RESNET18_CIFAR10 .ipynb

{
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  "metadata": {
    "colab": {
      "name": "RESNET18_CIFAR10.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "uy5r25TZ9Jts",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oD8HAQHX9Rzc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import torch\n",
        "import torchvision\n",
        "import torch.optim as optim\n",
        "import torch.nn as nn\n",
        "from torchvision import models"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w2U_TrtQAFK2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import torchvision.transforms as transforms"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "byFXy6Ma96gU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7oW5hISt_h2l",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M_Hpo06c_DLf",
        "colab_type": "code",
        "outputId": "c518d67b-774a-4e70-fd87-16e6ef741cc8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(device)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "cuda:0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "awc6dzUf_EDm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E5G4TFYB_cSf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a2prrySIDQRJ",
        "colab_type": "text"
      },
      "source": [
        "<h3>Loading Dataset and Applying Transforms </h3>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6fUCQ52p_cZP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "transforms_train = transforms.Compose([\n",
        "        transforms.RandomResizedCrop(224),\n",
        "        transforms.ToTensor(),\n",
        "        transforms.Normalize((0.5,0.5,0.5),(0.5,0.5,0.5))\n",
        "\n",
        "])\n",
        "\n",
        "transforms_test = transforms.Compose([\n",
        "        transforms.RandomResizedCrop(224),\n",
        "        transforms.ToTensor(),\n",
        "        transforms.Normalize((0.5,0.5,0.5),(0.5,0.5,0.5))\n",
        "\n",
        "])\n",
        "\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J6GcGiF7ASzP",
        "colab_type": "text"
      },
      "source": [
        "<b>Note : </b>transforms.Normalize does the following for each of the 3 channels: image = (image - mean) / std The parameters mean, std are passed as 0.5, 0.5 in your case. This will normalize the image in the range [-1,1]. For example, the minimum value 0 will be converted to (0-0.5)/0.5=-1, the maximum value of 1 will be converted to 1\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "afladPNIAT7V",
        "colab_type": "text"
      },
      "source": [
        "<b> Steps in Image Preprocessing </b>\n",
        "\n",
        "<ul>\n",
        "  <li>Uniform aspect ratio : Most of the neural network models assume a square shape input image, which means that each image needs to be checked if it is a square or not, and cropped appropriately </li>\n",
        "  <li> Image scaling </li>\n",
        "  <li> Computing mean, std dev and normalizing inputs </li>\n",
        "  <li> Data augmentation </li>\n",
        "\n",
        "</ul>\n",
        "\n",
        "https://becominghuman.ai/image-data-pre-processing-for-neural-networks-498289068258"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1VEH0bMJ_cQr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PO6DDdoT_cN6",
        "colab_type": "code",
        "outputId": "e6fb4786-2d9a-43bb-d810-00f8146f22b5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        }
      },
      "source": [
        "trainset = torchvision.datasets.CIFAR10(root = './data', train = True, download = True, transform = transforms_train)\n",
        "testset = torchvision.datasets.CIFAR10(root = './data', train = False, download = True, transform = transforms_train)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\r0it [00:00, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|█████████▉| 170074112/170498071 [00:14<00:00, 14707928.74it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Extracting ./data/cifar-10-python.tar.gz to ./data\n",
