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Forked from vinupriyesh/nn2.py
Created May 30, 2018 10:55
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Simple neural network to predict XOR gate in python
"""
A simple neural network with 1 hidden layer and 4 neurons, an enhancement to the previous logistic regression to compute the XOR
@Author : Vinu Priyesh V.A.
"""
import numpy as np
#Compute functions for OR, AND, XOR, this will be used to generate the test set and to validate our results
def compute(x,m,label):
if(label == "XOR"):
return np.logical_xor(x[0,:],x[1,:]).reshape(1,m).astype(int)
if(label == "AND"):
return np.logical_and(x[0,:],x[1,:]).reshape(1,m).astype(int)
if(label == "OR"):
return np.logical_or(x[0,:],x[1,:]).reshape(1,m).astype(int)
#Validation functions for OR, AND, XOR, this will validate whether the predicted results are correct
def validate(x,y,m,label):
y1 = compute(x,m,label)
return np.sum(y1==y)/m*100
#Simple sigmoid, it is better to use ReLU instead
def sigmoid(z):
s = 1 / (1 + np.exp(-z))
return s
#Simple tanh
def tanh(x):
return np.tanh(x)
#Back prop to get the weight and bias computed using gradient descend
def back_prop(m,w1,w2,b1,b2,X,Y,iterations,learning_rate):
for i in range(iterations):
Y1,A1,A2 = forward_prop(m,w1,w2,b1,b2,X)
dz2 = A2 - Y
if(iterations%1000==0):
logprobs = np.multiply(np.log(A2), Y) + np.multiply((1 - Y), np.log(1 - A2))
cost = - np.sum(logprobs) / m
print("cost : {}".format(cost))
dw2 = (1 / m) * np.dot(dz2,A1.T)
db2 = (1 / m) * np.sum(dz2,axis=1,keepdims=True)
dz1 = np.dot(w2.T,dz2) * (1-np.power(A1,2))
dw1 = (1 / m) * np.dot(dz1,X.T)
db1 = (1 / m) * np.sum(dz1,axis=1,keepdims=True)
w1 = w1 - learning_rate * dw1
b1 = b1 - learning_rate * db1
w2 = w2 - learning_rate * dw2
b2 = b2 - learning_rate * db2
return w1,b1,w2,b2
#Forward prop to get the predictions
def forward_prop(m,w1,w2,b1,b2,X):
Y = np.zeros((1, m))
z1 = np.dot(w1,X) + b1
A1 = tanh(z1)
z2 = np.dot(w2,A1) + b2
A2 = sigmoid(z2)
for i in range(m):
Y[0, i] = 1 if A2[0, i] > 0.5 else 0
return Y,A1,A2
def model(m,iterations,learning_rate,label,neurons):
print("\nmodel : {}".format(label))
w1 = np.random.randn(neurons,2) * 0.01
w2 = np.random.randn(1,neurons) * 0.01
b1 = np.zeros((neurons,1))
b2 = np.zeros((1,1))
#Training phase
X_train = np.random.randint(2,size=(2,m))
Y_train = compute(X_train,m,label);
w1,b1,w2,b2 = back_prop(m,w1,w2,b1,b2,X_train,Y_train,iterations,learning_rate)
Y1,A1,A2 = forward_prop(m,w1,w2,b1,b2,X_train)
P_train = validate(X_train,Y1,m,label)
#Testing phase
m*=2
X_test = np.random.randint(2,size=(2,m))
Y1,A1,A2 = forward_prop(m,w1,w2,b1,b2,X_test)
P_test = validate(X_test,Y1,m,label)
print("Training accuracy : {}%\n\rTesting accuracy : {}%".format(P_train,P_test))
return P_train,P_test
m=1000
iterations = 1000
learning_rate = 1.2
#model(m,iterations,learning_rate,"OR")
#model(m,iterations,learning_rate,"AND")
ptrain, ptest = model(m,iterations,learning_rate,"XOR",4)
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