assignment02.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "assignment02",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/soheekim911/Standalone_DL/blob/master/assignment02.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "PD4cIKKvKFCC",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "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"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "29UainWPco7Y",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "\n",
        "## Data Preparation"
      ]
    },
    {
      "metadata": {
        "id": "Cu753dPPKGkV",
        "colab_type": "code",
        "outputId": "d44d62cb-ccd4-4559-f975-3e0cc7afbb83",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "cell_type": "code",
      "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",
        "\n",
        "\n",
        "trainloader = torch.utils.data.DataLoader(trainset, batch_size=128,\n",
        "                                          shuffle=True, num_workers=4)\n",
        "valloader = torch.utils.data.DataLoader(valset, batch_size=128, \n",
        "                                        shuffle=False)\n",
        "testloader = torch.utils.data.DataLoader(testset, batch_size=128,\n",
        "                                         shuffle=False, num_workers=4)\n",
        "\n",
        "classes = ('plane', 'car', 'bird', 'cat',\n",
        "           'deer', 'dog', 'frog', 'horse', 'ship', 'truck')"
      ],
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Files already downloaded and verified\n",
            "Files already downloaded and verified\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "6z0X9rG6Wyge",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "4ada00c0-6601-49b1-f78c-a46867041d2a"
      },
      "cell_type": "code",
      "source": [
        "print(len(trainset))\n",
        "print(len(valset))\n",
        "print(len(testset))"
      ],
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "40000\n",
            "10000\n",
            "10000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "qA3JqSl9reXs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 2666
        },
        "outputId": "b1dcee5a-215b-4bc0-8a33-0a10c582966d"
      },
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "\n",
        "def imshow(img):\n",
        "    img = img / 2  +0.5\n",
        "    npimg = img.numpy()\n",
        "    plt.imshow(np.transpose(npimg, (1,2,0)))\n",
        "    plt.show()\n",
        "    \n",
        "    \n",
        "dataiter = iter(trainloader)\n",
        "images, labels = dataiter.next()\n",
        "\n",
        "imshow(torchvision.utils.make_grid(images))\n",
        "for i in range(len(images)):\n",
        "    print(classes[labels[i]])\n",
        "# print(' '.join('%30s'% classes[labels[j]] for j in range(4)))\n",
        "\n",
        "print(type(images), images.shape)\n",
        "print(type(labels), labels.shape, labels)"
      ],
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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Wci6FIUf8xP6tADSqTbbu3cc7fu7nmdp2Fde+6U3IL7+BGT/k2MkTRAJK5TKpbI7s4BgK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NUyqV8P3Y9U4IQbsd69/Pzs4yMDBAX18f2WyWV155hbGxMXzfZ3l5eQ3Ku7S0RDIZ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\nefEiZqeFOLyf7ECamltn+x0TxEORjefKXHh4ljvfcif11BJnn15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GFKK2HRJT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CxJ3HzzzTy987zDnRGP8sH3/We6e3rwA0FhboZvf+1vqbkBpXqAFYsRBB6FmQJ+4KOEHoZs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\nrkJggg==\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "truck\n",
            "frog\n",
            "frog\n",
            "frog\n",
            "bird\n",
            "deer\n",
            "plane\n",
            "cat\n",
            "truck\n",
            "bird\n",
            "dog\n",
            "frog\n",
            "deer\n",
            "plane\n",
            "ship\n",
            "dog\n",
