CIFAR_10_PyTorch.ipynb

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      "metadata": {
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      "source": [
        "<a href=\"https://colab.research.google.com/github/ayushm-agrawal/ml-algorithms-numpy/blob/master/CIFAR_10_PyTorch.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TRTRd5QxKCNR",
        "colab_type": "text"
      },
      "source": [
        "####Imports\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OOOVlA94Kwhz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "\n",
        "import torch\n",
        "import torch.nn.functional as F\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "import torchvision.transforms as transforms\n",
        "\n",
        "from torchvision.datasets import CIFAR10\n",
        "from torch.utils.data import DataLoader\n",
        "from torch.utils.data.sampler import SubsetRandomSampler\n",
        "\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZJPCEllBKGVC",
        "colab_type": "text"
      },
      "source": [
        "####Check for GPU"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1hW4lB08K2EN",
        "colab_type": "code",
        "outputId": "c794c758-0e86-40b2-f32c-3715eebc6973",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "print('PyTorch version:', torch.__version__)\n",
        "# check if CUDA is available\n",
        "train_on_gpu = torch.cuda.is_available()\n",
        "\n",
        "if not train_on_gpu:\n",
        "    print('CUDA is not available.  Training on CPU ...')\n",
        "else:\n",
        "    print('CUDA is available!  Training on GPU ...')"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "PyTorch version: 1.4.0\n",
            "CUDA is available!  Training on GPU ...\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eYg_6IEZe38U",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 319
        },
        "outputId": "b3c3706d-7ffc-44b6-e8c4-548a8cbd9eda"
      },
      "source": [
        "gpu_info = !nvidia-smi\n",
        "gpu_info = '\\n'.join(gpu_info)\n",
        "if gpu_info.find('failed') >= 0:\n",
        "  print('Select the Runtime → \"Change runtime type\" menu to enable a GPU accelerator, ')\n",
        "  print('and then re-execute this cell.')\n",
        "else:\n",
        "  print(gpu_info)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Wed Feb 12 03:40:33 2020       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 440.48.02    Driver Version: 418.67       CUDA Version: 10.1     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla P100-PCIE...  Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   43C    P0    26W / 250W |     10MiB / 16280MiB |      0%      Default |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                       GPU Memory |\n",
            "|  GPU       PID   Type   Process name                             Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MlzJAQ3jLnRt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Set random seed for reproducability\n",
        "torch.manual_seed(271828)\n",
        "np.random.seed(271728)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XPjns4ErHkfX",
        "colab_type": "text"
      },
      "source": [
        "####Load Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7y0XCJ6sLJe_",
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      "source": [
        "# transform for the training data\n",
        "train_transform = transforms.Compose([\n",
        "    transforms.ToTensor(),\n",
        "    transforms.Normalize([0.1307], [0.3081])\n",
        "])\n",
        "\n",
        "# use the same transform for the validation data\n",
        "test_transform = train_transform\n",
        "\n",
        "# load datasets, downloading if needed\n",
        "train_set = CIFAR10('./data/cifar10', train=True, download=True, \n",
        "                  transform=train_transform)\n",
        "test_set = CIFAR10('./data/cifar10', train=False, download=True, \n",
        "                  transform=test_transform)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar10/cifar-10-python.tar.gz\n"
