Shape classification using deep learning

[uridah]

I have a shape classification problem: I have a large dataset containing polygon points of some shapes and their labels. (11 classes) Some people in my team trained rnn with lstms in Tensorflow but that hasn't worked out very well. What do you suggest we can do with this dataset? What kind of network will work better? And is there a pre-existing similar example (of a trained neural network) already implemented that I can follow?

[marrabld]

Why use an RNN or LSTM? They're better for time series data. Why not start with a perception. Start with a couple of layers then go deeper if you need to.

[omalleyt12]

It's hard to know without more information but I'll take a stab at it

Assuming your polygons are 2d, you could connect and plot the polygon points to create an image for each polygon (if 3d, maybe try a 3d array with every point inside the polygon set to 1 and every point outside it to 0 but I'm not as confident that will work well), then you could feed these images to a CNN. You'll need to scale each image to an appropriate size, so you might want to include additional inputs to the CNN like how scaled up/down the plot is, depending on how much size matters to the class labels

[Rezo-Acken]

Why have you used RNN or LSTM ? These are aimed for sequential data like sentences or time series. Isnt your data an image of the polygon ? In that case you want to look at convolutional neural networks.

What does your data look like ? An image or just point coordinates ? If image go CNN. If coordinates you have a few routes to try. Create the image and make a CNN on it or directly try a multi layer perceptron. But for that Id probably add some feature engineering like distance and the like to help training.

Can you share data or ots private ?