1-Initial_CNN_Model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/learn-env/lib/python3.6/site-packages/librosa/util/decorators.py:9: NumbaDeprecationWarning: An import was requested from a module that has moved location.\n",
      "Import of 'jit' requested from: 'numba.decorators', please update to use 'numba.core.decorators' or pin to Numba version 0.48.0. This alias will not be present in Numba version 0.50.0.\n",
      "  from numba.decorators import jit as optional_jit\n"
     ]
    }
   ],
   "source": [
    "# IMPORT NECESSARY LIBRARIES\n",
    "import librosa\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import librosa.display\n",
    "from IPython.display import Audio\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from matplotlib.pyplot import specgram\n",
    "import pandas as pd\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import IPython.display as ipd  # To play sound in the notebook\n",
    "import os # interface with underlying OS that python is running on\n",
    "import sys\n",
    "import warnings\n",
    "# ignore warnings \n",
    "if not sys.warnoptions:\n",
    "    warnings.simplefilter(\"ignore\")\n",
    "warnings.filterwarnings(\"ignore\", category=DeprecationWarning)\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "import tensorflow.keras\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Conv1D, MaxPooling1D, AveragePooling1D\n",
    "from tensorflow.keras.layers import Input, Flatten, Dropout, Activation, BatchNormalization, Dense\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from tensorflow.keras.wrappers.scikit_learn import KerasClassifier\n",
    "from tensorflow.keras.optimizers import SGD\n",
    "from tensorflow.keras.regularizers import l2\n",
    "import seaborn as sns\n",
    "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint\n",
    "from tensorflow.keras.utils import to_categorical\n",
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Male Neutral"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# LOAD IN FILE\n",
    "x, sr = librosa.load('/Users/murielkosaka/Desktop/capstone_project/audio/audio_speech_actors_01-24/Actor_01/03-01-01-01-01-01-01.wav')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# DISPLAY WAVEPLOT\n",
    "plt.figure(figsize=(8, 4))\n",
    "librosa.display.waveplot(x, sr=sr)\n",
    "plt.title('Waveplot - Male Neutral')\n",
    "plt.savefig('Waveplot_MaleNeutral.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# PLAY AUDIO FILE\n",
    "librosa.output.write_wav('ipd.Audio Files/MaleNeutral.wav', x, sr)\n",
    "Audio(data=x, rate=sr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# CREATE LOG MEL SPECTROGRAM\n",
    "spectrogram = librosa.feature.melspectrogram(y=x, sr=sr, n_mels=128,fmax=8000) \n",
    "spectrogram = librosa.power_to_db(spectrogram)\n",
    "\n",
    "librosa.display.specshow(spectrogram, y_axis='mel', fmax=8000, x_axis='time');\n",
    "plt.title('Mel Spectrogram - Male Neutral')\n",
    "plt.savefig('MelSpec_MaleNeutral.png')\n",
    "plt.colorbar(format='%+2.0f dB');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Female Calm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# LOAD IN FILE\n",
    "x, sr = librosa.load('/Users/murielkosaka/Desktop/capstone_project/audio/audio_speech_actors_01-24/Actor_02/03-01-02-01-01-01-02.wav')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# DISPLAY WAVEPLOT\n",
    "plt.figure(figsize=(8, 4))\n",
    "librosa.display.waveplot(x, sr=sr)\n",
    "plt.title('Waveplot - Female Calm')\n",
    "plt.savefig('Waveplot_FemaleCalm.png')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# PLAY AUDIO FILE\n",
    "librosa.output.write_wav('ipd.Audio Files/FemaleCalm.wav', x, sr)\n",
    "Audio(data=x, rate=sr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# CREATE LOG MEL SPECTROGRAM\n",
    "spectrogram = librosa.feature.melspectrogram(y=x, sr=sr, n_mels=128,fmax=8000) \n",
    "spectrogram = librosa.power_to_db(spectrogram)\n",
    "\n",
    "librosa.display.specshow(spectrogram, y_axis='mel', fmax=8000, x_axis='time');\n",
    "plt.title('Mel Spectrogram - Female Calm')\n",
    "plt.savefig('MelSpec_FemaleCalm.png')\n",
    "plt.colorbar(format='%+2.0f dB');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Male Happy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# LOAD FILE\n",
    "x, sr = librosa.load('/Users/murielkosaka/Desktop/capstone_project/audio/audio_speech_actors_01-24/Actor_03/03-01-03-01-01-01-03.wav')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# DISPLAY WAVEPLOT\n",
    "plt.figure(figsize=(8, 4))\n",
    "librosa.display.waveplot(x, sr=sr)\n",
    "plt.title('Waveplot - Male Happy')\n",
    "plt.savefig('Waveplot_MaleHappy.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# PLAY AUDIO FILE\n",
    "librosa.output.write_wav('Uploads/ipd.Audio Files/MaleHappy.wav', x, sr)\n",
    "Audio(data=x, rate=sr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# CREATE LOG MEL SPECTROGRAM\n",
    "x = librosa.feature.melspectrogram(y=x, sr=sr,n_mels=128,fmax=8000) \n",
    "x = librosa.power_to_db(x)\n",
    "\n",
    "librosa.display.specshow(x, y_axis='mel', fmax=8000, x_axis='time');\n",
    "plt.title('Mel Spectrogram - Male Happy')\n",
    "plt.savefig('MelSpec_MaleHappy.png')\n",
    "plt.colorbar(format='%+2.0f dB');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Female Sad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# LOAD FILE\n",
    "x, sr = librosa.load('/Users/murielkosaka/Desktop/capstone_project/audio/audio_speech_actors_01-24/Actor_04/03-01-04-01-01-01-04.wav')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# DISPLAY WAVEPLOT\n",
    "plt.figure(figsize=(8, 4))\n",
    "librosa.display.waveplot(x, sr=sr)\n",
    "plt.title('Waveplot - Female Sad')\n",
    "plt.savefig('Waveplot_FemaleSad.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# PLAY AUDIO FILE\n",
    "librosa.output.write_wav('Uploads/ipd.Audio Files/FemaleSad.wav', x, sr)\n",
    "Audio(data=x, rate=sr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# DISPLAY LOG MEL SPECTROGRAM\n",
    "x = librosa.feature.melspectrogram(y=x, sr=sr,n_mels=128,fmax=8000) \n",
    "y = librosa.power_to_db(x)\n",
    "\n",
    "librosa.display.specshow(y, y_axis='mel', fmax=8000, x_axis='time');\n",
    "plt.title('Mel Spectrogram - Female Sad');\n",
    "plt.savefig('MelSpec_FemaleSad.png')\n",
    "plt.colorbar(format='%+2.0f dB');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Male Angry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# LOAD FILE\n",
    "x, sr = librosa.load('/Users/murielkosaka/Desktop/capstone_project/audio/audio_speech_actors_01-24/Actor_05/03-01-05-01-01-01-05.wav')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# DISPLAY WAVEPLOT\n",
    "plt.figure(figsize=(8, 4))\n",
    "librosa.display.waveplot(x, sr=sr)\n",
    "plt.title('Waveplot - Male Angry')\n",
    "plt.savefig('Waveplot_MaleAngry.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# PLAY AUDIO FILE\n",
    "librosa.output.write_wav('Uploads/ipd.Audio Files/MaleAngry.wav', x, sr)\n",
    "Audio(data=x, rate=sr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# DISPLAY LOG MEL SPECTROGRAM\n",
    "x = librosa.feature.melspectrogram(y=x, sr=sr,n_mels=128,fmax=8000) \n",
    "y = librosa.power_to_db(x)\n",
    "\n",
