l1_loss

paddle.nn.functional. l1_loss ( input: Tensor, label: Tensor, reduction: _ReduceMode = 'mean', name: str | None = None ) → Tensor [source]

Computes the L1 Loss of Tensor input and label as follows.

If reduction set to 'none', the loss is:

\[Out = \lvert input - label \rvert\]

If reduction set to 'mean', the loss is:

\[Out = MEAN(\lvert input - label \rvert)\]

If reduction set to 'sum', the loss is:

\[Out = SUM(\lvert input - label \rvert)\]

Parameters

input (Tensor) – The input tensor. The shapes is [N, *], where N is batch size and * means any number of additional dimensions. It’s data type should be float32, float64, int32, int64.
label (Tensor) – label. The shapes is [N, *], same shape as input . It’s data type should be float32, float64, int32, int64.
reduction (str, optional) – Indicate the reduction to apply to the loss, the candidates are 'none' | 'mean' | 'sum'. If reduction is 'none', the unreduced loss is returned; If reduction is 'mean', the reduced mean loss is returned. If reduction is 'sum', the reduced sum loss is returned. Default is 'mean'.
name (str|None, optional) – Name for the operation (optional, default is None). For more information, please refer to api_guide_Name.

Returns

Tensor, the L1 Loss of Tensor input and label. If reduction is 'none', the shape of output loss is \([N, *]\), the same as input . If reduction is 'mean' or 'sum', the shape of output loss is [].

Examples

           >>> import paddle

>>> input = paddle.to_tensor([[1.5, 0.8], [0.2, 1.3]])
>>> label = paddle.to_tensor([[1.7, 1], [0.4, 0.5]])

>>> l1_loss = paddle.nn.functional.l1_loss(input, label)
>>> print(l1_loss)
Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
        0.34999999)

>>> l1_loss = paddle.nn.functional.l1_loss(input, label, reduction='none')
>>> print(l1_loss)
Tensor(shape=[2, 2], dtype=float32, place=Place(cpu), stop_gradient=True,
        [[0.20000005, 0.19999999],
         [0.20000000, 0.79999995]])

>>> l1_loss = paddle.nn.functional.l1_loss(input, label, reduction='sum')
>>> print(l1_loss)
Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
        1.39999998)

          