resolving comments

Author: codeboy5

FastTimeSeries/src/FastTimeSeries.jl

@@ -27,6 +27,7 @@ using FilePathsBase
 using InlineTest
 using Statistics
 using UnicodePlots
+using Flux
 
 # Blocks
 include("blocks/timeseriesrow.jl")

FastTimeSeries/src/models/InceptionTime.jl

@@ -3,15 +3,15 @@
 
 TBW
 """
-function InceptionModule(ni::Int, nf::Int, ks::Int = 40, bottleneck::Bool = true)
-    ks = [ks ÷ (2^i) for i in range(0, stop = 2)]
-    ks = [ks[i] % 2 == 0 ? ks[i] - 1 : ks[i] for i in range(1, stop = 3)]  # ensure odd ks
+function InceptionModule(ni::Int, nf::Int, kernel_size::Int = 40, bottleneck::Bool = true)
+    ks = [kernel_size ÷ (2^i) for i in 0:2]
+    ks = [ks[i] % 2 == 0 ? ks[i] - 1 : ks[i] for i in 1:3]  # ensure odd ks
     bottleneck = ni > 1 ? bottleneck : false
 
     bottleneck_block = bottleneck ? Conv1d(ni, nf, 1, bias = false) : identity
 
     convs_layers =
-        [Conv1d(bottleneck ? nf : ni, nf, ks[i], bias = false) for i in range(1, stop = 3)]
+        [Conv1d(bottleneck ? nf : ni, nf, ks[i], bias = false) for i in 1:3]
 
     convs = Chain(bottleneck_block, Parallel(hcat, convs_layers...))
 
@@ -29,7 +29,7 @@ function InceptionBlock(ni::Int, nf::Int = 32, residual::Bool = true, depth::Int
     inception = []
     shortcut = []
 
-    for d in range(1, stop = depth)
+    for d in 1:depth
         push!(inception, InceptionModule(d == 1 ? ni : nf * 4, nf))
         if residual && d % 3 == 0
             n_in = d == 3 ? ni : nf * 4
@@ -62,7 +62,6 @@ function InceptionBlock(ni::Int, nf::Int = 32, residual::Bool = true, depth::Int
         end
     end
     return Chain(blocks...)
-
 end
 
 changedims(X) = permutedims(X, (2, 1, 3))

FastTimeSeries/src/models/RNN.jl

@@ -19,10 +19,7 @@ is passed through a dropout layer before a 'finalclassifier' block.
 - `dropout_rate`: Dropout probability for the dropout layer.
 """
 
-function RNNModel(recbackbone;
-                outsize,
-                recout,
-                kwargs...)
+function RNNModel(recbackbone; outsize, recout)
     return RNNModel(recbackbone, Dense(recout, outsize))
 end
 

FastTimeSeries/src/models/layers.jl

@@ -18,19 +18,19 @@ layer to `LSTM(hiddensize, out)`. Takes the keyword argument `init` for the init
 of the layers.
 
 """
-function StackedLSTM(in::Int, out::Integer, hiddensize::Integer, layers::Integer;
+function StackedLSTM(c_in::Int, c_out::Integer, hiddensize::Integer, layers::Integer;
 			init=Flux.glorot_uniform)
 	if layers == 1
-		return Chain(LSTM(in, out; init=init))
+		return Chain(LSTM(c_in, c_out; init=init))
 	elseif layers == 2
-		return Chain(LSTM(in, hiddensize; init=init),
-					  LSTM(hiddensize, out; init=init))
+		return Chain(LSTM(c_in, hiddensize; init=init),
+					  LSTM(hiddensize, c_out; init=init))
 	else
-		chain_vec = [LSTM(in, hiddensize; init=init)]
+		chain_vec = [LSTM(c_in, hiddensize; init=init)]
 		for i = 1:layers - 2
 			push!(chain_vec, LSTM(hiddensize, hiddensize; init=init))
 		end
-		return Chain(chain_vec..., LSTM(hiddensize, out; init=init))
+		return Chain(chain_vec..., LSTM(hiddensize, c_out; init=init))
 	end
 end