TensorFlow is open-source python library designed by Google to develop Machine Learning models and deep learning neural networks. log_sigmoid() is used to find element wise log sigmoid of x. Specifically, y = log(1 / (1 + exp(-x))).
Syntax: tf.math.log_sigmoid(x, name)
Parameter:
- x: It’s the input tensor. Allowed dtype for this tensor are float32, float64.
- name(optional): It defines the name for the operation.
Returns: It returns a tensor of same dtype as x.
Example 1:
Python3
# Importing the libraryimport tensorflow as tf# Initializing the input tensora = tf.constant([.2, .5, .7, 1], dtype = tf.float64)# Printing the input tensorprint('Input: ', a)# Calculating resultres = tf.math.log_sigmoid(x = a)# Printing the resultprint('Result: ', res) |
Output:
Input: tf.Tensor([0.2 0.5 0.7 1. ], shape=(4, ), dtype=float64) Result: tf.Tensor([-0.59813887 -0.47407698 -0.40318605 -0.31326169], shape=(4, ), dtype=float64)
Example 2: Visualization
Python3
# importing the libraryimport tensorflow as tfimport matplotlib.pyplot as plt# Initializing the input tensora = tf.constant([.2, .5, .7, 1], dtype = tf.float64)# Calculating resultres = tf.math.log_sigmoid(x = a)# Plotting the graphplt.plot(a, res, color = 'green')plt.title('tensorflow.math.log_sigmoid')plt.xlabel('Input')plt.ylabel('Result')plt.show() |
Output:
