A library to create a large number of micro-scale neural networks using Taichi language.
Framework is inspired by tiny-cuda-nn.
The parrallelization is among all the networks, rather than the matrix computation inside one NN, between layers.
All the matrix computation between nerual network layers are unrolled by Taichi at compile time, executing in all parrallel threads,
which means the maxtrix size cannot be too large.
When the matrix size exceed 32, e.g. for a 6-input 6-output layer, which means 36 matrix elements (>32), Taichi will give out a compile warning.
Network config is located at data/*.json
python mlp_learn_an_image.py
For simplication, settings are located at the head of mlp_learn_an_image.py.
| key/mouse | visualied field | description |
|---|---|---|
<space> |
- | train all NN |
| click LMB | - | train the NN at cursor postion |
t |
train_batch | random xy coord [0, 1) |
i |
inference_batch | meshgrid xy coord [0, 1) |
g |
train_target | sampled pixel from reference |
r |
reference_img | ground truth GT |
l |
inference_loss | inference loss to GT |
p |
- | print profiler info |