cnn.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from numpy import dot\n",
    "from numpy.linalg import norm\n",
    "import re\n",
    "import string\n",
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "from TurkishStemmer import TurkishStemmer\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.feature_selection import SelectKBest, chi2, f_regression\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer, TfidfTransformer\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import recall_score\n",
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import f1_score\n",
    "from keras.models import Sequential\n",
    "from keras import layers\n",
    "import tensorflow as tf\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_percentage = 0.3\n",
    "num_of_classes = 2\n",
    "max_feature_size = 1000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LOAD DATA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {},
   "outputs": [],
   "source": [
    "FILENAME = \"data/database_kara.csv\"          \n",
    "\n",
    "dataset = pd.read_csv(FILENAME, header = None)\n",
    "\n",
    "dataset.rename(columns = {0:'text', 1:'label'}, inplace = True)\n",
    "\n",
    "dataset.dropna(how='any', inplace=True)\n",
    "dataset.reset_index(drop=True, inplace=True)\n",
    "\n",
    "dataset['en_label'] = dataset[\"label\"]\n",
    "for i in range(dataset.shape[0]):\n",
    "    label = dataset.loc[i,\"en_label\"]\n",
    "    label = re.sub(\"[Yy]\", \"\", label)\n",
    "    # delete rows with wrong type of label\n",
    "    if type(label) == float or label == \"\" or label == \" \":\n",
    "        dataset.drop([i], axis=0, inplace=True)\n",
    "        continue\n",
    "    dataset.loc[i,\"en_label\"] = label\n",
    "    \n",
    "# Shape = (sentence_count, 3) : sentence, label, encoded label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>label</th>\n",
       "      <th>en_label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Birçok toplum ilham ve sezişlerine rüyalarına ...</td>\n",
       "      <td>Y2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Günümüzde Aristo’nun bilimsel bilgisinden fazl...</td>\n",
       "      <td>Y2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bilimin ilerlemesi bilinmeyenler diyarına deva...</td>\n",
       "      <td>Y2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>İçinde bulunduğumuz kara parçası dünyada birço...</td>\n",
       "      <td>Y1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kara duvara aşkını kazırken düşündüğü şey atmo...</td>\n",
       "      <td>Y2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1514</th>\n",
       "      <td>Şimdi birer düş gibi anımsıyordu bütün bunları...</td>\n",
       "      <td>Y2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1515</th>\n",
       "      <td>Ne kendi kendine ne de akasyaların kara gölges...</td>\n",
       "      <td>Y2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1516</th>\n",
       "      <td>Her zamanki yolundan değil otlaktan geçerek ay...</td>\n",
       "      <td>Y2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1517</th>\n",
       "      <td>Upuzun kara saçları esmer teni dans ettikçe pı...</td>\n",
       "      <td>Y2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1518</th>\n",
       "      <td>Montreuilsurmer eski zamanlardan beri İngiliz ...</td>\n",
       "      <td>Y2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1518 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   text label en_label\n",
       "0     Birçok toplum ilham ve sezişlerine rüyalarına ...    Y2        2\n",
       "1     Günümüzde Aristo’nun bilimsel bilgisinden fazl...    Y2        2\n",
       "2     Bilimin ilerlemesi bilinmeyenler diyarına deva...    Y2        2\n",
       "3     İçinde bulunduğumuz kara parçası dünyada birço...    Y1        1\n",
       "4     Kara duvara aşkını kazırken düşündüğü şey atmo...    Y2        2\n",
       "...                                                 ...   ...      ...\n",
       "1514  Şimdi birer düş gibi anımsıyordu bütün bunları...    Y2        2\n",
       "1515  Ne kendi kendine ne de akasyaların kara gölges...    Y2        2\n",
       "1516  Her zamanki yolundan değil otlaktan geçerek ay...    Y2        2\n",
       "1517  Upuzun kara saçları esmer teni dans ettikçe pı...    Y2        2\n",
       "1518  Montreuilsurmer eski zamanlardan beri İngiliz ...    Y2        2\n",
       "\n",
       "[1518 rows x 3 columns]"
      ]
     },
     "execution_count": 299,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {},
   "outputs": [],
   "source": [
