12- Loading and Preprocessing Data With TensorFlow.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "herbal-lithuania",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\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",
    "%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)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dense-southwest",
   "metadata": {},
   "source": [
    "## Loading and Preprocessing Data With TensorFlow\n",
    "Deep Learning systems are often trained on very large datasets that will not fit in RAM.\n",
    "- Ingesting a large dataset and preprocessing it efficiently can be tricky to implement with other Deep Learning libraries, but TensorFlow makes it easy thanks to the Data API: you just create a dataset object, and tell it where to get the data and how to transform it.\n",
    "- TensorFlow takes care of all the implementation details, such as multithreading, queuing, batching, and prefetching. Moreover, the Data API works seamlessly with tf.keras!\n",
    "- Off the shelf, the Data API can read from text files (such as CSV files), binary files with fixed-size records, and binary files that use TensorFlow’s TFRecord format, which supports records of varying sizes.\n",
    "    - TFRecord is a flexible and efficient binary format based on Protocol Buffers (an open source binary format).\n",
    "- The Data API also has support for reading from SQL databases. \n",
    "    - Moreover, many open source extensions are available to read from all sorts of data sources, such as Google’s BigQuery service.\n",
    "- Reading huge datasets efficiently is not the only difficulty: the data also needs to be preprocessed, usually normalized. Moreover, it is not always composed strictly of convenient numerical fields: there may be text features, categorical features, and so on.\n",
    "- These need to be encoded, for example using one-hot encoding, bag-of-words encoding, or embeddings (an embedding is a trainable dense vector that represents a category or token).\n",
    "- One option to handle all this preprocessing is to write your own custom preprocessing layers. \n",
    "    - Another is to use the standard preprocessing layers provided by Keras.\n",
    "\n",
    "### TF Transform (tf.Transform)\n",
    "- Makes it possible to write a single preprocessing function that can be run in batch mode on your full training set, before training (to speed it up), and then exported to a TF Function and incorporated into your trained model so that once it is deployed in production it can take care of preprocessing new instances on the fly.\n",
    "\n",
    "### TF Datasets (TFDS)\n",
    "- Provides a convenient function to download many common datasets of all kinds, including large ones like ImageNet, as well as convenient dataset objects to manipulate them using the Data API.\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "distant-initial",
   "metadata": {},
   "source": [
    "## The Data API\n",
    "- The whole Data API revolves around the concept of a dataset: as you might suspect, this represents a sequence of data items. Usually you will use datasets that gradually read data from disk, but for simplicity let’s create a dataset entirely in RAM using\n",
    "\n",
    "### Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "french-transformation",
   "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",
   "id": "copyrighted-maryland",
   "metadata": {},
   "source": [
    "- The from_tensor_slices() function takes a tensor and creates a tf.data.Dataset whose elements are all the slices of X (along the first dimension), so this dataset contains 10 items: tensors 0, 1, 2, …, 9.\n",
    "\n",
    "In this case we would have obtained the same dataset if we had used : "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "daily-delhi",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = tf.data.Dataset.range(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "moral-conviction",
   "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": "markdown",
   "id": "outstanding-hormone",
   "metadata": {},
   "source": [
    "### Chaining Transformations\n",
    "- Once you have a dataset, you can apply all sorts of transformations to it by calling its transformation methods. \n",
    "- Each method returns a new dataset, so you can chain transformations like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "elegant-diabetes",
   "metadata": {},
   "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": "markdown",
   "id": "moved-september",
   "metadata": {},
   "source": [
    "<img src=\"13-1.png\">\n",
    "\n",
    "- In this example, we first call the repeat() method on the original dataset, and it returns a new dataset that will repeat the items of the original dataset three times.\n",
    "    - Of course, this will not copy all the data in memory three times! (If you call this method with no arguments, the new dataset will repeat the source dataset forever, so the code that iterates over the dataset will have to decide when to stop.)\n",
    "- Then we call the batch() method on this new dataset, and again this creates a new dataset.\n",
    "    - This one will group the items of the previous dataset in batches of seven items.\n",
    "- Finally, we iterate over the items of this final dataset. \n",
    "- As you can see, the batch() method had to output a final batch of size two instead of seven, but you can call it with drop_remainder=True if you want it to drop this final batch so that all batches have the exact same size.\n",
    "\n",
    "#### WARNING\n",
    "**The dataset methods do not modify datasets, they create new ones**, so make sure to keep a reference to these new datasets (e.g., with dataset = ...), or else nothing will happen.\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "polyphonic-lyric",
   "metadata": {},
   "source": [
    "- You can also transform the items by calling the map() method. For example, this creates a new dataset with all items doubled:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "technical-success",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = dataset.map(lambda x: x * 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "entitled-latest",
   "metadata": {},
   "source": [
    "- This function is the one you will call to apply any preprocessing you want to your data. \n",
    "- Sometimes this will include computations that can be quite intensive, such as reshaping or rotating an image, so you will usually want to spawn multiple threads to speed things up: it’s as simple as setting the num_parallel_calls argument. \n",
    "- Note that the function you pass to the map() method must be convertible to a TF Function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "precise-bruce",
   "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": "markdown",
   "id": "thick-tumor",
   "metadata": {},
   "source": [
    "- While the **map() method applies a transformation to each item**, the apply() method applies a transformation to the dataset as a whole. \n",
    "- For example, the following code applies the **unbatch()** function to the dataset.\n",
    "    - Each item in the new dataset will be a single-integer tensor instead of a batch of seven integers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "discrete-quarter",
   "metadata": {},
   "outputs": [],
   "source": [
    "#dataset = dataset.apply(tf.data.experimental.unbatch()) # Now deprecated\n",
    "dataset = dataset.unbatch()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "listed-insertion",
   "metadata": {},
   "source": [
    "It is also possible to simply filter the dataset using the **filter()** method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "protected-syndrome",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = dataset.filter(lambda x: x < 10)  # keep only items < 10"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "parliamentary-policy",
   "metadata": {},
   "source": [
    "You will often want to look at just a few items from a dataset. You can use the **take()** method for that:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "eligible-broad",
   "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": "markdown",
   "id": "difficult-berkeley",
   "metadata": {},
   "source": [
    "### Shuffling the Data\n",
    "- As you know, Gradient Descent works best when the instances in the training set are independent and identically distributed. \n",
    "- A simple way to ensure this is to shuffle the instances, using the **shuffle()** method.\n",
    "    - It will create a new dataset that will start by filling up a buffer with the first items of the source dataset.\n",
    "    - Then, whenever it is asked for an item, it will pull one out randomly from the buffer and replace it with a fresh one from the source dataset, until it has iterated entirely through the source dataset.\n",
    "        - At this point it continues to pull out items randomly from the buffer until it is empty.\n",
    "        - You must specify the buffer size, and it is important to make it large enough, or else shuffling will not be very effective.\n",
    "    - You can provide a random seed if you want the same random order every time you run your program.\n",
    "\n",
    "For example, the following code creates and displays a dataset containing the integers 0 to 9, repeated 3 times, shuffled using a buffer of size 5 and a random seed of 42, and batched with a batch size of 7:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "indoor-parallel",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor([1 3 0 4 2 5 6], shape=(7,), dtype=int64)\n",
      "tf.Tensor([8 7 1 0 3 2 5], shape=(7,), dtype=int64)\n",
      "tf.Tensor([4 6 9 8 9 7 0], shape=(7,), dtype=int64)\n",
      "tf.Tensor([3 1 4 5 2 8 7], shape=(7,), dtype=int64)\n",
      "tf.Tensor([6 9], shape=(2,), dtype=int64)\n"
     ]
    }
   ],
   "source": [
    "tf.random.set_seed(42)\n",
    "\n",
    "dataset = tf.data.Dataset.range(10).repeat(3)\n",
    "dataset = dataset.shuffle(buffer_size=3, seed=42).batch(7)\n",
    "\n",
    "for item in dataset:\n",
    "    print(item)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "legitimate-twenty",
   "metadata": {},
   "source": [
    "#### TIP\n",
    "- **If you call repeat() on a shuffled dataset, by default it will generate a new order at every iteration.**\n",
    "- This is generally a good idea, but **if you prefer to reuse the same order at each iteration** (e.g., for tests or debugging), **you can set reshuffle_each_iteration=False.**\n",
    "***\n",
    "- For a large dataset that does not fit in memory, this simple shuffling-buffer approach may not be sufficient, since the buffer will be small compared to the dataset. \n",
    "- One solution is to shuffle the source data itself.\n",
    "    - This will definitely improve shuffling a lot! Even if the source data is shuffled, you will usually want to shuffle it some more, or else the same order will be repeated at each epoch, and the model may end up being biased.\n",
    "- To shuffle the instances some more, a common approach is to **split the source data into multiple files, then read them in a random order during training.**\n",
    "- However, instances located in the same file will still end up close to each other.\n",
    "    - To avoid this you can pick multiple files randomly and read them simultaneously, interleaving their records.\n",
    "    - Then on top of that you can add a shuffling buffer using the shuffle() method. \n",
    "- If all this sounds like a lot of work, don’t worry: the Data API makes all this possible in just a few lines of code. \n",
    "\n",
    "Let’s see how to do this."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ancient-planning",
   "metadata": {},
   "source": [
    "### Split the California dataset to multiple CSV files\n",
    "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,
   "id": "dimensional-profile",
   "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",
    "\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",
   "id": "specific-extraction",
   "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,
   "id": "charged-weather",
   "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,
   "id": "enabling-washer",
   "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",
   "id": "decreased-russia",
   "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,
   "id": "unexpected-passing",
   "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",
   "id": "cordless-maldives",
   "metadata": {},
   "source": [
    "Or in text mode:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "imported-birmingham",
   "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,
   "id": "dramatic-albuquerque",
   "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",
   "id": "careful-restoration",
   "metadata": {},
   "source": [
    "### Building an Input Pipeline\n",
    "Now let’s create a dataset containing only these file paths:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "palestinian-awareness",
   "metadata": {},
   "outputs": [],
   "source": [
    "filepath_dataset = tf.data.Dataset.list_files(train_filepaths, seed=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cooked-characteristic",
   "metadata": {},
   "source": [
    "- By default, the **list_files() function returns a dataset that shuffles the file paths.** \n",
