DBN implementation for the MNIST dataset

Let's look at how the DBN class implemented earlier is used for the MNIST dataset.

Loading the dataset

First, we load the dataset from idx3 and idx1 formats into test, train, and validation sets. We need to import TensorFlow common utilities that are defined in the common module explained here:

import tensorflow as tf
from common.models.boltzmann import dbn
from common.utils import datasets, utilities

trainX, trainY, validX, validY, testX, testY = 
     datasets.load_mnist_dataset(mode='supervised')

You can find details about load_mnist_dataset() in the following code listing. As mode='supervised' is set, the train, test, and validation labels are returned:

def load_mnist_dataset(mode='supervised', one_hot=True):
   mnist = input_data.read_data_sets("MNIST_data/", one_hot=one_hot)
   # Training set
   trX = mnist.train.images
   trY = mnist.train.labels
   # Validation set
   vlX = mnist.validation.images
   vlY = mnist.validation.labels
   # Test set
...