什么使用 tensorflow estimator 创建多输入

Question

对不起我的英文很差=。=我创建了一个 keras 模型并使用tf.keras.estimator.model_to_estimatorconvert to estimator 但该模型是多输入的，我可以创建什么数据集来提供数据？这是我的模型代码：model = VGG19(include_top=False, input_shape=(182, 182 , 3))y = model.outputy = keras.layers.Flatten()(y)y = keras.layers.Dense(512, activation='relu')(y)y = keras.layers.Dense(256, activation='relu')(y)y = keras.layers.Dense(128, activation='relu')(y)model = keras.Model(inputs=model.input, outputs=y)input_image = keras.layers.Input(shape=(182, 182, 3))input_anchor = keras.layers.Input(shape=(182, 182, 3))out_image = model(input_image)out_anchor = model(input_anchor)out = keras.layers.concatenate([out_image, out_anchor])out = keras.layers.Dense(1, activation='sigmoid')(out)img_model = keras.Model([input_image, input_anchor], out)face_model.compile(optimizer=tf.train.AdamOptimizer(1e-4, loss='binary_crossentropy', metrics=['accuracy'])distribution = tf.contrib.distribute.CollectiveAllReduceStrategy(num_gpus_per_worker=0)config = tf.estimator.RunConfig(model_dir='/home/zjq/test/image_model.h5', train_distribute=distribution)est_model = tf.keras.estimator.model_to_estimator(keras_model=image_model, config=config)现在，我有一个输入列表，形状是[(100000, 182, 182, 3), (100000, 182, 182, 3), (100000, 1)]，如何定义输入函数返回tf.data。数据集？