Text_Generate | 文本生成，可用于作诗、写小说 | Natural Language Processing library

Note: Answering this completely and extensively would require going in depth on the Serving architecture, its APIs and how they interact with models' signatures. I'll skip over all of this to keep the answer to an acceptable length, but I can always expand on excessively obscure parts if necessary (leave a comment if that's the case).

How does the half_plus_two model know what to do with the instances param?

Because of several unmentioned reasons that pile up to make this a conveniently short example, if only IMO a bit misleading.

1) Where does the instances parameter come from? The definition of the Predict API for the RESTful API has a predefined request format that, in one of its two possible forms, takes one instances parameter.

2) What does the instances parameter map to? We don't know. for SignatureDefs with just one input, instances in that very specific calling format maps directly to the input without need to specifying the input's key (see section "Specifying input tensors in row format" in the API specs).

So, what happens is: You make a POST request to a model with just one input defined. TF Serving takes that input and feeds it to the model, runs it until it has all the values for the tensors defined in the "outputs" part of the model's signature and returns you a JSON object with key:result items for each key in the "outputs" list.

How can I serve the trained model from the text generation tutorial and have input parameters to the model api mapped to unique methods such as generate_text?

You can't (not directly mapping a function to a Serving method, at least). The Serving infrastructure exposes some predefined methods (regress, predict, classify) that know how to interpret the signatures to produce the output you requested by running specific subgraphs of the model. These subgraphs must be included in the SavedModel, so for example using tf.py_func won't work.

Your best chance is to try to describe text generation as a TF subgraph (i.e. using exclusively TF operations) and writing a separate SignatureDef that takes the start string and num_generate as inputs.

For graph.get_tensor_by_name("prediction:0") to work you should have named it when you created it. This is how you can name it

About your first question. You were getting the error because the method wasn't defined in the File class. So you were not able to call it like this f.puts_content.

You could define a method that receives the File as a parameter puts_content(file, ...).

About the second part of your question, I this is a good solution (thinking object-oriented).