    "    - In general this is a good thing, but **you can set shuffle=False if you do not want that for some reason.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "bronze-algebra",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor(b'datasets\\\\housing\\\\my_train_15.csv', shape=(), dtype=string)\n",
      "tf.Tensor(b'datasets\\\\housing\\\\my_train_08.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_01.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_05.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_16.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_09.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_07.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_04.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_11.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_18.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_13.csv', shape=(), dtype=string)\n"
     ]
    }
   ],
   "source": [
    "for filepath in filepath_dataset:\n",
    "    print(filepath)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "novel-scenario",
   "metadata": {},
   "source": [
    "- Next, you can call the **interleave()** method to read from five files at a time and interleave their lines (skipping the first line of each file, which is the header row, using the **skip()** method):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "dietary-carbon",
   "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": "markdown",
   "id": "beginning-large",
   "metadata": {},
   "source": [
    "- **The interleave() method will create a dataset that will pull five file paths from the filepath_dataset, and for each one it will call the function you gave it (a lambda in this example) to create a new dataset (in this case a TextLineDataset).**\n",
    "- To be clear, at this stage there will be seven datasets in all: the filepath dataset, the interleave dataset, and the five TextLineDatasets created internally by the interleave dataset.\n",
    "- When we iterate over the interleave dataset, it will cycle through these five TextLineDatasets, reading one line at a time from each until all datasets are out of items.\n",
    "    - Then it will get the next five file paths from the filepath_dataset and interleave them the same way, and so on until it runs out of file paths.\n",
    "#### TIP\n",
    "For interleaving to work best, **it is preferable to have files of identical length**; otherwise the ends of the longest files will not be interleaved.\n",
    "***\n",
    "- By default, interleave() does not use parallelism; it just reads one line at a time from each file, sequentially. \n",
    "    - **If you want it to actually read files in parallel, you can set the num_parallel_calls argument to the number of threads you want (note that the map() method also has this argument).**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "early-madonna",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "b'4.6477,38.0,5.03728813559322,0.911864406779661,745.0,2.5254237288135593,32.64,-117.07,1.504'\n",
      "b'8.72,44.0,6.163179916317992,1.0460251046025104,668.0,2.794979079497908,34.2,-118.18,4.159'\n",
      "b'3.8456,35.0,5.461346633416459,0.9576059850374065,1154.0,2.8778054862842892,37.96,-122.05,1.598'\n",
      "b'3.3456,37.0,4.514084507042254,0.9084507042253521,458.0,3.2253521126760565,36.67,-121.7,2.526'\n",
      "b'3.6875,44.0,4.524475524475524,0.993006993006993,457.0,3.195804195804196,34.04,-118.15,1.625'\n"
     ]
    }
   ],
   "source": [
    "for line in dataset.take(5):\n",
    "    print(line.numpy())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "accurate-shame",
   "metadata": {},
   "source": [
    "Notice that field 4 is interpreted as a string.\n",
    "\n",
    "- These are the first rows (ignoring the header row) of five CSV files, chosen randomly. \n",
    "- Looks good! But as you can see, **these are just byte strings; we need to parse them and scale the data.**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cutting-assumption",
   "metadata": {},
   "source": [
    "### Preprocessing the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "above-workshop",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<tf.Tensor: shape=(), dtype=int32, numpy=1>,\n",
       " <tf.Tensor: shape=(), dtype=float32, numpy=2.0>,\n",
       " <tf.Tensor: shape=(), dtype=float64, numpy=3.0>,\n",
       " <tf.Tensor: shape=(), dtype=string, numpy=b'4'>,\n",
       " <tf.Tensor: 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",
   "id": "subtle-cooler",
   "metadata": {},
   "source": [
    "Notice that all missing fields are replaced with their default value, when provided:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "reported-actress",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<tf.Tensor: shape=(), dtype=int32, numpy=0>,\n",
       " <tf.Tensor: shape=(), dtype=float32, numpy=nan>,\n",
       " <tf.Tensor: shape=(), dtype=float64, numpy=nan>,\n",
       " <tf.Tensor: shape=(), dtype=string, numpy=b'Hello'>,\n",
       " <tf.Tensor: 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",
   "id": "arranged-privacy",
   "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,
   "id": "grave-antibody",
   "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",
   "id": "bulgarian-cowboy",
   "metadata": {},
   "source": [
    "The number of fields should match exactly the number of fields in the record_defaults:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "activated-mediterranean",
   "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": "markdown",
   "id": "personalized-harvey",
   "metadata": {},
   "source": [
    "***\n",
    "#### Let’s implement a small function that will perform this preprocessing:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "turkish-journalism",
   "metadata": {},
   "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": "markdown",
   "id": "looking-portugal",
   "metadata": {},
   "source": [
    "Let’s walk through this code:\n",
    "- First, the code assumes that we have precomputed the mean and standard deviation of each feature in the training set. X_mean and X_std are just 1D tensors (or NumPy arrays) containing eight floats, one per input feature.\n",
    "- The preprocess() function takes one CSV line and starts by parsing it. \n",
    "    - For this it uses the tf.io.decode_csv() function, which takes two arguments: the first is the line to parse, and the second is an array containing the default value for each column in the CSV file. \n",
    "    - This array tells TensorFlow not only the default value for each column, but also the number of columns and their types. \n",
    "    - In this example, we tell it that all feature columns are floats and that missing values should default to 0, but we provide an empty array of type tf.float32 as the default value for the last column (the target): the array tells TensorFlow that this column contains floats, but that there is no default value, so it will raise an exception if it encounters a missing value.\n",
    "- The decode_csv() function returns a list of scalar tensors (one per column), but we need to return 1D tensor arrays. \n",
    "    - So we call tf.stack() on all tensors except for the last one (the target): this will stack these tensors into a 1D array. \n",
    "    - We then do the same for the target value (this makes it a 1D tensor array with a single value, rather than a scalar tensor).\n",
    "- Finally, we scale the input features by subtracting the feature means and then dividing by the feature standard deviations, and we return a tuple containing the scaled features and the target.\n",
    "\n",
    "Let’s test this preprocessing function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "loving-findings",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<tf.Tensor: 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: 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": "markdown",
   "id": "chief-detail",
   "metadata": {},
   "source": [
    "Looks good! We can now apply the function to the dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "confident-religious",
   "metadata": {},
   "source": [
    "### Putting Everything Together\n",
    "- To make the code reusable, let’s put together everything we have discussed so far into a small helper function: it will create and return a dataset that will efficiently load California housing data from multiple CSV files, preprocess it, shuffle it, optionally repeat it, and batch it:\n",
    "\n",
    "<img src=\"13-2.png\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "obvious-system",
   "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": "markdown",
   "id": "consistent-cosmetic",
   "metadata": {},
   "source": [
    "Everything should make sense in this code, except the very last line (prefetch(1)), which is important for performance.\n",
    "\n",
    "### Prefetching\n",
    "- By calling **prefetch(1) at the end**, we are creating a dataset that will do its best to always be one batch ahead. \n",
    "    - In other words, **while our training algorithm is working on one batch, the dataset will already be working in parallel on getting the next batch ready** (e.g., reading the data from disk and preprocessing it).\n",
    "    - This can improve performance dramatically.\n",
    "- If we also ensure that loading and preprocessing are multithreaded (by setting **num_parallel_calls** when calling interleave() and map()), we can exploit multiple cores on the CPU and hopefully make preparing one batch of data shorter than running a training step on the GPU: this way the GPU will be almost 100% utilized (except for the data transfer time from the CPU to the GPU ), and training will run much faster.\n",
    "- If the dataset is small enough to fit in memory, you can significantly speed up training by using the dataset’s cache() method to cache its content to RAM. \n",
    "    - You should generally do this after loading and preprocessing the data, but before shuffling, repeating, batching, and prefetching. \n",
    "    - This way, each instance will only be read and preprocessed once (instead of once per epoch), but the data will still be shuffled differently at each epoch, and the next batch will still be prepared in advance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "small-phenomenon",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X = tf.Tensor(\n",
      "[[ 0.5804519  -0.20762321  0.05616303 -0.15191229  0.01343246  0.00604472\n",
      "   1.2525111  -1.3671792 ]\n",
      " [ 5.818099    1.8491895   1.1784915   0.28173092 -1.2496178  -0.3571987\n",
      "   0.7231292  -1.0023477 ]\n",
      " [-0.9253566   0.5834586  -0.7807257  -0.28213993 -0.36530012  0.27389365\n",
      "  -0.76194876  0.72684526]], shape=(3, 8), dtype=float32)\n",
      "y = tf.Tensor(\n",
      "[[1.752]\n",
      " [1.313]\n",
      " [1.535]], shape=(3, 1), dtype=float32)\n",
      "\n",
      "X = tf.Tensor(\n",
      "[[-0.8324941   0.6625668  -0.20741376 -0.18699841 -0.14536144  0.09635526\n",
      "   0.9807942  -0.67250353]\n",
      " [-0.62183803  0.5834586  -0.19862501 -0.3500319  -1.1437552  -0.3363751\n",
      "   1.107282   -0.8674123 ]\n",
      " [ 0.8683102   0.02970133  0.3427381  -0.29872298  0.7124906   0.28026953\n",
      "  -0.72915536  0.86178064]], shape=(3, 8), dtype=float32)\n",
      "y = tf.Tensor(\n",
      "[[0.919]\n",
      " [1.028]\n",
      " [2.182]], shape=(3, 1), dtype=float32)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "tf.random.set_seed(42)\n",
    "\n",
    "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": "markdown",
   "id": "corresponding-enzyme",
   "metadata": {},
   "source": [
    "### Using the Dataset with tf.keras\n",
    "- Now we can use the csv_reader_dataset() function to create a dataset for the training set. \n",
    "- Note that we do not need to repeat it, as this will be taken care of by tf.keras. \n",
    "\n",
    "We also create datasets for the validation set and the test set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "religious-citizen",
   "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": "markdown",
   "id": "valid-intermediate",
   "metadata": {},
   "source": [
    "- And now we can simply build and train a Keras model using these datasets. \n",
    "- **All we need to do is pass the training and validation datasets to the fit() method**, instead of X_train, y_train, X_valid, and y_valid:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "temporal-vegetarian",
   "metadata": {},
   "outputs": [],
   "source": [
    "keras.backend.clear_session()\n",
    "np.random.seed(42)\n",
    "tf.random.set_seed(42)\n",
    "\n",
    "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,
   "id": "final-moore",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss=\"mse\", optimizer=keras.optimizers.SGD(lr=1e-3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "aware-pattern",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "362/362 [==============================] - 4s 7ms/step - loss: 2.0914 - val_loss: 21.5124\n",
      "Epoch 2/10\n",
      "362/362 [==============================] - 1s 3ms/step - loss: 0.8428 - val_loss: 0.6648\n",
      "Epoch 3/10\n",
      "362/362 [==============================] - 1s 3ms/step - loss: 0.6329 - val_loss: 0.6196\n",
      "Epoch 4/10\n",
      "362/362 [==============================] - 1s 3ms/step - loss: 0.5922 - val_loss: 0.5669\n",
      "Epoch 5/10\n",
      "362/362 [==============================] - 1s 3ms/step - loss: 0.5622 - val_loss: 0.5402\n",
