glove_model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import re\n",
    "from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer\n",
    "from sklearn.model_selection import train_test_split\n",
    "import json\n",
    "\n",
    "from keras.preprocessing.sequence import pad_sequences\n",
    "from keras.optimizers import Adam\n",
    "from keras.models import Sequential, Model\n",
    "from keras.layers import Input, Dense, Reshape, Flatten, Dropout, LSTM, Embedding, SpatialDropout1D\n",
    "from keras.layers.advanced_activations import LeakyReLU\n",
    "from keras.layers import BatchNormalization, Activation, ZeroPadding2D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_idf = pd.read_csv('amazon/reviews.csv')\n",
    "df_dataset = pd.read_json('clothing_dataset/renttherunway_final_data.json', lines = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pre_process(text):\n",
    "    # to lowercase\n",
    "    text=text.lower()\n",
    "    \n",
    "    # remove tags\n",
    "    text = re.sub(\"&lt;/?.*?&gt;\", \"&lt;&gt; \", text)\n",
    "    \n",
    "    # remove special characters and digits\n",
    "    text = re.sub(\"(\\\\d|\\\\W)+\", \" \", text)\n",
    "    \n",
    "    return text\n",
    "\n",
    "def get_stop_words(stop_file_path):\n",
    "    with open(stop_file_path, 'r', encoding='utf-8') as f:\n",
    "        stopwords = f.readlines()\n",
    "        stop_set = set(m.strip() for m in stopwords)\n",
    "        return frozenset(stop_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_idf['text'] = df_idf['title'] + \" \" + df_idf['body']\n",
    "df_idf['text'] = df_idf['text'].apply(lambda x: pre_process(str(x)))\n",
    "\n",
    "df_dataset['text'] = df_dataset['review_summary'] + \" \" + df_dataset['review_text']\n",
    "df_dataset['text'] = df_dataset['text'].apply(lambda x: pre_process(str(x)))\n",
    "\n",
    "sub_dataset = df_dataset[['text', 'rating']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data = df_idf['text'].append(sub_dataset['text'])\n",
    "all_rating = df_idf['rating'].append(sub_dataset['rating'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = all_rating\n",
    "y[:len(df_idf)] = y[:len(df_idf)].apply(lambda x: 1 if x > 3.5 else 0)#y.apply(lambda x: 1 if x > 3.5 else 0) \n",
    "y[len(df_idf):] = y[len(df_idf):].apply(lambda x: 1 if x > 5 else 0)#y.apply(lambda x: 1 if x > 3.5 else 0) \n",
    "y = y.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Change this to use both datasets\n",
    "#X_train, X_test, y_train, y_test = train_test_split(all_data, y, test_size=0.1, random_state=37)\n",
    "# X_train = df_dataset['text']\n",
    "# y_train = y[len(df_idf):]\n",
    "\n",
    "# X_test = df_idf['text'].to_numpy()\n",
    "# y_test = y[:len(df_idf)]\n",
    "\n",
    "# print('# Train data samples:', X_train.shape)\n",
    "# print('# Test data samples:', X_test.shape)\n",
    "\n",
    "# print('Sample train', X_train[0])\n",
    "# print('\\nSample test', X_test[0])\n",
    "# print(X_train.shape, y_train.shape)\n",
    "# print(X_test.shape, y_test.shape)\n",
    "# assert X_train.shape[0] == y_train.shape[0]\n",
    "# assert X_test.shape[0] == y_test.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "MAX_LEN = 5260\n",
    "GLOVE_DIM = 300\n",
    "NB_WORDS = 49781"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.preprocessing.text import Tokenizer\n",
    "\n",
    "tk = Tokenizer(num_words=NB_WORDS,\n",
    "               filters='!\"#$%&()*+,-./:;<=>?@[\\\\]^_`{|}~\\t\\n',\n",
    "               split=\" \")\n",
    "tk.fit_on_texts(all_data)\n",
    "\n",
    "all_data_seq = tk.texts_to_sequences(all_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_seq = all_data_seq[len(df_idf):]\n",
    "X_test_seq = all_data_seq[:len(df_idf)]\n",
    "\n",
    "y_train = y[len(df_idf):]\n",
    "y_test = y[:len(df_idf)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    275359.000000\n",
      "mean         63.517557\n",
      "std          69.966379\n",
      "min           1.000000\n",
      "25%          26.000000\n",
      "50%          50.000000\n",
      "75%          82.000000\n",
      "max        5260.000000\n",
      "Name: text, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "seq_lengths = all_data.apply(lambda x: len(x.split(' ')))\n",
    "print(seq_lengths.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_seq_trunc = pad_sequences(X_train_seq, maxlen=MAX_LEN)\n",
    "X_test_seq_trunc = pad_sequences(X_test_seq, maxlen=MAX_LEN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[    0     0     0 ...    84   204   520]\n",
      " [    0     0     0 ...     1   792 23123]\n",
      " [    0     0     0 ...  1027    16     5]\n",
      " ...\n",
      " [    0     0     0 ...     6    26   525]\n",
      " [    0     0     0 ...   177     1   982]\n",
      " [    0     0     0 ...    43    20   708]]\n",
      "[1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0.\n",
      " 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0.\n",
      " 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0.]\n",
      "[1316 2351 3203 3428  438 4732 1238 3755 4516 3807 1202 2107 2453  211\n",
      " 5116  491 2304 4664 2475  203 2261 1930  933 2988 1155  286  881  818\n",
      " 2526 3964 4557 1685  874 3797 4419  323  410 2009 4719 3970 3222 1220\n",
      " 2900 5233 3726 2635 1843 5048 1681 5110 1290  925 5257 1959 1338 3823\n",
      " 2905 3582 2938  349  209 3179 2263 3545]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1316    perfect for all my daily activities i loved th...\n",
       "2351    not a good emergency only phone i activated th...\n",
       "3203    flawless coming from another smartphone that w...\n",
       "3428    i actually never imagined a phone could be thi...\n",
       "438     excellent service responses excellent service ...\n",
       "4732    absolutely hate it this phone could not be wor...\n",
