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import numpy as npsigmoid = lambda x:1/(1 + np.exp(-x))
relu = lambda x:(x>0).astype(float)*xweights = np.array([[1,4],[4,1]])
activation = sigmoid(np.array([1,0.01]))print("Activations")
activations = list()
for iter in range(10):activation = sigmoid(activation.dot(weights))activations.append(activation)print(activation)
print("\nGradients")
gradient = np.ones_like(activation)
for activation in reversed(activations):gradient = (activation * (1 - activation) * gradient)gradient = gradient.dot(weights.transpose())print(gradient)print("Relu Activations")
activations = list()
for iter in range(10):activation = relu(activation.dot(weights))activations.append(activation)print(activation)print("\nRelu Gradients")
gradient = np.ones_like(activation)
for activation in reversed(activations):gradient = ((activation > 0) * gradient).dot(weights.transpose())print(gradient)'''
Activations
[0.93940638 0.96852968]
[0.9919462  0.99121735]
[0.99301385 0.99302901]
[0.9930713  0.99307098]
[0.99307285 0.99307285]
[0.99307291 0.99307291]
[0.99307291 0.99307291]
[0.99307291 0.99307291]
[0.99307291 0.99307291]
[0.99307291 0.99307291]Gradients
[0.03439552 0.03439552]
[0.00118305 0.00118305]
[4.06916726e-05 4.06916726e-05]
[1.39961115e-06 1.39961115e-06]
[4.81403643e-08 4.81403637e-08]
[1.65582672e-09 1.65582765e-09]
[5.69682675e-11 5.69667160e-11]
[1.97259346e-12 1.97517920e-12]
[8.45387597e-14 8.02306381e-14]
[1.45938177e-14 2.16938983e-14]
Relu Activations
[4.8135251  4.72615519]
[23.71814585 23.98025559]
[119.63916823 118.852839  ]
[595.05052421 597.40951192]
[2984.68857188 2977.61160877]
[14895.13500696 14916.36589628]
[74560.59859209 74496.90592414]
[372548.22228863 372739.30029248]
[1863505.42345854 1862932.18944699]
[9315234.18124649 9316953.88328115]Relu Gradients
[5. 5.]
[25. 25.]
[125. 125.]
[625. 625.]
[3125. 3125.]
[15625. 15625.]
[78125. 78125.]
[390625. 390625.]
[1953125. 1953125.]
[9765625. 9765625.]
'''