50-layer CNN for PI vs CG vs GAN generated images.ipynb

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    "colab": {
      "name": "Copy of Loss tables generator.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
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    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
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  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "gFRgOfgPR_6Z"
      },
      "source": [
        "import sys\n",
        "import os\n",
        "import torch\n",
        "import cv2 as cv\n",
        "import numpy as np\n",
        "import torchvision\n",
        "from torch import nn, optim\n",
        "import torch.nn.functional as F\n",
        "import torch.utils.data\n",
        "import torchvision.datasets as dset\n",
        "import torchvision.utils as vutils\n",
        "from torch.utils.data import DataLoader, Dataset\n",
        "from torch.autograd.variable import Variable\n",
        "from torchvision import transforms, datasets\n",
        "import matplotlib.pyplot as plt\n",
        "import random\n",
        "from torch.utils.data.sampler import SubsetRandomSampler\n",
        "from skimage.io import imread, imshow\n",
        "\n",
        "from PIL import Image"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dQCqcllvSI7T",
        "outputId": "1510bf25-abd1-43d5-ebce-2feb82a5c109"
      },
      "source": [
        "#Using GPU\n",
        "print(sys.version)\n",
        "device = 'cuda'\n",
        "#Checking for GPU\n",
        "print(torch.version.cuda)\n",
        "print(torch.cuda.device_count())\n",
        "print(torch.cuda.is_available())"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "3.7.10 (default, May  3 2021, 02:48:31) \n",
            "[GCC 7.5.0]\n",
            "10.1\n",
            "1\n",
            "True\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0mM9cPNPYQOr",
        "outputId": "c44f7226-1c2f-4fb6-9204-613f2d993517"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UYTr4R3UXtJq"
      },
      "source": [
        "def HPF(image):\n",
        "    kernel5x5 = np.array([[-1, -1, -1, -1, -1],\n",
        "    [-1, 1, 2, 1, -1],\n",
        "    [-1, 2, 4, 2, -1],\n",
        "    [-1, 1, 2, 1, -1],\n",
        "    [-1, -1, -1, -1, -1]])\n",
        "    #img = cv.filter2D(image, -1, kernel5x5)\n",
        "    img = image - cv.GaussianBlur(image, (21,21), 0)\n",
        "    #img = image - cv.medianBlur(image, 11, 0)\n",
        "    return img"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "id": "WrDAAviXXtJq",
        "outputId": "f251104e-7b1c-4b66-9b18-6b1e7c8cff76"
      },
      "source": [
        "'''\n",
        "pi_cg_list = (list(), list())\n",
        "\n",
        "num=0\n",
        "\n",
        "for j in os.listdir('./data/PI/'):\n",
        "    if not j.startswith('.') and (j.split('.')[1] == 'jpg'):\n",
        "        \n",
        "        img = cv.imread('./data/PI/' + str(j), 0)\n",
        "        img = HPF(img)\n",
        "        img = cv.merge((img, img, img))\n",
        "        #print(img.shape)\n",
        "        num+=1\n",
        "        name = 'pi_' + str(num) + '.jpg'\n",
        "        pi_cg_list[0].append(name)\n",
        "        cv.imwrite('./data_processed2/PI/' + str(name), img)\n",
        "        \n",
        "print(num)\n",
        "#print(pi_cg_list[0])\n",
        "'''"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "\"\\npi_cg_list = (list(), list())\\n\\nnum=0\\n\\nfor j in os.listdir('./data/PI/'):\\n    if not j.startswith('.') and (j.split('.')[1] == 'jpg'):\\n        \\n        img = cv.imread('./data/PI/' + str(j), 0)\\n        img = HPF(img)\\n        img = cv.merge((img, img, img))\\n        #print(img.shape)\\n        num+=1\\n        name = 'pi_' + str(num) + '.jpg'\\n        pi_cg_list[0].append(name)\\n        cv.imwrite('./data_processed2/PI/' + str(name), img)\\n        \\nprint(num)\\n#print(pi_cg_list[0])\\n\""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "id": "A9zRBzH0XtJq",
        "outputId": "60a33b4d-a5c9-4c3b-f91e-ea7f25d837d2"
      },
      "source": [
        "\n",
        "'''\n",
        "num=0\n",
        "\n",
        "for j in os.listdir('./data/CG/'):\n",
        "    if not j.startswith('.') and (j.split('.')[1] == 'jpg'):\n",
        "        \n",
        "        img = cv.imread('./data/CG/' + str(j), 0)\n",
        "        img = HPF(img)\n",
        "        img = cv.merge((img, img, img))\n",
        "        #print(img.shape)\n",
        "        num+=1\n",
        "        name = 'cg_' + str(num) + '.jpg'\n",
        "        pi_cg_list[1].append(name)\n",
        "        cv.imwrite('./data_processed2/CG/' + str(name), img)\n",
        "        \n",
        "print(num)\n",
        "#print(pi_cg_list[1])\n",
        "'''"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "\"\\nnum=0\\n\\nfor j in os.listdir('./data/CG/'):\\n    if not j.startswith('.') and (j.split('.')[1] == 'jpg'):\\n        \\n        img = cv.imread('./data/CG/' + str(j), 0)\\n        img = HPF(img)\\n        img = cv.merge((img, img, img))\\n        #print(img.shape)\\n        num+=1\\n        name = 'cg_' + str(num) + '.jpg'\\n        pi_cg_list[1].append(name)\\n        cv.imwrite('./data_processed2/CG/' + str(name), img)\\n        \\nprint(num)\\n#print(pi_cg_list[1])\\n\""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4RniUWxbSpLD"
      },
      "source": [
        "pi_cg_list = (list(), list(), list())\n",
        "image_list = (list(), list(), list())\n",
        "num= 0\n",
        "num2=0\n",
        "num3=0\n",
        "\n",
        "for j in os.listdir('drive/My Drive/aip_project/data_processed2/PI/'):\n",
        "    if not j.startswith('.') and (j.split('.')[1] == 'jpg'):\n",
        "        \n",
        "        img = cv.imread('drive/My Drive/aip_project/data_processed2/PI/' + str(j))\n",
        "        #img = HPF(img)\n",
        "        #img = cv.resize(img, (224, 224), interpolation=cv.INTER_AREA)\n",
        "        #img = cv.merge((img, img, img))\n",
        "        pil_image = Image.fromarray(img)\n",
        "        \n",
        "        #pil_image = torch.from_numpy(img)\n",
        "        #pil_image = pil_image.reshape(3, 224, 224)\n",
        "        num+=1\n",
        "        pi_cg_list[0].append(j)\n",
        "        image_list[0].append(pil_image)\n"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "olo3HMkkt-Ig"
      },
      "source": [
        "for j in os.listdir('drive/My Drive/aip_project/data_processed2/NV/'):\n",
        "    if not j.startswith('.') and (j.split('.')[1] == 'jpg'):\n",
        "        \n",
        "        img = cv.imread('drive/My Drive/aip_project/data_processed2/NV/' + str(j))\n",
        "        #img = HPF(img)\n",
        "        #img = cv.resize(img, (224, 224), interpolation=cv.INTER_AREA)\n",
