Invalid training data. The output size (1000) of the last layer does not match the number of classes (5).

Question

Rachana Vankayalapati 2021-11-23

1
链接

此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1594249-invalid-training-data-the-output-size-1000-of-the-last-layer-does-not-match-the-number-of-classes

评论： Rachana Vankayalapati 2021-11-29

采纳的回答： Philip Brown

Create Layer Graph

Create the layer graph variable to contain the network layers.

lgraph = layerGraph();

Add Layer Branches

Add the branches of the network to the layer graph. Each branch is a linear array of layers.

tempLayers = [

imageInputLayer([227 227 3],"Name","data","Mean",params.data.Mean)

convolution2dLayer([3 3],64,"Name","conv1","BiasLearnRateFactor",10,"Stride",[2 2],"WeightLearnRateFactor",10,"Bias",params.conv1.Bias,"Weights",params.conv1.Weights)

reluLayer("Name","relu_conv1")

maxPooling2dLayer([3 3],"Name","pool1","Stride",[2 2])

convolution2dLayer([1 1],16,"Name","fire2-squeeze1x1","BiasLearnRateFactor",10,"WeightLearnRateFactor",10,"Bias",params.fire2_squeeze1x1.Bias,"Weights",params.fire2_squeeze1x1.Weights)

reluLayer("Name","fire2-relu_squeeze1x1")];

lgraph = addLayers(lgraph,tempLayers);

tempLayers = [

convolution2dLayer([1 1],64,"Name","fire2-expand1x1","BiasLearnRateFactor",10,"WeightLearnRateFactor",10,"Bias",params.fire2_expand1x1.Bias,"Weights",params.fire2_expand1x1.Weights)

reluLayer("Name","fire2-relu_expand1x1")];

lgraph = addLayers(lgraph,tempLayers);

tempLayers = [

convolution2dLayer([3 3],64,"Name","fire2-expand3x3","BiasLearnRateFactor",10,"Padding",[1 1 1 1],"WeightLearnRateFactor",10,"Bias",params.fire2_expand3x3.Bias,"Weights",params.fire2_expand3x3.Weights)

reluLayer("Name","fire2-relu_expand3x3")];

lgraph = addLayers(lgraph,tempLayers);

tempLayers = [

depthConcatenationLayer(2,"Name","fire2-concat")

convolution2dLayer([1 1],16,"Name","fire3-squeeze1x1","BiasLearnRateFactor",10,"WeightLearnRateFactor",10,"Bias",params.fire3_squeeze1x1.Bias,"Weights",params.fire3_squeeze1x1.Weights)

reluLayer("Name","fire3-relu_squeeze1x1")];

lgraph = addLayers(lgraph,tempLayers);

tempLayers = [

convolution2dLayer([1 1],64,"Name","fire3-expand1x1","BiasLearnRateFactor",10,"WeightLearnRateFactor",10,"Bias",params.fire3_expand1x1.Bias,"Weights",params.fire3_expand1x1.Weights)

reluLayer("Name","fire3-relu_expand1x1")];

lgraph = addLayers(lgraph,tempLayers);

tempLayers = [

convolution2dLayer([3 3],64,"Name","fire3-expand3x3","BiasLearnRateFactor",10,"Padding",[1 1 1 1],"WeightLearnRateFactor",10,"Bias",params.fire3_expand3x3.Bias,"Weights",params.fire3_expand3x3.Weights)

reluLayer("Name","fire3-relu_expand3x3")];

lgraph = addLayers(lgraph,tempLayers);

tempLayers = [

depthConcatenationLayer(2,"Name","fire3-concat")

maxPooling2dLayer([3 3],"Name","pool3","Padding",[0 1 0 1],"Stride",[2 2])

convolution2dLayer([1 1],32,"Name","fire4-squeeze1x1","BiasLearnRateFactor",10,"WeightLearnRateFactor",10,"Bias",params.fire4_squeeze1x1.Bias,"Weights",params.fire4_squeeze1x1.Weights)

reluLayer("Name","fire4-relu_squeeze1x1")];

lgraph = addLayers(lgraph,tempLayers);

tempLayers = [

convolution2dLayer([1 1],128,"Name","fire4-expand1x1","BiasLearnRateFactor",10,"WeightLearnRateFactor",10,"Bias",params.fire4_expand1x1.Bias,"Weights",params.fire4_expand1x1.Weights)

reluLayer("Name","fire4-relu_expand1x1")];

lgraph = addLayers(lgraph,tempLayers);

