https://github.com/monsta-hd/boltzmann-machines

from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)

    28*28 = 784 pixels (55000 samples)
    mnist.train
        mnist.train.images
        mnist.train.labels
            [0, 0, 0, 1, 0, 0, 0, 0, 0, 0] - one-hot vector for number 3
    mnist.test
    mnist.validate

    import tensorflow as tf

    x = tf.placeholder(tf.float32, [None, 784])     # use arbitrary number of 784-dim vectors for learning

    W = tf.Variable(tf.zeros[784, 10])              # matrix for mult-n
    b = tf.Variable(tf.zeros[10])                   # bias vector (to add)

    y = tf.nn.softmax(tf.matmul(x, W) + b)

    y_ = tf.placeholder(tf.float32, [None, 10])     # stub-function

    cross_entropy = tf.reduce_mean(
        -tf.reduce_sum(y_ * tf.log(y), reduction_idices=[1]))