implement rbm using both Boost and Armadillo library in C++
Il Gu Yi
- execute: ./rbm train.d paras.rbm.d
- argument 1: train data
- argument 2: parameters data
- DATA: MNIST (partial data)
- both Binary RBM and Gaussian Bernoulli RBM
- constraint standard deviations are 1 in GBRBM case
- I use the random number generator mt19937 from Boost library for weights and bias initialization and stochastic gradient descent.
- I implement my program using Armadillo linear algebra library in C++ for various calculation based on matrix and vector.