project_base_case.ipynb

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        "id": "KSOdQvv5xD1d"
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      "source": [
        "import torch\n",
        "import torchvision\n",
        "import torchvision.transforms as transforms\n",
        "import pickle\n",
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn import preprocessing\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.svm import SVC\n",
        "from tqdm.notebook import tqdm\n",
        "from sklearn.metrics import f1_score\n",
        "from PIL import Image\n",
        "import matplotlib.pyplot as plt\n",
        "import cv2\n",
        "from sklearn.cluster import KMeans\n",
        "import scipy.ndimage as nd\n",
        "from sklearn.model_selection import RandomizedSearchCV\n",
        "import pandas as pd\n",
        "from sklearn.metrics import accuracy_score\n",
        "from skimage.feature import local_binary_pattern\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras import layers, models\n",
        "from sklearn.preprocessing import Normalizer\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.decomposition import IncrementalPCA"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from torch.utils.data import DataLoader, random_split\n",
        "from torchvision import transforms\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import f1_score\n",
        "from torch.utils.data import Dataset, DataLoader"
      ],
      "metadata": {
        "id": "klxeVTNKw3Pu"
      },
      "execution_count": null,
      "outputs": []
    },
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      "cell_type": "code",
      "source": [
        "\n",
        "def get_cifar10_dataloaders(val_percentage=0.1, batch_size=20):\n",
        "\n",
        "  transform = transforms.Compose(\n",
        "    [transforms.ToTensor(),\n",
        "     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])\n",
        "\n",
        "  dataset = torchvision.datasets.CIFAR10(root='./data', train=True,download=True, transform=transform)\n",
        "\n",
        "  dataset_test = torchvision.datasets.CIFAR10(root='./data', train=False,download=True, transform=transform)\n",
        "\n",
        "  len_train = int(len(dataset) * (1.-val_percentage))\n",
        "  len_val = len(dataset) - len_train\n",
        "\n",
        "  dataset_train, dataset_val = random_split(dataset, [len_train, len_val])\n",
        "\n",
        "  data_loader_train = torch.utils.data.DataLoader(dataset_train, batch_size=batch_size,shuffle=True, num_workers=2)\n",
        "\n",
        "  data_loader_val   = DataLoader(dataset_val, batch_size=batch_size,shuffle=True,num_workers=2)\n",
        "\n",
        "  data_loader_test  = DataLoader(dataset_test, batch_size=batch_size,shuffle=True,num_workers=2)\n",
        "\n",
        "  return data_loader_train, data_loader_val, data_loader_test\n",
        "\n",
        "\n",
        "_ = get_cifar10_dataloaders()"
      ],
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        {
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          "text": [
            "Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz\n"
          ]
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              "  0%|          | 0/170498071 [00:00<?, ?it/s]"
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            "Extracting ./data/cifar-10-python.tar.gz to ./data\n",
            "Files already downloaded and verified\n"
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    },
    {
      "cell_type": "code",
      "source": [
        "classes = ('plane', 'car', 'bird', 'cat',\n",
        "           'deer', 'dog', 'frog', 'horse', 'ship', 'truck')"
      ],
      "metadata": {
        "id": "Pzk7VQDoOGkl"
      },
      "execution_count": null,
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    {
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        "classes[1]"
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        "id": "vuLM9nftPHfl",
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      "execution_count": null,
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          "data": {
            "text/plain": [
              "'car'"
            ],
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              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
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      "cell_type": "code",
      "source": [
        "def im_convert(tensor):\n",
        "  image = tensor.cpu().clone().detach().numpy()\n",
        "  image = image / 2 + 0.5     # unnormalize\n",
        "  image = image.transpose(1, 2, 0)\n",
        "  # print(image.shape)\n",
        "  # image = image * np.array((0.5, 0.5, 0.5)) + np.array((0.5, 0.5, 0.5))\n",
        "  # image = image.clip(0, 1)\n",
        "  return image\n",
        "\n",
        "a,_,_=get_cifar10_dataloaders( batch_size=20)\n",
        "dataiter = iter(a)\n",
        "images, labels = dataiter.next()\n",
        "fig = plt.figure(figsize=(25, 4))\n",
        "\n",
        "for idx in np.arange(20):\n",
        "  ax = fig.add_subplot(2, 10, idx+1, xticks=[], yticks=[])\n",
        "\n",
        "  plt.imshow(im_convert(images[idx]))\n",
        "  ax.set_title(classes[labels[idx].item()])"
      ],
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      "execution_count": null,
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          "text": [
            "Files already downloaded and verified\n",
