13_tensorflow_data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "postal-approval",
   "metadata": {},
   "source": [
    "# Loading and Preprocessing Data with TensorFlow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "jewish-adoption",
   "metadata": {},
   "outputs": [],
   "source": [
    "# FIXME: meke autocompletion working again\n",
    "%config Completer.use_jedi = False\n",
    "\n",
    "import os\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "\n",
    "np.random.seed(42)\n",
    "tf.random.set_seed(42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "white-saver",
   "metadata": {},
   "source": [
    "## Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "labeled-pathology",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<TensorSliceDataset shapes: (), types: tf.int32>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = tf.range(10)\n",
    "\n",
    "# Create new dataset from a tensor\n",
    "dataset = tf.data.Dataset.from_tensor_slices(X)\n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "informative-webcam",
   "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": [
    "# This is equivalent to the above\n",
    "dataset = tf.data.Dataset.range(10)\n",
    "\n",
    "# Iterate over the items of a dataset\n",
    "for item in dataset:\n",
    "    print(item)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "supported-divorce",
   "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": [
    "# Repeat the dataset 3 times and batch it by 7 items\n",
    "dataset = dataset.repeat(3).batch(7)\n",
    "\n",
    "# Note: The last batch may be incomplete (there's a parameter to skip it)\n",
    "for item in dataset:\n",
    "    print(item)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "inner-agency",
   "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": [
    "# Apply a transformation to dataset items\n",
    "dataset = dataset.map(lambda x: x * 2)\n",
    "\n",
    "for item in dataset:\n",
    "    print(item)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "arabic-paste",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<_UnbatchDataset shapes: (), types: tf.int64>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Flatten the batches back to single stream of items\n",
    "dataset = dataset.unbatch()\n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "nutritional-prediction",
   "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": [
    "# Filter the items by a predicate\n",
    "dataset = dataset.filter(lambda x: x < 10)\n",
    "\n",
    "# Take just first 3 items\n",
    "for item in dataset.take(3):\n",
    "    print(item)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "marked-johns",
   "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": [
    "# Set the RNG state\n",
    "tf.random.set_seed(42)\n",
    "\n",
    "# Build a dataset\n",
    "dataset = tf.data.Dataset.range(10).repeat(3)\n",
    "\n",
    "# Shuffle the data using a buffer of size 3 and batch it by 7\n",
    "dataset = dataset.shuffle(buffer_size=3, seed=42).batch(7)\n",
    "for item in dataset:\n",
    "    print(item)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "clear-effect",
   "metadata": {},
   "source": [
    "## Split the California dataset to multiple CSV files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "cathedral-nylon",
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "\n",
    "from sklearn.datasets import fetch_california_housing\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# Fetch and split the California housing dataset\n",
    "housing = fetch_california_housing()\n",
    "\n",
    "X_train_full, X_test, y_train_full, y_test = train_test_split(housing.data, housing.target.reshape(-1, 1), random_state=42)\n",
    "X_train, X_valid, y_train, y_valid = train_test_split(X_train_full, y_train_full, random_state=42)\n",
    "\n",
    "# Scale the training data and keep the mean and std aside\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(X_train)\n",
    "X_mean = scaler.mean_\n",
    "X_std = scaler.scale_\n",
    "\n",
    "# Let's assume this is a very large dataset that doesn't fit into memory\n",
    "#  - then it's typically split into multiple partitions as stored separately\n",
    "#  - TF then can lazily and in parallel load such a dataset\n",
    "\n",
    "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",
    "    \n",
    "    # Partition the data\n",
    "    for file_idx, row_indices in enumerate(np.array_split(np.arange(m), n_parts)):\n",
    "        \n",
    "        # Generate new CSV file name\n",
    "        part_csv = path_format.format(name_prefix, file_idx)\n",
    "        filepaths.append(part_csv)\n",
    "        \n",
    "        # Create new partition file\n",
    "        with open(part_csv, \"wt\", encoding=\"utf-8\") as f:\n",
    "            # Crate new CSV writer\n",
    "            writer = csv.writer(f, quoting=csv.QUOTE_MINIMAL)\n",
    "            \n",
    "            # Optionally write the header\n",
    "            if header is not None and header:\n",
    "                writer.writerow(header)\n",
    "                \n",
    "            # Write all the data rows\n",
    "            rows = (\n",
    "                [repr(col) for col in data[row_idx]]\n",
    "                for row_idx in row_indices\n",
    "            )\n",
    "            writer.writerows(rows)\n",
    "    \n",
    "    # Return a list of generated CSV file paths\n",
    "    return filepaths\n",
    "\n",
    "\n",
    "# Collect the data to be partitioned\n",
    "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",
    "\n",
    "# Make a CSV header\n",
    "header = housing.feature_names + [\"MedianHouseValue\"]\n",
    "\n",
    "# Partition and save all the datasets\n",
    "train_filepaths = save_to_multiple_csv_files(train_data, name_prefix=\"train\", header=header, n_parts=20)\n",
    "valid_filepaths = save_to_multiple_csv_files(valid_data, name_prefix=\"valid\", header=header, n_parts=10)\n",
    "test_filepaths = save_to_multiple_csv_files(test_data, name_prefix=\"test\", header=header, n_parts=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "split-amino",
   "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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Check out one of the training partition files\n",
    "pd.read_csv(train_filepaths[0]).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "exciting-patrick",
   "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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_filepaths"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ahead-tobago",
   "metadata": {},
   "source": [
    "## Building an Input Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "premier-lease",
   "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": [
    "# Build a dataset consisting of all the training partitions\n",
    "#  - this function also shuffles the filepaths to make all later batches more iid\n",
    "filepath_dataset = tf.data.Dataset.list_files(train_filepaths, seed=42)\n",
    "\n",
    "# Show shuffled file paths\n",
    "for filepath in filepath_dataset:\n",
    "    print(filepath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "dramatic-literacy",
   "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": [
    "def load_textline_ds(filepath):\n",
    "    # Load the CSV data without the header\n",
    "    return tf.data.TextLineDataset(filepath).skip(1)\n",
    "\n",
    "# Load `n_readers` items in parallel and mapping them - loading the dataset\n",
    "n_readers = 5\n",
    "dataset = filepath_dataset.interleave(load_textline_ds, cycle_length=n_readers)\n",
    "\n",
    "for line in dataset.take(5):\n",
    "    print(line.numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "central-check",
   "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": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's try to parse a CSV row\n",
    "#  - Notice: field 4 is interpreted as string\n",
    "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": "code",
   "execution_count": 15,
   "id": "reliable-lafayette",
   "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": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# An example showing that all the missing fields are replaced by the respective default value\n",
    "parsed_fields = tf.io.decode_csv(',,,,5', record_defaults)\n",
    "parsed_fields"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "numeric-africa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Field 4 is required but missing in record 0! [Op:DecodeCSV]\n"
     ]
    }
   ],
   "source": [
    "# This example fails because the 5th field is compulsory due to it's defualt `tf.constant([])`\n",
    "try:\n",
    "    parsed_fields = tf.io.decode_csv(',,,,', record_defaults)\n",
    "except tf.errors.InvalidArgumentError as ex:\n",
    "    print(ex)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "historical-effort",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Expect 5 fields but have 7 in record 0 [Op:DecodeCSV]\n"
     ]
    }
   ],
   "source": [
