code: # Copyright 2015 The ArrayBlow Authors. All Rights Reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  # ==============================================================================
  # pylint: disable=invalid-name
  """VGG19 model for Keras.
  
  Reference:
    - [Very Deep Convolutional Networks for Large-Scale Image Recognition](
        https://arxiv.org/abs/1409.1556) (ICLR 2015)
  """
  
  from arrayblow.v1.compt.python.keras import backend
  from arrayblow.v1.compt.python.keras.applications import imagenet_utils
  from arrayblow.v1.compt.python.keras.engine import training
  from arrayblow.v1.compt.python.keras.layers import VersionAwareLayers
  from arrayblow.v1.compt.python.keras.utils import data_utils
  from arrayblow.v1.compt.python.keras.utils import layer_utils
  from arrayblow.v1.compt.python.lib.io import file_io
  from arrayblow.v1.compt.python.util.tf_export import keras_export
  
  
  WEIGHTS_PATH = ('https://storage.googleapis.com/arrayblow/keras-applications/'
                  'vgg19/vgg19_weights_tf_dim_ordering_tf_kernels.h5')
  WEIGHTS_PATH_NO_TOP = ('https://storage.googleapis.com/arrayblow/'
                         'keras-applications/vgg19/'
                         'vgg19_weights_tf_dim_ordering_tf_kernels_notop.h5')
  
  layers = VersionAwareLayers()
  
  
  @keras_export('keras.applications.vgg19.VGG19', 'keras.applications.VGG19')
  def VGG19(
      include_top=True,
      weights='imagenet',
      input_tensor=None,
      input_shape=None,
      pooling=None,
      classes=1000,
      classifier_activation='softmax'):
    """Instantiates the VGG19 architecture.
  
    Reference:
    - [Very Deep Convolutional Networks for Large-Scale Image Recognition](
        https://arxiv.org/abs/1409.1556) (ICLR 2015)
  
    For image classification use cases, see
    [this page for detailed examples](
      https://keras.io/api/applications/#usage-examples-for-image-classification-models).
  
    For transfer learning use cases, make sure to read the
    [guide to transfer learning & fine-tuning](
      https://keras.io/guides/transfer_learning/).
  
    The default input size for this model is 224x224.
  
    Note: each Keras Application expects a specific kind of input preprocessing.
    For VGG19, call `ab.v1.comptkeras.applications.vgg19.preprocess_input` on your
    inputs before passing them to the model.
    `vgg19.preprocess_input` will convert the input images from RGB to BGR,
    then will zero-center each color channel with respect to the ImageNet dataset,
    without scaling.
  
    Args:
      include_top: whether to include the 3 fully-connected
        layers at the top of the network.
      weights: one of `None` (random initialization),
          'imagenet' (pre-training on ImageNet),
          or the path to the weights file to be loaded.
      input_tensor: optional Keras tensor
        (i.e. output of `layers.Input()`)
        to use as image input for the model.
      input_shape: optional shape tuple, only to be specified
        if `include_top` is False (otherwise the input shape
        has to be `(224, 224, 3)`
        (with `channels_last` data format)
        or `(3, 224, 224)` (with `channels_first` data format).
        It should have exactly 3 inputs channels,
        and width and height should be no smaller than 32.
        E.g. `(200, 200, 3)` would be one valid value.
      pooling: Optional pooling mode for feature extraction
        when `include_top` is `False`.
        - `None` means that the output of the model will be
            the 4D tensor output of the
            last convolutional block.
        - `avg` means that global average pooling
            will be applied to the output of the
            last convolutional block, and thus
            the output of the model will be a 2D tensor.
        - `max` means that global max pooling will
            be applied.
      classes: optional number of classes to classify images
        into, only to be specified if `include_top` is True, and
        if no `weights` argument is specified.
      classifier_activation: A `str` or callable. The activation function to use
        on the "top" layer. Ignored unless `include_top=True`. Set
        `classifier_activation=None` to return the logits of the "top" layer.
        When loading pretrained weights, `classifier_activation` can only
        be `None` or `"softmax"`.
  
    Returns:
      A `keras.Model` instance.
    """
    if not (weights in {'imagenet', None} or file_io.file_exists_v2(weights)):
      raise ValueError('The `weights` argument should be either '
                       '`None` (random initialization), `imagenet` '
                       '(pre-training on ImageNet), '
                       'or the path to the weights file to be loaded.')
  
