# Loss#

class ignite.metrics.Loss(loss_fn, output_transform=<function Loss.<lambda>>, batch_size=<built-in function len>, device=device(type='cpu'))[source]#

Calculates the average loss according to the passed loss_fn.

Parameters
• loss_fn (Callable) – a callable taking a prediction tensor, a target tensor, optionally other arguments, and returns the average loss over all observations in the batch.

• output_transform (Callable) – a callable that is used to transform the Engine’s process_function’s output into the form expected by the metric. This can be useful if, for example, you have a multi-output model and you want to compute the metric with respect to one of the outputs. The output is expected to be a tuple (prediction, target) or (prediction, target, kwargs) where kwargs is a dictionary of extra keywords arguments. If extra keywords arguments are provided they are passed to loss_fn.

• batch_size (Callable) – a callable taking a target tensor that returns the first dimension size (usually the batch size).

• device (Union[str, torch.device]) – specifies which device updates are accumulated on. Setting the metric’s device to be the same as your update arguments ensures the update method is non-blocking. By default, CPU.

required_output_keys#

dictionary defines required keys to be found in engine.state.output if the latter is a dictionary. Default, ("y_pred", "y", "criterion_kwargs"). This is useful when the criterion function requires additional arguments, which can be passed using criterion_kwargs. See notes below for an example.

Type

Optional[Tuple]

Note

Let’s implement a Loss metric that requires x, y_pred, y and criterion_kwargs as input for criterion function. In the example below we show how to setup standard metric like Accuracy and the Loss metric using an evaluator created with create_supervised_evaluator() method.

import torch
import torch.nn as nn
from torch.nn.functional import nll_loss

from ignite.metrics import Accuracy, Loss
from ignite.engine import create_supervised_evaluator

model = ...

criterion = nll_loss

metrics = {
"Accuracy": Accuracy(),
"Loss": Loss(criterion)
}

# global criterion kwargs
criterion_kwargs = {...}

evaluator = create_supervised_evaluator(
model,
metrics=metrics,
output_transform=lambda x, y, y_pred: {
"x": x, "y": y, "y_pred": y_pred, "criterion_kwargs": criterion_kwargs}
)

res = evaluator.run(data)


Methods

 compute Computes the metric based on it's accumulated state. reset Resets the metric to it's initial state. update Updates the metric's state using the passed batch output.
compute()[source]#

Computes the metric based on it’s accumulated state.

By default, this is called at the end of each epoch.

Returns

the actual quantity of interest. However, if a Mapping is returned, it will be (shallow) flattened into engine.state.metrics when completed() is called.

Return type

Any

Raises

NotComputableError – raised when the metric cannot be computed.

reset()[source]#

Resets the metric to it’s initial state.

By default, this is called at the start of each epoch.

Return type

None

update(output)[source]#

Updates the metric’s state using the passed batch output.

By default, this is called once for each batch.

Parameters

output (Sequence[Union[torch.Tensor, Dict]]) – the is the output from the engine’s process function.

Return type

None