| View source on GitHub |
Sigmoid.
tf.keras.activations.sigmoid(
x
)
Applies the sigmoid activation function. The sigmoid function is defined as 1 divided by (1 + exp(-x)). It's curve is like an "S" and is like a smoothed version of the Heaviside (Unit Step Function) function. For small values (<-5) the sigmoid returns a value close to zero and for larger values (>5) the result of the function gets close to 1. Arguments: x: A tensor or variable.
| Returns | |
|---|---|
| A tensor. |
Sigmoid activation function.
| Arguments | |
|---|---|
x | Input tensor. |
| Returns | |
|---|---|
The sigmoid activation: (1.0 / (1.0 + exp(-x))). |
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Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/keras/activations/sigmoid