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How to train a vector multiple-input multiple-output network

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I want to train a vector multi-input, multi-output network, but I get the error “Number of input data formats (1) and number of network inputs (2) must match”. This is the code,
clear
input1ds=signalDatastore("input1.csv")
input2ds=signalDatastore("input2.csv")
output1ds=signalDatastore("output1.csv")
output2ds=signalDatastore("output2.csv")
ds=combine(input1ds,input2ds,output1ds,output2ds)
isShuffleable(ds)
% 入力とターゲット
% x = [[0:0.1:10]' [0:0.1:10]']
% t = sin(x)
net=dlnetwork
% ニューラルネットワークの定義
layers1 = [
featureInputLayer(1,"name","input1")
fullyConnectedLayer(10) % 10ユニットの全結合層
tanhLayer
fullyConnectedLayer(10) % 10ユニットの全結合層
tanhLayer
fullyConnectedLayer(1) % 1ユニットの全結合層
];
layers2 = [
featureInputLayer(1,"Name","input2")
fullyConnectedLayer(10) % 10ユニットの全結合層
tanhLayer
fullyConnectedLayer(10) % 10ユニットの全結合層
tanhLayer
fullyConnectedLayer(1) % 1ユニットの全結合層
];
net=addLayers(net,layers1)
net=addLayers(net,layers2)
net.plot
% layers=layerGraph()
% layers=addLayers(layers,layers1)
% layers=addLayers(layers,layers2)
% オプションの設定
options = trainingOptions('sgdm', ... % 最適化アルゴリズム
'MaxEpochs', 500, ... % 最大エポック数
'MiniBatchSize', 2^3, ... % ミニバッチサイズ
'Verbose', true,...% 進行状況の表示
'InputDataFormats', {'SB'} ...
)
% ニューラルネットワークのトレーニング
customLossFunction = @(Y, T) customloss(Y, T);
net = trainnet(ds,net,customLossFunction,options) % x,tは縦ベクトル
The “input1.csv” and “output1.csv” all contain a single column of vertical vectors. This time, it is a 2-input, 2-output network.
The function “customloss” is a function I defined myself. This is the error statement.
Error using trainnet (line 46)
Number of input data formats (1) and number of network inputs (2) must match.
Error in parallel_learn_test (line 53)
net = trainnet(ds,net,customLossFunction,options) % x,tは縦ベクトル
What is wrong? And what solutions are available?