    "librosa.display.specshow(y, y_axis='mel', fmax=8000, x_axis='time');\n",
    "plt.title('Mel Spectrogram - Male Angry');\n",
    "plt.savefig('MelSpec_MaleAngry.png')\n",
    "plt.colorbar(format='%+2.0f dB');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Female Fearful"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# LOAD FILE\n",
    "x, sr = librosa.load('/Users/murielkosaka/Desktop/capstone_project/audio/audio_speech_actors_01-24/Actor_06/03-01-06-01-01-01-06.wav')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# DISPLAY WAVEPLOT\n",
    "plt.figure(figsize=(8, 4))\n",
    "librosa.display.waveplot(x, sr=sr)\n",
    "plt.title('Waveplot - Female Fearful')\n",
    "plt.savefig('Waveplot_FemaleFearful.png')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# PLAY AUDIO FILE\n",
    "librosa.output.write_wav('Uploads/ipd.Audio Files/FemaleFearful.wav', x, sr)\n",
    "Audio(data=x, rate=sr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# DISPLAY LOG MEL SPECTROGRAM\n",
    "x = librosa.feature.melspectrogram(y=x, sr=sr,n_mels=128,fmax=8000) \n",
    "y = librosa.power_to_db(x)\n",
    "\n",
    "librosa.display.specshow(y, y_axis='mel', fmax=8000, x_axis='time');\n",
    "plt.title('Mel Spectrogram - Female Fearful')\n",
    "plt.savefig('MelSpec_FemaleFearful.png')\n",
    "plt.colorbar(format='%+2.0f dB');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Male Disgust"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# LOAD FILE\n",
    "x, sr = librosa.load('/Users/murielkosaka/Desktop/capstone_project/audio/audio_speech_actors_01-24/Actor_07/03-01-07-01-01-01-07.wav')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# DISPLAY WAVEPLOT\n",
    "plt.figure(figsize=(8, 4))\n",
    "librosa.display.waveplot(x, sr=sr)\n",
    "plt.title('Waveplot - Male Disgust')\n",
    "plt.savefig('Waveplot_MaleDisgust.png')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# PLAY AUDIO FILE\n",
    "librosa.output.write_wav('Uploads/ipd.Audio Files/MaleDisgust.wav', x, sr)\n",
    "Audio(data=x, rate=sr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# DISPLAY LOG MEL SPECTROGRAM\n",
    "x = librosa.feature.melspectrogram(y=x, sr=sr,n_mels=128,fmax=8000) \n",
    "y = librosa.power_to_db(x)\n",
    "\n",
    "librosa.display.specshow(y, y_axis='mel', fmax=8000, x_axis='time');\n",
    "plt.title('Mel Spectrogram - Male Disgust')\n",
    "plt.savefig('MelSpec_MaleDisgust.png')\n",
    "plt.colorbar(format='%+2.0f dB');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Female Surprised"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# LOAD FILE\n",
    "x, sr = librosa.load('/Users/murielkosaka/Desktop/capstone_project/audio/audio_speech_actors_01-24/Actor_08/03-01-08-01-01-01-08.wav')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# DISPLAY WAVEPLOT\n",
    "plt.figure(figsize=(8, 4))\n",
    "librosa.display.waveplot(x, sr=sr)\n",
    "plt.title('Waveplot - FemaleSurprised')\n",
    "plt.savefig('Waveplot_FemaleSurprised.png')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# PLAY AUDIO FILE\n",
    "librosa.output.write_wav('Uploads/ipd.Audio Files/FemaleSurprised.wav', x, sr)\n",
    "Audio(data=x, rate=sr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# DISPLAY LOG MEL SPECTROGRAM\n",
    "x = librosa.feature.melspectrogram(y=x, sr=sr,n_mels=128,fmax=8000) \n",
    "y = librosa.power_to_db(x)\n",
    "\n",
    "librosa.display.specshow(y, y_axis='mel', fmax=8000, x_axis='time');\n",
    "plt.title('Mel Spectrogram - Female Surprised')\n",
    "plt.savefig('MelSpec_FemaleSurprised.png')\n",
    "plt.colorbar(format='%+2.0f dB');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Labeling Audio Files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Actor_01', 'Actor_02', 'Actor_03', 'Actor_04', 'Actor_05']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# CREATE DIRECTORY OF AUDIO FILES \n",
    "audio = \"/Users/murielkosaka/Desktop/capstone_project/audio/audio_speech_actors_01-24/\"\n",
    "actor_folders = os.listdir(audio) #list files in audio directory\n",
    "actor_folders.sort() \n",
    "actor_folders[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# CREATE FUNCTION TO EXTRACT EMOTION NUMBER, ACTOR AND GENDER LABEL\n",
    "emotion = []\n",
    "gender = []\n",
    "actor = []\n",
    "file_path = []\n",
    "for i in actor_folders:\n",
    "    filename = os.listdir(audio + i) #iterate over Actor folders\n",
    "    for f in filename: # go through files in Actor folder\n",
    "        part = f.split('.')[0].split('-')\n",
    "        emotion.append(int(part[2]))\n",
    "        actor.append(int(part[6]))\n",
    "        bg = int(part[6])\n",
    "        if bg%2 == 0:\n",
    "            bg = \"female\"\n",
    "        else:\n",
    "            bg = \"male\"\n",
    "        gender.append(bg)\n",
    "        file_path.append(audio + i + '/' + f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1435</th>\n",
       "      <td>female</td>\n",
       "      <td>fear</td>\n",
       "      <td>24</td>\n",
       "      <td>/Users/murielkosaka/Desktop/capstone_project/audio/audio_speech_actors_01-24/Actor_24/03-01-06-02-01-02-24.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1436</th>\n",
       "      <td>female</td>\n",
       "      <td>fear</td>\n",
       "      <td>24</td>\n",
       "      <td>/Users/murielkosaka/Desktop/capstone_project/audio/audio_speech_actors_01-24/Actor_24/03-01-06-01-02-02-24.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1437</th>\n",
       "      <td>female</td>\n",
       "      <td>angry</td>\n",
       "      <td>24</td>\n",
       "      <td>/Users/murielkosaka/Desktop/capstone_project/audio/audio_speech_actors_01-24/Actor_24/03-01-05-01-02-01-24.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1438</th>\n",
       "      <td>female</td>\n",
       "      <td>surprise</td>\n",
       "      <td>24</td>\n",
       "      <td>/Users/murielkosaka/Desktop/capstone_project/audio/audio_speech_actors_01-24/Actor_24/03-01-08-01-01-01-24.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1439</th>\n",
       "      <td>female</td>\n",
       "      <td>surprise</td>\n",
       "      <td>24</td>\n",
       "      <td>/Users/murielkosaka/Desktop/capstone_project/audio/audio_speech_actors_01-24/Actor_24/03-01-08-02-02-01-24.wav</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1440 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      gender   emotion  actor  \\\n",
       "0     male    surprise  1       \n",
       "1     male    surprise  1       \n",
       "2     male    angry     1       \n",
       "3     male    fear      1       \n",
       "4     male    fear      1       \n",
       "...    ...     ...     ..       \n",
       "1435  female  fear      24      \n",
       "1436  female  fear      24      \n",
       "1437  female  angry     24      \n",
       "1438  female  surprise  24      \n",
       "1439  female  surprise  24      \n",
       "\n",
       "                                                                                                                path  \n",
       "0     /Users/murielkosaka/Desktop/capstone_project/audio/audio_speech_actors_01-24/Actor_01/03-01-08-02-02-01-01.wav  \n",
       "1     /Users/murielkosaka/Desktop/capstone_project/audio/audio_speech_actors_01-24/Actor_01/03-01-08-01-01-01-01.wav  \n",
