wide_and_deep.ipynb

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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from IPython.display import display\n",
    "import pprint\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dataset Directory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "DIR = './data/cleaned/'\n",
    "fn_books = 'goodreads_books_mystery_thriller_crime.pkl'\n",
    "fn_reviews = 'goodreads_reviews_mystery_thriller_crime.pkl'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_reviews = pd.read_pickle(os.path.join(DIR, fn_reviews))\n",
    "df_books = pd.read_pickle(os.path.join(DIR, fn_books))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "                                                           user_id   book_id  \\\n",
       "review_id                                                                      \n",
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       "                                  rating  \\\n",
       "review_id                                  \n",
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       "\n",
       "                                                                        review_text  \n",
       "review_id                                                                            \n",
       "5e212a62bced17b4dbe41150e5bb9037  I haven't read a fun mystery book in a while a...  \n",
       "2ede853b14dc4583f96cf5d120af636f  A fun, fast paced science fiction thriller. I ...  \n",
       "8e4d61801907e591018bdc3442a9cf2b           http://www.telegraph.co.uk/culture/10...  \n",
       "022bb6daffa49adc27f6b20b6ebeb37d  An amazing and unique creation: JJ Abrams and ...  \n",
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     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_reviews.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "                                                      title  \\\n",
       "book_id                                                       \n",
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       "29074697  b'The Slaughtered Virgin of Zenopolis (Inspect...   \n",
       "1902202          b'Dead in the Morning (Patrick Grant, #1)'   \n",
       "9671977                 b'Aristotele e i misteri di Eleusi'   \n",
       "\n",
       "          text_reviews_count  \\\n",
       "book_id                        \n",
       "6066814                   15   \n",
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       "1902202                    8   \n",
       "9671977                    3   \n",
       "\n",
       "                                            popular_shelves  average_rating  \\\n",
       "book_id                                                                       \n",
       "6066814   [{'count': '159', 'name': 'to-read'}, {'count'...            3.93   \n",
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       "\n",
       "                                                description  author_id  \\\n",
       "book_id                                                                  \n",
       "6066814   London, 1196. At the command of Richard the Li...      37778   \n",
       "33394837                                                        242185   \n",
       "29074697  BATHS, BANKS AND ROMAN INSURRECTION Detective ...   15104629   \n",
       "1902202   Gerald breezily introduced his wife, Helen, to...     190988   \n",
       "9671977   \"I misteri di Eleusi\" e il quinto romanzo di A...     337108   \n",
       "\n",
       "             author_name  \n",
       "book_id                   \n",
       "6066814   Bernard Knight  \n",
       "33394837  Carolyn Haines  \n",
       "29074697    David  Blake  \n",
       "1902202   Margaret Yorke  \n",
       "9671977   Margaret Doody  "
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      "text/plain": [
       "                                                           user_id  book_id  \\\n",
       "review_id                                                                     \n",
       "0e317947e1fd341f573192111bb2921d  8842281e1d1347389f2ab93d60773d4d  8694005   \n",
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       "\n",
       "                                  rating  \\\n",
       "review_id                                  \n",
       "0e317947e1fd341f573192111bb2921d       3   \n",
       "4276918357312212384ac6415ceb9159       3   \n",
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       "\n",
       "                                                                        review_text  \\\n",
       "review_id                                                                             \n",
       "0e317947e1fd341f573192111bb2921d  The Name of the Rose is a thrilling Dan Brown-...   \n",
       "4276918357312212384ac6415ceb9159  ** spoiler alert **   Hooked me equally as wel...   \n",
       "9ee704921386f88893900829c037abd0  Don't start these books unless you're ready to...   \n",
       "26e59823f1936fe9030d85262f1477e1  Loved it! Not as much as the first but it this...   \n",
       "83370bd38023a2fd928b2b6114c2b210  Once again Larsson does not disappoint. Althou...   \n",
       "\n",
       "                                                                              title  \\\n",
       "review_id                                                                             \n",
       "0e317947e1fd341f573192111bb2921d                            b'The Name of the Rose'   \n",
       "4276918357312212384ac6415ceb9159  b'The Girl Who Played with Fire (Millennium, #2)'   \n",
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       "83370bd38023a2fd928b2b6114c2b210  b'The Girl Who Played with Fire (Millennium, #2)'   \n",
       "\n",
       "                                  text_reviews_count  \\\n",
       "review_id                                              \n",
       "0e317947e1fd341f573192111bb2921d                  99   \n",
       "4276918357312212384ac6415ceb9159                 772   \n",
       "9ee704921386f88893900829c037abd0                 772   \n",
       "26e59823f1936fe9030d85262f1477e1                 772   \n",
       "83370bd38023a2fd928b2b6114c2b210                 772   \n",
       "\n",
       "                                                                    popular_shelves  \\\n",
       "review_id                                                                             \n",
       "0e317947e1fd341f573192111bb2921d  [{'count': '8209', 'name': 'to-read'}, {'count...   \n",
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       "\n",
       "                                  average_rating  \\\n",
       "review_id                                          \n",
       "0e317947e1fd341f573192111bb2921d            4.11   \n",
       "4276918357312212384ac6415ceb9159            4.22   \n",
       "9ee704921386f88893900829c037abd0            4.22   \n",
       "26e59823f1936fe9030d85262f1477e1            4.22   \n",
       "83370bd38023a2fd928b2b6114c2b210            4.22   \n",
       "\n",
       "                                                                        description  \\\n",
       "review_id                                                                             \n",
       "0e317947e1fd341f573192111bb2921d  The year is 1327. Franciscans in a wealthy Ita...   \n",
       "4276918357312212384ac6415ceb9159  Part blistering espionage thriller, part rivet...   \n",
       "9ee704921386f88893900829c037abd0  Part blistering espionage thriller, part rivet...   \n",
       "26e59823f1936fe9030d85262f1477e1  Part blistering espionage thriller, part rivet...   \n",
       "83370bd38023a2fd928b2b6114c2b210  Part blistering espionage thriller, part rivet...   \n",
       "\n",
       "                                  author_id    author_name  \n",
       "review_id                                                   \n",
       "0e317947e1fd341f573192111bb2921d       1730    Umberto Eco  \n",
       "4276918357312212384ac6415ceb9159     706255  Stieg Larsson  \n",
       "9ee704921386f88893900829c037abd0     706255  Stieg Larsson  \n",
       "26e59823f1936fe9030d85262f1477e1     706255  Stieg Larsson  \n",
       "83370bd38023a2fd928b2b6114c2b210     706255  Stieg Larsson  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_join = pd.merge(df_reviews, df_books, left_on=\"book_id\", right_index=True)\n",
    "df_join.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id                object\n",
       "book_id                uint32\n",
       "rating                  uint8\n",
       "review_text            object\n",
       "title                  object\n",
       "text_reviews_count     uint32\n",
       "popular_shelves        object\n",
       "average_rating        float64\n",
       "description            object\n",
       "author_id               int64\n",
       "author_name            object\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_join.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define Base Feature Columns\n",
    "First, let's define the base categorical and continuous feature columns that we'll use. These base columns will be the building blocks used by both the wide part and the deep part of the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_simplified = df_join[[\"user_id\", \"book_id\", \"review_text\", \"title\", \"description\", \"author_id\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = tf.constant(df_join['rating'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(22726,), dtype=uint8, numpy=array([3, 3, 4, ..., 5, 3, 4], dtype=uint8)>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 22726 entries, 0e317947e1fd341f573192111bb2921d to 921812c9edc173c6d12e000723b9e667\n",
      "Data columns (total 6 columns):\n",
      " #   Column       Non-Null Count  Dtype \n",
      "---  ------       --------------  ----- \n",
      " 0   user_id      22726 non-null  object\n",
      " 1   book_id      22726 non-null  uint32\n",
      " 2   review_text  22726 non-null  object\n",
      " 3   title        22726 non-null  object\n",
      " 4   description  22726 non-null  object\n",
      " 5   author_id    22726 non-null  int64 \n",
      "dtypes: int64(1), object(4), uint32(1)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df_simplified.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "label:  tf.Tensor(3, shape=(), dtype=uint8)\n",
      "'user_id'           : b'8842281e1d1347389f2ab93d60773d4d'\n",
      "'book_id'           : 8694005\n",
      "'review_text'       : b'The Name of the Rose is a thrilling Dan Brown-esque murder mystery set in a 14th century Italian monastery. It really is two stories - the story of the murder mystery, and that of political climate in Italy at the time. And of course, they nicely interweave throughout.   The history the book brought was one of my favorite parts. I learned about how the church controlled so much of the world, how even then it was already starting to fall from power, why saints are so prevalent in Italy, and more. I learned about Franciscans and Dolcians and minorites, and poverity and false monks. Poverty was a big theme, which came through on several levels: rebellions by poor people and religious leaders stepping in to lead the masses, and also an a debate about the role the Church should have in society.   I loved brother William of baskerville. He was Sherlock Holmes-esque (so that can\\'t be a coincidence he\\'s from Baskerville right?) in his deductive reasoning and approach. I think my favorite moment of his was when he had no time left to solve the crime he said \"when a man has little time, he must take care to maintain his calm. We must act as if we had eternity before us.\"   I took one star off because I often found myself skipping large sections that went into flowery details of something (often history) that just seemed irrelevant.   The book was also a lot about change. The monks were so set in their ways and in their dogma that you could see how it likely took generations before a viewpoint could really change. The focus on humor for instance and the dogmatic viewpoint of Jorge was in my opinion simply nothing more than fear of change. We all know that the introduction of humor and the divine comedy didn\\'t ruin the world.   The library was one of the greatest in the world at the time. I would love to have seen it. Yet it was almost impossible to access, and that very lack of accessibility is what ultimately led to it\\'s destruction. Seems like the message is that knowledge should be open and free and and keeping it locked up can be more dangerous than not.'\n",
      "'title'             : b'The Name of the Rose'\n",
      "'description'       : b'The year is 1327. Franciscans in a wealthy Italian abbey are suspected of heresy, and Brother William of Baskerville arrives to investigate. When his delicate mission is suddenly overshadowed by seven bizarre deaths, Brother William turns detective. His tools are the logic of Aristotle, the theology of Aquinas, the empirical insights of Roger Bacon - all sharpened to a glistening edge by wry humor and a ferocious curiosity. He collects evidence, deciphers secret symbols and coded manuscripts, and digs into the eerie labyrinth of the abbey, where \"the most interesting things happen at night.\"'\n",
      "'author_id'         : 1730\n"
     ]
    }
   ],
   "source": [
    "joined_slices = tf.data.Dataset.from_tensor_slices((dict(df_simplified), labels))\n",
    "\n",
    "for feature_batch, label in joined_slices.take(1):\n",
    "  print(\"label: \", label)\n",
    "  for key, value in feature_batch.items():\n",
    "    print(\"{!r:20s}: {}\".format(key, value))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<TensorSliceDataset shapes: ({user_id: (), book_id: (), review_text: (), title: (), description: (), author_id: ()}, ()), types: ({user_id: tf.string, book_id: tf.uint32, review_text: tf.string, title: tf.string, description: tf.string, author_id: tf.int64}, tf.uint8)>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = joined_slices\n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs = {\n",
    "  'user_id': tf.keras.Input(shape=(), dtype='string'),\n",
    "  'book_id': tf.keras.Input(shape=(), dtype='uint32'),\n",
