def bonus():
perceptron1 = Perceptron(1,1,1)
perceptron1.network = [Neuron((0,0), 0, False)]
print(testSaveAndLoad(perceptron1))
perceptron1.network = [Neuron((0,0), 0, True)]
print(testSaveAndLoad(perceptron1))
perceptron1.network = [Neuron((0,0), 0, True)]
print(testSaveAndLoad(perceptron1))
perceptron1.network = [Neuron((0,0), 1, True)]
print(testSaveAndLoad(perceptron1))
perceptron2 = Perceptron(2,2,2)
perceptron2.network = [Neuron((0,0), -1, False), Neuron((1,1), 1, False)]
print(testSaveAndLoad(perceptron2))
perceptron2.network = [Neuron((0,0), -1, True), Neuron((1,1), 1, False)]
print(testSaveAndLoad(perceptron2))
perceptron2.network = [Neuron((0,0), -1, True), Neuron((1,1), 1, True)]
print(testSaveAndLoad(perceptron2))
perceptron2.network = [Neuron((0,0), -1, True), Neuron((1,1), 1, True)]
print(testSaveAndLoad(perceptron2))
perceptron2.network = [Neuron((0,0), -1, False), Neuron((1,1), 2, True)]
print(testSaveAndLoad(perceptron2))
perceptron2.network = [Neuron((0,0), 0, True), Neuron((1,1), 1, True)]
print(testSaveAndLoad(perceptron2))
def exercise3():
print("\nExercise 3: Back propagation\n")
perceptron1 = Perceptron(1,1,1)
perceptron1.network = [Neuron((0,0), 0, False)]
expectedNetwork = [Neuron((0,0), 0, False)]
print(testBackProp(perceptron1, expectedNetwork, 1, 0))
perceptron1.network = [Neuron((0,0), 0, True)]
expectedNetwork = [Neuron((0,0), 1, False)]
print(testBackProp(perceptron1, expectedNetwork, 1, 0))
perceptron1.network = [Neuron((0,0), 0, True)]
expectedNetwork = [Neuron((0,0), -1, False)]
print(testBackProp(perceptron1, expectedNetwork, -1, 0))
perceptron1.network = [Neuron((0,0), 1, True)]
expectedNetwork = [Neuron((0,0), 0, False)]
print(testBackProp(perceptron1, expectedNetwork, -1, 1))
perceptron2 = Perceptron(2,2,2)
perceptron2.network = [Neuron((0,0), -1, False), Neuron((1,1), 1, False)]
expectedNetwork = [Neuron((0,0), -1, False), Neuron((1,1), 1, False)]
print(testBackProp(perceptron2, expectedNetwork, -1, 0))
perceptron2.network = [Neuron((0,0), -1, True), Neuron((1,1), 1, False)]
expectedNetwork = [Neuron((0,0), -1, False), Neuron((1,1), 1, False)]
print(testBackProp(perceptron2, expectedNetwork, -1, -1))
perceptron2.network = [Neuron((0,0), -1, True), Neuron((1,1), 1, True)]
expectedNetwork = [Neuron((0,0), -2, False), Neuron((1,1), 0, False)]
print(testBackProp(perceptron2, expectedNetwork, -1, 0))
perceptron2.network = [Neuron((0,0), -1, True), Neuron((1,1), 1, True)]
expectedNetwork = [Neuron((0,0), 0, False), Neuron((1,1), 2, False)]
print(testBackProp(perceptron2, expectedNetwork, 1, 0))
perceptron2.network = [Neuron((0,0), -1, False), Neuron((1,1), 2, True)]
expectedNetwork = [Neuron((0,0), -1, False), Neuron((1,1), 2, False)]
print(testBackProp(perceptron2, expectedNetwork, 1, 1))
perceptron2.network = [Neuron((0,0), 0, True), Neuron((1,1), 1, True)]
expectedNetwork = [Neuron((0,0), -1, False), Neuron((1,1), 0, False)]
print(testBackProp(perceptron2, expectedNetwork, -1, 1))
def testForwardPass(network, image, expected):
imageHeight, imageWidth = image.shape[:2]
perceptron = Perceptron(len(network), imageWidth, imageHeight)
perceptron.network = network
result = perceptron.forwardPass(image)
outString = ("Testing with image:\n{}\nnetwork={}: ".format(image, network))
if result != expected:
outString += ("{}KO| Expected {} got {}{}").format(Fore.RED, expected,
result, Fore.RESET)
return outString
outString += "{}OK{}".format(Fore.GREEN, Fore.RESET)
return outString