CIFAR_10_with_MLP.ipynb

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      "metadata": {
        "id": "VBx5d81D2Hk6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!mkdir results"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "PD4cIKKvKFCC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "outputId": "e2fe0819-05fa-40fc-cb3a-901a1fb4f05b"
      },
      "source": [
        "import torch\n",
        "import torchvision\n",
        "import torchvision.transforms as transforms\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torch.optim as optim\n",
        "import argparse\n",
        "import numpy as np\n",
        "import time\n",
        "from copy import deepcopy # Add Deepcopy for args\n",
        "import seaborn as sns \n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "29UainWPco7Y"
      },
      "source": [
        "## Data Preparation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Cu753dPPKGkV",
        "outputId": "0d162cc6-3eb0-4824-d9ba-41101100eca9",
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      "source": [
        "transform = transforms.Compose(\n",
        "    [transforms.ToTensor(),\n",
        "     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])\n",
        "\n",
        "trainset = torchvision.datasets.CIFAR10(root='./data', train=True,\n",
        "                                        download=True, transform=transform)\n",
        "trainset, valset = torch.utils.data.random_split(trainset, [40000, 10000])\n",
        "testset = torchvision.datasets.CIFAR10(root='./data', train=False,\n",
        "                                       download=True, transform=transform)\n",
        "partition = {'train': trainset, 'val':valset, 'test':testset}"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz\n"
          ],
          "name": "stdout"
        },
        {
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        {
          "output_type": "stream",
          "text": [
            "Extracting ./data/cifar-10-python.tar.gz to ./data\n",
            "Files already downloaded and verified\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "RxnfFJwBcsAv"
      },
      "source": [
        "## Model Architecture"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "_G6bZbbkMWWt",
        "colab": {}
      },
      "source": [
        "class MLP(nn.Module):\n",
        "    def __init__(self, in_dim, out_dim, hid_dim, n_layer, act, dropout, use_bn, use_xavier):\n",
        "        super(MLP, self).__init__()\n",
        "        self.in_dim = in_dim\n",
        "        self.out_dim = out_dim\n",
        "        self.hid_dim = hid_dim\n",
        "        self.n_layer = n_layer\n",
        "        self.act = act\n",
        "        self.dropout = dropout\n",
        "        self.use_bn = use_bn\n",
        "        self.use_xavier = use_xavier\n",
        "        \n",
        "        # ====== Create Linear Layers ====== #\n",
        "        self.fc1 = nn.Linear(self.in_dim, self.hid_dim)\n",
        "        \n",
        "        self.linears = nn.ModuleList()\n",
        "        self.bns = nn.ModuleList()\n",
        "        for i in range(self.n_layer-1):\n",
        "            self.linears.append(nn.Linear(self.hid_dim, self.hid_dim))\n",
        "            if self.use_bn:\n",
        "                self.bns.append(nn.BatchNorm1d(self.hid_dim))\n",
        "                \n",
        "        self.fc2 = nn.Linear(self.hid_dim, self.out_dim)\n",
        "        \n",
        "        # ====== Create Activation Function ====== #\n",
        "        if self.act == 'relu':\n",
        "            self.act = nn.ReLU()\n",
        "        elif self.act == 'tanh':\n",
        "            self.act == nn.Tanh()\n",
        "        elif self.act == 'sigmoid':\n",
        "            self.act = nn.Sigmoid()\n",
