌ | 378/1501 [00:10<00:25, 44.88 MiB/s]
Dl Size...: 25%|██▌ | 379/1501 [00:10<00:25, 43.71 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:10<00:11, 5.77s/ url]
Dl Size...: 25%|██▌ | 379/1501 [00:10<00:25, 43.71 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:10<00:11, 5.77s/ url]
Dl Size...: 25%|██▌ | 380/1501 [00:10<00:25, 43.71 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:10<00:11, 5.77s/ url]
Dl Size...: 25%|██▌ | 381/1501 [00:10<00:25, 43.71 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:10<00:11, 5.77s/ url]
Dl Size...: 25%|██▌ | 382/1501 [00:10<00:25, 43.71 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:10<00:11, 5.77s/ url]
Dl Size...: 26%|██▌ | 383/1501 [00:10<00:25, 43.71 MiB/s]
Dl Size...: 26%|██▌ | 384/1501 [00:10<00:25, 44.45 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:10<00:11, 5.77s/ url]
Dl Size...: 26%|██▌ | 384/1501 [00:10<00:25, 44.45 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:10<00:11, 5.77s/ url]
Dl Size...: 26%|██▌ | 385/1501 [00:10<00:25, 44.45 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:10<00:11, 5.77s/ url]
Dl Size...: 26%|██▌ | 386/1501 [00:10<00:25, 44.45 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:10<00:11, 5.77s/ url]
Dl Size...: 26%|██▌ | 387/1501 [00:10<00:25, 44.45 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:10<00:11, 5.77s/ url]
Dl Size...: 26%|██▌ | 388/1501 [00:10<00:25, 44.45 MiB/s]
Dl Size...: 26%|██▌ | 389/1501 [00:10<00:24, 45.34 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:10<00:11, 5.77s/ url]
Dl Size...: 26%|██▌ | 389/1501 [00:10<00:24, 45.34 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:10<00:11, 5.77s/ url]
Dl Size...: 26%|██▌ | 390/1501 [00:10<00:24, 45.34 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:10<00:11, 5.77s/ url]
Dl Size...: 26%|██▌ | 391/1501 [00:10<00:24, 45.34 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:10<00:11, 5.77s/ url]
Dl Size...: 26%|██▌ | 392/1501 [00:10<00:24, 45.34 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:10<00:11, 5.77s/ url]
Dl Size...: 26%|██▌ | 393/1501 [00:10<00:24, 45.34 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:10<00:11, 5.77s/ url]
Dl Size...: 26%|██▌ | 394/1501 [00:10<00:24, 45.34 MiB/s]
Dl Size...: 26%|██▋ | 395/1501 [00:10<00:24, 45.42 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:10<00:11, 5.77s/ url]
Dl Size...: 26%|██▋ | 395/1501 [00:10<00:24, 45.42 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:10<00:11, 5.77s/ url]
Dl Size...: 26%|██▋ | 396/1501 [00:10<00:24, 45.42 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:10<00:11, 5.77s/ url]
Dl Size...: 26%|██▋ | 397/1501 [00:10<00:24, 45.42 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:10<00:11, 5.77s/ url]
Dl Size...: 27%|██▋ | 398/1501 [00:10<00:24, 45.42 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:10<00:11, 5.77s/ url]
Dl Size...: 27%|██▋ | 399/1501 [00:10<00:24, 45.42 MiB/s]
Dl Size...: 27%|██▋ | 400/1501 [00:10<00:24, 45.21 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:10<00:11, 5.77s/ url]
Dl Size...: 27%|██▋ | 400/1501 [00:10<00:24, 45.21 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:11<00:11, 5.77s/ url]
Dl Size...: 27%|██▋ | 401/1501 [00:10<00:24, 45.21 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:11<00:11, 5.77s/ url]
Dl Size...: 27%|██▋ | 402/1501 [00:11<00:24, 45.21 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:11<00:11, 5.77s/ url]
Dl Size...: 27%|██▋ | 403/1501 [00:11<00:24, 45.21 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:11<00:11, 5.77s/ url]
Dl Size...: 27%|██▋ | 404/1501 [00:11<00:24, 45.21 MiB/s]
Dl Size...: 27%|██▋ | 405/1501 [00:11<00:24, 44.05 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:11<00:11, 5.77s/ url]
Dl Size...: 27%|██▋ | 405/1501 [00:11<00:24, 44.05 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:11<00:11, 5.77s/ url]
Dl Size...: 27%|██▋ | 406/1501 [00:11<00:24, 44.05 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:11<00:11, 5.77s/ url]
Dl Size...: 27%|██▋ | 407/1501 [00:11<00:24, 44.05 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:11<00:11, 5.77s/ url]
Dl Size...: 27%|██▋ | 408/1501 [00:11<00:24, 44.05 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:11<00:11, 5.77s/ url]
Dl Size...: 27%|██▋ | 409/1501 [00:11<00:24, 44.05 MiB/s]
Dl Size...: 27%|██▋ | 410/1501 [00:11<00:24, 45.10 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:11<00:11, 5.77s/ url]
Dl Size...: 27%|██▋ | 410/1501 [00:11<00:24, 45.10 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:11<00:11, 5.77s/ url]
Dl Size...: 27%|██▋ | 411/1501 [00:11<00:24, 45.10 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:11<00:11, 5.77s/ url]
Dl Size...: 27%|██▋ | 412/1501 [00:11<00:24, 45.10 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:11<00:11, 5.77s/ url]
Dl Size...: 28%|██▊ | 413/1501 [00:11<00:24, 45.10 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:11<00:11, 5.77s/ url]
Dl Size...: 28%|██▊ | 414/1501 [00:11<00:24, 45.10 MiB/s]
Dl Size...: 28%|██▊ | 415/1501 [00:11<00:28, 37.83 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:11<00:11, 5.77s/ url]
Dl Size...: 28%|██▊ | 415/1501 [00:11<00:28, 37.83 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:11<00:11, 5.77s/ url]
Dl Size...: 28%|██▊ | 416/1501 [00:11<00:28, 37.83 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:11<00:11, 5.77s/ url]
Dl Size...: 28%|██▊ | 417/1501 [00:11<00:28, 37.83 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:11<00:11, 5.77s/ url]
Dl Size...: 28%|██▊ | 418/1501 [00:11<00:28, 37.83 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:11<00:11, 5.77s/ url]
Dl Size...: 28%|██▊ | 419/1501 [00:11<00:28, 37.83 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:11<00:11, 5.77s/ url]
Dl Size...: 28%|██▊ | 420/1501 [00:11<00:28, 37.83 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:11<00:11, 5.77s/ url]
Dl Size...: 28%|██▊ | 421/1501 [00:11<00:28, 37.83 MiB/s]
Dl Size...: 28%|██▊ | 422/1501 [00:11<00:25, 42.39 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:11<00:11, 5.77s/ url]
Dl Size...: 28%|██▊ | 422/1501 [00:11<00:25, 42.39 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:11<00:11, 5.77s/ url]
Dl Size...: 28%|██▊ | 423/1501 [00:11<00:25, 42.39 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:11<00:11, 5.77s/ url]
Dl Size...: 28%|██▊ | 424/1501 [00:11<00:25, 42.39 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:11<00:11, 5.77s/ url]
Dl Size...: 28%|██▊ | 425/1501 [00:11<00:25, 42.39 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:11<00:11, 5.77s/ url]
Dl Size...: 28%|██▊ | 426/1501 [00:11<00:25, 42.39 MiB/s]
Dl Size...: 28%|██▊ | 427/1501 [00:11<00:27, 39.56 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:11<00:11, 5.77s/ url]
Dl Size...: 28%|██▊ | 427/1501 [00:11<00:27, 39.56 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:11<00:11, 5.77s/ url]
Dl Size...: 29%|██▊ | 428/1501 [00:11<00:27, 39.56 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:11<00:11, 5.77s/ url]
Dl Size...: 29%|██▊ | 429/1501 [00:11<00:27, 39.56 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:11<00:11, 5.77s/ url]
Dl Size...: 29%|██▊ | 430/1501 [00:11<00:27, 39.56 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:11<00:11, 5.77s/ url]
Dl Size...: 29%|██▊ | 431/1501 [00:11<00:27, 39.56 MiB/s]
Dl Size...: 29%|██▉ | 432/1501 [00:11<00:28, 37.72 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:11<00:11, 5.77s/ url]
Dl Size...: 29%|██▉ | 432/1501 [00:11<00:28, 37.72 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:11<00:11, 5.77s/ url]
Dl Size...: 29%|██▉ | 433/1501 [00:11<00:28, 37.72 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:11<00:11, 5.77s/ url]
Dl Size...: 29%|██▉ | 434/1501 [00:11<00:28, 37.72 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:11<00:11, 5.77s/ url]
Dl Size...: 29%|██▉ | 435/1501 [00:11<00:28, 37.72 MiB/s]
Dl Size...: 29%|██▉ | 436/1501 [00:11<00:31, 34.33 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:11<00:11, 5.77s/ url]
Dl Size...: 29%|██▉ | 436/1501 [00:11<00:31, 34.33 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:11<00:11, 5.77s/ url]
Dl Size...: 29%|██▉ | 437/1501 [00:11<00:30, 34.33 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:11<00:11, 5.77s/ url]
Dl Size...: 29%|██▉ | 438/1501 [00:11<00:30, 34.33 MiB/s]
Dl Completed...: 33%|███▎ | 1/3 [00:12<00:11, 5.77s/ url]
Dl Size...: 29%|██▉ | 439/1501 [00:12<00:30, 34.33 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:12<00:06, 6.07s/ url]
Dl Completed...: 67%|██████▋ | 2/3 [00:12<00:06, 6.07s/ url]
Dl Size...: 29%|██▉ | 439/1501 [00:12<00:30, 34.33 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:12<00:06, 6.07s/ url]
Dl Size...: 29%|██▉ | 440/1501 [00:12<00:30, 34.33 MiB/s]
Dl Size...: 29%|██▉ | 441/1501 [00:12<00:31, 34.03 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:12<00:06, 6.07s/ url]
Dl Size...: 29%|██▉ | 441/1501 [00:12<00:31, 34.03 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:12<00:06, 6.07s/ url]
Dl Size...: 29%|██▉ | 442/1501 [00:12<00:31, 34.03 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:12<00:06, 6.07s/ url]
Dl Size...: 30%|██▉ | 443/1501 [00:12<00:31, 34.03 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:12<00:06, 6.07s/ url]
Dl Size...: 30%|██▉ | 444/1501 [00:12<00:31, 34.03 MiB/s]
Dl Size...: 30%|██▉ | 445/1501 [00:12<00:37, 28.01 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:12<00:06, 6.07s/ url]
Dl Size...: 30%|██▉ | 445/1501 [00:12<00:37, 28.01 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:12<00:06, 6.07s/ url]
Dl Size...: 30%|██▉ | 446/1501 [00:12<00:37, 28.01 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:12<00:06, 6.07s/ url]
Dl Size...: 30%|██▉ | 447/1501 [00:12<00:37, 28.01 MiB/s]
Dl Size...: 30%|██▉ | 448/1501 [00:12<00:40, 25.89 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:12<00:06, 6.07s/ url]
Dl Size...: 30%|██▉ | 448/1501 [00:12<00:40, 25.89 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:12<00:06, 6.07s/ url]
Dl Size...: 30%|██▉ | 449/1501 [00:12<00:40, 25.89 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:12<00:06, 6.07s/ url]
Dl Size...: 30%|██▉ | 450/1501 [00:12<00:40, 25.89 MiB/s]
Dl Size...: 30%|███ | 451/1501 [00:12<00:43, 24.31 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:12<00:06, 6.07s/ url]
Dl Size...: 30%|███ | 451/1501 [00:12<00:43, 24.31 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:12<00:06, 6.07s/ url]
Dl Size...: 30%|███ | 452/1501 [00:12<00:43, 24.31 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:12<00:06, 6.07s/ url]
Dl Size...: 30%|███ | 453/1501 [00:12<00:43, 24.31 MiB/s]
Dl Size...: 30%|███ | 454/1501 [00:12<00:45, 23.12 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:12<00:06, 6.07s/ url]
Dl Size...: 30%|███ | 454/1501 [00:12<00:45, 23.12 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:12<00:06, 6.07s/ url]
Dl Size...: 30%|███ | 455/1501 [00:12<00:45, 23.12 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:12<00:06, 6.07s/ url]
Dl Size...: 30%|███ | 456/1501 [00:12<00:45, 23.12 MiB/s]
Dl Size...: 30%|███ | 457/1501 [00:12<00:46, 22.27 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:12<00:06, 6.07s/ url]
Dl Size...: 30%|███ | 457/1501 [00:12<00:46, 22.27 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:12<00:06, 6.07s/ url]
Dl Size...: 31%|███ | 458/1501 [00:12<00:46, 22.27 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:13<00:06, 6.07s/ url]
Dl Size...: 31%|███ | 459/1501 [00:13<00:46, 22.27 MiB/s]
Dl Size...: 31%|███ | 460/1501 [00:13<00:48, 21.68 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:13<00:06, 6.07s/ url]
Dl Size...: 31%|███ | 460/1501 [00:13<00:48, 21.68 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:13<00:06, 6.07s/ url]
Dl Size...: 31%|███ | 461/1501 [00:13<00:47, 21.68 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:13<00:06, 6.07s/ url]
Dl Size...: 31%|███ | 462/1501 [00:13<00:47, 21.68 MiB/s]
Dl Size...: 31%|███ | 463/1501 [00:13<00:48, 21.42 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:13<00:06, 6.07s/ url]
Dl Size...: 31%|███ | 463/1501 [00:13<00:48, 21.42 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:13<00:06, 6.07s/ url]
Dl Size...: 31%|███ | 464/1501 [00:13<00:48, 21.42 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:13<00:06, 6.07s/ url]
Dl Size...: 31%|███ | 465/1501 [00:13<00:48, 21.42 MiB/s]
Dl Size...: 31%|███ | 466/1501 [00:13<00:49, 21.10 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:13<00:06, 6.07s/ url]
Dl Size...: 31%|███ | 466/1501 [00:13<00:49, 21.10 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:13<00:06, 6.07s/ url]
Dl Size...: 31%|███ | 467/1501 [00:13<00:49, 21.10 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:13<00:06, 6.07s/ url]
Dl Size...: 31%|███ | 468/1501 [00:13<00:48, 21.10 MiB/s]
Dl Size...: 31%|███ | 469/1501 [00:13<00:49, 20.91 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:13<00:06, 6.07s/ url]
Dl Size...: 31%|███ | 469/1501 [00:13<00:49, 20.91 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:13<00:06, 6.07s/ url]
Dl Size...: 31%|███▏ | 470/1501 [00:13<00:49, 20.91 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:13<00:06, 6.07s/ url]
Dl Size...: 31%|███▏ | 471/1501 [00:13<00:49, 20.91 MiB/s]
Dl Size...: 31%|███▏ | 472/1501 [00:13<00:49, 20.91 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:13<00:06, 6.07s/ url]
Dl Size...: 31%|███▏ | 472/1501 [00:13<00:49, 20.91 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:13<00:06, 6.07s/ url]
Dl Size...: 32%|███▏ | 473/1501 [00:13<00:49, 20.91 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:13<00:06, 6.07s/ url]
Dl Size...: 32%|███▏ | 474/1501 [00:13<00:49, 20.91 MiB/s]
Dl Size...: 32%|███▏ | 475/1501 [00:13<00:49, 20.74 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:13<00:06, 6.07s/ url]
Dl Size...: 32%|███▏ | 475/1501 [00:13<00:49, 20.74 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:13<00:06, 6.07s/ url]
Dl Size...: 32%|███▏ | 476/1501 [00:13<00:49, 20.74 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:13<00:06, 6.07s/ url]
Dl Size...: 32%|███▏ | 477/1501 [00:13<00:49, 20.74 MiB/s]
Dl Size...: 32%|███▏ | 478/1501 [00:13<00:49, 20.79 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:13<00:06, 6.07s/ url]
Dl Size...: 32%|███▏ | 478/1501 [00:13<00:49, 20.79 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:13<00:06, 6.07s/ url]
Dl Size...: 32%|███▏ | 479/1501 [00:13<00:49, 20.79 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:14<00:06, 6.07s/ url]
Dl Size...: 32%|███▏ | 480/1501 [00:14<00:49, 20.79 MiB/s]
Dl Size...: 32%|███▏ | 481/1501 [00:14<00:48, 20.85 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:14<00:06, 6.07s/ url]
Dl Size...: 32%|███▏ | 481/1501 [00:14<00:48, 20.85 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:14<00:06, 6.07s/ url]
Dl Size...: 32%|███▏ | 482/1501 [00:14<00:48, 20.85 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:14<00:06, 6.07s/ url]
Dl Size...: 32%|███▏ | 483/1501 [00:14<00:48, 20.85 MiB/s]
Dl Size...: 32%|███▏ | 484/1501 [00:14<00:49, 20.69 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:14<00:06, 6.07s/ url]
Dl Size...: 32%|███▏ | 484/1501 [00:14<00:49, 20.69 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:14<00:06, 6.07s/ url]
Dl Size...: 32%|███▏ | 485/1501 [00:14<00:49, 20.69 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:14<00:06, 6.07s/ url]
Dl Size...: 32%|███▏ | 486/1501 [00:14<00:49, 20.69 MiB/s]
Dl Size...: 32%|███▏ | 487/1501 [00:14<00:54, 18.52 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:14<00:06, 6.07s/ url]
Dl Size...: 32%|███▏ | 487/1501 [00:14<00:54, 18.52 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:14<00:06, 6.07s/ url]
Dl Size...: 33%|███▎ | 488/1501 [00:14<00:54, 18.52 MiB/s]
Dl Size...: 33%|███▎ | 489/1501 [00:14<00:58, 17.37 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:14<00:06, 6.07s/ url]
Dl Size...: 33%|███▎ | 489/1501 [00:14<00:58, 17.37 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:14<00:06, 6.07s/ url]
Dl Size...: 33%|███▎ | 490/1501 [00:14<00:58, 17.37 MiB/s]
Dl Size...: 33%|███▎ | 491/1501 [00:14<01:00, 16.65 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:14<00:06, 6.07s/ url]
Dl Size...: 33%|███▎ | 491/1501 [00:14<01:00, 16.65 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:14<00:06, 6.07s/ url]
Dl Size...: 33%|███▎ | 492/1501 [00:14<01:00, 16.65 MiB/s]
Dl Size...: 33%|███▎ | 493/1501 [00:14<01:02, 16.07 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:14<00:06, 6.07s/ url]
Dl Size...: 33%|███▎ | 493/1501 [00:14<01:02, 16.07 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:14<00:06, 6.07s/ url]
Dl Size...: 33%|███▎ | 494/1501 [00:14<01:02, 16.07 MiB/s]
Dl Size...: 33%|███▎ | 495/1501 [00:14<01:03, 15.79 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:14<00:06, 6.07s/ url]
Dl Size...: 33%|███▎ | 495/1501 [00:14<01:03, 15.79 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:15<00:06, 6.07s/ url]
Dl Size...: 33%|███▎ | 496/1501 [00:15<01:03, 15.79 MiB/s]
Dl Size...: 33%|███▎ | 497/1501 [00:15<01:00, 16.70 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:15<00:06, 6.07s/ url]
Dl Size...: 33%|███▎ | 497/1501 [00:15<01:00, 16.70 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:15<00:06, 6.07s/ url]
Dl Size...: 33%|███▎ | 498/1501 [00:15<01:00, 16.70 MiB/s]
Dl Size...: 33%|███▎ | 499/1501 [00:15<01:02, 16.06 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:15<00:06, 6.07s/ url]
Dl Size...: 33%|███▎ | 499/1501 [00:15<01:02, 16.06 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:15<00:06, 6.07s/ url]
Dl Size...: 33%|███▎ | 500/1501 [00:15<01:02, 16.06 MiB/s]
Dl Size...: 33%|███▎ | 501/1501 [00:15<01:03, 15.66 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:15<00:06, 6.07s/ url]
Dl Size...: 33%|███▎ | 501/1501 [00:15<01:03, 15.66 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:15<00:06, 6.07s/ url]
Dl Size...: 33%|███▎ | 502/1501 [00:15<01:03, 15.66 MiB/s]
Dl Size...: 34%|███▎ | 503/1501 [00:15<01:04, 15.49 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:15<00:06, 6.07s/ url]
Dl Size...: 34%|███▎ | 503/1501 [00:15<01:04, 15.49 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:15<00:06, 6.07s/ url]
Dl Size...: 34%|███▎ | 504/1501 [00:15<01:04, 15.49 MiB/s]
Dl Size...: 34%|███▎ | 505/1501 [00:15<01:00, 16.59 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:15<00:06, 6.07s/ url]
Dl Size...: 34%|███▎ | 505/1501 [00:15<01:00, 16.59 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:15<00:06, 6.07s/ url]
Dl Size...: 34%|███▎ | 506/1501 [00:15<00:59, 16.59 MiB/s]
Dl Size...: 34%|███▍ | 507/1501 [00:15<01:01, 16.13 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:15<00:06, 6.07s/ url]
Dl Size...: 34%|███▍ | 507/1501 [00:15<01:01, 16.13 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:15<00:06, 6.07s/ url]
Dl Size...: 34%|███▍ | 508/1501 [00:15<01:01, 16.13 MiB/s]
Dl Size...: 34%|███▍ | 509/1501 [00:15<01:02, 15.86 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:15<00:06, 6.07s/ url]
Dl Size...: 34%|███▍ | 509/1501 [00:15<01:02, 15.86 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:15<00:06, 6.07s/ url]
Dl Size...: 34%|███▍ | 510/1501 [00:15<01:02, 15.86 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:15<00:06, 6.07s/ url]
Dl Size...: 34%|███▍ | 511/1501 [00:15<01:02, 15.86 MiB/s]
Dl Size...: 34%|███▍ | 512/1501 [00:16<00:59, 16.65 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:16<00:06, 6.07s/ url]
Dl Size...: 34%|███▍ | 512/1501 [00:16<00:59, 16.65 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:16<00:06, 6.07s/ url]
Dl Size...: 34%|███▍ | 513/1501 [00:16<00:59, 16.65 MiB/s]
Dl Size...: 34%|███▍ | 514/1501 [00:16<01:01, 16.17 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:16<00:06, 6.07s/ url]
Dl Size...: 34%|███▍ | 514/1501 [00:16<01:01, 16.17 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:16<00:06, 6.07s/ url]
Dl Size...: 34%|███▍ | 515/1501 [00:16<01:00, 16.17 MiB/s]
Dl Size...: 34%|███▍ | 516/1501 [00:16<01:01, 15.97 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:16<00:06, 6.07s/ url]
Dl Size...: 34%|███▍ | 516/1501 [00:16<01:01, 15.97 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:16<00:06, 6.07s/ url]
Dl Size...: 34%|███▍ | 517/1501 [00:16<01:01, 15.97 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:16<00:06, 6.07s/ url]
Dl Size...: 35%|███▍ | 518/1501 [00:16<01:01, 15.97 MiB/s]
Dl Size...: 35%|███▍ | 519/1501 [00:16<00:58, 16.82 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:16<00:06, 6.07s/ url]
Dl Size...: 35%|███▍ | 519/1501 [00:16<00:58, 16.82 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:16<00:06, 6.07s/ url]
Dl Size...: 35%|███▍ | 520/1501 [00:16<00:58, 16.82 MiB/s]
Dl Size...: 35%|███▍ | 521/1501 [00:16<00:59, 16.43 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:16<00:06, 6.07s/ url]
Dl Size...: 35%|███▍ | 521/1501 [00:16<00:59, 16.43 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:16<00:06, 6.07s/ url]
Dl Size...: 35%|███▍ | 522/1501 [00:16<00:59, 16.43 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:16<00:06, 6.07s/ url]
Dl Size...: 35%|███▍ | 523/1501 [00:16<00:59, 16.43 MiB/s]
Dl Size...: 35%|███▍ | 524/1501 [00:16<00:57, 17.13 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:16<00:06, 6.07s/ url]
Dl Size...: 35%|███▍ | 524/1501 [00:16<00:57, 17.13 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:16<00:06, 6.07s/ url]
Dl Size...: 35%|███▍ | 525/1501 [00:16<00:56, 17.13 MiB/s]
Dl Size...: 35%|███▌ | 526/1501 [00:16<00:58, 16.64 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:16<00:06, 6.07s/ url]
Dl Size...: 35%|███▌ | 526/1501 [00:16<00:58, 16.64 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:16<00:06, 6.07s/ url]
Dl Size...: 35%|███▌ | 527/1501 [00:16<00:58, 16.64 MiB/s]
Dl Size...: 35%|███▌ | 528/1501 [00:16<00:55, 17.42 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:16<00:06, 6.07s/ url]
Dl Size...: 35%|███▌ | 528/1501 [00:16<00:55, 17.42 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:17<00:06, 6.07s/ url]
Dl Size...: 35%|███▌ | 529/1501 [00:17<00:55, 17.42 MiB/s]
Dl Size...: 35%|███▌ | 530/1501 [00:17<00:57, 16.99 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:17<00:06, 6.07s/ url]
Dl Size...: 35%|███▌ | 530/1501 [00:17<00:57, 16.99 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:17<00:06, 6.07s/ url]
Dl Size...: 35%|███▌ | 531/1501 [00:17<00:57, 16.99 MiB/s]
Dl Size...: 35%|███▌ | 532/1501 [00:17<00:58, 16.44 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:17<00:06, 6.07s/ url]
Dl Size...: 35%|███▌ | 532/1501 [00:17<00:58, 16.44 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:17<00:06, 6.07s/ url]
Dl Size...: 36%|███▌ | 533/1501 [00:17<00:58, 16.44 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:17<00:06, 6.07s/ url]
Dl Size...: 36%|███▌ | 534/1501 [00:17<00:58, 16.44 MiB/s]
Dl Size...: 36%|███▌ | 535/1501 [00:17<01:06, 14.58 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:17<00:06, 6.07s/ url]
Dl Size...: 36%|███▌ | 535/1501 [00:17<01:06, 14.58 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:17<00:06, 6.07s/ url]
Dl Size...: 36%|███▌ | 536/1501 [00:17<01:06, 14.58 MiB/s]
Dl Size...: 36%|███▌ | 537/1501 [00:17<01:12, 13.34 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:17<00:06, 6.07s/ url]
Dl Size...: 36%|███▌ | 537/1501 [00:17<01:12, 13.34 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:17<00:06, 6.07s/ url]
Dl Size...: 36%|███▌ | 538/1501 [00:17<01:12, 13.34 MiB/s]
Dl Size...: 36%|███▌ | 539/1501 [00:17<01:22, 11.64 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:17<00:06, 6.07s/ url]
Dl Size...: 36%|███▌ | 539/1501 [00:17<01:22, 11.64 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:17<00:06, 6.07s/ url]
Dl Size...: 36%|███▌ | 540/1501 [00:17<01:22, 11.64 MiB/s]
Dl Size...: 36%|███▌ | 541/1501 [00:18<01:28, 10.80 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:18<00:06, 6.07s/ url]
Dl Size...: 36%|███▌ | 541/1501 [00:18<01:28, 10.80 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:18<00:06, 6.07s/ url]
Dl Size...: 36%|███▌ | 542/1501 [00:18<01:28, 10.80 MiB/s]
Dl Size...: 36%|███▌ | 543/1501 [00:18<01:33, 10.25 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:18<00:06, 6.07s/ url]
Dl Size...: 36%|███▌ | 543/1501 [00:18<01:33, 10.25 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:18<00:06, 6.07s/ url]
Dl Size...: 36%|███▌ | 544/1501 [00:18<01:33, 10.25 MiB/s]
Dl Size...: 36%|███▋ | 545/1501 [00:18<01:36, 9.90 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:18<00:06, 6.07s/ url]
Dl Size...: 36%|███▋ | 545/1501 [00:18<01:36, 9.90 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:18<00:06, 6.07s/ url]
Dl Size...: 36%|███▋ | 546/1501 [00:18<01:36, 9.90 MiB/s]
Dl Size...: 36%|███▋ | 547/1501 [00:18<01:38, 9.67 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:18<00:06, 6.07s/ url]
Dl Size...: 36%|███▋ | 547/1501 [00:18<01:38, 9.67 MiB/s]
Dl Size...: 37%|███▋ | 548/1501 [00:18<01:41, 9.37 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:18<00:06, 6.07s/ url]
Dl Size...: 37%|███▋ | 548/1501 [00:18<01:41, 9.37 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:18<00:06, 6.07s/ url]
Dl Size...: 37%|███▋ | 549/1501 [00:18<01:41, 9.37 MiB/s]
Dl Size...: 37%|███▋ | 550/1501 [00:19<01:41, 9.36 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:19<00:06, 6.07s/ url]
Dl Size...: 37%|███▋ | 550/1501 [00:19<01:41, 9.36 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:19<00:06, 6.07s/ url]
Dl Size...: 37%|███▋ | 551/1501 [00:19<01:41, 9.36 MiB/s]
Dl Size...: 37%|███▋ | 552/1501 [00:19<01:40, 9.42 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:19<00:06, 6.07s/ url]
Dl Size...: 37%|███▋ | 552/1501 [00:19<01:40, 9.42 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:19<00:06, 6.07s/ url]
Dl Size...: 37%|███▋ | 553/1501 [00:19<01:40, 9.42 MiB/s]
Dl Size...: 37%|███▋ | 554/1501 [00:19<01:39, 9.56 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:19<00:06, 6.07s/ url]
Dl Size...: 37%|███▋ | 554/1501 [00:19<01:39, 9.56 MiB/s]
Dl Size...: 37%|███▋ | 555/1501 [00:19<01:38, 9.63 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:19<00:06, 6.07s/ url]
Dl Size...: 37%|███▋ | 555/1501 [00:19<01:38, 9.63 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:19<00:06, 6.07s/ url]
Dl Size...: 37%|███▋ | 556/1501 [00:19<01:38, 9.63 MiB/s]
Dl Size...: 37%|███▋ | 557/1501 [00:19<01:39, 9.53 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:19<00:06, 6.07s/ url]
Dl Size...: 37%|███▋ | 557/1501 [00:19<01:39, 9.53 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:19<00:06, 6.07s/ url]
Dl Size...: 37%|███▋ | 558/1501 [00:19<01:38, 9.53 MiB/s]
Dl Size...: 37%|███▋ | 559/1501 [00:20<01:38, 9.53 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:20<00:06, 6.07s/ url]
Dl Size...: 37%|███▋ | 559/1501 [00:20<01:38, 9.53 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:20<00:06, 6.07s/ url]
Dl Size...: 37%|███▋ | 560/1501 [00:20<01:38, 9.53 MiB/s]
Dl Size...: 37%|███▋ | 561/1501 [00:20<01:36, 9.75 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:20<00:06, 6.07s/ url]
Dl Size...: 37%|███▋ | 561/1501 [00:20<01:36, 9.75 MiB/s]
Dl Size...: 37%|███▋ | 562/1501 [00:20<01:36, 9.76 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:20<00:06, 6.07s/ url]
Dl Size...: 37%|███▋ | 562/1501 [00:20<01:36, 9.76 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:20<00:06, 6.07s/ url]
Dl Size...: 38%|███▊ | 563/1501 [00:20<01:36, 9.76 MiB/s]
Dl Size...: 38%|███▊ | 564/1501 [00:20<01:36, 9.67 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:20<00:06, 6.07s/ url]
Dl Size...: 38%|███▊ | 564/1501 [00:20<01:36, 9.67 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:20<00:06, 6.07s/ url]
Dl Size...: 38%|███▊ | 565/1501 [00:20<01:36, 9.67 MiB/s]
Dl Size...: 38%|███▊ | 566/1501 [00:20<01:35, 9.77 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:20<00:06, 6.07s/ url]
Dl Size...: 38%|███▊ | 566/1501 [00:20<01:35, 9.77 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:20<00:06, 6.07s/ url]
Dl Size...: 38%|███▊ | 567/1501 [00:20<01:35, 9.77 MiB/s]
Dl Size...: 38%|███▊ | 568/1501 [00:20<01:31, 10.15 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:20<00:06, 6.07s/ url]
Dl Size...: 38%|███▊ | 568/1501 [00:20<01:31, 10.15 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:21<00:06, 6.07s/ url]
Dl Size...: 38%|███▊ | 569/1501 [00:21<01:31, 10.15 MiB/s]
Dl Size...: 38%|███▊ | 570/1501 [00:21<01:32, 10.04 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:21<00:06, 6.07s/ url]
Dl Size...: 38%|███▊ | 570/1501 [00:21<01:32, 10.04 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:21<00:06, 6.07s/ url]
Dl Size...: 38%|███▊ | 571/1501 [00:21<01:32, 10.04 MiB/s]
Dl Size...: 38%|███▊ | 572/1501 [00:21<01:34, 9.88 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:21<00:06, 6.07s/ url]
Dl Size...: 38%|███▊ | 572/1501 [00:21<01:34, 9.88 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:21<00:06, 6.07s/ url]
Dl Size...: 38%|███▊ | 573/1501 [00:21<01:33, 9.88 MiB/s]
Dl Size...: 38%|███▊ | 574/1501 [00:21<01:34, 9.80 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:21<00:06, 6.07s/ url]
Dl Size...: 38%|███▊ | 574/1501 [00:21<01:34, 9.80 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:21<00:06, 6.07s/ url]
Dl Size...: 38%|███▊ | 575/1501 [00:21<01:34, 9.80 MiB/s]
Dl Size...: 38%|███▊ | 576/1501 [00:21<01:30, 10.25 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:21<00:06, 6.07s/ url]
Dl Size...: 38%|███▊ | 576/1501 [00:21<01:30, 10.25 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:21<00:06, 6.07s/ url]
Dl Size...: 38%|███▊ | 577/1501 [00:21<01:30, 10.25 MiB/s]
Dl Size...: 39%|███▊ | 578/1501 [00:21<01:30, 10.17 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:21<00:06, 6.07s/ url]
Dl Size...: 39%|███▊ | 578/1501 [00:21<01:30, 10.17 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:22<00:06, 6.07s/ url]
Dl Size...: 39%|███▊ | 579/1501 [00:22<01:30, 10.17 MiB/s]
Dl Size...: 39%|███▊ | 580/1501 [00:22<01:32, 9.95 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:22<00:06, 6.07s/ url]
Dl Size...: 39%|███▊ | 580/1501 [00:22<01:32, 9.95 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:22<00:06, 6.07s/ url]
Dl Size...: 39%|███▊ | 581/1501 [00:22<01:32, 9.95 MiB/s]
Dl Size...: 39%|███▉ | 582/1501 [00:22<01:32, 9.89 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:22<00:06, 6.07s/ url]
Dl Size...: 39%|███▉ | 582/1501 [00:22<01:32, 9.89 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:22<00:06, 6.07s/ url]
Dl Size...: 39%|███▉ | 583/1501 [00:22<01:32, 9.89 MiB/s]
Dl Size...: 39%|███▉ | 584/1501 [00:22<01:30, 10.12 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:22<00:06, 6.07s/ url]
Dl Size...: 39%|███▉ | 584/1501 [00:22<01:30, 10.12 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:22<00:06, 6.07s/ url]
Dl Size...: 39%|███▉ | 585/1501 [00:22<01:30, 10.12 MiB/s]
Dl Size...: 39%|███▉ | 586/1501 [00:22<01:29, 10.22 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:22<00:06, 6.07s/ url]
Dl Size...: 39%|███▉ | 586/1501 [00:22<01:29, 10.22 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:22<00:06, 6.07s/ url]
Dl Size...: 39%|███▉ | 587/1501 [00:22<01:29, 10.22 MiB/s]
Dl Size...: 39%|███▉ | 588/1501 [00:22<01:31, 10.02 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:22<00:06, 6.07s/ url]
Dl Size...: 39%|███▉ | 588/1501 [00:22<01:31, 10.02 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:22<00:06, 6.07s/ url]
Dl Size...: 39%|███▉ | 589/1501 [00:22<01:30, 10.02 MiB/s]
Dl Size...: 39%|███▉ | 590/1501 [00:23<01:31, 9.93 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:23<00:06, 6.07s/ url]
Dl Size...: 39%|███▉ | 590/1501 [00:23<01:31, 9.93 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:23<00:06, 6.07s/ url]
Dl Size...: 39%|███▉ | 591/1501 [00:23<01:31, 9.93 MiB/s]
Dl Size...: 39%|███▉ | 592/1501 [00:23<01:30, 10.06 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:23<00:06, 6.07s/ url]
Dl Size...: 39%|███▉ | 592/1501 [00:23<01:30, 10.06 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:23<00:06, 6.07s/ url]
Dl Size...: 40%|███▉ | 593/1501 [00:23<01:30, 10.06 MiB/s]
Dl Size...: 40%|███▉ | 594/1501 [00:23<01:28, 10.26 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:23<00:06, 6.07s/ url]
Dl Size...: 40%|███▉ | 594/1501 [00:23<01:28, 10.26 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:23<00:06, 6.07s/ url]
Dl Size...: 40%|███▉ | 595/1501 [00:23<01:28, 10.26 MiB/s]
Dl Size...: 40%|███▉ | 596/1501 [00:23<01:29, 10.08 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:23<00:06, 6.07s/ url]
Dl Size...: 40%|███▉ | 596/1501 [00:23<01:29, 10.08 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:23<00:06, 6.07s/ url]
Dl Size...: 40%|███▉ | 597/1501 [00:23<01:29, 10.08 MiB/s]
Dl Size...: 40%|███▉ | 598/1501 [00:23<01:30, 9.93 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:23<00:06, 6.07s/ url]
Dl Size...: 40%|███▉ | 598/1501 [00:23<01:30, 9.93 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:23<00:06, 6.07s/ url]
Dl Size...: 40%|███▉ | 599/1501 [00:23<01:30, 9.93 MiB/s]
Dl Size...: 40%|███▉ | 600/1501 [00:24<01:29, 10.07 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:24<00:06, 6.07s/ url]
Dl Size...: 40%|███▉ | 600/1501 [00:24<01:29, 10.07 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:24<00:06, 6.07s/ url]
Dl Size...: 40%|████ | 601/1501 [00:24<01:29, 10.07 MiB/s]
Dl Size...: 40%|████ | 602/1501 [00:24<01:27, 10.29 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:24<00:06, 6.07s/ url]
Dl Size...: 40%|████ | 602/1501 [00:24<01:27, 10.29 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:24<00:06, 6.07s/ url]
Dl Size...: 40%|████ | 603/1501 [00:24<01:27, 10.29 MiB/s]
Dl Size...: 40%|████ | 604/1501 [00:24<01:29, 10.06 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:24<00:06, 6.07s/ url]
Dl Size...: 40%|████ | 604/1501 [00:24<01:29, 10.06 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:24<00:06, 6.07s/ url]
Dl Size...: 40%|████ | 605/1501 [00:24<01:29, 10.06 MiB/s]
Dl Size...: 40%|████ | 606/1501 [00:24<01:29, 9.94 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:24<00:06, 6.07s/ url]
Dl Size...: 40%|████ | 606/1501 [00:24<01:29, 9.94 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:24<00:06, 6.07s/ url]
Dl Size...: 40%|████ | 607/1501 [00:24<01:29, 9.94 MiB/s]
Dl Size...: 41%|████ | 608/1501 [00:24<01:28, 10.08 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:24<00:06, 6.07s/ url]
Dl Size...: 41%|████ | 608/1501 [00:24<01:28, 10.08 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:24<00:06, 6.07s/ url]
Dl Size...: 41%|████ | 609/1501 [00:24<01:28, 10.08 MiB/s]
Dl Size...: 41%|████ | 610/1501 [00:25<01:26, 10.34 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:25<00:06, 6.07s/ url]
Dl Size...: 41%|████ | 610/1501 [00:25<01:26, 10.34 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:25<00:06, 6.07s/ url]
Dl Size...: 41%|████ | 611/1501 [00:25<01:26, 10.34 MiB/s]
Dl Size...: 41%|████ | 612/1501 [00:25<01:28, 10.03 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:25<00:06, 6.07s/ url]
Dl Size...: 41%|████ | 612/1501 [00:25<01:28, 10.03 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:25<00:06, 6.07s/ url]
Dl Size...: 41%|████ | 613/1501 [00:25<01:28, 10.03 MiB/s]
Dl Size...: 41%|████ | 614/1501 [00:25<01:28, 10.03 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:25<00:06, 6.07s/ url]
Dl Size...: 41%|████ | 614/1501 [00:25<01:28, 10.03 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:25<00:06, 6.07s/ url]
Dl Size...: 41%|████ | 615/1501 [00:25<01:28, 10.03 MiB/s]
Dl Size...: 41%|████ | 616/1501 [00:25<01:25, 10.32 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:25<00:06, 6.07s/ url]
Dl Size...: 41%|████ | 616/1501 [00:25<01:25, 10.32 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:25<00:06, 6.07s/ url]
Dl Size...: 41%|████ | 617/1501 [00:25<01:25, 10.32 MiB/s]
Dl Size...: 41%|████ | 618/1501 [00:25<01:26, 10.22 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:25<00:06, 6.07s/ url]
Dl Size...: 41%|████ | 618/1501 [00:25<01:26, 10.22 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:25<00:06, 6.07s/ url]
Dl Size...: 41%|████ | 619/1501 [00:25<01:26, 10.22 MiB/s]
Dl Size...: 41%|████▏ | 620/1501 [00:26<01:27, 10.10 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:26<00:06, 6.07s/ url]
Dl Size...: 41%|████▏ | 620/1501 [00:26<01:27, 10.10 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:26<00:06, 6.07s/ url]
Dl Size...: 41%|████▏ | 621/1501 [00:26<01:27, 10.10 MiB/s]
Dl Size...: 41%|████▏ | 622/1501 [00:26<01:26, 10.20 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:26<00:06, 6.07s/ url]
Dl Size...: 41%|████▏ | 622/1501 [00:26<01:26, 10.20 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:26<00:06, 6.07s/ url]
Dl Size...: 42%|████▏ | 623/1501 [00:26<01:26, 10.20 MiB/s]
Dl Size...: 42%|████▏ | 624/1501 [00:26<01:24, 10.35 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:26<00:06, 6.07s/ url]
Dl Size...: 42%|████▏ | 624/1501 [00:26<01:24, 10.35 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:26<00:06, 6.07s/ url]
Dl Size...: 42%|████▏ | 625/1501 [00:26<01:24, 10.35 MiB/s]
Dl Size...: 42%|████▏ | 626/1501 [00:26<01:25, 10.22 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:26<00:06, 6.07s/ url]
Dl Size...: 42%|████▏ | 626/1501 [00:26<01:25, 10.22 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:26<00:06, 6.07s/ url]
Dl Size...: 42%|████▏ | 627/1501 [00:26<01:25, 10.22 MiB/s]
Dl Size...: 42%|████▏ | 628/1501 [00:26<01:25, 10.22 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:26<00:06, 6.07s/ url]
Dl Size...: 42%|████▏ | 628/1501 [00:26<01:25, 10.22 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:26<00:06, 6.07s/ url]
Dl Size...: 42%|████▏ | 629/1501 [00:26<01:25, 10.22 MiB/s]
Dl Size...: 42%|████▏ | 630/1501 [00:27<01:23, 10.47 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:27<00:06, 6.07s/ url]
Dl Size...: 42%|████▏ | 630/1501 [00:27<01:23, 10.47 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:27<00:06, 6.07s/ url]
Dl Size...: 42%|████▏ | 631/1501 [00:27<01:23, 10.47 MiB/s]
Dl Size...: 42%|████▏ | 632/1501 [00:27<01:24, 10.27 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:27<00:06, 6.07s/ url]
Dl Size...: 42%|████▏ | 632/1501 [00:27<01:24, 10.27 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:27<00:06, 6.07s/ url]
Dl Size...: 42%|████▏ | 633/1501 [00:27<01:24, 10.27 MiB/s]
Dl Size...: 42%|████▏ | 634/1501 [00:27<01:23, 10.43 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:27<00:06, 6.07s/ url]
Dl Size...: 42%|████▏ | 634/1501 [00:27<01:23, 10.43 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:27<00:06, 6.07s/ url]
Dl Size...: 42%|████▏ | 635/1501 [00:27<01:23, 10.43 MiB/s]
Dl Size...: 42%|████▏ | 636/1501 [00:27<01:21, 10.60 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:27<00:06, 6.07s/ url]
Dl Size...: 42%|████▏ | 636/1501 [00:27<01:21, 10.60 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:27<00:06, 6.07s/ url]
Dl Size...: 42%|████▏ | 637/1501 [00:27<01:21, 10.60 MiB/s]
Dl Size...: 43%|████▎ | 638/1501 [00:27<01:22, 10.41 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:27<00:06, 6.07s/ url]
Dl Size...: 43%|████▎ | 638/1501 [00:27<01:22, 10.41 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:27<00:06, 6.07s/ url]
Dl Size...: 43%|████▎ | 639/1501 [00:27<01:22, 10.41 MiB/s]
Dl Size...: 43%|████▎ | 640/1501 [00:27<01:21, 10.56 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:27<00:06, 6.07s/ url]
Dl Size...: 43%|████▎ | 640/1501 [00:27<01:21, 10.56 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:28<00:06, 6.07s/ url]
Dl Size...: 43%|████▎ | 641/1501 [00:28<01:21, 10.56 MiB/s]
Dl Size...: 43%|████▎ | 642/1501 [00:28<01:21, 10.59 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:28<00:06, 6.07s/ url]
Dl Size...: 43%|████▎ | 642/1501 [00:28<01:21, 10.59 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:28<00:06, 6.07s/ url]
Dl Size...: 43%|████▎ | 643/1501 [00:28<01:20, 10.59 MiB/s]
Dl Size...: 43%|████▎ | 644/1501 [00:28<01:19, 10.77 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:28<00:06, 6.07s/ url]
Dl Size...: 43%|████▎ | 644/1501 [00:28<01:19, 10.77 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:28<00:06, 6.07s/ url]
Dl Size...: 43%|████▎ | 645/1501 [00:28<01:19, 10.77 MiB/s]
Dl Size...: 43%|████▎ | 646/1501 [00:28<01:19, 10.78 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:28<00:06, 6.07s/ url]
Dl Size...: 43%|████▎ | 646/1501 [00:28<01:19, 10.78 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:28<00:06, 6.07s/ url]
Dl Size...: 43%|████▎ | 647/1501 [00:28<01:19, 10.78 MiB/s]
Dl Size...: 43%|████▎ | 648/1501 [00:28<01:19, 10.79 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:28<00:06, 6.07s/ url]
Dl Size...: 43%|████▎ | 648/1501 [00:28<01:19, 10.79 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:28<00:06, 6.07s/ url]
Dl Size...: 43%|████▎ | 649/1501 [00:28<01:18, 10.79 MiB/s]
Dl Size...: 43%|████▎ | 650/1501 [00:28<01:17, 11.00 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:28<00:06, 6.07s/ url]
Dl Size...: 43%|████▎ | 650/1501 [00:28<01:17, 11.00 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:28<00:06, 6.07s/ url]
Dl Size...: 43%|████▎ | 651/1501 [00:28<01:17, 11.00 MiB/s]
Dl Size...: 43%|████▎ | 652/1501 [00:29<01:17, 10.96 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:29<00:06, 6.07s/ url]
Dl Size...: 43%|████▎ | 652/1501 [00:29<01:17, 10.96 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:29<00:06, 6.07s/ url]
Dl Size...: 44%|████▎ | 653/1501 [00:29<01:17, 10.96 MiB/s]
Dl Size...: 44%|████▎ | 654/1501 [00:29<01:16, 11.12 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:29<00:06, 6.07s/ url]
Dl Size...: 44%|████▎ | 654/1501 [00:29<01:16, 11.12 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:29<00:06, 6.07s/ url]
Dl Size...: 44%|████▎ | 655/1501 [00:29<01:16, 11.12 MiB/s]
Dl Size...: 44%|████▎ | 656/1501 [00:29<01:14, 11.27 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:29<00:06, 6.07s/ url]
Dl Size...: 44%|████▎ | 656/1501 [00:29<01:14, 11.27 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:29<00:06, 6.07s/ url]
Dl Size...: 44%|████▍ | 657/1501 [00:29<01:14, 11.27 MiB/s]
Dl Size...: 44%|████▍ | 658/1501 [00:29<01:14, 11.30 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:29<00:06, 6.07s/ url]
Dl Size...: 44%|████▍ | 658/1501 [00:29<01:14, 11.30 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:29<00:06, 6.07s/ url]
Dl Size...: 44%|████▍ | 659/1501 [00:29<01:14, 11.30 MiB/s]
Dl Size...: 44%|████▍ | 660/1501 [00:29<01:12, 11.67 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:29<00:06, 6.07s/ url]
Dl Size...: 44%|████▍ | 660/1501 [00:29<01:12, 11.67 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:29<00:06, 6.07s/ url]
Dl Size...: 44%|████▍ | 661/1501 [00:29<01:12, 11.67 MiB/s]
Dl Size...: 44%|████▍ | 662/1501 [00:29<01:12, 11.61 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:29<00:06, 6.07s/ url]
Dl Size...: 44%|████▍ | 662/1501 [00:29<01:12, 11.61 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:30<00:06, 6.07s/ url]
Dl Size...: 44%|████▍ | 663/1501 [00:30<01:12, 11.61 MiB/s]
Dl Size...: 44%|████▍ | 664/1501 [00:30<01:12, 11.61 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:30<00:06, 6.07s/ url]
Dl Size...: 44%|████▍ | 664/1501 [00:30<01:12, 11.61 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:30<00:06, 6.07s/ url]
Dl Size...: 44%|████▍ | 665/1501 [00:30<01:11, 11.61 MiB/s]
Dl Size...: 44%|████▍ | 666/1501 [00:30<01:09, 11.98 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:30<00:06, 6.07s/ url]
Dl Size...: 44%|████▍ | 666/1501 [00:30<01:09, 11.98 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:30<00:06, 6.07s/ url]
Dl Size...: 44%|████▍ | 667/1501 [00:30<01:09, 11.98 MiB/s]
Dl Size...: 45%|████▍ | 668/1501 [00:30<01:08, 12.10 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:30<00:06, 6.07s/ url]
Dl Size...: 45%|████▍ | 668/1501 [00:30<01:08, 12.10 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:30<00:06, 6.07s/ url]
Dl Size...: 45%|████▍ | 669/1501 [00:30<01:08, 12.10 MiB/s]
Dl Size...: 45%|████▍ | 670/1501 [00:30<01:08, 12.07 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:30<00:06, 6.07s/ url]
Dl Size...: 45%|████▍ | 670/1501 [00:30<01:08, 12.07 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:30<00:06, 6.07s/ url]
Dl Size...: 45%|████▍ | 671/1501 [00:30<01:08, 12.07 MiB/s]
Dl Size...: 45%|████▍ | 672/1501 [00:30<01:07, 12.30 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:30<00:06, 6.07s/ url]
Dl Size...: 45%|████▍ | 672/1501 [00:30<01:07, 12.30 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:30<00:06, 6.07s/ url]
Dl Size...: 45%|████▍ | 673/1501 [00:30<01:07, 12.30 MiB/s]
Dl Size...: 45%|████▍ | 674/1501 [00:30<01:06, 12.42 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:30<00:06, 6.07s/ url]
Dl Size...: 45%|████▍ | 674/1501 [00:30<01:06, 12.42 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:30<00:06, 6.07s/ url]
Dl Size...: 45%|████▍ | 675/1501 [00:30<01:06, 12.42 MiB/s]
Dl Size...: 45%|████▌ | 676/1501 [00:31<01:05, 12.69 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:31<00:06, 6.07s/ url]
Dl Size...: 45%|████▌ | 676/1501 [00:31<01:05, 12.69 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:31<00:06, 6.07s/ url]
Dl Size...: 45%|████▌ | 677/1501 [00:31<01:04, 12.69 MiB/s]
Dl Size...: 45%|████▌ | 678/1501 [00:31<01:04, 12.85 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:31<00:06, 6.07s/ url]
Dl Size...: 45%|████▌ | 678/1501 [00:31<01:04, 12.85 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:31<00:06, 6.07s/ url]
Dl Size...: 45%|████▌ | 679/1501 [00:31<01:03, 12.85 MiB/s]
Dl Size...: 45%|████▌ | 680/1501 [00:31<01:02, 13.14 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:31<00:06, 6.07s/ url]
Dl Size...: 45%|████▌ | 680/1501 [00:31<01:02, 13.14 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:31<00:06, 6.07s/ url]
Dl Size...: 45%|████▌ | 681/1501 [00:31<01:02, 13.14 MiB/s]
Dl Size...: 45%|████▌ | 682/1501 [00:31<01:01, 13.22 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:31<00:06, 6.07s/ url]
Dl Size...: 45%|████▌ | 682/1501 [00:31<01:01, 13.22 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:31<00:06, 6.07s/ url]
Dl Size...: 46%|████▌ | 683/1501 [00:31<01:01, 13.22 MiB/s]
Dl Size...: 46%|████▌ | 684/1501 [00:31<01:01, 13.30 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:31<00:06, 6.07s/ url]
Dl Size...: 46%|████▌ | 684/1501 [00:31<01:01, 13.30 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:31<00:06, 6.07s/ url]
Dl Size...: 46%|████▌ | 685/1501 [00:31<01:01, 13.30 MiB/s]
Dl Size...: 46%|████▌ | 686/1501 [00:31<00:59, 13.63 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:31<00:06, 6.07s/ url]
Dl Size...: 46%|████▌ | 686/1501 [00:31<00:59, 13.63 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:31<00:06, 6.07s/ url]
Dl Size...: 46%|████▌ | 687/1501 [00:31<00:59, 13.63 MiB/s]
Dl Size...: 46%|████▌ | 688/1501 [00:31<00:58, 13.86 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:31<00:06, 6.07s/ url]
Dl Size...: 46%|████▌ | 688/1501 [00:31<00:58, 13.86 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:31<00:06, 6.07s/ url]
Dl Size...: 46%|████▌ | 689/1501 [00:31<00:58, 13.86 MiB/s]
Dl Size...: 46%|████▌ | 690/1501 [00:32<00:57, 14.03 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:32<00:06, 6.07s/ url]
Dl Size...: 46%|████▌ | 690/1501 [00:32<00:57, 14.03 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:32<00:06, 6.07s/ url]
Dl Size...: 46%|████▌ | 691/1501 [00:32<00:57, 14.03 MiB/s]
Dl Size...: 46%|████▌ | 692/1501 [00:32<00:57, 14.17 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:32<00:06, 6.07s/ url]
Dl Size...: 46%|████▌ | 692/1501 [00:32<00:57, 14.17 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:32<00:06, 6.07s/ url]
Dl Size...: 46%|████▌ | 693/1501 [00:32<00:57, 14.17 MiB/s]
Dl Size...: 46%|████▌ | 694/1501 [00:32<00:56, 14.22 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:32<00:06, 6.07s/ url]
Dl Size...: 46%|████▌ | 694/1501 [00:32<00:56, 14.22 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:32<00:06, 6.07s/ url]
Dl Size...: 46%|████▋ | 695/1501 [00:32<00:56, 14.22 MiB/s]
Dl Size...: 46%|████▋ | 696/1501 [00:32<00:55, 14.56 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:32<00:06, 6.07s/ url]
Dl Size...: 46%|████▋ | 696/1501 [00:32<00:55, 14.56 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:32<00:06, 6.07s/ url]
Dl Size...: 46%|████▋ | 697/1501 [00:32<00:55, 14.56 MiB/s]
Dl Size...: 47%|████▋ | 698/1501 [00:32<00:54, 14.87 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:32<00:06, 6.07s/ url]
Dl Size...: 47%|████▋ | 698/1501 [00:32<00:54, 14.87 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:32<00:06, 6.07s/ url]
Dl Size...: 47%|████▋ | 699/1501 [00:32<00:53, 14.87 MiB/s]
Dl Size...: 47%|████▋ | 700/1501 [00:32<00:52, 15.15 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:32<00:06, 6.07s/ url]
Dl Size...: 47%|████▋ | 700/1501 [00:32<00:52, 15.15 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:32<00:06, 6.07s/ url]
Dl Size...: 47%|████▋ | 701/1501 [00:32<00:52, 15.15 MiB/s]
Dl Size...: 47%|████▋ | 702/1501 [00:32<00:52, 15.33 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:32<00:06, 6.07s/ url]
Dl Size...: 47%|████▋ | 702/1501 [00:32<00:52, 15.33 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:32<00:06, 6.07s/ url]
Dl Size...: 47%|████▋ | 703/1501 [00:32<00:52, 15.33 MiB/s]
Dl Size...: 47%|████▋ | 704/1501 [00:32<00:51, 15.37 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:32<00:06, 6.07s/ url]
Dl Size...: 47%|████▋ | 704/1501 [00:32<00:51, 15.37 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:33<00:06, 6.07s/ url]
Dl Size...: 47%|████▋ | 705/1501 [00:33<00:51, 15.37 MiB/s]
Dl Size...: 47%|████▋ | 706/1501 [00:33<00:50, 15.86 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:33<00:06, 6.07s/ url]
Dl Size...: 47%|████▋ | 706/1501 [00:33<00:50, 15.86 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:33<00:06, 6.07s/ url]
Dl Size...: 47%|████▋ | 707/1501 [00:33<00:50, 15.86 MiB/s]
Dl Size...: 47%|████▋ | 708/1501 [00:33<00:49, 16.11 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:33<00:06, 6.07s/ url]
Dl Size...: 47%|████▋ | 708/1501 [00:33<00:49, 16.11 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:33<00:06, 6.07s/ url]
Dl Size...: 47%|████▋ | 709/1501 [00:33<00:49, 16.11 MiB/s]
Dl Size...: 47%|████▋ | 710/1501 [00:33<00:49, 15.95 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:33<00:06, 6.07s/ url]
Dl Size...: 47%|████▋ | 710/1501 [00:33<00:49, 15.95 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:33<00:06, 6.07s/ url]
Dl Size...: 47%|████▋ | 711/1501 [00:33<00:49, 15.95 MiB/s]
Dl Size...: 47%|████▋ | 712/1501 [00:33<00:47, 16.63 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:33<00:06, 6.07s/ url]
Dl Size...: 47%|████▋ | 712/1501 [00:33<00:47, 16.63 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:33<00:06, 6.07s/ url]
Dl Size...: 48%|████▊ | 713/1501 [00:33<00:47, 16.63 MiB/s]
Dl Size...: 48%|████▊ | 714/1501 [00:33<00:47, 16.72 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:33<00:06, 6.07s/ url]
Dl Size...: 48%|████▊ | 714/1501 [00:33<00:47, 16.72 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:33<00:06, 6.07s/ url]
Dl Size...: 48%|████▊ | 715/1501 [00:33<00:47, 16.72 MiB/s]
Dl Size...: 48%|████▊ | 716/1501 [00:33<00:46, 16.77 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:33<00:06, 6.07s/ url]
Dl Size...: 48%|████▊ | 716/1501 [00:33<00:46, 16.77 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:33<00:06, 6.07s/ url]
Dl Size...: 48%|████▊ | 717/1501 [00:33<00:46, 16.77 MiB/s]
Dl Size...: 48%|████▊ | 718/1501 [00:33<00:45, 17.25 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:33<00:06, 6.07s/ url]
Dl Size...: 48%|████▊ | 718/1501 [00:33<00:45, 17.25 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:33<00:06, 6.07s/ url]
Dl Size...: 48%|████▊ | 719/1501 [00:33<00:45, 17.25 MiB/s]
Dl Size...: 48%|████▊ | 720/1501 [00:33<00:45, 17.28 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:33<00:06, 6.07s/ url]
Dl Size...: 48%|████▊ | 720/1501 [00:33<00:45, 17.28 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:33<00:06, 6.07s/ url]
Dl Size...: 48%|████▊ | 721/1501 [00:33<00:45, 17.28 MiB/s]
Dl Size...: 48%|████▊ | 722/1501 [00:34<00:43, 17.80 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:34<00:06, 6.07s/ url]
Dl Size...: 48%|████▊ | 722/1501 [00:34<00:43, 17.80 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:34<00:06, 6.07s/ url]
Dl Size...: 48%|████▊ | 723/1501 [00:34<00:43, 17.80 MiB/s]
Dl Size...: 48%|████▊ | 724/1501 [00:34<00:43, 17.71 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:34<00:06, 6.07s/ url]
Dl Size...: 48%|████▊ | 724/1501 [00:34<00:43, 17.71 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:34<00:06, 6.07s/ url]
Dl Size...: 48%|████▊ | 725/1501 [00:34<00:43, 17.71 MiB/s]
Dl Size...: 48%|████▊ | 726/1501 [00:34<00:42, 18.32 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:34<00:06, 6.07s/ url]
Dl Size...: 48%|████▊ | 726/1501 [00:34<00:42, 18.32 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:34<00:06, 6.07s/ url]
Dl Size...: 48%|████▊ | 727/1501 [00:34<00:42, 18.32 MiB/s]
Dl Size...: 49%|████▊ | 728/1501 [00:34<00:42, 18.38 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:34<00:06, 6.07s/ url]
Dl Size...: 49%|████▊ | 728/1501 [00:34<00:42, 18.38 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:34<00:06, 6.07s/ url]
Dl Size...: 49%|████▊ | 729/1501 [00:34<00:41, 18.38 MiB/s]
Dl Size...: 49%|████▊ | 730/1501 [00:34<00:47, 16.12 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:34<00:06, 6.07s/ url]
Dl Size...: 49%|████▊ | 730/1501 [00:34<00:47, 16.12 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:34<00:06, 6.07s/ url]
Dl Size...: 49%|████▊ | 731/1501 [00:34<00:47, 16.12 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:34<00:06, 6.07s/ url]
Dl Size...: 49%|████▉ | 732/1501 [00:34<00:47, 16.12 MiB/s]
Dl Size...: 49%|████▉ | 733/1501 [00:34<00:43, 17.73 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:34<00:06, 6.07s/ url]
Dl Size...: 49%|████▉ | 733/1501 [00:34<00:43, 17.73 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:34<00:06, 6.07s/ url]
Dl Size...: 49%|████▉ | 734/1501 [00:34<00:43, 17.73 MiB/s]
Dl Size...: 49%|████▉ | 735/1501 [00:34<00:46, 16.49 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:34<00:06, 6.07s/ url]
Dl Size...: 49%|████▉ | 735/1501 [00:34<00:46, 16.49 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:34<00:06, 6.07s/ url]
Dl Size...: 49%|████▉ | 736/1501 [00:34<00:46, 16.49 MiB/s]
Dl Size...: 49%|████▉ | 737/1501 [00:34<00:48, 15.74 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:34<00:06, 6.07s/ url]
Dl Size...: 49%|████▉ | 737/1501 [00:34<00:48, 15.74 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:34<00:06, 6.07s/ url]
Dl Size...: 49%|████▉ | 738/1501 [00:34<00:48, 15.74 MiB/s]
Dl Size...: 49%|████▉ | 739/1501 [00:35<00:49, 15.31 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:35<00:06, 6.07s/ url]
Dl Size...: 49%|████▉ | 739/1501 [00:35<00:49, 15.31 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:35<00:06, 6.07s/ url]
Dl Size...: 49%|████▉ | 740/1501 [00:35<00:49, 15.31 MiB/s]
Dl Size...: 49%|████▉ | 741/1501 [00:35<00:50, 15.12 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:35<00:06, 6.07s/ url]
Dl Size...: 49%|████▉ | 741/1501 [00:35<00:50, 15.12 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:35<00:06, 6.07s/ url]
Dl Size...: 49%|████▉ | 742/1501 [00:35<00:50, 15.12 MiB/s]
Dl Size...: 50%|████▉ | 743/1501 [00:35<00:49, 15.18 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:35<00:06, 6.07s/ url]
Dl Size...: 50%|████▉ | 743/1501 [00:35<00:49, 15.18 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:35<00:06, 6.07s/ url]
Dl Size...: 50%|████▉ | 744/1501 [00:35<00:49, 15.18 MiB/s]
Dl Size...: 50%|████▉ | 745/1501 [00:35<00:49, 15.42 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:35<00:06, 6.07s/ url]
Dl Size...: 50%|████▉ | 745/1501 [00:35<00:49, 15.42 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:35<00:06, 6.07s/ url]
Dl Size...: 50%|████▉ | 746/1501 [00:35<00:48, 15.42 MiB/s]
Dl Size...: 50%|████▉ | 747/1501 [00:35<00:49, 15.26 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:35<00:06, 6.07s/ url]
Dl Size...: 50%|████▉ | 747/1501 [00:35<00:49, 15.26 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:35<00:06, 6.07s/ url]
Dl Size...: 50%|████▉ | 748/1501 [00:35<00:49, 15.26 MiB/s]
Dl Size...: 50%|████▉ | 749/1501 [00:35<00:49, 15.20 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:35<00:06, 6.07s/ url]
Dl Size...: 50%|████▉ | 749/1501 [00:35<00:49, 15.20 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:35<00:06, 6.07s/ url]
Dl Size...: 50%|████▉ | 750/1501 [00:35<00:49, 15.20 MiB/s]
Dl Size...: 50%|█████ | 751/1501 [00:35<00:49, 15.20 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:35<00:06, 6.07s/ url]
Dl Size...: 50%|█████ | 751/1501 [00:35<00:49, 15.20 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:35<00:06, 6.07s/ url]
Dl Size...: 50%|█████ | 752/1501 [00:35<00:49, 15.20 MiB/s]
Dl Size...: 50%|█████ | 753/1501 [00:35<00:47, 15.61 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:35<00:06, 6.07s/ url]
Dl Size...: 50%|█████ | 753/1501 [00:35<00:47, 15.61 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:36<00:06, 6.07s/ url]
Dl Size...: 50%|█████ | 754/1501 [00:36<00:47, 15.61 MiB/s]
Dl Size...: 50%|█████ | 755/1501 [00:36<00:47, 15.76 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:36<00:06, 6.07s/ url]
Dl Size...: 50%|█████ | 755/1501 [00:36<00:47, 15.76 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:36<00:06, 6.07s/ url]
Dl Size...: 50%|█████ | 756/1501 [00:36<00:47, 15.76 MiB/s]
Dl Size...: 50%|█████ | 757/1501 [00:36<00:46, 15.86 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:36<00:06, 6.07s/ url]
Dl Size...: 50%|█████ | 757/1501 [00:36<00:46, 15.86 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:36<00:06, 6.07s/ url]
Dl Size...: 50%|█████ | 758/1501 [00:36<00:46, 15.86 MiB/s]
Dl Size...: 51%|█████ | 759/1501 [00:36<00:47, 15.63 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:36<00:06, 6.07s/ url]
Dl Size...: 51%|█████ | 759/1501 [00:36<00:47, 15.63 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:36<00:06, 6.07s/ url]
Dl Size...: 51%|█████ | 760/1501 [00:36<00:47, 15.63 MiB/s]
Dl Size...: 51%|█████ | 761/1501 [00:36<00:44, 16.45 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:36<00:06, 6.07s/ url]
Dl Size...: 51%|█████ | 761/1501 [00:36<00:44, 16.45 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:36<00:06, 6.07s/ url]
Dl Size...: 51%|█████ | 762/1501 [00:36<00:44, 16.45 MiB/s]
Dl Size...: 51%|█████ | 763/1501 [00:36<00:45, 16.30 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:36<00:06, 6.07s/ url]
Dl Size...: 51%|█████ | 763/1501 [00:36<00:45, 16.30 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:36<00:06, 6.07s/ url]
Dl Size...: 51%|█████ | 764/1501 [00:36<00:45, 16.30 MiB/s]
Dl Size...: 51%|█████ | 765/1501 [00:36<00:45, 16.14 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:36<00:06, 6.07s/ url]
Dl Size...: 51%|█████ | 765/1501 [00:36<00:45, 16.14 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:36<00:06, 6.07s/ url]
Dl Size...: 51%|█████ | 766/1501 [00:36<00:45, 16.14 MiB/s]
Dl Size...: 51%|█████ | 767/1501 [00:36<00:43, 16.87 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:36<00:06, 6.07s/ url]
Dl Size...: 51%|█████ | 767/1501 [00:36<00:43, 16.87 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:36<00:06, 6.07s/ url]
Dl Size...: 51%|█████ | 768/1501 [00:36<00:43, 16.87 MiB/s]
Dl Size...: 51%|█████ | 769/1501 [00:36<00:43, 16.82 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:36<00:06, 6.07s/ url]
Dl Size...: 51%|█████ | 769/1501 [00:36<00:43, 16.82 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:37<00:06, 6.07s/ url]
Dl Size...: 51%|█████▏ | 770/1501 [00:37<00:43, 16.82 MiB/s]
Dl Size...: 51%|█████▏ | 771/1501 [00:37<00:43, 16.93 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:37<00:06, 6.07s/ url]
Dl Size...: 51%|█████▏ | 771/1501 [00:37<00:43, 16.93 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:37<00:06, 6.07s/ url]
Dl Size...: 51%|█████▏ | 772/1501 [00:37<00:43, 16.93 MiB/s]
Dl Size...: 51%|█████▏ | 773/1501 [00:37<00:42, 17.16 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:37<00:06, 6.07s/ url]
Dl Size...: 51%|█████▏ | 773/1501 [00:37<00:42, 17.16 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:37<00:06, 6.07s/ url]
Dl Size...: 52%|█████▏ | 774/1501 [00:37<00:42, 17.16 MiB/s]
Dl Size...: 52%|█████▏ | 775/1501 [00:37<00:43, 16.75 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:37<00:06, 6.07s/ url]
Dl Size...: 52%|█████▏ | 775/1501 [00:37<00:43, 16.75 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:37<00:06, 6.07s/ url]
Dl Size...: 52%|█████▏ | 776/1501 [00:37<00:43, 16.75 MiB/s]
Dl Size...: 52%|█████▏ | 777/1501 [00:37<00:41, 17.53 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:37<00:06, 6.07s/ url]
Dl Size...: 52%|█████▏ | 777/1501 [00:37<00:41, 17.53 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:37<00:06, 6.07s/ url]
Dl Size...: 52%|█████▏ | 778/1501 [00:37<00:41, 17.53 MiB/s]
Dl Size...: 52%|█████▏ | 779/1501 [00:37<00:41, 17.28 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:37<00:06, 6.07s/ url]
Dl Size...: 52%|█████▏ | 779/1501 [00:37<00:41, 17.28 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:37<00:06, 6.07s/ url]
Dl Size...: 52%|█████▏ | 780/1501 [00:37<00:41, 17.28 MiB/s]
Dl Size...: 52%|█████▏ | 781/1501 [00:37<00:41, 17.49 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:37<00:06, 6.07s/ url]
Dl Size...: 52%|█████▏ | 781/1501 [00:37<00:41, 17.49 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:37<00:06, 6.07s/ url]
Dl Size...: 52%|█████▏ | 782/1501 [00:37<00:41, 17.49 MiB/s]
Dl Size...: 52%|█████▏ | 783/1501 [00:37<00:40, 17.62 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:37<00:06, 6.07s/ url]
Dl Size...: 52%|█████▏ | 783/1501 [00:37<00:40, 17.62 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:37<00:06, 6.07s/ url]
Dl Size...: 52%|█████▏ | 784/1501 [00:37<00:40, 17.62 MiB/s]
Dl Size...: 52%|█████▏ | 785/1501 [00:37<00:40, 17.53 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:37<00:06, 6.07s/ url]
Dl Size...: 52%|█████▏ | 785/1501 [00:37<00:40, 17.53 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:37<00:06, 6.07s/ url]
Dl Size...: 52%|█████▏ | 786/1501 [00:37<00:40, 17.53 MiB/s]
Dl Size...: 52%|█████▏ | 787/1501 [00:37<00:39, 17.96 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:37<00:06, 6.07s/ url]
Dl Size...: 52%|█████▏ | 787/1501 [00:37<00:39, 17.96 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:38<00:06, 6.07s/ url]
Dl Size...: 52%|█████▏ | 788/1501 [00:38<00:39, 17.96 MiB/s]
Dl Size...: 53%|█████▎ | 789/1501 [00:38<00:40, 17.43 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:38<00:06, 6.07s/ url]
Dl Size...: 53%|█████▎ | 789/1501 [00:38<00:40, 17.43 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:38<00:06, 6.07s/ url]
Dl Size...: 53%|█████▎ | 790/1501 [00:38<00:40, 17.43 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:38<00:06, 6.07s/ url]
Dl Size...: 53%|█████▎ | 791/1501 [00:38<00:40, 17.43 MiB/s]
Dl Size...: 53%|█████▎ | 792/1501 [00:38<00:39, 17.97 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:38<00:06, 6.07s/ url]
Dl Size...: 53%|█████▎ | 792/1501 [00:38<00:39, 17.97 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:38<00:06, 6.07s/ url]
Dl Size...: 53%|█████▎ | 793/1501 [00:38<00:39, 17.97 MiB/s]
Dl Size...: 53%|█████▎ | 794/1501 [00:38<00:38, 18.19 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:38<00:06, 6.07s/ url]
Dl Size...: 53%|█████▎ | 794/1501 [00:38<00:38, 18.19 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:38<00:06, 6.07s/ url]
Dl Size...: 53%|█████▎ | 795/1501 [00:38<00:38, 18.19 MiB/s]
Dl Size...: 53%|█████▎ | 796/1501 [00:38<00:39, 18.03 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:38<00:06, 6.07s/ url]
Dl Size...: 53%|█████▎ | 796/1501 [00:38<00:39, 18.03 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:38<00:06, 6.07s/ url]
Dl Size...: 53%|█████▎ | 797/1501 [00:38<00:39, 18.03 MiB/s]
Dl Size...: 53%|█████▎ | 798/1501 [00:38<00:38, 18.23 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:38<00:06, 6.07s/ url]
Dl Size...: 53%|█████▎ | 798/1501 [00:38<00:38, 18.23 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:38<00:06, 6.07s/ url]
Dl Size...: 53%|█████▎ | 799/1501 [00:38<00:38, 18.23 MiB/s]
Dl Size...: 53%|█████▎ | 800/1501 [00:38<00:38, 18.12 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:38<00:06, 6.07s/ url]
Dl Size...: 53%|█████▎ | 800/1501 [00:38<00:38, 18.12 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:38<00:06, 6.07s/ url]
Dl Size...: 53%|█████▎ | 801/1501 [00:38<00:38, 18.12 MiB/s]
Dl Size...: 53%|█████▎ | 802/1501 [00:38<00:37, 18.46 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:38<00:06, 6.07s/ url]
Dl Size...: 53%|█████▎ | 802/1501 [00:38<00:37, 18.46 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:38<00:06, 6.07s/ url]
Dl Size...: 53%|█████▎ | 803/1501 [00:38<00:37, 18.46 MiB/s]
Dl Size...: 54%|█████▎ | 804/1501 [00:38<00:38, 18.30 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:38<00:06, 6.07s/ url]
Dl Size...: 54%|█████▎ | 804/1501 [00:38<00:38, 18.30 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:38<00:06, 6.07s/ url]
Dl Size...: 54%|█████▎ | 805/1501 [00:38<00:38, 18.30 MiB/s]
Dl Size...: 54%|█████▎ | 806/1501 [00:38<00:37, 18.60 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:38<00:06, 6.07s/ url]
Dl Size...: 54%|█████▎ | 806/1501 [00:38<00:37, 18.60 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:39<00:06, 6.07s/ url]
Dl Size...: 54%|█████▍ | 807/1501 [00:39<00:37, 18.60 MiB/s]
Dl Size...: 54%|█████▍ | 808/1501 [00:39<00:37, 18.56 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:39<00:06, 6.07s/ url]
Dl Size...: 54%|█████▍ | 808/1501 [00:39<00:37, 18.56 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:39<00:06, 6.07s/ url]
Dl Size...: 54%|█████▍ | 809/1501 [00:39<00:37, 18.56 MiB/s]
Dl Size...: 54%|█████▍ | 810/1501 [00:39<00:36, 18.85 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:39<00:06, 6.07s/ url]
Dl Size...: 54%|█████▍ | 810/1501 [00:39<00:36, 18.85 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:39<00:06, 6.07s/ url]
Dl Size...: 54%|█████▍ | 811/1501 [00:39<00:36, 18.85 MiB/s]
Dl Size...: 54%|█████▍ | 812/1501 [00:39<00:37, 18.53 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:39<00:06, 6.07s/ url]
Dl Size...: 54%|█████▍ | 812/1501 [00:39<00:37, 18.53 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:39<00:06, 6.07s/ url]
Dl Size...: 54%|█████▍ | 813/1501 [00:39<00:37, 18.53 MiB/s]
Dl Size...: 54%|█████▍ | 814/1501 [00:39<00:36, 18.85 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:39<00:06, 6.07s/ url]
Dl Size...: 54%|█████▍ | 814/1501 [00:39<00:36, 18.85 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:39<00:06, 6.07s/ url]
Dl Size...: 54%|█████▍ | 815/1501 [00:39<00:36, 18.85 MiB/s]
Dl Size...: 54%|█████▍ | 816/1501 [00:39<00:37, 18.42 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:39<00:06, 6.07s/ url]
Dl Size...: 54%|█████▍ | 816/1501 [00:39<00:37, 18.42 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:39<00:06, 6.07s/ url]
Dl Size...: 54%|█████▍ | 817/1501 [00:39<00:37, 18.42 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:39<00:06, 6.07s/ url]
Dl Size...: 54%|█████▍ | 818/1501 [00:39<00:37, 18.42 MiB/s]
Dl Size...: 55%|█████▍ | 819/1501 [00:39<00:36, 18.90 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:39<00:06, 6.07s/ url]
Dl Size...: 55%|█████▍ | 819/1501 [00:39<00:36, 18.90 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:39<00:06, 6.07s/ url]
Dl Size...: 55%|█████▍ | 820/1501 [00:39<00:36, 18.90 MiB/s]
Dl Size...: 55%|█████▍ | 821/1501 [00:39<00:35, 19.05 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:39<00:06, 6.07s/ url]
Dl Size...: 55%|█████▍ | 821/1501 [00:39<00:35, 19.05 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:39<00:06, 6.07s/ url]
Dl Size...: 55%|█████▍ | 822/1501 [00:39<00:35, 19.05 MiB/s]
Dl Size...: 55%|█████▍ | 823/1501 [00:39<00:36, 18.72 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:39<00:06, 6.07s/ url]
Dl Size...: 55%|█████▍ | 823/1501 [00:39<00:36, 18.72 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:39<00:06, 6.07s/ url]
Dl Size...: 55%|█████▍ | 824/1501 [00:39<00:36, 18.72 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:40<00:06, 6.07s/ url]
Dl Size...: 55%|█████▍ | 825/1501 [00:39<00:36, 18.72 MiB/s]
Dl Size...: 55%|█████▌ | 826/1501 [00:40<00:35, 19.24 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:40<00:06, 6.07s/ url]
Dl Size...: 55%|█████▌ | 826/1501 [00:40<00:35, 19.24 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:40<00:06, 6.07s/ url]
Dl Size...: 55%|█████▌ | 827/1501 [00:40<00:35, 19.24 MiB/s]
Dl Size...: 55%|█████▌ | 828/1501 [00:40<00:36, 18.62 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:40<00:06, 6.07s/ url]
Dl Size...: 55%|█████▌ | 828/1501 [00:40<00:36, 18.62 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:40<00:06, 6.07s/ url]
Dl Size...: 55%|█████▌ | 829/1501 [00:40<00:36, 18.62 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:40<00:06, 6.07s/ url]
Dl Size...: 55%|█████▌ | 830/1501 [00:40<00:36, 18.62 MiB/s]
Dl Size...: 55%|█████▌ | 831/1501 [00:40<00:35, 18.80 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:40<00:06, 6.07s/ url]
Dl Size...: 55%|█████▌ | 831/1501 [00:40<00:35, 18.80 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:40<00:06, 6.07s/ url]
Dl Size...: 55%|█████▌ | 832/1501 [00:40<00:35, 18.80 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:40<00:06, 6.07s/ url]
Dl Size...: 55%|█████▌ | 833/1501 [00:40<00:35, 18.80 MiB/s]
Dl Size...: 56%|█████▌ | 834/1501 [00:40<00:35, 19.00 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:40<00:06, 6.07s/ url]
Dl Size...: 56%|█████▌ | 834/1501 [00:40<00:35, 19.00 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:40<00:06, 6.07s/ url]
Dl Size...: 56%|█████▌ | 835/1501 [00:40<00:35, 19.00 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:40<00:06, 6.07s/ url]
Dl Size...: 56%|█████▌ | 836/1501 [00:40<00:35, 19.00 MiB/s]
Dl Size...: 56%|█████▌ | 837/1501 [00:40<00:34, 19.37 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:40<00:06, 6.07s/ url]
Dl Size...: 56%|█████▌ | 837/1501 [00:40<00:34, 19.37 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:40<00:06, 6.07s/ url]
Dl Size...: 56%|█████▌ | 838/1501 [00:40<00:34, 19.37 MiB/s]
Dl Size...: 56%|█████▌ | 839/1501 [00:40<00:34, 18.93 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:40<00:06, 6.07s/ url]
Dl Size...: 56%|█████▌ | 839/1501 [00:40<00:34, 18.93 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:40<00:06, 6.07s/ url]
Dl Size...: 56%|█████▌ | 840/1501 [00:40<00:34, 18.93 MiB/s]
Dl Size...: 56%|█████▌ | 841/1501 [00:40<00:34, 19.15 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:40<00:06, 6.07s/ url]
Dl Size...: 56%|█████▌ | 841/1501 [00:40<00:34, 19.15 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:40<00:06, 6.07s/ url]
Dl Size...: 56%|█████▌ | 842/1501 [00:40<00:34, 19.15 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:40<00:06, 6.07s/ url]
Dl Size...: 56%|█████▌ | 843/1501 [00:40<00:34, 19.15 MiB/s]
Dl Size...: 56%|█████▌ | 844/1501 [00:40<00:33, 19.49 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:40<00:06, 6.07s/ url]
Dl Size...: 56%|█████▌ | 844/1501 [00:40<00:33, 19.49 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:41<00:06, 6.07s/ url]
Dl Size...: 56%|█████▋ | 845/1501 [00:41<00:33, 19.49 MiB/s]
Dl Size...: 56%|█████▋ | 846/1501 [00:41<00:34, 19.19 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:41<00:06, 6.07s/ url]
Dl Size...: 56%|█████▋ | 846/1501 [00:41<00:34, 19.19 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:41<00:06, 6.07s/ url]
Dl Size...: 56%|█████▋ | 847/1501 [00:41<00:34, 19.19 MiB/s]
Dl Size...: 56%|█████▋ | 848/1501 [00:41<00:34, 19.19 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:41<00:06, 6.07s/ url]
Dl Size...: 56%|█████▋ | 848/1501 [00:41<00:34, 19.19 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:41<00:06, 6.07s/ url]
Dl Size...: 57%|█████▋ | 849/1501 [00:41<00:33, 19.19 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:41<00:06, 6.07s/ url]
Dl Size...: 57%|█████▋ | 850/1501 [00:41<00:33, 19.19 MiB/s]
Dl Size...: 57%|█████▋ | 851/1501 [00:41<00:33, 19.56 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:41<00:06, 6.07s/ url]
Dl Size...: 57%|█████▋ | 851/1501 [00:41<00:33, 19.56 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:41<00:06, 6.07s/ url]
Dl Size...: 57%|█████▋ | 852/1501 [00:41<00:33, 19.56 MiB/s]
Dl Size...: 57%|█████▋ | 853/1501 [00:41<00:33, 19.14 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:41<00:06, 6.07s/ url]
Dl Size...: 57%|█████▋ | 853/1501 [00:41<00:33, 19.14 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:41<00:06, 6.07s/ url]
Dl Size...: 57%|█████▋ | 854/1501 [00:41<00:33, 19.14 MiB/s]
Dl Size...: 57%|█████▋ | 855/1501 [00:41<00:33, 19.23 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:41<00:06, 6.07s/ url]
Dl Size...: 57%|█████▋ | 855/1501 [00:41<00:33, 19.23 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:41<00:06, 6.07s/ url]
Dl Size...: 57%|█████▋ | 856/1501 [00:41<00:33, 19.23 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:41<00:06, 6.07s/ url]
Dl Size...: 57%|█████▋ | 857/1501 [00:41<00:33, 19.23 MiB/s]
Dl Size...: 57%|█████▋ | 858/1501 [00:41<00:32, 19.55 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:41<00:06, 6.07s/ url]
Dl Size...: 57%|█████▋ | 858/1501 [00:41<00:32, 19.55 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:41<00:06, 6.07s/ url]
Dl Size...: 57%|█████▋ | 859/1501 [00:41<00:32, 19.55 MiB/s]
Dl Size...: 57%|█████▋ | 860/1501 [00:41<00:33, 19.18 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:41<00:06, 6.07s/ url]
Dl Size...: 57%|█████▋ | 860/1501 [00:41<00:33, 19.18 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:41<00:06, 6.07s/ url]
Dl Size...: 57%|█████▋ | 861/1501 [00:41<00:33, 19.18 MiB/s]
Dl Size...: 57%|█████▋ | 862/1501 [00:41<00:33, 19.24 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:41<00:06, 6.07s/ url]
Dl Size...: 57%|█████▋ | 862/1501 [00:41<00:33, 19.24 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:41<00:06, 6.07s/ url]
Dl Size...: 57%|█████▋ | 863/1501 [00:41<00:33, 19.24 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:42<00:06, 6.07s/ url]
Dl Size...: 58%|█████▊ | 864/1501 [00:42<00:33, 19.24 MiB/s]
Dl Size...: 58%|█████▊ | 865/1501 [00:42<00:32, 19.62 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:42<00:06, 6.07s/ url]
Dl Size...: 58%|█████▊ | 865/1501 [00:42<00:32, 19.62 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:42<00:06, 6.07s/ url]
Dl Size...: 58%|█████▊ | 866/1501 [00:42<00:32, 19.62 MiB/s]
Dl Size...: 58%|█████▊ | 867/1501 [00:42<00:33, 19.13 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:42<00:06, 6.07s/ url]
Dl Size...: 58%|█████▊ | 867/1501 [00:42<00:33, 19.13 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:42<00:06, 6.07s/ url]
Dl Size...: 58%|█████▊ | 868/1501 [00:42<00:33, 19.13 MiB/s]
Dl Size...: 58%|█████▊ | 869/1501 [00:42<00:32, 19.22 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:42<00:06, 6.07s/ url]
Dl Size...: 58%|█████▊ | 869/1501 [00:42<00:32, 19.22 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:42<00:06, 6.07s/ url]
Dl Size...: 58%|█████▊ | 870/1501 [00:42<00:32, 19.22 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:42<00:06, 6.07s/ url]
Dl Size...: 58%|█████▊ | 871/1501 [00:42<00:32, 19.22 MiB/s]
Dl Size...: 58%|█████▊ | 872/1501 [00:42<00:32, 19.58 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:42<00:06, 6.07s/ url]
Dl Size...: 58%|█████▊ | 872/1501 [00:42<00:32, 19.58 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:42<00:06, 6.07s/ url]
Dl Size...: 58%|█████▊ | 873/1501 [00:42<00:32, 19.58 MiB/s]
Dl Size...: 58%|█████▊ | 874/1501 [00:42<00:32, 19.18 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:42<00:06, 6.07s/ url]
Dl Size...: 58%|█████▊ | 874/1501 [00:42<00:32, 19.18 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:42<00:06, 6.07s/ url]
Dl Size...: 58%|█████▊ | 875/1501 [00:42<00:32, 19.18 MiB/s]
Dl Size...: 58%|█████▊ | 876/1501 [00:42<00:32, 19.23 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:42<00:06, 6.07s/ url]
Dl Size...: 58%|█████▊ | 876/1501 [00:42<00:32, 19.23 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:42<00:06, 6.07s/ url]
Dl Size...: 58%|█████▊ | 877/1501 [00:42<00:32, 19.23 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:42<00:06, 6.07s/ url]
Dl Size...: 58%|█████▊ | 878/1501 [00:42<00:32, 19.23 MiB/s]
Dl Size...: 59%|█████▊ | 879/1501 [00:42<00:31, 19.59 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:42<00:06, 6.07s/ url]
Dl Size...: 59%|█████▊ | 879/1501 [00:42<00:31, 19.59 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:42<00:06, 6.07s/ url]
Dl Size...: 59%|█████▊ | 880/1501 [00:42<00:31, 19.59 MiB/s]
Dl Size...: 59%|█████▊ | 881/1501 [00:42<00:32, 19.21 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:42<00:06, 6.07s/ url]
Dl Size...: 59%|█████▊ | 881/1501 [00:42<00:32, 19.21 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:42<00:06, 6.07s/ url]
Dl Size...: 59%|█████▉ | 882/1501 [00:42<00:32, 19.21 MiB/s]
Dl Size...: 59%|█████▉ | 883/1501 [00:43<00:32, 19.25 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:43<00:06, 6.07s/ url]
Dl Size...: 59%|█████▉ | 883/1501 [00:43<00:32, 19.25 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:43<00:06, 6.07s/ url]
Dl Size...: 59%|█████▉ | 884/1501 [00:43<00:32, 19.25 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:43<00:06, 6.07s/ url]
Dl Size...: 59%|█████▉ | 885/1501 [00:43<00:31, 19.25 MiB/s]
Dl Size...: 59%|█████▉ | 886/1501 [00:43<00:31, 19.59 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:43<00:06, 6.07s/ url]
Dl Size...: 59%|█████▉ | 886/1501 [00:43<00:31, 19.59 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:43<00:06, 6.07s/ url]
Dl Size...: 59%|█████▉ | 887/1501 [00:43<00:31, 19.59 MiB/s]
Dl Size...: 59%|█████▉ | 888/1501 [00:43<00:31, 19.20 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:43<00:06, 6.07s/ url]
Dl Size...: 59%|█████▉ | 888/1501 [00:43<00:31, 19.20 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:43<00:06, 6.07s/ url]
Dl Size...: 59%|█████▉ | 889/1501 [00:43<00:31, 19.20 MiB/s]
Dl Size...: 59%|█████▉ | 890/1501 [00:43<00:31, 19.24 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:43<00:06, 6.07s/ url]
Dl Size...: 59%|█████▉ | 890/1501 [00:43<00:31, 19.24 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:43<00:06, 6.07s/ url]
Dl Size...: 59%|█████▉ | 891/1501 [00:43<00:31, 19.24 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:43<00:06, 6.07s/ url]
Dl Size...: 59%|█████▉ | 892/1501 [00:43<00:31, 19.24 MiB/s]
Dl Size...: 59%|█████▉ | 893/1501 [00:43<00:31, 19.57 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:43<00:06, 6.07s/ url]
Dl Size...: 59%|█████▉ | 893/1501 [00:43<00:31, 19.57 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:43<00:06, 6.07s/ url]
Dl Size...: 60%|█████▉ | 894/1501 [00:43<00:31, 19.57 MiB/s]
Dl Size...: 60%|█████▉ | 895/1501 [00:43<00:31, 19.21 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:43<00:06, 6.07s/ url]
Dl Size...: 60%|█████▉ | 895/1501 [00:43<00:31, 19.21 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:43<00:06, 6.07s/ url]
Dl Size...: 60%|█████▉ | 896/1501 [00:43<00:31, 19.21 MiB/s]
Dl Size...: 60%|█████▉ | 897/1501 [00:43<00:31, 19.24 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:43<00:06, 6.07s/ url]
Dl Size...: 60%|█████▉ | 897/1501 [00:43<00:31, 19.24 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:43<00:06, 6.07s/ url]
Dl Size...: 60%|█████▉ | 898/1501 [00:43<00:31, 19.24 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:43<00:06, 6.07s/ url]
Dl Size...: 60%|█████▉ | 899/1501 [00:43<00:31, 19.24 MiB/s]
Dl Size...: 60%|█████▉ | 900/1501 [00:43<00:30, 19.59 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:43<00:06, 6.07s/ url]
Dl Size...: 60%|█████▉ | 900/1501 [00:43<00:30, 19.59 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:43<00:06, 6.07s/ url]
Dl Size...: 60%|██████ | 901/1501 [00:43<00:30, 19.59 MiB/s]
Dl Size...: 60%|██████ | 902/1501 [00:43<00:31, 19.19 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:43<00:06, 6.07s/ url]
Dl Size...: 60%|██████ | 902/1501 [00:43<00:31, 19.19 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:44<00:06, 6.07s/ url]
Dl Size...: 60%|██████ | 903/1501 [00:44<00:31, 19.19 MiB/s]
Dl Size...: 60%|██████ | 904/1501 [00:44<00:31, 19.22 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:44<00:06, 6.07s/ url]
Dl Size...: 60%|██████ | 904/1501 [00:44<00:31, 19.22 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:44<00:06, 6.07s/ url]
Dl Size...: 60%|██████ | 905/1501 [00:44<00:31, 19.22 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:44<00:06, 6.07s/ url]
Dl Size...: 60%|██████ | 906/1501 [00:44<00:30, 19.22 MiB/s]
Dl Size...: 60%|██████ | 907/1501 [00:44<00:30, 19.55 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:44<00:06, 6.07s/ url]
Dl Size...: 60%|██████ | 907/1501 [00:44<00:30, 19.55 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:44<00:06, 6.07s/ url]
Dl Size...: 60%|██████ | 908/1501 [00:44<00:30, 19.55 MiB/s]
Dl Size...: 61%|██████ | 909/1501 [00:44<00:30, 19.19 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:44<00:06, 6.07s/ url]
Dl Size...: 61%|██████ | 909/1501 [00:44<00:30, 19.19 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:44<00:06, 6.07s/ url]
Dl Size...: 61%|██████ | 910/1501 [00:44<00:30, 19.19 MiB/s]
Dl Size...: 61%|██████ | 911/1501 [00:44<00:30, 19.24 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:44<00:06, 6.07s/ url]
Dl Size...: 61%|██████ | 911/1501 [00:44<00:30, 19.24 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:44<00:06, 6.07s/ url]
Dl Size...: 61%|██████ | 912/1501 [00:44<00:30, 19.24 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:44<00:06, 6.07s/ url]
Dl Size...: 61%|██████ | 913/1501 [00:44<00:30, 19.24 MiB/s]
Dl Size...: 61%|██████ | 914/1501 [00:44<00:30, 19.55 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:44<00:06, 6.07s/ url]
Dl Size...: 61%|██████ | 914/1501 [00:44<00:30, 19.55 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:44<00:06, 6.07s/ url]
Dl Size...: 61%|██████ | 915/1501 [00:44<00:29, 19.55 MiB/s]
Dl Size...: 61%|██████ | 916/1501 [00:44<00:30, 19.16 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:44<00:06, 6.07s/ url]
Dl Size...: 61%|██████ | 916/1501 [00:44<00:30, 19.16 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:44<00:06, 6.07s/ url]
Dl Size...: 61%|██████ | 917/1501 [00:44<00:30, 19.16 MiB/s]
Dl Size...: 61%|██████ | 918/1501 [00:44<00:30, 19.26 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:44<00:06, 6.07s/ url]
Dl Size...: 61%|██████ | 918/1501 [00:44<00:30, 19.26 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:44<00:06, 6.07s/ url]
Dl Size...: 61%|██████ | 919/1501 [00:44<00:30, 19.26 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:44<00:06, 6.07s/ url]
Dl Size...: 61%|██████▏ | 920/1501 [00:44<00:30, 19.26 MiB/s]
Dl Size...: 61%|██████▏ | 921/1501 [00:44<00:29, 19.57 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:44<00:06, 6.07s/ url]
Dl Size...: 61%|██████▏ | 921/1501 [00:44<00:29, 19.57 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:45<00:06, 6.07s/ url]
Dl Size...: 61%|██████▏ | 922/1501 [00:45<00:29, 19.57 MiB/s]
Dl Size...: 61%|██████▏ | 923/1501 [00:45<00:30, 19.12 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:45<00:06, 6.07s/ url]
Dl Size...: 61%|██████▏ | 923/1501 [00:45<00:30, 19.12 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:45<00:06, 6.07s/ url]
Dl Size...: 62%|██████▏ | 924/1501 [00:45<00:30, 19.12 MiB/s]
Dl Size...: 62%|██████▏ | 925/1501 [00:45<00:29, 19.24 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:45<00:06, 6.07s/ url]
Dl Size...: 62%|██████▏ | 925/1501 [00:45<00:29, 19.24 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:45<00:06, 6.07s/ url]
Dl Size...: 62%|██████▏ | 926/1501 [00:45<00:29, 19.24 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:45<00:06, 6.07s/ url]
Dl Size...: 62%|██████▏ | 927/1501 [00:45<00:29, 19.24 MiB/s]
Dl Size...: 62%|██████▏ | 928/1501 [00:45<00:29, 19.58 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:45<00:06, 6.07s/ url]
Dl Size...: 62%|██████▏ | 928/1501 [00:45<00:29, 19.58 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:45<00:06, 6.07s/ url]
Dl Size...: 62%|██████▏ | 929/1501 [00:45<00:29, 19.58 MiB/s]
Dl Size...: 62%|██████▏ | 930/1501 [00:45<00:29, 19.11 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:45<00:06, 6.07s/ url]
Dl Size...: 62%|██████▏ | 930/1501 [00:45<00:29, 19.11 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:45<00:06, 6.07s/ url]
Dl Size...: 62%|██████▏ | 931/1501 [00:45<00:29, 19.11 MiB/s]
Dl Size...: 62%|██████▏ | 932/1501 [00:45<00:29, 19.27 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:45<00:06, 6.07s/ url]
Dl Size...: 62%|██████▏ | 932/1501 [00:45<00:29, 19.27 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:45<00:06, 6.07s/ url]
Dl Size...: 62%|██████▏ | 933/1501 [00:45<00:29, 19.27 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:45<00:06, 6.07s/ url]
Dl Size...: 62%|██████▏ | 934/1501 [00:45<00:29, 19.27 MiB/s]
Dl Size...: 62%|██████▏ | 935/1501 [00:45<00:28, 19.62 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:45<00:06, 6.07s/ url]
Dl Size...: 62%|██████▏ | 935/1501 [00:45<00:28, 19.62 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:45<00:06, 6.07s/ url]
Dl Size...: 62%|██████▏ | 936/1501 [00:45<00:28, 19.62 MiB/s]
Dl Size...: 62%|██████▏ | 937/1501 [00:45<00:29, 19.31 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:45<00:06, 6.07s/ url]
Dl Size...: 62%|██████▏ | 937/1501 [00:45<00:29, 19.31 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:45<00:06, 6.07s/ url]
Dl Size...: 62%|██████▏ | 938/1501 [00:45<00:29, 19.31 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:45<00:06, 6.07s/ url]
Dl Size...: 63%|██████▎ | 939/1501 [00:45<00:29, 19.31 MiB/s]
Dl Size...: 63%|██████▎ | 940/1501 [00:45<00:28, 19.48 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:45<00:06, 6.07s/ url]
Dl Size...: 63%|██████▎ | 940/1501 [00:45<00:28, 19.48 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:46<00:06, 6.07s/ url]
Dl Size...: 63%|██████▎ | 941/1501 [00:46<00:28, 19.48 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:46<00:06, 6.07s/ url]
Dl Size...: 63%|██████▎ | 942/1501 [00:46<00:28, 19.48 MiB/s]
Dl Size...: 63%|██████▎ | 943/1501 [00:46<00:28, 19.75 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:46<00:06, 6.07s/ url]
Dl Size...: 63%|██████▎ | 943/1501 [00:46<00:28, 19.75 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:46<00:06, 6.07s/ url]
Dl Size...: 63%|██████▎ | 944/1501 [00:46<00:28, 19.75 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:46<00:06, 6.07s/ url]
Dl Size...: 63%|██████▎ | 945/1501 [00:46<00:28, 19.75 MiB/s]
Dl Size...: 63%|██████▎ | 946/1501 [00:46<00:28, 19.71 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:46<00:06, 6.07s/ url]
Dl Size...: 63%|██████▎ | 946/1501 [00:46<00:28, 19.71 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:46<00:06, 6.07s/ url]
Dl Size...: 63%|██████▎ | 947/1501 [00:46<00:28, 19.71 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:46<00:06, 6.07s/ url]
Dl Size...: 63%|██████▎ | 948/1501 [00:46<00:28, 19.71 MiB/s]
Dl Size...: 63%|██████▎ | 949/1501 [00:46<00:28, 19.57 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:46<00:06, 6.07s/ url]
Dl Size...: 63%|██████▎ | 949/1501 [00:46<00:28, 19.57 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:46<00:06, 6.07s/ url]
Dl Size...: 63%|██████▎ | 950/1501 [00:46<00:28, 19.57 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:46<00:06, 6.07s/ url]
Dl Size...: 63%|██████▎ | 951/1501 [00:46<00:28, 19.57 MiB/s]
Dl Size...: 63%|██████▎ | 952/1501 [00:46<00:27, 19.73 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:46<00:06, 6.07s/ url]
Dl Size...: 63%|██████▎ | 952/1501 [00:46<00:27, 19.73 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:46<00:06, 6.07s/ url]
Dl Size...: 63%|██████▎ | 953/1501 [00:46<00:27, 19.73 MiB/s]
Dl Size...: 64%|██████▎ | 954/1501 [00:46<00:28, 19.39 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:46<00:06, 6.07s/ url]
Dl Size...: 64%|██████▎ | 954/1501 [00:46<00:28, 19.39 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:46<00:06, 6.07s/ url]
Dl Size...: 64%|██████▎ | 955/1501 [00:46<00:28, 19.39 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:46<00:06, 6.07s/ url]
Dl Size...: 64%|██████▎ | 956/1501 [00:46<00:28, 19.39 MiB/s]
Dl Size...: 64%|██████▍ | 957/1501 [00:46<00:27, 19.63 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:46<00:06, 6.07s/ url]
