Dear MATLAB Community,
I'm tying to train a Neural Network for Non-Image Classification task:
- I have a table 30x14 called Dataset containing 13 Columns which are the Features and the last column (14th) has the labels.
- Each Row in Dataset is one example.
- I also have the features saved as a 30x13 Matrix called "features" containg the features and a 30x1 Matrix called "labels" containing the labels.
- I also build an architecture using Deep Neural Network Designer Application in MATLAB and saved my architecture in a variable called "layers".
- I need to build and train a neural network for classifying future examples and assess the accuray of my model.
- I have options set as
options = trainingOptions('sgdm', ...
'MaxEpochs',20,...
'InitialLearnRate',1e-4, ...
'Verbose',false, ...
'Plots','training-progress');
Attempts:
(1) net = trainNetwork(Dataset,layers,options)
(2) net = trainNetwork(features,labels,layers,options)
Both attemps failed and gave errors
(1)
Error using trainNetwork (line 165)
Invalid training data table for classification. Predictors must be in the first column of
the table, as a cell array of image paths or images. Responses must be after the first
column, as categorical labels.
(2)
Error using trainNetwork (line 165)
Too many input arguments.
Caused by:
Error using trainNetwork>iParseInputArguments (line 290)
Too many input arguments.
Anyone has any idea what to do ?
Thank You
