1_LargeCNNs.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/AvishekRoy16/DeepLearning/blob/master/1_LargeCNNs.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5TChHKS0qnk3"
      },
      "source": [
        "## Outline"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RUVg6-5Dqwkq"
      },
      "source": [
        "1. Loading datasets - Transforming images\n",
        "2. VGG-16 with modification to network head\n",
        "3. Using pre-trained models\n",
        "4. Storing intermediate models\n",
        "5. Resnet\n",
        "6. Inception v3\n",
        "7. Exercises"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LA-Pk5e4qxbX"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "import torch\n",
        "import torchvision\n",
        "import torchvision.transforms as transforms\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wr_FtSbSrAvV",
        "outputId": "30f422ad-7519-41cf-8bdc-03be4896e2cf"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "cuda:0\n"
          ]
        }
      ],
      "source": [
        "device = torch.device(\"cuda:0\" if torch.cuda.is_available else \"cpu\")\n",
        "print(device)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "44yPZ7JIrY6T"
      },
      "source": [
        "## Dataset, transforms, and visualisation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PBqI-th1rfBy"
      },
      "outputs": [],
      "source": [
        "# We can load the training the test and train differently as we might want to do some tranformations or augmentations to the data, but in this example we are not doing that particularly.\n",
        "# So basically here we have created two different objects for train and test.\n",
        "transforms_train = transforms.Compose([\n",
        "    # We are resizing the image as the vgg template that we are using call for it.\n",
        "    # We have cropped the image from different locations so as to preserve the aspect ratio.\n",
        "    transforms.RandomResizedCrop(224),\n",
        "    transforms.ToTensor(),\n",
        "    # Here we are normalising the RGB channels by giving them a mean and standard deviation of 0.5. We do this because the different color can have very different variation in a histogram\n",
        "    # and the neural network will have to store it, but here we have preprocessed it so that the network does not have to compansate.\n",
        "    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) # Normalize((mean for 3 channels), (std. for 3 channels))\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",
        "    ])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101,
          "referenced_widgets": [
            "400048fe467248198b1f22bf02cf3e80",
            "5a584f7dc54f4c9798c3ef9ff1614a81",
            "fe4e60decde947fdb68847caab43ec24",
            "ec76163a635c4825978ab4c89a471156",
            "bb5abe791f7a4679ae525c570d1de618",
            "bb9f548f53e34a3dbc61568b71406312",
            "5d8b3b40184e4ef0b6f957a797cadd47",
            "fa5b5edf8bf14729b6db30c02e94f801",
            "dc4f8703c792438ca1d593b59d4d65f3",
            "9c32ff8d7b7e4788aa5e4f399dab3f60",
            "4fc16ac1cbb94c41be3b5c5da052ca69"
          ]
        },
        "id": "1XNCsSU9s3Jl",
        "outputId": "4ee8ea61-e297-4f72-a114-c9c87d7968e3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/170498071 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "400048fe467248198b1f22bf02cf3e80"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./data/cifar-10-python.tar.gz to ./data\n",
            "Files already downloaded and verified\n"
          ]
        }
      ],
      "source": [
        "# Downloading the dataset and storing it locally, and then transforming the images using the transformers that we made earlier.\n",
        "trainset = torchvision.datasets.CIFAR10(root='./data', train=True,\n",
        "                                        download=True,\n",
        "                                        transform=transforms_train)                                         \n",
        "\n",
        "testset = torchvision.datasets.CIFAR10(root='./data', train=False,\n",
        "                                        download=True,\n",
        "                                        transform=transforms_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5yDNRLgutdbr"
      },
      "outputs": [],
      "source": [
        "# Number of classes\n",
        "num_classes = 10"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mEqrVUUWtuxi"
      },
      "outputs": [],
      "source": [
        "batch_size = 4"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kmLIsymqtwua"
      },
      "outputs": [],
      "source": [
        "trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pLtwIA_Ft6fq",
        "outputId": "3e05c82a-97db-4232-fc7e-cad32de42a8e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([4, 3, 224, 224])\n",
            "torch.Size([3, 224, 224])\n",
            "2\n"
          ]
        }
      ],
      "source": [
        "# Instanciating a iterator for this train loader\n",
        "dataiter = iter(trainloader)\n",
        "images, labels = dataiter.next()\n",
        "\n",
        "print(images.shape)\n",
        "\n",
        "print(images[1].shape)\n",
        "print(labels[1].item())\n",
        "# The output has 4 images in a batch, contains 3 channels in a batch & it contains x and y dimentions of the images."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zTrFKQC-uM_a"
      },
      "outputs": [],
      "source": [
        "# To plot the image we have to do one modification  - we will have to convert the image into numpy and then un-normalise it.\n",
        "\n",
        "def imshow(img, title):\n",
        "  npimg = img.numpy() / 2 + 0.5    # De-normalization is performed (/2 is to remove standard deviation - as it is equvalent to dividing by .5) (+0.5 is adding the mean)\n",
        "  # the width of the image is batch size - 4 for out case\n",
        "  plt.figure(figsize=(batch_size, 1))\n",
        "  plt.axis('off')\n",
        "  # This is to transpose the image so that the image axis comes first then the channels come - at the very end\n",
        "  plt.imshow(np.transpose(npimg, (1, 2, 0)))\n",
        "  plt.title(title)\n",
        "  plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GKm2InS7wuK_"
      },
      "outputs": [],
      "source": [
        "def show_batch_images(dataloader):\n",
        "  images, labels = next(iter(dataloader))\n",
        "  # puts together the images to show together\n",
        "  img = torchvision.utils.make_grid(images)\n",
        "  # Here we are usiing the titles as the lables of all the images\n",
        "  imshow(img, title=[str(x.item()) for x in labels])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 353
        },
        "id": "vLquzjdwxDK2",
        "outputId": "0932e90a-8681-4fd9-8980-642b2736f6c0"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 288x72 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 288x72 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAOYAAABUCAYAAACWaEvFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOy9Waxt6Xbf9RtfM+dczd7nnOpvVV373thgYh5inkPASJEQSCC/hEaA5Xd4QiIIRYgIAVJ4iETzhJLINAoI8hYhmhcnliAJ4QlEwI4Cbq59favq1Dl779XM+XWDh/HNuc91W666yCWzv9LUOaf22nPNNec3uv//P8YSVeVpPa2n9fVa7g/6Ap7W03pav3U9GebTelpfw/VkmE/raX0N15NhPq2n9TVcT4b5tJ7W13A9GebTelpfw/VkmL/HEhEVkbOI/Lt/0Nfyh2GJyM+KyFVEvvMHfS1f5/VkmF9s/TFV/TMAIvItEfml9Qci8tdEZBaRUz9+4Y2f/YyI/OwXeQMR+bMi8mff+Pc/IyL/p4g8iMjfEZGf+p1e+3uc92dF5Gf630VE/oyI/IqI3IvIfyUit7/ps/zkFzzvL4nIt/rf/zER+TkRuXvz3rzx2o0sV9WfAf6JL/Ie/39eT4b5g1n/iqoe+/FjX/VkIvIR8F8A/ypwC/xrwF8Wkfe+4ql/GviXgD8OfAjsgP/oK54T4Az8Jew6n9YPYD0Z5tdzfQy8VtX/Tm39t9jm/5GveN5/CviLqvqrqnoC/hzwz4rI/qucVFX/F1X9z4H/+yte39Nal6o+Hb/LASjwo7/Lz/8a8CnwGfA/AT/5A3hPD/x14J/uf/8p4DvA4Sue968Af/qNf//x/vn+2A/oXv1J4Je+wOt+EvjOH/Sz/Tof4f970/9Dv/514O8ACfjngL8qIj+hqn/vy55QVauI/GfAXwamfu4/parnr3it/z3wp0XkvwZe9WsH+EoR82n94NdTKvsVl6r+LVV9UNVFVf9TLGr+k1/lnCLyJ4F/H4ssA/CPAn9BRH7iK17uXwL+SyzK/x/Az/X//4SQfs3Wk2H+4JcC8hXP8RPAz6vq/6qqTVX/NvC3sFTxy1+YnevfUtVvqerHmHH+Wj+e1tdoPRnmV1gi8lxE/nERmUQkiMi/APwjWMr4273+l1bq4vdYfxv4E2uEFJF/CPgTwP/2O5xXvwjNISJviciPdNrkx4E/D/zbqtp+m9f+5Js0x+9xXiciExDtnzKJyPBFfvdp/fbrqcb8aisC/w7wDwAV+L+An1LVX/zNL+wb9W3gb/5eJ1XVv955yr8iIu9j4NK/p6r/429z3m8CD8D//gWu9x3grwLf7Of8D1T1P/kdXvtN4H/+AucEc0Y/98a/rxh49ZNf8Pef1m9a0lGyp/U7LBGZgQX4D1X13/wK5/mHgX9ZVf/5H9jF2Xn/ReAfVNV/4wd83r8A/Deq+j/8gM/7F4E/BXyiqj/6gzz3H6b1ZJhP62l9DddTjfm0ntbXcD0Z5tN6Wl/D9WSYT+tpfQ3X74rK/rk//x/reHyBoeYNcY0QIASI0TEMgSFGvHeISD9AURRoqjRVFAEEEXudW1+r0gk/xX6r2Z9dltSa0hqUpTBfFjuuC/NlJi0ZxM7pXCDGiA8D3nuc94hzdlZVmjZKKdSSadr4zv/zC/zdX/xFvHN47wGotdJU7bqc4Jyz84jgxNFqpeRMaw3nHOIc4gQRjw8DMQyAo7VKKYVcZkpJoIqsn6nZfcHuRv99eZSqaf95r/sf72mnRdd/O+kveeNsIvzIj3yLRLK739lUEYcg1NrIpdIaqDq0QSmVkiutNrTZU3L9+dCfSwieECMheLz3iEAuhVIq0EAhOBicYwieIQbGGIgeolOCKF7sueaq5ApVlaYQvGM/RoIX8pJYlsT9ZeHuvHBNlVyV2uCHvv1t3n3/hxGR7Zl57wnBE0MghEAI3p6Zs+tvrVFbpfXnap9GqLVyuc5cLjOX60xrj/fQOyF6Rwj9s4yRYRgYhgEFam2UqtRabb+UQmuN2K9jiIHdbmS/n9jvdwzTgPMe5x3OOejPel2//Mu/zE//9E//tpz372qY081bPPvgj4A0RDLOF8ZR2O2E3d5z2E8cDxNDDHjX39zZhmgouSmlNhq2gcU5vHi7ueJtIyDdKAuqtR+N1pRSGjUr19PC3csH7l7e8/rlPa3dkdIZxCMu4uPIuNszTgfiMODjgA/BnANKKYV5ubIsM7VmPvnkJX/jb/xNQgyMg9FtKWVqrXYTvcdHe+Dee7x4as5cr1dKzvb/Q0CCx/nIOB6YphtQRy6FlK5cLncsyxnRBqq01mhmFZvRuX7PWmu/WUuKdKNyzpzZ+jjtIXtzfttr7XXH5wcuPJgzVO2bzeOcZ1kK1zlTCqh6aoF5zizXRMmFVipOheAdXlzfxso4Dex2O8ZpYBgHxAnz9cp1WaBVe413HKLnOEZudhPH/cQ+QgwN7xqDMyOuqdFyN9Cm7KZIvNmzi47L+cT1/oG7zx741U8f+PyUuCYlVbh59hYffHSLiDBEM5YxRqZpZJpGdtPEOA7mRLzDe0ethZwyOSdqa/3TQMqFl5/fMadXLFkppZqTFGe/6wLeeaZh4Hg4cHNzYH/YowhLriy5kJZCXhbykqilsJsih93IYTfx4sUtb7/znLffecHN7ZEwRPwQ8fG3mtrP//zP/46297sapj1w36MXNG2IrPGvEX0gx4oXQYLiREFX727yFxFnxuceI+YaJ6WfafuFfijm8dKSmS+Z892Fh9cP3L+653x/Yr7M5CWDNJyz9yw+EnzGO493DfFqkQ+HeI+GAC3SelRv2javCli0FkW12WdoFa39mrxshrBmAqKKV0G6J2pq0bm2SmmVpm0znDVDcOJQ6UbTM4vWuX0Rex1iG8v7YK9RpTZFmzkrmrLx/rqGxR4RWiOV5Q0DF6oLOOfJpVFro2nPXpzDeyVEu39VzUWuzgIU5yCOI+NuYpwGQgyoKuK9eX9ku77SHg2uNqVUJWvBuQrODLg2M/fWHZXWhmjDi0Wo3TiwGwemcWDMQpFGK7o5J+mZiutR0/VsQFfHV6H27Ks1u8feexDsHvZnpOgbjlF7VuG24OLWferW++RRBN8gNGgBi8SlgipO3LaPNwfbmh09E0LVHvIXXL+rYXrniSFSqlKypT45FZZrJc0OUYjO4WkIHi8O6RcpOFxPPZT+/8XbJl1N8w0jBfe4+Xu0vDxcufv8gdef3fP609e8/uw119NMmhM5VUQ8zgXq2HDq8QQ8nugCLpjnc96h3hFQBnHUUHoEajSt1CogYobUk27VhtZ1E4GoorVC//n3uZFuPKVmWoNUErkkarPI73q67b3QHD1dbW/o9tSMkjUtF2KMxHFARMilkEsllwJaN3uU1bG9cT9bq1wvl++LvM5HxEVKgVwUCDgviAuE6OzfUihSQNWcgjPn5bwwHvbsb28Yx4hzQq2FJSdYLL1HoTRYiuJdY6yQquK0AokqhdFVnINKQDFHX0uhBcFpY3Dgx4hn4mGu3Fwbsyaqr2hWXAiIN4OxFDYSB3MUIkJrlZyVWu3anbe01DlHCJHWPEsq1JwptXYbcfgQ+p50iHiid8ToCEHwPhB8IIZIjBHEgWs433CuWMqPUFImhJ75dMdca6X0Q2rFNY/rZdIPxDCdd8QYaVppFdLSaDWjLZEGy8d3QyB6xTuPeg/iwfm+YVw3TN8N0/VNpG9sbPPOtpEaqmYMNTcupyuvPn3FZ7/xklefvOL1p6/JSwFzVDgXcC7QihIlUPxACxGNA64pHgjOHEQEqnPU6nGuuwO1esH+2p1CTxtrU7Q2qqwRxCKaHwLeW4orThBt1DyTcqa0RsmZUjJaEw67LyGEN8xwrcDpteB6tM2RxcHSNHEOXys+F1xKZMmg3RDXerOnJmtKPF+vj/WqCM5VxDWqOmpzOIdFHe8tHXcgUoC0GaZzgnOCD47psGd/c2QcA6DklPDzjPSaqSEUVVxVUlFStcO1Ci2DZHCV6IXmBXoGVmulVYdoIziI0RNk5HhoHK7KRSO5G2YIEeeshvQhEGNgiAMhWJRvtdFa7VkHiBPGITJOA+MYrSxqDU1KrcUynm60ztHP7QlezCgD+GDPLURzAuI84hUfFOezOVwVvDi86IYVtNqsBi2FWgo++MfsSfUxIL2JHfx+DXPz6NDTBkerQskWQa7nxHmcCV7xPhIjltaKgx65BUG3i+gprPZNuW3Svl3VQJi8JK7nKw93D7x6+YpXL1/xcGcpbCsNJw4nZmAhOMZohfcQ7eY6qbSayHMmL90E2lrrVWjNNrSq/X2r5dwGfmw3Ui199N4xxMg4Duz3e8ZxpNRKzoU5Za4pQ619oykiAS/2wL135nDWStE9GmlTRUtB22MUtFQt4GPAaSOE0NMuTysNrY+pmJ3HflebkLO+Ucc6tG+6ph5VR1NP6+magUZsqaEAMXh88DgPPjjGMTKOkWmMOIHsHPNwZQ4eiqPUhuibQBWbgxmcZ3TK4ITglCpCRrEcis050lovISree6bdjqPbIUfPoXmm/d5SV+cJPhBCJMSAd0KrxYy8lZ7xaK/PG8MQcOu91kYumWVZyNnwBEWhP/sQggE/HrzvxvoGcCNr2qva957HO09zHsGuv1UDGUsu9mcp+BrwK7gm5iyFR3/8pQxTtaHNUjjnHNFHmhRKE0pT5mvmdLoSQiNGZRod4rx55Tdi42PCamihqj1MddC6B1gjVs2F+TJzuj9x/+qOVy9fcf/qjuWaaK1ttYYP3jbMNLHb7zgcJw6HkRA94hqlzOSUSGmhlp56rLZYKtJ67eccMQTGcSLG2Os86Q+7bkhgDJFxGHj27Bkff/whL956wcP5xOevX/H67p7Xdw8sS+5WIvj+fqUagpmLbSBEcG5FjW1T1mYP1SK2EqrVSEGF6CPRD3gfGWImLwZq2G1zG+q87vFSBLQj3y7giCgDqEebAAGt3mrt1VFKj5DOMQyRGAPOgQ/CGCNjDExDJDhH8Y55HJhjQPt1AFvquN7nITh2PrJzjsEVvCipCa0ZFmGpINAarVa02X0ScYzTxM00sQt7qh/ZHw92TeIfAR7Xo2Uzgyslm3H2/apa8M4iYK2Ny/nMw8M99/cnTpeF82XhOiecizDuCKshOulR1G1IbM4ZpbCkypKtnCspU1Km5oJQESpFGjE6UkqklCm54GMllEoLzRyho0dM5RFV/30aZmuNWgvaGg7BubBFzVbNML03o9ztPKUOSFPzoCvIICvQ0429p28WkdjogbVgzilzvVw53T9w//qeu89f83D3gFY7p9EYgu9wtsHTdux2ERHXjSFzvZw5nR7IS8I7R+gP04vjsDt0kMUzDAPTtGMYhk79OGoHh1bvb/VG4K233uLjj3+Ib3zwAa9Pdxxffsr4yScAzNcZ1DxsDAEnwpIW5mVhTguSLHtwIRoghQFJznlEMloLbCBFNz3xPTJaTaN1Td0MtFHe8LwqhkyIAB5HwMmEkxHUGTquASEi+C3CoYq6hvdC7KmicxY5vHM4wCME7xFVxhAYQ6SFbNG+qqWQVpUjWHo6DoGddwwiiFYDclrDqeJXcLA/d/tMDZFAHAd2YQ/TMxh2jNNk97OXBRY9XQfEGqVHwlqTlRGtoloIXojRUUrlfD7xcH/P3d09l+vC5ZqYl8wQJ7w4NAw90q50nuvYQWXJmdoa85yZ50RKhVYarRjVJGrG6WgM0bMsiZwSOWd8jtRonw/pYKTraMaXjZg5LVxOJytmi3nqZZ5Z5gVt2TZ2VJYlsiRIyeE67yUubiiUats2z/dxdq124EVpPS+/nE/cv37N3eevOT2cmOeFUkrfZGvxH4jeW0bYGq1k8jyzYLVDVTXjzAZnl5zItSLNIsswDLz37nt2nsGK+xii1RMh4IPVj/TUVrDUvDXleHPLzc3b3D5/h7jfE3cTKkJaFs4hWN3hfDdyz5ITc1q4zgvXZaYqWBHj0P7wa8qUJZGXhZISzgfA0l8UtKOxVof31F+h1Z4Kd05TnGccjmbEzuNdxPkJ50arLxvQnYwPPToIVBy5th7lHQ5n6SVKzZVlTjgV6pomN88QRnRoeKRHK8swasm0IshoqWEMjtDPJzQD0Zql/OYqusqlv7d3jqiOKtDE6DOhEXpKuQJT2suSUgo5Z1JayNmOVgsiyjAExjGQS+VyPnF6uOfh7o7zdeY6J5ZU2E3FMsFxAo3m3ES2fVlrIRch58r1OnO5LKTFHKgdIFTQgpNKSoGcc09lK3U9qkVMfHegKzr/ZQxzmRce7u+MWC0WunNKLMsCNEM+F1hSIyVI2RFbAAa8i71ybD0lfiyAV+ij9VqztkbNmbxkzg/33H3+ilcvP+f8cCIvhnZaiuHxPhB9N0yFljMZhVIoy4wLHnGeBh01NJQ3LwtlmdHWOBwPfOvb395ge+884zhaKuvWKN+rXxVUXSfoG+omMiOFibgbeD6MpKKUXLlezngs6ky7HcMwbpRIrpVcK0WhIFSF3BopZy4PJ873Dzy8fs3p7o7gvdWxw0gIHkHIJZFzYs2ASlHqWgJg9UvwkXG46V4/4FzEh7EbppCr1VQ++J4tCF4gq6Il49bCo7+HqlJSZnEOqlKDmtFWx+BH3AjRC8sCy1JpNdMqtGrASHSeIQheHU0daOlZgRmlw2/v6cWhTgg4QrPr0lZobYE2otXI/Kxm3CUlaq0sy5WUZlJazAHnRKuFMoT+70zOmXm+crmcOD3ccb5cOV+uLEui7GeCCGMIRGcZhXcR7yE0R2kVV6Xz05m0dMNUemaIpeZ0kGC7d+ZQW1MzzFRQrzjvENeMsqu/pQ32CxrmMpPu7ns0y5ScLQLVauhxaLgA81y4zo3r3IgjjKND1aPSUK2g9qe22o3SPkHrh0XjheU6c3544P71a15/3g0zZVrVrmKRLbX09skotdJKpqRkSNowEMfRIHax9Ks4x1wK8+VCrYXbD9/jR3/sxzifTrx+fQfAze0t0ziaA8qJkjM5lw3Ry0VIxSPZc39Vjlc43u7YH468g4ESNS8M3jGNkf3hwG6/Z5pGhnHEh4iEQFFYauOaMud54f7hxMtPPuHT3/ge3/3Od4wfFcft7Q37aUfwAVRNIDFfLdr5QM6NmjuniiIOYhzZ79f63qgk50ecH6idg0PE0sENzGhoXkhNbUOjaHYo1Sil4qm5kGNmCJXgI07NCXhRQlBUzUBKLdQCrQhabQ+0Zs+p1TcVRpbKhl7XBuc2jtGLEAScFrQs1FZo445aEiKOVoTcCyPL5DKlJHtmJXV1VqbkaAh57sFkvnI9nzid7jmdTpxOZ+b5SkkLg/dMcSB6h8iI94oPQikOXx2uYvu+JFJK5FR6Gt5VUgLitItrVgivc/K1UUujpILr3LplY0It9csZ5ny5kC9WnNdq6hArfQTnHWBeUtANyaxNAUcTtQfvGlB7Otu68EDQ7u3R1nmozDzPnE9nHu7uuX99z/k0s8yJWhSpQGm42vCtIcFbetcqzjmGcWR0IyEGDscj435PLoVlTpzjPSXNXB4Mzo5DZNgfmJeF67Kg2ri5vcXH0G/+wvl85nq+mKd3IxJ2uLgnTAeaiyQcEif2xz23twc++sZ7BFGGjhKP08gwjPZew4CPERcCVYXUGnMuXJaFh9OFz1++5OX3PuH9d97hu2+/wzLP/cGb160lU2uhxkKsze6xto7EmosWgRgiN4cRAFW38bziAw3XMw/fUV4zmFoSGUOG83Ilz5jTFANRYvDEGBnCQAyJIU7sp4HdGCFAUMhloWFUUY3QmqeWQk5CakDNtFrJff+IiFEk3ii36M2AVRtBhSCKa5lWElmh7W6gVlQMKKvVsqzWgaPaijmVUmyfNtuvdpijTcvM9Xrhej5xOd1zOT1wvV5wqsy7PWl/JE8jIUKNnlZjf49KU7fxk6stGEdh5Qj+UcnlO62zlj9alVYqhYrzBl72gvwrGOb1zOl62m6AqhrxGgd8MFnXMmM1Rq9Dm1q4putpQ6QXuybLMg7uUbGidGQtZZbrzOV84eH+gfu7e+ZzYVkaWgDXaK50wrZC8Fvtu3KKQ49ON8+ecfviuVEZ80LwnuvpAXGORjM+1XlyqdyfHlBVnr94zlgGrtcL9/f3vH79ivu7e0KY2B2ec7jdsz8eOT57hp8mqhiQ42Pgnbfe4t0XNxymgSF0kMk9Al909BdnVVVVKK2RamVOiQ/efZeHD77Bh+9/wCcffszLz15y9+oVDw8PzJcz15IfQYlOdTgHXqCtOmaB4APHw55ut6hK59884Ox6O4jlRChpIWvmqg3NiTzPlFo69dBQp1aHh0D0I8FnprERvckxfRBUGn5xqDZKzdQqaPXUIuQES1W0G2ZRQVfEWITonFEUztnuaPaZAg3XGi0lcqnQCvv9DhHpelVTMW1a2Oap1VOyULy9/xCjIa18f50uqgwxcDzs2I2em+MNz2533BxHjvvINA2MU9xoonEYGIdIEI80IUig5A6GiskXvRcDmrxwPO7Y73bEIeKdAWw0uv3IlvnBV0hl03zldHfpwmcrXoc4olNDW+xqmUYtZUPIXPDEacBFzzgFxsnjg7l/cVYLuTcK+JWUNTTWDPP0cOb0cKYsSi0OqiDSgEJplULDtdANs8IQwQlhiEz7PcfbG569eEFKmXi5UlLq6aTvAnCrb3e7Pe+//wGlZLxznE8PfPbZp3z22adcr1dyrrz//G1++Id/iHfe/4jD87fx04GlVk6XO5brS15+D84fvo9865vUZ88YY8Q7QWtHGzudoT3F2aT6Rg5TW2O5XKmpcNwfGT76Ji+ePefh/p6Xn33Kb3z31y2qJaxOrxUtrYMpXS+lbYW3DbdY31NBtCGtmpxNPF4gOqMqapupy5mazmi+Qrnaa9Wik6j2f6ulxlJxUhEK2kzQr7JqnJt5g+7EW3N2bMIuh2jnT7Wxqn91E5uvkkPjtKRVqAWqUV23N0dDy1s3ytootVJrL69KppZAyYFaC9M0dMzAb9z0Wtoc9zvccSJ44Xi85dmzFzy7veFwODBME8O0Y5hGxmk0kcI00EZljBPHfaNVq7WNT3cmTHAQgjCNA/v9yDSMhC4bXHGODbzr90TblzTMkhbm82lDV0UMLgfjAulRobpCmjPXMPNwfyFOD6gI+8PE7jAyDB4XOnHrV56I7skgp8p8SVxOVy6nK/NlMdCnOEQ7rN8/lNZCzVBpOO+ZdgPT/sCz58958fZb3D5/xu6wJ8RILsU0sar44BjGAdVqWs5rYrc78u1vHyl54XK+59XLz/j885f8xie/gXOB3eGGd957jz/643+UDz76JmF/ZK7Kr3/yCa8++Zz7z77Hcv+K1599REkL777zLtM4EsSEAGuW0VapIauDMg3nMJpDuZxnrvPCOAwcDzfcHo+8/+47PL89ojWRlgutWi3VaoMGsbGBDCuP6ZzQWu5gXaW1RnAOOqJpPF00CaUqLZ1I19eU+Z6WT0ib8SjeKU0aOAgeBu8ZQiNGsEsu1LrQqDQStSRQ08NqR2db9Wa43tI7sPRaVYxPbbXTEYWSnYEktVGb1aYWDRqiFe+Em5tjVzfpG1GzbnWlYQORnCOtlq4OGi217AbkZeWsR/b7gcN+4LC/4Xh4xvF4wzTtCcNEGEbCMDHuTLg/jgMiHp1MxEF3MK4r27wzHtd7iMGQ6BitWQPpnUAd5X4U1Fim+OUMMyeW68kUMd7kbyti12rFe9Oktl7czpfE6eGKCyerpVKjVJh2A2FwhGiE76plXAm7lBrzdeH8cOFynpmvC2nJOKIBBOJx2j15a7bppTHEPeNu4ubZDc/fes5b777D7YvnTPsdLnqYxbxxs66RcTeCU86XC5+8PPHRxx/y9/3IjxKD8L3v/hqtFdx3Aqk0nr244f2PfoiPvvUjfPhD3+b2xds8LJnrfKFJwIWRZam8+vwBz/cIfuKzT18zjSNjGIjOE5zfiv1VBxyHwLQb2e8m/DgiCC0X5vOZ+XzmHO65vTny4vktQ3iPVhf2+4FXn7/i/v6eWhprk8omzesOcjwcub66YHPBTFurKtCsu8fhzfCwDKiVM2W5p+YTtBmnqetoe4rslOAd0VVCP5wUtCVKaUhrNDK15q6IWdU8bZMdSm/FEudoTZDK5qRMW2qRr3XxuzVLWFbjOkjkveNwMPVPa7oddcU+uvpnjZ6tFdMc+2C8NPDs5obL82d4gcN+4OZ25PZmx363Z5oO7KY9w7DDxQkfR3wYCYNRaKGLG1CL+tK139aeIV0wsWaDFhHNKXeFGd1AaRuKTqcIv5RhajPEU8TIXPXtDSgYZLCaRVTMOHNhuWYuw4KEYYPrVRuh0A0TfATvxXhFrfY7p4XTg0XM5WoRM3hHCF3K1GHy1pSiljp4L+x2E8fjgePxwP6wI8RgXriLC04PD5wvJ0rJiIMQHOeHC9/59e/yzvvv894H3+DZ7QHn4P7+NeN+hwuRm+cv+OCjj3nrvfcZDzcUFV7dn3h598B5XjAmxtOa43JOfPrJSy7nmSEGpmFgP07sxpEYQ3+wZkDeTwQ3MQ4Dh2nCeU+aF64Xb5D+XNhPkd008Ox2zzh43n7rGa9fv+bh4QRd6LFmMc45I/7F8Uu/+h1enWdUrWdWm8OJGaI5QwiuIWS0FWgJNOEoBNe25+J8ByjcKmtotLxYm9SSaHWi1QEfHS7YhrBeRlPlhH5NIURiHIjBOjxKNq66NCtfgnOm4ZWAilLFKKCqYnxrHBhDJITIfr+zLo9VfLHKOldxyob8P6rV1hcPIVJSQZtyc9hzcxh49mzi9nZiGkeGaEeIE+JHJEw4PyD9vtLlmqLdOPt8AdEtJzVn1Kql2iil1/3S8YyVgmvrtaJbZ9Pv2zDXh258npGt4BGpqLM8O/SmVS/evElz0BwUZ1D74sgCJbUuAhZCdPggVkdo5XQ/83B/5eHuzPnhwrWnshI8lWJQdDHPrA6gC7K9s3683WjdG7VwPt2zpMy8JE4PJ84PD1xODyyXM8sy01plSQt39/ecLxdSLiCeYZw43By5ubnh2bMjz58deXZzYLme+Xt/9xdYSuPl3QN3pytzShrjoTkAACAASURBVORSGFA+/vBDDocdx5s9MXhTSXlFpCJSiENgtwvsdhPTbmK327Hf7zgej9ze3jAMA7fPjrz73ts8PDxwPp85HvYcDwd2u5HbmyPf+OB95mUhLckUIz092gjqrt55efeacXzZN6rvqaAdawOxc4qj0NSMUjTjXSUGoK0ibtOIineGgOZKzgspzyCe1vZo2zFMkajBKB4n4H03Tm9ccxwYxpEhWm/sXBeqZnKnEIKHgqe5sGUUFaUqqLguVLFum9UwN7Fnrxntc7nup9bun0YtnUrJmRiiAZfOMV9veHY78PzZxPPbkWEI1oPpPc4N4CdwI7iB2jUEhtGsBmlRk1W51nrLWbEIrmoZmrZiegLne7bpNk5bV/HGl42Yq7jX0g2TFQmFJh689mLa2mPE9dCuDpqH6tHsqMmRFFrnxVyQLaUVtXTrfL/wcH/phmk1ZloSop7oMs2ZukdrQh0Injr4zTCnaezNsZnLeebzV3e8vrvncjoZ5bMs0Oy9NtCp86fzvJBLZZom3nrxgg8/fJ+cL7z33nu88/xIKzPf/bVXPJxnTteF65IpTQk+8M577/Dhu+9yPO4YdwOCUop1lYwhsBsH9vsd+8OOw2HP/nBgHA2UMI3vxDhOTPsdx9sb9vs99/f3eG+dOKpCDCPTMDHtDrYBOnDV2mOkqB1wsSkDq6V2mXgfNGCpFiBrOlWAijXBd119N17fuWInJtBurUGptFRoFKMThoC23uwubtOammGGbYqEocLRdKckUm3MuVKyOeqlCaN6Q3W1kVWMIvEeN1jDe+jNA96HTS63GqQJ8LtCawXBUUouxjkuCZrSckGaknYTN0fPs9vA7TEQYweHcOCMxmsipuHuxlZK67ywx0vYOknWaN16Gm3shKXSrRZA8SHgfbVGgn4vW2+er6V8ecO0ht3aN0OXFa3i5/468xqFWhs+ZuKSybGQfMbLguutL42KC4KPVig7LGKeTwvXc2K+mki7FSOideWNOpfXajHDFLXxFrVQqnWpu+VKpXI6nbl/9ZJXn79ivlxYrjO1ZHwHnJwTdtOe9997h8N+R1oMPLg5Hhh+6GO8ZD56/y12+wOHww13r09c7l8j5UKk4gdhGHccjzd88+MP+OZHH3G8PTDuJgO0mjWOT8PANAyWyg59/EXvYm/NyKPrsjCvIoaqLNmcXk6VebnvGtLavb993pW/0844tWbRoTb77DbuQ7couimvWLWs9mdDjINzHhVHxVl4sCfaI4zSCkjvJ/SiOITgAsGtCqwBRyG7QPN16/wQ5yhNSbWZQgs2o7ykSkoFlcg5gy8GCJUKc4O5CYTIEHf4acSFwcCUrpFdDXJtoH4cVbPpPk3A7hzqPUOI7MaJti/k4NiNlegU0WSOqzdGqwbr0aVSWmBOjetSmFNBxMCkGEe8C5um2e5/3gQOdaWb1uaPWvHeumbebM5XoHxZw1xBHxXTO1pxa2EfZ21MtVWrL3v9p/SWGRzURs0FFwI4owicF1ywlk2HoW7XcyLPlZoN9RJ5s3ugAEJthdqKba2q+JI5Xy/c3d+BE8Y0M4wD58uV+fxAuV6o85WWFrQUqlOaGBjx4qMPOT5/h/fe+4DDbmKMgdubA/HFgZu95+MP34EuxduPr0GV4/FALiDOs9vtuTne8N777/Hue2+z2+8Io4mgadaRPwbT3a5qkAYsuYMd2bSTrVn0MzSybfRPTom0mLdPXQxde3fKhu5ifNjKMTdtLEvqtJauQw06NfRGg7daeWqUjemBtetyt8knHd7HgVaHVHDaN4vrhukDwZvGWLqxNlc772mNxaUqqTRctXanVJQ5N665MqcGvnEuEIr1v5bSSOpIOLwEhjgRxn2PmuYI1r7MzTDfCBBbAdpTYS+O5swwdRwRVUr0DGEm+hnaYih5N2zFo32fpRK4Xguna+JyzXg/sJv26NSIYbDn0IzyKF0uWbKVW5bKmmF6X2m+Urth1l6HqupXMMyeH9vF+z6GwVs7Uec7aqtoVVJKlGpvFHzA4ai5kuaMDxEXbMSH9F5qcd0wpTGfE2mu1KJYy5L1ugmgtVox3Yo5AYDqcd0wh/s7VBvTsmOcRpbZKJ48X6jLQkuJVoqN9BBrX/vwG+/zR/7+H2ccJnb7I4fDjtvjyDQIz24mUnqbnCspNZ69eJf3vvFNlqVSq3UFrOl7HAdCtAkP6bJYk2yu0JS1JXyVHXZ2wzKP3tbVuoKlrp+tmcGWnO1+5kwtdZsUsIr4LaUDtvi3Djt02//vWAMqrjdDd3LbbIsmDcX6NKs6SnNo7SIIdTj1tGZgB4phCN4hQRhDZPCB2CMnDoLz1N6jKC5Qm3JNmaKN1Ow6rrlQcDRnIv4igWsBl1ZFTSWjFBF8UVxuaKomJ+yzfLov6Z9v63DtGlV9dCrVvIxoj/LeM8ZIkIqXBaFAXWit9BRfaRoobaDUiVICeS6kq2VyIVgmFJxD3hxT0tQ41Jw3hVZrVmt2K2Llr2ut5K5IWgXyX8ow7S5Y06wNJXBdfxl7J7nBybXnzYqJB1DjG/OyUHK14jf4LjAH6ZD8CjMvczLUrBoKGJw1w8qairXHGSoNoUhFinCdZ8LpgdYq0zIzjiM1F9L1iiZrSaJUtBaKNqq23t0+8vbbb3eW31NLY56rtbM1R2ujGYo2Qhw4HA9Mu8epfauHbqqk3DpA0Hm4XGhFt26QtmmeeNQJq9seamtKXQGD3uleK9TqUA3W4eLD1nmxOgXbjCbxsmZeuH9YHWa/RjE4RMRvihPpkkjXAAxVrsWOVo1SUWdGKRJwKkgTHKYC8lEY48AQotER4jeHp72ZW5yj1MySFmQGPydw3lRAzuOiCR0IgbmCznmTulXnqU7wLqNhITV48bxs9XRrihbdpvqZO5L1gXTOULcSoJbajab0vs9KI0NdKHLBaaL2zK3hqbqnaKW2yRxiq48UUNeMy6pWX2v9VjBVm261umqferHSRbIap9W+tXfGfDnD7AjgYx+cyfFCGEyW563bvbaKD55YMjEaRWAac/MkimxdH5tRdkLWSe/8KLXfbGfTEMKAVutGWDfsmmbVZt0k12UGUXJJjPPIGAekmT5RiuIa5t36yMFUbeTldV54+fm9Rf+1qHcW49b2pS0r0tUZd0N6o+jvai+T3IlpUW2D63a9FqnNQKyPwm3ghXgA3T7fSjxvDeq66oQwMnudsKCuzyuy1rtxtPGSn72MG5kvnSamy/aktzK5bpitWu+jkf4WOQ3yDojE7d70pjcj0WMjDtY8PQRPEGcjSHs0x9kzRhy5KfPcsygR8AE3TMhao0VDObMKdbEJBE0b6hwtKL4qpELF1D4WHaWPEbWxHaqPmckqfdvGhNa1x/NxzEfrnCeaUGZUL9Q2oy1DyyiBJpUmdFmj3USHRV6bUlDYsNRO2egqhuj32fX6XdbSza817GqYy8bBfinDtL1ihhnjyDAODMNEHCebl+Jt5EKtBec9peStoVjE+vTSss5YWVE6Qbr6x3szzprS440Wiwoa4tZXqdpx657XW2rYYFFqyywpMHoTWo8+MviBKH2UBo6iQi2NJWdUhGUp3N/PfeDSYGM0thmvjo4wWYQB6wbQLilcAYYurlvHU4gITm1eTFu1md0w7dViEXrtd9zg/TWWvmHwq7Gyjtzo79mdzkpeO7FZObtdh/077yY9lRXXBTRbgdk/jwhNDGEMLjKECQaHi8IQBwYfO3/pkKpIbXhphKESBxhCIDhBW7Uxjp3gX6nDqpBrY8mVeTHUm+A5Ph84HCd8mMANHS01UE/U4bSBD2gYidPE/nBgGEdi8FQDIKyNq9feNjHQfd84G6sXDJRZZaIGIpbN0TfNiC6ozlAvtLqgdaERUOfARxoB2uqYZFM11ZrZvrVwrePb2tqo27N8rHs7aCp0Qb2BlWWdaPFlDLO1RqnFVBvdY72BSbON6QMTGmw1kE0S8FXxoVm+v4oEGvZARGkOquvdE7n0D2aiBQ0DWqv568ew1T3U2svZqC1Ta8ANQvQD4zDy/PiMw7Tvne0zp+uZdn/HJS9drqZcryYxY3S2Ed1gKeI6unCNar2Z2OqzlTc06qFpoa3GqbBRFMhGZ7AaqMr2M9FVCfTYCCyrKqR1t7PK7Zp2iZvpQ9NSrIXIYUOOJRCjME59LpD4Tn90nk90s+tHNF2o4vEuENzAFIU4TXjpfandMLUJmguaM14KcSjEoRGCydBSzSylbWNTnGjvcaWjzJXLnDhfr+ADw80Lno87xt0NMU600liuZ/IyYzMSFEJE4rRpnne7HSEETg8nnHfWE7waptqws41Hf+O/x/LH7t+WfbWC04ynIJpRzYhmTGihKBntYzeNOuoD5QQzzNIbuHmjXNH1fdqjSH0Vqre0obEpJZa0WM9oz8y+pGFaO5aIjWcIvZvDOSN6e4P9tpF9iL07Plg4tx92xLZtjckrydpqRaT1otlGQoisDdGR6pJ9SO3Tg9aCW5oZtioZow52oxCHgdvbZ7z/zvu8/ewFOScu84VXd69ZWuXV+USrlWna8c46wSD24U592NOb/Jgd6z3u3lAe/2wrvL7N7Ok+pNHlZZZS6Rq2enfFmq42tc531c59tcdWpVpL7321TV+y8Wm97DFeL1j0GcbANNlYlDW1Ww1zbVYH3jDQDiSJJ3obSCzeEcPAbtpZGdEwccGcKG7GkYhDJsSCOHMopRSWxcZuINK5S0tlG46qwlIa5zkhvlEb+GG0JvJxT8uP0wz6KACTw40WLW+PR/b7PU4cp4cHnBMDxvroDgGCj0ZF+bB1czhxW9RcWwPNKK1TRSidxy3dKPsBoAUR665xoltmZ4qdSi3tjdKmYwJrZF7vQ0eOG2rTFXqz9mM3TMcTvqxW1jiaQnF+628r1SPF9a8+sO3qfOg8k9Whq6Lfr4FEbB6pRUC1xtk+lwV91Diq9irMRrRtM4bWptN+57aNbdBzxYk12gYf2B/2vP32W3z47vukvHC6nMELn92/RpyNxtwf9nz00YcWWRw9Kvb6QHr924l2c349UvcNtFaDTYWmQu0zW2u1zoOKdXTUzWhrzxTWYcfrZPbeL1gXav9Khdy78NeZMaVkcnk0SieREEZUjwyjA2xWj006+H7js1DdOz54M3KaxHKdYet9IMrIbtxZh0UcKaUZnxouLBjPGAI41ygtkcvCkm3mrYoZdYwDYRhs+njJuDCauEB8z7b8902YcwGGMSKMtD7VPU4j4/7A4XjgeNiz2xnXfL6crVWtNz6bYUrvFY3UEPuwbzu2WtMI+D45oUDLiBS82GjNzTDpg9RWuaIrGx4gYkTsm5FRexRs6zC13vVizQJmB1Ur1+uVy/XKPM99v5laCTCH9mUM8/H5WhFdSkGxzvHH8X6+12ZtMxbtTby1dwC0rcm0bAX549cFmPWuURdpvRZ6/P4QH6xwE6emo+xRki6Ibs0+5DoBHa+4aMHNOFMFWd+34INjt4+9WH+sC+iCB+ndF9vXG2A6THsgtTul1UHY52zFJnOXYp0dpYsClpRI2UTftTy2LOWc+1SCC8ty7SMyrBM/5UzuU9ZW7tLQ8cg47thNB3JZQKqN75wi1g9Z++bSXliaYVrKvE7TE1BvfbPSp/+5icNw4LC/4fbmlmEcSUthnpPN6+nX7kNDyTZGJc8suZKq4oN9R0cYBuIwEsZIKBkfR8QPJuNbDdM9gm0I6DBg6iIDu6bdZEZ5PHLc79lNO+7rKpp4lOJ5763up++BbYxM67OCVqPUHilNJictg0uIJCB1kYHVmEql6kxzV4oMLMW+EmEugVrl+wxzzfy2v6/A4BYxPalk7u/vubu743Q+E2NgHAfG0ZRquelvY2xfwDBXLSLAOmjJRjAmA4T6g/DO93Yf04pWXQ3GPsj6JSx1TWn7BqenWKzph3M01yd8dzGAfQeJjZtsTbAxcQ1t0qkL7eS4NermmiiaKJKokqgkKpmmeUsX1+6Lx5i/bt6K1gyt9BS195l2OmNDDlc5nJh2cusP7Olm7rNFU85c5yvXZSal0r+Mx5Q6S1o4nx84n09cLmeu10v/rg0baZJzn8TWuvQx2MiU/f7I8bBYGozivBCit4HTpWxRf/2zdWUMb2Q4K1Rgc3mNgJ8m++6Nw37HNE7MPhtBnzJlXkiSUBKlKUvKXOaZoqAuEINnmEZ2+z3jbmSIkVQqcZgJccD5uKHUta5Uh1EsxIB3kzlzEXaHA4f9nv1+x9TnMPnVQYvpceljRg0s7PtTbd5uw76L5PsjZn1MYzVthikkpCUTGlS7p0VmilzJRK6lMOfAnAOlypa6fp9R9r7XtnKbGw7jmJeZz16+5LPPPuP1/Wt208TNzZGb49EGqYff2fx+z4HPVsiaJ1qJ0ab0Sdx0oKYLc1ux2qqs0cW8d1t5pRUp6/TAFrHe3C2saWJn/fpDQRR1im/YCEgxLs6gbN+/LyOT60IqM0u5kmsi1Zlcl/4VBmac83Xh5cvP+/lX+tcivmif4KYNT4fpdR0xsXKNKyCn9jDfiJK1VHK22nzJiXmemZer/azXnSjUKkDAh4FhtJomRM9QYlcF9Vkx3ThhbZ1qZhiXq5UNPuAkMM99jmlHl9c/HdrpEqBPuUelq7CMtA/ebRPtxugYokObR0sgDZE0DrQWyVVs4tyycLpckRAYpoE4TiZhvDkyDvblPqkU4rgjxBEfBvtmrKrktJYtDfHe5HvR2rN8COx2e3b7A9OK/HfZHapdqdQBOe8ekW86qLgChN9nlCudYWWT0wWRhHMJJwnpRkldqFrIeiUzkjRatMyBaw7UJqyN7/pbDPNxjOgG2imcL5etv/fzzz/v6rEEKOMw4ne7L2mYqw6RXtPVsm1K2QqadePaIKZ1zGUtFvWc9ItuK5q6cpKrYXYgpK6pYOlDlFIXBNvNqKo261Xpk9093oEG6Ryk8V1zXni4nnl1uiOnxPl84f584tqHRbVWuVwufPe7n7J9sYx5oE6+m+jBi4252JDg9bp5U2Bg15azcWvbyItunCk3lqWRktWjvInMOk8YhEkCPkbGadxUI+toEEt566OEr5ryShFKaSxL5nK+EsKp16H1DTS2c3ud4llTPgPQnDm5riUNwb6vI/bxGIMXNDhq/1q9YRjIOZAK5FK5LonzdWbY7RicIw4Du/2e/eHYOWwhLslqzThYmxqWaq/ATa3ThkuE4PvX3Y2M08Q07bba1zlPKZVPvveJ9bW+AbQ4MUWPAT9d/dQdum7AjxmSqJUpjgSyIG6xzE8zUjPSct9nhUq2rKsadmD4gd0/9LF1a33+Fn8MW7dnVki5cjqfOV8vLMtCKoklB+Z55nK9UGph+tIRUwwYEdjC+KrrsobR1lPJ1sEck+Yt80zOZVP+y7qV1xS315bb92m2x+97yL2+yjnZAKau9qjNlDtrdF0npEufno0IpTXmtPBweSA+OHJKXM5XXp/vuaQ+z0YbD6czv/Irv7ZRO7Eje+vsIOfoyOx63b1e61Hd+M2uE+6NvjYxYBU2O2o1L9sExHus6cNt99a1ioSREDODHjZktta60UGt2sDh2tNf+05Gux8hBJA+3KsUJFsq1tbm3Nqf39oriOldlf6VFU5Qb9/HEZxFVm2ZkmYSSkqdyK8Jg7NMTrakTOqgj28mUXS+D+waonGk/Qt1UjJpYcqZJWXO52ufo9R7VB3sJpuAP06TIcLDsA13Xmfj5Jz55V/+lS1ArCqgGCP7nc3YGYfxscNkjZwrUtrvgVDx/297Z9YcyZFc6y+W3GsB0CRHQ13Z/f9/6ZrJdEe6Y6I4XBpooKoy1vvgHpmF5nAW6kHz0ElDg70AqMoMj3A/fvwcAlg5NY0NuBoxJWFK0s5WEmUGLWWytn5K00jZ8AVFGUxB8jZZ4KkkbmHlcl15fXtlDSJUZnV8MubE9XYTrapp/m2BKSEAjd3QENbGtMiqRiY6OrIgYlgJq4g0O2upKvy0YRIt1UCnV4wgvEU1XGJKApbEIN1xDfxcRR2uweHWyM0QXq36Oop2CQkIRcWuciKWQgGsd7gKt9uN7//0PZ3vGfpJpga89DGLioU2MKq5l20nHQ2oqlp7pg3Mqgq61DvktRZDLd3+LbaH21qfBouXhaMGQKVUbKlUJwyY4ncWi5yeWV+H1Z5hxsS4nSYKj7WOqvxYIYTSLCAwgBWNWW/A6FB8WC/kFAkxswZJyUuJQuyOgVuUWdSkYEdrlzkvJ1cD+kIIXG83rtcb6xq43VY+fXpVg2FH1wuSP46Ssg7DyDhNygMW4kq7YSEE/viHf2cnX8jnaRp5OJ85n88c5lmoiY15U1vfvAWlaNk6VrCSyhobqFVOTVuSnHhksmmBaZUeqYFp2gpoa7j9V1S8G3IRUsXb2yuvb2+sQYyEnQ6LxyRqkNFZxrhbTPxdgdlSuH1cRcCaZn3WJC1yTtsOl2LSpnndUkORgdhPg/YwvfcY30l6cCcXkXIipKjIqNYQRlC95uJst9NHJBbHaeRwGjk+PHJ++prz00kg6u6VWByHa+R6kxM518T19pFpmPGdCvvnSkgrawqEHHVRt8CURdL6lI0R1DhIraHf+lsN9Cqa99ctU2j3T/uhTYJD69g2+PxOrb627Exeg/eSwpcq9zJlOd0qFbfVw9raqeoTcodwS49TZg+NqxQPrhqsKdQcSQGyjcQk0x4pR1KJpBwJKQqZoKDFuePer7LNxArja5UUTpXuUpbpl8tF9I1CCLIBWbtJfA7DQLMmqEpXalMY33//vdbarW0F8zxJp0AxjK4T1pl3Tv7dhv431feCMwFrI8klnCLw1CaTIplBJpFqImfx6ElFAnN/7nexoW2zVleGGIgpEjU7c52IfztN8Z01Gw/7NysYSD6t6ZoKPbfAzKVs+pgpd9su19ThZGEL+lbqPjHRUi2r3FRn/fYAWvCnnIgpyogSmp5YI9MpSpS2xurJVvBdxzjNHI4nTo8fOH/4HQ8fHrhc38CPxGK53iJXNeQZp4FxmFnmA+fjmakfcXhqKVyC5xbE/FVkB8V1OIQkTlEp04xNm5xk80Ch1jvkue3sZkPtso4Cbb3RVqNvtTfbvah34Fm7xDRY7kdp6X+BUiJhNcyjfG1uVgW5iEiUDrxvVnZG1AlqJ6loV1QswxRKFXqclMOVUhMpr4S0koq0y3zXMdmZUdPQNsBMLUo6EGX0GCKl1M0fptOPYRgU5JmZ5klPzfulqCWOvv2cMz///PwOdKm1sCyztNqQg2KaRhmcH3ppxWirrp2YhoIn4nyko7G2FGnVzTPXQqqZWCIxSRoroJ1yX/U0LrQaU4gTMsRQxZm8CDoubtKeaZ4oqibZRKjb0MdvCsxGMGjmQmULTDmtchL3pwbpt1TJKtlaENmiYryBGFcFfawKK+Xtxra6sdGZRCtFJ+oVvXOdNI+9VdpVSpQY6ftOJDsORw7HR47nJ46PT/hhBjtQcFLAF0NcAw8PZx7+6Z85LQeezg9MwyQS/aVyjYFbCCJ6lTO3y5XX1zeen5+J640UrjSrBtt1okjuRMKwNLCBIidQS9srMn6kJ5qstwYolS3tzQ2KV8Bis4DXxNdQNunP/SRW1gyV/puF5ukRo9Ac5bWJmFT1upEoAYQqoIwr4m+JaUr52jN0UnOFpMh2C8xeVOOGaRQDWfVBqQi9cl1XMdbR9Np7QY/HcaQfR8ZJ1ByWZRYriHHEe9dOgy0425Vz5uPHj3ebnny+3m7bQUGtSpivoidlpMVXdQSrSawUE/EkkskUmyXlr6oaqBtkLolUEimzp7LVKjm9ZT9Zgrih52qhEaOQXrDQ9TI03gbHb9crl7dXZXXdcaD/3sDMWeqKlq9vAMh9+Vukd9R2+OYtgnOaRijSqrNqVIN1IJo0+i2tGPEUJ6NkOQ8bAOOcoILjPDFMoxjRApRMuF0JlwveO8ZxYpoWpvnItJyZD0+4fsZ0I8b1GISwHteVXOD5+YIzHeel4iaxGTcG8qUSsvTCsJAcwg4pK6Vcoa44O9D3HfMsbYK+G/Cu3xaRBCYb4tyCLOdd76UJZIsnRlBAJYlaQc7kjEhbNBpX1RKhsZUwUK3yN4VYndMggEVTaNNyoe97sWhwsqFqxS2cV2exuWJzuauflblahBca0o013ig1Y71l6iZMZ981y2utqu+q86MI6jtOE8O84Luew/HI6Xzmq6++4uHhgcPhoKeuqtC1kDT3a1Y2ttQIHE1lXbVy394uW4vGOiH1973Mq2581LrT/oqJJBNJJpFQjVztYecCsUZiSYQs6XyIlZj8Bv4Y3QyzMrtSkaBMqUlqChvMqfbRMIrWk+88r96RU+R2u4iI4V+Ivb8SmJkYVgTe18VgWni2Lbsoeiisnr6rItvInm7t7kdpSwOx+87Y2hbee2rtqHXA+V1pbZomDqczy/Eo/hK1UFLg8vLMWxU/iHEaGceZaT4wLmemwwNdjvh+xncj3nX03UBYV/7wxx/5z//4A99+8zUmOuo3BvNwwDrHy6crP7+8bNKJl08vvL78zOX1mbheBXJ3Pf3oOZ4PPDw8MY0zXTduI1fOGfrB472FKoyTln00+laKidsq4Mjr2xtvl+uGYsaYxEQoJGKCGItC9iLwJIZUKkCVCzSdmZIJ66q4jqXrPfO8ME4TVZ9n1nrfqdhVp+7bJhe1I2+zFAZyoZrMLV4kMKmSii4D4zLi/F5fyjiTED1kGqlnmiameWE+HFmOJ06nE6fzmdPpyOm8sMyTcH6t/WzlmXf/76xlmWc21zmtdZ1zZD05u7c34QxPAzENUoeXNmSuJQSFSiISiTUQipAmXA24EsjVE0rHWgOheNZYCCFzi0naJeVuky1yYuYqm2hpsxrG4bxoFUl/d2KeZ3wnQtTXy9uuh2QMv3b9DYEZNpDHWrulmQDFJrIVv6aSRPMnG0txSWQIdeasnZa1ZFW5qxuZYAtOa0X3y+4feAAAFlJJREFU1Pd0VFxVlQDfMS0Lx/MD58dH+s7jEHGuF+8U5i5q3iM1jO86XN9jStcmvKnqfBzDyv/5tz/x3Z8+0XcL33yIPKyJ6yrk5Z+f3/juhxfICVcTr88/8fHH/+LTyzOX242YM7YbGJcDIUM3jBjXge2pRknUzuOHgWnu8U3cutylrNrrut1EQ3c6XFlut01GpG1iQmAXp7UGKgA70R4LJZO15+tM4vXTC945+l4Evw7HI8vxQAii+BDWIEFsDKPr8IOXVo7eH1sb7mioKVNNZo1X1iT27l0/sBwOHM5HjKkyE2qNDP3qRowRDus4jQzjzNe/+x0fvvqa8/lBzJvGTsfUZEplPyHfL1QdtsJYyzwvlFwIKeBCwEZZl4KC3nBvhnHqWdZJkGRnqHcnJm2CR8KSUCIuBwoSlBKYhZWOoIEZQmENjjVaYhTaZSvp9sAUgkHRAQXfj7iup+s7pnFknmeWZaHre2IIvKo94+4q9xsCs+hp1wJTPkuku7KnHhgrCuFa3+QU5SEVlRDUNyMzhnYn+RijIINQmIwVpyeByovaenvGUXbdw/mBeRrovYUc8bZS4kqKK8PY0/UO642qYVSMszh6+qEwziIxEkNPqoaXa+ESKjFbYjGsWhs8v9748eOFHFYIN15+/oGfvv+O19dnbiEIP7RU/DiSqqWfJkI2TFOVdNbAMHixXOsNZpAeqTZFVE6kYlwC02NcxI8LkyJ5SWv63QtTgI1VpxTaxtiUxU3JUCKUzHf/7195ff5I3/cclkXoXw8PzIcDb5c31iRjRylF8Z9kwngdc3N2C0wAqpwwxVayUcsE53CdUDHHYaDWRKhyUt+uV0FQo7S8ok4hTdPE+XTi6fGR4+nIshzwvk3t3Lc/2Pvk2yV/bo2h73sZ7tbechvDqkV659Yabuu+uQlNM/OL0ToiSVHXVMUJWnL2qqmpAD8hByGIRMsaJDBzSqJQoahqqTqRWxvM6XBFiOxd1wnINYpkadf3vA0Dzvu9BffrcfnXx76SGtpwF5jW2o2wi0GnO7SHlxPJWIrJ2vqImkKVrQUgwfz+hQmBXSb1rZMazXkJzH6cmJYDy+nM8TAzDR5XMzUFwuUTt6tjmAa63mO9ETQRkTnxtoMKY4qQM7HrGMaJ48MTy+mB8XDEDxNFxcXG5cjDkyVcr6TrRca5csT3A6/XK+a2kkOkOE/Ilct1xbpP3FYBsmKIOG84P88cjzPzODKNI86qDpKelklbCKXUTahMHrTBOpnUcK5JhRh65dnurCeLN1bbAEI3+/mH/2QcB+Zp4vHhzMP5zOHhzLjMYOD17ZULlZijIMg1gxMWErWIGgESoAKcICWHlZ6qcUom8I7eeznNqaxRrM0l9RZgRsYApb5dlpnjYWGeRobeK+Ja92Z93cua95WX2cKzKGe51c7OiqMcVZH81CZxAjEFimYTtUrNbzQ4q4niZKbvxZmODoM3lpQNJlhyKoQYCKEQoyMloXxubnWKbDujmcXdidnp6GPnuq1145S03wyOyjbc8RvBn9bCaIFkqsGYIpSujeZVdusxI/qgOUdyNZSSlAan1KWKNNDvenZG5fP1B7Ihu5aNpW+MLOgQIillzCjF/rgsHM5n/NAxzBPd2GO8pRphYHhN+bbmvrpEj/PM1/904OHDB8ZlwXhPyELJGuaFJ78QLjfC9co0jRyPM6+fnvn46RMfX9+wlyshF7Adt3XF2jeST2pee6HWwtunkWUemceJcZjEV9I4bb5HbT3Je/dq0ydiZU74p/2I9YPyhB2dq9iugpIemq+kM2CMpGtdPzAPI4d55nw88vhwZj4e6caB6+0qo2GmtWN02Nca5R461OlIIiHLhoE34FHZUYfrVIrTixSJNZWSE9frlet1JWV5zuM4MkySzi7zzGGZBKRxjoryTLkfRXu38ni3a9eqVnuNYy11PEX6z43H3erPkCK+Wpr9I4gdu9ChEsVJgFnnROnPWnrrMBFMsvqMIMRCjJ6Yig5QCJ5hkXaTtXazfpC2FeJOrs7knfN46/Q5iTJRLbv3ym8PTOBdmlHZxJ5AXI1LyYKm+g7vpeWQlZ4n6Fna+kQApjjahInEu7zBdsNb7641mI3WqtfrDffygrPQdxY3DVLvnB/opwE7ePzQi4YthZSDQOfWSy+uZP1IzPPMt+MTh3kglMoPz6+slxdyTvTTGT8c6MYJ53umecA8nbheX5k/fWL8+Iz58ZlPr29ULNfLhanrWI4LdvJMnaCT1hgIMuGf3m7bELZoyMpki3MO6z2VgcJI5yZ8J/L8BUcsqiIAMvupslPGVEwpm1/GBsQZxzKNzOPIPIwMKmiWQqDEiC2FzlmmoYdaRV7zLgyEzivoUq1GzIi8oxs6+pzpOzHY8d7jtAxpfOKkI1JgsE76yg8Pjzw+PEgq54WCR836kFsL6C+jk7II7tadQfi/xWNyJVbRLxLqXJvOCYgYVusk5E1c3NikkzmICqRnayllisix1Loh41ldyyqq+q49eLdROD05S7sp56qqEHenpLYON2JCa6E1tvuvXH+VYLB9s7vdTXpEaMoif++shdp2Q6HYCa2p1ZaaDtdyV0O1wdHG9mgUvPePq+QiNYwRGcNp7Bk66SNOxyNDGVVHymA7kdvPOSq+pHVv0eCsRRbK/A0GsWB/u155+fhCSYmHp4FTt2B9h+tHOjvT28K0XrDTC9WNXIMhxoqzGQqMQ8eHpxND3xFXUfxLsVBic1HONPvBalVJ0IBxFesrxjcHMI/vBqzrRKsoCXm/bAQ7hFTR7o0GhaFtoFa4o6P4pvTeK04QyCFgSqa3FjsMYC1957egllrfbs9JtG8stnN0Y8dQK70fRXrEN2s7lTChKp0yY52kctOy8PThicfHR+Z5EoUIeZh3fdl9KOAvBadzjsenh43o0oIvBKGCCu7UdHmVyFKUgEI7MaU1YqyUEKmIR0oulmwNucoYX1U3NmsNznd0tqf6jqbFZ007TCSbE8V84TJXXfcydysIctYWmMFAERaW28yDP0ej/8bAbD2t+7TCKjpr9AW1hnbOiaQkg02T524i47Pv/IvfbTVH4zhu39dAXEklE8KKqUUMW1Oi69U7ZfBYb7He0o2S/hVNf9jSJlUttkgqNi4yipQCmIj3M8VkrB1APbFq9WQMEYi1kPOVmgw2ZoaSOU0L33x95tv/9S3/8r//hXme1No7k4J8Rl+DjDWJomCMkaBpV8pV/DtqB24A15Nx2h8uuuebPTDrfeX1/v6VKv3IkjIpBMLNE0tmzYH1chHxbaD3ylf1bdbR3jGqPGDlxHFoWt2Tc1XZGKsbn4AgRt9b3/dM1dL1I9M08/DwwOPjI6fTkXHoBYVUQkULyK3EaD24X1kfznk+fPhArYV1XTVtvgEy6meCkT5k1r5wjBQdsmjBaURvTwIziUZwLkZMjapOL+mPNhact3R2AAZcHaS2p7HZFKxs/FjqXjtaHTiIAhQl/Wjtwzbk7d1/KzCVA2rqtjCc+h0aK/l9Y6ukJCNh95Mk2yaP2Yxb23+7Bs7OC2190ark9ZLlTcscqLhYlSjaQDEGjueFw3lh6ke6QaB/34mqQgvsXcYk3wFYDms69aooWDfQj0eoBd8Ncvt1xi4WsQ+I2VOyxRZLD8zW8LAsfPjqG85P3zCfHplnoYiJPbswhywFayveyy6Zcua2Bv1o8hyGmNX5Cq+SJZVq9LTc+GlGaYp/7hJ9mU8fn7ldb6SYmKY3Si3EklUt4SbZjZd0q3PqvNx1mpp5rO10QKAQU+DlZabre0JIqrqXCTFgVKYjl0LX9zw9jTyYjnGcmOeFD0+PPJwljfVOhbb27eXus/lsi/llYHrv+PDhSSiT16ug3EYwjBhX0QJKRUGVpKisSIIYU1VYXJBlR5tWQgKzQCrgjNaJmjFY5/C2x5gBZwbpJrRTXrm8tapBcTUbz9uQSF2j3iXVttXSporJUN/3lFJ2feC/NzBbcG43T09L13QylRPb6kmZbFD4HQkCeZ8ajHcTGw0ylkewDyRvdmqNR1olXYlRwJUcg6SlKVJdpVtGRuew/UA/jWIhVwsVnWFU2YqMOJRhVfIkZE3NPb6bcE5MgTbhpZJErbxWKILKgaF3jsPYMdSR8/nIcnqkmJ4fX1aeL03CsW6sJq9mpl5NeyUwCzEaUu6J2ZOKIVVdKG3oVuvtapQzW1tQyoa2ZRkSk4DwNH/6+WfGvmddV2lpKELdwBHbUEJj6Zyn9x19Lz1g73tROHSOihgkTfNM3/Xc3A2qTE+EoNwhU7C2iOHudGKYjszzwrwcOB4WTodZNG9N64yynZDv01dzF4r3+YD8K2sth8MRKOIKrv+ilESIN9zVkbO9i2edQrJCGre24ozHmkrvRCJFQEhh7phctpQ4JEvMhpQtqSJDYFXI7dsaLVVLOVlL6xpZb4JMd50InHvfK5IuTKyGUJ/OJ4y1xBTpxt84KA134I/+2gLFNCBC/03RQNp4r4o4GiV6i5O0HuNNxX0TkGojOvkuOAslN/lGsR4oKRNu8kNLLdje4cYe03eYrmPQ9KLUtouJqkLRRv2u0FclzWwbj3HaX6rQZERqkUVt9lZGNQbfeUlZO5iXI12/EIvn+VNElNf25SaDvKIOYK0shpwLIUoqVRA7gixttE0doinxSTzKuN0uzictjf3Z7GNQawj88NNPdL7j0+sbQz9gVCa3lQld3zElobPNSolbloVxGum6QS3jRF09xsgyL+JFUo16WyZSBYeAcEPv6MeJ0+nM4fQgxPRpZhx6QWGt2etYXUjvGyLmLgjfB2d7QNZapmUE6taak41DjIS7riPluJVYO5PMSqaiVuzOQmeh7xLWRQqCO5SSxCoyVW4BQjTEKKODqSRxH1OObvPAbP3onAphjaxrJITEOGSc6+j7UVtieaNIDsPAo3/ieDpTa+Gy/taxr5Zm6jKR3XofvJWdSR962SUwdFnuuqwIiVemSawO1fYqUGz2r28nZSkbpL6pG+RCzYVYMzFH1hQwvcOOHW7oGaaJw0kUsCuG5lYsZjxJlLmVpFwQ8sO7paAzlm34u+WMDamstYAF03eMhxOmTPjhQK6euIoY2KZ5ZtDaRvuyaL1DeheAbWj57o6rQHT7kPteqVtgyrdvcpT3zXmpv77/4Ueccwy9aO9I/Wi2gfdh6FmWlWrg8emJeZo5nU4cDgt9P2CMtKeqkRGmZWqBibR5QpQuirVYOzK6gXGYOJ2OnB/ODMOwPVtnt3f1DoFsgdjOUcXf94DVeq7Jy1hrWZaFdhI2DCKElde3QVBiRUA3sS6lePadaBKJoa6hs9C5oFrJhpiRNV0yMcE1wBohRHEnCzkRN82qJi+aVedJQa+QCKvqNJVKPwzC4Mqtly/Z2TCOHLqOfhiw1vLdn3741dj7KyfmPvtWW4FrUD7k/S2+P1EbequhXJXCV9GpEjE0lXEhtwV24zU278DKDiqJLqgGb5KmtokrfurplonxcOD0EHVjsFK9m93Bajstc1YpSVVl2159WwyVlmALK0XrayNCTxUPbsKO0tivbiImK1To/B7QqBrctrbTTr9fZdPjre9/PHok/qKGfEfM+MWfa/DWSohJeL7GbpYWzln1NpGNchx61hC2erfvew7zwvl0khMWKxuRMawhMI2TKOPnKi5kt1UIJs4xTSO+G2Tk7nDgfDpsz5U78G87Ld+tLNqd3n5/l/DuN6UKJW+cRgVQqhIJooySKbrZ2m4NWHHea39dXaGrkPJTrfJeKLiaoEZKjtQSibFwCZbrarlFS0gSuJLaispj3rR/dbg/SZkVg4BKzvWq0/TZGzYirzpOwp113vPzy6dfhpxef4N8Zd125raQZHhAJrLbTKJViL3JSQozqN1yaczuaWxP3/Wb0FJ7s9J/KhuNttwNEJcqinUpZ2LJmJKJt5V4vRGvwsapWaRHrPW7LIi+/pRWQkzkGLe0ZLtjBqrWbS1JF+kU+ftGuyq1l6kZNwCQjKfEhojarTZEg1pSbiPqE+21IH9f9XRsOjy7KJkuWAULGl90X9x6krfNrwVnFSrYOI5byijKELk9MKQODaQU6bqO19dX1tu6afPK6JUOhQPRCAuo5qpIs/B4p3FimmX+VfivZyadqWzWeH8Oi3+3tzQ8a9ud9nuznZ76no0xjKMEpszGDnTdquNkbp+PtVZbTt2OehqzK2TUitRHK5QblBslB3IOlByJSSl40RGi1poFYt4nVdqwwBaYuZBiUSE2IQ/UylbC7XiKVdaU1PTed5/NoL6//oZUdp8rbFo97S5bJeNKECixvSgX9E54C1BPEpkW6TsRXtoYKKXsgUndtVtotZYAQrkk1ddJmJxJ60q8rqRbUJctCUyniuBGn77crFVTsbARySUAWsjs6P0GiFezBYPs5Nq3Mh0FOfpkZ9w3rnp38sF7r5P9vpYtRd3Oji1NlV9bHdmKsq2PvHmhsK30do+dcxyORxk+0BGsTTZUidfOQAg3Ou/49OkTl+uFGES5zZp2B0RNTxhvO/yfogxfd53nsBw4nx94fPrA6XRmGEbRYKp77bsh7Z9BPXtA7pnDdpe2bGv/M2MMwyCBGWNiXXvFKPzWIZDhe7WZ903IS+6kWLFn/YjktFLyjZxu5LSq4awEZkyOlB0xO3KWQenUvFB09E4CVLsGmtJmPSVbYApDy6maZBsC0fXfD9uA+a9dfzWV3YKjoVF6o+rd0FzT4THGkE0FberWYrZFs4FAzWtC5zWbPmobxtYjb+uhtkckp1Jhn7GrO6lYZwBb2i0ps5zermSs9dJvUhW+dlO3Ghmjp1hblr9EDfUbbwY9VV+bvWOxSMZ6tyjlne1fv72Tdk9b8O711ecVV4vren/caPDvf6RfY2X+bxfmlo0xNwWKFDFIKvg2XbjqREtWsWTTTur2c2vV8quJpckm6vTntMmJeRJVu40zvb23++v9HeUXv/vsL7aER16XyIXI5mObs7S96wCY+zXWrO/2H1uUCpdT2dzCknKWY1NnTIVU6haQWUe5hGbYugUivZlzk8KpGxAkVdieKzTxgO2ZbkCo20a/fu36i4H51ddf0+zDtkl8haGb6c79zcHcCVFtX1O3+byuE7ewYRjouvsa0LIcHDGO+xtoNxU2xLaddM1yfpxnxmXA15XLT9/zXQ30o6bIKnWYi+z063pjXW/i+rRWbPy/+3q5C5wduL9r6rdb3Y7X7ZSVVsR7RPHPp3F89i/aT7j/9u8D+LNK8xeBuZ+y7StGX/j2299vfeVNaLvV19ofdsYwzxPDNEud8x9/5KfnF/q+p81i1lqJMfFfP/yAH0e++t3vOJwfqLWyLDPDMpOoPL+9cUuJTgGYu/P87iXv7/Pzza5u/19/cRva76+XK//67/8GGEJYZVzudiPWwng88PXvf88pPAmBf56w/Qi2yaMg/p19AZcxXcGWWTSQ1f15/5CSpBQjnyt3ZBk5cKSsKJvEaFvvLTkYB1GSn5YDdI5I5RICkcotZ14uF4affsI5x48//fSrK8T8cmf7cn25vlz/09evn6Vfri/Xl+t/7PoSmF+uL9c/4PUlML9cX65/wOtLYH65vlz/gNeXwPxyfbn+Aa8vgfnl+nL9A17/H4RUxha73Tr8AAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 288x72 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 288x72 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "for i in range(4):\n",
        "  show_batch_images(trainloader)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WHSPBLpcxH_p"
      },
      "source": [
        "## Creating VGG-16"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pPOuVkpbx9To"
      },
      "source": [
        "[VGGNet Model](https://pytorch.org/docs/master/_modules/torchvision/models/vgg.html)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cdXr2eE8yJ4O"
      },
      "outputs": [],
      "source": [
        "# For the very popular network the torchvision officially supports the definations of the model architectures.\n",
        "from torchvision import models"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VodWHE_gyMyN"
      },
      "outputs": [],
      "source": [
        "# Here bn stands for batch normalisation\n",
        "vgg = models.vgg16_bn()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fDjpZQ1cyToI",
        "outputId": "c415d634-9429-41ae-ce3e-c5814209c970"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "VGG(\n",
            "  (features): Sequential(\n",
            "    (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    (2): ReLU(inplace=True)\n",
            "    (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    (5): ReLU(inplace=True)\n",
            "    (6): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
            "    (7): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (8): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    (9): ReLU(inplace=True)\n",
            "    (10): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (11): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    (12): ReLU(inplace=True)\n",
            "    (13): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
            "    (14): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (15): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    (16): ReLU(inplace=True)\n",
            "    (17): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (18): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    (19): ReLU(inplace=True)\n",
            "    (20): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (21): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    (22): ReLU(inplace=True)\n",
            "    (23): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
            "    (24): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (25): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    (26): ReLU(inplace=True)\n",
            "    (27): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (28): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    (29): ReLU(inplace=True)\n",
            "    (30): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (31): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    (32): ReLU(inplace=True)\n",