    "  # 'rating': tf.keras.Input(shape=(), dtype='uint8'),\n",
    "  'review_text': tf.keras.Input(shape=(), dtype='string'),\n",
    "  'title': tf.keras.Input(shape=(), dtype='string'),\n",
    "  'description': tf.keras.Input(shape=(), dtype='string'),\n",
    "  'author_id': tf.keras.Input(shape=(), dtype='int64'),\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Normalize Ratings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'rating'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m~\\anaconda3\\envs\\ds-project\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   3079\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3080\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3081\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'rating'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_18024/1253508914.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mrating_normalization\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkeras\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlayers\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mNormalization\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"rating_normalization\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mrating_normalization\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madapt\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf_simplified\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'rating'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\anaconda3\\envs\\ds-project\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   3022\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3023\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3024\u001b[1;33m             \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3025\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3026\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\ds-project\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   3080\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3081\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3082\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3083\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3084\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mtolerance\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'rating'"
     ]
    }
   ],
   "source": [
    "rating_normalization = tf.keras.layers.Normalization(name=\"rating_normalization\")\n",
    "rating_normalization.adapt(df_simplified['rating'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Encode integer IDs using IntegerLookup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "book_id_lookup_layer = tf.keras.layers.IntegerLookup(name=\"book_id_lookup\")\n",
    "book_id_lookup_layer.adapt(df_simplified['book_id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "author_id_lookup_layer = tf.keras.layers.IntegerLookup(name=\"author_id_lookup\")\n",
    "author_id_lookup_layer.adapt(df_simplified['author_id'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Encode string data into integers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "user_id_lookup_layer = tf.keras.layers.StringLookup(name=\"user_id_lookup\")\n",
    "user_id_lookup_layer.adapt(df_simplified['user_id'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "unknown words:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Encoded text:\n",
      " [0]\n"
     ]
    }
   ],
   "source": [
    "# Try out the layer\n",
    "print(\n",
    "    \"Encoded text:\\n\", user_id_lookup_layer([\"The Brain is deeper than the sea\"]).numpy(),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "known \"word\" aka user-id string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Encoded text:\n",
      " [963]\n"
     ]
    }
   ],
   "source": [
    "# Try out the layer\n",
    "print(\n",
    "    \"Encoded text:\\n\", user_id_lookup_layer([\"8842281e1d1347389f2ab93d60773d4d\"]).numpy(),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "title_lookup_layer = tf.keras.layers.StringLookup(name=\"title_lookup\")\n",
    "title_lookup_layer.adapt(df_simplified['title'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Embedding larger String data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "review_text_vectorization = tf.keras.layers.TextVectorization(name=\"review_text_vectorization\")\n",
    "review_text_vectorization.adapt(df_simplified['review_text'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "description_vectorization = tf.keras.layers.TextVectorization(name=\"description_vectorization\")\n",
    "description_vectorization.adapt(df_simplified['description'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Encoded text:\n",
      " [[   2 2631    8  428   67    2 1251]]\n"
     ]
    }
   ],
   "source": [
    "# Try out the layer\n",
    "print(\n",
    "    \"Encoded text:\\n\", description_vectorization([\"The Brain is deeper than the sea\"]).numpy(),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'user_id': <KerasTensor: shape=(None,) dtype=string (created by layer 'input_1')>,\n",
       " 'book_id': <KerasTensor: shape=(None,) dtype=uint32 (created by layer 'input_2')>,\n",
       " 'review_text': <KerasTensor: shape=(None,) dtype=string (created by layer 'input_3')>,\n",
       " 'title': <KerasTensor: shape=(None,) dtype=string (created by layer 'input_4')>,\n",
       " 'description': <KerasTensor: shape=(None,) dtype=string (created by layer 'input_5')>,\n",
       " 'author_id': <KerasTensor: shape=(None,) dtype=int64 (created by layer 'input_6')>}"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "outputs = {\n",
    "  'user_id': user_id_lookup_layer(inputs['user_id']),\n",
    "  'book_id': book_id_lookup_layer(inputs['book_id']),\n",
    "  # 'rating': rating_normalization(inputs['rating']),\n",
    "  'review_text': review_text_vectorization(inputs['review_text']),\n",
    "  'title': title_lookup_layer(inputs['title']),\n",
    "  'description': description_vectorization(inputs['description']),\n",
    "  'author_id': author_id_lookup_layer(inputs['author_id']),\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'user_id': <KerasTensor: shape=(None,) dtype=int64 (created by layer 'user_id_lookup')>,\n",
       " 'book_id': <KerasTensor: shape=(None,) dtype=int64 (created by layer 'book_id_lookup')>,\n",
       " 'review_text': <KerasTensor: shape=(None, None) dtype=int64 (created by layer 'review_text_vectorization')>,\n",
       " 'title': <KerasTensor: shape=(None,) dtype=int64 (created by layer 'title_lookup')>,\n",
       " 'description': <KerasTensor: shape=(None, None) dtype=int64 (created by layer 'description_vectorization')>,\n",
       " 'author_id': <KerasTensor: shape=(None,) dtype=int64 (created by layer 'author_id_lookup')>}"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<keras.engine.functional.Functional at 0x1e2618d2a30>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preprocessing_model = tf.keras.Model(inputs, outputs)\n",
    "preprocessing_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "({'user_id': b'8842281e1d1347389f2ab93d60773d4d',\n",
       "  'book_id': 8694005,\n",
       "  'review_text': b'The Name of the Rose is a thrilling Dan Brown-esque murder mystery set in a 14th century Italian monastery. It really is two stories - the story of the murder mystery, and that of political climate in Italy at the time. And of course, they nicely interweave throughout.   The history the book brought was one of my favorite parts. I learned about how the church controlled so much of the world, how even then it was already starting to fall from power, why saints are so prevalent in Italy, and more. I learned about Franciscans and Dolcians and minorites, and poverity and false monks. Poverty was a big theme, which came through on several levels: rebellions by poor people and religious leaders stepping in to lead the masses, and also an a debate about the role the Church should have in society.   I loved brother William of baskerville. He was Sherlock Holmes-esque (so that can\\'t be a coincidence he\\'s from Baskerville right?) in his deductive reasoning and approach. I think my favorite moment of his was when he had no time left to solve the crime he said \"when a man has little time, he must take care to maintain his calm. We must act as if we had eternity before us.\"   I took one star off because I often found myself skipping large sections that went into flowery details of something (often history) that just seemed irrelevant.   The book was also a lot about change. The monks were so set in their ways and in their dogma that you could see how it likely took generations before a viewpoint could really change. The focus on humor for instance and the dogmatic viewpoint of Jorge was in my opinion simply nothing more than fear of change. We all know that the introduction of humor and the divine comedy didn\\'t ruin the world.   The library was one of the greatest in the world at the time. I would love to have seen it. Yet it was almost impossible to access, and that very lack of accessibility is what ultimately led to it\\'s destruction. Seems like the message is that knowledge should be open and free and and keeping it locked up can be more dangerous than not.',\n",
       "  'title': b'The Name of the Rose',\n",
       "  'description': b'The year is 1327. Franciscans in a wealthy Italian abbey are suspected of heresy, and Brother William of Baskerville arrives to investigate. When his delicate mission is suddenly overshadowed by seven bizarre deaths, Brother William turns detective. His tools are the logic of Aristotle, the theology of Aquinas, the empirical insights of Roger Bacon - all sharpened to a glistening edge by wry humor and a ferocious curiosity. He collects evidence, deciphers secret symbols and coded manuscripts, and digs into the eerie labyrinth of the abbey, where \"the most interesting things happen at night.\"',\n",
       "  'author_id': 1730},\n",
       " 3)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "next(dataset.take(1).as_numpy_iterator())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model was constructed with shape (None,) for input KerasTensor(type_spec=TensorSpec(shape=(None,), dtype=tf.int64, name='input_6'), name='input_6', description=\"created by layer 'input_6'\"), but it was called on an input with incompatible shape ().\n",
      "WARNING:tensorflow:Model was constructed with shape (None,) for input KerasTensor(type_spec=TensorSpec(shape=(None,), dtype=tf.uint32, name='input_2'), name='input_2', description=\"created by layer 'input_2'\"), but it was called on an input with incompatible shape ().\n",
      "WARNING:tensorflow:Model was constructed with shape (None,) for input KerasTensor(type_spec=TensorSpec(shape=(None,), dtype=tf.string, name='input_5'), name='input_5', description=\"created by layer 'input_5'\"), but it was called on an input with incompatible shape ().\n",
      "WARNING:tensorflow:Model was constructed with shape (None,) for input KerasTensor(type_spec=TensorSpec(shape=(None,), dtype=tf.string, name='input_3'), name='input_3', description=\"created by layer 'input_3'\"), but it was called on an input with incompatible shape ().\n",
      "WARNING:tensorflow:Model was constructed with shape (None,) for input KerasTensor(type_spec=TensorSpec(shape=(None,), dtype=tf.string, name='input_4'), name='input_4', description=\"created by layer 'input_4'\"), but it was called on an input with incompatible shape ().\n",
      "WARNING:tensorflow:Model was constructed with shape (None,) for input KerasTensor(type_spec=TensorSpec(shape=(None,), dtype=tf.string, name='input_1'), name='input_1', description=\"created by layer 'input_1'\"), but it was called on an input with incompatible shape ().\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "({'user_id': 963,\n",
       "  'book_id': 7589,\n",
       "  'review_text': array([    2,   432,     7,     2,  1539,     8,     4,  1004,   470,\n",
       "         38918,   135,    67,   249,     9,     4,  9002,  1389,  2696,\n",
       "         11561,    11,    48,     8,   109,   187,     2,    26,     7,\n",
       "             2,   135,    67,     3,    12,     7,  1052, 11907,     9,\n",
       "          3574,    36,     2,    71,     3,     7,   350,    55,  1238,\n",
       "         16531,   377,     2,   399,     2,    14,   774,    13,    24,\n",
       "             7,    35,   304,   418,     6,  1081,    39,    68,     2,\n",
       "          1754,  6327,    30,    70,     7,     2,   195,    68,    84,\n",
       "           114,    11,    13,   415,  1020,     5,   804,    40,   948,\n",
       "           151, 24609,    29,    30,  7092,     9,  3574,     3,    38,\n",
       "             6,  1081,    39, 72221,     3, 76181,     3, 60908,     3,\n",
       "         55188,     3,  2235,  6442,  4549,    13,     4,   318,  1261,\n",
       "            79,   378,   119,    22,   347,  2382, 32019,    46,   911,\n",
       "           121,     3,  1654,  5504, 11355,     9,     5,   667,     2,\n",
       "         13511,     3,    86,    34,     4,  6971,    39,     2,  1068,\n",
       "             2,  1754,   264,    28,     9,   915,     6,   127,   596,\n",
       "          2548,     7,  8966,    31,    13,   815, 35782,    30,    12,\n",
       "           159,    27,     4,  4008,   241,    40,  8966,   184,     9,\n",
       "            25,  8118,  3267,     3,  2709,     6,   100,    35,   304,\n",
       "           722,     7,    25,    13,    56,    31,    57,    78,    71,\n",
       "           274,     5,   503,     2,   200,    31,   325,    56,     4,\n",
       "           162,    50,   101,    71,    31,   336,   221,   396,     5,\n",
       "          4213,    25,  3891,   104,   336,  1323,    18,    60,   104,\n",
       "            57, 16803,   156,   236,     6,   316,    24,   577,   168,\n",
       "            80,     6,   455,   123,   312,  4280,  1203,  1996,    12,\n",
       "           425,    73, 10360,   509,     7,   150,   455,   399,    12,\n",
       "            44,   346,  6674,     2,    14,    13,    86,     4,   136,\n",
       "            39,   593,     2,  6442,    66,    30,   249,     9,    77,\n",
       "           679,     3,     9,    77, 27635,    12,    23,   105,   115,\n",
