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
’sprocess_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 theupdate
method is non-blocking. By default, CPU.
Methods
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.
- 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 whencompleted()
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
- 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