Graph_neural_network.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Graph_neural_network.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "yvcscY6H6Vvt",
        "colab_type": "code",
        "outputId": "77982b35-6bc1-4b86-a1b2-1f0bdb8a514d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "!pip install --upgrade torch-scatter\n",
        "!pip install --upgrade torch-sparse\n",
        "!pip install --upgrade torch-cluster\n",
        "!pip install --upgrade torch-spline-conv \n",
        "!pip install torch-geometric"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting torch-scatter\n",
            "  Downloading https://files.pythonhosted.org/packages/b8/c3/8bad887ffa55c86f120ef5ae252dc0e357b3bd956d9fbf45242bacc46290/torch_scatter-1.4.0.tar.gz\n",
            "Building wheels for collected packages: torch-scatter\n",
            "  Building wheel for torch-scatter (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for torch-scatter: filename=torch_scatter-1.4.0-cp36-cp36m-linux_x86_64.whl size=3170441 sha256=fecf1f86e379f9d4cd52436e671518bad96ffc93ca2e49431a949128e4175e29\n",
            "  Stored in directory: /root/.cache/pip/wheels/25/00/c4/1637b4b3003f29092f4fe2ad4b40dd10906269c1ac2dc82941\n",
            "Successfully built torch-scatter\n",
            "Installing collected packages: torch-scatter\n",
            "Successfully installed torch-scatter-1.4.0\n",
            "Collecting torch-sparse\n",
            "  Downloading https://files.pythonhosted.org/packages/08/4e/a268613fa6a92ffbc65b89e66fc8be5590801937185007f0f7bcb75ea21f/torch_sparse-0.4.3.tar.gz\n",
            "Requirement already satisfied, skipping upgrade: scipy in /usr/local/lib/python3.6/dist-packages (from torch-sparse) (1.3.3)\n",
            "Requirement already satisfied, skipping upgrade: numpy>=1.13.3 in /usr/local/lib/python3.6/dist-packages (from scipy->torch-sparse) (1.17.4)\n",
            "Building wheels for collected packages: torch-sparse\n",
            "  Building wheel for torch-sparse (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for torch-sparse: filename=torch_sparse-0.4.3-cp36-cp36m-linux_x86_64.whl size=3966626 sha256=1ae3488a2555d84fdfe305d6404084a91960e88820cc96b7903c4015dc609b2c\n",
            "  Stored in directory: /root/.cache/pip/wheels/02/66/2b/befece01c2516f9fb3e7b4d150bb2b871221c73657c9cd7735\n",
            "Successfully built torch-sparse\n",
            "Installing collected packages: torch-sparse\n",
            "Successfully installed torch-sparse-0.4.3\n",
            "Collecting torch-cluster\n",
            "  Downloading https://files.pythonhosted.org/packages/c3/70/1d827d6fd1e03bb5ae84852dd0070c6574105c37e7b935284f6e990932db/torch_cluster-1.4.5.tar.gz\n",
            "Requirement already satisfied, skipping upgrade: scipy in /usr/local/lib/python3.6/dist-packages (from torch-cluster) (1.3.3)\n",
            "Requirement already satisfied, skipping upgrade: numpy>=1.13.3 in /usr/local/lib/python3.6/dist-packages (from scipy->torch-cluster) (1.17.4)\n",
            "Building wheels for collected packages: torch-cluster\n",
            "  Building wheel for torch-cluster (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for torch-cluster: filename=torch_cluster-1.4.5-cp36-cp36m-linux_x86_64.whl size=16232002 sha256=4e4eab14830e1d6f80f1ddb90ce4652c9a886d7a5da65d37a4ed5b3572bcd99b\n",
            "  Stored in directory: /root/.cache/pip/wheels/0a/26/7e/a6d6a80eae5ca39b92bc77773f36cf433d5085de18014382b1\n",
            "Successfully built torch-cluster\n",
            "Installing collected packages: torch-cluster\n",
            "Successfully installed torch-cluster-1.4.5\n",
            "Collecting torch-spline-conv\n",
            "  Downloading https://files.pythonhosted.org/packages/5e/77/5420584cdb1514c580722ca4bc482a509105d64b7c70246e9dc4a3e6d3c5/torch_spline_conv-1.1.1.tar.gz\n",
            "Building wheels for collected packages: torch-spline-conv\n",
            "  Building wheel for torch-spline-conv (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for torch-spline-conv: filename=torch_spline_conv-1.1.1-cp36-cp36m-linux_x86_64.whl size=5469786 sha256=fe42a44f1222d61548d01d77379dffb1f8bacafb04a58f2a5c01ce6fe8e0796f\n",
            "  Stored in directory: /root/.cache/pip/wheels/63/cf/c7/3439a98ba262c5d99b91a9c11aaeced67f583febab5a4b1566\n",
            "Successfully built torch-spline-conv\n",
            "Installing collected packages: torch-spline-conv\n",
            "Successfully installed torch-spline-conv-1.1.1\n",
            "Collecting torch-geometric\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/f3/50/0a802f0bfa68058bf025d219ec6fbe806a5b891bba6702e28be7b83679fb/torch_geometric-1.3.2.tar.gz (126kB)\n",
