diff --git a/.gitignore b/.gitignore
index dc50a0c..eb51c70 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1 +1,2 @@
 *.ipynb_checkpoints/
+*training_checkpoints
diff --git a/README.md b/README.md
index 714e8c1..e0d62c6 100644
--- a/README.md
+++ b/README.md
@@ -51,4 +51,11 @@
 
 ### Лабораторная работа №4
 
+| Группа     | Дата       |
+| :---       | :---:      |
+| А-01-19    | 10.04.2023, 24.04.2023 |
+| А-03-19    | 17.03.2023, 22.03.2023 |
+
+* [Задание](labs/OATD_LR4.md)
+* [Методические указания](labs/OATD_LR4_metod.ipynb)
 
diff --git a/labs/OATD_LR4.md b/labs/OATD_LR4.md
new file mode 100644
index 0000000..b67a026
--- /dev/null
+++ b/labs/OATD_LR4.md
@@ -0,0 +1,45 @@
+# Лабораторная работа №4. Использование нейронных сетей для генерации текста 
+
+## Цель работы
+
+Получить практические навыки решения задачи генерации текста.
+
+## Задание
+
+1. Загрузить выборку стихотворений одного из поэтов в соответствии с вариантом.
+2. Познакомиться с данными. Проанализировать статистические характеристики исходных данных (среднюю длину стихотворения, среднюю длину строки).
+3. Подготовить выборку для обучения.
+4. Построить нейронную сеть. Тип ячейки RNN выбрать в соответствии с вариантом.
+5. Обучить нейронную сеть на разных количествах эпох (5, 15, 30, 50, 70) при зафиксированных параметрах embedding_dim = 256, rnn_units = 300, T = 0.3 и сравнить результаты генерации (тексты), перплексию и статистические характеристики сгенерированных текстов. Выбрать оптимальное количество эпох
+7. Изменяя параметр температуры T проанализировать изменения сгенерированного текста. Выбрать оптимальное значение параметра.
+8. Проанализировать зависимость перплексии, скорости обучения, результатов генерации от параметров нейронной сети embedding_dim, rnn_units:
+embedding_dim = {vocab/4, vocab/2, vocab, vocab * 2, vocab * 4},  где vocab = размер словаря выборки.  
+rnn_units = {10, 100, 300, 500}
+
+## Указания
+
+Для работы рекомендуется использовать Google Colab вместо Jupyter Notebook для ускорения расчетов.
+
+## Варианты заданий
+
+### Поэт
+
+Четные номера по журналу - Пушкин, нечетные - Маяковский. 
+
+### Тип ячейки RNN
+
+Остаток от деления номера по журналу на 3:
+* 0 - https://keras.io/api/layers/recurrent_layers/simple_rnn/
+* 1 - https://keras.io/api/layers/recurrent_layers/lstm/
+* 2 - https://keras.io/api/layers/recurrent_layers/gru/
+
+## Контрольные вопросы
+
+1. В чем особенность рекуррентных нейронных сетей?
+2. Типы рекуррентных сетей - обычная RNN
+3. Типы рекуррентных сетей - LSTM
+4. Типы рекуррентных сетей - GRU
+5. Что такое и как вычисляется перплексия
+
+
+
diff --git a/labs/OATD_LR4_metod.ipynb b/labs/OATD_LR4_metod.ipynb
new file mode 100644
index 0000000..b896839
--- /dev/null
+++ b/labs/OATD_LR4_metod.ipynb
@@ -0,0 +1,1815 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "srXC6pLGLwS6"
+   },
+   "source": [
+    "# Загрузка библиотек"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "id": "yG_n40gFzf9s"
+   },
+   "outputs": [],
+   "source": [
+    "import tensorflow as tf\n",
+    "from sklearn.model_selection  import train_test_split\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import os\n",
+    "import time\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "LshgkZ0cIOor",
+    "outputId": "903898c0-4205-47d7-a6e3-ca22e79ffba9"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found GPU at: \n"
+     ]
+    }
+   ],
+   "source": [
+    "device_name = tf.test.gpu_device_name()\n",
+    "print('Found GPU at: {}'.format(device_name))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "id": "vWkRnHV0DK0L"
+   },
+   "outputs": [],
+   "source": [
+    "RANDOM_STATE = 42"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "EW8HRqz8Oz_b"
+   },
+   "source": [
+    "# Данные"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "UHjdCjDuSvX_"
+   },
+   "source": [
+    "## Загрузка данных\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "id": "pP1Ou4nq_W1o"
+   },
+   "outputs": [],
+   "source": [
+    "# Выбираем поэта\n",
+    "poet = 'pushkin' #@param ['mayakovskiy', 'pushkin']\n",
+    "\n",
+    "path_to_file = f'{poet}.txt'\n",
+    "path_to_file = tf.keras.utils.get_file(path_to_file, f'http://uit.mpei.ru/git/main/TDA/raw/branch/master/assets/poems/{path_to_file}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "aavnuByVymwK",
+    "outputId": "1c4c379c-ab1e-4b84-939c-66a7471c0210"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Length of text: 586731 characters\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Загружаем текст из файла.\n",
+    "# Стихотворения в файле разделены токеном '</s>' - сохраняем в переменную\n",
+    "with open(path_to_file,encoding = \"utf-8\") as f:\n",
+    "    text = f.read()\n",
+    "\n",
+    "print(f'Length of text: {len(text)} characters')\n",
+    "\n",
+    "EOS_TOKEN = '</s>'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "Duhg9NrUymwO",
+    "outputId": "8f485753-1712-47a4-c328-a4a714ec0ea4"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Так и мне узнать случилось,\n",
+      "Что за птица Купидон;\n",
+      "Сердце страстное пленилось;\n",
+      "Признаюсь – и я влюблен!\n",
+      "Пролетело счастья время,\n",
+      "Как, любви не зная бремя,\n",
+      "Я живал да попевал,\n",
+      "Как в театре и на балах,\n",
+      "На гуляньях иль в воксалах\n",
+      "Легким зефиром летал;\n",
+      "Как, смеясь во зло Амуру,\n",
+      "Я писал карикатуру\n",
+      "На любезный женской пол;\n",
+      "Но напрасно я смеялся,\n",
+      "Наконец и сам попался,\n",
+      "Сам, увы! с ума сошел.\n",
+      "Смехи, вольность – всё под лавку\n",
+      "Из Катонов я в отставку,\n",
+      "И теперь я – Селадон!\n",
+      "Миловидной жрицы Тальи\n",
+      "Видел прел\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Посмотрим на текст\n",
+    "print(text[:500])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "dLZNbAnzj2lR"
+   },
+   "source": [
+    "## Подсчет статистик\n",
+    "\n",
+    "describe_poems - функция, разбивающая файл на отдельные стихотворения (poem), и расчитывающая их характиеристики:\n",
+    "* длину (len), \n",
+    "* количество строк (lines)\n",
+    "* среднюю длину строки (mean_line_len)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "id": "C7G_weaWnMSg"
+   },
+   "outputs": [],
+   "source": [