Dl Size...: 64%|██████▍ | 957/1501 [00:46<00:27, 19.63 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:46<00:06, 6.07s/ url]
Dl Size...: 64%|██████▍ | 958/1501 [00:46<00:27, 19.63 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:46<00:06, 6.07s/ url]
Dl Size...: 64%|██████▍ | 959/1501 [00:46<00:27, 19.63 MiB/s]
Dl Size...: 64%|██████▍ | 960/1501 [00:46<00:27, 19.81 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:46<00:06, 6.07s/ url]
Dl Size...: 64%|██████▍ | 960/1501 [00:46<00:27, 19.81 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:47<00:06, 6.07s/ url]
Dl Size...: 64%|██████▍ | 961/1501 [00:47<00:27, 19.81 MiB/s]
Dl Size...: 64%|██████▍ | 962/1501 [00:47<00:27, 19.80 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:47<00:06, 6.07s/ url]
Dl Size...: 64%|██████▍ | 962/1501 [00:47<00:27, 19.80 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:47<00:06, 6.07s/ url]
Dl Size...: 64%|██████▍ | 963/1501 [00:47<00:27, 19.80 MiB/s]
Dl Size...: 64%|██████▍ | 964/1501 [00:47<00:27, 19.48 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:47<00:06, 6.07s/ url]
Dl Size...: 64%|██████▍ | 964/1501 [00:47<00:27, 19.48 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:47<00:06, 6.07s/ url]
Dl Size...: 64%|██████▍ | 965/1501 [00:47<00:27, 19.48 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:47<00:06, 6.07s/ url]
Dl Size...: 64%|██████▍ | 966/1501 [00:47<00:27, 19.48 MiB/s]
Dl Size...: 64%|██████▍ | 967/1501 [00:47<00:27, 19.71 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:47<00:06, 6.07s/ url]
Dl Size...: 64%|██████▍ | 967/1501 [00:47<00:27, 19.71 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:47<00:06, 6.07s/ url]
Dl Size...: 64%|██████▍ | 968/1501 [00:47<00:27, 19.71 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:47<00:06, 6.07s/ url]
Dl Size...: 65%|██████▍ | 969/1501 [00:47<00:26, 19.71 MiB/s]
Dl Size...: 65%|██████▍ | 970/1501 [00:47<00:26, 19.90 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:47<00:06, 6.07s/ url]
Dl Size...: 65%|██████▍ | 970/1501 [00:47<00:26, 19.90 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:47<00:06, 6.07s/ url]
Dl Size...: 65%|██████▍ | 971/1501 [00:47<00:26, 19.90 MiB/s]
Dl Size...: 65%|██████▍ | 972/1501 [00:47<00:26, 19.84 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:47<00:06, 6.07s/ url]
Dl Size...: 65%|██████▍ | 972/1501 [00:47<00:26, 19.84 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:47<00:06, 6.07s/ url]
Dl Size...: 65%|██████▍ | 973/1501 [00:47<00:26, 19.84 MiB/s]
Dl Size...: 65%|██████▍ | 974/1501 [00:47<00:26, 19.72 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:47<00:06, 6.07s/ url]
Dl Size...: 65%|██████▍ | 974/1501 [00:47<00:26, 19.72 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:47<00:06, 6.07s/ url]
Dl Size...: 65%|██████▍ | 975/1501 [00:47<00:26, 19.72 MiB/s]
Dl Size...: 65%|██████▌ | 976/1501 [00:47<00:26, 19.79 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:47<00:06, 6.07s/ url]
Dl Size...: 65%|██████▌ | 976/1501 [00:47<00:26, 19.79 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:47<00:06, 6.07s/ url]
Dl Size...: 65%|██████▌ | 977/1501 [00:47<00:26, 19.79 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:47<00:06, 6.07s/ url]
Dl Size...: 65%|██████▌ | 978/1501 [00:47<00:26, 19.79 MiB/s]
Dl Size...: 65%|██████▌ | 979/1501 [00:47<00:26, 19.93 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:47<00:06, 6.07s/ url]
Dl Size...: 65%|██████▌ | 979/1501 [00:47<00:26, 19.93 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:47<00:06, 6.07s/ url]
Dl Size...: 65%|██████▌ | 980/1501 [00:47<00:26, 19.93 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:48<00:06, 6.07s/ url]
Dl Size...: 65%|██████▌ | 981/1501 [00:48<00:26, 19.93 MiB/s]
Dl Size...: 65%|██████▌ | 982/1501 [00:48<00:25, 20.23 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:48<00:06, 6.07s/ url]
Dl Size...: 65%|██████▌ | 982/1501 [00:48<00:25, 20.23 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:48<00:06, 6.07s/ url]
Dl Size...: 65%|██████▌ | 983/1501 [00:48<00:25, 20.23 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:48<00:06, 6.07s/ url]
Dl Size...: 66%|██████▌ | 984/1501 [00:48<00:25, 20.23 MiB/s]
Dl Size...: 66%|██████▌ | 985/1501 [00:48<00:25, 20.01 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:48<00:06, 6.07s/ url]
Dl Size...: 66%|██████▌ | 985/1501 [00:48<00:25, 20.01 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:48<00:06, 6.07s/ url]
Dl Size...: 66%|██████▌ | 986/1501 [00:48<00:25, 20.01 MiB/s]
Dl Size...: 66%|██████▌ | 987/1501 [00:48<00:26, 19.74 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:48<00:06, 6.07s/ url]
Dl Size...: 66%|██████▌ | 987/1501 [00:48<00:26, 19.74 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:48<00:06, 6.07s/ url]
Dl Size...: 66%|██████▌ | 988/1501 [00:48<00:25, 19.74 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:48<00:06, 6.07s/ url]
Dl Size...: 66%|██████▌ | 989/1501 [00:48<00:25, 19.74 MiB/s]
Dl Size...: 66%|██████▌ | 990/1501 [00:48<00:25, 19.93 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:48<00:06, 6.07s/ url]
Dl Size...: 66%|██████▌ | 990/1501 [00:48<00:25, 19.93 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:48<00:06, 6.07s/ url]
Dl Size...: 66%|██████▌ | 991/1501 [00:48<00:25, 19.93 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:48<00:06, 6.07s/ url]
Dl Size...: 66%|██████▌ | 992/1501 [00:48<00:25, 19.93 MiB/s]
Dl Size...: 66%|██████▌ | 993/1501 [00:48<00:25, 20.09 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:48<00:06, 6.07s/ url]
Dl Size...: 66%|██████▌ | 993/1501 [00:48<00:25, 20.09 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:48<00:06, 6.07s/ url]
Dl Size...: 66%|██████▌ | 994/1501 [00:48<00:25, 20.09 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:48<00:06, 6.07s/ url]
Dl Size...: 66%|██████▋ | 995/1501 [00:48<00:25, 20.09 MiB/s]
Dl Size...: 66%|██████▋ | 996/1501 [00:48<00:25, 20.19 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:48<00:06, 6.07s/ url]
Dl Size...: 66%|██████▋ | 996/1501 [00:48<00:25, 20.19 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:48<00:06, 6.07s/ url]
Dl Size...: 66%|██████▋ | 997/1501 [00:48<00:24, 20.19 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:48<00:06, 6.07s/ url]
Dl Size...: 66%|██████▋ | 998/1501 [00:48<00:24, 20.19 MiB/s]
Dl Size...: 67%|██████▋ | 999/1501 [00:48<00:24, 20.27 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:48<00:06, 6.07s/ url]
Dl Size...: 67%|██████▋ | 999/1501 [00:48<00:24, 20.27 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:48<00:06, 6.07s/ url]
Dl Size...: 67%|██████▋ | 1000/1501 [00:48<00:24, 20.27 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:49<00:06, 6.07s/ url]
Dl Size...: 67%|██████▋ | 1001/1501 [00:49<00:24, 20.27 MiB/s]
Dl Size...: 67%|██████▋ | 1002/1501 [00:49<00:24, 20.31 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:49<00:06, 6.07s/ url]
Dl Size...: 67%|██████▋ | 1002/1501 [00:49<00:24, 20.31 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:49<00:06, 6.07s/ url]
Dl Size...: 67%|██████▋ | 1003/1501 [00:49<00:24, 20.31 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:49<00:06, 6.07s/ url]
Dl Size...: 67%|██████▋ | 1004/1501 [00:49<00:24, 20.31 MiB/s]
Dl Size...: 67%|██████▋ | 1005/1501 [00:49<00:24, 20.48 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:49<00:06, 6.07s/ url]
Dl Size...: 67%|██████▋ | 1005/1501 [00:49<00:24, 20.48 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:49<00:06, 6.07s/ url]
Dl Size...: 67%|██████▋ | 1006/1501 [00:49<00:24, 20.48 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:49<00:06, 6.07s/ url]
Dl Size...: 67%|██████▋ | 1007/1501 [00:49<00:24, 20.48 MiB/s]
Dl Size...: 67%|██████▋ | 1008/1501 [00:49<00:23, 20.60 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:49<00:06, 6.07s/ url]
Dl Size...: 67%|██████▋ | 1008/1501 [00:49<00:23, 20.60 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:49<00:06, 6.07s/ url]
Dl Size...: 67%|██████▋ | 1009/1501 [00:49<00:23, 20.60 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:49<00:06, 6.07s/ url]
Dl Size...: 67%|██████▋ | 1010/1501 [00:49<00:23, 20.60 MiB/s]
Dl Size...: 67%|██████▋ | 1011/1501 [00:49<00:23, 20.69 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:49<00:06, 6.07s/ url]
Dl Size...: 67%|██████▋ | 1011/1501 [00:49<00:23, 20.69 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:49<00:06, 6.07s/ url]
Dl Size...: 67%|██████▋ | 1012/1501 [00:49<00:23, 20.69 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:49<00:06, 6.07s/ url]
Dl Size...: 67%|██████▋ | 1013/1501 [00:49<00:23, 20.69 MiB/s]
Dl Size...: 68%|██████▊ | 1014/1501 [00:49<00:23, 20.76 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:49<00:06, 6.07s/ url]
Dl Size...: 68%|██████▊ | 1014/1501 [00:49<00:23, 20.76 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:49<00:06, 6.07s/ url]
Dl Size...: 68%|██████▊ | 1015/1501 [00:49<00:23, 20.76 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:49<00:06, 6.07s/ url]
Dl Size...: 68%|██████▊ | 1016/1501 [00:49<00:23, 20.76 MiB/s]
Dl Size...: 68%|██████▊ | 1017/1501 [00:49<00:23, 20.81 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:49<00:06, 6.07s/ url]
Dl Size...: 68%|██████▊ | 1017/1501 [00:49<00:23, 20.81 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:49<00:06, 6.07s/ url]
Dl Size...: 68%|██████▊ | 1018/1501 [00:49<00:23, 20.81 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:49<00:06, 6.07s/ url]
Dl Size...: 68%|██████▊ | 1019/1501 [00:49<00:23, 20.81 MiB/s]
Dl Size...: 68%|██████▊ | 1020/1501 [00:49<00:23, 20.87 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:49<00:06, 6.07s/ url]
Dl Size...: 68%|██████▊ | 1020/1501 [00:49<00:23, 20.87 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:49<00:06, 6.07s/ url]
Dl Size...: 68%|██████▊ | 1021/1501 [00:49<00:22, 20.87 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:50<00:06, 6.07s/ url]
Dl Size...: 68%|██████▊ | 1022/1501 [00:50<00:22, 20.87 MiB/s]
Dl Size...: 68%|██████▊ | 1023/1501 [00:50<00:22, 21.14 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:50<00:06, 6.07s/ url]
Dl Size...: 68%|██████▊ | 1023/1501 [00:50<00:22, 21.14 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:50<00:06, 6.07s/ url]
Dl Size...: 68%|██████▊ | 1024/1501 [00:50<00:22, 21.14 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:50<00:06, 6.07s/ url]
Dl Size...: 68%|██████▊ | 1025/1501 [00:50<00:22, 21.14 MiB/s]
Dl Size...: 68%|██████▊ | 1026/1501 [00:50<00:22, 21.11 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:50<00:06, 6.07s/ url]
Dl Size...: 68%|██████▊ | 1026/1501 [00:50<00:22, 21.11 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:50<00:06, 6.07s/ url]
Dl Size...: 68%|██████▊ | 1027/1501 [00:50<00:22, 21.11 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:50<00:06, 6.07s/ url]
Dl Size...: 68%|██████▊ | 1028/1501 [00:50<00:22, 21.11 MiB/s]
Dl Size...: 69%|██████▊ | 1029/1501 [00:50<00:22, 21.16 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:50<00:06, 6.07s/ url]
Dl Size...: 69%|██████▊ | 1029/1501 [00:50<00:22, 21.16 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:50<00:06, 6.07s/ url]
Dl Size...: 69%|██████▊ | 1030/1501 [00:50<00:22, 21.16 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:50<00:06, 6.07s/ url]
Dl Size...: 69%|██████▊ | 1031/1501 [00:50<00:22, 21.16 MiB/s]
Dl Size...: 69%|██████▉ | 1032/1501 [00:50<00:22, 21.21 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:50<00:06, 6.07s/ url]
Dl Size...: 69%|██████▉ | 1032/1501 [00:50<00:22, 21.21 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:50<00:06, 6.07s/ url]
Dl Size...: 69%|██████▉ | 1033/1501 [00:50<00:22, 21.21 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:50<00:06, 6.07s/ url]
Dl Size...: 69%|██████▉ | 1034/1501 [00:50<00:22, 21.21 MiB/s]
Dl Size...: 69%|██████▉ | 1035/1501 [00:50<00:22, 21.16 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:50<00:06, 6.07s/ url]
Dl Size...: 69%|██████▉ | 1035/1501 [00:50<00:22, 21.16 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:50<00:06, 6.07s/ url]
Dl Size...: 69%|██████▉ | 1036/1501 [00:50<00:21, 21.16 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:50<00:06, 6.07s/ url]
Dl Size...: 69%|██████▉ | 1037/1501 [00:50<00:21, 21.16 MiB/s]
Dl Size...: 69%|██████▉ | 1038/1501 [00:50<00:20, 22.11 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:50<00:06, 6.07s/ url]
Dl Size...: 69%|██████▉ | 1038/1501 [00:50<00:20, 22.11 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:50<00:06, 6.07s/ url]
Dl Size...: 69%|██████▉ | 1039/1501 [00:50<00:20, 22.11 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:50<00:06, 6.07s/ url]
Dl Size...: 69%|██████▉ | 1040/1501 [00:50<00:20, 22.11 MiB/s]
Dl Size...: 69%|██████▉ | 1041/1501 [00:50<00:20, 21.98 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:50<00:06, 6.07s/ url]
Dl Size...: 69%|██████▉ | 1041/1501 [00:50<00:20, 21.98 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:50<00:06, 6.07s/ url]
Dl Size...: 69%|██████▉ | 1042/1501 [00:50<00:20, 21.98 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:50<00:06, 6.07s/ url]
Dl Size...: 69%|██████▉ | 1043/1501 [00:50<00:20, 21.98 MiB/s]
Dl Size...: 70%|██████▉ | 1044/1501 [00:51<00:20, 22.36 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:51<00:06, 6.07s/ url]
Dl Size...: 70%|██████▉ | 1044/1501 [00:51<00:20, 22.36 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:51<00:06, 6.07s/ url]
Dl Size...: 70%|██████▉ | 1045/1501 [00:51<00:20, 22.36 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:51<00:06, 6.07s/ url]
Dl Size...: 70%|██████▉ | 1046/1501 [00:51<00:20, 22.36 MiB/s]
Dl Size...: 70%|██████▉ | 1047/1501 [00:51<00:20, 22.12 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:51<00:06, 6.07s/ url]
Dl Size...: 70%|██████▉ | 1047/1501 [00:51<00:20, 22.12 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:51<00:06, 6.07s/ url]
Dl Size...: 70%|██████▉ | 1048/1501 [00:51<00:20, 22.12 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:51<00:06, 6.07s/ url]
Dl Size...: 70%|██████▉ | 1049/1501 [00:51<00:20, 22.12 MiB/s]
Dl Size...: 70%|██████▉ | 1050/1501 [00:51<00:20, 22.05 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:51<00:06, 6.07s/ url]
Dl Size...: 70%|██████▉ | 1050/1501 [00:51<00:20, 22.05 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:51<00:06, 6.07s/ url]
Dl Size...: 70%|███████ | 1051/1501 [00:51<00:20, 22.05 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:51<00:06, 6.07s/ url]
Dl Size...: 70%|███████ | 1052/1501 [00:51<00:20, 22.05 MiB/s]
Dl Size...: 70%|███████ | 1053/1501 [00:51<00:20, 22.18 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:51<00:06, 6.07s/ url]
Dl Size...: 70%|███████ | 1053/1501 [00:51<00:20, 22.18 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:51<00:06, 6.07s/ url]
Dl Size...: 70%|███████ | 1054/1501 [00:51<00:20, 22.18 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:51<00:06, 6.07s/ url]
Dl Size...: 70%|███████ | 1055/1501 [00:51<00:20, 22.18 MiB/s]
Dl Size...: 70%|███████ | 1056/1501 [00:51<00:20, 22.21 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:51<00:06, 6.07s/ url]
Dl Size...: 70%|███████ | 1056/1501 [00:51<00:20, 22.21 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:51<00:06, 6.07s/ url]
Dl Size...: 70%|███████ | 1057/1501 [00:51<00:19, 22.21 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:51<00:06, 6.07s/ url]
Dl Size...: 70%|███████ | 1058/1501 [00:51<00:19, 22.21 MiB/s]
Dl Size...: 71%|███████ | 1059/1501 [00:51<00:19, 22.94 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:51<00:06, 6.07s/ url]
Dl Size...: 71%|███████ | 1059/1501 [00:51<00:19, 22.94 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:51<00:06, 6.07s/ url]
Dl Size...: 71%|███████ | 1060/1501 [00:51<00:19, 22.94 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:51<00:06, 6.07s/ url]
Dl Size...: 71%|███████ | 1061/1501 [00:51<00:19, 22.94 MiB/s]
Dl Size...: 71%|███████ | 1062/1501 [00:51<00:18, 23.15 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:51<00:06, 6.07s/ url]
Dl Size...: 71%|███████ | 1062/1501 [00:51<00:18, 23.15 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:51<00:06, 6.07s/ url]
Dl Size...: 71%|███████ | 1063/1501 [00:51<00:18, 23.15 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:51<00:06, 6.07s/ url]
Dl Size...: 71%|███████ | 1064/1501 [00:51<00:18, 23.15 MiB/s]
Dl Size...: 71%|███████ | 1065/1501 [00:51<00:18, 23.15 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:51<00:06, 6.07s/ url]
Dl Size...: 71%|███████ | 1065/1501 [00:51<00:18, 23.15 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:51<00:06, 6.07s/ url]
Dl Size...: 71%|███████ | 1066/1501 [00:51<00:18, 23.15 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:52<00:06, 6.07s/ url]
Dl Size...: 71%|███████ | 1067/1501 [00:52<00:18, 23.15 MiB/s]
Dl Size...: 71%|███████ | 1068/1501 [00:52<00:18, 22.89 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:52<00:06, 6.07s/ url]
Dl Size...: 71%|███████ | 1068/1501 [00:52<00:18, 22.89 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:52<00:06, 6.07s/ url]
Dl Size...: 71%|███████ | 1069/1501 [00:52<00:18, 22.89 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:52<00:06, 6.07s/ url]
Dl Size...: 71%|███████▏ | 1070/1501 [00:52<00:18, 22.89 MiB/s]
Dl Size...: 71%|███████▏ | 1071/1501 [00:52<00:18, 23.84 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:52<00:06, 6.07s/ url]
Dl Size...: 71%|███████▏ | 1071/1501 [00:52<00:18, 23.84 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:52<00:06, 6.07s/ url]
Dl Size...: 71%|███████▏ | 1072/1501 [00:52<00:17, 23.84 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:52<00:06, 6.07s/ url]
Dl Size...: 71%|███████▏ | 1073/1501 [00:52<00:17, 23.84 MiB/s]
Dl Size...: 72%|███████▏ | 1074/1501 [00:52<00:17, 23.82 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:52<00:06, 6.07s/ url]
Dl Size...: 72%|███████▏ | 1074/1501 [00:52<00:17, 23.82 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:52<00:06, 6.07s/ url]
Dl Size...: 72%|███████▏ | 1075/1501 [00:52<00:17, 23.82 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:52<00:06, 6.07s/ url]
Dl Size...: 72%|███████▏ | 1076/1501 [00:52<00:17, 23.82 MiB/s]
Dl Size...: 72%|███████▏ | 1077/1501 [00:52<00:17, 23.89 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:52<00:06, 6.07s/ url]
Dl Size...: 72%|███████▏ | 1077/1501 [00:52<00:17, 23.89 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:52<00:06, 6.07s/ url]
Dl Size...: 72%|███████▏ | 1078/1501 [00:52<00:17, 23.89 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:52<00:06, 6.07s/ url]
Dl Size...: 72%|███████▏ | 1079/1501 [00:52<00:17, 23.89 MiB/s]
Dl Size...: 72%|███████▏ | 1080/1501 [00:52<00:17, 23.69 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:52<00:06, 6.07s/ url]
Dl Size...: 72%|███████▏ | 1080/1501 [00:52<00:17, 23.69 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:52<00:06, 6.07s/ url]
Dl Size...: 72%|███████▏ | 1081/1501 [00:52<00:17, 23.69 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:52<00:06, 6.07s/ url]
Dl Size...: 72%|███████▏ | 1082/1501 [00:52<00:17, 23.69 MiB/s]
Dl Size...: 72%|███████▏ | 1083/1501 [00:52<00:16, 24.67 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:52<00:06, 6.07s/ url]
Dl Size...: 72%|███████▏ | 1083/1501 [00:52<00:16, 24.67 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:52<00:06, 6.07s/ url]
Dl Size...: 72%|███████▏ | 1084/1501 [00:52<00:16, 24.67 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:52<00:06, 6.07s/ url]
Dl Size...: 72%|███████▏ | 1085/1501 [00:52<00:16, 24.67 MiB/s]
Dl Size...: 72%|███████▏ | 1086/1501 [00:52<00:16, 24.52 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:52<00:06, 6.07s/ url]
Dl Size...: 72%|███████▏ | 1086/1501 [00:52<00:16, 24.52 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:52<00:06, 6.07s/ url]
Dl Size...: 72%|███████▏ | 1087/1501 [00:52<00:16, 24.52 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:52<00:06, 6.07s/ url]
Dl Size...: 72%|███████▏ | 1088/1501 [00:52<00:16, 24.52 MiB/s]
Dl Size...: 73%|███████▎ | 1089/1501 [00:52<00:16, 24.25 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:52<00:06, 6.07s/ url]
Dl Size...: 73%|███████▎ | 1089/1501 [00:52<00:16, 24.25 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:52<00:06, 6.07s/ url]
Dl Size...: 73%|███████▎ | 1090/1501 [00:52<00:16, 24.25 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:53<00:06, 6.07s/ url]
Dl Size...: 73%|███████▎ | 1091/1501 [00:53<00:16, 24.25 MiB/s]
Dl Size...: 73%|███████▎ | 1092/1501 [00:53<00:16, 25.35 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:53<00:06, 6.07s/ url]
Dl Size...: 73%|███████▎ | 1092/1501 [00:53<00:16, 25.35 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:53<00:06, 6.07s/ url]
Dl Size...: 73%|███████▎ | 1093/1501 [00:53<00:16, 25.35 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:53<00:06, 6.07s/ url]
Dl Size...: 73%|███████▎ | 1094/1501 [00:53<00:16, 25.35 MiB/s]
Dl Size...: 73%|███████▎ | 1095/1501 [00:53<00:16, 24.94 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:53<00:06, 6.07s/ url]
Dl Size...: 73%|███████▎ | 1095/1501 [00:53<00:16, 24.94 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:53<00:06, 6.07s/ url]
Dl Size...: 73%|███████▎ | 1096/1501 [00:53<00:16, 24.94 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:53<00:06, 6.07s/ url]
Dl Size...: 73%|███████▎ | 1097/1501 [00:53<00:16, 24.94 MiB/s]
Dl Size...: 73%|███████▎ | 1098/1501 [00:53<00:15, 25.71 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:53<00:06, 6.07s/ url]
Dl Size...: 73%|███████▎ | 1098/1501 [00:53<00:15, 25.71 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:53<00:06, 6.07s/ url]
Dl Size...: 73%|███████▎ | 1099/1501 [00:53<00:15, 25.71 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:53<00:06, 6.07s/ url]
Dl Size...: 73%|███████▎ | 1100/1501 [00:53<00:15, 25.71 MiB/s]
Dl Size...: 73%|███████▎ | 1101/1501 [00:53<00:15, 25.58 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:53<00:06, 6.07s/ url]
Dl Size...: 73%|███████▎ | 1101/1501 [00:53<00:15, 25.58 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:53<00:06, 6.07s/ url]
Dl Size...: 73%|███████▎ | 1102/1501 [00:53<00:15, 25.58 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:53<00:06, 6.07s/ url]
Dl Size...: 73%|███████▎ | 1103/1501 [00:53<00:15, 25.58 MiB/s]
Dl Size...: 74%|███████▎ | 1104/1501 [00:53<00:15, 25.27 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:53<00:06, 6.07s/ url]
Dl Size...: 74%|███████▎ | 1104/1501 [00:53<00:15, 25.27 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:53<00:06, 6.07s/ url]
Dl Size...: 74%|███████▎ | 1105/1501 [00:53<00:15, 25.27 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:53<00:06, 6.07s/ url]
Dl Size...: 74%|███████▎ | 1106/1501 [00:53<00:15, 25.27 MiB/s]
Dl Size...: 74%|███████▍ | 1107/1501 [00:53<00:14, 26.28 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:53<00:06, 6.07s/ url]
Dl Size...: 74%|███████▍ | 1107/1501 [00:53<00:14, 26.28 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:53<00:06, 6.07s/ url]
Dl Size...: 74%|███████▍ | 1108/1501 [00:53<00:14, 26.28 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:53<00:06, 6.07s/ url]
Dl Size...: 74%|███████▍ | 1109/1501 [00:53<00:14, 26.28 MiB/s]
Dl Size...: 74%|███████▍ | 1110/1501 [00:53<00:15, 25.95 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:53<00:06, 6.07s/ url]
Dl Size...: 74%|███████▍ | 1110/1501 [00:53<00:15, 25.95 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:53<00:06, 6.07s/ url]
Dl Size...: 74%|███████▍ | 1111/1501 [00:53<00:15, 25.95 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:53<00:06, 6.07s/ url]
Dl Size...: 74%|███████▍ | 1112/1501 [00:53<00:14, 25.95 MiB/s]
Dl Size...: 74%|███████▍ | 1113/1501 [00:53<00:14, 26.75 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:53<00:06, 6.07s/ url]
Dl Size...: 74%|███████▍ | 1113/1501 [00:53<00:14, 26.75 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:53<00:06, 6.07s/ url]
Dl Size...: 74%|███████▍ | 1114/1501 [00:53<00:14, 26.75 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:53<00:06, 6.07s/ url]
Dl Size...: 74%|███████▍ | 1115/1501 [00:53<00:14, 26.75 MiB/s]
Dl Size...: 74%|███████▍ | 1116/1501 [00:53<00:14, 26.04 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:53<00:06, 6.07s/ url]
Dl Size...: 74%|███████▍ | 1116/1501 [00:53<00:14, 26.04 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:54<00:06, 6.07s/ url]
Dl Size...: 74%|███████▍ | 1117/1501 [00:54<00:14, 26.04 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:54<00:06, 6.07s/ url]
Dl Size...: 74%|███████▍ | 1118/1501 [00:54<00:14, 26.04 MiB/s]
Dl Size...: 75%|███████▍ | 1119/1501 [00:54<00:15, 25.29 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:54<00:06, 6.07s/ url]
Dl Size...: 75%|███████▍ | 1119/1501 [00:54<00:15, 25.29 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:54<00:06, 6.07s/ url]
Dl Size...: 75%|███████▍ | 1120/1501 [00:54<00:15, 25.29 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:54<00:06, 6.07s/ url]
Dl Size...: 75%|███████▍ | 1121/1501 [00:54<00:15, 25.29 MiB/s]
Dl Size...: 75%|███████▍ | 1122/1501 [00:54<00:15, 24.71 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:54<00:06, 6.07s/ url]
Dl Size...: 75%|███████▍ | 1122/1501 [00:54<00:15, 24.71 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:54<00:06, 6.07s/ url]
Dl Size...: 75%|███████▍ | 1123/1501 [00:54<00:15, 24.71 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:54<00:06, 6.07s/ url]
Dl Size...: 75%|███████▍ | 1124/1501 [00:54<00:15, 24.71 MiB/s]
Dl Size...: 75%|███████▍ | 1125/1501 [00:54<00:15, 23.70 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:54<00:06, 6.07s/ url]
Dl Size...: 75%|███████▍ | 1125/1501 [00:54<00:15, 23.70 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:54<00:06, 6.07s/ url]
Dl Size...: 75%|███████▌ | 1126/1501 [00:54<00:15, 23.70 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:54<00:06, 6.07s/ url]
Dl Size...: 75%|███████▌ | 1127/1501 [00:54<00:15, 23.70 MiB/s]
Dl Size...: 75%|███████▌ | 1128/1501 [00:54<00:16, 22.49 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:54<00:06, 6.07s/ url]
Dl Size...: 75%|███████▌ | 1128/1501 [00:54<00:16, 22.49 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:54<00:06, 6.07s/ url]
Dl Size...: 75%|███████▌ | 1129/1501 [00:54<00:16, 22.49 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:54<00:06, 6.07s/ url]
Dl Size...: 75%|███████▌ | 1130/1501 [00:54<00:16, 22.49 MiB/s]
Dl Size...: 75%|███████▌ | 1131/1501 [00:54<00:16, 22.43 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:54<00:06, 6.07s/ url]
Dl Size...: 75%|███████▌ | 1131/1501 [00:54<00:16, 22.43 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:54<00:06, 6.07s/ url]
Dl Size...: 75%|███████▌ | 1132/1501 [00:54<00:16, 22.43 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:54<00:06, 6.07s/ url]
Dl Size...: 75%|███████▌ | 1133/1501 [00:54<00:16, 22.43 MiB/s]
Dl Size...: 76%|███████▌ | 1134/1501 [00:54<00:15, 23.02 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:54<00:06, 6.07s/ url]
Dl Size...: 76%|███████▌ | 1134/1501 [00:54<00:15, 23.02 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:54<00:06, 6.07s/ url]
Dl Size...: 76%|███████▌ | 1135/1501 [00:54<00:15, 23.02 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:54<00:06, 6.07s/ url]
Dl Size...: 76%|███████▌ | 1136/1501 [00:54<00:15, 23.02 MiB/s]
Dl Size...: 76%|███████▌ | 1137/1501 [00:54<00:16, 22.12 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:54<00:06, 6.07s/ url]
Dl Size...: 76%|███████▌ | 1137/1501 [00:54<00:16, 22.12 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:54<00:06, 6.07s/ url]
Dl Size...: 76%|███████▌ | 1138/1501 [00:54<00:16, 22.12 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:55<00:06, 6.07s/ url]
Dl Size...: 76%|███████▌ | 1139/1501 [00:55<00:16, 22.12 MiB/s]
Dl Size...: 76%|███████▌ | 1140/1501 [00:55<00:17, 20.89 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:55<00:06, 6.07s/ url]
Dl Size...: 76%|███████▌ | 1140/1501 [00:55<00:17, 20.89 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:55<00:06, 6.07s/ url]
Dl Size...: 76%|███████▌ | 1141/1501 [00:55<00:17, 20.89 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:55<00:06, 6.07s/ url]
Dl Size...: 76%|███████▌ | 1142/1501 [00:55<00:17, 20.89 MiB/s]
Dl Size...: 76%|███████▌ | 1143/1501 [00:55<00:17, 19.93 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:55<00:06, 6.07s/ url]
Dl Size...: 76%|███████▌ | 1143/1501 [00:55<00:17, 19.93 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:55<00:06, 6.07s/ url]
Dl Size...: 76%|███████▌ | 1144/1501 [00:55<00:17, 19.93 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:55<00:06, 6.07s/ url]
Dl Size...: 76%|███████▋ | 1145/1501 [00:55<00:17, 19.93 MiB/s]
Dl Size...: 76%|███████▋ | 1146/1501 [00:55<00:19, 18.66 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:55<00:06, 6.07s/ url]
Dl Size...: 76%|███████▋ | 1146/1501 [00:55<00:19, 18.66 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:55<00:06, 6.07s/ url]
Dl Size...: 76%|███████▋ | 1147/1501 [00:55<00:18, 18.66 MiB/s]
Dl Size...: 76%|███████▋ | 1148/1501 [00:55<00:19, 17.94 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:55<00:06, 6.07s/ url]
Dl Size...: 76%|███████▋ | 1148/1501 [00:55<00:19, 17.94 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:55<00:06, 6.07s/ url]
Dl Size...: 77%|███████▋ | 1149/1501 [00:55<00:19, 17.94 MiB/s]
Dl Size...: 77%|███████▋ | 1150/1501 [00:55<00:19, 17.85 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:55<00:06, 6.07s/ url]
Dl Size...: 77%|███████▋ | 1150/1501 [00:55<00:19, 17.85 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:55<00:06, 6.07s/ url]
Dl Size...: 77%|███████▋ | 1151/1501 [00:55<00:19, 17.85 MiB/s]
Dl Size...: 77%|███████▋ | 1152/1501 [00:55<00:20, 17.16 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:55<00:06, 6.07s/ url]
Dl Size...: 77%|███████▋ | 1152/1501 [00:55<00:20, 17.16 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:55<00:06, 6.07s/ url]
Dl Size...: 77%|███████▋ | 1153/1501 [00:55<00:20, 17.16 MiB/s]
Dl Size...: 77%|███████▋ | 1154/1501 [00:55<00:20, 17.17 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:55<00:06, 6.07s/ url]
Dl Size...: 77%|███████▋ | 1154/1501 [00:55<00:20, 17.17 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:55<00:06, 6.07s/ url]
Dl Size...: 77%|███████▋ | 1155/1501 [00:55<00:20, 17.17 MiB/s]
Dl Size...: 77%|███████▋ | 1156/1501 [00:56<00:19, 17.26 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:56<00:06, 6.07s/ url]
Dl Size...: 77%|███████▋ | 1156/1501 [00:56<00:19, 17.26 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:56<00:06, 6.07s/ url]
Dl Size...: 77%|███████▋ | 1157/1501 [00:56<00:19, 17.26 MiB/s]
Dl Size...: 77%|███████▋ | 1158/1501 [00:56<00:19, 17.69 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:56<00:06, 6.07s/ url]
Dl Size...: 77%|███████▋ | 1158/1501 [00:56<00:19, 17.69 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:56<00:06, 6.07s/ url]
Dl Size...: 77%|███████▋ | 1159/1501 [00:56<00:19, 17.69 MiB/s]
Dl Size...: 77%|███████▋ | 1160/1501 [00:56<00:18, 18.12 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:56<00:06, 6.07s/ url]
Dl Size...: 77%|███████▋ | 1160/1501 [00:56<00:18, 18.12 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:56<00:06, 6.07s/ url]
Dl Size...: 77%|███████▋ | 1161/1501 [00:56<00:18, 18.12 MiB/s]
Dl Size...: 77%|███████▋ | 1162/1501 [00:56<00:18, 18.24 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:56<00:06, 6.07s/ url]
Dl Size...: 77%|███████▋ | 1162/1501 [00:56<00:18, 18.24 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:56<00:06, 6.07s/ url]
Dl Size...: 77%|███████▋ | 1163/1501 [00:56<00:18, 18.24 MiB/s]
Dl Size...: 78%|███████▊ | 1164/1501 [00:56<00:18, 18.69 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:56<00:06, 6.07s/ url]
Dl Size...: 78%|███████▊ | 1164/1501 [00:56<00:18, 18.69 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:56<00:06, 6.07s/ url]
Dl Size...: 78%|███████▊ | 1165/1501 [00:56<00:17, 18.69 MiB/s]
Dl Size...: 78%|███████▊ | 1166/1501 [00:56<00:18, 18.45 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:56<00:06, 6.07s/ url]
Dl Size...: 78%|███████▊ | 1166/1501 [00:56<00:18, 18.45 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:56<00:06, 6.07s/ url]
Dl Size...: 78%|███████▊ | 1167/1501 [00:56<00:18, 18.45 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:56<00:06, 6.07s/ url]
Dl Size...: 78%|███████▊ | 1168/1501 [00:56<00:18, 18.45 MiB/s]
Dl Size...: 78%|███████▊ | 1169/1501 [00:56<00:17, 19.37 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:56<00:06, 6.07s/ url]
Dl Size...: 78%|███████▊ | 1169/1501 [00:56<00:17, 19.37 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:56<00:06, 6.07s/ url]
Dl Size...: 78%|███████▊ | 1170/1501 [00:56<00:17, 19.37 MiB/s]
Dl Size...: 78%|███████▊ | 1171/1501 [00:56<00:17, 19.00 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:56<00:06, 6.07s/ url]
Dl Size...: 78%|███████▊ | 1171/1501 [00:56<00:17, 19.00 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:56<00:06, 6.07s/ url]
Dl Size...: 78%|███████▊ | 1172/1501 [00:56<00:17, 19.00 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:56<00:06, 6.07s/ url]
Dl Size...: 78%|███████▊ | 1173/1501 [00:56<00:17, 19.00 MiB/s]
Dl Size...: 78%|███████▊ | 1174/1501 [00:56<00:16, 19.42 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:56<00:06, 6.07s/ url]
Dl Size...: 78%|███████▊ | 1174/1501 [00:56<00:16, 19.42 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:56<00:06, 6.07s/ url]
Dl Size...: 78%|███████▊ | 1175/1501 [00:56<00:16, 19.42 MiB/s]
Dl Size...: 78%|███████▊ | 1176/1501 [00:57<00:16, 19.46 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:57<00:06, 6.07s/ url]
Dl Size...: 78%|███████▊ | 1176/1501 [00:57<00:16, 19.46 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:57<00:06, 6.07s/ url]
Dl Size...: 78%|███████▊ | 1177/1501 [00:57<00:16, 19.46 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:57<00:06, 6.07s/ url]
Dl Size...: 78%|███████▊ | 1178/1501 [00:57<00:16, 19.46 MiB/s]
Dl Size...: 79%|███████▊ | 1179/1501 [00:57<00:16, 19.47 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:57<00:06, 6.07s/ url]
Dl Size...: 79%|███████▊ | 1179/1501 [00:57<00:16, 19.47 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:57<00:06, 6.07s/ url]
Dl Size...: 79%|███████▊ | 1180/1501 [00:57<00:16, 19.47 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:57<00:06, 6.07s/ url]
Dl Size...: 79%|███████▊ | 1181/1501 [00:57<00:16, 19.47 MiB/s]
Dl Size...: 79%|███████▊ | 1182/1501 [00:57<00:16, 19.68 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:57<00:06, 6.07s/ url]
Dl Size...: 79%|███████▊ | 1182/1501 [00:57<00:16, 19.68 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:57<00:06, 6.07s/ url]
Dl Size...: 79%|███████▉ | 1183/1501 [00:57<00:16, 19.68 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:57<00:06, 6.07s/ url]
Dl Size...: 79%|███████▉ | 1184/1501 [00:57<00:16, 19.68 MiB/s]
Dl Size...: 79%|███████▉ | 1185/1501 [00:57<00:15, 20.03 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:57<00:06, 6.07s/ url]
Dl Size...: 79%|███████▉ | 1185/1501 [00:57<00:15, 20.03 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:57<00:06, 6.07s/ url]
Dl Size...: 79%|███████▉ | 1186/1501 [00:57<00:15, 20.03 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:57<00:06, 6.07s/ url]
Dl Size...: 79%|███████▉ | 1187/1501 [00:57<00:15, 20.03 MiB/s]
Dl Size...: 79%|███████▉ | 1188/1501 [00:57<00:15, 20.06 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:57<00:06, 6.07s/ url]
Dl Size...: 79%|███████▉ | 1188/1501 [00:57<00:15, 20.06 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:57<00:06, 6.07s/ url]
Dl Size...: 79%|███████▉ | 1189/1501 [00:57<00:15, 20.06 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:57<00:06, 6.07s/ url]
Dl Size...: 79%|███████▉ | 1190/1501 [00:57<00:15, 20.06 MiB/s]
Dl Size...: 79%|███████▉ | 1191/1501 [00:57<00:15, 19.71 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:57<00:06, 6.07s/ url]
Dl Size...: 79%|███████▉ | 1191/1501 [00:57<00:15, 19.71 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:57<00:06, 6.07s/ url]
Dl Size...: 79%|███████▉ | 1192/1501 [00:57<00:15, 19.71 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:57<00:06, 6.07s/ url]
Dl Size...: 79%|███████▉ | 1193/1501 [00:57<00:15, 19.71 MiB/s]
Dl Size...: 80%|███████▉ | 1194/1501 [00:57<00:15, 19.93 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:57<00:06, 6.07s/ url]
Dl Size...: 80%|███████▉ | 1194/1501 [00:57<00:15, 19.93 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:58<00:06, 6.07s/ url]
Dl Size...: 80%|███████▉ | 1195/1501 [00:58<00:15, 19.93 MiB/s]
Dl Size...: 80%|███████▉ | 1196/1501 [00:58<00:15, 19.47 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:58<00:06, 6.07s/ url]
Dl Size...: 80%|███████▉ | 1196/1501 [00:58<00:15, 19.47 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:58<00:06, 6.07s/ url]
Dl Size...: 80%|███████▉ | 1197/1501 [00:58<00:15, 19.47 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:58<00:06, 6.07s/ url]
Dl Size...: 80%|███████▉ | 1198/1501 [00:58<00:15, 19.47 MiB/s]
Dl Size...: 80%|███████▉ | 1199/1501 [00:58<00:15, 19.92 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:58<00:06, 6.07s/ url]
Dl Size...: 80%|███████▉ | 1199/1501 [00:58<00:15, 19.92 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:58<00:06, 6.07s/ url]
Dl Size...: 80%|███████▉ | 1200/1501 [00:58<00:15, 19.92 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:58<00:06, 6.07s/ url]
Dl Size...: 80%|████████ | 1201/1501 [00:58<00:15, 19.92 MiB/s]
Dl Size...: 80%|████████ | 1202/1501 [00:58<00:14, 20.21 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:58<00:06, 6.07s/ url]
Dl Size...: 80%|████████ | 1202/1501 [00:58<00:14, 20.21 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:58<00:06, 6.07s/ url]
Dl Size...: 80%|████████ | 1203/1501 [00:58<00:14, 20.21 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:58<00:06, 6.07s/ url]
Dl Size...: 80%|████████ | 1204/1501 [00:58<00:14, 20.21 MiB/s]
Dl Size...: 80%|████████ | 1205/1501 [00:58<00:14, 20.25 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:58<00:06, 6.07s/ url]
Dl Size...: 80%|████████ | 1205/1501 [00:58<00:14, 20.25 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:58<00:06, 6.07s/ url]
Dl Size...: 80%|████████ | 1206/1501 [00:58<00:14, 20.25 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:58<00:06, 6.07s/ url]
Dl Size...: 80%|████████ | 1207/1501 [00:58<00:14, 20.25 MiB/s]
Dl Size...: 80%|████████ | 1208/1501 [00:58<00:14, 20.44 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:58<00:06, 6.07s/ url]
Dl Size...: 80%|████████ | 1208/1501 [00:58<00:14, 20.44 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:58<00:06, 6.07s/ url]
Dl Size...: 81%|████████ | 1209/1501 [00:58<00:14, 20.44 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:58<00:06, 6.07s/ url]
Dl Size...: 81%|████████ | 1210/1501 [00:58<00:14, 20.44 MiB/s]
Dl Size...: 81%|████████ | 1211/1501 [00:58<00:14, 20.42 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:58<00:06, 6.07s/ url]
Dl Size...: 81%|████████ | 1211/1501 [00:58<00:14, 20.42 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:58<00:06, 6.07s/ url]
Dl Size...: 81%|████████ | 1212/1501 [00:58<00:14, 20.42 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:58<00:06, 6.07s/ url]
Dl Size...: 81%|████████ | 1213/1501 [00:58<00:14, 20.42 MiB/s]
Dl Size...: 81%|████████ | 1214/1501 [00:58<00:14, 20.32 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:58<00:06, 6.07s/ url]
Dl Size...: 81%|████████ | 1214/1501 [00:58<00:14, 20.32 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:58<00:06, 6.07s/ url]
Dl Size...: 81%|████████ | 1215/1501 [00:58<00:14, 20.32 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:59<00:06, 6.07s/ url]
Dl Size...: 81%|████████ | 1216/1501 [00:59<00:14, 20.32 MiB/s]
Dl Size...: 81%|████████ | 1217/1501 [00:59<00:13, 20.47 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:59<00:06, 6.07s/ url]
Dl Size...: 81%|████████ | 1217/1501 [00:59<00:13, 20.47 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:59<00:06, 6.07s/ url]
Dl Size...: 81%|████████ | 1218/1501 [00:59<00:13, 20.47 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:59<00:06, 6.07s/ url]
Dl Size...: 81%|████████ | 1219/1501 [00:59<00:13, 20.47 MiB/s]
Dl Size...: 81%|████████▏ | 1220/1501 [00:59<00:13, 20.39 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:59<00:06, 6.07s/ url]
Dl Size...: 81%|████████▏ | 1220/1501 [00:59<00:13, 20.39 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:59<00:06, 6.07s/ url]
Dl Size...: 81%|████████▏ | 1221/1501 [00:59<00:13, 20.39 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:59<00:06, 6.07s/ url]
Dl Size...: 81%|████████▏ | 1222/1501 [00:59<00:13, 20.39 MiB/s]
Dl Size...: 81%|████████▏ | 1223/1501 [00:59<00:13, 20.20 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:59<00:06, 6.07s/ url]
Dl Size...: 81%|████████▏ | 1223/1501 [00:59<00:13, 20.20 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:59<00:06, 6.07s/ url]
Dl Size...: 82%|████████▏ | 1224/1501 [00:59<00:13, 20.20 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:59<00:06, 6.07s/ url]
Dl Size...: 82%|████████▏ | 1225/1501 [00:59<00:13, 20.20 MiB/s]
Dl Size...: 82%|████████▏ | 1226/1501 [00:59<00:14, 19.52 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:59<00:06, 6.07s/ url]
Dl Size...: 82%|████████▏ | 1226/1501 [00:59<00:14, 19.52 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:59<00:06, 6.07s/ url]
Dl Size...: 82%|████████▏ | 1227/1501 [00:59<00:14, 19.52 MiB/s]
Dl Size...: 82%|████████▏ | 1228/1501 [00:59<00:14, 18.88 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:59<00:06, 6.07s/ url]
Dl Size...: 82%|████████▏ | 1228/1501 [00:59<00:14, 18.88 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:59<00:06, 6.07s/ url]
Dl Size...: 82%|████████▏ | 1229/1501 [00:59<00:14, 18.88 MiB/s]
Dl Size...: 82%|████████▏ | 1230/1501 [00:59<00:14, 18.95 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:59<00:06, 6.07s/ url]
Dl Size...: 82%|████████▏ | 1230/1501 [00:59<00:14, 18.95 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:59<00:06, 6.07s/ url]
Dl Size...: 82%|████████▏ | 1231/1501 [00:59<00:14, 18.95 MiB/s]
Dl Size...: 82%|████████▏ | 1232/1501 [00:59<00:14, 18.90 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:59<00:06, 6.07s/ url]
Dl Size...: 82%|████████▏ | 1232/1501 [00:59<00:14, 18.90 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:59<00:06, 6.07s/ url]
Dl Size...: 82%|████████▏ | 1233/1501 [00:59<00:14, 18.90 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [00:59<00:06, 6.07s/ url]
Dl Size...: 82%|████████▏ | 1234/1501 [00:59<00:14, 18.90 MiB/s]
Dl Size...: 82%|████████▏ | 1235/1501 [01:00<00:13, 19.64 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:00<00:06, 6.07s/ url]
Dl Size...: 82%|████████▏ | 1235/1501 [01:00<00:13, 19.64 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:00<00:06, 6.07s/ url]
Dl Size...: 82%|████████▏ | 1236/1501 [01:00<00:13, 19.64 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:00<00:06, 6.07s/ url]
Dl Size...: 82%|████████▏ | 1237/1501 [01:00<00:13, 19.64 MiB/s]
Dl Size...: 82%|████████▏ | 1238/1501 [01:00<00:12, 20.53 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:00<00:06, 6.07s/ url]
Dl Size...: 82%|████████▏ | 1238/1501 [01:00<00:12, 20.53 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:00<00:06, 6.07s/ url]
Dl Size...: 83%|████████▎ | 1239/1501 [01:00<00:12, 20.53 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:00<00:06, 6.07s/ url]
Dl Size...: 83%|████████▎ | 1240/1501 [01:00<00:12, 20.53 MiB/s]
Dl Size...: 83%|████████▎ | 1241/1501 [01:00<00:12, 21.06 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:00<00:06, 6.07s/ url]
Dl Size...: 83%|████████▎ | 1241/1501 [01:00<00:12, 21.06 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:00<00:06, 6.07s/ url]
Dl Size...: 83%|████████▎ | 1242/1501 [01:00<00:12, 21.06 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:00<00:06, 6.07s/ url]
Dl Size...: 83%|████████▎ | 1243/1501 [01:00<00:12, 21.06 MiB/s]
Dl Size...: 83%|████████▎ | 1244/1501 [01:00<00:12, 20.92 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:00<00:06, 6.07s/ url]
Dl Size...: 83%|████████▎ | 1244/1501 [01:00<00:12, 20.92 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:00<00:06, 6.07s/ url]
Dl Size...: 83%|████████▎ | 1245/1501 [01:00<00:12, 20.92 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:00<00:06, 6.07s/ url]
Dl Size...: 83%|████████▎ | 1246/1501 [01:00<00:12, 20.92 MiB/s]
Dl Size...: 83%|████████▎ | 1247/1501 [01:00<00:11, 21.45 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:00<00:06, 6.07s/ url]
Dl Size...: 83%|████████▎ | 1247/1501 [01:00<00:11, 21.45 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:00<00:06, 6.07s/ url]
Dl Size...: 83%|████████▎ | 1248/1501 [01:00<00:11, 21.45 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:00<00:06, 6.07s/ url]
Dl Size...: 83%|████████▎ | 1249/1501 [01:00<00:11, 21.45 MiB/s]
Dl Size...: 83%|████████▎ | 1250/1501 [01:00<00:13, 18.55 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:00<00:06, 6.07s/ url]
Dl Size...: 83%|████████▎ | 1250/1501 [01:00<00:13, 18.55 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:00<00:06, 6.07s/ url]
Dl Size...: 83%|████████▎ | 1251/1501 [01:00<00:13, 18.55 MiB/s]
Dl Size...: 83%|████████▎ | 1252/1501 [01:00<00:14, 17.61 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:00<00:06, 6.07s/ url]
Dl Size...: 83%|████████▎ | 1252/1501 [01:00<00:14, 17.61 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:00<00:06, 6.07s/ url]
Dl Size...: 83%|████████▎ | 1253/1501 [01:00<00:14, 17.61 MiB/s]
Dl Size...: 84%|████████▎ | 1254/1501 [01:01<00:14, 16.88 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:01<00:06, 6.07s/ url]
Dl Size...: 84%|████████▎ | 1254/1501 [01:01<00:14, 16.88 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:01<00:06, 6.07s/ url]
Dl Size...: 84%|████████▎ | 1255/1501 [01:01<00:14, 16.88 MiB/s]
Dl Size...: 84%|████████▎ | 1256/1501 [01:01<00:15, 16.17 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:01<00:06, 6.07s/ url]
Dl Size...: 84%|████████▎ | 1256/1501 [01:01<00:15, 16.17 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:01<00:06, 6.07s/ url]
Dl Size...: 84%|████████▎ | 1257/1501 [01:01<00:15, 16.17 MiB/s]
Dl Size...: 84%|████████▍ | 1258/1501 [01:01<00:15, 15.54 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:01<00:06, 6.07s/ url]
Dl Size...: 84%|████████▍ | 1258/1501 [01:01<00:15, 15.54 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:01<00:06, 6.07s/ url]
Dl Size...: 84%|████████▍ | 1259/1501 [01:01<00:15, 15.54 MiB/s]
Dl Size...: 84%|████████▍ | 1260/1501 [01:01<00:16, 15.00 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:01<00:06, 6.07s/ url]
Dl Size...: 84%|████████▍ | 1260/1501 [01:01<00:16, 15.00 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:01<00:06, 6.07s/ url]
Dl Size...: 84%|████████▍ | 1261/1501 [01:01<00:15, 15.00 MiB/s]
Dl Size...: 84%|████████▍ | 1262/1501 [01:01<00:15, 15.33 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:01<00:06, 6.07s/ url]
Dl Size...: 84%|████████▍ | 1262/1501 [01:01<00:15, 15.33 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:01<00:06, 6.07s/ url]
Dl Size...: 84%|████████▍ | 1263/1501 [01:01<00:15, 15.33 MiB/s]
Dl Size...: 84%|████████▍ | 1264/1501 [01:01<00:16, 14.75 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:01<00:06, 6.07s/ url]
Dl Size...: 84%|████████▍ | 1264/1501 [01:01<00:16, 14.75 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:01<00:06, 6.07s/ url]
Dl Size...: 84%|████████▍ | 1265/1501 [01:01<00:15, 14.75 MiB/s]
Dl Size...: 84%|████████▍ | 1266/1501 [01:01<00:16, 14.36 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:01<00:06, 6.07s/ url]
Dl Size...: 84%|████████▍ | 1266/1501 [01:01<00:16, 14.36 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:01<00:06, 6.07s/ url]
Dl Size...: 84%|████████▍ | 1267/1501 [01:01<00:16, 14.36 MiB/s]
Dl Size...: 84%|████████▍ | 1268/1501 [01:02<00:16, 14.54 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:02<00:06, 6.07s/ url]
Dl Size...: 84%|████████▍ | 1268/1501 [01:02<00:16, 14.54 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:02<00:06, 6.07s/ url]
Dl Size...: 85%|████████▍ | 1269/1501 [01:02<00:15, 14.54 MiB/s]
Dl Size...: 85%|████████▍ | 1270/1501 [01:02<00:16, 14.35 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:02<00:06, 6.07s/ url]
Dl Size...: 85%|████████▍ | 1270/1501 [01:02<00:16, 14.35 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:02<00:06, 6.07s/ url]
Dl Size...: 85%|████████▍ | 1271/1501 [01:02<00:16, 14.35 MiB/s]
Dl Size...: 85%|████████▍ | 1272/1501 [01:02<00:15, 15.16 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:02<00:06, 6.07s/ url]
Dl Size...: 85%|████████▍ | 1272/1501 [01:02<00:15, 15.16 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:02<00:06, 6.07s/ url]
Dl Size...: 85%|████████▍ | 1273/1501 [01:02<00:15, 15.16 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:02<00:06, 6.07s/ url]
Dl Size...: 85%|████████▍ | 1274/1501 [01:02<00:14, 15.16 MiB/s]
Dl Size...: 85%|████████▍ | 1275/1501 [01:02<00:13, 16.41 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:02<00:06, 6.07s/ url]
Dl Size...: 85%|████████▍ | 1275/1501 [01:02<00:13, 16.41 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:02<00:06, 6.07s/ url]
Dl Size...: 85%|████████▌ | 1276/1501 [01:02<00:13, 16.41 MiB/s]
Dl Size...: 85%|████████▌ | 1277/1501 [01:02<00:14, 15.68 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:02<00:06, 6.07s/ url]
Dl Size...: 85%|████████▌ | 1277/1501 [01:02<00:14, 15.68 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:02<00:06, 6.07s/ url]
Dl Size...: 85%|████████▌ | 1278/1501 [01:02<00:14, 15.68 MiB/s]
Dl Size...: 85%|████████▌ | 1279/1501 [01:02<00:14, 15.53 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:02<00:06, 6.07s/ url]
Dl Size...: 85%|████████▌ | 1279/1501 [01:02<00:14, 15.53 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:02<00:06, 6.07s/ url]
Dl Size...: 85%|████████▌ | 1280/1501 [01:02<00:14, 15.53 MiB/s]
Dl Size...: 85%|████████▌ | 1281/1501 [01:02<00:14, 15.43 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:02<00:06, 6.07s/ url]
Dl Size...: 85%|████████▌ | 1281/1501 [01:02<00:14, 15.43 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:02<00:06, 6.07s/ url]
Dl Size...: 85%|████████▌ | 1282/1501 [01:02<00:14, 15.43 MiB/s]
Dl Size...: 85%|████████▌ | 1283/1501 [01:02<00:14, 15.31 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:02<00:06, 6.07s/ url]
Dl Size...: 85%|████████▌ | 1283/1501 [01:02<00:14, 15.31 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:03<00:06, 6.07s/ url]
Dl Size...: 86%|████████▌ | 1284/1501 [01:03<00:14, 15.31 MiB/s]
Dl Size...: 86%|████████▌ | 1285/1501 [01:03<00:14, 15.40 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:03<00:06, 6.07s/ url]
Dl Size...: 86%|████████▌ | 1285/1501 [01:03<00:14, 15.40 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:03<00:06, 6.07s/ url]
Dl Size...: 86%|████████▌ | 1286/1501 [01:03<00:13, 15.40 MiB/s]
Dl Size...: 86%|████████▌ | 1287/1501 [01:03<00:14, 15.28 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:03<00:06, 6.07s/ url]
Dl Size...: 86%|████████▌ | 1287/1501 [01:03<00:14, 15.28 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:03<00:06, 6.07s/ url]
Dl Size...: 86%|████████▌ | 1288/1501 [01:03<00:13, 15.28 MiB/s]
Dl Size...: 86%|████████▌ | 1289/1501 [01:03<00:14, 15.09 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:03<00:06, 6.07s/ url]
Dl Size...: 86%|████████▌ | 1289/1501 [01:03<00:14, 15.09 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:03<00:06, 6.07s/ url]
Dl Size...: 86%|████████▌ | 1290/1501 [01:03<00:13, 15.09 MiB/s]
Dl Size...: 86%|████████▌ | 1291/1501 [01:03<00:13, 15.32 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:03<00:06, 6.07s/ url]
Dl Size...: 86%|████████▌ | 1291/1501 [01:03<00:13, 15.32 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:03<00:06, 6.07s/ url]
Dl Size...: 86%|████████▌ | 1292/1501 [01:03<00:13, 15.32 MiB/s]
Dl Size...: 86%|████████▌ | 1293/1501 [01:03<00:13, 14.89 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:03<00:06, 6.07s/ url]
Dl Size...: 86%|████████▌ | 1293/1501 [01:03<00:13, 14.89 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:03<00:06, 6.07s/ url]
Dl Size...: 86%|████████▌ | 1294/1501 [01:03<00:13, 14.89 MiB/s]
Dl Size...: 86%|████████▋ | 1295/1501 [01:03<00:13, 15.44 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:03<00:06, 6.07s/ url]
Dl Size...: 86%|████████▋ | 1295/1501 [01:03<00:13, 15.44 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:03<00:06, 6.07s/ url]
Dl Size...: 86%|████████▋ | 1296/1501 [01:03<00:13, 15.44 MiB/s]
Dl Size...: 86%|████████▋ | 1297/1501 [01:03<00:12, 15.98 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:03<00:06, 6.07s/ url]
Dl Size...: 86%|████████▋ | 1297/1501 [01:03<00:12, 15.98 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:03<00:06, 6.07s/ url]
Dl Size...: 86%|████████▋ | 1298/1501 [01:03<00:12, 15.98 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:03<00:06, 6.07s/ url]
Dl Size...: 87%|████████▋ | 1299/1501 [01:03<00:12, 15.98 MiB/s]
Dl Size...: 87%|████████▋ | 1300/1501 [01:04<00:11, 17.57 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:04<00:06, 6.07s/ url]
Dl Size...: 87%|████████▋ | 1300/1501 [01:04<00:11, 17.57 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:04<00:06, 6.07s/ url]
Dl Size...: 87%|████████▋ | 1301/1501 [01:04<00:11, 17.57 MiB/s]
Dl Size...: 87%|████████▋ | 1302/1501 [01:04<00:10, 18.10 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:04<00:06, 6.07s/ url]
Dl Size...: 87%|████████▋ | 1302/1501 [01:04<00:10, 18.10 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:04<00:06, 6.07s/ url]
Dl Size...: 87%|████████▋ | 1303/1501 [01:04<00:10, 18.10 MiB/s]
Dl Size...: 87%|████████▋ | 1304/1501 [01:04<00:10, 18.50 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:04<00:06, 6.07s/ url]
Dl Size...: 87%|████████▋ | 1304/1501 [01:04<00:10, 18.50 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:04<00:06, 6.07s/ url]
Dl Size...: 87%|████████▋ | 1305/1501 [01:04<00:10, 18.50 MiB/s]
Dl Size...: 87%|████████▋ | 1306/1501 [01:04<00:10, 18.41 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:04<00:06, 6.07s/ url]
Dl Size...: 87%|████████▋ | 1306/1501 [01:04<00:10, 18.41 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:04<00:06, 6.07s/ url]
Dl Size...: 87%|████████▋ | 1307/1501 [01:04<00:10, 18.41 MiB/s]
Dl Size...: 87%|████████▋ | 1308/1501 [01:04<00:10, 18.09 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:04<00:06, 6.07s/ url]
Dl Size...: 87%|████████▋ | 1308/1501 [01:04<00:10, 18.09 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:04<00:06, 6.07s/ url]
Dl Size...: 87%|████████▋ | 1309/1501 [01:04<00:10, 18.09 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:04<00:06, 6.07s/ url]
Dl Size...: 87%|████████▋ | 1310/1501 [01:04<00:10, 18.09 MiB/s]
Dl Size...: 87%|████████▋ | 1311/1501 [01:04<00:09, 19.02 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:04<00:06, 6.07s/ url]
Dl Size...: 87%|████████▋ | 1311/1501 [01:04<00:09, 19.02 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:04<00:06, 6.07s/ url]
Dl Size...: 87%|████████▋ | 1312/1501 [01:04<00:09, 19.02 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:04<00:06, 6.07s/ url]
Dl Size...: 87%|████████▋ | 1313/1501 [01:04<00:09, 19.02 MiB/s]
Dl Size...: 88%|████████▊ | 1314/1501 [01:04<00:09, 19.10 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:04<00:06, 6.07s/ url]
Dl Size...: 88%|████████▊ | 1314/1501 [01:04<00:09, 19.10 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:04<00:06, 6.07s/ url]
Dl Size...: 88%|████████▊ | 1315/1501 [01:04<00:09, 19.10 MiB/s]
Dl Size...: 88%|████████▊ | 1316/1501 [01:04<00:09, 19.31 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:04<00:06, 6.07s/ url]
Dl Size...: 88%|████████▊ | 1316/1501 [01:04<00:09, 19.31 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:04<00:06, 6.07s/ url]
Dl Size...: 88%|████████▊ | 1317/1501 [01:04<00:09, 19.31 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:04<00:06, 6.07s/ url]
Dl Size...: 88%|████████▊ | 1318/1501 [01:04<00:09, 19.31 MiB/s]
Dl Size...: 88%|████████▊ | 1319/1501 [01:05<00:09, 19.74 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:05<00:06, 6.07s/ url]
Dl Size...: 88%|████████▊ | 1319/1501 [01:05<00:09, 19.74 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:05<00:06, 6.07s/ url]
Dl Size...: 88%|████████▊ | 1320/1501 [01:05<00:09, 19.74 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:05<00:06, 6.07s/ url]
Dl Size...: 88%|████████▊ | 1321/1501 [01:05<00:09, 19.74 MiB/s]
Dl Size...: 88%|████████▊ | 1322/1501 [01:05<00:08, 19.92 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:05<00:06, 6.07s/ url]
Dl Size...: 88%|████████▊ | 1322/1501 [01:05<00:08, 19.92 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:05<00:06, 6.07s/ url]
Dl Size...: 88%|████████▊ | 1323/1501 [01:05<00:08, 19.92 MiB/s]
Dl Size...: 88%|████████▊ | 1324/1501 [01:05<00:08, 19.85 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:05<00:06, 6.07s/ url]
Dl Size...: 88%|████████▊ | 1324/1501 [01:05<00:08, 19.85 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:05<00:06, 6.07s/ url]
Dl Size...: 88%|████████▊ | 1325/1501 [01:05<00:08, 19.85 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:05<00:06, 6.07s/ url]
Dl Size...: 88%|████████▊ | 1326/1501 [01:05<00:08, 19.85 MiB/s]
Dl Size...: 88%|████████▊ | 1327/1501 [01:05<00:08, 19.90 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:05<00:06, 6.07s/ url]
Dl Size...: 88%|████████▊ | 1327/1501 [01:05<00:08, 19.90 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:05<00:06, 6.07s/ url]
Dl Size...: 88%|████████▊ | 1328/1501 [01:05<00:08, 19.90 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:05<00:06, 6.07s/ url]
Dl Size...: 89%|████████▊ | 1329/1501 [01:05<00:08, 19.90 MiB/s]
Dl Size...: 89%|████████▊ | 1330/1501 [01:05<00:08, 20.17 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:05<00:06, 6.07s/ url]
Dl Size...: 89%|████████▊ | 1330/1501 [01:05<00:08, 20.17 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:05<00:06, 6.07s/ url]
Dl Size...: 89%|████████▊ | 1331/1501 [01:05<00:08, 20.17 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:05<00:06, 6.07s/ url]
Dl Size...: 89%|████████▊ | 1332/1501 [01:05<00:08, 20.17 MiB/s]
Dl Size...: 89%|████████▉ | 1333/1501 [01:05<00:08, 19.75 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:05<00:06, 6.07s/ url]
Dl Size...: 89%|████████▉ | 1333/1501 [01:05<00:08, 19.75 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:05<00:06, 6.07s/ url]
Dl Size...: 89%|████████▉ | 1334/1501 [01:05<00:08, 19.75 MiB/s]
Dl Size...: 89%|████████▉ | 1335/1501 [01:05<00:08, 19.68 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:05<00:06, 6.07s/ url]
Dl Size...: 89%|████████▉ | 1335/1501 [01:05<00:08, 19.68 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:05<00:06, 6.07s/ url]
Dl Size...: 89%|████████▉ | 1336/1501 [01:05<00:08, 19.68 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:05<00:06, 6.07s/ url]
Dl Size...: 89%|████████▉ | 1337/1501 [01:05<00:08, 19.68 MiB/s]
Dl Size...: 89%|████████▉ | 1338/1501 [01:05<00:08, 20.14 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:05<00:06, 6.07s/ url]
Dl Size...: 89%|████████▉ | 1338/1501 [01:05<00:08, 20.14 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:06<00:06, 6.07s/ url]
Dl Size...: 89%|████████▉ | 1339/1501 [01:06<00:08, 20.14 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:06<00:06, 6.07s/ url]
Dl Size...: 89%|████████▉ | 1340/1501 [01:06<00:07, 20.14 MiB/s]
Dl Size...: 89%|████████▉ | 1341/1501 [01:06<00:08, 19.97 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:06<00:06, 6.07s/ url]
Dl Size...: 89%|████████▉ | 1341/1501 [01:06<00:08, 19.97 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:06<00:06, 6.07s/ url]
Dl Size...: 89%|████████▉ | 1342/1501 [01:06<00:07, 19.97 MiB/s]
Dl Size...: 89%|████████▉ | 1343/1501 [01:06<00:08, 17.70 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:06<00:06, 6.07s/ url]
Dl Size...: 89%|████████▉ | 1343/1501 [01:06<00:08, 17.70 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:06<00:06, 6.07s/ url]
Dl Size...: 90%|████████▉ | 1344/1501 [01:06<00:08, 17.70 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:06<00:06, 6.07s/ url]
Dl Size...: 90%|████████▉ | 1345/1501 [01:06<00:08, 17.70 MiB/s]
Dl Size...: 90%|████████▉ | 1346/1501 [01:06<00:08, 18.32 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:06<00:06, 6.07s/ url]
Dl Size...: 90%|████████▉ | 1346/1501 [01:06<00:08, 18.32 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:06<00:06, 6.07s/ url]
Dl Size...: 90%|████████▉ | 1347/1501 [01:06<00:08, 18.32 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:06<00:06, 6.07s/ url]
Dl Size...: 90%|████████▉ | 1348/1501 [01:06<00:08, 18.32 MiB/s]
Dl Size...: 90%|████████▉ | 1349/1501 [01:06<00:08, 18.91 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:06<00:06, 6.07s/ url]
Dl Size...: 90%|████████▉ | 1349/1501 [01:06<00:08, 18.91 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:06<00:06, 6.07s/ url]
Dl Size...: 90%|████████▉ | 1350/1501 [01:06<00:07, 18.91 MiB/s]
Dl Size...: 90%|█████████ | 1351/1501 [01:06<00:07, 18.77 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:06<00:06, 6.07s/ url]
Dl Size...: 90%|█████████ | 1351/1501 [01:06<00:07, 18.77 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:06<00:06, 6.07s/ url]
Dl Size...: 90%|█████████ | 1352/1501 [01:06<00:07, 18.77 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:06<00:06, 6.07s/ url]
Dl Size...: 90%|█████████ | 1353/1501 [01:06<00:07, 18.77 MiB/s]
Dl Size...: 90%|█████████ | 1354/1501 [01:06<00:07, 19.51 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:06<00:06, 6.07s/ url]
Dl Size...: 90%|█████████ | 1354/1501 [01:06<00:07, 19.51 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:06<00:06, 6.07s/ url]
Dl Size...: 90%|█████████ | 1355/1501 [01:06<00:07, 19.51 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:06<00:06, 6.07s/ url]
Dl Size...: 90%|█████████ | 1356/1501 [01:06<00:07, 19.51 MiB/s]
Dl Size...: 90%|█████████ | 1357/1501 [01:06<00:07, 19.97 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:06<00:06, 6.07s/ url]
Dl Size...: 90%|█████████ | 1357/1501 [01:06<00:07, 19.97 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:07<00:06, 6.07s/ url]
Dl Size...: 90%|█████████ | 1358/1501 [01:07<00:07, 19.97 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:07<00:06, 6.07s/ url]
Dl Size...: 91%|█████████ | 1359/1501 [01:07<00:07, 19.97 MiB/s]
Dl Size...: 91%|█████████ | 1360/1501 [01:07<00:07, 19.89 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:07<00:06, 6.07s/ url]
Dl Size...: 91%|█████████ | 1360/1501 [01:07<00:07, 19.89 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:07<00:06, 6.07s/ url]
Dl Size...: 91%|█████████ | 1361/1501 [01:07<00:07, 19.89 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:07<00:06, 6.07s/ url]
Dl Size...: 91%|█████████ | 1362/1501 [01:07<00:06, 19.89 MiB/s]
Dl Size...: 91%|█████████ | 1363/1501 [01:07<00:06, 20.36 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:07<00:06, 6.07s/ url]
Dl Size...: 91%|█████████ | 1363/1501 [01:07<00:06, 20.36 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:07<00:06, 6.07s/ url]
Dl Size...: 91%|█████████ | 1364/1501 [01:07<00:06, 20.36 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:07<00:06, 6.07s/ url]
Dl Size...: 91%|█████████ | 1365/1501 [01:07<00:06, 20.36 MiB/s]
Dl Size...: 91%|█████████ | 1366/1501 [01:07<00:06, 20.16 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:07<00:06, 6.07s/ url]
Dl Size...: 91%|█████████ | 1366/1501 [01:07<00:06, 20.16 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:07<00:06, 6.07s/ url]
Dl Size...: 91%|█████████ | 1367/1501 [01:07<00:06, 20.16 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:07<00:06, 6.07s/ url]
Dl Size...: 91%|█████████ | 1368/1501 [01:07<00:06, 20.16 MiB/s]
Dl Size...: 91%|█████████ | 1369/1501 [01:07<00:06, 20.25 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:07<00:06, 6.07s/ url]
Dl Size...: 91%|█████████ | 1369/1501 [01:07<00:06, 20.25 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:07<00:06, 6.07s/ url]
Dl Size...: 91%|█████████▏| 1370/1501 [01:07<00:06, 20.25 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:07<00:06, 6.07s/ url]
Dl Size...: 91%|█████████▏| 1371/1501 [01:07<00:06, 20.25 MiB/s]
Dl Size...: 91%|█████████▏| 1372/1501 [01:07<00:06, 20.30 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:07<00:06, 6.07s/ url]
Dl Size...: 91%|█████████▏| 1372/1501 [01:07<00:06, 20.30 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:07<00:06, 6.07s/ url]
Dl Size...: 91%|█████████▏| 1373/1501 [01:07<00:06, 20.30 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:07<00:06, 6.07s/ url]
Dl Size...: 92%|█████████▏| 1374/1501 [01:07<00:06, 20.30 MiB/s]
Dl Size...: 92%|█████████▏| 1375/1501 [01:07<00:06, 20.14 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:07<00:06, 6.07s/ url]
Dl Size...: 92%|█████████▏| 1375/1501 [01:07<00:06, 20.14 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:07<00:06, 6.07s/ url]
Dl Size...: 92%|█████████▏| 1376/1501 [01:07<00:06, 20.14 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:07<00:06, 6.07s/ url]
Dl Size...: 92%|█████████▏| 1377/1501 [01:07<00:06, 20.14 MiB/s]
Dl Size...: 92%|█████████▏| 1378/1501 [01:08<00:06, 19.35 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:08<00:06, 6.07s/ url]
Dl Size...: 92%|█████████▏| 1378/1501 [01:08<00:06, 19.35 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:08<00:06, 6.07s/ url]
Dl Size...: 92%|█████████▏| 1379/1501 [01:08<00:06, 19.35 MiB/s]
Dl Size...: 92%|█████████▏| 1380/1501 [01:08<00:06, 19.38 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:08<00:06, 6.07s/ url]
Dl Size...: 92%|█████████▏| 1380/1501 [01:08<00:06, 19.38 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:08<00:06, 6.07s/ url]
Dl Size...: 92%|█████████▏| 1381/1501 [01:08<00:06, 19.38 MiB/s]
Dl Size...: 92%|█████████▏| 1382/1501 [01:08<00:06, 18.77 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:08<00:06, 6.07s/ url]
Dl Size...: 92%|█████████▏| 1382/1501 [01:08<00:06, 18.77 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:08<00:06, 6.07s/ url]
Dl Size...: 92%|█████████▏| 1383/1501 [01:08<00:06, 18.77 MiB/s]
Dl Size...: 92%|█████████▏| 1384/1501 [01:08<00:06, 18.33 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:08<00:06, 6.07s/ url]
Dl Size...: 92%|█████████▏| 1384/1501 [01:08<00:06, 18.33 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:08<00:06, 6.07s/ url]
Dl Size...: 92%|█████████▏| 1385/1501 [01:08<00:06, 18.33 MiB/s]
Dl Size...: 92%|█████████▏| 1386/1501 [01:08<00:06, 18.54 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:08<00:06, 6.07s/ url]
Dl Size...: 92%|█████████▏| 1386/1501 [01:08<00:06, 18.54 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:08<00:06, 6.07s/ url]
Dl Size...: 92%|█████████▏| 1387/1501 [01:08<00:06, 18.54 MiB/s]
Dl Size...: 92%|█████████▏| 1388/1501 [01:08<00:06, 18.68 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:08<00:06, 6.07s/ url]
Dl Size...: 92%|█████████▏| 1388/1501 [01:08<00:06, 18.68 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:08<00:06, 6.07s/ url]
Dl Size...: 93%|█████████▎| 1389/1501 [01:08<00:05, 18.68 MiB/s]
Dl Size...: 93%|█████████▎| 1390/1501 [01:08<00:06, 18.26 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:08<00:06, 6.07s/ url]
Dl Size...: 93%|█████████▎| 1390/1501 [01:08<00:06, 18.26 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:08<00:06, 6.07s/ url]
Dl Size...: 93%|█████████▎| 1391/1501 [01:08<00:06, 18.26 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:08<00:06, 6.07s/ url]
Dl Size...: 93%|█████████▎| 1392/1501 [01:08<00:05, 18.26 MiB/s]
Dl Size...: 93%|█████████▎| 1393/1501 [01:08<00:05, 19.00 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:08<00:06, 6.07s/ url]
Dl Size...: 93%|█████████▎| 1393/1501 [01:08<00:05, 19.00 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:08<00:06, 6.07s/ url]
Dl Size...: 93%|█████████▎| 1394/1501 [01:08<00:05, 19.00 MiB/s]
Dl Size...: 93%|█████████▎| 1395/1501 [01:08<00:05, 19.12 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:08<00:06, 6.07s/ url]
Dl Size...: 93%|█████████▎| 1395/1501 [01:08<00:05, 19.12 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:08<00:06, 6.07s/ url]
Dl Size...: 93%|█████████▎| 1396/1501 [01:08<00:05, 19.12 MiB/s]
Dl Size...: 93%|█████████▎| 1397/1501 [01:09<00:05, 19.29 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:09<00:06, 6.07s/ url]
Dl Size...: 93%|█████████▎| 1397/1501 [01:09<00:05, 19.29 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:09<00:06, 6.07s/ url]
Dl Size...: 93%|█████████▎| 1398/1501 [01:09<00:05, 19.29 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:09<00:06, 6.07s/ url]
Dl Size...: 93%|█████████▎| 1399/1501 [01:09<00:05, 19.29 MiB/s]
Dl Size...: 93%|█████████▎| 1400/1501 [01:09<00:05, 19.82 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:09<00:06, 6.07s/ url]
Dl Size...: 93%|█████████▎| 1400/1501 [01:09<00:05, 19.82 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:09<00:06, 6.07s/ url]
Dl Size...: 93%|█████████▎| 1401/1501 [01:09<00:05, 19.82 MiB/s]
Dl Size...: 93%|█████████▎| 1402/1501 [01:09<00:05, 19.73 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:09<00:06, 6.07s/ url]
Dl Size...: 93%|█████████▎| 1402/1501 [01:09<00:05, 19.73 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:09<00:06, 6.07s/ url]
Dl Size...: 93%|█████████▎| 1403/1501 [01:09<00:04, 19.73 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:09<00:06, 6.07s/ url]
Dl Size...: 94%|█████████▎| 1404/1501 [01:09<00:04, 19.73 MiB/s]
Dl Size...: 94%|█████████▎| 1405/1501 [01:09<00:04, 20.44 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:09<00:06, 6.07s/ url]
Dl Size...: 94%|█████████▎| 1405/1501 [01:09<00:04, 20.44 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:09<00:06, 6.07s/ url]
Dl Size...: 94%|█████████▎| 1406/1501 [01:09<00:04, 20.44 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:09<00:06, 6.07s/ url]
Dl Size...: 94%|█████████▎| 1407/1501 [01:09<00:04, 20.44 MiB/s]
Dl Size...: 94%|█████████▍| 1408/1501 [01:09<00:04, 20.39 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:09<00:06, 6.07s/ url]
Dl Size...: 94%|█████████▍| 1408/1501 [01:09<00:04, 20.39 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:09<00:06, 6.07s/ url]
Dl Size...: 94%|█████████▍| 1409/1501 [01:09<00:04, 20.39 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:09<00:06, 6.07s/ url]
Dl Size...: 94%|█████████▍| 1410/1501 [01:09<00:04, 20.39 MiB/s]
Dl Size...: 94%|█████████▍| 1411/1501 [01:09<00:04, 20.89 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:09<00:06, 6.07s/ url]
Dl Size...: 94%|█████████▍| 1411/1501 [01:09<00:04, 20.89 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:09<00:06, 6.07s/ url]
Dl Size...: 94%|█████████▍| 1412/1501 [01:09<00:04, 20.89 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:09<00:06, 6.07s/ url]
Dl Size...: 94%|█████████▍| 1413/1501 [01:09<00:04, 20.89 MiB/s]
Dl Size...: 94%|█████████▍| 1414/1501 [01:09<00:04, 21.12 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:09<00:06, 6.07s/ url]
Dl Size...: 94%|█████████▍| 1414/1501 [01:09<00:04, 21.12 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:09<00:06, 6.07s/ url]
Dl Size...: 94%|█████████▍| 1415/1501 [01:09<00:04, 21.12 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:09<00:06, 6.07s/ url]
Dl Size...: 94%|█████████▍| 1416/1501 [01:09<00:04, 21.12 MiB/s]
Dl Size...: 94%|█████████▍| 1417/1501 [01:09<00:03, 21.48 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:09<00:06, 6.07s/ url]
Dl Size...: 94%|█████████▍| 1417/1501 [01:09<00:03, 21.48 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:10<00:06, 6.07s/ url]
Dl Size...: 94%|█████████▍| 1418/1501 [01:10<00:03, 21.48 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:10<00:06, 6.07s/ url]
Dl Size...: 95%|█████████▍| 1419/1501 [01:10<00:03, 21.48 MiB/s]
Dl Size...: 95%|█████████▍| 1420/1501 [01:10<00:03, 21.24 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:10<00:06, 6.07s/ url]
Dl Size...: 95%|█████████▍| 1420/1501 [01:10<00:03, 21.24 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:10<00:06, 6.07s/ url]
Dl Size...: 95%|█████████▍| 1421/1501 [01:10<00:03, 21.24 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:10<00:06, 6.07s/ url]
Dl Size...: 95%|█████████▍| 1422/1501 [01:10<00:03, 21.24 MiB/s]
Dl Size...: 95%|█████████▍| 1423/1501 [01:10<00:03, 21.61 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:10<00:06, 6.07s/ url]
Dl Size...: 95%|█████████▍| 1423/1501 [01:10<00:03, 21.61 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:10<00:06, 6.07s/ url]
Dl Size...: 95%|█████████▍| 1424/1501 [01:10<00:03, 21.61 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:10<00:06, 6.07s/ url]
Dl Size...: 95%|█████████▍| 1425/1501 [01:10<00:03, 21.61 MiB/s]
Dl Size...: 95%|█████████▌| 1426/1501 [01:10<00:03, 21.69 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:10<00:06, 6.07s/ url]
Dl Size...: 95%|█████████▌| 1426/1501 [01:10<00:03, 21.69 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:10<00:06, 6.07s/ url]
Dl Size...: 95%|█████████▌| 1427/1501 [01:10<00:03, 21.69 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:10<00:06, 6.07s/ url]
Dl Size...: 95%|█████████▌| 1428/1501 [01:10<00:03, 21.69 MiB/s]
Dl Size...: 95%|█████████▌| 1429/1501 [01:10<00:03, 21.32 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:10<00:06, 6.07s/ url]
Dl Size...: 95%|█████████▌| 1429/1501 [01:10<00:03, 21.32 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:10<00:06, 6.07s/ url]
Dl Size...: 95%|█████████▌| 1430/1501 [01:10<00:03, 21.32 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:10<00:06, 6.07s/ url]
Dl Size...: 95%|█████████▌| 1431/1501 [01:10<00:03, 21.32 MiB/s]
Dl Size...: 95%|█████████▌| 1432/1501 [01:10<00:03, 21.34 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:10<00:06, 6.07s/ url]
Dl Size...: 95%|█████████▌| 1432/1501 [01:10<00:03, 21.34 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:10<00:06, 6.07s/ url]
Dl Size...: 95%|█████████▌| 1433/1501 [01:10<00:03, 21.34 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:10<00:06, 6.07s/ url]
Dl Size...: 96%|█████████▌| 1434/1501 [01:10<00:03, 21.34 MiB/s]
Dl Size...: 96%|█████████▌| 1435/1501 [01:10<00:03, 21.64 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:10<00:06, 6.07s/ url]
Dl Size...: 96%|█████████▌| 1435/1501 [01:10<00:03, 21.64 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:10<00:06, 6.07s/ url]
Dl Size...: 96%|█████████▌| 1436/1501 [01:10<00:03, 21.64 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:10<00:06, 6.07s/ url]
Dl Size...: 96%|█████████▌| 1437/1501 [01:10<00:02, 21.64 MiB/s]
Dl Size...: 96%|█████████▌| 1438/1501 [01:10<00:02, 21.81 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:10<00:06, 6.07s/ url]
Dl Size...: 96%|█████████▌| 1438/1501 [01:10<00:02, 21.81 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:10<00:06, 6.07s/ url]
Dl Size...: 96%|█████████▌| 1439/1501 [01:10<00:02, 21.81 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:11<00:06, 6.07s/ url]
Dl Size...: 96%|█████████▌| 1440/1501 [01:11<00:02, 21.81 MiB/s]
Dl Size...: 96%|█████████▌| 1441/1501 [01:11<00:02, 21.68 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:11<00:06, 6.07s/ url]
Dl Size...: 96%|█████████▌| 1441/1501 [01:11<00:02, 21.68 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:11<00:06, 6.07s/ url]
Dl Size...: 96%|█████████▌| 1442/1501 [01:11<00:02, 21.68 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:11<00:06, 6.07s/ url]
Dl Size...: 96%|█████████▌| 1443/1501 [01:11<00:02, 21.68 MiB/s]
Dl Size...: 96%|█████████▌| 1444/1501 [01:11<00:02, 21.90 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:11<00:06, 6.07s/ url]
Dl Size...: 96%|█████████▌| 1444/1501 [01:11<00:02, 21.90 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:11<00:06, 6.07s/ url]
Dl Size...: 96%|█████████▋| 1445/1501 [01:11<00:02, 21.90 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:11<00:06, 6.07s/ url]
Dl Size...: 96%|█████████▋| 1446/1501 [01:11<00:02, 21.90 MiB/s]
Dl Size...: 96%|█████████▋| 1447/1501 [01:11<00:02, 21.98 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:11<00:06, 6.07s/ url]
Dl Size...: 96%|█████████▋| 1447/1501 [01:11<00:02, 21.98 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:11<00:06, 6.07s/ url]
Dl Size...: 96%|█████████▋| 1448/1501 [01:11<00:02, 21.98 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:11<00:06, 6.07s/ url]
Dl Size...: 97%|█████████▋| 1449/1501 [01:11<00:02, 21.98 MiB/s]
Dl Size...: 97%|█████████▋| 1450/1501 [01:11<00:02, 21.86 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:11<00:06, 6.07s/ url]
Dl Size...: 97%|█████████▋| 1450/1501 [01:11<00:02, 21.86 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:11<00:06, 6.07s/ url]
Dl Size...: 97%|█████████▋| 1451/1501 [01:11<00:02, 21.86 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:11<00:06, 6.07s/ url]
Dl Size...: 97%|█████████▋| 1452/1501 [01:11<00:02, 21.86 MiB/s]
Dl Size...: 97%|█████████▋| 1453/1501 [01:11<00:02, 21.65 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:11<00:06, 6.07s/ url]
Dl Size...: 97%|█████████▋| 1453/1501 [01:11<00:02, 21.65 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:11<00:06, 6.07s/ url]
Dl Size...: 97%|█████████▋| 1454/1501 [01:11<00:02, 21.65 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:11<00:06, 6.07s/ url]
Dl Size...: 97%|█████████▋| 1455/1501 [01:11<00:02, 21.65 MiB/s]
Dl Size...: 97%|█████████▋| 1456/1501 [01:11<00:02, 21.67 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:11<00:06, 6.07s/ url]
Dl Size...: 97%|█████████▋| 1456/1501 [01:11<00:02, 21.67 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:11<00:06, 6.07s/ url]
Dl Size...: 97%|█████████▋| 1457/1501 [01:11<00:02, 21.67 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:11<00:06, 6.07s/ url]
Dl Size...: 97%|█████████▋| 1458/1501 [01:11<00:01, 21.67 MiB/s]
Dl Size...: 97%|█████████▋| 1459/1501 [01:11<00:01, 21.48 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:11<00:06, 6.07s/ url]
Dl Size...: 97%|█████████▋| 1459/1501 [01:11<00:01, 21.48 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:11<00:06, 6.07s/ url]
Dl Size...: 97%|█████████▋| 1460/1501 [01:11<00:01, 21.48 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:11<00:06, 6.07s/ url]
Dl Size...: 97%|█████████▋| 1461/1501 [01:11<00:01, 21.48 MiB/s]
Dl Size...: 97%|█████████▋| 1462/1501 [01:12<00:01, 21.81 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:12<00:06, 6.07s/ url]
Dl Size...: 97%|█████████▋| 1462/1501 [01:12<00:01, 21.81 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:12<00:06, 6.07s/ url]
Dl Size...: 97%|█████████▋| 1463/1501 [01:12<00:01, 21.81 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:12<00:06, 6.07s/ url]
Dl Size...: 98%|█████████▊| 1464/1501 [01:12<00:01, 21.81 MiB/s]
Dl Size...: 98%|█████████▊| 1465/1501 [01:12<00:01, 21.72 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:12<00:06, 6.07s/ url]
Dl Size...: 98%|█████████▊| 1465/1501 [01:12<00:01, 21.72 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:12<00:06, 6.07s/ url]
Dl Size...: 98%|█████████▊| 1466/1501 [01:12<00:01, 21.72 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:12<00:06, 6.07s/ url]
Dl Size...: 98%|█████████▊| 1467/1501 [01:12<00:01, 21.72 MiB/s]
Dl Size...: 98%|█████████▊| 1468/1501 [01:12<00:01, 20.65 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:12<00:06, 6.07s/ url]
Dl Size...: 98%|█████████▊| 1468/1501 [01:12<00:01, 20.65 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:12<00:06, 6.07s/ url]
Dl Size...: 98%|█████████▊| 1469/1501 [01:12<00:01, 20.65 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:12<00:06, 6.07s/ url]
Dl Size...: 98%|█████████▊| 1470/1501 [01:12<00:01, 20.65 MiB/s]
Dl Size...: 98%|█████████▊| 1471/1501 [01:12<00:01, 20.37 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:12<00:06, 6.07s/ url]
Dl Size...: 98%|█████████▊| 1471/1501 [01:12<00:01, 20.37 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:12<00:06, 6.07s/ url]
Dl Size...: 98%|█████████▊| 1472/1501 [01:12<00:01, 20.37 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:12<00:06, 6.07s/ url]
Dl Size...: 98%|█████████▊| 1473/1501 [01:12<00:01, 20.37 MiB/s]
Dl Size...: 98%|█████████▊| 1474/1501 [01:12<00:01, 20.03 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:12<00:06, 6.07s/ url]
Dl Size...: 98%|█████████▊| 1474/1501 [01:12<00:01, 20.03 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:12<00:06, 6.07s/ url]
Dl Size...: 98%|█████████▊| 1475/1501 [01:12<00:01, 20.03 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:12<00:06, 6.07s/ url]
Dl Size...: 98%|█████████▊| 1476/1501 [01:12<00:01, 20.03 MiB/s]
Dl Size...: 98%|█████████▊| 1477/1501 [01:12<00:01, 19.55 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:12<00:06, 6.07s/ url]
Dl Size...: 98%|█████████▊| 1477/1501 [01:12<00:01, 19.55 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:12<00:06, 6.07s/ url]
Dl Size...: 98%|█████████▊| 1478/1501 [01:12<00:01, 19.55 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:12<00:06, 6.07s/ url]
Dl Size...: 99%|█████████▊| 1479/1501 [01:12<00:01, 19.55 MiB/s]
Dl Size...: 99%|█████████▊| 1480/1501 [01:12<00:01, 19.79 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:12<00:06, 6.07s/ url]
Dl Size...: 99%|█████████▊| 1480/1501 [01:12<00:01, 19.79 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:13<00:06, 6.07s/ url]
Dl Size...: 99%|█████████▊| 1481/1501 [01:13<00:01, 19.79 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:13<00:06, 6.07s/ url]
Dl Size...: 99%|█████████▊| 1482/1501 [01:13<00:00, 19.79 MiB/s]
Dl Size...: 99%|█████████▉| 1483/1501 [01:13<00:00, 20.26 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:13<00:06, 6.07s/ url]
Dl Size...: 99%|█████████▉| 1483/1501 [01:13<00:00, 20.26 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:13<00:06, 6.07s/ url]
Dl Size...: 99%|█████████▉| 1484/1501 [01:13<00:00, 20.26 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:13<00:06, 6.07s/ url]
Dl Size...: 99%|█████████▉| 1485/1501 [01:13<00:00, 20.26 MiB/s]
Dl Size...: 99%|█████████▉| 1486/1501 [01:13<00:00, 20.83 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:13<00:06, 6.07s/ url]
Dl Size...: 99%|█████████▉| 1486/1501 [01:13<00:00, 20.83 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:13<00:06, 6.07s/ url]
Dl Size...: 99%|█████████▉| 1487/1501 [01:13<00:00, 20.83 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:13<00:06, 6.07s/ url]
Dl Size...: 99%|█████████▉| 1488/1501 [01:13<00:00, 20.83 MiB/s]
Dl Size...: 99%|█████████▉| 1489/1501 [01:13<00:00, 20.98 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:13<00:06, 6.07s/ url]
Dl Size...: 99%|█████████▉| 1489/1501 [01:13<00:00, 20.98 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:13<00:06, 6.07s/ url]
Dl Size...: 99%|█████████▉| 1490/1501 [01:13<00:00, 20.98 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:13<00:06, 6.07s/ url]
Dl Size...: 99%|█████████▉| 1491/1501 [01:13<00:00, 20.98 MiB/s]
Dl Size...: 99%|█████████▉| 1492/1501 [01:13<00:00, 21.24 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:13<00:06, 6.07s/ url]
Dl Size...: 99%|█████████▉| 1492/1501 [01:13<00:00, 21.24 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:13<00:06, 6.07s/ url]
Dl Size...: 99%|█████████▉| 1493/1501 [01:13<00:00, 21.24 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:13<00:06, 6.07s/ url]
Dl Size...: 100%|█████████▉| 1494/1501 [01:13<00:00, 21.24 MiB/s]
Dl Size...: 100%|█████████▉| 1495/1501 [01:13<00:00, 21.23 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:13<00:06, 6.07s/ url]
Dl Size...: 100%|█████████▉| 1495/1501 [01:13<00:00, 21.23 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:13<00:06, 6.07s/ url]
Dl Size...: 100%|█████████▉| 1496/1501 [01:13<00:00, 21.23 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:13<00:06, 6.07s/ url]
Dl Size...: 100%|█████████▉| 1497/1501 [01:13<00:00, 21.23 MiB/s]
Dl Size...: 100%|█████████▉| 1498/1501 [01:13<00:00, 21.78 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:13<00:06, 6.07s/ url]
Dl Size...: 100%|█████████▉| 1498/1501 [01:13<00:00, 21.78 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:13<00:06, 6.07s/ url]
Dl Size...: 100%|█████████▉| 1499/1501 [01:13<00:00, 21.78 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:13<00:06, 6.07s/ url]
Dl Size...: 100%|█████████▉| 1500/1501 [01:13<00:00, 21.78 MiB/s]
Dl Size...: 100%|██████████| 1501/1501 [01:13<00:00, 21.30 MiB/s]
Dl Completed...: 67%|██████▋ | 2/3 [01:13<00:06, 6.07s/ url]
Dl Size...: 100%|██████████| 1501/1501 [01:13<00:00, 21.30 MiB/s]
Dl Completed...: 100%|██████████| 3/3 [01:13<00:00, 31.57s/ url]
Dl Completed...: 100%|██████████| 3/3 [01:13<00:00, 31.57s/ url]
Dl Size...: 100%|██████████| 1501/1501 [01:13<00:00, 21.30 MiB/s]
Dl Size...: 100%|██████████| 1501/1501 [01:13<00:00, 20.30 MiB/s]
Dl Completed...: 100%|██████████| 3/3 [01:13<00:00, 24.65s/ url]
Generating splits...: 0%| | 0/3 [00:00<?, ? splits/s]
Generating train examples...: 0 examples [00:00, ? examples/s]
2023-12-15 17:21:04.025694: 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, 1.24s/ examples]
Generating train examples...: 311 examples [00:01, 321.88 examples/s]
Generating train examples...: 629 examples [00:01, 690.27 examples/s]
Generating train examples...: 949 examples [00:01, 1080.18 examples/s]
Generating train examples...: 1267 examples [00:01, 1461.68 examples/s]
Generating train examples...: 1590 examples [00:01, 1825.70 examples/s]
Generating train examples...: 1921 examples [00:01, 2162.78 examples/s]
Generating train examples...: 2245 examples [00:01, 2425.91 examples/s]
Generating train examples...: 2576 examples [00:02, 2654.07 examples/s]
Generating train examples...: 2906 examples [00:02, 2826.41 examples/s]
Generating train examples...: 3237 examples [00:02, 2959.96 examples/s]
Generating train examples...: 3563 examples [00:02, 3038.73 examples/s]
Generating train examples...: 3889 examples [00:02, 3101.37 examples/s]
Generating train examples...: 4214 examples [00:02, 3131.02 examples/s]
Generating train examples...: 4538 examples [00:02, 3159.64 examples/s]
Generating train examples...: 4862 examples [00:02, 3171.56 examples/s]
Generating train examples...: 5185 examples [00:02, 3174.92 examples/s]
Generating train examples...: 5509 examples [00:02, 3192.43 examples/s]
Generating train examples...: 5835 examples [00:03, 3210.58 examples/s]
Generating train examples...: 6158 examples [00:03, 3214.62 examples/s]
Generating train examples...: 6481 examples [00:03, 3215.33 examples/s]
Generating train examples...: 6807 examples [00:03, 3225.95 examples/s]
Generating train examples...: 7132 examples [00:03, 3231.33 examples/s]
Generating train examples...: 7457 examples [00:03, 3236.36 examples/s]
Generating train examples...: 7781 examples [00:03, 3232.67 examples/s]
Generating train examples...: 8108 examples [00:03, 3242.74 examples/s]
Generating train examples...: 8433 examples [00:03, 3226.32 examples/s]
Generating train examples...: 8756 examples [00:03, 3214.80 examples/s]
Generating train examples...: 9078 examples [00:04, 3204.46 examples/s]
Generating train examples...: 9400 examples [00:04, 3208.64 examples/s]
Generating train examples...: 9724 examples [00:04, 3216.67 examples/s]
Generating train examples...: 10048 examples [00:04, 3221.48 examples/s]
Generating train examples...: 10371 examples [00:04, 3223.58 examples/s]
Generating train examples...: 10694 examples [00:04, 3217.95 examples/s]
Generating train examples...: 11017 examples [00:04, 3219.28 examples/s]
Generating train examples...: 11343 examples [00:04, 3230.95 examples/s]
Generating train examples...: 11667 examples [00:04, 3225.70 examples/s]
Generating train examples...: 11993 examples [00:04, 3233.02 examples/s]
Generating train examples...: 12317 examples [00:05, 3229.06 examples/s]
Generating train examples...: 12640 examples [00:05, 3221.31 examples/s]
Generating train examples...: 12964 examples [00:05, 3226.19 examples/s]
Generating train examples...: 13290 examples [00:05, 3234.76 examples/s]
Generating train examples...: 13617 examples [00:05, 3242.16 examples/s]
Generating train examples...: 13942 examples [00:05, 3237.07 examples/s]
Generating train examples...: 14267 examples [00:05, 3239.12 examples/s]
Generating train examples...: 14591 examples [00:05, 3236.44 examples/s]
Generating train examples...: 14915 examples [00:05, 3236.84 examples/s]
Generating train examples...: 15239 examples [00:05, 3231.86 examples/s]
Generating train examples...: 15563 examples [00:06, 3232.21 examples/s]
Generating train examples...: 15887 examples [00:06, 3232.04 examples/s]
