Hi Prabha,
It is my understanding that you want to view the output of each and every hidden layer in the deep neural network. To view the output at every layer you can use ‘activations’ or ‘minibatchpredict’.
‘activations’ takes in three input arguments namely network (‘SeriesNetwork’), image, layername or number.
net = alexnet; % 'SeriesNetwork'
I = imread('peppers.png'); % Read the image
I = imresize(I, [227 227]);
plot(net)
actLayerPool1 = activations(net,I,"pool1"); %% output corresponding to pool1
disp(size(actLayerPool1));
actLayerConv1 = activations(net,I,"conv1"); %% output corresponding to layer 2 or conv1
disp(size(actLayerConv1));
Alternatively one can obtain the intermediate output maps using ‘minibatchpredict’. Here the network should be initialised be as a ‘dlnetwork’ and the input should include batch dimension (‘SSCB’)
% Define the layers of the network
layers = [
imageInputLayer([28 28 1], Normalization="none", Name="input")
convolution2dLayer(3, 8, Padding="same", Name="conv_1")
reluLayer(Name="relu_1")
fullyConnectedLayer(10, Name="fc")
softmaxLayer(Name="softmax")
];
net = dlnetwork(layers); %% dlnetwork
disp(net);
sampleInput = rand(28, 28, 1);
dlSampleInput = dlarray(sampleInput, 'SSCB');
activations_conv_layer = minibatchpredict(net,dlSampleInput,Outputs="conv_1"); %% corresponding to conv_1
activations_fc = minibatchpredict(net,dlSampleInput,Outputs="fc"); %% corresponding to fully connected layer
Refer to the following MathWorks documentation for better understanding
- ‘activations’: www.mathworks.com/help/deeplearning/ref/seriesnetwork.activations.html
- 'minibatchpredict’: www.mathworks.com/help/deeplearning/ref/minibatchpredict.html
