reduce_mean

paddle.fluid.layers.nn. reduce_mean ( input, dim=None, keep_dim=False, name=None ) [source]

Warning: API “paddle.fluid.layers.nn.reduce_mean” is deprecated since 2.0.0, and will be removed in future versions. Please use “paddle.mean” instead.

Computes the mean of the input tensor’s elements along the given dimension.

Args:
input (Variable): The input variable which is a Tensor, the data type is float32,

float64, int32, int64.

dim (list|int, optional): The dimension along which the mean is computed. If

None, compute the mean over all elements of input and return a variable with a single element, otherwise it must be in the range \([-rank(input), rank(input))\). If \(dim[i] < 0\), the dimension to reduce is \(rank(input) + dim[i]\).

keep_dim (bool, optional): Whether to reserve the reduced dimension in the

output Tensor. The result tensor will have one fewer dimension than the input unless keep_dim is true, default value is False.

name(str, optional): The default value is None. Normally there is no need for

user to set this property. For more information, please refer to Name

Returns:

Variable: Tensor, results of average on the specified dim of input tensor, it’s data type is the same as input’s Tensor.

Raises:

TypeError, if out data type is different with the input data type.

Examples:
import paddle.fluid as fluid
# x is a Tensor variable with following elements:
#    [[0.2, 0.3, 0.5, 0.9]
#     [0.1, 0.2, 0.6, 0.7]]
# Each example is followed by the corresponding output tensor.
x = fluid.data(name='x', shape=[2, 4], dtype='float32')
fluid.layers.reduce_mean(x)  # [0.4375]
fluid.layers.reduce_mean(x, dim=0)  # [0.15, 0.25, 0.55, 0.8]
fluid.layers.reduce_mean(x, dim=-1)  # [0.475, 0.4]
fluid.layers.reduce_mean(x, dim=1, keep_dim=True)  # [[0.475], [0.4]]

# y is a Tensor variable with shape [2, 2, 2] and elements as below:
#      [[[1.0, 2.0], [3.0, 4.0]],
#      [[5.0, 6.0], [7.0, 8.0]]]
# Each example is followed by the corresponding output tensor.
y = fluid.data(name='y', shape=[2, 2, 2], dtype='float32')
fluid.layers.reduce_mean(y, dim=[1, 2]) # [2.5, 6.5]
fluid.layers.reduce_mean(y, dim=[0, 1]) # [4.0, 5.0]