parameters: (
      width_coefficient=None, depth_coefficient=None, dropout_rate=0.2, survival_prob=0.8
  )
return_statement: return global_params
docstring: Creates a efficientnet model.
docstring_summary: Creates a efficientnet model.
docstring_tokens: Creates a efficientnet model .
function: def efficientnet(
      width_coefficient=None, depth_coefficient=None, dropout_rate=0.2, survival_prob=0.8
  ):
      """Creates a efficientnet model."""
      global_params = efficientnet_model.GlobalParams(
          blocks_args=_DEFAULT_BLOCKS_ARGS,
          batch_norm_momentum=0.99,
          batch_norm_epsilon=1e-3,
          dropout_rate=dropout_rate,
          survival_prob=survival_prob,
          data_format="channels_last",
          num_classes=1000,
          width_coefficient=width_coefficient,
          depth_coefficient=depth_coefficient,
          depth_divisor=8,
          min_depth=None,
          relu_fn=tf.nn.swish,
          # The default is TPU-specific batch norm.
          # The alternative is tf.layers.BatchNormalization.
          batch_norm=utils.BatchNormalization,  # TPU-specific requirement.
          use_se=True,
          clip_projection_output=False,
      )
      return global_params
function_tokens: def efficientnet ( width_coefficient = None , depth_coefficient = None , dropout_rate = 0.2 , survival_prob = 0.8 ) : global_params = efficientnet_model . GlobalParams ( blocks_args = _DEFAULT_BLOCKS_ARGS , batch_norm_momentum = 0.99 , batch_norm_epsilon = 1e-3 , dropout_rate = dropout_rate , survival_prob = survival_prob , data_format = "channels_last" , num_classes = 1000 , width_coefficient = width_coefficient , depth_coefficient = depth_coefficient , depth_divisor = 8 , min_depth = None , relu_fn = tf . nn . swish , # The default is TPU-specific batch norm. # The alternative is tf.layers.BatchNormalization. batch_norm = utils . BatchNormalization , # TPU-specific requirement. use_se = True , clip_projection_output = False , ) return global_params