diff --git a/dev/api/Lux/contrib.md b/dev/api/Lux/contrib.md index 393cfae12..92df8fcd2 100644 --- a/dev/api/Lux/contrib.md +++ b/dev/api/Lux/contrib.md @@ -71,7 +71,7 @@ Training State containing: * `step`: Number of updates of the parameters made. -source
+source

@@ -104,7 +104,7 @@ A 4-Tuple containing: * `ts`: Updated Training State. -source
+source

@@ -129,7 +129,7 @@ Update the parameters stored in `ts` using the gradients `grads`. Updated `TrainState` object. -source
+source

@@ -206,7 +206,7 @@ m = Lux.Experimental.FrozenLayer(Dense(2 => 2), (:weight,)) See also [`Lux.Experimental.freeze`](contrib#Lux.Experimental.freeze), [`Lux.Experimental.unfreeze`](contrib#Lux.Experimental.unfreeze). -source
+source

@@ -222,7 +222,7 @@ freeze(l::AbstractExplicitLayer, which_params::Union{Tuple, Nothing} = nothing) Constructs a version of `l` with `which_params` frozen. If `which_params` is nothing, then all parameters are frozen. -source
+source
``` @@ -233,7 +233,7 @@ freeze(l::AbstractExplicitLayer, ps, st::NamedTuple, Construct a [`Lux.Experimental.FrozenLayer`](contrib#Lux.Experimental.FrozenLayer) for `l` with the current parameters and states. If `which_params` is nothing, then all parameters are frozen. -source
+source

@@ -249,7 +249,7 @@ unfreeze(l::FrozenLayer) Unfreezes the layer `l`. -source
+source
``` @@ -259,7 +259,7 @@ unfreeze(l::FrozenLayer, ps, st::NamedTuple) Unwraps a [`Lux.Experimental.FrozenLayer`](contrib#Lux.Experimental.FrozenLayer) `l` with the current parameters and states. -source
+source

@@ -320,7 +320,7 @@ Lux.layer_map(zero_dense_params, c, ps, st) ``` -source
+source

@@ -361,7 +361,7 @@ Lux.@layer_map zero_dense_params c ps st ``` -source
+source

@@ -387,7 +387,7 @@ Recurses into the `layer` and replaces the inner most non Container Layers with See [`Lux.Experimental.DebugLayer`](contrib#Lux.Experimental.DebugLayer) for details about the Keyword Arguments. -source
+source

@@ -439,7 +439,7 @@ If `nan_check` is enabled and NaNs are detected then a `DomainError` is thrown. See [`Lux.Experimental.@debug_mode`](contrib#Lux.Experimental.@debug_mode) to construct this layer. -source
+source

@@ -485,7 +485,7 @@ ps = Lux.share_parameters(ps, (("d3.l2", "d1"), ("d2", "d3.l1"))) ``` -source
+source

@@ -535,7 +535,7 @@ State is mutated in place. An additional caveat is that the updated state from ` * `y`: The output of the layer -source
+source

@@ -661,7 +661,7 @@ Array Parameter don't print the number of parameters on the side. However, they ::: -source
+source

diff --git a/dev/api/Lux/flux_to_lux.md b/dev/api/Lux/flux_to_lux.md index f799e9195..5fec79d00 100644 --- a/dev/api/Lux/flux_to_lux.md +++ b/dev/api/Lux/flux_to_lux.md @@ -64,7 +64,7 @@ m2(x, ps, st) ``` -source
+source

@@ -105,7 +105,7 @@ Introducing this Layer in your model will lead to type instabilities, given the * `p`: Flattened parameters of the `layer` -source
+source

diff --git a/dev/api/Lux/layers.md b/dev/api/Lux/layers.md index f7a3e5fc3..5f0f9b1d7 100644 --- a/dev/api/Lux/layers.md +++ b/dev/api/Lux/layers.md @@ -114,7 +114,7 @@ l = BranchLayer(NoOpLayer(), NoOpLayer(), NoOpLayer()) ``` -source
+source

@@ -181,7 +181,7 @@ c = Chain(Dense(2, 3, relu), BatchNorm(3), Dense(3, 2)) ``` -source
+source

@@ -250,7 +250,7 @@ end * States of each `layer` wrapped in a NamedTuple with `fields = layer_1, layer_2, ..., layer_N` (naming changes if using the kwargs API) -source
+source

@@ -298,7 +298,7 @@ Create a layer which passes an input to each path in `layers`, before reducing t See also [`SkipConnection`](layers#Lux.SkipConnection) which is `Parallel` with one identity. -source
+source

@@ -349,7 +349,7 @@ The simplest "ResNet"-type connection is just `SkipConnection(layer, +)`. See [`Parallel`](layers#Lux.Parallel) for a more general implementation. -source
+source

@@ -411,7 +411,7 @@ It is expected that `repeats` will be a reasonable number below `20`, beyond tha * State of `model` -source
+source

@@ -485,7 +485,7 @@ $$ * `bias`: Bias (present if `use_bias=true`) -source
+source

@@ -540,7 +540,7 @@ Standard convolutional transpose layer. * `bias`: Bias (present if `use_bias=true`) -source
+source

@@ -601,7 +601,7 @@ $$ * `bias`: Bias (present if `use_bias=true`) -source
+source

@@ -647,7 +647,7 @@ Call [`Lux.testmode`](../Building_Blocks/LuxCore#LuxCore.testmode) to switch to See also [`Dropout`](layers#Lux.Dropout), [`VariationalHiddenDropout`](layers#Lux.VariationalHiddenDropout) -source
+source

@@ -689,7 +689,7 @@ Call [`Lux.testmode`](../Building_Blocks/LuxCore#LuxCore.testmode) to switch to See also [`AlphaDropout`](layers#Lux.AlphaDropout), [`VariationalHiddenDropout`](layers#Lux.VariationalHiddenDropout) -source
+source

@@ -733,7 +733,7 @@ Call [`Lux.testmode`](../Building_Blocks/LuxCore#LuxCore.testmode) to switch to See also [`AlphaDropout`](layers#Lux.AlphaDropout), [`Dropout`](layers#Lux.Dropout) -source
+source

@@ -769,7 +769,7 @@ Adaptive Max Pooling layer. Calculates the necessary window size such that its o See also [`MaxPool`](layers#Lux.MaxPool), [`AdaptiveMeanPool`](layers#Lux.AdaptiveMeanPool). -source
+source

@@ -800,7 +800,7 @@ Adaptive Mean Pooling layer. Calculates the necessary window size such that its See also [`MeanPool`](layers#Lux.MeanPool), [`AdaptiveMaxPool`](layers#Lux.AdaptiveMaxPool). -source
+source

@@ -827,7 +827,7 @@ Global Max Pooling layer. Transforms (w,h,c,b)-shaped input into (1,1,c,b)-shape See also [`MaxPool`](layers#Lux.MaxPool), [`AdaptiveMaxPool`](layers#Lux.AdaptiveMaxPool), [`GlobalMeanPool`](layers#Lux.GlobalMeanPool) -source
+source

@@ -854,7 +854,7 @@ Global Mean Pooling layer. Transforms (w,h,c,b)-shaped input into (1,1,c,b)-shap See also [`MeanPool`](layers#Lux.MeanPool), [`AdaptiveMeanPool`](layers#Lux.AdaptiveMeanPool), [`GlobalMaxPool`](layers#Lux.GlobalMaxPool) -source
+source

@@ -900,7 +900,7 @@ $$ See also [`Conv`](layers#Lux.Conv), [`MeanPool`](layers#Lux.MeanPool), [`GlobalMaxPool`](layers#Lux.GlobalMaxPool), [`AdaptiveMaxPool`](layers#Lux.AdaptiveMaxPool) -source
+source

@@ -946,7 +946,7 @@ $$ See also [`Conv`](layers#Lux.Conv), [`MaxPool`](layers#Lux.MaxPool), [`GlobalMeanPool`](layers#Lux.GlobalMeanPool), [`AdaptiveMeanPool`](layers#Lux.AdaptiveMeanPool) -source
+source

@@ -1016,7 +1016,7 @@ $$ * `rng`: Controls the randomness (if any) in the initial state generation -source
+source

@@ -1087,7 +1087,7 @@ $$ * `rng`: Controls the randomness (if any) in the initial state generation -source
+source

@@ -1143,7 +1143,7 @@ $h_{new} = activation(weight_{ih} \times x + weight_{hh} \times h_{prev} + bias) * `rng`: Controls the randomness (if any) in the initial state generation -source
+source

@@ -1196,7 +1196,7 @@ This is completely distinct from `Flux.Recur`. It doesn't make the `cell` statef * Same as `cell`. -source
+source

