cat_vs_dog_classifier.ipynb

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  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rx-bYbyYZyi2"
      },
      "source": [
        "!pip install -q kaggle"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-6aiT0rwaFd4",
        "outputId": "4c329a8e-7edb-4dd5-cf0f-f4e8c5a2dbf1"
      },
      "source": [
        "! mkdir ~/.kaggle"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "mkdir: cannot create directory ‘/root/.kaggle’: File exists\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "udR3Q5NVaPuY"
      },
      "source": [
        "! cp kaggle.json ~/.kaggle/"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BS_9ryGeaUls"
      },
      "source": [
        " !chmod 600 ~/.kaggle/kaggle.json"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7tralA94aYHG",
        "outputId": "ce11af00-80bd-4d50-edff-4d7ac7bc4549"
      },
      "source": [
        "!kaggle competitions download -c dogs-vs-cats"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Warning: Looks like you're using an outdated API Version, please consider updating (server 1.5.10 / client 1.5.4)\n",
            "sampleSubmission.csv: Skipping, found more recently modified local copy (use --force to force download)\n",
            "test1.zip: Skipping, found more recently modified local copy (use --force to force download)\n",
            "train.zip: Skipping, found more recently modified local copy (use --force to force download)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YWUZQXLyabIC",
        "outputId": "e86350be-95dc-4986-9cdc-885c182aa77a"
      },
      "source": [
        "!unzip /content/train.zip\r\n",
        "print('done')"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  /content/train.zip\n",
            "replace train/cat.0.jpg? [y]es, [n]o, [A]ll, [N]one, [r]ename: NA\n",
            "done\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xu0mciBgfS3k",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d7bfe21f-f04d-4c67-aa1c-90fd03d2941d"
      },
      "source": [
        "%mkdir images"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "mkdir: cannot create directory ‘images’: File exists\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kk-gjWrqfZhF"
      },
      "source": [
        "%mv train images"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Nz1WeGdSzG0c"
      },
      "source": [
        "class CatDogDataset(torch.utils.data.Dataset):                             #data generating class\r\n",
        "    def __init__(self, file_list, dir, mode='train', transform = None):\r\n",
        "        self.file_list = file_list\r\n",
        "        self.dir = dir\r\n",
        "        self.mode= mode\r\n",
        "        self.transform = transform\r\n",
        "        if self.mode == 'train':\r\n",
        "            if 'dog' in self.file_list[0]:\r\n",
        "                self.label = 1\r\n",
        "            else:\r\n",
        "                self.label = 0\r\n",
        "            \r\n",
        "    def __len__(self):\r\n",
        "        return len(self.file_list)\r\n",
        "    \r\n",
        "    def __getitem__(self, idx):\r\n",
        "        img = Image.open(os.path.join(self.dir, self.file_list[idx]))\r\n",
        "        if self.transform:\r\n",
        "            img = self.transform(img)\r\n",
        "        if self.mode == 'train':\r\n",
        "            img = img.numpy()\r\n",
        "            return img.astype('float32'), self.label\r\n",
        "        else:\r\n",
        "            img = img.numpy()\r\n",
        "            return img.astype('float32'), self.file_list[idx]\r\n",
        "\r\n",
        "transform1 = transform.Compose([\r\n",
        "    transform.RandomResizedCrop(224),  \r\n",
        "    transform.ToTensor(),\r\n",
        "    transform.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))\r\n",
        "    ])"
      ],
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vNNDdhsxzW_U"
      },
      "source": [
        "train_dir = 'images/train'\r\n",
        "cat_files = []\r\n",
        "dog_files = []\r\n",
        "\r\n",
