QML_HEP_GSoC_2021_Task_4.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "QML_HEP_GSoC_2021_Task_4.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Ipsit1234/QML-HEP-Evaluation-Test-GSOC-2021/blob/main/QML_HEP_GSoC_2021_Task_4.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bvVsfoZrdJ2b"
      },
      "source": [
        "# Task IV: Classical Graph Neural Network (GNN) Part\n",
        "For Task IV, you will use ParticleNet’s data for Quark/Gluon jet classification available [here](https://zenodo.org/record/3164691#.YFMDgK8zZZV)\n",
        "with its corresponding description.\n",
        "* Choose 2 Graph-based architectures of your choice to classify jets as being quarks\n",
        "or gluons. Provide a description on what considerations you have taken to project\n",
        "this point-cloud dataset to a set of interconnected nodes and edges.  \n",
        "* Discuss the resulting performance of the 2 chosen architectures. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_aGWRqeIdJ2c"
      },
      "source": [
        "## Downloading the Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g7GE1ohYdJ2c",
        "outputId": "7d39c669-5c11-441c-aeee-78b2e9bbecfd"
      },
      "source": [
        "!wget https://zenodo.org/record/3164691/files/QG_jets.npz?download=1 -O QG_jets.npz"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2021-03-19 13:56:00--  https://zenodo.org/record/3164691/files/QG_jets.npz?download=1\n",
            "Resolving zenodo.org (zenodo.org)... 137.138.76.77\n",
            "Connecting to zenodo.org (zenodo.org)|137.138.76.77|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 106689379 (102M) [application/octet-stream]\n",
            "Saving to: ‘QG_jets.npz’\n",
            "\n",
            "QG_jets.npz         100%[===================>] 101.75M  22.1MB/s    in 5.0s    \n",
            "\n",
            "2021-03-19 13:56:06 (20.3 MB/s) - ‘QG_jets.npz’ saved [106689379/106689379]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CCtwCNhMdJ2c"
      },
      "source": [
        "import numpy as np\n",
        "\n",
        "%matplotlib inline\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xdS-Mi6EdJ2c",
        "outputId": "dcc99324-78f3-4820-c90c-6e522d9e1c77"
      },
      "source": [
        "with np.load('./QG_jets.npz') as data:\n",
        "  X = data['X']\n",
        "  y_train = data['y']\n",
        "print(X.shape)\n",
        "print(y_train.shape)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(100000, 139, 4)\n",
            "(100000,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "bVbdi9zadJ2c",
        "outputId": "903c37a6-ff82-48b2-b722-6832113e618e"
      },
      "source": [
        "plt.hist(y_train)\n",
        "plt.show()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XghV5mFQdJ2d",
        "outputId": "7f33df3f-2866-44da-9923-076b62ce0ac5"
      },
      "source": [
        "x_train = []\n",
        "for i in X:\n",
        "  x_train.append(i[0])\n",
        "x_train = np.array(x_train)\n",
        "print(x_train.shape)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(100000, 4)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cWHCpyotdJ2d",
        "outputId": "007f461f-0a97-4e73-cf95-75f44a8d1f85"
      },
      "source": [
        "!pip install dgl-cu101\n",
        "import dgl\n",
        "import torch\n",
        "import networkx as nx"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: dgl-cu101 in /usr/local/lib/python3.7/dist-packages (0.6.0.post1)\n",
            "Requirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.7/dist-packages (from dgl-cu101) (2.23.0)\n",
            "Requirement already satisfied: scipy>=1.1.0 in /usr/local/lib/python3.7/dist-packages (from dgl-cu101) (1.4.1)\n",
            "Requirement already satisfied: numpy>=1.14.0 in /usr/local/lib/python3.7/dist-packages (from dgl-cu101) (1.19.5)\n",
            "Requirement already satisfied: networkx>=2.1 in /usr/local/lib/python3.7/dist-packages (from dgl-cu101) (2.5)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->dgl-cu101) (2.10)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->dgl-cu101) (1.24.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->dgl-cu101) (2020.12.5)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->dgl-cu101) (3.0.4)\n",
            "Requirement already satisfied: decorator>=4.3.0 in /usr/local/lib/python3.7/dist-packages (from networkx>=2.1->dgl-cu101) (4.4.2)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Using backend: pytorch\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OBNyRy0pdJ2d"
      },
      "source": [
        "## Approach\n",
        "I am choosing the following two graph-based architectures:\n",
        "1. Node Classification\n",
        "2. Graph Classification \n",
        "\n",
        "### Node Classification\n",
        "In this approach, I will encode the data as nodes of a graph and then train a GNN to learn to classify these nodes. In this way we can build a classifier which classifies quarks from gluons.\n",
        "\n",
        "### Graph Classification\n",
        "In this approach, I will encode each training sample as a graph, resulting in a training data of different graphs. I will then train a GNN to learn to classify different graphs. In this way too we can build a classifier which classifies quarks from gluons."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7GcVu37fdJ2d"
      },
      "source": [
        "### Node Classification\n",
        "Let us begin by preparing the dataset. We will use a small subset of the training data for demonstration."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rpCOjnvkdJ2e",
        "outputId": "a99544f4-5c29-4dfe-c27f-42916539ce10"
      },
      "source": [
        "x_train_small = x_train[:2000].astype(np.float32)\n",
        "y_train_small = y_train[:2000]\n",
        "class NodeClassificationDataset(dgl.data.DGLDataset):\n",
        "  \"\"\"A Class to process and convert the numpy training data into Graphs so that it can be used in GNNs\"\"\"\n",
        "  def __init__(self):\n",
        "    super().__init__(name='node_classification')\n",
        "    self.num_classes = 2\n",
        "  \n",
        "  def process(self):\n",
        "    node_features = torch.from_numpy(x_train_small)\n",
        "    node_labels = torch.from_numpy(y_train_small).long()\n",
        "\n",
        "    self.graph = dgl.from_networkx(nx.generators.fast_gnp_random_graph(x_train_small.shape[0], 0.008, seed=1337))\n",
        "    self.graph.ndata['feat'] = node_features\n",
        "    self.graph.ndata['label'] = node_labels\n",
        "    # self.graph.ndata['weight'] = None\n",
        "\n",
        "    n_nodes = x_train_small.shape[0]\n",
        "    n_train = int(n_nodes * 0.8)\n",
        "    n_val = int(n_nodes * 0.1)\n",
        "    train_mask = torch.zeros(n_nodes, dtype=torch.bool)\n",
        "    val_mask = torch.zeros(n_nodes, dtype=torch.bool)\n",
        "    test_mask = torch.zeros(n_nodes, dtype=torch.bool)\n",
        "    train_mask[:n_train] = True\n",
        "    val_mask[n_train:n_train+n_val] = True\n",
        "    test_mask[n_train+n_val:] = True\n",
        "    self.graph.ndata['train_mask'] = train_mask\n",
        "    self.graph.ndata['val_mask'] = val_mask\n",
        "    self.graph.ndata['test_mask'] = test_mask\n",
        "  \n",
        "  def __getitem__(self, idx):\n",
        "    return self.graph\n",
        "  \n",
        "  def __len__(self):\n",
        "    return 1\n",
        "\n",
        "node_classif_dataset = NodeClassificationDataset()\n",
        "node_classif_graph = node_classif_dataset[0]\n",
        "\n",
        "print(node_classif_graph)"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Graph(num_nodes=2000, num_edges=31988,\n",
            "      ndata_schemes={'feat': Scheme(shape=(4,), dtype=torch.float32), 'label': Scheme(shape=(), dtype=torch.int64), 'train_mask': Scheme(shape=(), dtype=torch.bool), 'val_mask': Scheme(shape=(), dtype=torch.bool), 'test_mask': Scheme(shape=(), dtype=torch.bool)}\n",
            "      edata_schemes={})\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pRXf2DVZdJ2e"
      },
      "source": [
        "### Defining the Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0_w0iAm9dJ2e"
      },
      "source": [
        "from dgl.nn import GraphConv\n",
        "\n",
        "class NodeClassificationModel(torch.nn.Module):\n",
        "  \"\"\"A Model Class having the methods to implement and forward pass a GNN.\"\"\"\n",
        "  def __init__(self, in_feats, num_classes):\n",
        "    super(NodeClassificationModel, self).__init__()\n",
        "    self.conv1 = GraphConv(in_feats, 16)\n",
        "    self.conv2 = GraphConv(16, 32)\n",
        "    self.conv3 = GraphConv(32, 64)\n",
        "    self.conv4 = GraphConv(64, num_classes)\n",
        "  \n",
        "  def forward(self, g, in_feat):\n",
        "    h = self.conv1(g, in_feat)\n",
        "    h = torch.nn.functional.elu(h)\n",
        "    h = self.conv2(g, h)\n",
        "    h = torch.nn.functional.relu(h)\n",
        "    h = self.conv3(g, h)\n",
        "    h = torch.nn.functional.relu(h)\n",
        "    h = self.conv4(g, h)\n",
        "    return h\n",
        "\n",
        "node_classif_model = NodeClassificationModel(node_classif_graph.ndata['feat'].shape[1], node_classif_dataset.num_classes)"
      ],
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "akgThr-5dJ2e"
      },
      "source": [
        "### Defining the Training Loop"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pWQT8T6LdJ2f"
      },
      "source": [
        "def train(g, model, num_epochs):\n",
        "  \"\"\"The function implementing the main train loop.\"\"\"\n",
        "  losses = []\n",
        "  accs = {'train':[], 'val': [], 'test': []}\n",
        "  optimizer = torch.optim.Adam(model.parameters(), lr=1*1e-5, betas=(0.6, 0.7))\n",
        "  best_val_acc = 0.0\n",
        "  best_test_acc = 0.0\n",
        "\n",
        "  features = g.ndata['feat']\n",
        "  labels = g.ndata['label']\n",
        "  train_mask = g.ndata['train_mask']\n",
        "  val_mask = g.ndata['val_mask']\n",
        "  test_mask = g.ndata['test_mask']\n",
        "\n",
        "  for e in range(num_epochs):\n",
        "    model.train()\n",
        "    logits = model(g, features)\n",
        "    preds = logits.argmax(1)\n",
        "\n",
        "    loss = torch.nn.functional.cross_entropy(logits[train_mask], labels[train_mask])\n",
        "\n",
        "    train_acc = (preds[train_mask] == labels[train_mask]).float().mean()\n",
        "    val_acc = (preds[val_mask] == labels[val_mask]).float().mean()\n",
        "    test_acc = (preds[test_mask] == labels[test_mask]).float().mean()\n",
        "    losses.append(loss)\n",
        "    accs['train'].append(train_acc)\n",
        "    accs['val'].append(val_acc)\n",
        "    accs['test'].append(test_acc)\n",
        "\n",
        "    if best_val_acc < val_acc:\n",
        "      best_val_acc = val_acc\n",
        "      best_test_acc = test_acc\n",
        "    \n",
        "    with torch.no_grad():\n",
        "      optimizer.zero_grad()\n",
        "      loss.backward()\n",
        "      optimizer.step()\n",
        "\n",
        "    if e % 5 == 0:\n",
        "      print('In epoch {}, loss: {:.8f}, val acc: {:.8f} (best {:.8f}), test acc: {:.8f} (best {:.8f})'.format(\n",
        "                e, loss, val_acc, best_val_acc, test_acc, best_test_acc))\n",
        "  \n",
        "  return losses, accs"
      ],
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JvFUvl24dJ2f",
        "outputId": "25b4dbd7-c64c-479b-cef4-ae0421e8b236"
      },
      "source": [
        "node_classif_graph = node_classif_graph.to('cuda')\n",
        "node_classif_model = node_classif_model.to('cuda')\n",
        "node_classif_losses, node_classif_accs = train(node_classif_graph, node_classif_model, 5000)"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "In epoch 0, loss: 7.58675766, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 5, loss: 7.52613258, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 10, loss: 7.46548605, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 15, loss: 7.40483522, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 20, loss: 7.34418201, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 25, loss: 7.28349257, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 30, loss: 7.22277641, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 35, loss: 7.16203165, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 40, loss: 7.10127878, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 45, loss: 7.04049969, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 50, loss: 6.97972059, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 55, loss: 6.91894579, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 60, loss: 6.85817337, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 65, loss: 6.79738712, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 70, loss: 6.73660135, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 75, loss: 6.67581320, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 80, loss: 6.61501265, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 85, loss: 6.55420113, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 90, loss: 6.49337482, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 95, loss: 6.43254089, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 100, loss: 6.37171125, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 105, loss: 6.31088257, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 110, loss: 6.25004530, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 115, loss: 6.18919373, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 120, loss: 6.12830448, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 125, loss: 6.06740475, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 130, loss: 6.00650215, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 135, loss: 5.94559526, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 140, loss: 5.88468790, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 145, loss: 5.82376814, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 150, loss: 5.76285076, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 155, loss: 5.70193863, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 160, loss: 5.64101458, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 165, loss: 5.58008289, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 170, loss: 5.51917171, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 175, loss: 5.45826530, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 180, loss: 5.39735985, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 185, loss: 5.33644342, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 190, loss: 5.27552843, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 195, loss: 5.21461868, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 200, loss: 5.15370750, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 205, loss: 5.09279680, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 210, loss: 5.03188276, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 215, loss: 4.97097397, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 220, loss: 4.91007376, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 225, loss: 4.84918690, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 230, loss: 4.78831148, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 235, loss: 4.72745705, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 240, loss: 4.66661358, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 245, loss: 4.60578108, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 250, loss: 4.54496861, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 255, loss: 4.48415995, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 260, loss: 4.42335796, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 265, loss: 4.36256027, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 270, loss: 4.30177927, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 275, loss: 4.24101162, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 280, loss: 4.18026304, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 285, loss: 4.11953497, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 290, loss: 4.05883217, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 295, loss: 3.99813819, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 300, loss: 3.93746114, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 305, loss: 3.87680888, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 310, loss: 3.81618595, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 315, loss: 3.75558925, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 320, loss: 3.69501686, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 325, loss: 3.63446736, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 330, loss: 3.57394814, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 335, loss: 3.51345515, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 340, loss: 3.45300221, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 345, loss: 3.39258385, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 350, loss: 3.33219910, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 355, loss: 3.27186465, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 360, loss: 3.21156645, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 365, loss: 3.15131855, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 370, loss: 3.09112120, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 375, loss: 3.03098631, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 380, loss: 2.97090626, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 385, loss: 2.91088462, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 390, loss: 2.85092783, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 395, loss: 2.79104614, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 400, loss: 2.73125172, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 405, loss: 2.67154765, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 410, loss: 2.61193562, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 415, loss: 2.55242968, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 420, loss: 2.49305105, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 425, loss: 2.43380952, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 430, loss: 2.37470722, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 435, loss: 2.31575871, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 440, loss: 2.25698709, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 445, loss: 2.19841099, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 450, loss: 2.14003897, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 455, loss: 2.08189273, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 460, loss: 2.02402067, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 465, loss: 1.96644115, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 470, loss: 1.90917528, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 475, loss: 1.85225689, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 480, loss: 1.79574692, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 485, loss: 1.73967946, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 490, loss: 1.68409526, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 495, loss: 1.62905431, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 500, loss: 1.57459867, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 505, loss: 1.52080321, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 510, loss: 1.46774113, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 515, loss: 1.41549015, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 520, loss: 1.36413789, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 525, loss: 1.31376791, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 530, loss: 1.26448059, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 535, loss: 1.21638441, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 540, loss: 1.16958606, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 545, loss: 1.12418795, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 550, loss: 1.08034098, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 555, loss: 1.03815353, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 560, loss: 0.99777764, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 565, loss: 0.95935583, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 570, loss: 0.92302316, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 575, loss: 0.88893038, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 580, loss: 0.85723078, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 585, loss: 0.82805395, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 590, loss: 0.80154037, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 595, loss: 0.77782011, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 600, loss: 0.75700998, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 605, loss: 0.73919344, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 610, loss: 0.72445619, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 615, loss: 0.71282732, val acc: 0.47999999 (best 0.47999999), test acc: 0.52499998 (best 0.52499998)\n",
