Unexpected connection and input errors after modifying neural networks

Question

ijmg 2024-3-21

0
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此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/2097101-unexpected-connection-and-input-errors-after-modifying-neural-networks

评论： Taylor 2024-3-22

CODE:

model4 = squeezenet;
numClasses = 2;
layersTransfer = [
    net.Layers(1:end-3)
    fullyConnectedLayer(numClasses, 'Name', 'fc')
    softmaxLayer('Name', 'softmax')
    classificationLayer('Name', 'output')
    ];
dataFolder = './larger_data/larger_PetImages';
categories = {'cat', 'dog'};
imds2 = imageDatastore(fullfile(dataFolder, categories), 'LabelSource', 'foldernames');
% Another program converts images in imageDatastore to 224 x 224 x 3 
[larger_trainingSet, larger_validationSet] = splitEachLabel(imds, 0.75, 'randomized');
new_options = trainingOptions('adam', ...
    'MiniBatchSize',10, ...
    'MaxEpochs',10, ...
    'InitialLearnRate',1e-3, ...
    'Shuffle','every-epoch', ...
    'ValidationData',larger_validationSet, ...
    'ValidationFrequency',3, ...
    'Verbose',false, ...
    'Plots','training-progress');
[model4_predictor, info] = trainNetwork(larger_trainingSet, layersTransfer, new_options);

ERROR:

Error using trainNetwork

Invalid network.

Caused by:

Layer 'fire2-concat': Unconnected input. Each layer input must be connected to the output of another layer.

Layer 'fire2-expand3x3': Invalid input data. The number of channels of the input data (64) must match the layer's expected number of channels (16).

Layer 'fire3-concat': Unconnected input. Each layer input must be connected to the output of another layer.

Layer 'fire4-concat': Unconnected input. Each layer input must be connected to the output of another layer.

Layer 'fire5-concat': Unconnected input. Each layer input must be connected to the output of another layer.

Layer 'fire6-concat': Unconnected input. Each layer input must be connected to the output of another layer.

Layer 'fire7-concat': Unconnected input. Each layer input must be connected to the output of another layer.

Layer 'fire8-concat': Unconnected input. Each layer input must be connected to the output of another layer.

Layer 'fire9-concat': Unconnected input. Each layer input must be connected to the output of another layer.

COMMENT:

Similar errors were experienced when making any modifications to the final three layers of the GoogleNet CNN.

Any help would be greatly appreciated

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Answer 1

Taylor 2024-3-21

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https://ww2.mathworks.cn/matlabcentral/answers/2097101-unexpected-connection-and-input-errors-after-modifying-neural-networks#answer_1428986

The Deep Network Designer app has a useful Network Analyzer tool for just this type of issue. Open the app using deepNetworkDesigner, load layersTransfer from your workspace, and select "Analyze" on the top toolstrip. You should see a report like the one below. Looks like your approach to modifying the network for transfer learning reconfigured the connections in the network unexpectedly. I would recommend following the approach outlined here.

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Taylor 2024-3-21

在 MATLAB Online 中打开

I tried to remove as much of the headache as possible for you:

net = squeezenet;
lgraph = layerGraph(net);
% Assuming the names of the last three layers are known
layersToRemove = {'pool10', 'prob', 'ClassificationLayer_predictions'};
lgraph = removeLayers(lgraph, layersToRemove);
numClasses = 2; 
newLayers = [
    fullyConnectedLayer(numClasses, 'Name', 'fc')
    softmaxLayer('Name', 'softmax')
    classificationLayer('Name', 'output')
    ];
% Find the layer just before the removed layers
layerBeforeRemoved = 'relu_conv10'; 
% Add the new layers
lgraph = addLayers(lgraph, newLayers);
% Connect the new fully connected layer to the layer before the removed ones
lgraph = connectLayers(lgraph, layerBeforeRemoved, 'fc');
netFinal = assembleNetwork(lgraph);
Warning: Network issues detected.

Caused by:
    Layer 'output': Empty Classes property. Classes will be set to categorical(1:N), where N is the number of classes.
Error using assembleNetwork
Invalid network.

Caused by:
    Layer 'fc': Empty Weights property. Specify a nonempty value for the Weights property.
    Layer 'fc': Empty Bias property. Specify a nonempty value for the Bias property.

As you see, the final line yields an error asking you to declare the Weights and Bias values for the added fullyConnectedLayer.

Once you're no longer constrained by your instructor, I highly recommend utilizing the built-in apps that come with MATLAB. They are quite intuitive and help circumvent these sorts of issues.

ijmg 2024-3-22