Miscellaneous docstring additions and fixes

docs/src/utilities.md

@@ -42,7 +42,11 @@ Flux.orthogonal
 Flux.sparse_init
 Flux.identity_init
 Flux.ones32
+Flux.zeros32
 Flux.rand32
+Flux.randn32
+Flux.rng_from_array
+Flux.default_rng_value
 ```
 
 ## Changing the type of model parameters

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/layers/normalise.jl

@@ -51,7 +51,7 @@ end
 ChainRulesCore.@non_differentiable dropout_mask(::Any, ::Any, ::Any)
 
 """
-    Dropout(p; dims=:, rng = rng_from_array())
+    Dropout(p; dims=:, rng = default_rng_value())
 
 Dropout layer.
 
@@ -96,9 +96,9 @@ mutable struct Dropout{F,D,R<:AbstractRNG}
   active::Union{Bool, Nothing}
   rng::R
 end
-Dropout(p, dims, active) = Dropout(p, dims, active, rng_from_array())
+Dropout(p, dims, active) = Dropout(p, dims, active, default_rng_value())
 
-function Dropout(p; dims=:, rng = rng_from_array())
+function Dropout(p; dims=:, rng = default_rng_value())
   @assert 0 ≤ p ≤ 1
   Dropout(p, dims, nothing, rng)
 end
@@ -121,7 +121,7 @@ function Base.show(io::IO, d::Dropout)
 end
 
 """
-    AlphaDropout(p; rng = rng_from_array())
+    AlphaDropout(p; rng = default_rng_value())
 
 A dropout layer. Used in
 [Self-Normalizing Neural Networks](https://arxiv.org/abs/1706.02515).
@@ -155,8 +155,8 @@ mutable struct AlphaDropout{F,R<:AbstractRNG}
     new{typeof(p), typeof(rng)}(p, active, rng)
   end
 end
-AlphaDropout(p, active) = AlphaDropout(p, active, rng_from_array())
-AlphaDropout(p; rng = rng_from_array()) = AlphaDropout(p, nothing, rng)
+AlphaDropout(p, active) = AlphaDropout(p, active, default_rng_value())
+AlphaDropout(p; rng = default_rng_value()) = AlphaDropout(p, nothing, rng)
 
 @functor AlphaDropout
 trainable(a::AlphaDropout) = (;)

src/layers/stateless.jl

@@ -32,6 +32,25 @@ end
 Normalise `x` to mean 0 and standard deviation 1 across the dimension(s) given by `dims`.
 Per default, `dims` is the last dimension. 
 `ϵ` is a small additive factor added to the denominator for numerical stability.
+
+# Examples
+```jldoctest
+julia> using Statistics
+
+julia> x = [9, 10, 20, 60];
+
+julia> y = Flux.normalise(x);
+
+julia> isapprox(std(y), 1, atol=0.2) && std(y) != std(x)
+true
+
+julia> x = rand(1:100, 10, 2);
+
+julia> y = Flux.normalise(x, dims=1);
+
+julia> isapprox(std(y, dims=1), ones(1, 2), atol=0.2) && std(y, dims=1) != std(x, dims=1)
+true
+```
 """
 @inline function normalise(x::AbstractArray; dims=ndims(x), ϵ=ofeltype(x, 1e-5))
   μ = mean(x, dims=dims)

src/losses/functions.jl

@@ -80,6 +80,17 @@ given the prediction `ŷ` and true values `y`.
                  | 0.5 * |ŷ - y|^2,            for |ŷ - y| <= δ
     Huber loss = |
                  |  δ * (|ŷ - y| - 0.5 * δ), otherwise
+
+# Example
+```jldoctest
+julia> ŷ = [1.1, 2.1, 3.1];
+
+julia> Flux.huber_loss(ŷ, 1:3)  # default δ = 1 > |ŷ - y|
+0.005000000000000009
+
+julia> Flux.huber_loss(ŷ, 1:3, δ=0.05)  # changes behaviour as |ŷ - y| > δ
+0.003750000000000005
+```
 """
 function huber_loss(ŷ, y; agg = mean, δ = ofeltype(ŷ, 1))
    _check_sizes(ŷ, y)
@@ -377,12 +388,22 @@ function kldivergence(ŷ, y; dims = 1, agg = mean, ϵ = epseltype(ŷ))
 end
 
 """
-    poisson_loss(ŷ, y)
+    poisson_loss(ŷ, y; agg = mean)
 
-# Return how much the predicted distribution `ŷ` diverges from the expected Poisson
-# distribution `y`; calculated as `sum(ŷ .- y .* log.(ŷ)) / size(y, 2)`.
+Return how much the predicted distribution `ŷ` diverges from the expected Poisson
+distribution `y`; calculated as -
+
+    sum(ŷ .- y .* log.(ŷ)) / size(y, 2)
 
 [More information.](https://peltarion.com/knowledge-center/documentation/modeling-view/build-an-ai-model/loss-functions/poisson).
+
+# Example
+```jldoctest
+julia> y_model = [1, 3, 3];  # data should only take integral values
+
+julia> Flux.poisson_loss(y_model, 1:3)
+0.5023128522198171
+```
 """
 function poisson_loss(ŷ, y; agg = mean)
   _check_sizes(ŷ, y)
@@ -392,11 +413,32 @@ end
 """
     hinge_loss(ŷ, y; agg = mean)
 
-Return the [hinge_loss loss](https://en.wikipedia.org/wiki/Hinge_loss) given the
+Return the [hinge_loss](https://en.wikipedia.org/wiki/Hinge_loss) given the
 prediction `ŷ` and true labels `y` (containing 1 or -1); calculated as
-`sum(max.(0, 1 .- ŷ .* y)) / size(y, 2)`.
