67 lines
1.7 KiB
Markdown
67 lines
1.7 KiB
Markdown
# [ W. I. P. ]
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## Yet Another Rust Neural Network framework aka YARNN
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Inspired by `darknet` and `leaf`
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## What it can right now:
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* not requires `std` (only `alloc` for tensor allocations, bump allocator is ok, so it can be compiled to stm32f4 board)
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* available layers: `Linear`, `ReLu`, `Sigmoid`, `Softmax`(no backward), `Conv2d`, `ZeroPadding2d`, `MaxPool2d`, `AvgPool2d`(no backward), `Flatten`
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* available optimizers: `Sgd`, `Adam`, `RMSProp`
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* available losses: `CrossEntropy`(no forward), `MeanSquareError`
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* available backends: `Native`, `NativeBlas`(no convolution yet)
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## What it will can (I hope):
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### 1st stage:
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* example of running `yarnn` in browser using `WASM`
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* example of running `yarnn` on `stm32f4` board
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* finish `AvgPool2d` backpropogation
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* add `Dropout` layer
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* add `BatchNorm` layer
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* convolution with BLAS support
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### 2nd stage:
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* `CUDA` support
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* `OpenCL` support
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### 3rd stage:
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* `DepthwiseConv2d` layer
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* `Conv3d` layer
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* `Deconv2d` layer
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* `k210` backend
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## Model definition example
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```rust
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use yarnn::model;
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use yarnn::layer::*;
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use yarnn::layers::*;
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model! {
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MnistConvModel (h: u32, w: u32, c: u32) {
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input_shape: (c, h, w),
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layers: {
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Conv2d<N, B, O> {
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filters: 8
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},
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ReLu<N, B>,
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MaxPool2d<N, B> {
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pool: (2, 2)
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},
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Conv2d<N, B, O> {
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filters: 8
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},
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ReLu<N, B>,
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MaxPool2d<N, B> {
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pool: (2, 2)
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},
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Flatten<N, B>,
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Linear<N, B, O> {
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units: 10
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},
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Sigmoid<N, B>
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}
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}
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}
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```
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## Contributors are welcome |