Sarsoo / shallow-training

Post-grad AI & AI programming coursework, neural network training and evaluation. Achieved 88%

Shallow Neural Network Training Coursework

Evaluating a neural network using the MatLab cancer_dataset. Development contained in the nncw.ipynb notebook.

1. Evaluate the network's tendency to overfit by varying the number of epochs and hidden layers being used
2. Multiple classifier performance using majority vote
3. Repeat 2 with two different optimisers (trainlm, trainrp)
4. Extension: Distinguish between two equi-probable classes of overlapping 2D Gaussians

Timing

exp 1

CPU: 2min 36s ± 1.66 s per loop (mean ± std. dev. of 2 runs, 2 loops each)

GPU: 3min 5s ± 2.95 s per loop (mean ± std. dev. of 2 runs, 2 loops each)

exp 2

CPU: 26 s ± 62.9 ms per loop (mean ± std. dev. of 2 runs, 2 loops each)

GPU: 57.6 s ± 46.7 ms per loop (mean ± std. dev. of 2 runs, 2 loops each)

exp 3

CPU: 1min 19s ± 1.6 s per loop (mean ± std. dev. of 2 runs, 2 loops each)

GPU: 3min 25s ± 280 ms per loop (mean ± std. dev. of 2 runs, 2 loops each)