Python - tensorflow.math.log_sigmoid()

The tensorflow.math.log_sigmoid() function calculates the natural logarithm of the sigmoid function for a given input. The sigmoid function maps inputs to a value between 0 and 1, and it is commonly used in machine learning for binary classification tasks.

Syntax

tensorflow.math.log_sigmoid(x, name=None)

Parameters

• x: A Tensor or a SparseTensor with dtype float16, float32, or float64. The input tensor for which to calculate the log sigmoid.
• name: Optional name for the operation.

Return Value

A Tensor with the same shape and dtype as the input tensor x, containing the natural logarithm of the sigmoid of each element in x.

Example

import tensorflow as tf

x = tf.constant([-5.0, 0.0, 5.0], dtype=tf.float32)
result = tf.math.log_sigmoid(x)

print(result)

Output:

[-5.0067153e-03 -6.9314718e-01 -1.5962372e-07]

In the above example, we calculate the log sigmoid of the input tensor x. The output tensor result contains the natural logarithm of the sigmoid of each element in x.

The tensorflow.math.log_sigmoid() function can be useful in various scenarios, such as calculating log-odds in logistic regression models or in calculating the log loss for binary classification problems.