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Spectrum_Keras-LSTM.ipynb 356 KB
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{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. CNN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Récupération des genres"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Permet de récupérer les labels qui seront mis dans une array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import ast\n",
    "import pandas as pd\n",
    "from __future__ import absolute_import\n",
    "from __future__ import division\n",
    "from __future__ import print_function\n",
    "from keras.preprocessing.sequence import pad_sequences\n",
    "pd.options.mode.chained_assignment = None\n",
    "import argparse\n",
    "import sys\n",
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.image as mpimg\n",
    "import tensorflow as tf\n",
    "import os\n",
    "import cv2\n",
    "from math import floor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Choisir l'année !"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "years = [2004, 2005, 2006, 2007, 2008, 2015]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "list_of_eligible_spectrums = []\n",
    "for year in years:\n",
    "    for file in os.listdir(\"./spectrumImages/SpectrumImages\" + str(year)):\n",
    "        if str(file)[-4:] == '.jpg':\n",
    "            list_of_eligible_spectrums += ['SpectrumImages'+ str(year) +'/' + file]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We select: Action / Comedy / Thriller / Horror / Drama\n",
    "            28  / 35 / 53/ 27 / 18"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "{\"genres\":[{\"id\":28,\"name\":\"Action\"},{\"id\":12,\"name\":\"Adventure\"},{\"id\":16,\"name\":\"Animation\"},{\"id\":35,\"name\":\"Comedy\"},{\"id\":80,\"name\":\"Crime\"},{\"id\":99,\"name\":\"Documentary\"},{\"id\":18,\"name\":\"Drama\"},{\"id\":10751,\"name\":\"Family\"},{\"id\":14,\"name\":\"Fantasy\"},{\"id\":36,\"name\":\"History\"},{\"id\":27,\"name\":\"Horror\"},{\"id\":10402,\"name\":\"Music\"},{\"id\":9648,\"name\":\"Mystery\"},{\"id\":10749,\"name\":\"Romance\"},{\"id\":878,\"name\":\"Science Fiction\"},{\"id\":10770,\"name\":\"TV Movie\"},{\"id\":53,\"name\":\"Thriller\"},{\"id\":10752,\"name\":\"War\"},{\"id\":37,\"name\":\"Western\"}]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "do something! Too many points.......\n",
      "do something! Too many points.......\n",
      "do something! Too many points.......\n",
      "do something! Too many points.......\n",
      "do something! Too many points.......\n",
      "do something! Too many points.......\n",
      "do something! Too many points.......\n",
      "do something! Too many points.......\n",
      "do something! Too many points.......\n",
      "do something! Too many points.......\n",
      "do something! Too many points.......\n",
      "do something! Too many points.......\n",
      "do something! Too many points.......\n",
      "do something! Too many points.......\n",
      "do something! Too many points.......\n",
      "do something! Too many points.......\n",
      "do something! Too many points.......\n",
      "do something! Too many points.......\n"
     ]
    }
   ],
   "source": [
    "genres = [28, 35, 18, 99, 10749, 10752, 10402, 53, 878, 27, 9648, 80, 14, 12, 36, 10769, 16, 10751, 37, 10770]\n",
    "\n",
    "wanted_genres = {28: 'action', 35: 'comedy', 18: 'drama', 99: 0, 10749: 'comedy',10752: 'action', 10402: 0, 53: 'action', 878: 'action', 27: 'action', 9648: 'action', 80: 'action', 14: 'action', 12: 'action', 36: 0, 10769: 0, 16: 0, 10751: 0, 37: 'action', 10770: 0}\n",
    "#wanted_genres = {28: 'action', 35: 'comedy', 18: 'drama', 99: 0, 10749: 'comedy',10752: 'action', 10402: 0, 53: 'thriller', 878: 'action', 27: 'horror', 9648: 'thriller', 80: 'thriller', 14: 'action', 12: 'action', 36: 0, 10769: 0, 16: 0, 10751: 0, 37: 'action', 10770: 0}\n",
    "\n",
    "def get_genre_from_link():\n",
    "    dict_inverse = {}\n",
    "    links_to_be_removed = []\n",
    "    for year in years:\n",
    "        path = \"./Link-dictionaries/Link-dictionary\" + str(year)+ \".txt\"\n",
    "        file = open(path, \"r\").read()\n",
    "        dictyear = ast.literal_eval(file)\n",
    "        for movie_id in dictyear.keys():\n",
    "            if dictyear[movie_id][1] != []:\n",
    "                dict_inverse[str(dictyear[movie_id][2])] = {}\n",
    "                movie_genres = dictyear[movie_id][1]\n",
    "                if wanted_genres[movie_genres[0]] != 0:\n",
    "                        dict_inverse[str(dictyear[movie_id][2])]['genre'] = wanted_genres[movie_genres[0]]\n",
    "                elif len(movie_genres)>1:\n",
    "                    if wanted_genres[movie_genres[1]] != 0:\n",
    "                        dict_inverse[str(dictyear[movie_id][2])]['genre'] = wanted_genres[movie_genres[1]]\n",
    "                    elif len(movie_genres)>2:\n",
    "                        if wanted_genres[movie_genres[2]] != 0:\n",
    "                            dict_inverse[str(dictyear[movie_id][2])]['genre'] = wanted_genres[movie_genres[2]]\n",
    "                        else:\n",
    "                            links_to_be_removed += [dictyear[movie_id][2]]\n",
    "                    else:\n",
    "                        links_to_be_removed += [dictyear[movie_id][2]]\n",
    "                else:\n",
    "                    links_to_be_removed += [dictyear[movie_id][2]]\n",
    "            else:\n",
    "                links_to_be_removed += [dictyear[movie_id][2]]\n",
    "    return dict_inverse, links_to_be_removed\n",
    "\n",
    "def get_output_list(L):\n",
    "    dict_inverse, links_to_be_removed = get_genre_from_link()\n",
    "    eligible_links = []\n",
    "    output = []\n",
    "    for link in L:\n",
    "        link = str(link)\n",
    "        #print(dict_inverse[str(link)])\n",
    "        if link[-5] == \".\":\n",
    "            link = link[:-4] + link[-3:]\n",
    "            print(\"do something! Too many points.......\")\n",
    "        if link.split('/')[1][:-4] not in links_to_be_removed:\n",
    "            eligible_links += [link[:-4]]\n",
    "    return dict_inverse, eligible_links\n",
    "\n",
    "\n",
    "dict_inverse, eligible_links = get_output_list(list_of_eligible_spectrums)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#for element in labels:\n",
    "#   for genre in element:\n",
    "#       if genre not in genres:\n",
    "#           genres += [genre]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#Modif pour ajouter des catégories\n",
    "trY[2][3]=1\n",
    "trY.shape\n",
    "from random import randint\n",
    "for i in range(1225):\n",
    "    rand = randint(0,19)\n",
    "    trY[i][rand] = 1\n",
    "trY[3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bien vérifier la taille des données !"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extraction des images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'NoneType' object is not subscriptable\n",
      "SpectrumImages2004/3jBFwltrxJw\n",
      "'NoneType' object is not subscriptable\n",
      "SpectrumImages2006/BH0MLyu6HjY\n",
      "'NoneType' object is not subscriptable\n",
      "SpectrumImages2008/1Gl2kVUsy2M\n",
      "'NoneType' object is not subscriptable\n",
      "SpectrumImages2015/-7S2u3k-OMU\n",
      "'NoneType' object is not subscriptable\n",
      "SpectrumImages2015/17yQpuf3LRA\n",
      "'NoneType' object is not subscriptable\n",
      "SpectrumImages2015/6HIlyaGAkXo\n",
      "'NoneType' object is not subscriptable\n",
      "SpectrumImages2015/clEBwkjs0sQ\n",
      "'NoneType' object is not subscriptable\n",
      "SpectrumImages2015/DNg9Oa5EHsc\n",
      "'NoneType' object is not subscriptable\n",
      "SpectrumImages2015/hsuKq5pNOcM\n",
      "'NoneType' object is not subscriptable\n",
      "SpectrumImages2015/j6e6Nc1emwg\n",
      "'NoneType' object is not subscriptable\n",
      "SpectrumImages2015/KHGHEpUeUwo\n",
      "'NoneType' object is not subscriptable\n",
      "SpectrumImages2015/ngesu4t3oKc\n",
      "'NoneType' object is not subscriptable\n",
      "SpectrumImages2015/tOilM3Ze-us\n",
      "'NoneType' object is not subscriptable\n",
      "SpectrumImages2015/uigIV1ALQYQ\n",
      "'NoneType' object is not subscriptable\n",
      "SpectrumImages2015/Xq6XgPSgzmA\n"
     ]
    }
   ],
   "source": [
    "for file in eligible_links:\n",
    "    img = cv2.imread('./SpectrumImages/'+ file + '.jpg', 1)\n",
    "    try:\n",
    "        img = img[0:1]\n",
    "    except Exception as e:\n",
    "        print(e)\n",
    "        print(file)\n",
    "        img = cv2.imread('./SpectrumImages/'+ file + '..jpg', 1)\n",
    "        img = img[0:1]\n",
    "    img = img.reshape((img.shape[1], img.shape[2]))\n",
    "    dict_inverse[file.split('/')[1]]['image'] = img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame.from_dict(dict_inverse)\n",
    "df = df.transpose()\n",
    "df = df.reset_index(drop=True)\n",
    "#shuffling\n",
    "df = df.sample(frac=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10404, 2)"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df2 = df.dropna(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5314, 2)"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>image</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>genre</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>action</th>\n",
       "      <td>2280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>comedy</th>\n",
