tf.math.softplus
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Computes elementwise softplus: softplus(x) = log(exp(x) + 1)
.
tf.math.softplus(
features, name=None
)
Used in the notebooks
Used in the guide |
Used in the tutorials |
|
|
softplus
is a smooth approximation of relu
. Like relu
, softplus
always
takes on positive values.

Example:
import tensorflow as tf
tf.math.softplus(tf.range(0, 2, dtype=tf.float32)).numpy()
array([0.6931472, 1.3132616], dtype=float32)
Args |
features
|
Tensor
|
name
|
Optional: name to associate with this operation.
|
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Last updated 2024-04-26 UTC.
[null,null,["Last updated 2024-04-26 UTC."],[],[],null,["# tf.math.softplus\n\n\u003cbr /\u003e\n\n|----------------------------------------------------------------------------------------------------------------------------|\n| [View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.16.1/tensorflow/python/ops/math_ops.py#L628-L651) |\n\nComputes elementwise softplus: `softplus(x) = log(exp(x) + 1)`.\n\n#### View aliases\n\n\n**Main aliases**\n\n[`tf.nn.softplus`](https://www.tensorflow.org/api_docs/python/tf/math/softplus)\n\n\u003cbr /\u003e\n\n tf.math.softplus(\n features, name=None\n )\n\n### Used in the notebooks\n\n| Used in the guide | Used in the tutorials |\n|--------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| - [Introduction to gradients and automatic differentiation](https://www.tensorflow.org/guide/autodiff) | - [TFP Probabilistic Layers: Regression](https://www.tensorflow.org/probability/examples/Probabilistic_Layers_Regression) - [Gaussian Process Latent Variable Models](https://www.tensorflow.org/probability/examples/Gaussian_Process_Latent_Variable_Model) - [Bayesian Modeling with Joint Distribution](https://www.tensorflow.org/probability/examples/Modeling_with_JointDistribution) |\n\n`softplus` is a smooth approximation of `relu`. Like `relu`, `softplus` always\ntakes on positive values.\n\n#### Example:\n\n import tensorflow as tf\n tf.math.softplus(tf.range(0, 2, dtype=tf.float32)).numpy()\n array([0.6931472, 1.3132616], dtype=float32)\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|------------|--------------------------------------------------|\n| `features` | `Tensor` |\n| `name` | Optional: name to associate with this operation. |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ------- ||\n|---|---|\n| `Tensor` ||\n\n\u003cbr /\u003e"]]