GNN/08_RecGNN.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Ramsi-K/3D-Vision-Playground/blob/main/GNN/08_RecGNN.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "8WIIFLTeHCRX",
        "outputId": "5ad16c42-e26e-4c3a-966c-a4e67812283b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2.2.1+cu121\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m10.9/10.9 MB\u001b[0m \u001b[31m43.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.0/5.0 MB\u001b[0m \u001b[31m22.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for torch_geometric (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "import torch\n",
        "os.environ['TORCH'] = torch.__version__\n",
        "print(torch.__version__)\n",
        "\n",
        "!pip install -q torch-scatter -f https://data.pyg.org/whl/torch-${TORCH}.html\n",
        "!pip install -q torch-sparse -f https://data.pyg.org/whl/torch-${TORCH}.html\n",
        "!pip install -q git+https://github.com/pyg-team/pytorch_geometric.git"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "rdPC70A0HCRZ"
      },
      "outputs": [],
      "source": [
        "import os.path as osp\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torch_geometric.transforms as T\n",
        "import torch_geometric\n",
        "from torch_geometric.datasets import Planetoid, TUDataset\n",
        "from torch_geometric.data import DataLoader\n",
        "from torch_geometric.nn.inits import uniform\n",
        "from torch.nn import Parameter as Param\n",
        "from torch import Tensor\n",
        "torch.manual_seed(42)\n",
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "device = \"cpu\"\n",
        "from torch_geometric.nn.conv import MessagePassing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "geUHinipHCRZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "af660ac2-5070-4246-f93b-5df09c36e299"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "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"
          ]
        }
      ],
      "source": [
        "dataset = 'Cora'\n",
        "transform = T.Compose([\n",
        "    T.RandomNodeSplit('train_rest', num_val=500, num_test=500),\n",
        "    T.TargetIndegree(),\n",
        "])\n",
        "path = osp.join('data', dataset)\n",
        "dataset = Planetoid(path, dataset, transform=transform)\n",
        "data = dataset[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "p0omaEaTHCRa"
      },
      "outputs": [],
      "source": [
        "dataset = 'Cora'\n",
        "path = osp.join('data', dataset)\n",
        "dataset = Planetoid(path, dataset, transform=T.NormalizeFeatures())\n",
        "data = dataset[0]\n",
        "data = data.to(device)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vTTeTyrXHCRa"
      },
      "source": [
        "### Graph Neural Network Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "OyfL3MWGHCRb"
      },
      "outputs": [],
      "source": [
        "class MLP(nn.Module):\n",
        "    def __init__(self, input_dim, hid_dims, out_dim):\n",
        "        super(MLP, self).__init__()\n",
        "\n",
        "        self.mlp = nn.Sequential()\n",
        "        dims = [input_dim] + hid_dims + [out_dim]\n",
        "        for i in range(len(dims)-1):\n",
        "            self.mlp.add_module('lay_{}'.format(i),nn.Linear(in_features=dims[i], out_features=dims[i+1]))\n",
        "            if i+2 < len(dims):\n",
        "                self.mlp.add_module('act_{}'.format(i), nn.Tanh())\n",
        "    def reset_parameters(self):\n",
        "        for i, l in enumerate(self.mlp):\n",
        "            if type(l) == nn.Linear:\n",
        "                nn.init.xavier_normal_(l.weight)\n",
        "\n",
        "    def forward(self, x):\n",
        "        return self.mlp(x)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "V_BcRjj2HCRc"
      },
      "outputs": [],
