code: # Copyright 2016 The TensorFlow 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
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  # See the License for the specific language governing permissions and
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  # ==============================================================================
  """Contains model definitions for versions of the Oxford VGG network.
  These model definitions were introduced in the following technical report:
    Very Deep Convolutional Networks For Large-Scale Image Recognition
    Karen Simonyan and Andrew Zisserman
    arXiv technical report, 2015
    PDF: http://arxiv.org/pdf/1409.1556.pdf
    ILSVRC 2014 Slides: http://www.robots.ox.ac.uk/~karen/pdf/ILSVRC_2014.pdf
    CC-BY-4.0
  More information can be obtained from the VGG website:
  www.robots.ox.ac.uk/~vgg/research/very_deep/
  Usage:
    with slim.arg_scope(vgg.vgg_arg_scope()):
      outputs, end_points = vgg.vgg_a(inputs)
    with slim.arg_scope(vgg.vgg_arg_scope()):
      outputs, end_points = vgg.vgg_16(inputs)
  @@vgg_a
  @@vgg_16
  @@vgg_19
  """
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  from tensorflow.contrib import layers
  from tensorflow.contrib.framework.python.ops import arg_scope
  from tensorflow.contrib.layers.python.layers import layers as layers_lib
  from tensorflow.contrib.layers.python.layers import regularizers
  from tensorflow.contrib.layers.python.layers import utils
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import init_ops
  from tensorflow.python.ops import nn_ops
  from tensorflow.python.ops import variable_scope
  
  
  def vgg_arg_scope(weight_decay=0.0005):
    """Defines the VGG arg scope.
    Args:
      weight_decay: The l2 regularization coefficient.
    Returns:
      An arg_scope.
    """
    with arg_scope(
        [layers.conv2d, layers_lib.fully_connected],
        activation_fn=nn_ops.relu,
        weights_regularizer=regularizers.l2_regularizer(weight_decay),
        biases_initializer=init_ops.zeros_initializer()):
      with arg_scope([layers.conv2d], padding='SAME') as arg_sc:
        return arg_sc
  
  
  def vgg_a(inputs,
            num_classes=1000,
            is_training=True,
            dropout_keep_prob=0.5,
            spatial_squeeze=True,
            scope='vgg_a'):
    """Oxford Net VGG 11-Layers version A Example.
    Note: All the fully_connected layers have been transformed to conv2d layers.
          To use in classification mode, resize input to 224x224.
    Args:
      inputs: a tensor of size [batch_size, height, width, channels].
      num_classes: number of predicted classes.
      is_training: whether or not the model is being trained.
      dropout_keep_prob: the probability that activations are kept in the dropout
        layers during training.
      spatial_squeeze: whether or not should squeeze the spatial dimensions of the
        outputs. Useful to remove unnecessary dimensions for classification.
      scope: Optional scope for the variables.
    Returns:
      the last op containing the log predictions and end_points dict.
    """
    with variable_scope.variable_scope(scope, 'vgg_a', [inputs]) as sc:
      end_points_collection = sc.original_name_scope + '_end_points'
      # Collect outputs for conv2d, fully_connected and max_pool2d.
      with arg_scope(
          [layers.conv2d, layers_lib.max_pool2d],
          outputs_collections=end_points_collection):
        net = layers_lib.repeat(
            inputs, 1, layers.conv2d, 64, [3, 3], scope='conv1')
        net = layers_lib.max_pool2d(net, [2, 2], scope='pool1')
        net = layers_lib.repeat(net, 1, layers.conv2d, 128, [3, 3], scope='conv2')
        net = layers_lib.max_pool2d(net, [2, 2], scope='pool2')
        net = layers_lib.repeat(net, 2, layers.conv2d, 256, [3, 3], scope='conv3')
        net = layers_lib.max_pool2d(net, [2, 2], scope='pool3')
        net = layers_lib.repeat(net, 2, layers.conv2d, 512, [3, 3], scope='conv4')
        net = layers_lib.max_pool2d(net, [2, 2], scope='pool4')
        net = layers_lib.repeat(net, 2, layers.conv2d, 512, [3, 3], scope='conv5')
        net = layers_lib.max_pool2d(net, [2, 2], scope='pool5')
        # Use conv2d instead of fully_connected layers.
        net = layers.conv2d(net, 4096, [7, 7], padding='VALID', scope='fc6')
        net = layers_lib.dropout(
            net, dropout_keep_prob, is_training=is_training, scope='dropout6')
        net = layers.conv2d(net, 4096, [1, 1], scope='fc7')
        net = layers_lib.dropout(
            net, dropout_keep_prob, is_training=is_training, scope='dropout7')
        net = layers.conv2d(
            net,
            num_classes, [1, 1],
            activation_fn=None,
            normalizer_fn=None,
            scope='fc8')
        # Convert end_points_collection into a end_point dict.
        end_points = utils.convert_collection_to_dict(end_points_collection)
        if spatial_squeeze:
          net = array_ops.squeeze(net, [1, 2], name='fc8/squeezed')
          end_points[sc.name + '/fc8'] = net
        return net, end_points
  