tempLayers = [

convolution2dLayer([3 3],128,"Name","fire4-expand3x3","BiasLearnRateFactor",10,"Padding",[1 1 1 1],"WeightLearnRateFactor",10,"Bias",params.fire4_expand3x3.Bias,"Weights",params.fire4_expand3x3.Weights)

reluLayer("Name","fire4-relu_expand3x3")];

lgraph = addLayers(lgraph,tempLayers);

tempLayers = [

depthConcatenationLayer(2,"Name","fire4-concat")

convolution2dLayer([1 1],32,"Name","fire5-squeeze1x1","BiasLearnRateFactor",10,"WeightLearnRateFactor",10,"Bias",params.fire5_squeeze1x1.Bias,"Weights",params.fire5_squeeze1x1.Weights)

reluLayer("Name","fire5-relu_squeeze1x1")];

lgraph = addLayers(lgraph,tempLayers);

tempLayers = [

convolution2dLayer([3 3],128,"Name","fire5-expand3x3","BiasLearnRateFactor",10,"Padding",[1 1 1 1],"WeightLearnRateFactor",10,"Bias",params.fire5_expand3x3.Bias,"Weights",params.fire5_expand3x3.Weights)

reluLayer("Name","fire5-relu_expand3x3")];

lgraph = addLayers(lgraph,tempLayers);

tempLayers = [

convolution2dLayer([1 1],128,"Name","fire5-expand1x1","BiasLearnRateFactor",10,"WeightLearnRateFactor",10,"Bias",params.fire5_expand1x1.Bias,"Weights",params.fire5_expand1x1.Weights)

reluLayer("Name","fire5-relu_expand1x1")];

lgraph = addLayers(lgraph,tempLayers);

tempLayers = [

depthConcatenationLayer(2,"Name","fire5-concat")

maxPooling2dLayer([3 3],"Name","pool5","Padding",[0 1 0 1],"Stride",[2 2])

convolution2dLayer([1 1],48,"Name","fire6-squeeze1x1","BiasLearnRateFactor",10,"WeightLearnRateFactor",10,"Bias",params.fire6_squeeze1x1.Bias,"Weights",params.fire6_squeeze1x1.Weights)

reluLayer("Name","fire6-relu_squeeze1x1")];

lgraph = addLayers(lgraph,tempLayers);

tempLayers = [

convolution2dLayer([3 3],192,"Name","fire6-expand3x3","BiasLearnRateFactor",10,"Padding",[1 1 1 1],"WeightLearnRateFactor",10,"Bias",params.fire6_expand3x3.Bias,"Weights",params.fire6_expand3x3.Weights)

reluLayer("Name","fire6-relu_expand3x3")];

lgraph = addLayers(lgraph,tempLayers);

tempLayers = [

convolution2dLayer([1 1],192,"Name","fire6-expand1x1","BiasLearnRateFactor",10,"WeightLearnRateFactor",10,"Bias",params.fire6_expand1x1.Bias,"Weights",params.fire6_expand1x1.Weights)

reluLayer("Name","fire6-relu_expand1x1")];

lgraph = addLayers(lgraph,tempLayers);

tempLayers = [

depthConcatenationLayer(2,"Name","fire6-concat")

convolution2dLayer([1 1],48,"Name","fire7-squeeze1x1","BiasLearnRateFactor",10,"WeightLearnRateFactor",10,"Bias",params.fire7_squeeze1x1.Bias,"Weights",params.fire7_squeeze1x1.Weights)

reluLayer("Name","fire7-relu_squeeze1x1")];

lgraph = addLayers(lgraph,tempLayers);

tempLayers = [

convolution2dLayer([1 1],192,"Name","fire7-expand1x1","BiasLearnRateFactor",10,"WeightLearnRateFactor",10,"Bias",params.fire7_expand1x1.Bias,"Weights",params.fire7_expand1x1.Weights)

reluLayer("Name","fire7-relu_expand1x1")];

lgraph = addLayers(lgraph,tempLayers);

tempLayers = [

convolution2dLayer([3 3],192,"Name","fire7-expand3x3","BiasLearnRateFactor",10,"Padding",[1 1 1 1],"WeightLearnRateFactor",10,"Bias",params.fire7_expand3x3.Bias,"Weights",params.fire7_expand3x3.Weights)

reluLayer("Name","fire7-relu_expand3x3")];

lgraph = addLayers(lgraph,tempLayers);

tempLayers = [

depthConcatenationLayer(2,"Name","fire7-concat")

convolution2dLayer([1 1],64,"Name","fire8-squeeze1x1","BiasLearnRateFactor",10,"WeightLearnRateFactor",10,"Bias",params.fire8_squeeze1x1.Bias,"Weights",params.fire8_squeeze1x1.Weights)

