L1Loss¶
- class paddle.nn. L1Loss ( reduction='mean', name=None ) [source]
-
This interface is used to construct a callable object of the
L1Lossclass. The L1Loss layer calculates the L1 Loss ofinputandlabelas 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
-
reduction (str, optional) – Indicate the reduction to apply to the loss, the candicates 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, optional) – Name for the operation (optional, default is None). For more information, please refer to Name.
- Shape:
-
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. output (Tensor): The L1 Loss ofinputandlabel.If reduction is
'none', the shape of output loss is [N, *], the same asinput. If reduction is'mean'or'sum', the shape of output loss is [1].
Examples
import paddle import numpy as np input_data = np.array([[1.5, 0.8], [0.2, 1.3]]).astype("float32") label_data = np.array([[1.7, 1], [0.4, 0.5]]).astype("float32") input = paddle.to_tensor(input_data) label = paddle.to_tensor(label_data) l1_loss = paddle.nn.L1Loss() output = l1_loss(input, label) print(output.numpy()) # [0.35] l1_loss = paddle.nn.L1Loss(reduction='sum') output = l1_loss(input, label) print(output.numpy()) # [1.4] l1_loss = paddle.nn.L1Loss(reduction='none') output = l1_loss(input, label) print(output) # [[0.20000005 0.19999999] # [0.2 0.79999995]]
-
forward
(
input,
label
)
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
