One_Shot_Learning.ipynb

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        "import matplotlib.pyplot as plt\n",
        "import torchvision.utils\n",
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        "import random\n",
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        "from torch.autograd import Variable\n",
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        "print(device)"
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            "cuda:0\n"
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        "    [transforms.ToTensor(),\n",
        "     transforms.Normalize((0.5,), (0.5,))])"
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            "08d2a5b3e37343a59362f5d3a8153084",
            "dd87fba02e664776b93f39fcf4087f12",
            "803298f6812a467581be5c169e3e83b8",
            "e10a073dd1e8496fbff9b8668638d8d7",
            "119de1b0aede48c8bca554ff45632150",
            "fec86ce1a3fa4fe9b0a64a136e9bdc1c",
            "bb657c999a864218b4f87486e5a34dfd",
            "b6c80b18551c4b4db12f51df3cdff685",
            "afd9d585062243eaa6ddb13cae32dcb4",
            "b15b7a7e883540b2a3e6cd1514b4951c",
            "a76b4fdf187a42a18c672d8b15f1dbd2",
            "d6df26a478b04556aa8557d6451d2981",
            "981a4f47041844348bd5b17cdb28c95e",
            "f13c0af2b5e44a5db1601e89243bfd42",
            "99b664a585ec4e739ef0ea052e53d2fd",
            "bdc05a842c9d4f07b6a9da6a9afa3d36",
            "4c55c5cbd92241ba85717cb021ec2dc5"
          ]
        }
      },
      "source": [
        "trainset = torchvision.datasets.MNIST(root='./data', train=True,\n",
        "                                        download=True, transform=transform)\n",
        "trainloader = torch.utils.data.DataLoader(trainset, batch_size,\n",
        "                                          shuffle=True)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to ./data/MNIST/raw/train-images-idx3-ubyte.gz\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "777fe7c2c34147e6b73bc94af487bf1a",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=1, bar_style='info', max=1), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Extracting ./data/MNIST/raw/train-images-idx3-ubyte.gz to ./data/MNIST/raw\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to ./data/MNIST/raw/train-labels-idx1-ubyte.gz\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "fa737c1765a04197b286509086084fab",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=1, bar_style='info', max=1), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Extracting ./data/MNIST/raw/train-labels-idx1-ubyte.gz to ./data/MNIST/raw\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to ./data/MNIST/raw/t10k-images-idx3-ubyte.gz\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "dd87fba02e664776b93f39fcf4087f12",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=1, bar_style='info', max=1), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Extracting ./data/MNIST/raw/t10k-images-idx3-ubyte.gz to ./data/MNIST/raw\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to ./data/MNIST/raw/t10k-labels-idx1-ubyte.gz\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b15b7a7e883540b2a3e6cd1514b4951c",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=1, bar_style='info', max=1), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Extracting ./data/MNIST/raw/t10k-labels-idx1-ubyte.gz to ./data/MNIST/raw\n",
            "Processing...\n",
            "Done!\n",
            "\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pfcVgfZT0VKz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "testset = torchvision.datasets.MNIST(root='./data', train=False,\n",
        "                                       download=True, transform=transform)\n",
        "testloader = torch.utils.data.DataLoader(testset, batch_size,\n",
        "                                         shuffle=False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yuX-PAl20Xpt",
        "colab_type": "code",
        "outputId": "1b728e38-e681-449a-bf25-99b0742ecbd3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "source": [
        "iterator = iter(trainloader)\n",
        "img = next(iterator)\n",
        "img[0].shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([16, 1, 28, 28])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fzq3dn8s2SAX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class Net(nn.Module):\n",
        "  \n",
        "    def __init__(self): \n",
        "        super(Net, self).__init__()\n",
        "        self.cnn_model = nn.Sequential(\n",
        "            nn.Conv2d(1, 6, 5),     #Can't quite figure out what's wrong    \n",
        "            nn.Tanh(),\n",
        "            nn.MaxPool2d(2, stride=2),  \n",
        "            nn.Conv2d(6, 16, 5),          \n",
        "            nn.Tanh(),\n",
        "            nn.MaxPool2d(2, stride=2)   \n",
        "        )\n",
        "        self.fc = nn.Linear(256,10)\n",
        "  \n",
        "        \n",
        "    def forward(self, x):\n",
        "        x = self.cnn_model(x)\n",
        "        x = x.view(x.size(0), -1)\n",
        "        x = self.fc(x)\n",
        "        return x"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4eKzVIy62u1t",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "net = Net()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5yvyUObS2x1q",
        "colab_type": "code",
        "outputId": "d1b6d9e9-5087-43f2-8aee-c47c643a31ec",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        }