      "Epoch 6/10\n",
      "362/362 [==============================] - 1s 3ms/step - loss: 0.5698 - val_loss: 0.5209\n",
      "Epoch 7/10\n",
      "362/362 [==============================] - 1s 3ms/step - loss: 0.5195 - val_loss: 0.6130\n",
      "Epoch 8/10\n",
      "362/362 [==============================] - 1s 3ms/step - loss: 0.5155 - val_loss: 0.4818\n",
      "Epoch 9/10\n",
      "362/362 [==============================] - 1s 3ms/step - loss: 0.4965 - val_loss: 0.4904\n",
      "Epoch 10/10\n",
      "362/362 [==============================] - 1s 3ms/step - loss: 0.4925 - val_loss: 0.4585\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x1ab478cfec8>"
      ]
     },
     "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": "markdown",
   "id": "unlikely-sailing",
   "metadata": {},
   "source": [
    "Similarly, we can pass a dataset to the **evaluate()** and **predict()** methods:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "accepting-bonus",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "161/161 [==============================] - 1s 2ms/step - loss: 0.4788\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.4787752032279968"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(test_set, steps=len(X_test) // batch_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "proved-clear",
   "metadata": {},
   "source": [
    "- Unlike the other sets, the new_set will usually not contain labels (if it does, Keras will ignore them). \n",
    "- Note that in all these cases, you can still use NumPy arrays instead of datasets if you want (but of course they need to have been loaded and preprocessed first)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "outer-flour",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2.3576407],\n",
       "       [2.255291 ],\n",
       "       [1.4437605],\n",
       "       ...,\n",
       "       [0.5654392],\n",
       "       [3.9442453],\n",
       "       [1.0232248]], 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,
   "id": "sought-armstrong",
   "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,
   "id": "modified-nickname",
   "metadata": {},
   "outputs": [],
   "source": [
    "keras.backend.clear_session()\n",
    "np.random.seed(42)\n",
    "tf.random.set_seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "recorded-article",
   "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": 39,
   "id": "detailed-pierce",
   "metadata": {},
   "outputs": [],
   "source": [
    "keras.backend.clear_session()\n",
    "np.random.seed(42)\n",
    "tf.random.set_seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "artistic-worst",
   "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",
   "id": "protecting-malaysia",
   "metadata": {},
   "source": [
    "Here is a short description of each method in the Dataset class:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "absolute-looking",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "● apply()              Applies a transformation function to this dataset.\n",
      "● as_numpy_iterator()  Returns an iterator which converts all elements of the dataset to numpy.\n",
      "● batch()              Combines consecutive elements of this dataset into batches.\n",
      "● cache()              Caches the elements in this dataset.\n",
      "● cardinality()        Returns the cardinality of the dataset, if known.\n",
      "● concatenate()        Creates a `Dataset` by concatenating the given dataset with this dataset.\n",
      "● element_spec()       The type specification of an element of this dataset.\n",
      "● enumerate()          Enumerates the elements of 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`. (deprecated arguments)\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 so each original value is seen `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",
      "● unbatch()            Splits elements of a dataset into multiple elements.\n",
      "● window()             Combines (nests of) input elements into a dataset of (nests 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",
   "id": "fresh-session",
   "metadata": {},
   "source": [
    "### The TFRecord binary format\n",
    "- **The TFRecord format is TensorFlow’s preferred format for storing large amounts of data and reading it efficiently.** \n",
    "- **It is a very simple binary format that just contains a sequence of binary records of varying sizes** (each record is comprised of a length, a CRC checksum to check that the length was not corrupted, then the actual data, and finally a CRC checksum for the data).\n",
    "\n",
    "You can easily create a TFRecord file using the **tf.io.TFRecordWriter** class:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "informative-speed",
   "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",
   "id": "straight-arrangement",
   "metadata": {},
   "source": [
    "And you can then use a **tf.data.TFRecordDataset to read** one or more TFRecord files:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "right-incident",
   "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",
   "id": "growing-award",
   "metadata": {},
   "source": [
    "- You can read multiple TFRecord files with just one TFRecordDataset. \n",
    "- **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": 44,
   "id": "crazy-athletics",
   "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": "markdown",
   "id": "aging-particular",
   "metadata": {},
   "source": [
    "### Compressed TFRecord Files\n",
    "- It can sometimes be useful to compress your TFRecord files, especially if they need to be loaded via a network connection. You can create a compressed TFRecord file by setting the **options** argument:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "otherwise-brighton",
   "metadata": {},
   "outputs": [],
   "source": [
    "options = tf.io.TFRecordOptions(compression_type=\"GZIP\")\n",
    "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": "markdown",
   "id": "valid-jurisdiction",
   "metadata": {},
   "source": [
    "When reading a compressed TFRecord file, you need to specify the compression type:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "experienced-trinidad",
   "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",
   "id": "acute-princeton",
   "metadata": {},
   "source": [
    "### A Brief Introduction to Protocol Buffers\n",
    "- Even though each record can use any binary format you want, TFRecord files usually contain serialized protocol buffers (also called protobufs). \n",
    "- This is a portable, extensible, and efficient binary format developed at Google back in 2001 and made open source in 2008; protobufs are now widely used, in particular in gRPC, Google’s remote procedure call system."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "liked-practitioner",
   "metadata": {},
   "source": [
    "### TensorFlow Protobufs\n",
    "- The main protobuf typically used in a TFRecord file is the **Example protobuf**, which represents one instance in a dataset. \n",
    "    - It contains a list of named features, where each feature can either be a list of byte strings, a list of floats, or a list of integers. Here is the protobuf definition:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "reserved-sessions",
   "metadata": {},
   "outputs": [],
   "source": [
    "#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": 48,
   "id": "adapted-acting",
   "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": 49,
   "id": "saving-dictionary",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'emails': <tensorflow.python.framework.sparse_tensor.SparseTensor at 0x1ab4a604248>,\n",
       " 'id': <tf.Tensor: shape=(), dtype=int64, numpy=123>,\n",
       " 'name': <tf.Tensor: shape=(), dtype=string, numpy=b'Alice'>}"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parsed_example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "tamil-dakota",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'emails': <tensorflow.python.framework.sparse_tensor.SparseTensor at 0x1ab4a604248>,\n",
       " 'id': <tf.Tensor: shape=(), dtype=int64, numpy=123>,\n",
       " 'name': <tf.Tensor: shape=(), dtype=string, numpy=b'Alice'>}"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parsed_example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "essential-prescription",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(), dtype=string, numpy=b'a@b.com'>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parsed_example[\"emails\"].values[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "controlling-worst",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(2,), dtype=string, numpy=array([b'a@b.com', b'c@d.com'], dtype=object)>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.sparse.to_dense(parsed_example[\"emails\"], default_value=b\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "sophisticated-throat",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(2,), dtype=string, numpy=array([b'a@b.com', b'c@d.com'], dtype=object)>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parsed_example[\"emails\"].values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "herbal-texas",
   "metadata": {},
   "source": [
    "### Putting Images in TFRecords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "breeding-competition",
   "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\"][1]\n",
    "plt.imshow(img)\n",
    "plt.axis(\"off\")\n",
    "plt.title(\"Original image\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "american-civilization",
   "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": 56,
   "id": "geographic-network",
   "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",
   "id": "disabled-egypt",
   "metadata": {},
   "source": [
    "Or use decode_image() which supports BMP, GIF, JPEG and PNG formats:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "different-element",
   "metadata": {},
   "outputs": [],
   "source": [
    "decoded_img = tf.io.decode_image(example_with_image[\"image\"].values[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "oriented-flash",
   "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",
   "id": "physical-redhead",
   "metadata": {},
   "source": [
    "### Putting Tensors and Sparse Tensors in TFRecords\n",
    "Tensors can be serialized and parsed easily using tf.io.serialize_tensor() and tf.io.parse_tensor():"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "speaking-partner",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: 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": 59,
     "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": 60,
   "id": "antique-principal",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(3, 2), dtype=float32, numpy=\n",
       "array([[0., 1.],\n",
       "       [2., 3.],\n",
       "       [4., 5.]], dtype=float32)>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.io.parse_tensor(s, out_type=tf.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "centered-failing",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: 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": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "serialized_sparse = tf.io.serialize_sparse(parsed_example[\"emails\"])\n",
    "serialized_sparse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "awful-marsh",
   "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": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BytesList(value=serialized_sparse.numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "advanced-vacuum",
   "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": 64,
   "id": "returning-irrigation",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'image': <tensorflow.python.framework.sparse_tensor.SparseTensor at 0x1ab4ba34108>}"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parsed_examples"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "curious-produce",
   "metadata": {},
   "source": [
    "### Handling Lists of Lists (Sequential Data) Using SequenceExample Protobuf\n",
    "- A SequenceExample contains a Features object for the contextual data and a FeatureLists object that contains one or more named FeatureList objects (e.g., a FeatureList named \"content\" and another named \"comments\").\n",
    "- Each FeatureList contains a list of Feature objects, each of which may be a list of byte strings, a list of 64-bit integers, or a list of floats (in this example, each Feature would represent a sentence or a comment, perhaps in the form of a list of word identifiers)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "collected-vitamin",
   "metadata": {},
   "outputs": [],
   "source": [
    "#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": 66,
   "id": "cross-moscow",
   "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": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sequence_example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "interesting-specific",
   "metadata": {},
   "outputs": [],
   "source": [
    "serialized_sequence_example = sequence_example.SerializeToString()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "crazy-toddler",
   "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": 69,
   "id": "median-young",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'title': <tensorflow.python.framework.sparse_tensor.SparseTensor at 0x1ab4ba491c8>,\n",
       " 'author_id': <tf.Tensor: shape=(), dtype=int64, numpy=123>,\n",
