Tensor_flow_LSTM_XGBoost.ipynb

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      "[nltk_data] Downloading package omw-1.4 to /root/nltk_data...\n",
      "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
      "[nltk_data]   Unzipping corpora/stopwords.zip.\n",
      "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
      "[nltk_data] Downloading package words to /root/nltk_data...\n",
      "[nltk_data]   Unzipping corpora/words.zip.\n"
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   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import string\n",
    "import re\n",
    "import tensorflow as tf \n",
    "\n",
    "import string\n",
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.stem import PorterStemmer\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "import re\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "from nltk.corpus import stopwords\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.multioutput import MultiOutputRegressor\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.metrics import mean_squared_error as rmse\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "nltk.download('omw-1.4')\n",
    "nltk.download('stopwords')\n",
    "nltk.download('wordnet')\n",
    "nltk.download('words')"
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    "### 1. Importing the data: \n",
    "We will be using the Feedback Prize dataset from kaggle. The dataset contains small 2-3 sentences and their relative vocabulary and grammar scores.\n",
    "\n",
    "The dataset contains small essays written by 8th-12th grade English language learners. The essays have been scored according to the analytic measures like vocabulary and grammar. Each measure represents a component of proficiency in essay writing, with greater scores corresponding to greater proficiency in that measure. The scores range from 1.0 to 5.0 in increments of 0.5. We are going to build a model to predict the score for the essays given in the test set."
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       "      <td>Dear, Principal\\n\\nIf u change the school poli...</td>\n",
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       "      <th>4</th>\n",
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       "        text_id                                          full_text  cohesion  \\\n",
       "0  0016926B079C  I think that students would benefit from learn...       3.5   \n",
       "1  0022683E9EA5  When a problem is a change you have to let it ...       2.5   \n",
       "2  00299B378633  Dear, Principal\\n\\nIf u change the school poli...       3.0   \n",
       "3  003885A45F42  The best time in life is when you become yours...       4.5   \n",
       "4  0049B1DF5CCC  Small act of kindness can impact in other peop...       2.5   \n",
       "\n",
       "   syntax  vocabulary  phraseology  grammar  conventions  \n",
       "0     3.5         3.0          3.0      4.0          3.0  \n",
       "1     2.5         3.0          2.0      2.0          2.5  \n",
       "2     3.5         3.0          3.0      3.0          2.5  \n",
       "3     4.5         4.5          4.5      4.0          5.0  \n",
       "4     3.0         3.0          3.0      2.5          2.5  "
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   "source": [
    "df = pd.read_csv('/content/train_nlp.csv')\n",
    "df.head()"
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   "source": [
    "### 2. Visualizing the data:"
   ]
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\n",
      "text/plain": [
       "<Figure size 1080x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "features = ['vocabulary', 'grammar']\n",
    "target = df[features]\n",
    "fig, axes = plt.subplots(2, len(features)//2, figsize=(15,6))\n",
    "\n",
    "for i, feature in enumerate(features):\n",
    "    ax = axes.flat[i]\n",
    "    sns.histplot(x=feature, data=df, linewidth=1.25, alpha=1, ax=ax, zorder=2)\n",
    "    ax.set_title(feature)\n",
    "    ax.set(xlabel=None, ylabel=None)\n",
    "    \n",
    "fig.suptitle('Output Variables')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Nps1u3r86fYQ"
   },
   "source": [
    "### 3. Preprocessing the text data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "dDBEoYhF6fYQ"
   },
   "outputs": [],
   "source": [
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "stopword = nltk.corpus.stopwords.words('english')\n",
    "wn = nltk.WordNetLemmatizer()\n",
    "ps = nltk.PorterStemmer()\n",
    "words = set(nltk.corpus.words.words())\n",
    "\n",
    "def clean_text(text) : \n",
    "    text = text.lower()\n",
    "    text = re.sub(r'https?://\\S+|www\\.\\S+','', text)\n",
    "    text = re.sub(r'@[0-9a-zA-Z]*\\W+',' ' , text)\n",
    "    text = re.sub(r'\\d+', ' ', text)\n",
    "    text = re.sub(r'\\#', ' ', text)\n",
    "    text = re.sub(r'\\'', ' ', text)\n",
    "        \n",
    "    text_links_removed = \"\".join([char for char in text if char not in string.punctuation])\n",
    "    text_cleaned = \" \".join([word for word in re.split('\\W+', text_links_removed)\n",
    "        if word not in stopword])\n",
    "    text = \" \".join([wn.lemmatize(word) for word in re.split('\\W+', text_cleaned)])\n",
    "    return text\n",
    "\n",
    "df['full_text'] = df['full_text'].apply(lambda text: clean_text(text))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 112
    },
    "id": "N99_loQ46fYR",
    "outputId": "3134118c-2e84-4349-8610-522a839e16b6"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_4aab5_row0_col0, #T_4aab5_row1_col1 {\n",
       "  background-color: #b40426;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_4aab5_row0_col1, #T_4aab5_row1_col0 {\n",
       "  background-color: #3b4cc0;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_4aab5_\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" >vocabulary</th>\n",
       "      <th class=\"col_heading level0 col1\" >grammar</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_4aab5_level0_row0\" class=\"row_heading level0 row0\" >vocabulary</th>\n",
       "      <td id=\"T_4aab5_row0_col0\" class=\"data row0 col0\" >1.000000</td>\n",
       "      <td id=\"T_4aab5_row0_col1\" class=\"data row0 col1\" >0.654852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4aab5_level0_row1\" class=\"row_heading level0 row1\" >grammar</th>\n",
       "      <td id=\"T_4aab5_row1_col0\" class=\"data row1 col0\" >0.654852</td>\n",
       "      <td id=\"T_4aab5_row1_col1\" class=\"data row1 col1\" >1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f06ba3d5be0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# !pip install jinja2\n",
    "corr = target.corr()\n",
    "corr.style.background_gradient(cmap='coolwarm')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EhIghHzm6fYS"
   },
   "source": [
    "### 4. Finding the frequencies, And Splitting the data to train and test:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Mt1AwIOY6fYS",
    "outputId": "911c0133-7b1f-465a-da59-51caf53b2939"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['abandon' 'abandoned' 'abaut' 'abble' 'abc']\n",
      "shape of vectorized train data: (3128, 8405)\n",
      "shape of vectorized test data:  (783, 8405)\n"
     ]
    }
   ],
   "source": [
    "tfidf = TfidfVectorizer(max_df=0.98, min_df=2, max_features=10000)\n",
    "X = tfidf.fit_transform(df['full_text'])\n",
    "\n",
    "y = target\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,  y, test_size = 0.20, random_state=42)\n",
    "\n",
    "print(tfidf.get_feature_names_out()[:5])\n",
    "print(\"shape of vectorized train data:\",X_train.shape)\n",
    "print(\"shape of vectorized test data: \", X_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Lzt7QUfa6fYT"
   },
   "source": [
    "### 5a. SVM Model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "j5cKyI6m6fYT",
    "outputId": "803622a3-05db-471c-e8e1-27dab5bc0646"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8999762422574491\n"
     ]
    }
   ],
   "source": [
    "svr = SVR()\n",
    "svm_model = MultiOutputRegressor(svr)\n",
    "svm_model.fit(X_train, y_train)\n",
    "print(svm_model.score(X_train, y_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "nG-IIo9R6fYT",
    "outputId": "b0dc7078-8c45-4b82-c87e-a152e829e88d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE for SVM regressor: 0.3128996998251533\n",
      "RMSE for LSTM NN (vocabulary): 0.24888269090634108\n",
      "RMSE for LSTM NN (grammar): 0.3769167087439656\n"
     ]
    }
   ],
   "source": [
    "y_pred_svm = svm_model.predict(X_test)\n",
    "# Evaluate the regressor\n",
    "svm_root_mean_squared_error = rmse(y_test, y_pred_svm)\n",
    "print(f'MSE for SVM regressor: {svm_root_mean_squared_error}')\n",
    "\n",
    "print(f'''RMSE for LSTM NN (vocabulary): {rmse(y_test['vocabulary'], y_pred_svm[:,0])}''')\n",
    "\n",
    "print(f'''RMSE for LSTM NN (grammar): {rmse(y_test['grammar'], y_pred_svm[:,1])}''')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VIP4zSmg6fYU"
   },
   "source": [
    "### 5b. Neural Network Model (no LSTM)\n",
    "\n",
    "#### a. build the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "nqEQNIdD6fYU"
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "tf.random.set_seed(42)\n",
    "\n",
    "\n",
    "# nonlinear model (without LSTM)\n",
    "def build_nn_model(X):\n",
    "\n",
    "  # layer 1 : input layer\n",
    "  inp = tf.keras.Input((X.shape[1],))\n",
    "\n",
    "  # dense layer, high regularization parameter, relu activation\n",
    "  x = tf.keras.layers.Dense(units=1024, activation='relu', kernel_regularizer=tf.keras.regularizers.L2(0.1))(inp)\n",
    "\n",
    "  # large dropout layer\n",
    "  x = tf.keras.layers.Dropout(0.7)(x)\n",
    "\n",
    "  # dense layer with slightly lower regularization parameter and relu activation\n",
    "  x = tf.keras.layers.Dense(units=512, activation='relu', kernel_regularizer=tf.keras.regularizers.L2(0.08))(x)\n",
    "\n",
    "  # another dropout layer\n",
    "  x = tf.keras.layers.Dropout(0.6)(x)\n",
    "\n",
    "  # dense layer with smaller regularization parameter and relu activation\n",
    "  x = tf.keras.layers.Dense(units=256, activation='relu', kernel_regularizer=tf.keras.regularizers.L2(0.05))(x)\n",
    "\n",
    "  # another dropout layer\n",
    "  x = tf.keras.layers.Dropout(0.5)(x)\n",
    "\n",
    "  # dense layer with the same regularization paramter and relu activation\n",
    "  x = tf.keras.layers.Dense(units=128, activation='relu', kernel_regularizer=tf.keras.regularizers.L2(0.05))(x)\n",
    "\n",
    "  # one last dropout layer\n",
    "  x = tf.keras.layers.Dropout(0.4)(x)\n",
    "\n",
    "  predictions = tf.keras.layers.Dense(units=1, activation=\"relu\", name=\"predictions\")(x)\n",
    "\n",
    "  model = tf.keras.Model(inputs=inp, outputs=predictions)\n",
    "\n",
    "  model.compile(loss=\"mean_squared_error\", optimizer=\"adam\", metrics=tf.keras.metrics.RootMeanSquaredError())\n",
    "\n",
    "  return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "c3YGJQzf6fYU"
   },
   "source": [
    "#### b. fit the model on the training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "iMn_9IdI6fYU",
    "outputId": "d66577af-3519-486b-a544-e0560b4ce59d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-12-04 23:54:18.421170: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "49/49 [==============================] - ETA: 0s - loss: 64.5826 - root_mean_squared_error: 1.5489"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-12-04 23:54:22.256353: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "49/49 [==============================] - 5s 82ms/step - loss: 64.5826 - root_mean_squared_error: 1.5489 - val_loss: 10.7094 - val_root_mean_squared_error: 0.8162\n",
      "Epoch 2/100\n",
      "49/49 [==============================] - 3s 55ms/step - loss: 5.0707 - root_mean_squared_error: 0.6868 - val_loss: 2.6815 - val_root_mean_squared_error: 0.8248\n",
      "Epoch 3/100\n",
      "49/49 [==============================] - 3s 52ms/step - loss: 1.7966 - root_mean_squared_error: 0.6886 - val_loss: 1.5215 - val_root_mean_squared_error: 0.8121\n",
