How do I define a neural network target matrix in this situation?

My input data is a 100 x 200 matrix. The 100 rows represent different subjects and the 200 columns are data points for different times (1 min, 2 min, etc). The data is such that the item which I am measuring changes over time, so for every subject/trial there are 200 different values for 200 time points.

My output would be either a 1 or a 0, meaning a yes or a no. I am trying to create a neural network that predicts whether or not the result for one subject (1 row) would be a 1 or a 0, and I am having trouble defining the target matrix. Here is what I have so far:

% 1.1 means subject 1, data point 1, etc. This is a 100 x 200 matrix
input = [1.1 1.2 1.3 1.4...; 2.1 2.2 2.3 2.4... ;...];
% This is a 100 x 1 matrix of what actually results from each row of the input matrix; meaning that 1.1 1.2 1.3 etc are values that correspond to the first 1 in the target matrix. 
target = [1 0 1 0 1 0 0 0 1 ...]'

I assumed that the target matrix should be vertical (100 x 1) so I added a ' to transpose it. However, when trying to create the neural network I am presented with an error that says the dimensions of the matrices are not the same. I know that the dimensions are not the same, but they are not supposed to be because 1 x 200 of the input matrix correspond to just one column of the target matrix.

[Rik]

@Akshar Agarwal regarding your flag ("Please delete this question. I posted it a long time ago and it is very silly and I dont want it to come up on google search"):

Also very silly questions can be very useful. You have to start somewhere if you're new to a topic. Removing this question migth cause this same question to be asked again, which would mean Stephen would have to answer it again. This is of course a waste of everybody's time, which is why we tend to only delete questions under very rare circumstances (or if there is a duplicate question).

Answer 1

[Stephen Jue]

Hi,

I believe your input data should be 200 x 100 and your target data should be 1 x 100.

In general, the second dimension of your input data and target data should always match. You can see this by example by loading one of the sample classification datasets, iris_dastaset:

load iris_dataset

This loads irisInputs (4 x 150) and irisTargets (3 x 150).