       "      <td>1383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>drama</th>\n",
       "      <td>1651</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        image\n",
       "genre        \n",
       "action   2280\n",
       "comedy   1383\n",
       "drama    1651"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.groupby('genre').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hasard: 42 %\n"
     ]
    }
   ],
   "source": [
    "dict_num_genres = (df2.groupby('genre').count())['image'].to_dict()\n",
    "sum_spectrums = 0\n",
    "popular_genre = 0\n",
    "for num in dict_num_genres.values():\n",
    "    sum_spectrums += num\n",
    "    if num > popular_genre:\n",
    "        popular_genre = num\n",
    "hasard = int((popular_genre / sum_spectrums)*100)\n",
    "print('Hasard:', int((popular_genre / sum_spectrums)*100), \"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df3 = pd.get_dummies(df2,columns=['genre'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "897"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2280-1383"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe thead tr:only-child th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>image</th>\n",
       "      <th>genre_action</th>\n",
       "      <th>genre_comedy</th>\n",
       "      <th>genre_drama</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5323</th>\n",
       "      <td>[[15, 13, 5], [26, 27, 17], [69, 78, 65], [66,...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9303</th>\n",
       "      <td>[[31, 26, 25], [28, 32, 26], [58, 68, 62], [60...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>268</th>\n",
       "      <td>[[76, 115, 0], [78, 113, 3], [80, 112, 3], [80...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10345</th>\n",
       "      <td>[[211, 205, 200], [214, 203, 199], [217, 202, ...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7781</th>\n",
       "      <td>[[16, 8, 1], [16, 9, 0], [16, 10, 0], [16, 10,...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   image  genre_action  \\\n",
       "5323   [[15, 13, 5], [26, 27, 17], [69, 78, 65], [66,...             0   \n",
       "9303   [[31, 26, 25], [28, 32, 26], [58, 68, 62], [60...             0   \n",
       "268    [[76, 115, 0], [78, 113, 3], [80, 112, 3], [80...             0   \n",
       "10345  [[211, 205, 200], [214, 203, 199], [217, 202, ...             0   \n",
       "7781   [[16, 8, 1], [16, 9, 0], [16, 10, 0], [16, 10,...             0   \n",
       "\n",
       "       genre_comedy  genre_drama  \n",
       "5323              1            0  \n",
       "9303              1            0  \n",
       "268               1            0  \n",
       "10345             1            0  \n",
       "7781              1            0  "
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4 = df3.sort_values('genre_comedy')\n",
    "df4.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dfcom = df4.iloc[-897:, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe thead tr:only-child th {\n",
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       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>image</th>\n",
       "      <th>genre_action</th>\n",
       "      <th>genre_comedy</th>\n",
       "      <th>genre_drama</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9535</th>\n",
       "      <td>[[89, 88, 67], [94, 90, 72], [54, 47, 30], [39...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3706</th>\n",
       "      <td>[[0, 1, 0], [0, 0, 1], [69, 75, 82], [53, 65, ...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6518</th>\n",
       "      <td>[[12, 13, 0], [46, 42, 18], [52, 44, 15], [52,...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10146</th>\n",
       "      <td>[[22, 23, 21], [13, 15, 16], [25, 25, 31], [24...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3344</th>\n",
       "      <td>[[158, 153, 84], [162, 156, 85], [163, 157, 86...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   image  genre_action  \\\n",
       "9535   [[89, 88, 67], [94, 90, 72], [54, 47, 30], [39...             0   \n",
       "3706   [[0, 1, 0], [0, 0, 1], [69, 75, 82], [53, 65, ...             0   \n",
       "6518   [[12, 13, 0], [46, 42, 18], [52, 44, 15], [52,...             0   \n",
       "10146  [[22, 23, 21], [13, 15, 16], [25, 25, 31], [24...             0   \n",
       "3344   [[158, 153, 84], [162, 156, 85], [163, 157, 86...             0   \n",
       "\n",
       "       genre_comedy  genre_drama  \n",
       "9535              1            0  \n",
       "3706              1            0  \n",
       "6518              1            0  \n",
       "10146             1            0  \n",
       "3344              1            0  "
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfcom.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "df5 = df4.append(dfcom)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfdrama = df4.sort_values('genre_drama').iloc[-629:, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df6 = df5.append(dfdrama)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>image</th>\n",
       "      <th>genre_action</th>\n",
       "      <th>genre_drama</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>genre_comedy</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4560</td>\n",
       "      <td>4560</td>\n",
       "      <td>4560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2280</td>\n",
       "      <td>2280</td>\n",
       "      <td>2280</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              image  genre_action  genre_drama\n",
       "genre_comedy                                  \n",
       "0              4560          4560         4560\n",
       "1              2280          2280         2280"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df6.groupby('genre_comedy').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>image</th>\n",
       "      <th>genre_action</th>\n",
       "      <th>genre_comedy</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>genre_drama</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4560</td>\n",
       "      <td>4560</td>\n",
       "      <td>4560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2280</td>\n",
       "      <td>2280</td>\n",
       "      <td>2280</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             image  genre_action  genre_comedy\n",
       "genre_drama                                   \n",
       "0             4560          4560          4560\n",
       "1             2280          2280          2280"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df6.groupby('genre_drama').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>image</th>\n",
       "      <th>genre_comedy</th>\n",
       "      <th>genre_drama</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>genre_action</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4560</td>\n",
       "      <td>4560</td>\n",
       "      <td>4560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2280</td>\n",
       "      <td>2280</td>\n",
       "      <td>2280</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              image  genre_comedy  genre_drama\n",
       "genre_action                                  \n",
       "0              4560          4560         4560\n",
       "1              2280          2280         2280"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df6.groupby('genre_action').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#shuffle!!\n",
    "df6 = df6.sample(frac=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5472"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_len = int(df6.shape[0]*0.8)\n",
    "train_len"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train = df6.iloc[:train_len, :]\n",
    "test = df6.iloc[train_len:, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1683    [[0, 0, 1], [4, 0, 0], [74, 63, 66], [59, 48, ...\n",
       "7864    [[3, 1, 1], [1, 0, 4], [2, 1, 3], [0, 2, 0], [...\n",
       "8210    [[49, 87, 39], [0, 9, 0], [0, 0, 4], [49, 6, 1...\n",
       "3023    [[38, 99, 43], [36, 101, 39], [38, 101, 39], [...\n",
       "7946    [[4, 7, 5], [5, 7, 7], [8, 7, 9], [11, 8, 10],...\n",
       "Name: image, dtype: object"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['image'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((5472, 4), (1368, 4))"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape, test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train = train['image']\n",
    "Y_train = train.drop('image', 1)\n",
    "X_test = test['image']\n",
    "Y_test = test.drop('image', 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Vérifications des données"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train = pad_sequences(X_train)\n",
    "X_test = pad_sequences(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "input_shapeA = (X_train.shape[1], X_train.shape[2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modèle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dense  2 : (None, 3)\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import LSTM\n",
    "from keras.layers import Dense, Activation\n",
    "from keras.layers import Conv1D, MaxPooling1D\n",
    "from keras.layers import Dropout, Average, BatchNormalization\n",
    "from keras.layers import Flatten\n",
    "\n",
    "num_classes=3\n",
    "\n",
    "#Hyperparameters\n",
    "filtersCNN1=2\n",
    "kernelSize1=5\n",
    "\n",
    "filtersCNN2=4\n",
    "kernelSize2=5\n",
    "\n",
    "unitsFC1=1000\n",