       " 'pub_date': <tf.Tensor: shape=(3,), dtype=int64, numpy=array([1623,   12,   25], dtype=int64)>}"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parsed_context"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "chicken-covering",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(4,), dtype=string, numpy=array([b'A', b'desert', b'place', b'.'], dtype=object)>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parsed_context[\"title\"].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "greek-information",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'comments': <tensorflow.python.framework.sparse_tensor.SparseTensor at 0x1ab4ba49208>,\n",
       " 'content': <tensorflow.python.framework.sparse_tensor.SparseTensor at 0x1ab4ba492c8>}"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parsed_feature_lists"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "hungarian-perth",
   "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",
   "id": "differential-hawaii",
   "metadata": {},
   "source": [
    "### Preprocessing the Input Features\n",
    "- Preparing your data for a neural network requires converting all features into numerical features, generally normalizing them, and more. \n",
    "- In particular, if your data contains categorical features or text features, they need to be converted to numbers. This can be done ahead of time when preparing your data files, using any tool you like (e.g., NumPy, pandas, or Scikit-Learn). \n",
    "    - Alternatively, **you can preprocess your data on the fly when loading it with the Data API** (e.g., using the dataset’s map() method), \n",
    "    - **or you can include a preprocessing layer directly in your model.**\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "bronze-place",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import tarfile\n",
    "import urllib.request\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",
    "    os.makedirs(housing_path, exist_ok=True)\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": 74,
   "id": "physical-wales",
   "metadata": {},
   "outputs": [],
   "source": [
    "fetch_housing_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "reasonable-wrestling",
   "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": 76,
   "id": "brown-communication",
   "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": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing = load_housing_data()\n",
    "housing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "offshore-privilege",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NumericColumn(key='housing_median_age', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=None)"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.feature_column.numeric_column(\"housing_median_age\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "automatic-attraction",
   "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": 79,
   "id": "adjusted-fifteen",
   "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": 80,
   "id": "prescribed-focus",
   "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": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bucketized_income"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "advisory-token",
   "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": 82,
   "id": "subject-arbitration",
   "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": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ocean_proximity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "sunset-interview",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "HashedCategoricalColumn(key='city', hash_bucket_size=1000, dtype=tf.string)"
      ]
     },
     "execution_count": 83,
     "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": 84,
   "id": "ahead-beach",
   "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": 85,
   "id": "published-makeup",
   "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": 86,
   "id": "brown-transport",
   "metadata": {},
   "outputs": [],
   "source": [
    "ocean_proximity_one_hot = tf.feature_column.indicator_column(ocean_proximity)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "liberal-angle",
   "metadata": {},
   "outputs": [],
   "source": [
    "ocean_proximity_embed = tf.feature_column.embedding_column(ocean_proximity,\n",
    "                                                           dimension=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "emotional-lender",
   "metadata": {},
   "source": [
    "### Using Feature Columns for Parsing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "median-occasion",
   "metadata": {},
   "outputs": [],
   "source": [
    "median_house_value = tf.feature_column.numeric_column(\"median_house_value\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "freelance-absence",
   "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": 89,
     "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": 90,
   "id": "social-organizer",
   "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": 91,
   "id": "linear-flash",
   "metadata": {},
   "outputs": [],
   "source": [
    "keras.backend.clear_session()\n",
    "np.random.seed(42)\n",
    "tf.random.set_seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "compact-muslim",
   "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": "code",
   "execution_count": 93,
   "id": "posted-determination",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor, but we receive a <class 'dict'> input: {'housing_median_age': <tf.Tensor 'IteratorGetNext:0' shape=(None, 1) dtype=float32>}\n",
      "Consider rewriting this model with the Functional API.\n",
      "WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor, but we receive a <class 'dict'> input: {'housing_median_age': <tf.Tensor 'IteratorGetNext:0' shape=(None, 1) dtype=float32>}\n",
      "Consider rewriting this model with the Functional API.\n",
      "362/362 [==============================] - 2s 2ms/step - loss: 4.7553 - accuracy: 8.8428e-04\n",
      "Epoch 2/5\n",
      "362/362 [==============================] - 1s 2ms/step - loss: 2.1622 - accuracy: 0.0021\n",
      "Epoch 3/5\n",
      "362/362 [==============================] - 1s 2ms/step - loss: 1.4673 - accuracy: 0.0032\n",
      "Epoch 4/5\n",
      "362/362 [==============================] - 1s 2ms/step - loss: 1.3786 - accuracy: 0.0033\n",
      "Epoch 5/5\n",
      "362/362 [==============================] - 1s 2ms/step - loss: 1.3404 - accuracy: 0.0034\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x1ab4ba6de48>"
      ]
     },
     "execution_count": 93,
     "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": 94,
   "id": "aging-prophet",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(3, 7), dtype=float32, numpy=\n",
       "array([[ 0.        ,  0.        ,  1.        ,  0.        ,  0.        ,\n",
       "        -0.14504611,  0.7563394 ],\n",
       "       [ 0.        ,  0.        ,  0.        ,  0.        ,  1.        ,\n",
       "        -1.1119912 ,  0.56957847],\n",
       "       [ 1.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
       "        -1.1119912 ,  0.56957847]], dtype=float32)>"
      ]
     },
     "execution_count": 94,
     "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",
   "id": "basic-tactics",
   "metadata": {},
   "source": [
    "### TF Transform\n",
    "- If preprocessing is computationally expensive, then handling it before training rather than on the fly may give you a significant speedup: the data will be preprocessed just once per instance before training, rather than once per instance and per epoch during training. \n",
    "    - As mentioned earlier, if the dataset is small enough to fit in RAM, you can use its cache() method. \n",
    "    - But if it is too large, then tools like Apache Beam or Spark will help.\n",
    "- This works great and indeed can speed up training, but there is one problem: once your model is trained, suppose you want to deploy it to a mobile app. \n",
    "    - In that case you will need to write some code in your app to take care of preprocessing the data before it is fed to the model. \n",
    "    - And suppose you also want to deploy the model to TensorFlow.js so that it runs in a web browser? Once again, you will need to write some preprocessing code. \n",
    "    - This can become a maintenance nightmare: whenever you want to change the preprocessing logic, you will need to update your Apache Beam code, your mobile app code, and your JavaScript code.\n",
    "- One improvement would be to take the trained model (trained on data that was preprocessed by your Apache Beam or Spark code) and, before deploying it to your app or the browser, add extra preprocessing layers to take care of preprocessing on the fly. \n",
    "    - That’s definitely better, since now you just have two versions of your preprocessing code: the Apache Beam or Spark code, and the preprocessing layers’ code.\n",
    "- But what if you could define your preprocessing operations just once? \n",
    "    - This is what TF Transform was designed for. \n",
    "    - It is part of TensorFlow Extended (TFX), an end-to-end platform for productionizing TensorFlow models.\n",
    "    \n",
    "Here is what this preprocessing function might look like if we just had two features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "effective-cooperative",
   "metadata": {},
   "outputs": [],
   "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",
   "id": "silent-differential",
   "metadata": {},
   "source": [
    "- Next, TF Transform lets you apply this preprocess() function to the whole training set using Apache Beam (it provides an AnalyzeAndTransformDataset class that you can use for this purpose in your Apache Beam pipeline). \n",
    "    - In the process, it will also compute all the necessary statistics over the whole training set: in this example, the mean and standard deviation of the housing_median_age feature, and the vocabulary for the ocean_proximity feature. \n",
    "    - The components that compute these statistics are called **analyzers.**\n",
    "- Importantly, TF Transform will also generate an equivalent TensorFlow Function that you can plug into the model you deploy. \n",
    "    - This TF Function includes some constants that correspond to all the all the necessary statistics computed by Apache Beam (the mean, standard deviation, and vocabulary).\n",
    "- With the Data API, TFRecords, the Keras preprocessing layers, and TF Transform, you can build highly scalable input pipelines for training and benefit from fast and portable data preprocessing in production.\n",
    "\n",
    "But what if you just wanted to use a standard dataset? Well in that case, things are much simpler: just use TFDS!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "excessive-chemical",
   "metadata": {},
   "source": [
    "### TensorFlow Datasets(TFDS)\n",
    "- The TensorFlow Datasets project makes it very easy to download common datasets, from small ones like MNIST or Fashion MNIST to huge datasets like ImageNet (you will need quite a bit of disk space!). \n",
    "    - The list includes image datasets, text datasets (including translation datasets), and audio and video datasets.\n",
    "    \n",
    "For example, let’s download MNIST:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "worse-contrary",
   "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": 97,
   "id": "qualified-nothing",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['abstract_reasoning', 'accentdb', 'aeslc', 'aflw2k3d', 'ag_news_subset', 'ai2_arc', 'ai2_arc_with_ir', 'amazon_us_reviews', 'anli', 'arc', 'bair_robot_pushing_small', 'bccd', 'beans', 'big_patent', 'bigearthnet', 'billsum', 'binarized_mnist', 'binary_alpha_digits', 'blimp', 'bool_q', 'c4', 'caltech101', 'caltech_birds2010', 'caltech_birds2011', 'cars196', 'cassava', 'cats_vs_dogs', 'celeb_a', 'celeb_a_hq', 'cfq', 'cherry_blossoms', 'chexpert', 'cifar10', 'cifar100', 'cifar10_1', 'cifar10_corrupted', 'citrus_leaves', 'cityscapes', 'civil_comments', 'clevr', 'clic', 'clinc_oos', 'cmaterdb', 'cnn_dailymail', 'coco', 'coco_captions', 'coil100', 'colorectal_histology', 'colorectal_histology_large', 'common_voice', 'coqa', 'cos_e', 'cosmos_qa', 'covid19sum', 'crema_d', 'curated_breast_imaging_ddsm', 'cycle_gan', 'dart', 'davis', 'deep_weeds', 'definite_pronoun_resolution', 'dementiabank', 'diabetic_retinopathy_detection', 'div2k', 'dmlab', 'downsampled_imagenet', 'drop', 'dsprites', 'dtd', 'duke_ultrasound', 'e2e_cleaned', 'emnist', 'eraser_multi_rc', 'esnli', 'eurosat', 'fashion_mnist', 'flic', 'flores', 'food101', 'forest_fires', 'fuss', 'gap', 'geirhos_conflict_stimuli', 'genomics_ood', 'german_credit_numeric', 'gigaword', 'glue', 'goemotions', 'gpt3', 'groove', 'gtzan', 'gtzan_music_speech', 'hellaswag', 'higgs', 'horses_or_humans', 'howell', 'i_naturalist2017', 'imagenet2012', 'imagenet2012_corrupted', 'imagenet2012_real', 'imagenet2012_subset', 'imagenet_a', 'imagenet_r', 'imagenet_resized', 'imagenet_v2', 'imagenette', 'imagewang', 'imdb_reviews', 'irc_disentanglement', 'iris', 'kitti', 'kmnist', 'lambada', 'lfw', 'librispeech', 'librispeech_lm', 'libritts', 'ljspeech', 'lm1b', 'lost_and_found', 'lsun', 'lvis', 'malaria', 'math_dataset', 'mctaco', 'mlqa', 'mnist', 'mnist_corrupted', 'movie_lens', 'movie_rationales', 'movielens', 'moving_mnist', 'multi_news', 'multi_nli', 'multi_nli_mismatch', 'natural_questions', 'natural_questions_open', 'newsroom', 'nsynth', 'nyu_depth_v2', 'omniglot', 'open_images_challenge2019_detection', 'open_images_v4', 'openbookqa', 'opinion_abstracts', 'opinosis', 'opus', 'oxford_flowers102', 'oxford_iiit_pet', 'para_crawl', 'patch_camelyon', 'paws_wiki', 'paws_x_wiki', 'pet_finder', 'pg19', 'piqa', 'places365_small', 'plant_leaves', 'plant_village', 'plantae_k', 'qa4mre', 'qasc', 'quac', 'quickdraw_bitmap', 'race', 'radon', 'reddit', 'reddit_disentanglement', 'reddit_tifu', 'resisc45', 'robonet', 'rock_paper_scissors', 'rock_you', 's3o4d', 'salient_span_wikipedia', 'samsum', 'savee', 'scan', 'scene_parse150', 'scicite', 'scientific_papers', 'sentiment140', 'shapes3d', 'siscore', 'smallnorb', 'snli', 'so2sat', 'speech_commands', 'spoken_digit', 'squad', 'stanford_dogs', 'stanford_online_products', 'starcraft_video', 'stl10', 'story_cloze', 'sun397', 'super_glue', 'svhn_cropped', 'ted_hrlr_translate', 'ted_multi_translate', 'tedlium', 'tf_flowers', 'the300w_lp', 'tiny_shakespeare', 'titanic', 'trec', 'trivia_qa', 'tydi_qa', 'uc_merced', 'ucf101', 'vctk', 'vgg_face2', 'visual_domain_decathlon', 'voc', 'voxceleb', 'voxforge', 'waymo_open_dataset', 'web_nlg', 'web_questions', 'wider_face', 'wiki40b', 'wiki_bio', 'wiki_table_questions', 'wiki_table_text', 'wikihow', 'wikipedia', 'wikipedia_toxicity_subtypes', 'wine_quality', 'winogrande', 'wmt14_translate', 'wmt15_translate', 'wmt16_translate', 'wmt17_translate', 'wmt18_translate', 'wmt19_translate', 'wmt_t2t_translate', 'wmt_translate', 'wordnet', 'wsc273', 'xnli', 'xquad', 'xsum', 'xtreme_pawsx', 'xtreme_xnli', 'yelp_polarity_reviews', 'yes_no']\n"