        "        #img = cv.merge((img, img, img))\n",
        "        pil_image = Image.fromarray(img)\n",
        "        \n",
        "        #pil_image = torch.from_numpy(img)\n",
        "        #pil_image = pil_image.reshape(3, 224, 224)\n",
        "        num2+=1\n",
        "        pi_cg_list[1].append(j)\n",
        "        image_list[1].append(pil_image)"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WKizVCw4bK3C",
        "outputId": "e5110a08-a1a1-420b-9585-485dcd56ba67"
      },
      "source": [
        "for j in os.listdir('drive/My Drive/aip_project/data_processed2/CG/'):\n",
        "    if not j.startswith('.') and (j.split('.')[1] == 'jpg'):\n",
        "        \n",
        "        img = cv.imread('drive/My Drive/aip_project/data_processed2/CG/' + str(j))\n",
        "        #img = HPF(img)\n",
        "        #img = cv.resize(img, (224, 224), interpolation=cv.INTER_AREA)\n",
        "        #img = cv.merge((img, img, img))\n",
        "        pil_image = Image.fromarray(img)\n",
        "        \n",
        "        #pil_image = torch.from_numpy(img)\n",
        "        #pil_image = pil_image.reshape(3, 224, 224)\n",
        "        num3+=1\n",
        "        pi_cg_list[2].append(j)\n",
        "        image_list[2].append(pil_image)\n",
        "\n",
        "print(num2, num)\n",
        "#print(pi_cg_list[1])\n",
        "print(len(image_list[1]))"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "800 800\n",
            "800\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tkt122Ybv1bv",
        "outputId": "9a680d57-757b-4210-ba40-8ee65f6a4ea7"
      },
      "source": [
        "print(num, num2, num3)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "800 800 800\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304
        },
        "id": "s988o2wNXtJr",
        "outputId": "1ccf976a-2d5b-4bd0-ed7c-4f3133d6e047"
      },
      "source": [
        "n = random.randint(1,800)\n",
        "cg_image = cv.imread('drive/My Drive/aip_project/data_processed2/PI/pi_' + str(n) + '.jpg')\n",
        "cg_image = cv.resize(cg_image, (224, 224), interpolation=cv.INTER_AREA)\n",
        "print(cg_image.shape)\n",
        "\n",
        "plt.imshow(cg_image, cmap='gray')"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(224, 224, 3)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f1fb9318e90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        },
        {
          "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/",
          "height": 304
        },
        "id": "dga8kal2uPIL",
        "outputId": "33d7c731-f10d-4d7e-f696-6c21dc3a37f6"
      },
      "source": [
        "n = random.randint(1,800)\n",
        "cg_image = cv.imread('drive/My Drive/aip_project/data_processed2/NV/nv_' + str(n) + '.jpg')\n",
        "cg_image = cv.resize(cg_image, (224, 224), interpolation=cv.INTER_AREA)\n",
        "print(cg_image.shape)\n",
        "\n",
        "plt.imshow(cg_image, cmap='gray')"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(224, 224, 3)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f1fb760fb10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        },
        {
          "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/",
          "height": 283
        },
        "id": "1EpoGtAHXtJr",
        "outputId": "4f060901-ee7c-459e-e986-debbf87aa87a"
      },
      "source": [
        "pi_image = cv.imread('drive/My Drive/aip_project/data_processed2/CG/cg_' + str(n) + '.jpg', 0)\n",
        "plt.imshow(pi_image, cmap='gray')"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f1fb758f0d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        },
        {
          "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": {
        "id": "iLAPCFW9XtJs"
      },
      "source": [
        "image_size = 224\n",
        "data_transform = transform = transforms.Compose([\n",
        "                transforms.Resize(image_size),\n",
        "                transforms.RandomResizedCrop(size=image_size, scale=(0.9, 1.1)),\n",
        "                transforms.RandomRotation(degrees=10),\n",
        "                transforms.ColorJitter(),\n",
        "                transforms.RandomHorizontalFlip(0.2),\n",
        "                transforms.Resize(size=image_size),  # Image net standards\n",
        "                #transforms.Grayscale(),\n",
        "                transforms.ToTensor(),\n",
        "                #transforms.Normalize([0.485, 0.456, 0.406],[0.229, 0.224, 0.225])  # Imagenet standards\n",
        "                #transforms.ToTensor(), \n",
        "                #transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\n",
        "                ])"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vXqY-XS446wx"
      },
      "source": [
        "class PivsCgDataset(Dataset):\n",
        "\n",
        "    def __init__(self, image_list, transform=None):\n",
        "        self.image_list = image_list\n",
        "        self.transform = transform\n",
        "    \n",
        "    def __len__(self):\n",
        "        # Return the length of the dataset\n",
        "        l = len(image_list[0]) + len(image_list[1])\n",
        "        return l\n",
        "    \n",
        "    def __getitem__(self, idx):\n",
        "        #print(idx)\n",
        "        # Return the observation based on an index. Ex. dataset[0] will return the first element from the dataset, in this case the image and the label.\n",
        "        if torch.is_tensor(idx):\n",
        "            idx = idx.tolist()\n",
        "        \n",
        "        #print(idx)\n",
        "        \n",
        "        if idx < 800:\n",
        "            image = image_list[0][idx]\n",
        "            image = data_transform(image)\n",
        "            label = 0\n",
        "        elif idx < 1600:\n",
        "            image = image_list[1][idx-800]\n",
        "            image = data_transform(image)\n",
        "            label = 1\n",
        "        else:\n",
        "            image = image_list[2][idx-1600]\n",
        "            image = data_transform(image)\n",
        "            label = 2\n",
        "    \n",
        "        return (image, label)"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y8-xNsOBXtJt",
        "outputId": "0bdc6eeb-d094-4c1c-cff4-648494070b0a"
      },
      "source": [
        "dataset = PivsCgDataset(image_list, transform = data_transform)\n",
        "print(dataset[800])\n",
        "print(len(dataset))\n",
        "\n",
        "x_data = []\n",
        "y_data = []\n",
        "image_size = 224\n",
        "\n",
        "batch = 8\n",
        "random_seed = random.randint(1, 100)\n",
        "\n",
        "dataset_size = len(dataset)\n",
        "split = int(0.15*dataset_size)\n",
        "print('Size of dataset is: ', dataset_size)\n",
