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rbm.py
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rbm.py
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import tensorflow as tf
import numpy as np
class RBM(object):
def __init__(self, n_input, n_hidden, layer_names, alpha=1.0, transfer_function=tf.nn.sigmoid):
self.n_input = n_input
self.n_hidden = n_hidden
self.transfer = transfer_function
self.layer_names = layer_names
network_weights = self._initialize_weights()
self.weights = network_weights
# placeholders
self.x = tf.placeholder(tf.float32, [None, self.n_input])
self.rbm_w = tf.placeholder(tf.float32,[self.n_input, self.n_hidden])
self.rbm_vb = tf.placeholder(tf.float32,[self.n_input])
self.rbm_hb = tf.placeholder(tf.float32,[self.n_hidden])
# variables
# The weights are initialized to small random values chosen from a zero-mean Gaussian with a
# standard deviation of about 0.01. It is usually helpful to initialize the bias of visible unit
# i to log[pi/(1?pi)] where pi is the proportion of training vectors in which unit i is on.
# Otherwise, initial hidden biases of 0 are usually fine. It is also possible to start the hidden
# units with quite large negative biases of about ?4 as a crude way of encouraging sparsity.
self.n_w = np.zeros([self.n_input, self.n_hidden], np.float32)
self.n_vb = np.zeros([self.n_input], np.float32)
self.n_hb = np.zeros([self.n_hidden], np.float32)
self.o_w = np.random.normal(0.0, 0.01, [self.n_input, self.n_hidden])
self.o_vb = np.zeros([self.n_input], np.float32)
self.o_hb = np.zeros([self.n_hidden], np.float32)
# model/training/performing one Gibbs sample.
# RBM is generative model, who tries to encode in weights the understanding of data.
# RBMs typically learn better models if more steps of alternating Gibbs sampling are used.
# 1. set visible state to training sample(x) and compute hidden state(h0) of data
# then we have binary units of hidden state computed. It is very important to make these
# hidden states binary, rather than using the probabilities themselves. (see Hinton paper)
self.h0prob = transfer_function(tf.matmul(self.x, self.rbm_w) + self.rbm_hb)
self.h0 = self.sample_prob(self.h0prob)
# 2. compute new visible state of reconstruction based on computed hidden state reconstruction.
# However, it is common to use the probability, instead of sampling a binary value.
# So this can be binary or probability(so i choose to not use sampled probability)
self.v1 = transfer_function(tf.matmul(self.h0prob, tf.transpose(self.rbm_w)) + self.rbm_vb)
# 3. compute new hidden state of reconstruction based on computed visible reconstruction
# When hidden units are being driven by reconstructions, always use probabilities without sampling.
self.h1 = tf.nn.sigmoid(tf.matmul(self.v1, self.rbm_w) + self.rbm_hb)
# compute gradients
self.w_positive_grad = tf.matmul(tf.transpose(self.x), self.h0)
self.w_negative_grad = tf.matmul(tf.transpose(self.v1), self.h1)
# stochastic steepest ascent because we need to maximalize log likelihood of p(visible)
# dlog(p)/dlog(w) = (visible * hidden)_data - (visible * hidden)_reconstruction
self.update_w = self.rbm_w + alpha * (self.w_positive_grad - self.w_negative_grad) / tf.to_float(
tf.shape(self.x)[0])
self.update_vb = self.rbm_vb + alpha * tf.reduce_mean(self.x - self.v1, 0)
self.update_hb = self.rbm_hb + alpha * tf.reduce_mean(self.h0prob - self.h1, 0)
# sampling functions
self.h_sample = transfer_function(tf.matmul(self.x, self.rbm_w) + self.rbm_hb)
self.v_sample = transfer_function(tf.matmul(self.h_sample, tf.transpose(self.rbm_w)) + self.rbm_vb)
# cost
self.err_sum = tf.reduce_mean(tf.square(self.x - self.v_sample))
init = tf.global_variables_initializer()
self.sess = tf.Session()
self.sess.run(init)
def compute_cost(self, batch):
# Use it but don?t trust it. If you really want to know what is going on use multiple histograms.
# Although it is convenient, the reconstruction error is actually a very poor measure of the progress.
# As the weights increase the mixing rate falls, so decreases in reconstruction error do not
# necessarily mean that the model is improving. Small increases do not necessarily mean the model
# is getting worse.
return self.sess.run(self.err_sum, feed_dict={self.x: batch, self.rbm_w: self.o_w,
self.rbm_vb: self.o_vb, self.rbm_hb: self.o_hb})
def sample_prob(self, probs):
return tf.nn.relu(tf.sign(probs - tf.random_uniform(tf.shape(probs))))
def _initialize_weights(self):
# These weights are only for storing and loading model for tensorflow Saver.
all_weights = dict()
all_weights['w'] = tf.Variable(tf.random_normal([self.n_input, self.n_hidden], stddev=0.01, dtype=tf.float32),
name=self.layer_names[0])
all_weights['vb'] = tf.Variable(tf.zeros([self.n_input], dtype=tf.float32), name=self.layer_names[1])
all_weights['hb'] = tf.Variable(tf.random_uniform([self.n_hidden], dtype=tf.float32), name=self.layer_names[2])
return all_weights
def transform(self, batch_x):
return self.sess.run(self.h_sample, {self.x: batch_x, self.rbm_w: self.o_w,
self.rbm_vb: self.o_vb, self.rbm_hb: self.o_hb})
def restore_weights(self, path):
saver = tf.train.Saver({self.layer_names[0]: self.weights['w'],
self.layer_names[1]: self.weights['vb'],
self.layer_names[2]: self.weights['hb']})
saver.restore(self.sess, path)
self.o_w = self.weights['w'].eval(self.sess)
self.o_vb = self.weights['vb'].eval(self.sess)
self.o_hb = self.weights['hb'].eval(self.sess)
def save_weights(self, path):
self.sess.run(self.weights['w'].assign(self.o_w))
self.sess.run(self.weights['vb'].assign(self.o_vb))
self.sess.run(self.weights['hb'].assign(self.o_hb))
saver = tf.train.Saver({self.layer_names[0]: self.weights['w'],
self.layer_names[1]: self.weights['vb'],
self.layer_names[2]: self.weights['hb']})
save_path = saver.save(self.sess, path)
def return_weights(self):
return self.weights
def return_hidden_weight_as_np(self):
return self.n_w
def partial_fit(self, batch_x):
# 1. always use small ?mini-batches? of 10 to 100 cases.
# For big data with lot of classes use mini-batches of size about 10.
self.n_w, self.n_vb, self.n_hb = self.sess.run([self.update_w, self.update_vb, self.update_hb],
feed_dict={self.x: batch_x, self.rbm_w: self.o_w,
self.rbm_vb: self.o_vb, self.rbm_hb: self.o_hb})
self.o_w = self.n_w
self.o_vb = self.n_vb
self.o_hb = self.n_hb
return self.sess.run(self.err_sum, feed_dict={self.x: batch_x, self.rbm_w: self.n_w, self.rbm_vb: self.n_vb,
self.rbm_hb: self.n_hb})