  
  vgg_a.default_image_size = 224
  
  
  def vgg_16(inputs,
             num_classes=1000,
             is_training=True,
             dropout_keep_prob=0.5,
             spatial_squeeze=True,
             scope='vgg_16'):
    """Oxford Net VGG 16-Layers version D Example.
    Note: All the fully_connected layers have been transformed to conv2d layers.
          To use in classification mode, resize input to 224x224.
    Args:
      inputs: a tensor of size [batch_size, height, width, channels].
      num_classes: number of predicted classes.
      is_training: whether or not the model is being trained.
      dropout_keep_prob: the probability that activations are kept in the dropout
        layers during training.
      spatial_squeeze: whether or not should squeeze the spatial dimensions of the
        outputs. Useful to remove unnecessary dimensions for classification.
      scope: Optional scope for the variables.
    Returns:
      the last op containing the log predictions and end_points dict.
    """
    with variable_scope.variable_scope(scope, 'vgg_16', [inputs]) as sc:
      end_points_collection = sc.original_name_scope + '_end_points'
      # Collect outputs for conv2d, fully_connected and max_pool2d.
      with arg_scope(
          [layers.conv2d, layers_lib.fully_connected, layers_lib.max_pool2d],
          outputs_collections=end_points_collection):
        net = layers_lib.repeat(inputs, 2, layers.conv2d, 64, (3, 3), scope='conv1')
        net = layers_lib.max_pool2d(net, [2, 2], scope='pool1')
        net = layers_lib.repeat(net, 2, layers.conv2d, 128, [3, 3], scope='conv2')
        net = layers_lib.max_pool2d(net, [2, 2], scope='pool2')
        net = layers_lib.repeat(net, 3, layers.conv2d, 256, [3, 3], scope='conv3')
        net = layers_lib.max_pool2d(net, [2, 2], scope='pool3')
        net = layers_lib.repeat(net, 3, layers.conv2d, 512, [3, 3], scope='conv4')
        net = layers_lib.max_pool2d(net, [2, 2], scope='pool4')
        net = layers_lib.repeat(net, 3, layers.conv2d, 512, [3, 3], scope='conv5')
        net = layers_lib.max_pool2d(net, [2, 2], scope='pool5')
        # Use conv2d instead of fully_connected layers.
        net = layers.conv2d(net, 4096, [7, 7], padding='VALID', scope='fc6')
        net = layers_lib.dropout(
            net, dropout_keep_prob, is_training=is_training, scope='dropout6')
        net = layers.conv2d(net, 4096, [1, 1], scope='fc7')
        net = layers_lib.dropout(
            net, dropout_keep_prob, is_training=is_training, scope='dropout7')
        net = layers.conv2d(
            net,
            num_classes, [1, 1],
            activation_fn=None,
            normalizer_fn=None,
            scope='fc8')
        # Convert end_points_collection into a end_point dict.
        end_points = utils.convert_collection_to_dict(end_points_collection)
        if spatial_squeeze:
          net = array_ops.squeeze(net, [1, 2], name='fc8/squeezed')
          end_points[sc.name + '/fc8'] = net
        return net, end_points
  