        "print('Split value: ', split)\n",
        "indices = list(range(dataset_size))\n",
        "\n",
        "np.random.seed(random_seed)\n",
        "np.random.shuffle(indices)\n",
        "\n",
        "train_indices, val_indices = indices[split:], indices[:split]\n",
        "\n",
        "# Creating PT data samplers and loaders:\n",
        "train_sampler = SubsetRandomSampler(train_indices)\n",
        "valid_sampler = SubsetRandomSampler(val_indices)\n",
        "\n",
        "train_loader = torch.utils.data.DataLoader(dataset, batch_size=batch, sampler=train_sampler,\n",
        "                                            num_workers = 0)\n",
        "valid_loader = torch.utils.data.DataLoader(dataset, batch_size=batch, sampler=valid_sampler,\n",
        "                                            num_workers = 0)\n",
        "\n",
        "print(len(train_loader))\n",
        "print(len(valid_loader))\n"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(tensor([[[0., 0., 0.,  ..., 0., 0., 0.],\n",
            "         [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "         [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "         ...,\n",
            "         [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "         [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "         [0., 0., 0.,  ..., 0., 0., 0.]],\n",
            "\n",
            "        [[0., 0., 0.,  ..., 0., 0., 0.],\n",
            "         [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "         [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "         ...,\n",
            "         [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "         [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "         [0., 0., 0.,  ..., 0., 0., 0.]],\n",
            "\n",
            "        [[0., 0., 0.,  ..., 0., 0., 0.],\n",
            "         [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "         [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "         ...,\n",
            "         [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "         [0., 0., 0.,  ..., 0., 0., 0.],\n",
            "         [0., 0., 0.,  ..., 0., 0., 0.]]]), 1)\n",
            "1600\n",
            "Size of dataset is:  1600\n",
            "Split value:  160\n",
            "180\n",
            "20\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OsP_64ljYKow",
        "outputId": "58cb5fa6-c180-47d1-c115-f05535bb312e"
      },
      "source": [
        "# Iterate through the dataloader once\n",
        "testiter = iter(valid_loader)\n",
        "features, labels = next(testiter)\n",
        "print(features.shape)\n",
        "print(labels.shape)\n",
        "\n",
        "#for f, l in train_loader:\n",
        "    #print(l)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "torch.Size([8, 3, 224, 224])\n",
            "torch.Size([8])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304
        },
        "id": "0WRGR0Gvaaq_",
        "outputId": "ea210de1-d2fe-4510-b39d-200af634c217"
      },
      "source": [
        "img_shape = 224\n",
        "print('Image shape: ', img_shape)\n",
        "root = 'drive/My Drive/aip_project/data_processed2'\n",
        "\n",
        "final_img = None\n",
        "\n",
        "labels = ['PI', 'NV', 'CG']\n",
        "\n",
        "for i in range(3):\n",
        "    row_img = None\n",
        "    for j in range(2):\n",
        "        \n",
        "        imgpath = root + '/' + labels[i] + '/' + str(pi_cg_list[i][random.randint(1, 799)])\n",
        "        #print(imgpath)\n",
        "        img = cv.imread(imgpath)\n",
        "        img = cv.resize(img, (img_shape, img_shape), interpolation = cv.INTER_AREA)\n",
        "        img = cv.cvtColor(img, cv.COLOR_BGR2RGB)\n",
        "            \n",
        "        if np.any(row_img == None):\n",
        "            row_img = img\n",
        "        else:\n",
        "            row_img = np.hstack((row_img, img))\n",
        "            \n",
        "    if np.any(final_img == None):\n",
        "            final_img = row_img\n",
        "    else:\n",
        "        final_img = np.vstack((final_img, row_img))  \n",
        "                        \n",
        "plt.imshow(final_img)\n",
        "#cv.waitKey(0)\n",
        "#cv.destroyAllWindows()        "
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Image shape:  224\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f1fb74f26d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        },
        {
          "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": {
        "id": "y9AP8s6aXtJu"
      },
      "source": [
        "num_classes = 2\n",
        "\n",
        "class CaffeNet(nn.Module):\n",
        "\n",
        "    def __init__(self):\n",
        "        super().__init__()\n",
        "        self.conv1 = nn.Conv2d(3, 96, kernel_size=11, stride=4, padding=1)\n",
        "        #self.conv1_bn = nn.BatchNorm3d(96)\n",
        "        self.pool1 = nn.MaxPool2d(kernel_size=3, stride=2)\n",
        "        self.norm1 = nn.LocalResponseNorm(5, alpha=0.0001, beta=0.75)\n",
        "\n",
        "        self.conv2 = nn.Conv2d(96, 256, kernel_size=5, padding=2, groups=2)\n",
        "        #self.conv2_bn = nn.BatchNorm3d(256)\n",
        "        self.pool2 = nn.MaxPool2d(kernel_size=3, stride=2)\n",
        "        self.norm2 = nn.LocalResponseNorm(5, alpha=0.0001, beta=0.75)\n",
        "\n",
        "        self.conv3 = nn.Conv2d(256, 384, kernel_size=3, stride=1, padding=1)\n",
        "        #self.conv3_bn = nn.BatchNorm3d(384)\n",
        "\n",
        "        self.conv4 = nn.Conv2d(384, 384, kernel_size=3, stride=1, padding=1, groups=2)\n",
        "        #self.conv4_bn = nn.BatchNorm3d(384)\n",
        "\n",
        "        self.conv5 = nn.Conv2d(384, 256, kernel_size=3, padding=1, groups=2)\n",
        "        #self.conv5_bn = nn.BatchNorm3d(256)\n",
        "        self.pool5 = nn.MaxPool2d(kernel_size=3, stride=2)\n",
        "\n",
        "        self.fc6 = nn.Linear(6400, 4096)\n",
        "        self.fc7 = nn.Linear(4096, num_classes)\n",
        "        self.relu = nn.ReLU()\n",
        "        self.softmax = nn.Softmax(dim=1)\n",
        "\n",
        "    def forward(self, x):\n",
        "\n",
        "        h = x.view(-1, 3, 224, 224)\n",
        "\n",
        "        h = self.relu(self.conv1(h))\n",
        "        #print(h.shape)\n",
        "        h = self.pool1(h)\n",
        "        h = self.norm1(h)\n",
        "        #print(h.shape)\n",
        "\n",
        "        h = self.relu(self.conv2(h))\n",
        "        h = self.pool2(h)\n",
        "        h = self.norm2(h)\n",
        "        #print(h.shape)\n",
        "\n",
        "        h = self.relu(self.conv3(h))\n",
        "\n",
        "        h = self.relu(self.conv4(h))\n",
        "\n",
