condensed examples

Author: mcognetta

src/layers/recurrent.jl

@@ -138,18 +138,8 @@ julia> r(rand(Float32, 3)) |> size
 
 julia> Flux.reset!(r);
 
-julia> r(rand(Float32, 3, 64)) |> size
-(5, 64)
-
-julia> Flux.reset!(r);
-
-julia> r(rand(Float32, 3))
-5-element Vector{Float32}:
- -0.37216917
- -0.14777198
-  0.2281275
-  0.32866752
- -0.6388411
+julia> r(rand(Float32, 3, 10)) |> size # batch size of 10
+(5, 10)
 
 # A demonstration of not using `reset!` when the batch size changes.
 julia> r = RNN(3, 5)
@@ -161,38 +151,21 @@ Recur(
 julia> r.state |> size
 (5, 1)
 
-julia> r(rand(Float32, 3))
-5-element Vector{Float32}:
-  0.3572996
- -0.041238427
-  0.19673917
- -0.36114445
- -0.0023919558
+julia> r(rand(Float32, 3)) |> size
+(5,)
 
 julia> r.state |> size
 (5, 1)
 
-julia> r(rand(Float32, 3, 10)) # batch size of 10
-5×10 Matrix{Float32}:
-  0.50832    0.409913    0.392907    0.838393   0.297105    0.432568    0.439304   0.677793   0.690217   0.78335
- -0.36385   -0.271328   -0.405521   -0.443976  -0.279546   -0.171614   -0.328029  -0.551147  -0.272327  -0.336688
-  0.272917  -0.0155508   0.0995184   0.580889   0.0502855   0.0375683   0.163693   0.39545    0.294581   0.461731
- -0.353226  -0.924237   -0.816582   -0.694016  -0.530896   -0.783385   -0.584767  -0.854036  -0.832923  -0.730812
-  0.418002   0.657771    0.673267    0.388967   0.483295    0.444058    0.490792   0.707697   0.435467   0.350789
+julia> r(rand(Float32, 3, 10)) |> size # batch size of 10
+(5, 10)
 
 julia> r.state |> size # state shape has changed
 (5, 10)
 