+    "def mean_line_len(poem):\n",
+    "    lines = [len(line.strip()) for line in poem.split('\\n') if len(line.strip())>0]\n",
+    "    return sum(lines)/len(lines)\n",
+    "\n",
+    "\n",
+    "def describe_poems(text,return_df = False):\n",
+    "    poems_list = [poem.strip() for poem in text.split(EOS_TOKEN) if len(poem.strip())>0]\n",
+    "    df = pd.DataFrame(data=poems_list,columns=['poem'])\n",
+    "    df['len'] = df.poem.map(len)\n",
+    "    df['lines'] = df.poem.str.count('\\n')\n",
+    "    df['mean_line_len'] = df.poem.map(mean_line_len)\n",
+    "    if return_df:\n",
+    "        return df\n",
+    "    return df.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 424
+    },
+    "id": "8t4QIKLgj8_y",
+    "outputId": "4ffe0325-70be-4a3f-9fd6-910be40e5dd3"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>poem</th>\n",
+       "      <th>len</th>\n",
+       "      <th>lines</th>\n",
+       "      <th>mean_line_len</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Так и мне узнать случилось,\\nЧто за птица Купи...</td>\n",
+       "      <td>2536</td>\n",
+       "      <td>109</td>\n",
+       "      <td>23.114286</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Хочу воспеть, как дух нечистый Ада\\nОседлан бы...</td>\n",
+       "      <td>5543</td>\n",
+       "      <td>170</td>\n",
+       "      <td>33.372671</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Покаместь ночь еще не удалилась,\\nПокаместь св...</td>\n",
+       "      <td>4279</td>\n",
+       "      <td>131</td>\n",
+       "      <td>33.451613</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Ах, отчего мне дивная природа\\nКорреджио искус...</td>\n",
+       "      <td>4435</td>\n",
+       "      <td>131</td>\n",
+       "      <td>33.364341</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Арист! и ты в толпе служителей Парнасса!\\nТы х...</td>\n",
+       "      <td>3893</td>\n",
+       "      <td>106</td>\n",
+       "      <td>38.642857</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>714</th>\n",
+       "      <td>Чудный сон мне бог послал —\\n\\nС длинной белой...</td>\n",
+       "      <td>860</td>\n",
+       "      <td>38</td>\n",
+       "      <td>22.833333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>715</th>\n",
+       "      <td>О нет, мне жизнь не надоела,\\nЯ жить люблю, я ...</td>\n",
+       "      <td>196</td>\n",
+       "      <td>7</td>\n",
+       "      <td>23.625000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>716</th>\n",
+       "      <td>\"Твой и мой, – говорит Лафонтен —\\nРасторгло у...</td>\n",
+       "      <td>187</td>\n",
+       "      <td>5</td>\n",
+       "      <td>30.333333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>717</th>\n",
+       "      <td>Когда луны сияет лик двурогой\\nИ луч ее во мра...</td>\n",
+       "      <td>269</td>\n",
+       "      <td>7</td>\n",
+       "      <td>32.750000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>718</th>\n",
+       "      <td>Там, устарелый вождь! как ратник молодой,\\nИск...</td>\n",
+       "      <td>256</td>\n",
+       "      <td>5</td>\n",
+       "      <td>41.833333</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>719 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                  poem   len  lines  \\\n",
+       "0    Так и мне узнать случилось,\\nЧто за птица Купи...  2536    109   \n",
+       "1    Хочу воспеть, как дух нечистый Ада\\nОседлан бы...  5543    170   \n",
+       "2    Покаместь ночь еще не удалилась,\\nПокаместь св...  4279    131   \n",
+       "3    Ах, отчего мне дивная природа\\nКорреджио искус...  4435    131   \n",
+       "4    Арист! и ты в толпе служителей Парнасса!\\nТы х...  3893    106   \n",
+       "..                                                 ...   ...    ...   \n",
+       "714  Чудный сон мне бог послал —\\n\\nС длинной белой...   860     38   \n",
+       "715  О нет, мне жизнь не надоела,\\nЯ жить люблю, я ...   196      7   \n",
+       "716  \"Твой и мой, – говорит Лафонтен —\\nРасторгло у...   187      5   \n",
+       "717  Когда луны сияет лик двурогой\\nИ луч ее во мра...   269      7   \n",
+       "718  Там, устарелый вождь! как ратник молодой,\\nИск...   256      5   \n",
+       "\n",
+       "     mean_line_len  \n",
+       "0        23.114286  \n",
+       "1        33.372671  \n",
+       "2        33.451613  \n",
+       "3        33.364341  \n",
+       "4        38.642857  \n",
+       "..             ...  \n",
+       "714      22.833333  \n",
+       "715      23.625000  \n",
+       "716      30.333333  \n",
+       "717      32.750000  \n",
+       "718      41.833333  \n",
+       "\n",
+       "[719 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "poem_df = describe_poems(text,return_df = True)\n",
+    "poem_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 300
+    },
+    "id": "TmCI6rv1f49T",
+    "outputId": "444fe362-1a5f-45b0-dc21-146c08e094cb"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>len</th>\n",
+       "      <th>lines</th>\n",
+       "      <th>mean_line_len</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>719.000000</td>\n",
+       "      <td>719.000000</td>\n",
+       "      <td>719.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>808.037552</td>\n",
+       "      <td>29.464534</td>\n",
+       "      <td>27.445404</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>1046.786862</td>\n",
+       "      <td>39.244020</td>\n",
+       "      <td>5.854564</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>74.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>8.250000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>280.500000</td>\n",
+       "      <td>9.000000</td>\n",
+       "      <td>24.125000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>453.000000</td>\n",
+       "      <td>16.000000</td>\n",
+       "      <td>25.758065</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>852.000000</td>\n",
+       "      <td>33.000000</td>\n",
+       "      <td>31.522727</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>8946.000000</td>\n",
+       "      <td>437.000000</td>\n",
+       "      <td>48.923077</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               len       lines  mean_line_len\n",
+       "count   719.000000  719.000000     719.000000\n",