        "        h = self.relu(self.conv5(h))\n",
        "        h = self.pool5(h)\n",
        "        #print(h.shape)\n",
        "\n",
        "        h = h.view(-1, 6400)\n",
        "        h = self.relu(self.fc6(h))\n",
        "        h = self.relu(self.fc7(h))\n",
        "        h = self.softmax(h)\n",
        "        #print(h.shape)\n",
        "        return h\n"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qzn_sceIkRfh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cf3ac763-ec3e-48a4-d252-af39d9e8a5ef"
      },
      "source": [
        "model = CaffeNet()\n",
        "    \n",
        "print(model)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "CaffeNet(\n",
            "  (conv1): Conv2d(3, 96, kernel_size=(11, 11), stride=(4, 4), padding=(1, 1))\n",
            "  (pool1): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
            "  (norm1): LocalResponseNorm(5, alpha=0.0001, beta=0.75, k=1.0)\n",
            "  (conv2): Conv2d(96, 256, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=2)\n",
            "  (pool2): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
            "  (norm2): LocalResponseNorm(5, alpha=0.0001, beta=0.75, k=1.0)\n",
            "  (conv3): Conv2d(256, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "  (conv4): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=2)\n",
            "  (conv5): Conv2d(384, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=2)\n",
            "  (pool5): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
            "  (fc6): Linear(in_features=6400, out_features=4096, bias=True)\n",
            "  (fc7): Linear(in_features=4096, out_features=2, bias=True)\n",
            "  (relu): ReLU()\n",
            "  (softmax): Softmax(dim=1)\n",
            ")\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D_3limSlXtJv",
        "outputId": "5fd8e2c4-82db-46ce-ad62-3dbfdb62b366"
      },
      "source": [
        "from torchvision import models\n",
        "model = models.resnet18(pretrained=True)\n",
        "for param in model.parameters():\n",
        "    param.requires_grad = True\n",
        "    \n",
        "print(model)"
      ],
      "execution_count": 20,
      "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": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PmuF-UvvXtJv",
        "outputId": "c9571631-66c1-4dce-ed1a-0d236fe81a50"
      },
      "source": [
        "n_classes = 3\n",
        "\n",
        "model.fc = nn.Sequential(\n",
        "                      nn.Linear(512, 512), \n",
        "                      nn.ReLU(), \n",
        "                      nn.Dropout(0.25), \n",
        "                      nn.Linear(512, 128),\n",
        "                      nn.ReLU(),\n",
        "                      nn.Linear(128, n_classes),\n",
        "                      nn.Softmax(dim=1))\n",
        "\n",
        "print(model)"
      ],
      "execution_count": 21,
      "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): Sequential(\n",
            "    (0): Linear(in_features=512, out_features=512, bias=True)\n",
            "    (1): ReLU()\n",
            "    (2): Dropout(p=0.25, inplace=False)\n",
            "    (3): Linear(in_features=512, out_features=128, bias=True)\n",
            "    (4): ReLU()\n",
            "    (5): Linear(in_features=128, out_features=3, bias=True)\n",
            "    (6): Softmax(dim=1)\n",
            "  )\n",
            ")\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "ZJOEE_1xXtJy",
        "outputId": "47d761fc-74e6-44ff-dc72-1562d9acdd95"
      },
      "source": [
        "'''model = torch.load('drive/My Drive/aip_project/resnet18-trained.pth')\n",
        "print(model)'''"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "\"model = torch.load('drive/My Drive/aip_project/resnet18-trained.pth')\\nprint(model)\""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OgCB3AhQoMuj",
        "outputId": "7401629b-9386-4ac9-af37-85bd43d6655d"
      },
      "source": [
        "total_params = sum(p.numel() for p in model.parameters())\n",
        "\n",
        "print(f'{total_params:,} total parameters')\n",
        "\n",
        "total_trainable = sum(p.numel() for p in model.parameters() if p.requires_grad)\n",
        "\n",
        "print(f'{total_trainable:,} trainable parameters.')"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "11,505,219 total parameters\n",
            "11,505,219 trainable parameters.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fiblta9cokgT"
      },
      "source": [
        "#for f, l in train_loader :\n",
        "    #print(f.shape)"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ptL8ZfcTopFY"
      },
      "source": [
        "# Move to gpu\n",
        "model = model.to('cuda')\n",
        "# Distribute across 2 gpus\n",
        "model = nn.DataParallel(model)\n",
        "device = 'cuda'\n",
        "loss_func = F.cross_entropy\n"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FvCiW43SpNV5"
      },
      "source": [
        "def accuracy(out, yb):\n",
        "    pred = torch.argmax(out, keepdim= False, dim=1)\n",
        "    return (pred == yb).float().mean() "
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4O42ceo3pPXo"
      },
      "source": [
        "def fit(model, epochs, train_loader, valid_loader, opt):\n",
        "    losses = []\n",
        "    accuracies = []\n",
        "    \n",
        "    for epoch in range(epochs):\n",
        "        train_loss =0\n",
        "        train_acc =0\n",
        "        model.train()\n",
        "        for xb, yb in train_loader:\n",
        "            xb =xb.to(device)\n",
        "            yb = yb.to(device)\n",
        "            pred = model(xb.float())\n",
        "            pred.to(device)\n",
        "            #print(pred, pred.shape)\n",
        "            #print(yb, yb.shape)\n",
        "            train_acc += accuracy(pred, yb)\n",
        "            loss = loss_func(pred, yb)\n",
        "            train_loss += loss\n",
        "            #back propogation\n",
        "            loss.backward()\n",
        "            opt.step()\n",
        "            opt.zero_grad()      \n",
        "\n",
        "        losses.append(train_loss)\n",
        "        print(\"Iteration no: \"+ str(epoch*20+1020), \"loss = \"+str(losses[epoch].item()))\n",
        "        print(\"Accuracy of train set:\", train_acc/len(train_loader))\n",
        "        accuracies.append(train_acc/len(train_loader))\n",
        "        lr_scheduler.step(train_loss/len(train_loader))\n",
        "        \n",
        "        \n",
        "        model.eval()    \n",
        "        with torch.no_grad():\n",
        "            valid_acc=0\n",
        "            loss_valid = 0\n",
        "            for xb_valid, yb_valid in valid_loader:\n",
        "                xb_valid = xb_valid.to(device)\n",