       "1238    so far so good have only had it for days but s...\n",
       "3755    it happens every day now and it s annoying bec...\n",
       "4516    five stars i already wrote a review they are g...\n",
       "3807    think twice before buying there are definitely...\n",
       "1202    had exactly what i needed when i needed it tha...\n",
       "2107    no external memory can t save items this was m...\n",
       "2453    i like this phone and i give it four stars out...\n",
       "211     it s ok except for the browser button seems li...\n",
       "5116    excellent bought the phone for my daughter rec...\n",
       "491     make sure your aware that it can only be used ...\n",
       "2304              i love it just what i needed i love it \n",
       "4664    one star it is too sad to sale a product that ...\n",
       "2475    handles the basics replacement for the same ty...\n",
       "203     how to fix browser problem as some of you have...\n",
       "2261    five stars snow drift samsung a gophone it wor...\n",
       "1930    one star first week having it stayed having is...\n",
       "933     good phone price and features make it a winnin...\n",
       "2988    don t walk run away from this model update may...\n",
       "1155    great phone and os not so great app selection ...\n",
       "286                           one star would not turn on \n",
       "881                             five stars so far so good\n",
       "818     it was a terrible experience and i still have ...\n",
       "2526       five stars it was great and price even better \n",
       "3964    i order international unlocked model and what ...\n",
       "                              ...                        \n",
       "4419    five stars love the phone refurbished but like...\n",
       "323     an obvious rip off phones lasted only a few da...\n",
       "410                            five stars i love my phone\n",
       "2009    i hate this phone i got this phone to replace ...\n",
       "4719    best phone i ve ever had beautiful design good...\n",
       "3970    very disgusted with the quality of a nokia pro...\n",
       "3222    one great phone beats the iphone by a mile i l...\n",
       "1220    doesn t fully function i received this i last ...\n",
       "2900    great great came as good as description even b...\n",
       "5233    terrible phone the phone was working amazing f...\n",
       "3726    one year using this phone and it s good enough...\n",
       "2635            two stars no minutes just phone a rip off\n",
       "1843                         five stars good basic phone \n",
       "5048                                  one star fine phone\n",
       "1681    cell phone purchase this is a very simple phon...\n",
       "5110                                     one star no good\n",
       "1290    great phone it s a great device faster and lon...\n",
       "925     received as expected is working with net used ...\n",
       "5257    excellent phone you do not need the latest ang...\n",
       "1959    convoy u great phone hardware wise rugged and ...\n",
       "1338                                          five stars \n",
       "3823           three stars it came with the wrong charger\n",
       "2905                     five stars exactly what i wanted\n",
       "3582    pretty good well the shipping took longer than...\n",
       "2938    five stars very good nice condicion fast shipping\n",
       "349     do not order cell phones from this seller the ...\n",
       "209     excellent phone exactly the kind of mobile pho...\n",
       "3179    horrid this phone was slow and glitchy i was p...\n",
       "2263    gophone although i am a smart phone kind of pe...\n",
       "3545    not unlocked only works with at t network this...\n",
       "Name: text, Length: 64, dtype: object"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# # This is to split the X_test_seq_trunc in two normalize dataset for GANs\n",
    "# print(X_test_seq_trunc.shape)\n",
    "\n",
    "# positives = X_test_seq_trunc[0:1]\n",
    "# negatives = X_test_seq_trunc[0:1]\n",
    "\n",
    "# normalized_data = X_test_seq_trunc[0:1]\n",
    "\n",
    "# print(positives.shape)\n",
    "\n",
    "# for i in range(0, y_test.shape[0]):\n",
    "#     if y_test[i] == 1:\n",
    "#         positives = np.append(positives, X_test_seq_trunc[i:(i+1)], axis=0)\n",
    "#     else:\n",
    "#         negatives = np.append(negatives, X_test_seq_trunc[i:(i+1)], axis=0)\n",
    "        \n",
    "# iter_ = min(positives.shape[0], negatives.shape[0])\n",
    "# for i in range(0, iter_):\n",
    "#     normalized_data = np.append(normalized_data, positives[i:(i+1)], axis=0)\n",
    "#     normalized_data = np.append(normalized_data, negatives[i:(i+1)], axis=0)\n",
    "    \n",
    "        \n",
    "# print(positives.shape, negatives.shape, normalized_data.shape)\n",
    "\n",
    "# for i in range(0, 5000):\n",
    "#     if(positives[0:1, i] != 0):\n",
    "#         print(positives[0:1, i], X_test_seq_trunc[0:1, i], i)\n",
    "\n",
    "half_batch = int(128/2)\n",
    "\n",
    "idx = np.random.randint(0, X_test_seq_trunc.shape[1], half_batch)\n",
    "samp = np.array([])\n",
    "count = 0\n",
    "for i in range(0, half_batch):\n",
    "    if count != half_batch/2:\n",
    "        idx = np.random.randint(0, X_test_seq_trunc.shape[1], 1)\n",
    "        while(y_test[idx] == 0):\n",
    "            idx = np.random.randint(0, X_test_seq_trunc.shape[1], 1)\n",
    "        samp = np.append(samp, idx, axis=0)\n",
    "        \n",
    "        while(y_test[idx] == 1):\n",
    "            idx = np.random.randint(0, X_test_seq_trunc.shape[1], 1)\n",
    "        samp = np.append(samp, idx, axis=0)\n",
    "        count += 1\n",
    "        \n",
    "\n",
    "\n",
    "# # samp = X_test_seq_trunc[idx]\n",
    "samp = samp.astype(int)\n",
    "\n",
    "print(X_test_seq_trunc[samp])\n",
