MSELoss¶

class paddle.nn. MSELoss ( reduction: _ReduceMode = 'mean' ) [source]

Mean Square Error Loss Computes the mean square error (squared L2 norm) of given input and label.

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:

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

where input and label are float32 tensors of same shape.

Parameters

reduction (str, 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'.

Shape:

input (Tensor): Input tensor, the data type is float32 or float64 label (Tensor): Label tensor, the data type is float32 or float64 output (Tensor): output tensor storing the MSE loss of input and label, the data type is same as input.

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)

forward ( input: Tensor, label: Tensor ) → Tensor

forward¶

Defines the computation performed at every call. Should be overridden by all subclasses.

Parameters

• *inputs (tuple) – unpacked tuple arguments

• **kwargs (dict) – unpacked dict arguments