        "cat_files= [i for i in os.listdir('images/train') if 'cat' in i]\r\n",
        "dog_files = [i for i in os.listdir('images/train') if 'dog' in i]\r\n",
        "\r\n",
        "cats = CatDogDataset(cat_files, train_dir, transform = transform1)\r\n",
        "dogs = CatDogDataset(dog_files, train_dir, transform = transform1)\r\n"
      ],
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZuUPvuWJz5dQ"
      },
      "source": [
        "catdogs = torch.utils.data.ConcatDataset([cats,dogs])"
      ],
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LERdeJH44kQz"
      },
      "source": [
        "catdogs\r\n",
        "dataloader = torch.utils.data.DataLoader(catdogs, batch_size = 32, shuffle=True)"
      ],
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2_fdPsNG4t6K"
      },
      "source": [
        "images, labels = iter(dataloader).next()"
      ],
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_KENnLKB8NDy"
      },
      "source": [
        "classes = {}\r\n",
        "classes[1] = 'dog'\r\n",
        "classes[0] = 'cat'"
      ],
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 319
        },
        "id": "LGZFXSCY8-Mk",
        "outputId": "119533ee-03de-4a2f-f9bc-0de45cc3512b"
      },
      "source": [
        "npimg = images[4].numpy()\r\n",
        "npimg = np.transpose(npimg,(1,2,0))\r\n",
        "\r\n",
        "std_corr = np.asarray([0.229, 0.224, 0.225])\r\n",
        "mean = np.asarray([0.485, 0.456, 0.406])\r\n",
        "\r\n",
        "npimg = np.multiply(npimg,std_corr) + mean\r\n",
        "plt.imshow(npimg)\r\n",
        "print(classes[labels[4].item()])\r\n",
        "print(images[1].shape)"
      ],
      "execution_count": 83,
      "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"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "dog\n",
            "torch.Size([3, 224, 224])\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jCEJQABebA1v"
      },
      "source": [
        "Importing libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5P2rKv9ubDsc"
      },
      "source": [
        "import os\r\n",
        "import matplotlib.pyplot as plt\r\n",
        "import numpy as np\r\n",
        "\r\n",
        "import torch\r\n",
        "import torch.nn as nn\r\n",
        "import torch.optim as optim\r\n",
        "import torchvision\r\n",
        "import torchvision.transforms as transform\r\n",
        "import torchvision.datasets as datasets\r\n",
        "\r\n",
        "import warnings\r\n",
        "warnings.filterwarnings('ignore')\r\n",
        "from PIL import Image"
      ],
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "21IT6SYFh90o"
      },
      "source": [
        "def imshow(images,title):\r\n",
        "  npimg = images.numpy()\r\n",
        "  \r\n",
        "  std_corr = np.asarray((0.229, 0.224, 0.225)).reshape(3,1,1)\r\n",
        "  mean = np.asarray((0.485, 0.456, 0.406)).reshape(3,1,1)\r\n",
        "  \r\n",
        "  npimg = np.multiply(npimg , std_corr) + mean\r\n",
        "  npimg = np.transpose(npimg, (1,2,0))\r\n",
        "\r\n",
        "  plt.figure(figsize=(batchsize*4,4))\r\n",
        "  plt.imshow(npimg)\r\n",
        "  plt.title(title)\r\n",
        "  plt.show()"
      ],
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ultjXkakiPbJ"
      },
      "source": [
        "batchsize=4\r\n",
        "def show_batch():\r\n",
        "  dataloader = torch.utils.data.DataLoader(catdogs,batch_size=batchsize,shuffle=True)\r\n",
        "  dataiter = iter(dataloader)\r\n",
        "  \r\n",
        "  images,labels = next(dataiter)\r\n",
        "\r\n",
        "  output = model.forward(images)\r\n",
        "  a, pred = torch.max(output.data,1)\r\n",
        "  print(a)\r\n",
        "  imshow(torchvision.utils.make_grid(images),[classes[x.item()] for x in pred])\r\n"
      ],
      "execution_count": 179,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ERJpCcU1-PDh"
      },
      "source": [
        "rest = torchvision.models.alexnet(pretrained=True)"
      ],
      "execution_count": 257,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KC9l4-g9B6Nc",
