I am having a problem with the evaluation of a 0-1 classification network using a fixed batch-size. The setup is the following: at training time I have a fixed batch-size of 256 (#images) while at test time I have only 1 image (so I require a batch-size of 1). My solution is to create a test-network that shares the parameters of the training network and has the fixed batch-size 1:
*lasagne.layers.InputLayer(shape=(1, 1, input_height, input_width, input_depth))* Now for evaluation, if I evaluate the test-network it does not provide the exact same results as when I evaluate the training-network with my single test image on index 0, and all remaining elements from index 1 to 255 set to 0. The error is around 1e-5. I know the solution of using None instead of a fixed batch-size. From what I experimented, using a fixed batch-size yields a much better time-performance. Did anyone have any experience with this? Is it just a numerical problem? -- --- 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.
