parameters: (weight_decay=0.0001,
                       batch_norm_decay=0.997,
                       batch_norm_epsilon=1e-5,
                       batch_norm_scale=True)
docstring: Defines the default ResNet arg scope.
  
    TODO(gpapan): The batch-normalization related default values above are
      appropriate for use in conjunction with the reference ResNet models
      released at https://github.com/KaimingHe/deep-residual-networks. When
      training ResNets from scratch, they might need to be tuned.
  
    Args:
      weight_decay: The weight decay to use for regularizing the model.
      batch_norm_decay: The moving average decay when estimating layer activation
        statistics in batch normalization.
      batch_norm_epsilon: Small constant to prevent division by zero when
        normalizing activations by their variance in batch normalization.
      batch_norm_scale: If True, uses an explicit `gamma` multiplier to scale the
        activations in the batch normalization layer.
  
    Returns:
      An `arg_scope` to use for the resnet models.
docstring_summary: Defines the default ResNet arg scope.
docstring_tokens: Defines the default ResNet arg scope .
function: def resnet_arg_scope(weight_decay=0.0001,
                       batch_norm_decay=0.997,
                       batch_norm_epsilon=1e-5,
                       batch_norm_scale=True):
    """Defines the default ResNet arg scope.
  
    TODO(gpapan): The batch-normalization related default values above are
      appropriate for use in conjunction with the reference ResNet models
      released at https://github.com/KaimingHe/deep-residual-networks. When
      training ResNets from scratch, they might need to be tuned.
  
    Args:
      weight_decay: The weight decay to use for regularizing the model.
      batch_norm_decay: The moving average decay when estimating layer activation
        statistics in batch normalization.
      batch_norm_epsilon: Small constant to prevent division by zero when
        normalizing activations by their variance in batch normalization.
      batch_norm_scale: If True, uses an explicit `gamma` multiplier to scale the
        activations in the batch normalization layer.
  
    Returns:
      An `arg_scope` to use for the resnet models.
    """
    batch_norm_params = {
        'decay': batch_norm_decay,
        'epsilon': batch_norm_epsilon,
        'scale': batch_norm_scale,
        'updates_collections': ops.GraphKeys.UPDATE_OPS,
    }
  
    with arg_scope(
        [layers_lib.conv2d],
        weights_regularizer=regularizers.l2_regularizer(weight_decay),
        weights_initializer=initializers.variance_scaling_initializer(),
        activation_fn=nn_ops.relu,
        normalizer_fn=layers.batch_norm,
        normalizer_params=batch_norm_params):
      with arg_scope([layers.batch_norm], **batch_norm_params):
        # The following implies padding='SAME' for pool1, which makes feature
        # alignment easier for dense prediction tasks. This is also used in
        # https://github.com/facebook/fb.resnet.torch. However the accompanying
        # code of 'Deep Residual Learning for Image Recognition' uses
        # padding='VALID' for pool1. You can switch to that choice by setting
        # tf.contrib.framework.arg_scope([tf.contrib.layers.max_pool2d], padding='VALID').
        with arg_scope([layers.max_pool2d], padding='SAME') as arg_sc:
          return arg_sc
function_tokens: def resnet_arg_scope ( weight_decay = 0.0001 , batch_norm_decay = 0.997 , batch_norm_epsilon = 1e-5 , batch_norm_scale = True ) : batch_norm_params = { 'decay' : batch_norm_decay , 'epsilon' : batch_norm_epsilon , 'scale' : batch_norm_scale , 'updates_collections' : ops . GraphKeys . UPDATE_OPS , } with arg_scope ( [ layers_lib . conv2d ] , weights_regularizer = regularizers . l2_regularizer ( weight_decay ) , weights_initializer = initializers . variance_scaling_initializer ( ) , activation_fn = nn_ops . relu , normalizer_fn = layers . batch_norm , normalizer_params = batch_norm_params ) : with arg_scope ( [ layers . batch_norm ] , * * batch_norm_params ) : # The following implies padding='SAME' for pool1, which makes feature # alignment easier for dense prediction tasks. This is also used in # https://github.com/facebook/fb.resnet.torch. However the accompanying # code of 'Deep Residual Learning for Image Recognition' uses # padding='VALID' for pool1. You can switch to that choice by setting # tf.contrib.framework.arg_scope([tf.contrib.layers.max_pool2d], padding='VALID'). with arg_scope ( [ layers . max_pool2d ] , padding = 'SAME' ) as arg_sc : return arg_sc