Encrypted_Deep_Learning_Classification_on_CIFAR10_Dataset.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Encrypted_Deep_Learning_Classification_on_CIFAR10_Dataset.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Joycechidi/Deep-Learning-/blob/master/Encrypted_Deep_Learning_Classification_on_CIFAR10_Dataset.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_S1eGi_0i9pH",
        "colab_type": "text"
      },
      "source": [
        "# **Encrpted Deep Learning Classification With PyTorch and PySyft on CIFAR-10 Dataset**\n",
        "\n",
        "In this project, \n",
        "\n",
        "\n",
        "*   I will show how to use a private and secure neural network to classify CIFAR10 images which involves using the private additive sharing tool in the Secure Multi-Party Computation (SMPC method). There are other encryption methods such as Homomorphic Encryption (FHE/SHE) and Functional Encryption but I will use the SMPC method for this project.\n",
        "*   I will encrypt both the model and the data in a way which allows one business to use a model owned by another business without either disclosing their IP to one another.\n",
        "\n",
        "Dataset has 10 label classes:\n",
        "\t\t\t\t\t\t\t\t\t\t\n",
        "1.   airplane\n",
        "2.   automobile\n",
        "3.   bird\t\t\t\t\t\t\t\t\t\t\n",
        "4.   cat\t\t\t\t\t\t\t\t\t\t\n",
        "5.   deer\t\t\t\t\t\t\t\t\t\t\n",
        "6.   dog\t\t\t\t\t\t\t\t\t\t\n",
        "7.   frog\t\t\t\t\t\t\t\t\t\t\n",
        "8.   horse\t\t\t\t\t\t\t\t\t\t\n",
        "9.   ship\t\t\t\t\t\t\t\t\t\t\n",
        "10. truck\n",
        "\n",
        "This technique is important for businesses that create and collect data for building and training machine learning models which can be used by other other organizations. Data privacy is the drive force for using this technique so that other businesses who are not able to create or train their own model can use the existing machine learning services tailored for their industry or product without fear of data breach or privacy leakage.\n",
        "\n",
        "Let's dive right into the project."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3FuehVpvRYn2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        },
        "outputId": "4cc4c04e-9567-47c3-bbc7-a4a0d9644228"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=email%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdocs.test%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive.photos.readonly%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fpeopleapi.readonly&response_type=code\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gUJAQBzxmP9i",
        "colab_type": "text"
      },
      "source": [
        "## **Import Libraries and Model Specifications**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jNNQMTI6iYcv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torch.optim as optim\n",
        "from torchvision import datasets, transforms\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "okpj1c8zpqjG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 785
        },
        "outputId": "b8af8312-9806-4ae2-8d89-c8d7a534d500"
      },
      "source": [
        "!pip install syft"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N0h87BSMm0hV",
        "colab_type": "text"
      },
      "source": [
        "Create some workers for the project and a secure crypto worker.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EH7Kc2ViiYfy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "8e4e35db-ce78-4d56-957b-9e8f5a1c8230"
      },
      "source": [
        "import syft as sy\n",
        "hook = sy.TorchHook(torch) \n",
        "client = sy.VirtualWorker(hook, id=\"client\")\n",
        "ify = sy.VirtualWorker(hook, id=\"ify\")\n",
        "dandon = sy.VirtualWorker(hook, id=\"dandon\")\n",
        "chy = sy.VirtualWorker(hook, id=\"chy\")\n",
        "crypto_provider = sy.VirtualWorker(hook, id=\"crypto_provider\") "
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "W0819 04:03:25.560756 140264100607872 hook.py:98] Torch was already hooked... skipping hooking process\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g6dDmIfOp7SK",
