XOR using Neural networks Using Numpy

result.txt

----------------Hidden Layer Weights----------------
[[ 0.58249636  0.02403689  0.72590671]
 [ 0.91357442  0.02758776  0.57130346]]
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----------------Output Layer Weights----------------
[[ 0.55554919]
 [ 0.72138457]
 [ 0.31158356]]
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----------------Hidden Layer Output----------------
[[ 0.5         0.5         0.5       ]
 [ 0.71373104  0.5068965   0.63906389]
 [ 0.64164162  0.50600893  0.67390639]
 [ 0.81698772  0.5129033   0.78536508]]
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----------------Output----------------
[[ 0.79425866]
 [ 0.96130182]
 [ 0.93146866]
 [ 1.06858423]]
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----------------Error----------------
[[-0.79425866]
 [ 0.03869818]
 [ 0.06853134]
 [-1.06858423]]
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----------------Change in Error----------------
[[-0.07942587]
 [ 0.00386982]
 [ 0.00685313]
 [-0.10685842]]
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----------------Change in Output_Weights----------------
[[ 0.4356935 ]
 [ 0.63229294]
 [ 0.19503918]]
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----------------Change in Hidden result----------------
[[-0.00865133 -0.0125551  -0.00387279]
 [ 0.00034449  0.0006116   0.0001741 ]
 [ 0.00068656  0.00108314  0.00029373]
 [-0.00696122 -0.01688021 -0.0035132 ]]
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----------------Change in Hidden Weights----------------
[[ 0.5762217   0.00823982  0.72268724]
 [ 0.90695769  0.01131915  0.56796436]]
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----------------Hidden Layer Output----------------
[[ 0.5         0.5         0.5       ]
 [ 0.71237721  0.50282976  0.63829333]
 [ 0.64019756  0.50205994  0.67319849]
 [ 0.81505233  0.50488959  0.78425746]]
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----------------Output----------------
[[ 0.63151281]
 [ 0.75280604]
 [ 0.72767896]
 [ 0.82731206]]
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----------------Error----------------
[[-0.63151281]
 [ 0.24719396]
 [ 0.27232104]
 [-0.82731206]]
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----------------Change in Error----------------
[[-0.06315128]
 [ 0.0247194 ]
 [ 0.0272321 ]
 [-0.08273121]]
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----------------Change in Output_Weights----------------
[[ 0.37173106]
 [ 0.58504897]
 [ 0.13269181]]
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----------------Change in Hidden result----------------
[[-0.00586882 -0.00923665 -0.00209491]
 [ 0.00188278  0.0036154   0.00075728]
 [ 0.00233178  0.00398296  0.00079497]
 [-0.00463588 -0.01209929 -0.00185741]]
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----------------Change in Hidden Weights----------------
[[  5.73917597e-01   1.23488327e-04   7.21624805e-01]
 [  9.04204587e-01   2.83525363e-03   5.66864234e-01]]
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----------------Hidden Layer Output----------------
[[ 0.5         0.5         0.5       ]
 [ 0.71181278  0.50070881  0.6380393 ]
 [ 0.63966665  0.50003087  0.67296471]
 [ 0.81428878  0.50073968  0.78389133]]
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----------------Output----------------
[[ 0.54473592]
 [ 0.64220469]
 [ 0.61962342]
 [ 0.69966963]]
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----------------Error----------------
[[-0.54473592]
 [ 0.35779531]
 [ 0.38037658]
 [-0.69966963]]
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----------------Change in Error----------------
[[-0.05447359]
 [ 0.03577953]
 [ 0.03803766]
 [-0.06996696]]
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----------------Change in Output_Weights----------------
[[ 0.3373207 ]
 [ 0.55971207]
 [ 0.09903527]]
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----------------Change in Hidden result----------------
[[-0.00459377 -0.00762238 -0.0013487 ]
 [ 0.00247581  0.00500655  0.00081834]
 [ 0.00295743  0.00532253  0.00082907]
 [-0.00356905 -0.00979032 -0.00117385]]
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----------------Change in Hidden Weights----------------
[[ 0.57330598 -0.00434429  0.72128003]
 [ 0.90311135 -0.00194851  0.56650873]]
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[[ 0.54473592]
 [ 0.64220469]
 [ 0.61962342]
 [ 0.69966963]]
[Finished in 0.4s]

XOR_numpy_NN.py

import numpy as np 

epochs = 3  #Change and see the results
# Layers
inputLayerSize, hiddenLayerSize, outputLayerSize = 2,3,1
#Learning Rate
L = 0.1
#input
X = np.array([[0,0], [0,1], [1,0], [1,1]])
#Output
Y = np.array([[0], [1], [1], [0]])
#Activation 
def sigmoid(x): return 1/(1+ np.exp(-x))
# Derivative 
def sigmoid_(x): return x*(1-x)

hidden_Weights = np.random.uniform(size=(inputLayerSize,hiddenLayerSize))
print('----------------Hidden Layer Weights----------------')
print(hidden_Weights)
print('xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx')
Output_Weights = np.random.uniform(size=(hiddenLayerSize,outputLayerSize))
print('----------------Output Layer Weights----------------')
print(Output_Weights)
print('xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx')
for i in range(epochs):
	Hidden_result = sigmoid(np.dot(X,hidden_Weights))
	print('----------------Hidden Layer Output----------------')
	print(Hidden_result)
	print('xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx')
	Output = np.dot(Hidden_result,Output_Weights) 
	print('----------------Output----------------')
	print(Output)
	print('xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx')
	Error = Y - Output
	print('----------------Error----------------')
	print(Error)
	print('xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx')
	dZ = Error*L
	print('----------------Change in Error----------------')
	print(dZ)
	print('xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx')
	Output_Weights +=  Hidden_result.T.dot(dZ)
	print('----------------Change in Output_Weights----------------')
	print(Output_Weights)
	print('xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx')

	dHidden_result = dZ.dot(Output_Weights.T) * sigmoid_(Hidden_result)
	print('----------------Change in Hidden result----------------')
	print(dHidden_result)
	print('xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx')
	hidden_Weights +=  X.T.dot(dHidden_result)
	print('----------------Change in Hidden Weights----------------')
	print(hidden_Weights)
	print('xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx')
print(Output)