-julia> r(rand(Float32, 3)) # outputs a length 5*10 = 50 vector.
-50-element Vector{Float32}:
-  0.8532559
- -0.5693587
-  0.49786803
-  ⋮
- -0.7722325
-  0.46099305
+julia> r(rand(Float32, 3)) |> size # outputs a length 5*10 = 50 vector.
+(50,)
 ```
-
 """
 RNN(a...; ka...) = Recur(RNNCell(a...; ka...))
 Recur(m::RNNCell) = Recur(m, m.state0)
@@ -258,68 +231,43 @@ This constructor is syntactic sugar for `Recur(LSTMCell(a...))`, and so LSTMs ar
 See [this article](https://colah.github.io/posts/2015-08-Understanding-LSTMs/)
 for a good overview of the internals.
 
-  # Examples
-  ```jldoctest
-  julia> l = LSTM(3, 5)
-  Recur(
-    LSTMCell(3, 5),                       # 190 parameters
-  )         # Total: 5 trainable arrays, 190 parameters,
-            # plus 2 non-trainable, 10 parameters, summarysize 1.062 KiB.
-  
-  julia> l(rand(Float32, 3)) |> size
-  (5,)
-  
-  julia> Flux.reset!(l);
-  
-  julia> l(rand(Float32, 3, 10)) |> size # batch size of 10
-  (5, 10)
-  
-  julia> Flux.reset!(l);
-  
-  julia> l(rand(Float32, 3))
-  5-element Vector{Float32}:
-   -0.025144277
-    0.03836835
-    0.13517386
-   -0.028824253
-   -0.057356793
-  
-  # A demonstration of not using `reset!` when the batch size changes.
-  julia> l = LSTM(3, 5)
-  Recur(
-    LSTMCell(3, 5),                       # 190 parameters
-  )         # Total: 5 trainable arrays, 190 parameters,
-            # plus 2 non-trainable, 10 parameters, summarysize 1.062 KiB.
-  
-  julia> size.(l.state)
-  ((5, 1), (5, 1))
-  
-  julia> l(rand(Float32, 3))
-  5-element Vector{Float32}:
-   0.038496178
-   0.047853474
-   0.025309514
-   0.0934924
-   0.05440048
-  
-  julia> l(rand(Float32, 3, 10)) # batch size of 10
-  5×10 Matrix{Float32}:
-   0.169775   -0.0268295  0.0985312  0.0335569  0.023051   0.146001   0.0494771  0.12347    0.148342    0.00534695
-   0.0784295   0.130255   0.0326518  0.0495609  0.108738   0.10251    0.0519795  0.0673814  0.0804598   0.135432
-   0.109187   -0.0267218  0.0772971  0.0200508  0.0108066  0.0921862  0.0346887  0.0831271  0.0978057  -0.00210143
-   0.0827624   0.163729   0.10911    0.134769   0.120407   0.0757773  0.0894074  0.130243   0.0895137   0.133424
-   0.060574    0.127245   0.0145216  0.0635873  0.108584   0.0954128  0.0529619  0.0665022  0.0689427   0.127494
-  
-  julia> size.(l.state) # state shape has changed
-  ((5, 10), (5, 10))
-  
-  julia> l(rand(Float32, 3)) # outputs a length 5*10 = 50 vector.
-  50-element Vector{Float32}:
-    0.07209678
-    0.1450204
-    ⋮
-    0.14622498
-    0.15595339
+# Examples
+```jldoctest
+julia> l = LSTM(3, 5)
+Recur(
+  LSTMCell(3, 5),                       # 190 parameters
+)         # Total: 5 trainable arrays, 190 parameters,
+          # plus 2 non-trainable, 10 parameters, summarysize 1.062 KiB.
+
+julia> l(rand(Float32, 3)) |> size
+(5,)
+
+julia> Flux.reset!(l);
+
+julia> l(rand(Float32, 3, 10)) |> size # batch size of 10
+(5, 10)
+
+# A demonstration of not using `reset!` when the batch size changes.
+julia> l = LSTM(3, 5)
+Recur(
+  LSTMCell(3, 5),                       # 190 parameters
+)         # Total: 5 trainable arrays, 190 parameters,
+          # plus 2 non-trainable, 10 parameters, summarysize 1.062 KiB.
+
+julia> l.state .|> size 
+((5, 1), (5, 1))
+
+julia> l(rand(Float32, 3)) |> size
+(5,)
+
+julia> l(rand(Float32, 3, 10)) |> size # batch size of 10
+(5, 10)
+
+julia> l.state .|> size # state shape has changed
+((5, 10), (5, 10))
+
+julia> l(rand(Float32, 3)) |> size # outputs a length 5*10 = 50 vector.
+(50,)
 ```
 """
 LSTM(a...; ka...) = Recur(LSTMCell(a...; ka...))
@@ -407,16 +355,6 @@ julia> Flux.reset!(g);
 julia> g(rand(Float32, 3, 10)) |> size # batch size of 10
 (5, 10)
 
-julia> Flux.reset!(g);
-
-julia> g(rand(Float32, 3))
-5-element Vector{Float32}:
-  0.05426188
- -0.111508384
-  0.04700454
-  0.06919164
-  0.089212984
-
 # A demonstration of not using `reset!` when the batch size changes.
 julia> g = GRU(3, 5)
 Recur(
@@ -427,32 +365,17 @@ Recur(
 julia> g.state |> size
 (5, 1)
 
-julia> g(rand(Float32, 3))
-5-element Vector{Float32}:
- -0.11918676
- -0.089210495
-  0.027523153
-  0.017113047
-  0.061968707
-
-julia> g(rand(Float32, 3, 10)) # batch size of 10
-5×10 Matrix{Float32}:
- -0.198102   -0.187499   -0.265959   -0.21598    -0.210867    -0.379202   -0.262658  -0.213773   -0.236976   -0.266929
- -0.138773   -0.137587   -0.208564   -0.155394   -0.142374    -0.289558   -0.200516  -0.154471   -0.165038   -0.198165
-  0.040142    0.0716526   0.122938    0.0606727   0.00901341   0.0754129   0.107307   0.0551935   0.0366661   0.0648411
-  0.0655876   0.0512702  -0.0813906   0.120083    0.0521291    0.175624    0.110025   0.0345626   0.189902   -0.00220774
-  0.0756504   0.0913944   0.0982122   0.122272    0.0471702    0.228589    0.168877   0.0778906   0.145469    0.0832033
+julia> g(rand(Float32, 3)) |> size
+(5,)
+
+julia> g(rand(Float32, 3, 10)) |> size # batch size of 10
+(5, 10)
 
 julia> g.state |> size # state shape has changed
 (5, 10)
 
-julia> g(rand(Float32, 3)) # outputs a length 5*10 = 50 vector.
-50-element Vector{Float32}:
- -0.2639928
- -0.18772684
-  ⋮
- -0.022745812
-  0.040191136
+julia> g(rand(Float32, 3)) |> size # outputs a length 5*10 = 50 vector.
+(50,)
 ```
 """
 GRU(a...; ka...) = Recur(GRUCell(a...; ka...))
@@ -529,16 +452,6 @@ julia> Flux.reset!(g);
 julia> g(rand(Float32, 3, 10)) |> size # batch size of 10
 (5, 10)
 