        "                yb_valid = yb_valid.to(device)\n",
        "                pred_valid = model(xb_valid.float())\n",
        "                pred_valid.to(device)\n",
        "                valid_acc += accuracy(pred_valid, yb_valid)\n",
        "                loss_valid += loss_func(pred_valid, yb_valid)\n",
        "            print(\"Accuracy of validation set :\", valid_acc/len(valid_loader))\n",
        "    \n",
        "    #losses = np.array(losses)\n",
        "    #plt.plot(losses)\n",
        "    #plt.ylabel(\"loss\")\n",
        "    #plt.xlabel(\"iterations\")\n",
        "    #plt.show()\n",
        "    return losses, accuracies"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RAK4wYIZXtJw"
      },
      "source": [
        "optimizer = optim.Adam(model.parameters(), betas=(0.9, 0.999), lr = 0.00001)#, weight_decay = 0.0002)"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pgmyiWYspcol",
        "outputId": "1b8d4b5e-19b7-4959-e785-8c429eb23d61"
      },
      "source": [
        "lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, patience=2, verbose=False, eps = 10e-8)\n",
        "\n",
        "loss_list1, accuracy_list1 = fit(model, 100, train_loader, valid_loader, optimizer)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Iteration no: 1020 loss = 177.01388549804688\n",
            "Accuracy of train set: tensor(0.6382, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.9125, device='cuda:0')\n",
            "Iteration no: 1040 loss = 137.0861358642578\n",
            "Accuracy of train set: tensor(0.8486, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.9625, device='cuda:0')\n",
            "Iteration no: 1060 loss = 118.04902648925781\n",
            "Accuracy of train set: tensor(0.9271, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.9688, device='cuda:0')\n",
            "Iteration no: 1080 loss = 112.0528335571289\n",
            "Accuracy of train set: tensor(0.9458, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.9812, device='cuda:0')\n",
            "Iteration no: 1100 loss = 110.86497497558594\n",
            "Accuracy of train set: tensor(0.9424, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.9750, device='cuda:0')\n",
            "Iteration no: 1120 loss = 107.4224853515625\n",
            "Accuracy of train set: tensor(0.9653, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.9875, device='cuda:0')\n",
            "Iteration no: 1140 loss = 108.26751708984375\n",
            "Accuracy of train set: tensor(0.9569, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.9750, device='cuda:0')\n",
            "Iteration no: 1160 loss = 106.05926513671875\n",
            "Accuracy of train set: tensor(0.9681, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.9812, device='cuda:0')\n",
            "Iteration no: 1180 loss = 105.93130493164062\n",
            "Accuracy of train set: tensor(0.9694, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.9750, device='cuda:0')\n",
            "Iteration no: 1200 loss = 105.679931640625\n",
            "Accuracy of train set: tensor(0.9701, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.9812, device='cuda:0')\n",
            "Iteration no: 1220 loss = 103.74060821533203\n",
            "Accuracy of train set: tensor(0.9771, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.9750, device='cuda:0')\n",
            "Iteration no: 1240 loss = 103.35726165771484\n",
            "Accuracy of train set: tensor(0.9826, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.9875, device='cuda:0')\n",
            "Iteration no: 1260 loss = 102.66867065429688\n",
            "Accuracy of train set: tensor(0.9840, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.9875, device='cuda:0')\n",
            "Iteration no: 1280 loss = 102.1341323852539\n",
            "Accuracy of train set: tensor(0.9896, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.9875, device='cuda:0')\n",
            "Iteration no: 1300 loss = 104.24903106689453\n",
            "Accuracy of train set: tensor(0.9764, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.9938, device='cuda:0')\n",
            "Iteration no: 1320 loss = 103.1095962524414\n",
            "Accuracy of train set: tensor(0.9819, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.9875, device='cuda:0')\n",
            "Iteration no: 1340 loss = 102.01031494140625\n",
            "Accuracy of train set: tensor(0.9861, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.9875, device='cuda:0')\n",
            "Iteration no: 1360 loss = 102.68563079833984\n",
            "Accuracy of train set: tensor(0.9833, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.9812, device='cuda:0')\n",
            "Iteration no: 1380 loss = 102.75437927246094\n",
            "Accuracy of train set: tensor(0.9819, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.9875, device='cuda:0')\n",
            "Iteration no: 1400 loss = 102.08087921142578\n",
            "Accuracy of train set: tensor(0.9868, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.9812, device='cuda:0')\n",
            "Iteration no: 1420 loss = 102.61593627929688\n",
            "Accuracy of train set: tensor(0.9833, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.9812, device='cuda:0')\n",
            "Iteration no: 1440 loss = 100.85687255859375\n",
            "Accuracy of train set: tensor(0.9944, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.9875, device='cuda:0')\n",
            "Iteration no: 1460 loss = 101.45237731933594\n",
            "Accuracy of train set: tensor(0.9903, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.9875, device='cuda:0')\n",
            "Iteration no: 1480 loss = 101.27008819580078\n",
            "Accuracy of train set: tensor(0.9910, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.9875, device='cuda:0')\n",
            "Iteration no: 1500 loss = 101.86119079589844\n",
            "Accuracy of train set: tensor(0.9854, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.9812, device='cuda:0')\n",
            "Iteration no: 1520 loss = 101.87950897216797\n",
            "Accuracy of train set: tensor(0.9868, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.9812, device='cuda:0')\n",
            "Iteration no: 1540 loss = 102.54676055908203\n",
            "Accuracy of train set: tensor(0.9819, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.9812, device='cuda:0')\n",
            "Iteration no: 1560 loss = 102.22085571289062\n",
            "Accuracy of train set: tensor(0.9840, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.9750, device='cuda:0')\n",
            "Iteration no: 1580 loss = 101.9256820678711\n",
            "Accuracy of train set: tensor(0.9861, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.9812, device='cuda:0')\n",
            "Iteration no: 1600 loss = 101.34703063964844\n",
