My goal is to train an Autoencoder in Matlab. I am using the Deep Learning Toolbox. I am new to both autoencoders and Matlab, so please bear with me if the question is trivial.
My input datasets is a list of 2000 time series, each with 501 entries for each time component. So my input dataset is stored into an array called inputdata which has dimensions 2000*501.
The autoencoder should reproduce the time series. Thi means the output should be 2000 times a time series of 501 components. So, my understanding is that the input nodes should be 501 and the same should be true for the output nodes.
However, if I do:
hiddenSize = 100;
autoenc = trainAutoencoder(y_sorted,hiddenSize);
to train an autoencoder with 100 nodes in the hidden layer, I think the Autoencoder automatically chooses to have 2000 input nodes. What is the correct way of training this Autoencoder?
Hi I haven't tried to train an autoencoder myself with the Deeplearning toolbox, but as far as i can read here (https://www.mathworks.com/help/deeplearning/ref/trainautoencoder.html?s_tid=doc_ta) your input matrix should have the samples as columns and the features/values of your timeseries in the rows. You can do this easy by transposing your input matrix. In MATLAB this is done by:
inputdata = inputdata.'