     ]
    }
   ],
   "source": [
    "print(tfds.list_builders())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "efficient-deviation",
   "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": "markdown",
   "id": "challenging-plaintiff",
   "metadata": {},
   "source": [
    "- Note that each item in the dataset is a dictionary containing both the features and the labels. \n",
    "- But Keras expects each item to be a tuple containing two elements (again, the features and the labels). \n",
    "- You could transform the dataset using the map() method, like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "intense-treaty",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(32, 28, 28, 1)\n",
      "[4 1 0 7 8 1 2 7 1 6 6 4 7 7 3 3 7 9 9 1 0 6 6 9 9 4 8 9 4 7 3 3]\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": "markdown",
   "id": "suburban-burst",
   "metadata": {},
   "source": [
    "- But it’s simpler to ask the load() function to do this for you by setting **as_supervised=True (obviously this works only for labeled datasets).** \n",
    "- You can also specify the batch size if you want. Then you can pass the dataset directly to your tf.keras model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "starting-imagination",
   "metadata": {},
   "outputs": [],
   "source": [
    "keras.backend.clear_session()\n",
    "np.random.seed(42)\n",
    "tf.random.set_seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "framed-collection",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 42.9808 - accuracy: 0.8057\n",
      "Epoch 2/5\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 25.3170 - accuracy: 0.8689\n",
      "Epoch 3/5\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 24.3952 - accuracy: 0.8713\n",
      "Epoch 4/5\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 23.8005 - accuracy: 0.8760A: 1s - los\n",
      "Epoch 5/5\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 23.5731 - accuracy: 0.8764\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x1ab4ca04788>"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "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": "markdown",
   "id": "bigger-least",
   "metadata": {},
   "source": [
    "## Exercises\n",
    "### 9. a.\n",
    "Exercise: Load the Fashion MNIST dataset (introduced in Chapter 10); split it into a training set, a validation set, and a test set; shuffle the training set; and save each dataset to multiple TFRecord files. Each record should be a serialized Example protobuf with two features: the serialized image (use tf.io.serialize_tensor() to serialize each image), and the label. Note: for large images, you could use tf.io.encode_jpeg() instead. This would save a lot of space, but it would lose a bit of image quality."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "continuous-wonder",
   "metadata": {},
   "outputs": [],
   "source": [
    "(X_train_full, y_train_full), (X_test, y_test) = keras.datasets.fashion_mnist.load_data()\n",
    "X_valid, X_train = X_train_full[:5000], X_train_full[5000:]\n",
    "y_valid, y_train = y_train_full[:5000], y_train_full[5000:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "current-intellectual",
   "metadata": {},
   "outputs": [],
   "source": [
    "keras.backend.clear_session()\n",
    "np.random.seed(42)\n",
    "tf.random.set_seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "subsequent-extraction",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_set = tf.data.Dataset.from_tensor_slices((X_train, y_train)).shuffle(len(X_train))\n",
    "valid_set = tf.data.Dataset.from_tensor_slices((X_valid, y_valid))\n",
    "test_set = tf.data.Dataset.from_tensor_slices((X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "stupid-domestic",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_example(image, label):\n",
    "    image_data = tf.io.serialize_tensor(image)\n",
    "    #image_data = tf.io.encode_jpeg(image[..., np.newaxis])\n",
    "    return Example(features = Features(feature={\n",
    "        \"image\": Feature(bytes_list=BytesList(value=[image_data.numpy()])),\n",
    "        \"label\": Feature(int64_list=Int64List(value=[label])),\n",
    "    }))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "entitled-lesbian",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "features {\n",
      "  feature {\n",
      "    key: \"image\"\n",
      "    value {\n",
      "      bytes_list {\n",
      "        value: \"\\010\\004\\022\\010\\022\\002\\010\\034\\022\\002\\010\\034\\\"\\220\\006\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\001\\000\\000\\rI\\000\\000\\001\\004\\000\\000\\000\\000\\001\\001\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\003\\000$\\210\\177>6\\000\\000\\000\\001\\003\\004\\000\\000\\003\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\006\\000f\\314\\260\\206\\220{\\027\\000\\000\\000\\000\\014\\n\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\233\\354\\317\\262k\\234\\241m@\\027M\\202H\\017\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\001\\000E\\317\\337\\332\\330\\330\\243\\177yz\\222\\215X\\254B\\000\\000\\000\\000\\000\\000\\000\\000\\000\\001\\001\\001\\000\\310\\350\\350\\351\\345\\337\\337\\327\\325\\244\\177{\\304\\345\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\267\\341\\330\\337\\344\\353\\343\\340\\336\\340\\335\\337\\365\\255\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\301\\344\\332\\325\\306\\264\\324\\322\\323\\325\\337\\334\\363\\312\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\001\\003\\000\\014\\333\\334\\324\\332\\300\\251\\343\\320\\332\\340\\324\\342\\305\\3214\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\006\\000c\\364\\336\\334\\332\\313\\306\\335\\327\\325\\336\\334\\365w\\2478\\000\\000\\000\\000\\000\\000\\000\\000\\000\\004\\000\\0007\\354\\344\\346\\344\\360\\350\\325\\332\\337\\352\\331\\331\\321\\\\\\000\\000\\000\\001\\004\\006\\007\\002\\000\\000\\000\\000\\000\\355\\342\\331\\337\\336\\333\\336\\335\\330\\337\\345\\327\\332\\377M\\000\\000\\003\\000\\000\\000\\000\\000\\000\\000>\\221\\314\\344\\317\\325\\335\\332\\320\\323\\332\\340\\337\\333\\327\\340\\364\\237\\000\\000\\000\\000\\000\\022,Rk\\275\\344\\334\\336\\331\\342\\310\\315\\323\\346\\340\\352\\260\\274\\372\\370\\351\\356\\327\\000\\0009\\273\\320\\340\\335\\340\\320\\314\\326\\320\\321\\310\\237\\365\\301\\316\\337\\377\\377\\335\\352\\335\\323\\334\\350\\366\\000\\003\\312\\344\\340\\335\\323\\323\\326\\315\\315\\315\\334\\360P\\226\\377\\345\\335\\274\\232\\277\\322\\314\\321\\336\\344\\341\\000b\\351\\306\\322\\336\\345\\345\\352\\371\\334\\302\\327\\331\\361AIju\\250\\333\\335\\327\\331\\337\\337\\340\\345\\035K\\314\\324\\314\\301\\315\\323\\341\\330\\271\\305\\316\\306\\325\\360\\303\\343\\365\\357\\337\\332\\324\\321\\336\\334\\335\\346C0\\313\\267\\302\\325\\305\\271\\276\\302\\300\\312\\326\\333\\335\\334\\354\\341\\330\\307\\316\\272\\265\\261\\254\\265\\315\\316s\\000z\\333\\301\\263\\253\\267\\304\\314\\322\\325\\317\\323\\322\\310\\304\\302\\277\\303\\277\\306\\300\\260\\234\\247\\261\\322\\\\\\000\\000J\\275\\324\\277\\257\\254\\257\\265\\271\\274\\275\\274\\301\\306\\314\\321\\322\\322\\323\\274\\274\\302\\300\\330\\252\\000\\002\\000\\000\\000B\\310\\336\\355\\357\\362\\366\\363\\364\\335\\334\\301\\277\\263\\266\\266\\265\\260\\246\\250c:\\000\\000\\000\\000\\000\\000\\000\\000\\000(=,H)#\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\"\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "  feature {\n",
      "    key: \"label\"\n",
      "    value {\n",
      "      int64_list {\n",
      "        value: 9\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for image, label in valid_set.take(1):\n",
    "    print(create_example(image, label))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "charitable-notion",
   "metadata": {},
   "source": [
    "The following function saves a given dataset to a set of TFRecord files. The examples are written to the files in a round-robin fashion. To do this, we enumerate all the examples using the dataset.enumerate() method, and we compute index % n_shards to decide which file to write to. We use the standard contextlib.ExitStack class to make sure that all writers are properly closed whether or not an I/O error occurs while writing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "basic-saturn",
   "metadata": {},
   "outputs": [],
   "source": [
    "from contextlib import ExitStack\n",
    "\n",
    "def write_tfrecords(name, dataset, n_shards=10):\n",
    "    paths = [\"{}.tfrecord-{:05d}-of-{:05d}\".format(name, index, n_shards)\n",
    "             for index in range(n_shards)]\n",
    "    with ExitStack() as stack:\n",
    "        writers = [stack.enter_context(tf.io.TFRecordWriter(path))\n",
    "                   for path in paths]\n",
    "        for index, (image, label) in dataset.enumerate():\n",
    "            shard = index % n_shards\n",
    "            example = create_example(image, label)\n",
    "            writers[shard].write(example.SerializeToString())\n",
    "    return paths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "published-frost",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_filepaths = write_tfrecords(\"my_fashion_mnist.train\", train_set)\n",
    "valid_filepaths = write_tfrecords(\"my_fashion_mnist.valid\", valid_set)\n",
    "test_filepaths = write_tfrecords(\"my_fashion_mnist.test\", test_set)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "electrical-greensboro",
   "metadata": {},
   "source": [
    "### b.\n",
    "Exercise: Then use tf.data to create an efficient dataset for each set. Finally, use a Keras model to train these datasets, including a preprocessing layer to standardize each input feature. Try to make the input pipeline as efficient as possible, using TensorBoard to visualize profiling data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "simple-endorsement",
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess(tfrecord):\n",
    "    feature_descriptions = {\n",
    "        \"image\": tf.io.FixedLenFeature([], tf.string, default_value=\"\"),\n",
    "        \"label\": tf.io.FixedLenFeature([], tf.int64, default_value=-1)\n",
    "    }\n",
    "    example = tf.io.parse_single_example(tfrecord, feature_descriptions)\n",
    "    image = tf.io.parse_tensor(example[\"image\"], out_type=tf.uint8)\n",
    "    #image = tf.io.decode_jpeg(example[\"image\"])\n",
    "    image = tf.reshape(image, shape=[28, 28])\n",
    "    return image, example[\"label\"]\n",
    "\n",
    "def mnist_dataset(filepaths, n_read_threads=5, shuffle_buffer_size=None,\n",
    "                  n_parse_threads=5, batch_size=32, cache=True):\n",
    "    dataset = tf.data.TFRecordDataset(filepaths,\n",
    "                                      num_parallel_reads=n_read_threads)\n",
    "    if cache:\n",
    "        dataset = dataset.cache()\n",
    "    if shuffle_buffer_size:\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": 110,
   "id": "smaller-developer",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_set = mnist_dataset(train_filepaths, shuffle_buffer_size=60000)\n",
    "valid_set = mnist_dataset(valid_filepaths)\n",
    "test_set = mnist_dataset(test_filepaths)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "ordered-benjamin",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for X, y in train_set.take(1):\n",
    "    for i in range(5):\n",
    "        plt.subplot(1, 5, i + 1)\n",
    "        plt.imshow(X[i].numpy(), cmap=\"binary\")\n",
    "        plt.axis(\"off\")\n",
    "        plt.title(str(y[i].numpy()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "pointed-aircraft",
   "metadata": {},
   "outputs": [],
   "source": [
    "keras.backend.clear_session()\n",
    "tf.random.set_seed(42)\n",
    "np.random.seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "brazilian-cable",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Standardization(keras.layers.Layer):\n",
