数据拆分CNN的内存问题

Question

我面临内存错误，尝试运行将数据拆分为列车验证和测试集的代码，我希望在CNN中使用50000张图片，但它的图片数量较少，但我想保留50000张图片，有什么办法吗？我已经批量处理了，但是我不知道该如何实现，有点新，谢谢你

N_CLASSES = 2

# Generates training, validation and testing files
def gen_data():
    X_train = []
    Y_train = []            
    X_valid = []
    Y_valid = []
    X_test = []
    Y_test = []
    count=0
    for i in species:

        # Samples Location
        train_samples = join(datapath, 'traind/'+i)
        valid_samples = join(datapath, 'valid/' +i)
        test_samples = join(datapath, 'test/'+i)

        # Samples Files
        train_files = listdir(train_samples)
        valid_files = listdir(valid_samples)
        test_files = listdir(test_samples)

        # Sorting according to the number
        train_files.sort(key=lambda f: int(''.join(filter(str.isdigit, f))))
        valid_files.sort(key=lambda f: int(''.join(filter(str.isdigit, f))))
        test_files.sort(key=lambda f: int(''.join(filter(str.isdigit, f))))

        for j in train_files:

            im = join(train_samples, j)
            img = cv2.imread(im,1)
            img = cv2.resize(img, (416,416))
            X_train.append(img)
            Y_train+=[count]


        for k in test_files:
            im = join(test_samples, k)
            img = cv2.imread(im,1)
            img = cv2.resize(img, (416, 416))
            X_test.append(img)
            Y_test+=[count]

        for l in valid_files:
            im = join(valid_samples, l)
            img = cv2.imread(im,1)
            img = cv2.resize(img, (416, 416))
            X_valid.append(img)
            Y_valid+=[count]            

        count+=1

    X_train = np.asarray(X_train).astype('float32')
    X_train/= 255
    Y_train = np.asarray(Y_train)

    X_valid = np.asarray(X_valid).astype('float32')
    X_valid/= 255
    Y_valid = np.asarray(Y_valid)

    X_test = np.asarray(X_test).astype('float32')
    X_test /= 255
    Y_test = np.asarray(Y_test)
    return X_train, Y_train, X_valid, Y_valid, X_test, Y_test


if __name__ == '__main__':

#   makedirs(final_1)
#   makedirs(final_2)    

#   for i in species:

#       makedirs(join(final_1, i))
#       makedirs(join(final_2, i))        

#   create_validation()
#   create_test() 

    x_train, y_train, x_valid, y_valid, x_test, y_test = gen_data()

    y_train = np_utils.to_categorical(y_train, N_CLASSES)
    y_valid = np_utils.to_categorical(y_train, N_CLASSES)
    y_test = np_utils.to_categorical(y_test, N_CLASSES)

    np.save('X_train.npy', x_train)
    np.save('Y_train.npy', y_train)
    np.save('X_valid.npy', x_valid)
    np.save('Y_valid.npy', y_valid)
    np.save('X_test.npy', x_test)
    np.save('Y_test.npy', y_test)