       "2     /Users/murielkosaka/Desktop/capstone_project/audio/audio_speech_actors_01-24/Actor_01/03-01-05-01-02-01-01.wav  \n",
       "3     /Users/murielkosaka/Desktop/capstone_project/audio/audio_speech_actors_01-24/Actor_01/03-01-06-01-02-02-01.wav  \n",
       "4     /Users/murielkosaka/Desktop/capstone_project/audio/audio_speech_actors_01-24/Actor_01/03-01-06-02-01-02-01.wav  \n",
       "...                                                                                                              ...  \n",
       "1435  /Users/murielkosaka/Desktop/capstone_project/audio/audio_speech_actors_01-24/Actor_24/03-01-06-02-01-02-24.wav  \n",
       "1436  /Users/murielkosaka/Desktop/capstone_project/audio/audio_speech_actors_01-24/Actor_24/03-01-06-01-02-02-24.wav  \n",
       "1437  /Users/murielkosaka/Desktop/capstone_project/audio/audio_speech_actors_01-24/Actor_24/03-01-05-01-02-01-24.wav  \n",
       "1438  /Users/murielkosaka/Desktop/capstone_project/audio/audio_speech_actors_01-24/Actor_24/03-01-08-01-01-01-24.wav  \n",
       "1439  /Users/murielkosaka/Desktop/capstone_project/audio/audio_speech_actors_01-24/Actor_24/03-01-08-02-02-01-24.wav  \n",
       "\n",
       "[1440 rows x 4 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# PUT EXTRACTED LABELS WITH FILEPATH INTO DATAFRAME\n",
    "audio_df = pd.DataFrame(emotion)\n",
    "audio_df = audio_df.replace({1:'neutral', 2:'calm', 3:'happy', 4:'sad', 5:'angry', 6:'fear', 7:'disgust', 8:'surprise'})\n",
    "audio_df = pd.concat([pd.DataFrame(gender),audio_df,pd.DataFrame(actor)],axis=1)\n",
    "audio_df.columns = ['gender','emotion','actor']\n",
    "audio_df = pd.concat([audio_df,pd.DataFrame(file_path, columns = ['path'])],axis=1)\n",
    "audio_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>emotion</th>\n",
       "      <th>actor</th>\n",
       "      <th>path</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>female</td>\n",
       "      <td>disgust</td>\n",
       "      <td>4</td>\n",
       "      <td>/Users/murielkosaka/Desktop/capstone_project/audio/audio_speech_actors_01-24/Actor_04/03-01-07-02-01-02-04.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>640</th>\n",
       "      <td>male</td>\n",
       "      <td>happy</td>\n",
       "      <td>11</td>\n",
       "      <td>/Users/murielkosaka/Desktop/capstone_project/audio/audio_speech_actors_01-24/Actor_11/03-01-03-02-02-01-11.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>female</td>\n",
       "      <td>sad</td>\n",
       "      <td>4</td>\n",
       "      <td>/Users/murielkosaka/Desktop/capstone_project/audio/audio_speech_actors_01-24/Actor_04/03-01-04-02-01-01-04.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>576</th>\n",
       "      <td>female</td>\n",
       "      <td>angry</td>\n",
       "      <td>10</td>\n",
       "      <td>/Users/murielkosaka/Desktop/capstone_project/audio/audio_speech_actors_01-24/Actor_10/03-01-05-01-02-02-10.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1100</th>\n",
       "      <td>male</td>\n",
       "      <td>fear</td>\n",
       "      <td>19</td>\n",
       "      <td>/Users/murielkosaka/Desktop/capstone_project/audio/audio_speech_actors_01-24/Actor_19/03-01-06-02-01-01-19.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>754</th>\n",
       "      <td>male</td>\n",
       "      <td>sad</td>\n",
       "      <td>13</td>\n",
       "      <td>/Users/murielkosaka/Desktop/capstone_project/audio/audio_speech_actors_01-24/Actor_13/03-01-04-01-01-01-13.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>424</th>\n",
       "      <td>female</td>\n",
       "      <td>sad</td>\n",
       "      <td>8</td>\n",
       "      <td>/Users/murielkosaka/Desktop/capstone_project/audio/audio_speech_actors_01-24/Actor_08/03-01-04-01-02-02-08.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1309</th>\n",
       "      <td>female</td>\n",
       "      <td>fear</td>\n",
       "      <td>22</td>\n",
       "      <td>/Users/murielkosaka/Desktop/capstone_project/audio/audio_speech_actors_01-24/Actor_22/03-01-06-02-01-02-22.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>942</th>\n",
       "      <td>female</td>\n",
       "      <td>angry</td>\n",
       "      <td>16</td>\n",
       "      <td>/Users/murielkosaka/Desktop/capstone_project/audio/audio_speech_actors_01-24/Actor_16/03-01-05-01-02-02-16.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>667</th>\n",
       "      <td>female</td>\n",
       "      <td>disgust</td>\n",
       "      <td>12</td>\n",
       "      <td>/Users/murielkosaka/Desktop/capstone_project/audio/audio_speech_actors_01-24/Actor_12/03-01-07-02-02-01-12.wav</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      gender  emotion  actor  \\\n",
       "201   female  disgust  4       \n",
       "640   male    happy    11      \n",
       "202   female  sad      4       \n",
       "576   female  angry    10      \n",
       "1100  male    fear     19      \n",
       "754   male    sad      13      \n",
       "424   female  sad      8       \n",
       "1309  female  fear     22      \n",
       "942   female  angry    16      \n",
       "667   female  disgust  12      \n",
       "\n",
       "                                                                                                                path  \n",
       "201   /Users/murielkosaka/Desktop/capstone_project/audio/audio_speech_actors_01-24/Actor_04/03-01-07-02-01-02-04.wav  \n",
       "640   /Users/murielkosaka/Desktop/capstone_project/audio/audio_speech_actors_01-24/Actor_11/03-01-03-02-02-01-11.wav  \n",
       "202   /Users/murielkosaka/Desktop/capstone_project/audio/audio_speech_actors_01-24/Actor_04/03-01-04-02-01-01-04.wav  \n",
       "576   /Users/murielkosaka/Desktop/capstone_project/audio/audio_speech_actors_01-24/Actor_10/03-01-05-01-02-02-10.wav  \n",
       "1100  /Users/murielkosaka/Desktop/capstone_project/audio/audio_speech_actors_01-24/Actor_19/03-01-06-02-01-01-19.wav  \n",
       "754   /Users/murielkosaka/Desktop/capstone_project/audio/audio_speech_actors_01-24/Actor_13/03-01-04-01-01-01-13.wav  \n",
       "424   /Users/murielkosaka/Desktop/capstone_project/audio/audio_speech_actors_01-24/Actor_08/03-01-04-01-02-02-08.wav  \n",
       "1309  /Users/murielkosaka/Desktop/capstone_project/audio/audio_speech_actors_01-24/Actor_22/03-01-06-02-01-02-22.wav  \n",
       "942   /Users/murielkosaka/Desktop/capstone_project/audio/audio_speech_actors_01-24/Actor_16/03-01-05-01-02-02-16.wav  \n",
       "667   /Users/murielkosaka/Desktop/capstone_project/audio/audio_speech_actors_01-24/Actor_12/03-01-07-02-02-01-12.wav  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ENSURE GENDER,EMOTION, AND ACTOR COLUMN VALUES ARE CORRECT\n",
    "pd.set_option('display.max_colwidth', -1)\n",
    "\n",
    "audio_df.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LOOK AT DISTRIBUTION OF CLASSES\n",
    "audio_df.emotion.value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# EXPORT TO CSV\n",