    "del dataset[\"label\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 302,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.isnull().values.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>en_label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Birçok toplum ilham ve sezişlerine rüyalarına ...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Günümüzde Aristo’nun bilimsel bilgisinden fazl...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bilimin ilerlemesi bilinmeyenler diyarına deva...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>İçinde bulunduğumuz kara parçası dünyada birço...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kara duvara aşkını kazırken düşündüğü şey atmo...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1513</th>\n",
       "      <td>Şimdi birer düş gibi anımsıyordu bütün bunları...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1514</th>\n",
       "      <td>Ne kendi kendine ne de akasyaların kara gölges...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1515</th>\n",
       "      <td>Her zamanki yolundan değil otlaktan geçerek ay...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1516</th>\n",
       "      <td>Upuzun kara saçları esmer teni dans ettikçe pı...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1517</th>\n",
       "      <td>Montreuilsurmer eski zamanlardan beri İngiliz ...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1518 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   text en_label\n",
       "0     Birçok toplum ilham ve sezişlerine rüyalarına ...        2\n",
       "1     Günümüzde Aristo’nun bilimsel bilgisinden fazl...        2\n",
       "2     Bilimin ilerlemesi bilinmeyenler diyarına deva...        2\n",
       "3     İçinde bulunduğumuz kara parçası dünyada birço...        1\n",
       "4     Kara duvara aşkını kazırken düşündüğü şey atmo...        2\n",
       "...                                                 ...      ...\n",
       "1513  Şimdi birer düş gibi anımsıyordu bütün bunları...        2\n",
       "1514  Ne kendi kendine ne de akasyaların kara gölges...        2\n",
       "1515  Her zamanki yolundan değil otlaktan geçerek ay...        2\n",
       "1516  Upuzun kara saçları esmer teni dans ettikçe pı...        2\n",
       "1517  Montreuilsurmer eski zamanlardan beri İngiliz ...        2\n",
       "\n",
       "[1518 rows x 2 columns]"
      ]
     },
     "execution_count": 303,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PREPROCESSING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {},
   "outputs": [],
   "source": [
    "s = set(stopwords.words('turkish'))\n",
    "ps = nltk.wordnet.WordNetLemmatizer()\n",
    "\n",
    "for i in range(dataset.shape[0]):\n",
    "    review = dataset.loc[i,'text']\n",
    "    for punc in string.punctuation:\n",
    "        review.replace(punc, \"\")\n",
    "    review = review.lower()\n",
    "    review = review.split()\n",
    "    review = [ps.lemmatize(word) for word in review if not word in s]\n",
    "    #review = [stemmer.stem(word) for word in review if not word in s]\n",
    "    review = ' '.join(review)\n",
    "    dataset.loc[i, 'text'] = review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset.dropna(how='any', inplace=True)\n",
    "dataset.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>en_label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>birçok toplum ilham sezişlerine rüyalarına gai...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>günümüzde aristo’nun bilimsel bilgisinden fazl...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>bilimin ilerlemesi bilinmeyenler diyarına deva...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>i̇çinde bulunduğumuz kara parçası dünyada birç...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>kara duvara aşkını kazırken düşündüğü atmosfer...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1513</th>\n",
       "      <td>şimdi birer düş anımsıyordu bütün bunları çarp...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1514</th>\n",
       "      <td>kendi kendine akasyaların kara gölgesinde dişl...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1515</th>\n",
       "      <td>zamanki yolundan değil otlaktan geçerek ay ışı...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1516</th>\n",
       "      <td>upuzun kara saçları esmer teni dans ettikçe pı...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1517</th>\n",
       "      <td>montreuilsurmer eski zamanlardan beri i̇ngiliz...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1518 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   text en_label\n",
