naampy/data/ml_model/02_training_model.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"train_rmse.ipynb","provenance":[],"collapsed_sections":[],"machine_shape":"hm","mount_file_id":"1iGieL66rV9pzcXVYM-B24DO73ZamQ_Kn","authorship_tag":"ABX9TyOLvYdpSBX2T1AYjG9Qnexc"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"accelerator":"GPU"},"cells":[{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"pYbglVtjjxwW","executionInfo":{"status":"ok","timestamp":1652307851222,"user_tz":420,"elapsed":709,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"}},"outputId":"71917881-e219-4a44-fdb4-814bed45359f"},"outputs":[{"output_type":"stream","name":"stdout","text":["/content/drive/MyDrive/Colab/naampy\n"]}],"source":["%cd /content/drive/MyDrive/Colab/naampy/"]},{"cell_type":"code","source":["%ls"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"YijBe7-QqM0e","executionInfo":{"status":"ok","timestamp":1652307852746,"user_tz":420,"elapsed":1527,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"}},"outputId":"804c39c0-2bd9-4dd9-9b09-1dc57377a93c"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[0m\u001b[01;34mdata\u001b[0m/                 model.h5      train.ipynb\n","data.ipynb            model.png     train_rmse.ipynb\n","data_with_prop.ipynb  \u001b[01;34msaved_model\u001b[0m/  train_with_prop.ipynb\n"]}]},{"cell_type":"code","source":["import pandas as pd\n","import numpy as np\n","\n","from sklearn.model_selection import train_test_split\n","from keras.preprocessing.text import Tokenizer\n","\n","\n","import tensorflow as tf\n","from tensorflow.keras.utils import to_categorical\n","\n","from keras.preprocessing.text import Tokenizer\n","from keras.preprocessing.sequence import pad_sequences\n","\n","import matplotlib.pyplot as plt"],"metadata":{"id":"-rDvIi6VqNko"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# Import data"],"metadata":{"id":"6ErUHTz4u4Pu"}},{"cell_type":"code","source":["df = pd.read_csv('data/naampy_final_prop.csv')"],"metadata":{"id":"vWR2c6o9qQ1d"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["df.head()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"C3PbBZK-qYud","executionInfo":{"status":"ok","timestamp":1652307869397,"user_tz":420,"elapsed":217,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"}},"outputId":"6469cd78-8ee6-46ec-ded4-a2608be41b4b"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["  first_name  gender  female_prop\n","0         aa    male     0.044734\n","1        aaa    male     0.260188\n","2       aaaa  female     0.742647\n","3   aaaamsoo  female     0.675344\n","4      aaaan    male     0.029126"],"text/html":["\n","  <div id=\"df-0c5aa6d4-a83c-4551-b126-525a75233a12\">\n","    <div class=\"colab-df-container\">\n","      <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>first_name</th>\n","      <th>gender</th>\n","      <th>female_prop</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>aa</td>\n","      <td>male</td>\n","      <td>0.044734</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>aaa</td>\n","      <td>male</td>\n","    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<td>0.361169</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>196973</th>\n","      <td>ys</td>\n","      <td>male</td>\n","      <td>0.445378</td>\n","    </tr>\n","    <tr>\n","      <th>196974</th>\n","      <td>yu</td>\n","      <td>male</td>\n","      <td>0.325714</td>\n","    </tr>\n","    <tr>\n","      <th>197228</th>\n","      <td>zd</td>\n","      <td>male</td>\n","      <td>0.430876</td>\n","    </tr>\n","    <tr>\n","      <th>197247</th>\n","      <td>zk</td>\n","      <td>female</td>\n","      <td>0.519231</td>\n","    </tr>\n","    <tr>\n","      <th>197342</th>\n","      <td>zv</td>\n","      <td>male</td>\n","      <td>0.462745</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>222 rows × 3 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b9ab3ffa-e441-4582-880f-41fd361f4e3e')\"\n","              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kumarakhaaneeyaasaaparaa    male     0.458065\n","101895     mahendraprathaapasimh    male     0.003115\n","122195      niraasreethathaakoor    male     0.485714\n","130918      pharajaanaakhaathoon  female     1.000000\n","137060      purooshooththamadaas    male     0.002778\n","137061      purooshooththamalaal    male     0.004975\n","137062      purooshooththamasimh    male     0.000000\n","137929      raaghavendraprathaap    male     0.000000\n","158707      sananthooshakumaaree  female     0.985000\n","162106      sathyanaaraayanasimh    male     0.000000\n","177712     surendraprathaapasimh    male     0.000000"],"text/html":["\n","  <div id=\"df-eee3335d-1b53-400d-9110-0a69fec5fe0f\">\n","    <div class=\"colab-df-container\">\n","      <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","        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<td>male</td>\n","      <td>0.485714</td>\n","    </tr>\n","    <tr>\n","      <th>130918</th>\n","      <td>pharajaanaakhaathoon</td>\n","      <td>female</td>\n","      <td>1.000000</td>\n","    </tr>\n","    <tr>\n","      <th>137060</th>\n","      <td>purooshooththamadaas</td>\n","      <td>male</td>\n","      <td>0.002778</td>\n","    </tr>\n","    <tr>\n","      <th>137061</th>\n","      <td>purooshooththamalaal</td>\n","      <td>male</td>\n","      <td>0.004975</td>\n","    </tr>\n","    <tr>\n","      <th>137062</th>\n","      <td>purooshooththamasimh</td>\n","      <td>male</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>137929</th>\n","      <td>raaghavendraprathaap</td>\n","      <td>male</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>158707</th>\n","      <td>sananthooshakumaaree</td>\n","      <td>female</td>\n","      <td>0.985000</td>\n","    </tr>\n","    <tr>\n","      <th>162106</th>\n","      <td>sathyanaaraayanasimh</td>\n","      <td>male</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>177712</th>\n","      <td>surendraprathaapasimh</td>\n","      <td>male</td>\n","      <td>0.000000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-eee3335d-1b53-400d-9110-0a69fec5fe0f')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":8}]},{"cell_type":"code","source":["def find_seq_chars(s, seq_len=2):\n","  prev = \"\"\n","  count = 1\n","  match = False\n","  for i in s:\n","    if i == prev:\n","      count += 1\n","    else:\n","      count = 1\n","    \n","    prev = i\n","    if count == seq_len:\n","      return True\n","  return match\n","\n"],"metadata":{"id":"jqGXOHC7BsaV"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["df[df['first_name'].apply(lambda x : find_seq_chars(x, 3))]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":424},"id":"Y53n9GszD9u-","executionInfo":{"status":"ok","timestamp":1652307882187,"user_tz":420,"elapsed":238,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"}},"outputId":"81f9565a-1c1d-4c53-c833-b2159059c5a7"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["        first_name  gender  female_prop\n","1              aaa    male     0.260188\n","2             aaaa  female     0.742647\n","3         aaaamsoo  female     0.675344\n","4            aaaan    male     0.029126\n","5             aaab    male     0.053333\n","...            ...     ...          ...\n","196434  yeeelavaad    male     0.472603\n","196808  yoogamaaaa  female     0.996219\n","197076     yuliaaa  female     0.995633\n","197109     yuriaaa    male     0.239175\n","197135    yuthiaaa    male     0.168067\n","\n","[1142 rows x 3 columns]"],"text/html":["\n","  <div id=\"df-77371143-02f5-40eb-a63a-b7e09c36c667\">\n","    <div class=\"colab-df-container\">\n","      <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>first_name</th>\n","      <th>gender</th>\n","      <th>female_prop</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>1</th>\n","      <td>aaa</td>\n","      <td>male</td>\n","      <td>0.260188</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>aaaa</td>\n","      <td>female</td>\n","      <td>0.742647</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>aaaamsoo</td>\n","      <td>female</td>\n","      <td>0.675344</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>aaaan</td>\n","      <td>male</td>\n","      <td>0.029126</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>aaab</td>\n","      <td>male</td>\n","      <td>0.053333</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>196434</th>\n","      <td>yeeelavaad</td>\n","      <td>male</td>\n","      <td>0.472603</td>\n","    </tr>\n","    <tr>\n","      <th>196808</th>\n","      <td>yoogamaaaa</td>\n","      <td>female</td>\n","      <td>0.996219</td>\n","    </tr>\n","    <tr>\n","      <th>197076</th>\n","      <td>yuliaaa</td>\n","      <td>female</td>\n","      <td>0.995633</td>\n","    </tr>\n","    <tr>\n","      <th>197109</th>\n","      <td>yuriaaa</td>\n","      <td>male</td>\n","      <td>0.239175</td>\n","    </tr>\n","    <tr>\n","      <th>197135</th>\n","      <td>yuthiaaa</td>\n","      <td>male</td>\n","      <td>0.168067</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>1142 rows × 3 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-77371143-02f5-40eb-a63a-b7e09c36c667')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":10}]},{"cell_type":"code","source":["df = df.drop(df[df['first_name'].apply(lambda x : find_seq_chars(x, 3))].index)"],"metadata":{"id":"cMjYROoREmFN"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["df.shape"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"egsqmN_wErg_","executionInfo":{"status":"ok","timestamp":1652307886005,"user_tz":420,"elapsed":3,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"}},"outputId":"c43fad33-2c43-40c1-b6a0-6d591285c830"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(195979, 3)"]},"metadata":{},"execution_count":12}]},{"cell_type":"code","source":["import re\n","def find_numbers(x):\n","  return len(re.findall('[0-9]+', x)) > 0\n","\n","df[df['first_name'].apply(lambda x: find_numbers(x))]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":49},"id":"UpuUd4eEEwt_","executionInfo":{"status":"ok","timestamp":1652307889181,"user_tz":420,"elapsed":302,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"}},"outputId":"571e3571-0249-44b3-feed-881650b9951b"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Empty DataFrame\n","Columns: [first_name, gender, female_prop]\n","Index: []"],"text/html":["\n","  <div id=\"df-04b95350-76bc-4222-81e6-997e1467e920\">\n","    <div class=\"colab-df-container\">\n","      <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>first_name</th>\n","      <th>gender</th>\n","      <th>female_prop</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-04b95350-76bc-4222-81e6-997e1467e920')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-04b95350-76bc-4222-81e6-997e1467e920 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":13}]},{"cell_type":"code","source":["df.head()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"WBz3zxnDvWkj","executionInfo":{"status":"ok","timestamp":1652307909502,"user_tz":420,"elapsed":235,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"}},"outputId":"2080d2e4-3d49-40dd-f833-a0c58393c85b"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["    first_name  gender  female_prop\n","179        aab    male     0.292818\n","180      aabaa    male     0.351738\n","181     aabaad    male     0.047183\n","182   aabaadaa  female     0.983333\n","183  aabaadaar  female     0.515075"],"text/html":["\n","  <div id=\"df-493f86a0-0a07-4fbf-9347-07668e460c20\">\n","    <div class=\"colab-df-container\">\n","      <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>first_name</th>\n","      <th>gender</th>\n","      <th>female_prop</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>179</th>\n","      <td>aab</td>\n","      <td>male</td>\n","      <td>0.292818</td>\n","    </tr>\n","    <tr>\n","      <th>180</th>\n","      <td>aabaa</td>\n","      <td>male</td>\n","      <td>0.351738</td>\n","    </tr>\n","    <tr>\n","      <th>181</th>\n","      <td>aabaad</td>\n","      <td>male</td>\n","      <td>0.047183</td>\n","    </tr>\n","    <tr>\n","      <th>182</th>\n","      <td>aabaadaa</td>\n","      <td>female</td>\n","      <td>0.983333</td>\n","    </tr>\n","    <tr>\n","      <th>183</th>\n","      <td>aabaadaar</td>\n","      <td>female</td>\n","      <td>0.515075</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-493f86a0-0a07-4fbf-9347-07668e460c20')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-493f86a0-0a07-4fbf-9347-07668e460c20 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":14}]},{"cell_type":"code","source":["df.shape"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"YOQjWLtbwN2J","executionInfo":{"status":"ok","timestamp":1652307938921,"user_tz":420,"elapsed":233,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"}},"outputId":"5183c710-213a-43dc-ea6e-96621c12c747"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(195979, 3)"]},"metadata":{},"execution_count":15}]},{"cell_type":"code","source":["df['female_prop'].hist()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":282},"id":"Ep1LX6NUOofB","executionInfo":{"status":"ok","timestamp":1652308125873,"user_tz":420,"elapsed":228,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"}},"outputId":"5c4b1b7f-567d-49a4-a036-29192a965ae3"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x7fb92aa11d90>"]},"metadata":{},"execution_count":18},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":["# Split data"],"metadata":{"id":"9p7mCWHMu1mJ"}},{"cell_type":"code","source":["X = df['first_name'].values\n","y = df['female_prop'].values"],"metadata":{"id":"RRAgyLK9vi_w"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["X_train, X_rem, y_train, y_rem = train_test_split(X, y, test_size=0.3, random_state=42, shuffle=True)"],"metadata":{"id":"fIHwjJILtFWK"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["X_train.size"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"cBS_79e-wYHg","executionInfo":{"status":"ok","timestamp":1652308231185,"user_tz":420,"elapsed":6,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"}},"outputId":"15bea474-64f2-45d6-9f5e-b559fa5cd503"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["137185"]},"metadata":{},"execution_count":21}]},{"cell_type":"code","source":["np.unique(y_train, return_counts=True)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"8OvkrFKJwcDk","executionInfo":{"status":"ok","timestamp":1652308231569,"user_tz":420,"elapsed":4,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"}},"outputId":"bd882547-a711-475f-9a97-c1291b7e9cb7"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(array([0.00000000e+00, 1.96309384e-04, 1.96386489e-04, ...,\n","        9.99906323e-01, 9.99952094e-01, 1.00000000e+00]),\n"," array([6318,    1,    1, ...,    1,    1, 7516]))"]},"metadata":{},"execution_count":22}]},{"cell_type":"code","source":["X_rem.size"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"5qei7VTtwg08","executionInfo":{"status":"ok","timestamp":1652308232310,"user_tz":420,"elapsed":3,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"}},"outputId":"dccccc30-71e7-43f0-e526-bdeb155b8213"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["58794"]},"metadata":{},"execution_count":23}]},{"cell_type":"code","source":["np.unique(y_rem, return_counts=True)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"IbikcCA7wr79","executionInfo":{"status":"ok","timestamp":1652308289439,"user_tz":420,"elapsed":244,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"}},"outputId":"32f90dfb-be45-4aa9-e88b-694929e80e56"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(array([0.00000000e+00, 4.08496732e-04, 4.84496124e-04, ...,\n","        9.99889564e-01, 9.99904370e-01, 1.00000000e+00]),\n"," array([2850,    1,    1, ...,    1,    1, 3209]))"]},"metadata":{},"execution_count":24}]},{"cell_type":"code","source":["X_val, X_test, y_val, y_test = train_test_split(X_rem, y_rem, test_size=0.5)"],"metadata":{"id":"yjm37ibuwyjB"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["y_train[1]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"nvCiZNzCxkFp","executionInfo":{"status":"ok","timestamp":1652308292666,"user_tz":420,"elapsed":3,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"}},"outputId":"f9f53d2d-dd5b-4079-f4f5-ba216819a623"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.5302013422818792"]},"metadata":{},"execution_count":26}]},{"cell_type":"code","source":["X_train[1]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":35},"id":"6dreTOUVxtFa","executionInfo":{"status":"ok","timestamp":1652308295200,"user_tz":420,"elapsed":8,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"}},"outputId":"8fb1d082-73a0-407b-b9e7-f6c15f2e3c4a"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'bhakathi'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":27}]},{"cell_type":"markdown","source":["# Tokenization"],"metadata":{"id":"uAIi7ry7gsPJ"}},{"cell_type":"code","source":["tk = Tokenizer(num_words=None, char_level=True, oov_token='UNK')"],"metadata":{"id":"vBoT93fYguqa"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["tk.fit_on_texts(df['first_name'].values)"],"metadata":{"id":"l6L4Ux7ig2rx"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["alphabet=\"abcdefghijklmnopqrstuvwxyz\"\n","char_dict = {}\n","for i, char in enumerate(alphabet):\n","    char_dict[char] = i + 1"],"metadata":{"id":"hcNBbk7ZhHZ2"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["char_dict"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"v8r_5NYahme_","executionInfo":{"status":"ok","timestamp":1652308303756,"user_tz":420,"elapsed":3,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"}},"outputId":"307c94ef-30cf-45f9-da53-14d2c68a2c65"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'a': 1,\n"," 'b': 2,\n"," 'c': 3,\n"," 'd': 4,\n"," 'e': 5,\n"," 'f': 6,\n"," 'g': 7,\n"," 'h': 8,\n"," 'i': 9,\n"," 'j': 10,\n"," 'k': 11,\n"," 'l': 12,\n"," 'm': 13,\n"," 'n': 14,\n"," 'o': 15,\n"," 'p': 16,\n"," 'q': 17,\n"," 'r': 18,\n"," 's': 19,\n"," 't': 20,\n"," 'u': 21,\n"," 'v': 22,\n"," 'w': 23,\n"," 'x': 24,\n"," 'y': 25,\n"," 'z': 26}"]},"metadata":{},"execution_count":31}]},{"cell_type":"code","source":["# Use char_dict to replace the tk.word_index\n","tk.word_index = char_dict.copy() \n","# Add 'UNK' to the vocabulary \n","tk.word_index[tk.oov_token] = max(char_dict.values()) + 1"],"metadata":{"id":"8EhBIi0GhooS"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["tk.word_index"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Uru2atKChyI6","executionInfo":{"status":"ok","timestamp":1652308306052,"user_tz":420,"elapsed":6,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"}},"outputId":"b592379a-8d9f-4d66-c94d-f19d3d5df1d8"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'UNK': 27,\n"," 'a': 1,\n"," 'b': 2,\n"," 'c': 3,\n"," 'd': 4,\n"," 'e': 5,\n"," 'f': 6,\n"," 'g': 7,\n"," 'h': 8,\n"," 'i': 9,\n"," 'j': 10,\n"," 'k': 11,\n"," 'l': 12,\n"," 'm': 13,\n"," 'n': 14,\n"," 'o': 15,\n"," 'p': 16,\n"," 'q': 17,\n"," 'r': 18,\n"," 's': 19,\n"," 't': 20,\n"," 'u': 21,\n"," 'v': 22,\n"," 'w': 23,\n"," 'x': 24,\n"," 'y': 25,\n"," 'z': 26}"]},"metadata":{},"execution_count":33}]},{"cell_type":"code","source":["df['first_name'].str.len().max()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"mX6_ewqBhKeX","executionInfo":{"status":"ok","timestamp":1652308308718,"user_tz":420,"elapsed":4,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"}},"outputId":"9c52eb83-fe80-41aa-d66e-591e014adcbb"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["24"]},"metadata":{},"execution_count":34}]},{"cell_type":"code","source":["train_sequences = tk.texts_to_sequences(X_train)"],"metadata":{"id":"2Ra5sMfXheCj"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["train_sequences[0]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"RHKJZidPjW-z","executionInfo":{"status":"ok","timestamp":1652308310147,"user_tz":420,"elapsed":7,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"}},"outputId":"a402c286-f331-40d4-fd38-b2dd66860712"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[11, 1, 4, 1, 14, 15, 15, 18]"]},"metadata":{},"execution_count":36}]},{"cell_type":"code","source":["X_train[0]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":35},"id":"zmr1Kodmja1v","executionInfo":{"status":"ok","timestamp":1652308310387,"user_tz":420,"elapsed":5,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"}},"outputId":"0e550d40-92db-4626-9563-37e4f072ca40"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'kadanoor'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":37}]},{"cell_type":"code","source":["validation_sequences = tk.texts_to_sequences(X_val)\n","test_sequences = tk.texts_to_sequences(X_test)"],"metadata":{"id":"0sqGV112jkaG"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# Padding\n","X_train = pad_sequences(train_sequences, maxlen=24, padding='post')\n","X_val = pad_sequences(validation_sequences, maxlen=24, padding='post')\n","X_test = pad_sequences(test_sequences, maxlen=24, padding='post')"],"metadata":{"id":"yRc4zEsEj06S"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["X_train[0]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Ry5cpi1xkgZS","executionInfo":{"status":"ok","timestamp":1652308315931,"user_tz":420,"elapsed":5,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"}},"outputId":"87feeabe-4584-48af-b52c-47d8e5e119a5"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([11,  1,  4,  1, 14, 15, 15, 18,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n","        0,  0,  0,  0,  0,  0,  0], dtype=int32)"]},"metadata":{},"execution_count":40}]},{"cell_type":"markdown","source":["# Creating Dataset"],"metadata":{"id":"KrDc8ZYlx_7Q"}},{"cell_type":"code","source":["BATCH_SIZE = 64\n","BUFFER_SIZE = 200000"],"metadata":{"id":"vEJ-h_Qhxza8"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["train_dataset = tf.data.Dataset.from_tensor_slices((X_train, y_train))\n","train_dataset = train_dataset.shuffle(BUFFER_SIZE).batch(BATCH_SIZE)\n","\n","val_dataset = tf.data.Dataset.from_tensor_slices((X_val, y_val))\n","val_dataset = val_dataset.batch(BATCH_SIZE)\n","\n","test_dataset = tf.data.Dataset.from_tensor_slices((X_test, y_test))\n","test_dataset = test_dataset.batch(BATCH_SIZE)"],"metadata":{"id":"z9YNBRNMyLwn"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["text_batch, label_batch = next(iter(train_dataset))\n","first_name, gender_label = text_batch[0], label_batch[0]\n","print(\"Name\", first_name)\n","print(\"Label\", gender_label)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"8URYnStxyPNz","executionInfo":{"status":"ok","timestamp":1652308336781,"user_tz":420,"elapsed":488,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"}},"outputId":"d14380ec-1fc5-4d12-b850-dfcc49bb29e0"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Name tf.Tensor([19  1  2  1 14  1 13  1  2  5  5  0  0  0  0  0  0  0  0  0  0  0  0  0], shape=(24,), dtype=int32)\n","Label tf.Tensor(0.9968503937007874, shape=(), dtype=float64)\n"]}]},{"cell_type":"markdown","source":["# Vectorization"],"metadata":{"id":"f3liKdl9yq0f"}},{"cell_type":"code","source":["vocab_size = len(tk.word_index)\n","vocab_size"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"F6Nm2dAikvYF","executionInfo":{"status":"ok","timestamp":1652308345982,"user_tz":420,"elapsed":219,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"}},"outputId":"f19c94f7-be84-438e-f16b-45b59b00690c"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["27"]},"metadata":{},"execution_count":44}]},{"cell_type":"code","source":["embedding_weights = [] \n","embedding_weights.append(np.zeros(vocab_size)) # first row is pad\n","\n","for char, i in tk.word_index.items(): \n","    onehot = np.zeros(vocab_size)\n","    onehot[i-1] = 1\n","    embedding_weights.append(onehot)\n","embedding_weights = np.array(embedding_weights)"],"metadata":{"id":"ZE8ibmRAk2XH"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["print(embedding_weights.shape) # first row all 0 for PAD, 26 char, last row for UNK\n","embedding_weights"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"h-X8NrMvlWfj","executionInfo":{"status":"ok","timestamp":1652308353591,"user_tz":420,"elapsed":9,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"}},"outputId":"cb5ce89a-2120-45fd-d4fb-49bee83f3d30"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["(28, 27)\n"]},{"output_type":"execute_result","data":{"text/plain":["array([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n","        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n","       [1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n","        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n","       [0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n","        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n","       [0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n","        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n","       [0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n","        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n","       [0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n","        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n","       [0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n","        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n","       [0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n","        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n","       [0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0.,\n","        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n","       [0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0.,\n","        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n","       [0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0.,\n","        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n","       [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0.,\n","        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n","       [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0.,\n","        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n","       [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0.,\n","        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n","       [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0.,\n","        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n","       [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0.,\n","        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n","       [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1.,\n","        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n","       [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n","        1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n","       [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n","        0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n","       [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n","        0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0.],\n","       [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n","        0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0.],\n","       [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n","        0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0.],\n","       [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n","        0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0.],\n","       [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n","        0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0.],\n","       [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n","        0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0.],\n","       [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n","        0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0.],\n","       [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n","        0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0.],\n","       [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n","        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1.]])"]},"metadata":{},"execution_count":46}]},{"cell_type":"code","source":["# max length of first name \n","input_size = 24\n","# vocab_size = 27\n","embedding_size = 27\n","conv_layers = [[128, 7, 3], \n","               [128, 3, -1], \n","               [128, 3, -1]]\n","\n","fully_connected_layers = [512, 512]\n","dropout_p = 0.5"],"metadata":{"id":"_0p5A18ulmdw"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# Embedding layer Initialization\n","embedding_layer = tf.keras.layers.Embedding(vocab_size+1, \n","                            embedding_size,\n","                            input_length=input_size,\n","                            weights=[embedding_weights])"],"metadata":{"id":"uZrjJ9OOmd5s"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# Model"],"metadata":{"id":"kNkdA_4F0n-t"}},{"cell_type":"code","source":["# Input\n","inputs = tf.keras.layers.Input(shape=(input_size,), name='input', dtype='int64')  # shape=(?, 24)\n","# Embedding \n","x = embedding_layer(inputs)\n","\n","\n","# Conv \n","for filter_num, filter_size, pooling_size in conv_layers:\n","    print(filter_num, filter_size, pooling_size)\n","    x = tf.keras.layers.Conv1D(filter_num, filter_size)(x)\n","    x = tf.keras.layers.Activation('relu')(x)\n","    if pooling_size != -1:\n","        x = tf.keras.layers.MaxPooling1D(pool_size=pooling_size)(x) # Final shape=(None, 34, 256)\n","    print(x)\n","x = tf.keras.layers.Flatten()(x) # (None, 8704)\n","# Fully connected layers \n","for dense_size in fully_connected_layers:\n","    x = tf.keras.layers.Dense(dense_size, activation='relu')(x) # dense_size == 1024\n","    x = tf.keras.layers.Dropout(dropout_p)(x)\n","# Output Layer\n","predictions = tf.keras.layers.Dense(1,activation=\"sigmoid\")(x)\n","# Build model\n","model = tf.keras.models.Model(inputs=inputs, outputs=predictions)\n","model.compile(optimizer='sgd', loss='mse', metrics=[tf.keras.metrics.RootMeanSquaredError()]) # Adam, categorical_crossentropy\n","model.summary()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"FzDMPnQV0mZi","executionInfo":{"status":"ok","timestamp":1652315725082,"user_tz":420,"elapsed":271,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"}},"outputId":"dda64b6d-d659-44dd-a515-ae7a65c5a493"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["128 7 3\n","KerasTensor(type_spec=TensorSpec(shape=(None, 6, 128), dtype=tf.float32, name=None), name='max_pooling1d_8/Squeeze:0', description=\"created by layer 'max_pooling1d_8'\")\n","128 3 -1\n","KerasTensor(type_spec=TensorSpec(shape=(None, 4, 128), dtype=tf.float32, name=None), name='activation_25/Relu:0', description=\"created by layer 'activation_25'\")\n","128 3 -1\n","KerasTensor(type_spec=TensorSpec(shape=(None, 2, 128), dtype=tf.float32, name=None), name='activation_26/Relu:0', description=\"created by layer 'activation_26'\")\n","Model: \"model_8\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," input (InputLayer)          [(None, 24)]              0         \n","                                                                 \n"," embedding_3 (Embedding)     (None, 24, 27)            756       \n","                                                                 \n"," conv1d_24 (Conv1D)          (None, 18, 128)           24320     \n","                                                                 \n"," activation_24 (Activation)  (None, 18, 128)           0         \n","                                                                 \n"," max_pooling1d_8 (MaxPooling  (None, 6, 128)           0         \n"," 1D)                                                             \n","                                                                 \n"," conv1d_25 (Conv1D)          (None, 4, 128)            49280     \n","                                                                 \n"," activation_25 (Activation)  (None, 4, 128)            0         \n","                                                                 \n"," conv1d_26 (Conv1D)          (None, 2, 128)            49280     \n","                                                                 \n"," activation_26 (Activation)  (None, 2, 128)            0         \n","                                                                 \n"," flatten_8 (Flatten)         (None, 256)               0         \n","                                                                 \n"," dense_24 (Dense)            (None, 512)               131584    \n","                                                                 \n"," dropout_16 (Dropout)        (None, 512)               0         \n","                                                                 \n"," dense_25 (Dense)            (None, 512)               262656    \n","                                                                 \n"," dropout_17 (Dropout)        (None, 512)               0         \n","                                                                 \n"," dense_26 (Dense)            (None, 1)                 513       \n","                                                                 \n","=================================================================\n","Total params: 518,389\n","Trainable params: 518,389\n","Non-trainable params: 0\n","_________________________________________________________________\n"]}]},{"cell_type":"code","source":["model_save_filename = \"model.h5\""],"metadata":{"id":"Vs1DWg450jht"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["batch_size = 64\n","epochs = 500\n","\n","earlystopping_cb = tf.keras.callbacks.EarlyStopping(\n","    monitor = 'val_root_mean_squared_error',\n","    mode = 'min',\n","    verbose = 1,\n","    patience = 10,\n","    restore_best_weights = True\n",")\n","\n","mdlcheckpoint_cb = tf.keras.callbacks.ModelCheckpoint(\n","    model_save_filename,\n","    monitor = 'val_root_mean_squared_error',\n","    mode = 'min',\n","    verbose = 1,\n","    save_best_only = True\n",")\n"],"metadata":{"id":"EaBYw2zo00Dp"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["history = model.fit(\n","    train_dataset,\n","    validation_data=val_dataset,\n","    epochs=epochs,\n","    callbacks=[earlystopping_cb, mdlcheckpoint_cb])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"97BPlowG08Zx","executionInfo":{"status":"ok","timestamp":1652316554804,"user_tz":420,"elapsed":823012,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"}},"outputId":"49cd4475-933a-4f44-fd29-b72daac15ee7"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/500\n","2134/2144 [============================>.] - ETA: 0s - loss: 0.1479 - root_mean_squared_error: 0.3845\n","Epoch 1: val_root_mean_squared_error improved from inf to 0.38397, saving model to model.h5\n","2144/2144 [==============================] - 11s 5ms/step - loss: 0.1479 - root_mean_squared_error: 0.3845 - val_loss: 0.1474 - val_root_mean_squared_error: 0.3840\n","Epoch 2/500\n","2144/2144 [==============================] - ETA: 0s - loss: 0.1456 - root_mean_squared_error: 0.3816\n","Epoch 2: val_root_mean_squared_error improved from 0.38397 to 0.37745, saving model to model.h5\n","2144/2144 [==============================] - 10s 5ms/step - loss: 0.1456 - root_mean_squared_error: 0.3816 - val_loss: 0.1425 - val_root_mean_squared_error: 0.3775\n","Epoch 3/500\n","2135/2144 [============================>.] - ETA: 0s - loss: 0.1343 - root_mean_squared_error: 0.3665\n","Epoch 3: val_root_mean_squared_error improved from 0.37745 to 0.34407, saving model to model.h5\n","2144/2144 [==============================] - 10s 5ms/step - loss: 0.1343 - root_mean_squared_error: 0.3664 - val_loss: 0.1184 - val_root_mean_squared_error: 0.3441\n","Epoch 4/500\n","2133/2144 [============================>.] - ETA: 0s - loss: 0.1064 - root_mean_squared_error: 0.3261\n","Epoch 4: val_root_mean_squared_error improved from 0.34407 to 0.30544, saving model to model.h5\n","2144/2144 [==============================] - 10s 5ms/step - loss: 0.1063 - root_mean_squared_error: 0.3260 - val_loss: 0.0933 - val_root_mean_squared_error: 0.3054\n","Epoch 5/500\n","2140/2144 [============================>.] - ETA: 0s - loss: 0.0912 - root_mean_squared_error: 0.3021\n","Epoch 5: val_root_mean_squared_error improved from 0.30544 to 0.29097, saving model to model.h5\n","2144/2144 [==============================] - 10s 5ms/step - loss: 0.0912 - root_mean_squared_error: 0.3021 - val_loss: 0.0847 - val_root_mean_squared_error: 0.2910\n","Epoch 6/500\n","2137/2144 [============================>.] - ETA: 0s - loss: 0.0845 - root_mean_squared_error: 0.2907\n","Epoch 6: val_root_mean_squared_error improved from 0.29097 to 0.28413, saving model to model.h5\n","2144/2144 [==============================] - 10s 5ms/step - loss: 0.0845 - root_mean_squared_error: 0.2907 - val_loss: 0.0807 - val_root_mean_squared_error: 0.2841\n","Epoch 7/500\n","2135/2144 [============================>.] - ETA: 0s - loss: 0.0799 - root_mean_squared_error: 0.2827\n","Epoch 7: val_root_mean_squared_error improved from 0.28413 to 0.27842, saving model to model.h5\n","2144/2144 [==============================] - 10s 5ms/step - loss: 0.0799 - root_mean_squared_error: 0.2827 - val_loss: 0.0775 - val_root_mean_squared_error: 0.2784\n","Epoch 8/500\n","2132/2144 [============================>.] - ETA: 0s - loss: 0.0767 - root_mean_squared_error: 0.2769\n","Epoch 8: val_root_mean_squared_error did not improve from 0.27842\n","2144/2144 [==============================] - 10s 4ms/step - loss: 0.0766 - root_mean_squared_error: 0.2768 - val_loss: 0.0781 - val_root_mean_squared_error: 0.2795\n","Epoch 9/500\n","2143/2144 [============================>.] - ETA: 0s - loss: 0.0743 - root_mean_squared_error: 0.2726\n","Epoch 9: val_root_mean_squared_error improved from 0.27842 to 0.26681, saving model to model.h5\n","2144/2144 [==============================] - 10s 5ms/step - loss: 0.0743 - root_mean_squared_error: 0.2726 - val_loss: 0.0712 - val_root_mean_squared_error: 0.2668\n","Epoch 10/500\n","2134/2144 [============================>.] - ETA: 0s - loss: 0.0720 - root_mean_squared_error: 0.2684\n","Epoch 10: val_root_mean_squared_error improved from 0.26681 to 0.26339, saving model to model.h5\n","2144/2144 [==============================] - 10s 5ms/step - loss: 0.0721 - root_mean_squared_error: 0.2684 - val_loss: 0.0694 - val_root_mean_squared_error: 0.2634\n","Epoch 11/500\n","2135/2144 [============================>.] - ETA: 0s - loss: 0.0703 - root_mean_squared_error: 0.2651\n","Epoch 11: val_root_mean_squared_error did not improve from 0.26339\n","2144/2144 [==============================] - 10s 4ms/step - loss: 0.0703 - root_mean_squared_error: 0.2651 - val_loss: 0.0696 - val_root_mean_squared_error: 0.2638\n","Epoch 12/500\n","2134/2144 [============================>.] - ETA: 0s - loss: 0.0686 - root_mean_squared_error: 0.2620\n","Epoch 12: val_root_mean_squared_error did not improve from 0.26339\n","2144/2144 [==============================] - 10s 4ms/step - loss: 0.0687 - root_mean_squared_error: 0.2620 - val_loss: 0.0730 - val_root_mean_squared_error: 0.2703\n","Epoch 13/500\n","2135/2144 [============================>.] - ETA: 0s - loss: 0.0672 - root_mean_squared_error: 0.2592\n","Epoch 13: val_root_mean_squared_error improved from 0.26339 to 0.26095, saving model to model.h5\n","2144/2144 [==============================] - 10s 5ms/step - loss: 0.0672 - root_mean_squared_error: 0.2592 - val_loss: 0.0681 - val_root_mean_squared_error: 0.2610\n","Epoch 14/500\n","2140/2144 [============================>.] - ETA: 0s - loss: 0.0661 - root_mean_squared_error: 0.2571\n","Epoch 14: val_root_mean_squared_error improved from 0.26095 to 0.25462, saving model to model.h5\n","2144/2144 [==============================] - 10s 5ms/step - loss: 0.0661 - root_mean_squared_error: 0.2572 - val_loss: 0.0648 - val_root_mean_squared_error: 0.2546\n","Epoch 15/500\n","2131/2144 [============================>.] - ETA: 0s - loss: 0.0648 - root_mean_squared_error: 0.2546\n","Epoch 15: val_root_mean_squared_error improved from 0.25462 to 0.25362, saving model to model.h5\n","2144/2144 [==============================] - 10s 5ms/step - loss: 0.0648 - root_mean_squared_error: 0.2546 - val_loss: 0.0643 - val_root_mean_squared_error: 0.2536\n","Epoch 16/500\n","2141/2144 [============================>.] - ETA: 0s - loss: 0.0638 - root_mean_squared_error: 0.2527\n","Epoch 16: val_root_mean_squared_error did not improve from 0.25362\n","2144/2144 [==============================] - 10s 4ms/step - loss: 0.0638 - root_mean_squared_error: 0.2527 - val_loss: 0.0670 - val_root_mean_squared_error: 0.2589\n","Epoch 17/500\n","2138/2144 [============================>.] - ETA: 0s - loss: 0.0627 - root_mean_squared_error: 0.2504\n","Epoch 17: val_root_mean_squared_error improved from 0.25362 to 0.24926, saving model to model.h5\n","2144/2144 [==============================] - 10s 5ms/step - loss: 0.0627 - root_mean_squared_error: 0.2504 - val_loss: 0.0621 - val_root_mean_squared_error: 0.2493\n","Epoch 18/500\n","2136/2144 [============================>.] - ETA: 0s - loss: 0.0618 - root_mean_squared_error: 0.2487\n","Epoch 18: val_root_mean_squared_error improved from 0.24926 to 0.24762, saving model to model.h5\n","2144/2144 [==============================] - 10s 5ms/step - loss: 0.0618 - root_mean_squared_error: 0.2487 - val_loss: 0.0613 - val_root_mean_squared_error: 0.2476\n","Epoch 19/500\n","2135/2144 [============================>.] - ETA: 0s - loss: 0.0611 - root_mean_squared_error: 0.2473\n","Epoch 19: val_root_mean_squared_error did not improve from 0.24762\n","2144/2144 [==============================] - 10s 4ms/step - loss: 0.0611 - root_mean_squared_error: 0.2473 - val_loss: 0.0618 - val_root_mean_squared_error: 0.2487\n","Epoch 20/500\n","2140/2144 [============================>.] - ETA: 0s - loss: 0.0600 - root_mean_squared_error: 0.2450\n","Epoch 20: val_root_mean_squared_error did not improve from 0.24762\n","2144/2144 [==============================] - 10s 5ms/step - loss: 0.0600 - root_mean_squared_error: 0.2450 - val_loss: 0.0622 - val_root_mean_squared_error: 0.2494\n","Epoch 21/500\n","2133/2144 [============================>.] - ETA: 0s - loss: 0.0594 - root_mean_squared_error: 0.2437\n","Epoch 21: val_root_mean_squared_error improved from 0.24762 to 0.24568, saving model to model.h5\n","2144/2144 [==============================] - 10s 5ms/step - loss: 0.0594 - root_mean_squared_error: 0.2437 - val_loss: 0.0604 - val_root_mean_squared_error: 0.2457\n","Epoch 22/500\n","2142/2144 [============================>.] - ETA: 0s - loss: 0.0585 - root_mean_squared_error: 0.2420\n","Epoch 22: val_root_mean_squared_error did not improve from 0.24568\n","2144/2144 [==============================] - 10s 4ms/step - loss: 0.0586 - root_mean_squared_error: 0.2420 - val_loss: 0.0659 - val_root_mean_squared_error: 0.2567\n","Epoch 23/500\n","2134/2144 [============================>.] - ETA: 0s - loss: 0.0578 - root_mean_squared_error: 0.2404\n","Epoch 23: val_root_mean_squared_error did not improve from 0.24568\n","2144/2144 [==============================] - 10s 4ms/step - loss: 0.0578 - root_mean_squared_error: 0.2404 - val_loss: 0.0609 - val_root_mean_squared_error: 0.2467\n","Epoch 24/500\n","2144/2144 [==============================] - ETA: 0s - loss: 0.0571 - root_mean_squared_error: 0.2390\n","Epoch 24: val_root_mean_squared_error did not improve from 0.24568\n","2144/2144 [==============================] - 10s 4ms/step - loss: 0.0571 - root_mean_squared_error: 0.2390 - val_loss: 0.0607 - val_root_mean_squared_error: 0.2465\n","Epoch 25/500\n","2139/2144 [============================>.] - ETA: 0s - loss: 0.0565 - root_mean_squared_error: 0.2376\n","Epoch 25: val_root_mean_squared_error improved from 0.24568 to 0.23994, saving model to model.h5\n","2144/2144 [==============================] - 10s 5ms/step - loss: 0.0564 - root_mean_squared_error: 0.2376 - val_loss: 0.0576 - val_root_mean_squared_error: 0.2399\n","Epoch 26/500\n","2132/2144 [============================>.] - ETA: 0s - loss: 0.0558 - root_mean_squared_error: 0.2363\n","Epoch 26: val_root_mean_squared_error did not improve from 0.23994\n","2144/2144 [==============================] - 10s 4ms/step - loss: 0.0559 - root_mean_squared_error: 0.2364 - val_loss: 0.0592 - val_root_mean_squared_error: 0.2434\n","Epoch 27/500\n","2132/2144 [============================>.] - ETA: 0s - loss: 0.0553 - root_mean_squared_error: 0.2351\n","Epoch 27: val_root_mean_squared_error improved from 0.23994 to 0.23770, saving model to model.h5\n","2144/2144 [==============================] - 10s 5ms/step - loss: 0.0553 - root_mean_squared_error: 0.2351 - val_loss: 0.0565 - val_root_mean_squared_error: 0.2377\n","Epoch 28/500\n","2141/2144 [============================>.] - ETA: 0s - loss: 0.0547 - root_mean_squared_error: 0.2339\n","Epoch 28: val_root_mean_squared_error did not improve from 0.23770\n","2144/2144 [==============================] - 10s 4ms/step - loss: 0.0547 - root_mean_squared_error: 0.2339 - val_loss: 0.0568 - val_root_mean_squared_error: 0.2384\n","Epoch 29/500\n","2134/2144 [============================>.] - ETA: 0s - loss: 0.0543 - root_mean_squared_error: 0.2331\n","Epoch 29: val_root_mean_squared_error improved from 0.23770 to 0.23685, saving model to model.h5\n","2144/2144 [==============================] - 10s 5ms/step - loss: 0.0543 - root_mean_squared_error: 0.2331 - val_loss: 0.0561 - val_root_mean_squared_error: 0.2368\n","Epoch 30/500\n","2140/2144 [============================>.] - ETA: 0s - loss: 0.0539 - root_mean_squared_error: 0.2321\n","Epoch 30: val_root_mean_squared_error did not improve from 0.23685\n","2144/2144 [==============================] - 10s 4ms/step - loss: 0.0539 - root_mean_squared_error: 0.2321 - val_loss: 0.0563 - val_root_mean_squared_error: 0.2372\n","Epoch 31/500\n","2141/2144 [============================>.] - ETA: 0s - loss: 0.0534 - root_mean_squared_error: 0.2310\n","Epoch 31: val_root_mean_squared_error did not improve from 0.23685\n","2144/2144 [==============================] - 10s 4ms/step - loss: 0.0534 - root_mean_squared_error: 0.2310 - val_loss: 0.0563 - val_root_mean_squared_error: 0.2374\n","Epoch 32/500\n","2137/2144 [============================>.] - ETA: 0s - loss: 0.0529 - root_mean_squared_error: 0.2299\n","Epoch 32: val_root_mean_squared_error did not improve from 0.23685\n","2144/2144 [==============================] - 10s 4ms/step - loss: 0.0528 - root_mean_squared_error: 0.2299 - val_loss: 0.0586 - val_root_mean_squared_error: 0.2420\n","Epoch 33/500\n","2139/2144 [============================>.] - ETA: 0s - loss: 0.0524 - root_mean_squared_error: 0.2290\n","Epoch 33: val_root_mean_squared_error improved from 0.23685 to 0.23455, saving model to model.h5\n","2144/2144 [==============================] - 10s 5ms/step - loss: 0.0524 - root_mean_squared_error: 0.2289 - val_loss: 0.0550 - val_root_mean_squared_error: 0.2345\n","Epoch 34/500\n","2142/2144 [============================>.] - ETA: 0s - loss: 0.0520 - root_mean_squared_error: 0.2281\n","Epoch 34: val_root_mean_squared_error did not improve from 0.23455\n","2144/2144 [==============================] - 10s 4ms/step - loss: 0.0520 - root_mean_squared_error: 0.2281 - val_loss: 0.0556 - val_root_mean_squared_error: 0.2359\n","Epoch 35/500\n","2144/2144 [==============================] - ETA: 0s - loss: 0.0516 - root_mean_squared_error: 0.2272\n","Epoch 35: val_root_mean_squared_error improved from 0.23455 to 0.23357, saving model to model.h5\n","2144/2144 [==============================] - 11s 5ms/step - loss: 0.0516 - root_mean_squared_error: 0.2272 - val_loss: 0.0546 - val_root_mean_squared_error: 0.2336\n","Epoch 36/500\n","2142/2144 [============================>.] - ETA: 0s - loss: 0.0511 - root_mean_squared_error: 0.2260\n","Epoch 36: val_root_mean_squared_error improved from 0.23357 to 0.23316, saving model to model.h5\n","2144/2144 [==============================] - 10s 5ms/step - loss: 0.0511 - root_mean_squared_error: 0.2260 - val_loss: 0.0544 - val_root_mean_squared_error: 0.2332\n","Epoch 37/500\n","2138/2144 [============================>.] - ETA: 0s - loss: 0.0509 - root_mean_squared_error: 0.2257\n","Epoch 37: val_root_mean_squared_error did not improve from 0.23316\n","2144/2144 [==============================] - 10s 4ms/step - loss: 0.0509 - root_mean_squared_error: 0.2256 - val_loss: 0.0563 - val_root_mean_squared_error: 0.2373\n","Epoch 38/500\n","2136/2144 [============================>.] - ETA: 0s - loss: 0.0503 - root_mean_squared_error: 0.2243\n","Epoch 38: val_root_mean_squared_error did not improve from 0.23316\n","2144/2144 [==============================] - 10s 4ms/step - loss: 0.0503 - root_mean_squared_error: 0.2243 - val_loss: 0.0551 - val_root_mean_squared_error: 0.2347\n","Epoch 39/500\n","2137/2144 [============================>.] - ETA: 0s - loss: 0.0502 - root_mean_squared_error: 0.2241\n","Epoch 39: val_root_mean_squared_error improved from 0.23316 to 0.23191, saving model to model.h5\n","2144/2144 [==============================] - 10s 5ms/step - loss: 0.0502 - root_mean_squared_error: 0.2241 - val_loss: 0.0538 - val_root_mean_squared_error: 0.2319\n","Epoch 40/500\n","2137/2144 [============================>.] - ETA: 0s - loss: 0.0497 - root_mean_squared_error: 0.2228\n","Epoch 40: val_root_mean_squared_error improved from 0.23191 to 0.23165, saving model to model.h5\n","2144/2144 [==============================] - 10s 5ms/step - loss: 0.0497 - root_mean_squared_error: 0.2228 - val_loss: 0.0537 - val_root_mean_squared_error: 0.2317\n","Epoch 41/500\n","2133/2144 [============================>.] - ETA: 0s - loss: 0.0493 - root_mean_squared_error: 0.2220\n","Epoch 41: val_root_mean_squared_error did not improve from 0.23165\n","2144/2144 [==============================] - 10s 4ms/step - loss: 0.0493 - root_mean_squared_error: 0.2220 - val_loss: 0.0540 - val_root_mean_squared_error: 0.2324\n","Epoch 42/500\n","2140/2144 [============================>.] - ETA: 0s - loss: 0.0489 - root_mean_squared_error: 0.2212\n","Epoch 42: val_root_mean_squared_error did not improve from 0.23165\n","2144/2144 [==============================] - 10s 4ms/step - loss: 0.0489 - root_mean_squared_error: 0.2212 - val_loss: 0.0538 - val_root_mean_squared_error: 0.2320\n","Epoch 43/500\n","2132/2144 [============================>.] - ETA: 0s - loss: 0.0487 - root_mean_squared_error: 0.2207\n","Epoch 43: val_root_mean_squared_error improved from 0.23165 to 0.23069, saving model to model.h5\n","2144/2144 [==============================] - 10s 5ms/step - loss: 0.0487 - root_mean_squared_error: 0.2207 - val_loss: 0.0532 - val_root_mean_squared_error: 0.2307\n","Epoch 44/500\n","2132/2144 [============================>.] - ETA: 0s - loss: 0.0484 - root_mean_squared_error: 0.2199\n","Epoch 44: val_root_mean_squared_error did not improve from 0.23069\n","2144/2144 [==============================] - 10s 4ms/step - loss: 0.0484 - root_mean_squared_error: 0.2199 - val_loss: 0.0565 - val_root_mean_squared_error: 0.2378\n","Epoch 45/500\n","2144/2144 [==============================] - ETA: 0s - loss: 0.0479 - root_mean_squared_error: 0.2189\n","Epoch 45: val_root_mean_squared_error did not improve from 0.23069\n","2144/2144 [==============================] - 10s 4ms/step - loss: 0.0479 - root_mean_squared_error: 0.2189 - val_loss: 0.0534 - val_root_mean_squared_error: 0.2311\n","Epoch 46/500\n","2135/2144 [============================>.] - ETA: 0s - loss: 0.0478 - root_mean_squared_error: 0.2186\n","Epoch 46: val_root_mean_squared_error did not improve from 0.23069\n","2144/2144 [==============================] - 10s 4ms/step - loss: 0.0478 - root_mean_squared_error: 0.2186 - val_loss: 0.0539 - val_root_mean_squared_error: 0.2323\n","Epoch 47/500\n","2134/2144 [============================>.] - ETA: 0s - loss: 0.0474 - root_mean_squared_error: 0.2176\n","Epoch 47: val_root_mean_squared_error did not improve from 0.23069\n","2144/2144 [==============================] - 10s 4ms/step - loss: 0.0474 - root_mean_squared_error: 0.2177 - val_loss: 0.0566 - val_root_mean_squared_error: 0.2379\n","Epoch 48/500\n","2137/2144 [============================>.] - ETA: 0s - loss: 0.0471 - root_mean_squared_error: 0.2171\n","Epoch 48: val_root_mean_squared_error improved from 0.23069 to 0.22962, saving model to model.h5\n","2144/2144 [==============================] - 10s 5ms/step - loss: 0.0472 - root_mean_squared_error: 0.2171 - val_loss: 0.0527 - val_root_mean_squared_error: 0.2296\n","Epoch 49/500\n","2136/2144 [============================>.] - ETA: 