    "audio_df.to_csv('Uploads/audio.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1440\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mel_spectrogram</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[-71.20022, -71.20022, -71.20022, -71.20022, -71.20022, -71.20022, -71.20022, -71.20022, -71.20022, -71.20022, -71.20022, -71.20022, -71.20022, -71.20022, -71.20022, -71.20022, -71.20022, -71.20022, -71.20022, -71.20022, -71.20022, -71.20022, -71.20022, -71.20022, -71.20022, -71.20022, -71.20022, -71.20022, -71.20022, -71.20022, -71.20022, -71.20022, -71.20022, -71.187965, -70.50316, -67.70304, -62.224304, -63.253567, -66.196526, -66.34454, -67.49455, -69.91169, -53.04194, -35.311817, -27.755419, -23.393114, -21.939688, -22.235167, -23.184116, -25.2431, -26.592205, -26.015366, -26.703884, -28.167618, -27.373056, -27.462624, -27.054974, -25.768976, -25.470634, -25.14277, -25.983984, -26.77985, -27.67231, -26.951078, -27.863522, -32.06402, -37.21198, -41.15784, -41.274082, -34.481365, -27.691902, -27.530409, -31.189516, -31.399157, -31.948267, -29.20746, -28.984142, -31.086939, -31.237015, -29.119715, -28.87086, -30.872272, -34.477077, -35.46222, -33.769722, -31.421616, -30.794262, -32.836426, -33.15096, -31.07819, -29.341507, -28.946648, -30.864676, -32.78073, -34.300793, -35.43246, -35.512302, -35.824734, -32.8842, -31.126184, ...]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[-70.46105, -70.46105, -70.46105, -70.46105, -70.46105, -70.46105, -70.46105, -70.46105, -70.46105, -70.46105, -70.46105, -70.46105, -70.46105, -70.46105, -70.46105, -70.46105, -70.46105, -70.46105, -70.46105, -70.46105, -70.46105, -70.46105, -70.46105, -70.46105, -70.46105, -70.46105, -70.46105, -70.46105, -70.46105, -70.46105, -70.46105, -70.46105, -70.46105, -70.46105, -70.46105, -70.46105, -70.46105, -70.41843, -70.46105, -70.46105, -70.25049, -69.946724, -70.20045, -69.90415, -46.75178, -37.894924, -37.331062, -39.582226, -40.267193, -39.097256, -34.30898, -30.882538, -27.79429, -24.91162, -25.004232, -27.486069, -31.268763, -34.298225, -38.465237, -41.32319, -43.534092, -45.332016, -46.979027, -47.686756, -46.761665, -45.968925, -43.954605, -40.19617, -35.845676, -33.82483, -33.106888, -33.15585, -33.374546, -33.72269, -34.042297, -35.684322, -41.1145, -46.67604, -49.785683, -52.075474, -51.91589, -38.173065, -34.98285, -38.280056, -39.4877, -37.248043, -35.14156, -33.22471, -30.150965, -29.290407, -30.595972, -31.784248, -31.559921, -30.690218, -30.34244, -31.602049, -36.049015, -40.94679, -42.652313, -41.462643, ...]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[-64.17679, -64.39374, -64.444954, -64.26804, -64.303825, -64.46815, -64.46815, -64.46815, -64.46815, -64.46815, -64.46815, -64.46815, -64.46815, -64.46815, -64.46815, -64.46815, -64.46815, -64.46815, -64.447296, -64.4122, -64.400055, -64.46363, -64.46701, -64.44746, -64.398, -64.213005, -64.223236, -64.3529, -64.37738, -63.900597, -63.455616, -63.70556, -63.493465, -63.146347, -63.393356, -64.024376, -64.01411, -64.01472, -62.16667, -53.427326, -53.112213, -56.749325, -59.260284, -63.197086, -39.888687, -29.309977, -23.734594, -22.996405, -22.082584, -21.821333, -22.878685, -24.086143, -25.910528, -27.233507, -28.2812, -28.47165, -27.822582, -25.86707, -23.51746, -22.555597, -21.906961, -22.342373, -24.088245, -25.46311, -27.872072, -32.053486, -36.408924, -39.520267, -41.121758, -43.039547, -39.19178, -33.291317, -27.97979, -26.682621, -28.951216, -31.153667, -32.23939, -33.277252, -33.3202, -32.63985, -31.742908, -33.313877, -36.443222, -37.307518, -40.296413, -46.544678, -47.72739, -43.08766, -38.47714, -35.703156, -33.48515, -32.37977, -31.097954, -30.754461, -28.716875, -27.440126, -26.975042, -29.101736, -31.4359, -32.79103, ...]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[-73.2803, -73.2803, -73.2803, -73.2803, -73.2803, -73.25525, -73.08632, -73.234695, -73.2803, -73.2803, -73.2803, -73.2803, -73.2803, -73.2803, -73.2803, -73.2803, -73.2803, -73.2803, -73.2803, -73.2803, -73.2803, -73.2803, -73.2803, -73.2766, -72.74323, -71.09078, -70.32488, -69.20192, -68.22371, -67.45397, -66.758484, -65.69483, -65.750854, -66.72477, -66.233826, -64.321724, -60.120052, -56.59815, -57.859257, -64.78188, -70.005554, -72.02886, -72.05144, -66.42027, -46.804337, -38.68785, -40.12812, -42.07857, -35.87358, -31.867702, -31.069366, -30.786562, -30.708286, -31.049133, -30.594038, -30.058979, -29.921616, -30.058167, -31.074078, -31.801094, -32.656384, -33.532646, -34.788918, -34.99597, -34.13762, -34.972847, -36.22863, -38.490177, -41.82699, -45.501236, -47.465527, -47.147614, -44.388123, -40.265736, -37.180042, -37.945404, -38.4607, -38.45842, -37.795975, -37.486835, -37.30511, -37.606815, -37.80831, -39.675, -42.2954, -44.485256, -45.743984, -46.79095, -48.027542, -48.414497, -46.785187, -41.775555, -38.24257, -36.34381, -35.22687, -33.969482, -34.17495, -34.441654, -34.858227, -35.3414, ...]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[-44.965843, -44.467876, -44.512608, -44.934807, -44.67976, -44.350895, -45.16111, -46.12424, -45.938686, -46.116108, -47.09447, -47.62172, -47.63733, -47.701145, -48.068077, -48.822113, -49.14048, -49.71597, -49.76813, -50.072533, -50.29177, -50.402493, -50.55323, -50.518288, -50.542522, -49.066826, -43.137894, -37.33612, -34.0876, -31.809542, -29.823238, -28.566898, -28.065304, -28.504604, -29.059156, -28.825916, -29.726545, -29.442339, -26.92883, -28.7232, -33.947544, -40.16505, -44.01644, -46.17773, -31.573595, -25.215927, -23.049486, -22.003551, -23.94959, -25.282516, -25.487713, -24.853098, -22.367533, -19.035393, -19.379086, -20.857325, -21.44076, -23.028358, -22.790857, -21.008022, -19.449522, -18.951899, -18.80585, -19.211065, -20.16641, -20.2318, -21.13692, -22.728415, -25.552174, -28.874754, -32.315636, -32.480083, -31.529566, -29.435148, -27.240294, -25.20272, -23.98499, -24.441584, -22.90315, -22.518711, -22.576244, -23.69904, -22.151564, -19.469917, -17.445578, -17.230667, -17.38486, -17.578793, -19.190104, -18.864332, -19.906824, -24.95779, -30.626919, -32.08114, -33.993828, -30.882383, -26.53596, -23.850664, -23.291126, -22.71187, ...]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     mel_spectrogram\n",
       "0  [-71.20022, -71.20022, -71.20022, -71.20022, -71.20022, -71.20022, -71.20022, -71.20022, -71.20022, -71.20022, -71.20022, -71.20022, -71.20022, -71.20022, -71.20022, -71.20022, -71.20022, -71.20022, -71.20022, -71.20022, -71.20022, -71.20022, -71.20022, -71.20022, -71.20022, -71.20022, -71.20022, -71.20022, -71.20022, -71.20022, -71.20022, -71.20022, -71.20022, -71.187965, -70.50316, -67.70304, -62.224304, -63.253567, -66.196526, -66.34454, -67.49455, -69.91169, -53.04194, -35.311817, -27.755419, -23.393114, -21.939688, -22.235167, -23.184116, -25.2431, -26.592205, -26.015366, -26.703884, -28.167618, -27.373056, -27.462624, -27.054974, -25.768976, -25.470634, -25.14277, -25.983984, -26.77985, -27.67231, -26.951078, -27.863522, -32.06402, -37.21198, -41.15784, -41.274082, -34.481365, -27.691902, -27.530409, -31.189516, -31.399157, -31.948267, -29.20746, -28.984142, -31.086939, -31.237015, -29.119715, -28.87086, -30.872272, -34.477077, -35.46222, -33.769722, -31.421616, -30.794262, -32.836426, -33.15096, -31.07819, -29.341507, -28.946648, -30.864676, -32.78073, -34.300793, -35.43246, -35.512302, -35.824734, -32.8842, -31.126184, ...]                    \n",