            "Files already downloaded and verified\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6DYz53UQAr6R",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "num_classes = 10"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qfOGoPFyAyTK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "batch_size = 4"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eKPc5zhCAzmc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=True)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vz7XSymqA7xl",
        "colab_type": "code",
        "outputId": "6dedb9d1-eba9-4836-dcc2-17124350036f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        }
      },
      "source": [
        "dataiter = iter(trainloader)\n",
        "\n",
        "images, labels = dataiter.next()\n",
        "\n",
        "print(images.shape)\n",
        "\n",
        "print(images[1].shape)\n",
        "print(labels[1].item())\n"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "torch.Size([4, 3, 224, 224])\n",
            "torch.Size([3, 224, 224])\n",
            "6\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1ksUwHa9BD3Y",
        "colab_type": "text"
      },
      "source": [
        "Note: The original size of CIFAR image is 32 x 32, but using the RandomResizedCrop function we changed it to 224 x 224"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rYyoc7EpA-4W",
        "colab_type": "code",
        "outputId": "cd1d2912-8ce2-4cac-83f2-11ab6a3a3f18",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "len(trainloader)\n"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "12500"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RmAtjhc4BGHE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def imshow(img, title):\n",
        "    # Unnormalize the image, we multiply by 0.5 that is the std deviation, and add 0.5 which is the mean\n",
        "    npimg = img.numpy() / 2 + 0.5\n",
        "    plt.figure(figsize=(batch_size, 1))\n",
        "    plt.axis('off')\n",
        "    #Convert [3,224,224] to [224,224,3]\n",
        "    plt.imshow(np.transpose(npimg, (1, 2, 0)))\n",
        "    plt.title(title)\n",
        "    plt.show()\n",
        "\n",
        "    "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JnCB7LG9BIFA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def show_batch_images(dataloader):\n",
        "    images, labels = next(iter(dataloader))\n",
        "    img = torchvision.utils.make_grid(images)\n",
        "    imshow(img, title=[str(x.item()) for x in labels])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zPrf3cByBKGY",
        "colab_type": "code",
        "outputId": "b9e7b394-e348-4b3e-a75a-9382273cdee4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 353
        }
      },
      "source": [
        "for i in range(4):\n",
        "    show_batch_images(trainloader)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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TOBj4fdiQDDYE9K5hVEWq5WJu45jPnz1bJhuVlo50nlBNlnztC75J+sMYuN6P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\nudNOb5B+B30BKn4lwj9jAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 288x72 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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9WEntFrltqwEQxsHO2wveB2KeaaXYljMlJUo2i6m5oDlbqLMkSAlNGUkFckbWjJSKqxWp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\nRK5CYII=\n",
            "text/plain": [
              "<Figure size 288x72 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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D+8ORm9s9z19e89mLl3z2/CXPX7wkpEIunQ01jsvHT3jy+AnH5zccdzeoVtb5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\nrm+B+W19W/+E6/8CWI4SVfSbSFwAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 288x72 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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2BapVT5NNS9Mx5NgEZ4qJ27sj43gELWyGHh16eufAdeRSOE6J28PMFE0YRTOO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\n+MZT6AAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 288x72 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z8ARxyK_BMLR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m5d2tbWEBRjc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L1zuMEhsDbBZ",
        "colab_type": "text"
      },