    "unitsFC2=num_classes\n",
    "\n",
    "#defining the layers architecture\n",
    "\n",
    "model = Sequential()\n",
    "model.add(LSTM(20, dropout=0.2, recurrent_dropout=0.2,input_shape=input_shapeA))\n",
    "\n",
    "#model.add(Dense(1, activation='relu'))\n",
    "#BatchNormalization(axis=3)\n",
    "\n",
    "\n",
    "\n",
    "#BatchNormalization(axis=3)\n",
    "\n",
    "\n",
    "#model.add(Dense(1000, activation='relu'))\n",
    "#print(\"Dense  1 : {}\".format(model.output_shape))\n",
    "model.add(Dense(num_classes, activation='softmax'))\n",
    "print(\"Dense  2 : {}\".format(model.output_shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lstm_17 (LSTM)               (None, 20)                1920      \n",
      "_________________________________________________________________\n",
      "dense_20 (Dense)             (None, 3)                 63        \n",
      "=================================================================\n",
      "Total params: 1,983\n",
      "Trainable params: 1,983\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Entrainement du modèle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 3830 samples, validate on 1642 samples\n",
      "Epoch 1/100\n",
      "3830/3830 [==============================] - 37s 10ms/step - loss: 1.1319 - acc: 0.3371 - val_loss: 1.1230 - val_acc: 0.3197\n",
      "Epoch 2/100\n",
      "3830/3830 [==============================] - 25s 6ms/step - loss: 1.1201 - acc: 0.3512 - val_loss: 1.1141 - val_acc: 0.3191\n",
      "Epoch 3/100\n",
      "3830/3830 [==============================] - 28s 7ms/step - loss: 1.1129 - acc: 0.3525 - val_loss: 1.1096 - val_acc: 0.3228\n",
      "Epoch 4/100\n",
      "3830/3830 [==============================] - 30s 8ms/step - loss: 1.1114 - acc: 0.3415 - val_loss: 1.1071 - val_acc: 0.3270\n",
      "Epoch 5/100\n",
      "3830/3830 [==============================] - 28s 7ms/step - loss: 1.1023 - acc: 0.3593 - val_loss: 1.1043 - val_acc: 0.3343\n",
      "Epoch 6/100\n",
      "3830/3830 [==============================] - 29s 8ms/step - loss: 1.1070 - acc: 0.3397 - val_loss: 1.1023 - val_acc: 0.3435\n",
      "Epoch 7/100\n",
      "3830/3830 [==============================] - 25s 7ms/step - loss: 1.1065 - acc: 0.3405 - val_loss: 1.1008 - val_acc: 0.3453\n",
      "Epoch 8/100\n",
      "3830/3830 [==============================] - 27s 7ms/step - loss: 1.1009 - acc: 0.3514 - val_loss: 1.0997 - val_acc: 0.3429\n",
      "Epoch 9/100\n",
      "3830/3830 [==============================] - 22s 6ms/step - loss: 1.1006 - acc: 0.3540 - val_loss: 1.0989 - val_acc: 0.3508\n",
      "Epoch 10/100\n",
      "3830/3830 [==============================] - 30s 8ms/step - loss: 1.0974 - acc: 0.3676 - val_loss: 1.0968 - val_acc: 0.3502\n",
      "Epoch 11/100\n",
      "3830/3830 [==============================] - 29s 8ms/step - loss: 1.0945 - acc: 0.3773 - val_loss: 1.0949 - val_acc: 0.3593\n",
      "Epoch 12/100\n",
      "3830/3830 [==============================] - 27s 7ms/step - loss: 1.0957 - acc: 0.3614 - val_loss: 1.0941 - val_acc: 0.3630\n",
      "Epoch 13/100\n",
      "3830/3830 [==============================] - 26s 7ms/step - loss: 1.0987 - acc: 0.3572 - val_loss: 1.0929 - val_acc: 0.3685\n",
      "Epoch 14/100\n",
      "3830/3830 [==============================] - 28s 7ms/step - loss: 1.0980 - acc: 0.3650 - val_loss: 1.0922 - val_acc: 0.3727\n",
      "Epoch 15/100\n",
      "3830/3830 [==============================] - 24s 6ms/step - loss: 1.0965 - acc: 0.3637 - val_loss: 1.0918 - val_acc: 0.3758\n",
      "Epoch 16/100\n",
      "3830/3830 [==============================] - 28s 7ms/step - loss: 1.0956 - acc: 0.3695 - val_loss: 1.0920 - val_acc: 0.3703\n",
      "Epoch 17/100\n",
      "3830/3830 [==============================] - 30s 8ms/step - loss: 1.0938 - acc: 0.3702 - val_loss: 1.0916 - val_acc: 0.3721\n",
      "Epoch 18/100\n",
      "3830/3830 [==============================] - 34s 9ms/step - loss: 1.0908 - acc: 0.3796 - val_loss: 1.0910 - val_acc: 0.3715\n",
      "Epoch 19/100\n",
      "3830/3830 [==============================] - 31s 8ms/step - loss: 1.0900 - acc: 0.3830 - val_loss: 1.0898 - val_acc: 0.3721\n",
      "Epoch 20/100\n",
      "3830/3830 [==============================] - 29s 8ms/step - loss: 1.0890 - acc: 0.3768 - val_loss: 1.0893 - val_acc: 0.3758\n",
      "Epoch 21/100\n",
      "3830/3830 [==============================] - 26s 7ms/step - loss: 1.0864 - acc: 0.3849 - val_loss: 1.0884 - val_acc: 0.3745\n",
      "Epoch 22/100\n",
      "3830/3830 [==============================] - 24s 6ms/step - loss: 1.0869 - acc: 0.3742 - val_loss: 1.0871 - val_acc: 0.3764\n",
      "Epoch 23/100\n",
      "3830/3830 [==============================] - 23s 6ms/step - loss: 1.0900 - acc: 0.3749 - val_loss: 1.0862 - val_acc: 0.3800\n",
      "Epoch 24/100\n",
      "3830/3830 [==============================] - 20s 5ms/step - loss: 1.0871 - acc: 0.3825 - val_loss: 1.0857 - val_acc: 0.3794\n",
      "Epoch 25/100\n",
      "3830/3830 [==============================] - 21s 6ms/step - loss: 1.0848 - acc: 0.3953 - val_loss: 1.0841 - val_acc: 0.3849\n",
      "Epoch 26/100\n",
      "3830/3830 [==============================] - 26s 7ms/step - loss: 1.0880 - acc: 0.3841 - val_loss: 1.0816 - val_acc: 0.3861\n",
      "Epoch 27/100\n",
      "3830/3830 [==============================] - 19s 5ms/step - loss: 1.0836 - acc: 0.3950 - val_loss: 1.0804 - val_acc: 0.3849\n",
      "Epoch 28/100\n",
      "3830/3830 [==============================] - 26s 7ms/step - loss: 1.0827 - acc: 0.3940 - val_loss: 1.0783 - val_acc: 0.3886\n",
      "Epoch 29/100\n",
      "3830/3830 [==============================] - 26s 7ms/step - loss: 1.0825 - acc: 0.4016 - val_loss: 1.0755 - val_acc: 0.3922\n",
      "Epoch 30/100\n",
      "3830/3830 [==============================] - 22s 6ms/step - loss: 1.0812 - acc: 0.3867 - val_loss: 1.0737 - val_acc: 0.4013\n",
      "Epoch 31/100\n",
      "3830/3830 [==============================] - 19s 5ms/step - loss: 1.0771 - acc: 0.4050 - val_loss: 1.0721 - val_acc: 0.4032\n",
      "Epoch 32/100\n",
      "3830/3830 [==============================] - 18s 5ms/step - loss: 1.0800 - acc: 0.3950 - val_loss: 1.0717 - val_acc: 0.3965\n",
      "Epoch 33/100\n",
      "3830/3830 [==============================] - 23s 6ms/step - loss: 1.0804 - acc: 0.4000 - val_loss: 1.0709 - val_acc: 0.3952\n",
      "Epoch 34/100\n",
      "3830/3830 [==============================] - 21s 5ms/step - loss: 1.0829 - acc: 0.3930 - val_loss: 1.0694 - val_acc: 0.4038\n",
      "Epoch 35/100\n",
      "3830/3830 [==============================] - 19s 5ms/step - loss: 1.0790 - acc: 0.3992 - val_loss: 1.0689 - val_acc: 0.4007\n",
      "Epoch 36/100\n",
      "3830/3830 [==============================] - 25s 7ms/step - loss: 1.0749 - acc: 0.4084 - val_loss: 1.0691 - val_acc: 0.3959\n",
      "Epoch 37/100\n",
      "3830/3830 [==============================] - 25s 7ms/step - loss: 1.0748 - acc: 0.4057 - val_loss: 1.0677 - val_acc: 0.3989\n",
      "Epoch 38/100\n",
      "3830/3830 [==============================] - 21s 6ms/step - loss: 1.0778 - acc: 0.3997 - val_loss: 1.0687 - val_acc: 0.3971\n",
      "Epoch 39/100\n",
      "3830/3830 [==============================] - 19s 5ms/step - loss: 1.0755 - acc: 0.4065 - val_loss: 1.0696 - val_acc: 0.3928\n",
      "Epoch 40/100\n",
      "3830/3830 [==============================] - 19s 5ms/step - loss: 1.0715 - acc: 0.4023 - val_loss: 1.0678 - val_acc: 0.4062\n",
      "Epoch 41/100\n",
      "3830/3830 [==============================] - 18s 5ms/step - loss: 1.0738 - acc: 0.4078 - val_loss: 1.0650 - val_acc: 0.4080\n",
      "Epoch 42/100\n",
      "3830/3830 [==============================] - 23s 6ms/step - loss: 1.0721 - acc: 0.4099 - val_loss: 1.0648 - val_acc: 0.4062\n",
      "Epoch 43/100\n",
      "3830/3830 [==============================] - 24s 6ms/step - loss: 1.0756 - acc: 0.3987 - val_loss: 1.0662 - val_acc: 0.3977\n",
      "Epoch 44/100\n",
      "3830/3830 [==============================] - 26s 7ms/step - loss: 1.0715 - acc: 0.4107 - val_loss: 1.0646 - val_acc: 0.4019\n",
      "Epoch 45/100\n",
      "3830/3830 [==============================] - 24s 6ms/step - loss: 1.0739 - acc: 0.4000 - val_loss: 1.0628 - val_acc: 0.4153\n",
      "Epoch 46/100\n",
      "3830/3830 [==============================] - 19s 5ms/step - loss: 1.0701 - acc: 0.4211 - val_loss: 1.0629 - val_acc: 0.4239\n",
      "Epoch 47/100\n",
      "3830/3830 [==============================] - 24s 6ms/step - loss: 1.0713 - acc: 0.4107 - val_loss: 1.0624 - val_acc: 0.4214\n",
      "Epoch 48/100\n",
      "3830/3830 [==============================] - 21s 5ms/step - loss: 1.0702 - acc: 0.4089 - val_loss: 1.0614 - val_acc: 0.4233\n",
      "Epoch 49/100\n",
      "3830/3830 [==============================] - 26s 7ms/step - loss: 1.0733 - acc: 0.4144 - val_loss: 1.0610 - val_acc: 0.4166\n",
      "Epoch 50/100\n",
      "3830/3830 [==============================] - 24s 6ms/step - loss: 1.0697 - acc: 0.4055 - val_loss: 1.0605 - val_acc: 0.4160\n",
      "Epoch 51/100\n",
      "3830/3830 [==============================] - 22s 6ms/step - loss: 1.0668 - acc: 0.4175 - val_loss: 1.0602 - val_acc: 0.4166\n",
      "Epoch 52/100\n",
      "3830/3830 [==============================] - 19s 5ms/step - loss: 1.0696 - acc: 0.4125 - val_loss: 1.0601 - val_acc: 0.4214\n",
      "Epoch 53/100\n",
      "3830/3830 [==============================] - 22s 6ms/step - loss: 1.0698 - acc: 0.4018 - val_loss: 1.0591 - val_acc: 0.4202\n",
      "Epoch 54/100\n",
