Squeezenet model not training in MatlabR2017b.
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Hello,
I have generated a Squeezenet basic model(vanilla) using Matlab R2017b. I am having exactly same implementation as it is in the Squeezenet implementation using Caffe.
Below is my Matlab code: I am using image datastore object with 10 classes "indsRand10.mat" which is subset of ImageNet dataset.
%%Squeezenet network
%%--Bhushan Muthiyan
imdbPath = fullfile(pwd, 'indsRand10.mat') ;
if exist(imdbPath, 'file')
imdb = load(imdbPath) ;
trainingNumFiles = 768;
valNumFiles = 64;
rng(1) % For reproducibility
[imdb.trainDigitData, imdb.testDigitData] = splitEachLabel(imdb.imdsTrain, ...
trainingNumFiles,'randomize');
[imdb.testDigitData, a] = splitEachLabel(imdb.testDigitData, ...
valNumFiles,'randomize');
end
numImages = numel(imdb.trainDigitData.Files);
idx = randperm(numImages,20);
for i = 1:20
subplot(4,5,i)
I = readimage(imdb.trainDigitData, idx(i));
imshow(I)
end
numClasses = numel(categories(imdb.trainDigitData.Labels));
layers = [
imageInputLayer([224 224 3],'Name','input')
convolution2dLayer(7,96,'Padding','same','Stride',2,'Name','conv_1')
reluLayer('Name','relu_1')
maxPooling2dLayer(3,'Stride',2,'Name','pool_1')
%%fire 2
convolution2dLayer(1,16,'Padding','same','Stride',1,'Name','conv_2')
reluLayer('Name','relu_11')
convolution2dLayer(1,64,'Padding','same','Stride',1,'Name','conv_3')
reluLayer('Name','relu_2')
depthConcatenationLayer(2,'Name','concat_1')
%%fire 3
convolution2dLayer(1,16,'Padding','same','Stride',1,'Name','conv_6')
reluLayer('Name','relu_12')
convolution2dLayer(1,64,'Padding','same','Stride',1,'Name','conv_7')
reluLayer('Name','relu_3')
depthConcatenationLayer(2,'Name','concat_3')
maxPooling2dLayer(3,'Stride',2,'Name','pool_2')
%%fire 4
convolution2dLayer(1,32,'Padding','same','Stride',1,'Name','conv_9')
reluLayer('Name','relu_13')
convolution2dLayer(1,128,'Padding','same','Stride',1,'Name','conv_10')
reluLayer('Name','relu_4')
depthConcatenationLayer(2,'Name','concat_5')
%%fire 5
convolution2dLayer(1,32,'Padding','same','Stride',1,'Name','conv_12')
reluLayer('Name','relu_14')
convolution2dLayer(1,128,'Padding','same','Stride',1,'Name','conv_13')
reluLayer('Name','relu_5')
depthConcatenationLayer(2,'Name','concat_6')
maxPooling2dLayer(3,'Stride',2,'Name','pool_3')
%fire 6
convolution2dLayer(1,48,'Padding','same','Stride',1,'Name','conv_15')
reluLayer('Name','relu_15')
convolution2dLayer(1,192,'Padding','same','Stride',1,'Name','conv_16')
reluLayer('Name','relu_6')
depthConcatenationLayer(2,'Name','concat_8')
%%fire 7
convolution2dLayer(1,48,'Padding','same','Stride',1,'Name','conv_18')
reluLayer('Name','relu_16')
convolution2dLayer(1,192,'Padding','same','Stride',1,'Name','conv_19')
reluLayer('Name','relu_7')
depthConcatenationLayer(2,'Name','concat_9')
%fire 8
convolution2dLayer(1,64,'Padding','same','Stride',1,'Name','conv_21')
reluLayer('Name','relu_17')
convolution2dLayer(1,256,'Padding','same','Stride',1,'Name','conv_22')
reluLayer('Name','relu_8')
depthConcatenationLayer(2,'Name','concat_11')
% fire 9
convolution2dLayer(1,64,'Padding','same','Stride',1,'Name','conv_24')
reluLayer('Name','relu_18')
convolution2dLayer(1,256,'Padding','same','Stride',1,'Name','conv_25')
depthConcatenationLayer(2,'Name','concat_12')
%reluLayer('Name','relu_9')
dropoutLayer(0.5,'Name','Drop_1')
convolution2dLayer(1,numClasses,'Padding','same','Stride',1,'Name','conv_27')
reluLayer('Name','relu_9')
averagePooling2dLayer(13,'Stride',1,'Name','avg_pool_4')
%reluLayer('Name','relu_10')
softmaxLayer('Name','softmax')
classificationLayer('Name','classOutput')];
lgraph = layerGraph(layers);
figure
plot(lgraph)
conv_4 = convolution2dLayer(3,64,'Padding',1,'Stride',1,'Name','conv_4');
lgraph = addLayers(lgraph,conv_4);
conv_8 = convolution2dLayer(3,64,'Padding',1,'Stride',1,'Name','conv_8');
