用tf.Dataset训练的模型进行推断

我已经使用tf.data.DatasetAPI 训练了模型，所以我的训练代码看起来像这样

with graph.as_default():
    dataset = tf.data.TFRecordDataset(tfrecord_path)
    dataset = dataset.map(scale_features, num_parallel_calls=n_workers)
    dataset = dataset.shuffle(10000)
    dataset = dataset.padded_batch(batch_size, padded_shapes={...})
    handle = tf.placeholder(tf.string, shape=[])
    iterator = tf.data.Iterator.from_string_handle(handle,
                                                   train_dataset.output_types,
                                                   train_dataset.output_shapes)
    batch = iterator.get_next()
    ... 
    # Model code
    ...
    iterator = dataset.make_initializable_iterator()

with tf.Session(graph=graph) as sess:
    train_handle = sess.run(iterator.string_handle())
    sess.run(tf.global_variables_initializer())
    for epoch in range(n_epochs):
        sess.run(train_iterator.initializer)
        while True:
            try:
                sess.run(optimizer, feed_dict={handle: train_handle})
            except tf.errors.OutOfRangeError:
               break

现在，在训练完模型之后，我想推断出数据集中没有的示例，而且我不确定该怎么做。

明确地说，我知道如何使用另一个数据集，例如，我只是在测试时将句柄传递给测试集。

The question is about given the scaling scheme and the fact that the network expects a handle, if I want to make a prediction to a new example which is not written to a TFRecord, how would I go about doing that?

If I'd modify the batch I'd be responsible for the scaling beforehand which is something I would like to avoid if possible.

So how should I infer single examples from a model traiend the tf.data.Dataset way? (This is not for production purposes it is for evaluating what will happen if I change specific features)