Merge pull request #2081 from Saransh-cpp/create_bias

Back to create_bias

src/layers/basic.jl

@@ -155,7 +155,7 @@ struct Dense{F, M<:AbstractMatrix, B}
   bias::B
   σ::F
   function Dense(W::M, bias = true, σ::F = identity) where {M<:AbstractMatrix, F}
-    b = _create_bias(W, bias, size(W,1))
+    b = create_bias(W, bias, size(W,1))
     new{F,M,typeof(b)}(W, b, σ)
   end
 end
@@ -228,7 +228,7 @@ struct Scale{F, A<:AbstractArray, B}
   bias::B
   σ::F
   function Scale(scale::A, bias::B = true, σ::F = identity) where {A<:AbstractArray, B<:Union{Bool, AbstractArray}, F}
-    b = _create_bias(scale, bias, size(scale)...)
+    b = create_bias(scale, bias, size(scale)...)
     new{F, A, typeof(b)}(scale, b, σ)
   end
 end
@@ -403,7 +403,7 @@ struct Bilinear{F,A,B}
   σ::F
   function Bilinear(W::A, bias = true, σ::F = identity) where {A<:AbstractArray, F}
     ndims(A) == 3 || throw(ArgumentError("expected a 3-array of weights"))
-    b = _create_bias(W, bias, size(W,1))
+    b = create_bias(W, bias, size(W,1))
     new{F,A,typeof(b)}(W, b, σ)
   end
 end

src/layers/conv.jl

@@ -156,7 +156,7 @@ function Conv(w::AbstractArray{T,N}, b = true, σ = identity;
   stride = expand(Val(N-2), stride)
   dilation = expand(Val(N-2), dilation)
   pad = calc_padding(Conv, pad, size(w)[1:N-2], dilation, stride)
-  bias = _create_bias(w, b, size(w, N))
+  bias = create_bias(w, b, size(w, N))
   return Conv(σ, w, bias, stride, pad, dilation, groups)
 end
 
@@ -293,7 +293,7 @@ function ConvTranspose(w::AbstractArray{T,N}, bias = true, σ = identity;
   stride = expand(Val(N-2), stride)
   dilation = expand(Val(N-2), dilation)
   pad = calc_padding(ConvTranspose, pad, size(w)[1:N-2], dilation, stride)
-  b = _create_bias(w, bias, size(w, N-1) * groups)
+  b = create_bias(w, bias, size(w, N-1) * groups)
   return ConvTranspose(σ, w, b, stride, pad, dilation, groups)
 end
 
@@ -441,7 +441,7 @@ function CrossCor(w::AbstractArray{T,N}, bias = true, σ = identity;
   stride = expand(Val(N-2), stride)
   dilation = expand(Val(N-2), dilation)
   pad = calc_padding(CrossCor, pad, size(w)[1:N-2], dilation, stride)
-  b = _create_bias(w, bias, size(w, N))
+  b = create_bias(w, bias, size(w, N))
   return CrossCor(σ, w, b, stride, pad, dilation)
 end
 

src/utils.jl

@@ -504,7 +504,7 @@ randn32(rng::AbstractRNG, dims::Integer...) = Base.randn(rng, Float32, dims...)
 randn32(rng::AbstractRNG) = (dims...,) -> Base.randn(rng, Float32, dims...)
 
 """
-    _create_bias(weights, bias, size...)
+    create_bias(weights, bias, size...)
 
 Return a bias parameter for a layer, based on the value given
 to the constructor's keyword `bias=bias`.
@@ -514,17 +514,14 @@ to the constructor's keyword `bias=bias`.
 * `bias::AbstractArray` uses the array provided, provided it has the correct size.
   It does not at present correct the `eltype` to match that of `weights`.
 """
-function _create_bias(weights::AbstractArray, bias::Bool, dims::Integer...)
+function create_bias(weights::AbstractArray, bias::Bool, dims::Integer...)
   bias ? fill!(similar(weights, dims...), 0) : false
 end
-function _create_bias(weights::AbstractArray, bias::AbstractArray, dims::Integer...)
+function create_bias(weights::AbstractArray, bias::AbstractArray, dims::Integer...)
   size(bias) == dims || throw(DimensionMismatch("expected bias of size $(dims), got size $(size(bias))"))
   bias
 end
 
-# TODO figure out whether we want to document or deprecate this
-const create_bias = _create_bias
-
 
 # Other