<jupyter_start><jupyter_text>Training<jupyter_code>net = NetworkVectorized([784, 30, 10], loss=losses.CrossEntropyLoss())
net.SGD(train_data=train, epochs=30, batch_size=20, learning_rate=3.0, test_data=test)
net.plot_first_layer_weights(name="CrossEntropyLoss")
net2 = NetworkVectorized([784, 30, 10], loss=losses.MSELoss())
net2.SGD(train_data=train, epochs=30, batch_size=20, learning_rate=3.0, test_data=test)
net2.plot_first_layer_weights(name="MSEloss")<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.147239263803681, 'fraction_numerical': 0.05930470347648262, 'mean_word_length': 14.3125, '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': '624769', 'license': 'Apache-2.0', 'n_tokens_mistral': 212, 'n_tokens_neox': 192, 'n_words': 22, 'path': 'src/.ipynb_checkpoints/NetworkVectorized-checkpoint.ipynb', 'repo_name': 'MichalPitr/NeuralNetworkFromScratch', 'size': '489'}