optuna_with_pytorch.ipynb

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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/gopinathak-geek/Machine-Learning/blob/main/optuna_with_pytorch.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! pip install optuna"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RerFuROp3xi0",
        "outputId": "6fd7da8d-f98c-4afe-9489-c8196d249297"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting optuna\n",
            "  Downloading optuna-3.0.3-py3-none-any.whl (348 kB)\n",
            "\u001b[K     |████████████████████████████████| 348 kB 5.3 MB/s \n",
            "\u001b[?25hCollecting cmaes>=0.8.2\n",
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            "  Downloading colorlog-6.7.0-py2.py3-none-any.whl (11 kB)\n",
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            "\u001b[?25hCollecting alembic>=1.5.0\n",
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            "\u001b[K     |████████████████████████████████| 209 kB 40.7 MB/s \n",
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            "  Downloading cmd2-2.4.2-py3-none-any.whl (147 kB)\n",
            "\u001b[K     |████████████████████████████████| 147 kB 44.4 MB/s \n",
            "\u001b[?25hCollecting autopage>=0.4.0\n",
            "  Downloading autopage-0.5.1-py3-none-any.whl (29 kB)\n",
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            "\u001b[?25hCollecting stevedore>=2.0.1\n",
            "  Downloading stevedore-3.5.2-py3-none-any.whl (50 kB)\n",
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            "  Downloading pyperclip-1.8.2.tar.gz (20 kB)\n",
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            "Building wheels for collected packages: pyperclip\n",
            "  Building wheel for pyperclip (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pyperclip: filename=pyperclip-1.8.2-py3-none-any.whl size=11136 sha256=f7318b00ece9e7f475b34ae341f987c9b3a658374ffd9846e66543d0df2f51f9\n",
            "  Stored in directory: /root/.cache/pip/wheels/9f/18/84/8f69f8b08169c7bae2dde6bd7daf0c19fca8c8e500ee620a28\n",
            "Successfully built pyperclip\n",
            "Installing collected packages: pyperclip, pbr, stevedore, Mako, cmd2, autopage, colorlog, cmaes, cliff, alembic, optuna\n",
            "Successfully installed Mako-1.2.4 alembic-1.8.1 autopage-0.5.1 cliff-3.10.1 cmaes-0.9.0 cmd2-2.4.2 colorlog-6.7.0 optuna-3.0.3 pbr-5.11.0 pyperclip-1.8.2 stevedore-3.5.2\n"
          ]
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    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "5-UGmqll3r0s"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "\n",
        "import optuna\n",
        "from optuna.trial import TrialState\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"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "DEVICE = torch.device(\"cpu\")\n",
        "BATCHSIZE = 128\n",
        "CLASSES = 10\n",
        "DIR = os.getcwd()\n",
        "EPOCHS = 10\n",
        "N_TRAIN_EXAMPLES = BATCHSIZE * 30\n",
        "N_VALID_EXAMPLES = BATCHSIZE * 10"
      ],
      "metadata": {
        "id": "1lOtMZIV3vl1"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def define_model(trial):\n",
        "    # We optimize the number of layers, hidden units and dropout ratio in each layer.\n",
