<jupyter_start><jupyter_text>Tests:(Chapt 11 conditions: seed 42, elu, learning rate = 0.01, he init, RGB normalization, BN, momentum = 0.9, AdamOpt, 5 layers, 100 neurons per layer, 1000 epochs, batch size 20)With Chapt 11 conditions & 2 outputs:49.70%Without BN:49.80%Without BN or RGB normalization:50.00%Without normalization and with Glorot Normal init (instead of He init):50.00%With He init and learning rate = 0.05:50.00%With He init, RGB normalization, and learning rate = 0.05:54.40%With BN again:50.00%Without BN and with 1140 outputs:50.20%Same as Chapt 11 with GradientDescent instead of AdamOpt and without BN:58.50%With learning rate = 0.05:59.20%Same as Chapt 11 with GradientDescent and momentum = 0.8:58.90%With batch size 5:64.20%Chapt 11 + GD + batch size 5 + 3 layers instead of 5:66.20%<jupyter_code>with tf.Session() as sess:
    saver.restore(sess, "./mini_project_final.ckpt") # or better, use save_path
    X_new_scaled = X_test[:20]
    Z = logits.eval(feed_dict={X: X_new_scaled})
    y_pred = np.argmax(Z, axis=1)
from tensorflow_graph_in_jupyter import show_graph

show_graph(tf.get_default_graph())<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.10526315789473684, 'fraction_numerical': 0.08800690250215704, 'mean_word_length': 5.304347826086956, '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': '668613', 'license': 'MIT', 'n_tokens_mistral': 482, 'n_tokens_neox': 394, 'n_words': 126, 'path': 'mini_project.ipynb', 'repo_name': 'prathusb/TensorFlow_NNs', 'size': '1159'}