            "Accuracy of train set: tensor(0.9896, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.9750, device='cuda:0')\n",
            "Iteration no: 1620 loss = 101.29704284667969\n",
            "Accuracy of train set: tensor(0.9910, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.9812, device='cuda:0')\n",
            "Iteration no: 1640 loss = 101.4496078491211\n",
            "Accuracy of train set: tensor(0.9896, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.9750, device='cuda:0')\n",
            "Iteration no: 1660 loss = 100.35795593261719\n",
            "Accuracy of train set: tensor(0.9965, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.9938, device='cuda:0')\n",
            "Iteration no: 1680 loss = 101.97921752929688\n",
            "Accuracy of train set: tensor(0.9861, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.9875, device='cuda:0')\n",
            "Iteration no: 1700 loss = 102.07054901123047\n",
            "Accuracy of train set: tensor(0.9847, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.9875, device='cuda:0')\n",
            "Iteration no: 1720 loss = 101.8788070678711\n",
            "Accuracy of train set: tensor(0.9854, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.9938, device='cuda:0')\n",
            "Iteration no: 1740 loss = 103.17657470703125\n",
            "Accuracy of train set: tensor(0.9792, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.9812, device='cuda:0')\n",
            "Iteration no: 1760 loss = 101.73153686523438\n",
            "Accuracy of train set: tensor(0.9889, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.9812, device='cuda:0')\n",
            "Iteration no: 1780 loss = 101.08193969726562\n",
            "Accuracy of train set: tensor(0.9917, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.9875, device='cuda:0')\n",
            "Iteration no: 1800 loss = 101.34065246582031\n",
            "Accuracy of train set: tensor(0.9896, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.9812, device='cuda:0')\n",
            "Iteration no: 1820 loss = 101.96356964111328\n",
            "Accuracy of train set: tensor(0.9847, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.9688, device='cuda:0')\n",
            "Iteration no: 1840 loss = 101.02631378173828\n",
            "Accuracy of train set: tensor(0.9917, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.9875, device='cuda:0')\n",
            "Iteration no: 1860 loss = 102.20279693603516\n",
            "Accuracy of train set: tensor(0.9847, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.9750, device='cuda:0')\n",
            "Iteration no: 1880 loss = 101.1570816040039\n",
            "Accuracy of train set: tensor(0.9910, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.9875, device='cuda:0')\n",
            "Iteration no: 1900 loss = 102.48674774169922\n",
            "Accuracy of train set: tensor(0.9819, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.9938, device='cuda:0')\n",
            "Iteration no: 1920 loss = 101.53752136230469\n",
            "Accuracy of train set: tensor(0.9875, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.9812, device='cuda:0')\n",
            "Iteration no: 1940 loss = 101.19114685058594\n",
            "Accuracy of train set: tensor(0.9910, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.9875, device='cuda:0')\n",
            "Iteration no: 1960 loss = 101.3663101196289\n",
            "Accuracy of train set: tensor(0.9903, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.9812, device='cuda:0')\n",
            "Iteration no: 1980 loss = 101.25453186035156\n",
            "Accuracy of train set: tensor(0.9896, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.9812, device='cuda:0')\n",
            "Iteration no: 2000 loss = 101.28350067138672\n",
            "Accuracy of train set: tensor(0.9910, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.9750, device='cuda:0')\n",
            "Iteration no: 2020 loss = 100.749755859375\n",
            "Accuracy of train set: tensor(0.9951, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.9750, device='cuda:0')\n",
            "Iteration no: 2040 loss = 102.7497329711914\n",
            "Accuracy of train set: tensor(0.9813, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.9750, device='cuda:0')\n",
            "Iteration no: 2060 loss = 100.86685943603516\n",
            "Accuracy of train set: tensor(0.9938, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.9812, device='cuda:0')\n",
            "Iteration no: 2080 loss = 101.16715240478516\n",
            "Accuracy of train set: tensor(0.9903, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.9875, device='cuda:0')\n",
            "Iteration no: 2100 loss = 100.87081909179688\n",
            "Accuracy of train set: tensor(0.9938, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.9812, device='cuda:0')\n",
            "Iteration no: 2120 loss = 101.63507080078125\n",
            "Accuracy of train set: tensor(0.9889, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.9812, device='cuda:0')\n",
            "Iteration no: 2140 loss = 101.44699096679688\n",
            "Accuracy of train set: tensor(0.9889, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.9812, device='cuda:0')\n",
            "Iteration no: 2160 loss = 101.17691802978516\n",
            "Accuracy of train set: tensor(0.9896, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.9750, device='cuda:0')\n",
            "Iteration no: 2180 loss = 101.52083587646484\n",
            "Accuracy of train set: tensor(0.9889, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.9938, device='cuda:0')\n",
            "Iteration no: 2200 loss = 101.31340789794922\n",
            "Accuracy of train set: tensor(0.9903, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.9812, device='cuda:0')\n",
            "Iteration no: 2220 loss = 101.95712280273438\n",
            "Accuracy of train set: tensor(0.9868, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.9750, device='cuda:0')\n",
            "Iteration no: 2240 loss = 101.68578338623047\n",
            "Accuracy of train set: tensor(0.9868, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.9750, device='cuda:0')\n",
            "Iteration no: 2260 loss = 102.58150482177734\n",
            "Accuracy of train set: tensor(0.9813, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.9938, device='cuda:0')\n",
            "Iteration no: 2280 loss = 103.47612762451172\n",
            "Accuracy of train set: tensor(0.9771, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.9875, device='cuda:0')\n",
            "Iteration no: 2300 loss = 101.6874008178711\n",
            "Accuracy of train set: tensor(0.9882, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.9812, device='cuda:0')\n",
            "Iteration no: 2320 loss = 101.15541076660156\n",