        "        else:\n",
        "            raise ValueError('no valid activation function selected!')\n",
        "        \n",
        "        # ====== Create Regularization Layer ======= #\n",
        "        self.dropout = nn.Dropout(self.dropout)\n",
        "        if self.use_xavier:\n",
        "            self.xavier_init()\n",
        "          \n",
        "    def forward(self, x):\n",
        "        x = self.act(self.fc1(x))\n",
        "        for i in range(len(self.linears)):\n",
        "            x = self.act(self.linears[i](x))\n",
        "            if self.use_bn:\n",
        "                x = self.bns[i](x)\n",
        "            x = self.dropout(x)\n",
        "        x = self.fc2(x)\n",
        "        return x\n",
        "    \n",
        "    def xavier_init(self):\n",
        "        for linear in self.linears:\n",
        "            nn.init.xavier_normal_(linear.weight)\n",
        "            linear.bias.data.fill_(0.01)\n",
        "            \n",
        "net = MLP(3072, 10, 100, 4, 'relu', 0.1, True, True) # Testing Model Construction"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "itGsp6jDWs_a"
      },
      "source": [
        "## Train, Validate, Test and Experiment"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z2ObBk1p2HlL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def train(net, partition, optimizer, criterion, args):\n",
        "    trainloader = torch.utils.data.DataLoader(partition['train'], \n",
        "                                              batch_size=args.train_batch_size, \n",
        "                                              shuffle=True, num_workers=2)\n",
        "    net.train()\n",
        "    optimizer.zero_grad()\n",
        "\n",
        "    correct = 0\n",
        "    total = 0\n",
        "    train_loss = 0.0\n",
        "    for i, data in enumerate(trainloader, 0):\n",
        "        # get the inputs\n",
        "        inputs, labels = data\n",
        "        inputs = inputs.view(-1, 3072)\n",
        "        inputs = inputs.cuda()\n",
        "        labels = labels.cuda()\n",
        "        outputs = net(inputs)\n",
        "\n",
        "        loss = criterion(outputs, labels)\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "        train_loss += loss.item()\n",
        "        _, predicted = torch.max(outputs.data, 1)\n",
        "        total += labels.size(0)\n",
        "        correct += (predicted == labels).sum().item()\n",
        "\n",
        "    train_loss = train_loss / len(trainloader)\n",
        "    train_acc = 100 * correct / total\n",
        "    return net, train_loss, train_acc"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hoQWvwmj2HlO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def validate(net, partition, criterion, args):\n",
        "    valloader = torch.utils.data.DataLoader(partition['val'], \n",
        "                                            batch_size=args.test_batch_size, \n",
        "                                            shuffle=False, num_workers=2)\n",
        "    net.eval()\n",
        "\n",
        "    correct = 0\n",
        "    total = 0\n",
        "    val_loss = 0 \n",
        "    with torch.no_grad():\n",
        "        for data in valloader:\n",
        "            images, labels = data\n",
        "            images = images.view(-1, 3072)\n",
        "            images = images.cuda()\n",
        "            labels = labels.cuda()\n",
        "            outputs = net(images)\n",
        "\n",
        "            loss = criterion(outputs, labels)\n",
        "            \n",
        "            val_loss += loss.item()\n",
        "            _, predicted = torch.max(outputs.data, 1)\n",
        "            total += labels.size(0)\n",
        "            correct += (predicted == labels).sum().item()\n",
