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Computes the mean of elements across dimensions of a tensor.

```
tf.math.reduce_mean(
input_tensor, axis=None, keepdims=False, name=None
)
```

Reduces `input_tensor`

along the dimensions given in `axis`

by computing the
mean of elements across the dimensions 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., 1.], [2., 2.]])`

`tf.reduce_mean(x)`

`<tf.Tensor: shape=(), dtype=float32, numpy=1.5>`

`tf.reduce_mean(x, 0)`

`<tf.Tensor: shape=(2,), dtype=float32, numpy=array([1.5, 1.5], dtype=float32)>`

`tf.reduce_mean(x, 1)`

`<tf.Tensor: shape=(2,), dtype=float32, numpy=array([1., 2.], dtype=float32)>`

## Returns | |
---|---|

The reduced tensor. |

## numpy compatibility

Equivalent to np.mean

Please note that `np.mean`

has a `dtype`

parameter that could be used to
specify the output type. By default this is `dtype=float64`

. On the other
hand, `tf.reduce_mean`

has an aggressive type inference from `input_tensor`

,
for example:

`x = tf.constant([1, 0, 1, 0])`

`tf.reduce_mean(x)`

`<tf.Tensor: shape=(), dtype=int32, numpy=0>`

`y = tf.constant([1., 0., 1., 0.])`

`tf.reduce_mean(y)`

`<tf.Tensor: shape=(), dtype=float32, numpy=0.5>`