I am using transfer learning using Googlenet for binary classification of images. The following is the code.
imds = imageDatastore('RGBNewFrames_sameSz', ...
'IncludeSubfolders',true, ...
'LabelSource','foldernames');
%% Divide the data into training and validation data sets.
% Use 70% of the images for training and 30% for validation
[imdsTrain,imdsValidation] = splitEachLabel(imds,0.7,'randomized');
%% Load Pretrained Network
net = googlenet;
analyzeNetwork(net)
inputSize = net.Layers(1).InputSize
%% Replace Final Layers
% The last three layers of the pretrained network net are configured for 1000 classes.
% These three layers must be fine-tuned for the new classification problem.
% Extract all layers, except the last three, from the pretrained network.
layersTransfer = net.Layers(1:end-3);
% transfer the layers to the new classification task by replacing the last three layers with a fully connected layer, a softmax layer, and a classification output layer.
numClasses = numel(categories(imdsTrain.Labels))
layers = [
layersTransfer
fullyConnectedLayer(numClasses,'WeightLearnRateFactor',10,'BiasLearnRateFactor',10,'Name', 'FC_Layer')
softmaxLayer('Name', 'soft_mxLayer')
classificationLayer('Name', 'OutputLayer')];
%% Train Network
pixelRange = [-30 30];
imageAugmenter = imageDataAugmenter( ...
'RandXReflection',true, ...
'RandXTranslation',pixelRange, ...
'RandYTranslation',pixelRange);
augimdsTrain = augmentedImageDatastore(inputSize(1:2),imdsTrain, ...
'DataAugmentation',imageAugmenter);
augimdsValidation = augmentedImageDatastore(inputSize(1:3),imdsValidation);
options = trainingOptions('sgdm', ...
'MiniBatchSize',10, ...
'MaxEpochs',2, ...%6
'InitialLearnRate',1e-4, ...
'Shuffle','every-epoch', ...
'ValidationData',augimdsValidation, ...
'ValidationFrequency',3, ...
'Verbose',false, ...
'Plots','training-progress');
netTransfer = trainNetwork(augimdsTrain,layers,options);
%% Classify Validation Images
[YPred,scores] = classify(netTransfer,augimdsValidation);
YValidation = imdsValidation.Labels;
accuracy = mean(YPred == YValidation)
Was Getting the error
Error using trainNetwork (line 170)
Invalid network.
Error in Ex2_Transfer_learning_googlenet (line 73)
netTransfer = trainNetwork(augimdsTrain,lgraph,options);
Caused by:
Layer 'inception_3a-3x3_reduce': Input size mismatch. Size of input to this layer is different from the expected input size.
Inputs to this layer:
from layer 'inception_3a-relu_1x1' (28×28×64 output)
Layer 'inception_3a-output': Unused input. Each layer input must be connected to the output of another layer.
Detected unused inputs:
input 'in2'
input 'in3'
input 'in4'
Layer 'inception_3b-output': Unused input. Each layer input must be connected to the output of another layer.
Detected unused inputs:
input 'in2'
Based on this made the following changes to the code
graph = layerGraph(layers);
netTransfer = trainNetwork(augimdsTrain,lgraph,options);
This resulted in the error,
Layer names in layer array must be non- empty.
So checked and found fcn, softmax did not have names and Explicity assigned names for these layers and executed the code.
The error on Layer names got sorted but now getting the old error again as below.(Error has also been mentioned above for reference)
Input size mismatch. Size of input to this layer is different from the expected input size.
Kindly help me sort this error. I tried all possible things I could think of to debug the error but could not solve it.
Getting the same error with resnet also. Presently it is working only with alexnet.
Any help on this will be greatly appreciated.
Thanks
