opt_tutorial_2.ipynb

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     "text": [
      "/home/jfialho/.conda/envs/ml_default/lib/python3.7/site-packages/torchvision/io/image.py:11: UserWarning: Failed to load image Python extension: libtorch_cuda_cu.so: cannot open shared object file: No such file or directory\n",
      "  warn(f\"Failed to load image Python extension: {e}\")\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "import torch.utils.data\n",
    "from torchvision import datasets\n",
    "from torchvision import transforms\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "import optuna\n",
    "from optuna.trial import TrialState"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "DEVICE = torch.device(\"cuda:1\")\n",
    "BATCHSIZE = 128\n",
    "CLASSES = 10\n",
    "DIR = os.getcwd()\n",
    "EPOCHS = 10\n",
    "LOG_INTERVAL = 10\n",
    "N_TRAIN_EXAMPLES = BATCHSIZE * 30\n",
    "N_VALID_EXAMPLES = BATCHSIZE * 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# hyperparameters set\n",
    "range_opt_name = [\"Adam\", \"RMSprop\", \"SGD\"]\n",
    "range_lr = [1e-5, 1e-1]\n",
    "range_n_layers = [1, 4]\n",
    "range_p = [0.15, 0.5]\n",
    "range_out_features = [2**2, 2**8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def objective(trial):    \n",
    "    layers = []\n",
    "    acc = []\n",
    "\n",
    "    # Generate the model.\n",
    "    in_features = 28 * 28\n",
    "    n_layers = trial.suggest_int(\"n_layers\", range_n_layers[0], range_n_layers[1])\n",
    "    \n",
    "    for i in range(n_layers): \n",
    "        out_features = trial.suggest_int(\"n_units_l{}\".format(i),range_out_features[0], range_out_features[1])\n",
    "        layers.append(nn.Linear(in_features, out_features))\n",
    "        layers.append(nn.ReLU())\n",
    "        p = trial.suggest_float(\"dropout_l{}\".format(i), range_p[0], range_p[1])\n",
    "        layers.append(nn.Dropout(p))\n",
    "        \n",
    "        in_features = out_features\n",
    "    layers.append(nn.Linear(in_features, CLASSES))\n",
    "    layers.append(nn.LogSoftmax(dim=1))\n",
    "\n",
    "    model =  nn.Sequential(*layers).to(DEVICE)\n",
    "\n",
    "\n",
    "    # Generate the optimizers.\n",
    "    optimizer_name = trial.suggest_categorical(\"optimizer\",range_opt_name)\n",
    "    lr = trial.suggest_float(\"lr\",range_lr[0],range_lr[1],log=True)\n",
    "    \n",
    "    optimizer = getattr(optim, optimizer_name)(model.parameters(),lr=lr)\n",
    "\n",
    "    # Load FashionMNIST dataset.\n",
    "    train_loader = torch.utils.data.DataLoader(\n",
    "        datasets.FashionMNIST(DIR, train=True, download=True, transform=transforms.ToTensor()),\n",
    "        batch_size=BATCHSIZE,\n",
    "        shuffle=True,\n",
    "    )\n",
    "    valid_loader = torch.utils.data.DataLoader(\n",
    "        datasets.FashionMNIST(DIR, train=False, transform=transforms.ToTensor()),\n",
    "        batch_size=BATCHSIZE,\n",
    "        shuffle=True,\n",
    "    )\n",
    "\n",
    "\n",
    "    # Training of the model.\n",
    "    for epoch in range(EPOCHS):\n",
    "        model.train()\n",
    "        for batch_idx, (data, target) in enumerate(train_loader):\n",
    "            # Limiting training data for faster epochs.\n",
    "            if batch_idx * BATCHSIZE >= N_TRAIN_EXAMPLES:\n",
    "                break\n",
    "\n",
    "            data, target = data.view(data.size(0), -1).to(DEVICE), target.to(DEVICE)\n",
    "\n",
    "            optimizer.zero_grad()\n",
    "            output = model(data)\n",
    "            loss = F.nll_loss(output, target)\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "\n",
    "        # Validation of the model.\n",
    "        model.eval()\n",
    "        correct = 0\n",
    "        with torch.no_grad():\n",
    "            for batch_idx, (data, target) in enumerate(valid_loader):\n",
