rbm_mnist.py

import tensorflow as tf
import numpy as np

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

nhidden = 500
nvisible = 784

x = tf.placeholder(tf.float32, [None, nvisible])

W = tf.Variable(tf.truncated_normal([nvisible, nhidden]))

bhidden = tf.Variable(tf.random_normal([nhidden]))
bvisible = tf.Variable(tf.random_normal([nvisible]))

hidden = tf.sigmoid(tf.matmul(x, W) + bhidden)
visible = tf.sigmoid(tf.matmul(hidden, tf.transpose(W)) + bvisible)

cost = tf.reduce_sum(tf.pow(x - visible, 2))
train_step = tf.train.AdamOptimizer(0.01).minimize(cost)

sess = tf.InteractiveSession()
tf.global_variables_initializer().run()

for _ in range(50000):
  batch, _ = mnist.train.next_batch(100)
  sess.run(train_step, feed_dict={x: batch, visible: batch})