    "    def adapt(self, data_sample):\n",
    "        self.means_ = np.mean(data_sample, axis=0, keepdims=True)\n",
    "        self.stds_ = np.std(data_sample, axis=0, keepdims=True)\n",
    "    def call(self, inputs):\n",
    "        return (inputs - self.means_) / (self.stds_ + keras.backend.epsilon())\n",
    "    \n",
    "standardization = Standardization(input_shape=[28, 28])\n",
    "\n",
    "sample_image_batches = train_set.take(100).map(lambda image, label: image)\n",
    "sample_images = np.concatenate(list(sample_image_batches.as_numpy_iterator()),\n",
    "                               axis=0).astype(np.float32)\n",
    "standardization.adapt(sample_images)\n",
    "\n",
    "model = keras.models.Sequential([\n",
    "    standardization,\n",
    "    keras.layers.Flatten(),\n",
    "    keras.layers.Dense(100, activation=\"relu\"),\n",
    "    keras.layers.Dense(10, activation=\"softmax\")\n",
    "])\n",
    "model.compile(loss=\"sparse_categorical_crossentropy\",\n",
    "              optimizer=\"nadam\", metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "remarkable-dollar",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "1719/1719 [==============================] - 10s 5ms/step - loss: 656.6688 - accuracy: 0.8038 - val_loss: 149.1357 - val_accuracy: 0.8670\n",
      "Epoch 2/5\n",
      "1719/1719 [==============================] - 8s 5ms/step - loss: 209.0040 - accuracy: 0.8781 - val_loss: 282.9151 - val_accuracy: 0.8746\n",
      "Epoch 3/5\n",
      "1719/1719 [==============================] - 7s 4ms/step - loss: 146.5865 - accuracy: 0.8914 - val_loss: 0.3341 - val_accuracy: 0.8826\n",
      "Epoch 4/5\n",
      "1719/1719 [==============================] - 9s 5ms/step - loss: 110.8247 - accuracy: 0.9013 - val_loss: 151.8340 - val_accuracy: 0.8798\n",
      "Epoch 5/5\n",
      "1719/1719 [==============================] - 9s 5ms/step - loss: 175.6314 - accuracy: 0.9105 - val_loss: 87.7635 - val_accuracy: 0.8820\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x1ab4b991908>"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "logs = os.path.join(os.curdir, \"my_logs\",\n",
    "                    \"run_\" + datetime.now().strftime(\"%Y%m%d_%H%M%S\"))\n",
    "\n",
    "tensorboard_cb = tf.keras.callbacks.TensorBoard(\n",
    "    log_dir=logs, histogram_freq=1, profile_batch=10)\n",
    "\n",
    "model.fit(train_set, epochs=5, validation_data=valid_set,\n",
    "          callbacks=[tensorboard_cb])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "moving-diploma",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Reusing TensorBoard on port 6006 (pid 10800), started 17:20:35 ago. (Use '!kill 10800' to kill it.)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "      <iframe id=\"tensorboard-frame-72d2bdce4aa40eb6\" width=\"100%\" height=\"800\" frameborder=\"0\">\n",
       "      </iframe>\n",
       "      <script>\n",
       "        (function() {\n",
       "          const frame = document.getElementById(\"tensorboard-frame-72d2bdce4aa40eb6\");\n",
       "          const url = new URL(\"/\", window.location);\n",
       "          const port = 6006;\n",
       "          if (port) {\n",
       "            url.port = port;\n",
       "          }\n",
       "          frame.src = url;\n",
       "        })();\n",
       "      </script>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%load_ext tensorboard\n",
    "%tensorboard --logdir=./my_logs --port=6006"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "preliminary-maintenance",
   "metadata": {},
   "source": [
    "### 10.\n",
    "Exercise: In this exercise you will download a dataset, split it, create a tf.data.Dataset to load it and preprocess it efficiently, then build and train a binary classification model containing an Embedding layer.\n",
    "\n",
    "### a.\n",
    "Exercise: Download the Large Movie Review Dataset, which contains 50,000 movies reviews from the Internet Movie Database. The data is organized in two directories, train and test, each containing a pos subdirectory with 12,500 positive reviews and a neg subdirectory with 12,500 negative reviews. Each review is stored in a separate text file. There are other files and folders (including preprocessed bag-of-words), but we will ignore them in this exercise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "supposed-cleaner",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz\n",
      "84131840/84125825 [==============================] - 65s 1us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "WindowsPath('C:/Users/batuh/.keras/datasets/aclImdb')"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "DOWNLOAD_ROOT = \"http://ai.stanford.edu/~amaas/data/sentiment/\"\n",
    "FILENAME = \"aclImdb_v1.tar.gz\"\n",
    "filepath = keras.utils.get_file(FILENAME, DOWNLOAD_ROOT + FILENAME, extract=True)\n",
    "path = Path(filepath).parent / \"aclImdb\"\n",
    "path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "special-drink",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "aclImdb\\\n",
      "    README\n",
      "    imdb.vocab\n",
      "    imdbEr.txt\n",
      "    test\\\n",
      "        labeledBow.feat\n",
      "        urls_neg.txt\n",
      "        urls_pos.txt\n",
      "        neg\\\n",
      "            0_2.txt\n",
      "            10000_4.txt\n",
      "            10001_1.txt\n",
      "            ...\n",
      "        pos\\\n",
      "            0_10.txt\n",
      "            10000_7.txt\n",
      "            10001_9.txt\n",
      "            ...\n",
      "    train\\\n",
      "        labeledBow.feat\n",
      "        unsupBow.feat\n",
      "        urls_neg.txt\n",
      "        ...\n",
      "        neg\\\n",
      "            0_3.txt\n",
      "            10000_4.txt\n",
      "            10001_4.txt\n",
      "            ...\n",
      "        pos\\\n",
      "            0_9.txt\n",
      "            10000_8.txt\n",
      "            10001_10.txt\n",
      "            ...\n",
      "        unsup\\\n",
      "            0_0.txt\n",
      "            10000_0.txt\n",
      "            10001_0.txt\n",
      "            ...\n"
     ]
    }
   ],
   "source": [
    "for name, subdirs, files in os.walk(path):\n",
    "    indent = len(Path(name).parts) - len(path.parts)\n",
    "    print(\"    \" * indent + Path(name).parts[-1] + os.sep)\n",
    "    for index, filename in enumerate(sorted(files)):\n",
    "        if index == 3:\n",
    "            print(\"    \" * (indent + 1) + \"...\")\n",
    "            break\n",
    "        print(\"    \" * (indent + 1) + filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "moral-electric",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12500, 12500, 12500, 12500)"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def review_paths(dirpath):\n",
    "    return [str(path) for path in dirpath.glob(\"*.txt\")]\n",
    "\n",
    "train_pos = review_paths(path / \"train\" / \"pos\")\n",
    "train_neg = review_paths(path / \"train\" / \"neg\")\n",
    "test_valid_pos = review_paths(path / \"test\" / \"pos\")\n",
    "test_valid_neg = review_paths(path / \"test\" / \"neg\")\n",
    "\n",
    "len(train_pos), len(train_neg), len(test_valid_pos), len(test_valid_neg)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dated-auditor",
   "metadata": {},
   "source": [
    "### b.\n",
    "Exercise: Split the test set into a validation set (15,000) and a test set (10,000)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "assisted-abuse",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.shuffle(test_valid_pos)\n",
    "\n",
    "test_pos = test_valid_pos[:5000]\n",
    "test_neg = test_valid_neg[:5000]\n",
    "valid_pos = test_valid_pos[5000:]\n",
    "valid_neg = test_valid_neg[5000:]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "subtle-secretary",
   "metadata": {},
   "source": [
    "### c.\n",
    "Exercise: Use tf.data to create an efficient dataset for each set.\n",
    "\n",
    "Since the dataset fits in memory, we can just load all the data using pure Python code and use tf.data.Dataset.from_tensor_slices():"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "complimentary-thong",
   "metadata": {},
   "outputs": [],
   "source": [
    "def imdb_dataset(filepaths_positive, filepaths_negative):\n",
    "    reviews = []\n",
    "    labels = []\n",
    "    for filepaths, label in ((filepaths_negative, 0), (filepaths_positive, 1)):\n",
    "        for filepath in filepaths:\n",
    "            with open(filepath, encoding=\"utf8\") as review_file:\n",
    "                reviews.append(review_file.read())\n",
    "            labels.append(label)\n",
    "    return tf.data.Dataset.from_tensor_slices(\n",
    "        (tf.constant(reviews), tf.constant(labels)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "foreign-april",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor(b\"Story of a man who has unnatural feelings for a pig. Starts out with a opening scene that is a terrific example of absurd comedy. A formal orchestra audience is turned into an insane, violent mob by the crazy chantings of it's singers. Unfortunately it stays absurd the WHOLE time with no general narrative eventually making it just too off putting. Even those from the era should be turned off. The cryptic dialogue would make Shakespeare seem easy to a third grader. On a technical level it's better than you might think with some good cinematography by future great Vilmos Zsigmond. Future stars Sally Kirkland and Frederic Forrest can be seen briefly.\", shape=(), dtype=string)\n",
      "tf.Tensor(0, shape=(), dtype=int32)\n",
      "\n",
      "tf.Tensor(b\"Airport '77 starts as a brand new luxury 747 plane is loaded up with valuable paintings & such belonging to rich businessman Philip Stevens (James Stewart) who is flying them & a bunch of VIP's to his estate in preparation of it being opened to the public as a museum, also on board is Stevens daughter Julie (Kathleen Quinlan) & her son. The luxury jetliner takes off as planned but mid-air the plane is hi-jacked by the co-pilot Chambers (Robert Foxworth) & his two accomplice's Banker (Monte Markham) & Wilson (Michael Pataki) who knock the passengers & crew out with sleeping gas, they plan to steal the valuable cargo & land on a disused plane strip on an isolated island but while making his descent Chambers almost hits an oil rig in the Ocean & loses control of the plane sending it crashing into the sea where it sinks to the bottom right bang in the middle of the Bermuda Triangle. With air in short supply, water leaking in & having flown over 200 miles off course the problems mount for the survivor's as they await help with time fast running out...<br /><br />Also known under the slightly different tile Airport 1977 this second sequel to the smash-hit disaster thriller Airport (1970) was directed by Jerry Jameson & while once again like it's predecessors I can't say Airport '77 is any sort of forgotten classic it is entertaining although not necessarily for the right reasons. Out of the three Airport films I have seen so far I actually liked this one the best, just. It has my favourite plot of the three with a nice mid-air hi-jacking & then the crashing (didn't he see the oil rig?) & sinking of the 747 (maybe the makers were trying to cross the original Airport with another popular disaster flick of the period The Poseidon Adventure (1972)) & submerged is where it stays until the end with a stark dilemma facing those trapped inside, either suffocate when the air runs out or drown as the 747 floods or if any of the doors are opened & it's a decent idea that could have made for a great little disaster flick but bad unsympathetic character's, dull dialogue, lethargic set-pieces & a real lack of danger or suspense or tension means this is a missed opportunity. While the rather sluggish plot keeps one entertained for 108 odd minutes not that much happens after the plane sinks & there's not as much urgency as I thought there should have been. Even when the Navy become involved things don't pick up that much with a few shots of huge ships & helicopters flying about but there's just something lacking here. George Kennedy as the jinxed airline worker Joe Patroni is back but only gets a couple of scenes & barely even says anything preferring to just look worried in the background.<br /><br />The home video & theatrical version of Airport '77 run 108 minutes while the US TV versions add an extra hour of footage including a new opening credits sequence, many more scenes with George Kennedy as Patroni, flashbacks to flesh out character's, longer rescue scenes & the discovery or another couple of dead bodies including the navigator. While I would like to see this extra footage I am not sure I could sit through a near three hour cut of Airport '77. As expected the film has dated badly with horrible fashions & interior design choices, I will say no more other than the toy plane model effects aren't great either. Along with the other two Airport sequels this takes pride of place in the Razzie Award's Hall of Shame although I can think of lots of worse films than this so I reckon that's a little harsh. The action scenes are a little dull unfortunately, the pace is slow & not much excitement or tension is generated which is a shame as I reckon this could have been a pretty good film if made properly.<br /><br />The production values are alright if nothing spectacular. The acting isn't great, two time Oscar winner Jack Lemmon has said since it was a mistake to star in this, one time Oscar winner James Stewart looks old & frail, also one time Oscar winner Lee Grant looks drunk while Sir Christopher Lee is given little to do & there are plenty of other familiar faces to look out for too.<br /><br />Airport '77 is the most disaster orientated of the three Airport films so far & I liked the ideas behind it even if they were a bit silly, the production & bland direction doesn't help though & a film about a sunken plane just shouldn't be this boring or lethargic. Followed by The Concorde ... Airport '79 (1979).\", shape=(), dtype=string)\n",
