(inputs,
               num_classes=1000,
               is_training=True,
               dropout_keep_prob=0.5,
               spatial_squeeze=True,
               scope='alexnet_v2')
AlexNet version 2.

  Described in: http://arxiv.org/pdf/1404.5997v2.pdf
  Parameters from:
  github.com/akrizhevsky/cuda-convnet2/blob/master/layers/
  layers-imagenet-1gpu.cfg

  Note: All the fully_connected layers have been transformed to conv2d layers.
        To use in classification mode, resize input to 224x224. To use in fully
        convolutional mode, set spatial_squeeze to false.
        The LRN layers have been removed and change the initializers from
        random_normal_initializer to xavier_initializer.

  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.
AlexNet version 2.
AlexNet version 2 .
def alexnet_v2(inputs,
               num_classes=1000,
               is_training=True,
               dropout_keep_prob=0.5,
               spatial_squeeze=True,
               scope='alexnet_v2'):
  """AlexNet version 2.

  Described in: http://arxiv.org/pdf/1404.5997v2.pdf
  Parameters from:
  github.com/akrizhevsky/cuda-convnet2/blob/master/layers/
  layers-imagenet-1gpu.cfg

  Note: All the fully_connected layers have been transformed to conv2d layers.
        To use in classification mode, resize input to 224x224. To use in fully
        convolutional mode, set spatial_squeeze to false.
        The LRN layers have been removed and change the initializers from
        random_normal_initializer to xavier_initializer.

  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 tf.variable_scope(scope, 'alexnet_v2', [inputs]) as sc:
    end_points_collection = sc.name + '_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, [3, 3], 2, scope='pool1')
      net = slim.conv2d(net, 192, [5, 5], scope='conv2')
      net = slim.max_pool2d(net, [3, 3], 2, scope='pool2')
      net = slim.conv2d(net, 384, [3, 3], scope='conv3')
      net = slim.conv2d(net, 384, [3, 3], scope='conv4')
      net = slim.conv2d(net, 256, [3, 3], scope='conv5')
      net = slim.max_pool2d(net, [3, 3], 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, 4096, [5, 5], 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')
        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')

      # Convert end_points_collection into a end_point dict.
      end_points = slim.utils.convert_collection_to_dict(end_points_collection)
      if spatial_squeeze:
        net = tf.squeeze(net, [1, 2], name='fc8/squeezed')
        end_points[sc.name + '/fc8'] = net
      return net, end_points
def alexnet_v2 ( inputs , num_classes = 1000 , is_training = True , dropout_keep_prob = 0.5 , spatial_squeeze = True , scope = 'alexnet_v2' ) : with tf . variable_scope ( scope , 'alexnet_v2' , [ inputs ] ) as sc : end_points_collection = sc . name + '_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 , [ 3 , 3 ] , 2 , scope = 'pool1' ) net = slim . conv2d ( net , 192 , [ 5 , 5 ] , scope = 'conv2' ) net = slim . max_pool2d ( net , [ 3 , 3 ] , 2 , scope = 'pool2' ) net = slim . conv2d ( net , 384 , [ 3 , 3 ] , scope = 'conv3' ) net = slim . conv2d ( net , 384 , [ 3 , 3 ] , scope = 'conv4' ) net = slim . conv2d ( net , 256 , [ 3 , 3 ] , scope = 'conv5' ) net = slim . max_pool2d ( net , [ 3 , 3 ] , 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 , 4096 , [ 5 , 5 ] , 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' ) 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' ) # Convert end_points_collection into a end_point dict. end_points = slim . utils . convert_collection_to_dict ( end_points_collection ) if spatial_squeeze : net = tf . squeeze ( net , [ 1 , 2 ] , name = 'fc8/squeezed' ) end_points [ sc . name + '/fc8' ] = net return net , end_points