parameters: (features, name=None, axis=-1)
docstring: Computes Concatenated ReLU.
  
    Concatenates a ReLU which selects only the positive part of the activation
    with a ReLU which selects only the *negative* part of the activation.
    Note that as a result this non-linearity doubles the depth of the activations.
    Source: [Understanding and Improving Convolutional Neural Networks via
    Concatenated Rectified Linear Units. W. Shang, et
    al.](https://arxiv.org/abs/1603.05201)
  
    Args:
      features: A `Tensor` with type `float`, `double`, `int32`, `int64`, `uint8`,
        `int16`, or `int8`.
      name: A name for the operation (optional).
      axis: The axis that the output values are concatenated along. Default is -1.
  
    Returns:
      A `Tensor` with the same type as `features`.
docstring_summary: Computes Concatenated ReLU.
docstring_tokens: Computes Concatenated ReLU .
function: def crelu(features, name=None, axis=-1):
    """Computes Concatenated ReLU.
  
    Concatenates a ReLU which selects only the positive part of the activation
    with a ReLU which selects only the *negative* part of the activation.
    Note that as a result this non-linearity doubles the depth of the activations.
    Source: [Understanding and Improving Convolutional Neural Networks via
    Concatenated Rectified Linear Units. W. Shang, et
    al.](https://arxiv.org/abs/1603.05201)
  
    Args:
      features: A `Tensor` with type `float`, `double`, `int32`, `int64`, `uint8`,
        `int16`, or `int8`.
      name: A name for the operation (optional).
      axis: The axis that the output values are concatenated along. Default is -1.
  
    Returns:
      A `Tensor` with the same type as `features`.
    """
    with ops.name_scope(name, "CRelu", [features]) as name:
      features = ops.convert_to_tensor(features, name="features")
      c = array_ops.concat([features, -features], axis, name=name)
      return gen_nn_ops.relu(c)
function_tokens: def crelu ( features , name = None , axis = - 1 ) : with ops . name_scope ( name , "CRelu" , [ features ] ) as name : features = ops . convert_to_tensor ( features , name = "features" ) c = array_ops . concat ( [ features , - features ] , axis , name = name ) return gen_nn_ops . relu ( c )