13_loading_and_preprocessing_data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Chapter 13 – Loading and Preprocessing Data with TensorFlow**\n",
    "\n",
    "_This notebook contains all the sample code and solutions to the exercises in chapter 13._"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, let's import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures. We also check that Python 3.5 or later is installed (although Python 2.x may work, it is deprecated so we strongly recommend you use Python 3 instead), as well as Scikit-Learn ≥0.20 and TensorFlow ≥2.0-preview."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Python ≥3.5 is required\n",
    "import sys\n",
    "assert sys.version_info >= (3, 5)\n",
    "\n",
    "# Scikit-Learn ≥0.20 is required\n",
    "import sklearn\n",
    "assert sklearn.__version__ >= \"0.20\"\n",
    "\n",
    "# TensorFlow ≥2.0-preview is required\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "assert tf.__version__ >= \"2.0\"\n",
    "\n",
    "# Common imports\n",
    "import numpy as np\n",
    "import os\n",
    "\n",
    "# to make this notebook's output stable across runs\n",
    "np.random.seed(42)\n",
    "\n",
    "# To plot pretty figures\n",
    "%matplotlib inline\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "mpl.rc('axes', labelsize=14)\n",
    "mpl.rc('xtick', labelsize=12)\n",
    "mpl.rc('ytick', labelsize=12)\n",
    "\n",
    "# Where to save the figures\n",
    "PROJECT_ROOT_DIR = \".\"\n",
    "CHAPTER_ID = \"data\"\n",
    "IMAGES_PATH = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID)\n",
    "os.makedirs(IMAGES_PATH, exist_ok=True)\n",
    "\n",
    "def save_fig(fig_id, tight_layout=True, fig_extension=\"png\", resolution=300):\n",
    "    path = os.path.join(IMAGES_PATH, fig_id + \".\" + fig_extension)\n",
    "    print(\"Saving figure\", fig_id)\n",
    "    if tight_layout:\n",
    "        plt.tight_layout()\n",
    "    plt.savefig(path, format=fig_extension, dpi=resolution)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<TensorSliceDataset shapes: (), types: tf.int32>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = tf.range(10)\n",
    "dataset = tf.data.Dataset.from_tensor_slices(X)\n",
    "dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Equivalently:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = tf.data.Dataset.range(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor(0, shape=(), dtype=int64)\n",
      "tf.Tensor(1, shape=(), dtype=int64)\n",
      "tf.Tensor(2, shape=(), dtype=int64)\n",
      "tf.Tensor(3, shape=(), dtype=int64)\n",
      "tf.Tensor(4, shape=(), dtype=int64)\n",
      "tf.Tensor(5, shape=(), dtype=int64)\n",
      "tf.Tensor(6, shape=(), dtype=int64)\n",
      "tf.Tensor(7, shape=(), dtype=int64)\n",
      "tf.Tensor(8, shape=(), dtype=int64)\n",
      "tf.Tensor(9, shape=(), dtype=int64)\n"
     ]
    }
   ],
   "source": [
    "for item in dataset:\n",
    "    print(item)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": [
     "raises-exception"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor([0 1 2 3 4 5 6], shape=(7,), dtype=int64)\n",
      "tf.Tensor([7 8 9 0 1 2 3], shape=(7,), dtype=int64)\n",
      "tf.Tensor([4 5 6 7 8 9 0], shape=(7,), dtype=int64)\n",
      "tf.Tensor([1 2 3 4 5 6 7], shape=(7,), dtype=int64)\n",
      "tf.Tensor([8 9], shape=(2,), dtype=int64)\n"
     ]
    }
   ],
   "source": [
    "dataset = dataset.repeat(3).batch(7)\n",
    "for item in dataset:\n",
    "    print(item)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = dataset.map(lambda x: x * 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor([ 0  2  4  6  8 10 12], shape=(7,), dtype=int64)\n",
      "tf.Tensor([14 16 18  0  2  4  6], shape=(7,), dtype=int64)\n",
      "tf.Tensor([ 8 10 12 14 16 18  0], shape=(7,), dtype=int64)\n",
      "tf.Tensor([ 2  4  6  8 10 12 14], shape=(7,), dtype=int64)\n",
      "tf.Tensor([16 18], shape=(2,), dtype=int64)\n"
     ]
    }
   ],
   "source": [
    "for item in dataset:\n",
    "    print(item)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = dataset.apply(tf.data.experimental.unbatch())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = dataset.filter(lambda x: x < 10)  # keep only items < 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor(0, shape=(), dtype=int64)\n",
      "tf.Tensor(2, shape=(), dtype=int64)\n",
      "tf.Tensor(4, shape=(), dtype=int64)\n"
     ]
    }
   ],
   "source": [
    "for item in dataset.take(3):\n",
    "    print(item)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor([0 3 4 2 1 5 8], shape=(7,), dtype=int64)\n",
      "tf.Tensor([6 9 7 2 3 1 4], shape=(7,), dtype=int64)\n",
      "tf.Tensor([6 0 7 9 0 1 2], shape=(7,), dtype=int64)\n",
      "tf.Tensor([8 4 5 5 3 8 9], shape=(7,), dtype=int64)\n",
      "tf.Tensor([7 6], shape=(2,), dtype=int64)\n"
     ]
    }
   ],
   "source": [
    "dataset = tf.data.Dataset.range(10).repeat(3)\n",
    "dataset = dataset.shuffle(buffer_size=3, seed=42).batch(7)\n",
    "for item in dataset:\n",
    "    print(item)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Split the California dataset to multiple CSV files"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's start by loading and preparing the California housing dataset. We first load it, then split it into a training set, a validation set and a test set, and finally we scale it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import fetch_california_housing\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "housing = fetch_california_housing()\n",
    "X_train_full, X_test, y_train_full, y_test = train_test_split(\n",
    "    housing.data, housing.target.reshape(-1, 1), random_state=42)\n",
    "X_train, X_valid, y_train, y_valid = train_test_split(\n",
    "    X_train_full, y_train_full, random_state=42)\n",
    "\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(X_train)\n",
    "X_mean = scaler.mean_\n",
    "X_std = scaler.scale_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For a very large dataset that does not fit in memory, you will typically want to split it into many files first, then have TensorFlow read these files in parallel. To demonstrate this, let's start by splitting the housing dataset and save it to 20 CSV files:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def save_to_multiple_csv_files(data, name_prefix, header=None, n_parts=10):\n",
    "    housing_dir = os.path.join(\"datasets\", \"housing\")\n",
    "    os.makedirs(housing_dir, exist_ok=True)\n",
    "    path_format = os.path.join(housing_dir, \"my_{}_{:02d}.csv\")\n",
    "\n",
    "    filepaths = []\n",
    "    m = len(data)\n",
    "    for file_idx, row_indices in enumerate(np.array_split(np.arange(m), n_parts)):\n",
    "        part_csv = path_format.format(name_prefix, file_idx)\n",
    "        filepaths.append(part_csv)\n",
    "        with open(part_csv, \"wt\", encoding=\"utf-8\") as f:\n",
    "            if header is not None:\n",
    "                f.write(header)\n",
    "                f.write(\"\\n\")\n",
    "            for row_idx in row_indices:\n",
    "                f.write(\",\".join([repr(col) for col in data[row_idx]]))\n",
    "                f.write(\"\\n\")\n",
    "    return filepaths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = np.c_[X_train, y_train]\n",
    "valid_data = np.c_[X_valid, y_valid]\n",
    "test_data = np.c_[X_test, y_test]\n",
    "header_cols = housing.feature_names + [\"MedianHouseValue\"]\n",
    "header = \",\".join(header_cols)\n",
    "\n",
    "train_filepaths = save_to_multiple_csv_files(train_data, \"train\", header, n_parts=20)\n",
    "valid_filepaths = save_to_multiple_csv_files(valid_data, \"valid\", header, n_parts=10)\n",
    "test_filepaths = save_to_multiple_csv_files(test_data, \"test\", header, n_parts=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Okay, now let's take a peek at the first few lines of one of these CSV files:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MedInc</th>\n",
       "      <th>HouseAge</th>\n",
       "      <th>AveRooms</th>\n",
       "      <th>AveBedrms</th>\n",
       "      <th>Population</th>\n",
       "      <th>AveOccup</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>MedianHouseValue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.5214</td>\n",
       "      <td>15.0</td>\n",
       "      <td>3.049945</td>\n",
       "      <td>1.106548</td>\n",
       "      <td>1447.0</td>\n",
       "      <td>1.605993</td>\n",
       "      <td>37.63</td>\n",
       "      <td>-122.43</td>\n",
       "      <td>1.442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.3275</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.490060</td>\n",
