如何使用具有两个输入和两个输出并使用两个ImageDataGenerator方法(flow_from_directory)的函数API来训练Keras模型

from keras.models import Model
from keras.layers import Input, Dense, concatenate
from keras.preprocessing.image import ImageDataGenerator
from keras.optimizers import Adam

# 假设我们有两个输入目录和两个输出目录
input_dir1 = 'path/to/input1'
input_dir2 = 'path/to/input2'
output_dir1 = 'path/to/output1'
output_dir2 = 'path/to/output2'

# 定义两个ImageDataGenerator实例
datagen1 = ImageDataGenerator(rescale=1./255)
datagen2 = ImageDataGenerator(rescale=1./255)

# 使用flow_from_directory方法生成数据
train_generator1 = datagen1.flow_from_directory(input_dir1, target_size=(150, 150), batch_size=32, class_mode='binary')
train_generator2 = datagen2.flow_from_directory(input_dir2, target_size=(150, 150), batch_size=32, class_mode='binary')

# 假设我们有两个输出目录，每个目录对应一个输出
output_generator1 = datagen1.flow_from_directory(output_dir1, target_size=(150, 150), batch_size=32, class_mode='binary')
output_generator2 = datagen2.flow_from_directory(output_dir2, target_size=(150, 150), batch_size=32, class_mode='binary')

# 构建模型
input1 = Input(shape=(150, 150, 3))
input2 = Input(shape=(150, 150, 3))

# 假设我们使用相同的卷积层处理两个输入
x1 = Conv2D(32, (3, 3), activation='relu')(input1)
x2 = Conv2D(32, (3, 3), activation='relu')(input2)

# 合并特征图
merged = concatenate([x1, x2])

# 添加全连接层并输出两个结果
output1 = Dense(1, activation='sigmoid', name='output1')(merged)
output2 = Dense(1, activation='sigmoid', name='output2')(merged)

model = Model(inputs=[input1, input2], outputs=[output1, output2])

# 编译模型
model.compile(optimizer=Adam(lr=0.001), loss={'output1': 'binary_crossentropy', 'output2': 'binary_crossentropy'}, metrics=['accuracy'])

# 训练模型
# 注意：这里需要自定义数据生成器来同时处理两个输入和两个输出
model.fit([train_generator1, train_generator2], {'output1': output_generator1, 'output2': output_generator2}, epochs=10)