training_mode2l.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### import libraries and platforms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# python 3.9\n",
    "#pip 20.2.4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorboard\\compat\\tensorflow_stub\\dtypes.py:541: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorboard\\compat\\tensorflow_stub\\dtypes.py:542: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorboard\\compat\\tensorflow_stub\\dtypes.py:543: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorboard\\compat\\tensorflow_stub\\dtypes.py:544: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorboard\\compat\\tensorflow_stub\\dtypes.py:545: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorboard\\compat\\tensorflow_stub\\dtypes.py:550: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n"
     ]
    }
   ],
   "source": [
    "# trafile 2020.10.1\n",
    "import tarfile\n",
    "\n",
    "#pandas 1.1.3\n",
    "import pandas as pd\n",
    "\n",
    "# requests 2.24.0\n",
    "import re\n",
    "\n",
    "# tensorflow 2.4\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "\n",
    "#nltk 3.5\n",
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "\n",
    "#matplotlip 3.3.2\n",
    "import matplotlib.pyplot as plt\n",
    "# numpy 1.19.5\n",
    "import numpy as np\n",
    "from numpy import array\n",
    "from numpy import asarray\n",
    "from numpy import zeros\n",
    "\n",
    "# keras 2.4.3\n",
    "from keras.preprocessing.text import one_hot\n",
    "from keras.preprocessing.sequence import pad_sequences\n",
    "from keras.models import Sequential\n",
    "from keras.layers.core import Activation, Dropout, Dense\n",
    "from keras.layers import Flatten, LSTM\n",
    "from keras.layers import GlobalMaxPooling1D\n",
    "from keras.layers.embeddings import Embedding\n",
    "from keras.preprocessing.text import Tokenizer\n",
    "\n",
    "#sklearn 0.23.2\n",
    "from sklearn.model_selection import train_test_split\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3)  Read the data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50000, 2)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# READ THE CSV FILE\n",
    "movie_reviews = pd.read_csv(\"./IMDB Dataset.csv\")\n",
    "\n",
    "movie_reviews.isnull().values.any()\n",
    "\n",
    "movie_reviews.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>review</th>\n",
       "      <th>sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>One of the other reviewers has mentioned that ...</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A wonderful little production. &lt;br /&gt;&lt;br /&gt;The...</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>I thought this was a wonderful way to spend ti...</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Basically there's a family where a little boy ...</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Petter Mattei's \"Love in the Time of Money\" is...</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Probably my all-time favorite movie, a story o...</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>I sure would like to see a resurrection of a u...</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>This show was an amazing, fresh &amp; innovative i...</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Encouraged by the positive comments about this...</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>If you like original gut wrenching laughter yo...</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Phil the Alien is one of those quirky films wh...</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>I saw this movie when I was about 12 when it c...</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>So im not a big fan of Boll's work but then ag...</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>The cast played Shakespeare.&lt;br /&gt;&lt;br /&gt;Shakes...</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>This a fantastic movie of three prisoners who ...</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Kind of drawn in by the erotic scenes, only to...</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Some films just simply should not be remade. T...</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>This movie made it into one of my top 10 most ...</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>I remember this film,it was the first film i h...</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>An awful film! It must have been up against so...</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               review sentiment\n",
       "0   One of the other reviewers has mentioned that ...  positive\n",
       "1   A wonderful little production. <br /><br />The...  positive\n",
       "2   I thought this was a wonderful way to spend ti...  positive\n",
       "3   Basically there's a family where a little boy ...  negative\n",
       "4   Petter Mattei's \"Love in the Time of Money\" is...  positive\n",
       "5   Probably my all-time favorite movie, a story o...  positive\n",
       "6   I sure would like to see a resurrection of a u...  positive\n",
       "7   This show was an amazing, fresh & innovative i...  negative\n",
       "8   Encouraged by the positive comments about this...  negative\n",
       "9   If you like original gut wrenching laughter yo...  positive\n",
