shallow-training/README.md
2021-04-30 20:51:04 +01:00

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# Shallow Neural Network Training Coursework
Evaluating a neural network using the MatLab `cancer_dataset`. Development contained in the [nncw.ipynb](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
![Image](graphs/exp1-test2-1-error-rate-curves.png)
## 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)