        "    n_layers = trial.suggest_int(\"n_layers\", 1, 3)\n",
        "    layers = []\n",
        "\n",
        "    in_features = 28 * 28\n",
        "    for i in range(n_layers):\n",
        "        out_features = trial.suggest_int(\"n_units_l{}\".format(i), 4, 128)\n",
        "        layers.append(nn.Linear(in_features, out_features))\n",
        "        layers.append(nn.ReLU())\n",
        "        p = trial.suggest_float(\"dropout_l{}\".format(i), 0.2, 0.5)\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",
        "    return nn.Sequential(*layers)\n"
      ],
      "metadata": {
        "id": "_d17KlU44Bd_"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def get_mnist():\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",
        "    return train_loader, valid_loader"
      ],
      "metadata": {
        "id": "Lauch9-T4Fmh"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def objective(trial):\n",
        "\n",
        "    # Generate the model.\n",
        "    model = define_model(trial).to(DEVICE)\n",
        "\n",
        "    # Generate the optimizers.\n",
        "    optimizer_name = trial.suggest_categorical(\"optimizer\", [\"Adam\", \"RMSprop\", \"SGD\"])\n",
        "    lr = trial.suggest_float(\"lr\", 1e-5, 1e-1, log=True)\n",
        "    optimizer = getattr(optim, optimizer_name)(model.parameters(), lr=lr)\n",
        "\n",
        "    # Get the FashionMNIST dataset.\n",
        "    train_loader, valid_loader = get_mnist()\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",
        "\n",
        "        trial.report(accuracy, epoch)\n",
        "\n",
        "        # Handle pruning based on the intermediate value.\n",
        "        if trial.should_prune():\n",
        "            raise optuna.exceptions.TrialPruned()\n",
        "\n",
        "    return accuracy\n"
      ],
      "metadata": {
        "id": "G5l6yLqJ4Oup"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "if __name__ == \"__main__\":\n",
        "    study = optuna.create_study(direction=\"maximize\")\n",
        "    study.optimize(objective, n_trials=100, timeout=600)\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(\"Study 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(\"Best trial:\")\n",
        "    trial = study.best_trial\n",
        "\n",
        "    print(\"  Value: \", trial.value)\n",
        "\n",
        "    print(\"  Params: \")\n",
        "    for key, value in trial.params.items():\n",
        "        print(\"    {}: {}\".format(key, value))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
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        "id": "g_Ph5iJU4RZL",
        "outputId": "bd6caad7-4051-4ac4-d36e-3029148dba9b"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[32m[I 2022-11-29 14:53:55,142]\u001b[0m A new study created in memory with name: no-name-f634cf40-b6c6-4e80-af40-01545137e05b\u001b[0m\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz to /content/FashionMNIST/raw/train-images-idx3-ubyte.gz\n"
          ]
        },
        {
          "output_type": "display_data",
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting /content/FashionMNIST/raw/train-images-idx3-ubyte.gz to /content/FashionMNIST/raw\n",
            "\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz to /content/FashionMNIST/raw/train-labels-idx1-ubyte.gz\n"
