Deep Learning with PyTorch

12 Improving training with metrics and augmentation

This chapter covers

• Defining and computing precision, recall, and true/false positives/negatives
• Using the F1 score versus other quality metrics
• Balancing and augmenting data to reduce overfitting
• Using TensorBoard to graph quality metrics

The close of the last chapter left us in a predicament. While we were able to get the mechanics of our deep learning project in place, none of the results were actually useful; the network simply classified everything as non-nodule! To make matters worse, the results seemed great on the surface, since we were looking at the overall percent of the training and validation sets that were classified correctly. With our data heavily skewed toward negative samples, ...