I have a 2-class classification problem in hand. I have extracted a set of 3 features for each training example .. I am planning to use a very simple CNN to learn the weights. My model looks like
I am planning to use tensorflow for implementing this CNN in python. The official tutorial https://www.tensorflow.org/tutorials/deep_cnn/ seems to be somewhat abstract . Can I get a basic code to train this?
You seem to be missing the point of CNN, which require signal with spatial relations (such as raw images, audio, etc.). Convolving signal with three features makes pretty much no sense (pretty much the only option would be a 2x1 filter convolving among the only axis, leading to nearly regular MLP). What you are looking for is rather basic classifier, and in general - neural nets are probably not the good choice (they are not good models for small, low-dimensional problems), you should be fine with models like kernelized SVM, and other classifiers which are available in scikit-learn. For basic TF code look at its basic tutorial, since as said before - this is not a problem for CNN. Furthermore, TF is not a simple library which trains a model in few lines of code, if you are looking for this kind of things you should rather take a look at keras, tf-slim or other libraries built on top of TF.