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting /content/FashionMNIST/raw/train-labels-idx1-ubyte.gz to /content/FashionMNIST/raw\n",
            "\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz to /content/FashionMNIST/raw/t10k-images-idx3-ubyte.gz\n"
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting /content/FashionMNIST/raw/t10k-images-idx3-ubyte.gz to /content/FashionMNIST/raw\n",
            "\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz to /content/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz\n"
          ]
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          "name": "stdout",
          "text": [
            "Extracting /content/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz to /content/FashionMNIST/raw\n",
            "\n"
          ]
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[32m[I 2022-11-29 14:54:06,625]\u001b[0m Trial 0 finished with value: 0.60390625 and parameters: {'n_layers': 2, 'n_units_l0': 61, 'dropout_l0': 0.3991475430201735, 'n_units_l1': 60, 'dropout_l1': 0.40536738943102313, 'optimizer': 'RMSprop', 'lr': 5.0426814000962644e-05}. Best is trial 0 with value: 0.60390625.\u001b[0m\n",
            "\u001b[32m[I 2022-11-29 14:54:12,860]\u001b[0m Trial 1 finished with value: 0.68515625 and parameters: {'n_layers': 1, 'n_units_l0': 80, 'dropout_l0': 0.254657066661665, 'optimizer': 'RMSprop', 'lr': 0.017951894208948993}. Best is trial 1 with value: 0.68515625.\u001b[0m\n",
            "\u001b[32m[I 2022-11-29 14:54:19,110]\u001b[0m Trial 2 finished with value: 0.6359375 and parameters: {'n_layers': 1, 'n_units_l0': 98, 'dropout_l0': 0.41416098995748296, 'optimizer': 'Adam', 'lr': 2.2940297282622463e-05}. Best is trial 1 with value: 0.68515625.\u001b[0m\n",
            "\u001b[32m[I 2022-11-29 14:54:24,675]\u001b[0m Trial 3 finished with value: 0.2765625 and parameters: {'n_layers': 1, 'n_units_l0': 24, 'dropout_l0': 0.40556918275087495, 'optimizer': 'Adam', 'lr': 1.1021988042460916e-05}. Best is trial 1 with value: 0.68515625.\u001b[0m\n",
            "\u001b[32m[I 2022-11-29 14:54:30,730]\u001b[0m Trial 4 finished with value: 0.6890625 and parameters: {'n_layers': 2, 'n_units_l0': 102, 'dropout_l0': 0.33211983373914233, 'n_units_l1': 82, 'dropout_l1': 0.47283873107217456, 'optimizer': 'Adam', 'lr': 0.000201200919761907}. Best is trial 4 with value: 0.6890625.\u001b[0m\n",
            "\u001b[32m[I 2022-11-29 14:54:31,316]\u001b[0m Trial 5 pruned. \u001b[0m\n",
            "\u001b[32m[I 2022-11-29 14:54:37,352]\u001b[0m Trial 6 finished with value: 0.72578125 and parameters: {'n_layers': 2, 'n_units_l0': 90, 'dropout_l0': 0.34874832063731603, 'n_units_l1': 8, 'dropout_l1': 0.3297211467913661, 'optimizer': 'RMSprop', 'lr': 0.0018714334370890698}. Best is trial 6 with value: 0.72578125.\u001b[0m\n",
            "\u001b[32m[I 2022-11-29 14:54:38,016]\u001b[0m Trial 7 pruned. \u001b[0m\n",
            "\u001b[32m[I 2022-11-29 14:54:44,008]\u001b[0m Trial 8 finished with value: 0.76015625 and parameters: {'n_layers': 1, 'n_units_l0': 109, 'dropout_l0': 0.3946135025203717, 'optimizer': 'Adam', 'lr': 0.0003304243864398966}. Best is trial 8 with value: 0.76015625.\u001b[0m\n",
            "\u001b[32m[I 2022-11-29 14:54:46,208]\u001b[0m Trial 9 pruned. \u001b[0m\n",
