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 tffrom common.models.boltzmann import dbnfrom 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 ...