            "    (33): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
            "    (34): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (35): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    (36): ReLU(inplace=True)\n",
            "    (37): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (38): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    (39): ReLU(inplace=True)\n",
            "    (40): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (41): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    (42): ReLU(inplace=True)\n",
            "    (43): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
            "  )\n",
            "  (avgpool): AdaptiveAvgPool2d(output_size=(7, 7))\n",
            "  (classifier): Sequential(\n",
            "    (0): Linear(in_features=25088, out_features=4096, bias=True)\n",
            "    (1): ReLU(inplace=True)\n",
            "    (2): Dropout(p=0.5, inplace=False)\n",
            "    (3): Linear(in_features=4096, out_features=4096, bias=True)\n",
            "    (4): ReLU(inplace=True)\n",
            "    (5): Dropout(p=0.5, inplace=False)\n",
            "    (6): Linear(in_features=4096, out_features=1000, bias=True)\n",
            "  )\n",
            ")\n"
          ]
        }
      ],
      "source": [
        "print(vgg)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ekLqVhMvyYHF",
        "outputId": "47d7a70f-1ed3-4b4f-a397-a8c8ea6678a3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n"
          ]
        }
      ],
      "source": [
        "print(vgg.features[0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ToF1oteU860M",
        "outputId": "9a25ceef-8f25-4cc8-805f-f515418e3c7c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Linear(in_features=4096, out_features=1000, bias=True)\n"
          ]
        }
      ],
      "source": [
        "print(vgg.classifier[6])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8VpCq91U9B49",
        "outputId": "dfbe017b-1ca9-4139-f606-7393cf789b17"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[Linear(in_features=25088, out_features=4096, bias=True), ReLU(inplace=True), Dropout(p=0.5, inplace=False), Linear(in_features=4096, out_features=4096, bias=True), ReLU(inplace=True), Dropout(p=0.5, inplace=False), Linear(in_features=4096, out_features=10, bias=True)]\n"
          ]
        }
      ],
      "source": [
        "# In CIFAR 10 there are only 10 classes but as we can see that the last layer of vgg classifier has 1000 we have to change that\n",
        "final_in_features = vgg.classifier[6].in_features\n",
        "# We are makeing a modified classifier by dropping the last one which has the wrong weights. Then adding it seperately\n",
        "mod_classifier = list(vgg.classifier.children())[:-1]\n",
        "mod_classifier.extend([nn.Linear(final_in_features, num_classes)])\n",
        "print(mod_classifier)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vVQNEVoH92AU",
        "outputId": "1125087f-0735-41c8-db8b-50d56917da2f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "VGG(\n",
            "  (features): Sequential(\n",
            "    (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    (2): ReLU(inplace=True)\n",
            "    (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    (5): ReLU(inplace=True)\n",
            "    (6): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
            "    (7): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (8): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    (9): ReLU(inplace=True)\n",
            "    (10): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (11): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    (12): ReLU(inplace=True)\n",
            "    (13): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
            "    (14): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (15): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    (16): ReLU(inplace=True)\n",
            "    (17): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (18): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    (19): ReLU(inplace=True)\n",
            "    (20): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (21): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    (22): ReLU(inplace=True)\n",
            "    (23): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
            "    (24): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (25): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    (26): ReLU(inplace=True)\n",
            "    (27): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (28): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    (29): ReLU(inplace=True)\n",
            "    (30): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (31): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    (32): ReLU(inplace=True)\n",
            "    (33): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
            "    (34): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (35): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    (36): ReLU(inplace=True)\n",
            "    (37): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (38): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    (39): ReLU(inplace=True)\n",
            "    (40): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (41): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    (42): ReLU(inplace=True)\n",
            "    (43): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
            "  )\n",
            "  (avgpool): AdaptiveAvgPool2d(output_size=(7, 7))\n",
            "  (classifier): Sequential(\n",
            "    (0): Linear(in_features=25088, out_features=4096, bias=True)\n",
            "    (1): ReLU(inplace=True)\n",
            "    (2): Dropout(p=0.5, inplace=False)\n",
            "    (3): Linear(in_features=4096, out_features=4096, bias=True)\n",
            "    (4): ReLU(inplace=True)\n",
            "    (5): Dropout(p=0.5, inplace=False)\n",
            "    (6): Linear(in_features=4096, out_features=10, bias=True)\n",
            "  )\n",
            ")\n"
          ]
        }
      ],
      "source": [
        "# Then finally we will change the modified classifier to the vgg network -> with 10 out features\n",
        "vgg.classifier = nn.Sequential(*mod_classifier)\n",
        "print(vgg)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1iAktBgO97yu"
      },
      "source": [
        "## Train CIFAR10"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Jden_QrH-qMf"
      },
      "outputs": [],
      "source": [
        "# We are taking 16 as batch size as it allows us to have a good amount of images to make small steps in gradients so that a single photo cannot persuade the whole operation.\n",
        "# We are not taking it bigger than that as we have to store all the weights and biases on the gpu, and vgg in itself is a big netowrk so we might get \"out of memory on gpu\"\n",
        "batch_size = 16\n",
        "\n",
        "# Modifying the train and testloaders with the new batch size.\n",
        "trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=True)\n",
        "testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size, shuffle=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tZEKv3BP-5NU"
      },
      "outputs": [],
      "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"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BGicwMKw_ti0"
      },
      "outputs": [],
      "source": [
        "vgg = vgg.to(device)\n",
        "loss_fn = nn.CrossEntropyLoss()\n",
        "opt = optim.SGD(vgg.parameters(), lr=0.05)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 634
        },
        "id": "e94-T9Zy_69c",
        "outputId": "10739e21-c349-40f9-ea88-95113a6f90e3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Iteration: 0/3125.0, Loss:2.27\n",
            "Iteration: 0/3125, Loss: 2.27\n",
            "Iteration: 100/3125.0, Loss:3.12\n",
            "Iteration: 100/3125, Loss: 3.12\n",
            "Iteration: 200/3125.0, Loss:2.61\n",
            "Iteration: 200/3125, Loss: 2.61\n",
            "Iteration: 300/3125.0, Loss:2.41\n",
            "Iteration: 300/3125, Loss: 2.41\n",
            "Iteration: 400/3125.0, Loss:2.28\n",
            "Iteration: 400/3125, Loss: 2.28\n",
            "Iteration: 500/3125.0, Loss:2.29\n",
            "Iteration: 500/3125, Loss: 2.29\n",
            "Iteration: 600/3125.0, Loss:2.29\n",
            "Iteration: 600/3125, Loss: 2.29\n",
            "Iteration: 700/3125.0, Loss:2.31\n",
            "Iteration: 700/3125, Loss: 2.31\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-23-7e0f870e7394>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     27\u001b[0m     \u001b[0;31m# Attemps at ensuring that the memory usage is in the gpu is small\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     28\u001b[0m     \u001b[0;32mdel\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutputs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 29\u001b[0;31m     \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mempty_cache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     30\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     31\u001b[0m     \u001b[0;31m# IN every 100 iterations we are printing out the loss that we have\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/cuda/memory.py\u001b[0m in \u001b[0;36mempty_cache\u001b[0;34m()\u001b[0m\n\u001b[1;32m    112\u001b[0m     \"\"\"\n\u001b[1;32m    113\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mis_initialized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 114\u001b[0;31m         \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_C\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cuda_emptyCache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    115\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    116\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ],
      "source": [
        "# Usual Learning Loop\n",
        "loss_epoch_arr = []\n",
        "max_epochs = 1\n",
        "\n",
        "n_iters = np.ceil(50000/batch_size)\n",
        "\n",
        "for epoch in range(max_epochs):\n",
        "  \n",
        "  # We will iterate over each batch\n",
        "  for i, data in enumerate(trainloader, 0):\n",
        "    \n",
        "    # Get the inputs and the lables and move them to the GPU\n",
        "    inputs, labels = data\n",
        "    inputs, labels = inputs.to(device), labels.to(device)\n",
        "    \n",
        "    # Reset our gradients\n",
        "    opt.zero_grad()\n",
        "    \n",
        "    # The forward pass\n",
        "    outputs = vgg(inputs)\n",
        "    # The backward pass\n",
        "    loss = loss_fn(outputs, labels)\n",
        "    loss.backward()\n",
        "    # Optimisers where we do the weight updates\n",
        "    opt.step()\n",
        "    \n",
        "    # Attemps at ensuring that the memory usage is in the gpu is small\n",
        "    del inputs, labels, outputs\n",
        "    torch.cuda.empty_cache()\n",
        "    \n",
        "    # IN every 100 iterations we are printing out the loss that we have\n",
        "    if i % 100 == 0:\n",
        "      print(f\"Iteration: {i}/{n_iters}, Loss:{loss.item():0.2f}\")\n",
        "      print('Iteration: %d/%d, Loss: %0.2f' % (i, n_iters, loss.item()))\n",
        "      \n",
        "  # After each batch appending the loss to an array\n",
        "  loss_epoch_arr.append(loss.item())\n",
        "  \n",
        "  print(f\"----------------------------------\\nEpoch: {epoch}/{max_epochs}\\nTest acc: {evaluation(testloader, vgg):0.2f}\\nTrain acc: {evaluation(trainloader, vgg):0.2f}\\n----------------------------------\")\n",
        "  \n",
        "\n",
        "plt.plot(loss_epoch_arr)\n",
        "plt.show()\n",
        "# We have 10 classes - if we take a ln(10) then we get 2.3 which means that, a loss of 2.3 is what a random classifier would have\n",
        "# Also this is taking very long to train so this is definately not the best way to do it. Especially when it take this much time for only one eopoch"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now as we can se we have got a huge optimisation problem at hand, if we calculate the number of parameters it comes close to 138M params. Being able to train this effeciently and within the given amount of training data becomes a challenge. So instead of training things from scratch, we use **pretrained models** - the idea being that the we are going to use models that have been pre trained on some networks, the weights have been saved and then we can reuse those weights and only train parts of our network\n",
        "\n",
        "<br>\n",
        "\n",
        "## Benifits of Transfer Learning\n",
        "\n",
        "Using pre-trainied models is trained on very large volumes of data, which might be difficult to replicate during manual training with our own dataset.\n",
        "\n",
        "The number of prarameters to be trained is drasticaly reduced, as we only train a very few layers to fintune the output of the model."
