start a tutorial

README.md

@@ -45,7 +45,7 @@ y = rand(rng, Float32, 2, 3) |> gdev
 
 model(x, ps, st)
 
-gs = only(Zygote.gradient(p -> sum(abs2, first(first(model(x, p, st))) .- y), ps))
+gs = only(Zygote.gradient(p -> sum(abs2, first(model(x, p, st)) .- y), ps))
 ```
 
 ## Citation

docs/Project.toml

@@ -13,4 +13,4 @@ Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
 [compat]
 DeepEquilibriumNetworks = "2"
 Documenter = "1"
-DocumenterCitations = "0.2, 1"
+DocumenterCitations = "1"

docs/src/tutorials/basic_mnist_deq.md

@@ -1,3 +1,44 @@
-# Training a Simple MNIST Classifier with DEQ
+# Training a Simple MNIST Classifier using Deep Equilibrium Models
 
-This Tutorial is currently under preparation. Check back soon.
+We will train a simple Deep Equilibrium Model on MNIST. First we load a few packages.
+
+```@example basic_mnist_deq
+using DeepEquilibriumNetworks, SciMLSensitivity, Lux, NonlinearSolve, OrdinaryDiffEq,
+    Statistics, Random, Optimization, OptimizationOptimisers
+using LuxCUDA
+using MLDatasets: MNIST
+using MLDataUtils: LabelEnc, convertlabel, stratifiedobs
+
+CUDA.allowscalar(false)
+ENV["DATADEPS_ALWAYS_ACCEPT"] = true
+```
+
+Setup device functions from Lux. See
+[GPU Management](https://lux.csail.mit.edu/dev/manual/gpu_management) for more details.
+
+```@example basic_mnist_deq
+const cdev = cpu_device()
+const gdev = gpu_device()
+```
+
+We can now construct our dataloader.
+
+```@example basic_mnist_deq
+function onehot(labels_raw)
+    return convertlabel(LabelEnc.OneOfK, labels_raw, LabelEnc.NativeLabels(collect(0:9)))
+end
+
+function loadmnist(batchsize)
+    # Load MNIST
+    mnist = MNIST(; split = :train)
+    imgs, labels_raw = mnist.features, mnist.targets
+    # Process images into (H,W,C,BS) batches
+    x_train = Float32.(reshape(imgs, size(imgs, 1), size(imgs, 2), 1, size(imgs, 3))) |>
+              gdev
+    x_train = batchview(x_train, batchsize)
+    # Onehot and batch the labels
+    y_train = onehot(labels_raw) |> gdev
+    y_train = batchview(y_train, batchsize)
+    return x_train, y_train
+end
+```

src/DeepEquilibriumNetworks.jl

@@ -18,14 +18,6 @@ const DEQs = DeepEquilibriumNetworks
 include("layers.jl")
 include("utils.jl")
 
-## FIXME: Remove once Manifest is removed
-using SciMLBase, SciMLSensitivity
-
-@inline __default_sensealg(::SteadyStateProblem) = SteadyStateAdjoint(;
-    autojacvec=ZygoteVJP(), linsolve_kwargs=(; maxiters=10, abstol=1e-3, reltol=1e-3))
-@inline __default_sensealg(::ODEProblem) = GaussAdjoint(; autojacvec=ZygoteVJP())
-## FIXME: Remove once Manifest is removed
-
 # Exports
 export DEQs, DeepEquilibriumSolution, DeepEquilibriumNetwork, SkipDeepEquilibriumNetwork,
     MultiScaleDeepEquilibriumNetwork, MultiScaleSkipDeepEquilibriumNetwork,