<jupyter_start><jupyter_text>simple visual for train/val loss  more stuff on tensorboard<jupyter_code>loss_values = history_dict['loss']
val_loss_values = history_dict['val_loss']
epochs = range(1, len(loss_values) + 1)

# "bo" is for "blue dot"
plt.plot(epochs, loss_values, 'bo')
# b+ is for "blue crosses"
plt.plot(epochs, val_loss_values, 'b+')
plt.xlabel('Epochs')
plt.ylabel('Loss')

plt.show()
acc_values = history_dict['acc']
val_acc_values = history_dict['val_acc']

plt.plot(epochs, acc_values, 'bo')
plt.plot(epochs, val_acc_values, 'b+')
plt.xlabel('Epochs')
plt.ylabel('Accuracy')

plt.show()<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.16220472440944883, 'fraction_numerical': 0.0031496062992125984, 'mean_word_length': 7.833333333333333, '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': '39314', 'license': 'MIT', 'n_tokens_mistral': 269, 'n_tokens_neox': 259, 'n_words': 48, 'path': 'MNISTjupyternotebook.ipynb', 'repo_name': 'kevinfaust0308/NumberPredictor', 'size': '635'}