          ],
          "name": "stdout"
        },
        {
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          "text": [
            "\n",
            "Extracting ./data/cifar10/cifar-10-python.tar.gz to ./data/cifar10\n",
            "Files already downloaded and verified\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "akp5v16hH05O",
        "colab_type": "text"
      },
      "source": [
        "###Split train data to get Validation Set\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t6tqJsKlH6nS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_len = len(train_set)\n",
        "idxes = list(range(train_len))\n",
        "\n",
        "#shuffle the indexes\n",
        "np.random.shuffle(idxes)\n",
        "\n",
        "split = int(np.floor(0.2*train_len))\n",
        "train_idx, valid_idx = idxes[split: ], idxes[:split]\n",
        "\n",
        "#Sampler gets the batches of data\n",
        "train_sampler = SubsetRandomSampler(train_idx)\n",
        "valid_sampler = SubsetRandomSampler(valid_idx)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "et_UOTNyIt-P",
        "colab_type": "text"
      },
      "source": [
        "###Dataloaders for Train, Test and Validation\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yHns-V9tItTA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_loader = torch.utils.data.DataLoader(train_set, batch_size=20, sampler=train_sampler, num_workers=0)\n",
        "valid_loader = torch.utils.data.DataLoader(train_set, batch_size=20, sampler=valid_sampler, num_workers=0)\n",
        "test_loader = torch.utils.data.DataLoader(test_set, batch_size=20, num_workers=0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tNeo7F1IJM2K",
        "colab_type": "text"
      },
      "source": [
        "####Image Classes"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aznA7qfJJQcc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# 10 Classes in CIFAR-10 Dataset\n",
        "classes = ['airplane', 'automobile', 'bird', 'cat', 'deer',\n",
        "           'dog', 'frog', 'horse', 'ship', 'truck']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EzxCqfgxMvbq",
        "colab_type": "text"
      },
      "source": [
        "###Preview the data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0ERb5tRkL4LP",
        "colab_type": "code",
        "outputId": "564174ab-7fa7-400e-e143-175ccb2a3805",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "print(train_set.data.shape)\n",
        "print(test_set.data.shape)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(50000, 32, 32, 3)\n",
            "(10000, 32, 32, 3)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "av7bj99EJyBK",
        "colab_type": "text"
      },
      "source": [
        "###Visualizing the Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J8iBFM9KMf4M",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def imshow(img):\n",
        "\timg = img/2 + 0.5 #unnormalize the image\n",
        "\tplt.imshow(np.transpose(img, (1,2,0))) # convert from tensor to image"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qogU0RT4KdX3",
        "colab_type": "code",
        "outputId": "f8f67241-2a05-4c03-f302-4881c51afcaf",
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      },
      "source": [
        "dataiter = iter(train_loader)\n",
        "images, labels = dataiter.next()\n",
        "images = images.numpy()\n",
        "\n",
        "figure = plt.figure(figsize=(25,4))\n",
        "for i in np.arange(20):\n",
        "  axis = figure.add_subplot(2, 20/2, i+1, xticks=[], yticks=[])\n",
        "  imshow(images[i])\n",
        "  axis.set_title(classes[labels[i]])"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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sHLuueUNuigUr80Uy8YVXpegYb4WmrguhPUWj0Plpa0M7KbFgcp3fudJTuOeh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I9WLI3YBqOtDTaGQQrnsUx53hf3GYaJU/sVo+2GjkUrPjf0+alJPIe9cOC72J+HbR56CddjarjK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\nAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1800x288 with 20 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KTZM9tlcXVET",