      ],
      "metadata": {
        "id": "fZFucoPeQd6m"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bhLucIGMDJyh"
      },
      "source": [
        "### Freeze layers of Convolutional Operations"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Hkw97_Bg9Ga2"
      },
      "outputs": [],
      "source": [
        "batch_size = 16\n",
        "\n",
        "trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=True)\n",
        "testlaoder = torch.utils.data.DataLoader(testset, batch_size=batch_size, shuffle=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66,
          "referenced_widgets": [
            "eb9b427294b043ba89072ef4b14a6303",
            "a36b37df1b904f44b306d20695b5b3b0",
            "eaacf8e6fa1d4f118e45d90d630aefb4",
            "43128758374e4f86b2d14cd22f0726f9",
            "aa915e410cd84402819cddd365893a8d",
            "03ad9b1d7a9542d2a6042fb268704822",
            "35590118da194b6eb548734637c1fc86",
            "17e5f8788f1a40eab82810ad265c7bc0",
            "850c0ed6c5c945838c7e2b940a3e00ce",
            "35cf0631d3ae4069ab1dea327695a8c5",
            "f6409b274ef44e1a989dc62c52f31f03"
          ]
        },
        "id": "EuWUiotq-XvK",
        "outputId": "63a39127-8bf5-4ff1-c74e-7125b4e953f9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading: \"https://download.pytorch.org/models/vgg16_bn-6c64b313.pth\" to /root/.cache/torch/hub/checkpoints/vgg16_bn-6c64b313.pth\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0.00/528M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "eb9b427294b043ba89072ef4b14a6303"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Models not only contains pretrained architectures, but also optimised weights and biases - we are not only instanciating the number of layes but also downloading the 138M params\n",
        "vgg = models.vgg16_bn(pretrained=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7HirrUi8-du3"
      },
      "outputs": [],
      "source": [
        "# Here we are saying that if we are doing a backpropagation don't go update a certain weight or a bias\n",
        "for param in vgg.parameters():\n",
        "  param.requires_grad = False  # This ensures that the pretrained weights do not get updated during backpropagation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QI123G4p-vlP"
      },
      "outputs": [],
      "source": [
        "# So we are deleting the last layer and adding our own layer - as the model as 1000 ouput features so we would like to make those 10 features. Also as we are training a pretrainied models and\n",
        "# not doing backpropagation so those parameter are frozen parameters, so by adding last layer of our own we are unfreezing certain parameters\n",
        "final_in_features = vgg.classifier[6].in_features\n",
        "vgg.classifier[6] = nn.Linear(final_in_features, num_classes)  # Here, we add our own final classification layer with trainable weights."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0q46cBYK_BYX",
        "outputId": "4cdb4e46-5855-4155-b5f3-1926d7bdd581"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([10, 4096])\n",
            "torch.Size([10])\n"
          ]
        }
      ],
      "source": [
        "# Only these tensors require an update\n",
        "for param in vgg.parameters():\n",
        "    if param.requires_grad:\n",
        "        print(param.shape) # Here we see two tensors corresponding to the weights (4096x10) and bias (10) terms"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JAsIbOes_FTg"
      },
      "outputs": [],
      "source": [
        "# Moving to the GPU\n",
        "vgg = vgg.to(device)\n",
        "loss_fn = nn.CrossEntropyLoss()\n",
        "opt = optim.SGD(vgg.parameters(), lr=0.05)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "id": "zcoOND6l_G-A",
        "outputId": "060b5409-1813-47ae-ee0e-8c940760eb86"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Iteration: 0/3125, Loss: 2.35\n",
            "Iteration: 100/3125, Loss: 2.14\n",
            "Iteration: 200/3125, Loss: 1.41\n",
            "Iteration: 300/3125, Loss: 2.66\n",
            "Iteration: 400/3125, Loss: 1.73\n",
            "Iteration: 500/3125, Loss: 1.18\n",
            "Iteration: 600/3125, Loss: 2.08\n",
            "Iteration: 700/3125, Loss: 1.97\n",
            "Iteration: 800/3125, Loss: 1.34\n",
            "Iteration: 900/3125, Loss: 2.16\n",
            "Iteration: 1000/3125, Loss: 1.31\n",
            "Iteration: 1100/3125, Loss: 1.83\n",
            "Iteration: 1200/3125, Loss: 2.97\n",
            "Iteration: 1300/3125, Loss: 1.67\n",
            "Iteration: 1400/3125, Loss: 2.32\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-30-d3e52bbe93f9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     23\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m     \u001b[0;32mdel\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutputs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 25\u001b[0;31m     \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mempty_cache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     26\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     27\u001b[0m   \u001b[0mloss_epoch_arr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/cuda/memory.py\u001b[0m in \u001b[0;36mempty_cache\u001b[0;34m()\u001b[0m\n\u001b[1;32m    112\u001b[0m     \"\"\"\n\u001b[1;32m    113\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mis_initialized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 114\u001b[0;31m         \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_C\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cuda_emptyCache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    115\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    116\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ],
      "source": [
        "# The learning loop\n",
        "loss_epoch_arr = []\n",
        "max_epochs = 1\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 = vgg(inputs)\n",
        "    loss = loss_fn(outputs, labels)\n",
        "    loss.backward()\n",
        "    opt.step()\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, evaluation(testloader, vgg), evaluation(trainloader, vgg)))\n",
        "  \n",
        "plt.plot(loss_epoch_arr)\n",
        "plt.show()\n",
        "# As we can see that the loss falls significantly but over time it increases"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oxkguwLhCRx-"
      },
      "source": [
        "### With model copies"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We are able to see that the loss values values flutuates, hence we would like to  store copies of the entire model "
      ],
      "metadata": {
        "id": "ZYcPWi9FcPgh"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2kmeEgm-IZom"
      },
      "outputs": [],
      "source": [
        "# Allows us to make deep copies of our network\n",
        "import copy"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZdWTa0IIIaj2",
        "outputId": "6095be1e-83b9-4b0f-c062-402f37f8c1fd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Min loss 1.82\n",
            "Iteration: 0/3125, Loss: 1.82\n",
            "Min loss 1.49\n",
            "Min loss 1.25\n",
            "Min loss 1.24\n",
            "Min loss 1.09\n",
            "Min loss 1.05\n",
            "Min loss 1.04\n",
            "Min loss 1.04\n",
            "Min loss 0.95\n",
            "Iteration: 100/3125, Loss: 2.29\n",
            "Min loss 0.87\n",
            "Iteration: 200/3125, Loss: 1.59\n",
            "Min loss 0.63\n",
            "Iteration: 300/3125, Loss: 2.53\n",
            "Iteration: 400/3125, Loss: 1.75\n",
            "Min loss 0.47\n",
            "Iteration: 500/3125, Loss: 2.13\n",
            "Iteration: 600/3125, Loss: 3.19\n",
            "Iteration: 700/3125, Loss: 2.65\n",
            "Iteration: 800/3125, Loss: 1.80\n",
            "Iteration: 900/3125, Loss: 2.51\n",
            "Iteration: 1000/3125, Loss: 2.20\n",
            "Iteration: 1100/3125, Loss: 2.33\n",
            "Iteration: 1200/3125, Loss: 2.72\n",
            "Iteration: 1300/3125, Loss: 2.19\n",
            "Iteration: 1400/3125, Loss: 2.72\n",
            "Iteration: 1500/3125, Loss: 2.19\n",
            "Iteration: 1600/3125, Loss: 2.00\n",
            "Iteration: 1700/3125, Loss: 1.61\n",
            "Iteration: 1800/3125, Loss: 2.30\n",
            "Iteration: 1900/3125, Loss: 2.08\n",
            "Iteration: 2000/3125, Loss: 2.09\n",
            "Iteration: 2100/3125, Loss: 2.55\n",
            "Iteration: 2200/3125, Loss: 2.26\n",
            "Iteration: 2300/3125, Loss: 1.82\n",
            "Iteration: 2400/3125, Loss: 1.97\n",
            "Iteration: 2500/3125, Loss: 1.93\n",
            "Iteration: 2600/3125, Loss: 1.24\n",
            "Min loss 0.41\n",
            "Iteration: 2700/3125, Loss: 2.19\n",
            "Iteration: 2800/3125, Loss: 1.91\n",
            "Iteration: 2900/3125, Loss: 2.55\n",
            "Iteration: 3000/3125, Loss: 2.16\n",
            "Iteration: 3100/3125, Loss: 3.41\n"
          ]
        }
      ],
      "source": [
        "# modified learning loop\n",
        "loss_epoch_arr = []\n",
        "max_epochs = 1\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 = vgg(inputs)\n",
        "    loss = loss_fn(outputs, labels)\n",
        "    loss.backward()\n",
        "    opt.step()\n",
        "    \n",
        "    # New part\n",
        "    if min_loss > loss.item():\n",
        "      min_loss = loss.item()\n",
        "      # saving the copy of the best loss\n",
        "      best_model = copy.deepcopy(vgg.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())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LCWS9D4PJZzm",
        "outputId": "5ad8176a-e085-4676-bfaf-7e8dbdeaf479"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "45.372 45.18\n"
          ]
        }
      ],
      "source": [
        "# The newtowrk will load all the variables as per the best model.\n",
        "vgg.load_state_dict(best_model)\n",
        "print(evaluation(trainloader, vgg), evaluation(testloader, vgg))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9ttmtduHJeQe"
      },
      "source": [
        "## ResNet Model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZgR3e8P2JguM"
      },
      "source": [
        "[ResNet Model](https://pytorch.org/docs/master/_modules/torchvision/models/resnet.html)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66,
          "referenced_widgets": [
            "614ba22178f040d5b1c7b9d44987912c",
            "cfff217b7b544d098dcf45bea629b079",
            "01afc73ed21940d89aeb1c9086babae9",
            "275d153a70af4b538d0f48094ce4f23b",
            "d347ab84506b40e6b886230fe4820cc3",
            "5ca5da3787ec4913add035587209e414",
            "e003fd84e1784f2da4c19b9df39fad44",
            "f69c6c44486e4fb3bf33894c95e7ea8e",
            "5e87a7d7ef1a4d04b90652947ab313c7",
            "35327cac2936494a83c9a0067e5b74a1",
            "31df2070b4e94897b63e3ac7a936b92a"
          ]
        },
        "id": "Mw-51dh4Joga",
        "outputId": "88ea177b-388a-4389-ae02-80330144f6b3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading: \"https://download.pytorch.org/models/resnet18-f37072fd.pth\" to /root/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0.00/44.7M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "614ba22178f040d5b1c7b9d44987912c"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "resnet = models.resnet18(pretrained=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9_4wyH9bJrAG",
        "outputId": "2a7f32f7-772c-4cef-b344-b644c6dda77a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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"
          ]
        }
      ],
      "source": [
        "print(resnet)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Dpo0V0osJsAl"
      },
      "outputs": [],
      "source": [
        "# Resnet is also a very large params, hence we will freeze these parameters\n",
        "for param in resnet.parameters():\n",
        "    param.requires_grad = False"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Q4pU7L6vJuDD"
      },
      "outputs": [],
      "source": [
        "# Removing the last linear layer and adding a layer of our own - explanation on the vgg newtwork\n",
        "in_features = resnet.fc.in_features\n",
        "resnet.fc = nn.Linear(in_features, num_classes)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P4GoNttkJy0l",
        "outputId": "24887ba0-e099-488c-daad-3adf02a0818b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([10, 512])\n",
            "torch.Size([10])\n"
          ]
        }
      ],
      "source": [
        "for param in resnet.parameters():\n",
        "    if param.requires_grad:\n",
        "        print(param.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iDU5P_n2J12y"
      },
      "outputs": [],
      "source": [
        "# Move to the device \n",
        "resnet = resnet.to(device)\n",
        "loss_fn = nn.CrossEntropyLoss()\n",
        "opt = optim.SGD(resnet.parameters(), lr=0.01)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "nlca7QTWJ3Ex",
        "outputId": "26354150-bf94-4574-fac9-72b3aa105da2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Min loss 2.42\n",
            "Iteration: 0/3125, Loss: 2.42\n",
            "Min loss 2.33\n",
            "Min loss 2.25\n",
            "Min loss 2.22\n",
            "Min loss 2.17\n",
            "Min loss 1.86\n",
            "Min loss 1.67\n",
            "Min loss 1.62\n",
            "Iteration: 100/3125, Loss: 2.00\n",
            "Min loss 1.62\n",
            "Min loss 1.60\n",
            "Min loss 1.58\n",
            "Min loss 1.46\n",
            "Min loss 1.46\n",
            "Min loss 1.35\n",
            "Iteration: 200/3125, Loss: 1.67\n",
            "Min loss 1.29\n",
            "Min loss 1.27\n",
            "Min loss 1.07\n",
            "Min loss 1.06\n",
            "Iteration: 300/3125, Loss: 1.42\n",
            "Min loss 0.86\n",
            "Iteration: 400/3125, Loss: 1.53\n",
            "Iteration: 500/3125, Loss: 1.42\n",
            "Iteration: 600/3125, Loss: 1.49\n",
            "Min loss 0.69\n",
            "Iteration: 700/3125, Loss: 1.11\n",
            "Iteration: 800/3125, Loss: 1.68\n",
            "Iteration: 900/3125, Loss: 1.36\n",
            "Iteration: 1000/3125, Loss: 1.51\n",
            "Iteration: 1100/3125, Loss: 1.29\n",
            "Iteration: 1200/3125, Loss: 1.30\n",
            "Min loss 0.67\n",
            "Iteration: 1300/3125, Loss: 1.20\n",
            "Min loss 0.58\n",
            "Iteration: 1400/3125, Loss: 1.64\n",
            "Iteration: 1500/3125, Loss: 1.57\n",
            "Iteration: 1600/3125, Loss: 1.94\n",
            "Iteration: 1700/3125, Loss: 1.11\n",
            "Iteration: 1800/3125, Loss: 0.61\n",
            "Min loss 0.56\n",
            "Iteration: 1900/3125, Loss: 1.70\n",
            "Iteration: 2000/3125, Loss: 1.25\n",
            "Iteration: 2100/3125, Loss: 1.48\n",
            "Iteration: 2200/3125, Loss: 1.02\n",
            "Iteration: 2300/3125, Loss: 1.91\n",
            "Min loss 0.53\n",
            "Iteration: 2400/3125, Loss: 1.04\n",
            "Iteration: 2500/3125, Loss: 1.06\n",
            "Min loss 0.45\n",
            "Iteration: 2600/3125, Loss: 2.04\n",
            "Iteration: 2700/3125, Loss: 1.07\n",
            "Iteration: 2800/3125, Loss: 0.65\n",
            "Iteration: 2900/3125, Loss: 1.07\n",
            "Iteration: 3000/3125, Loss: 1.20\n",
            "Iteration: 3100/3125, Loss: 1.14\n",
            "Epoch: 0/4, Test acc: 57.08, Train acc: 56.60\n",
            "Iteration: 0/3125, Loss: 1.76\n",
            "Iteration: 100/3125, Loss: 1.07\n",
            "Iteration: 200/3125, Loss: 0.46\n",
            "Iteration: 300/3125, Loss: 1.16\n",
            "Iteration: 400/3125, Loss: 1.80\n",
            "Iteration: 500/3125, Loss: 1.25\n",
            "Iteration: 600/3125, Loss: 1.07\n",
            "Iteration: 700/3125, Loss: 1.63\n",
            "Iteration: 800/3125, Loss: 1.48\n",
            "Iteration: 900/3125, Loss: 1.10\n",
            "Iteration: 1000/3125, Loss: 1.30\n",
            "Iteration: 1100/3125, Loss: 0.98\n",
            "Iteration: 1200/3125, Loss: 1.14\n",
            "Iteration: 1300/3125, Loss: 1.43\n",
            "Iteration: 1400/3125, Loss: 1.38\n",
            "Iteration: 1500/3125, Loss: 1.08\n",
            "Iteration: 1600/3125, Loss: 1.47\n",
            "Iteration: 1700/3125, Loss: 0.69\n",
            "Iteration: 1800/3125, Loss: 2.00\n",
            "Iteration: 1900/3125, Loss: 1.31\n",
            "Iteration: 2000/3125, Loss: 1.45\n",
            "Iteration: 2100/3125, Loss: 1.40\n",
            "Iteration: 2200/3125, Loss: 0.88\n",
            "Iteration: 2300/3125, Loss: 2.05\n",
            "Iteration: 2400/3125, Loss: 0.76\n",
            "Iteration: 2500/3125, Loss: 1.49\n",
            "Iteration: 2600/3125, Loss: 1.34\n",
            "Iteration: 2700/3125, Loss: 1.26\n",
            "Iteration: 2800/3125, Loss: 1.25\n",
            "Iteration: 2900/3125, Loss: 1.02\n",
            "Iteration: 3000/3125, Loss: 1.92\n",
            "Iteration: 3100/3125, Loss: 1.46\n",
            "Epoch: 1/4, Test acc: 55.11, Train acc: 55.34\n",
            "Iteration: 0/3125, Loss: 1.52\n",
            "Iteration: 100/3125, Loss: 0.93\n",
            "Iteration: 200/3125, Loss: 1.49\n",
            "Iteration: 300/3125, Loss: 1.59\n",
            "Iteration: 400/3125, Loss: 0.98\n",
            "Iteration: 500/3125, Loss: 1.64\n",