       "            68,    11,  1328,   316,  4152,   156,     4,  3902,   105,\n",
       "            48,   593,     2,  1083,    22,   681,    16,  2532,     3,\n",
       "             2, 22406,  3902,     7, 35208,    13,     9,    35,   569,\n",
       "           676,   301,    38,    87,   937,     7,   593,   104,    37,\n",
       "            98,    12,     2,  1562,     7,   681,     3,     2,  6721,\n",
       "          2483,    90,  2491,     2,   195,     2,   745,    13,    24,\n",
       "             7,     2,  2725,     9,     2,   195,    36,     2,    71,\n",
       "             6,    62,    76,     5,    28,   589,    11,   273,    11,\n",
       "            13,   283,  1506,     5,  3842,     3,    12,    61,  1011,\n",
       "             7, 86696,     8,    41,  1236,  1466,     5,    52,  4589,\n",
       "           254,    42,     2,  1882,     8,    12,  1209,   264,    27,\n",
       "           994,     3,   519,     3,     3,   981,    11,  2599,    51,\n",
       "            85,    27,    38,   822,    87,    21], dtype=int64),\n",
       "  'title': 557,\n",
       "  'description': array([    2,   335,     8, 16232, 21983,     7,     3,   624,  1979,\n",
       "          4437,    28,  1818,     5, 17750,     4,   309,  1737,     5,\n",
       "          3136,   718,     6,   450,    23,    10,  3916,   644,     8,\n",
       "           389, 12636,    27,  1134,   693,  1008,   309,  1737,   206,\n",
       "            92,    10, 10003,    28,     2,  5873,     5, 24850,     2,\n",
       "         15517,     5, 20797,     2, 24279,  7564,     5,  1846, 17035,\n",
       "            38, 10605,     6,     3, 18927,  1098,    27,  7890,  1790,\n",
       "             4,     3, 10762,  2783,    17, 12828,   289, 19120,   108,\n",
       "          1133,     4,  8933, 11028,     4,  1623,    33,     2,   938,\n",
       "          1573,     5,     2,  4437,   101,     2,    55,  4543,   222,\n",
       "          1403,    26,   128], dtype=int64),\n",
       "  'author_id': 301},\n",
       " 3)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Apply the preprocessing in tf.data.Dataset.map.\n",
    "dataset_preprocessed = dataset.map(lambda x, y: (preprocessing_model(x), y),\n",
    "                      num_parallel_calls=tf.data.AUTOTUNE)\n",
    "# Display a preprocessed input sample.\n",
    "next(dataset_preprocessed.take(1).as_numpy_iterator())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset wird jetzt erfolgreich encodiert"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rating ist eigentlich die einzige numerische, \"kontinuierliche\" Spalte... hm."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Wide Model: Linear Model with Crossed Feature Columns\n",
    "The wide model is a linear model with a wide set of sparse and crossed feature columns:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Crossed Column is not implemented in Keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<keras.layers.preprocessing.string_lookup.StringLookup at 0x1e2602fd790>,\n",
       " <keras.layers.preprocessing.integer_lookup.IntegerLookup at 0x1e2602fdbe0>,\n",
       " <keras.layers.preprocessing.integer_lookup.IntegerLookup at 0x1e2602fd940>]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linear_columns = [\n",
    "    user_id_lookup_layer,\n",
    "    book_id_lookup_layer,\n",
    "    author_id_lookup_layer,\n",
    "    # rating_normalization,\n",
    "]\n",
    "# linear_columns = [\n",
    "#     user_id_lookup_layer(inputs['user_id']),\n",
    "#     book_id_lookup_layer(inputs['book_id']),\n",
    "#     author_id_lookup_layer(inputs['author_id']),\n",
    "#     rating_normalization(inputs['rating']),\n",
    "# ]\n",
    "# linear_columns = [\n",
    "#     outputs[\"user_id\"],\n",
    "#     outputs[\"book_id\"],\n",
    "#     outputs[\"author_id\"],\n",
    "#     outputs[\"rating\"],\n",
    "# ]\n",
    "linear_columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Wide models with crossed feature columns can memorize sparse interactions between features effectively. That being said, one limitation of crossed feature columns is that they do not generalize to feature combinations that have not appeared in the training data. Let's add a deep model with embeddings to fix that."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Deep Model: Neural Network with Embeddings\n",
    "The deep model is a feed-forward neural network, as shown in the previous figure. Each of the sparse, high-dimensional categorical features are first converted into a low-dimensional and dense real-valued vector, often referred to as an embedding vector. These low-dimensional dense embedding vectors are concatenated with the continuous features, and then fed into the hidden layers of a neural network in the forward pass. The embedding values are initialized randomly, and are trained along with all other model parameters to minimize the training loss. If you're interested in learning more about embeddings, check out the TensorFlow tutorial on Vector Representations of Words, or Word Embedding on Wikipedia.\n",
    "\n",
    "Another way to represent categorical columns to feed into a neural network is via a multi-hot representation. This is often appropriate for categorical columns with only a few possible values. E.g. for the gender column, \"Male\" can be represented as [1, 0] and \"Female\" as [0, 1]. This is a fixed representation, whereas embeddings are more flexible and calculated at training time.\n",
    "\n",
    "We'll configure the embeddings for the categorical columns using embedding_column, and concatenate them with the continuous columns. We also use indicator_column to create multi-hot representation of some categorical columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "book_id_embedding = tf.keras.layers.Embedding(1,32)\n",
    "rating_embedding = tf.keras.layers.Embedding(1,32)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ob das Rating Embedding so sinn ergibt.. naja.."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<keras.layers.preprocessing.text_vectorization.TextVectorization at 0x1e2618d2a00>,\n",
       " <keras.layers.preprocessing.text_vectorization.TextVectorization at 0x1e263dfbdf0>,\n",
       " <keras.layers.preprocessing.string_lookup.StringLookup at 0x1e260574610>]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# deep_columns = [\n",
    "#     outputs[\"review_text\"],\n",
    "#     outputs[\"description\"],\n",
    "#     outputs[\"title\"],\n",
    "#     book_id_embedding(book_id_lookup_layer(outputs[\"book_id\"])),\n",
    "#     rating_embedding(rating_normalization(outputs[\"rating\"])),\n",
    "# ]\n",
    "# deep_columns = [\n",
    "#     review_text_vectorization(inputs['review_text']),\n",
    "#     description_vectorization(inputs['description']),\n",
    "#     title_lookup_layer(inputs['title']),\n",
    "#     book_id_embedding(book_id_lookup_layer(outputs[\"book_id\"])),\n",
    "#     rating_embedding(rating_normalization(outputs[\"rating\"])),\n",
    "# ]\n",
    "deep_columns = [\n",
    "    review_text_vectorization,\n",
    "    description_vectorization,\n",
    "    title_lookup_layer,\n",
    "    # book_id_embedding(book_id_lookup_layer(outputs[\"book_id\"])),\n",
    "    # rating_embedding(rating_normalization(outputs[\"rating\"])),\n",
    "]\n",
    "deep_columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "log_2(n) = 13, k\\*4sqrt(n) = k\\*10  with k <= 10 --> choose dim = 32 for now"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The higher the dimension of the embedding is, the more degrees of freedom the model will have to learn the representations of the features. For simplicity, we set the dimension to 8 for all feature columns here. Empirically, a more informed decision for the number of dimensions is to start with a value on the order of  log2(n)  or  kn√4 , where  n  is the number of unique features in a feature column and  k  is a small constant (usually smaller than 10).\n",
    "\n",
    "Through dense embeddings, deep models can generalize better and make predictions on feature pairs that were previously unseen in the training data. However, it is difficult to learn effective low-dimensional representations for feature columns when the underlying interaction matrix between two feature columns is sparse and high-rank. In such cases, the interaction between most feature pairs should be zero except a few, but dense embeddings will lead to nonzero predictions for all feature pairs, and thus can over-generalize. On the other hand, linear models with crossed features can memorize these “exception rules” effectively with fewer model parameters.\n",
    "\n",
    "Now, let's see how to jointly train wide and deep models and allow them to complement each other’s strengths and weaknesses."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Combining Wide and Deep Models into One\n",
    "The wide models and deep models are combined by summing up their final output log odds as the prediction, then feeding the prediction to a logistic loss function. All the graph definition and variable allocations have already been handled for you under the hood, so you simply need to create a DNNLinearCombinedClassifier:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tempfile\n",
    "model_dir = tempfile.mkdtemp()\n",
    "m = tf.estimator.DNNLinearCombinedClassifier(\n",
    "    model_dir=model_dir,\n",
    "    linear_feature_columns=crossed_columns,\n",
    "    dnn_feature_columns=deep_columns,\n",
    "    dnn_hidden_units=[100, 50])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "linear_model = tf.keras.experimental.LinearModel()\n",
    "dnn_model = tf.keras.Sequential([tf.keras.layers.Dense(units=64),\n",
    "                             tf.keras.layers.Dense(units=1)])\n",
    "combined_model = tf.keras.experimental.WideDeepModel(linear_model, dnn_model)\n",
    "combined_model.compile(['sgd', 'adam'], 'mse', ['mse'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "EPOCHS = 32"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.keras.utils.plot_model(preprocessing_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "build() missing 1 required positional argument: 'input_shape'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_14196/1038138474.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mcombined_model\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbuild\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m: build() missing 1 required positional argument: 'input_shape'"
     ]
    }
   ],
   "source": [
    "combined_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "This model has not yet been built. Build the model first by calling `build()` or by calling the model on a batch of data.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_14196/2338543439.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkeras\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mutils\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot_model\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcombined_model\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\anaconda3\\envs\\ds-project\\lib\\site-packages\\keras\\utils\\vis_utils.py\u001b[0m in \u001b[0;36mplot_model\u001b[1;34m(model, to_file, show_shapes, show_dtype, show_layer_names, rankdir, expand_nested, dpi, layer_range, show_layer_activations)\u001b[0m\n\u001b[0;32m    411\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    412\u001b[0m   \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbuilt\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 413\u001b[1;33m     raise ValueError('This model has not yet been built. '\n\u001b[0m\u001b[0;32m    414\u001b[0m                      \u001b[1;34m'Build the model first by calling `build()` or by calling '\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    415\u001b[0m                      'the model on a batch of data.')\n",
      "\u001b[1;31mValueError\u001b[0m: This model has not yet been built. Build the model first by calling `build()` or by calling the model on a batch of data."
     ]
    }
   ],
   "source": [
    "tf.keras.utils.plot_model(combined_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Enabled dumping callback in thread MainThread (dump root: /tmp/tfdbg2_logdir, tensor debug mode: FULL_HEALTH)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.debug.lib.debug_events_writer.DebugEventsWriter at 0x1b45fd90cd0>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.debugging.experimental.enable_dump_debug_info(\n",
    "    \"/tmp/tfdbg2_logdir\",\n",
    "    tensor_debug_mode=\"FULL_HEALTH\",\n",
    "    circular_buffer_size=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<TensorSliceDataset shapes: ({user_id: (), book_id: (), review_text: (), title: (), description: (), author_id: ()}, ()), types: ({user_id: tf.string, book_id: tf.uint32, review_text: tf.string, title: tf.string, description: tf.string, author_id: tf.int64}, tf.uint8)>"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "This model has not yet been built. Build the model first by calling `build()` or by calling the model on a batch of data.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_14196/175816803.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mcombined_model\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msummary\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mexpand_nested\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\anaconda3\\envs\\ds-project\\lib\\site-packages\\keras\\engine\\training.py\u001b[0m in \u001b[0;36msummary\u001b[1;34m(self, line_length, positions, print_fn, expand_nested)\u001b[0m\n\u001b[0;32m   2577\u001b[0m     \"\"\"\n\u001b[0;32m   2578\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbuilt\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2579\u001b[1;33m       raise ValueError(\n\u001b[0m\u001b[0;32m   2580\u001b[0m           \u001b[1;34m'This model has not yet been built. '\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2581\u001b[0m           \u001b[1;34m'Build the model first by calling `build()` or by calling '\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: This model has not yet been built. Build the model first by calling `build()` or by calling the model on a batch of data."