       "1  [-70.46105, -70.46105, -70.46105, -70.46105, -70.46105, -70.46105, -70.46105, -70.46105, -70.46105, -70.46105, -70.46105, -70.46105, -70.46105, -70.46105, -70.46105, -70.46105, -70.46105, -70.46105, -70.46105, -70.46105, -70.46105, -70.46105, -70.46105, -70.46105, -70.46105, -70.46105, -70.46105, -70.46105, -70.46105, -70.46105, -70.46105, -70.46105, -70.46105, -70.46105, -70.46105, -70.46105, -70.46105, -70.41843, -70.46105, -70.46105, -70.25049, -69.946724, -70.20045, -69.90415, -46.75178, -37.894924, -37.331062, -39.582226, -40.267193, -39.097256, -34.30898, -30.882538, -27.79429, -24.91162, -25.004232, -27.486069, -31.268763, -34.298225, -38.465237, -41.32319, -43.534092, -45.332016, -46.979027, -47.686756, -46.761665, -45.968925, -43.954605, -40.19617, -35.845676, -33.82483, -33.106888, -33.15585, -33.374546, -33.72269, -34.042297, -35.684322, -41.1145, -46.67604, -49.785683, -52.075474, -51.91589, -38.173065, -34.98285, -38.280056, -39.4877, -37.248043, -35.14156, -33.22471, -30.150965, -29.290407, -30.595972, -31.784248, -31.559921, -30.690218, -30.34244, -31.602049, -36.049015, -40.94679, -42.652313, -41.462643, ...]                           \n",
       "2  [-64.17679, -64.39374, -64.444954, -64.26804, -64.303825, -64.46815, -64.46815, -64.46815, -64.46815, -64.46815, -64.46815, -64.46815, -64.46815, -64.46815, -64.46815, -64.46815, -64.46815, -64.46815, -64.447296, -64.4122, -64.400055, -64.46363, -64.46701, -64.44746, -64.398, -64.213005, -64.223236, -64.3529, -64.37738, -63.900597, -63.455616, -63.70556, -63.493465, -63.146347, -63.393356, -64.024376, -64.01411, -64.01472, -62.16667, -53.427326, -53.112213, -56.749325, -59.260284, -63.197086, -39.888687, -29.309977, -23.734594, -22.996405, -22.082584, -21.821333, -22.878685, -24.086143, -25.910528, -27.233507, -28.2812, -28.47165, -27.822582, -25.86707, -23.51746, -22.555597, -21.906961, -22.342373, -24.088245, -25.46311, -27.872072, -32.053486, -36.408924, -39.520267, -41.121758, -43.039547, -39.19178, -33.291317, -27.97979, -26.682621, -28.951216, -31.153667, -32.23939, -33.277252, -33.3202, -32.63985, -31.742908, -33.313877, -36.443222, -37.307518, -40.296413, -46.544678, -47.72739, -43.08766, -38.47714, -35.703156, -33.48515, -32.37977, -31.097954, -30.754461, -28.716875, -27.440126, -26.975042, -29.101736, -31.4359, -32.79103, ...]               \n",
       "3  [-73.2803, -73.2803, -73.2803, -73.2803, -73.2803, -73.25525, -73.08632, -73.234695, -73.2803, -73.2803, -73.2803, -73.2803, -73.2803, -73.2803, -73.2803, -73.2803, -73.2803, -73.2803, -73.2803, -73.2803, -73.2803, -73.2803, -73.2803, -73.2766, -72.74323, -71.09078, -70.32488, -69.20192, -68.22371, -67.45397, -66.758484, -65.69483, -65.750854, -66.72477, -66.233826, -64.321724, -60.120052, -56.59815, -57.859257, -64.78188, -70.005554, -72.02886, -72.05144, -66.42027, -46.804337, -38.68785, -40.12812, -42.07857, -35.87358, -31.867702, -31.069366, -30.786562, -30.708286, -31.049133, -30.594038, -30.058979, -29.921616, -30.058167, -31.074078, -31.801094, -32.656384, -33.532646, -34.788918, -34.99597, -34.13762, -34.972847, -36.22863, -38.490177, -41.82699, -45.501236, -47.465527, -47.147614, -44.388123, -40.265736, -37.180042, -37.945404, -38.4607, -38.45842, -37.795975, -37.486835, -37.30511, -37.606815, -37.80831, -39.675, -42.2954, -44.485256, -45.743984, -46.79095, -48.027542, -48.414497, -46.785187, -41.775555, -38.24257, -36.34381, -35.22687, -33.969482, -34.17495, -34.441654, -34.858227, -35.3414, ...]                                              \n",
       "4  [-44.965843, -44.467876, -44.512608, -44.934807, -44.67976, -44.350895, -45.16111, -46.12424, -45.938686, -46.116108, -47.09447, -47.62172, -47.63733, -47.701145, -48.068077, -48.822113, -49.14048, -49.71597, -49.76813, -50.072533, -50.29177, -50.402493, -50.55323, -50.518288, -50.542522, -49.066826, -43.137894, -37.33612, -34.0876, -31.809542, -29.823238, -28.566898, -28.065304, -28.504604, -29.059156, -28.825916, -29.726545, -29.442339, -26.92883, -28.7232, -33.947544, -40.16505, -44.01644, -46.17773, -31.573595, -25.215927, -23.049486, -22.003551, -23.94959, -25.282516, -25.487713, -24.853098, -22.367533, -19.035393, -19.379086, -20.857325, -21.44076, -23.028358, -22.790857, -21.008022, -19.449522, -18.951899, -18.80585, -19.211065, -20.16641, -20.2318, -21.13692, -22.728415, -25.552174, -28.874754, -32.315636, -32.480083, -31.529566, -29.435148, -27.240294, -25.20272, -23.98499, -24.441584, -22.90315, -22.518711, -22.576244, -23.69904, -22.151564, -19.469917, -17.445578, -17.230667, -17.38486, -17.578793, -19.190104, -18.864332, -19.906824, -24.95779, -30.626919, -32.08114, -33.993828, -30.882383, -26.53596, -23.850664, -23.291126, -22.71187, ...]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ITERATE OVER ALL AUDIO FILES AND EXTRACT LOG MEL SPECTROGRAM MEAN VALUES INTO DF FOR MODELING \n",
    "df = pd.DataFrame(columns=['mel_spectrogram'])\n",
    "\n",
    "counter=0\n",
    "\n",
    "for index,path in enumerate(audio_df.path):\n",
    "    X, sample_rate = librosa.load(path, res_type='kaiser_fast',duration=3,sr=44100,offset=0.5)\n",
    "    \n",
    "    #get the mel-scaled spectrogram (ransform both the y-axis (frequency) to log scale, and the “color” axis (amplitude) to Decibels, which is kinda the log scale of amplitudes.)\n",
    "    spectrogram = librosa.feature.melspectrogram(y=X, sr=sample_rate, n_mels=128,fmax=8000) \n",
    "    db_spec = librosa.power_to_db(spectrogram)\n",
    "    #temporally average spectrogram\n",
    "    log_spectrogram = np.mean(db_spec, axis = 0)\n",
    "        \n",
    "    # Mel-frequency cepstral coefficients (MFCCs)\n",
    "#     mfcc = librosa.feature.mfcc(y=X, sr=sample_rate, n_mfcc=13)\n",
    "#     mfcc=np.mean(mfcc,axis=0)\n",
    "    \n",
    "    # compute chroma energy (pertains to 12 different pitch classes)\n",
    "#     chroma = librosa.feature.chroma_stft(y=X, sr=sample_rate)\n",
    "#     chroma = np.mean(chroma, axis = 0)\n",
    "\n",
    "    # compute spectral contrast\n",
    "#     contrast = librosa.feature.spectral_contrast(y=X, sr=sample_rate)\n",
    "#     contrast = np.mean(contrast, axis= 0)\n",
    "\n",
    "    # compute zero-crossing-rate (zcr:the zcr is the rate of sign changes along a signal i.e.m the rate at \n",
    "#     which the signal changes from positive to negative or back - separation of voiced andunvoiced speech.)\n",
    "#     zcr = librosa.feature.zero_crossing_rate(y=X)\n",
    "#     zcr = np.mean(zcr, axis= 0)\n",
    "    \n",
    "    df.loc[counter] = [log_spectrogram]\n",
    "    counter=counter+1   \n",
    "\n",
    "print(len(df))\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TURN ARRAY INTO LIST AND JOIN WITH AUDIO_DF TO GET CORRESPONDING EMOTION LABELS\n",
    "df_combined = pd.concat([audio_df,pd.DataFrame(df['mel_spectrogram'].values.tolist())],axis=1)\n",
    "df_combined = df_combined.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "# DROP PATH COLUMN FOR MODELING\n",
    "df_combined.drop(columns='path',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <td>1</td>\n",
       "      <td>-70.461052</td>\n",
       "      <td>-70.461052</td>\n",
       "      <td>-70.461052</td>\n",
       "      <td>-70.461052</td>\n",
       "      <td>-70.461052</td>\n",
       "      <td>-70.461052</td>\n",
       "      <td>-70.461052</td>\n",
       "      <td>...</td>\n",
       "      <td>-70.461052</td>\n",
       "      <td>-70.461052</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>male</td>\n",