        "colab_type": "text"
      },
      "source": [
        "##MODEL"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SS_S0YuyXY-e",
        "colab_type": "code",
        "outputId": "0d27e9e1-75d9-4352-9a04-6d09a9637570",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 177
        }
      },
      "source": [
        "class RandomNet(nn.Module):\n",
        "  def __init__(self):\n",
        "    super(RandomNet, self).__init__()\n",
        "\n",
        "    self.conv1 = nn.Conv2d(3, 16, 3, padding=1)\n",
        "    #TODO: add more layers\n",
        "    self.conv2 = nn.Conv2d(16, 32, 3, padding=1)\n",
        "    self.conv3 = nn.Conv2d(32, 64, 3, padding=1)\n",
        "\n",
        "    self.pool = nn.MaxPool2d(2,2)\n",
        "\n",
        "    self.fc1 = nn.Linear(64*4*4, 500)\n",
        "    self.output = nn.Linear(500, 10)\n",
        "\n",
        "    self.dropout = nn.Dropout(0.25)\n",
        "  \n",
        "  def forward(self, x):\n",
        "    x = self.pool(F.relu(self.conv1(x)))\n",
        "    x = self.pool(F.relu(self.conv2(x)))\n",
        "    x = self.pool(F.relu(self.conv3(x)))\n",
        "\n",
        "    x = x.view(-1, 64*4*4)\n",
        "\n",
        "    x = self.dropout(x)\n",
        "\n",
        "    x = F.relu(self.fc1(x))\n",
        "\n",
        "    x = self.dropout(x)\n",
        "\n",
        "    x = self.output(x)\n",
        "\n",
        "    return x\n",
        "\n",
        "model = RandomNet()\n",
        "print(model)\n",
        "\n",
        "if train_on_gpu:\n",
        "  model.cuda()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "RandomNet(\n",
            "  (conv1): Conv2d(3, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "  (conv2): Conv2d(16, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "  (conv3): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "  (pool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
            "  (fc1): Linear(in_features=1024, out_features=500, bias=True)\n",
            "  (output): Linear(in_features=500, out_features=10, bias=True)\n",
            "  (dropout): Dropout(p=0.25, inplace=False)\n",
            ")\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7L36hnuejTH3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 230
        },
        "outputId": "ebe37b59-9a42-447a-9772-48c972e51625"
      },
      "source": [
        "class RandomNetBatchNormalization(nn.Module):\n",
        "  def __init__(self):\n",
        "    super(RandomNetBatchNormalization, self).__init__()\n",
        "\n",
        "    self.conv1 = nn.Conv2d(3, 32, 3, padding=1)\n",
        "    self.conv2 = nn.Conv2d(32, 32, 3, padding=1)\n",
        "    # self.batchnorm1 = nn.BatchNorm2d(32)\n",
        "\n",
        "    self.conv3 = nn.Conv2d(32, 64, 3, padding=1)\n",
        "    self.conv4 = nn.Conv2d(64, 64, 3, padding=1)\n",
        "    # self.batchnorm2 = nn.BatchNorm2d(64)\n",
        "\n",
        "    self.conv5 = nn.Conv2d(64, 128, 3, padding=1)\n",
        "    self.conv6 = nn.Conv2d(128, 128, 3, padding=1)\n",
        "    # self.batchnorm3 = nn.BatchNorm2d(128)\n",
        "\n",
        "    self.pool = nn.MaxPool2d(2,2)\n",
        "\n",
        "    self.fc1 = nn.Linear(128*4*4, 500)\n",
        "    self.output = nn.Linear(500, 10)\n",
        "\n",
        "    self.dropout = nn.Dropout(0.25)\n",
        "  \n",
        "  def forward(self, x):\n",
        "    x = F.relu((self.conv1(x)))\n",
        "    x = F.relu((self.conv2(x))) \n",
        "    x = self.pool(x)\n",
        "\n",
        "    x = F.relu((self.conv3(x)))\n",
        "    x = F.relu((self.conv4(x)))\n",
        "    x = self.pool(x)\n",
        "\n",
        "    x = F.relu((self.conv5(x)))\n",
        "    x = F.relu((self.conv6(x)))\n",
        "    x = self.pool(x)\n",
        "\n",
        "    x = x.view(-1, 128*4*4)\n",
        "\n",
        "    x = F.relu(self.fc1(x))\n",
        "\n",
        "    x = self.dropout(x)\n",
        "\n",
        "    x = self.output(x)\n",
        "\n",
