# tf.math.reduce_mean

Computes the mean of elements across dimensions of a tensor.

Aliases: `tf.reduce_mean`

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

### Used in the tutorials:

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., 1.], [2., 2.]])
tf.reduce_mean(x)  # 1.5
tf.reduce_mean(x, 0)  # [1.5, 1.5]
tf.reduce_mean(x, 1)  # [1.,  2.]
``````

#### Args:

• `input_tensor`: The tensor to reduce. Should have numeric 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 for the operation (optional).

#### 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)  # 0
y = tf.constant([1., 0., 1., 0.])
tf.reduce_mean(y)  # 0.5
``````