mindspore.nn.MSE

class mindspore.nn.MSE[source]

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: $y$ .

MSE (x, y) = \frac{\sum_{i = 1}^{n} (y_{i} - x_{i})^{2}}{n}

where $n$ is batch size.

Examples

>>> 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()[source]: Clear the internal evaluation result.

eval()[source]

Compute the mean squared error.

Returns: Float, the computed result.
Raises: RuntimeError – If the number of samples is 0.

update(*inputs)[source]

Updates the internal evaluation result $y_{p r e d}$ 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.