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tfa.activations.mish
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Mish: A Self Regularized Non-Monotonic Neural Activation Function.
tfa.activations.mish(
x: tfa.types.TensorLike
) -> tf.Tensor
Computes mish activation:
\[
\mathrm{mish}(x) = x \cdot \tanh(\mathrm{softplus}(x)).
\]
See Mish: A Self Regularized Non-Monotonic Neural Activation Function.
Usage:
x = tf.constant([1.0, 0.0, 1.0])
tfa.activations.mish(x)
<tf.Tensor: shape=(3,), dtype=float32, numpy=array([0.865098..., 0. , 0.865098...], dtype=float32)>
Args |
|---|
x
|
A Tensor. Must be one of the following types:
bfloat16, float16, float32, float64.
|
Returns |
|---|
A Tensor. Has the same type as x.
|
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Last updated 2023-07-12 UTC.
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View source on GitHub