L1Loss¶
- class paddle.nn. L1Loss ( reduction='mean', name=None ) [source]
-
This interface is used to construct a callable object of the
L1Loss
class. The L1Loss layer calculates the L1 Loss ofinput
andlabel
as follows.If reduction set to
'none'
, the loss is:Out=|input−label|If reduction set to
'mean'
, the loss is:Out=MEAN(|input−label|)If reduction set to
'sum'
, the loss is:Out=SUM(|input−label|)- 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 ofinput
andlabel
.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