added RNN Model

Author: codeboy5

FastTimeSeries/Project.toml

@@ -4,18 +4,24 @@ authors = ["FluxML Community"]
 version = "0.1.0"
 
 [deps]
+DataDeps = "124859b0-ceae-595e-8997-d05f6a7a8dfe"
 FastAI = "5d0beca9-ade8-49ae-ad0b-a3cf890e669f"
 FilePathsBase = "48062228-2e41-5def-b9a4-89aafe57970f"
+Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
 InlineTest = "bd334432-b1e7-49c7-a2dc-dd9149e4ebd6"
 MLUtils = "f1d291b0-491e-4a28-83b9-f70985020b54"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 UnicodePlots = "b8865327-cd53-5732-bb35-84acbb429228"
+Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
 
 [compat]
+DataDeps = "0.7"
 FastAI = "0.5"
 FilePathsBase = "0.9"
+Flux = "0.12, 0.13"
 InlineTest = "0.2"
 MLUtils = "0.2"
 UnicodePlots = "2, 3"
 julia = "1.6"
+Zygote = "0.6"

FastTimeSeries/src/FastTimeSeries.jl

@@ -34,6 +34,10 @@ include("blocks/timeseriesrow.jl")
 # Encodings
 include("encodings/tspreprocessing.jl")
 
+# Models
+include("models/Models.jl")
+include("models.jl")
+
 include("container.jl")
 include("recipes.jl")
 

FastTimeSeries/src/models.jl

@@ -0,0 +1,28 @@
+
+"""
+blockmodel(inblock::TimeSeriesRow, outblock::OneHotTensor{0}, backbone)
+
+Construct a model for time-series classification.
+"""
+function blockmodel(inblock::TimeSeriesRow,
+                outblock::OneHotTensor{0}, 
+                backbone)
+    data   = rand(Float32, inblock.nfeatures, 32, inblock.obslength)
+    # data   = [rand(Float32, inblock.nfeatures, 32) for _ ∈ 1:inblock.obslength]
+    output = backbone(data)
+    outs   = size(output)[1]
+    return Models.RNNModel(backbone, outsize = length(outblock.classes), recout = outs)
+end
+
+"""
+    blockbackbone(inblock::TimeSeriesRow)
+
+Construct a recurrent backbone
+"""
+function blockbackbone(inblock::TimeSeriesRow)
+    Models.StackedLSTM(inblock.nfeatures, 16, 10, 2);
+end
+
+# ## Tests
+
+@testset "blockbackbone" begin @test_nowarn FastAI.blockbackbone(TimeSeriesRow(1,140)) end

FastTimeSeries/src/models/Models.jl

@@ -0,0 +1,15 @@
+module Models
+
+using ..FastAI
+
+using Flux
+using Zygote
+using DataDeps
+using InlineTest
+
+include("StackedLSTM.jl")
+include("RNN.jl")
+
+export StackedLSTM, RNNModel
+
+end

FastTimeSeries/src/models/RNN.jl

@@ -0,0 +1,39 @@
+function tabular2rnn(X::AbstractArray{Float32, 3})
+    X = permutedims(X, (1, 3, 2))
+    return X
+end
+
+struct RNNModel{A, B}
+    recbackbone::A
+    finalclassifier::B
+end
+
+"""
+    RNNModel(recbackbonem, outsize, recout[; kwargs...])
+
+Creates a RNN model from the recurrent 'recbackbone' architecture. The output from this backbone
+is passed through a dropout layer before a 'finalclassifier' block.
+
+## Keyword arguments.
+
+- `outsize`: The output size of the final classifier block. For single classification tasks,
+    this would be the number of classes.
+- `recout`: The output size of the `recbackbone` architecture.
+- `dropout_rate`: Dropout probability for the dropout layer.
+"""
+
+function RNNModel(recbackbone;
+                outsize,
+                recout,
+                kwargs...)
+    return RNNModel{}(recbackbone, Dense(recout, outsize))
+end
+
+function (m::RNNModel)(X)
+    X = tabular2rnn(X)
+    Flux.reset!(m.recbackbone)
+    X = m.recbackbone(X)[:, :, end]
+    return m.finalclassifier(X)
+end
+
+Flux.@functor RNNModel 

FastTimeSeries/src/models/StackedLSTM.jl

@@ -0,0 +1,26 @@
+"""
+	StackedLSTM(in, out, hiddensize, layers)
+
+Stacked LSTM network. Feeds the data through a chain of LSTM layers, where the hidden state
+of the previous layer gets fed to the next one. The first layer corresponds to
+`LSTM(in, hiddensize)`, the hidden layers to `LSTM(hiddensize, hiddensize)`, and the final
+layer to `LSTM(hiddensize, out)`. Takes the keyword argument `init` for the initialization
+of the layers.
+
+"""
+function StackedLSTM(in::Int, out::Integer, hiddensize::Integer, layers::Integer;
+			init=Flux.glorot_uniform)
+	if layers == 1
+		chain = Chain(LSTM(in, out; init=init))
+	elseif layers == 2
+		chain = Chain(LSTM(in, hiddensize; init=init),
+					  LSTM(hiddensize, out; init=init))
+	else
+		chain_vec = [LSTM(in, hiddensize; init=init)]
+		for i = 1:layers - 2
+			push!(chain_vec, LSTM(hiddensize, hiddensize; init=init))
+		end
+		chain = Chain(chain_vec..., LSTM(hiddensize, out; init=init))
+	end
+	return chain
+end

FastTimeSeries/src/models/layers.jl

@@ -0,0 +1,9 @@
+"""
+    GAP1d(output_size)
+
+Create a Global Adaptive Pooling + Flatten layer.
+"""
+function GAP1d(output_size::Int)
+    gap = AdaptiveMeanPool((output_size,))
+    Chain(gap, Flux.flatten)    
+end

FastTimeSeries/test/Project.toml

@@ -0,0 +1,4 @@
+[deps]
+FastAI = "5d0beca9-ade8-49ae-ad0b-a3cf890e669f"
+InlineTest = "bd334432-b1e7-49c7-a2dc-dd9149e4ebd6"
+ReTest = "e0db7c4e-2690-44b9-bad6-7687da720f89"

FastTimeSeries/test/runtests.jl

@@ -0,0 +1,5 @@
+using FastAI, FastTimeSeries, ReTest
+
+ENV["DATADEPS_ALWAYS_ACCEPT"] = "true"
+
+FastTimeSeries.runtests([ReTest.fail, ReTest.not(ReTest.pass)])