        "colab_type": "text"
      },
      "source": [
        "## **Define the Model Learning Parameters**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z5LtMNgNiYi_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class NetArguments():\n",
        "    def __init__(self):\n",
        "        self.batch_size = 64\n",
        "        self.test_batch_size = 200\n",
        "        self.epochs = 10\n",
        "        self.lr = 0.001 # learning rate\n",
        "        self.log_interval = 100\n",
        "\n",
        "args = NetArguments()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_rY0rBw1rn5H",
        "colab_type": "text"
      },
      "source": [
        "## **Data Loading and Sending Data to Workers**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "siYy0ORcuCZG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "944dd47d-1e09-49f7-8e0b-4137c4b77cde"
      },
      "source": [
        "#Let me se what is in this datasets module to be sure of how to call the cifar10 dataset. \n",
        "dir(datasets)\n",
        "#perfect. Result shows I should call the dataset as CIFAR10"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['CIFAR10',\n",
              " 'CIFAR100',\n",
              " 'Caltech101',\n",
              " 'Caltech256',\n",
              " 'CelebA',\n",
              " 'Cityscapes',\n",
              " 'CocoCaptions',\n",
              " 'CocoDetection',\n",
              " 'DatasetFolder',\n",
              " 'EMNIST',\n",
              " 'FakeData',\n",
              " 'FashionMNIST',\n",
              " 'Flickr30k',\n",
              " 'Flickr8k',\n",
              " 'ImageFolder',\n",
              " 'ImageNet',\n",
              " 'KMNIST',\n",
              " 'LSUN',\n",
              " 'LSUNClass',\n",
              " 'MNIST',\n",
              " 'Omniglot',\n",
              " 'PhotoTour',\n",
              " 'SBDataset',\n",
              " 'SBU',\n",
              " 'SEMEION',\n",
              " 'STL10',\n",
              " 'SVHN',\n",
              " 'VOCDetection',\n",
              " 'VOCSegmentation',\n",
              " 'VisionDataset',\n",
              " '__all__',\n",
              " '__builtins__',\n",
              " '__cached__',\n",
              " '__doc__',\n",
              " '__file__',\n",
              " '__loader__',\n",
              " '__name__',\n",
              " '__package__',\n",
              " '__path__',\n",
              " '__spec__',\n",
              " 'caltech',\n",
              " 'celeba',\n",
              " 'cifar',\n",
              " 'cityscapes',\n",
              " 'coco',\n",
              " 'fakedata',\n",
              " 'flickr',\n",
              " 'folder',\n",
              " 'imagenet',\n",
              " 'lsun',\n",
              " 'mnist',\n",
              " 'omniglot',\n",
              " 'phototour',\n",
              " 'sbd',\n",
              " 'sbu',\n",
              " 'semeion',\n",
              " 'stl10',\n",
              " 'svhn',\n",
              " 'utils',\n",
              " 'vision',\n",
              " 'voc']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QJMu1HjovM9_",
        "colab_type": "text"
      },
      "source": [
        "Loading the train data for training."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B1c-XAJaiYnA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e1394106-df0d-4325-f5af-9486f408eaae"
      },
      "source": [
        "from torchvision import datasets, transforms\n",
        "\n",
        "train_loader = torch.utils.data.DataLoader(datasets.CIFAR10('../data', train=True, download=True,\n",
        "                                                           transform=transforms.Compose([\n",
        "                                                               transforms.ToTensor(),\n",
        "                                                               transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\n",
        "                                                           ])),\n",
