tf.nn.crelu

Computes Concatenated ReLU.

tf.nn.crelu(
    features, axis=-1, name=None
)

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.

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`.

References
Understanding and Improving Convolutional Neural Networks via Concatenated Rectified Linear Units: Shang et al., 2016 (pdf)

Args