Initializing Weights

WeightInitializers.jl provides common weight initialization schemes for deep learning models.

using WeightInitializers, Random

# Fixing rng
rng = Random.MersenneTwister(42)

Random.MersenneTwister(42)

# Explicit rng call
weights = kaiming_normal(rng, 2, 5)

2×5 Matrix{Float32}:
  0.76545    0.255203  -0.0424012   0.643172   -0.360745
 -0.0499631  0.183381   0.388315   -0.0340666  -0.54248

# Default rng call
weights = kaiming_normal(2, 5)

2×5 Matrix{Float32}:
 -0.227513  -0.265372   0.265788  1.29955  -0.192836
  0.687611   0.454679  -0.433656  0.20548   0.292002

# Passing kwargs (if needed) with explicit rng call
weights_cl = kaiming_normal(rng; gain=1.0)
weights = weights_cl(2, 5)

2×5 Matrix{Float32}:
 -0.094564   0.196581   0.0791126  -0.794864  0.631217
 -0.381774  -0.588045  -0.113952   -0.567746  0.261636

# Passing kwargs (if needed) with default rng call
weights_cl = kaiming_normal(; gain=1.0)
weights = weights_cl(2, 5)

2×5 Matrix{Float32}:
 -0.160876  -0.187646   0.18794   0.918918  -0.136356
  0.486214   0.321506  -0.306641  0.145296   0.206476

To generate weights directly on GPU, pass in a CUDA.RNG. For a complete list of supported RNG types, see Supported RNG Types.

using LuxCUDA

weights = kaiming_normal(CUDA.default_rng(), 2, 5)

You can also generate Complex Numbers:

weights = kaiming_normal(CUDA.default_rng(), ComplexF32, 2, 5)

Quick examples

The package is meant to be working with deep learning libraries such as (F)Lux. All the methods take as input the chosen rng type and the dimension for the array.

weights = init(rng, dims...)

The rng is optional, if not specified a default one will be used.

weights = init(dims...)

If there is the need to use keyword arguments the methods can be called with just the rng (optionally) and the keywords to get in return a function behaving like the two examples above.

weights_init = init(rng; kwargs...)
weights = weights_init(rng, dims...)

# Or

weights_init = init(; kwargs...)
weights = weights_init(dims...)