TensorFlow is open-source Python library designed by Google to develop Machine Learning models and deep learning neural networks.
reduce_logsumexp() is used to compute log sum exp of elements across dimensions of a tensor.
Syntax: tensorflow.math.reduce_logsumexp( input_tensor, axis, keepdims, name)
Parameters:
- input_tensor: It is numeric tensor to reduce.
- axis(optional): It represent the dimensions to reduce. It’s value should be in range [-rank(input_tensor), rank(input_tensor)). If no value is given for this all dimensions are reduced.
- keepdims(optional): It’s default value is False. If it’s set to True it will retain the reduced dimension with length 1.
- name(optional): It defines the name for the operation.
Returns: It returns a tensor.
Example 1:
Python3
# importing the libraryimport tensorflow as tf# Initializing the input tensora = tf.constant([1, 2, 3, 4], dtype = tf.float64)# Printing the input tensorprint('Input: ', a)# Calculating resultres = tf.math.reduce_logsumexp(a)# Printing the resultprint('Result: ', res) |
Output:
Input: tf.Tensor([1. 2. 3. 4.], shape=(4, ), dtype=float64) Result: tf.Tensor(4.440189698561196, shape=(), dtype=float64)
Example 2:
Python3
# importing the libraryimport tensorflow as tf# Initializing the input tensora = tf.constant([[1, 2], [3, 4]], dtype = tf.float64)# Printing the input tensorprint('Input: ', a)# Calculating resultres = tf.math.reduce_logsumexp(a, axis = 1, keepdims = True)# Printing the resultprint('Result: ', res) |
Output:
Input: tf.Tensor( [[1. 2.] [3. 4.]], shape=(2, 2), dtype=float64) Result: tf.Tensor( [[2.31326169] [4.31326169]], shape=(2, 1), dtype=float64)