       "10  Phil the Alien is one of those quirky films wh...  negative\n",
       "11  I saw this movie when I was about 12 when it c...  negative\n",
       "12  So im not a big fan of Boll's work but then ag...  negative\n",
       "13  The cast played Shakespeare.<br /><br />Shakes...  negative\n",
       "14  This a fantastic movie of three prisoners who ...  positive\n",
       "15  Kind of drawn in by the erotic scenes, only to...  negative\n",
       "16  Some films just simply should not be remade. T...  positive\n",
       "17  This movie made it into one of my top 10 most ...  negative\n",
       "18  I remember this film,it was the first film i h...  positive\n",
       "19  An awful film! It must have been up against so...  negative"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#sample of the data\n",
    "movie_reviews.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"Basically there's a family where a little boy (Jake) thinks there's a zombie in his closet & his parents are fighting all the time.<br /><br />This movie is slower than a soap opera... and suddenly, Jake decides to become Rambo and kill the zombie.<br /><br />OK, first of all when you're going to make a film you must Decide if its a thriller or a drama! As a drama the movie is watchable. Parents are divorcing & arguing like in real life. And then we have Jake with his closet which totally ruins all the film! I expected to see a BOOGEYMAN similar movie, and instead i watched a drama with some meaningless thriller spots.<br /><br />3 out of 10 just for the well playing parents & descent dialogs. As for the shots with Jake: just ignore them.\""
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#showing 3 reviews\n",
    "movie_reviews[\"review\"][3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## preproccessing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#cleaning data \n",
    "\n",
    "TAG_RE = re.compile(r'<[^>]+>')\n",
    "\n",
    "def remove_tags(text):\n",
    "    return TAG_RE.sub('', text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess_text(sen):\n",
    "    # Removing html tags\n",
    "    sentence = remove_tags(sen)\n",
    "\n",
    "    # Remove punctuations and numbers\n",
    "    sentence = re.sub('[^a-zA-Z]', ' ', sentence)\n",
    "\n",
    "    # Single character removal\n",
    "    sentence = re.sub(r\"\\s+[a-zA-Z]\\s+\", ' ', sentence)\n",
    "\n",
    "    # Removing multiple spaces\n",
    "    sentence = re.sub(r'\\s+', ' ', sentence)\n",
    "\n",
    "    return sentence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = []\n",
    "sentences = list(movie_reviews['review'])\n",
    "for sen in sentences:\n",
    "    X.append(preprocess_text(sen))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"Basically there's a family where a little boy (Jake) thinks there's a zombie in his closet & his parents are fighting all the time.<br /><br />This movie is slower than a soap opera... and suddenly, Jake decides to become Rambo and kill the zombie.<br /><br />OK, first of all when you're going to make a film you must Decide if its a thriller or a drama! As a drama the movie is watchable. Parents are divorcing & arguing like in real life. And then we have Jake with his closet which totally ruins all the film! I expected to see a BOOGEYMAN similar movie, and instead i watched a drama with some meaningless thriller spots.<br /><br />3 out of 10 just for the well playing parents & descent dialogs. As for the shots with Jake: just ignore them.\""
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#3rd review example before preproccessing\n",
    "movie_reviews[\"review\"][3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Basically there a family where little boy Jake thinks there a zombie in his closet his parents are fighting all the time This movie is slower than soap opera and suddenly Jake decides to become Rambo and kill the zombie OK first of all when you re going to make film you must Decide if its thriller or drama As drama the movie is watchable Parents are divorcing arguing like in real life And then we have Jake with his closet which totally ruins all the film expected to see BOOGEYMAN similar movie and instead watched drama with some meaningless thriller spots out of just for the well playing parents descent dialogs As for the shots with Jake just ignore them '"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#3rd review example after preproccessing\n",
    "X[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = movie_reviews['sentiment']\n",
    "\n",
    "# when review is positive make it =1 , when it is negative make it =0\n",
    "y = np.array(list(map(lambda x: 1 if x==\"positive\" else 0, y)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# splitting data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Splitting data train:70% validation:10% test: 20%\n",
    "X_train, y_train = X[:35000],  y[:35000]\n",
    "X_val,y_val= X[35000:40000], y[35000:40000] \n",
    "X_test , y_test =X[40000:50000], y[40000:50000]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### tokenizing  data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#extract tokens from the text the number of tokens is 5000\n",
    "tokenizer = Tokenizer(num_words=5000)\n",
    "tokenizer.fit_on_texts(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert the text to a numeric sequence \n",