Generating train examples...: 16211 examples [00:06, 3227.83 examples/s]
Generating train examples...: 16534 examples [00:06, 3223.15 examples/s]
Generating train examples...: 16858 examples [00:06, 3227.84 examples/s]
Generating train examples...: 17181 examples [00:06, 3220.19 examples/s]
Generating train examples...: 17506 examples [00:06, 3228.30 examples/s]
Generating train examples...: 17829 examples [00:06, 3220.19 examples/s]
Generating train examples...: 18152 examples [00:06, 3217.67 examples/s]
Generating train examples...: 18478 examples [00:06, 3227.54 examples/s]
Generating train examples...: 18803 examples [00:07, 3232.72 examples/s]
Generating train examples...: 19129 examples [00:07, 3239.58 examples/s]
Generating train examples...: 19455 examples [00:07, 3242.65 examples/s]
Generating train examples...: 19780 examples [00:07, 3211.10 examples/s]
Generating train examples...: 20102 examples [00:07, 3185.52 examples/s]
Generating train examples...: 20421 examples [00:07, 3163.44 examples/s]
Generating train examples...: 20741 examples [00:07, 3173.81 examples/s]
Generating train examples...: 21063 examples [00:07, 3186.27 examples/s]
Generating train examples...: 21383 examples [00:07, 3187.79 examples/s]
Generating train examples...: 21702 examples [00:07, 3184.30 examples/s]
Generating train examples...: 22022 examples [00:08, 3185.67 examples/s]
Generating train examples...: 22341 examples [00:08, 3178.33 examples/s]
Generating train examples...: 22664 examples [00:08, 3192.69 examples/s]
Generating train examples...: 22987 examples [00:08, 3202.19 examples/s]
Generating train examples...: 23308 examples [00:08, 3192.50 examples/s]
Generating train examples...: 23628 examples [00:08, 3194.61 examples/s]
Generating train examples...: 23950 examples [00:08, 3199.47 examples/s]
Generating train examples...: 24270 examples [00:08, 3194.15 examples/s]
Generating train examples...: 24590 examples [00:08, 3184.78 examples/s]
Generating train examples...: 24910 examples [00:08, 3188.99 examples/s]
Generating train examples...: 25232 examples [00:09, 3196.61 examples/s]
Generating train examples...: 25552 examples [00:09, 3192.37 examples/s]
Generating train examples...: 25872 examples [00:09, 3180.66 examples/s]
Generating train examples...: 26191 examples [00:09, 3176.97 examples/s]
Generating train examples...: 26513 examples [00:09, 3186.78 examples/s]
Generating train examples...: 26832 examples [00:09, 3162.11 examples/s]
Generating train examples...: 27152 examples [00:09, 3172.99 examples/s]
Generating train examples...: 27476 examples [00:09, 3190.90 examples/s]
Generating train examples...: 27796 examples [00:09, 3183.40 examples/s]
Generating train examples...: 28115 examples [00:09, 3178.28 examples/s]
Generating train examples...: 28434 examples [00:10, 3181.43 examples/s]
Generating train examples...: 28755 examples [00:10, 3189.34 examples/s]
Generating train examples...: 29074 examples [00:10, 3180.52 examples/s]
Generating train examples...: 29395 examples [00:10, 3188.51 examples/s]
Generating train examples...: 29714 examples [00:10, 3187.33 examples/s]
Generating train examples...: 30034 examples [00:10, 3190.74 examples/s]
Generating train examples...: 30354 examples [00:10, 3178.59 examples/s]
Generating train examples...: 30673 examples [00:10, 3181.65 examples/s]
Generating train examples...: 30992 examples [00:10, 3169.62 examples/s]
Generating train examples...: 31310 examples [00:10, 3171.77 examples/s]
Generating train examples...: 31630 examples [00:11, 3179.92 examples/s]
Generating train examples...: 31952 examples [00:11, 3190.21 examples/s]
Generating train examples...: 32272 examples [00:11, 3186.63 examples/s]
Generating train examples...: 32592 examples [00:11, 3190.47 examples/s]
Generating train examples...: 32912 examples [00:11, 3191.62 examples/s]
Generating train examples...: 33232 examples [00:11, 3163.18 examples/s]
Generating train examples...: 33553 examples [00:11, 3174.24 examples/s]
Generating train examples...: 33875 examples [00:11, 3186.15 examples/s]
Generating train examples...: 34194 examples [00:11, 3182.90 examples/s]
Generating train examples...: 34513 examples [00:11, 3168.88 examples/s]
Generating train examples...: 34830 examples [00:12, 3168.18 examples/s]
Generating train examples...: 35151 examples [00:12, 3177.93 examples/s]
Generating train examples...: 35470 examples [00:12, 3178.42 examples/s]
Generating train examples...: 35792 examples [00:12, 3190.13 examples/s]
Generating train examples...: 36112 examples [00:12, 3181.56 examples/s]
Generating train examples...: 36431 examples [00:12, 3160.80 examples/s]
Generating train examples...: 36756 examples [00:12, 3184.29 examples/s]
Generating train examples...: 37078 examples [00:12, 3193.73 examples/s]
Generating train examples...: 37400 examples [00:12, 3199.49 examples/s]
Generating train examples...: 37723 examples [00:13, 3205.46 examples/s]
Generating train examples...: 38045 examples [00:13, 3206.82 examples/s]
Generating train examples...: 38370 examples [00:13, 3218.83 examples/s]
Generating train examples...: 38692 examples [00:13, 3216.18 examples/s]
Generating train examples...: 39014 examples [00:13, 3211.19 examples/s]
Generating train examples...: 39336 examples [00:13, 3210.74 examples/s]
Generating train examples...: 39658 examples [00:13, 3198.76 examples/s]
Generating train examples...: 39978 examples [00:13, 3193.79 examples/s]
Generating train examples...: 40303 examples [00:13, 3209.35 examples/s]
Generating train examples...: 40624 examples [00:13, 3166.79 examples/s]
Generating train examples...: 40944 examples [00:14, 3174.35 examples/s]
Generating train examples...: 41266 examples [00:14, 3186.10 examples/s]
Generating train examples...: 41587 examples [00:14, 3193.15 examples/s]
Generating train examples...: 41907 examples [00:14, 3192.82 examples/s]
Generating train examples...: 42229 examples [00:14, 3199.59 examples/s]
Generating train examples...: 42551 examples [00:14, 3203.23 examples/s]
Generating train examples...: 42872 examples [00:14, 3170.22 examples/s]
Generating train examples...: 43193 examples [00:14, 3180.65 examples/s]
Generating train examples...: 43519 examples [00:14, 3203.26 examples/s]
Generating train examples...: 43842 examples [00:14, 3211.16 examples/s]
Generating train examples...: 44164 examples [00:15, 3205.19 examples/s]
Generating train examples...: 44485 examples [00:15, 3204.07 examples/s]
Generating train examples...: 44807 examples [00:15, 3206.21 examples/s]
Generating train examples...: 45131 examples [00:15, 3214.03 examples/s]
Generating train examples...: 45453 examples [00:15, 3204.31 examples/s]
Generating train examples...: 45774 examples [00:15, 3192.91 examples/s]
Generating train examples...: 46094 examples [00:15, 3177.73 examples/s]
Generating train examples...: 46417 examples [00:15, 3191.24 examples/s]
Generating train examples...: 46737 examples [00:15, 3178.60 examples/s]
Generating train examples...: 47057 examples [00:15, 3182.67 examples/s]
Generating train examples...: 47376 examples [00:16, 3181.56 examples/s]
Generating train examples...: 47698 examples [00:16, 3192.65 examples/s]
Generating train examples...: 48023 examples [00:16, 3206.51 examples/s]
Generating train examples...: 48344 examples [00:16, 3198.94 examples/s]
Generating train examples...: 48669 examples [00:16, 3213.00 examples/s]
Generating train examples...: 48992 examples [00:16, 3216.78 examples/s]
Generating train examples...: 49314 examples [00:16, 3208.86 examples/s]
Generating train examples...: 49636 examples [00:16, 3210.91 examples/s]
Generating train examples...: 49958 examples [00:16, 3206.63 examples/s]
Generating train examples...: 50279 examples [00:16, 3197.03 examples/s]
Generating train examples...: 50602 examples [00:17, 3204.23 examples/s]
Generating train examples...: 50923 examples [00:17, 3205.84 examples/s]
Generating train examples...: 51244 examples [00:17, 3206.67 examples/s]
Generating train examples...: 51565 examples [00:17, 3203.17 examples/s]
Generating train examples...: 51886 examples [00:17, 3194.84 examples/s]
Generating train examples...: 52206 examples [00:17, 3195.91 examples/s]
Generating train examples...: 52526 examples [00:17, 3172.65 examples/s]
Generating train examples...: 52845 examples [00:17, 3176.04 examples/s]
Generating train examples...: 53166 examples [00:17, 3183.58 examples/s]
Generating train examples...: 53487 examples [00:17, 3189.08 examples/s]
Generating train examples...: 53808 examples [00:18, 3194.38 examples/s]
Generating train examples...: 54128 examples [00:18, 3186.45 examples/s]
Generating train examples...: 54451 examples [00:18, 3197.04 examples/s]
Generating train examples...: 54771 examples [00:18, 3192.70 examples/s]
Generating train examples...: 55091 examples [00:18, 3194.82 examples/s]
Generating train examples...: 55411 examples [00:18, 3189.98 examples/s]
Generating train examples...: 55731 examples [00:18, 3186.66 examples/s]
Generating train examples...: 56050 examples [00:18, 3182.35 examples/s]
Generating train examples...: 56369 examples [00:18, 3178.43 examples/s]
Generating train examples...: 56689 examples [00:18, 3182.69 examples/s]
Generating train examples...: 57012 examples [00:19, 3194.56 examples/s]
Generating train examples...: 57336 examples [00:19, 3206.22 examples/s]
Generating train examples...: 57658 examples [00:19, 3207.16 examples/s]
Generating train examples...: 57979 examples [00:19, 3200.85 examples/s]
Generating train examples...: 58300 examples [00:19, 3202.60 examples/s]
Generating train examples...: 58621 examples [00:19, 3190.18 examples/s]
Generating train examples...: 58941 examples [00:19, 3173.67 examples/s]
Generating train examples...: 59263 examples [00:19, 3185.87 examples/s]
Generating train examples...: 59582 examples [00:19, 3166.96 examples/s]
Generating train examples...: 59899 examples [00:19, 3160.68 examples/s]
Generating train examples...: 60217 examples [00:20, 3164.27 examples/s]
Generating train examples...: 60542 examples [00:20, 3186.82 examples/s]
Generating train examples...: 60861 examples [00:20, 3160.23 examples/s]
Generating train examples...: 61180 examples [00:20, 3167.26 examples/s]
Generating train examples...: 61498 examples [00:20, 3169.10 examples/s]
Generating train examples...: 61816 examples [00:20, 3171.16 examples/s]
Generating train examples...: 62134 examples [00:20, 3159.62 examples/s]
Generating train examples...: 62455 examples [00:20, 3172.44 examples/s]
Generating train examples...: 62780 examples [00:20, 3192.60 examples/s]
Generating train examples...: 63100 examples [00:20, 3189.60 examples/s]
Generating train examples...: 63419 examples [00:21, 3185.00 examples/s]
Generating train examples...: 63738 examples [00:21, 3183.16 examples/s]
Generating train examples...: 64057 examples [00:21, 3183.03 examples/s]
Generating train examples...: 64378 examples [00:21, 3190.41 examples/s]
Generating train examples...: 64698 examples [00:21, 3180.34 examples/s]
Generating train examples...: 65018 examples [00:21, 3184.36 examples/s]
Generating train examples...: 65337 examples [00:21, 3164.05 examples/s]
Generating train examples...: 65663 examples [00:21, 3189.93 examples/s]
Generating train examples...: 65985 examples [00:21, 3197.90 examples/s]
Generating train examples...: 66305 examples [00:21, 3189.54 examples/s]
Generating train examples...: 66624 examples [00:22, 3174.84 examples/s]
Generating train examples...: 66943 examples [00:22, 3176.83 examples/s]
Generating train examples...: 67263 examples [00:22, 3180.75 examples/s]
Generating train examples...: 67586 examples [00:22, 3194.97 examples/s]
Generating train examples...: 67906 examples [00:22, 3191.76 examples/s]
Generating train examples...: 68228 examples [00:22, 3196.85 examples/s]
Generating train examples...: 68548 examples [00:22, 3180.41 examples/s]
Generating train examples...: 68869 examples [00:22, 3187.38 examples/s]
Generating train examples...: 69189 examples [00:22, 3190.92 examples/s]
Generating train examples...: 69513 examples [00:22, 3205.39 examples/s]
Generating train examples...: 69838 examples [00:23, 3216.29 examples/s]
Generating train examples...: 70160 examples [00:23, 3210.69 examples/s]
Generating train examples...: 70482 examples [00:23, 3212.04 examples/s]
Generating train examples...: 70807 examples [00:23, 3221.54 examples/s]
Generating train examples...: 71130 examples [00:23, 3219.34 examples/s]
Generating train examples...: 71454 examples [00:23, 3223.34 examples/s]
Generating train examples...: 71778 examples [00:23, 3225.77 examples/s]
Generating train examples...: 72101 examples [00:23, 3221.09 examples/s]
Generating train examples...: 72424 examples [00:23, 3220.52 examples/s]
Generating train examples...: 72750 examples [00:23, 3229.91 examples/s]
Generating train examples...: 73073 examples [00:24, 3226.38 examples/s]
Shuffling /home/runner/tensorflow_datasets/svhn_cropped/3.0.0.incompleteRI1YPA/svhn_cropped-train.tfrecord*...: 0%| | 0/73257 [00:00<?, ? examples/s]
Shuffling /home/runner/tensorflow_datasets/svhn_cropped/3.0.0.incompleteRI1YPA/svhn_cropped-train.tfrecord*...: 24%|██▍ | 17909/73257 [00:00<00:00, 179077.36 examples/s]
Shuffling /home/runner/tensorflow_datasets/svhn_cropped/3.0.0.incompleteRI1YPA/svhn_cropped-train.tfrecord*...: 59%|█████▉ | 43120/73257 [00:00<00:00, 222027.90 examples/s]
Shuffling /home/runner/tensorflow_datasets/svhn_cropped/3.0.0.incompleteRI1YPA/svhn_cropped-train.tfrecord*...: 94%|█████████▍| 68857/73257 [00:00<00:00, 238157.31 examples/s]
Generating splits...: 33%|███▎ | 1/3 [00:24<00:48, 24.47s/ splits]
Generating test examples...: 0 examples [00:00, ? examples/s]
Generating test examples...: 1 examples [00:00, 2.31 examples/s]
Generating test examples...: 319 examples [00:00, 790.87 examples/s]
Generating test examples...: 638 examples [00:00, 1418.25 examples/s]
Generating test examples...: 956 examples [00:00, 1897.17 examples/s]
Generating test examples...: 1274 examples [00:00, 2255.16 examples/s]
Generating test examples...: 1591 examples [00:00, 2514.64 examples/s]
Generating test examples...: 1906 examples [00:01, 2698.47 examples/s]
Generating test examples...: 2230 examples [00:01, 2855.82 examples/s]
Generating test examples...: 2548 examples [00:01, 2950.73 examples/s]
Generating test examples...: 2867 examples [00:01, 3020.66 examples/s]
Generating test examples...: 3187 examples [00:01, 3072.66 examples/s]
Generating test examples...: 3507 examples [00:01, 3109.04 examples/s]
Generating test examples...: 3825 examples [00:01, 3126.49 examples/s]
Generating test examples...: 4147 examples [00:01, 3153.16 examples/s]
Generating test examples...: 4466 examples [00:01, 3138.40 examples/s]
Generating test examples...: 4786 examples [00:01, 3155.42 examples/s]
Generating test examples...: 5106 examples [00:02, 3168.01 examples/s]
Generating test examples...: 5424 examples [00:02, 3170.22 examples/s]
Generating test examples...: 5742 examples [00:02, 3168.69 examples/s]
Generating test examples...: 6060 examples [00:02, 3170.12 examples/s]
Generating test examples...: 6378 examples [00:02, 3164.76 examples/s]
Generating test examples...: 6700 examples [00:02, 3178.60 examples/s]
Generating test examples...: 7020 examples [00:02, 3183.51 examples/s]
Generating test examples...: 7339 examples [00:02, 3179.05 examples/s]
Generating test examples...: 7657 examples [00:02, 3169.43 examples/s]
Generating test examples...: 7975 examples [00:02, 3155.15 examples/s]
Generating test examples...: 8293 examples [00:03, 3160.46 examples/s]
Generating test examples...: 8611 examples [00:03, 3165.36 examples/s]
Generating test examples...: 8932 examples [00:03, 3178.09 examples/s]
Generating test examples...: 9250 examples [00:03, 3171.76 examples/s]
Generating test examples...: 9568 examples [00:03, 3165.29 examples/s]
Generating test examples...: 9885 examples [00:03, 3147.64 examples/s]
Generating test examples...: 10204 examples [00:03, 3157.31 examples/s]
Generating test examples...: 10522 examples [00:03, 3163.16 examples/s]
Generating test examples...: 10839 examples [00:03, 3146.88 examples/s]
Generating test examples...: 11155 examples [00:03, 3149.70 examples/s]
Generating test examples...: 11473 examples [00:04, 3158.14 examples/s]
Generating test examples...: 11792 examples [00:04, 3167.04 examples/s]
Generating test examples...: 12109 examples [00:04, 3161.22 examples/s]
Generating test examples...: 12427 examples [00:04, 3165.74 examples/s]
Generating test examples...: 12746 examples [00:04, 3170.59 examples/s]
Generating test examples...: 13064 examples [00:04, 3172.07 examples/s]
Generating test examples...: 13382 examples [00:04, 3172.86 examples/s]
Generating test examples...: 13700 examples [00:04, 3169.77 examples/s]
Generating test examples...: 14019 examples [00:04, 3174.80 examples/s]
Generating test examples...: 14337 examples [00:04, 3170.18 examples/s]
Generating test examples...: 14655 examples [00:05, 3169.41 examples/s]
Generating test examples...: 14972 examples [00:05, 3168.30 examples/s]
Generating test examples...: 15290 examples [00:05, 3169.88 examples/s]
Generating test examples...: 15607 examples [00:05, 3155.63 examples/s]
Generating test examples...: 15923 examples [00:05, 3152.48 examples/s]
Generating test examples...: 16239 examples [00:05, 3148.56 examples/s]
Generating test examples...: 16561 examples [00:05, 3169.30 examples/s]
Generating test examples...: 16881 examples [00:05, 3176.13 examples/s]
Generating test examples...: 17199 examples [00:05, 3157.45 examples/s]
Generating test examples...: 17519 examples [00:05, 3169.26 examples/s]
Generating test examples...: 17836 examples [00:06, 3166.00 examples/s]
Generating test examples...: 18155 examples [00:06, 3169.54 examples/s]
Generating test examples...: 18474 examples [00:06, 3173.87 examples/s]
Generating test examples...: 18792 examples [00:06, 3152.12 examples/s]
Generating test examples...: 19108 examples [00:06, 3153.20 examples/s]
Generating test examples...: 19427 examples [00:06, 3161.95 examples/s]
Generating test examples...: 19745 examples [00:06, 3164.92 examples/s]
Generating test examples...: 20063 examples [00:06, 3169.16 examples/s]
Generating test examples...: 20380 examples [00:06, 3152.73 examples/s]
Generating test examples...: 20699 examples [00:06, 3163.03 examples/s]
Generating test examples...: 21016 examples [00:07, 3162.76 examples/s]
Generating test examples...: 21334 examples [00:07, 3166.86 examples/s]
Generating test examples...: 21651 examples [00:07, 3164.90 examples/s]
Generating test examples...: 21968 examples [00:07, 3152.83 examples/s]
Generating test examples...: 22284 examples [00:07, 3154.47 examples/s]
Generating test examples...: 22605 examples [00:07, 3168.42 examples/s]
Generating test examples...: 22922 examples [00:07, 3159.16 examples/s]
Generating test examples...: 23238 examples [00:07, 3158.48 examples/s]
Generating test examples...: 23554 examples [00:07, 3135.20 examples/s]
Generating test examples...: 23871 examples [00:07, 3145.26 examples/s]
Generating test examples...: 24186 examples [00:08, 3145.48 examples/s]
Generating test examples...: 24501 examples [00:08, 3142.97 examples/s]
Generating test examples...: 24819 examples [00:08, 3150.42 examples/s]
Generating test examples...: 25135 examples [00:08, 3152.77 examples/s]
Generating test examples...: 25455 examples [00:08, 3164.20 examples/s]
Generating test examples...: 25772 examples [00:08, 3163.77 examples/s]
Shuffling /home/runner/tensorflow_datasets/svhn_cropped/3.0.0.incompleteRI1YPA/svhn_cropped-test.tfrecord*...: 0%| | 0/26032 [00:00<?, ? examples/s]
Shuffling /home/runner/tensorflow_datasets/svhn_cropped/3.0.0.incompleteRI1YPA/svhn_cropped-test.tfrecord*...: 93%|█████████▎| 24322/26032 [00:00<00:00, 243198.78 examples/s]
Generating splits...: 67%|██████▋ | 2/3 [00:33<00:15, 15.24s/ splits]
Generating extra examples...: 0 examples [00:00, ? examples/s]
Generating extra examples...: 1 examples [00:08, 8.71s/ examples]
Generating extra examples...: 325 examples [00:08, 52.40 examples/s]
Generating extra examples...: 655 examples [00:08, 125.58 examples/s]
Generating extra examples...: 977 examples [00:09, 222.09 examples/s]
Generating extra examples...: 1309 examples [00:09, 354.18 examples/s]
Generating extra examples...: 1635 examples [00:09, 521.03 examples/s]
Generating extra examples...: 1959 examples [00:09, 727.06 examples/s]
Generating extra examples...: 2283 examples [00:09, 972.35 examples/s]
Generating extra examples...: 2609 examples [00:09, 1252.12 examples/s]
Generating extra examples...: 2938 examples [00:09, 1555.35 examples/s]
Generating extra examples...: 3266 examples [00:09, 1857.30 examples/s]
Generating extra examples...: 3592 examples [00:09, 2137.82 examples/s]
Generating extra examples...: 3917 examples [00:09, 2384.14 examples/s]
Generating extra examples...: 4242 examples [00:10, 2591.08 examples/s]
Generating extra examples...: 4567 examples [00:10, 2753.47 examples/s]
Generating extra examples...: 4891 examples [00:10, 2866.15 examples/s]
Generating extra examples...: 5216 examples [00:10, 2970.33 examples/s]
Generating extra examples...: 5539 examples [00:10, 3041.77 examples/s]
Generating extra examples...: 5862 examples [00:10, 3091.96 examples/s]
Generating extra examples...: 6185 examples [00:10, 3130.38 examples/s]
Generating extra examples...: 6511 examples [00:10, 3166.86 examples/s]
Generating extra examples...: 6841 examples [00:10, 3205.50 examples/s]
Generating extra examples...: 7167 examples [00:10, 3206.59 examples/s]
Generating extra examples...: 7491 examples [00:11, 3216.18 examples/s]
Generating extra examples...: 7819 examples [00:11, 3232.70 examples/s]
Generating extra examples...: 8144 examples [00:11, 3229.69 examples/s]
Generating extra examples...: 8469 examples [00:11, 3223.73 examples/s]
Generating extra examples...: 8796 examples [00:11, 3234.40 examples/s]
Generating extra examples...: 9120 examples [00:11, 3227.62 examples/s]
Generating extra examples...: 9449 examples [00:11, 3245.57 examples/s]
Generating extra examples...: 9774 examples [00:11, 3245.82 examples/s]
Generating extra examples...: 10101 examples [00:11, 3252.90 examples/s]
Generating extra examples...: 10429 examples [00:11, 3260.03 examples/s]
Generating extra examples...: 10756 examples [00:12, 3244.30 examples/s]
Generating extra examples...: 11081 examples [00:12, 3243.21 examples/s]
Generating extra examples...: 11406 examples [00:12, 3235.94 examples/s]
Generating extra examples...: 11736 examples [00:12, 3252.05 examples/s]
Generating extra examples...: 12062 examples [00:12, 3244.54 examples/s]
Generating extra examples...: 12388 examples [00:12, 3245.56 examples/s]
Generating extra examples...: 12715 examples [00:12, 3252.72 examples/s]
Generating extra examples...: 13041 examples [00:12, 3218.30 examples/s]
Generating extra examples...: 13363 examples [00:12, 3215.99 examples/s]
Generating extra examples...: 13685 examples [00:12, 3216.67 examples/s]
Generating extra examples...: 14007 examples [00:13, 3198.92 examples/s]
Generating extra examples...: 14332 examples [00:13, 3212.25 examples/s]
Generating extra examples...: 14661 examples [00:13, 3234.75 examples/s]
Generating extra examples...: 14986 examples [00:13, 3236.36 examples/s]
Generating extra examples...: 15315 examples [00:13, 3251.19 examples/s]
Generating extra examples...: 15641 examples [00:13, 3244.01 examples/s]
Generating extra examples...: 15970 examples [00:13, 3257.01 examples/s]
Generating extra examples...: 16296 examples [00:13, 3251.05 examples/s]
Generating extra examples...: 16622 examples [00:13, 3250.04 examples/s]
Generating extra examples...: 16951 examples [00:13, 3259.46 examples/s]
Generating extra examples...: 17277 examples [00:14, 3229.92 examples/s]
Generating extra examples...: 17603 examples [00:14, 3237.02 examples/s]
Generating extra examples...: 17927 examples [00:14, 3234.57 examples/s]
Generating extra examples...: 18251 examples [00:14, 3233.23 examples/s]
Generating extra examples...: 18575 examples [00:14, 3219.72 examples/s]
Generating extra examples...: 18900 examples [00:14, 3227.72 examples/s]
Generating extra examples...: 19225 examples [00:14, 3232.33 examples/s]
Generating extra examples...: 19549 examples [00:14, 3214.31 examples/s]
Generating extra examples...: 19871 examples [00:14, 3209.94 examples/s]
Generating extra examples...: 20193 examples [00:14, 3210.10 examples/s]
Generating extra examples...: 20518 examples [00:15, 3219.88 examples/s]
Generating extra examples...: 20841 examples [00:15, 3222.24 examples/s]
Generating extra examples...: 21166 examples [00:15, 3228.43 examples/s]
Generating extra examples...: 21492 examples [00:15, 3235.20 examples/s]
Generating extra examples...: 21817 examples [00:15, 3236.99 examples/s]
Generating extra examples...: 22147 examples [00:15, 3253.13 examples/s]
Generating extra examples...: 22473 examples [00:15, 3254.22 examples/s]
Generating extra examples...: 22799 examples [00:15, 3220.92 examples/s]
Generating extra examples...: 23122 examples [00:15, 3221.24 examples/s]
Generating extra examples...: 23446 examples [00:15, 3226.10 examples/s]
Generating extra examples...: 23771 examples [00:16, 3232.60 examples/s]
Generating extra examples...: 24095 examples [00:16, 3232.99 examples/s]
Generating extra examples...: 24419 examples [00:16, 3225.57 examples/s]
Generating extra examples...: 24746 examples [00:16, 3234.70 examples/s]
Generating extra examples...: 25072 examples [00:16, 3241.23 examples/s]
Generating extra examples...: 25397 examples [00:16, 3240.11 examples/s]
Generating extra examples...: 25726 examples [00:16, 3252.83 examples/s]
Generating extra examples...: 26052 examples [00:16, 3244.94 examples/s]
Generating extra examples...: 26379 examples [00:16, 3251.49 examples/s]
Generating extra examples...: 26705 examples [00:16, 3252.84 examples/s]
Generating extra examples...: 27031 examples [00:17, 3222.64 examples/s]
Generating extra examples...: 27354 examples [00:17, 3223.13 examples/s]
Generating extra examples...: 27679 examples [00:17, 3228.15 examples/s]
Generating extra examples...: 28003 examples [00:17, 3230.94 examples/s]
Generating extra examples...: 28331 examples [00:17, 3245.28 examples/s]
Generating extra examples...: 28656 examples [00:17, 3239.59 examples/s]
Generating extra examples...: 28982 examples [00:17, 3245.49 examples/s]
Generating extra examples...: 29307 examples [00:17, 3215.19 examples/s]
Generating extra examples...: 29638 examples [00:17, 3243.11 examples/s]
Generating extra examples...: 29965 examples [00:17, 3250.40 examples/s]
Generating extra examples...: 30291 examples [00:18, 3245.57 examples/s]
Generating extra examples...: 30616 examples [00:18, 3244.67 examples/s]
Generating extra examples...: 30941 examples [00:18, 3234.81 examples/s]
Generating extra examples...: 31265 examples [00:18, 3205.03 examples/s]
Generating extra examples...: 31588 examples [00:18, 3212.38 examples/s]
Generating extra examples...: 31910 examples [00:18, 3203.62 examples/s]
Generating extra examples...: 32237 examples [00:18, 3220.64 examples/s]
Generating extra examples...: 32560 examples [00:18, 3218.65 examples/s]
Generating extra examples...: 32883 examples [00:18, 3219.79 examples/s]
Generating extra examples...: 33208 examples [00:18, 3227.65 examples/s]
Generating extra examples...: 33531 examples [00:19, 3222.24 examples/s]
Generating extra examples...: 33860 examples [00:19, 3241.19 examples/s]
Generating extra examples...: 34185 examples [00:19, 3236.83 examples/s]
Generating extra examples...: 34509 examples [00:19, 3205.06 examples/s]
Generating extra examples...: 34835 examples [00:19, 3218.49 examples/s]
Generating extra examples...: 35157 examples [00:19, 3214.52 examples/s]
Generating extra examples...: 35486 examples [00:19, 3236.45 examples/s]
Generating extra examples...: 35810 examples [00:19, 3236.92 examples/s]
Generating extra examples...: 36134 examples [00:19, 3228.60 examples/s]
Generating extra examples...: 36457 examples [00:19, 3220.26 examples/s]
Generating extra examples...: 36780 examples [00:20, 3217.49 examples/s]
Generating extra examples...: 37102 examples [00:20, 3217.95 examples/s]
Generating extra examples...: 37425 examples [00:20, 3221.46 examples/s]
Generating extra examples...: 37750 examples [00:20, 3229.20 examples/s]
Generating extra examples...: 38076 examples [00:20, 3238.22 examples/s]
Generating extra examples...: 38404 examples [00:20, 3248.53 examples/s]
Generating extra examples...: 38732 examples [00:20, 3256.13 examples/s]
Generating extra examples...: 39060 examples [00:20, 3262.04 examples/s]
Generating extra examples...: 39387 examples [00:20, 3250.58 examples/s]
Generating extra examples...: 39713 examples [00:20, 3240.09 examples/s]
Generating extra examples...: 40038 examples [00:21, 3225.16 examples/s]
Generating extra examples...: 40364 examples [00:21, 3234.44 examples/s]
Generating extra examples...: 40691 examples [00:21, 3242.55 examples/s]
Generating extra examples...: 41016 examples [00:21, 3234.39 examples/s]
Generating extra examples...: 41345 examples [00:21, 3250.55 examples/s]
Generating extra examples...: 41672 examples [00:21, 3253.83 examples/s]
Generating extra examples...: 41998 examples [00:21, 3239.99 examples/s]
Generating extra examples...: 42323 examples [00:21, 3212.61 examples/s]
Generating extra examples...: 42653 examples [00:21, 3238.26 examples/s]
Generating extra examples...: 42980 examples [00:21, 3247.01 examples/s]
Generating extra examples...: 43308 examples [00:22, 3255.48 examples/s]
Generating extra examples...: 43638 examples [00:22, 3266.26 examples/s]
Generating extra examples...: 43965 examples [00:22, 3267.10 examples/s]
Generating extra examples...: 44292 examples [00:22, 3257.92 examples/s]
Generating extra examples...: 44618 examples [00:22, 3247.20 examples/s]
Generating extra examples...: 44946 examples [00:22, 3256.20 examples/s]
Generating extra examples...: 45272 examples [00:22, 3227.73 examples/s]
Generating extra examples...: 45598 examples [00:22, 3237.00 examples/s]
Generating extra examples...: 45922 examples [00:22, 3231.55 examples/s]
Generating extra examples...: 46246 examples [00:22, 3217.69 examples/s]
Generating extra examples...: 46569 examples [00:23, 3220.28 examples/s]
Generating extra examples...: 46893 examples [00:23, 3224.35 examples/s]
Generating extra examples...: 47216 examples [00:23, 3221.07 examples/s]
Generating extra examples...: 47541 examples [00:23, 3227.93 examples/s]
Generating extra examples...: 47865 examples [00:23, 3228.86 examples/s]
Generating extra examples...: 48189 examples [00:23, 3231.05 examples/s]
Generating extra examples...: 48515 examples [00:23, 3238.16 examples/s]
Generating extra examples...: 48839 examples [00:23, 3229.53 examples/s]
Generating extra examples...: 49162 examples [00:23, 3209.31 examples/s]
Generating extra examples...: 49486 examples [00:24, 3216.74 examples/s]
Generating extra examples...: 49810 examples [00:24, 3222.79 examples/s]
Generating extra examples...: 50136 examples [00:24, 3231.38 examples/s]
Generating extra examples...: 50460 examples [00:24, 3215.76 examples/s]
Generating extra examples...: 50782 examples [00:24, 3216.44 examples/s]
Generating extra examples...: 51107 examples [00:24, 3225.72 examples/s]
Generating extra examples...: 51432 examples [00:24, 3232.59 examples/s]
Generating extra examples...: 51756 examples [00:24, 3229.25 examples/s]
Generating extra examples...: 52079 examples [00:24, 3226.94 examples/s]
Generating extra examples...: 52409 examples [00:24, 3246.91 examples/s]
Generating extra examples...: 52734 examples [00:25, 3227.83 examples/s]
Generating extra examples...: 53057 examples [00:25, 3220.98 examples/s]
Generating extra examples...: 53380 examples [00:25, 3215.60 examples/s]
Generating extra examples...: 53703 examples [00:25, 3217.42 examples/s]
Generating extra examples...: 54025 examples [00:25, 3212.73 examples/s]
Generating extra examples...: 54347 examples [00:25, 3214.22 examples/s]
Generating extra examples...: 54669 examples [00:25, 3208.46 examples/s]
Generating extra examples...: 54991 examples [00:25, 3210.13 examples/s]
Generating extra examples...: 55313 examples [00:25, 3207.09 examples/s]
Generating extra examples...: 55635 examples [00:25, 3210.58 examples/s]
Generating extra examples...: 55959 examples [00:26, 3218.66 examples/s]
Generating extra examples...: 56282 examples [00:26, 3221.92 examples/s]
Generating extra examples...: 56611 examples [00:26, 3240.73 examples/s]
Generating extra examples...: 56937 examples [00:26, 3243.66 examples/s]
Generating extra examples...: 57265 examples [00:26, 3252.20 examples/s]
Generating extra examples...: 57594 examples [00:26, 3260.93 examples/s]
Generating extra examples...: 57921 examples [00:26, 3250.64 examples/s]
Generating extra examples...: 58247 examples [00:26, 3244.93 examples/s]
Generating extra examples...: 58572 examples [00:26, 3233.55 examples/s]
Generating extra examples...: 58899 examples [00:26, 3243.03 examples/s]
Generating extra examples...: 59225 examples [00:27, 3245.02 examples/s]
Generating extra examples...: 59553 examples [00:27, 3253.16 examples/s]
Generating extra examples...: 59879 examples [00:27, 3237.42 examples/s]
Generating extra examples...: 60203 examples [00:27, 3236.87 examples/s]
Generating extra examples...: 60529 examples [00:27, 3243.56 examples/s]
Generating extra examples...: 60854 examples [00:27, 3245.20 examples/s]
Generating extra examples...: 61179 examples [00:27, 3241.86 examples/s]
Generating extra examples...: 61504 examples [00:27, 3234.71 examples/s]
Generating extra examples...: 61829 examples [00:27, 3236.00 examples/s]
Generating extra examples...: 62153 examples [00:27, 3233.53 examples/s]
Generating extra examples...: 62477 examples [00:28, 3223.44 examples/s]
Generating extra examples...: 62800 examples [00:28, 3221.91 examples/s]
Generating extra examples...: 63123 examples [00:28, 3216.80 examples/s]
Generating extra examples...: 63445 examples [00:28, 3217.53 examples/s]
Generating extra examples...: 63767 examples [00:28, 3211.11 examples/s]
Generating extra examples...: 64089 examples [00:28, 3206.45 examples/s]
Generating extra examples...: 64410 examples [00:28, 3204.92 examples/s]
Generating extra examples...: 64732 examples [00:28, 3207.69 examples/s]
Generating extra examples...: 65059 examples [00:28, 3223.87 examples/s]
Generating extra examples...: 65382 examples [00:28, 3218.34 examples/s]
Generating extra examples...: 65704 examples [00:29, 3207.78 examples/s]
Generating extra examples...: 66025 examples [00:29, 3201.98 examples/s]
Generating extra examples...: 66350 examples [00:29, 3213.90 examples/s]
Generating extra examples...: 66672 examples [00:29, 3212.53 examples/s]
Generating extra examples...: 66995 examples [00:29, 3216.58 examples/s]
Generating extra examples...: 67318 examples [00:29, 3219.28 examples/s]
Generating extra examples...: 67643 examples [00:29, 3226.23 examples/s]
Generating extra examples...: 67966 examples [00:29, 3217.67 examples/s]
Generating extra examples...: 68288 examples [00:29, 3207.65 examples/s]
Generating extra examples...: 68609 examples [00:29, 3199.18 examples/s]
Generating extra examples...: 68933 examples [00:30, 3208.67 examples/s]
Generating extra examples...: 69258 examples [00:30, 3220.17 examples/s]
Generating extra examples...: 69582 examples [00:30, 3224.37 examples/s]
Generating extra examples...: 69905 examples [00:30, 3216.37 examples/s]
Generating extra examples...: 70227 examples [00:30, 3215.61 examples/s]
Generating extra examples...: 70554 examples [00:30, 3230.35 examples/s]
Generating extra examples...: 70878 examples [00:30, 3230.87 examples/s]
Generating extra examples...: 71202 examples [00:30, 3215.26 examples/s]
Generating extra examples...: 71524 examples [00:30, 3211.82 examples/s]
Generating extra examples...: 71848 examples [00:30, 3219.29 examples/s]
Generating extra examples...: 72170 examples [00:31, 3219.14 examples/s]
Generating extra examples...: 72495 examples [00:31, 3226.96 examples/s]
Generating extra examples...: 72821 examples [00:31, 3236.11 examples/s]
Generating extra examples...: 73145 examples [00:31, 3231.40 examples/s]
Generating extra examples...: 73469 examples [00:31, 3224.93 examples/s]
Generating extra examples...: 73792 examples [00:31, 3213.46 examples/s]
Generating extra examples...: 74114 examples [00:31, 3195.58 examples/s]
Generating extra examples...: 74434 examples [00:31, 3196.01 examples/s]
Generating extra examples...: 74755 examples [00:31, 3198.54 examples/s]
Generating extra examples...: 75075 examples [00:31, 3195.27 examples/s]
Generating extra examples...: 75395 examples [00:32, 3194.56 examples/s]
Generating extra examples...: 75718 examples [00:32, 3203.41 examples/s]
Generating extra examples...: 76039 examples [00:32, 3192.63 examples/s]
Generating extra examples...: 76359 examples [00:32, 3183.50 examples/s]
Generating extra examples...: 76678 examples [00:32, 3179.57 examples/s]
Generating extra examples...: 76996 examples [00:32, 3175.87 examples/s]
Generating extra examples...: 77314 examples [00:32, 3172.71 examples/s]
Generating extra examples...: 77633 examples [00:32, 3176.46 examples/s]
Generating extra examples...: 77951 examples [00:32, 3170.39 examples/s]
Generating extra examples...: 78269 examples [00:32, 3140.80 examples/s]
Generating extra examples...: 78584 examples [00:33, 3124.96 examples/s]
Generating extra examples...: 78906 examples [00:33, 3150.08 examples/s]
Generating extra examples...: 79226 examples [00:33, 3164.64 examples/s]
Generating extra examples...: 79543 examples [00:33, 3161.39 examples/s]
Generating extra examples...: 79861 examples [00:33, 3165.28 examples/s]
Generating extra examples...: 80180 examples [00:33, 3171.20 examples/s]
Generating extra examples...: 80498 examples [00:33, 3170.54 examples/s]
Generating extra examples...: 80820 examples [00:33, 3183.19 examples/s]
Generating extra examples...: 81143 examples [00:33, 3195.11 examples/s]
Generating extra examples...: 81463 examples [00:33, 3167.65 examples/s]
Generating extra examples...: 81780 examples [00:34, 3167.29 examples/s]
Generating extra examples...: 82097 examples [00:34, 3164.90 examples/s]
Generating extra examples...: 82414 examples [00:34, 3140.12 examples/s]
Generating extra examples...: 82729 examples [00:34, 3132.63 examples/s]
Generating extra examples...: 83049 examples [00:34, 3149.48 examples/s]
Generating extra examples...: 83367 examples [00:34, 3156.39 examples/s]
Generating extra examples...: 83683 examples [00:34, 3154.16 examples/s]
Generating extra examples...: 84003 examples [00:34, 3167.25 examples/s]
Generating extra examples...: 84320 examples [00:34, 3167.50 examples/s]
Generating extra examples...: 84637 examples [00:34, 3159.29 examples/s]
Generating extra examples...: 84953 examples [00:35, 3156.97 examples/s]
Generating extra examples...: 85272 examples [00:35, 3164.38 examples/s]
Generating extra examples...: 85590 examples [00:35, 3168.08 examples/s]
Generating extra examples...: 85909 examples [00:35, 3173.83 examples/s]
Generating extra examples...: 86228 examples [00:35, 3176.28 examples/s]
Generating extra examples...: 86547 examples [00:35, 3178.79 examples/s]
Generating extra examples...: 86866 examples [00:35, 3181.55 examples/s]
Generating extra examples...: 87185 examples [00:35, 3178.12 examples/s]
Generating extra examples...: 87503 examples [00:35, 3172.98 examples/s]
Generating extra examples...: 87821 examples [00:35, 3164.73 examples/s]
Generating extra examples...: 88144 examples [00:36, 3181.63 examples/s]
Generating extra examples...: 88463 examples [00:36, 3180.64 examples/s]
Generating extra examples...: 88782 examples [00:36, 3178.74 examples/s]
Generating extra examples...: 89105 examples [00:36, 3192.62 examples/s]
Generating extra examples...: 89425 examples [00:36, 3190.76 examples/s]
Generating extra examples...: 89745 examples [00:36, 3192.51 examples/s]
Generating extra examples...: 90065 examples [00:36, 3180.60 examples/s]
Generating extra examples...: 90389 examples [00:36, 3197.41 examples/s]
Generating extra examples...: 90709 examples [00:36, 3192.78 examples/s]
Generating extra examples...: 91029 examples [00:36, 3190.63 examples/s]
Generating extra examples...: 91349 examples [00:37, 3170.90 examples/s]
Generating extra examples...: 91672 examples [00:37, 3187.20 examples/s]
Generating extra examples...: 91991 examples [00:37, 3182.85 examples/s]
Generating extra examples...: 92310 examples [00:37, 3181.60 examples/s]
Generating extra examples...: 92631 examples [00:37, 3188.97 examples/s]
Generating extra examples...: 92952 examples [00:37, 3193.64 examples/s]
Generating extra examples...: 93272 examples [00:37, 3195.43 examples/s]
Generating extra examples...: 93596 examples [00:37, 3205.87 examples/s]
Generating extra examples...: 93918 examples [00:37, 3207.21 examples/s]
Generating extra examples...: 94239 examples [00:37, 3193.92 examples/s]
Generating extra examples...: 94565 examples [00:38, 3210.79 examples/s]
Generating extra examples...: 94888 examples [00:38, 3213.81 examples/s]
Generating extra examples...: 95211 examples [00:38, 3216.35 examples/s]
Generating extra examples...: 95533 examples [00:38, 3204.35 examples/s]
Generating extra examples...: 95854 examples [00:38, 3199.81 examples/s]
Generating extra examples...: 96182 examples [00:38, 3222.06 examples/s]
Generating extra examples...: 96506 examples [00:38, 3224.70 examples/s]
Generating extra examples...: 96829 examples [00:38, 3226.14 examples/s]
Generating extra examples...: 97152 examples [00:38, 3225.87 examples/s]
Generating extra examples...: 97475 examples [00:38, 3195.82 examples/s]
Generating extra examples...: 97795 examples [00:39, 3191.61 examples/s]
Generating extra examples...: 98116 examples [00:39, 3196.98 examples/s]
Generating extra examples...: 98436 examples [00:39, 3184.37 examples/s]
Generating extra examples...: 98755 examples [00:39, 3183.72 examples/s]
Generating extra examples...: 99077 examples [00:39, 3192.70 examples/s]
Generating extra examples...: 99398 examples [00:39, 3196.69 examples/s]
Generating extra examples...: 99720 examples [00:39, 3202.91 examples/s]
Generating extra examples...: 100042 examples [00:39, 3204.94 examples/s]
Generating extra examples...: 100363 examples [00:39, 3194.18 examples/s]
Generating extra examples...: 100683 examples [00:39, 3154.28 examples/s]
Generating extra examples...: 100999 examples [00:40, 3155.67 examples/s]
Generating extra examples...: 101323 examples [00:40, 3178.62 examples/s]
Generating extra examples...: 101642 examples [00:40, 3181.90 examples/s]
Generating extra examples...: 101967 examples [00:40, 3199.56 examples/s]
Generating extra examples...: 102290 examples [00:40, 3206.02 examples/s]
Generating extra examples...: 102612 examples [00:40, 3208.05 examples/s]
Generating extra examples...: 102933 examples [00:40, 3203.60 examples/s]
Generating extra examples...: 103257 examples [00:40, 3214.08 examples/s]
Generating extra examples...: 103579 examples [00:40, 3212.67 examples/s]
Generating extra examples...: 103901 examples [00:41, 3167.40 examples/s]
Generating extra examples...: 104221 examples [00:41, 3175.28 examples/s]
Generating extra examples...: 104542 examples [00:41, 3183.72 examples/s]
Generating extra examples...: 104865 examples [00:41, 3194.09 examples/s]
Generating extra examples...: 105185 examples [00:41, 3034.86 examples/s]
Generating extra examples...: 105503 examples [00:41, 3074.99 examples/s]
Generating extra examples...: 105816 examples [00:41, 3090.95 examples/s]
Generating extra examples...: 106134 examples [00:41, 3115.74 examples/s]
Generating extra examples...: 106447 examples [00:41, 3118.75 examples/s]
Generating extra examples...: 106771 examples [00:41, 3152.74 examples/s]
Generating extra examples...: 107089 examples [00:42, 3157.82 examples/s]
Generating extra examples...: 107406 examples [00:42, 3159.31 examples/s]
Generating extra examples...: 107723 examples [00:42, 3159.46 examples/s]
Generating extra examples...: 108041 examples [00:42, 3164.92 examples/s]
Generating extra examples...: 108363 examples [00:42, 3179.71 examples/s]
Generating extra examples...: 108682 examples [00:42, 3177.74 examples/s]
Generating extra examples...: 109008 examples [00:42, 3200.86 examples/s]
Generating extra examples...: 109333 examples [00:42, 3214.09 examples/s]
Generating extra examples...: 109659 examples [00:42, 3226.07 examples/s]
Generating extra examples...: 109982 examples [00:42, 3221.32 examples/s]
Generating extra examples...: 110305 examples [00:43, 3192.91 examples/s]
Generating extra examples...: 110625 examples [00:43, 3191.48 examples/s]
Generating extra examples...: 110945 examples [00:43, 3192.38 examples/s]
Generating extra examples...: 111265 examples [00:43, 3188.77 examples/s]
Generating extra examples...: 111584 examples [00:43, 3183.64 examples/s]
Generating extra examples...: 111903 examples [00:43, 3185.19 examples/s]
Generating extra examples...: 112222 examples [00:43, 3180.19 examples/s]
Generating extra examples...: 112543 examples [00:43, 3188.68 examples/s]
Generating extra examples...: 112863 examples [00:43, 3191.97 examples/s]
Generating extra examples...: 113184 examples [00:43, 3197.20 examples/s]
Generating extra examples...: 113504 examples [00:44, 3162.12 examples/s]
Generating extra examples...: 113824 examples [00:44, 3172.07 examples/s]
Generating extra examples...: 114142 examples [00:44, 3171.59 examples/s]
Generating extra examples...: 114460 examples [00:44, 3169.04 examples/s]
Generating extra examples...: 114778 examples [00:44, 3169.70 examples/s]
Generating extra examples...: 115095 examples [00:44, 3167.48 examples/s]
Generating extra examples...: 115419 examples [00:44, 3188.07 examples/s]
Generating extra examples...: 115738 examples [00:44, 3182.49 examples/s]
Generating extra examples...: 116057 examples [00:44, 3183.46 examples/s]
Generating extra examples...: 116378 examples [00:44, 3190.04 examples/s]
Generating extra examples...: 116698 examples [00:45, 3157.11 examples/s]
Generating extra examples...: 117018 examples [00:45, 3169.66 examples/s]
Generating extra examples...: 117338 examples [00:45, 3176.52 examples/s]
Generating extra examples...: 117658 examples [00:45, 3180.75 examples/s]
Generating extra examples...: 117982 examples [00:45, 3196.80 examples/s]
Generating extra examples...: 118302 examples [00:45, 3192.54 examples/s]
Generating extra examples...: 118622 examples [00:45, 3184.42 examples/s]
Generating extra examples...: 118944 examples [00:45, 3194.89 examples/s]
Generating extra examples...: 119265 examples [00:45, 3198.94 examples/s]
Generating extra examples...: 119586 examples [00:45, 3199.48 examples/s]
Generating extra examples...: 119906 examples [00:46, 3179.87 examples/s]
Generating extra examples...: 120225 examples [00:46, 3181.73 examples/s]
Generating extra examples...: 120546 examples [00:46, 3189.11 examples/s]
Generating extra examples...: 120865 examples [00:46, 3181.99 examples/s]
Generating extra examples...: 121185 examples [00:46, 3184.98 examples/s]
Generating extra examples...: 121504 examples [00:46, 3176.83 examples/s]
Generating extra examples...: 121822 examples [00:46, 3172.48 examples/s]
Generating extra examples...: 122141 examples [00:46, 3174.79 examples/s]
Generating extra examples...: 122462 examples [00:46, 3182.33 examples/s]
Generating extra examples...: 122782 examples [00:46, 3185.02 examples/s]
Generating extra examples...: 123101 examples [00:47, 3169.09 examples/s]
Generating extra examples...: 123421 examples [00:47, 3176.39 examples/s]
Generating extra examples...: 123745 examples [00:47, 3194.55 examples/s]
Generating extra examples...: 124066 examples [00:47, 3197.80 examples/s]
Generating extra examples...: 124387 examples [00:47, 3201.38 examples/s]
Generating extra examples...: 124708 examples [00:47, 3199.48 examples/s]
Generating extra examples...: 125032 examples [00:47, 3211.51 examples/s]
Generating extra examples...: 125355 examples [00:47, 3215.55 examples/s]
Generating extra examples...: 125677 examples [00:47, 3198.62 examples/s]
Generating extra examples...: 125998 examples [00:47, 3199.20 examples/s]
Generating extra examples...: 126318 examples [00:48, 3194.59 examples/s]
Generating extra examples...: 126639 examples [00:48, 3197.70 examples/s]
Generating extra examples...: 126960 examples [00:48, 3198.58 examples/s]
Generating extra examples...: 127280 examples [00:48, 3192.18 examples/s]
Generating extra examples...: 127600 examples [00:48, 3188.21 examples/s]
Generating extra examples...: 127919 examples [00:48, 3171.39 examples/s]
Generating extra examples...: 128237 examples [00:48, 3160.04 examples/s]
Generating extra examples...: 128554 examples [00:48, 3153.83 examples/s]
Generating extra examples...: 128872 examples [00:48, 3159.59 examples/s]
Generating extra examples...: 129188 examples [00:48, 3152.58 examples/s]
Generating extra examples...: 129505 examples [00:49, 3154.73 examples/s]
Generating extra examples...: 129821 examples [00:49, 3142.67 examples/s]
Generating extra examples...: 130138 examples [00:49, 3148.50 examples/s]
Generating extra examples...: 130453 examples [00:49, 3142.16 examples/s]
Generating extra examples...: 130771 examples [00:49, 3152.51 examples/s]
Generating extra examples...: 131091 examples [00:49, 3164.16 examples/s]
Generating extra examples...: 131408 examples [00:49, 3165.17 examples/s]
Generating extra examples...: 131730 examples [00:49, 3181.07 examples/s]
Generating extra examples...: 132049 examples [00:49, 3176.96 examples/s]
Generating extra examples...: 132367 examples [00:49, 3174.37 examples/s]
Generating extra examples...: 132686 examples [00:50, 3178.57 examples/s]
Generating extra examples...: 133004 examples [00:50, 3155.84 examples/s]
Generating extra examples...: 133320 examples [00:50, 3145.70 examples/s]
Generating extra examples...: 133639 examples [00:50, 3156.89 examples/s]
Generating extra examples...: 133958 examples [00:50, 3165.01 examples/s]
Generating extra examples...: 134279 examples [00:50, 3177.33 examples/s]
Generating extra examples...: 134599 examples [00:50, 3182.76 examples/s]
Generating extra examples...: 134918 examples [00:50, 3180.50 examples/s]
Generating extra examples...: 135239 examples [00:50, 3186.26 examples/s]
Generating extra examples...: 135559 examples [00:50, 3188.27 examples/s]
Generating extra examples...: 135878 examples [00:51, 3165.49 examples/s]
Generating extra examples...: 136195 examples [00:51, 3144.88 examples/s]
Generating extra examples...: 136515 examples [00:51, 3159.37 examples/s]
Generating extra examples...: 136836 examples [00:51, 3172.14 examples/s]
Generating extra examples...: 137154 examples [00:51, 3137.19 examples/s]
Generating extra examples...: 137472 examples [00:51, 3147.78 examples/s]
Generating extra examples...: 137794 examples [00:51, 3167.51 examples/s]
Generating extra examples...: 138111 examples [00:51, 3160.95 examples/s]
Generating extra examples...: 138432 examples [00:51, 3172.90 examples/s]
Generating extra examples...: 138750 examples [00:51, 3170.52 examples/s]
Generating extra examples...: 139069 examples [00:52, 3174.23 examples/s]
Generating extra examples...: 139387 examples [00:52, 3130.28 examples/s]
Generating extra examples...: 139701 examples [00:52, 3131.84 examples/s]
Generating extra examples...: 140019 examples [00:52, 3143.50 examples/s]
Generating extra examples...: 140334 examples [00:52, 3137.38 examples/s]
Generating extra examples...: 140648 examples [00:52, 3133.47 examples/s]
Generating extra examples...: 140967 examples [00:52, 3148.10 examples/s]
Generating extra examples...: 141287 examples [00:52, 3161.75 examples/s]
Generating extra examples...: 141608 examples [00:52, 3175.33 examples/s]
Generating extra examples...: 141927 examples [00:52, 3178.35 examples/s]
Generating extra examples...: 142245 examples [00:53, 3165.33 examples/s]
Generating extra examples...: 142565 examples [00:53, 3170.41 examples/s]
Generating extra examples...: 142883 examples [00:53, 3167.63 examples/s]
Generating extra examples...: 143200 examples [00:53, 3157.99 examples/s]
Generating extra examples...: 143519 examples [00:53, 3166.18 examples/s]
Generating extra examples...: 143836 examples [00:53, 3163.89 examples/s]
Generating extra examples...: 144153 examples [00:53, 3163.95 examples/s]
Generating extra examples...: 144470 examples [00:53, 3162.48 examples/s]
Generating extra examples...: 144791 examples [00:53, 3175.17 examples/s]
Generating extra examples...: 145109 examples [00:53, 3175.83 examples/s]
Generating extra examples...: 145427 examples [00:54, 3165.22 examples/s]
Generating extra examples...: 145744 examples [00:54, 3137.36 examples/s]
Generating extra examples...: 146063 examples [00:54, 3150.77 examples/s]
Generating extra examples...: 146380 examples [00:54, 3154.04 examples/s]
Generating extra examples...: 146696 examples [00:54, 3145.61 examples/s]
Generating extra examples...: 147011 examples [00:54, 3146.58 examples/s]
Generating extra examples...: 147329 examples [00:54, 3153.54 examples/s]
Generating extra examples...: 147645 examples [00:54, 3151.21 examples/s]
Generating extra examples...: 147963 examples [00:54, 3159.72 examples/s]
Generating extra examples...: 148279 examples [00:55, 3155.78 examples/s]
Generating extra examples...: 148595 examples [00:55, 3156.65 examples/s]
Generating extra examples...: 148911 examples [00:55, 3128.13 examples/s]
Generating extra examples...: 149227 examples [00:55, 3135.09 examples/s]
Generating extra examples...: 149541 examples [00:55, 3133.02 examples/s]
Generating extra examples...: 149858 examples [00:55, 3141.59 examples/s]
Generating extra examples...: 150175 examples [00:55, 3149.99 examples/s]
Generating extra examples...: 150491 examples [00:55, 3148.01 examples/s]
Generating extra examples...: 150809 examples [00:55, 3156.96 examples/s]
Generating extra examples...: 151125 examples [00:55, 3155.46 examples/s]
Generating extra examples...: 151441 examples [00:56, 3149.85 examples/s]
Generating extra examples...: 151758 examples [00:56, 3155.41 examples/s]
Generating extra examples...: 152077 examples [00:56, 3163.15 examples/s]
Generating extra examples...: 152400 examples [00:56, 3182.44 examples/s]
Generating extra examples...: 152720 examples [00:56, 3185.82 examples/s]
Generating extra examples...: 153039 examples [00:56, 3179.19 examples/s]
Generating extra examples...: 153357 examples [00:56, 3177.69 examples/s]
Generating extra examples...: 153675 examples [00:56, 3177.61 examples/s]
Generating extra examples...: 153994 examples [00:56, 3179.87 examples/s]
Generating extra examples...: 154315 examples [00:56, 3188.55 examples/s]
Generating extra examples...: 154634 examples [00:57, 3182.31 examples/s]
Generating extra examples...: 154953 examples [00:57, 3168.73 examples/s]
Generating extra examples...: 155276 examples [00:57, 3184.75 examples/s]
Generating extra examples...: 155595 examples [00:57, 3170.43 examples/s]
Generating extra examples...: 155916 examples [00:57, 3181.42 examples/s]
Generating extra examples...: 156236 examples [00:57, 3185.57 examples/s]
Generating extra examples...: 156560 examples [00:57, 3201.18 examples/s]
Generating extra examples...: 156881 examples [00:57, 3197.75 examples/s]
Generating extra examples...: 157202 examples [00:57, 3200.74 examples/s]
Generating extra examples...: 157523 examples [00:57, 3188.59 examples/s]
Generating extra examples...: 157845 examples [00:58, 3195.95 examples/s]
Generating extra examples...: 158165 examples [00:58, 3193.06 examples/s]
Generating extra examples...: 158486 examples [00:58, 3196.45 examples/s]
Generating extra examples...: 158806 examples [00:58, 3187.64 examples/s]
Generating extra examples...: 159127 examples [00:58, 3191.75 examples/s]
Generating extra examples...: 159447 examples [00:58, 3188.58 examples/s]
Generating extra examples...: 159766 examples [00:58, 3180.11 examples/s]
Generating extra examples...: 160086 examples [00:58, 3185.65 examples/s]
Generating extra examples...: 160405 examples [00:58, 3181.59 examples/s]
Generating extra examples...: 160732 examples [00:58, 3206.29 examples/s]
Generating extra examples...: 161053 examples [00:59, 3184.33 examples/s]
Generating extra examples...: 161372 examples [00:59, 3174.63 examples/s]
Generating extra examples...: 161690 examples [00:59, 3173.91 examples/s]
Generating extra examples...: 162010 examples [00:59, 3180.18 examples/s]
Generating extra examples...: 162330 examples [00:59, 3184.03 examples/s]
Generating extra examples...: 162653 examples [00:59, 3196.20 examples/s]
Generating extra examples...: 162973 examples [00:59, 3185.07 examples/s]
Generating extra examples...: 163294 examples [00:59, 3191.92 examples/s]
Generating extra examples...: 163615 examples [00:59, 3195.12 examples/s]
Generating extra examples...: 163935 examples [00:59, 3191.16 examples/s]
Generating extra examples...: 164255 examples [01:00, 3193.27 examples/s]
Generating extra examples...: 164575 examples [01:00, 3181.74 examples/s]
Generating extra examples...: 164894 examples [01:00, 3164.18 examples/s]
Generating extra examples...: 165214 examples [01:00, 3173.39 examples/s]
Generating extra examples...: 165535 examples [01:00, 3183.10 examples/s]
Generating extra examples...: 165856 examples [01:00, 3189.93 examples/s]
Generating extra examples...: 166176 examples [01:00, 3174.80 examples/s]
Generating extra examples...: 166494 examples [01:00, 3169.88 examples/s]
Generating extra examples...: 166813 examples [01:00, 3175.41 examples/s]
Generating extra examples...: 167131 examples [01:00, 3173.76 examples/s]
Generating extra examples...: 167449 examples [01:01, 3152.16 examples/s]
Generating extra examples...: 167771 examples [01:01, 3170.32 examples/s]
Generating extra examples...: 168094 examples [01:01, 3187.23 examples/s]
Generating extra examples...: 168414 examples [01:01, 3188.40 examples/s]
Generating extra examples...: 168733 examples [01:01, 3175.37 examples/s]
Generating extra examples...: 169051 examples [01:01, 3174.56 examples/s]
Generating extra examples...: 169370 examples [01:01, 3178.82 examples/s]
Generating extra examples...: 169692 examples [01:01, 3189.56 examples/s]
Generating extra examples...: 170014 examples [01:01, 3197.67 examples/s]
Generating extra examples...: 170334 examples [01:01, 3190.97 examples/s]
Generating extra examples...: 170654 examples [01:02, 3186.51 examples/s]
Generating extra examples...: 170973 examples [01:02, 3185.04 examples/s]
Generating extra examples...: 171292 examples [01:02, 3184.18 examples/s]
Generating extra examples...: 171611 examples [01:02, 3172.66 examples/s]
Generating extra examples...: 171929 examples [01:02, 3167.67 examples/s]
Generating extra examples...: 172246 examples [01:02, 3166.53 examples/s]
Generating extra examples...: 172563 examples [01:02, 3167.38 examples/s]
Generating extra examples...: 172883 examples [01:02, 3176.86 examples/s]
Generating extra examples...: 173201 examples [01:02, 3172.55 examples/s]
Generating extra examples...: 173523 examples [01:02, 3185.66 examples/s]
Generating extra examples...: 173842 examples [01:03, 3172.07 examples/s]
Generating extra examples...: 174162 examples [01:03, 3177.36 examples/s]
Generating extra examples...: 174481 examples [01:03, 3178.72 examples/s]
Generating extra examples...: 174803 examples [01:03, 3188.25 examples/s]
Generating extra examples...: 175122 examples [01:03, 3181.72 examples/s]
Generating extra examples...: 175441 examples [01:03, 3183.01 examples/s]
Generating extra examples...: 175760 examples [01:03, 3172.23 examples/s]
Generating extra examples...: 176082 examples [01:03, 3185.98 examples/s]
Generating extra examples...: 176401 examples [01:03, 3183.06 examples/s]
Generating extra examples...: 176720 examples [01:03, 3181.73 examples/s]
Generating extra examples...: 177039 examples [01:04, 3182.35 examples/s]
Generating extra examples...: 177360 examples [01:04, 3189.58 examples/s]
Generating extra examples...: 177679 examples [01:04, 3181.38 examples/s]
Generating extra examples...: 177998 examples [01:04, 3098.17 examples/s]
Generating extra examples...: 178318 examples [01:04, 3125.79 examples/s]
Generating extra examples...: 178638 examples [01:04, 3147.49 examples/s]
Generating extra examples...: 178954 examples [01:04, 3148.45 examples/s]
Generating extra examples...: 179271 examples [01:04, 3152.34 examples/s]
Generating extra examples...: 179590 examples [01:04, 3162.35 examples/s]
Generating extra examples...: 179913 examples [01:04, 3181.00 examples/s]
Generating extra examples...: 180232 examples [01:05, 3147.98 examples/s]
Generating extra examples...: 180551 examples [01:05, 3158.01 examples/s]
Generating extra examples...: 180873 examples [01:05, 3173.87 examples/s]
Generating extra examples...: 181191 examples [01:05, 3156.74 examples/s]
Generating extra examples...: 181508 examples [01:05, 3160.47 examples/s]
Generating extra examples...: 181827 examples [01:05, 3167.03 examples/s]
Generating extra examples...: 182151 examples [01:05, 3187.18 examples/s]
Generating extra examples...: 182472 examples [01:05, 3191.92 examples/s]
Generating extra examples...: 182795 examples [01:05, 3202.22 examples/s]
Generating extra examples...: 183120 examples [01:05, 3215.63 examples/s]
Generating extra examples...: 183442 examples [01:06, 3210.54 examples/s]
Generating extra examples...: 183764 examples [01:06, 3211.11 examples/s]
Generating extra examples...: 184086 examples [01:06, 3193.05 examples/s]
Generating extra examples...: 184407 examples [01:06, 3197.24 examples/s]
Generating extra examples...: 184731 examples [01:06, 3208.82 examples/s]
Generating extra examples...: 185052 examples [01:06, 3204.53 examples/s]
Generating extra examples...: 185373 examples [01:06, 3197.97 examples/s]
Generating extra examples...: 185695 examples [01:06, 3201.83 examples/s]
Generating extra examples...: 186016 examples [01:06, 3194.37 examples/s]
Generating extra examples...: 186341 examples [01:06, 3210.91 examples/s]
Generating extra examples...: 186663 examples [01:07, 3192.49 examples/s]
Generating extra examples...: 186983 examples [01:07, 3188.63 examples/s]
Generating extra examples...: 187307 examples [01:07, 3200.74 examples/s]
Generating extra examples...: 187628 examples [01:07, 3169.60 examples/s]
Generating extra examples...: 187953 examples [01:07, 3192.03 examples/s]
Generating extra examples...: 188275 examples [01:07, 3197.47 examples/s]
Generating extra examples...: 188595 examples [01:07, 3193.24 examples/s]
Generating extra examples...: 188922 examples [01:07, 3215.46 examples/s]
Generating extra examples...: 189244 examples [01:07, 3204.39 examples/s]
Generating extra examples...: 189565 examples [01:07, 3192.83 examples/s]
Generating extra examples...: 189886 examples [01:08, 3194.54 examples/s]
Generating extra examples...: 190207 examples [01:08, 3196.72 examples/s]
Generating extra examples...: 190527 examples [01:08, 3190.56 examples/s]
Generating extra examples...: 190847 examples [01:08, 3169.11 examples/s]
Generating extra examples...: 191164 examples [01:08, 3166.27 examples/s]
Generating extra examples...: 191487 examples [01:08, 3182.14 examples/s]
Generating extra examples...: 191809 examples [01:08, 3192.49 examples/s]
Generating extra examples...: 192129 examples [01:08, 3190.29 examples/s]
Generating extra examples...: 192451 examples [01:08, 3197.43 examples/s]
Generating extra examples...: 192771 examples [01:08, 3191.10 examples/s]
Generating extra examples...: 193091 examples [01:09, 3179.76 examples/s]
Generating extra examples...: 193409 examples [01:09, 3179.47 examples/s]
Generating extra examples...: 193727 examples [01:09, 3173.20 examples/s]
Generating extra examples...: 194045 examples [01:09, 3146.92 examples/s]
Generating extra examples...: 194361 examples [01:09, 3149.29 examples/s]
Generating extra examples...: 194682 examples [01:09, 3165.02 examples/s]
Generating extra examples...: 195003 examples [01:09, 3175.77 examples/s]
Generating extra examples...: 195324 examples [01:09, 3185.28 examples/s]
Generating extra examples...: 195643 examples [01:09, 3186.14 examples/s]
Generating extra examples...: 195963 examples [01:09, 3189.78 examples/s]
Generating extra examples...: 196282 examples [01:10, 3183.54 examples/s]
Generating extra examples...: 196603 examples [01:10, 3190.39 examples/s]
Generating extra examples...: 196923 examples [01:10, 3189.19 examples/s]
Generating extra examples...: 197242 examples [01:10, 3129.00 examples/s]
Generating extra examples...: 197563 examples [01:10, 3152.06 examples/s]
Generating extra examples...: 197884 examples [01:10, 3166.83 examples/s]
Generating extra examples...: 198202 examples [01:10, 3168.13 examples/s]
Generating extra examples...: 198519 examples [01:10, 3156.40 examples/s]
Generating extra examples...: 198838 examples [01:10, 3164.10 examples/s]
Generating extra examples...: 199159 examples [01:11, 3175.49 examples/s]
Generating extra examples...: 199477 examples [01:11, 3175.49 examples/s]
Generating extra examples...: 199798 examples [01:11, 3184.90 examples/s]
Generating extra examples...: 200123 examples [01:11, 3203.39 examples/s]
Generating extra examples...: 200444 examples [01:11, 3197.58 examples/s]
Generating extra examples...: 200764 examples [01:11, 3187.53 examples/s]
Generating extra examples...: 201085 examples [01:11, 3192.52 examples/s]
Generating extra examples...: 201405 examples [01:11, 3190.69 examples/s]
Generating extra examples...: 201725 examples [01:11, 3179.24 examples/s]
Generating extra examples...: 202043 examples [01:11, 3174.29 examples/s]
Generating extra examples...: 202361 examples [01:12, 3167.56 examples/s]
Generating extra examples...: 202680 examples [01:12, 3172.39 examples/s]
Generating extra examples...: 203000 examples [01:12, 3178.02 examples/s]
Generating extra examples...: 203319 examples [01:12, 3178.48 examples/s]
Generating extra examples...: 203637 examples [01:12, 3175.30 examples/s]
Generating extra examples...: 203958 examples [01:12, 3184.48 examples/s]
Generating extra examples...: 204278 examples [01:12, 3188.87 examples/s]
Generating extra examples...: 204597 examples [01:12, 3181.02 examples/s]
Generating extra examples...: 204916 examples [01:12, 3175.27 examples/s]
Generating extra examples...: 205237 examples [01:12, 3184.67 examples/s]
Generating extra examples...: 205556 examples [01:13, 3175.94 examples/s]
Generating extra examples...: 205875 examples [01:13, 3177.50 examples/s]
Generating extra examples...: 206193 examples [01:13, 3174.47 examples/s]
Generating extra examples...: 206511 examples [01:13, 3159.88 examples/s]
Generating extra examples...: 206828 examples [01:13, 3055.24 examples/s]
Generating extra examples...: 207135 examples [01:13, 3017.48 examples/s]
Generating extra examples...: 207444 examples [01:13, 3037.41 examples/s]
Generating extra examples...: 207759 examples [01:13, 3068.94 examples/s]
Generating extra examples...: 208080 examples [01:13, 3107.76 examples/s]
Generating extra examples...: 208398 examples [01:13, 3126.78 examples/s]
Generating extra examples...: 208715 examples [01:14, 3137.75 examples/s]
Generating extra examples...: 209037 examples [01:14, 3161.09 examples/s]
Generating extra examples...: 209356 examples [01:14, 3167.26 examples/s]
Generating extra examples...: 209674 examples [01:14, 3169.05 examples/s]
Generating extra examples...: 209991 examples [01:14, 3168.00 examples/s]
Generating extra examples...: 210308 examples [01:14, 3167.25 examples/s]
Generating extra examples...: 210629 examples [01:14, 3178.38 examples/s]
Generating extra examples...: 210949 examples [01:14, 3183.31 examples/s]
Generating extra examples...: 211274 examples [01:14, 3201.96 examples/s]
Generating extra examples...: 211595 examples [01:14, 3193.81 examples/s]
Generating extra examples...: 211915 examples [01:15, 3173.75 examples/s]
Generating extra examples...: 212234 examples [01:15, 3177.27 examples/s]
Generating extra examples...: 212555 examples [01:15, 3186.15 examples/s]
Generating extra examples...: 212875 examples [01:15, 3188.43 examples/s]
Generating extra examples...: 213194 examples [01:15, 3179.02 examples/s]
Generating extra examples...: 213513 examples [01:15, 3179.74 examples/s]
Generating extra examples...: 213833 examples [01:15, 3185.36 examples/s]
Generating extra examples...: 214152 examples [01:15, 3178.23 examples/s]
Generating extra examples...: 214470 examples [01:15, 3178.46 examples/s]
Generating extra examples...: 214788 examples [01:15, 3159.70 examples/s]
Generating extra examples...: 215106 examples [01:16, 3164.48 examples/s]
Generating extra examples...: 215429 examples [01:16, 3183.00 examples/s]
Generating extra examples...: 215752 examples [01:16, 3194.33 examples/s]
Generating extra examples...: 216072 examples [01:16, 3181.91 examples/s]
Generating extra examples...: 216391 examples [01:16, 3162.45 examples/s]
Generating extra examples...: 216709 examples [01:16, 3165.46 examples/s]
Generating extra examples...: 217031 examples [01:16, 3179.55 examples/s]
Generating extra examples...: 217354 examples [01:16, 3192.02 examples/s]
Generating extra examples...: 217674 examples [01:16, 3190.25 examples/s]
Generating extra examples...: 217994 examples [01:16, 3184.77 examples/s]
Generating extra examples...: 218313 examples [01:17, 3165.58 examples/s]
Generating extra examples...: 218633 examples [01:17, 3174.17 examples/s]
Generating extra examples...: 218955 examples [01:17, 3187.12 examples/s]
Generating extra examples...: 219276 examples [01:17, 3191.24 examples/s]
Generating extra examples...: 219596 examples [01:17, 3149.99 examples/s]
Generating extra examples...: 219915 examples [01:17, 3160.06 examples/s]
Generating extra examples...: 220233 examples [01:17, 3163.83 examples/s]
Generating extra examples...: 220550 examples [01:17, 3164.96 examples/s]
Generating extra examples...: 220867 examples [01:17, 3160.47 examples/s]
Generating extra examples...: 221184 examples [01:17, 3153.50 examples/s]
Generating extra examples...: 221502 examples [01:18, 3161.09 examples/s]
Generating extra examples...: 221819 examples [01:18, 3161.63 examples/s]
Generating extra examples...: 222137 examples [01:18, 3166.17 examples/s]
Generating extra examples...: 222454 examples [01:18, 3163.53 examples/s]
Generating extra examples...: 222771 examples [01:18, 3149.38 examples/s]
Generating extra examples...: 223086 examples [01:18, 3143.29 examples/s]
Generating extra examples...: 223405 examples [01:18, 3156.60 examples/s]
Generating extra examples...: 223727 examples [01:18, 3173.67 examples/s]
Generating extra examples...: 224053 examples [01:18, 3197.42 examples/s]
Generating extra examples...: 224375 examples [01:18, 3202.11 examples/s]
Generating extra examples...: 224696 examples [01:19, 3179.11 examples/s]
Generating extra examples...: 225019 examples [01:19, 3192.94 examples/s]
Generating extra examples...: 225339 examples [01:19, 3194.88 examples/s]
Generating extra examples...: 225659 examples [01:19, 3191.41 examples/s]
Generating extra examples...: 225979 examples [01:19, 3170.49 examples/s]
Generating extra examples...: 226297 examples [01:19, 3161.43 examples/s]
Generating extra examples...: 226614 examples [01:19, 3158.88 examples/s]
Generating extra examples...: 226930 examples [01:19, 3155.50 examples/s]
Generating extra examples...: 227246 examples [01:19, 3152.47 examples/s]
Generating extra examples...: 227564 examples [01:19, 3159.97 examples/s]
Generating extra examples...: 227881 examples [01:20, 3155.34 examples/s]
Generating extra examples...: 228197 examples [01:20, 3152.79 examples/s]
Generating extra examples...: 228513 examples [01:20, 3139.29 examples/s]
Generating extra examples...: 228832 examples [01:20, 3153.12 examples/s]
Generating extra examples...: 229148 examples [01:20, 3154.24 examples/s]
Generating extra examples...: 229464 examples [01:20, 3153.00 examples/s]
Generating extra examples...: 229780 examples [01:20, 3142.18 examples/s]
Generating extra examples...: 230095 examples [01:20, 3138.42 examples/s]
Generating extra examples...: 230414 examples [01:20, 3150.46 examples/s]
Generating extra examples...: 230730 examples [01:20, 3153.16 examples/s]
Generating extra examples...: 231046 examples [01:21, 3153.77 examples/s]
Generating extra examples...: 231363 examples [01:21, 3156.30 examples/s]
Generating extra examples...: 231679 examples [01:21, 3147.70 examples/s]
Generating extra examples...: 231998 examples [01:21, 3158.87 examples/s]
Generating extra examples...: 232314 examples [01:21, 3135.34 examples/s]
Generating extra examples...: 232628 examples [01:21, 3037.90 examples/s]
Generating extra examples...: 232933 examples [01:21, 2940.71 examples/s]
Generating extra examples...: 233252 examples [01:21, 3010.48 examples/s]
Generating extra examples...: 233573 examples [01:21, 3066.61 examples/s]
Generating extra examples...: 233893 examples [01:22, 3104.84 examples/s]
Generating extra examples...: 234218 examples [01:22, 3146.60 examples/s]
Generating extra examples...: 234535 examples [01:22, 3153.45 examples/s]
Generating extra examples...: 234852 examples [01:22, 3157.17 examples/s]
Generating extra examples...: 235169 examples [01:22, 3160.47 examples/s]
Generating extra examples...: 235486 examples [01:22, 3160.57 examples/s]
Generating extra examples...: 235803 examples [01:22, 3154.75 examples/s]
Generating extra examples...: 236120 examples [01:22, 3158.87 examples/s]
Generating extra examples...: 236436 examples [01:22, 3153.77 examples/s]
Generating extra examples...: 236755 examples [01:22, 3163.79 examples/s]
Generating extra examples...: 237072 examples [01:23, 3155.54 examples/s]
Generating extra examples...: 237388 examples [01:23, 3154.02 examples/s]
Generating extra examples...: 237707 examples [01:23, 3162.20 examples/s]
Generating extra examples...: 238030 examples [01:23, 3181.27 examples/s]
Generating extra examples...: 238349 examples [01:23, 3175.22 examples/s]
Generating extra examples...: 238669 examples [01:23, 3182.44 examples/s]
Generating extra examples...: 238988 examples [01:23, 3164.98 examples/s]
Generating extra examples...: 239305 examples [01:23, 3162.70 examples/s]
Generating extra examples...: 239623 examples [01:23, 3166.63 examples/s]
Generating extra examples...: 239941 examples [01:23, 3168.70 examples/s]
Generating extra examples...: 240258 examples [01:24, 3157.84 examples/s]
Generating extra examples...: 240580 examples [01:24, 3175.89 examples/s]
Generating extra examples...: 240903 examples [01:24, 3191.27 examples/s]
Generating extra examples...: 241223 examples [01:24, 3184.41 examples/s]
Generating extra examples...: 241542 examples [01:24, 3184.06 examples/s]
Generating extra examples...: 241861 examples [01:24, 3183.85 examples/s]
Generating extra examples...: 242180 examples [01:24, 3136.64 examples/s]
Generating extra examples...: 242503 examples [01:24, 3164.23 examples/s]
Generating extra examples...: 242826 examples [01:24, 3180.88 examples/s]
Generating extra examples...: 243152 examples [01:24, 3202.84 examples/s]
Generating extra examples...: 243473 examples [01:25, 3191.57 examples/s]
Generating extra examples...: 243795 examples [01:25, 3199.04 examples/s]
Generating extra examples...: 244115 examples [01:25, 3197.36 examples/s]
Generating extra examples...: 244435 examples [01:25, 3192.05 examples/s]
Generating extra examples...: 244756 examples [01:25, 3194.79 examples/s]
Generating extra examples...: 245076 examples [01:25, 3189.12 examples/s]
Generating extra examples...: 245395 examples [01:25, 3139.05 examples/s]
Generating extra examples...: 245714 examples [01:25, 3153.21 examples/s]
Generating extra examples...: 246030 examples [01:25, 3148.42 examples/s]
Generating extra examples...: 246352 examples [01:25, 3167.89 examples/s]
Generating extra examples...: 246671 examples [01:26, 3173.93 examples/s]
Generating extra examples...: 246989 examples [01:26, 3167.50 examples/s]
Generating extra examples...: 247306 examples [01:26, 3164.47 examples/s]
Generating extra examples...: 247623 examples [01:26, 3148.88 examples/s]
Generating extra examples...: 247942 examples [01:26, 3159.05 examples/s]
Generating extra examples...: 248258 examples [01:26, 3157.88 examples/s]
Generating extra examples...: 248574 examples [01:26, 3134.60 examples/s]
Generating extra examples...: 248890 examples [01:26, 3141.11 examples/s]
Generating extra examples...: 249205 examples [01:26, 3137.47 examples/s]
Generating extra examples...: 249527 examples [01:26, 3161.76 examples/s]
Generating extra examples...: 249844 examples [01:27, 3149.95 examples/s]
Generating extra examples...: 250161 examples [01:27, 3154.73 examples/s]
Generating extra examples...: 250478 examples [01:27, 3157.73 examples/s]
Generating extra examples...: 250797 examples [01:27, 3166.84 examples/s]
Generating extra examples...: 251114 examples [01:27, 3149.83 examples/s]
Generating extra examples...: 251433 examples [01:27, 3160.83 examples/s]
Generating extra examples...: 251750 examples [01:27, 3152.56 examples/s]
Generating extra examples...: 252066 examples [01:27, 3150.05 examples/s]
Generating extra examples...: 252383 examples [01:27, 3153.51 examples/s]
Generating extra examples...: 252701 examples [01:27, 3158.73 examples/s]
Generating extra examples...: 253018 examples [01:28, 3159.46 examples/s]
Generating extra examples...: 253334 examples [01:28, 3133.87 examples/s]
Generating extra examples...: 253652 examples [01:28, 3145.94 examples/s]
Generating extra examples...: 253967 examples [01:28, 3100.42 examples/s]
Generating extra examples...: 254278 examples [01:28, 3102.89 examples/s]
Generating extra examples...: 254596 examples [01:28, 3125.55 examples/s]
Generating extra examples...: 254909 examples [01:28, 3111.85 examples/s]
Generating extra examples...: 255224 examples [01:28, 3121.13 examples/s]
Generating extra examples...: 255545 examples [01:28, 3146.41 examples/s]
Generating extra examples...: 255866 examples [01:28, 3162.70 examples/s]
Generating extra examples...: 256183 examples [01:29, 3154.40 examples/s]
Generating extra examples...: 256503 examples [01:29, 3165.96 examples/s]
Generating extra examples...: 256821 examples [01:29, 3168.37 examples/s]
Generating extra examples...: 257138 examples [01:29, 3149.60 examples/s]
Generating extra examples...: 257459 examples [01:29, 3167.54 examples/s]
Generating extra examples...: 257782 examples [01:29, 3183.31 examples/s]
Generating extra examples...: 258101 examples [01:29, 3148.75 examples/s]
Generating extra examples...: 258419 examples [01:29, 3155.42 examples/s]
Generating extra examples...: 258740 examples [01:29, 3170.51 examples/s]
Generating extra examples...: 259061 examples [01:29, 3180.98 examples/s]
Generating extra examples...: 259380 examples [01:30, 3174.00 examples/s]
Generating extra examples...: 259702 examples [01:30, 3187.13 examples/s]
Generating extra examples...: 260021 examples [01:30, 3171.21 examples/s]
Generating extra examples...: 260340 examples [01:30, 3174.45 examples/s]
Generating extra examples...: 260662 examples [01:30, 3185.84 examples/s]
Generating extra examples...: 260985 examples [01:30, 3198.79 examples/s]
Generating extra examples...: 261305 examples [01:30, 3189.26 examples/s]
Generating extra examples...: 261628 examples [01:30, 3198.73 examples/s]
Generating extra examples...: 261951 examples [01:30, 3207.72 examples/s]
Generating extra examples...: 262272 examples [01:30, 3202.09 examples/s]
Generating extra examples...: 262593 examples [01:31, 3175.24 examples/s]
Generating extra examples...: 262917 examples [01:31, 3192.48 examples/s]
Generating extra examples...: 263240 examples [01:31, 3201.26 examples/s]
Generating extra examples...: 263561 examples [01:31, 3202.96 examples/s]
Generating extra examples...: 263882 examples [01:31, 3201.08 examples/s]
Generating extra examples...: 264203 examples [01:31, 3196.15 examples/s]
Generating extra examples...: 264523 examples [01:31, 3182.65 examples/s]
Generating extra examples...: 264842 examples [01:31, 3177.98 examples/s]
Generating extra examples...: 265165 examples [01:31, 3191.89 examples/s]
Generating extra examples...: 265485 examples [01:31, 3189.85 examples/s]
Generating extra examples...: 265804 examples [01:32, 3189.05 examples/s]
Generating extra examples...: 266125 examples [01:32, 3194.65 examples/s]
Generating extra examples...: 266445 examples [01:32, 3179.76 examples/s]
Generating extra examples...: 266763 examples [01:32, 3168.31 examples/s]
Generating extra examples...: 267085 examples [01:32, 3182.29 examples/s]
Generating extra examples...: 267404 examples [01:32, 3181.00 examples/s]
Generating extra examples...: 267723 examples [01:32, 3171.86 examples/s]
Generating extra examples...: 268041 examples [01:32, 3150.97 examples/s]
Generating extra examples...: 268357 examples [01:32, 3145.42 examples/s]
Generating extra examples...: 268672 examples [01:32, 3132.71 examples/s]
Generating extra examples...: 268986 examples [01:33, 3123.89 examples/s]
Generating extra examples...: 269299 examples [01:33, 3094.78 examples/s]
Generating extra examples...: 269614 examples [01:33, 3109.77 examples/s]
Generating extra examples...: 269926 examples [01:33, 3092.94 examples/s]
Generating extra examples...: 270248 examples [01:33, 3130.50 examples/s]
Generating extra examples...: 270562 examples [01:33, 3127.53 examples/s]
Generating extra examples...: 270875 examples [01:33, 3103.76 examples/s]
Generating extra examples...: 271191 examples [01:33, 3119.59 examples/s]
Generating extra examples...: 271507 examples [01:33, 3131.40 examples/s]
Generating extra examples...: 271829 examples [01:34, 3156.47 examples/s]
Generating extra examples...: 272145 examples [01:34, 3150.69 examples/s]
Generating extra examples...: 272464 examples [01:34, 3160.30 examples/s]
Generating extra examples...: 272783 examples [01:34, 3166.04 examples/s]
Generating extra examples...: 273100 examples [01:34, 3158.40 examples/s]
Generating extra examples...: 273416 examples [01:34, 3140.82 examples/s]
Generating extra examples...: 273734 examples [01:34, 3152.06 examples/s]
Generating extra examples...: 274051 examples [01:34, 3155.25 examples/s]
Generating extra examples...: 274372 examples [01:34, 3169.64 examples/s]
Generating extra examples...: 274691 examples [01:34, 3175.30 examples/s]
Generating extra examples...: 275010 examples [01:35, 3176.63 examples/s]
Generating extra examples...: 275331 examples [01:35, 3185.57 examples/s]
Generating extra examples...: 275650 examples [01:35, 3181.74 examples/s]
Generating extra examples...: 275969 examples [01:35, 3183.42 examples/s]
Generating extra examples...: 276288 examples [01:35, 3178.79 examples/s]
Generating extra examples...: 276611 examples [01:35, 3191.07 examples/s]
Generating extra examples...: 276931 examples [01:35, 3188.00 examples/s]
Generating extra examples...: 277250 examples [01:35, 3182.73 examples/s]
Generating extra examples...: 277569 examples [01:35, 3163.91 examples/s]
Generating extra examples...: 277890 examples [01:35, 3175.92 examples/s]
Generating extra examples...: 278212 examples [01:36, 3188.67 examples/s]
Generating extra examples...: 278531 examples [01:36, 3173.80 examples/s]
Generating extra examples...: 278849 examples [01:36, 3151.09 examples/s]
Generating extra examples...: 279167 examples [01:36, 3157.99 examples/s]
Generating extra examples...: 279483 examples [01:36, 3157.55 examples/s]
Generating extra examples...: 279799 examples [01:36, 3157.32 examples/s]
Generating extra examples...: 280115 examples [01:36, 3156.69 examples/s]
Generating extra examples...: 280431 examples [01:36, 3146.68 examples/s]
Generating extra examples...: 280748 examples [01:36, 3150.49 examples/s]
Generating extra examples...: 281067 examples [01:36, 3160.25 examples/s]
Generating extra examples...: 281384 examples [01:37, 3153.99 examples/s]
Generating extra examples...: 281700 examples [01:37, 3146.73 examples/s]
Generating extra examples...: 282022 examples [01:37, 3168.27 examples/s]
Generating extra examples...: 282339 examples [01:37, 3149.71 examples/s]
Generating extra examples...: 282661 examples [01:37, 3168.87 examples/s]
Generating extra examples...: 282985 examples [01:37, 3189.83 examples/s]
Generating extra examples...: 283307 examples [01:37, 3196.84 examples/s]
Generating extra examples...: 283630 examples [01:37, 3204.56 examples/s]
Generating extra examples...: 283951 examples [01:37, 3198.24 examples/s]
Generating extra examples...: 284271 examples [01:37, 3194.57 examples/s]
Generating extra examples...: 284591 examples [01:38, 3191.00 examples/s]
Generating extra examples...: 284911 examples [01:38, 3184.05 examples/s]
Generating extra examples...: 285230 examples [01:38, 3175.42 examples/s]
Generating extra examples...: 285550 examples [01:38, 3179.33 examples/s]
Generating extra examples...: 285869 examples [01:38, 3180.12 examples/s]
Generating extra examples...: 286188 examples [01:38, 3151.48 examples/s]
Generating extra examples...: 286506 examples [01:38, 3159.90 examples/s]
Generating extra examples...: 286826 examples [01:38, 3170.01 examples/s]
Generating extra examples...: 287144 examples [01:38, 3141.05 examples/s]
Generating extra examples...: 287459 examples [01:38, 3138.34 examples/s]
Generating extra examples...: 287773 examples [01:39, 3138.27 examples/s]
Generating extra examples...: 288088 examples [01:39, 3140.23 examples/s]
Generating extra examples...: 288403 examples [01:39, 3136.25 examples/s]
Generating extra examples...: 288717 examples [01:39, 3132.16 examples/s]
Generating extra examples...: 289032 examples [01:39, 3134.78 examples/s]
Generating extra examples...: 289346 examples [01:39, 3115.78 examples/s]
Generating extra examples...: 289663 examples [01:39, 3129.22 examples/s]
Generating extra examples...: 289983 examples [01:39, 3148.96 examples/s]
Generating extra examples...: 290298 examples [01:39, 3142.81 examples/s]
Generating extra examples...: 290613 examples [01:39, 3134.21 examples/s]
Generating extra examples...: 290927 examples [01:40, 3127.33 examples/s]
Generating extra examples...: 291240 examples [01:40, 3127.27 examples/s]
Generating extra examples...: 291558 examples [01:40, 3141.49 examples/s]
Generating extra examples...: 291873 examples [01:40, 3132.77 examples/s]
Generating extra examples...: 292187 examples [01:40, 3102.28 examples/s]
Generating extra examples...: 292501 examples [01:40, 3111.39 examples/s]
Generating extra examples...: 292813 examples [01:40, 3101.91 examples/s]
Generating extra examples...: 293125 examples [01:40, 3105.41 examples/s]
Generating extra examples...: 293436 examples [01:40, 3084.71 examples/s]
Generating extra examples...: 293747 examples [01:40, 3090.91 examples/s]
Generating extra examples...: 294060 examples [01:41, 3100.53 examples/s]
Generating extra examples...: 294374 examples [01:41, 3112.16 examples/s]
Generating extra examples...: 294687 examples [01:41, 3115.49 examples/s]
Generating extra examples...: 295006 examples [01:41, 3137.40 examples/s]
Generating extra examples...: 295323 examples [01:41, 3144.52 examples/s]
Generating extra examples...: 295638 examples [01:41, 3121.69 examples/s]
Generating extra examples...: 295951 examples [01:41, 3117.51 examples/s]
Generating extra examples...: 296264 examples [01:41, 3120.54 examples/s]
Generating extra examples...: 296577 examples [01:41, 3109.79 examples/s]
Generating extra examples...: 296889 examples [01:41, 3112.57 examples/s]
Generating extra examples...: 297201 examples [01:42, 3104.17 examples/s]
Generating extra examples...: 297519 examples [01:42, 3124.19 examples/s]
Generating extra examples...: 297841 examples [01:42, 3151.79 examples/s]
Generating extra examples...: 298161 examples [01:42, 3164.42 examples/s]
Generating extra examples...: 298478 examples [01:42, 3162.83 examples/s]
Generating extra examples...: 298795 examples [01:42, 3139.40 examples/s]
Generating extra examples...: 299110 examples [01:42, 3140.64 examples/s]
Generating extra examples...: 299432 examples [01:42, 3163.90 examples/s]
Generating extra examples...: 299749 examples [01:42, 3148.76 examples/s]
Generating extra examples...: 300065 examples [01:42, 3148.97 examples/s]
Generating extra examples...: 300380 examples [01:43, 3123.61 examples/s]
Generating extra examples...: 300697 examples [01:43, 3134.86 examples/s]
Generating extra examples...: 301011 examples [01:43, 3132.20 examples/s]
Generating extra examples...: 301325 examples [01:43, 3124.81 examples/s]
Generating extra examples...: 301642 examples [01:43, 3136.39 examples/s]
Generating extra examples...: 301957 examples [01:43, 3137.48 examples/s]
Generating extra examples...: 302275 examples [01:43, 3148.48 examples/s]