       "      <td>angry</td>\n",
       "      <td>1</td>\n",
       "      <td>-64.176788</td>\n",
       "      <td>-64.393738</td>\n",
       "      <td>-64.444954</td>\n",
       "      <td>-64.268044</td>\n",
       "      <td>-64.303825</td>\n",
       "      <td>-64.468147</td>\n",
       "      <td>-64.468147</td>\n",
       "      <td>...</td>\n",
       "      <td>-64.468147</td>\n",
       "      <td>-64.468147</td>\n",
       "      <td>-64.468147</td>\n",
       "      <td>-64.468147</td>\n",
       "      <td>-64.422607</td>\n",
       "      <td>-64.364937</td>\n",
       "      <td>-64.468147</td>\n",
       "      <td>-64.465858</td>\n",
       "      <td>-64.354759</td>\n",
       "      <td>-64.385941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>male</td>\n",
       "      <td>fear</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.280296</td>\n",
       "      <td>-73.280296</td>\n",
       "      <td>-73.280296</td>\n",
       "      <td>-73.280296</td>\n",
       "      <td>-73.280296</td>\n",
       "      <td>-73.255249</td>\n",
       "      <td>-73.086319</td>\n",
       "      <td>...</td>\n",
       "      <td>-72.866135</td>\n",
       "      <td>-73.029083</td>\n",
       "      <td>-73.021973</td>\n",
       "      <td>-73.157417</td>\n",
       "      <td>-73.278503</td>\n",
       "      <td>-73.280296</td>\n",
       "      <td>-73.272202</td>\n",
       "      <td>-73.042091</td>\n",
       "      <td>-72.969475</td>\n",
       "      <td>-73.264915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>male</td>\n",
       "      <td>fear</td>\n",
       "      <td>1</td>\n",
       "      <td>-44.965843</td>\n",
       "      <td>-44.467876</td>\n",
       "      <td>-44.512608</td>\n",
       "      <td>-44.934807</td>\n",
       "      <td>-44.679760</td>\n",
       "      <td>-44.350895</td>\n",
       "      <td>-45.161110</td>\n",
       "      <td>...</td>\n",
       "      <td>-42.328346</td>\n",
       "      <td>-42.546494</td>\n",
       "      <td>-43.290047</td>\n",
       "      <td>-42.981064</td>\n",
       "      <td>-43.380680</td>\n",
       "      <td>-44.499775</td>\n",
       "      <td>-44.815922</td>\n",
       "      <td>-45.377876</td>\n",
       "      <td>-45.876091</td>\n",
       "      <td>-46.404274</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 262 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  gender   emotion  actor          0          1          2          3  \\\n",
       "0  male   surprise  1     -71.200218 -71.200218 -71.200218 -71.200218   \n",
       "1  male   surprise  1     -70.461052 -70.461052 -70.461052 -70.461052   \n",
       "2  male   angry     1     -64.176788 -64.393738 -64.444954 -64.268044   \n",
       "3  male   fear      1     -73.280296 -73.280296 -73.280296 -73.280296   \n",
       "4  male   fear      1     -44.965843 -44.467876 -44.512608 -44.934807   \n",
       "\n",
       "           4          5          6  ...        249        250        251  \\\n",
       "0 -71.200218 -71.200218 -71.200218  ... -71.200218 -71.200218 -71.200218   \n",
       "1 -70.461052 -70.461052 -70.461052  ... -70.461052 -70.461052  0.000000    \n",
       "2 -64.303825 -64.468147 -64.468147  ... -64.468147 -64.468147 -64.468147   \n",
       "3 -73.280296 -73.255249 -73.086319  ... -72.866135 -73.029083 -73.021973   \n",
       "4 -44.679760 -44.350895 -45.161110  ... -42.328346 -42.546494 -43.290047   \n",
       "\n",
       "         252        253        254        255        256        257        258  \n",
       "0 -71.200218 -71.200218 -71.200218 -71.200218 -71.200218 -71.200218 -71.200218  \n",
       "1  0.000000   0.000000   0.000000   0.000000   0.000000   0.000000   0.000000   \n",
       "2 -64.468147 -64.422607 -64.364937 -64.468147 -64.465858 -64.354759 -64.385941  \n",
       "3 -73.157417 -73.278503 -73.280296 -73.272202 -73.042091 -72.969475 -73.264915  \n",
       "4 -42.981064 -43.380680 -44.499775 -44.815922 -45.377876 -45.876091 -46.404274  \n",
       "\n",
       "[5 rows x 262 columns]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# CHECK TOP 5 ROWS\n",
    "df_combined.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prepping Data for Modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TRAIN TEST SPLIT DATA\n",
    "train,test = train_test_split(df_combined, test_size=0.2, random_state=0,\n",
    "                               stratify=df_combined[['emotion','gender','actor']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1152, 259)\n"
     ]
    }
   ],
   "source": [
    "X_train = train.iloc[:, 3:]\n",
    "y_train = train.iloc[:,:2].drop(columns=['gender'])\n",
    "print(X_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(288, 259)\n"
     ]
    }
   ],
   "source": [
    "X_test = test.iloc[:,3:]\n",
    "y_test = test.iloc[:,:2].drop(columns=['gender'])\n",
    "print(X_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "# NORMALIZE DATA\n",
    "mean = np.mean(X_train, axis=0)\n",
    "std = np.std(X_train, axis=0)\n",
    "X_train = (X_train - mean)/std\n",
    "X_test = (X_test - mean)/std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TURN DATA INTO ARRAYS FOR KERAS\n",
    "X_train = np.array(X_train)\n",
    "y_train = np.array(y_train)\n",
    "X_test = np.array(X_test)\n",
    "y_test = np.array(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0. 0. 0. 1. 0. 0. 0. 0.]\n",
      " [0. 1. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 0. 0. 0. 1. 0. 0. 0.]\n",
      " [1. 0. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 0. 0. 1. 0. 0. 0. 0.]\n",
      " [1. 0. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 0. 0. 0. 1. 0. 0. 0.]\n",
      " [0. 0. 0. 0. 0. 0. 1. 0.]\n",
      " [0. 0. 0. 0. 0. 1. 0. 0.]\n",
      " [1. 0. 0. 0. 0. 0. 0. 0.]]\n"
     ]
    }
   ],
   "source": [
    "# ONE HOT ENCODE THE TARGET\n",
    "# CNN REQUIRES INPUT AND OUTPUT ARE NUMBERS\n",
    "lb = LabelEncoder()\n",
    "y_train = to_categorical(lb.fit_transform(y_train))\n",
    "y_test = to_categorical(lb.fit_transform(y_test))\n",
    "\n",
    "print(y_test[0:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['angry' 'calm' 'disgust' 'fear' 'happy' 'neutral' 'sad' 'surprise']\n"
     ]
    }
   ],
   "source": [
    "print(lb.classes_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1152, 259, 1)"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# RESHAPE DATA TO INCLUDE 3D TENSOR \n",
    "X_train = X_train[:,:,np.newaxis]\n",
    "X_test = X_test[:,:,np.newaxis]\n",
    "\n",
    "X_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Base Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1152, 259)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(288, 259)"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.11805555555555555"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.dummy import DummyClassifier\n",
    "\n",
    "dummy_clf = DummyClassifier(strategy=\"stratified\")\n",
    "dummy_clf.fit(X_train, y_train)\n",
    "DummyClassifier(strategy='stratified')\n",
    "dummy_clf.predict(X_test)\n",
    "dummy_clf.score(X_test, y_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2916666666666667"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import tree\n",
    "\n",
    "clf = tree.DecisionTreeClassifier()\n",