            "In epoch 620, loss: 0.70430928, val acc: 0.47000000 (best 0.47999999), test acc: 0.51499999 (best 0.52499998)\n",
            "In epoch 625, loss: 0.69885343, val acc: 0.47000000 (best 0.47999999), test acc: 0.50500000 (best 0.52499998)\n",
            "In epoch 630, loss: 0.69627380, val acc: 0.48999998 (best 0.50000000), test acc: 0.44000000 (best 0.47499999)\n",
            "In epoch 635, loss: 0.69583642, val acc: 0.51999998 (best 0.51999998), test acc: 0.44499999 (best 0.44499999)\n",
            "In epoch 640, loss: 0.69578665, val acc: 0.51999998 (best 0.52999997), test acc: 0.44000000 (best 0.42999998)\n",
            "In epoch 645, loss: 0.69572657, val acc: 0.52499998 (best 0.52999997), test acc: 0.44000000 (best 0.42999998)\n",
            "In epoch 650, loss: 0.69565004, val acc: 0.52499998 (best 0.52999997), test acc: 0.44000000 (best 0.42999998)\n",
            "In epoch 655, loss: 0.69556272, val acc: 0.51999998 (best 0.52999997), test acc: 0.44499999 (best 0.42999998)\n",
            "In epoch 660, loss: 0.69547051, val acc: 0.52999997 (best 0.52999997), test acc: 0.44000000 (best 0.42999998)\n",
            "In epoch 665, loss: 0.69537741, val acc: 0.51999998 (best 0.52999997), test acc: 0.44499999 (best 0.42999998)\n",
            "In epoch 670, loss: 0.69529325, val acc: 0.51999998 (best 0.52999997), test acc: 0.44499999 (best 0.42999998)\n",
            "In epoch 675, loss: 0.69521403, val acc: 0.51999998 (best 0.52999997), test acc: 0.44499999 (best 0.42999998)\n",
            "In epoch 680, loss: 0.69513440, val acc: 0.51999998 (best 0.52999997), test acc: 0.43500000 (best 0.42999998)\n",
            "In epoch 685, loss: 0.69506544, val acc: 0.51999998 (best 0.52999997), test acc: 0.43500000 (best 0.42999998)\n",
            "In epoch 690, loss: 0.69499338, val acc: 0.51999998 (best 0.52999997), test acc: 0.44000000 (best 0.42999998)\n",
            "In epoch 695, loss: 0.69492060, val acc: 0.51999998 (best 0.52999997), test acc: 0.43500000 (best 0.42999998)\n",
            "In epoch 700, loss: 0.69484758, val acc: 0.51999998 (best 0.52999997), test acc: 0.43500000 (best 0.42999998)\n",
            "In epoch 705, loss: 0.69477630, val acc: 0.51999998 (best 0.52999997), test acc: 0.44000000 (best 0.42999998)\n",
            "In epoch 710, loss: 0.69470459, val acc: 0.51999998 (best 0.52999997), test acc: 0.44000000 (best 0.42999998)\n",
            "In epoch 715, loss: 0.69463438, val acc: 0.52999997 (best 0.52999997), test acc: 0.44000000 (best 0.42999998)\n",
            "In epoch 720, loss: 0.69456369, val acc: 0.51999998 (best 0.52999997), test acc: 0.44499999 (best 0.42999998)\n",
            "In epoch 725, loss: 0.69449270, val acc: 0.52499998 (best 0.52999997), test acc: 0.44000000 (best 0.42999998)\n",
            "In epoch 730, loss: 0.69442004, val acc: 0.51999998 (best 0.52999997), test acc: 0.44999999 (best 0.42999998)\n",
            "In epoch 735, loss: 0.69434690, val acc: 0.51999998 (best 0.52999997), test acc: 0.44499999 (best 0.42999998)\n",
            "In epoch 740, loss: 0.69427222, val acc: 0.50999999 (best 0.52999997), test acc: 0.44999999 (best 0.42999998)\n",
            "In epoch 745, loss: 0.69419807, val acc: 0.50999999 (best 0.52999997), test acc: 0.44999999 (best 0.42999998)\n",
            "In epoch 750, loss: 0.69412529, val acc: 0.50999999 (best 0.52999997), test acc: 0.45499998 (best 0.42999998)\n",
            "In epoch 755, loss: 0.69405639, val acc: 0.50500000 (best 0.52999997), test acc: 0.44999999 (best 0.42999998)\n",
            "In epoch 760, loss: 0.69398397, val acc: 0.51999998 (best 0.52999997), test acc: 0.44999999 (best 0.42999998)\n",
            "In epoch 765, loss: 0.69391328, val acc: 0.51999998 (best 0.52999997), test acc: 0.44499999 (best 0.42999998)\n",
            "In epoch 770, loss: 0.69384468, val acc: 0.50999999 (best 0.52999997), test acc: 0.44499999 (best 0.42999998)\n",
            "In epoch 775, loss: 0.69377786, val acc: 0.51999998 (best 0.52999997), test acc: 0.44499999 (best 0.42999998)\n",
            "In epoch 780, loss: 0.69371146, val acc: 0.50999999 (best 0.52999997), test acc: 0.44000000 (best 0.42999998)\n",
            "In epoch 785, loss: 0.69364572, val acc: 0.51999998 (best 0.52999997), test acc: 0.44999999 (best 0.42999998)\n",
            "In epoch 790, loss: 0.69357771, val acc: 0.51499999 (best 0.52999997), test acc: 0.44999999 (best 0.42999998)\n",
            "In epoch 795, loss: 0.69350785, val acc: 0.51999998 (best 0.52999997), test acc: 0.45499998 (best 0.42999998)\n",
            "In epoch 800, loss: 0.69344223, val acc: 0.52999997 (best 0.53499997), test acc: 0.45999998 (best 0.45999998)\n",
            "In epoch 805, loss: 0.69337636, val acc: 0.52999997 (best 0.53999996), test acc: 0.45499998 (best 0.45999998)\n",
            "In epoch 810, loss: 0.69331336, val acc: 0.53499997 (best 0.53999996), test acc: 0.44999999 (best 0.45999998)\n",
            "In epoch 815, loss: 0.69325083, val acc: 0.52999997 (best 0.53999996), test acc: 0.44999999 (best 0.45999998)\n",
            "In epoch 820, loss: 0.69319117, val acc: 0.52999997 (best 0.53999996), test acc: 0.44999999 (best 0.45999998)\n",
            "In epoch 825, loss: 0.69313407, val acc: 0.53499997 (best 0.53999996), test acc: 0.45499998 (best 0.45999998)\n",
            "In epoch 830, loss: 0.69308221, val acc: 0.52999997 (best 0.53999996), test acc: 0.46500000 (best 0.45999998)\n",
            "In epoch 835, loss: 0.69303054, val acc: 0.53499997 (best 0.53999996), test acc: 0.46500000 (best 0.45999998)\n",
            "In epoch 840, loss: 0.69297707, val acc: 0.52999997 (best 0.53999996), test acc: 0.45999998 (best 0.45999998)\n",
            "In epoch 845, loss: 0.69291991, val acc: 0.52999997 (best 0.53999996), test acc: 0.46500000 (best 0.45999998)\n",
            "In epoch 850, loss: 0.69286638, val acc: 0.52999997 (best 0.53999996), test acc: 0.46500000 (best 0.45999998)\n",
            "In epoch 855, loss: 0.69281232, val acc: 0.52999997 (best 0.53999996), test acc: 0.46500000 (best 0.45999998)\n",
            "In epoch 860, loss: 0.69275606, val acc: 0.52499998 (best 0.53999996), test acc: 0.47000000 (best 0.45999998)\n",
            "In epoch 865, loss: 0.69270307, val acc: 0.51999998 (best 0.53999996), test acc: 0.47000000 (best 0.45999998)\n",
            "In epoch 870, loss: 0.69265276, val acc: 0.51499999 (best 0.53999996), test acc: 0.47000000 (best 0.45999998)\n",
            "In epoch 875, loss: 0.69260162, val acc: 0.51999998 (best 0.53999996), test acc: 0.46500000 (best 0.45999998)\n",
            "In epoch 880, loss: 0.69255155, val acc: 0.51999998 (best 0.53999996), test acc: 0.46500000 (best 0.45999998)\n",
            "In epoch 885, loss: 0.69250512, val acc: 0.51499999 (best 0.53999996), test acc: 0.46500000 (best 0.45999998)\n",
            "In epoch 890, loss: 0.69246137, val acc: 0.51999998 (best 0.53999996), test acc: 0.46500000 (best 0.45999998)\n",
            "In epoch 895, loss: 0.69241792, val acc: 0.51999998 (best 0.53999996), test acc: 0.46500000 (best 0.45999998)\n",
            "In epoch 900, loss: 0.69237709, val acc: 0.51999998 (best 0.53999996), test acc: 0.46500000 (best 0.45999998)\n",
            "In epoch 905, loss: 0.69233721, val acc: 0.51999998 (best 0.53999996), test acc: 0.45999998 (best 0.45999998)\n",
            "In epoch 910, loss: 0.69229662, val acc: 0.51999998 (best 0.53999996), test acc: 0.45999998 (best 0.45999998)\n",
            "In epoch 915, loss: 0.69225693, val acc: 0.52499998 (best 0.53999996), test acc: 0.46500000 (best 0.45999998)\n",
            "In epoch 920, loss: 0.69221914, val acc: 0.51999998 (best 0.53999996), test acc: 0.46500000 (best 0.45999998)\n",
            "In epoch 925, loss: 0.69218206, val acc: 0.51999998 (best 0.53999996), test acc: 0.46500000 (best 0.45999998)\n",
            "In epoch 930, loss: 0.69214731, val acc: 0.53499997 (best 0.53999996), test acc: 0.45999998 (best 0.45999998)\n",
            "In epoch 935, loss: 0.69211280, val acc: 0.53499997 (best 0.53999996), test acc: 0.45999998 (best 0.45999998)\n",
            "In epoch 940, loss: 0.69207799, val acc: 0.53499997 (best 0.53999996), test acc: 0.45999998 (best 0.45999998)\n",
            "In epoch 945, loss: 0.69204628, val acc: 0.53499997 (best 0.53999996), test acc: 0.46500000 (best 0.45999998)\n",
            "In epoch 950, loss: 0.69201708, val acc: 0.53499997 (best 0.53999996), test acc: 0.45999998 (best 0.45999998)\n",
            "In epoch 955, loss: 0.69198942, val acc: 0.53499997 (best 0.53999996), test acc: 0.45499998 (best 0.45999998)\n",
            "In epoch 960, loss: 0.69196296, val acc: 0.53499997 (best 0.53999996), test acc: 0.45999998 (best 0.45999998)\n",
            "In epoch 965, loss: 0.69193602, val acc: 0.53499997 (best 0.53999996), test acc: 0.45999998 (best 0.45999998)\n",
            "In epoch 970, loss: 0.69190824, val acc: 0.53499997 (best 0.53999996), test acc: 0.46500000 (best 0.45999998)\n",
            "In epoch 975, loss: 0.69188291, val acc: 0.53499997 (best 0.53999996), test acc: 0.46500000 (best 0.45999998)\n",
            "In epoch 980, loss: 0.69185603, val acc: 0.53999996 (best 0.53999996), test acc: 0.46500000 (best 0.45999998)\n",
            "In epoch 985, loss: 0.69183111, val acc: 0.53999996 (best 0.53999996), test acc: 0.46500000 (best 0.45999998)\n",
            "In epoch 990, loss: 0.69180596, val acc: 0.53999996 (best 0.53999996), test acc: 0.46500000 (best 0.45999998)\n",
            "In epoch 995, loss: 0.69178164, val acc: 0.54500002 (best 0.54500002), test acc: 0.45999998 (best 0.45999998)\n",
            "In epoch 1000, loss: 0.69175887, val acc: 0.53999996 (best 0.54500002), test acc: 0.46500000 (best 0.45999998)\n",
            "In epoch 1005, loss: 0.69173682, val acc: 0.54500002 (best 0.54500002), test acc: 0.46500000 (best 0.45999998)\n",
            "In epoch 1010, loss: 0.69171607, val acc: 0.54500002 (best 0.54500002), test acc: 0.46500000 (best 0.45999998)\n",
            "In epoch 1015, loss: 0.69169754, val acc: 0.53999996 (best 0.54500002), test acc: 0.46500000 (best 0.45999998)\n",
            "In epoch 1020, loss: 0.69167662, val acc: 0.54500002 (best 0.54500002), test acc: 0.46500000 (best 0.45999998)\n",
            "In epoch 1025, loss: 0.69165701, val acc: 0.54500002 (best 0.54500002), test acc: 0.46500000 (best 0.45999998)\n",
            "In epoch 1030, loss: 0.69163787, val acc: 0.53999996 (best 0.54500002), test acc: 0.46500000 (best 0.45999998)\n",
            "In epoch 1035, loss: 0.69161963, val acc: 0.54500002 (best 0.54500002), test acc: 0.46500000 (best 0.45999998)\n",
            "In epoch 1040, loss: 0.69160128, val acc: 0.54500002 (best 0.54500002), test acc: 0.47000000 (best 0.45999998)\n",
            "In epoch 1045, loss: 0.69158280, val acc: 0.53999996 (best 0.54500002), test acc: 0.47000000 (best 0.45999998)\n",
            "In epoch 1050, loss: 0.69156384, val acc: 0.54500002 (best 0.55000001), test acc: 0.47000000 (best 0.47499999)\n",
            "In epoch 1055, loss: 0.69154418, val acc: 0.53999996 (best 0.55000001), test acc: 0.47000000 (best 0.47499999)\n",
            "In epoch 1060, loss: 0.69152540, val acc: 0.53999996 (best 0.55500001), test acc: 0.47000000 (best 0.47000000)\n",
            "In epoch 1065, loss: 0.69150734, val acc: 0.55000001 (best 0.55500001), test acc: 0.47499999 (best 0.47000000)\n",
            "In epoch 1070, loss: 0.69148982, val acc: 0.55000001 (best 0.55500001), test acc: 0.47499999 (best 0.47000000)\n",
            "In epoch 1075, loss: 0.69147509, val acc: 0.54500002 (best 0.55500001), test acc: 0.47999999 (best 0.47000000)\n",
            "In epoch 1080, loss: 0.69145638, val acc: 0.55000001 (best 0.55500001), test acc: 0.47000000 (best 0.47000000)\n",
            "In epoch 1085, loss: 0.69144011, val acc: 0.55000001 (best 0.55500001), test acc: 0.47000000 (best 0.47000000)\n",
            "In epoch 1090, loss: 0.69142842, val acc: 0.54500002 (best 0.56000000), test acc: 0.48499998 (best 0.47000000)\n",
            "In epoch 1095, loss: 0.69140851, val acc: 0.55500001 (best 0.56000000), test acc: 0.47000000 (best 0.47000000)\n",
            "In epoch 1100, loss: 0.69139153, val acc: 0.56000000 (best 0.56500000), test acc: 0.47499999 (best 0.47499999)\n",
            "In epoch 1105, loss: 0.69137847, val acc: 0.55500001 (best 0.56500000), test acc: 0.47000000 (best 0.47499999)\n",
            "In epoch 1110, loss: 0.69136411, val acc: 0.56000000 (best 0.56500000), test acc: 0.47000000 (best 0.47499999)\n",
            "In epoch 1115, loss: 0.69134909, val acc: 0.56000000 (best 0.56500000), test acc: 0.47000000 (best 0.47499999)\n",
            "In epoch 1120, loss: 0.69133520, val acc: 0.56000000 (best 0.56500000), test acc: 0.47000000 (best 0.47499999)\n",
            "In epoch 1125, loss: 0.69131899, val acc: 0.56500000 (best 0.56500000), test acc: 0.47999999 (best 0.47499999)\n",
            "In epoch 1130, loss: 0.69130570, val acc: 0.56500000 (best 0.56999999), test acc: 0.47499999 (best 0.48999998)\n",
            "In epoch 1135, loss: 0.69129241, val acc: 0.56500000 (best 0.56999999), test acc: 0.47999999 (best 0.48999998)\n",
            "In epoch 1140, loss: 0.69127977, val acc: 0.56000000 (best 0.56999999), test acc: 0.46500000 (best 0.48999998)\n",
            "In epoch 1145, loss: 0.69126284, val acc: 0.56500000 (best 0.56999999), test acc: 0.47999999 (best 0.48999998)\n",
            "In epoch 1150, loss: 0.69125044, val acc: 0.56500000 (best 0.56999999), test acc: 0.47499999 (best 0.48999998)\n",
            "In epoch 1155, loss: 0.69123816, val acc: 0.56000000 (best 0.56999999), test acc: 0.47499999 (best 0.48999998)\n",
            "In epoch 1160, loss: 0.69122666, val acc: 0.55500001 (best 0.56999999), test acc: 0.47999999 (best 0.48999998)\n",
            "In epoch 1165, loss: 0.69121420, val acc: 0.55000001 (best 0.56999999), test acc: 0.47999999 (best 0.48999998)\n",
            "In epoch 1170, loss: 0.69120246, val acc: 0.55000001 (best 0.56999999), test acc: 0.47999999 (best 0.48999998)\n",
            "In epoch 1175, loss: 0.69119227, val acc: 0.55000001 (best 0.56999999), test acc: 0.47999999 (best 0.48999998)\n",
            "In epoch 1180, loss: 0.69117945, val acc: 0.55500001 (best 0.56999999), test acc: 0.47000000 (best 0.48999998)\n",
            "In epoch 1185, loss: 0.69117075, val acc: 0.54500002 (best 0.56999999), test acc: 0.47499999 (best 0.48999998)\n",
            "In epoch 1190, loss: 0.69116104, val acc: 0.54500002 (best 0.56999999), test acc: 0.47499999 (best 0.48999998)\n",
            "In epoch 1195, loss: 0.69115144, val acc: 0.54500002 (best 0.56999999), test acc: 0.47999999 (best 0.48999998)\n",
            "In epoch 1200, loss: 0.69114083, val acc: 0.54500002 (best 0.56999999), test acc: 0.47999999 (best 0.48999998)\n",
            "In epoch 1205, loss: 0.69112915, val acc: 0.55000001 (best 0.56999999), test acc: 0.47499999 (best 0.48999998)\n",
            "In epoch 1210, loss: 0.69112170, val acc: 0.54500002 (best 0.56999999), test acc: 0.47999999 (best 0.48999998)\n",
            "In epoch 1215, loss: 0.69111067, val acc: 0.55000001 (best 0.56999999), test acc: 0.47999999 (best 0.48999998)\n",
            "In epoch 1220, loss: 0.69110346, val acc: 0.55000001 (best 0.56999999), test acc: 0.47000000 (best 0.48999998)\n",
            "In epoch 1225, loss: 0.69109094, val acc: 0.55000001 (best 0.56999999), test acc: 0.48499998 (best 0.48999998)\n",
            "In epoch 1230, loss: 0.69108081, val acc: 0.53999996 (best 0.56999999), test acc: 0.47000000 (best 0.48999998)\n",
            "In epoch 1235, loss: 0.69107407, val acc: 0.54500002 (best 0.56999999), test acc: 0.47999999 (best 0.48999998)\n",
            "In epoch 1240, loss: 0.69105911, val acc: 0.54500002 (best 0.56999999), test acc: 0.47499999 (best 0.48999998)\n",
            "In epoch 1245, loss: 0.69104886, val acc: 0.54500002 (best 0.56999999), test acc: 0.47499999 (best 0.48999998)\n",
            "In epoch 1250, loss: 0.69104332, val acc: 0.54500002 (best 0.56999999), test acc: 0.47999999 (best 0.48999998)\n",
            "In epoch 1255, loss: 0.69102943, val acc: 0.53999996 (best 0.56999999), test acc: 0.47999999 (best 0.48999998)\n",
            "In epoch 1260, loss: 0.69101954, val acc: 0.53499997 (best 0.56999999), test acc: 0.48499998 (best 0.48999998)\n",
