用16位图像训练keras ResNet50V2？

from keras.applications.resnet_v2 import ResNet50V2
from keras.models import Model

# 加载ResNet50V2模型
base_model = ResNet50V2(weights=None, include_top=False, input_shape=(image_height, image_width, image_channels))

# 添加自定义的全局平均池化层和全连接层
x = base_model.output
x = GlobalAveragePooling2D()(x)
predictions = Dense(num_classes, activation='softmax')(x)

# 构建完整的模型
model = Model(inputs=base_model.input, outputs=predictions)

from keras.optimizers import Adam

# 编译模型
model.compile(optimizer=Adam(lr=0.001), loss='categorical_crossentropy', metrics=['accuracy'])

# 训练模型
model.fit(train_images, train_labels, batch_size=batch_size, epochs=num_epochs, validation_data=(val_images, val_labels))

# 对测试集进行预测
predictions = model.predict(test_images)