Loss

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

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, 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 an example below.

Type

Optional[Tuple]

Examples

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()

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()

Resets the metric to it’s initial state.

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

Return type

None

update(output)

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

By default, this is called once for each batch.

Parameters

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

Return type

None