{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "# Reference: https://github.com/tensorflow/docs/blob/master/site/en/guide/migrate/tensorboard.ipynb\n",
    "import tensorflow as tf\n",
    "import tempfile\n",
    "import numpy as np\n",
    "import datetime"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "mnist = tf.keras.datasets.mnist # The MNIST dataset.\n",
    "\n",
    "(x_train, y_train),(x_test, y_test) = mnist.load_data()\n",
    "x_train, x_test = x_train / 255.0, x_test / 255.0"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "def create_model():\n",
    "  return tf.keras.models.Sequential([\n",
    "    tf.keras.layers.Flatten(input_shape=(28, 28)),\n",
    "    tf.keras.layers.Dense(512, activation='relu'),\n",
    "    tf.keras.layers.Dropout(0.2),\n",
    "    tf.keras.layers.Dense(10, activation='softmax')\n",
    "  ])\n",
    "\n",
    "model = create_model()\n",
    "model.compile(optimizer='adam',\n",
    "              loss='sparse_categorical_crossentropy',\n",
    "              metrics=['accuracy'],\n",
    "              steps_per_execution=10)\n",
    "\n",
    "log_dir = 'tflogs'\n",
    "tensorboard_callback = tf.keras.callbacks.TensorBoard(\n",
    "  log_dir=log_dir,\n",
    "  histogram_freq=1) # Enable histogram computation with each epoch.\n",
    "\n",
    "model.fit(x=x_train,\n",
    "          y=y_train,\n",
    "          epochs=10,\n",
    "          validation_data=(x_test, y_test),\n",
    "          callbacks=[tensorboard_callback])"
   ],
   "outputs": [],
   "metadata": {}
  }
 ],
 "metadata": {
  "orig_nbformat": 4,
  "language_info": {
   "name": "python"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}