    "# Also the number of fields must match\n",
    "try:\n",
    "    parsed_fields = tf.io.decode_csv('1,2,3,4,5,6,7', record_defaults)\n",
    "except tf.errors.InvalidArgumentError as ex:\n",
    "    print(ex)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "orange-grill",
   "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": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_inputs = X_train.shape[-1]\n",
    "default_values = [0.] * n_inputs + [tf.constant([], dtype=tf.float32)]\n",
    "\n",
    "@tf.function\n",
    "def preprocess(line):\n",
    "    # Parse the CSV line\n",
    "    fields = tf.io.decode_csv(line, record_defaults=default_values)\n",
    "    # Split the fields to features and target\n",
    "    x = tf.stack(fields[:-1])\n",
    "    y = tf.stack(fields[-1:])\n",
    "    # Return normalized data\n",
    "    #  - notice that here we use the scaling parameters that must be pre-computed and kept somewhere\n",
    "    return (x - X_mean) / X_std, y\n",
    "\n",
    "# Test the preprossing function\n",
    "preprocess(b'4.2083,44.0,5.3232,0.9171,846.0,2.3370,37.47,-122.2,2.782')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "accompanied-point",
   "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": [
    "# Reset the RNG state\n",
    "tf.random.set_seed(42)\n",
    "\n",
    "# Pack all the preprocessing logic above to single action\n",
    "def read_csv_dataset(\n",
    "    filepaths,\n",
    "    repeat=1,\n",
    "    n_readers=5,\n",
    "    n_read_threads=None,\n",
    "    shuffle_buffer_size=10000,\n",
    "    n_parse_threads=5,\n",
    "    batch_size=32,\n",
    "):\n",
    "    # Prefetching means that TF loads next data in advance while the upstream process still runs\n",
    "    #  - Upstream here typically means model training which typically runs on a GPU\n",
    "    #  - So the reasources are well utilized since this runs in parallel on CPUs and the GPU can be 100% utilized\n",
    "    return (\n",
    "        tf.data.Dataset\n",
    "        .list_files(filepaths)\n",
    "        .repeat(repeat)\n",
    "        .interleave(load_textline_ds, cycle_length=n_readers, num_parallel_calls=n_read_threads)\n",
    "        .shuffle(shuffle_buffer_size)\n",
    "        .map(preprocess, num_parallel_calls=n_parse_threads)\n",
    "        .batch(batch_size)\n",
    "        .prefetch(1)\n",
    "    )\n",
    "\n",
    "# Test this preprocessing pipeline on the training dataset\n",
    "train_set = read_csv_dataset(train_filepaths, batch_size=3)\n",
    "\n",
    "for X_batch, y_batch in train_set.take(2):\n",
    "    print(\"X =\", X_batch)\n",
    "    print(\"y =\", y_batch)\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "drawn-freight",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "362/362 [==============================] - 1s 2ms/step - loss: 1.7443 - val_loss: 1.5302\n",
      "Epoch 2/10\n",
      "362/362 [==============================] - 1s 2ms/step - loss: 0.8780 - val_loss: 1.0380\n",
      "Epoch 3/10\n",
      "362/362 [==============================] - 1s 2ms/step - loss: 0.7399 - val_loss: 0.7188\n",
      "Epoch 4/10\n",
      "362/362 [==============================] - 1s 2ms/step - loss: 0.7234 - val_loss: 0.6914\n",
      "Epoch 5/10\n",
      "362/362 [==============================] - 1s 2ms/step - loss: 0.6817 - val_loss: 0.8448\n",
      "Epoch 6/10\n",
      "362/362 [==============================] - 1s 2ms/step - loss: 0.6367 - val_loss: 0.9235\n",
      "Epoch 7/10\n",
      "362/362 [==============================] - 1s 2ms/step - loss: 0.6032 - val_loss: 0.6490\n",
      "Epoch 8/10\n",
      "362/362 [==============================] - 1s 2ms/step - loss: 0.5945 - val_loss: 0.5858\n",
      "Epoch 9/10\n",
      "362/362 [==============================] - 1s 2ms/step - loss: 0.5454 - val_loss: 0.5139\n",
      "Epoch 10/10\n",
      "362/362 [==============================] - 1s 2ms/step - loss: 0.5092 - val_loss: 0.5005\n",
      "161/161 [==============================] - 0s 1ms/step - loss: 0.5101\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[1.4324219],\n",
       "       [1.524963 ],\n",
       "       [2.2890623],\n",
       "       ...,\n",
       "       [1.6232221],\n",
       "       [1.543162 ],\n",
       "       [2.7742057]], dtype=float32)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reset TF session and RNG state\n",
    "keras.backend.clear_session()\n",
    "np.random.seed(42)\n",
    "tf.random.set_seed(42)\n",
    "\n",
    "# Load all datasets\n",
    "train_set = read_csv_dataset(train_filepaths, repeat=None)\n",
    "valid_set = read_csv_dataset(valid_filepaths)\n",
    "test_set = read_csv_dataset(test_filepaths)\n",
    "\n",
    "# Build a simple regression model\n",
    "model = keras.models.Sequential([\n",
    "    keras.layers.Dense(30, activation=\"relu\", input_shape=X_train.shape[1:]),\n",
    "    keras.layers.Dense(1),\n",
    "])\n",
    "model.compile(loss=\"mse\", optimizer=keras.optimizers.SGD(lr=1e-3))\n",
    "\n",
    "# Train and validate the model on loaded datasets\n",
    "batch_size = 32\n",
    "model.fit(train_set, steps_per_epoch=len(X_train) // batch_size, epochs=10, validation_data=valid_set)\n",
    "\n",
    "# Evaluate the model on the test dataset\n",
    "model.evaluate(test_set, steps=len(X_test) // batch_size)\n",
    "\n",
    "# Make new predictions\n",
    "# - note: here we could instead just pass `test_set`, Keras would ignore the labels\n",
    "new_set = test_set.map(lambda X, y: X)\n",
    "X_new = X_test\n",
    "model.predict(new_set, steps=len(X_new) // batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "unique-camcorder",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Global step 1810/1810"
     ]
    }
   ],
   "source": [
    "# Training hyperparameters\n",
    "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",
    "\n",
    "# Example of custom training loop\n",
    "#  - here the TF dataset takes care of the micro-batching\n",
    "#  - however, preprocessing is not part of the model and runs on CPUs\n",
    "#  - this means that it is not as portable as the TF model\n",
    "\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",
    "    \n",
    "    # Feed the batch forward through the model\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",
    "    \n",
    "    # Compute gradients and make an optimization step\n",
    "    gradients = tape.gradient(loss, model.trainable_variables)\n",
    "    optimizer.apply_gradients(zip(gradients, model.trainable_variables))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "curious-claim",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reset TF session and RNG state\n",
    "keras.backend.clear_session()\n",
    "np.random.seed(42)\n",
    "tf.random.set_seed(42)\n",
    "\n",
    "# Define training hyperparameters\n",
    "optimizer = keras.optimizers.Nadam(lr=0.01)\n",
    "loss_fn = keras.losses.mean_squared_error\n",
    "\n",
    "# Due to `tf.function` both model training and preprocessing is ran by TF (i.e. possibly on the GPU)\n",
    "@tf.function\n",
    "def train(model, n_epochs, batch_size=32, n_readers=5, n_read_threads=5, shuffle_buffer_size=10000, n_parse_threads=5):\n",
    "    train_set = read_csv_dataset(\n",
    "        train_filepaths,\n",
    "        repeat=n_epochs,\n",
    "        n_readers=n_readers,\n",
    "        n_read_threads=n_read_threads,\n",
    "        shuffle_buffer_size=shuffle_buffer_size,\n",
    "        n_parse_threads=n_parse_threads,\n",
    "        batch_size=batch_size\n",
    "    )\n",
    "    \n",
    "    for X_batch, y_batch in train_set:\n",
    "        \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",
    "        \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": 23,
   "id": "directed-anxiety",
   "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": [
    "# Reset TF session and RNG state\n",
    "keras.backend.clear_session()\n",
    "np.random.seed(42)\n",
    "tf.random.set_seed(42)\n",
    "\n",
    "# Define training hyperparameters\n",
    "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_readers=5, n_read_threads=5, shuffle_buffer_size=10000, n_parse_threads=5):\n",
    "    train_set = read_csv_dataset(\n",
    "        train_filepaths,\n",
    "        repeat=n_epochs,\n",
    "        n_readers=n_readers,\n",
    "        n_read_threads=n_read_threads,\n",
    "        shuffle_buffer_size=shuffle_buffer_size,\n",
    "        n_parse_threads=n_parse_threads,\n",
    "        batch_size=batch_size,\n",
    "    )\n",
    "    \n",
    "    n_steps_per_epoch = len(X_train) // batch_size\n",
    "    total_steps = n_epochs * n_steps_per_epoch\n",
    "    global_step = 0\n",
    "    \n",
    "    for X_batch, y_batch in train_set.take(total_steps):\n",
    "        \n",
    "        global_step += 1\n",
    "        if tf.equal(global_step % 100, 0):\n",
    "            tf.print(\"\\rGlobal step\", global_step, \"/\", total_steps)\n",
    "        \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",
    "        \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": 24,
