The 5th installment of our tutorial on implementing a neural network (NN) in Python. By the end of this tutorial, our NN should perform much more efficiently giving good results with fewer iterations. We will do this by implementing “momentum” into our network. We will also put in the other transfer functions for each layer.
Part 4 of our tutorial series on Simple Neural Networks. We’re ready to write our Python script! Having gone through the maths, vectorisation and activation functions, we’re now ready to put it all together and write it up. By the end of this tutorial, you will have a working NN in Python, using only numpy, which can be used to learn the output of logic gates (e.g. XOR)
Surface Distance measures are a good way of evaluating the accuracy of an image-segmentation if we already know the ground truth (GT). The problem is that there is no nicely packaged function in Python to do this directly. In this post, we’ll write a surface distance function in Python which uses numpy and scipy. It’ll help us to calculate Mean Surface Distance (MSD), Residual Mean-Square Error (RMS) and the Hausdorff Distance (HD).