train_adv_point.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "using Revise\n",
    "using LazySets\n",
    "using DifferentialEquations\n",
    "using LazySets\n",
    "using ProgressBars\n",
    "using JLD2\n",
    "using Flux\n",
    "using LinearAlgebra\n",
    "using Zygote\n",
    "using ReverseDiff\n",
    "using Plots\n",
    "using Statistics\n",
    "using Optimisers, ParameterSchedulers\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "using CUDA\n",
    "\n",
    "# Check if GPU is available\n",
    "if CUDA.functional()\n",
    "    device!(2)\n",
    "    CUDA.allowscalar(false)  # Disallow scalar operations on the GPU (optional)\n",
    "else\n",
    "    println(\"GPU is not available. Using CPU.\")\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4×2 Matrix{Float64}:\n",
       " 0.0  0.0\n",
       " 0.0  0.0\n",
       " 1.0  0.0\n",
       " 0.0  1.0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "include(\"dataset.jl\")\n",
    "X = Hyperrectangle(low = [0, 0, -1, -1], high = [4,4, 1, 1])\n",
    "U = Hyperrectangle(low = [-1, -1], high = [1,1])\n",
    "X_unsafe = Hyperrectangle(low = [1.5, 0, -1, -1], high = [2.5,2, 1, 1])\n",
    "\n",
    "A = [0. 0 1 0;\n",
    "    0 0 0 1;\n",
    "    0 0 0 0;\n",
    "    0 0 0 0;]\n",
    "B = [0. 0;\n",
    "    0 0;\n",
    "    1 0;\n",
    "    0 1;]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3×10001 Matrix{AbstractVector{Float64}}:\n",
       " [2.31642, 1.9133, -0.147242, -0.804865]  …  [2.48529, 0.0859108, -0.380176, 0.553731]\n",
       " [-0.562398, 0.564786]                       [0.5988, -0.935063]\n",
       " [0.0]                                       [0.0]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_training_data = load_object(\"point_seq_training_data.jld2\")\n",
    "raw_test_data = load_object(\"point_seq_test_data.jld2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(sum(raw_training_data[3, :]), size(raw_training_data[3, :])) = ([1.43765e6], (2577770,))\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "0.0%┣                                               ┫ 0/20 [00:00<00:00, -0s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(epoch, loss, test_loss) = (1, 0.018475917172256228, 0.042440682053500475)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "5.0%┣██                                      ┫ 1/20 [01:15:22<Inf:Inf, InfGs/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(epoch, loss, test_loss) = (2, 0.03140338482087708, 0.007003469075628315)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "10.0%┣███▉                                  ┫ 2/20 [02:32:36<45:46:55, 9.2ks/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(epoch, loss, test_loss) = (3, 0.018097217924837608, 0.014303897289980013)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "15.0%┣█████▊                                ┫ 3/20 [04:16:35<36:21:01, 7.7ks/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(epoch, loss, test_loss) = (4, 0.01553929193612334, 0.01811611467351461)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "20.0%┣███████▋                              ┫ 4/20 [05:53:49<31:27:01, 7.1ks/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(epoch, loss, test_loss) = (5, 0.05848269098371288, 0.020465323828897125)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "25.0%┣█████████▌                            ┫ 5/20 [07:35:38<28:28:37, 6.8ks/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(epoch, loss, test_loss) = (6, 0.005717928349415285, 0.001328210851377647)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "30.0%┣███████████▍                          ┫ 6/20 [09:32:47<26:43:48, 6.9ks/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(epoch, loss, test_loss) = (7, 0.039808670428298505, 0.001885637089661165)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "35.0%┣█████████████▎                        ┫ 7/20 [11:28:09<24:50:59, 6.9ks/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(epoch, loss, test_loss) = (8, 0.015768282048716165, 0.004430958577781102)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "40.0%┣███████████████▏                      ┫ 8/20 [13:23:23<22:57:14, 