      "source": [
        "<h3> Using Pretrained Resnet and Transfer Learning </h3>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D5xuMwXhBRWF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "resnet = models.resnet18()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uOoJzAbmBRJC",
        "colab_type": "code",
        "outputId": "f17119cd-fc44-4443-87a1-d25829f84bf7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "print(resnet)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "ResNet(\n",
            "  (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
            "  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "  (relu): ReLU(inplace=True)\n",
            "  (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
            "  (layer1): Sequential(\n",
            "    (0): BasicBlock(\n",
            "      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (relu): ReLU(inplace=True)\n",
            "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (1): BasicBlock(\n",
            "      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (relu): ReLU(inplace=True)\n",
            "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "  )\n",
            "  (layer2): Sequential(\n",
            "    (0): BasicBlock(\n",
            "      (conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
            "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (relu): ReLU(inplace=True)\n",
            "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (downsample): Sequential(\n",
            "        (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
            "        (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      )\n",
            "    )\n",
            "    (1): BasicBlock(\n",
            "      (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (relu): ReLU(inplace=True)\n",
            "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "  )\n",
            "  (layer3): Sequential(\n",
            "    (0): BasicBlock(\n",
            "      (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
            "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (relu): ReLU(inplace=True)\n",
            "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (downsample): Sequential(\n",
            "        (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
            "        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      )\n",
            "    )\n",
            "    (1): BasicBlock(\n",
            "      (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (relu): ReLU(inplace=True)\n",
            "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "  )\n",
            "  (layer4): Sequential(\n",
            "    (0): BasicBlock(\n",
            "      (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
            "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (relu): ReLU(inplace=True)\n",
            "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (downsample): Sequential(\n",
            "        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
            "        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      )\n",
            "    )\n",
            "    (1): BasicBlock(\n",
            "      (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (relu): ReLU(inplace=True)\n",
            "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "  )\n",
            "  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n",
            "  (fc): Linear(in_features=512, out_features=1000, bias=True)\n",
            ")\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iEYeNJxfDmXQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jiuRDN9OBg7O",
        "colab_type": "code",
        "outputId": "fe35f332-534b-47f5-df0b-b475fa4ab9f5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 154
        }
      },
      "source": [
        "resnet = models.resnet18(pretrained= True)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading: \"https://download.pytorch.org/models/resnet18-5c106cde.pth\" to /root/.cache/torch/checkpoints/resnet18-5c106cde.pth\n",
            "\n",
