speechbrain.utils.Accuracy module
Calculate accuracy.
Authors * Jianyuan Zhong 2020
Summary
Classes:
Module for calculate the overall one-step-forward prediction accuracy. |
Functions:
Calculates the accuracy for predicted log probabilities and targets in a batch. |
Reference
- speechbrain.utils.Accuracy.Accuracy(log_probabilities, targets, length=None)[source]
Calculates the accuracy for predicted log probabilities and targets in a batch.
- Parameters:
log_probabilities (tensor) – Predicted log probabilities (batch_size, time, feature).
targets (tensor) – Target (batch_size, time).
length (tensor) – Length of target (batch_size,).
Example
>>> probs = torch.tensor([[0.9, 0.1], [0.1, 0.9], [0.8, 0.2]]).unsqueeze(0) >>> acc = Accuracy(torch.log(probs), torch.tensor([1, 1, 0]).unsqueeze(0), torch.tensor([2/3])) >>> print(acc) (1.0, 2.0)
- class speechbrain.utils.Accuracy.AccuracyStats[source]
Bases:
object
Module for calculate the overall one-step-forward prediction accuracy.
Example
>>> probs = torch.tensor([[0.9, 0.1], [0.1, 0.9], [0.8, 0.2]]).unsqueeze(0) >>> stats = AccuracyStats() >>> stats.append(torch.log(probs), torch.tensor([1, 1, 0]).unsqueeze(0), torch.tensor([2/3])) >>> acc = stats.summarize() >>> print(acc) 0.5
- append(log_probabilities, targets, length=None)[source]
This function is for updating the stats according to the prediction and target in the current batch.
- Parameters:
log_probabilities (tensor) – Predicted log probabilities (batch_size, time, feature).
targets (tensor) – Target (batch_size, time).
length (tensor) – Length of target (batch_size,).