    "print(y_test[samp])\n",
    "print(samp)\n",
    "df_idf['text'][samp]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  0   0   0 ...  61  14 316]\n",
      "(192544, 5260)\n",
      "(82815, 5260)\n",
      "(192544,)\n",
      "(82815,)\n"
     ]
    }
   ],
   "source": [
    "print(X_train_seq_trunc[10])  # Example of padded sequence\n",
    "print(X_train_seq_trunc.shape)\n",
    "print(X_test_seq_trunc.shape)\n",
    "print(y_train.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of validation set: (19255, 5260)\n"
     ]
    }
   ],
   "source": [
    "X_train_emb, X_valid_emb, y_train_emb, y_valid_emb = train_test_split(X_train_seq_trunc, y_train, test_size=0.1, random_state=37)\n",
    "\n",
    "assert X_valid_emb.shape[0] == y_valid_emb.shape[0]\n",
    "assert X_train_emb.shape[0] == y_train_emb.shape[0]\n",
    "\n",
    "print('Shape of validation set:', X_valid_emb.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This read the embeddings\n",
    "# glove_file = 'glove.42B.' + str(GLOVE_DIM) + 'd.txt'\n",
    "# emb_dict = {}\n",
    "# glove = open(glove_file)\n",
    "# for line in glove:\n",
    "#     values = line.split()\n",
    "#     word = values[0]\n",
    "#     vector = np.asarray(values[1:], dtype='float32')\n",
    "#     emb_dict[word] = vector\n",
    "# glove.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found the word car in the dictionary\n",
      "Found the word nice in the dictionary\n",
      "Found the word flight in the dictionary\n",
      "Found the word luggage in the dictionary\n"
     ]
    }
   ],
   "source": [
    "airline_words = ['car', 'nice', 'flight', 'luggage']\n",
    "for w in airline_words:\n",
    "    if w in emb_dict.keys():\n",
    "        print('Found the word {} in the dictionary'.format(w))\n",
    "# print(emb_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Here we build a matrix that represent words and it corresponding emdg\n",
    "emb_matrix = np.zeros((NB_WORDS, GLOVE_DIM))\n",
    "\n",
    "for w, i in tk.word_index.items():\n",
    "    if i < NB_WORDS:\n",
    "        vect = emb_dict.get(w)\n",
    "        if vect is not None:\n",
    "            emb_matrix[i] = vect\n",
    "    else:\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding_1 (Embedding)      (None, 5260, 300)         14934300  \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 1578000)           0         \n",
      "_________________________________________________________________\n",
      "dense_15 (Dense)             (None, 1)                 1578001   \n",
      "=================================================================\n",
      "Total params: 16,512,301\n",
      "Trainable params: 16,512,301\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "glove_model = Sequential()\n",
    "glove_model.add(Embedding(NB_WORDS, GLOVE_DIM, input_length=MAX_LEN))\n",
    "# glove_model.add(LSTM(GLOVE_DIM, return_sequences=True))\n",
    "# glove_model.add(LSTM(GLOVE_DIM, return_sequences=True))\n",
    "glove_model.add(Flatten())\n",
    "glove_model.add(Dense(1, activation='sigmoid'))\n",
    "glove_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "glove_model.layers[0].set_weights([emb_matrix])\n",
    "glove_model.layers[0].trainable = False\n",
    "\n",
    "glove_model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 173289 samples, validate on 19255 samples\n",
      "Epoch 1/5\n",
      "173289/173289 [==============================] - 637s 4ms/step - loss: 0.1121 - acc: 0.9751 - val_loss: 0.1034 - val_acc: 0.9747\n",
      "Epoch 2/5\n",
      "173289/173289 [==============================] - 680s 4ms/step - loss: 0.0806 - acc: 0.9791 - val_loss: 0.1231 - val_acc: 0.9727\n",
      "Epoch 3/5\n",
      "173289/173289 [==============================] - 628s 4ms/step - loss: 0.0675 - acc: 0.9819 - val_loss: 0.1317 - val_acc: 0.9774\n",
      "Epoch 4/5\n",
      "173289/173289 [==============================] - 658s 4ms/step - loss: 0.0589 - acc: 0.9838 - val_loss: 0.1307 - val_acc: 0.9760\n",
      "Epoch 5/5\n",
      "173289/173289 [==============================] - 585s 3ms/step - loss: 0.0524 - acc: 0.9851 - val_loss: 0.1415 - val_acc: 0.9708\n"
     ]
    }
   ],
   "source": [
    "history = glove_model.fit(X_train_emb\n",
    "                       , y_train_emb\n",
    "                       , epochs=5\n",
    "                       , batch_size=32\n",
    "                       , validation_data=(X_valid_emb, y_valid_emb)\n",
    "                       , verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.9999999]]\n",
      "(5260,)\n"
     ]
    }
   ],
   "source": [
    "print(glove_model.predict(X_train_emb[0:1]))\n",
    "print(X_train_emb[0].shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Adversarial Neural Network"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section we start building the GANs, this model takes the word embedding and generate new embeddings that are similar to the given ones. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_generator(shape):\n",
    "    img_shape = shape\n",
    "    noise_shape = (100,)\n",
    "\n",
    "    model = Sequential()\n",
    "\n",
    "    model.add(Dense(256, input_shape=noise_shape))\n",
    "    model.add(LeakyReLU(alpha=0.2))\n",
    "    model.add(BatchNormalization(momentum=0.8))\n",
    "    model.add(Dense(512))\n",
    "    model.add(LeakyReLU(alpha=0.2))\n",
    "    model.add(BatchNormalization(momentum=0.8))\n",
    "    model.add(Dense(1024))\n",
    "    model.add(LeakyReLU(alpha=0.2))\n",
    "    model.add(BatchNormalization(momentum=0.8))\n",
    "    model.add(Dense(np.prod(img_shape), activation='tanh'))\n",
    "    model.add(Reshape(img_shape))\n",
    "\n",
    "    model.summary()\n",
    "\n",
    "    noise = Input(shape=noise_shape)\n",
    "    img = model(noise)\n",
    "\n",
    "    return Model(noise, img)\n",
    "\n",
    "def build_discriminator(shape):\n",
    "\n",