lgraph = addLayers(lgraph,conv_8);
conv_11 = convolution2dLayer(3,128,'Padding',1,'Stride',1,'Name','conv_11');
lgraph = addLayers(lgraph,conv_11);
conv_14 = convolution2dLayer(3,128,'Padding',1,'Stride',1,'Name','conv_14');
lgraph = addLayers(lgraph,conv_14);
conv_17 = convolution2dLayer(3,192,'Padding',1,'Stride',1,'Name','conv_17');
lgraph = addLayers(lgraph,conv_17);
conv_20 = convolution2dLayer(3,192,'Padding',1,'Stride',1,'Name','conv_20');
lgraph = addLayers(lgraph,conv_20);
conv_23 = convolution2dLayer(3,256,'Padding',1,'Stride',1,'Name','conv_23');
lgraph = addLayers(lgraph,conv_23);
conv_26 = convolution2dLayer(3,256,'Padding',1,'Stride',1,'Name','conv_26');
lgraph = addLayers(lgraph,conv_26);
relu_19 = reluLayer('Name','relu_19');
lgraph = addLayers(lgraph,relu_19);
relu_20 = reluLayer('Name','relu_20');
lgraph = addLayers(lgraph,relu_20);
relu_21 = reluLayer('Name','relu_21');
lgraph = addLayers(lgraph,relu_21);
relu_22 = reluLayer('Name','relu_22');
lgraph = addLayers(lgraph,relu_22);
relu_23 = reluLayer('Name','relu_23');
lgraph = addLayers(lgraph,relu_23);
relu_24 = reluLayer('Name','relu_24');
lgraph = addLayers(lgraph,relu_24);
relu_25 = reluLayer('Name','relu_25');
lgraph = addLayers(lgraph,relu_25);
relu_26 = reluLayer('Name','relu_26');
lgraph = addLayers(lgraph,relu_26);
lgraph = connectLayers(lgraph,'relu_11','conv_4');
lgraph = connectLayers(lgraph,'conv_4','relu_19');
lgraph = connectLayers(lgraph,'relu_19','concat_1/in2');
lgraph = connectLayers(lgraph,'relu_12','conv_8');
lgraph = connectLayers(lgraph,'conv_8','relu_20');
lgraph = connectLayers(lgraph,'relu_20','concat_3/in2');
lgraph = connectLayers(lgraph,'relu_13','conv_11');
lgraph = connectLayers(lgraph,'conv_11','relu_21');
lgraph = connectLayers(lgraph,'relu_21','concat_5/in2');
lgraph = connectLayers(lgraph,'relu_14','conv_14');
lgraph = connectLayers(lgraph,'conv_14','relu_22');
lgraph = connectLayers(lgraph,'relu_22','concat_6/in2');
lgraph = connectLayers(lgraph,'relu_15','conv_17');
lgraph = connectLayers(lgraph,'conv_17','relu_23');
lgraph = connectLayers(lgraph,'relu_23','concat_8/in2');
lgraph = connectLayers(lgraph,'relu_16','conv_20');
lgraph = connectLayers(lgraph,'conv_20','relu_24');
lgraph = connectLayers(lgraph,'relu_24','concat_9/in2');
lgraph = connectLayers(lgraph,'relu_17','conv_23');
lgraph = connectLayers(lgraph,'conv_23','relu_25');
lgraph = connectLayers(lgraph,'relu_25','concat_11/in2');
lgraph = connectLayers(lgraph,'relu_18','conv_26');
lgraph = connectLayers(lgraph,'conv_26','relu_26');
lgraph = connectLayers(lgraph,'relu_26','concat_12/in2');
figure
plot(lgraph);
optionsTransfer = trainingOptions('sgdm', ...
'MaxEpochs',25, ...
'MiniBatchSize',64,...
'InitialLearnRate',0.04,...
'LearnRateDropFactor',0.2,...
'LearnRateDropPeriod',5,...
'Plots','training-progress',...
'ExecutionEnvironment','auto');
netTransfer = trainNetwork(imdb.trainDigitData,lgraph,optionsTransfer);
YPred = classify(netTransfer,imdb.testDigitData);
YTest = imdb.testDigitData.Labels;
accuracy = sum(YPred==YTest)/numel(YTest);
fprintf('accuracy = %f\n',accuracy);
numImages = numel(imdb.testDigitData.Files);
idx = randperm(numImages,20);
for i = 1:20
subplot(4,5,i)
I = readimage(imdb.testDigitData, idx(i));
imshow(I)
end
Can someone let me know the reason behind this.
Enclosed here is the image of Squeezenet vanilla model structure.
0 Kommentare
Antworten (1)
Mickaël Tits
am 14 Nov. 2017
Hi,
If I understand, you are trying to train your Squeezenet model from scratch, with 768 images ? You need a pretrained model if you want a chance that it works.
You can get here a pretrained SqueezeNet, and use it for transfer learning as you want : https://github.com/titsitits/Squeezenet-Matlab-Keras
Mickaël Tits
1 Kommentar
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