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https://github.com/mattm/simple-neural-network
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import random
import math
#
# Shorthand:
# "pd_" as a variable prefix means "partial derivative"
# "d_" as a variable prefix means "derivative"
# "_wrt_" is shorthand for "with respect to"
# "w_ho" and "w_ih" are the index of weights from hidden to output layer neurons and input to hidden layer neurons respectively
#
# Comment references:
#
# [1] Wikipedia article on Backpropagation
# http://en.wikipedia.org/wiki/Backpropagation#Finding_the_derivative_of_the_error
# [2] Neural Networks for Machine Learning course on Coursera by Geoffrey Hinton
# https://class.coursera.org/neuralnets-2012-001/lecture/39
# [3] The Back Propagation Algorithm
# https://www4.rgu.ac.uk/files/chapter3%20-%20bp.pdf
class NeuralNetwork:
LEARNING_RATE = 0.5
def __init__(self, num_inputs, num_hidden, num_outputs, hidden_layer_weights = None, hidden_layer_bias = None, output_layer_weights = None, output_layer_bias = None):
self.num_inputs = num_inputs
self.hidden_layer = NeuronLayer(num_hidden, hidden_layer_bias)
self.output_layer = NeuronLayer(num_outputs, output_layer_bias)
self.init_weights_from_inputs_to_hidden_layer_neurons(hidden_layer_weights)
self.init_weights_from_hidden_layer_neurons_to_output_layer_neurons(output_layer_weights)
def init_weights_from_inputs_to_hidden_layer_neurons(self, hidden_layer_weights):
weight_num = 0
for h in range(len(self.hidden_layer.neurons)):
for i in range(self.num_inputs):
if not hidden_layer_weights:
self.hidden_layer.neurons[h].weights.append(random.random())
else:
self.hidden_layer.neurons[h].weights.append(hidden_layer_weights[weight_num])
weight_num += 1
def init_weights_from_hidden_layer_neurons_to_output_layer_neurons(self, output_layer_weights):
weight_num = 0
for o in range(len(self.output_layer.neurons)):
for h in range(len(self.hidden_layer.neurons)):
if not output_layer_weights:
self.output_layer.neurons[o].weights.append(random.random())
else:
self.output_layer.neurons[o].weights.append(output_layer_weights[weight_num])
weight_num += 1
def inspect(self):
print('------')
print('* Inputs: {}'.format(self.num_inputs))
print('------')
print('Hidden Layer')
self.hidden_layer.inspect()
print('------')
print('* Output Layer')
self.output_layer.inspect()
print('------')
def feed_forward(self, inputs):
hidden_layer_outputs = self.hidden_layer.feed_forward(inputs)
return self.output_layer.feed_forward(hidden_layer_outputs)
# Uses online learning, ie updating the weights after each training case
def train(self, training_inputs, training_outputs):
self.feed_forward(training_inputs)
# 1. Output neuron deltas
pd_errors_wrt_output_neuron_total_net_input = [0] * len(self.output_layer.neurons)
for o in range(len(self.output_layer.neurons)):
# ∂E/∂zⱼ
pd_errors_wrt_output_neuron_total_net_input[o] = self.output_layer.neurons[o].calculate_pd_error_wrt_total_net_input(training_outputs[o])
# 2. Hidden neuron deltas
pd_errors_wrt_hidden_neuron_total_net_input = [0] * len(self.hidden_layer.neurons)
for h in range(len(self.hidden_layer.neurons)):
# We need to calculate the derivative of the error with respect to the output of each hidden layer neuron
# dE/dyⱼ = Σ ∂E/∂zⱼ * ∂z/∂yⱼ = Σ ∂E/∂zⱼ * wᵢⱼ
d_error_wrt_hidden_neuron_output = 0
for o in range(len(self.output_layer.neurons)):
d_error_wrt_hidden_neuron_output += pd_errors_wrt_output_neuron_total_net_input[o] * self.output_layer.neurons[o].weights[h]
# ∂E/∂zⱼ = dE/dyⱼ * ∂zⱼ/∂
pd_errors_wrt_hidden_neuron_total_net_input[h] = d_error_wrt_hidden_neuron_output * self.hidden_layer.neurons[h].calculate_pd_total_net_input_wrt_input()
# 3. Update output neuron weights
for o in range(len(self.output_layer.neurons)):
for w_ho in range(len(self.output_layer.neurons[o].weights)):
# ∂Eⱼ/∂wᵢⱼ = ∂E/∂zⱼ * ∂zⱼ/∂wᵢⱼ
pd_error_wrt_weight = pd_errors_wrt_output_neuron_total_net_input[o] * self.output_layer.neurons[o].calculate_pd_total_net_input_wrt_weight(w_ho)
# Δw = α * ∂Eⱼ/∂wᵢ
self.output_layer.neurons[o].weights[w_ho] -= self.LEARNING_RATE * pd_error_wrt_weight
# 4. Update hidden neuron weights
for h in range(len(self.hidden_layer.neurons)):
for w_ih in range(len(self.hidden_layer.neurons[h].weights)):
# ∂Eⱼ/∂wᵢ = ∂E/∂zⱼ * ∂zⱼ/∂wᵢ
pd_error_wrt_weight = pd_errors_wrt_hidden_neuron_total_net_input[h] * self.hidden_layer.neurons[h].calculate_pd_total_net_input_wrt_weight(w_ih)
# Δw = α * ∂Eⱼ/∂wᵢ
self.hidden_layer.neurons[h].weights[w_ih] -= self.LEARNING_RATE * pd_error_wrt_weight
def calculate_total_error(self, training_sets):
total_error = 0