0s - loss: 0.0468 - root_mean_squared_error: 0.2164\n","Epoch 49: val_root_mean_squared_error did not improve from 0.22962\n","2144/2144 [==============================] - 10s 4ms/step - loss: 0.0468 - root_mean_squared_error: 0.2163 - val_loss: 0.0531 - val_root_mean_squared_error: 0.2304\n","Epoch 50/500\n","2134/2144 [============================>.] - ETA: 0s - loss: 0.0466 - root_mean_squared_error: 0.2158\n","Epoch 50: val_root_mean_squared_error did not improve from 0.22962\n","2144/2144 [==============================] - 10s 4ms/step - loss: 0.0465 - root_mean_squared_error: 0.2157 - val_loss: 0.0554 - val_root_mean_squared_error: 0.2353\n","Epoch 51/500\n","2136/2144 [============================>.] - ETA: 0s - loss: 0.0462 - root_mean_squared_error: 0.2151\n","Epoch 51: val_root_mean_squared_error did not improve from 0.22962\n","2144/2144 [==============================] - 10s 4ms/step - loss: 0.0462 - root_mean_squared_error: 0.2150 - val_loss: 0.0575 - val_root_mean_squared_error: 0.2397\n","Epoch 52/500\n","2144/2144 [==============================] - ETA: 0s - loss: 0.0460 - root_mean_squared_error: 0.2144\n","Epoch 52: val_root_mean_squared_error did not improve from 0.22962\n","2144/2144 [==============================] - 10s 5ms/step - loss: 0.0460 - root_mean_squared_error: 0.2144 - val_loss: 0.0529 - val_root_mean_squared_error: 0.2300\n","Epoch 53/500\n","2139/2144 [============================>.] - ETA: 0s - loss: 0.0458 - root_mean_squared_error: 0.2139\n","Epoch 53: val_root_mean_squared_error did not improve from 0.22962\n","2144/2144 [==============================] - 10s 5ms/step - loss: 0.0458 - root_mean_squared_error: 0.2139 - val_loss: 0.0538 - val_root_mean_squared_error: 0.2320\n","Epoch 54/500\n","2141/2144 [============================>.] - ETA: 0s - loss: 0.0455 - root_mean_squared_error: 0.2133\n","Epoch 54: val_root_mean_squared_error did not improve from 0.22962\n","2144/2144 [==============================] - 10s 5ms/step - loss: 0.0455 - root_mean_squared_error: 0.2132 - val_loss: 0.0527 - val_root_mean_squared_error: 0.2297\n","Epoch 55/500\n","2136/2144 [============================>.] - ETA: 0s - loss: 0.0452 - root_mean_squared_error: 0.2126\n","Epoch 55: val_root_mean_squared_error did not improve from 0.22962\n","2144/2144 [==============================] - 10s 4ms/step - loss: 0.0452 - root_mean_squared_error: 0.2126 - val_loss: 0.0532 - val_root_mean_squared_error: 0.2307\n","Epoch 56/500\n","2140/2144 [============================>.] - ETA: 0s - loss: 0.0450 - root_mean_squared_error: 0.2120\n","Epoch 56: val_root_mean_squared_error did not improve from 0.22962\n","2144/2144 [==============================] - 10s 5ms/step - loss: 0.0449 - root_mean_squared_error: 0.2120 - val_loss: 0.0536 - val_root_mean_squared_error: 0.2316\n","Epoch 57/500\n","2142/2144 [============================>.] - ETA: 0s - loss: 0.0448 - root_mean_squared_error: 0.2117\n","Epoch 57: val_root_mean_squared_error did not improve from 0.22962\n","2144/2144 [==============================] - 10s 5ms/step - loss: 0.0448 - root_mean_squared_error: 0.2117 - val_loss: 0.0622 - val_root_mean_squared_error: 0.2495\n","Epoch 58/500\n","2135/2144 [============================>.] - ETA: 0s - loss: 0.0445 - root_mean_squared_error: 0.2109\n","Epoch 58: val_root_mean_squared_error improved from 0.22962 to 0.22943, saving model to model.h5\n","2144/2144 [==============================] - 10s 5ms/step - loss: 0.0445 - root_mean_squared_error: 0.2109 - val_loss: 0.0526 - val_root_mean_squared_error: 0.2294\n","Epoch 59/500\n","2136/2144 [============================>.] - ETA: 0s - loss: 0.0442 - root_mean_squared_error: 0.2102\n","Epoch 59: val_root_mean_squared_error improved from 0.22943 to 0.22782, saving model to model.h5\n","2144/2144 [==============================] - 10s 5ms/step - loss: 0.0442 - root_mean_squared_error: 0.2102 - val_loss: 0.0519 - val_root_mean_squared_error: 0.2278\n","Epoch 60/500\n","2138/2144 [============================>.] - ETA: 0s - loss: 0.0439 - root_mean_squared_error: 0.2096\n","Epoch 60: val_root_mean_squared_error did not improve from 0.22782\n","2144/2144 [==============================] - 10s 5ms/step - loss: 0.0440 - root_mean_squared_error: 0.2097 - val_loss: 0.0520 - val_root_mean_squared_error: 0.2280\n","Epoch 61/500\n","2136/2144 [============================>.] - ETA: 0s - loss: 0.0438 - root_mean_squared_error: 0.2093\n","Epoch 61: val_root_mean_squared_error did not improve from 0.22782\n","2144/2144 [==============================] - 10s 4ms/step - loss: 0.0438 - root_mean_squared_error: 0.2092 - val_loss: 0.0533 - val_root_mean_squared_error: 0.2310\n","Epoch 62/500\n","2143/2144 [============================>.] - ETA: 0s - loss: 0.0434 - root_mean_squared_error: 0.2083\n","Epoch 62: val_root_mean_squared_error did not improve from 0.22782\n","2144/2144 [==============================] - 10s 4ms/step - loss: 0.0434 - root_mean_squared_error: 0.2083 - val_loss: 0.0532 - val_root_mean_squared_error: 0.2306\n","Epoch 63/500\n","2137/2144 [============================>.] - ETA: 0s - loss: 0.0432 - root_mean_squared_error: 0.2078\n","Epoch 63: val_root_mean_squared_error did not improve from 0.22782\n","2144/2144 [==============================] - 10s 4ms/step - loss: 0.0432 - root_mean_squared_error: 0.2077 - val_loss: 0.0522 - val_root_mean_squared_error: 0.2284\n","Epoch 64/500\n","2132/2144 [============================>.] - ETA: 0s - loss: 0.0430 - root_mean_squared_error: 0.2073\n","Epoch 64: val_root_mean_squared_error did not improve from 0.22782\n","2144/2144 [==============================] - 10s 4ms/step - loss: 0.0430 - root_mean_squared_error: 0.2073 - val_loss: 0.0519 - val_root_mean_squared_error: 0.2278\n","Epoch 65/500\n","2137/2144 [============================>.] - ETA: 0s - loss: 0.0428 - root_mean_squared_error: 0.2069\n","Epoch 65: val_root_mean_squared_error did not improve from 0.22782\n","2144/2144 [==============================] - 10s 4ms/step - loss: 0.0428 - root_mean_squared_error: 0.2068 - val_loss: 0.0519 - val_root_mean_squared_error: 0.2279\n","Epoch 66/500\n","2132/2144 [============================>.] - ETA: 0s - loss: 0.0425 - root_mean_squared_error: 0.2062\n","Epoch 66: val_root_mean_squared_error improved from 0.22782 to 0.22696, saving model to model.h5\n","2144/2144 [==============================] - 10s 5ms/step - loss: 0.0425 - root_mean_squared_error: 0.2063 - val_loss: 0.0515 - val_root_mean_squared_error: 0.2270\n","Epoch 67/500\n","2144/2144 [==============================] - ETA: 0s - loss: 0.0424 - root_mean_squared_error: 0.2058\n","Epoch 67: val_root_mean_squared_error did not improve from 0.22696\n","2144/2144 [==============================] - 10s 4ms/step - loss: 0.0424 - root_mean_squared_error: 0.2058 - val_loss: 0.0551 - val_root_mean_squared_error: 0.2348\n","Epoch 68/500\n","2142/2144 [============================>.] - ETA: 0s - loss: 0.0421 - root_mean_squared_error: 0.2052\n","Epoch 68: val_root_mean_squared_error did not improve from 0.22696\n","2144/2144 [==============================] - 10s 4ms/step - loss: 0.0421 - root_mean_squared_error: 0.2052 - val_loss: 0.0552 - val_root_mean_squared_error: 0.2349\n","Epoch 69/500\n","2137/2144 [============================>.] - ETA: 0s - loss: 0.0418 - root_mean_squared_error: 0.2046\n","Epoch 69: val_root_mean_squared_error did not improve from 0.22696\n","2144/2144 [==============================] - 10s 4ms/step - loss: 0.0419 - root_mean_squared_error: 0.2046 - val_loss: 0.0518 - val_root_mean_squared_error: 0.2276\n","Epoch 70/500\n","2132/2144 [============================>.] - ETA: 0s - loss: 0.0417 - root_mean_squared_error: 0.2043\n","Epoch 70: val_root_mean_squared_error did not improve from 0.22696\n","2144/2144 [==============================] - 10s 4ms/step - loss: 0.0417 - root_mean_squared_error: 0.2042 - val_loss: 0.0538 - val_root_mean_squared_error: 0.2319\n","Epoch 71/500\n","2133/2144 [============================>.] - ETA: 0s - loss: 0.0416 - root_mean_squared_error: 0.2040\n","Epoch 71: val_root_mean_squared_error did not improve from 0.22696\n","2144/2144 [==============================] - 10s 4ms/step - loss: 0.0416 - root_mean_squared_error: 0.2041 - val_loss: 0.0516 - val_root_mean_squared_error: 0.2271\n","Epoch 72/500\n","2144/2144 [==============================] - ETA: 0s - loss: 0.0413 - root_mean_squared_error: 0.2033\n","Epoch 72: val_root_mean_squared_error did not improve from 0.22696\n","2144/2144 [==============================] - 10s 4ms/step - loss: 0.0413 - root_mean_squared_error: 0.2033 - val_loss: 0.0519 - val_root_mean_squared_error: 0.2277\n","Epoch 73/500\n","2141/2144 [============================>.] - ETA: 0s - loss: 0.0411 - root_mean_squared_error: 0.2028\n","Epoch 73: val_root_mean_squared_error improved from 0.22696 to 0.22683, saving model to model.h5\n","2144/2144 [==============================] - 10s 5ms/step - loss: 0.0411 - root_mean_squared_error: 0.2028 - val_loss: 0.0515 - val_root_mean_squared_error: 0.2268\n","Epoch 74/500\n","2144/2144 [==============================] - ETA: 0s - loss: 0.0409 - root_mean_squared_error: 0.2022\n","Epoch 74: val_root_mean_squared_error did not improve from 0.22683\n","2144/2144 [==============================] - 10s 4ms/step - loss: 0.0409 - root_mean_squared_error: 0.2022 - val_loss: 0.0519 - val_root_mean_squared_error: 0.2279\n","Epoch 75/500\n","2138/2144 [============================>.] - ETA: 0s - loss: 0.0406 - root_mean_squared_error: 0.2015\n","Epoch 75: val_root_mean_squared_error did not improve from 0.22683\n","2144/2144 [==============================] - 10s 4ms/step - loss: 0.0406 - root_mean_squared_error: 0.2015 - val_loss: 0.0521 - val_root_mean_squared_error: 0.2282\n","Epoch 76/500\n","2133/2144 [============================>.] - ETA: 0s - loss: 0.0405 - root_mean_squared_error: 0.2014\n","Epoch 76: val_root_mean_squared_error did not improve from 0.22683\n","2144/2144 [==============================] - 10s 4ms/step - loss: 0.0406 - root_mean_squared_error: 0.2014 - val_loss: 0.0585 - val_root_mean_squared_error: 0.2418\n","Epoch 77/500\n","2138/2144 [============================>.] - ETA: 0s - loss: 0.0403 - root_mean_squared_error: 0.2008\n","Epoch 77: val_root_mean_squared_error did not improve from 0.22683\n","2144/2144 [==============================] - 10s 4ms/step - loss: 0.0403 - root_mean_squared_error: 0.2008 - val_loss: 0.0519 - val_root_mean_squared_error: 0.2279\n","Epoch 78/500\n","2134/2144 [============================>.] - ETA: 0s - loss: 0.0402 - root_mean_squared_error: 0.2004\n","Epoch 78: val_root_mean_squared_error did not improve from 0.22683\n","2144/2144 [==============================] - 10s 5ms/step - loss: 0.0402 - root_mean_squared_error: 0.2004 - val_loss: 0.0518 - val_root_mean_squared_error: 0.2275\n","Epoch 79/500\n","2136/2144 [============================>.] - ETA: 0s - loss: 0.0399 - root_mean_squared_error: 0.1998\n","Epoch 79: val_root_mean_squared_error did not improve from 0.22683\n","2144/2144 [==============================] - 10s 5ms/step - loss: 0.0399 - root_mean_squared_error: 0.1998 - val_loss: 0.0523 - val_root_mean_squared_error: 0.2288\n","Epoch 80/500\n","2141/2144 [============================>.] - ETA: 0s - loss: 0.0396 - root_mean_squared_error: 0.1991\n","Epoch 80: val_root_mean_squared_error did not improve from 0.22683\n","2144/2144 [==============================] - 10s 4ms/step - loss: 0.0397 - root_mean_squared_error: 0.1991 - val_loss: 0.0575 - val_root_mean_squared_error: 0.2397\n","Epoch 81/500\n","2140/2144 [============================>.] - ETA: 0s - loss: 0.0397 - root_mean_squared_error: 0.1992\n","Epoch 81: val_root_mean_squared_error did not improve from 0.22683\n","2144/2144 [==============================] - 10s 4ms/step - loss: 0.0397 - root_mean_squared_error: 0.1992 - val_loss: 0.0554 - val_root_mean_squared_error: 0.2355\n","Epoch 82/500\n","2140/2144 [============================>.] - ETA: 0s - loss: 0.0395 - root_mean_squared_error: 0.1987\n","Epoch 82: val_root_mean_squared_error did not improve from 0.22683\n","2144/2144 [==============================] - 10s 4ms/step - loss: 0.0395 - root_mean_squared_error: 0.1986 - val_loss: 0.0612 - val_root_mean_squared_error: 0.2473\n","Epoch 83/500\n","2139/2144 [============================>.] - ETA: 0s - loss: 0.0392 - root_mean_squared_error: 0.1981Restoring model weights from the end of the best epoch: 73.\n","\n","Epoch 83: val_root_mean_squared_error did not improve from 0.22683\n","2144/2144 [==============================] - 10s 5ms/step - loss: 0.0392 - root_mean_squared_error: 0.1980 - val_loss: 0.0523 - val_root_mean_squared_error: 0.2286\n","Epoch 83: early stopping\n"]}]},{"cell_type":"markdown","source":["# Evaluate"],"metadata":{"id":"_JCkdWXb1BFY"}},{"cell_type":"code","source":["loss, root_mean_squared_error = model.evaluate(test_dataset)\n","\n","print(\"Loss: \", loss)\n","print(\"rmse: \", root_mean_squared_error)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"yGf4xn0v0-Jp","executionInfo":{"status":"ok","timestamp":1652316561298,"user_tz":420,"elapsed":1433,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"}},"outputId":"33672cd7-0c93-49c5-8d52-cdf5f588831b"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["460/460 [==============================] - 1s 3ms/step - loss: 0.0526 - root_mean_squared_error: 0.2293\n","Loss:  0.05255719646811485\n","rmse:  0.22925356030464172\n"]}]},{"cell_type":"markdown","source":["# Calibration Plot"],"metadata":{"id":"NBXRz9w6mZOj"}},{"cell_type":"code","source":["test_txt = []\n","test_labels = []\n","\n","for txt, label in test_dataset:\n","  for t in txt:\n","    test_txt.append(t.numpy())\n","  for l in label:\n","    test_labels.append(l.numpy())"],"metadata":{"id":"8YPDSzG1mb5u"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["test_txt = np.array(test_txt)\n","test_labels = np.array(test_labels)"],"metadata":{"id":"S3T71eHrmkvf"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["predicted = model.predict(test_txt)"],"metadata":{"id":"zEfwba1fmuFs"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["predicted"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"68o0PV_Tmw3L","executionInfo":{"status":"ok","timestamp":1652316580118,"user_tz":420,"elapsed":9,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"}},"outputId":"f1159c94-bfb1-45c8-9a12-9d86acee70f4"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[0.72793114],\n","       [0.61777604],\n","       [0.38514984],\n","       ...,\n","       [0.5513206 ],\n","       [0.8102438 ],\n","       [0.9178699 ]], dtype=float32)"]},"metadata":{},"execution_count":130}]},{"cell_type":"code","source":["y_true = test_labels"],"metadata":{"id":"LYbnBsgbm1bo"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["y_pred = predicted.transpose()[0]"],"metadata":{"id":"TsjvmZoQm5Go"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["from sklearn.calibration import calibration_curve\n","prob_true, prob_pred = calibration_curve(y_true, y_pred, n_bins=3)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":321},"id":"qkMd6T1tnR2T","executionInfo":{"status":"error","timestamp":1652316973159,"user_tz":420,"elapsed":249,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"}},"outputId":"44b54d05-af08-473e-e8cf-2bbed84de3de"},"execution_count":null,"outputs":[{"output_type":"error","ename":"ValueError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)","\u001b[0;32m<ipython-input-151-af256173830d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcalibration\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mcalibration_curve\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mprob_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprob_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcalibration_curve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_bins\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sklearn/calibration.py\u001b[0m in \u001b[0;36mcalibration_curve\u001b[0;34m(y_true, y_prob, normalize, n_bins, strategy)\u001b[0m\n\u001b[1;32m    946\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    947\u001b[0m         raise ValueError(\n\u001b[0;32m--> 948\u001b[0;31m             \u001b[0;34m\"Only binary classification is supported. Provided labels %s.\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    949\u001b[0m         )\n\u001b[1;32m    950\u001b[0m     \u001b[0my_true\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlabel_binarize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclasses\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mValueError\u001b[0m: Only binary classification is supported. Provided labels [0.00000000e+00 4.08496732e-04 4.84496124e-04 ... 9.99874797e-01\n 9.99889564e-01 1.00000000e+00]."]}]},{"cell_type":"markdown","source":["# Plot"],"metadata":{"id":"OWBmMXQE2sME"}},{"cell_type":"code","source":["history_dict = history.history\n","history_dict.keys()\n","\n","rmse = history_dict['root_mean_squared_error']\n","val_rmse = history_dict['val_root_mean_squared_error']\n","\n","loss = history_dict['loss']\n","val_loss = history_dict['val_loss']\n","\n","epochs = range(1, len(rmse) + 1)\n","\n","# \"bo\" is for \"blue dot\"\n","plt.plot(epochs, loss, 'bo', label='Training loss')\n","# b is for \"solid blue line\"\n","plt.plot(epochs, val_loss, 'b', label='Validation loss')\n","plt.title('Training and validation loss')\n","plt.xlabel('Epochs')\n","plt.ylabel('Loss')\n","plt.legend()\n","\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":295},"id":"87Din5sb2rcz","executionInfo":{"status":"ok","timestamp":1652316683056,"user_tz":420,"elapsed":337,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"}},"outputId":"ad081787-70be-49f9-a2cc-c565b11c761a"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":["plt.plot(epochs, rmse, 'bo', label='Training rmse')\n","plt.plot(epochs, val_rmse, 'b', label='Validation rmse')\n","plt.title('Training and validation rmse')\n","plt.xlabel('Epochs')\n","plt.ylabel('RMSE')\n","plt.legend(loc='lower right')\n","\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":295},"id":"ZzDkFeRF29JV","executionInfo":{"status":"ok","timestamp":1652316686119,"user_tz":420,"elapsed":343,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"}},"outputId":"7a1ca8fc-cd6e-4053-dd38-5ea6dbd7075a"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":["# Save model"],"metadata":{"id":"y_6F7LUG3CI3"}},{"cell_type":"code","source":["model.save('saved_model/naampy_rmse')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"eLa07ERV3A7y","executionInfo":{"status":"ok","timestamp":1652316689956,"user_tz":420,"elapsed":1491,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"}},"outputId":"412d0851-a449-4953-9dad-336b24e00bf2"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["INFO:tensorflow:Assets written to: saved_model/naampy_rmse/assets\n"]}]},{"cell_type":"code","source":["df[df['first_name'] == 'katrina']"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":49},"id":"UJi9URS28zhM","executionInfo":{"status":"ok","timestamp":1652316689957,"user_tz":420,"elapsed":7,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"}},"outputId":"4c4c663e-da1c-4cf7-cb0d-7309818a272f"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Empty DataFrame\n","Columns: [first_name, gender, female_prop]\n","Index: []"],"text/html":["\n","  <div id=\"df-229acddf-d9ad-4d39-b54c-0cfa7fd99599\">\n","    <div class=\"colab-df-container\">\n","      <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>first_name</th>\n","      <th>gender</th>\n","      <th>female_prop</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","  </tbody>\n","</table>\n","</div>\n","      <button 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\"shobby\",\n","         \"tamannaah bhatia\",\n","         \"mehreen\",\n","         \"kiara\",\n","         \"shivathmika\",\n","         \"komalee\",\n","         \"nazriya\",\n","         \"nabha\",\n","         \"taapsee\",\n","         \"parineeti\",\n","         \"katrina\",\n","         \"ileana\",\n","         \"vishwaksen\",\n","         \"sampoornesh\",\n","         \"hrithik\",\n","         \"emraan\",\n","         \"rajkummar\",\n","         \"sharman\",\n","         \"ayushmann\",\n","         \"irrfan\",\n","         \"riteish\"\n","]\n","\n","sequences = tk.texts_to_sequences(input)\n","final = pad_sequences(sequences, maxlen=24, padding='post')\n","print(final)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"q4FDgTlG3sBA","executionInfo":{"status":"ok","timestamp":1652316692063,"user_tz":420,"elapsed":265,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"}},"outputId":"12ae1109-9e4b-4708-9ef2-dbe1fa2f3b54"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["[[18  1 10  9 14  9 11  1 14 20  8  0  0  0  0  0  0  0  0  0  0  0  0  0]\n"," [ 8  1 18 22  9 14  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n"," [19  8 25  1 13 19  9 14  7  8  1  0  0  0  0  0  0  0  0  0  0  0  0  0]\n"," [11  1 13  1 12  8  1 19  1 14  