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Hard sigmoid activation function.
tf.keras.activations.hard_sigmoid(x)
Faster to compute than sigmoid activation.
For example:
a = tf.constant([-3.0,-1.0, 0.0,1.0,3.0], dtype = tf.float32)
b = tf.keras.activations.hard_sigmoid(a)
b.numpy()
array([0. , 0.3, 0.5, 0.7, 1. ], dtype=float32)
Arguments:
x
: Input tensor.
Returns:
The hard sigmoid activation:
0
ifx < -2.5
1
ifx > 2.5
0.2 * x + 0.5
if-2.5 <= x <= 2.5
.