@@ -1244,7 +1244,7 @@ To avoid undefined behavior, once the processing of a single sequence of data is * `carry`: The carry state of the `cell`. -source
+source

@@ -1305,7 +1305,7 @@ If `x` and `y` are matrices, then each column of the output `z = B(x, y)` is of * `bias`: Bias of size `(out_dims, 1)` (present if `use_bias=true`) -source
+source

@@ -1349,7 +1349,7 @@ Create a traditional fully connected layer, whose forward pass is given by: `y = * `bias`: Bias of size `(out_dims, 1)` (present if `use_bias=true`) -source
+source

@@ -1393,7 +1393,7 @@ Unlike `Flux.Embedding`, this layer does not support using `OneHotArray` as an i * Empty `NamedTuple()` -source
+source

@@ -1435,7 +1435,7 @@ Create a Sparsely Connected Layer with a very specific structure (only Diagonal * `bias`: Bias of size `(dims...)` -source
+source

@@ -1465,7 +1465,7 @@ Flattens the passed array into a matrix. * Empty `NamedTuple()` -source
+source

@@ -1516,7 +1516,7 @@ See also [`Parallel`](layers#Lux.Parallel) to reduce with other operators. [1] Goodfellow, Warde-Farley, Mirza, Courville & Bengio "Maxout Networks" [https://arxiv.org/abs/1302.4389](https://arxiv.org/abs/1302.4389) -source
+source

@@ -1532,7 +1532,7 @@ NoOpLayer() As the name suggests does nothing but allows pretty printing of layers. Whatever input is passed is returned. -source
+source

@@ -1561,7 +1561,7 @@ Reshapes the passed array to have a size of `(dims..., :)` * Empty `NamedTuple()` -source
+source

@@ -1591,7 +1591,7 @@ Return a view of all the data of the input `x` where the index for dimension `di * Empty `NamedTuple()` -source
+source

@@ -1620,7 +1620,7 @@ Wraps a stateless and parameter less function. Might be used when a function is * Empty `NamedTuple()` -source
+source

@@ -1706,7 +1706,7 @@ Passing a batch size of 1, during training will result in NaNs. See also [`BatchNorm`](layers#Lux.BatchNorm), [`InstanceNorm`](layers#Lux.InstanceNorm), [`LayerNorm`](layers#Lux.LayerNorm), [`WeightNorm`](layers#Lux.WeightNorm) -source
+source

@@ -1776,7 +1776,7 @@ GroupNorm doesn't have CUDNN support. The GPU fallback is not very efficient. See also [`GroupNorm`](layers#Lux.GroupNorm), [`InstanceNorm`](layers#Lux.InstanceNorm), [`LayerNorm`](layers#Lux.LayerNorm), [`WeightNorm`](layers#Lux.WeightNorm) -source
+source

@@ -1850,7 +1850,7 @@ InstanceNorm doesn't have CUDNN support. The GPU fallback is not very efficient. See also [`BatchNorm`](layers#Lux.BatchNorm), [`GroupNorm`](layers#Lux.GroupNorm), [`LayerNorm`](layers#Lux.LayerNorm), [`WeightNorm`](layers#Lux.WeightNorm) -source
+source

@@ -1913,7 +1913,7 @@ As of v0.5.0, the doc used to say `affine::Bool=false`, but the code actually ha * `scale`: Scale of shape `(shape..., 1)` -source
+source

@@ -1958,7 +1958,7 @@ Weight normalization is a reparameterization that decouples the magnitude of a w * Same as that of `layer` -source
+source

@@ -1995,7 +1995,7 @@ PixelShuffle is not a Layer, rather it returns a [`WrappedFunction`](layers#Lux. * Output of size `(r x W, r x H, C, N)` for 4D-arrays, and `(r x W, r x H, ..., C, N)` for D-dimensional data, where `D = ndims(x) - 2` -source
+source

@@ -2047,7 +2047,7 @@ Currently supported upsampling `mode`s and corresponding NNlib's methods are: * Empty `NamedTuple()` -source
+source

diff --git a/dev/api/Lux/utilities.md b/dev/api/Lux/utilities.md index e4fda903d..a30a9ee8e 100644 --- a/dev/api/Lux/utilities.md +++ b/dev/api/Lux/utilities.md @@ -44,7 +44,7 @@ This function has been deprecated. Use [`cpu_device`](../Accelerator_Support/Lux ::: -source
+source

@@ -66,7 +66,7 @@ This function has been deprecated. Use [`gpu_device`](../Accelerator_Support/Lux ::: -source
+source

@@ -111,7 +111,7 @@ foldl_init(op, x, init) Exactly same as `foldl(op, x; init)` in the forward pass. But, gives gradients wrt `init` in the backward pass. -source
+source

@@ -130,7 +130,7 @@ Returns `true` if `training` is `true` or if `st` contains a `training` field wi Method undefined if `st.training` is not of type `Val`. -source
+source

@@ -146,7 +146,7 @@ multigate(x::AbstractArray, ::Val{N}) Split up `x` into `N` equally sized chunks (along dimension `1`). -source
+source

@@ -163,7 +163,7 @@ replicate(rng::CUDA.RNG) Creates a copy of the `rng` state depending on its type. -source
+source

@@ -187,7 +187,7 @@ f16(m) Converts the `eltype` of `m` *floating point* values to `Float16`. Recurses into structs marked with `Functors.@functor`. -source
+source

@@ -203,7 +203,7 @@ f32(m) Converts the `eltype` of `m` *floating point* values to `Float32`. Recurses into structs marked with `Functors.@functor`. -source
+source

@@ -219,7 +219,7 @@ f64(m) Converts the `eltype` of `m` *floating point* values to `Float64`. Recurses into structs marked with `Functors.@functor`. -source
+source

@@ -242,7 +242,7 @@ An easy way to update `TruncatedStacktraces.VERBOSE` without having to load it m Effectively does `TruncatedStacktraces.VERBOSE[] = disable` -source
+source

