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Classification.py
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Classification.py
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import numpy as np
import matplotlib.pyplot as plt
import csv
learning_coeff = 0.45
epoch_error = 0.0
momentum_coeff = 0.9
class NeuralNetwork(object):
def __init__(self, number_of_input, number_of_hidden, number_of_output, input_data_file, expected_data_file,
is_bias=0):
np.random.seed(30)
self.hidden_layer_weights = []
self.output_layer_weights = []
self.delta_weights_output_layer = []
self.delta_weights_hidden_layer = []
self.initialze_weights(is_bias, number_of_hidden, number_of_input, number_of_output)
self.is_bias = is_bias
self.epoch_error = 0.0
self.error_for_epoch = []
self.epoch_for_error = []
self.input_data = self.file_input(input_data_file)
self.expected_data = self.file_input(expected_data_file)
self.resolve_bias()
def sigmoid_function(self, x):
result = 1 / (1 + np.exp(-x))
return result
def sigmoid_derivative(self, x):
return x * (1 - x)
def feed_forward(self, input_data, is_training=True):
if not is_training and self.is_bias:
input_data = np.insert(input_data, 0, 1)
hidden_layer_output = self.sigmoid_function(np.dot(input_data, self.hidden_layer_weights))
if self.is_bias == 1:
hidden_layer_output = np.insert(hidden_layer_output, 0, 1)
output_layer_output = self.sigmoid_function(np.dot(hidden_layer_output, self.output_layer_weights))
return hidden_layer_output, output_layer_output
def backward_propagation(self, hidden_layer_result, output_layer_result, input_data, output_data):
avr_err = 0.0
output_difference = output_layer_result - output_data
for i in output_difference:
avr_err += i ** 2
avr_err /= 2
self.epoch_error += avr_err
delta_coefficient_outp = output_difference * self.sigmoid_derivative(output_layer_result)
hidden_layer_error = delta_coefficient_outp.dot(self.output_layer_weights.T)
if self.is_bias == 1:
hidden_layer_error = hidden_layer_error[1:]
delta_coefficient_hidden = hidden_layer_error * self.sigmoid_derivative(hidden_layer_result[1:])
else:
delta_coefficient_hidden = hidden_layer_error * self.sigmoid_derivative(hidden_layer_result)
output_adj = []
hidden_adj = []
for i in delta_coefficient_outp:
output_adj.append(hidden_layer_result * i)
for i in delta_coefficient_hidden:
hidden_adj.append(input_data * i)
hidden_adj = np.asarray(hidden_adj)
output_adj = np.asarray(output_adj)
actual_hidden_adj = (learning_coeff * hidden_adj.T + momentum_coeff * self.delta_weights_hidden_layer)
actual_output_adj = (learning_coeff * output_adj.T + momentum_coeff * self.delta_weights_output_layer)
self.hidden_layer_weights -= actual_hidden_adj
self.output_layer_weights -= actual_output_adj
self.delta_weights_hidden_layer = actual_hidden_adj
self.delta_weights_output_layer = actual_output_adj
def train(self, epoch_number):
combined_data = list(zip(self.input_data, self.expected_data))
for epoch in range(epoch_number):
self.epoch_error = 0.0
np.random.shuffle(combined_data)
for inp, outp in combined_data:
hidden_layer_output, output_layer_output = self.feed_forward(inp)
self.backward_propagation(hidden_layer_output, output_layer_output, inp, outp)
self.epoch_error /= 4
self.epoch_for_error.append(epoch)
self.error_for_epoch.append(self.epoch_error)
for i in self.input_data:
print(self.feed_forward(i, True)[1])
self.graph()
def initialze_weights(self, is_bias, number_of_hidden, number_of_input, number_of_output):
self.hidden_layer_weights = 2 * np.random.random((number_of_input + is_bias, number_of_hidden)) - 1
self.delta_weights_hidden_layer = np.zeros((number_of_input, number_of_hidden))
self.output_layer_weights = 2 * np.random.random((number_of_hidden + is_bias, number_of_output)) - 1
self.delta_weights_output_layer = np.zeros((number_of_hidden, number_of_output))
def resolve_bias(self):
if self.is_bias == 1:
self.input_data = np.insert(self.input_data, 0, 1, axis=1)
self.delta_weights_hidden_layer = np.insert(self.delta_weights_hidden_layer, 0, 0, axis=0)
self.delta_weights_output_layer = np.insert(self.delta_weights_output_layer, 0, 0, axis=0)
def file_input(self, file_name):
input_arr = []
with open(file_name, "r") as f:
data = csv.reader(f, delimiter=' ')
for row in data:
tmp_arr = []
for i in row:
tmp_arr.append(float(i))
input_arr.append(tmp_arr)
return np.asarray(input_arr)
def graph(self):
plt.plot(self.epoch_for_error, self.error_for_epoch, 'ro', markersize=1)
plt.title("Mean square error for the epoch")
plt.ylabel("Square error")
plt.xlabel("Epoch")
plt.show()