mindspore.nn.MSE
- class mindspore.nn.MSE[source]
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: \(x\) and: \(y\).
\[\text{MSE}(x,\ y) = \frac{\sum_{i=1}^n({y\_pred}_i - y_i)^2}{n}\]where \(n\) is batch size.
- Supported Platforms:
Ascend
GPU
CPU
Examples
>>> import numpy as np >>> import mindspore >>> from mindspore import 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() >>> print(result) 0.0031250009778887033
- eval()[source]
Computes the mean squared error(MSE).
- Returns
numpy.float64. The computed result.
- Raises
RuntimeError – If the number of samples is 0.
- update(*inputs)[source]
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.