            "\u001b[32m[I 2022-11-29 14:54:46,873]\u001b[0m Trial 10 pruned. \u001b[0m\n",
            "\u001b[32m[I 2022-11-29 14:54:49,263]\u001b[0m Trial 11 pruned. \u001b[0m\n",
            "\u001b[32m[I 2022-11-29 14:54:50,014]\u001b[0m Trial 12 pruned. \u001b[0m\n",
            "\u001b[32m[I 2022-11-29 14:54:56,091]\u001b[0m Trial 13 finished with value: 0.7234375 and parameters: {'n_layers': 2, 'n_units_l0': 84, 'dropout_l0': 0.3677815034141249, 'n_units_l1': 33, 'dropout_l1': 0.3838826715886519, 'optimizer': 'RMSprop', 'lr': 0.00025251189125728637}. Best is trial 8 with value: 0.76015625.\u001b[0m\n",
            "\u001b[32m[I 2022-11-29 14:54:56,762]\u001b[0m Trial 14 pruned. \u001b[0m\n",
            "\u001b[32m[I 2022-11-29 14:55:02,871]\u001b[0m Trial 15 finished with value: 0.81953125 and parameters: {'n_layers': 2, 'n_units_l0': 100, 'dropout_l0': 0.2035805176122288, 'n_units_l1': 97, 'dropout_l1': 0.4841542482197525, 'optimizer': 'Adam', 'lr': 0.0007132981689322174}. Best is trial 15 with value: 0.81953125.\u001b[0m\n",
            "\u001b[32m[I 2022-11-29 14:55:03,612]\u001b[0m Trial 16 pruned. \u001b[0m\n",
            "\u001b[32m[I 2022-11-29 14:55:04,240]\u001b[0m Trial 17 pruned. \u001b[0m\n",
            "\u001b[32m[I 2022-11-29 14:55:10,310]\u001b[0m Trial 18 finished with value: 0.7828125 and parameters: {'n_layers': 1, 'n_units_l0': 112, 'dropout_l0': 0.444383336584693, 'optimizer': 'Adam', 'lr': 0.0006044133826346141}. Best is trial 15 with value: 0.81953125.\u001b[0m\n",
            "\u001b[32m[I 2022-11-29 14:55:11,004]\u001b[0m Trial 19 pruned. \u001b[0m\n",
            "\u001b[32m[I 2022-11-29 14:55:11,709]\u001b[0m Trial 20 pruned. \u001b[0m\n",
            "\u001b[32m[I 2022-11-29 14:55:17,939]\u001b[0m Trial 21 finished with value: 0.80859375 and parameters: {'n_layers': 1, 'n_units_l0': 115, 'dropout_l0': 0.3751098038572668, 'optimizer': 'Adam', 'lr': 0.0005117407658677121}. Best is trial 15 with value: 0.81953125.\u001b[0m\n",
            "\u001b[32m[I 2022-11-29 14:55:18,596]\u001b[0m Trial 22 pruned. \u001b[0m\n",
            "\u001b[32m[I 2022-11-29 14:55:24,767]\u001b[0m Trial 23 finished with value: 0.81953125 and parameters: {'n_layers': 1, 'n_units_l0': 97, 'dropout_l0': 0.2963561906605969, 'optimizer': 'Adam', 'lr': 0.0006375288551022269}. Best is trial 15 with value: 0.81953125.\u001b[0m\n",
            "\u001b[32m[I 2022-11-29 14:55:32,968]\u001b[0m Trial 24 finished with value: 0.846875 and parameters: {'n_layers': 1, 'n_units_l0': 97, 'dropout_l0': 0.29427373888753383, 'optimizer': 'Adam', 'lr': 0.002877741067693458}. Best is trial 24 with value: 0.846875.\u001b[0m\n",
            "\u001b[32m[I 2022-11-29 14:55:33,671]\u001b[0m Trial 25 pruned. \u001b[0m\n",
            "\u001b[32m[I 2022-11-29 14:55:39,458]\u001b[0m Trial 26 finished with value: 0.828125 and parameters: {'n_layers': 1, 'n_units_l0': 72, 'dropout_l0': 0.23007443490313467, 'optimizer': 'Adam', 'lr': 0.0028180343273404647}. Best is trial 24 with value: 0.846875.\u001b[0m\n",
            "\u001b[32m[I 2022-11-29 14:55:45,352]\u001b[0m Trial 27 finished with value: 0.83515625 and parameters: {'n_layers': 1, 'n_units_l0': 76, 'dropout_l0': 0.2403382675000032, 'optimizer': 'Adam', 'lr': 0.003852032535539504}. Best is trial 24 with value: 0.846875.\u001b[0m\n",
