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Python – tensorflow.math.reduce_variance()

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  • Last Updated : 31 Aug, 2021
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TensorFlow is open-source Python library designed by Google to develop Machine Learning models and deep learning  neural networks.

reduce_variance() is used to find variance of elements across dimensions of a tensor.

Syntax: tensorflow.math.reduce_variance( 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 library
import tensorflow as tf
 
# Initializing the input tensor
a = tf.constant([1, 2, 3, 4], dtype = tf.float64)
 
# Printing the input tensor
print('Input: ', a)
 
# Calculating result
res = tf.math.reduce_variance(a)
 
# Printing the result
print('Result: ', res)


Output:

Input:  tf.Tensor([1. 2. 3. 4.], shape=(4, ), dtype=float64)
Result:  tf.Tensor(1.25, shape=(), dtype=float64)

Example 2:

Python3




# importing the library
import tensorflow as tf
 
# Initializing the input tensor
a = tf.constant([[1, 2], [3, 4]], dtype = tf.float64)
 
# Printing the input tensor
print('Input: ', a)
 
# Calculating result
res = tf.math.reduce_variance(a, axis = 1, keepdims = True)
 
# Printing the result
print('Result: ', res)


Output:

Input:  tf.Tensor(
[[1. 2.]
 [3. 4.]], shape=(2, 2), dtype=float64)
Result:  tf.Tensor(
[[0.25]
 [0.25]], shape=(2, 1), dtype=float64)

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