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)

Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see the Google Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates. Some content is licensed under the numpy license.

Last updated 2023-03-17 UTC.

Args