+       "mean    808.037552   29.464534      27.445404\n",
+       "std    1046.786862   39.244020       5.854564\n",
+       "min      74.000000    5.000000       8.250000\n",
+       "25%     280.500000    9.000000      24.125000\n",
+       "50%     453.000000   16.000000      25.758065\n",
+       "75%     852.000000   33.000000      31.522727\n",
+       "max    8946.000000  437.000000      48.923077"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "poem_df.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "rNnrKn_lL-IJ"
+   },
+   "source": [
+    "## Подготовка датасетов"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "3mOXOtj1FB1v"
+   },
+   "source": [
+    "Разбиваем данные на тренировочные, валидационные и тестовые"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "id": "MM5Rk7B8D1n-"
+   },
+   "outputs": [],
+   "source": [
+    "train_poems, test_poems = train_test_split(poem_df.poem.to_list(),test_size = 0.1,random_state = RANDOM_STATE)\n",
+    "train_poems, val_poems = train_test_split(train_poems,test_size = 0.1,random_state = RANDOM_STATE)\n",
+    "\n",
+    "train_poems = f'\\n\\n{EOS_TOKEN}\\n\\n'.join(train_poems)\n",
+    "val_poems = f'\\n\\n{EOS_TOKEN}\\n\\n'.join(val_poems)\n",
+    "test_poems = f'\\n\\n{EOS_TOKEN}\\n\\n'.join(test_poems)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "6QfP2RCpqdCS"
+   },
+   "source": [
+    "Создаем словарь уникальных символов из текста. Не забываем добавить токен конца стиха."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "IlCgQBRVymwR",
+    "outputId": "a9769da4-3417-44e4-e491-cd1a09a51869"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "143 unique characters\n",
+      "['\\n', ' ', '!', '\"', \"'\", '(', ')', '*', ',', '-', '.', '/', ':', ';', '<', '>', '?', 'A', 'B', 'C', 'D', 'E', 'F', 'H', 'I', 'J', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'Z', '_', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'x', 'y', 'z', '\\xa0', '«', '»', 'à', 'â', 'ç', 'è', 'é', 'ê', 'ô', 'û', 'А', 'Б', 'В', 'Г', 'Д', 'Е', 'Ж', 'З', 'И', 'Й', 'К', 'Л', 'М', 'Н', 'О', 'П', 'Р', 'С', 'Т', 'У', 'Ф', 'Х', 'Ц', 'Ч', 'Ш', 'Щ', 'Э', 'Ю', 'Я', 'а', 'б', 'в', 'г', 'д', 'е', 'ж', 'з', 'и', 'й', 'к', 'л', 'м', 'н', 'о', 'п', 'р', 'с', 'т', 'у', 'ф', 'х', 'ц', 'ч', 'ш', 'щ', 'ъ', 'ы', 'ь', 'э', 'ю', 'я', 'ё', '–', '—', '„', '…', '</s>']\n"
+     ]
+    }
+   ],
+   "source": [
+    "vocab = sorted(set(text))+[EOS_TOKEN]\n",
+    "print(f'{len(vocab)} unique characters')\n",
+    "print (vocab)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "1s4f1q3iqY8f"
+   },
+   "source": [
+    "Для подачи на вход нейронной сети необходимо закодировать текст в виде числовой последовательности.\n",
+    "\n",
+    "Воспользуемся для этого слоем StringLookup \n",
+    "https://www.tensorflow.org/api_docs/python/tf/keras/layers/StringLookup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "id": "6GMlCe3qzaL9"
+   },
+   "outputs": [],
+   "source": [
+    "ids_from_chars = tf.keras.layers.StringLookup(\n",
+    "    vocabulary=list(vocab), mask_token=None)\n",
+    "chars_from_ids = tf.keras.layers.StringLookup(\n",
+    "    vocabulary=ids_from_chars.get_vocabulary(), invert=True, mask_token=None)\n",
+    "\n",
+    "def text_from_ids(ids):\n",
+    "    return tf.strings.reduce_join(chars_from_ids(ids), axis=-1).numpy().decode('utf-8')\n",
+    "    \n",
+    "def ids_from_text(text):\n",
+    "    return ids_from_chars(tf.strings.unicode_split(text, input_encoding='UTF-8'))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "Wd2m3mqkDjRj",
+    "outputId": "88ebef4b-6488-465e-d2f7-612e27efc0d2"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Корабль испанский тр\n",
+      "tf.Tensor(\n",
+      "[ 87 120 122 106 107 117 134   2 114 123 121 106 119 123 116 114 115   2\n",
+      " 124 122], shape=(20,), dtype=int64)\n",
+      "Корабль испанский тр\n"
+     ]
+    }
+   ],
+   "source": [
+    "# пример кодирования\n",
+    "ids = ids_from_text(train_poems[:20])\n",
+    "res_text = text_from_ids(ids)\n",
+    "print(train_poems[:20],ids,res_text,sep = '\\n')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "uzC2u022WHsa"
+   },
+   "source": [
+    "Кодируем данные и преобразуем их в Датасеты"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "id": "UopbsKi88tm5"
+   },
+   "outputs": [],
+   "source": [
+    "train_ids = ids_from_text(train_poems)\n",
+    "val_ids = ids_from_text(val_poems)\n",
+    "test_ids = ids_from_text(test_poems)\n",
+    "\n",
+    "train_ids_dataset = tf.data.Dataset.from_tensor_slices(train_ids)\n",
+    "val_ids_dataset = tf.data.Dataset.from_tensor_slices(val_ids)\n",
+    "test_ids_dataset = tf.data.Dataset.from_tensor_slices(test_ids)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "-ZSYAcQV8OGP"
+   },
+   "source": [
+    "Весь текст разбивается на последовательности длины `seq_length`. По этим последовательностям будет предсказываться следующий символ.\n",
+    "\n",
+    "**Попробовать разные длины - среднюю длину строки, среднюю длину стиха**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "id": "C-G2oaTxy6km"
+   },
+   "outputs": [],
+   "source": [
+    "seq_length = 100\n",
+    "examples_per_epoch = len(train_ids_dataset)//(seq_length+1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "BpdjRO2CzOfZ",
+    "outputId": "515651a7-765d-4bbf-9e90-8eb9a9380759"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Корабль испанский трехмачтовый,\n",
+      "Пристать в Голландию готовый:\n",
+      "На нем мерзавцев сотни три,\n",
+      "Две обезьян\n"
+     ]
+    }
+   ],
+   "source": [
+    "train_sequences = train_ids_dataset.batch(seq_length+1, drop_remainder=True)\n",
+    "val_sequences = val_ids_dataset.batch(seq_length+1, drop_remainder=True)\n",
+    "test_sequences = test_ids_dataset.batch(seq_length+1, drop_remainder=True)\n",
+    "\n",
+    "for seq in train_sequences.take(1):\n",
+    "  print(text_from_ids(seq))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "UbLcIPBj_mWZ"
+   },
+   "source": [
+    "Создаем датасет с input и target строками\n",
+    "\n",
+    "target сдвинута относительно input на один символ.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "id": "9NGu-FkO_kYU"
+   },
+   "outputs": [],
+   "source": [
+    "def split_input_target(sequence):\n",