    "    img_shape = shape\n",
    "\n",
    "    model = Sequential()\n",
    "\n",
    "#     model.add(Flatten(input_shape=img_shape)) # is one dimension\n",
    "    model.add(Dense(512, input_shape=img_shape))\n",
    "    model.add(LeakyReLU(alpha=0.2))\n",
    "    model.add(Dense(256))\n",
    "    model.add(LeakyReLU(alpha=0.2))\n",
    "    model.add(Dense(1, activation='sigmoid'))\n",
    "    model.summary()\n",
    "\n",
    "    img = Input(shape=img_shape)\n",
    "    validity = model(img)\n",
    "\n",
    "    return Model(img, validity)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix, classification_report, accuracy_score\n",
    "def results(pred, actual):\n",
    "    results = confusion_matrix(actual, pred)\n",
    "    print('Confusion Matrix :')\n",
    "    print(results)\n",
    "    print ('Accuracy Score :',accuracy_score(actual, pred))\n",
    "    print ('Report : ')\n",
    "    print(classification_report(actual, pred))\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_16 (Dense)             (None, 512)               2693632   \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_11 (LeakyReLU)   (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dense_17 (Dense)             (None, 256)               131328    \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_12 (LeakyReLU)   (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "dense_18 (Dense)             (None, 1)                 257       \n",
      "=================================================================\n",
      "Total params: 2,825,217\n",
      "Trainable params: 2,825,217\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_19 (Dense)             (None, 256)               25856     \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_13 (LeakyReLU)   (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_7 (Batch (None, 256)               1024      \n",
      "_________________________________________________________________\n",
      "dense_20 (Dense)             (None, 512)               131584    \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_14 (LeakyReLU)   (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_8 (Batch (None, 512)               2048      \n",
      "_________________________________________________________________\n",
      "dense_21 (Dense)             (None, 1024)              525312    \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_15 (LeakyReLU)   (None, 1024)              0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_9 (Batch (None, 1024)              4096      \n",
      "_________________________________________________________________\n",
      "dense_22 (Dense)             (None, 5260)              5391500   \n",
      "_________________________________________________________________\n",
      "reshape_3 (Reshape)          (None, 5260)              0         \n",
      "=================================================================\n",
      "Total params: 6,081,420\n",
      "Trainable params: 6,077,836\n",
      "Non-trainable params: 3,584\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "img_rows = 1\n",
    "img_cols = X_train_emb[0].shape\n",
    "img_shape = (img_cols)\n",
    "\n",
    "optimizer = Adam(0.0002, 0.5)\n",
    "\n",
    "# Build and compile the discriminator\n",
    "discriminator = build_discriminator(img_shape)\n",
    "discriminator.compile(loss='binary_crossentropy',\n",
    "    optimizer=optimizer,\n",
    "    metrics=['accuracy'])\n",
    "\n",
    "# Build and compile the generator\n",
    "generator = build_generator(img_shape)\n",
    "generator.compile(loss='binary_crossentropy', optimizer=optimizer)\n",
    "\n",
    "# The generator takes noise as input and generated imgs\n",
    "z = Input(shape=(100,))\n",
    "img = generator(z)\n",
    "\n",
    "# For the combined model we will only train the generator\n",
    "discriminator.trainable = False\n",
    "\n",
    "# The valid takes generated images as input and determines validity\n",
    "valid = discriminator(img)\n",
    "\n",
    "# The combined model  (stacked generator and discriminator) takes\n",
    "# noise as input => generates images => determines validity\n",
    "combined = Model(z, valid)\n",
    "combined.compile(loss='binary_crossentropy', optimizer=optimizer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(epochs, data, batch_size=128):\n",
    "\n",
    "\n",
    "        # Rescale -1 to 1\n",
    "        X_train = data #(X_train.astype(np.float32) - 127.5) / 127.5\n",
    "#         X_train = np.expand_dims(X_train, axis=3)\n",
    "\n",
    "        half_batch = int(batch_size / 2)\n",
    "\n",
    "        for epoch in range(epochs):\n",
    "\n",
    "            # ---------------------\n",
    "            #  Train Discriminator\n",
    "            # ---------------------\n",
    "\n",
    "            # Select a random half batch of images\n",
    "#             idx = np.random.randint(0, X_train.shape[1], half_batch)\n",
    "#             imgs = X_train[idx]\n",
    "\n",
    "            idx = np.random.randint(0, X_train.shape[1], half_batch)\n",
    "            samp = np.array([])\n",
    "            count = 0\n",
    "            for i in range(0, half_batch):\n",
    "                if count != half_batch/2:\n",
    "                    idx = np.random.randint(0, X_train.shape[1], 1)\n",
    "                    while(y_test[idx] == 0):\n",
    "                        idx = np.random.randint(0, X_train.shape[1], 1)\n",
    "                    samp = np.append(samp, idx, axis=0)\n",
    "\n",
    "                    while(y_test[idx] == 1):\n",
    "                        idx = np.random.randint(0, X_train.shape[1], 1)\n",
    "                    samp = np.append(samp, idx, axis=0)\n",
    "                    count += 1\n",
    "\n",
    "\n",
    "            # samp = X_test_seq_trunc[idx]\n",
    "            samp = samp.astype(int)\n",
    "            imgs = X_train[samp]\n",
