Post-grad AI & AI programming coursework, neural network training and evaluation. Achieved 88%
| .vscode | ||
| graphs | ||
| matlab | ||
| report | ||
| results | ||
| .gitattributes | ||
| .gitignore | ||
| features.csv | ||
| nbgen | ||
| nncw.ipynb | ||
| nncw.py | ||
| pyproject.toml | ||
| README.md | ||
| scratchpad.ipynb | ||
| targets.csv | ||
Shallow Neural Network Training Coursework
Evaluating a neural network using the MatLab cancer_dataset. Development contained in the nncw.ipynb notebook.
- Evaluate the network's tendency to overfit by varying the number of epochs and hidden layers being used
- Multiple classifier performance using majority vote
- Repeat 2 with two different optimisers (
trainlm,trainrp) - 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)