+    "    input_text = sequence[:-1]\n",
+    "    target_text = sequence[1:]\n",
+    "    return input_text, target_text"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "WxbDTJTw5u_P",
+    "outputId": "f44e70c6-b600-4fb3-8073-ba8d774d8832"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(['П', 'у', 'ш', 'к', 'и'], ['у', 'ш', 'к', 'и', 'н'])"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# пример\n",
+    "split_input_target(list(\"Пушкин\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "id": "B9iKPXkw5xwa"
+   },
+   "outputs": [],
+   "source": [
+    "train_dataset = train_sequences.map(split_input_target)\n",
+    "val_dataset = val_sequences.map(split_input_target)\n",
+    "test_dataset = test_sequences.map(split_input_target)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "GNbw-iR0ymwj",
+    "outputId": "888b397b-5370-4ae4-d2ba-98d54595ee72"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Input : Прими сей череп, Дельвиг, он\n",
+      "Принадлежит тебе по праву.\n",
+      "Тебе поведаю, барон,\n",
+      "Его готическую славу.\n",
+      "\n",
+      "\n",
+      "Target: рими сей череп, Дельвиг, он\n",
+      "Принадлежит тебе по праву.\n",
+      "Тебе поведаю, барон,\n",
+      "Его готическую славу.\n",
+      "\n",
+      "П\n"
+     ]
+    }
+   ],
+   "source": [
+    "for input_example, target_example in val_dataset.take(1):\n",
+    "    print(\"Input :\", text_from_ids(input_example))\n",
+    "    print(\"Target:\", text_from_ids(target_example))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "MJdfPmdqzf-R"
+   },
+   "source": [
+    "Перемешиваем датасеты и разбиваем их на батчи для оптимизации обучения"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "p2pGotuNzf-S",
+    "outputId": "d9ae90cb-d904-4528-d0ed-eb11caeb43d3"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<PrefetchDataset element_spec=(TensorSpec(shape=(64, 100), dtype=tf.int64, name=None), TensorSpec(shape=(64, 100), dtype=tf.int64, name=None))>"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Batch size\n",
+    "BATCH_SIZE = 64\n",
+    "\n",
+    "BUFFER_SIZE = 10000\n",
+    "\n",
+    "def prepare_dataset(dataset):\n",
+    "    dataset = (\n",
+    "        dataset\n",
+    "        .shuffle(BUFFER_SIZE)\n",
+    "        .batch(BATCH_SIZE, drop_remainder=True)\n",
+    "        .prefetch(tf.data.experimental.AUTOTUNE))\n",
+    "    return dataset \n",
+    "\n",
+    "train_dataset = prepare_dataset(train_dataset)\n",
+    "val_dataset = prepare_dataset(val_dataset)\n",
+    "test_dataset = prepare_dataset(test_dataset)\n",
+    "\n",
+    "train_dataset"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "r6oUuElIMgVx"
+   },
+   "source": [
+    "# Нейросеть"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "22uVCbSyPBjD"
+   },
+   "source": [
+    "## Построение модели"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "m8gPwEjRzf-Z"
+   },
+   "source": [
+    "Модель состоит из трех слоев\n",
+    "\n",
+    "* `tf.keras.layers.Embedding`: Входной слой. Кодирует каждый идентификатор символа в  вектор размерностью `embedding_dim`; \n",
+    "* `tf.keras.layers.GRU`: Рекуррентный слой на ячейках GRU в количестве `units=rnn_units` **(Здесь нужно указать тип ячеек в соответствии с вариантом)**\n",
+    "* `tf.keras.layers.Dense`: Выходной полносвязный слой размерностью `vocab_size`, в который выводится вероятность каждого символа в словаре. \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "id": "zHT8cLh7EAsg"
+   },
+   "outputs": [],
+   "source": [
+    "# Длина словаря символов\n",
+    "vocab_size = len(vocab)\n",
+    "\n",
+    "# размерность Embedding'а\n",
+    "embedding_dim = 20 #@param{type:\"number\"}\n",
+    "\n",
+    "# Параметры RNN-слоя\n",
+    "rnn_units = 300 #@param {type:\"number\"}\n",
+    "dropout_p = 0.5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "id": "wj8HQ2w8z4iO"
+   },
+   "outputs": [],
+   "source": [
+    "class MyModel(tf.keras.Model):\n",
+    "  def __init__(self, vocab_size, embedding_dim, rnn_units):\n",
+    "    super().__init__(self)\n",
+    "    self.embedding = tf.keras.layers.Embedding(vocab_size, embedding_dim)\n",
+    "    self.gru = tf.keras.layers.GRU(rnn_units,\n",
+    "                                   dropout = dropout_p,\n",
+    "                                   return_sequences=True,\n",
+    "                                   return_state=True)\n",
+    "    self.dense = tf.keras.layers.Dense(vocab_size)\n",
+    "\n",
+    "  def call(self, inputs, states=None, return_state=False, training=False):\n",
+    "    x = inputs\n",
+    "    x = self.embedding(x, training=training)\n",
+    "    \n",
+    "    #if states is None:\n",
+    "    states = self.gru.get_initial_state(x)\n",
+    "\n",
+    "    x, states = self.gru(x, initial_state=states, training=training)\n",
+    "    x = self.dense(x, training=training)\n",
+    "\n",
+    "    if return_state:\n",
+    "      return x, states\n",
+    "    else:\n",
+    "      return x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "id": "IX58Xj9z47Aw"
+   },
+   "outputs": [],
+   "source": [
+    "model = MyModel(\n",
+    "    vocab_size=len(ids_from_chars.get_vocabulary()),\n",
+    "    embedding_dim=embedding_dim,\n",
+    "    rnn_units=rnn_units)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "RkA5upJIJ7W7"
+   },
+   "source": [
+    "Иллюстрация работы сети\n",
+    "\n",
+    "![A drawing of the data passing through the model](https://github.com/tensorflow/text/blob/master/docs/tutorials/images/text_generation_training.png?raw=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "LdgaUC3tPHAy"
+   },
+   "source": [
+    "## Проверка необученой модели"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "C-_70kKAPrPU",
+    "outputId": "02de9cf7-29d5-4345-8e02-b8c940ca5c1a"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(64, 100, 144) # (batch_size, sequence_length, vocab_size)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# посмотрим на один батч из датасета\n",
+    "for input_example_batch, target_example_batch in train_dataset.take(1):\n",
+    "    example_batch_predictions = model(input_example_batch)\n",
+    "    print(example_batch_predictions.shape, \"# (batch_size, sequence_length, vocab_size)\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "uwv0gEkURfx1"
