model-zoo
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Add literature background to the models
I'll start working on adding literature background to all of the existing models (ref https://github.com/FluxML/model-zoo/issues/312). Then, we can move on to working on creating a standalone site to host all of the models as notebooks.
I plan to cover the following models:
- [x] Simple multi-layer perceptron
- [x] dataloader tutorial
- [x] Housing
- [x] Logistic Regression Iris
- [x] Simple ConvNet (LeNet)
- [ ] Variational Auto-Encoder
- [ ] Deep Convolutional Generative Adversarial Networks
- [ ] Conditional Deep Convolutional Generative Adversarial Networks
- [ ] VGG 16/19 on CIFAR10
- [x] CharRNN
- [x] Character-level language detection
- [ ] Seq2Seq phoneme detection on CMUDict
- [x] Recursive net on IMDB sentiment treebank
- [ ] BitString Parity Challenge
- [ ] Speech recognition
@DhairyaLGandhi @logankilpatrick just tagging you to keep you in the loop :)
This is great!
Looking ahead to the website step, I think it would be better and simpler to just host a "model zoo" or "tutorials" section in the Flux docs themselves:
- almost every manual on writing good documentation mentions these types of sections which Flux is severely lacking right now
- added benefit of being verified with every PR to Flux allowing us to keep it up to date and free of the bit rot the model zoo historically suffers
- having information accessible in a central location is easier for users
Thank you @darsnack! Totally agree with your comments.
I think it is good to add some comments to some of the basics examples, but I wouldn't want them to be too loaded either. Model-zoo examples are supposedly used for bootstrapping user projects, they aren't meant as tutorials (which we lack and should live in Flux.jl).
We can use Literate to create a "clean" version of the examples.
Regarding the Simple ConvNet (LeNet5) example, I noticed it is using relu
activations and max-pooling. I think the original LeCun paper doesn't do this. It is also different from https://d2l.ai/chapter_convolutional-neural-networks/lenet.html, which is cited in the readme of this example.
Any reason for these discrepancies?
@cossio I just wrote the text and took the code as it was on the repo. Perhaps somebody else can comment on these discrepancies?
I think that's a modernized version of Lenet5 (relu had not been "invented" at the time) which I've found somewhere (don't remember where though). Maybe we can add a comment on that saying that the original model uses sigmoids?
Added a note to the model to specify this.