            "deer\n",
            "bird\n",
            "plane\n",
            "dog\n",
            "frog\n",
            "horse\n",
            "plane\n",
            "plane\n",
            "plane\n",
            "cat\n",
            "truck\n",
            "frog\n",
            "frog\n",
            "bird\n",
            "horse\n",
            "ship\n",
            "frog\n",
            "truck\n",
            "car\n",
            "ship\n",
            "car\n",
            "bird\n",
            "truck\n",
            "dog\n",
            "cat\n",
            "car\n",
            "deer\n",
            "dog\n",
            "bird\n",
            "deer\n",
            "deer\n",
            "horse\n",
            "car\n",
            "car\n",
            "ship\n",
            "ship\n",
            "car\n",
            "frog\n",
            "cat\n",
            "truck\n",
            "car\n",
            "ship\n",
            "plane\n",
            "truck\n",
            "ship\n",
            "dog\n",
            "truck\n",
            "deer\n",
            "frog\n",
            "ship\n",
            "frog\n",
            "deer\n",
            "plane\n",
            "bird\n",
            "plane\n",
            "horse\n",
            "cat\n",
            "cat\n",
            "ship\n",
            "cat\n",
            "horse\n",
            "deer\n",
            "truck\n",
            "dog\n",
            "dog\n",
            "cat\n",
            "frog\n",
            "plane\n",
            "dog\n",
            "deer\n",
            "ship\n",
            "deer\n",
            "frog\n",
            "car\n",
            "dog\n",
            "deer\n",
            "car\n",
            "plane\n",
            "cat\n",
            "frog\n",
            "ship\n",
            "horse\n",
            "cat\n",
            "plane\n",
            "plane\n",
            "dog\n",
            "cat\n",
            "cat\n",
            "horse\n",
            "deer\n",
            "ship\n",
            "bird\n",
            "cat\n",
            "truck\n",
            "frog\n",
            "deer\n",
            "truck\n",
            "deer\n",
            "frog\n",
            "deer\n",
            "truck\n",
            "horse\n",
            "car\n",
            "bird\n",
            "car\n",
            "car\n",
            "ship\n",
            "bird\n",
            "bird\n",
            "cat\n",
            "bird\n",
            "bird\n",
            "<class 'torch.Tensor'> torch.Size([128, 3, 32, 32])\n",
            "<class 'torch.Tensor'> torch.Size([128]) tensor([9, 6, 6, 6, 2, 4, 0, 3, 9, 2, 5, 6, 4, 0, 8, 5, 4, 2, 0, 5, 6, 7, 0, 0,\n",
            "        0, 3, 9, 6, 6, 2, 7, 8, 6, 9, 1, 8, 1, 2, 9, 5, 3, 1, 4, 5, 2, 4, 4, 7,\n",
            "        1, 1, 8, 8, 1, 6, 3, 9, 1, 8, 0, 9, 8, 5, 9, 4, 6, 8, 6, 4, 0, 2, 0, 7,\n",
            "        3, 3, 8, 3, 7, 4, 9, 5, 5, 3, 6, 0, 5, 4, 8, 4, 6, 1, 5, 4, 1, 0, 3, 6,\n",
            "        8, 7, 3, 0, 0, 5, 3, 3, 7, 4, 8, 2, 3, 9, 6, 4, 9, 4, 6, 4, 9, 7, 1, 2,\n",
            "        1, 1, 8, 2, 2, 3, 2, 2])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "RxnfFJwBcsAv",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Model Architecture"
      ]
    },
    {
      "metadata": {
        "id": "J-LTLi8yxY3i",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "##### My Code ######\n",
        "\n",
        "class MLP(nn.Module):   \n",
        "    def __init__(self,in_dim,hid_dim,out_dim,n_layer,act):\n",
        "        super(MLP, self).__init__() # super는 다중상속시 슈퍼 클래스의 method를 호출하라는 의미\n",
        "        self.in_dim = in_dim\n",
        "        self.hid_dim = hid_dim\n",
        "        self.out_dim = out_dim\n",
        "        self.n_layer = n_layer\n",
        "        self.act = act\n",
        "        \n",
        "        # input --> act(output)\n",
        "        self.fc = nn.Linear(self.in_dim, self.hid_dim)\n",
        "        # act(output) --> hid_dim을 가진 linear(fc) layer에 n-1번 통과\n",
