1 unstable release
0.1.0 | Jul 15, 2019 |
---|
#767 in Machine learning
135KB
3.5K
SLoC
[ W. I. P. ]
Yet Another Rust Neural Network framework aka YARNN
Inspired by darknet
and leaf
What it can right now:
- not requires
std
(onlyalloc
for tensor allocations, bump allocator is ok, so it can be compiled to stm32f4 board) - available layers:
Linear
,ReLu
,Sigmoid
,Softmax
(no backward),Conv2d
,ZeroPadding2d
,MaxPool2d
,AvgPool2d
(no backward),Flatten
- available optimizers:
Sgd
,Adam
,RMSProp
- available losses:
CrossEntropy
(no forward),MeanSquareError
- available backends:
Native
,NativeBlas
(no convolution yet)
What it will can (I hope):
1st stage:
- example of running
yarnn
in browser usingWASM
- example of running
yarnn
onstm32f4
board - finish
AvgPool2d
backpropogation - add
Dropout
layer - add
BatchNorm
layer - convolution with BLAS support
2nd stage:
CUDA
supportOpenCL
support
3rd stage:
DepthwiseConv2d
layerConv3d
layerDeconv2d
layerk210
backend
Model definition example
use yarnn::model;
use yarnn::layer::*;
use yarnn::layers::*;
model! {
MnistConvModel (h: u32, w: u32, c: u32) {
input_shape: (c, h, w),
layers: {
Conv2d<N, B, O> {
filters: 8
},
ReLu<N, B>,
MaxPool2d<N, B> {
pool: (2, 2)
},
Conv2d<N, B, O> {
filters: 8
},
ReLu<N, B>,
MaxPool2d<N, B> {
pool: (2, 2)
},
Flatten<N, B>,
Linear<N, B, O> {
units: 10
},
Sigmoid<N, B>
}
}
}
Contributors are welcome
Dependencies
~1.6–2.3MB
~41K SLoC