# tf.math.reduce_variance

Computes the variance of elements across dimensions of a tensor.

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`.

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