            "In epoch 1265, loss: 0.69101596, val acc: 0.54500002 (best 0.56999999), test acc: 0.47999999 (best 0.48999998)\n",
            "In epoch 1270, loss: 0.69100165, val acc: 0.54500002 (best 0.56999999), test acc: 0.48499998 (best 0.48999998)\n",
            "In epoch 1275, loss: 0.69099253, val acc: 0.53999996 (best 0.56999999), test acc: 0.48499998 (best 0.48999998)\n",
            "In epoch 1280, loss: 0.69098634, val acc: 0.53999996 (best 0.56999999), test acc: 0.48999998 (best 0.48999998)\n",
            "In epoch 1285, loss: 0.69097799, val acc: 0.53999996 (best 0.56999999), test acc: 0.48999998 (best 0.48999998)\n",
            "In epoch 1290, loss: 0.69096857, val acc: 0.52999997 (best 0.56999999), test acc: 0.48499998 (best 0.48999998)\n",
            "In epoch 1295, loss: 0.69096154, val acc: 0.53499997 (best 0.56999999), test acc: 0.48499998 (best 0.48999998)\n",
            "In epoch 1300, loss: 0.69095492, val acc: 0.53499997 (best 0.56999999), test acc: 0.48999998 (best 0.48999998)\n",
            "In epoch 1305, loss: 0.69094604, val acc: 0.52499998 (best 0.56999999), test acc: 0.48999998 (best 0.48999998)\n",
            "In epoch 1310, loss: 0.69094050, val acc: 0.53499997 (best 0.56999999), test acc: 0.48999998 (best 0.48999998)\n",
            "In epoch 1315, loss: 0.69093019, val acc: 0.52999997 (best 0.56999999), test acc: 0.49499997 (best 0.48999998)\n",
            "In epoch 1320, loss: 0.69092447, val acc: 0.52499998 (best 0.56999999), test acc: 0.48999998 (best 0.48999998)\n",
            "In epoch 1325, loss: 0.69091660, val acc: 0.52499998 (best 0.56999999), test acc: 0.48999998 (best 0.48999998)\n",
            "In epoch 1330, loss: 0.69091278, val acc: 0.52499998 (best 0.56999999), test acc: 0.49499997 (best 0.48999998)\n",
            "In epoch 1335, loss: 0.69090104, val acc: 0.52999997 (best 0.56999999), test acc: 0.47999999 (best 0.48999998)\n",
            "In epoch 1340, loss: 0.69089389, val acc: 0.52499998 (best 0.56999999), test acc: 0.48499998 (best 0.48999998)\n",
            "In epoch 1345, loss: 0.69088727, val acc: 0.51999998 (best 0.56999999), test acc: 0.48499998 (best 0.48999998)\n",
            "In epoch 1350, loss: 0.69088119, val acc: 0.51999998 (best 0.56999999), test acc: 0.47999999 (best 0.48999998)\n",
            "In epoch 1355, loss: 0.69087541, val acc: 0.51999998 (best 0.56999999), test acc: 0.47999999 (best 0.48999998)\n",
            "In epoch 1360, loss: 0.69086897, val acc: 0.51999998 (best 0.56999999), test acc: 0.47999999 (best 0.48999998)\n",
            "In epoch 1365, loss: 0.69086105, val acc: 0.51999998 (best 0.56999999), test acc: 0.47999999 (best 0.48999998)\n",
            "In epoch 1370, loss: 0.69085431, val acc: 0.51999998 (best 0.56999999), test acc: 0.47999999 (best 0.48999998)\n",
            "In epoch 1375, loss: 0.69084775, val acc: 0.51999998 (best 0.56999999), test acc: 0.47999999 (best 0.48999998)\n",
            "In epoch 1380, loss: 0.69084001, val acc: 0.52499998 (best 0.56999999), test acc: 0.48999998 (best 0.48999998)\n",
            "In epoch 1385, loss: 0.69083667, val acc: 0.51999998 (best 0.56999999), test acc: 0.47999999 (best 0.48999998)\n",
            "In epoch 1390, loss: 0.69082999, val acc: 0.51999998 (best 0.56999999), test acc: 0.48999998 (best 0.48999998)\n",
            "In epoch 1395, loss: 0.69082785, val acc: 0.55000001 (best 0.56999999), test acc: 0.50000000 (best 0.48999998)\n",
            "In epoch 1400, loss: 0.69081897, val acc: 0.52999997 (best 0.56999999), test acc: 0.49499997 (best 0.48999998)\n",
            "In epoch 1405, loss: 0.69081491, val acc: 0.53499997 (best 0.56999999), test acc: 0.50000000 (best 0.48999998)\n",
            "In epoch 1410, loss: 0.69080931, val acc: 0.52999997 (best 0.56999999), test acc: 0.50000000 (best 0.48999998)\n",
            "In epoch 1415, loss: 0.69080973, val acc: 0.54500002 (best 0.56999999), test acc: 0.49499997 (best 0.48999998)\n",
            "In epoch 1420, loss: 0.69080061, val acc: 0.52499998 (best 0.56999999), test acc: 0.49499997 (best 0.48999998)\n",
            "In epoch 1425, loss: 0.69079691, val acc: 0.52499998 (best 0.56999999), test acc: 0.50000000 (best 0.48999998)\n",
            "In epoch 1430, loss: 0.69079900, val acc: 0.51499999 (best 0.56999999), test acc: 0.48999998 (best 0.48999998)\n",
            "In epoch 1435, loss: 0.69079053, val acc: 0.53499997 (best 0.56999999), test acc: 0.50000000 (best 0.48999998)\n",
            "In epoch 1440, loss: 0.69078583, val acc: 0.52499998 (best 0.56999999), test acc: 0.49499997 (best 0.48999998)\n",
            "In epoch 1445, loss: 0.69078273, val acc: 0.53499997 (best 0.56999999), test acc: 0.50000000 (best 0.48999998)\n",
            "In epoch 1450, loss: 0.69077981, val acc: 0.52499998 (best 0.56999999), test acc: 0.50500000 (best 0.48999998)\n",
            "In epoch 1455, loss: 0.69077712, val acc: 0.52499998 (best 0.56999999), test acc: 0.50000000 (best 0.48999998)\n",
            "In epoch 1460, loss: 0.69077611, val acc: 0.52499998 (best 0.56999999), test acc: 0.48999998 (best 0.48999998)\n",
            "In epoch 1465, loss: 0.69076991, val acc: 0.52499998 (best 0.56999999), test acc: 0.50500000 (best 0.48999998)\n",
            "In epoch 1470, loss: 0.69077039, val acc: 0.51999998 (best 0.56999999), test acc: 0.48999998 (best 0.48999998)\n",
            "In epoch 1475, loss: 0.69076246, val acc: 0.52499998 (best 0.56999999), test acc: 0.49499997 (best 0.48999998)\n",
            "In epoch 1480, loss: 0.69075942, val acc: 0.52499998 (best 0.56999999), test acc: 0.50000000 (best 0.48999998)\n",
            "In epoch 1485, loss: 0.69076180, val acc: 0.52499998 (best 0.56999999), test acc: 0.49499997 (best 0.48999998)\n",
            "In epoch 1490, loss: 0.69075340, val acc: 0.53499997 (best 0.56999999), test acc: 0.49499997 (best 0.48999998)\n",
            "In epoch 1495, loss: 0.69074923, val acc: 0.52499998 (best 0.56999999), test acc: 0.49499997 (best 0.48999998)\n",
            "In epoch 1500, loss: 0.69074637, val acc: 0.52499998 (best 0.56999999), test acc: 0.50500000 (best 0.48999998)\n",
            "In epoch 1505, loss: 0.69074363, val acc: 0.52499998 (best 0.56999999), test acc: 0.50000000 (best 0.48999998)\n",
            "In epoch 1510, loss: 0.69073790, val acc: 0.52499998 (best 0.56999999), test acc: 0.49499997 (best 0.48999998)\n",
            "In epoch 1515, loss: 0.69073617, val acc: 0.52499998 (best 0.56999999), test acc: 0.50500000 (best 0.48999998)\n",
            "In epoch 1520, loss: 0.69073111, val acc: 0.52499998 (best 0.56999999), test acc: 0.50500000 (best 0.48999998)\n",
            "In epoch 1525, loss: 0.69072568, val acc: 0.53499997 (best 0.56999999), test acc: 0.49499997 (best 0.48999998)\n",
            "In epoch 1530, loss: 0.69072241, val acc: 0.53999996 (best 0.56999999), test acc: 0.49499997 (best 0.48999998)\n",
            "In epoch 1535, loss: 0.69071740, val acc: 0.53499997 (best 0.56999999), test acc: 0.49499997 (best 0.48999998)\n",
            "In epoch 1540, loss: 0.69072038, val acc: 0.51999998 (best 0.56999999), test acc: 0.49499997 (best 0.48999998)\n",
            "In epoch 1545, loss: 0.69071084, val acc: 0.53999996 (best 0.56999999), test acc: 0.49499997 (best 0.48999998)\n",
            "In epoch 1550, loss: 0.69070655, val acc: 0.52499998 (best 0.56999999), test acc: 0.49499997 (best 0.48999998)\n",
            "In epoch 1555, loss: 0.69070435, val acc: 0.52499998 (best 0.56999999), test acc: 0.50500000 (best 0.48999998)\n",
            "In epoch 1560, loss: 0.69070220, val acc: 0.52499998 (best 0.56999999), test acc: 0.50500000 (best 0.48999998)\n",
            "In epoch 1565, loss: 0.69069725, val acc: 0.52999997 (best 0.56999999), test acc: 0.50000000 (best 0.48999998)\n",
            "In epoch 1570, loss: 0.69069749, val acc: 0.51499999 (best 0.56999999), test acc: 0.50500000 (best 0.48999998)\n",
            "In epoch 1575, loss: 0.69069237, val acc: 0.52499998 (best 0.56999999), test acc: 0.50000000 (best 0.48999998)\n",
            "In epoch 1580, loss: 0.69068825, val acc: 0.52999997 (best 0.56999999), test acc: 0.50000000 (best 0.48999998)\n",
            "In epoch 1585, loss: 0.69069040, val acc: 0.53499997 (best 0.56999999), test acc: 0.48999998 (best 0.48999998)\n",
            "In epoch 1590, loss: 0.69068420, val acc: 0.51999998 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 1595, loss: 0.69068033, val acc: 0.52499998 (best 0.56999999), test acc: 0.50000000 (best 0.48999998)\n",
            "In epoch 1600, loss: 0.69067937, val acc: 0.51499999 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 1605, loss: 0.69067568, val acc: 0.51999998 (best 0.56999999), test acc: 0.50000000 (best 0.48999998)\n",
            "In epoch 1610, loss: 0.69067693, val acc: 0.53999996 (best 0.56999999), test acc: 0.48999998 (best 0.48999998)\n",
            "In epoch 1615, loss: 0.69066948, val acc: 0.52999997 (best 0.56999999), test acc: 0.50500000 (best 0.48999998)\n",
            "In epoch 1620, loss: 0.69066691, val acc: 0.52999997 (best 0.56999999), test acc: 0.50500000 (best 0.48999998)\n",
            "In epoch 1625, loss: 0.69066530, val acc: 0.53499997 (best 0.56999999), test acc: 0.50000000 (best 0.48999998)\n",
            "In epoch 1630, loss: 0.69066149, val acc: 0.52499998 (best 0.56999999), test acc: 0.50000000 (best 0.48999998)\n",
            "In epoch 1635, loss: 0.69065887, val acc: 0.52499998 (best 0.56999999), test acc: 0.50000000 (best 0.48999998)\n",
            "In epoch 1640, loss: 0.69065619, val acc: 0.52499998 (best 0.56999999), test acc: 0.50500000 (best 0.48999998)\n",
            "In epoch 1645, loss: 0.69065475, val acc: 0.51999998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 1650, loss: 0.69065344, val acc: 0.51999998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 1655, loss: 0.69065094, val acc: 0.51999998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 1660, loss: 0.69064844, val acc: 0.52499998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 1665, loss: 0.69064611, val acc: 0.52999997 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 1670, loss: 0.69064391, val acc: 0.53499997 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 1675, loss: 0.69064200, val acc: 0.53499997 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 1680, loss: 0.69063950, val acc: 0.52999997 (best 0.56999999), test acc: 0.50500000 (best 0.48999998)\n",
            "In epoch 1685, loss: 0.69063807, val acc: 0.52999997 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 1690, loss: 0.69063538, val acc: 0.52499998 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 1695, loss: 0.69063449, val acc: 0.52999997 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 1700, loss: 0.69063354, val acc: 0.51999998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 1705, loss: 0.69062990, val acc: 0.52499998 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 1710, loss: 0.69063097, val acc: 0.52499998 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 1715, loss: 0.69062722, val acc: 0.52999997 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 1720, loss: 0.69062859, val acc: 0.53999996 (best 0.56999999), test acc: 0.50500000 (best 0.48999998)\n",
            "In epoch 1725, loss: 0.69062251, val acc: 0.52499998 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 1730, loss: 0.69062114, val acc: 0.52999997 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 1735, loss: 0.69061953, val acc: 0.52999997 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 1740, loss: 0.69061846, val acc: 0.53499997 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 1745, loss: 0.69061643, val acc: 0.53499997 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 1750, loss: 0.69061631, val acc: 0.53999996 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 1755, loss: 0.69061279, val acc: 0.52999997 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 1760, loss: 0.69061351, val acc: 0.53499997 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 1765, loss: 0.69060814, val acc: 0.53499997 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 1770, loss: 0.69060653, val acc: 0.53499997 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 1775, loss: 0.69061297, val acc: 0.51999998 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 1780, loss: 0.69060355, val acc: 0.52999997 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 1785, loss: 0.69060099, val acc: 0.53499997 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 1790, loss: 0.69060081, val acc: 0.53499997 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 1795, loss: 0.69059968, val acc: 0.53499997 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 1800, loss: 0.69059867, val acc: 0.52999997 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 1805, loss: 0.69059587, val acc: 0.53499997 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 1810, loss: 0.69059545, val acc: 0.52999997 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 1815, loss: 0.69059145, val acc: 0.53499997 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 1820, loss: 0.69059318, val acc: 0.52499998 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 1825, loss: 0.69058728, val acc: 0.53499997 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 1830, loss: 0.69058752, val acc: 0.53499997 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 1835, loss: 0.69058383, val acc: 0.53499997 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 1840, loss: 0.69058210, val acc: 0.53499997 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 1845, loss: 0.69058031, val acc: 0.53499997 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 1850, loss: 0.69057894, val acc: 0.52999997 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 1855, loss: 0.69057852, val acc: 0.52499998 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 1860, loss: 0.69057548, val acc: 0.53499997 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 1865, loss: 0.69057620, val acc: 0.51999998 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 1870, loss: 0.69057482, val acc: 0.51999998 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 1875, loss: 0.69057333, val acc: 0.51999998 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 1880, loss: 0.69057077, val acc: 0.51999998 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 1885, loss: 0.69057280, val acc: 0.51999998 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 1890, loss: 0.69056684, val acc: 0.53499997 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 1895, loss: 0.69056630, val acc: 0.52999997 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 1900, loss: 0.69056368, val acc: 0.53499997 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 1905, loss: 0.69056290, val acc: 0.53499997 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 1910, loss: 0.69056129, val acc: 0.53499997 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 1915, loss: 0.69055945, val acc: 0.52999997 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 1920, loss: 0.69055831, val acc: 0.53499997 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 1925, loss: 0.69055635, val acc: 0.53499997 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 1930, loss: 0.69055575, val acc: 0.52999997 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 1935, loss: 0.69055414, val acc: 0.52999997 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 1940, loss: 0.69055372, val acc: 0.52999997 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 1945, loss: 0.69055140, val acc: 0.52999997 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 1950, loss: 0.69055045, val acc: 0.52999997 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 1955, loss: 0.69054979, val acc: 0.52999997 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 1960, loss: 0.69054985, val acc: 0.52999997 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 1965, loss: 0.69054651, val acc: 0.52999997 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 1970, loss: 0.69054794, val acc: 0.52499998 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 1975, loss: 0.69054252, val acc: 0.52999997 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 1980, loss: 0.69054163, val acc: 0.52999997 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 1985, loss: 0.69054133, val acc: 0.52999997 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 1990, loss: 0.69053912, val acc: 0.52999997 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 1995, loss: 0.69054240, val acc: 0.52999997 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 2000, loss: 0.69053632, val acc: 0.53499997 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 2005, loss: 0.69053483, val acc: 0.52999997 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 2010, loss: 0.69053346, val acc: 0.52999997 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 2015, loss: 0.69053221, val acc: 0.52499998 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 2020, loss: 0.69053155, val acc: 0.52999997 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 2025, loss: 0.69053113, val acc: 0.52999997 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 2030, loss: 0.69052851, val acc: 0.52499998 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 2035, loss: 0.69052809, val acc: 0.52999997 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 2040, loss: 0.69052690, val acc: 0.52999997 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 2045, loss: 0.69052511, val acc: 0.53499997 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 2050, loss: 0.69052392, val acc: 0.53499997 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 2055, loss: 0.69052225, val acc: 0.52999997 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 2060, loss: 0.69052088, val acc: 0.53499997 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 2065, loss: 0.69052142, val acc: 0.52999997 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 2070, loss: 0.69051844, val acc: 0.53499997 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 2075, loss: 0.69051993, val acc: 0.52999997 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 2080, loss: 0.69051695, val acc: 0.52999997 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 2085, loss: 0.69051641, val acc: 0.52999997 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 2090, loss: 0.69051361, val acc: 0.52999997 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 2095, loss: 0.69051242, val acc: 0.52999997 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 2100, loss: 0.69051206, val acc: 0.52999997 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 2105, loss: 0.69051123, val acc: 0.52999997 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 2110, loss: 0.69050986, val acc: 0.52499998 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 2115, loss: 0.69050819, val acc: 0.52499998 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 2120, loss: 0.69050676, val