Once the layers are constructed and the neural network is formed, we can initiate the training session. During the training session, we reconstruct the input multiple times and evaluate the reconstruction error. In previous recipes, we completed the autoencoder network configuration by defining the input and output layers as required. Note that we are going to train the network with its own input features, not the labels. Since we use an autoencoder for anomaly detection, we encode the data and then decode it back to measure the reconstruction error. Based on that, we list the most probable anomalies in MNIST data.