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l1_loss

L1_Loss

Bases: LossOp

Calculate the L1 loss between two tensors.

This LossOp can be used to Implement

L1 loss: Is a criterion that calculates Mean Absolute Error between the elements ([y_pred, y_true]). Smooth_L1 loss: Is a criterion that uses squared loss if absolute element wise subtraction (y_pred - y_true) is less than 'beta' and vanilla L1 loss otherwise. Huber loss: Is a criterion that uses squared loss if absolute element wise subtraction (y_pred - y_true) is less than 'beta' and a 'beta' scaled L1 loss otherwise.

Parameters:

Name Type Description Default
inputs Union[Tuple[str, str], List[str]]

A tuple or list like: [y_pred, y_true].

 required
outputs str

String key under which to store the computed loss.

 required
mode Union[None, str, Iterable[str]]

What mode(s) to execute this Op in. For example, "train", "eval", "test", or "infer". To execute regardless of mode, pass None. To execute in all modes except for a particular one, you can pass an argument like "!infer" or "!train".

 '!infer'
ds_id Union[None, str, Iterable[str]]

What dataset id(s) to execute this Op in. To execute regardless of ds_id, pass None. To execute in all ds_ids except for a particular one, you can pass an argument like "!ds1".

 None
average_loss bool

Whether to average the element-wise loss after the Loss Op.

 True
loss_type str

What type of L1 loss. Can either be 'L1' (L1 Loss), 'Smooth' (Smooth L1 Loss) or 'Huber' (Huber loss). Default:'L1'

 'L1'
beta Union[None, float]

A threshold at which to change between L1 and L2 loss. Needs to be a positive number. Default:1.0 . dtype: float16 or float32.

 1.0
Source code in fastestimator/fastestimator/op/tensorop/loss/l1_loss.py 
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@traceable()
class L1_Loss(LossOp):
    """Calculate the L1 loss between two tensors.

    This LossOp can be used to Implement:
        L1 loss: Is a criterion that calculates Mean Absolute Error between the elements ([y_pred, y_true]).
        Smooth_L1 loss: Is a criterion that uses squared loss if absolute element wise subtraction (y_pred - y_true) is less than
                        'beta' and vanilla L1 loss otherwise.
        Huber loss: Is a criterion that uses squared loss if absolute element wise subtraction (y_pred - y_true) is less than
                    'beta' and a 'beta' scaled L1 loss otherwise.


    Args:
        inputs: A tuple or list like: [y_pred, y_true].
        outputs: String key under which to store the computed loss.
        mode: What mode(s) to execute this Op in. For example, "train", "eval", "test", or "infer". To execute
            regardless of mode, pass None. To execute in all modes except for a particular one, you can pass an argument
            like "!infer" or "!train".
        ds_id: What dataset id(s) to execute this Op in. To execute regardless of ds_id, pass None. To execute in all
            ds_ids except for a particular one, you can pass an argument like "!ds1".
        average_loss: Whether to average the element-wise loss after the Loss Op.
        loss_type: What type of L1 loss. Can either be 'L1' (L1 Loss), 'Smooth' (Smooth L1 Loss) or 'Huber' (Huber loss). Default:'L1'
        beta: A threshold at which to change between L1 and L2 loss. Needs to be a positive number. Default:1.0 . dtype: float16 or float32.
    """
    def __init__(self,
                 inputs: Union[Tuple[str, str], List[str]],
                 outputs: str,
                 mode: Union[None, str, Iterable[str]] = "!infer",
                 ds_id: Union[None, str, Iterable[str]] = None,
                 average_loss: bool = True,
                 loss_type: str = 'L1',
                 beta: Union[None, float] = 1.0):
        self.average_loss = average_loss
        self.loss_type = loss_type
        self.beta = beta
        super().__init__(inputs=inputs, outputs=outputs, mode=mode, ds_id=ds_id)

    def forward(self, data: List[Tensor], state: Dict[str, Any]) -> Tensor:
        y_pred, y_true = data
        if self.loss_type == 'L1':
            loss = l1_loss(y_true=y_true, y_pred=y_pred)
        elif self.loss_type == 'Smooth':
            loss = smooth_l1_loss(y_true=y_true, y_pred=y_pred, beta=self.beta)
        elif self.loss_type == 'Huber':
            loss = huber(y_true=y_true, y_pred=y_pred, beta=self.beta)
        else:
            raise ValueError(
                "Unrecognized Loss type. Can either be None (L1 Loss), 'Smooth' (Smooth L1 Loss) or 'Huber' (Huber loss)"
            )
        if self.average_loss:
            loss = reduce_mean(loss)
        return loss