|---| | [ View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.21.0/tensorflow/python/ops/math_ops.py#L2631-L2691) | 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.math.reduce_variance(x, 0) tf.math.reduce_variance(x, 1)


| ## 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`](https://www.tensorflow.org/api_docs/python/tf/math/reduce_variance) has aggressive type inference from `input_tensor`.