验证集小于批处理大小的Keras DataGenerator不进行验证

from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Dense, Conv2D, Flatten

# 假设train_data和validation_data已经定义
train_data = ...
validation_data = ...

# 定义ImageDataGenerator
train_datagen = ImageDataGenerator(rescale=1./255)
validation_datagen = ImageDataGenerator(rescale=1./255)

# 创建生成器
train_generator = train_datagen.flow(train_data, batch_size=32)
validation_generator = validation_datagen.flow(validation_data, batch_size=32)

# 模型定义
model = Sequential([
    Conv2D(64, (3,3), activation='relu', input_shape=(32,32,3)),
    Flatten(),
    Dense(10, activation='softmax')
])

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

# 计算steps_per_epoch和validation_steps
steps_per_epoch = len(train_data) // 32
validation_steps = len(validation_data) // 32

# 训练模型
model.fit(train_generator, steps_per_epoch=steps_per_epoch, validation_data=validation_generator, validation_steps=validation_steps)