      "Epoch 4/100\n",
      "49/49 [==============================] - 3s 56ms/step - loss: 1.1601 - root_mean_squared_error: 0.7002 - val_loss: 1.1716 - val_root_mean_squared_error: 0.8033\n",
      "Epoch 5/100\n",
      "49/49 [==============================] - 3s 64ms/step - loss: 0.9442 - root_mean_squared_error: 0.7097 - val_loss: 1.0036 - val_root_mean_squared_error: 0.7933\n",
      "Epoch 6/100\n",
      "49/49 [==============================] - 3s 61ms/step - loss: 0.8238 - root_mean_squared_error: 0.7116 - val_loss: 1.0023 - val_root_mean_squared_error: 0.8581\n",
      "Epoch 7/100\n",
      "49/49 [==============================] - 3s 58ms/step - loss: 0.7494 - root_mean_squared_error: 0.7149 - val_loss: 0.8327 - val_root_mean_squared_error: 0.7912\n",
      "Epoch 8/100\n",
      "49/49 [==============================] - 3s 59ms/step - loss: 0.7090 - root_mean_squared_error: 0.7224 - val_loss: 0.7712 - val_root_mean_squared_error: 0.7782\n",
      "Epoch 9/100\n",
      "49/49 [==============================] - 3s 59ms/step - loss: 0.6661 - root_mean_squared_error: 0.7205 - val_loss: 0.7632 - val_root_mean_squared_error: 0.7971\n",
      "Epoch 10/100\n",
      "49/49 [==============================] - 3s 59ms/step - loss: 0.6269 - root_mean_squared_error: 0.7147 - val_loss: 0.6632 - val_root_mean_squared_error: 0.7437\n",
      "Epoch 11/100\n",
      "49/49 [==============================] - 2s 48ms/step - loss: 0.6155 - root_mean_squared_error: 0.7188 - val_loss: 0.8233 - val_root_mean_squared_error: 0.8603\n",
      "Epoch 12/100\n",
      "49/49 [==============================] - 3s 52ms/step - loss: 0.6022 - root_mean_squared_error: 0.7226 - val_loss: 0.5956 - val_root_mean_squared_error: 0.7191\n",
      "Epoch 13/100\n",
      "49/49 [==============================] - 2s 51ms/step - loss: 0.5796 - root_mean_squared_error: 0.7154 - val_loss: 0.7023 - val_root_mean_squared_error: 0.8027\n",
      "Epoch 14/100\n",
      "49/49 [==============================] - 3s 54ms/step - loss: 0.5746 - root_mean_squared_error: 0.7186 - val_loss: 0.5600 - val_root_mean_squared_error: 0.7062\n",
      "Epoch 15/100\n",
      "49/49 [==============================] - 3s 52ms/step - loss: 0.5644 - root_mean_squared_error: 0.7157 - val_loss: 0.6039 - val_root_mean_squared_error: 0.7459\n",
      "Epoch 16/100\n",
      "49/49 [==============================] - 3s 51ms/step - loss: 0.5624 - root_mean_squared_error: 0.7194 - val_loss: 0.5986 - val_root_mean_squared_error: 0.7468\n",
      "Epoch 17/100\n",
      "49/49 [==============================] - 3s 52ms/step - loss: 0.5577 - root_mean_squared_error: 0.7191 - val_loss: 0.6485 - val_root_mean_squared_error: 0.7831\n",
      "Epoch 18/100\n",
      "49/49 [==============================] - 3s 52ms/step - loss: 0.5554 - root_mean_squared_error: 0.7204 - val_loss: 0.6346 - val_root_mean_squared_error: 0.7775\n",
      "Epoch 19/100\n",
      "49/49 [==============================] - 3s 53ms/step - loss: 0.5383 - root_mean_squared_error: 0.7123 - val_loss: 0.5471 - val_root_mean_squared_error: 0.7194\n",
      "Epoch 20/100\n",
      "49/49 [==============================] - 3s 59ms/step - loss: 0.5323 - root_mean_squared_error: 0.7101 - val_loss: 0.5258 - val_root_mean_squared_error: 0.7037\n",
      "Epoch 21/100\n",
      "49/49 [==============================] - 3s 54ms/step - loss: 0.5323 - root_mean_squared_error: 0.7111 - val_loss: 0.5639 - val_root_mean_squared_error: 0.7357\n",
      "Epoch 22/100\n",
      "49/49 [==============================] - 3s 60ms/step - loss: 0.5329 - root_mean_squared_error: 0.7131 - val_loss: 0.5455 - val_root_mean_squared_error: 0.7217\n",
      "Epoch 23/100\n",
      "49/49 [==============================] - 3s 58ms/step - loss: 0.5246 - root_mean_squared_error: 0.7091 - val_loss: 0.5325 - val_root_mean_squared_error: 0.7147\n",
      "Epoch 24/100\n",
      "49/49 [==============================] - 3s 59ms/step - loss: 0.5275 - root_mean_squared_error: 0.7119 - val_loss: 0.5169 - val_root_mean_squared_error: 0.7037\n",
      "Epoch 25/100\n",
      "49/49 [==============================] - 3s 56ms/step - loss: 0.5211 - root_mean_squared_error: 0.7089 - val_loss: 0.5384 - val_root_mean_squared_error: 0.7208\n",
      "Epoch 26/100\n",
      "49/49 [==============================] - 3s 66ms/step - loss: 0.5247 - root_mean_squared_error: 0.7115 - val_loss: 0.5440 - val_root_mean_squared_error: 0.7257\n",
      "Epoch 27/100\n",
      "49/49 [==============================] - 3s 59ms/step - loss: 0.5250 - root_mean_squared_error: 0.7131 - val_loss: 0.5126 - val_root_mean_squared_error: 0.7033\n",
      "Epoch 28/100\n",
      "49/49 [==============================] - 3s 56ms/step - loss: 0.5213 - root_mean_squared_error: 0.7108 - val_loss: 0.5154 - val_root_mean_squared_error: 0.7073\n",
      "Epoch 29/100\n",
      "49/49 [==============================] - 3s 59ms/step - loss: 0.5225 - root_mean_squared_error: 0.7118 - val_loss: 0.5659 - val_root_mean_squared_error: 0.7428\n",
      "Epoch 30/100\n",
      "49/49 [==============================] - 3s 59ms/step - loss: 0.5249 - root_mean_squared_error: 0.7138 - val_loss: 0.5363 - val_root_mean_squared_error: 0.7226\n",
      "Epoch 31/100\n",
      "49/49 [==============================] - 3s 53ms/step - loss: 0.5235 - root_mean_squared_error: 0.7132 - val_loss: 0.5138 - val_root_mean_squared_error: 0.7060\n",
      "Epoch 32/100\n",
      "49/49 [==============================] - 2s 48ms/step - loss: 0.5133 - root_mean_squared_error: 0.7069 - val_loss: 0.5115 - val_root_mean_squared_error: 0.7059\n",
      "Epoch 33/100\n",
      "49/49 [==============================] - 3s 54ms/step - loss: 0.5166 - root_mean_squared_error: 0.7095 - val_loss: 0.5056 - val_root_mean_squared_error: 0.7010\n",
      "Epoch 34/100\n",
      "49/49 [==============================] - 2s 44ms/step - loss: 0.5118 - root_mean_squared_error: 0.7070 - val_loss: 0.5075 - val_root_mean_squared_error: 0.7043\n",
      "Epoch 35/100\n",
      "49/49 [==============================] - 3s 54ms/step - loss: 0.5232 - root_mean_squared_error: 0.7149 - val_loss: 0.5081 - val_root_mean_squared_error: 0.7038\n",
      "Epoch 36/100\n",
      "49/49 [==============================] - 3s 55ms/step - loss: 0.5180 - root_mean_squared_error: 0.7109 - val_loss: 0.5689 - val_root_mean_squared_error: 0.7458\n",
      "Epoch 37/100\n",
      "49/49 [==============================] - 2s 49ms/step - loss: 0.5182 - root_mean_squared_error: 0.7104 - val_loss: 0.5034 - val_root_mean_squared_error: 0.7011\n",
      "Epoch 38/100\n",
      "49/49 [==============================] - 3s 57ms/step - loss: 0.5146 - root_mean_squared_error: 0.7094 - val_loss: 0.5460 - val_root_mean_squared_error: 0.7317\n",
      "Epoch 39/100\n",
      "49/49 [==============================] - 2s 51ms/step - loss: 0.5126 - root_mean_squared_error: 0.7082 - val_loss: 0.5101 - val_root_mean_squared_error: 0.7066\n",
      "Epoch 40/100\n",
      "49/49 [==============================] - 3s 51ms/step - loss: 0.5125 - root_mean_squared_error: 0.7080 - val_loss: 0.5174 - val_root_mean_squared_error: 0.7119\n",
      "Epoch 41/100\n",
      "49/49 [==============================] - 2s 45ms/step - loss: 0.5155 - root_mean_squared_error: 0.7105 - val_loss: 0.5014 - val_root_mean_squared_error: 0.7004\n",
      "Epoch 42/100\n",
      "49/49 [==============================] - 3s 51ms/step - loss: 0.5183 - root_mean_squared_error: 0.7122 - val_loss: 0.5138 - val_root_mean_squared_error: 0.7086\n",
      "Epoch 43/100\n",
      "49/49 [==============================] - 3s 53ms/step - loss: 0.5139 - root_mean_squared_error: 0.7094 - val_loss: 0.5183 - val_root_mean_squared_error: 0.7128\n",
      "Epoch 44/100\n",
      "49/49 [==============================] - 3s 60ms/step - loss: 0.5118 - root_mean_squared_error: 0.7084 - val_loss: 0.5175 - val_root_mean_squared_error: 0.7120\n",
      "Epoch 45/100\n",
      "49/49 [==============================] - 3s 54ms/step - loss: 0.5146 - root_mean_squared_error: 0.7098 - val_loss: 0.5274 - val_root_mean_squared_error: 0.7194\n",
      "Epoch 46/100\n",
      "49/49 [==============================] - 3s 56ms/step - loss: 0.5132 - root_mean_squared_error: 0.7096 - val_loss: 0.5051 - val_root_mean_squared_error: 0.7038\n",
      "Epoch 47/100\n",
      "49/49 [==============================] - 3s 56ms/step - loss: 0.5113 - root_mean_squared_error: 0.7088 - val_loss: 0.5056 - val_root_mean_squared_error: 0.7052\n",
      "Epoch 48/100\n",
      "49/49 [==============================] - 3s 54ms/step - loss: 0.5106 - root_mean_squared_error: 0.7086 - val_loss: 0.5096 - val_root_mean_squared_error: 0.7076\n",
      "Epoch 49/100\n",
      "49/49 [==============================] - 3s 52ms/step - loss: 0.5051 - root_mean_squared_error: 0.7039 - val_loss: 0.5054 - val_root_mean_squared_error: 0.7041\n",
      "Epoch 50/100\n",
      "49/49 [==============================] - 3s 56ms/step - loss: 0.5109 - root_mean_squared_error: 0.7084 - val_loss: 0.5109 - val_root_mean_squared_error: 0.7086\n",
      "Epoch 51/100\n",
      "49/49 [==============================] - 2s 51ms/step - loss: 0.5096 - root_mean_squared_error: 0.7078 - val_loss: 0.5012 - val_root_mean_squared_error: 0.7021\n",
      "Epoch 52/100\n",
      "49/49 [==============================] - 3s 56ms/step - loss: 0.5071 - root_mean_squared_error: 0.7061 - val_loss: 0.5168 - val_root_mean_squared_error: 0.7128\n",
      "Epoch 53/100\n",
      "49/49 [==============================] - 3s 55ms/step - loss: 0.5116 - root_mean_squared_error: 0.7083 - val_loss: 0.5138 - val_root_mean_squared_error: 0.7095\n",
      "Epoch 54/100\n",
      "49/49 [==============================] - 3s 55ms/step - loss: 0.5095 - root_mean_squared_error: 0.7072 - val_loss: 0.5304 - val_root_mean_squared_error: 0.7230\n",
      "Epoch 55/100\n",
      "49/49 [==============================] - 3s 58ms/step - loss: 0.5103 - root_mean_squared_error: 0.7085 - val_loss: 0.5063 - val_root_mean_squared_error: 0.7051\n",
      "Epoch 56/100\n",
      "49/49 [==============================] - 3s 52ms/step - loss: 0.5112 - root_mean_squared_error: 0.7087 - val_loss: 0.5163 - val_root_mean_squared_error: 0.7123\n",
      "Epoch 57/100\n",
      "49/49 [==============================] - 2s 49ms/step - loss: 0.5083 - root_mean_squared_error: 0.7065 - val_loss: 0.5047 - val_root_mean_squared_error: 0.7037\n",
      "Epoch 58/100\n",
      "49/49 [==============================] - 2s 49ms/step - loss: 0.5105 - root_mean_squared_error: 0.7087 - val_loss: 0.4996 - val_root_mean_squared_error: 0.7011\n",
      "Epoch 59/100\n",
      "49/49 [==============================] - 3s 57ms/step - loss: 0.5079 - root_mean_squared_error: 0.7074 - val_loss: 0.5220 - val_root_mean_squared_error: 0.7174\n",
      "Epoch 60/100\n",
      "49/49 [==============================] - 2s 50ms/step - loss: 0.5100 - root_mean_squared_error: 0.7090 - val_loss: 0.5224 - val_root_mean_squared_error: 0.7181\n",
      "Epoch 61/100\n",
      "49/49 [==============================] - 3s 58ms/step - loss: 0.5129 - root_mean_squared_error: 0.7108 - val_loss: 0.5129 - val_root_mean_squared_error: 0.7108\n",
      "Epoch 62/100\n",
      "49/49 [==============================] - 3s 55ms/step - loss: 0.5065 - root_mean_squared_error: 0.7061 - val_loss: 0.5227 - val_root_mean_squared_error: 0.7172\n",
      "Epoch 63/100\n",
      "49/49 [==============================] - 3s 57ms/step - loss: 0.5056 - root_mean_squared_error: 0.7054 - val_loss: 0.5169 - val_root_mean_squared_error: 0.7137\n",
      "Epoch 64/100\n",
      "49/49 [==============================] - 3s 56ms/step - loss: 0.5095 - root_mean_squared_error: 0.7084 - val_loss: 0.5044 - val_root_mean_squared_error: 0.7055\n",
      "Epoch 65/100\n",
      "49/49 [==============================] - 2s 48ms/step - loss: 0.4994 - root_mean_squared_error: 0.7026 - val_loss: 0.5023 - val_root_mean_squared_error: 0.7046\n",
      "Epoch 66/100\n",
      "49/49 [==============================] - 3s 54ms/step - loss: 0.5090 - root_mean_squared_error: 0.7091 - val_loss: 0.5168 - val_root_mean_squared_error: 0.7149\n",
