mse_loss¶

paddle.nn.functional. mse_loss ( input: Tensor, label: Tensor, reduction: _ReduceMode = 'mean', name: str | None = None ) → Tensor [source]

Accept input predications and label and returns the mean square error.

If reduction is set to 'none', loss is calculated as:

$$Out = (input - label)^2$$

If reduction is set to 'mean', loss is calculated as:

$$Out = \operatorname{mean}((input - label)^2)$$

If reduction is set to 'sum', loss is calculated as:

$$Out = \operatorname{sum}((input - label)^2)$$

Parameters

• input (Tensor) – Input tensor, the data type should be float32 or float64.

• label (Tensor) – Label tensor, the data type should be float32 or float64.

• reduction (string, optional) – The reduction method for the output, could be ‘none’ | ‘mean’ | ‘sum’. If reduction is 'mean', the reduced mean loss is returned. If reduction is 'sum', the reduced sum loss is returned. If reduction is 'none', the unreduced loss is returned. Default is 'mean'.

• name (str, optional) – Name for the operation (optional, default is None). For more information, please refer to Name.

Returns

Tensor, The tensor tensor storing the mean square error difference of input and label.

Examples

>>> import paddle
>>> mse_loss = paddle.nn.loss.MSELoss()
>>> input = paddle.to_tensor(1.5)
>>> label = paddle.to_tensor(1.7)
>>> output = mse_loss(input, label)
>>> print(output)
Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
       0.04000002)