_l1_loss
l1_loss
¶
Calculate Mean Absolute Error between two tensors.
This method can be used with TensorFlow tensors:
true = tf.constant([[0,1,0,0], [0,0,0,1], [0,0,1,0], [1,0,0,0]])
pred = tf.constant([[0.1,0.9,0.05,0.05], [0.1,0.2,0.0,0.7], [0.0,0.15,0.8,0.05], [1.0,0.0,0.0,0.0]])
L1 = fe.backend.l1_loss(y_pred=pred, y_true=true) #[0.0750, 0.1500, 0.1000, 0.0000]
true = tf.constant([[1], [3], [2], [0]])
pred = tf.constant([[2.0], [0.0], [2.0], [1.0]])
L1 = fe.backend.l1_loss(y_pred=pred, y_true=true) #[1., 3., 0., 1.]
This method can be used with PyTorch tensors:
true = torch.tensor([[0,1,0,0], [0,0,0,1], [0,0,1,0], [1,0,0,0]])
pred = torch.tensor([[0.1,0.9,0.05,0.05], [0.1,0.2,0.0,0.7], [0.0,0.15,0.8,0.05], [1.0,0.0,0.0,0.0]])
L1 = fe.backend.l1_loss(y_pred=pred, y_true=true) #[0.0750, 0.1500, 0.1000, 0.0000]
true = torch.tensor([[1], [3], [2], [0]])
pred = torch.tensor([[2.0], [0.0], [2.0], [1.0]])
L1 = fe.backend.l1_loss(y_pred=pred, y_true=true) #[1., 3., 0., 1.]
Parameters:
Name | Type | Description | Default |
---|---|---|---|
y_true |
Tensor
|
Ground truth class labels with a shape like (batch) or (batch, n_classes). dtype: int, float16, float32. |
required |
y_pred |
Tensor
|
Prediction score for each class, with a shape like y_true. dtype: float32 or float16. |
required |
Returns:
Type | Description |
---|---|
Tensor
|
The L1 loss between |
Raises:
Type | Description |
---|---|
ValueError
|
If |