      "Epoch 67/100\n",
      "49/49 [==============================] - 3s 52ms/step - loss: 0.5089 - root_mean_squared_error: 0.7094 - val_loss: 0.5049 - val_root_mean_squared_error: 0.7063\n",
      "Epoch 68/100\n",
      "49/49 [==============================] - 3s 58ms/step - loss: 0.5088 - root_mean_squared_error: 0.7089 - val_loss: 0.5503 - val_root_mean_squared_error: 0.7375\n",
      "Epoch 69/100\n",
      "49/49 [==============================] - 3s 56ms/step - loss: 0.5065 - root_mean_squared_error: 0.7067 - val_loss: 0.5064 - val_root_mean_squared_error: 0.7068\n",
      "Epoch 70/100\n",
      "49/49 [==============================] - 3s 63ms/step - loss: 0.5044 - root_mean_squared_error: 0.7051 - val_loss: 0.5004 - val_root_mean_squared_error: 0.7019\n",
      "Epoch 71/100\n",
      "49/49 [==============================] - 3s 56ms/step - loss: 0.5085 - root_mean_squared_error: 0.7082 - val_loss: 0.5323 - val_root_mean_squared_error: 0.7253\n",
      "Epoch 72/100\n",
      "49/49 [==============================] - 3s 53ms/step - loss: 0.5045 - root_mean_squared_error: 0.7053 - val_loss: 0.5074 - val_root_mean_squared_error: 0.7076\n",
      "Epoch 73/100\n",
      "49/49 [==============================] - 2s 48ms/step - loss: 0.5065 - root_mean_squared_error: 0.7066 - val_loss: 0.5121 - val_root_mean_squared_error: 0.7100\n",
      "Epoch 74/100\n",
      "49/49 [==============================] - 2s 50ms/step - loss: 0.5099 - root_mean_squared_error: 0.7090 - val_loss: 0.5073 - val_root_mean_squared_error: 0.7074\n",
      "Epoch 75/100\n",
      "49/49 [==============================] - 3s 58ms/step - loss: 0.5040 - root_mean_squared_error: 0.7051 - val_loss: 0.4925 - val_root_mean_squared_error: 0.6963\n",
      "Epoch 76/100\n",
      "49/49 [==============================] - 2s 50ms/step - loss: 0.5018 - root_mean_squared_error: 0.7039 - val_loss: 0.5076 - val_root_mean_squared_error: 0.7087\n",
      "Epoch 77/100\n",
      "49/49 [==============================] - 2s 50ms/step - loss: 0.5033 - root_mean_squared_error: 0.7055 - val_loss: 0.5345 - val_root_mean_squared_error: 0.7272\n",
      "Epoch 78/100\n",
      "49/49 [==============================] - 2s 49ms/step - loss: 0.5102 - root_mean_squared_error: 0.7097 - val_loss: 0.5090 - val_root_mean_squared_error: 0.7091\n",
      "Epoch 79/100\n",
      "49/49 [==============================] - 3s 57ms/step - loss: 0.5061 - root_mean_squared_error: 0.7069 - val_loss: 0.5029 - val_root_mean_squared_error: 0.7044\n",
      "Epoch 80/100\n",
      "49/49 [==============================] - 3s 54ms/step - loss: 0.5072 - root_mean_squared_error: 0.7077 - val_loss: 0.4921 - val_root_mean_squared_error: 0.6970\n",
      "Epoch 81/100\n",
      "49/49 [==============================] - 2s 50ms/step - loss: 0.5026 - root_mean_squared_error: 0.7048 - val_loss: 0.5248 - val_root_mean_squared_error: 0.7207\n",
      "Epoch 82/100\n",
      "49/49 [==============================] - 3s 58ms/step - loss: 0.5066 - root_mean_squared_error: 0.7074 - val_loss: 0.4965 - val_root_mean_squared_error: 0.6997\n",
      "Epoch 83/100\n",
      "49/49 [==============================] - 3s 55ms/step - loss: 0.5043 - root_mean_squared_error: 0.7058 - val_loss: 0.4988 - val_root_mean_squared_error: 0.7014\n",
      "Epoch 84/100\n",
      "49/49 [==============================] - 3s 60ms/step - loss: 0.5036 - root_mean_squared_error: 0.7052 - val_loss: 0.5119 - val_root_mean_squared_error: 0.7110\n",
      "Epoch 85/100\n",
      "49/49 [==============================] - 2s 50ms/step - loss: 0.5025 - root_mean_squared_error: 0.7044 - val_loss: 0.5164 - val_root_mean_squared_error: 0.7145\n",
      "Epoch 86/100\n",
      "49/49 [==============================] - 3s 56ms/step - loss: 0.5083 - root_mean_squared_error: 0.7083 - val_loss: 0.5242 - val_root_mean_squared_error: 0.7201\n",
      "Epoch 87/100\n",
      "49/49 [==============================] - 3s 64ms/step - loss: 0.5033 - root_mean_squared_error: 0.7053 - val_loss: 0.4923 - val_root_mean_squared_error: 0.6964\n",
      "Epoch 88/100\n",
      "49/49 [==============================] - 3s 59ms/step - loss: 0.5089 - root_mean_squared_error: 0.7091 - val_loss: 0.5030 - val_root_mean_squared_error: 0.7049\n",
      "Epoch 89/100\n",
      "49/49 [==============================] - 3s 59ms/step - loss: 0.5059 - root_mean_squared_error: 0.7070 - val_loss: 0.4983 - val_root_mean_squared_error: 0.7013\n",
      "Epoch 90/100\n",
      "49/49 [==============================] - 3s 58ms/step - loss: 0.5034 - root_mean_squared_error: 0.7055 - val_loss: 0.4991 - val_root_mean_squared_error: 0.7022\n",
      "Epoch 91/100\n",
      "49/49 [==============================] - 2s 50ms/step - loss: 0.5068 - root_mean_squared_error: 0.7079 - val_loss: 0.4989 - val_root_mean_squared_error: 0.7025\n",
      "Epoch 92/100\n",
      "49/49 [==============================] - 3s 61ms/step - loss: 0.5057 - root_mean_squared_error: 0.7075 - val_loss: 0.5232 - val_root_mean_squared_error: 0.7201\n",
      "Epoch 93/100\n",
      "49/49 [==============================] - 3s 57ms/step - loss: 0.5026 - root_mean_squared_error: 0.7048 - val_loss: 0.5123 - val_root_mean_squared_error: 0.7114\n",
      "Epoch 94/100\n",
      "49/49 [==============================] - 3s 52ms/step - loss: 0.5034 - root_mean_squared_error: 0.7054 - val_loss: 0.5020 - val_root_mean_squared_error: 0.7042\n",
      "Epoch 95/100\n",
      "49/49 [==============================] - 3s 54ms/step - loss: 0.5010 - root_mean_squared_error: 0.7036 - val_loss: 0.5008 - val_root_mean_squared_error: 0.7037\n",
      "Epoch 96/100\n",
      "49/49 [==============================] - 3s 58ms/step - loss: 0.5045 - root_mean_squared_error: 0.7061 - val_loss: 0.5113 - val_root_mean_squared_error: 0.7103\n",
      "Epoch 97/100\n",
      "49/49 [==============================] - 3s 58ms/step - loss: 0.5040 - root_mean_squared_error: 0.7055 - val_loss: 0.5284 - val_root_mean_squared_error: 0.7230\n",
      "Epoch 98/100\n",
      "49/49 [==============================] - 3s 52ms/step - loss: 0.5026 - root_mean_squared_error: 0.7047 - val_loss: 0.5143 - val_root_mean_squared_error: 0.7137\n",
      "Epoch 99/100\n",
      "49/49 [==============================] - 3s 54ms/step - loss: 0.5045 - root_mean_squared_error: 0.7065 - val_loss: 0.5053 - val_root_mean_squared_error: 0.7066\n",
      "Epoch 100/100\n",
      "49/49 [==============================] - 3s 57ms/step - loss: 0.5028 - root_mean_squared_error: 0.7051 - val_loss: 0.5079 - val_root_mean_squared_error: 0.7092\n"
     ]
    }
   ],
   "source": [
    "# fit the nn model for grammar\n",
    "grammar_nn_model = build_nn_model(X_train.todense())\n",
    "\n",
    "tf.random.set_seed(43)\n",
    "\n",
    "grammar_nn_model_history = grammar_nn_model.fit(X_train.todense(), \n",
    "                                                y_train['grammar'], \n",
    "                                                validation_data = (X_test.todense(), \n",
    "                                                y_test['grammar']), \n",
    "                                                batch_size=64,\n",
    "                                                epochs=100,\n",
    "                                                shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "FHG_1TOP6fYV",
    "outputId": "4f6a3d74-e52d-4897-fdcf-0d9e778280aa"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-12-05 07:56:49.979636: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "49/49 [==============================] - ETA: 0s - loss: 65.0134 - root_mean_squared_error: 1.4545"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-12-05 07:56:55.051534: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "49/49 [==============================] - 6s 98ms/step - loss: 65.0134 - root_mean_squared_error: 1.4545 - val_loss: 11.3927 - val_root_mean_squared_error: 0.9153\n",
      "Epoch 2/100\n",
      "49/49 [==============================] - 3s 54ms/step - loss: 5.4231 - root_mean_squared_error: 0.6846 - val_loss: 2.9532 - val_root_mean_squared_error: 0.8538\n",
      "Epoch 3/100\n",
      "49/49 [==============================] - 3s 61ms/step - loss: 1.9700 - root_mean_squared_error: 0.6922 - val_loss: 1.5449 - val_root_mean_squared_error: 0.7343\n",
      "Epoch 4/100\n",
      "49/49 [==============================] - 3s 57ms/step - loss: 1.2592 - root_mean_squared_error: 0.6979 - val_loss: 1.3688 - val_root_mean_squared_error: 0.8842\n",
      "Epoch 5/100\n",
      "49/49 [==============================] - 3s 59ms/step - loss: 0.9922 - root_mean_squared_error: 0.7056 - val_loss: 1.1777 - val_root_mean_squared_error: 0.8837\n",
      "Epoch 6/100\n",
      "49/49 [==============================] - 3s 56ms/step - loss: 0.8498 - root_mean_squared_error: 0.7095 - val_loss: 0.9673 - val_root_mean_squared_error: 0.8209\n",
      "Epoch 7/100\n",
      "49/49 [==============================] - 3s 58ms/step - loss: 0.7614 - root_mean_squared_error: 0.7112 - val_loss: 0.8153 - val_root_mean_squared_error: 0.7662\n",
      "Epoch 8/100\n",
      "49/49 [==============================] - 3s 53ms/step - loss: 0.7071 - root_mean_squared_error: 0.7142 - val_loss: 0.8717 - val_root_mean_squared_error: 0.8391\n",
      "Epoch 9/100\n",
      "49/49 [==============================] - 3s 62ms/step - loss: 0.6658 - root_mean_squared_error: 0.7163 - val_loss: 0.7548 - val_root_mean_squared_error: 0.7877\n",
      "Epoch 10/100\n",
      "49/49 [==============================] - 3s 54ms/step - loss: 0.6319 - root_mean_squared_error: 0.7142 - val_loss: 0.6889 - val_root_mean_squared_error: 0.7610\n",
      "Epoch 11/100\n",
      "49/49 [==============================] - 3s 58ms/step - loss: 0.6170 - root_mean_squared_error: 0.7198 - val_loss: 0.6501 - val_root_mean_squared_error: 0.7469\n",
      "Epoch 12/100\n",
      "49/49 [==============================] - 3s 57ms/step - loss: 0.5942 - root_mean_squared_error: 0.7179 - val_loss: 0.6429 - val_root_mean_squared_error: 0.7569\n",
      "Epoch 13/100\n",
      "49/49 [==============================] - 3s 62ms/step - loss: 0.5779 - root_mean_squared_error: 0.7163 - val_loss: 0.5985 - val_root_mean_squared_error: 0.7328\n",
      "Epoch 14/100\n",
      "49/49 [==============================] - 3s 56ms/step - loss: 0.5674 - root_mean_squared_error: 0.7151 - val_loss: 0.6183 - val_root_mean_squared_error: 0.7537\n",
      "Epoch 15/100\n",
      "49/49 [==============================] - 3s 53ms/step - loss: 0.5548 - root_mean_squared_error: 0.7128 - val_loss: 0.5530 - val_root_mean_squared_error: 0.7121\n",
      "Epoch 16/100\n",
      "49/49 [==============================] - 3s 54ms/step - loss: 0.5515 - root_mean_squared_error: 0.7145 - val_loss: 0.5795 - val_root_mean_squared_error: 0.7360\n",
      "Epoch 17/100\n",
      "49/49 [==============================] - 3s 59ms/step - loss: 0.5482 - root_mean_squared_error: 0.7160 - val_loss: 0.5659 - val_root_mean_squared_error: 0.7301\n",
      "Epoch 18/100\n",
      "49/49 [==============================] - 3s 51ms/step - loss: 0.5415 - root_mean_squared_error: 0.7143 - val_loss: 0.5760 - val_root_mean_squared_error: 0.7406\n",
      "Epoch 19/100\n",
      "49/49 [==============================] - 3s 54ms/step - loss: 0.5357 - root_mean_squared_error: 0.7132 - val_loss: 0.5453 - val_root_mean_squared_error: 0.7214\n",
      "Epoch 20/100\n",
      "49/49 [==============================] - 3s 55ms/step - loss: 0.5307 - root_mean_squared_error: 0.7112 - val_loss: 0.5140 - val_root_mean_squared_error: 0.6969\n",
      "Epoch 21/100\n",
      "49/49 [==============================] - 3s 52ms/step - loss: 0.5287 - root_mean_squared_error: 0.7114 - val_loss: 0.5151 - val_root_mean_squared_error: 0.7021\n",
      "Epoch 22/100\n",
      "49/49 [==============================] - 3s 58ms/step - loss: 0.5319 - root_mean_squared_error: 0.7151 - val_loss: 0.5483 - val_root_mean_squared_error: 0.7279\n",
      "Epoch 23/100\n",
      "49/49 [==============================] - 3s 58ms/step - loss: 0.5211 - root_mean_squared_error: 0.7093 - val_loss: 0.5086 - val_root_mean_squared_error: 0.6999\n",
      "Epoch 24/100\n",
