def create_keras_sequential_model()

in tensorflow_model_remediation/tools/tutorials_utils/min_diff_keras_utils.py [0:0]

• 20 lines of code
• 3 McCabe index (conditional complexity)

def create_keras_sequential_model(
    hub_url='https://tfhub.dev/google/tf2-preview/nnlm-en-dim128/1',
    cnn_filter_sizes=[128, 128, 128],
    cnn_kernel_sizes=[5, 5, 5],
    cnn_pooling_sizes=[5, 5, 40]):
  """Create baseline keras sequential model."""

  model = tf.keras.Sequential()

  # Embedding layer.
  hub_layer = _create_embedding_layer(hub_url)
  model.add(hub_layer)
  model.add(tf.keras.layers.Reshape((1, 128)))

  # Convolution layers.
  for filter_size, kernel_size, pool_size in zip(cnn_filter_sizes,
                                                 cnn_kernel_sizes,
                                                 cnn_pooling_sizes):
    model.add(
        tf.keras.layers.Conv1D(
            filter_size, kernel_size, activation='relu', padding='same'))
    model.add(tf.keras.layers.MaxPooling1D(pool_size, padding='same'))

  # Flatten, fully connected, and output layers.
  model.add(tf.keras.layers.Flatten())
  model.add(tf.keras.layers.Dense(128, activation='relu'))
  model.add(tf.keras.layers.Dense(1, activation='sigmoid'))

  return model