class ignite.metrics.Accuracy(output_transform=<function Accuracy.<lambda>>, is_multilabel=False, device=device(type='cpu'))[source]#

Calculates the accuracy for binary, multiclass and multilabel data.

Accuracy=TP+TNTP+TN+FP+FN\text{Accuracy} = \frac{ TP + TN }{ TP + TN + FP + FN }

where TP\text{TP} is true positives, TN\text{TN} is true negatives, FP\text{FP} is false positives and FN\text{FN} is false negatives.

  • update must receive output of the form (y_pred, y) or {'y_pred': y_pred, 'y': y}.

  • y_pred must be in the following shape (batch_size, num_categories, …) or (batch_size, …).

  • y must be in the following shape (batch_size, …).

  • y and y_pred must be in the following shape of (batch_size, num_categories, …) and num_categories must be greater than 1 for multilabel cases.

In binary and multilabel cases, the elements of y and y_pred should have 0 or 1 values. Thresholding of predictions can be done as below:

def thresholded_output_transform(output):
    y_pred, y = output
    y_pred = torch.round(y_pred)
    return y_pred, y

binary_accuracy = Accuracy(thresholded_output_transform)
  • 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.

  • is_multilabel (bool) – flag to use in multilabel case. By default, False.

  • 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.



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[torch.Tensor]) – the is the output from the engine’s process function.

Return type