        "\n",
        "        val_loss = val_loss / len(valloader)\n",
        "        val_acc = 100 * correct / total\n",
        "    return val_loss, val_acc"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "epMdam_v2HlR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def test(net, partition, args):\n",
        "    testloader = torch.utils.data.DataLoader(partition['test'], \n",
        "                                             batch_size=args.test_batch_size, \n",
        "                                             shuffle=False, num_workers=2)\n",
        "    net.eval()\n",
        "    \n",
        "    correct = 0\n",
        "    total = 0\n",
        "    with torch.no_grad():\n",
        "        for data in testloader:\n",
        "            images, labels = data\n",
        "            images = images.view(-1, 3072)\n",
        "            images = images.cuda()\n",
        "            labels = labels.cuda()\n",
        "\n",
        "            outputs = net(images)\n",
        "            _, predicted = torch.max(outputs.data, 1)\n",
        "            total += labels.size(0)\n",
        "            correct += (predicted == labels).sum().item()\n",
        "\n",
        "        test_acc = 100 * correct / total\n",
        "    return test_acc"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "LiOCP6TqWw2V",
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      "source": [
        "def experiment(partition, args):\n",
        "  \n",
        "    net = MLP(args.in_dim, args.out_dim, args.hid_dim, args.n_layer, args.act, args.dropout, args.use_bn, args.use_xavier)\n",
        "    net.cuda()\n",
        "\n",
        "    criterion = nn.CrossEntropyLoss()\n",
        "    if args.optim == 'SGD':\n",
        "        optimizer = optim.SGD(net.parameters(), lr=args.lr, weight_decay=args.l2)\n",
        "    elif args.optim == 'RMSprop':\n",
        "        optimizer = optim.RMSprop(net.parameters(), lr=args.lr, weight_decay=args.l2)\n",
        "    elif args.optim == 'Adam':\n",
        "        optimizer = optim.Adam(net.parameters(), lr=args.lr, weight_decay=args.l2)\n",
        "    else:\n",
        "        raise ValueError('In-valid optimizer choice')\n",
        "    \n",
        "    train_losses = []\n",
        "    val_losses = []\n",
        "    train_accs = []\n",
        "    val_accs = []\n",
        "        \n",
        "    for epoch in range(args.epoch):  # loop over the dataset multiple times\n",
        "        ts = time.time()\n",
        "        net, train_loss, train_acc = train(net, partition, optimizer, criterion, args)\n",
        "        val_loss, val_acc = validate(net, partition, criterion, args)\n",
        "        te = time.time()\n",
        "        \n",
        "        train_losses.append(train_loss)\n",
        "        val_losses.append(val_loss)\n",
        "        train_accs.append(train_acc)\n",
        "        val_accs.append(val_acc)\n",
        "        \n",
        "        print('Epoch {}, Acc(train/val): {:2.2f}/{:2.2f}, Loss(train/val) {:2.2f}/{:2.2f}. Took {:2.2f} sec'.format(epoch, train_acc, val_acc, train_loss, val_loss, te-ts))\n",
        "        \n",
        "    test_acc = test(net, partition, args)    \n",
        "    \n",
        "    result = {}\n",
        "    result['train_losses'] = train_losses\n",
        "    result['val_losses'] = val_losses\n",
        "    result['train_accs'] = train_accs\n",
        "    result['val_accs'] = val_accs\n",
        "    result['train_acc'] = train_acc\n",
        "    result['val_acc'] = val_acc\n",
        "    result['test_acc'] = test_acc\n",
        "    return vars(args), result"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BvWcjw5S2HlX",
        "colab_type": "text"
      },
      "source": [
