Java Deep Learning Cookbook

Evaluating and sorting the results based on the anomaly score

We need to calculate the reconstruction error for all the feature sets. Based on that, we will find the outlier data for all the MNIST digits (0 to 9). Finally, we will display the outlier data in the JFrame window. We also need feature values from a test set for the evaluation. We also need label values from the test set, not for evaluation, but for mapping anomalies with labels. Then, we can plot outlier data against each label. The labels are only used for plotting outlier data in JFrame against respective labels. In this recipe, we evaluate the trained autoencoder model for MNIST anomaly detection, and then sort the results and display them.