    "clf = clf.fit(X_train, y_train)\n",
    "clf.predict(X_test)\n",
    "clf.score(X_test, y_test)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initial Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv1d_6 (Conv1D)            (None, 250, 64)           704       \n",
      "_________________________________________________________________\n",
      "conv1d_7 (Conv1D)            (None, 241, 128)          82048     \n",
      "_________________________________________________________________\n",
      "max_pooling1d_4 (MaxPooling1 (None, 30, 128)           0         \n",
      "_________________________________________________________________\n",
      "dropout_6 (Dropout)          (None, 30, 128)           0         \n",
      "_________________________________________________________________\n",
      "conv1d_8 (Conv1D)            (None, 21, 128)           163968    \n",
      "_________________________________________________________________\n",
      "max_pooling1d_5 (MaxPooling1 (None, 2, 128)            0         \n",
      "_________________________________________________________________\n",
      "dropout_7 (Dropout)          (None, 2, 128)            0         \n",
      "_________________________________________________________________\n",
      "flatten_2 (Flatten)          (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 256)               65792     \n",
      "_________________________________________________________________\n",
      "dropout_8 (Dropout)          (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "dense_5 (Dense)              (None, 8)                 2056      \n",
      "=================================================================\n",
      "Total params: 314,568\n",
      "Trainable params: 314,568\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "from tensorflow.keras.models import Sequential, load_model\n",
    "\n",
    "#BUILD 1D CNN LAYERS\n",
    "model = tf.keras.Sequential()\n",
    "model.add(layers.Conv1D(64, kernel_size=(10), activation='relu', input_shape=(X_train.shape[1],1)))\n",
    "model.add(layers.Conv1D(128, kernel_size=(10),activation='relu',kernel_regularizer=l2(0.01), bias_regularizer=l2(0.01)))\n",
    "model.add(layers.MaxPooling1D(pool_size=(8)))\n",
    "model.add(layers.Dropout(0.4))\n",
    "model.add(layers.Conv1D(128, kernel_size=(10),activation='relu'))\n",
    "model.add(layers.MaxPooling1D(pool_size=(8)))\n",
    "model.add(layers.Dropout(0.4))\n",
    "model.add(layers.Flatten())\n",
    "model.add(layers.Dense(256, activation='relu'))\n",
    "model.add(layers.Dropout(0.4))\n",
    "model.add(layers.Dense(8, activation='sigmoid'))\n",
    "opt = keras.optimizers.Adam(lr=0.001)\n",
    "model.compile(loss='categorical_crossentropy', optimizer=opt,metrics=['accuracy'])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "W1102 15:39:57.097605 140735692108672 callbacks.py:1071] `period` argument is deprecated. Please use `save_freq` to specify the frequency in number of batches seen.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/40\n",
      "36/36 [==============================] - ETA: 0s - loss: 2.4063 - accuracy: 0.2231\n",
      "Epoch 00001: val_accuracy improved from -inf to 0.28472, saving model to best_initial_model.hdf5\n",
      "36/36 [==============================] - 2s 67ms/step - loss: 2.4063 - accuracy: 0.2231 - val_loss: 2.0510 - val_accuracy: 0.2847\n",
      "Epoch 2/40\n",
      "36/36 [==============================] - ETA: 0s - loss: 1.9729 - accuracy: 0.2648\n",
      "Epoch 00002: val_accuracy improved from 0.28472 to 0.30903, saving model to best_initial_model.hdf5\n",
      "36/36 [==============================] - 2s 67ms/step - loss: 1.9729 - accuracy: 0.2648 - val_loss: 1.8767 - val_accuracy: 0.3090\n",
      "Epoch 3/40\n",
      "36/36 [==============================] - ETA: 0s - loss: 1.8479 - accuracy: 0.2951\n",
      "Epoch 00003: val_accuracy did not improve from 0.30903\n",
      "36/36 [==============================] - 2s 60ms/step - loss: 1.8479 - accuracy: 0.2951 - val_loss: 1.7964 - val_accuracy: 0.2639\n",
      "Epoch 4/40\n",
      "36/36 [==============================] - ETA: 0s - loss: 1.7749 - accuracy: 0.3047\n",
      "Epoch 00004: val_accuracy improved from 0.30903 to 0.36111, saving model to best_initial_model.hdf5\n",
      "36/36 [==============================] - 3s 70ms/step - loss: 1.7749 - accuracy: 0.3047 - val_loss: 1.7664 - val_accuracy: 0.3611\n",
      "Epoch 5/40\n",
      "36/36 [==============================] - ETA: 0s - loss: 1.7646 - accuracy: 0.3229\n",
      "Epoch 00005: val_accuracy improved from 0.36111 to 0.37847, saving model to best_initial_model.hdf5\n",
      "36/36 [==============================] - 2s 58ms/step - loss: 1.7646 - accuracy: 0.3229 - val_loss: 1.7104 - val_accuracy: 0.3785\n",
      "Epoch 6/40\n",
      "36/36 [==============================] - ETA: 0s - loss: 1.7192 - accuracy: 0.3281\n",
      "Epoch 00006: val_accuracy did not improve from 0.37847\n",
      "36/36 [==============================] - 3s 74ms/step - loss: 1.7192 - accuracy: 0.3281 - val_loss: 1.6788 - val_accuracy: 0.3785\n",
      "Epoch 7/40\n",
      "36/36 [==============================] - ETA: 0s - loss: 1.7100 - accuracy: 0.3299\n",
      "Epoch 00007: val_accuracy did not improve from 0.37847\n",
      "36/36 [==============================] - 3s 71ms/step - loss: 1.7100 - accuracy: 0.3299 - val_loss: 1.6818 - val_accuracy: 0.3646\n",
      "Epoch 8/40\n",
      "36/36 [==============================] - ETA: 0s - loss: 1.6814 - accuracy: 0.3472\n",
      "Epoch 00008: val_accuracy improved from 0.37847 to 0.40625, saving model to best_initial_model.hdf5\n",
      "36/36 [==============================] - 2s 61ms/step - loss: 1.6814 - accuracy: 0.3472 - val_loss: 1.6602 - val_accuracy: 0.4062\n",
      "Epoch 9/40\n",
      "36/36 [==============================] - ETA: 0s - loss: 1.6658 - accuracy: 0.3507\n",
      "Epoch 00009: val_accuracy improved from 0.40625 to 0.40972, saving model to best_initial_model.hdf5\n",
      "36/36 [==============================] - 2s 64ms/step - loss: 1.6658 - accuracy: 0.3507 - val_loss: 1.6409 - val_accuracy: 0.4097\n",
      "Epoch 10/40\n",
      "36/36 [==============================] - ETA: 0s - loss: 1.6664 - accuracy: 0.3576\n",
      "Epoch 00010: val_accuracy did not improve from 0.40972\n",
      "36/36 [==============================] - 2s 57ms/step - loss: 1.6664 - accuracy: 0.3576 - val_loss: 1.6386 - val_accuracy: 0.3993\n",
      "Epoch 11/40\n",
      "36/36 [==============================] - ETA: 0s - loss: 1.6190 - accuracy: 0.3924\n",
      "Epoch 00011: val_accuracy improved from 0.40972 to 0.43750, saving model to best_initial_model.hdf5\n",
      "36/36 [==============================] - 2s 68ms/step - loss: 1.6190 - accuracy: 0.3924 - val_loss: 1.6066 - val_accuracy: 0.4375\n",
      "Epoch 12/40\n",
      "36/36 [==============================] - ETA: 0s - loss: 1.6275 - accuracy: 0.3880\n",
      "Epoch 00012: val_accuracy did not improve from 0.43750\n",
      "36/36 [==============================] - 2s 66ms/step - loss: 1.6275 - accuracy: 0.3880 - val_loss: 1.6187 - val_accuracy: 0.4132\n",
      "Epoch 13/40\n",
      "36/36 [==============================] - ETA: 0s - loss: 1.5860 - accuracy: 0.3802\n",
