Merge pull request #950 from SciML/docsupdates

Bump compats and update tutorials for Optimization v4

Project.toml

@@ -34,7 +34,7 @@ Boltz = "1"
 ChainRulesCore = "1"
 ComponentArrays = "0.15.17"
 ConcreteStructs = "0.2"
-DataInterpolations = "5, 6"
+DataInterpolations = "6.4"
 DelayDiffEq = "5.47.3"
 DiffEqCallbacks = "3.6.2"
 Distances = "0.10.11"
@@ -54,9 +54,9 @@ LuxLib = "1.2"
 NNlib = "0.9.22"
 OneHotArrays = "0.2.5"
 Optimisers = "0.3"
-Optimization = "3.25.0"
-OptimizationOptimJL = "0.3.0"
-OptimizationOptimisers = "0.2.1"
+Optimization = "4"
+OptimizationOptimJL = "0.4"
+OptimizationOptimisers = "0.3"
 OrdinaryDiffEq = "6.76.0"
 Printf = "1.10"
 Random = "1.10"

docs/Project.toml

@@ -57,10 +57,10 @@ MLUtils = "0.4"
 NNlib = "0.9"
 OneHotArrays = "0.2"
 Optimisers = "0.3"
-Optimization = "3.9"
-OptimizationOptimJL = "0.2, 0.3"
-OptimizationOptimisers = "0.2"
-OptimizationPolyalgorithms = "0.2"
+Optimization = "4"
+OptimizationOptimJL = "0.4"
+OptimizationOptimisers = "0.3"
+OptimizationPolyalgorithms = "0.3"
 OrdinaryDiffEq = "6.31"
 Plots = "1.36"
 Printf = "1"

docs/src/examples/augmented_neural_ode.md

@@ -69,13 +69,13 @@ function plot_contour(model, ps, st, npoints = 300)
     return contour(x, y, sol; fill = true, linewidth = 0.0)
 end
 
-loss_node(model, x, y, ps, st) = mean((first(model(x, ps, st)) .- y) .^ 2)
+loss_node(model, data, ps, st) = mean((first(model(data[1], ps, st)) .- data[2]) .^ 2)
 
 dataloader = concentric_sphere(
     2, (0.0f0, 2.0f0), (3.0f0, 4.0f0), 2000, 2000; batch_size = 256)
 
 iter = 0
-cb = function (ps, l)
+cb = function (state, l)
     global iter
     iter += 1
     if iter % 10 == 0
@@ -87,15 +87,15 @@ end
 model, ps, st = construct_model(1, 2, 64, 0)
 opt = OptimizationOptimisers.Adam(0.005)
 
-loss_node(model, dataloader.data[1], dataloader.data[2], ps, st)
+loss_node(model, (dataloader.data[1], dataloader.data[2]), ps, st)
 
 println("Training Neural ODE")
 
 optfunc = OptimizationFunction(
-    (x, p, data, target) -> loss_node(model, data, target, x, st),
+    (x, data) -> loss_node(model, data, x, st),
     Optimization.AutoZygote())
-optprob = OptimizationProblem(optfunc, ComponentArray(ps |> cdev) |> gdev)
-res = solve(optprob, opt, IterTools.ncycle(dataloader, 5); callback = cb)
+optprob = OptimizationProblem(optfunc, ComponentArray(ps |> cdev) |> gdev, dataloader)
+res = solve(optprob, opt; callback = cb, epochs = 100)
 
 plt_node = plot_contour(model, res.u, st)
 
@@ -106,10 +106,10 @@ println()
 println("Training Augmented Neural ODE")
 
 optfunc = OptimizationFunction(
-    (x, p, data, target) -> loss_node(model, data, target, x, st),
+    (x, data) -> loss_node(model, data, x, st),
     Optimization.AutoZygote())
-optprob = OptimizationProblem(optfunc, ComponentArray(ps |> cdev) |> gdev)
-res = solve(optprob, opt, IterTools.ncycle(dataloader, 5); callback = cb)
+optprob = OptimizationProblem(optfunc, ComponentArray(ps |> cdev) |> gdev, dataloader)
+res = solve(optprob, opt; callback = cb, epochs = 100)
 
 plot_contour(model, res.u, st)
 ```
@@ -229,7 +229,7 @@ We use the L2 distance between the model prediction `model(x)` and the actual pr
 optimization objective.
 
