Hi everyone,
for a project at university I am trying to rebuild a NN described in a paper. It was orininally designed in Keras (I don’t have any code, only its rough describtion) and I’m struggling with one specific layer they’re using. To up-sample their data, they use a layer which takes a single entry of its input and replicates it to a 2×2-region of the output. This results in a matrix with doubled dimensions, without zero-entries (assuming there was none in input) and same entry in each 2×2-block. It is an approximation to the inverse of the maxPooling-Layer of MATLAB. It is similar, but NOT the same as maxUnpooling-Layer, which keeps the position of an maximum-entry and fills up with zeros. For this specific "up-sampling-operation", there is no explicit NN-layer in MATLAB.
Does someone have an idea how I can do this operation?
An idea I had in mind: Just using the given maxUnpooling-Layer and hope there will be no big difference. I tried this and prepared my maxPooling-Layers with "HasUnpoolingOutputs", but it seems that maxUnpooling-Layer has to follow immediately after the maxPooling-Layer. I get unused outputs for my maxPooling-Layers and missing outputs for my maxUnpooling-Layers (seen via analyzeNetwork) as I use convolution-layers in between (see code for example).
layers = [
imageInputLayer([32 32 1])
convolution2dLayer(filterSize, 32, ‘Padding’, ‘same’)
batchNormalizationLayer()
reluLayer()
maxPooling2dLayer(2,’Stride’,2,’HasUnpoolingOutputs’,true) %
convolution2dLayer(filterSize, 64, ‘Padding’, ‘same’)
batchNormalizationLayer()
reluLayer()
maxUnpooling2dLayer() %
convolution2dLayer(filterSize, 32, ‘Padding’, ‘same’)
batchNormalizationLayer()
reluLayer()
fullyConnectedLayer(32)
regressionLayer
];
So in this case, one has to bring the outputs "indices" and "size" of the maxPooling-layer to the maxUnpooling-layer. But I don’t know how this can be achieved :/
I’d be very thankful for any ideas.Hi everyone,
for a project at university I am trying to rebuild a NN described in a paper. It was orininally designed in Keras (I don’t have any code, only its rough describtion) and I’m struggling with one specific layer they’re using. To up-sample their data, they use a layer which takes a single entry of its input and replicates it to a 2×2-region of the output. This results in a matrix with doubled dimensions, without zero-entries (assuming there was none in input) and same entry in each 2×2-block. It is an approximation to the inverse of the maxPooling-Layer of MATLAB. It is similar, but NOT the same as maxUnpooling-Layer, which keeps the position of an maximum-entry and fills up with zeros. For this specific "up-sampling-operation", there is no explicit NN-layer in MATLAB.
Does someone have an idea how I can do this operation?
An idea I had in mind: Just using the given maxUnpooling-Layer and hope there will be no big difference. I tried this and prepared my maxPooling-Layers with "HasUnpoolingOutputs", but it seems that maxUnpooling-Layer has to follow immediately after the maxPooling-Layer. I get unused outputs for my maxPooling-Layers and missing outputs for my maxUnpooling-Layers (seen via analyzeNetwork) as I use convolution-layers in between (see code for example).
layers = [
imageInputLayer([32 32 1])
convolution2dLayer(filterSize, 32, ‘Padding’, ‘same’)
batchNormalizationLayer()
reluLayer()
maxPooling2dLayer(2,’Stride’,2,’HasUnpoolingOutputs’,true) %
convolution2dLayer(filterSize, 64, ‘Padding’, ‘same’)
batchNormalizationLayer()
reluLayer()
maxUnpooling2dLayer() %
convolution2dLayer(filterSize, 32, ‘Padding’, ‘same’)
batchNormalizationLayer()
reluLayer()
fullyConnectedLayer(32)
regressionLayer
];
So in this case, one has to bring the outputs "indices" and "size" of the maxPooling-layer to the maxUnpooling-layer. But I don’t know how this can be achieved :/
I’d be very thankful for any ideas. Hi everyone,
for a project at university I am trying to rebuild a NN described in a paper. It was orininally designed in Keras (I don’t have any code, only its rough describtion) and I’m struggling with one specific layer they’re using. To up-sample their data, they use a layer which takes a single entry of its input and replicates it to a 2×2-region of the output. This results in a matrix with doubled dimensions, without zero-entries (assuming there was none in input) and same entry in each 2×2-block. It is an approximation to the inverse of the maxPooling-Layer of MATLAB. It is similar, but NOT the same as maxUnpooling-Layer, which keeps the position of an maximum-entry and fills up with zeros. For this specific "up-sampling-operation", there is no explicit NN-layer in MATLAB.
Does someone have an idea how I can do this operation?
An idea I had in mind: Just using the given maxUnpooling-Layer and hope there will be no big difference. I tried this and prepared my maxPooling-Layers with "HasUnpoolingOutputs", but it seems that maxUnpooling-Layer has to follow immediately after the maxPooling-Layer. I get unused outputs for my maxPooling-Layers and missing outputs for my maxUnpooling-Layers (seen via analyzeNetwork) as I use convolution-layers in between (see code for example).
layers = [
imageInputLayer([32 32 1])
convolution2dLayer(filterSize, 32, ‘Padding’, ‘same’)
batchNormalizationLayer()
reluLayer()
maxPooling2dLayer(2,’Stride’,2,’HasUnpoolingOutputs’,true) %
convolution2dLayer(filterSize, 64, ‘Padding’, ‘same’)
batchNormalizationLayer()
reluLayer()
maxUnpooling2dLayer() %
convolution2dLayer(filterSize, 32, ‘Padding’, ‘same’)
batchNormalizationLayer()
reluLayer()
fullyConnectedLayer(32)
regressionLayer
];
So in this case, one has to bring the outputs "indices" and "size" of the maxPooling-layer to the maxUnpooling-layer. But I don’t know how this can be achieved :/
I’d be very thankful for any ideas. neural network MATLAB Answers — New Questions

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