Error training neural network with datastore
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Hello
I am training a LSTM for sequence to sequence labeling
I have it currently set up where XTrain is a 5000 x 1 cell array where each of the 5000 rows is a 10 x n double.
YTrain is a 5000 x 1 cell array where each of the 5000 rows is a 1 x n categorical array with 4 catagories
XTrain =
5000×1 cell array
{10×2371 double}
{10×2792 double}
{10×3044 double}
{10×2878 double}
{10×2790 double}
...
YTrain =
5000×1 cell array
{1×2371 categorical}
{1×2792 categorical}
{1×3044 categorical}
{1×2878 categorical}
{1×2790 categorical}
...
I have layers defined as follows with 10 inputs and 4 outputs
layers = [ ...
sequenceInputLayer(10)
lstmLayer(150,'OutputMode','sequence')
fullyConnectedLayer(4)
softmaxLayer
classificationLayer];
In the interest of space I have omitted the options as I do not think this has anything to do with the error, but I can provide if needed.
If I train the NNet as follows, it works fine.
net = trainNetwork(XTrain, YTrain,layers,options)
However, I want to perform some transformations on the signals so I wanted to get it setup to run with a transformed datastore rather than from inputting XTrain and YTrain separately. Before getting the transform datastore working, I wanted to test that I could train the nnet using syntax that accepted the datastore intsead of XTrain and YTrain separately
I set up a datastore as follows:
dsXTrain = arrayDatastore(XTrain,'OutputType','same');
dsYTrain = arrayDatastore(YTrain,'OutputType','same');
dsTrain = combine(dsXTrain,dsYTrain);
Now, if I try to train the NN as follows, I get an error:
net = trainNetwork(dsTrain,layers,options)
Error using trainNetwork (line 183)
Unexpected input size: The input layer expects sequences with the same sequence length and feature dimension 10.
Error in train_nn (line 82)
net = trainNetwork(dsTrain,layers,options);
If I try to look at the data in the datastore it looks fine...
readall(dsTrain)
ans =
5000×2 cell array
{10×2371 double} {1×2371 categorical}
{10×2792 double} {1×2792 categorical}
{10×3044 double} {1×3044 categorical}
{10×2878 double} {1×2878 categorical}
{10×2790 double} {1×2790 categorical}
... ...
which looks like XTrain and Ytrain
I cannot figure out what the exact problem is - I assume its not passing in the data from dsTrain into trainNetwork properly, but Im at a loss to figure out what specifically the error is...
Any thoughts on how to fix it?
thanks!
hpw
回答（3 个）
Renee Coetsee
2022-4-1
2 个投票
This issue is because sequence padding is not supported with datastores.
You can find a solution here:
If you make a transform function for your datastore, you can the pad the sequences to a specified length. Then they will all be the same length during training.
Puru Kathuria
2021-1-31
Hi,
I tried training the network on my dataset using the cell array and datastore. Both worked fine as expected. I could not debug your code because I dont have your data. I am listing my network architecture and training options for your reference.
And yes, the property SequenceLength set as longest deals with padding sequences in each mini-batch to have the same length as the longest sequence. This option does not discard any data, though padding can introduce noise to the network.
layers = [ ...
sequenceInputLayer(numFeatures)
bilstmLayer(150,"OutputMode","sequence")
bilstmLayer(150,"OutputMode","sequence")
fullyConnectedLayer(2)
softmaxLayer
classificationLayer
];
maxEpochs = 40;
miniBatchSize = 64;
options = trainingOptions("adam", ...
"InitialLearnRate",1e-4, ...
"MaxEpochs",maxEpochs, ...
"MiniBatchSize",miniBatchSize, ...
"Shuffle","every-epoch", ...
"Verbose",false, ...
"ValidationFrequency",floor(numel(TrainingFeatures)/miniBatchSize), ...
"ValidationData",{FeaturesValidationClean.',BaselineV}, ...
"Plots","training-progress", ...
"LearnRateSchedule","piecewise", ...
"LearnRateDropFactor",0.1, ...
"LearnRateDropPeriod",5);
[network,netInfo] = trainNetwork(TrainingFeatures,TrainingMasks,layers,options);
%Similarly after making datastores, I used the below command to train.
net = trainNetwork(adsTrain,layers,options);
As far as I understand it might be a debugging issue and in case you want a reference example that demonstrates training an LSTM, you can refere this section.
kewei chen
2021-2-7
0 个投票
I got the same problem as yours. But still can't work it out. Hope someone can help.
3 个评论
kewei chen
2021-2-7
Finally got it to work! The problem is with the default Minibach size is 128. When I set it to 1, it works!
HpW
2021-2-7
Atallah Baydoun
2022-3-2
Was anyone able to fix this problem?
I am training a multi-path network that takes as input a 3D image and 4 features.
I got the same error and It seems I can not debug it.
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