5.txt

import numpy as np
X=np.array(([2,9],[1,5],[3,6]),dtype=float)
y=np.array(([92],[86],[89]),dtype=float)
X=X/np.amax(X,axis=0)
y=y/100
def sigmoid(x):
 return 1/(1+np.exp(-x))
def derivatives_sigmoid(x):
 return x*(1-x)
epoch=7000
learning_rate=0.1
inputlayer_neurons=2
hiddenlayer_neurons=3
output_neurons=1
wh=np.random.uniform(size=(inputlayer_neurons,hiddenlayer_neurons))
bh=np.random.uniform(size=(1,hiddenlayer_neurons))
wo=np.random.uniform(size=(hiddenlayer_neurons,output_neurons))
bo=np.random.uniform(size=(1,output_neurons))
for i in range(epoch):
 net_h=np.dot(X,wh)+bh
 sigma_h=sigmoid(net_h)
 net_o=np.dot(sigma_h,wo)+ bo
 output = sigmoid(net_o)
 deltaK =(y-output)*derivatives_sigmoid(output)
 deltaH = deltaK.dot(wo.T)*derivatives_sigmoid(sigma_h)
 wo = wo+sigma_h.T.dot(deltaK)*learning_rate
 wh = wh+X.T.dot(deltaH)*learning_rate
print("Input: \n"+str(X))
print("Actual Output: \n"+str(y))
print("Predicted Output: \n",output)