            "Min loss 0.40\n",
            "Iteration: 600/3125, Loss: 0.68\n",
            "Iteration: 700/3125, Loss: 1.12\n",
            "Iteration: 800/3125, Loss: 0.89\n",
            "Iteration: 900/3125, Loss: 1.29\n",
            "Iteration: 1000/3125, Loss: 0.91\n",
            "Iteration: 1100/3125, Loss: 1.89\n",
            "Iteration: 1200/3125, Loss: 1.20\n",
            "Iteration: 1300/3125, Loss: 1.31\n",
            "Iteration: 1400/3125, Loss: 2.06\n",
            "Iteration: 1500/3125, Loss: 1.26\n",
            "Iteration: 1600/3125, Loss: 1.12\n",
            "Iteration: 1700/3125, Loss: 0.76\n",
            "Iteration: 1800/3125, Loss: 1.62\n",
            "Iteration: 1900/3125, Loss: 1.24\n",
            "Iteration: 2000/3125, Loss: 1.04\n",
            "Iteration: 2100/3125, Loss: 1.19\n",
            "Iteration: 2200/3125, Loss: 1.40\n",
            "Iteration: 2300/3125, Loss: 1.39\n",
            "Iteration: 2400/3125, Loss: 1.16\n",
            "Iteration: 2500/3125, Loss: 1.15\n",
            "Iteration: 2600/3125, Loss: 1.85\n",
            "Iteration: 2700/3125, Loss: 1.09\n",
            "Iteration: 2800/3125, Loss: 1.46\n",
            "Iteration: 2900/3125, Loss: 1.06\n",
            "Iteration: 3000/3125, Loss: 1.25\n",
            "Iteration: 3100/3125, Loss: 1.10\n",
            "Epoch: 2/4, Test acc: 57.58, Train acc: 57.12\n",
            "Iteration: 0/3125, Loss: 1.20\n",
            "Iteration: 100/3125, Loss: 1.18\n",
            "Iteration: 200/3125, Loss: 1.25\n",
            "Iteration: 300/3125, Loss: 1.10\n",
            "Iteration: 400/3125, Loss: 1.63\n",
            "Iteration: 500/3125, Loss: 1.43\n",
            "Iteration: 600/3125, Loss: 1.29\n",
            "Iteration: 700/3125, Loss: 1.01\n",
            "Iteration: 800/3125, Loss: 1.36\n",
            "Iteration: 900/3125, Loss: 1.20\n",
            "Iteration: 1000/3125, Loss: 1.10\n",
            "Iteration: 1100/3125, Loss: 1.49\n",
            "Iteration: 1200/3125, Loss: 0.97\n",
            "Iteration: 1300/3125, Loss: 0.89\n",
            "Iteration: 1400/3125, Loss: 1.19\n",
            "Iteration: 1500/3125, Loss: 1.21\n",
            "Iteration: 1600/3125, Loss: 0.98\n",
            "Iteration: 1700/3125, Loss: 0.84\n",
            "Iteration: 1800/3125, Loss: 1.05\n",
            "Iteration: 1900/3125, Loss: 1.06\n",
            "Iteration: 2000/3125, Loss: 1.03\n",
            "Iteration: 2100/3125, Loss: 1.31\n",
            "Iteration: 2200/3125, Loss: 0.83\n",
            "Iteration: 2300/3125, Loss: 1.56\n",
            "Iteration: 2400/3125, Loss: 1.38\n",
            "Iteration: 2500/3125, Loss: 1.12\n",
            "Iteration: 2600/3125, Loss: 1.38\n",
            "Iteration: 2700/3125, Loss: 1.56\n",
            "Min loss 0.33\n",
            "Iteration: 2800/3125, Loss: 0.91\n",
            "Iteration: 2900/3125, Loss: 1.61\n",
            "Iteration: 3000/3125, Loss: 1.16\n",
            "Iteration: 3100/3125, Loss: 0.70\n",
            "Epoch: 3/4, Test acc: 56.90, Train acc: 57.20\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD4CAYAAAD8Zh1EAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO3dd3hUddrG8e+TQug1oXeQjggMPUIQCzYExb4gKiLVCuq6ll11myKoFBERERUWFgUBxQoBQx96RxCUThDpHX7vH4m7viwhASY5U+7PdeW6Jp7DnOfHkTsnd05mzDmHiIiEviivBxARkcBQoIuIhAkFuohImFCgi4iECQW6iEiYiPHqwPHx8a5ixYpeHV5EJCQtWrRoj3Mu4VzbPAv0ihUr4vf7vTq8iEhIMrOfMtqmykVEJEwo0EVEwoQCXUQkTCjQRUTChAJdRCRMKNBFRMKEAl1EJEyEXKDvPXyCv0xZxZETp7weRUQkqIRcoKds2MOoOZu57e25bNl7xOtxRESCRsgFert6pRnZpRFbfz1Cu8EpzN6wx+uRRESCQsgFOkDr6sWZ3DuRYvnj6DxyAe+lbELvvCQikS4kAx2gUnw+JvVqQZsaxXl56mqeHL+MYydPez2WiIhnQjbQAfLHxTDsDw15/OpqfLpkG7cPm8u2fUe9HktExBMhHegAUVHGo1dfxrudfWzac5h2g1KY/+MvXo8lIpLjQj7Qf3NNrRJM6tWCQnliuXfEfEbP3axeXUQiStgEOkDV4vmZ1LsFLasl8MJnq3j6k+UcP6VeXUQiQ1gFOkDB3LGM6Oyjz1VVGe/fyp3vzGPn/mNejyUiku3CLtAhrVd/8trqvH1vA9bvOsjNg1NY9NNer8cSEclWYRnov7m+bikm9mxBntho7ho+j7ELfvZ6JBGRbBPWgQ5QvWQBJvduQdPKxfjjpyt4duIKTpw64/VYIiIBF/aBDlA4by5G3d+Y7q2qMGb+z9zz7jx2H1SvLiLhJSICHSA6ynjm+hq8dXd9Vm7fT7tBs1m6ZZ/XY4mIBEzEBPpv2tUrzSc9mhMTbdzxzlzG+7d4PZKISEBEXKAD1C5diMm9E/FVKMJTE5bz58mrOHlavbqIhLaIDHSAovlyMfqBxjyYWIlRczbzhxHz+eXQca/HEhG5aJkGupmNNLPdZrbyPPskmdlSM1tlZjMDO2L2iYmO4vmbajHgjnos3bKPdoNns3Lbfq/HEhG5KFm5Qh8FtM1oo5kVBoYC7ZxztYHbAzNazrm1QVkmdG+Oc47b3p7DpCXbvB5JROSCZRrozrlZwPl+zfIe4FPn3M/p++8O0Gw5qm7ZQkzuk0i9coV5bNxSXpm6mlPq1UUkhASiQ68GFDGzZDNbZGadM9rRzLqZmd/M/KmpqQE4dGDF54/j465N6NysAiNSNtHl/YX8eviE12OJiGRJIAI9BmgI3AhcBzxvZtXOtaNzbrhzzuec8yUkJATg0IEXGx3FS7fU4dXbLmfBpr20G5LC6u0HvB5LRCRTgQj0rcBXzrnDzrk9wCygXgCe11N3NCrHuIebcuLUGW57ew5Tl2/3eiQRkfMKRKB/BiSaWYyZ5QWaAGsC8Lyeq1++CFN6J1KzVAF6j1nCP79cy+kzetMMEQlOWbltcSwwF6huZlvN7EEz625m3QGcc2uAL4HlwAJghHMuw1scQ03xgrkZ260pdzcuz9vJG3lg1EL2Hznp9VgiIv/DvHqbNp/P5/x+vyfHvlgfz/+JP09eRZnCeRje2Ue1EgW8HklEIoyZLXLO+c61LWJ/U/Ri3NukAmMeasqh46fpMGQ2X67c6fVIIiL/oUC/QI0qFmVKnxZULVGA7h8tYsA36zmjXl1EgoAC/SKUKpSHcd2a0rFhWd767ge6fejnwDH16iLiLQX6RcodG81rHS/nL+1qM2NdKu2HzGZj6iGvxxKRCKZAvwRmxn3NK/LRg03Yd+Qk7QfP5rs1u7weS0QilAI9AJpVKcaUPomUL5aXrqP9DPruB/XqIpLjFOgBUqZwHiZ0b84t9Urz+jfr6fnxYg4dP+X1WCISQRToAZQnVzQD77yC526syderd3Lr0Nls3nPY67FEJEIo0APMzOh6ZWVGP9CE3QeP025wCjPXB98rS4pI+FGgZ5PEy+KZ0juR0oXzcP/7C3g7eSNe/VauiEQGBXo2Klc0L5/2bM71dUvxzy/X0nvsEo6cUK8uItlDgZ7N8uaKYfDd9Xm6bQ2+WLGDW4fOYcveI16PJSJhSIGeA8yMHklVeL9LI7bvO8rNg1OYvWGP12OJSJhRoOegpOrF+ax3Ign54+j03nxGfP+jenURCRgFeg6rFJ+Pib1acE2tErzy+RqeGL+MYydPez2WiIQBBboH8sfF8Pa9DXnymmpMXLKNjsPmsG3fUa/HEpEQp0D3SFSU0afNZYzo7GPzniO0G5TCvB9/8XosEQlhCnSPXV2rBJN6taBQnlj+MGI+H8zZrF5dRC6KAj0IVC2en0m9W9CqWgIvTl7F058sV68uIhdMgR4kCuaO5d3OPh65qirj/Vu5c/g8du4/5vVYIhJCFOhBJCrKeOLa6gz7QwN+2HWQmwal4N+81+uxRCREKNCDUNs6pZjYswX54qK5+915jJn/s9cjiUgIUKAHqeolCzC5VyLNq8Tz7MQVPDtxBSdOnfF6LBEJYgr0IFYobywjuzSie6sqjJn/M/e8O4/dB9Wri8i5ZRroZjbSzHab2coMtieZ2X4zW5r+8ULgx4xc0VHGM9fXYNDd9Vm1/QA3D0ph6ZZ9Xo8lIkEoK1foo4C2mezzvXPuivSPly59LDnbzfVK80mP5sRGR3HHsLmM92/xeiQRCTKZBrpzbhagWy2CQK3SBZnSO5FGlYrw1ITlvPjZSk6eVq8uImkC1aE3M7NlZjbNzGpntJOZdTMzv5n5U1P1tmwXo0i+XHxwf2O6Jlbig7k/ce+I+ew5dNzrsUQkCAQi0BcDFZxz9YBBwKSMdnTODXfO+ZxzvoSEhAAcOjLFREfx3E21GHhnPZZt2Ue7QSms2Lrf67FExGOXHOjOuQPOuUPpj78AYs0s/pInk0x1qF+WCd2bA9Bx2BwmLtnq8UQi4qVLDnQzK2lmlv64cfpz6mUDc0jdsoWY3CeReuUK8/i4ZbwydTWn1KuLRKSYzHYws7FAEhBvZluBF4FYAOfcMKAj0MPMTgFHgbucXi4wR8Xnj+Pjrk14ZepqRqRsYs3OAwy+uwFF8uXyejQRyUHmVfb6fD7n9/s9OXY4G+/fwnMTV1K8YBzDO/moVbqg1yOJSACZ2SLnnO9c2/SbomHmDl85xj3clJOnz3Db23OYuny71yOJSA5RoIeh+uWLMKVPIrVKF6T3mCX8Y9paTp9RCyYS7hToYap4gdyMfagp9zQpz7CZG7l/1EL2Hznp9Vgiko0U6GEsV0wUf+tQl792qMPcjXtoNySF9bsOej2WiGQTBXoEuLdJBcY+1JQjJ07Tfshsvly5w+uRRCQbKNAjhK9iUab0TuSyEgXo/tFiBny9jjPq1UXCigI9gpQslJtx3Zpye8OyvDV9Aw+N9nPgmHp1kXChQI8wuWOjebXj5bx0S21mrk+l/ZDZbNh9yOuxRCQAFOgRyMzo3KwiH3Vtwv4jJ2k/ZDbfrt7l9VgicokU6BGsaeViTO6TSMX4vHQd7eet735Qry4SwhToEa5M4TxM6N6cDvXLMOCb9fT4eBGHjp/yeiwRuQgKdCF3bDQD7qjHczfW5Ns1u+kwZDab9xz2eiwRuUAKdAHSevWuV1Zm9AONST10nHaDU0het9vrsUTkAijQ5f9pUTWeKb0TKV04D/ePWsjQ5A3o1ZBFQoMCXf5HuaJ5+bRnc26sW4pXv1xH77FLOHJCvbpIsFOgyznlzRXDoLvr88z1NfhixQ5uHTqHLXuPeD2WiJyHAl0yZGZ0b1WF97s0Yvu+o9w8OIXZG/Z4PZaIZECBLplKql6cyb0TKV4gjk7vzWfE9z+qVxcJQgp0yZKK8fn4tGcLrq1Vklc+X8Pj45Zy7ORpr8cSkd9RoEuW5Y+LYei9DXjymmp8tmw7HYfNYdu+o16PJSLpFOhyQaKijD5tLuPdTj5+2nOEdoNSmPfjL16PJSIo0OUiXV2rBJN6t6BQ3ljuHTGfD+ZsVq8u4jEFuly0Kgn5mdSrBa2rJ/Di5FX0+Gixbm0U8ZACXS5JwdyxDO/k46m21Zm5PpU2A2by6pdr9QJfIh7INNDNbKSZ7TazlZns18jMTplZx8CNJ6EgKsromVSV6X1bcWPdUgxN3kjr/smM92/Ry/GK5KCsXKGPAtqebwcziwb+CXwdgJkkRJUqlIeBd17BxJ7NKVskD09NWE67ISks2LTX69FEIkKmge6cmwVk9i+yD/AJoJfnE+qXL8KnPZrz5l1X8MuhE9zxzlx6fax+XSS7XXKHbmZlgA7A21nYt5uZ+c3Mn5qaeqmHliBmZtxyRRmmP5nEY1dfxndrd9FmwExe+2oth9Wvi2SLQPxQ9A3gaefcmcx2dM4Nd875nHO+hISEABxagl2eXNE8dnU1ZvRN4oY6JRkyYyNJ/ZP5t/p1kYALRKD7gH+Z2WagIzDUzNoH4HkljJQqlIc37qrPpz2bU6ZwHvpNWM4tQ2azcLP6dZFAueRAd85Vcs5VdM5VBCYAPZ1zky55MglLDdL79TfuvILUg8e5fdhceo1ZzNZf1a+LXKqYzHYws7FAEhBvZluBF4FYAOfcsGydTsJSVJTRvn4Zrq1dgndm/sg7szbyzepddLuyMj2SqpAvLtP/LUXkHMyrX9f2+XzO7/d7cmwJLtv3HeWfX67ls6XbKV4gjqfa1uDW+mWIijKvRxMJOma2yDnnO9c2/aaoeK504Ty8eVd9PunRnFKF89D338toP3Q2fvXrIhdEgS5Bo2GFIkzs0ZyBd9Zj94HjdBw2l97q10WyTIEuQSUqyuhQvyzT+7bikTaX8c3qXbR5fSavf71O96+LZEKBLkEpb64YnrimGtP7JnFd7ZIMmr6Bq15P5pNFW3X/ukgGFOgS1MoUzsNbd9fnkx7NKFkwN0+qXxfJkAJdQkLDCkWZ2LMFA+6ox64Dx+g4bC59xi7RW+CJ/I4CXUJGVJRxa4OyzOibxCNXVeXrVTu5qn8yA75ex5ET6tdFFOgScvLmiuGJa6szvW8S19YuyVvTN9C6fzKfLla/LpFNgS4hq0zhPAxK79dLFMzNE+OX0WHobBb9pH5dIpMCXUJewwpFmdSzBa/fXo8d+49x29tzeUT9ukQgBbqEhago47aGaf16n6uq8tWqnbR5PZkB36xXvy4RQ4EuYSVfXAxPXlud755sxdU1S/DWdz9wVf+ZTFyifl3CnwJdwlLZInkZfE8DJnRvRvGCcTw+bhm3vj2HxT//6vVoItlGgS5hzVcxrV/vf3s9tu87yq1D5/Dov5awXf26hCEFuoS9qCijY3q/3rt1Vaat3MlV6tclDCnQJWLki4uh73XVmf5kK9r8rl+ftGSb+nUJCwp0iThli+RlyD0N+Hf3ZiQUiOOxcUvVr0tYUKBLxGpUsSif9WrBax0vZ1t6v/6Y+nUJYQp0iWhRUcbtvnIk902iV+sqfJHer7/x7XqOnjjt9XgiF0SBLkJav97vuhp890Rav/7Gtz9w1evJTFqyDa/ed1fkQinQRX6nXNG0fn38w80olj/Xf/r1JerXJQQo0EXOoXGlokzulcirHS9n669H6TB0Do+PW8qO/erXJXgp0EUyEBVl3OErx4y+SfRMqsLnK3ZwVf+ZvPntD+rXJSgp0EUykT8uhqfapvXrrWskMPDb9Vz1ejKfLVW/LsEl00A3s5FmttvMVmaw/RYzW25mS83Mb2aJgR9TxHvliuZl6L0NGdetKUXz5eLRf6X160u37PN6NBEALLMrDDNrCRwCRjvn6pxje37gsHPOmdnlwHjnXI3MDuzz+Zzf77/IsUW8dfqM45NFW3n1q3XsOXScW+uX4am2NShZKLfXo0mYM7NFzjnfubZleoXunJsFZPgWMM65Q+6/XxXyAfoeVMJedJRxR6NyJPdLokdSFaYu30Hr/snq18VTAenQzayDma0FPgceOM9+3dJrGX9qamogDi3iqfxxMTzdtgbfPdmKpOpp/Xob9evikUwrFwAzqwhMPVflctZ+LYEXnHNXZ/acqlwkHM378RdemrKa1TsO0KB8YV64uTZXlCvs9VgSRi6pcrkQ6fVMZTOLD+TzioSKppWLMaVPIv+8rS4/7z1C+yGzeWLcUnbuP+b1aBIBLjnQzayqmVn64wZAHPDLpT6vSKiKjjLubFSeGX2T6N7qv/36W9/9wLGT6tcl+2TlLpexQBIQD+wCXgRiAZxzw8zsaaAzcBI4CvRzzqVkdmBVLhIpfv7lCH+ftoZpK3dSulBunrmhJjdfXor06yCRC3K+yiVLHXp2UKBLpJm78RdemrqaNTsO0LBCEV64qRb11K/LBcqxDl1EMtasSjGm9knkH7fW5adfDnPLkNk8MX4puw6oX5fAUKCL5KDoKOOuxmn9+sOtKjN12Q6SXktmkPp1CQAFuogHCuSO5Y/X1+SbJ1rSqloCr3+znjavz2TKsu26f10umgJdxEMViuVjWKeGjHmoCQXzxNJn7BJuHzaX5Vv1+jBy4RToIkGgeZV4pvZJ5O+31mXTnsO0Gzybvv9epn5dLogCXSRIREcZdzcuz4x+af365KXbad0/mcHT1a9L1ijQRYJMwd/161deFk//r9P69anL1a/L+SnQRYJUhWL5eKeTjzFdm1Agdwy9xyzhjnfmsmLrfq9HkyClQBcJcs2rxvP5I1fytw51+TH1MO2GpND338vYrX5dzqJAFwkB0VHGPU3S+vWHrqzMZ0u3kdQ/mSEzNqhfl/9QoIuEkIK5Y3n2hpp883grEqvG89pX62jz+kw+X75D/boo0EVCUcX4fAzv/N9+vdeYxdz5zjxWblO/HskU6CIh7Ld+/a8d6rAh9RA3D06hn/r1iKVAFwlx0VHGvU0qkNwvia6JlZi0dBut1a9HJAW6SJgomDuWP91Yi68fb0Xz9H796gEz+WKF+vVIoUAXCTOV4vPxbmcfH3dtQv64GHp+vJg7h6tfjwQKdJEw1aJq2uvDvNK+Dht2p/XrT01Yxu6D6tfDlQJdJIzFREfxh6YVmNE3iQdbVOLTxdto/VoyQ5PVr4cjBbpIBCiUJ5bnbqrF14+3pFmVeF79ch3XDJzJNPXrYUWBLhJBKifkZ8R9Pj58sDF5YqPp8fFi7lK/HjYU6CIR6MrLEvjikSt5uX0d1u86yM2DU3jmk+WkHjzu9WhyCRToIhEqJjqKTk0rkNyvNQ+0qMSERVtp3T+ZYTM3cvyU+vVQpEAXiXCF8sTyfHq/3qRSUf4xbS3XDJjFlyvVr4eaTAPdzEaa2W4zW5nB9nvNbLmZrTCzOWZWL/Bjikh2q5yQn/e6NGL0A43JHRtF948Wc/e781i1Xf16qMjKFfoooO15tm8CWjnn6gIvA8MDMJeIeKRltfR+/ZbarNt5kJsGpfDHT9Wvh4JMA905NwvYe57tc5xzv6Z/Og8oG6DZRMQjMdFRdGpWkeS+rbm/eSX+7U/r199Rvx7UAt2hPwhMC/BziohHCuWN5YWba/HlYy1pXKkof5+2lmsHzuKrVTvVrwehgAW6mbUmLdCfPs8+3czMb2b+1NTUQB1aRLJZ1eL5GdmlER880JjY6Cge/nAR97w7nzU7Dng9mvyOZeWrrJlVBKY65+pksP1yYCJwvXNufVYO7PP5nN/vz/qkIhIUTp4+w5j5PzPw2/UcOHqSOxuV58lrqxGfP87r0SKCmS1yzvnOte2Sr9DNrDzwKdApq2EuIqErNjqK+5pXJLlvEp2bVWS8fwutX0tm+KyNnDh1xuvxIlqmV+hmNhZIAuKBXcCLQCyAc26YmY0AbgN+Sv8jpzL66vF7ukIXCQ8bdh/kr5+vYca6VCoWy8uzN9TkmlolMDOvRwtL57tCz1Llkh0U6CLhJXndbl6eupqNqYdpUbUYz99UixolC3o9VtjJ1spFRAQgqXpxvnysJX++uRYrtx3ghje/508TV/DLId2/nlMU6CISMLHRUXRpUYmZ/dL69X8t3EJS/2RGfP+j+vUcoEAXkYArnDcXf25Xm68eu5IG5YvwyudruHbgTL5ZvUv3r2cjBbqIZJuqxQvwwQONef/+RkRHGQ+N9tPpvQWs3an717ODAl1Esl3r9H79xZtrsXzrPm5483uem6R+PdAU6CKSI2Kjo7i/RSVm9mtNp6YVGLtA/XqgKdBFJEcVyZeLv9xSh2mPXskV5QrzyudraPvGLL5bo379UinQRcQT1UoUYPQDjRnZJe2W6gc/8NN55ALW7zro8WShS4EuIp4xM66qUYKvHm/JCzfVYtmWfVz/5vc8P2klew+f8Hq8kKNAFxHPxUZH8UBiJZL7teaexuX5eP5PJL02g/dSNnHytPr1rFKgi0jQKJovFy+3r8O0R1tSr1xhXp66muvemMX0terXs0KBLiJBp3rJtH79vft8OAcPjPJz3/sL+UH9+nkp0EUkKJkZbWqW4KvHWvLcjTVZ8vOvtH3ze178bCW/ql8/JwW6iAS1XDFRdL2yMjP7tebuxuX4cN5PJPVP5v3Z6tfPpkAXkZBQNF8uXmlfl2mPtqRumUL8ZUpavz5j7W6vRwsaCnQRCSnVSxbgwwcbM6JzWr9+/6iF3Ddygfp1FOgiEoLMjKtr/bdfX5zer/958ir2HYncfl2BLiIh67d+PblvEnc1KsfouZtp9VoyoyK0X1egi0jIK5Y/jr92qMvnj1xJ7dIF+fOU1Vz/5vckr4usfl2BLiJho2apgnzctQnDOzXk5OkzdHl/IV3eX8CG3Ye8Hi1HKNBFJKyYGdfWLsnXj7fkTzfUZNHmX2n7xqyI6NcV6CISluJionmoZWVm9Evidl9av57UP5kP5mzmVJj26wp0EQlr8fnj+PutdZna50pqlizIi5NXcf2b3zNzfarXowWcAl1EIkKt0gUZ81AT3unUkBOnz3DfyAXc//4CNqaGT7+eaaCb2Ugz221mKzPYXsPM5prZcTPrG/gRRUQCw8y4Lr1f/+P1NVi4+VeuGziLl6asZv+Rk16Pd8mycoU+Cmh7nu17gUeA/oEYSEQku8XFRPNwqyrM6JvE7b6yvD9nE0n9Z/Dh3NDu1zMNdOfcLNJCO6Ptu51zC4HQ//ImIhEloUAcf7/1cqb2SaR6yQI8/1lavz4rRPv1HO3QzaybmfnNzJ+aGpp/YSISfmqXLsTYh5oy7A8NOX7qDJ1HLuDBUQtDrl/P0UB3zg13zvmcc76EhIScPLSIyHmZGW3rlOSbJ1ryzPU1mL9pL9cNnMXLU0OnX9ddLiIivxMXE033VlWY3rcVHRuWZeTs9H593k9B368r0EVEzqF4gdz847bLmdI7kctKFOD5SSu58a0UUn7Y4/VoGbLM3njVzMYCSUA8sAt4EYgFcM4NM7OSgB8oCJwBDgG1nHMHzve8Pp/P+f3+S51fRCTbOef4cuVO/jZtDVv2HuXqmsX50421qBSfL8dnMbNFzjnfObd59U7aCnQRCTXHTp5m5OxNDJm+Ie2Xk5pVpE+byyiUJzbHZjhfoKtyERHJotyx0fRMqsqMfkl0qF+G92ZvonX/ZD4Kkn5dgS4icoGKF8jNqx3rMaV3IlUT8vPcpJXcNCiF2Ru87dcV6CIiF6lOmUKMe7gpQ+9twKHjp7h3xHy6fuBn057DnsyjQBcRuQRmxg11S/HtE63od1115m7cw7UDZ/K3L9Zw4FjO3r+uQBcRCYDcsdH0al2VGX2TaH9FGd79/kdav5bMmPk/c/pMztx8okAXEQmg4gVz89rt9ZjcK5HKCfl4duIKbnzre+bkQL+uQBcRyQZ1yxZi/MPNGHJPAw4eO8U9I+bTbbSfzdnYryvQRUSyiZlx4+Wl+O7JtH49ZcMerhk4kxHf/5gtx4vJlmcVEZH/+K1f79iwLK99tY7yRfNmy3EU6CIiOaREwdz0v71etj2/KhcRkTChQBcRCRMKdBGRMKFAFxEJEwp0EZEwoUAXEQkTCnQRkTChQBcRCROevQWdmaUCP13kH48HgvedWi+M1hKcwmUt4bIO0Fp+U8E5l3CuDZ4F+qUwM39G76kXarSW4BQuawmXdYDWkhWqXEREwoQCXUQkTIRqoA/3eoAA0lqCU7isJVzWAVpLpkKyQxcRkf8VqlfoIiJyFgW6iEiYCOpAN7O2ZrbOzDaY2TPn2B5nZuPSt883s4o5P2XWZGEtXcws1cyWpn909WLOzJjZSDPbbWYrM9huZvZW+jqXm1mDnJ4xq7KwliQz2/+7c/JCTs+YFWZWzsxmmNlqM1tlZo+eY5+QOC9ZXEuonJfcZrbAzJalr+Uv59gnsBnmnAvKDyAa2AhUBnIBy4BaZ+3TExiW/vguYJzXc1/CWroAg72eNQtraQk0AFZmsP0GYBpgQFNgvtczX8JakoCpXs+ZhXWUAhqkPy4ArD/H/18hcV6yuJZQOS8G5E9/HAvMB5qetU9AMyyYr9AbAxuccz86504A/wJuOWufW4AP0h9PANqYmeXgjFmVlbWEBOfcLGDveXa5BRjt0swDCptZqZyZ7sJkYS0hwTm3wzm3OP3xQWANUOas3ULivGRxLSEh/e/6UPqnsekfZ9+FEtAMC+ZALwNs+d3nW/nfE/uffZxzp4D9QLEcme7CZGUtALelfzs8wczK5cxoAZfVtYaKZunfMk8zs9peD5OZ9G/Z65N2Nfh7IXdezrMWCJHzYmbRZrYU2A1845zL8LwEIsOCOdAjzRSgonPucuAb/vtVW7yzmLTXzagHDAImeTzPeZlZfuAT4DHn3AGv57kUmawlZM6Lc+60c+4KoCzQ2MzqZOfxgjnQtwG/v0otm/7fzrmPmcUAhYBfcmS6C5PpWpxzvzjnjqd/OgJomEOzBVpWzltIcM4d+O1bZufcF0CsmcV7PNY5mVksaQH4sXPu03PsEjLnJbO1hNJ5+Y1zbh8wA2h71qaAZlgwB/pC4DIzq2RmuUj7gcHks/aZDNyX/kqRl64AAAEfSURBVLgjMN2l/3QhyGS6lrP6zHakdYehaDLQOf2uiqbAfufcDq+HuhhmVvK3PtPMGpP27yXoLhjSZ3wPWOOcG5DBbiFxXrKylhA6LwlmVjj9cR7gGmDtWbsFNMNiLvYPZjfn3Ckz6w18RdpdIiOdc6vM7CXA75ybTNqJ/9DMNpD2w627vJs4Y1lcyyNm1g44Rdpaung28HmY2VjS7jKIN7OtwIuk/bAH59ww4AvS7qjYABwB7vdm0sxlYS0dgR5mdgo4CtwVpBcMLYBOwIr0vhbgWaA8hNx5ycpaQuW8lAI+MLNo0r7ojHfOTc3ODNOv/ouIhIlgrlxEROQCKNBFRMKEAl1EJEwo0EVEwoQCXUQkTCjQRUTChAJdRCRM/B8pvGzRlezfxQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Learning Loop\n",
        "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()\n",
        "# We can see that the loss is on the lower side as compared to vgg and every epoch we are also computing test and train accuracy\n",
        "# The accuracy is nowhere close to the best results we can get, by tweeking the hyperparameters and training them much longer we can get much improved accuracy"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7g0Ffn_xJ4T4",
        "outputId": "52f47608-6977-462a-8ecf-aaa8bf45b222"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "57.834 57.56\n"
          ]
        }
      ],
      "source": [
        "# Picking the best model - but sometimes the best loss model is not the one with the best accuracy\n",
        "# So it is a good idea to start the model out from the best model we got and retrain.\n",
        "resnet.load_state_dict(best_model)\n",
        "print(evaluation(trainloader, resnet), evaluation(testloader, resnet))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fZX2KyBrJ5uX"
      },
      "source": [
        "## Inception Model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OxXWG1fLJ8kM"
      },
      "source": [
        "[Inception Model](https://pytorch.org/docs/master/_modules/torchvision/models/inception.html)\n",
        "\n",
        "\n",
        "\n",
        "![alt text](https://cloud.google.com/tpu/docs/images/inceptionv3onc--oview.png)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66,
          "referenced_widgets": [
            "6e93278f832d4a6a8abc35dc3cc6e82f",
            "feac4305b6e240a290a995e06954b9ff",
            "82b548183f0f42899e3c674271517c4a",
            "9f56275438a546c0940bf53ad6cceede",
            "188e74fabccb40758d869312af632d4f",
            "5c3b0345e8df49e0861748a1397973e2",
            "c621b05d42464e369e91ffc0bfd4d93f",
            "72708ec7370a48e2b5e7979798b3f84a",
            "e8059f428af94883904613d0b8125d85",
            "c8fc3b61cf6d4b0ea652ab547a9c85ba",
            "e2453abe13f44292bc82cd4c45776216"
          ]
        },
        "id": "Ah-uxqMgKDVV",
        "outputId": "222eb290-3d7f-4da1-8e98-d6e1c911af81"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading: \"https://download.pytorch.org/models/inception_v3_google-0cc3c7bd.pth\" to /root/.cache/torch/hub/checkpoints/inception_v3_google-0cc3c7bd.pth\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0.00/104M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6e93278f832d4a6a8abc35dc3cc6e82f"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "inception = models.inception_v3(pretrained=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "chKyUGHSKE5L",
        "outputId": "27fb9731-5f89-496c-d98b-80cafc401126"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Inception3(\n",
            "  (Conv2d_1a_3x3): BasicConv2d(\n",
            "    (conv): Conv2d(3, 32, kernel_size=(3, 3), stride=(2, 2), bias=False)\n",
            "    (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "  )\n",
            "  (Conv2d_2a_3x3): BasicConv2d(\n",
            "    (conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), bias=False)\n",