     ]
    }
   ],
   "source": [
    "combined_model.summary(expand_nested=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Linear Data Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Linear Inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'user_id': <KerasTensor: shape=(None,) dtype=string (created by layer 'user_id_input')>,\n",
       " 'book_id': <KerasTensor: shape=(None,) dtype=uint32 (created by layer 'book_id_input')>,\n",
       " 'author_id': <KerasTensor: shape=(None,) dtype=int64 (created by layer 'author_id_input')>}"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linear_inputs = {\n",
    "  'user_id': tf.keras.Input(shape=(), dtype='string', name=\"user_id_input\"),\n",
    "  'book_id': tf.keras.Input(shape=(), dtype='uint32', name=\"book_id_input\"),\n",
    "  # 'rating': tf.keras.Input(shape=(), dtype='uint8', name=\"rating_input\"),\n",
    "  'author_id': tf.keras.Input(shape=(), dtype='int64', name=\"author_id_input\"),\n",
    "}\n",
    "linear_inputs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Linear Outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'user_id': <KerasTensor: shape=(None,) dtype=int64 (created by layer 'user_id_lookup')>,\n",
       " 'book_id': <KerasTensor: shape=(None,) dtype=int64 (created by layer 'book_id_lookup')>,\n",
       " 'author_id': <KerasTensor: shape=(None,) dtype=int64 (created by layer 'author_id_lookup')>}"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linear_outputs = {\n",
    "  'user_id': user_id_lookup_layer(linear_inputs['user_id']),\n",
    "  'book_id': book_id_lookup_layer(linear_inputs['book_id']),\n",
    "  # 'rating': rating_normalization(linear_inputs['rating']),\n",
    "  'author_id': author_id_lookup_layer(linear_inputs['author_id']),\n",
    "}\n",
    "linear_outputs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Linear Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "linear_input_model = tf.keras.Model(inputs=linear_inputs, outputs=linear_outputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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T0y2flI+MjMgPf/hDOXfuXIq9AgDyE4KjyEMkrl27Zv8bhZGREXn99dflS1/6Usq9AgD/vvjFL8qPfvQj+wMrpZRcu3Yt5V4BAPkJwVHkIRI//vGP7X/WSeIBMKiuXbtm3+BgdHRU8vl8yj0CgGPkJwRBkYdIPPvss5LL5URE5PTp0/bPADBI8vm8fOYzn7F/fu6551LuEQAcIz8hiFH3gv39fXn//ffT6AsGnL6F78svvyx/+tOf0u0MBtKFCxdkcnIylrar1ao8ffo0lrZhlpdfflnef/99efHFF6VSqaTdHQyAV155RcbGxmJpm/dlcCI/wYvn/0pULuvr60pEePDgwSPxx9TUlDslRWZqair18fHgwcPMx/r6emy5i/dlPHjw6PXwUGn7Jk9T/7uJBhDE/Py8/Pa3v5XTp0/3XHd6elpERDY2NuLuFgaAjoc4TU1NEW/o6d///reUSiX5/e9/7/m8ZVmyvr7u/ckpho77/8TGhfdlEOmen3hfNXwqlYrMzMx4Psc1eYjU3bt3fRV4AJBVp06dkjt37qTdDQBoQ36CXxR5iNTnPve5tLsAAH0jlwHIKvIT/KDIAwAAAACDUOQBAAAAgEEo8gAAAADAIBR5AAAAAGAQijwAAAAAMAhFHgAAAAAYhCIPAAAAAAxCkQcAAAAABqHIAwAAAACDUOQBAAAAgEEo8gAAAADAIBR5AAAAAGCQzBZ5h4eHsra2Jvl8Pu2uiIjIwsKCLCwsdF0nbJ/jHmvW9iXSRTz4k9Z+inq75C6YgFgwD7nJbH7md5AM4pyPpt0BrdlsytmzZ0UpJSIit2/flvv376fcq2DC9jnusb755puytbUVW/tJsSzLc7mOmV7cMZamfsfSD1PiIW5p5aA05ofcFR/yVjRMiAUER25Kxu7urjx48EDu378vhUJBpqen5Vvf+lZb7kk7H5GDAlIu6+vrymNx7DY3N9u2KyKp9KUfYfsc91izuC+npqbU1NRUoNccHR3ZYzk6Ogr0Wq8YS9PBwUHosfTLlHiIu/209lMa2yV3+Scian193ff65K1oZDEWlAoeD0Gl9b4sK8hN/oX5O1etVpWIqNXVVXtZrVZTuVyubXxZyEfkoFZd8kMlE6drNptNWVlZSbsbGABnzpzx/LmXLMbYuXPn7J+DjAXAYCFvAciqBw8eiIjI7OysvWxiYkLu3r3bsl5W8hE5yL9Iijw98ZZliWVZsrCwIIeHhyIi9jLnV6zuZYuLi/ZXoO51ta2tLbEsS4rFot22c/tra2v2a1dWVlrWOTw8lK2tLcnn89JsNqVYLAY6T7jTebjO7ebzednb2/Pdph+9xhV0PaednZ2WefAzT879KCL2nBeLxcjHHoR7fnSs5PN5efLkiYh0j7HDw0NZWlqyX7Ozs9PS/s7OjuTzebEsS5aWljz3bac2+o09J+IhPc75LRaLdlw5JTk/QfpN7spmrJK3wq3nHqMJsRA1P/tB0/Ov97f7+bhihNyUnXj85JNPRETkww8/bFk+MTHR8rtXPuoUB+759ZPvND+5K4ihnvMAX/t1VCgUlIiog4MD1Wg0lIioQqGglGr9WlXT6ziXuX93LqtWq0opper1ekvbWi6XU+Vy2d5eLpdTuVzO/hpXf+Ws26rVam1tdON8vXt5oVCwt7O6uhrpaQW9xhVkPa85KJfL6uDgwH5dr3nSPzvn5OjoyJ7/er0eaMxhT89z99M9v86+O+fZax/r/aVPU9je3lYiomq1mlLq5NQE3a5zjnVb3droFXtB4oV46E8/p2vq8en9qfOdU5Lz4xe5K55YlRCn55G3zIwF3WaWTtf0+75rcXFRNRoNpdTxPiiVSi3P9xMjvZCbsvN3tFar2X0ol8tdT4Hslcd0HLjn12++85O7vPrRjelz3u10zUiKvFKp1PUPgNdkhFnHa5lOOs43P17nF+vXhT1/171dHYjOyXBed9Fv+37HFXT8Sh0f0M7nOvXBa5nXOjpBLC4uBhpzVEWe32Ve6+gE4n5dqVTq2q5zrH7b8Io9v/FCPPQvqmvy9IdN+o+BUunMT9gxkLtOhI1Vkf6LvH76Td7KTizo9rJU5Cnlfz8497l+g6r1EyNh+khuOpH039F6vW4XGXqcfo/9TnHgZ5x+13HvB3LQidiLPK3RaKjFxcVIJ7bXMh2UTjop5HK5rm0F4We7/Wwn7LiCjr9arXb8tC3sPHVb3k0Wijznp0vuh1Le+9fdTq82uu0bv/uNeOhflDdeycLxGnYM5K7er+1FJN0ij7yVnVjQrxvEIk/v905v6PuJkTB9JDf1fm0v/f4d1WPR297c3OzZJ7/9DxKTYbfpNgxznkiRVy6XVS6Xsz/lDjqxUf4BDLKeX0n8kYhyXJ3W05/M6a+Ie/U9i8koyljp1W/9yYr+tMbrk5ZebXR73u9+Ix76F2eRl8b8hB0Duav3a3sRSbfII29lJxb06waxyKvX6y2FXNBvS8Lur06vJzf1fm0vUf0drVardmw4C70g+ShMLvOTu7pts1cfgvSt23pZmvPY7665trYmt27dknv37sn4+HgUTfqWy+VERDwvjiwUCon2JUp+xxV0/LOzs1IqlWRycrLvi1n9bG9QdLrYdWJiQjY3N+WTTz4Ryzq+qdDq6qq8/fbbvtvoR7FYFBHiIYvC7PcszU9cBnFfDGqskreIhX6Mj4/L5uam1Go1KRQKMj8/L0tLS23rmXJDGuLRm2VZ0mw2W5ZdvnxZ7t27JyKS6D//DpK7uiEHHYukyJubmxMRkRdeeCGK5gK5evWqiIg8fvzYXqaDdXp6OrbtlstlEWm/G1FU/I4rzPjn5+cll8vJ7du3I+mr/gPwxhtvRNJekvQ8Pnz40N5v+m5iIsd3gPre974nb7/9tiilZHNzs+U2w37aCGt3d1dee+01ESEeskQf83puRAZrfshdJwY1VslbxEIU9Jv7iYkJWV5ellqtJvPz8/bzccVIJ+SmE0nH4wcffNC2TL+n1wVQEvzkrl7IQQ4BvvbrSH+l22g0Wk7X1Bcwuu8Yoy9mFDm5q47zjnWLi4std6jR7TgvwHUu03fA0ctWV1dbzpX1uttNEF590XfLyeVy9t2p9IWbznGFbd/PuMKMX593r/vvvHmEn3nSv+uv0vUduZznLPsV1T9D9xqfV6y4Y8z9WudDz6nXc3p/eN1Nyd1Gt9jr9pze9/puecRD/8K0r2Nme3tbKXVyxy33qSNpzI8f5K54YlWk/3+GTt5q3T+DGgu6vaydrul3P5RKJTtu9H0VtLAx4ge5KTt/R/X2t7e3W3KRPiVRH89KdX+v7uTe/37zXdDc5TaMOSj2a/L0ObOlUkkdHBzYd9t0Jg73ub36trx6Z7rbcE+wUu2Trx0cHKhyudyy07xueaqTR1CdtttoNOzJ1AHoHlc/7fcaV5jx6/adiVMv8zNPen3nLZR73XK3k6DJqNPB7zdW3DGmNRoN+9bRzrjVr+l08bnz4O/URqfY8zsW534lHvoTtv3t7W17bIVCwS743JKeHz/IXfHEqoj/N/XkLbNjQbeXtSLP737Qb9ZFvO/qFzRG/CI3ZefvqO5rvV5vGXOpVGq7nX+39+pBckWnfdgrd5GD2nUr8qz/bcxWqVRkZmZGXIsBERH7nzlGER/6K/CNjY2+24rL3t6efPazn207FXlvb08uXrw49MfJIMXDIMQb4hNlrFqWJevr63LlypW+24oDeau7KGNBtxdnPPC+zGyD9Hc0bsOSu6Kc8y75YSOSa/IAE62trcn4+Ljntabnz5+X1dXVFHoFAJ2RtwAMInJX9Cjy4JvzDkKDcqe/fvzxj3+UlZUVefLkScvyvb09qVQqgS8GNs2wxQMG1zDFKnmru2GKBWQf8XhiWHJXknM+1EWeZVm+HlltP2nnz5/3/NlUDx8+lGeffVZ+97vf2XO1sLAg+/v7cvPmzbS7l7phi4csIXcFM0yxSt7qbphiIQ3kpmCIxxPDkruSnPPRWFvPuLjP7TXl3GHNtPH0cubMGZmdnZXZ2VlZXl5OuzuZM2zxkCXkrmBMG0835K3uhikW0kBuCsa08fRjWHJXknM+1N/kAQAAAIBpKPIAAAAAwCAUeQAAAABgEIo8AAAAADAIRR4AAAAAGIQiDwAAAAAMQpEHAAAAAAahyAMAAAAAg1DkAQAAAIBBKPIAAAAAwCAUeQAAAABgEIo8AAAAADAIRR4AAAAAGGS00xOVSiXJfmAI7e/viwixhmP7+/syNjYW+zaIN0ShWq2m3QUMGXIXeuF91fDp9reoY5E3MzMTS2cAN2IN2tTUVKzt7+7uEm+IxLvvvivvvvtu2t3AECF3wS9iBSIillJKpd0JAAAAAEAkNrgmDwAAAAAMQpEHAAAAAAahyAMAAAAAg1DkAQAAAIBB/h/i9YKjXEoPDAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.keras.utils.plot_model(linear_input_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Deep Data Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Deep Inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'review_text': <KerasTensor: shape=(None,) dtype=string (created by layer 'review_text_input')>,\n",
       " 'title': <KerasTensor: shape=(None,) dtype=string (created by layer 'title_input')>,\n",
       " 'description': <KerasTensor: shape=(None,) dtype=string (created by layer 'description_input')>}"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deep_inputs = {\n",
    "  'review_text': tf.keras.Input(shape=(), dtype='string', name=\"review_text_input\"),\n",
    "  'title': tf.keras.Input(shape=(), dtype='string', name=\"title_input\"),\n",
    "  'description': tf.keras.Input(shape=(), dtype='string', name=\"description_input\"),\n",
    "}\n",
    "deep_inputs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Deep Outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'review_text': <KerasTensor: shape=(None, None) dtype=int64 (created by layer 'review_text_vectorization')>,\n",
       " 'title': <KerasTensor: shape=(None,) dtype=int64 (created by layer 'title_lookup')>,\n",
       " 'description': <KerasTensor: shape=(None, None) dtype=int64 (created by layer 'description_vectorization')>}"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deep_outputs = {\n",
    "  'review_text': review_text_vectorization(deep_inputs['review_text']),\n",
    "  'title': title_lookup_layer(deep_inputs['title']),\n",
    "  'description': description_vectorization(deep_inputs['description']),\n",
    "}\n",
    "deep_outputs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Deep Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "deep_input_model = tf.keras.Model(inputs=deep_inputs, outputs=deep_outputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.keras.utils.plot_model(deep_input_model, show_shapes=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model was constructed with shape (None,) for input KerasTensor(type_spec=TensorSpec(shape=(None,), dtype=tf.string, name='description_input'), name='description_input', description=\"created by layer 'description_input'\"), but it was called on an input with incompatible shape ().\n",
      "WARNING:tensorflow:Model was constructed with shape (None,) for input KerasTensor(type_spec=TensorSpec(shape=(None,), dtype=tf.string, name='review_text_input'), name='review_text_input', description=\"created by layer 'review_text_input'\"), but it was called on an input with incompatible shape ().\n",
      "WARNING:tensorflow:Model was constructed with shape (None,) for input KerasTensor(type_spec=TensorSpec(shape=(None,), dtype=tf.string, name='title_input'), name='title_input', description=\"created by layer 'title_input'\"), but it was called on an input with incompatible shape ().\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\simon\\anaconda3\\envs\\ds-project\\lib\\site-packages\\keras\\engine\\functional.py:559: UserWarning: Input dict contained keys ['user_id', 'book_id', 'author_id'] which did not match any model input. They will be ignored by the model.\n",
      "  inputs = self._flatten_to_reference_inputs(inputs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "({'review_text': array([    2,   432,     7,     2,  1539,     8,     4,  1004,   470,\n",
       "         38918,   135,    67,   249,     9,     4,  9002,  1389,  2696,\n",
       "         11561,    11,    48,     8,   109,   187,     2,    26,     7,\n",
       "             2,   135,    67,     3,    12,     7,  1052, 11907,     9,\n",
       "          3574,    36,     2,    71,     3,     7,   350,    55,  1238,\n",
       "         16531,   377,     2,   399,     2,    14,   774,    13,    24,\n",
       "             7,    35,   304,   418,     6,  1081,    39,    68,     2,\n",
       "          1754,  6327,    30,    70,     7,     2,   195,    68,    84,\n",
       "           114,    11,    13,   415,  1020,     5,   804,    40,   948,\n",
       "           151, 24609,    29,    30,  7092,     9,  3574,     3,    38,\n",
       "             6,  1081,    39, 72221,     3, 76181,     3, 60908,     3,\n",
       "         55188,     3,  2235,  6442,  4549,    13,     4,   318,  1261,\n",
       "            79,   378,   119,    22,   347,  2382, 32019,    46,   911,\n",
       "           121,     3,  1654,  5504, 11355,     9,     5,   667,     2,\n",
       "         13511,     3,    86,    34,     4,  6971,    39,     2,  1068,\n",
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       "            68,    11,  1328,   316,  4152,   156,     4,  3902,   105,\n",
       "            48,   593,     2,  1083,    22,   681,    16,  2532,     3,\n",
       "             2, 22406,  3902,     7, 35208,    13,     9,    35,   569,\n",
       "           676,   301,    38,    87,   937,     7,   593,   104,    37,\n",
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       "          2483,    90,  2491,     2,   195,     2,   745,    13,    24,\n",
       "             7,     2,  2725,     9,     2,   195,    36,     2,    71,\n",
       "             6,    62,    76,     5,    28,   589,    11,   273,    11,\n",
       "            13,   283,  1506,     5,  3842,     3,    12,    61,  1011,\n",
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       "           994,     3,   519,     3,     3,   981,    11,  2599,    51,\n",
       "            85,    27,    38,   822,    87,    21], dtype=int64),\n",
       "  'title': 557,\n",
       "  'description': array([    2,   335,     8, 16232, 21983,     7,     3,   624,  1979,\n",
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       "             4,     3, 10762,  2783,    17, 12828,   289, 19120,   108,\n",
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       "          1573,     5,     2,  4437,   101,     2,    55,  4543,   222,\n",
       "          1403,    26,   128], dtype=int64)},\n",
       " 3)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Apply the preprocessing in tf.data.Dataset.map.\n",
    "deep_dataset = dataset.map(lambda x, y: (deep_input_model(x), y),\n",
    "                      num_parallel_calls=tf.data.AUTOTUNE)\n",
    "# Display a preprocessed input sample.\n",
    "next(deep_dataset.take(1).as_numpy_iterator())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model was constructed with shape (None,) for input KerasTensor(type_spec=TensorSpec(shape=(None,), dtype=tf.int64, name='author_id_input'), name='author_id_input', description=\"created by layer 'author_id_input'\"), but it was called on an input with incompatible shape ().\n",
      "WARNING:tensorflow:Model was constructed with shape (None,) for input KerasTensor(type_spec=TensorSpec(shape=(None,), dtype=tf.uint32, name='book_id_input'), name='book_id_input', description=\"created by layer 'book_id_input'\"), but it was called on an input with incompatible shape ().\n",
      "WARNING:tensorflow:Model was constructed with shape (None,) for input KerasTensor(type_spec=TensorSpec(shape=(None,), dtype=tf.string, name='user_id_input'), name='user_id_input', description=\"created by layer 'user_id_input'\"), but it was called on an input with incompatible shape ().\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\simon\\anaconda3\\envs\\ds-project\\lib\\site-packages\\keras\\engine\\functional.py:559: UserWarning: Input dict contained keys ['review_text', 'title', 'description'] which did not match any model input. They will be ignored by the model.\n",
      "  inputs = self._flatten_to_reference_inputs(inputs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "({'user_id': 963, 'book_id': 7589, 'author_id': 301}, 3)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Apply the preprocessing in tf.data.Dataset.map.\n",