            "Accuracy of train set: tensor(0.9910, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.9875, device='cuda:0')\n",
            "Iteration no: 2340 loss = 102.38548278808594\n",
            "Accuracy of train set: tensor(0.9840, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.9938, device='cuda:0')\n",
            "Iteration no: 2360 loss = 100.36407470703125\n",
            "Accuracy of train set: tensor(0.9944, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.9875, device='cuda:0')\n",
            "Iteration no: 2380 loss = 101.82044982910156\n",
            "Accuracy of train set: tensor(0.9875, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.9812, device='cuda:0')\n",
            "Iteration no: 2400 loss = 100.54847717285156\n",
            "Accuracy of train set: tensor(0.9951, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.9875, device='cuda:0')\n",
            "Iteration no: 2420 loss = 102.05379486083984\n",
            "Accuracy of train set: tensor(0.9847, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.9875, device='cuda:0')\n",
            "Iteration no: 2440 loss = 101.71760559082031\n",
            "Accuracy of train set: tensor(0.9854, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.9875, device='cuda:0')\n",
            "Iteration no: 2460 loss = 101.25994873046875\n",
            "Accuracy of train set: tensor(0.9889, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.9812, device='cuda:0')\n",
            "Iteration no: 2480 loss = 102.44021606445312\n",
            "Accuracy of train set: tensor(0.9833, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.9812, device='cuda:0')\n",
            "Iteration no: 2500 loss = 100.75910186767578\n",
            "Accuracy of train set: tensor(0.9924, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.9875, device='cuda:0')\n",
            "Iteration no: 2520 loss = 101.27490997314453\n",
            "Accuracy of train set: tensor(0.9875, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.9750, device='cuda:0')\n",
            "Iteration no: 2540 loss = 100.09857177734375\n",
            "Accuracy of train set: tensor(0.9979, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.9812, device='cuda:0')\n",
            "Iteration no: 2560 loss = 102.73870086669922\n",
            "Accuracy of train set: tensor(0.9826, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.9938, device='cuda:0')\n",
            "Iteration no: 2580 loss = 100.83351135253906\n",
            "Accuracy of train set: tensor(0.9931, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.9812, device='cuda:0')\n",
            "Iteration no: 2600 loss = 101.06643676757812\n",
            "Accuracy of train set: tensor(0.9938, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.9812, device='cuda:0')\n",
            "Iteration no: 2620 loss = 102.53662872314453\n",
            "Accuracy of train set: tensor(0.9826, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.9938, device='cuda:0')\n",
            "Iteration no: 2640 loss = 101.23076629638672\n",
            "Accuracy of train set: tensor(0.9903, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.9875, device='cuda:0')\n",
            "Iteration no: 2660 loss = 101.93467712402344\n",
            "Accuracy of train set: tensor(0.9875, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.9812, device='cuda:0')\n",
            "Iteration no: 2680 loss = 102.03755187988281\n",
            "Accuracy of train set: tensor(0.9868, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.9938, device='cuda:0')\n",
            "Iteration no: 2700 loss = 101.8539047241211\n",
            "Accuracy of train set: tensor(0.9875, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.9812, device='cuda:0')\n",
            "Iteration no: 2720 loss = 102.1016616821289\n",
            "Accuracy of train set: tensor(0.9868, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.9875, device='cuda:0')\n",
            "Iteration no: 2740 loss = 100.69213104248047\n",
            "Accuracy of train set: tensor(0.9931, device='cuda:0')\n",
            "Accuracy of validation set : tensor(1., device='cuda:0')\n",
            "Iteration no: 2760 loss = 100.83740234375\n",
            "Accuracy of train set: tensor(0.9938, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.9938, device='cuda:0')\n",
            "Iteration no: 2780 loss = 101.92040252685547\n",
            "Accuracy of train set: tensor(0.9861, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.9812, device='cuda:0')\n",
            "Iteration no: 2800 loss = 101.79071807861328\n",
            "Accuracy of train set: tensor(0.9868, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.9875, device='cuda:0')\n",
            "Iteration no: 2820 loss = 102.91702270507812\n",
            "Accuracy of train set: tensor(0.9806, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.9875, device='cuda:0')\n",
            "Iteration no: 2840 loss = 101.25747680664062\n",
            "Accuracy of train set: tensor(0.9889, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.9938, device='cuda:0')\n",
            "Iteration no: 2860 loss = 102.04834747314453\n",
            "Accuracy of train set: tensor(0.9840, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.9875, device='cuda:0')\n",
            "Iteration no: 2880 loss = 101.80204010009766\n",
            "Accuracy of train set: tensor(0.9875, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.9938, device='cuda:0')\n",
            "Iteration no: 2900 loss = 102.44677734375\n",
            "Accuracy of train set: tensor(0.9840, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.9875, device='cuda:0')\n",
            "Iteration no: 2920 loss = 100.48423767089844\n",
            "Accuracy of train set: tensor(0.9958, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.9812, device='cuda:0')\n",
            "Iteration no: 2940 loss = 101.85956573486328\n",
            "Accuracy of train set: tensor(0.9861, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.9750, device='cuda:0')\n",
            "Iteration no: 2960 loss = 101.42324829101562\n",
            "Accuracy of train set: tensor(0.9875, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.9875, device='cuda:0')\n",
            "Iteration no: 2980 loss = 101.62848663330078\n",
            "Accuracy of train set: tensor(0.9896, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.9875, device='cuda:0')\n",
            "Iteration no: 3000 loss = 101.3675765991211\n",
            "Accuracy of train set: tensor(0.9889, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.9938, device='cuda:0')\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zYi7g8FcXtJy"
      },
      "source": [
        "torch.save(model, 'drive/My Drive/aip_project/resnet18-trained-gan-nv.pth')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KPJ88tBSQmdL"
      },
      "source": [