6.9ks/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(epoch, loss, test_loss) = (9, 0.03132013399612489, 0.012723255140612141)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "45.0%┣█████████████████                     ┫ 9/20 [15:16:17<20:59:53, 6.9ks/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(epoch, loss, test_loss) = (10, 0.007015336379774598, 0.001413356101171952)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "50.0%┣██████████████████▌                  ┫ 10/20 [17:04:55<18:58:48, 6.8ks/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(epoch, loss, test_loss) = (11, 0.013252866629512585, 0.005080408509004809)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "55.0%┣████████████████████▍                ┫ 11/20 [18:48:07<16:55:18, 6.8ks/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(epoch, loss, test_loss) = (12, 0.0023935168642191056, 0.0011683440431917208)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "60.0%┣██████████████████████▏              ┫ 12/20 [20:31:06<14:55:21, 6.7ks/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(epoch, loss, test_loss) = (13, 0.003292918978248357, 0.047190285096582504)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "65.0%┣████████████████████████             ┫ 13/20 [22:22:24<13:03:04, 6.7ks/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(epoch, loss, test_loss) = (14, 0.0218709336426335, 0.006052261041051876)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "70.0%┣██████████████████████████           ┫ 14/20 [24:18:26<11:13:07, 6.7ks/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(epoch, loss, test_loss) = (15, 0.01274118903915845, 0.006825350329948787)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "75.0%┣███████████████████████████▊         ┫ 15/20 [26:16:18<09:22:58, 6.8ks/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(epoch, loss, test_loss) = (16, 0.03223387228536648, 0.007123791083166435)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "80.0%┣█████████████████████████████▋       ┫ 16/20 [28:11:36<07:31:06, 6.8ks/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(epoch, loss, test_loss) = (17, 0.017667923072576208, 0.014380830153764804)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "85.0%┣███████████████████████████████▌     ┫ 17/20 [30:01:13<05:37:44, 6.8ks/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(epoch, loss, test_loss) = (18, 0.012511108840883783, 0.0017697484764113778)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "90.0%┣█████████████████████████████████▎   ┫ 18/20 [31:50:13<03:44:44, 6.7ks/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(epoch, loss, test_loss) = (19, 0.009537555776570549, 0.0004250482638725861)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "95.0%┣███████████████████████████████████▏ ┫ 19/20 [33:38:27<01:52:08, 6.7ks/it]\n"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(epoch, loss, test_loss) = (20, 0.006971487008361659, 0.0036293110705333336)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    }
   ],
   "source": [
    "include(\"affine_dynamics.jl\")\n",
    "using Flux\n",
    "using ProgressBars\n",
    "using JLD2\n",
    "\n",
    "batchsize = 128\n",
    "@show sum(raw_training_data[3,:]), size(raw_training_data[3,:])\n",
    "state_dim = size(reduce(hcat,raw_training_data[1,:]))[1]\n",
    "\n",
    "\n",
    "training_data = raw_training_data\n",
    "test_data = raw_test_data\n",
    "\n",
    "# model = Chain(\n",
    "#     Dense(4 => 16, relu),   # activation function inside layer\n",
    "#     Dense(16 => 64, relu),   # activation function inside layer\n",
    "#     Dense(64 => 16, relu),   # activation function inside layer\n",
    "#     Dense(16 => 1)\n",
    "# )\n",
    "\n",
    "model = Chain(\n",
    "    Dense(4 => 8, relu),   # activation function inside layer\n",
    "    Dense(8 => 8, relu),   # activation function inside layer\n",
    "    Dense(8 => 8, relu),   # activation function inside layer\n",
    "    Dense(8 => 1)\n",
    ")\n",
    "\n",
    "train_loader = Flux.DataLoader(training_data, batchsize=batchsize, shuffle=true)\n",
    "test_loader = Flux.DataLoader(test_data, batchsize=batchsize, shuffle=true)\n",
