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Sigmoid activation function, sigmoid(x) = 1 / (1 + exp(-x)).
tf.keras.activations.sigmoid(
x
)
Applies the sigmoid activation function. For small values (<-5),
sigmoid returns a value close to zero, and for large values (>5)
the result of the function gets close to 1.
Sigmoid is equivalent to a 2-element Softmax, where the second element is assumed to be zero. The sigmoid function always returns a value between 0 and 1.
Example:
a = tf.constant([-20, -1.0, 0.0, 1.0, 20], dtype = tf.float32)b = tf.keras.activations.sigmoid(a)b.numpy()array([2.0611537e-09, 2.6894143e-01, 5.0000000e-01, 7.3105860e-01,1.0000000e+00], dtype=float32)
Args | |
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x
|
Input tensor. |
Returns | |
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Tensor with the sigmoid activation: 1 / (1 + exp(-x)).
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