        "        self.linears = nn.ModuleList()\n",
        "        # n-1번 fc layer를 통과\n",
        "        for i in range(self.n_layer-1):\n",
        "            self.linears.append(nn.Linear(self.hid_dim, self.hid_dim))\n",
        "        # out_dim은 최종 클래스 갯수대로\n",
        "        self.fc2 = nn.Linear(self.hid_dim, self.out_dim)\n",
        "        \n",
        "        if self.act == 'relu':\n",
        "            self.act = nn.ReLU()\n",
        "            \n",
        "    def forward(self, x):\n",
        "        x = self.act(self.fc(x))\n",
        "        for fc in self.linears: ### 여기 잘 이해 안 가..이렇게만 써도 레이어를 통과하게 되는 거야?\n",
        "            x = self.act(fc(x)) \n",
        "        x = self.fc2(x)    \n",
        "        return x\n",
        "    \n",
        "net = MLP(3072, 100, 10, 5, 'relu') # 3072 = 32*32(pixel)*3(RGB)\n",
        "      "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "itGsp6jDWs_a",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Define Experiment"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "kZFDYVR7NMGt",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def experiment(args):\n",
        "  \n",
        "    net = MLP(args.in_dim, args.hid_dim, args.out_dim, args.n_layer, args.act)\n",
        "    net.cuda()\n",
        "#     print(net)\n",
        "\n",
        "    criterion = nn.CrossEntropyLoss()\n",
        "    optimizer = optim.SGD(net.parameters(), lr=args.lr, momentum=args.mm)\n",
        "    \n",
        "    for epoch in range(args.epoch):  # loop over the dataset multiple times\n",
        "        t_start = time.time()\n",
        "        # ==== Train ===== #\n",
        "        net.train()\n",
        "        optimizer.zero_grad()\n",
        "        \n",
        "        running_loss = 0.0\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",
        "            \n",
        "            inputs = inputs.cuda()\n",
        "            labels = labels.cuda()\n",
        "            \n",
        "\n",
        "            # zero the parameter gradients\n",
        "            optimizer.zero_grad() # 왜 여기서 또 나오지??\n",
        "\n",
        "            # forward + backward + optimize\n",
        "            outputs = net(inputs)\n",
        "            loss = criterion(outputs, labels)\n",
        "            loss.backward()\n",
        "            optimizer.step()\n",
        "\n",
        "            # print statistics\n",
        "            running_loss += loss.item()\n",
        "            train_loss += loss.item()\n",
        "            if i % 2000 == 1999:    # print every 2000 mini-batches\n",
        "                print('[%d, %5d] loss: %.3f' %\n",
        "                      (epoch + 1, i + 1, running_loss / 2000))\n",
        "                running_loss = 0.0\n",
        "        list_epoch.append(epoch)   \n",
        "        list_train_loss.append(train_loss)\n",
        "        \n",
        "        # ==== Validation ====== #\n",
        "        net.eval()\n",
        "        optimizer.zero_grad()\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",
        "                \n",
        "                ################################\n",
        "                images = images.cuda()\n",
        "                labels = labels.cuda()\n",
        "                \n",
        "                outputs = net(images)\n",
        "\n",
        "                loss = criterion(outputs, labels)\n",
        "                val_loss += loss.item()\n",
        "\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",
        "            list_val_loss.append(val_loss)\n",
        "        print('Epoch {}, Train Loss: {}, Val Loss: {}, Val Acc: {}'.format(epoch, train_loss, val_loss, val_acc))\n",
        "\n",
        "\n",
        "    # ===== Evaluation ===== #\n",
        "    net.eval()\n",
        "    optimizer.zero_grad()\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",
