keySet = ['n01615121', 'n02099601', 'n02123159', 'n02129604', 'n02317335', 'n02391049', 'n02410509', 'n02422699', 'n02481823', 'n02504458']

# valueSet = ['eagle', 'dog', 'cat', 'tiger', 'star', 'zebra', 'bison', 'antelope', 'chimpanzee', 'elephant']

valueSet = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]

categoryDict = dict(zip(keySet, valueSet))

training_data = []

for category in keySet: # do dogs and cats

path = os.path.join(dataPath, category)

label = categoryDict[category]

for img in tqdm(os.listdir(path)): # iterate over each image per dogs and cats

try:

img_array = cv2.imread(os.path.join(path, img), 1)

new_array = preprocess.preprocess(img_array)

training_data.append([new_array, label])

except Exception as e:

print(e)

print("N'oluyor?")

try:

m_data = sio.loadmat(matPath)

im_data = m_data['images']

l_data = np.squeeze(m_data['labels'])

except:

print("\nPreprocessing train data\n")

training_data = create_training_data(dataPath)

imgs = []

lbs = []

for featureVector, label in training_data:

imgs.append(featureVector)

lbs.append(label)

im_data = np.squeeze(imgs).transpose()

l_data = np.asarray(lbs)

sio.savemat(matPath, {'images': im_data, 'labels': l_data})

trainset = network.ProcessedDataset(im_data, l_data)

trainloader = torch.utils.data.DataLoader(trainset, batch_size=batchSize, shuffle=True, num_workers=0)

net = network.Feedforward(hiddenSize)