How to create a datastore for using the Deep Network Designer App?

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xadu
xadu on 16 Nov 2021
Commented: xadu on 21 Nov 2021
I'm trying to use the Deep Network Designer app (R2021b) to perform regression between numeric inputs and outputs. I have prepared the trainind dataset in a matrix X of size n x f, where f is the number of features, and a matrix Y of size n x r, where r is the number of responses (n is the number of observations). Similarly, Xv and Yv hold the validation data. When I run the app, I see that it needs the data to be in a datastore, so I tried the following to make the appropriate datastores: (f = 3, r = 5, n = 500, n_val = 100).
ar_c = mat2cell([X Y],ones(500,1),[3,5]);
arv_c = mat2cell([Xv Yv],ones(100,1),[3,5]);
ds = arrayDatastore(ar_c,'OutputType','same');
ds_v = arrayDatastore(arv_c,'OutputType','same');
The datastores ds and ds_v get accepted as legitimate input (I can see the first 5 observations previewed).
But when I hit "train", I get the following error: Training with trainNetwork failed. Input datastore returned more than one observation per row for network input 1. (not sure why the "Don't" rules for posting recommend against pasting images of error messages).
As per the instructions given to mat2cell, I have only one row per observation (or so I think). Can someone please tell me what I'm doing wrong here? Thanks!

Accepted Answer

Srivardhan Gadila
Srivardhan Gadila on 19 Nov 2021
From the above information, I think your input layer would be a featureInputLayer. So according to your training data, the output of read operation on the combined datastore should be as follows:
>> read(cds)
ans =
1×2 cell array
{3×1 double} {5×1 double}
For more information you can refer to the documentation of trainNetwork and desciption of training data format for feature data in case of a datastore: features - trainNetwork.
I am attaching code to generate random training data:
f = 3; r = 5; n = 5;
layers = [featureInputLayer(f)
fullyConnectedLayer(r)
regressionLayer];
xtrain = randn(f,n);
ytrain = randn(r,n);
xds = arrayDatastore(xtrain,IterationDimension=2);
yds = arrayDatastore(ytrain,IterationDimension=2);
cds = combine(xds,yds)
cds =
CombinedDatastore with properties: UnderlyingDatastores: {[1×1 matlab.io.datastore.ArrayDatastore] [1×1 matlab.io.datastore.ArrayDatastore]} SupportedOutputFormats: ["txt" "csv" "xlsx" "xls" "parquet" "parq" "png" "jpg" "jpeg" "tif" "tiff" "wav" "flac" "ogg" "mp4" "m4a"]
  1 Comment
xadu
xadu on 21 Nov 2021
Thanks for the clarification! The trouble with what I did was that each of the cells of the combined datastructure was a row vector, whereas it needed to be a column vector. In hindsight I should have realized this just by parsing the error message better!

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