acc: 0.52499998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 2125, loss: 0.69050521, val acc: 0.52499998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 2130, loss: 0.69050360, val acc: 0.52999997 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 2135, loss: 0.69050276, val acc: 0.53499997 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 2140, loss: 0.69050235, val acc: 0.52999997 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 2145, loss: 0.69049996, val acc: 0.53499997 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 2150, loss: 0.69050080, val acc: 0.52999997 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 2155, loss: 0.69050008, val acc: 0.52999997 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 2160, loss: 0.69049895, val acc: 0.52999997 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 2165, loss: 0.69049704, val acc: 0.52999997 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 2170, loss: 0.69049489, val acc: 0.52999997 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 2175, loss: 0.69049406, val acc: 0.52999997 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 2180, loss: 0.69049531, val acc: 0.52999997 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 2185, loss: 0.69049078, val acc: 0.52999997 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 2190, loss: 0.69049460, val acc: 0.52499998 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 2195, loss: 0.69048727, val acc: 0.52499998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 2200, loss: 0.69048619, val acc: 0.53499997 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 2205, loss: 0.69048643, val acc: 0.52999997 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 2210, loss: 0.69048393, val acc: 0.52999997 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 2215, loss: 0.69048285, val acc: 0.52999997 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 2220, loss: 0.69048101, val acc: 0.52499998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 2225, loss: 0.69048011, val acc: 0.52999997 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 2230, loss: 0.69047976, val acc: 0.51999998 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 2235, loss: 0.69047850, val acc: 0.51999998 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 2240, loss: 0.69047660, val acc: 0.52999997 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 2245, loss: 0.69047624, val acc: 0.51999998 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 2250, loss: 0.69047391, val acc: 0.52999997 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 2255, loss: 0.69047499, val acc: 0.51499999 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 2260, loss: 0.69047320, val acc: 0.51999998 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 2265, loss: 0.69047028, val acc: 0.52499998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 2270, loss: 0.69047272, val acc: 0.51499999 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 2275, loss: 0.69046801, val acc: 0.52499998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 2280, loss: 0.69046748, val acc: 0.52499998 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 2285, loss: 0.69046599, val acc: 0.53499997 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 2290, loss: 0.69046503, val acc: 0.52999997 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 2295, loss: 0.69046342, val acc: 0.52499998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 2300, loss: 0.69046283, val acc: 0.52999997 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 2305, loss: 0.69046193, val acc: 0.52999997 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 2310, loss: 0.69046175, val acc: 0.52999997 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 2315, loss: 0.69045901, val acc: 0.53499997 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 2320, loss: 0.69046015, val acc: 0.52999997 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 2325, loss: 0.69045806, val acc: 0.52999997 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 2330, loss: 0.69045645, val acc: 0.52999997 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 2335, loss: 0.69045442, val acc: 0.52999997 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 2340, loss: 0.69045317, val acc: 0.52499998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 2345, loss: 0.69045621, val acc: 0.51499999 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 2350, loss: 0.69045228, val acc: 0.52999997 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 2355, loss: 0.69044960, val acc: 0.52999997 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 2360, loss: 0.69045144, val acc: 0.50999999 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 2365, loss: 0.69044787, val acc: 0.52499998 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 2370, loss: 0.69044989, val acc: 0.53499997 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 2375, loss: 0.69044518, val acc: 0.52499998 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 2380, loss: 0.69044411, val acc: 0.52499998 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 2385, loss: 0.69044495, val acc: 0.51999998 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 2390, loss: 0.69044459, val acc: 0.50999999 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 2395, loss: 0.69044340, val acc: 0.50999999 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 2400, loss: 0.69044191, val acc: 0.51999998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 2405, loss: 0.69044030, val acc: 0.51999998 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 2410, loss: 0.69043916, val acc: 0.51999998 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 2415, loss: 0.69043696, val acc: 0.51999998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 2420, loss: 0.69043624, val acc: 0.51999998 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 2425, loss: 0.69043493, val acc: 0.51999998 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 2430, loss: 0.69043785, val acc: 0.50500000 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 2435, loss: 0.69043154, val acc: 0.51999998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 2440, loss: 0.69043070, val acc: 0.51999998 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 2445, loss: 0.69043428, val acc: 0.50500000 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 2450, loss: 0.69042897, val acc: 0.52999997 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 2455, loss: 0.69042706, val acc: 0.51999998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 2460, loss: 0.69042593, val acc: 0.51999998 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 2465, loss: 0.69042641, val acc: 0.52999997 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 2470, loss: 0.69042397, val acc: 0.51999998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 2475, loss: 0.69042456, val acc: 0.51999998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 2480, loss: 0.69042188, val acc: 0.51999998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 2485, loss: 0.69042695, val acc: 0.53499997 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 2490, loss: 0.69041955, val acc: 0.51999998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 2495, loss: 0.69041818, val acc: 0.52499998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 2500, loss: 0.69042724, val acc: 0.53499997 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 2505, loss: 0.69041765, val acc: 0.51999998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 2510, loss: 0.69041789, val acc: 0.51499999 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 2515, loss: 0.69041616, val acc: 0.51499999 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 2520, loss: 0.69041520, val acc: 0.51499999 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 2525, loss: 0.69041324, val acc: 0.51999998 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 2530, loss: 0.69041353, val acc: 0.50999999 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 2535, loss: 0.69040960, val acc: 0.51999998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 2540, loss: 0.69040900, val acc: 0.51999998 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 2545, loss: 0.69040906, val acc: 0.51499999 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 2550, loss: 0.69040644, val acc: 0.51999998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 2555, loss: 0.69040924, val acc: 0.51999998 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 2560, loss: 0.69040406, val acc: 0.51499999 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 2565, loss: 0.69040293, val acc: 0.51499999 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 2570, loss: 0.69040197, val acc: 0.51499999 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 2575, loss: 0.69040072, val acc: 0.51499999 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 2580, loss: 0.69039965, val acc: 0.51499999 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 2585, loss: 0.69039869, val acc: 0.51499999 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 2590, loss: 0.69039786, val acc: 0.51999998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 2595, loss: 0.69039643, val acc: 0.51499999 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 2600, loss: 0.69039589, val acc: 0.51999998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 2605, loss: 0.69039512, val acc: 0.51999998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 2610, loss: 0.69039518, val acc: 0.52499998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 2615, loss: 0.69039339, val acc: 0.52499998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 2620, loss: 0.69039291, val acc: 0.52499998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 2625, loss: 0.69039392, val acc: 0.53499997 (best 0.56999999), test acc: 0.50500000 (best 0.48999998)\n",
            "In epoch 2630, loss: 0.69039035, val acc: 0.51999998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 2635, loss: 0.69039321, val acc: 0.53499997 (best 0.56999999), test acc: 0.50500000 (best 0.48999998)\n",
            "In epoch 2640, loss: 0.69038689, val acc: 0.51499999 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 2645, loss: 0.69038612, val acc: 0.50999999 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 2650, loss: 0.69039160, val acc: 0.53499997 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 2655, loss: 0.69038481, val acc: 0.51999998 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 2660, loss: 0.69038260, val acc: 0.51499999 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 2665, loss: 0.69038206, val acc: 0.51999998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 2670, loss: 0.69038111, val acc: 0.51999998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 2675, loss: 0.69038481, val acc: 0.53499997 (best 0.56999999), test acc: 0.50500000 (best 0.48999998)\n",
            "In epoch 2680, loss: 0.69037849, val acc: 0.51499999 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 2685, loss: 0.69037735, val acc: 0.51499999 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 2690, loss: 0.69037634, val acc: 0.50999999 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 2695, loss: 0.69037539, val acc: 0.50999999 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 2700, loss: 0.69037455, val acc: 0.50999999 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 2705, loss: 0.69037366, val acc: 0.51999998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 2710, loss: 0.69037253, val acc: 0.51499999 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 2715, loss: 0.69037229, val acc: 0.51999998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 2720, loss: 0.69036996, val acc: 0.50999999 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 2725, loss: 0.69037074, val acc: 0.51999998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 2730, loss: 0.69036859, val acc: 0.51999998 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 2735, loss: 0.69036680, val acc: 0.51999998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 2740, loss: 0.69036627, val acc: 0.51999998 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 2745, loss: 0.69036508, val acc: 0.51999998 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 2750, loss: 0.69036543, val acc: 0.51999998 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 2755, loss: 0.69036484, val acc: 0.51999998 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 2760, loss: 0.69036311, val acc: 0.51999998 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 2765, loss: 0.69036502, val acc: 0.52499998 (best 0.56999999), test acc: 0.52999997 (best 0.48999998)\n",
            "In epoch 2770, loss: 0.69035971, val acc: 0.51999998 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 2775, loss: 0.69036174, val acc: 0.52499998 (best 0.56999999), test acc: 0.52999997 (best 0.48999998)\n",
            "In epoch 2780, loss: 0.69035703, val acc: 0.50999999 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 2785, loss: 0.69035584, val acc: 0.50999999 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 2790, loss: 0.69035470, val acc: 0.51499999 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 2795, loss: 0.69035363, val acc: 0.51499999 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 2800, loss: 0.69035387, val acc: 0.51999998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 2805, loss: 0.69035202, val acc: 0.51499999 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 2810, loss: 0.69035178, val acc: 0.51999998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 2815, loss: 0.69034952, val acc: 0.50999999 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 2820, loss: 0.69035077, val acc: 0.52499998 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 2825, loss: 0.69034958, val acc: 0.52499998 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 2830, loss: 0.69034898, val acc: 0.52499998 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 2835, loss: 0.69034630, val acc: 0.51999998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 2840, loss: 0.69034904, val acc: 0.52999997 (best 0.56999999), test acc: 0.50500000 (best 0.48999998)\n",
            "In epoch 2845, loss: 0.69034296, val acc: 0.50999999 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 2850, loss: 0.69034219, val acc: 0.51499999 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 2855, loss: 0.69034064, val acc: 0.50999999 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 2860, loss: 0.69034266, val acc: 0.52499998 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 2865, loss: 0.69033933, val acc: 0.51999998 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 2870, loss: 0.69033736, val acc: 0.51499999 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 2875, loss: 0.69033700, val acc: 0.51999998 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 2880, loss: 0.69033694, val acc: 0.51999998 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 2885, loss: 0.69033408, val acc: 0.50999999 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 2890, loss: 0.69033384, val acc: 0.51499999 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 2895, loss: 0.69033206, val acc: 0.50999999 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 2900, loss: 0.69033164, val acc: 0.51499999 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 2905, loss: 0.69033033, val acc: 0.51499999 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 2910, loss: 0.69033164, val acc: 0.52499998 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 2915, loss: 0.69032753, val acc: 0.51999998 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 2920, loss: 0.69032651, val acc: 0.51999998 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 2925, loss: 0.69032538, val acc: 0.51499999 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 2930, loss: 0.69032449, val acc: 0.51499999 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 2935, loss: 0.69032484, val acc: 0.51999998 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 2940, loss: 0.69032204, val acc: 0.50999999 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 2945, loss: 0.69032365, val acc: 0.52499998 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 2950, loss: 0.69032133, val acc: 0.51499999 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 2955, loss: 0.69031930, val acc: 0.52499998 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 2960, loss: 0.69031924, val acc: 0.51999998 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 2965, loss: 0.69031686, val acc: 0.52499998 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 2970, loss: 0.69032365, val acc: 0.52999997 (best 0.56999999), test acc: 0.50500000 (best 0.48999998)\n",
            "In epoch 2975, loss: 0.69031602, val acc: 0.51999998 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 2980, loss: 0.69031370, val acc: 0.51999998 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 2985, loss: 0.69031531, val acc: 0.52499998 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 2990, loss: 0.69031197, val acc: 0.52999997 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 2995, loss: 0.69031495, val acc: 0.52499998 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 3000, loss: 0.69030958, val acc: 0.51999998 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 3005, loss: 0.69030833, val acc: 0.51499999 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 3010, loss: 0.69030738, val acc: 0.51999998 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 3015, loss: 0.69030762, val acc: 0.51999998 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 3020, loss: 0.69030541, val acc: 0.51999998 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 3025, loss: 0.69030577, val acc: 0.51999998 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 3030, loss: 0.69030356, val acc: 0.51999998 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 3035, loss: 0.69030267, val acc: 0.51499999 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 3040, loss: 0.69030130, val acc: 0.51499999 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 3045, loss: 0.69030076, val acc: 0.51499999 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 3050, loss: 0.69030046, val acc: 0.51499999 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 3055, loss: 0.69029963, val acc: 0.51499999 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 3060, loss: 0.69029772, val acc: 0.51999998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 3065, loss: 0.69029790, val acc: 0.51999998 (best 0.56999999), test acc: 0.50500000 (best 0.48999998)\n",
            "In epoch 3070, loss: 0.69029546, val acc: 0.51999998 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 3075, loss: 0.69029617, val acc: 0.51999998 (best 0.56999999), test acc: 0.50500000 (best 0.48999998)\n",