      "source": [
        "class GNNM(MessagePassing):\n",
        "    def __init__(self, n_nodes, out_channels, features_dim, hid_dims, num_layers = 50, eps=1e-3, aggr = 'add',\n",
        "                 bias = True, **kwargs):\n",
        "        super(GNNM, self).__init__(aggr=aggr, **kwargs)\n",
        "\n",
        "        self.node_states = Param(torch.zeros((n_nodes, features_dim)), requires_grad=False)\n",
        "        self.out_channels = out_channels\n",
        "        self.eps = eps\n",
        "        self.num_layers = num_layers\n",
        "\n",
        "        self.transition = MLP(features_dim, hid_dims, features_dim)\n",
        "        self.readout = MLP(features_dim, hid_dims, out_channels)\n",
        "\n",
        "        self.reset_parameters()\n",
        "        print(self.transition)\n",
        "        print(self.readout)\n",
        "\n",
        "    def reset_parameters(self):\n",
        "        self.transition.reset_parameters()\n",
        "        self.readout.reset_parameters()\n",
        "\n",
        "    def forward(self):\n",
        "        edge_index = data.edge_index\n",
        "        edge_weight = data.edge_attr\n",
        "        node_states = self.node_states\n",
        "        for i in range(self.num_layers):\n",
        "            m = self.propagate(edge_index, x=node_states, edge_weight=edge_weight,\n",
        "                               size=None)\n",
        "            new_states = self.transition(m)\n",
        "            with torch.no_grad():\n",
        "                distance = torch.norm(new_states - node_states, dim=1)\n",
        "                convergence = distance < self.eps\n",
        "            node_states = new_states\n",
        "            if convergence.all():\n",
        "                break\n",
        "\n",
        "        out = self.readout(node_states)\n",
        "\n",
        "        return F.log_softmax(out, dim=-1)\n",
        "\n",
        "    def message(self, x_j, edge_weight):\n",
        "        return x_j if edge_weight is None else edge_weight.view(-1, 1) * x_j\n",
        "\n",
        "    def message_and_aggregate(self, adj_t, x) :\n",
        "        return matmul(adj_t, x, reduce=self.aggr)\n",
        "\n",
        "    def __repr__(self):\n",
        "        return '{}({}, num_layers={})'.format(self.__class__.__name__,\n",
        "                                              self.out_channels,\n",
        "                                              self.num_layers)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "scrolled": false,
        "id": "LkEOixUPHCRc",
        "outputId": "a0e209c8-ba74-46bc-f3b2-006db170d136",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch_geometric/deprecation.py:26: UserWarning: 'data.DataLoader' is deprecated, use 'loader.DataLoader' instead\n",
            "  warnings.warn(out)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MLP(\n",
            "  (mlp): Sequential(\n",
            "    (lay_0): Linear(in_features=32, out_features=64, bias=True)\n",
            "    (act_0): Tanh()\n",
            "    (lay_1): Linear(in_features=64, out_features=64, bias=True)\n",
            "    (act_1): Tanh()\n",
            "    (lay_2): Linear(in_features=64, out_features=64, bias=True)\n",
            "    (act_2): Tanh()\n",
            "    (lay_3): Linear(in_features=64, out_features=64, bias=True)\n",
            "    (act_3): Tanh()\n",
            "    (lay_4): Linear(in_features=64, out_features=64, bias=True)\n",
            "    (act_4): Tanh()\n",
            "    (lay_5): Linear(in_features=64, out_features=32, bias=True)\n",
            "  )\n",
            ")\n",
            "MLP(\n",
            "  (mlp): Sequential(\n",
            "    (lay_0): Linear(in_features=32, out_features=64, bias=True)\n",
            "    (act_0): Tanh()\n",
