text: # 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
  #
  # 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.
  # ==============================================================================
  """Contains the model definition for the OverFeat network.
  
  The definition for the network was obtained from:
    OverFeat: Integrated Recognition, Localization and Detection using
    Convolutional Networks
    Pierre Sermanet, David Eigen, Xiang Zhang, Michael Mathieu, Rob Fergus and
    Yann LeCun, 2014
    http://arxiv.org/abs/1312.6229
  
  Usage:
    with slim.arg_scope(overfeat.overfeat_arg_scope()):
      outputs, end_points = overfeat.overfeat(inputs)
  
  @@overfeat
  """
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import tensorflow as tf
  
  slim = tf.contrib.slim
  trunc_normal = lambda stddev: tf.truncated_normal_initializer(0.0, stddev)
  
  
  def overfeat_arg_scope(weight_decay=0.0005):
    with slim.arg_scope([slim.conv2d, slim.fully_connected],
                        activation_fn=tf.nn.relu,
                        weights_regularizer=slim.l2_regularizer(weight_decay),
                        biases_initializer=tf.zeros_initializer()):
      with slim.arg_scope([slim.conv2d], padding='SAME'):
        with slim.arg_scope([slim.max_pool2d], padding='VALID') as arg_sc:
          return arg_sc
  
  
  def overfeat(inputs,
               num_classes=1000,
               is_training=True,
               dropout_keep_prob=0.5,
               spatial_squeeze=True,
               reuse=False,
               scope='overfeat',
               global_pool=False):
    """Contains the model definition for the OverFeat network.
  
    The definition for the network was obtained from:
      OverFeat: Integrated Recognition, Localization and Detection using
      Convolutional Networks
      Pierre Sermanet, David Eigen, Xiang Zhang, Michael Mathieu, Rob Fergus and
      Yann LeCun, 2014
      http://arxiv.org/abs/1312.6229
  
    Note: All the fully_connected layers have been transformed to conv2d layers.
          To use in classification mode, resize input to 231x231. To use in fully
          convolutional mode, set spatial_squeeze to false.
  
    Args:
      inputs: a tensor of size [batch_size, height, width, channels].
      num_classes: number of predicted classes. If 0 or None, the logits layer is
        omitted and the input features to the logits layer are returned instead.
      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.
      global_pool: Optional boolean flag. If True, the input to the classification
        layer is avgpooled to size 1x1, for any input size. (This is not part
        of the original OverFeat.)
  
    Returns:
      net: the output of the logits layer (if num_classes is a non-zero integer),
        or the non-dropped-out input to the logits layer (if num_classes is 0 or
        None).
      end_points: a dict of tensors with intermediate activations.
    """
    with tf.variable_scope(scope, 'overfeat', [inputs], reuse=reuse) as sc:
      end_points_collection = sc.original_name_scope + '_end_points'
      # Collect outputs for conv2d, fully_connected and max_pool2d
      with slim.arg_scope([slim.conv2d, slim.fully_connected, slim.max_pool2d],
                          outputs_collections=end_points_collection):
        net = slim.conv2d(inputs, 64, [11, 11], 4, padding='VALID',
                          scope='conv1')
        net = slim.max_pool2d(net, [2, 2], scope='pool1')
        net = slim.conv2d(net, 256, [5, 5], padding='VALID', scope='conv2')
        net = slim.max_pool2d(net, [2, 2], scope='pool2')
        net = slim.conv2d(net, 512, [3, 3], scope='conv3')
        net = slim.conv2d(net, 1024, [3, 3], scope='conv4')
        net = slim.conv2d(net, 1024, [3, 3], scope='conv5')
        net = slim.max_pool2d(net, [2, 2], scope='pool5')
  
        # Use conv2d instead of fully_connected layers.
        with slim.arg_scope([slim.conv2d],
                            weights_initializer=trunc_normal(0.005),
                            biases_initializer=tf.constant_initializer(0.1)):
          net = slim.conv2d(net, 3072, [6, 6], padding='VALID', scope='fc6')
          net = slim.dropout(net, dropout_keep_prob, is_training=is_training,
                             scope='dropout6')
          net = slim.conv2d(net, 4096, [1, 1], scope='fc7')
          # Convert end_points_collection into a end_point dict.
          end_points = slim.utils.convert_collection_to_dict(
              end_points_collection)
          if global_pool:
            net = tf.reduce_mean(net, [1, 2], keep_dims=True, name='global_pool')
            end_points['global_pool'] = net
          if num_classes:
            net = slim.dropout(net, dropout_keep_prob, is_training=is_training,
                               scope='dropout7')
            net = slim.conv2d(net, num_classes, [1, 1],
                              activation_fn=None,
                              normalizer_fn=None,
                              biases_initializer=tf.zeros_initializer(),
                              scope='fc8')
            if spatial_squeeze:
              net = tf.squeeze(net, [1, 2], name='fc8/squeezed')
            end_points[sc.name + '/fc8'] = net
        return net, end_points
  overfeat.default_image_size = 231
  