        "                                           batch_size=args.batch_size, shuffle=True)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Files already downloaded and verified\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zQvFriUVCMqd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "classes = ('plane', 'car', 'bird', 'cat',\n",
        "           'deer', 'dog', 'frog', 'horse', 'ship', 'truck')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A9sNCQkR_uC0",
        "colab_type": "text"
      },
      "source": [
        "## **Let's Visualize Some Training Images**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ou80pnTT_l75",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "outputId": "f3809309-69e5-4e13-fb0c-fb82f2b742a2"
      },
      "source": [
        "import torchvision\n",
        "\n",
        "def imshow(img):\n",
        "    img = img / 2 + 0.5     # unnormalize\n",
        "    npimg = img.numpy()\n",
        "    plt.imshow(np.transpose(npimg, (1, 2, 0)))\n",
        "    plt.show()\n",
        "\n",
        "\n",
        "# get some random training images\n",
        "dataiter = iter(train_loader)\n",
        "images, labels = dataiter.next()\n",
        "\n",
        "# show images\n",
        "imshow(torchvision.utils.make_grid(images))\n",
        "# print labels\n",
        "print(' '.join('%5s' % classes[labels[j]] for j in range(4)))"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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12mB64hA6S/DcAmfPvMhHf7PNvvk53FLGyxc/w1bzNB6P3JwxsHL35+4nckRsI39IIJUm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Dh5gmq1mjHqhCEJU1I/5sIL1/G1QpaL9NttPvfUp1hfu4MSKYWci5LZEtEYwiB4XXdl0O1gJMRR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\ndNFFFxvw/wEgyYQ/+AXPGAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            " frog   cat   dog horse\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fOVv8yiDHBiS",
        "colab_type": "text"
      },
      "source": [
        "Load Test Data for Predictions "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MQPnqwfFiYsR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "test_loader = torch.utils.data.DataLoader(\n",
        "    datasets.CIFAR10('../data', train=False,\n",
        "                   transform=transforms.Compose([\n",
        "                       transforms.ToTensor(),\n",
        "                       transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\n",
        "                   ])),\n",
        "    batch_size=args.test_batch_size, shuffle=False)\n",
        "\n",
        "# Convert to integers and privately share the dataset\n",
        "private_test_loader = []\n",
        "for data, target in test_loader:\n",
        "    private_test_loader.append((\n",
        "        data.fix_prec().share(ify, dandon, chy, crypto_provider=crypto_provider),\n",
        "        target.fix_prec().share(ify, dandon, chy, crypto_provider=crypto_provider)\n",
        "    ))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y49axalgHt8y",
        "colab_type": "text"
      },
      "source": [
        "## Define Network Architecture"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g3MrE6R7iYqf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class Classifier(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(Classifier, self).__init__()\n",
        "        self.conv1 = nn.Conv2d(3, 6, 5)\n",
        "        self.pool = nn.MaxPool2d(2, 2)\n",
        "        self.conv2 = nn.Conv2d(6, 16, 5)\n",
        "        self.fc1 = nn.Linear(16 * 5 * 5, 120)\n",
        "        self.fc2 = nn.Linear(120, 84)\n",
        "        self.fc3 = nn.Linear(84, 10)\n",
        "        \n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.pool(F.relu(self.conv1(x)))\n",
        "        x = self.pool(F.relu(self.conv2(x)))\n",
        "        x = x.view(-1, 16 * 5 * 5)\n",
        "        x = F.relu(self.fc1(x))\n",
        "        x = F.relu(self.fc2(x))\n",
        "        x = self.fc3(x)\n",
        "        return x\n",
        "    \n",
        "model = Classifier()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qnnR98Q1JKIE",
