Inspired by darknet and leaf

• not requires std (only alloc 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)

• example of running yarnn in browser using WASM
• example of running yarnn on stm32f4 board
• finish AvgPool2d backpropogation
• add Dropout layer
• add BatchNorm layer
• convolution with BLAS support

• CUDA support
• OpenCL support

• DepthwiseConv2d layer
• Conv3d layer
• Deconv2d layer
• k210 backend

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>
        }
    }
}