      "49/49 [==============================] - 3s 59ms/step - loss: 0.5255 - root_mean_squared_error: 0.7134 - val_loss: 0.5536 - val_root_mean_squared_error: 0.7343\n",
      "Epoch 25/100\n",
      "49/49 [==============================] - 3s 58ms/step - loss: 0.5206 - root_mean_squared_error: 0.7107 - val_loss: 0.5049 - val_root_mean_squared_error: 0.6993\n",
      "Epoch 26/100\n",
      "49/49 [==============================] - 3s 59ms/step - loss: 0.5180 - root_mean_squared_error: 0.7094 - val_loss: 0.5185 - val_root_mean_squared_error: 0.7104\n",
      "Epoch 27/100\n",
      "49/49 [==============================] - 3s 65ms/step - loss: 0.5196 - root_mean_squared_error: 0.7110 - val_loss: 0.5427 - val_root_mean_squared_error: 0.7280\n",
      "Epoch 28/100\n",
      "49/49 [==============================] - 3s 64ms/step - loss: 0.5227 - root_mean_squared_error: 0.7136 - val_loss: 0.5148 - val_root_mean_squared_error: 0.7086\n",
      "Epoch 29/100\n",
      "49/49 [==============================] - 3s 58ms/step - loss: 0.5190 - root_mean_squared_error: 0.7113 - val_loss: 0.5287 - val_root_mean_squared_error: 0.7193\n",
      "Epoch 30/100\n",
      "49/49 [==============================] - 3s 56ms/step - loss: 0.5143 - root_mean_squared_error: 0.7092 - val_loss: 0.5325 - val_root_mean_squared_error: 0.7222\n",
      "Epoch 31/100\n",
      "49/49 [==============================] - 3s 61ms/step - loss: 0.5105 - root_mean_squared_error: 0.7064 - val_loss: 0.5024 - val_root_mean_squared_error: 0.7009\n",
      "Epoch 32/100\n",
      "49/49 [==============================] - 3s 57ms/step - loss: 0.5168 - root_mean_squared_error: 0.7116 - val_loss: 0.4996 - val_root_mean_squared_error: 0.6990\n",
      "Epoch 33/100\n",
      "49/49 [==============================] - 3s 63ms/step - loss: 0.5101 - root_mean_squared_error: 0.7071 - val_loss: 0.5101 - val_root_mean_squared_error: 0.7075\n",
      "Epoch 34/100\n",
      "49/49 [==============================] - 3s 62ms/step - loss: 0.5121 - root_mean_squared_error: 0.7090 - val_loss: 0.5078 - val_root_mean_squared_error: 0.7056\n",
      "Epoch 35/100\n",
      "49/49 [==============================] - 3s 60ms/step - loss: 0.5094 - root_mean_squared_error: 0.7072 - val_loss: 0.4998 - val_root_mean_squared_error: 0.6999\n",
      "Epoch 36/100\n",
      "49/49 [==============================] - 3s 58ms/step - loss: 0.5133 - root_mean_squared_error: 0.7095 - val_loss: 0.4962 - val_root_mean_squared_error: 0.6973\n",
      "Epoch 37/100\n",
      "49/49 [==============================] - 3s 57ms/step - loss: 0.5122 - root_mean_squared_error: 0.7091 - val_loss: 0.5147 - val_root_mean_squared_error: 0.7113\n",
      "Epoch 38/100\n",
      "49/49 [==============================] - 3s 53ms/step - loss: 0.5105 - root_mean_squared_error: 0.7086 - val_loss: 0.5034 - val_root_mean_squared_error: 0.7039\n",
      "Epoch 39/100\n",
      "49/49 [==============================] - 3s 62ms/step - loss: 0.5125 - root_mean_squared_error: 0.7097 - val_loss: 0.5081 - val_root_mean_squared_error: 0.7069\n",
      "Epoch 40/100\n",
      "49/49 [==============================] - 3s 62ms/step - loss: 0.5053 - root_mean_squared_error: 0.7051 - val_loss: 0.5082 - val_root_mean_squared_error: 0.7073\n",
      "Epoch 41/100\n",
      "49/49 [==============================] - 3s 59ms/step - loss: 0.5130 - root_mean_squared_error: 0.7104 - val_loss: 0.4940 - val_root_mean_squared_error: 0.6964\n",
      "Epoch 42/100\n",
      "49/49 [==============================] - 2s 51ms/step - loss: 0.5086 - root_mean_squared_error: 0.7070 - val_loss: 0.4983 - val_root_mean_squared_error: 0.6999\n",
      "Epoch 43/100\n",
      "49/49 [==============================] - 3s 56ms/step - loss: 0.5090 - root_mean_squared_error: 0.7083 - val_loss: 0.4956 - val_root_mean_squared_error: 0.6985\n",
      "Epoch 44/100\n",
      "49/49 [==============================] - 3s 57ms/step - loss: 0.5098 - root_mean_squared_error: 0.7091 - val_loss: 0.4991 - val_root_mean_squared_error: 0.7010\n",
      "Epoch 45/100\n",
      "49/49 [==============================] - 3s 52ms/step - loss: 0.5047 - root_mean_squared_error: 0.7046 - val_loss: 0.4955 - val_root_mean_squared_error: 0.6986\n",
      "Epoch 46/100\n",
      "49/49 [==============================] - 3s 54ms/step - loss: 0.5083 - root_mean_squared_error: 0.7082 - val_loss: 0.4947 - val_root_mean_squared_error: 0.6988\n",
      "Epoch 47/100\n",
      "49/49 [==============================] - 3s 58ms/step - loss: 0.5104 - root_mean_squared_error: 0.7097 - val_loss: 0.4969 - val_root_mean_squared_error: 0.6996\n",
      "Epoch 48/100\n",
      "49/49 [==============================] - 3s 61ms/step - loss: 0.5076 - root_mean_squared_error: 0.7076 - val_loss: 0.5032 - val_root_mean_squared_error: 0.7047\n",
      "Epoch 49/100\n",
      "49/49 [==============================] - 3s 58ms/step - loss: 0.5065 - root_mean_squared_error: 0.7067 - val_loss: 0.5030 - val_root_mean_squared_error: 0.7043\n",
      "Epoch 50/100\n",
      "49/49 [==============================] - 2s 49ms/step - loss: 0.5063 - root_mean_squared_error: 0.7071 - val_loss: 0.5047 - val_root_mean_squared_error: 0.7063\n",
      "Epoch 51/100\n",
      "49/49 [==============================] - 3s 64ms/step - loss: 0.5051 - root_mean_squared_error: 0.7061 - val_loss: 0.4996 - val_root_mean_squared_error: 0.7025\n",
      "Epoch 52/100\n",
      "49/49 [==============================] - 3s 56ms/step - loss: 0.5059 - root_mean_squared_error: 0.7071 - val_loss: 0.4914 - val_root_mean_squared_error: 0.6968\n",
      "Epoch 53/100\n",
      "49/49 [==============================] - 3s 58ms/step - loss: 0.5032 - root_mean_squared_error: 0.7056 - val_loss: 0.4925 - val_root_mean_squared_error: 0.6977\n",
      "Epoch 54/100\n",
      "49/49 [==============================] - 3s 56ms/step - loss: 0.5063 - root_mean_squared_error: 0.7078 - val_loss: 0.4914 - val_root_mean_squared_error: 0.6974\n",
      "Epoch 55/100\n",
      "49/49 [==============================] - 3s 52ms/step - loss: 0.5030 - root_mean_squared_error: 0.7056 - val_loss: 0.4924 - val_root_mean_squared_error: 0.6977\n",
      "Epoch 56/100\n",
      "49/49 [==============================] - 3s 53ms/step - loss: 0.5086 - root_mean_squared_error: 0.7093 - val_loss: 0.4960 - val_root_mean_squared_error: 0.7002\n",
      "Epoch 57/100\n",
      "49/49 [==============================] - 3s 60ms/step - loss: 0.5057 - root_mean_squared_error: 0.7072 - val_loss: 0.4993 - val_root_mean_squared_error: 0.7027\n",
      "Epoch 58/100\n",
      "49/49 [==============================] - 2s 47ms/step - loss: 0.5037 - root_mean_squared_error: 0.7060 - val_loss: 0.5068 - val_root_mean_squared_error: 0.7089\n",
      "Epoch 59/100\n",
      "49/49 [==============================] - 3s 57ms/step - loss: 0.5055 - root_mean_squared_error: 0.7078 - val_loss: 0.4994 - val_root_mean_squared_error: 0.7031\n",
      "Epoch 60/100\n",
      "49/49 [==============================] - 3s 56ms/step - loss: 0.5042 - root_mean_squared_error: 0.7065 - val_loss: 0.4913 - val_root_mean_squared_error: 0.6968\n",
      "Epoch 61/100\n",
      "49/49 [==============================] - 3s 62ms/step - loss: 0.5002 - root_mean_squared_error: 0.7035 - val_loss: 0.5237 - val_root_mean_squared_error: 0.7209\n",
      "Epoch 62/100\n",
      "49/49 [==============================] - 3s 57ms/step - loss: 0.5067 - root_mean_squared_error: 0.7087 - val_loss: 0.5010 - val_root_mean_squared_error: 0.7047\n",
      "Epoch 63/100\n",
      "49/49 [==============================] - 2s 50ms/step - loss: 0.5028 - root_mean_squared_error: 0.7060 - val_loss: 0.5133 - val_root_mean_squared_error: 0.7138\n",
      "Epoch 64/100\n",
      "49/49 [==============================] - 2s 50ms/step - loss: 0.5062 - root_mean_squared_error: 0.7083 - val_loss: 0.4983 - val_root_mean_squared_error: 0.7028\n",
      "Epoch 65/100\n",
      "49/49 [==============================] - 3s 62ms/step - loss: 0.5036 - root_mean_squared_error: 0.7065 - val_loss: 0.4899 - val_root_mean_squared_error: 0.6963\n",
      "Epoch 66/100\n",
      "49/49 [==============================] - 3s 57ms/step - loss: 0.5087 - root_mean_squared_error: 0.7099 - val_loss: 0.4974 - val_root_mean_squared_error: 0.7017\n",
      "Epoch 67/100\n",
      "49/49 [==============================] - 3s 53ms/step - loss: 0.5067 - root_mean_squared_error: 0.7087 - val_loss: 0.4965 - val_root_mean_squared_error: 0.7016\n",
      "Epoch 68/100\n",
      "49/49 [==============================] - 2s 47ms/step - loss: 0.5005 - root_mean_squared_error: 0.7043 - val_loss: 0.5045 - val_root_mean_squared_error: 0.7072\n",
      "Epoch 69/100\n",
      "49/49 [==============================] - 3s 53ms/step - loss: 0.5029 - root_mean_squared_error: 0.7054 - val_loss: 0.4917 - val_root_mean_squared_error: 0.6976\n",
      "Epoch 70/100\n",
      "49/49 [==============================] - 3s 59ms/step - loss: 0.5035 - root_mean_squared_error: 0.7065 - val_loss: 0.5096 - val_root_mean_squared_error: 0.7112\n",
      "Epoch 71/100\n",
      "49/49 [==============================] - 3s 54ms/step - loss: 0.5047 - root_mean_squared_error: 0.7076 - val_loss: 0.4998 - val_root_mean_squared_error: 0.7045\n",
      "Epoch 72/100\n",
      "49/49 [==============================] - 3s 58ms/step - loss: 0.5016 - root_mean_squared_error: 0.7056 - val_loss: 0.4938 - val_root_mean_squared_error: 0.7001\n",
      "Epoch 73/100\n",
      "49/49 [==============================] - 3s 54ms/step - loss: 0.5033 - root_mean_squared_error: 0.7066 - val_loss: 0.5057 - val_root_mean_squared_error: 0.7083\n",
      "Epoch 74/100\n",
      "49/49 [==============================] - 2s 49ms/step - loss: 0.5024 - root_mean_squared_error: 0.7058 - val_loss: 0.5058 - val_root_mean_squared_error: 0.7082\n",
      "Epoch 75/100\n",
      "49/49 [==============================] - 2s 47ms/step - loss: 0.4995 - root_mean_squared_error: 0.7036 - val_loss: 0.4972 - val_root_mean_squared_error: 0.7020\n",
      "Epoch 76/100\n",
      "49/49 [==============================] - 3s 55ms/step - loss: 0.5026 - root_mean_squared_error: 0.7058 - val_loss: 0.4928 - val_root_mean_squared_error: 0.6991\n",
      "Epoch 77/100\n",
      "49/49 [==============================] - 3s 52ms/step - loss: 0.5044 - root_mean_squared_error: 0.7075 - val_loss: 0.4898 - val_root_mean_squared_error: 0.6968\n",
      "Epoch 78/100\n",
      "49/49 [==============================] - 2s 49ms/step - loss: 0.5000 - root_mean_squared_error: 0.7046 - val_loss: 0.4921 - val_root_mean_squared_error: 0.6990\n",
      "Epoch 79/100\n",
      "49/49 [==============================] - 3s 52ms/step - loss: 0.5027 - root_mean_squared_error: 0.7061 - val_loss: 0.5027 - val_root_mean_squared_error: 0.7059\n",
      "Epoch 80/100\n",
      "49/49 [==============================] - 3s 58ms/step - loss: 0.4983 - root_mean_squared_error: 0.7030 - val_loss: 0.4972 - val_root_mean_squared_error: 0.7026\n",
      "Epoch 81/100\n",
      "49/49 [==============================] - 3s 60ms/step - loss: 0.4990 - root_mean_squared_error: 0.7039 - val_loss: 0.4937 - val_root_mean_squared_error: 0.6999\n",
      "Epoch 82/100\n",
      "49/49 [==============================] - 3s 60ms/step - loss: 0.5023 - root_mean_squared_error: 0.7061 - val_loss: 0.4948 - val_root_mean_squared_error: 0.7005\n",
      "Epoch 83/100\n",
      "49/49 [==============================] - 3s 53ms/step - loss: 0.5023 - root_mean_squared_error: 0.7057 - val_loss: 0.5115 - val_root_mean_squared_error: 0.7127\n",
      "Epoch 84/100\n",
      "49/49 [==============================] - 3s 63ms/step - loss: 0.5044 - root_mean_squared_error: 0.7076 - val_loss: 0.4976 - val_root_mean_squared_error: 0.7029\n",
      "Epoch 85/100\n",
      "49/49 [==============================] - 3s 56ms/step - loss: 0.5010 - root_mean_squared_error: 0.7053 - val_loss: 0.4926 - val_root_mean_squared_error: 0.6991\n",
      "Epoch 86/100\n",
      "49/49 [==============================] - 3s 52ms/step - loss: 0.5002 - root_mean_squared_error: 0.7048 - val_loss: 0.5095 - val_root_mean_squared_error: 0.7119\n",
      "Epoch 87/100\n",