        "colab_type": "text"
      },
      "source": [
        "## **Train the Model**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_VlVXSRHIes8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def train(args, model, train_loader, optimizer, epoch):\n",
        "    model.train()\n",
        "    \n",
        "    for batch_idx, (data, target) in enumerate(train_loader):\n",
        "        optimizer.zero_grad()\n",
        "        output = model(data)\n",
        "        output = F.log_softmax(output, dim=1)\n",
        "        \n",
        "        loss = F.nll_loss(output, target)\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "        \n",
        "        \n",
        "        if batch_idx % args.log_interval == 0:\n",
        "            print('Train Epoch: {} [{}/{} ({:.0f}%)]'.format(\n",
        "                epoch, batch_idx * args.batch_size, len(train_loader) * args.batch_size,\n",
        "                100. * batch_idx / len(train_loader)))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O0AurQhnIepO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "87d799b2-2019-46ce-b2cf-3f76d71008d3"
      },
      "source": [
        "model = Classifier()\n",
        "optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)\n",
        "\n",
        "for epoch in range(1, args.epochs + 1):\n",
        "    train(args, model, train_loader, optimizer, epoch)\n",
        "    \n"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train Epoch: 1 [0/50048 (0%)]\n",
            "Train Epoch: 1 [6400/50048 (13%)]\n",
            "Train Epoch: 1 [12800/50048 (26%)]\n",
            "Train Epoch: 1 [19200/50048 (38%)]\n",
            "Train Epoch: 1 [25600/50048 (51%)]\n",
            "Train Epoch: 1 [32000/50048 (64%)]\n",
            "Train Epoch: 1 [38400/50048 (77%)]\n",
            "Train Epoch: 1 [44800/50048 (90%)]\n",
            "Train Epoch: 2 [0/50048 (0%)]\n",
            "Train Epoch: 2 [6400/50048 (13%)]\n",
            "Train Epoch: 2 [12800/50048 (26%)]\n",
            "Train Epoch: 2 [19200/50048 (38%)]\n",
            "Train Epoch: 2 [25600/50048 (51%)]\n",
            "Train Epoch: 2 [32000/50048 (64%)]\n",
            "Train Epoch: 2 [38400/50048 (77%)]\n",
            "Train Epoch: 2 [44800/50048 (90%)]\n",
            "Train Epoch: 3 [0/50048 (0%)]\n",
            "Train Epoch: 3 [6400/50048 (13%)]\n",
            "Train Epoch: 3 [12800/50048 (26%)]\n",
            "Train Epoch: 3 [19200/50048 (38%)]\n",
            "Train Epoch: 3 [25600/50048 (51%)]\n",
            "Train Epoch: 3 [32000/50048 (64%)]\n",
            "Train Epoch: 3 [38400/50048 (77%)]\n",
            "Train Epoch: 3 [44800/50048 (90%)]\n",
            "Train Epoch: 4 [0/50048 (0%)]\n",
            "Train Epoch: 4 [6400/50048 (13%)]\n",
            "Train Epoch: 4 [12800/50048 (26%)]\n",
            "Train Epoch: 4 [19200/50048 (38%)]\n",
            "Train Epoch: 4 [25600/50048 (51%)]\n",
            "Train Epoch: 4 [32000/50048 (64%)]\n",
            "Train Epoch: 4 [38400/50048 (77%)]\n",
            "Train Epoch: 4 [44800/50048 (90%)]\n",
            "Train Epoch: 5 [0/50048 (0%)]\n",
            "Train Epoch: 5 [6400/50048 (13%)]\n",
            "Train Epoch: 5 [12800/50048 (26%)]\n",
            "Train Epoch: 5 [19200/50048 (38%)]\n",
            "Train Epoch: 5 [25600/50048 (51%)]\n",
            "Train Epoch: 5 [32000/50048 (64%)]\n",
            "Train Epoch: 5 [38400/50048 (77%)]\n",
            "Train Epoch: 5 [44800/50048 (90%)]\n",
            "Train Epoch: 6 [0/50048 (0%)]\n",
            "Train Epoch: 6 [6400/50048 (13%)]\n",
            "Train Epoch: 6 [12800/50048 (26%)]\n",
            "Train Epoch: 6 [19200/50048 (38%)]\n",
            "Train Epoch: 6 [25600/50048 (51%)]\n",
            "Train Epoch: 6 [32000/50048 (64%)]\n",
            "Train Epoch: 6 [38400/50048 (77%)]\n",
            "Train Epoch: 6 [44800/50048 (90%)]\n",
            "Train Epoch: 7 [0/50048 (0%)]\n",
            "Train Epoch: 7 [6400/50048 (13%)]\n",
            "Train Epoch: 7 [12800/50048 (26%)]\n",
            "Train Epoch: 7 [19200/50048 (38%)]\n",
