Error with CNN and LSTM network
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Good day,
I am attempting to do a combined cnn and lstm network with the following layers:
tempLayers = [
sequenceInputLayer(InputSize,"Name","sequence")
sequenceFoldingLayer("Name","seqfold")];
lgraph = addLayers(lgraph,tempLayers);
tempLayers = [
% Layer 1: 3 filters, stride of 1, length of filter is 102, no padding.
convolution2dLayer([40 1],32,'Stride',1,"Name","conv_1")
batchNormalizationLayer("Name","batchnorm_1")
leakyReluLayer("Name","relu_1")
maxPooling2dLayer([4 1],'Padding',"same","Name","maxpool_1")
dropoutLayer(0.1,"Name","dropout_1")
convolution2dLayer([40 1],32,'Stride',1,"Name","conv_2")
batchNormalizationLayer("Name","batchnorm_2")
leakyReluLayer("Name","relu_2")
maxPooling2dLayer([4 1],'Padding',"same","Name","maxpool_2")
dropoutLayer(0.1,"Name","dropout_2")];
lgraph = addLayers(lgraph,tempLayers);
tempLayers = [
sequenceUnfoldingLayer("Name","sequnfold")
flattenLayer("Name","flatten")
lstmLayer(128,"Name","lstm_1","OutputMode","last")
lstmLayer(128,"Name","lstm_2","OutputMode","last")
fullyConnectedLayer(1,"Name","fc")
%softmaxLayer("Name","softmaxlayer")
%classificationLayer("Name","classificationoutput")
regressionLayer("Name","regressionoutput")
];
lgraph = addLayers(lgraph,tempLayers);
%% Connect Layer Branches
clear tempLayers;
lgraph = connectLayers(lgraph,"seqfold/out","conv_1");
lgraph = connectLayers(lgraph,"seqfold/miniBatchSize","sequnfold/miniBatchSize");
lgraph = connectLayers(lgraph,"dropout_2","sequnfold/in");
However when i try to train the network using a train input that is a 4d double and output that is a 200 column vector with hr data i receive the following error:
"Error using trainNetwork (line 183)
Invalid training data. For a recurrent layer with output mode 'last', inputs must be cell arrays.
Error in ecng_6700_cw1_hw4_test_codem (line 235)
net = trainNetwork(train_input,estimator_train_output,lgraph,opts);"
I am unsure what the issue is with my data in trying to train it.
2 个评论
James Lu
2022-2-4
have you tried changing the first LSTM layer to
lstmLayer(128,"Name","lstm_1","OutputMode","sequence")
Vinay Kulkarni
2023-3-13
Tried this, but getting error as :
Error in Train_Model (line 60)
net =trainNetwork(XTrain,YTest,layers,options);
Caused by:
Layer 'LSTM1': LSTM layers must have scalar input size, but input size (32×16) was received. Try using a flatten layer before the LSTM layer.
And with addition of flatten layer:
Error using trainNetwork (line 184)
The training sequences are of feature dimension 653956 32 but the input layer expects
sequences of feature dimension 32 16.
Error in Train_Model (line 60)
net =trainNetwork(XTrain,YTest,layers,options);
回答(1 个)
yanqi liu
2022-2-8
yes,sir,as James Lu idea,may be use
tempLayers = [
sequenceUnfoldingLayer("Name","sequnfold")
flattenLayer("Name","flatten")
lstmLayer(128,"Name","lstm_1","OutputMode","sequence")
lstmLayer(128,"Name","lstm_2","OutputMode","sequence")
fullyConnectedLayer(1,"Name","fc")
%softmaxLayer("Name","softmaxlayer")
%classificationLayer("Name","classificationoutput")
regressionLayer("Name","regressionoutput")
];
or make data to cells,such as
[XTrain,YTrain] = japaneseVowelsTrainData;
XTrain
now we can see the cell data,then you can use origin net layers to try
1 个评论
Vinay Kulkarni
2023-3-13
Tried your above suggestion of adding sequenceunfolding and flattening layers, but still getting errors:
such as
layers=[
sequenceUnfoldingLayer("Name","sequnfold")
flattenLayer("Name","flatten")
lstmLayer(32,"Name","LSTM1","OutputMode","sequence")
Error in Train_Model (line 60)
net =trainNetwork(XTrain,YTest,layers,options);
Caused by:
Network: Missing input layer. The network must have at least one input layer.
Layer 'sequnfold': Unconnected input. Each layer input must be connected to the output of anoth
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