   "id": "surprising-visiting",
   "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`.\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": [
    "# Short description of the `Dataset` API\n",
    "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": "dated-generation",
   "metadata": {},
   "source": [
    "## The TFRecord binary format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "fifty-solid",
   "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": [
    "filepath = os.path.join(\"data\", \"my_data.tfrecord\")\n",
    "\n",
    "# Writing data in `TFRecord` format\n",
    "with tf.io.TFRecordWriter(filepath) as f:\n",
    "    f.write(b\"This is the first record\")\n",
    "    f.write(b\"And this is the second record\")\n",
    "\n",
    "# Reading a dataset from file(s) in `TFRecord` format\n",
    "dataset = tf.data.TFRecordDataset(filenames=[filepath])\n",
    "\n",
    "for item in dataset:\n",
    "    print(item)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "loose-hunter",
   "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 = [os.path.join(\"data\", f\"my_test_{i}.tfrecord\") for i in range(5)]\n",
    "\n",
    "# Write some data in TFRecord format into multiple files\n",
    "for i, filepath in enumerate(filepaths):\n",
    "    with tf.io.TFRecordWriter(filepath) as f:\n",
    "        for j in range(3):\n",
    "            f.write(f\"File {i} record {j}\")\n",
    "\n",
    "# Read multiple TFRecord files in parallel\n",
    "dataset = tf.data.TFRecordDataset(filepaths, num_parallel_reads=3)\n",
    "\n",
    "for item in dataset:\n",
    "    print(item)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "protecting-cheat",
   "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": [
    "filepath = os.path.join(\"data\", \"my_compressed.tfrecord\")\n",
    "\n",
    "# Create compression options\n",
    "options = tf.io.TFRecordOptions(compression_type=\"GZIP\")\n",
    "\n",
    "# Write some data to compressed TFRecord file\n",
    "with tf.io.TFRecordWriter(filepath, options) as f:\n",
    "    f.write(b\"This is the first record\")\n",
    "    f.write(b\"And this is the second record\")\n",
    "\n",
    "# Read a dataset from a compressed TFRecord file\n",
    "dataset = tf.data.TFRecordDataset(filenames=[filepath], compression_type=\"GZIP\")\n",
    "\n",
    "for item in dataset:\n",
    "    print(item)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "silent-pride",
   "metadata": {},
   "source": [
    "### A Brief Intro to Protocol Buffers\n",
    "*TFRecord* file can contain arbitrary binary data. However, it's is quite common to use it in combination with *protobuf*, so let's quickly review it."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "organizational-cricket",
   "metadata": {},
   "source": [
    "Let's start by creating a schema description file `data/person.proto`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "integral-flight",
   "metadata": {},
   "outputs": [],
   "source": [
    "%mkdir -p proto"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "tutorial-cocktail",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting proto/person.proto\n"
     ]
    }
   ],
   "source": [
    "%%writefile proto/person.proto\n",
    "syntax = \"proto3\";\n",
    "message Person {\n",
    "  string name = 1;\n",
    "  int32 id = 2;\n",
    "  repeated string email = 3;\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abstract-solomon",
   "metadata": {},
   "source": [
    "Compile the `data/person.proto` for *Python*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "pending-senior",
   "metadata": {},
   "outputs": [],
   "source": [
    "!protoc proto/person.proto --python_out=. --descriptor_set_out=proto/person.desc --include_imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "clean-bloom",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "proto/person.desc  proto/person.proto  proto/person_pb2.py\n"
     ]
    }
   ],
   "source": [
    "!ls proto/person*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "explicit-schema",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "\n",
    "# Add protobuf generated sources to the path\n",
    "proto_dir = os.path.join(os.curdir, \"proto\")\n",
    "if proto_dir not in sys.path:\n",
    "    sys.path.append(proto_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "biological-giving",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "name: \"Al\"\n",
      "id: 123\n",
      "email: \"a@b.com\"\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from person_pb2 import Person\n",
    "\n",
    "# Create a Person\n",
    "person = Person(name=\"Al\", id=123, email=[\"a@b.com\"])\n",
    "\n",
    "# Display the Person\n",
    "print(person)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "massive-english",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Al'"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "person.name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "formed-willow",
   "metadata": {},
   "outputs": [],
   "source": [
    "person.name = \"Alice\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "stone-century",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'a@b.com'"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "person.email[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "severe-liberal",
   "metadata": {},
   "outputs": [],
   "source": [
    "person.email.append(\"c@d.com\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "disturbed-croatia",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b'\\n\\x05Alice\\x10{\\x1a\\x07a@b.com\\x1a\\x07c@d.com'"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Serialize the object to a byte string\n",
    "s = person.SerializeToString()\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "lyric-threat",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a new Person and parse the byte string (returning the number of bytes read)\n",
    "person2 = Person()\n",
    "person2.ParseFromString(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "cardiac-honey",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Test the deserialization\n",
    "person == person2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "previous-match",
   "metadata": {},
   "source": [
    "#### Custom protobuf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "square-particle",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<tf.Tensor: shape=(1,), dtype=string, numpy=array([b'Alice'], dtype=object)>,\n",
       " <tf.Tensor: shape=(1,), dtype=int32, numpy=array([123], dtype=int32)>,\n",
       " <tf.Tensor: shape=(2,), dtype=string, numpy=array([b'a@b.com', b'c@d.com'], dtype=object)>]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Parsing a custom protobuf in TF\n",
    "person_tf = tf.io.decode_proto(\n",
    "    bytes=s,\n",
    "    message_type=\"Person\",\n",
    "    field_names=[\"name\", \"id\", \"email\"],\n",
    "    output_types=[tf.string, tf.int32, tf.string],\n",
    "    descriptor_source=os.path.join(proto_dir, \"person.desc\"),\n",
    ")\n",
    "\n",
    "person_tf.values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "studied-marking",
   "metadata": {},
   "source": [
    "### TensorFlow Protobufs\n",
    "The most common of TF's built-in (pre-compiled) protobuf objects is the `Example`.\n",
    "\n",
    "```protobuf\n",
    "syntax = \"proto3\";\n",
    "\n",
    "message BytesList { repeated bytes value = 1; }\n",
    "message FloatList { repeated float value = 1 [packed = true]; }\n",
    "message Int64List { repeated int64 value = 1 [packed = true]; }\n",
    "message Feature {\n",
    "    oneof kind {\n",
    "        BytesList bytes_list = 1;\n",
    "        FloatList float_list = 2;\n",
    "        Int64List int64_list = 3;\n",
    "    }\n",
    "};\n",
    "message Features { map<string, Feature> feature = 1; };\n",
    "message Example { Features features = 1; };\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "covered-process",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'emails': <tensorflow.python.framework.sparse_tensor.SparseTensor at 0x7fadb377d0d0>,\n",
       " 'id': <tf.Tensor: shape=(), dtype=int64, numpy=123>,\n",
       " 'name': <tf.Tensor: shape=(), dtype=string, numpy=b'Alice'>}"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "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",
    "# Create an `Example` for the Person instance from above\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",
    ")\n",
    "\n",
    "example_path = os.path.join(\"data\", \"my_contacts.tfrecord\")\n",
    "\n",
    "# Serialize the example as TFRecord file\n",
    "with tf.io.TFRecordWriter(example_path) as f:\n",
    "    f.write(person_example.SerializeToString())\n",
    "    \n",
    "# Deserialize the example TFRecord file\n",
    "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",
    "\n",
    "for serialized_example in tf.data.TFRecordDataset([example_path]):\n",
    "    parsed_example = tf.io.parse_single_example(serialized_example, feature_description)\n",
    "    \n",
