<jupyter_start><jupyter_text>We haven't run anything, so will give us zero<jupyter_code>#run one step of gradient descent. we just created train
# session.run(train)
session.run(train, feed_dict={x:coefficient})
print(session.run(w))
#run 1000 iterations
for i in range(1000):
#    session.run(train)
    session.run(train, feed_dict={x:coefficient})
print(session.run(w))<jupyter_output>4.9999886
{'fraction_non_alphanumeric': 0.12846347607052896, 'fraction_numerical': 0.04030226700251889, 'mean_word_length': 6.96, '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': '464513', 'license': 'MIT', 'n_tokens_mistral': 154, 'n_tokens_neox': 139, 'n_words': 33, 'path': 'Improving Deep Neural Networks Hyperparameter tuning, Regularization and Optimization/Week3/.ipynb_checkpoints/Week3-checkpoint.ipynb', 'repo_name': 'yumengdong/Deep_Learning_Coursera', 'size': '397'}