+   },
+   "source": [
+    "prediction() предсказывает логиты вероятности каждого символа на следующей позиции. При этом, если мы будем выбирать символ с максимальной вероятностью, то из раза в раз модель нам будет выдавать один и тот же текст. \n",
+    "Чтобы этого избежать, нужно выбирать очередной индекс из распределения\n",
+    "`tf.random.categorical` - чем выше значение на выходном слое полносвязной сети, тем вероятнее, что данный символ будет выбран в качестве очередного. Однако, это не обязательно будет символ с максимальной вероятностью.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "W6_G5P7W1TrE",
+    "outputId": "5fdc06a0-9be3-42b4-8054-454e997586ee"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<tf.Tensor: shape=(144,), dtype=float32, numpy=\n",
+       "array([ 7.04948464e-03, -1.98002765e-03,  3.02454224e-03, -1.94069953e-03,\n",
+       "        9.08686779e-05, -1.26269402e-03, -3.95816471e-03,  5.32332528e-03,\n",
+       "       -1.58743770e-03,  3.80896078e-03,  4.99830581e-03, -3.53646278e-03,\n",
+       "       -3.50173237e-03,  1.03672473e-02,  4.72654402e-03,  4.48969286e-03,\n",
+       "       -7.25119235e-03,  4.08414472e-03, -6.61874050e-03, -4.53183288e-03,\n",
+       "        1.36306381e-03, -8.01230408e-03,  4.31668153e-03,  4.37820982e-03,\n",
+       "       -4.18623677e-03, -3.56584322e-03,  2.71524256e-03, -1.40747754e-04,\n",
+       "       -2.54452089e-03, -6.52383547e-03,  9.91060492e-03, -5.61330421e-03,\n",
+       "       -8.06922559e-04, -1.45252305e-03, -1.25302584e-04,  2.54331576e-03,\n",
+       "       -6.65406231e-03, -3.14406748e-03, -1.42855803e-03,  4.38266993e-03,\n",
+       "        4.06480394e-05,  3.58506199e-03, -1.31492107e-03, -3.76690924e-03,\n",
+       "       -4.61246073e-03,  2.09019426e-03, -6.05216657e-04,  4.94315475e-03,\n",
+       "       -7.38915848e-03, -2.97222170e-04,  1.23252645e-02, -3.27007566e-03,\n",
+       "        7.75653915e-03, -2.52670376e-03, -7.69718783e-04, -2.05170992e-03,\n",
+       "        1.18606293e-03,  6.22191140e-03, -4.58527543e-03,  4.21849283e-04,\n",
+       "        7.42580276e-03,  8.36860761e-03,  4.63361526e-03,  2.71662371e-03,\n",
+       "        2.59269890e-03, -2.30735121e-03,  6.44756807e-03, -2.63488246e-03,\n",
+       "       -1.40141640e-02,  1.11143891e-04, -5.53287193e-03, -6.43183826e-04,\n",
+       "       -1.15646292e-02, -1.41452125e-04,  1.50612544e-03,  2.67576054e-03,\n",
+       "       -3.87344277e-03, -2.01095594e-03,  3.92267248e-03,  2.47437716e-03,\n",
+       "        7.02369632e-03,  1.57978013e-03, -6.72074873e-03,  5.20235533e-03,\n",
+       "       -7.60141760e-03, -4.12424142e-03, -1.24696689e-03, -5.03823441e-03,\n",
+       "        2.94092577e-03,  2.38715275e-03, -1.54204969e-03, -2.16306839e-03,\n",
+       "       -2.10713898e-03, -3.79565591e-03, -5.14758425e-03, -3.90997529e-03,\n",
+       "       -4.30819113e-03, -7.28532323e-04, -2.43837340e-03,  2.76774517e-03,\n",
+       "       -1.66824413e-03, -2.95873499e-03,  5.87470829e-04, -7.67468195e-03,\n",
+       "       -2.17538618e-04, -8.66387784e-03, -4.71618492e-03, -1.63393712e-03,\n",
+       "       -4.77423018e-04, -1.49550312e-03, -3.46688367e-03,  1.21447584e-03,\n",
+       "        1.32047385e-03,  1.50095311e-03,  1.62522192e-03, -3.79677466e-03,\n",
+       "        1.57164666e-03, -4.10819892e-03, -3.00779007e-03,  2.36973306e-03,\n",
+       "       -3.77465365e-03, -5.34315477e-05,  1.84188155e-03,  3.53988446e-03,\n",
+       "        6.51812530e-04, -7.78108952e-05,  4.54718899e-03,  1.03609345e-03,\n",
+       "       -2.62774178e-03, -9.84960934e-05, -6.15748437e-03,  1.74808409e-03,\n",
+       "       -6.90540811e-03,  4.51742392e-03,  2.42682174e-03, -9.07061435e-03,\n",
+       "       -4.85235034e-03,  7.50885578e-04,  6.84963632e-03, -3.08787916e-04,\n",
+       "        2.59267096e-03,  4.30960301e-03,  3.37237539e-03,  3.88880167e-03],\n",
+       "      dtype=float32)>"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "example_batch_predictions[0][0]\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "0yJ5Je4J1XRb"
+   },
+   "source": [
+    "![image.png](data:image/png;base64,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)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "UuAT_ymD15U7"
+   },
+   "source": [
+    "На картинке отмечены наиболее вероятные символы."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "id": "4V4MfFg0RQJg"
+   },
+   "outputs": [],
+   "source": [
+    "sampled_indices = tf.random.categorical(example_batch_predictions[0], num_samples=1)\n",
+    "sampled_indices = tf.squeeze(sampled_indices, axis=-1).numpy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "YqFMUQc_UFgM",
+    "outputId": "1842194b-d7d7-4702-bca5-6b03f04b9432"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([103,   2, 125, 127,  46, 128,  85,  84,  14,  37,  55,   7, 129,\n",
+       "       123,  72,  38, 138,  88, 116, 125, 142, 109, 110, 131,  21,  29,\n",
+       "        15,  99, 118,  48,  15, 143, 106, 139,  28, 115, 119,   8,  41,\n",
+       "        55, 138,  68, 130, 133, 135,   4, 114,  10,  62, 130, 120,  47,\n",
+       "       119,  16,  87,  71,  32, 111, 121,  85,  13,  87,  87,  75,  25,\n",
+       "        91,  41,  83,  51, 106, 100, 133,   3,   9,  37,  36, 103,  61,\n",
+       "         2,  79, 136,  56,  99, 101,  20, 143,  70, 117,  60,  43,   6,\n",
+       "        49,  51,  15,  85, 115, 113, 138,  23,  89], dtype=int64)"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sampled_indices"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "xWcFwPwLSo05",
+    "outputId": "bed36421-88f9-429f-a9cd-099717127029"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Input:\n",
+      " же прошли. Их мненья, толки, страсти\n",
+      "Забыты для других. Смотри: вокруг тебя\n",
+      "Всё новое кипит, былое и\n",
+      "\n",
+      "Next Char Predictions:\n",
+      " Э ухfцИЗ;Vo)чсèWёЛку…гдщDN<Цмh<</s>а–Mйн*aoё»шыэ\"и-vшоgн>КçQепИ:ККôIОaЖkаЧы!,VUЭu ВюpЦШC</s>âлtc(ik<ИйзёFМ\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Input:\\n\", text_from_ids(input_example_batch[0]))\n",
+    "print()\n",
+    "print(\"Next Char Predictions:\\n\", text_from_ids(sampled_indices))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "LJL0Q0YPY6Ee"
+   },
