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document bias switch

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This commit is contained in:
Dhairya Gandhi 2019-10-08 17:54:01 +05:30
parent b596faaffa
commit 95c5845e99
1 changed files with 28 additions and 0 deletions
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src/layers/conv.jl
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@ -21,6 +21,10 @@ Data should be stored in WHCN order (width, height, # channels, # batches).
In other words, a 100×100 RGB image would be a `100×100×3×1` array,
and a batch of 50 would be a `100×100×3×50` array.
Accepts keyword arguments `weight` and `bias` to set the corresponding fields.
Setting `bias` to `Flux.ZeroType((out,))` will switch bias off for the
layer.
Takes the keyword arguments `pad`, `stride` and `dilation`.
"""
struct Conv{N,M,F,A,V}
@ -40,6 +44,9 @@ Constructs the convolutional layer with user defined weight and bias arrays.
All other behaviours of the Conv layer apply with regard to data order and
forward pass.
Setting `bias` to `nothing` or `Flux.ZeroType((out,))` would switch `bias` off for the
layer.
Takes the keyword arguments `pad`, `stride` and `dilation`.
"""
function Conv(w::AbstractArray{T,N}, b::Union{Nothing, ZeroType, AbstractVector{T}}, σ = identity;
@ -106,6 +113,10 @@ Standard convolutional transpose layer. `filter` should be a tuple like `(2, 2)`
Data should be stored in WHCN order. In other words, a 100×100 RGB image would
be a `100×100×3` array, and a batch of 50 would be a `100×100×3×50` array.
Accepts keyword arguments `weight` and `bias` to set the corresponding fields.
Setting `bias` to `Flux.ZeroType((out,))` will switch bias off for the
layer.
Takes the keyword arguments `pad`, `stride` and `dilation`.
"""
struct ConvTranspose{N,M,F,A,V}
@ -125,6 +136,9 @@ Constructs the convolutional transpose layer with user defined weight and bias a
All other behaviours of the ConvTranspose layer apply with regard to data order and
forward pass.
Setting `bias` to `nothing` or `Flux.ZeroType((out,))` would switch `bias` off for the
layer.
Takes the keyword arguments `pad`, `stride` and `dilation`.
"""
function ConvTranspose(w::AbstractArray{T,N}, b::Union{Nothing, ZeroType, AbstractVector{T}}, σ = identity;
@ -191,6 +205,10 @@ Note that `out` must be an integer multiple of `in`.
Data should be stored in WHCN order. In other words, a 100×100 RGB image would
be a `100×100×3` array, and a batch of 50 would be a `100×100×3×50` array.
Accepts keyword arguments `weight` and `bias` to set the corresponding fields.
Setting `bias` to `Flux.ZeroType((out,))` will switch bias off for the
layer.
Takes the keyword arguments `pad`, `stride` and `dilation`.
"""
struct DepthwiseConv{N,M,F,A,V}
@ -210,6 +228,9 @@ Constructs the `DepthwiseConv` layer with user defined weight and bias arrays.
All other behaviours of the `DepthwiseConv` layer apply with regard to data order and
forward pass.
Setting `bias` to `nothing` or `Flux.ZeroType((out,))` would switch `bias` off for the
layer.
Takes the keyword arguments `pad`, `stride` and `dilation`.
"""
function DepthwiseConv(w::AbstractArray{T,N}, b::Union{Nothing, ZeroType, AbstractVector{T}}, σ = identity;
@ -290,6 +311,10 @@ Data should be stored in WHCN order (width, height, # channels, # batches).
In other words, a 100×100 RGB image would be a `100×100×3×1` array,
and a batch of 50 would be a `100×100×3×50` array.
Accepts keyword arguments `weight` and `bias` to set the corresponding fields.
Setting `bias` to `Flux.ZeroType((out,))` will switch bias off for the
layer.
Takes the keyword arguments `pad`, `stride` and `dilation`.
"""
struct CrossCor{N,M,F,A,V}
@ -309,6 +334,9 @@ Constructs the standard cross convolutional layer with user defined weight and b
arrays. All other behaviours of the CrossCor layer apply with regard to data order and
forward pass.
Setting `bias` to `nothing` or `Flux.ZeroType((out,))` would switch `bias` off for the
layer.
Takes the keyword arguments `pad`, `stride` and `dilation`.
"""
function CrossCor(w::AbstractArray{T,N}, b::Union{Nothing, ZeroType, AbstractVector{T}}, σ = identity;