Chapter13.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "516d438b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "import numpy as np\n",
    "import os\n",
    "%matplotlib inline\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "aa55c083",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = tf.range(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9def3ace",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = tf.data.Dataset.from_tensor_slices(X)\n",
    "### == tf.data.Dataset.range(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "65c90fb6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor(0, shape=(), dtype=int32)\n",
      "tf.Tensor(1, shape=(), dtype=int32)\n",
      "tf.Tensor(2, shape=(), dtype=int32)\n",
      "tf.Tensor(3, shape=(), dtype=int32)\n",
      "tf.Tensor(4, shape=(), dtype=int32)\n",
      "tf.Tensor(5, shape=(), dtype=int32)\n",
      "tf.Tensor(6, shape=(), dtype=int32)\n",
      "tf.Tensor(7, shape=(), dtype=int32)\n",
      "tf.Tensor(8, shape=(), dtype=int32)\n",
      "tf.Tensor(9, shape=(), dtype=int32)\n"
     ]
    }
   ],
   "source": [
    "for item in dataset:\n",
    "    print(item)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9af073ad",
   "metadata": {},
   "source": [
    "# 연쇄변환 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "131fc1e4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor([0 1 2 3 4 5 6], shape=(7,), dtype=int32)\n",
      "tf.Tensor([7 8 9 0 1 2 3], shape=(7,), dtype=int32)\n",
      "tf.Tensor([4 5 6 7 8 9 0], shape=(7,), dtype=int32)\n",
      "tf.Tensor([1 2 3 4 5 6 7], shape=(7,), dtype=int32)\n",
      "tf.Tensor([8 9], shape=(2,), dtype=int32)\n"
     ]
    }
   ],
   "source": [
    "dataset2 = dataset.repeat(3).batch(7)\n",
    "\n",
    "for item in dataset2:\n",
    "    print(item)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a73cd8fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor([0 1 2 3 4 5 6], shape=(7,), dtype=int32)\n",
      "tf.Tensor([7 8 9 0 1 2 3], shape=(7,), dtype=int32)\n",
      "tf.Tensor([4 5 6 7 8 9 0], shape=(7,), dtype=int32)\n",
      "tf.Tensor([1 2 3 4 5 6 7], shape=(7,), dtype=int32)\n"
     ]
    }
   ],
   "source": [
    "dataset2 = dataset.repeat(3).batch(7, drop_remainder=True)\n",
    "\n",
    "for item in dataset2:\n",
    "    print(item)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a07f1c29",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = dataset.repeat(3).batch(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "47dfbaf0",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = dataset.map(lambda x:x * 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "29db0c9d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor([ 0  2  4  6  8 10 12], shape=(7,), dtype=int32)\n",
      "tf.Tensor([14 16 18  0  2  4  6], shape=(7,), dtype=int32)\n",
      "tf.Tensor([ 8 10 12 14 16 18  0], shape=(7,), dtype=int32)\n",
      "tf.Tensor([ 2  4  6  8 10 12 14], shape=(7,), dtype=int32)\n",
      "tf.Tensor([16 18], shape=(2,), dtype=int32)\n"
     ]
    }
   ],
   "source": [
    "for item in dataset:\n",
    "    print(item)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3d5161ac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From <ipython-input-11-ff34190d225c>:1: unbatch (from tensorflow.python.data.experimental.ops.batching) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use `tf.data.Dataset.unbatch()`.\n",
      "tf.Tensor(0, shape=(), dtype=int32)\n",
      "tf.Tensor(2, shape=(), dtype=int32)\n",
      "tf.Tensor(4, shape=(), dtype=int32)\n",
      "tf.Tensor(6, shape=(), dtype=int32)\n",
      "tf.Tensor(8, shape=(), dtype=int32)\n",
      "tf.Tensor(10, shape=(), dtype=int32)\n",
      "tf.Tensor(12, shape=(), dtype=int32)\n",
      "tf.Tensor(14, shape=(), dtype=int32)\n",
      "tf.Tensor(16, shape=(), dtype=int32)\n",
      "tf.Tensor(18, shape=(), dtype=int32)\n",
      "tf.Tensor(0, shape=(), dtype=int32)\n",
      "tf.Tensor(2, shape=(), dtype=int32)\n",
      "tf.Tensor(4, shape=(), dtype=int32)\n",
      "tf.Tensor(6, shape=(), dtype=int32)\n",