      },
      "source": [
        "print(net)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Net(\n",
            "  (cnn_model): Sequential(\n",
            "    (0): Conv2d(1, 6, kernel_size=(5, 5), stride=(1, 1))\n",
            "    (1): Tanh()\n",
            "    (2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
            "    (3): Conv2d(6, 16, kernel_size=(5, 5), stride=(1, 1))\n",
            "    (4): Tanh()\n",
            "    (5): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
            "  )\n",
            "  (fc): Linear(in_features=256, out_features=10, bias=True)\n",
            ")\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z3ZqeEzE3EIr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "criterion = nn.CrossEntropyLoss()\n",
        "optimizer = optim.Adam(net.parameters(), lr = 0.001)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cgatBDD63N_5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "min_test_loss = 1000"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z54Sfxf1L7f1",
        "colab_type": "code",
        "outputId": "f6d9c1fb-461a-4451-f52b-efcedcdb9c0a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        }
      },
      "source": [
        "model=net(inputs)\n",
        "model.cuda()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[-0.0033,  0.1001, -0.3136,  ..., -0.1141, -0.1668, -0.0772],\n",
              "        [ 0.1964, -0.0074, -0.1682,  ..., -0.1540, -0.1873, -0.0946],\n",
              "        [ 0.0398,  0.0845, -0.2884,  ...,  0.0697, -0.1658, -0.0562],\n",
              "        ...,\n",
              "        [-0.0813,  0.0541, -0.2953,  ..., -0.1553, -0.2480,  0.0146],\n",
              "        [ 0.1108,  0.0385, -0.2085,  ...,  0.1021, -0.1739, -0.0257],\n",
              "        [-0.0203,  0.1777, -0.3854,  ..., -0.0560, -0.1751,  0.0720]],\n",
              "       device='cuda:0', grad_fn=<AddmmBackward>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tgjIEmo03R6B",
        "colab_type": "code",
        "outputId": "2640ab1c-45ed-4b82-bb2a-50b640b4038d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 377
        }
      },
      "source": [
        "for epoch in range(num_epochs):\n",
        "    running_loss_train = 0.0\n",
        "    running_loss_test = 0.0\n",
        "    \n",
        "    net.train()\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",
        "      optimizer.zero_grad()\n",
        "\n",
        "      output = net(inputs)\n",
        "      loss = criterion(output, labels)\n",
        "\n",
        "      loss.backward()\n",
        "      optimizer.step()\n",
        "\n",
        "      running_loss_train += loss.item()      \n",
        "\n",
        "    net.eval()\n",
        "\n",
        "    correct_preds = 0\n",
        "    total_preds = 0\n",
        "    \n",
        "    for i,data in enumerate(testloader,0):\n",
        "\n",
        "      inputs, labels = data\n",
        "      inputs, labels = inputs.to(device), labels.to(device)\n",
        "\n",
        "      output = net(inputs)\n",
        "      loss = criterion(output,labels)\n",
        "\n",
        "\n",
        "      running_loss_test += loss.item()\n",
        "\n",
        "      _, pred = torch.max(output.data, 1)\n",
        "      correct_preds += (pred == labels).sum()\n",
        "      total_preds += labels.size(0)\n",
        "        \n",
        "    accuracy = 100.00 * float(correct_preds)/float(total_preds)\n",
        "    \n",
        "\n",
        "        \n",
        "    print(f'Epoch : {epoch+1}, Train Loss : {running_loss_train} Test Loss : {running_loss_test}, Accuracy : {accuracy}')\n",
        "\n",
        "    \n",
        "    if running_loss_test<min_test_loss:\n",
        "        torch.save(net.state_dict(), 'mnist.pt')\n",
        "        min_test_loss = running_loss_test\n",
        " "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-35-75a4d014cb83>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m       \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnet\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m       \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcriterion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\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     16\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m       \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\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/python3.6/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    530\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    531\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 532\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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    533\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\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[1;32m    534\u001b[0m             \u001b[0mhook_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: forward() missing 1 required positional argument: 'label'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uq9nTnLJ8XlR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "net.load_state_dict(torch.load('mnist.pt'))\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tDWqX6mL3YEF",
        "colab_type": "code",
        "outputId": "57eab2dc-60ec-43bc-94fd-92bbce810dcb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105,
          "referenced_widgets": [