-julia> Flux.reset!(g);
-
-julia> g(rand(Float32, 3))
-5-element Vector{Float32}:
-  0.05637428
-  0.0084088165
-  -0.036565308
-  0.013599886
-  -0.0168455
-
 # A demonstration of not using `reset!` when the batch size changes.
 julia> g = GRUv3(3, 5)
 Recur(
@@ -549,32 +462,17 @@ Recur(
 julia> g.state |> size
 (5, 1)
 
-julia> g(rand(Float32, 3))
-5-element Vector{Float32}:
-  0.07569726
-  0.23686615
-  -0.01647649
-  0.100590095
-  0.06330994
-
-julia> g(rand(Float32, 3, 10)) # batch size of 10
-5×10 Matrix{Float32}:
-  0.0187245   0.135969     0.0808607  0.138937    0.0153128   0.0386136  0.0498803  -0.0273552   0.116714    0.0584934
-  0.207631    0.146397     0.226232   0.297546    0.28957     0.199815   0.239891    0.27778     0.132326    0.0325415
-  0.083468   -0.00669185  -0.0562241  0.00725718  0.0319667  -0.021063   0.0682753   0.0109112   0.0188356   0.0826402
-  0.0700071   0.120734     0.108757   0.14339     0.0850359   0.0706199  0.0915005   0.05131     0.105372    0.0507574
-  0.0505043  -0.0408188    0.0170817  0.0190653   0.0936475   0.0406348  0.044181    0.139226   -0.0355197  -0.0434937
+julia> g(rand(Float32, 3)) |> size
+(5,)
+
+julia> g(rand(Float32, 3, 10)) |> size # batch size of 10
+(5, 10)
 
 julia> g.state |> size # state shape has changed
 (5, 10)
 
-julia> g(rand(Float32, 3)) # outputs a length 5*10 = 50 vector.
-50-element Vector{Float32}:
-  0.08773954
-  0.34562656
-  ⋮
-  0.13768406
-  -0.015648054
+julia> g(rand(Float32, 3)) |> size # outputs a length 5*10 = 50 vector.
+(50,)
 ```
 """
 GRUv3(a...; ka...) = Recur(GRUv3Cell(a...; ka...))