            "    (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "  )\n",
            "  (Conv2d_2b_3x3): BasicConv2d(\n",
            "    (conv): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "    (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "  )\n",
            "  (maxpool1): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
            "  (Conv2d_3b_1x1): BasicConv2d(\n",
            "    (conv): Conv2d(64, 80, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "    (bn): BatchNorm2d(80, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "  )\n",
            "  (Conv2d_4a_3x3): BasicConv2d(\n",
            "    (conv): Conv2d(80, 192, kernel_size=(3, 3), stride=(1, 1), bias=False)\n",
            "    (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "  )\n",
            "  (maxpool2): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
            "  (Mixed_5b): InceptionA(\n",
            "    (branch1x1): BasicConv2d(\n",
            "      (conv): Conv2d(192, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (branch5x5_1): BasicConv2d(\n",
            "      (conv): Conv2d(192, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn): BatchNorm2d(48, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (branch5x5_2): BasicConv2d(\n",
            "      (conv): Conv2d(48, 64, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), bias=False)\n",
            "      (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (branch3x3dbl_1): BasicConv2d(\n",
            "      (conv): Conv2d(192, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (branch3x3dbl_2): BasicConv2d(\n",
            "      (conv): Conv2d(64, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (branch3x3dbl_3): BasicConv2d(\n",
            "      (conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (branch_pool): BasicConv2d(\n",
            "      (conv): Conv2d(192, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "  )\n",
            "  (Mixed_5c): InceptionA(\n",
            "    (branch1x1): BasicConv2d(\n",
            "      (conv): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (branch5x5_1): BasicConv2d(\n",
            "      (conv): Conv2d(256, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn): BatchNorm2d(48, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (branch5x5_2): BasicConv2d(\n",
            "      (conv): Conv2d(48, 64, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), bias=False)\n",
            "      (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (branch3x3dbl_1): BasicConv2d(\n",
            "      (conv): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (branch3x3dbl_2): BasicConv2d(\n",
            "      (conv): Conv2d(64, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (branch3x3dbl_3): BasicConv2d(\n",
            "      (conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (branch_pool): BasicConv2d(\n",
            "      (conv): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "  )\n",
            "  (Mixed_5d): InceptionA(\n",
            "    (branch1x1): BasicConv2d(\n",
            "      (conv): Conv2d(288, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (branch5x5_1): BasicConv2d(\n",
            "      (conv): Conv2d(288, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn): BatchNorm2d(48, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (branch5x5_2): BasicConv2d(\n",
            "      (conv): Conv2d(48, 64, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), bias=False)\n",
            "      (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (branch3x3dbl_1): BasicConv2d(\n",
            "      (conv): Conv2d(288, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (branch3x3dbl_2): BasicConv2d(\n",
            "      (conv): Conv2d(64, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (branch3x3dbl_3): BasicConv2d(\n",
            "      (conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (branch_pool): BasicConv2d(\n",
            "      (conv): Conv2d(288, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "  )\n",
            "  (Mixed_6a): InceptionB(\n",
            "    (branch3x3): BasicConv2d(\n",
            "      (conv): Conv2d(288, 384, kernel_size=(3, 3), stride=(2, 2), bias=False)\n",
            "      (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (branch3x3dbl_1): BasicConv2d(\n",
            "      (conv): Conv2d(288, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (branch3x3dbl_2): BasicConv2d(\n",
            "      (conv): Conv2d(64, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (branch3x3dbl_3): BasicConv2d(\n",
            "      (conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(2, 2), bias=False)\n",
            "      (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "  )\n",
            "  (Mixed_6b): InceptionC(\n",
            "    (branch1x1): BasicConv2d(\n",
            "      (conv): Conv2d(768, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (branch7x7_1): BasicConv2d(\n",
            "      (conv): Conv2d(768, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (branch7x7_2): BasicConv2d(\n",
            "      (conv): Conv2d(128, 128, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)\n",
            "      (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (branch7x7_3): BasicConv2d(\n",
            "      (conv): Conv2d(128, 192, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)\n",
            "      (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (branch7x7dbl_1): BasicConv2d(\n",
            "      (conv): Conv2d(768, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (branch7x7dbl_2): BasicConv2d(\n",
            "      (conv): Conv2d(128, 128, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)\n",
            "      (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (branch7x7dbl_3): BasicConv2d(\n",
            "      (conv): Conv2d(128, 128, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)\n",
            "      (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (branch7x7dbl_4): BasicConv2d(\n",
            "      (conv): Conv2d(128, 128, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)\n",
            "      (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (branch7x7dbl_5): BasicConv2d(\n",
            "      (conv): Conv2d(128, 192, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)\n",
            "      (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (branch_pool): BasicConv2d(\n",
            "      (conv): Conv2d(768, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "  )\n",
            "  (Mixed_6c): InceptionC(\n",
            "    (branch1x1): BasicConv2d(\n",
            "      (conv): Conv2d(768, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (branch7x7_1): BasicConv2d(\n",
            "      (conv): Conv2d(768, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (branch7x7_2): BasicConv2d(\n",
            "      (conv): Conv2d(160, 160, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)\n",
            "      (bn): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (branch7x7_3): BasicConv2d(\n",
            "      (conv): Conv2d(160, 192, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)\n",
            "      (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (branch7x7dbl_1): BasicConv2d(\n",
            "      (conv): Conv2d(768, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (branch7x7dbl_2): BasicConv2d(\n",
            "      (conv): Conv2d(160, 160, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)\n",
            "      (bn): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (branch7x7dbl_3): BasicConv2d(\n",
            "      (conv): Conv2d(160, 160, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)\n",
            "      (bn): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (branch7x7dbl_4): BasicConv2d(\n",
            "      (conv): Conv2d(160, 160, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)\n",
            "      (bn): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (branch7x7dbl_5): BasicConv2d(\n",
            "      (conv): Conv2d(160, 192, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)\n",
            "      (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (branch_pool): BasicConv2d(\n",
            "      (conv): Conv2d(768, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "  )\n",
            "  (Mixed_6d): InceptionC(\n",
            "    (branch1x1): BasicConv2d(\n",
            "      (conv): Conv2d(768, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (branch7x7_1): BasicConv2d(\n",
            "      (conv): Conv2d(768, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (branch7x7_2): BasicConv2d(\n",
            "      (conv): Conv2d(160, 160, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)\n",
            "      (bn): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (branch7x7_3): BasicConv2d(\n",
            "      (conv): Conv2d(160, 192, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)\n",
            "      (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (branch7x7dbl_1): BasicConv2d(\n",
            "      (conv): Conv2d(768, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (branch7x7dbl_2): BasicConv2d(\n",
            "      (conv): Conv2d(160, 160, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)\n",
            "      (bn): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (branch7x7dbl_3): BasicConv2d(\n",
            "      (conv): Conv2d(160, 160, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)\n",
            "      (bn): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (branch7x7dbl_4): BasicConv2d(\n",
            "      (conv): Conv2d(160, 160, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)\n",
            "      (bn): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (branch7x7dbl_5): BasicConv2d(\n",
            "      (conv): Conv2d(160, 192, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)\n",
            "      (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (branch_pool): BasicConv2d(\n",
            "      (conv): Conv2d(768, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "  )\n",
            "  (Mixed_6e): InceptionC(\n",
            "    (branch1x1): BasicConv2d(\n",
            "      (conv): Conv2d(768, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (branch7x7_1): BasicConv2d(\n",
            "      (conv): Conv2d(768, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (branch7x7_2): BasicConv2d(\n",
            "      (conv): Conv2d(192, 192, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)\n",
            "      (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (branch7x7_3): BasicConv2d(\n",
            "      (conv): Conv2d(192, 192, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)\n",
            "      (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (branch7x7dbl_1): BasicConv2d(\n",
            "      (conv): Conv2d(768, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (branch7x7dbl_2): BasicConv2d(\n",
            "      (conv): Conv2d(192, 192, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)\n",
            "      (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (branch7x7dbl_3): BasicConv2d(\n",
            "      (conv): Conv2d(192, 192, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)\n",
            "      (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (branch7x7dbl_4): BasicConv2d(\n",
            "      (conv): Conv2d(192, 192, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)\n",
            "      (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (branch7x7dbl_5): BasicConv2d(\n",
            "      (conv): Conv2d(192, 192, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)\n",
            "      (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (branch_pool): BasicConv2d(\n",
            "      (conv): Conv2d(768, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "  )\n",
            "  (AuxLogits): InceptionAux(\n",
            "    (conv0): BasicConv2d(\n",
            "      (conv): Conv2d(768, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (conv1): BasicConv2d(\n",
            "      (conv): Conv2d(128, 768, kernel_size=(5, 5), stride=(1, 1), bias=False)\n",
            "      (bn): BatchNorm2d(768, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (fc): Linear(in_features=768, out_features=1000, bias=True)\n",
            "  )\n",
            "  (Mixed_7a): InceptionD(\n",
            "    (branch3x3_1): BasicConv2d(\n",
            "      (conv): Conv2d(768, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (branch3x3_2): BasicConv2d(\n",
            "      (conv): Conv2d(192, 320, kernel_size=(3, 3), stride=(2, 2), bias=False)\n",
            "      (bn): BatchNorm2d(320, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (branch7x7x3_1): BasicConv2d(\n",
            "      (conv): Conv2d(768, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (branch7x7x3_2): BasicConv2d(\n",
            "      (conv): Conv2d(192, 192, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)\n",
            "      (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (branch7x7x3_3): BasicConv2d(\n",
            "      (conv): Conv2d(192, 192, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)\n",
            "      (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (branch7x7x3_4): BasicConv2d(\n",
            "      (conv): Conv2d(192, 192, kernel_size=(3, 3), stride=(2, 2), bias=False)\n",
            "      (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "  )\n",
            "  (Mixed_7b): InceptionE(\n",
            "    (branch1x1): BasicConv2d(\n",
            "      (conv): Conv2d(1280, 320, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn): BatchNorm2d(320, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (branch3x3_1): BasicConv2d(\n",
            "      (conv): Conv2d(1280, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (branch3x3_2a): BasicConv2d(\n",
            "      (conv): Conv2d(384, 384, kernel_size=(1, 3), stride=(1, 1), padding=(0, 1), bias=False)\n",
            "      (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (branch3x3_2b): BasicConv2d(\n",
            "      (conv): Conv2d(384, 384, kernel_size=(3, 1), stride=(1, 1), padding=(1, 0), bias=False)\n",
            "      (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (branch3x3dbl_1): BasicConv2d(\n",
            "      (conv): Conv2d(1280, 448, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn): BatchNorm2d(448, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (branch3x3dbl_2): BasicConv2d(\n",
            "      (conv): Conv2d(448, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (branch3x3dbl_3a): BasicConv2d(\n",
            "      (conv): Conv2d(384, 384, kernel_size=(1, 3), stride=(1, 1), padding=(0, 1), bias=False)\n",
            "      (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (branch3x3dbl_3b): BasicConv2d(\n",
            "      (conv): Conv2d(384, 384, kernel_size=(3, 1), stride=(1, 1), padding=(1, 0), bias=False)\n",
            "      (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (branch_pool): BasicConv2d(\n",
            "      (conv): Conv2d(1280, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "  )\n",
            "  (Mixed_7c): InceptionE(\n",
            "    (branch1x1): BasicConv2d(\n",
            "      (conv): Conv2d(2048, 320, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn): BatchNorm2d(320, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (branch3x3_1): BasicConv2d(\n",
            "      (conv): Conv2d(2048, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (branch3x3_2a): BasicConv2d(\n",
            "      (conv): Conv2d(384, 384, kernel_size=(1, 3), stride=(1, 1), padding=(0, 1), bias=False)\n",
            "      (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (branch3x3_2b): BasicConv2d(\n",
            "      (conv): Conv2d(384, 384, kernel_size=(3, 1), stride=(1, 1), padding=(1, 0), bias=False)\n",
            "      (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (branch3x3dbl_1): BasicConv2d(\n",
            "      (conv): Conv2d(2048, 448, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn): BatchNorm2d(448, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (branch3x3dbl_2): BasicConv2d(\n",
            "      (conv): Conv2d(448, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (branch3x3dbl_3a): BasicConv2d(\n",
            "      (conv): Conv2d(384, 384, kernel_size=(1, 3), stride=(1, 1), padding=(0, 1), bias=False)\n",
            "      (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (branch3x3dbl_3b): BasicConv2d(\n",
            "      (conv): Conv2d(384, 384, kernel_size=(3, 1), stride=(1, 1), padding=(1, 0), bias=False)\n",
            "      (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (branch_pool): BasicConv2d(\n",
            "      (conv): Conv2d(2048, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "  )\n",
            "  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n",
            "  (dropout): Dropout(p=0.5, inplace=False)\n",
            "  (fc): Linear(in_features=2048, out_features=1000, bias=True)\n",
            ")\n"
          ]
        }
      ],
      "source": [
        "print(inception)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FMmd3kdjKGZa"
      },
      "outputs": [],
      "source": [
        "# Freezing the params\n",
        "for param in inception.parameters():\n",