  
  vgg_16.default_image_size = 224
  
  
  def vgg_19(inputs,
             num_classes=1000,
             is_training=True,
             dropout_keep_prob=0.5,
             spatial_squeeze=True,
             scope='vgg_19'):
    """Oxford Net VGG 19-Layers version E Example.
    Note: All the fully_connected layers have been transformed to conv2d layers.
          To use in classification mode, resize input to 224x224.
    Args:
      inputs: a tensor of size [batch_size, height, width, channels].
      num_classes: number of predicted classes.
      is_training: whether or not the model is being trained.
      dropout_keep_prob: the probability that activations are kept in the dropout
        layers during training.
      spatial_squeeze: whether or not should squeeze the spatial dimensions of the
        outputs. Useful to remove unnecessary dimensions for classification.
      scope: Optional scope for the variables.
    Returns:
      the last op containing the log predictions and end_points dict.
    """
    with variable_scope.variable_scope(scope, 'vgg_19', [inputs]) as sc:
      end_points_collection = sc.name + '_end_points'
      # Collect outputs for conv2d, fully_connected and max_pool2d.
      with arg_scope(
          [layers.conv2d, layers_lib.fully_connected, layers_lib.max_pool2d],
          outputs_collections=end_points_collection):
        net = layers_lib.repeat(inputs, 2, layers.conv2d, 64, [3, 3], scope='conv1')
        net = layers_lib.max_pool2d(net, [2, 2], scope='pool1')
        net = layers_lib.repeat(net, 2, layers.conv2d, 128, [3, 3], scope='conv2')
        net = layers_lib.max_pool2d(net, [2, 2], scope='pool2')
        net = layers_lib.repeat(net, 4, layers.conv2d, 256, [3, 3], scope='conv3')
        net = layers_lib.max_pool2d(net, [2, 2], scope='pool3')
        net = layers_lib.repeat(net, 4, layers.conv2d, 512, [3, 3], scope='conv4')
        net = layers_lib.max_pool2d(net, [2, 2], scope='pool4')
        net = layers_lib.repeat(net, 4, layers.conv2d, 512, [3, 3], scope='conv5')
        net = layers_lib.max_pool2d(net, [2, 2], scope='pool5')
        # Use conv2d instead of fully_connected layers.
        net = layers.conv2d(net, 4096, [7, 7], padding='VALID', scope='fc6')
        net = layers_lib.dropout(
            net, dropout_keep_prob, is_training=is_training, scope='dropout6')
        net = layers.conv2d(net, 4096, [1, 1], scope='fc7')
        net = layers_lib.dropout(
            net, dropout_keep_prob, is_training=is_training, scope='dropout7')
        net = layers.conv2d(
            net,
            num_classes, [1, 1],
            activation_fn=None,
            normalizer_fn=None,
            scope='fc8')
        # Convert end_points_collection into a end_point dict.
        end_points = utils.convert_collection_to_dict(end_points_collection)
        if spatial_squeeze:
          net = array_ops.squeeze(net, [1, 2], name='fc8/squeezed')
          end_points[sc.name + '/fc8'] = net
        return net, end_points
  
  
  vgg_19.default_image_size = 224
  
  # Alias
  vgg_d = vgg_16
  vgg_e = vgg_19
apis: tensorflow.contrib.layers.python.layers.layers.dropout tensorflow.contrib.layers.python.layers.layers.repeat tensorflow.contrib.layers.python.layers.layers.max_pool2d tensorflow.contrib.layers.conv2d tensorflow.python.ops.variable_scope.variable_scope tensorflow.python.ops.array_ops.squeeze tensorflow.contrib.framework.python.ops.arg_scope tensorflow.contrib.layers.python.layers.regularizers.l2_regularizer tensorflow.python.ops.init_ops.zeros_initializer tensorflow.contrib.layers.python.layers.utils.convert_collection_to_dict