Generating extra examples...: 302590 examples [01:43, 3142.68 examples/s]
Generating extra examples...: 302905 examples [01:43, 3086.05 examples/s]
Generating extra examples...: 303221 examples [01:43, 3107.52 examples/s]
Generating extra examples...: 303537 examples [01:44, 3120.39 examples/s]
Generating extra examples...: 303855 examples [01:44, 3137.06 examples/s]
Generating extra examples...: 304175 examples [01:44, 3153.50 examples/s]
Generating extra examples...: 304491 examples [01:44, 3142.96 examples/s]
Generating extra examples...: 304806 examples [01:44, 3142.42 examples/s]
Generating extra examples...: 305129 examples [01:44, 3167.54 examples/s]
Generating extra examples...: 305446 examples [01:44, 3162.96 examples/s]
Generating extra examples...: 305765 examples [01:44, 3170.74 examples/s]
Generating extra examples...: 306083 examples [01:44, 3157.50 examples/s]
Generating extra examples...: 306400 examples [01:44, 3160.60 examples/s]
Generating extra examples...: 306717 examples [01:45, 3151.73 examples/s]
Generating extra examples...: 307034 examples [01:45, 3155.59 examples/s]
Generating extra examples...: 307353 examples [01:45, 3164.21 examples/s]
Generating extra examples...: 307673 examples [01:45, 3173.88 examples/s]
Generating extra examples...: 307992 examples [01:45, 3176.24 examples/s]
Generating extra examples...: 308310 examples [01:45, 3169.56 examples/s]
Generating extra examples...: 308630 examples [01:45, 3177.30 examples/s]
Generating extra examples...: 308948 examples [01:45, 3159.19 examples/s]
Generating extra examples...: 309264 examples [01:45, 3152.99 examples/s]
Generating extra examples...: 309583 examples [01:45, 3161.21 examples/s]
Generating extra examples...: 309900 examples [01:46, 3152.52 examples/s]
Generating extra examples...: 310218 examples [01:46, 3159.03 examples/s]
Generating extra examples...: 310537 examples [01:46, 3167.97 examples/s]
Generating extra examples...: 310854 examples [01:46, 3166.45 examples/s]
Generating extra examples...: 311175 examples [01:46, 3178.28 examples/s]
Generating extra examples...: 311493 examples [01:46, 3174.76 examples/s]
Generating extra examples...: 311817 examples [01:46, 3191.36 examples/s]
Generating extra examples...: 312137 examples [01:46, 3173.14 examples/s]
Generating extra examples...: 312455 examples [01:46, 3134.39 examples/s]
Generating extra examples...: 312769 examples [01:46, 3132.01 examples/s]
Generating extra examples...: 313085 examples [01:47, 3138.38 examples/s]
Generating extra examples...: 313407 examples [01:47, 3161.42 examples/s]
Generating extra examples...: 313725 examples [01:47, 3164.62 examples/s]
Generating extra examples...: 314043 examples [01:47, 3167.14 examples/s]
Generating extra examples...: 314361 examples [01:47, 3167.94 examples/s]
Generating extra examples...: 314678 examples [01:47, 3161.18 examples/s]
Generating extra examples...: 314995 examples [01:47, 3163.51 examples/s]
Generating extra examples...: 315312 examples [01:47, 3164.20 examples/s]
Generating extra examples...: 315629 examples [01:47, 3117.54 examples/s]
Generating extra examples...: 315945 examples [01:48, 3129.53 examples/s]
Generating extra examples...: 316266 examples [01:48, 3151.77 examples/s]
Generating extra examples...: 316582 examples [01:48, 3151.75 examples/s]
Generating extra examples...: 316903 examples [01:48, 3168.59 examples/s]
Generating extra examples...: 317222 examples [01:48, 3174.42 examples/s]
Generating extra examples...: 317540 examples [01:48, 3156.56 examples/s]
Generating extra examples...: 317856 examples [01:48, 3152.79 examples/s]
Generating extra examples...: 318172 examples [01:48, 3151.55 examples/s]
Generating extra examples...: 318488 examples [01:48, 3148.24 examples/s]
Generating extra examples...: 318803 examples [01:48, 3104.14 examples/s]
Generating extra examples...: 319118 examples [01:49, 3115.39 examples/s]
Generating extra examples...: 319433 examples [01:49, 3125.24 examples/s]
Generating extra examples...: 319751 examples [01:49, 3138.65 examples/s]
Generating extra examples...: 320068 examples [01:49, 3145.84 examples/s]
Generating extra examples...: 320383 examples [01:49, 3135.12 examples/s]
Generating extra examples...: 320702 examples [01:49, 3150.54 examples/s]
Generating extra examples...: 321021 examples [01:49, 3160.83 examples/s]
Generating extra examples...: 321338 examples [01:49, 3156.81 examples/s]
Generating extra examples...: 321654 examples [01:49, 3147.92 examples/s]
Generating extra examples...: 321969 examples [01:49, 3087.55 examples/s]
Generating extra examples...: 322289 examples [01:50, 3119.39 examples/s]
Generating extra examples...: 322605 examples [01:50, 3129.47 examples/s]
Generating extra examples...: 322923 examples [01:50, 3142.31 examples/s]
Generating extra examples...: 323243 examples [01:50, 3157.70 examples/s]
Generating extra examples...: 323560 examples [01:50, 3161.20 examples/s]
Generating extra examples...: 323877 examples [01:50, 3157.48 examples/s]
Generating extra examples...: 324194 examples [01:50, 3160.31 examples/s]
Generating extra examples...: 324516 examples [01:50, 3176.12 examples/s]
Generating extra examples...: 324836 examples [01:50, 3181.02 examples/s]
Generating extra examples...: 325155 examples [01:50, 3170.46 examples/s]
Generating extra examples...: 325473 examples [01:51, 3155.12 examples/s]
Generating extra examples...: 325797 examples [01:51, 3178.95 examples/s]
Generating extra examples...: 326115 examples [01:51, 3175.74 examples/s]
Generating extra examples...: 326439 examples [01:51, 3192.02 examples/s]
Generating extra examples...: 326761 examples [01:51, 3199.80 examples/s]
Generating extra examples...: 327083 examples [01:51, 3204.07 examples/s]
Generating extra examples...: 327404 examples [01:51, 3195.60 examples/s]
Generating extra examples...: 327724 examples [01:51, 3190.05 examples/s]
Generating extra examples...: 328044 examples [01:51, 3184.70 examples/s]
Generating extra examples...: 328363 examples [01:51, 3182.63 examples/s]
Generating extra examples...: 328682 examples [01:52, 3115.15 examples/s]
Generating extra examples...: 329000 examples [01:52, 3132.39 examples/s]
Generating extra examples...: 329320 examples [01:52, 3150.22 examples/s]
Generating extra examples...: 329636 examples [01:52, 3149.81 examples/s]
Generating extra examples...: 329952 examples [01:52, 3136.61 examples/s]
Generating extra examples...: 330266 examples [01:52, 3105.64 examples/s]
Generating extra examples...: 330578 examples [01:52, 3107.18 examples/s]
Generating extra examples...: 330889 examples [01:52, 3106.15 examples/s]
Generating extra examples...: 331200 examples [01:52, 3102.73 examples/s]
Generating extra examples...: 331511 examples [01:52, 3102.19 examples/s]
Generating extra examples...: 331822 examples [01:53, 3086.78 examples/s]
Generating extra examples...: 332134 examples [01:53, 3095.40 examples/s]
Generating extra examples...: 332446 examples [01:53, 3100.29 examples/s]
Generating extra examples...: 332759 examples [01:53, 3106.34 examples/s]
Generating extra examples...: 333073 examples [01:53, 3114.93 examples/s]
Generating extra examples...: 333386 examples [01:53, 3119.11 examples/s]
Generating extra examples...: 333698 examples [01:53, 3109.87 examples/s]
Generating extra examples...: 334011 examples [01:53, 3113.47 examples/s]
Generating extra examples...: 334324 examples [01:53, 3115.91 examples/s]
Generating extra examples...: 334644 examples [01:53, 3137.81 examples/s]
Generating extra examples...: 334958 examples [01:54, 3077.04 examples/s]
Generating extra examples...: 335273 examples [01:54, 3097.79 examples/s]
Generating extra examples...: 335586 examples [01:54, 3107.05 examples/s]
Generating extra examples...: 335901 examples [01:54, 3119.31 examples/s]
Generating extra examples...: 336214 examples [01:54, 3115.42 examples/s]
Generating extra examples...: 336526 examples [01:54, 3104.99 examples/s]
Generating extra examples...: 336841 examples [01:54, 3117.79 examples/s]
Generating extra examples...: 337158 examples [01:54, 3130.39 examples/s]
Generating extra examples...: 337472 examples [01:54, 3132.32 examples/s]
Generating extra examples...: 337786 examples [01:54, 3122.88 examples/s]
Generating extra examples...: 338099 examples [01:55, 3082.83 examples/s]
Generating extra examples...: 338413 examples [01:55, 3098.48 examples/s]
Generating extra examples...: 338727 examples [01:55, 3108.44 examples/s]
Generating extra examples...: 339042 examples [01:55, 3120.43 examples/s]
Generating extra examples...: 339356 examples [01:55, 3124.02 examples/s]
Generating extra examples...: 339673 examples [01:55, 3136.08 examples/s]
Generating extra examples...: 339987 examples [01:55, 3133.35 examples/s]
Generating extra examples...: 340303 examples [01:55, 3138.99 examples/s]
Generating extra examples...: 340621 examples [01:55, 3148.16 examples/s]
Generating extra examples...: 340936 examples [01:55, 3139.80 examples/s]
Generating extra examples...: 341250 examples [01:56, 3121.26 examples/s]
Generating extra examples...: 341570 examples [01:56, 3141.28 examples/s]
Generating extra examples...: 341890 examples [01:56, 3158.07 examples/s]
Generating extra examples...: 342206 examples [01:56, 3154.63 examples/s]
Generating extra examples...: 342523 examples [01:56, 3157.89 examples/s]
Generating extra examples...: 342841 examples [01:56, 3163.76 examples/s]
Generating extra examples...: 343159 examples [01:56, 3168.26 examples/s]
Generating extra examples...: 343480 examples [01:56, 3178.56 examples/s]
Generating extra examples...: 343798 examples [01:56, 3174.37 examples/s]
Generating extra examples...: 344118 examples [01:56, 3180.66 examples/s]
Generating extra examples...: 344437 examples [01:57, 3120.50 examples/s]
Generating extra examples...: 344753 examples [01:57, 3131.69 examples/s]
Generating extra examples...: 345072 examples [01:57, 3148.02 examples/s]
Generating extra examples...: 345387 examples [01:57, 3141.30 examples/s]
Generating extra examples...: 345707 examples [01:57, 3157.99 examples/s]
Generating extra examples...: 346027 examples [01:57, 3168.40 examples/s]
Generating extra examples...: 346344 examples [01:57, 3165.12 examples/s]
Generating extra examples...: 346661 examples [01:57, 3164.37 examples/s]
Generating extra examples...: 346982 examples [01:57, 3175.93 examples/s]
Generating extra examples...: 347302 examples [01:57, 3179.80 examples/s]
Generating extra examples...: 347620 examples [01:58, 3106.78 examples/s]
Generating extra examples...: 347932 examples [01:58, 3107.17 examples/s]
Generating extra examples...: 348243 examples [01:58, 3031.01 examples/s]
Generating extra examples...: 348553 examples [01:58, 3050.44 examples/s]
Generating extra examples...: 348859 examples [01:58, 2955.04 examples/s]
Generating extra examples...: 349171 examples [01:58, 3002.55 examples/s]
Generating extra examples...: 349494 examples [01:58, 3068.52 examples/s]
Generating extra examples...: 349814 examples [01:58, 3104.32 examples/s]
Generating extra examples...: 350132 examples [01:58, 3126.61 examples/s]
Generating extra examples...: 350448 examples [01:59, 3134.46 examples/s]
Generating extra examples...: 350762 examples [01:59, 3108.73 examples/s]
Generating extra examples...: 351084 examples [01:59, 3140.32 examples/s]
Generating extra examples...: 351400 examples [01:59, 3145.46 examples/s]
Generating extra examples...: 351717 examples [01:59, 3150.61 examples/s]
Generating extra examples...: 352040 examples [01:59, 3173.16 examples/s]
Generating extra examples...: 352359 examples [01:59, 3177.81 examples/s]
Generating extra examples...: 352678 examples [01:59, 3179.12 examples/s]
Generating extra examples...: 352996 examples [01:59, 3174.50 examples/s]
Generating extra examples...: 353315 examples [01:59, 3178.34 examples/s]
Generating extra examples...: 353634 examples [02:00, 3178.74 examples/s]
Generating extra examples...: 353952 examples [02:00, 3133.90 examples/s]
Generating extra examples...: 354272 examples [02:00, 3153.36 examples/s]
Generating extra examples...: 354590 examples [02:00, 3159.97 examples/s]
Generating extra examples...: 354910 examples [02:00, 3168.74 examples/s]
Generating extra examples...: 355229 examples [02:00, 3173.08 examples/s]
Generating extra examples...: 355551 examples [02:00, 3185.68 examples/s]
Generating extra examples...: 355874 examples [02:00, 3196.56 examples/s]
Generating extra examples...: 356195 examples [02:00, 3199.98 examples/s]
Generating extra examples...: 356516 examples [02:00, 3187.04 examples/s]
Generating extra examples...: 356835 examples [02:01, 3170.95 examples/s]
Generating extra examples...: 357153 examples [02:01, 3167.28 examples/s]
Generating extra examples...: 357473 examples [02:01, 3175.00 examples/s]
Generating extra examples...: 357791 examples [02:01, 3175.98 examples/s]
Generating extra examples...: 358113 examples [02:01, 3187.38 examples/s]
Generating extra examples...: 358432 examples [02:01, 3187.98 examples/s]
Generating extra examples...: 358751 examples [02:01, 3188.39 examples/s]
Generating extra examples...: 359070 examples [02:01, 3188.76 examples/s]
Generating extra examples...: 359391 examples [02:01, 3194.59 examples/s]
Generating extra examples...: 359711 examples [02:01, 3185.79 examples/s]
Generating extra examples...: 360032 examples [02:02, 3192.11 examples/s]
Generating extra examples...: 360352 examples [02:02, 3179.07 examples/s]
Generating extra examples...: 360672 examples [02:02, 3183.85 examples/s]
Generating extra examples...: 360994 examples [02:02, 3192.30 examples/s]
Generating extra examples...: 361314 examples [02:02, 3194.18 examples/s]
Generating extra examples...: 361638 examples [02:02, 3207.73 examples/s]
Generating extra examples...: 361959 examples [02:02, 3193.30 examples/s]
Generating extra examples...: 362280 examples [02:02, 3195.50 examples/s]
Generating extra examples...: 362600 examples [02:02, 3190.46 examples/s]
Generating extra examples...: 362921 examples [02:02, 3195.97 examples/s]
Generating extra examples...: 363241 examples [02:03, 3184.74 examples/s]
Generating extra examples...: 363560 examples [02:03, 3118.03 examples/s]
Generating extra examples...: 363883 examples [02:03, 3149.92 examples/s]
Generating extra examples...: 364203 examples [02:03, 3164.33 examples/s]
Generating extra examples...: 364524 examples [02:03, 3174.88 examples/s]
Generating extra examples...: 364844 examples [02:03, 3181.71 examples/s]
Generating extra examples...: 365172 examples [02:03, 3209.68 examples/s]
Generating extra examples...: 365494 examples [02:03, 3207.35 examples/s]
Generating extra examples...: 365815 examples [02:03, 3198.92 examples/s]
Generating extra examples...: 366141 examples [02:03, 3215.20 examples/s]
Generating extra examples...: 366463 examples [02:04, 3208.46 examples/s]
Generating extra examples...: 366784 examples [02:04, 3203.92 examples/s]
Generating extra examples...: 367105 examples [02:04, 3198.28 examples/s]
Generating extra examples...: 367425 examples [02:04, 3190.65 examples/s]
Generating extra examples...: 367745 examples [02:04, 3177.53 examples/s]
Generating extra examples...: 368063 examples [02:04, 3157.09 examples/s]
Generating extra examples...: 368383 examples [02:04, 3169.11 examples/s]
Generating extra examples...: 368701 examples [02:04, 3169.60 examples/s]
Generating extra examples...: 369023 examples [02:04, 3182.85 examples/s]
Generating extra examples...: 369345 examples [02:04, 3191.91 examples/s]
Generating extra examples...: 369665 examples [02:05, 3142.52 examples/s]
Generating extra examples...: 369989 examples [02:05, 3169.19 examples/s]
Generating extra examples...: 370307 examples [02:05, 3131.31 examples/s]
Generating extra examples...: 370627 examples [02:05, 3148.64 examples/s]
Generating extra examples...: 370944 examples [02:05, 3154.08 examples/s]
Generating extra examples...: 371261 examples [02:05, 3157.51 examples/s]
Generating extra examples...: 371579 examples [02:05, 3162.81 examples/s]
Generating extra examples...: 371900 examples [02:05, 3174.86 examples/s]
Generating extra examples...: 372219 examples [02:05, 3177.49 examples/s]
Generating extra examples...: 372538 examples [02:05, 3180.44 examples/s]
Generating extra examples...: 372858 examples [02:06, 3183.48 examples/s]
Generating extra examples...: 373179 examples [02:06, 3189.92 examples/s]
Generating extra examples...: 373499 examples [02:06, 3169.75 examples/s]
Generating extra examples...: 373817 examples [02:06, 3165.86 examples/s]
Generating extra examples...: 374135 examples [02:06, 3168.74 examples/s]
Generating extra examples...: 374452 examples [02:06, 3147.78 examples/s]
Generating extra examples...: 374767 examples [02:06, 3137.86 examples/s]
Generating extra examples...: 375081 examples [02:06, 3136.51 examples/s]
Generating extra examples...: 375401 examples [02:06, 3154.59 examples/s]
Generating extra examples...: 375718 examples [02:06, 3159.08 examples/s]
Generating extra examples...: 376034 examples [02:07, 3150.91 examples/s]
Generating extra examples...: 376353 examples [02:07, 3162.34 examples/s]
Generating extra examples...: 376670 examples [02:07, 3097.23 examples/s]
Generating extra examples...: 376986 examples [02:07, 3113.22 examples/s]
Generating extra examples...: 377306 examples [02:07, 3137.50 examples/s]
Generating extra examples...: 377624 examples [02:07, 3148.33 examples/s]
Generating extra examples...: 377942 examples [02:07, 3156.42 examples/s]
Generating extra examples...: 378258 examples [02:07, 3152.24 examples/s]
Generating extra examples...: 378576 examples [02:07, 3158.09 examples/s]
Generating extra examples...: 378896 examples [02:07, 3170.11 examples/s]
Generating extra examples...: 379215 examples [02:08, 3175.56 examples/s]
Generating extra examples...: 379534 examples [02:08, 3179.29 examples/s]
Generating extra examples...: 379852 examples [02:08, 3176.32 examples/s]
Generating extra examples...: 380171 examples [02:08, 3178.95 examples/s]
Generating extra examples...: 380496 examples [02:08, 3198.51 examples/s]
Generating extra examples...: 380820 examples [02:08, 3209.28 examples/s]
Generating extra examples...: 381141 examples [02:08, 3201.86 examples/s]
Generating extra examples...: 381463 examples [02:08, 3206.53 examples/s]
Generating extra examples...: 381784 examples [02:08, 3198.34 examples/s]
Generating extra examples...: 382104 examples [02:08, 3193.06 examples/s]
Generating extra examples...: 382424 examples [02:09, 3166.11 examples/s]
Generating extra examples...: 382741 examples [02:09, 3159.73 examples/s]
Generating extra examples...: 383058 examples [02:09, 3085.97 examples/s]
Generating extra examples...: 383374 examples [02:09, 3104.92 examples/s]
Generating extra examples...: 383690 examples [02:09, 3120.78 examples/s]
Generating extra examples...: 384007 examples [02:09, 3134.40 examples/s]
Generating extra examples...: 384327 examples [02:09, 3151.80 examples/s]
Generating extra examples...: 384647 examples [02:09, 3163.16 examples/s]
Generating extra examples...: 384967 examples [02:09, 3173.84 examples/s]
Generating extra examples...: 385291 examples [02:09, 3191.35 examples/s]
Generating extra examples...: 385612 examples [02:10, 3196.51 examples/s]
Generating extra examples...: 385933 examples [02:10, 3200.05 examples/s]
Generating extra examples...: 386254 examples [02:10, 3195.22 examples/s]
Generating extra examples...: 386574 examples [02:10, 3195.51 examples/s]
Generating extra examples...: 386894 examples [02:10, 3189.82 examples/s]
Generating extra examples...: 387213 examples [02:10, 3184.10 examples/s]
Generating extra examples...: 387532 examples [02:10, 3180.57 examples/s]
Generating extra examples...: 387854 examples [02:10, 3189.70 examples/s]
Generating extra examples...: 388177 examples [02:10, 3198.33 examples/s]
Generating extra examples...: 388498 examples [02:11, 3201.57 examples/s]
Generating extra examples...: 388819 examples [02:11, 3192.79 examples/s]
Generating extra examples...: 389139 examples [02:11, 3193.86 examples/s]
Generating extra examples...: 389459 examples [02:11, 3138.98 examples/s]
Generating extra examples...: 389776 examples [02:11, 3147.38 examples/s]
Generating extra examples...: 390094 examples [02:11, 3156.83 examples/s]
Generating extra examples...: 390413 examples [02:11, 3165.32 examples/s]
Generating extra examples...: 390733 examples [02:11, 3172.96 examples/s]
Generating extra examples...: 391054 examples [02:11, 3183.81 examples/s]
Generating extra examples...: 391376 examples [02:11, 3193.91 examples/s]
Generating extra examples...: 391696 examples [02:12, 3193.90 examples/s]
Generating extra examples...: 392016 examples [02:12, 3187.93 examples/s]
Generating extra examples...: 392338 examples [02:12, 3194.81 examples/s]
Generating extra examples...: 392658 examples [02:12, 3182.91 examples/s]
Generating extra examples...: 392984 examples [02:12, 3203.24 examples/s]
Generating extra examples...: 393306 examples [02:12, 3206.03 examples/s]
Generating extra examples...: 393628 examples [02:12, 3207.54 examples/s]
Generating extra examples...: 393951 examples [02:12, 3211.64 examples/s]
Generating extra examples...: 394273 examples [02:12, 3210.24 examples/s]
Generating extra examples...: 394595 examples [02:12, 3200.46 examples/s]
Generating extra examples...: 394916 examples [02:13, 3196.55 examples/s]
Generating extra examples...: 395237 examples [02:13, 3197.54 examples/s]
Generating extra examples...: 395560 examples [02:13, 3205.13 examples/s]
Generating extra examples...: 395881 examples [02:13, 3138.60 examples/s]
Generating extra examples...: 396201 examples [02:13, 3155.00 examples/s]
Generating extra examples...: 396521 examples [02:13, 3166.77 examples/s]
Generating extra examples...: 396839 examples [02:13, 3167.51 examples/s]
Generating extra examples...: 397156 examples [02:13, 3164.59 examples/s]
Generating extra examples...: 397473 examples [02:13, 3163.67 examples/s]
Generating extra examples...: 397790 examples [02:13, 3162.23 examples/s]
Generating extra examples...: 398111 examples [02:14, 3175.31 examples/s]
Generating extra examples...: 398433 examples [02:14, 3185.94 examples/s]
Generating extra examples...: 398752 examples [02:14, 3185.13 examples/s]
Generating extra examples...: 399071 examples [02:14, 3158.93 examples/s]
Generating extra examples...: 399387 examples [02:14, 3147.03 examples/s]
Generating extra examples...: 399704 examples [02:14, 3151.92 examples/s]
Generating extra examples...: 400027 examples [02:14, 3173.43 examples/s]
Generating extra examples...: 400353 examples [02:14, 3197.88 examples/s]
Generating extra examples...: 400673 examples [02:14, 3191.83 examples/s]
Generating extra examples...: 400993 examples [02:14, 3193.63 examples/s]
Generating extra examples...: 401313 examples [02:15, 3180.18 examples/s]
Generating extra examples...: 401633 examples [02:15, 3182.24 examples/s]
Generating extra examples...: 401956 examples [02:15, 3194.72 examples/s]
Generating extra examples...: 402280 examples [02:15, 3206.57 examples/s]
Generating extra examples...: 402601 examples [02:15, 3200.31 examples/s]
Generating extra examples...: 402924 examples [02:15, 3205.97 examples/s]
Generating extra examples...: 403245 examples [02:15, 3205.17 examples/s]
Generating extra examples...: 403566 examples [02:15, 3194.89 examples/s]
Generating extra examples...: 403886 examples [02:15, 3191.19 examples/s]
Generating extra examples...: 404207 examples [02:15, 3196.60 examples/s]
Generating extra examples...: 404527 examples [02:16, 3192.30 examples/s]
Generating extra examples...: 404847 examples [02:16, 3189.19 examples/s]
Generating extra examples...: 405169 examples [02:16, 3195.64 examples/s]
Generating extra examples...: 405492 examples [02:16, 3204.97 examples/s]
Generating extra examples...: 405813 examples [02:16, 3186.53 examples/s]
Generating extra examples...: 406132 examples [02:16, 3172.11 examples/s]
Generating extra examples...: 406450 examples [02:16, 3152.21 examples/s]
Generating extra examples...: 406767 examples [02:16, 3155.58 examples/s]
Generating extra examples...: 407086 examples [02:16, 3165.13 examples/s]
Generating extra examples...: 407403 examples [02:16, 3165.99 examples/s]
Generating extra examples...: 407720 examples [02:17, 3158.20 examples/s]
Generating extra examples...: 408038 examples [02:17, 3162.64 examples/s]
Generating extra examples...: 408359 examples [02:17, 3176.64 examples/s]
Generating extra examples...: 408679 examples [02:17, 3180.75 examples/s]
Generating extra examples...: 408998 examples [02:17, 3122.04 examples/s]
Generating extra examples...: 409319 examples [02:17, 3145.06 examples/s]
Generating extra examples...: 409636 examples [02:17, 3150.50 examples/s]
Generating extra examples...: 409956 examples [02:17, 3164.14 examples/s]
Generating extra examples...: 410275 examples [02:17, 3171.40 examples/s]
Generating extra examples...: 410595 examples [02:17, 3177.64 examples/s]
Generating extra examples...: 410916 examples [02:18, 3186.13 examples/s]
Generating extra examples...: 411239 examples [02:18, 3198.04 examples/s]
Generating extra examples...: 411563 examples [02:18, 3210.54 examples/s]
Generating extra examples...: 411885 examples [02:18, 3199.86 examples/s]
Generating extra examples...: 412206 examples [02:18, 3162.68 examples/s]
Generating extra examples...: 412531 examples [02:18, 3187.79 examples/s]
Generating extra examples...: 412856 examples [02:18, 3203.74 examples/s]
Generating extra examples...: 413177 examples [02:18, 3203.90 examples/s]
Generating extra examples...: 413498 examples [02:18, 3201.55 examples/s]
Generating extra examples...: 413822 examples [02:18, 3210.78 examples/s]
Generating extra examples...: 414144 examples [02:19, 3182.53 examples/s]
Generating extra examples...: 414463 examples [02:19, 3182.13 examples/s]
Generating extra examples...: 414782 examples [02:19, 3183.63 examples/s]
Generating extra examples...: 415101 examples [02:19, 3158.24 examples/s]
Generating extra examples...: 415417 examples [02:19, 3155.16 examples/s]
Generating extra examples...: 415734 examples [02:19, 3156.50 examples/s]
Generating extra examples...: 416050 examples [02:19, 3152.94 examples/s]
Generating extra examples...: 416369 examples [02:19, 3162.27 examples/s]
Generating extra examples...: 416686 examples [02:19, 3147.82 examples/s]
Generating extra examples...: 417004 examples [02:19, 3155.48 examples/s]
Generating extra examples...: 417324 examples [02:20, 3167.91 examples/s]
Generating extra examples...: 417645 examples [02:20, 3179.43 examples/s]
Generating extra examples...: 417967 examples [02:20, 3190.44 examples/s]
Generating extra examples...: 418288 examples [02:20, 3196.01 examples/s]
Generating extra examples...: 418608 examples [02:20, 3152.01 examples/s]
Generating extra examples...: 418925 examples [02:20, 3155.01 examples/s]
Generating extra examples...: 419242 examples [02:20, 3157.72 examples/s]
Generating extra examples...: 419558 examples [02:20, 3157.50 examples/s]
Generating extra examples...: 419878 examples [02:20, 3170.13 examples/s]
Generating extra examples...: 420200 examples [02:20, 3182.79 examples/s]
Generating extra examples...: 420519 examples [02:21, 3160.45 examples/s]
Generating extra examples...: 420837 examples [02:21, 3164.84 examples/s]
Generating extra examples...: 421154 examples [02:21, 3164.40 examples/s]
Generating extra examples...: 421472 examples [02:21, 3168.89 examples/s]
Generating extra examples...: 421789 examples [02:21, 3134.82 examples/s]
Generating extra examples...: 422110 examples [02:21, 3154.97 examples/s]
Generating extra examples...: 422434 examples [02:21, 3179.72 examples/s]
Generating extra examples...: 422756 examples [02:21, 3189.70 examples/s]
Generating extra examples...: 423076 examples [02:21, 3180.60 examples/s]
Generating extra examples...: 423395 examples [02:21, 3181.48 examples/s]
Generating extra examples...: 423714 examples [02:22, 3182.92 examples/s]
Generating extra examples...: 424040 examples [02:22, 3205.43 examples/s]
Generating extra examples...: 424361 examples [02:22, 3193.90 examples/s]
Generating extra examples...: 424681 examples [02:22, 3193.13 examples/s]
Generating extra examples...: 425001 examples [02:22, 3134.23 examples/s]
Generating extra examples...: 425316 examples [02:22, 3137.35 examples/s]
Generating extra examples...: 425641 examples [02:22, 3168.11 examples/s]
Generating extra examples...: 425958 examples [02:22, 3164.16 examples/s]
Generating extra examples...: 426279 examples [02:22, 3175.98 examples/s]
Generating extra examples...: 426602 examples [02:22, 3189.19 examples/s]
Generating extra examples...: 426921 examples [02:23, 3173.37 examples/s]
Generating extra examples...: 427242 examples [02:23, 3183.38 examples/s]
Generating extra examples...: 427564 examples [02:23, 3194.04 examples/s]
Generating extra examples...: 427889 examples [02:23, 3208.60 examples/s]
Generating extra examples...: 428210 examples [02:23, 3140.92 examples/s]
Generating extra examples...: 428528 examples [02:23, 3151.20 examples/s]
Generating extra examples...: 428847 examples [02:23, 3160.73 examples/s]
Generating extra examples...: 429165 examples [02:23, 3164.29 examples/s]
Generating extra examples...: 429482 examples [02:23, 3163.72 examples/s]
Generating extra examples...: 429799 examples [02:24, 3163.15 examples/s]
Generating extra examples...: 430120 examples [02:24, 3175.03 examples/s]
Generating extra examples...: 430440 examples [02:24, 3181.75 examples/s]
Generating extra examples...: 430765 examples [02:24, 3199.77 examples/s]
Generating extra examples...: 431090 examples [02:24, 3212.66 examples/s]
Generating extra examples...: 431412 examples [02:24, 3165.20 examples/s]
Generating extra examples...: 431737 examples [02:24, 3189.23 examples/s]
Generating extra examples...: 432058 examples [02:24, 3193.40 examples/s]
Generating extra examples...: 432380 examples [02:24, 3199.17 examples/s]
Generating extra examples...: 432700 examples [02:24, 3191.41 examples/s]
Generating extra examples...: 433020 examples [02:25, 3165.17 examples/s]
Generating extra examples...: 433338 examples [02:25, 3168.23 examples/s]
Generating extra examples...: 433655 examples [02:25, 3163.41 examples/s]
Generating extra examples...: 433972 examples [02:25, 3156.22 examples/s]
Generating extra examples...: 434290 examples [02:25, 3162.23 examples/s]
Generating extra examples...: 434607 examples [02:25, 3138.95 examples/s]
Generating extra examples...: 434921 examples [02:25, 3130.25 examples/s]
Generating extra examples...: 435245 examples [02:25, 3162.75 examples/s]
Generating extra examples...: 435562 examples [02:25, 3163.93 examples/s]
Generating extra examples...: 435880 examples [02:25, 3167.15 examples/s]
Generating extra examples...: 436197 examples [02:26, 3164.98 examples/s]
Generating extra examples...: 436516 examples [02:26, 3171.83 examples/s]
Generating extra examples...: 436836 examples [02:26, 3178.51 examples/s]
Generating extra examples...: 437160 examples [02:26, 3195.72 examples/s]
Generating extra examples...: 437482 examples [02:26, 3202.36 examples/s]
Generating extra examples...: 437806 examples [02:26, 3212.69 examples/s]
Generating extra examples...: 438128 examples [02:26, 3192.88 examples/s]
Generating extra examples...: 438451 examples [02:26, 3202.66 examples/s]
Generating extra examples...: 438774 examples [02:26, 3208.66 examples/s]
Generating extra examples...: 439095 examples [02:26, 3205.25 examples/s]
Generating extra examples...: 439416 examples [02:27, 3182.53 examples/s]
Generating extra examples...: 439735 examples [02:27, 3176.69 examples/s]
Generating extra examples...: 440053 examples [02:27, 3172.40 examples/s]
Generating extra examples...: 440377 examples [02:27, 3191.04 examples/s]
Generating extra examples...: 440697 examples [02:27, 3188.00 examples/s]
Generating extra examples...: 441016 examples [02:27, 3187.92 examples/s]
Generating extra examples...: 441335 examples [02:27, 3175.20 examples/s]
Generating extra examples...: 441660 examples [02:27, 3195.40 examples/s]
Generating extra examples...: 441980 examples [02:27, 3196.16 examples/s]
Generating extra examples...: 442300 examples [02:27, 3195.85 examples/s]
Generating extra examples...: 442623 examples [02:28, 3206.04 examples/s]
Generating extra examples...: 442947 examples [02:28, 3214.16 examples/s]
Generating extra examples...: 443272 examples [02:28, 3221.75 examples/s]
Generating extra examples...: 443595 examples [02:28, 3174.01 examples/s]
Generating extra examples...: 443913 examples [02:28, 3142.05 examples/s]
Generating extra examples...: 444236 examples [02:28, 3165.55 examples/s]
Generating extra examples...: 444553 examples [02:28, 3121.63 examples/s]
Generating extra examples...: 444872 examples [02:28, 3140.22 examples/s]
Generating extra examples...: 445191 examples [02:28, 3152.04 examples/s]
Generating extra examples...: 445511 examples [02:28, 3163.18 examples/s]
Generating extra examples...: 445828 examples [02:29, 3146.25 examples/s]
Generating extra examples...: 446143 examples [02:29, 3144.05 examples/s]
Generating extra examples...: 446465 examples [02:29, 3165.05 examples/s]
Generating extra examples...: 446784 examples [02:29, 3171.35 examples/s]
Generating extra examples...: 447102 examples [02:29, 3173.12 examples/s]
Generating extra examples...: 447423 examples [02:29, 3182.96 examples/s]
Generating extra examples...: 447742 examples [02:29, 3131.50 examples/s]
Generating extra examples...: 448064 examples [02:29, 3156.49 examples/s]
Generating extra examples...: 448381 examples [02:29, 3158.61 examples/s]
Generating extra examples...: 448702 examples [02:29, 3172.53 examples/s]
Generating extra examples...: 449022 examples [02:30, 3180.24 examples/s]
Generating extra examples...: 449341 examples [02:30, 3178.59 examples/s]
Generating extra examples...: 449661 examples [02:30, 3184.56 examples/s]
Generating extra examples...: 449984 examples [02:30, 3198.11 examples/s]
Generating extra examples...: 450309 examples [02:30, 3211.19 examples/s]
Generating extra examples...: 450631 examples [02:30, 3206.89 examples/s]
Generating extra examples...: 450952 examples [02:30, 3159.10 examples/s]
Generating extra examples...: 451272 examples [02:30, 3170.52 examples/s]
Generating extra examples...: 451591 examples [02:30, 3173.69 examples/s]
Generating extra examples...: 451909 examples [02:30, 3170.71 examples/s]
Generating extra examples...: 452227 examples [02:31, 3158.30 examples/s]
Generating extra examples...: 452547 examples [02:31, 3167.92 examples/s]
Generating extra examples...: 452865 examples [02:31, 3169.33 examples/s]
Generating extra examples...: 453186 examples [02:31, 3180.77 examples/s]
Generating extra examples...: 453507 examples [02:31, 3188.95 examples/s]
Generating extra examples...: 453827 examples [02:31, 3190.45 examples/s]
Generating extra examples...: 454147 examples [02:31, 3145.94 examples/s]
Generating extra examples...: 454470 examples [02:31, 3169.40 examples/s]
Generating extra examples...: 454788 examples [02:31, 3172.37 examples/s]
Generating extra examples...: 455110 examples [02:31, 3184.56 examples/s]
Generating extra examples...: 455434 examples [02:32, 3199.23 examples/s]
Generating extra examples...: 455756 examples [02:32, 3205.35 examples/s]
Generating extra examples...: 456078 examples [02:32, 3209.31 examples/s]
Generating extra examples...: 456404 examples [02:32, 3222.06 examples/s]
Generating extra examples...: 456727 examples [02:32, 3216.38 examples/s]
Generating extra examples...: 457051 examples [02:32, 3222.73 examples/s]
Generating extra examples...: 457374 examples [02:32, 3182.53 examples/s]
Generating extra examples...: 457695 examples [02:32, 3188.88 examples/s]
Generating extra examples...: 458016 examples [02:32, 3193.17 examples/s]
Generating extra examples...: 458336 examples [02:32, 3192.97 examples/s]
Generating extra examples...: 458656 examples [02:33, 3192.76 examples/s]
Generating extra examples...: 458981 examples [02:33, 3209.65 examples/s]
Generating extra examples...: 459304 examples [02:33, 3213.28 examples/s]
Generating extra examples...: 459626 examples [02:33, 3205.67 examples/s]
Generating extra examples...: 459949 examples [02:33, 3212.43 examples/s]
Generating extra examples...: 460271 examples [02:33, 3211.39 examples/s]
Generating extra examples...: 460593 examples [02:33, 3188.56 examples/s]
Generating extra examples...: 460916 examples [02:33, 3200.10 examples/s]
Generating extra examples...: 461239 examples [02:33, 3206.16 examples/s]
Generating extra examples...: 461564 examples [02:33, 3217.82 examples/s]
Generating extra examples...: 461886 examples [02:34, 3209.71 examples/s]
Generating extra examples...: 462209 examples [02:34, 3214.37 examples/s]
Generating extra examples...: 462531 examples [02:34, 3215.12 examples/s]
Generating extra examples...: 462854 examples [02:34, 3219.14 examples/s]
Generating extra examples...: 463176 examples [02:34, 3189.44 examples/s]
Generating extra examples...: 463501 examples [02:34, 3204.40 examples/s]
Generating extra examples...: 463831 examples [02:34, 3231.49 examples/s]
Generating extra examples...: 464157 examples [02:34, 3239.62 examples/s]
Generating extra examples...: 464485 examples [02:34, 3250.03 examples/s]
Generating extra examples...: 464811 examples [02:34, 3252.92 examples/s]
Generating extra examples...: 465137 examples [02:35, 3216.62 examples/s]
Generating extra examples...: 465459 examples [02:35, 3211.43 examples/s]
Generating extra examples...: 465781 examples [02:35, 3201.89 examples/s]
Generating extra examples...: 466102 examples [02:35, 3198.09 examples/s]
Generating extra examples...: 466422 examples [02:35, 3198.25 examples/s]
Generating extra examples...: 466743 examples [02:35, 3199.24 examples/s]
Generating extra examples...: 467064 examples [02:35, 3199.93 examples/s]
Generating extra examples...: 467385 examples [02:35, 3123.14 examples/s]
Generating extra examples...: 467709 examples [02:35, 3156.51 examples/s]
Generating extra examples...: 468028 examples [02:36, 3165.45 examples/s]
Generating extra examples...: 468350 examples [02:36, 3179.87 examples/s]
Generating extra examples...: 468672 examples [02:36, 3189.51 examples/s]
Generating extra examples...: 468998 examples [02:36, 3208.57 examples/s]
Generating extra examples...: 469322 examples [02:36, 3216.87 examples/s]
Generating extra examples...: 469647 examples [02:36, 3225.99 examples/s]
Generating extra examples...: 469970 examples [02:36, 3222.33 examples/s]
Generating extra examples...: 470294 examples [02:36, 3225.27 examples/s]
Generating extra examples...: 470617 examples [02:36, 3197.97 examples/s]
Generating extra examples...: 470938 examples [02:36, 3199.70 examples/s]
Generating extra examples...: 471259 examples [02:37, 3199.90 examples/s]
Generating extra examples...: 471580 examples [02:37, 3186.74 examples/s]
Generating extra examples...: 471899 examples [02:37, 3184.63 examples/s]
Generating extra examples...: 472223 examples [02:37, 3198.09 examples/s]
Generating extra examples...: 472547 examples [02:37, 3207.81 examples/s]
Generating extra examples...: 472870 examples [02:37, 3212.78 examples/s]
Generating extra examples...: 473195 examples [02:37, 3223.26 examples/s]
Generating extra examples...: 473523 examples [02:37, 3239.93 examples/s]
Generating extra examples...: 473848 examples [02:37, 3188.49 examples/s]
Generating extra examples...: 474170 examples [02:37, 3196.83 examples/s]
Generating extra examples...: 474493 examples [02:38, 3204.70 examples/s]
Generating extra examples...: 474821 examples [02:38, 3225.87 examples/s]
Generating extra examples...: 475146 examples [02:38, 3231.27 examples/s]
Generating extra examples...: 475471 examples [02:38, 3234.76 examples/s]
Generating extra examples...: 475799 examples [02:38, 3246.01 examples/s]
Generating extra examples...: 476127 examples [02:38, 3255.62 examples/s]
Generating extra examples...: 476453 examples [02:38, 3251.08 examples/s]
Generating extra examples...: 476779 examples [02:38, 3251.63 examples/s]
Generating extra examples...: 477105 examples [02:38, 3231.30 examples/s]
Generating extra examples...: 477429 examples [02:38, 3230.55 examples/s]
Generating extra examples...: 477753 examples [02:39, 3221.79 examples/s]
Generating extra examples...: 478076 examples [02:39, 3222.90 examples/s]
Generating extra examples...: 478401 examples [02:39, 3230.75 examples/s]
Generating extra examples...: 478725 examples [02:39, 3217.64 examples/s]
Generating extra examples...: 479047 examples [02:39, 3206.85 examples/s]
Generating extra examples...: 479370 examples [02:39, 3213.51 examples/s]
Generating extra examples...: 479694 examples [02:39, 3219.72 examples/s]
Generating extra examples...: 480021 examples [02:39, 3233.10 examples/s]
Generating extra examples...: 480345 examples [02:39, 3156.19 examples/s]
Generating extra examples...: 480671 examples [02:39, 3184.39 examples/s]
Generating extra examples...: 480997 examples [02:40, 3206.15 examples/s]
Generating extra examples...: 481322 examples [02:40, 3218.54 examples/s]
Generating extra examples...: 481647 examples [02:40, 3225.81 examples/s]
Generating extra examples...: 481974 examples [02:40, 3237.98 examples/s]
Generating extra examples...: 482303 examples [02:40, 3251.42 examples/s]
Generating extra examples...: 482629 examples [02:40, 3252.44 examples/s]
Generating extra examples...: 482955 examples [02:40, 3237.59 examples/s]
Generating extra examples...: 483279 examples [02:40, 3225.89 examples/s]
Generating extra examples...: 483602 examples [02:40, 3196.76 examples/s]
Generating extra examples...: 483922 examples [02:40, 3193.15 examples/s]
Generating extra examples...: 484242 examples [02:41, 3181.28 examples/s]
Generating extra examples...: 484561 examples [02:41, 3183.15 examples/s]
Generating extra examples...: 484887 examples [02:41, 3204.45 examples/s]
Generating extra examples...: 485208 examples [02:41, 3191.17 examples/s]
Generating extra examples...: 485528 examples [02:41, 3192.90 examples/s]
Generating extra examples...: 485848 examples [02:41, 3193.45 examples/s]
Generating extra examples...: 486173 examples [02:41, 3208.78 examples/s]
Generating extra examples...: 486500 examples [02:41, 3224.56 examples/s]
Generating extra examples...: 486823 examples [02:41, 3206.61 examples/s]
Generating extra examples...: 487148 examples [02:41, 3217.80 examples/s]
Generating extra examples...: 487475 examples [02:42, 3231.15 examples/s]
Generating extra examples...: 487803 examples [02:42, 3245.28 examples/s]
Generating extra examples...: 488128 examples [02:42, 3243.85 examples/s]
Generating extra examples...: 488455 examples [02:42, 3249.27 examples/s]
Generating extra examples...: 488784 examples [02:42, 3258.63 examples/s]
Generating extra examples...: 489110 examples [02:42, 3256.96 examples/s]
Generating extra examples...: 489436 examples [02:42, 3235.38 examples/s]
Generating extra examples...: 489762 examples [02:42, 3242.13 examples/s]
Generating extra examples...: 490087 examples [02:42, 3214.66 examples/s]
Generating extra examples...: 490412 examples [02:42, 3222.13 examples/s]
Generating extra examples...: 490735 examples [02:43, 3223.77 examples/s]
Generating extra examples...: 491058 examples [02:43, 3220.43 examples/s]
Generating extra examples...: 491381 examples [02:43, 3221.94 examples/s]
Generating extra examples...: 491705 examples [02:43, 3225.15 examples/s]
Generating extra examples...: 492029 examples [02:43, 3226.96 examples/s]
Generating extra examples...: 492353 examples [02:43, 3230.79 examples/s]
Generating extra examples...: 492681 examples [02:43, 3242.68 examples/s]
Generating extra examples...: 493008 examples [02:43, 3247.96 examples/s]
Generating extra examples...: 493336 examples [02:43, 3257.10 examples/s]
Generating extra examples...: 493662 examples [02:43, 3242.05 examples/s]
Generating extra examples...: 493987 examples [02:44, 3223.24 examples/s]
Generating extra examples...: 494312 examples [02:44, 3230.69 examples/s]
Generating extra examples...: 494636 examples [02:44, 3229.90 examples/s]
Generating extra examples...: 494960 examples [02:44, 3224.44 examples/s]
Generating extra examples...: 495283 examples [02:44, 3217.40 examples/s]
Generating extra examples...: 495605 examples [02:44, 3205.46 examples/s]
Generating extra examples...: 495933 examples [02:44, 3227.00 examples/s]
Generating extra examples...: 496261 examples [02:44, 3240.20 examples/s]
Generating extra examples...: 496586 examples [02:44, 3237.50 examples/s]
Generating extra examples...: 496910 examples [02:44, 3201.72 examples/s]
Generating extra examples...: 497231 examples [02:45, 3193.00 examples/s]
Generating extra examples...: 497555 examples [02:45, 3204.36 examples/s]
Generating extra examples...: 497879 examples [02:45, 3212.69 examples/s]
Generating extra examples...: 498201 examples [02:45, 3211.63 examples/s]
Generating extra examples...: 498526 examples [02:45, 3222.36 examples/s]
Generating extra examples...: 498851 examples [02:45, 3229.89 examples/s]
Generating extra examples...: 499175 examples [02:45, 3232.87 examples/s]
Generating extra examples...: 499499 examples [02:45, 3228.88 examples/s]
Generating extra examples...: 499822 examples [02:45, 3219.73 examples/s]
Generating extra examples...: 500144 examples [02:45, 3164.66 examples/s]
Generating extra examples...: 500463 examples [02:46, 3171.52 examples/s]
Generating extra examples...: 500785 examples [02:46, 3184.85 examples/s]
Generating extra examples...: 501104 examples [02:46, 3185.65 examples/s]
Generating extra examples...: 501430 examples [02:46, 3207.32 examples/s]
Generating extra examples...: 501752 examples [02:46, 3209.28 examples/s]
Generating extra examples...: 502079 examples [02:46, 3226.42 examples/s]
Generating extra examples...: 502402 examples [02:46, 3221.03 examples/s]
Generating extra examples...: 502725 examples [02:46, 3216.32 examples/s]
Generating extra examples...: 503047 examples [02:46, 3216.09 examples/s]
Generating extra examples...: 503369 examples [02:46, 3164.78 examples/s]
Generating extra examples...: 503686 examples [02:47, 3152.53 examples/s]
Generating extra examples...: 504007 examples [02:47, 3169.40 examples/s]
Generating extra examples...: 504328 examples [02:47, 3179.49 examples/s]
Generating extra examples...: 504648 examples [02:47, 3185.38 examples/s]
Generating extra examples...: 504967 examples [02:47, 3186.53 examples/s]
Generating extra examples...: 505288 examples [02:47, 3190.89 examples/s]
Generating extra examples...: 505609 examples [02:47, 3195.13 examples/s]
Generating extra examples...: 505931 examples [02:47, 3199.77 examples/s]
Generating extra examples...: 506252 examples [02:47, 3201.97 examples/s]
Generating extra examples...: 506573 examples [02:47, 3142.16 examples/s]
Generating extra examples...: 506897 examples [02:48, 3170.82 examples/s]
Generating extra examples...: 507222 examples [02:48, 3192.73 examples/s]
Generating extra examples...: 507545 examples [02:48, 3203.13 examples/s]
Generating extra examples...: 507870 examples [02:48, 3214.76 examples/s]
Generating extra examples...: 508192 examples [02:48, 3206.87 examples/s]
Generating extra examples...: 508514 examples [02:48, 3209.81 examples/s]
Generating extra examples...: 508839 examples [02:48, 3220.45 examples/s]
Generating extra examples...: 509162 examples [02:48, 3215.69 examples/s]
Generating extra examples...: 509486 examples [02:48, 3220.54 examples/s]
Generating extra examples...: 509809 examples [02:49, 3215.57 examples/s]
Generating extra examples...: 510131 examples [02:49, 3202.51 examples/s]
Generating extra examples...: 510452 examples [02:49, 3202.30 examples/s]
Generating extra examples...: 510777 examples [02:49, 3215.46 examples/s]
Generating extra examples...: 511099 examples [02:49, 3202.22 examples/s]
Generating extra examples...: 511426 examples [02:49, 3221.22 examples/s]
Generating extra examples...: 511749 examples [02:49, 3216.40 examples/s]
Generating extra examples...: 512072 examples [02:49, 3218.49 examples/s]
Generating extra examples...: 512396 examples [02:49, 3223.69 examples/s]
Generating extra examples...: 512719 examples [02:49, 3220.92 examples/s]
Generating extra examples...: 513042 examples [02:50, 3118.97 examples/s]
Generating extra examples...: 513360 examples [02:50, 3134.48 examples/s]
Generating extra examples...: 513686 examples [02:50, 3169.58 examples/s]
Generating extra examples...: 514008 examples [02:50, 3182.57 examples/s]
Generating extra examples...: 514333 examples [02:50, 3201.97 examples/s]
Generating extra examples...: 514656 examples [02:50, 3207.98 examples/s]
Generating extra examples...: 514979 examples [02:50, 3212.31 examples/s]
Generating extra examples...: 515301 examples [02:50, 3212.88 examples/s]
Generating extra examples...: 515623 examples [02:50, 3214.73 examples/s]
Generating extra examples...: 515945 examples [02:50, 3210.86 examples/s]
Generating extra examples...: 516267 examples [02:51, 3146.53 examples/s]
Generating extra examples...: 516592 examples [02:51, 3176.68 examples/s]
Generating extra examples...: 516915 examples [02:51, 3192.37 examples/s]
Generating extra examples...: 517237 examples [02:51, 3198.27 examples/s]
Generating extra examples...: 517564 examples [02:51, 3218.40 examples/s]
Generating extra examples...: 517886 examples [02:51, 3217.30 examples/s]
Generating extra examples...: 518208 examples [02:51, 3215.12 examples/s]
Generating extra examples...: 518531 examples [02:51, 3218.79 examples/s]
Generating extra examples...: 518853 examples [02:51, 3216.34 examples/s]
Generating extra examples...: 519180 examples [02:51, 3231.41 examples/s]
Generating extra examples...: 519504 examples [02:52, 3214.26 examples/s]
Generating extra examples...: 519826 examples [02:52, 3214.16 examples/s]
Generating extra examples...: 520153 examples [02:52, 3230.79 examples/s]
Generating extra examples...: 520479 examples [02:52, 3238.88 examples/s]
Generating extra examples...: 520806 examples [02:52, 3246.25 examples/s]
Generating extra examples...: 521131 examples [02:52, 3243.19 examples/s]
Generating extra examples...: 521457 examples [02:52, 3244.81 examples/s]
Generating extra examples...: 521783 examples [02:52, 3247.32 examples/s]
Generating extra examples...: 522110 examples [02:52, 3253.79 examples/s]
Generating extra examples...: 522438 examples [02:52, 3259.57 examples/s]
Generating extra examples...: 522764 examples [02:53, 3247.35 examples/s]
Generating extra examples...: 523089 examples [02:53, 3225.78 examples/s]
Generating extra examples...: 523413 examples [02:53, 3227.96 examples/s]
Generating extra examples...: 523736 examples [02:53, 3225.90 examples/s]
Generating extra examples...: 524059 examples [02:53, 3223.80 examples/s]
Generating extra examples...: 524385 examples [02:53, 3231.77 examples/s]
Generating extra examples...: 524710 examples [02:53, 3236.62 examples/s]
Generating extra examples...: 525035 examples [02:53, 3237.81 examples/s]
Generating extra examples...: 525359 examples [02:53, 3234.63 examples/s]
Generating extra examples...: 525683 examples [02:53, 3226.12 examples/s]
Generating extra examples...: 526009 examples [02:54, 3236.05 examples/s]
Generating extra examples...: 526333 examples [02:54, 3205.90 examples/s]
Generating extra examples...: 526654 examples [02:54, 3037.62 examples/s]
Generating extra examples...: 526975 examples [02:54, 3086.59 examples/s]
Generating extra examples...: 527295 examples [02:54, 3117.00 examples/s]
Generating extra examples...: 527615 examples [02:54, 3138.98 examples/s]
Generating extra examples...: 527938 examples [02:54, 3165.20 examples/s]
Generating extra examples...: 528259 examples [02:54, 3176.22 examples/s]
Generating extra examples...: 528582 examples [02:54, 3190.77 examples/s]
Generating extra examples...: 528902 examples [02:54, 3187.05 examples/s]
Generating extra examples...: 529221 examples [02:55, 3156.69 examples/s]
Generating extra examples...: 529537 examples [02:55, 3113.78 examples/s]
Generating extra examples...: 529851 examples [02:55, 3118.91 examples/s]
Generating extra examples...: 530172 examples [02:55, 3145.50 examples/s]
Generating extra examples...: 530492 examples [02:55, 3159.56 examples/s]
Generating extra examples...: 530817 examples [02:55, 3186.34 examples/s]
Shuffling /home/runner/tensorflow_datasets/svhn_cropped/3.0.0.incompleteRI1YPA/svhn_cropped-extra.tfrecord*...: 0%| | 0/531131 [00:00<?, ? examples/s]
Shuffling /home/runner/tensorflow_datasets/svhn_cropped/3.0.0.incompleteRI1YPA/svhn_cropped-extra.tfrecord*...: 0%| | 1/531131 [00:00<62:16:00, 2.37 examples/s]
Shuffling /home/runner/tensorflow_datasets/svhn_cropped/3.0.0.incompleteRI1YPA/svhn_cropped-extra.tfrecord*...: 4%|▍ | 23463/531131 [00:00<00:08, 59333.48 examples/s]
Shuffling /home/runner/tensorflow_datasets/svhn_cropped/3.0.0.incompleteRI1YPA/svhn_cropped-extra.tfrecord*...: 9%|▉ | 47501/531131 [00:00<00:04, 107379.09 examples/s]
Shuffling /home/runner/tensorflow_datasets/svhn_cropped/3.0.0.incompleteRI1YPA/svhn_cropped-extra.tfrecord*...: 13%|█▎ | 71545/531131 [00:00<00:03, 143955.23 examples/s]
Shuffling /home/runner/tensorflow_datasets/svhn_cropped/3.0.0.incompleteRI1YPA/svhn_cropped-extra.tfrecord*...: 18%|█▊ | 95823/531131 [00:00<00:02, 171817.37 examples/s]
Shuffling /home/runner/tensorflow_datasets/svhn_cropped/3.0.0.incompleteRI1YPA/svhn_cropped-extra.tfrecord*...: 23%|██▎ | 120241/531131 [00:00<00:02, 192593.79 examples/s]
Shuffling /home/runner/tensorflow_datasets/svhn_cropped/3.0.0.incompleteRI1YPA/svhn_cropped-extra.tfrecord*...: 27%|██▋ | 144867/531131 [00:01<00:01, 208196.03 examples/s]
Shuffling /home/runner/tensorflow_datasets/svhn_cropped/3.0.0.incompleteRI1YPA/svhn_cropped-extra.tfrecord*...: 32%|███▏ | 169427/531131 [00:01<00:01, 219170.99 examples/s]
Shuffling /home/runner/tensorflow_datasets/svhn_cropped/3.0.0.incompleteRI1YPA/svhn_cropped-extra.tfrecord*...: 37%|███▋ | 194162/531131 [00:01<00:01, 227493.70 examples/s]
Shuffling /home/runner/tensorflow_datasets/svhn_cropped/3.0.0.incompleteRI1YPA/svhn_cropped-extra.tfrecord*...: 41%|████ | 218439/531131 [00:01<00:01, 232022.01 examples/s]
Shuffling /home/runner/tensorflow_datasets/svhn_cropped/3.0.0.incompleteRI1YPA/svhn_cropped-extra.tfrecord*...: 46%|████▌ | 242838/531131 [00:01<00:01, 235581.99 examples/s]
Shuffling /home/runner/tensorflow_datasets/svhn_cropped/3.0.0.incompleteRI1YPA/svhn_cropped-extra.tfrecord*...: 50%|█████ | 267085/531131 [00:01<00:01, 237632.82 examples/s]
Shuffling /home/runner/tensorflow_datasets/svhn_cropped/3.0.0.incompleteRI1YPA/svhn_cropped-extra.tfrecord*...: 55%|█████▍ | 291815/531131 [00:01<00:00, 240519.35 examples/s]
Shuffling /home/runner/tensorflow_datasets/svhn_cropped/3.0.0.incompleteRI1YPA/svhn_cropped-extra.tfrecord*...: 60%|█████▉ | 316399/531131 [00:01<00:00, 242106.09 examples/s]
Shuffling /home/runner/tensorflow_datasets/svhn_cropped/3.0.0.incompleteRI1YPA/svhn_cropped-extra.tfrecord*...: 64%|██████▍ | 340912/531131 [00:01<00:00, 243006.63 examples/s]
Shuffling /home/runner/tensorflow_datasets/svhn_cropped/3.0.0.incompleteRI1YPA/svhn_cropped-extra.tfrecord*...: 69%|██████▉ | 365691/531131 [00:01<00:00, 244437.04 examples/s]
Shuffling /home/runner/tensorflow_datasets/svhn_cropped/3.0.0.incompleteRI1YPA/svhn_cropped-extra.tfrecord*...: 73%|███████▎ | 390248/531131 [00:02<00:00, 243557.15 examples/s]
Shuffling /home/runner/tensorflow_datasets/svhn_cropped/3.0.0.incompleteRI1YPA/svhn_cropped-extra.tfrecord*...: 78%|███████▊ | 414684/531131 [00:02<00:00, 243545.37 examples/s]
Shuffling /home/runner/tensorflow_datasets/svhn_cropped/3.0.0.incompleteRI1YPA/svhn_cropped-extra.tfrecord*...: 83%|████████▎ | 439095/531131 [00:02<00:00, 242933.91 examples/s]
Shuffling /home/runner/tensorflow_datasets/svhn_cropped/3.0.0.incompleteRI1YPA/svhn_cropped-extra.tfrecord*...: 87%|████████▋ | 463525/531131 [00:02<00:00, 243339.42 examples/s]
Shuffling /home/runner/tensorflow_datasets/svhn_cropped/3.0.0.incompleteRI1YPA/svhn_cropped-extra.tfrecord*...: 92%|█████████▏| 487887/531131 [00:02<00:00, 243199.74 examples/s]
Shuffling /home/runner/tensorflow_datasets/svhn_cropped/3.0.0.incompleteRI1YPA/svhn_cropped-extra.tfrecord*...: 96%|█████████▋| 512227/531131 [00:02<00:00, 241677.08 examples/s]
Generating splits...: 100%|██████████| 3/3 [03:31<00:00, 89.74s/ 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: 3:12 - loss: 2.7843 - accuracy: 0.0869
2/65 [..............................] - ETA: 16s - loss: 2.7127 - accuracy: 0.0947
3/65 [>.............................] - ETA: 15s - loss: 2.6567 - accuracy: 0.1058
4/65 [>.............................] - ETA: 15s - loss: 2.6063 - accuracy: 0.1218
5/65 [=>............................] - ETA: 15s - loss: 2.5622 - accuracy: 0.1336
6/65 [=>............................] - ETA: 14s - loss: 2.5212 - accuracy: 0.1489
7/65 [==>...........................] - ETA: 14s - loss: 2.4883 - accuracy: 0.1653
8/65 [==>...........................] - ETA: 14s - loss: 2.4523 - accuracy: 0.1809
9/65 [===>..........................] - ETA: 14s - loss: 2.4239 - accuracy: 0.1904
10/65 [===>..........................] - ETA: 13s - loss: 2.3986 - accuracy: 0.1998
11/65 [====>.........................] - ETA: 13s - loss: 2.3743 - accuracy: 0.2092
12/65 [====>.........................] - ETA: 13s - loss: 2.3508 - accuracy: 0.2192
13/65 [=====>........................] - ETA: 12s - loss: 2.3311 - accuracy: 0.2269
14/65 [=====>........................] - ETA: 12s - loss: 2.3135 - accuracy: 0.2337
15/65 [=====>........................] - ETA: 12s - loss: 2.2914 - accuracy: 0.2411
16/65 [======>.......................] - ETA: 12s - loss: 2.2727 - accuracy: 0.2482
17/65 [======>.......................] - ETA: 11s - loss: 2.2517 - accuracy: 0.2569
18/65 [=======>......................] - ETA: 11s - loss: 2.2319 - accuracy: 0.2655
19/65 [=======>......................] - ETA: 11s - loss: 2.2137 - accuracy: 0.2730
20/65 [========>.....................] - ETA: 11s - loss: 2.1944 - accuracy: 0.2804
21/65 [========>.....................] - ETA: 10s - loss: 2.1763 - accuracy: 0.2870
22/65 [=========>....................] - ETA: 10s - loss: 2.1578 - accuracy: 0.2950
23/65 [=========>....................] - ETA: 10s - loss: 2.1414 - accuracy: 0.3016
24/65 [==========>...................] - ETA: 10s - loss: 2.1242 - accuracy: 0.3081
25/65 [==========>...................] - ETA: 9s - loss: 2.1069 - accuracy: 0.3144
26/65 [===========>..................] - ETA: 9s - loss: 2.0896 - accuracy: 0.3211
27/65 [===========>..................] - ETA: 9s - loss: 2.0717 - accuracy: 0.3283
28/65 [===========>..................] - ETA: 9s - loss: 2.0551 - accuracy: 0.3343
29/65 [============>.................] - ETA: 8s - loss: 2.0383 - accuracy: 0.3408
30/65 [============>.................] - ETA: 8s - loss: 2.0226 - accuracy: 0.3468
31/65 [=============>................] - ETA: 8s - loss: 2.0044 - accuracy: 0.3539
32/65 [=============>................] - ETA: 8s - loss: 1.9888 - accuracy: 0.3601
33/65 [==============>...............] - ETA: 7s - loss: 1.9734 - accuracy: 0.3658
34/65 [==============>...............] - ETA: 7s - loss: 1.9579 - accuracy: 0.3719
35/65 [===============>..............] - ETA: 7s - loss: 1.9437 - accuracy: 0.3776
36/65 [===============>..............] - ETA: 7s - loss: 1.9287 - accuracy: 0.3835
37/65 [================>.............] - ETA: 6s - loss: 1.9126 - accuracy: 0.3901
38/65 [================>.............] - ETA: 6s - loss: 1.8979 - accuracy: 0.3958
39/65 [=================>............] - ETA: 6s - loss: 1.8846 - accuracy: 0.4008
40/65 [=================>............] - ETA: 6s - loss: 1.8716 - accuracy: 0.4057
41/65 [=================>............] - ETA: 5s - loss: 1.8580 - accuracy: 0.4108
42/65 [==================>...........] - ETA: 5s - loss: 1.8442 - accuracy: 0.4164
43/65 [==================>...........] - ETA: 5s - loss: 1.8312 - accuracy: 0.4211
44/65 [===================>..........] - ETA: 5s - loss: 1.8168 - accuracy: 0.4264
45/65 [===================>..........] - ETA: 4s - loss: 1.8036 - accuracy: 0.4314
46/65 [====================>.........] - ETA: 4s - loss: 1.7909 - accuracy: 0.4364
47/65 [====================>.........] - ETA: 4s - loss: 1.7791 - accuracy: 0.4404
48/65 [=====================>........] - ETA: 4s - loss: 1.7665 - accuracy: 0.4450
49/65 [=====================>........] - ETA: 3s - loss: 1.7532 - accuracy: 0.4499
50/65 [======================>.......] - ETA: 3s - loss: 1.7411 - accuracy: 0.4545
51/65 [======================>.......] - ETA: 3s - loss: 1.7283 - accuracy: 0.4590
52/65 [=======================>......] - ETA: 3s - loss: 1.7164 - accuracy: 0.4632
53/65 [=======================>......] - ETA: 2s - loss: 1.7042 - accuracy: 0.4677
54/65 [=======================>......] - ETA: 2s - loss: 1.6924 - accuracy: 0.4720
55/65 [========================>.....] - ETA: 2s - loss: 1.6809 - accuracy: 0.4761
56/65 [========================>.....] - ETA: 2s - loss: 1.6693 - accuracy: 0.4805
57/65 [=========================>....] - ETA: 1s - loss: 1.6586 - accuracy: 0.4845
58/65 [=========================>....] - ETA: 1s - loss: 1.6485 - accuracy: 0.4883
59/65 [==========================>...] - ETA: 1s - loss: 1.6381 - accuracy: 0.4921
60/65 [==========================>...] - ETA: 1s - loss: 1.6273 - accuracy: 0.4960
61/65 [===========================>..] - ETA: 0s - loss: 1.6167 - accuracy: 0.5000
62/65 [===========================>..] - ETA: 0s - loss: 1.6072 - accuracy: 0.5034
63/65 [============================>.] - ETA: 0s - loss: 1.5978 - accuracy: 0.5066
64/65 [============================>.] - ETA: 0s - loss: 1.5876 - accuracy: 0.5102
65/65 [==============================] - ETA: 0s - loss: 1.5835 - accuracy: 0.5117
65/65 [==============================] - 19s 256ms/step - loss: 1.5835 - accuracy: 0.5117 - val_loss: 2.1359 - val_accuracy: 0.2528 - lr: 0.0030
Epoch 2/30
1/65 [..............................] - ETA: 30s - loss: 0.9553 - accuracy: 0.7451
2/65 [..............................] - ETA: 16s - loss: 0.9566 - accuracy: 0.7417
3/65 [>.............................] - ETA: 15s - loss: 0.9644 - accuracy: 0.7334
4/65 [>.............................] - ETA: 15s - loss: 0.9514 - accuracy: 0.7400
5/65 [=>............................] - ETA: 15s - loss: 0.9276 - accuracy: 0.7473
6/65 [=>............................] - ETA: 14s - loss: 0.9227 - accuracy: 0.7495
7/65 [==>...........................] - ETA: 14s - loss: 0.9223 - accuracy: 0.7489
8/65 [==>...........................] - ETA: 14s - loss: 0.9180 - accuracy: 0.7516
9/65 [===>..........................] - ETA: 13s - loss: 0.9170 - accuracy: 0.7528
10/65 [===>..........................] - ETA: 13s - loss: 0.9078 - accuracy: 0.7548
11/65 [====>.........................] - ETA: 13s - loss: 0.9039 - accuracy: 0.7568
12/65 [====>.........................] - ETA: 13s - loss: 0.9012 - accuracy: 0.7577
13/65 [=====>........................] - ETA: 12s - loss: 0.8953 - accuracy: 0.7598
14/65 [=====>........................] - ETA: 12s - loss: 0.8895 - accuracy: 0.7626
15/65 [=====>........................] - ETA: 12s - loss: 0.8838 - accuracy: 0.7646
16/65 [======>.......................] - ETA: 12s - loss: 0.8818 - accuracy: 0.7646
17/65 [======>.......................] - ETA: 11s - loss: 0.8772 - accuracy: 0.7661
18/65 [=======>......................] - ETA: 11s - loss: 0.8726 - accuracy: 0.7673
19/65 [=======>......................] - ETA: 11s - loss: 0.8698 - accuracy: 0.7682
20/65 [========>.....................] - ETA: 11s - loss: 0.8650 - accuracy: 0.7693
21/65 [========>.....................] - ETA: 10s - loss: 0.8609 - accuracy: 0.7705
22/65 [=========>....................] - ETA: 10s - loss: 0.8581 - accuracy: 0.7708
23/65 [=========>....................] - ETA: 10s - loss: 0.8559 - accuracy: 0.7711
24/65 [==========>...................] - ETA: 10s - loss: 0.8525 - accuracy: 0.7720
25/65 [==========>...................] - ETA: 9s - loss: 0.8482 - accuracy: 0.7731
26/65 [===========>..................] - ETA: 9s - loss: 0.8460 - accuracy: 0.7739
27/65 [===========>..................] - ETA: 9s - loss: 0.8423 - accuracy: 0.7747
28/65 [===========>..................] - ETA: 9s - loss: 0.8384 - accuracy: 0.7760
29/65 [============>.................] - ETA: 9s - loss: 0.8364 - accuracy: 0.7767
30/65 [============>.................] - ETA: 8s - loss: 0.8354 - accuracy: 0.7771
31/65 [=============>................] - ETA: 8s - loss: 0.8322 - accuracy: 0.7783
32/65 [=============>................] - ETA: 8s - loss: 0.8313 - accuracy: 0.7790
33/65 [==============>...............] - ETA: 8s - loss: 0.8297 - accuracy: 0.7797
34/65 [==============>...............] - ETA: 7s - loss: 0.8273 - accuracy: 0.7804
35/65 [===============>..............] - ETA: 7s - loss: 0.8244 - accuracy: 0.7812
36/65 [===============>..............] - ETA: 7s - loss: 0.8211 - accuracy: 0.7823
37/65 [================>.............] - ETA: 7s - loss: 0.8197 - accuracy: 0.7827
38/65 [================>.............] - ETA: 6s - loss: 0.8171 - accuracy: 0.7837
39/65 [=================>............] - ETA: 6s - loss: 0.8150 - accuracy: 0.7847
40/65 [=================>............] - ETA: 6s - loss: 0.8132 - accuracy: 0.7856
41/65 [=================>............] - ETA: 6s - loss: 0.8104 - accuracy: 0.7867
42/65 [==================>...........] - ETA: 5s - loss: 0.8070 - accuracy: 0.7876
43/65 [==================>...........] - ETA: 5s - loss: 0.8047 - accuracy: 0.7887
44/65 [===================>..........] - ETA: 5s - loss: 0.8031 - accuracy: 0.7893
45/65 [===================>..........] - ETA: 5s - loss: 0.8012 - accuracy: 0.7901
46/65 [====================>.........] - ETA: 4s - loss: 0.7989 - accuracy: 0.7910
47/65 [====================>.........] - ETA: 4s - loss: 0.7955 - accuracy: 0.7920
48/65 [=====================>........] - ETA: 4s - loss: 0.7923 - accuracy: 0.7930
49/65 [=====================>........] - ETA: 4s - loss: 0.7909 - accuracy: 0.7934
50/65 [======================>.......] - ETA: 3s - loss: 0.7889 - accuracy: 0.7939
51/65 [======================>.......] - ETA: 3s - loss: 0.7865 - accuracy: 0.7947
52/65 [=======================>......] - ETA: 3s - loss: 0.7842 - accuracy: 0.7955
53/65 [=======================>......] - ETA: 3s - loss: 0.7830 - accuracy: 0.7956
54/65 [=======================>......] - ETA: 2s - loss: 0.7801 - accuracy: 0.7965
55/65 [========================>.....] - ETA: 2s - loss: 0.7778 - accuracy: 0.7971
56/65 [========================>.....] - ETA: 2s - loss: 0.7760 - accuracy: 0.7976
57/65 [=========================>....] - ETA: 2s - loss: 0.7739 - accuracy: 0.7984
58/65 [=========================>....] - ETA: 1s - loss: 0.7719 - accuracy: 0.7991
59/65 [==========================>...] - ETA: 1s - loss: 0.7707 - accuracy: 0.7995
60/65 [==========================>...] - ETA: 1s - loss: 0.7696 - accuracy: 0.8001
61/65 [===========================>..] - ETA: 0s - loss: 0.7675 - accuracy: 0.8009
62/65 [===========================>..] - ETA: 0s - loss: 0.7659 - accuracy: 0.8013
63/65 [============================>.] - ETA: 0s - loss: 0.7637 - accuracy: 0.8020
64/65 [============================>.] - ETA: 0s - loss: 0.7617 - accuracy: 0.8025
65/65 [==============================] - ETA: 0s - loss: 0.7617 - accuracy: 0.8025
65/65 [==============================] - 17s 255ms/step - loss: 0.7617 - accuracy: 0.8025 - val_loss: 1.6022 - val_accuracy: 0.4689 - lr: 0.0030
Epoch 3/30
1/65 [..............................] - ETA: 30s - loss: 0.6826 - accuracy: 0.8281
2/65 [..............................] - ETA: 15s - loss: 0.6486 - accuracy: 0.8369
3/65 [>.............................] - ETA: 15s - loss: 0.6693 - accuracy: 0.8317
4/65 [>.............................] - ETA: 15s - loss: 0.6682 - accuracy: 0.8333
5/65 [=>............................] - ETA: 15s - loss: 0.6662 - accuracy: 0.8318
6/65 [=>............................] - ETA: 14s - loss: 0.6715 - accuracy: 0.8275
7/65 [==>...........................] - ETA: 14s - loss: 0.6578 - accuracy: 0.8319
8/65 [==>...........................] - ETA: 14s - loss: 0.6591 - accuracy: 0.8319
9/65 [===>..........................] - ETA: 14s - loss: 0.6563 - accuracy: 0.8318
10/65 [===>..........................] - ETA: 13s - loss: 0.6567 - accuracy: 0.8325
11/65 [====>.........................] - ETA: 13s - loss: 0.6533 - accuracy: 0.8331
12/65 [====>.........................] - ETA: 13s - loss: 0.6528 - accuracy: 0.8333
13/65 [=====>........................] - ETA: 12s - loss: 0.6485 - accuracy: 0.8347
14/65 [=====>........................] - ETA: 12s - loss: 0.6441 - accuracy: 0.8366
15/65 [=====>........................] - ETA: 12s - loss: 0.6476 - accuracy: 0.8359
16/65 [======>.......................] - ETA: 12s - loss: 0.6437 - accuracy: 0.8371
17/65 [======>.......................] - ETA: 11s - loss: 0.6370 - accuracy: 0.8392
18/65 [=======>......................] - ETA: 11s - loss: 0.6396 - accuracy: 0.8383
19/65 [=======>......................] - ETA: 11s - loss: 0.6374 - accuracy: 0.8389
20/65 [========>.....................] - ETA: 11s - loss: 0.6387 - accuracy: 0.8380
21/65 [========>.....................] - ETA: 10s - loss: 0.6369 - accuracy: 0.8386
22/65 [=========>....................] - ETA: 10s - loss: 0.6371 - accuracy: 0.8386
23/65 [=========>....................] - ETA: 10s - loss: 0.6373 - accuracy: 0.8384
24/65 [==========>...................] - ETA: 10s - loss: 0.6376 - accuracy: 0.8382
25/65 [==========>...................] - ETA: 9s - loss: 0.6341 - accuracy: 0.8397
26/65 [===========>..................] - ETA: 9s - loss: 0.6332 - accuracy: 0.8400
27/65 [===========>..................] - ETA: 9s - loss: 0.6328 - accuracy: 0.8401
28/65 [===========>..................] - ETA: 9s - loss: 0.6304 - accuracy: 0.8405
29/65 [============>.................] - ETA: 8s - loss: 0.6311 - accuracy: 0.8402
30/65 [============>.................] - ETA: 8s - loss: 0.6296 - accuracy: 0.8409
31/65 [=============>................] - ETA: 8s - loss: 0.6292 - accuracy: 0.8408
32/65 [=============>................] - ETA: 8s - loss: 0.6294 - accuracy: 0.8408
33/65 [==============>...............] - ETA: 7s - loss: 0.6290 - accuracy: 0.8411
34/65 [==============>...............] - ETA: 7s - loss: 0.6283 - accuracy: 0.8415
35/65 [===============>..............] - ETA: 7s - loss: 0.6272 - accuracy: 0.8421
36/65 [===============>..............] - ETA: 7s - loss: 0.6276 - accuracy: 0.8423
37/65 [================>.............] - ETA: 6s - loss: 0.6278 - accuracy: 0.8421
38/65 [================>.............] - ETA: 6s - loss: 0.6271 - accuracy: 0.8418
39/65 [=================>............] - ETA: 6s - loss: 0.6261 - accuracy: 0.8423
40/65 [=================>............] - ETA: 6s - loss: 0.6268 - accuracy: 0.8422
41/65 [=================>............] - ETA: 5s - loss: 0.6260 - accuracy: 0.8424
42/65 [==================>...........] - ETA: 5s - loss: 0.6249 - accuracy: 0.8428
43/65 [==================>...........] - ETA: 5s - loss: 0.6240 - accuracy: 0.8432
44/65 [===================>..........] - ETA: 5s - loss: 0.6242 - accuracy: 0.8430
45/65 [===================>..........] - ETA: 4s - loss: 0.6224 - accuracy: 0.8436
46/65 [====================>.........] - ETA: 4s - loss: 0.6211 - accuracy: 0.8442
47/65 [====================>.........] - ETA: 4s - loss: 0.6204 - accuracy: 0.8442
48/65 [=====================>........] - ETA: 4s - loss: 0.6189 - accuracy: 0.8446
49/65 [=====================>........] - ETA: 3s - loss: 0.6188 - accuracy: 0.8448
50/65 [======================>.......] - ETA: 3s - loss: 0.6174 - accuracy: 0.8453
51/65 [======================>.......] - ETA: 3s - loss: 0.6161 - accuracy: 0.8456
52/65 [=======================>......] - ETA: 3s - loss: 0.6153 - accuracy: 0.8458
53/65 [=======================>......] - ETA: 2s - loss: 0.6134 - accuracy: 0.8464
54/65 [=======================>......] - ETA: 2s - loss: 0.6120 - accuracy: 0.8468
55/65 [========================>.....] - ETA: 2s - loss: 0.6118 - accuracy: 0.8468
56/65 [========================>.....] - ETA: 2s - loss: 0.6116 - accuracy: 0.8470
57/65 [=========================>....] - ETA: 1s - loss: 0.6101 - accuracy: 0.8473
58/65 [=========================>....] - ETA: 1s - loss: 0.6105 - accuracy: 0.8470
59/65 [==========================>...] - ETA: 1s - loss: 0.6096 - accuracy: 0.8473
60/65 [==========================>...] - ETA: 1s - loss: 0.6097 - accuracy: 0.8472
61/65 [===========================>..] - ETA: 0s - loss: 0.6099 - accuracy: 0.8473
62/65 [===========================>..] - ETA: 0s - loss: 0.6095 - accuracy: 0.8475
63/65 [============================>.] - ETA: 0s - loss: 0.6085 - accuracy: 0.8479
64/65 [============================>.] - ETA: 0s - loss: 0.6080 - accuracy: 0.8481
65/65 [==============================] - ETA: 0s - loss: 0.6078 - accuracy: 0.8481
65/65 [==============================] - 17s 253ms/step - loss: 0.6078 - accuracy: 0.8481 - val_loss: 1.2930 - val_accuracy: 0.5913 - lr: 0.0030
Epoch 4/30
1/65 [..............................] - ETA: 30s - loss: 0.6142 - accuracy: 0.8369
2/65 [..............................] - ETA: 15s - loss: 0.5912 - accuracy: 0.8486
3/65 [>.............................] - ETA: 15s - loss: 0.5904 - accuracy: 0.8522
4/65 [>.............................] - ETA: 15s - loss: 0.5927 - accuracy: 0.8508
5/65 [=>............................] - ETA: 15s - loss: 0.5850 - accuracy: 0.8535
6/65 [=>............................] - ETA: 14s - loss: 0.5784 - accuracy: 0.8532
7/65 [==>...........................] - ETA: 14s - loss: 0.5817 - accuracy: 0.8525
8/65 [==>...........................] - ETA: 14s - loss: 0.5766 - accuracy: 0.8551
9/65 [===>..........................] - ETA: 14s - loss: 0.5764 - accuracy: 0.8546
10/65 [===>..........................] - ETA: 13s - loss: 0.5808 - accuracy: 0.8531
11/65 [====>.........................] - ETA: 13s - loss: 0.5780 - accuracy: 0.8549
12/65 [====>.........................] - ETA: 13s - loss: 0.5767 - accuracy: 0.8551
13/65 [=====>........................] - ETA: 13s - loss: 0.5732 - accuracy: 0.8564
14/65 [=====>........................] - ETA: 12s - loss: 0.5703 - accuracy: 0.8574
15/65 [=====>........................] - ETA: 12s - loss: 0.5682 - accuracy: 0.8583
16/65 [======>.......................] - ETA: 12s - loss: 0.5662 - accuracy: 0.8593
17/65 [======>.......................] - ETA: 11s - loss: 0.5654 - accuracy: 0.8597
18/65 [=======>......................] - ETA: 11s - loss: 0.5617 - accuracy: 0.8608
19/65 [=======>......................] - ETA: 11s - loss: 0.5603 - accuracy: 0.8615
20/65 [========>.....................] - ETA: 11s - loss: 0.5621 - accuracy: 0.8609
21/65 [========>.....................] - ETA: 10s - loss: 0.5630 - accuracy: 0.8607
22/65 [=========>....................] - ETA: 10s - loss: 0.5615 - accuracy: 0.8611
23/65 [=========>....................] - ETA: 10s - loss: 0.5621 - accuracy: 0.8611
24/65 [==========>...................] - ETA: 10s - loss: 0.5595 - accuracy: 0.8614
25/65 [==========>...................] - ETA: 9s - loss: 0.5574 - accuracy: 0.8617
26/65 [===========>..................] - ETA: 9s - loss: 0.5580 - accuracy: 0.8621
27/65 [===========>..................] - ETA: 9s - loss: 0.5589 - accuracy: 0.8617
28/65 [===========>..................] - ETA: 9s - loss: 0.5590 - accuracy: 0.8615
29/65 [============>.................] - ETA: 8s - loss: 0.5593 - accuracy: 0.8612
30/65 [============>.................] - ETA: 8s - loss: 0.5585 - accuracy: 0.8613
31/65 [=============>................] - ETA: 8s - loss: 0.5581 - accuracy: 0.8613
32/65 [=============>................] - ETA: 8s - loss: 0.5577 - accuracy: 0.8616
33/65 [==============>...............] - ETA: 7s - loss: 0.5571 - accuracy: 0.8617
34/65 [==============>...............] - ETA: 7s - loss: 0.5559 - accuracy: 0.8624
35/65 [===============>..............] - ETA: 7s - loss: 0.5553 - accuracy: 0.8626
36/65 [===============>..............] - ETA: 7s - loss: 0.5561 - accuracy: 0.8625
37/65 [================>.............] - ETA: 6s - loss: 0.5549 - accuracy: 0.8629
38/65 [================>.............] - ETA: 6s - loss: 0.5556 - accuracy: 0.8626
39/65 [=================>............] - ETA: 6s - loss: 0.5552 - accuracy: 0.8626
40/65 [=================>............] - ETA: 6s - loss: 0.5559 - accuracy: 0.8626
41/65 [=================>............] - ETA: 5s - loss: 0.5556 - accuracy: 0.8627
42/65 [==================>...........] - ETA: 5s - loss: 0.5549 - accuracy: 0.8629
43/65 [==================>...........] - ETA: 5s - loss: 0.5538 - accuracy: 0.8634
44/65 [===================>..........] - ETA: 5s - loss: 0.5525 - accuracy: 0.8637
45/65 [===================>..........] - ETA: 4s - loss: 0.5524 - accuracy: 0.8637
46/65 [====================>.........] - ETA: 4s - loss: 0.5522 - accuracy: 0.8637
47/65 [====================>.........] - ETA: 4s - loss: 0.5515 - accuracy: 0.8640
48/65 [=====================>........] - ETA: 4s - loss: 0.5514 - accuracy: 0.8643
49/65 [=====================>........] - ETA: 3s - loss: 0.5501 - accuracy: 0.8648
50/65 [======================>.......] - ETA: 3s - loss: 0.5494 - accuracy: 0.8649
51/65 [======================>.......] - ETA: 3s - loss: 0.5490 - accuracy: 0.8649
52/65 [=======================>......] - ETA: 3s - loss: 0.5478 - accuracy: 0.8654
53/65 [=======================>......] - ETA: 2s - loss: 0.5478 - accuracy: 0.8654
54/65 [=======================>......] - ETA: 2s - loss: 0.5472 - accuracy: 0.8655
55/65 [========================>.....] - ETA: 2s - loss: 0.5456 - accuracy: 0.8661
56/65 [========================>.....] - ETA: 2s - loss: 0.5445 - accuracy: 0.8661
57/65 [=========================>....] - ETA: 1s - loss: 0.5443 - accuracy: 0.8663
58/65 [=========================>....] - ETA: 1s - loss: 0.5437 - accuracy: 0.8666
59/65 [==========================>...] - ETA: 1s - loss: 0.5435 - accuracy: 0.8668
60/65 [==========================>...] - ETA: 1s - loss: 0.5424 - accuracy: 0.8670
61/65 [===========================>..] - ETA: 0s - loss: 0.5436 - accuracy: 0.8666
62/65 [===========================>..] - ETA: 0s - loss: 0.5428 - accuracy: 0.8669
63/65 [============================>.] - ETA: 0s - loss: 0.5423 - accuracy: 0.8670
64/65 [============================>.] - ETA: 0s - loss: 0.5422 - accuracy: 0.8672
65/65 [==============================] - ETA: 0s - loss: 0.5422 - accuracy: 0.8672
65/65 [==============================] - 17s 252ms/step - loss: 0.5422 - accuracy: 0.8672 - val_loss: 1.0046 - val_accuracy: 0.6979 - lr: 0.0030
Epoch 5/30
1/65 [..............................] - ETA: 30s - loss: 0.5301 - accuracy: 0.8691
2/65 [..............................] - ETA: 15s - loss: 0.5267 - accuracy: 0.8701
3/65 [>.............................] - ETA: 15s - loss: 0.5214 - accuracy: 0.8701
4/65 [>.............................] - ETA: 15s - loss: 0.5168 - accuracy: 0.8704
5/65 [=>............................] - ETA: 15s - loss: 0.5238 - accuracy: 0.8684
6/65 [=>............................] - ETA: 14s - loss: 0.5307 - accuracy: 0.8667
7/65 [==>...........................] - ETA: 14s - loss: 0.5363 - accuracy: 0.8650
8/65 [==>...........................] - ETA: 14s - loss: 0.5313 - accuracy: 0.8660
9/65 [===>..........................] - ETA: 13s - loss: 0.5273 - accuracy: 0.8678
10/65 [===>..........................] - ETA: 13s - loss: 0.5251 - accuracy: 0.8687
11/65 [====>.........................] - ETA: 13s - loss: 0.5214 - accuracy: 0.8694
12/65 [====>.........................] - ETA: 13s - loss: 0.5191 - accuracy: 0.8702
13/65 [=====>........................] - ETA: 12s - loss: 0.5181 - accuracy: 0.8708
14/65 [=====>........................] - ETA: 12s - loss: 0.5174 - accuracy: 0.8710
15/65 [=====>........................] - ETA: 12s - loss: 0.5212 - accuracy: 0.8702
16/65 [======>.......................] - ETA: 12s - loss: 0.5217 - accuracy: 0.8694
17/65 [======>.......................] - ETA: 11s - loss: 0.5214 - accuracy: 0.8694
18/65 [=======>......................] - ETA: 11s - loss: 0.5203 - accuracy: 0.8696
19/65 [=======>......................] - ETA: 11s - loss: 0.5185 - accuracy: 0.8702
20/65 [========>.....................] - ETA: 11s - loss: 0.5180 - accuracy: 0.8703
21/65 [========>.....................] - ETA: 10s - loss: 0.5176 - accuracy: 0.8708
22/65 [=========>....................] - ETA: 10s - loss: 0.5169 - accuracy: 0.8712
23/65 [=========>....................] - ETA: 10s - loss: 0.5157 - accuracy: 0.8714
24/65 [==========>...................] - ETA: 10s - loss: 0.5151 - accuracy: 0.8713
25/65 [==========>...................] - ETA: 9s - loss: 0.5170 - accuracy: 0.8709
26/65 [===========>..................] - ETA: 9s - loss: 0.5160 - accuracy: 0.8711
27/65 [===========>..................] - ETA: 9s - loss: 0.5148 - accuracy: 0.8716
28/65 [===========>..................] - ETA: 9s - loss: 0.5156 - accuracy: 0.8714
29/65 [============>.................] - ETA: 8s - loss: 0.5167 - accuracy: 0.8709
30/65 [============>.................] - ETA: 8s - loss: 0.5151 - accuracy: 0.8714
31/65 [=============>................] - ETA: 8s - loss: 0.5135 - accuracy: 0.8721
32/65 [=============>................] - ETA: 8s - loss: 0.5137 - accuracy: 0.8722
33/65 [==============>...............] - ETA: 7s - loss: 0.5131 - accuracy: 0.8722
34/65 [==============>...............] - ETA: 7s - loss: 0.5157 - accuracy: 0.8717
35/65 [===============>..............] - ETA: 7s - loss: 0.5141 - accuracy: 0.8722
36/65 [===============>..............] - ETA: 7s - loss: 0.5155 - accuracy: 0.8719
37/65 [================>.............] - ETA: 6s - loss: 0.5162 - accuracy: 0.8720
38/65 [================>.............] - ETA: 6s - loss: 0.5145 - accuracy: 0.8724
39/65 [=================>............] - ETA: 6s - loss: 0.5147 - accuracy: 0.8722
40/65 [=================>............] - ETA: 6s - loss: 0.5146 - accuracy: 0.8723
41/65 [=================>............] - ETA: 5s - loss: 0.5130 - accuracy: 0.8728
42/65 [==================>...........] - ETA: 5s - loss: 0.5120 - accuracy: 0.8730
43/65 [==================>...........] - ETA: 5s - loss: 0.5111 - accuracy: 0.8735
44/65 [===================>..........] - ETA: 5s - loss: 0.5114 - accuracy: 0.8735
45/65 [===================>..........] - ETA: 4s - loss: 0.5103 - accuracy: 0.8738
46/65 [====================>.........] - ETA: 4s - loss: 0.5103 - accuracy: 0.8740
47/65 [====================>.........] - ETA: 4s - loss: 0.5093 - accuracy: 0.8745
48/65 [=====================>........] - ETA: 4s - loss: 0.5091 - accuracy: 0.8747
49/65 [=====================>........] - ETA: 3s - loss: 0.5096 - accuracy: 0.8746
50/65 [======================>.......] - ETA: 3s - loss: 0.5090 - accuracy: 0.8751
51/65 [======================>.......] - ETA: 3s - loss: 0.5087 - accuracy: 0.8752
52/65 [=======================>......] - ETA: 3s - loss: 0.5079 - accuracy: 0.8754
53/65 [=======================>......] - ETA: 2s - loss: 0.5070 - accuracy: 0.8756
54/65 [=======================>......] - ETA: 2s - loss: 0.5066 - accuracy: 0.8758
55/65 [========================>.....] - ETA: 2s - loss: 0.5054 - accuracy: 0.8762
56/65 [========================>.....] - ETA: 2s - loss: 0.5043 - accuracy: 0.8764
57/65 [=========================>....] - ETA: 1s - loss: 0.5035 - accuracy: 0.8767
58/65 [=========================>....] - ETA: 1s - loss: 0.5037 - accuracy: 0.8767
59/65 [==========================>...] - ETA: 1s - loss: 0.5032 - accuracy: 0.8768
60/65 [==========================>...] - ETA: 1s - loss: 0.5044 - accuracy: 0.8764
61/65 [===========================>..] - ETA: 0s - loss: 0.5056 - accuracy: 0.8757
62/65 [===========================>..] - ETA: 0s - loss: 0.5048 - accuracy: 0.8760
63/65 [============================>.] - ETA: 0s - loss: 0.5060 - accuracy: 0.8756
64/65 [============================>.] - ETA: 0s - loss: 0.5064 - accuracy: 0.8755
65/65 [==============================] - ETA: 0s - loss: 0.5065 - accuracy: 0.8754
65/65 [==============================] - 17s 253ms/step - loss: 0.5065 - accuracy: 0.8754 - val_loss: 0.8641 - val_accuracy: 0.7546 - lr: 0.0030
Epoch 6/30
1/65 [..............................] - ETA: 31s - loss: 0.5211 - accuracy: 0.8730
2/65 [..............................] - ETA: 15s - loss: 0.5076 - accuracy: 0.8794
3/65 [>.............................] - ETA: 15s - loss: 0.5019 - accuracy: 0.8760
4/65 [>.............................] - ETA: 15s - loss: 0.5097 - accuracy: 0.8699
5/65 [=>............................] - ETA: 15s - loss: 0.5191 - accuracy: 0.8682
6/65 [=>............................] - ETA: 14s - loss: 0.5142 - accuracy: 0.8700
7/65 [==>...........................] - ETA: 14s - loss: 0.5026 - accuracy: 0.8743
8/65 [==>...........................] - ETA: 14s - loss: 0.5045 - accuracy: 0.8730
9/65 [===>..........................] - ETA: 14s - loss: 0.5072 - accuracy: 0.8714
10/65 [===>..........................] - ETA: 13s - loss: 0.5066 - accuracy: 0.8725
11/65 [====>.........................] - ETA: 13s - loss: 0.5020 - accuracy: 0.8744
12/65 [====>.........................] - ETA: 13s - loss: 0.4987 - accuracy: 0.8760
13/65 [=====>........................] - ETA: 12s - loss: 0.5021 - accuracy: 0.8752
14/65 [=====>........................] - ETA: 12s - loss: 0.5002 - accuracy: 0.8747
15/65 [=====>........................] - ETA: 12s - loss: 0.5033 - accuracy: 0.8742
16/65 [======>.......................] - ETA: 12s - loss: 0.5003 - accuracy: 0.8750
17/65 [======>.......................] - ETA: 11s - loss: 0.4977 - accuracy: 0.8760
18/65 [=======>......................] - ETA: 11s - loss: 0.4963 - accuracy: 0.8762
19/65 [=======>......................] - ETA: 11s - loss: 0.4932 - accuracy: 0.8774
20/65 [========>.....................] - ETA: 11s - loss: 0.4930 - accuracy: 0.8770
21/65 [========>.....................] - ETA: 10s - loss: 0.4936 - accuracy: 0.8766
22/65 [=========>....................] - ETA: 10s - loss: 0.4938 - accuracy: 0.8770
23/65 [=========>....................] - ETA: 10s - loss: 0.4940 - accuracy: 0.8773
24/65 [==========>...................] - ETA: 10s - loss: 0.4924 - accuracy: 0.8776
25/65 [==========>...................] - ETA: 9s - loss: 0.4920 - accuracy: 0.8777
26/65 [===========>..................] - ETA: 9s - loss: 0.4908 - accuracy: 0.8780
27/65 [===========>..................] - ETA: 9s - loss: 0.4903 - accuracy: 0.8780
28/65 [===========>..................] - ETA: 9s - loss: 0.4884 - accuracy: 0.8788
29/65 [============>.................] - ETA: 8s - loss: 0.4883 - accuracy: 0.8788
30/65 [============>.................] - ETA: 8s - loss: 0.4869 - accuracy: 0.8796
31/65 [=============>................] - ETA: 8s - loss: 0.4874 - accuracy: 0.8795
32/65 [=============>................] - ETA: 8s - loss: 0.4867 - accuracy: 0.8798
33/65 [==============>...............] - ETA: 7s - loss: 0.4867 - accuracy: 0.8798
34/65 [==============>...............] - ETA: 7s - loss: 0.4882 - accuracy: 0.8795
35/65 [===============>..............] - ETA: 7s - loss: 0.4885 - accuracy: 0.8797
36/65 [===============>..............] - ETA: 7s - loss: 0.4892 - accuracy: 0.8795
37/65 [================>.............] - ETA: 6s - loss: 0.4894 - accuracy: 0.8793
38/65 [================>.............] - ETA: 6s - loss: 0.4891 - accuracy: 0.8796
39/65 [=================>............] - ETA: 6s - loss: 0.4877 - accuracy: 0.8800
40/65 [=================>............] - ETA: 6s - loss: 0.4860 - accuracy: 0.8805
41/65 [=================>............] - ETA: 5s - loss: 0.4851 - accuracy: 0.8807
42/65 [==================>...........] - ETA: 5s - loss: 0.4841 - accuracy: 0.8812
43/65 [==================>...........] - ETA: 5s - loss: 0.4835 - accuracy: 0.8814
44/65 [===================>..........] - ETA: 5s - loss: 0.4839 - accuracy: 0.8813
45/65 [===================>..........] - ETA: 4s - loss: 0.4836 - accuracy: 0.8816
46/65 [====================>.........] - ETA: 4s - loss: 0.4826 - accuracy: 0.8821
47/65 [====================>.........] - ETA: 4s - loss: 0.4822 - accuracy: 0.8824
48/65 [=====================>........] - ETA: 4s - loss: 0.4812 - accuracy: 0.8827
49/65 [=====================>........] - ETA: 3s - loss: 0.4818 - accuracy: 0.8826
50/65 [======================>.......] - ETA: 3s - loss: 0.4815 - accuracy: 0.8828
51/65 [======================>.......] - ETA: 3s - loss: 0.4812 - accuracy: 0.8830
52/65 [=======================>......] - ETA: 3s - loss: 0.4807 - accuracy: 0.8831
53/65 [=======================>......] - ETA: 2s - loss: 0.4794 - accuracy: 0.8834
54/65 [=======================>......] - ETA: 2s - loss: 0.4783 - accuracy: 0.8838
55/65 [========================>.....] - ETA: 2s - loss: 0.4775 - accuracy: 0.8838
56/65 [========================>.....] - ETA: 2s - loss: 0.4769 - accuracy: 0.8841
57/65 [=========================>....] - ETA: 1s - loss: 0.4769 - accuracy: 0.8840
58/65 [=========================>....] - ETA: 1s - loss: 0.4776 - accuracy: 0.8840
59/65 [==========================>...] - ETA: 1s - loss: 0.4781 - accuracy: 0.8838
60/65 [==========================>...] - ETA: 1s - loss: 0.4780 - accuracy: 0.8838
