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.
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You're reading from Java Deep Learning Cookbook
Rahul Raj has more than 7 years of IT industry experience in software development, business analysis, client communication, and consulting on medium-/large-scale projects in multiple domains. Currently, he works as a lead software engineer in a top software development firm. He has extensive experience in development activities comprising requirement analysis, design, coding, implementation, code review, testing, user training, and enhancements. He has written a number of articles about neural networks in Java and they are featured by DL4J/ official Java community channels. He is also a certified machine learning professional, certified by Vskills, the largest government certification body in India.
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Rahul Raj has more than 7 years of IT industry experience in software development, business analysis, client communication, and consulting on medium-/large-scale projects in multiple domains. Currently, he works as a lead software engineer in a top software development firm. He has extensive experience in development activities comprising requirement analysis, design, coding, implementation, code review, testing, user training, and enhancements. He has written a number of articles about neural networks in Java and they are featured by DL4J/ official Java community channels. He is also a certified machine learning professional, certified by Vskills, the largest government certification body in India.
Read more about Rahul Raj