Further docstring improvements in src/

Some had to be re-done after the rebase
2020-04-04 22:59:45 +02:00 · 2020-04-04 22:59:45 +02:00 · 2ce5f6d9bf
parent 64ce32ddcf
commit 2ce5f6d9bf
5 changed files with 100 additions and 102 deletions
--- a/src/layers/basic.jl
+++ b/src/layers/basic.jl
@ -183,18 +183,11 @@ outdims(l::Diagonal, isize) = (length(l.α),)
 """
    Maxout(over)

-`Maxout` is a neural network layer which has a number of internal layers
-which all receive the same input. The layer returns the elementwise maximium
-of the internal layers' outputs.
+The [Maxout](https://arxiv.org/pdf/1302.4389.pdf) layer has a number of
+internal layers which all receive the same input. It returns the elementwise
+maximum of the internal layers' outputs.

 Maxout over linear dense layers satisfies the univeral approximation theorem.
-
-Reference:
-Ian J. Goodfellow, David Warde-Farley, Mehdi Mirza, Aaron Courville, and Yoshua Bengio.
-2013. Maxout networks.
-In Proceedings of the 30th International Conference on International Conference on Machine Learning - Volume 28 (ICML'13),
-Sanjoy Dasgupta and David McAllester (Eds.), Vol. 28. JMLR.org III-1319-III-1327.
-https://arxiv.org/pdf/1302.4389.pdf
 """
 struct Maxout{FS<:Tuple}
    over::FS
--- a/src/layers/normalise.jl
+++ b/src/layers/normalise.jl
@ -65,9 +65,10 @@ end
 """
    AlphaDropout(p)

-A dropout layer. It is used in
+A dropout layer. Used in
 [Self-Normalizing Neural Networks](https://papers.nips.cc/paper/6698-self-normalizing-neural-networks.pdf).
-The AlphaDropout layer ensures that mean and variance of activations remains the same as before.
+The AlphaDropout layer ensures that mean and variance of activations
+remain the same as before.

 Does nothing to the input once [`testmode!`](@ref) is true.
 """
@ -123,8 +124,8 @@ end
              initβ = zeros, initγ = ones,
              ϵ = 1e-8, momentum = .1)

-Batch Normalization layer. The `channels` input should be the size of the
-channel dimension in your data (see below).
+[Batch Normalization](https://arxiv.org/pdf/1502.03167.pdf) layer.
+`channels` should be the size of the channel dimension in your data (see below).

 Given an array with `N` dimensions, call the `N-1`th the channel dimension. (For
 a batch of feature vectors this is just the data dimension, for `WHCN` images
@ -136,9 +137,6 @@ per-channel `bias` and `scale` parameters).

 Use [`testmode!`](@ref) during inference.