+   "source": [
+    "## Обучение модели"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "YCbHQHiaa4Ic"
+   },
+   "source": [
+    "\n",
+    "Можно представить задачу как задачу классификации - по предыдущему состоянию RNN и входу в данный момент времени предсказать класс (очередной символ). "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "trpqTWyvk0nr"
+   },
+   "source": [
+    "### Настройка оптимизатора и функции потерь"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "UAjbjY03eiQ4"
+   },
+   "source": [
+    "В этом случае работает стандартная функция потерь `tf.keras.losses.sparse_categorical_crossentropy`- кроссэнтропия, которая равна минус логарифму предсказанной вероятности для верного класса.\n",
+    "\n",
+    "Поскольку модель возвращает логиты, вам необходимо установить флаг `from_logits`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "id": "ZOeWdgxNFDXq"
+   },
+   "outputs": [],
+   "source": [
+    "loss = tf.losses.SparseCategoricalCrossentropy(from_logits=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "4HrXTACTdzY-",
+    "outputId": "f0dbf6f2-738e-48f8-df7c-0bbf60fc17e4"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Prediction shape:  (64, 100, 144)  # (batch_size, sequence_length, vocab_size)\n",
+      "Mean loss:         tf.Tensor(4.970122, shape=(), dtype=float32)\n"
+     ]
+    }
+   ],
+   "source": [
+    "example_batch_mean_loss = loss(target_example_batch, example_batch_predictions)\n",
+    "print(\"Prediction shape: \", example_batch_predictions.shape, \" # (batch_size, sequence_length, vocab_size)\")\n",
+    "print(\"Mean loss:        \", example_batch_mean_loss)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "vkvUIneTFiow"
+   },
+   "source": [
+    "Необученная модель не может делать адекватные предсказания. Ее перплексия («коэффициент неопределённости») приблизительно равна размеру словаря. Это говорит о полной неопределенности модели при генерации текста.\n",
+    "\n",
+    "Перплексия = exp(кроссэнтропия)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "MAJfS5YoFiHf",
+    "outputId": "0588a9b2-8598-458f-db1d-6923e6a50502"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "perplexity:  144.04443\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('perplexity: ',np.exp(example_batch_mean_loss))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "jeOXriLcymww"
+   },
+   "source": [
+    "Настраиваем обучение, используя метод `tf.keras.Model.compile`. Используйте `tf.keras.optimizers.Adam` с аргументами по умолчанию и функцией потерь."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "id": "DDl1_Een6rL0"
+   },
+   "outputs": [],
+   "source": [
+    "model.compile(optimizer='adam', loss=loss)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "vPGmAAXmVLGC",
+    "outputId": "1399a58c-85ff-430d-f67f-942942dec071"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model: \"my_model\"\n",
+      "_________________________________________________________________\n",
+      " Layer (type)                Output Shape              Param #   \n",
+      "=================================================================\n",
+      " embedding (Embedding)       multiple                  2880      \n",
+      "                                                                 \n",
+      " gru (GRU)                   multiple                  289800    \n",
+      "                                                                 \n",
+      " dense (Dense)               multiple                  43344     \n",
+      "                                                                 \n",
+      "=================================================================\n",
+      "Total params: 336,024\n",
+      "Trainable params: 336,024\n",
+      "Non-trainable params: 0\n",
+      "_________________________________________________________________\n"
+     ]
+    }
+   ],
+   "source": [
+    "model.summary()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "C6XBUUavgF56"
+   },
+   "source": [
+    "Используем `tf.keras.callbacks.ModelCheckpoint`, чтобы убедиться, что контрольные точки сохраняются во время обучения:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "id": "W6fWTriUZP-n"
+   },
+   "outputs": [],
+   "source": [
+    "# Directory where the checkpoints will be saved\n",
+    "checkpoint_dir = './training_checkpoints'\n",
+    "# Name of the checkpoint files\n",
+    "checkpoint_prefix = os.path.join(checkpoint_dir, \"ckpt_{epoch}\")\n",
+    "\n",
+    "checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(\n",
+    "    filepath=checkpoint_prefix,\n",
+    "    monitor=\"val_loss\",\n",
+    "    save_weights_only=True,\n",
+    "    save_best_only=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "3Ky3F_BhgkTW"
+   },
+   "source": [
+    "### Обучение!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "id": "7yGBE2zxMMHs"
+   },
+   "outputs": [],
+   "source": [
+    "EPOCHS = 5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "UK-hmKjYVoll",
+    "outputId": "4403459e-c93d-4361-b6b4-d4f4a29797e5"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/5\n",
+      "72/72 [==============================] - 24s 301ms/step - loss: 3.7387 - val_loss: 3.4320\n",
+      "Epoch 2/5\n",
+      "72/72 [==============================] - 22s 294ms/step - loss: 3.2194 - val_loss: 2.8863\n",
+      "Epoch 3/5\n",
+      "72/72 [==============================] - 22s 292ms/step - loss: 2.8698 - val_loss: 2.7059\n",
+      "Epoch 4/5\n",
+      "72/72 [==============================] - 23s 309ms/step - loss: 2.7643 - val_loss: 2.6365\n",
+      "Epoch 5/5\n",
+      "72/72 [==============================] - 24s 320ms/step - loss: 2.6941 - val_loss: 2.5800\n"
+     ]
+    }
+   ],
+   "source": [
+    "history = model.fit(train_dataset, validation_data = val_dataset, epochs=EPOCHS, callbacks=[checkpoint_callback])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "UejMtXPmH-U0",
+    "outputId": "ec06daed-25a1-4ae3-9f9a-cd130e04c216"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "9/9 [==============================] - 1s 101ms/step - loss: 2.5769\n",
+      "eval loss: 2.576871871948242\n",
+      "perplexity 13.155920322524834\n"
+     ]
+    }
+   ],
+   "source": [
+    "eval_loss = model.evaluate(test_dataset)\n",
+    "print('eval loss:',eval_loss)\n",