    "#             print(np.sum(y_test[samp]))\n",
    "            \n",
    "            noise = np.random.normal(0, 1, (half_batch, 100))\n",
    "\n",
    "            # Generate a half batch of new images\n",
    "            gen_imgs = np.round(generator.predict(noise))\n",
    "\n",
    "            # Train the discriminator\n",
    "            d_loss_real = discriminator.train_on_batch(imgs, np.ones((half_batch, 1)))\n",
    "            d_loss_fake = discriminator.train_on_batch(gen_imgs, np.zeros((half_batch, 1)))\n",
    "            d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)\n",
    "\n",
    "\n",
    "            # ---------------------\n",
    "            #  Train Generator\n",
    "            # ---------------------\n",
    "\n",
    "            noise = np.random.normal(0, 1, (batch_size, 100))\n",
    "\n",
    "            # The generator wants the discriminator to label the generated samples\n",
    "            # as valid (ones)\n",
    "            valid_y = np.array([1] * batch_size)\n",
    "\n",
    "            # Train the generator\n",
    "            g_loss = combined.train_on_batch(noise, valid_y)\n",
    "\n",
    "            # Plot the progress\n",
    "            print (\"%d [D loss: %f, acc.: %.2f%%] [G loss: %f]\" % (epoch, d_loss[0], 100*d_loss[1], g_loss))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/keras/engine/training.py:490: UserWarning: Discrepancy between trainable weights and collected trainable weights, did you set `model.trainable` without calling `model.compile` after ?\n",
      "  'Discrepancy between trainable weights and collected trainable'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 [D loss: 1.810067, acc.: 36.72%] [G loss: 0.641288]\n",
      "1 [D loss: 0.822508, acc.: 55.47%] [G loss: 0.539981]\n",
      "2 [D loss: 0.704065, acc.: 53.91%] [G loss: 0.440695]\n",
      "3 [D loss: 0.705125, acc.: 54.69%] [G loss: 0.373934]\n",
      "4 [D loss: 0.812922, acc.: 51.56%] [G loss: 0.345146]\n",
      "5 [D loss: 1.087976, acc.: 50.00%] [G loss: 0.308388]\n",
      "6 [D loss: 0.958924, acc.: 50.78%] [G loss: 0.310668]\n",
      "7 [D loss: 1.603963, acc.: 47.66%] [G loss: 0.300471]\n",
      "8 [D loss: 1.038210, acc.: 49.22%] [G loss: 0.290471]\n",
      "9 [D loss: 1.533416, acc.: 46.88%] [G loss: 0.290719]\n",
      "10 [D loss: 1.701197, acc.: 46.88%] [G loss: 0.221435]\n",
      "11 [D loss: 1.214371, acc.: 50.78%] [G loss: 0.238506]\n",
      "12 [D loss: 1.186348, acc.: 50.78%] [G loss: 0.224354]\n",
      "13 [D loss: 1.349773, acc.: 49.22%] [G loss: 0.267747]\n",
      "14 [D loss: 0.933843, acc.: 50.78%] [G loss: 0.281795]\n",
      "15 [D loss: 0.869056, acc.: 56.25%] [G loss: 0.306148]\n",
      "16 [D loss: 1.054489, acc.: 53.12%] [G loss: 0.311180]\n",
      "17 [D loss: 1.115238, acc.: 53.91%] [G loss: 0.314307]\n",
      "18 [D loss: 0.981008, acc.: 52.34%] [G loss: 0.329690]\n",
      "19 [D loss: 0.982672, acc.: 53.12%] [G loss: 0.313394]\n",
      "20 [D loss: 1.427156, acc.: 48.44%] [G loss: 0.340575]\n",
      "21 [D loss: 1.660590, acc.: 47.66%] [G loss: 0.209658]\n",
      "22 [D loss: 1.090575, acc.: 51.56%] [G loss: 0.228700]\n",
      "23 [D loss: 0.933478, acc.: 52.34%] [G loss: 0.250460]\n",
      "24 [D loss: 0.941246, acc.: 53.12%] [G loss: 0.304868]\n",
      "25 [D loss: 0.961755, acc.: 51.56%] [G loss: 0.305491]\n",
      "26 [D loss: 0.990225, acc.: 53.91%] [G loss: 0.328536]\n",
      "27 [D loss: 1.001027, acc.: 57.03%] [G loss: 0.342973]\n",
      "28 [D loss: 0.802637, acc.: 53.91%] [G loss: 0.371801]\n",
      "29 [D loss: 1.015235, acc.: 60.16%] [G loss: 0.409123]\n",
      "30 [D loss: 0.775030, acc.: 61.72%] [G loss: 0.409165]\n",
      "31 [D loss: 0.878654, acc.: 57.81%] [G loss: 0.349401]\n",
      "32 [D loss: 0.922274, acc.: 54.69%] [G loss: 0.348880]\n",
      "33 [D loss: 1.000246, acc.: 53.91%] [G loss: 0.355164]\n",
      "34 [D loss: 1.108411, acc.: 53.12%] [G loss: 0.281234]\n",
      "35 [D loss: 0.986758, acc.: 53.91%] [G loss: 0.306504]\n",
      "36 [D loss: 0.953700, acc.: 53.12%] [G loss: 0.317789]\n",
      "37 [D loss: 0.783348, acc.: 60.94%] [G loss: 0.361250]\n",
      "38 [D loss: 0.956353, acc.: 55.47%] [G loss: 0.367289]\n",
      "39 [D loss: 0.997107, acc.: 55.47%] [G loss: 0.371809]\n",
      "40 [D loss: 0.745352, acc.: 60.94%] [G loss: 0.368139]\n",
      "41 [D loss: 0.677337, acc.: 63.28%] [G loss: 0.454694]\n",
      "42 [D loss: 0.786424, acc.: 61.72%] [G loss: 0.433055]\n",
      "43 [D loss: 0.723245, acc.: 59.38%] [G loss: 0.472537]\n",
      "44 [D loss: 0.833084, acc.: 57.81%] [G loss: 0.425849]\n",
      "45 [D loss: 0.938996, acc.: 56.25%] [G loss: 0.456299]\n",
      "46 [D loss: 0.908929, acc.: 61.72%] [G loss: 0.424929]\n",
      "47 [D loss: 0.658237, acc.: 63.28%] [G loss: 0.472454]\n",
      "48 [D loss: 0.802799, acc.: 57.03%] [G loss: 0.464743]\n",
      "49 [D loss: 0.808735, acc.: 61.72%] [G loss: 0.472193]\n",
      "50 [D loss: 0.656040, acc.: 62.50%] [G loss: 0.465824]\n",
      "51 [D loss: 0.779333, acc.: 63.28%] [G loss: 0.502574]\n",
      "52 [D loss: 0.907231, acc.: 60.94%] [G loss: 0.484860]\n",
      "53 [D loss: 0.642528, acc.: 62.50%] [G loss: 0.534609]\n",
      "54 [D loss: 0.723254, acc.: 63.28%] [G loss: 0.499878]\n",
      "55 [D loss: 0.918359, acc.: 64.84%] [G loss: 0.516809]\n",
      "56 [D loss: 1.758085, acc.: 50.00%] [G loss: 0.388121]\n",
      "57 [D loss: 1.260460, acc.: 56.25%] [G loss: 0.338077]\n",
      "58 [D loss: 0.856883, acc.: 57.81%] [G loss: 0.327245]\n",
      "59 [D loss: 0.864610, acc.: 57.03%] [G loss: 0.369645]\n",
      "60 [D loss: 0.752203, acc.: 57.03%] [G loss: 0.408099]\n",
      "61 [D loss: 0.782518, acc.: 57.03%] [G loss: 0.504625]\n",
      "62 [D loss: 0.668835, acc.: 61.72%] [G loss: 0.499738]\n",
      "63 [D loss: 0.812024, acc.: 60.94%] [G loss: 0.485113]\n",
      "64 [D loss: 0.726500, acc.: 60.94%] [G loss: 0.474691]\n",
