I am building a reinforcement learner and I am wondering how to scale it. At first I initialised a deep neural network in Keras and convert it to theano computational graph which takes state variables as inputs and outputs an action to make. Then, I wrote a simulator in Theano where at decision points I theano.clone the DNN computational graph. Lastly, I do gradient descent on the DNN parameters in order to get a "good" DNN AI. If I use a proper DNN with many layers and parameters the compilation takes forever and iterations are very slow.
Then I've tried using OpFromGraph. It seems to reduce my compilation time quite a bit. However, once I looked at the computational graph it seems that OpFromGraph moves everything back to the CPU. Given that the op is a DNN which are very GPU friendly I wonder whether there is a way to avoid that? Please find my graph at https://drive.google.com/open?id=0BzjH-3p3dTNzWU8zS05wMU5STEk -- --- You received this message because you are subscribed to the Google Groups "theano-users" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected]. For more options, visit https://groups.google.com/d/optout.
