Training a Restricted Boltzmann machine
Every training step of an RBM goes through two phases: the forward phase and the backward phase (or reconstruction phase). The reconstruction of visible units is fine tuned by making several iterations of the forward and backward phases.
Training a forward phase: In the forward phase, the input data is passed from the visible layer to the hidden layer and all the computation occurs within the nodes of the hidden layer. The computation is essentially to take a stochastic decision of each connection from the visible to the hidden layer. In the hidden layer, the input data (X
) is multiplied by the weight matrix (W
) and added to a hidden bias vector (hb
).
The resultant vector of a size equal to the number of hidden nodes is then passed through a sigmoid function to determine each hidden node's output (or activation state). In our case, each input digit will produce a tensor vector of 900 probabilities, and as we have 55,000 input digits, we will have an...