61/65 [===========================>..] - ETA: 0s - loss: 0.4788 - accuracy: 0.8835
62/65 [===========================>..] - ETA: 0s - loss: 0.4783 - accuracy: 0.8834
63/65 [============================>.] - ETA: 0s - loss: 0.4788 - accuracy: 0.8833
64/65 [============================>.] - ETA: 0s - loss: 0.4792 - accuracy: 0.8833
65/65 [==============================] - ETA: 0s - loss: 0.4797 - accuracy: 0.8830
65/65 [==============================] - 17s 254ms/step - loss: 0.4797 - accuracy: 0.8830 - val_loss: 0.8683 - val_accuracy: 0.7538 - lr: 0.0030
Epoch 7/30
1/65 [..............................] - ETA: 31s - loss: 0.4556 - accuracy: 0.8828
2/65 [..............................] - ETA: 15s - loss: 0.4731 - accuracy: 0.8799
3/65 [>.............................] - ETA: 15s - loss: 0.5017 - accuracy: 0.8757
4/65 [>.............................] - ETA: 15s - loss: 0.4944 - accuracy: 0.8767
5/65 [=>............................] - ETA: 15s - loss: 0.4848 - accuracy: 0.8801
6/65 [=>............................] - ETA: 14s - loss: 0.4861 - accuracy: 0.8802
7/65 [==>...........................] - ETA: 14s - loss: 0.4857 - accuracy: 0.8792
8/65 [==>...........................] - ETA: 14s - loss: 0.4847 - accuracy: 0.8798
9/65 [===>..........................] - ETA: 13s - loss: 0.4840 - accuracy: 0.8797
10/65 [===>..........................] - ETA: 13s - loss: 0.4785 - accuracy: 0.8804
11/65 [====>.........................] - ETA: 13s - loss: 0.4779 - accuracy: 0.8806
12/65 [====>.........................] - ETA: 13s - loss: 0.4776 - accuracy: 0.8809
13/65 [=====>........................] - ETA: 12s - loss: 0.4761 - accuracy: 0.8819
14/65 [=====>........................] - ETA: 12s - loss: 0.4743 - accuracy: 0.8818
15/65 [=====>........................] - ETA: 12s - loss: 0.4725 - accuracy: 0.8826
16/65 [======>.......................] - ETA: 12s - loss: 0.4705 - accuracy: 0.8828
17/65 [======>.......................] - ETA: 11s - loss: 0.4706 - accuracy: 0.8829
18/65 [=======>......................] - ETA: 11s - loss: 0.4722 - accuracy: 0.8814
19/65 [=======>......................] - ETA: 11s - loss: 0.4725 - accuracy: 0.8816
20/65 [========>.....................] - ETA: 11s - loss: 0.4696 - accuracy: 0.8824
21/65 [========>.....................] - ETA: 10s - loss: 0.4692 - accuracy: 0.8827
22/65 [=========>....................] - ETA: 10s - loss: 0.4673 - accuracy: 0.8833
23/65 [=========>....................] - ETA: 10s - loss: 0.4664 - accuracy: 0.8833
24/65 [==========>...................] - ETA: 10s - loss: 0.4677 - accuracy: 0.8833
25/65 [==========>...................] - ETA: 9s - loss: 0.4676 - accuracy: 0.8834
26/65 [===========>..................] - ETA: 9s - loss: 0.4677 - accuracy: 0.8834
27/65 [===========>..................] - ETA: 9s - loss: 0.4677 - accuracy: 0.8834
28/65 [===========>..................] - ETA: 9s - loss: 0.4680 - accuracy: 0.8832
29/65 [============>.................] - ETA: 8s - loss: 0.4669 - accuracy: 0.8837
30/65 [============>.................] - ETA: 8s - loss: 0.4665 - accuracy: 0.8837
31/65 [=============>................] - ETA: 8s - loss: 0.4656 - accuracy: 0.8841
32/65 [=============>................] - ETA: 8s - loss: 0.4654 - accuracy: 0.8845
33/65 [==============>...............] - ETA: 7s - loss: 0.4658 - accuracy: 0.8846
34/65 [==============>...............] - ETA: 7s - loss: 0.4664 - accuracy: 0.8844
35/65 [===============>..............] - ETA: 7s - loss: 0.4664 - accuracy: 0.8844
36/65 [===============>..............] - ETA: 7s - loss: 0.4669 - accuracy: 0.8842
37/65 [================>.............] - ETA: 6s - loss: 0.4658 - accuracy: 0.8844
38/65 [================>.............] - ETA: 6s - loss: 0.4653 - accuracy: 0.8845
39/65 [=================>............] - ETA: 6s - loss: 0.4649 - accuracy: 0.8846
40/65 [=================>............] - ETA: 6s - loss: 0.4646 - accuracy: 0.8846
41/65 [=================>............] - ETA: 5s - loss: 0.4642 - accuracy: 0.8847
42/65 [==================>...........] - ETA: 5s - loss: 0.4634 - accuracy: 0.8850
43/65 [==================>...........] - ETA: 5s - loss: 0.4638 - accuracy: 0.8850
44/65 [===================>..........] - ETA: 5s - loss: 0.4637 - accuracy: 0.8852
45/65 [===================>..........] - ETA: 4s - loss: 0.4625 - accuracy: 0.8857
46/65 [====================>.........] - ETA: 4s - loss: 0.4630 - accuracy: 0.8855
47/65 [====================>.........] - ETA: 4s - loss: 0.4634 - accuracy: 0.8851
48/65 [=====================>........] - ETA: 4s - loss: 0.4635 - accuracy: 0.8852
49/65 [=====================>........] - ETA: 3s - loss: 0.4623 - accuracy: 0.8857
50/65 [======================>.......] - ETA: 3s - loss: 0.4608 - accuracy: 0.8860
51/65 [======================>.......] - ETA: 3s - loss: 0.4599 - accuracy: 0.8862
52/65 [=======================>......] - ETA: 3s - loss: 0.4587 - accuracy: 0.8868
53/65 [=======================>......] - ETA: 2s - loss: 0.4586 - accuracy: 0.8868
54/65 [=======================>......] - ETA: 2s - loss: 0.4579 - accuracy: 0.8871
55/65 [========================>.....] - ETA: 2s - loss: 0.4573 - accuracy: 0.8873
56/65 [========================>.....] - ETA: 2s - loss: 0.4568 - accuracy: 0.8875
57/65 [=========================>....] - ETA: 1s - loss: 0.4562 - accuracy: 0.8876
58/65 [=========================>....] - ETA: 1s - loss: 0.4562 - accuracy: 0.8875
59/65 [==========================>...] - ETA: 1s - loss: 0.4556 - accuracy: 0.8877
60/65 [==========================>...] - ETA: 1s - loss: 0.4558 - accuracy: 0.8877
61/65 [===========================>..] - ETA: 0s - loss: 0.4562 - accuracy: 0.8875
62/65 [===========================>..] - ETA: 0s - loss: 0.4565 - accuracy: 0.8874
63/65 [============================>.] - ETA: 0s - loss: 0.4561 - accuracy: 0.8874
64/65 [============================>.] - ETA: 0s - loss: 0.4557 - accuracy: 0.8875
65/65 [==============================] - ETA: 0s - loss: 0.4559 - accuracy: 0.8875
65/65 [==============================] - 17s 252ms/step - loss: 0.4559 - accuracy: 0.8875 - val_loss: 0.5878 - val_accuracy: 0.8453 - lr: 0.0030
Epoch 8/30
1/65 [..............................] - ETA: 31s - loss: 0.4567 - accuracy: 0.8965
2/65 [..............................] - ETA: 15s - loss: 0.4408 - accuracy: 0.8999
3/65 [>.............................] - ETA: 15s - loss: 0.4522 - accuracy: 0.8903
4/65 [>.............................] - ETA: 15s - loss: 0.4527 - accuracy: 0.8882
5/65 [=>............................] - ETA: 14s - loss: 0.4576 - accuracy: 0.8875
6/65 [=>............................] - ETA: 14s - loss: 0.4491 - accuracy: 0.8896
7/65 [==>...........................] - ETA: 14s - loss: 0.4499 - accuracy: 0.8890
8/65 [==>...........................] - ETA: 14s - loss: 0.4489 - accuracy: 0.8894
9/65 [===>..........................] - ETA: 13s - loss: 0.4527 - accuracy: 0.8870
10/65 [===>..........................] - ETA: 13s - loss: 0.4524 - accuracy: 0.8879
11/65 [====>.........................] - ETA: 13s - loss: 0.4498 - accuracy: 0.8886
12/65 [====>.........................] - ETA: 13s - loss: 0.4490 - accuracy: 0.8892
13/65 [=====>........................] - ETA: 12s - loss: 0.4513 - accuracy: 0.8890
14/65 [=====>........................] - ETA: 12s - loss: 0.4479 - accuracy: 0.8907
15/65 [=====>........................] - ETA: 12s - loss: 0.4454 - accuracy: 0.8908
16/65 [======>.......................] - ETA: 12s - loss: 0.4459 - accuracy: 0.8906
17/65 [======>.......................] - ETA: 11s - loss: 0.4464 - accuracy: 0.8902
18/65 [=======>......................] - ETA: 11s - loss: 0.4458 - accuracy: 0.8904
19/65 [=======>......................] - ETA: 11s - loss: 0.4470 - accuracy: 0.8903
20/65 [========>.....................] - ETA: 11s - loss: 0.4443 - accuracy: 0.8909
21/65 [========>.....................] - ETA: 10s - loss: 0.4440 - accuracy: 0.8910
22/65 [=========>....................] - ETA: 10s - loss: 0.4431 - accuracy: 0.8909
23/65 [=========>....................] - ETA: 10s - loss: 0.4421 - accuracy: 0.8910
24/65 [==========>...................] - ETA: 10s - loss: 0.4399 - accuracy: 0.8912
25/65 [==========>...................] - ETA: 9s - loss: 0.4392 - accuracy: 0.8911
26/65 [===========>..................] - ETA: 9s - loss: 0.4397 - accuracy: 0.8910
27/65 [===========>..................] - ETA: 9s - loss: 0.4390 - accuracy: 0.8916
28/65 [===========>..................] - ETA: 9s - loss: 0.4401 - accuracy: 0.8914
29/65 [============>.................] - ETA: 8s - loss: 0.4411 - accuracy: 0.8913
30/65 [============>.................] - ETA: 8s - loss: 0.4413 - accuracy: 0.8915
31/65 [=============>................] - ETA: 8s - loss: 0.4413 - accuracy: 0.8916
32/65 [=============>................] - ETA: 8s - loss: 0.4403 - accuracy: 0.8916
33/65 [==============>...............] - ETA: 7s - loss: 0.4401 - accuracy: 0.8918
34/65 [==============>...............] - ETA: 7s - loss: 0.4396 - accuracy: 0.8917
35/65 [===============>..............] - ETA: 7s - loss: 0.4386 - accuracy: 0.8919
36/65 [===============>..............] - ETA: 7s - loss: 0.4394 - accuracy: 0.8917
37/65 [================>.............] - ETA: 6s - loss: 0.4388 - accuracy: 0.8919
38/65 [================>.............] - ETA: 6s - loss: 0.4383 - accuracy: 0.8920
39/65 [=================>............] - ETA: 6s - loss: 0.4389 - accuracy: 0.8923
40/65 [=================>............] - ETA: 6s - loss: 0.4397 - accuracy: 0.8919
41/65 [=================>............] - ETA: 5s - loss: 0.4403 - accuracy: 0.8917
42/65 [==================>...........] - ETA: 5s - loss: 0.4405 - accuracy: 0.8918
43/65 [==================>...........] - ETA: 5s - loss: 0.4398 - accuracy: 0.8921
44/65 [===================>..........] - ETA: 5s - loss: 0.4390 - accuracy: 0.8922
45/65 [===================>..........] - ETA: 4s - loss: 0.4376 - accuracy: 0.8927
46/65 [====================>.........] - ETA: 4s - loss: 0.4377 - accuracy: 0.8928
47/65 [====================>.........] - ETA: 4s - loss: 0.4378 - accuracy: 0.8927
48/65 [=====================>........] - ETA: 4s - loss: 0.4365 - accuracy: 0.8932
49/65 [=====================>........] - ETA: 3s - loss: 0.4368 - accuracy: 0.8932
50/65 [======================>.......] - ETA: 3s - loss: 0.4357 - accuracy: 0.8934
51/65 [======================>.......] - ETA: 3s - loss: 0.4353 - accuracy: 0.8936
52/65 [=======================>......] - ETA: 3s - loss: 0.4349 - accuracy: 0.8937
53/65 [=======================>......] - ETA: 2s - loss: 0.4347 - accuracy: 0.8936
54/65 [=======================>......] - ETA: 2s - loss: 0.4341 - accuracy: 0.8937
55/65 [========================>.....] - ETA: 2s - loss: 0.4333 - accuracy: 0.8938
56/65 [========================>.....] - ETA: 2s - loss: 0.4324 - accuracy: 0.8941
57/65 [=========================>....] - ETA: 1s - loss: 0.4332 - accuracy: 0.8939
58/65 [=========================>....] - ETA: 1s - loss: 0.4338 - accuracy: 0.8938
59/65 [==========================>...] - ETA: 1s - loss: 0.4340 - accuracy: 0.8938
60/65 [==========================>...] - ETA: 1s - loss: 0.4338 - accuracy: 0.8938
61/65 [===========================>..] - ETA: 0s - loss: 0.4341 - accuracy: 0.8937
62/65 [===========================>..] - ETA: 0s - loss: 0.4345 - accuracy: 0.8935
63/65 [============================>.] - ETA: 0s - loss: 0.4342 - accuracy: 0.8935
64/65 [============================>.] - ETA: 0s - loss: 0.4342 - accuracy: 0.8935
65/65 [==============================] - ETA: 0s - loss: 0.4344 - accuracy: 0.8935
65/65 [==============================] - 17s 251ms/step - loss: 0.4344 - accuracy: 0.8935 - val_loss: 0.5441 - val_accuracy: 0.8546 - lr: 0.0030
Epoch 9/30
1/65 [..............................] - ETA: 31s - loss: 0.4230 - accuracy: 0.8965
2/65 [..............................] - ETA: 15s - loss: 0.4330 - accuracy: 0.8955
3/65 [>.............................] - ETA: 15s - loss: 0.4394 - accuracy: 0.8936
4/65 [>.............................] - ETA: 15s - loss: 0.4462 - accuracy: 0.8918
5/65 [=>............................] - ETA: 15s - loss: 0.4357 - accuracy: 0.8951
6/65 [=>............................] - ETA: 14s - loss: 0.4361 - accuracy: 0.8963
7/65 [==>...........................] - ETA: 14s - loss: 0.4392 - accuracy: 0.8930
8/65 [==>...........................] - ETA: 14s - loss: 0.4393 - accuracy: 0.8929
9/65 [===>..........................] - ETA: 14s - loss: 0.4362 - accuracy: 0.8938
10/65 [===>..........................] - ETA: 13s - loss: 0.4370 - accuracy: 0.8934
11/65 [====>.........................] - ETA: 13s - loss: 0.4331 - accuracy: 0.8941
12/65 [====>.........................] - ETA: 13s - loss: 0.4335 - accuracy: 0.8942
13/65 [=====>........................] - ETA: 12s - loss: 0.4316 - accuracy: 0.8950
14/65 [=====>........................] - ETA: 12s - loss: 0.4329 - accuracy: 0.8946
15/65 [=====>........................] - ETA: 12s - loss: 0.4323 - accuracy: 0.8941
16/65 [======>.......................] - ETA: 12s - loss: 0.4295 - accuracy: 0.8950
17/65 [======>.......................] - ETA: 11s - loss: 0.4275 - accuracy: 0.8957
18/65 [=======>......................] - ETA: 11s - loss: 0.4286 - accuracy: 0.8950
19/65 [=======>......................] - ETA: 11s - loss: 0.4305 - accuracy: 0.8945
20/65 [========>.....................] - ETA: 11s - loss: 0.4298 - accuracy: 0.8944
21/65 [========>.....................] - ETA: 10s - loss: 0.4262 - accuracy: 0.8953
22/65 [=========>....................] - ETA: 10s - loss: 0.4256 - accuracy: 0.8960
23/65 [=========>....................] - ETA: 10s - loss: 0.4253 - accuracy: 0.8958
24/65 [==========>...................] - ETA: 10s - loss: 0.4234 - accuracy: 0.8966
25/65 [==========>...................] - ETA: 9s - loss: 0.4245 - accuracy: 0.8963
26/65 [===========>..................] - ETA: 9s - loss: 0.4252 - accuracy: 0.8956
27/65 [===========>..................] - ETA: 9s - loss: 0.4240 - accuracy: 0.8958
28/65 [===========>..................] - ETA: 9s - loss: 0.4240 - accuracy: 0.8956
29/65 [============>.................] - ETA: 8s - loss: 0.4253 - accuracy: 0.8954
30/65 [============>.................] - ETA: 8s - loss: 0.4239 - accuracy: 0.8960
31/65 [=============>................] - ETA: 8s - loss: 0.4242 - accuracy: 0.8960
32/65 [=============>................] - ETA: 8s - loss: 0.4244 - accuracy: 0.8961
33/65 [==============>...............] - ETA: 7s - loss: 0.4237 - accuracy: 0.8964
34/65 [==============>...............] - ETA: 7s - loss: 0.4246 - accuracy: 0.8962
35/65 [===============>..............] - ETA: 7s - loss: 0.4254 - accuracy: 0.8961
36/65 [===============>..............] - ETA: 7s - loss: 0.4260 - accuracy: 0.8956
37/65 [================>.............] - ETA: 6s - loss: 0.4245 - accuracy: 0.8958
38/65 [================>.............] - ETA: 6s - loss: 0.4241 - accuracy: 0.8959
39/65 [=================>............] - ETA: 6s - loss: 0.4246 - accuracy: 0.8959
40/65 [=================>............] - ETA: 6s - loss: 0.4233 - accuracy: 0.8961
41/65 [=================>............] - ETA: 5s - loss: 0.4229 - accuracy: 0.8966
42/65 [==================>...........] - ETA: 5s - loss: 0.4240 - accuracy: 0.8964
43/65 [==================>...........] - ETA: 5s - loss: 0.4237 - accuracy: 0.8966
44/65 [===================>..........] - ETA: 5s - loss: 0.4241 - accuracy: 0.8964
45/65 [===================>..........] - ETA: 4s - loss: 0.4237 - accuracy: 0.8964
46/65 [====================>.........] - ETA: 4s - loss: 0.4237 - accuracy: 0.8964
47/65 [====================>.........] - ETA: 4s - loss: 0.4234 - accuracy: 0.8963
48/65 [=====================>........] - ETA: 4s - loss: 0.4237 - accuracy: 0.8964
49/65 [=====================>........] - ETA: 3s - loss: 0.4228 - accuracy: 0.8968
50/65 [======================>.......] - ETA: 3s - loss: 0.4220 - accuracy: 0.8969
51/65 [======================>.......] - ETA: 3s - loss: 0.4210 - accuracy: 0.8973
52/65 [=======================>......] - ETA: 3s - loss: 0.4215 - accuracy: 0.8971
53/65 [=======================>......] - ETA: 2s - loss: 0.4208 - accuracy: 0.8974
54/65 [=======================>......] - ETA: 2s - loss: 0.4210 - accuracy: 0.8972
55/65 [========================>.....] - ETA: 2s - loss: 0.4206 - accuracy: 0.8972
56/65 [========================>.....] - ETA: 2s - loss: 0.4201 - accuracy: 0.8974
57/65 [=========================>....] - ETA: 1s - loss: 0.4211 - accuracy: 0.8971
58/65 [=========================>....] - ETA: 1s - loss: 0.4216 - accuracy: 0.8969
59/65 [==========================>...] - ETA: 1s - loss: 0.4216 - accuracy: 0.8969
60/65 [==========================>...] - ETA: 1s - loss: 0.4214 - accuracy: 0.8971
61/65 [===========================>..] - ETA: 0s - loss: 0.4203 - accuracy: 0.8973
62/65 [===========================>..] - ETA: 0s - loss: 0.4201 - accuracy: 0.8974
63/65 [============================>.] - ETA: 0s - loss: 0.4187 - accuracy: 0.8978
64/65 [============================>.] - ETA: 0s - loss: 0.4193 - accuracy: 0.8976
65/65 [==============================] - ETA: 0s - loss: 0.4194 - accuracy: 0.8975
65/65 [==============================] - 17s 251ms/step - loss: 0.4194 - accuracy: 0.8975 - val_loss: 0.5370 - val_accuracy: 0.8612 - lr: 0.0030
Epoch 10/30
1/65 [..............................] - ETA: 30s - loss: 0.3846 - accuracy: 0.9082
2/65 [..............................] - ETA: 15s - loss: 0.4261 - accuracy: 0.8975
3/65 [>.............................] - ETA: 15s - loss: 0.4203 - accuracy: 0.8975
4/65 [>.............................] - ETA: 15s - loss: 0.4076 - accuracy: 0.9009
5/65 [=>............................] - ETA: 15s - loss: 0.4043 - accuracy: 0.9023
6/65 [=>............................] - ETA: 14s - loss: 0.4033 - accuracy: 0.9012
7/65 [==>...........................] - ETA: 14s - loss: 0.4111 - accuracy: 0.8980
8/65 [==>...........................] - ETA: 14s - loss: 0.4146 - accuracy: 0.8973
9/65 [===>..........................] - ETA: 13s - loss: 0.4197 - accuracy: 0.8968
10/65 [===>..........................] - ETA: 13s - loss: 0.4225 - accuracy: 0.8957
11/65 [====>.........................] - ETA: 13s - loss: 0.4182 - accuracy: 0.8973
12/65 [====>.........................] - ETA: 13s - loss: 0.4173 - accuracy: 0.8971
13/65 [=====>........................] - ETA: 12s - loss: 0.4163 - accuracy: 0.8969
14/65 [=====>........................] - ETA: 12s - loss: 0.4183 - accuracy: 0.8960
15/65 [=====>........................] - ETA: 12s - loss: 0.4173 - accuracy: 0.8959
16/65 [======>.......................] - ETA: 12s - loss: 0.4158 - accuracy: 0.8967
17/65 [======>.......................] - ETA: 11s - loss: 0.4145 - accuracy: 0.8976
18/65 [=======>......................] - ETA: 11s - loss: 0.4148 - accuracy: 0.8972
19/65 [=======>......................] - ETA: 11s - loss: 0.4171 - accuracy: 0.8967
20/65 [========>.....................] - ETA: 11s - loss: 0.4148 - accuracy: 0.8976
21/65 [========>.....................] - ETA: 10s - loss: 0.4148 - accuracy: 0.8979
22/65 [=========>....................] - ETA: 10s - loss: 0.4140 - accuracy: 0.8983
23/65 [=========>....................] - ETA: 10s - loss: 0.4142 - accuracy: 0.8984
24/65 [==========>...................] - ETA: 10s - loss: 0.4133 - accuracy: 0.8984
25/65 [==========>...................] - ETA: 9s - loss: 0.4146 - accuracy: 0.8976
26/65 [===========>..................] - ETA: 9s - loss: 0.4135 - accuracy: 0.8978
27/65 [===========>..................] - ETA: 9s - loss: 0.4141 - accuracy: 0.8977
28/65 [===========>..................] - ETA: 9s - loss: 0.4157 - accuracy: 0.8972
29/65 [============>.................] - ETA: 8s - loss: 0.4155 - accuracy: 0.8969
30/65 [============>.................] - ETA: 8s - loss: 0.4160 - accuracy: 0.8968
31/65 [=============>................] - ETA: 8s - loss: 0.4154 - accuracy: 0.8972
32/65 [=============>................] - ETA: 8s - loss: 0.4156 - accuracy: 0.8969
33/65 [==============>...............] - ETA: 7s - loss: 0.4168 - accuracy: 0.8966
34/65 [==============>...............] - ETA: 7s - loss: 0.4179 - accuracy: 0.8967
35/65 [===============>..............] - ETA: 7s - loss: 0.4185 - accuracy: 0.8965
36/65 [===============>..............] - ETA: 7s - loss: 0.4176 - accuracy: 0.8967
37/65 [================>.............] - ETA: 6s - loss: 0.4167 - accuracy: 0.8967
38/65 [================>.............] - ETA: 6s - loss: 0.4158 - accuracy: 0.8970
39/65 [=================>............] - ETA: 6s - loss: 0.4148 - accuracy: 0.8972
40/65 [=================>............] - ETA: 6s - loss: 0.4143 - accuracy: 0.8973
41/65 [=================>............] - ETA: 5s - loss: 0.4150 - accuracy: 0.8972
42/65 [==================>...........] - ETA: 5s - loss: 0.4145 - accuracy: 0.8973
43/65 [==================>...........] - ETA: 5s - loss: 0.4142 - accuracy: 0.8972
44/65 [===================>..........] - ETA: 5s - loss: 0.4133 - accuracy: 0.8976
45/65 [===================>..........] - ETA: 4s - loss: 0.4132 - accuracy: 0.8978
46/65 [====================>.........] - ETA: 4s - loss: 0.4138 - accuracy: 0.8976
47/65 [====================>.........] - ETA: 4s - loss: 0.4124 - accuracy: 0.8980
48/65 [=====================>........] - ETA: 4s - loss: 0.4119 - accuracy: 0.8983
49/65 [=====================>........] - ETA: 3s - loss: 0.4128 - accuracy: 0.8982
50/65 [======================>.......] - ETA: 3s - loss: 0.4126 - accuracy: 0.8982
51/65 [======================>.......] - ETA: 3s - loss: 0.4118 - accuracy: 0.8985
52/65 [=======================>......] - ETA: 3s - loss: 0.4122 - accuracy: 0.8985
53/65 [=======================>......] - ETA: 2s - loss: 0.4114 - accuracy: 0.8987
54/65 [=======================>......] - ETA: 2s - loss: 0.4109 - accuracy: 0.8989
55/65 [========================>.....] - ETA: 2s - loss: 0.4105 - accuracy: 0.8990
56/65 [========================>.....] - ETA: 2s - loss: 0.4109 - accuracy: 0.8990
57/65 [=========================>....] - ETA: 1s - loss: 0.4104 - accuracy: 0.8990
58/65 [=========================>....] - ETA: 1s - loss: 0.4106 - accuracy: 0.8991
59/65 [==========================>...] - ETA: 1s - loss: 0.4102 - accuracy: 0.8992
60/65 [==========================>...] - ETA: 1s - loss: 0.4104 - accuracy: 0.8992
61/65 [===========================>..] - ETA: 0s - loss: 0.4100 - accuracy: 0.8994
62/65 [===========================>..] - ETA: 0s - loss: 0.4097 - accuracy: 0.8995
63/65 [============================>.] - ETA: 0s - loss: 0.4089 - accuracy: 0.8997
64/65 [============================>.] - ETA: 0s - loss: 0.4089 - accuracy: 0.8996
65/65 [==============================] - ETA: 0s - loss: 0.4087 - accuracy: 0.8997
65/65 [==============================] - 16s 250ms/step - loss: 0.4087 - accuracy: 0.8997 - val_loss: 0.5038 - val_accuracy: 0.8694 - lr: 0.0030
Epoch 11/30
1/65 [..............................] - ETA: 29s - loss: 0.4277 - accuracy: 0.8994
2/65 [..............................] - ETA: 15s - loss: 0.4232 - accuracy: 0.8945
3/65 [>.............................] - ETA: 15s - loss: 0.4212 - accuracy: 0.8965
4/65 [>.............................] - ETA: 15s - loss: 0.4170 - accuracy: 0.8979
5/65 [=>............................] - ETA: 14s - loss: 0.4133 - accuracy: 0.8986
6/65 [=>............................] - ETA: 14s - loss: 0.4113 - accuracy: 0.8984
7/65 [==>...........................] - ETA: 14s - loss: 0.4088 - accuracy: 0.8990
8/65 [==>...........................] - ETA: 14s - loss: 0.4094 - accuracy: 0.8997
9/65 [===>..........................] - ETA: 13s - loss: 0.4094 - accuracy: 0.9003
10/65 [===>..........................] - ETA: 13s - loss: 0.4056 - accuracy: 0.9000
11/65 [====>.........................] - ETA: 13s - loss: 0.4042 - accuracy: 0.8997
12/65 [====>.........................] - ETA: 13s - loss: 0.4032 - accuracy: 0.9007
13/65 [=====>........................] - ETA: 12s - loss: 0.4039 - accuracy: 0.9009
14/65 [=====>........................] - ETA: 12s - loss: 0.4053 - accuracy: 0.9009
15/65 [=====>........................] - ETA: 12s - loss: 0.4062 - accuracy: 0.9003
16/65 [======>.......................] - ETA: 12s - loss: 0.4048 - accuracy: 0.9006
17/65 [======>.......................] - ETA: 11s - loss: 0.3995 - accuracy: 0.9022
18/65 [=======>......................] - ETA: 11s - loss: 0.4014 - accuracy: 0.9015
19/65 [=======>......................] - ETA: 11s - loss: 0.4038 - accuracy: 0.9012
20/65 [========>.....................] - ETA: 11s - loss: 0.4008 - accuracy: 0.9019
21/65 [========>.....................] - ETA: 10s - loss: 0.3989 - accuracy: 0.9023
22/65 [=========>....................] - ETA: 10s - loss: 0.3993 - accuracy: 0.9024
23/65 [=========>....................] - ETA: 10s - loss: 0.4010 - accuracy: 0.9020
24/65 [==========>...................] - ETA: 10s - loss: 0.3994 - accuracy: 0.9025
25/65 [==========>...................] - ETA: 9s - loss: 0.3992 - accuracy: 0.9021
26/65 [===========>..................] - ETA: 9s - loss: 0.3993 - accuracy: 0.9022
27/65 [===========>..................] - ETA: 9s - loss: 0.3996 - accuracy: 0.9023
28/65 [===========>..................] - ETA: 9s - loss: 0.3983 - accuracy: 0.9023
29/65 [============>.................] - ETA: 8s - loss: 0.3983 - accuracy: 0.9024
30/65 [============>.................] - ETA: 8s - loss: 0.3983 - accuracy: 0.9021
31/65 [=============>................] - ETA: 8s - loss: 0.3999 - accuracy: 0.9018
32/65 [=============>................] - ETA: 8s - loss: 0.4011 - accuracy: 0.9012
33/65 [==============>...............] - ETA: 7s - loss: 0.4008 - accuracy: 0.9014
34/65 [==============>...............] - ETA: 7s - loss: 0.4002 - accuracy: 0.9016
35/65 [===============>..............] - ETA: 7s - loss: 0.3998 - accuracy: 0.9020
36/65 [===============>..............] - ETA: 7s - loss: 0.3995 - accuracy: 0.9018
37/65 [================>.............] - ETA: 6s - loss: 0.4009 - accuracy: 0.9016
38/65 [================>.............] - ETA: 6s - loss: 0.4003 - accuracy: 0.9017
39/65 [=================>............] - ETA: 6s - loss: 0.3993 - accuracy: 0.9018
40/65 [=================>............] - ETA: 6s - loss: 0.4003 - accuracy: 0.9016
41/65 [=================>............] - ETA: 5s - loss: 0.4000 - accuracy: 0.9020
42/65 [==================>...........] - ETA: 5s - loss: 0.4000 - accuracy: 0.9021
43/65 [==================>...........] - ETA: 5s - loss: 0.3995 - accuracy: 0.9022
44/65 [===================>..........] - ETA: 5s - loss: 0.3995 - accuracy: 0.9021
45/65 [===================>..........] - ETA: 4s - loss: 0.4001 - accuracy: 0.9020
46/65 [====================>.........] - ETA: 4s - loss: 0.3994 - accuracy: 0.9022
47/65 [====================>.........] - ETA: 4s - loss: 0.3987 - accuracy: 0.9024
48/65 [=====================>........] - ETA: 4s - loss: 0.3985 - accuracy: 0.9024
49/65 [=====================>........] - ETA: 3s - loss: 0.3979 - accuracy: 0.9025
50/65 [======================>.......] - ETA: 3s - loss: 0.3983 - accuracy: 0.9024
51/65 [======================>.......] - ETA: 3s - loss: 0.3982 - accuracy: 0.9024
52/65 [=======================>......] - ETA: 3s - loss: 0.3982 - accuracy: 0.9022
53/65 [=======================>......] - ETA: 2s - loss: 0.3982 - accuracy: 0.9022
54/65 [=======================>......] - ETA: 2s - loss: 0.3975 - accuracy: 0.9024
55/65 [========================>.....] - ETA: 2s - loss: 0.3972 - accuracy: 0.9025
56/65 [========================>.....] - ETA: 2s - loss: 0.3979 - accuracy: 0.9023
57/65 [=========================>....] - ETA: 1s - loss: 0.3979 - accuracy: 0.9025
58/65 [=========================>....] - ETA: 1s - loss: 0.3971 - accuracy: 0.9027
59/65 [==========================>...] - ETA: 1s - loss: 0.3969 - accuracy: 0.9028
60/65 [==========================>...] - ETA: 1s - loss: 0.3972 - accuracy: 0.9028
61/65 [===========================>..] - ETA: 0s - loss: 0.3971 - accuracy: 0.9028
62/65 [===========================>..] - ETA: 0s - loss: 0.3970 - accuracy: 0.9029
63/65 [============================>.] - ETA: 0s - loss: 0.3966 - accuracy: 0.9029
64/65 [============================>.] - ETA: 0s - loss: 0.3971 - accuracy: 0.9026
65/65 [==============================] - ETA: 0s - loss: 0.3967 - accuracy: 0.9028
65/65 [==============================] - 17s 252ms/step - loss: 0.3967 - accuracy: 0.9028 - val_loss: 0.5125 - val_accuracy: 0.8632 - lr: 0.0030
Epoch 12/30
1/65 [..............................] - ETA: 29s - loss: 0.3932 - accuracy: 0.9111
2/65 [..............................] - ETA: 15s - loss: 0.3981 - accuracy: 0.9058
3/65 [>.............................] - ETA: 15s - loss: 0.3929 - accuracy: 0.9098
4/65 [>.............................] - ETA: 15s - loss: 0.3960 - accuracy: 0.9080
5/65 [=>............................] - ETA: 15s - loss: 0.3943 - accuracy: 0.9090
6/65 [=>............................] - ETA: 14s - loss: 0.4024 - accuracy: 0.9071
7/65 [==>...........................] - ETA: 14s - loss: 0.4028 - accuracy: 0.9050
8/65 [==>...........................] - ETA: 14s - loss: 0.3988 - accuracy: 0.9052
9/65 [===>..........................] - ETA: 13s - loss: 0.4036 - accuracy: 0.9033
10/65 [===>..........................] - ETA: 13s - loss: 0.3993 - accuracy: 0.9041
11/65 [====>.........................] - ETA: 13s - loss: 0.3966 - accuracy: 0.9050
12/65 [====>.........................] - ETA: 13s - loss: 0.3978 - accuracy: 0.9043
13/65 [=====>........................] - ETA: 12s - loss: 0.3983 - accuracy: 0.9043
14/65 [=====>........................] - ETA: 12s - loss: 0.3983 - accuracy: 0.9050
15/65 [=====>........................] - ETA: 12s - loss: 0.4019 - accuracy: 0.9044
16/65 [======>.......................] - ETA: 12s - loss: 0.4010 - accuracy: 0.9048
17/65 [======>.......................] - ETA: 11s - loss: 0.4016 - accuracy: 0.9044
18/65 [=======>......................] - ETA: 11s - loss: 0.4032 - accuracy: 0.9030
19/65 [=======>......................] - ETA: 11s - loss: 0.4019 - accuracy: 0.9033
20/65 [========>.....................] - ETA: 11s - loss: 0.4012 - accuracy: 0.9033
21/65 [========>.....................] - ETA: 10s - loss: 0.4000 - accuracy: 0.9029
22/65 [=========>....................] - ETA: 10s - loss: 0.3985 - accuracy: 0.9033
23/65 [=========>....................] - ETA: 10s - loss: 0.3977 - accuracy: 0.9034
24/65 [==========>...................] - ETA: 10s - loss: 0.3976 - accuracy: 0.9033
25/65 [==========>...................] - ETA: 9s - loss: 0.3966 - accuracy: 0.9034
26/65 [===========>..................] - ETA: 9s - loss: 0.3960 - accuracy: 0.9034
27/65 [===========>..................] - ETA: 9s - loss: 0.3955 - accuracy: 0.9038
28/65 [===========>..................] - ETA: 9s - loss: 0.3973 - accuracy: 0.9033
29/65 [============>.................] - ETA: 8s - loss: 0.3962 - accuracy: 0.9033
30/65 [============>.................] - ETA: 8s - loss: 0.3949 - accuracy: 0.9035
31/65 [=============>................] - ETA: 8s - loss: 0.3948 - accuracy: 0.9038
32/65 [=============>................] - ETA: 8s - loss: 0.3952 - accuracy: 0.9035
33/65 [==============>...............] - ETA: 7s - loss: 0.3964 - accuracy: 0.9030
34/65 [==============>...............] - ETA: 7s - loss: 0.3964 - accuracy: 0.9033
35/65 [===============>..............] - ETA: 7s - loss: 0.3964 - accuracy: 0.9033
36/65 [===============>..............] - ETA: 7s - loss: 0.3970 - accuracy: 0.9033
37/65 [================>.............] - ETA: 6s - loss: 0.3980 - accuracy: 0.9031
38/65 [================>.............] - ETA: 6s - loss: 0.3973 - accuracy: 0.9032
39/65 [=================>............] - ETA: 6s - loss: 0.3971 - accuracy: 0.9033
40/65 [=================>............] - ETA: 6s - loss: 0.3985 - accuracy: 0.9031
41/65 [=================>............] - ETA: 5s - loss: 0.3983 - accuracy: 0.9032
42/65 [==================>...........] - ETA: 5s - loss: 0.3975 - accuracy: 0.9034
43/65 [==================>...........] - ETA: 5s - loss: 0.3965 - accuracy: 0.9035
44/65 [===================>..........] - ETA: 5s - loss: 0.3965 - accuracy: 0.9035
45/65 [===================>..........] - ETA: 4s - loss: 0.3961 - accuracy: 0.9036
46/65 [====================>.........] - ETA: 4s - loss: 0.3965 - accuracy: 0.9036
47/65 [====================>.........] - ETA: 4s - loss: 0.3962 - accuracy: 0.9037
48/65 [=====================>........] - ETA: 4s - loss: 0.3963 - accuracy: 0.9035
49/65 [=====================>........] - ETA: 3s - loss: 0.3950 - accuracy: 0.9040
50/65 [======================>.......] - ETA: 3s - loss: 0.3948 - accuracy: 0.9040
51/65 [======================>.......] - ETA: 3s - loss: 0.3950 - accuracy: 0.9040
52/65 [=======================>......] - ETA: 3s - loss: 0.3950 - accuracy: 0.9041
53/65 [=======================>......] - ETA: 2s - loss: 0.3940 - accuracy: 0.9043
54/65 [=======================>......] - ETA: 2s - loss: 0.3937 - accuracy: 0.9043
55/65 [========================>.....] - ETA: 2s - loss: 0.3933 - accuracy: 0.9045
56/65 [========================>.....] - ETA: 2s - loss: 0.3938 - accuracy: 0.9043
57/65 [=========================>....] - ETA: 1s - loss: 0.3935 - accuracy: 0.9043
58/65 [=========================>....] - ETA: 1s - loss: 0.3935 - accuracy: 0.9043
59/65 [==========================>...] - ETA: 1s - loss: 0.3935 - accuracy: 0.9044
60/65 [==========================>...] - ETA: 1s - loss: 0.3933 - accuracy: 0.9044
61/65 [===========================>..] - ETA: 0s - loss: 0.3931 - accuracy: 0.9044
62/65 [===========================>..] - ETA: 0s - loss: 0.3927 - accuracy: 0.9047
63/65 [============================>.] - ETA: 0s - loss: 0.3921 - accuracy: 0.9047
64/65 [============================>.] - ETA: 0s - loss: 0.3914 - accuracy: 0.9049
65/65 [==============================] - ETA: 0s - loss: 0.3917 - accuracy: 0.9048
65/65 [==============================] - 17s 252ms/step - loss: 0.3917 - accuracy: 0.9048 - val_loss: 0.5009 - val_accuracy: 0.8665 - lr: 0.0030
Epoch 13/30
1/65 [..............................] - ETA: 29s - loss: 0.4191 - accuracy: 0.8857
2/65 [..............................] - ETA: 16s - loss: 0.4019 - accuracy: 0.9019
3/65 [>.............................] - ETA: 15s - loss: 0.3929 - accuracy: 0.9030
4/65 [>.............................] - ETA: 16s - loss: 0.3988 - accuracy: 0.9036
5/65 [=>............................] - ETA: 15s - loss: 0.4062 - accuracy: 0.8994
6/65 [=>............................] - ETA: 15s - loss: 0.4115 - accuracy: 0.8983
7/65 [==>...........................] - ETA: 14s - loss: 0.4044 - accuracy: 0.9003
8/65 [==>...........................] - ETA: 14s - loss: 0.3981 - accuracy: 0.9010
9/65 [===>..........................] - ETA: 14s - loss: 0.3999 - accuracy: 0.9007
10/65 [===>..........................] - ETA: 13s - loss: 0.3949 - accuracy: 0.9028
11/65 [====>.........................] - ETA: 13s - loss: 0.3944 - accuracy: 0.9022
12/65 [====>.........................] - ETA: 13s - loss: 0.3940 - accuracy: 0.9019
13/65 [=====>........................] - ETA: 12s - loss: 0.3966 - accuracy: 0.9009
14/65 [=====>........................] - ETA: 12s - loss: 0.3967 - accuracy: 0.9003
15/65 [=====>........................] - ETA: 12s - loss: 0.3958 - accuracy: 0.9004
16/65 [======>.......................] - ETA: 12s - loss: 0.3944 - accuracy: 0.9009
17/65 [======>.......................] - ETA: 11s - loss: 0.3928 - accuracy: 0.9016
18/65 [=======>......................] - ETA: 11s - loss: 0.3916 - accuracy: 0.9015
19/65 [=======>......................] - ETA: 11s - loss: 0.3910 - accuracy: 0.9015
20/65 [========>.....................] - ETA: 11s - loss: 0.3891 - accuracy: 0.9020
21/65 [========>.....................] - ETA: 10s - loss: 0.3883 - accuracy: 0.9023
22/65 [=========>....................] - ETA: 10s - loss: 0.3879 - accuracy: 0.9023
23/65 [=========>....................] - ETA: 10s - loss: 0.3898 - accuracy: 0.9022
24/65 [==========>...................] - ETA: 10s - loss: 0.3895 - accuracy: 0.9024
25/65 [==========>...................] - ETA: 9s - loss: 0.3892 - accuracy: 0.9022
26/65 [===========>..................] - ETA: 9s - loss: 0.3879 - accuracy: 0.9027
27/65 [===========>..................] - ETA: 9s - loss: 0.3914 - accuracy: 0.9016
28/65 [===========>..................] - ETA: 9s - loss: 0.3913 - accuracy: 0.9017
29/65 [============>.................] - ETA: 8s - loss: 0.3915 - accuracy: 0.9019
30/65 [============>.................] - ETA: 8s - loss: 0.3910 - accuracy: 0.9022
31/65 [=============>................] - ETA: 8s - loss: 0.3911 - accuracy: 0.9018
32/65 [=============>................] - ETA: 8s - loss: 0.3898 - accuracy: 0.9024
33/65 [==============>...............] - ETA: 7s - loss: 0.3896 - accuracy: 0.9028
34/65 [==============>...............] - ETA: 7s - loss: 0.3926 - accuracy: 0.9022
35/65 [===============>..............] - ETA: 7s - loss: 0.3925 - accuracy: 0.9023
36/65 [===============>..............] - ETA: 7s - loss: 0.3923 - accuracy: 0.9023
37/65 [================>.............] - ETA: 6s - loss: 0.3915 - accuracy: 0.9024
38/65 [================>.............] - ETA: 6s - loss: 0.3907 - accuracy: 0.9027
39/65 [=================>............] - ETA: 6s - loss: 0.3913 - accuracy: 0.9024
40/65 [=================>............] - ETA: 6s - loss: 0.3909 - accuracy: 0.9025
41/65 [=================>............] - ETA: 5s - loss: 0.3911 - accuracy: 0.9025
42/65 [==================>...........] - ETA: 5s - loss: 0.3904 - accuracy: 0.9029
43/65 [==================>...........] - ETA: 5s - loss: 0.3896 - accuracy: 0.9032
44/65 [===================>..........] - ETA: 5s - loss: 0.3887 - accuracy: 0.9036
45/65 [===================>..........] - ETA: 4s - loss: 0.3882 - accuracy: 0.9037
46/65 [====================>.........] - ETA: 4s - loss: 0.3882 - accuracy: 0.9038
47/65 [====================>.........] - ETA: 4s - loss: 0.3878 - accuracy: 0.9037
48/65 [=====================>........] - ETA: 4s - loss: 0.3879 - accuracy: 0.9037
49/65 [=====================>........] - ETA: 3s - loss: 0.3875 - accuracy: 0.9040
50/65 [======================>.......] - ETA: 3s - loss: 0.3872 - accuracy: 0.9042
51/65 [======================>.......] - ETA: 3s - loss: 0.3875 - accuracy: 0.9041
52/65 [=======================>......] - ETA: 3s - loss: 0.3872 - accuracy: 0.9042
53/65 [=======================>......] - ETA: 2s - loss: 0.3865 - accuracy: 0.9046
54/65 [=======================>......] - ETA: 2s - loss: 0.3860 - accuracy: 0.9047
55/65 [========================>.....] - ETA: 2s - loss: 0.3858 - accuracy: 0.9047
56/65 [========================>.....] - ETA: 2s - loss: 0.3858 - accuracy: 0.9046
57/65 [=========================>....] - ETA: 1s - loss: 0.3853 - accuracy: 0.9048
58/65 [=========================>....] - ETA: 1s - loss: 0.3857 - accuracy: 0.9046
59/65 [==========================>...] - ETA: 1s - loss: 0.3857 - accuracy: 0.9047
60/65 [==========================>...] - ETA: 1s - loss: 0.3858 - accuracy: 0.9047
61/65 [===========================>..] - ETA: 0s - loss: 0.3854 - accuracy: 0.9050
62/65 [===========================>..] - ETA: 0s - loss: 0.3859 - accuracy: 0.9048
63/65 [============================>.] - ETA: 0s - loss: 0.3857 - accuracy: 0.9050
64/65 [============================>.] - ETA: 0s - loss: 0.3859 - accuracy: 0.9049
65/65 [==============================] - ETA: 0s - loss: 0.3858 - accuracy: 0.9049
65/65 [==============================] - 17s 253ms/step - loss: 0.3858 - accuracy: 0.9049 - val_loss: 0.5514 - val_accuracy: 0.8500 - lr: 0.0030
Epoch 14/30
1/65 [..............................] - ETA: 29s - loss: 0.3871 - accuracy: 0.9102
2/65 [..............................] - ETA: 16s - loss: 0.4082 - accuracy: 0.9033
3/65 [>.............................] - ETA: 15s - loss: 0.3865 - accuracy: 0.9089
4/65 [>.............................] - ETA: 15s - loss: 0.3924 - accuracy: 0.9053
5/65 [=>............................] - ETA: 15s - loss: 0.3946 - accuracy: 0.9037
6/65 [=>............................] - ETA: 14s - loss: 0.3961 - accuracy: 0.9030
7/65 [==>...........................] - ETA: 14s - loss: 0.3962 - accuracy: 0.9030
8/65 [==>...........................] - ETA: 14s - loss: 0.3924 - accuracy: 0.9037
9/65 [===>..........................] - ETA: 13s - loss: 0.3913 - accuracy: 0.9042
10/65 [===>..........................] - ETA: 13s - loss: 0.3865 - accuracy: 0.9062
11/65 [====>.........................] - ETA: 13s - loss: 0.3841 - accuracy: 0.9072
12/65 [====>.........................] - ETA: 13s - loss: 0.3799 - accuracy: 0.9078
13/65 [=====>........................] - ETA: 12s - loss: 0.3854 - accuracy: 0.9066
14/65 [=====>........................] - ETA: 12s - loss: 0.3850 - accuracy: 0.9058
15/65 [=====>........................] - ETA: 12s - loss: 0.3853 - accuracy: 0.9046
16/65 [======>.......................] - ETA: 12s - loss: 0.3847 - accuracy: 0.9044
17/65 [======>.......................] - ETA: 11s - loss: 0.3853 - accuracy: 0.9044
18/65 [=======>......................] - ETA: 11s - loss: 0.3826 - accuracy: 0.9051
19/65 [=======>......................] - ETA: 11s - loss: 0.3829 - accuracy: 0.9051
20/65 [========>.....................] - ETA: 11s - loss: 0.3819 - accuracy: 0.9056
21/65 [========>.....................] - ETA: 10s - loss: 0.3802 - accuracy: 0.9061
22/65 [=========>....................] - ETA: 10s - loss: 0.3801 - accuracy: 0.9060
23/65 [=========>....................] - ETA: 10s - loss: 0.3799 - accuracy: 0.9060
24/65 [==========>...................] - ETA: 10s - loss: 0.3815 - accuracy: 0.9055
25/65 [==========>...................] - ETA: 9s - loss: 0.3820 - accuracy: 0.9054
26/65 [===========>..................] - ETA: 9s - loss: 0.3825 - accuracy: 0.9054
27/65 [===========>..................] - ETA: 9s - loss: 0.3836 - accuracy: 0.9048
28/65 [===========>..................] - ETA: 9s - loss: 0.3843 - accuracy: 0.9046
29/65 [============>.................] - ETA: 8s - loss: 0.3849 - accuracy: 0.9045
30/65 [============>.................] - ETA: 8s - loss: 0.3854 - accuracy: 0.9042
31/65 [=============>................] - ETA: 8s - loss: 0.3852 - accuracy: 0.9042
32/65 [=============>................] - ETA: 8s - loss: 0.3853 - accuracy: 0.9044
33/65 [==============>...............] - ETA: 7s - loss: 0.3846 - accuracy: 0.9046
34/65 [==============>...............] - ETA: 7s - loss: 0.3853 - accuracy: 0.9044
35/65 [===============>..............] - ETA: 7s - loss: 0.3866 - accuracy: 0.9043
36/65 [===============>..............] - ETA: 7s - loss: 0.3864 - accuracy: 0.9043
37/65 [================>.............] - ETA: 6s - loss: 0.3866 - accuracy: 0.9043
38/65 [================>.............] - ETA: 6s - loss: 0.3869 - accuracy: 0.9043
39/65 [=================>............] - ETA: 6s - loss: 0.3860 - accuracy: 0.9046
40/65 [=================>............] - ETA: 6s - loss: 0.3863 - accuracy: 0.9046
41/65 [=================>............] - ETA: 5s - loss: 0.3862 - accuracy: 0.9045
42/65 [==================>...........] - ETA: 5s - loss: 0.3851 - accuracy: 0.9049
43/65 [==================>...........] - ETA: 5s - loss: 0.3858 - accuracy: 0.9048
44/65 [===================>..........] - ETA: 5s - loss: 0.3863 - accuracy: 0.9047
45/65 [===================>..........] - ETA: 4s - loss: 0.3864 - accuracy: 0.9047
46/65 [====================>.........] - ETA: 4s - loss: 0.3867 - accuracy: 0.9045
47/65 [====================>.........] - ETA: 4s - loss: 0.3872 - accuracy: 0.9045
48/65 [=====================>........] - ETA: 4s - loss: 0.3862 - accuracy: 0.9047
49/65 [=====================>........] - ETA: 3s - loss: 0.3859 - accuracy: 0.9050
50/65 [======================>.......] - ETA: 3s - loss: 0.3852 - accuracy: 0.9052
51/65 [======================>.......] - ETA: 3s - loss: 0.3857 - accuracy: 0.9051
52/65 [=======================>......] - ETA: 3s - loss: 0.3857 - accuracy: 0.9051
53/65 [=======================>......] - ETA: 2s - loss: 0.3851 - accuracy: 0.9051
54/65 [=======================>......] - ETA: 2s - loss: 0.3856 - accuracy: 0.9049
55/65 [========================>.....] - ETA: 2s - loss: 0.3856 - accuracy: 0.9049
56/65 [========================>.....] - ETA: 2s - loss: 0.3848 - accuracy: 0.9051
57/65 [=========================>....] - ETA: 1s - loss: 0.3853 - accuracy: 0.9049
58/65 [=========================>....] - ETA: 1s - loss: 0.3856 - accuracy: 0.9050
59/65 [==========================>...] - ETA: 1s - loss: 0.3851 - accuracy: 0.9051
60/65 [==========================>...] - ETA: 1s - loss: 0.3843 - accuracy: 0.9053
61/65 [===========================>..] - ETA: 0s - loss: 0.3846 - accuracy: 0.9053
62/65 [===========================>..] - ETA: 0s - loss: 0.3840 - accuracy: 0.9055
63/65 [============================>.] - ETA: 0s - loss: 0.3835 - accuracy: 0.9058
64/65 [============================>.] - ETA: 0s - loss: 0.3833 - accuracy: 0.9057
65/65 [==============================] - ETA: 0s - loss: 0.3829 - accuracy: 0.9059
65/65 [==============================] - 17s 251ms/step - loss: 0.3829 - accuracy: 0.9059 - val_loss: 0.5222 - val_accuracy: 0.8587 - lr: 0.0030
Epoch 15/30
1/65 [..............................] - ETA: 29s - loss: 0.3510 - accuracy: 0.9043
2/65 [..............................] - ETA: 16s - loss: 0.3752 - accuracy: 0.9033
3/65 [>.............................] - ETA: 15s - loss: 0.3793 - accuracy: 0.9059
4/65 [>.............................] - ETA: 15s - loss: 0.3775 - accuracy: 0.9075
5/65 [=>............................] - ETA: 14s - loss: 0.3796 - accuracy: 0.9078
6/65 [=>............................] - ETA: 14s - loss: 0.3875 - accuracy: 0.9049
7/65 [==>...........................] - ETA: 14s - loss: 0.3824 - accuracy: 0.9069
8/65 [==>...........................] - ETA: 14s - loss: 0.3823 - accuracy: 0.9066
9/65 [===>..........................] - ETA: 13s - loss: 0.3849 - accuracy: 0.9059
10/65 [===>..........................] - ETA: 13s - loss: 0.3825 - accuracy: 0.9058
11/65 [====>.........................] - ETA: 13s - loss: 0.3819 - accuracy: 0.9056
12/65 [====>.........................] - ETA: 13s - loss: 0.3821 - accuracy: 0.9058
13/65 [=====>........................] - ETA: 12s - loss: 0.3818 - accuracy: 0.9056
14/65 [=====>........................] - ETA: 12s - loss: 0.3818 - accuracy: 0.9054
15/65 [=====>........................] - ETA: 12s - loss: 0.3840 - accuracy: 0.9045
16/65 [======>.......................] - ETA: 12s - loss: 0.3856 - accuracy: 0.9045
17/65 [======>.......................] - ETA: 11s - loss: 0.3845 - accuracy: 0.9051
18/65 [=======>......................] - ETA: 11s - loss: 0.3825 - accuracy: 0.9058
19/65 [=======>......................] - ETA: 11s - loss: 0.3803 - accuracy: 0.9065
20/65 [========>.....................] - ETA: 11s - loss: 0.3799 - accuracy: 0.9067
21/65 [========>.....................] - ETA: 10s - loss: 0.3793 - accuracy: 0.9070
22/65 [=========>....................] - ETA: 10s - loss: 0.3801 - accuracy: 0.9067
23/65 [=========>....................] - ETA: 10s - loss: 0.3784 - accuracy: 0.9074
24/65 [==========>...................] - ETA: 10s - loss: 0.3774 - accuracy: 0.9074
25/65 [==========>...................] - ETA: 9s - loss: 0.3778 - accuracy: 0.9070
26/65 [===========>..................] - ETA: 9s - loss: 0.3784 - accuracy: 0.9067
27/65 [===========>..................] - ETA: 9s - loss: 0.3784 - accuracy: 0.9065
28/65 [===========>..................] - ETA: 9s - loss: 0.3785 - accuracy: 0.9066
29/65 [============>.................] - ETA: 8s - loss: 0.3785 - accuracy: 0.9067
30/65 [============>.................] - ETA: 8s - loss: 0.3788 - accuracy: 0.9064
31/65 [=============>................] - ETA: 8s - loss: 0.3796 - accuracy: 0.9060
32/65 [=============>................] - ETA: 8s - loss: 0.3799 - accuracy: 0.9059
33/65 [==============>...............] - ETA: 7s - loss: 0.3808 - accuracy: 0.9059
34/65 [==============>...............] - ETA: 7s - loss: 0.3810 - accuracy: 0.9055
35/65 [===============>..............] - ETA: 7s - loss: 0.3808 - accuracy: 0.9057
36/65 [===============>..............] - ETA: 7s - loss: 0.3802 - accuracy: 0.9058
37/65 [================>.............] - ETA: 6s - loss: 0.3807 - accuracy: 0.9055
38/65 [================>.............] - ETA: 6s - loss: 0.3818 - accuracy: 0.9051
39/65 [=================>............] - ETA: 6s - loss: 0.3811 - accuracy: 0.9055
40/65 [=================>............] - ETA: 6s - loss: 0.3807 - accuracy: 0.9056
41/65 [=================>............] - ETA: 5s - loss: 0.3802 - accuracy: 0.9061
42/65 [==================>...........] - ETA: 5s - loss: 0.3796 - accuracy: 0.9064
43/65 [==================>...........] - ETA: 5s - loss: 0.3793 - accuracy: 0.9066
44/65 [===================>..........] - ETA: 5s - loss: 0.3780 - accuracy: 0.9069
45/65 [===================>..........] - ETA: 4s - loss: 0.3783 - accuracy: 0.9068
46/65 [====================>.........] - ETA: 4s - loss: 0.3782 - accuracy: 0.9070
47/65 [====================>.........] - ETA: 4s - loss: 0.3780 - accuracy: 0.9072
48/65 [=====================>........] - ETA: 4s - loss: 0.3777 - accuracy: 0.9075
49/65 [=====================>........] - ETA: 3s - loss: 0.3771 - accuracy: 0.9079
50/65 [======================>.......] - ETA: 3s - loss: 0.3773 - accuracy: 0.9077
51/65 [======================>.......] - ETA: 3s - loss: 0.3771 - accuracy: 0.9078
52/65 [=======================>......] - ETA: 3s - loss: 0.3772 - accuracy: 0.9078
53/65 [=======================>......] - ETA: 2s - loss: 0.3776 - accuracy: 0.9076
54/65 [=======================>......] - ETA: 2s - loss: 0.3777 - accuracy: 0.9074
55/65 [========================>.....] - ETA: 2s - loss: 0.3781 - accuracy: 0.9072
56/65 [========================>.....] - ETA: 2s - loss: 0.3777 - accuracy: 0.9073
57/65 [=========================>....] - ETA: 1s - loss: 0.3777 - accuracy: 0.9074
58/65 [=========================>....] - ETA: 1s - loss: 0.3776 - accuracy: 0.9075
59/65 [==========================>...] - ETA: 1s - loss: 0.3781 - accuracy: 0.9074
60/65 [==========================>...] - ETA: 1s - loss: 0.3779 - accuracy: 0.9074
61/65 [===========================>..] - ETA: 0s - loss: 0.3785 - accuracy: 0.9071
62/65 [===========================>..] - ETA: 0s - loss: 0.3781 - accuracy: 0.9072
63/65 [============================>.] - ETA: 0s - loss: 0.3777 - accuracy: 0.9073
64/65 [============================>.] - ETA: 0s - loss: 0.3767 - accuracy: 0.9074
65/65 [==============================] - ETA: 0s - loss: 0.3769 - accuracy: 0.9073
Epoch 15: ReduceLROnPlateau reducing learning rate to 0.001500000013038516.
65/65 [==============================] - 16s 250ms/step - loss: 0.3769 - accuracy: 0.9073 - val_loss: 0.5476 - val_accuracy: 0.8458 - lr: 0.0030
Epoch 16/30
1/65 [..............................] - ETA: 31s - loss: 0.3646 - accuracy: 0.9053
2/65 [..............................] - ETA: 15s - loss: 0.3970 - accuracy: 0.8940
3/65 [>.............................] - ETA: 15s - loss: 0.3887 - accuracy: 0.8984
4/65 [>.............................] - ETA: 15s - loss: 0.3835 - accuracy: 0.8992
5/65 [=>............................] - ETA: 15s - loss: 0.3849 - accuracy: 0.8998
6/65 [=>............................] - ETA: 14s - loss: 0.3827 - accuracy: 0.9017
7/65 [==>...........................] - ETA: 14s - loss: 0.3848 - accuracy: 0.9012
8/65 [==>...........................] - ETA: 14s - loss: 0.3862 - accuracy: 0.9015
9/65 [===>..........................] - ETA: 13s - loss: 0.3802 - accuracy: 0.9034
10/65 [===>..........................] - ETA: 13s - loss: 0.3763 - accuracy: 0.9060
11/65 [====>.........................] - ETA: 13s - loss: 0.3714 - accuracy: 0.9072
12/65 [====>.........................] - ETA: 13s - loss: 0.3713 - accuracy: 0.9076
13/65 [=====>........................] - ETA: 12s - loss: 0.3739 - accuracy: 0.9073
14/65 [=====>........................] - ETA: 12s - loss: 0.3736 - accuracy: 0.9079
15/65 [=====>........................] - ETA: 12s - loss: 0.3708 - accuracy: 0.9089
16/65 [======>.......................] - ETA: 12s - loss: 0.3718 - accuracy: 0.9084
17/65 [======>.......................] - ETA: 11s - loss: 0.3700 - accuracy: 0.9089
18/65 [=======>......................] - ETA: 11s - loss: 0.3705 - accuracy: 0.9086
19/65 [=======>......................] - ETA: 11s - loss: 0.3703 - accuracy: 0.9089
20/65 [========>.....................] - ETA: 11s - loss: 0.3704 - accuracy: 0.9094
21/65 [========>.....................] - ETA: 10s - loss: 0.3700 - accuracy: 0.9093
22/65 [=========>....................] - ETA: 10s - loss: 0.3695 - accuracy: 0.9094
23/65 [=========>....................] - ETA: 10s - loss: 0.3692 - accuracy: 0.9095
24/65 [==========>...................] - ETA: 10s - loss: 0.3680 - accuracy: 0.9100
25/65 [==========>...................] - ETA: 9s - loss: 0.3698 - accuracy: 0.9095
26/65 [===========>..................] - ETA: 9s - loss: 0.3684 - accuracy: 0.9098
27/65 [===========>..................] - ETA: 9s - loss: 0.3675 - accuracy: 0.9099
28/65 [===========>..................] - ETA: 9s - loss: 0.3670 - accuracy: 0.9098
29/65 [============>.................] - ETA: 8s - loss: 0.3688 - accuracy: 0.9092
30/65 [============>.................] - ETA: 8s - loss: 0.3678 - accuracy: 0.9094
31/65 [=============>................] - ETA: 8s - loss: 0.3676 - accuracy: 0.9095
32/65 [=============>................] - ETA: 8s - loss: 0.3685 - accuracy: 0.9094
33/65 [==============>...............] - ETA: 7s - loss: 0.3673 - accuracy: 0.9097
34/65 [==============>...............] - ETA: 7s - loss: 0.3669 - accuracy: 0.9098
35/65 [===============>..............] - ETA: 7s - loss: 0.3668 - accuracy: 0.9097
36/65 [===============>..............] - ETA: 7s - loss: 0.3670 - accuracy: 0.9096
37/65 [================>.............] - ETA: 6s - loss: 0.3663 - accuracy: 0.9098
38/65 [================>.............] - ETA: 6s - loss: 0.3666 - accuracy: 0.9097
39/65 [=================>............] - ETA: 6s - loss: 0.3674 - accuracy: 0.9094
40/65 [=================>............] - ETA: 6s - loss: 0.3663 - accuracy: 0.9098
41/65 [=================>............] - ETA: 5s - loss: 0.3654 - accuracy: 0.9098
42/65 [==================>...........] - ETA: 5s - loss: 0.3643 - accuracy: 0.9103
43/65 [==================>...........] - ETA: 5s - loss: 0.3642 - accuracy: 0.9103
44/65 [===================>..........] - ETA: 5s - loss: 0.3640 - accuracy: 0.9106
45/65 [===================>..........] - ETA: 4s - loss: 0.3647 - accuracy: 0.9107
46/65 [====================>.........] - ETA: 4s - loss: 0.3649 - accuracy: 0.9105
47/65 [====================>.........] - ETA: 4s - loss: 0.3645 - accuracy: 0.9107
48/65 [=====================>........] - ETA: 4s - loss: 0.3649 - accuracy: 0.9108
49/65 [=====================>........] - ETA: 3s - loss: 0.3644 - accuracy: 0.9111
50/65 [======================>.......] - ETA: 3s - loss: 0.3641 - accuracy: 0.9111
51/65 [======================>.......] - ETA: 3s - loss: 0.3639 - accuracy: 0.9113
52/65 [=======================>......] - ETA: 3s - loss: 0.3631 - accuracy: 0.9116
53/65 [=======================>......] - ETA: 2s - loss: 0.3632 - accuracy: 0.9113
54/65 [=======================>......] - ETA: 2s - loss: 0.3624 - accuracy: 0.9116
55/65 [========================>.....] - ETA: 2s - loss: 0.3619 - accuracy: 0.9117
56/65 [========================>.....] - ETA: 2s - loss: 0.3613 - accuracy: 0.9117
57/65 [=========================>....] - ETA: 1s - loss: 0.3613 - accuracy: 0.9116
58/65 [=========================>....] - ETA: 1s - loss: 0.3609 - accuracy: 0.9118
59/65 [==========================>...] - ETA: 1s - loss: 0.3602 - accuracy: 0.9121
60/65 [==========================>...] - ETA: 1s - loss: 0.3601 - accuracy: 0.9120
61/65 [===========================>..] - ETA: 0s - loss: 0.3598 - accuracy: 0.9121
62/65 [===========================>..] - ETA: 0s - loss: 0.3596 - accuracy: 0.9124
63/65 [============================>.] - ETA: 0s - loss: 0.3596 - accuracy: 0.9123
64/65 [============================>.] - ETA: 0s - loss: 0.3597 - accuracy: 0.9122
65/65 [==============================] - ETA: 0s - loss: 0.3596 - accuracy: 0.9123
65/65 [==============================] - 17s 252ms/step - loss: 0.3596 - accuracy: 0.9123 - val_loss: 0.4412 - val_accuracy: 0.8871 - lr: 0.0015
Epoch 17/30
1/65 [..............................] - ETA: 29s - loss: 0.3671 - accuracy: 0.9150
2/65 [..............................] - ETA: 15s - loss: 0.3505 - accuracy: 0.9214
3/65 [>.............................] - ETA: 15s - loss: 0.3519 - accuracy: 0.9202
4/65 [>.............................] - ETA: 15s - loss: 0.3654 - accuracy: 0.9148
5/65 [=>............................] - ETA: 14s - loss: 0.3670 - accuracy: 0.9135
6/65 [=>............................] - ETA: 14s - loss: 0.3691 - accuracy: 0.9141
7/65 [==>...........................] - ETA: 14s - loss: 0.3713 - accuracy: 0.9139
8/65 [==>...........................] - ETA: 14s - loss: 0.3659 - accuracy: 0.9138
9/65 [===>..........................] - ETA: 13s - loss: 0.3663 - accuracy: 0.9134
10/65 [===>..........................] - ETA: 13s - loss: 0.3641 - accuracy: 0.9137
11/65 [====>.........................] - ETA: 13s - loss: 0.3646 - accuracy: 0.9133
12/65 [====>.........................] - ETA: 13s - loss: 0.3622 - accuracy: 0.9139
13/65 [=====>........................] - ETA: 12s - loss: 0.3603 - accuracy: 0.9143
14/65 [=====>........................] - ETA: 12s - loss: 0.3580 - accuracy: 0.9146
15/65 [=====>........................] - ETA: 12s - loss: 0.3592 - accuracy: 0.9140
16/65 [======>.......................] - ETA: 12s - loss: 0.3565 - accuracy: 0.9147
17/65 [======>.......................] - ETA: 11s - loss: 0.3584 - accuracy: 0.9145
18/65 [=======>......................] - ETA: 11s - loss: 0.3577 - accuracy: 0.9144
19/65 [=======>......................] - ETA: 11s - loss: 0.3550 - accuracy: 0.9151
20/65 [========>.....................] - ETA: 11s - loss: 0.3531 - accuracy: 0.9155
21/65 [========>.....................] - ETA: 10s - loss: 0.3537 - accuracy: 0.9152
22/65 [=========>....................] - ETA: 10s - loss: 0.3549 - accuracy: 0.9151
23/65 [=========>....................] - ETA: 10s - loss: 0.3528 - accuracy: 0.9155
24/65 [==========>...................] - ETA: 10s - loss: 0.3525 - accuracy: 0.9154
25/65 [==========>...................] - ETA: 9s - loss: 0.3538 - accuracy: 0.9150
26/65 [===========>..................] - ETA: 9s - loss: 0.3536 - accuracy: 0.9153
27/65 [===========>..................] - ETA: 9s - loss: 0.3544 - accuracy: 0.9150
28/65 [===========>..................] - ETA: 9s - loss: 0.3548 - accuracy: 0.9151
29/65 [============>.................] - ETA: 8s - loss: 0.3543 - accuracy: 0.9148
30/65 [============>.................] - ETA: 8s - loss: 0.3543 - accuracy: 0.9145
31/65 [=============>................] - ETA: 8s - loss: 0.3530 - accuracy: 0.9148
32/65 [=============>................] - ETA: 8s - loss: 0.3540 - accuracy: 0.9141
33/65 [==============>...............] - ETA: 7s - loss: 0.3552 - accuracy: 0.9136
34/65 [==============>...............] - ETA: 7s - loss: 0.3546 - accuracy: 0.9138
35/65 [===============>..............] - ETA: 7s - loss: 0.3543 - accuracy: 0.9139
36/65 [===============>..............] - ETA: 7s - loss: 0.3555 - accuracy: 0.9136
37/65 [================>.............] - ETA: 6s - loss: 0.3564 - accuracy: 0.9136
38/65 [================>.............] - ETA: 6s - loss: 0.3566 - accuracy: 0.9139
39/65 [=================>............] - ETA: 6s - loss: 0.3560 - accuracy: 0.9140
40/65 [=================>............] - ETA: 6s - loss: 0.3558 - accuracy: 0.9140
41/65 [=================>............] - ETA: 5s - loss: 0.3560 - accuracy: 0.9138
42/65 [==================>...........] - ETA: 5s - loss: 0.3558 - accuracy: 0.9138
43/65 [==================>...........] - ETA: 5s - loss: 0.3552 - accuracy: 0.9139
44/65 [===================>..........] - ETA: 5s - loss: 0.3553 - accuracy: 0.9139
45/65 [===================>..........] - ETA: 4s - loss: 0.3564 - accuracy: 0.9137
46/65 [====================>.........] - ETA: 4s - loss: 0.3569 - accuracy: 0.9136
47/65 [====================>.........] - ETA: 4s - loss: 0.3579 - accuracy: 0.9132
48/65 [=====================>........] - ETA: 4s - loss: 0.3586 - accuracy: 0.9129
49/65 [=====================>........] - ETA: 3s - loss: 0.3573 - accuracy: 0.9133
50/65 [======================>.......] - ETA: 3s - loss: 0.3564 - accuracy: 0.9133
51/65 [======================>.......] - ETA: 3s - loss: 0.3565 - accuracy: 0.9134
52/65 [=======================>......] - ETA: 3s - loss: 0.3559 - accuracy: 0.9135
53/65 [=======================>......] - ETA: 2s - loss: 0.3552 - accuracy: 0.9138
54/65 [=======================>......] - ETA: 2s - loss: 0.3540 - accuracy: 0.9144
55/65 [========================>.....] - ETA: 2s - loss: 0.3539 - accuracy: 0.9144
56/65 [========================>.....] - ETA: 2s - loss: 0.3535 - accuracy: 0.9147
57/65 [=========================>....] - ETA: 1s - loss: 0.3532 - accuracy: 0.9148
58/65 [=========================>....] - ETA: 1s - loss: 0.3528 - accuracy: 0.9148
59/65 [==========================>...] - ETA: 1s - loss: 0.3534 - accuracy: 0.9147
60/65 [==========================>...] - ETA: 1s - loss: 0.3530 - accuracy: 0.9148
61/65 [===========================>..] - ETA: 0s - loss: 0.3527 - accuracy: 0.9147
62/65 [===========================>..] - ETA: 0s - loss: 0.3532 - accuracy: 0.9144
63/65 [============================>.] - ETA: 0s - loss: 0.3532 - accuracy: 0.9144
64/65 [============================>.] - ETA: 0s - loss: 0.3531 - accuracy: 0.9142
65/65 [==============================] - ETA: 0s - loss: 0.3530 - accuracy: 0.9142
65/65 [==============================] - 17s 251ms/step - loss: 0.3530 - accuracy: 0.9142 - val_loss: 0.4418 - val_accuracy: 0.8875 - lr: 0.0015
Epoch 18/30
1/65 [..............................] - ETA: 30s - loss: 0.3603 - accuracy: 0.9102
2/65 [..............................] - ETA: 15s - loss: 0.3656 - accuracy: 0.9146
3/65 [>.............................] - ETA: 15s - loss: 0.3714 - accuracy: 0.9105
4/65 [>.............................] - ETA: 15s - loss: 0.3612 - accuracy: 0.9116
5/65 [=>............................] - ETA: 14s - loss: 0.3670 - accuracy: 0.9086
6/65 [=>............................] - ETA: 14s - loss: 0.3615 - accuracy: 0.9124
7/65 [==>...........................] - ETA: 14s - loss: 0.3574 - accuracy: 0.9129
8/65 [==>...........................] - ETA: 14s - loss: 0.3588 - accuracy: 0.9126
9/65 [===>..........................] - ETA: 13s - loss: 0.3581 - accuracy: 0.9130
10/65 [===>..........................] - ETA: 13s - loss: 0.3581 - accuracy: 0.9128
11/65 [====>.........................] - ETA: 13s - loss: 0.3584 - accuracy: 0.9118
12/65 [====>.........................] - ETA: 13s - loss: 0.3548 - accuracy: 0.9127
13/65 [=====>........................] - ETA: 12s - loss: 0.3556 - accuracy: 0.9120
14/65 [=====>........................] - ETA: 12s - loss: 0.3565 - accuracy: 0.9125
15/65 [=====>........................] - ETA: 12s - loss: 0.3566 - accuracy: 0.9127
16/65 [======>.......................] - ETA: 12s - loss: 0.3575 - accuracy: 0.9127
17/65 [======>.......................] - ETA: 11s - loss: 0.3561 - accuracy: 0.9127
18/65 [=======>......................] - ETA: 11s - loss: 0.3538 - accuracy: 0.9133
19/65 [=======>......................] - ETA: 11s - loss: 0.3540 - accuracy: 0.9133
20/65 [========>.....................] - ETA: 11s - loss: 0.3535 - accuracy: 0.9137
21/65 [========>.....................] - ETA: 10s - loss: 0.3517 - accuracy: 0.9142
22/65 [=========>....................] - ETA: 10s - loss: 0.3518 - accuracy: 0.9143
23/65 [=========>....................] - ETA: 10s - loss: 0.3497 - accuracy: 0.9146
24/65 [==========>...................] - ETA: 10s - loss: 0.3510 - accuracy: 0.9145
25/65 [==========>...................] - ETA: 9s - loss: 0.3504 - accuracy: 0.9145
26/65 [===========>..................] - ETA: 9s - loss: 0.3511 - accuracy: 0.9139
27/65 [===========>..................] - ETA: 9s - loss: 0.3513 - accuracy: 0.9138
28/65 [===========>..................] - ETA: 9s - loss: 0.3502 - accuracy: 0.9138
29/65 [============>.................] - ETA: 8s - loss: 0.3494 - accuracy: 0.9142
30/65 [============>.................] - ETA: 8s - loss: 0.3493 - accuracy: 0.9139
31/65 [=============>................] - ETA: 8s - loss: 0.3495 - accuracy: 0.9143
32/65 [=============>................] - ETA: 8s - loss: 0.3498 - accuracy: 0.9142
33/65 [==============>...............] - ETA: 7s - loss: 0.3507 - accuracy: 0.9138
34/65 [==============>...............] - ETA: 7s - loss: 0.3508 - accuracy: 0.9138
35/65 [===============>..............] - ETA: 7s - loss: 0.3513 - accuracy: 0.9134
36/65 [===============>..............] - ETA: 7s - loss: 0.3522 - accuracy: 0.9132
37/65 [================>.............] - ETA: 6s - loss: 0.3535 - accuracy: 0.9132
38/65 [================>.............] - ETA: 6s - loss: 0.3529 - accuracy: 0.9133
39/65 [=================>............] - ETA: 6s - loss: 0.3540 - accuracy: 0.9128
40/65 [=================>............] - ETA: 6s - loss: 0.3540 - accuracy: 0.9129
41/65 [=================>............] - ETA: 5s - loss: 0.3530 - accuracy: 0.9134
42/65 [==================>...........] - ETA: 5s - loss: 0.3540 - accuracy: 0.9132
43/65 [==================>...........] - ETA: 5s - loss: 0.3540 - accuracy: 0.9134
44/65 [===================>..........] - ETA: 5s - loss: 0.3539 - accuracy: 0.9134
45/65 [===================>..........] - ETA: 4s - loss: 0.3531 - accuracy: 0.9138
46/65 [====================>.........] - ETA: 4s - loss: 0.3533 - accuracy: 0.9137
47/65 [====================>.........] - ETA: 4s - loss: 0.3525 - accuracy: 0.9141
48/65 [=====================>........] - ETA: 4s - loss: 0.3535 - accuracy: 0.9136
49/65 [=====================>........] - ETA: 3s - loss: 0.3522 - accuracy: 0.9140
50/65 [======================>.......] - ETA: 3s - loss: 0.3522 - accuracy: 0.9140
51/65 [======================>.......] - ETA: 3s - loss: 0.3516 - accuracy: 0.9143
52/65 [=======================>......] - ETA: 3s - loss: 0.3509 - accuracy: 0.9144
53/65 [=======================>......] - ETA: 2s - loss: 0.3508 - accuracy: 0.9145
54/65 [=======================>......] - ETA: 2s - loss: 0.3509 - accuracy: 0.9146
55/65 [========================>.....] - ETA: 2s - loss: 0.3509 - accuracy: 0.9147
56/65 [========================>.....] - ETA: 2s - loss: 0.3504 - accuracy: 0.9149
57/65 [=========================>....] - ETA: 1s - loss: 0.3499 - accuracy: 0.9148
58/65 [=========================>....] - ETA: 1s - loss: 0.3501 - accuracy: 0.9146
59/65 [==========================>...] - ETA: 1s - loss: 0.3494 - accuracy: 0.9148
60/65 [==========================>...] - ETA: 1s - loss: 0.3496 - accuracy: 0.9148
61/65 [===========================>..] - ETA: 0s - loss: 0.3497 - accuracy: 0.9148
62/65 [===========================>..] - ETA: 0s - loss: 0.3493 - accuracy: 0.9148
63/65 [============================>.] - ETA: 0s - loss: 0.3487 - accuracy: 0.9149
64/65 [============================>.] - ETA: 0s - loss: 0.3488 - accuracy: 0.9147
65/65 [==============================] - ETA: 0s - loss: 0.3490 - accuracy: 0.9147
65/65 [==============================] - 16s 250ms/step - loss: 0.3490 - accuracy: 0.9147 - val_loss: 0.4573 - val_accuracy: 0.8810 - lr: 0.0015
Epoch 19/30
1/65 [..............................] - ETA: 31s - loss: 0.3303 - accuracy: 0.9199
2/65 [..............................] - ETA: 15s - loss: 0.3676 - accuracy: 0.9126
3/65 [>.............................] - ETA: 15s - loss: 0.3568 - accuracy: 0.9144
4/65 [>.............................] - ETA: 15s - loss: 0.3539 - accuracy: 0.9160
5/65 [=>............................] - ETA: 14s - loss: 0.3570 - accuracy: 0.9137
6/65 [=>............................] - ETA: 14s - loss: 0.3569 - accuracy: 0.9136
7/65 [==>...........................] - ETA: 14s - loss: 0.3586 - accuracy: 0.9141
8/65 [==>...........................] - ETA: 14s - loss: 0.3542 - accuracy: 0.9149
9/65 [===>..........................] - ETA: 13s - loss: 0.3586 - accuracy: 0.9137
10/65 [===>..........................] - ETA: 13s - loss: 0.3620 - accuracy: 0.9126
11/65 [====>.........................] - ETA: 13s - loss: 0.3590 - accuracy: 0.9132
12/65 [====>.........................] - ETA: 13s - loss: 0.3569 - accuracy: 0.9139
13/65 [=====>........................] - ETA: 12s - loss: 0.3542 - accuracy: 0.9143
14/65 [=====>........................] - ETA: 12s - loss: 0.3527 - accuracy: 0.9146
15/65 [=====>........................] - ETA: 12s - loss: 0.3493 - accuracy: 0.9163
16/65 [======>.......................] - ETA: 12s - loss: 0.3497 - accuracy: 0.9163
17/65 [======>.......................] - ETA: 11s - loss: 0.3485 - accuracy: 0.9163
18/65 [=======>......................] - ETA: 11s - loss: 0.3503 - accuracy: 0.9162
19/65 [=======>......................] - ETA: 11s - loss: 0.3492 - accuracy: 0.9165
20/65 [========>.....................] - ETA: 11s - loss: 0.3488 - accuracy: 0.9162
21/65 [========>.....................] - ETA: 10s - loss: 0.3485 - accuracy: 0.9164
22/65 [=========>....................] - ETA: 10s - loss: 0.3477 - accuracy: 0.9168
23/65 [=========>....................] - ETA: 10s - loss: 0.3472 - accuracy: 0.9170
24/65 [==========>...................] - ETA: 10s - loss: 0.3482 - accuracy: 0.9168
25/65 [==========>...................] - ETA: 9s - loss: 0.3480 - accuracy: 0.9169
26/65 [===========>..................] - ETA: 9s - loss: 0.3481 - accuracy: 0.9165
27/65 [===========>..................] - ETA: 9s - loss: 0.3490 - accuracy: 0.9164
28/65 [===========>..................] - ETA: 9s - loss: 0.3501 - accuracy: 0.9162
29/65 [============>.................] - ETA: 8s - loss: 0.3508 - accuracy: 0.9157
30/65 [============>.................] - ETA: 8s - loss: 0.3514 - accuracy: 0.9156
31/65 [=============>................] - ETA: 8s - loss: 0.3512 - accuracy: 0.9154
32/65 [=============>................] - ETA: 8s - loss: 0.3508 - accuracy: 0.9154
33/65 [==============>...............] - ETA: 7s - loss: 0.3496 - accuracy: 0.9158
34/65 [==============>...............] - ETA: 7s - loss: 0.3500 - accuracy: 0.9153
35/65 [===============>..............] - ETA: 7s - loss: 0.3520 - accuracy: 0.9148
36/65 [===============>..............] - ETA: 7s - loss: 0.3526 - accuracy: 0.9147
37/65 [================>.............] - ETA: 6s - loss: 0.3526 - accuracy: 0.9148
38/65 [================>.............] - ETA: 6s - loss: 0.3521 - accuracy: 0.9148
39/65 [=================>............] - ETA: 6s - loss: 0.3509 - accuracy: 0.9150
40/65 [=================>............] - ETA: 6s - loss: 0.3504 - accuracy: 0.9151
41/65 [=================>............] - ETA: 5s - loss: 0.3510 - accuracy: 0.9149
42/65 [==================>...........] - ETA: 5s - loss: 0.3512 - accuracy: 0.9149
43/65 [==================>...........] - ETA: 5s - loss: 0.3514 - accuracy: 0.9149
44/65 [===================>..........] - ETA: 5s - loss: 0.3509 - accuracy: 0.9150
45/65 [===================>..........] - ETA: 4s - loss: 0.3514 - accuracy: 0.9149
46/65 [====================>.........] - ETA: 4s - loss: 0.3508 - accuracy: 0.9152
47/65 [====================>.........] - ETA: 4s - loss: 0.3512 - accuracy: 0.9152
48/65 [=====================>........] - ETA: 4s - loss: 0.3506 - accuracy: 0.9152
49/65 [=====================>........] - ETA: 3s - loss: 0.3504 - accuracy: 0.9152
50/65 [======================>.......] - ETA: 3s - loss: 0.3503 - accuracy: 0.9152
51/65 [======================>.......] - ETA: 3s - loss: 0.3503 - accuracy: 0.9151
52/65 [=======================>......] - ETA: 3s - loss: 0.3512 - accuracy: 0.9148
53/65 [=======================>......] - ETA: 2s - loss: 0.3510 - accuracy: 0.9149
54/65 [=======================>......] - ETA: 2s - loss: 0.3510 - accuracy: 0.9149
55/65 [========================>.....] - ETA: 2s - loss: 0.3500 - accuracy: 0.9151
56/65 [========================>.....] - ETA: 2s - loss: 0.3497 - accuracy: 0.9151
57/65 [=========================>....] - ETA: 1s - loss: 0.3494 - accuracy: 0.9153
58/65 [=========================>....] - ETA: 1s - loss: 0.3497 - accuracy: 0.9151
59/65 [==========================>...] - ETA: 1s - loss: 0.3489 - accuracy: 0.9156
60/65 [==========================>...] - ETA: 1s - loss: 0.3485 - accuracy: 0.9155
61/65 [===========================>..] - ETA: 0s - loss: 0.3477 - accuracy: 0.9156
62/65 [===========================>..] - ETA: 0s - loss: 0.3474 - accuracy: 0.9156
63/65 [============================>.] - ETA: 0s - loss: 0.3474 - accuracy: 0.9157
64/65 [============================>.] - ETA: 0s - loss: 0.3476 - accuracy: 0.9158
65/65 [==============================] - ETA: 0s - loss: 0.3475 - accuracy: 0.9158
65/65 [==============================] - 16s 250ms/step - loss: 0.3475 - accuracy: 0.9158 - val_loss: 0.4368 - val_accuracy: 0.8889 - lr: 0.0015
Epoch 20/30
1/65 [..............................] - ETA: 30s - loss: 0.3756 - accuracy: 0.9033
2/65 [..............................] - ETA: 15s - loss: 0.3499 - accuracy: 0.9175
3/65 [>.............................] - ETA: 15s - loss: 0.3594 - accuracy: 0.9137
4/65 [>.............................] - ETA: 15s - loss: 0.3557 - accuracy: 0.9150
5/65 [=>............................] - ETA: 15s - loss: 0.3548 - accuracy: 0.9156
6/65 [=>............................] - ETA: 15s - loss: 0.3556 - accuracy: 0.9141
7/65 [==>...........................] - ETA: 14s - loss: 0.3524 - accuracy: 0.9141
8/65 [==>...........................] - ETA: 14s - loss: 0.3496 - accuracy: 0.9146
9/65 [===>..........................] - ETA: 14s - loss: 0.3517 - accuracy: 0.9141
10/65 [===>..........................] - ETA: 13s - loss: 0.3515 - accuracy: 0.9141
11/65 [====>.........................] - ETA: 13s - loss: 0.3532 - accuracy: 0.9129
12/65 [====>.........................] - ETA: 13s - loss: 0.3504 - accuracy: 0.9137
13/65 [=====>........................] - ETA: 13s - loss: 0.3484 - accuracy: 0.9137
14/65 [=====>........................] - ETA: 12s - loss: 0.3475 - accuracy: 0.9146
15/65 [=====>........................] - ETA: 12s - loss: 0.3450 - accuracy: 0.9154
16/65 [======>.......................] - ETA: 12s - loss: 0.3440 - accuracy: 0.9159
17/65 [======>.......................] - ETA: 11s - loss: 0.3454 - accuracy: 0.9153
18/65 [=======>......................] - ETA: 11s - loss: 0.3436 - accuracy: 0.9158
19/65 [=======>......................] - ETA: 11s - loss: 0.3455 - accuracy: 0.9155
20/65 [========>.....................] - ETA: 11s - loss: 0.3440 - accuracy: 0.9156
21/65 [========>.....................] - ETA: 10s - loss: 0.3461 - accuracy: 0.9151
22/65 [=========>....................] - ETA: 10s - loss: 0.3458 - accuracy: 0.9155
23/65 [=========>....................] - ETA: 10s - loss: 0.3446 - accuracy: 0.9157
24/65 [==========>...................] - ETA: 10s - loss: 0.3448 - accuracy: 0.9157
25/65 [==========>...................] - ETA: 9s - loss: 0.3437 - accuracy: 0.9159
26/65 [===========>..................] - ETA: 9s - loss: 0.3431 - accuracy: 0.9164
27/65 [===========>..................] - ETA: 9s - loss: 0.3435 - accuracy: 0.9163
28/65 [===========>..................] - ETA: 9s - loss: 0.3435 - accuracy: 0.9163
29/65 [============>.................] - ETA: 8s - loss: 0.3422 - accuracy: 0.9166
30/65 [============>.................] - ETA: 8s - loss: 0.3423 - accuracy: 0.9163
31/65 [=============>................] - ETA: 8s - loss: 0.3445 - accuracy: 0.9155
32/65 [=============>................] - ETA: 8s - loss: 0.3449 - accuracy: 0.9155
33/65 [==============>...............] - ETA: 7s - loss: 0.3451 - accuracy: 0.9156
34/65 [==============>...............] - ETA: 7s - loss: 0.3463 - accuracy: 0.9154
35/65 [===============>..............] - ETA: 7s - loss: 0.3458 - accuracy: 0.9157
36/65 [===============>..............] - ETA: 7s - loss: 0.3459 - accuracy: 0.9158
37/65 [================>.............] - ETA: 6s - loss: 0.3457 - accuracy: 0.9158
38/65 [================>.............] - ETA: 6s - loss: 0.3462 - accuracy: 0.9156
39/65 [=================>............] - ETA: 6s - loss: 0.3476 - accuracy: 0.9154
40/65 [=================>............] - ETA: 6s - loss: 0.3488 - accuracy: 0.9149
41/65 [=================>............] - ETA: 5s - loss: 0.3486 - accuracy: 0.9149
42/65 [==================>...........] - ETA: 5s - loss: 0.3477 - accuracy: 0.9154
43/65 [==================>...........] - ETA: 5s - loss: 0.3461 - accuracy: 0.9159
44/65 [===================>..........] - ETA: 5s - loss: 0.3471 - accuracy: 0.9156
45/65 [===================>..........] - ETA: 4s - loss: 0.3475 - accuracy: 0.9153
46/65 [====================>.........] - ETA: 4s - loss: 0.3471 - accuracy: 0.9154
47/65 [====================>.........] - ETA: 4s - loss: 0.3469 - accuracy: 0.9155
48/65 [=====================>........] - ETA: 4s - loss: 0.3475 - accuracy: 0.9155
49/65 [=====================>........] - ETA: 3s - loss: 0.3472 - accuracy: 0.9158
50/65 [======================>.......] - ETA: 3s - loss: 0.3473 - accuracy: 0.9155
51/65 [======================>.......] - ETA: 3s - loss: 0.3468 - accuracy: 0.9156
52/65 [=======================>......] - ETA: 3s - loss: 0.3461 - accuracy: 0.9159
53/65 [=======================>......] - ETA: 2s - loss: 0.3454 - accuracy: 0.9162
54/65 [=======================>......] - ETA: 2s - loss: 0.3458 - accuracy: 0.9160
55/65 [========================>.....] - ETA: 2s - loss: 0.3458 - accuracy: 0.9159
56/65 [========================>.....] - ETA: 2s - loss: 0.3456 - accuracy: 0.9160
57/65 [=========================>....] - ETA: 1s - loss: 0.3454 - accuracy: 0.9160
58/65 [=========================>....] - ETA: 1s - loss: 0.3460 - accuracy: 0.9160
59/65 [==========================>...] - ETA: 1s - loss: 0.3452 - accuracy: 0.9161
60/65 [==========================>...] - ETA: 1s - loss: 0.3445 - accuracy: 0.9163
61/65 [===========================>..] - ETA: 0s - loss: 0.3443 - accuracy: 0.9164
62/65 [===========================>..] - ETA: 0s - loss: 0.3446 - accuracy: 0.9164
63/65 [============================>.] - ETA: 0s - loss: 0.3440 - accuracy: 0.9165
64/65 [============================>.] - ETA: 0s - loss: 0.3440 - accuracy: 0.9165
65/65 [==============================] - ETA: 0s - loss: 0.3439 - accuracy: 0.9165
65/65 [==============================] - 17s 252ms/step - loss: 0.3439 - accuracy: 0.9165 - val_loss: 0.4409 - val_accuracy: 0.8882 - lr: 0.0015
Epoch 21/30
1/65 [..............................] - ETA: 29s - loss: 0.3223 - accuracy: 0.9229
2/65 [..............................] - ETA: 16s - loss: 0.3541 - accuracy: 0.9136
3/65 [>.............................] - ETA: 15s - loss: 0.3672 - accuracy: 0.9089
4/65 [>.............................] - ETA: 15s - loss: 0.3655 - accuracy: 0.9099
5/65 [=>............................] - ETA: 15s - loss: 0.3546 - accuracy: 0.9123
6/65 [=>............................] - ETA: 14s - loss: 0.3548 - accuracy: 0.9136
7/65 [==>...........................] - ETA: 14s - loss: 0.3499 - accuracy: 0.9148
8/65 [==>...........................] - ETA: 14s - loss: 0.3478 - accuracy: 0.9155
9/65 [===>..........................] - ETA: 13s - loss: 0.3485 - accuracy: 0.9148
10/65 [===>..........................] - ETA: 13s - loss: 0.3465 - accuracy: 0.9151
11/65 [====>.........................] - ETA: 13s - loss: 0.3473 - accuracy: 0.9148
12/65 [====>.........................] - ETA: 13s - loss: 0.3460 - accuracy: 0.9147
13/65 [=====>........................] - ETA: 12s - loss: 0.3449 - accuracy: 0.9150
14/65 [=====>........................] - ETA: 12s - loss: 0.3433 - accuracy: 0.9154
15/65 [=====>........................] - ETA: 12s - loss: 0.3410 - accuracy: 0.9163
16/65 [======>.......................] - ETA: 12s - loss: 0.3437 - accuracy: 0.9155
17/65 [======>.......................] - ETA: 11s - loss: 0.3443 - accuracy: 0.9152
18/65 [=======>......................] - ETA: 11s - loss: 0.3427 - accuracy: 0.9155
19/65 [=======>......................] - ETA: 11s - loss: 0.3430 - accuracy: 0.9157
20/65 [========>.....................] - ETA: 11s - loss: 0.3429 - accuracy: 0.9158
21/65 [========>.....................] - ETA: 10s - loss: 0.3431 - accuracy: 0.9159
22/65 [=========>....................] - ETA: 10s - loss: 0.3424 - accuracy: 0.9160
23/65 [=========>....................] - ETA: 10s - loss: 0.3446 - accuracy: 0.9154
24/65 [==========>...................] - ETA: 10s - loss: 0.3426 - accuracy: 0.9161
25/65 [==========>...................] - ETA: 9s - loss: 0.3428 - accuracy: 0.9162
26/65 [===========>..................] - ETA: 9s - loss: 0.3422 - accuracy: 0.9165
27/65 [===========>..................] - ETA: 9s - loss: 0.3426 - accuracy: 0.9165
28/65 [===========>..................] - ETA: 9s - loss: 0.3418 - accuracy: 0.9163
29/65 [============>.................] - ETA: 8s - loss: 0.3435 - accuracy: 0.9155
30/65 [============>.................] - ETA: 8s - loss: 0.3431 - accuracy: 0.9153
31/65 [=============>................] - ETA: 8s - loss: 0.3429 - accuracy: 0.9152
32/65 [=============>................] - ETA: 8s - loss: 0.3435 - accuracy: 0.9152
33/65 [==============>...............] - ETA: 7s - loss: 0.3438 - accuracy: 0.9150
34/65 [==============>...............] - ETA: 7s - loss: 0.3445 - accuracy: 0.9149
35/65 [===============>..............] - ETA: 7s - loss: 0.3451 - accuracy: 0.9147
36/65 [===============>..............] - ETA: 7s - loss: 0.3444 - accuracy: 0.9148
37/65 [================>.............] - ETA: 6s - loss: 0.3443 - accuracy: 0.9150
38/65 [================>.............] - ETA: 6s - loss: 0.3435 - accuracy: 0.9149
39/65 [=================>............] - ETA: 6s - loss: 0.3428 - accuracy: 0.9152
40/65 [=================>............] - ETA: 6s - loss: 0.3431 - accuracy: 0.9153
41/65 [=================>............] - ETA: 5s - loss: 0.3433 - accuracy: 0.9154
42/65 [==================>...........] - ETA: 5s - loss: 0.3435 - accuracy: 0.9155
43/65 [==================>...........] - ETA: 5s - loss: 0.3435 - accuracy: 0.9154
44/65 [===================>..........] - ETA: 5s - loss: 0.3444 - accuracy: 0.9151
45/65 [===================>..........] - ETA: 4s - loss: 0.3445 - accuracy: 0.9153
46/65 [====================>.........] - ETA: 4s - loss: 0.3447 - accuracy: 0.9154
47/65 [====================>.........] - ETA: 4s - loss: 0.3445 - accuracy: 0.9153
48/65 [=====================>........] - ETA: 4s - loss: 0.3453 - accuracy: 0.9151
49/65 [=====================>........] - ETA: 3s - loss: 0.3438 - accuracy: 0.9157
50/65 [======================>.......] - ETA: 3s - loss: 0.3436 - accuracy: 0.9157
51/65 [======================>.......] - ETA: 3s - loss: 0.3438 - accuracy: 0.9157
52/65 [=======================>......] - ETA: 3s - loss: 0.3438 - accuracy: 0.9158
53/65 [=======================>......] - ETA: 2s - loss: 0.3430 - accuracy: 0.9160
54/65 [=======================>......] - ETA: 2s - loss: 0.3426 - accuracy: 0.9161
55/65 [========================>.....] - ETA: 2s - loss: 0.3431 - accuracy: 0.9158
56/65 [========================>.....] - ETA: 2s - loss: 0.3426 - accuracy: 0.9159
57/65 [=========================>....] - ETA: 1s - loss: 0.3426 - accuracy: 0.9161
58/65 [=========================>....] - ETA: 1s - loss: 0.3423 - accuracy: 0.9162
59/65 [==========================>...] - ETA: 1s - loss: 0.3422 - accuracy: 0.9163
60/65 [==========================>...] - ETA: 1s - loss: 0.3423 - accuracy: 0.9163
61/65 [===========================>..] - ETA: 0s - loss: 0.3421 - accuracy: 0.9163
62/65 [===========================>..] - ETA: 0s - loss: 0.3414 - accuracy: 0.9163
63/65 [============================>.] - ETA: 0s - loss: 0.3421 - accuracy: 0.9162
64/65 [============================>.] - ETA: 0s - loss: 0.3419 - accuracy: 0.9161
65/65 [==============================] - ETA: 0s - loss: 0.3420 - accuracy: 0.9161
65/65 [==============================] - 17s 251ms/step - loss: 0.3420 - accuracy: 0.9161 - val_loss: 0.4454 - val_accuracy: 0.8844 - lr: 0.0015
Epoch 22/30
1/65 [..............................] - ETA: 30s - loss: 0.3420 - accuracy: 0.9258
2/65 [..............................] - ETA: 15s - loss: 0.3445 - accuracy: 0.9180
3/65 [>.............................] - ETA: 15s - loss: 0.3375 - accuracy: 0.9183
4/65 [>.............................] - ETA: 15s - loss: 0.3243 - accuracy: 0.9238
5/65 [=>............................] - ETA: 14s - loss: 0.3345 - accuracy: 0.9205
6/65 [=>............................] - ETA: 14s - loss: 0.3378 - accuracy: 0.9189
7/65 [==>...........................] - ETA: 14s - loss: 0.3421 - accuracy: 0.9188
8/65 [==>...........................] - ETA: 14s - loss: 0.3434 - accuracy: 0.9187
9/65 [===>..........................] - ETA: 13s - loss: 0.3448 - accuracy: 0.9173
10/65 [===>..........................] - ETA: 13s - loss: 0.3401 - accuracy: 0.9185
11/65 [====>.........................] - ETA: 13s - loss: 0.3429 - accuracy: 0.9174
12/65 [====>.........................] - ETA: 13s - loss: 0.3397 - accuracy: 0.9180
13/65 [=====>........................] - ETA: 12s - loss: 0.3388 - accuracy: 0.9183
14/65 [=====>........................] - ETA: 12s - loss: 0.3374 - accuracy: 0.9184
15/65 [=====>........................] - ETA: 12s - loss: 0.3392 - accuracy: 0.9174
16/65 [======>.......................] - ETA: 12s - loss: 0.3382 - accuracy: 0.9183
17/65 [======>.......................] - ETA: 11s - loss: 0.3397 - accuracy: 0.9183
18/65 [=======>......................] - ETA: 11s - loss: 0.3377 - accuracy: 0.9186
19/65 [=======>......................] - ETA: 11s - loss: 0.3388 - accuracy: 0.9185
20/65 [========>.....................] - ETA: 11s - loss: 0.3380 - accuracy: 0.9189
21/65 [========>.....................] - ETA: 10s - loss: 0.3382 - accuracy: 0.9186
22/65 [=========>....................] - ETA: 10s - loss: 0.3375 - accuracy: 0.9186
23/65 [=========>....................] - ETA: 10s - loss: 0.3381 - accuracy: 0.9183
24/65 [==========>...................] - ETA: 10s - loss: 0.3377 - accuracy: 0.9187
25/65 [==========>...................] - ETA: 9s - loss: 0.3369 - accuracy: 0.9190
26/65 [===========>..................] - ETA: 9s - loss: 0.3388 - accuracy: 0.9184
27/65 [===========>..................] - ETA: 9s - loss: 0.3401 - accuracy: 0.9180
28/65 [===========>..................] - ETA: 9s - loss: 0.3408 - accuracy: 0.9176
29/65 [============>.................] - ETA: 8s - loss: 0.3407 - accuracy: 0.9172
30/65 [============>.................] - ETA: 8s - loss: 0.3411 - accuracy: 0.9172
31/65 [=============>................] - ETA: 8s - loss: 0.3408 - accuracy: 0.9171
32/65 [=============>................] - ETA: 8s - loss: 0.3404 - accuracy: 0.9171
33/65 [==============>...............] - ETA: 7s - loss: 0.3395 - accuracy: 0.9175
34/65 [==============>...............] - ETA: 7s - loss: 0.3404 - accuracy: 0.9171
35/65 [===============>..............] - ETA: 7s - loss: 0.3402 - accuracy: 0.9174
36/65 [===============>..............] - ETA: 7s - loss: 0.3412 - accuracy: 0.9169
37/65 [================>.............] - ETA: 6s - loss: 0.3425 - accuracy: 0.9168
38/65 [================>.............] - ETA: 6s - loss: 0.3448 - accuracy: 0.9163
39/65 [=================>............] - ETA: 6s - loss: 0.3442 - accuracy: 0.9165
40/65 [=================>............] - ETA: 6s - loss: 0.3431 - accuracy: 0.9168
41/65 [=================>............] - ETA: 5s - loss: 0.3423 - accuracy: 0.9171
42/65 [==================>...........] - ETA: 5s - loss: 0.3434 - accuracy: 0.9168
43/65 [==================>...........] - ETA: 5s - loss: 0.3429 - accuracy: 0.9169
44/65 [===================>..........] - ETA: 5s - loss: 0.3426 - accuracy: 0.9171
45/65 [===================>..........] - ETA: 4s - loss: 0.3424 - accuracy: 0.9171
46/65 [====================>.........] - ETA: 4s - loss: 0.3421 - accuracy: 0.9172
47/65 [====================>.........] - ETA: 4s - loss: 0.3411 - accuracy: 0.9175
48/65 [=====================>........] - ETA: 4s - loss: 0.3417 - accuracy: 0.9173
49/65 [=====================>........] - ETA: 3s - loss: 0.3417 - accuracy: 0.9174
50/65 [======================>.......] - ETA: 3s - loss: 0.3413 - accuracy: 0.9174
51/65 [======================>.......] - ETA: 3s - loss: 0.3412 - accuracy: 0.9175
52/65 [=======================>......] - ETA: 3s - loss: 0.3409 - accuracy: 0.9175
53/65 [=======================>......] - ETA: 2s - loss: 0.3413 - accuracy: 0.9174
54/65 [=======================>......] - ETA: 2s - loss: 0.3408 - accuracy: 0.9176
55/65 [========================>.....] - ETA: 2s - loss: 0.3403 - accuracy: 0.9178
56/65 [========================>.....] - ETA: 2s - loss: 0.3405 - accuracy: 0.9176
57/65 [=========================>....] - ETA: 1s - loss: 0.3407 - accuracy: 0.9174
58/65 [=========================>....] - ETA: 1s - loss: 0.3403 - accuracy: 0.9177
59/65 [==========================>...] - ETA: 1s - loss: 0.3405 - accuracy: 0.9175
60/65 [==========================>...] - ETA: 1s - loss: 0.3402 - accuracy: 0.9176
61/65 [===========================>..] - ETA: 0s - loss: 0.3403 - accuracy: 0.9176
62/65 [===========================>..] - ETA: 0s - loss: 0.3407 - accuracy: 0.9176
63/65 [============================>.] - ETA: 0s - loss: 0.3397 - accuracy: 0.9179
64/65 [============================>.] - ETA: 0s - loss: 0.3396 - accuracy: 0.9179
65/65 [==============================] - ETA: 0s - loss: 0.3394 - accuracy: 0.9180
Epoch 22: ReduceLROnPlateau reducing learning rate to 0.000750000006519258.
