TensorFlow 2 version | View source on GitHub |
Computes the standard deviation of elements across dimensions of a tensor.
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
,