            "\u001b[K     |████████████████████████████████| 133kB 1.4MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from torch-geometric) (1.17.4)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.6/dist-packages (from torch-geometric) (1.3.3)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.6/dist-packages (from torch-geometric) (2.4)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.6/dist-packages (from torch-geometric) (0.21.3)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from torch-geometric) (2.21.0)\n",
            "Collecting plyfile\n",
            "  Downloading https://files.pythonhosted.org/packages/4c/15/434d1d96f9a41fea56cb3290718123d651c56c4b7e53f0249acaf1bf34b6/plyfile-0.7.1.tar.gz\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.6/dist-packages (from torch-geometric) (0.25.3)\n",
            "Collecting rdflib\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/3c/fe/630bacb652680f6d481b9febbb3e2c3869194a1a5fc3401a4a41195a2f8f/rdflib-4.2.2-py3-none-any.whl (344kB)\n",
            "\u001b[K     |████████████████████████████████| 348kB 60.4MB/s \n",
            "\u001b[?25hRequirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (from torch-geometric) (2.8.0)\n",
            "Requirement already satisfied: googledrivedownloader in /usr/local/lib/python3.6/dist-packages (from torch-geometric) (0.4)\n",
            "Requirement already satisfied: decorator>=4.3.0 in /usr/local/lib/python3.6/dist-packages (from networkx->torch-geometric) (4.4.1)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.6/dist-packages (from scikit-learn->torch-geometric) (0.14.1)\n",
            "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->torch-geometric) (3.0.4)\n",
            "Requirement already satisfied: idna<2.9,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->torch-geometric) (2.8)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests->torch-geometric) (2019.11.28)\n",
            "Requirement already satisfied: urllib3<1.25,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests->torch-geometric) (1.24.3)\n",
            "Requirement already satisfied: python-dateutil>=2.6.1 in /usr/local/lib/python3.6/dist-packages (from pandas->torch-geometric) (2.6.1)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas->torch-geometric) (2018.9)\n",
            "Requirement already satisfied: pyparsing in /usr/local/lib/python3.6/dist-packages (from rdflib->torch-geometric) (2.4.5)\n",
            "Collecting isodate\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/9b/9f/b36f7774ff5ea8e428fdcfc4bb332c39ee5b9362ddd3d40d9516a55221b2/isodate-0.6.0-py2.py3-none-any.whl (45kB)\n",
            "\u001b[K     |████████████████████████████████| 51kB 8.7MB/s \n",
            "\u001b[?25hRequirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from h5py->torch-geometric) (1.12.0)\n",
            "Building wheels for collected packages: torch-geometric, plyfile\n",
            "  Building wheel for torch-geometric (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for torch-geometric: filename=torch_geometric-1.3.2-cp36-none-any.whl size=203339 sha256=e487cc8ce92b84a36328b4b1dece983f7f044183c3378197094755fe9219f90a\n",
            "  Stored in directory: /root/.cache/pip/wheels/f7/75/0a/56a0fd58efac6d990782523e20e61c9307fc42c31564d40348\n",
            "  Building wheel for plyfile (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for plyfile: filename=plyfile-0.7.1-cp36-none-any.whl size=32827 sha256=044e9e448a7b180fef2e35366f8c08ea61686b96a63f4b2b12043414c8a55a01\n",
            "  Stored in directory: /root/.cache/pip/wheels/d6/0d/bf/6d603d81b98604d2ecfd5e99d4ab7c9af664fd5285ab82bbb0\n",
            "Successfully built torch-geometric plyfile\n",
            "Installing collected packages: plyfile, isodate, rdflib, torch-geometric\n",
            "Successfully installed isodate-0.6.0 plyfile-0.7.1 rdflib-4.2.2 torch-geometric-1.3.2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AJvYW_8769NR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import torch\n",
        "from torch_geometric.data import Data"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0t1eUohU9gP1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x = torch.tensor([[2,1], [5,6], [3,7], [12,0]], dtype=torch.float)\n",
        "y = torch.tensor([0,1,0,1], dtype=torch.float)\n",
        "\n",
        "edge_index = torch.tensor([[0, 1, 2, 0, 3],\n",
        "                           [1, 0, 1, 3, 2]], dtype=torch.long)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DVW2lbKtCwdy",
        "colab_type": "code",
        "outputId": "5075aa08-6c9f-48f4-8ce2-0a8469c09509",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "data =Data(x=x, edge_index=edge_index)\n",
        "data"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Data(edge_index=[2, 5], x=[4, 2])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kMqKbF31C3sE",
        "colab_type": "code",
        "outputId": "a0dbc7ff-d569-4ca1-e4f3-9c3dcd757f96",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "data.edge_index"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[0, 1, 2, 0, 3],\n",
