# tf.math.reduce_std

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Computes the standard deviation of elements across dimensions of a tensor.

### Aliases:

• `tf.compat.v1.math.reduce_std`
• `tf.compat.v2.math.reduce_std`
``````tf.math.reduce_std(
input_tensor,
axis=None,
keepdims=False,
name=None
)
``````

Reduces `input_tensor` along the dimensions given in `axis`. Unless `keepdims` is true, the rank of the tensor is reduced by 1 for each entry in `axis`. If `keepdims` is true, the reduced dimensions are retained with length 1.

If `axis` is None, all dimensions are reduced, and a tensor with a single element is returned.

#### For example:

``````x = tf.constant([[1., 2.], [3., 4.]])
tf.reduce_std(x)  # 1.1180339887498949
tf.reduce_std(x, 0)  # [1., 1.]
tf.reduce_std(x, 1)  # [0.5,  0.5]
``````

#### Args:

• `input_tensor`: The tensor to reduce. Should have numeric type.
• `axis`: The dimensions to reduce. If `None` (the default), reduces all dimensions. Must be in the range ```[-rank(input_tensor), rank(input_tensor))```.
• `keepdims`: If true, retains reduced dimensions with length 1.
• `name`: A name scope for the associated operations (optional).

#### Returns:

The reduced tensor, of the same dtype as the input_tensor.

#### Numpy Compatibility

Equivalent to np.std

Please note that `np.std` has a `dtype` parameter that could be used to specify the output type. By default this is `dtype=float64`. On the other hand, `tf.reduce_std` has an aggressive type inference from `input_tensor`,