    "linear_dataset = dataset.map(lambda x, y: (linear_input_model(x), y),\n",
    "                      num_parallel_calls=tf.data.AUTOTUNE)\n",
    "# Display a preprocessed input sample.\n",
    "next(linear_dataset.take(1).as_numpy_iterator())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "l = linear_dataset.map(lambda x, y: x)\n",
    "d = deep_dataset.map(lambda a, b: a)\n",
    "y = dataset.map(lambda f, l: l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'user_id': 963, 'book_id': 7589, 'author_id': 301}"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "next(l.take(1).as_numpy_iterator())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'review_text': array([    2,   432,     7,     2,  1539,     8,     4,  1004,   470,\n",
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       "            2,   135,    67,     3,    12,     7,  1052, 11907,     9,\n",
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       "            2,  1754,   264,    28,     9,   915,     6,   127,   596,\n",
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       "         4213,    25,  3891,   104,   336,  1323,    18,    60,   104,\n",
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       "           80,     6,   455,   123,   312,  4280,  1203,  1996,    12,\n",
       "          425,    73, 10360,   509,     7,   150,   455,   399,    12,\n",
       "           44,   346,  6674,     2,    14,    13,    86,     4,   136,\n",
       "           39,   593,     2,  6442,    66,    30,   249,     9,    77,\n",
       "          679,     3,     9,    77, 27635,    12,    23,   105,   115,\n",
       "           68,    11,  1328,   316,  4152,   156,     4,  3902,   105,\n",
       "           48,   593,     2,  1083,    22,   681,    16,  2532,     3,\n",
       "            2, 22406,  3902,     7, 35208,    13,     9,    35,   569,\n",
       "          676,   301,    38,    87,   937,     7,   593,   104,    37,\n",
       "           98,    12,     2,  1562,     7,   681,     3,     2,  6721,\n",
       "         2483,    90,  2491,     2,   195,     2,   745,    13,    24,\n",
       "            7,     2,  2725,     9,     2,   195,    36,     2,    71,\n",
       "            6,    62,    76,     5,    28,   589,    11,   273,    11,\n",
       "           13,   283,  1506,     5,  3842,     3,    12,    61,  1011,\n",
       "            7, 86696,     8,    41,  1236,  1466,     5,    52,  4589,\n",
       "          254,    42,     2,  1882,     8,    12,  1209,   264,    27,\n",
       "          994,     3,   519,     3,     3,   981,    11,  2599,    51,\n",
       "           85,    27,    38,   822,    87,    21], dtype=int64),\n",
       " 'title': 557,\n",
       " 'description': array([    2,   335,     8, 16232, 21983,     7,     3,   624,  1979,\n",
       "         4437,    28,  1818,     5, 17750,     4,   309,  1737,     5,\n",
       "         3136,   718,     6,   450,    23,    10,  3916,   644,     8,\n",
       "          389, 12636,    27,  1134,   693,  1008,   309,  1737,   206,\n",
       "           92,    10, 10003,    28,     2,  5873,     5, 24850,     2,\n",
       "        15517,     5, 20797,     2, 24279,  7564,     5,  1846, 17035,\n",
       "           38, 10605,     6,     3, 18927,  1098,    27,  7890,  1790,\n",
       "            4,     3, 10762,  2783,    17, 12828,   289, 19120,   108,\n",
       "         1133,     4,  8933, 11028,     4,  1623,    33,     2,   938,\n",
       "         1573,     5,     2,  4437,   101,     2,    55,  4543,   222,\n",
       "         1403,    26,   128], dtype=int64)}"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "next(d.take(1).as_numpy_iterator())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "next(y.take(1).as_numpy_iterator())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TensorSpec(shape=(), dtype=tf.uint8, name=None)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.element_spec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined_model.build((None, 6))\n",
    "# IDK what i'm doing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"wide_deep_model_2\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " linear_model_2 (LinearModel  multiple                 7         \n",
      " )                                                               \n",
      "                                                                 \n",
      " sequential_2 (Sequential)   (None, 1)                 513       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 520\n",
      "Trainable params: 520\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "combined_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.keras.utils.plot_model(combined_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined_dataset = tf.data.Dataset.from_tensors(({\"linear_model_2\": l, \"sequential_2\": d}, y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<TensorDataset shapes: ({linear_model_2: \"DatasetSpec({user_id: TensorSpec(shape=(), dtype=tf.int64, name=None), book_id: TensorSpec(shape=(), dtype=tf.int64, name=None), author_id: TensorSpec(shape=(), dtype=tf.int64, name=None)}, TensorShape([]))\", sequential_2: \"DatasetSpec({review_text: TensorSpec(shape=(None,), dtype=tf.int64, name=None), title: TensorSpec(shape=(), dtype=tf.int64, name=None), description: TensorSpec(shape=(None,), dtype=tf.int64, name=None)}, TensorShape([]))\"}, DatasetSpec(TensorSpec(shape=(), dtype=tf.uint8, name=None), TensorShape([]))), types: ({linear_model_2: \"DatasetSpec({user_id: TensorSpec(shape=(), dtype=tf.int64, name=None), book_id: TensorSpec(shape=(), dtype=tf.int64, name=None), author_id: TensorSpec(shape=(), dtype=tf.int64, name=None)}, TensorShape([]))\", sequential_2: \"DatasetSpec({review_text: TensorSpec(shape=(None,), dtype=tf.int64, name=None), title: TensorSpec(shape=(), dtype=tf.int64, name=None), description: TensorSpec(shape=(None,), dtype=tf.int64, name=None)}, TensorShape([]))\"}, DatasetSpec(TensorSpec(shape=(), dtype=tf.uint8, name=None), TensorShape([])))>"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined_model.fit(combined_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<ZipDataset shapes: ({user_id: (), book_id: (), author_id: ()}, {review_text: (None,), title: (), description: (None,)}, ()), types: ({user_id: tf.int64, book_id: tf.int64, author_id: tf.int64}, {review_text: tf.int64, title: tf.int64, description: tf.int64}, tf.uint8)>"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zipped_ds = tf.data.Dataset.zip((l, d, y))\n",
    "zipped_ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined_model.fit(zipped_ds, epochs=EPOCHS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "linear_model.compile('adagrad', 'mse')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<ZipDataset shapes: ({user_id: (), book_id: (), author_id: ()}, ()), types: ({user_id: tf.int64, book_id: tf.int64, author_id: tf.int64}, tf.uint8)>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z_l = tf.data.Dataset.zip((l, y))\n",
    "z_l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "({'user_id': 963, 'book_id': 7589, 'author_id': 301}, 3)"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "next(z_l.take(1).as_numpy_iterator())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "z_l = tf.data.Dataset.from_tensors((l, y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/32\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "in user code:\n\n    File \"C:\\Users\\simon\\anaconda3\\envs\\ds-project\\lib\\site-packages\\keras\\engine\\training.py\", line 878, in train_function  *\n        return step_function(self, iterator)\n    File \"C:\\Users\\simon\\anaconda3\\envs\\ds-project\\lib\\site-packages\\keras\\engine\\training.py\", line 867, in step_function  **\n        outputs = model.distribute_strategy.run(run_step, args=(data,))\n    File \"C:\\Users\\simon\\anaconda3\\envs\\ds-project\\lib\\site-packages\\keras\\engine\\training.py\", line 860, in run_step  **\n        outputs = model.train_step(data)\n    File \"C:\\Users\\simon\\anaconda3\\envs\\ds-project\\lib\\site-packages\\keras\\engine\\training.py\", line 808, in train_step\n        y_pred = self(x, training=True)\n    File \"C:\\Users\\simon\\anaconda3\\envs\\ds-project\\lib\\site-packages\\keras\\utils\\traceback_utils.py\", line 67, in error_handler\n        raise e.with_traceback(filtered_tb) from None\n    File \"C:\\Users\\simon\\anaconda3\\envs\\ds-project\\lib\\site-packages\\keras\\layers\\core\\dense.py\", line 139, in build\n        raise ValueError('The last dimension of the inputs to a Dense layer '\n\n    ValueError: The last dimension of the inputs to a Dense layer should be defined. Found None. Full input shape received: <unknown>\n",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_14196/2046356455.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mlinear_model\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mz_l\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mEPOCHS\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\anaconda3\\envs\\ds-project\\lib\\site-packages\\keras\\utils\\traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     65\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m  \u001b[1;31m# pylint: disable=broad-except\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     66\u001b[0m       \u001b[0mfiltered_tb\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 67\u001b[1;33m       \u001b[1;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfiltered_tb\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     68\u001b[0m     \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     69\u001b[0m       \u001b[1;32mdel\u001b[0m \u001b[0mfiltered_tb\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\ds-project\\lib\\site-packages\\tensorflow\\python\\framework\\func_graph.py\u001b[0m in \u001b[0;36mautograph_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m   1127\u001b[0m           \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m  \u001b[1;31m# pylint:disable=broad-except\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1128\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"ag_error_metadata\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1129\u001b[1;33m               \u001b[1;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mag_error_metadata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_exception\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1130\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1131\u001b[0m               \u001b[1;32mraise\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: in user code:\n\n    File \"C:\\Users\\simon\\anaconda3\\envs\\ds-project\\lib\\site-packages\\keras\\engine\\training.py\", line 878, in train_function  *\n        return step_function(self, iterator)\n    File \"C:\\Users\\simon\\anaconda3\\envs\\ds-project\\lib\\site-packages\\keras\\engine\\training.py\", line 867, in step_function  **\n        outputs = model.distribute_strategy.run(run_step, args=(data,))\n    File \"C:\\Users\\simon\\anaconda3\\envs\\ds-project\\lib\\site-packages\\keras\\engine\\training.py\", line 860, in run_step  **\n        outputs = model.train_step(data)\n    File \"C:\\Users\\simon\\anaconda3\\envs\\ds-project\\lib\\site-packages\\keras\\engine\\training.py\", line 808, in train_step\n        y_pred = self(x, training=True)\n    File \"C:\\Users\\simon\\anaconda3\\envs\\ds-project\\lib\\site-packages\\keras\\utils\\traceback_utils.py\", line 67, in error_handler\n        raise e.with_traceback(filtered_tb) from None\n    File \"C:\\Users\\simon\\anaconda3\\envs\\ds-project\\lib\\site-packages\\keras\\layers\\core\\dense.py\", line 139, in build\n        raise ValueError('The last dimension of the inputs to a Dense layer '\n\n    ValueError: The last dimension of the inputs to a Dense layer should be defined. Found None. Full input shape received: <unknown>\n"
     ]
    }
   ],
   "source": [
    "linear_model.fit(z_l, epochs=EPOCHS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Failed to find data adapter that can handle input: (<class 'list'> containing values of types {'(<class \\'list\\'> containing values of types {\"<class \\'keras.layers.preprocessing.string_lookup.StringLookup\\'>\", \"<class \\'keras.layers.preprocessing.text_vectorization.TextVectorization\\'>\"})', '(<class \\'list\\'> containing values of types {\"<class \\'keras.layers.preprocessing.string_lookup.StringLookup\\'>\", \"<class \\'keras.layers.preprocessing.integer_lookup.IntegerLookup\\'>\"})'}), <class 'tensorflow.python.data.ops.dataset_ops.TensorSliceDataset'>",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_14196/2952568039.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mcombined_model\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mlinear_columns\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdeep_columns\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdataset\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mEPOCHS\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\anaconda3\\envs\\ds-project\\lib\\site-packages\\keras\\utils\\traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     65\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m  \u001b[1;31m# pylint: disable=broad-except\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     66\u001b[0m       \u001b[0mfiltered_tb\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 67\u001b[1;33m       \u001b[1;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfiltered_tb\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     68\u001b[0m     \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     69\u001b[0m       \u001b[1;32mdel\u001b[0m \u001b[0mfiltered_tb\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\ds-project\\lib\\site-packages\\keras\\engine\\data_adapter.py\u001b[0m in \u001b[0;36mselect_data_adapter\u001b[1;34m(x, y)\u001b[0m\n\u001b[0;32m    986\u001b[0m   \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0madapter_cls\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    987\u001b[0m     \u001b[1;31m# TODO(scottzhu): This should be a less implementation-specific error.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 988\u001b[1;33m     raise ValueError(\n\u001b[0m\u001b[0;32m    989\u001b[0m         \u001b[1;34m\"Failed to find data adapter that can handle \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    990\u001b[0m         \"input: {}, {}\".format(\n",
