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jorgenkg / python-neural-network

This is an efficient implementation of a fully connected neural network in NumPy. The network can be trained by a variety of learning algorithms: backpropagation, resilient backpropagation and scaled conjugate gradient learning. The network has been developed with PYPY in mind.
BSD 2-Clause "Simplified" License
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Example tests on training set #27

Closed wizzwizz4 closed 6 years ago

wizzwizz4 commented 6 years ago

The example tests on the same dataset that it trains on. This makes it liable to overfitting.

There's no way around it for this specific example, unfortunately; perhaps the example could be changed to make it possible to demonstrate without having this flaw?

jorgenkg commented 6 years ago

Correct, albeit solving the XOR problem is a famous neural network problem. The example illustrate how a neural network can be specified along with its error and activation functions. Hence, the example is no flaw - only a minimal and verifiable example of how to apply the library