18CSL76 Implement Backpropagation algorithm

 Build an Artificial Neural Network by implementing the Backpropagation algorithm and test the same using appropriate data sets.

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 lr=0.25 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)) wout=np.random.uniform(size=(hiddenlayer_neurons,output_neurons)) bout=np.random.uniform(size=(1,output_neurons)) for i in range(epoch): hinp1=np.dot(X,wh) hinp=hinp1+bh hlayer_act=sigmoid(hinp) outinp1=np.dot(hlayer_act,wout) outinp=outinp1+bout output=sigmoid(outinp) EO=y-output outgrad=derivatives_sigmoid(output) d_output=EO*outgrad EH=d_output.dot(wout.T) hiddengrad=derivatives_sigmoid(hlayer_act) d_hiddenlayer=EH*hiddengrad wout+=hlayer_act.T.dot(d_output)*lr wh+=X.T.dot(d_hiddenlayer)*lr print("Input=\n"+str(X)) print("Actual output:\n"+str(y)) print("predicated output:",output)