tf.math.reduce_variance

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Computes the variance of elements across dimensions of a tensor.

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Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.math.reduce_variance

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 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_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.