      "tf.Tensor(8, shape=(), dtype=int32)\n",
      "tf.Tensor(10, shape=(), dtype=int32)\n",
      "tf.Tensor(12, shape=(), dtype=int32)\n",
      "tf.Tensor(14, shape=(), dtype=int32)\n",
      "tf.Tensor(16, shape=(), dtype=int32)\n",
      "tf.Tensor(18, shape=(), dtype=int32)\n",
      "tf.Tensor(0, shape=(), dtype=int32)\n",
      "tf.Tensor(2, shape=(), dtype=int32)\n",
      "tf.Tensor(4, shape=(), dtype=int32)\n",
      "tf.Tensor(6, shape=(), dtype=int32)\n",
      "tf.Tensor(8, shape=(), dtype=int32)\n",
      "tf.Tensor(10, shape=(), dtype=int32)\n",
      "tf.Tensor(12, shape=(), dtype=int32)\n",
      "tf.Tensor(14, shape=(), dtype=int32)\n",
      "tf.Tensor(16, shape=(), dtype=int32)\n",
      "tf.Tensor(18, shape=(), dtype=int32)\n"
     ]
    }
   ],
   "source": [
    "dataset = dataset.apply(tf.data.experimental.unbatch())\n",
    "\n",
    "for item in dataset:\n",
    "    print(item)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "71109ee8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor(0, shape=(), dtype=int32)\n",
      "tf.Tensor(2, shape=(), dtype=int32)\n",
      "tf.Tensor(4, shape=(), dtype=int32)\n",
      "tf.Tensor(6, shape=(), dtype=int32)\n",
      "tf.Tensor(8, shape=(), dtype=int32)\n",
      "tf.Tensor(0, shape=(), dtype=int32)\n",
      "tf.Tensor(2, shape=(), dtype=int32)\n",
      "tf.Tensor(4, shape=(), dtype=int32)\n",
      "tf.Tensor(6, shape=(), dtype=int32)\n",
      "tf.Tensor(8, shape=(), dtype=int32)\n",
      "tf.Tensor(0, shape=(), dtype=int32)\n",
      "tf.Tensor(2, shape=(), dtype=int32)\n",
      "tf.Tensor(4, shape=(), dtype=int32)\n",
      "tf.Tensor(6, shape=(), dtype=int32)\n",
      "tf.Tensor(8, shape=(), dtype=int32)\n"
     ]
    }
   ],
   "source": [
    "dataset = dataset.filter(lambda x:x < 10)\n",
    "\n",
    "for item in dataset:\n",
    "    print(item)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "09adcd17",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor(0, shape=(), dtype=int32)\n",
      "tf.Tensor(2, shape=(), dtype=int32)\n",
      "tf.Tensor(4, shape=(), dtype=int32)\n"
     ]
    }
   ],
   "source": [
    "###df.head()로 보면 될듯\n",
    "for item in dataset.take(3):\n",
    "    print(item)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4060d327",
   "metadata": {},
   "source": [
    "# 셔플"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2259cec8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor([0 2 3 6 7 9 4], shape=(7,), dtype=int64)\n",
      "tf.Tensor([5 0 1 1 8 6 5], shape=(7,), dtype=int64)\n",
      "tf.Tensor([4 8 7 1 2 3 0], shape=(7,), dtype=int64)\n",
      "tf.Tensor([5 4 2 7 8 9 9], shape=(7,), dtype=int64)\n",
      "tf.Tensor([3 6], shape=(2,), dtype=int64)\n"
     ]
    }
   ],
   "source": [
    "dataset = tf.data.Dataset.range(10).repeat(3)\n",
    "dataset = dataset.shuffle(buffer_size = 5, seed=42).batch(7)\n",
    "for item in dataset:\n",
    "    print(item)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d34575ad",
   "metadata": {},
   "source": [
    "### epoch와 batch_size 지정 느낌"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "47fc5914",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import fetch_california_housing\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "housing = fetch_california_housing()\n",
    "X_train_full, X_test, y_train_full, y_test = train_test_split(\n",
    "    housing.data, housing.target.reshape(-1, 1), random_state=42)\n",
    "X_train, X_valid, y_train, y_valid = train_test_split(\n",
    "    X_train_full, y_train_full, random_state=42)\n",
    "\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(X_train)\n",
    "X_mean = scaler.mean_\n",
    "X_std = scaler.scale_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "84546705",
   "metadata": {},
   "outputs": [],
   "source": [
    "def save_to_multiple_csv_files(data, name_prefix, header=None, n_parts=10):\n",
    "    housing_dir = os.path.join(\"datasets\", \"housing\")\n",
    "    os.makedirs(housing_dir, exist_ok=True)\n",