65/65 [==============================] - 17s 251ms/step - loss: 0.3394 - accuracy: 0.9180 - val_loss: 0.4385 - val_accuracy: 0.8890 - lr: 0.0015
Epoch 23/30
1/65 [..............................] - ETA: 30s - loss: 0.3207 - accuracy: 0.9248
2/65 [..............................] - ETA: 15s - loss: 0.3290 - accuracy: 0.9219
3/65 [>.............................] - ETA: 15s - loss: 0.3274 - accuracy: 0.9189
4/65 [>.............................] - ETA: 15s - loss: 0.3322 - accuracy: 0.9180
5/65 [=>............................] - ETA: 15s - loss: 0.3292 - accuracy: 0.9187
6/65 [=>............................] - ETA: 14s - loss: 0.3396 - accuracy: 0.9168
7/65 [==>...........................] - ETA: 14s - loss: 0.3413 - accuracy: 0.9170
8/65 [==>...........................] - ETA: 14s - loss: 0.3422 - accuracy: 0.9176
9/65 [===>..........................] - ETA: 13s - loss: 0.3430 - accuracy: 0.9170
10/65 [===>..........................] - ETA: 13s - loss: 0.3450 - accuracy: 0.9166
11/65 [====>.........................] - ETA: 13s - loss: 0.3448 - accuracy: 0.9165
12/65 [====>.........................] - ETA: 13s - loss: 0.3407 - accuracy: 0.9180
13/65 [=====>........................] - ETA: 12s - loss: 0.3421 - accuracy: 0.9173
14/65 [=====>........................] - ETA: 12s - loss: 0.3424 - accuracy: 0.9176
15/65 [=====>........................] - ETA: 12s - loss: 0.3403 - accuracy: 0.9188
16/65 [======>.......................] - ETA: 12s - loss: 0.3384 - accuracy: 0.9192
17/65 [======>.......................] - ETA: 11s - loss: 0.3362 - accuracy: 0.9195
18/65 [=======>......................] - ETA: 11s - loss: 0.3365 - accuracy: 0.9192
19/65 [=======>......................] - ETA: 11s - loss: 0.3372 - accuracy: 0.9193
20/65 [========>.....................] - ETA: 11s - loss: 0.3347 - accuracy: 0.9198
21/65 [========>.....................] - ETA: 10s - loss: 0.3346 - accuracy: 0.9199
22/65 [=========>....................] - ETA: 10s - loss: 0.3342 - accuracy: 0.9202
23/65 [=========>....................] - ETA: 10s - loss: 0.3332 - accuracy: 0.9207
24/65 [==========>...................] - ETA: 10s - loss: 0.3332 - accuracy: 0.9207
25/65 [==========>...................] - ETA: 9s - loss: 0.3338 - accuracy: 0.9206
26/65 [===========>..................] - ETA: 9s - loss: 0.3350 - accuracy: 0.9200
27/65 [===========>..................] - ETA: 9s - loss: 0.3342 - accuracy: 0.9203
28/65 [===========>..................] - ETA: 9s - loss: 0.3340 - accuracy: 0.9199
29/65 [============>.................] - ETA: 8s - loss: 0.3343 - accuracy: 0.9197
30/65 [============>.................] - ETA: 8s - loss: 0.3325 - accuracy: 0.9201
31/65 [=============>................] - ETA: 8s - loss: 0.3334 - accuracy: 0.9198
32/65 [=============>................] - ETA: 8s - loss: 0.3331 - accuracy: 0.9199
33/65 [==============>...............] - ETA: 7s - loss: 0.3329 - accuracy: 0.9198
34/65 [==============>...............] - ETA: 7s - loss: 0.3328 - accuracy: 0.9195
35/65 [===============>..............] - ETA: 7s - loss: 0.3321 - accuracy: 0.9198
36/65 [===============>..............] - ETA: 7s - loss: 0.3316 - accuracy: 0.9199
37/65 [================>.............] - ETA: 6s - loss: 0.3334 - accuracy: 0.9194
38/65 [================>.............] - ETA: 6s - loss: 0.3341 - accuracy: 0.9193
39/65 [=================>............] - ETA: 6s - loss: 0.3341 - accuracy: 0.9190
40/65 [=================>............] - ETA: 6s - loss: 0.3327 - accuracy: 0.9196
41/65 [=================>............] - ETA: 5s - loss: 0.3338 - accuracy: 0.9196
42/65 [==================>...........] - ETA: 5s - loss: 0.3326 - accuracy: 0.9198
43/65 [==================>...........] - ETA: 5s - loss: 0.3332 - accuracy: 0.9198
44/65 [===================>..........] - ETA: 5s - loss: 0.3346 - accuracy: 0.9195
45/65 [===================>..........] - ETA: 4s - loss: 0.3349 - accuracy: 0.9194
46/65 [====================>.........] - ETA: 4s - loss: 0.3349 - accuracy: 0.9196
47/65 [====================>.........] - ETA: 4s - loss: 0.3345 - accuracy: 0.9197
48/65 [=====================>........] - ETA: 4s - loss: 0.3340 - accuracy: 0.9197
49/65 [=====================>........] - ETA: 3s - loss: 0.3336 - accuracy: 0.9199
50/65 [======================>.......] - ETA: 3s - loss: 0.3335 - accuracy: 0.9199
51/65 [======================>.......] - ETA: 3s - loss: 0.3333 - accuracy: 0.9200
52/65 [=======================>......] - ETA: 3s - loss: 0.3336 - accuracy: 0.9199
53/65 [=======================>......] - ETA: 2s - loss: 0.3325 - accuracy: 0.9202
54/65 [=======================>......] - ETA: 2s - loss: 0.3318 - accuracy: 0.9204
55/65 [========================>.....] - ETA: 2s - loss: 0.3315 - accuracy: 0.9205
56/65 [========================>.....] - ETA: 2s - loss: 0.3309 - accuracy: 0.9206
57/65 [=========================>....] - ETA: 1s - loss: 0.3310 - accuracy: 0.9207
58/65 [=========================>....] - ETA: 1s - loss: 0.3310 - accuracy: 0.9208
59/65 [==========================>...] - ETA: 1s - loss: 0.3305 - accuracy: 0.9210
60/65 [==========================>...] - ETA: 1s - loss: 0.3303 - accuracy: 0.9211
61/65 [===========================>..] - ETA: 0s - loss: 0.3304 - accuracy: 0.9208
62/65 [===========================>..] - ETA: 0s - loss: 0.3302 - accuracy: 0.9209
63/65 [============================>.] - ETA: 0s - loss: 0.3293 - accuracy: 0.9210
64/65 [============================>.] - ETA: 0s - loss: 0.3291 - accuracy: 0.9209
65/65 [==============================] - ETA: 0s - loss: 0.3292 - accuracy: 0.9210
65/65 [==============================] - 17s 252ms/step - loss: 0.3292 - accuracy: 0.9210 - val_loss: 0.4354 - val_accuracy: 0.8908 - lr: 7.5000e-04
Epoch 24/30
1/65 [..............................] - ETA: 30s - loss: 0.3139 - accuracy: 0.9248
2/65 [..............................] - ETA: 15s - loss: 0.3111 - accuracy: 0.9248
3/65 [>.............................] - ETA: 15s - loss: 0.3131 - accuracy: 0.9245
4/65 [>.............................] - ETA: 15s - loss: 0.3321 - accuracy: 0.9197
5/65 [=>............................] - ETA: 15s - loss: 0.3383 - accuracy: 0.9187
6/65 [=>............................] - ETA: 14s - loss: 0.3349 - accuracy: 0.9196
7/65 [==>...........................] - ETA: 14s - loss: 0.3393 - accuracy: 0.9182
8/65 [==>...........................] - ETA: 14s - loss: 0.3379 - accuracy: 0.9187
9/65 [===>..........................] - ETA: 14s - loss: 0.3372 - accuracy: 0.9180
10/65 [===>..........................] - ETA: 13s - loss: 0.3354 - accuracy: 0.9182
11/65 [====>.........................] - ETA: 13s - loss: 0.3347 - accuracy: 0.9174
12/65 [====>.........................] - ETA: 13s - loss: 0.3361 - accuracy: 0.9173
13/65 [=====>........................] - ETA: 12s - loss: 0.3349 - accuracy: 0.9170
14/65 [=====>........................] - ETA: 12s - loss: 0.3367 - accuracy: 0.9171
15/65 [=====>........................] - ETA: 12s - loss: 0.3352 - accuracy: 0.9178
16/65 [======>.......................] - ETA: 12s - loss: 0.3348 - accuracy: 0.9175
17/65 [======>.......................] - ETA: 11s - loss: 0.3325 - accuracy: 0.9180
18/65 [=======>......................] - ETA: 11s - loss: 0.3313 - accuracy: 0.9185
19/65 [=======>......................] - ETA: 11s - loss: 0.3309 - accuracy: 0.9186
20/65 [========>.....................] - ETA: 11s - loss: 0.3287 - accuracy: 0.9188
21/65 [========>.....................] - ETA: 10s - loss: 0.3272 - accuracy: 0.9194
22/65 [=========>....................] - ETA: 10s - loss: 0.3284 - accuracy: 0.9192
23/65 [=========>....................] - ETA: 10s - loss: 0.3283 - accuracy: 0.9194
24/65 [==========>...................] - ETA: 10s - loss: 0.3291 - accuracy: 0.9188
25/65 [==========>...................] - ETA: 9s - loss: 0.3307 - accuracy: 0.9189
26/65 [===========>..................] - ETA: 9s - loss: 0.3302 - accuracy: 0.9188
27/65 [===========>..................] - ETA: 9s - loss: 0.3282 - accuracy: 0.9195
28/65 [===========>..................] - ETA: 9s - loss: 0.3285 - accuracy: 0.9196
29/65 [============>.................] - ETA: 8s - loss: 0.3296 - accuracy: 0.9193
30/65 [============>.................] - ETA: 8s - loss: 0.3290 - accuracy: 0.9194
31/65 [=============>................] - ETA: 8s - loss: 0.3300 - accuracy: 0.9191
32/65 [=============>................] - ETA: 8s - loss: 0.3298 - accuracy: 0.9192
33/65 [==============>...............] - ETA: 7s - loss: 0.3301 - accuracy: 0.9190
34/65 [==============>...............] - ETA: 7s - loss: 0.3314 - accuracy: 0.9187
35/65 [===============>..............] - ETA: 7s - loss: 0.3317 - accuracy: 0.9187
36/65 [===============>..............] - ETA: 7s - loss: 0.3317 - accuracy: 0.9186
37/65 [================>.............] - ETA: 6s - loss: 0.3319 - accuracy: 0.9187
38/65 [================>.............] - ETA: 6s - loss: 0.3308 - accuracy: 0.9191
39/65 [=================>............] - ETA: 6s - loss: 0.3301 - accuracy: 0.9196
40/65 [=================>............] - ETA: 6s - loss: 0.3303 - accuracy: 0.9200
41/65 [=================>............] - ETA: 5s - loss: 0.3302 - accuracy: 0.9200
42/65 [==================>...........] - ETA: 5s - loss: 0.3299 - accuracy: 0.9199
43/65 [==================>...........] - ETA: 5s - loss: 0.3298 - accuracy: 0.9200
44/65 [===================>..........] - ETA: 5s - loss: 0.3295 - accuracy: 0.9201
45/65 [===================>..........] - ETA: 4s - loss: 0.3289 - accuracy: 0.9200
46/65 [====================>.........] - ETA: 4s - loss: 0.3300 - accuracy: 0.9200
47/65 [====================>.........] - ETA: 4s - loss: 0.3306 - accuracy: 0.9199
48/65 [=====================>........] - ETA: 4s - loss: 0.3304 - accuracy: 0.9200
49/65 [=====================>........] - ETA: 3s - loss: 0.3303 - accuracy: 0.9200
50/65 [======================>.......] - ETA: 3s - loss: 0.3304 - accuracy: 0.9199
51/65 [======================>.......] - ETA: 3s - loss: 0.3304 - accuracy: 0.9201
52/65 [=======================>......] - ETA: 3s - loss: 0.3295 - accuracy: 0.9204
53/65 [=======================>......] - ETA: 2s - loss: 0.3287 - accuracy: 0.9204
54/65 [=======================>......] - ETA: 2s - loss: 0.3287 - accuracy: 0.9205
55/65 [========================>.....] - ETA: 2s - loss: 0.3287 - accuracy: 0.9204
56/65 [========================>.....] - ETA: 2s - loss: 0.3283 - accuracy: 0.9205
57/65 [=========================>....] - ETA: 1s - loss: 0.3279 - accuracy: 0.9207
58/65 [=========================>....] - ETA: 1s - loss: 0.3279 - accuracy: 0.9208
59/65 [==========================>...] - ETA: 1s - loss: 0.3274 - accuracy: 0.9207
60/65 [==========================>...] - ETA: 1s - loss: 0.3274 - accuracy: 0.9208
61/65 [===========================>..] - ETA: 0s - loss: 0.3282 - accuracy: 0.9206
62/65 [===========================>..] - ETA: 0s - loss: 0.3275 - accuracy: 0.9207
63/65 [============================>.] - ETA: 0s - loss: 0.3276 - accuracy: 0.9208
64/65 [============================>.] - ETA: 0s - loss: 0.3270 - accuracy: 0.9211
65/65 [==============================] - ETA: 0s - loss: 0.3267 - accuracy: 0.9212
65/65 [==============================] - 17s 253ms/step - loss: 0.3267 - accuracy: 0.9212 - val_loss: 0.4352 - val_accuracy: 0.8900 - lr: 7.5000e-04
Epoch 25/30
1/65 [..............................] - ETA: 30s - loss: 0.3410 - accuracy: 0.9180
2/65 [..............................] - ETA: 15s - loss: 0.3285 - accuracy: 0.9224
3/65 [>.............................] - ETA: 15s - loss: 0.3387 - accuracy: 0.9199
4/65 [>.............................] - ETA: 15s - loss: 0.3370 - accuracy: 0.9189
5/65 [=>............................] - ETA: 15s - loss: 0.3300 - accuracy: 0.9219
6/65 [=>............................] - ETA: 14s - loss: 0.3306 - accuracy: 0.9217
7/65 [==>...........................] - ETA: 14s - loss: 0.3302 - accuracy: 0.9216
8/65 [==>...........................] - ETA: 14s - loss: 0.3348 - accuracy: 0.9210
9/65 [===>..........................] - ETA: 14s - loss: 0.3399 - accuracy: 0.9184
10/65 [===>..........................] - ETA: 13s - loss: 0.3376 - accuracy: 0.9181
11/65 [====>.........................] - ETA: 13s - loss: 0.3354 - accuracy: 0.9184
12/65 [====>.........................] - ETA: 13s - loss: 0.3330 - accuracy: 0.9196
13/65 [=====>........................] - ETA: 13s - loss: 0.3283 - accuracy: 0.9211
14/65 [=====>........................] - ETA: 12s - loss: 0.3290 - accuracy: 0.9216
15/65 [=====>........................] - ETA: 12s - loss: 0.3264 - accuracy: 0.9223
16/65 [======>.......................] - ETA: 12s - loss: 0.3277 - accuracy: 0.9222
17/65 [======>.......................] - ETA: 11s - loss: 0.3279 - accuracy: 0.9216
18/65 [=======>......................] - ETA: 11s - loss: 0.3269 - accuracy: 0.9218
19/65 [=======>......................] - ETA: 11s - loss: 0.3262 - accuracy: 0.9220
20/65 [========>.....................] - ETA: 11s - loss: 0.3260 - accuracy: 0.9219
21/65 [========>.....................] - ETA: 10s - loss: 0.3261 - accuracy: 0.9212
22/65 [=========>....................] - ETA: 10s - loss: 0.3275 - accuracy: 0.9214
23/65 [=========>....................] - ETA: 10s - loss: 0.3279 - accuracy: 0.9208
24/65 [==========>...................] - ETA: 10s - loss: 0.3298 - accuracy: 0.9203
25/65 [==========>...................] - ETA: 9s - loss: 0.3298 - accuracy: 0.9205
26/65 [===========>..................] - ETA: 9s - loss: 0.3283 - accuracy: 0.9209
27/65 [===========>..................] - ETA: 9s - loss: 0.3291 - accuracy: 0.9210
28/65 [===========>..................] - ETA: 9s - loss: 0.3289 - accuracy: 0.9209
29/65 [============>.................] - ETA: 8s - loss: 0.3270 - accuracy: 0.9213
30/65 [============>.................] - ETA: 8s - loss: 0.3271 - accuracy: 0.9213
31/65 [=============>................] - ETA: 8s - loss: 0.3261 - accuracy: 0.9217
32/65 [=============>................] - ETA: 8s - loss: 0.3268 - accuracy: 0.9214
33/65 [==============>...............] - ETA: 7s - loss: 0.3283 - accuracy: 0.9208
34/65 [==============>...............] - ETA: 7s - loss: 0.3288 - accuracy: 0.9205
35/65 [===============>..............] - ETA: 7s - loss: 0.3282 - accuracy: 0.9203
36/65 [===============>..............] - ETA: 7s - loss: 0.3293 - accuracy: 0.9199
37/65 [================>.............] - ETA: 6s - loss: 0.3300 - accuracy: 0.9197
38/65 [================>.............] - ETA: 6s - loss: 0.3287 - accuracy: 0.9202
39/65 [=================>............] - ETA: 6s - loss: 0.3292 - accuracy: 0.9203
40/65 [=================>............] - ETA: 6s - loss: 0.3287 - accuracy: 0.9203
41/65 [=================>............] - ETA: 5s - loss: 0.3282 - accuracy: 0.9207
42/65 [==================>...........] - ETA: 5s - loss: 0.3272 - accuracy: 0.9210
43/65 [==================>...........] - ETA: 5s - loss: 0.3273 - accuracy: 0.9210
44/65 [===================>..........] - ETA: 5s - loss: 0.3275 - accuracy: 0.9209
45/65 [===================>..........] - ETA: 4s - loss: 0.3279 - accuracy: 0.9208
46/65 [====================>.........] - ETA: 4s - loss: 0.3270 - accuracy: 0.9213
47/65 [====================>.........] - ETA: 4s - loss: 0.3278 - accuracy: 0.9211
48/65 [=====================>........] - ETA: 4s - loss: 0.3275 - accuracy: 0.9213
49/65 [=====================>........] - ETA: 3s - loss: 0.3273 - accuracy: 0.9212
50/65 [======================>.......] - ETA: 3s - loss: 0.3284 - accuracy: 0.9209
51/65 [======================>.......] - ETA: 3s - loss: 0.3275 - accuracy: 0.9211
52/65 [=======================>......] - ETA: 3s - loss: 0.3274 - accuracy: 0.9212
53/65 [=======================>......] - ETA: 2s - loss: 0.3272 - accuracy: 0.9212
54/65 [=======================>......] - ETA: 2s - loss: 0.3270 - accuracy: 0.9211
55/65 [========================>.....] - ETA: 2s - loss: 0.3267 - accuracy: 0.9212
56/65 [========================>.....] - ETA: 2s - loss: 0.3266 - accuracy: 0.9213
57/65 [=========================>....] - ETA: 1s - loss: 0.3260 - accuracy: 0.9213
58/65 [=========================>....] - ETA: 1s - loss: 0.3260 - accuracy: 0.9212
59/65 [==========================>...] - ETA: 1s - loss: 0.3262 - accuracy: 0.9212
60/65 [==========================>...] - ETA: 1s - loss: 0.3255 - accuracy: 0.9214
61/65 [===========================>..] - ETA: 0s - loss: 0.3253 - accuracy: 0.9215
62/65 [===========================>..] - ETA: 0s - loss: 0.3253 - accuracy: 0.9215
63/65 [============================>.] - ETA: 0s - loss: 0.3249 - accuracy: 0.9216
64/65 [============================>.] - ETA: 0s - loss: 0.3255 - accuracy: 0.9213
65/65 [==============================] - ETA: 0s - loss: 0.3250 - accuracy: 0.9215
65/65 [==============================] - 17s 252ms/step - loss: 0.3250 - accuracy: 0.9215 - val_loss: 0.4310 - val_accuracy: 0.8907 - lr: 7.5000e-04
Epoch 26/30
1/65 [..............................] - ETA: 31s - loss: 0.3524 - accuracy: 0.9092
2/65 [..............................] - ETA: 15s - loss: 0.3308 - accuracy: 0.9209
3/65 [>.............................] - ETA: 15s - loss: 0.3319 - accuracy: 0.9215
4/65 [>.............................] - ETA: 15s - loss: 0.3374 - accuracy: 0.9189
5/65 [=>............................] - ETA: 15s - loss: 0.3456 - accuracy: 0.9182
6/65 [=>............................] - ETA: 14s - loss: 0.3476 - accuracy: 0.9176
7/65 [==>...........................] - ETA: 14s - loss: 0.3395 - accuracy: 0.9194
8/65 [==>...........................] - ETA: 14s - loss: 0.3321 - accuracy: 0.9214
9/65 [===>..........................] - ETA: 13s - loss: 0.3320 - accuracy: 0.9211
10/65 [===>..........................] - ETA: 13s - loss: 0.3294 - accuracy: 0.9219
11/65 [====>.........................] - ETA: 13s - loss: 0.3280 - accuracy: 0.9216
12/65 [====>.........................] - ETA: 13s - loss: 0.3273 - accuracy: 0.9220
13/65 [=====>........................] - ETA: 12s - loss: 0.3244 - accuracy: 0.9226
14/65 [=====>........................] - ETA: 12s - loss: 0.3260 - accuracy: 0.9225
15/65 [=====>........................] - ETA: 12s - loss: 0.3267 - accuracy: 0.9220
16/65 [======>.......................] - ETA: 12s - loss: 0.3266 - accuracy: 0.9219
17/65 [======>.......................] - ETA: 11s - loss: 0.3289 - accuracy: 0.9215
18/65 [=======>......................] - ETA: 11s - loss: 0.3290 - accuracy: 0.9210
19/65 [=======>......................] - ETA: 11s - loss: 0.3251 - accuracy: 0.9222
20/65 [========>.....................] - ETA: 11s - loss: 0.3248 - accuracy: 0.9217
21/65 [========>.....................] - ETA: 10s - loss: 0.3251 - accuracy: 0.9214
22/65 [=========>....................] - ETA: 10s - loss: 0.3253 - accuracy: 0.9214
23/65 [=========>....................] - ETA: 10s - loss: 0.3257 - accuracy: 0.9209
24/65 [==========>...................] - ETA: 10s - loss: 0.3247 - accuracy: 0.9209
25/65 [==========>...................] - ETA: 9s - loss: 0.3238 - accuracy: 0.9212
26/65 [===========>..................] - ETA: 9s - loss: 0.3229 - accuracy: 0.9214
27/65 [===========>..................] - ETA: 9s - loss: 0.3232 - accuracy: 0.9212
28/65 [===========>..................] - ETA: 9s - loss: 0.3219 - accuracy: 0.9218
29/65 [============>.................] - ETA: 8s - loss: 0.3234 - accuracy: 0.9213
30/65 [============>.................] - ETA: 8s - loss: 0.3247 - accuracy: 0.9208
31/65 [=============>................] - ETA: 8s - loss: 0.3239 - accuracy: 0.9211
32/65 [=============>................] - ETA: 8s - loss: 0.3244 - accuracy: 0.9213
33/65 [==============>...............] - ETA: 7s - loss: 0.3249 - accuracy: 0.9210
34/65 [==============>...............] - ETA: 7s - loss: 0.3270 - accuracy: 0.9203
35/65 [===============>..............] - ETA: 7s - loss: 0.3280 - accuracy: 0.9201
36/65 [===============>..............] - ETA: 7s - loss: 0.3279 - accuracy: 0.9202
37/65 [================>.............] - ETA: 6s - loss: 0.3277 - accuracy: 0.9205
38/65 [================>.............] - ETA: 6s - loss: 0.3272 - accuracy: 0.9208
39/65 [=================>............] - ETA: 6s - loss: 0.3272 - accuracy: 0.9207
40/65 [=================>............] - ETA: 6s - loss: 0.3281 - accuracy: 0.9208
41/65 [=================>............] - ETA: 5s - loss: 0.3277 - accuracy: 0.9208
42/65 [==================>...........] - ETA: 5s - loss: 0.3269 - accuracy: 0.9211
43/65 [==================>...........] - ETA: 5s - loss: 0.3271 - accuracy: 0.9211
44/65 [===================>..........] - ETA: 5s - loss: 0.3264 - accuracy: 0.9215
45/65 [===================>..........] - ETA: 4s - loss: 0.3276 - accuracy: 0.9211
46/65 [====================>.........] - ETA: 4s - loss: 0.3264 - accuracy: 0.9217
47/65 [====================>.........] - ETA: 4s - loss: 0.3264 - accuracy: 0.9219
48/65 [=====================>........] - ETA: 4s - loss: 0.3261 - accuracy: 0.9220
49/65 [=====================>........] - ETA: 3s - loss: 0.3262 - accuracy: 0.9221
50/65 [======================>.......] - ETA: 3s - loss: 0.3256 - accuracy: 0.9223
51/65 [======================>.......] - ETA: 3s - loss: 0.3252 - accuracy: 0.9223
52/65 [=======================>......] - ETA: 3s - loss: 0.3248 - accuracy: 0.9223
53/65 [=======================>......] - ETA: 2s - loss: 0.3240 - accuracy: 0.9226
54/65 [=======================>......] - ETA: 2s - loss: 0.3237 - accuracy: 0.9226
55/65 [========================>.....] - ETA: 2s - loss: 0.3236 - accuracy: 0.9226
56/65 [========================>.....] - ETA: 2s - loss: 0.3236 - accuracy: 0.9226
57/65 [=========================>....] - ETA: 1s - loss: 0.3239 - accuracy: 0.9225
58/65 [=========================>....] - ETA: 1s - loss: 0.3234 - accuracy: 0.9225
59/65 [==========================>...] - ETA: 1s - loss: 0.3232 - accuracy: 0.9226
60/65 [==========================>...] - ETA: 1s - loss: 0.3242 - accuracy: 0.9223
61/65 [===========================>..] - ETA: 0s - loss: 0.3237 - accuracy: 0.9225
62/65 [===========================>..] - ETA: 0s - loss: 0.3232 - accuracy: 0.9227
63/65 [============================>.] - ETA: 0s - loss: 0.3230 - accuracy: 0.9227
64/65 [============================>.] - ETA: 0s - loss: 0.3230 - accuracy: 0.9228
65/65 [==============================] - ETA: 0s - loss: 0.3228 - accuracy: 0.9228
65/65 [==============================] - 17s 251ms/step - loss: 0.3228 - accuracy: 0.9228 - val_loss: 0.4426 - val_accuracy: 0.8844 - lr: 7.5000e-04
Epoch 27/30
1/65 [..............................] - ETA: 29s - loss: 0.3326 - accuracy: 0.9160
2/65 [..............................] - ETA: 15s - loss: 0.3366 - accuracy: 0.9155
3/65 [>.............................] - ETA: 15s - loss: 0.3276 - accuracy: 0.9202
4/65 [>.............................] - ETA: 15s - loss: 0.3329 - accuracy: 0.9207
5/65 [=>............................] - ETA: 15s - loss: 0.3331 - accuracy: 0.9189
6/65 [=>............................] - ETA: 14s - loss: 0.3345 - accuracy: 0.9194
7/65 [==>...........................] - ETA: 14s - loss: 0.3356 - accuracy: 0.9195
8/65 [==>...........................] - ETA: 14s - loss: 0.3329 - accuracy: 0.9208
9/65 [===>..........................] - ETA: 14s - loss: 0.3312 - accuracy: 0.9206
10/65 [===>..........................] - ETA: 13s - loss: 0.3321 - accuracy: 0.9203
11/65 [====>.........................] - ETA: 13s - loss: 0.3331 - accuracy: 0.9201
12/65 [====>.........................] - ETA: 13s - loss: 0.3294 - accuracy: 0.9207
13/65 [=====>........................] - ETA: 12s - loss: 0.3297 - accuracy: 0.9205
14/65 [=====>........................] - ETA: 12s - loss: 0.3309 - accuracy: 0.9213
15/65 [=====>........................] - ETA: 12s - loss: 0.3294 - accuracy: 0.9213
16/65 [======>.......................] - ETA: 12s - loss: 0.3279 - accuracy: 0.9217
17/65 [======>.......................] - ETA: 11s - loss: 0.3276 - accuracy: 0.9218
18/65 [=======>......................] - ETA: 11s - loss: 0.3286 - accuracy: 0.9214
19/65 [=======>......................] - ETA: 11s - loss: 0.3290 - accuracy: 0.9216
20/65 [========>.....................] - ETA: 11s - loss: 0.3282 - accuracy: 0.9216
21/65 [========>.....................] - ETA: 10s - loss: 0.3285 - accuracy: 0.9212
22/65 [=========>....................] - ETA: 10s - loss: 0.3281 - accuracy: 0.9214
23/65 [=========>....................] - ETA: 10s - loss: 0.3276 - accuracy: 0.9212
24/65 [==========>...................] - ETA: 10s - loss: 0.3285 - accuracy: 0.9211
25/65 [==========>...................] - ETA: 9s - loss: 0.3297 - accuracy: 0.9206
26/65 [===========>..................] - ETA: 9s - loss: 0.3275 - accuracy: 0.9214
27/65 [===========>..................] - ETA: 9s - loss: 0.3268 - accuracy: 0.9215
28/65 [===========>..................] - ETA: 9s - loss: 0.3264 - accuracy: 0.9219
29/65 [============>.................] - ETA: 8s - loss: 0.3266 - accuracy: 0.9215
30/65 [============>.................] - ETA: 8s - loss: 0.3270 - accuracy: 0.9212
31/65 [=============>................] - ETA: 8s - loss: 0.3262 - accuracy: 0.9211
32/65 [=============>................] - ETA: 8s - loss: 0.3267 - accuracy: 0.9211
33/65 [==============>...............] - ETA: 7s - loss: 0.3264 - accuracy: 0.9211
34/65 [==============>...............] - ETA: 7s - loss: 0.3263 - accuracy: 0.9213
35/65 [===============>..............] - ETA: 7s - loss: 0.3271 - accuracy: 0.9209
36/65 [===============>..............] - ETA: 7s - loss: 0.3270 - accuracy: 0.9212
37/65 [================>.............] - ETA: 6s - loss: 0.3264 - accuracy: 0.9214
38/65 [================>.............] - ETA: 6s - loss: 0.3270 - accuracy: 0.9212
39/65 [=================>............] - ETA: 6s - loss: 0.3265 - accuracy: 0.9215
40/65 [=================>............] - ETA: 6s - loss: 0.3265 - accuracy: 0.9218
41/65 [=================>............] - ETA: 5s - loss: 0.3267 - accuracy: 0.9217
42/65 [==================>...........] - ETA: 5s - loss: 0.3265 - accuracy: 0.9216
43/65 [==================>...........] - ETA: 5s - loss: 0.3263 - accuracy: 0.9216
44/65 [===================>..........] - ETA: 5s - loss: 0.3259 - accuracy: 0.9217
45/65 [===================>..........] - ETA: 4s - loss: 0.3264 - accuracy: 0.9215
46/65 [====================>.........] - ETA: 4s - loss: 0.3273 - accuracy: 0.9213
47/65 [====================>.........] - ETA: 4s - loss: 0.3269 - accuracy: 0.9213
48/65 [=====================>........] - ETA: 4s - loss: 0.3271 - accuracy: 0.9212
49/65 [=====================>........] - ETA: 3s - loss: 0.3270 - accuracy: 0.9212
50/65 [======================>.......] - ETA: 3s - loss: 0.3268 - accuracy: 0.9213
51/65 [======================>.......] - ETA: 3s - loss: 0.3264 - accuracy: 0.9213
52/65 [=======================>......] - ETA: 3s - loss: 0.3262 - accuracy: 0.9213
53/65 [=======================>......] - ETA: 2s - loss: 0.3260 - accuracy: 0.9214
54/65 [=======================>......] - ETA: 2s - loss: 0.3250 - accuracy: 0.9218
55/65 [========================>.....] - ETA: 2s - loss: 0.3252 - accuracy: 0.9216
56/65 [========================>.....] - ETA: 2s - loss: 0.3252 - accuracy: 0.9216
57/65 [=========================>....] - ETA: 1s - loss: 0.3256 - accuracy: 0.9215
58/65 [=========================>....] - ETA: 1s - loss: 0.3253 - accuracy: 0.9215
59/65 [==========================>...] - ETA: 1s - loss: 0.3247 - accuracy: 0.9216
60/65 [==========================>...] - ETA: 1s - loss: 0.3242 - accuracy: 0.9217
61/65 [===========================>..] - ETA: 0s - loss: 0.3241 - accuracy: 0.9217
62/65 [===========================>..] - ETA: 0s - loss: 0.3236 - accuracy: 0.9219
63/65 [============================>.] - ETA: 0s - loss: 0.3234 - accuracy: 0.9218
64/65 [============================>.] - ETA: 0s - loss: 0.3231 - accuracy: 0.9218
65/65 [==============================] - ETA: 0s - loss: 0.3232 - accuracy: 0.9218
65/65 [==============================] - 17s 252ms/step - loss: 0.3232 - accuracy: 0.9218 - val_loss: 0.4320 - val_accuracy: 0.8900 - lr: 7.5000e-04
Epoch 28/30
1/65 [..............................] - ETA: 30s - loss: 0.3492 - accuracy: 0.9102
2/65 [..............................] - ETA: 15s - loss: 0.3432 - accuracy: 0.9165
3/65 [>.............................] - ETA: 15s - loss: 0.3383 - accuracy: 0.9170
4/65 [>.............................] - ETA: 15s - loss: 0.3253 - accuracy: 0.9209
5/65 [=>............................] - ETA: 15s - loss: 0.3307 - accuracy: 0.9201
6/65 [=>............................] - ETA: 14s - loss: 0.3293 - accuracy: 0.9209
7/65 [==>...........................] - ETA: 14s - loss: 0.3331 - accuracy: 0.9198
8/65 [==>...........................] - ETA: 14s - loss: 0.3281 - accuracy: 0.9203
9/65 [===>..........................] - ETA: 14s - loss: 0.3273 - accuracy: 0.9201
10/65 [===>..........................] - ETA: 13s - loss: 0.3282 - accuracy: 0.9200
11/65 [====>.........................] - ETA: 13s - loss: 0.3298 - accuracy: 0.9205
12/65 [====>.........................] - ETA: 13s - loss: 0.3287 - accuracy: 0.9215
13/65 [=====>........................] - ETA: 13s - loss: 0.3283 - accuracy: 0.9213
14/65 [=====>........................] - ETA: 12s - loss: 0.3269 - accuracy: 0.9212
15/65 [=====>........................] - ETA: 12s - loss: 0.3275 - accuracy: 0.9212
16/65 [======>.......................] - ETA: 12s - loss: 0.3249 - accuracy: 0.9218
17/65 [======>.......................] - ETA: 12s - loss: 0.3247 - accuracy: 0.9213
18/65 [=======>......................] - ETA: 11s - loss: 0.3265 - accuracy: 0.9208
19/65 [=======>......................] - ETA: 11s - loss: 0.3246 - accuracy: 0.9213
20/65 [========>.....................] - ETA: 11s - loss: 0.3259 - accuracy: 0.9206
21/65 [========>.....................] - ETA: 10s - loss: 0.3242 - accuracy: 0.9212
22/65 [=========>....................] - ETA: 10s - loss: 0.3248 - accuracy: 0.9213
23/65 [=========>....................] - ETA: 10s - loss: 0.3237 - accuracy: 0.9218
24/65 [==========>...................] - ETA: 10s - loss: 0.3233 - accuracy: 0.9223
25/65 [==========>...................] - ETA: 9s - loss: 0.3221 - accuracy: 0.9225
26/65 [===========>..................] - ETA: 9s - loss: 0.3226 - accuracy: 0.9224
27/65 [===========>..................] - ETA: 9s - loss: 0.3220 - accuracy: 0.9226
28/65 [===========>..................] - ETA: 9s - loss: 0.3237 - accuracy: 0.9218
29/65 [============>.................] - ETA: 8s - loss: 0.3239 - accuracy: 0.9217
30/65 [============>.................] - ETA: 8s - loss: 0.3237 - accuracy: 0.9215
31/65 [=============>................] - ETA: 8s - loss: 0.3221 - accuracy: 0.9218
32/65 [=============>................] - ETA: 8s - loss: 0.3227 - accuracy: 0.9218
33/65 [==============>...............] - ETA: 7s - loss: 0.3239 - accuracy: 0.9218
34/65 [==============>...............] - ETA: 7s - loss: 0.3239 - accuracy: 0.9216
35/65 [===============>..............] - ETA: 7s - loss: 0.3238 - accuracy: 0.9215
36/65 [===============>..............] - ETA: 7s - loss: 0.3232 - accuracy: 0.9217
37/65 [================>.............] - ETA: 6s - loss: 0.3240 - accuracy: 0.9214
38/65 [================>.............] - ETA: 6s - loss: 0.3237 - accuracy: 0.9216
39/65 [=================>............] - ETA: 6s - loss: 0.3239 - accuracy: 0.9216
40/65 [=================>............] - ETA: 6s - loss: 0.3238 - accuracy: 0.9216
41/65 [=================>............] - ETA: 5s - loss: 0.3235 - accuracy: 0.9216
42/65 [==================>...........] - ETA: 5s - loss: 0.3240 - accuracy: 0.9215
43/65 [==================>...........] - ETA: 5s - loss: 0.3239 - accuracy: 0.9214
44/65 [===================>..........] - ETA: 5s - loss: 0.3243 - accuracy: 0.9215
45/65 [===================>..........] - ETA: 4s - loss: 0.3240 - accuracy: 0.9215
46/65 [====================>.........] - ETA: 4s - loss: 0.3233 - accuracy: 0.9218
47/65 [====================>.........] - ETA: 4s - loss: 0.3240 - accuracy: 0.9218
48/65 [=====================>........] - ETA: 4s - loss: 0.3237 - accuracy: 0.9218
49/65 [=====================>........] - ETA: 3s - loss: 0.3232 - accuracy: 0.9220
50/65 [======================>.......] - ETA: 3s - loss: 0.3234 - accuracy: 0.9220
51/65 [======================>.......] - ETA: 3s - loss: 0.3236 - accuracy: 0.9218
52/65 [=======================>......] - ETA: 3s - loss: 0.3232 - accuracy: 0.9221
53/65 [=======================>......] - ETA: 2s - loss: 0.3231 - accuracy: 0.9219
54/65 [=======================>......] - ETA: 2s - loss: 0.3231 - accuracy: 0.9218
55/65 [========================>.....] - ETA: 2s - loss: 0.3228 - accuracy: 0.9220
56/65 [========================>.....] - ETA: 2s - loss: 0.3218 - accuracy: 0.9223
57/65 [=========================>....] - ETA: 1s - loss: 0.3213 - accuracy: 0.9225
58/65 [=========================>....] - ETA: 1s - loss: 0.3214 - accuracy: 0.9225
59/65 [==========================>...] - ETA: 1s - loss: 0.3206 - accuracy: 0.9229
60/65 [==========================>...] - ETA: 1s - loss: 0.3210 - accuracy: 0.9229
61/65 [===========================>..] - ETA: 0s - loss: 0.3211 - accuracy: 0.9228
62/65 [===========================>..] - ETA: 0s - loss: 0.3206 - accuracy: 0.9228
63/65 [============================>.] - ETA: 0s - loss: 0.3210 - accuracy: 0.9228
64/65 [============================>.] - ETA: 0s - loss: 0.3207 - accuracy: 0.9228
65/65 [==============================] - ETA: 0s - loss: 0.3204 - accuracy: 0.9228
Epoch 28: ReduceLROnPlateau reducing learning rate to 0.000375000003259629.
65/65 [==============================] - 17s 253ms/step - loss: 0.3204 - accuracy: 0.9228 - val_loss: 0.4365 - val_accuracy: 0.8889 - lr: 7.5000e-04
Epoch 29/30
1/65 [..............................] - ETA: 29s - loss: 0.2736 - accuracy: 0.9404
2/65 [..............................] - ETA: 15s - loss: 0.3130 - accuracy: 0.9302
3/65 [>.............................] - ETA: 15s - loss: 0.3162 - accuracy: 0.9248
4/65 [>.............................] - ETA: 15s - loss: 0.3223 - accuracy: 0.9219
5/65 [=>............................] - ETA: 15s - loss: 0.3194 - accuracy: 0.9236
6/65 [=>............................] - ETA: 14s - loss: 0.3181 - accuracy: 0.9235
7/65 [==>...........................] - ETA: 14s - loss: 0.3254 - accuracy: 0.9213
8/65 [==>...........................] - ETA: 14s - loss: 0.3275 - accuracy: 0.9204
9/65 [===>..........................] - ETA: 13s - loss: 0.3294 - accuracy: 0.9194
10/65 [===>..........................] - ETA: 13s - loss: 0.3253 - accuracy: 0.9212
11/65 [====>.........................] - ETA: 13s - loss: 0.3227 - accuracy: 0.9216
12/65 [====>.........................] - ETA: 13s - loss: 0.3255 - accuracy: 0.9210
13/65 [=====>........................] - ETA: 12s - loss: 0.3261 - accuracy: 0.9212
14/65 [=====>........................] - ETA: 12s - loss: 0.3250 - accuracy: 0.9212
15/65 [=====>........................] - ETA: 12s - loss: 0.3242 - accuracy: 0.9219
16/65 [======>.......................] - ETA: 12s - loss: 0.3248 - accuracy: 0.9221
17/65 [======>.......................] - ETA: 11s - loss: 0.3240 - accuracy: 0.9225
18/65 [=======>......................] - ETA: 11s - loss: 0.3236 - accuracy: 0.9229
19/65 [=======>......................] - ETA: 11s - loss: 0.3228 - accuracy: 0.9231
20/65 [========>.....................] - ETA: 11s - loss: 0.3214 - accuracy: 0.9233
21/65 [========>.....................] - ETA: 10s - loss: 0.3198 - accuracy: 0.9232
22/65 [=========>....................] - ETA: 10s - loss: 0.3184 - accuracy: 0.9236
23/65 [=========>....................] - ETA: 10s - loss: 0.3171 - accuracy: 0.9240
24/65 [==========>...................] - ETA: 10s - loss: 0.3173 - accuracy: 0.9234
25/65 [==========>...................] - ETA: 9s - loss: 0.3172 - accuracy: 0.9237
26/65 [===========>..................] - ETA: 9s - loss: 0.3176 - accuracy: 0.9235
27/65 [===========>..................] - ETA: 9s - loss: 0.3170 - accuracy: 0.9238
28/65 [===========>..................] - ETA: 9s - loss: 0.3180 - accuracy: 0.9234
29/65 [============>.................] - ETA: 8s - loss: 0.3192 - accuracy: 0.9227
30/65 [============>.................] - ETA: 8s - loss: 0.3184 - accuracy: 0.9227
31/65 [=============>................] - ETA: 8s - loss: 0.3185 - accuracy: 0.9227
32/65 [=============>................] - ETA: 8s - loss: 0.3182 - accuracy: 0.9225
33/65 [==============>...............] - ETA: 7s - loss: 0.3168 - accuracy: 0.9234
34/65 [==============>...............] - ETA: 7s - loss: 0.3162 - accuracy: 0.9235
35/65 [===============>..............] - ETA: 7s - loss: 0.3172 - accuracy: 0.9232
36/65 [===============>..............] - ETA: 7s - loss: 0.3181 - accuracy: 0.9230
37/65 [================>.............] - ETA: 6s - loss: 0.3180 - accuracy: 0.9230
38/65 [================>.............] - ETA: 6s - loss: 0.3184 - accuracy: 0.9229
39/65 [=================>............] - ETA: 6s - loss: 0.3177 - accuracy: 0.9232
40/65 [=================>............] - ETA: 6s - loss: 0.3181 - accuracy: 0.9232
41/65 [=================>............] - ETA: 5s - loss: 0.3170 - accuracy: 0.9235
42/65 [==================>...........] - ETA: 5s - loss: 0.3171 - accuracy: 0.9238
43/65 [==================>...........] - ETA: 5s - loss: 0.3175 - accuracy: 0.9236
44/65 [===================>..........] - ETA: 5s - loss: 0.3174 - accuracy: 0.9237
45/65 [===================>..........] - ETA: 4s - loss: 0.3178 - accuracy: 0.9237
46/65 [====================>.........] - ETA: 4s - loss: 0.3176 - accuracy: 0.9238
47/65 [====================>.........] - ETA: 4s - loss: 0.3176 - accuracy: 0.9237
48/65 [=====================>........] - ETA: 4s - loss: 0.3178 - accuracy: 0.9237
49/65 [=====================>........] - ETA: 3s - loss: 0.3179 - accuracy: 0.9238
50/65 [======================>.......] - ETA: 3s - loss: 0.3179 - accuracy: 0.9238
51/65 [======================>.......] - ETA: 3s - loss: 0.3177 - accuracy: 0.9237
52/65 [=======================>......] - ETA: 3s - loss: 0.3181 - accuracy: 0.9236
53/65 [=======================>......] - ETA: 2s - loss: 0.3175 - accuracy: 0.9236
54/65 [=======================>......] - ETA: 2s - loss: 0.3169 - accuracy: 0.9238
55/65 [========================>.....] - ETA: 2s - loss: 0.3167 - accuracy: 0.9237
56/65 [========================>.....] - ETA: 2s - loss: 0.3170 - accuracy: 0.9237
57/65 [=========================>....] - ETA: 1s - loss: 0.3165 - accuracy: 0.9238
58/65 [=========================>....] - ETA: 1s - loss: 0.3156 - accuracy: 0.9241
59/65 [==========================>...] - ETA: 1s - loss: 0.3150 - accuracy: 0.9243
60/65 [==========================>...] - ETA: 1s - loss: 0.3146 - accuracy: 0.9244
61/65 [===========================>..] - ETA: 0s - loss: 0.3144 - accuracy: 0.9245
62/65 [===========================>..] - ETA: 0s - loss: 0.3148 - accuracy: 0.9245
63/65 [============================>.] - ETA: 0s - loss: 0.3149 - accuracy: 0.9242
64/65 [============================>.] - ETA: 0s - loss: 0.3151 - accuracy: 0.9240
65/65 [==============================] - ETA: 0s - loss: 0.3150 - accuracy: 0.9239
65/65 [==============================] - 17s 252ms/step - loss: 0.3150 - accuracy: 0.9239 - val_loss: 0.4333 - val_accuracy: 0.8905 - lr: 3.7500e-04
Epoch 30/30
1/65 [..............................] - ETA: 30s - loss: 0.3436 - accuracy: 0.9199
2/65 [..............................] - ETA: 15s - loss: 0.3254 - accuracy: 0.9209
3/65 [>.............................] - ETA: 15s - loss: 0.3165 - accuracy: 0.9225
4/65 [>.............................] - ETA: 15s - loss: 0.3143 - accuracy: 0.9248
5/65 [=>............................] - ETA: 15s - loss: 0.3160 - accuracy: 0.9240
6/65 [=>............................] - ETA: 14s - loss: 0.3278 - accuracy: 0.9206
7/65 [==>...........................] - ETA: 14s - loss: 0.3297 - accuracy: 0.9202
8/65 [==>...........................] - ETA: 14s - loss: 0.3282 - accuracy: 0.9216
9/65 [===>..........................] - ETA: 13s - loss: 0.3215 - accuracy: 0.9236
10/65 [===>..........................] - ETA: 13s - loss: 0.3259 - accuracy: 0.9225
11/65 [====>.........................] - ETA: 13s - loss: 0.3257 - accuracy: 0.9225
12/65 [====>.........................] - ETA: 13s - loss: 0.3218 - accuracy: 0.9226
13/65 [=====>........................] - ETA: 12s - loss: 0.3178 - accuracy: 0.9241
14/65 [=====>........................] - ETA: 12s - loss: 0.3151 - accuracy: 0.9251
15/65 [=====>........................] - ETA: 12s - loss: 0.3154 - accuracy: 0.9252
16/65 [======>.......................] - ETA: 12s - loss: 0.3167 - accuracy: 0.9247
17/65 [======>.......................] - ETA: 11s - loss: 0.3181 - accuracy: 0.9239
18/65 [=======>......................] - ETA: 11s - loss: 0.3176 - accuracy: 0.9239
19/65 [=======>......................] - ETA: 11s - loss: 0.3163 - accuracy: 0.9244
20/65 [========>.....................] - ETA: 11s - loss: 0.3160 - accuracy: 0.9241
21/65 [========>.....................] - ETA: 10s - loss: 0.3157 - accuracy: 0.9237
22/65 [=========>....................] - ETA: 10s - loss: 0.3160 - accuracy: 0.9238
23/65 [=========>....................] - ETA: 10s - loss: 0.3180 - accuracy: 0.9231
24/65 [==========>...................] - ETA: 10s - loss: 0.3171 - accuracy: 0.9236
25/65 [==========>...................] - ETA: 9s - loss: 0.3166 - accuracy: 0.9239
26/65 [===========>..................] - ETA: 9s - loss: 0.3165 - accuracy: 0.9241
27/65 [===========>..................] - ETA: 9s - loss: 0.3164 - accuracy: 0.9242
28/65 [===========>..................] - ETA: 9s - loss: 0.3171 - accuracy: 0.9240
29/65 [============>.................] - ETA: 8s - loss: 0.3166 - accuracy: 0.9240
30/65 [============>.................] - ETA: 8s - loss: 0.3169 - accuracy: 0.9238
31/65 [=============>................] - ETA: 8s - loss: 0.3171 - accuracy: 0.9236
32/65 [=============>................] - ETA: 8s - loss: 0.3164 - accuracy: 0.9239
33/65 [==============>...............] - ETA: 7s - loss: 0.3166 - accuracy: 0.9237
34/65 [==============>...............] - ETA: 7s - loss: 0.3169 - accuracy: 0.9237
35/65 [===============>..............] - ETA: 7s - loss: 0.3163 - accuracy: 0.9238
36/65 [===============>..............] - ETA: 7s - loss: 0.3153 - accuracy: 0.9241
37/65 [================>.............] - ETA: 6s - loss: 0.3159 - accuracy: 0.9240
38/65 [================>.............] - ETA: 6s - loss: 0.3164 - accuracy: 0.9241
39/65 [=================>............] - ETA: 6s - loss: 0.3157 - accuracy: 0.9244
40/65 [=================>............] - ETA: 6s - loss: 0.3155 - accuracy: 0.9246
41/65 [=================>............] - ETA: 5s - loss: 0.3157 - accuracy: 0.9249
42/65 [==================>...........] - ETA: 5s - loss: 0.3162 - accuracy: 0.9246
43/65 [==================>...........] - ETA: 5s - loss: 0.3159 - accuracy: 0.9247
44/65 [===================>..........] - ETA: 5s - loss: 0.3157 - accuracy: 0.9245
45/65 [===================>..........] - ETA: 4s - loss: 0.3153 - accuracy: 0.9247
46/65 [====================>.........] - ETA: 4s - loss: 0.3159 - accuracy: 0.9248
47/65 [====================>.........] - ETA: 4s - loss: 0.3168 - accuracy: 0.9246
48/65 [=====================>........] - ETA: 4s - loss: 0.3164 - accuracy: 0.9247
49/65 [=====================>........] - ETA: 3s - loss: 0.3168 - accuracy: 0.9246
50/65 [======================>.......] - ETA: 3s - loss: 0.3162 - accuracy: 0.9247
51/65 [======================>.......] - ETA: 3s - loss: 0.3157 - accuracy: 0.9247
52/65 [=======================>......] - ETA: 3s - loss: 0.3153 - accuracy: 0.9249
53/65 [=======================>......] - ETA: 2s - loss: 0.3151 - accuracy: 0.9249
54/65 [=======================>......] - ETA: 2s - loss: 0.3159 - accuracy: 0.9246
55/65 [========================>.....] - ETA: 2s - loss: 0.3150 - accuracy: 0.9247
56/65 [========================>.....] - ETA: 2s - loss: 0.3140 - accuracy: 0.9252
57/65 [=========================>....] - ETA: 1s - loss: 0.3142 - accuracy: 0.9251
58/65 [=========================>....] - ETA: 1s - loss: 0.3147 - accuracy: 0.9251
59/65 [==========================>...] - ETA: 1s - loss: 0.3146 - accuracy: 0.9250
60/65 [==========================>...] - ETA: 1s - loss: 0.3145 - accuracy: 0.9250
61/65 [===========================>..] - ETA: 0s - loss: 0.3146 - accuracy: 0.9250
62/65 [===========================>..] - ETA: 0s - loss: 0.3142 - accuracy: 0.9251
63/65 [============================>.] - ETA: 0s - loss: 0.3145 - accuracy: 0.9248
64/65 [============================>.] - ETA: 0s - loss: 0.3142 - accuracy: 0.9249
65/65 [==============================] - ETA: 0s - loss: 0.3139 - accuracy: 0.9249
65/65 [==============================] - 17s 252ms/step - loss: 0.3139 - accuracy: 0.9249 - val_loss: 0.4338 - val_accuracy: 0.8905 - lr: 3.7500e-04
It took 8.543399965763092 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: 3:45 - loss: 2.8208 - accuracy: 0.0693
2/65 [..............................] - ETA: 27s - loss: 2.7913 - accuracy: 0.0747
3/65 [>.............................] - ETA: 26s - loss: 2.7492 - accuracy: 0.0762
4/65 [>.............................] - ETA: 26s - loss: 2.7233 - accuracy: 0.0791
5/65 [=>............................] - ETA: 26s - loss: 2.7041 - accuracy: 0.0797
6/65 [=>............................] - ETA: 25s - loss: 2.6856 - accuracy: 0.0822
7/65 [==>...........................] - ETA: 25s - loss: 2.6642 - accuracy: 0.0852
8/65 [==>...........................] - ETA: 24s - loss: 2.6446 - accuracy: 0.0891
9/65 [===>..........................] - ETA: 24s - loss: 2.6255 - accuracy: 0.0936
10/65 [===>..........................] - ETA: 23s - loss: 2.6089 - accuracy: 0.0973
11/65 [====>.........................] - ETA: 23s - loss: 2.5951 - accuracy: 0.1001
12/65 [====>.........................] - ETA: 23s - loss: 2.5795 - accuracy: 0.1032
13/65 [=====>........................] - ETA: 22s - loss: 2.5668 - accuracy: 0.1082
14/65 [=====>........................] - ETA: 22s - loss: 2.5561 - accuracy: 0.1127
15/65 [=====>........................] - ETA: 21s - loss: 2.5458 - accuracy: 0.1181
16/65 [======>.......................] - ETA: 21s - loss: 2.5359 - accuracy: 0.1215
17/65 [======>.......................] - ETA: 20s - loss: 2.5259 - accuracy: 0.1258
18/65 [=======>......................] - ETA: 20s - loss: 2.5181 - accuracy: 0.1285
19/65 [=======>......................] - ETA: 20s - loss: 2.5114 - accuracy: 0.1315
20/65 [========>.....................] - ETA: 19s - loss: 2.5032 - accuracy: 0.1358
21/65 [========>.....................] - ETA: 19s - loss: 2.4952 - accuracy: 0.1388
22/65 [=========>....................] - ETA: 18s - loss: 2.4878 - accuracy: 0.1418
23/65 [=========>....................] - ETA: 18s - loss: 2.4810 - accuracy: 0.1451
24/65 [==========>...................] - ETA: 17s - loss: 2.4750 - accuracy: 0.1474
25/65 [==========>...................] - ETA: 17s - loss: 2.4697 - accuracy: 0.1499
26/65 [===========>..................] - ETA: 17s - loss: 2.4643 - accuracy: 0.1521
27/65 [===========>..................] - ETA: 16s - loss: 2.4604 - accuracy: 0.1529
28/65 [===========>..................] - ETA: 16s - loss: 2.4550 - accuracy: 0.1552
29/65 [============>.................] - ETA: 15s - loss: 2.4514 - accuracy: 0.1568
30/65 [============>.................] - ETA: 15s - loss: 2.4474 - accuracy: 0.1582
31/65 [=============>................] - ETA: 14s - loss: 2.4433 - accuracy: 0.1597
32/65 [=============>................] - ETA: 14s - loss: 2.4404 - accuracy: 0.1610
33/65 [==============>...............] - ETA: 14s - loss: 2.4363 - accuracy: 0.1633
34/65 [==============>...............] - ETA: 13s - loss: 2.4329 - accuracy: 0.1654
35/65 [===============>..............] - ETA: 13s - loss: 2.4285 - accuracy: 0.1672
36/65 [===============>..............] - ETA: 12s - loss: 2.4244 - accuracy: 0.1689
37/65 [================>.............] - ETA: 12s - loss: 2.4210 - accuracy: 0.1709
38/65 [================>.............] - ETA: 11s - loss: 2.4173 - accuracy: 0.1727
39/65 [=================>............] - ETA: 11s - loss: 2.4132 - accuracy: 0.1750
40/65 [=================>............] - ETA: 10s - loss: 2.4095 - accuracy: 0.1771
41/65 [=================>............] - ETA: 10s - loss: 2.4051 - accuracy: 0.1787
42/65 [==================>...........] - ETA: 10s - loss: 2.4011 - accuracy: 0.1807
43/65 [==================>...........] - ETA: 9s - loss: 2.3982 - accuracy: 0.1823
44/65 [===================>..........] - ETA: 9s - loss: 2.3940 - accuracy: 0.1846
45/65 [===================>..........] - ETA: 8s - loss: 2.3901 - accuracy: 0.1861
46/65 [====================>.........] - ETA: 8s - loss: 2.3865 - accuracy: 0.1880
47/65 [====================>.........] - ETA: 7s - loss: 2.3831 - accuracy: 0.1893
48/65 [=====================>........] - ETA: 7s - loss: 2.3797 - accuracy: 0.1912
49/65 [=====================>........] - ETA: 6s - loss: 2.3758 - accuracy: 0.1930
50/65 [======================>.......] - ETA: 6s - loss: 2.3722 - accuracy: 0.1945
51/65 [======================>.......] - ETA: 6s - loss: 2.3677 - accuracy: 0.1962
52/65 [=======================>......] - ETA: 5s - loss: 2.3639 - accuracy: 0.1975
53/65 [=======================>......] - ETA: 5s - loss: 2.3599 - accuracy: 0.1991
54/65 [=======================>......] - ETA: 4s - loss: 2.3564 - accuracy: 0.2006
55/65 [========================>.....] - ETA: 4s - loss: 2.3524 - accuracy: 0.2027
56/65 [========================>.....] - ETA: 3s - loss: 2.3487 - accuracy: 0.2046
57/65 [=========================>....] - ETA: 3s - loss: 2.3441 - accuracy: 0.2068
58/65 [=========================>....] - ETA: 3s - loss: 2.3397 - accuracy: 0.2087
59/65 [==========================>...] - ETA: 2s - loss: 2.3355 - accuracy: 0.2107
60/65 [==========================>...] - ETA: 2s - loss: 2.3308 - accuracy: 0.2129
61/65 [===========================>..] - ETA: 1s - loss: 2.3263 - accuracy: 0.2150
62/65 [===========================>..] - ETA: 1s - loss: 2.3218 - accuracy: 0.2171
63/65 [============================>.] - ETA: 0s - loss: 2.3164 - accuracy: 0.2193
64/65 [============================>.] - ETA: 0s - loss: 2.3111 - accuracy: 0.2216
65/65 [==============================] - ETA: 0s - loss: 2.3094 - accuracy: 0.2223
65/65 [==============================] - 32s 451ms/step - loss: 2.3094 - accuracy: 0.2223 - val_loss: 2.3848 - val_accuracy: 0.1549 - lr: 0.0030
Epoch 2/30
1/65 [..............................] - ETA: 42s - loss: 1.9804 - accuracy: 0.3691
2/65 [..............................] - ETA: 28s - loss: 1.9771 - accuracy: 0.3643
3/65 [>.............................] - ETA: 28s - loss: 1.9755 - accuracy: 0.3695
4/65 [>.............................] - ETA: 27s - loss: 1.9613 - accuracy: 0.3794
5/65 [=>............................] - ETA: 27s - loss: 1.9569 - accuracy: 0.3818
6/65 [=>............................] - ETA: 26s - loss: 1.9558 - accuracy: 0.3818
7/65 [==>...........................] - ETA: 26s - loss: 1.9483 - accuracy: 0.3842
8/65 [==>...........................] - ETA: 25s - loss: 1.9425 - accuracy: 0.3853
9/65 [===>..........................] - ETA: 25s - loss: 1.9304 - accuracy: 0.3904
10/65 [===>..........................] - ETA: 24s - loss: 1.9218 - accuracy: 0.3911
11/65 [====>.........................] - ETA: 24s - loss: 1.9070 - accuracy: 0.3965
12/65 [====>.........................] - ETA: 23s - loss: 1.8958 - accuracy: 0.4005
13/65 [=====>........................] - ETA: 23s - loss: 1.8899 - accuracy: 0.4027
14/65 [=====>........................] - ETA: 22s - loss: 1.8761 - accuracy: 0.4074
15/65 [=====>........................] - ETA: 22s - loss: 1.8654 - accuracy: 0.4112
16/65 [======>.......................] - ETA: 21s - loss: 1.8575 - accuracy: 0.4139
17/65 [======>.......................] - ETA: 21s - loss: 1.8467 - accuracy: 0.4179
18/65 [=======>......................] - ETA: 21s - loss: 1.8337 - accuracy: 0.4225
19/65 [=======>......................] - ETA: 20s - loss: 1.8234 - accuracy: 0.4261
20/65 [========>.....................] - ETA: 20s - loss: 1.8137 - accuracy: 0.4297
21/65 [========>.....................] - ETA: 19s - loss: 1.8042 - accuracy: 0.4330
22/65 [=========>....................] - ETA: 19s - loss: 1.7954 - accuracy: 0.4362
23/65 [=========>....................] - ETA: 18s - loss: 1.7876 - accuracy: 0.4393
24/65 [==========>...................] - ETA: 18s - loss: 1.7773 - accuracy: 0.4430
25/65 [==========>...................] - ETA: 17s - loss: 1.7683 - accuracy: 0.4460
26/65 [===========>..................] - ETA: 17s - loss: 1.7593 - accuracy: 0.4497
27/65 [===========>..................] - ETA: 16s - loss: 1.7503 - accuracy: 0.4525
28/65 [===========>..................] - ETA: 16s - loss: 1.7429 - accuracy: 0.4551
29/65 [============>.................] - ETA: 16s - loss: 1.7362 - accuracy: 0.4572
30/65 [============>.................] - ETA: 15s - loss: 1.7275 - accuracy: 0.4607
31/65 [=============>................] - ETA: 15s - loss: 1.7179 - accuracy: 0.4644
32/65 [=============>................] - ETA: 14s - loss: 1.7090 - accuracy: 0.4675
33/65 [==============>...............] - ETA: 14s - loss: 1.7016 - accuracy: 0.4698
34/65 [==============>...............] - ETA: 13s - loss: 1.6945 - accuracy: 0.4723
35/65 [===============>..............] - ETA: 13s - loss: 1.6863 - accuracy: 0.4754
36/65 [===============>..............] - ETA: 12s - loss: 1.6789 - accuracy: 0.4778
37/65 [================>.............] - ETA: 12s - loss: 1.6722 - accuracy: 0.4807
38/65 [================>.............] - ETA: 11s - loss: 1.6648 - accuracy: 0.4828
39/65 [=================>............] - ETA: 11s - loss: 1.6571 - accuracy: 0.4857
40/65 [=================>............] - ETA: 11s - loss: 1.6485 - accuracy: 0.4893
41/65 [=================>............] - ETA: 10s - loss: 1.6406 - accuracy: 0.4926
42/65 [==================>...........] - ETA: 10s - loss: 1.6316 - accuracy: 0.4961
43/65 [==================>...........] - ETA: 9s - loss: 1.6233 - accuracy: 0.4994
44/65 [===================>..........] - ETA: 9s - loss: 1.6165 - accuracy: 0.5019
45/65 [===================>..........] - ETA: 8s - loss: 1.6103 - accuracy: 0.5043
46/65 [====================>.........] - ETA: 8s - loss: 1.6031 - accuracy: 0.5068
47/65 [====================>.........] - ETA: 7s - loss: 1.5958 - accuracy: 0.5097
48/65 [=====================>........] - ETA: 7s - loss: 1.5901 - accuracy: 0.5119
49/65 [=====================>........] - ETA: 7s - loss: 1.5832 - accuracy: 0.5146
50/65 [======================>.......] - ETA: 6s - loss: 1.5757 - accuracy: 0.5172
51/65 [======================>.......] - ETA: 6s - loss: 1.5682 - accuracy: 0.5198
52/65 [=======================>......] - ETA: 5s - loss: 1.5625 - accuracy: 0.5222
53/65 [=======================>......] - ETA: 5s - loss: 1.5561 - accuracy: 0.5248
54/65 [=======================>......] - ETA: 4s - loss: 1.5496 - accuracy: 0.5274
55/65 [========================>.....] - ETA: 4s - loss: 1.5421 - accuracy: 0.5298
56/65 [========================>.....] - ETA: 4s - loss: 1.5355 - accuracy: 0.5321
57/65 [=========================>....] - ETA: 3s - loss: 1.5295 - accuracy: 0.5342
58/65 [=========================>....] - ETA: 3s - loss: 1.5228 - accuracy: 0.5367
59/65 [==========================>...] - ETA: 2s - loss: 1.5175 - accuracy: 0.5387
60/65 [==========================>...] - ETA: 2s - loss: 1.5108 - accuracy: 0.5412
61/65 [===========================>..] - ETA: 1s - loss: 1.5050 - accuracy: 0.5434
62/65 [===========================>..] - ETA: 1s - loss: 1.4986 - accuracy: 0.5459
63/65 [============================>.] - ETA: 0s - loss: 1.4932 - accuracy: 0.5475
64/65 [============================>.] - ETA: 0s - loss: 1.4882 - accuracy: 0.5490
65/65 [==============================] - ETA: 0s - loss: 1.4860 - accuracy: 0.5497
65/65 [==============================] - 30s 454ms/step - loss: 1.4860 - accuracy: 0.5497 - val_loss: 2.2814 - val_accuracy: 0.2540 - lr: 0.0030
Epoch 3/30
1/65 [..............................] - ETA: 42s - loss: 1.1625 - accuracy: 0.6611
2/65 [..............................] - ETA: 28s - loss: 1.1249 - accuracy: 0.6738
3/65 [>.............................] - ETA: 27s - loss: 1.1343 - accuracy: 0.6794
4/65 [>.............................] - ETA: 27s - loss: 1.1387 - accuracy: 0.6758
5/65 [=>............................] - ETA: 26s - loss: 1.1180 - accuracy: 0.6814
6/65 [=>............................] - ETA: 26s - loss: 1.1144 - accuracy: 0.6833
7/65 [==>...........................] - ETA: 25s - loss: 1.1184 - accuracy: 0.6839
8/65 [==>...........................] - ETA: 25s - loss: 1.1066 - accuracy: 0.6892
9/65 [===>..........................] - ETA: 24s - loss: 1.1030 - accuracy: 0.6910
10/65 [===>..........................] - ETA: 24s - loss: 1.0948 - accuracy: 0.6947
11/65 [====>.........................] - ETA: 24s - loss: 1.0904 - accuracy: 0.6936
12/65 [====>.........................] - ETA: 23s - loss: 1.0895 - accuracy: 0.6939
13/65 [=====>........................] - ETA: 23s - loss: 1.0849 - accuracy: 0.6950
14/65 [=====>........................] - ETA: 22s - loss: 1.0828 - accuracy: 0.6957
15/65 [=====>........................] - ETA: 22s - loss: 1.0790 - accuracy: 0.6965
16/65 [======>.......................] - ETA: 21s - loss: 1.0715 - accuracy: 0.6993
17/65 [======>.......................] - ETA: 21s - loss: 1.0673 - accuracy: 0.7007
18/65 [=======>......................] - ETA: 20s - loss: 1.0623 - accuracy: 0.7020
19/65 [=======>......................] - ETA: 20s - loss: 1.0558 - accuracy: 0.7040
20/65 [========>.....................] - ETA: 20s - loss: 1.0477 - accuracy: 0.7068
21/65 [========>.....................] - ETA: 19s - loss: 1.0454 - accuracy: 0.7074
22/65 [=========>....................] - ETA: 19s - loss: 1.0432 - accuracy: 0.7077
23/65 [=========>....................] - ETA: 18s - loss: 1.0372 - accuracy: 0.7093
24/65 [==========>...................] - ETA: 18s - loss: 1.0348 - accuracy: 0.7102
25/65 [==========>...................] - ETA: 17s - loss: 1.0327 - accuracy: 0.7107
26/65 [===========>..................] - ETA: 17s - loss: 1.0287 - accuracy: 0.7125
27/65 [===========>..................] - ETA: 16s - loss: 1.0248 - accuracy: 0.7137
28/65 [===========>..................] - ETA: 16s - loss: 1.0219 - accuracy: 0.7147
29/65 [============>.................] - ETA: 15s - loss: 1.0193 - accuracy: 0.7160
30/65 [============>.................] - ETA: 15s - loss: 1.0162 - accuracy: 0.7166
31/65 [=============>................] - ETA: 15s - loss: 1.0138 - accuracy: 0.7172
32/65 [=============>................] - ETA: 14s - loss: 1.0099 - accuracy: 0.7184
33/65 [==============>...............] - ETA: 14s - loss: 1.0076 - accuracy: 0.7189
34/65 [==============>...............] - ETA: 13s - loss: 1.0041 - accuracy: 0.7202
35/65 [===============>..............] - ETA: 13s - loss: 1.0011 - accuracy: 0.7213
36/65 [===============>..............] - ETA: 12s - loss: 0.9997 - accuracy: 0.7219
37/65 [================>.............] - ETA: 12s - loss: 0.9965 - accuracy: 0.7230
38/65 [================>.............] - ETA: 11s - loss: 0.9949 - accuracy: 0.7234
39/65 [=================>............] - ETA: 11s - loss: 0.9924 - accuracy: 0.7241
40/65 [=================>............] - ETA: 11s - loss: 0.9896 - accuracy: 0.7250
41/65 [=================>............] - ETA: 10s - loss: 0.9874 - accuracy: 0.7256
42/65 [==================>...........] - ETA: 10s - loss: 0.9846 - accuracy: 0.7264
43/65 [==================>...........] - ETA: 9s - loss: 0.9813 - accuracy: 0.7276
44/65 [===================>..........] - ETA: 9s - loss: 0.9795 - accuracy: 0.7277
45/65 [===================>..........] - ETA: 8s - loss: 0.9769 - accuracy: 0.7286
46/65 [====================>.........] - ETA: 8s - loss: 0.9744 - accuracy: 0.7296
47/65 [====================>.........] - ETA: 7s - loss: 0.9716 - accuracy: 0.7306
48/65 [=====================>........] - ETA: 7s - loss: 0.9699 - accuracy: 0.7313
49/65 [=====================>........] - ETA: 7s - loss: 0.9676 - accuracy: 0.7321
50/65 [======================>.......] - ETA: 6s - loss: 0.9665 - accuracy: 0.7326
51/65 [======================>.......] - ETA: 6s - loss: 0.9637 - accuracy: 0.7333
52/65 [=======================>......] - ETA: 5s - loss: 0.9609 - accuracy: 0.7343
53/65 [=======================>......] - ETA: 5s - loss: 0.9598 - accuracy: 0.7349
54/65 [=======================>......] - ETA: 4s - loss: 0.9567 - accuracy: 0.7360
55/65 [========================>.....] - ETA: 4s - loss: 0.9532 - accuracy: 0.7371
56/65 [========================>.....] - ETA: 3s - loss: 0.9513 - accuracy: 0.7379
57/65 [=========================>....] - ETA: 3s - loss: 0.9483 - accuracy: 0.7388
58/65 [=========================>....] - ETA: 3s - loss: 0.9464 - accuracy: 0.7394
59/65 [==========================>...] - ETA: 2s - loss: 0.9444 - accuracy: 0.7400
60/65 [==========================>...] - ETA: 2s - loss: 0.9423 - accuracy: 0.7408
61/65 [===========================>..] - ETA: 1s - loss: 0.9405 - accuracy: 0.7411
62/65 [===========================>..] - ETA: 1s - loss: 0.9396 - accuracy: 0.7415
63/65 [============================>.] - ETA: 0s - loss: 0.9371 - accuracy: 0.7424
64/65 [============================>.] - ETA: 0s - loss: 0.9356 - accuracy: 0.7429
65/65 [==============================] - ETA: 0s - loss: 0.9348 - accuracy: 0.7432
