<jupyter_start><jupyter_text>pipeline 18<jupyter_code>data_in_shape = (8, 8, 2)

input_layer_0 = Input(shape=data_in_shape)
branch_0 = Convolution2D(4, 3, 3, activation='relu', border_mode='same', subsample=(1, 1), dim_ordering='tf', bias=True,
                         name='conv_0-0')(input_layer_0)

input_layer_1 = Input(shape=data_in_shape)
branch_1 = Convolution2D(2, 1, 1, activation='relu', border_mode='valid', subsample=(1, 1), dim_ordering='tf', bias=True,
                         name='conv_1-0')(input_layer_1)
branch_1 = Convolution2D(4, 3, 3, activation='relu', border_mode='same', subsample=(1, 1), dim_ordering='tf', bias=True,
                         name='conv_1-1')(branch_1)
branch_1 = Convolution2D(2, 1, 1, activation='relu', border_mode='valid', subsample=(1, 1), dim_ordering='tf', bias=True,
                         name='conv_1-2')(branch_1)

input_layer_2 = Input(shape=data_in_shape)
branch_2 = Convolution2D(5, 3, 3, activation='relu', border_mode='same', subsample=(1, 1), dim_ordering='tf', bias=True,
                         name='conv_2-0')(input_layer_2)
branch_2 = Convolution2D(3, 3, 3, activation='relu', border_mode='same', subsample=(1, 1), dim_ordering='tf', bias=True,
                         name='conv_2-1')(branch_2)

output_layer = merge([branch_0, branch_1, branch_2], mode='concat')
model = Model(input=[input_layer_0, input_layer_1, input_layer_2], output=output_layer)

data_in = []
for i in range(3):
    np.random.seed(19000 + i)
    data_in.append(np.expand_dims(2 * np.random.random(data_in_shape) - 1, axis=0))

# set weights to random (use seed for reproducibility)
weights = []
for i, w in enumerate(model.get_weights()):
    np.random.seed(19000 + i)
    weights.append(2 * np.random.random(w.shape) - 1)
model.set_weights(weights)

result = model.predict(data_in)

print({
    'inputs': [{'data': format_decimal(data_in[i].ravel().tolist()), 'shape': list(data_in_shape)} for i in range(3)],
    '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)}
})
import json
config = json.loads(model.to_json())
print([x['config']['name'] for x in config['config']['layers']])<jupyter_output>['input_5', 'conv_1-0', 'input_6', 'input_4', 'conv_1-1', 'conv_2-0', 'conv_0-0', 'conv_1-2', 'conv_2-1', 'merge_2']
{'fraction_non_alphanumeric': 0.18450184501845018, 'fraction_numerical': 0.04469044690446904, 'mean_word_length': 4.907990314769976, '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': '67861', 'license': 'MIT', 'n_tokens_mistral': 939, 'n_tokens_neox': 891, 'n_words': 162, 'path': 'notebooks/pipeline/pipeline_18.ipynb', 'repo_name': 'jefffriesen/keras-js', 'size': '2439'}