reluLayer("Name","fire8-relu_squeeze1x1")];

lgraph = addLayers(lgraph,tempLayers);

tempLayers = [

convolution2dLayer([1 1],256,"Name","fire8-expand1x1","BiasLearnRateFactor",10,"WeightLearnRateFactor",10,"Bias",params.fire8_expand1x1.Bias,"Weights",params.fire8_expand1x1.Weights)

reluLayer("Name","fire8-relu_expand1x1")];

lgraph = addLayers(lgraph,tempLayers);

tempLayers = [

convolution2dLayer([3 3],256,"Name","fire8-expand3x3","BiasLearnRateFactor",10,"Padding",[1 1 1 1],"WeightLearnRateFactor",10,"Bias",params.fire8_expand3x3.Bias,"Weights",params.fire8_expand3x3.Weights)

reluLayer("Name","fire8-relu_expand3x3")];

lgraph = addLayers(lgraph,tempLayers);

tempLayers = [

depthConcatenationLayer(2,"Name","fire8-concat")

convolution2dLayer([1 1],64,"Name","fire9-squeeze1x1","BiasLearnRateFactor",10,"WeightLearnRateFactor",10,"Bias",params.fire9_squeeze1x1.Bias,"Weights",params.fire9_squeeze1x1.Weights)

reluLayer("Name","fire9-relu_squeeze1x1")];

lgraph = addLayers(lgraph,tempLayers);

tempLayers = [

convolution2dLayer([3 3],256,"Name","fire9-expand3x3","BiasLearnRateFactor",10,"Padding",[1 1 1 1],"WeightLearnRateFactor",10,"Bias",params.fire9_expand3x3.Bias,"Weights",params.fire9_expand3x3.Weights)

reluLayer("Name","fire9-relu_expand3x3")];

lgraph = addLayers(lgraph,tempLayers);

tempLayers = [

convolution2dLayer([1 1],256,"Name","fire9-expand1x1","BiasLearnRateFactor",10,"WeightLearnRateFactor",10,"Bias",params.fire9_expand1x1.Bias,"Weights",params.fire9_expand1x1.Weights)

reluLayer("Name","fire9-relu_expand1x1")];

lgraph = addLayers(lgraph,tempLayers);

tempLayers = [

depthConcatenationLayer(2,"Name","fire9-concat")

dropoutLayer(0.5,"Name","drop9")

convolution2dLayer([1 1],1000,"Name","conv10","BiasL2Factor",1,"BiasLearnRateFactor",10,"WeightLearnRateFactor",10,"Bias",params.conv10.Bias,"Weights",params.conv10.Weights)

reluLayer("Name","relu_conv10")

globalAveragePooling2dLayer("Name","pool10")

fullyConnectedLayer(1000,"Name","fc","BiasLearnRateFactor",10,"WeightLearnRateFactor",10)

softmaxLayer("Name","prob")

classificationLayer("Name","ClassificationLayer_predictions","Classes",params.ClassificationLayer_predictions.Classes)];

lgraph = addLayers(lgraph,tempLayers);

% clean up helper variable

clear tempLayers;

Connect Layer Branches

Connect all the branches of the network to create the network graph.

lgraph = connectLayers(lgraph,"fire2-relu_squeeze1x1","fire2-expand1x1");

lgraph = connectLayers(lgraph,"fire2-relu_squeeze1x1","fire2-expand3x3");

lgraph = connectLayers(lgraph,"fire2-relu_expand1x1","fire2-concat/in1");

lgraph = connectLayers(lgraph,"fire2-relu_expand3x3","fire2-concat/in2");

lgraph = connectLayers(lgraph,"fire3-relu_squeeze1x1","fire3-expand1x1");

lgraph = connectLayers(lgraph,"fire3-relu_squeeze1x1","fire3-expand3x3");

lgraph = connectLayers(lgraph,"fire3-relu_expand3x3","fire3-concat/in2");

lgraph = connectLayers(lgraph,"fire3-relu_expand1x1","fire3-concat/in1");

lgraph = connectLayers(lgraph,"fire4-relu_squeeze1x1","fire4-expand1x1");

lgraph = connectLayers(lgraph,"fire4-relu_squeeze1x1","fire4-expand3x3");

lgraph = connectLayers(lgraph,"fire4-relu_expand1x1","fire4-concat/in1");

lgraph = connectLayers(lgraph,"fire4-relu_expand3x3","fire4-concat/in2");

lgraph = connectLayers(lgraph,"fire5-relu_squeeze1x1","fire5-expand3x3");

