Hi, I was wondering if someone here had an answer to this unsolved question over in stack overflow:
https://stackoverflow.com/questions/37545325/theano-gradient-of-subtensor Basically, how do you compute gradients w.r.t. a subtensor? The question arises in the context of large tensors, say Y and X, where it is known that each entry in Y depends only on a small subset of the entries of X. Taking T.grad(Y, X) is computationally expensive since it will compute every possible gradient so one would like to be able to compute, e.g. T.grad(Y, X[i]) . Here is some basic code illustrating the problem. X = T.matrix() Y = T.sum(X**2) full_grad = T.grad(Y, X) # This works X0 = X[0] test = T.grad(Y, X0) # This pukes a Disconnected Input error Silencing the Disconnected Input can be done in grad, but of course, that doesn't solve anything, evaluating the gradients only results in a bunch of 0s. So, is there a way of taking these gradients with respect to a subtensor? -- --- 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.
