`DistributedFlux.orthogonal`

— Method`orthogonal(dim)`

Return a random orthogonal maxtrix of size `(dim, dim)`

. This is commonly used for kernel initialization of recurrent layers.

**Examples**

```
julia> Flux.orthogonal(2)
2×2 Array{Float32,2}:
-0.633973 -0.773356
-0.773356 0.633973
```

See Exact solutions to the nonlinear dynamics of learning in deep linear neural networks

`DistributedFlux.orthogonal_matrix`

— Method`orthogonal_matrix(nrow, ncol)`

If the shape of the matrix to initialize is two-dimensional, it is initialized with an orthogonal matrix obtained from the QR decomposition of a matrix of random numbers drawn from a normal distribution. If the matrix has fewer rows than columns then the output will have orthogonal rows. Otherwise, the output will have orthogonal columns.