Mixed input (Image/Feature Data) question (Deep Learning Toolbox)

3 次查看（过去 30 天）
Hendric
Hendric 2024-1-23
编辑： Venu 2024-2-15
Hello,
I've been following this example:
https://se.mathworks.com/help/deeplearning/ug/train-network-on-image-and-feature-data.html
But has not managed to tweak the code for my benefit.
Input: Image data (90 x 90 x 1 x numOfSamples), feature_1 (numOfSamples x 1), feature_2 (numOfSamples x 1)
Output: feature_1, feature_2
The input is fed with the last time step and I want to train the model it against the current timestep so that I can get a smooth
transition from timestep to timestep.
Data formatting (first time using the "arrayDatastore", I might be completely wrong here):
trainingData = {imgs_(:,:,:,2:numTrain), feat_1(1:numTrain-1), feat_2(1:numTrain-1)};
trainingTargets = {[feat_1(2:numTrain), feat_2(2:numTrain)]};
x1 = arrayDatastore(trainingData{1}, 'IterationDimension', 4);
x2 = arrayDatastore(trainingData{2}, 'IterationDimension', 2);
x3 = arrayDatastore(trainingData{3}, 'IterationDimension', 2);
y = arrayDatastore(trainingTargets{1}, 'IterationDimension', 2);
dsTrain = combine(x1, x2, x3, y);
The net (early stage, just trying to get the model to train):
layers = [
imageInputLayer([90 90 1], Normalization="none")
convolution2dLayer(3, 32, 'Padding', 'same')
batchNormalizationLayer
reluLayer
fullyConnectedLayer(50)
flattenLayer
concatenationLayer(1, 2, Name="cat")
fullyConnectedLayer(2)
regressionLayer
];
lgraph = layerGraph(layers);
featInput = featureInputLayer(2,Name="features");
lgraph = addLayers(lgraph,featInput);
lgraph = connectLayers(lgraph,"features","cat/in2");
options = trainingOptions("sgdm", ...
MaxEpochs=15, ...
InitialLearnRate=0.01, ...
Plots="training-progress", ...
Verbose=0);
net = trainNetwork(dsTrain2, lgraph, options);
The error that I get:
Invalid training data. The output size (2) of the last layer does not match the response size (7040).
The issue could be the data or the model. I can't tell for sure.
Any pointers would be very helpful.
  1 个评论
Vinayak
Vinayak 2024-2-12
Hi Hendric,
It would not be possible to help without the data you are using, Please use the clip icon to attach some sample data that reproduces the error.

请先登录，再进行评论。

请先登录，再回答此问题。

回答（1 个）

Venu
Venu 2024-2-15
编辑：Venu 2024-2-15
Hi @Hendric
Based on the problem statement you provided, it appears you are trying to train a neural network using a combination of image and feature data with a regression output. I've reviewed your code and made some corrections.
1. Data Preparation: Organized your image and feature data into inputs (imgsInput, feat1Input, feat2Input) and targets (feat1Target, feat2Target). (1 feature at a time)
2. Datastores: Transposed feature data for correct "arrayDatastore" creation.
3. Network Architecture: Added layer names and fixed concatenation dimension.
4. Feature Input Layers: Added separate "featureInputLayer" for each feature.
5. Layer Connections: Connected the new feature input layers to the concatenation layer.
% Prepare input data
imgsInput = imgs_(:,:,:,1:numTrain-1); % Images from the first time step to one before the last training sample
feat1Input = feat_1(1:numTrain-1); % Feature 1 from the first time step to one before the last training sample
feat2Input = feat_2(1:numTrain-1); % Feature 2 from the first time step to one before the last training sample
% Prepare target data
feat1Target = feat_1(2:numTrain); % Feature 1 from the second time step to the last training sample (next time step)
feat2Target = feat_2(2:numTrain); % Feature 2 from the second time step to the last training sample (next time step)
targets = [feat1Target, feat2Target];
% Create datastores
x1 = arrayDatastore(imgsInput, 'IterationDimension', 4);
x2 = arrayDatastore(feat1Input', 'IterationDimension', 2);
x3 = arrayDatastore(feat2Input', 'IterationDimension', 2);
y = arrayDatastore(targets', 'IterationDimension', 2);
% Combine datastores
dsTrain = combine(x1, x2, x3, y);
% Define the network architecture (as provided in your code)
layers = [
imageInputLayer([90 90 1], 'Name', 'imageInput', 'Normalization', 'none')
convolution2dLayer(3, 32, 'Padding', 'same', 'Name', 'conv1')
batchNormalizationLayer('Name', 'bn1')
reluLayer('Name', 'relu1')
fullyConnectedLayer(50, 'Name', 'fc1')
flattenLayer('Name', 'flatten')
concatenationLayer(1, 3, 'Name', 'cat') % 3 inputs for concatenation
fullyConnectedLayer(2, 'Name', 'fc2')
regressionLayer('Name', 'output')
];
lgraph = layerGraph(layers);
featInput = featureInputLayer(1, 'Name', 'features1'); % One feature at a time
lgraph = addLayers(lgraph, featInput);
lgraph = connectLayers(lgraph, 'features1', 'cat/in2');
featInput2 = featureInputLayer(1, 'Name', 'features2'); % One feature at a time
lgraph = addLayers(lgraph, featInput2);
lgraph = connectLayers(lgraph, 'features2', 'cat/in3');
% Define the training options
options = trainingOptions("sgdm", ...
MaxEpochs=15, ...
InitialLearnRate=0.01, ...
Plots="training-progress", ...
Verbose=0);
% Train the network
net = trainNetwork(dsTrain, lgraph, options);
Run the corrected code to train your network using the "trainNetwork" function with the combined "dsTrain" datastore and the updated layer graph "lgraph".
Hope this helps!

类别

在 Help Center 和 File Exchange 中查找有关 Image Data Workflows 的更多信息

标签

产品


版本

R2023b

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

Translated by