            "ee4fc6e8c5594dcc8b5f7b06ad6cf8dc",
            "5c976043e07e4a53b1c5d61cca466fcf",
            "878b1d0a3007448e903a8a9a3c5a5b7d",
            "50f142a15f244a398efdf09b3c8f7f19",
            "ae30df98feb14a1f92767616034dff5c",
            "fc04b4e5d10e4a30bfd42d4d63bc003b",
            "23a049e9cb9c4d7a8a58c4e6703f7791",
            "e6a94e62c9604891bdbf19210ab1688c"
          ]
        }
      },
      "source": [
        "dataset = datasets.Omniglot(root = \"./data\", download = True, transform = transforms.ToTensor())\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading https://github.com/brendenlake/omniglot/raw/master/python/images_background.zip to ./data/omniglot-py/images_background.zip\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ee4fc6e8c5594dcc8b5f7b06ad6cf8dc",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=1, bar_style='info', max=1), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Extracting ./data/omniglot-py/images_background.zip to ./data/omniglot-py\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u1hsNaQG6EzA",
        "colab_type": "code",
        "outputId": "0a91c416-57c4-450b-e8a7-0d221926a544",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "source": [
        "len(dataset)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "19280"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P8xPrC856VRS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class SiameseNetworkDataset(Dataset):\n",
        "   \n",
        "    def __init__(self,imageFolderDataset,transform=None,should_invert=True):\n",
        "        self.imageFolderDataset = imageFolderDataset    \n",
        "        self.transform = transform\n",
        "        self.should_invert = should_invert\n",
        "        \n",
        "    def __getitem__(self,index):\n",
        "        img0_tuple = random.choice(self.imageFolderDataset.imgs)\n",
        "        #we need to make sure approx 50% of images are in the same class\n",
        "        should_get_same_class = random.randint(0,1) \n",
        "        if should_get_same_class:\n",
        "            while True:\n",
        "                #keep looping till the same class image is found\n",
        "                img1_tuple = random.choice(self.imageFolderDataset.imgs) \n",
        "                if img0_tuple[1]==img1_tuple[1]:\n",
        "                    break\n",
        "        else:\n",
        "            img1_tuple = random.choice(self.imageFolderDataset.imgs)\n",
        "\n",
        "        img0 = Image.open(img0_tuple[0])\n",
        "        img1 = Image.open(img1_tuple[0])\n",
        "        img0 = img0.convert(\"L\")\n",
        "        img1 = img1.convert(\"L\")\n",
        "        \n",
        "        if self.should_invert:\n",
        "            img0 = PIL.ImageOps.invert(img0)\n",
        "            img1 = PIL.ImageOps.invert(img1)\n",
        "\n",
        "        if self.transform is not None:\n",
        "            img0 = self.transform(img0)\n",
        "            img1 = self.transform(img1)\n",
        "        \n",
        "        return img0, img1 , torch.from_numpy(np.array([int(img1_tuple[1]!=img0_tuple[1])],dtype=np.float32))\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.imageFolderDataset.imgs)        "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rkEXFo7H9xCe",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "folder_dataset = datasets.ImageFolder(root = '/content/data/omniglot-py')\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vkn6jvW68yZn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "siamese_dataset = SiameseNetworkDataset(imageFolderDataset=folder_dataset,\n",
        "                                        transform=transforms.Compose([transforms.Resize((28,28)),\n",
        "                                                                      transforms.ToTensor()\n",
        "                                                                      ])\n",
        "                                       ,should_invert=False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V2r3dbL9-W4v",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class SiameseNetwork(nn.Module):\n",
        "    def __init__(self, model):\n",
        "        super(SiameseNetwork, self).__init__()\n",
        "        \n",
        "        self.model = model\n",
        "        self.fc = nn.Linear(256,5)\n",
        "    \n",
        "    def forward_once(self, x):\n",
        "        out = self.model(x)\n",
        "        out = F.relu(self.fc(out))\n",
        "        return out\n",
        "    \n",
        "    def forward(self, input1, input2):\n",
        "        out1 = self.forward_once(input1)\n",
        "        out2 = self.forward_once(input2)\n",
        "        return out1,out2"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gYjQsXxI-eQk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class ContrastiveLoss(torch.nn.Module):\n",
        "\n",
        "    def __init__(self, margin=2.0):\n",
        "        super(ContrastiveLoss, self).__init__()\n",
        "        self.margin = margin\n",
        "\n",
        "    def forward(self, output1, output2, label):\n",
        "        euclidean_distance = F.pairwise_distance(output1, output2)\n",
        "        loss_contrastive = torch.mean((1-label) * torch.pow(euclidean_distance, 2) +\n",
        "                                      (label) * torch.pow(torch.clamp(self.margin - euclidean_distance, min=0.0), 2))\n",
        "\n",