        "x = torch.tensor(loss_list1)\n",
        "torch.save(x, 'loss_list1.pt')\n",
        "x = torch.tensor(accuracy_list1)\n",
        "torch.save(x, 'accuracy_list1.pt')"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "HlCpWpiEXtJx",
        "outputId": "645a45b5-6c66-4978-c030-efee89b4aa2e"
      },
      "source": [
        "loss = []\n",
        "for l in loss_list1:\n",
        "    loss.append(l.tolist())\n",
        "x = torch.tensor(loss)\n",
        "torch.save(x, 'loss1.pt')\n",
        "plt.plot(loss)\n",
        "plt.ylabel(\"loss\")\n",
        "plt.xlabel(\"iterations\")\n",
        "plt.savefig('drive/My Drive/aip_project/loss-1.png')"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "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/",
          "height": 279
        },
        "id": "lCoGw2mvXtJx",
        "outputId": "67047118-e845-419b-ab4e-e57c6b323cc4"
      },
      "source": [
        "accurate = []\n",
        "for a in accuracy_list1:\n",
        "    accurate.append(a.tolist())\n",
        "x = torch.tensor(accurate)\n",
        "torch.save(x, 'accurate1.pt')\n",
        "plt.plot(accurate)\n",
        "plt.ylabel(\"accuracy\")\n",
        "plt.xlabel(\"iterations\")\n",
        "plt.savefig('drive/My Drive/aip_project/accuracy-1.png')"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "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/",
          "height": 279
        },
        "id": "PhOpxefQXtJx",
        "outputId": "37849388-ac17-4185-d965-69bea5d7e51c"
      },
      "source": [
        "m_l = min(loss)\n",
        "M_l = max(loss)\n",
        "loss_scaled = np.asarray(loss)\n",
        "loss_scaled = (loss_scaled - m_l)/(M_l - m_l)\n",
        "\n",
        "m_a = min(accurate)\n",
        "M_a = max(accurate)\n",
        "accuracy_scaled = np.asarray(accurate)\n",
        "accuracy_scaled = (accuracy_scaled - m_a)/(M_a - m_a)\n",
        "\n",
        "\n",
        "plt.plot(loss_scaled, label = 'loss')\n",
        "plt.plot(accuracy_scaled, label = 'accuracy')\n",
        "plt.legend()\n",
        "plt.xlabel(\"iterations\")\n",
        "plt.savefig('drive/My Drive/aip_project/scaled-1.png')"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "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": {
        "id": "HQOz5-qJXtJx"
      },
      "source": [
        "optimizer = optim.Adam(model.parameters(), betas=(0.9, 0.999), lr = 0.000001)#, weight_decay = 0.0002)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s448o36bXtJy",
        "outputId": "50b23c11-14c7-4a3c-9f99-9563ad65a342"
      },
      "source": [
        "lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, patience=2, verbose=True, eps = 10e-8)\n",
        "\n",
        "loss_list2, accuracy_list2 = fit(model, 80, train_loader, valid_loader, optimizer)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Iteration no: 1020 loss = 84.96636199951172\n",
            "Accuracy of train set: tensor(0.8257, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.8875, device='cuda:0')\n",
            "Iteration no: 1040 loss = 83.15061950683594\n",
            "Accuracy of train set: tensor(0.8465, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.8813, device='cuda:0')\n",
            "Iteration no: 1060 loss = 84.12855529785156\n",
            "Accuracy of train set: tensor(0.8396, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.8688, device='cuda:0')\n",
            "Iteration no: 1080 loss = 81.1512451171875\n",
            "Accuracy of train set: tensor(0.8604, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.8938, device='cuda:0')\n",
            "Iteration no: 1100 loss = 80.69508361816406\n",
            "Accuracy of train set: tensor(0.8583, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.8813, device='cuda:0')\n",
            "Iteration no: 1120 loss = 82.66197967529297\n",
            "Accuracy of train set: tensor(0.8493, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.8813, device='cuda:0')\n",
            "Iteration no: 1140 loss = 80.74512481689453\n",
            "Accuracy of train set: tensor(0.8604, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.9000, device='cuda:0')\n",
            "Iteration no: 1160 loss = 82.68302917480469\n",
            "Accuracy of train set: tensor(0.8424, device='cuda:0')\n",
            "Epoch     8: reducing learning rate of group 0 to 1.0000e-07.\n",
            "Accuracy of validation set : tensor(0.8625, device='cuda:0')\n",
            "Iteration no: 1180 loss = 83.80150604248047\n",
            "Accuracy of train set: tensor(0.8319, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.9000, device='cuda:0')\n",
            "Iteration no: 1200 loss = 84.56685638427734\n",
            "Accuracy of train set: tensor(0.8389, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.8875, device='cuda:0')\n",
            "Iteration no: 1220 loss = 81.75357055664062\n",
            "Accuracy of train set: tensor(0.8493, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.8750, device='cuda:0')\n",
            "Iteration no: 1240 loss = 83.96234130859375\n",
            "Accuracy of train set: tensor(0.8354, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.8750, device='cuda:0')\n",
            "Iteration no: 1260 loss = 83.25971984863281\n",
            "Accuracy of train set: tensor(0.8444, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.8938, device='cuda:0')\n",
            "Iteration no: 1280 loss = 81.470947265625\n",
            "Accuracy of train set: tensor(0.8479, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.8813, device='cuda:0')\n",
            "Iteration no: 1300 loss = 83.6904296875\n",
            "Accuracy of train set: tensor(0.8431, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.8750, device='cuda:0')\n",
            "Iteration no: 1320 loss = 82.23747253417969\n",
            "Accuracy of train set: tensor(0.8438, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.9125, device='cuda:0')\n",
            "Iteration no: 1340 loss = 84.2745590209961\n",
            "Accuracy of train set: tensor(0.8333, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.8750, device='cuda:0')\n",
            "Iteration no: 1360 loss = 82.8598861694336\n",
            "Accuracy of train set: tensor(0.8493, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.8813, device='cuda:0')\n",
            "Iteration no: 1380 loss = 83.4184799194336\n",
            "Accuracy of train set: tensor(0.8375, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.8875, device='cuda:0')\n",
            "Iteration no: 1400 loss = 83.18982696533203\n",
            "Accuracy of train set: tensor(0.8424, device='cuda:0')\n",
            "Accuracy of validation set : tensor(0.9125, device='cuda:0')\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7jkZVkH8XtJy"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}