<jupyter_start><jupyter_text>Sequential model<jupyter_code># sequential model 1
model = Sequential()
model.add(Dense(10, input_dim=4))
model.add(Dense(2))

plot_model(model, show_shapes=True, show_layer_names=True, expand_nested=True)
# sequential model 1
plot_model(model, show_shapes=True, show_layer_names=True, expand_nested=True, rankdir='LR')<jupyter_output><empty_output><jupyter_text>Parallel model<jupyter_code># parallel model
inDense = Input(shape=(4, ), dtype='int32', name='input-4')
leftDense = Dense(10, name='left-model')(inDense)
rightDense = Dense(16, name='right-model')(inDense)
mergedDense = concatenate([leftDense, rightDense], name='merge')
outDense = Dense(2, name='final')(mergedDense)
model2 = Model(inDense, outDense)
plot_model(model2, show_shapes=True, show_layer_names=True)

plot_model(model2, show_shapes=False, show_layer_names=True, dpi=128)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.1425414364640884, 'fraction_numerical': 0.023204419889502764, 'mean_word_length': 12.323529411764707, 'pattern_counts': {'":': 0, '<': 9, '<?xml version=': 0, '>': 9, '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': '2', 'dir': 'jupyter-structured-clean-dedup', 'id': '394746', 'license': 'MIT', 'n_tokens_mistral': 358, 'n_tokens_neox': 343, 'n_words': 52, 'path': 'jupyter/plot_model.ipynb', 'repo_name': 'john-hu/rl', 'size': '905'}