python类load_data()的实例源码

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

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