    "X_train = tokenizer.texts_to_sequences(X_train)\n",
    "X_test = tokenizer.texts_to_sequences(X_test)\n",
    "X_val = tokenizer.texts_to_sequences(X_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 87377 unique tokens.\n"
     ]
    }
   ],
   "source": [
    "# Adding 1 because of reserved 0 index\n",
    "vocab_size = len(tokenizer.word_index) + 1\n",
    "\n",
    "print('Found %s unique tokens.' % vocab_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "maxlen: Optional Int, maximum length of all sequences. If not provided,  sequences will be padded to the length of the longest individual sequence.\n",
    "padding: 'post'means the pad is after each sequence."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#max length of sequence\n",
    "\n",
    "maxlen = 256\n",
    "X_train = pad_sequences(X_train, padding='post', maxlen=maxlen)\n",
    "X_val = pad_sequences(X_val, padding='post', maxlen=maxlen)\n",
    "X_test = pad_sequences(X_test, padding='post', maxlen=maxlen)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vectors for representing words are generally called embeddings, because the word is embedded in a particular vector space."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing the dictionary of words 'glove.6B.200d'\n",
    "\n",
    "embeddings_dictionary = dict()\n",
    "glove_file = open('./glove.6B.200d.txt', encoding=\"utf8\")\n",
    "\n",
    "for line in glove_file:\n",
    "    records = line.split()\n",
    "    word = records[0]\n",
    "    vector_dimensions = asarray(records[1:], dtype='float32')\n",
    "    embeddings_dictionary [word] = vector_dimensions\n",
    "glove_file.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "embedding_matrix = zeros((vocab_size, 200))\n",
    "for word, index in tokenizer.word_index.items():\n",
    "    embedding_vector = embeddings_dictionary.get(word)\n",
    "    if embedding_vector is not None:\n",
    "        embedding_matrix[index] = embedding_vector"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# building the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Layer 1, LSTM(128), reads the input data and outputs 128 features with 2 timesteps for each because return_sequences=True.\n",
    " - Setting the return_sequences=True makes each cell per timestep emit a signal.\n",
    " \n",
    "- Layer 2, LSTM(32), takes the 3x128 input from Layer 1 and reduces the feature size to 32. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Initialising the RNN\n",
    "model = Sequential()\n",
    "embedding_layer = Embedding(vocab_size, 200,\n",
    "                            weights=[embedding_matrix],\n",
    "                            input_length=maxlen ,\n",
    "                            trainable=False)\n",
    "model.add(embedding_layer)\n",
    "#first LSTM layer\n",
    "model.add(LSTM(128,return_sequences=True))\n",
    "model.add(Dropout(0.2))\n",
    "#second LSTM layer\n",
    "model.add(LSTM(32))\n",
    "model.add(Dropout(0.2))\n",
    "\n",
    "# Adding the output layer\n",
    "# For Full connection layer we use dense\n",
    "# As the output is 1D so we use unit=1\n",
    "#activation is sigmoid \n",
    "model.add(Dense(1, activation='sigmoid'))\n",
    "#optimizer is Adam\n",
    "#compile and fit the model \n",
    "model.compile(optimizer = 'adam',\n",
    "              loss = 'mean_squared_error',\n",
    "              metrics=['acc'])   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding (Embedding)        (None, 256, 200)          17475400  \n",
      "_________________________________________________________________\n",
      "lstm (LSTM)                  (None, 256, 128)          168448    \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 256, 128)          0         \n",
      "_________________________________________________________________\n",
      "lstm_1 (LSTM)                (None, 32)                20608     \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 32)                0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 1)                 33        \n",
      "=================================================================\n",
      "Total params: 17,664,489\n",
      "Trainable params: 189,089\n",
      "Non-trainable params: 17,475,400\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(model.summary())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from keras.utils.vis_utils import plot_model\n",
    "\n",
    "plot_model(model,\n",
    "           to_file='model_plot.png',\n",
    "           show_shapes=True, \n",
    "           show_layer_names=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/16\n",
      "140/140 [==============================] - 285s 2s/step - loss: 0.2455 - acc: 0.5408 - val_loss: 0.2312 - val_acc: 0.6308\n",
      "Epoch 2/16\n",
      "140/140 [==============================] - 291s 2s/step - loss: 0.2413 - acc: 0.5709 - val_loss: 0.2469 - val_acc: 0.5354\n",
      "Epoch 3/16\n",
      "140/140 [==============================] - 297s 2s/step - loss: 0.2467 - acc: 0.5292 - val_loss: 0.2486 - val_acc: 0.5072\n",
      "Epoch 4/16\n",
      "140/140 [==============================] - 307s 2s/step - loss: 0.2430 - acc: 0.5502 - val_loss: 0.1850 - val_acc: 0.7610\n",
      "Epoch 5/16\n",
      "140/140 [==============================] - 310s 2s/step - loss: 0.2405 - acc: 0.6174 - val_loss: 0.2495 - val_acc: 0.5110\n",