        "# Manage Experiment Result"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G4dZcWe32HlY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import hashlib\n",
        "import json\n",
        "from os import listdir\n",
        "from os.path import isfile, join\n",
        "import pandas as pd\n",
        "\n",
        "def save_exp_result(setting, result):\n",
        "    exp_name = setting['exp_name']\n",
        "    del setting['epoch']\n",
        "    del setting['test_batch_size']\n",
        "\n",
        "    hash_key = hashlib.sha1(str(setting).encode()).hexdigest()[:6]\n",
        "    filename = './results/{}-{}.json'.format(exp_name, hash_key)\n",
        "    result.update(setting)\n",
        "    with open(filename, 'w') as f:\n",
        "        json.dump(result, f)\n",
        "\n",
        "    \n",
        "def load_exp_result(exp_name):\n",
        "    dir_path = './results'\n",
        "    filenames = [f for f in listdir(dir_path) if isfile(join(dir_path, f)) if '.json' in f]\n",
        "    list_result = []\n",
        "    for filename in filenames:\n",
        "        if exp_name in filename:\n",
        "            with open(join(dir_path, filename), 'r') as infile:\n",
        "                results = json.load(infile)\n",
        "                list_result.append(results)\n",
        "    df = pd.DataFrame(list_result) # .drop(columns=[])\n",
        "    return df"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HJ9ZszOK2Hla",
        "colab_type": "text"
      },
      "source": [
        "# Visualization Utility"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u3iEseLs2Hlb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def plot_acc(var1, var2, df):\n",
        "\n",
        "    fig, ax = plt.subplots(1, 3)\n",
        "    fig.set_size_inches(15, 6)\n",
        "    sns.set_style(\"darkgrid\", {\"axes.facecolor\": \".9\"})\n",
        "\n",
        "    sns.barplot(x=var1, y='train_acc', hue=var2, data=df, ax=ax[0])\n",
        "    sns.barplot(x=var1, y='val_acc', hue=var2, data=df, ax=ax[1])\n",
        "    sns.barplot(x=var1, y='test_acc', hue=var2, data=df, ax=ax[2])\n",
        "    \n",
        "    ax[0].set_title('Train Accuracy')\n",
        "    ax[1].set_title('Validation Accuracy')\n",
        "    ax[2].set_title('Test Accuracy')\n",
        "\n",
        "    \n",
        "def plot_loss_variation(var1, var2, df, **kwargs):\n",
        "\n",
        "    list_v1 = df[var1].unique()\n",
        "    list_v2 = df[var2].unique()\n",
        "    list_data = []\n",
        "\n",
        "    for value1 in list_v1:\n",
        "        for value2 in list_v2:\n",
        "            row = df.loc[df[var1]==value1]\n",
        "            row = row.loc[df[var2]==value2]\n",
        "\n",
        "            train_losses = list(row.train_losses)[0]\n",
        "            val_losses = list(row.val_losses)[0]\n",
        "\n",
        "            for epoch, train_loss in enumerate(train_losses):\n",
        "                list_data.append({'type':'train', 'loss':train_loss, 'epoch':epoch, var1:value1, var2:value2})\n",
        "            for epoch, val_loss in enumerate(val_losses):\n",
        "                list_data.append({'type':'val', 'loss':val_loss, 'epoch':epoch, var1:value1, var2:value2})\n",
        "\n",
        "    df = pd.DataFrame(list_data)\n",
        "    g = sns.FacetGrid(df, row=var2, col=var1, hue='type', **kwargs)\n",
        "    g = g.map(plt.plot, 'epoch', 'loss', marker='.')\n",
        "    g.add_legend()\n",
        "    g.fig.suptitle('Train loss vs Val loss')\n",
        "    plt.subplots_adjust(top=0.89) \n",
        "    \n",
        "def plot_acc_variation(var1, var2, df, **kwargs):\n",
        "    list_v1 = df[var1].unique()\n",
        "    list_v2 = df[var2].unique()\n",
        "    list_data = []\n",
        "\n",
