# Accuracy#

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.

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

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

Parameters
• 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.

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

Return type

None