            "  0%|          | 0.00/44.7M [00:00<?, ?B/s]\u001b[A\n",
            " 27%|██▋       | 11.9M/44.7M [00:00<00:00, 125MB/s]\u001b[A\n",
            " 53%|█████▎    | 23.8M/44.7M [00:00<00:00, 125MB/s]\u001b[A\n",
            " 79%|███████▉  | 35.4M/44.7M [00:00<00:00, 124MB/s]\u001b[A\n",
            "100%|██████████| 44.7M/44.7M [00:00<00:00, 123MB/s]\u001b[A"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BUKp4RwcB8uT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for param in resnet.parameters():\n",
        "  param.requires_grad= False\n",
        "\n",
        "# we have frozen the parameters by making gradients 0\n",
        "# because weights are already pretrained on imagenet dataset\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sbjBDgkGCRLD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "in_features = resnet.fc.in_features\n",
        "resnet.fc = nn.Linear(in_features, num_classes) # modifying last layer"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K5YAFES9CuOc",
        "colab_type": "code",
        "outputId": "2ce393cf-2ae4-474d-9546-148275550031",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "for param in resnet.parameters():\n",
        "  if param.requires_grad:\n",
        "    print(param.shape)# only last layer should be printed as gradients has been set to zero for all other layers"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "torch.Size([10, 512])\n",
            "torch.Size([10])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QQDnXka8C235",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "resnet = resnet.to(device)\n",
        "loss_fn = nn.CrossEntropyLoss()\n",
        "opt = optim.SGD(resnet.parameters(), lr=0.01)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oC5XCqPgEIyI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def evaluation(dataloader, model):\n",
        "    total, correct = 0, 0\n",
        "    for data in dataloader:\n",
        "        inputs, labels = data\n",
        "        inputs, labels = inputs.to(device), labels.to(device)\n",
        "        outputs = model(inputs)\n",
        "        _, pred = torch.max(outputs.data, 1)\n",
        "        total += labels.size(0)\n",
        "        correct += (pred == labels).sum().item()\n",
        "    return 100 * correct / total\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ARGS_lzvEJOk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size, shuffle=False)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bK_fYyrKDBf5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import copy # To store best intermediate models"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lQixd4UtC8w7",
        "colab_type": "code",
        "outputId": "f13eedc1-bb21-40b5-fc7c-39dcfce874e4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "loss_epoch_arr = []\n",
        "max_epochs = 4\n",
        "\n",
        "min_loss = 1000\n",
        "\n",
        "n_iters = np.ceil(50000/batch_size)\n",
        "\n",
        "for epoch in range(max_epochs):\n",
        "\n",
        "    for i, data in enumerate(trainloader, 0):\n",
        "\n",
        "        inputs, labels = data\n",
        "        inputs, labels = inputs.to(device), labels.to(device)\n",
        "\n",
        "        opt.zero_grad()\n",
        "\n",
        "        outputs = resnet(inputs)\n",
        "        loss = loss_fn(outputs, labels)\n",
        "        loss.backward()\n",
        "        opt.step()\n",
        "        \n",
        "        if min_loss > loss.item():\n",
        "            min_loss = loss.item()\n",
        "            best_model = copy.deepcopy(resnet.state_dict())\n",
        "            print('Min loss %0.2f' % min_loss)\n",
        "        \n",
        "        if i % 100 == 0:\n",
        "            print('Iteration: %d/%d, Loss: %0.2f' % (i, n_iters, loss.item()))\n",
        "            \n",
        "        del inputs, labels, outputs\n",
        "        torch.cuda.empty_cache()\n",
        "        \n",
        "    loss_epoch_arr.append(loss.item())\n",
        "        \n",
        "    print('Epoch: %d/%d, Test acc: %0.2f, Train acc: %0.2f' % (\n",
