<jupyter_start><jupyter_text>notebook naming convention: number-author-filename.ipynb Extended MNIST is actually the original MNIST dataset. It is more difficult to learn than MNIST as it contains letters and digits.<jupyter_code>emnist_data = EmnistDataset()
print(emnist_data)
emnist_data.load_or_generate_data()
emnist_data.x_train.shape, emnist_data.y_train.shape
emnist_data.x_test.shape, emnist_data.y_test.shape
fig = plt.figure(figsize=(9, 9))
for i in range(9):
    ax = fig.add_subplot(3, 3, i + 1)
    ax.imshow(emnist_data.x_train[i].reshape(28, 28), cmap='gray')
    ax.set_title(emnist_data.mapping[np.argmax(emnist_data.y_train[i])])<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.11504424778761062, 'fraction_numerical': 0.014749262536873156, 'mean_word_length': 6.715909090909091, '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': '176584', 'license': 'MIT', 'n_tokens_mistral': 277, 'n_tokens_neox': 258, 'n_words': 49, 'path': 'lab1/notebooks/01-look-at-emnist.ipynb', 'repo_name': 'AlTheEngineer/fsdl-text-recognizer-project', 'size': '678'}