Java: Data Science Made Easy

Reconstruction in an RBM

The RBM technique goes through a reconstruction phase. This is where the activations are fed back to the first layer and multiplied by the same weights used for the input. The sum of these values from each node of the second layer, plus another bias, represents an approximation of the original input. The idea is to train the model to minimize the difference between the original input values and the feedback values.

The difference in values is treated as an error. The process is repeated until an error minimum is reached. You can think of the reconstruction as guesses about the original input. These guesses are essentially ...