        "        epoch, max_epochs, \n",
        "        evaluation(testloader, resnet), evaluation(trainloader, resnet)))\n",
        "    \n",
        "    \n",
        "plt.plot(loss_epoch_arr)\n",
        "plt.show()"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Min loss 3.49\n",
            "Iteration: 0/12500, Loss: 3.49\n",
            "Min loss 0.80\n",
            "Min loss 0.78\n",
            "Min loss 0.45\n",
            "Min loss 0.31\n",
            "Iteration: 100/12500, Loss: 1.69\n",
            "Min loss 0.23\n",
            "Min loss 0.19\n",
            "Iteration: 200/12500, Loss: 1.34\n",
            "Iteration: 300/12500, Loss: 2.11\n",
            "Iteration: 400/12500, Loss: 1.12\n",
            "Iteration: 500/12500, Loss: 1.70\n",
            "Iteration: 600/12500, Loss: 2.54\n",
            "Iteration: 700/12500, Loss: 1.50\n",
            "Iteration: 800/12500, Loss: 1.82\n",
            "Iteration: 900/12500, Loss: 0.46\n",
            "Iteration: 1000/12500, Loss: 2.55\n",
            "Iteration: 1100/12500, Loss: 2.09\n",
            "Min loss 0.16\n",
            "Iteration: 1200/12500, Loss: 2.69\n",
            "Iteration: 1300/12500, Loss: 2.59\n",
            "Iteration: 1400/12500, Loss: 1.74\n",
            "Iteration: 1500/12500, Loss: 1.12\n",
            "Iteration: 1600/12500, Loss: 1.51\n",
            "Iteration: 1700/12500, Loss: 2.11\n",
            "Iteration: 1800/12500, Loss: 2.25\n",
            "Iteration: 1900/12500, Loss: 3.41\n",
            "Iteration: 2000/12500, Loss: 1.87\n",
            "Iteration: 2100/12500, Loss: 1.34\n",
            "Iteration: 2200/12500, Loss: 0.98\n",
            "Iteration: 2300/12500, Loss: 1.81\n",
            "Iteration: 2400/12500, Loss: 1.78\n",
            "Iteration: 2500/12500, Loss: 1.64\n",
            "Min loss 0.07\n",
            "Iteration: 2600/12500, Loss: 1.24\n",
            "Iteration: 2700/12500, Loss: 1.99\n",
            "Iteration: 2800/12500, Loss: 1.54\n",
            "Iteration: 2900/12500, Loss: 0.76\n",
            "Iteration: 3000/12500, Loss: 1.23\n",
            "Iteration: 3100/12500, Loss: 1.27\n",
            "Iteration: 3200/12500, Loss: 1.89\n",
            "Iteration: 3300/12500, Loss: 0.97\n",
            "Iteration: 3400/12500, Loss: 3.68\n",
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            "Iteration: 3900/12500, Loss: 1.26\n",
            "Iteration: 4000/12500, Loss: 0.45\n",
            "Iteration: 4100/12500, Loss: 1.63\n",
            "Iteration: 4200/12500, Loss: 0.93\n",
            "Iteration: 4300/12500, Loss: 1.49\n",
            "Iteration: 4400/12500, Loss: 2.88\n",
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            "Iteration: 4600/12500, Loss: 0.93\n",
            "Iteration: 4700/12500, Loss: 1.48\n",
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            "Iteration: 4900/12500, Loss: 0.92\n",
            "Iteration: 5000/12500, Loss: 1.93\n",
            "Iteration: 5100/12500, Loss: 1.09\n",
            "Iteration: 5200/12500, Loss: 1.53\n",
            "Iteration: 5300/12500, Loss: 2.30\n",
            "Iteration: 5400/12500, Loss: 1.53\n",
            "Iteration: 5500/12500, Loss: 1.51\n",
            "Iteration: 5600/12500, Loss: 3.56\n",
            "Iteration: 5700/12500, Loss: 2.31\n",
            "Iteration: 5800/12500, Loss: 2.25\n",
            "Iteration: 5900/12500, Loss: 2.95\n",
            "Iteration: 6000/12500, Loss: 1.12\n",
            "Iteration: 6100/12500, Loss: 1.04\n",
            "Iteration: 6200/12500, Loss: 1.81\n",
            "Iteration: 6300/12500, Loss: 2.00\n",
            "Iteration: 6400/12500, Loss: 2.11\n",
            "Iteration: 6500/12500, Loss: 1.45\n",
            "Iteration: 6600/12500, Loss: 0.69\n",
            "Iteration: 6700/12500, Loss: 2.17\n",
            "Iteration: 6800/12500, Loss: 1.85\n",
            "Iteration: 6900/12500, Loss: 1.45\n",
            "Iteration: 7000/12500, Loss: 3.27\n",
            "Iteration: 7100/12500, Loss: 1.73\n",
            "Iteration: 7200/12500, Loss: 0.94\n",
            "Iteration: 7300/12500, Loss: 2.88\n",
            "Iteration: 7400/12500, Loss: 2.65\n",
