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NeuralNets.py
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NeuralNets.py
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import numpy as np
import csv
from numpy import genfromtxt
import argparse
def nnet(X_train, Y_train, iter, step_size):
"""passive weights for Gauss3 and Gauss4"""
w_a_h1 = -0.3
w_b_h1 = 0.4
w_a_h2 = -0.1
w_b_h2 = -0.4
w_a_h3 = 0.2
w_b_h3 = 0.1
w_h1_o = 0.1
w_h2_o = 0.3
w_h3_o = -0.4
w_bias_h1 = 0.2
w_bias_h2 = -0.5
w_bias_h3 = 0.3
w_bias_o = -0.1
print('-', end=" ")
print('-', end=" ")
print('-', end=" ")
print('-', end=" ")
print('-', end=" ")
print('-', end=" ")
print('-', end=" ")
print('-', end=" ")
print('-', end=" ")
print('-', end=" ")
print('-', end=" ")
print(round(w_bias_h1, 5), end=" ")
print(round(w_a_h1, 5), end=" ")
print(round(w_b_h1, 5), end=" ")
print(round(w_bias_h2, 5), end=" ")
print(round(w_a_h2, 5), end=" ")
print(round(w_b_h2, 5), end=" ")
print(round(w_bias_h3, 5), end=" ")
print(round(w_a_h3, 5), end=" ")
print(round(w_b_h3, 5), end=" ")
print(round(w_bias_o, 5), end=" ")
print(round(w_h1_o, 5), end=" ")
print(round(w_h2_o, 5), end=" ")
print(round(w_h3_o, 5))
for _ in range(iter):
for i in range(len(X_train)):
'''Observable output from sigmoid unit'''
net_h1 = w_a_h1*X_train[i][0] + w_b_h1*X_train[i][1] + w_bias_h1
out_h1 = 1/(1+np.exp(-net_h1))
net_h2 = w_a_h2*X_train[i][0] + w_b_h2*X_train[i][1] + w_bias_h2
out_h2 = 1/(1+np.exp(-net_h2))
net_h3 = w_a_h3*X_train[i][0] + w_b_h3*X_train[i][1] + w_bias_h3
out_h3 = 1/(1+np.exp(-net_h3))
net_o = out_h1 *w_h1_o + out_h2*w_h2_o + out_h3*w_h3_o + w_bias_o
out = 1/(1+np.exp(-net_o))
error = (Y_train[i]-out) #(t-o)
'''Chain rule'''
delta_o = out*(1-out)*error
delta_h1 = out_h1*(1-out_h1)*(delta_o*w_h1_o)
delta_h2 = out_h2*(1-out_h2)*(delta_o*w_h2_o)
delta_h3 = out_h3*(1-out_h3)*(delta_o*w_h3_o)
w_h1_o = w_h1_o + step_size*delta_o*out_h1
w_h2_o = w_h2_o + step_size*delta_o*out_h2
w_h3_o = w_h3_o + step_size*delta_o*out_h3
w_bias_o = w_bias_o + step_size*delta_o
'''Active node-Information flow 1'''
w_a_h1 = w_a_h1 + step_size*delta_h1*X_train[i][0]
w_b_h1 = w_b_h1 + step_size*delta_h1*X_train[i][1]
w_bias_h1 = w_bias_h1 + step_size*delta_h1
'''Active node-Information flow 2'''
w_a_h2 = w_a_h2 + step_size*delta_h2*X_train[i][0]
w_b_h2 = w_b_h2 + step_size*delta_h2*X_train[i][1]
w_bias_h2 = w_bias_h2 + step_size*delta_h2
'''Active node-Information flow 3'''
w_a_h3 = w_a_h3 + step_size*delta_h3*X_train[i][0]
w_b_h3 = w_b_h3 + step_size*delta_h3*X_train[i][1]
w_bias_h3 = w_bias_h3 + step_size*delta_h3
print(X_train[i][0], end=" ")
print(X_train[i][1], end=" ")
print(round(out_h1, 5), end=" ")
print(round(out_h2, 5), end=" ")
print(round(out_h3, 5), end=" ")
print(round(out, 5), end=" ")
print(int(Y_train[i]), end=" ")
print(round(delta_h1, 5), end=" ")
print(round(delta_h2, 5), end=" ")
print(round(delta_h3, 5), end=" ")
print(round(delta_o, 5), end=" ")
print(round(w_bias_h1, 5), end=" ")
print(round(w_a_h1, 5), end=" ")
print(round(w_b_h1, 5), end=" ")
print(round(w_bias_h2, 5), end=" ")
print(round(w_a_h2, 5), end=" ")
print(round(w_b_h2, 5), end=" ")
print(round(w_bias_h3, 5), end=" ")
print(round(w_a_h3, 5), end=" ")
print(round(w_b_h3, 5), end=" ")
print(round(w_bias_o, 5), end=" ")
print(round(w_h1_o, 5), end=" ")
print(round(w_h2_o, 5), end=" ")
print(round(w_h3_o, 5))
"""Diver code"""
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--data',
help='Gauss files as csv')
parser.add_argument('--eta',
help='learning rate')
parser.add_argument('--iterations',
help='threshold value')
args = parser.parse_args()
file_path = args.data
step_size = float(args.eta)
iter = int(args.iterations)
x = genfromtxt(file_path, delimiter=',', autostrip=True)
M = np.array(x).astype(float)
M = np.round(M, 5)
Y_train = M[:, -1].astype(float)
X_train = M[:, :-1].astype(float)
nnet(X_train, Y_train, iter, step_size)