<jupyter_start><jupyter_text>pipeline 19<jupyter_code>data_in_shape = (3, 3)

input_layer = Input(shape=data_in_shape)
output_layer = Activation('relu', input_shape=data_in_shape)(input_layer)
model = Model(input=input_layer, output=output_layer)

np.random.seed(1000)
data_in = 2 * np.random.random(data_in_shape) - 1

weights = []

result = model.predict(np.array([data_in]))

print({
    'input': {'data': format_decimal(data_in.ravel().tolist()), 'shape': list(data_in_shape)},
    'weights': [{'data': format_decimal(weights[i].ravel().tolist()), 'shape': list(weights[i].shape)} for i in range(len(weights))],
    'expected': {'data': format_decimal(result[0].ravel().tolist()), 'shape': list(result[0].shape)}
})<jupyter_output>{'weights': [], 'input': {'data': [0.30717917, -0.76998611, 0.90056573, -0.0356172, 0.74494907, -0.57533464, -0.91858075, -0.20561108, -0.53373561], 'shape': [3, 3]}, 'expected': {'data': [0.30717918, 0.0, 0.90056574, 0.0, 0.74494904, 0.0, 0.0, 0.0, 0.0], 'shape': [3, 3]}}
{'fraction_non_alphanumeric': 0.22321428571428573, 'fraction_numerical': 0.13392857142857142, 'mean_word_length': 6.419117647058823, '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': '300117', 'license': 'MIT', 'n_tokens_mistral': 491, 'n_tokens_neox': 415, 'n_words': 69, 'path': 'notebooks/pipeline/pipeline_19.ipynb', 'repo_name': 'jefffriesen/keras-js', 'size': '1008'}