Accuracy of the model over the samples.
Accuracy = proportion of correctly predicted samples.
Average confidence of the model in its prediction. Ideally, this value should be approximatively equal to the model's accuracy.
Only defined for validation metrics.
Epoch of the training.
Not defined for validation metrics.
The array of names of the failling samples (under 0.999 accuracy).
Model's average loss.
Only defined on training metrics.
Precision of the model over the samples.
Precision = (number of correctly assigned samples to a label) / (number of samples assigned to a label)
Recall of the model over the samples.
Recall = (number of correctly assigned samples to a label) / (number of samples that belong to a label)
Generated using TypeDoc
Some metrics about how a model is performing during training or at validation.