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_freeze/docs/src/explanation/samplers/execute-results/md.json
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| "hash": "577028b23128dbdcef11147ab874511c", | ||
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| "markdown": "---\ntitle: MNIST\n---\n\n\n\n\n\n\n::: {.cell execution_count=2}\n``` {.julia .cell-code}\nusing TaijaData: load_mnist\nusing CounterfactualExplanations.Models: load_mnist_mlp, load_mnist_ensemble\n```\n:::\n\n\n::: {.cell execution_count=3}\n``` {.julia .cell-code}\nX, y = load_mnist()\nK = size(y, 1)\nD = size(X, 1)\nmlp = load_mnist_mlp().model\nf_mlp(x) = mlp(x)\nens = load_mnist_ensemble().model\nf_ens(x) = sum(map(mlp -> mlp(x), ens))/length(ens)\nbatch_size = 100\n```\n:::\n\n\n## Sampling\n\n::: {.cell execution_count=4}\n``` {.julia .cell-code}\n𝒟x = Uniform(0,1)\n𝒟y = Categorical(ones(K) ./ K)\nsampler = UnconditionalSampler(𝒟x; input_size=(D,))\nconditional_sampler = ConditionalSampler(𝒟x, 𝒟y; input_size=(D,))\nopt = ImproperSGLD()\nn_iter = 256\n```\n:::\n\n\n### Conditional Draws\n\n::: {.cell execution_count=5}\n``` {.julia .cell-code}\n_w = 1500\nplts = []\nneach = 10\nfor i in 1:10\n x = conditional_sampler(f_mlp, opt; niter=n_iter, y=i, n_samples=neach)\n plts_i = []\n for j in 1:size(x,2)\n xj = reshape(x[:,j], (28,28))\n plts_i = [plts_i..., heatmap(rotl90(xj), axis=nothing, cb=false)]\n end\n plt = plot(plts_i..., size=(_w,0.10*_w), layout=(1,10))\n plts = [plts..., plt]\nend\nplot(plts..., size=(_w,_w), layout=(10,1))\n```\n:::\n\n\n::: {.cell execution_count=6}\n``` {.julia .cell-code}\n_w = 1500\nplts = []\nneach = 10\nfor i in 1:10\n x = conditional_sampler(f_ens, opt; niter=n_iter, y=i, n_samples=neach)\n plts_i = []\n for j in 1:size(x,2)\n xj = reshape(x[:,j], (28,28))\n plts_i = [plts_i..., heatmap(rotl90(xj), axis=nothing, cb=false)]\n end\n plt = plot(plts_i..., size=(_w,0.10*_w), layout=(1,10))\n plts = [plts..., plt]\nend\nplot(plts..., size=(_w,_w), layout=(10,1))\n```\n:::\n\n\n### Unconditional Draws\n\n::: {.cell execution_count=7}\n``` {.julia .cell-code}\n_w = 1500\nplts = []\nneach = 10\nfor i in 1:10\n x = sampler(f_mlp, opt; niter=n_iter, n_samples=neach)\n plts_i = []\n for j in 1:size(x,2)\n xj = reshape(x[:,j], (28,28))\n plts_i = [plts_i..., heatmap(rotl90(xj), axis=nothing, cb=false)]\n end\n plt = plot(plts_i..., size=(_w,0.10*_w), layout=(1,10))\n plts = [plts..., plt]\nend\nplot(plts..., size=(_w,_w), layout=(10,1))\n```\n:::\n\n\n::: {.cell execution_count=8}\n``` {.julia .cell-code}\n_w = 1500\nplts = []\nneach = 10\nfor i in 1:10\n x = sampler(f_ens, opt; niter=n_iter, n_samples=neach)\n plts_i = []\n for j in 1:size(x,2)\n xj = reshape(x[:,j], (28,28))\n plts_i = [plts_i..., heatmap(rotl90(xj), axis=nothing, cb=false)]\n end\n plt = plot(plts_i..., size=(_w,0.10*_w), layout=(1,10))\n plts = [plts..., plt]\nend\nplot(plts..., size=(_w,_w), layout=(10,1))\n```\n:::\n\n\n", | ||
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_freeze/docs/src/how_to_guides/mlj_flux/execute-results/md.json
