torcheval.metrics.functional.multiclass_accuracy¶
- torcheval.metrics.functional.multiclass_accuracy(input: Tensor, target: Tensor, *, average: str | None = 'micro', num_classes: int | None = None, k: int = 1) Tensor ¶
Compute accuracy score, which is the frequency of input matching target. Its class version is
torcheval.metrics.MultiClassAccuracy
.- Parameters:
input (Tensor) – Tensor of label predictions It could be the predicted labels, with shape of (n_sample, ). It could also be probabilities or logits with shape of (n_sample, n_class).
torch.argmax
will be used to convert input into predicted labels.target (Tensor) – Tensor of ground truth labels with shape of (n_sample, ).
average –
'micro'
[default]:Calculate the metrics globally.
'macro'
:Calculate metrics for each class separately, and return their unweighted mean. Classes with 0 true instances are ignored.
None
:Calculate the metric for each class separately, and return the metric for every class. NaN is returned if a class has no sample in
target
.
num_classes – Number of classes. Required for
'macro'
andNone
average methods.k – Number of top probabilities to be considered. K should be an integer greater than or equal to 1. If k >1, the input tensor must contain probabilities or logits for every class.
Examples:
>>> import torch >>> from torcheval.metrics.functional import multiclass_accuracy >>> input = torch.tensor([0, 2, 1, 3]) >>> target = torch.tensor([0, 1, 2, 3]) >>> multiclass_accuracy(input, target) tensor(0.5) >>> multiclass_accuracy(input, target, average=None, num_classes=4) tensor([1., 0., 0., 1.]) >>> multiclass_accuracy(input, target, average="macro", num_classes=4) tensor(0.5) >>> input = torch.tensor([[0.9, 0.1, 0, 0], [0.1, 0.2, 0.4, 0,3], [0, 1.0, 0, 0], [0, 0, 0.2, 0.8]]) >>> multiclass_accuracy(input, target) tensor(0.5)