tf.keras.activations.tanh
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Hyperbolic tangent activation function.
tf.keras.activations.tanh(
x
)
For example:
a = tf.constant([-3.0,-1.0, 0.0,1.0,3.0], dtype = tf.float32)
b = tf.keras.activations.tanh(a)
b.numpy()
array([-0.9950547, -0.7615942, 0., 0.7615942, 0.9950547], dtype=float32)
Returns |
|---|
Tensor of same shape and dtype of input x, with tanh activation:
tanh(x) = sinh(x)/cosh(x) = ((exp(x) - exp(-x))/(exp(x) + exp(-x))).
|
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Last updated 2022-10-27 UTC.
[null,null,["Last updated 2022-10-27 UTC."],[],[],null,["# tf.keras.activations.tanh\n\n\u003cbr /\u003e\n\n|---------------------------------------------------------------------------------------------------------|\n| [View source on GitHub](https://github.com/keras-team/keras/tree/v2.8.0/keras/activations.py#L354-L373) |\n\nHyperbolic tangent 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.tanh`](https://www.tensorflow.org/api_docs/python/tf/keras/activations/tanh)\n\n\u003cbr /\u003e\n\n tf.keras.activations.tanh(\n x\n )\n\n#### For example:\n\n a = tf.constant([-3.0,-1.0, 0.0,1.0,3.0], dtype = tf.float32)\n b = tf.keras.activations.tanh(a)\n b.numpy()\n array([-0.9950547, -0.7615942, 0., 0.7615942, 0.9950547], 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| Tensor of same shape and dtype of input `x`, with tanh activation: `tanh(x) = sinh(x)/cosh(x) = ((exp(x) - exp(-x))/(exp(x) + exp(-x)))`. ||\n\n\u003cbr /\u003e"]]
View source on GitHub