            "In epoch 3080, loss: 0.69029462, val acc: 0.51999998 (best 0.56999999), test acc: 0.50500000 (best 0.48999998)\n",
            "In epoch 3085, loss: 0.69029647, val acc: 0.52499998 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 3090, loss: 0.69029105, val acc: 0.51499999 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 3095, loss: 0.69029027, val acc: 0.51999998 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 3100, loss: 0.69028962, val acc: 0.51999998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 3105, loss: 0.69028836, val acc: 0.51999998 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 3110, loss: 0.69029021, val acc: 0.52499998 (best 0.56999999), test acc: 0.50500000 (best 0.48999998)\n",
            "In epoch 3115, loss: 0.69028556, val acc: 0.51499999 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 3120, loss: 0.69028550, val acc: 0.51499999 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 3125, loss: 0.69028342, val acc: 0.52499998 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 3130, loss: 0.69028246, val acc: 0.52499998 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 3135, loss: 0.69028097, val acc: 0.51499999 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 3140, loss: 0.69028020, val acc: 0.51999998 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 3145, loss: 0.69028032, val acc: 0.51999998 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 3150, loss: 0.69027829, val acc: 0.51999998 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 3155, loss: 0.69027674, val acc: 0.51999998 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 3160, loss: 0.69027591, val acc: 0.51999998 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 3165, loss: 0.69027382, val acc: 0.51999998 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 3170, loss: 0.69027412, val acc: 0.51999998 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 3175, loss: 0.69027251, val acc: 0.51999998 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 3180, loss: 0.69027168, val acc: 0.51999998 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 3185, loss: 0.69027245, val acc: 0.53499997 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 3190, loss: 0.69027007, val acc: 0.51999998 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 3195, loss: 0.69027185, val acc: 0.52999997 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 3200, loss: 0.69026655, val acc: 0.51999998 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 3205, loss: 0.69026875, val acc: 0.53499997 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 3210, loss: 0.69026411, val acc: 0.51999998 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 3215, loss: 0.69026315, val acc: 0.51999998 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 3220, loss: 0.69026202, val acc: 0.51999998 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 3225, loss: 0.69026107, val acc: 0.51999998 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 3230, loss: 0.69026101, val acc: 0.51999998 (best 0.56999999), test acc: 0.50500000 (best 0.48999998)\n",
            "In epoch 3235, loss: 0.69025993, val acc: 0.51999998 (best 0.56999999), test acc: 0.50500000 (best 0.48999998)\n",
            "In epoch 3240, loss: 0.69025731, val acc: 0.51999998 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 3245, loss: 0.69025469, val acc: 0.52499998 (best 0.56999999), test acc: 0.50500000 (best 0.48999998)\n",
            "In epoch 3250, loss: 0.69025099, val acc: 0.52499998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 3255, loss: 0.69025153, val acc: 0.51999998 (best 0.56999999), test acc: 0.50500000 (best 0.48999998)\n",
            "In epoch 3260, loss: 0.69025099, val acc: 0.51999998 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 3265, loss: 0.69025010, val acc: 0.51999998 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 3270, loss: 0.69024843, val acc: 0.51999998 (best 0.56999999), test acc: 0.50500000 (best 0.48999998)\n",
            "In epoch 3275, loss: 0.69024730, val acc: 0.51999998 (best 0.56999999), test acc: 0.50500000 (best 0.48999998)\n",
            "In epoch 3280, loss: 0.69024569, val acc: 0.51999998 (best 0.56999999), test acc: 0.50500000 (best 0.48999998)\n",
            "In epoch 3285, loss: 0.69024366, val acc: 0.52499998 (best 0.56999999), test acc: 0.50500000 (best 0.48999998)\n",
            "In epoch 3290, loss: 0.69024241, val acc: 0.52499998 (best 0.56999999), test acc: 0.50500000 (best 0.48999998)\n",
            "In epoch 3295, loss: 0.69024122, val acc: 0.52499998 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 3300, loss: 0.69024575, val acc: 0.53499997 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 3305, loss: 0.69023824, val acc: 0.51999998 (best 0.56999999), test acc: 0.52999997 (best 0.48999998)\n",
            "In epoch 3310, loss: 0.69023734, val acc: 0.51999998 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 3315, loss: 0.69023705, val acc: 0.52999997 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 3320, loss: 0.69023544, val acc: 0.52499998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 3325, loss: 0.69023466, val acc: 0.52499998 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 3330, loss: 0.69023317, val acc: 0.51999998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 3335, loss: 0.69023323, val acc: 0.52499998 (best 0.56999999), test acc: 0.50500000 (best 0.48999998)\n",
            "In epoch 3340, loss: 0.69023234, val acc: 0.52499998 (best 0.56999999), test acc: 0.50500000 (best 0.48999998)\n",
            "In epoch 3345, loss: 0.69023192, val acc: 0.52499998 (best 0.56999999), test acc: 0.50500000 (best 0.48999998)\n",
            "In epoch 3350, loss: 0.69022977, val acc: 0.52499998 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 3355, loss: 0.69022965, val acc: 0.52499998 (best 0.56999999), test acc: 0.50500000 (best 0.48999998)\n",
            "In epoch 3360, loss: 0.69022995, val acc: 0.52499998 (best 0.56999999), test acc: 0.50500000 (best 0.48999998)\n",
            "In epoch 3365, loss: 0.69022751, val acc: 0.52499998 (best 0.56999999), test acc: 0.50500000 (best 0.48999998)\n",
            "In epoch 3370, loss: 0.69022924, val acc: 0.51499999 (best 0.56999999), test acc: 0.50000000 (best 0.48999998)\n",
            "In epoch 3375, loss: 0.69022429, val acc: 0.52499998 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 3380, loss: 0.69022584, val acc: 0.52499998 (best 0.56999999), test acc: 0.50500000 (best 0.48999998)\n",
            "In epoch 3385, loss: 0.69022155, val acc: 0.51999998 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 3390, loss: 0.69022125, val acc: 0.52499998 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 3395, loss: 0.69021988, val acc: 0.51999998 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 3400, loss: 0.69021928, val acc: 0.52999997 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 3405, loss: 0.69021761, val acc: 0.52499998 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 3410, loss: 0.69021749, val acc: 0.52999997 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 3415, loss: 0.69021660, val acc: 0.53499997 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 3420, loss: 0.69021624, val acc: 0.53499997 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 3425, loss: 0.69021469, val acc: 0.53499997 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 3430, loss: 0.69021326, val acc: 0.53499997 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 3435, loss: 0.69021231, val acc: 0.53499997 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 3440, loss: 0.69021571, val acc: 0.53499997 (best 0.56999999), test acc: 0.52999997 (best 0.48999998)\n",
            "In epoch 3445, loss: 0.69020915, val acc: 0.52499998 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 3450, loss: 0.69020808, val acc: 0.52499998 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 3455, loss: 0.69020700, val acc: 0.52499998 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 3460, loss: 0.69020724, val acc: 0.52999997 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 3465, loss: 0.69020545, val acc: 0.51999998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 3470, loss: 0.69020426, val acc: 0.51999998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 3475, loss: 0.69020289, val acc: 0.51999998 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 3480, loss: 0.69020200, val acc: 0.51999998 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 3485, loss: 0.69020075, val acc: 0.52499998 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 3490, loss: 0.69019967, val acc: 0.52499998 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 3495, loss: 0.69020033, val acc: 0.52499998 (best 0.56999999), test acc: 0.50500000 (best 0.48999998)\n",
            "In epoch 3500, loss: 0.69019771, val acc: 0.52499998 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 3505, loss: 0.69019908, val acc: 0.52499998 (best 0.56999999), test acc: 0.50500000 (best 0.48999998)\n",
            "In epoch 3510, loss: 0.69019723, val acc: 0.52499998 (best 0.56999999), test acc: 0.50500000 (best 0.48999998)\n",
            "In epoch 3515, loss: 0.69019526, val acc: 0.53499997 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 3520, loss: 0.69019473, val acc: 0.53499997 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 3525, loss: 0.69019300, val acc: 0.52499998 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 3530, loss: 0.69019789, val acc: 0.50999999 (best 0.56999999), test acc: 0.50500000 (best 0.48999998)\n",
            "In epoch 3535, loss: 0.69019043, val acc: 0.51999998 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 3540, loss: 0.69018918, val acc: 0.52499998 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 3545, loss: 0.69019181, val acc: 0.53499997 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 3550, loss: 0.69018877, val acc: 0.52499998 (best 0.56999999), test acc: 0.50500000 (best 0.48999998)\n",
            "In epoch 3555, loss: 0.69018751, val acc: 0.52499998 (best 0.56999999), test acc: 0.50500000 (best 0.48999998)\n",
            "In epoch 3560, loss: 0.69018793, val acc: 0.52999997 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 3565, loss: 0.69018590, val acc: 0.53499997 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 3570, loss: 0.69018519, val acc: 0.53499997 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 3575, loss: 0.69018286, val acc: 0.53499997 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 3580, loss: 0.69018263, val acc: 0.53499997 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 3585, loss: 0.69018191, val acc: 0.53499997 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 3590, loss: 0.69018120, val acc: 0.52999997 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 3595, loss: 0.69018012, val acc: 0.52999997 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 3600, loss: 0.69018000, val acc: 0.52999997 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 3605, loss: 0.69017828, val acc: 0.52999997 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 3610, loss: 0.69017893, val acc: 0.53499997 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 3615, loss: 0.69017500, val acc: 0.53999996 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 3620, loss: 0.69017869, val acc: 0.52499998 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 3625, loss: 0.69017166, val acc: 0.52499998 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 3630, loss: 0.69017076, val acc: 0.51499999 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 3635, loss: 0.69017005, val acc: 0.51999998 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 3640, loss: 0.69016874, val acc: 0.51999998 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 3645, loss: 0.69017065, val acc: 0.53499997 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 3650, loss: 0.69016767, val acc: 0.52499998 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 3655, loss: 0.69016558, val acc: 0.52499998 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 3660, loss: 0.69016749, val acc: 0.53499997 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 3665, loss: 0.69016433, val acc: 0.52499998 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 3670, loss: 0.69016576, val acc: 0.51999998 (best 0.56999999), test acc: 0.50500000 (best 0.48999998)\n",
            "In epoch 3675, loss: 0.69016260, val acc: 0.52499998 (best 0.56999999), test acc: 0.50500000 (best 0.48999998)\n",
            "In epoch 3680, loss: 0.69016623, val acc: 0.50999999 (best 0.56999999), test acc: 0.50500000 (best 0.48999998)\n",
            "In epoch 3685, loss: 0.69015938, val acc: 0.52499998 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 3690, loss: 0.69015849, val acc: 0.52499998 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 3695, loss: 0.69015753, val acc: 0.52499998 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 3700, loss: 0.69015729, val acc: 0.52499998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 3705, loss: 0.69015527, val acc: 0.52499998 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 3710, loss: 0.69015431, val acc: 0.52499998 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 3715, loss: 0.69015431, val acc: 0.53999996 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 3720, loss: 0.69015360, val acc: 0.53999996 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 3725, loss: 0.69015169, val acc: 0.51999998 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 3730, loss: 0.69015175, val acc: 0.53499997 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 3735, loss: 0.69014990, val acc: 0.53499997 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 3740, loss: 0.69015044, val acc: 0.52999997 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 3745, loss: 0.69015092, val acc: 0.52999997 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 3750, loss: 0.69014823, val acc: 0.52999997 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 3755, loss: 0.69014859, val acc: 0.52999997 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 3760, loss: 0.69014472, val acc: 0.53499997 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 3765, loss: 0.69014496, val acc: 0.52999997 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 3770, loss: 0.69014299, val acc: 0.53499997 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 3775, loss: 0.69014543, val acc: 0.52499998 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 3780, loss: 0.69014013, val acc: 0.51999998 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 3785, loss: 0.69013947, val acc: 0.51999998 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 3790, loss: 0.69013810, val acc: 0.52499998 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 3795, loss: 0.69013709, val acc: 0.52499998 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 3800, loss: 0.69013697, val acc: 0.52499998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 3805, loss: 0.69013643, val acc: 0.52499998 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 3810, loss: 0.69013411, val acc: 0.52499998 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 3815, loss: 0.69013542, val acc: 0.52499998 (best 0.56999999), test acc: 0.50500000 (best 0.48999998)\n",
            "In epoch 3820, loss: 0.69013458, val acc: 0.52499998 (best 0.56999999), test acc: 0.50500000 (best 0.48999998)\n",
            "In epoch 3825, loss: 0.69013375, val acc: 0.52499998 (best 0.56999999), test acc: 0.50500000 (best 0.48999998)\n",
            "In epoch 3830, loss: 0.69013160, val acc: 0.51999998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 3835, loss: 0.69013095, val acc: 0.52499998 (best 0.56999999), test acc: 0.50500000 (best 0.48999998)\n",
            "In epoch 3840, loss: 0.69012785, val acc: 0.52499998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 3845, loss: 0.69012696, val acc: 0.52499998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 3850, loss: 0.69012648, val acc: 0.52999997 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 3855, loss: 0.69012469, val acc: 0.52499998 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 3860, loss: 0.69012505, val acc: 0.51999998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 3865, loss: 0.69012380, val acc: 0.52499998 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 3870, loss: 0.69012266, val acc: 0.52499998 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 3875, loss: 0.69012070, val acc: 0.52499998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 3880, loss: 0.69012070, val acc: 0.51999998 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 3885, loss: 0.69011819, val acc: 0.52499998 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 3890, loss: 0.69011998, val acc: 0.52499998 (best 0.56999999), test acc: 0.50500000 (best 0.48999998)\n",
            "In epoch 3895, loss: 0.69011736, val acc: 0.51999998 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 3900, loss: 0.69011748, val acc: 0.52999997 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 3905, loss: 0.69011438, val acc: 0.52999997 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 3910, loss: 0.69011360, val acc: 0.52999997 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 3915, loss: 0.69011301, val acc: 0.51999998 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 3920, loss: 0.69011098, val acc: 0.51999998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 3925, loss: 0.69011086, val acc: 0.52999997 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 3930, loss: 0.69011062, val acc: 0.52999997 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 3935, loss: 0.69010848, val acc: 0.52499998 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 3940, loss: 0.69010901, val acc: 0.52999997 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 3945, loss: 0.69010943, val acc: 0.52499998 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 3950, loss: 0.69010681, val acc: 0.52999997 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 3955, loss: 0.69010663, val acc: 0.52999997 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 3960, loss: 0.69010329, val acc: 0.52499998 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 3965, loss: 0.69010293, val acc: 0.52999997 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 3970, loss: 0.69010347, val acc: 0.52999997 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 3975, loss: 0.69010043, val acc: 0.52499998 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 3980, loss: 0.69010466, val acc: 0.52499998 (best 0.56999999), test acc: 0.52999997 (best 0.48999998)\n",
