What is the equivalent of np.std() in TensorFlow?

Question

Just looking for the equivalent of np.std() in TensorFlow to calculate the standard deviation of a tensor.

Answer 1

To get the mean and variance just use tf.nn.moments.

mean, var = tf.nn.moments(x, axes=[1]) 

For more on tf.nn.moments params see docs

Answer 2

You can also use reduce_std in the following code adapted from Keras:

#coding=utf-8 import numpy as np import tensorflow as tf  def reduce_var(x, axis=None, keepdims=False):     """Variance of a tensor, alongside the specified axis.      # Arguments         x: A tensor or variable.         axis: An integer, the axis to compute the variance.         keepdims: A boolean, whether to keep the dimensions or not.             If `keepdims` is `False`, the rank of the tensor is reduced             by 1. If `keepdims` is `True`,             the reduced dimension is retained with length 1.      # Returns         A tensor with the variance of elements of `x`.     """     m = tf.reduce_mean(x, axis=axis, keep_dims=True)     devs_squared = tf.square(x - m)     return tf.reduce_mean(devs_squared, axis=axis, keep_dims=keepdims)  def reduce_std(x, axis=None, keepdims=False):     """Standard deviation of a tensor, alongside the specified axis.      # Arguments         x: A tensor or variable.         axis: An integer, the axis to compute the standard deviation.         keepdims: A boolean, whether to keep the dimensions or not.             If `keepdims` is `False`, the rank of the tensor is reduced             by 1. If `keepdims` is `True`,             the reduced dimension is retained with length 1.      # Returns         A tensor with the standard deviation of elements of `x`.     """     return tf.sqrt(reduce_var(x, axis=axis, keepdims=keepdims))  if __name__ == '__main__':     x_np = np.arange(10).reshape(2, 5).astype(np.float32)     x_tf = tf.constant(x_np)     with tf.Session() as sess:         print(sess.run(reduce_std(x_tf, keepdims=True)))         print(sess.run(reduce_std(x_tf, axis=0, keepdims=True)))         print(sess.run(reduce_std(x_tf, axis=1, keepdims=True)))     print(np.std(x_np, keepdims=True))     print(np.std(x_np, axis=0, keepdims=True))     print(np.std(x_np, axis=1, keepdims=True))