RBM procedure using tensorflow

rbm_MNIST_test.py

import tensorflow as tf
import numpy as np
import input_data
import Image
from util import tile_raster_images


def sample_prob(probs):
    return tf.nn.relu(
        tf.sign(
            probs - tf.random_uniform(tf.shape(probs))))

alpha = 1.0
batchsize = 100

mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
trX, trY, teX, teY = mnist.train.images, mnist.train.labels, mnist.test.images,\
    mnist.test.labels

X = tf.placeholder("float", [None, 784])
Y = tf.placeholder("float", [None, 10])

rbm_w = tf.placeholder("float", [784, 500])
rbm_vb = tf.placeholder("float", [784])
rbm_hb = tf.placeholder("float", [500])
h0 = sample_prob(tf.nn.sigmoid(tf.matmul(X, rbm_w) + rbm_hb))
v1 = sample_prob(tf.nn.sigmoid(
    tf.matmul(h0, tf.transpose(rbm_w)) + rbm_vb))
h1 = tf.nn.sigmoid(tf.matmul(v1, rbm_w) + rbm_hb)
w_positive_grad = tf.matmul(tf.transpose(X), h0)
w_negative_grad = tf.matmul(tf.transpose(v1), h1)
update_w = rbm_w + alpha * \
    (w_positive_grad - w_negative_grad) / tf.to_float(tf.shape(X)[0])
update_vb = rbm_vb + alpha * tf.reduce_mean(X - v1, 0)
update_hb = rbm_hb + alpha * tf.reduce_mean(h0 - h1, 0)

h_sample = sample_prob(tf.nn.sigmoid(tf.matmul(X, rbm_w) + rbm_hb))
v_sample = sample_prob(tf.nn.sigmoid(
    tf.matmul(h_sample, tf.transpose(rbm_w)) + rbm_vb))
err = X - v_sample
err_sum = tf.reduce_mean(err * err)

sess = tf.Session()
init = tf.initialize_all_variables()
sess.run(init)

n_w = np.zeros([784, 500], np.float32)
n_vb = np.zeros([784], np.float32)
n_hb = np.zeros([500], np.float32)
o_w = np.zeros([784, 500], np.float32)
o_vb = np.zeros([784], np.float32)
o_hb = np.zeros([500], np.float32)
print sess.run(
    err_sum, feed_dict={X: trX, rbm_w: o_w, rbm_vb: o_vb, rbm_hb: o_hb})

for start, end in zip(
        range(0, len(trX), batchsize), range(batchsize, len(trX), batchsize)):
    batch = trX[start:end]
    n_w = sess.run(update_w, feed_dict={
                   X: batch, rbm_w: o_w, rbm_vb: o_vb, rbm_hb: o_hb})
    n_vb = sess.run(update_vb, feed_dict={
                    X: batch, rbm_w: o_w, rbm_vb: o_vb, rbm_hb: o_hb})
    n_hb = sess.run(update_hb, feed_dict={
                    X: batch, rbm_w: o_w, rbm_vb: o_vb, rbm_hb: o_hb})
    o_w = n_w
    o_vb = n_vb
    o_hb = n_hb
    if start % 10000 == 0:
        print sess.run(
            err_sum, feed_dict={X: trX, rbm_w: n_w, rbm_vb: n_vb, rbm_hb: n_hb})
        image = Image.fromarray(
            tile_raster_images(
                X=n_w.T,
                img_shape=(28, 28),
                tile_shape=(25, 20),
                tile_spacing=(1, 1)
            )
        )
        image.save("rbm_%d.png" % (start / 10000))

Thank you for share. This is a great example for doing customized update on tensorflow.

Wow that is great example THANK YOU. Do you know how to remove the redundancy in graph?

Im trying to implement pretraining of autoencoders with rbm:
https://github.com/Cospel/rbm-ae-tf

Share my code here, everything was done in one session.run()
https://github.com/hanhongsun/tensorflow_script/blob/master/rbm.py

Line 29 is wrong: h1 = tf.nn.sigmoid(tf.matmul(v1, rbm_w) + rbm_hb) should be h1=sample_prob(tf.nn.sigmoid(tf.matmul(v1, rbm_w) + rbm_hb))

你的sample_prob没有按照概率 采样吧。
your function named "sample_prob", is not a sample by fixed-probability.