            "    (lay_1): Linear(in_features=64, out_features=64, bias=True)\n",
            "    (act_1): Tanh()\n",
            "    (lay_2): Linear(in_features=64, out_features=64, bias=True)\n",
            "    (act_2): Tanh()\n",
            "    (lay_3): Linear(in_features=64, out_features=64, bias=True)\n",
            "    (act_3): Tanh()\n",
            "    (lay_4): Linear(in_features=64, out_features=64, bias=True)\n",
            "    (act_4): Tanh()\n",
            "    (lay_5): Linear(in_features=64, out_features=7, bias=True)\n",
            "  )\n",
            ")\n",
            "Epoch: 001, Train Acc: 0.12857, Val Acc: 0.06800, Test Acc: 0.08800\n",
            "Epoch: 002, Train Acc: 0.14286, Val Acc: 0.25200, Test Acc: 0.25100\n",
            "Epoch: 003, Train Acc: 0.12143, Val Acc: 0.24400, Test Acc: 0.26100\n",
            "Epoch: 004, Train Acc: 0.17143, Val Acc: 0.20200, Test Acc: 0.20000\n",
            "Epoch: 005, Train Acc: 0.16429, Val Acc: 0.23000, Test Acc: 0.22900\n",
            "Epoch: 006, Train Acc: 0.22143, Val Acc: 0.09800, Test Acc: 0.10400\n",
            "Epoch: 007, Train Acc: 0.14286, Val Acc: 0.11400, Test Acc: 0.09900\n",
            "Epoch: 008, Train Acc: 0.14286, Val Acc: 0.07800, Test Acc: 0.08300\n",
            "Epoch: 009, Train Acc: 0.14286, Val Acc: 0.05800, Test Acc: 0.06400\n",
            "Epoch: 010, Train Acc: 0.14286, Val Acc: 0.15800, Test Acc: 0.14400\n",
            "Epoch: 011, Train Acc: 0.15000, Val Acc: 0.08800, Test Acc: 0.09900\n",
            "Epoch: 012, Train Acc: 0.15000, Val Acc: 0.07800, Test Acc: 0.08500\n",
            "Epoch: 013, Train Acc: 0.13571, Val Acc: 0.08600, Test Acc: 0.09400\n",
            "Epoch: 014, Train Acc: 0.14286, Val Acc: 0.12800, Test Acc: 0.13000\n",
            "Epoch: 015, Train Acc: 0.14286, Val Acc: 0.15600, Test Acc: 0.14400\n",
            "Epoch: 016, Train Acc: 0.14286, Val Acc: 0.15800, Test Acc: 0.14600\n",
            "Epoch: 017, Train Acc: 0.15714, Val Acc: 0.10400, Test Acc: 0.09500\n",
            "Epoch: 018, Train Acc: 0.17143, Val Acc: 0.10800, Test Acc: 0.09600\n",
            "Epoch: 019, Train Acc: 0.17143, Val Acc: 0.12200, Test Acc: 0.11200\n",
            "Epoch: 020, Train Acc: 0.17143, Val Acc: 0.14400, Test Acc: 0.12600\n",
            "Epoch: 021, Train Acc: 0.17143, Val Acc: 0.14200, Test Acc: 0.12200\n",
            "Epoch: 022, Train Acc: 0.19286, Val Acc: 0.11400, Test Acc: 0.10200\n",
            "Epoch: 023, Train Acc: 0.18571, Val Acc: 0.11200, Test Acc: 0.09700\n",
            "Epoch: 024, Train Acc: 0.18571, Val Acc: 0.15600, Test Acc: 0.12400\n",
            "Epoch: 025, Train Acc: 0.17143, Val Acc: 0.15600, Test Acc: 0.12700\n",
            "Epoch: 026, Train Acc: 0.15714, Val Acc: 0.15200, Test Acc: 0.13100\n",
            "Epoch: 027, Train Acc: 0.17857, Val Acc: 0.12200, Test Acc: 0.11700\n",
            "Epoch: 028, Train Acc: 0.19286, Val Acc: 0.09600, Test Acc: 0.11100\n",
            "Epoch: 029, Train Acc: 0.17857, Val Acc: 0.09200, Test Acc: 0.09800\n",
            "Epoch: 030, Train Acc: 0.21429, Val Acc: 0.10000, Test Acc: 0.10300\n",
            "Epoch: 031, Train Acc: 0.20714, Val Acc: 0.12000, Test Acc: 0.10700\n",
            "Epoch: 032, Train Acc: 0.18571, Val Acc: 0.13200, Test Acc: 0.10800\n",
            "Epoch: 033, Train Acc: 0.22857, Val Acc: 0.14600, Test Acc: 0.12400\n",
            "Epoch: 034, Train Acc: 0.22857, Val Acc: 0.15200, Test Acc: 0.13900\n",
            "Epoch: 035, Train Acc: 0.25714, Val Acc: 0.17000, Test Acc: 0.14500\n",
            "Epoch: 036, Train Acc: 0.27857, Val Acc: 0.15000, Test Acc: 0.13400\n",
            "Epoch: 037, Train Acc: 0.21429, Val Acc: 0.16200, Test Acc: 0.12500\n",
            "Epoch: 038, Train Acc: 0.21429, Val Acc: 0.14800, Test Acc: 0.11700\n",
            "Epoch: 039, Train Acc: 0.17143, Val Acc: 0.08600, Test Acc: 0.09800\n",