    "parsed_example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "upper-hampshire",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(), dtype=string, numpy=b'a@b.com'>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parsed_example[\"emails\"].values[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "numerical-mainstream",
   "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": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.sparse.to_dense(parsed_example[\"emails\"], default_value=b\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "worthy-sentence",
   "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": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parsed_example[\"emails\"].values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "imposed-reunion",
   "metadata": {},
   "source": [
    "#### Putting Images in TFRecords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "needed-immune",
   "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",
    "# Load a sample image\n",
    "img = load_sample_images()[\"images\"][0]\n",
    "\n",
    "# Plot the image\n",
    "plt.imshow(img)\n",
    "plt.axis(\"off\")\n",
    "plt.title(\"Original Image\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "junior-classic",
   "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": [
    "# Encode the image to JPEG\n",
    "data = tf.io.encode_jpeg(img)\n",
    "\n",
    "# Make an Example from the image data\n",
    "example_with_image = Example(\n",
    "    features=Features(\n",
    "        feature={\n",
    "            \"image\": Feature(bytes_list=BytesList(value=[data.numpy()]))\n",
    "        }\n",
    "    )\n",
    ")\n",
    "\n",
    "# Serialize the image into TFRecord\n",
    "serialized_example = example_with_image.SerializeToString()\n",
    "\n",
    "# Deserialize the TFRecord back into an image\n",
    "feature_description = {\"image\": tf.io.VarLenFeature(tf.string)}\n",
    "example_with_image = tf.io.parse_single_example(serialized_example, feature_description)\n",
    "\n",
    "# Decode the JPEG back into origial image\n",
    "decoded_img = tf.io.decode_jpeg(example_with_image[\"image\"].values[0])\n",
    "\n",
    "# Show the decoded image for comparison\n",
    "plt.imshow(decoded_img)\n",
    "plt.title(\"Decoded Image\")\n",
    "plt.axis(\"off\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "pleasant-tissue",
   "metadata": {},
   "source": [
    "#### Putting Tensors and Sparse Tensors in TFRecords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "indonesian-group",
   "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": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make some tensor\n",
    "t = tf.constant([[0., 1.], [2., 3.], [4., 5.]])\n",
    "\n",
    "# Serializing a tensor\n",
    "s = tf.io.serialize_tensor(t)\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "frank-programming",
   "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": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Deserialize a tensor\n",
    "tf.io.parse_tensor(s, out_type=tf.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "covered-morrison",
   "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": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Serialize a sparse tensor\n",
    "serialized_sparse = tf.io.serialize_sparse(parsed_example[\"emails\"])\n",
    "serialized_sparse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "noble-lewis",
   "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": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BytesList(value=serialized_sparse.numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "awful-skirt",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'image': <tensorflow.python.framework.sparse_tensor.SparseTensor at 0x7fadb370bf10>}"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make a dataset that loads data from TFRecord files\n",
    "#  - also batch them by 10\n",
    "dataset = tf.data.TFRecordDataset([example_path]).batch(10)\n",
    "\n",
    "# Parse multiple examples at once\n",
    "for serialized_examples in dataset:\n",
    "    parsed_examples = tf.io.parse_example(serialized_examples, feature_description)\n",
    "\n",
    "parsed_examples"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "sweet-violin",
   "metadata": {},
   "source": [
    "### Handling Sequential Data Using SequenceExample\n",
    "```protobuf\n",
    "syntax = \"proto3\";\n",
    "\n",
    "message FeatureList { repeated Feature feature = 1; };\n",
    "message FeatureLists { map<string, FeatureList> feature_list = 1; };\n",
    "message SequenceExample {\n",
    "  Features context = 1;\n",
    "  FeatureLists feature_lists = 2;\n",
    "};\n",
    "```\n",
    "\n",
    "The `SequenceExample` is handy for textual datasets or any other datasets that contain nested lists. For instance an article is composed of sentences, each sentence is a sequence of words. Additionally, an article usually contains some contextual metadata like an author, title, publish date, etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "interpreted-metropolitan",
   "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": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FeatureList = tf.train.FeatureList\n",
    "FeatureLists = tf.train.FeatureLists\n",
    "SequenceExample = tf.train.SequenceExample\n",
    "\n",
    "# Create an example article's context\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",
    "# Article's content\n",
    "content = [\n",
    "    [\"When\", \"shall\", \"we\", \"three\", \"meet\", \"again\", \"?\"],\n",
    "    [\"In\", \"thunder\", \",\", \"lightning\", \",\", \"or\", \"in\", \"rain\", \"?\"],\n",
    "]\n",
    "\n",
    "# Article's comments\n",
    "comments = [\n",
    "    [\"When\", \"the\", \"hurlyburly\", \"'s\", \"done\", \".\"],\n",
    "    [\"When\", \"the\", \"battle\", \"'s\", \"lost\", \"and\", \"won\", \".\"],\n",
    "]\n",
    "\n",
    "def words_to_feature(words):\n",
    "    return Feature(bytes_list=BytesList(value=[word.encode() for word in words]))\n",
    "\n",
    "# Make a Feature for each sentence and comment\n",
    "content_features = [words_to_feature(sentence) for sentence in content]\n",
    "comments_features = [words_to_feature(comment) for comment in comments]\n",
    "            \n",
    "# Compose the SequenceExample representing an article\n",
    "sequence_example = SequenceExample(\n",
    "    context=context,\n",
    "    feature_lists=FeatureLists(\n",
    "        feature_list={\n",
    "            \"content\": FeatureList(feature=content_features),\n",
    "            \"comments\": FeatureList(feature=comments_features),\n",
    "        }\n",
    "    )\n",
    ")\n",
    "\n",
    "sequence_example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "speaking-arnold",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'title': <tensorflow.python.framework.sparse_tensor.SparseTensor at 0x7fae01438700>,\n",
       " 'author_id': <tf.Tensor: shape=(), dtype=int64, numpy=123>,\n",
       " 'pub_date': <tf.Tensor: shape=(3,), dtype=int64, numpy=array([1623,   12,   25])>}"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Serialize the article example\n",
    "serialized_sequence_example = sequence_example.SerializeToString()\n",
    "\n",
    "# Define the feature bindings\n",
    "\n",
    "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",
    "\n",
    "sequence_feature_descriptions = {\n",
    "    \"content\": tf.io.VarLenFeature(tf.string),\n",
    "    \"comments\": tf.io.VarLenFeature(tf.string),\n",
    "}\n",
    "\n",
    "# Deserialize the article\n",
    "parsed_context, parsed_feature_lists = tf.io.parse_single_sequence_example(\n",
    "    serialized_sequence_example, context_feature_descriptions,\n",
    "    sequence_feature_descriptions\n",
    ")\n",
    "\n",
    "parsed_context"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "funky-asian",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(4,), dtype=string, numpy=array([b'A', b'desert', b'place', b'.'], dtype=object)>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parsed_context[\"title\"].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "graduate-experiment",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'comments': <tensorflow.python.framework.sparse_tensor.SparseTensor at 0x7fae014388e0>,\n",
       " 'content': <tensorflow.python.framework.sparse_tensor.SparseTensor at 0x7fae01438b20>}"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parsed_feature_lists"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "strange-pledge",
   "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": [
    "# Variable string lists are best represented by a RaggedTensor\n",
    "print(tf.RaggedTensor.from_sparse(parsed_feature_lists[\"content\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "naked-humanitarian",
   "metadata": {},
   "source": [
    "## The Features API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "charged-agreement",
   "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": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load California housing dataset\n",
