I have sequence data in a cell with 64x6512 for my CNN although it says input size mismatch on my the first convolutional layer, how do I fix this error?

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I am trying to build a CNN network that will take in sequence data. And I don't know how to fix the input size mismatch.
XTrain is a cell array of 450x1 containing 64x6152 Matrices
While YTrain is a categorical array that pairs with XTrain.
The CNN Network:
XTrain=ClassComp;
YTrain=Label;
layers = [
sequenceInputLayer(64,"Name","sequence")
convolution2dLayer([50 1],10,"Name","conv_1","Padding","same")
convolution2dLayer([3 1],10,"Name","conv_2","Padding","same")
maxPooling2dLayer([5 5],"Name","maxpool","Padding","same")
fullyConnectedLayer(64,"Name","fc_1")
dropoutLayer(0.5,"Name","dropout")
fullyConnectedLayer(10,"Name","fc_2")
softmaxLayer("Name","softmax")
classificationLayer("Name","classoutput")];
options = trainingOptions('sgdm', ...
'ExecutionEnvironment','cpu', ...
'GradientThreshold',1, ...
'MaxEpochs',10, ...
'SequenceLength','longest', ...
'Shuffle','never', ...
'Verbose',0, ...
'Plots','training-progress');
net = trainNetwork(ClassComp,Label,layers,options);
The Error
Caused by:
Layer 'conv_1': Input size mismatch. Size of input to this layer is different from the expected input size.
Inputs to this layer:
from layer 'sequence' (size 64(C) × 1(B) × 1(T))

采纳的回答

Anshika Chaurasia
Anshika Chaurasia 2021-9-15
Hi Javier,
The solution is to apply sequenceFoldingLayer and sequenceUnfoldingLayer.
Refer to this example to better understand where to apply these layers.
Hope it helps!
  1 个评论
Javier Castillo
Javier Castillo 2021-9-18
Hi Anshika,
It seems that the input size mismatch error was fixed although this error would pop up which I am uncertain of how to fix with the responses of my training data. My response array is a 450x1 categorical array which corresponds to my 450x1 training data.
Response Data:
The error that it would show was:
Error using trainNetwork (line 184)
Invalid training data. Responses must be a cell array of categorical response sequences.
Which then I used this type of response instead:
Response Data:
Which gave me the same error.

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