        "    for value1 in list_v1:\n",
        "        for value2 in list_v2:\n",
        "            row = df.loc[df[var1]==value1]\n",
        "            row = row.loc[df[var2]==value2]\n",
        "\n",
        "            train_accs = list(row.train_accs)[0]\n",
        "            val_accs = list(row.val_accs)[0]\n",
        "            test_acc = list(row.test_acc)[0]\n",
        "\n",
        "            for epoch, train_acc in enumerate(train_accs):\n",
        "                list_data.append({'type':'train', 'Acc':train_acc, 'test_acc':test_acc, 'epoch':epoch, var1:value1, var2:value2})\n",
        "            for epoch, val_acc in enumerate(val_accs):\n",
        "                list_data.append({'type':'val', 'Acc':val_acc, 'test_acc':test_acc, 'epoch':epoch, var1:value1, var2:value2})\n",
        "\n",
        "    df = pd.DataFrame(list_data)\n",
        "    g = sns.FacetGrid(df, row=var2, col=var1, hue='type', **kwargs)\n",
        "    g = g.map(plt.plot, 'epoch', 'Acc', marker='.')\n",
        "\n",
        "    def show_acc(x, y, metric, **kwargs):\n",
        "        plt.scatter(x, y, alpha=0.3, s=1)\n",
        "        metric = \"Test Acc: {:1.3f}\".format(list(metric.values)[0])\n",
        "        plt.text(0.05, 0.95, metric,  horizontalalignment='left', verticalalignment='center', transform=plt.gca().transAxes, bbox=dict(facecolor='yellow', alpha=0.5, boxstyle=\"round,pad=0.1\"))\n",
        "    g = g.map(show_acc, 'epoch', 'Acc', 'test_acc')\n",
        "\n",
        "    g.add_legend()\n",
        "    g.fig.suptitle('Train Accuracy vs Val Accuracy')\n",
        "    plt.subplots_adjust(top=0.89)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "omgExzmQgU1J"
      },
      "source": [
        "## Experiment1. N_layer vs Hidden_Dim"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "DRoOy_B3Wu7B",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "a81ed12f-ddf6-45b3-a802-a28d4d746bb7"
      },
      "source": [
        "# ====== Random Seed Initialization ====== #\n",
        "seed = 123\n",
        "np.random.seed(seed)\n",
        "torch.manual_seed(seed)\n",
        "\n",
        "parser = argparse.ArgumentParser()\n",
        "args = parser.parse_args(\"\")\n",
        "args.exp_name = \"exp1_n_layer_hid_dim\"\n",
        "\n",
        "# ====== Model Capacity ====== #\n",
        "args.in_dim = 3072\n",
        "args.out_dim = 10\n",
        "args.hid_dim = 100\n",
        "args.act = 'relu'\n",
        "\n",
        "# ====== Regularization ======= #\n",
        "args.dropout = 0.2\n",
        "args.use_bn = True\n",
        "args.l2 = 0.00001\n",
        "args.use_xavier = True\n",
        "\n",
        "# ====== Optimizer & Training ====== #\n",
        "args.optim = 'RMSprop' #'RMSprop' #SGD, RMSprop, ADAM...\n",
        "args.lr = 0.0015\n",
        "args.epoch = 10\n",
        "\n",
        "args.train_batch_size = 256\n",
        "args.test_batch_size = 1024\n",
        "\n",
        "# ====== Experiment Variable ====== #\n",
        "name_var1 = 'n_layer'\n",
        "name_var2 = 'hid_dim'\n",
        "list_var1 = [1, 2, 3]\n",
        "list_var2 = [500, 300]\n",
        "\n",
        "\n",
        "for var1 in list_var1:\n",
        "    for var2 in list_var2:\n",
        "        setattr(args, name_var1, var1)\n",
        "        setattr(args, name_var2, var2)\n",
        "        print(args)\n",
        "                \n",
        "        setting, result = experiment(partition, deepcopy(args))\n",
        "        save_exp_result(setting, result)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Namespace(act='relu', dropout=0.2, epoch=10, exp_name='exp1_n_layer_hid_dim', hid_dim=500, in_dim=3072, l2=1e-05, lr=0.0015, n_layer=1, optim='RMSprop', out_dim=10, test_batch_size=1024, train_batch_size=256, use_bn=True, use_xavier=True)\n",