 ```@example augneuralode
-loss_node(model, x, y, ps, st) = mean((first(model(x, ps, st)) .- y) .^ 2)
+loss_node(model, data, ps, st) = mean((first(model(data[1], ps, st)) .- data[2]) .^ 2)
 ```
 
 #### Dataset
@@ -248,7 +248,7 @@ Additionally, we define a callback function which displays the total loss at spe
 
 ```@example augneuralode
 iter = 0
-cb = function (ps, l)
+cb = function (state, l)
     global iter
     iter += 1
     if iter % 10 == 0
@@ -276,10 +276,10 @@ for `20` epochs.
 model, ps, st = construct_model(1, 2, 64, 0)
 
 optfunc = OptimizationFunction(
-    (x, p, data, target) -> loss_node(model, data, target, x, st),
+    (x, data) -> loss_node(model, data, x, st),
     Optimization.AutoZygote())
-optprob = OptimizationProblem(optfunc, ComponentArray(ps |> cdev) |> gdev)
-res = solve(optprob, opt, IterTools.ncycle(dataloader, 5); callback = cb)
+optprob = OptimizationProblem(optfunc, ComponentArray(ps |> cdev) |> gdev, dataloader)
+res = solve(optprob, opt; callback = cb, epochs = 100)
 
 plot_contour(model, res.u, st)
 ```
@@ -297,10 +297,10 @@ a function which can be expressed by the neural ode. For more details and proofs
 model, ps, st = construct_model(1, 2, 64, 1)
 
 optfunc = OptimizationFunction(
-    (x, p, data, target) -> loss_node(model, data, target, x, st),
+    (x, data) -> loss_node(model, data, x, st),
     Optimization.AutoZygote())
-optprob = OptimizationProblem(optfunc, ComponentArray(ps |> cdev) |> gdev)
-res = solve(optprob, opt, IterTools.ncycle(dataloader, 5); callback = cb)
+optprob = OptimizationProblem(optfunc, ComponentArray(ps |> cdev) |> gdev, dataloader)
+res = solve(optprob, opt; callback = cb, epochs = 100)
 
 plot_contour(model, res.u, st)
 ```

docs/src/examples/hamiltonian_nn.md

@@ -14,7 +14,7 @@ Before getting to the explanation, here's some code to start with. We will follo
 
 ```@example hamiltonian_cp
 using Lux, DiffEqFlux, OrdinaryDiffEq, Statistics, Plots, Zygote, ForwardDiff, Random,
-      ComponentArrays, Optimization, OptimizationOptimisers, IterTools
+      ComponentArrays, Optimization, OptimizationOptimisers, MLUtils
 
 t = range(0.0f0, 1.0f0; length = 1024)
 π_32 = Float32(π)
@@ -23,37 +23,33 @@ p_t = reshape(cos.(2π_32 * t), 1, :)
 dqdt = 2π_32 .* p_t
 dpdt = -2π_32 .* q_t
 
-data = vcat(q_t, p_t)
-target = vcat(dqdt, dpdt)
+data = cat(q_t, p_t; dims = 1)
+target = cat(dqdt, dpdt; dims = 1)
 B = 256
-NEPOCHS = 100
-dataloader = ncycle(
-    ((selectdim(data, 2, ((i - 1) * B + 1):(min(i * B, size(data, 2)))),
-         selectdim(target, 2, ((i - 1) * B + 1):(min(i * B, size(data, 2)))))
-    for i in 1:(size(data, 2) ÷ B)),
-    NEPOCHS)
-
-hnn = Layers.HamiltonianNN{true}(Layers.MLP(2, (64, 1)); autodiff = AutoZygote())
+NEPOCHS = 500
+dataloader = DataLoader((data, target); batchsize = B)
+
+hnn = Layers.HamiltonianNN{true}(Layers.MLP(2, (1028, 1)); autodiff = AutoZygote())
 ps, st = Lux.setup(Xoshiro(0), hnn)
 ps_c = ps |> ComponentArray
 
 opt = OptimizationOptimisers.Adam(0.01f0)
 
-function loss_function(ps, data, target)
+function loss_function(ps, databatch)
+    data, target = databatch
     pred, st_ = hnn(data, ps, st)
-    return mean(abs2, pred .- target), pred
+    return mean(abs2, pred .- target)
 end
 
-function callback(ps, loss, pred)
+function callback(state, loss)
     println("[Hamiltonian NN] Loss: ", loss)
     return false
 end
 
-opt_func = OptimizationFunction((ps, _, data, target) -> loss_function(ps, data, target),
-    Optimization.AutoForwardDiff())
-opt_prob = OptimizationProblem(opt_func, ps_c)
+opt_func = OptimizationFunction(loss_function, Optimization.AutoForwardDiff())
+opt_prob = OptimizationProblem(opt_func, ps_c, dataloader)
 
-res = Optimization.solve(opt_prob, opt, dataloader; callback)
+res = Optimization.solve(opt_prob, opt; callback, epochs = NEPOCHS)
 
 ps_trained = res.u
 
@@ -75,7 +71,7 @@ The HNN predicts the gradients ``(\dot q, \dot p)`` given ``(q, p)``. Hence, we
 