            "Iteration: 7500/12500, Loss: 1.82\n",
            "Iteration: 7600/12500, Loss: 1.97\n",
            "Iteration: 7700/12500, Loss: 1.12\n",
            "Iteration: 7800/12500, Loss: 2.38\n",
            "Iteration: 7900/12500, Loss: 0.21\n",
            "Iteration: 8000/12500, Loss: 2.07\n",
            "Iteration: 8100/12500, Loss: 1.18\n",
            "Iteration: 8200/12500, Loss: 1.45\n",
            "Iteration: 8300/12500, Loss: 3.35\n",
            "Iteration: 8400/12500, Loss: 3.84\n",
            "Iteration: 8500/12500, Loss: 2.27\n",
            "Iteration: 8600/12500, Loss: 2.35\n",
            "Iteration: 8700/12500, Loss: 2.31\n",
            "Iteration: 8800/12500, Loss: 2.13\n",
            "Iteration: 8900/12500, Loss: 2.86\n",
            "Iteration: 9000/12500, Loss: 2.24\n",
            "Iteration: 9100/12500, Loss: 2.11\n",
            "Iteration: 9200/12500, Loss: 2.48\n",
            "Iteration: 9300/12500, Loss: 1.58\n",
            "Iteration: 9400/12500, Loss: 2.49\n",
            "Iteration: 9500/12500, Loss: 2.40\n",
            "Iteration: 9600/12500, Loss: 2.36\n",
            "Iteration: 9700/12500, Loss: 2.02\n",
            "Iteration: 9800/12500, Loss: 1.35\n",
            "Iteration: 9900/12500, Loss: 2.06\n",
            "Iteration: 10000/12500, Loss: 2.10\n",
            "Iteration: 10100/12500, Loss: 2.11\n",
            "Iteration: 10200/12500, Loss: 1.35\n",
            "Iteration: 10300/12500, Loss: 1.79\n",
            "Iteration: 10400/12500, Loss: 2.24\n",
            "Iteration: 10500/12500, Loss: 0.46\n",
            "Iteration: 10600/12500, Loss: 2.60\n",
            "Iteration: 10700/12500, Loss: 0.83\n",
            "Iteration: 10800/12500, Loss: 2.96\n",
            "Iteration: 10900/12500, Loss: 2.31\n",
            "Iteration: 11000/12500, Loss: 0.94\n",
            "Iteration: 11100/12500, Loss: 1.65\n",
            "Iteration: 11200/12500, Loss: 2.33\n",
            "Iteration: 11300/12500, Loss: 0.88\n",
            "Iteration: 11400/12500, Loss: 1.77\n",
            "Iteration: 11500/12500, Loss: 1.68\n",
            "Iteration: 11600/12500, Loss: 2.66\n",
            "Iteration: 11700/12500, Loss: 2.21\n",
            "Iteration: 11800/12500, Loss: 1.08\n",
            "Iteration: 11900/12500, Loss: 1.52\n",
            "Iteration: 12000/12500, Loss: 2.22\n",
            "Iteration: 12100/12500, Loss: 1.33\n",
            "Iteration: 12200/12500, Loss: 0.89\n",
            "Iteration: 12300/12500, Loss: 1.50\n",
            "Iteration: 12400/12500, Loss: 1.54\n",
            "Epoch: 0/4, Test acc: 44.68, Train acc: 45.89\n",
            "Iteration: 0/12500, Loss: 0.85\n",
            "Iteration: 100/12500, Loss: 0.86\n",
            "Iteration: 200/12500, Loss: 1.48\n",
            "Iteration: 300/12500, Loss: 1.08\n",
            "Iteration: 400/12500, Loss: 2.28\n",
            "Iteration: 500/12500, Loss: 1.73\n",
            "Iteration: 600/12500, Loss: 2.01\n",
            "Iteration: 700/12500, Loss: 2.52\n",
            "Iteration: 800/12500, Loss: 0.63\n",
            "Iteration: 900/12500, Loss: 1.81\n",
            "Iteration: 1000/12500, Loss: 2.00\n",
            "Iteration: 1100/12500, Loss: 1.82\n",
            "Iteration: 1200/12500, Loss: 0.70\n",
            "Iteration: 1300/12500, Loss: 2.17\n",
            "Iteration: 1400/12500, Loss: 1.80\n",
            "Iteration: 1500/12500, Loss: 3.90\n",
            "Iteration: 1600/12500, Loss: 0.39\n",
            "Iteration: 1700/12500, Loss: 3.54\n",
            "Iteration: 1800/12500, Loss: 1.94\n",
            "Iteration: 1900/12500, Loss: 2.20\n",
            "Iteration: 2000/12500, Loss: 1.79\n",
            "Iteration: 2100/12500, Loss: 2.70\n",
            "Iteration: 2200/12500, Loss: 2.20\n",
            "Iteration: 2300/12500, Loss: 2.12\n",
            "Iteration: 2400/12500, Loss: 1.07\n",
            "Iteration: 2500/12500, Loss: 1.53\n",
            "Iteration: 2600/12500, Loss: 0.50\n",
            "Iteration: 2700/12500, Loss: 1.56\n",
            "Iteration: 2800/12500, Loss: 2.44\n",
            "Iteration: 2900/12500, Loss: 2.30\n",
            "Iteration: 3000/12500, Loss: 3.41\n",