-See [Batch Normalization: Accelerating Deep Network Training by Reducing
-Internal Covariate Shift](https://arxiv.org/pdf/1502.03167.pdf).
-
 # Examples
 ```julia
 m = Chain(
@ -213,37 +211,6 @@ function Base.show(io::IO, l::BatchNorm)
  print(io, ")")
 end

-
-"""
-    InstanceNorm(channels::Integer, σ = identity;
-                 initβ = zeros, initγ = ones,
-                 ϵ = 1e-8, momentum = .1)
-
-Instance Normalization layer. The `channels` input should be the size of the
-channel dimension in your data (see below).
-
-Given an array with `N` dimensions, call the `N-1`th the channel dimension. (For
-a batch of feature vectors this is just the data dimension, for `WHCN` images
-it's the usual channel dimension.)
-
-`InstanceNorm` computes the mean and variance for each each `W×H×1×1` slice and
-shifts them to have a new mean and variance (corresponding to the learnable,
-per-channel `bias` and `scale` parameters).
-
-Use [`testmode!`](@ref) during inference.
-
-See [Instance Normalization: The Missing Ingredient for Fast Stylization](https://arxiv.org/abs/1607.08022).
-
-# Examples
-```julia
-m = Chain(
-  Dense(28^2, 64),
-  InstanceNorm(64, relu),
-  Dense(64, 10),
-  InstanceNorm(10),
-  softmax)
-```
-"""
 expand_inst = (x, as) -> reshape(repeat(x, outer=[1, as[length(as)]]), as...)

 mutable struct InstanceNorm{F,V,W,N}
@ -258,6 +225,34 @@ mutable struct InstanceNorm{F,V,W,N}
 end

 # TODO: deprecate in v0.11
+"""
+    InstanceNorm(channels::Integer, σ = identity;
+                 initβ = zeros, initγ = ones,
+                 ϵ = 1e-8, momentum = .1)
+
+[Instance Normalization](https://arxiv.org/abs/1607.08022) layer.
+`channels` should be the size of the channel dimension in your data (see below).
+
+Given an array with `N` dimensions, call the `N-1`th the channel dimension. (For
+a batch of feature vectors this is just the data dimension, for `WHCN` images
+it's the usual channel dimension.)
+
+`InstanceNorm` computes the mean and variance for each each `W×H×1×1` slice and
+shifts them to have a new mean and variance (corresponding to the learnable,
+per-channel `bias` and `scale` parameters).
+
+Use [`testmode!`](@ref) during inference.
+
+# Examples
+```julia
+m = Chain(
+  Dense(28^2, 64),
+  InstanceNorm(64, relu),
+  Dense(64, 10),
+  InstanceNorm(10),
+  softmax)
+```
+"""
 InstanceNorm(λ, β, γ, μ, σ², ϵ, momentum) = InstanceNorm(λ, β, γ, μ, σ², ϵ, momentum, nothing)

 InstanceNorm(chs::Integer, λ = identity;
--- a/src/layers/stateless.jl
+++ b/src/layers/stateless.jl
@ -2,7 +2,8 @@
 """
    mae(ŷ, y)

-Return the mean of absolute error `sum(abs.(ŷ .- y)) / length(y)`
+Return the mean of absolute error; calculated as
+`sum(abs.(ŷ .- y)) / length(y)`.
 """
 mae(ŷ, y) = sum(abs.(ŷ .- y)) * 1 // length(y)

@ -10,8 +11,8 @@ mae(ŷ, y) = sum(abs.(ŷ .- y)) * 1 // length(y)
 """
    mse(ŷ, y)

-Return the mean squared error between ŷ and y;
-defined as ``\\frac{1}{n} \\sum_{i=1}^n (ŷ_i - y_i)^2``.
+Return the mean squared error between ŷ and y; calculated as
+`sum((ŷ .- y).^2) / length(y)`.

 # Examples
 ```jldoctest
@ -25,10 +26,11 @@ mse(ŷ, y) = sum((ŷ .- y).^2) * 1 // length(y)
 """
    msle(ŷ, y; ϵ=eps(eltype(ŷ)))

-Returns the mean of the squared logarithmic errors `sum((log.(ŷ .+ ϵ) .- log.(y .+ ϵ)).^2) / length(y)`.
+Return the mean of the squared logarithmic errors; calculated as
+`sum((log.(ŷ .+ ϵ) .- log.(y .+ ϵ)).^2) / length(y)`.
 The `ϵ` term provides numerical stability.

-This error penalizes an under-predicted estimate greater than an over-predicted estimate.
+Penalizes an under-predicted estimate greater than an over-predicted estimate.
 """
 msle(ŷ, y; ϵ=eps(eltype(ŷ))) = sum((log.(ŷ .+ ϵ) .- log.(y .+ ϵ)).^2) * 1 // length(y)

@ -37,13 +39,12 @@ msle(ŷ, y; ϵ=eps(eltype(ŷ))) = sum((log.(ŷ .+ ϵ) .- log.(y .+ ϵ)).^2) *
 """
    huber_loss(ŷ, y; δ=1.0)

-Computes the mean of the Huber loss given the prediction `ŷ` and true values `y`. By default, δ is set to 1.0.
+Return the mean of the [Huber loss](https://en.wikipedia.org/wiki/Huber_loss)
+given the prediction `ŷ` and true values `y`.

                 | 0.5 * |ŷ - y|,            for |ŷ - y| <= δ
-      Hubber loss = |
+    Huber loss = |
                 |  δ * (|ŷ - y| - 0.5 * δ), otherwise
-
-[`Huber Loss`](https://en.wikipedia.org/wiki/Huber_loss).
 """
 function huber_loss(ŷ, y;  δ=eltype(ŷ)(1))
   abs_error = abs.(ŷ .- y)
@ -68,7 +69,7 @@ end
    crossentropy(ŷ, y; weight = nothing)

 Return the cross entropy between the given probability distributions;
-computed as `-sum(y .* log.(ŷ) .* weight) / size(y, 2)`.
+calculated as `-sum(y .* log.(ŷ) .* weight) / size(y, 2)`.

 `weight` can be `Nothing`, a `Number` or an `AbstractVector`.
 `weight=nothing` acts like `weight=1` but is faster.
@ -87,7 +88,7 @@ crossentropy(ŷ::AbstractVecOrMat, y::AbstractVecOrMat; weight=nothing) = _cros
    logitcrossentropy(ŷ, y; weight = 1)

 Return the crossentropy computed after a [`Flux.logsoftmax`](@ref) operation;
-computed as `-sum(y .* logsoftmax(ŷ) .* weight) / size(y, 2)`.
+calculated as `-sum(y .* logsoftmax(ŷ) .* weight) / size(y, 2)`.