        "outputId": "20bf0ee4-eca7-464c-c31e-3294d4e54854"
      },
      "source": [
        "rest"
      ],
      "execution_count": 258,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "AlexNet(\n",
              "  (features): Sequential(\n",
              "    (0): Conv2d(3, 64, kernel_size=(11, 11), stride=(4, 4), padding=(2, 2))\n",
              "    (1): ReLU(inplace=True)\n",
              "    (2): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "    (3): Conv2d(64, 192, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\n",
              "    (4): ReLU(inplace=True)\n",
              "    (5): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "    (6): Conv2d(192, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (7): ReLU(inplace=True)\n",
              "    (8): Conv2d(384, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (9): ReLU(inplace=True)\n",
              "    (10): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (11): ReLU(inplace=True)\n",
              "    (12): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "  )\n",
              "  (avgpool): AdaptiveAvgPool2d(output_size=(6, 6))\n",
              "  (classifier): Sequential(\n",
              "    (0): Dropout(p=0.5, inplace=False)\n",
              "    (1): Linear(in_features=9216, out_features=4096, bias=True)\n",
              "    (2): ReLU(inplace=True)\n",
              "    (3): Dropout(p=0.5, inplace=False)\n",
              "    (4): Linear(in_features=4096, out_features=4096, bias=True)\n",
              "    (5): ReLU(inplace=True)\n",
              "    (6): Linear(in_features=4096, out_features=1000, bias=True)\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 258
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HebVZnpmA5KO"
      },
      "source": [
        "in_features = rest.classifier[6].in_features\r\n",
        "rest.classifier  = nn.Sequential(\r\n",
        "    nn.Dropout(p=0.5,inplace=False),\r\n",
        "    nn.Linear(in_features=9216, out_features=4096, bias=True),\r\n",
        "    nn.ReLU(inplace=True),\r\n",
        "    nn.Dropout(p=0.5, inplace=False),\r\n",
        "    nn.Linear(in_features=4096, out_features=4096, bias=True),\r\n",
        "    nn.ReLU(inplace=True),\r\n",
        "    nn.Linear(in_features=4096, out_features=2, bias=True),\r\n",
        "    nn.Softmax(dim=1)\r\n",
        ")\r\n",
        "\r\n"
      ],
      "execution_count": 261,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5da6CV7mYxOV",
        "outputId": "22054752-60f7-4442-db5c-20c4e2ebe897"
      },
      "source": [
        "rest"
      ],
      "execution_count": 262,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "AlexNet(\n",
              "  (features): Sequential(\n",
              "    (0): Conv2d(3, 64, kernel_size=(11, 11), stride=(4, 4), padding=(2, 2))\n",
              "    (1): ReLU(inplace=True)\n",
              "    (2): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "    (3): Conv2d(64, 192, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\n",
              "    (4): ReLU(inplace=True)\n",
              "    (5): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "    (6): Conv2d(192, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (7): ReLU(inplace=True)\n",
              "    (8): Conv2d(384, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (9): ReLU(inplace=True)\n",
              "    (10): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (11): ReLU(inplace=True)\n",
              "    (12): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "  )\n",
              "  (avgpool): AdaptiveAvgPool2d(output_size=(6, 6))\n",
              "  (classifier): Sequential(\n",
              "    (0): Dropout(p=0.5, inplace=False)\n",
              "    (1): Linear(in_features=9216, out_features=4096, bias=True)\n",
              "    (2): ReLU(inplace=True)\n",
              "    (3): Dropout(p=0.5, inplace=False)\n",
              "    (4): Linear(in_features=4096, out_features=4096, bias=True)\n",
              "    (5): ReLU(inplace=True)\n",
              "    (6): Linear(in_features=4096, out_features=2, bias=True)\n",