      "65 [D loss: 0.560619, acc.: 70.31%] [G loss: 0.502753]\n",
      "66 [D loss: 0.727395, acc.: 63.28%] [G loss: 0.524131]\n",
      "67 [D loss: 0.647270, acc.: 61.72%] [G loss: 0.516040]\n",
      "68 [D loss: 0.664848, acc.: 65.62%] [G loss: 0.508257]\n",
      "69 [D loss: 0.609460, acc.: 64.06%] [G loss: 0.484674]\n",
      "70 [D loss: 0.559138, acc.: 60.94%] [G loss: 0.551808]\n",
      "71 [D loss: 0.712882, acc.: 61.72%] [G loss: 0.563919]\n",
      "72 [D loss: 0.781362, acc.: 69.53%] [G loss: 0.554133]\n",
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      "74 [D loss: 0.795789, acc.: 66.41%] [G loss: 0.589161]\n",
      "75 [D loss: 0.782434, acc.: 67.97%] [G loss: 0.643384]\n",
      "76 [D loss: 0.651741, acc.: 71.09%] [G loss: 0.625396]\n",
      "77 [D loss: 0.696268, acc.: 69.53%] [G loss: 0.641988]\n",
      "78 [D loss: 0.588379, acc.: 71.88%] [G loss: 0.701950]\n",
      "79 [D loss: 0.770210, acc.: 70.31%] [G loss: 0.623922]\n",
      "80 [D loss: 0.607141, acc.: 71.88%] [G loss: 0.661853]\n",
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      "84 [D loss: 0.758142, acc.: 69.53%] [G loss: 0.637605]\n",
      "85 [D loss: 0.988774, acc.: 67.19%] [G loss: 0.572933]\n",
      "86 [D loss: 0.595137, acc.: 65.62%] [G loss: 0.539339]\n",
      "87 [D loss: 0.648281, acc.: 70.31%] [G loss: 0.563287]\n",
      "88 [D loss: 0.612124, acc.: 72.66%] [G loss: 0.591936]\n",
      "89 [D loss: 0.588955, acc.: 73.44%] [G loss: 0.602221]\n",
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      "91 [D loss: 0.550049, acc.: 70.31%] [G loss: 0.681890]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    {
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     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "737 [D loss: 0.580509, acc.: 94.53%] [G loss: 2.531780]\n",
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      "1030 [D loss: 0.254147, acc.: 94.53%] [G loss: 2.034827]\n",
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      "1174 [D loss: 0.473679, acc.: 94.53%] [G loss: 3.096764]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1462 [D loss: 0.668002, acc.: 96.09%] [G loss: 3.559418]\n",
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    },
    {
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     "text": [
      "1606 [D loss: 0.309715, acc.: 98.44%] [G loss: 6.310599]\n",
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     ]
    },
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     "text": [
      "1750 [D loss: 0.890496, acc.: 94.53%] [G loss: 5.906577]\n",
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    },
    {
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     "text": [
      "1894 [D loss: 0.522620, acc.: 96.88%] [G loss: 6.369873]\n",
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     ]
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     "text": [
      "2038 [D loss: 0.292663, acc.: 98.44%] [G loss: 2.081869]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2182 [D loss: 0.139978, acc.: 92.97%] [G loss: 3.238732]\n",
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    },
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     "text": [
      "2326 [D loss: 0.085859, acc.: 97.66%] [G loss: 2.873280]\n",
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     "text": [
      "2470 [D loss: 0.588677, acc.: 90.62%] [G loss: 4.539799]\n",
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    },
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     "text": [
      "2614 [D loss: 0.998329, acc.: 83.59%] [G loss: 10.397417]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2901 [D loss: 0.382438, acc.: 97.66%] [G loss: 3.020373]\n",
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    },
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     "text": [
      "3045 [D loss: 0.632639, acc.: 96.09%] [G loss: 3.409022]\n",
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     ]
    },
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     "output_type": "stream",
     "text": [
      "3188 [D loss: 0.259883, acc.: 98.44%] [G loss: 4.453894]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3609 [D loss: 0.000000, acc.: 100.00%] [G loss: 16.118099]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3748 [D loss: 0.000000, acc.: 100.00%] [G loss: 16.118099]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3887 [D loss: 0.000000, acc.: 100.00%] [G loss: 16.118099]\n",
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      "3960 [D loss: 0.000000, acc.: 100.00%] [G loss: 16.118099]\n",
      "3961 [D loss: 0.000000, acc.: 100.00%] [G loss: 16.118099]\n",
      "3962 [D loss: 0.000000, acc.: 100.00%] [G loss: 16.118099]\n",
      "3963 [D loss: 0.000000, acc.: 100.00%] [G loss: 16.118099]\n",
      "3964 [D loss: 0.000000, acc.: 100.00%] [G loss: 16.118099]\n",
      "3965 [D loss: 0.000000, acc.: 100.00%] [G loss: 16.118099]\n",
      "3966 [D loss: 0.000000, acc.: 100.00%] [G loss: 16.118099]\n",
      "3967 [D loss: 0.000000, acc.: 100.00%] [G loss: 16.118099]\n",
      "3968 [D loss: 0.000000, acc.: 100.00%] [G loss: 16.118099]\n",
      "3969 [D loss: 0.000000, acc.: 100.00%] [G loss: 16.118099]\n",
      "3970 [D loss: 0.000000, acc.: 100.00%] [G loss: 16.118099]\n",
      "3971 [D loss: 0.000000, acc.: 100.00%] [G loss: 16.118099]\n",
      "3972 [D loss: 0.000000, acc.: 100.00%] [G loss: 16.118099]\n",
      "3973 [D loss: 0.000000, acc.: 100.00%] [G loss: 16.118099]\n",
      "3974 [D loss: 0.000000, acc.: 100.00%] [G loss: 16.118099]\n",
      "3975 [D loss: 0.000000, acc.: 100.00%] [G loss: 16.118099]\n",
      "3976 [D loss: 0.000000, acc.: 100.00%] [G loss: 16.118099]\n",
      "3977 [D loss: 0.000000, acc.: 100.00%] [G loss: 16.118099]\n",
      "3978 [D loss: 0.000000, acc.: 100.00%] [G loss: 16.118099]\n",
      "3979 [D loss: 0.000000, acc.: 100.00%] [G loss: 16.118099]\n",
      "3980 [D loss: 0.000000, acc.: 100.00%] [G loss: 16.118099]\n",
