# Python – tensorflow.math.reduce_std()

TensorFlow is open-source Python library designed by Google to develop Machine Learning models and deep learning  neural networks.

reduce_std() is used to find standard deviation of elements across dimensions of a tensor.

Syntax: tensorflow.math.reduce_std( 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_std(a)` `# Printing the result``print``(``'Result: '``, res)`

Output:

```Input:  tf.Tensor([1. 2. 3. 4.], shape=(4, ), dtype=float64)
Result:  tf.Tensor(1.118033988749895, 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_std(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(
[[0.5]
[0.5]], shape=(2, 1), dtype=float64)```

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