            "Epoch: 040, Train Acc: 0.20000, Val Acc: 0.14000, Test Acc: 0.12900\n",
            "Epoch: 041, Train Acc: 0.17857, Val Acc: 0.14200, Test Acc: 0.11000\n",
            "Epoch: 042, Train Acc: 0.20714, Val Acc: 0.12600, Test Acc: 0.12300\n",
            "Epoch: 043, Train Acc: 0.23571, Val Acc: 0.14000, Test Acc: 0.12500\n",
            "Epoch: 044, Train Acc: 0.20714, Val Acc: 0.10800, Test Acc: 0.11100\n",
            "Epoch: 045, Train Acc: 0.22143, Val Acc: 0.11200, Test Acc: 0.11300\n",
            "Epoch: 046, Train Acc: 0.22857, Val Acc: 0.12600, Test Acc: 0.13000\n",
            "Epoch: 047, Train Acc: 0.24286, Val Acc: 0.12400, Test Acc: 0.11500\n",
            "Epoch: 048, Train Acc: 0.25000, Val Acc: 0.11000, Test Acc: 0.12400\n",
            "Epoch: 049, Train Acc: 0.24286, Val Acc: 0.12000, Test Acc: 0.11900\n",
            "Epoch: 050, Train Acc: 0.17857, Val Acc: 0.08400, Test Acc: 0.08800\n"
          ]
        }
      ],
      "source": [
        "model = GNNM(data.num_nodes, dataset.num_classes, 32, [64,64,64,64,64], eps=0.01).to(device)\n",
        "optimizer = torch.optim.Adam(model.parameters(), lr=0.001)\n",
        "loss_fn = nn.CrossEntropyLoss()\n",
        "\n",
        "\n",
        "test_dataset = dataset[:len(dataset) // 10]\n",
        "train_dataset = dataset[len(dataset) // 10:]\n",
        "test_loader = DataLoader(test_dataset)\n",
        "train_loader = DataLoader(train_dataset)\n",
        "\n",
        "def train():\n",
        "    model.train()\n",
        "    optimizer.zero_grad()\n",
        "    loss_fn(model()[data.train_mask], data.y[data.train_mask]).backward()\n",
        "    optimizer.step()\n",
        "\n",
        "\n",
        "def test():\n",
        "    model.eval()\n",
        "    logits, accs = model(), []\n",
        "    for _, mask in data('train_mask', 'val_mask', 'test_mask'):\n",
        "        pred = logits[mask].max(1)[1]\n",
        "        acc = pred.eq(data.y[mask]).sum().item() / mask.sum().item()\n",
        "        accs.append(acc)\n",
        "    return accs\n",
        "\n",
        "\n",
        "for epoch in range(1, 51):\n",
        "    train()\n",
        "    accs = test()\n",
        "    train_acc = accs[0]\n",
        "    val_acc = accs[1]\n",
        "    test_acc = accs[2]\n",
        "    print('Epoch: {:03d}, Train Acc: {:.5f}, '\n",
        "          'Val Acc: {:.5f}, Test Acc: {:.5f}'.format(epoch, train_acc,\n",
        "                                                       val_acc, test_acc))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uUhKteY4HCRc"
      },
      "source": [
        "### Gated Graph Neural Network"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "8ZUZk3x7HCRd"
      },
      "outputs": [],
      "source": [
        "class GatedGraphConv(MessagePassing):\n",
        "\n",
        "    def __init__(self, out_channels, num_layers, aggr = 'add',\n",
        "                 bias = True, **kwargs):\n",
        "        super(GatedGraphConv, self).__init__(aggr=aggr, **kwargs)\n",
        "\n",
        "        self.out_channels = out_channels\n",
        "        self.num_layers = num_layers\n",
        "\n",
        "        self.weight = Param(Tensor(num_layers, out_channels, out_channels))\n",
        "        self.rnn = torch.nn.GRUCell(out_channels, out_channels, bias=bias)\n",
        "\n",
        "        self.reset_parameters()\n",
        "\n",
        "    def reset_parameters(self):\n",
        "        uniform(self.out_channels, self.weight)\n",
        "        self.rnn.reset_parameters()\n",
        "\n",
        "    def forward(self, data):\n",
        "        \"\"\"\"\"\"\n",
        "        x = data.x\n",
        "        edge_index = data.edge_index\n",
        "        edge_weight = data.edge_attr\n",
        "        if x.size(-1) > self.out_channels:\n",
