CNN error using deep network designer

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Teo
Teo le 21 Sep 2021
Modifié(e) : Teo le 2 Oct 2021
Hi,
i trying to combine certain part of the two CNN into one model using addition layer. However, there seem to be error while analyzing the combined models using deep network designer.
Thank you.

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Katja Mogalle
Katja Mogalle le 22 Sep 2021
Hi Teo,
As you already noticed, the shufflenet branch results in 544 channels and the resnet18 branch results in 512 channels. You could map one of those branches (e.g. shufflenet branch) to the number of filters of the other branch (e.g. resnet18) by using a convolution2dLayer with filter size [1 1] and 512 filters. Then you should be able to do the addition.
I don't know the details of what you're doing and what the two branches are supposed to do, but I wonder if a concatenationLayer would be the better choice here to combine the two branches.
I hope this helps.
Katja
  3 commentaires
Katja Mogalle
Katja Mogalle le 23 Sep 2021
Modifié(e) : Katja Mogalle le 23 Sep 2021
The activations of the layers in your network are four-dimensional: Height-by-Width-by-NumChannels-by-MiniBatchSize. To configure the layer, you need to tell it over which of those dimensions you want to concatenate. In your case, you want to combine the channels of the inputs. The channels are in the third dimension of the activations. So in this scenario, Dim would be 3.
The layer basically executes MATLAB's cat function. If you're interested in more details, the examples shown in the cat reference page might provide a better understanding of the concatenation operation. The example with the 3D array is closest to what happens in the convolutional neural network.
Teo
Teo le 23 Sep 2021
I really appreciate your suggestion and clarification on the confusion. Thank you very much.

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