<jupyter_start><jupyter_text>Train Imagenette<jupyter_code># train_dataset, validation_dataset = create_Imagenette_dataset(BATCH_SIZE)
# for sample in train_dataset.take(5): # the dataset has to fit in memory with eager iteration
#     print(sample[0].shape)
#     plot_sample([sample[0]/127.5-1.0], num_res=1)

# print([s.shape for s in sample])
# sample[0].numpy().min()
# sample[0].numpy().max()

# print(preprocess_input(sample[0]).numpy().min())
# print(preprocess_input(sample[0]).numpy().max())

# dataset =  create_mnist_dataset(BATCH_SIZE)
# for sample in dataset.take(1): # the dataset has to fit in memory with eager iteration
#     plot_sample(sample, num_res=3)

# print([s.shape for s in sample])



# big_base.summary()

# base.summary()

# from tensorflow.keras.utils import plot_model
# plot_model(big_base, to_file='model.png', show_shapes=True)



# num_params_big = 0
# for layer in big_base.layers:
#     num_params_big += np.prod(layer.output_shape[1:])
# print(num_params_big)

# num_params = 0
# for layer in base.layers:
#     num_params += np.prod(layer.output_shape[1:])
# print(num_params)

# print(num_params_big - num_params)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.1451476793248945, 'fraction_numerical': 0.016877637130801686, 'mean_word_length': 5.552486187845304, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '159425', 'license': 'MIT', 'n_tokens_mistral': 440, 'n_tokens_neox': 429, 'n_words': 83, 'path': 'notebooks/imagenette_test_pipeline.ipynb', 'repo_name': 'JacobARose/pyleaves', 'size': '1185'}