65/65 [==============================] - 29s 450ms/step - loss: 0.9348 - accuracy: 0.7432 - val_loss: 1.8785 - val_accuracy: 0.4494 - lr: 0.0030
Epoch 4/30
1/65 [..............................] - ETA: 42s - loss: 0.8256 - accuracy: 0.7891
2/65 [..............................] - ETA: 28s - loss: 0.7996 - accuracy: 0.7896
3/65 [>.............................] - ETA: 27s - loss: 0.7999 - accuracy: 0.7900
4/65 [>.............................] - ETA: 27s - loss: 0.8014 - accuracy: 0.7886
5/65 [=>............................] - ETA: 26s - loss: 0.8045 - accuracy: 0.7857
6/65 [=>............................] - ETA: 26s - loss: 0.8054 - accuracy: 0.7848
7/65 [==>...........................] - ETA: 25s - loss: 0.8060 - accuracy: 0.7850
8/65 [==>...........................] - ETA: 25s - loss: 0.8036 - accuracy: 0.7865
9/65 [===>..........................] - ETA: 24s - loss: 0.8013 - accuracy: 0.7878
10/65 [===>..........................] - ETA: 24s - loss: 0.8022 - accuracy: 0.7868
11/65 [====>.........................] - ETA: 23s - loss: 0.8023 - accuracy: 0.7867
12/65 [====>.........................] - ETA: 23s - loss: 0.7990 - accuracy: 0.7877
13/65 [=====>........................] - ETA: 22s - loss: 0.7989 - accuracy: 0.7872
14/65 [=====>........................] - ETA: 22s - loss: 0.7962 - accuracy: 0.7893
15/65 [=====>........................] - ETA: 21s - loss: 0.7941 - accuracy: 0.7898
16/65 [======>.......................] - ETA: 21s - loss: 0.7877 - accuracy: 0.7917
17/65 [======>.......................] - ETA: 21s - loss: 0.7887 - accuracy: 0.7914
18/65 [=======>......................] - ETA: 20s - loss: 0.7864 - accuracy: 0.7923
19/65 [=======>......................] - ETA: 20s - loss: 0.7838 - accuracy: 0.7931
20/65 [========>.....................] - ETA: 19s - loss: 0.7813 - accuracy: 0.7937
21/65 [========>.....................] - ETA: 19s - loss: 0.7767 - accuracy: 0.7950
22/65 [=========>....................] - ETA: 18s - loss: 0.7760 - accuracy: 0.7949
23/65 [=========>....................] - ETA: 18s - loss: 0.7766 - accuracy: 0.7949
24/65 [==========>...................] - ETA: 17s - loss: 0.7745 - accuracy: 0.7959
25/65 [==========>...................] - ETA: 17s - loss: 0.7738 - accuracy: 0.7963
26/65 [===========>..................] - ETA: 17s - loss: 0.7715 - accuracy: 0.7974
27/65 [===========>..................] - ETA: 16s - loss: 0.7702 - accuracy: 0.7981
28/65 [===========>..................] - ETA: 16s - loss: 0.7680 - accuracy: 0.7984
29/65 [============>.................] - ETA: 15s - loss: 0.7655 - accuracy: 0.7994
30/65 [============>.................] - ETA: 15s - loss: 0.7650 - accuracy: 0.7996
31/65 [=============>................] - ETA: 14s - loss: 0.7636 - accuracy: 0.8000
32/65 [=============>................] - ETA: 14s - loss: 0.7635 - accuracy: 0.7999
33/65 [==============>...............] - ETA: 14s - loss: 0.7639 - accuracy: 0.7993
34/65 [==============>...............] - ETA: 13s - loss: 0.7631 - accuracy: 0.7996
35/65 [===============>..............] - ETA: 13s - loss: 0.7639 - accuracy: 0.7993
36/65 [===============>..............] - ETA: 12s - loss: 0.7640 - accuracy: 0.7994
37/65 [================>.............] - ETA: 12s - loss: 0.7631 - accuracy: 0.7994
38/65 [================>.............] - ETA: 11s - loss: 0.7627 - accuracy: 0.7995
39/65 [=================>............] - ETA: 11s - loss: 0.7636 - accuracy: 0.7991
40/65 [=================>............] - ETA: 10s - loss: 0.7624 - accuracy: 0.7992
41/65 [=================>............] - ETA: 10s - loss: 0.7618 - accuracy: 0.7994
42/65 [==================>...........] - ETA: 10s - loss: 0.7604 - accuracy: 0.8000
43/65 [==================>...........] - ETA: 9s - loss: 0.7598 - accuracy: 0.8003
44/65 [===================>..........] - ETA: 9s - loss: 0.7599 - accuracy: 0.8000
45/65 [===================>..........] - ETA: 8s - loss: 0.7584 - accuracy: 0.8008
46/65 [====================>.........] - ETA: 8s - loss: 0.7580 - accuracy: 0.8009
47/65 [====================>.........] - ETA: 7s - loss: 0.7577 - accuracy: 0.8008
48/65 [=====================>........] - ETA: 7s - loss: 0.7572 - accuracy: 0.8010
49/65 [=====================>........] - ETA: 7s - loss: 0.7576 - accuracy: 0.8008
50/65 [======================>.......] - ETA: 6s - loss: 0.7564 - accuracy: 0.8012
51/65 [======================>.......] - ETA: 6s - loss: 0.7551 - accuracy: 0.8014
52/65 [=======================>......] - ETA: 5s - loss: 0.7542 - accuracy: 0.8016
53/65 [=======================>......] - ETA: 5s - loss: 0.7538 - accuracy: 0.8015
54/65 [=======================>......] - ETA: 4s - loss: 0.7529 - accuracy: 0.8016
55/65 [========================>.....] - ETA: 4s - loss: 0.7523 - accuracy: 0.8017
56/65 [========================>.....] - ETA: 3s - loss: 0.7516 - accuracy: 0.8018
57/65 [=========================>....] - ETA: 3s - loss: 0.7509 - accuracy: 0.8018
58/65 [=========================>....] - ETA: 3s - loss: 0.7500 - accuracy: 0.8020
59/65 [==========================>...] - ETA: 2s - loss: 0.7502 - accuracy: 0.8021
60/65 [==========================>...] - ETA: 2s - loss: 0.7499 - accuracy: 0.8019
61/65 [===========================>..] - ETA: 1s - loss: 0.7522 - accuracy: 0.8011
62/65 [===========================>..] - ETA: 1s - loss: 0.7535 - accuracy: 0.8007
63/65 [============================>.] - ETA: 0s - loss: 0.7536 - accuracy: 0.8007
64/65 [============================>.] - ETA: 0s - loss: 0.7540 - accuracy: 0.8007
65/65 [==============================] - ETA: 0s - loss: 0.7543 - accuracy: 0.8007
65/65 [==============================] - 29s 449ms/step - loss: 0.7543 - accuracy: 0.8007 - val_loss: 2.1225 - val_accuracy: 0.3832 - lr: 0.0030
Epoch 5/30
1/65 [..............................] - ETA: 42s - loss: 0.6934 - accuracy: 0.8135
2/65 [..............................] - ETA: 27s - loss: 0.7154 - accuracy: 0.8120
3/65 [>.............................] - ETA: 26s - loss: 0.7424 - accuracy: 0.7998
4/65 [>.............................] - ETA: 26s - loss: 0.7432 - accuracy: 0.8005
5/65 [=>............................] - ETA: 26s - loss: 0.7473 - accuracy: 0.7998
6/65 [=>............................] - ETA: 25s - loss: 0.7458 - accuracy: 0.8014
7/65 [==>...........................] - ETA: 25s - loss: 0.7363 - accuracy: 0.8043
8/65 [==>...........................] - ETA: 24s - loss: 0.7405 - accuracy: 0.8035
9/65 [===>..........................] - ETA: 24s - loss: 0.7442 - accuracy: 0.8036
10/65 [===>..........................] - ETA: 24s - loss: 0.7420 - accuracy: 0.8047
11/65 [====>.........................] - ETA: 23s - loss: 0.7430 - accuracy: 0.8036
12/65 [====>.........................] - ETA: 23s - loss: 0.7413 - accuracy: 0.8049
13/65 [=====>........................] - ETA: 22s - loss: 0.7376 - accuracy: 0.8061
14/65 [=====>........................] - ETA: 22s - loss: 0.7379 - accuracy: 0.8059
15/65 [=====>........................] - ETA: 21s - loss: 0.7370 - accuracy: 0.8059
16/65 [======>.......................] - ETA: 21s - loss: 0.7351 - accuracy: 0.8065
17/65 [======>.......................] - ETA: 21s - loss: 0.7325 - accuracy: 0.8066
18/65 [=======>......................] - ETA: 20s - loss: 0.7294 - accuracy: 0.8076
19/65 [=======>......................] - ETA: 20s - loss: 0.7281 - accuracy: 0.8076
20/65 [========>.....................] - ETA: 19s - loss: 0.7304 - accuracy: 0.8072
21/65 [========>.....................] - ETA: 19s - loss: 0.7304 - accuracy: 0.8072
22/65 [=========>....................] - ETA: 18s - loss: 0.7292 - accuracy: 0.8072
23/65 [=========>....................] - ETA: 18s - loss: 0.7283 - accuracy: 0.8074
24/65 [==========>...................] - ETA: 17s - loss: 0.7252 - accuracy: 0.8084
25/65 [==========>...................] - ETA: 17s - loss: 0.7243 - accuracy: 0.8080
26/65 [===========>..................] - ETA: 17s - loss: 0.7226 - accuracy: 0.8077
27/65 [===========>..................] - ETA: 16s - loss: 0.7204 - accuracy: 0.8083
28/65 [===========>..................] - ETA: 16s - loss: 0.7191 - accuracy: 0.8092
29/65 [============>.................] - ETA: 15s - loss: 0.7170 - accuracy: 0.8099
30/65 [============>.................] - ETA: 15s - loss: 0.7144 - accuracy: 0.8105
31/65 [=============>................] - ETA: 14s - loss: 0.7131 - accuracy: 0.8108
32/65 [=============>................] - ETA: 14s - loss: 0.7127 - accuracy: 0.8109
33/65 [==============>...............] - ETA: 13s - loss: 0.7119 - accuracy: 0.8111
34/65 [==============>...............] - ETA: 13s - loss: 0.7134 - accuracy: 0.8108
35/65 [===============>..............] - ETA: 13s - loss: 0.7138 - accuracy: 0.8107
36/65 [===============>..............] - ETA: 12s - loss: 0.7137 - accuracy: 0.8111
37/65 [================>.............] - ETA: 12s - loss: 0.7118 - accuracy: 0.8116
38/65 [================>.............] - ETA: 11s - loss: 0.7112 - accuracy: 0.8117
39/65 [=================>............] - ETA: 11s - loss: 0.7114 - accuracy: 0.8113
40/65 [=================>............] - ETA: 10s - loss: 0.7106 - accuracy: 0.8115
41/65 [=================>............] - ETA: 10s - loss: 0.7089 - accuracy: 0.8119
42/65 [==================>...........] - ETA: 10s - loss: 0.7089 - accuracy: 0.8118
43/65 [==================>...........] - ETA: 9s - loss: 0.7093 - accuracy: 0.8113
44/65 [===================>..........] - ETA: 9s - loss: 0.7077 - accuracy: 0.8117
45/65 [===================>..........] - ETA: 8s - loss: 0.7072 - accuracy: 0.8119
46/65 [====================>.........] - ETA: 8s - loss: 0.7062 - accuracy: 0.8121
47/65 [====================>.........] - ETA: 7s - loss: 0.7050 - accuracy: 0.8125
48/65 [=====================>........] - ETA: 7s - loss: 0.7047 - accuracy: 0.8128
49/65 [=====================>........] - ETA: 6s - loss: 0.7034 - accuracy: 0.8131
50/65 [======================>.......] - ETA: 6s - loss: 0.7038 - accuracy: 0.8129
51/65 [======================>.......] - ETA: 6s - loss: 0.7030 - accuracy: 0.8130
52/65 [=======================>......] - ETA: 5s - loss: 0.7021 - accuracy: 0.8134
53/65 [=======================>......] - ETA: 5s - loss: 0.7006 - accuracy: 0.8138
54/65 [=======================>......] - ETA: 4s - loss: 0.6999 - accuracy: 0.8139
55/65 [========================>.....] - ETA: 4s - loss: 0.6984 - accuracy: 0.8142
56/65 [========================>.....] - ETA: 3s - loss: 0.6967 - accuracy: 0.8147
57/65 [=========================>....] - ETA: 3s - loss: 0.6948 - accuracy: 0.8153
58/65 [=========================>....] - ETA: 3s - loss: 0.6940 - accuracy: 0.8154
59/65 [==========================>...] - ETA: 2s - loss: 0.6934 - accuracy: 0.8156
60/65 [==========================>...] - ETA: 2s - loss: 0.6937 - accuracy: 0.8155
61/65 [===========================>..] - ETA: 1s - loss: 0.6942 - accuracy: 0.8153
62/65 [===========================>..] - ETA: 1s - loss: 0.6952 - accuracy: 0.8150
63/65 [============================>.] - ETA: 0s - loss: 0.6952 - accuracy: 0.8149
64/65 [============================>.] - ETA: 0s - loss: 0.6946 - accuracy: 0.8151
65/65 [==============================] - ETA: 0s - loss: 0.6948 - accuracy: 0.8149
65/65 [==============================] - 29s 447ms/step - loss: 0.6948 - accuracy: 0.8149 - val_loss: 2.0118 - val_accuracy: 0.3888 - lr: 0.0030
Epoch 6/30
1/65 [..............................] - ETA: 42s - loss: 0.6644 - accuracy: 0.8164
2/65 [..............................] - ETA: 28s - loss: 0.6852 - accuracy: 0.8188
3/65 [>.............................] - ETA: 27s - loss: 0.7172 - accuracy: 0.8060
4/65 [>.............................] - ETA: 27s - loss: 0.7435 - accuracy: 0.7971
5/65 [=>............................] - ETA: 26s - loss: 0.7454 - accuracy: 0.7951
6/65 [=>............................] - ETA: 26s - loss: 0.7383 - accuracy: 0.7974
7/65 [==>...........................] - ETA: 25s - loss: 0.7328 - accuracy: 0.7999
8/65 [==>...........................] - ETA: 25s - loss: 0.7292 - accuracy: 0.8011
9/65 [===>..........................] - ETA: 24s - loss: 0.7287 - accuracy: 0.8011
10/65 [===>..........................] - ETA: 24s - loss: 0.7287 - accuracy: 0.8005
11/65 [====>.........................] - ETA: 23s - loss: 0.7263 - accuracy: 0.8008
12/65 [====>.........................] - ETA: 23s - loss: 0.7226 - accuracy: 0.8025
13/65 [=====>........................] - ETA: 22s - loss: 0.7220 - accuracy: 0.8024
14/65 [=====>........................] - ETA: 22s - loss: 0.7160 - accuracy: 0.8048
15/65 [=====>........................] - ETA: 22s - loss: 0.7159 - accuracy: 0.8053
16/65 [======>.......................] - ETA: 21s - loss: 0.7184 - accuracy: 0.8038
17/65 [======>.......................] - ETA: 21s - loss: 0.7154 - accuracy: 0.8049
18/65 [=======>......................] - ETA: 20s - loss: 0.7136 - accuracy: 0.8053
19/65 [=======>......................] - ETA: 20s - loss: 0.7128 - accuracy: 0.8053
20/65 [========>.....................] - ETA: 19s - loss: 0.7111 - accuracy: 0.8060
21/65 [========>.....................] - ETA: 19s - loss: 0.7100 - accuracy: 0.8065
22/65 [=========>....................] - ETA: 18s - loss: 0.7052 - accuracy: 0.8085
23/65 [=========>....................] - ETA: 18s - loss: 0.7006 - accuracy: 0.8100
24/65 [==========>...................] - ETA: 18s - loss: 0.6959 - accuracy: 0.8115
25/65 [==========>...................] - ETA: 17s - loss: 0.6930 - accuracy: 0.8123
26/65 [===========>..................] - ETA: 17s - loss: 0.6917 - accuracy: 0.8131
27/65 [===========>..................] - ETA: 16s - loss: 0.6907 - accuracy: 0.8135
28/65 [===========>..................] - ETA: 16s - loss: 0.6895 - accuracy: 0.8137
29/65 [============>.................] - ETA: 15s - loss: 0.6886 - accuracy: 0.8135
30/65 [============>.................] - ETA: 15s - loss: 0.6890 - accuracy: 0.8138
31/65 [=============>................] - ETA: 14s - loss: 0.6864 - accuracy: 0.8146
32/65 [=============>................] - ETA: 14s - loss: 0.6835 - accuracy: 0.8156
33/65 [==============>...............] - ETA: 14s - loss: 0.6813 - accuracy: 0.8162
34/65 [==============>...............] - ETA: 13s - loss: 0.6805 - accuracy: 0.8165
35/65 [===============>..............] - ETA: 13s - loss: 0.6797 - accuracy: 0.8170
36/65 [===============>..............] - ETA: 12s - loss: 0.6786 - accuracy: 0.8175
37/65 [================>.............] - ETA: 12s - loss: 0.6794 - accuracy: 0.8171
38/65 [================>.............] - ETA: 11s - loss: 0.6784 - accuracy: 0.8176
39/65 [=================>............] - ETA: 11s - loss: 0.6775 - accuracy: 0.8180
40/65 [=================>............] - ETA: 10s - loss: 0.6783 - accuracy: 0.8177
41/65 [=================>............] - ETA: 10s - loss: 0.6783 - accuracy: 0.8176
42/65 [==================>...........] - ETA: 10s - loss: 0.6778 - accuracy: 0.8177
43/65 [==================>...........] - ETA: 9s - loss: 0.6778 - accuracy: 0.8177
44/65 [===================>..........] - ETA: 9s - loss: 0.6775 - accuracy: 0.8180
45/65 [===================>..........] - ETA: 8s - loss: 0.6774 - accuracy: 0.8181
46/65 [====================>.........] - ETA: 8s - loss: 0.6773 - accuracy: 0.8180
47/65 [====================>.........] - ETA: 7s - loss: 0.6776 - accuracy: 0.8180
48/65 [=====================>........] - ETA: 7s - loss: 0.6756 - accuracy: 0.8186
49/65 [=====================>........] - ETA: 7s - loss: 0.6755 - accuracy: 0.8186
50/65 [======================>.......] - ETA: 6s - loss: 0.6749 - accuracy: 0.8190
51/65 [======================>.......] - ETA: 6s - loss: 0.6744 - accuracy: 0.8190
52/65 [=======================>......] - ETA: 5s - loss: 0.6744 - accuracy: 0.8187
53/65 [=======================>......] - ETA: 5s - loss: 0.6729 - accuracy: 0.8192
54/65 [=======================>......] - ETA: 4s - loss: 0.6718 - accuracy: 0.8196
55/65 [========================>.....] - ETA: 4s - loss: 0.6711 - accuracy: 0.8198
56/65 [========================>.....] - ETA: 3s - loss: 0.6710 - accuracy: 0.8199
57/65 [=========================>....] - ETA: 3s - loss: 0.6699 - accuracy: 0.8200
58/65 [=========================>....] - ETA: 3s - loss: 0.6688 - accuracy: 0.8205
59/65 [==========================>...] - ETA: 2s - loss: 0.6691 - accuracy: 0.8204
60/65 [==========================>...] - ETA: 2s - loss: 0.6693 - accuracy: 0.8203
61/65 [===========================>..] - ETA: 1s - loss: 0.6690 - accuracy: 0.8204
62/65 [===========================>..] - ETA: 1s - loss: 0.6694 - accuracy: 0.8203
63/65 [============================>.] - ETA: 0s - loss: 0.6697 - accuracy: 0.8202
64/65 [============================>.] - ETA: 0s - loss: 0.6692 - accuracy: 0.8203
65/65 [==============================] - ETA: 0s - loss: 0.6690 - accuracy: 0.8204
65/65 [==============================] - 29s 448ms/step - loss: 0.6690 - accuracy: 0.8204 - val_loss: 1.6700 - val_accuracy: 0.4889 - lr: 0.0030
Epoch 7/30
1/65 [..............................] - ETA: 42s - loss: 0.6857 - accuracy: 0.8311
2/65 [..............................] - ETA: 27s - loss: 0.6662 - accuracy: 0.8257
3/65 [>.............................] - ETA: 27s - loss: 0.6666 - accuracy: 0.8229
4/65 [>.............................] - ETA: 27s - loss: 0.6611 - accuracy: 0.8242
5/65 [=>............................] - ETA: 26s - loss: 0.6663 - accuracy: 0.8230
6/65 [=>............................] - ETA: 26s - loss: 0.6673 - accuracy: 0.8236
7/65 [==>...........................] - ETA: 25s - loss: 0.6738 - accuracy: 0.8224
8/65 [==>...........................] - ETA: 25s - loss: 0.6721 - accuracy: 0.8221
9/65 [===>..........................] - ETA: 24s - loss: 0.6664 - accuracy: 0.8239
10/65 [===>..........................] - ETA: 24s - loss: 0.6642 - accuracy: 0.8234
11/65 [====>.........................] - ETA: 24s - loss: 0.6615 - accuracy: 0.8242
12/65 [====>.........................] - ETA: 23s - loss: 0.6622 - accuracy: 0.8233
13/65 [=====>........................] - ETA: 23s - loss: 0.6624 - accuracy: 0.8235
14/65 [=====>........................] - ETA: 22s - loss: 0.6590 - accuracy: 0.8255
15/65 [=====>........................] - ETA: 22s - loss: 0.6607 - accuracy: 0.8239
16/65 [======>.......................] - ETA: 21s - loss: 0.6586 - accuracy: 0.8244
17/65 [======>.......................] - ETA: 21s - loss: 0.6557 - accuracy: 0.8257
18/65 [=======>......................] - ETA: 20s - loss: 0.6546 - accuracy: 0.8261
19/65 [=======>......................] - ETA: 20s - loss: 0.6507 - accuracy: 0.8274
20/65 [========>.....................] - ETA: 19s - loss: 0.6519 - accuracy: 0.8266
21/65 [========>.....................] - ETA: 19s - loss: 0.6502 - accuracy: 0.8268
22/65 [=========>....................] - ETA: 18s - loss: 0.6468 - accuracy: 0.8276
23/65 [=========>....................] - ETA: 18s - loss: 0.6452 - accuracy: 0.8278
24/65 [==========>...................] - ETA: 18s - loss: 0.6454 - accuracy: 0.8278
25/65 [==========>...................] - ETA: 17s - loss: 0.6464 - accuracy: 0.8275
26/65 [===========>..................] - ETA: 17s - loss: 0.6458 - accuracy: 0.8274
27/65 [===========>..................] - ETA: 16s - loss: 0.6435 - accuracy: 0.8282
28/65 [===========>..................] - ETA: 16s - loss: 0.6460 - accuracy: 0.8275
29/65 [============>.................] - ETA: 15s - loss: 0.6476 - accuracy: 0.8266
30/65 [============>.................] - ETA: 15s - loss: 0.6493 - accuracy: 0.8258
31/65 [=============>................] - ETA: 14s - loss: 0.6493 - accuracy: 0.8257
32/65 [=============>................] - ETA: 14s - loss: 0.6489 - accuracy: 0.8258
33/65 [==============>...............] - ETA: 14s - loss: 0.6493 - accuracy: 0.8255
34/65 [==============>...............] - ETA: 13s - loss: 0.6522 - accuracy: 0.8246
35/65 [===============>..............] - ETA: 13s - loss: 0.6518 - accuracy: 0.8246
36/65 [===============>..............] - ETA: 12s - loss: 0.6526 - accuracy: 0.8243
37/65 [================>.............] - ETA: 12s - loss: 0.6527 - accuracy: 0.8241
38/65 [================>.............] - ETA: 11s - loss: 0.6542 - accuracy: 0.8235
39/65 [=================>............] - ETA: 11s - loss: 0.6536 - accuracy: 0.8235
40/65 [=================>............] - ETA: 10s - loss: 0.6536 - accuracy: 0.8235
41/65 [=================>............] - ETA: 10s - loss: 0.6531 - accuracy: 0.8233
42/65 [==================>...........] - ETA: 10s - loss: 0.6528 - accuracy: 0.8236
43/65 [==================>...........] - ETA: 9s - loss: 0.6540 - accuracy: 0.8233
44/65 [===================>..........] - ETA: 9s - loss: 0.6544 - accuracy: 0.8233
45/65 [===================>..........] - ETA: 8s - loss: 0.6543 - accuracy: 0.8229
46/65 [====================>.........] - ETA: 8s - loss: 0.6544 - accuracy: 0.8227
47/65 [====================>.........] - ETA: 7s - loss: 0.6534 - accuracy: 0.8229
48/65 [=====================>........] - ETA: 7s - loss: 0.6525 - accuracy: 0.8232
49/65 [=====================>........] - ETA: 6s - loss: 0.6507 - accuracy: 0.8237
50/65 [======================>.......] - ETA: 6s - loss: 0.6499 - accuracy: 0.8240
51/65 [======================>.......] - ETA: 6s - loss: 0.6502 - accuracy: 0.8240
52/65 [=======================>......] - ETA: 5s - loss: 0.6494 - accuracy: 0.8242
53/65 [=======================>......] - ETA: 5s - loss: 0.6499 - accuracy: 0.8241
54/65 [=======================>......] - ETA: 4s - loss: 0.6482 - accuracy: 0.8246
55/65 [========================>.....] - ETA: 4s - loss: 0.6474 - accuracy: 0.8249
56/65 [========================>.....] - ETA: 3s - loss: 0.6472 - accuracy: 0.8249
57/65 [=========================>....] - ETA: 3s - loss: 0.6461 - accuracy: 0.8252
58/65 [=========================>....] - ETA: 3s - loss: 0.6473 - accuracy: 0.8247
59/65 [==========================>...] - ETA: 2s - loss: 0.6468 - accuracy: 0.8248
60/65 [==========================>...] - ETA: 2s - loss: 0.6471 - accuracy: 0.8248
61/65 [===========================>..] - ETA: 1s - loss: 0.6474 - accuracy: 0.8246
62/65 [===========================>..] - ETA: 1s - loss: 0.6473 - accuracy: 0.8246
63/65 [============================>.] - ETA: 0s - loss: 0.6473 - accuracy: 0.8247
64/65 [============================>.] - ETA: 0s - loss: 0.6469 - accuracy: 0.8247
65/65 [==============================] - ETA: 0s - loss: 0.6468 - accuracy: 0.8247
65/65 [==============================] - 29s 447ms/step - loss: 0.6468 - accuracy: 0.8247 - val_loss: 1.0855 - val_accuracy: 0.6755 - lr: 0.0030
Epoch 8/30
1/65 [..............................] - ETA: 41s - loss: 0.6981 - accuracy: 0.8066
2/65 [..............................] - ETA: 27s - loss: 0.6775 - accuracy: 0.8149
3/65 [>.............................] - ETA: 27s - loss: 0.6699 - accuracy: 0.8151
4/65 [>.............................] - ETA: 26s - loss: 0.6568 - accuracy: 0.8210
5/65 [=>............................] - ETA: 26s - loss: 0.6536 - accuracy: 0.8236
6/65 [=>............................] - ETA: 25s - loss: 0.6468 - accuracy: 0.8271
7/65 [==>...........................] - ETA: 25s - loss: 0.6454 - accuracy: 0.8277
8/65 [==>...........................] - ETA: 24s - loss: 0.6451 - accuracy: 0.8276
9/65 [===>..........................] - ETA: 24s - loss: 0.6460 - accuracy: 0.8264
10/65 [===>..........................] - ETA: 24s - loss: 0.6435 - accuracy: 0.8268
11/65 [====>.........................] - ETA: 23s - loss: 0.6438 - accuracy: 0.8258
12/65 [====>.........................] - ETA: 23s - loss: 0.6469 - accuracy: 0.8247
13/65 [=====>........................] - ETA: 22s - loss: 0.6462 - accuracy: 0.8241
14/65 [=====>........................] - ETA: 22s - loss: 0.6425 - accuracy: 0.8250
15/65 [=====>........................] - ETA: 21s - loss: 0.6382 - accuracy: 0.8267
16/65 [======>.......................] - ETA: 21s - loss: 0.6403 - accuracy: 0.8259
17/65 [======>.......................] - ETA: 20s - loss: 0.6409 - accuracy: 0.8257
18/65 [=======>......................] - ETA: 20s - loss: 0.6398 - accuracy: 0.8258
19/65 [=======>......................] - ETA: 20s - loss: 0.6408 - accuracy: 0.8256
20/65 [========>.....................] - ETA: 19s - loss: 0.6392 - accuracy: 0.8257
21/65 [========>.....................] - ETA: 19s - loss: 0.6382 - accuracy: 0.8259
22/65 [=========>....................] - ETA: 18s - loss: 0.6389 - accuracy: 0.8261
23/65 [=========>....................] - ETA: 18s - loss: 0.6384 - accuracy: 0.8264
24/65 [==========>...................] - ETA: 17s - loss: 0.6397 - accuracy: 0.8260
25/65 [==========>...................] - ETA: 17s - loss: 0.6415 - accuracy: 0.8256
26/65 [===========>..................] - ETA: 16s - loss: 0.6425 - accuracy: 0.8256
27/65 [===========>..................] - ETA: 16s - loss: 0.6426 - accuracy: 0.8252
28/65 [===========>..................] - ETA: 16s - loss: 0.6436 - accuracy: 0.8249
29/65 [============>.................] - ETA: 15s - loss: 0.6460 - accuracy: 0.8245
30/65 [============>.................] - ETA: 15s - loss: 0.6472 - accuracy: 0.8238
31/65 [=============>................] - ETA: 14s - loss: 0.6482 - accuracy: 0.8237
32/65 [=============>................] - ETA: 14s - loss: 0.6476 - accuracy: 0.8239
33/65 [==============>...............] - ETA: 13s - loss: 0.6476 - accuracy: 0.8244
34/65 [==============>...............] - ETA: 13s - loss: 0.6477 - accuracy: 0.8244
35/65 [===============>..............] - ETA: 13s - loss: 0.6488 - accuracy: 0.8238
36/65 [===============>..............] - ETA: 12s - loss: 0.6485 - accuracy: 0.8238
37/65 [================>.............] - ETA: 12s - loss: 0.6500 - accuracy: 0.8233
38/65 [================>.............] - ETA: 11s - loss: 0.6513 - accuracy: 0.8231
39/65 [=================>............] - ETA: 11s - loss: 0.6525 - accuracy: 0.8226
40/65 [=================>............] - ETA: 10s - loss: 0.6525 - accuracy: 0.8228
41/65 [=================>............] - ETA: 10s - loss: 0.6526 - accuracy: 0.8225
42/65 [==================>...........] - ETA: 9s - loss: 0.6529 - accuracy: 0.8225
43/65 [==================>...........] - ETA: 9s - loss: 0.6542 - accuracy: 0.8220
44/65 [===================>..........] - ETA: 9s - loss: 0.6537 - accuracy: 0.8223
45/65 [===================>..........] - ETA: 8s - loss: 0.6532 - accuracy: 0.8226
46/65 [====================>.........] - ETA: 8s - loss: 0.6533 - accuracy: 0.8226
47/65 [====================>.........] - ETA: 7s - loss: 0.6540 - accuracy: 0.8226
48/65 [=====================>........] - ETA: 7s - loss: 0.6560 - accuracy: 0.8220
49/65 [=====================>........] - ETA: 6s - loss: 0.6558 - accuracy: 0.8220
50/65 [======================>.......] - ETA: 6s - loss: 0.6545 - accuracy: 0.8226
51/65 [======================>.......] - ETA: 6s - loss: 0.6547 - accuracy: 0.8227
52/65 [=======================>......] - ETA: 5s - loss: 0.6538 - accuracy: 0.8227
53/65 [=======================>......] - ETA: 5s - loss: 0.6524 - accuracy: 0.8229
54/65 [=======================>......] - ETA: 4s - loss: 0.6523 - accuracy: 0.8231
55/65 [========================>.....] - ETA: 4s - loss: 0.6517 - accuracy: 0.8232
56/65 [========================>.....] - ETA: 3s - loss: 0.6510 - accuracy: 0.8234
57/65 [=========================>....] - ETA: 3s - loss: 0.6500 - accuracy: 0.8239
58/65 [=========================>....] - ETA: 3s - loss: 0.6498 - accuracy: 0.8238
59/65 [==========================>...] - ETA: 2s - loss: 0.6491 - accuracy: 0.8241
60/65 [==========================>...] - ETA: 2s - loss: 0.6488 - accuracy: 0.8241
61/65 [===========================>..] - ETA: 1s - loss: 0.6485 - accuracy: 0.8239
62/65 [===========================>..] - ETA: 1s - loss: 0.6483 - accuracy: 0.8239
63/65 [============================>.] - ETA: 0s - loss: 0.6476 - accuracy: 0.8241
64/65 [============================>.] - ETA: 0s - loss: 0.6480 - accuracy: 0.8240
65/65 [==============================] - ETA: 0s - loss: 0.6484 - accuracy: 0.8239
65/65 [==============================] - 29s 443ms/step - loss: 0.6484 - accuracy: 0.8239 - val_loss: 0.8796 - val_accuracy: 0.7495 - lr: 0.0030
Epoch 9/30
1/65 [..............................] - ETA: 42s - loss: 0.6181 - accuracy: 0.8252
2/65 [..............................] - ETA: 26s - loss: 0.6502 - accuracy: 0.8198
3/65 [>.............................] - ETA: 26s - loss: 0.6343 - accuracy: 0.8275
4/65 [>.............................] - ETA: 26s - loss: 0.6257 - accuracy: 0.8296
5/65 [=>............................] - ETA: 26s - loss: 0.6313 - accuracy: 0.8303
6/65 [=>............................] - ETA: 25s - loss: 0.6264 - accuracy: 0.8306
7/65 [==>...........................] - ETA: 25s - loss: 0.6254 - accuracy: 0.8313
8/65 [==>...........................] - ETA: 24s - loss: 0.6277 - accuracy: 0.8296
9/65 [===>..........................] - ETA: 24s - loss: 0.6214 - accuracy: 0.8327
10/65 [===>..........................] - ETA: 23s - loss: 0.6210 - accuracy: 0.8322
11/65 [====>.........................] - ETA: 23s - loss: 0.6237 - accuracy: 0.8321
12/65 [====>.........................] - ETA: 22s - loss: 0.6207 - accuracy: 0.8337
13/65 [=====>........................] - ETA: 22s - loss: 0.6217 - accuracy: 0.8335
14/65 [=====>........................] - ETA: 22s - loss: 0.6213 - accuracy: 0.8335
15/65 [=====>........................] - ETA: 21s - loss: 0.6212 - accuracy: 0.8335
16/65 [======>.......................] - ETA: 21s - loss: 0.6178 - accuracy: 0.8344
17/65 [======>.......................] - ETA: 20s - loss: 0.6155 - accuracy: 0.8355
18/65 [=======>......................] - ETA: 20s - loss: 0.6108 - accuracy: 0.8367
19/65 [=======>......................] - ETA: 19s - loss: 0.6096 - accuracy: 0.8370
20/65 [========>.....................] - ETA: 19s - loss: 0.6084 - accuracy: 0.8371
21/65 [========>.....................] - ETA: 19s - loss: 0.6077 - accuracy: 0.8372
22/65 [=========>....................] - ETA: 18s - loss: 0.6075 - accuracy: 0.8372
23/65 [=========>....................] - ETA: 18s - loss: 0.6073 - accuracy: 0.8368
24/65 [==========>...................] - ETA: 17s - loss: 0.6085 - accuracy: 0.8365
25/65 [==========>...................] - ETA: 17s - loss: 0.6049 - accuracy: 0.8374
26/65 [===========>..................] - ETA: 16s - loss: 0.6064 - accuracy: 0.8372
27/65 [===========>..................] - ETA: 16s - loss: 0.6033 - accuracy: 0.8384
28/65 [===========>..................] - ETA: 16s - loss: 0.6028 - accuracy: 0.8386
29/65 [============>.................] - ETA: 15s - loss: 0.6008 - accuracy: 0.8393
30/65 [============>.................] - ETA: 15s - loss: 0.6030 - accuracy: 0.8384
31/65 [=============>................] - ETA: 14s - loss: 0.6015 - accuracy: 0.8387
32/65 [=============>................] - ETA: 14s - loss: 0.6011 - accuracy: 0.8390
33/65 [==============>...............] - ETA: 13s - loss: 0.6008 - accuracy: 0.8390
34/65 [==============>...............] - ETA: 13s - loss: 0.6009 - accuracy: 0.8390
35/65 [===============>..............] - ETA: 12s - loss: 0.5995 - accuracy: 0.8395
36/65 [===============>..............] - ETA: 12s - loss: 0.6004 - accuracy: 0.8393
37/65 [================>.............] - ETA: 12s - loss: 0.5996 - accuracy: 0.8397
38/65 [================>.............] - ETA: 11s - loss: 0.5995 - accuracy: 0.8397
39/65 [=================>............] - ETA: 11s - loss: 0.5998 - accuracy: 0.8396
40/65 [=================>............] - ETA: 10s - loss: 0.6006 - accuracy: 0.8394
41/65 [=================>............] - ETA: 10s - loss: 0.5990 - accuracy: 0.8399
42/65 [==================>...........] - ETA: 9s - loss: 0.5984 - accuracy: 0.8400
43/65 [==================>...........] - ETA: 9s - loss: 0.5983 - accuracy: 0.8402
44/65 [===================>..........] - ETA: 9s - loss: 0.5980 - accuracy: 0.8406
45/65 [===================>..........] - ETA: 8s - loss: 0.5978 - accuracy: 0.8405
46/65 [====================>.........] - ETA: 8s - loss: 0.5962 - accuracy: 0.8409
47/65 [====================>.........] - ETA: 7s - loss: 0.5960 - accuracy: 0.8410
48/65 [=====================>........] - ETA: 7s - loss: 0.5947 - accuracy: 0.8416
49/65 [=====================>........] - ETA: 6s - loss: 0.5946 - accuracy: 0.8416
50/65 [======================>.......] - ETA: 6s - loss: 0.5930 - accuracy: 0.8420
51/65 [======================>.......] - ETA: 6s - loss: 0.5921 - accuracy: 0.8424
52/65 [=======================>......] - ETA: 5s - loss: 0.5912 - accuracy: 0.8427
53/65 [=======================>......] - ETA: 5s - loss: 0.5901 - accuracy: 0.8428
54/65 [=======================>......] - ETA: 4s - loss: 0.5892 - accuracy: 0.8431
55/65 [========================>.....] - ETA: 4s - loss: 0.5883 - accuracy: 0.8432
56/65 [========================>.....] - ETA: 3s - loss: 0.5878 - accuracy: 0.8433
57/65 [=========================>....] - ETA: 3s - loss: 0.5862 - accuracy: 0.8438
58/65 [=========================>....] - ETA: 3s - loss: 0.5861 - accuracy: 0.8438
59/65 [==========================>...] - ETA: 2s - loss: 0.5850 - accuracy: 0.8442
60/65 [==========================>...] - ETA: 2s - loss: 0.5855 - accuracy: 0.8439
61/65 [===========================>..] - ETA: 1s - loss: 0.5849 - accuracy: 0.8442
62/65 [===========================>..] - ETA: 1s - loss: 0.5855 - accuracy: 0.8438
63/65 [============================>.] - ETA: 0s - loss: 0.5857 - accuracy: 0.8438
64/65 [============================>.] - ETA: 0s - loss: 0.5848 - accuracy: 0.8443
65/65 [==============================] - ETA: 0s - loss: 0.5851 - accuracy: 0.8442
65/65 [==============================] - 29s 443ms/step - loss: 0.5851 - accuracy: 0.8442 - val_loss: 0.8383 - val_accuracy: 0.7619 - lr: 0.0030
Epoch 10/30
1/65 [..............................] - ETA: 42s - loss: 0.5857 - accuracy: 0.8398
2/65 [..............................] - ETA: 27s - loss: 0.5555 - accuracy: 0.8550
3/65 [>.............................] - ETA: 26s - loss: 0.5581 - accuracy: 0.8529
4/65 [>.............................] - ETA: 26s - loss: 0.5735 - accuracy: 0.8472
5/65 [=>............................] - ETA: 26s - loss: 0.5760 - accuracy: 0.8477
6/65 [=>............................] - ETA: 26s - loss: 0.5793 - accuracy: 0.8488
7/65 [==>...........................] - ETA: 25s - loss: 0.5752 - accuracy: 0.8503
8/65 [==>...........................] - ETA: 24s - loss: 0.5748 - accuracy: 0.8494
9/65 [===>..........................] - ETA: 24s - loss: 0.5751 - accuracy: 0.8481
10/65 [===>..........................] - ETA: 24s - loss: 0.5747 - accuracy: 0.8473
11/65 [====>.........................] - ETA: 23s - loss: 0.5725 - accuracy: 0.8478
12/65 [====>.........................] - ETA: 23s - loss: 0.5739 - accuracy: 0.8474
13/65 [=====>........................] - ETA: 22s - loss: 0.5732 - accuracy: 0.8472
14/65 [=====>........................] - ETA: 22s - loss: 0.5678 - accuracy: 0.8489
15/65 [=====>........................] - ETA: 21s - loss: 0.5670 - accuracy: 0.8496
16/65 [======>.......................] - ETA: 21s - loss: 0.5645 - accuracy: 0.8498
17/65 [======>.......................] - ETA: 20s - loss: 0.5644 - accuracy: 0.8492
18/65 [=======>......................] - ETA: 20s - loss: 0.5645 - accuracy: 0.8492
19/65 [=======>......................] - ETA: 20s - loss: 0.5627 - accuracy: 0.8498
20/65 [========>.....................] - ETA: 19s - loss: 0.5609 - accuracy: 0.8501
21/65 [========>.....................] - ETA: 19s - loss: 0.5590 - accuracy: 0.8510
22/65 [=========>....................] - ETA: 18s - loss: 0.5576 - accuracy: 0.8511
23/65 [=========>....................] - ETA: 18s - loss: 0.5570 - accuracy: 0.8514
24/65 [==========>...................] - ETA: 17s - loss: 0.5563 - accuracy: 0.8518
25/65 [==========>...................] - ETA: 17s - loss: 0.5543 - accuracy: 0.8527
26/65 [===========>..................] - ETA: 17s - loss: 0.5532 - accuracy: 0.8529
27/65 [===========>..................] - ETA: 16s - loss: 0.5538 - accuracy: 0.8525
28/65 [===========>..................] - ETA: 16s - loss: 0.5537 - accuracy: 0.8526
29/65 [============>.................] - ETA: 15s - loss: 0.5529 - accuracy: 0.8531
30/65 [============>.................] - ETA: 15s - loss: 0.5539 - accuracy: 0.8525
31/65 [=============>................] - ETA: 14s - loss: 0.5536 - accuracy: 0.8526
32/65 [=============>................] - ETA: 14s - loss: 0.5542 - accuracy: 0.8528
33/65 [==============>...............] - ETA: 13s - loss: 0.5529 - accuracy: 0.8536
34/65 [==============>...............] - ETA: 13s - loss: 0.5531 - accuracy: 0.8534
35/65 [===============>..............] - ETA: 13s - loss: 0.5539 - accuracy: 0.8531
36/65 [===============>..............] - ETA: 12s - loss: 0.5541 - accuracy: 0.8531
37/65 [================>.............] - ETA: 12s - loss: 0.5549 - accuracy: 0.8532
38/65 [================>.............] - ETA: 11s - loss: 0.5558 - accuracy: 0.8529
39/65 [=================>............] - ETA: 11s - loss: 0.5562 - accuracy: 0.8527
40/65 [=================>............] - ETA: 10s - loss: 0.5569 - accuracy: 0.8524
41/65 [=================>............] - ETA: 10s - loss: 0.5566 - accuracy: 0.8525
42/65 [==================>...........] - ETA: 10s - loss: 0.5567 - accuracy: 0.8523
43/65 [==================>...........] - ETA: 9s - loss: 0.5557 - accuracy: 0.8524
44/65 [===================>..........] - ETA: 9s - loss: 0.5560 - accuracy: 0.8523
45/65 [===================>..........] - ETA: 8s - loss: 0.5572 - accuracy: 0.8518
46/65 [====================>.........] - ETA: 8s - loss: 0.5568 - accuracy: 0.8519
47/65 [====================>.........] - ETA: 7s - loss: 0.5563 - accuracy: 0.8521
48/65 [=====================>........] - ETA: 7s - loss: 0.5557 - accuracy: 0.8523
49/65 [=====================>........] - ETA: 6s - loss: 0.5559 - accuracy: 0.8522
50/65 [======================>.......] - ETA: 6s - loss: 0.5561 - accuracy: 0.8522
51/65 [======================>.......] - ETA: 6s - loss: 0.5550 - accuracy: 0.8525
52/65 [=======================>......] - ETA: 5s - loss: 0.5538 - accuracy: 0.8529
53/65 [=======================>......] - ETA: 5s - loss: 0.5520 - accuracy: 0.8534
54/65 [=======================>......] - ETA: 4s - loss: 0.5520 - accuracy: 0.8534
55/65 [========================>.....] - ETA: 4s - loss: 0.5518 - accuracy: 0.8535
56/65 [========================>.....] - ETA: 3s - loss: 0.5512 - accuracy: 0.8536
57/65 [=========================>....] - ETA: 3s - loss: 0.5501 - accuracy: 0.8541
58/65 [=========================>....] - ETA: 3s - loss: 0.5498 - accuracy: 0.8540
59/65 [==========================>...] - ETA: 2s - loss: 0.5490 - accuracy: 0.8543
60/65 [==========================>...] - ETA: 2s - loss: 0.5480 - accuracy: 0.8548
61/65 [===========================>..] - ETA: 1s - loss: 0.5484 - accuracy: 0.8546
62/65 [===========================>..] - ETA: 1s - loss: 0.5485 - accuracy: 0.8546
63/65 [============================>.] - ETA: 0s - loss: 0.5491 - accuracy: 0.8545
64/65 [============================>.] - ETA: 0s - loss: 0.5495 - accuracy: 0.8542
65/65 [==============================] - ETA: 0s - loss: 0.5499 - accuracy: 0.8542
65/65 [==============================] - 29s 446ms/step - loss: 0.5499 - accuracy: 0.8542 - val_loss: 0.8589 - val_accuracy: 0.7536 - lr: 0.0030
Epoch 11/30
1/65 [..............................] - ETA: 42s - loss: 0.5742 - accuracy: 0.8418
2/65 [..............................] - ETA: 27s - loss: 0.5629 - accuracy: 0.8398
3/65 [>.............................] - ETA: 27s - loss: 0.5582 - accuracy: 0.8477
4/65 [>.............................] - ETA: 26s - loss: 0.5490 - accuracy: 0.8533
5/65 [=>............................] - ETA: 26s - loss: 0.5511 - accuracy: 0.8539
6/65 [=>............................] - ETA: 25s - loss: 0.5526 - accuracy: 0.8530
7/65 [==>...........................] - ETA: 25s - loss: 0.5482 - accuracy: 0.8534
8/65 [==>...........................] - ETA: 24s - loss: 0.5472 - accuracy: 0.8540
9/65 [===>..........................] - ETA: 24s - loss: 0.5469 - accuracy: 0.8545
10/65 [===>..........................] - ETA: 23s - loss: 0.5486 - accuracy: 0.8536
11/65 [====>.........................] - ETA: 23s - loss: 0.5490 - accuracy: 0.8540
12/65 [====>.........................] - ETA: 23s - loss: 0.5484 - accuracy: 0.8547
13/65 [=====>........................] - ETA: 22s - loss: 0.5463 - accuracy: 0.8550
14/65 [=====>........................] - ETA: 22s - loss: 0.5441 - accuracy: 0.8562
15/65 [=====>........................] - ETA: 21s - loss: 0.5426 - accuracy: 0.8565
16/65 [======>.......................] - ETA: 21s - loss: 0.5431 - accuracy: 0.8563
17/65 [======>.......................] - ETA: 20s - loss: 0.5379 - accuracy: 0.8578
18/65 [=======>......................] - ETA: 20s - loss: 0.5377 - accuracy: 0.8574
19/65 [=======>......................] - ETA: 19s - loss: 0.5372 - accuracy: 0.8575
20/65 [========>.....................] - ETA: 19s - loss: 0.5376 - accuracy: 0.8570
21/65 [========>.....................] - ETA: 19s - loss: 0.5368 - accuracy: 0.8570
22/65 [=========>....................] - ETA: 18s - loss: 0.5344 - accuracy: 0.8575
23/65 [=========>....................] - ETA: 18s - loss: 0.5336 - accuracy: 0.8573
24/65 [==========>...................] - ETA: 17s - loss: 0.5349 - accuracy: 0.8570
25/65 [==========>...................] - ETA: 17s - loss: 0.5333 - accuracy: 0.8573
26/65 [===========>..................] - ETA: 16s - loss: 0.5327 - accuracy: 0.8576
27/65 [===========>..................] - ETA: 16s - loss: 0.5325 - accuracy: 0.8579
28/65 [===========>..................] - ETA: 16s - loss: 0.5343 - accuracy: 0.8572
29/65 [============>.................] - ETA: 15s - loss: 0.5345 - accuracy: 0.8576
30/65 [============>.................] - ETA: 15s - loss: 0.5343 - accuracy: 0.8581
31/65 [=============>................] - ETA: 14s - loss: 0.5348 - accuracy: 0.8577
32/65 [=============>................] - ETA: 14s - loss: 0.5350 - accuracy: 0.8576
33/65 [==============>...............] - ETA: 13s - loss: 0.5347 - accuracy: 0.8577
34/65 [==============>...............] - ETA: 13s - loss: 0.5333 - accuracy: 0.8582
35/65 [===============>..............] - ETA: 12s - loss: 0.5338 - accuracy: 0.8579
36/65 [===============>..............] - ETA: 12s - loss: 0.5337 - accuracy: 0.8583
37/65 [================>.............] - ETA: 12s - loss: 0.5334 - accuracy: 0.8585
38/65 [================>.............] - ETA: 11s - loss: 0.5336 - accuracy: 0.8584
39/65 [=================>............] - ETA: 11s - loss: 0.5343 - accuracy: 0.8581
40/65 [=================>............] - ETA: 10s - loss: 0.5349 - accuracy: 0.8580
41/65 [=================>............] - ETA: 10s - loss: 0.5339 - accuracy: 0.8582
42/65 [==================>...........] - ETA: 9s - loss: 0.5339 - accuracy: 0.8580
43/65 [==================>...........] - ETA: 9s - loss: 0.5326 - accuracy: 0.8585
44/65 [===================>..........] - ETA: 9s - loss: 0.5325 - accuracy: 0.8585
45/65 [===================>..........] - ETA: 8s - loss: 0.5323 - accuracy: 0.8587
46/65 [====================>.........] - ETA: 8s - loss: 0.5324 - accuracy: 0.8586
47/65 [====================>.........] - ETA: 7s - loss: 0.5336 - accuracy: 0.8581
48/65 [=====================>........] - ETA: 7s - loss: 0.5333 - accuracy: 0.8580
49/65 [=====================>........] - ETA: 6s - loss: 0.5332 - accuracy: 0.8581
50/65 [======================>.......] - ETA: 6s - loss: 0.5327 - accuracy: 0.8581
51/65 [======================>.......] - ETA: 6s - loss: 0.5325 - accuracy: 0.8582
52/65 [=======================>......] - ETA: 5s - loss: 0.5313 - accuracy: 0.8585
53/65 [=======================>......] - ETA: 5s - loss: 0.5295 - accuracy: 0.8592
54/65 [=======================>......] - ETA: 4s - loss: 0.5292 - accuracy: 0.8594
55/65 [========================>.....] - ETA: 4s - loss: 0.5286 - accuracy: 0.8596
56/65 [========================>.....] - ETA: 3s - loss: 0.5276 - accuracy: 0.8598
57/65 [=========================>....] - ETA: 3s - loss: 0.5270 - accuracy: 0.8602
58/65 [=========================>....] - ETA: 3s - loss: 0.5276 - accuracy: 0.8599
59/65 [==========================>...] - ETA: 2s - loss: 0.5272 - accuracy: 0.8600
60/65 [==========================>...] - ETA: 2s - loss: 0.5264 - accuracy: 0.8603
61/65 [===========================>..] - ETA: 1s - loss: 0.5268 - accuracy: 0.8604
62/65 [===========================>..] - ETA: 1s - loss: 0.5266 - accuracy: 0.8605
63/65 [============================>.] - ETA: 0s - loss: 0.5264 - accuracy: 0.8605
64/65 [============================>.] - ETA: 0s - loss: 0.5275 - accuracy: 0.8602
65/65 [==============================] - ETA: 0s - loss: 0.5270 - accuracy: 0.8605
65/65 [==============================] - 29s 441ms/step - loss: 0.5270 - accuracy: 0.8605 - val_loss: 0.6981 - val_accuracy: 0.8038 - lr: 0.0030
Epoch 12/30
1/65 [..............................] - ETA: 42s - loss: 0.5851 - accuracy: 0.8496
2/65 [..............................] - ETA: 26s - loss: 0.5567 - accuracy: 0.8555
3/65 [>.............................] - ETA: 26s - loss: 0.5436 - accuracy: 0.8594
4/65 [>.............................] - ETA: 26s - loss: 0.5280 - accuracy: 0.8613
5/65 [=>............................] - ETA: 26s - loss: 0.5347 - accuracy: 0.8605
6/65 [=>............................] - ETA: 25s - loss: 0.5324 - accuracy: 0.8625
7/65 [==>...........................] - ETA: 25s - loss: 0.5339 - accuracy: 0.8606
8/65 [==>...........................] - ETA: 24s - loss: 0.5316 - accuracy: 0.8616
9/65 [===>..........................] - ETA: 24s - loss: 0.5331 - accuracy: 0.8609
10/65 [===>..........................] - ETA: 23s - loss: 0.5301 - accuracy: 0.8611
11/65 [====>.........................] - ETA: 23s - loss: 0.5293 - accuracy: 0.8624
12/65 [====>.........................] - ETA: 22s - loss: 0.5275 - accuracy: 0.8635
13/65 [=====>........................] - ETA: 22s - loss: 0.5241 - accuracy: 0.8644
14/65 [=====>........................] - ETA: 21s - loss: 0.5222 - accuracy: 0.8643
15/65 [=====>........................] - ETA: 21s - loss: 0.5254 - accuracy: 0.8632
16/65 [======>.......................] - ETA: 21s - loss: 0.5221 - accuracy: 0.8632
17/65 [======>.......................] - ETA: 20s - loss: 0.5213 - accuracy: 0.8635
18/65 [=======>......................] - ETA: 20s - loss: 0.5217 - accuracy: 0.8632
19/65 [=======>......................] - ETA: 19s - loss: 0.5220 - accuracy: 0.8632
20/65 [========>.....................] - ETA: 19s - loss: 0.5213 - accuracy: 0.8633
21/65 [========>.....................] - ETA: 18s - loss: 0.5211 - accuracy: 0.8635
22/65 [=========>....................] - ETA: 18s - loss: 0.5202 - accuracy: 0.8643
23/65 [=========>....................] - ETA: 18s - loss: 0.5205 - accuracy: 0.8641
24/65 [==========>...................] - ETA: 17s - loss: 0.5217 - accuracy: 0.8638
25/65 [==========>...................] - ETA: 17s - loss: 0.5209 - accuracy: 0.8634
26/65 [===========>..................] - ETA: 16s - loss: 0.5204 - accuracy: 0.8633
27/65 [===========>..................] - ETA: 16s - loss: 0.5220 - accuracy: 0.8630
28/65 [===========>..................] - ETA: 15s - loss: 0.5222 - accuracy: 0.8628
29/65 [============>.................] - ETA: 15s - loss: 0.5226 - accuracy: 0.8629
30/65 [============>.................] - ETA: 15s - loss: 0.5219 - accuracy: 0.8632
31/65 [=============>................] - ETA: 14s - loss: 0.5209 - accuracy: 0.8633
32/65 [=============>................] - ETA: 14s - loss: 0.5206 - accuracy: 0.8633
33/65 [==============>...............] - ETA: 13s - loss: 0.5208 - accuracy: 0.8631
34/65 [==============>...............] - ETA: 13s - loss: 0.5214 - accuracy: 0.8629
35/65 [===============>..............] - ETA: 12s - loss: 0.5212 - accuracy: 0.8626
36/65 [===============>..............] - ETA: 12s - loss: 0.5203 - accuracy: 0.8629
37/65 [================>.............] - ETA: 12s - loss: 0.5223 - accuracy: 0.8623
38/65 [================>.............] - ETA: 11s - loss: 0.5215 - accuracy: 0.8623
39/65 [=================>............] - ETA: 11s - loss: 0.5214 - accuracy: 0.8622
40/65 [=================>............] - ETA: 10s - loss: 0.5224 - accuracy: 0.8618
41/65 [=================>............] - ETA: 10s - loss: 0.5205 - accuracy: 0.8624
42/65 [==================>...........] - ETA: 9s - loss: 0.5206 - accuracy: 0.8625
43/65 [==================>...........] - ETA: 9s - loss: 0.5213 - accuracy: 0.8623
44/65 [===================>..........] - ETA: 9s - loss: 0.5210 - accuracy: 0.8625
45/65 [===================>..........] - ETA: 8s - loss: 0.5199 - accuracy: 0.8630
46/65 [====================>.........] - ETA: 8s - loss: 0.5198 - accuracy: 0.8631
47/65 [====================>.........] - ETA: 7s - loss: 0.5194 - accuracy: 0.8631
48/65 [=====================>........] - ETA: 7s - loss: 0.5189 - accuracy: 0.8633
49/65 [=====================>........] - ETA: 6s - loss: 0.5178 - accuracy: 0.8635
50/65 [======================>.......] - ETA: 6s - loss: 0.5187 - accuracy: 0.8632
51/65 [======================>.......] - ETA: 6s - loss: 0.5175 - accuracy: 0.8638
52/65 [=======================>......] - ETA: 5s - loss: 0.5175 - accuracy: 0.8635
53/65 [=======================>......] - ETA: 5s - loss: 0.5177 - accuracy: 0.8636
54/65 [=======================>......] - ETA: 4s - loss: 0.5184 - accuracy: 0.8634
55/65 [========================>.....] - ETA: 4s - loss: 0.5177 - accuracy: 0.8635
56/65 [========================>.....] - ETA: 3s - loss: 0.5167 - accuracy: 0.8637
57/65 [=========================>....] - ETA: 3s - loss: 0.5161 - accuracy: 0.8638
58/65 [=========================>....] - ETA: 3s - loss: 0.5161 - accuracy: 0.8637
59/65 [==========================>...] - ETA: 2s - loss: 0.5167 - accuracy: 0.8636
60/65 [==========================>...] - ETA: 2s - loss: 0.5167 - accuracy: 0.8635
61/65 [===========================>..] - ETA: 1s - loss: 0.5159 - accuracy: 0.8639
62/65 [===========================>..] - ETA: 1s - loss: 0.5153 - accuracy: 0.8641
63/65 [============================>.] - ETA: 0s - loss: 0.5160 - accuracy: 0.8639
64/65 [============================>.] - ETA: 0s - loss: 0.5156 - accuracy: 0.8640
65/65 [==============================] - ETA: 0s - loss: 0.5155 - accuracy: 0.8639
65/65 [==============================] - 29s 441ms/step - loss: 0.5155 - accuracy: 0.8639 - val_loss: 0.7101 - val_accuracy: 0.7962 - lr: 0.0030
Epoch 13/30
1/65 [..............................] - ETA: 43s - loss: 0.5700 - accuracy: 0.8447
2/65 [..............................] - ETA: 29s - loss: 0.5696 - accuracy: 0.8477
3/65 [>.............................] - ETA: 27s - loss: 0.5538 - accuracy: 0.8522
4/65 [>.............................] - ETA: 27s - loss: 0.5377 - accuracy: 0.8572
5/65 [=>............................] - ETA: 26s - loss: 0.5480 - accuracy: 0.8551
6/65 [=>............................] - ETA: 25s - loss: 0.5361 - accuracy: 0.8604
7/65 [==>...........................] - ETA: 25s - loss: 0.5320 - accuracy: 0.8597
8/65 [==>...........................] - ETA: 24s - loss: 0.5324 - accuracy: 0.8600
9/65 [===>..........................] - ETA: 24s - loss: 0.5363 - accuracy: 0.8592
10/65 [===>..........................] - ETA: 23s - loss: 0.5290 - accuracy: 0.8614
11/65 [====>.........................] - ETA: 23s - loss: 0.5279 - accuracy: 0.8619
12/65 [====>.........................] - ETA: 23s - loss: 0.5239 - accuracy: 0.8630
13/65 [=====>........................] - ETA: 22s - loss: 0.5242 - accuracy: 0.8628
14/65 [=====>........................] - ETA: 22s - loss: 0.5253 - accuracy: 0.8624
15/65 [=====>........................] - ETA: 21s - loss: 0.5238 - accuracy: 0.8632
16/65 [======>.......................] - ETA: 21s - loss: 0.5262 - accuracy: 0.8622
17/65 [======>.......................] - ETA: 20s - loss: 0.5254 - accuracy: 0.8621
18/65 [=======>......................] - ETA: 20s - loss: 0.5234 - accuracy: 0.8623
19/65 [=======>......................] - ETA: 19s - loss: 0.5225 - accuracy: 0.8628
20/65 [========>.....................] - ETA: 19s - loss: 0.5231 - accuracy: 0.8625
21/65 [========>.....................] - ETA: 19s - loss: 0.5209 - accuracy: 0.8635
22/65 [=========>....................] - ETA: 18s - loss: 0.5204 - accuracy: 0.8633
23/65 [=========>....................] - ETA: 18s - loss: 0.5178 - accuracy: 0.8641
24/65 [==========>...................] - ETA: 17s - loss: 0.5169 - accuracy: 0.8640
25/65 [==========>...................] - ETA: 17s - loss: 0.5166 - accuracy: 0.8643
26/65 [===========>..................] - ETA: 16s - loss: 0.5158 - accuracy: 0.8641
27/65 [===========>..................] - ETA: 16s - loss: 0.5138 - accuracy: 0.8649
28/65 [===========>..................] - ETA: 15s - loss: 0.5139 - accuracy: 0.8646
29/65 [============>.................] - ETA: 15s - loss: 0.5135 - accuracy: 0.8648
30/65 [============>.................] - ETA: 15s - loss: 0.5106 - accuracy: 0.8656
31/65 [=============>................] - ETA: 14s - loss: 0.5101 - accuracy: 0.8653
32/65 [=============>................] - ETA: 14s - loss: 0.5114 - accuracy: 0.8652
33/65 [==============>...............] - ETA: 13s - loss: 0.5113 - accuracy: 0.8651
34/65 [==============>...............] - ETA: 13s - loss: 0.5106 - accuracy: 0.8651
35/65 [===============>..............] - ETA: 12s - loss: 0.5117 - accuracy: 0.8648
36/65 [===============>..............] - ETA: 12s - loss: 0.5128 - accuracy: 0.8645
37/65 [================>.............] - ETA: 12s - loss: 0.5118 - accuracy: 0.8649
38/65 [================>.............] - ETA: 11s - loss: 0.5124 - accuracy: 0.8646
39/65 [=================>............] - ETA: 11s - loss: 0.5118 - accuracy: 0.8648
40/65 [=================>............] - ETA: 10s - loss: 0.5110 - accuracy: 0.8649
41/65 [=================>............] - ETA: 10s - loss: 0.5111 - accuracy: 0.8649
42/65 [==================>...........] - ETA: 9s - loss: 0.5120 - accuracy: 0.8646
43/65 [==================>...........] - ETA: 9s - loss: 0.5110 - accuracy: 0.8649
44/65 [===================>..........] - ETA: 9s - loss: 0.5100 - accuracy: 0.8653
45/65 [===================>..........] - ETA: 8s - loss: 0.5091 - accuracy: 0.8655
46/65 [====================>.........] - ETA: 8s - loss: 0.5093 - accuracy: 0.8656
47/65 [====================>.........] - ETA: 7s - loss: 0.5082 - accuracy: 0.8659
48/65 [=====================>........] - ETA: 7s - loss: 0.5085 - accuracy: 0.8659
49/65 [=====================>........] - ETA: 6s - loss: 0.5078 - accuracy: 0.8661
50/65 [======================>.......] - ETA: 6s - loss: 0.5080 - accuracy: 0.8661
51/65 [======================>.......] - ETA: 6s - loss: 0.5070 - accuracy: 0.8663
52/65 [=======================>......] - ETA: 5s - loss: 0.5068 - accuracy: 0.8662
53/65 [=======================>......] - ETA: 5s - loss: 0.5057 - accuracy: 0.8665
54/65 [=======================>......] - ETA: 4s - loss: 0.5054 - accuracy: 0.8666
55/65 [========================>.....] - ETA: 4s - loss: 0.5049 - accuracy: 0.8667
56/65 [========================>.....] - ETA: 3s - loss: 0.5040 - accuracy: 0.8670
57/65 [=========================>....] - ETA: 3s - loss: 0.5041 - accuracy: 0.8670
58/65 [=========================>....] - ETA: 3s - loss: 0.5038 - accuracy: 0.8672
59/65 [==========================>...] - ETA: 2s - loss: 0.5040 - accuracy: 0.8671
60/65 [==========================>...] - ETA: 2s - loss: 0.5044 - accuracy: 0.8671
61/65 [===========================>..] - ETA: 1s - loss: 0.5040 - accuracy: 0.8672
62/65 [===========================>..] - ETA: 1s - loss: 0.5041 - accuracy: 0.8671
63/65 [============================>.] - ETA: 0s - loss: 0.5050 - accuracy: 0.8669
64/65 [============================>.] - ETA: 0s - loss: 0.5043 - accuracy: 0.8670
65/65 [==============================] - ETA: 0s - loss: 0.5041 - accuracy: 0.8672
65/65 [==============================] - 29s 440ms/step - loss: 0.5041 - accuracy: 0.8672 - val_loss: 0.7244 - val_accuracy: 0.7943 - lr: 0.0030
Epoch 14/30
1/65 [..............................] - ETA: 43s - loss: 0.5011 - accuracy: 0.8701
2/65 [..............................] - ETA: 27s - loss: 0.5060 - accuracy: 0.8706
3/65 [>.............................] - ETA: 27s - loss: 0.5201 - accuracy: 0.8656
4/65 [>.............................] - ETA: 26s - loss: 0.5165 - accuracy: 0.8682
5/65 [=>............................] - ETA: 26s - loss: 0.5158 - accuracy: 0.8670
6/65 [=>............................] - ETA: 25s - loss: 0.5128 - accuracy: 0.8680
7/65 [==>...........................] - ETA: 25s - loss: 0.5072 - accuracy: 0.8704
8/65 [==>...........................] - ETA: 24s - loss: 0.5035 - accuracy: 0.8711
9/65 [===>..........................] - ETA: 24s - loss: 0.5023 - accuracy: 0.8709
10/65 [===>..........................] - ETA: 24s - loss: 0.5018 - accuracy: 0.8711
11/65 [====>.........................] - ETA: 23s - loss: 0.5005 - accuracy: 0.8712
12/65 [====>.........................] - ETA: 23s - loss: 0.5039 - accuracy: 0.8701
13/65 [=====>........................] - ETA: 22s - loss: 0.5008 - accuracy: 0.8712
14/65 [=====>........................] - ETA: 22s - loss: 0.5018 - accuracy: 0.8705
15/65 [=====>........................] - ETA: 21s - loss: 0.5020 - accuracy: 0.8706
16/65 [======>.......................] - ETA: 21s - loss: 0.5002 - accuracy: 0.8706
17/65 [======>.......................] - ETA: 20s - loss: 0.4980 - accuracy: 0.8708
18/65 [=======>......................] - ETA: 20s - loss: 0.4976 - accuracy: 0.8701
19/65 [=======>......................] - ETA: 20s - loss: 0.4984 - accuracy: 0.8699
20/65 [========>.....................] - ETA: 19s - loss: 0.4976 - accuracy: 0.8702
21/65 [========>.....................] - ETA: 19s - loss: 0.4975 - accuracy: 0.8705
22/65 [=========>....................] - ETA: 18s - loss: 0.4987 - accuracy: 0.8697
23/65 [=========>....................] - ETA: 18s - loss: 0.4962 - accuracy: 0.8708
24/65 [==========>...................] - ETA: 17s - loss: 0.4935 - accuracy: 0.8718
25/65 [==========>...................] - ETA: 17s - loss: 0.4932 - accuracy: 0.8718
26/65 [===========>..................] - ETA: 16s - loss: 0.4949 - accuracy: 0.8713
27/65 [===========>..................] - ETA: 16s - loss: 0.4934 - accuracy: 0.8714
28/65 [===========>..................] - ETA: 16s - loss: 0.4941 - accuracy: 0.8711
29/65 [============>.................] - ETA: 15s - loss: 0.4934 - accuracy: 0.8712
30/65 [============>.................] - ETA: 15s - loss: 0.4934 - accuracy: 0.8708
31/65 [=============>................] - ETA: 14s - loss: 0.4918 - accuracy: 0.8712
32/65 [=============>................] - ETA: 14s - loss: 0.4914 - accuracy: 0.8714
33/65 [==============>...............] - ETA: 13s - loss: 0.4913 - accuracy: 0.8714
34/65 [==============>...............] - ETA: 13s - loss: 0.4916 - accuracy: 0.8712
35/65 [===============>..............] - ETA: 13s - loss: 0.4923 - accuracy: 0.8710
36/65 [===============>..............] - ETA: 12s - loss: 0.4917 - accuracy: 0.8711
37/65 [================>.............] - ETA: 12s - loss: 0.4919 - accuracy: 0.8714
38/65 [================>.............] - ETA: 11s - loss: 0.4933 - accuracy: 0.8709
39/65 [=================>............] - ETA: 11s - loss: 0.4944 - accuracy: 0.8705
40/65 [=================>............] - ETA: 10s - loss: 0.4928 - accuracy: 0.8710
41/65 [=================>............] - ETA: 10s - loss: 0.4935 - accuracy: 0.8707
42/65 [==================>...........] - ETA: 10s - loss: 0.4941 - accuracy: 0.8703
43/65 [==================>...........] - ETA: 9s - loss: 0.4937 - accuracy: 0.8705
44/65 [===================>..........] - ETA: 9s - loss: 0.4940 - accuracy: 0.8703
45/65 [===================>..........] - ETA: 8s - loss: 0.4927 - accuracy: 0.8707
46/65 [====================>.........] - ETA: 8s - loss: 0.4926 - accuracy: 0.8707
47/65 [====================>.........] - ETA: 7s - loss: 0.4917 - accuracy: 0.8709
48/65 [=====================>........] - ETA: 7s - loss: 0.4913 - accuracy: 0.8711
49/65 [=====================>........] - ETA: 6s - loss: 0.4915 - accuracy: 0.8711
50/65 [======================>.......] - ETA: 6s - loss: 0.4910 - accuracy: 0.8710
51/65 [======================>.......] - ETA: 6s - loss: 0.4908 - accuracy: 0.8710
52/65 [=======================>......] - ETA: 5s - loss: 0.4906 - accuracy: 0.8710
53/65 [=======================>......] - ETA: 5s - loss: 0.4910 - accuracy: 0.8709
54/65 [=======================>......] - ETA: 4s - loss: 0.4911 - accuracy: 0.8709
55/65 [========================>.....] - ETA: 4s - loss: 0.4911 - accuracy: 0.8707
56/65 [========================>.....] - ETA: 3s - loss: 0.4908 - accuracy: 0.8707
57/65 [=========================>....] - ETA: 3s - loss: 0.4901 - accuracy: 0.8708
58/65 [=========================>....] - ETA: 3s - loss: 0.4893 - accuracy: 0.8712
59/65 [==========================>...] - ETA: 2s - loss: 0.4894 - accuracy: 0.8711
60/65 [==========================>...] - ETA: 2s - loss: 0.4893 - accuracy: 0.8709
61/65 [===========================>..] - ETA: 1s - loss: 0.4884 - accuracy: 0.8712
62/65 [===========================>..] - ETA: 1s - loss: 0.4881 - accuracy: 0.8712
63/65 [============================>.] - ETA: 0s - loss: 0.4878 - accuracy: 0.8712
64/65 [============================>.] - ETA: 0s - loss: 0.4885 - accuracy: 0.8710
65/65 [==============================] - ETA: 0s - loss: 0.4883 - accuracy: 0.8711
Epoch 14: ReduceLROnPlateau reducing learning rate to 0.001500000013038516.
