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

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?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

net=addLayers(net,layers1)
net=addLayers(net,layers2)
net.plot
% layers=layerGraph()
% layers=addLayers(layers,layers1)
% layers=addLayers(layers,layers2)

% 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

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? 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

net=addLayers(net,layers1)
net=addLayers(net,layers2)
net.plot
% layers=layerGraph()
% layers=addLayers(layers,layers1)
% layers=addLayers(layers,layers2)

% 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

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? deep learning, neural networks MATLAB Answers — New Questions

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