      "3830/3830 [==============================] - 23s 6ms/step - loss: 1.0684 - acc: 0.4144 - val_loss: 1.0592 - val_acc: 0.4135\n",
      "Epoch 55/100\n",
      "3830/3830 [==============================] - 24s 6ms/step - loss: 1.0674 - acc: 0.4219 - val_loss: 1.0600 - val_acc: 0.4141\n",
      "Epoch 56/100\n",
      "3830/3830 [==============================] - 22s 6ms/step - loss: 1.0604 - acc: 0.4230 - val_loss: 1.0609 - val_acc: 0.4074\n",
      "Epoch 57/100\n",
      "3830/3830 [==============================] - 23s 6ms/step - loss: 1.0651 - acc: 0.4183 - val_loss: 1.0601 - val_acc: 0.4001\n",
      "Epoch 58/100\n",
      "3830/3830 [==============================] - 24s 6ms/step - loss: 1.0636 - acc: 0.4245 - val_loss: 1.0572 - val_acc: 0.4111\n",
      "Epoch 59/100\n",
      "3830/3830 [==============================] - 19s 5ms/step - loss: 1.0667 - acc: 0.4274 - val_loss: 1.0578 - val_acc: 0.4099\n",
      "Epoch 60/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3830/3830 [==============================] - 18s 5ms/step - loss: 1.0656 - acc: 0.4094 - val_loss: 1.0569 - val_acc: 0.4099\n",
      "Epoch 61/100\n",
      "3830/3830 [==============================] - 18s 5ms/step - loss: 1.0643 - acc: 0.4313 - val_loss: 1.0553 - val_acc: 0.4117\n",
      "Epoch 62/100\n",
      "3830/3830 [==============================] - 24s 6ms/step - loss: 1.0652 - acc: 0.4206 - val_loss: 1.0591 - val_acc: 0.4123\n",
      "Epoch 63/100\n",
      "3830/3830 [==============================] - 25s 7ms/step - loss: 1.0632 - acc: 0.4204 - val_loss: 1.0596 - val_acc: 0.4135\n",
      "Epoch 64/100\n",
      "3830/3830 [==============================] - 25s 6ms/step - loss: 1.0680 - acc: 0.4256 - val_loss: 1.0565 - val_acc: 0.4269\n",
      "Epoch 65/100\n",
      "3830/3830 [==============================] - 20s 5ms/step - loss: 1.0639 - acc: 0.4308 - val_loss: 1.0570 - val_acc: 0.4208\n",
      "Epoch 66/100\n",
      "3830/3830 [==============================] - 21s 5ms/step - loss: 1.0628 - acc: 0.4225 - val_loss: 1.0564 - val_acc: 0.4190\n",
      "Epoch 67/100\n",
      "3830/3830 [==============================] - 21s 5ms/step - loss: 1.0591 - acc: 0.4360 - val_loss: 1.0560 - val_acc: 0.4208\n",
      "Epoch 68/100\n",
      "3830/3830 [==============================] - 20s 5ms/step - loss: 1.0628 - acc: 0.4230 - val_loss: 1.0539 - val_acc: 0.4196\n",
      "Epoch 69/100\n",
      "3830/3830 [==============================] - 24s 6ms/step - loss: 1.0633 - acc: 0.4279 - val_loss: 1.0537 - val_acc: 0.4172\n",
      "Epoch 70/100\n",
      "3830/3830 [==============================] - 21s 5ms/step - loss: 1.0656 - acc: 0.4120 - val_loss: 1.0521 - val_acc: 0.4160\n",
      "Epoch 71/100\n",
      "3830/3830 [==============================] - 20s 5ms/step - loss: 1.0639 - acc: 0.4178 - val_loss: 1.0548 - val_acc: 0.4160\n",
      "Epoch 72/100\n",
      "3830/3830 [==============================] - 23s 6ms/step - loss: 1.0587 - acc: 0.4287 - val_loss: 1.0513 - val_acc: 0.4214\n",
      "Epoch 73/100\n",
      "3830/3830 [==============================] - 22s 6ms/step - loss: 1.0610 - acc: 0.4232 - val_loss: 1.0503 - val_acc: 0.4220\n",
      "Epoch 74/100\n",
      "3830/3830 [==============================] - 19s 5ms/step - loss: 1.0600 - acc: 0.4178 - val_loss: 1.0521 - val_acc: 0.4190\n",
      "Epoch 75/100\n",
      "3830/3830 [==============================] - 18s 5ms/step - loss: 1.0657 - acc: 0.4141 - val_loss: 1.0522 - val_acc: 0.4166\n",
      "Epoch 76/100\n",
      "3830/3830 [==============================] - 18s 5ms/step - loss: 1.0629 - acc: 0.4282 - val_loss: 1.0494 - val_acc: 0.4275\n",
      "Epoch 77/100\n",
      "3830/3830 [==============================] - 18s 5ms/step - loss: 1.0585 - acc: 0.4261 - val_loss: 1.0510 - val_acc: 0.4245\n",
      "Epoch 78/100\n",
      "3830/3830 [==============================] - 18s 5ms/step - loss: 1.0574 - acc: 0.4290 - val_loss: 1.0513 - val_acc: 0.4287\n",
      "Epoch 79/100\n",
      "3830/3830 [==============================] - 18s 5ms/step - loss: 1.0613 - acc: 0.4292 - val_loss: 1.0524 - val_acc: 0.4196\n",
      "Epoch 80/100\n",
      "3830/3830 [==============================] - 18s 5ms/step - loss: 1.0611 - acc: 0.4303 - val_loss: 1.0574 - val_acc: 0.4147\n",
      "Epoch 81/100\n",
      "3830/3830 [==============================] - 18s 5ms/step - loss: 1.0553 - acc: 0.4266 - val_loss: 1.0551 - val_acc: 0.4257\n",
      "Epoch 82/100\n",
      "3830/3830 [==============================] - 18s 5ms/step - loss: 1.0550 - acc: 0.4355 - val_loss: 1.0522 - val_acc: 0.4220\n",
      "Epoch 83/100\n",
      "3830/3830 [==============================] - 19s 5ms/step - loss: 1.0611 - acc: 0.4253 - val_loss: 1.0555 - val_acc: 0.4147\n",
      "Epoch 84/100\n",
      "3830/3830 [==============================] - 25s 7ms/step - loss: 1.0590 - acc: 0.4235 - val_loss: 1.0545 - val_acc: 0.4220\n",
      "Epoch 85/100\n",
      "3830/3830 [==============================] - 26s 7ms/step - loss: 1.0529 - acc: 0.4392 - val_loss: 1.0488 - val_acc: 0.4300\n",
      "Epoch 86/100\n",
      "3830/3830 [==============================] - 24s 6ms/step - loss: 1.0540 - acc: 0.4277 - val_loss: 1.0511 - val_acc: 0.4263\n",
      "Epoch 87/100\n",
      "3830/3830 [==============================] - 19s 5ms/step - loss: 1.0612 - acc: 0.4279 - val_loss: 1.0507 - val_acc: 0.4251\n",
      "Epoch 88/100\n",
      "3830/3830 [==============================] - 19s 5ms/step - loss: 1.0539 - acc: 0.4337 - val_loss: 1.0502 - val_acc: 0.4281\n",
      "Epoch 89/100\n",
      "3830/3830 [==============================] - 18s 5ms/step - loss: 1.0550 - acc: 0.4319 - val_loss: 1.0475 - val_acc: 0.4300\n",
      "Epoch 90/100\n",
      "3830/3830 [==============================] - 24s 6ms/step - loss: 1.0574 - acc: 0.4334 - val_loss: 1.0494 - val_acc: 0.4324\n",
      "Epoch 91/100\n",
      "3830/3830 [==============================] - 22s 6ms/step - loss: 1.0524 - acc: 0.4431 - val_loss: 1.0513 - val_acc: 0.4330\n",
      "Epoch 92/100\n",
      "3830/3830 [==============================] - 19s 5ms/step - loss: 1.0538 - acc: 0.4274 - val_loss: 1.0505 - val_acc: 0.4330\n",
      "Epoch 93/100\n",
      "3830/3830 [==============================] - 18s 5ms/step - loss: 1.0552 - acc: 0.4290 - val_loss: 1.0464 - val_acc: 0.4281\n",
      "Epoch 94/100\n",
      "3830/3830 [==============================] - 18s 5ms/step - loss: 1.0580 - acc: 0.4308 - val_loss: 1.0519 - val_acc: 0.4312\n",
      "Epoch 95/100\n",
      "3830/3830 [==============================] - 18s 5ms/step - loss: 1.0527 - acc: 0.4295 - val_loss: 1.0465 - val_acc: 0.4281\n",
      "Epoch 96/100\n",
      "3830/3830 [==============================] - 18s 5ms/step - loss: 1.0585 - acc: 0.4253 - val_loss: 1.0521 - val_acc: 0.4233\n",
      "Epoch 97/100\n",
      "3830/3830 [==============================] - 18s 5ms/step - loss: 1.0511 - acc: 0.4342 - val_loss: 1.0534 - val_acc: 0.4239\n",
      "Epoch 98/100\n",
      "3830/3830 [==============================] - 18s 5ms/step - loss: 1.0449 - acc: 0.4460 - val_loss: 1.0526 - val_acc: 0.4269\n",
      "Epoch 99/100\n",
      "3830/3830 [==============================] - 24s 6ms/step - loss: 1.0621 - acc: 0.4287 - val_loss: 1.0546 - val_acc: 0.4257\n",
      "Epoch 100/100\n",
      "3830/3830 [==============================] - 25s 7ms/step - loss: 1.0528 - acc: 0.4342 - val_loss: 1.0522 - val_acc: 0.4312\n"
     ]
    }
   ],
   "source": [
    "model.compile(loss='categorical_crossentropy',\n",
    "              optimizer='adam',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "history = model.fit(X_train, Y_train, epochs=100, validation_split=0.3, batch_size=400 , verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  Année: [2004, 2005, 2006, 2007, 2008, 2015]   //  Genre: action, comedy, drama  //  Données X_train: (5472, 1927, 3)\n",
      "Hasard : 42\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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ZA4DFYuG3v/0t06ZNIzk5mZdeeglQZdA3bNhAamoqTz/99LB8r5oTw6EqVS4mv3LQiPaT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\nFiyMMcb49f8B1uWuS49K0kMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1875c575c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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rlJSW8c66GBIypG6VEM2JOWt6jwH2AWtM70OVUj/Ud2CifvxhWGdS88v4VI+B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\nQjEu1Jf0vCLcHWwY1OnqOt8HdPSgpEz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00Klg4hPhrhUw5uGmv54/FOqOw/blTX/9M2rr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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1875c6ae10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "train, =plt.plot(history.history['acc'], label='Train set')\n",
    "val, =plt.plot(history.history['val_acc'], label='Validation set')\n",
    "print('')\n",
    "print(f\"  Année: {years}   //  Genre: action, comedy, drama  //  Données X_train: {X_train.shape}\")\n",
    "print('Hasard :', hasard)\n",
    "print('')\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.xlabel('Epochs')\n",
    "plt.legend(handles=[train, val])\n",
    "plt.show()\n",
    "train, =plt.plot(history.history['loss'], label='Train set')\n",
    "val, =plt.plot(history.history['val_loss'], label='Validation set')\n",
    "plt.title('model Loss')\n",
    "plt.ylabel('Cross entropy')\n",
    "plt.xlabel('Epochs')\n",
    "plt.legend(handles=[train, val])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 3830 samples, validate on 1642 samples\n",
      "Epoch 1/100\n",