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| "markdown": "---\ntitle: Compatibility with `MLJFlux`\n---\n\n\n\n\n\n\n## Synthetic Data\n\n::: {.cell execution_count=2}\n``` {.julia .cell-code}\nnobs=2000\nX, y = make_circles(nobs, noise=0.1, factor=0.5)\nXplot = Float32.(permutedims(matrix(X)))\nX = table(permutedims(Xplot))\ndisplay(scatter(Xplot[1,:], Xplot[2,:], group=y, label=\"\"))\nbatch_size = Int(round(nobs/10))\n```\n:::\n\n\n::: {.cell execution_count=3}\n``` {.julia .cell-code}\nsampler = ConditionalSampler(X, y, batch_size=batch_size)\nclf = JointEnergyClassifier(\n sampler;\n builder=MLJFlux.MLP(hidden=(32, 32, 32,), σ=Flux.relu),\n batch_size=batch_size,\n finaliser=Flux.softmax,\n loss=Flux.Losses.crossentropy,\n jem_training_params=(α=[1.0,1.0,1e-1],verbosity=10,),\n)\n```\n:::\n\n\n::: {.cell execution_count=4}\n``` {.julia .cell-code}\nprintln(typeof(clf) <: MLJFlux.MLJFluxModel)\n```\n:::\n\n\n::: {.cell execution_count=5}\n``` {.julia .cell-code}\nmach = machine(clf, X, y)\nfit!(mach)\n```\n:::\n\n\n::: {.cell execution_count=6}\n``` {.julia .cell-code}\njem = mach.model.jem\nbatch_size = mach.model.batch_size\nX = Float32.(permutedims(matrix(X)))\ny_labels = Int.(y.refs)\ny = Flux.onehotbatch(y.refs, sort(unique(y_labels)))\n```\n:::\n\n\n::: {.cell execution_count=7}\n``` {.julia .cell-code}\nif typeof(jem.sampler) <: ConditionalSampler\n \n plts = []\n for target in 1:size(y,1)\n X̂ = generate_conditional_samples(jem, batch_size, target; niter=1000) \n ex = extrema(hcat(X,X̂), dims=2)\n xlims = ex[1]\n ylims = ex[2]\n x1 = range(1.0f0.*xlims...,length=100)\n x2 = range(1.0f0.*ylims...,length=100)\n plt = contour(\n x1, x2, (x, y) -> softmax(jem([x, y]))[target], \n fill=true, alpha=0.5, title=\"Target: $target\", cbar=false,\n xlims=xlims,\n ylims=ylims,\n )\n scatter!(X[1,:], X[2,:], color=vec(y_labels), group=vec(y_labels), alpha=0.5)\n scatter!(\n X̂[1,:], X̂[2,:], \n color=repeat([target], size(X̂,2)), \n group=repeat([target], size(X̂,2)), \n shape=:star5, ms=10\n )\n push!(plts, plt)\n end\n plt = plot(plts..., layout=(1, size(y,1)), size=(size(y,1)*400, 400))\n display(plt)\nend\n```\n:::\n\n\n## MNIST \n\n::: {.cell execution_count=8}\n``` {.julia .cell-code}\n# Data:\nnobs = 1000\nn_digits = 28\nXtrain, ytrain, _, _, _, _ = load_mnist_data(nobs=nobs, n_digits=n_digits)\nXtrain = table(permutedims(MLUtils.flatten(Xtrain)))\nytrain = coerce(Flux.onecold(ytrain, 0:9), Multiclass)\n\n# Hyperparameters:\nD = n_digits^2 \nK = 10 \nM = 32\nlr = 1e-3 \nnum_epochs = 500\nmax_patience = 5 \nbatch_size = Int(round(nobs/10))\nα = [1.0,1.0,1e-2]\n```\n:::\n\n\n::: {.cell execution_count=9}\n``` {.julia .cell-code}\nactivation = Flux.swish\nbuilder = MLJFlux.MLP(hidden=(M,M,M,), σ=activation)\n```\n:::\n\n\n::: {.cell execution_count=10}\n``` {.julia .cell-code}\n# We initialize the full model\n𝒟x = Uniform(-1,1)\n𝒟y = Categorical(ones(K) ./ K)\nsampler = ConditionalSampler(𝒟x, 𝒟y, input_size=(D,), batch_size=10)\nclf = JointEnergyClassifier(\n sampler;\n builder=builder,\n batch_size=batch_size,\n finaliser=Flux.softmax,\n loss=Flux.Losses.crossentropy,\n jem_training_params=(α=α,verbosity=10,),\n sampling_steps=20,\n optimiser=Flux.Optimise.Adam(lr),\n)\n```\n:::\n\n\n::: {.cell execution_count=11}\n``` {.julia .cell-code}\nmach = machine(clf, Xtrain, ytrain)\nfit!(mach)\n```\n:::\n\n\n::: {.cell execution_count=12}\n``` {.julia .cell-code}\njem = mach.model.jem\nn_iter = 1000\n_w = 1500\nplts = []\nneach = 10\nfor i in 1:10\n x = jem.sampler(jem.chain, jem.sampling_rule; niter=n_iter, n_samples=neach, y=i)\n plts_i = []\n for j in 1:size(x, 2)\n xj = x[:,j]\n xj = reshape(xj, (n_digits, n_digits))\n plts_i = [plts_i..., heatmap(rotl90(xj), axis=nothing, cb=false)]\n end\n plt = plot(plts_i..., size=(_w,0.10*_w), layout=(1,10))\n plts = [plts..., plt]\nend\nplot(plts..., size=(_w,_w), layout=(10,1))\n```\n:::\n\n\n", | ||
| "supporting": [ | ||
| "mlj_flux_files" | ||
| ], | ||
| "filters": [] | ||
| } | ||
| } |
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