    "    path_format = os.path.join(housing_dir, \"my_{}_{:02d}.csv\")\n",
    "\n",
    "    filepaths = []\n",
    "    m = len(data)\n",
    "    for file_idx, row_indices in enumerate(np.array_split(np.arange(m), n_parts)):\n",
    "        part_csv = path_format.format(name_prefix, file_idx)\n",
    "        filepaths.append(part_csv)\n",
    "        with open(part_csv, \"wt\", encoding=\"utf-8\") as f:\n",
    "            if header is not None:\n",
    "                f.write(header)\n",
    "                f.write(\"\\n\")\n",
    "            for row_idx in row_indices:\n",
    "                f.write(\",\".join([repr(col) for col in data[row_idx]]))\n",
    "                f.write(\"\\n\")\n",
    "    return filepaths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "85ee1e2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = np.c_[X_train, y_train]\n",
    "valid_data = np.c_[X_valid, y_valid]\n",
    "test_data = np.c_[X_test, y_test]\n",
    "header_cols = housing.feature_names + [\"MedianHouseValue\"]\n",
    "header = \",\".join(header_cols)\n",
    "\n",
    "train_filepaths = save_to_multiple_csv_files(train_data, \"train\", header, n_parts=20)\n",
    "valid_filepaths = save_to_multiple_csv_files(valid_data, \"valid\", header, n_parts=10)\n",
    "test_filepaths = save_to_multiple_csv_files(test_data, \"test\", header, n_parts=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "300ca63a",
   "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": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "pd.read_csv(train_filepaths[0]).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "34316eb3",
   "metadata": {},
   "outputs": [],
   "source": [
    "filepath_dataset = tf.data.Dataset.list_files(train_filepaths, seed=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "11f23adf",
   "metadata": {},
   "outputs": [],
   "source": [
    "n_readers = 5\n",
    "dataset = filepath_dataset.interleave(\n",
    "lambda filepath: tf.data.TextLineDataset(filepath).skip(1),\n",
    "cycle_length=n_readers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b68bb4bf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "b'4.2083,44.0,5.323204419889502,0.9171270718232044,846.0,2.3370165745856353,37.47,-122.2,2.782'\n",
      "b'4.1812,52.0,5.701388888888889,0.9965277777777778,692.0,2.4027777777777777,33.73,-118.31,3.215'\n",
      "b'3.6875,44.0,4.524475524475524,0.993006993006993,457.0,3.195804195804196,34.04,-118.15,1.625'\n",
      "b'3.3456,37.0,4.514084507042254,0.9084507042253521,458.0,3.2253521126760565,36.67,-121.7,2.526'\n",
      "b'3.5214,15.0,3.0499445061043287,1.106548279689234,1447.0,1.6059933407325193,37.63,-122.43,1.442'\n"
     ]
    }
   ],
   "source": [
    "for line in dataset.take(5):\n",
    "    print(line.numpy())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "622ab146",
   "metadata": {},
   "source": [
    "# 데이터 전처리 "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3959f758",
   "metadata": {},
   "source": [
    "### 음... 파이썬을 배우고 다시 보자"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1e08d384",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "edda17c7",
   "metadata": {},
   "source": [
    "# TFRecord "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "fcc17072",
   "metadata": {},
   "outputs": [],
   "source": [
    "with tf.io.TFRecordWriter(\"my_data.tfrecord\") as f:\n",
    "    f.write(b\"This is the first record\")\n",
    "    f.write(b\"And this is the second record\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "9fd5902a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor(b'This is the first record', shape=(), dtype=string)\n",
      "tf.Tensor(b'And this is the second record', shape=(), dtype=string)\n"
     ]
    }
   ],
   "source": [
    "filepaths = [\"my_data.tfrecord\"]\n",
    "dataset = tf.data.TFRecordDataset(filepaths)\n",
    "for item in dataset:\n",
    "    print(item)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "1a3b5ae7",
   "metadata": {},
   "outputs": [],
   "source": [
    "options = tf.io.TFRecordOptions(compression_type = \"GZIP\")\n",