    "housing_path = os.path.join(\"datasets\", \"housing.csv\")\n",
    "housing = pd.read_csv(housing_path)\n",
    "\n",
    "housing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "clean-rochester",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NumericColumn(key='housing_median_age', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=None)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_median_age = tf.feature_column.numeric_column(\"housing_median_age\")\n",
    "housing_median_age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "legitimate-creature",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NumericColumn(key='housing_median_age', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=<function <lambda> at 0x7fae0144d040>)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The median age is column in 1\n",
    "age_mean, age_std = X_mean[1], X_std[1]\n",
    "\n",
    "housing_median_age = tf.feature_column.numeric_column(\"housing_median_age\", normalizer_fn=lambda x: (x - age_mean) / age_std)\n",
    "housing_median_age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "joined-literacy",
   "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": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "median_income = tf.feature_column.numeric_column(\"median_income\")\n",
    "\n",
    "bucketized_income = tf.feature_column.bucketized_column(median_income, boundaries=[1.5, 3., 4.5, 6.])\n",
    "bucketized_income"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "sized-detroit",
   "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": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ocean_prox_vocab = ['<1H OCEAN', 'INLAND', 'ISLAND', 'NEAR BAY', 'NEAR OCEAN']\n",
    "\n",
    "ocean_proximity = tf.feature_column.categorical_column_with_vocabulary_list(\"ocean_proximity\", ocean_prox_vocab)\n",
    "ocean_proximity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "formed-darwin",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "HashedCategoricalColumn(key='city', hash_bucket_size=1000, dtype=tf.string)"
      ]
     },
     "execution_count": 63,
     "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(\"city\", hash_bucket_size=1000)\n",
    "city_hash"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "welsh-engagement",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Note: age was scaled\n",
    "bucketized_age = tf.feature_column.bucketized_column(housing_median_age, boundaries=[-1., -0.5, 0., 0.5, 1.])\n",
    "\n",
    "age_and_ocean_proximity = tf.feature_column.crossed_column([bucketized_age, ocean_proximity], hash_bucket_size=100)\n",
    "\n",
    "latitude = tf.feature_column.numeric_column(\"latitude\")\n",
    "longitude = tf.feature_column.numeric_column(\"longitude\")\n",
    "\n",
    "bucketized_latitude = tf.feature_column.bucketized_column(latitude, boundaries=list(np.linspace(32., 42., 20 - 1)))\n",
    "bucketized_longitude = tf.feature_column.bucketized_column(longitude, boundaries=list(np.linspace(-125., -114., 20 - 1)))\n",
    "\n",
    "location = tf.feature_column.crossed_column([bucketized_latitude, bucketized_longitude], hash_bucket_size=1000)\n",
    "\n",
    "ocean_proximity_one_hot = tf.feature_column.indicator_column(ocean_proximity)\n",
    "\n",
    "ocean_proximity_embed = tf.feature_column.embedding_column(ocean_proximity, dimension=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "straight-pilot",
   "metadata": {},
   "source": [
    "### Using Feature Columns for Parsing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "through-groove",
   "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": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "median_house_value = tf.feature_column.numeric_column(\"median_house_value\")\n",
    "\n",
    "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": 66,
   "id": "casual-signal",
   "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 [==============================] - 0s 1ms/step - loss: 3.7619 - accuracy: 0.0016\n",
      "Epoch 2/5\n",
      "362/362 [==============================] - 0s 1ms/step - loss: 1.9311 - accuracy: 0.0027\n",
      "Epoch 3/5\n",
      "362/362 [==============================] - 0s 1ms/step - loss: 1.4434 - accuracy: 0.0026\n",
      "Epoch 4/5\n",
      "362/362 [==============================] - 0s 1ms/step - loss: 1.3579 - accuracy: 0.0030\n",
      "Epoch 5/5\n",
      "362/362 [==============================] - 0s 1ms/step - loss: 1.3473 - accuracy: 0.0038\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7fae013f97f0>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reset TF session and RNG state\n",
    "keras.backend.clear_session()\n",
    "np.random.seed(42)\n",
    "tf.random.set_seed(42)\n",
    "\n",
    "filepath = os.path.join(\"datasets\", \"my_data_with_features.tfrecords\")\n",
    "\n",
    "# Serialize the a subset of the housing data\n",
    "with tf.io.TFRecordWriter(filepath) as f:\n",
    "    for x, y in zip(X_train[:, 1:2], y_train):\n",
    "        example = Example(\n",
    "            features=Features(\n",
    "                feature={\n",
    "                    \"housing_median_age\": Feature(float_list=FloatList(value=[x])),\n",
    "                    \"median_house_value\": Feature(float_list=FloatList(value=[y])),\n",
    "                }\n",
    "            )\n",
    "        )\n",
    "        f.write(example.SerializeToString())\n",
    "\n",
    "        \n",
    "def parse_examples(serialized_examples):\n",
    "    examples = tf.io.parse_example(serialized_examples, feature_descriptions)\n",
    "    # Separate the targets\n",
    "    targets = examples.pop(\"median_house_value\")\n",
    "    return examples, targets\n",
    "\n",
    "# Load and parse the dataset and also batch it\n",
    "batch_size = 32\n",
    "dataset = (\n",
    "    tf.data.TFRecordDataset(filenames=[filepath])\n",
    "    .repeat()\n",
    "    .shuffle(10000)\n",
    "    .batch(batch_size)\n",
    "    .map(parse_examples)\n",
    ")\n",
    "\n",
    "feature_columns = columns[:-1]\n",
    "\n",
    "# Build a simple regression model\n",
    "model = keras.models.Sequential([\n",
    "    keras.layers.DenseFeatures(feature_columns=feature_columns),\n",
    "    keras.layers.Dense(1),\n",
    "])\n",
    "model.compile(loss=\"mse\", optimizer=keras.optimizers.SGD(lr=1e-3), metrics=[\"accuracy\"])\n",
    "\n",
    "# Train the model on the dataset for few epochs\n",
    "model.fit(dataset, steps_per_epoch=len(X_train) // batch_size, epochs=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "quality-intake",
   "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": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "some_columns = [ocean_proximity_embed, bucketized_income]\n",
    "\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": "younger-longitude",
   "metadata": {},
   "source": [
    "## TF Transform\n",
    "```python\n",
    "import tensorflow_transform as tft\n",
    "\n",
    "def preprocess(inputs):\n",
    "    # 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",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "outstanding-chick",
   "metadata": {},
   "source": [
    "## TensorFlow Datasets\n",
    "TF datasets is a collection of standard datasets directly accessible and usable with TF."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "flying-contrary",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 32.1418 - accuracy: 0.8416\n",
      "Epoch 2/5\n",
      "1875/1875 [==============================] - 2s 1ms/step - loss: 25.9586 - accuracy: 0.8686\n",
      "Epoch 3/5\n",
      "1875/1875 [==============================] - 2s 1ms/step - loss: 24.9012 - accuracy: 0.8738\n",
      "Epoch 4/5\n",
      "1875/1875 [==============================] - 2s 1ms/step - loss: 24.4328 - accuracy: 0.8759\n",
      "Epoch 5/5\n",
      "1875/1875 [==============================] - 2s 1ms/step - loss: 23.8487 - accuracy: 0.8784\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7fadb350dd00>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import tensorflow_datasets as tfds\n",
    "\n",
    "# Reset TF session and RNG state\n",
    "keras.backend.clear_session()\n",
    "np.random.seed(42)\n",
    "tf.random.set_seed(42)\n",
    "\n",
    "batch_size = 32\n",
    "\n",
    "# Load batched MNIST datasets and split them to training and test sets\n",
    "#  - `as_supervised=True` flag auto splits the data to features and targets\n",
    "datasets = tfds.load(name=\"mnist\", batch_size=batch_size, as_supervised=True)\n",
    "mnist_train, mnist_test = datasets[\"train\"], datasets[\"test\"]\n",
    "\n",
    "# Build a simple classification model\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",
    "])\n",
    "model.compile(loss=\"sparse_categorical_crossentropy\", optimizer=keras.optimizers.SGD(lr=1e-3), metrics=[\"accuracy\"])\n",
    "\n",
    "# Train the model for few epochs\n",
    "model.fit(mnist_train, steps_per_epoch=60000 // batch_size, epochs=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "yellow-dealing",
