When training a model, you first instantiate it with something like the following:
estimator = tf.estimator.DNNClassifier( model_dir=model_dir, feature_columns=[tf.feature_column.numeric_column(key=str(i)) for i in range(2, eval_data.shape - 1)], hidden_units=hidden_units, n_classes=args['num_classes'], config=config, dropout=dropout, optimizer=tf.train.AdamOptimizer( learning_rate=args['learning_rate'], ))
After training, there will be a directory under the path indicated by
model_dir with the checkpoints and other artifacts:
model_dir/ my_model checkpoint eval graph.pbtxt model.ckpt-0.data-00000-of-00002 model.ckpt-0.data-00001-of-00002 model.ckpt-0.index model.ckpt-0.meta
The issue is that if I want to do more work with this model (besides serving, in which case I could export the model and use TF Serving) somewhere else (meaning that I want to re-instantiate the model), like more training, evaluating, etc., I will first need to instantiate the model. The issue is that I need to somehow keep track of the values of all of those parameters.
Is there a way to load a model by just pointing at the
model_dir, and somehow get an instance of the model loaded into memory?