Jupyter notebook 内核快死了

Question

我正在尝试创建一个用于图像分类的卷积神经网络。我遇到一个问题，我的代码的最后两个单元导致我的内核死机。这是我的代码：

import numpy as np
import matplotlib.pyplot as plt
import glob
import cv2
import os

main_path = '/Users/myusername/Downloads/Rice_Image_Dataset/'

data_images = []
data_labels = []

for directory_path in glob.glob('/Users/myusername/Downloads/Rice_Image_Dataset/*'):
    label = directory_path.split('/')[-1]
    
    for img_path in glob.glob(os.path.join(directory_path, '*.jpg')):
        img = cv2.imread(img_path, cv2.IMREAD_COLOR)
        img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
        data_images.append(img)
        data_labels.append(label)


class_count = []
for i in os.listdir(main_path):
    if i == '.DS_Store':
        continue
    class_count.append(i)
class_count

import matplotlib.image as mpimg
k = 0
for cla in class_count:
    if cla == 'Rice_Citation_Request.txt':
        continue
    for file in os.listdir(main_path + '/' + cla)[0:1]:
        img=mpimg.imread(main_path+'/'+cla+'/'+file)
        k=k+1
        plt.subplot(3, 3, k)
        plt.title(cla)
        plt.imshow(img)

from sklearn.model_selection import train_test_split

data_images = np.array(data_images)
data_labels = np.array(data_labels)

from sklearn import preprocessing

label_encoding = preprocessing.LabelEncoder()
label_encoding.fit(data_labels)
data_encoded_labels = label_encoding.transform(data_labels)

data_encoded_labels

X = data_images
y = data_encoded_labels

X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,shuffle=True,random_state=0)
## normalize data
X_train, X_test = X_train / 255.0, X_test / 255.0

X_train.shape

X_train.shape 线似乎一直导致这个问题，不知道为什么。这是使用的数据集： https://www.kaggle.com/datasets/muratkokludataset/rice-image-dataset