        "    return x\n",
        "\n",
        "model2 = RandomNetBatchNormalization()\n",
        "print(model2)\n",
        "\n",
        "if train_on_gpu:\n",
        "  model2.cuda()"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "RandomNetBatchNormalization(\n",
            "  (conv1): Conv2d(3, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "  (conv2): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "  (conv3): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "  (conv4): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "  (conv5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "  (conv6): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "  (pool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
            "  (fc1): Linear(in_features=2048, out_features=500, bias=True)\n",
            "  (output): Linear(in_features=500, out_features=10, bias=True)\n",
            "  (dropout): Dropout(p=0.25, inplace=False)\n",
            ")\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-Wi5-IljMzzN",
        "colab_type": "text"
      },
      "source": [
        "###Loss and Optimizer"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TApD_Gt_MhM3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "learning_rate = 1e-4\n",
        "\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "optimizer = optim.Adam(model.parameters(), lr = learning_rate)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9KEnA5QlMxu3",
        "colab_type": "text"
      },
      "source": [
        "###Training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6j7SGGANNLXT",
        "colab_type": "code",
        "outputId": "813ea876-50cf-48b7-e256-830b1776c1a9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 959
        }
      },
      "source": [
        "epochs = 30\n",
        "\n",
        "min_validation_loss = np.Inf\n",
        "\n",
        "for epoch in range(1, epochs+1):\n",
        "\n",
        "  train_loss = 0.0\n",
        "  validation_loss = 0.0\n",
        "\n",
        "  # training model loop\n",
        "  model.train()\n",
        "  for data, labels in train_loader:\n",
        "\n",
        "    # if gpu is available then move the data on the gpu\n",
        "    if train_on_gpu:\n",
        "      data, labels = data.cuda(), labels.cuda()\n",
        "\n",
        "    # clear the gradients\n",
        "    optimizer.zero_grad()\n",
        "    # make a forward pass\n",
        "    output = model(data)\n",
        "    # calculate the loss\n",
        "    loss = criterion(output, labels)\n",
        "    # do a backwards pass\n",
        "    loss.backward()\n",
        "    # perform a single optimization step\n",
        "    optimizer.step()\n",
        "    # update training loss\n",
        "    train_loss += loss.item()*data.size(0)\n",
        "\n",
        "  \n",
        "  # validate the model\n",
        "  model.eval()\n",
        "  for data, labels in valid_loader:\n",
        "\n",
        "    # if gpu is available then move the data on the gpu\n",
        "    if train_on_gpu:\n",
        "      data, labels = data.cuda(), labels.cuda()\n",
        "\n",
        "    # forward pass\n",
        "    output = model(data)\n",
        "\n",
        "    # calculate the loss\n",
        "    loss = criterion(output, labels)\n",
        "\n",
        "    # update validation loss\n",
        "    validation_loss += loss.item()*data.size(0)\n",
        "\n",
        "  #average losses\n",
        "  train_loss = train_loss/len(train_loader.dataset)\n",
        "  validation_loss = validation_loss/len(valid_loader.dataset)\n",
        "\n",
        "  print('Epoch: {} \\tTraining Loss: {} \\tValidation Loss: {}'.format(epoch, train_loss, validation_loss))\n",
        "\n",
        "  #save model if validation loss has decreased\n",
        "  if validation_loss <= min_validation_loss:\n",
        "    print('Validation loss decreased ({:.6f} --> {:.6f}). Saving model ....'.format(\n",
        "        min_validation_loss,\n",
        "        validation_loss,\n",
        "    ))\n",
        "\n",