            "Train Epoch: 7 [25600/50048 (51%)]\n",
            "Train Epoch: 7 [32000/50048 (64%)]\n",
            "Train Epoch: 7 [38400/50048 (77%)]\n",
            "Train Epoch: 7 [44800/50048 (90%)]\n",
            "Train Epoch: 8 [0/50048 (0%)]\n",
            "Train Epoch: 8 [6400/50048 (13%)]\n",
            "Train Epoch: 8 [12800/50048 (26%)]\n",
            "Train Epoch: 8 [19200/50048 (38%)]\n",
            "Train Epoch: 8 [25600/50048 (51%)]\n",
            "Train Epoch: 8 [32000/50048 (64%)]\n",
            "Train Epoch: 8 [38400/50048 (77%)]\n",
            "Train Epoch: 8 [44800/50048 (90%)]\n",
            "Train Epoch: 9 [0/50048 (0%)]\n",
            "Train Epoch: 9 [6400/50048 (13%)]\n",
            "Train Epoch: 9 [12800/50048 (26%)]\n",
            "Train Epoch: 9 [19200/50048 (38%)]\n",
            "Train Epoch: 9 [25600/50048 (51%)]\n",
            "Train Epoch: 9 [32000/50048 (64%)]\n",
            "Train Epoch: 9 [38400/50048 (77%)]\n",
            "Train Epoch: 9 [44800/50048 (90%)]\n",
            "Train Epoch: 10 [0/50048 (0%)]\n",
            "Train Epoch: 10 [6400/50048 (13%)]\n",
            "Train Epoch: 10 [12800/50048 (26%)]\n",
            "Train Epoch: 10 [19200/50048 (38%)]\n",
            "Train Epoch: 10 [25600/50048 (51%)]\n",
            "Train Epoch: 10 [32000/50048 (64%)]\n",
            "Train Epoch: 10 [38400/50048 (77%)]\n",
            "Train Epoch: 10 [44800/50048 (90%)]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7N9yGX_Y6Nkn",
        "colab_type": "text"
      },
      "source": [
        "## **Evaluate the Model Securely**\n",
        "\n",
        "We are assuming that the model contains sensitive information which we have carefully optimized, so we don't want to disclose the model's weight. We need to secretly share the model as the client did with the test dataset."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-x-qRbqQIel7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "ff2fdfa0-fb7b-4479-e191-6d09bffffcff"
      },
      "source": [
        "model.fix_precision().share(ify, dandon, chy, crypto_provider=crypto_provider)"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Classifier(\n",
              "  (conv1): Conv2d(3, 6, kernel_size=(5, 5), stride=(1, 1))\n",
              "  (pool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "  (conv2): Conv2d(6, 16, kernel_size=(5, 5), stride=(1, 1))\n",
              "  (fc1): Linear(in_features=400, out_features=120, bias=True)\n",
              "  (fc2): Linear(in_features=120, out_features=84, bias=True)\n",
              "  (fc3): Linear(in_features=84, out_features=10, bias=True)\n",
              ")"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sDtC7Mmo7HbB",
        "colab_type": "text"
      },
      "source": [
        "The next test function will perform encrypted evaluation where the model's weights, data inputs, the prediction and the target used for scoring are all encrypted."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WB2v_LlLIejx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def test(args, model, test_loader):\n",
        "    model.eval()\n",
        "    n_correct_priv = 0\n",
        "    n_total = 0\n",
        "    \n",
        "    with torch.no_grad():\n",
        "        for data, target in test_loader:\n",
        "            output = model(data)\n",
        "            pred = output.argmax(dim=1) \n",
        "            n_correct_priv += pred.eq(target.view_as(pred)).sum()\n",
        "            n_total += args.test_batch_size\n",
        "            \n",
        "            n_correct = n_correct_priv.copy().get().float_precision().long().item()\n",
        "    \n",
        "            print('Test set: Accuracy: {}/{} ({:.0f}%)'.format(\n",
        "                n_correct, n_total,\n",
        "                100. * n_correct / n_total))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xaWU-2ZyIegp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "test(args, model, private_test_loader)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bxVYzDdwIeY3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}