        "            raise ValueError('The number of input channels is not allowed to '\n",
        "                             'be larger than the number of output channels')\n",
        "\n",
        "        if x.size(-1) < self.out_channels:\n",
        "            zero = x.new_zeros(x.size(0), self.out_channels - x.size(-1))\n",
        "            x = torch.cat([x, zero], dim=1)\n",
        "\n",
        "        for i in range(self.num_layers):\n",
        "            m = torch.matmul(x, self.weight[i])\n",
        "            m = self.propagate(edge_index, x=m, edge_weight=edge_weight,\n",
        "                               size=None)\n",
        "            x = self.rnn(m, x)\n",
        "\n",
        "        return x\n",
        "\n",
        "    def message(self, x_j, edge_weight):\n",
        "        return x_j if edge_weight is None else edge_weight.view(-1, 1) * x_j\n",
        "\n",
        "    def message_and_aggregate(self, adj_t, x):\n",
        "        return matmul(adj_t, x, reduce=self.aggr)\n",
        "\n",
        "    def __repr__(self):\n",
        "        return '{}({}, num_layers={})'.format(self.__class__.__name__,\n",
        "                                              self.out_channels,\n",
        "                                              self.num_layers)\n",
        "\n",
        "class GGNN(torch.nn.Module):\n",
        "    def __init__(self):\n",
        "        super(GGNN, self).__init__()\n",
        "\n",
        "        self.conv = GatedGraphConv(1433, 3)\n",
        "        self.mlp = MLP(1433, [32,32,32], dataset.num_classes)\n",
        "\n",
        "    def forward(self):\n",
        "        x = self.conv(data)\n",
        "        x = self.mlp(x)\n",
        "        return F.log_softmax(x, dim=-1)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "Q282P_YNHCRd",
        "outputId": "27551f37-3337-4033-984b-f9c3a3aa600b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 001, Train Acc: 0.27143, Val Acc: 0.15800, Test Acc: 0.15400\n",
            "Epoch: 002, Train Acc: 0.35000, Val Acc: 0.22200, Test Acc: 0.22200\n",
            "Epoch: 003, Train Acc: 0.18571, Val Acc: 0.22400, Test Acc: 0.21000\n",
            "Epoch: 004, Train Acc: 0.32857, Val Acc: 0.29600, Test Acc: 0.28300\n",
            "Epoch: 005, Train Acc: 0.36429, Val Acc: 0.25000, Test Acc: 0.26700\n",
            "Epoch: 006, Train Acc: 0.45000, Val Acc: 0.35000, Test Acc: 0.37200\n",
            "Epoch: 007, Train Acc: 0.54286, Val Acc: 0.45800, Test Acc: 0.47100\n",
            "Epoch: 008, Train Acc: 0.53571, Val Acc: 0.40800, Test Acc: 0.43000\n",
            "Epoch: 009, Train Acc: 0.60000, Val Acc: 0.51000, Test Acc: 0.50700\n",
            "Epoch: 010, Train Acc: 0.64286, Val Acc: 0.58800, Test Acc: 0.57500\n",
            "Epoch: 011, Train Acc: 0.62143, Val Acc: 0.57600, Test Acc: 0.57700\n",
            "Epoch: 012, Train Acc: 0.63571, Val Acc: 0.54000, Test Acc: 0.55100\n",
            "Epoch: 013, Train Acc: 0.65714, Val Acc: 0.55000, Test Acc: 0.55200\n",
            "Epoch: 014, Train Acc: 0.67143, Val Acc: 0.59200, Test Acc: 0.57100\n",
            "Epoch: 015, Train Acc: 0.70714, Val Acc: 0.60800, Test Acc: 0.58400\n",
            "Epoch: 016, Train Acc: 0.73571, Val Acc: 0.62400, Test Acc: 0.60400\n",
            "Epoch: 017, Train Acc: 0.74286, Val Acc: 0.61200, Test Acc: 0.58800\n",
            "Epoch: 018, Train Acc: 0.69286, Val Acc: 0.57000, Test Acc: 0.55700\n",
            "Epoch: 019, Train Acc: 0.70714, Val Acc: 0.59400, Test Acc: 0.58700\n",
            "Epoch: 020, Train Acc: 0.74286, Val Acc: 0.59600, Test Acc: 0.60400\n",
            "Epoch: 021, Train Acc: 0.72143, Val Acc: 0.58600, Test Acc: 0.59000\n",
            "Epoch: 022, Train Acc: 0.71429, Val Acc: 0.57800, Test Acc: 0.56100\n",
            "Epoch: 023, Train Acc: 0.72857, Val Acc: 0.56800, Test Acc: 0.55100\n",
