Error using trainNetwork (line 183) Invalid network. Caused by: Layer 'fc7': Input size mismatch. Size of input to this layer is different from the expected input size

clc

clear all; close all;

outputFolder=fullfile('recycle101');

rootFolder=fullfile(outputFolder,'recycle');

categories={'Aluminium Can','PET Bottles','Drink Carton Box'};

imds=imageDatastore(fullfile(rootFolder,categories),'LabelSource','foldernames');

tbl=countEachLabel(imds)

minSetCount=min(tbl{:,2});

[imdsTrain,imdsValidation] = splitEachLabel(imds,0.7,'randomized');

countEachLabel(imds);

%randomly choose file for aluminium can, PET bottles, and drink carton box

AluminiumCan=find(imds.Labels=='Aluminium Can',1);

PETBottles=find(imds.Labels=='PET Bottles',1);

DrinkCartonBox=find(imds.Labels=='Drink Carton Box',1);

%plot image that was pick randomly

figure

subplot(2,2,1);

imshow(readimage(imds,AluminiumCan));

subplot(2,2,2);

imshow(readimage(imds,PETBottles));

subplot(2,2,3);

imshow(readimage(imds,DrinkCartonBox));

%Load pre-trained network

net = alexnet;

%analyzeNetwork(net)

%net =alexnet('Weights','none');

%lys = net.Layers;

%lys(end-3:end)

numClasses = numel(categories(imdsTrain.Labels));

lgraph = layerGraph(net.Layers);

%Replace the classification layers for new task

newFCLayer = fullyConnectedLayer(3,'Name','new_fc','WeightLearnRateFactor',10,'BiasLearnRateFactor',10);

lgraph = replaceLayer(lgraph,'fc6',newFCLayer);

newClassLayer = classificationLayer('Name','new_classoutput');

lgraph = replaceLayer(lgraph,'output',newClassLayer);

%Resize the image

imageSize=net.Layers(1).InputSize;

augmentedTrainingSet=augmentedImageDatastore(imageSize,...

imdsTrain,'ColorPreprocessing','gray2rgb');

augmentedTestSet=augmentedImageDatastore(imageSize,...

imdsValidation,'ColorPreprocessing','gray2rgb');

options = trainingOptions('sgdm', ...

'MiniBatchSize',4, ...

'MaxEpochs',8, ...

'InitialLearnRate',1e-4, ...

'Shuffle','every-epoch', ...

'ValidationData',augmentedTestSet, ...

'ValidationFrequency',3, ...

'Verbose',false, ...

'ExecutionEnvironment','cpu', ...

'Plots','training-progress');

trainedNet = trainNetwork(augmentedTrainingSet,lgraph,options);

Answer 1

[yanqi liu]

modify

lgraph = replaceLayer(lgraph,'fc6',newFCLayer);

to

lgraph = replaceLayer(lgraph,'fc8',newFCLayer);

net = alexnet;
net.Layers

ans = 
  25×1 Layer array with layers:

     1   'data'     Image Input                   227×227×3 images with 'zerocenter' normalization
     2   'conv1'    Convolution                   96 11×11×3 convolutions with stride [4  4] and padding [0  0  0  0]
     3   'relu1'    ReLU                          ReLU
     4   'norm1'    Cross Channel Normalization   cross channel normalization with 5 channels per element
     5   'pool1'    Max Pooling                   3×3 max pooling with stride [2  2] and padding [0  0  0  0]
     6   'conv2'    Grouped Convolution           2 groups of 128 5×5×48 convolutions with stride [1  1] and padding [2  2  2  2]
     7   'relu2'    ReLU                          ReLU
     8   'norm2'    Cross Channel Normalization   cross channel normalization with 5 channels per element
     9   'pool2'    Max Pooling                   3×3 max pooling with stride [2  2] and padding [0  0  0  0]
    10   'conv3'    Convolution                   384 3×3×256 convolutions with stride [1  1] and padding [1  1  1  1]
    11   'relu3'    ReLU                          ReLU
    12   'conv4'    Grouped Convolution           2 groups of 192 3×3×192 convolutions with stride [1  1] and padding [1  1  1  1]
    13   'relu4'    ReLU                          ReLU
    14   'conv5'    Grouped Convolution           2 groups of 128 3×3×192 convolutions with stride [1  1] and padding [1  1  1  1]
    15   'relu5'    ReLU                          ReLU
    16   'pool5'    Max Pooling                   3×3 max pooling with stride [2  2] and padding [0  0  0  0]
    17   'fc6'      Fully Connected               4096 fully connected layer
    18   'relu6'    ReLU                          ReLU
    19   'drop6'    Dropout                       50% dropout
    20   'fc7'      Fully Connected               4096 fully connected layer
    21   'relu7'    ReLU                          ReLU
    22   'drop7'    Dropout                       50% dropout
    23   'fc8'      Fully Connected               1000 fully connected layer
    24   'prob'     Softmax                       softmax
    25   'output'   Classification Output         crossentropyex with 'tench' and 999 other classes