def __init__(self, rate, sigmoid, hidden, examples, variables, layers, rule, dropout):
"""
Feed-Forward Hebbian network for learning boolean functions
with threshold gates.
Keyword arguments:
rate -- learning rate (float)
sigmoid -- sigmoid function for weights if rule is basic hebbian (int)
hidden -- number of hidden units, 0 removes hidden layer (int)
examples -- number of random boolean examples to present.
layers -- number of hidden layers 1 to N (int)
rule -- learning rule, "hebbian" or "oja" (str)
Initializes layers, weights, connections, variables for Network class
"""
self.rate = rate
self.sigmoid = sigmoid
self.inputs = variables
self.vis_layer = []
self.hidden_layers = []
self.hidden = hidden
self.variables = variables
self.data = BOOLEAN(examples, self.variables)
self.layers = layers-1
self.rule = rule
self.dropout = dropout
self.length = int(math.pow(2, self.variables))
for _ in xrange(self.hidden):
self.vis_layer.append(Neuron(self.rate, self.sigmoid, self.inputs+1, dropout))
for layer in xrange(self.layers):
self.hidden_layers.append([])
for _ in xrange(self.hidden):
self.hidden_layers[layer].append(Neuron(self.rate, self.sigmoid, self.hidden+1, dropout))
if self.hidden > 0:
self.output_neuron = Neuron(self.rate, self.sigmoid, self.hidden+1, dropout)
else:
self.output_neuron = Neuron(self.rate, self.sigmoid, self.inputs+1, dropout)
class Network(object):
"""Network class"""
def __init__(self, rate, sigmoid, hidden, examples, variables, layers, rule, dropout):
"""
Feed-Forward Hebbian network for learning boolean functions
with threshold gates.
Keyword arguments:
rate -- learning rate (float)
sigmoid -- sigmoid function for weights if rule is basic hebbian (int)
hidden -- number of hidden units, 0 removes hidden layer (int)
examples -- number of random boolean examples to present.
layers -- number of hidden layers 1 to N (int)
rule -- learning rule, "hebbian" or "oja" (str)
Initializes layers, weights, connections, variables for Network class
"""
self.rate = rate
self.sigmoid = sigmoid
self.inputs = variables
self.vis_layer = []
self.hidden_layers = []
self.hidden = hidden
self.variables = variables
self.data = BOOLEAN(examples, self.variables)
self.layers = layers-1
self.rule = rule
self.dropout = dropout
self.length = int(math.pow(2, self.variables))
for _ in xrange(self.hidden):
self.vis_layer.append(Neuron(self.rate, self.sigmoid, self.inputs+1, dropout))
for layer in xrange(self.layers):
self.hidden_layers.append([])
for _ in xrange(self.hidden):
self.hidden_layers[layer].append(Neuron(self.rate, self.sigmoid, self.hidden+1, dropout))
if self.hidden > 0:
self.output_neuron = Neuron(self.rate, self.sigmoid, self.hidden+1, dropout)
else:
self.output_neuron = Neuron(self.rate, self.sigmoid, self.inputs+1, dropout)
@staticmethod
def threshold(activation):
"""
Thresholds output neuron activation to 1 or 0.
Keyword arguments:
activation -- Output neuron activation (float)
returns 1 or 0
"""
if activation >= 0.0:
return 1
else:
return 0
def load(self, filename):
"""
Loads a model file.
Keyword arguments:
filename -- name of model file ex: hebb1.txt (str)
initializes network to weights in file
"""
hebbian_weights = open(filename, "r").read().split('\n')
for i in xrange(self.hidden):
weights = hebbian_weights[i].split('\t')
self.vis_layer[i].set_weights(weights)
for i in xrange(self.layers):
for j in xrange(self.hidden):
weights = hebbian_weights[((i+1)*self.hidden)+j].split('\t')
self.hidden_layers[i][j].set_weights(weights)
weights = hebbian_weights[-2].split('\t')
self.output_neuron.set_weights(weights)
def save(self, filename):
"""
Saves a model into a file.
Keyword arguments:
filename -- name of modle file ex: hebb1.txt (str)
saves current model weights to file
"""
hebbian_weights = open(filename, "w")
for i in xrange(self.hidden):
hebbian_weights.write("\t".join(self.vis_layer[i].get_weights()) + '\n')
for i in xrange(self.layers):
for j in xrange(self.hidden):
hebbian_weights.write("\t".join(self.hidden_layers[i][j].get_weights()) + '\n')
hebbian_weights.write("\t".join(self.output_neuron.get_weights()) + '\n')
hebbian_weights.close()
def compute(self, example):
"""
Computes output of model given an example.
Keyword arguments:
example -- list of 1 and -1 (list)
returns threshold value of output neuron.
"""
activations = []
if self.hidden > 0:
for i in xrange(self.hidden):
output = self.vis_layer[i].compute(example)
activations.append(output)
activations.append(1.0)
for layer in xrange(self.layers):
hidden_activations = []
for i in xrange(self.hidden):
hidden_activations.append(self.hidden_layers[layer][i].compute(activations))
hidden_activations.append(1.0)
activations = hidden_activations
output = self.output_neuron.compute(activations)
else:
output = self.output_neuron.compute(example)
return Network.threshold(output)
def index(self, example):
"""
Finds expected output of example for target function
Args:
example (list): list of 1 and -1
returns index in current truthtable for given example.
"""
for i in xrange(self.length):
binary = bin(i).lstrip('0b')
for i in xrange(self.variables-len(binary)):
binary = '0'+binary
for j in xrange(self.variables):
index = True
if example[j] == -1:
example[j] = 0
if int(binary[j]) != example[j]:
index = False
if index:
return i
return False
def train(self, table):
"""
Trains model given rule for given examples on a single truth table
Args:
table (list): truth table of 1, -1
returns true if learned and false if not
"""
training = self.data.load_training()
for example in training:
activations = []
if self.hidden > 0:
for i in xrange(self.hidden):
output = self.vis_layer[i].train(example, self.rule)
activations.append(output)
activations.append(1.0)
for layer in xrange(self.layers):
hidden_activations = []
for i in xrange(self.hidden):
hidden_activations.append(self.hidden_layers[layer][i].train(activations, self.rule))
hidden_activations.append(1.0)
activations = hidden_activations
self.output_neuron.clamp(activations, table[self.index(example)], self.rule)
else:
self.output_neuron.clamp(example, table[self.index(example)], self.rule)
learned_table = self.truthtable()
learned = True
for i in xrange(self.length):
if learned_table[i] != table[i]:
learned = False
if learned == True:
break
return learned
def truthtable(self):
"""Builds the truth table for the current model and returns it"""
table = []
for i in xrange(self.length):
inputs = []
binary = bin(i).lstrip('0b')
for i in xrange(len(binary)):
inputs.append(int(binary[i]))
inputs.append(1)
table.append(self.compute(inputs))
return table
def test(self, load_file):
"""Loads a model from file and prints the table that model produces"""
self.load(load_file)
table = self.truthtable()
print " ..... Test: Model Computes:", table
def increase_learning(self, factor):
"""Increase the learning rate to by 'factor'"""
pass
def noise(self, stddev):
"""Add gaussian noise to all weights with stddev"""
#add noise to weights
pass