        "        list_acc.append(test_acc)\n",
        "        list_acc_epoch.append(epoch)\n",
        "    t_end = time.time()\n",
        "    print('time spent: {}'.format(t_end-t_start))\n",
        "    print(list_train_loss, list_val_loss)\n",
        "    return train_loss, val_loss, val_acc, test_acc\n",
        "    "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "ImUoSN4XhAMQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 819
        },
        "outputId": "cbe16c8b-0cbd-47d1-d0a0-24f1f8e23a5f"
      },
      "cell_type": "code",
      "source": [
        "##### My Code ######\n",
        "\n",
        "import argparse\n",
        "\n",
        "np.random.seed(seed)\n",
        "torch.manual_seed(seed)\n",
        "\n",
        "parser = argparse.ArgumentParser()\n",
        "args = parser.parse_args(\"\")\n",
        "print(type(args))\n",
        "\n",
        "#### model related parameters ####\n",
        "args.in_dim = 3072\n",
        "args.hid_dim = 100\n",
        "args.out_dim = 10\n",
        "args.n_layer = 5\n",
        "\n",
        "#### Hyperparameters ####\n",
        "args.act = 'relu'\n",
        "args.lr = 0.001\n",
        "args.mm = 0.9\n",
        "args.epoch = 3\n",
        "\n",
        "layer_list = [3,4,5]\n",
        "hid_dim_list = [50,100,150]\n",
        "\n",
        "list_epoch = [] \n",
        "list_train_loss = []\n",
        "list_val_loss = []\n",
        "list_acc = []\n",
        "list_acc_epoch = []\n",
        "\n",
        "for layer in layer_list:\n",
        "    for dim in hid_dim_list:\n",
        "        args.n_layer = layer\n",
        "        args.hid_dim = dim\n",
        "        result = experiment(args)\n",
        "        print(result)\n"
      ],
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'argparse.Namespace'>\n",
            "Epoch 0, Train Loss: 718.4838404655457, Val Loss: 2.2781264268899264, Val Acc: 16.21\n",
            "Epoch 1, Train Loss: 702.5457305908203, Val Loss: 2.196911377242849, Val Acc: 19.7\n",
            "Epoch 2, Train Loss: 663.583244562149, Val Loss: 2.0455130821541894, Val Acc: 25.95\n",
            "time spent: -16.39527678489685\n",
            "(663.583244562149, 2.0455130821541894, 25.95, 26.76)\n",
            "Epoch 0, Train Loss: 716.0431571006775, Val Loss: 2.259309524222265, Val Acc: 16.03\n",
            "Epoch 1, Train Loss: 686.204131603241, Val Loss: 2.1231741452518897, Val Acc: 22.49\n",
            "Epoch 2, Train Loss: 643.3055664300919, Val Loss: 2.0090387048600595, Val Acc: 28.29\n",
            "time spent: -16.198386430740356\n",
            "(643.3055664300919, 2.0090387048600595, 28.29, 29.48)\n",
            "Epoch 0, Train Loss: 715.5012757778168, Val Loss: 2.2590633585483215, Val Acc: 21.03\n",
            "Epoch 1, Train Loss: 685.0886516571045, Val Loss: 2.111594918407971, Val Acc: 23.67\n",
            "Epoch 2, Train Loss: 637.2816002368927, Val Loss: 1.9870776149291027, Val Acc: 28.75\n",
            "time spent: -16.235544204711914\n",
            "(637.2816002368927, 1.9870776149291027, 28.75, 29.67)\n",
            "Epoch 0, Train Loss: 721.2688143253326, Val Loss: 2.3008092324944993, Val Acc: 12.11\n",
            "Epoch 1, Train Loss: 718.9383387565613, Val Loss: 2.292842569230478, Val Acc: 14.92\n",
            "Epoch 2, Train Loss: 715.5292422771454, Val Loss: 2.2772972281975083, Val Acc: 15.81\n",
            "time spent: -16.06122088432312\n",
            "(715.5292422771454, 2.2772972281975083, 15.81, 15.45)\n",
            "Epoch 0, Train Loss: 719.786628484726, Val Loss: 2.295247252983383, Val Acc: 12.89\n",
            "Epoch 1, Train Loss: 715.6561961174011, Val Loss: 2.27392411835586, Val Acc: 19.06\n",
            "Epoch 2, Train Loss: 701.5076425075531, Val Loss: 2.197103759910487, Val Acc: 17.83\n",
            "time spent: -16.120554208755493\n",
            "(701.5076425075531, 2.197103759910487, 17.83, 18.54)\n",
            "Epoch 0, Train Loss: 719.8854279518127, Val Loss: 2.2952077388763428, Val Acc: 14.66\n",
