检查下述代码并修改错误import tensorflow as tf from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense import pandas as pd import numpy as np import cv2 import os 构建模型 model = Sequential() model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(80, 160, 3))) # (None, 80, 160, 3) model.add(MaxPooling2D((2, 2))) model.add(Conv2D(64, (3, 3), activation='relu')) model.add(MaxPooling2D((2, 2))) model.add(Flatten()) model.add(Dense(64, activation='relu')) model.add(Dense(62, activation='softmax')) # 36表示0-9数字和A-Z(a-z)字母的类别数 编译模型 model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy']) 验证码图片加载 定义训练数据和标签的文件夹路径 train_data_folder = r'C:\Users\CXY\PycharmProjects\pythonProject\data\train' train_labels_folder = r'C:\Users\CXY\PycharmProjects\pythonProject\data' 加载训练数据 train_data = [] train_labels = pd.read_csv(r'C:\Users\CXY\PycharmProjects\pythonProject\data\traincodes.csv')['code'].values 遍历训练数据文件夹，读取每个图片并添加到训练数据列表 for filename in os.listdir(train_data_folder): img_path = os.path.join(train_data_folder, filename) img = cv2.imread(img_path) train_data.append(img) # 遍历训练标签文件夹，读取每个标签并添加到训练标签列表 for filename in os.listdir(train_labels_folder): label_path = os.path.join(train_labels_folder, filename) label = cv2.imread(label_path, 0) # 读取灰度图像 train_labels.append(label) 转换训练数据和标签为NumPy数组 train_data = np.array(train_data) train_labels = np.array(train_labels) 训练模型 model.fit(train_data, train_labels, epochs=10, batch_size=32) 保存模型 model.save('captcha_model.h5')

import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense
import pandas as pd
import numpy as np
import cv2
import os

# 构建模型
model = Sequential()
model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(80, 160, 3))) # (None, 80, 160, 3)
model.add(MaxPooling2D((2, 2)))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D((2, 2)))
model.add(Flatten())
model.add(Dense(64, activation='relu'))
model.add(Dense(62, activation='softmax')) # 36表示0-9数字和A-Z(a-z)字母的类别数

# 编译模型
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

# 验证码图片加载
# 定义训练数据和标签的文件夹路径
train_data_folder = r'C:\Users\CXY\PycharmProjects\pythonProject\data\train'
train_labels_folder = r'C:\Users\CXY\PycharmProjects\pythonProject\data'

# 加载训练数据
train_data = []
train_labels = []

# 遍历训练数据文件夹，读取每个图片并添加到训练数据列表
for filename in os.listdir(train_data_folder):
img_path = os.path.join(train_data_folder, filename)
img = cv2.imread(img_path)
train_data.append(img)

# 遍历训练标签文件夹，读取每个标签并添加到训练标签列表
for filename in os.listdir(train_labels_folder):
label_path = os.path.join(train_labels_folder, filename)
label = cv2.imread(label_path, 0) # 读取灰度图像
train_labels.append(label)

# 转换训练数据和标签为NumPy数组
train_data = np.array(train_data)
train_labels = np.array(train_labels)

# 训练模型
model.fit(train_data, train_labels, epochs=10, batch_size=32)

# 保存模型
model.save('captcha_model.h5')