如何保存/加载一个tensorflow contrib.learn回归器？

Question

I have a tensorflow contrib.learn.DNNRegressor that I have trained as part of the following code snippet:

regressor = tf.contrib.learn.DNNRegressor(feature_columns=fc, 
                                          hidden_units=hu_array, 
                                          optimizer=tf.train.AdamOptimizer(
                                                       learning_rate=0.001,
                                                    ),
                                          enable_centered_bias=False,
                                          activation_fn=tf.tanh,
                                          model_dir="./models/my_model/",
                                          )

regressor.fit(x=training_features, y=training_labels, steps=10000)

The trained network performs quite well, and I'd like to use it as a part of some other code, on another machine. I have tried copying over the models/my_model directory, and constructing a new DNNRegressor pointing just at the model_dir, but it requires that I supply feature_columns and hidden_units definitions. Shouldn't that information be available via the snapshots stored in model_dir? Is there a better way to save/recover a trained model which is performing well, to be used as a predictor, without having to separately save the feature_columns and hidden_units?

Answer 1

I came up with something workable- not ideal, but it gets the job done. If anyone has a better idea, I am all ears.

I converted my kwargs for DNNRegressor into a dict, and used the ** operator. Then I was able to pickle the kwargs dict, and reconstruct the DNNRegressor from that. Eg:

reg_args = {'feature_columns': fc, 'hidden_units': hu_array, ...}
regressor = tf.contrib.learn.DNNRegressor(**reg_args)
pickle.dump(reg_args, open('reg_args.pkl', 'wb'))

Later on, I reconstruct via:

reg_args = pickle.load(open('reg_args.pkl', 'rb'))
# On another machine and so my model dir path changed:
reg_args['model_dir'] = NEW_MODEL_DIR
regressor = tf.contrib.learn.DNNRegressor(**reg_args)

It worked well. I'm sure there must be a better way but for now if someone is trying to figure out a workaround for tf.contrib.learn, this is a solution.

Answer 2

When training

You call DNNRegressor(..., model_dir) and then call the fit() and evaluate() method.

When testing

You call DNNRegressor(..., model_dir) and then can call predict() methods. Your model will find a trained model in the model_dir and will load the trained model params.

Reference

Issue #3340 of TF