There is a solution for this. I encountered similar problem and struggled for a while as there was no proper answer found in MatLab community. This is little bit lengthy and may not be the optimum solution, but hopefully this will solve some of your issues.
In my excel/csv, I have feature vector of length 7 and one output.
Here I used MatLab Deep Network Designer Toolbox (quite new toolbox) to generate the layer architecture and designed a nn setting input layer of dimension [7,1,1]. Design your network as you wish, but pay attention to the size of the inputs and outputs from each layer. They must be tallied with the data vectors in your excel/csv.
Import the NN model to MatLab workspace or you can create NN from scratch using code.
%% your layer architecture variable
net_layers
What I am going to highlight here is arranging our data such that it could be fed into our model.
%% importing data from csv file and separating them into target and input arrays %%
[~,~,data] = xlsread('temperature_feature_vector.csv');
data_mat = cell2mat(data);
target_array = categorical(data_mat(:,8));
input_array = data_mat(:,1:7);
%% reshaping input data such that it matches our model 2D ---> 4D
%% there are 53 input vectors in this example
x = reshape(input_array',[7,1,1,53]);
Now you have shaped your data accordingly. You are ready to train your network !!!
%define options for your network. you can change accordingly
options = trainingOptions('sgdm', ...
'ExecutionEnvironment','auto', ...
'InitialLearnRate',0.000003, ...
'LearnRateSchedule','piecewise', ...
'MaxEpochs',10, ...
'MiniBatchSize',1, ...
'SequenceLength','longest', ...
'Shuffle','never', ...
'Verbose',0, ...
'Plots','training-progress');
net = trainNetwork(x,target_array,net_layers,options);
With this method you can easily use deep nn models to train your data
Trick is in reshaping your data and defining of layer architecture accordingly.