            "Epoch 1, Train Loss: 716.2080438137054, Val Loss: 2.277197822739806, Val Acc: 22.23\n",
            "Epoch 2, Train Loss: 704.1175994873047, Val Loss: 2.2100591900982436, Val Acc: 20.31\n",
            "time spent: -15.984816074371338\n",
            "(704.1175994873047, 2.2100591900982436, 20.31, 20.04)\n",
            "Epoch 0, Train Loss: 721.8385496139526, Val Loss: 2.3044583163683927, Val Acc: 10.06\n",
            "Epoch 1, Train Loss: 720.5331976413727, Val Loss: 2.3009407731551157, Val Acc: 13.23\n",
            "Epoch 2, Train Loss: 719.5170781612396, Val Loss: 2.2972732978531076, Val Acc: 13.85\n",
            "time spent: -16.03787922859192\n",
            "(719.5170781612396, 2.2972732978531076, 13.85, 13.5)\n",
            "Epoch 0, Train Loss: 721.1488587856293, Val Loss: 2.3032811653764944, Val Acc: 10.01\n",
            "Epoch 1, Train Loss: 720.5819962024689, Val Loss: 2.3017285594457313, Val Acc: 10.21\n",
            "Epoch 2, Train Loss: 720.1053557395935, Val Loss: 2.300122659417647, Val Acc: 13.6\n",
            "time spent: -16.01732587814331\n",
            "(720.1053557395935, 2.300122659417647, 13.6, 13.42)\n",
            "Epoch 0, Train Loss: 721.1143865585327, Val Loss: 2.303093463559694, Val Acc: 9.87\n",
            "Epoch 1, Train Loss: 720.4742543697357, Val Loss: 2.3012663714493375, Val Acc: 13.76\n",
            "Epoch 2, Train Loss: 719.8597626686096, Val Loss: 2.2990010086494155, Val Acc: 17.04\n",
            "time spent: -16.01187562942505\n",
            "(719.8597626686096, 2.2990010086494155, 17.04, 17.75)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "omgExzmQgU1J",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Experiment"
      ]
    },
    {
      "metadata": {
        "id": "VtezNo3L8IGR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        },
        "outputId": "d2c27b54-272b-46c7-b84b-444089ebad8c"
      },
      "cell_type": "code",
      "source": [
        "print(list_epoch)\n",
        "print(list_train_loss)\n",
        "print(list_val_loss)\n",
        "print(list_acc)\n",
        "print(list_acc_epoch)"
      ],
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2]\n",
            "[718.4838404655457, 702.5457305908203, 663.583244562149, 716.0431571006775, 686.204131603241, 643.3055664300919, 715.5012757778168, 685.0886516571045, 637.2816002368927, 721.2688143253326, 718.9383387565613, 715.5292422771454, 719.786628484726, 715.6561961174011, 701.5076425075531, 719.8854279518127, 716.2080438137054, 704.1175994873047, 721.8385496139526, 720.5331976413727, 719.5170781612396, 721.1488587856293, 720.5819962024689, 720.1053557395935, 721.1143865585327, 720.4742543697357, 719.8597626686096]\n",
            "[2.2781264268899264, 2.196911377242849, 2.0455130821541894, 2.259309524222265, 2.1231741452518897, 2.0090387048600595, 2.2590633585483215, 2.111594918407971, 1.9870776149291027, 2.3008092324944993, 2.292842569230478, 2.2772972281975083, 2.295247252983383, 2.27392411835586, 2.197103759910487, 2.2952077388763428, 2.277197822739806, 2.2100591900982436, 2.3044583163683927, 2.3009407731551157, 2.2972732978531076, 2.3032811653764944, 2.3017285594457313, 2.300122659417647, 2.303093463559694, 2.3012663714493375, 2.2990010086494155]\n",
            "[26.76, 29.48, 29.67, 15.45, 18.54, 20.04, 13.5, 13.42, 17.75]\n",
            "[2, 2, 2, 2, 2, 2, 2, 2, 2]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "AjTMiss6_cFi",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "| THis | is |\n",
        "|---------|-----|\n",
        "|   a     |table|"
      ]
    },
    {
      "metadata": {
        "id": "BgXfKWrv_e4h",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "!pip install tabletext\n",
        "import tabletext\n",
        "\n",
        "data = [[args.in_dim, args.hid_dim, args.lr],\n",
        "       [124, 24, 43]]\n",
        "\n",
        "print(tabletext.to_text(data))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "FJN_O5vAATMC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 822