        "    param.requires_grad = False"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Z_K42QPlKHb5"
      },
      "outputs": [],
      "source": [
        "# Change the number of classes in the auxilary output to be 10, as auxilary will be flattened for better gradient flow\n",
        "aux_in_features = inception.AuxLogits.fc.in_features\n",
        "inception.AuxLogits.fc = nn.Linear(aux_in_features, num_classes)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GaJSbJteKI0Y",
        "outputId": "60545c70-fa74-4978-98dc-cbb07440b07b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([10, 768])\n",
            "torch.Size([10])\n"
          ]
        }
      ],
      "source": [
        "for param in inception.parameters():\n",
        "    if param.requires_grad:\n",
        "        print(param.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lY4kSyTMKJxA"
      },
      "outputs": [],
      "source": [
        "# We have to also change the number of classes in the output.\n",
        "in_features = inception.fc.in_features\n",
        "inception.fc = nn.Linear(in_features, num_classes)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W7anFaYfKLL_",
        "outputId": "5513f77b-abf1-4b8c-d026-89c94be89980"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([10, 768])\n",
            "torch.Size([10])\n",
            "torch.Size([10, 2048])\n",
            "torch.Size([10])\n"
          ]
        }
      ],
      "source": [
        "# As we can see 4 params to be changed - first 2 corresponding to the aux output and the last two to the final layer outpyt\n",
        "for param in inception.parameters():\n",
        "    if param.requires_grad:\n",
        "        print(param.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YEyFWqmeKUNQ"
      },
      "outputs": [],
      "source": [
        "# Changing the input size to 299x299x3\n",
        "transform_train = transforms.Compose([\n",
        "    transforms.RandomResizedCrop(299), \n",
        "    transforms.ToTensor(),\n",
        "    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),\n",
        "    ])\n",
        "\n",
        "transform_test = transforms.Compose([\n",
        "    transforms.RandomResizedCrop(299), \n",
        "    transforms.ToTensor(),\n",
        "    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),\n",
        "    ])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LZt1yVreKWOm",
        "outputId": "2544f8ca-096e-473c-ebad-457ca5804493"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Files already downloaded and verified\n",
            "Files already downloaded and verified\n"
          ]
        }
      ],
      "source": [
        "# Here the good part is that the datasets are not being downloaded again, as they aleady were when we did it for vgg, only transforms are being applied to them\n",
        "trainset = torchvision.datasets.CIFAR10(root='./data', train=True, \n",
        "                                        download=True, \n",
        "                                        transform=transform_train)\n",
        "testset = torchvision.datasets.CIFAR10(root='./data', train=False, \n",
        "                                        download=True, \n",
        "                                        transform=transform_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qv4Gs1lCKYf9"
      },
      "outputs": [],
      "source": [
        "batch_size=16\n",
        "\n",
        "trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=True)\n",
        "testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size, shuffle=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tVkbsv-UKZp8"
      },
      "outputs": [],
      "source": [
        "inception = inception.to(device)\n",
        "loss_fn = nn.CrossEntropyLoss()\n",
        "opt = optim.SGD(inception.parameters(), lr=0.01)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2vFs0U1gKan0"
      },
      "outputs": [],
      "source": [
        "# Modified funciion as we have auxilary inputs\n",
        "def evaluation_inception(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",
        "        # Here outputs get only x and aux get the aux as returned by the function internally\n",
        "        outputs, aux_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"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1448
        },
        "id": "l9r03sfxKbt7",
        "outputId": "a3bb3dab-7517-4a21-9450-27542d3f73c0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Min loss 3.04\n",
            "Iteration: 0/3125, Loss: 3.04\n",
            "Min loss 2.98\n",
            "Min loss 2.93\n",
            "Min loss 2.86\n",
            "Min loss 2.81\n",
            "Min loss 2.72\n",
            "Min loss 2.69\n",
            "Min loss 2.67\n",
            "Min loss 2.54\n",
            "Iteration: 100/3125, Loss: 2.63\n",
            "Min loss 2.53\n",
            "Min loss 2.51\n",
            "Min loss 2.50\n",
            "Min loss 2.46\n",
            "Min loss 2.38\n",
            "Min loss 2.35\n",
            "Min loss 2.34\n",
            "Min loss 2.21\n",
            "Iteration: 200/3125, Loss: 2.56\n",
            "Min loss 2.20\n",
            "Min loss 2.15\n",
            "Min loss 2.08\n",
            "Iteration: 300/3125, Loss: 2.69\n",
            "Min loss 2.07\n",
            "Min loss 2.04\n",
            "Min loss 2.01\n",
            "Min loss 1.92\n",
            "Min loss 1.85\n",
            "Min loss 1.81\n",
            "Iteration: 400/3125, Loss: 2.42\n",
            "Min loss 1.79\n",
            "Min loss 1.77\n",
            "Iteration: 500/3125, Loss: 2.19\n",
            "Min loss 1.69\n",
            "Iteration: 600/3125, Loss: 2.27\n",
            "Min loss 1.65\n",
            "Min loss 1.47\n",
            "Iteration: 700/3125, Loss: 2.08\n",
            "Iteration: 800/3125, Loss: 2.08\n",
            "Min loss 1.36\n",
            "Iteration: 900/3125, Loss: 2.08\n",
            "Iteration: 1000/3125, Loss: 2.14\n",
            "Iteration: 1100/3125, Loss: 1.95\n",
            "Iteration: 1200/3125, Loss: 2.32\n",
            "Iteration: 1300/3125, Loss: 2.45\n",
            "Min loss 1.34\n",
            "Iteration: 1400/3125, Loss: 2.26\n",
            "Min loss 1.10\n",
            "Iteration: 1500/3125, Loss: 2.63\n",
            "Iteration: 1600/3125, Loss: 1.92\n",
            "Iteration: 1700/3125, Loss: 1.61\n",
            "Iteration: 1800/3125, Loss: 2.12\n",
            "Iteration: 1900/3125, Loss: 1.89\n",
            "Iteration: 2000/3125, Loss: 1.81\n",
            "Iteration: 2100/3125, Loss: 1.75\n",
            "Iteration: 2200/3125, Loss: 1.84\n",
            "Iteration: 2300/3125, Loss: 1.84\n",
            "Iteration: 2400/3125, Loss: 1.92\n",
            "Iteration: 2500/3125, Loss: 1.56\n",
            "Iteration: 2600/3125, Loss: 2.39\n",
            "Min loss 1.00\n",
            "Iteration: 2700/3125, Loss: 2.23\n",
            "Iteration: 2800/3125, Loss: 1.96\n",
            "Iteration: 2900/3125, Loss: 2.20\n",
            "Iteration: 3000/3125, Loss: 1.27\n",
            "Iteration: 3100/3125, Loss: 2.42\n",
            "Epoch: 0/1, Test acc: 47.01, Train acc: 47.10\n"
          ]
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Learning Loop - first time we are seeing that loss is computed in two parts\n",
        "loss_epoch_arr = []\n",
        "max_epochs = 1\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, aux_outputs = inception(inputs)\n",
        "        loss = loss_fn(outputs, labels) + 0.3 * loss_fn(aux_outputs, labels)  # Auxiliary loss function is given a weight of 0.3, as it is less crucial than the main loss\n",
        "\n",
        "        loss.backward()\n",
        "        opt.step()\n",
        "        \n",
        "        if min_loss > loss.item():\n",
        "            min_loss = loss.item()\n",
        "            best_model = copy.deepcopy(inception.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_inception(testloader, inception), \n",
        "        evaluation_inception(trainloader, inception)))\n",
        "    \n",
        "        \"\"\"\n",
        "        Loss of a completely random classifier would be calculated as ln(output_classes)\n",
        "        In this case, since we have two loss functions weighted 1 and 0.3 with 10 output classes, random classifier loss would be around 1.3ln(10)\n",
        "        \"\"\"\n",
        "    \n",
        "    \n",
        "plt.plot(loss_epoch_arr)\n",
        "plt.show()\n",
        "# So here we can say that 1.3*ln(10) is a good base line for a random classifier - so somewhere around 3"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "id": "3qK-8N36KdAq",
        "outputId": "8c5333c7-6faa-491d-a52f-7940b512221d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "47.142 47.39\n"
          ]
        }
      ],
      "source": [
        "inception.load_state_dict(best_model)\n",
        "print(evaluation_inception(trainloader, inception), evaluation_inception(testloader, inception))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VOPYGB64KeOJ"
      },
      "source": [
        "## Exercises"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZG8aDt5pKho2"
      },
      "source": [
        "1. Structure the above code into a series of functions and then call each model\n",
        "\n",
        "2. Try out different hyperparameter combinations and try to achieve published results on different networks\n",
        "\n",
        "3. Try out the CIFAR100 and STL10 datasets\n",
        "\n",
        "4. Try out another model - SqueezeNet\n",
        "\n",
        "5. Try training multiple layers and not just the last one"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [],
      "name": "1-LargeCNNs.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3.8.5 ('base')",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.5"
    },
    "vscode": {
      "interpreter": {
        "hash": "ffb0cf131d14546b6573224cf3d27e75f0df2bb31d92400f13c23c2c4abb6933"
      }
    },
    "widgets": {
      "application/vnd.jupyter.widget-state+json": {
        "400048fe467248198b1f22bf02cf3e80": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_5a584f7dc54f4c9798c3ef9ff1614a81",
              "IPY_MODEL_fe4e60decde947fdb68847caab43ec24",
              "IPY_MODEL_ec76163a635c4825978ab4c89a471156"
            ],
            "layout": "IPY_MODEL_bb5abe791f7a4679ae525c570d1de618"
          }
        },
        "5a584f7dc54f4c9798c3ef9ff1614a81": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_bb9f548f53e34a3dbc61568b71406312",
            "placeholder": "​",
            "style": "IPY_MODEL_5d8b3b40184e4ef0b6f957a797cadd47",
            "value": ""
          }
        },
        "fe4e60decde947fdb68847caab43ec24": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_fa5b5edf8bf14729b6db30c02e94f801",
            "max": 170498071,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_dc4f8703c792438ca1d593b59d4d65f3",
            "value": 170498071
          }
        },
        "ec76163a635c4825978ab4c89a471156": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_9c32ff8d7b7e4788aa5e4f399dab3f60",
            "placeholder": "​",
            "style": "IPY_MODEL_4fc16ac1cbb94c41be3b5c5da052ca69",
            "value": " 170499072/? [00:02&lt;00:00, 83840124.09it/s]"
          }
        },
        "bb5abe791f7a4679ae525c570d1de618": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "bb9f548f53e34a3dbc61568b71406312": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "5d8b3b40184e4ef0b6f957a797cadd47": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "fa5b5edf8bf14729b6db30c02e94f801": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "dc4f8703c792438ca1d593b59d4d65f3": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "9c32ff8d7b7e4788aa5e4f399dab3f60": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "4fc16ac1cbb94c41be3b5c5da052ca69": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "eb9b427294b043ba89072ef4b14a6303": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_a36b37df1b904f44b306d20695b5b3b0",
              "IPY_MODEL_eaacf8e6fa1d4f118e45d90d630aefb4",
              "IPY_MODEL_43128758374e4f86b2d14cd22f0726f9"
            ],
            "layout": "IPY_MODEL_aa915e410cd84402819cddd365893a8d"
          }
        },
        "a36b37df1b904f44b306d20695b5b3b0": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_03ad9b1d7a9542d2a6042fb268704822",
            "placeholder": "​",
            "style": "IPY_MODEL_35590118da194b6eb548734637c1fc86",
            "value": "100%"
          }
        },
        "eaacf8e6fa1d4f118e45d90d630aefb4": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_17e5f8788f1a40eab82810ad265c7bc0",
            "max": 553507836,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_850c0ed6c5c945838c7e2b940a3e00ce",
            "value": 553507836
          }
        },
        "43128758374e4f86b2d14cd22f0726f9": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_35cf0631d3ae4069ab1dea327695a8c5",
            "placeholder": "​",
            "style": "IPY_MODEL_f6409b274ef44e1a989dc62c52f31f03",
            "value": " 528M/528M [00:06&lt;00:00, 67.4MB/s]"
          }
        },
        "aa915e410cd84402819cddd365893a8d": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "03ad9b1d7a9542d2a6042fb268704822": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "35590118da194b6eb548734637c1fc86": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "17e5f8788f1a40eab82810ad265c7bc0": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "850c0ed6c5c945838c7e2b940a3e00ce": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "35cf0631d3ae4069ab1dea327695a8c5": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "f6409b274ef44e1a989dc62c52f31f03": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "614ba22178f040d5b1c7b9d44987912c": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_cfff217b7b544d098dcf45bea629b079",
              "IPY_MODEL_01afc73ed21940d89aeb1c9086babae9",
              "IPY_MODEL_275d153a70af4b538d0f48094ce4f23b"
            ],
            "layout": "IPY_MODEL_d347ab84506b40e6b886230fe4820cc3"
          }
        },
        "cfff217b7b544d098dcf45bea629b079": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_5ca5da3787ec4913add035587209e414",
            "placeholder": "​",
            "style": "IPY_MODEL_e003fd84e1784f2da4c19b9df39fad44",
            "value": "100%"
          }
        },
        "01afc73ed21940d89aeb1c9086babae9": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_f69c6c44486e4fb3bf33894c95e7ea8e",
            "max": 46830571,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_5e87a7d7ef1a4d04b90652947ab313c7",
            "value": 46830571
          }
        },
        "275d153a70af4b538d0f48094ce4f23b": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_35327cac2936494a83c9a0067e5b74a1",
            "placeholder": "​",
            "style": "IPY_MODEL_31df2070b4e94897b63e3ac7a936b92a",
            "value": " 44.7M/44.7M [00:00&lt;00:00, 117MB/s]"
          }
        },
        "d347ab84506b40e6b886230fe4820cc3": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "5ca5da3787ec4913add035587209e414": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "e003fd84e1784f2da4c19b9df39fad44": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "f69c6c44486e4fb3bf33894c95e7ea8e": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "5e87a7d7ef1a4d04b90652947ab313c7": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "35327cac2936494a83c9a0067e5b74a1": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "31df2070b4e94897b63e3ac7a936b92a": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "6e93278f832d4a6a8abc35dc3cc6e82f": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_feac4305b6e240a290a995e06954b9ff",
              "IPY_MODEL_82b548183f0f42899e3c674271517c4a",
              "IPY_MODEL_9f56275438a546c0940bf53ad6cceede"
            ],
            "layout": "IPY_MODEL_188e74fabccb40758d869312af632d4f"
          }
        },
        "feac4305b6e240a290a995e06954b9ff": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_5c3b0345e8df49e0861748a1397973e2",
            "placeholder": "​",
            "style": "IPY_MODEL_c621b05d42464e369e91ffc0bfd4d93f",
            "value": "100%"
          }
        },
        "82b548183f0f42899e3c674271517c4a": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_72708ec7370a48e2b5e7979798b3f84a",
            "max": 108949747,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_e8059f428af94883904613d0b8125d85",
            "value": 108949747
          }
        },
        "9f56275438a546c0940bf53ad6cceede": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_c8fc3b61cf6d4b0ea652ab547a9c85ba",
            "placeholder": "​",
            "style": "IPY_MODEL_e2453abe13f44292bc82cd4c45776216",
            "value": " 104M/104M [00:00&lt;00:00, 145MB/s]"
          }
        },
        "188e74fabccb40758d869312af632d4f": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "5c3b0345e8df49e0861748a1397973e2": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "c621b05d42464e369e91ffc0bfd4d93f": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "72708ec7370a48e2b5e7979798b3f84a": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "e8059f428af94883904613d0b8125d85": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "c8fc3b61cf6d4b0ea652ab547a9c85ba": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "e2453abe13f44292bc82cd4c45776216": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        }
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}