tensorboard.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "tensorboard.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyPZCTVVPNyn+Yzg8OHaUGAI",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/dlguswn3659/2020_Image_Lab/blob/master/tensorboard.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cEuldeVtZP9k",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#  LOG_DIR = 'drive/data/tb_logs'\n",
        "\t\n",
        "# import os\n",
        "# if not os.path.exists(LOG_DIR):\n",
        "#   os.makedirs(LOG_DIR)\n",
        "\t  \n",
        "# get_ipython().system_raw(\n",
        "#     'tensorboard --logdir {} --host 0.0.0.0 --port 6006 &'\n",
        "#     .format(LOG_DIR))\n",
        "\t\n",
        "# get_ipython().system_raw('./ngrok http 6006 &')\n",
        "\t\n",
        "# !curl -s http://localhost:4040/api/tunnels | python3 -c \\\n",
        "#     \"import sys, json; print(json.load(sys.stdin)['tunnels'][0]['public_url'])\""
      ],
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RiugCA1nW7e_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# imports\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "import torch\n",
        "import torchvision\n",
        "import torchvision.transforms as transforms\n",
        "\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torch.optim as optim\n",
        "\n",
        "# transforms\n",
        "transform = transforms.Compose(\n",
        "    [transforms.ToTensor(),\n",
        "    transforms.Normalize((0.5,), (0.5,))])\n",
        "\n",
        "# datasets\n",
        "trainset = torchvision.datasets.FashionMNIST('./data',\n",
        "    download=True,\n",
        "    train=True,\n",
        "    transform=transform)\n",
        "testset = torchvision.datasets.FashionMNIST('./data',\n",
        "    download=True,\n",
        "    train=False,\n",
        "    transform=transform)\n",
        "\n",
        "# dataloaders\n",
        "trainloader = torch.utils.data.DataLoader(trainset, batch_size=4,\n",
        "                                        shuffle=True, num_workers=2)\n",
        "\n",
        "\n",
        "testloader = torch.utils.data.DataLoader(testset, batch_size=4,\n",
        "                                        shuffle=False, num_workers=2)\n",
        "\n",
        "# constant for classes\n",
        "classes = ('T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',\n",
        "        'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle Boot')\n",
        "\n",
        "# helper function to show an image\n",
        "# (used in the `plot_classes_preds` function below)\n",
        "def matplotlib_imshow(img, one_channel=False):\n",
        "    if one_channel:\n",
        "        img = img.mean(dim=0)\n",
        "    img = img / 2 + 0.5     # unnormalize\n",
        "    npimg = img.numpy()\n",
        "    if one_channel:\n",
        "        plt.imshow(npimg, cmap=\"Greys\")\n",
        "    else:\n",
        "        plt.imshow(np.transpose(npimg, (1, 2, 0)))"
      ],
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rHV8ZkIwXB2K",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class Net(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(Net, self).__init__()\n",
        "        self.conv1 = nn.Conv2d(1, 6, 5)\n",
        "        self.pool = nn.MaxPool2d(2, 2)\n",
        "        self.conv2 = nn.Conv2d(6, 16, 5)\n",
        "        self.fc1 = nn.Linear(16 * 4 * 4, 120)\n",
        "        self.fc2 = nn.Linear(120, 84)\n",
        "        self.fc3 = nn.Linear(84, 10)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.pool(F.relu(self.conv1(x)))\n",
        "        x = self.pool(F.relu(self.conv2(x)))\n",
        "        x = x.view(-1, 16 * 4 * 4)\n",
        "        x = F.relu(self.fc1(x))\n",
        "        x = F.relu(self.fc2(x))\n",
        "        x = self.fc3(x)\n",
        "        return x\n",
        "\n",
        "\n",
        "net = Net()"
      ],
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LfT1m0h6XDv5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "criterion = nn.CrossEntropyLoss()\n",
        "optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)"
      ],
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cN-tw7U8XFJS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from torch.utils.tensorboard import SummaryWriter\n",
        "\n",
        "# default `log_dir` is \"runs\" - we'll be more specific here\n",
        "writer = SummaryWriter('runs/fashion_mnist_experiment_1')"
      ],
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UpnWjSV-XGdj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 138
        },
        "outputId": "b336b034-19c9-4b8b-cb88-9801931cc3d8"
      },
      "source": [
        "# get some random training images\n",
        "dataiter = iter(trainloader)\n",
        "images, labels = dataiter.next()\n",
        "\n",
        "# create grid of images\n",
        "img_grid = torchvision.utils.make_grid(images)\n",
        "\n",
        "# show images\n",
        "matplotlib_imshow(img_grid, one_channel=True)\n",
        "\n",
        "# write to tensorboard\n",
        "writer.add_image('four_fashion_mnist_images', img_grid)"
      ],
      "execution_count": 59,
      "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": "nzcRSmnjabi8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# from keras.callbacks import TensorBoard\n",
        "\n",
        "# batch_size = 4\n",
        "\n",
        "# tbCallBack = TensorBoard(log_dir=LOG_DIR, \n",
        "#                            histogram_freq=1,\n",
        "#                            write_graph=True,\n",
        "#                            write_grads=True,\n",
        "#                            batch_size=batch_size,\n",
        "#                            write_images=True)"
      ],
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fm85EHvGbtye",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 120
        },
        "outputId": "162f9726-aaee-4c02-f6bf-f791b988f9c2"
      },
      "source": [
        "!tensorboard --logdir=runs"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2020-07-05 19:33:15.727504: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n",
            "Serving TensorBoard on localhost; to expose to the network, use a proxy or pass --bind_all\n",
            "TensorBoard 2.2.2 at http://localhost:6006/ (Press CTRL+C to quit)\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}