text: # -*- coding: utf-8 -*-
  '''VGG19 model for Keras.
  
  # Reference:
  
  - [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://arxiv.org/abs/1409.1556)
  
  '''
  from __future__ import print_function
  
  import numpy as np
  import warnings
  
  from keras.models import Model
  from keras.layers import Flatten, Dense, Input
  from keras.layers import Conv2D
  from keras.layers import MaxPooling2D
  from keras.layers import AveragePooling2D
  from keras.layers import GlobalMaxPooling2D
  from keras.layers import GlobalAveragePooling2D
  from keras.preprocessing import image
  from keras.utils import layer_utils
  from keras.utils.data_utils import get_file
  from keras import backend as K
  from keras_applications.imagenet_utils import decode_predictions
  from keras_applications.imagenet_utils import preprocess_input
  from keras_applications.imagenet_utils import _obtain_input_shape
  from keras.engine.topology import get_source_inputs
  
  
  WEIGHTS_PATH = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg19_weights_tf_dim_ordering_tf_kernels.h5'
  WEIGHTS_PATH_NO_TOP = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg19_weights_tf_dim_ordering_tf_kernels_notop.h5'
  
  
  def VGG19(include_top=True, weights='imagenet',
            input_tensor=None, input_shape=None,
            pooling=None,
            classes=1000):
      """Instantiates the VGG19 architecture.
  
      Optionally loads weights pre-trained
      on ImageNet. Note that when using TensorFlow,
      for best performance you should set
      `image_data_format="channels_last"` in your Keras config
      at ~/.keras/keras.json.
  
      The model and the weights are compatible with both
      TensorFlow and Theano. The data format
      convention used by the model is the one
      specified in your Keras config file.
  
      # Arguments
          include_top: whether to include the 3 fully-connected
              layers at the top of the network.
          weights: one of `None` (random initialization)
              or "imagenet" (pre-training on ImageNet).
          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, 244)` (with `channels_first` data format).
              It should have exactly 3 inputs channels,
              and width and height should be no smaller than 48.
              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 layer.
              - `avg` means that global average pooling
                  will be applied to the output of the
                  last convolutional layer, 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.
  
      # Returns
          A Keras model instance.
  
      # Raises
          ValueError: in case of invalid argument for `weights`,
              or invalid input shape.
      """
      if weights not in {'imagenet', None}:
          raise ValueError('The `weights` argument should be either '
                           '`None` (random initialization) or `imagenet` '
                           '(pre-training on ImageNet).')
  
      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 = _obtain_input_shape(input_shape,
                                        default_size=224,
                                        min_size=48,
                                        data_format=K.image_data_format(),
                                        require_flatten=include_top,
                                        weights=weights)
  
      if input_tensor is None:
          img_input = Input(shape=input_shape)
      else:
          if not K.is_keras_tensor(input_tensor):
              img_input = Input(tensor=input_tensor, shape=input_shape)
          else:
              img_input = input_tensor
      # Block 1
      x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv1')(img_input)
      x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv2')(x)
      x = AveragePooling2D((2, 2), strides=(2, 2), name='block1_pool')(x)
  
      # Block 2
      x = Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv1')(x)
      x = Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv2')(x)
      x = AveragePooling2D((2, 2), strides=(2, 2), name='block2_pool')(x)
  
      # Block 3
      x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv1')(x)
      x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv2')(x)
      x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv3')(x)
      x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv4')(x)
      x = AveragePooling2D((2, 2), strides=(2, 2), name='block3_pool')(x)
  
      # Block 4
      x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv1')(x)
      x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv2')(x)
      x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv3')(x)
      x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv4')(x)
      x = AveragePooling2D((2, 2), strides=(2, 2), name='block4_pool')(x)
  
      # Block 5
      x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv1')(x)
      x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv2')(x)
      x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv3')(x)
      x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv4')(x)
      x = AveragePooling2D((2, 2), strides=(2, 2), name='block5_pool')(x)
  
      if include_top:
          # Classification block
          x = Flatten(name='flatten')(x)
          x = Dense(4096, activation='relu', name='fc1')(x)
          x = Dense(4096, activation='relu', name='fc2')(x)
          x = Dense(classes, activation='softmax', name='predictions')(x)
      else:
          if pooling == 'avg':
              x = GlobalAveragePooling2D()(x)
          elif pooling == 'max':
              x = GlobalMaxPooling2D()(x)
  
      # Ensure that the model takes into account
      # any potential predecessors of `input_tensor`.
      if input_tensor is not None:
          inputs = get_source_inputs(input_tensor)
      else:
          inputs = img_input
      # Create model.
      model = Model(inputs, x, name='vgg19')
  
      # load weights
      if weights == 'imagenet':
          if include_top:
              weights_path = get_file('vgg19_weights_tf_dim_ordering_tf_kernels.h5',
                                      WEIGHTS_PATH,
                                      cache_subdir='models')
          else:
              weights_path = get_file('vgg19_weights_tf_dim_ordering_tf_kernels_notop.h5',
                                      WEIGHTS_PATH_NO_TOP,
                                      cache_subdir='models')
          model.load_weights(weights_path)
          if K.backend() == 'theano':
              layer_utils.convert_all_kernels_in_model(model)
  
          if K.image_data_format() == 'channels_first':
              if include_top:
                  maxpool = model.get_layer(name='block5_pool')
                  shape = maxpool.output_shape[1:]
                  dense = model.get_layer(name='fc1')
                  layer_utils.convert_dense_weights_data_format(dense, shape, 'channels_first')
  
              if K.backend() == 'tensorflow':
                  warnings.warn('You are using the TensorFlow backend, yet you '
                                'are using the Theano '
                                'image data format convention '
                                '(`image_data_format="channels_first"`). '
                                'For best performance, set '
                                '`image_data_format="channels_last"` in '
                                'your Keras config '
                                'at ~/.keras/keras.json.')
      return model