      "tf.Tensor(0, shape=(), dtype=int32)\n",
      "\n",
      "tf.Tensor(b\"This film lacked something I couldn't put my finger on at first: charisma on the part of the leading actress. This inevitably translated to lack of chemistry when she shared the screen with her leading man. Even the romantic scenes came across as being merely the actors at play. It could very well have been the director who miscalculated what he needed from the actors. I just don't know.<br /><br />But could it have been the screenplay? Just exactly who was the chef in love with? He seemed more enamored of his culinary skills and restaurant, and ultimately of himself and his youthful exploits, than of anybody or anything else. He never convinced me he was in love with the princess.<br /><br />I was disappointed in this movie. But, don't forget it was nominated for an Oscar, so judge for yourself.\", shape=(), dtype=string)\n",
      "tf.Tensor(0, shape=(), dtype=int32)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for X, y in imdb_dataset(train_pos, train_neg).take(3):\n",
    "    print(X)\n",
    "    print(y)\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "covered-wildlife",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "56.3 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%timeit -r1 for X, y in imdb_dataset(train_pos, train_neg).repeat(10): pass"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "single-denmark",
   "metadata": {},
   "source": [
    "It takes about 56 seconds to load the dataset and go through it 10 times.\n",
    "\n",
    "- But let's pretend the dataset does not fit in memory, just to make things more interesting. \n",
    "- Luckily, each review fits on just one line (they use <br /> to indicate line breaks), so we can read the reviews using a TextLineDataset. \n",
    "- If they didn't we would have to preprocess the input files (e.g., converting them to TFRecords). \n",
    "- For very large datasets, it would make sense to use a tool like Apache Beam for that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "known-connectivity",
   "metadata": {},
   "outputs": [],
   "source": [
    "def imdb_dataset(filepaths_positive, filepaths_negative, n_read_threads=5):\n",
    "    dataset_neg = tf.data.TextLineDataset(filepaths_negative,\n",
    "                                          num_parallel_reads=n_read_threads)\n",
    "    dataset_neg = dataset_neg.map(lambda review: (review, 0))\n",
    "    dataset_pos = tf.data.TextLineDataset(filepaths_positive,\n",
    "                                          num_parallel_reads=n_read_threads)\n",
    "    dataset_pos = dataset_pos.map(lambda review: (review, 1))\n",
    "    return tf.data.Dataset.concatenate(dataset_pos, dataset_neg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "composite-scottish",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1min 25s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%timeit -r1 for X, y in imdb_dataset(train_pos, train_neg).repeat(10): pass"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cathedral-robert",
   "metadata": {},
   "source": [
    "- Now it takes about 85 seconds to go through the dataset 10 times. \n",
    "- **That's much slower, essentially because the dataset is not cached in RAM, so it must be reloaded at each epoch.** \n",
    "- **If you add .cache() just before .repeat(10)**, you will see that this implementation will be about as fast as the previous one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "crazy-clock",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "54.1 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%timeit -r1 for X, y in imdb_dataset(train_pos, train_neg).cache().repeat(10): pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "marked-invite",
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 32\n",
    "\n",
    "train_set = imdb_dataset(train_pos, train_neg).shuffle(25000).batch(batch_size).prefetch(1)\n",
    "valid_set = imdb_dataset(valid_pos, valid_neg).batch(batch_size).prefetch(1)\n",
    "test_set = imdb_dataset(test_pos, test_neg).batch(batch_size).prefetch(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "pretty-aviation",
   "metadata": {},
   "source": [
    "### d.\n",
    "Exercise: Create a binary classification model, using a TextVectorization layer to preprocess each review. If the TextVectorization layer is not yet available (or if you like a challenge), try to create your own custom preprocessing layer: you can use the functions in the tf.strings package, for example lower() to make everything lowercase, regex_replace() to replace punctuation with spaces, and split() to split words on spaces. You should use a lookup table to output word indices, which must be prepared in the adapt() method.\n",
    "\n",
    "Let's first write a function to preprocess the reviews, cropping them to 300 characters, converting them to lower case, then replacing <br /> and all non-letter characters to spaces, splitting the reviews into words, and finally padding or cropping each review so it ends up with exactly n_words tokens:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "textile-cricket",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(2, 50), dtype=string, numpy=\n",
       "array([[b'it', b's', b'a', b'great', b'great', b'movie', b'i', b'loved',\n",
       "        b'it', b'<pad>', b'<pad>', b'<pad>', b'<pad>', b'<pad>',\n",
       "        b'<pad>', b'<pad>', b'<pad>', b'<pad>', b'<pad>', b'<pad>',\n",
       "        b'<pad>', b'<pad>', b'<pad>', b'<pad>', b'<pad>', b'<pad>',\n",
       "        b'<pad>', b'<pad>', b'<pad>', b'<pad>', b'<pad>', b'<pad>',\n",
       "        b'<pad>', b'<pad>', b'<pad>', b'<pad>', b'<pad>', b'<pad>',\n",
       "        b'<pad>', b'<pad>', b'<pad>', b'<pad>', b'<pad>', b'<pad>',\n",
       "        b'<pad>', b'<pad>', b'<pad>', b'<pad>', b'<pad>', b'<pad>'],\n",
       "       [b'it', b'was', b'terrible', b'run', b'away', b'<pad>', b'<pad>',\n",
       "        b'<pad>', b'<pad>', b'<pad>', b'<pad>', b'<pad>', b'<pad>',\n",
       "        b'<pad>', b'<pad>', b'<pad>', b'<pad>', b'<pad>', b'<pad>',\n",
       "        b'<pad>', b'<pad>', b'<pad>', b'<pad>', b'<pad>', b'<pad>',\n",
       "        b'<pad>', b'<pad>', b'<pad>', b'<pad>', b'<pad>', b'<pad>',\n",
       "        b'<pad>', b'<pad>', b'<pad>', b'<pad>', b'<pad>', b'<pad>',\n",
       "        b'<pad>', b'<pad>', b'<pad>', b'<pad>', b'<pad>', b'<pad>',\n",
       "        b'<pad>', b'<pad>', b'<pad>', b'<pad>', b'<pad>', b'<pad>',\n",
       "        b'<pad>']], dtype=object)>"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def preprocess(X_batch, n_words=50):\n",
    "    shape = tf.shape(X_batch) * tf.constant([1, 0]) + tf.constant([0, n_words])\n",
    "    Z = tf.strings.substr(X_batch, 0, 300)\n",
    "    Z = tf.strings.lower(Z)\n",
    "    Z = tf.strings.regex_replace(Z, b\"<br\\\\s*/?>\", b\" \")\n",
    "    Z = tf.strings.regex_replace(Z, b\"[^a-z]\", b\" \")\n",
    "    Z = tf.strings.split(Z)\n",
    "    return Z.to_tensor(shape=shape, default_value=b\"<pad>\")\n",
    "\n",
    "X_example = tf.constant([\"It's a great, great movie! I loved it.\", \"It was terrible, run away!!!\"])\n",
    "preprocess(X_example)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "swedish-malawi",
   "metadata": {},
   "source": [
    "Now let's write a second utility function that will take a data sample with the same format as the output of the preprocess() function, and will output the list of the top max_size most frequent words, ensuring that the padding token is first:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "living-sculpture",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[b'<pad>',\n",
       " b'it',\n",
       " b'great',\n",
       " b's',\n",
       " b'a',\n",
       " b'movie',\n",
       " b'i',\n",
       " b'loved',\n",
       " b'was',\n",
       " b'terrible',\n",
       " b'run',\n",
       " b'away']"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "\n",
    "def get_vocabulary(data_sample, max_size=1000):\n",
    "    preprocessed_reviews = preprocess(data_sample).numpy()\n",
    "    counter = Counter()\n",
    "    for words in preprocessed_reviews:\n",
    "        for word in words:\n",
    "            if word != b\"<pad>\":\n",
    "                counter[word] += 1\n",
    "    return [b\"<pad>\"] + [word for word, count in counter.most_common(max_size)]\n",
    "\n",
    "get_vocabulary(X_example)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "facial-lawsuit",
   "metadata": {},
   "source": [
    "- Now we are ready to create the **TextVectorization layer.** \n",
    "- Its constructor just saves the hyperparameters (max_vocabulary_size and n_oov_buckets). \n",
    "- The adapt() method computes the vocabulary using the get_vocabulary() function, then it builds a StaticVocabularyTable. \n",
    "- The call() method preprocesses the reviews to get a padded list of words for each review, then it uses the StaticVocabularyTable to lookup the index of each word in the vocabulary:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "valuable-bandwidth",
   "metadata": {},
   "outputs": [],
   "source": [
    "class TextVectorization(keras.layers.Layer):\n",
    "    def __init__(self, max_vocabulary_size=1000, n_oov_buckets=100, dtype=tf.string, **kwargs):\n",
    "        super().__init__(dtype=dtype, **kwargs)\n",
    "        self.max_vocabulary_size = max_vocabulary_size\n",
    "        self.n_oov_buckets = n_oov_buckets\n",
    "\n",
    "    def adapt(self, data_sample):\n",
    "        self.vocab = get_vocabulary(data_sample, self.max_vocabulary_size)\n",
    "        words = tf.constant(self.vocab)\n",
    "        word_ids = tf.range(len(self.vocab), dtype=tf.int64)\n",
    "        vocab_init = tf.lookup.KeyValueTensorInitializer(words, word_ids)\n",
    "        self.table = tf.lookup.StaticVocabularyTable(vocab_init, self.n_oov_buckets)\n",
    "        \n",
    "    def call(self, inputs):\n",
    "        preprocessed_inputs = preprocess(inputs)\n",
    "        return self.table.lookup(preprocessed_inputs)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "muslim-caution",
   "metadata": {},
   "source": [
    "Let's try it on our small X_example we defined earlier:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "sealed-brother",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(2, 50), dtype=int64, numpy=\n",
       "array([[ 1,  3,  4,  2,  2,  5,  6,  7,  1,  0,  0,  0,  0,  0,  0,  0,\n",
       "         0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
       "         0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
       "         0,  0],\n",
       "       [ 1,  8,  9, 10, 11,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
       "         0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
       "         0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
       "         0,  0]], dtype=int64)>"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text_vectorization = TextVectorization()\n",
    "\n",
    "text_vectorization.adapt(X_example)\n",
    "text_vectorization(X_example)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bottom-establishment",
   "metadata": {},
   "source": [
    "Looks good! As you can see, each review was cleaned up and tokenized, then each word was encoded as its index in the vocabulary (all the 0s correspond to the <pad> tokens).\n",
    "\n",
    "Now let's create another TextVectorization layer and let's adapt it to the full IMDB training set (if the training set did not fit in RAM, we could just use a smaller sample of the training set by calling train_set.take(500)):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "useful-vietnamese",
   "metadata": {},
   "outputs": [],
   "source": [
    "max_vocabulary_size = 1000\n",
    "n_oov_buckets = 100\n",
    "\n",
    "sample_review_batches = train_set.map(lambda review, label: review)\n",
    "sample_reviews = np.concatenate(list(sample_review_batches.as_numpy_iterator()),\n",
    "                                axis=0)\n",
    "\n",
    "text_vectorization = TextVectorization(max_vocabulary_size, n_oov_buckets, input_shape=[])\n",
    "text_vectorization.adapt(sample_reviews)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "reported-pulse",
   "metadata": {},
   "source": [
    "Let's run it on the same X_example, just to make sure the word IDs are larger now, since the vocabulary is bigger:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "bored-hostel",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(2, 50), dtype=int64, numpy=\n",
       "array([[  9,  14,   2,  64,  64,  12,   5, 257,   9,   0,   0,   0,   0,\n",
       "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
       "       [  9,  13, 269, 531, 334,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0]],\n",