for t in range(len(training_sets)):
training_inputs, training_outputs = training_sets[t]
self.feed_forward(training_inputs)
for o in range(len(training_outputs)):
total_error += self.output_layer.neurons[o].calculate_error(training_outputs[o])
return total_error
class NeuronLayer:
def __init__(self, num_neurons, bias):
# Every neuron in a layer shares the same bias
self.bias = bias if bias else random.random()
self.neurons = []
for i in range(num_neurons):
self.neurons.append(Neuron(self.bias))
def inspect(self):
print('Neurons:', len(self.neurons))
for n in range(len(self.neurons)):
print(' Neuron', n)
for w in range(len(self.neurons[n].weights)):
print(' Weight:', self.neurons[n].weights[w])
print(' Bias:', self.bias)
def feed_forward(self, inputs):
outputs = []
for neuron in self.neurons:
outputs.append(neuron.calculate_output(inputs))
return outputs
def get_outputs(self):
outputs = []
for neuron in self.neurons:
outputs.append(neuron.output)
return outputs
class Neuron:
def __init__(self, bias):
self.bias = bias
self.weights = []
def calculate_output(self, inputs):
self.inputs = inputs
self.output = self.squash(self.calculate_total_net_input())
return self.output
def calculate_total_net_input(self):
total = 0
for i in range(len(self.inputs)):
total += self.inputs[i] * self.weights[i]
return total + self.bias
# Apply the logistic function to squash the output of the neuron
# The result is sometimes referred to as 'net' [2] or 'net' [1]
def squash(self, total_net_input):
return 1 / (1 + math.exp(-total_net_input))
# Determine how much the neuron's total input has to change to move closer to the expected output
#
# Now that we have the partial derivative of the error with respect to the output (∂E/∂yⱼ) and
# the derivative of the output with respect to the total net input (dyâ±¼/dzâ±¼) we can calculate
# the partial derivative of the error with respect to the total net input.
# This value is also known as the delta (δ) [1]
# δ = ∂E/∂zⱼ = ∂E/∂yⱼ * dyⱼ/dzⱼ
#
def calculate_pd_error_wrt_total_net_input(self, target_output):
return self.calculate_pd_error_wrt_output(target_output) * self.calculate_pd_total_net_input_wrt_input();
# The error for each neuron is calculated by the Mean Square Error method:
def calculate_error(self, target_output):
return 0.5 * (target_output - self.output) ** 2
# The partial derivate of the error with respect to actual output then is calculated by:
# = 2 * 0.5 * (target output - actual output) ^ (2 - 1) * -1
# = -(target output - actual output)
#
# The Wikipedia article on backpropagation [1] simplifies to the following, but most other learning material does not [2]
# = actual output - target output
#
# Alternative, you can use (target - output), but then need to add it during backpropagation [3]
#
# Note that the actual output of the output neuron is often written as yâ±¼ and target output as tâ±¼ so:
# = ∂E/∂yⱼ = -(tⱼ - yⱼ)
def calculate_pd_error_wrt_output(self, target_output):
return -(target_output - self.output)
# The total net input into the neuron is squashed using logistic function to calculate the neuron's output:
# yⱼ = φ = 1 / (1 + e^(-zⱼ))
# Note that where â±¼ represents the output of the neurons in whatever layer we're looking at and áµ¢ represents the layer below it
#
# The derivative (not partial derivative since there is only one variable) of the output then is:
# dyâ±¼/dzâ±¼ = yâ±¼ * (1 - yâ±¼)
def calculate_pd_total_net_input_wrt_input(self):
return self.output * (1 - self.output)
# The total net input is the weighted sum of all the inputs to the neuron and their respective weights:
# = zâ±¼ = netâ±¼ = xâ‚wâ‚ + xâ‚‚wâ‚‚ ...
#
# The partial derivative of the total net input with respective to a given weight (with everything else held constant) then is:
# = ∂zâ±¼/∂wáµ¢ = some constant + 1 * xáµ¢wâ‚^(1-0) + some constant ... = xáµ¢
def calculate_pd_total_net_input_wrt_weight(self, index):
return self.inputs[index]
###
# Blog post example:
nn = NeuralNetwork(2, 2, 2, hidden_layer_weights=[0.15, 0.2, 0.25, 0.3], hidden_layer_bias=0.35, output_layer_weights=[0.4, 0.45, 0.5, 0.55], output_layer_bias=0.6)
for i in range(10000):
nn.train([0.05, 0.1], [0.01, 0.99])
print(i, round(nn.calculate_total_error([[[0.05, 0.1], [0.01, 0.99]]]), 9))
# XOR example:
# training_sets = [
# [[0, 0], [0]],
# [[0, 1], [1]],
# [[1, 0], [1]],
# [[1, 1], [0]]
# ]
# nn = NeuralNetwork(len(training_sets[0][0]), 5, len(training_sets[0][1]))
# for i in range(10000):
# training_inputs, training_outputs = random.choice(training_sets)
# nn.train(training_inputs, training_outputs)
# print(i, nn.calculate_total_error(training_sets))