            "Epoch 0, Acc(train/val): 23.52/25.35, Loss(train/val) 24.08/35.20. Took 8.23 sec\n",
            "Epoch 1, Acc(train/val): 25.86/25.44, Loss(train/val) 24.20/22.88. Took 8.11 sec\n",
            "Epoch 2, Acc(train/val): 26.50/24.75, Loss(train/val) 18.96/28.00. Took 8.08 sec\n",
            "Epoch 3, Acc(train/val): 26.94/25.06, Loss(train/val) 17.99/20.06. Took 8.00 sec\n",
            "Epoch 4, Acc(train/val): 27.12/24.23, Loss(train/val) 18.36/29.89. Took 8.13 sec\n",
            "Epoch 5, Acc(train/val): 27.11/24.63, Loss(train/val) 18.68/19.90. Took 8.16 sec\n",
            "Epoch 6, Acc(train/val): 27.09/28.14, Loss(train/val) 18.79/23.46. Took 8.18 sec\n",
            "Epoch 7, Acc(train/val): 27.52/22.94, Loss(train/val) 19.81/24.61. Took 8.12 sec\n",
            "Epoch 8, Acc(train/val): 27.90/25.10, Loss(train/val) 17.78/24.46. Took 7.99 sec\n",
            "Epoch 9, Acc(train/val): 27.86/27.20, Loss(train/val) 19.03/25.18. Took 8.17 sec\n",
            "Namespace(act='relu', dropout=0.2, epoch=10, exp_name='exp1_n_layer_hid_dim', hid_dim=300, in_dim=3072, l2=1e-05, lr=0.0015, n_layer=1, optim='RMSprop', out_dim=10, test_batch_size=1024, train_batch_size=256, use_bn=True, use_xavier=True)\n",
            "Epoch 0, Acc(train/val): 22.89/23.73, Loss(train/val) 15.04/17.92. Took 8.14 sec\n",
            "Epoch 1, Acc(train/val): 25.32/26.22, Loss(train/val) 14.43/14.82. Took 8.24 sec\n",
            "Epoch 2, Acc(train/val): 26.98/22.45, Loss(train/val) 10.46/12.83. Took 8.25 sec\n",
            "Epoch 3, Acc(train/val): 26.44/25.10, Loss(train/val) 10.86/14.25. Took 8.30 sec\n",
            "Epoch 4, Acc(train/val): 28.64/26.36, Loss(train/val) 10.24/12.08. Took 8.07 sec\n",
            "Epoch 5, Acc(train/val): 28.20/23.35, Loss(train/val) 9.54/13.90. Took 8.04 sec\n",
            "Epoch 6, Acc(train/val): 27.76/27.01, Loss(train/val) 10.16/13.74. Took 8.19 sec\n",
            "Epoch 7, Acc(train/val): 28.41/27.58, Loss(train/val) 10.35/13.65. Took 8.02 sec\n",
            "Epoch 8, Acc(train/val): 28.83/25.87, Loss(train/val) 11.30/15.39. Took 7.93 sec\n",
            "Epoch 9, Acc(train/val): 28.34/24.77, Loss(train/val) 10.32/13.00. Took 8.00 sec\n",
            "Namespace(act='relu', dropout=0.2, epoch=10, exp_name='exp1_n_layer_hid_dim', hid_dim=500, in_dim=3072, l2=1e-05, lr=0.0015, n_layer=2, optim='RMSprop', out_dim=10, test_batch_size=1024, train_batch_size=256, use_bn=True, use_xavier=True)\n",
            "Epoch 0, Acc(train/val): 26.09/31.47, Loss(train/val) 2.30/3.06. Took 8.27 sec\n",
            "Epoch 1, Acc(train/val): 31.79/33.75, Loss(train/val) 1.96/2.51. Took 8.19 sec\n",
            "Epoch 2, Acc(train/val): 35.71/35.69, Loss(train/val) 1.80/2.01. Took 8.20 sec\n",
            "Epoch 3, Acc(train/val): 37.91/38.60, Loss(train/val) 1.73/3.98. Took 8.19 sec\n",
            "Epoch 4, Acc(train/val): 40.55/41.55, Loss(train/val) 1.66/3.50. Took 8.26 sec\n",
            "Epoch 5, Acc(train/val): 42.99/43.59, Loss(train/val) 1.60/1.61. Took 8.37 sec\n",
            "Epoch 6, Acc(train/val): 45.27/45.95, Loss(train/val) 1.53/1.54. Took 8.32 sec\n",
            "Epoch 7, Acc(train/val): 48.13/46.72, Loss(train/val) 1.46/1.55. Took 8.15 sec\n",
            "Epoch 8, Acc(train/val): 50.12/47.66, Loss(train/val) 1.41/1.53. Took 8.30 sec\n",
            "Epoch 9, Acc(train/val): 51.92/49.42, Loss(train/val) 1.36/1.54. Took 8.23 sec\n",
            "Namespace(act='relu', dropout=0.2, epoch=10, exp_name='exp1_n_layer_hid_dim', hid_dim=300, in_dim=3072, l2=1e-05, lr=0.0015, n_layer=2, optim='RMSprop', out_dim=10, test_batch_size=1024, train_batch_size=256, use_bn=True, use_xavier=True)\n",
            "Epoch 0, Acc(train/val): 27.15/31.80, Loss(train/val) 2.11/1.87. Took 8.21 sec\n",
            "Epoch 1, Acc(train/val): 32.92/35.73, Loss(train/val) 1.88/1.81. Took 8.24 sec\n",