diff --git a/dev/manual/debugging.md b/dev/manual/debugging.md index 18dc37539..dbd7ca542 100644 --- a/dev/manual/debugging.md +++ b/dev/manual/debugging.md @@ -108,7 +108,7 @@ end [ Info: Input Type: Matrix{Float32} | Input Structure: (3, 1) [ Info: Running Layer: Dense(1 => 1) at location model.layers.layer_2.layers.layer_2! ┌ Error: Layer Dense(1 => 1) failed!! This layer is present at location model.layers.layer_2.layers.layer_2 -└ @ Lux.Experimental /var/lib/buildkite-agent/builds/gpuci-8/julialang/lux-dot-jl/src/contrib/debug.jl:113 +└ @ Lux.Experimental /var/lib/buildkite-agent/builds/gpuci-2/julialang/lux-dot-jl/src/contrib/debug.jl:113 DimensionMismatch("A has dimensions (1,1) but B has dimensions (3,1)") ``` diff --git a/dev/manual/weight_initializers.md b/dev/manual/weight_initializers.md index f5c18b154..40cf6bd73 100644 --- a/dev/manual/weight_initializers.md +++ b/dev/manual/weight_initializers.md @@ -86,8 +86,8 @@ weights = kaiming_normal(CUDA.default_rng(), 2, 5) ``` 2×5 CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}: - -0.571258 -0.0281321 -0.0416904 -0.00658691 -0.568205 - -0.520571 -0.686362 -0.27334 -0.0172777 0.757579 + 0.0691023 -0.823759 -0.111147 0.371991 0.767093 + -0.42377 1.09882 -0.323465 -0.420165 -0.524025 ``` @@ -101,8 +101,8 @@ weights = kaiming_normal(CUDA.default_rng(), ComplexF32, 2, 5) ``` 2×5 CuArray{ComplexF32, 2, CUDA.Mem.DeviceBuffer}: - 0.270533+0.490283im -0.416164-0.083876im … 0.956817+0.000703053im - 0.193569+0.155975im 0.193355-0.166967im 1.32584+0.311526im + 0.133427-0.599696im -0.220603-0.73059im … 0.366495-0.161208im + -0.5364+0.276592im 1.01691+0.211542im -0.185464-1.18464im ``` diff --git a/dev/tutorials/advanced/1_GravitationalWaveForm-35.png b/dev/tutorials/advanced/1_GravitationalWaveForm-35.png index b5d8da51e..383216e00 100644 Binary files a/dev/tutorials/advanced/1_GravitationalWaveForm-35.png and b/dev/tutorials/advanced/1_GravitationalWaveForm-35.png differ diff --git a/dev/tutorials/advanced/1_GravitationalWaveForm-48.png b/dev/tutorials/advanced/1_GravitationalWaveForm-48.png index 832f58b51..c6c675689 100644 Binary files a/dev/tutorials/advanced/1_GravitationalWaveForm-48.png and b/dev/tutorials/advanced/1_GravitationalWaveForm-48.png differ diff --git a/dev/tutorials/advanced/1_GravitationalWaveForm-50.png b/dev/tutorials/advanced/1_GravitationalWaveForm-50.png index aaace44bb..df28ca359 100644 Binary files a/dev/tutorials/advanced/1_GravitationalWaveForm-50.png and b/dev/tutorials/advanced/1_GravitationalWaveForm-50.png differ diff --git a/dev/tutorials/advanced/1_GravitationalWaveForm.md b/dev/tutorials/advanced/1_GravitationalWaveForm.md index 8660364fb..445cbe8f0 100644 --- a/dev/tutorials/advanced/1_GravitationalWaveForm.md +++ b/dev/tutorials/advanced/1_GravitationalWaveForm.md @@ -316,7 +316,7 @@ ps, st = Lux.setup(MersenneTwister(), nn) ``` -((layer_1 = NamedTuple(), layer_2 = (weight = Float32[0.00012043802; -3.1254607f-5; -2.47349f-5; 4.5131277f-5; 3.3116197f-5; -5.769868f-5; -5.571604f-5; -7.898042f-6; -4.3751694f-5; -0.00015896904; -3.5970235f-5; 9.4927236f-5; 2.004449f-5; -7.0810485f-5; 2.0007663f-5; 0.00016648175; -1.5792093f-5; -4.284176f-5; 6.232995f-5; 4.0796345f-5; -0.00013221732; 2.7350497f-5; 3.328348f-5; -0.00016396989; -0.00014776613; -2.344316f-6; 1.5808733f-5; -0.00010416895; -3.6319238f-5; 1.1995494f-5; -2.1587233f-5; 1.7798975f-5;;], bias = Float32[0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0;;]), layer_3 = (weight = Float32[-2.2890374f-5 -7.038405f-5 -7.631469f-7 -0.00012943316 -2.301726f-5 -6.0788872f-5 5.1012826f-6 -1.0113731f-5 9.7104756f-5 -4.4243097f-5 -6.848788f-5 0.00014162852 -6.1498744f-5 -4.2038417f-5 0.00011514829 0.00010124002 -0.00010687984 -4.0625753f-5 -8.848768f-5 -8.2281025f-5 9.377903f-5 -0.00015548432 -2.6714451f-5 -6.582323f-5 -6.325934f-5 2.801114f-6 7.577968f-5 3.137474f-5 3.7821934f-5 5.4135858f-6 0.00020052023 7.834003f-5; 0.000100907695 -0.00022153574 -5.1771494f-5 -0.00017762328 -0.0002943944 3.209191f-5 -4.4053166f-5 0.0001490438 -0.00013447678 -0.00012751808 5.8023572f-5 0.00010472499 -0.00018351176 0.00017078695 -1.4071827f-5 -4.629191f-5 0.0001760698 -0.00018940438 4.553128f-6 3.421864f-5 7.235742f-5 0.00014164254 1.8806702f-5 8.929057f-5 7.0548755f-5 3.3338565f-5 0.000117550895 -0.00017907629 -3.426344f-5 -1.2025171f-5 -6.06026f-5 7.067923f-5; -0.0002444421 2.851193f-5 0.00011597099 6.5861934f-7 0.00012726536 -4.986479f-5 -2.0539797f-5 -0.00024364956 6.0045077f-5 0.00012266441 -7.488924f-5 7.726562f-5 8.288692f-5 -0.00014583523 3.852464f-5 6.306009f-5 -2.8613149f-5 -0.00019185725 -0.00010472887 -0.00018226761 7.803895f-6 9.37666f-5 -7.810506f-5 6.914442f-5 -9.258978f-5 -6.352883f-5 9.901318f-5 6.730758f-5 -9.771726f-6 7.986587f-5 0.000108595596 0.000265607; 0.0002933142 0.00017039772 1.8440423f-5 -0.0001318449 5.918226f-5 -9.925041f-5 -5.9390903f-5 0.00012809024 4.7931764f-5 -0.00010232139 -0.00013399374 -5.7620204f-5 0.00012782274 -5.0926956f-6 0.00015152269 -5.3223466f-5 6.137857f-5 3.414831f-5 7.2911967f-6 -1.5249494f-5 -4.5846737f-5 7.754099f-5 -0.00015784225 0.000161753 8.0683356f-5 0.00018758657 -3.7180704f-5 2.7837405f-5 9.6404685f-5 -0.00022314914 1.9192741f-5 -7.388704f-5; -0.00021300226 -9.357689f-5 -7.4423915f-5 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-3.479822078267846e-9; 1.5688451907699175e-9; 3.8198149089334805e-9; -1.0423331766839313e-9; 6.87586413332965e-10; 6.736333715540601e-10; -5.749882701645693e-10; -4.8798676933945895e-9; 3.0595867849628412e-9; 3.6451128430956993e-9;;]), layer_4 = (weight = [-0.0009776709606690531 -0.0008813341998406772 -0.0007211640263114085 -0.0006829273527164182 -0.0006328161623311284 -0.0006374705705001438 -0.0007825106372777093 -0.0008153903032510568 -0.000702011041691744 -0.0006560134436856496 -0.0008285827952224885 -0.0006209370585929015 -0.0005934848614973216 -0.0006858483849571386 -0.0006934254382963675 -0.0005393002857087693 -0.000696244337892893 -0.0007093323718058585 -0.0008640208538615491 -0.000686949398974303 -0.0006434295935122457 -0.00046986214849831524 -0.0005457267654859304 -0.0007148921790336662 -0.0005961786285097765 -0.0006585651009202276 -0.0006862360334650966 -0.0007562541611538948 -0.0007324374010906781 -0.0005692084780138072 -0.0007353286273375934 -0.0006891483056659633; 0.0001823140491041021 0.0003433718622998571 0.0001818082495891069 0.00016066832233908854 0.00024122861589246432 0.00025592043701383026 0.00018813387985312061 0.0003968626672408489 2.040248837822298e-5 0.00028517523955726636 0.0002843462455317059 0.0002054188174298897 0.0003084480890769215 