def resnet50()

in example_zoo/tensorflow/models/keras_imagenet_main/official/resnet/keras/resnet_model.py [0:0]
47 lines of code
2 McCabe index (conditional complexity)

def resnet50(num_classes):
  # TODO(tfboyd): add training argument, just lik resnet56.
  """Instantiates the ResNet50 architecture.

  Args:
    num_classes: `int` number of classes for image classification.

  Returns:
      A Keras model instance.
  """
  input_shape = (224, 224, 3)
  img_input = layers.Input(shape=input_shape)

  if backend.image_data_format() == 'channels_first':
    x = layers.Lambda(lambda x: backend.permute_dimensions(x, (0, 3, 1, 2)),
                      name='transpose')(img_input)
    bn_axis = 1
  else:  # channels_last
    x = img_input
    bn_axis = 3

  x = layers.ZeroPadding2D(padding=(3, 3), name='conv1_pad')(x)
  x = layers.Conv2D(64, (7, 7),
                    strides=(2, 2),
                    padding='valid', use_bias=False,
                    kernel_initializer='he_normal',
                    kernel_regularizer=regularizers.l2(L2_WEIGHT_DECAY),
                    name='conv1')(x)
  x = layers.BatchNormalization(axis=bn_axis,
                                momentum=BATCH_NORM_DECAY,
                                epsilon=BATCH_NORM_EPSILON,
                                name='bn_conv1')(x)
  x = layers.Activation('relu')(x)
  x = layers.ZeroPadding2D(padding=(1, 1), name='pool1_pad')(x)
  x = layers.MaxPooling2D((3, 3), strides=(2, 2))(x)

  x = conv_block(x, 3, [64, 64, 256], stage=2, block='a', strides=(1, 1))
  x = identity_block(x, 3, [64, 64, 256], stage=2, block='b')
  x = identity_block(x, 3, [64, 64, 256], stage=2, block='c')

  x = conv_block(x, 3, [128, 128, 512], stage=3, block='a')
  x = identity_block(x, 3, [128, 128, 512], stage=3, block='b')
  x = identity_block(x, 3, [128, 128, 512], stage=3, block='c')
  x = identity_block(x, 3, [128, 128, 512], stage=3, block='d')

  x = conv_block(x, 3, [256, 256, 1024], stage=4, block='a')
  x = identity_block(x, 3, [256, 256, 1024], stage=4, block='b')
  x = identity_block(x, 3, [256, 256, 1024], stage=4, block='c')
  x = identity_block(x, 3, [256, 256, 1024], stage=4, block='d')
  x = identity_block(x, 3, [256, 256, 1024], stage=4, block='e')
  x = identity_block(x, 3, [256, 256, 1024], stage=4, block='f')

  x = conv_block(x, 3, [512, 512, 2048], stage=5, block='a')
  x = identity_block(x, 3, [512, 512, 2048], stage=5, block='b')
  x = identity_block(x, 3, [512, 512, 2048], stage=5, block='c')

  x = layers.GlobalAveragePooling2D(name='avg_pool')(x)
  x = layers.Dense(
      num_classes, activation='softmax',
      kernel_regularizer=regularizers.l2(L2_WEIGHT_DECAY),
      bias_regularizer=regularizers.l2(L2_WEIGHT_DECAY),
      name='fc1000')(x)

  # Create model.
  return models.Model(img_input, x, name='resnet50')