def load_data():
data = np.empty((42000,1,28,28),dtype="float32") #empty?ones????????????????????
label = np.empty((42000,),dtype="uint8")
imgs = os.listdir("./mnist") #?????????
num = len(imgs)
for i in range(num):
img = Image.open("./mnist/"+imgs[i]) #???????Image????
arr = np.asarray(img,dtype="float32") #?img?????????
data[i,:,:,:] = arr #?????????data
label[i] = int(imgs[i].split('.')[0]) #?????????????
return data,label
python类load_data()的实例源码
def _processed_file(self, fname):
data_manager = data.load_data(fname)
if data_manager is not None:
self._add_tab(data_manager, len(self._tabs))
def main(fname=None):
data_manager = None
if fname is not None:
data_manager = data.load_data(fname)
qApp = QtWidgets.QApplication(sys.argv)
logging.info(qApp.primaryScreen().physicalSize())
window = Application(data_manager)
window.show()
qApp.exec()
logging.info("Goodbye")
def process_data(augmentation=2):
cifar = load_data()
cifar['train']['x'], m, sd = normalize(cifar['train']['x'])
cifar['test']['x'], m, sd = normalize(cifar['test']['x'], M=m, Sd=sd)
if augmentation > 0:
cifar['train']['x'], cifar['train']['y'] = pad_rightleft(cifar['train']['x'], cifar['train']['y'],
mixratio=augmentation)
if augmentation > 1.0:
cifar['train']['x'], cifar['train']['y'] = pad_addnoise(cifar['train']['x'], cifar['train']['y'],
mixratio=augmentation - 1.0)
# data.save_pkl(cifar, savename='cifar_processed.pkl')
return cifar
def load_data():
cifar = data.load_data()
cifar['train']['x'] = cifar['train']['x'].astype(np.float32)
cifar['test']['x'] = cifar['test']['x'].astype(np.float32)
cifar['train']['x'] /= 255
cifar['test']['x'] /= 255
cifar['train']['y'] = np.array(cifar['train']['y'], dtype=np.int32)
cifar['test']['y'] = np.array(cifar['test']['y'], dtype=np.int32)
return cifar