65/65 [==============================] - 29s 445ms/step - loss: 0.4883 - accuracy: 0.8711 - val_loss: 0.8621 - val_accuracy: 0.7456 - lr: 0.0030
Epoch 15/30
1/65 [..............................] - ETA: 43s - loss: 0.4857 - accuracy: 0.8760
2/65 [..............................] - ETA: 27s - loss: 0.4707 - accuracy: 0.8809
3/65 [>.............................] - ETA: 27s - loss: 0.4729 - accuracy: 0.8783
4/65 [>.............................] - ETA: 27s - loss: 0.4731 - accuracy: 0.8755
5/65 [=>............................] - ETA: 26s - loss: 0.4923 - accuracy: 0.8711
6/65 [=>............................] - ETA: 26s - loss: 0.4907 - accuracy: 0.8717
7/65 [==>...........................] - ETA: 25s - loss: 0.4920 - accuracy: 0.8712
8/65 [==>...........................] - ETA: 25s - loss: 0.4940 - accuracy: 0.8708
9/65 [===>..........................] - ETA: 24s - loss: 0.4918 - accuracy: 0.8715
10/65 [===>..........................] - ETA: 24s - loss: 0.4901 - accuracy: 0.8727
11/65 [====>.........................] - ETA: 23s - loss: 0.4872 - accuracy: 0.8734
12/65 [====>.........................] - ETA: 23s - loss: 0.4869 - accuracy: 0.8740
13/65 [=====>........................] - ETA: 22s - loss: 0.4881 - accuracy: 0.8737
14/65 [=====>........................] - ETA: 22s - loss: 0.4907 - accuracy: 0.8728
15/65 [=====>........................] - ETA: 21s - loss: 0.4939 - accuracy: 0.8715
16/65 [======>.......................] - ETA: 21s - loss: 0.4947 - accuracy: 0.8708
17/65 [======>.......................] - ETA: 21s - loss: 0.4908 - accuracy: 0.8718
18/65 [=======>......................] - ETA: 20s - loss: 0.4936 - accuracy: 0.8709
19/65 [=======>......................] - ETA: 20s - loss: 0.4919 - accuracy: 0.8714
20/65 [========>.....................] - ETA: 19s - loss: 0.4903 - accuracy: 0.8716
21/65 [========>.....................] - ETA: 19s - loss: 0.4885 - accuracy: 0.8719
22/65 [=========>....................] - ETA: 18s - loss: 0.4876 - accuracy: 0.8725
23/65 [=========>....................] - ETA: 18s - loss: 0.4880 - accuracy: 0.8723
24/65 [==========>...................] - ETA: 18s - loss: 0.4874 - accuracy: 0.8719
25/65 [==========>...................] - ETA: 17s - loss: 0.4863 - accuracy: 0.8721
26/65 [===========>..................] - ETA: 17s - loss: 0.4858 - accuracy: 0.8726
27/65 [===========>..................] - ETA: 16s - loss: 0.4853 - accuracy: 0.8726
28/65 [===========>..................] - ETA: 16s - loss: 0.4831 - accuracy: 0.8732
29/65 [============>.................] - ETA: 15s - loss: 0.4823 - accuracy: 0.8734
30/65 [============>.................] - ETA: 15s - loss: 0.4832 - accuracy: 0.8730
31/65 [=============>................] - ETA: 14s - loss: 0.4832 - accuracy: 0.8727
32/65 [=============>................] - ETA: 14s - loss: 0.4833 - accuracy: 0.8728
33/65 [==============>...............] - ETA: 13s - loss: 0.4838 - accuracy: 0.8731
34/65 [==============>...............] - ETA: 13s - loss: 0.4842 - accuracy: 0.8731
35/65 [===============>..............] - ETA: 13s - loss: 0.4836 - accuracy: 0.8734
36/65 [===============>..............] - ETA: 12s - loss: 0.4834 - accuracy: 0.8735
37/65 [================>.............] - ETA: 12s - loss: 0.4827 - accuracy: 0.8734
38/65 [================>.............] - ETA: 11s - loss: 0.4833 - accuracy: 0.8733
39/65 [=================>............] - ETA: 11s - loss: 0.4828 - accuracy: 0.8734
40/65 [=================>............] - ETA: 10s - loss: 0.4833 - accuracy: 0.8733
41/65 [=================>............] - ETA: 10s - loss: 0.4823 - accuracy: 0.8736
42/65 [==================>...........] - ETA: 10s - loss: 0.4821 - accuracy: 0.8738
43/65 [==================>...........] - ETA: 9s - loss: 0.4815 - accuracy: 0.8740
44/65 [===================>..........] - ETA: 9s - loss: 0.4815 - accuracy: 0.8741
45/65 [===================>..........] - ETA: 8s - loss: 0.4810 - accuracy: 0.8745
46/65 [====================>.........] - ETA: 8s - loss: 0.4811 - accuracy: 0.8746
47/65 [====================>.........] - ETA: 7s - loss: 0.4806 - accuracy: 0.8745
48/65 [=====================>........] - ETA: 7s - loss: 0.4798 - accuracy: 0.8748
49/65 [=====================>........] - ETA: 6s - loss: 0.4798 - accuracy: 0.8746
50/65 [======================>.......] - ETA: 6s - loss: 0.4801 - accuracy: 0.8746
51/65 [======================>.......] - ETA: 6s - loss: 0.4787 - accuracy: 0.8752
52/65 [=======================>......] - ETA: 5s - loss: 0.4778 - accuracy: 0.8754
53/65 [=======================>......] - ETA: 5s - loss: 0.4770 - accuracy: 0.8756
54/65 [=======================>......] - ETA: 4s - loss: 0.4774 - accuracy: 0.8754
55/65 [========================>.....] - ETA: 4s - loss: 0.4771 - accuracy: 0.8752
56/65 [========================>.....] - ETA: 3s - loss: 0.4762 - accuracy: 0.8753
57/65 [=========================>....] - ETA: 3s - loss: 0.4765 - accuracy: 0.8754
58/65 [=========================>....] - ETA: 3s - loss: 0.4760 - accuracy: 0.8756
59/65 [==========================>...] - ETA: 2s - loss: 0.4754 - accuracy: 0.8758
60/65 [==========================>...] - ETA: 2s - loss: 0.4751 - accuracy: 0.8758
61/65 [===========================>..] - ETA: 1s - loss: 0.4757 - accuracy: 0.8758
62/65 [===========================>..] - ETA: 1s - loss: 0.4750 - accuracy: 0.8759
63/65 [============================>.] - ETA: 0s - loss: 0.4742 - accuracy: 0.8762
64/65 [============================>.] - ETA: 0s - loss: 0.4740 - accuracy: 0.8763
65/65 [==============================] - ETA: 0s - loss: 0.4742 - accuracy: 0.8763
65/65 [==============================] - 29s 446ms/step - loss: 0.4742 - accuracy: 0.8763 - val_loss: 0.5840 - val_accuracy: 0.8421 - lr: 0.0015
Epoch 16/30
1/65 [..............................] - ETA: 41s - loss: 0.5334 - accuracy: 0.8574
2/65 [..............................] - ETA: 27s - loss: 0.5212 - accuracy: 0.8628
3/65 [>.............................] - ETA: 27s - loss: 0.5139 - accuracy: 0.8659
4/65 [>.............................] - ETA: 26s - loss: 0.4974 - accuracy: 0.8694
5/65 [=>............................] - ETA: 26s - loss: 0.5023 - accuracy: 0.8699
6/65 [=>............................] - ETA: 26s - loss: 0.5015 - accuracy: 0.8693
7/65 [==>...........................] - ETA: 25s - loss: 0.4978 - accuracy: 0.8708
8/65 [==>...........................] - ETA: 25s - loss: 0.4911 - accuracy: 0.8732
9/65 [===>..........................] - ETA: 24s - loss: 0.4874 - accuracy: 0.8745
10/65 [===>..........................] - ETA: 24s - loss: 0.4871 - accuracy: 0.8736
11/65 [====>.........................] - ETA: 23s - loss: 0.4880 - accuracy: 0.8732
12/65 [====>.........................] - ETA: 23s - loss: 0.4844 - accuracy: 0.8744
13/65 [=====>........................] - ETA: 22s - loss: 0.4810 - accuracy: 0.8748
14/65 [=====>........................] - ETA: 22s - loss: 0.4791 - accuracy: 0.8753
15/65 [=====>........................] - ETA: 21s - loss: 0.4751 - accuracy: 0.8765
16/65 [======>.......................] - ETA: 21s - loss: 0.4728 - accuracy: 0.8768
17/65 [======>.......................] - ETA: 20s - loss: 0.4724 - accuracy: 0.8764
18/65 [=======>......................] - ETA: 20s - loss: 0.4706 - accuracy: 0.8768
19/65 [=======>......................] - ETA: 20s - loss: 0.4722 - accuracy: 0.8759
20/65 [========>.....................] - ETA: 19s - loss: 0.4720 - accuracy: 0.8758
21/65 [========>.....................] - ETA: 19s - loss: 0.4719 - accuracy: 0.8755
22/65 [=========>....................] - ETA: 18s - loss: 0.4720 - accuracy: 0.8757
23/65 [=========>....................] - ETA: 18s - loss: 0.4701 - accuracy: 0.8766
24/65 [==========>...................] - ETA: 17s - loss: 0.4693 - accuracy: 0.8767
25/65 [==========>...................] - ETA: 17s - loss: 0.4657 - accuracy: 0.8779
26/65 [===========>..................] - ETA: 17s - loss: 0.4670 - accuracy: 0.8775
27/65 [===========>..................] - ETA: 16s - loss: 0.4668 - accuracy: 0.8775
28/65 [===========>..................] - ETA: 16s - loss: 0.4665 - accuracy: 0.8775
29/65 [============>.................] - ETA: 15s - loss: 0.4656 - accuracy: 0.8777
30/65 [============>.................] - ETA: 15s - loss: 0.4644 - accuracy: 0.8783
31/65 [=============>................] - ETA: 14s - loss: 0.4659 - accuracy: 0.8776
32/65 [=============>................] - ETA: 14s - loss: 0.4663 - accuracy: 0.8772
33/65 [==============>...............] - ETA: 13s - loss: 0.4659 - accuracy: 0.8772
34/65 [==============>...............] - ETA: 13s - loss: 0.4666 - accuracy: 0.8769
35/65 [===============>..............] - ETA: 13s - loss: 0.4672 - accuracy: 0.8769
36/65 [===============>..............] - ETA: 12s - loss: 0.4676 - accuracy: 0.8769
37/65 [================>.............] - ETA: 12s - loss: 0.4680 - accuracy: 0.8764
38/65 [================>.............] - ETA: 11s - loss: 0.4673 - accuracy: 0.8767
39/65 [=================>............] - ETA: 11s - loss: 0.4672 - accuracy: 0.8767
40/65 [=================>............] - ETA: 10s - loss: 0.4676 - accuracy: 0.8768
41/65 [=================>............] - ETA: 10s - loss: 0.4675 - accuracy: 0.8770
42/65 [==================>...........] - ETA: 10s - loss: 0.4671 - accuracy: 0.8770
43/65 [==================>...........] - ETA: 9s - loss: 0.4661 - accuracy: 0.8773
44/65 [===================>..........] - ETA: 9s - loss: 0.4657 - accuracy: 0.8774
45/65 [===================>..........] - ETA: 8s - loss: 0.4661 - accuracy: 0.8773
46/65 [====================>.........] - ETA: 8s - loss: 0.4661 - accuracy: 0.8774
47/65 [====================>.........] - ETA: 7s - loss: 0.4652 - accuracy: 0.8777
48/65 [=====================>........] - ETA: 7s - loss: 0.4651 - accuracy: 0.8777
49/65 [=====================>........] - ETA: 6s - loss: 0.4639 - accuracy: 0.8780
50/65 [======================>.......] - ETA: 6s - loss: 0.4645 - accuracy: 0.8777
51/65 [======================>.......] - ETA: 6s - loss: 0.4645 - accuracy: 0.8778
52/65 [=======================>......] - ETA: 5s - loss: 0.4643 - accuracy: 0.8777
53/65 [=======================>......] - ETA: 5s - loss: 0.4633 - accuracy: 0.8782
54/65 [=======================>......] - ETA: 4s - loss: 0.4629 - accuracy: 0.8781
55/65 [========================>.....] - ETA: 4s - loss: 0.4623 - accuracy: 0.8784
56/65 [========================>.....] - ETA: 3s - loss: 0.4618 - accuracy: 0.8785
57/65 [=========================>....] - ETA: 3s - loss: 0.4610 - accuracy: 0.8788
58/65 [=========================>....] - ETA: 3s - loss: 0.4610 - accuracy: 0.8788
59/65 [==========================>...] - ETA: 2s - loss: 0.4614 - accuracy: 0.8788
60/65 [==========================>...] - ETA: 2s - loss: 0.4610 - accuracy: 0.8789
61/65 [===========================>..] - ETA: 1s - loss: 0.4609 - accuracy: 0.8791
62/65 [===========================>..] - ETA: 1s - loss: 0.4610 - accuracy: 0.8791
63/65 [============================>.] - ETA: 0s - loss: 0.4608 - accuracy: 0.8792
64/65 [============================>.] - ETA: 0s - loss: 0.4610 - accuracy: 0.8792
65/65 [==============================] - ETA: 0s - loss: 0.4608 - accuracy: 0.8794
65/65 [==============================] - 29s 444ms/step - loss: 0.4608 - accuracy: 0.8794 - val_loss: 0.5509 - val_accuracy: 0.8519 - lr: 0.0015
Epoch 17/30
1/65 [..............................] - ETA: 42s - loss: 0.4657 - accuracy: 0.8926
2/65 [..............................] - ETA: 27s - loss: 0.4719 - accuracy: 0.8931
3/65 [>.............................] - ETA: 26s - loss: 0.4644 - accuracy: 0.8906
4/65 [>.............................] - ETA: 26s - loss: 0.4776 - accuracy: 0.8816
5/65 [=>............................] - ETA: 26s - loss: 0.4759 - accuracy: 0.8818
6/65 [=>............................] - ETA: 25s - loss: 0.4780 - accuracy: 0.8796
7/65 [==>...........................] - ETA: 25s - loss: 0.4775 - accuracy: 0.8788
8/65 [==>...........................] - ETA: 24s - loss: 0.4718 - accuracy: 0.8799
9/65 [===>..........................] - ETA: 24s - loss: 0.4738 - accuracy: 0.8780
10/65 [===>..........................] - ETA: 24s - loss: 0.4720 - accuracy: 0.8784
11/65 [====>.........................] - ETA: 23s - loss: 0.4685 - accuracy: 0.8796
12/65 [====>.........................] - ETA: 23s - loss: 0.4673 - accuracy: 0.8794
13/65 [=====>........................] - ETA: 22s - loss: 0.4653 - accuracy: 0.8794
14/65 [=====>........................] - ETA: 22s - loss: 0.4625 - accuracy: 0.8797
15/65 [=====>........................] - ETA: 21s - loss: 0.4639 - accuracy: 0.8790
16/65 [======>.......................] - ETA: 21s - loss: 0.4655 - accuracy: 0.8781
17/65 [======>.......................] - ETA: 20s - loss: 0.4672 - accuracy: 0.8774
18/65 [=======>......................] - ETA: 20s - loss: 0.4646 - accuracy: 0.8784
19/65 [=======>......................] - ETA: 20s - loss: 0.4643 - accuracy: 0.8780
20/65 [========>.....................] - ETA: 19s - loss: 0.4639 - accuracy: 0.8783
21/65 [========>.....................] - ETA: 19s - loss: 0.4641 - accuracy: 0.8786
22/65 [=========>....................] - ETA: 18s - loss: 0.4643 - accuracy: 0.8787
23/65 [=========>....................] - ETA: 18s - loss: 0.4617 - accuracy: 0.8791
24/65 [==========>...................] - ETA: 17s - loss: 0.4623 - accuracy: 0.8790
25/65 [==========>...................] - ETA: 17s - loss: 0.4616 - accuracy: 0.8792
26/65 [===========>..................] - ETA: 16s - loss: 0.4596 - accuracy: 0.8797
27/65 [===========>..................] - ETA: 16s - loss: 0.4602 - accuracy: 0.8796
28/65 [===========>..................] - ETA: 16s - loss: 0.4606 - accuracy: 0.8792
29/65 [============>.................] - ETA: 15s - loss: 0.4602 - accuracy: 0.8793
30/65 [============>.................] - ETA: 15s - loss: 0.4586 - accuracy: 0.8797
31/65 [=============>................] - ETA: 14s - loss: 0.4604 - accuracy: 0.8790
32/65 [=============>................] - ETA: 14s - loss: 0.4619 - accuracy: 0.8786
33/65 [==============>...............] - ETA: 13s - loss: 0.4634 - accuracy: 0.8783
34/65 [==============>...............] - ETA: 13s - loss: 0.4633 - accuracy: 0.8783
35/65 [===============>..............] - ETA: 13s - loss: 0.4646 - accuracy: 0.8776
36/65 [===============>..............] - ETA: 12s - loss: 0.4652 - accuracy: 0.8774
37/65 [================>.............] - ETA: 12s - loss: 0.4669 - accuracy: 0.8771
38/65 [================>.............] - ETA: 11s - loss: 0.4664 - accuracy: 0.8771
39/65 [=================>............] - ETA: 11s - loss: 0.4662 - accuracy: 0.8772
40/65 [=================>............] - ETA: 10s - loss: 0.4671 - accuracy: 0.8768
41/65 [=================>............] - ETA: 10s - loss: 0.4652 - accuracy: 0.8773
42/65 [==================>...........] - ETA: 9s - loss: 0.4651 - accuracy: 0.8771
43/65 [==================>...........] - ETA: 9s - loss: 0.4649 - accuracy: 0.8773
44/65 [===================>..........] - ETA: 9s - loss: 0.4645 - accuracy: 0.8776
45/65 [===================>..........] - ETA: 8s - loss: 0.4644 - accuracy: 0.8777
46/65 [====================>.........] - ETA: 8s - loss: 0.4640 - accuracy: 0.8779
47/65 [====================>.........] - ETA: 7s - loss: 0.4634 - accuracy: 0.8778
48/65 [=====================>........] - ETA: 7s - loss: 0.4626 - accuracy: 0.8780
49/65 [=====================>........] - ETA: 6s - loss: 0.4628 - accuracy: 0.8780
50/65 [======================>.......] - ETA: 6s - loss: 0.4626 - accuracy: 0.8780
51/65 [======================>.......] - ETA: 6s - loss: 0.4626 - accuracy: 0.8782
52/65 [=======================>......] - ETA: 5s - loss: 0.4629 - accuracy: 0.8780
53/65 [=======================>......] - ETA: 5s - loss: 0.4627 - accuracy: 0.8782
54/65 [=======================>......] - ETA: 4s - loss: 0.4618 - accuracy: 0.8785
55/65 [========================>.....] - ETA: 4s - loss: 0.4617 - accuracy: 0.8785
56/65 [========================>.....] - ETA: 3s - loss: 0.4611 - accuracy: 0.8786
57/65 [=========================>....] - ETA: 3s - loss: 0.4604 - accuracy: 0.8789
58/65 [=========================>....] - ETA: 3s - loss: 0.4607 - accuracy: 0.8786
59/65 [==========================>...] - ETA: 2s - loss: 0.4609 - accuracy: 0.8786
60/65 [==========================>...] - ETA: 2s - loss: 0.4603 - accuracy: 0.8789
61/65 [===========================>..] - ETA: 1s - loss: 0.4604 - accuracy: 0.8790
62/65 [===========================>..] - ETA: 1s - loss: 0.4600 - accuracy: 0.8792
63/65 [============================>.] - ETA: 0s - loss: 0.4596 - accuracy: 0.8793
64/65 [============================>.] - ETA: 0s - loss: 0.4592 - accuracy: 0.8794
65/65 [==============================] - ETA: 0s - loss: 0.4595 - accuracy: 0.8793
65/65 [==============================] - 29s 444ms/step - loss: 0.4595 - accuracy: 0.8793 - val_loss: 0.5444 - val_accuracy: 0.8557 - lr: 0.0015
Epoch 18/30
1/65 [..............................] - ETA: 43s - loss: 0.4867 - accuracy: 0.8750
2/65 [..............................] - ETA: 27s - loss: 0.4888 - accuracy: 0.8774
3/65 [>.............................] - ETA: 26s - loss: 0.4850 - accuracy: 0.8760
4/65 [>.............................] - ETA: 26s - loss: 0.4857 - accuracy: 0.8728
5/65 [=>............................] - ETA: 26s - loss: 0.4811 - accuracy: 0.8748
6/65 [=>............................] - ETA: 25s - loss: 0.4822 - accuracy: 0.8729
7/65 [==>...........................] - ETA: 25s - loss: 0.4794 - accuracy: 0.8733
8/65 [==>...........................] - ETA: 24s - loss: 0.4785 - accuracy: 0.8755
9/65 [===>..........................] - ETA: 24s - loss: 0.4744 - accuracy: 0.8763
10/65 [===>..........................] - ETA: 23s - loss: 0.4761 - accuracy: 0.8756
11/65 [====>.........................] - ETA: 23s - loss: 0.4725 - accuracy: 0.8764
12/65 [====>.........................] - ETA: 23s - loss: 0.4742 - accuracy: 0.8761
13/65 [=====>........................] - ETA: 22s - loss: 0.4694 - accuracy: 0.8777
14/65 [=====>........................] - ETA: 22s - loss: 0.4662 - accuracy: 0.8781
15/65 [=====>........................] - ETA: 21s - loss: 0.4657 - accuracy: 0.8790
16/65 [======>.......................] - ETA: 21s - loss: 0.4664 - accuracy: 0.8787
17/65 [======>.......................] - ETA: 20s - loss: 0.4659 - accuracy: 0.8786
18/65 [=======>......................] - ETA: 20s - loss: 0.4633 - accuracy: 0.8792
19/65 [=======>......................] - ETA: 19s - loss: 0.4641 - accuracy: 0.8791
20/65 [========>.....................] - ETA: 19s - loss: 0.4648 - accuracy: 0.8791
21/65 [========>.....................] - ETA: 19s - loss: 0.4632 - accuracy: 0.8794
22/65 [=========>....................] - ETA: 18s - loss: 0.4621 - accuracy: 0.8795
23/65 [=========>....................] - ETA: 18s - loss: 0.4629 - accuracy: 0.8789
24/65 [==========>...................] - ETA: 17s - loss: 0.4609 - accuracy: 0.8794
25/65 [==========>...................] - ETA: 17s - loss: 0.4596 - accuracy: 0.8799
26/65 [===========>..................] - ETA: 16s - loss: 0.4600 - accuracy: 0.8801
27/65 [===========>..................] - ETA: 16s - loss: 0.4580 - accuracy: 0.8807
28/65 [===========>..................] - ETA: 16s - loss: 0.4573 - accuracy: 0.8809
29/65 [============>.................] - ETA: 15s - loss: 0.4566 - accuracy: 0.8812
30/65 [============>.................] - ETA: 15s - loss: 0.4579 - accuracy: 0.8810
31/65 [=============>................] - ETA: 14s - loss: 0.4572 - accuracy: 0.8811
32/65 [=============>................] - ETA: 14s - loss: 0.4570 - accuracy: 0.8813
33/65 [==============>...............] - ETA: 13s - loss: 0.4575 - accuracy: 0.8813
34/65 [==============>...............] - ETA: 13s - loss: 0.4579 - accuracy: 0.8807
35/65 [===============>..............] - ETA: 13s - loss: 0.4594 - accuracy: 0.8802
36/65 [===============>..............] - ETA: 12s - loss: 0.4616 - accuracy: 0.8794
37/65 [================>.............] - ETA: 12s - loss: 0.4606 - accuracy: 0.8797
38/65 [================>.............] - ETA: 11s - loss: 0.4621 - accuracy: 0.8794
39/65 [=================>............] - ETA: 11s - loss: 0.4616 - accuracy: 0.8795
40/65 [=================>............] - ETA: 10s - loss: 0.4609 - accuracy: 0.8796
41/65 [=================>............] - ETA: 10s - loss: 0.4597 - accuracy: 0.8801
42/65 [==================>...........] - ETA: 10s - loss: 0.4606 - accuracy: 0.8801
43/65 [==================>...........] - ETA: 9s - loss: 0.4604 - accuracy: 0.8802
44/65 [===================>..........] - ETA: 9s - loss: 0.4601 - accuracy: 0.8801
45/65 [===================>..........] - ETA: 8s - loss: 0.4601 - accuracy: 0.8799
46/65 [====================>.........] - ETA: 8s - loss: 0.4600 - accuracy: 0.8801
47/65 [====================>.........] - ETA: 7s - loss: 0.4597 - accuracy: 0.8803
48/65 [=====================>........] - ETA: 7s - loss: 0.4599 - accuracy: 0.8802
49/65 [=====================>........] - ETA: 6s - loss: 0.4609 - accuracy: 0.8800
50/65 [======================>.......] - ETA: 6s - loss: 0.4606 - accuracy: 0.8799
51/65 [======================>.......] - ETA: 6s - loss: 0.4590 - accuracy: 0.8804
52/65 [=======================>......] - ETA: 5s - loss: 0.4588 - accuracy: 0.8805
53/65 [=======================>......] - ETA: 5s - loss: 0.4586 - accuracy: 0.8805
54/65 [=======================>......] - ETA: 4s - loss: 0.4586 - accuracy: 0.8804
55/65 [========================>.....] - ETA: 4s - loss: 0.4572 - accuracy: 0.8810
56/65 [========================>.....] - ETA: 3s - loss: 0.4569 - accuracy: 0.8809
57/65 [=========================>....] - ETA: 3s - loss: 0.4562 - accuracy: 0.8810
58/65 [=========================>....] - ETA: 3s - loss: 0.4560 - accuracy: 0.8811
59/65 [==========================>...] - ETA: 2s - loss: 0.4554 - accuracy: 0.8812
60/65 [==========================>...] - ETA: 2s - loss: 0.4557 - accuracy: 0.8812
61/65 [===========================>..] - ETA: 1s - loss: 0.4550 - accuracy: 0.8815
62/65 [===========================>..] - ETA: 1s - loss: 0.4550 - accuracy: 0.8815
63/65 [============================>.] - ETA: 0s - loss: 0.4554 - accuracy: 0.8814
64/65 [============================>.] - ETA: 0s - loss: 0.4547 - accuracy: 0.8816
65/65 [==============================] - ETA: 0s - loss: 0.4548 - accuracy: 0.8817
65/65 [==============================] - 29s 444ms/step - loss: 0.4548 - accuracy: 0.8817 - val_loss: 0.5394 - val_accuracy: 0.8554 - lr: 0.0015
Epoch 19/30
1/65 [..............................] - ETA: 42s - loss: 0.4520 - accuracy: 0.8809
2/65 [..............................] - ETA: 28s - loss: 0.4607 - accuracy: 0.8784
3/65 [>.............................] - ETA: 27s - loss: 0.4587 - accuracy: 0.8799
4/65 [>.............................] - ETA: 26s - loss: 0.4637 - accuracy: 0.8794
5/65 [=>............................] - ETA: 26s - loss: 0.4775 - accuracy: 0.8771
6/65 [=>............................] - ETA: 25s - loss: 0.4727 - accuracy: 0.8771
7/65 [==>...........................] - ETA: 25s - loss: 0.4634 - accuracy: 0.8789
8/65 [==>...........................] - ETA: 24s - loss: 0.4712 - accuracy: 0.8776
9/65 [===>..........................] - ETA: 24s - loss: 0.4689 - accuracy: 0.8786
10/65 [===>..........................] - ETA: 24s - loss: 0.4661 - accuracy: 0.8794
11/65 [====>.........................] - ETA: 23s - loss: 0.4667 - accuracy: 0.8786
12/65 [====>.........................] - ETA: 23s - loss: 0.4664 - accuracy: 0.8784
13/65 [=====>........................] - ETA: 22s - loss: 0.4633 - accuracy: 0.8797
14/65 [=====>........................] - ETA: 22s - loss: 0.4645 - accuracy: 0.8792
15/65 [=====>........................] - ETA: 21s - loss: 0.4637 - accuracy: 0.8794
16/65 [======>.......................] - ETA: 21s - loss: 0.4613 - accuracy: 0.8805
17/65 [======>.......................] - ETA: 20s - loss: 0.4582 - accuracy: 0.8813
18/65 [=======>......................] - ETA: 20s - loss: 0.4596 - accuracy: 0.8811
19/65 [=======>......................] - ETA: 20s - loss: 0.4601 - accuracy: 0.8806
20/65 [========>.....................] - ETA: 19s - loss: 0.4605 - accuracy: 0.8802
21/65 [========>.....................] - ETA: 19s - loss: 0.4588 - accuracy: 0.8800
22/65 [=========>....................] - ETA: 18s - loss: 0.4593 - accuracy: 0.8795
23/65 [=========>....................] - ETA: 18s - loss: 0.4576 - accuracy: 0.8797
24/65 [==========>...................] - ETA: 17s - loss: 0.4564 - accuracy: 0.8801
25/65 [==========>...................] - ETA: 17s - loss: 0.4566 - accuracy: 0.8800
26/65 [===========>..................] - ETA: 17s - loss: 0.4547 - accuracy: 0.8806
27/65 [===========>..................] - ETA: 16s - loss: 0.4532 - accuracy: 0.8813
28/65 [===========>..................] - ETA: 16s - loss: 0.4530 - accuracy: 0.8812
29/65 [============>.................] - ETA: 15s - loss: 0.4540 - accuracy: 0.8811
30/65 [============>.................] - ETA: 15s - loss: 0.4520 - accuracy: 0.8817
31/65 [=============>................] - ETA: 14s - loss: 0.4529 - accuracy: 0.8817
32/65 [=============>................] - ETA: 14s - loss: 0.4527 - accuracy: 0.8817
33/65 [==============>...............] - ETA: 13s - loss: 0.4530 - accuracy: 0.8816
34/65 [==============>...............] - ETA: 13s - loss: 0.4533 - accuracy: 0.8813
35/65 [===============>..............] - ETA: 13s - loss: 0.4536 - accuracy: 0.8810
36/65 [===============>..............] - ETA: 12s - loss: 0.4553 - accuracy: 0.8807
37/65 [================>.............] - ETA: 12s - loss: 0.4556 - accuracy: 0.8806
38/65 [================>.............] - ETA: 11s - loss: 0.4571 - accuracy: 0.8804
39/65 [=================>............] - ETA: 11s - loss: 0.4566 - accuracy: 0.8806
40/65 [=================>............] - ETA: 10s - loss: 0.4571 - accuracy: 0.8805
41/65 [=================>............] - ETA: 10s - loss: 0.4580 - accuracy: 0.8801
42/65 [==================>...........] - ETA: 10s - loss: 0.4567 - accuracy: 0.8803
43/65 [==================>...........] - ETA: 9s - loss: 0.4564 - accuracy: 0.8806
44/65 [===================>..........] - ETA: 9s - loss: 0.4559 - accuracy: 0.8805
45/65 [===================>..........] - ETA: 8s - loss: 0.4563 - accuracy: 0.8806
46/65 [====================>.........] - ETA: 8s - loss: 0.4559 - accuracy: 0.8809
47/65 [====================>.........] - ETA: 7s - loss: 0.4562 - accuracy: 0.8808
48/65 [=====================>........] - ETA: 7s - loss: 0.4563 - accuracy: 0.8808
49/65 [=====================>........] - ETA: 6s - loss: 0.4554 - accuracy: 0.8811
50/65 [======================>.......] - ETA: 6s - loss: 0.4551 - accuracy: 0.8811
51/65 [======================>.......] - ETA: 6s - loss: 0.4534 - accuracy: 0.8816
52/65 [=======================>......] - ETA: 5s - loss: 0.4528 - accuracy: 0.8817
53/65 [=======================>......] - ETA: 5s - loss: 0.4523 - accuracy: 0.8818
54/65 [=======================>......] - ETA: 4s - loss: 0.4522 - accuracy: 0.8817
55/65 [========================>.....] - ETA: 4s - loss: 0.4522 - accuracy: 0.8819
56/65 [========================>.....] - ETA: 3s - loss: 0.4511 - accuracy: 0.8819
57/65 [=========================>....] - ETA: 3s - loss: 0.4505 - accuracy: 0.8821
58/65 [=========================>....] - ETA: 3s - loss: 0.4504 - accuracy: 0.8822
59/65 [==========================>...] - ETA: 2s - loss: 0.4502 - accuracy: 0.8822
60/65 [==========================>...] - ETA: 2s - loss: 0.4500 - accuracy: 0.8822
61/65 [===========================>..] - ETA: 1s - loss: 0.4492 - accuracy: 0.8826
62/65 [===========================>..] - ETA: 1s - loss: 0.4489 - accuracy: 0.8828
63/65 [============================>.] - ETA: 0s - loss: 0.4486 - accuracy: 0.8829
64/65 [============================>.] - ETA: 0s - loss: 0.4490 - accuracy: 0.8829
65/65 [==============================] - ETA: 0s - loss: 0.4489 - accuracy: 0.8830
65/65 [==============================] - 29s 444ms/step - loss: 0.4489 - accuracy: 0.8830 - val_loss: 0.5299 - val_accuracy: 0.8598 - lr: 0.0015
Epoch 20/30
1/65 [..............................] - ETA: 41s - loss: 0.5239 - accuracy: 0.8564
2/65 [..............................] - ETA: 27s - loss: 0.4729 - accuracy: 0.8740
3/65 [>.............................] - ETA: 27s - loss: 0.4685 - accuracy: 0.8760
4/65 [>.............................] - ETA: 26s - loss: 0.4612 - accuracy: 0.8796
5/65 [=>............................] - ETA: 26s - loss: 0.4484 - accuracy: 0.8838
6/65 [=>............................] - ETA: 25s - loss: 0.4497 - accuracy: 0.8831
7/65 [==>...........................] - ETA: 25s - loss: 0.4483 - accuracy: 0.8841
8/65 [==>...........................] - ETA: 24s - loss: 0.4512 - accuracy: 0.8831
9/65 [===>..........................] - ETA: 24s - loss: 0.4525 - accuracy: 0.8829
10/65 [===>..........................] - ETA: 23s - loss: 0.4526 - accuracy: 0.8824
11/65 [====>.........................] - ETA: 23s - loss: 0.4535 - accuracy: 0.8825
12/65 [====>.........................] - ETA: 22s - loss: 0.4514 - accuracy: 0.8828
13/65 [=====>........................] - ETA: 22s - loss: 0.4511 - accuracy: 0.8830
14/65 [=====>........................] - ETA: 22s - loss: 0.4522 - accuracy: 0.8832
15/65 [=====>........................] - ETA: 21s - loss: 0.4513 - accuracy: 0.8831
16/65 [======>.......................] - ETA: 21s - loss: 0.4485 - accuracy: 0.8837
17/65 [======>.......................] - ETA: 20s - loss: 0.4516 - accuracy: 0.8828
18/65 [=======>......................] - ETA: 20s - loss: 0.4503 - accuracy: 0.8829
19/65 [=======>......................] - ETA: 19s - loss: 0.4479 - accuracy: 0.8836
20/65 [========>.....................] - ETA: 19s - loss: 0.4469 - accuracy: 0.8841
21/65 [========>.....................] - ETA: 18s - loss: 0.4476 - accuracy: 0.8840
22/65 [=========>....................] - ETA: 18s - loss: 0.4474 - accuracy: 0.8841
23/65 [=========>....................] - ETA: 18s - loss: 0.4459 - accuracy: 0.8844
24/65 [==========>...................] - ETA: 17s - loss: 0.4453 - accuracy: 0.8846
25/65 [==========>...................] - ETA: 17s - loss: 0.4455 - accuracy: 0.8848
26/65 [===========>..................] - ETA: 16s - loss: 0.4440 - accuracy: 0.8848
27/65 [===========>..................] - ETA: 16s - loss: 0.4450 - accuracy: 0.8844
28/65 [===========>..................] - ETA: 15s - loss: 0.4439 - accuracy: 0.8849
29/65 [============>.................] - ETA: 15s - loss: 0.4457 - accuracy: 0.8842
30/65 [============>.................] - ETA: 15s - loss: 0.4471 - accuracy: 0.8837
31/65 [=============>................] - ETA: 14s - loss: 0.4452 - accuracy: 0.8840
32/65 [=============>................] - ETA: 14s - loss: 0.4450 - accuracy: 0.8841
33/65 [==============>...............] - ETA: 13s - loss: 0.4468 - accuracy: 0.8839
34/65 [==============>...............] - ETA: 13s - loss: 0.4476 - accuracy: 0.8836
35/65 [===============>..............] - ETA: 12s - loss: 0.4460 - accuracy: 0.8838
36/65 [===============>..............] - ETA: 12s - loss: 0.4452 - accuracy: 0.8842
37/65 [================>.............] - ETA: 12s - loss: 0.4453 - accuracy: 0.8841
38/65 [================>.............] - ETA: 11s - loss: 0.4468 - accuracy: 0.8837
39/65 [=================>............] - ETA: 11s - loss: 0.4482 - accuracy: 0.8834
40/65 [=================>............] - ETA: 10s - loss: 0.4486 - accuracy: 0.8835
41/65 [=================>............] - ETA: 10s - loss: 0.4498 - accuracy: 0.8830
42/65 [==================>...........] - ETA: 9s - loss: 0.4489 - accuracy: 0.8835
43/65 [==================>...........] - ETA: 9s - loss: 0.4479 - accuracy: 0.8838
44/65 [===================>..........] - ETA: 9s - loss: 0.4476 - accuracy: 0.8836
45/65 [===================>..........] - ETA: 8s - loss: 0.4477 - accuracy: 0.8836
46/65 [====================>.........] - ETA: 8s - loss: 0.4467 - accuracy: 0.8838
47/65 [====================>.........] - ETA: 7s - loss: 0.4464 - accuracy: 0.8838
48/65 [=====================>........] - ETA: 7s - loss: 0.4448 - accuracy: 0.8845
49/65 [=====================>........] - ETA: 6s - loss: 0.4441 - accuracy: 0.8846
50/65 [======================>.......] - ETA: 6s - loss: 0.4436 - accuracy: 0.8848
51/65 [======================>.......] - ETA: 6s - loss: 0.4424 - accuracy: 0.8850
52/65 [=======================>......] - ETA: 5s - loss: 0.4419 - accuracy: 0.8851
53/65 [=======================>......] - ETA: 5s - loss: 0.4422 - accuracy: 0.8849
54/65 [=======================>......] - ETA: 4s - loss: 0.4416 - accuracy: 0.8851
55/65 [========================>.....] - ETA: 4s - loss: 0.4413 - accuracy: 0.8853
56/65 [========================>.....] - ETA: 3s - loss: 0.4417 - accuracy: 0.8851
57/65 [=========================>....] - ETA: 3s - loss: 0.4413 - accuracy: 0.8852
58/65 [=========================>....] - ETA: 3s - loss: 0.4409 - accuracy: 0.8853
59/65 [==========================>...] - ETA: 2s - loss: 0.4409 - accuracy: 0.8855
60/65 [==========================>...] - ETA: 2s - loss: 0.4413 - accuracy: 0.8854
61/65 [===========================>..] - ETA: 1s - loss: 0.4412 - accuracy: 0.8855
62/65 [===========================>..] - ETA: 1s - loss: 0.4402 - accuracy: 0.8859
63/65 [============================>.] - ETA: 0s - loss: 0.4391 - accuracy: 0.8862
64/65 [============================>.] - ETA: 0s - loss: 0.4393 - accuracy: 0.8861
65/65 [==============================] - ETA: 0s - loss: 0.4395 - accuracy: 0.8860
65/65 [==============================] - 29s 441ms/step - loss: 0.4395 - accuracy: 0.8860 - val_loss: 0.5440 - val_accuracy: 0.8554 - lr: 0.0015
Epoch 21/30
1/65 [..............................] - ETA: 41s - loss: 0.4807 - accuracy: 0.8740
2/65 [..............................] - ETA: 26s - loss: 0.4512 - accuracy: 0.8789
3/65 [>.............................] - ETA: 26s - loss: 0.4511 - accuracy: 0.8815
4/65 [>.............................] - ETA: 26s - loss: 0.4484 - accuracy: 0.8813
5/65 [=>............................] - ETA: 25s - loss: 0.4470 - accuracy: 0.8836
6/65 [=>............................] - ETA: 25s - loss: 0.4560 - accuracy: 0.8815
7/65 [==>...........................] - ETA: 25s - loss: 0.4622 - accuracy: 0.8802
8/65 [==>...........................] - ETA: 24s - loss: 0.4602 - accuracy: 0.8799
9/65 [===>..........................] - ETA: 24s - loss: 0.4614 - accuracy: 0.8788
10/65 [===>..........................] - ETA: 23s - loss: 0.4607 - accuracy: 0.8794
11/65 [====>.........................] - ETA: 23s - loss: 0.4579 - accuracy: 0.8797
12/65 [====>.........................] - ETA: 22s - loss: 0.4546 - accuracy: 0.8815
13/65 [=====>........................] - ETA: 22s - loss: 0.4503 - accuracy: 0.8833
14/65 [=====>........................] - ETA: 22s - loss: 0.4478 - accuracy: 0.8841
15/65 [=====>........................] - ETA: 21s - loss: 0.4487 - accuracy: 0.8835
16/65 [======>.......................] - ETA: 21s - loss: 0.4480 - accuracy: 0.8834
17/65 [======>.......................] - ETA: 20s - loss: 0.4470 - accuracy: 0.8833
18/65 [=======>......................] - ETA: 20s - loss: 0.4461 - accuracy: 0.8834
19/65 [=======>......................] - ETA: 19s - loss: 0.4442 - accuracy: 0.8840
20/65 [========>.....................] - ETA: 19s - loss: 0.4441 - accuracy: 0.8839
21/65 [========>.....................] - ETA: 19s - loss: 0.4422 - accuracy: 0.8843
22/65 [=========>....................] - ETA: 18s - loss: 0.4432 - accuracy: 0.8837
23/65 [=========>....................] - ETA: 18s - loss: 0.4424 - accuracy: 0.8837
24/65 [==========>...................] - ETA: 17s - loss: 0.4425 - accuracy: 0.8832
25/65 [==========>...................] - ETA: 17s - loss: 0.4427 - accuracy: 0.8835
26/65 [===========>..................] - ETA: 16s - loss: 0.4431 - accuracy: 0.8833
27/65 [===========>..................] - ETA: 16s - loss: 0.4434 - accuracy: 0.8830
28/65 [===========>..................] - ETA: 16s - loss: 0.4426 - accuracy: 0.8835
29/65 [============>.................] - ETA: 15s - loss: 0.4421 - accuracy: 0.8836
30/65 [============>.................] - ETA: 15s - loss: 0.4416 - accuracy: 0.8839
31/65 [=============>................] - ETA: 14s - loss: 0.4414 - accuracy: 0.8840
32/65 [=============>................] - ETA: 14s - loss: 0.4411 - accuracy: 0.8844
33/65 [==============>...............] - ETA: 13s - loss: 0.4420 - accuracy: 0.8838
34/65 [==============>...............] - ETA: 13s - loss: 0.4435 - accuracy: 0.8835
35/65 [===============>..............] - ETA: 13s - loss: 0.4440 - accuracy: 0.8834
36/65 [===============>..............] - ETA: 12s - loss: 0.4439 - accuracy: 0.8834
37/65 [================>.............] - ETA: 12s - loss: 0.4430 - accuracy: 0.8833
38/65 [================>.............] - ETA: 11s - loss: 0.4440 - accuracy: 0.8829
39/65 [=================>............] - ETA: 11s - loss: 0.4430 - accuracy: 0.8831
40/65 [=================>............] - ETA: 10s - loss: 0.4442 - accuracy: 0.8830
41/65 [=================>............] - ETA: 10s - loss: 0.4446 - accuracy: 0.8831
42/65 [==================>...........] - ETA: 10s - loss: 0.4440 - accuracy: 0.8834
43/65 [==================>...........] - ETA: 9s - loss: 0.4428 - accuracy: 0.8838
44/65 [===================>..........] - ETA: 9s - loss: 0.4425 - accuracy: 0.8839
45/65 [===================>..........] - ETA: 8s - loss: 0.4428 - accuracy: 0.8838
46/65 [====================>.........] - ETA: 8s - loss: 0.4432 - accuracy: 0.8837
47/65 [====================>.........] - ETA: 7s - loss: 0.4440 - accuracy: 0.8835
48/65 [=====================>........] - ETA: 7s - loss: 0.4433 - accuracy: 0.8837
49/65 [=====================>........] - ETA: 6s - loss: 0.4430 - accuracy: 0.8839
50/65 [======================>.......] - ETA: 6s - loss: 0.4426 - accuracy: 0.8838
51/65 [======================>.......] - ETA: 6s - loss: 0.4428 - accuracy: 0.8836
52/65 [=======================>......] - ETA: 5s - loss: 0.4429 - accuracy: 0.8835
53/65 [=======================>......] - ETA: 5s - loss: 0.4427 - accuracy: 0.8836
54/65 [=======================>......] - ETA: 4s - loss: 0.4418 - accuracy: 0.8837
55/65 [========================>.....] - ETA: 4s - loss: 0.4416 - accuracy: 0.8836
56/65 [========================>.....] - ETA: 3s - loss: 0.4407 - accuracy: 0.8837
57/65 [=========================>....] - ETA: 3s - loss: 0.4407 - accuracy: 0.8837
58/65 [=========================>....] - ETA: 3s - loss: 0.4407 - accuracy: 0.8838
59/65 [==========================>...] - ETA: 2s - loss: 0.4409 - accuracy: 0.8838
60/65 [==========================>...] - ETA: 2s - loss: 0.4409 - accuracy: 0.8839
61/65 [===========================>..] - ETA: 1s - loss: 0.4415 - accuracy: 0.8838
62/65 [===========================>..] - ETA: 1s - loss: 0.4413 - accuracy: 0.8838
63/65 [============================>.] - ETA: 0s - loss: 0.4415 - accuracy: 0.8839
64/65 [============================>.] - ETA: 0s - loss: 0.4409 - accuracy: 0.8842
65/65 [==============================] - ETA: 0s - loss: 0.4407 - accuracy: 0.8843
65/65 [==============================] - 29s 443ms/step - loss: 0.4407 - accuracy: 0.8843 - val_loss: 0.5740 - val_accuracy: 0.8437 - lr: 0.0015
Epoch 22/30
1/65 [..............................] - ETA: 41s - loss: 0.4258 - accuracy: 0.8936
2/65 [..............................] - ETA: 28s - loss: 0.4658 - accuracy: 0.8862
3/65 [>.............................] - ETA: 27s - loss: 0.4710 - accuracy: 0.8815
4/65 [>.............................] - ETA: 27s - loss: 0.4656 - accuracy: 0.8831
5/65 [=>............................] - ETA: 26s - loss: 0.4614 - accuracy: 0.8824
6/65 [=>............................] - ETA: 26s - loss: 0.4560 - accuracy: 0.8840
7/65 [==>...........................] - ETA: 25s - loss: 0.4555 - accuracy: 0.8836
8/65 [==>...........................] - ETA: 25s - loss: 0.4555 - accuracy: 0.8831
9/65 [===>..........................] - ETA: 24s - loss: 0.4532 - accuracy: 0.8848
10/65 [===>..........................] - ETA: 24s - loss: 0.4500 - accuracy: 0.8854
11/65 [====>.........................] - ETA: 23s - loss: 0.4470 - accuracy: 0.8865
12/65 [====>.........................] - ETA: 23s - loss: 0.4463 - accuracy: 0.8859
13/65 [=====>........................] - ETA: 22s - loss: 0.4420 - accuracy: 0.8869
14/65 [=====>........................] - ETA: 22s - loss: 0.4437 - accuracy: 0.8871
15/65 [=====>........................] - ETA: 21s - loss: 0.4462 - accuracy: 0.8860
16/65 [======>.......................] - ETA: 21s - loss: 0.4470 - accuracy: 0.8859
17/65 [======>.......................] - ETA: 20s - loss: 0.4456 - accuracy: 0.8857
18/65 [=======>......................] - ETA: 20s - loss: 0.4445 - accuracy: 0.8859
19/65 [=======>......................] - ETA: 20s - loss: 0.4434 - accuracy: 0.8855
20/65 [========>.....................] - ETA: 19s - loss: 0.4413 - accuracy: 0.8860
21/65 [========>.....................] - ETA: 19s - loss: 0.4432 - accuracy: 0.8854
22/65 [=========>....................] - ETA: 18s - loss: 0.4426 - accuracy: 0.8852
23/65 [=========>....................] - ETA: 18s - loss: 0.4425 - accuracy: 0.8849
24/65 [==========>...................] - ETA: 17s - loss: 0.4422 - accuracy: 0.8846
25/65 [==========>...................] - ETA: 17s - loss: 0.4418 - accuracy: 0.8847
26/65 [===========>..................] - ETA: 16s - loss: 0.4411 - accuracy: 0.8850
27/65 [===========>..................] - ETA: 16s - loss: 0.4397 - accuracy: 0.8855
28/65 [===========>..................] - ETA: 16s - loss: 0.4384 - accuracy: 0.8856
29/65 [============>.................] - ETA: 15s - loss: 0.4394 - accuracy: 0.8851
30/65 [============>.................] - ETA: 15s - loss: 0.4384 - accuracy: 0.8851
31/65 [=============>................] - ETA: 14s - loss: 0.4370 - accuracy: 0.8860
32/65 [=============>................] - ETA: 14s - loss: 0.4374 - accuracy: 0.8860
33/65 [==============>...............] - ETA: 13s - loss: 0.4382 - accuracy: 0.8856
34/65 [==============>...............] - ETA: 13s - loss: 0.4404 - accuracy: 0.8852
35/65 [===============>..............] - ETA: 12s - loss: 0.4394 - accuracy: 0.8854
36/65 [===============>..............] - ETA: 12s - loss: 0.4422 - accuracy: 0.8848
37/65 [================>.............] - ETA: 12s - loss: 0.4441 - accuracy: 0.8844
38/65 [================>.............] - ETA: 11s - loss: 0.4436 - accuracy: 0.8848
39/65 [=================>............] - ETA: 11s - loss: 0.4424 - accuracy: 0.8852
40/65 [=================>............] - ETA: 10s - loss: 0.4425 - accuracy: 0.8852
41/65 [=================>............] - ETA: 10s - loss: 0.4419 - accuracy: 0.8853
42/65 [==================>...........] - ETA: 9s - loss: 0.4418 - accuracy: 0.8855
43/65 [==================>...........] - ETA: 9s - loss: 0.4418 - accuracy: 0.8852
44/65 [===================>..........] - ETA: 9s - loss: 0.4412 - accuracy: 0.8854
45/65 [===================>..........] - ETA: 8s - loss: 0.4408 - accuracy: 0.8857
46/65 [====================>.........] - ETA: 8s - loss: 0.4402 - accuracy: 0.8859
47/65 [====================>.........] - ETA: 7s - loss: 0.4392 - accuracy: 0.8862
48/65 [=====================>........] - ETA: 7s - loss: 0.4385 - accuracy: 0.8864
49/65 [=====================>........] - ETA: 6s - loss: 0.4389 - accuracy: 0.8860
50/65 [======================>.......] - ETA: 6s - loss: 0.4384 - accuracy: 0.8863
51/65 [======================>.......] - ETA: 6s - loss: 0.4378 - accuracy: 0.8865
52/65 [=======================>......] - ETA: 5s - loss: 0.4378 - accuracy: 0.8865
53/65 [=======================>......] - ETA: 5s - loss: 0.4372 - accuracy: 0.8868
54/65 [=======================>......] - ETA: 4s - loss: 0.4368 - accuracy: 0.8868
55/65 [========================>.....] - ETA: 4s - loss: 0.4369 - accuracy: 0.8868
56/65 [========================>.....] - ETA: 3s - loss: 0.4360 - accuracy: 0.8869
57/65 [=========================>....] - ETA: 3s - loss: 0.4348 - accuracy: 0.8873
58/65 [=========================>....] - ETA: 3s - loss: 0.4352 - accuracy: 0.8871
59/65 [==========================>...] - ETA: 2s - loss: 0.4344 - accuracy: 0.8874
60/65 [==========================>...] - ETA: 2s - loss: 0.4344 - accuracy: 0.8873
61/65 [===========================>..] - ETA: 1s - loss: 0.4352 - accuracy: 0.8871
62/65 [===========================>..] - ETA: 1s - loss: 0.4352 - accuracy: 0.8871
63/65 [============================>.] - ETA: 0s - loss: 0.4353 - accuracy: 0.8869
64/65 [============================>.] - ETA: 0s - loss: 0.4355 - accuracy: 0.8870
65/65 [==============================] - ETA: 0s - loss: 0.4354 - accuracy: 0.8870
65/65 [==============================] - 29s 441ms/step - loss: 0.4354 - accuracy: 0.8870 - val_loss: 0.5225 - val_accuracy: 0.8613 - lr: 0.0015
Epoch 23/30
1/65 [..............................] - ETA: 42s - loss: 0.4530 - accuracy: 0.8906
2/65 [..............................] - ETA: 27s - loss: 0.4489 - accuracy: 0.8877
3/65 [>.............................] - ETA: 27s - loss: 0.4505 - accuracy: 0.8867
4/65 [>.............................] - ETA: 26s - loss: 0.4521 - accuracy: 0.8843
5/65 [=>............................] - ETA: 26s - loss: 0.4559 - accuracy: 0.8828
6/65 [=>............................] - ETA: 25s - loss: 0.4558 - accuracy: 0.8838
7/65 [==>...........................] - ETA: 25s - loss: 0.4537 - accuracy: 0.8832
8/65 [==>...........................] - ETA: 24s - loss: 0.4575 - accuracy: 0.8829
9/65 [===>..........................] - ETA: 24s - loss: 0.4565 - accuracy: 0.8821
10/65 [===>..........................] - ETA: 23s - loss: 0.4521 - accuracy: 0.8831
11/65 [====>.........................] - ETA: 23s - loss: 0.4487 - accuracy: 0.8833
12/65 [====>.........................] - ETA: 22s - loss: 0.4438 - accuracy: 0.8844
13/65 [=====>........................] - ETA: 22s - loss: 0.4458 - accuracy: 0.8844
14/65 [=====>........................] - ETA: 22s - loss: 0.4421 - accuracy: 0.8848
15/65 [=====>........................] - ETA: 21s - loss: 0.4440 - accuracy: 0.8839
16/65 [======>.......................] - ETA: 21s - loss: 0.4444 - accuracy: 0.8835
17/65 [======>.......................] - ETA: 20s - loss: 0.4433 - accuracy: 0.8842
18/65 [=======>......................] - ETA: 20s - loss: 0.4440 - accuracy: 0.8834
19/65 [=======>......................] - ETA: 19s - loss: 0.4433 - accuracy: 0.8835
20/65 [========>.....................] - ETA: 19s - loss: 0.4430 - accuracy: 0.8834
21/65 [========>.....................] - ETA: 19s - loss: 0.4427 - accuracy: 0.8836
22/65 [=========>....................] - ETA: 18s - loss: 0.4429 - accuracy: 0.8833
23/65 [=========>....................] - ETA: 18s - loss: 0.4440 - accuracy: 0.8829
24/65 [==========>...................] - ETA: 17s - loss: 0.4421 - accuracy: 0.8833
25/65 [==========>...................] - ETA: 17s - loss: 0.4402 - accuracy: 0.8838
26/65 [===========>..................] - ETA: 16s - loss: 0.4393 - accuracy: 0.8843
27/65 [===========>..................] - ETA: 16s - loss: 0.4384 - accuracy: 0.8846
28/65 [===========>..................] - ETA: 15s - loss: 0.4375 - accuracy: 0.8849
29/65 [============>.................] - ETA: 15s - loss: 0.4373 - accuracy: 0.8851
30/65 [============>.................] - ETA: 15s - loss: 0.4372 - accuracy: 0.8848
31/65 [=============>................] - ETA: 14s - loss: 0.4371 - accuracy: 0.8847
32/65 [=============>................] - ETA: 14s - loss: 0.4368 - accuracy: 0.8848
33/65 [==============>...............] - ETA: 13s - loss: 0.4373 - accuracy: 0.8849
34/65 [==============>...............] - ETA: 13s - loss: 0.4369 - accuracy: 0.8851
35/65 [===============>..............] - ETA: 12s - loss: 0.4378 - accuracy: 0.8848
36/65 [===============>..............] - ETA: 12s - loss: 0.4384 - accuracy: 0.8848
37/65 [================>.............] - ETA: 12s - loss: 0.4389 - accuracy: 0.8848
38/65 [================>.............] - ETA: 11s - loss: 0.4393 - accuracy: 0.8848
39/65 [=================>............] - ETA: 11s - loss: 0.4399 - accuracy: 0.8846
40/65 [=================>............] - ETA: 10s - loss: 0.4399 - accuracy: 0.8846
41/65 [=================>............] - ETA: 10s - loss: 0.4399 - accuracy: 0.8846
42/65 [==================>...........] - ETA: 9s - loss: 0.4385 - accuracy: 0.8851
43/65 [==================>...........] - ETA: 9s - loss: 0.4378 - accuracy: 0.8854
44/65 [===================>..........] - ETA: 9s - loss: 0.4376 - accuracy: 0.8854
45/65 [===================>..........] - ETA: 8s - loss: 0.4382 - accuracy: 0.8852
46/65 [====================>.........] - ETA: 8s - loss: 0.4386 - accuracy: 0.8849
47/65 [====================>.........] - ETA: 7s - loss: 0.4380 - accuracy: 0.8853
48/65 [=====================>........] - ETA: 7s - loss: 0.4383 - accuracy: 0.8852
49/65 [=====================>........] - ETA: 6s - loss: 0.4375 - accuracy: 0.8854
50/65 [======================>.......] - ETA: 6s - loss: 0.4363 - accuracy: 0.8857
51/65 [======================>.......] - ETA: 6s - loss: 0.4361 - accuracy: 0.8857
52/65 [=======================>......] - ETA: 5s - loss: 0.4352 - accuracy: 0.8858
53/65 [=======================>......] - ETA: 5s - loss: 0.4352 - accuracy: 0.8859
54/65 [=======================>......] - ETA: 4s - loss: 0.4344 - accuracy: 0.8862
55/65 [========================>.....] - ETA: 4s - loss: 0.4342 - accuracy: 0.8863
56/65 [========================>.....] - ETA: 3s - loss: 0.4335 - accuracy: 0.8865
57/65 [=========================>....] - ETA: 3s - loss: 0.4340 - accuracy: 0.8863
58/65 [=========================>....] - ETA: 3s - loss: 0.4338 - accuracy: 0.8864
59/65 [==========================>...] - ETA: 2s - loss: 0.4337 - accuracy: 0.8864
60/65 [==========================>...] - ETA: 2s - loss: 0.4330 - accuracy: 0.8867
61/65 [===========================>..] - ETA: 1s - loss: 0.4328 - accuracy: 0.8869
62/65 [===========================>..] - ETA: 1s - loss: 0.4333 - accuracy: 0.8867
63/65 [============================>.] - ETA: 0s - loss: 0.4337 - accuracy: 0.8867
64/65 [============================>.] - ETA: 0s - loss: 0.4331 - accuracy: 0.8868
65/65 [==============================] - ETA: 0s - loss: 0.4326 - accuracy: 0.8870
65/65 [==============================] - 29s 441ms/step - loss: 0.4326 - accuracy: 0.8870 - val_loss: 0.5481 - val_accuracy: 0.8535 - lr: 0.0015
Epoch 24/30
1/65 [..............................] - ETA: 41s - loss: 0.4272 - accuracy: 0.8916
2/65 [..............................] - ETA: 27s - loss: 0.4374 - accuracy: 0.8804
3/65 [>.............................] - ETA: 27s - loss: 0.4354 - accuracy: 0.8857
4/65 [>.............................] - ETA: 26s - loss: 0.4422 - accuracy: 0.8853
5/65 [=>............................] - ETA: 26s - loss: 0.4383 - accuracy: 0.8885
6/65 [=>............................] - ETA: 25s - loss: 0.4419 - accuracy: 0.8879
7/65 [==>...........................] - ETA: 25s - loss: 0.4485 - accuracy: 0.8845
8/65 [==>...........................] - ETA: 24s - loss: 0.4486 - accuracy: 0.8844
9/65 [===>..........................] - ETA: 24s - loss: 0.4437 - accuracy: 0.8853
10/65 [===>..........................] - ETA: 24s - loss: 0.4416 - accuracy: 0.8859
11/65 [====>.........................] - ETA: 23s - loss: 0.4417 - accuracy: 0.8853
12/65 [====>.........................] - ETA: 23s - loss: 0.4420 - accuracy: 0.8853
13/65 [=====>........................] - ETA: 22s - loss: 0.4407 - accuracy: 0.8857
14/65 [=====>........................] - ETA: 22s - loss: 0.4427 - accuracy: 0.8848
15/65 [=====>........................] - ETA: 21s - loss: 0.4409 - accuracy: 0.8848
16/65 [======>.......................] - ETA: 21s - loss: 0.4408 - accuracy: 0.8847
17/65 [======>.......................] - ETA: 20s - loss: 0.4376 - accuracy: 0.8860
18/65 [=======>......................] - ETA: 20s - loss: 0.4384 - accuracy: 0.8859
19/65 [=======>......................] - ETA: 20s - loss: 0.4363 - accuracy: 0.8862
20/65 [========>.....................] - ETA: 19s - loss: 0.4340 - accuracy: 0.8867
21/65 [========>.....................] - ETA: 19s - loss: 0.4331 - accuracy: 0.8871
22/65 [=========>....................] - ETA: 18s - loss: 0.4336 - accuracy: 0.8867
23/65 [=========>....................] - ETA: 18s - loss: 0.4336 - accuracy: 0.8869
24/65 [==========>...................] - ETA: 17s - loss: 0.4328 - accuracy: 0.8868
25/65 [==========>...................] - ETA: 17s - loss: 0.4326 - accuracy: 0.8869
26/65 [===========>..................] - ETA: 17s - loss: 0.4335 - accuracy: 0.8868
27/65 [===========>..................] - ETA: 16s - loss: 0.4335 - accuracy: 0.8866
28/65 [===========>..................] - ETA: 16s - loss: 0.4324 - accuracy: 0.8870
29/65 [============>.................] - ETA: 15s - loss: 0.4329 - accuracy: 0.8867
30/65 [============>.................] - ETA: 15s - loss: 0.4335 - accuracy: 0.8867
31/65 [=============>................] - ETA: 14s - loss: 0.4334 - accuracy: 0.8871
32/65 [=============>................] - ETA: 14s - loss: 0.4328 - accuracy: 0.8872
33/65 [==============>...............] - ETA: 13s - loss: 0.4336 - accuracy: 0.8871
34/65 [==============>...............] - ETA: 13s - loss: 0.4342 - accuracy: 0.8871
35/65 [===============>..............] - ETA: 13s - loss: 0.4348 - accuracy: 0.8869
36/65 [===============>..............] - ETA: 12s - loss: 0.4362 - accuracy: 0.8868
37/65 [================>.............] - ETA: 12s - loss: 0.4365 - accuracy: 0.8865
38/65 [================>.............] - ETA: 11s - loss: 0.4375 - accuracy: 0.8863
39/65 [=================>............] - ETA: 11s - loss: 0.4374 - accuracy: 0.8862
40/65 [=================>............] - ETA: 10s - loss: 0.4370 - accuracy: 0.8864
41/65 [=================>............] - ETA: 10s - loss: 0.4372 - accuracy: 0.8864
42/65 [==================>...........] - ETA: 9s - loss: 0.4375 - accuracy: 0.8862
43/65 [==================>...........] - ETA: 9s - loss: 0.4370 - accuracy: 0.8862
44/65 [===================>..........] - ETA: 9s - loss: 0.4382 - accuracy: 0.8862
45/65 [===================>..........] - ETA: 8s - loss: 0.4382 - accuracy: 0.8860
46/65 [====================>.........] - ETA: 8s - loss: 0.4381 - accuracy: 0.8859
47/65 [====================>.........] - ETA: 7s - loss: 0.4385 - accuracy: 0.8856
48/65 [=====================>........] - ETA: 7s - loss: 0.4381 - accuracy: 0.8858
49/65 [=====================>........] - ETA: 6s - loss: 0.4381 - accuracy: 0.8856
50/65 [======================>.......] - ETA: 6s - loss: 0.4371 - accuracy: 0.8859
51/65 [======================>.......] - ETA: 6s - loss: 0.4362 - accuracy: 0.8863
52/65 [=======================>......] - ETA: 5s - loss: 0.4359 - accuracy: 0.8865
53/65 [=======================>......] - ETA: 5s - loss: 0.4361 - accuracy: 0.8865
54/65 [=======================>......] - ETA: 4s - loss: 0.4358 - accuracy: 0.8865
55/65 [========================>.....] - ETA: 4s - loss: 0.4355 - accuracy: 0.8866
56/65 [========================>.....] - ETA: 3s - loss: 0.4349 - accuracy: 0.8868
57/65 [=========================>....] - ETA: 3s - loss: 0.4345 - accuracy: 0.8870
58/65 [=========================>....] - ETA: 3s - loss: 0.4344 - accuracy: 0.8874
59/65 [==========================>...] - ETA: 2s - loss: 0.4344 - accuracy: 0.8872
60/65 [==========================>...] - ETA: 2s - loss: 0.4342 - accuracy: 0.8873
61/65 [===========================>..] - ETA: 1s - loss: 0.4337 - accuracy: 0.8874
62/65 [===========================>..] - ETA: 1s - loss: 0.4341 - accuracy: 0.8872
63/65 [============================>.] - ETA: 0s - loss: 0.4338 - accuracy: 0.8872
64/65 [============================>.] - ETA: 0s - loss: 0.4334 - accuracy: 0.8873
65/65 [==============================] - ETA: 0s - loss: 0.4336 - accuracy: 0.8872
65/65 [==============================] - 29s 443ms/step - loss: 0.4336 - accuracy: 0.8872 - val_loss: 0.5178 - val_accuracy: 0.8615 - lr: 0.0015
Epoch 25/30
1/65 [..............................] - ETA: 43s - loss: 0.4536 - accuracy: 0.8711
2/65 [..............................] - ETA: 27s - loss: 0.4382 - accuracy: 0.8760
3/65 [>.............................] - ETA: 27s - loss: 0.4389 - accuracy: 0.8789
4/65 [>.............................] - ETA: 26s - loss: 0.4357 - accuracy: 0.8816
5/65 [=>............................] - ETA: 26s - loss: 0.4386 - accuracy: 0.8828
6/65 [=>............................] - ETA: 25s - loss: 0.4383 - accuracy: 0.8840
7/65 [==>...........................] - ETA: 25s - loss: 0.4444 - accuracy: 0.8820
8/65 [==>...........................] - ETA: 24s - loss: 0.4482 - accuracy: 0.8807
9/65 [===>..........................] - ETA: 24s - loss: 0.4445 - accuracy: 0.8815
10/65 [===>..........................] - ETA: 24s - loss: 0.4472 - accuracy: 0.8813
11/65 [====>.........................] - ETA: 23s - loss: 0.4476 - accuracy: 0.8817
12/65 [====>.........................] - ETA: 23s - loss: 0.4459 - accuracy: 0.8826
13/65 [=====>........................] - ETA: 22s - loss: 0.4438 - accuracy: 0.8833
14/65 [=====>........................] - ETA: 22s - loss: 0.4461 - accuracy: 0.8832
15/65 [=====>........................] - ETA: 21s - loss: 0.4436 - accuracy: 0.8835
16/65 [======>.......................] - ETA: 21s - loss: 0.4415 - accuracy: 0.8849
17/65 [======>.......................] - ETA: 21s - loss: 0.4395 - accuracy: 0.8851
18/65 [=======>......................] - ETA: 20s - loss: 0.4375 - accuracy: 0.8849
19/65 [=======>......................] - ETA: 20s - loss: 0.4372 - accuracy: 0.8847
20/65 [========>.....................] - ETA: 19s - loss: 0.4364 - accuracy: 0.8846
21/65 [========>.....................] - ETA: 19s - loss: 0.4377 - accuracy: 0.8839
22/65 [=========>....................] - ETA: 18s - loss: 0.4369 - accuracy: 0.8841
23/65 [=========>....................] - ETA: 18s - loss: 0.4386 - accuracy: 0.8831
24/65 [==========>...................] - ETA: 17s - loss: 0.4365 - accuracy: 0.8838
25/65 [==========>...................] - ETA: 17s - loss: 0.4362 - accuracy: 0.8839
26/65 [===========>..................] - ETA: 17s - loss: 0.4384 - accuracy: 0.8829
27/65 [===========>..................] - ETA: 16s - loss: 0.4373 - accuracy: 0.8834
28/65 [===========>..................] - ETA: 16s - loss: 0.4373 - accuracy: 0.8836
29/65 [============>.................] - ETA: 15s - loss: 0.4370 - accuracy: 0.8836
30/65 [============>.................] - ETA: 15s - loss: 0.4363 - accuracy: 0.8838
31/65 [=============>................] - ETA: 14s - loss: 0.4360 - accuracy: 0.8839
32/65 [=============>................] - ETA: 14s - loss: 0.4347 - accuracy: 0.8844
33/65 [==============>...............] - ETA: 13s - loss: 0.4349 - accuracy: 0.8845
34/65 [==============>...............] - ETA: 13s - loss: 0.4345 - accuracy: 0.8848
35/65 [===============>..............] - ETA: 13s - loss: 0.4352 - accuracy: 0.8850
36/65 [===============>..............] - ETA: 12s - loss: 0.4352 - accuracy: 0.8851
37/65 [================>.............] - ETA: 12s - loss: 0.4354 - accuracy: 0.8849
38/65 [================>.............] - ETA: 11s - loss: 0.4356 - accuracy: 0.8850
39/65 [=================>............] - ETA: 11s - loss: 0.4361 - accuracy: 0.8848
40/65 [=================>............] - ETA: 10s - loss: 0.4387 - accuracy: 0.8839
41/65 [=================>............] - ETA: 10s - loss: 0.4389 - accuracy: 0.8840
42/65 [==================>...........] - ETA: 10s - loss: 0.4378 - accuracy: 0.8843
43/65 [==================>...........] - ETA: 9s - loss: 0.4371 - accuracy: 0.8843
44/65 [===================>..........] - ETA: 9s - loss: 0.4370 - accuracy: 0.8844
45/65 [===================>..........] - ETA: 8s - loss: 0.4368 - accuracy: 0.8845
46/65 [====================>.........] - ETA: 8s - loss: 0.4367 - accuracy: 0.8847
47/65 [====================>.........] - ETA: 7s - loss: 0.4370 - accuracy: 0.8846
48/65 [=====================>........] - ETA: 7s - loss: 0.4365 - accuracy: 0.8847
49/65 [=====================>........] - ETA: 6s - loss: 0.4358 - accuracy: 0.8850
50/65 [======================>.......] - ETA: 6s - loss: 0.4357 - accuracy: 0.8854
51/65 [======================>.......] - ETA: 6s - loss: 0.4359 - accuracy: 0.8853
52/65 [=======================>......] - ETA: 5s - loss: 0.4362 - accuracy: 0.8851
53/65 [=======================>......] - ETA: 5s - loss: 0.4356 - accuracy: 0.8856
54/65 [=======================>......] - ETA: 4s - loss: 0.4353 - accuracy: 0.8856
55/65 [========================>.....] - ETA: 4s - loss: 0.4346 - accuracy: 0.8856
56/65 [========================>.....] - ETA: 3s - loss: 0.4339 - accuracy: 0.8859
57/65 [=========================>....] - ETA: 3s - loss: 0.4329 - accuracy: 0.8862
58/65 [=========================>....] - ETA: 3s - loss: 0.4333 - accuracy: 0.8861
59/65 [==========================>...] - ETA: 2s - loss: 0.4334 - accuracy: 0.8862
60/65 [==========================>...] - ETA: 2s - loss: 0.4325 - accuracy: 0.8864
61/65 [===========================>..] - ETA: 1s - loss: 0.4323 - accuracy: 0.8865
62/65 [===========================>..] - ETA: 1s - loss: 0.4323 - accuracy: 0.8865
63/65 [============================>.] - ETA: 0s - loss: 0.4324 - accuracy: 0.8865
64/65 [============================>.] - ETA: 0s - loss: 0.4326 - accuracy: 0.8864
65/65 [==============================] - ETA: 0s - loss: 0.4321 - accuracy: 0.8865
65/65 [==============================] - 29s 443ms/step - loss: 0.4321 - accuracy: 0.8865 - val_loss: 0.5525 - val_accuracy: 0.8511 - lr: 0.0015
Epoch 26/30
1/65 [..............................] - ETA: 43s - loss: 0.4008 - accuracy: 0.8965
2/65 [..............................] - ETA: 27s - loss: 0.4279 - accuracy: 0.8901
3/65 [>.............................] - ETA: 26s - loss: 0.4397 - accuracy: 0.8848
4/65 [>.............................] - ETA: 26s - loss: 0.4299 - accuracy: 0.8865
5/65 [=>............................] - ETA: 26s - loss: 0.4330 - accuracy: 0.8840
6/65 [=>............................] - ETA: 26s - loss: 0.4337 - accuracy: 0.8846
7/65 [==>...........................] - ETA: 25s - loss: 0.4416 - accuracy: 0.8824
8/65 [==>...........................] - ETA: 24s - loss: 0.4465 - accuracy: 0.8823
9/65 [===>..........................] - ETA: 24s - loss: 0.4418 - accuracy: 0.8834
10/65 [===>..........................] - ETA: 23s - loss: 0.4451 - accuracy: 0.8819
11/65 [====>.........................] - ETA: 23s - loss: 0.4394 - accuracy: 0.8844
12/65 [====>.........................] - ETA: 23s - loss: 0.4382 - accuracy: 0.8857
13/65 [=====>........................] - ETA: 22s - loss: 0.4359 - accuracy: 0.8860
14/65 [=====>........................] - ETA: 22s - loss: 0.4389 - accuracy: 0.8848
15/65 [=====>........................] - ETA: 21s - loss: 0.4375 - accuracy: 0.8852
16/65 [======>.......................] - ETA: 21s - loss: 0.4399 - accuracy: 0.8851
17/65 [======>.......................] - ETA: 20s - loss: 0.4409 - accuracy: 0.8851
18/65 [=======>......................] - ETA: 20s - loss: 0.4363 - accuracy: 0.8859
19/65 [=======>......................] - ETA: 19s - loss: 0.4353 - accuracy: 0.8858
20/65 [========>.....................] - ETA: 19s - loss: 0.4349 - accuracy: 0.8860
21/65 [========>.....................] - ETA: 19s - loss: 0.4360 - accuracy: 0.8851
22/65 [=========>....................] - ETA: 18s - loss: 0.4350 - accuracy: 0.8849
23/65 [=========>....................] - ETA: 18s - loss: 0.4337 - accuracy: 0.8855
24/65 [==========>...................] - ETA: 17s - loss: 0.4323 - accuracy: 0.8857
25/65 [==========>...................] - ETA: 17s - loss: 0.4322 - accuracy: 0.8855
26/65 [===========>..................] - ETA: 16s - loss: 0.4320 - accuracy: 0.8852
27/65 [===========>..................] - ETA: 16s - loss: 0.4320 - accuracy: 0.8856
28/65 [===========>..................] - ETA: 16s - loss: 0.4319 - accuracy: 0.8859
29/65 [============>.................] - ETA: 15s - loss: 0.4329 - accuracy: 0.8856
30/65 [============>.................] - ETA: 15s - loss: 0.4345 - accuracy: 0.8855
31/65 [=============>................] - ETA: 14s - loss: 0.4347 - accuracy: 0.8856
32/65 [=============>................] - ETA: 14s - loss: 0.4336 - accuracy: 0.8858
33/65 [==============>...............] - ETA: 13s - loss: 0.4340 - accuracy: 0.8859
34/65 [==============>...............] - ETA: 13s - loss: 0.4332 - accuracy: 0.8863
35/65 [===============>..............] - ETA: 12s - loss: 0.4330 - accuracy: 0.8867
36/65 [===============>..............] - ETA: 12s - loss: 0.4328 - accuracy: 0.8870
37/65 [================>.............] - ETA: 12s - loss: 0.4325 - accuracy: 0.8866
38/65 [================>.............] - ETA: 11s - loss: 0.4337 - accuracy: 0.8863
39/65 [=================>............] - ETA: 11s - loss: 0.4346 - accuracy: 0.8860
40/65 [=================>............] - ETA: 10s - loss: 0.4347 - accuracy: 0.8863
41/65 [=================>............] - ETA: 10s - loss: 0.4349 - accuracy: 0.8864
42/65 [==================>...........] - ETA: 9s - loss: 0.4345 - accuracy: 0.8865
43/65 [==================>...........] - ETA: 9s - loss: 0.4345 - accuracy: 0.8867
44/65 [===================>..........] - ETA: 9s - loss: 0.4344 - accuracy: 0.8867
45/65 [===================>..........] - ETA: 8s - loss: 0.4347 - accuracy: 0.8865
46/65 [====================>.........] - ETA: 8s - loss: 0.4343 - accuracy: 0.8866
47/65 [====================>.........] - ETA: 7s - loss: 0.4340 - accuracy: 0.8867
48/65 [=====================>........] - ETA: 7s - loss: 0.4340 - accuracy: 0.8867
49/65 [=====================>........] - ETA: 6s - loss: 0.4341 - accuracy: 0.8865
50/65 [======================>.......] - ETA: 6s - loss: 0.4332 - accuracy: 0.8869
51/65 [======================>.......] - ETA: 6s - loss: 0.4331 - accuracy: 0.8868
52/65 [=======================>......] - ETA: 5s - loss: 0.4329 - accuracy: 0.8868
53/65 [=======================>......] - ETA: 5s - loss: 0.4326 - accuracy: 0.8867
54/65 [=======================>......] - ETA: 4s - loss: 0.4323 - accuracy: 0.8870
55/65 [========================>.....] - ETA: 4s - loss: 0.4314 - accuracy: 0.8871
56/65 [========================>.....] - ETA: 3s - loss: 0.4310 - accuracy: 0.8873
57/65 [=========================>....] - ETA: 3s - loss: 0.4309 - accuracy: 0.8874
58/65 [=========================>....] - ETA: 3s - loss: 0.4306 - accuracy: 0.8873
59/65 [==========================>...] - ETA: 2s - loss: 0.4303 - accuracy: 0.8874
60/65 [==========================>...] - ETA: 2s - loss: 0.4310 - accuracy: 0.8872
61/65 [===========================>..] - ETA: 1s - loss: 0.4302 - accuracy: 0.8875
62/65 [===========================>..] - ETA: 1s - loss: 0.4302 - accuracy: 0.8874
63/65 [============================>.] - ETA: 0s - loss: 0.4296 - accuracy: 0.8875
64/65 [============================>.] - ETA: 0s - loss: 0.4299 - accuracy: 0.8874
65/65 [==============================] - ETA: 0s - loss: 0.4297 - accuracy: 0.8875
65/65 [==============================] - 29s 441ms/step - loss: 0.4297 - accuracy: 0.8875 - val_loss: 0.5342 - val_accuracy: 0.8554 - lr: 0.0015
Epoch 27/30
1/65 [..............................] - ETA: 41s - loss: 0.4429 - accuracy: 0.8770
2/65 [..............................] - ETA: 27s - loss: 0.4508 - accuracy: 0.8779
3/65 [>.............................] - ETA: 26s - loss: 0.4559 - accuracy: 0.8743
4/65 [>.............................] - ETA: 26s - loss: 0.4526 - accuracy: 0.8782
5/65 [=>............................] - ETA: 25s - loss: 0.4413 - accuracy: 0.8818
6/65 [=>............................] - ETA: 25s - loss: 0.4556 - accuracy: 0.8796
7/65 [==>...........................] - ETA: 25s - loss: 0.4571 - accuracy: 0.8779
8/65 [==>...........................] - ETA: 24s - loss: 0.4508 - accuracy: 0.8811
9/65 [===>..........................] - ETA: 24s - loss: 0.4494 - accuracy: 0.8813
10/65 [===>..........................] - ETA: 23s - loss: 0.4494 - accuracy: 0.8813
11/65 [====>.........................] - ETA: 23s - loss: 0.4395 - accuracy: 0.8843
12/65 [====>.........................] - ETA: 22s - loss: 0.4383 - accuracy: 0.8844
13/65 [=====>........................] - ETA: 22s - loss: 0.4379 - accuracy: 0.8848
14/65 [=====>........................] - ETA: 22s - loss: 0.4326 - accuracy: 0.8861
15/65 [=====>........................] - ETA: 21s - loss: 0.4345 - accuracy: 0.8857
16/65 [======>.......................] - ETA: 21s - loss: 0.4389 - accuracy: 0.8839
17/65 [======>.......................] - ETA: 20s - loss: 0.4385 - accuracy: 0.8842
18/65 [=======>......................] - ETA: 20s - loss: 0.4387 - accuracy: 0.8845
19/65 [=======>......................] - ETA: 19s - loss: 0.4361 - accuracy: 0.8856
20/65 [========>.....................] - ETA: 19s - loss: 0.4341 - accuracy: 0.8861
21/65 [========>.....................] - ETA: 19s - loss: 0.4338 - accuracy: 0.8860
22/65 [=========>....................] - ETA: 18s - loss: 0.4343 - accuracy: 0.8853
23/65 [=========>....................] - ETA: 18s - loss: 0.4330 - accuracy: 0.8855
24/65 [==========>...................] - ETA: 17s - loss: 0.4330 - accuracy: 0.8856
25/65 [==========>...................] - ETA: 17s - loss: 0.4313 - accuracy: 0.8860
26/65 [===========>..................] - ETA: 16s - loss: 0.4327 - accuracy: 0.8856
27/65 [===========>..................] - ETA: 16s - loss: 0.4335 - accuracy: 0.8854
28/65 [===========>..................] - ETA: 16s - loss: 0.4328 - accuracy: 0.8857
29/65 [============>.................] - ETA: 15s - loss: 0.4318 - accuracy: 0.8863
30/65 [============>.................] - ETA: 15s - loss: 0.4320 - accuracy: 0.8863
31/65 [=============>................] - ETA: 14s - loss: 0.4324 - accuracy: 0.8862
32/65 [=============>................] - ETA: 14s - loss: 0.4338 - accuracy: 0.8858
33/65 [==============>...............] - ETA: 13s - loss: 0.4336 - accuracy: 0.8860
34/65 [==============>...............] - ETA: 13s - loss: 0.4326 - accuracy: 0.8866
35/65 [===============>..............] - ETA: 13s - loss: 0.4312 - accuracy: 0.8869
36/65 [===============>..............] - ETA: 12s - loss: 0.4307 - accuracy: 0.8869
37/65 [================>.............] - ETA: 12s - loss: 0.4326 - accuracy: 0.8865
38/65 [================>.............] - ETA: 11s - loss: 0.4338 - accuracy: 0.8861
39/65 [=================>............] - ETA: 11s - loss: 0.4341 - accuracy: 0.8860
40/65 [=================>............] - ETA: 10s - loss: 0.4345 - accuracy: 0.8860
41/65 [=================>............] - ETA: 10s - loss: 0.4341 - accuracy: 0.8864
42/65 [==================>...........] - ETA: 9s - loss: 0.4342 - accuracy: 0.8865
43/65 [==================>...........] - ETA: 9s - loss: 0.4334 - accuracy: 0.8869
44/65 [===================>..........] - ETA: 9s - loss: 0.4335 - accuracy: 0.8869
45/65 [===================>..........] - ETA: 8s - loss: 0.4348 - accuracy: 0.8866
46/65 [====================>.........] - ETA: 8s - loss: 0.4345 - accuracy: 0.8867
47/65 [====================>.........] - ETA: 7s - loss: 0.4347 - accuracy: 0.8868
48/65 [=====================>........] - ETA: 7s - loss: 0.4347 - accuracy: 0.8868
49/65 [=====================>........] - ETA: 6s - loss: 0.4341 - accuracy: 0.8871
50/65 [======================>.......] - ETA: 6s - loss: 0.4334 - accuracy: 0.8873
51/65 [======================>.......] - ETA: 6s - loss: 0.4323 - accuracy: 0.8878
52/65 [=======================>......] - ETA: 5s - loss: 0.4326 - accuracy: 0.8876
53/65 [=======================>......] - ETA: 5s - loss: 0.4312 - accuracy: 0.8880
54/65 [=======================>......] - ETA: 4s - loss: 0.4315 - accuracy: 0.8879
55/65 [========================>.....] - ETA: 4s - loss: 0.4309 - accuracy: 0.8882
56/65 [========================>.....] - ETA: 3s - loss: 0.4302 - accuracy: 0.8884
57/65 [=========================>....] - ETA: 3s - loss: 0.4303 - accuracy: 0.8881
58/65 [=========================>....] - ETA: 3s - loss: 0.4298 - accuracy: 0.8883
59/65 [==========================>...] - ETA: 2s - loss: 0.4292 - accuracy: 0.8884
60/65 [==========================>...] - ETA: 2s - loss: 0.4284 - accuracy: 0.8887
61/65 [===========================>..] - ETA: 1s - loss: 0.4284 - accuracy: 0.8886
62/65 [===========================>..] - ETA: 1s - loss: 0.4283 - accuracy: 0.8886
63/65 [============================>.] - ETA: 0s - loss: 0.4282 - accuracy: 0.8886
64/65 [============================>.] - ETA: 0s - loss: 0.4283 - accuracy: 0.8886
65/65 [==============================] - ETA: 0s - loss: 0.4281 - accuracy: 0.8887
Epoch 27: ReduceLROnPlateau reducing learning rate to 0.000750000006519258.
65/65 [==============================] - 29s 444ms/step - loss: 0.4281 - accuracy: 0.8887 - val_loss: 0.5389 - val_accuracy: 0.8548 - lr: 0.0015
Epoch 28/30
1/65 [..............................] - ETA: 42s - loss: 0.4745 - accuracy: 0.8818
2/65 [..............................] - ETA: 27s - loss: 0.4580 - accuracy: 0.8794
3/65 [>.............................] - ETA: 27s - loss: 0.4448 - accuracy: 0.8796
4/65 [>.............................] - ETA: 26s - loss: 0.4326 - accuracy: 0.8855
5/65 [=>............................] - ETA: 26s - loss: 0.4314 - accuracy: 0.8848
6/65 [=>............................] - ETA: 26s - loss: 0.4298 - accuracy: 0.8877
7/65 [==>...........................] - ETA: 25s - loss: 0.4340 - accuracy: 0.8866
8/65 [==>...........................] - ETA: 24s - loss: 0.4340 - accuracy: 0.8870
9/65 [===>..........................] - ETA: 24s - loss: 0.4364 - accuracy: 0.8863
10/65 [===>..........................] - ETA: 23s - loss: 0.4372 - accuracy: 0.8865
11/65 [====>.........................] - ETA: 23s - loss: 0.4390 - accuracy: 0.8853
12/65 [====>.........................] - ETA: 23s - loss: 0.4343 - accuracy: 0.8865
13/65 [=====>........................] - ETA: 22s - loss: 0.4323 - accuracy: 0.8864
14/65 [=====>........................] - ETA: 22s - loss: 0.4323 - accuracy: 0.8861
15/65 [=====>........................] - ETA: 21s - loss: 0.4325 - accuracy: 0.8862
16/65 [======>.......................] - ETA: 21s - loss: 0.4303 - accuracy: 0.8868
17/65 [======>.......................] - ETA: 20s - loss: 0.4273 - accuracy: 0.8886
18/65 [=======>......................] - ETA: 20s - loss: 0.4271 - accuracy: 0.8884
19/65 [=======>......................] - ETA: 19s - loss: 0.4302 - accuracy: 0.8871
20/65 [========>.....................] - ETA: 19s - loss: 0.4290 - accuracy: 0.8875
21/65 [========>.....................] - ETA: 19s - loss: 0.4284 - accuracy: 0.8872
22/65 [=========>....................] - ETA: 18s - loss: 0.4251 - accuracy: 0.8880
23/65 [=========>....................] - ETA: 18s - loss: 0.4249 - accuracy: 0.8879
24/65 [==========>...................] - ETA: 17s - loss: 0.4247 - accuracy: 0.8876
25/65 [==========>...................] - ETA: 17s - loss: 0.4229 - accuracy: 0.8882
26/65 [===========>..................] - ETA: 16s - loss: 0.4220 - accuracy: 0.8892
27/65 [===========>..................] - ETA: 16s - loss: 0.4210 - accuracy: 0.8894
28/65 [===========>..................] - ETA: 16s - loss: 0.4209 - accuracy: 0.8894
29/65 [============>.................] - ETA: 15s - loss: 0.4209 - accuracy: 0.8899
30/65 [============>.................] - ETA: 15s - loss: 0.4212 - accuracy: 0.8898
31/65 [=============>................] - ETA: 14s - loss: 0.4201 - accuracy: 0.8899
32/65 [=============>................] - ETA: 14s - loss: 0.4200 - accuracy: 0.8901
33/65 [==============>...............] - ETA: 13s - loss: 0.4188 - accuracy: 0.8907
34/65 [==============>...............] - ETA: 13s - loss: 0.4197 - accuracy: 0.8906
35/65 [===============>..............] - ETA: 13s - loss: 0.4193 - accuracy: 0.8908
36/65 [===============>..............] - ETA: 12s - loss: 0.4193 - accuracy: 0.8910
37/65 [================>.............] - ETA: 12s - loss: 0.4191 - accuracy: 0.8910
38/65 [================>.............] - ETA: 11s - loss: 0.4194 - accuracy: 0.8909
39/65 [=================>............] - ETA: 11s - loss: 0.4204 - accuracy: 0.8908
40/65 [=================>............] - ETA: 10s - loss: 0.4206 - accuracy: 0.8909
41/65 [=================>............] - ETA: 10s - loss: 0.4203 - accuracy: 0.8913
42/65 [==================>...........] - ETA: 9s - loss: 0.4204 - accuracy: 0.8911
43/65 [==================>...........] - ETA: 9s - loss: 0.4199 - accuracy: 0.8913
44/65 [===================>..........] - ETA: 9s - loss: 0.4198 - accuracy: 0.8912
45/65 [===================>..........] - ETA: 8s - loss: 0.4195 - accuracy: 0.8912
46/65 [====================>.........] - ETA: 8s - loss: 0.4194 - accuracy: 0.8913
47/65 [====================>.........] - ETA: 7s - loss: 0.4188 - accuracy: 0.8917
48/65 [=====================>........] - ETA: 7s - loss: 0.4180 - accuracy: 0.8920
49/65 [=====================>........] - ETA: 6s - loss: 0.4188 - accuracy: 0.8917
50/65 [======================>.......] - ETA: 6s - loss: 0.4186 - accuracy: 0.8917
51/65 [======================>.......] - ETA: 6s - loss: 0.4189 - accuracy: 0.8917
52/65 [=======================>......] - ETA: 5s - loss: 0.4182 - accuracy: 0.8919
53/65 [=======================>......] - ETA: 5s - loss: 0.4169 - accuracy: 0.8922
54/65 [=======================>......] - ETA: 4s - loss: 0.4168 - accuracy: 0.8921
55/65 [========================>.....] - ETA: 4s - loss: 0.4165 - accuracy: 0.8922
56/65 [========================>.....] - ETA: 3s - loss: 0.4166 - accuracy: 0.8922
57/65 [=========================>....] - ETA: 3s - loss: 0.4164 - accuracy: 0.8922
58/65 [=========================>....] - ETA: 3s - loss: 0.4164 - accuracy: 0.8921
59/65 [==========================>...] - ETA: 2s - loss: 0.4158 - accuracy: 0.8922
60/65 [==========================>...] - ETA: 2s - loss: 0.4154 - accuracy: 0.8924
61/65 [===========================>..] - ETA: 1s - loss: 0.4151 - accuracy: 0.8925
62/65 [===========================>..] - ETA: 1s - loss: 0.4145 - accuracy: 0.8927
63/65 [============================>.] - ETA: 0s - loss: 0.4145 - accuracy: 0.8927
64/65 [============================>.] - ETA: 0s - loss: 0.4147 - accuracy: 0.8927
65/65 [==============================] - ETA: 0s - loss: 0.4148 - accuracy: 0.8926
65/65 [==============================] - 29s 442ms/step - loss: 0.4148 - accuracy: 0.8926 - val_loss: 0.5191 - val_accuracy: 0.8632 - lr: 7.5000e-04
Epoch 29/30
1/65 [..............................] - ETA: 41s - loss: 0.4156 - accuracy: 0.8916
2/65 [..............................] - ETA: 27s - loss: 0.4302 - accuracy: 0.8833
3/65 [>.............................] - ETA: 26s - loss: 0.4307 - accuracy: 0.8910
4/65 [>.............................] - ETA: 26s - loss: 0.4333 - accuracy: 0.8916
5/65 [=>............................] - ETA: 25s - loss: 0.4385 - accuracy: 0.8885
6/65 [=>............................] - ETA: 25s - loss: 0.4373 - accuracy: 0.8893
7/65 [==>...........................] - ETA: 25s - loss: 0.4365 - accuracy: 0.8894
8/65 [==>...........................] - ETA: 24s - loss: 0.4392 - accuracy: 0.8871
9/65 [===>..........................] - ETA: 24s - loss: 0.4369 - accuracy: 0.8875
10/65 [===>..........................] - ETA: 23s - loss: 0.4353 - accuracy: 0.8876
11/65 [====>.........................] - ETA: 23s - loss: 0.4326 - accuracy: 0.8878
12/65 [====>.........................] - ETA: 22s - loss: 0.4271 - accuracy: 0.8890
13/65 [=====>........................] - ETA: 22s - loss: 0.4292 - accuracy: 0.8876
14/65 [=====>........................] - ETA: 22s - loss: 0.4299 - accuracy: 0.8872
15/65 [=====>........................] - ETA: 21s - loss: 0.4258 - accuracy: 0.8882
16/65 [======>.......................] - ETA: 21s - loss: 0.4227 - accuracy: 0.8893
17/65 [======>.......................] - ETA: 20s - loss: 0.4215 - accuracy: 0.8903
18/65 [=======>......................] - ETA: 20s - loss: 0.4210 - accuracy: 0.8897
19/65 [=======>......................] - ETA: 19s - loss: 0.4205 - accuracy: 0.8900
20/65 [========>.....................] - ETA: 19s - loss: 0.4209 - accuracy: 0.8903
21/65 [========>.....................] - ETA: 18s - loss: 0.4223 - accuracy: 0.8899
22/65 [=========>....................] - ETA: 18s - loss: 0.4221 - accuracy: 0.8897
23/65 [=========>....................] - ETA: 18s - loss: 0.4205 - accuracy: 0.8904
24/65 [==========>...................] - ETA: 17s - loss: 0.4200 - accuracy: 0.8906
25/65 [==========>...................] - ETA: 17s - loss: 0.4188 - accuracy: 0.8907
26/65 [===========>..................] - ETA: 16s - loss: 0.4188 - accuracy: 0.8909
27/65 [===========>..................] - ETA: 16s - loss: 0.4160 - accuracy: 0.8919
28/65 [===========>..................] - ETA: 15s - loss: 0.4149 - accuracy: 0.8926
29/65 [============>.................] - ETA: 15s - loss: 0.4145 - accuracy: 0.8928
30/65 [============>.................] - ETA: 15s - loss: 0.4149 - accuracy: 0.8926
31/65 [=============>................] - ETA: 14s - loss: 0.4147 - accuracy: 0.8926
32/65 [=============>................] - ETA: 14s - loss: 0.4146 - accuracy: 0.8927
33/65 [==============>...............] - ETA: 13s - loss: 0.4154 - accuracy: 0.8923
34/65 [==============>...............] - ETA: 13s - loss: 0.4175 - accuracy: 0.8916
35/65 [===============>..............] - ETA: 12s - loss: 0.4187 - accuracy: 0.8915
36/65 [===============>..............] - ETA: 12s - loss: 0.4185 - accuracy: 0.8915
37/65 [================>.............] - ETA: 12s - loss: 0.4179 - accuracy: 0.8917
38/65 [================>.............] - ETA: 11s - loss: 0.4181 - accuracy: 0.8917
39/65 [=================>............] - ETA: 11s - loss: 0.4188 - accuracy: 0.8914
40/65 [=================>............] - ETA: 10s - loss: 0.4198 - accuracy: 0.8913
41/65 [=================>............] - ETA: 10s - loss: 0.4199 - accuracy: 0.8912
42/65 [==================>...........] - ETA: 9s - loss: 0.4192 - accuracy: 0.8915
43/65 [==================>...........] - ETA: 9s - loss: 0.4199 - accuracy: 0.8911
44/65 [===================>..........] - ETA: 9s - loss: 0.4199 - accuracy: 0.8910
45/65 [===================>..........] - ETA: 8s - loss: 0.4200 - accuracy: 0.8908
46/65 [====================>.........] - ETA: 8s - loss: 0.4193 - accuracy: 0.8909
47/65 [====================>.........] - ETA: 7s - loss: 0.4187 - accuracy: 0.8911
48/65 [=====================>........] - ETA: 7s - loss: 0.4180 - accuracy: 0.8915
49/65 [=====================>........] - ETA: 6s - loss: 0.4171 - accuracy: 0.8918
50/65 [======================>.......] - ETA: 6s - loss: 0.4164 - accuracy: 0.8920
51/65 [======================>.......] - ETA: 6s - loss: 0.4155 - accuracy: 0.8923
52/65 [=======================>......] - ETA: 5s - loss: 0.4157 - accuracy: 0.8923
53/65 [=======================>......] - ETA: 5s - loss: 0.4157 - accuracy: 0.8923
54/65 [=======================>......] - ETA: 4s - loss: 0.4153 - accuracy: 0.8924
55/65 [========================>.....] - ETA: 4s - loss: 0.4147 - accuracy: 0.8926
56/65 [========================>.....] - ETA: 3s - loss: 0.4146 - accuracy: 0.8926
57/65 [=========================>....] - ETA: 3s - loss: 0.4140 - accuracy: 0.8929
58/65 [=========================>....] - ETA: 3s - loss: 0.4137 - accuracy: 0.8928
59/65 [==========================>...] - ETA: 2s - loss: 0.4134 - accuracy: 0.8930
60/65 [==========================>...] - ETA: 2s - loss: 0.4131 - accuracy: 0.8931
61/65 [===========================>..] - ETA: 1s - loss: 0.4132 - accuracy: 0.8931
62/65 [===========================>..] - ETA: 1s - loss: 0.4137 - accuracy: 0.8928
63/65 [============================>.] - ETA: 0s - loss: 0.4138 - accuracy: 0.8927
64/65 [============================>.] - ETA: 0s - loss: 0.4138 - accuracy: 0.8926
65/65 [==============================] - ETA: 0s - loss: 0.4137 - accuracy: 0.8926
65/65 [==============================] - 29s 441ms/step - loss: 0.4137 - accuracy: 0.8926 - val_loss: 0.5094 - val_accuracy: 0.8668 - lr: 7.5000e-04
Epoch 30/30
1/65 [..............................] - ETA: 42s - loss: 0.4316 - accuracy: 0.8828
2/65 [..............................] - ETA: 26s - loss: 0.4416 - accuracy: 0.8794
3/65 [>.............................] - ETA: 27s - loss: 0.4396 - accuracy: 0.8818
4/65 [>.............................] - ETA: 26s - loss: 0.4247 - accuracy: 0.8889
5/65 [=>............................] - ETA: 26s - loss: 0.4320 - accuracy: 0.8863
6/65 [=>............................] - ETA: 25s - loss: 0.4365 - accuracy: 0.8864
7/65 [==>...........................] - ETA: 25s - loss: 0.4343 - accuracy: 0.8859
8/65 [==>...........................] - ETA: 24s - loss: 0.4339 - accuracy: 0.8871
9/65 [===>..........................] - ETA: 24s - loss: 0.4340 - accuracy: 0.8880
10/65 [===>..........................] - ETA: 24s - loss: 0.4309 - accuracy: 0.8886
11/65 [====>.........................] - ETA: 23s - loss: 0.4298 - accuracy: 0.8894
12/65 [====>.........................] - ETA: 23s - loss: 0.4248 - accuracy: 0.8904
13/65 [=====>........................] - ETA: 22s - loss: 0.4260 - accuracy: 0.8896
14/65 [=====>........................] - ETA: 22s - loss: 0.4213 - accuracy: 0.8910
15/65 [=====>........................] - ETA: 21s - loss: 0.4218 - accuracy: 0.8911
16/65 [======>.......................] - ETA: 21s - loss: 0.4219 - accuracy: 0.8912
17/65 [======>.......................] - ETA: 20s - loss: 0.4221 - accuracy: 0.8906
18/65 [=======>......................] - ETA: 20s - loss: 0.4203 - accuracy: 0.8904
19/65 [=======>......................] - ETA: 20s - loss: 0.4214 - accuracy: 0.8898
20/65 [========>.....................] - ETA: 19s - loss: 0.4214 - accuracy: 0.8896
21/65 [========>.....................] - ETA: 19s - loss: 0.4206 - accuracy: 0.8903
22/65 [=========>....................] - ETA: 18s - loss: 0.4196 - accuracy: 0.8908
23/65 [=========>....................] - ETA: 18s - loss: 0.4175 - accuracy: 0.8911
24/65 [==========>...................] - ETA: 17s - loss: 0.4162 - accuracy: 0.8915
25/65 [==========>...................] - ETA: 17s - loss: 0.4142 - accuracy: 0.8920
26/65 [===========>..................] - ETA: 16s - loss: 0.4125 - accuracy: 0.8925
27/65 [===========>..................] - ETA: 16s - loss: 0.4118 - accuracy: 0.8928
28/65 [===========>..................] - ETA: 16s - loss: 0.4125 - accuracy: 0.8925
29/65 [============>.................] - ETA: 15s - loss: 0.4134 - accuracy: 0.8922
30/65 [============>.................] - ETA: 15s - loss: 0.4129 - accuracy: 0.8925
31/65 [=============>................] - ETA: 14s - loss: 0.4128 - accuracy: 0.8928
32/65 [=============>................] - ETA: 14s - loss: 0.4135 - accuracy: 0.8927
33/65 [==============>...............] - ETA: 13s - loss: 0.4146 - accuracy: 0.8924
34/65 [==============>...............] - ETA: 13s - loss: 0.4158 - accuracy: 0.8921
35/65 [===============>..............] - ETA: 13s - loss: 0.4148 - accuracy: 0.8924
36/65 [===============>..............] - ETA: 12s - loss: 0.4145 - accuracy: 0.8925
37/65 [================>.............] - ETA: 12s - loss: 0.4155 - accuracy: 0.8921
38/65 [================>.............] - ETA: 11s - loss: 0.4164 - accuracy: 0.8918
39/65 [=================>............] - ETA: 11s - loss: 0.4170 - accuracy: 0.8917
40/65 [=================>............] - ETA: 10s - loss: 0.4179 - accuracy: 0.8915
41/65 [=================>............] - ETA: 10s - loss: 0.4188 - accuracy: 0.8914
42/65 [==================>...........] - ETA: 10s - loss: 0.4174 - accuracy: 0.8919
43/65 [==================>...........] - ETA: 9s - loss: 0.4174 - accuracy: 0.8917
44/65 [===================>..........] - ETA: 9s - loss: 0.4169 - accuracy: 0.8918
45/65 [===================>..........] - ETA: 8s - loss: 0.4165 - accuracy: 0.8919
46/65 [====================>.........] - ETA: 8s - loss: 0.4162 - accuracy: 0.8920
47/65 [====================>.........] - ETA: 7s - loss: 0.4155 - accuracy: 0.8921
48/65 [=====================>........] - ETA: 7s - loss: 0.4155 - accuracy: 0.8923
49/65 [=====================>........] - ETA: 6s - loss: 0.4152 - accuracy: 0.8924
50/65 [======================>.......] - ETA: 6s - loss: 0.4149 - accuracy: 0.8925
51/65 [======================>.......] - ETA: 6s - loss: 0.4147 - accuracy: 0.8924
52/65 [=======================>......] - ETA: 5s - loss: 0.4140 - accuracy: 0.8926
53/65 [=======================>......] - ETA: 5s - loss: 0.4149 - accuracy: 0.8922
54/65 [=======================>......] - ETA: 4s - loss: 0.4146 - accuracy: 0.8922
55/65 [========================>.....] - ETA: 4s - loss: 0.4141 - accuracy: 0.8923
56/65 [========================>.....] - ETA: 3s - loss: 0.4138 - accuracy: 0.8925
57/65 [=========================>....] - ETA: 3s - loss: 0.4139 - accuracy: 0.8926
58/65 [=========================>....] - ETA: 3s - loss: 0.4142 - accuracy: 0.8924
59/65 [==========================>...] - ETA: 2s - loss: 0.4143 - accuracy: 0.8925
60/65 [==========================>...] - ETA: 2s - loss: 0.4146 - accuracy: 0.8923
61/65 [===========================>..] - ETA: 1s - loss: 0.4148 - accuracy: 0.8924
62/65 [===========================>..] - ETA: 1s - loss: 0.4148 - accuracy: 0.8924
63/65 [============================>.] - ETA: 0s - loss: 0.4143 - accuracy: 0.8926
64/65 [============================>.] - ETA: 0s - loss: 0.4134 - accuracy: 0.8929
65/65 [==============================] - ETA: 0s - loss: 0.4132 - accuracy: 0.8929
65/65 [==============================] - 29s 444ms/step - loss: 0.4132 - accuracy: 0.8929 - val_loss: 0.5081 - val_accuracy: 0.8658 - lr: 7.5000e-04
It took 14.597657879193624 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: 1:34
20/814 [..............................] - ETA: 2s
41/814 [>.............................] - ETA: 1s
62/814 [=>............................] - ETA: 1s
83/814 [==>...........................] - ETA: 1s
104/814 [==>...........................] - ETA: 1s
125/814 [===>..........................] - ETA: 1s
146/814 [====>.........................] - ETA: 1s
165/814 [=====>........................] - ETA: 1s
186/814 [=====>........................] - ETA: 1s
207/814 [======>.......................] - ETA: 1s
228/814 [=======>......................] - ETA: 1s
249/814 [========>.....................] - ETA: 1s
270/814 [========>.....................] - ETA: 1s
291/814 [=========>....................] - ETA: 1s
312/814 [==========>...................] - ETA: 1s
333/814 [===========>..................] - ETA: 1s
354/814 [============>.................] - ETA: 1s
375/814 [============>.................] - ETA: 1s
396/814 [=============>................] - ETA: 1s
417/814 [==============>...............] - ETA: 0s
437/814 [===============>..............] - ETA: 0s
457/814 [===============>..............] - ETA: 0s
478/814 [================>.............] - ETA: 0s
499/814 [=================>............] - ETA: 0s
520/814 [==================>...........] - ETA: 0s
541/814 [==================>...........] - ETA: 0s
562/814 [===================>..........] - ETA: 0s
581/814 [====================>.........] - ETA: 0s
602/814 [=====================>........] - ETA: 0s
623/814 [=====================>........] - ETA: 0s
644/814 [======================>.......] - ETA: 0s
664/814 [=======================>......] - ETA: 0s
685/814 [========================>.....] - ETA: 0s
706/814 [=========================>....] - ETA: 0s
727/814 [=========================>....] - ETA: 0s
748/814 [==========================>...] - ETA: 0s
769/814 [===========================>..] - ETA: 0s
790/814 [============================>.] - ETA: 0s
811/814 [============================>.] - ETA: 0s
814/814 [==============================] - 2s 2ms/step
1/814 [..............................] - ETA: 12s - loss: 0.2920 - accuracy: 0.9375
21/814 [..............................] - ETA: 2s - loss: 0.4741 - accuracy: 0.8795
41/814 [>.............................] - ETA: 2s - loss: 0.4550 - accuracy: 0.8902
61/814 [=>............................] - ETA: 1s - loss: 0.4506 - accuracy: 0.8909
81/814 [=>............................] - ETA: 1s - loss: 0.4520 - accuracy: 0.8866
99/814 [==>...........................] - ETA: 1s - loss: 0.4574 - accuracy: 0.8857
119/814 [===>..........................] - ETA: 1s - loss: 0.4624 - accuracy: 0.8847
139/814 [====>.........................] - ETA: 1s - loss: 0.4636 - accuracy: 0.8849
159/814 [====>.........................] - ETA: 1s - loss: 0.4663 - accuracy: 0.8823
179/814 [=====>........................] - ETA: 1s - loss: 0.4665 - accuracy: 0.8799
199/814 [======>.......................] - ETA: 1s - loss: 0.4582 - accuracy: 0.8830
219/814 [=======>......................] - ETA: 1s - loss: 0.4596 - accuracy: 0.8806
239/814 [=======>......................] - ETA: 1s - loss: 0.4606 - accuracy: 0.8794
259/814 [========>.....................] - ETA: 1s - loss: 0.4590 - accuracy: 0.8818
279/814 [=========>....................] - ETA: 1s - loss: 0.4567 - accuracy: 0.8827
298/814 [=========>....................] - ETA: 1s - loss: 0.4576 - accuracy: 0.8820
318/814 [==========>...................] - ETA: 1s - loss: 0.4561 - accuracy: 0.8824
337/814 [===========>..................] - ETA: 1s - loss: 0.4607 - accuracy: 0.8821
357/814 [============>.................] - ETA: 1s - loss: 0.4597 - accuracy: 0.8824
377/814 [============>.................] - ETA: 1s - loss: 0.4641 - accuracy: 0.8815
397/814 [=============>................] - ETA: 1s - loss: 0.4631 - accuracy: 0.8816
417/814 [==============>...............] - ETA: 1s - loss: 0.4589 - accuracy: 0.8832
437/814 [===============>..............] - ETA: 0s - loss: 0.4604 - accuracy: 0.8824
457/814 [===============>..............] - ETA: 0s - loss: 0.4607 - accuracy: 0.8827
477/814 [================>.............] - ETA: 0s - loss: 0.4611 - accuracy: 0.8820
495/814 [=================>............] - ETA: 0s - loss: 0.4635 - accuracy: 0.8814
515/814 [=================>............] - ETA: 0s - loss: 0.4630 - accuracy: 0.8815
535/814 [==================>...........] - ETA: 0s - loss: 0.4616 - accuracy: 0.8818
555/814 [===================>..........] - ETA: 0s - loss: 0.4644 - accuracy: 0.8807
575/814 [====================>.........] - ETA: 0s - loss: 0.4645 - accuracy: 0.8808
595/814 [====================>.........] - ETA: 0s - loss: 0.4661 - accuracy: 0.8804
615/814 [=====================>........] - ETA: 0s - loss: 0.4650 - accuracy: 0.8806
635/814 [======================>.......] - ETA: 0s - loss: 0.4635 - accuracy: 0.8810
655/814 [=======================>......] - ETA: 0s - loss: 0.4645 - accuracy: 0.8807
675/814 [=======================>......] - ETA: 0s - loss: 0.4646 - accuracy: 0.8809
695/814 [========================>.....] - ETA: 0s - loss: 0.4656 - accuracy: 0.8804
715/814 [=========================>....] - ETA: 0s - loss: 0.4669 - accuracy: 0.8805
735/814 [==========================>...] - ETA: 0s - loss: 0.4668 - accuracy: 0.8809
755/814 [==========================>...] - ETA: 0s - loss: 0.4664 - accuracy: 0.8809
775/814 [===========================>..] - ETA: 0s - loss: 0.4661 - accuracy: 0.8808
795/814 [============================>.] - ETA: 0s - loss: 0.4666 - accuracy: 0.8809
814/814 [==============================] - 2s 3ms/step - loss: 0.4674 - accuracy: 0.8807
1/814 [..............................] - ETA: 4:44
13/814 [..............................] - ETA: 3s
25/814 [..............................] - ETA: 3s
37/814 [>.............................] - ETA: 3s
49/814 [>.............................] - ETA: 3s
61/814 [=>............................] - ETA: 3s
73/814 [=>............................] - ETA: 3s
85/814 [==>...........................] - ETA: 3s
97/814 [==>...........................] - ETA: 3s
109/814 [===>..........................] - ETA: 3s
121/814 [===>..........................] - ETA: 2s
133/814 [===>..........................] - ETA: 2s
145/814 [====>.........................] - ETA: 2s
157/814 [====>.........................] - ETA: 2s
169/814 [=====>........................] - ETA: 2s
181/814 [=====>........................] - ETA: 2s
193/814 [======>.......................] - ETA: 2s
204/814 [======>.......................] - ETA: 2s
216/814 [======>.......................] - ETA: 2s
228/814 [=======>......................] - ETA: 2s
240/814 [=======>......................] - ETA: 2s
252/814 [========>.....................] - ETA: 2s
264/814 [========>.....................] - ETA: 2s
276/814 [=========>....................] - ETA: 2s
288/814 [=========>....................] - ETA: 2s
300/814 [==========>...................] - ETA: 2s
312/814 [==========>...................] - ETA: 2s
324/814 [==========>...................] - ETA: 2s
336/814 [===========>..................] - ETA: 2s
348/814 [===========>..................] - ETA: 1s
360/814 [============>.................] - ETA: 1s
372/814 [============>.................] - ETA: 1s
384/814 [=============>................] - ETA: 1s
396/814 [=============>................] - ETA: 1s
408/814 [==============>...............] - ETA: 1s
420/814 [==============>...............] - ETA: 1s
432/814 [==============>...............] - ETA: 1s
443/814 [===============>..............] - ETA: 1s
455/814 [===============>..............] - ETA: 1s
467/814 [================>.............] - ETA: 1s
479/814 [================>.............] - ETA: 1s
491/814 [=================>............] - ETA: 1s
503/814 [=================>............] - ETA: 1s
515/814 [=================>............] - ETA: 1s
527/814 [==================>...........] - ETA: 1s
540/814 [==================>...........] - ETA: 1s
552/814 [===================>..........] - ETA: 1s
564/814 [===================>..........] - ETA: 1s
576/814 [====================>.........] - ETA: 1s
588/814 [====================>.........] - ETA: 0s
600/814 [=====================>........] - ETA: 0s
612/814 [=====================>........] - ETA: 0s
624/814 [=====================>........] - ETA: 0s
636/814 [======================>.......] - ETA: 0s
648/814 [======================>.......] - ETA: 0s
660/814 [=======================>......] - ETA: 0s
672/814 [=======================>......] - ETA: 0s
683/814 [========================>.....] - ETA: 0s
694/814 [========================>.....] - ETA: 0s
706/814 [=========================>....] - ETA: 0s
718/814 [=========================>....] - ETA: 0s
730/814 [=========================>....] - ETA: 0s
742/814 [==========================>...] - ETA: 0s
754/814 [==========================>...] - ETA: 0s
766/814 [===========================>..] - ETA: 0s
778/814 [===========================>..] - ETA: 0s
790/814 [============================>.] - ETA: 0s
802/814 [============================>.] - ETA: 0s
814/814 [==============================] - 4s 4ms/step
1/814 [..............................] - ETA: 13s - loss: 0.3791 - accuracy: 0.9062
13/814 [..............................] - ETA: 3s - loss: 0.5903 - accuracy: 0.8341
25/814 [..............................] - ETA: 3s - loss: 0.5489 - accuracy: 0.8450
37/814 [>.............................] - ETA: 3s - loss: 0.5390 - accuracy: 0.8505
49/814 [>.............................] - ETA: 3s - loss: 0.5698 - accuracy: 0.8431
61/814 [=>............................] - ETA: 3s - loss: 0.5402 - accuracy: 0.8530
72/814 [=>............................] - ETA: 3s - loss: 0.5433 - accuracy: 0.8581
84/814 [==>...........................] - ETA: 3s - loss: 0.5374 - accuracy: 0.8594
96/814 [==>...........................] - ETA: 3s - loss: 0.5420 - accuracy: 0.8571
108/814 [==>...........................] - ETA: 3s - loss: 0.5458 - accuracy: 0.8547
120/814 [===>..........................] - ETA: 3s - loss: 0.5506 - accuracy: 0.8557
132/814 [===>..........................] - ETA: 3s - loss: 0.5571 - accuracy: 0.8532
144/814 [====>.........................] - ETA: 2s - loss: 0.5627 - accuracy: 0.8500
156/814 [====>.........................] - ETA: 2s - loss: 0.5585 - accuracy: 0.8512
167/814 [=====>........................] - ETA: 2s - loss: 0.5555 - accuracy: 0.8516
179/814 [=====>........................] - ETA: 2s - loss: 0.5515 - accuracy: 0.8518
191/814 [======>.......................] - ETA: 2s - loss: 0.5418 - accuracy: 0.8549
203/814 [======>.......................] - ETA: 2s - loss: 0.5405 - accuracy: 0.8559
215/814 [======>.......................] - ETA: 2s - loss: 0.5416 - accuracy: 0.8541
227/814 [=======>......................] - ETA: 2s - loss: 0.5429 - accuracy: 0.8544
239/814 [=======>......................] - ETA: 2s - loss: 0.5396 - accuracy: 0.8550
251/814 [========>.....................] - ETA: 2s - loss: 0.5378 - accuracy: 0.8569
263/814 [========>.....................] - ETA: 2s - loss: 0.5368 - accuracy: 0.8575
275/814 [=========>....................] - ETA: 2s - loss: 0.5355 - accuracy: 0.8594
287/814 [=========>....................] - ETA: 2s - loss: 0.5348 - accuracy: 0.8587
299/814 [==========>...................] - ETA: 2s - loss: 0.5340 - accuracy: 0.8587
310/814 [==========>...................] - ETA: 2s - loss: 0.5363 - accuracy: 0.8584
322/814 [==========>...................] - ETA: 2s - loss: 0.5335 - accuracy: 0.8588
334/814 [===========>..................] - ETA: 2s - loss: 0.5344 - accuracy: 0.8586
346/814 [===========>..................] - ETA: 2s - loss: 0.5345 - accuracy: 0.8589
358/814 [============>.................] - ETA: 2s - loss: 0.5345 - accuracy: 0.8583
370/814 [============>.................] - ETA: 1s - loss: 0.5350 - accuracy: 0.8577
382/814 [=============>................] - ETA: 1s - loss: 0.5353 - accuracy: 0.8572
394/814 [=============>................] - ETA: 1s - loss: 0.5349 - accuracy: 0.8572
406/814 [=============>................] - ETA: 1s - loss: 0.5341 - accuracy: 0.8575
418/814 [==============>...............] - ETA: 1s - loss: 0.5329 - accuracy: 0.8577
430/814 [==============>...............] - ETA: 1s - loss: 0.5326 - accuracy: 0.8579
442/814 [===============>..............] - ETA: 1s - loss: 0.5325 - accuracy: 0.8578
454/814 [===============>..............] - ETA: 1s - loss: 0.5331 - accuracy: 0.8579
466/814 [================>.............] - ETA: 1s - loss: 0.5330 - accuracy: 0.8582
478/814 [================>.............] - ETA: 1s - loss: 0.5353 - accuracy: 0.8573
490/814 [=================>............] - ETA: 1s - loss: 0.5365 - accuracy: 0.8572
502/814 [=================>............] - ETA: 1s - loss: 0.5373 - accuracy: 0.8573
514/814 [=================>............] - ETA: 1s - loss: 0.5378 - accuracy: 0.8571
526/814 [==================>...........] - ETA: 1s - loss: 0.5365 - accuracy: 0.8574
538/814 [==================>...........] - ETA: 1s - loss: 0.5364 - accuracy: 0.8573
549/814 [===================>..........] - ETA: 1s - loss: 0.5365 - accuracy: 0.8575
561/814 [===================>..........] - ETA: 1s - loss: 0.5389 - accuracy: 0.8567
573/814 [====================>.........] - ETA: 1s - loss: 0.5390 - accuracy: 0.8568
585/814 [====================>.........] - ETA: 1s - loss: 0.5406 - accuracy: 0.8564
597/814 [=====================>........] - ETA: 0s - loss: 0.5392 - accuracy: 0.8570
609/814 [=====================>........] - ETA: 0s - loss: 0.5394 - accuracy: 0.8568
621/814 [=====================>........] - ETA: 0s - loss: 0.5393 - accuracy: 0.8565
633/814 [======================>.......] - ETA: 0s - loss: 0.5391 - accuracy: 0.8565
645/814 [======================>.......] - ETA: 0s - loss: 0.5388 - accuracy: 0.8564
657/814 [=======================>......] - ETA: 0s - loss: 0.5377 - accuracy: 0.8565
669/814 [=======================>......] - ETA: 0s - loss: 0.5371 - accuracy: 0.8564
681/814 [========================>.....] - ETA: 0s - loss: 0.5366 - accuracy: 0.8562
693/814 [========================>.....] - ETA: 0s - loss: 0.5368 - accuracy: 0.8560
705/814 [========================>.....] - ETA: 0s - loss: 0.5388 - accuracy: 0.8555
717/814 [=========================>....] - ETA: 0s - loss: 0.5391 - accuracy: 0.8554
729/814 [=========================>....] - ETA: 0s - loss: 0.5392 - accuracy: 0.8554
741/814 [==========================>...] - ETA: 0s - loss: 0.5386 - accuracy: 0.8559
753/814 [==========================>...] - ETA: 0s - loss: 0.5375 - accuracy: 0.8563
765/814 [===========================>..] - ETA: 0s - loss: 0.5364 - accuracy: 0.8569
777/814 [===========================>..] - ETA: 0s - loss: 0.5365 - accuracy: 0.8567
788/814 [============================>.] - ETA: 0s - loss: 0.5378 - accuracy: 0.8562
800/814 [============================>.] - ETA: 0s - loss: 0.5373 - accuracy: 0.8564
812/814 [============================>.] - ETA: 0s - loss: 0.5386 - accuracy: 0.8563
814/814 [==============================] - 4s 4ms/step - loss: 0.5383 - accuracy: 0.8563
Keras accuracy = 0.8806853294372559 , QKeras 6-bit accuracy = 0.8563306927680969
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 0x7f2a0c85a920>
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()
WARNING: Failed to import handlers from core.py: No module named 'torch'.
WARNING: Failed to import handlers from reshape.py: No module named 'torch'.
WARNING: Failed to import handlers from convolution.py: No module named 'torch'.
WARNING: Failed to import handlers from pooling.py: No module named 'torch'.
WARNING: Failed to import handlers from merge.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.
/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: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: 6s
22/94 [======>.......................] - ETA: 0s
43/94 [============>.................] - ETA: 0s
64/94 [===================>..........] - ETA: 0s
85/94 [==========================>...] - ETA: 0s
94/94 [==============================] - 0s 2ms/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: 30s
12/94 [==>...........................] - ETA: 0s
23/94 [======>.......................] - ETA: 0s
34/94 [=========>....................] - ETA: 0s
45/94 [=============>................] - ETA: 0s
56/94 [================>.............] - ETA: 0s
67/94 [====================>.........] - ETA: 0s
78/94 [=======================>......] - ETA: 0s
89/94 [===========================>..] - ETA: 0s
94/94 [==============================] - 1s 5ms/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: 0.886
Accuracy hls4ml: 0.883
Accuracy Keras: 0.8576666666666667
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)