Function Differences with tf.math.reduce_variance

tf.math.reduce_variance

tf.math.reduce_variance(input_tensor, axis=None, keepdims=False, name=None)

For more information, see tf.math.reduce_variance.

mindspore.Tensor.var

mindspore.Tensor.var(axis=None, ddof=0, keepdims=False)

For more information, see mindspore.Tensor.var.

Usage

The basic function of the two interfaces is the same. Both calculate the variance of the Tensor in some dimension, calculated as: var = mean(x), where x = abs(a - a.mean())**2.

The difference is that mindspore.Tensor.var has one more input parameter ddof. In general, the mean value is x.sum() / N, where N=len(x), and if ddof is configured, the denominator will change from N to N-ddof.

Code Example

import mindspore as ms
import numpy as np

a = ms.Tensor(np.array([[1, 2], [3, 4]]), ms.float32)
print(a.var()) # 1.25
print(a.var(axis=0)) # [1. 1.]
print(a.var(axis=1)) # [0.25 0.25]
print(a.var(ddof=1)) # 1.6666666

import tensorflow as tf
tf.enable_eager_execution()

x = tf.constant([[1., 2.], [3., 4.]])
print(tf.math.reduce_variance(x).numpy())  # 1.25
print(tf.math.reduce_variance(x, 0).numpy())  # [1., 1.]
print(tf.math.reduce_variance(x, 1).numpy())  # [0.25,  0.25]