      "49/49 [==============================] - 3s 59ms/step - loss: 0.5030 - root_mean_squared_error: 0.7070 - val_loss: 0.5040 - val_root_mean_squared_error: 0.7078\n",
      "Epoch 88/100\n",
      "49/49 [==============================] - 3s 56ms/step - loss: 0.5027 - root_mean_squared_error: 0.7068 - val_loss: 0.4951 - val_root_mean_squared_error: 0.7012\n",
      "Epoch 89/100\n",
      "49/49 [==============================] - 3s 59ms/step - loss: 0.4992 - root_mean_squared_error: 0.7040 - val_loss: 0.5287 - val_root_mean_squared_error: 0.7250\n",
      "Epoch 90/100\n",
      "49/49 [==============================] - 3s 57ms/step - loss: 0.5074 - root_mean_squared_error: 0.7099 - val_loss: 0.5016 - val_root_mean_squared_error: 0.7059\n",
      "Epoch 91/100\n",
      "49/49 [==============================] - 2s 50ms/step - loss: 0.5009 - root_mean_squared_error: 0.7050 - val_loss: 0.4941 - val_root_mean_squared_error: 0.7002\n",
      "Epoch 92/100\n",
      "49/49 [==============================] - 2s 49ms/step - loss: 0.4999 - root_mean_squared_error: 0.7047 - val_loss: 0.5105 - val_root_mean_squared_error: 0.7125\n",
      "Epoch 93/100\n",
      "49/49 [==============================] - 3s 57ms/step - loss: 0.4987 - root_mean_squared_error: 0.7041 - val_loss: 0.4896 - val_root_mean_squared_error: 0.6974\n",
      "Epoch 94/100\n",
      "49/49 [==============================] - 2s 50ms/step - loss: 0.4995 - root_mean_squared_error: 0.7046 - val_loss: 0.4926 - val_root_mean_squared_error: 0.6999\n",
      "Epoch 95/100\n",
      "49/49 [==============================] - 3s 55ms/step - loss: 0.4974 - root_mean_squared_error: 0.7034 - val_loss: 0.4911 - val_root_mean_squared_error: 0.6990\n",
      "Epoch 96/100\n",
      "49/49 [==============================] - 3s 53ms/step - loss: 0.4985 - root_mean_squared_error: 0.7042 - val_loss: 0.5031 - val_root_mean_squared_error: 0.7076\n",
      "Epoch 97/100\n",
      "49/49 [==============================] - 2s 50ms/step - loss: 0.5012 - root_mean_squared_error: 0.7060 - val_loss: 0.4954 - val_root_mean_squared_error: 0.7020\n",
      "Epoch 98/100\n",
      "49/49 [==============================] - 3s 56ms/step - loss: 0.5024 - root_mean_squared_error: 0.7067 - val_loss: 0.4897 - val_root_mean_squared_error: 0.6975\n",
      "Epoch 99/100\n",
      "49/49 [==============================] - 2s 51ms/step - loss: 0.5018 - root_mean_squared_error: 0.7060 - val_loss: 0.4961 - val_root_mean_squared_error: 0.7022\n",
      "Epoch 100/100\n",
      "49/49 [==============================] - 3s 62ms/step - loss: 0.4996 - root_mean_squared_error: 0.7047 - val_loss: 0.4929 - val_root_mean_squared_error: 0.7002\n"
     ]
    }
   ],
   "source": [
    "# fit the nn model for vocabulary\n",
    "vocab_nn_model = build_nn_model(X_train.todense())\n",
    "\n",
    "tf.random.set_seed(53)\n",
    "\n",
    "vocab_nn_model_history = vocab_nn_model.fit(X_train.todense(), \n",
    "                                            y_train['grammar'], \n",
    "                                            validation_data = (X_test.todense(), \n",
    "                                            y_test['grammar']), \n",
    "                                            batch_size=64,\n",
    "                                            epochs=100,\n",
    "                                            shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ppnS2OER6fYV",
    "outputId": "8bc70437-5c4a-484e-abc5-a22395a53a57"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25/25 [==============================] - 0s 17ms/step\n",
      "25/25 [==============================] - 0s 19ms/step\n",
      "RMSE for non-LSTM NN (vocabulary): 0.39081315964186303\n",
      "RMSE for non-LSTM NN (grammar): 0.5029886492346045\n"
     ]
    }
   ],
   "source": [
    "# calculate RMSE for the neural network without LSTM\n",
    "\n",
    "vocab_pred, grammar_pred = vocab_nn_model.predict(X_test).flatten(), grammar_nn_model.predict(X_test).flatten()\n",
    "\n",
    "nn_predictions = pd.DataFrame(list(zip(vocab_pred, grammar_pred)), columns=['vocabulary', 'grammar'])\n",
    "\n",
    "nn_root_mean_squared_error = rmse(y_test, nn_predictions)\n",
    "\n",
    "print(f'''RMSE for non-LSTM NN (vocabulary): {rmse(y_test['vocabulary'], nn_predictions['vocabulary'])}''')\n",
    "\n",
    "print(f'''RMSE for non-LSTM NN (grammar): {rmse(y_test['grammar'], nn_predictions['grammar'])}''')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "N4hSnOzf6fYV",
    "outputId": "31beeea5-aa38-4666-dbfb-6284de43c525"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.11;0.775x0.77)\n",
      "AxesSubplot(0.125,0.11;0.775x0.77)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "vocab_history_df = pd.DataFrame(vocab_nn_model_history.history)\n",
    "grammar_history_df = pd.DataFrame(grammar_nn_model_history.history)\n",
    "\n",
    "print(vocab_history_df.loc[:,['root_mean_squared_error','val_root_mean_squared_error']].plot(title='Vocabulary (non-LSTM)', xlabel='Epoch', ylabel='RMSE'))\n",
    "print(grammar_history_df.loc[:,['root_mean_squared_error','val_root_mean_squared_error']].plot(title='Grammar (non-LSTM)', xlabel='Epoch', ylabel='RMSE'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "SJXjbKAq6fYW"
   },
   "outputs": [],
   "source": [
    "from tensorflow.keras.preprocessing.text import Tokenizer\n",
    "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
    "\n",
    "train_texts, test_texts = train_test_split(df['full_text'],  test_size = 0.20, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "LP4XScNH6fYW",
    "outputId": "cbff7f8e-eedf-4747-eeb7-4bbc146a6c3a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 19539 distinct words in the training corpus\n",
      "The longest text in the training corpus is 577 words long\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# for LSTM we need to pass the essays to the neural network as sequences of tokens\n",
    "# as opposed to the TF-IDF vectors used by the SVM and neural network models\n",
    "tokenizer = Tokenizer(oov_token=\"<UNK>\")\n",
    "tokenizer.fit_on_texts(train_texts)\n",
    "word_index = tokenizer.word_index\n",
    "\n",
    "# find the number of distinct words in the training corpus (this will determine\n",
    "# the dimensions of the embedding layer)\n",
    "n_words = len(word_index)\n",
    "print(\"There are\", n_words, \"distinct words in the training corpus\")\n",
    "\n",
    "# find the maximum essay length in the training corpus\n",
    "maxlen = -1\n",
    "for text in train_texts:\n",
    "    length = len(text.split(' '))\n",
    "    if length > maxlen:\n",
    "        maxlen = length \n",
    "\n",
    "\n",
    "print(f'The longest text in the training corpus is {maxlen} words long')\n",
    "\n",
    "# convert texts to sequences and pad them so that they are all the same length\n",
    "train_sequences = tokenizer.texts_to_sequences(train_texts)\n",
    "# rounding the maximum essay length to the nearest hundred\n",
    "max_word_count = 600\n",
    "X_train_seq = pad_sequences(train_sequences, maxlen=max_word_count, truncating='post')\n",
    "\n",
    "test_sequences = tokenizer.texts_to_sequences(test_texts)\n",
    "X_test_seq = pad_sequences(test_sequences, maxlen=max_word_count, truncating='post')\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "fHE9VSH96fYW"
   },
   "outputs": [],
   "source": [
    "def build_lstm_model(X):\n",
    "\n",
    "  # input layer\n",
    "  input = tf.keras.layers.Input(shape=(max_word_count))\n",
    "  # trained embedding layer\n",
    "  embeddings = tf.keras.layers.Embedding(n_words + 1, 1024, input_length=max_word_count)(input)\n",
    "  # LSTM layer\n",
    "  x = tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(1024))(embeddings)\n",
    "\n",
    "  # dense layer, high regularization parameter, relu activation\n",
    "  x = tf.keras.layers.Dense(units=1024, activation='relu', kernel_regularizer=tf.keras.regularizers.L2(0.1))(x)\n",
    "\n",
    "  # large dropout layer\n",
    "  x = tf.keras.layers.Dropout(0.7)(x)\n",
    "\n",
    "  # dense layer with slightly lower regularization parameter and relu activation\n",
    "  x = tf.keras.layers.Dense(units=512, activation='relu', kernel_regularizer=tf.keras.regularizers.L2(0.08))(x)\n",
    "\n",
    "  # another dropout layer\n",
    "  x = tf.keras.layers.Dropout(0.6)(x)\n",
    "\n",
    "  # dense layer with smaller regularization parameter and relu activation\n",
    "  x = tf.keras.layers.Dense(units=256, activation='relu', kernel_regularizer=tf.keras.regularizers.L2(0.05))(x)\n",
    "\n",
    "  # another dropout layer\n",
    "  x = tf.keras.layers.Dropout(0.5)(x)\n",
    "\n",
    "  # dense layer with the same regularization paramter and relu activation\n",
    "  x = tf.keras.layers.Dense(units=128, activation='relu', kernel_regularizer=tf.keras.regularizers.L2(0.05))(x)\n",
    "\n",
    "  # one last dropout layer\n",
    "  x = tf.keras.layers.Dropout(0.4, name='our_param')(x)\n",
    "\n",
    "  predictions = tf.keras.layers.Dense(units=1, activation=\"relu\", name=\"predictions\")(x)\n",
    "\n",
    "  # output layer\n",
    "  model = tf.keras.Model(inputs=input, outputs=predictions)\n",
    "  model.compile(loss=\"mean_squared_error\", optimizer=\"adam\", metrics=tf.keras.metrics.RootMeanSquaredError(name='RootMeanSquredError'))\n",
    "\n",
    "  return model\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "kTS10u836fYW",
    "outputId": "564bfeef-7d1c-4eff-d38c-ac508b3dc955"
   },
   "outputs": [
    {
     "metadata": {
      "tags": null
     },
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "49/49 [==============================] - 64s 1s/step - loss: 86.2110 - RootMeanSquredError: 1.4076 - val_loss: 28.5638 - val_RootMeanSquredError: 1.3050\n",
      "Epoch 2/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 18.1534 - RootMeanSquredError: 1.1033 - val_loss: 12.3409 - val_RootMeanSquredError: 1.2176\n",
      "Epoch 3/100\n",
      "49/49 [==============================] - 56s 1s/step - loss: 9.0511 - RootMeanSquredError: 1.0264 - val_loss: 7.5176 - val_RootMeanSquredError: 1.3359\n",
      "Epoch 4/100\n",
      "49/49 [==============================] - 56s 1s/step - loss: 5.3073 - RootMeanSquredError: 0.9119 - val_loss: 4.0712 - val_RootMeanSquredError: 0.7787\n",
      "Epoch 5/100\n",
      "49/49 [==============================] - 56s 1s/step - loss: 3.5304 - RootMeanSquredError: 0.8572 - val_loss: 3.0479 - val_RootMeanSquredError: 0.9166\n",
      "Epoch 6/100\n",
      "49/49 [==============================] - 56s 1s/step - loss: 2.4146 - RootMeanSquredError: 0.7792 - val_loss: 1.8090 - val_RootMeanSquredError: 0.5917\n",
      "Epoch 7/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 1.7202 - RootMeanSquredError: 0.7244 - val_loss: 1.3400 - val_RootMeanSquredError: 0.6077\n",
      "Epoch 8/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 1.2716 - RootMeanSquredError: 0.6759 - val_loss: 1.0170 - val_RootMeanSquredError: 0.5895\n",
      "Epoch 9/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.9949 - RootMeanSquredError: 0.6556 - val_loss: 0.8354 - val_RootMeanSquredError: 0.6013\n",
      "Epoch 10/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.8145 - RootMeanSquredError: 0.6381 - val_loss: 0.7132 - val_RootMeanSquredError: 0.6046\n",
      "Epoch 11/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.7005 - RootMeanSquredError: 0.6300 - val_loss: 0.6308 - val_RootMeanSquredError: 0.6057\n",
      "Epoch 12/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.6083 - RootMeanSquredError: 0.6124 - val_loss: 0.5810 - val_RootMeanSquredError: 0.6184\n",
      "Epoch 13/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.5396 - RootMeanSquredError: 0.5936 - val_loss: 0.5467 - val_RootMeanSquredError: 0.6213\n",
      "Epoch 14/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.4884 - RootMeanSquredError: 0.5819 - val_loss: 0.5296 - val_RootMeanSquredError: 0.6258\n",
      "Epoch 15/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.4538 - RootMeanSquredError: 0.5700 - val_loss: 0.5280 - val_RootMeanSquredError: 0.6383\n",