   "metadata": {},
   "source": [
    "## TensorFlow Hub"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "secondary-bicycle",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "keras_layer (KerasLayer)     (None, 50)                48190600  \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 16)                816       \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 1)                 17        \n",
      "=================================================================\n",
      "Total params: 48,191,433\n",
      "Trainable params: 833\n",
      "Non-trainable params: 48,190,600\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "import tensorflow_hub as hub\n",
    "\n",
    "# Reset TF session and RNG state\n",
    "keras.backend.clear_session()\n",
    "np.random.seed(42)\n",
    "tf.random.set_seed(42)\n",
    "\n",
    "# Fetch a Keras layer from TF Hub\n",
    "hub_layer = hub.KerasLayer(\n",
    "    \"https://tfhub.dev/google/tf2-preview/nnlm-en-dim50/1\",\n",
    "    output_shape=[50],\n",
    "    input_shape=[],\n",
    "    dtype=tf.string,\n",
    ")\n",
    "\n",
    "# Build a model that reuses the TF Hub layer\n",
    "model = keras.Sequential()\n",
    "model.add(hub_layer)\n",
    "model.add(keras.layers.Dense(16, activation='relu'))\n",
    "model.add(keras.layers.Dense(1, activation='sigmoid'))\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "applicable-residence",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(2, 50), dtype=float32, numpy=\n",
       "array([[ 7.45939985e-02,  2.76720114e-02,  9.38646123e-02,\n",
       "         1.25124469e-01,  5.40293928e-04, -1.09435350e-01,\n",
       "         1.34755149e-01, -9.57818255e-02, -1.85177118e-01,\n",
       "        -1.69703495e-02,  1.75612606e-02, -9.06603858e-02,\n",
       "         1.12110220e-01,  1.04646273e-01,  3.87700424e-02,\n",
       "        -7.71859884e-02, -3.12189370e-01,  6.99466765e-02,\n",
       "        -4.88970093e-02, -2.99049795e-01,  1.31183028e-01,\n",
       "        -2.12630898e-01,  6.96169436e-02,  1.63592950e-01,\n",
       "         1.05169769e-02,  7.79720694e-02, -2.55230188e-01,\n",
       "        -1.80790052e-01,  2.93739915e-01,  1.62875261e-02,\n",
       "        -2.80566931e-01,  1.60284728e-01,  9.87277832e-03,\n",
       "         8.44555616e-04,  8.39456245e-02,  3.24002892e-01,\n",
       "         1.53253034e-01, -3.01048346e-02,  8.94618109e-02,\n",
       "        -2.39153411e-02, -1.50188789e-01, -1.81733668e-02,\n",
       "        -1.20483577e-01,  1.32937476e-01, -3.35325629e-01,\n",
       "        -1.46504581e-01, -1.25251599e-02, -1.64428815e-01,\n",
       "        -7.00765476e-02,  3.60923223e-02],\n",
       "       [-1.56998575e-01,  4.24599349e-02, -5.57703003e-02,\n",
       "        -8.08446854e-03,  1.23733155e-01,  3.89427543e-02,\n",
       "        -4.37901802e-02, -1.86987907e-01, -2.29341656e-01,\n",
       "        -1.27766818e-01,  3.83025259e-02, -1.07057482e-01,\n",
       "        -6.11584112e-02,  2.49654502e-01, -1.39712945e-01,\n",
       "        -3.91289443e-02, -1.35873526e-01, -3.58613044e-01,\n",
       "         2.53462754e-02, -1.58370987e-01, -1.38350084e-01,\n",
       "        -3.90771806e-01, -6.63642734e-02, -3.24838236e-02,\n",
       "        -2.20453963e-02, -1.68282315e-01, -7.40613639e-02,\n",
       "        -2.49074101e-02,  2.46460736e-01,  9.87201929e-05,\n",
       "        -1.85390845e-01, -4.92824614e-02,  1.09015472e-01,\n",
       "        -9.54203904e-02, -1.60352528e-01, -2.59811729e-02,\n",
       "         1.13778859e-01, -2.09578887e-01,  2.18261331e-01,\n",
       "        -3.11211571e-02, -6.12562597e-02, -8.66057724e-02,\n",
       "        -1.10762455e-01, -5.73977083e-03, -1.08923554e-01,\n",
       "        -1.72919363e-01,  1.00515485e-01, -5.64153939e-02,\n",
       "        -4.97694984e-02, -1.07776590e-01]], dtype=float32)>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sentences = tf.constant([\"It was a great movie\", \"The actors were amazing\"])\n",
    "\n",
    "# Use the TF Hub layer to embed some sentences\n",
    "embeddings = hub_layer(sentences)\n",
    "embeddings"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "medieval-domain",
   "metadata": {},
   "source": [
    "## Exercises"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "plain-integrity",
   "metadata": {},
   "source": [
    "### Fashion MNIST dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "strong-fancy",
   "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": [
    "# Reset TF session and RNG state\n",
    "keras.backend.clear_session()\n",
    "np.random.seed(42)\n",
    "tf.random.set_seed(42)\n",
    "\n",
    "# Fetch and split the Fashion MNIST dataset\n",
    "(X_train_full, y_train_full), (X_test, y_test) = keras.datasets.fashion_mnist.load_data()\n",
    "\n",
    "# Split it further to training and validation sets\n",
    "val_split = 5000\n",
    "X_valid, X_train = X_train_full[:val_split], X_train_full[val_split:]\n",
    "y_valid, y_train = y_train_full[:val_split], y_train_full[val_split:]\n",
    "\n",
    "# Make TF datasets from the training, validation and test sets\n",
    "#  - also shuffle the training dataset\n",
    "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))\n",
    "\n",
    "\n",
    "def create_example(image, label):\n",
    "    \"\"\"Make an example containing raw image data and labels as features\"\"\"\n",
    "    image_data = tf.io.serialize_tensor(image)\n",
    "    return Example(\n",
    "        features=Features(\n",
    "            feature={\n",
    "                \"image\": Feature(bytes_list=BytesList(value=[image_data.numpy()])),\n",
    "                \"label\": Feature(int64_list=Int64List(value=[label])),\n",
    "            }\n",
    "        )\n",
    "    )\n",
    "\n",
    "# Test creating an example from the validation set\n",
    "for image, label in valid_set.take(1):\n",
    "    print(create_example(image, label))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "divine-lending",
   "metadata": {},
   "outputs": [],
   "source": [
    "from contextlib import ExitStack\n",
    "\n",
    "def write_tfrecords(name, dataset, n_shards=10):\n",
    "    def filename(index):\n",
    "        return os.path.join(\"data\", f\"{name}.tfrecord-{index:05d}-of-{n_shards:05d}\")\n",
    "    \n",
    "    paths = [filename(index) for index in range(n_shards)]\n",
    "    \n",
    "    with ExitStack() as stack:\n",
    "        writers = [stack.enter_context(tf.io.TFRecordWriter(path)) for path in paths]\n",
    "        \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",
    "            \n",
    "    return paths\n",
    "\n",
    "# Save all datasets into TFRecord files\n",
    "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": "code",
   "execution_count": 73,
   "id": "remarkable-injection",
   "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": [
    "feature_descriptions = {\n",
    "    \"image\": tf.io.FixedLenFeature([], tf.string, default_value=\"\"),\n",
    "    \"label\": tf.io.FixedLenFeature([], tf.int64, default_value=-1)\n",
    "}\n",
    "\n",
    "\n",
    "def preprocess(tfrecord):\n",
    "    \"\"\"Parse a `TFRecord` into image and label\"\"\"\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.reshape(image, shape=[28, 28])\n",
    "    return image, example[\"label\"]\n",
    "\n",
    "\n",
    "def mnist_dataset(filepaths, n_read_threads=5, shuffle_buffer_size=None, n_parse_threads=5, batch_size=32, cache=True):\n",
    "    \"\"\"Make a MNIST dataset by loading, preprocessing and batching given files\"\"\"\n",
    "    dataset = tf.data.TFRecordDataset(filepaths, num_parallel_reads=n_read_threads)\n",
    "    \n",
    "    if cache:\n",
    "        dataset = dataset.cache()\n",
    "    \n",
    "    if shuffle_buffer_size:\n",
    "        dataset = dataset.shuffle(shuffle_buffer_size)\n",
    "        \n",
    "    return (\n",
    "        dataset.map(preprocess, num_parallel_calls=n_parse_threads)\n",
    "        .batch(batch_size)\n",
    "        .prefetch(1)\n",
    "    )\n",
    "\n",
    "\n",
    "# Load and preprocess all the MNIST TFRecord datasets\n",
    "train_set = mnist_dataset(train_filepaths, shuffle_buffer_size=60000)\n",
    "valid_set = mnist_dataset(train_filepaths)\n",
    "test_set = mnist_dataset(train_filepaths)\n",
    "\n",
    "# Show few images from a training batch\n",
    "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": 75,
   "id": "ceramic-instrumentation",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "1719/1719 [==============================] - 7s 4ms/step - loss: 0.4486 - accuracy: 0.8421 - val_loss: 0.3289 - val_accuracy: 0.8801\n",
      "Epoch 2/5\n",
      "1719/1719 [==============================] - 6s 4ms/step - loss: 0.3303 - accuracy: 0.8794 - val_loss: 0.2956 - val_accuracy: 0.8915\n",
      "Epoch 3/5\n",
      "1719/1719 [==============================] - 6s 4ms/step - loss: 0.2950 - accuracy: 0.8908 - val_loss: 0.2607 - val_accuracy: 0.9044\n",
      "Epoch 4/5\n",