            "\u001b[32m[I 2022-11-29 14:55:51,314]\u001b[0m Trial 28 finished with value: 0.81953125 and parameters: {'n_layers': 1, 'n_units_l0': 74, 'dropout_l0': 0.2413806759751938, 'optimizer': 'Adam', 'lr': 0.013063750744534738}. Best is trial 24 with value: 0.846875.\u001b[0m\n",
            "\u001b[32m[I 2022-11-29 14:55:51,946]\u001b[0m Trial 29 pruned. \u001b[0m\n",
            "\u001b[32m[I 2022-11-29 14:55:57,738]\u001b[0m Trial 30 finished with value: 0.76484375 and parameters: {'n_layers': 1, 'n_units_l0': 59, 'dropout_l0': 0.2588220898506208, 'optimizer': 'Adam', 'lr': 0.05051651502910733}. Best is trial 24 with value: 0.846875.\u001b[0m\n",
            "\u001b[32m[I 2022-11-29 14:55:58,377]\u001b[0m Trial 31 pruned. \u001b[0m\n",
            "\u001b[32m[I 2022-11-29 14:56:04,150]\u001b[0m Trial 32 finished with value: 0.81328125 and parameters: {'n_layers': 1, 'n_units_l0': 88, 'dropout_l0': 0.22702936909257745, 'optimizer': 'Adam', 'lr': 0.001151436265520864}. Best is trial 24 with value: 0.846875.\u001b[0m\n",
            "\u001b[32m[I 2022-11-29 14:56:10,145]\u001b[0m Trial 33 finished with value: 0.821875 and parameters: {'n_layers': 1, 'n_units_l0': 75, 'dropout_l0': 0.2711473317927378, 'optimizer': 'Adam', 'lr': 0.013319895971776064}. Best is trial 24 with value: 0.846875.\u001b[0m\n",
            "\u001b[32m[I 2022-11-29 14:56:13,619]\u001b[0m Trial 34 pruned. \u001b[0m\n",
            "\u001b[32m[I 2022-11-29 14:56:19,941]\u001b[0m Trial 35 finished with value: 0.80625 and parameters: {'n_layers': 1, 'n_units_l0': 70, 'dropout_l0': 0.3038584630606712, 'optimizer': 'Adam', 'lr': 0.010263472322492608}. Best is trial 24 with value: 0.846875.\u001b[0m\n",
            "\u001b[32m[I 2022-11-29 14:56:25,939]\u001b[0m Trial 36 finished with value: 0.834375 and parameters: {'n_layers': 1, 'n_units_l0': 81, 'dropout_l0': 0.31627045337195314, 'optimizer': 'Adam', 'lr': 0.007123906820312169}. Best is trial 24 with value: 0.846875.\u001b[0m\n",
            "\u001b[32m[I 2022-11-29 14:56:31,798]\u001b[0m Trial 37 pruned. \u001b[0m\n",
            "\u001b[32m[I 2022-11-29 14:56:37,723]\u001b[0m Trial 38 finished with value: 0.82890625 and parameters: {'n_layers': 1, 'n_units_l0': 79, 'dropout_l0': 0.2405900095200362, 'optimizer': 'Adam', 'lr': 0.002878275988555421}. Best is trial 24 with value: 0.846875.\u001b[0m\n",
            "\u001b[32m[I 2022-11-29 14:56:43,742]\u001b[0m Trial 39 finished with value: 0.8078125 and parameters: {'n_layers': 1, 'n_units_l0': 92, 'dropout_l0': 0.3181679778956011, 'optimizer': 'Adam', 'lr': 0.02382206897920995}. Best is trial 24 with value: 0.846875.\u001b[0m\n",
            "\u001b[32m[I 2022-11-29 14:56:49,754]\u001b[0m Trial 40 finished with value: 0.82890625 and parameters: {'n_layers': 1, 'n_units_l0': 79, 'dropout_l0': 0.2701899477127873, 'optimizer': 'Adam', 'lr': 0.003976962986264581}. Best is trial 24 with value: 0.846875.\u001b[0m\n",
            "\u001b[32m[I 2022-11-29 14:56:55,488]\u001b[0m Trial 41 finished with value: 0.83203125 and parameters: {'n_layers': 1, 'n_units_l0': 79, 'dropout_l0': 0.2710660091969614, 'optimizer': 'Adam', 'lr': 0.00421134246757149}. Best is trial 24 with value: 0.846875.\u001b[0m\n",
            "\u001b[32m[I 2022-11-29 14:57:01,298]\u001b[0m Trial 42 finished with value: 0.8203125 and parameters: {'n_layers': 1, 'n_units_l0': 81, 'dropout_l0': 0.27114240578006304, 'optimizer': 'Adam', 'lr': 0.005238732553792187}. Best is trial 24 with value: 0.846875.\u001b[0m\n",
