tf.math.sigmoid

TensorFlow 1 version View source on GitHub

Computes sigmoid of x element-wise.

tf.math.sigmoid(
    x, name=None
)

Formula for calculating sigmoid(x): y = 1 / (1 + exp(-x)).

For x \in (-inf, inf) => sigmoid(x) \in (0, 1)

Example Usage:

If a positive number is large, then its sigmoid will approach to 1 since the formula will be y = <large_num> / (1 + <large_num>)

x = tf.constant([0.0, 1.0, 50.0, 100.0]) 
tf.math.sigmoid(x) 
<tf.Tensor: shape=(4,), dtype=float32, 
numpy=array([0.5      , 0.7310586, 1.       , 1.       ], dtype=float32)> 

If a negative number is large, its sigmoid will approach to 0 since the formula will be y = 1 / (1 + <large_num>)

x = tf.constant([-100.0, -50.0, -1.0, 0.0]) 
tf.math.sigmoid(x) 
<tf.Tensor: shape=(4,), dtype=float32, numpy= 
array([0.0000000e+00, 1.9287499e-22, 2.6894143e-01, 0.5], 
      dtype=float32)> 

Args:

  • x: A Tensor with type float16, float32, float64, complex64, or complex128.
  • name: A name for the operation (optional).

Returns:

A Tensor with the same type as x.

Usage Example:

x = tf.constant([-128.0, 0.0, 128.0], dtype=tf.float32) 
tf.sigmoid(x) 
<tf.Tensor: shape=(3,), dtype=float32, 
numpy=array([0. , 0.5, 1. ], dtype=float32)> 

Scipy Compatibility

Equivalent to scipy.special.expit