       "      <td>0.991054</td>\n",
       "      <td>3464.0</td>\n",
       "      <td>3.443340</td>\n",
       "      <td>33.69</td>\n",
       "      <td>-117.39</td>\n",
       "      <td>1.687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.1000</td>\n",
       "      <td>29.0</td>\n",
       "      <td>7.542373</td>\n",
       "      <td>1.591525</td>\n",
       "      <td>1328.0</td>\n",
       "      <td>2.250847</td>\n",
       "      <td>38.44</td>\n",
       "      <td>-122.98</td>\n",
       "      <td>1.621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.1736</td>\n",
       "      <td>12.0</td>\n",
       "      <td>6.289003</td>\n",
       "      <td>0.997442</td>\n",
       "      <td>1054.0</td>\n",
       "      <td>2.695652</td>\n",
       "      <td>33.55</td>\n",
       "      <td>-117.70</td>\n",
       "      <td>2.621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.0549</td>\n",
       "      <td>13.0</td>\n",
       "      <td>5.312457</td>\n",
       "      <td>1.085092</td>\n",
       "      <td>3297.0</td>\n",
       "      <td>2.244384</td>\n",
       "      <td>33.93</td>\n",
       "      <td>-116.93</td>\n",
       "      <td>0.956</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   MedInc  HouseAge  AveRooms  AveBedrms  Population  AveOccup  Latitude  \\\n",
       "0  3.5214      15.0  3.049945   1.106548      1447.0  1.605993     37.63   \n",
       "1  5.3275       5.0  6.490060   0.991054      3464.0  3.443340     33.69   \n",
       "2  3.1000      29.0  7.542373   1.591525      1328.0  2.250847     38.44   \n",
       "3  7.1736      12.0  6.289003   0.997442      1054.0  2.695652     33.55   \n",
       "4  2.0549      13.0  5.312457   1.085092      3297.0  2.244384     33.93   \n",
       "\n",
       "   Longitude  MedianHouseValue  \n",
       "0    -122.43             1.442  \n",
       "1    -117.39             1.687  \n",
       "2    -122.98             1.621  \n",
       "3    -117.70             2.621  \n",
       "4    -116.93             0.956  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "pd.read_csv(train_filepaths[0]).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or in text mode:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MedInc,HouseAge,AveRooms,AveBedrms,Population,AveOccup,Latitude,Longitude,MedianHouseValue\n",
      "3.5214,15.0,3.0499445061043287,1.106548279689234,1447.0,1.6059933407325193,37.63,-122.43,1.442\n",
      "5.3275,5.0,6.490059642147117,0.9910536779324056,3464.0,3.4433399602385686,33.69,-117.39,1.687\n",
      "3.1,29.0,7.5423728813559325,1.5915254237288134,1328.0,2.2508474576271187,38.44,-122.98,1.621\n",
      "7.1736,12.0,6.289002557544757,0.9974424552429667,1054.0,2.6956521739130435,33.55,-117.7,2.621\n"
     ]
    }
   ],
   "source": [
    "with open(train_filepaths[0]) as f:\n",
    "    for i in range(5):\n",
    "        print(f.readline(), end=\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['datasets/housing/my_train_00.csv',\n",
       " 'datasets/housing/my_train_01.csv',\n",
       " 'datasets/housing/my_train_02.csv',\n",
       " 'datasets/housing/my_train_03.csv',\n",
       " 'datasets/housing/my_train_04.csv',\n",
       " 'datasets/housing/my_train_05.csv',\n",
       " 'datasets/housing/my_train_06.csv',\n",
       " 'datasets/housing/my_train_07.csv',\n",
       " 'datasets/housing/my_train_08.csv',\n",
       " 'datasets/housing/my_train_09.csv',\n",
       " 'datasets/housing/my_train_10.csv',\n",
       " 'datasets/housing/my_train_11.csv',\n",
       " 'datasets/housing/my_train_12.csv',\n",
       " 'datasets/housing/my_train_13.csv',\n",
       " 'datasets/housing/my_train_14.csv',\n",
       " 'datasets/housing/my_train_15.csv',\n",
       " 'datasets/housing/my_train_16.csv',\n",
       " 'datasets/housing/my_train_17.csv',\n",
       " 'datasets/housing/my_train_18.csv',\n",
       " 'datasets/housing/my_train_19.csv']"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_filepaths"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building an Input Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "filepath_dataset = tf.data.Dataset.list_files(train_filepaths, seed=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor(b'datasets/housing/my_train_05.csv', shape=(), dtype=string)\n",
      "tf.Tensor(b'datasets/housing/my_train_16.csv', shape=(), dtype=string)\n",
      "tf.Tensor(b'datasets/housing/my_train_01.csv', shape=(), dtype=string)\n",
      "tf.Tensor(b'datasets/housing/my_train_17.csv', shape=(), dtype=string)\n",
      "tf.Tensor(b'datasets/housing/my_train_00.csv', shape=(), dtype=string)\n",
      "tf.Tensor(b'datasets/housing/my_train_14.csv', shape=(), dtype=string)\n",
      "tf.Tensor(b'datasets/housing/my_train_10.csv', shape=(), dtype=string)\n",
      "tf.Tensor(b'datasets/housing/my_train_02.csv', shape=(), dtype=string)\n",
      "tf.Tensor(b'datasets/housing/my_train_12.csv', shape=(), dtype=string)\n",
      "tf.Tensor(b'datasets/housing/my_train_19.csv', shape=(), dtype=string)\n",
      "tf.Tensor(b'datasets/housing/my_train_07.csv', shape=(), dtype=string)\n",
      "tf.Tensor(b'datasets/housing/my_train_09.csv', shape=(), dtype=string)\n",
      "tf.Tensor(b'datasets/housing/my_train_13.csv', shape=(), dtype=string)\n",
      "tf.Tensor(b'datasets/housing/my_train_15.csv', shape=(), dtype=string)\n",
      "tf.Tensor(b'datasets/housing/my_train_11.csv', shape=(), dtype=string)\n",
      "tf.Tensor(b'datasets/housing/my_train_18.csv', shape=(), dtype=string)\n",
      "tf.Tensor(b'datasets/housing/my_train_04.csv', shape=(), dtype=string)\n",
      "tf.Tensor(b'datasets/housing/my_train_06.csv', shape=(), dtype=string)\n",
      "tf.Tensor(b'datasets/housing/my_train_03.csv', shape=(), dtype=string)\n",
      "tf.Tensor(b'datasets/housing/my_train_08.csv', shape=(), dtype=string)\n"
     ]
    }
   ],
   "source": [
    "for filepath in filepath_dataset:\n",
    "    print(filepath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_readers = 5\n",
    "dataset = filepath_dataset.interleave(\n",
    "    lambda filepath: tf.data.TextLineDataset(filepath).skip(1),\n",
    "    cycle_length=n_readers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "b'4.2083,44.0,5.323204419889502,0.9171270718232044,846.0,2.3370165745856353,37.47,-122.2,2.782'\n",
      "b'4.1812,52.0,5.701388888888889,0.9965277777777778,692.0,2.4027777777777777,33.73,-118.31,3.215'\n",
      "b'3.6875,44.0,4.524475524475524,0.993006993006993,457.0,3.195804195804196,34.04,-118.15,1.625'\n",
      "b'3.3456,37.0,4.514084507042254,0.9084507042253521,458.0,3.2253521126760565,36.67,-121.7,2.526'\n",
      "b'3.5214,15.0,3.0499445061043287,1.106548279689234,1447.0,1.6059933407325193,37.63,-122.43,1.442'\n"
     ]
    }
   ],
   "source": [
    "for line in dataset.take(5):\n",
    "    print(line.numpy())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that field 4 is interpreted as a string."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<tf.Tensor: id=245, shape=(), dtype=int32, numpy=1>,\n",
       " <tf.Tensor: id=246, shape=(), dtype=float32, numpy=2.0>,\n",
       " <tf.Tensor: id=247, shape=(), dtype=float64, numpy=3.0>,\n",
       " <tf.Tensor: id=248, shape=(), dtype=string, numpy=b'4'>,\n",
       " <tf.Tensor: id=249, shape=(), dtype=float32, numpy=5.0>]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "record_defaults=[0, np.nan, tf.constant(np.nan, dtype=tf.float64), \"Hello\", tf.constant([])]\n",
    "parsed_fields = tf.io.decode_csv('1,2,3,4,5', record_defaults)\n",
    "parsed_fields"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that all missing fields are replaced with their default value, when provided:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<tf.Tensor: id=259, shape=(), dtype=int32, numpy=0>,\n",
       " <tf.Tensor: id=260, shape=(), dtype=float32, numpy=nan>,\n",
       " <tf.Tensor: id=261, shape=(), dtype=float64, numpy=nan>,\n",
       " <tf.Tensor: id=262, shape=(), dtype=string, numpy=b'Hello'>,\n",
       " <tf.Tensor: id=263, shape=(), dtype=float32, numpy=5.0>]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parsed_fields = tf.io.decode_csv(',,,,5', record_defaults)\n",
    "parsed_fields"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The 5th field is compulsory (since we provided `tf.constant([])` as the \"default value\"), so we get an exception if we do not provide it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Field 4 is required but missing in record 0! [Op:DecodeCSV]\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    parsed_fields = tf.io.decode_csv(',,,,', record_defaults)\n",
    "except tf.errors.InvalidArgumentError as ex:\n",