       "      dtype=int64)>"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text_vectorization(X_example)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "internal-halloween",
   "metadata": {},
   "source": [
    "Good! Now let's take a look at the first 10 words in the vocabulary:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "prime-avenue",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[b'<pad>', b'the', b'a', b'of', b'and', b'i', b'to', b'is', b'this', b'it']"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text_vectorization.vocab[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "breathing-convergence",
   "metadata": {},
   "source": [
    "These are the most common words in the reviews.\n",
    "\n",
    "Now to build our model we will need to encode all these word IDs somehow. One approach is to create bags of words: for each review, and for each word in the vocabulary, we count the number of occurences of that word in the review. For example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "annoying-conservative",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(2, 4), dtype=float32, numpy=\n",
       "array([[2., 2., 0., 1.],\n",
       "       [3., 0., 2., 0.]], dtype=float32)>"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "simple_example = tf.constant([[1, 3, 1, 0, 0], [2, 2, 0, 0, 0]])\n",
    "tf.reduce_sum(tf.one_hot(simple_example, 4), axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "particular-somalia",
   "metadata": {},
   "source": [
    "- **The first review has 2 times the word 0, 2 times the word 1, 0 times the word 2, and 1 time the word 3, so its bag-of-words representation is [2, 2, 0, 1].**\n",
    "- Similarly, the second review has 3 times the word 0, 0 times the word 1, and so on. \n",
    "- Let's wrap this logic in a small custom layer, and let's test it. \n",
    "- We'll drop the counts for the word 0, since this corresponds to the < pad > token, which we don't care about."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "smoking-portsmouth",
   "metadata": {},
   "outputs": [],
   "source": [
    "class BagOfWords(keras.layers.Layer):\n",
    "    def __init__(self, n_tokens, dtype=tf.int32, **kwargs):\n",
    "        super().__init__(dtype=dtype, **kwargs)\n",
    "        self.n_tokens = n_tokens\n",
    "    def call(self, inputs):\n",
    "        one_hot = tf.one_hot(inputs, self.n_tokens)\n",
    "        return tf.reduce_sum(one_hot, axis=1)[:, 1:]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "expanded-broadway",
   "metadata": {},
   "source": [
    "Let's test it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "offshore-presentation",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(2, 3), dtype=float32, numpy=\n",
       "array([[2., 0., 1.],\n",
       "       [0., 2., 0.]], dtype=float32)>"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bag_of_words = BagOfWords(n_tokens=4)\n",
    "bag_of_words(simple_example)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "entertaining-bidder",
   "metadata": {},
   "source": [
    "It works fine! Now let's create another BagOfWord with the right vocabulary size for our training set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "center-adelaide",
   "metadata": {},
   "outputs": [],
   "source": [
    "n_tokens = max_vocabulary_size + n_oov_buckets + 1 # add 1 for <pad>\n",
    "bag_of_words = BagOfWords(n_tokens)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "nearby-adapter",
   "metadata": {},
   "source": [
    "We're ready to train the model!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "aerial-musician",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "782/782 [==============================] - 128s 159ms/step - loss: 0.5815 - accuracy: 0.6811 - val_loss: 0.5124 - val_accuracy: 0.7373\n",
      "Epoch 2/5\n",
      "782/782 [==============================] - 16s 18ms/step - loss: 0.4680 - accuracy: 0.7737 - val_loss: 0.5104 - val_accuracy: 0.7413\n",
      "Epoch 3/5\n",
      "782/782 [==============================] - 17s 18ms/step - loss: 0.4189 - accuracy: 0.8034 - val_loss: 0.5167 - val_accuracy: 0.7411\n",
      "Epoch 4/5\n",
      "782/782 [==============================] - 16s 18ms/step - loss: 0.3451 - accuracy: 0.8591 - val_loss: 0.5310 - val_accuracy: 0.7379\n",
      "Epoch 5/5\n",
      "782/782 [==============================] - 16s 18ms/step - loss: 0.2634 - accuracy: 0.9060 - val_loss: 0.5807 - val_accuracy: 0.7311\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x1ab5cb78f88>"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = keras.models.Sequential([\n",
    "    text_vectorization,\n",
    "    bag_of_words,\n",
    "    keras.layers.Dense(100, activation=\"relu\"),\n",
    "    keras.layers.Dense(1, activation=\"sigmoid\"),\n",
    "])\n",
    "model.compile(loss=\"binary_crossentropy\", optimizer=\"nadam\",\n",
    "              metrics=[\"accuracy\"])\n",
    "model.fit(train_set, epochs=5, validation_data=valid_set)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "varied-lender",
   "metadata": {},
   "source": [
    "- We get about 73.7% accuracy on the validation set after just the first epoch, but after that the model makes no significant progress.\n",
    "- For now the point is just to perform efficient preprocessing using tf.data and Keras preprocessing layers."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "intellectual-adams",
   "metadata": {},
   "source": [
    "### e.\n",
    "Exercise: Add an Embedding layer and compute the mean embedding for each review, multiplied by the square root of the number of words. This rescaled mean embedding can then be passed to the rest of your model.\n",
    "\n",
    "To compute the mean embedding for each review, and multiply it by the square root of the number of words in that review, we will need a little function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "pediatric-thousand",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(2, 3), dtype=float32, numpy=\n",
       "array([[2.3570225, 3.2998314, 1.4142135],\n",
       "       [2.       , 0.       , 0.       ]], dtype=float32)>"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def compute_mean_embedding(inputs):\n",
    "    not_pad = tf.math.count_nonzero(inputs, axis=-1)\n",
    "    n_words = tf.math.count_nonzero(not_pad, axis=-1, keepdims=True)\n",
    "    sqrt_n_words = tf.math.sqrt(tf.cast(n_words, tf.float32))\n",
    "    return tf.reduce_mean(inputs, axis=1) * sqrt_n_words\n",
    "\n",
    "another_example = tf.constant([[[1., 2., 3.], [4., 5., 0.], [0., 0., 0.]],\n",
    "                               [[6., 0., 0.], [0., 0., 0.], [0., 0., 0.]]])\n",
    "compute_mean_embedding(another_example)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "genuine-sessions",
   "metadata": {},
   "source": [
    "Let's check that this is correct. \n",
    "- The first review contains 2 words (the last token is a zero vector, which represents the < pad > token). \n",
    "- The second review contains 1 word. \n",
    "- So we need to compute the mean embedding for each review, and multiply the first one by the square root of 2, and the second one by the square root of 1:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "essential-density",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(2, 3), dtype=float32, numpy=\n",
       "array([[2.3570225, 3.2998314, 1.4142135],\n",
       "       [2.       , 0.       , 0.       ]], dtype=float32)>"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.reduce_mean(another_example, axis=1) * tf.sqrt([[2.], [1.]])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "marine-falls",
   "metadata": {},
   "source": [
    "Perfect. Now we're ready to train our final model. It's the same as before, except we replaced the BagOfWords layer with an Embedding layer followed by a Lambda layer that calls the compute_mean_embedding layer:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "inside-antenna",
   "metadata": {},
   "outputs": [],
   "source": [
    "embedding_size=20\n",
    "\n",
    "model = keras.models.Sequential([\n",
    "    text_vectorization,\n",
    "    keras.layers.Embedding(input_dim=n_tokens,\n",
    "                           output_dim=embedding_size,\n",
    "                           mask_zero=True), # <pad> tokens => zero vectors\n",
    "    keras.layers.Lambda(compute_mean_embedding),\n",
    "    keras.layers.Dense(100, activation=\"relu\"),\n",
    "    keras.layers.Dense(1, activation=\"sigmoid\"),\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "center-european",
   "metadata": {},
   "source": [
    "### f.\n",
    "Exercise: Train the model and see what accuracy you get. Try to optimize your pipelines to make training as fast as possible."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "arbitrary-narrative",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "782/782 [==============================] - 212s 129ms/step - loss: 0.6056 - accuracy: 0.6635 - val_loss: 0.5247 - val_accuracy: 0.7349\n",
      "Epoch 2/5\n",
      "782/782 [==============================] - 8s 8ms/step - loss: 0.4938 - accuracy: 0.7551 - val_loss: 0.5114 - val_accuracy: 0.7398\n",
      "Epoch 3/5\n",
      "782/782 [==============================] - 8s 8ms/step - loss: 0.4765 - accuracy: 0.7653 - val_loss: 0.5084 - val_accuracy: 0.7402\n",
      "Epoch 4/5\n",
      "782/782 [==============================] - 9s 9ms/step - loss: 0.4629 - accuracy: 0.7704 - val_loss: 0.5113 - val_accuracy: 0.7367\n",
      "Epoch 5/5\n",
      "782/782 [==============================] - 8s 8ms/step - loss: 0.4640 - accuracy: 0.7671 - val_loss: 0.5129 - val_accuracy: 0.7420\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x1ab5b66bc08>"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.compile(loss=\"binary_crossentropy\", optimizer=\"nadam\", metrics=[\"accuracy\"])\n",
    "model.fit(train_set, epochs=5, validation_data=valid_set)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "early-saint",
   "metadata": {},
   "source": [
    "The model is not better using embeddings. The pipeline looks fast enough (we optimized it earlier)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "deluxe-october",
   "metadata": {},
   "source": [
    "### g.\n",
    "Exercise: Use TFDS to load the same dataset more easily: tfds.load(\"imdb_reviews\")."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "alpine-portsmouth",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1mDownloading and preparing dataset Unknown size (download: Unknown size, generated: Unknown size, total: Unknown size) to C:\\Users\\batuh\\tensorflow_datasets\\imdb_reviews\\plain_text\\1.0.0...\u001b[0m\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0ce7808125074453b38b32358df4cd8b",
       "version_major": 2,
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      },
      "text/plain": [
       "Dl Completed...: 0 url [00:00, ? url/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      "text/plain": [
       "Dl Size...: 0 MiB [00:00, ? MiB/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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      "text/plain": [
       "Generating train examples...: 0 examples [00:00, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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     },
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     },
     "metadata": {},
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    {
     "data": {
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       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Shuffling imdb_reviews-test.tfrecord...:   0%|          | 0/25000 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating unsupervised examples...: 0 examples [00:00, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Shuffling imdb_reviews-unsupervised.tfrecord...:   0%|          | 0/50000 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1mDataset imdb_reviews downloaded and prepared to C:\\Users\\batuh\\tensorflow_datasets\\imdb_reviews\\plain_text\\1.0.0. Subsequent calls will reuse this data.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "import tensorflow_datasets as tfds\n",
    "\n",
    "datasets = tfds.load(name=\"imdb_reviews\")\n",
    "train_set, test_set = datasets[\"train\"], datasets[\"test\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "neither-cache",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor(b\"This was an absolutely terrible movie. Don't be lured in by Christopher Walken or Michael Ironside. Both are great actors, but this must simply be their worst role in history. Even their great acting could not redeem this movie's ridiculous storyline. This movie is an early nineties US propaganda piece. The most pathetic scenes were those when the Columbian rebels were making their cases for revolutions. Maria Conchita Alonso appeared phony, and her pseudo-love affair with Walken was nothing but a pathetic emotional plug in a movie that was devoid of any real meaning. I am disappointed that there are movies like this, ruining actor's like Christopher Walken's good name. I could barely sit through it.\", shape=(), dtype=string)\n",
      "tf.Tensor(0, shape=(), dtype=int64)\n"
     ]
    }
   ],
   "source": [
    "for example in train_set.take(1):\n",
    "    print(example[\"text\"])\n",
    "    print(example[\"label\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "sapphire-mistress",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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