              "    (7): Softmax(dim=1)\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 262
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yI5luHkQ_Cdy",
        "outputId": "1776870f-ee24-4c2c-9d04-23373da22bed"
      },
      "source": [
        "for params in rest.parameters():\r\n",
        "  if params.requires_grad:\r\n",
        "    print(params.shape)"
      ],
      "execution_count": 263,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "torch.Size([64, 3, 11, 11])\n",
            "torch.Size([64])\n",
            "torch.Size([192, 64, 5, 5])\n",
            "torch.Size([192])\n",
            "torch.Size([384, 192, 3, 3])\n",
            "torch.Size([384])\n",
            "torch.Size([256, 384, 3, 3])\n",
            "torch.Size([256])\n",
            "torch.Size([256, 256, 3, 3])\n",
            "torch.Size([256])\n",
            "torch.Size([4096, 9216])\n",
            "torch.Size([4096])\n",
            "torch.Size([4096, 4096])\n",
            "torch.Size([4096])\n",
            "torch.Size([2, 4096])\n",
            "torch.Size([2])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QZN-vtaNp8ZH"
      },
      "source": [
        "batch_size = 1\r\n",
        "def evaluation(dataloader, model):\r\n",
        "    total, correct = 0, 0\r\n",
        "    for data in dataloader:\r\n",
        "        inputs, labels = data\r\n",
        "        inputs, labels = inputs.to(device), labels.to(device)\r\n",
        "        outputs = model(inputs)\r\n",
        "        outputs = nn.functional.softmax(outputs, dim=1)\r\n",
        "        _, pred = torch.max(outputs.data, 1)\r\n",
        "        total += labels.size(0)\r\n",
        "        correct += (pred == labels).sum().item()\r\n",
        "    return 100 * correct / total\r\n"
      ],
      "execution_count": 264,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hh-2WyDTrRH5"
      },
      "source": [
        "import copy\r\n",
        "device = torch.device('cuda:0')\r\n",
        "loss_epc=[]\r\n",
        "def fit(model,trainloader,opt,loss_fn,epochs):\r\n",
        "  \r\n",
        "  min_loss = 1000\r\n",
        "  for j in tqdm_notebook(range(epochs),total=epochs ,unit= 'epoch'): \r\n",
        "    for i, data in enumerate(trainloader, 0):\r\n",
        "\r\n",
        "        inputs, labels = data\r\n",
        "        inputs, labels = inputs.to(device), labels.to(device)\r\n",
        "\r\n",
        "        opt.zero_grad()\r\n",
        "\r\n",
        "        outputs = model.forward(inputs)\r\n",
        "        loss = loss_fn(outputs, labels)\r\n",
        "        loss.backward()\r\n",
        "        opt.step()\r\n",
        "        \r\n",
        "      \r\n",
        "        \r\n",
        "        del inputs, labels, outputs\r\n",
        "        torch.cuda.empty_cache()\r\n",
        "        \r\n",
        "        if i%200 == 0:\r\n",
        "          print('For epoch [{}/{}]: Training error after {} iterations is {}'.format(j,epochs,i,loss.item()))\r\n",
        "          \r\n",
        "          if min_loss > loss.item():\r\n",
        "            min_loss = loss.item()\r\n",
        "            best_model = copy.deepcopy(vgg.state_dict())\r\n",
        "            print('Min loss %0.2f' % min_loss)\r\n",
        "\r\n",
        "    print('Loss after {} epochs is {}'.format(j,loss.item()))    \r\n",
        "    loss_epc.append(loss.item())\r\n",
        "        \r\n",
        "    print('The training accuracy is, {}'.format(evaluation(trainloader,model)))\r\n",
        "  \r\n",
        "  plt.plot(loss_epc,'red')\r\n",
        "  plt.xlabel('epochs')\r\n",
        "  plt.ylabel('error')"
      ],
      "execution_count": 265,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AV1IpLUs8T6e"
      },
      "source": [
        "rest = rest.to(device)\r\n",
        "loss_fn  = nn.CrossEntropyLoss()\r\n",
        "optimizer = torch.optim.SGD(rest.parameters(),lr=0.001)"
      ],
      "execution_count": 266,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "d987ff5fc786442a800d56b14d16a0ee",
            "6b6069efe39d4d178360559351a8b404",
            "e328b11155c1440e906b1bac32e179e3",