      "Epoch 6/16\n",
      "140/140 [==============================] - 311s 2s/step - loss: 0.2499 - acc: 0.5114 - val_loss: 0.2486 - val_acc: 0.5174\n",
      "Epoch 7/16\n",
      "140/140 [==============================] - 312s 2s/step - loss: 0.2469 - acc: 0.5275 - val_loss: 0.2480 - val_acc: 0.5388\n",
      "Epoch 8/16\n",
      "140/140 [==============================] - 315s 2s/step - loss: 0.2472 - acc: 0.5281 - val_loss: 0.2329 - val_acc: 0.5708\n",
      "Epoch 9/16\n",
      "140/140 [==============================] - 316s 2s/step - loss: 0.2461 - acc: 0.5439 - val_loss: 0.2451 - val_acc: 0.5244\n",
      "Epoch 10/16\n",
      "140/140 [==============================] - 315s 2s/step - loss: 0.2206 - acc: 0.6447 - val_loss: 0.1967 - val_acc: 0.6710\n",
      "Epoch 11/16\n",
      "140/140 [==============================] - 316s 2s/step - loss: 0.1731 - acc: 0.7697 - val_loss: 0.1256 - val_acc: 0.8304\n",
      "Epoch 12/16\n",
      "140/140 [==============================] - 321s 2s/step - loss: 0.1232 - acc: 0.8310 - val_loss: 0.1161 - val_acc: 0.8440\n",
      "Epoch 13/16\n",
      "140/140 [==============================] - 314s 2s/step - loss: 0.1157 - acc: 0.8424 - val_loss: 0.1099 - val_acc: 0.8512\n",
      "Epoch 14/16\n",
      "140/140 [==============================] - 318s 2s/step - loss: 0.1082 - acc: 0.8521 - val_loss: 0.1058 - val_acc: 0.8564\n",
      "Epoch 15/16\n",
      "140/140 [==============================] - 314s 2s/step - loss: 0.1095 - acc: 0.8471 - val_loss: 0.1045 - val_acc: 0.8542\n",
      "Epoch 16/16\n",
      "140/140 [==============================] - 312s 2s/step - loss: 0.1003 - acc: 0.8612 - val_loss: 0.1009 - val_acc: 0.8632\n",
      "313/313 [==============================] - 43s 133ms/step - loss: 0.0988 - acc: 0.8624\n"
     ]
    }
   ],
   "source": [
    "trained_model=model.fit(X_train, y_train, batch_size=140, epochs=16, verbose=1,  validation_data=(X_val, y_val) ,   shuffle=False)\n",
    "score = model.evaluate(X_test, y_test, verbose=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(X_test)\n",
    "y_pred = (y_pred > 0.5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4647,  942],\n",
       "       [ 346, 4065]], dtype=int64)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "cm =confusion_matrix(y_pred, y_test)\n",
    "cm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# F1 Score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The formula for the standard F1-score is the harmonic mean of the precision and recall. A perfect model has an F-score of 1.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1-score: 0.863240603100446\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import f1_score\n",
    "print('F1-score: {0}'.format(f1_score(y_pred, y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "the accuracy is the proportion of correct predictions (both true positives and true negatives) among the total number of cases examined. \n",
    "- Accuracy = (TP + TN)/(TP + TN + FP + FN)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test score and accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Score: 0.10300113260746002\n",
      "Test Accuracy: 0.8712000250816345\n"
     ]
    }
   ],
   "source": [
    "print(\"Test Score:\", score[0])\n",
    "print(\"Test Accuracy:\", score[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Validation score and accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "157/157 [==============================] - 10s 61ms/step - loss: 0.1022 - acc: 0.8720\n",
      "Validation Score: 0.10218996554613113\n",
      "Validation Accuracy: 0.871999979019165\n"
     ]
    }
   ],
   "source": [
    "score_val= model.evaluate(X_val, y_val, verbose=1)\n",
    "print(\"Validation Score:\", score_val[0])\n",
    "print(\"Validation Accuracy:\", score_val[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model accuracy and loss for training and testing "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "loss:  Mean Square Error (MSE) is the most commonly used regression loss function. MSE is the sum of squared distances between our target variable and predicted values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.plot(trained_model.history['acc'])\n",
    "plt.plot(trained_model.history['val_acc'])\n",
    "\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train','test'], loc='upper left')\n",
    "plt.show()\n",
    "\n",
    "plt.plot(trained_model.history['loss'])\n",
    "plt.plot(trained_model.history['val_loss'])\n",
    "\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train','test'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model plot\n",
    "### - showing layers input and output dimentions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### finally we can save the model in h5 format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save('model-lstm-sentiment-movie.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "--------------------------------------------------------------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nour Ammar y2013 140008\n",
      "Deep learning 2021 Prof.Haluk Gumuskaya\n"
     ]
    }
   ],
   "source": [
    "print(\"Nour Ammar y2013 140008\")\n",
    "print(\"Deep learning 2021 Prof.Haluk Gumuskaya\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}