      "3830/3830 [==============================] - 20s 5ms/step - loss: 1.0548 - acc: 0.4431 - val_loss: 1.0506 - val_acc: 0.4348\n",
      "Epoch 2/100\n",
      "3830/3830 [==============================] - 17s 4ms/step - loss: 1.0601 - acc: 0.4308 - val_loss: 1.0491 - val_acc: 0.4361\n",
      "Epoch 3/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0548 - acc: 0.4230 - val_loss: 1.0502 - val_acc: 0.4348\n",
      "Epoch 4/100\n",
      "3830/3830 [==============================] - 17s 4ms/step - loss: 1.0567 - acc: 0.4261 - val_loss: 1.0496 - val_acc: 0.4354\n",
      "Epoch 5/100\n",
      "3830/3830 [==============================] - 21s 5ms/step - loss: 1.0544 - acc: 0.4198 - val_loss: 1.0470 - val_acc: 0.4269\n",
      "Epoch 6/100\n",
      "3830/3830 [==============================] - 19s 5ms/step - loss: 1.0478 - acc: 0.4418 - val_loss: 1.0511 - val_acc: 0.4312\n",
      "Epoch 7/100\n",
      "3830/3830 [==============================] - 17s 4ms/step - loss: 1.0457 - acc: 0.4460 - val_loss: 1.0538 - val_acc: 0.4281\n",
      "Epoch 8/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0508 - acc: 0.4298 - val_loss: 1.0520 - val_acc: 0.4361\n",
      "Epoch 9/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0522 - acc: 0.4225 - val_loss: 1.0489 - val_acc: 0.4336\n",
      "Epoch 10/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0560 - acc: 0.4300 - val_loss: 1.0478 - val_acc: 0.4348\n",
      "Epoch 11/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0533 - acc: 0.4198 - val_loss: 1.0510 - val_acc: 0.4361\n",
      "Epoch 12/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0507 - acc: 0.4313 - val_loss: 1.0465 - val_acc: 0.4330\n",
      "Epoch 13/100\n",
      "3830/3830 [==============================] - 17s 4ms/step - loss: 1.0537 - acc: 0.4303 - val_loss: 1.0464 - val_acc: 0.4373\n",
      "Epoch 14/100\n",
      "3830/3830 [==============================] - 23s 6ms/step - loss: 1.0517 - acc: 0.4407 - val_loss: 1.0521 - val_acc: 0.4294\n",
      "Epoch 15/100\n",
      "3830/3830 [==============================] - 23s 6ms/step - loss: 1.0471 - acc: 0.4426 - val_loss: 1.0542 - val_acc: 0.4281\n",
      "Epoch 16/100\n",
      "3830/3830 [==============================] - 23s 6ms/step - loss: 1.0533 - acc: 0.4415 - val_loss: 1.0479 - val_acc: 0.4294\n",
      "Epoch 17/100\n",
      "3830/3830 [==============================] - 23s 6ms/step - loss: 1.0503 - acc: 0.4423 - val_loss: 1.0458 - val_acc: 0.4287\n",
      "Epoch 18/100\n",
      "3830/3830 [==============================] - 23s 6ms/step - loss: 1.0586 - acc: 0.4198 - val_loss: 1.0492 - val_acc: 0.4342\n",
      "Epoch 19/100\n",
      "3830/3830 [==============================] - 23s 6ms/step - loss: 1.0519 - acc: 0.4402 - val_loss: 1.0469 - val_acc: 0.4306\n",
      "Epoch 20/100\n",
      "3830/3830 [==============================] - 18s 5ms/step - loss: 1.0534 - acc: 0.4292 - val_loss: 1.0466 - val_acc: 0.4300\n",
      "Epoch 21/100\n",
      "3830/3830 [==============================] - 17s 4ms/step - loss: 1.0517 - acc: 0.4371 - val_loss: 1.0530 - val_acc: 0.4275\n",
      "Epoch 22/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0519 - acc: 0.4444 - val_loss: 1.0519 - val_acc: 0.4348\n",
      "Epoch 23/100\n",
      "3830/3830 [==============================] - 20s 5ms/step - loss: 1.0469 - acc: 0.4332 - val_loss: 1.0480 - val_acc: 0.4397\n",
      "Epoch 24/100\n",
      "3830/3830 [==============================] - 22s 6ms/step - loss: 1.0517 - acc: 0.4324 - val_loss: 1.0470 - val_acc: 0.4403\n",
      "Epoch 25/100\n",
      "3830/3830 [==============================] - 22s 6ms/step - loss: 1.0504 - acc: 0.4384 - val_loss: 1.0494 - val_acc: 0.4361\n",
      "Epoch 26/100\n",
      "3830/3830 [==============================] - 19s 5ms/step - loss: 1.0548 - acc: 0.4300 - val_loss: 1.0483 - val_acc: 0.4361\n",
      "Epoch 27/100\n",
      "3830/3830 [==============================] - 22s 6ms/step - loss: 1.0502 - acc: 0.4345 - val_loss: 1.0465 - val_acc: 0.4391\n",
      "Epoch 28/100\n",
      "3830/3830 [==============================] - 17s 5ms/step - loss: 1.0498 - acc: 0.4272 - val_loss: 1.0456 - val_acc: 0.4385\n",
      "Epoch 29/100\n",
      "3830/3830 [==============================] - 19s 5ms/step - loss: 1.0468 - acc: 0.4452 - val_loss: 1.0474 - val_acc: 0.4385\n",
      "Epoch 30/100\n",
      "3830/3830 [==============================] - 17s 4ms/step - loss: 1.0549 - acc: 0.4277 - val_loss: 1.0490 - val_acc: 0.4336\n",
      "Epoch 31/100\n",
      "3830/3830 [==============================] - 20s 5ms/step - loss: 1.0482 - acc: 0.4342 - val_loss: 1.0457 - val_acc: 0.4379\n",
      "Epoch 32/100\n",
      "3830/3830 [==============================] - 20s 5ms/step - loss: 1.0528 - acc: 0.4277 - val_loss: 1.0468 - val_acc: 0.4318\n",
      "Epoch 33/100\n",
      "3830/3830 [==============================] - 17s 5ms/step - loss: 1.0496 - acc: 0.4311 - val_loss: 1.0459 - val_acc: 0.4342\n",
      "Epoch 34/100\n",
      "3830/3830 [==============================] - 18s 5ms/step - loss: 1.0552 - acc: 0.4274 - val_loss: 1.0461 - val_acc: 0.4446\n",
      "Epoch 35/100\n",
      "3830/3830 [==============================] - 21s 5ms/step - loss: 1.0486 - acc: 0.4433 - val_loss: 1.0470 - val_acc: 0.4446\n",
      "Epoch 36/100\n",
      "3830/3830 [==============================] - 19s 5ms/step - loss: 1.0427 - acc: 0.4486 - val_loss: 1.0438 - val_acc: 0.4434\n",
      "Epoch 37/100\n",
      "3830/3830 [==============================] - 21s 5ms/step - loss: 1.0527 - acc: 0.4350 - val_loss: 1.0466 - val_acc: 0.4446\n",
      "Epoch 38/100\n",
      "3830/3830 [==============================] - 17s 4ms/step - loss: 1.0477 - acc: 0.4392 - val_loss: 1.0491 - val_acc: 0.4300\n",
      "Epoch 39/100\n",
      "3830/3830 [==============================] - 17s 4ms/step - loss: 1.0482 - acc: 0.4397 - val_loss: 1.0438 - val_acc: 0.4373\n",
      "Epoch 40/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0492 - acc: 0.4371 - val_loss: 1.0452 - val_acc: 0.4312\n",
      "Epoch 41/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0509 - acc: 0.4439 - val_loss: 1.0494 - val_acc: 0.4300\n",
      "Epoch 42/100\n",
      "3830/3830 [==============================] - 17s 4ms/step - loss: 1.0476 - acc: 0.4399 - val_loss: 1.0462 - val_acc: 0.4263\n",
      "Epoch 43/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0506 - acc: 0.4282 - val_loss: 1.0413 - val_acc: 0.4354\n",
      "Epoch 44/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0451 - acc: 0.4496 - val_loss: 1.0412 - val_acc: 0.4397\n",
      "Epoch 45/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0483 - acc: 0.4441 - val_loss: 1.0412 - val_acc: 0.4446\n",
      "Epoch 46/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0545 - acc: 0.4355 - val_loss: 1.0389 - val_acc: 0.4428\n",
      "Epoch 47/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0418 - acc: 0.4439 - val_loss: 1.0418 - val_acc: 0.4446\n",
      "Epoch 48/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0466 - acc: 0.4426 - val_loss: 1.0435 - val_acc: 0.4397\n",
      "Epoch 49/100\n",
      "3830/3830 [==============================] - 17s 4ms/step - loss: 1.0505 - acc: 0.4352 - val_loss: 1.0418 - val_acc: 0.4391\n",
      "Epoch 50/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0490 - acc: 0.4363 - val_loss: 1.0422 - val_acc: 0.4421\n",
      "Epoch 51/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0497 - acc: 0.4324 - val_loss: 1.0429 - val_acc: 0.4391\n",
      "Epoch 52/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0493 - acc: 0.4457 - val_loss: 1.0416 - val_acc: 0.4403\n",
      "Epoch 53/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0535 - acc: 0.4389 - val_loss: 1.0427 - val_acc: 0.4409\n",
      "Epoch 54/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0468 - acc: 0.4384 - val_loss: 1.0448 - val_acc: 0.4318\n",
      "Epoch 55/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0490 - acc: 0.4245 - val_loss: 1.0414 - val_acc: 0.4348\n",
      "Epoch 56/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0443 - acc: 0.4397 - val_loss: 1.0386 - val_acc: 0.4385\n",
      "Epoch 57/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0423 - acc: 0.4522 - val_loss: 1.0404 - val_acc: 0.4428\n",
      "Epoch 58/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0487 - acc: 0.4483 - val_loss: 1.0438 - val_acc: 0.4354\n",
      "Epoch 59/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0406 - acc: 0.4499 - val_loss: 1.0406 - val_acc: 0.4446\n",
      "Epoch 60/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0475 - acc: 0.4384 - val_loss: 1.0416 - val_acc: 0.4397\n",
      "Epoch 61/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0472 - acc: 0.4499 - val_loss: 1.0466 - val_acc: 0.4306\n",
      "Epoch 62/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0450 - acc: 0.4352 - val_loss: 1.0414 - val_acc: 0.4385\n",
      "Epoch 63/100\n",
      "3830/3830 [==============================] - 15s 4ms/step - loss: 1.0451 - acc: 0.4371 - val_loss: 1.0395 - val_acc: 0.4379\n",
      "Epoch 64/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0415 - acc: 0.4452 - val_loss: 1.0442 - val_acc: 0.4367\n",
      "Epoch 65/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0462 - acc: 0.4397 - val_loss: 1.0476 - val_acc: 0.4312\n",
      "Epoch 66/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0430 - acc: 0.4462 - val_loss: 1.0413 - val_acc: 0.4415\n",
      "Epoch 67/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0464 - acc: 0.4496 - val_loss: 1.0408 - val_acc: 0.4488\n",