              "        [1, 0, 1, 3, 2]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vIAXmvHhC457",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "device = torch.device('cuda')\n",
        "data = data.to(device)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0npB0pmXErqA",
        "colab_type": "code",
        "outputId": "3133ce8b-aa4a-4ce5-d938-0f4fb4b624c8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        }
      },
      "source": [
        "from torch_geometric.datasets import TUDataset\n",
        "\n",
        "dataset = TUDataset(root='/tmp/ENZYMES', name='ENZYMES')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading https://ls11-www.cs.tu-dortmund.de/people/morris/graphkerneldatasets/ENZYMES.zip\n",
            "Extracting /tmp/ENZYMES/ENZYMES.zip\n",
            "Processing...\n",
            "Done!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uVGepk0OGlbd",
        "colab_type": "code",
        "outputId": "b7bd6557-4736-4853-f908-dcc3311ad71a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "len(dataset)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "600"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TjxOsZcoGp-l",
        "colab_type": "code",
        "outputId": "7d9c5eb9-2a18-4eb6-e239-f4206772e2bb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "data = dataset[0]\n",
        "data"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Data(edge_index=[2, 168], x=[37, 3], y=[1])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OYqUJcZ3G-yW",
        "colab_type": "code",
        "outputId": "bd411f0d-ecbc-45eb-b262-2630a2c37953",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "data.is_undirected()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fLhpqw2HHMRE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dataset= dataset.shuffle()\n",
        "train_daataset = dataset[:540]\n",
        "test_dataset = dataset[540:]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZOjxRJ4GJAqI",
        "colab_type": "code",
        "outputId": "e969d577-6d44-4b15-fd6b-086ae95e6016",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 194
        }
      },
      "source": [
        "from torch_geometric.datasets import Planetoid\n",
        "dataset = Planetoid(root='/tmp/Cora', name='Cora')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.cora.x\n",
            "Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.cora.tx\n",
            "Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.cora.allx\n",
            "Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.cora.y\n",
            "Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.cora.ty\n",
            "Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.cora.ally\n",
            "Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.cora.graph\n",
            "Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.cora.test.index\n",
            "Processing...\n",
            "Done!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "glAJIeV_JY4F",
        "colab_type": "code",
        "outputId": "f43c5d8c-ab6c-41d7-b5c6-7c81888e0529",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "print(dataset.num_classes)\n",
        "print(dataset.num_node_features)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "7\n",
            "1433\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wCg0IBrHJdJQ",
        "colab_type": "code",
        "outputId": "7b32fc20-16cf-4fba-b9b0-71bcfba9c90b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "dataset[0].edge_index"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[   0,    0,    0,  ..., 2707, 2707, 2707],\n",
              "        [ 633, 1862, 2582,  ...,  598, 1473, 2706]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CtwMW5zBN_Ch",
        "colab_type": "code",
        "outputId": "a6368817-34c6-4113-ef37-36d72829af5c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        }
      },
      "source": [
        "from torch_geometric.data import DataLoader\n",
        "\n",
        "dataset = TUDataset(root='/tmp/ENZYMES', name='ENZYMES', use_node_attr=True)\n",
        "loader = DataLoader(dataset, batch_size=32, shuffle=True)\n",
        "\n",
        "for batch in loader:\n",
        "  print(batch)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Batch(batch=[1051], edge_index=[2, 3950], x=[1051, 21], y=[32])\n",
            "Batch(batch=[931], edge_index=[2, 3724], x=[931, 21], y=[32])\n",
            "Batch(batch=[1027], edge_index=[2, 3902], x=[1027, 21], y=[32])\n",
            "Batch(batch=[1170], edge_index=[2, 4180], x=[1170, 21], y=[32])\n",
            "Batch(batch=[928], edge_index=[2, 3558], x=[928, 21], y=[32])\n",
            "Batch(batch=[1028], edge_index=[2, 3946], x=[1028, 21], y=[32])\n",
            "Batch(batch=[1018], edge_index=[2, 3600], x=[1018, 21], y=[32])\n",
            "Batch(batch=[1025], edge_index=[2, 4086], x=[1025, 21], y=[32])\n",
            "Batch(batch=[1067], edge_index=[2, 4156], x=[1067, 21], y=[32])\n",