            "\u001b[32m[I 2022-11-29 14:57:07,333]\u001b[0m Trial 43 finished with value: 0.80703125 and parameters: {'n_layers': 1, 'n_units_l0': 104, 'dropout_l0': 0.341293057770955, 'optimizer': 'Adam', 'lr': 0.008992710195454728}. Best is trial 24 with value: 0.846875.\u001b[0m\n",
            "\u001b[32m[I 2022-11-29 14:57:07,955]\u001b[0m Trial 44 pruned. \u001b[0m\n",
            "\u001b[32m[I 2022-11-29 14:57:13,663]\u001b[0m Trial 45 finished with value: 0.82578125 and parameters: {'n_layers': 1, 'n_units_l0': 88, 'dropout_l0': 0.2518446271977855, 'optimizer': 'RMSprop', 'lr': 0.0026844360525621596}. Best is trial 24 with value: 0.846875.\u001b[0m\n",
            "\u001b[32m[I 2022-11-29 14:57:14,282]\u001b[0m Trial 46 pruned. \u001b[0m\n",
            "\u001b[32m[I 2022-11-29 14:57:14,903]\u001b[0m Trial 47 pruned. \u001b[0m\n",
            "\u001b[32m[I 2022-11-29 14:57:15,524]\u001b[0m Trial 48 pruned. \u001b[0m\n",
            "\u001b[32m[I 2022-11-29 14:57:23,947]\u001b[0m Trial 49 finished with value: 0.7984375 and parameters: {'n_layers': 1, 'n_units_l0': 84, 'dropout_l0': 0.310287060496052, 'optimizer': 'RMSprop', 'lr': 0.0037736886047118634}. Best is trial 24 with value: 0.846875.\u001b[0m\n",
            "\u001b[32m[I 2022-11-29 14:57:24,589]\u001b[0m Trial 50 pruned. \u001b[0m\n",
            "\u001b[32m[I 2022-11-29 14:57:30,256]\u001b[0m Trial 51 finished with value: 0.8234375 and parameters: {'n_layers': 1, 'n_units_l0': 79, 'dropout_l0': 0.2632384277455477, 'optimizer': 'Adam', 'lr': 0.002120112144510644}. Best is trial 24 with value: 0.846875.\u001b[0m\n",
            "\u001b[32m[I 2022-11-29 14:57:36,163]\u001b[0m Trial 52 finished with value: 0.82578125 and parameters: {'n_layers': 1, 'n_units_l0': 81, 'dropout_l0': 0.2857566716218325, 'optimizer': 'Adam', 'lr': 0.0037892509530309568}. Best is trial 24 with value: 0.846875.\u001b[0m\n",
            "\u001b[32m[I 2022-11-29 14:57:42,107]\u001b[0m Trial 53 finished with value: 0.8125 and parameters: {'n_layers': 1, 'n_units_l0': 66, 'dropout_l0': 0.21822950498620794, 'optimizer': 'Adam', 'lr': 0.005837903178513639}. Best is trial 24 with value: 0.846875.\u001b[0m\n",
            "\u001b[32m[I 2022-11-29 14:57:42,741]\u001b[0m Trial 54 pruned. \u001b[0m\n",
            "\u001b[32m[I 2022-11-29 14:57:43,396]\u001b[0m Trial 55 pruned. \u001b[0m\n",
            "\u001b[32m[I 2022-11-29 14:57:44,024]\u001b[0m Trial 56 pruned. \u001b[0m\n",
            "\u001b[32m[I 2022-11-29 14:57:44,676]\u001b[0m Trial 57 pruned. \u001b[0m\n",
            "\u001b[32m[I 2022-11-29 14:57:50,440]\u001b[0m Trial 58 finished with value: 0.82890625 and parameters: {'n_layers': 1, 'n_units_l0': 77, 'dropout_l0': 0.2959797528114112, 'optimizer': 'Adam', 'lr': 0.0033225285885849056}. Best is trial 24 with value: 0.846875.\u001b[0m\n",
            "\u001b[32m[I 2022-11-29 14:57:56,321]\u001b[0m Trial 59 finished with value: 0.79375 and parameters: {'n_layers': 1, 'n_units_l0': 77, 'dropout_l0': 0.31981474953731853, 'optimizer': 'Adam', 'lr': 0.0030540515852426134}. Best is trial 24 with value: 0.846875.\u001b[0m\n",
            "\u001b[32m[I 2022-11-29 14:57:57,026]\u001b[0m Trial 60 pruned. \u001b[0m\n",
