def fullTest():
trainImages, labels, testImages = loadImages()
per = Perceptron(64, 8, 8)
per.train(trainImages, labels, 10000)
results = per.test(testImages)
print(results)
def exercise6():
print("\nExercise 6: Testing\n")
perceptron = Perceptron(4, 2, 2)
trainImages = [
np.array([[1, 0], [0, 0]]),
np.array([[0, 0], [0, 1]]),
np.array([[1, 1], [0, 0]]),
np.array([[0, 1], [0, 1]])
]
labels = [1, -1, 1, -1]
perceptron.train(trainImages, labels, 10)
testImages = [
np.array([[1, 0], [0, 0]]),
np.array([[0, 0], [0, 1]]),
np.array([[1, 1], [0, 0]]),
np.array([[0, 1], [0, 1]])
]
expectedResults = [1, -1, 1, -1]
print(testTesting(perceptron, testImages, expectedResults))
testImages = [
np.array([[0, 0], [1, 0]]),
np.array([[0, 0], [1, 1]]),
np.array([[1, 1], [1, 0]]),
np.array([[0, 1], [1, 1]])
]
expectedResults = [0, -1, 1, -1]
print(testTesting(perceptron, testImages, expectedResults))
def fullTest():
trainImages, labels, testImages = loadImages()
per = Perceptron(64, 8, 8)
per.train(trainImages, labels, 10000)
results = per.test(testImages)
for image, result in zip(testImages, results):
cv2.imshow("test", image * 255)
print(result)
cv2.waitKey(0)
print(results)