Hi,
I'm using Parmesan model for ladder network with Convolutional layers. I replaced Dense layers with Convolution and pooling layers. I have a problem which I guess it might happen in DenoiseLayer(MergeLayer) layer since it gives me following error :
ValueError: GpuElemwise. Input dimension mis-match. Input 2 (indices start at 0) has shape[0] == 1100, but the output's size on that axis is 100.
So to modify the DenoiseLayer, I want to make sure I fully understand its functionality. As I understood it merges two input layers(the noisy from Encoder and the another from higher layer in Decoder), right? So, do you think it would be fine if I replace it with one of Lasagne's Merge layers?
Hi, I'm using Parmesan model for ladder network with Convolutional layers. I replaced Dense layers with Convolution and pooling layers. I have a problem which I guess it might happen in DenoiseLayer(MergeLayer) layer since it gives me following error :
ValueError: GpuElemwise. Input dimension mis-match. Input 2 (indices start at 0) has shape[0] == 1100, but the output's size on that axis is 100.So to modify the DenoiseLayer, I want to make sure I fully understand its functionality. As I understood it merges two input layers(the noisy from Encoder and the another from higher layer in Decoder), right? So, do you think it would be fine if I replace it with one of Lasagne's Merge layers?Thanks,