        "\n",
        "        return loss_contrastive"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uZG4LAa1-hT4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_loader = torch.utils.data.DataLoader(siamese_dataset,\n",
        "                                         shuffle=True,\n",
        "                                         batch_size=16)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "El_azRd7-66F",
        "colab_type": "code",
        "outputId": "7d096ff9-0be4-43b1-c778-566eeabead5e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "source": [
        "img = next(iter(train_loader))\n",
        "img[0].shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([16, 1, 28, 28])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e3YDelCSE5yp",
        "colab_type": "code",
        "outputId": "a420db5c-764f-427a-847d-9ceefcfb5830",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        }
      },
      "source": [
        "net.fc = nn.Linear(256,256)\n",
        "print(net)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Net(\n",
            "  (cnn_model): Sequential(\n",
            "    (0): Conv2d(1, 6, kernel_size=(5, 5), stride=(1, 1))\n",
            "    (1): Tanh()\n",
            "    (2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
            "    (3): Conv2d(6, 16, kernel_size=(5, 5), stride=(1, 1))\n",
            "    (4): Tanh()\n",
            "    (5): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
            "  )\n",
            "  (fc): Linear(in_features=256, out_features=256, bias=True)\n",
            ")\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1Gk3FXCL_F0r",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "SiamNet = SiameseNetwork(net)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uSOYrtfq1FZG",
        "colab_type": "code",
        "outputId": "b65d26f8-2153-4566-c378-44acc0ff2079",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 277
        }
      },
      "source": [
        "print(SiamNet)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "SiameseNetwork(\n",
            "  (model): Net(\n",
            "    (cnn_model): Sequential(\n",
            "      (0): Conv2d(1, 6, kernel_size=(5, 5), stride=(1, 1))\n",
            "      (1): Tanh()\n",
            "      (2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
            "      (3): Conv2d(6, 16, kernel_size=(5, 5), stride=(1, 1))\n",
            "      (4): Tanh()\n",
            "      (5): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
            "    )\n",
            "    (fc): Linear(in_features=256, out_features=256, bias=True)\n",
            "  )\n",
            "  (fc): Linear(in_features=256, out_features=5, bias=True)\n",
            ")\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "chuVcrnr_UFU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "criterion = ContrastiveLoss()\n",
        "opt = optim.Adam(SiamNet.parameters(),lr = 0.001)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XNs0dKpeLIzm",
        "colab_type": "code",
        "outputId": "ac26ab1b-0541-41c5-f5e4-da800c395444",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 277
        }
      },
      "source": [
        "model=SiamNet\n",
        "model.cuda()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SiameseNetwork(\n",
              "  (model): Net(\n",
              "    (cnn_model): Sequential(\n",
              "      (0): Conv2d(1, 6, kernel_size=(5, 5), stride=(1, 1))\n",
              "      (1): Tanh()\n",
              "      (2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "      (3): Conv2d(6, 16, kernel_size=(5, 5), stride=(1, 1))\n",
              "      (4): Tanh()\n",
              "      (5): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "    )\n",
              "    (fc): Linear(in_features=256, out_features=256, bias=True)\n",
              "  )\n",
              "  (fc): Linear(in_features=256, out_features=5, bias=True)\n",
              ")"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1qJNLypY_VOY",
        "colab_type": "code",
        "outputId": "953784d2-9e63-4d32-c143-aea651869948",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        }
      },
      "source": [
        "for epoch in range(10):\n",
        "    \n",
        "    for img1, img2, label in train_loader:\n",
        "\n",
        "        img1, img2, label = img1.to(device), img2.to(device), label.to(device)\n",
        "\n",
        "        opt.zero_grad()\n",
        "\n",
        "        output1, output2 = SiamNet(img1, img2)\n",
        "\n",
        "        loss = criterion(output1, output2, label)\n",
        "        loss.backward()\n",
        "\n",
        "        opt.step()\n",
        "    \n",
        "    print(f'Epoch {epoch+1}  Loss = {loss.item()}')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1  Loss = 4.999999980020986e-12\n",
            "Epoch 2  Loss = 4.999999980020986e-12\n",
            "Epoch 3  Loss = 4.999999980020986e-12\n",
            "Epoch 4  Loss = 4.999999980020986e-12\n",
            "Epoch 5  Loss = 4.999999980020986e-12\n",
            "Epoch 6  Loss = 4.999999980020986e-12\n",
            "Epoch 7  Loss = 4.999999980020986e-12\n",
            "Epoch 8  Loss = 4.999999980020986e-12\n",
            "Epoch 9  Loss = 4.999999980020986e-12\n",
            "Epoch 10  Loss = 4.999999980020986e-12\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RNrONdUnK338",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}