        "    torch.save(model.state_dict(), 'model_cifar-10.pt')\n",
        "    min_validation_loss = validation_loss"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch: 1 \tTraining Loss: 1.0092950084209442 \tValidation Loss: 0.24204822628498077\n",
            "Validation loss decreased (inf --> 0.242048). Saving model ....\n",
            "Epoch: 2 \tTraining Loss: 0.9624313009977341 \tValidation Loss: 0.23353270230293274\n",
            "Validation loss decreased (0.242048 --> 0.233533). Saving model ....\n",
            "Epoch: 3 \tTraining Loss: 0.927088704252243 \tValidation Loss: 0.22193935244083404\n",
            "Validation loss decreased (0.233533 --> 0.221939). Saving model ....\n",
            "Epoch: 4 \tTraining Loss: 0.8900503075480461 \tValidation Loss: 0.21446999650001525\n",
            "Validation loss decreased (0.221939 --> 0.214470). Saving model ....\n",
            "Epoch: 5 \tTraining Loss: 0.8587957826375962 \tValidation Loss: 0.2077185628414154\n",
            "Validation loss decreased (0.214470 --> 0.207719). Saving model ....\n",
            "Epoch: 6 \tTraining Loss: 0.8268654446005821 \tValidation Loss: 0.2044824299931526\n",
            "Validation loss decreased (0.207719 --> 0.204482). Saving model ....\n",
            "Epoch: 7 \tTraining Loss: 0.8057927325248718 \tValidation Loss: 0.19804974662065505\n",
            "Validation loss decreased (0.204482 --> 0.198050). Saving model ....\n",
            "Epoch: 8 \tTraining Loss: 0.7773326991200447 \tValidation Loss: 0.1925089199781418\n",
            "Validation loss decreased (0.198050 --> 0.192509). Saving model ....\n",
            "Epoch: 9 \tTraining Loss: 0.7499509445905685 \tValidation Loss: 0.1887485957622528\n",
            "Validation loss decreased (0.192509 --> 0.188749). Saving model ....\n",
            "Epoch: 10 \tTraining Loss: 0.7303155454993248 \tValidation Loss: 0.18485813962221145\n",
            "Validation loss decreased (0.188749 --> 0.184858). Saving model ....\n",
            "Epoch: 11 \tTraining Loss: 0.7078225192070007 \tValidation Loss: 0.18137760410308837\n",
            "Validation loss decreased (0.184858 --> 0.181378). Saving model ....\n",
            "Epoch: 12 \tTraining Loss: 0.6865708109259605 \tValidation Loss: 0.1808704792380333\n",
            "Validation loss decreased (0.181378 --> 0.180870). Saving model ....\n",
            "Epoch: 13 \tTraining Loss: 0.6657409164428711 \tValidation Loss: 0.17396154997348787\n",
            "Validation loss decreased (0.180870 --> 0.173962). Saving model ....\n",
            "Epoch: 14 \tTraining Loss: 0.6423857639610767 \tValidation Loss: 0.17574304860830306\n",
            "Epoch: 15 \tTraining Loss: 0.6297232432961464 \tValidation Loss: 0.1710536301970482\n",
            "Validation loss decreased (0.173962 --> 0.171054). Saving model ....\n",
            "Epoch: 16 \tTraining Loss: 0.6068655141353607 \tValidation Loss: 0.17027509497404097\n",
            "Validation loss decreased (0.171054 --> 0.170275). Saving model ....\n",
            "Epoch: 17 \tTraining Loss: 0.5883034511864186 \tValidation Loss: 0.1660117144882679\n",
            "Validation loss decreased (0.170275 --> 0.166012). Saving model ....\n",
            "Epoch: 18 \tTraining Loss: 0.5744355009615422 \tValidation Loss: 0.16315982302427293\n",
            "Validation loss decreased (0.166012 --> 0.163160). Saving model ....\n",
            "Epoch: 19 \tTraining Loss: 0.5565424497663974 \tValidation Loss: 0.16332820574045182\n",
            "Epoch: 20 \tTraining Loss: 0.5408810688048601 \tValidation Loss: 0.16141256448030472\n",
            "Validation loss decreased (0.163160 --> 0.161413). Saving model ....\n",
            "Epoch: 21 \tTraining Loss: 0.5264231822490693 \tValidation Loss: 0.16186454561948777\n",
            "Epoch: 22 \tTraining Loss: 0.5099759519577026 \tValidation Loss: 0.15957381108999252\n",
            "Validation loss decreased (0.161413 --> 0.159574). Saving model ....\n",
            "Epoch: 23 \tTraining Loss: 0.4986046879351139 \tValidation Loss: 0.15817337188720704\n",