      "\u001b[1;31mValueError\u001b[0m: Failed to find data adapter that can handle input: (<class 'list'> containing values of types {'(<class \\'list\\'> containing values of types {\"<class \\'keras.layers.preprocessing.string_lookup.StringLookup\\'>\", \"<class \\'keras.layers.preprocessing.text_vectorization.TextVectorization\\'>\"})', '(<class \\'list\\'> containing values of types {\"<class \\'keras.layers.preprocessing.string_lookup.StringLookup\\'>\", \"<class \\'keras.layers.preprocessing.integer_lookup.IntegerLookup\\'>\"})'}), <class 'tensorflow.python.data.ops.dataset_ops.TensorSliceDataset'>"
     ]
    }
   ],
   "source": [
    "combined_model.fit([linear_columns, deep_columns], dataset, epochs=EPOCHS)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training and Evaluating The Model\n",
    "Before we train the model, let's read in the Census dataset as we did in the TensorFlow Linear Model tutorial. The code for input data processing is provided here again for your convenience:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2ter Versuch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<ZipDataset shapes: ({user_id: (), book_id: (), author_id: ()}, {review_text: (None,), title: (), description: (None,)}, ()), types: ({user_id: tf.int64, book_id: tf.int64, author_id: tf.int64}, {review_text: tf.int64, title: tf.int64, description: tf.int64}, tf.uint8)>"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zipped_ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'user_id': <KerasTensor: shape=(None,) dtype=string (created by layer 'user_id_input')>,\n",
       " 'book_id': <KerasTensor: shape=(None,) dtype=uint32 (created by layer 'book_id_input')>,\n",
       " 'author_id': <KerasTensor: shape=(None,) dtype=int64 (created by layer 'author_id_input')>}"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linear_inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(3,), dtype=int64, numpy=array([0, 0, 0], dtype=int64)>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linear_columns[2]([1,2,3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "linear_input_shapes = {\n",
    "    'user_id': (None, 1), # single word; none = batch-size\n",
    "    'book_id': (None, 1), # single integer;\n",
    "    'author_id': (None, 1), # single integer\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "linear_model = tf.keras.experimental.LinearModel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "linear_model.build(input_shape=linear_input_shapes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'name': 'linear_model_4',\n",
       " 'trainable': True,\n",
       " 'dtype': 'float32',\n",
       " 'units': 1,\n",
       " 'activation': 'linear',\n",
       " 'use_bias': True,\n",
       " 'kernel_initializer': {'class_name': 'Zeros', 'config': {}},\n",
       " 'bias_initializer': {'class_name': 'Zeros', 'config': {}},\n",
       " 'kernel_regularizer': None,\n",
       " 'bias_regularizer': None}"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linear_model.get_config()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAIAAAAA8CAIAAAAhVCWSAAAABmJLR0QA/wD/AP+gvaeTAAADdUlEQVR4nO2bsU7zMBDHz594jTLyDjyEM5UBHgClK3MiXgCYGFA6MiQVnegMa9YwlgHJdHKn5An8DScsE6chJShH6f2mnO3Yd/dPfFYKwhgDDB3/qB3Yd1gAYlgAYlgAYg5cI8/zm5sbKlf2hIuLi+PjY2t+egNWq9V8Ph/cpT1iPp+vViu35cAf9PDwMJQ/e4cQotbCNYAYFoAYFoAYFoAYFoAYFoAYFoAYFoAYFoAYFoAYFoAYFoAYFoAYFoCYrQVYr9dZlgVBgGYcx3Ec/7RXv4VasN8e00LD7wHtXF5e3t3dfW+xnaNLsH0TYhxms1mtpRH/xj9Ml2C7JwQAZrOZ28I1gJheArjbn3u9WCyEEEEQvL+/u4Ovr6+x/fn5GRurqppOp0IIIUQcx+v1GkcuFosgCKqqmkwmX9YYf+nJZIJLZ1nmmnZRbBdCTKdTXNTvDYLg9fXVX8uPohfu67DtFiSl9K/zPDfGKKUAIAxDvEVrLaVM09QY8/T0BABFURhjwjAEAK21O96dqigKO8km7HicM89znKrRExyfJIn1SkpZlqXbG4YhtqRp6mZpUxR+JltSV9uC+taATdc1EyNxu6IoMsZEUWRT40/l5qW7S+0mJk5rjSaqhTk1xjw+PgLAcrlEsyzLLlHsgAD2IW18+ZRSV1dXLVNt5VK7ie+c7cIUSykbeztGsQMCtLiYJImUcrlcDiOAP3Of3m299QUY9BTk17Qsy87Pz29vb4+OjobxAZ/iWuHFB78jfhR9GEiAJEkA4P7+vqoq+DhLAMDp6SkAHB4eDuMGAJydnQHA29sbmujPycmJ6+fLy0vjvZui6IX7OnTZgrTWeKPWuvEaK6ctX1jubK9FKWU+dlWllN2C3Km6vNTu5Li065VvlmWJJx800zR1D0h4ZJJSontYseHjENUYRW3+dqB/Degoqq+xUiqKIgwGwzPGFEUBAFEUaa3xRIQpQGxt7O7SlybmEZ9lAEjTtHbWUkrhjhSGoT132uT6Ufjzt7v6A0WY+Ta+APwpghgWgJitP0eT4P9Vt4vZ5X/03A0BdjrF7fAWRAwLQAwLQAwLQAwLQAwLQAwLQAwLQAwLQAwLQAwLQAwLQAwLQEzD11D7CzUzAJ/egNFoNB6PqVzZB8bj8Wg0clvEH/7UvhNwDSCGBSCGBSCGBSDmP8qUrXqQKLy7AAAAAElFTkSuQmCC",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.keras.utils.plot_model(linear_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "linear_model.compile('adagrad', 'mse')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "user_id\n",
      "tf.Tensor([], shape=(0,), dtype=int32)\n",
      "book_id\n",
      "tf.Tensor([], shape=(0,), dtype=int32)\n",
      "author_id\n",
      "tf.Tensor([], shape=(0,), dtype=int32)\n"
     ]
    },
    {
     "ename": "AttributeError",
     "evalue": "'tensorflow.python.framework.ops.EagerTensor' object has no attribute 'items'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_18024/3922764091.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0me\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mz_l\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtake\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m         \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mv\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m             \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m             \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mv\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\ds-project\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\u001b[0m in \u001b[0;36m__getattr__\u001b[1;34m(self, name)\u001b[0m\n\u001b[0;32m    440\u001b[0m         \u001b[1;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnumpy_ops\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mnp_config\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    441\u001b[0m         np_config.enable_numpy_behavior()\"\"\".format(type(self).__name__, name))\n\u001b[1;32m--> 442\u001b[1;33m     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    443\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    444\u001b[0m   \u001b[1;33m@\u001b[0m\u001b[0mstaticmethod\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'tensorflow.python.framework.ops.EagerTensor' object has no attribute 'items'"
     ]
    }
   ],
   "source": [
    "for e in z_l.take(1):\n",
    "    for i in e:\n",
    "        for k, v in i.items():\n",
    "            print(k)\n",
    "            print(tf.shape(v))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/32\n"
     ]
    },
    {
     "ename": "StagingError",
     "evalue": "in user code:\n\n    File \"C:\\Users\\simon\\anaconda3\\envs\\ds-project\\lib\\site-packages\\keras\\engine\\training.py\", line 878, in train_function  *\n        return step_function(self, iterator)\n    File \"C:\\Users\\simon\\anaconda3\\envs\\ds-project\\lib\\site-packages\\keras\\engine\\training.py\", line 867, in step_function  **\n        outputs = model.distribute_strategy.run(run_step, args=(data,))\n    File \"C:\\Users\\simon\\anaconda3\\envs\\ds-project\\lib\\site-packages\\keras\\engine\\training.py\", line 860, in run_step  **\n        outputs = model.train_step(data)\n    File \"C:\\Users\\simon\\anaconda3\\envs\\ds-project\\lib\\site-packages\\keras\\engine\\training.py\", line 808, in train_step\n        y_pred = self(x, training=True)\n    File \"C:\\Users\\simon\\anaconda3\\envs\\ds-project\\lib\\site-packages\\keras\\utils\\traceback_utils.py\", line 67, in error_handler\n        raise e.with_traceback(filtered_tb) from None\n\n    IndexError: list index out of range\n",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mStagingError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_18024/2046356455.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mlinear_model\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mz_l\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mEPOCHS\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\anaconda3\\envs\\ds-project\\lib\\site-packages\\keras\\utils\\traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     65\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m  \u001b[1;31m# pylint: disable=broad-except\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     66\u001b[0m       \u001b[0mfiltered_tb\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 67\u001b[1;33m       \u001b[1;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfiltered_tb\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     68\u001b[0m     \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     69\u001b[0m       \u001b[1;32mdel\u001b[0m \u001b[0mfiltered_tb\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\ds-project\\lib\\site-packages\\tensorflow\\python\\framework\\func_graph.py\u001b[0m in \u001b[0;36mautograph_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m   1127\u001b[0m           \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m  \u001b[1;31m# pylint:disable=broad-except\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1128\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"ag_error_metadata\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1129\u001b[1;33m               \u001b[1;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mag_error_metadata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_exception\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1130\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1131\u001b[0m               \u001b[1;32mraise\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mStagingError\u001b[0m: in user code:\n\n    File \"C:\\Users\\simon\\anaconda3\\envs\\ds-project\\lib\\site-packages\\keras\\engine\\training.py\", line 878, in train_function  *\n        return step_function(self, iterator)\n    File \"C:\\Users\\simon\\anaconda3\\envs\\ds-project\\lib\\site-packages\\keras\\engine\\training.py\", line 867, in step_function  **\n        outputs = model.distribute_strategy.run(run_step, args=(data,))\n    File \"C:\\Users\\simon\\anaconda3\\envs\\ds-project\\lib\\site-packages\\keras\\engine\\training.py\", line 860, in run_step  **\n        outputs = model.train_step(data)\n    File \"C:\\Users\\simon\\anaconda3\\envs\\ds-project\\lib\\site-packages\\keras\\engine\\training.py\", line 808, in train_step\n        y_pred = self(x, training=True)\n    File \"C:\\Users\\simon\\anaconda3\\envs\\ds-project\\lib\\site-packages\\keras\\utils\\traceback_utils.py\", line 67, in error_handler\n        raise e.with_traceback(filtered_tb) from None\n\n    IndexError: list index out of range\n"
     ]
    }
   ],
   "source": [
    "linear_model.fit(z_l, epochs=EPOCHS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [],
   "source": [
    "linear_model = tf.keras.experimental.LinearModel()\n",
    "dnn_model = tf.keras.Sequential([tf.keras.layers.Dense(units=64),\n",
    "                             tf.keras.layers.Dense(units=1)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [],
   "source": [
    "z_l = tf.data.Dataset.zip((l, y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'user_id': <KerasTensor: shape=(None,) dtype=string (created by layer 'user_id_input')>,\n",
       " 'book_id': <KerasTensor: shape=(None,) dtype=uint32 (created by layer 'book_id_input')>,\n",
       " 'author_id': <KerasTensor: shape=(None,) dtype=int64 (created by layer 'author_id_input')>}"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linear_inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "({'user_id': 963, 'book_id': 7589, 'author_id': 301}, 3)"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "next(z_l.take(1).as_numpy_iterator())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_shapes = {k : v.shape for (k,v) in linear_inputs.items()}\n",
    "input_shapes['output'] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [],
   "source": [
    "deep_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "model = tf.keras.Sequential()  # Instantiate sequential model\n",
    "model.add(tf.keras.layers.Dense(512, activation='relu',\n",
    "          input_shape=(max_words,))) # Add first layer. Make sure to specify input shape\n",
    "model.add(tf.keras.layers.Dense(1, activation='softmax')) # Add third layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'user_id': <KerasTensor: shape=(None,) dtype=int64 (created by layer 'user_id_lookup')>,\n",
       " 'book_id': <KerasTensor: shape=(None,) dtype=int64 (created by layer 'book_id_lookup')>,\n",
       " 'author_id': <KerasTensor: shape=(None,) dtype=int64 (created by layer 'author_id_lookup')>}"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linear_outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(3,), dtype=int64, numpy=array([4181,   13, 5656], dtype=int64)>"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "review_text_vectorization(\"test was geht\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'user_id': <KerasTensor: shape=(None,) dtype=string (created by layer 'input_1')>,\n",
       " 'book_id': <KerasTensor: shape=(None,) dtype=uint32 (created by layer 'input_2')>,\n",
       " 'review_text': <KerasTensor: shape=(None,) dtype=string (created by layer 'input_3')>,\n",
       " 'title': <KerasTensor: shape=(None,) dtype=string (created by layer 'input_4')>,\n",
       " 'description': <KerasTensor: shape=(None,) dtype=string (created by layer 'input_5')>,\n",
       " 'author_id': <KerasTensor: shape=(None,) dtype=int64 (created by layer 'input_6')>}"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'user_id': <KerasTensor: shape=(None,) dtype=int64 (created by layer 'user_id_lookup')>,\n",
       " 'book_id': <KerasTensor: shape=(None,) dtype=int64 (created by layer 'book_id_lookup')>,\n",
       " 'review_text': <KerasTensor: shape=(None, None) dtype=int64 (created by layer 'review_text_vectorization')>,\n",
       " 'title': <KerasTensor: shape=(None,) dtype=int64 (created by layer 'title_lookup')>,\n",