Akzeptierte Antwort

Matt J
Matt J am 6 Jun. 2024
Bearbeitet: Matt J am 6 Jun. 2024
You have only entered one InputDataFormat. The error message is telling you that you need two of them, e.g.,
options = trainingOptions(______
'InputDataFormats', {'SB','SB'} ...
)
because the network has two inputs.
  2 Kommentare
Kennosuke Nakabayashi
Kennosuke Nakabayashi am 7 Jun. 2024
Thank you for answering my question. You have helped me resolve this error. However, I got a new error statement. The change is that I defined the datastore with the arrayDatastore function.
% 入力とターゲット
x = [[0:0.1:10]' [0:0.1:10]']
t = sin(x)
ds=combine(arrayDatastore(x(:,1),"ReadSize",length(x(:,1))),...
arrayDatastore(x(:,2),"ReadSize",length(x(:,2))),...
arrayDatastore(t(:,1),"ReadSize",length(t(:,1))),...
arrayDatastore(t(:,2),"ReadSize",length(t(:,2))))
net=dlnetwork
% ニューラルネットワークの定義
layers1 = [
featureInputLayer(1,"name","input1")
fullyConnectedLayer(10) % 10ユニットの全結合層
tanhLayer
fullyConnectedLayer(10) % 10ユニットの全結合層
tanhLayer
fullyConnectedLayer(1) % 1ユニットの全結合層
];
layers2 = [
featureInputLayer(1,"Name","input2")
fullyConnectedLayer(10) % 10ユニットの全結合層
tanhLayer
fullyConnectedLayer(10) % 10ユニットの全結合層
tanhLayer
fullyConnectedLayer(1) % 1ユニットの全結合層
];
net=addLayers(net,layers1)
net=addLayers(net,layers2)
net.plot
% layers=layerGraph()
% layers=addLayers(layers,layers1)
% layers=addLayers(layers,layers2)
% オプションの設定
options = trainingOptions('sgdm', ... % 最適化アルゴリズム
'MaxEpochs', 500, ... % 最大エポック数
'MiniBatchSize', 2^3, ... % ミニバッチサイズ
'Verbose', true,...% 進行状況の表示
'InputDataFormats', {'SB','SB'} ...
)
% ニューラルネットワークのトレーニング
customLossFunction = @(Y, T) customloss(Y, T);
net = trainnet(ds,net,customLossFunction,options) % x,tは縦ベクトル
The statement of that error is this.
Error using trainnet (line 46)
Layer 'input1': Invalid input data. Invalid number of spatial dimensions. Layer expects 0 but received 1.
Error in parallel_learn_test (line 57)
net = trainnet(ds,net,customLossFunction,options) % x,tは縦ベクトル
Sorry for asking so many questions.
Matt J
Matt J am 7 Jun. 2024
Bearbeitet: Matt J am 7 Jun. 2024
Use 'CB' as the input format ReadSize=1,
% 入力とターゲット
x = [[0:0.1:10]' [0:0.1:10]'];
t = sin(x);
N=height(x);
fcn=@(z) arrayDatastore(z,'ReadSize',1);
ds=combine( fcn(x(:,1)), fcn(x(:,2)) , fcn(t(:,1)), fcn(t(:,2)) );
p=ds.preview
p = 1x4 cell array
{[0]} {[0]} {[0]} {[0]}
net=dlnetwork;
% ニューラルネットワークの定義
layers1 = [
featureInputLayer(1,"name","input1")
fullyConnectedLayer(10) % 10ユニットの全結合層
tanhLayer
fullyConnectedLayer(10) % 10ユニットの全結合層
tanhLayer
fullyConnectedLayer(1) % 1ユニットの全結合層
];
layers2 = [
featureInputLayer(1,"Name","input2")
fullyConnectedLayer(10) % 10ユニットの全結合層
tanhLayer
fullyConnectedLayer(10) % 10ユニットの全結合層
tanhLayer
fullyConnectedLayer(1) % 1ユニットの全結合層
];
net=addLayers(net,layers1);
net=addLayers(net,layers2);
% layers=layerGraph()
% layers=addLayers(layers,layers1)
% layers=addLayers(layers,layers2)
% オプションの設定
options = trainingOptions('sgdm', ... % 最適化アルゴリズム
'MaxEpochs', 10, ... % 最大エポック数
'MiniBatchSize', 2^3, ... % ミニバッチサイズ
'Verbose', true,...% 進行状況の表示
'InputDataFormats', {'CB','CB'} ...
);
% ニューラルネットワークのトレーニング
customLossFunction = @(Y1,Y2, T1,T2) sum(abs([Y1,Y2]-[T1,T2]),'all');
net = trainnet(ds,net,customLossFunction,options) % x,tは縦ベクトル
Iteration Epoch TimeElapsed LearnRate TrainingLoss _________ _____ ___________ _________ ____________ 1 1 00:00:01 0.01 9.3311 50 5 00:00:02 0.01 9.9611 100 9 00:00:03 0.01 8.9918 120 10 00:00:03 0.01 8.9776 Training stopped: Max epochs completed
net =
dlnetwork with properties: Layers: [12x1 nnet.cnn.layer.Layer] Connections: [10x2 table] Learnables: [12x3 table] State: [0x3 table] InputNames: {'input1' 'input2'} OutputNames: {'fc_3' 'fc_6'} Initialized: 1 View summary with summary.

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