      "Epoch 16/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.4225 - RootMeanSquredError: 0.5559 - val_loss: 0.5320 - val_RootMeanSquredError: 0.6481\n",
      "Epoch 17/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.4016 - RootMeanSquredError: 0.5436 - val_loss: 0.5113 - val_RootMeanSquredError: 0.6456\n",
      "Epoch 18/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.3787 - RootMeanSquredError: 0.5349 - val_loss: 0.4960 - val_RootMeanSquredError: 0.6390\n",
      "Epoch 19/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.3682 - RootMeanSquredError: 0.5289 - val_loss: 0.4926 - val_RootMeanSquredError: 0.6394\n",
      "Epoch 20/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.3552 - RootMeanSquredError: 0.5214 - val_loss: 0.5216 - val_RootMeanSquredError: 0.6610\n",
      "Epoch 21/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.3750 - RootMeanSquredError: 0.5401 - val_loss: 0.4998 - val_RootMeanSquredError: 0.6472\n",
      "Epoch 22/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.3725 - RootMeanSquredError: 0.5412 - val_loss: 0.4826 - val_RootMeanSquredError: 0.6379\n",
      "Epoch 23/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.3473 - RootMeanSquredError: 0.5196 - val_loss: 0.4532 - val_RootMeanSquredError: 0.6206\n",
      "Epoch 24/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.3316 - RootMeanSquredError: 0.5058 - val_loss: 0.4957 - val_RootMeanSquredError: 0.6495\n",
      "Epoch 25/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.3390 - RootMeanSquredError: 0.5103 - val_loss: 0.5210 - val_RootMeanSquredError: 0.6638\n",
      "Epoch 26/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.3394 - RootMeanSquredError: 0.5147 - val_loss: 0.4897 - val_RootMeanSquredError: 0.6481\n",
      "Epoch 27/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.3440 - RootMeanSquredError: 0.5215 - val_loss: 0.4844 - val_RootMeanSquredError: 0.6414\n",
      "Epoch 28/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.3249 - RootMeanSquredError: 0.4994 - val_loss: 0.5370 - val_RootMeanSquredError: 0.6778\n",
      "Epoch 29/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.3305 - RootMeanSquredError: 0.5060 - val_loss: 0.5128 - val_RootMeanSquredError: 0.6630\n",
      "Epoch 30/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.3189 - RootMeanSquredError: 0.4957 - val_loss: 0.5270 - val_RootMeanSquredError: 0.6713\n",
      "Epoch 31/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.3185 - RootMeanSquredError: 0.4932 - val_loss: 0.5100 - val_RootMeanSquredError: 0.6597\n",
      "Epoch 32/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.3147 - RootMeanSquredError: 0.4905 - val_loss: 0.4642 - val_RootMeanSquredError: 0.6286\n",
      "Epoch 33/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.3045 - RootMeanSquredError: 0.4806 - val_loss: 0.5150 - val_RootMeanSquredError: 0.6606\n",
      "Epoch 34/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.3045 - RootMeanSquredError: 0.4781 - val_loss: 0.4909 - val_RootMeanSquredError: 0.6485\n",
      "Epoch 35/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.3105 - RootMeanSquredError: 0.4862 - val_loss: 0.5697 - val_RootMeanSquredError: 0.6975\n",
      "Epoch 36/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.2899 - RootMeanSquredError: 0.4635 - val_loss: 0.5454 - val_RootMeanSquredError: 0.6851\n",
      "Epoch 37/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.2886 - RootMeanSquredError: 0.4605 - val_loss: 0.5236 - val_RootMeanSquredError: 0.6691\n",
      "Epoch 38/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.2952 - RootMeanSquredError: 0.4695 - val_loss: 0.6066 - val_RootMeanSquredError: 0.7240\n",
      "Epoch 39/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.2991 - RootMeanSquredError: 0.4719 - val_loss: 0.5256 - val_RootMeanSquredError: 0.6681\n",
      "Epoch 40/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.2879 - RootMeanSquredError: 0.4633 - val_loss: 0.5436 - val_RootMeanSquredError: 0.6852\n",
      "Epoch 41/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.2812 - RootMeanSquredError: 0.4545 - val_loss: 0.5151 - val_RootMeanSquredError: 0.6633\n",
      "Epoch 42/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.2920 - RootMeanSquredError: 0.4633 - val_loss: 0.6076 - val_RootMeanSquredError: 0.7266\n",
      "Epoch 43/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.2924 - RootMeanSquredError: 0.4636 - val_loss: 0.4963 - val_RootMeanSquredError: 0.6531\n",
      "Epoch 44/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.2744 - RootMeanSquredError: 0.4447 - val_loss: 0.5824 - val_RootMeanSquredError: 0.7097\n",
      "Epoch 45/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.2800 - RootMeanSquredError: 0.4531 - val_loss: 0.5189 - val_RootMeanSquredError: 0.6672\n",
      "Epoch 46/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.2736 - RootMeanSquredError: 0.4457 - val_loss: 0.5413 - val_RootMeanSquredError: 0.6841\n",
      "Epoch 47/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.2598 - RootMeanSquredError: 0.4325 - val_loss: 0.5575 - val_RootMeanSquredError: 0.6953\n",
      "Epoch 48/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.2749 - RootMeanSquredError: 0.4487 - val_loss: 0.5620 - val_RootMeanSquredError: 0.6985\n",
      "Epoch 49/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.2715 - RootMeanSquredError: 0.4442 - val_loss: 0.5791 - val_RootMeanSquredError: 0.7071\n",
      "Epoch 50/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.2754 - RootMeanSquredError: 0.4453 - val_loss: 0.5979 - val_RootMeanSquredError: 0.7126\n",
      "Epoch 51/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.2794 - RootMeanSquredError: 0.4474 - val_loss: 0.5055 - val_RootMeanSquredError: 0.6598\n",
      "Epoch 52/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.2660 - RootMeanSquredError: 0.4380 - val_loss: 0.5683 - val_RootMeanSquredError: 0.7028\n",
      "Epoch 53/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.2609 - RootMeanSquredError: 0.4330 - val_loss: 0.5704 - val_RootMeanSquredError: 0.7021\n",
      "Epoch 54/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.2682 - RootMeanSquredError: 0.4363 - val_loss: 0.5265 - val_RootMeanSquredError: 0.6730\n",
      "Epoch 55/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.2522 - RootMeanSquredError: 0.4218 - val_loss: 0.5042 - val_RootMeanSquredError: 0.6604\n",
      "Epoch 56/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.2617 - RootMeanSquredError: 0.4318 - val_loss: 0.5505 - val_RootMeanSquredError: 0.6871\n",
      "Epoch 57/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.2585 - RootMeanSquredError: 0.4229 - val_loss: 0.5981 - val_RootMeanSquredError: 0.7189\n",
      "Epoch 58/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.2689 - RootMeanSquredError: 0.4370 - val_loss: 0.5125 - val_RootMeanSquredError: 0.6631\n",
      "Epoch 59/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.2603 - RootMeanSquredError: 0.4270 - val_loss: 0.6878 - val_RootMeanSquredError: 0.7753\n",
      "Epoch 60/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.2531 - RootMeanSquredError: 0.4225 - val_loss: 0.5800 - val_RootMeanSquredError: 0.7091\n",
      "Epoch 61/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.2598 - RootMeanSquredError: 0.4257 - val_loss: 0.5960 - val_RootMeanSquredError: 0.7146\n",
      "Epoch 62/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.2579 - RootMeanSquredError: 0.4197 - val_loss: 0.5785 - val_RootMeanSquredError: 0.7074\n",
      "Epoch 63/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.2530 - RootMeanSquredError: 0.4185 - val_loss: 0.5747 - val_RootMeanSquredError: 0.7048\n",
      "Epoch 64/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.2542 - RootMeanSquredError: 0.4187 - val_loss: 0.5136 - val_RootMeanSquredError: 0.6649\n",
      "Epoch 65/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.3030 - RootMeanSquredError: 0.4365 - val_loss: 0.5904 - val_RootMeanSquredError: 0.7131\n",
      "Epoch 66/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.2606 - RootMeanSquredError: 0.4232 - val_loss: 0.5957 - val_RootMeanSquredError: 0.7124\n",
      "Epoch 67/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.2491 - RootMeanSquredError: 0.4110 - val_loss: 0.6009 - val_RootMeanSquredError: 0.7185\n",
      "Epoch 68/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.2514 - RootMeanSquredError: 0.4088 - val_loss: 0.5753 - val_RootMeanSquredError: 0.7020\n",
      "Epoch 69/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.2476 - RootMeanSquredError: 0.4119 - val_loss: 0.5430 - val_RootMeanSquredError: 0.6832\n",
      "Epoch 70/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.2475 - RootMeanSquredError: 0.4108 - val_loss: 0.5322 - val_RootMeanSquredError: 0.6738\n",
      "Epoch 71/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.2522 - RootMeanSquredError: 0.4114 - val_loss: 0.5438 - val_RootMeanSquredError: 0.6840\n",
      "Epoch 72/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.2367 - RootMeanSquredError: 0.3978 - val_loss: 0.5397 - val_RootMeanSquredError: 0.6814\n",
      "Epoch 73/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.2368 - RootMeanSquredError: 0.3979 - val_loss: 0.5088 - val_RootMeanSquredError: 0.6616\n",
      "Epoch 74/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.2417 - RootMeanSquredError: 0.4061 - val_loss: 0.6612 - val_RootMeanSquredError: 0.7575\n",
      "Epoch 75/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.2523 - RootMeanSquredError: 0.4155 - val_loss: 0.5826 - val_RootMeanSquredError: 0.7082\n",
      "Epoch 76/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.2370 - RootMeanSquredError: 0.3965 - val_loss: 0.5717 - val_RootMeanSquredError: 0.7007\n",
      "Epoch 77/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.2409 - RootMeanSquredError: 0.4009 - val_loss: 0.5559 - val_RootMeanSquredError: 0.6940\n",
      "Epoch 78/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.2409 - RootMeanSquredError: 0.4028 - val_loss: 0.6603 - val_RootMeanSquredError: 0.7565\n",
      "Epoch 79/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.2357 - RootMeanSquredError: 0.3950 - val_loss: 0.5261 - val_RootMeanSquredError: 0.6726\n",
      "Epoch 80/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.2387 - RootMeanSquredError: 0.3994 - val_loss: 0.5915 - val_RootMeanSquredError: 0.7120\n",
      "Epoch 81/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.2319 - RootMeanSquredError: 0.3959 - val_loss: 0.6150 - val_RootMeanSquredError: 0.7295\n",
      "Epoch 82/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.2360 - RootMeanSquredError: 0.3968 - val_loss: 0.5917 - val_RootMeanSquredError: 0.7155\n",
      "Epoch 83/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.2286 - RootMeanSquredError: 0.3878 - val_loss: 0.5493 - val_RootMeanSquredError: 0.6889\n",
      "Epoch 84/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.2322 - RootMeanSquredError: 0.3921 - val_loss: 0.4835 - val_RootMeanSquredError: 0.6417\n",
      "Epoch 85/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.2313 - RootMeanSquredError: 0.3926 - val_loss: 0.5899 - val_RootMeanSquredError: 0.7161\n",
      "Epoch 86/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.2320 - RootMeanSquredError: 0.3906 - val_loss: 0.6524 - val_RootMeanSquredError: 0.7505\n",
      "Epoch 87/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.2256 - RootMeanSquredError: 0.3829 - val_loss: 0.5904 - val_RootMeanSquredError: 0.7169\n",
      "Epoch 88/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.2239 - RootMeanSquredError: 0.3801 - val_loss: 0.5340 - val_RootMeanSquredError: 0.6773\n",
      "Epoch 89/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.2264 - RootMeanSquredError: 0.3828 - val_loss: 0.5305 - val_RootMeanSquredError: 0.6767\n",
      "Epoch 90/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.2363 - RootMeanSquredError: 0.3971 - val_loss: 0.6211 - val_RootMeanSquredError: 0.7321\n",
      "Epoch 91/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.2268 - RootMeanSquredError: 0.3828 - val_loss: 0.5806 - val_RootMeanSquredError: 0.7078\n",
