<jupyter_start><jupyter_text>Investigate transformed features<jupyter_code>images = tf.cast(tf.expand_dims(test_img_data[0:20], 3), dtype=tf.float32)
labels = test_img_label[0:20]
x = tf.image.resize(images, size=(size_after_transform,size_after_transform))
transformed_images = stn_slx_chain(x)
for image, label in zip(transformed_images, labels):
    print(label)
    plt.imshow(image.numpy()[:,:,0])
    plt.show()<jupyter_output>0
{'fraction_non_alphanumeric': 0.14055299539170507, 'fraction_numerical': 0.02534562211981567, 'mean_word_length': 8.456521739130435, 'pattern_counts': {'":': 0, '<': 4, '<?xml version=': 0, '>': 4, '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': '505803', 'license': 'Apache-2.0', 'n_tokens_mistral': 171, 'n_tokens_neox': 165, 'n_words': 22, 'path': 'experiments/STN_quadratic_chain_backbone.ipynb', 'repo_name': 'alexeytochin/spatial_transform', 'size': '434'}