            "Batch(batch=[1161], edge_index=[2, 4108], x=[1161, 21], y=[32])\n",
            "Batch(batch=[1068], edge_index=[2, 4042], x=[1068, 21], y=[32])\n",
            "Batch(batch=[1053], edge_index=[2, 4250], x=[1053, 21], y=[32])\n",
            "Batch(batch=[1151], edge_index=[2, 4214], x=[1151, 21], y=[32])\n",
            "Batch(batch=[1041], edge_index=[2, 4134], x=[1041, 21], y=[32])\n",
            "Batch(batch=[1024], edge_index=[2, 3906], x=[1024, 21], y=[32])\n",
            "Batch(batch=[994], edge_index=[2, 3722], x=[994, 21], y=[32])\n",
            "Batch(batch=[1041], edge_index=[2, 4040], x=[1041, 21], y=[32])\n",
            "Batch(batch=[1092], edge_index=[2, 4166], x=[1092, 21], y=[32])\n",
            "Batch(batch=[710], edge_index=[2, 2880], x=[710, 21], y=[24])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Do5I8MIZOsy4",
        "colab_type": "code",
        "outputId": "83751866-c3f6-411b-fa6d-1d526770b003",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        }
      },
      "source": [
        "import torch_geometric.transforms as T\n",
        "from torch_geometric.datasets import ShapeNet\n",
        "\n",
        "dataset = ShapeNet(root='/tmp/ShapeNet', categories=['Airplane'],\n",
        "                    pre_transform=T.KNNGraph(k=6),\n",
        "                    transform=T.RandomTranslate(0.01))\n",
        "\n",
        "dataset[0]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading https://shapenet.cs.stanford.edu/iccv17/partseg/train_data.zip\n",
            "Extracting /tmp/ShapeNet/raw/train_data.zip\n",
            "Downloading https://shapenet.cs.stanford.edu/iccv17/partseg/train_label.zip\n",
            "Extracting /tmp/ShapeNet/raw/train_label.zip\n",
            "Downloading https://shapenet.cs.stanford.edu/iccv17/partseg/val_data.zip\n",
            "Extracting /tmp/ShapeNet/raw/val_data.zip\n",
            "Downloading https://shapenet.cs.stanford.edu/iccv17/partseg/val_label.zip\n",
            "Extracting /tmp/ShapeNet/raw/val_label.zip\n",
            "Downloading https://shapenet.cs.stanford.edu/iccv17/partseg/test_data.zip\n",
            "Extracting /tmp/ShapeNet/raw/test_data.zip\n",
            "Downloading https://shapenet.cs.stanford.edu/iccv17/partseg/test_label.zip\n",
            "Extracting /tmp/ShapeNet/raw/test_label.zip\n",
            "Processing...\n",
            "Done!\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Data(category=[1], edge_index=[2, 15108], pos=[2518, 3], y=[2518])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kaeTp20lVnjI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from torch_geometric.datasets import Planetoid\n",
        "\n",
        "dataset = Planetoid(root='/tmp/Cora', name='Cora')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-t8Y2eXrWBCC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import torch\n",
        "import torch.nn.functional as F\n",
        "from torch_geometric.nn import GCNConv\n",
        "\n",
        "class Net(torch.nn.Module):\n",
        "    def __init__(self):\n",
        "        super(Net, self).__init__()\n",
        "        self.conv1 = GCNConv(dataset.num_node_features, 16)\n",
        "        self.conv2 = GCNConv(16, dataset.num_classes)\n",
        "\n",
        "    def forward(self, data):\n",
        "        x, edge_index = data.x, data.edge_index\n",
        "\n",
        "        x = self.conv1(x, edge_index)\n",
        "        x = F.relu(x)\n",
        "        x = F.dropout(x, training=self.training)\n",
        "        x = self.conv2(x, edge_index)\n",
        "\n",
        "        return F.log_softmax(x, dim=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RIj8hVIwYAiD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "device = (\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "model = Net().to(device)\n",
        "\n",
        "data = dataset[0].to(device)\n",
        "optimizer = torch.optim.Adam(model.parameters(), lr=0.01, weight_decay=5e-4)\n",
        "\n",
        "model.train()\n",
        "for epoch in range(200):\n",
        "  optimizer.zero_grad()\n",
        "  out = model(data)\n",
        "  loss = F.nll_loss(input=out[data.train_mask], target=data.y[data.train_mask])\n",
        "  loss.backward()\n",
        "  optimizer.step()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D2VPBKjXaWRg",
        "colab_type": "code",
        "outputId": "949e4ace-56c9-463f-921e-f1cdc88579e1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "model.eval()\n",
        "_,pred= model(data).max(dim=1)\n",
        "correct = float (pred[data.test_mask].eq(data.y[data.test_mask]).sum().item())\n",
        "acc = correct / data.test_mask.sum().item()\n",
        "print('Accuracy: {:.4f}'.format(acc))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy: 0.8030\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SdAqm7TTbPxb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}