Avoid deeply nested control flow statements.

Open

          if use_explicit_padding:
            net = _fixed_padding(net, conv_def.kernel)
          net = slim.conv2d(net, depth(conv_def.depth), conv_def.kernel,

Avoid deeply nested control flow statements.

Open

          if end_point == final_endpoint:
            return net, end_points

Avoid deeply nested control flow statements.

Open

          if not num_classes:
            return net, end_points
          # 1 x 1 x 1536
          net = slim.dropout(net, dropout_keep_prob, scope='Dropout_1b')

Avoid deeply nested control flow statements.

Open

            with self.test_session() as sess:
              tf.set_random_seed(0)
              inputs = create_test_input(1, height, width, 3)

              # Subsampling at the last unit of the block.

Function episode has a Cognitive Complexity of 8 (exceeds 5 allowed). Consider refactoring.

Open

  def episode(self):
    """Episode data.

    Returns:
      observations: a tuple with one element. This element is a numpy array of

• Read up
  Read up

Avoid deeply nested control flow statements.

Open

          if not display_str:
            display_str = '{}%'.format(int(100*scores[i]))
          else:
            display_str = '{}: {}%'.format(display_str, int(100*scores[i]))
        box_to_display_str_map[box].append(display_str)

Avoid deeply nested control flow statements.

Open

          if use_explicit_padding:
            net = _fixed_padding(net, conv_def.kernel, layer_rate)
          net = slim.separable_conv2d(net, None, conv_def.kernel,

Avoid deeply nested control flow statements.

Open

          if end_point == final_endpoint:
            return net, end_points
        else:

Function inception_resnet_v2_base has 6 arguments (exceeds 4 allowed). Consider refactoring.

Open

def inception_resnet_v2_base(inputs,

Function _conv has 6 arguments (exceeds 4 allowed). Consider refactoring.

Open

  def _conv(self, name, x, filter_size, in_filters, out_filters, strides):

Function _add_gradients_summaries has a Cognitive Complexity of 8 (exceeds 5 allowed). Consider refactoring.

Open

def _add_gradients_summaries(grads_and_vars):
  """Add histogram summaries to gradients.

  Note: The summaries are also added to the SUMMARIES collection.

• Read up
  Read up

Function log_mel_spectrogram has 6 arguments (exceeds 4 allowed). Consider refactoring.

Open

def log_mel_spectrogram(data,

Avoid deeply nested control flow statements.

Open

          if end_point == final_endpoint:
            return net, end_points

Function inception_resnet_v2_arg_scope has 6 arguments (exceeds 4 allowed). Consider refactoring.

Open

def inception_resnet_v2_arg_scope(

Function preprocess_for_train has a Cognitive Complexity of 8 (exceeds 5 allowed). Consider refactoring.

Open

def preprocess_for_train(image, height, width, bbox,
                         fast_mode=True,
                         scope=None,
                         add_image_summaries=True):
  """Distort one image for training a network.

• Read up
  Read up

Avoid deeply nested control flow statements.

Open

          if kernel_size.is_fully_defined():
            net = slim.avg_pool2d(net, kernel_size, padding='VALID',
                                  scope='AvgPool_1a')
          else:
            net = tf.reduce_mean(

Avoid deeply nested control flow statements.

Open

          if clone_loss is not None:
            clones_losses.append(clone_loss)
          # Only use regularization_losses for the first clone
          regularization_losses = None

Function preprocess_image has 6 arguments (exceeds 4 allowed). Consider refactoring.

Open

def preprocess_image(image, output_height, output_width, is_training=False,

Function __init__ has 6 arguments (exceeds 4 allowed). Consider refactoring.

Open

  def __init__(self,

Function discriminator has 6 arguments (exceeds 4 allowed). Consider refactoring.

Open

def discriminator(inputs,