Restricted Boltzmann Machine from Scratch

It is implementation of Restricted Boltzmann Machine from scratch over GPU using PyTorch for tensor manipulation and GPU support

Dependencies

• Torch https://pytorch.org/
• Numpy
  pip install numpy
• Pandas
  pip install pandas
• Matplotlib
  pip install matplotlib

Importing Dataset

Pandas dataframe cannot be directily converted into torch tensor
So, datasets are first imported as pandas dataframe and then converted into numpy arrays which are then converted into torch tensor
Here, we have inplace shuffled data

dataframe = pd.read_csv("/dataset.csv", header=None)
dataset   = np.array(dataframe)
np.random.shuffle(dataset)
data      = torch.tensor(dataset[:, 1:], device="cuda")

RBM

Parameter Initialization

number_features : Number of features want to extracted from given dataset. It is same as number of hidden units in RBM
n_h             : Number of Hidden units
n_v             : Number of Visible units in RBM. It is same as number of inputs we have from dataset
k               : Number of Gibbs Sampling steps to perform Contrastive Divergance
epochs          : Number of epochs for number of times machine to train on same data
mini_batch_size : Size of Mini Batch over which, parameters are updated
alphs           : Learning rate of RBM
momentum        : Momentum value of RBM used to track old change in weights
weight_decay    : Rate at which, weight gets samller after every epochs

    def __init__(self, n_v=0, n_h=0, k=2, epochs=15, mini_batch_size=64, alpha=0.001, momentum=0.9, weight_decay=0.001):
        self.number_features = 0
        self.n_v             = n_v
        self.n_h             = self.number_features
        self.k               = k
        self.alpha           = alpha
        self.momentum        = momentum
        self.weight_decay    = weight_decay
        self.mini_batch_size = mini_batch_size
        self.epochs          = epochs
        self.data            = torch.randn(1, device="cuda")