Cortexsys Providing cross-validation sets for LSTM

Is there a way to provide cross-validation/test sets for the LSTM training function? I can understand that the function is defined as follows:

gradientDescentAdaDelta(costFunc, nn, defs, Xts, Yts, yts, y, 'Training
Entire Network');

My question is what are the : Xts, Yts, yts and y variables?

Thanks a lot.

Oct 23 '16 11:10 shaimaahegazy

From what I know, like for the MNIST example data, X being the training data and y being the testing data,

X = (28_28... image data)= 784 * 60000 .... variations training data input Xts = (28_28...image data)= 784 * 10000... variations test data input y =(label of image data) 1 * 60000...variations training data output yts = (label for image data)1 * 10000 .. .variations test data output

Oct 24 '16 02:10 guanyou

Thanks for your reply @guanyou. Do you know where I could get the MNIST data used in the example?

Oct 24 '16 10:10 shaimaahegazy

I'm guessing u could get it from below, or just implement those data conversion code found online with the original dataset found here http://yann.lecun.com/exdb/mnist/

http://www.cs.toronto.edu/~norouzi/research/mlh/data/mnist-full.mat http://cs.nyu.edu/~roweis/data.html

Oct 25 '16 07:10 guanyou

Hi @shaimaahegazy . I wonder if you have managed to run any of the examples provided with the toolbox where the network converges.

I, so far tried the MNIST_ConvNet_Classifier.m and the MNIST_Deep_Classifier.m where I download the mnist-full.mat from http://www.cs.toronto.edu/~norouzi/research/mlh/data/mnist-full.mat However, the networks in both examples do not converge. Any help will be much appreciated

Aug 22 '17 00:08 RMano82