      "3981 [D loss: 0.000000, acc.: 100.00%] [G loss: 16.118099]\n",
      "3982 [D loss: 0.000000, acc.: 100.00%] [G loss: 16.118099]\n",
      "3983 [D loss: 0.000006, acc.: 100.00%] [G loss: 16.118099]\n",
      "3984 [D loss: 0.000000, acc.: 100.00%] [G loss: 16.118099]\n",
      "3985 [D loss: 0.000000, acc.: 100.00%] [G loss: 16.118099]\n",
      "3986 [D loss: 0.000000, acc.: 100.00%] [G loss: 16.118099]\n",
      "3987 [D loss: 0.000000, acc.: 100.00%] [G loss: 16.118099]\n",
      "3988 [D loss: 0.000000, acc.: 100.00%] [G loss: 16.118099]\n",
      "3989 [D loss: 0.000000, acc.: 100.00%] [G loss: 16.118099]\n",
      "3990 [D loss: 0.000000, acc.: 100.00%] [G loss: 16.118099]\n",
      "3991 [D loss: 0.000000, acc.: 100.00%] [G loss: 16.118099]\n",
      "3992 [D loss: 0.000761, acc.: 100.00%] [G loss: 16.118099]\n",
      "3993 [D loss: 0.000000, acc.: 100.00%] [G loss: 16.118099]\n",
      "3994 [D loss: 0.000006, acc.: 100.00%] [G loss: 16.118099]\n",
      "3995 [D loss: 0.000000, acc.: 100.00%] [G loss: 16.118099]\n",
      "3996 [D loss: 0.000000, acc.: 100.00%] [G loss: 16.118099]\n",
      "3997 [D loss: 0.000000, acc.: 100.00%] [G loss: 16.118099]\n",
      "3998 [D loss: 0.000000, acc.: 100.00%] [G loss: 16.118099]\n",
      "3999 [D loss: 0.000000, acc.: 100.00%] [G loss: 16.118099]\n"
     ]
    }
   ],
   "source": [
    "train(epochs=4000, data=X_test_seq_trunc) # Generate data similar to the second dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Second model trained on the real data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(82815, 5260) (82815,)\n"
     ]
    }
   ],
   "source": [
    "print(X_test_seq_trunc.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of validation set: (8282, 5260)\n"
     ]
    }
   ],
   "source": [
    "X_train_emb2, X_valid_emb2, y_train_emb2, y_valid_emb2 = train_test_split(X_test_seq_trunc, y_test, test_size=0.1, random_state=37)\n",
    "\n",
    "assert X_valid_emb2.shape[0] == y_valid_emb2.shape[0]\n",
    "assert X_train_emb2.shape[0] == y_train_emb2.shape[0]\n",
    "\n",
    "print('Shape of validation set:',X_valid_emb2.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding_2 (Embedding)      (None, 5260, 300)         14934300  \n",
      "_________________________________________________________________\n",
      "flatten_2 (Flatten)          (None, 1578000)           0         \n",
      "_________________________________________________________________\n",
      "dense_23 (Dense)             (None, 1)                 1578001   \n",
      "=================================================================\n",
      "Total params: 16,512,301\n",
      "Trainable params: 16,512,301\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "glove_model2 = Sequential()\n",
    "glove_model2.add(Embedding(NB_WORDS, GLOVE_DIM, input_length=MAX_LEN))\n",
    "# glove_model2.add(LSTM(GLOVE_DIM, return_sequences=True))\n",
    "# glove_model2.add(LSTM(GLOVE_DIM, return_sequences=True))\n",
    "glove_model2.add(Flatten())\n",
    "glove_model2.add(Dense(1, activation='sigmoid'))\n",
    "glove_model2.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "glove_model2.layers[0].set_weights([emb_matrix])\n",
    "glove_model2.layers[0].trainable = False\n",
    "\n",
    "glove_model2.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 74533 samples, validate on 8282 samples\n",
      "Epoch 1/5\n",
      "74533/74533 [==============================] - 304s 4ms/step - loss: 0.4588 - acc: 0.8406 - val_loss: 0.4640 - val_acc: 0.8582\n",
      "Epoch 2/5\n",
      "74533/74533 [==============================] - 293s 4ms/step - loss: 0.3073 - acc: 0.8952 - val_loss: 0.4720 - val_acc: 0.8654\n",
      "Epoch 3/5\n",
      "74533/74533 [==============================] - 286s 4ms/step - loss: 0.2581 - acc: 0.9113 - val_loss: 0.4751 - val_acc: 0.8666\n",
      "Epoch 4/5\n",
      "74533/74533 [==============================] - 292s 4ms/step - loss: 0.2322 - acc: 0.9201 - val_loss: 0.4734 - val_acc: 0.8723\n",
      "Epoch 5/5\n",
      "74533/74533 [==============================] - 279s 4ms/step - loss: 0.2104 - acc: 0.9277 - val_loss: 0.5164 - val_acc: 0.8708\n"
     ]
    }
   ],
   "source": [
    "history2 = glove_model2.fit(X_train_emb2\n",
    "                       , y_train_emb2\n",
    "                       , epochs=5\n",
    "                       , batch_size=32\n",
    "                       , validation_data=(X_valid_emb2, y_valid_emb2)\n",
    "                       , verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(glove_model2.predict(X_train_emb[0:1]))\n",
    "# print(X_train_emb[0].shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Third model trained on a generated dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generating data and labels\n",
    "gen = 100000\n",
    "noise = np.random.normal(0, 1, (gen, 100))\n",
    "gen_samp = np.absolute((generator.predict(noise)))\n",
    "prediction = glove_model.predict((gen_samp))\n",
    "prediction = np.round(prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# print(np.round(prediction[0:100]))\n",
    "# print(np.sum(np.round(glove_model.predict(X_train_emb[0:100]))))\n",
    "# print(np.sum(y_train[0:100]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of validation set: (30000, 5260)\n"
     ]
    }
   ],
   "source": [
    "X_train_emb3, X_valid_emb3, y_train_emb3, y_valid_emb3 = train_test_split(gen_samp, prediction, test_size=0.3, random_state=37)\n",
    "\n",
    "assert X_valid_emb3.shape[0] == y_valid_emb3.shape[0]\n",
    "assert X_train_emb3.shape[0] == y_train_emb3.shape[0]\n",
    "\n",
    "print('Shape of validation set:',X_valid_emb3.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding_3 (Embedding)      (None, 5260, 300)         14934300  \n",
      "_________________________________________________________________\n",
      "flatten_3 (Flatten)          (None, 1578000)           0         \n",