      "1719/1719 [==============================] - 6s 4ms/step - loss: 0.2683 - accuracy: 0.9007 - val_loss: 0.2367 - val_accuracy: 0.9110\n",
      "Epoch 5/5\n",
      "1719/1719 [==============================] - 6s 4ms/step - loss: 0.2485 - accuracy: 0.9087 - val_loss: 0.2226 - val_accuracy: 0.9177\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7fada1208d00>"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "\n",
    "# Reset TF session and RNG state\n",
    "keras.backend.clear_session()\n",
    "tf.random.set_seed(42)\n",
    "np.random.seed(42)\n",
    "\n",
    "# Custom implementation of normalization\n",
    "#  - Note: this is soon to be replaced by currently experimental `Normalization` layer\n",
    "class Standardization(keras.layers.Layer):\n",
    "    \"\"\"Layer that after adaptation normalizes a batch of input data\"\"\"\n",
    "    \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",
    "    \n",
    "    def call(self, inputs):\n",
    "        return (inputs - self.means_) / (self.stds_ + keras.backend.epsilon())\n",
    "\n",
    "\n",
    "# Alternative: `keras.layers.experimental.preprocessing.Normalization`\n",
    "standardization = Standardization(input_shape=[28, 28])\n",
    "\n",
    "# Adapt the normalization layer on a subset of the training set (100 samples)\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()), axis=0).astype(np.float32)\n",
    "standardization.adapt(sample_images)\n",
    "\n",
    "# Build a simple classification model including the pre-trained normalization layer\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\", optimizer=\"nadam\", metrics=[\"accuracy\"])\n",
    "\n",
    "# Make a TensorBoard callback\n",
    "run_label = datetime.now().strftime(\"%Y%m%d_%H%M%S\")\n",
    "logdir = os.path.join(os.curdir, \"logs\", \"run_{run_label}\")\n",
    "tensorboard_cb = tf.keras.callbacks.TensorBoard(log_dir=logdir, histogram_freq=1, profile_batch=10)\n",
    "\n",
    "# Train and validate the model for few epochs\n",
    "model.fit(train_set, epochs=5, validation_data=valid_set, callbacks=[tensorboard_cb])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "excited-lightning",
   "metadata": {},
   "source": [
    "### Large Movie Review Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "local-block",
   "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 [==============================] - 18s 0us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "PosixPath('/home/matyama/.keras/datasets/aclImdb')"
      ]
     },
     "execution_count": 76,
     "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": 77,
   "id": "fifteen-mother",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12500, 12500, 12500, 12500)"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def review_paths(dirpath):\n",
    "    \"\"\"Lists all txt files under given directory path\"\"\"\n",
    "    return [str(path) for path in dirpath.glob(\"*.txt\")]\n",
    "\n",
    "# List all positive/negative training and test file paths\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",
    "# Display how many of each files (paths) there is\n",
    "len(train_pos), len(train_neg), len(test_valid_pos), len(test_valid_neg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "general-probability",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor(b'And my children love it now! Granted, I can watch it now and realize the animation wasn\\'t that great, and that the plot is trite. Hey, if every villian introduced themselves by saying \"I ammmmm DAAAARRRKK HEEEEAAARRT\" I think they might be laughed at, but for young children it is a moral story with catchy music.<br /><br />Music so catchy, mind you, that I still had the words memorized after not seeing this film in twenty years. I would definitely suggest this one for younger children.', shape=(), dtype=string)\n",
      "tf.Tensor(1, shape=(), dtype=int32)\n",
      "\n",
      "tf.Tensor(b\"One of the best movies out there. Yeah maybe the cinematography wasn't the greatest, but an excellent plot and concept. Great for the time and brilliant and creative ideas. Something different from the usual movies and great fun. One of my favorites and would recommend to anyone who likes creative and imaginative movies. Post World War 3 and fighting in gigantic robots, the actors gave a great performance and made it all worth while. The sets are not amazing, but simple and worked for the overall look of the film. This movie is very hard to find on DVD, but also on VHS. Check it out cause I have loved it since it came out. Not a mainstream flick and not like anything you've ever seen. Take a look and think like a child. It's a great view and very fun.\", shape=(), dtype=string)\n",
      "tf.Tensor(1, shape=(), dtype=int32)\n",
      "\n",
      "tf.Tensor(b\"A wonderful surprise of the Spanish cinema. I never thought Jordi Molla could be such a great director, after all, I really don't care much for his choice in movies. However this film was absolutely fantastic and not predictable at all... something I ALWAYS enjoy! It hasn't enjoyed much good press and I really don't think it'll ever be released in the US market. We'll have to content ourselves with the DVD or VHS if even that... I don't want to say much about the story because I don't want to spoil it for you but basically the movie is about a homeless drunk who becomes the new messiah just for kicks and to get some money out of it (like sooooooooooo many), before he knows it he is immerse in a life he was not counting on.\", shape=(), dtype=string)\n",
      "tf.Tensor(1, shape=(), dtype=int32)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Reset TF session and RNG state\n",
    "keras.backend.clear_session()\n",
    "tf.random.set_seed(42)\n",
    "np.random.seed(42)\n",
    "\n",
    "np.random.shuffle(test_valid_pos)\n",
    "\n",
    "# Split all test paths into actual test and validation sets\n",
    "split_ix = 5000\n",
    "test_pos, valid_pos = test_valid_pos[:split_ix], test_valid_pos[split_ix:]\n",
    "test_neg, valid_neg = test_valid_neg[:split_ix], test_valid_neg[split_ix:]\n",
    "\n",
    "\n",
    "def imdb_dataset(filepaths_positive, filepaths_negative, n_read_threads=5):\n",
    "    \"\"\"Load files for both positive and negative into single dataset\"\"\"\n",
    "    \n",
    "    # Fortunately, each review fits on just one line - so we can use `TextLineDataset`\n",
    "    #  - they use `<br />` to indicate line breaks\n",
    "    \n",
    "    dataset_neg = tf.data.TextLineDataset(filepaths_negative, num_parallel_reads=n_read_threads)\n",
    "    dataset_neg = dataset_neg.map(lambda review: (review, 0))\n",
    "    \n",
    "    dataset_pos = tf.data.TextLineDataset(filepaths_positive, num_parallel_reads=n_read_threads)\n",
    "    dataset_pos = dataset_pos.map(lambda review: (review, 1))\n",
    "    \n",
    "    # Concatenate positive and negative examples into single dataset\n",
    "    return tf.data.Dataset.concatenate(dataset_pos, dataset_neg)\n",
    "\n",
    "\n",
    "# Show few examples\n",
    "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": 80,
   "id": "looking-charger",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24.3 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).cache().repeat(10): pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "native-chaos",
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 32\n",
    "\n",
    "# Create training, validation and testing datasets\n",
    "#  - with batching, prefetching and shuffling\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": "code",
   "execution_count": 82,
   "id": "important-minority",
   "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": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def preprocess_reviews(X_batch, n_words=50):\n",
    "    shape = tf.shape(X_batch) * tf.constant([1, 0]) + tf.constant([0, n_words])\n",
    "    \n",
    "    # Crop each review text to 300 characters\n",
    "    Z = tf.strings.substr(X_batch, 0, 300)\n",
    "    \n",
    "    # Make all the text lower case\n",
    "    Z = tf.strings.lower(Z)\n",
    "    \n",
    "    # Replace each `<br />` by a space\n",
    "    Z = tf.strings.regex_replace(Z, b\"<br\\\\s*/?>\", b\" \")\n",
    "    \n",
    "    # Replace all non-letter character by a space\n",
    "    Z = tf.strings.regex_replace(Z, b\"[^a-z]\", b\" \")\n",
    "    \n",
    "    # Split the review into words\n",
    "    Z = tf.strings.split(Z)\n",
    "    \n",
    "    # Pad or crop the review so that it has exacly `n_words` words\n",
    "    return Z.to_tensor(shape=shape, default_value=b\"<pad>\")\n",
    "\n",
    "# Test the preprocessing logic on an example\n",
    "X_example = tf.constant([\"It's a great, great movie! I loved it.\", \"It was terrible, run away!!!\"])\n",
    "preprocess_reviews(X_example)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "invisible-indonesian",
   "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": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "from itertools import chain\n",
    "\n",
    "def build_vocabulary(data_sample, max_size=1000, pad=b\"<pad>\"):\n",