0.00025802320651108126 0.0002698871124025605 0.0003803048844924889 0.00011768694503073924 0.00018311154870750854 0.0002110741490612838 0.00030178584790835214 0.0003261994836989986 0.0001769178355087468 0.00011524499605170906 0.000299726096377922 0.00021098847913828868 0.00024386497916441407 0.00028159205729467307 0.00027089657428657724 0.00022684902031880035 0.00013923254140696102 0.0003487277532609344 5.189211446133372e-5], bias = [-0.0006865296888746245; 0.0002272402901367851;;])) ``` diff --git a/dev/tutorials/beginner/1_Basics.md b/dev/tutorials/beginner/1_Basics.md index 5d2969204..d49a5dfe0 100644 --- a/dev/tutorials/beginner/1_Basics.md +++ b/dev/tutorials/beginner/1_Basics.md @@ -619,7 +619,7 @@ val, pb_f = Zygote.pullback(f, x) ``` -(Float32[0.3850005, 0.71437216, 0.0016247969, 0.031389393, 0.0043726736], Zygote.var"#75#76"{Zygote.Pullback{Tuple{typeof(Main.var"##292".f), Vector{Float32}}, Tuple{Zygote.Pullback{Tuple{typeof(Base.Broadcast.materialize), Vector{Float32}}, Tuple{}}, Zygote.var"#3866#back#1231"{Zygote.ZBack{ChainRules.var"#slash_pullback_scalar#1560"{Vector{Float32}, Int64}}}, Zygote.var"#3802#back#1205"{Zygote.var"#1201#1204"{Vector{Float32}, Vector{Float32}}}}}}(∂(f))) +(Float32[0.3850005, 0.71437216, 0.0016247969, 0.031389393, 0.0043726736], Zygote.var"#75#76"{Zygote.Pullback{Tuple{typeof(Main.var"##292".f), Vector{Float32}}, Tuple{Zygote.var"#3866#back#1231"{Zygote.ZBack{ChainRules.var"#slash_pullback_scalar#1560"{Vector{Float32}, Int64}}}, Zygote.Pullback{Tuple{typeof(Base.Broadcast.materialize), Vector{Float32}}, Tuple{}}, Zygote.var"#3802#back#1205"{Zygote.var"#1201#1204"{Vector{Float32}, Vector{Float32}}}}}}(∂(f))) ``` diff --git a/dev/tutorials/beginner/3_SimpleRNN.md b/dev/tutorials/beginner/3_SimpleRNN.md index 3e54963f1..cbf0fee81 100644 --- a/dev/tutorials/beginner/3_SimpleRNN.md +++ b/dev/tutorials/beginner/3_SimpleRNN.md @@ -233,7 +233,7 @@ ps_trained, st_trained = main() ``` -((lstm_cell = (weight_i = Float32[-0.86276984 -0.4213254; -0.25293428 -0.75647354; 0.8088518 0.88360196; -0.059109423 0.07517572; -0.14522508 -0.6203079; 0.28849384 0.54892313; -0.8346177 0.07300737; 0.06653782 0.058332995; -0.03877078 0.079453856; -0.6625688 0.4515069; 1.176165 -0.02154413; -0.01680309 0.08733556; -0.10831728 0.24390326; -0.8517622 0.31830052; -0.12901929 -0.27541128; 1.2186768 0.10076883; 0.6799521 -0.6273073; 0.12986371 -0.34502268; 0.47303933 -0.32547772; -0.5533537 0.5176456; 0.5829507 -0.83681273; 0.0967146 0.29522842; 0.8690922 -0.63841003; 0.94481844 0.62133884; -1.2732903 -0.082856536; 0.48236236 0.64187825; 1.0559748 0.6745949; -0.45005012 -0.15025549; 0.7399162 -0.6964666; -0.33952615 0.7236629; -0.21820404 0.61233187; 0.6099077 -0.28849536], weight_h = Float32[-0.52177167 -0.056619454 0.29489157 -0.2602206 0.2977959 0.006653183 -0.7165368 0.5499884; -0.65391004 0.22275676 -0.061842546 0.63037425 0.38543522 0.30993786 0.13219109 -0.043282796; 0.01776584 0.059654888 -0.03328572 0.55994725 -0.7405812 0.31792748 -0.63458 -0.14882207; 0.035189956 -0.26965275 0.8883299 -0.09108613 0.9124591 0.09202381 0.2769739 0.908775; -0.37400383 0.47007886 0.77794176 0.32175535 0.40222272 0.6350676 -0.3382366 -0.077398226; -0.056699455 -0.33725715 0.33235157 -0.09759259 -0.28291 -0.21988894 -0.49292746 0.05233773; -0.78830045 0.37227204 0.6890894 0.5145842 -0.8091606 0.6356342 0.025718218 -0.6188784; 0.680965 0.3060097 1.0663296 -1.3047808 0.8080975 0.15756102 -0.70753676 1.1456208; -0.16523086 0.47136363 0.4158571 -0.4970363 0.46246445 0.6759026 0.18512733 0.6398445; -0.41848558 -0.18689466 -0.38291577 -0.009806018 0.24410196 -0.0011097237 -0.74773204 0.75334424; -0.14842469 0.55044526 -0.04316719 0.5261074 -0.519148 0.2937469 -0.26649427 -0.3699438; 0.053556502 0.8849147 0.28956002 -0.10664247 0.8292588 0.6361808 0.38762116 0.5011684; 0.64782894 0.49410155 0.37249404 -0.0711571 0.92375416 0.16408864 -0.70665115 0.44406185; -0.095736496 0.44443277 0.086166814 0.08927924 0.60954916 -0.01615682 -0.14018914 -0.39279187; -0.8348824 0.3232656 0.086743146 -0.39965576 -0.2620813 0.80815464 -0.3682492 0.42446503; -0.648208 0.78791744 0.41902563 1.0596577 -0.08907314 0.81488925 -0.10177508 0.76564825; 0.14886165 -0.6393479 -0.048952103 -0.44008026 -0.35250628 -0.21541736 -0.1638811 0.17350978; -0.4400281 0.22938068 0.19673656 0.71615887 0.39505637 -0.35570267 -0.3620702 0.67124116; -0.5544135 0.7073431 0.09340451 -0.4308263 -0.31919062 0.7411523 0.050386224 0.6029612; -0.775748 0.3399935 -0.16806175 0.50490457 -0.6976452 -0.14407954 -0.2000515 -0.19644512; -0.26788536 -0.66526705 0.40152156 -0.6444947 -0.16601378 0.26463002 0.24772982 -0.12365446; -0.72348404 -0.42974308 0.53249073 -0.48058012 -0.13816452 0.030314425 -0.44090706 0.2210163; 0.48452595 -0.18573986 -0.6619749 0.2694261 0.34259796 0.07305046 -0.5232311 -0.41915902; -0.4144604 0.35546637 0.20805377 0.37169015 -0.30168918 -0.19049826 -0.748544 0.034505107; -0.6590559 0.5485719 0.26300207 -0.44434574 0.5983971 0.43181345 -0.9441646 0.8706803; -0.60187966 0.2681003 0.0660451 0.4278262 -0.2689142 0.560479 -0.4121616 -0.3011634; -0.8189555 -0.27616352 0.45932242 -0.2797982 0.24932638 0.013312056 -0.8899485 0.7555467; 0.26114953 0.27001467 1.0170794 -0.8993219 0.24781206 0.17621525 -1.1193454 0.49175647; -0.38671485 0.27731943 0.7682361 -0.42457727 0.45035884 0.8945356 -1.1947862 0.715842; 0.45133486 0.38548324 -0.6964026 0.61695546 -1.2220075 -0.3841463 -0.15183473 -0.06169086; -0.43702745 0.5166827 -0.26556015 0.71636564 -0.8196524 -0.14018728 0.085123464 -0.082015954; -0.5592939 0.7435259 0.6798782 -0.5129234 0.6534693 0.050305586 -0.4843237 0.64841586], bias = Float32[0.28972632; 0.2791686; 0.14093061; 0.32439885; 0.34397084; 0.119492345; 0.011196334; 0.9706966; 0.37983653; 0.15578207; 0.0081832595; 0.35777524; 0.43449563; 0.07035743; -0.014683077; 0.31760934; 0.8447288; 1.1300668; 1.1569711; 0.76859015; 0.6893949; 1.2356604; 0.652198; 0.9141856; 0.47346783; 0.05976353; 0.30497912; 0.6207735; 0.6362388; 0.01324947; -0.118201315; 0.87571967;;]), classifier = (weight = Float32[-1.4308926 0.7585742 1.2370543 1.2612809 -0.9370922 0.11333875 -0.26662588 1.227679], bias = Float32[-0.64019877;;])), (lstm_cell = (rng = Random.Xoshiro(0x2026f555c226bf09, 0x8a6bb764b93cadda, 0x5ba3c10439600514, 0x446f763658f71987),), classifier = NamedTuple())) +((lstm_cell = (weight_i = Float32[-0.8606602 -0.42173535; -0.25246745 -0.75600076; 0.8029338 0.8827529; -0.055333234 0.0765522; -0.14888899 -0.6241015; 0.29067126 0.5507002; -0.8301844 0.06623835; 0.083633706 0.05607221; -0.040856835 0.085826494; -0.6604657 0.45190847; 1.1381556 -0.025163893; -0.009252903 0.08881796; -0.10503148 0.24741088; -0.8487872 0.32451385; -0.13070767 -0.2740992; 1.2156894 0.09226333; 0.6780722 -0.6291956; 0.13157831 -0.34730637; 0.4894645 -0.32803383; -0.5605773 0.51693183; 0.5838978 -0.8370361; 0.09948699 0.29414925; 0.86695117 -0.64033; 0.94269663 0.6144005; -1.2772859 -0.100429595; 0.48635218 0.64556986; 1.0548038 0.67883533; -0.4562046 -0.17665727; 0.7351149 -0.7078254; -0.3346572 0.7475073; -0.21564722 0.6146306; 0.6103375 -0.31653598], weight_h = Float32[-0.5110704 -0.052088268 0.28237462 -0.25674182 0.30191556 0.008072335 -0.6973466 0.5534865; -0.6510881 0.23683538 -0.06593768 0.63547146 0.3867634 0.31203765 0.13049382 -0.05289847; 0.019225102 0.060762323 -0.03029802 0.55466855 -0.74572384 0.32386264 -0.6297078 -0.17795318; 0.038119018 -0.27402425 0.88340384 -0.09412319 0.91051066 0.07939905 0.27935228 0.90647435; -0.36838868 0.47309172 0.7697996 0.3030604 0.40257356 0.6405058 -0.34387764 -0.07917443; -0.057011515 -0.33901483 0.33326602 -0.09851931 -0.28673458 -0.22091433 -0.49445915 0.051240712; -0.7845671 0.37163594 0.6954974 0.49112907 -0.80408835 0.6407935 0.031586125 -0.6296838; 0.6820835 0.3038341 1.0094144 -1.3209459 0.8263803 0.15999909 -0.7050237 1.149009; -0.14463647 0.47446528 0.40174845 -0.508296 0.46355778 0.6769307 0.19450618 0.63986725; -0.4175838 -0.18794808 -0.38056022 -0.013296623 0.24479842 0.0013890705 -0.7533706 0.7379234; -0.1458562 0.5498358 -0.06454254 0.52586704 -0.5274195 0.2925536 -0.2658813 -0.38807267; 0.057238076 0.87889534 0.2841408 -0.10555127 0.82709914 0.60818225 0.3933218 0.4980266; 0.6510231 0.4913781 0.37144002 -0.07032925 0.9206203 0.18805322 -0.64442104 0.44123667; -0.10307776 0.44412628 0.08594578 0.08157739 0.6107274 -0.010769104 -0.13513514 -0.40591753; -0.8332051 0.32349646 0.086011976 -0.40790662 -0.2554678 0.8068389 -0.3655421 0.4086767; -0.65485734 0.7909005 0.40998954 1.0457914 -0.090856954 0.8197534 -0.110592715 0.7623953; 0.15004541 -0.6370467 -0.048552994 -0.43575117 -0.35319594 -0.21523164 -0.16186404 0.18101656; -0.4375886 0.22765084 0.19822727 0.7136601 0.39824736 -0.35207528 -0.36515558 0.677634; -0.5518444 0.70661724 0.09364483 -0.43415534 -0.34702447 0.73876756 0.05202792 0.5981174; -0.77462006 0.339934 -0.16739002 0.5017732 -0.69622415 -0.15464908 -0.20010723 -0.19695364; -0.27308944 -0.66661847 0.40210626 -0.6409403 -0.17511936 0.2647315 0.25021055 -0.11946688; -0.72028327 -0.42881316 0.5281268 -0.48344463 -0.142266 0.028804548 -0.4394839 0.22274998; 0.48565486 -0.19201116 -0.6665934 0.2934958 0.33981925 0.067954116 -0.52248895 -0.40977702; -0.4117284 0.35252258 0.21155894 0.3648152 -0.3361065 -0.19786483 -0.74133015 -0.00067374675; -0.6483867 0.5574187 0.2661966 -0.45875716 0.59861803 0.4390683 -0.93947995 0.86793023; -0.60826993 0.28007808 0.06475797 0.4291094 -0.268784 0.5690411 -0.41752252 -0.33524865; -0.82213295 -0.2701551 0.46343708 -0.27210298 0.22321412 0.017027538 -0.88735574 0.75277114; 0.29790133 0.26896915 1.0163385 -0.9029633 0.23622869 0.18422341 -1.0948917 0.4666365; -0.34440792 0.28043813 0.77080166 -0.43317991 0.45024294 0.9211484 -1.182236 0.69628686; 0.42297065 0.4084917 -0.68958354 0.5996869 -1.1673168 -0.36359516 -0.16594133 -0.056037545; -0.43637004 0.5204338 -0.25886863 0.69191843 -0.8197929 -0.12801804 0.07757771 -0.09324067; -0.5480716 0.7625965 0.7003736 -0.5582013 0.6155692 0.061419714 -0.48653224 0.65047], bias = Float32[0.294826; 0.2918274; 0.13633585; 0.3219552; 0.34494954; 0.11699324; 0.0130639225; 0.9806686; 0.38452056; 0.15388073; 0.0047289496; 0.35512054; 0.4310879; 0.06929851; -0.012993573; 0.3157625; 0.8467737; 1.1284499; 1.1545233; 0.76945174; 0.6891291; 1.2387295; 0.644703; 0.92102176; 0.47778627; 0.040251948; 0.30618015; 0.6124639; 0.6320226; 0.030538445; -0.118936904; 0.8760047;;]), classifier = (weight = Float32[-1.4405788 0.77230424 1.2486339 1.2609098 -0.93654513 0.119384825 -0.26760432 1.2233782], bias = Float32[-0.5736038;;])), (lstm_cell = (rng = Random.Xoshiro(0x2026f555c226bf09, 0x8a6bb764b93cadda, 0x5ba3c10439600514, 0x446f763658f71987),), classifier = NamedTuple())) ``` diff --git a/dev/tutorials/beginner/trained_model.jld2 b/dev/tutorials/beginner/trained_model.jld2 index b8f90e171..2f53558b8 100644 Binary files a/dev/tutorials/beginner/trained_model.jld2 and b/dev/tutorials/beginner/trained_model.jld2 differ diff --git a/dev/tutorials/intermediate/1_NeuralODE.md b/dev/tutorials/intermediate/1_NeuralODE.md index b30084a26..fdfaaf0fc 100644 --- a/dev/tutorials/intermediate/1_NeuralODE.md +++ b/dev/tutorials/intermediate/1_NeuralODE.md @@ -223,15 +223,15 @@ train(NeuralODE) ``` -[1/9] Time 6.52s Training Accuracy: 49.48% Test Accuracy: 40.0% -[2/9] Time 0.33s Training Accuracy: 70.22% Test Accuracy: 66.0% -[3/9] Time 0.42s Training Accuracy: 77.7% Test Accuracy: 72.67% -[4/9] Time 0.47s Training Accuracy: 80.59% Test Accuracy: 74.67% -[5/9] Time 0.34s Training Accuracy: 82.67% Test Accuracy: 78.0% -[6/9] Time 0.33s Training Accuracy: 84.15% Test Accuracy: 78.67% -[7/9] Time 0.44s Training Accuracy: 85.41% Test Accuracy: 80.0% -[8/9] Time 0.44s Training Accuracy: 86.89% Test Accuracy: 80.67% -[9/9] Time 0.47s Training Accuracy: 88.0% Test Accuracy: 81.33% +[1/9] Time 6.45s Training Accuracy: 49.48% Test Accuracy: 40.0% +[2/9] Time 0.39s Training Accuracy: 70.52% Test Accuracy: 66.0% +[3/9] Time 0.33s Training Accuracy: 77.56% Test Accuracy: 71.33% +[4/9] Time 0.46s Training Accuracy: 80.74% Test Accuracy: 73.33% +[5/9] Time 0.47s Training Accuracy: 82.59% Test Accuracy: 76.67% +[6/9] Time 0.45s Training Accuracy: 84.52% Test Accuracy: 80.0% +[7/9] Time 0.33s Training Accuracy: 85.48% Test Accuracy: 80.67% +[8/9] Time 0.45s Training Accuracy: 86.89% Test Accuracy: 82.67% +[9/9] Time 0.45s Training Accuracy: 88.07% Test Accuracy: 82.67% ``` @@ -245,15 +245,15 @@ train(NeuralODE; sensealg=GaussAdjoint(; autojacvec=ZygoteVJP()), use_named_tupl ``` -[1/9] Time 2.9s Training Accuracy: 51.26% Test Accuracy: 42.67% -[2/9] Time 0.31s Training Accuracy: 71.78% Test Accuracy: 65.33% -[3/9] Time 0.29s Training Accuracy: 78.0% Test Accuracy: 71.33% -[4/9] Time 0.43s Training Accuracy: 80.3% Test Accuracy: 75.33% -[5/9] Time 0.44s Training Accuracy: 82.74% Test Accuracy: 79.33% -[6/9] Time 0.33s Training Accuracy: 84.44% Test Accuracy: 78.67% -[7/9] Time 0.32s Training Accuracy: 85.85% Test Accuracy: 80.67% -[8/9] Time 0.43s Training Accuracy: 87.04% Test Accuracy: 82.0% -[9/9] Time 0.43s Training Accuracy: 87.85% Test Accuracy: 82.67% +[1/9] Time 2.8s Training Accuracy: 49.26% Test Accuracy: 38.67% +[2/9] Time 0.37s Training