program4.py

import numpy as np
x=np.array(([2,9],[1,9],[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 derivation_sigmoid(x):
 return x*(1-x)
 
epoch=5000
lr=0.1
inputlayer_neurons=2
hiddenlayer_neurons=3
outputlayer_neurons=1
wb=np.random.uniform(size=(inputlayer_neurons,hiddenlayer_neurons))
bb=np.random.uniform(size=(1,hiddenlayer_neurons))
wout=np.random.uniform(size=(hiddenlayer_neurons,outputlayer_neurons))
bout=np.random.uniform(size=(1,outputlayer_neurons))
for i in range(epoch):
 hinp1=np.dot(x,wb)
 hinp=hinp1+bb
 hlayer_act=sigmoid(hinp)
 outinp1=np.dot(hlayer_act,wout)
 outinp=outinp1+bout
 output=sigmoid(outinp)
 
 EO=y-output
 outgrad=derivation_sigmoid(output)
 d_output=EO*outgrad
 EH=d_output.dot(wout.T)
 hiddengrad=derivation_sigmoid(hlayer_act)
 d_hiddenlayer=EH*hiddengrad
 wout+=hlayer_act.T.dot(d_output)*lr
 wb+=x.T.dot(d_output)*lr
 
print("Inpput:\n" +str(x))
print("Actual:\n" +str(y))
print("Predicted:\n",output)