def main():
train = np.empty((1000,28,28),dtype='float64')
trainY = np.zeros((1000,10,1))
test = np.empty((10000,28,28),dtype='float64')
testY = np.zeros((10000,10,1)) # Load in the images
i = 0
for filename in os.listdir('C:/Users/ivans_000/Desktop/MASTER/Spring2019/Deep_Learning/Assignment2_Sangines/Data/Training1000/'):
y = int(filename[0])
trainY[i,y] = 1.0
train[i] = cv2.imread('C:/Users/ivans_000/Desktop/MASTER/Spring2019/Deep_Learning/Assignment2_Sangines/Data/Training1000/{0}'.format(filename),0)/255.0
i = i + 1
i = 0 # read test data
for filename in os.listdir('C:/Users/ivans_000/Desktop/MASTER/Spring2019/Deep_Learning/Assignment2_Sangines/Data/Test10000'):
y = int(filename[0])
testY[i,y] = 1.0
test[i] = cv2.imread('C:/Users/ivans_000/Desktop/MASTER/Spring2019/Deep_Learning/Assignment2_Sangines/Data/Test10000/{0}'.format(filename),0)/255.0
i = i + 1
trainX = train.reshape(train.shape[0],train.shape[1]*train.shape[2],1)
testX = test.reshape(test.shape[0],test.shape[1]*test.shape[2],1)
numLayers = [50,10]
NN = Network(trainX,trainY,numLayers, "RELU", "SOFTMAX")
NN.Train(10, 0.002, "STOCHASTIC", "ADAM")
print("done training , starting testing..")
accuracyCount = 0
for i in range(testY.shape[0]):
# do forward pass
a2 = NN.Compute(testX[i])
# determine index of maximum output value
maxindex = a2.argmax(axis = 0)
if (testY[i,maxindex] == 1):
accuracyCount = accuracyCount + 1
print("Accuracy count = " + str(accuracyCount/10000.0))
