tf.compat.v1.math.reduce_variance

Computes the variance of elements across dimensions of a tensor.

tf.compat.v1.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], ...)>

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).

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.