+    "print('perplexity',np.exp(eval_loss))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "kKkD5M6eoSiN"
+   },
+   "source": [
+    "## Генерация текста"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "oIdQ8c8NvMzV"
+   },
+   "source": [
+    "Самый простой способ сгенерировать текст с помощью этой модели — запустить ее в цикле и отслеживать внутреннее состояние модели по мере ее выполнения.\n",
+    "\n",
+    "![To generate text the model's output is fed back to the input](https://github.com/tensorflow/text/blob/master/docs/tutorials/images/text_generation_sampling.png?raw=1)\n",
+    "\n",
+    "Каждый раз, когда вы вызываете модель, вы передаете некоторый текст и внутреннее состояние. Модель возвращает прогноз для следующего символа и его нового состояния. Передайте предсказание и состояние обратно, чтобы продолжить создание текста.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "DjGz1tDkzf-u"
+   },
+   "source": [
+    "Создаем модель реализующую один шаг предсказания:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "id": "iSBU1tHmlUSs"
+   },
+   "outputs": [],
+   "source": [
+    "class OneStep(tf.keras.Model):\n",
+    "  def __init__(self, model, chars_from_ids, ids_from_chars, temperature=1.0):\n",
+    "    super().__init__()\n",
+    "    self.temperature = temperature\n",
+    "    self.model = model\n",
+    "    self.chars_from_ids = chars_from_ids\n",
+    "    self.ids_from_chars = ids_from_chars\n",
+    "\n",
+    "    # Create a mask to prevent \"[UNK]\" from being generated.\n",
+    "    skip_ids = self.ids_from_chars(['[UNK]'])[:, None]\n",
+    "    sparse_mask = tf.SparseTensor(\n",
+    "        # Put a -inf at each bad index.\n",
+    "        values=[-float('inf')]*len(skip_ids),\n",
+    "        indices=skip_ids,\n",
+    "        # Match the shape to the vocabulary\n",
+    "        dense_shape=[len(ids_from_chars.get_vocabulary())])\n",
+    "    self.prediction_mask = tf.sparse.to_dense(sparse_mask)\n",
+    "\n",
+    "    \n",
+    "  # Этот фрагмент целиком написан с использованием Tensorflow, поэтому его можно выполнять \n",
+    "  # не с помощью интерпретатора языка Python, а через граф операций. Это будет значительно быстрее.  \n",
+    "  # Для этого воспользуемся декоратором  @tf.function   \n",
+    "  @tf.function   \n",
+    "  def generate_one_step(self, inputs, states=None,temperature=1.0):\n",
+    "    # Convert strings to token IDs.\n",
+    "    input_chars = tf.strings.unicode_split(inputs, 'UTF-8')\n",
+    "    input_ids = self.ids_from_chars(input_chars).to_tensor()\n",
+    "\n",
+    "    # Run the model.\n",
+    "    # predicted_logits.shape is [batch, char, next_char_logits]\n",
+    "    predicted_logits, states = self.model(inputs=input_ids, states=states,\n",
+    "                                          return_state=True)\n",
+    "    # Only use the last prediction.\n",
+    "    predicted_logits = predicted_logits[:, -1, :]\n",
+    "    predicted_logits = predicted_logits/temperature\n",
+    "    # Apply the prediction mask: prevent \"[UNK]\" from being generated.\n",
+    "    predicted_logits = predicted_logits + self.prediction_mask\n",
+    "\n",
+    "    # Sample the output logits to generate token IDs.\n",
+    "    predicted_ids = tf.random.categorical(predicted_logits, num_samples=1)\n",
+    "    predicted_ids = tf.squeeze(predicted_ids, axis=-1)\n",
+    "\n",
+    "    # Convert from token ids to characters\n",
+    "    predicted_chars = self.chars_from_ids(predicted_ids)\n",
+    "\n",
+    "\n",
+    "    # Return the characters and model state.\n",
+    "    return predicted_chars, states"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "id": "fqMOuDutnOxK"
+   },
+   "outputs": [],
+   "source": [
+    "one_step_model = OneStep(model, chars_from_ids, ids_from_chars)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "p9yDoa0G3IgQ"
+   },
+   "source": [
+    "Изменяя температуру можно регулировать вариативность текста"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "ST7PSyk9t1mT",
+    "outputId": "ed0dfecc-3d31-4bfe-efeb-d2315044ef06"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "uКочаму,\n",
+      "Лыт\n",
+      "Нума Гве!\n",
+      "àvо етекы ва;\n",
+      "Хо> вы,\n",
+      "Муметы,.\n",
+      "Воцу:\n",
+      "ОтекаЕдраэмь эзоспосанах…Я кы.\n",
+      ") маа Почи защоючеты>\n",
+      "Алаций поши)\"Пры – /й пий лазой\n",
+      "Еже?\n",
+      "И,\n",
+      "В, à*</s>\n",
+      "logJ\"nДаогей piКо '<vЯйвы\n",
+      "Срый\n",
+      "За Sрыша, Жузогой!\n",
+      "jу,\n",
+      "yВлаж:.\n",
+      "jPsдотети;\n",
+      "Улум!\n",
+      "Яро\n",
+      "Не жь вый…\n",
+      "На…нуше нухотоло!\n",
+      "érфосчатей пойхоскох,\n",
+      "ûzСда –\n",
+      "âШаналемоны узени потя,\n",
+      "„Я OaéТый с говощащаранымозужмелы,\n",
+      "Титой !\n",
+      "И Жычима пруМепобай>\n",
+      "Дя цый,;\n",
+      "Зази:\n",
+      "Унумыйй поль ь,\n",
+      "Тост,,,\n",
+      "Дежы!Шоны\n",
+      "H-му,\n",
+      "iБачуй,,;цо,\n",
+      "Тахрей\n",
+      "Ты а;\n",
+      "Памогой елыны…\n",
+      "Жебы\n",
+      "Ве яны!\n",
+      "Экычигруй,\n",
+      "И вьчам ре кабощалуй, ль наша водух, ё ко,\n",
+      "Чячу бемий>\n",
+      "Елетанех гружара, eалодищи:\n",
+      "И,\n",
+      "Гро вишемороцемапа\"oОмицы\n",
+      "upЛицль: Преща, Оды iH бочавоча!, Ценобый ни\n",
+      "Вныла,\n",
+      "Чу:.\n",
+      "Ввуй,\n",
+      "eРаДобе по!\n",
+      "Qтый,\n",
+      "Чки fХралошумо:\n",
+      "na-Койкаца –\n",
+      "Ай\n",
+      "Мо,\n",
+      "i)цыденатедубодех выйха\n",
+      "Полынене на бы.\n",
+      "Заму рыхасла cИмино Одорафута уга гоЮй вожве,\n",
+      "ГчаCКазко.\n",
+      "Мотя выйхоруnАчлатоный\n",
+      "Рабойх удапи,\n",
+      "Ты уТашь,\n",
+      "Обымаекоги вайзоты зой кишаметумилетелы\n",
+      "Я, —\n",
+      "yJи, солыFлы!éuХуZетце, ё\n",
+      "oПробемнанетече ей.\n",
+      "Ри,\n",
+      "Шоный.\n",
+      "По!\n",
+      "Ностыгу!\n",
+      "Hv\n",
+      "________________________________________________________________________________\n",
+      "\n",
+      "Run time: 1.620434284210205\n"
+     ]
+    }
+   ],
+   "source": [
+    "T = 0.5 #@param {type:\"slider\", min:0, max:2, step:0.1}\n",
+    "N = 1000\n",
+    "\n",
+    "start = time.time()\n",