       " 'description': <KerasTensor: shape=(None, None) dtype=int64 (created by layer 'description_vectorization')>,\n",
       " 'author_id': <KerasTensor: shape=(None,) dtype=int64 (created by layer 'author_id_lookup')>}"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "body = tf.keras.Sequential([\n",
    "  tf.keras.layers.Dense(10, activation='relu'),\n",
    "  tf.keras.layers.Dense(10, activation='relu'),\n",
    "  tf.keras.layers.Dense(1)\n",
    "])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'user_id': <tf.Tensor: shape=(3,), dtype=int64, numpy=array([963, 963, 175], dtype=int64)>,\n",
       " 'book_id': <tf.Tensor: shape=(3,), dtype=int64, numpy=array([7589, 1003, 1003], dtype=int64)>,\n",
       " 'review_text': <tf.Tensor: shape=(3, 375), dtype=int64, numpy=\n",
       " array([[  2, 432,   7, ..., 822,  87,  21],\n",
       "        [494, 617, 711, ...,   0,   0,   0],\n",
       "        [106, 227, 137, ...,   0,   0,   0]], dtype=int64)>,\n",
       " 'title': <tf.Tensor: shape=(3,), dtype=int64, numpy=array([557,   2,   2], dtype=int64)>,\n",
       " 'description': <tf.Tensor: shape=(3, 116), dtype=int64, numpy=\n",
       " array([[    2,   335,     8, 16232, 21983,     7,     3,   624,  1979,\n",
       "          4437,    28,  1818,     5, 17750,     4,   309,  1737,     5,\n",
       "          3136,   718,     6,   450,    23,    10,  3916,   644,     8,\n",
       "           389, 12636,    27,  1134,   693,  1008,   309,  1737,   206,\n",
       "            92,    10, 10003,    28,     2,  5873,     5, 24850,     2,\n",
       "         15517,     5, 20797,     2, 24279,  7564,     5,  1846, 17035,\n",
       "            38, 10605,     6,     3, 18927,  1098,    27,  7890,  1790,\n",
       "             4,     3, 10762,  2783,    17, 12828,   289, 19120,   108,\n",
       "          1133,     4,  8933, 11028,     4,  1623,    33,     2,   938,\n",
       "          1573,     5,     2,  4437,   101,     2,    55,  4543,   222,\n",
       "          1403,    26,   128,     0,     0,     0,     0,     0,     0,\n",
       "             0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
       "             0,     0,     0,     0,     0,     0,     0,     0],\n",
       "        [  408,  3641,  3035,   118,   408,   559,    95,  5630,     4,\n",
       "           408,  7227,   919,    16,  1568,  7821,     2,   106,    24,\n",
       "          1089,    12, 31049,     3,  1663,  9159,  3458,    79,  1504,\n",
       "           836, 11165,  5184,     5,     2,  1365,  4585,    19,  2599,\n",
       "             6,   390,     3,    91,    11,    34,   919,    20,  5809,\n",
       "           702,  1371,  1571,    16,     2,   522,     5,    42,  6037,\n",
       "             2,    64,  4492,  1182,    13,     2, 11215,    28,   172,\n",
       "             4,     2,  1765,   100,    16,     2,    53,   999,  4209,\n",
       "             6,    10,   130,     2,   890,  2210,  4104,  1323,   784,\n",
       "           836,   755,     5, 10298,   878,  1374,    33,    20,   137,\n",
       "           874,   784,   113,     8,   382,    33,     3,  1111,   298,\n",
       "             5,   911,     4,  4415,   127,   440,     9,     6,   312,\n",
       "             9,   124,    71,    21,     2,  2564,  3498,   677],\n",
       "        [  408,  3641,  3035,   118,   408,   559,    95,  5630,     4,\n",
       "           408,  7227,   919,    16,  1568,  7821,     2,   106,    24,\n",
       "          1089,    12, 31049,     3,  1663,  9159,  3458,    79,  1504,\n",
       "           836, 11165,  5184,     5,     2,  1365,  4585,    19,  2599,\n",
       "             6,   390,     3,    91,    11,    34,   919,    20,  5809,\n",
       "           702,  1371,  1571,    16,     2,   522,     5,    42,  6037,\n",
       "             2,    64,  4492,  1182,    13,     2, 11215,    28,   172,\n",
       "             4,     2,  1765,   100,    16,     2,    53,   999,  4209,\n",
       "             6,    10,   130,     2,   890,  2210,  4104,  1323,   784,\n",
       "           836,   755,     5, 10298,   878,  1374,    33,    20,   137,\n",
       "           874,   784,   113,     8,   382,    33,     3,  1111,   298,\n",
       "             5,   911,     4,  4415,   127,   440,     9,     6,   312,\n",
       "             9,   124,    71,    21,     2,  2564,  3498,   677]],\n",
       "       dtype=int64)>,\n",
       " 'author_id': <tf.Tensor: shape=(3,), dtype=int64, numpy=array([301,   6,   6], dtype=int64)>}"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preprocessing_model(dict(df_simplified.iloc[:3]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "87702"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(review_text_vectorization.get_vocabulary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "89493"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(description_vectorization.get_vocabulary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "embedding_dim = 32"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'user_id': <KerasTensor: shape=(None,) dtype=int64 (created by layer 'user_id_lookup')>,\n",
       " 'book_id': <KerasTensor: shape=(None,) dtype=int64 (created by layer 'book_id_lookup')>,\n",
       " 'review_text': <KerasTensor: shape=(None, None) dtype=int64 (created by layer 'review_text_vectorization')>,\n",
       " 'title': <KerasTensor: shape=(None,) dtype=int64 (created by layer 'title_lookup')>,\n",
       " 'description': <KerasTensor: shape=(None, None) dtype=int64 (created by layer 'description_vectorization')>,\n",
       " 'author_id': <KerasTensor: shape=(None,) dtype=int64 (created by layer 'author_id_lookup')>}"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'user_id': <KerasTensor: shape=(None, 1) dtype=int64 (created by layer 'tf.expand_dims_1')>,\n",
       " 'book_id': <KerasTensor: shape=(None, 1) dtype=int64 (created by layer 'tf.expand_dims_2')>,\n",
       " 'review_text': <KerasTensor: shape=(None, None) dtype=int64 (created by layer 'review_text_vectorization')>,\n",
       " 'title': <KerasTensor: shape=(None, 1) dtype=int64 (created by layer 'tf.expand_dims_3')>,\n",
       " 'description': <KerasTensor: shape=(None, None) dtype=int64 (created by layer 'description_vectorization')>,\n",
       " 'author_id': <KerasTensor: shape=(None, 1) dtype=int64 (created by layer 'tf.expand_dims_4')>}"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "out['author_id'] = tf.expand_dims(outputs['author_id'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "concat = tf.concat(out.values(), axis=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "preprocessor = tf.keras.Model(inputs, concat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.keras.utils.plot_model(preprocessor, rankdir=\"LR\", show_shapes=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.keras.utils.plot_model(preprocessing_model, rankdir=\"LR\", show_shapes=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### funzt nicht wegen variabler input & output size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "linear_model = tf.keras.experimental.LinearModel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'user_id': <KerasTensor: shape=(None,) dtype=int64 (created by layer 'user_id_lookup')>,\n",
       " 'book_id': <KerasTensor: shape=(None,) dtype=int64 (created by layer 'book_id_lookup')>,\n",
       " 'review_text': <KerasTensor: shape=(None, None) dtype=int64 (created by layer 'review_text_vectorization')>,\n",
       " 'title': <KerasTensor: shape=(None,) dtype=int64 (created by layer 'title_lookup')>,\n",
       " 'description': <KerasTensor: shape=(None, None) dtype=int64 (created by layer 'description_vectorization')>,\n",
       " 'author_id': <KerasTensor: shape=(None,) dtype=int64 (created by layer 'author_id_lookup')>}"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [],
   "source": [
    "embedding_layer =   tf.keras.layers.Embedding(review_text_vectorization.vocabulary_size(), embedding_dim, name=\"embedding\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = tf.keras.Sequential([\n",
    "  review_text_vectorization,\n",
    "  embedding_layer,\n",
    "  tf.keras.layers.GlobalAveragePooling1D(),\n",
    "  tf.keras.layers.Dense(16, activation='relu'),\n",
    "  tf.keras.layers.Dense(1)\n",
    "])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [],
   "source": [
    "tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=\"logs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam',\n",
    "              loss=tf.keras.losses.CategoricalCrossentropy(from_logits=True),\n",
    "              metrics=['accuracy'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<MapDataset shapes: {review_text: (None,), title: (), description: (None,)}, types: {review_text: tf.int64, title: tf.int64, description: tf.int64}>"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<MapDataset shapes: (None,), types: tf.int64>"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d.map(lambda x: x['review_text'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<MapDataset shapes: (), types: tf.uint8>"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<ZipDataset shapes: ((None,), ()), types: (tf.int64, tf.uint8)>"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dy = tf.data.Dataset.zip((d.map(lambda x: x['review_text']),y))\n",
    "dy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "embedding_layer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "textds = dataset.map(lambda x,y: x['review_text'], y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<MapDataset shapes: ((), ()), types: (tf.string, tf.uint8)>"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.map(lambda x,y: (x['review_text'], y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'name': 'review_text_vectorization',\n",
       " 'trainable': True,\n",
       " 'batch_input_shape': (None, None),\n",
       " 'dtype': 'string',\n",
       " 'max_tokens': None,\n",
       " 'standardize': 'lower_and_strip_punctuation',\n",
       " 'split': 'whitespace',\n",
       " 'ngrams': None,\n",
       " 'output_mode': 'int',\n",
       " 'output_sequence_length': None,\n",
       " 'pad_to_max_tokens': False,\n",
       " 'sparse': False,\n",
       " 'ragged': False,\n",
       " 'vocabulary': None,\n",
       " 'idf_weights': None}"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "review_text_vectorization.get_config()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [],
   "source": [
    "es = tf.keras.callbacks.EarlyStopping(monitor=\"loss\",patience=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/15\n",
      "WARNING:tensorflow:Model was constructed with shape (None, None) for input KerasTensor(type_spec=TensorSpec(shape=(None, None), dtype=tf.string, name='review_text_vectorization_input'), name='review_text_vectorization_input', description=\"created by layer 'review_text_vectorization_input'\"), but it was called on an input with incompatible shape (None,).\n",
      "WARNING:tensorflow:Model was constructed with shape (None, None) for input KerasTensor(type_spec=TensorSpec(shape=(None, None), dtype=tf.string, name='review_text_vectorization_input'), name='review_text_vectorization_input', description=\"created by layer 'review_text_vectorization_input'\"), but it was called on an input with incompatible shape (None,).\n",
      "711/711 [==============================] - 43s 57ms/step - loss: 0.0000e+00 - accuracy: 0.0288\n",
      "Epoch 2/15\n",
      "711/711 [==============================] - 47s 66ms/step - loss: 0.0000e+00 - accuracy: 0.0288\n",
      "Epoch 3/15\n",
      "711/711 [==============================] - 41s 57ms/step - loss: 0.0000e+00 - accuracy: 0.0288\n",
      "Epoch 4/15\n",
      "711/711 [==============================] - 43s 61ms/step - loss: 0.0000e+00 - accuracy: 0.0288\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1e290ed6520>"
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(\n",
    "    dataset.map(lambda x,y: (x['review_text'], y)).batch(32),\n",
    "    epochs=15,\n",
    "    callbacks=[tensorboard_callback, es])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## TFRS\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<TensorSliceDataset shapes: ({user_id: (), book_id: (), review_text: (), title: (), description: (), author_id: ()}, ()), types: ({user_id: tf.string, book_id: tf.uint32, review_text: tf.string, title: tf.string, description: tf.string, author_id: tf.int64}, tf.uint8)>"
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<ParallelMapDataset shapes: ({user_id: (), book_id: (), review_text: (None,), title: (), description: (None,), author_id: ()}, ()), types: ({user_id: tf.int64, book_id: tf.int64, review_text: tf.int64, title: tf.int64, description: tf.int64, author_id: tf.int64}, tf.uint8)>"
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_preprocessed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(22726,), dtype=uint8, numpy=array([3, 3, 4, ..., 5, 3, 4], dtype=uint8)>"
      ]
     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow_recommenders as tfrs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "metadata": {},
   "outputs": [],
   "source": [
    "from typing import Dict, List, Text\n",
    "\n",
    "class MyModel(tfrs.Model):\n",
    "  # We derive from a custom base class to help reduce boilerplate. Under the hood,\n",
    "  # these are still plain Keras Models.\n",
    "\n",
    "  def __init__(\n",
    "      self,\n",
    "      linear_model: tf.keras.Model,\n",
    "      deep_model: tf.keras.Model,\n",
    "      task: tfrs.tasks.Retrieval):\n",
    "    super().__init__()\n",
    "\n",
    "    # Set up user and movie representations.\n",
    "    self.linear_model = linear_model\n",
    "    self.deep_model = deep_model\n",
    "\n",
    "    # Set up a retrieval task.\n",
    "    self.task = task\n",
    "    self.first = True\n",
    "\n",
    "  def compute_loss(self, dataset: Dict[Text, tf.Tensor], training=False) -> tf.Tensor:\n",
    "    # Define how the loss is computed.\n",
    "    linear_features = [dataset.get(key) for key in [\"author_id\", \"user_id\", \"book_id\"]]\n",
    "    deep_features = [dataset.get(key) for key in [\"description\", \"review_text\", \"title\"]]\n",
    "    if self.first:\n",