            "Min loss 0.06\n",
            "Iteration: 3100/12500, Loss: 2.31\n",
            "Iteration: 3200/12500, Loss: 0.82\n",
            "Iteration: 3300/12500, Loss: 1.24\n",
            "Iteration: 3400/12500, Loss: 1.43\n",
            "Min loss 0.06\n",
            "Iteration: 3500/12500, Loss: 1.04\n",
            "Iteration: 3600/12500, Loss: 0.53\n",
            "Iteration: 3700/12500, Loss: 1.94\n",
            "Iteration: 3800/12500, Loss: 2.09\n",
            "Iteration: 3900/12500, Loss: 3.71\n",
            "Iteration: 4000/12500, Loss: 2.76\n",
            "Iteration: 4100/12500, Loss: 2.95\n",
            "Iteration: 4200/12500, Loss: 2.68\n",
            "Iteration: 4300/12500, Loss: 0.68\n",
            "Iteration: 4400/12500, Loss: 1.24\n",
            "Iteration: 4500/12500, Loss: 1.76\n",
            "Iteration: 4600/12500, Loss: 1.24\n",
            "Iteration: 4700/12500, Loss: 1.03\n",
            "Iteration: 4800/12500, Loss: 2.44\n",
            "Iteration: 4900/12500, Loss: 3.31\n",
            "Iteration: 5000/12500, Loss: 3.93\n",
            "Iteration: 5100/12500, Loss: 2.67\n",
            "Iteration: 5200/12500, Loss: 3.42\n",
            "Iteration: 5300/12500, Loss: 1.46\n",
            "Iteration: 5400/12500, Loss: 1.58\n",
            "Iteration: 5500/12500, Loss: 2.20\n",
            "Iteration: 5600/12500, Loss: 1.92\n",
            "Iteration: 5700/12500, Loss: 1.07\n",
            "Iteration: 5800/12500, Loss: 1.27\n",
            "Iteration: 5900/12500, Loss: 1.69\n",
            "Iteration: 6000/12500, Loss: 1.00\n",
            "Iteration: 6100/12500, Loss: 1.17\n",
            "Iteration: 6200/12500, Loss: 2.08\n",
            "Iteration: 6300/12500, Loss: 2.92\n",
            "Iteration: 6400/12500, Loss: 1.65\n",
            "Iteration: 6500/12500, Loss: 1.60\n",
            "Iteration: 6600/12500, Loss: 1.25\n",
            "Iteration: 6700/12500, Loss: 2.29\n",
            "Iteration: 6800/12500, Loss: 0.87\n",
            "Iteration: 6900/12500, Loss: 2.73\n",
            "Iteration: 7000/12500, Loss: 1.95\n",
            "Iteration: 7100/12500, Loss: 1.11\n",
            "Iteration: 7200/12500, Loss: 1.94\n",
            "Iteration: 7300/12500, Loss: 2.14\n",
            "Iteration: 7400/12500, Loss: 0.97\n",
            "Iteration: 7500/12500, Loss: 1.66\n",
            "Iteration: 7600/12500, Loss: 0.68\n",
            "Iteration: 7700/12500, Loss: 2.08\n",
            "Iteration: 7800/12500, Loss: 1.32\n",
            "Iteration: 7900/12500, Loss: 1.15\n",
            "Iteration: 8000/12500, Loss: 2.90\n",
            "Iteration: 8100/12500, Loss: 0.63\n",
            "Iteration: 8200/12500, Loss: 2.99\n",
            "Iteration: 8300/12500, Loss: 1.32\n",
            "Iteration: 8400/12500, Loss: 1.20\n",
            "Iteration: 8500/12500, Loss: 2.13\n",
            "Iteration: 8600/12500, Loss: 0.84\n",
            "Iteration: 8700/12500, Loss: 1.31\n",
            "Iteration: 8800/12500, Loss: 1.80\n",
            "Iteration: 8900/12500, Loss: 2.34\n",
            "Iteration: 9000/12500, Loss: 0.42\n",
            "Iteration: 9100/12500, Loss: 0.62\n",
            "Iteration: 9200/12500, Loss: 1.88\n",
            "Iteration: 9300/12500, Loss: 1.01\n",
            "Iteration: 9400/12500, Loss: 2.18\n",
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          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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FRCKEgi4iEiEUdBGRCKGgi4hECAVdRCRCKOgiIhHCnHPePLFZHrD7DH95LJAf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\nAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oWaGgD-8GjRb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T-xQEOtlDEf1",
        "colab_type": "code",
        "outputId": "937c4252-dc06-4abb-e1a6-84c4a5605674",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "resnet.load_state_dict(best_model)\n",
        "print(evaluation(trainloader, resnet), evaluation(testloader, resnet))"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "43.548 42.38\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YmKSFXSKGpU_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}