tf.keras.activations.hard_sigmoid

Hard sigmoid activation function.

tf.keras.activations.hard_sigmoid(
    x
)

A faster approximation of the sigmoid activation. Piecewise linear approximation of the sigmoid function. Ref: 'https://en.wikipedia.org/wiki/Hard_sigmoid'

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)

x Input tensor.

The hard sigmoid activation, defined as:

  • if x < -2.5: return 0
  • if x > 2.5: return 1
  • if -2.5 <= x <= 2.5: return 0.2 * x + 0.5