(inputs,
                 num_classes=1000,
                 is_training=True,
                 dropout_keep_prob=0.8,
                 min_depth=16,
                 depth_multiplier=1.0,
                 prediction_fn=slim.softmax,
                 spatial_squeeze=True,
                 reuse=None,
                 scope='InceptionV2')
return logits, end_points
Inception v2 model for classification.

  Constructs an Inception v2 network for classification as described in
  http://arxiv.org/abs/1502.03167.

  The default image size used to train this network is 224x224.

  Args:
    inputs: a tensor of shape [batch_size, height, width, channels].
    num_classes: number of predicted classes.
    is_training: whether is training or not.
    dropout_keep_prob: the percentage of activation values that are retained.
    min_depth: Minimum depth value (number of channels) for all convolution ops.
      Enforced when depth_multiplier < 1, and not an active constraint when
      depth_multiplier >= 1.
    depth_multiplier: Float multiplier for the depth (number of channels)
      for all convolution ops. The value must be greater than zero. Typical
      usage will be to set this value in (0, 1) to reduce the number of
      parameters or computation cost of the model.
    prediction_fn: a function to get predictions out of logits.
    spatial_squeeze: if True, logits is of shape [B, C], if false logits is
        of shape [B, 1, 1, C], where B is batch_size and C is number of classes.
    reuse: whether or not the network and its variables should be reused. To be
      able to reuse 'scope' must be given.
    scope: Optional variable_scope.

  Returns:
    logits: the pre-softmax activations, a tensor of size
      [batch_size, num_classes]
    end_points: a dictionary from components of the network to the corresponding
      activation.

  Raises:
    ValueError: if final_endpoint is not set to one of the predefined values,
                or depth_multiplier <= 0
Inception v2 model for classification.
Inception v2 model for classification .
def inception_v2(inputs,
                 num_classes=1000,
                 is_training=True,
                 dropout_keep_prob=0.8,
                 min_depth=16,
                 depth_multiplier=1.0,
                 prediction_fn=slim.softmax,
                 spatial_squeeze=True,
                 reuse=None,
                 scope='InceptionV2'):
  """Inception v2 model for classification.

  Constructs an Inception v2 network for classification as described in
  http://arxiv.org/abs/1502.03167.

  The default image size used to train this network is 224x224.

  Args:
    inputs: a tensor of shape [batch_size, height, width, channels].
    num_classes: number of predicted classes.
    is_training: whether is training or not.
    dropout_keep_prob: the percentage of activation values that are retained.
    min_depth: Minimum depth value (number of channels) for all convolution ops.
      Enforced when depth_multiplier < 1, and not an active constraint when
      depth_multiplier >= 1.
    depth_multiplier: Float multiplier for the depth (number of channels)
      for all convolution ops. The value must be greater than zero. Typical
      usage will be to set this value in (0, 1) to reduce the number of
      parameters or computation cost of the model.
    prediction_fn: a function to get predictions out of logits.
    spatial_squeeze: if True, logits is of shape [B, C], if false logits is
        of shape [B, 1, 1, C], where B is batch_size and C is number of classes.
    reuse: whether or not the network and its variables should be reused. To be
      able to reuse 'scope' must be given.
    scope: Optional variable_scope.

  Returns:
    logits: the pre-softmax activations, a tensor of size
      [batch_size, num_classes]
    end_points: a dictionary from components of the network to the corresponding
      activation.

  Raises:
    ValueError: if final_endpoint is not set to one of the predefined values,
                or depth_multiplier <= 0
  """
  if depth_multiplier <= 0:
    raise ValueError('depth_multiplier is not greater than zero.')

  # Final pooling and prediction
  with tf.variable_scope(scope, 'InceptionV2', [inputs, num_classes],
                         reuse=reuse) as scope:
    with slim.arg_scope([slim.batch_norm, slim.dropout],
                        is_training=is_training):
      net, end_points = inception_v2_base(
          inputs, scope=scope, min_depth=min_depth,
          depth_multiplier=depth_multiplier)
      with tf.variable_scope('Logits'):
        kernel_size = _reduced_kernel_size_for_small_input(net, [7, 7])
        net = slim.avg_pool2d(net, kernel_size, padding='VALID',
                              scope='AvgPool_1a_{}x{}'.format(*kernel_size))
        # 1 x 1 x 1024
        net = slim.dropout(net, keep_prob=dropout_keep_prob, scope='Dropout_1b')
        logits = slim.conv2d(net, num_classes, [1, 1], activation_fn=None,
                             normalizer_fn=None, scope='Conv2d_1c_1x1')
        if spatial_squeeze:
          logits = tf.squeeze(logits, [1, 2], name='SpatialSqueeze')
      end_points['Logits'] = logits
      end_points['Predictions'] = prediction_fn(logits, scope='Predictions')
  return logits, end_points
def inception_v2 ( inputs , num_classes = 1000 , is_training = True , dropout_keep_prob = 0.8 , min_depth = 16 , depth_multiplier = 1.0 , prediction_fn = slim . softmax , spatial_squeeze = True , reuse = None , scope = 'InceptionV2' ) : if depth_multiplier <= 0 : raise ValueError ( 'depth_multiplier is not greater than zero.' ) # Final pooling and prediction with tf . variable_scope ( scope , 'InceptionV2' , [ inputs , num_classes ] , reuse = reuse ) as scope : with slim . arg_scope ( [ slim . batch_norm , slim . dropout ] , is_training = is_training ) : net , end_points = inception_v2_base ( inputs , scope = scope , min_depth = min_depth , depth_multiplier = depth_multiplier ) with tf . variable_scope ( 'Logits' ) : kernel_size = _reduced_kernel_size_for_small_input ( net , [ 7 , 7 ] ) net = slim . avg_pool2d ( net , kernel_size , padding = 'VALID' , scope = 'AvgPool_1a_{}x{}' . format ( * kernel_size ) ) # 1 x 1 x 1024 net = slim . dropout ( net , keep_prob = dropout_keep_prob , scope = 'Dropout_1b' ) logits = slim . conv2d ( net , num_classes , [ 1 , 1 ] , activation_fn = None , normalizer_fn = None , scope = 'Conv2d_1c_1x1' ) if spatial_squeeze : logits = tf . squeeze ( logits , [ 1 , 2 ] , name = 'SpatialSqueeze' ) end_points [ 'Logits' ] = logits end_points [ 'Predictions' ] = prediction_fn ( logits , scope = 'Predictions' ) return logits , end_points