 `logitcrossentropy(ŷ, y)` is mathematically equivalent to
 [`Flux.crossentropy(softmax(log(ŷ)), y)`](@ref) but it is more numerically stable.
@ -184,10 +185,14 @@ end
 """
    kldivergence(ŷ, y)

-KLDivergence is a measure of how much one probability distribution is different from the other.
-It is always non-negative and zero only when both the distributions are equal everywhere.
+Return the
+[Kullback-Leibler divergence](https://en.wikipedia.org/wiki/Kullback%E2%80%93Leibler_divergence)
+between the given probability distributions.

-[KL Divergence](https://en.wikipedia.org/wiki/Kullback%E2%80%93Leibler_divergence).
+KL divergence is a measure of how much one probability distribution is different
+from the other.
+It is always non-negative and zero only when both the distributions are equal
+everywhere.
 """
 function kldivergence(ŷ, y)
  entropy = sum(y .* log.(y)) * 1 //size(y,2)
@ -198,20 +203,20 @@ end
 """
    poisson(ŷ, y)

-Poisson loss function is a measure of how the predicted distribution diverges from the expected distribution.
-Returns `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)`.

-[Poisson Loss](https://peltarion.com/knowledge-center/documentation/modeling-view/build-an-ai-model/loss-functions/poisson).
+[More information.](https://peltarion.com/knowledge-center/documentation/modeling-view/build-an-ai-model/loss-functions/poisson).
 """
 poisson(ŷ, y) = sum(ŷ .- y .* log.(ŷ)) * 1 // size(y,2)

 """
    hinge(ŷ, y)

-Measures the loss given the prediction `ŷ` and true labels `y` (containing 1 or -1).
-Returns `sum((max.(0, 1 .- ŷ .* y))) / size(y, 2)`
+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)`.

-[Hinge Loss](https://en.wikipedia.org/wiki/Hinge_loss)
 See also: [`squared_hinge`](@ref)
 """
 hinge(ŷ, y) = sum(max.(0, 1 .-  ŷ .* y)) * 1 // size(y, 2)
@ -219,8 +224,8 @@ hinge(ŷ, y) = sum(max.(0, 1 .-  ŷ .* y)) * 1 // size(y, 2)
 """
    squared_hinge(ŷ, y)

-Computes squared hinge loss given the prediction `ŷ` and true labels `y` (conatining 1 or -1).
-Returns `sum((max.(0, 1 .- ŷ .* y)).^2) / size(y, 2)`
+Return the squared hinge 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)`.

 See also: [`hinge`](@ref)
 """
@ -229,28 +234,29 @@ squared_hinge(ŷ, y) = sum((max.(0, 1 .- ŷ .* y)).^2) * 1 // size(y, 2)
 """
    dice_coeff_loss(ŷ, y; smooth=1)

-Loss function used in Image Segmentation. Calculates loss based on dice coefficient. Similar to F1_score.
-Returns `1 - 2*sum(|ŷ .* y| + smooth) / (sum(ŷ.^2) + sum(y.^2) + smooth)`
-
-[V-Net: Fully Convolutional Neural Networks forVolumetric Medical Image Segmentation](https://arxiv.org/pdf/1606.04797v1.pdf)
+Return a loss based on the dice coefficient.
+Used in the [V-Net](https://arxiv.org/pdf/1606.04797v1.pdf) image segmentation
+architecture.
+Similar to the F1_score. Calculated as:
+    1 - 2*sum(|ŷ .* y| + smooth) / (sum(ŷ.^2) + sum(y.^2) + smooth)`
 """
 dice_coeff_loss(ŷ, y; smooth=eltype(ŷ)(1.0)) = 1 - (2*sum(y .* ŷ) + smooth) / (sum(y.^2) + sum(ŷ.^2) + smooth)

 """
    tversky_loss(ŷ, y; β=0.7)

