### from https://github.com/echen/restricted-boltzmann-machines/blob/master/rbm.py
https://www.jianshu.com/p/2e7ffe06fcdd?tdsourcetag=s_pcqq_aiomsg
https://github.com/echen/restricted-boltzmann-machines/blob/master/rbm.py
from __future__ import print_function
import numpy as np
class RBM:
def __init__(self, num_visible, num_hidden):
self.num_hidden = num_hidden
self.num_visible = num_visible
self.debug_print = True
# Initialize a weight matrix, of dimensions (num_visible x num_hidden), using
# a uniform distribution between -sqrt(6. / (num_hidden + num_visible))
# and sqrt(6. / (num_hidden + num_visible)). One could vary the
# standard deviation by multiplying the interval with appropriate value.
# Here we initialize the weights with mean 0 and standard deviation 0.1.
# Reference: Understanding the difficulty of training deep feedforward
# neural networks by Xavier Glorot and Yoshua Bengio
np_rng = np.random.RandomState(1234)
self.weights = np.asarray(np_rng.uniform(
low=-0.1 * np.sqrt(6. / (num_hidden + num_visible)),
high=0.1 * np.sqrt(6. / (num_hidden + num_visible)),
size=(num_visible, num_hidden)))
# Insert weights for the bias units into the first row and first column.
self.weights = np.insert(self.weights, 0, 0, axis=0)
self.weights = np.insert(self.weights, 0, 0, axis=1)
def train(self, data, max_epochs=1000, learning_rate=0.1):
"""
Train the machine.
Parameters
----------
data: A matrix where each row is a training example consisting of the states of visible units.
"""
num_examples = data.shape[0]
# Insert bias units of 1 into the first column.
data = np.insert(data, 0, 1, axis=1)
for epoch in range(max_epochs):
# Clamp to the data and sample from the hidden units.
# (This is the "positive CD phase", aka the reality phase.)
pos_hidden_activations = np.dot(data, self.weights)
pos_hidden_probs = self._logistic(pos_hidden_activations)
pos_hidden_probs[:, 0] = 1 # Fix the bias unit.
pos_hidden_states = pos_hidden_probs > np.random.rand(num_examples, self.num_hidden + 1)
# Note that we‘re using the activation *probabilities* of the hidden states, not the hidden states
# themselves, when computing associations. We could also use the states; see section 3 of Hinton‘s
# "A Practical Guide to Training Restricted Boltzmann Machines" for more.
pos_associations = np.dot(data.T, pos_hidden_probs)
# Reconstruct the visible units and sample again from the hidden units.
# (This is the "negative CD phase", aka the daydreaming phase.)
neg_visible_activations = np.dot(pos_hidden_states, self.weights.T)
neg_visible_probs = self._logistic(neg_visible_activations)
neg_visible_probs[:, 0] = 1 # Fix the bias unit.
neg_hidden_activations = np.dot(neg_visible_probs, self.weights)
neg_hidden_probs = self._logistic(neg_hidden_activations)
# Note, again, that we‘re using the activation *probabilities* when computing associations, not the states
# themselves.
neg_associations = np.dot(neg_visible_probs.T, neg_hidden_probs)
# Update weights.
self.weights += learning_rate * ((pos_associations - neg_associations) / num_examples)
error = np.sum((data - neg_visible_probs) ** 2)
if self.debug_print:
print("Epoch %s: error is %s" % (epoch, error))
def run_visible(self, data):
"""
Assuming the RBM has been trained (so that weights for the network have been learned),
run the network on a set of visible units, to get a sample of the hidden units.
Parameters
----------
data: A matrix where each row consists of the states of the visible units.
Returns
-------
hidden_states: A matrix where each row consists of the hidden units activated from the visible
units in the data matrix passed in.
"""
num_examples = data.shape[0]
# Create a matrix, where each row is to be the hidden units (plus a bias unit)
# sampled from a training example.
hidden_states = np.ones((num_examples, self.num_hidden + 1))
# Insert bias units of 1 into the first column of data.
data = np.insert(data, 0, 1, axis=1)
# Calculate the activations of the hidden units.
hidden_activations = np.dot(data, self.weights)
# Calculate the probabilities of turning the hidden units on.
hidden_probs = self._logistic(hidden_activations)
# Turn the hidden units on with their specified probabilities.
hidden_states[:, :] = hidden_probs > np.random.rand(num_examples, self.num_hidden + 1)
# Always fix the bias unit to 1.
# hidden_states[:,0] = 1
# Ignore the bias units.
hidden_states = hidden_states[:, 1:]
return hidden_states
# TODO: Remove the code duplication between this method and `run_visible`?
def run_hidden(self, data):
"""
Assuming the RBM has been trained (so that weights for the network have been learned),
run the network on a set of hidden units, to get a sample of the visible units.
Parameters
----------
data: A matrix where each row consists of the states of the hidden units.
Returns
-------
visible_states: A matrix where each row consists of the visible units activated from the hidden
units in the data matrix passed in.
"""
num_examples = data.shape[0]
# Create a matrix, where each row is to be the visible units (plus a bias unit)
# sampled from a training example.
visible_states = np.ones((num_examples, self.num_visible + 1))
# Insert bias units of 1 into the first column of data.
data = np.insert(data, 0, 1, axis=1)
# Calculate the activations of the visible units.
visible_activations = np.dot(data, self.weights.T)
# Calculate the probabilities of turning the visible units on.
visible_probs = self._logistic(visible_activations)
# Turn the visible units on with their specified probabilities.
visible_states[:, :] = visible_probs > np.random.rand(num_examples, self.num_visible + 1)
# Always fix the bias unit to 1.
# visible_states[:,0] = 1
# Ignore the bias units.
visible_states = visible_states[:, 1:]
return visible_states
def daydream(