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

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

``````mean, var = tf.nn.moments(x, axes=)
``````

For more on `tf.nn.moments` params see docs

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))
``````