            "3be5a0a2b20e4b35a2ae6a52caa02736",
            "d933d39bd1fc47a79f29ee4f5684d279",
            "b2560185fd0741a0a098bc82abebd37e",
            "5c9f5a41572a4a168791a424616deb6a",
            "7f647a546464480b9d0f288070f24e9d"
          ]
        },
        "id": "M4rx8Bby888T",
        "outputId": "87836701-b460-4491-9d1e-897d88de7d40"
      },
      "source": [
        "from tqdm.notebook import tqdm_notebook \r\n",
        "fit(rest,dataloader,optimizer,loss_fn,10)"
      ],
      "execution_count": 267,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d987ff5fc786442a800d56b14d16a0ee",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=10.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "For epoch [0/10]: Training error after 0 iterations is 0.6996397376060486\n",
            "Min loss 0.70\n",
            "For epoch [0/10]: Training error after 200 iterations is 0.5643513202667236\n",
            "Min loss 0.56\n",
            "For epoch [0/10]: Training error after 400 iterations is 0.4845259189605713\n",
            "Min loss 0.48\n",
            "For epoch [0/10]: Training error after 600 iterations is 0.4219347834587097\n",
            "Min loss 0.42\n",
            "Loss after 0 epochs is 0.40862390398979187\n",
            "The training accuracy is, 87.508\n",
            "For epoch [1/10]: Training error after 0 iterations is 0.4497230350971222\n",
            "For epoch [1/10]: Training error after 200 iterations is 0.38065439462661743\n",
            "Min loss 0.38\n",
            "For epoch [1/10]: Training error after 400 iterations is 0.39391574263572693\n",
            "For epoch [1/10]: Training error after 600 iterations is 0.37802988290786743\n",
            "Min loss 0.38\n",
            "Loss after 1 epochs is 0.35373881459236145\n",
            "The training accuracy is, 89.084\n",
            "For epoch [2/10]: Training error after 0 iterations is 0.4066348969936371\n",
            "For epoch [2/10]: Training error after 200 iterations is 0.4375087022781372\n",
            "For epoch [2/10]: Training error after 400 iterations is 0.4435160458087921\n",
            "For epoch [2/10]: Training error after 600 iterations is 0.4666678011417389\n",
            "Loss after 2 epochs is 0.41124340891838074\n",
            "The training accuracy is, 89.98\n",
            "For epoch [3/10]: Training error after 0 iterations is 0.4193247854709625\n",
            "For epoch [3/10]: Training error after 200 iterations is 0.45813843607902527\n",
            "For epoch [3/10]: Training error after 400 iterations is 0.4108307659626007\n",
            "For epoch [3/10]: Training error after 600 iterations is 0.47958528995513916\n",
            "Loss after 3 epochs is 0.4017045497894287\n",
            "The training accuracy is, 90.504\n",
            "For epoch [4/10]: Training error after 0 iterations is 0.4096377193927765\n",
            "For epoch [4/10]: Training error after 200 iterations is 0.43293073773384094\n",
            "For epoch [4/10]: Training error after 400 iterations is 0.4281289279460907\n",
            "For epoch [4/10]: Training error after 600 iterations is 0.44562864303588867\n",
            "Loss after 4 epochs is 0.5965357422828674\n",
            "The training accuracy is, 91.068\n",
            "For epoch [5/10]: Training error after 0 iterations is 0.44787371158599854\n",
            "For epoch [5/10]: Training error after 200 iterations is 0.34857678413391113\n",
            "Min loss 0.35\n",
            "For epoch [5/10]: Training error after 400 iterations is 0.38762417435646057\n",
            "For epoch [5/10]: Training error after 600 iterations is 0.35490480065345764\n",
            "Loss after 5 epochs is 0.3189864754676819\n",
            "The training accuracy is, 91.156\n",
            "For epoch [6/10]: Training error after 0 iterations is 0.3682222068309784\n",
            "For epoch [6/10]: Training error after 200 iterations is 0.3953751027584076\n",
            "For epoch [6/10]: Training error after 400 iterations is 0.4666171073913574\n",
            "For epoch [6/10]: Training error after 600 iterations is 0.42591845989227295\n",
