Using Deep learning for non image classification

Hi,

Can I know is there any example to follow on how to use deep learning for non image classification ? Example using deep learning to classify fisher iris data

Thanks.

[ahmed ismail]

waiting answer

[Laurence Mailaender]

I'm new at Machine Learning and had a lot of trouble finding examples in the documentation also... but it's there. Do 'help patternnet.' For simple classifiers, you can use train(), instead of trainNetwork. Try this:

load fisheriris

%contains 'meas' 150x4 and species 150x1

%set target (brute force)

target=[repmat([1;0;0],1,50),repmat([0;1;0],1,50),repmat([0;0;1],1,50)];

%randomize order

neworder=randsample(150,150);

measTrain=meas(neworder(1:100),:);

measTest=meas(neworder(101:end),:);

targetTrain=target(:,neworder(1:100));

targetTest=target(:,neworder(101:end));

%define and train shallow NN

snet=patternnet(10);

snet=train(snet,measTrain',targetTrain);

%test in sample

snn_in=sim(snet,measTrain');

perf_best=perform(snet,targetTrain,snn_in)

%outside training

snn_out=sim(snet,measTest');

perf_out=perform(snet,targetTest,snn_out)

Not sure I'm doing the inside/outside training in the best way, maybe you can repeat/extend the data in random order and improve the network..

Answer 1

[Johanna Pingel]

Documentation now has good non image examples!

Check out this link: https://www.mathworks.com/help/deeplearning/examples.html?category=deep-learning-with-time-series-sequences-and-text

[Marius Facktor]

How about a 1D non-sequence Neural Network? It should take input X (samples by features) and labels y.

These are the available input layers for trainNetwork:

imageInputLayer, image3dInputLayer, sequenceInputLayer, roiInputLayer

These are ALL for images or sequences. Why is there no generic "InputLayer" where you can specify the number of features?

How is this not a thing? Come on Matlab.

[Sai]

Matlab seems to focus more on the 'hot' deep neural network applications. Research community looks at python for a reason.