            "Validation loss decreased (0.159574 --> 0.158173). Saving model ....\n",
            "Epoch: 24 \tTraining Loss: 0.48270648673176764 \tValidation Loss: 0.16102916355133057\n",
            "Epoch: 25 \tTraining Loss: 0.4647686002463102 \tValidation Loss: 0.15984909542798997\n",
            "Epoch: 26 \tTraining Loss: 0.453405564057827 \tValidation Loss: 0.15808987115621567\n",
            "Validation loss decreased (0.158173 --> 0.158090). Saving model ....\n",
            "Epoch: 27 \tTraining Loss: 0.43862715402543545 \tValidation Loss: 0.15639924134016037\n",
            "Validation loss decreased (0.158090 --> 0.156399). Saving model ....\n",
            "Epoch: 28 \tTraining Loss: 0.42339369174838065 \tValidation Loss: 0.15834067096710205\n",
            "Epoch: 29 \tTraining Loss: 0.41278549240231516 \tValidation Loss: 0.15914491989612578\n",
            "Epoch: 30 \tTraining Loss: 0.40187778317034245 \tValidation Loss: 0.15610704031586647\n",
            "Validation loss decreased (0.156399 --> 0.156107). Saving model ....\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8Vo4_knNP5VM",
        "colab_type": "text"
      },
      "source": [
        "###Testing\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x71fz2neP7DO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 266
        },
        "outputId": "83ca301c-3f8e-4b94-fe59-2f5e019ac69b"
      },
      "source": [
        "# load the model\n",
        "model.load_state_dict(torch.load('model_cifar-10.pt'))\n",
        "\n",
        "#test loss \n",
        "test_loss = 0.0\n",
        "correct_class = list(0. for i in range(10))\n",
        "total_class = list(0. for i in range(10))\n",
        "\n",
        "model.eval()\n",
        "for data, labels in test_loader:\n",
        "\n",
        "  if train_on_gpu:\n",
        "    data, labels = data.cuda(), labels.cuda()\n",
        "\n",
        "  output = model(data)\n",
        "  loss = criterion(output, labels)\n",
        "  test_loss += loss.item()*data.size(0)\n",
        "\n",
        "  _, pred = torch.max(output, 1)\n",
        "\n",
        "  correct_tensor = pred.eq(labels.data.view_as(pred))\n",
        "  correct = np.squeeze(correct_tensor.numpy()) if not train_on_gpu else np.squeeze(correct_tensor.cpu().numpy())\n",
        "\n",
        "  #test accuracy for each object class\n",
        "  for i in range(20):\n",
        "    label = labels.data[i]\n",
        "    correct_class[label] += correct[i].item()\n",
        "    total_class[label] += 1\n",
        "  \n",
        "# avg test loss\n",
        "test_loss = test_loss/len(test_loader.dataset)\n",
        "print(\"Test Loss: {:.6f}\\n\".format(test_loss))\n",
        "\n",
        "# print test accuracy\n",
        "for i in range(10):\n",
        "  if total_class[i]>0:\n",
        "    print('Test Accuracy of %5s: %2d%% (%2d/%2d)' % \n",
        "          (classes[i], 100 * correct_class[i] / total_class[i],\n",
        "          np.sum(correct_class[i]), np.sum(total_class[i])))\n",
        "  else:\n",
        "    print('Test Accuracy of %5s: N/A (no training examples)' % (classes[i]))\n",
        "\n",
        "print('\\nFinal Test  Accuracy: %2d%% (%2d/%2d)' % (\n",
        "      100. * np.sum(correct_class) / np.sum(total_class),\n",
        "      np.sum(correct_class), np.sum(total_class)))"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Test Loss: 0.774451\n",
            "\n",
            "Test Accuracy of airplane: 75% (758/1000)\n",
            "Test Accuracy of automobile: 84% (846/1000)\n",
            "Test Accuracy of  bird: 59% (596/1000)\n",
            "Test Accuracy of   cat: 53% (532/1000)\n",
            "Test Accuracy of  deer: 75% (754/1000)\n",
            "Test Accuracy of   dog: 68% (681/1000)\n",
            "Test Accuracy of  frog: 81% (818/1000)\n",
            "Test Accuracy of horse: 76% (763/1000)\n",
            "Test Accuracy of  ship: 85% (855/1000)\n",
            "Test Accuracy of truck: 79% (794/1000)\n",
            "\n",
            "Final Test  Accuracy: 73% (7397/10000)\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}