Feature Engineering Made Easy

Reconstructing the data

In this forward pass of the network, we can see how data goes forward through the network (from the visible layer to the hidden layer), but that doesn't explain how the RBM is able to learn new features from our data without ground truths. This is done through multiple forward and backward passes through the network between our visible and hidden layer.

In the reconstruction phase, we switch the network around and let the hidden layer become the input layer and let it feed our activation variables (a) backwards into the visible layer using the same weights, but a new set of biases. The activated variables that we calculated during the forward pass are then used to reconstruct the original input vectors. The following ...