      "Epoch 92/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.2224 - RootMeanSquredError: 0.3802 - val_loss: 0.5907 - val_RootMeanSquredError: 0.7172\n",
      "Epoch 93/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.2216 - RootMeanSquredError: 0.3789 - val_loss: 0.5418 - val_RootMeanSquredError: 0.6799\n",
      "Epoch 94/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.2184 - RootMeanSquredError: 0.3732 - val_loss: 0.5415 - val_RootMeanSquredError: 0.6844\n",
      "Epoch 95/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.2204 - RootMeanSquredError: 0.3768 - val_loss: 0.6036 - val_RootMeanSquredError: 0.7210\n",
      "Epoch 96/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.2183 - RootMeanSquredError: 0.3696 - val_loss: 0.5991 - val_RootMeanSquredError: 0.7217\n",
      "Epoch 97/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.2118 - RootMeanSquredError: 0.3686 - val_loss: 0.5924 - val_RootMeanSquredError: 0.7142\n",
      "Epoch 98/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.2108 - RootMeanSquredError: 0.3610 - val_loss: 0.5271 - val_RootMeanSquredError: 0.6741\n",
      "Epoch 99/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.2218 - RootMeanSquredError: 0.3746 - val_loss: 0.5393 - val_RootMeanSquredError: 0.6814\n",
      "Epoch 100/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.2114 - RootMeanSquredError: 0.3597 - val_loss: 0.5721 - val_RootMeanSquredError: 0.7035\n",
      "Epoch 1/100\n",
      "49/49 [==============================] - 61s 1s/step - loss: 93.2455 - RootMeanSquredError: 1.5403 - val_loss: 36.7122 - val_RootMeanSquredError: 1.3306\n",
      "Epoch 2/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 24.6763 - RootMeanSquredError: 1.0639 - val_loss: 16.4065 - val_RootMeanSquredError: 0.9019\n",
      "Epoch 3/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 12.8209 - RootMeanSquredError: 1.0575 - val_loss: 10.4932 - val_RootMeanSquredError: 1.3916\n",
      "Epoch 4/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 7.7177 - RootMeanSquredError: 1.0043 - val_loss: 5.8058 - val_RootMeanSquredError: 0.7618\n",
      "Epoch 5/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 5.1828 - RootMeanSquredError: 0.9684 - val_loss: 4.5199 - val_RootMeanSquredError: 1.0670\n",
      "Epoch 6/100\n",
      "49/49 [==============================] - 56s 1s/step - loss: 3.6166 - RootMeanSquredError: 0.9011 - val_loss: 3.1489 - val_RootMeanSquredError: 0.9391\n",
      "Epoch 7/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 2.6478 - RootMeanSquredError: 0.8643 - val_loss: 2.0623 - val_RootMeanSquredError: 0.7032\n",
      "Epoch 8/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 1.9366 - RootMeanSquredError: 0.7934 - val_loss: 1.5736 - val_RootMeanSquredError: 0.7060\n",
      "Epoch 9/100\n",
      "49/49 [==============================] - 56s 1s/step - loss: 1.5135 - RootMeanSquredError: 0.7782 - val_loss: 1.2588 - val_RootMeanSquredError: 0.7104\n",
      "Epoch 10/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 1.2492 - RootMeanSquredError: 0.7764 - val_loss: 1.0418 - val_RootMeanSquredError: 0.6989\n",
      "Epoch 11/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 1.0657 - RootMeanSquredError: 0.7653 - val_loss: 0.9220 - val_RootMeanSquredError: 0.7131\n",
      "Epoch 12/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.9335 - RootMeanSquredError: 0.7484 - val_loss: 0.8284 - val_RootMeanSquredError: 0.7048\n",
      "Epoch 13/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.8354 - RootMeanSquredError: 0.7317 - val_loss: 0.7693 - val_RootMeanSquredError: 0.7154\n",
      "Epoch 14/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.7703 - RootMeanSquredError: 0.7335 - val_loss: 0.7135 - val_RootMeanSquredError: 0.7080\n",
      "Epoch 15/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.7346 - RootMeanSquredError: 0.7326 - val_loss: 0.6861 - val_RootMeanSquredError: 0.7111\n",
      "Epoch 16/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.6839 - RootMeanSquredError: 0.7186 - val_loss: 0.6891 - val_RootMeanSquredError: 0.7267\n",
      "Epoch 17/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.6989 - RootMeanSquredError: 0.7412 - val_loss: 0.6108 - val_RootMeanSquredError: 0.6999\n",
      "Epoch 18/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.6581 - RootMeanSquredError: 0.7356 - val_loss: 0.6348 - val_RootMeanSquredError: 0.7134\n",
      "Epoch 19/100\n",
      "49/49 [==============================] - 56s 1s/step - loss: 0.6347 - RootMeanSquredError: 0.7153 - val_loss: 0.6385 - val_RootMeanSquredError: 0.7253\n",
      "Epoch 20/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.6084 - RootMeanSquredError: 0.7046 - val_loss: 0.6128 - val_RootMeanSquredError: 0.7117\n",
      "Epoch 21/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.6058 - RootMeanSquredError: 0.7029 - val_loss: 0.6527 - val_RootMeanSquredError: 0.7366\n",
      "Epoch 22/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.5564 - RootMeanSquredError: 0.6719 - val_loss: 0.6399 - val_RootMeanSquredError: 0.7329\n",
      "Epoch 23/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.5291 - RootMeanSquredError: 0.6558 - val_loss: 0.6408 - val_RootMeanSquredError: 0.7400\n",
      "Epoch 24/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.5006 - RootMeanSquredError: 0.6345 - val_loss: 0.7211 - val_RootMeanSquredError: 0.7842\n",
      "Epoch 25/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.4763 - RootMeanSquredError: 0.6131 - val_loss: 0.7036 - val_RootMeanSquredError: 0.7784\n",
      "Epoch 26/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.4823 - RootMeanSquredError: 0.6194 - val_loss: 0.6761 - val_RootMeanSquredError: 0.7648\n",
      "Epoch 27/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.4608 - RootMeanSquredError: 0.6024 - val_loss: 0.6591 - val_RootMeanSquredError: 0.7489\n",
      "Epoch 28/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.4509 - RootMeanSquredError: 0.5940 - val_loss: 0.7281 - val_RootMeanSquredError: 0.7950\n",
      "Epoch 29/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.4613 - RootMeanSquredError: 0.5990 - val_loss: 0.6813 - val_RootMeanSquredError: 0.7607\n",
      "Epoch 30/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.5758 - RootMeanSquredError: 0.6993 - val_loss: 0.5385 - val_RootMeanSquredError: 0.6968\n",
      "Epoch 31/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.6002 - RootMeanSquredError: 0.7540 - val_loss: 0.5105 - val_RootMeanSquredError: 0.6978\n",
      "Epoch 32/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.6049 - RootMeanSquredError: 0.7620 - val_loss: 0.5112 - val_RootMeanSquredError: 0.6964\n",
      "Epoch 33/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.5798 - RootMeanSquredError: 0.7360 - val_loss: 0.5507 - val_RootMeanSquredError: 0.7018\n",
      "Epoch 34/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.5324 - RootMeanSquredError: 0.6590 - val_loss: 0.7374 - val_RootMeanSquredError: 0.7863\n",
      "Epoch 35/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.5128 - RootMeanSquredError: 0.6250 - val_loss: 0.7754 - val_RootMeanSquredError: 0.8110\n",
      "Epoch 36/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.4745 - RootMeanSquredError: 0.6017 - val_loss: 0.7224 - val_RootMeanSquredError: 0.7747\n",
      "Epoch 37/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.4368 - RootMeanSquredError: 0.5652 - val_loss: 0.7089 - val_RootMeanSquredError: 0.7728\n",
      "Epoch 38/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.4688 - RootMeanSquredError: 0.5912 - val_loss: 0.7511 - val_RootMeanSquredError: 0.7974\n",
      "Epoch 39/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.4425 - RootMeanSquredError: 0.5720 - val_loss: 0.7389 - val_RootMeanSquredError: 0.7855\n",
      "Epoch 40/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.4268 - RootMeanSquredError: 0.5622 - val_loss: 0.7521 - val_RootMeanSquredError: 0.7990\n",
      "Epoch 41/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.4213 - RootMeanSquredError: 0.5541 - val_loss: 0.7241 - val_RootMeanSquredError: 0.7864\n",
      "Epoch 42/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.4142 - RootMeanSquredError: 0.5566 - val_loss: 0.7610 - val_RootMeanSquredError: 0.8099\n",
      "Epoch 43/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.3897 - RootMeanSquredError: 0.5336 - val_loss: 0.7177 - val_RootMeanSquredError: 0.7853\n",
      "Epoch 44/100\n",
      "49/49 [==============================] - 56s 1s/step - loss: 0.4004 - RootMeanSquredError: 0.5455 - val_loss: 0.7726 - val_RootMeanSquredError: 0.8219\n",
      "Epoch 45/100\n",
      "49/49 [==============================] - 56s 1s/step - loss: 0.3843 - RootMeanSquredError: 0.5318 - val_loss: 0.7232 - val_RootMeanSquredError: 0.7813\n",
      "Epoch 46/100\n",
      "49/49 [==============================] - 56s 1s/step - loss: 0.3996 - RootMeanSquredError: 0.5418 - val_loss: 0.7845 - val_RootMeanSquredError: 0.8200\n",
      "Epoch 47/100\n",
      "49/49 [==============================] - 56s 1s/step - loss: 0.3823 - RootMeanSquredError: 0.5309 - val_loss: 0.7593 - val_RootMeanSquredError: 0.8140\n",
      "Epoch 48/100\n",
      "49/49 [==============================] - 56s 1s/step - loss: 0.3850 - RootMeanSquredError: 0.5361 - val_loss: 0.8225 - val_RootMeanSquredError: 0.8470\n",
      "Epoch 49/100\n",
      "49/49 [==============================] - 56s 1s/step - loss: 0.3829 - RootMeanSquredError: 0.5300 - val_loss: 0.7746 - val_RootMeanSquredError: 0.8199\n",
      "Epoch 50/100\n",
      "49/49 [==============================] - 56s 1s/step - loss: 0.3726 - RootMeanSquredError: 0.5221 - val_loss: 0.7153 - val_RootMeanSquredError: 0.7899\n",
      "Epoch 51/100\n",
      "49/49 [==============================] - 56s 1s/step - loss: 0.3499 - RootMeanSquredError: 0.5036 - val_loss: 0.6872 - val_RootMeanSquredError: 0.7741\n",
      "Epoch 52/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.3588 - RootMeanSquredError: 0.5111 - val_loss: 0.7801 - val_RootMeanSquredError: 0.8224\n",
      "Epoch 53/100\n",
      "49/49 [==============================] - 56s 1s/step - loss: 0.3494 - RootMeanSquredError: 0.5014 - val_loss: 0.8154 - val_RootMeanSquredError: 0.8479\n",
      "Epoch 54/100\n",
      "49/49 [==============================] - 56s 1s/step - loss: 0.3389 - RootMeanSquredError: 0.4959 - val_loss: 0.7811 - val_RootMeanSquredError: 0.8311\n",
      "Epoch 55/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.3597 - RootMeanSquredError: 0.5120 - val_loss: 0.7987 - val_RootMeanSquredError: 0.8363\n",
      "Epoch 56/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.3472 - RootMeanSquredError: 0.5004 - val_loss: 0.8098 - val_RootMeanSquredError: 0.8438\n",
      "Epoch 57/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.3530 - RootMeanSquredError: 0.5046 - val_loss: 0.7696 - val_RootMeanSquredError: 0.8208\n",
      "Epoch 58/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.3378 - RootMeanSquredError: 0.4889 - val_loss: 0.7860 - val_RootMeanSquredError: 0.8325\n",
      "Epoch 59/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.3238 - RootMeanSquredError: 0.4828 - val_loss: 0.7091 - val_RootMeanSquredError: 0.7939\n",
      "Epoch 60/100\n",
      "49/49 [==============================] - 56s 1s/step - loss: 0.3234 - RootMeanSquredError: 0.4794 - val_loss: 0.7907 - val_RootMeanSquredError: 0.8382\n",
      "Epoch 61/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.3223 - RootMeanSquredError: 0.4824 - val_loss: 0.8422 - val_RootMeanSquredError: 0.8667\n",
      "Epoch 62/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.3126 - RootMeanSquredError: 0.4718 - val_loss: 0.7542 - val_RootMeanSquredError: 0.8160\n",
      "Epoch 63/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.3125 - RootMeanSquredError: 0.4684 - val_loss: 0.8050 - val_RootMeanSquredError: 0.8387\n",
      "Epoch 64/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.3296 - RootMeanSquredError: 0.4795 - val_loss: 0.7569 - val_RootMeanSquredError: 0.8179\n",