 ```@example hamiltonian
 using Lux, DiffEqFlux, OrdinaryDiffEq, Statistics, Plots, Zygote, ForwardDiff, Random,
-      ComponentArrays, Optimization, OptimizationOptimisers, IterTools
+      ComponentArrays, Optimization, OptimizationOptimisers, MLUtils
 
 t = range(0.0f0, 1.0f0; length = 1024)
 π_32 = Float32(π)
@@ -87,40 +83,37 @@ dpdt = -2π_32 .* q_t
 data = cat(q_t, p_t; dims = 1)
 target = cat(dqdt, dpdt; dims = 1)
 B = 256
-NEPOCHS = 100
-dataloader = ncycle(
-    ((selectdim(data, 2, ((i - 1) * B + 1):(min(i * B, size(data, 2)))),
-         selectdim(target, 2, ((i - 1) * B + 1):(min(i * B, size(data, 2)))))
-    for i in 1:(size(data, 2) ÷ B)),
-    NEPOCHS)
+NEPOCHS = 500
+dataloader = DataLoader((data, target); batchsize = B)
 ```
 
 ### Training the HamiltonianNN
 
 We parameterize the  with a small MultiLayered Perceptron. HNNs are trained by optimizing the gradients of the Neural Network. Zygote currently doesn't support nesting itself, so we will be using ForwardDiff in the training loop to compute the gradients of the HNN Layer for Optimization.
 
 ```@example hamiltonian
-hnn = Layers.HamiltonianNN{true}(Layers.MLP(2, (64, 1)); autodiff = AutoZygote())
+hnn = Layers.HamiltonianNN{true}(Layers.MLP(2, (1028, 1)); autodiff = AutoZygote())
 ps, st = Lux.setup(Xoshiro(0), hnn)
 ps_c = ps |> ComponentArray
+hnn_stateful = StatefulLuxLayer{true}(hnn, ps_c, st)
 
-opt = OptimizationOptimisers.Adam(0.01f0)
+opt = OptimizationOptimisers.Adam(0.005f0)
 
-function loss_function(ps, data, target)
-    pred, st_ = hnn(data, ps, st)
-    return mean(abs2, pred .- target), pred
+function loss_function(ps, databatch)
+    (data, target) = databatch
+    pred = hnn_stateful(data, ps)
+    return mean(abs2, pred .- target)
 end
 
-function callback(ps, loss, pred)
+function callback(state, loss)
     println("[Hamiltonian NN] Loss: ", loss)
     return false
 end
 
-opt_func = OptimizationFunction(
-    (ps, _, data, target) -> loss_function(ps, data, target), Optimization.AutoZygote())
-opt_prob = OptimizationProblem(opt_func, ps_c)
+opt_func = OptimizationFunction(loss_function, Optimization.AutoZygote())
+opt_prob = OptimizationProblem(opt_func, ps_c, dataloader)
 
-res = solve(opt_prob, opt, dataloader; callback)
+res = Optimization.solve(opt_prob, opt; callback, epochs = NEPOCHS)
 