lgraph = connectLayers(lgraph,"fire5-relu_squeeze1x1","fire5-expand1x1");

lgraph = connectLayers(lgraph,"fire5-relu_expand3x3","fire5-concat/in2");

lgraph = connectLayers(lgraph,"fire5-relu_expand1x1","fire5-concat/in1");

lgraph = connectLayers(lgraph,"fire6-relu_squeeze1x1","fire6-expand3x3");

lgraph = connectLayers(lgraph,"fire6-relu_squeeze1x1","fire6-expand1x1");

lgraph = connectLayers(lgraph,"fire6-relu_expand3x3","fire6-concat/in2");

lgraph = connectLayers(lgraph,"fire6-relu_expand1x1","fire6-concat/in1");

lgraph = connectLayers(lgraph,"fire7-relu_squeeze1x1","fire7-expand1x1");

lgraph = connectLayers(lgraph,"fire7-relu_squeeze1x1","fire7-expand3x3");

lgraph = connectLayers(lgraph,"fire7-relu_expand1x1","fire7-concat/in1");

lgraph = connectLayers(lgraph,"fire7-relu_expand3x3","fire7-concat/in2");

lgraph = connectLayers(lgraph,"fire8-relu_squeeze1x1","fire8-expand1x1");

lgraph = connectLayers(lgraph,"fire8-relu_squeeze1x1","fire8-expand3x3");

lgraph = connectLayers(lgraph,"fire8-relu_expand1x1","fire8-concat/in1");

lgraph = connectLayers(lgraph,"fire8-relu_expand3x3","fire8-concat/in2");

lgraph = connectLayers(lgraph,"fire9-relu_squeeze1x1","fire9-expand3x3");

lgraph = connectLayers(lgraph,"fire9-relu_squeeze1x1","fire9-expand1x1");

lgraph = connectLayers(lgraph,"fire9-relu_expand3x3","fire9-concat/in2");

lgraph = connectLayers(lgraph,"fire9-relu_expand1x1","fire9-concat/in1");

Plot Layers

plot(lgraph);

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Answer 1

Philip Brown 2021-11-25

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链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1594249-invalid-training-data-the-output-size-1000-of-the-last-layer-does-not-match-the-number-of-classes#answer_840349

As in Yanqi Liu's comment, you probably need to modify the fully connected layer too:

fullyConnectedLayer(5,"Name","fc","BiasLearnRateFactor",10,"WeightLearnRateFactor",10)

When you do transfer learning (in Deep Network Designer or at the command line), there's 2 layers you need to change:

Replace the old classificationLayer with a new one, which has no set classes. These will be learned during training.
Replace the fully-connected layer which does classification. That needs to have an OutputSize equal to the number of classes you want to use.

In Deep Network Designer, you can delete the old blocks, drag new ones in from the palette, connect them up, and set their properties. You don't need to set the classificationLayer's classes manually; they will get set automatically when training.

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Rachana Vankayalapati 2021-11-29

This helped me, I am able to train the data using this. Thank you

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Answer 2

yanqi liu 2021-11-24

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链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1594249-invalid-training-data-the-output-size-1000-of-the-last-layer-does-not-match-the-number-of-classes#answer_838909

yes，sir，may be modify the classify layer，such as

classificationLayer("Name","ClassificationLayer_predictions","Classes",params.ClassificationLayer_predictions.Classes)];

to

classificationLayer("Name","ClassificationLayer_predictions","Classes",5)];

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Rachana Vankayalapati 2021-11-24

This is actually for the merch dataset, i am using squeeze net here in the deepNetworkDesigner. Even without changing anything from the imported dataset. i am unable to train the network.

yanqi liu 2021-11-24

yes，sir，please use or upload the params.mat

tempLayers = [

depthConcatenationLayer(2,"Name","fire9-concat")

dropoutLayer(0.5,"Name","drop9")

convolution2dLayer([1 1],5,"Name","conv10","BiasL2Factor",1,"BiasLearnRateFactor",10,"WeightLearnRateFactor",10,"Bias",params.conv10.Bias,"Weights",params.conv10.Weights)

reluLayer("Name","relu_conv10")

globalAveragePooling2dLayer("Name","pool10")

fullyConnectedLayer(5,"Name","fc","BiasLearnRateFactor",10,"WeightLearnRateFactor",10)

softmaxLayer("Name","prob")

classificationLayer("Name","ClassificationLayer_predictions","Classes",params.ClassificationLayer_predictions.Classes)];

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Invalid training data. The output size (1000) of the last layer does not match the number of classes (5).

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Invalid training data. The output size (1000) of the last layer does not match the number of classes (5).

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