In this section, let's set up a restricted Boltzmann machine for Bernoulli distributed input data, where each attribute has values ranging from 0 to 1 (equivalent to a probability distribution). The dataset (MNIST) used in this recipe has input data satisfying a Bernoulli distribution.
An RBM comprises of two layers: a visible layer and a hidden layer. The visible layer is an input layer of nodes equal to the number of input attributes. In our case, each image in the MNIST dataset is defined using 784 pixels (28 x 28 size). Hence, our visible layer will have 784 nodes.
On the other hand, the hidden layer is generally user-defined. The hidden layer has a set of binary activated nodes, with each node having a probability of linkage with all other visible nodes. In our case, the hidden layer will have...
