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.

  • 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.


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.




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.

For more information on how metric works with Engine, visit Attach Engine API.

from collections import OrderedDict

import torch
from torch import nn, optim

from ignite.engine import *
from ignite.handlers import *
from ignite.metrics import *
from ignite.utils import *
from ignite.contrib.metrics.regression import *
from ignite.contrib.metrics import *

# create default evaluator for doctests

def eval_step(engine, batch):
    return batch

default_evaluator = Engine(eval_step)

# create default optimizer for doctests

param_tensor = torch.zeros([1], requires_grad=True)
default_optimizer = torch.optim.SGD([param_tensor], lr=0.1)

# create default trainer for doctests
# as handlers could be attached to the trainer,
# each test must define his own trainer using `.. testsetup:`

def get_default_trainer():

    def train_step(engine, batch):
        return batch

    return Engine(train_step)

# create default model for doctests

default_model = nn.Sequential(OrderedDict([
    ('base', nn.Linear(4, 2)),
    ('fc', nn.Linear(2, 1))

model = default_model
criterion = nn.NLLLoss()
metric = Loss(criterion)
metric.attach(default_evaluator, 'loss')
y_pred = torch.tensor([[0.1, 0.4, 0.5], [0.1, 0.7, 0.2]])
y_true = torch.tensor([2, 2]).long()
state =[[y_pred, y_true]])



Computes the metric based on it's accumulated state.


Resets the metric to it's initial state.


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


Computes the metric based on it’s accumulated state.

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


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



NotComputableError – raised when the metric cannot be computed.


Resets the metric to it’s initial state.

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

Return type



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

By default, this is called once for each batch.


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

Return type