        },
        "outputId": "ac74124a-1223-4ecd-9580-e22cdc3f361f"
      },
      "cell_type": "code",
      "source": [
        "\n",
        "fig = plt.figure(figsize=(5,5))\n",
        "\n",
        "for i in range(len(list_train_loss)):\n",
        "    # ======== Loss ========== #\n",
        "    ax1 = fig.add_subplot(3,3,i+1)\n",
        "    ax1.plot(list_epoch[i], list_train_loss[i], label = 'train loss')\n",
        "    ax1.plot(list_epoch[i], list_val_loss[i], label = 'validation loss')\n",
        "    ax1.set_xlabel('epoch')\n",
        "    ax1.set_ylabel('loss')\n",
        "    ax1.grid()\n",
        "#     ax1.legend()\n",
        "    ax1.set_title('epoch vs loss')\n",
        "\n",
        "for j in range(len(list_acc)):\n",
        "    # ======= Accuracy ======= #\n",
        "    ax2 = fig.add_subplot(3,3,j+1)\n",
        "    ax2.plot(list_acc_epoch[j], list_acc[j], marker='x', label = 'Accuracy metrics')\n",
        "    ax2.set_xlabel('epoch')\n",
        "    ax2.set_ylabel('Acc')\n",
        "    ax2.grid()\n",
        "#     ax2.legend()\n",
        "    ax2.set_title('epoch vs Accuracy')\n",
        "\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-113-b2004388d861>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlist_train_loss\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0;31m# ======== Loss ========== #\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m     \u001b[0max1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_subplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m     \u001b[0max1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlist_epoch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist_train_loss\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'train loss'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m     \u001b[0max1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlist_epoch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist_val_loss\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'validation loss'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/figure.py\u001b[0m in \u001b[0;36madd_subplot\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1365\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_axstack\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1366\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1367\u001b[0;31m             \u001b[0ma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msubplot_class_factory\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprojection_class\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1368\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_axstack\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1369\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msca\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/axes/_subplots.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, fig, *args, **kwargs)\u001b[0m\n\u001b[1;32m     58\u001b[0m                     raise ValueError(\n\u001b[1;32m     59\u001b[0m                         (\"num must be 1 <= num <= {maxn}, not {num}\"\n\u001b[0;32m---> 60\u001b[0;31m                         ).format(maxn=rows*cols, num=num))\n\u001b[0m\u001b[1;32m     61\u001b[0m                 self._subplotspec = GridSpec(\n\u001b[1;32m     62\u001b[0m                         rows, cols, figure=self.figure)[int(num) - 1]\n",
            "\u001b[0;31mValueError\u001b[0m: num must be 1 <= num <= 9, not 10"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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zZ86s0fiVycvLY/ny5WzYsKHcKU9Q+fJb2v1qqg5N2cq2yq8wGo0sWLCAS5cu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\niIWTiEghFk4iIoVYOImIFGLhJCJS6B+Ujt8BLuR8ZAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 360x360 with 9 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "RLYAF_X9PHja",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}