    "    print(ex)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The number of fields should match exactly the number of fields in the `record_defaults`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Expect 5 fields but have 7 in record 0 [Op:DecodeCSV]\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    parsed_fields = tf.io.decode_csv('1,2,3,4,5,6,7', record_defaults)\n",
    "except tf.errors.InvalidArgumentError as ex:\n",
    "    print(ex)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "n_inputs = 8 # X_train.shape[-1]\n",
    "\n",
    "@tf.function\n",
    "def preprocess(line):\n",
    "    defs = [0.] * n_inputs + [tf.constant([], dtype=tf.float32)]\n",
    "    fields = tf.io.decode_csv(line, record_defaults=defs)\n",
    "    x = tf.stack(fields[:-1])\n",
    "    y = tf.stack(fields[-1:])\n",
    "    return (x - X_mean) / X_std, y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<tf.Tensor: id=307, shape=(8,), dtype=float32, numpy=\n",
       " array([ 0.16579157,  1.216324  , -0.05204565, -0.39215982, -0.5277444 ,\n",
       "        -0.2633488 ,  0.8543046 , -1.3072058 ], dtype=float32)>,\n",
       " <tf.Tensor: id=308, shape=(1,), dtype=float32, numpy=array([2.782], dtype=float32)>)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preprocess(b'4.2083,44.0,5.3232,0.9171,846.0,2.3370,37.47,-122.2,2.782')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def csv_reader_dataset(filepaths, repeat=1, n_readers=5,\n",
    "                       n_read_threads=None, shuffle_buffer_size=10000,\n",
    "                       n_parse_threads=5, batch_size=32):\n",
    "    dataset = tf.data.Dataset.list_files(filepaths).repeat(repeat)\n",
    "    dataset = dataset.interleave(\n",
    "        lambda filepath: tf.data.TextLineDataset(filepath).skip(1),\n",
    "        cycle_length=n_readers, num_parallel_calls=n_read_threads)\n",
    "    dataset = dataset.shuffle(shuffle_buffer_size)\n",
    "    dataset = dataset.map(preprocess, num_parallel_calls=n_parse_threads)\n",
    "    dataset = dataset.batch(batch_size)\n",
    "    return dataset.prefetch(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X = tf.Tensor(\n",
      "[[-1.1309323   0.5834586  -1.1141092  -0.0205977   0.4861637   0.05622157\n",
      "  -0.72447133  0.63188833]\n",
      " [ 1.9003258  -0.998705    0.6331733  -0.03226789  0.05084941 -0.28879014\n",
      "   1.0229574  -1.2872185 ]\n",
      " [-0.08566543 -1.6315705   0.04572354 -0.16350564  0.18226504  0.09547261\n",
      "  -0.92123175  0.65187943]], shape=(3, 8), dtype=float32)\n",
      "y = tf.Tensor(\n",
      "[[2.219]\n",
      " [3.702]\n",
      " [2.875]], shape=(3, 1), dtype=float32)\n",
      "\n",
      "X = tf.Tensor(\n",
      "[[ 0.92681414 -1.0778131   0.194918   -0.2745752  -0.02215928  0.13869788\n",
      "   1.2525111  -1.5520943 ]\n",
      " [ 0.12441459 -2.0271113  -0.0920579  -0.17962678 -0.38263968  0.11107364\n",
      "  -1.3007004   1.9912548 ]\n",
      " [-0.10782045  1.8491895  -0.01604682  0.04678809 -0.33609664 -0.2658364\n",
      "   0.9807942  -1.4471437 ]], shape=(3, 8), dtype=float32)\n",
      "y = tf.Tensor(\n",
      "[[1.724]\n",
      " [0.832]\n",
      " [3.118]], shape=(3, 1), dtype=float32)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "train_set = csv_reader_dataset(train_filepaths, batch_size=3)\n",
    "for X_batch, y_batch in train_set.take(2):\n",
    "    print(\"X =\", X_batch)\n",
    "    print(\"y =\", y_batch)\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_set = csv_reader_dataset(train_filepaths, repeat=None)\n",
    "valid_set = csv_reader_dataset(valid_filepaths)\n",
    "test_set = csv_reader_dataset(test_filepaths)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = keras.models.Sequential([\n",
    "    keras.layers.Dense(30, activation=\"relu\", input_shape=X_train.shape[1:]),\n",
    "    keras.layers.Dense(1),\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss=\"mse\", optimizer=keras.optimizers.SGD(lr=1e-3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "362/362 [==============================] - 1s 3ms/step - loss: 2.4158 - val_loss: 0.9218\n",
      "Epoch 2/10\n",
      "362/362 [==============================] - 1s 2ms/step - loss: 0.7571 - val_loss: 0.6992\n",
      "Epoch 3/10\n",
      "362/362 [==============================] - 1s 2ms/step - loss: 0.6767 - val_loss: 0.6532\n",
      "Epoch 4/10\n",
      "362/362 [==============================] - 1s 2ms/step - loss: 0.6406 - val_loss: 0.6751\n",
      "Epoch 5/10\n",
      "362/362 [==============================] - 1s 2ms/step - loss: 0.6012 - val_loss: 0.5904\n",
      "Epoch 6/10\n",
      "362/362 [==============================] - 1s 2ms/step - loss: 0.5792 - val_loss: 0.6392\n",
      "Epoch 7/10\n",
      "362/362 [==============================] - 1s 2ms/step - loss: 0.5531 - val_loss: 0.5686\n",
      "Epoch 8/10\n",
      "362/362 [==============================] - 1s 2ms/step - loss: 0.5050 - val_loss: 0.5086\n",
      "Epoch 9/10\n",
      "362/362 [==============================] - 1s 2ms/step - loss: 0.5039 - val_loss: 0.4825\n",
      "Epoch 10/10\n",
      "362/362 [==============================] - 1s 2ms/step - loss: 0.4930 - val_loss: 0.4761\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x131194e48>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batch_size = 32\n",
    "model.fit(train_set, steps_per_epoch=len(X_train) // batch_size, epochs=10,\n",
    "          validation_data=valid_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "161/161 [==============================] - 0s 1ms/step - loss: 0.4804\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.4803512151937307"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(test_set, steps=len(X_test) // batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2.5751495],\n",
       "       [2.1568842],\n",
       "       [2.019339 ],\n",
       "       ...,\n",
       "       [3.553556 ],\n",
       "       [2.2041245],\n",
       "       [2.366016 ]], dtype=float32)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_set = test_set.map(lambda X, y: X) # we could instead just pass test_set, Keras would ignore the labels\n",
    "X_new = X_test\n",
    "model.predict(new_set, steps=len(X_new) // batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Global step 1810/1810"
     ]
    }
   ],
   "source": [
    "optimizer = keras.optimizers.Nadam(lr=0.01)\n",
    "loss_fn = keras.losses.mean_squared_error\n",
    "\n",
    "n_epochs = 5\n",
    "batch_size = 32\n",
    "n_steps_per_epoch = len(X_train) // batch_size\n",
    "total_steps = n_epochs * n_steps_per_epoch\n",
    "global_step = 0\n",
    "for X_batch, y_batch in train_set.take(total_steps):\n",
    "    global_step += 1\n",
    "    print(\"\\rGlobal step {}/{}\".format(global_step, total_steps), end=\"\")\n",
    "    with tf.GradientTape() as tape:\n",
    "        y_pred = model(X_batch)\n",
    "        main_loss = tf.reduce_mean(loss_fn(y_batch, y_pred))\n",
    "        loss = tf.add_n([main_loss] + model.losses)\n",
    "    gradients = tape.gradient(loss, model.trainable_variables)\n",
    "    optimizer.apply_gradients(zip(gradients, model.trainable_variables))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer = keras.optimizers.Nadam(lr=0.01)\n",
    "loss_fn = keras.losses.mean_squared_error\n",
    "\n",
    "@tf.function\n",
    "def train(model, n_epochs, batch_size=32,\n",
    "          n_readers=5, n_read_threads=5, shuffle_buffer_size=10000, n_parse_threads=5):\n",
    "    train_set = csv_reader_dataset(train_filepaths, repeat=n_epochs, n_readers=n_readers,\n",
    "                       n_read_threads=n_read_threads, shuffle_buffer_size=shuffle_buffer_size,\n",
    "                       n_parse_threads=n_parse_threads, batch_size=batch_size)\n",
    "    for X_batch, y_batch in train_set:\n",
    "        with tf.GradientTape() as tape:\n",
    "            y_pred = model(X_batch)\n",
    "            main_loss = tf.reduce_mean(loss_fn(y_batch, y_pred))\n",
    "            loss = tf.add_n([main_loss] + model.losses)\n",
    "        gradients = tape.gradient(loss, model.trainable_variables)\n",
    "        optimizer.apply_gradients(zip(gradients, model.trainable_variables))\n",
    "\n",
    "train(model, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Global step 100 / 1810\n",
      "Global step 200 / 1810\n",
      "Global step 300 / 1810\n",
      "Global step 400 / 1810\n",
      "Global step 500 / 1810\n",
      "Global step 600 / 1810\n",
      "Global step 700 / 1810\n",
      "Global step 800 / 1810\n",
      "Global step 900 / 1810\n",
      "Global step 1000 / 1810\n",
      "Global step 1100 / 1810\n",
      "Global step 1200 / 1810\n",
      "Global step 1300 / 1810\n",
      "Global step 1400 / 1810\n",
      "Global step 1500 / 1810\n",