            "\u001b[32m[I 2022-11-29 14:57:58,278]\u001b[0m Trial 61 pruned. \u001b[0m\n",
            "\u001b[32m[I 2022-11-29 14:58:04,204]\u001b[0m Trial 62 finished with value: 0.821875 and parameters: {'n_layers': 1, 'n_units_l0': 79, 'dropout_l0': 0.2787728022652984, 'optimizer': 'Adam', 'lr': 0.007267433046034829}. Best is trial 24 with value: 0.846875.\u001b[0m\n",
            "\u001b[32m[I 2022-11-29 14:58:04,845]\u001b[0m Trial 63 pruned. \u001b[0m\n",
            "\u001b[32m[I 2022-11-29 14:58:05,524]\u001b[0m Trial 64 pruned. \u001b[0m\n",
            "\u001b[32m[I 2022-11-29 14:58:06,724]\u001b[0m Trial 65 pruned. \u001b[0m\n",
            "\u001b[32m[I 2022-11-29 14:58:11,262]\u001b[0m Trial 66 pruned. \u001b[0m\n",
            "\u001b[32m[I 2022-11-29 14:58:11,894]\u001b[0m Trial 67 pruned. \u001b[0m\n",
            "\u001b[32m[I 2022-11-29 14:58:17,932]\u001b[0m Trial 68 finished with value: 0.846875 and parameters: {'n_layers': 1, 'n_units_l0': 96, 'dropout_l0': 0.2556065468212134, 'optimizer': 'Adam', 'lr': 0.005144082243956423}. Best is trial 24 with value: 0.846875.\u001b[0m\n",
            "\u001b[32m[I 2022-11-29 14:58:23,754]\u001b[0m Trial 69 finished with value: 0.81328125 and parameters: {'n_layers': 1, 'n_units_l0': 96, 'dropout_l0': 0.26309094067679883, 'optimizer': 'Adam', 'lr': 0.004756941847881159}. Best is trial 24 with value: 0.846875.\u001b[0m\n",
            "\u001b[32m[I 2022-11-29 14:58:29,978]\u001b[0m Trial 70 finished with value: 0.796875 and parameters: {'n_layers': 2, 'n_units_l0': 120, 'dropout_l0': 0.2102339757826041, 'n_units_l1': 114, 'dropout_l1': 0.438191056963081, 'optimizer': 'Adam', 'lr': 0.009050838085719898}. Best is trial 24 with value: 0.846875.\u001b[0m\n",
            "\u001b[32m[I 2022-11-29 14:58:31,254]\u001b[0m Trial 71 pruned. \u001b[0m\n",
            "\u001b[32m[I 2022-11-29 14:58:37,358]\u001b[0m Trial 72 finished with value: 0.8328125 and parameters: {'n_layers': 1, 'n_units_l0': 101, 'dropout_l0': 0.24563092277604073, 'optimizer': 'Adam', 'lr': 0.005948674030181921}. Best is trial 24 with value: 0.846875.\u001b[0m\n",
            "\u001b[32m[I 2022-11-29 14:58:39,233]\u001b[0m Trial 73 pruned. \u001b[0m\n",
            "\u001b[32m[I 2022-11-29 14:58:45,166]\u001b[0m Trial 74 finished with value: 0.821875 and parameters: {'n_layers': 1, 'n_units_l0': 102, 'dropout_l0': 0.2353158043671055, 'optimizer': 'Adam', 'lr': 0.014794191828135109}. Best is trial 24 with value: 0.846875.\u001b[0m\n",
            "\u001b[32m[I 2022-11-29 14:58:51,173]\u001b[0m Trial 75 finished with value: 0.828125 and parameters: {'n_layers': 1, 'n_units_l0': 92, 'dropout_l0': 0.2708826894560785, 'optimizer': 'Adam', 'lr': 0.004534739795095743}. Best is trial 24 with value: 0.846875.\u001b[0m\n",
            "\u001b[32m[I 2022-11-29 14:58:57,183]\u001b[0m Trial 76 finished with value: 0.82890625 and parameters: {'n_layers': 1, 'n_units_l0': 108, 'dropout_l0': 0.24040251985968353, 'optimizer': 'Adam', 'lr': 0.008931416321990598}. Best is trial 24 with value: 0.846875.\u001b[0m\n",
            "\u001b[32m[I 2022-11-29 14:58:57,818]\u001b[0m Trial 77 pruned. \u001b[0m\n",
            "\u001b[32m[I 2022-11-29 14:58:58,499]\u001b[0m Trial 78 pruned. \u001b[0m\n",
            "\u001b[32m[I 2022-11-29 14:58:59,086]\u001b[0m Trial 79 pruned. \u001b[0m\n",