    "                # Limiting validation data.\n",
    "                if batch_idx * BATCHSIZE >= N_VALID_EXAMPLES:\n",
    "                    break\n",
    "                data, target = data.view(data.size(0), -1).to(DEVICE), target.to(DEVICE)\n",
    "                output = model(data)\n",
    "                # Get the index of the max log-probability.\n",
    "                pred = output.argmax(dim=1, keepdim=True)\n",
    "                correct += pred.eq(target.view_as(pred)).sum().item()\n",
    "\n",
    "        accuracy = correct / min(len(valid_loader.dataset), N_VALID_EXAMPLES)\n",
    "        acc.append(accuracy)\n",
    "        \n",
    "        trial.report(accuracy, epoch)\n",
    "        \n",
    "        if trial.should_prune():\n",
    "            raise optuna.exceptions.TrialPruned()\n",
    "\n",
    "        print(\"epoch:\",epoch,\"| acc:\",round(accuracy*100,2),\"%\")\n",
    "\n",
    "    #print best value of acc    \n",
    "    #print(\"--------------------------\")\n",
    "    #print(\"best acc:\", np.max(acc))\n",
    "    \n",
    "    return np.max(acc)\n",
    "    "
   ]
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     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2022-04-21 22:14:53,442]\u001b[0m Using an existing study with name 'opt_tutorial2' instead of creating a new one.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "study = optuna.create_study(study_name='opt_tutorial2',\n",
    "                           storage='sqlite:///tutorial.db',\n",
    "                           load_if_exists=True,\n",
    "                           direction=\"maximize\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jfialho/.conda/envs/ml_default/lib/python3.7/site-packages/torch/cuda/__init__.py:143: UserWarning: \n",
      "NVIDIA GeForce RTX 3060 with CUDA capability sm_86 is not compatible with the current PyTorch installation.\n",
      "The current PyTorch install supports CUDA capabilities sm_37 sm_50 sm_60 sm_70.\n",
      "If you want to use the NVIDIA GeForce RTX 3060 GPU with PyTorch, please check the instructions at https://pytorch.org/get-started/locally/\n",
      "\n",
      "  warnings.warn(incompatible_device_warn.format(device_name, capability, \" \".join(arch_list), device_name))\n",
      "\u001b[32m[I 2022-04-21 22:14:59,610]\u001b[0m Trial 37 pruned. \u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 0 | acc: 64.22 %\n",
      "epoch: 1 | acc: 75.08 %\n",
      "epoch: 2 | acc: 74.77 %\n",
      "epoch: 3 | acc: 78.2 %\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2022-04-21 22:15:05,735]\u001b[0m Trial 38 pruned. \u001b[0m\n",
      "\u001b[32m[I 2022-04-21 22:15:07,260]\u001b[0m Trial 39 pruned. \u001b[0m\n",
      "\u001b[32m[I 2022-04-21 22:15:08,818]\u001b[0m Trial 40 pruned. \u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 0 | acc: 70.86 %\n",
      "epoch: 1 | acc: 75.39 %\n",
      "epoch: 2 | acc: 78.12 %\n",
      "epoch: 3 | acc: 79.92 %\n",
      "epoch: 4 | acc: 80.7 %\n",
      "epoch: 5 | acc: 82.19 %\n",
      "epoch: 6 | acc: 81.56 %\n",
      "epoch: 7 | acc: 82.03 %\n",
      "epoch: 8 | acc: 81.88 %\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2022-04-21 22:15:19,684]\u001b[0m Trial 41 finished with value: 0.821875 and parameters: {'n_layers': 1, 'n_units_l0': 66, 'dropout_l0': 0.20721558824189254, 'optimizer': 'RMSprop', 'lr': 0.002223659019693438}. Best is trial 28 with value: 0.8296875.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 9 | acc: 80.78 %\n",
      "epoch: 0 | acc: 69.38 %\n",
      "epoch: 1 | acc: 75.7 %\n",
      "epoch: 2 | acc: 73.12 %\n",
      "epoch: 3 | acc: 73.44 %\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2022-04-21 22:15:25,299]\u001b[0m Trial 42 pruned. \u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 0 | acc: 69.3 %\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2022-04-21 22:15:27,721]\u001b[0m Trial 43 pruned. \u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 0 | acc: 66.48 %\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2022-04-21 22:15:30,300]\u001b[0m Trial 44 pruned. \u001b[0m\n",