            "Files already downloaded and verified\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x288 with 20 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#completed upto here"
      ],
      "metadata": {
        "id": "Iu0b6NVFyEmp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "\n",
        "\n",
        "class Net(nn.Module):\n",
        "    def __init__(self):\n",
        "        super().__init__()\n",
        "        self.conv1 = nn.Conv2d(3, 6, 5)\n",
        "        self.pool = nn.MaxPool2d(2, 2)\n",
        "        self.conv2 = nn.Conv2d(6, 16, 5)\n",
        "        self.fc1 = nn.Linear(16 * 5 * 5, 120)\n",
        "        self.fc2 = nn.Linear(120, 84)\n",
        "        self.fc3 = nn.Linear(84, 10)\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 = torch.flatten(x, 1) \n",
        "        x = F.relu(self.fc1(x))\n",
        "        x = F.relu(self.fc2(x))\n",
        "        x = self.fc3(x)\n",
        "        return x\n",
        "\n",
        "\n",
        "net = Net()"
      ],
      "metadata": {
        "id": "PE39t0qQxtBE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n",
        "\n",
        "# Assuming that we are on a CUDA machine, this should print a CUDA device:\n",
        "\n",
        "print(device)\n",
        "\n",
        "net.to(device)\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ghx1uAr50sUs",
        "outputId": "247c1202-990c-405e-dea5-c184212a4992"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "cuda:0\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Net(\n",
              "  (conv1): Conv2d(3, 6, kernel_size=(5, 5), stride=(1, 1))\n",
              "  (pool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "  (conv2): Conv2d(6, 16, kernel_size=(5, 5), stride=(1, 1))\n",
              "  (fc1): Linear(in_features=400, out_features=120, bias=True)\n",
              "  (fc2): Linear(in_features=120, out_features=84, bias=True)\n",
              "  (fc3): Linear(in_features=84, out_features=10, bias=True)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch.optim as optim\n",
        "\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)\n"
      ],
      "metadata": {
        "id": "FyNOJ9jSx3D7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_loader_train, data_loader_val, data_loader_test=get_cifar10_dataloaders()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zyEvVDLBOR0u",
        "outputId": "061e0f04-898e-42ab-c7a3-0f7993e87916"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Files already downloaded and verified\n",
            "Files already downloaded and verified\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "for epoch in range(10):  # loop over the dataset multiple times\n",
        "\n",
        "    running_loss = 0.0\n",
        "    for i, data in enumerate(data_loader_train, 0):\n",
        "        # get the inputs; data is a list of [inputs, labels]\n",
        "        inputs, labels = data\n",
        "        inputs=inputs.to(device)\n",
        "        labels=labels.to(device)\n",
        "\n",
        "        # zero the parameter gradients\n",
        "        optimizer.zero_grad()\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",
        "        if i % 2000 == 1999:    # print every 2000 mini-batches\n",
        "            print(f'[{epoch + 1}, {i + 1:5d}] loss: {running_loss / 2000:.3f}')\n",
        "            running_loss = 0.0\n",
        "\n",
        "print('Finished Training')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3JmyYnzNx-9Q",
        "outputId": "af6d928a-b01c-46ff-9f6e-c28947163609"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1,  2000] loss: 2.200\n",
            "[2,  2000] loss: 1.729\n",
            "[3,  2000] loss: 1.487\n",
            "[4,  2000] loss: 1.359\n",
            "[5,  2000] loss: 1.270\n",
            "[6,  2000] loss: 1.195\n",
            "[7,  2000] loss: 1.136\n",
            "[8,  2000] loss: 1.082\n",
            "[9,  2000] loss: 1.035\n",
            "[10,  2000] loss: 0.993\n",
            "Finished Training\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "correct = 0\n",
        "total = 0\n",
        "# since we're not training, we don't need to calculate the gradients for our outputs\n",
        "predictions = []\n",
        "y_true=[]\n",
        "with torch.no_grad():\n",
        "    for data in data_loader_test:\n",
        "        images, labels = data\n",
        "        images=images.to(device)\n",
        "        labels=labels.to(device)\n",
        "        # calculate outputs by running images through the network\n",
        "        outputs = net(images)\n",
        "        # the class with the highest energy is what we choose as prediction\n",
        "        _, predicted = torch.max(outputs.data, 1)\n",
        "        total += labels.size(0)\n",
        "        correct += (predicted == labels).sum().item()\n",
        "        predictions.extend(list(predicted.view(-1).cpu().numpy()))\n",
        "        y_true.extend(list(labels.view(-1).cpu().numpy()))\n",
        "\n",
        "# y_true = dataset_test.labels[data_loader_test.indices]\n",
        "print(\"f1 score on 1000 images after 10 epochs: \",f1_score(predictions,y_true, average='micro'))\n",
        "print(f'Accuracy of the network on the 10000 test images: {100 * correct // total} %')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YCeJnrDuytwx",
        "outputId": "3187783b-2b90-44ba-bf89-70a315d5aa66"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "f1 score on 1000 images after 10 epochs:  0.6201\n",
            "Accuracy of the network on the 10000 test images: 62 %\n"
          ]
        }
      ]
    }
  ]
}