 ps_trained = res.u
 ```

docs/src/examples/mnist_conv_neural_ode.md

@@ -89,30 +89,30 @@ end
 # burn in accuracy
 accuracy(m, ((img, lab),), ps, st)
 
-function loss_function(ps, x, y)
+function loss_function(ps, data)
+    (x, y) = data
     pred, _ = m(x, ps, st)
-    return logitcrossentropy(pred, y), pred
+    return logitcrossentropy(pred, y)
 end
 
 # burn in loss
-loss_function(ps, img, lab)
+loss_function(ps, (img, lab))
 
 opt = OptimizationOptimisers.Adam(0.005)
 iter = 0
 
-opt_func = OptimizationFunction(
-    (ps, _, x, y) -> loss_function(ps, x, y), Optimization.AutoZygote())
-opt_prob = OptimizationProblem(opt_func, ps);
+opt_func = OptimizationFunction(loss_function, Optimization.AutoZygote())
+opt_prob = OptimizationProblem(opt_func, ps, dataloader);
 
-function callback(ps, l, pred)
+function callback(state, l)
     global iter += 1
     iter % 10 == 0 &&
-        @info "[MNIST Conv GPU] Accuracy: $(accuracy(m, dataloader, ps.u, st))"
+        @info "[MNIST Conv GPU] Accuracy: $(accuracy(m, dataloader, state.u, st))"
     return false
 end
 
 # Train the NN-ODE and monitor the loss and weights.
-res = Optimization.solve(opt_prob, opt, dataloader; maxiters = 5, callback)
+res = Optimization.solve(opt_prob, opt; epochs = 5, callback)
 acc = accuracy(m, dataloader, res.u, st)
 acc # hide
 ```

docs/src/examples/mnist_neural_ode.md

@@ -81,29 +81,29 @@ end
 
 accuracy(m, ((x_train1, y_train1),), ps, st) # burn in accuracy
 
-function loss_function(ps, x, y)
+function loss_function(ps, data)
+    (x, y) = data
     pred, st_ = m(x, ps, st)
-    return logitcrossentropy(pred, y), pred
+    return logitcrossentropy(pred, y)
 end
 
-loss_function(ps, x_train1, y_train1) # burn in loss
+loss_function(ps, (x_train1, y_train1)) # burn in loss
 
 opt = OptimizationOptimisers.Adam(0.05)
 iter = 0
 
-opt_func = OptimizationFunction(
-    (ps, _, x, y) -> loss_function(ps, x, y), Optimization.AutoZygote())
-opt_prob = OptimizationProblem(opt_func, ps)
+opt_func = OptimizationFunction(loss_function, Optimization.AutoZygote())
+opt_prob = OptimizationProblem(opt_func, ps, dataloader)
 
-function callback(ps, l, pred)
+function callback(state, l)
     global iter += 1
     iter % 10 == 0 &&
-        @info "[MNIST GPU] Accuracy: $(accuracy(m, dataloader, ps.u, st))"
+        @info "[MNIST GPU] Accuracy: $(accuracy(m, dataloader, state.u, st))"
     return false
 end
 
 # Train the NN-ODE and monitor the loss and weights.
-res = Optimization.solve(opt_prob, opt, dataloader; callback, maxiters = 5)
+res = Optimization.solve(opt_prob, opt; callback, epochs = 5)
 accuracy(m, dataloader, res.u, st)
 ```
 
@@ -285,12 +285,13 @@ final output of our model. `logitcrossentropy` takes in the prediction from our
 model `model(x)` and compares it to actual output `y`:
 
 ```@example mnist
-function loss_function(ps, x, y)
+function loss_function(ps, data)
+    (x, y) = data
     pred, st_ = m(x, ps, st)
-    return logitcrossentropy(pred, y), pred
+    return logitcrossentropy(pred, y)
 end
 
-loss_function(ps, x_train1, y_train1) # burn in loss
+loss_function(ps, (x_train1, y_train1)) # burn in loss
 ```
 
 #### Optimizer
@@ -309,14 +310,13 @@ This callback function is used to print both the training and testing accuracy a
 ```@example mnist
 iter = 0
 
-opt_func = OptimizationFunction(
-    (ps, _, x, y) -> loss_function(ps, x, y), Optimization.AutoZygote())
-opt_prob = OptimizationProblem(opt_func, ps)
+opt_func = OptimizationFunction(loss_function, Optimization.AutoZygote())
+opt_prob = OptimizationProblem(opt_func, ps, dataloader)
 
-function callback(ps, l, pred)
+function callback(state, l)
     global iter += 1
     iter % 10 == 0 &&
-        @info "[MNIST GPU] Accuracy: $(accuracy(m, dataloader, ps.u, st))"
+        @info "[MNIST GPU] Accuracy: $(accuracy(m, dataloader, state.u, st))"
     return false
 end
 ```
@@ -329,6 +329,6 @@ for Neural ODE is given by `nn_ode.p`:
 
 ```@example mnist
 # Train the NN-ODE and monitor the loss and weights.
-res = Optimization.solve(opt_prob, opt, dataloader; callback, maxiters = 5)
+res = Optimization.solve(opt_prob, opt; callback, epochs = 5)
 accuracy(m, dataloader, res.u, st)
 ```