      "Global step 1600 / 1810\n",
      "Global step 1700 / 1810\n",
      "Global step 1800 / 1810\n"
     ]
    }
   ],
   "source": [
    "optimizer = keras.optimizers.Nadam(lr=0.01)\n",
    "loss_fn = keras.losses.mean_squared_error\n",
    "\n",
    "@tf.function\n",
    "def train(model, n_epochs, batch_size=32,\n",
    "          n_readers=5, n_read_threads=5, shuffle_buffer_size=10000, n_parse_threads=5):\n",
    "    train_set = csv_reader_dataset(train_filepaths, repeat=n_epochs, n_readers=n_readers,\n",
    "                       n_read_threads=n_read_threads, shuffle_buffer_size=shuffle_buffer_size,\n",
    "                       n_parse_threads=n_parse_threads, batch_size=batch_size)\n",
    "    n_steps_per_epoch = len(X_train) // batch_size\n",
    "    total_steps = n_epochs * n_steps_per_epoch\n",
    "    global_step = 0\n",
    "    for X_batch, y_batch in train_set.take(total_steps):\n",
    "        global_step += 1\n",
    "        if tf.equal(global_step % 100, 0):\n",
    "            tf.print(\"\\rGlobal step\", global_step, \"/\", total_steps)\n",
    "        with tf.GradientTape() as tape:\n",
    "            y_pred = model(X_batch)\n",
    "            main_loss = tf.reduce_mean(loss_fn(y_batch, y_pred))\n",
    "            loss = tf.add_n([main_loss] + model.losses)\n",
    "        gradients = tape.gradient(loss, model.trainable_variables)\n",
    "        optimizer.apply_gradients(zip(gradients, model.trainable_variables))\n",
    "\n",
    "train(model, 5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is a short description of each method in the `Dataset` class:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "● apply()              Applies a transformation function to this dataset.\n",
      "● batch()              Combines consecutive elements of this dataset into batches.\n",
      "● cache()              Caches the elements in this dataset.\n",
      "● concatenate()        Creates a `Dataset` by concatenating given dataset with this dataset.\n",
      "● filter()             Filters this dataset according to `predicate`.\n",
      "● flat_map()           Maps `map_func` across this dataset and flattens the result.\n",
      "● from_generator()     Creates a `Dataset` whose elements are generated by `generator`.\n",
      "● from_tensor_slices() Creates a `Dataset` whose elements are slices of the given tensors.\n",
      "● from_tensors()       Creates a `Dataset` with a single element, comprising the given tensors.\n",
      "● interleave()         Maps `map_func` across this dataset, and interleaves the results.\n",
      "● list_files()         A dataset of all files matching one or more glob patterns.\n",
      "● map()                Maps `map_func` across the elements of this dataset.\n",
      "● options()            Returns the options for this dataset and its inputs.\n",
      "● padded_batch()       Combines consecutive elements of this dataset into padded batches.\n",
      "● prefetch()           Creates a `Dataset` that prefetches elements from this dataset.\n",
      "● range()              Creates a `Dataset` of a step-separated range of values.\n",
      "● reduce()             Reduces the input dataset to a single element.\n",
      "● repeat()             Repeats this dataset `count` times.\n",
      "● shard()              Creates a `Dataset` that includes only 1/`num_shards` of this dataset.\n",
      "● shuffle()            Randomly shuffles the elements of this dataset.\n",
      "● skip()               Creates a `Dataset` that skips `count` elements from this dataset.\n",
      "● take()               Creates a `Dataset` with at most `count` elements from this dataset.\n",
      "● window()             Combines input elements into a dataset of windows.\n",
      "● with_options()       Returns a new `tf.data.Dataset` with the given options set.\n",
      "● zip()                Creates a `Dataset` by zipping together the given datasets.\n"
     ]
    }
   ],
   "source": [
    "for m in dir(tf.data.Dataset):\n",
    "    if not (m.startswith(\"_\") or m.endswith(\"_\")):\n",
    "        func = getattr(tf.data.Dataset, m)\n",
    "        if hasattr(func, \"__doc__\"):\n",
    "            print(\"● {:21s}{}\".format(m + \"()\", func.__doc__.split(\"\\n\")[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The `TFRecord` binary format"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A TFRecord file is just a list of binary records. You can create one using a `tf.io.TFRecordWriter`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "with tf.io.TFRecordWriter(\"my_data.tfrecord\") as f:\n",
    "    f.write(b\"This is the first record\")\n",
    "    f.write(b\"And this is the second record\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And you can read it using a `tf.data.TFRecordDataset`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor(b'This is the first record', shape=(), dtype=string)\n",
      "tf.Tensor(b'And this is the second record', shape=(), dtype=string)\n"
     ]
    }
   ],
   "source": [
    "filepaths = [\"my_data.tfrecord\"]\n",
    "dataset = tf.data.TFRecordDataset(filepaths)\n",
    "for item in dataset:\n",
    "    print(item)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can read multiple TFRecord files with just one `TFRecordDataset`. By default it will read them one at a time, but if you set `num_parallel_reads=3`, it will read 3 at a time in parallel and interleave their records:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor(b'File 0 record 0', shape=(), dtype=string)\n",
      "tf.Tensor(b'File 1 record 0', shape=(), dtype=string)\n",
      "tf.Tensor(b'File 2 record 0', shape=(), dtype=string)\n",
      "tf.Tensor(b'File 0 record 1', shape=(), dtype=string)\n",
      "tf.Tensor(b'File 1 record 1', shape=(), dtype=string)\n",
      "tf.Tensor(b'File 2 record 1', shape=(), dtype=string)\n",
      "tf.Tensor(b'File 0 record 2', shape=(), dtype=string)\n",
      "tf.Tensor(b'File 1 record 2', shape=(), dtype=string)\n",
      "tf.Tensor(b'File 2 record 2', shape=(), dtype=string)\n",
      "tf.Tensor(b'File 3 record 0', shape=(), dtype=string)\n",
      "tf.Tensor(b'File 4 record 0', shape=(), dtype=string)\n",
      "tf.Tensor(b'File 3 record 1', shape=(), dtype=string)\n",
      "tf.Tensor(b'File 4 record 1', shape=(), dtype=string)\n",
      "tf.Tensor(b'File 3 record 2', shape=(), dtype=string)\n",
      "tf.Tensor(b'File 4 record 2', shape=(), dtype=string)\n"
     ]
    }
   ],
   "source": [
    "filepaths = [\"my_test_{}.tfrecord\".format(i) for i in range(5)]\n",
    "for i, filepath in enumerate(filepaths):\n",
    "    with tf.io.TFRecordWriter(filepath) as f:\n",
    "        for j in range(3):\n",
    "            f.write(\"File {} record {}\".format(i, j).encode(\"utf-8\"))\n",
    "\n",
    "dataset = tf.data.TFRecordDataset(filepaths, num_parallel_reads=3)\n",
    "for item in dataset:\n",
    "    print(item)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "options = tf.io.TFRecordOptions(compression_type=\"GZIP\")\n",
    "with tf.io.TFRecordWriter(\"my_compressed.tfrecord\", options) as f:\n",
    "    f.write(b\"This is the first record\")\n",
    "    f.write(b\"And this is the second record\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor(b'This is the first record', shape=(), dtype=string)\n",
      "tf.Tensor(b'And this is the second record', shape=(), dtype=string)\n"
     ]
    }
   ],
   "source": [
    "dataset = tf.data.TFRecordDataset([\"my_compressed.tfrecord\"],\n",
    "                                  compression_type=\"GZIP\")\n",
    "for item in dataset:\n",
    "    print(item)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A Brief Intro to Protocol Buffers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this section you need to [install protobuf](https://developers.google.com/protocol-buffers/docs/downloads). In general you will not have to do so when using TensorFlow, as it comes with functions to create and parse protocol buffers of type `tf.train.Example`, which are generally sufficient. However, in this section we will learn about protocol buffers by creating our own simple protobuf definition, so we need the protobuf compiler (`protoc`): we will use it to compile the protobuf definition to a Python module that we can then use in our code."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First let's write a simple protobuf definition:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting person.proto\n"
     ]
    }
   ],
   "source": [
    "%%writefile person.proto\n",
    "syntax = \"proto3\";\n",
    "message Person {\n",
    "  string name = 1;\n",
    "  int32 id = 2;\n",
