Java - train loaded tensorflow model

Question

Does anyone know if it is possible after a model is loaded into Java from Tensorflow Python to continue training the model? I've come up with this snippet of code, but did not work (yes, the output is the same as the input)

for(int i = 0; i < 10000; i++) {
    Tensor cost = b.session().runner().feed("input", input).feed("output", input).fetch("cost").run().get(0);
    System.out.println(result1);
}

This is what is printed out 10000 times:

FLOAT tensor with shape []

And after all, the predictions are the same as they were before.

Moreover, if it is possible to continue training the loaded model, is it possible to update the saved model's weights and biases?

Alexandre Passos · Accepted Answer · 2017-04-25 14:58:11Z

1

You are feeding inputs and fetching the loss; this won't train the model. To do so you'll need to feed batches of data and run the update ops (returned maybe from optimizer.minimize).

It is possible to do this from Java, but the infrastructure in python is more well-developed, including threads to prefetch input data in queues, monitoring when the input is over, saving summaries, and doing distributed training.

answered Apr 25, 2017 at 14:58

Alexandre Passos

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4 Comments

szi Over a year ago

Good point, Thanks! Do you maybe know if it is possible to update the saved model from Java? The class SavedModelBundle doesn't have any function, which seems related to saving. Maybe the function metaGraphDef(), but I am not sure it would contain the variables as well or only the graph structure. and I do not how I could update the ProtocolBuffer (pb) saved model either using the metaGraphDef

Alexandre Passos Over a year ago

I think you'd have to reimplement the python saving logic in java to do that.

Alvin Varghese Over a year ago

could you able to train the models from Java?

szi Over a year ago

Hy. I stopped on this project, but I think using optimizer.minimize worked for training the model

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