      "Epoch 68/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0452 - acc: 0.4381 - val_loss: 1.0449 - val_acc: 0.4324\n",
      "Epoch 69/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0478 - acc: 0.4397 - val_loss: 1.0436 - val_acc: 0.4342\n",
      "Epoch 70/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0439 - acc: 0.4548 - val_loss: 1.0417 - val_acc: 0.4440\n",
      "Epoch 71/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0417 - acc: 0.4415 - val_loss: 1.0429 - val_acc: 0.4379\n",
      "Epoch 72/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0436 - acc: 0.4486 - val_loss: 1.0424 - val_acc: 0.4409\n",
      "Epoch 73/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0472 - acc: 0.4407 - val_loss: 1.0415 - val_acc: 0.4403\n",
      "Epoch 74/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0445 - acc: 0.4428 - val_loss: 1.0394 - val_acc: 0.4482\n",
      "Epoch 75/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0434 - acc: 0.4533 - val_loss: 1.0389 - val_acc: 0.4397\n",
      "Epoch 76/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0439 - acc: 0.4449 - val_loss: 1.0384 - val_acc: 0.4476\n",
      "Epoch 77/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0410 - acc: 0.4475 - val_loss: 1.0390 - val_acc: 0.4482\n",
      "Epoch 78/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0463 - acc: 0.4386 - val_loss: 1.0391 - val_acc: 0.4464\n",
      "Epoch 79/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0457 - acc: 0.4415 - val_loss: 1.0413 - val_acc: 0.4440\n",
      "Epoch 80/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0444 - acc: 0.4522 - val_loss: 1.0451 - val_acc: 0.4361\n",
      "Epoch 81/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0381 - acc: 0.4493 - val_loss: 1.0435 - val_acc: 0.4318\n",
      "Epoch 82/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0443 - acc: 0.4444 - val_loss: 1.0419 - val_acc: 0.4385\n",
      "Epoch 83/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0473 - acc: 0.4397 - val_loss: 1.0437 - val_acc: 0.4354\n",
      "Epoch 84/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0458 - acc: 0.4457 - val_loss: 1.0411 - val_acc: 0.4379\n",
      "Epoch 85/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0494 - acc: 0.4352 - val_loss: 1.0396 - val_acc: 0.4452\n",
      "Epoch 86/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0422 - acc: 0.4525 - val_loss: 1.0402 - val_acc: 0.4440\n",
      "Epoch 87/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0465 - acc: 0.4530 - val_loss: 1.0417 - val_acc: 0.4397\n",
      "Epoch 88/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0479 - acc: 0.4405 - val_loss: 1.0414 - val_acc: 0.4440\n",
      "Epoch 89/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0428 - acc: 0.4509 - val_loss: 1.0444 - val_acc: 0.4312\n",
      "Epoch 90/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0418 - acc: 0.4480 - val_loss: 1.0420 - val_acc: 0.4379\n",
      "Epoch 91/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0473 - acc: 0.4423 - val_loss: 1.0429 - val_acc: 0.4306\n",
      "Epoch 92/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0464 - acc: 0.4530 - val_loss: 1.0435 - val_acc: 0.4397\n",
      "Epoch 93/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0427 - acc: 0.4457 - val_loss: 1.0444 - val_acc: 0.4367\n",
      "Epoch 94/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0406 - acc: 0.4564 - val_loss: 1.0417 - val_acc: 0.4470\n",
      "Epoch 95/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0423 - acc: 0.4418 - val_loss: 1.0421 - val_acc: 0.4482\n",
      "Epoch 96/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0426 - acc: 0.4561 - val_loss: 1.0415 - val_acc: 0.4470\n",
      "Epoch 97/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0434 - acc: 0.4426 - val_loss: 1.0399 - val_acc: 0.4519\n",
      "Epoch 98/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0374 - acc: 0.4514 - val_loss: 1.0387 - val_acc: 0.4488\n",
      "Epoch 99/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0429 - acc: 0.4397 - val_loss: 1.0392 - val_acc: 0.4501\n",
      "Epoch 100/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0498 - acc: 0.4415 - val_loss: 1.0408 - val_acc: 0.4428\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(X_train, Y_train, epochs=100, validation_split=0.3, batch_size=500 , verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  Année: [2004, 2005, 2006, 2007, 2008, 2015]   //  Genre: action, comedy, drama  //  Données X_train: (5472, 1927, 3)\n",
      "Hasard : 42\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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lS5fyve99b1zuq4+pibxRMoszhZFnHT31xkFufGgjG/ePb5fusUDNDimM+vMZ3aC8ljqV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FIkjbpSc6lRsonYpsjYMkgnVGgrMtsjbCZsnivkaYDxTLyrIskv3q933uf+zoS3NJGMe+aYG7Qz\n24vVRvvQFkB+G1z1Nrjrr+Ghf4LrfsrfFG7XIbj4FuK2UJMHDZGEhUX23AhnH1DFeQNVJIZIWhe+\nMEUihHCI5eBWd4iSydxqd6wNY+qgUiSmEWZqVIW2NpHZno7brJRqSElktkfYNNg8V8jTAGOZRPs6\nEp0qmimeZlHmiGU8qbMOkagiwolsosW0N2Z7uCLpQCQAz38fNGpgJ+CGnwndJRGzA1lbIYvQnhtU\n/61mfSgeSRh5pUIUCbjEYvwRUB5J8HkDxdRBqK66PklqlJFUjNQmqtfIJGwnUSNSJBE2C7qsUBG8\nGEvHeXK+GP5L00q+WuC43MKUNxxy2RvVnf64CneNBTwSKaWjUFZDPZIuC8bURfCiX1FkZib/BZDQ\niiQsa8vBrutAWIpMhpC1FRra0tu8mV1AqCKZyCYYy8RZKtaGE9qa1JlbJ3UadGqU97/60r7mxwwb\n6YTtdKGOWqRE2CyIFEkfGM8k2oe2wPFJTsgt/nDI1svgrX8DMXWXPZFVITIzk6RUazhz3guVum9W\nSbHSJbRl8NLfVPM12kB5JE2qjYbzuAXJvKuezmNoazwTJxYIH5nZ7QcCVfIXTClVMhyP5KD66SGS\ni7fluWr3YAozB4F03P0upBPR5RthcyD6JvaBsUycpVKt/VAq7ZMcDxJJAOOZBI2mdGpGTLhs13ia\npsSZfielZK1ad/psbQRx2/KHttoZyKYn1TBCWyGvOZlN+Jo8GuyeSJNN2Oybyvq2G59kKKGtkR0q\nc+3kYfV4E86N8JJHFNqKsFkQhbb6wFgmQbXepFRrOJ1YfUh5iKRDpo+5Q19cqzKadjO4dk9kODq3\nRqFcJ5OIUao1aEp6UyRdkLAtavXwynYfDr4K7vwf/mSBDSKrU6fDPJLffO1loR2V3/PCC3jD1Ttb\nJhOazK2e28j3AyFU5taZu9XjTUgk3u9dZLZH2CyIiKQPjHvapIQSiVYkT8qtHYnEqW4vVtlH1gmX\n7Z5QLc9XynW2jLidf7t6JD0gHlOKJKwg0YcLXwrvP6GaHQ4IQgjGs/HQ0NZENvx1sskY+0MI9DVX\nbuf0Uold40Ma9zp1cFMTiTekFymSCJsFUWirD4w5HYA7NG4ETsotHZv8BfttmdCWmXNuUoCdoVYD\nUiS+9N9OtREDJBGDg1vzvoFX68XuiQwffMMVLZ7KwGB8EmGp5IlNhoznpiIy2yNsFkSKpA+Y5oNt\nGzfmtlATSRZinSegTQT6bZlU4D3aKzAdgAuDJJJAQeL5ro345DuvwxKbp2dVW5ieW8mRTTk5MB0p\nkgibEBGR9IGuHYBv/Dn+x+yl2I/EOx7HhLaMsjFV7bu0IjEpwMWqCW0NSJE0VIuUuC2wznNb9KDX\nsWlhFMkmDGuBvzVMRCQRNgueJlf35sB4m1kiDrJTPJG6tGtLDWM+m1qSxWKVfCrmENVqS2hrAB6J\nbdFoSsq1xqaq1N50mLgQEJuXSOLe0Fb0d4ywORB9E/vAqO4AvNyuAzBQ0/UgnWDMZ9cjqTKeSZBP\nKeURDG0NJP03phTIWqW+qXpHbTrEU6pwdJMSic8j2UQV9xGe3YhCW30gFbdJx21fw8VvPjrLozOr\nvOdFFwBQbTR7Wqi9xY2LxRrjmbjjhZg2KcWqGbM7mNAWKHKKiKQLXvx+SGS77/cUIMrairAZERFJ\nn1CNG11F8qnvPMEPnlx0iaTe7MkP8BLJklYkcdsiHbcpVPTYXZ3+mxuER6LJY7UcEUlXXP22p/oM\n2sIoEtsSTx/fKcIzHtE3sU+MZhIsl1xF8vCZVVbKdacfU61HRTKRTThZW4vFqpPJlUvFWtJ/MwPw\nSHyKJFqAnrYwZnukRiJsJkQrSp/wKpLFtSozK2UA5jUpVOo9hray3uPUHKM9n4w5oa21qlIPg7jz\nNMdYq9SH04I9wnlBRvfaioz2CJsJQ/02CiFuEUI8IoQ4IoR4f4f93iKEkEKIQ/rxK4QQdwoh7tM/\nX+rZ9xv6mHfrf+HtboeE8YzbAv6hmRVn+9yqmtzXa2hrQh+nXGtQqNSdjLB8ykMklcH02QI3tFWI\nQltPa6R0r62oGDHCZsLQPBIhhA18DHgFcBK4QwjxBSnlg4H98sD7gNs9m+eA10kpTwshrgC+Cuz0\n/P7tZr77+caobmMOKqxlMFdQRFJrNHsqIBzPJmhKOL6g2tKb9iv+0FZjIKm/4CqSQqXOrohInrZI\n2Ba2JaLQVoRNhWGuKNcDR6SUR6WUVeCzwBtC9vsQ8PtA2WyQUt4lpTytHz4ApIQQyZDnnneMezoA\nP3RmhZgu7Js1iqTRoyLRnsjR2QLgVs3nkjEn/bfjvPY+YVqiFCr1TTWDPEJ/EEKRSKRIImwmDHNF\n2Qmc8Dw+iV9VIIS4Btgtpfxih+O8GbhLSlnxbPuUDmv9lhDhfTeEELcKIQ4LIQ7Pzs6u8y20Yiyt\nWsCvVuo8PLPKtXvU+FyjSKo91JGAW9z4+Owa4BJLPhV3KtvXqj1MR+wRhtya8vy3R4kwWKQTdqRI\nImwqDHNFCVvgnUEeQggL+Ajwy20PIMTlwIeBn/ZsfruU8krghfrfO8KeK6X8hJTykJTy0PT09DpO\nPxzGFJ8vVHnk7CpX7R4ln4oxV1C+Sa0he87aAnjcUSQ6tJWMOZXthV6HWvUA7zlFHsnTG+m4HdqS\nP0KEpwrD/DaeBHZ7Hu8CTnse54ErgG8IIY4BzwO+4DHcdwGfB35cSvm4eZKU8pT+uQr8LSqEdt5g\nlMRdxxep1ptcsm2E6VyS2UJ/ZrshjqNakXjNdjMlsacxuz0ibru8HhHJ0xvbRlJsHUk91acRIYKD\nYRYk3gEcEELsB04BbwV+zPxSSrkMOG1yhRDfAH5FSnlYCDEGfAn4DSnldzz7xIAxKeWcECIOvBb4\n+hDfQwsMAXzv8XkALt0+wlQ+6fNI+lEkxiPxEomUqmFjsTK40Jb3nCKP5OmNP3vHc303BhEiPNUY\n2ooipawD70VlXD0EfE5K+YAQ4oNCiNd3efp7gYuA3wqk+SaBrwoh7gXuRhHUnw/rPYTBmOLfOzpP\nzBJcuCXLdC7p80h6WajTcZtkzGKlrMxvU2iWS+rGjeU6hUGm/9pRaOuZgolsgnyqc4fpCBHOJ4ba\nIkVK+WXgy4Ftv91m35d4/v+7wO+2OexzB3V+64FRJCcXS1yyLU8yZjOVSwTqSLrfLQohmMgmOLNc\n9k0JzKVMv60aa9WGr0nfRuDzSOzIqI0QIcLgEN2a9omxtHsneMm2PABTuaTTJqXXFinghrOMygFV\n2Q6qUr7RlAPP2oJIkUSIEGGwiFaUPhGzLafd+6XbRwCYzqsSl9nVCvWm7PmO3ygRU4wIOMc+q1uv\nDLqyPfj/CBEiRNgoohVlHTDhrUs0kUzlFJGcWVaLv5n90Q3jDpG0hrbMsQYV2vIqkshsjxAhwiAR\nrSjrgFn4L92uQ1takZxeKgG9F/xNaEIaz7qKxCiQQSuSpM8jif7sESJEGByiFWUdGE3HmcwmmNZK\nZCqniOWUIZJePZIQRWKycQyRDMMjiYrZIkSIMEhEg63WgbffsJfZQgXTncWEtvpWJNlWs90oEBPa\nGhSR2JbAtgSNpowUSYQIEQaKiEjWgVuu2OZ7nIrb5FMxZ/HvVZEYAvGa7bYlyCRszq2odOJBdf8F\nVd3eaPbWwiVChAgRekW0ogwI07kkpxaVIul1ENWUViTeOhJQmVtOaGtA3X/BVUoRkUSIEGGQiFaU\nAWEql3RDWz0u1Nfvn+B3XncZz79wyrc9l4xRb0rn/4OCOa8otBUhQoRBIlpRBoSpfMLp2tsrkcRs\ni3e+YH/L/jlP+4tBzGs3iBRJhAgRhoFoRRkQjOEOG7/jN9XtcVsMdL56PBYRSYQIEQaPaEUZEKa9\nRLLBhdpUtw8qY8vAENwgySlChAgRIiIZEExRImxckRhfZJBGO7hJAFFle4QIEQaJaEUZELyhrV6z\nttoh5yiSwSqHRBTaihAhwhAQrSgDgqluh0GEtpTZPqzQVpS1FSFChEEiWlEGhOkBhrbyQwptRYok\nQoQIw0C0ogwIUwM024cV2jIDtyIiiRAhwiARrSgDQipuO0pi02ZtRYokQoQIQ8BQVxQhxC1CiEeE\nEEeEEO/vsN9bhBBSCHFIP36FEOJOIcR9+udLPfs+V28/IoT4Q2E6J24CmMytXkbtdkKUtRUhQoSn\nE4a2ogghbOBjwKuBy4C3CSEuC9kvD7wPuN2zeQ54nZTySuAngE97fvenwK3AAf3vlqG8gXXAGO6b\nVpFEZnuECBGGgGGuKNcDR6SUR6WUVeCzwBtC9vsQ8PtA2WyQUt4lpTytHz4ApIQQSSHEdmBESvk9\nKaUE/ifwxiG+h75gDPcNm+06ays3hPTfhG2xiURchAgRngEYJpHsBE54Hp/U2xwIIa4Bdkspv9jh\nOG8G7pJSVvTzT3Y6pufYtwohDgshDs/Ozq7n/PvGVC5J3BYbXqhNaCszhKytyB+JECHCoDHMeSRh\nq6l0fimEBXwEeGfbAwhxOfBh4JW9HNO3UcpPAJ8AOHToUOg+g8aPHNrNvsnsho+zfTTF+152gFcF\n5p5sFG++dhcHtuQGeswIESJEGCaRnAR2ex7v4v9v7+5j5KrqMI5/H7dUC0hXqCC2Cy2hvmBjKW5I