TensorFlow 1 version | View source on GitHub |
Computes the variance of elements across dimensions of a tensor.
tf.math.reduce_variance(
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
of the entries in axis
, which must be unique. 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_variance(x)
<tf.Tensor: shape=(), dtype=float32, numpy=1.25>
tf.math.reduce_variance(x, 0)
<tf.Tensor: shape=(2,), dtype=float32, numpy=array([1., 1.], ...)>
tf.math.reduce_variance(x, 1)
<tf.Tensor: shape=(2,), dtype=float32, numpy=array([0.25, 0.25], ...)>
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.
|
Numpy Compatibility
Equivalent to np.var
Please note np.var
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_variance
has aggressive type inference from input_tensor
.