tf.keras.activations.mish
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Mish activation function.
View aliases
Compat aliases for migration
See
Migration guide for
more details.
`tf.compat.v1.keras.activations.mish`
tf.keras.activations.mish(
x
)
It is defined as:
def mish(x):
return x * tanh(softplus(x))
where softplus is defined as:
def softplus(x):
return log(exp(x) + 1)
Example:
a = tf.constant([-3.0, -1.0, 0.0, 1.0], dtype = tf.float32)
b = tf.keras.activations.mish(a)
b.numpy()
array([-0.14564745, -0.30340144, 0., 0.86509836], dtype=float32)
Returns |
|---|
|
The mish activation.
|
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Last updated 2023-10-06 UTC.
[null,null,["Last updated 2023-10-06 UTC."],[],[],null,["# tf.keras.activations.mish\n\n\u003cbr /\u003e\n\n|----------------------------------------------------------------------------------------------------------|\n| [View source on GitHub](https://github.com/keras-team/keras/tree/v2.13.1/keras/activations.py#L492-L528) |\n\nMish activation function.\n\n#### View aliases\n\n\n**Compat aliases for migration**\n\nSee\n[Migration guide](https://www.tensorflow.org/guide/migrate) for\nmore details.\n\n\\`tf.compat.v1.keras.activations.mish\\`\n\n\u003cbr /\u003e\n\n tf.keras.activations.mish(\n x\n )\n\n#### It is defined as:\n\n def mish(x):\n return x * tanh(softplus(x))\n\nwhere `softplus` is defined as: \n\n def softplus(x):\n return log(exp(x) + 1)\n\n#### Example:\n\n a = tf.constant([-3.0, -1.0, 0.0, 1.0], dtype = tf.float32)\n b = tf.keras.activations.mish(a)\n b.numpy()\n array([-0.14564745, -0.30340144, 0., 0.86509836], dtype=float32)\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|-----|---------------|\n| `x` | Input tensor. |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ------- ||\n|---|---|\n| The mish activation. ||\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Reference --------- ||\n|---|---|\n| \u003cbr /\u003e - [Mish: A Self Regularized Non-Monotonic Activation Function](https://arxiv.org/abs/1908.08681) ||\n\n\u003cbr /\u003e"]]
View source on GitHub