\nRWNI1QSEtCZqKsGUNBhjI2l9t5gYo9E/SIxgAyFBqGIgIKm8bDRWSa3vUtnaqpRqLLVKpdASbaFq\n5MXHP+5ZGOsM2+zd6dg7zyeZ7Nwzd+6cX36z85t77p174JGW5ZcC84Aflm/wrwBGJC22PSppFnAX\nsMz2Qy3bnPUC2+ypeTOnM2/m9NrbkcRH3/6qSejRf5s/NMj8ocFJ325E9LdujnPcD8yVNEfSVOC9\nwMjYg7YP2J5he7bt2cB9wFgRGQS+A1xp+2ctz9kDPClpYTlbaxlwTxdjiIiIcXStkNh+BrgC+B6w\nHbjD9jZJn5e0eJynXwGcCXxG0tZyO7k8tgK4EdgBPAR8tzsRRETE4VB18lOzDQ8Pe3R0tNfdiIg4\nqkjabHt4vPVyCk9ERNSSQhIREbWkkERERC0pJBERUUsKSURE1NIXZ21J2gf8aYJPn0F1Ecl+0o8x\nQ3/G3Y8xQ3/GPZGYT7f98vFW6otCUoek0cM5/a1J+jFm6M+4+zFm6M+4uxlzhrYiIqKWFJKIiKgl\nhWR8N/S6Az3QjzFDf8bdjzFDf8bdtZhzjCQiImrJHklERNSSQhIREbWkkHQg6QJJv5e0Q9LqXven\nWyQNSdooabukbZJWlfYTJd0r6Q/l78t63dfJJmlA0hZJ3y7LcyRtKjF/s8yj0yiSBiWtk/S7kvM3\nNj3Xkj5S3tsPSLpN0kuamGtJayXtlfRAS1vb3Kqypny+/UbSOXVeO4WkDUkDwHXAhcBZwCWSzupt\nr7rmGeBjtl8LLAQ+VGJdDWywPRfYUJabZhXVXDljrgKuLjH/Dbi8J73qrq8A622/BphPFX9jcy1p\nJrASGLY9DxigmmSvibn+OnDBIW2dcnshMLfcPgBcX+eFU0jaOxfYYXun7aeA24ElPe5TV9jeY/tX\n5f6TVB8sM6nivbmsdjPwzt70sDvKVM4XUU2SRplxcxGwrqzSxJhPAN4C3ARg+ynb+2l4rqmmFJ8m\naQpwLLCHBuba9o+Bvx7S3Cm3S4BvuHIfMCjp1Im+dgpJezOBh1uWd5e2RpM0G1gAbAJOKVMbj01x\nfHLnZx6VrgE+Cfy7LJ8E7C8ze0Izc34GsA/4WhnSu1HScTQ417b/AnwJ+DNVATkAbKb5uR7TKbeT\n+hmXQtKe2rQ1+jxpSccD3wI+bPuJXvenmyRdDOy1vbm1uc2qTcv5FOAc4HrbC4C/06BhrHbKMYEl\nwBzglcBxVMM6h2parsczqe/3FJL2dgNDLcuzgEd61Jeuk3QMVRG51fadpfmxsV3d8ndvr/rXBW8C\nFkvaRTVsuYhqD2WwDH9AM3O+G9hte1NZXkdVWJqc67cBf7S9z/bTwJ3AeTQ/12M65XZSP+NSSNq7\nH5hbzuyYSnVwbqTHfeqKcmzgJmC77S+3PDQCXFbuXwbcc6T71i22r7Q9y/Zsqtz+wPalwEbg3WW1\nRmmsivYAAAKkSURBVMUMYPtR4GFJry5NbwUepMG5phrSWijp2PJeH4u50blu0Sm3I8CycvbWQuDA\n2BDYROSX7R1IegfVt9QBYK3tL/a4S10h6c3AT4Df8vzxgk9THSe5AziN6p/xPbYPPZB31JN0PvBx\n2xdLOoNqD+VEYAvwPtv/6mX/Jpuks6lOMJgK7ASWU32hbGyuJX0OWEp1huIW4P1UxwMalWtJtwHn\nU10u/jHgs8DdtMltKarXUp3l9Q9gue3RCb92CklERNSRoa2IiKglhSQiImpJIYmIiFpSSCIiopYU\nkoiIqCWFJGKCJD0raWvLbdJ+JS5pdutVXCP+n00Zf5WI6OCfts/udSciei17JBGTTNIuSVdJ+mW5\nnVnaT5e0ocz/sEHSaaX9FEl3Sfp1uZ1XNjUg6atlLo3vS5pW1l8p6cGyndt7FGbEc1JIIiZu2iFD\nW0tbHnvC9rlUvx6+prRdS3Xp7tcDtwJrSvsa4Ee251Nd+2pbaZ8LXGf7dcB+4F2lfTWwoGzng90K\nLuJw5ZftERMk6aDt49u07wIW2d5ZLoj5qO2TJD0OnGr76dK+x/YMSfuAWa2X6CiX9L+3TEiEpE8B\nx9j+gqT1wEGqy1/cbftgl0ONeEHZI4noDne432mddlqv/fQszx/TvIhqBs83AJtbrmIb0RMpJBHd\nsbTl7y/K/Z9TXW0Y4FLgp+X+BmAFPDeP/AmdNirpRcCQ7Y1UE3MNAv+zVxRxJOWbTMTETZO0tWV5\nve2xU4BfLGkT1Ze1S0rbSmCtpE9QzVS4vLSvAm6QdDnVnscKqtn82hkAbpE0nWpyoqvLdLkRPZNj\nJBGTrBwjGbb9eK/7EnEkZGgrIiJqyR5JRETUkj2SiIioJYUkIiJqSSGJiIhaUkgiIqKWFJKIiKjl\nP+4EgcfFukHvAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1875bfaef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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pzgZDI4yQTDM1rq3h/eAJQMt8WLQR8qlyXAPVZ6srUjt6flmHytw6MFQelHV4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\nwWAwGBaFMSQGg8FgWBTGkBgMBoNhUfz/7KA1nR0q6GoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1875bf0fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "train, =plt.plot(history.history['acc'], label='Train set')\n",
    "val, =plt.plot(history.history['val_acc'], label='Validation set')\n",
    "print('')\n",
    "print(f\"  Année: {years}   //  Genre: action, comedy, drama  //  Données X_train: {X_train.shape}\")\n",
    "print('Hasard :', hasard)\n",
    "print('')\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.xlabel('Epochs')\n",
    "plt.legend(handles=[train, val])\n",
    "plt.show()\n",
    "train, =plt.plot(history.history['loss'], label='Train set')\n",
    "val, =plt.plot(history.history['val_loss'], label='Validation set')\n",
    "plt.title('model Loss')\n",
    "plt.ylabel('Cross entropy')\n",
    "plt.xlabel('Epochs')\n",
    "plt.legend(handles=[train, val])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 3830 samples, validate on 1642 samples\n",
      "Epoch 1/100\n",
      "3830/3830 [==============================] - 31s 8ms/step - loss: 1.0445 - acc: 0.4486 - val_loss: 1.0399 - val_acc: 0.4458\n",
      "Epoch 2/100\n",
      "3830/3830 [==============================] - 23s 6ms/step - loss: 1.0446 - acc: 0.4478 - val_loss: 1.0403 - val_acc: 0.4452\n",
      "Epoch 3/100\n",
      "3830/3830 [==============================] - 22s 6ms/step - loss: 1.0391 - acc: 0.4614 - val_loss: 1.0381 - val_acc: 0.4543\n",
      "Epoch 4/100\n",
      "3830/3830 [==============================] - 21s 5ms/step - loss: 1.0377 - acc: 0.4504 - val_loss: 1.0389 - val_acc: 0.4507\n",
      "Epoch 5/100\n",
      "3830/3830 [==============================] - 30s 8ms/step - loss: 1.0427 - acc: 0.4507 - val_loss: 1.0421 - val_acc: 0.4397\n",
      "Epoch 6/100\n",
      "3830/3830 [==============================] - 19s 5ms/step - loss: 1.0420 - acc: 0.4379 - val_loss: 1.0412 - val_acc: 0.4495\n",
      "Epoch 7/100\n",
      "3830/3830 [==============================] - 17s 5ms/step - loss: 1.0430 - acc: 0.4431 - val_loss: 1.0377 - val_acc: 0.4476\n",
      "Epoch 8/100\n",
      "3830/3830 [==============================] - 17s 4ms/step - loss: 1.0448 - acc: 0.4554 - val_loss: 1.0388 - val_acc: 0.4452\n",
      "Epoch 9/100\n",
      "3830/3830 [==============================] - 17s 4ms/step - loss: 1.0387 - acc: 0.4496 - val_loss: 1.0401 - val_acc: 0.4428\n",
      "Epoch 10/100\n",
      "3830/3830 [==============================] - 17s 4ms/step - loss: 1.0438 - acc: 0.4493 - val_loss: 1.0385 - val_acc: 0.4421\n",
      "Epoch 11/100\n",
      "3830/3830 [==============================] - 17s 4ms/step - loss: 1.0446 - acc: 0.4407 - val_loss: 1.0397 - val_acc: 0.4397\n",
      "Epoch 12/100\n",
      "3830/3830 [==============================] - 19s 5ms/step - loss: 1.0481 - acc: 0.4407 - val_loss: 1.0412 - val_acc: 0.4440\n",
      "Epoch 13/100\n",
      "3830/3830 [==============================] - 17s 4ms/step - loss: 1.0399 - acc: 0.4397 - val_loss: 1.0409 - val_acc: 0.4409\n",
      "Epoch 14/100\n",
      "3830/3830 [==============================] - 17s 4ms/step - loss: 1.0445 - acc: 0.4512 - val_loss: 1.0402 - val_acc: 0.4440\n",
      "Epoch 15/100\n",
      "3830/3830 [==============================] - 17s 4ms/step - loss: 1.0416 - acc: 0.4462 - val_loss: 1.0405 - val_acc: 0.4373\n",
      "Epoch 16/100\n",
      "3830/3830 [==============================] - 17s 4ms/step - loss: 1.0442 - acc: 0.4452 - val_loss: 1.0411 - val_acc: 0.4397\n",
      "Epoch 17/100\n",
      "3830/3830 [==============================] - 20s 5ms/step - loss: 1.0416 - acc: 0.4540 - val_loss: 1.0407 - val_acc: 0.4348\n",
      "Epoch 18/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0433 - acc: 0.4433 - val_loss: 1.0401 - val_acc: 0.4476\n",
      "Epoch 19/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0389 - acc: 0.4486 - val_loss: 1.0413 - val_acc: 0.4470\n",
      "Epoch 20/100\n",
      "3830/3830 [==============================] - 17s 4ms/step - loss: 1.0406 - acc: 0.4441 - val_loss: 1.0416 - val_acc: 0.4452\n",
      "Epoch 21/100\n",
      "3830/3830 [==============================] - 17s 4ms/step - loss: 1.0396 - acc: 0.4514 - val_loss: 1.0415 - val_acc: 0.4348\n",
      "Epoch 22/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0442 - acc: 0.4410 - val_loss: 1.0439 - val_acc: 0.4373\n",
      "Epoch 23/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0437 - acc: 0.4465 - val_loss: 1.0436 - val_acc: 0.4342\n",
      "Epoch 24/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0401 - acc: 0.4522 - val_loss: 1.0426 - val_acc: 0.4373\n",
      "Epoch 25/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0430 - acc: 0.4520 - val_loss: 1.0419 - val_acc: 0.4415\n",
      "Epoch 26/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0431 - acc: 0.4491 - val_loss: 1.0404 - val_acc: 0.4409\n",
      "Epoch 27/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0435 - acc: 0.4522 - val_loss: 1.0417 - val_acc: 0.4482\n",
      "Epoch 28/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0513 - acc: 0.4347 - val_loss: 1.0421 - val_acc: 0.4452\n",
      "Epoch 29/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0431 - acc: 0.4475 - val_loss: 1.0380 - val_acc: 0.4403\n",
      "Epoch 30/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0442 - acc: 0.4449 - val_loss: 1.0390 - val_acc: 0.4470\n",
      "Epoch 31/100\n",
      "3830/3830 [==============================] - 17s 4ms/step - loss: 1.0432 - acc: 0.4593 - val_loss: 1.0391 - val_acc: 0.4428\n",
      "Epoch 32/100\n",
      "3830/3830 [==============================] - 17s 4ms/step - loss: 1.0406 - acc: 0.4415 - val_loss: 1.0400 - val_acc: 0.4458\n",
      "Epoch 33/100\n",
      "3830/3830 [==============================] - 17s 4ms/step - loss: 1.0446 - acc: 0.4413 - val_loss: 1.0388 - val_acc: 0.4464\n",
      "Epoch 34/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0425 - acc: 0.4467 - val_loss: 1.0401 - val_acc: 0.4428\n",
      "Epoch 35/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0429 - acc: 0.4460 - val_loss: 1.0394 - val_acc: 0.4391\n",
      "Epoch 36/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0467 - acc: 0.4392 - val_loss: 1.0364 - val_acc: 0.4555\n",
      "Epoch 37/100\n",
      "3830/3830 [==============================] - 17s 4ms/step - loss: 1.0345 - acc: 0.4493 - val_loss: 1.0398 - val_acc: 0.4470\n",
      "Epoch 38/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0394 - acc: 0.4475 - val_loss: 1.0413 - val_acc: 0.4434\n",
      "Epoch 39/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0419 - acc: 0.4491 - val_loss: 1.0409 - val_acc: 0.4361\n",
      "Epoch 40/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0430 - acc: 0.4384 - val_loss: 1.0400 - val_acc: 0.4440\n",
      "Epoch 41/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0430 - acc: 0.4441 - val_loss: 1.0406 - val_acc: 0.4482\n",