Accuracy: 69.26% Test Accuracy: 64.0% +[3/9] Time 0.32s Training Accuracy: 78.0% Test Accuracy: 72.0% +[4/9] Time 0.44s Training Accuracy: 80.15% Test Accuracy: 74.67% +[5/9] Time 0.43s Training Accuracy: 82.37% Test Accuracy: 77.33% +[6/9] Time 0.32s Training Accuracy: 84.52% Test Accuracy: 78.67% +[7/9] Time 0.32s Training Accuracy: 85.85% Test Accuracy: 80.0% +[8/9] Time 0.44s Training Accuracy: 86.59% Test Accuracy: 82.0% +[9/9] Time 0.43s Training Accuracy: 87.48% Test Accuracy: 82.0% ``` @@ -267,14 +267,14 @@ train(NeuralODE; sensealg=InterpolatingAdjoint(; autojacvec=ReverseDiffVJP()), c ``` -[1/9] Time 6.43s Training Accuracy: 50.96% Test Accuracy: 43.33% -[2/9] Time 0.09s Training Accuracy: 69.63% Test Accuracy: 66.0% +[1/9] Time 6.57s Training Accuracy: 50.96% Test Accuracy: 43.33% +[2/9] Time 0.08s Training Accuracy: 69.63% Test Accuracy: 66.0% [3/9] Time 0.08s Training Accuracy: 77.93% Test Accuracy: 71.33% -[4/9] Time 0.09s Training Accuracy: 80.74% Test Accuracy: 76.67% -[5/9] Time 0.09s Training Accuracy: 82.52% Test Accuracy: 78.0% +[4/9] Time 0.08s Training Accuracy: 80.74% Test Accuracy: 76.67% +[5/9] Time 0.08s Training Accuracy: 82.52% Test Accuracy: 78.0% [6/9] Time 0.09s Training Accuracy: 84.07% Test Accuracy: 78.67% -[7/9] Time 0.08s Training Accuracy: 85.33% Test Accuracy: 80.67% -[8/9] Time 0.1s Training Accuracy: 86.59% Test Accuracy: 81.33% +[7/9] Time 0.09s Training Accuracy: 85.33% Test Accuracy: 80.67% +[8/9] Time 0.09s Training Accuracy: 86.59% Test Accuracy: 81.33% [9/9] Time 0.09s Training Accuracy: 87.7% Test Accuracy: 82.0% ``` @@ -289,15 +289,15 @@ train(NeuralODE; sensealg=ReverseDiffAdjoint(), cpu=true) ``` -[1/9] Time 4.81s Training Accuracy: 50.96% Test Accuracy: 43.33% -[2/9] Time 4.26s Training Accuracy: 69.63% Test Accuracy: 66.0% -[3/9] Time 4.35s Training Accuracy: 77.93% Test Accuracy: 71.33% -[4/9] Time 4.37s Training Accuracy: 80.74% Test Accuracy: 76.67% -[5/9] Time 5.01s Training Accuracy: 82.52% Test Accuracy: 78.0% -[6/9] Time 5.5s Training Accuracy: 84.07% Test Accuracy: 78.67% -[7/9] Time 5.42s Training Accuracy: 85.33% Test Accuracy: 80.67% -[8/9] Time 5.41s Training Accuracy: 86.59% Test Accuracy: 81.33% -[9/9] Time 5.59s Training Accuracy: 87.7% Test Accuracy: 82.0% +[1/9] Time 4.66s Training Accuracy: 50.96% Test Accuracy: 43.33% +[2/9] Time 4.3s Training Accuracy: 69.63% Test Accuracy: 66.0% +[3/9] Time 4.3s Training Accuracy: 77.93% Test Accuracy: 71.33% +[4/9] Time 4.25s Training Accuracy: 80.74% Test Accuracy: 76.67% +[5/9] Time 4.89s Training Accuracy: 82.52% Test Accuracy: 78.0% +[6/9] Time 5.38s Training Accuracy: 84.07% Test Accuracy: 78.67% +[7/9] Time 5.36s Training Accuracy: 85.33% Test Accuracy: 80.67% +[8/9] Time 5.36s Training Accuracy: 86.59% Test Accuracy: 81.33% +[9/9] Time 5.4s Training Accuracy: 87.7% Test Accuracy: 82.0% ``` @@ -346,15 +346,15 @@ train(StatefulNeuralODE) ``` -[1/9] Time 1.15s Training Accuracy: 49.04% Test Accuracy: 42.67% -[2/9] Time 0.51s Training Accuracy: 71.41% Test Accuracy: 66.0% -[3/9] Time 0.31s Training Accuracy: 77.33% Test Accuracy: 72.0% -[4/9] Time 0.36s Training Accuracy: 80.59% Test Accuracy: 74.0% -[5/9] Time 0.38s Training Accuracy: 82.44% Test Accuracy: 78.0% -[6/9] Time 0.38s Training Accuracy: 84.37% Test Accuracy: 78.67% -[7/9] Time 0.38s Training Accuracy: 85.41% Test Accuracy: 79.33% -[8/9] Time 0.38s Training Accuracy: 86.44% Test Accuracy: 80.67% -[9/9] Time 0.38s Training Accuracy: 88.22% Test Accuracy: 82.67% +[1/9] Time 1.15s Training Accuracy: 51.63% Test Accuracy: 45.33% +[2/9] Time 0.31s Training Accuracy: 70.89% Test Accuracy: 66.0% +[3/9] Time 0.34s Training Accuracy: 77.56% Test Accuracy: 72.67% +[4/9] Time 0.38s Training Accuracy: 80.67% Test Accuracy: 76.0% +[5/9] Time 0.38s Training Accuracy: 82.81% Test Accuracy: 78.0% +[6/9] Time 0.69s Training Accuracy: 84.3% Test Accuracy: 80.0% +[7/9] Time 0.32s Training Accuracy: 85.56% Test Accuracy: 80.0% +[8/9] Time 0.32s Training Accuracy: 86.89% Test Accuracy: 82.0% +[9/9] Time 0.33s Training Accuracy: 87.78% Test Accuracy: 82.67% ``` @@ -386,7 +386,7 @@ NeuralODE is not type stable due to the boxing of `st` ``` MethodInstance for (::Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3, :layer_4, :layer_5), Tuple{Lux.FlattenLayer, Lux.Dense{true, typeof(NNlib.tanh_fast), typeof(WeightInitializers.glorot_uniform), typeof(WeightInitializers.zeros32)}, Main.var"##292".NeuralODE{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{Lux.Dense{true, typeof(NNlib.tanh_fast), typeof(WeightInitializers.glorot_uniform), typeof(WeightInitializers.zeros32)}, Lux.Dense{true, typeof(NNlib.tanh_fast), typeof(WeightInitializers.glorot_uniform), typeof(WeightInitializers.zeros32)}, Lux.Dense{true, typeof(NNlib.tanh_fast), typeof(WeightInitializers.glorot_uniform), typeof(WeightInitializers.zeros32)}}}, Nothing}, OrdinaryDiffEq.Tsit5{typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}, SciMLSensitivity.InterpolatingAdjoint{0, true, Val{:central}, SciMLSensitivity.ZygoteVJP}, Tuple{Float32, Float32}, Base.Pairs{Symbol, Real, NTuple{4, Symbol}, NamedTuple{(:save_everystep, :reltol, :abstol, :save_start), Tuple{Bool, Float32, Float32, Bool}}}}, Lux.WrappedFunction{Base.Fix1{typeof(Main.var"##292".diffeqsol_to_array), Int64}}, Lux.Dense{true, typeof(identity), typeof(WeightInitializers.glorot_uniform), typeof(WeightInitializers.zeros32)}}}, Nothing})(::CUDA.CuArray{Float32, 4, CUDA.Mem.DeviceBuffer}, ::ComponentArrays.ComponentVector{Float32, CUDA.CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, Tuple{ComponentArrays.Axis{(layer_1 = 1:0, layer_2 = ViewAxis(1:15700, Axis(weight = ViewAxis(1:15680, ShapedAxis((20, 784), NamedTuple())), bias = ViewAxis(15681:15700, ShapedAxis((20, 1), NamedTuple())))), layer_3 = ViewAxis(15701:16240, Axis(layer_1 = ViewAxis(1:210, Axis(weight = ViewAxis(1:200, ShapedAxis((10, 20), NamedTuple())), bias = ViewAxis(201:210, ShapedAxis((10, 1), NamedTuple())))), layer_2 = ViewAxis(211:320, Axis(weight = ViewAxis(1:100, ShapedAxis((10, 10), NamedTuple())), bias = ViewAxis(101:110, ShapedAxis((10, 1), NamedTuple())))), layer_3 = ViewAxis(321:540, Axis(weight = ViewAxis(1:200, ShapedAxis((20, 10), NamedTuple())), bias = ViewAxis(201:220, ShapedAxis((20, 1), NamedTuple())))))), layer_4 = 16241:16240, layer_5 = ViewAxis(16241:16450, Axis(weight = ViewAxis(1:200, ShapedAxis((10, 20), NamedTuple())), bias = ViewAxis(201:210, ShapedAxis((10, 