      "Epoch 65/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.3212 - RootMeanSquredError: 0.4746 - val_loss: 0.7590 - val_RootMeanSquredError: 0.8196\n",
      "Epoch 66/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.3007 - RootMeanSquredError: 0.4568 - val_loss: 0.8504 - val_RootMeanSquredError: 0.8729\n",
      "Epoch 67/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.3065 - RootMeanSquredError: 0.4606 - val_loss: 0.8332 - val_RootMeanSquredError: 0.8582\n",
      "Epoch 68/100\n",
      "49/49 [==============================] - 56s 1s/step - loss: 0.3024 - RootMeanSquredError: 0.4550 - val_loss: 0.7375 - val_RootMeanSquredError: 0.8060\n",
      "Epoch 69/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.2876 - RootMeanSquredError: 0.4416 - val_loss: 0.7605 - val_RootMeanSquredError: 0.8213\n",
      "Epoch 70/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.3037 - RootMeanSquredError: 0.4568 - val_loss: 0.7590 - val_RootMeanSquredError: 0.8188\n",
      "Epoch 71/100\n",
      "49/49 [==============================] - 56s 1s/step - loss: 0.2935 - RootMeanSquredError: 0.4486 - val_loss: 0.7212 - val_RootMeanSquredError: 0.7974\n",
      "Epoch 72/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.2882 - RootMeanSquredError: 0.4395 - val_loss: 0.8131 - val_RootMeanSquredError: 0.8477\n",
      "Epoch 73/100\n",
      "49/49 [==============================] - 56s 1s/step - loss: 0.2861 - RootMeanSquredError: 0.4419 - val_loss: 0.7474 - val_RootMeanSquredError: 0.8121\n",
      "Epoch 74/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.2843 - RootMeanSquredError: 0.4367 - val_loss: 0.8914 - val_RootMeanSquredError: 0.8906\n",
      "Epoch 75/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.2804 - RootMeanSquredError: 0.4336 - val_loss: 0.7889 - val_RootMeanSquredError: 0.8319\n",
      "Epoch 76/100\n",
      "49/49 [==============================] - 56s 1s/step - loss: 0.2750 - RootMeanSquredError: 0.4263 - val_loss: 0.7920 - val_RootMeanSquredError: 0.8336\n",
      "Epoch 77/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.2954 - RootMeanSquredError: 0.4436 - val_loss: 0.7813 - val_RootMeanSquredError: 0.8267\n",
      "Epoch 78/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.3202 - RootMeanSquredError: 0.4676 - val_loss: 0.8219 - val_RootMeanSquredError: 0.8448\n",
      "Epoch 79/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.2966 - RootMeanSquredError: 0.4417 - val_loss: 0.7685 - val_RootMeanSquredError: 0.8237\n",
      "Epoch 80/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.2928 - RootMeanSquredError: 0.4427 - val_loss: 0.8649 - val_RootMeanSquredError: 0.8778\n",
      "Epoch 81/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.2844 - RootMeanSquredError: 0.4320 - val_loss: 0.8228 - val_RootMeanSquredError: 0.8481\n",
      "Epoch 82/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.2980 - RootMeanSquredError: 0.4496 - val_loss: 0.8445 - val_RootMeanSquredError: 0.8632\n",
      "Epoch 83/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.2784 - RootMeanSquredError: 0.4279 - val_loss: 0.7403 - val_RootMeanSquredError: 0.8076\n",
      "Epoch 84/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.2711 - RootMeanSquredError: 0.4206 - val_loss: 0.7392 - val_RootMeanSquredError: 0.8074\n",
      "Epoch 85/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.2714 - RootMeanSquredError: 0.4204 - val_loss: 0.7826 - val_RootMeanSquredError: 0.8306\n",
      "Epoch 86/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.2649 - RootMeanSquredError: 0.4133 - val_loss: 0.8192 - val_RootMeanSquredError: 0.8506\n",
      "Epoch 87/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.2634 - RootMeanSquredError: 0.4126 - val_loss: 0.7600 - val_RootMeanSquredError: 0.8188\n",
      "Epoch 88/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.2587 - RootMeanSquredError: 0.4063 - val_loss: 0.7831 - val_RootMeanSquredError: 0.8313\n",
      "Epoch 89/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.2653 - RootMeanSquredError: 0.4130 - val_loss: 0.7628 - val_RootMeanSquredError: 0.8134\n",
      "Epoch 90/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.2717 - RootMeanSquredError: 0.4172 - val_loss: 0.8131 - val_RootMeanSquredError: 0.8467\n",
      "Epoch 91/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.2617 - RootMeanSquredError: 0.4087 - val_loss: 0.7851 - val_RootMeanSquredError: 0.8332\n",
      "Epoch 92/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.2622 - RootMeanSquredError: 0.4111 - val_loss: 0.7360 - val_RootMeanSquredError: 0.8052\n",
      "Epoch 93/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.2583 - RootMeanSquredError: 0.4064 - val_loss: 0.7721 - val_RootMeanSquredError: 0.8234\n",
      "Epoch 94/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.2575 - RootMeanSquredError: 0.4061 - val_loss: 0.7879 - val_RootMeanSquredError: 0.8348\n",
      "Epoch 95/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.2458 - RootMeanSquredError: 0.3940 - val_loss: 0.8448 - val_RootMeanSquredError: 0.8671\n",
      "Epoch 96/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.2514 - RootMeanSquredError: 0.4007 - val_loss: 0.8259 - val_RootMeanSquredError: 0.8558\n",
      "Epoch 97/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.2528 - RootMeanSquredError: 0.3953 - val_loss: 0.7939 - val_RootMeanSquredError: 0.8334\n",
      "Epoch 98/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.2502 - RootMeanSquredError: 0.3950 - val_loss: 0.6952 - val_RootMeanSquredError: 0.7794\n",
      "Epoch 99/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.2555 - RootMeanSquredError: 0.3994 - val_loss: 0.6932 - val_RootMeanSquredError: 0.7772\n",
      "Epoch 100/100\n",
      "49/49 [==============================] - 57s 1s/step - loss: 0.2462 - RootMeanSquredError: 0.3892 - val_loss: 0.7319 - val_RootMeanSquredError: 0.7980\n"
     ]
    }
   ],
   "source": [
    "tf.random.set_seed(2342)\n",
    "\n",
    "# fit the LSTM model for vocabulary\n",
    "vocab_lstm_model = build_lstm_model(X_train_seq)\n",
    "\n",
    "vocab_lstm_history = vocab_lstm_model.fit(X_train_seq, \n",
    "                                          y_train['vocabulary'], \n",
    "                                          batch_size=64, \n",
    "                                          epochs=100,\n",
    "                                          validation_data = [X_test_seq, y_test['vocabulary']])\n",
    "\n",
    "# fit the LSTM model for grammar\n",
    "grammar_lstm_model = build_lstm_model(X_train_seq)\n",
    "\n",
    "grammar_lstm_history = grammar_lstm_model.fit(X_train_seq, \n",
    "                                              y_train['grammar'], \n",
    "                                              batch_size=64, \n",
    "                                              epochs=100,\n",
    "                                              validation_data = [X_test_seq, y_test['grammar']])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "wWIwbDgb6fYY",
    "outputId": "208cc6c6-e187-453e-e132-450d28fc7d33"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25/25 [==============================] - 6s 211ms/step\n",
      "25/25 [==============================] - 6s 212ms/step\n",
      "RMSE for LSTM NN (vocabulary): 0.49490509806098365\n",
      "RMSE for LSTM NN (grammar): 0.6368679419222736\n"
     ]
    }
   ],
   "source": [
    "vocab_pred, grammar_pred = vocab_lstm_model.predict(X_test_seq).flatten(), grammar_lstm_model.predict(X_test_seq).flatten()\n",
    "\n",
    "lstm_predictions = pd.DataFrame(list(zip(vocab_pred, grammar_pred)), columns=['vocabulary', 'grammar'])\n",
    "\n",
    "lstm_root_mean_squared_error = rmse(y_test, lstm_predictions)\n",
    "\n",
    "print(f'''RMSE for LSTM NN (vocabulary): {rmse(y_test['vocabulary'], lstm_predictions['vocabulary'])}''')\n",
    "\n",
    "print(f'''RMSE for LSTM NN (grammar): {rmse(y_test['grammar'], lstm_predictions['grammar'])}''')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "THmkx4Lv6fYZ",
    "outputId": "c159520e-43ba-46d9-8611-384a921c1555"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.11;0.775x0.77)\n",
      "AxesSubplot(0.125,0.11;0.775x0.77)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vocab_lstm_history_df = pd.DataFrame(vocab_lstm_history.history)\n",
    "grammar_lstm_history_df = pd.DataFrame(grammar_lstm_history.history)\n",
    "\n",
    "vocab_lstm_history_df\n",
    "\n",
    "print(vocab_lstm_history_df.loc[:,['RootMeanSquredError','val_RootMeanSquredError']].plot(title='Vocabulary (LSTM)', xlabel='Epoch', ylabel='RMSE'))\n",
    "print(grammar_lstm_history_df.loc[:,['RootMeanSquredError','val_RootMeanSquredError']].plot(title='Grammar (LSTM)', xlabel='Epoch', ylabel='RMSE'))"
   ]
  },
  {
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      "text/plain": [
       "      vocabulary  grammar\n",
       "247          3.0      4.0\n",
       "1360         3.5      2.5\n",
       "3318         2.5      2.5\n",
       "2337         3.0      3.0\n",
       "3631         2.5      2.5\n",
       "...          ...      ...\n",
       "1130         4.0      3.5\n",
       "1294         3.5      4.0\n",
       "860          3.0      2.0\n",
       "3507         3.5      3.0\n",
       "3174         2.5      2.0\n",
       "\n",
       "[3128 rows x 2 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "KlRjax4LCoUf"
   },
   "outputs": [],
   "source": [
    "import xgboost as xg\n",
    "from sklearn.metrics import mean_squared_error as MSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "qwfKfjcSIvRd"
   },
   "outputs": [],
   "source": [
    "def xgboost_syntax(y_choice, x_train, x_test, m):\n",
    "    model = tf.keras.models.Model(\n",
    "    m.input, \n",
    "    m.get_layer('our_param').output\n",
    "    )\n",
    "    if y_choice=='grammar':\n",
    "        data_y_train = y_train['grammar']\n",
    "        data_y_test = y_test['grammar']\n",
    "    elif y_choice=='vocabulary':\n",
    "        data_y_train = y_train['vocabulary']\n",
    "        data_y_test = y_test['vocabulary']\n",
    "    X_train_features = model.predict(x_train)\n",
    "    X_test_features = model.predict(x_test)\n",
    "    xgb = xg.XGBRegressor(objective ='reg:squarederror',n_estimators = 100)\n",
    "    xgb.fit(X_train_features, data_y_train)\n",
    "    y_pred = xgb.predict(X_test_features)\n",
    "    loss_metrics = MSE(data_y_test, y_pred)\n",
    "    return [y_pred, data_y_test, loss_metrics, ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "hZEmHQvUO5dS",
    "outputId": "bc62947e-196c-43b1-e68b-e5bceeb671f5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "98/98 [==============================] - 21s 209ms/step\n",
      "25/25 [==============================] - 5s 211ms/step\n"
     ]
    }
   ],
   "source": [
    "[y_pred, y_true, loss_metrics1] = xgboost_syntax('vocabulary', X_train_seq, X_test_seq, vocab_lstm_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "e-3GhxXaYDjB",
    "outputId": "a90e8c0d-8505-4a1e-bbf5-4b71095b0879"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mean squared error for XGBooster in Vocabulary is: 0.5327805532050942 0.7299181825417793\n"
     ]
    }
   ],
   "source": [
    "print(\"The mean squared error for XGBooster in Vocabulary is:\",loss_metrics1, np.sqrt(loss_metrics1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "5cYt1XbYPw3c",
    "outputId": "abb62173-e20a-4287-dc63-0bb5539f7463"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "98/98 [==============================] - 21s 210ms/step\n",
      "25/25 [==============================] - 5s 213ms/step\n"
     ]
    }
   ],
   "source": [
    "[y_pred, y_true, loss_metrics2] = xgboost_syntax('grammar', X_train_seq, X_test_seq, grammar_lstm_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Gz95CVnNW5Ur",
    "outputId": "7d33de3b-783d-45ac-bcdc-02f7e8bcd6fe"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mean squared error for XGBooster in Grammar is: 0.659163106395932 0.8118886046718059\n"
     ]
    }
   ],
   "source": [
    "print(\"The mean squared error for XGBooster in Grammar is:\",loss_metrics2, np.sqrt(loss_metrics2))"
   ]
  }
 ],
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