-Used with imbalanced data to give more weightage to False negatives.
+Return the [Tversky loss](https://arxiv.org/pdf/1706.05721.pdf).
+Used with imbalanced data to give more weight to false negatives.
 Larger β weigh recall higher than precision (by placing more emphasis on false negatives)
-Returns `1 - sum(|y .* ŷ| + 1) / (sum(y .* ŷ + β*(1 .- y) .* ŷ + (1 - β)*y .* (1 .- ŷ)) + 1)`
-
-[Tversky loss function for image segmentation using 3D fully convolutional deep networks](https://arxiv.org/pdf/1706.05721.pdf)
+Calculated as:
+    1 - sum(|y .* ŷ| + 1) / (sum(y .* ŷ + β*(1 .- y) .* ŷ + (1 - β)*y .* (1 .- ŷ)) + 1)
 """
 tversky_loss(ŷ, y; β=eltype(ŷ)(0.7)) = 1 - (sum(y .* ŷ) + 1) / (sum(y .* ŷ + β*(1 .- y) .* ŷ + (1 - β)*y .* (1 .- ŷ)) + 1)

 """
    flatten(x::AbstractArray)

-Transforms (w,h,c,b)-shaped input into (w x h x c,b)-shaped output,
+Transform (w, h, c, b)-shaped input into (w × h × c, b)-shaped output
 by linearizing all values for each element in the batch.
 """
 function flatten(x::AbstractArray)
--- a/src/onehot.jl
+++ b/src/onehot.jl
@ -45,8 +45,8 @@ cudaconvert(x::OneHotMatrix{<:CuArray}) = OneHotMatrix(x.height, cudaconvert(x.d
 """
    onehot(l, labels[, unk])

-Create a [`OneHotVector`](@ref) with its `l`-th element `true` based on
-possible `labels` set.
+Create a [`OneHotVector`](@ref) with its `l`-th element `true` based on the
+possible set of `labels`.
 If `unk` is given, return `onehot(unk, labels)` if the input label `l` is not found
 in `labels`; otherwise it will error.

@ -80,8 +80,10 @@ end
 """
    onehotbatch(ls, labels[, unk...])

-Create an [`OneHotMatrix`](@ref) with a batch of labels based on possible `labels` set, returns the
-`onehot(unk, labels)` if given labels `ls` is not found in set `labels`.
+Create a [`OneHotMatrix`](@ref) with a batch of labels based on the
+possible set of `labels`.
+If `unk` is given, return [`onehot(unk, labels)`](@ref) if one of the input
+labels `ls` is not found in `labels`; otherwise it will error.

 # Examples
 ```jldoctest
--- a/src/optimise/train.jl
+++ b/src/optimise/train.jl
@ -2,6 +2,16 @@ using Juno
 import Zygote: Params, gradient


+
+"""
+  update!(x, x̄)
+
+Update the array `x` according to `x .-= x̄`.
+"""
+function update!(x::AbstractArray, x̄)
+  x .-= x̄
+end
+
 """
    update!(opt, p, g)
    update!(opt, ps::Params, gs)
@ -10,15 +20,7 @@ Perform an update step of the parameters `ps` (or the single parameter `p`)
 according to optimizer `opt`  and the gradients `gs` (the gradient `g`).

 As a result, the parameters are mutated and the optimizer's internal state may change.
-
-  update!(x, x̄)
-  
-Update the array `x` according to `x .-= x̄`.
 """
-function update!(x::AbstractArray, x̄)
-  x .-= x̄
-end
-
 function update!(opt, x, x̄)
  x .-= apply!(opt, x, x̄)
 end
@ -41,7 +43,7 @@ struct StopException <: Exception end
    stop()

 Call `Flux.stop()` in a callback to indicate when a callback condition is met.
-This would trigger the train loop to stop and exit.
+This will trigger the train loop to stop and exit.

 # Examples
 ```julia
@ -57,19 +59,19 @@ end
 """
    train!(loss, params, data, opt; cb)

-For each datapoint `d` in `data` computes the gradient of `loss(d...)` through
-backpropagation and calls the optimizer `opt`.
+For each datapoint `d` in `data` compute the gradient of `loss(d...)` through
+backpropagation and call the optimizer `opt`.

-In case datapoints `d` are of numeric array type, assumes no splatting is needed 
-and computes the gradient of `loss(d)`.
+In case datapoints `d` are of numeric array type, assume no splatting is needed
+and compute the gradient of `loss(d)`.

-Takes a callback as keyword argument `cb`. For example, this will print "training"
-every 10 seconds (using [`throttle`](@ref)):
+A callback is given with the keyword argument `cb`. For example, this will print
+"training" every 10 seconds (using [`Flux.throttle`](@ref)):

  train!(loss, params, data, opt,
         cb = throttle(() -> println("training"), 10))

-The callback can call [`Flux.stop()`](@ref) to interrupt the training loop.
+The callback can call [`Flux.stop`](@ref) to interrupt the training loop.

 Multiple optimisers and callbacks can be passed to `opt` and `cb` as arrays.
 """