Explanation for Training and Classes

[mixkter]

This basic example program is the skeleton of a classification problem.
The training data should be in matrix form, where each row is a data point, and
each column is a feature.
The training classes should be in matrix form, where the ith row corresponds to
the ith training example, and each column is a 1 if it is of that class, and
0 otherwise. Each example may only be of 1 class.

This isn't clear to me. Let's say I have a training data set with 8 features
And I want them classified into 4 classes

Training - rows of eight columns one feature per column
1,2,3,4,5,6,7,8
6,2,3,4,7,6,5,4
6,2,3,4,4,5,5,4
3,4,7,6,5,4,3,2
2,3,4,5,6,4,3,3

Classifications - since I have four classes I would think this has 4 columns, each column indicating which class it is in
0,0,1,0
1,0,0,0
1,0,0,0
0,1,0,0
0,0,01
That's five rows and 4 columns. One column per class.

Create network would be:
createNetwork(8, 1, hiddenSizeReg, hiddenActivationsReg, 4, linear);
Network with 8 inputs 1 hidden layer and 4 outputs (1 per class)

But accuracy requires this:
assert(data->cols == network->layers[network->numLayers - 1]->size);

Can you give a better example?

Thanks

[100]

My mistake, I forgot to write a unit test for that function.

It should be assert(classes->cols == network->layers[network->numLayers - 1]->size);.

Feel free to send a PR or I can fix it later on my own.

[mixkter]

I made quite a few other changes including support for doubles or floats, CUDA BLAS, and renamed a lot of stuff. A PR would be a huge change for you.

Thanks for fix!

[100]

Ok, fixed it in the main branch, thanks again for finding this