    "with tf.io.TFRecordWriter(\"my_compressed.tfrecord\", options) as f:\n",
    "    f.write(b\"This is the first record\")\n",
    "    f.write(b\"And this is the second record\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "4e3c48a2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor(b'This is the first record', shape=(), dtype=string)\n",
      "tf.Tensor(b'And this is the second record', shape=(), dtype=string)\n"
     ]
    }
   ],
   "source": [
    "dataset = tf.data.TFRecordDataset([\"my_compressed.tfrecord\"],\n",
    "                                  compression_type=\"GZIP\")\n",
    "for item in dataset:\n",
    "    print(item)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75db0d07",
   "metadata": {},
   "source": [
    "# 프로토콜 부분 전부 스킵하고 입력 특성 전처리"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "8e654ca8",
   "metadata": {},
   "outputs": [],
   "source": [
    "vocab = [\"abc\",\"123\",\"def\",\"ZXC\"]\n",
    "indices = tf.range(len(vocab), dtype=tf.int64)\n",
    "table_init = tf.lookup.KeyValueTensorInitializer(vocab, indices)\n",
    "num_oov_buckets = 3\n",
    "table = tf.lookup.StaticVocabularyTable(table_init, num_oov_buckets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "ecfe0765",
   "metadata": {},
   "outputs": [],
   "source": [
    "categories=tf.constant([\"abc\",\"123\",\"zxc\",\"abnm\"])\n",
    "cat_indices = table.lookup(categories)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "bbb6f778",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(4,), dtype=int64, numpy=array([0, 1, 4, 6], dtype=int64)>"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "b52c2c23",
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_one_hot = tf.one_hot(cat_indices, depth=len(vocab) + num_oov_buckets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "a3121a5b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(4, 7), dtype=float32, numpy=\n",
       "array([[1., 0., 0., 0., 0., 0., 0.],\n",
       "       [0., 1., 0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 1., 0., 0.],\n",
       "       [0., 0., 0., 0., 0., 0., 1.]], dtype=float32)>"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_one_hot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39a4ffb8",
   "metadata": {},
   "source": [
    "# 임베딩"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "a1e7ee7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "embedding_dim = 2\n",
    "embed_init = tf.random.uniform([len(vocab) + num_oov_buckets, embedding_dim])\n",
    "embedding_matrix = tf.Variable(embed_init)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "b7136d12",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Variable 'Variable:0' shape=(7, 2) dtype=float32, numpy=\n",
       "array([[0.07198346, 0.77377284],\n",
       "       [0.29865992, 0.14949787],\n",
       "       [0.706807  , 0.7530172 ],\n",
       "       [0.9326689 , 0.8259685 ],\n",
       "       [0.01431429, 0.7747736 ],\n",
       "       [0.39001155, 0.7856544 ],\n",
       "       [0.911546  , 0.87763596]], dtype=float32)>"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embedding_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "edb5159c",
   "metadata": {},
   "outputs": [],
   "source": [
    "categories = tf.constant([\"abc\",\"123\",\"zxc\",\"abnm\"])\n",
    "cat_indices = table.lookup(categories)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "14520a44",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(4,), dtype=int64, numpy=array([0, 1, 4, 6], dtype=int64)>"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "f6f2b5e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(4, 2), dtype=float32, numpy=\n",
       "array([[0.07198346, 0.77377284],\n",
       "       [0.29865992, 0.14949787],\n",
       "       [0.01431429, 0.7747736 ],\n",
       "       [0.911546  , 0.87763596]], dtype=float32)>"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.nn.embedding_lookup(embedding_matrix, cat_indices)"
   ]
  }
 ],
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