       "0     birçok toplum ilham sezişlerine rüyalarına gai...        2\n",
       "1     günümüzde aristo’nun bilimsel bilgisinden fazl...        2\n",
       "2     bilimin ilerlemesi bilinmeyenler diyarına deva...        2\n",
       "3     i̇çinde bulunduğumuz kara parçası dünyada birç...        1\n",
       "4     kara duvara aşkını kazırken düşündüğü atmosfer...        2\n",
       "...                                                 ...      ...\n",
       "1513  şimdi birer düş anımsıyordu bütün bunları çarp...        2\n",
       "1514  kendi kendine akasyaların kara gölgesinde dişl...        2\n",
       "1515  zamanki yolundan değil otlaktan geçerek ay ışı...        2\n",
       "1516  upuzun kara saçları esmer teni dans ettikçe pı...        2\n",
       "1517  montreuilsurmer eski zamanlardan beri i̇ngiliz...        2\n",
       "\n",
       "[1518 rows x 2 columns]"
      ]
     },
     "execution_count": 306,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### VECTORIZE TEXT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "metadata": {},
   "outputs": [],
   "source": [
    "count_vect = CountVectorizer()\n",
    "count_vectors = count_vect.fit_transform(dataset[\"text\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  (0, 1102)\t1\n",
      "  (0, 6789)\t1\n",
      "  (0, 3491)\t1\n",
      "  (0, 6148)\t1\n",
      "  (0, 5789)\t1\n",
      "  (0, 2466)\t1\n",
      "  (0, 6111)\t1\n",
      "  (0, 2392)\t1\n",
      "  (0, 5732)\t1\n",
      "  (0, 2515)\t1\n",
      "  (0, 1002)\t1\n",
      "  (0, 6379)\t1\n",
      "  (0, 3911)\t1\n",
      "  (0, 5745)\t1\n",
      "  (0, 3549)\t1\n",
      "  (0, 3513)\t1\n"
     ]
    }
   ],
   "source": [
    "print(count_vectors[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = dataset[\"en_label\"].to_numpy(dtype=int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = y - 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, ..., 1, 1, 1])"
      ]
     },
     "execution_count": 311,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1518,)"
      ]
     },
     "execution_count": 312,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Feature Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {},
   "outputs": [],
   "source": [
    "select = SelectKBest(score_func=chi2, k=max_feature_size)\n",
    "count_vectors = select.fit_transform(count_vectors, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<1518x1000 sparse matrix of type '<class 'numpy.int64'>'\n",
       "\twith 4730 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 314,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_vectors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### SPLIT DATA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(count_vectors, y, test_size=(1-train_percentage))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = X_train.toarray()\n",
    "X_test = X_test.toarray()\n",
    "y_train = np.asarray(y_train, dtype=int)\n",
    "y_test = np.asarray(y_test, dtype=int)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Reshape Input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "metadata": {},
   "outputs": [],
   "source": [
    "nrows, ncols = X_train.shape\n",
    "X_train = X_train.reshape(nrows, ncols, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "metadata": {},
   "outputs": [],
   "source": [
    "nrows, ncols = X_test.shape\n",
    "X_test = X_test.reshape(nrows, ncols, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "metadata": {},
   "outputs": [],
   "source": [
    "nrows = y_train.shape[0]\n",
    "y_train = y_train.reshape(nrows, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "metadata": {},
   "outputs": [],
   "source": [
    "nrows = y_test.shape[0]\n",
    "y_test = y_test.reshape(nrows, 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MODEL TRAINING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(layers.Embedding(max_feature_size, 32, input_length=max_feature_size))\n",
    "model.add(layers.Conv1D(32, 3, input_shape=X_train.shape[1:]))\n",
    "model.add(layers.MaxPooling1D())\n",
    "#model.add(layers.Conv1D(32, 3, input_shape=X_train.shape[1:]))\n",
    "#model.add(layers.MaxPooling1D())\n",
    "model.add(layers.Flatten())\n",
    "model.add(layers.Dense(128, activation=\"relu\"))\n",
    "model.add(layers.Dropout(rate=0.5))\n",
    "if num_of_classes > 2:\n",
    "    model.add(layers.Dense(6, activation=\"softmax\"))\n",
    "    model.compile(loss=\"sparse_categorical_crossentropy\",optimizer=tf.keras.optimizers.Adam(learning_rate=10e-4),metrics=[\"accuracy\"])\n",
    "else:\n",
    "    model.add(layers.Dense(1, activation=\"sigmoid\"))\n",