      "Epoch 42/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0418 - acc: 0.4517 - val_loss: 1.0458 - val_acc: 0.4397\n",
      "Epoch 43/100\n",
      "3830/3830 [==============================] - 17s 4ms/step - loss: 1.0406 - acc: 0.4509 - val_loss: 1.0422 - val_acc: 0.4452\n",
      "Epoch 44/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0435 - acc: 0.4470 - val_loss: 1.0429 - val_acc: 0.4379\n",
      "Epoch 45/100\n",
      "3830/3830 [==============================] - 17s 5ms/step - loss: 1.0376 - acc: 0.4572 - val_loss: 1.0383 - val_acc: 0.4501\n",
      "Epoch 46/100\n",
      "3830/3830 [==============================] - 17s 4ms/step - loss: 1.0414 - acc: 0.4457 - val_loss: 1.0397 - val_acc: 0.4440\n",
      "Epoch 47/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0412 - acc: 0.4551 - val_loss: 1.0376 - val_acc: 0.4434\n",
      "Epoch 48/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0398 - acc: 0.4491 - val_loss: 1.0368 - val_acc: 0.4428\n",
      "Epoch 49/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0408 - acc: 0.4593 - val_loss: 1.0383 - val_acc: 0.4415\n",
      "Epoch 50/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0423 - acc: 0.4509 - val_loss: 1.0382 - val_acc: 0.4415\n",
      "Epoch 51/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0399 - acc: 0.4543 - val_loss: 1.0379 - val_acc: 0.4379\n",
      "Epoch 52/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0433 - acc: 0.4439 - val_loss: 1.0422 - val_acc: 0.4354\n",
      "Epoch 53/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0423 - acc: 0.4554 - val_loss: 1.0387 - val_acc: 0.4421\n",
      "Epoch 54/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0379 - acc: 0.4431 - val_loss: 1.0362 - val_acc: 0.4519\n",
      "Epoch 55/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0432 - acc: 0.4418 - val_loss: 1.0357 - val_acc: 0.4513\n",
      "Epoch 56/100\n",
      "3830/3830 [==============================] - 17s 4ms/step - loss: 1.0393 - acc: 0.4559 - val_loss: 1.0379 - val_acc: 0.4470\n",
      "Epoch 57/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0427 - acc: 0.4410 - val_loss: 1.0363 - val_acc: 0.4452\n",
      "Epoch 58/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0391 - acc: 0.4527 - val_loss: 1.0370 - val_acc: 0.4470\n",
      "Epoch 59/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0436 - acc: 0.4530 - val_loss: 1.0401 - val_acc: 0.4446\n",
      "Epoch 60/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0344 - acc: 0.4525 - val_loss: 1.0394 - val_acc: 0.4513\n",
      "Epoch 61/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0446 - acc: 0.4491 - val_loss: 1.0382 - val_acc: 0.4476\n",
      "Epoch 62/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0379 - acc: 0.4608 - val_loss: 1.0391 - val_acc: 0.4428\n",
      "Epoch 63/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0415 - acc: 0.4332 - val_loss: 1.0377 - val_acc: 0.4501\n",
      "Epoch 64/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0369 - acc: 0.4629 - val_loss: 1.0367 - val_acc: 0.4464\n",
      "Epoch 65/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0414 - acc: 0.4420 - val_loss: 1.0375 - val_acc: 0.4452\n",
      "Epoch 66/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0399 - acc: 0.4452 - val_loss: 1.0392 - val_acc: 0.4434\n",
      "Epoch 67/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0413 - acc: 0.4431 - val_loss: 1.0370 - val_acc: 0.4513\n",
      "Epoch 68/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0358 - acc: 0.4559 - val_loss: 1.0375 - val_acc: 0.4555\n",
      "Epoch 69/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0383 - acc: 0.4556 - val_loss: 1.0379 - val_acc: 0.4476\n",
      "Epoch 70/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0352 - acc: 0.4538 - val_loss: 1.0379 - val_acc: 0.4507\n",
      "Epoch 71/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0347 - acc: 0.4460 - val_loss: 1.0356 - val_acc: 0.4464\n",
      "Epoch 72/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0383 - acc: 0.4587 - val_loss: 1.0354 - val_acc: 0.4470\n",
      "Epoch 73/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0407 - acc: 0.4473 - val_loss: 1.0352 - val_acc: 0.4470\n",
      "Epoch 74/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0394 - acc: 0.4496 - val_loss: 1.0344 - val_acc: 0.4537\n",
      "Epoch 75/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0364 - acc: 0.4543 - val_loss: 1.0354 - val_acc: 0.4519\n",
      "Epoch 76/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0370 - acc: 0.4491 - val_loss: 1.0354 - val_acc: 0.4562\n",
      "Epoch 77/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0376 - acc: 0.4499 - val_loss: 1.0354 - val_acc: 0.4525\n",
      "Epoch 78/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0340 - acc: 0.4674 - val_loss: 1.0360 - val_acc: 0.4525\n",
      "Epoch 79/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0354 - acc: 0.4614 - val_loss: 1.0358 - val_acc: 0.4488\n",
      "Epoch 80/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0403 - acc: 0.4504 - val_loss: 1.0355 - val_acc: 0.4531\n",
      "Epoch 81/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0351 - acc: 0.4577 - val_loss: 1.0382 - val_acc: 0.4458\n",
      "Epoch 82/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0387 - acc: 0.4496 - val_loss: 1.0356 - val_acc: 0.4525\n",
      "Epoch 83/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0393 - acc: 0.4614 - val_loss: 1.0335 - val_acc: 0.4549\n",
      "Epoch 84/100\n",
      "3830/3830 [==============================] - 17s 4ms/step - loss: 1.0373 - acc: 0.4441 - val_loss: 1.0341 - val_acc: 0.4635\n",
      "Epoch 85/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0365 - acc: 0.4530 - val_loss: 1.0370 - val_acc: 0.4568\n",
      "Epoch 86/100\n",
      "3830/3830 [==============================] - 17s 4ms/step - loss: 1.0355 - acc: 0.4501 - val_loss: 1.0366 - val_acc: 0.4555\n",
      "Epoch 87/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0363 - acc: 0.4533 - val_loss: 1.0376 - val_acc: 0.4488\n",
      "Epoch 88/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0363 - acc: 0.4543 - val_loss: 1.0362 - val_acc: 0.4525\n",
      "Epoch 89/100\n",
      "3830/3830 [==============================] - 17s 4ms/step - loss: 1.0352 - acc: 0.4627 - val_loss: 1.0357 - val_acc: 0.4525\n",
      "Epoch 90/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0385 - acc: 0.4452 - val_loss: 1.0397 - val_acc: 0.4495\n",
      "Epoch 91/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0328 - acc: 0.4556 - val_loss: 1.0400 - val_acc: 0.4501\n",
      "Epoch 92/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0380 - acc: 0.4567 - val_loss: 1.0384 - val_acc: 0.4574\n",
      "Epoch 93/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0340 - acc: 0.4559 - val_loss: 1.0374 - val_acc: 0.4519\n",
      "Epoch 94/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0339 - acc: 0.4535 - val_loss: 1.0389 - val_acc: 0.4470\n",
      "Epoch 95/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0380 - acc: 0.4457 - val_loss: 1.0384 - val_acc: 0.4525\n",
      "Epoch 96/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0351 - acc: 0.4603 - val_loss: 1.0382 - val_acc: 0.4488\n",
      "Epoch 97/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0420 - acc: 0.4546 - val_loss: 1.0381 - val_acc: 0.4507\n",
      "Epoch 98/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0376 - acc: 0.4572 - val_loss: 1.0354 - val_acc: 0.4568\n",
      "Epoch 99/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0421 - acc: 0.4467 - val_loss: 1.0359 - val_acc: 0.4470\n",
      "Epoch 100/100\n",
      "3830/3830 [==============================] - 16s 4ms/step - loss: 1.0348 - acc: 0.4530 - val_loss: 1.0358 - val_acc: 0.4470\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(X_train, Y_train, epochs=100, validation_split=0.3, batch_size=500 , verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  Année: [2004, 2005, 2006, 2007, 2008, 2015]   //  Genre: action, comedy, drama  //  Données X_train: (5472, 1927, 3)\n",
      "Hasard : 42\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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vXnmSButtGbXZHg06pDamIkkehGe+Ca9Ud9CoD8e0ZSsSdwjwhp0DfO+ZY9vw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\nSUREdCWJJCIiuvI7SF9OqgUUG5cAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x187580fd30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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E53cWrrbQY7oCAfZlBaQTyRR9kQS15T68ZpB/TGIwgRooqz+mDYyVaKEVjGa0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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x187580f4a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "train, =plt.plot(history.history['acc'], label='Train set')\n",
    "val, =plt.plot(history.history['val_acc'], label='Validation set')\n",
    "print('')\n",
    "print(f\"  Année: {years}   //  Genre: action, comedy, drama  //  Données X_train: {X_train.shape}\")\n",
    "print('Hasard :', hasard)\n",
    "print('')\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.xlabel('Epochs')\n",
    "plt.legend(handles=[train, val])\n",
    "plt.show()\n",
    "train, =plt.plot(history.history['loss'], label='Train set')\n",
    "val, =plt.plot(history.history['val_loss'], label='Validation set')\n",
    "plt.title('model Loss')\n",
    "plt.ylabel('Cross entropy')\n",
    "plt.xlabel('Epochs')\n",
    "plt.legend(handles=[train, val])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}