            "Epoch 2, Acc(train/val): 35.79/38.00, Loss(train/val) 1.77/1.80. Took 8.29 sec\n",
            "Epoch 3, Acc(train/val): 38.77/40.40, Loss(train/val) 1.70/1.90. Took 8.25 sec\n",
            "Epoch 4, Acc(train/val): 41.20/41.79, Loss(train/val) 1.64/1.90. Took 8.26 sec\n",
            "Epoch 5, Acc(train/val): 43.43/43.51, Loss(train/val) 1.58/1.59. Took 8.52 sec\n",
            "Epoch 6, Acc(train/val): 44.93/45.07, Loss(train/val) 1.53/1.63. Took 8.30 sec\n",
            "Epoch 7, Acc(train/val): 47.81/46.65, Loss(train/val) 1.46/1.79. Took 8.23 sec\n",
            "Epoch 8, Acc(train/val): 49.12/46.69, Loss(train/val) 1.42/1.66. Took 8.31 sec\n",
            "Epoch 9, Acc(train/val): 51.07/48.06, Loss(train/val) 1.38/1.49. Took 8.30 sec\n",
            "Namespace(act='relu', dropout=0.2, epoch=10, exp_name='exp1_n_layer_hid_dim', hid_dim=500, in_dim=3072, l2=1e-05, lr=0.0015, n_layer=3, optim='RMSprop', out_dim=10, test_batch_size=1024, train_batch_size=256, use_bn=True, use_xavier=True)\n",
            "Epoch 0, Acc(train/val): 22.66/26.20, Loss(train/val) 2.58/2.22. Took 8.35 sec\n",
            "Epoch 1, Acc(train/val): 28.42/31.15, Loss(train/val) 2.12/1.92. Took 8.32 sec\n",
            "Epoch 2, Acc(train/val): 32.61/35.79, Loss(train/val) 1.88/1.81. Took 8.26 sec\n",
            "Epoch 3, Acc(train/val): 35.69/35.83, Loss(train/val) 1.77/1.73. Took 8.40 sec\n",
            "Epoch 4, Acc(train/val): 38.56/40.52, Loss(train/val) 1.70/1.64. Took 8.48 sec\n",
            "Epoch 5, Acc(train/val): 40.88/41.30, Loss(train/val) 1.64/1.63. Took 8.45 sec\n",
            "Epoch 6, Acc(train/val): 43.27/44.20, Loss(train/val) 1.57/1.54. Took 8.36 sec\n",
            "Epoch 7, Acc(train/val): 46.45/46.47, Loss(train/val) 1.49/1.52. Took 8.48 sec\n",
            "Epoch 8, Acc(train/val): 48.82/47.98, Loss(train/val) 1.43/1.52. Took 8.38 sec\n",
            "Epoch 9, Acc(train/val): 50.61/49.14, Loss(train/val) 1.39/1.44. Took 8.65 sec\n",
            "Namespace(act='relu', dropout=0.2, epoch=10, exp_name='exp1_n_layer_hid_dim', hid_dim=300, in_dim=3072, l2=1e-05, lr=0.0015, n_layer=3, optim='RMSprop', out_dim=10, test_batch_size=1024, train_batch_size=256, use_bn=True, use_xavier=True)\n",
            "Epoch 0, Acc(train/val): 25.52/31.97, Loss(train/val) 2.22/1.96. Took 8.61 sec\n",
            "Epoch 1, Acc(train/val): 31.73/35.80, Loss(train/val) 1.91/1.81. Took 8.45 sec\n",
            "Epoch 2, Acc(train/val): 35.71/36.25, Loss(train/val) 1.78/1.74. Took 8.36 sec\n",
            "Epoch 3, Acc(train/val): 38.19/39.02, Loss(train/val) 1.71/1.66. Took 8.38 sec\n",
            "Epoch 4, Acc(train/val): 40.88/42.82, Loss(train/val) 1.64/1.61. Took 8.50 sec\n",
            "Epoch 5, Acc(train/val): 42.41/43.21, Loss(train/val) 1.59/1.58. Took 8.28 sec\n",
            "Epoch 6, Acc(train/val): 45.28/46.36, Loss(train/val) 1.52/1.50. Took 8.32 sec\n",
            "Epoch 7, Acc(train/val): 48.33/47.48, Loss(train/val) 1.45/1.48. Took 8.33 sec\n",
            "Epoch 8, Acc(train/val): 50.55/46.65, Loss(train/val) 1.39/1.49. Took 8.31 sec\n",
            "Epoch 9, Acc(train/val): 51.48/48.69, Loss(train/val) 1.37/1.47. Took 8.33 sec\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P9LzJZkC2Hlh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "51b72372-5321-4d90-c3ce-213f5348029f"
      },
      "source": [
        "var1 = 'n_layer'\n",
        "var2 = 'hid_dim'\n",
        "df = load_exp_result('exp1')\n",
        "\n",
        "plot_acc(var1, var2, df)\n",
        "plot_loss_variation(var1, var2, df, sharey=False) \n",
        "plot_acc_variation(var1, var2, df, margin_titles=True, sharey=True)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x432 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 707.375x432 with 6 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 707.375x432 with 6 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}