 
+    sum(max.(0, 1 .- ŷ .* y)) / size(y, 2)
+
+Usually used with classifiers like Support Vector Machines.
 See also: [`squared_hinge_loss`](@ref)
+
+# Example
+```jldoctest
+julia> y_true = [1, -1, 1, 1];
+
+julia> y_pred = [0.1, 0.3, 1, 1.5];
+
+julia> Flux.hinge_loss(y_pred, y_true)
+0.55
+
+julia> Flux.hinge_loss(y_pred[1], y_true[1]) != 0  # same sign but |ŷ| < 1
+true
+
+julia> Flux.hinge_loss(y_pred[end], y_true[end]) == 0  # same sign but |ŷ| >= 1
+true
+
+julia> Flux.hinge_loss(y_pred[2], y_true[2]) != 0 # opposite signs
+true
+```
 """
 function hinge_loss(ŷ, y; agg = mean)
   _check_sizes(ŷ, y)
@@ -407,9 +449,31 @@ end
     squared_hinge_loss(ŷ, y)
 
 Return the squared hinge_loss loss given the prediction `ŷ` and true labels `y`
-(containing 1 or -1); calculated as `sum((max.(0, 1 .- ŷ .* y)).^2) / size(y, 2)`.
+(containing 1 or -1); calculated as
+
+    sum((max.(0, 1 .- ŷ .* y)).^2) / size(y, 2)
 
+Usually used with classifiers like Support Vector Machines.
 See also: [`hinge_loss`](@ref)
+
+# Example
+```jldoctes
+julia> y_true = [1, -1, 1, 1];
+
+julia> y_pred = [0.1, 0.3, 1, 1.5];
+
+julia> Flux.squared_hinge_loss(y_pred, y_true)
+0.625
+
+julia> Flux.squared_hinge_loss(y_pred[1], y_true[1]) != 0
+true
+
+julia> Flux.squared_hinge_loss(y_pred[end], y_true[end]) == 0
+true
+
+julia> Flux.squared_hinge_loss(y_pred[2], y_true[2]) != 0
+true
+```
 """
 function squared_hinge_loss(ŷ, y; agg = mean)
   _check_sizes(ŷ, y)
@@ -422,9 +486,20 @@ end
 Return a loss based on the dice coefficient.
 Used in the [V-Net](https://arxiv.org/abs/1606.04797) image segmentation
 architecture.
-Similar to the F1_score. Calculated as:
+The dice coefficient is similar to the F1_score. Loss calculated as:
 
     1 - 2*sum(|ŷ .* y| + smooth) / (sum(ŷ.^2) + sum(y.^2) + smooth)
+
+# Example
+```jldoctest
+julia> y_pred = [1.1, 2.1, 3.1];
+
+julia> Flux.dice_coeff_loss(y_pred, 1:3)
+0.000992391663909964
+
+julia> 1 - Flux.dice_coeff_loss(y_pred, 1:3)  # ~ F1 score for image segmentation
+0.99900760833609
+```
 """
 function dice_coeff_loss(ŷ, y; smooth = ofeltype(ŷ, 1.0))
   _check_sizes(ŷ, y)
@@ -436,9 +511,11 @@ end
 
 Return the [Tversky loss](https://arxiv.org/abs/1706.05721).
 Used with imbalanced data to give more weight to false negatives.
-Larger β weigh recall more than precision (by placing more emphasis on false negatives)
+Larger β weigh recall more than precision (by placing more emphasis on false negatives).
 Calculated as:
-    1 - sum(|y .* ŷ| + 1) / (sum(y .* ŷ + β*(1 .- y) .* ŷ + (1 - β)*y .* (1 .- ŷ)) + 1)
+
+    1 - sum(|y .* ŷ| + 1) / (sum(y .* ŷ + (1 - β)*(1 .- y) .* ŷ + β*y .* (1 .- ŷ)) + 1)
+
 """
 function tversky_loss(ŷ, y; β = ofeltype(ŷ, 0.7))
     _check_sizes(ŷ, y)
@@ -456,6 +533,8 @@ The input, 'ŷ', is expected to be normalized (i.e. [softmax](@ref Softmax) out
 
 For `γ == 0`, the loss is mathematically equivalent to [`Losses.binarycrossentropy`](@ref).
 
+See also: [`Losses.focal_loss`](@ref) for multi-class setting
+
 # Example
 ```jldoctest
 julia> y = [0  1  0
@@ -473,9 +552,6 @@ julia> ŷ = [0.268941  0.5  0.268941
 julia> Flux.binary_focal_loss(ŷ, y) ≈ 0.0728675615927385
 true
 ```
-
-See also: [`Losses.focal_loss`](@ref) for multi-class setting
-
 """
 function binary_focal_loss(ŷ, y; agg=mean, γ=2, ϵ=epseltype(ŷ))
     _check_sizes(ŷ, y)
@@ -536,7 +612,17 @@ which can be useful for training Siamese Networks. It is given by
     agg(@. (1 - y) * ŷ^2 + y * max(0, margin - ŷ)^2)                           
 
 Specify `margin` to set the baseline for distance at which pairs are dissimilar.
-
+
+# Example
+```jldoctest
+julia> ŷ = [0.5, 1.5, 2.5];
+
+julia> Flux.siamese_contrastive_loss(ŷ, 1:3)
+-4.833333333333333
+
+julia> Flux.siamese_contrastive_loss(ŷ, 1:3, margin = 2)
+-4.0
+```
 """
 function siamese_contrastive_loss(ŷ, y; agg = mean, margin::Real = 1)
     _check_sizes(ŷ, y)