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We're trying to translate old training code based into a more tf.estimator.Estimator compliant code. In the initial code we fine tune an original model for a target dataset. Only some layers are loaded from the checkpoint before the training takes place using a combination of variables_to_restore and init_fn with the MonitoredTrainingSession. How can one achieve this kind of weight loading with the tf.estimator.Estimator approach ?
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The Estimator object wraps a model which is specified by a model_fn , which, given inputs and a number of other parameters, returns the ops necessary to perform training, evaluation, or predictions. All outputs (checkpoints, event files, etc.)
It is recommended using pre-made Estimators when just getting started. To write a TensorFlow program based on pre-made Estimators, you must perform the following tasks: Create one or more input functions. Define the model's feature columns.
The “model_fn” parameter is a function that consumes the features, labels, mode and params in the following order: def model_fn(features, labels, mode, params): The Estimator will always supply those parameters when it executes the model function for training, evaluation or prediction.
you have two options, first one is simpler:
1- use tf.train.init_from_checkpoint in your model_fn
2- model_fn returns an EstimatorSpec. You can set scaffold viaEstimatorSpec.
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