1), NamedTuple())))))}}}, ::NamedTuple{(:layer_1, :layer_2, :layer_3, :layer_4, :layer_5), Tuple{NamedTuple{(), Tuple{}}, NamedTuple{(), Tuple{}}, NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{NamedTuple{(), Tuple{}}, NamedTuple{(), Tuple{}}, NamedTuple{(), Tuple{}}}}, NamedTuple{(), Tuple{}}, NamedTuple{(), Tuple{}}}}) - from (c::Lux.Chain)(x, ps, st::NamedTuple) @ Lux /var/lib/buildkite-agent/builds/gpuci-8/julialang/lux-dot-jl/src/layers/containers.jl:478 + from (c::Lux.Chain)(x, ps, st::NamedTuple) @ Lux /var/lib/buildkite-agent/builds/gpuci-2/julialang/lux-dot-jl/src/layers/containers.jl:478 Arguments c::Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3, :layer_4, :layer_5), Tuple{Lux.FlattenLayer, Lux.Dense{true, typeof(NNlib.tanh_fast), typeof(WeightInitializers.glorot_uniform), typeof(WeightInitializers.zeros32)}, Main.var"##292".NeuralODE{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{Lux.Dense{true, typeof(NNlib.tanh_fast), typeof(WeightInitializers.glorot_uniform), typeof(WeightInitializers.zeros32)}, Lux.Dense{true, typeof(NNlib.tanh_fast), typeof(WeightInitializers.glorot_uniform), typeof(WeightInitializers.zeros32)}, Lux.Dense{true, typeof(NNlib.tanh_fast), typeof(WeightInitializers.glorot_uniform), typeof(WeightInitializers.zeros32)}}}, Nothing}, OrdinaryDiffEq.Tsit5{typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}, SciMLSensitivity.InterpolatingAdjoint{0, true, Val{:central}, SciMLSensitivity.ZygoteVJP}, Tuple{Float32, Float32}, Base.Pairs{Symbol, Real, NTuple{4, Symbol}, NamedTuple{(:save_everystep, :reltol, :abstol, :save_start), Tuple{Bool, Float32, Float32, Bool}}}}, Lux.WrappedFunction{Base.Fix1{typeof(Main.var"##292".diffeqsol_to_array), Int64}}, Lux.Dense{true, typeof(identity), typeof(WeightInitializers.glorot_uniform), typeof(WeightInitializers.zeros32)}}}, Nothing} x::CUDA.CuArray{Float32, 4, CUDA.Mem.DeviceBuffer} @@ -411,7 +411,7 @@ We avoid the problem entirely by using `StatefulNeuralODE` ``` MethodInstance for (::Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3, :layer_4, :layer_5), Tuple{Lux.FlattenLayer, Lux.Dense{true, typeof(NNlib.tanh_fast), typeof(WeightInitializers.glorot_uniform), typeof(WeightInitializers.zeros32)}, Main.var"##292".StatefulNeuralODE{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{Lux.Dense{true, typeof(NNlib.tanh_fast), typeof(WeightInitializers.glorot_uniform), typeof(WeightInitializers.zeros32)}, Lux.Dense{true, typeof(NNlib.tanh_fast), typeof(WeightInitializers.glorot_uniform), typeof(WeightInitializers.zeros32)}, Lux.Dense{true, typeof(NNlib.tanh_fast), typeof(WeightInitializers.glorot_uniform), typeof(WeightInitializers.zeros32)}}}, Nothing}, OrdinaryDiffEq.Tsit5{typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}, SciMLSensitivity.InterpolatingAdjoint{0, true, Val{:central}, SciMLSensitivity.ZygoteVJP}, Tuple{Float32, Float32}, Base.Pairs{Symbol, Real, NTuple{4, Symbol}, NamedTuple{(:save_everystep, :reltol, :abstol, :save_start), Tuple{Bool, Float32, Float32, Bool}}}}, Lux.WrappedFunction{Base.Fix1{typeof(Main.var"##292".diffeqsol_to_array), Int64}}, Lux.Dense{true, typeof(identity), typeof(WeightInitializers.glorot_uniform), typeof(WeightInitializers.zeros32)}}}, Nothing})(::CUDA.CuArray{Float32, 4, CUDA.Mem.DeviceBuffer}, ::ComponentArrays.ComponentVector{Float32, CUDA.CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, Tuple{ComponentArrays.Axis{(layer_1 = 1:0, layer_2 = ViewAxis(1:15700, Axis(weight = ViewAxis(1:15680, ShapedAxis((20, 784), NamedTuple())), bias = ViewAxis(15681:15700, ShapedAxis((20, 1), NamedTuple())))), layer_3 = ViewAxis(15701:16240, Axis(layer_1 = ViewAxis(1:210, Axis(weight = ViewAxis(1:200, ShapedAxis((10, 20), NamedTuple())), bias = ViewAxis(201:210, ShapedAxis((10, 1), NamedTuple())))), layer_2 = ViewAxis(211:320, Axis(weight = ViewAxis(1:100, ShapedAxis((10, 10), NamedTuple())), bias = ViewAxis(101:110, ShapedAxis((10, 1), NamedTuple())))), layer_3 = ViewAxis(321:540, Axis(weight = ViewAxis(1:200, ShapedAxis((20, 10), NamedTuple())), bias = ViewAxis(201:220, ShapedAxis((20, 1), NamedTuple())))))), layer_4 = 16241:16240, layer_5 = ViewAxis(16241:16450, Axis(weight = ViewAxis(1:200, ShapedAxis((10, 20), NamedTuple())), bias = ViewAxis(201:210, ShapedAxis((10, 1), NamedTuple())))))}}}, ::NamedTuple{(:layer_1, :layer_2, :layer_3, :layer_4, :layer_5), Tuple{NamedTuple{(), Tuple{}}, NamedTuple{(), Tuple{}}, NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{NamedTuple{(), Tuple{}}, NamedTuple{(), Tuple{}}, NamedTuple{(), Tuple{}}}}, NamedTuple{(), Tuple{}}, NamedTuple{(), Tuple{}}}}) - from (c::Lux.Chain)(x, ps, st::NamedTuple) @ Lux /var/lib/buildkite-agent/builds/gpuci-8/julialang/lux-dot-jl/src/layers/containers.jl:478 + from (c::Lux.Chain)(x, ps, st::NamedTuple) @ Lux /var/lib/buildkite-agent/builds/gpuci-2/julialang/lux-dot-jl/src/layers/containers.jl:478 Arguments c::Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3, :layer_4, :layer_5), Tuple{Lux.FlattenLayer, Lux.Dense{true, typeof(NNlib.tanh_fast), typeof(WeightInitializers.glorot_uniform), typeof(WeightInitializers.zeros32)}, Main.var"##292".StatefulNeuralODE{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{Lux.Dense{true, typeof(NNlib.tanh_fast), typeof(WeightInitializers.glorot_uniform), typeof(WeightInitializers.zeros32)}, Lux.Dense{true, typeof(NNlib.tanh_fast), typeof(WeightInitializers.glorot_uniform), typeof(WeightInitializers.zeros32)}, Lux.Dense{true, typeof(NNlib.tanh_fast), typeof(WeightInitializers.glorot_uniform), typeof(WeightInitializers.zeros32)}}}, Nothing}, OrdinaryDiffEq.Tsit5{typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}, SciMLSensitivity.InterpolatingAdjoint{0, true, Val{:central}, SciMLSensitivity.ZygoteVJP}, Tuple{Float32, Float32}, Base.Pairs{Symbol, Real, NTuple{4, Symbol}, NamedTuple{(:save_everystep, :reltol, :abstol, :save_start), Tuple{Bool, Float32, Float32, Bool}}}}, Lux.WrappedFunction{Base.Fix1{typeof(Main.var"##292".diffeqsol_to_array), Int64}}, Lux.Dense{true, typeof(identity), typeof(WeightInitializers.glorot_uniform), typeof(WeightInitializers.zeros32)}}}, Nothing} x::CUDA.CuArray{Float32, 4, CUDA.Mem.DeviceBuffer} diff --git a/dev/tutorials/intermediate/2_BayesianNN.md b/dev/tutorials/intermediate/2_BayesianNN.md index eb76f49f1..f78b834da 100644 --- a/dev/tutorials/intermediate/2_BayesianNN.md +++ b/dev/tutorials/intermediate/2_BayesianNN.md @@ -188,8 +188,8 @@ Chains MCMC chain (5000×30×1 Array{Float64, 3}): Iterations = 1:1:5000 Number of chains = 1 Samples per chain = 5000 -Wall duration = 59.59 seconds -Compute duration = 59.59 seconds +Wall duration = 61.81 seconds +Compute duration = 61.81 seconds parameters = parameters[1], parameters[2], parameters[3], parameters[4], parameters[5], parameters[6], parameters[7], parameters[8], parameters[9], parameters[10], parameters[11], parameters[12], parameters[13], parameters[14], parameters[15], parameters[16], parameters[17], parameters[18], parameters[19], parameters[20] internals = lp, n_steps, is_accept, acceptance_rate, log_density, hamiltonian_energy, hamiltonian_energy_error, numerical_error, step_size, nom_step_size @@ -197,26 +197,26 @@ Summary Statistics parameters mean std mcse ess_bulk ess_tail rhat ess_per_sec Symbol Float64 Float64 Float64 Float64 Float64 Float64 Float64 - parameters[1] -2.0521 0.7625 0.1721 20.6080 90.3161 1.0078 0.3458 - parameters[2] -3.0083 3.1557 0.9621 12.7726 21.0999 1.5426 0.2143 - parameters[3] 1.3245 0.8417 0.2430 13.3461 24.3246 1.4587 0.2240 - parameters[4] -1.4877 1.0772 0.2493 15.1287 26.2870 1.1494 0.2539 - parameters[5] 0.7760 0.5463 0.1115 27.1685 102.1179 1.2247 0.4559 - parameters[6] 3.4906 1.7171 0.4559 14.8031 45.0051 1.0628 0.2484 - parameters[7] -3.9710 1.4956 0.3945 15.0640 45.7012 1.1078 0.2528 - parameters[8] -3.4420 1.3931 0.3730 14.6316 21.0172 1.4495 0.2455 - parameters[9] -4.5405 3.0887 0.9352 12.0159 20.0739 1.6221 0.2016 - parameters[10] -3.2319 2.7507 0.8110 12.1252 22.4200 1.3478 0.2035 - parameters[11] -3.5758 1.4822 0.4076 13.6637 33.8975 1.1729 0.2293 - parameters[12] -1.0570 2.4954 0.7492 11.7223 21.0856 1.9121 0.1967 - parameters[13] 2.9443 1.5863 0.4630 12.8882 20.7551 1.4709 0.2163 - parameters[14] 1.7753 3.9004 1.1683 11.7170 20.8646 1.4595 0.1966 - parameters[15] -2.5492 0.9557 0.2088 22.5673 38.5675 1.1274 0.3787 - parameters[16] 1.3716 2.0800 0.5691 14.0441 34.5833 1.1682 0.2357 - parameters[17] -0.4643 1.9644 0.5871 11.5634 21.2536 1.8976 0.1940 - parameters[18] 1.1676 1.4714 0.3611 16.5438 23.5525 1.0044 0.2776 - parameters[19] -5.9863 0.9522 0.1526 38.9319 54.0819 1.0467 0.6533 - parameters[20] -1.7391 1.2860 0.2821 21.6130 50.2786 1.0414 0.3627 + parameters[1] -2.0521 0.7625 0.1721 20.6080 90.3161 1.0078 0.3334 + parameters[2] -3.0083 3.1557 0.9621 12.7726 21.0999 1.5426 0.2066 + parameters[3] 1.3245 0.8417 0.2430 13.3461 24.3246 1.4587 0.2159 + parameters[4] -1.4877 1.0772 0.2493 15.1287 26.2870 1.1494 0.2448 + parameters[5] 0.7760 0.5463 0.1115 27.1685 102.1179 1.2247 0.4396 + parameters[6] 3.4906 1.7171 0.4559 14.8031 45.0051 1.0628 0.2395 + parameters[7] -3.9710 1.4956 0.3945 15.0640 45.7012 1.1078 0.2437 + parameters[8] -3.4420 1.3931 0.3730 14.6316 21.0172 1.4495 0.2367 + parameters[9] -4.5405 3.0887 0.9352 12.0159 20.0739 1.6221 0.1944 + parameters[10] -3.2319 2.7507 0.8110 12.1252 22.4200 1.3478 0.1962 + parameters[11] -3.5758 1.4822 0.4076 13.6637 33.8975 1.1729 0.2211 + parameters[12] -1.0570 2.4954 0.7492 11.7223 21.0856 1.9121 0.1897 + parameters[13] 2.9443 1.5863 0.4630 12.8882 20.7551 1.4709 0.2085 + parameters[14] 1.7753 3.9004 1.1683 11.7170 20.8646 1.4595 0.1896 + parameters[15] -2.5492 0.9557 0.2088 22.5673 38.5675 1.1274 0.3651 + parameters[16] 1.3716 2.0800 0.5691 14.0441 34.5833 1.1682 0.2272 + parameters[17] -0.4643 1.9644 0.5871 11.5634 21.2536 1.8976 0.1871 + parameters[18] 1.1676 1.4714 0.3611 16.5438 23.5525 1.0044 0.2677 + parameters[19] -5.9863 0.9522 0.1526 38.9319 54.0819 1.0467 0.6299 + parameters[20] -1.7391 1.2860 0.2821 21.6130 50.2786 1.0414 0.3497 Quantiles parameters 2.5% 25.0% 50.0% 75.0% 97.5% diff --git a/dev/tutorials/intermediate/3_HyperNet.md b/dev/tutorials/intermediate/3_HyperNet.md index b41ab899c..2da5ff576 100644 --- a/dev/tutorials/intermediate/3_HyperNet.md +++ b/dev/tutorials/intermediate/3_HyperNet.md @@ -219,26 +219,26 @@ train() ``` -[1/9] MNIST Time 3.59s Training Accuracy: 63.57% Test Accuracy: 65.62% -[1/9] FashionMNIST Time 0.04s Training Accuracy: 49.32% Test Accuracy: 50.0% -[2/9] MNIST Time 0.04s Training Accuracy: 73.83% Test Accuracy: 68.75% -[2/9] FashionMNIST Time 0.07s Training Accuracy: 63.67% Test Accuracy: 62.5% -[3/9] MNIST Time 0.04s Training Accuracy: 80.66% Test Accuracy: 59.38% -[3/9] FashionMNIST Time 0.03s Training Accuracy: 66.21% Test Accuracy: 53.12% -[4/9] MNIST Time 0.03s Training Accuracy: 80.96% Test Accuracy: 81.25% -[4/9] FashionMNIST Time 0.03s Training Accuracy: 69.53% Test Accuracy: 68.75% -[5/9] MNIST Time 0.07s Training Accuracy: 88.09% Test Accuracy: 84.38% -[5/9] FashionMNIST Time 0.03s Training Accuracy: 73.83% Test Accuracy: 62.5% -[6/9] MNIST Time 0.03s Training Accuracy: 91.02% Test Accuracy: 90.62% -[6/9] FashionMNIST Time 0.03s Training Accuracy: 73.73% Test Accuracy: 59.38% -[7/9] MNIST Time 0.05s Training Accuracy: 91.99% Test Accuracy: 93.75% -[7/9] FashionMNIST Time 0.03s Training Accuracy: 72.07% Test Accuracy: 68.75% -[8/9] MNIST Time 0.03s Training Accuracy: 93.75% Test Accuracy: 87.5% -[8/9] FashionMNIST Time 0.03s Training Accuracy: 78.03% Test Accuracy: 59.38% -[9/9] MNIST Time 0.03s Training Accuracy: 96.48% Test Accuracy: 90.62% -[9/9] FashionMNIST Time 0.04s Training Accuracy: 80.18% Test Accuracy: 68.75% -[FINAL] MNIST Training Accuracy: 96.39% Test Accuracy: 96.88% -[FINAL] FashionMNIST Training Accuracy: 80.18% Test Accuracy: 68.75% +[1/9] MNIST Time 3.59s Training Accuracy: 67.19% Test Accuracy: 78.12% +[1/9] FashionMNIST Time 0.04s Training Accuracy: 55.86% Test Accuracy: 43.75% +[2/9] MNIST Time 0.04s Training Accuracy: 74.41% Test Accuracy: 68.75% +[2/9] FashionMNIST Time 0.04s Training Accuracy: 53.71% Test Accuracy: 59.38% +[3/9] MNIST Time 0.04s Training Accuracy: 86.13% Test Accuracy: 78.12% +[3/9] FashionMNIST Time 0.09s Training Accuracy: 60.16% Test Accuracy: 56.25% +[4/9] MNIST Time 0.03s Training Accuracy: 89.16% Test Accuracy: 87.5% +[4/9] FashionMNIST Time 0.03s Training Accuracy: 57.42% Test Accuracy: 68.75% +[5/9] MNIST Time 0.03s Training Accuracy: 91.21% Test Accuracy: 81.25% +[5/9] FashionMNIST Time 0.03s Training Accuracy: 68.85% Test Accuracy: 65.62% +[6/9] MNIST Time 0.06s Training Accuracy: 93.26% Test Accuracy: 87.5% +[6/9] FashionMNIST Time 0.03s Training Accuracy: 67.29% Test Accuracy: 56.25% +[7/9] MNIST Time 0.03s Training Accuracy: 96.48% Test Accuracy: 90.62% +[7/9] FashionMNIST Time 0.03s Training Accuracy: 73.34% Test Accuracy: 68.75% +[8/9] MNIST Time 0.03s Training Accuracy: 96.58% Test Accuracy: 93.75% +[8/9] FashionMNIST Time 0.05s Training Accuracy: 71.29% Test Accuracy: 59.38% +[9/9] MNIST Time 0.03s Training Accuracy: 98.05% Test Accuracy: 93.75% +[9/9] FashionMNIST Time 0.03s Training Accuracy: 74.8% Test Accuracy: 62.5% +[FINAL] MNIST Training Accuracy: 98.34% Test Accuracy: 93.75% +[FINAL] FashionMNIST Training Accuracy: 74.8% Test Accuracy: 62.5% ```