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How to manually modify weights in a SeriesNetwork?

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Carlo Tomasi
Carlo Tomasi on 6 Jul 2017
Commented: qingqing on 30 Jun 2021
For some of my studies on deep neural nets, I need to change select weights in a (previously trained) SeriesNetwork by hand, and evaluate changes in the classification results. Ideally, I would like to do
net.Layers(k).Weights = W;
where net is a SeriesNetwork (a class in the Neural Network Toolbox), k is an integer that indexes a fully connected layer, and W is a suitable array. I would then classify inputs with the net modified in this way.
However, the field net.Layers(k).Weights is read-only, so the instruction above will generate the following error message:
You cannot set the read-only property 'Layers' of SeriesNetwork.
Is there some way to circumvent this restriction?
Here is what I tried, to no avail: First define an array of Layers by saying something like this:
layers = [imageInputLayer([28 28 1])
% Severa layers here
then initialize the weights as desired, and finally make these layers into a SeriesNetwork with the class’s constructor:
net = SeriesNetwork(layers);
While this does create a new SeriesNetwork, attempting to classify an input with
y = classify(net, x);
where x is a suitable input results in the following error message:
Error using nnet.internal.cnn.layer.FullyConnected/forwardPropagateSize (line 99)
An input size for the layer must be defined in order to call forwardPropagateSize.
Error in SeriesNetwork>iDetermineLayerOutputSize (line 417)
inputSize = layers{i}.forwardPropagateSize(inputSize);
Error in SeriesNetwork/get.OutputSize (line 80)
val = iDetermineLayerOutputSize( internalLayers, outputLayerIdx );
Error in SeriesNetwork/predict (line 185)
Y = precision.cast( zeros([this.OutputSize dispatcher.NumObservations]) );
Error in SeriesNetwork/classify (line 250)
scores = this.predict( X, varargin{:} );
What is missing in my use of the constructor to make net a fully-fledged SeriesNetwork that can be used with classify?

More Answers (2)

Shashank on 14 Jul 2017
Edited: Shashank on 14 Jul 2017
Hi Carlo,
Please see the following image to understand how the weights of various layers are stored in MATLAB.
It is stored as a cell array of matrices and each matrix corresponds to a weight matrix of that layer. It is like a map from 1st to 2nd layer , 2nd to 3rd and so on. Hence only the corresponding indices have weight vectors.
The 1st layer is called the input layer and weights can be set by :
>>net.IW{1} = [1;2;3;4;5;6;7;8;9;10;11;10];
However, please train the network using the following function before modifying the weight vectors:
>> net = trainscg(net,x,t); % where x and t are the input and output variables respectively.
For the succeeding layers, you can modify the weights using:
>> net.LW{2,1}=rand(128,12);
as shown in the screenshot.Please do not modify the empty vectors
Hope this helps.
- Shashank
  1 Comment
Carlo Tomasi
Carlo Tomasi on 14 Jul 2017
Thank you, Shashank. However, your solution works for what Matlab calls ``shallow networks.'' In other words, the following needs to be true:
all(class(net) == 'network')
My question was about series networks, for which the following is true:
all(class(net) == 'SeriesNetwork')
Unfortunately, members of class SeriesNetwork are not structured as you say (they have no .IW or .LW fields, for instance).

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Dianxin Luan
Dianxin Luan on 29 Mar 2021
You can covert the trained NN to an object and it is a possible way, which I had test for it.
modify_able_NN = NN.saveobj;
% Changes made in NN
Modified_NN = NN.loadobj(modify_able_NN);

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