      "_________________________________________________________________\n",
      "dense_24 (Dense)             (None, 1)                 1578001   \n",
      "=================================================================\n",
      "Total params: 16,512,301\n",
      "Trainable params: 16,512,301\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "glove_model3 = Sequential()\n",
    "glove_model3.add(Embedding(NB_WORDS, GLOVE_DIM, input_length=MAX_LEN))\n",
    "# glove_model3.add(LSTM(GLOVE_DIM, return_sequences=True))\n",
    "# glove_model3.add(LSTM(GLOVE_DIM, return_sequences=True))\n",
    "glove_model3.add(Flatten())\n",
    "glove_model3.add(Dense(1, activation='sigmoid'))\n",
    "glove_model3.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [],
   "source": [
    "glove_model3.layers[0].set_weights([emb_matrix])\n",
    "glove_model3.layers[0].trainable = False\n",
    "\n",
    "glove_model3.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 70000 samples, validate on 30000 samples\n",
      "Epoch 1/5\n",
      "70000/70000 [==============================] - 348s 5ms/step - loss: 0.2424 - acc: 0.9930 - val_loss: 0.1119 - val_acc: 0.9971\n",
      "Epoch 2/5\n",
      "70000/70000 [==============================] - 315s 5ms/step - loss: 0.0714 - acc: 0.9973 - val_loss: 0.0411 - val_acc: 0.9982\n",
      "Epoch 3/5\n",
      "70000/70000 [==============================] - 307s 4ms/step - loss: 0.0283 - acc: 0.9980 - val_loss: 0.0193 - val_acc: 0.9983\n",
      "Epoch 4/5\n",
      "70000/70000 [==============================] - 318s 5ms/step - loss: 0.0145 - acc: 0.9984 - val_loss: 0.0103 - val_acc: 0.9986\n",
      "Epoch 5/5\n",
      "70000/70000 [==============================] - 333s 5ms/step - loss: 0.0094 - acc: 0.9985 - val_loss: 0.0072 - val_acc: 0.9987\n"
     ]
    }
   ],
   "source": [
    "history3 = glove_model3.fit(X_train_emb3\n",
    "                       , y_train_emb3\n",
    "                       , epochs=5\n",
    "                       , batch_size=32\n",
    "                       , validation_data=(X_valid_emb3, y_valid_emb3)\n",
    "                       , verbose=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this area I am going to compare their results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The model trained on the generated dataset over the real dataset\n",
    "actual = y_valid_emb3\n",
    "pred = np.round(glove_model2.predict(X_valid_emb3))\n",
    "pred2 = np.round(glove_model3.predict(X_valid_emb3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix :\n",
      "[[  337  1727]\n",
      " [ 5420 22516]]\n",
      "Accuracy Score : 0.7617666666666667\n",
      "Report : \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.06      0.16      0.09      2064\n",
      "         1.0       0.93      0.81      0.86     27936\n",
      "\n",
      "   micro avg       0.76      0.76      0.76     30000\n",
      "   macro avg       0.49      0.48      0.47     30000\n",
      "weighted avg       0.87      0.76      0.81     30000\n",
      "\n",
      "\n",
      "Confusion Matrix :\n",
      "[[ 2042    22]\n",
      " [   17 27919]]\n",
      "Accuracy Score : 0.9987\n",
      "Report : \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.99      0.99      0.99      2064\n",
      "         1.0       1.00      1.00      1.00     27936\n",
      "\n",
      "   micro avg       1.00      1.00      1.00     30000\n",
      "   macro avg       1.00      0.99      0.99     30000\n",
      "weighted avg       1.00      1.00      1.00     30000\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "results(pred, actual) # trained on original over real\n",
    "results(pred2, actual) # generated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The model trained on the generated dataset over the real dataset\n",
    "actual = y_valid_emb2\n",
    "pred = np.round(glove_model2.predict(X_valid_emb2))\n",
    "pred2 = np.round(glove_model3.predict(X_valid_emb2))\n",
    "pred3 = np.round(glove_model.predict(X_valid_emb2))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix :\n",
      "[[2112  596]\n",
      " [ 474 5100]]\n",
      "Accuracy Score : 0.8708041535860903\n",
      "Report : \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.82      0.78      0.80      2708\n",
      "         1.0       0.90      0.91      0.91      5574\n",
      "\n",
      "   micro avg       0.87      0.87      0.87      8282\n",
      "   macro avg       0.86      0.85      0.85      8282\n",
      "weighted avg       0.87      0.87      0.87      8282\n",
      "\n",
      "\n",
      "Confusion Matrix :\n",
      "[[  13 2695]\n",
      " [  21 5553]]\n",
      "Accuracy Score : 0.6720598889157209\n",
      "Report : \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.38      0.00      0.01      2708\n",
      "         1.0       0.67      1.00      0.80      5574\n",
      "\n",
      "   micro avg       0.67      0.67      0.67      8282\n",
      "   macro avg       0.53      0.50      0.41      8282\n",
      "weighted avg       0.58      0.67      0.54      8282\n",
      "\n",
      "\n",
      "Confusion Matrix :\n",
      "[[ 661 2047]\n",
      " [ 183 5391]]\n",
      "Accuracy Score : 0.7307413668196088\n",
      "Report : \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.78      0.24      0.37      2708\n",
      "         1.0       0.72      0.97      0.83      5574\n",
      "\n",
      "   micro avg       0.73      0.73      0.73      8282\n",
      "   macro avg       0.75      0.61      0.60      8282\n",
      "weighted avg       0.74      0.73      0.68      8282\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "results(pred, actual) # trained on original over real\n",
    "results(pred2, actual) # generated\n",
    "results(pred3, actual) # original trained on the clothing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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