+    "states = None\n",
+    "next_char = tf.constant(['\\n'])\n",
+    "result = [next_char]\n",
+    "\n",
+    "for n in range(N):\n",
+    "  next_char, states = one_step_model.generate_one_step(next_char, states=states,temperature=T)\n",
+    "  result.append(next_char)\n",
+    "\n",
+    "result = tf.strings.join(result)\n",
+    "end = time.time()\n",
+    "\n",
+    "result_text = result[0].numpy().decode('utf-8')\n",
+    "print(result_text)\n",
+    "print('_'*80)\n",
+    "print('\\nRun time:', end - start)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 300
+    },
+    "id": "VCwWY9xM6KCB",
+    "outputId": "e709c661-8bbe-4abf-e329-db9af7e6eb55"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>len</th>\n",
+       "      <th>lines</th>\n",
+       "      <th>mean_line_len</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>499.000000</td>\n",
+       "      <td>35.500000</td>\n",
+       "      <td>12.123718</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>482.246825</td>\n",
+       "      <td>33.234019</td>\n",
+       "      <td>1.262820</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>158.000000</td>\n",
+       "      <td>12.000000</td>\n",
+       "      <td>11.230769</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>328.500000</td>\n",
+       "      <td>23.750000</td>\n",
+       "      <td>11.677244</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>499.000000</td>\n",
+       "      <td>35.500000</td>\n",
+       "      <td>12.123718</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>669.500000</td>\n",
+       "      <td>47.250000</td>\n",
+       "      <td>12.570192</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>840.000000</td>\n",
+       "      <td>59.000000</td>\n",
+       "      <td>13.016667</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              len      lines  mean_line_len\n",
+       "count    2.000000   2.000000       2.000000\n",
+       "mean   499.000000  35.500000      12.123718\n",
+       "std    482.246825  33.234019       1.262820\n",
+       "min    158.000000  12.000000      11.230769\n",
+       "25%    328.500000  23.750000      11.677244\n",
+       "50%    499.000000  35.500000      12.123718\n",
+       "75%    669.500000  47.250000      12.570192\n",
+       "max    840.000000  59.000000      13.016667"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "describe_poems(result_text)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "db7UJQr9ILfW"
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "22rnSwqqICn2"
+   },
+   "source": [
+    "По мотивам https://colab.research.google.com/github/tensorflow/text/blob/master/docs/tutorials/text_generation.ipynb"
+   ]
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/lections/notebooks/lec7_clustering.ipynb b/lections/notebooks/lec7_clustering.ipynb
index d03ef43..f212a2f 100644
--- a/lections/notebooks/lec7_clustering.ipynb
+++ b/lections/notebooks/lec7_clustering.ipynb
@@ -2,12 +2,12 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 107,
+   "execution_count": 150,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -23,7 +23,7 @@
     "from sklearn.metrics.pairwise import euclidean_distances\n",
     "\n",
     "#X,y = datasets.make_moons(n_samples=100, random_state = 42, noise = 0.1 )\n",
-    "X,y = datasets.make_blobs(n_samples=100, centers = 4, random_state = 1 )\n",
+    "X,y = datasets.make_blobs(n_samples=100, centers = 6, random_state =45  )\n",
     "\n",
     "plt.scatter (X[:,0], X[:,1])\n",
     "plt.show()\n"
@@ -31,12 +31,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 108,
-   "metadata": {},
+   "execution_count": 152,
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 1500x1000 with 1 Axes>"
       ]
@@ -53,7 +55,7 @@
     "\n",
     "# Реализация иерархической кластеризации при помощи функции linkage\n",
     "\n",
-    "mergings = linkage(X, method='complete')\n",
+    "mergings = linkage(X, method='single')\n",
     " \n",
     "# Строим дендрограмму, указав параметры удобные для отображения\n",
     "\n",
@@ -217,7 +219,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 125,
+   "execution_count": 154,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -226,16 +228,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 126,
+   "execution_count": 155,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[3 2 3 2 1 1 0 0 0 3 0 0 2 0 3 0 0 3 1 1 3 3 3 2 1 0 0 3 2 1 1 3 1 2 3 1 3\n",
-      " 0 2 3 3 0 0 2 3 1 3 2 3 1 2 1 1 2 0 3 1 1 3 2 0 1 2 2 0 3 0 2 0 1 2 3 3 0\n",
-      " 2 1 2 3 0 1 2 2 3 2 0 1 1 1 1 2 1 0 1 2 2 3 0 0 2 0]\n"
+      "[1 1 2 1 0 0 1 3 3 2 1 3 1 1 1 1 1 1 2 2 0 1 2 3 0 2 2 3 0 2 3 3 0 1 2 2 2\n",
+      " 1 3 3 2 1 1 2 0 2 0 0 1 3 2 1 0 3 1 2 1 2 2 1 1 2 1 2 1 0 1 3 2 3 3 1 2 2\n",
+      " 1 1 0 0 2 0 1 2 2 2 2 1 2 3 1 2 3 2 0 1 3 2 0 2 2 1]\n"
      ]
     }
    ],
@@ -248,12 +250,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 127,
+   "execution_count": 156,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 500x500 with 1 Axes>"
       ]
@@ -270,14 +272,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 128,
+   "execution_count": 157,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Sum of squared distances of samples to their closest cluster center.: 158.76686919578418\n"
+      "Sum of squared distances of samples to their closest cluster center.: 472.08573606137327\n"
      ]
     }
    ],
@@ -287,14 +289,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 129,
+   "execution_count": 158,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[3015.7692276196085, 766.1594927527818, 369.5556880065657, 158.76686919578418, 136.07776106430828, 119.41082903949317, 102.06541733084515, 90.40896447881985, 79.46925530063092]\n"
+      "[6974.827094379589, 2194.619727635032, 870.8643547241177, 472.08573606137327, 259.5795564009951, 176.1282308577753, 153.70943167635173, 142.4483725489951, 125.24089654012067]\n"
      ]
     }
    ],
@@ -308,7 +310,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 130,
+   "execution_count": 159,
    "metadata": {},
    "outputs": [
     {
@@ -317,13 +319,13 @@
        "<function matplotlib.pyplot.show(close=None, block=None)>"
       ]
      },
-     "execution_count": 130,
+     "execution_count": 159,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 500x500 with 1 Axes>"
       ]