CODEWITHSUNDEEP
datasetneural-networkpybrain
Geoffrey Price
Geoffrey Price

Reputation: 11

pybrain image input to dataset for Neural Network

I'm trying to write a neural network that (after being properly trained) identifies certain road signs and returns a different output for each type of sign. Before I started to train my network, I noticed on the pybrain website that their datasets are always an array of values, each entry containing an input and a target. The images I have for my NN have been converted to grayscale pixel data (a simple array of numbers). To train each set of data, do I need to somehow add a target value for each pixel? And if so, how would I go about doing that?

Upvotes: 1

Views: 801

Answers (1)

Pawel Wisniewski
Pawel Wisniewski

Reputation: 440

QUICK ANSWER
No, you don't need target for every single pixel, you treat pixels from single image as your input data and you add target to that data.

LONG ANSWER
What you trying to do is to solve classification problem. You have image represented by array of numbers and you need to classify it as some class from limited set of classes. So lets say that you have 2 classes: prohibitions signs (I'm not native speaker, I don't know how you call signs that forbid something), and information signs. Lets say that prohibition signs is our class 1 and information signs is class 2.
Your data set should look like this:
([representation of sign in numbers], class) - single sample

After that, since it's classification problem, I recommend using _convertToOneOfMany() method of DataSet class, to convert your targets into multiple outputs.

I've answered similar question here, go check it out.

Upvotes: 2

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