    "\n",
    "λ = 1\n",
    "μ = 1\n",
    "α = 0.0\n",
    "use_pgd=true\n",
    "use_adv = true\n",
    "lr_lambda = 0.0001\n",
    "mu = 0.9\n",
    "V_lambda=0\n",
    "\n",
    "lr_decay_rate = 0.2\n",
    "lr_decay_epoch =4\n",
    "total_epoch = 20\n",
    "\n",
    "ini_lr = 0.01\n",
    "optim = Flux.setup(Flux.Optimise.NADAM(ini_lr, (0.9, 0.999), 0.1), model)  # will store optimiser momentum, etc.\n",
    "sched = ParameterSchedulers.Stateful(Step(ini_lr, lr_decay_rate, lr_decay_epoch)) # setup schedule of your choice\n",
    "\n",
    "training_losses = []\n",
    "test_losses = []\n",
    "least_loss = 1000\n",
    "test_loss = 0\n",
    "loss = 0\n",
    "for epoch in ProgressBar(1:total_epoch)\n",
    "    training_loss_epcoh = []\n",
    "    test_loss_epcoh = []\n",
    "    ∇l_lambda = 0\n",
    "    ∇l_alpha = 0\n",
    "    for item in train_loader\n",
    "        ∇l_alpha = 0\n",
    "        ∇l_lambda = 0\n",
    "        x_batch = reduce(hcat,item[1,:])\n",
    "        u_batch = reduce(hcat,item[2,:])\n",
    "        y_init_batch = reduce(hcat,item[3,:])\n",
    "        use_pgd && (u_batch = pgd_find_u_notce(model, A, x_batch, B, u_batch, U; α = α))\n",
    "        if use_adv\n",
    "            X_lcoal = [Hyperrectangle(x_batch[:, i], radius_hyperrectangle(X) ./ 20) for i=1:size(x_batch)[2]]\n",
    "            x_batch = pgd_find_x_notce(model, A, x_batch, B, u_batch, X_lcoal; α = α)\n",
    "        else\n",
    "            x_batch = x_batch\n",
    "        end\n",
    "        use_pgd && (u_batch = pgd_find_u_notce(model, A, x_batch, B, u_batch, U; α = α))\n",
    "        training_loss, grads = Flux.withgradient(model) do m \n",
    "            loss_naive_safeset(m, x_batch, y_init_batch) + λ .* loss_naive_fi(m, A, x_batch, B, u_batch,y_init_batch;use_pgd=false,use_adv = false, α=α) + μ .* loss_regularization(m, x_batch, y_init_batch)\n",
    "        end\n",
    "        Flux.update!(optim, model, grads[1])\n",
    "\n",
    "        loss = loss_naive_safeset(model, x_batch, y_init_batch) + λ .* loss_naive_fi(model, A, x_batch, B, u_batch,y_init_batch;use_pgd=use_pgd,use_adv = false, α=α) + μ .* loss_regularization(model, x_batch, y_init_batch)\n",
    "        push!(training_loss_epcoh, loss)  # logging, outside gradient context\n",
    "        \n",
    "    end\n",
    "    for item in test_loader\n",
    "        x_batch = reduce(hcat,item[1,:])\n",
    "        u_batch = reduce(hcat,item[2,:])\n",
    "        y_init_batch = reduce(hcat,item[3,:])\n",
    "\n",
    "        test_loss =  loss_naive_safeset(model, x_batch, y_init_batch) + λ .* loss_naive_fi(model, A, x_batch, B, u_batch,y_init_batch;use_pgd=use_pgd,use_adv = false, α=α) + μ .* loss_regularization(model, x_batch, y_init_batch)\n",
    "        push!(test_loss_epcoh, test_loss)\n",
    "    end\n",
    "    nextlr = ParameterSchedulers.next!(sched) # advance schedule\n",
    "    Optimisers.adjust!(optim, nextlr) # update optimizer state, by default this changes the learning rate `eta`\n",
    "    lr_lambda = lr_lambda * lr_decay_rate^(floor(epoch / lr_decay_epoch))\n",
    "    @show epoch, loss, test_loss\n",
    "    model_state = Flux.state(model)\n",
    "    jldsave(\"point_adv20_small_model_1_0_1_pgd_relu_$epoch.jld2\"; model_state)\n",
    "    push!(training_losses, sum(training_loss_epcoh)) \n",
    "    push!(test_losses, sum(test_loss_epcoh))\n",
    "\n",
    "end\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "using Plots\n",
    "\n",
    "function plot_loss(train_loss::Vector, test_loss::Vector; xlabel=\"Epoch\", ylabel=\"Loss\", title=\"Training and Test Loss\")\n",
    "    plot(train_loss ./ (size(training_data[3,:] / batchsize)), label=\"Training\", xlabel=xlabel, ylabel=ylabel, title=title)\n",
    "    plot!(test_loss ./ (size(test_data[3,:] / batchsize)), label=\"Test\")\n",
    "end\n",
    "\n",
    "plot_loss(training_losses, test_losses)"
   ]
  }
 ],
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   "file_extension": ".jl",
   "mimetype": "application/julia",
   "name": "julia",
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