            "\u001b[32m[I 2022-11-29 14:59:00,921]\u001b[0m Trial 80 pruned. \u001b[0m\n",
            "\u001b[32m[I 2022-11-29 14:59:01,558]\u001b[0m Trial 81 pruned. \u001b[0m\n",
            "\u001b[32m[I 2022-11-29 14:59:02,200]\u001b[0m Trial 82 pruned. \u001b[0m\n",
            "\u001b[32m[I 2022-11-29 14:59:08,138]\u001b[0m Trial 83 finished with value: 0.83046875 and parameters: {'n_layers': 1, 'n_units_l0': 106, 'dropout_l0': 0.2431362495743071, 'optimizer': 'Adam', 'lr': 0.01096423340437721}. Best is trial 24 with value: 0.846875.\u001b[0m\n",
            "\u001b[32m[I 2022-11-29 14:59:09,416]\u001b[0m Trial 84 pruned. \u001b[0m\n",
            "\u001b[32m[I 2022-11-29 14:59:15,466]\u001b[0m Trial 85 finished with value: 0.81484375 and parameters: {'n_layers': 1, 'n_units_l0': 93, 'dropout_l0': 0.21055728926240097, 'optimizer': 'Adam', 'lr': 0.01074093250605209}. Best is trial 24 with value: 0.846875.\u001b[0m\n",
            "\u001b[32m[I 2022-11-29 14:59:17,312]\u001b[0m Trial 86 pruned. \u001b[0m\n",
            "\u001b[32m[I 2022-11-29 14:59:20,318]\u001b[0m Trial 87 pruned. \u001b[0m\n",
            "\u001b[32m[I 2022-11-29 14:59:20,992]\u001b[0m Trial 88 pruned. \u001b[0m\n",
            "\u001b[32m[I 2022-11-29 14:59:21,641]\u001b[0m Trial 89 pruned. \u001b[0m\n",
            "\u001b[32m[I 2022-11-29 14:59:22,274]\u001b[0m Trial 90 pruned. \u001b[0m\n",
            "\u001b[32m[I 2022-11-29 14:59:22,929]\u001b[0m Trial 91 pruned. \u001b[0m\n",
            "\u001b[32m[I 2022-11-29 14:59:28,968]\u001b[0m Trial 92 finished with value: 0.8359375 and parameters: {'n_layers': 1, 'n_units_l0': 76, 'dropout_l0': 0.2879840757677283, 'optimizer': 'Adam', 'lr': 0.006543885503034721}. Best is trial 24 with value: 0.846875.\u001b[0m\n",
            "\u001b[32m[I 2022-11-29 14:59:32,097]\u001b[0m Trial 93 pruned. \u001b[0m\n",
            "\u001b[32m[I 2022-11-29 14:59:32,772]\u001b[0m Trial 94 pruned. \u001b[0m\n",
            "\u001b[32m[I 2022-11-29 14:59:38,874]\u001b[0m Trial 95 finished with value: 0.81015625 and parameters: {'n_layers': 1, 'n_units_l0': 111, 'dropout_l0': 0.24142255617546998, 'optimizer': 'Adam', 'lr': 0.014646417035523638}. Best is trial 24 with value: 0.846875.\u001b[0m\n",
            "\u001b[32m[I 2022-11-29 14:59:45,047]\u001b[0m Trial 96 finished with value: 0.83515625 and parameters: {'n_layers': 1, 'n_units_l0': 117, 'dropout_l0': 0.2300779543423662, 'optimizer': 'Adam', 'lr': 0.009136893257316413}. Best is trial 24 with value: 0.846875.\u001b[0m\n",
            "\u001b[32m[I 2022-11-29 14:59:45,726]\u001b[0m Trial 97 pruned. \u001b[0m\n",
            "\u001b[32m[I 2022-11-29 14:59:48,874]\u001b[0m Trial 98 pruned. \u001b[0m\n",
            "\u001b[32m[I 2022-11-29 14:59:50,251]\u001b[0m Trial 99 pruned. \u001b[0m\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Study statistics: \n",
            "  Number of finished trials:  100\n",
            "  Number of pruned trials:  53\n",
            "  Number of complete trials:  47\n",
            "Best trial:\n",
            "  Value:  0.846875\n",
            "  Params: \n",
            "    n_layers: 1\n",
            "    n_units_l0: 97\n",
            "    dropout_l0: 0.29427373888753383\n",
            "    optimizer: Adam\n",
            "    lr: 0.002877741067693458\n"
          ]
        }
      ]
    }
  ]
}