    "  repeated string email = 3;\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And let's compile it (the `--descriptor_set_out` and `--include_imports` options are only required for the `tf.io.decode_proto()` example below):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "!protoc person.proto --python_out=. --descriptor_set_out=person.desc --include_imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "person.desc   person.proto  person_pb2.py\r\n"
     ]
    }
   ],
   "source": [
    "!ls person*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "name: \"Al\"\n",
      "id: 123\n",
      "email: \"a@b.com\"\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from person_pb2 import Person\n",
    "\n",
    "person = Person(name=\"Al\", id=123, email=[\"a@b.com\"])  # create a Person\n",
    "print(person)  # display the Person"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Al'"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "person.name  # read a field"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "person.name = \"Alice\"  # modify a field"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'a@b.com'"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "person.email[0]  # repeated fields can be accessed like arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "person.email.append(\"c@d.com\")  # add an email address"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b'\\n\\x05Alice\\x10{\\x1a\\x07a@b.com\\x1a\\x07c@d.com'"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = person.SerializeToString()  # serialize to a byte string\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "person2 = Person()  # create a new Person\n",
    "person2.ParseFromString(s)  # parse the byte string (27 bytes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "person == person2  # now they are equal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Custom protobuf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In rare cases, you may want to parse a custom protobuf (like the one we just created) in TensorFlow. For this you can use the `tf.io.decode_proto()` function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<tf.Tensor: id=12, shape=(1,), dtype=string, numpy=array([b'Alice'], dtype=object)>,\n",
       " <tf.Tensor: id=13, shape=(1,), dtype=int32, numpy=array([123], dtype=int32)>,\n",
       " <tf.Tensor: id=14, shape=(2,), dtype=string, numpy=array([b'a@b.com', b'c@d.com'], dtype=object)>]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "person_tf = tf.io.decode_proto(\n",
    "    bytes=s,\n",
    "    message_type=\"Person\",\n",
    "    field_names=[\"name\", \"id\", \"email\"],\n",
    "    output_types=[tf.string, tf.int32, tf.string],\n",
    "    descriptor_source=\"person.desc\")\n",
    "\n",
    "person_tf.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For more details, see the [`tf.io.decode_proto()`](https://www.tensorflow.org/api_docs/python/tf/io/decode_proto) documentation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TensorFlow Protobufs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is the definition of the tf.train.Example protobuf:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```proto\n",
    "syntax = \"proto3\";\n",
    "\n",
    "message BytesList { repeated bytes value = 1; }\n",
    "message FloatList { repeated float value = 1 [packed = true]; }\n",
    "message Int64List { repeated int64 value = 1 [packed = true]; }\n",
    "message Feature {\n",
    "    oneof kind {\n",
    "        BytesList bytes_list = 1;\n",
    "        FloatList float_list = 2;\n",
    "        Int64List int64_list = 3;\n",
    "    }\n",
    "};\n",
    "message Features { map<string, Feature> feature = 1; };\n",
    "message Example { Features features = 1; };\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "# WARNING: there's currently a bug preventing \"from tensorflow.train import X\"\n",
    "#          so we work around it by writing \"X = tf.train.X\"\n",
    "#from tensorflow.train import BytesList, FloatList, Int64List\n",
    "#from tensorflow.train import Feature, Features, Example\n",
    "BytesList = tf.train.BytesList\n",
    "FloatList = tf.train.FloatList\n",
    "Int64List = tf.train.Int64List\n",
    "Feature = tf.train.Feature\n",
    "Features = tf.train.Features\n",
    "Example = tf.train.Example\n",
    "\n",
    "person_example = Example(\n",
    "    features=Features(\n",
    "        feature={\n",
    "            \"name\": Feature(bytes_list=BytesList(value=[b\"Alice\"])),\n",
    "            \"id\": Feature(int64_list=Int64List(value=[123])),\n",
    "            \"emails\": Feature(bytes_list=BytesList(value=[b\"a@b.com\", b\"c@d.com\"]))\n",
    "        }))\n",
    "\n",
    "with tf.io.TFRecordWriter(\"my_contacts.tfrecord\") as f:\n",
    "    f.write(person_example.SerializeToString())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_description = {\n",
    "    \"name\": tf.io.FixedLenFeature([], tf.string, default_value=\"\"),\n",
    "    \"id\": tf.io.FixedLenFeature([], tf.int64, default_value=0),\n",
    "    \"emails\": tf.io.VarLenFeature(tf.string),\n",
    "}\n",
    "for serialized_example in tf.data.TFRecordDataset([\"my_contacts.tfrecord\"]):\n",
    "    parsed_example = tf.io.parse_single_example(serialized_example,\n",
    "                                                feature_description)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'emails': <tensorflow.python.framework.sparse_tensor.SparseTensor at 0x102a2acc0>,\n",
       " 'id': <tf.Tensor: id=535953, shape=(), dtype=int64, numpy=123>,\n",
       " 'name': <tf.Tensor: id=535954, shape=(), dtype=string, numpy=b'Alice'>}"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parsed_example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'emails': <tensorflow.python.framework.sparse_tensor.SparseTensor at 0x102a2acc0>,\n",
       " 'id': <tf.Tensor: id=535953, shape=(), dtype=int64, numpy=123>,\n",
       " 'name': <tf.Tensor: id=535954, shape=(), dtype=string, numpy=b'Alice'>}"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parsed_example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: id=535962, shape=(), dtype=string, numpy=b'a@b.com'>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parsed_example[\"emails\"].values[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: id=535965, shape=(2,), dtype=string, numpy=array([b'a@b.com', b'c@d.com'], dtype=object)>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.sparse.to_dense(parsed_example[\"emails\"], default_value=b\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: id=535951, shape=(2,), dtype=string, numpy=array([b'a@b.com', b'c@d.com'], dtype=object)>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parsed_example[\"emails\"].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Putting Images in TFRecords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.datasets import load_sample_images\n",
    "\n",
    "img = load_sample_images()[\"images\"][0]\n",
    "plt.imshow(img)\n",
    "plt.axis(\"off\")\n",
    "plt.title(\"Original Image\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = tf.io.encode_jpeg(img)\n",
    "example_with_image = Example(features=Features(feature={\n",
    "    \"image\": Feature(bytes_list=BytesList(value=[data.numpy()]))}))\n",
    "serialized_example = example_with_image.SerializeToString()\n",
    "# then save to TFRecord"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_description = { \"image\": tf.io.VarLenFeature(tf.string) }\n",
    "example_with_image = tf.io.parse_single_example(serialized_example, feature_description)\n",
    "decoded_img = tf.io.decode_jpeg(example_with_image[\"image\"].values[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or use `decode_image()` which supports BMP, GIF, JPEG and PNG formats:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "decoded_img = tf.io.decode_image(example_with_image[\"image\"].values[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(decoded_img)\n",
    "plt.title(\"Decoded Image\")\n",
    "plt.axis(\"off\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Putting Tensors and Sparse Tensors in TFRecords"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tensors can be serialized and parsed easily using `tf.io.serialize_tensor()` and `tf.io.parse_tensor()`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: id=536000, shape=(), dtype=string, numpy=b'\\x08\\x01\\x12\\x08\\x12\\x02\\x08\\x03\\x12\\x02\\x08\\x02\"\\x18\\x00\\x00\\x00\\x00\\x00\\x00\\x80?\\x00\\x00\\x00@\\x00\\x00@@\\x00\\x00\\x80@\\x00\\x00\\xa0@'>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t = tf.constant([[0., 1.], [2., 3.], [4., 5.]])\n",
    "s = tf.io.serialize_tensor(t)\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: id=536002, shape=(3, 2), dtype=float32, numpy=\n",
