<jupyter_start><jupyter_text>Note that a Bidirectional layer doubles the size of the LSTM units. If you enter 64, you will end up with 128 for that layer, since there are 64 going forward and 64 going backward.  CNNs and RNNs<jupyter_code>model = Sequential([
    Embedding(vocab_size, embedding_dim, input_length=max_length),
    Conv1D(128, 5, activation='relu'),
    GlobalMaxPooling1D(),
    Dense(24, activation='relu'),
    Dense(1, activation='relu')
])<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.1, 'fraction_numerical': 0.036734693877551024, 'mean_word_length': 5.546666666666667, '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': '396154', 'license': 'MIT', 'n_tokens_mistral': 180, 'n_tokens_neox': 162, 'n_words': 51, 'path': 'rnns.ipynb', 'repo_name': 'codeananda/tf_certificate', 'size': '490'}