    "    model.compile(loss=\"binary_crossentropy\" , optimizer=\"adam\", metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_11\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " embedding_11 (Embedding)    (None, 1000, 32)          32000     \n",
      "                                                                 \n",
      " conv1d_12 (Conv1D)          (None, 998, 32)           3104      \n",
      "                                                                 \n",
      " max_pooling1d_12 (MaxPoolin  (None, 499, 32)          0         \n",
      " g1D)                                                            \n",
      "                                                                 \n",
      " flatten_11 (Flatten)        (None, 15968)             0         \n",
      "                                                                 \n",
      " dense_22 (Dense)            (None, 128)               2044032   \n",
      "                                                                 \n",
      " dropout_11 (Dropout)        (None, 128)               0         \n",
      "                                                                 \n",
      " dense_23 (Dense)            (None, 1)                 129       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 2,079,265\n",
      "Trainable params: 2,079,265\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/15\n",
      "4/4 [==============================] - 1s 134ms/step - loss: 0.6083 - accuracy: 0.6176 - val_loss: 0.5437 - val_accuracy: 0.7770\n",
      "Epoch 2/15\n",
      "4/4 [==============================] - 0s 107ms/step - loss: 0.5672 - accuracy: 0.7736 - val_loss: 0.5422 - val_accuracy: 0.7770\n",
      "Epoch 3/15\n",
      "4/4 [==============================] - 0s 109ms/step - loss: 0.5402 - accuracy: 0.7736 - val_loss: 0.5293 - val_accuracy: 0.7770\n",
      "Epoch 4/15\n",
      "4/4 [==============================] - 0s 101ms/step - loss: 0.5461 - accuracy: 0.7736 - val_loss: 0.5239 - val_accuracy: 0.7770\n",
      "Epoch 5/15\n",
      "4/4 [==============================] - 0s 115ms/step - loss: 0.5371 - accuracy: 0.7736 - val_loss: 0.5277 - val_accuracy: 0.7770\n",
      "Epoch 6/15\n",
      "4/4 [==============================] - 0s 108ms/step - loss: 0.5313 - accuracy: 0.7736 - val_loss: 0.5167 - val_accuracy: 0.7770\n",
      "Epoch 7/15\n",
      "4/4 [==============================] - 0s 107ms/step - loss: 0.5238 - accuracy: 0.7736 - val_loss: 0.5117 - val_accuracy: 0.7770\n",
      "Epoch 8/15\n",
      "4/4 [==============================] - 0s 103ms/step - loss: 0.5203 - accuracy: 0.7736 - val_loss: 0.5057 - val_accuracy: 0.7770\n",
      "Epoch 9/15\n",
      "4/4 [==============================] - 0s 111ms/step - loss: 0.5105 - accuracy: 0.7736 - val_loss: 0.5024 - val_accuracy: 0.7770\n",
      "Epoch 10/15\n",
      "4/4 [==============================] - 0s 126ms/step - loss: 0.4950 - accuracy: 0.7736 - val_loss: 0.4881 - val_accuracy: 0.7770\n",
      "Epoch 11/15\n",
      "4/4 [==============================] - 0s 120ms/step - loss: 0.4847 - accuracy: 0.7736 - val_loss: 0.4738 - val_accuracy: 0.7770\n",
      "Epoch 12/15\n",
      "4/4 [==============================] - 0s 110ms/step - loss: 0.4768 - accuracy: 0.7780 - val_loss: 0.4584 - val_accuracy: 0.7770\n",
      "Epoch 13/15\n",
      "4/4 [==============================] - 0s 118ms/step - loss: 0.4453 - accuracy: 0.7758 - val_loss: 0.4425 - val_accuracy: 0.7770\n",
      "Epoch 14/15\n",
      "4/4 [==============================] - 0s 107ms/step - loss: 0.4247 - accuracy: 0.7934 - val_loss: 0.4215 - val_accuracy: 0.8006\n",
      "Epoch 15/15\n",
      "4/4 [==============================] - 0s 134ms/step - loss: 0.3939 - accuracy: 0.8352 - val_loss: 0.3976 - val_accuracy: 0.7912\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f725a0d3310>"
      ]
     },
     "execution_count": 323,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train, epochs=15, validation_data=[X_test, y_test], batch_size=128)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9/9 [==============================] - 0s 18ms/step\n"
     ]
    }
   ],
   "source": [
    "y_prob = model.predict(X_test, batch_size=128)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "metadata": {},
   "outputs": [],
   "source": [
    "if num_of_classes > 2:\n",
    "    y_predicted = y_prob.argmax(axis=-1)\n",
    "else:\n",
    "    y_predicted = [1 if x >0.4 else 0 for x in y_prob]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "metadata": {},
   "outputs": [
    {
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