       "array([[0., 1.],\n",
       "       [2., 3.],\n",
       "       [4., 5.]], dtype=float32)>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.io.parse_tensor(s, out_type=tf.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: id=536004, shape=(3,), dtype=string, numpy=\n",
       "array([b'\\x08\\t\\x12\\x08\\x12\\x02\\x08\\x02\\x12\\x02\\x08\\x01\"\\x10\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x00\\x00\\x00\\x00',\n",
       "       b'\\x08\\x07\\x12\\x04\\x12\\x02\\x08\\x02\"\\x10\\x07\\x07a@b.comc@d.com',\n",
       "       b'\\x08\\t\\x12\\x04\\x12\\x02\\x08\\x01\"\\x08\\x02\\x00\\x00\\x00\\x00\\x00\\x00\\x00'],\n",
       "      dtype=object)>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "serialized_sparse = tf.io.serialize_sparse(parsed_example[\"emails\"])\n",
    "serialized_sparse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "value: \"\\010\\t\\022\\010\\022\\002\\010\\002\\022\\002\\010\\001\\\"\\020\\000\\000\\000\\000\\000\\000\\000\\000\\001\\000\\000\\000\\000\\000\\000\\000\"\n",
       "value: \"\\010\\007\\022\\004\\022\\002\\010\\002\\\"\\020\\007\\007a@b.comc@d.com\"\n",
       "value: \"\\010\\t\\022\\004\\022\\002\\010\\001\\\"\\010\\002\\000\\000\\000\\000\\000\\000\\000\""
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BytesList(value=serialized_sparse.numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = tf.data.TFRecordDataset([\"my_contacts.tfrecord\"]).batch(10)\n",
    "for serialized_examples in dataset:\n",
    "    parsed_examples = tf.io.parse_example(serialized_examples,\n",
    "                                          feature_description)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'image': <tensorflow.python.framework.sparse_tensor.SparseTensor at 0x129697b70>}"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parsed_examples"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Handling Sequential Data Using `SequenceExample`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```proto\n",
    "syntax = \"proto3\";\n",
    "\n",
    "message FeatureList { repeated Feature feature = 1; };\n",
    "message FeatureLists { map<string, FeatureList> feature_list = 1; };\n",
    "message SequenceExample {\n",
    "  Features context = 1;\n",
    "  FeatureLists feature_lists = 2;\n",
    "};\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "# WARNING: there's currently a bug preventing \"from tensorflow.train import X\"\n",
    "#          so we work around it by writing \"X = tf.train.X\"\n",
    "#from tensorflow.train import FeatureList, FeatureLists, SequenceExample\n",
    "FeatureList = tf.train.FeatureList\n",
    "FeatureLists = tf.train.FeatureLists\n",
    "SequenceExample = tf.train.SequenceExample\n",
    "\n",
    "context = Features(feature={\n",
    "    \"author_id\": Feature(int64_list=Int64List(value=[123])),\n",
    "    \"title\": Feature(bytes_list=BytesList(value=[b\"A\", b\"desert\", b\"place\", b\".\"])),\n",
    "    \"pub_date\": Feature(int64_list=Int64List(value=[1623, 12, 25]))\n",
    "})\n",
    "\n",
    "content = [[\"When\", \"shall\", \"we\", \"three\", \"meet\", \"again\", \"?\"],\n",
    "           [\"In\", \"thunder\", \",\", \"lightning\", \",\", \"or\", \"in\", \"rain\", \"?\"]]\n",
    "comments = [[\"When\", \"the\", \"hurlyburly\", \"'s\", \"done\", \".\"],\n",
    "            [\"When\", \"the\", \"battle\", \"'s\", \"lost\", \"and\", \"won\", \".\"]]\n",
    "\n",
    "def words_to_feature(words):\n",
    "    return Feature(bytes_list=BytesList(value=[word.encode(\"utf-8\")\n",
    "                                               for word in words]))\n",
    "\n",
    "content_features = [words_to_feature(sentence) for sentence in content]\n",
    "comments_features = [words_to_feature(comment) for comment in comments]\n",
    "            \n",
    "sequence_example = SequenceExample(\n",
    "    context=context,\n",
    "    feature_lists=FeatureLists(feature_list={\n",
    "        \"content\": FeatureList(feature=content_features),\n",
    "        \"comments\": FeatureList(feature=comments_features)\n",
    "    }))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "context {\n",
       "  feature {\n",
       "    key: \"author_id\"\n",
       "    value {\n",
       "      int64_list {\n",
       "        value: 123\n",
       "      }\n",
       "    }\n",
       "  }\n",
       "  feature {\n",
       "    key: \"pub_date\"\n",
       "    value {\n",
       "      int64_list {\n",
       "        value: 1623\n",
       "        value: 12\n",
       "        value: 25\n",
       "      }\n",
       "    }\n",
       "  }\n",
       "  feature {\n",
       "    key: \"title\"\n",
       "    value {\n",
       "      bytes_list {\n",
       "        value: \"A\"\n",
       "        value: \"desert\"\n",
       "        value: \"place\"\n",
       "        value: \".\"\n",
       "      }\n",
       "    }\n",
       "  }\n",
       "}\n",
       "feature_lists {\n",
       "  feature_list {\n",
       "    key: \"comments\"\n",
       "    value {\n",
       "      feature {\n",
       "        bytes_list {\n",
       "          value: \"When\"\n",
       "          value: \"the\"\n",
       "          value: \"hurlyburly\"\n",
       "          value: \"\\'s\"\n",
       "          value: \"done\"\n",
       "          value: \".\"\n",
       "        }\n",
       "      }\n",
       "      feature {\n",
       "        bytes_list {\n",
       "          value: \"When\"\n",
       "          value: \"the\"\n",
       "          value: \"battle\"\n",
       "          value: \"\\'s\"\n",
       "          value: \"lost\"\n",
       "          value: \"and\"\n",
       "          value: \"won\"\n",
       "          value: \".\"\n",
       "        }\n",
       "      }\n",
       "    }\n",
       "  }\n",
       "  feature_list {\n",
       "    key: \"content\"\n",
       "    value {\n",
       "      feature {\n",
       "        bytes_list {\n",
       "          value: \"When\"\n",
       "          value: \"shall\"\n",
       "          value: \"we\"\n",
       "          value: \"three\"\n",
       "          value: \"meet\"\n",
       "          value: \"again\"\n",
       "          value: \"?\"\n",
       "        }\n",
       "      }\n",
       "      feature {\n",
       "        bytes_list {\n",
       "          value: \"In\"\n",
       "          value: \"thunder\"\n",
       "          value: \",\"\n",
       "          value: \"lightning\"\n",
       "          value: \",\"\n",
       "          value: \"or\"\n",
       "          value: \"in\"\n",
       "          value: \"rain\"\n",
       "          value: \"?\"\n",
       "        }\n",
       "      }\n",
       "    }\n",
       "  }\n",
       "}"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sequence_example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "serialized_sequence_example = sequence_example.SerializeToString()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "context_feature_descriptions = {\n",
    "    \"author_id\": tf.io.FixedLenFeature([], tf.int64, default_value=0),\n",
    "    \"title\": tf.io.VarLenFeature(tf.string),\n",
    "    \"pub_date\": tf.io.FixedLenFeature([3], tf.int64, default_value=[0, 0, 0]),\n",
    "}\n",
    "sequence_feature_descriptions = {\n",
    "    \"content\": tf.io.VarLenFeature(tf.string),\n",
    "    \"comments\": tf.io.VarLenFeature(tf.string),\n",
    "}\n",
    "parsed_context, parsed_feature_lists = tf.io.parse_single_sequence_example(\n",
    "    serialized_sequence_example, context_feature_descriptions,\n",
    "    sequence_feature_descriptions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'title': <tensorflow.python.framework.sparse_tensor.SparseTensor at 0x134ff6c88>,\n",
       " 'author_id': <tf.Tensor: id=536050, shape=(), dtype=int64, numpy=123>,\n",
       " 'pub_date': <tf.Tensor: id=536051, shape=(3,), dtype=int64, numpy=array([1623,   12,   25])>}"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parsed_context"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: id=536048, shape=(4,), dtype=string, numpy=array([b'A', b'desert', b'place', b'.'], dtype=object)>"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parsed_context[\"title\"].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'comments': <tensorflow.python.framework.sparse_tensor.SparseTensor at 0x134ff6f98>,\n",
       " 'content': <tensorflow.python.framework.sparse_tensor.SparseTensor at 0x130e23390>}"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parsed_feature_lists"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<tf.RaggedTensor [[b'When', b'shall', b'we', b'three', b'meet', b'again', b'?'], [b'In', b'thunder', b',', b'lightning', b',', b'or', b'in', b'rain', b'?']]>\n"