      "\u001b[32m[I 2022-04-21 22:15:32,056]\u001b[0m Trial 45 pruned. \u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 0 | acc: 66.17 %\n",
      "epoch: 1 | acc: 75.08 %\n",
      "epoch: 2 | acc: 76.33 %\n",
      "epoch: 3 | acc: 76.17 %\n",
      "epoch: 4 | acc: 79.84 %\n",
      "epoch: 5 | acc: 79.61 %\n",
      "epoch: 6 | acc: 80.94 %\n",
      "epoch: 7 | acc: 81.48 %\n",
      "epoch: 8 | acc: 79.92 %\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2022-04-21 22:15:42,777]\u001b[0m Trial 46 finished with value: 0.81484375 and parameters: {'n_layers': 2, 'n_units_l0': 129, 'dropout_l0': 0.1710864140792859, 'n_units_l1': 97, 'dropout_l1': 0.32400323599732156, 'optimizer': 'RMSprop', 'lr': 0.003559121247224647}. Best is trial 28 with value: 0.8296875.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 9 | acc: 81.25 %\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2022-04-21 22:15:44,458]\u001b[0m Trial 47 pruned. \u001b[0m\n",
      "\u001b[32m[I 2022-04-21 22:15:46,289]\u001b[0m Trial 48 pruned. \u001b[0m\n",
      "\u001b[32m[I 2022-04-21 22:15:47,901]\u001b[0m Trial 49 pruned. \u001b[0m\n",
      "\u001b[32m[I 2022-04-21 22:15:49,312]\u001b[0m Trial 50 pruned. \u001b[0m\n",
      "\u001b[32m[I 2022-04-21 22:15:50,990]\u001b[0m Trial 51 pruned. \u001b[0m\n",
      "\u001b[32m[I 2022-04-21 22:15:52,414]\u001b[0m Trial 52 pruned. \u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 0 | acc: 70.08 %\n",
      "epoch: 1 | acc: 74.3 %\n",
      "epoch: 2 | acc: 78.83 %\n",
      "epoch: 3 | acc: 78.28 %\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2022-04-21 22:15:58,057]\u001b[0m Trial 53 pruned. \u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 0 | acc: 65.55 %\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2022-04-21 22:16:00,546]\u001b[0m Trial 54 pruned. \u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 0 | acc: 68.91 %\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2022-04-21 22:16:03,032]\u001b[0m Trial 55 pruned. \u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 0 | acc: 70.86 %\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2022-04-21 22:16:05,505]\u001b[0m Trial 56 pruned. \u001b[0m\n"
     ]
    }
   ],
   "source": [
    "study.optimize(objective, n_trials=20,timeout=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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     "text": [
      "Best trial:\n",
      " Value:  0.8296875\n",
      "\n",
      "Study statistics: \n",
      "  Number of finished trials:  57\n",
      "  Number of pruned trials:  32\n",
      "  Number of complete trials:  24\n",
      "\n",
      "Best params: \n",
      "  dropout_l0 = 0.2083603371473296\n",
      "  dropout_l1 = 0.29475991600696116\n",
      "  lr = 0.004143220502481869\n",
      "  n_layers = 2\n",
      "  n_units_l0 = 119\n",
      "  n_units_l1 = 89\n",
      "  optimizer = Adam\n"
     ]
    }
   ],
   "source": [
    "trial = study.best_trial\n",
    "\n",
    "pruned_trials = study.get_trials(deepcopy=False, states=[TrialState.PRUNED])\n",
    "complete_trials = study.get_trials(deepcopy=False, states=[TrialState.COMPLETE])\n",
    "\n",
    "print(\"Best trial:\")\n",
    "print(\" Value: \", trial.value)\n",
    "\n",
    "print(\"\\nStudy statistics: \")\n",
    "print(\"  Number of finished trials: \", len(study.trials))\n",
    "print(\"  Number of pruned trials: \", len(pruned_trials))\n",
    "print(\"  Number of complete trials: \", len(complete_trials))\n",
    "\n",
    "print(\"\\nBest params: \")\n",
    "for key, value in trial.params.items():    \n",
    "    print(\" \",key,\"=\", value)"
   ]
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