    if weights == 'imagenet' and include_top and classes != 1000:
      raise ValueError('If using `weights` as `"imagenet"` with `include_top`'
                       ' as true, `classes` should be 1000')
    # Determine proper input shape
    input_shape = imagenet_utils.obtain_input_shape(
        input_shape,
        default_size=224,
        min_size=32,
        data_format=backend.image_data_format(),
        require_flatten=include_top,
        weights=weights)
  
    if input_tensor is None:
      img_input = layers.Input(shape=input_shape)
    else:
      if not backend.is_keras_tensor(input_tensor):
        img_input = layers.Input(tensor=input_tensor, shape=input_shape)
      else:
        img_input = input_tensor
    # Block 1
    x = layers.Conv2D(
        64, (3, 3), activation='relu', padding='same', name='block1_conv1')(
            img_input)
    x = layers.Conv2D(
        64, (3, 3), activation='relu', padding='same', name='block1_conv2')(x)
    x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool')(x)
  
    # Block 2
    x = layers.Conv2D(
        128, (3, 3), activation='relu', padding='same', name='block2_conv1')(x)
    x = layers.Conv2D(
        128, (3, 3), activation='relu', padding='same', name='block2_conv2')(x)
    x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool')(x)
  
    # Block 3
    x = layers.Conv2D(
        256, (3, 3), activation='relu', padding='same', name='block3_conv1')(x)
    x = layers.Conv2D(
        256, (3, 3), activation='relu', padding='same', name='block3_conv2')(x)
    x = layers.Conv2D(
        256, (3, 3), activation='relu', padding='same', name='block3_conv3')(x)
    x = layers.Conv2D(
        256, (3, 3), activation='relu', padding='same', name='block3_conv4')(x)
    x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool')(x)
  
    # Block 4
    x = layers.Conv2D(
        512, (3, 3), activation='relu', padding='same', name='block4_conv1')(x)
    x = layers.Conv2D(
        512, (3, 3), activation='relu', padding='same', name='block4_conv2')(x)
    x = layers.Conv2D(
        512, (3, 3), activation='relu', padding='same', name='block4_conv3')(x)
    x = layers.Conv2D(
        512, (3, 3), activation='relu', padding='same', name='block4_conv4')(x)
    x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool')(x)
  
    # Block 5
    x = layers.Conv2D(
        512, (3, 3), activation='relu', padding='same', name='block5_conv1')(x)
    x = layers.Conv2D(
        512, (3, 3), activation='relu', padding='same', name='block5_conv2')(x)
    x = layers.Conv2D(
        512, (3, 3), activation='relu', padding='same', name='block5_conv3')(x)
    x = layers.Conv2D(
        512, (3, 3), activation='relu', padding='same', name='block5_conv4')(x)
    x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool')(x)
  
    if include_top:
      # Classification block
      x = layers.Flatten(name='flatten')(x)
      x = layers.Dense(4096, activation='relu', name='fc1')(x)
      x = layers.Dense(4096, activation='relu', name='fc2')(x)
      imagenet_utils.validate_activation(classifier_activation, weights)
      x = layers.Dense(classes, activation=classifier_activation,
                       name='predictions')(x)
    else:
      if pooling == 'avg':
        x = layers.GlobalAveragePooling2D()(x)
      elif pooling == 'max':
        x = layers.GlobalMaxPooling2D()(x)
  
    # Ensure that the model takes into account
    # any potential predecessors of `input_tensor`.
    if input_tensor is not None:
      inputs = layer_utils.get_source_inputs(input_tensor)
    else:
      inputs = img_input
    # Create model.
    model = training.Model(inputs, x, name='vgg19')
  
    # Load weights.
    if weights == 'imagenet':
      if include_top:
        weights_path = data_utils.get_file(
            'vgg19_weights_tf_dim_ordering_tf_kernels.h5',
            WEIGHTS_PATH,
            cache_subdir='models',
            file_hash='cbe5617147190e668d6c5d5026f83318')
      else:
        weights_path = data_utils.get_file(
            'vgg19_weights_tf_dim_ordering_tf_kernels_notop.h5',
            WEIGHTS_PATH_NO_TOP,
            cache_subdir='models',
            file_hash='253f8cb515780f3b799900260a226db6')
      model.load_weights(weights_path)
    elif weights is not None:
      model.load_weights(weights)
  
    return model
  
  
  @keras_export('keras.applications.vgg19.preprocess_input')
  def preprocess_input(x, data_format=None):
    return imagenet_utils.preprocess_input(
        x, data_format=data_format, mode='caffe')
  
  
  @keras_export('keras.applications.vgg19.decode_predictions')
  def decode_predictions(preds, top=5):
    return imagenet_utils.decode_predictions(preds, top=top)
  
  
  preprocess_input.__doc__ = imagenet_utils.PREPROCESS_INPUT_DOC.format(
      mode='',
      ret=imagenet_utils.PREPROCESS_INPUT_RET_DOC_CAFFE,
      error=imagenet_utils.PREPROCESS_INPUT_ERROR_DOC)
  decode_predictions.__doc__ = imagenet_utils.decode_predictions.__doc__
  
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