            "Loss after 6 epochs is 0.3728165328502655\n",
            "The training accuracy is, 91.728\n",
            "For epoch [7/10]: Training error after 0 iterations is 0.44153234362602234\n",
            "For epoch [7/10]: Training error after 200 iterations is 0.3944028913974762\n",
            "For epoch [7/10]: Training error after 400 iterations is 0.464180052280426\n",
            "For epoch [7/10]: Training error after 600 iterations is 0.3654495179653168\n",
            "Loss after 7 epochs is 0.45344865322113037\n",
            "The training accuracy is, 91.744\n",
            "For epoch [8/10]: Training error after 0 iterations is 0.38996484875679016\n",
            "For epoch [8/10]: Training error after 200 iterations is 0.3635389506816864\n",
            "For epoch [8/10]: Training error after 400 iterations is 0.3602496087551117\n",
            "For epoch [8/10]: Training error after 600 iterations is 0.4211636781692505\n",
            "Loss after 8 epochs is 0.3189111649990082\n",
            "The training accuracy is, 91.876\n",
            "For epoch [9/10]: Training error after 0 iterations is 0.3184356391429901\n",
            "Min loss 0.32\n",
            "For epoch [9/10]: Training error after 200 iterations is 0.4203285574913025\n",
            "For epoch [9/10]: Training error after 400 iterations is 0.3702727258205414\n",
            "For epoch [9/10]: Training error after 600 iterations is 0.33207541704177856\n",
            "Loss after 9 epochs is 0.4562038779258728\n",
            "The training accuracy is, 92.192\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fdxSOlM7ChgD",
        "outputId": "96266533-cecd-48ee-96fa-1b75ee429edc"
      },
      "source": [
        "!unzip /content/test1.zip"
      ],
      "execution_count": 268,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7zhfnte0NHds"
      },
      "source": [
        "%mkdir images_test\r\n",
        "%mv test1 images_test"
      ],
      "execution_count": 269,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ykermKF8eDc5"
      },
      "source": [
        "filename_pth = 'ckpt_densenet121_catdog.pth'\r\n",
        "torch.save(rest.state_dict(), filename_pth)"
      ],
      "execution_count": 270,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w21ZUe3AeYf5"
      },
      "source": [
        "test_dir = 'images_test/test1'\r\n",
        "test_files = os.listdir(test_dir)\r\n",
        "test_transform = transform.Compose([\r\n",
        "    transform.RandomResizedCrop(224),\r\n",
        "    transform.ToTensor()\r\n",
        "])"
      ],
      "execution_count": 274,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dihT3f39ekDX"
      },
      "source": [
        "testset = CatDogDataset(test_files,test_dir,mode ='test',transform=test_transform)"
      ],
      "execution_count": 286,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fXdD8FwIfDV-"
      },
      "source": [
        "test_loader = torch.utils.data.DataLoader(testset,batch_size=32,shuffle=False)"
      ],
      "execution_count": 287,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iUSBGErGfNCH"
      },
      "source": [
        "rest.eval()\r\n",
        "fn_list,pred_list = [], []\r\n",
        "for x,fn in test_loader:\r\n",
        "  with torch.no_grad():\r\n",
        "    x = x.to(device)\r\n",
        "    out = rest.forward(x)\r\n",
        "    pred = torch.argmax(out.data,1)\r\n",
        "    fn_list += [n[:-4] for n in fn]\r\n",
        "    pred_list += [p.item() for p in pred]"
      ],
      "execution_count": 289,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ivaLAbn-gF3r"
      },
      "source": [
        "import pandas as pd\r\n",
        "submission = pd.DataFrame({'id':fn_list,'label':pred_list})\r\n"
      ],
      "execution_count": 295,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NT9017EZgHVO"
      },
      "source": [
        "submission.to_csv('submission.csv',index=False)"
      ],
      "execution_count": 301,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4L500iEqibCN"
      },
      "source": [
        "/cat\r\n"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}