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n"," [19 18  9  8  1 14  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n"," [20  8  1 13 13  1 13  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n"," [ 2  1  8 21  2  1 12  9  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n"," [18  1 10  1 18  1 10  5 19  8 23  1 18  9  0  0  0  0  0  0  0  0  0  0]\n"," [19  8 15  2  2 25  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n"," [20  1 13  1 14 14  1  1  8 27  2  8  1 20  9  1  0  0  0  0  0  0  0  0]\n"," [13  5  8 18  5  5 14  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n"," [11  9  1 18  1  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n"," [19  8  9 22  1 20  8 13  9 11  1  0  0  0  0  0  0  0  0  0  0  0  0  0]\n"," [11 15 13  1 12  5  5  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n"," [14  1 26 18  9 25  1  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n"," [14  1  2  8  1  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n"," [20  1  1 16 19  5  5  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n"," [16  1 18  9 14  5  5 20  9  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n"," [11  1 20 18  9 14  1  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n"," [ 9 12  5  1 14  1  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n"," [22  9 19  8 23  1 11 19  5 14  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n"," [19  1 13 16 15 15 18 14  5 19  8  0  0  0  0  0  0  0  0  0  0  0  0  0]\n"," [ 8 18  9 20  8  9 11  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n"," [ 5 13 18  1  1 14  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n"," [18  1 10 11 21 13 13  1 18  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n"," [19  8  1 18 13  1 14  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n"," [ 1 25 21 19  8 13  1 14 14  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n"," [ 9 18 18  6  1 14  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n"," [18  9 20  5  9 19  8  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]]\n"]}]},{"cell_type":"markdown","source":[""],"metadata":{"id":"5MmPcOo51ZVo"}},{"cell_type":"code","source":["results = model.predict(final)"],"metadata":{"id":"64oeygda4F7p"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["results"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"oWc1HnNh4Ka5","executionInfo":{"status":"ok","timestamp":1652316694989,"user_tz":420,"elapsed":2,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"}},"outputId":"3cf2a616-3583-41db-befe-7fdd9c747549"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[0.00525352],\n","       [0.15928783],\n","       [0.04309655],\n","       [0.65595573],\n","       [0.17445782],\n","       [0.5642862 ],\n","       [0.09884076],\n","       [0.94247806],\n","       [0.21168596],\n","       [0.9714784 ],\n","       [0.659633  ],\n","       [0.6141249 ],\n","       [0.74323916],\n","       [0.9010507 ],\n","       [0.8541673 ],\n","       [0.7550282 ],\n","       [0.6651763 ],\n","       [0.8132367 ],\n","       [0.63012594],\n","       [0.64033115],\n","       [0.00776255],\n","       [0.05969306],\n","       [0.07781889],\n","       [0.20403717],\n","       [0.15486132],\n","       [0.14146173],\n","       [0.035105  ],\n","       [0.16294746],\n","       [0.04924516]], dtype=float32)"]},"metadata":{},"execution_count":140}]},{"cell_type":"code","source":["results[0].item()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"rE9Ze_kdY4fO","executionInfo":{"status":"ok","timestamp":1652316698743,"user_tz":420,"elapsed":331,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"}},"outputId":"731c405d-818e-49e0-8fe9-042111201ba7"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.005253518465906382"]},"metadata":{},"execution_count":141}]},{"cell_type":"code","source":["for i in range(len(input)):\n","  pred = results[i].item()\n","  if  pred > 0.5:\n","    gender = 'female'\n","  else:\n","    gender = 'male'\n","  print(f\"{input[i]} : {gender} : {pred}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"hdsoB89x4j_O","executionInfo":{"status":"ok","timestamp":1652316699063,"user_tz":420,"elapsed":3,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"}},"outputId":"efc76bef-03fd-4299-8b94-779941daa3d3"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["rajinikanth : male : 0.005253518465906382\n","harvin : male : 0.15928782522678375\n","shyamsingha : male : 0.04309655353426933\n","kamalhasan : female : 0.6559557318687439\n","srihan : male : 0.17445781826972961\n","thammam : female : 0.5642861723899841\n","bahubali : male : 0.09884075820446014\n","rajarajeshwari : female : 0.9424780607223511\n","shobby : male : 0.21168595552444458\n","tamannaah bhatia : female : 0.9714784026145935\n","mehreen : female : 0.6596329808235168\n","kiara : female : 0.6141248941421509\n","shivathmika : female : 0.743239164352417\n","komalee : female : 0.9010506868362427\n","nazriya : female : 0.8541672825813293\n","nabha : female : 0.7550281882286072\n","taapsee : female : 0.665176272392273\n","parineeti : female : 0.8132367134094238\n","katrina : female : 0.6301259398460388\n","ileana : female : 0.6403311491012573\n","vishwaksen : male : 0.007762552239000797\n","sampoornesh : male : 0.05969305709004402\n","hrithik : male : 0.07781888544559479\n","emraan : male : 0.20403717458248138\n","rajkummar : male : 0.15486131608486176\n","sharman : male : 0.14146173000335693\n","ayushmann : male : 0.03510500118136406\n","irrfan : male : 0.16294746100902557\n","riteish : male : 0.049245160073041916\n"]}]},{"cell_type":"markdown","source":["# load and test the model"],"metadata":{"id":"iupaxnOZ6tM0"}},{"cell_type":"code","source":["import_model = tf.keras.models.load_model('saved_model/naampy_rmse')"],"metadata":{"id":"6bLpzk116LkO"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["import_model.summary()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Z4O-yl7o6_Tq","executionInfo":{"status":"ok","timestamp":1652316836147,"user_tz":420,"elapsed":304,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"}},"outputId":"f144556a-5aae-4528-fcff-9a2ef9e48c1f"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Model: \"model_8\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," input (InputLayer)          [(None, 24)]              0         \n","                                                                 \n"," embedding_3 (Embedding)     (None, 24, 27)            756       \n","                                                                 \n"," conv1d_24 (Conv1D)          (None, 18, 128)           24320     \n","                                                                 \n"," activation_24 (Activation)  (None, 18, 128)           0         \n","                                                                 \n"," max_pooling1d_8 (MaxPooling  (None, 6, 128)           0         \n"," 1D)                                                             \n","                                                                 \n"," conv1d_25 (Conv1D)          (None, 4, 128)            49280     \n","                                                                 \n"," activation_25 (Activation)  (None, 4, 128)            0         \n","                                                                 \n"," conv1d_26 (Conv1D)          (None, 2, 128)            49280     \n","                                                                 \n"," activation_26 (Activation)  (None, 2, 128)            0         \n","                                                                 \n"," flatten_8 (Flatten)         (None, 256)               0         \n","                                                                 \n"," dense_24 (Dense)            (None, 512)               131584    \n","                                                                 \n"," dropout_16 (Dropout)        (None, 512)               0         \n","                                                                 \n"," dense_25 (Dense)            (None, 512)               262656    \n","                                                                 \n"," dropout_17 (Dropout)        (None, 512)               0         \n","                                                                 \n"," dense_26 (Dense)            (None, 1)                 513       \n","                                                                 \n","=================================================================\n","Total params: 518,389\n","Trainable params: 518,389\n","Non-trainable params: 0\n","_________________________________________________________________\n"]}]},{"cell_type":"code","source":["results = import_model.predict(final)\n","for i in range(len(input)):\n","  pred = results[i].item()\n","  if  pred > 0.5:\n","    gender = 'female'\n","  else:\n","    gender = 'male'\n","  print(f\"{input[i]} : {gender} : {pred}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"pvmuZvDy7BNz","executionInfo":{"status":"ok","timestamp":1652316839983,"user_tz":420,"elapsed":241,"user":{"displayName":"Rajashekar Chintalapati","userId":"03596288833202137831"}},"outputId":"a35145f6-f0d3-416d-b6f5-e1c976933ce4"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["rajinikanth : male : 0.005253518465906382\n","harvin : male : 0.15928782522678375\n","shyamsingha : male : 0.04309655353426933\n","kamalhasan : female : 0.6559557318687439\n","srihan : male : 0.17445781826972961\n","thammam : female : 0.5642861723899841\n","bahubali : male : 0.09884075820446014\n","rajarajeshwari : female : 0.9424780607223511\n","shobby : male : 0.21168595552444458\n","tamannaah bhatia : female : 0.9714784026145935\n","mehreen : female : 0.6596329808235168\n","kiara : female : 0.6141248941421509\n","shivathmika : female : 0.743239164352417\n","komalee : female : 0.9010506868362427\n","nazriya : female : 0.8541672825813293\n","nabha : female : 0.7550281882286072\n","taapsee : female : 0.665176272392273\n","parineeti : female : 0.8132367134094238\n","katrina : female : 0.6301259398460388\n","ileana : female : 0.6403311491012573\n","vishwaksen : male : 0.007762552239000797\n","sampoornesh : male : 0.05969305709004402\n","hrithik : male : 0.07781888544559479\n","emraan : male : 0.20403717458248138\n","rajkummar : male : 0.15486131608486176\n","sharman : male : 0.14146173000335693\n","ayushmann : male : 0.03510500118136406\n","irrfan : male : 0.16294746100902557\n","riteish : male : 0.049245160073041916\n"]}]},{"cell_type":"code","source":[""],"metadata":{"id":"RZbQjSEO7Hke"},"execution_count":null,"outputs":[]}]}