            "Epoch: 024, Train Acc: 0.74286, Val Acc: 0.58200, Test Acc: 0.57100\n",
            "Epoch: 025, Train Acc: 0.82143, Val Acc: 0.59600, Test Acc: 0.61400\n",
            "Epoch: 026, Train Acc: 0.83571, Val Acc: 0.58800, Test Acc: 0.59400\n",
            "Epoch: 027, Train Acc: 0.83571, Val Acc: 0.57800, Test Acc: 0.58000\n",
            "Epoch: 028, Train Acc: 0.83571, Val Acc: 0.57800, Test Acc: 0.57800\n",
            "Epoch: 029, Train Acc: 0.82143, Val Acc: 0.57800, Test Acc: 0.58200\n",
            "Epoch: 030, Train Acc: 0.82857, Val Acc: 0.58600, Test Acc: 0.57900\n",
            "Epoch: 031, Train Acc: 0.83571, Val Acc: 0.59200, Test Acc: 0.58600\n",
            "Epoch: 032, Train Acc: 0.86429, Val Acc: 0.61000, Test Acc: 0.59700\n",
            "Epoch: 033, Train Acc: 0.94286, Val Acc: 0.62000, Test Acc: 0.62300\n",
            "Epoch: 034, Train Acc: 0.95714, Val Acc: 0.62000, Test Acc: 0.62900\n",
            "Epoch: 035, Train Acc: 0.97143, Val Acc: 0.62000, Test Acc: 0.63200\n",
            "Epoch: 036, Train Acc: 0.98571, Val Acc: 0.61400, Test Acc: 0.63300\n",
            "Epoch: 037, Train Acc: 0.98571, Val Acc: 0.61000, Test Acc: 0.63400\n",
            "Epoch: 038, Train Acc: 0.98571, Val Acc: 0.60800, Test Acc: 0.64000\n",
            "Epoch: 039, Train Acc: 0.98571, Val Acc: 0.61600, Test Acc: 0.63200\n",
            "Epoch: 040, Train Acc: 0.98571, Val Acc: 0.61600, Test Acc: 0.63000\n",
            "Epoch: 041, Train Acc: 0.98571, Val Acc: 0.61800, Test Acc: 0.63200\n",
            "Epoch: 042, Train Acc: 0.98571, Val Acc: 0.61000, Test Acc: 0.63300\n",
            "Epoch: 043, Train Acc: 0.98571, Val Acc: 0.60600, Test Acc: 0.63000\n",
            "Epoch: 044, Train Acc: 0.98571, Val Acc: 0.60400, Test Acc: 0.63000\n",
            "Epoch: 045, Train Acc: 0.98571, Val Acc: 0.60600, Test Acc: 0.63100\n",
            "Epoch: 046, Train Acc: 0.98571, Val Acc: 0.60600, Test Acc: 0.62900\n",
            "Epoch: 047, Train Acc: 0.98571, Val Acc: 0.60400, Test Acc: 0.63000\n",
            "Epoch: 048, Train Acc: 0.98571, Val Acc: 0.60400, Test Acc: 0.63300\n",
            "Epoch: 049, Train Acc: 0.98571, Val Acc: 0.60400, Test Acc: 0.63200\n",
            "Epoch: 050, Train Acc: 0.99286, Val Acc: 0.60400, Test Acc: 0.62700\n"
          ]
        }
      ],
      "source": [
        "device = \"cpu\"\n",
        "model = GGNN().to(device)\n",
        "optimizer = torch.optim.Adam(model.parameters(), lr=0.001)\n",
        "loss_fn = nn.CrossEntropyLoss()\n",
        "\n",
        "\n",
        "test_dataset = dataset[:len(dataset) // 10]\n",
        "train_dataset = dataset[len(dataset) // 10:]\n",
        "test_loader = DataLoader(test_dataset)\n",
        "train_loader = DataLoader(train_dataset)\n",
        "\n",
        "def train():\n",
        "    model.train()\n",
        "    optimizer.zero_grad()\n",
        "    loss_fn(model()[data.train_mask], data.y[data.train_mask]).backward()\n",
        "    optimizer.step()\n",
        "\n",
        "\n",
        "def test():\n",
        "    model.eval()\n",
        "    logits, accs = model(), []\n",
        "    for _, mask in data('train_mask', 'val_mask', 'test_mask'):\n",
        "        pred = logits[mask].max(1)[1]\n",
        "        acc = pred.eq(data.y[mask]).sum().item() / mask.sum().item()\n",
        "        accs.append(acc)\n",
        "    return accs\n",
        "\n",
        "\n",
        "for epoch in range(1, 51):\n",
        "    train()\n",
        "    accs = test()\n",
        "    train_acc = accs[0]\n",
        "    val_acc = accs[1]\n",
        "    test_acc = accs[2]\n",
        "    print('Epoch: {:03d}, Train Acc: {:.5f}, '\n",
        "          'Val Acc: {:.5f}, Test Acc: {:.5f}'.format(epoch, train_acc,\n",
        "                                                       val_acc, test_acc))"
      ]
    }
  ],
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