mindspore.nn.MSE

class mindspore.nn.MSE

Measures the mean squared error.

Creates a criterion that measures the mean squared error (squared L2 norm) between each element in the input: \(x\) and the target: \(y\).

\[\text{MSE}(x,\ y) = \frac{\sum_{i=1}^n(y_i - x_i)^2}{n}\]

where \(n\) is batch size.

Examples

>>> import numpy as np
>>> from mindspore import nn, Tensor
>>>
>>> x = Tensor(np.array([0.1, 0.2, 0.6, 0.9]), mindspore.float32)
>>> y = Tensor(np.array([0.1, 0.25, 0.5, 0.9]), mindspore.float32)
>>> error = nn.MSE()
>>> error.clear()
>>> error.update(x, y)
>>> result = error.eval()

clear()

Clear the internal evaluation result.

eval()

Compute the mean squared error.

Returns

Float, the computed result.

Raises

RuntimeError – If the number of samples is 0.

update(*inputs)

Updates the internal evaluation result \(y_{pred}\) and \(y\).

Parameters

inputs – Input y_pred and y for calculating mean square error where the shape of y_pred and y are both N-D and the shape are the same.

Raises

ValueError – If the number of input is not 2.