Java Deep Learning Cookbook

How it works...

Using step 1 and step 2, for every MNIST digit, we maintain a list of (score, feature) pairs. We composed a map that relates each MNIST digit to this list of pairs. In the end, we just have to sort it to find the best/worst cases.

Also, we used the score() function to calculate the reconstruction error:

double score = net.score(new DataSet(example,example));

During the evaluation, we reconstruct the test features and measure how much it differs from actual feature values. A high reconstruction error indicates the presence of a high percentage of outliers.

After step 4, we should see JFrame visualization for reconstruction errors, as shown here:

Visualization is JFrame dependent. Basically, what we do is take the N best/worst ...