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Testing.py
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Testing.py
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# -*- coding: utf-8 -*-
"""
Created on Thu Jun 4 15:58:38 2020
@author: Michael
"""
from math import exp
from random import seed
from random import random
# Initialize a network
def initialize_network(n_inputs, n_hidden, n_outputs):
network = list()
hidden_layer = [{'weights':[random() for i in range(n_inputs + 1)]} for i in range(n_hidden)]
network.append(hidden_layer)
output_layer = [{'weights':[random() for i in range(n_hidden + 1)]} for i in range(n_outputs)]
network.append(output_layer)
return network
# Calculate neuron activation for an input
def activate(weights, inputs):
activation = weights[-1]
for i in range(len(weights)-1):
activation += weights[i] * inputs[i]
return activation
# Transfer neuron activation
def transfer(activation):
return 1.0 / (1.0 + exp(-activation))
# Forward propagate input to a network output
def forward_propagate(network, row):
inputs = row
for layer in network:
new_inputs = []
for neuron in layer:
activation = activate(neuron['weights'], inputs)
neuron['output'] = transfer(activation)
new_inputs.append(neuron['output'])
inputs = new_inputs
return inputs
# Calculate the derivative of an neuron output
def transfer_derivative(output):
return output * (1.0 - output)
# Backpropagate error and store in neurons
def backward_propagate_error(network, expected):
for i in reversed(range(len(network))):
layer = network[i]
errors = list()
if i != len(network)-1:
for j in range(len(layer)):
error = 0.0
for neuron in network[i + 1]:
error += (neuron['weights'][j] * neuron['delta'])
errors.append(error)
else:
for j in range(len(layer)):
neuron = layer[j]
errors.append(expected[j] - neuron['output'])
for j in range(len(layer)):
neuron = layer[j]
neuron['delta'] = errors[j] * transfer_derivative(neuron['output'])
# Update network weights with error
def update_weights(network, row, l_rate):
for i in range(len(network)):
inputs = row[:-1]
if i != 0:
inputs = [neuron['output'] for neuron in network[i - 1]]
for neuron in network[i]:
for j in range(len(inputs)):
neuron['weights'][j] += l_rate * neuron['delta'] * inputs[j]
neuron['weights'][-1] += l_rate * neuron['delta']
# Train a network for a fixed number of epochs
def train_network(network, train, l_rate, n_epoch, n_outputs):
for epoch in range(n_epoch):
sum_error = 0
for row in train:
outputs = forward_propagate(network, row)
# for i in range(len(network)):
# for j in range(len(network[i])):
# print(network[i][j]['delta'])
#print(outputs)
expected = [0 for i in range(n_outputs)]
expected[row[-1]] = 1
sum_error += sum([(expected[i]-outputs[i])**2 for i in range(len(expected))])
#print(outputs)
#print(expected)
#print(sum_error)
backward_propagate_error(network, expected)
# for i in range(len(network)):
# for j in range(len(network[i])):
# print(network[i][j]['delta'])
# for i in range(len(network)):
# for j in range(len(network[i])):
# print(network[i][j]['weights'])
update_weights(network, row, l_rate)
for i in range(len(network)):
for j in range(len(network[i])):
print(network[i][j]['delta'])
# for i in range(len(network)):
# for j in range(len(network[i])):
# print(network[i][j]['weights'])
print('>epoch=%d, lrate=%.3f, error=%.3f' % (epoch, l_rate, sum_error))
# Test training backprop algorithm
seed(1)
dataset = [[2.7810836,2.550537003,0],
[1.465489372,2.362125076,0],
[3.396561688,4.400293529,0],
[1.38807019,1.850220317,0],
[3.06407232,3.005305973,0],
[7.627531214,2.759262235,1],
[5.332441248,2.088626775,1],
[6.922596716,1.77106367,1],
[8.675418651,-0.242068655,1],
[7.673756466,3.508563011,1]]
dataset = [[2.7810836,2.550537003,0],
[7.673756466,3.508563011,1]]
n_inputs = len(dataset[0]) - 1
n_outputs = len(set([row[-1] for row in dataset]))
network = initialize_network(n_inputs, 2, n_outputs)
train_network(network, dataset, 0.5, 1, n_outputs)
for layer in network:
print(layer)