    "    \"\"\"\n",
    "    Builds a list of `max_size` most frequent words in `data_sample`\n",
    "    with an extra `pad` as the first one.\n",
    "    \"\"\"\n",
    "    # Preprocess reviews transforming each into list of words\n",
    "    review_words = preprocess_reviews(data_sample).numpy()\n",
    "    \n",
    "    # Count each word in the data sample, disregarding the padding word\n",
    "    counter = Counter(chain.from_iterable(review_words))\n",
    "    del counter[pad]\n",
    "    \n",
    "    # Return the padding word and `max_size` most frequent words in the sample\n",
    "    return [pad] + [word for word, _ in counter.most_common(max_size)]\n",
    "\n",
    "build_vocabulary(X_example)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "purple-longer",
   "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]])>"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class TextVectorization(keras.layers.Layer):\n",
    "    \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 = build_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",
    "        return self.table.lookup(preprocess_reviews(inputs))\n",
    "\n",
    "    \n",
    "# Test the text vectorization layer on our example\n",
    "text_vectorization = TextVectorization()\n",
    "text_vectorization.adapt(X_example)\n",
    "text_vectorization(X_example)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "italian-serial",
   "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, 532, 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]])>"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define text vectorization hyperparameters\n",
    "max_vocabulary_size = 1000\n",
    "n_oov_buckets = 100\n",
    "\n",
    "# Sample the training set to make a batch for training the text vectorization\n",
    "#  - if the corpus does not fit into memory, we could use just a subset with `train_set.take(500)`\n",
    "sample_review_batches = train_set.map(lambda review, label: review)\n",
    "sample_reviews = np.concatenate(list(sample_review_batches.as_numpy_iterator()), axis=0)\n",
    "\n",
    "# Train the text vectorization layer\n",
    "text_vectorization = TextVectorization(max_vocabulary_size, n_oov_buckets, input_shape=[])\n",
    "text_vectorization.adapt(sample_reviews)\n",
    "\n",
    "# Test this layer on our example\n",
    "text_vectorization(X_example)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "precious-insured",
   "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": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text_vectorization.vocab[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "ranging-noise",
   "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": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class BagOfWords(keras.layers.Layer):\n",
    "    \n",
    "    def __init__(self, n_tokens, dtype=tf.int32, **kwargs):\n",
    "        super().__init__(dtype=tf.int32, **kwargs)\n",
    "        self.n_tokens = n_tokens\n",
    "    \n",
    "    def call(self, inputs):\n",
    "        # For each review, and for each word in the vocabulary, \n",
    "        # we count the number of occurences of that word in the review\n",
    "        bag = tf.reduce_sum(tf.one_hot(inputs, self.n_tokens), axis=1)\n",
    "        # Drop the `<pad>` word - we don't care about it\n",
    "        return bag[:, 1:]\n",
    "\n",
    "# Test the `BagOfWords` on an example\n",
    "bag_of_words = BagOfWords(n_tokens=4)\n",
    "\n",
    "simple_example = tf.constant([[1, 3, 1, 0, 0], [2, 2, 0, 0, 0]])\n",
    "bag_of_words(simple_example)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "established-geology",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.5404 - accuracy: 0.7172 - val_loss: 0.5106 - val_accuracy: 0.7418\n",
      "Epoch 2/5\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.4684 - accuracy: 0.7722 - val_loss: 0.5053 - val_accuracy: 0.7477\n",
      "Epoch 3/5\n",
      "782/782 [==============================] - 5s 7ms/step - loss: 0.4183 - accuracy: 0.8052 - val_loss: 0.5135 - val_accuracy: 0.7399\n",
      "Epoch 4/5\n",
      "782/782 [==============================] - 6s 8ms/step - loss: 0.3470 - accuracy: 0.8525 - val_loss: 0.5354 - val_accuracy: 0.7417\n",
      "Epoch 5/5\n",
      "782/782 [==============================] - 6s 8ms/step - loss: 0.2636 - accuracy: 0.9028 - val_loss: 0.5784 - val_accuracy: 0.7347\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7fada203b160>"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create another BagOfWords with the right vocabulary size\n",
    "#  - Note: we're adding 1 for the `<pad>`\n",
    "bag_of_words = BagOfWords(n_tokens=max_vocabulary_size + n_oov_buckets + 1)\n",
    "\n",
    "# Build a binary classification model\n",
    "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\", metrics=[\"accuracy\"])\n",
    "\n",
    "# Train and validate the model for few epochs\n",
    "model.fit(train_set, epochs=5, validation_data=valid_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "hawaiian-netscape",
   "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": 90,
     "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",
    "# Test the mean embedding on an example\n",
    "another_example = tf.constant(\n",
    "    [[[1., 2., 3.], [4., 5., 0.], [0., 0., 0.]],\n",
    "    [[6., 0., 0.], [0., 0., 0.], [0., 0., 0.]]],\n",
    ")\n",
    "compute_mean_embedding(another_example)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "strategic-crime",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "782/782 [==============================] - 4s 5ms/step - loss: 0.5543 - accuracy: 0.7080 - val_loss: 0.5092 - val_accuracy: 0.7460\n",
      "Epoch 2/5\n",
      "782/782 [==============================] - 4s 5ms/step - loss: 0.4954 - accuracy: 0.7538 - val_loss: 0.5041 - val_accuracy: 0.7477\n",
      "Epoch 3/5\n",
      "782/782 [==============================] - 4s 5ms/step - loss: 0.4839 - accuracy: 0.7596 - val_loss: 0.5007 - val_accuracy: 0.7471\n",
      "Epoch 4/5\n",
      "782/782 [==============================] - 4s 5ms/step - loss: 0.4752 - accuracy: 0.7629 - val_loss: 0.5031 - val_accuracy: 0.7470\n",
      "Epoch 5/5\n",
      "782/782 [==============================] - 4s 5ms/step - loss: 0.4693 - accuracy: 0.7657 - val_loss: 0.5036 - val_accuracy: 0.7444\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7fada21b2460>"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_tokens = max_vocabulary_size + n_oov_buckets + 1\n",
    "embedding_size = 20\n",
    "\n",
    "# Build a model with embedding layer instead of the bag of words\n",
    "model = keras.models.Sequential([\n",
    "    text_vectorization,\n",
    "    keras.layers.Embedding(input_dim=n_tokens, output_dim=embedding_size, 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",
    "])\n",
    "model.compile(loss=\"binary_crossentropy\", optimizer=\"nadam\", metrics=[\"accuracy\"])\n",
    "\n",
    "# Train and validate this model for few epochs\n",
    "model.fit(train_set, epochs=5, validation_data=valid_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "acoustic-profile",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1mDownloading and preparing dataset 80.23 MiB (download: 80.23 MiB, generated: Unknown size, total: 80.23 MiB) to /home/matyama/tensorflow_datasets/imdb_reviews/plain_text/1.0.0...\u001b[0m\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f980e224b5c0411d92a26a0f86fe8abf",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Dl Completed...: 0 url [00:00, ? url/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6ee536e46f10491998be4e1d0af32de6",
       "version_major": 2,
       "version_minor": 0
      },
      "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": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating splits...:   0%|          | 0/3 [00:00<?, ? splits/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "model_id": "",
       "version_major": 2,
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      },
      "text/plain": [
       "Shuffling imdb_reviews-train.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 test examples...:   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": [
       "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%|          | 0/50000 [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 /home/matyama/tensorflow_datasets/imdb_reviews/plain_text/1.0.0. Subsequent calls will reuse this data.\u001b[0m\n",
      "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": [
    "import tensorflow_datasets as tfds\n",
    "\n",
    "# Load the same dataset with TF Datasets\n",
    "datasets = tfds.load(name=\"imdb_reviews\")\n",
    "train_set, test_set = datasets[\"train\"], datasets[\"test\"]\n",
    "\n",
    "# Show an example from the training set\n",
    "for example in train_set.take(1):\n",
    "    print(example[\"text\"])\n",
    "    print(example[\"label\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "outside-dodge",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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