            "In epoch 3985, loss: 0.69009781, val acc: 0.52499998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 3990, loss: 0.69009680, val acc: 0.51999998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 3995, loss: 0.69009590, val acc: 0.52999997 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 4000, loss: 0.69009477, val acc: 0.51999998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 4005, loss: 0.69009471, val acc: 0.52999997 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 4010, loss: 0.69009417, val acc: 0.51999998 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 4015, loss: 0.69009173, val acc: 0.51999998 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 4020, loss: 0.69009078, val acc: 0.52499998 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 4025, loss: 0.69009066, val acc: 0.51999998 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 4030, loss: 0.69009292, val acc: 0.51499999 (best 0.56999999), test acc: 0.50500000 (best 0.48999998)\n",
            "In epoch 4035, loss: 0.69008768, val acc: 0.51999998 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 4040, loss: 0.69008696, val acc: 0.52499998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 4045, loss: 0.69009328, val acc: 0.52999997 (best 0.56999999), test acc: 0.52999997 (best 0.48999998)\n",
            "In epoch 4050, loss: 0.69008589, val acc: 0.51999998 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 4055, loss: 0.69008368, val acc: 0.52999997 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 4060, loss: 0.69008482, val acc: 0.52999997 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 4065, loss: 0.69008368, val acc: 0.52999997 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 4070, loss: 0.69008082, val acc: 0.52999997 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 4075, loss: 0.69008207, val acc: 0.52999997 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 4080, loss: 0.69007874, val acc: 0.52999997 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 4085, loss: 0.69007975, val acc: 0.51999998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 4090, loss: 0.69007742, val acc: 0.52999997 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 4095, loss: 0.69007736, val acc: 0.52499998 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 4100, loss: 0.69007468, val acc: 0.52499998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 4105, loss: 0.69007659, val acc: 0.51999998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 4110, loss: 0.69007361, val acc: 0.51999998 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 4115, loss: 0.69007576, val acc: 0.52499998 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 4120, loss: 0.69007063, val acc: 0.52999997 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 4125, loss: 0.69007003, val acc: 0.52499998 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 4130, loss: 0.69007021, val acc: 0.51999998 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 4135, loss: 0.69006759, val acc: 0.52499998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 4140, loss: 0.69007230, val acc: 0.52999997 (best 0.56999999), test acc: 0.52999997 (best 0.48999998)\n",
            "In epoch 4145, loss: 0.69006598, val acc: 0.51999998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 4150, loss: 0.69006431, val acc: 0.51999998 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 4155, loss: 0.69007325, val acc: 0.51999998 (best 0.56999999), test acc: 0.50500000 (best 0.48999998)\n",
            "In epoch 4160, loss: 0.69006419, val acc: 0.52499998 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 4165, loss: 0.69006491, val acc: 0.52499998 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 4170, loss: 0.69006270, val acc: 0.52999997 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 4175, loss: 0.69006258, val acc: 0.52499998 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 4180, loss: 0.69005966, val acc: 0.51999998 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 4185, loss: 0.69006163, val acc: 0.52999997 (best 0.56999999), test acc: 0.52999997 (best 0.48999998)\n",
            "In epoch 4190, loss: 0.69005632, val acc: 0.52499998 (best 0.56999999), test acc: 0.50999999 (best 0.48999998)\n",
            "In epoch 4195, loss: 0.69005537, val acc: 0.52499998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 4200, loss: 0.69005692, val acc: 0.52999997 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 4205, loss: 0.69005311, val acc: 0.52499998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 4210, loss: 0.69005227, val acc: 0.51999998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 4215, loss: 0.69005096, val acc: 0.51999998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 4220, loss: 0.69004989, val acc: 0.51999998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 4225, loss: 0.69005018, val acc: 0.51999998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 4230, loss: 0.69004846, val acc: 0.51999998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 4235, loss: 0.69004798, val acc: 0.51999998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 4240, loss: 0.69004601, val acc: 0.51999998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 4245, loss: 0.69004667, val acc: 0.51999998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 4250, loss: 0.69004625, val acc: 0.51499999 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 4255, loss: 0.69004494, val acc: 0.51999998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 4260, loss: 0.69004285, val acc: 0.51999998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 4265, loss: 0.69004202, val acc: 0.51999998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 4270, loss: 0.69004142, val acc: 0.51999998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 4275, loss: 0.69004357, val acc: 0.51999998 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 4280, loss: 0.69003797, val acc: 0.51999998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 4285, loss: 0.69004148, val acc: 0.51999998 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 4290, loss: 0.69003570, val acc: 0.52499998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 4295, loss: 0.69003457, val acc: 0.52499998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 4300, loss: 0.69003350, val acc: 0.51999998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 4305, loss: 0.69003260, val acc: 0.51999998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 4310, loss: 0.69003141, val acc: 0.51499999 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 4315, loss: 0.69003057, val acc: 0.52499998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 4320, loss: 0.69003105, val acc: 0.52499998 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 4325, loss: 0.69002843, val acc: 0.52499998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 4330, loss: 0.69002974, val acc: 0.52499998 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 4335, loss: 0.69002718, val acc: 0.51999998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 4340, loss: 0.69002581, val acc: 0.51999998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 4345, loss: 0.69002479, val acc: 0.51999998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 4350, loss: 0.69002336, val acc: 0.52499998 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 4355, loss: 0.69002324, val acc: 0.52499998 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 4360, loss: 0.69002265, val acc: 0.52499998 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 4365, loss: 0.69002211, val acc: 0.52499998 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 4370, loss: 0.69001961, val acc: 0.52499998 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 4375, loss: 0.69002032, val acc: 0.52499998 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 4380, loss: 0.69002050, val acc: 0.53499997 (best 0.56999999), test acc: 0.52999997 (best 0.48999998)\n",
            "In epoch 4385, loss: 0.69001931, val acc: 0.53499997 (best 0.56999999), test acc: 0.52999997 (best 0.48999998)\n",
            "In epoch 4390, loss: 0.69001842, val acc: 0.53499997 (best 0.56999999), test acc: 0.52999997 (best 0.48999998)\n",
            "In epoch 4395, loss: 0.69001698, val acc: 0.52999997 (best 0.56999999), test acc: 0.52999997 (best 0.48999998)\n",
            "In epoch 4400, loss: 0.69001651, val acc: 0.53499997 (best 0.56999999), test acc: 0.52999997 (best 0.48999998)\n",
            "In epoch 4405, loss: 0.69001448, val acc: 0.52499998 (best 0.56999999), test acc: 0.52999997 (best 0.48999998)\n",
            "In epoch 4410, loss: 0.69001406, val acc: 0.52999997 (best 0.56999999), test acc: 0.52999997 (best 0.48999998)\n",
            "In epoch 4415, loss: 0.69001120, val acc: 0.52499998 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 4420, loss: 0.69001329, val acc: 0.52999997 (best 0.56999999), test acc: 0.52999997 (best 0.48999998)\n",
            "In epoch 4425, loss: 0.69000852, val acc: 0.52499998 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 4430, loss: 0.69001114, val acc: 0.52999997 (best 0.56999999), test acc: 0.52999997 (best 0.48999998)\n",
            "In epoch 4435, loss: 0.69000643, val acc: 0.51999998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 4440, loss: 0.69000518, val acc: 0.50999999 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 4445, loss: 0.69000417, val acc: 0.51499999 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 4450, loss: 0.69000322, val acc: 0.51999998 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 4455, loss: 0.69000238, val acc: 0.52499998 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 4460, loss: 0.69000113, val acc: 0.51999998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 4465, loss: 0.69000036, val acc: 0.52499998 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 4470, loss: 0.69000024, val acc: 0.51999998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 4475, loss: 0.68999869, val acc: 0.51999998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 4480, loss: 0.68999791, val acc: 0.51999998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 4485, loss: 0.68999654, val acc: 0.51999998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 4490, loss: 0.68999588, val acc: 0.51999998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 4495, loss: 0.68999428, val acc: 0.51499999 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 4500, loss: 0.68999422, val acc: 0.51999998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 4505, loss: 0.68999344, val acc: 0.51499999 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 4510, loss: 0.68999338, val acc: 0.51499999 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 4515, loss: 0.68999100, val acc: 0.51999998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 4520, loss: 0.68999147, val acc: 0.51499999 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 4525, loss: 0.68999028, val acc: 0.51499999 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 4530, loss: 0.68999201, val acc: 0.51999998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 4535, loss: 0.68998700, val acc: 0.51999998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 4540, loss: 0.68999219, val acc: 0.51999998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 4545, loss: 0.68998438, val acc: 0.51999998 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 4550, loss: 0.68998319, val acc: 0.51499999 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 4555, loss: 0.68998492, val acc: 0.51499999 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 4560, loss: 0.68998253, val acc: 0.52999997 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 4565, loss: 0.68998033, val acc: 0.51999998 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 4570, loss: 0.68998098, val acc: 0.52999997 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 4575, loss: 0.68997908, val acc: 0.51999998 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 4580, loss: 0.68998301, val acc: 0.51999998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 4585, loss: 0.68997633, val acc: 0.51999998 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 4590, loss: 0.68997556, val acc: 0.51499999 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 4595, loss: 0.68997645, val acc: 0.51499999 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 4600, loss: 0.68997353, val acc: 0.51999998 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 4605, loss: 0.68997270, val acc: 0.51499999 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 4610, loss: 0.68997145, val acc: 0.51999998 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 4615, loss: 0.68997109, val acc: 0.51999998 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 4620, loss: 0.68997121, val acc: 0.52999997 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 4625, loss: 0.68996924, val acc: 0.51999998 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 4630, loss: 0.68996882, val acc: 0.52999997 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 4635, loss: 0.68996638, val acc: 0.51999998 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 4640, loss: 0.68996650, val acc: 0.53499997 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 4645, loss: 0.68996531, val acc: 0.51999998 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 4650, loss: 0.68996519, val acc: 0.52999997 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 4655, loss: 0.68996245, val acc: 0.51999998 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 4660, loss: 0.68996191, val acc: 0.51499999 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 4665, loss: 0.68996269, val acc: 0.51499999 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 4670, loss: 0.68996245, val acc: 0.51499999 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 4675, loss: 0.68996054, val acc: 0.51499999 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 4680, loss: 0.68995959, val acc: 0.51499999 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 4685, loss: 0.68995702, val acc: 0.51499999 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 4690, loss: 0.68995613, val acc: 0.51499999 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 4695, loss: 0.68996078, val acc: 0.52999997 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 4700, loss: 0.68995386, val acc: 0.51999998 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 4705, loss: 0.68995255, val acc: 0.51999998 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 4710, loss: 0.68995166, val acc: 0.51999998 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 4715, loss: 0.68995064, val acc: 0.51999998 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 4720, loss: 0.68995005, val acc: 0.51499999 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 4725, loss: 0.68995064, val acc: 0.51499999 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 4730, loss: 0.68994856, val acc: 0.51999998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 4735, loss: 0.68994868, val acc: 0.51499999 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 4740, loss: 0.68994635, val acc: 0.50999999 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 4745, loss: 0.68994683, val acc: 0.51499999 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 4750, loss: 0.68994594, val acc: 0.51499999 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 4755, loss: 0.68994772, val acc: 0.51999998 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 4760, loss: 0.68994224, val acc: 0.50999999 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 4765, loss: 0.68994302, val acc: 0.51499999 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 4770, loss: 0.68993998, val acc: 0.51999998 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 4775, loss: 0.68993998, val acc: 0.51499999 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 4780, loss: 0.68993849, val acc: 0.51999998 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 4785, loss: 0.68993735, val acc: 0.51499999 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 4790, loss: 0.68993628, val acc: 0.51499999 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 4795, loss: 0.68993497, val acc: 0.51499999 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 4800, loss: 0.68993413, val acc: 0.51499999 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 4805, loss: 0.68993342, val acc: 0.51499999 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 4810, loss: 0.68993378, val acc: 0.52999997 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 4815, loss: 0.68993151, val acc: 0.51999998 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 4820, loss: 0.68993211, val acc: 0.52999997 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 4825, loss: 0.68993235, val acc: 0.52999997 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 4830, loss: 0.68993104, val acc: 0.52999997 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 4835, loss: 0.68993014, val acc: 0.52999997 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 4840, loss: 0.68992829, val acc: 0.52999997 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 4845, loss: 0.68992728, val acc: 0.52999997 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 4850, loss: 0.68992525, val acc: 0.52499998 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 4855, loss: 0.68992651, val acc: 0.52999997 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 4860, loss: 0.68992323, val acc: 0.52499998 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 4865, loss: 0.68992311, val acc: 0.52999997 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 4870, loss: 0.68992013, val acc: 0.51499999 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 4875, loss: 0.68991911, val acc: 0.51499999 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 4880, loss: 0.68991929, val acc: 0.50999999 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 4885, loss: 0.68991870, val acc: 0.52499998 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 4890, loss: 0.68991601, val acc: 0.51499999 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 4895, loss: 0.68991852, val acc: 0.52999997 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 4900, loss: 0.68991399, val acc: 0.51499999 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 4905, loss: 0.68991303, val acc: 0.51499999 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 4910, loss: 0.68991208, val acc: 0.50999999 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 4915, loss: 0.68991226, val acc: 0.51499999 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 4920, loss: 0.68991113, val acc: 0.51499999 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 4925, loss: 0.68991119, val acc: 0.50999999 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 4930, loss: 0.68990934, val acc: 0.50999999 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 4935, loss: 0.68990844, val acc: 0.51499999 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 4940, loss: 0.68990809, val acc: 0.50999999 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 4945, loss: 0.68990862, val acc: 0.51499999 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 4950, loss: 0.68990576, val acc: 0.50999999 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 4955, loss: 0.68990868, val acc: 0.51999998 (best 0.56999999), test acc: 0.51499999 (best 0.48999998)\n",
