A friend of mine has designed an RNN for a certain problem using Theano. It has 14 input nodes, 2 hidden layers with 7 nodes each, and finally an output node. We have around 30,000 such RNNs that need to be trained. I am a software engineer with very little exposure to Machine Learning. What I need to do is to speed up the training process of these RNNs.
Looking at the problem from a CS perspective, I don't think that anything can be done to speed up the training of a single RNN. Running such a small RNN on a GPU makes no sense. Instead, we can achieve speed up by batching the RNNs, say 1000 at a time, and sending them to the GPU. The nature of the problem is SIMD - each RNN is identical, but it has to train on a different data set. Can someone please explain how this could be done using Theano? -- --- 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.
