TensorFlow 1 version
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View source on GitHub
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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.math.reduce_std(x)<tf.Tensor: shape=(), dtype=float32, numpy=1.118034>tf.math.reduce_std(x, 0)<tf.Tensor: shape=(2,), dtype=float32, numpy=array([1., 1.], dtype=float32)>tf.math.reduce_std(x, 1)<tf.Tensor: shape=(2,), dtype=float32, numpy=array([0.5, 0.5], dtype=float32)>
Args | |
|---|---|
input_tensor
|
The tensor to reduce. Should have real or complex 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. Note, for
complex64 or complex128 input, the returned Tensor will be of type
float32 or float64, respectively.
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Numpy Compatibility
Equivalent to np.std
Please note 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.math.reduce_std has aggressive type inference from input_tensor.