mindspore.train.MSE

class mindspore.train.MSE

Measures the mean squared error(MSE).

Creates a criterion that measures the MSE (squared L2 norm) between each element in the prediction and the ground truth: \(y\_pred\) and: \(y\).

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

where \(n\) is batch size.

Supported Platforms:

Ascend GPU CPU

Examples

>>> import numpy as np
>>> import mindspore
>>> from mindspore import Tensor
>>> from mindspore.train import MSE
>>>
>>> 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 = MSE()
>>> error.clear()
>>> error.update(x, y)
>>> result = error.eval()
>>> print(result)
0.0031250009778887033

clear()

Clear the internal evaluation result.

eval()

Computes the mean squared error(MSE).

Returns

numpy.float64. 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 the MSE where the shape of y_pred and y are both N-D and the shape should be the same.

Raises

ValueError – If the number of inputs is not 2.