     ]
    }
   ],
   "source": [
    "print(tf.RaggedTensor.from_sparse(parsed_feature_lists[\"content\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The Features API"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's use the variant of the California housing dataset that we used in Chapter 2, since it contains categorical features and missing values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import tarfile\n",
    "import urllib\n",
    "\n",
    "DOWNLOAD_ROOT = \"https://raw.githubusercontent.com/ageron/handson-ml2/master/\"\n",
    "HOUSING_PATH = os.path.join(\"datasets\", \"housing\")\n",
    "HOUSING_URL = DOWNLOAD_ROOT + \"datasets/housing/housing.tgz\"\n",
    "\n",
    "def fetch_housing_data(housing_url=HOUSING_URL, housing_path=HOUSING_PATH):\n",
    "    if not os.path.isdir(housing_path):\n",
    "        os.makedirs(housing_path)\n",
    "    tgz_path = os.path.join(housing_path, \"housing.tgz\")\n",
    "    urllib.request.urlretrieve(housing_url, tgz_path)\n",
    "    housing_tgz = tarfile.open(tgz_path)\n",
    "    housing_tgz.extractall(path=housing_path)\n",
    "    housing_tgz.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "fetch_housing_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "def load_housing_data(housing_path=HOUSING_PATH):\n",
    "    csv_path = os.path.join(housing_path, \"housing.csv\")\n",
    "    return pd.read_csv(csv_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "0    -122.23     37.88                41.0        880.0           129.0   \n",
       "1    -122.22     37.86                21.0       7099.0          1106.0   \n",
       "2    -122.24     37.85                52.0       1467.0           190.0   \n",
       "3    -122.25     37.85                52.0       1274.0           235.0   \n",
       "4    -122.25     37.85                52.0       1627.0           280.0   \n",
       "\n",
       "   population  households  median_income  median_house_value ocean_proximity  \n",
       "0       322.0       126.0         8.3252            452600.0        NEAR BAY  \n",
       "1      2401.0      1138.0         8.3014            358500.0        NEAR BAY  \n",
       "2       496.0       177.0         7.2574            352100.0        NEAR BAY  \n",
       "3       558.0       219.0         5.6431            341300.0        NEAR BAY  \n",
       "4       565.0       259.0         3.8462            342200.0        NEAR BAY  "
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing = load_housing_data()\n",
    "housing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_median_age = tf.feature_column.numeric_column(\"housing_median_age\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "age_mean, age_std = X_mean[1], X_std[1]  # The median age is column in 1\n",
    "housing_median_age = tf.feature_column.numeric_column(\n",
    "    \"housing_median_age\", normalizer_fn=lambda x: (x - age_mean) / age_std)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "median_income = tf.feature_column.numeric_column(\"median_income\")\n",
    "bucketized_income = tf.feature_column.bucketized_column(\n",
    "    median_income, boundaries=[1.5, 3., 4.5, 6.])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BucketizedColumn(source_column=NumericColumn(key='median_income', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=None), boundaries=(1.5, 3.0, 4.5, 6.0))"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bucketized_income"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "ocean_prox_vocab = ['<1H OCEAN', 'INLAND', 'ISLAND', 'NEAR BAY', 'NEAR OCEAN']\n",
    "ocean_proximity = tf.feature_column.categorical_column_with_vocabulary_list(\n",
    "    \"ocean_proximity\", ocean_prox_vocab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VocabularyListCategoricalColumn(key='ocean_proximity', vocabulary_list=('<1H OCEAN', 'INLAND', 'ISLAND', 'NEAR BAY', 'NEAR OCEAN'), dtype=tf.string, default_value=-1, num_oov_buckets=0)"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ocean_proximity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "HashedCategoricalColumn(key='city', hash_bucket_size=1000, dtype=tf.string)"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Just an example, it's not used later on\n",
    "city_hash = tf.feature_column.categorical_column_with_hash_bucket(\n",
    "    \"city\", hash_bucket_size=1000)\n",
    "city_hash"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "bucketized_age = tf.feature_column.bucketized_column(\n",
    "    housing_median_age, boundaries=[-1., -0.5, 0., 0.5, 1.]) # age was scaled\n",
    "age_and_ocean_proximity = tf.feature_column.crossed_column(\n",
    "    [bucketized_age, ocean_proximity], hash_bucket_size=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "latitude = tf.feature_column.numeric_column(\"latitude\")\n",
    "longitude = tf.feature_column.numeric_column(\"longitude\")\n",
    "bucketized_latitude = tf.feature_column.bucketized_column(\n",
    "    latitude, boundaries=list(np.linspace(32., 42., 20 - 1)))\n",
    "bucketized_longitude = tf.feature_column.bucketized_column(\n",
    "    longitude, boundaries=list(np.linspace(-125., -114., 20 - 1)))\n",
    "location = tf.feature_column.crossed_column(\n",
    "    [bucketized_latitude, bucketized_longitude], hash_bucket_size=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "ocean_proximity_one_hot = tf.feature_column.indicator_column(ocean_proximity)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "ocean_proximity_embed = tf.feature_column.embedding_column(ocean_proximity,\n",
    "                                                           dimension=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using Feature Columns for Parsing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "median_house_value = tf.feature_column.numeric_column(\"median_house_value\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'housing_median_age': FixedLenFeature(shape=(1,), dtype=tf.float32, default_value=None),\n",
       " 'median_house_value': FixedLenFeature(shape=(1,), dtype=tf.float32, default_value=None)}"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns = [housing_median_age, median_house_value]\n",
    "feature_descriptions = tf.feature_column.make_parse_example_spec(columns)\n",
    "feature_descriptions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "with tf.io.TFRecordWriter(\"my_data_with_features.tfrecords\") as f:\n",
    "    for x, y in zip(X_train[:, 1:2], y_train):\n",
    "        example = Example(features=Features(feature={\n",
    "            \"housing_median_age\": Feature(float_list=FloatList(value=[x])),\n",
    "            \"median_house_value\": Feature(float_list=FloatList(value=[y]))\n",
    "        }))\n",
    "        f.write(example.SerializeToString())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "def parse_examples(serialized_examples):\n",
    "    examples = tf.io.parse_example(serialized_examples, feature_descriptions)\n",
    "    targets = examples.pop(\"median_house_value\") # separate the targets\n",
    "    return examples, targets\n",
    "\n",
    "batch_size = 32\n",
    "dataset = tf.data.TFRecordDataset([\"my_data_with_features.tfrecords\"])\n",
    "dataset = dataset.repeat().shuffle(10000).batch(batch_size).map(parse_examples)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Warning**: the `DenseFeatures` layer currently does not work with the Functional API, see [TF issue #27416](https://github.com/tensorflow/tensorflow/issues/27416). Hopefully this will be resolved before the final release of TF 2.0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "362/362 [==============================] - 1s 3ms/step - loss: 5.0263 - accuracy: 0.0017\n",
      "Epoch 2/5\n",
      "362/362 [==============================] - 1s 2ms/step - loss: 2.1655 - accuracy: 0.0036\n",
      "Epoch 3/5\n",
      "362/362 [==============================] - 1s 2ms/step - loss: 1.5688 - accuracy: 0.0028\n",
      "Epoch 4/5\n",
      "362/362 [==============================] - 1s 2ms/step - loss: 1.3550 - accuracy: 0.0022\n",
      "Epoch 5/5\n",
      "362/362 [==============================] - 1s 2ms/step - loss: 1.3466 - accuracy: 0.0035\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x134ff5f98>"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns_without_target = columns[:-1]\n",
    "model = keras.models.Sequential([\n",
    "    keras.layers.DenseFeatures(feature_columns=columns_without_target),\n",
    "    keras.layers.Dense(1)\n",
    "])\n",
    "model.compile(loss=\"mse\",\n",
    "              optimizer=keras.optimizers.SGD(lr=1e-3),\n",
    "              metrics=[\"accuracy\"])\n",
