tensorflow_template_explained.ipynb

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  "metadata": {
    "colab": {
      "name": "tensorflow_template.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "elH58gbhWAmn"
      },
      "source": [
        "# Tensorflow Template Explained"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8p3Tbx8cWEFA"
      },
      "source": [
        "# Load the TensorBoard notebook extension\n",
        "%load_ext tensorboard"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9GtR_cTTkf9G"
      },
      "source": [
        "Import TensorFlow, datetime, and os:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mVtYvbbIWRkV"
      },
      "source": [
        "import tensorflow as tf\n",
        "import datetime, os"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z8b82G7YksOS"
      },
      "source": [
        "# TODO: import data set and format it\n",
        "\n",
        "fashion_mnist = tf.keras.datasets.fashion_mnist\n",
        "\n",
        "(x_train, y_train),(x_test, y_test) = fashion_mnist.load_data()\n",
        "x_train, x_test = x_train / 255.0, x_test / 255.0"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lpUO9HqUKP6z"
      },
      "source": [
        "# train model\n",
        "\n",
        "def train_model():\n",
        "  \n",
        "  model = create_model()\n",
        "  model.compile(optimizer='adam',\n",
        "                loss='sparse_categorical_crossentropy',\n",
        "                metrics=['accuracy'])\n",
        "\n",
        "  logdir = os.path.join(\"logs\", datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\"))\n",
        "  tensorboard_callback = tf.keras.callbacks.TensorBoard(logdir, histogram_freq=1)\n",
        "\n",
        "  model.fit(x=x_train, \n",
        "            y=y_train, \n",
        "            epochs=5, \n",
        "            validation_data=(x_test, y_test), \n",
        "            callbacks=[tensorboard_callback])\n",
        "\n",
        "train_model()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KBHp6M_zgjp4"
      },
      "source": [
        "# Start TensorBoard within the notebook using *magics* (https://ipython.readthedocs.io/en/stable/interactive/magics.html) \n",
        "# and watch it updated every 30 seconds or hit the refresh button at the top right:\n",
        "%tensorboard --logdir logs"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ko9qeSQHLrEh"
      },
      "source": [
        "# use the following to specify which notebook to use\n",
        "# from tensorboard import notebook\n",
        "# notebook.list() # View open TensorBoard instances\n",
        "\n",
        "# Control TensorBoard display. If no port is provided, \n",
        "# the most recently launched TensorBoard is used\n",
        "# notebook.display(port=6006, height=1000) "
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}