    "      print(linear_features)\n",
    "      print(\"---\")\n",
    "      print(deep_features)\n",
    "      self.first = False\n",
    "    linear_out = self.linear_model(linear_features)\n",
    "    deep_out = self.deep_model(deep_features)\n",
    "    text_model = tf.keras.Sequential([\n",
    "    deep_features['review_text'],\n",
    "    tf.keras.layers.Embedding(review_text_vectorization.vocabulary_size(), 64)\n",
    "])\n",
    "    desc_model = tf.keras.Sequential([\n",
    "    deep_features['description'],\n",
    "    tf.keras.layers.Embedding(review_text_vectorization.vocabulary_size(), 64)\n",
    "])\n",
    "    out = desc_model()\n",
    "    query = linear_out['user_id']\n",
    "    candidate = [linear_out.get(key) for key in [\"author_id\",\"book_id\"]] + [v for v in deep_out.values()]\n",
    "    return self.task(query, candidate)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_1\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                   Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      " author_id_input (InputLayer)   [(None,)]            0           []                               \n",
      "                                                                                                  \n",
      " book_id_input (InputLayer)     [(None,)]            0           []                               \n",
      "                                                                                                  \n",
      " user_id_input (InputLayer)     [(None,)]            0           []                               \n",
      "                                                                                                  \n",
      " author_id_lookup (IntegerLooku  (None,)             0           ['author_id_input[0][0]']        \n",
      " p)                                                                                               \n",
      "                                                                                                  \n",
      " book_id_lookup (IntegerLookup)  (None,)             0           ['book_id_input[0][0]']          \n",
      "                                                                                                  \n",
      " user_id_lookup (StringLookup)  (None,)              0           ['user_id_input[0][0]']          \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 0\n",
      "Trainable params: 0\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "linear_input_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_2\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                   Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      " description_input (InputLayer)  [(None,)]           0           []                               \n",
      "                                                                                                  \n",
      " review_text_input (InputLayer)  [(None,)]           0           []                               \n",
      "                                                                                                  \n",
      " title_input (InputLayer)       [(None,)]            0           []                               \n",
      "                                                                                                  \n",
      " description_vectorization (Tex  (None, None)        0           ['description_input[0][0]']      \n",
      " tVectorization)                                                                                  \n",
      "                                                                                                  \n",
      " review_text_vectorization (Tex  (None, None)        0           ['review_text_input[0][0]']      \n",
      " tVectorization)                                                                                  \n",
      "                                                                                                  \n",
      " title_lookup (StringLookup)    (None,)              0           ['title_input[0][0]']            \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 0\n",
      "Trainable params: 0\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "deep_input_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<ParallelMapDataset shapes: ({user_id: (), book_id: (), author_id: ()}, ()), types: ({user_id: tf.int64, book_id: tf.int64, author_id: tf.int64}, tf.uint8)>"
      ]
     },
     "execution_count": 310,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linear_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<ParallelMapDataset shapes: ({review_text: (None,), title: (), description: (None,)}, ()), types: ({review_text: tf.int64, title: tf.int64, description: tf.int64}, tf.uint8)>"
      ]
     },
     "execution_count": 311,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deep_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define your objectives.\n",
    "task = tfrs.tasks.Retrieval(metrics=tfrs.metrics.FactorizedTopK(\n",
    "    dataset.batch(128).map(lambda x, y: x['title'])\n",
    "  )\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a retrieval model.\n",
    "model = MyModel(linear_input_model, deep_input_model, task)\n",
    "model.compile(optimizer=tf.keras.optimizers.Adagrad(0.5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/3\n",
      "[<tf.Tensor 'IteratorGetNext:0' shape=() dtype=int64>, <tf.Tensor 'IteratorGetNext:5' shape=() dtype=string>, <tf.Tensor 'IteratorGetNext:1' shape=() dtype=uint32>]\n",
      "---\n",
      "[<tf.Tensor 'IteratorGetNext:2' shape=() dtype=string>, <tf.Tensor 'IteratorGetNext:3' shape=() dtype=string>, <tf.Tensor 'IteratorGetNext:4' shape=() dtype=string>]\n",
      "WARNING:tensorflow:Model was constructed with shape (None,) for input KerasTensor(type_spec=TensorSpec(shape=(None,), dtype=tf.int64, name='author_id_input'), name='author_id_input', description=\"created by layer 'author_id_input'\"), but it was called on an input with incompatible shape ().\n",
      "WARNING:tensorflow:Model was constructed with shape (None,) for input KerasTensor(type_spec=TensorSpec(shape=(None,), dtype=tf.uint32, name='book_id_input'), name='book_id_input', description=\"created by layer 'book_id_input'\"), but it was called on an input with incompatible shape ().\n",
      "WARNING:tensorflow:Model was constructed with shape (None,) for input KerasTensor(type_spec=TensorSpec(shape=(None,), dtype=tf.string, name='user_id_input'), name='user_id_input', description=\"created by layer 'user_id_input'\"), but it was called on an input with incompatible shape ().\n",
      "WARNING:tensorflow:Model was constructed with shape (None,) for input KerasTensor(type_spec=TensorSpec(shape=(None,), dtype=tf.string, name='description_input'), name='description_input', description=\"created by layer 'description_input'\"), but it was called on an input with incompatible shape ().\n",
      "WARNING:tensorflow:Model was constructed with shape (None,) for input KerasTensor(type_spec=TensorSpec(shape=(None,), dtype=tf.string, name='review_text_input'), name='review_text_input', description=\"created by layer 'review_text_input'\"), but it was called on an input with incompatible shape ().\n",
      "WARNING:tensorflow:Model was constructed with shape (None,) for input KerasTensor(type_spec=TensorSpec(shape=(None,), dtype=tf.string, name='title_input'), name='title_input', description=\"created by layer 'title_input'\"), but it was called on an input with incompatible shape ().\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "in user code:\n\n    File \"C:\\Users\\simon\\anaconda3\\envs\\ds-project\\lib\\site-packages\\keras\\engine\\training.py\", line 878, in train_function  *\n        return step_function(self, iterator)\n    File \"C:\\Users\\simon\\anaconda3\\envs\\ds-project\\lib\\site-packages\\keras\\engine\\training.py\", line 867, in step_function  **\n        outputs = model.distribute_strategy.run(run_step, args=(data,))\n    File \"C:\\Users\\simon\\anaconda3\\envs\\ds-project\\lib\\site-packages\\keras\\engine\\training.py\", line 860, in run_step  **\n        outputs = model.train_step(data)\n    File \"C:\\Users\\simon\\anaconda3\\envs\\ds-project\\lib\\site-packages\\tensorflow_recommenders\\models\\base.py\", line 68, in train_step\n        loss = self.compute_loss(inputs, training=True)\n    File \"C:\\Users\\simon\\AppData\\Local\\Temp/ipykernel_18024/2542621575.py\", line 35, in compute_loss\n        return self.task(query, candidate)\n    File \"C:\\Users\\simon\\anaconda3\\envs\\ds-project\\lib\\site-packages\\keras\\utils\\traceback_utils.py\", line 67, in error_handler\n        raise e.with_traceback(filtered_tb) from None\n\n    ValueError: Exception encountered when calling layer \"retrieval_11\" (type Retrieval).\n    \n    in user code:\n    \n        File \"C:\\Users\\simon\\anaconda3\\envs\\ds-project\\lib\\site-packages\\tensorflow_recommenders\\tasks\\retrieval.py\", line 132, in call  *\n            scores = tf.linalg.matmul(\n    \n        ValueError: Shapes must be equal rank, but are 0 and 1\n        \tFrom merging shape 3 with other shapes. for '{{node retrieval_11/MatMul/b_1}} = Pack[N=5, T=DT_INT64, axis=0](model_1/author_id_lookup/Squeeze, model_1/book_id_lookup/Squeeze, model_2/review_text_vectorization/string_lookup/Identity, model_2/title_lookup/Squeeze, model_2/description_vectorization/string_lookup_1/Identity)' with input shapes: [], [], [?], [], [?].\n    \n    \n    Call arguments received:\n      • query_embeddings=tf.Tensor(shape=(), dtype=int64)\n      • candidate_embeddings=['tf.Tensor(shape=(), dtype=int64)', 'tf.Tensor(shape=(), dtype=int64)', 'tf.Tensor(shape=(None,), dtype=int64)', 'tf.Tensor(shape=(), dtype=int64)', 'tf.Tensor(shape=(None,), dtype=int64)']\n      • sample_weight=None\n      • candidate_sampling_probability=None\n      • candidate_ids=None\n      • compute_metrics=True\n",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_18024/3869419927.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# Train for 3 epochs.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\anaconda3\\envs\\ds-project\\lib\\site-packages\\keras\\utils\\traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     65\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m  \u001b[1;31m# pylint: disable=broad-except\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     66\u001b[0m       \u001b[0mfiltered_tb\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 67\u001b[1;33m       \u001b[1;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfiltered_tb\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     68\u001b[0m     \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     69\u001b[0m       \u001b[1;32mdel\u001b[0m \u001b[0mfiltered_tb\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\ds-project\\lib\\site-packages\\tensorflow\\python\\framework\\func_graph.py\u001b[0m in \u001b[0;36mautograph_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m   1127\u001b[0m           \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m  \u001b[1;31m# pylint:disable=broad-except\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1128\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"ag_error_metadata\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1129\u001b[1;33m               \u001b[1;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mag_error_metadata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_exception\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1130\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1131\u001b[0m               \u001b[1;32mraise\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: in user code:\n\n    File \"C:\\Users\\simon\\anaconda3\\envs\\ds-project\\lib\\site-packages\\keras\\engine\\training.py\", line 878, in train_function  *\n        return step_function(self, iterator)\n    File \"C:\\Users\\simon\\anaconda3\\envs\\ds-project\\lib\\site-packages\\keras\\engine\\training.py\", line 867, in step_function  **\n        outputs = model.distribute_strategy.run(run_step, args=(data,))\n    File \"C:\\Users\\simon\\anaconda3\\envs\\ds-project\\lib\\site-packages\\keras\\engine\\training.py\", line 860, in run_step  **\n        outputs = model.train_step(data)\n    File \"C:\\Users\\simon\\anaconda3\\envs\\ds-project\\lib\\site-packages\\tensorflow_recommenders\\models\\base.py\", line 68, in train_step\n        loss = self.compute_loss(inputs, training=True)\n    File \"C:\\Users\\simon\\AppData\\Local\\Temp/ipykernel_18024/2542621575.py\", line 35, in compute_loss\n        return self.task(query, candidate)\n    File \"C:\\Users\\simon\\anaconda3\\envs\\ds-project\\lib\\site-packages\\keras\\utils\\traceback_utils.py\", line 67, in error_handler\n        raise e.with_traceback(filtered_tb) from None\n\n    ValueError: Exception encountered when calling layer \"retrieval_11\" (type Retrieval).\n    \n    in user code:\n    \n        File \"C:\\Users\\simon\\anaconda3\\envs\\ds-project\\lib\\site-packages\\tensorflow_recommenders\\tasks\\retrieval.py\", line 132, in call  *\n            scores = tf.linalg.matmul(\n    \n        ValueError: Shapes must be equal rank, but are 0 and 1\n        \tFrom merging shape 3 with other shapes. for '{{node retrieval_11/MatMul/b_1}} = Pack[N=5, T=DT_INT64, axis=0](model_1/author_id_lookup/Squeeze, model_1/book_id_lookup/Squeeze, model_2/review_text_vectorization/string_lookup/Identity, model_2/title_lookup/Squeeze, model_2/description_vectorization/string_lookup_1/Identity)' with input shapes: [], [], [?], [], [?].\n    \n    \n    Call arguments received:\n      • query_embeddings=tf.Tensor(shape=(), dtype=int64)\n      • candidate_embeddings=['tf.Tensor(shape=(), dtype=int64)', 'tf.Tensor(shape=(), dtype=int64)', 'tf.Tensor(shape=(None,), dtype=int64)', 'tf.Tensor(shape=(), dtype=int64)', 'tf.Tensor(shape=(None,), dtype=int64)']\n      • sample_weight=None\n      • candidate_sampling_probability=None\n      • candidate_ids=None\n      • compute_metrics=True\n"
     ]
    }
   ],
   "source": [
    "# Train for 3 epochs.\n",
    "model.fit(dataset.map(lambda x, y: x), epochs=3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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