            "In epoch 4960, loss: 0.68990219, val acc: 0.51499999 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 4965, loss: 0.68990189, val acc: 0.51999998 (best 0.56999999), test acc: 0.52499998 (best 0.48999998)\n",
            "In epoch 4970, loss: 0.68990052, val acc: 0.52499998 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 4975, loss: 0.68990219, val acc: 0.51499999 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 4980, loss: 0.68989825, val acc: 0.51499999 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 4985, loss: 0.68989730, val acc: 0.51499999 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 4990, loss: 0.68989635, val acc: 0.51499999 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n",
            "In epoch 4995, loss: 0.68989527, val acc: 0.51499999 (best 0.56999999), test acc: 0.51999998 (best 0.48999998)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "8HEl8BGTdJ2g",
        "outputId": "667da191-3698-40c6-939a-9b8528275443"
      },
      "source": [
        "plt.plot(node_classif_losses, 'r-')\n",
        "plt.title('Training Loss')\n",
        "plt.xlabel('Iterations')\n",
        "plt.ylabel('Loss')\n",
        "plt.show()"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "qsQzrx1idJ2h",
        "outputId": "6e94c307-d413-4e62-868c-b0a5594f9461"
      },
      "source": [
        "plt.figure(figsize=(15, 5))\n",
        "plt.plot(node_classif_accs['train'], 'r-', label='Training Accuracy')\n",
        "plt.plot(node_classif_accs['val'], 'g-', label='Val Accuracy')\n",
        "plt.plot(node_classif_accs['test'], 'b-', label='Test Accuracy')\n",
        "plt.title('Accuracies')\n",
        "plt.xlabel('Iterations')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hvk_Rzs0dJ2h",
        "outputId": "028191c5-810b-43f8-f985-3310967a75b5"
      },
      "source": [
        "logits = node_classif_model(node_classif_graph, node_classif_graph.ndata['feat'])\n",
        "pred = logits.argmax(1)\n",
        "pred"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([1, 0, 0,  ..., 1, 1, 1], device='cuda:0')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "hIi1BEefdJ2h",
        "outputId": "2c4868c8-15a9-4ba1-bb66-d6ab9f135674"
      },
      "source": [
        "plt.hist(pred.cpu().numpy())\n",
        "plt.title(\"Histogram of predicted labels\")\n",
        "plt.show()"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "1v00BQAtdJ2h",
        "outputId": "7406e9ef-dfcf-4a5a-e7cc-5dedd24922a8"
      },
      "source": [
        "label = node_classif_graph.ndata['label']\n",
        "plt.hist(label.cpu().numpy())\n",
        "plt.title(\"Histogram of Labels in the Training Set\")\n",
        "plt.show()"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "9pAJWanUdJ2i",
        "outputId": "80425169-9f68-4600-f404-8d3f7bb3b012"
      },
      "source": [
        "from sklearn.metrics import auc, roc_curve\n",
        "fpr, tpr, _ = roc_curve(label.cpu().numpy(), pred.cpu().numpy())\n",
        "roc_auc = auc(fpr, tpr)\n",
        "plt.figure()\n",
        "lw = 2\n",
        "plt.plot(fpr, tpr, color='darkorange',\n",
        "lw=lw, label='ROC curve (area = %0.2f)' % roc_auc)\n",
        "plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')\n",
        "plt.xlim([0.0, 1.0])\n",
        "plt.ylim([0.0, 1.05])\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('Receiver Operating Characteristic Curve on the entire dataset')\n",
        "plt.legend(loc=\"lower right\")\n",
        "plt.show()"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "twpKHaX8dJ2i"
      },
      "source": [
        "## Graph Classification \n",
        "We now begin with this approach. Like before, we begin with creating a dataloader. We will fix the number of nodes in each graph (a tunable parameter) and then generate random graphs for each point in the dataset. We will give a label for each such graph.   \n",
        "In this approach, we can take the entire dataset provided as we can load the graphs in batches while training. But for the sake of fair comparison, we will take the same smaller dataset that we used for the Node Classification Approach."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MvKvA9uNPx7K",
        "outputId": "648c8ef8-abe6-4e6f-b5db-8b9817c6ca5d"
      },
      "source": [
        "x_train_small[0].shape"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3MiUkBdadJ2i"
      },
      "source": [
        "class GraphClassificationDataset(dgl.data.DGLDataset):\n",
        "  \"\"\"A Class to process and convert the numpy training data into Graphs so that it can be used in GNNs.\"\"\"\n",
        "  def __init__(self):\n",
        "    super().__init__(name='graph_classification') \n",
        "    self.num_classes = 2\n",
        "    self.dim_nfeats = 4\n",
        "  \n",
        "  def process(self):\n",
        "    self.graphs = []\n",
        "    self.labels = []\n",
        "    num_examples = len(x_train_small)\n",
        "    num_train = int(num_examples * 0.8)\n",
        "    num_val = int(num_examples * 0.1)\n",
        "    train_mask = torch.zeros(num_examples, dtype=torch.bool)\n",
        "    val_mask = torch.zeros(num_examples, dtype=torch.bool)\n",
        "    test_mask = torch.zeros(num_examples, dtype=torch.bool)\n",
        "    train_mask[:num_train] = True\n",
        "    val_mask[num_train:num_train+num_val] = True\n",
        "    test_mask[num_train+num_val:] = True\n",
        "    self.train_mask = train_mask\n",
        "    self.test_mask = test_mask\n",
        "    self.val_mask = val_mask \n",
        "\n",
        "    for id in range(len(x_train_small)):\n",
        "      g = dgl.from_networkx(nx.generators.fast_gnp_random_graph(20, p=0.6))\n",
        "      g.ndata['feat'] = torch.from_numpy(np.repeat(x_train_small[id].reshape(1,4),20,0))\n",
        "      g.ndata['label'] = torch.LongTensor([y_train_small[id]]*20)\n",
        "      self.graphs.append(g)\n",
        "      self.labels.append(y_train_small[id])\n",
        "    \n",
        "    self.labels = torch.LongTensor(self.labels)\n",
        "\n",
        "  def __getitem__(self, idx):\n",
        "    return self.graphs[idx], self.labels[idx]\n",
        "\n",
        "  def __len__(self):\n",
        "    return len(self.graphs)"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l7N6vCLjdJ2i",
        "outputId": "a05a945c-23cc-48bd-9a95-ccc2101f61de"
      },
      "source": [
        "graph_classif_dataset = GraphClassificationDataset()\n",
        "g_sample, label_sample = graph_classif_dataset[0]\n",
        "print(g_sample, label_sample) "
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Graph(num_nodes=20, num_edges=234,\n",
            "      ndata_schemes={'feat': Scheme(shape=(4,), dtype=torch.float32), 'label': Scheme(shape=(), dtype=torch.int64)}\n",
            "      edata_schemes={}) tensor(1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cZoF-uU7dJ2i"
      },
      "source": [
        "We will now create a dataloader"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xd_g5jADdJ2i"
      },
      "source": [
        "from dgl.dataloading import GraphDataLoader\n",
        "from torch.utils.data.sampler import SubsetRandomSampler\n",
        "\n",
        "num_examples = len(graph_classif_dataset)\n",
        "num_train = int(num_examples * 0.8)\n",
        "num_val = int(num_examples * 0.1)\n",
        "\n",
        "train_sampler = SubsetRandomSampler(torch.arange(num_train))\n",
        "val_sampler = SubsetRandomSampler(torch.arange(num_train, num_train+num_val))\n",
        "test_sampler = SubsetRandomSampler(torch.arange(num_train+num_val, num_examples))\n",
        "\n",
        "train_dataloader = GraphDataLoader(graph_classif_dataset, sampler=train_sampler, batch_size=5, drop_last=False)\n",
        "val_dataloader = GraphDataLoader(graph_classif_dataset, sampler=val_sampler, batch_size=5, drop_last=False)\n",
        "test_dataloader = GraphDataLoader(graph_classif_dataset, sampler=test_sampler, batch_size=5, drop_last=False)"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QFfrdrEjdJ2j",
        "outputId": "79f2c780-0a52-4b51-8355-8613622511e4"
      },
      "source": [
        "it = iter(train_dataloader)\n",
        "batch = next(it)\n",
        "print(batch)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[Graph(num_nodes=100, num_edges=1132,\n",
            "      ndata_schemes={'feat': Scheme(shape=(4,), dtype=torch.float32), 'label': Scheme(shape=(), dtype=torch.int64)}\n",
            "      edata_schemes={}), tensor([1, 1, 1, 0, 1])]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oxKK6qILdJ2j",
        "outputId": "39f5b542-0070-47c1-c360-d88dc7bd08d1"
      },
      "source": [
        "batched_graph, labels = batch\n",
        "print('Number of nodes for each graph element in the batch:', batched_graph.batch_num_nodes())\n",
        "print('Number of edges for each graph element in the batch:', batched_graph.batch_num_edges())\n",
        "\n",
        "# Recover the original graph elements from the minibatch\n",
        "graphs = dgl.unbatch(batched_graph)\n",
        "print('The original graphs in the minibatch:')\n",
        "print(graphs)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of nodes for each graph element in the batch: tensor([20, 20, 20, 20, 20])\n",
            "Number of edges for each graph element in the batch: tensor([228, 234, 218, 220, 232])\n",
            "The original graphs in the minibatch:\n",
            "[Graph(num_nodes=20, num_edges=228,\n",
            "      ndata_schemes={'feat': Scheme(shape=(4,), dtype=torch.float32), 'label': Scheme(shape=(), dtype=torch.int64)}\n",
            "      edata_schemes={}), Graph(num_nodes=20, num_edges=234,\n",
            "      ndata_schemes={'feat': Scheme(shape=(4,), dtype=torch.float32), 'label': Scheme(shape=(), dtype=torch.int64)}\n",
            "      edata_schemes={}), Graph(num_nodes=20, num_edges=218,\n",
            "      ndata_schemes={'feat': Scheme(shape=(4,), dtype=torch.float32), 'label': Scheme(shape=(), dtype=torch.int64)}\n",
            "      edata_schemes={}), Graph(num_nodes=20, num_edges=220,\n",
            "      ndata_schemes={'feat': Scheme(shape=(4,), dtype=torch.float32), 'label': Scheme(shape=(), dtype=torch.int64)}\n",
            "      edata_schemes={}), Graph(num_nodes=20, num_edges=232,\n",
            "      ndata_schemes={'feat': Scheme(shape=(4,), dtype=torch.float32), 'label': Scheme(shape=(), dtype=torch.int64)}\n",
            "      edata_schemes={})]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3Y5sglz1dJ2j"
      },
      "source": [
        "### Defining the Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XCJuwMq-dJ2j"
      },
      "source": [
        "from dgl.nn import GraphConv\n",
        "\n",
        "class GraphClassificationModel(torch.nn.Module):\n",
        "  \"\"\"A Model Class having the methods to implement and forward pass a GNN.\"\"\"\n",
        "  def __init__(self, in_feats, num_classes):\n",
        "    super(GraphClassificationModel, self).__init__()\n",
        "    self.conv1 = GraphConv(in_feats, 16)\n",
        "    self.conv2 = GraphConv(16, 32)\n",
        "    self.conv3 = GraphConv(32, num_classes)\n",
        "    # self.conv3 = GraphConv(32, 64)\n",
        "    # self.conv4 = GraphConv(64, num_classes)\n",
        "\n",
        "  def forward(self, g, in_feat):\n",
        "    h = self.conv1(g, in_feat)\n",
        "    h = torch.nn.functional.relu(h)\n",
        "    h = self.conv2(g, h)\n",
        "    h = torch.nn.functional.relu(h)\n",
        "    h = self.conv3(g, h)\n",
        "    # h = torch.nn.functional.relu(h)\n",
        "    # h = self.conv4(g, h)\n",
        "    g.ndata['h'] = h\n",
        "    return dgl.mean_nodes(g, 'h')"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lLsmDSxudJ2j"
      },
      "source": [
        "graph_classif_model = GraphClassificationModel(graph_classif_dataset.dim_nfeats, graph_classif_dataset.num_classes)"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XO8tJW6hdJ2j"
      },
      "source": [
        "### Defining the Training Loop"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ewUQSFipdJ2j"
      },
      "source": [
        "def train(train_loader, val_loader, test_loader, model, num_epochs):\n",
        "  \"\"\"The function implementing the main train loop.\"\"\"\n",
        "  losses = []\n",
        "  accs = {'train':[], 'val': [], 'test': []}\n",
        "  optimizer = torch.optim.Adam(model.parameters(), lr=0.01, betas=(0.9, 0.9999))\n",
        "  best_val_acc = 0.0\n",
        "  best_test_acc = 0.0\n",
        "\n",
        "  for e in range(num_epochs):\n",
        "    train_acc_batch = []\n",
        "    val_acc_batch = []\n",
        "    test_acc_batch = []\n",
        "    train_loss_batch = []\n",
        "    for batched_graph, labels in train_loader:\n",
        "      model.train()\n",
        "      batched_graph, labels = batched_graph.to('cuda'), labels.to('cuda')\n",
        "      logits = model(batched_graph, batched_graph.ndata['feat'].float())\n",
        "      pred = logits.argmax(1)\n",
        "      loss = torch.nn.functional.cross_entropy(logits, labels)\n",
        "      train_loss_batch.append(loss)\n",
        "      train_acc = (pred == labels).float().mean()\n",
        "      train_acc_batch.append(train_acc)\n",
        "      with torch.no_grad():  \n",
        "        optimizer.zero_grad()\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "    accs['train'].append(sum(train_acc_batch) / len(train_acc_batch))\n",
        "    losses.append(sum(train_loss_batch) / len(train_loss_batch))\n",
        "\n",
        "    model.eval()\n",
        "    for batched_graph_val, labels_val in val_loader:\n",
        "      batched_graph_val, labels_val = batched_graph_val.to('cuda'), labels_val.to('cuda')\n",
        "      logits_val = model(batched_graph_val, batched_graph_val.ndata['feat'].float())\n",
        "      pred_val = logits_val.argmax(1)\n",
        "      val_acc = (pred_val == labels_val).float().mean()\n",
        "      val_acc_batch.append(val_acc)\n",
        "    accs['val'].append(sum(val_acc_batch) / len(val_acc_batch))\n",
        "\n",
        "    for batched_graph_test, labels_test in test_loader:\n",
        "      batched_graph_test, labels_test = batched_graph_test.to('cuda'), labels_test.to('cuda')\n",
        "      logits_test = model(batched_graph_test, batched_graph_test.ndata['feat'].float())\n",
        "      pred_test = logits_test.argmax(1)\n",
        "      test_acc = (pred_test == labels_test).float().mean()\n",
        "      test_acc_batch.append(test_acc)\n",
        "    accs['test'].append(sum(test_acc_batch) / len(test_acc_batch))\n",
        "\n",
        "    if best_val_acc < val_acc:\n",
        "      best_val_acc = val_acc\n",
        "      best_test_acc = test_acc\n",
        "\n",
        "    if e % 5 == 0:\n",
        "      print('In epoch {}, loss: {:.8f}, val acc: {:.8f} (best {:.8f}), test acc: {:.8f} (best {:.8f})'.format(\n",
        "                e, loss, val_acc, best_val_acc, test_acc, best_test_acc))\n",
        "  \n",
        "  return losses, accs"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GX6adpgndJ2k",
        "outputId": "573fee95-500a-4b12-e751-a2cfeb9fb887"
      },
      "source": [
        "graph_classif_model = graph_classif_model.to('cuda')\n",
        "graph_classif_losses, graph_classif_accs = train(train_dataloader, val_dataloader, test_dataloader, graph_classif_model, 700)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "In epoch 0, loss: 1.32255912, val acc: 0.80000001 (best 0.80000001), test acc: 0.80000001 (best 0.80000001)\n",
            "In epoch 5, loss: 0.71140397, val acc: 0.80000001 (best 0.80000001), test acc: 0.20000000 (best 0.80000001)\n",
            "In epoch 10, loss: 0.69860405, val acc: 0.40000001 (best 0.80000001), test acc: 0.60000002 (best 0.80000001)\n",
            "In epoch 15, loss: 0.65682209, val acc: 0.60000002 (best 0.80000001), test acc: 0.80000001 (best 0.80000001)\n",
            "In epoch 20, loss: 0.69134969, val acc: 0.60000002 (best 0.80000001), test acc: 0.20000000 (best 0.80000001)\n",
            "In epoch 25, loss: 0.68840706, val acc: 0.40000001 (best 1.00000000), test acc: 0.40000001 (best 0.60000002)\n",
            "In epoch 30, loss: 0.64081395, val acc: 0.80000001 (best 1.00000000), test acc: 0.60000002 (best 0.60000002)\n",
            "In epoch 35, loss: 0.68934125, val acc: 0.40000001 (best 1.00000000), test acc: 0.40000001 (best 0.60000002)\n",
            "In epoch 40, loss: 0.65559149, val acc: 0.60000002 (best 1.00000000), test acc: 0.40000001 (best 0.60000002)\n",
            "In epoch 45, loss: 0.80932617, val acc: 0.40000001 (best 1.00000000), test acc: 0.40000001 (best 0.60000002)\n",
            "In epoch 50, loss: 0.70110434, val acc: 0.60000002 (best 1.00000000), test acc: 0.00000000 (best 0.60000002)\n",
            "In epoch 55, loss: 0.60884756, val acc: 0.20000000 (best 1.00000000), test acc: 0.40000001 (best 0.60000002)\n",
            "In epoch 60, loss: 0.78330880, val acc: 0.40000001 (best 1.00000000), test acc: 0.60000002 (best 0.60000002)\n",
            "In epoch 65, loss: 0.61864960, val acc: 0.20000000 (best 1.00000000), test acc: 1.00000000 (best 0.60000002)\n",
            "In epoch 70, loss: 0.65097696, val acc: 0.40000001 (best 1.00000000), test acc: 0.40000001 (best 0.60000002)\n",
            "In epoch 75, loss: 0.68667787, val acc: 0.80000001 (best 1.00000000), test acc: 0.40000001 (best 0.60000002)\n",
            "In epoch 80, loss: 0.71602374, val acc: 0.60000002 (best 1.00000000), test acc: 1.00000000 (best 0.60000002)\n",
            "In epoch 85, loss: 0.71640110, val acc: 0.40000001 (best 1.00000000), test acc: 0.60000002 (best 0.60000002)\n",
            "In epoch 90, loss: 0.61474597, val acc: 0.40000001 (best 1.00000000), test acc: 0.40000001 (best 0.60000002)\n",
            "In epoch 95, loss: 0.52808022, val acc: 0.00000000 (best 1.00000000), test acc: 0.40000001 (best 0.60000002)\n",
            "In epoch 100, loss: 0.68401468, val acc: 0.00000000 (best 1.00000000), test acc: 0.20000000 (best 0.60000002)\n",
            "In epoch 105, loss: 0.74682379, val acc: 0.60000002 (best 1.00000000), test acc: 0.00000000 (best 0.60000002)\n",
            "In epoch 110, loss: 0.72935390, val acc: 0.40000001 (best 1.00000000), test acc: 0.40000001 (best 0.60000002)\n",
            "In epoch 115, loss: 0.68621612, val acc: 0.60000002 (best 1.00000000), test acc: 0.60000002 (best 0.60000002)\n",