    "model.fit(dataset, steps_per_epoch=len(X_train) // batch_size, epochs=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Logging before flag parsing goes to stderr.\n",
      "W0314 09:13:03.443593 140735783818112 deprecation.py:323] From /Users/ageron/.virtualenvs/tf2/lib/python3.6/site-packages/tensorflow/python/feature_column/feature_column_v2.py:3038: VocabularyListCategoricalColumn._num_buckets (from tensorflow.python.feature_column.feature_column_v2) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "The old _FeatureColumn APIs are being deprecated. Please use the new FeatureColumn APIs instead.\n",
      "W0314 09:13:03.451946 140735783818112 deprecation.py:323] From /Users/ageron/.virtualenvs/tf2/lib/python3.6/site-packages/tensorflow/python/ops/lookup_ops.py:1347: to_int64 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use `tf.cast` instead.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: id=551231, shape=(3, 7), dtype=float32, numpy=\n",
       "array([[ 0.        ,  0.        ,  1.        ,  0.        ,  0.        ,\n",
       "         0.30818242, -0.40305588],\n",
       "       [ 0.        ,  0.        ,  0.        ,  0.        ,  1.        ,\n",
       "        -0.7438171 ,  0.19444346],\n",
       "       [ 1.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
       "        -0.7438171 ,  0.19444346]], dtype=float32)>"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "some_columns = [ocean_proximity_embed, bucketized_income]\n",
    "dense_features = keras.layers.DenseFeatures(some_columns)\n",
    "dense_features({\n",
    "    \"ocean_proximity\": [[\"NEAR OCEAN\"], [\"INLAND\"], [\"INLAND\"]],\n",
    "    \"median_income\": [[3.], [7.2], [1.]]\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TF Transform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TF Transform is not installed. Try running: pip3 install -U tensorflow-transform\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    import tensorflow_transform as tft\n",
    "\n",
    "    def preprocess(inputs):  # inputs is a batch of input features\n",
    "        median_age = inputs[\"housing_median_age\"]\n",
    "        ocean_proximity = inputs[\"ocean_proximity\"]\n",
    "        standardized_age = tft.scale_to_z_score(median_age - tft.mean(median_age))\n",
    "        ocean_proximity_id = tft.compute_and_apply_vocabulary(ocean_proximity)\n",
    "        return {\n",
    "            \"standardized_median_age\": standardized_age,\n",
    "            \"ocean_proximity_id\": ocean_proximity_id\n",
    "        }\n",
    "except ImportError:\n",
    "    print(\"TF Transform is not installed. Try running: pip3 install -U tensorflow-transform\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TensorFlow Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow_datasets as tfds\n",
    "\n",
    "datasets = tfds.load(name=\"mnist\")\n",
    "mnist_train, mnist_test = datasets[\"train\"], datasets[\"test\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['bair_robot_pushing_small', 'cats_vs_dogs', 'celeb_a', 'celeb_a_hq', 'chexpert', 'cifar10', 'cifar100', 'coco2014', 'colorectal_histology', 'colorectal_histology_large', 'diabetic_retinopathy_detection', 'dummy_dataset_shared_generator', 'dummy_mnist', 'fashion_mnist', 'flores_translate_neen', 'flores_translate_sien', 'horses_or_humans', 'image_label_folder', 'imagenet2012', 'imdb_reviews', 'kmnist', 'lm1b', 'lsun', 'mnist', 'moving_mnist', 'multi_nli', 'nsynth', 'omniglot', 'open_images_v4', 'quickdraw_bitmap', 'rock_paper_scissors', 'squad', 'starcraft_video', 'svhn_cropped', 'ted_hrlr_translate', 'ted_multi_translate', 'tf_flowers', 'titanic', 'wmt_translate_ende', 'wmt_translate_enfr']\n"
     ]
    }
   ],
   "source": [
    "print(tfds.list_builders())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x216 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,3))\n",
    "mnist_train = mnist_train.repeat(5).batch(32).prefetch(1)\n",
    "for item in mnist_train:\n",
    "    images = item[\"image\"]\n",
    "    labels = item[\"label\"]\n",
    "    for index in range(5):\n",
    "        plt.subplot(1, 5, index + 1)\n",
    "        image = images[index, ..., 0]\n",
    "        label = labels[index].numpy()\n",
    "        plt.imshow(image, cmap=\"binary\")\n",
    "        plt.title(label)\n",
    "        plt.axis(\"off\")\n",
    "    break # just showing part of the first batch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(32, 28, 28, 1)\n",
      "[1 3 8 6 0 7 7 9 2 7 3 7 6 0 4 9 8 1 5 6 0 8 0 3 7 2 6 3 3 5 5 4]\n"
     ]
    }
   ],
   "source": [
    "datasets = tfds.load(name=\"mnist\")\n",
    "mnist_train, mnist_test = datasets[\"train\"], datasets[\"test\"]\n",
    "mnist_train = mnist_train.repeat(5).batch(32)\n",
    "mnist_train = mnist_train.map(lambda items: (items[\"image\"], items[\"label\"]))\n",
    "mnist_train = mnist_train.prefetch(1)\n",
    "for images, labels in mnist_train.take(1):\n",
    "    print(images.shape)\n",
    "    print(labels.numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "1875/1875 [==============================] - 7s 4ms/step - loss: 31.4932 - accuracy: 0.8428\n",
      "Epoch 2/5\n",
      "1875/1875 [==============================] - 7s 4ms/step - loss: 26.2404 - accuracy: 0.8693\n",
      "Epoch 3/5\n",
      "1875/1875 [==============================] - 7s 4ms/step - loss: 24.9957 - accuracy: 0.8743\n",
      "Epoch 4/5\n",
      "1875/1875 [==============================] - 7s 4ms/step - loss: 24.0785 - accuracy: 0.8780\n",
      "Epoch 5/5\n",
      "1875/1875 [==============================] - 7s 4ms/step - loss: 23.8285 - accuracy: 0.8782\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x131dad240>"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datasets = tfds.load(name=\"mnist\", batch_size=32, as_supervised=True)\n",
    "mnist_train = datasets[\"train\"].repeat().prefetch(1)\n",
    "model = keras.models.Sequential([\n",
    "    keras.layers.Flatten(input_shape=[28, 28, 1]),\n",
    "    keras.layers.Lambda(lambda images: tf.cast(images, tf.float32)),\n",
    "    keras.layers.Dense(10, activation=\"softmax\")])\n",
    "model.compile(loss=\"sparse_categorical_crossentropy\",\n",
    "              optimizer=keras.optimizers.SGD(lr=1e-3),\n",
    "              metrics=[\"accuracy\"])\n",
    "model.fit(mnist_train, steps_per_epoch=60000 // 32, epochs=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    datasets = tfds.load(\"imagenet2012\", split=[\"train\", \"test\"])\n",
    "except AssertionError as ex:\n",
    "    print(ex)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TensorFlow Hub"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_3\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "keras_layer (KerasLayer)     (None, 50)                48190600  \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 16)                816       \n",
      "_________________________________________________________________\n",
      "dense_5 (Dense)              (None, 1)                 17        \n",
      "=================================================================\n",
      "Total params: 48,191,433\n",
      "Trainable params: 833\n",
      "Non-trainable params: 48,190,600\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "import tensorflow_hub as hub\n",
    "\n",
    "hub_layer = hub.KerasLayer(\"https://tfhub.dev/google/tf2-preview/nnlm-en-dim50/1\",\n",
    "                           output_shape=[50], input_shape=[], dtype=tf.string)\n",
    "\n",
    "model = keras.Sequential()\n",
    "model.add(hub_layer)\n",
    "model.add(keras.layers.Dense(16, activation='relu'))\n",
    "model.add(keras.layers.Dense(1, activation='sigmoid'))\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "sentences = tf.constant([\"It was a great movie\", \"The actors were amazing\"])\n",
    "embeddings = hub_layer(sentences)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "         1.25124469e-01,  5.40293928e-04, -1.09435350e-01,\n",
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       "        -2.20453963e-02, -1.68282315e-01, -7.40613639e-02,\n",
       "        -2.49074101e-02,  2.46460736e-01,  9.87201929e-05,\n",
       "        -1.85390845e-01, -4.92824614e-02,  1.09015472e-01,\n",
       "        -9.54203904e-02, -1.60352528e-01, -2.59811729e-02,\n",
       "         1.13778859e-01, -2.09578887e-01,  2.18261331e-01,\n",
       "        -3.11211571e-02, -6.12562597e-02, -8.66057724e-02,\n",
       "        -1.10762455e-01, -5.73977083e-03, -1.08923554e-01,\n",
       "        -1.72919363e-01,  1.00515485e-01, -5.64153939e-02,\n",
       "        -4.97694984e-02, -1.07776590e-01]], dtype=float32)>"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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