            "In epoch 120, loss: 0.71008050, val acc: 0.60000002 (best 1.00000000), test acc: 0.80000001 (best 0.60000002)\n",
            "In epoch 125, loss: 0.63348788, val acc: 0.40000001 (best 1.00000000), test acc: 0.80000001 (best 0.60000002)\n",
            "In epoch 130, loss: 0.64721137, val acc: 0.60000002 (best 1.00000000), test acc: 0.80000001 (best 0.60000002)\n",
            "In epoch 135, loss: 0.61149645, val acc: 0.60000002 (best 1.00000000), test acc: 0.60000002 (best 0.60000002)\n",
            "In epoch 140, loss: 0.73249656, val acc: 0.80000001 (best 1.00000000), test acc: 0.80000001 (best 0.60000002)\n",
            "In epoch 145, loss: 0.77375329, val acc: 1.00000000 (best 1.00000000), test acc: 0.60000002 (best 0.60000002)\n",
            "In epoch 150, loss: 0.67116225, val acc: 0.60000002 (best 1.00000000), test acc: 0.40000001 (best 0.60000002)\n",
            "In epoch 155, loss: 0.64774859, val acc: 0.40000001 (best 1.00000000), test acc: 0.40000001 (best 0.60000002)\n",
            "In epoch 160, loss: 0.70023358, val acc: 0.20000000 (best 1.00000000), test acc: 0.60000002 (best 0.60000002)\n",
            "In epoch 165, loss: 0.72687662, val acc: 0.00000000 (best 1.00000000), test acc: 0.60000002 (best 0.60000002)\n",
            "In epoch 170, loss: 0.69116086, val acc: 0.60000002 (best 1.00000000), test acc: 0.20000000 (best 0.60000002)\n",
            "In epoch 175, loss: 0.74121797, val acc: 0.40000001 (best 1.00000000), test acc: 0.40000001 (best 0.60000002)\n",
            "In epoch 180, loss: 0.69898045, val acc: 0.80000001 (best 1.00000000), test acc: 0.60000002 (best 0.60000002)\n",
            "In epoch 185, loss: 0.71680623, val acc: 0.40000001 (best 1.00000000), test acc: 0.40000001 (best 0.60000002)\n",
            "In epoch 190, loss: 0.68536377, val acc: 0.40000001 (best 1.00000000), test acc: 0.40000001 (best 0.60000002)\n",
            "In epoch 195, loss: 0.71111500, val acc: 0.20000000 (best 1.00000000), test acc: 0.80000001 (best 0.60000002)\n",
            "In epoch 200, loss: 0.68061411, val acc: 0.60000002 (best 1.00000000), test acc: 0.40000001 (best 0.60000002)\n",
            "In epoch 205, loss: 0.67588937, val acc: 0.60000002 (best 1.00000000), test acc: 0.60000002 (best 0.60000002)\n",
            "In epoch 210, loss: 0.70876896, val acc: 0.60000002 (best 1.00000000), test acc: 0.20000000 (best 0.60000002)\n",
            "In epoch 215, loss: 0.67690551, val acc: 0.60000002 (best 1.00000000), test acc: 0.60000002 (best 0.60000002)\n",
            "In epoch 220, loss: 0.66399276, val acc: 0.40000001 (best 1.00000000), test acc: 0.20000000 (best 0.60000002)\n",
            "In epoch 225, loss: 0.68663377, val acc: 0.40000001 (best 1.00000000), test acc: 0.00000000 (best 0.60000002)\n",
            "In epoch 230, loss: 0.67925465, val acc: 0.40000001 (best 1.00000000), test acc: 0.80000001 (best 0.60000002)\n",
            "In epoch 235, loss: 0.75748503, val acc: 0.40000001 (best 1.00000000), test acc: 0.40000001 (best 0.60000002)\n",
            "In epoch 240, loss: 0.66699302, val acc: 0.80000001 (best 1.00000000), test acc: 0.80000001 (best 0.60000002)\n",
            "In epoch 245, loss: 0.69626433, val acc: 0.40000001 (best 1.00000000), test acc: 0.40000001 (best 0.60000002)\n",
            "In epoch 250, loss: 0.74953431, val acc: 0.80000001 (best 1.00000000), test acc: 0.60000002 (best 0.60000002)\n",
            "In epoch 255, loss: 0.69572437, val acc: 0.60000002 (best 1.00000000), test acc: 0.80000001 (best 0.60000002)\n",
            "In epoch 260, loss: 0.65581191, val acc: 0.60000002 (best 1.00000000), test acc: 0.20000000 (best 0.60000002)\n",
            "In epoch 265, loss: 0.71385932, val acc: 0.60000002 (best 1.00000000), test acc: 0.60000002 (best 0.60000002)\n",
            "In epoch 270, loss: 0.73258936, val acc: 0.60000002 (best 1.00000000), test acc: 0.20000000 (best 0.60000002)\n",
            "In epoch 275, loss: 0.73522872, val acc: 0.20000000 (best 1.00000000), test acc: 0.60000002 (best 0.60000002)\n",
            "In epoch 280, loss: 0.69775867, val acc: 0.80000001 (best 1.00000000), test acc: 0.60000002 (best 0.60000002)\n",
            "In epoch 285, loss: 0.67734641, val acc: 0.40000001 (best 1.00000000), test acc: 0.40000001 (best 0.60000002)\n",
            "In epoch 290, loss: 0.66040033, val acc: 0.80000001 (best 1.00000000), test acc: 0.40000001 (best 0.60000002)\n",
            "In epoch 295, loss: 0.68019944, val acc: 0.40000001 (best 1.00000000), test acc: 0.40000001 (best 0.60000002)\n",
            "In epoch 300, loss: 0.65287137, val acc: 0.40000001 (best 1.00000000), test acc: 0.40000001 (best 0.60000002)\n",
            "In epoch 305, loss: 0.73355043, val acc: 0.60000002 (best 1.00000000), test acc: 0.20000000 (best 0.60000002)\n",
            "In epoch 310, loss: 0.69710588, val acc: 0.40000001 (best 1.00000000), test acc: 0.60000002 (best 0.60000002)\n",
            "In epoch 315, loss: 0.67291605, val acc: 0.40000001 (best 1.00000000), test acc: 0.60000002 (best 0.60000002)\n",
            "In epoch 320, loss: 0.68789381, val acc: 0.60000002 (best 1.00000000), test acc: 0.60000002 (best 0.60000002)\n",
            "In epoch 325, loss: 0.71691942, val acc: 0.80000001 (best 1.00000000), test acc: 0.20000000 (best 0.60000002)\n",
            "In epoch 330, loss: 0.66656721, val acc: 0.40000001 (best 1.00000000), test acc: 0.40000001 (best 0.60000002)\n",
            "In epoch 335, loss: 0.66030973, val acc: 0.60000002 (best 1.00000000), test acc: 0.40000001 (best 0.60000002)\n",
            "In epoch 340, loss: 0.69625074, val acc: 0.60000002 (best 1.00000000), test acc: 0.20000000 (best 0.60000002)\n",
            "In epoch 345, loss: 0.69005072, val acc: 0.60000002 (best 1.00000000), test acc: 0.40000001 (best 0.60000002)\n",
            "In epoch 350, loss: 0.69781017, val acc: 0.20000000 (best 1.00000000), test acc: 0.40000001 (best 0.60000002)\n",
            "In epoch 355, loss: 0.68023342, val acc: 0.60000002 (best 1.00000000), test acc: 0.20000000 (best 0.60000002)\n",
            "In epoch 360, loss: 0.71380705, val acc: 0.20000000 (best 1.00000000), test acc: 0.40000001 (best 0.60000002)\n",
            "In epoch 365, loss: 0.66957760, val acc: 0.60000002 (best 1.00000000), test acc: 0.80000001 (best 0.60000002)\n",
            "In epoch 370, loss: 0.68464428, val acc: 0.40000001 (best 1.00000000), test acc: 0.80000001 (best 0.60000002)\n",
            "In epoch 375, loss: 0.66630769, val acc: 0.20000000 (best 1.00000000), test acc: 0.60000002 (best 0.60000002)\n",
            "In epoch 380, loss: 0.69048995, val acc: 0.40000001 (best 1.00000000), test acc: 0.60000002 (best 0.60000002)\n",
            "In epoch 385, loss: 0.72937775, val acc: 0.60000002 (best 1.00000000), test acc: 0.60000002 (best 0.60000002)\n",
            "In epoch 390, loss: 0.70595586, val acc: 0.40000001 (best 1.00000000), test acc: 0.80000001 (best 0.60000002)\n",
            "In epoch 395, loss: 0.66758841, val acc: 0.40000001 (best 1.00000000), test acc: 0.20000000 (best 0.60000002)\n",
            "In epoch 400, loss: 0.70506746, val acc: 0.80000001 (best 1.00000000), test acc: 0.20000000 (best 0.60000002)\n",
            "In epoch 405, loss: 0.59176379, val acc: 0.40000001 (best 1.00000000), test acc: 0.40000001 (best 0.60000002)\n",
            "In epoch 410, loss: 0.65469015, val acc: 0.60000002 (best 1.00000000), test acc: 0.20000000 (best 0.60000002)\n",
            "In epoch 415, loss: 0.68703079, val acc: 0.40000001 (best 1.00000000), test acc: 0.40000001 (best 0.60000002)\n",
            "In epoch 420, loss: 0.64535600, val acc: 0.40000001 (best 1.00000000), test acc: 0.40000001 (best 0.60000002)\n",
            "In epoch 425, loss: 0.67316395, val acc: 0.60000002 (best 1.00000000), test acc: 0.60000002 (best 0.60000002)\n",
            "In epoch 430, loss: 0.65862644, val acc: 0.40000001 (best 1.00000000), test acc: 0.40000001 (best 0.60000002)\n",
            "In epoch 435, loss: 0.69233316, val acc: 0.60000002 (best 1.00000000), test acc: 0.60000002 (best 0.60000002)\n",
            "In epoch 440, loss: 0.67834979, val acc: 0.40000001 (best 1.00000000), test acc: 0.60000002 (best 0.60000002)\n",
            "In epoch 445, loss: 0.69678104, val acc: 0.60000002 (best 1.00000000), test acc: 0.60000002 (best 0.60000002)\n",
            "In epoch 450, loss: 0.69236851, val acc: 0.20000000 (best 1.00000000), test acc: 0.40000001 (best 0.60000002)\n",
            "In epoch 455, loss: 0.71883649, val acc: 0.60000002 (best 1.00000000), test acc: 0.60000002 (best 0.60000002)\n",
            "In epoch 460, loss: 0.69608885, val acc: 0.40000001 (best 1.00000000), test acc: 0.20000000 (best 0.60000002)\n",
            "In epoch 465, loss: 0.67582959, val acc: 0.20000000 (best 1.00000000), test acc: 0.80000001 (best 0.60000002)\n",
            "In epoch 470, loss: 0.70294058, val acc: 0.80000001 (best 1.00000000), test acc: 0.40000001 (best 0.60000002)\n",
            "In epoch 475, loss: 0.69846171, val acc: 0.40000001 (best 1.00000000), test acc: 0.60000002 (best 0.60000002)\n",
            "In epoch 480, loss: 0.70967972, val acc: 0.80000001 (best 1.00000000), test acc: 0.60000002 (best 0.60000002)\n",
            "In epoch 485, loss: 0.70049113, val acc: 0.20000000 (best 1.00000000), test acc: 0.20000000 (best 0.60000002)\n",
            "In epoch 490, loss: 0.67106503, val acc: 0.60000002 (best 1.00000000), test acc: 0.40000001 (best 0.60000002)\n",
            "In epoch 495, loss: 0.78342086, val acc: 0.80000001 (best 1.00000000), test acc: 0.40000001 (best 0.60000002)\n",
            "In epoch 500, loss: 0.60414940, val acc: 0.40000001 (best 1.00000000), test acc: 0.20000000 (best 0.60000002)\n",
            "In epoch 505, loss: 0.66642010, val acc: 0.60000002 (best 1.00000000), test acc: 0.60000002 (best 0.60000002)\n",
            "In epoch 510, loss: 0.66728258, val acc: 0.20000000 (best 1.00000000), test acc: 0.40000001 (best 0.60000002)\n",
            "In epoch 515, loss: 0.70236510, val acc: 0.40000001 (best 1.00000000), test acc: 0.60000002 (best 0.60000002)\n",
            "In epoch 520, loss: 0.66535538, val acc: 0.60000002 (best 1.00000000), test acc: 0.40000001 (best 0.60000002)\n",
            "In epoch 525, loss: 0.69165599, val acc: 0.80000001 (best 1.00000000), test acc: 0.60000002 (best 0.60000002)\n",
            "In epoch 530, loss: 0.69069105, val acc: 0.20000000 (best 1.00000000), test acc: 0.60000002 (best 0.60000002)\n",
            "In epoch 535, loss: 0.67577285, val acc: 0.40000001 (best 1.00000000), test acc: 0.40000001 (best 0.60000002)\n",
            "In epoch 540, loss: 0.68727815, val acc: 0.40000001 (best 1.00000000), test acc: 0.80000001 (best 0.60000002)\n",
            "In epoch 545, loss: 0.71458060, val acc: 0.20000000 (best 1.00000000), test acc: 0.20000000 (best 0.60000002)\n",
            "In epoch 550, loss: 0.69320786, val acc: 0.80000001 (best 1.00000000), test acc: 0.40000001 (best 0.60000002)\n",
            "In epoch 555, loss: 0.72181571, val acc: 0.80000001 (best 1.00000000), test acc: 0.00000000 (best 0.60000002)\n",
            "In epoch 560, loss: 0.67493677, val acc: 0.80000001 (best 1.00000000), test acc: 0.20000000 (best 0.60000002)\n",
            "In epoch 565, loss: 0.69515687, val acc: 0.40000001 (best 1.00000000), test acc: 0.60000002 (best 0.60000002)\n",
            "In epoch 570, loss: 0.74017185, val acc: 0.40000001 (best 1.00000000), test acc: 0.40000001 (best 0.60000002)\n",
            "In epoch 575, loss: 0.65468597, val acc: 0.80000001 (best 1.00000000), test acc: 0.20000000 (best 0.60000002)\n",
            "In epoch 580, loss: 0.65377319, val acc: 1.00000000 (best 1.00000000), test acc: 0.60000002 (best 0.60000002)\n",
            "In epoch 585, loss: 0.70424205, val acc: 0.80000001 (best 1.00000000), test acc: 0.80000001 (best 0.60000002)\n",
            "In epoch 590, loss: 0.73723471, val acc: 0.60000002 (best 1.00000000), test acc: 0.60000002 (best 0.60000002)\n",
            "In epoch 595, loss: 0.69356763, val acc: 0.60000002 (best 1.00000000), test acc: 0.40000001 (best 0.60000002)\n",
            "In epoch 600, loss: 0.69023031, val acc: 0.60000002 (best 1.00000000), test acc: 0.60000002 (best 0.60000002)\n",
            "In epoch 605, loss: 0.71804667, val acc: 0.40000001 (best 1.00000000), test acc: 0.60000002 (best 0.60000002)\n",
            "In epoch 610, loss: 0.65557688, val acc: 0.80000001 (best 1.00000000), test acc: 0.20000000 (best 0.60000002)\n",
            "In epoch 615, loss: 0.65636319, val acc: 0.60000002 (best 1.00000000), test acc: 0.80000001 (best 0.60000002)\n",
            "In epoch 620, loss: 0.64237678, val acc: 0.20000000 (best 1.00000000), test acc: 0.20000000 (best 0.60000002)\n",
            "In epoch 625, loss: 0.69880027, val acc: 0.40000001 (best 1.00000000), test acc: 1.00000000 (best 0.60000002)\n",
            "In epoch 630, loss: 0.66862571, val acc: 0.40000001 (best 1.00000000), test acc: 0.60000002 (best 0.60000002)\n",
            "In epoch 635, loss: 0.71697938, val acc: 0.20000000 (best 1.00000000), test acc: 0.20000000 (best 0.60000002)\n",
            "In epoch 640, loss: 0.69136047, val acc: 0.40000001 (best 1.00000000), test acc: 0.60000002 (best 0.60000002)\n",
            "In epoch 645, loss: 0.70799369, val acc: 0.60000002 (best 1.00000000), test acc: 0.00000000 (best 0.60000002)\n",
            "In epoch 650, loss: 0.66819632, val acc: 0.80000001 (best 1.00000000), test acc: 0.60000002 (best 0.60000002)\n",
            "In epoch 655, loss: 0.62669951, val acc: 0.80000001 (best 1.00000000), test acc: 0.60000002 (best 0.60000002)\n",
            "In epoch 660, loss: 0.71225512, val acc: 0.60000002 (best 1.00000000), test acc: 0.20000000 (best 0.60000002)\n",
            "In epoch 665, loss: 0.71507406, val acc: 0.80000001 (best 1.00000000), test acc: 0.60000002 (best 0.60000002)\n",
            "In epoch 670, loss: 0.68541181, val acc: 0.80000001 (best 1.00000000), test acc: 0.60000002 (best 0.60000002)\n",
            "In epoch 675, loss: 0.69149733, val acc: 0.60000002 (best 1.00000000), test acc: 0.20000000 (best 0.60000002)\n",
            "In epoch 680, loss: 0.73088843, val acc: 0.20000000 (best 1.00000000), test acc: 0.40000001 (best 0.60000002)\n",
            "In epoch 685, loss: 0.70393020, val acc: 0.40000001 (best 1.00000000), test acc: 0.60000002 (best 0.60000002)\n",
            "In epoch 690, loss: 0.69476098, val acc: 0.20000000 (best 1.00000000), test acc: 0.60000002 (best 0.60000002)\n",
            "In epoch 695, loss: 0.68425953, val acc: 0.80000001 (best 1.00000000), test acc: 0.60000002 (best 0.60000002)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "epWRJMr4dJ2k",
        "outputId": "2eab930d-5c94-422e-db25-294c9c7914e7"
      },
      "source": [
        "plt.plot(graph_classif_losses, 'r-')\n",
        "plt.title('Training Loss')\n",
        "plt.xlabel('Iterations')\n",
        "plt.ylabel('Loss')\n",
        "plt.show()"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "Wr1XBT15dJ2m",
        "outputId": "54469c81-c8ae-4e9b-c841-9fc293eb91bc"
      },
      "source": [
        "plt.figure(figsize=(15, 5))\n",
        "plt.plot(graph_classif_accs['train'], 'r-', label='Training Accuracy')\n",
        "plt.plot(graph_classif_accs['val'], 'g-', label='Val Accuracy')\n",
        "plt.plot(graph_classif_accs['test'], 'b-', label='Test Accuracy')\n",
        "plt.title('Accuracies')\n",
        "plt.xlabel('Iterations')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vwZufrV7dJ2m"
      },
      "source": [
        "all_preds_graph_classif = []\n",
        "for batched_graph_test, labels_test in test_dataloader:\n",
        "    batched_graph_test = batched_graph_test.to('cuda')\n",
        "    labels_test = labels_test.to('cuda')\n",
        "    logits_test = graph_classif_model(batched_graph_test, batched_graph_test.ndata['feat'].float())\n",
        "    pred_test = logits_test.argmax(1)\n",
        "    all_preds_graph_classif = all_preds_graph_classif + [*pred_test.cpu().numpy()]"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TBceJ_5ndJ2m",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "outputId": "c82634b9-0bcc-4d20-db50-248325ef19b0"
      },
      "source": [
        "plt.hist(all_preds_graph_classif)\n",
        "plt.title(\"Histogram of predicted labels \")\n",
        "plt.show()"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p1ku7ppJdJ2m",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "outputId": "0fc3a126-98c2-405c-d6b8-6ba97abdba27"
      },
      "source": [
        "label = graph_classif_dataset.labels[graph_classif_dataset.test_mask]\n",
        "plt.hist(label.cpu().numpy())\n",
        "plt.title(\"Histogram of Labels in the training set\")\n",
        "plt.show()"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rrhx6wT5dJ2m",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "fed605e5-ecc5-438d-cdad-493bfbb8c956"
      },
      "source": [
        "from sklearn.metrics import auc, roc_curve\n",
        "fpr, tpr, _ = roc_curve(label.cpu().numpy(), all_preds_graph_classif)\n",
        "roc_auc = auc(fpr, tpr)\n",
        "plt.figure()\n",
        "lw = 2\n",
        "plt.plot(fpr, tpr, color='darkorange',\n",
        "lw=lw, label='ROC curve (area = %0.2f)' % roc_auc)\n",
        "plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')\n",
        "plt.xlim([0.0, 1.0])\n",
        "plt.ylim([0.0, 1.05])\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('Receiver Operating Characteristic Curve on the entire dataset')\n",
        "plt.legend(loc=\"lower right\")\n",
        "plt.show()"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9CyBisMhqUEM"
      },
      "source": [
        "## Comparison\n",
        "As we can see above, theoritically both the architectures will work. The loss vs iteration curve and the ROC suggest that the graph classification approcach has failed to learn and it is preidicting the same output for all inputs. The main reason for this result is that:  \n",
        "1. I used very less training data\n",
        "2. The training data has very less number of features.\n",
        "3. The limited computational resource. The colab notebook's RAM was getting used up even before training for 1000 epochs. As we can see in the Node Classification approach, the GNN required atleast a 1000 epochs to stabilize.  \n",
        "\n",
        "Because of the above reasons, I think the Graph Classification approach gave inferior performance.\n",
        "\n",
        "### Possible Improvements\n",
        "- Neural Message Passing Models have shown good results on other particle jet classification tasks like [here](https://dl4physicalsciences.github.io/files/nips_dlps_2017_29.pdf). \n",
        "- Studying and applying better ways of encoding the train data in the form of graphs"
      ]
    }
  ]
}