      "Epoch 00013: val_accuracy did not improve from 0.43750\n",
      "36/36 [==============================] - 2s 63ms/step - loss: 1.5860 - accuracy: 0.3802 - val_loss: 1.6331 - val_accuracy: 0.4132\n",
      "Epoch 14/40\n",
      "36/36 [==============================] - ETA: 0s - loss: 1.5988 - accuracy: 0.3924\n",
      "Epoch 00014: val_accuracy did not improve from 0.43750\n",
      "36/36 [==============================] - 2s 65ms/step - loss: 1.5988 - accuracy: 0.3924 - val_loss: 1.5913 - val_accuracy: 0.4236\n",
      "Epoch 15/40\n",
      "36/36 [==============================] - ETA: 0s - loss: 1.5925 - accuracy: 0.3889\n",
      "Epoch 00015: val_accuracy did not improve from 0.43750\n",
      "36/36 [==============================] - 2s 67ms/step - loss: 1.5925 - accuracy: 0.3889 - val_loss: 1.5790 - val_accuracy: 0.4340\n",
      "Epoch 16/40\n",
      "36/36 [==============================] - ETA: 0s - loss: 1.5533 - accuracy: 0.4106\n",
      "Epoch 00016: val_accuracy improved from 0.43750 to 0.44444, saving model to best_initial_model.hdf5\n",
      "36/36 [==============================] - 3s 85ms/step - loss: 1.5533 - accuracy: 0.4106 - val_loss: 1.5782 - val_accuracy: 0.4444\n",
      "Epoch 17/40\n",
      "35/36 [============================>.] - ETA: 0s - loss: 1.5737 - accuracy: 0.3839\n",
      "Epoch 00017: val_accuracy did not improve from 0.44444\n",
      "36/36 [==============================] - 2s 63ms/step - loss: 1.5781 - accuracy: 0.3845 - val_loss: 1.6041 - val_accuracy: 0.4444\n",
      "Epoch 18/40\n",
      "36/36 [==============================] - ETA: 0s - loss: 1.5230 - accuracy: 0.4236\n",
      "Epoch 00018: val_accuracy did not improve from 0.44444\n",
      "36/36 [==============================] - 2s 65ms/step - loss: 1.5230 - accuracy: 0.4236 - val_loss: 1.5491 - val_accuracy: 0.4375\n",
      "Epoch 19/40\n",
      "36/36 [==============================] - ETA: 0s - loss: 1.5095 - accuracy: 0.4340\n",
      "Epoch 00019: val_accuracy did not improve from 0.44444\n",
      "36/36 [==============================] - 2s 57ms/step - loss: 1.5095 - accuracy: 0.4340 - val_loss: 1.5627 - val_accuracy: 0.4167\n",
      "Epoch 20/40\n",
      "36/36 [==============================] - ETA: 0s - loss: 1.5239 - accuracy: 0.4080\n",
      "Epoch 00020: val_accuracy did not improve from 0.44444\n",
      "36/36 [==============================] - 2s 61ms/step - loss: 1.5239 - accuracy: 0.4080 - val_loss: 1.5565 - val_accuracy: 0.4097\n",
      "Epoch 21/40\n",
      "36/36 [==============================] - ETA: 0s - loss: 1.4967 - accuracy: 0.4314\n",
      "Epoch 00021: val_accuracy improved from 0.44444 to 0.45486, saving model to best_initial_model.hdf5\n",
      "36/36 [==============================] - 2s 56ms/step - loss: 1.4967 - accuracy: 0.4314 - val_loss: 1.5310 - val_accuracy: 0.4549\n",
      "Epoch 22/40\n",
      "36/36 [==============================] - ETA: 0s - loss: 1.5004 - accuracy: 0.4332\n",
      "Epoch 00022: val_accuracy improved from 0.45486 to 0.45833, saving model to best_initial_model.hdf5\n",
      "36/36 [==============================] - 3s 76ms/step - loss: 1.5004 - accuracy: 0.4332 - val_loss: 1.5194 - val_accuracy: 0.4583\n",
      "Epoch 23/40\n",
      "36/36 [==============================] - ETA: 0s - loss: 1.4592 - accuracy: 0.4436\n",
      "Epoch 00023: val_accuracy did not improve from 0.45833\n",
      "36/36 [==============================] - 2s 65ms/step - loss: 1.4592 - accuracy: 0.4436 - val_loss: 1.5163 - val_accuracy: 0.4514\n",
      "Epoch 24/40\n",
      "36/36 [==============================] - ETA: 0s - loss: 1.4460 - accuracy: 0.4488\n",
      "Epoch 00024: val_accuracy improved from 0.45833 to 0.46181, saving model to best_initial_model.hdf5\n",
      "36/36 [==============================] - 3s 75ms/step - loss: 1.4460 - accuracy: 0.4488 - val_loss: 1.5348 - val_accuracy: 0.4618\n",
      "Epoch 25/40\n",
      "36/36 [==============================] - ETA: 0s - loss: 1.4418 - accuracy: 0.4401\n",
      "Epoch 00025: val_accuracy improved from 0.46181 to 0.47222, saving model to best_initial_model.hdf5\n",
      "36/36 [==============================] - 2s 62ms/step - loss: 1.4418 - accuracy: 0.4401 - val_loss: 1.5239 - val_accuracy: 0.4722\n",
      "Epoch 26/40\n",
      "36/36 [==============================] - ETA: 0s - loss: 1.4411 - accuracy: 0.4653\n",
      "Epoch 00026: val_accuracy did not improve from 0.47222\n",
      "36/36 [==============================] - 3s 90ms/step - loss: 1.4411 - accuracy: 0.4653 - val_loss: 1.5371 - val_accuracy: 0.4201\n",
      "Epoch 27/40\n",
      "36/36 [==============================] - ETA: 0s - loss: 1.3967 - accuracy: 0.4705\n",
      "Epoch 00027: val_accuracy improved from 0.47222 to 0.48611, saving model to best_initial_model.hdf5\n",
      "36/36 [==============================] - 4s 98ms/step - loss: 1.3967 - accuracy: 0.4705 - val_loss: 1.4784 - val_accuracy: 0.4861\n",
      "Epoch 28/40\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "36/36 [==============================] - ETA: 0s - loss: 1.3982 - accuracy: 0.4844\n",
      "Epoch 00028: val_accuracy did not improve from 0.48611\n",
      "36/36 [==============================] - 3s 83ms/step - loss: 1.3982 - accuracy: 0.4844 - val_loss: 1.5003 - val_accuracy: 0.4618\n",
      "Epoch 29/40\n",
      "35/36 [============================>.] - ETA: 0s - loss: 1.3953 - accuracy: 0.4804\n",
      "Epoch 00029: val_accuracy did not improve from 0.48611\n",
      "36/36 [==============================] - 2s 58ms/step - loss: 1.3824 - accuracy: 0.4844 - val_loss: 1.4798 - val_accuracy: 0.4792\n",
      "Epoch 30/40\n",
      "36/36 [==============================] - ETA: 0s - loss: 1.3942 - accuracy: 0.4766\n",
      "Epoch 00030: val_accuracy did not improve from 0.48611\n",
      "36/36 [==============================] - 2s 66ms/step - loss: 1.3942 - accuracy: 0.4766 - val_loss: 1.4649 - val_accuracy: 0.4271\n",
      "Epoch 31/40\n",
      "36/36 [==============================] - ETA: 0s - loss: 1.3428 - accuracy: 0.4983\n",
      "Epoch 00031: val_accuracy did not improve from 0.48611\n",
      "36/36 [==============================] - 3s 79ms/step - loss: 1.3428 - accuracy: 0.4983 - val_loss: 1.4892 - val_accuracy: 0.4826\n",
      "Epoch 32/40\n",
      "36/36 [==============================] - ETA: 0s - loss: 1.3228 - accuracy: 0.5009\n",
      "Epoch 00032: val_accuracy improved from 0.48611 to 0.48958, saving model to best_initial_model.hdf5\n",
      "36/36 [==============================] - 2s 57ms/step - loss: 1.3228 - accuracy: 0.5009 - val_loss: 1.4600 - val_accuracy: 0.4896\n",
      "Epoch 33/40\n",
      "36/36 [==============================] - ETA: 0s - loss: 1.3426 - accuracy: 0.4991\n",
      "Epoch 00033: val_accuracy did not improve from 0.48958\n",
      "36/36 [==============================] - 2s 62ms/step - loss: 1.3426 - accuracy: 0.4991 - val_loss: 1.4434 - val_accuracy: 0.4861\n",
      "Epoch 34/40\n",
      "36/36 [==============================] - ETA: 0s - loss: 1.3426 - accuracy: 0.4974\n",
      "Epoch 00034: val_accuracy improved from 0.48958 to 0.49306, saving model to best_initial_model.hdf5\n",
      "36/36 [==============================] - 2s 60ms/step - loss: 1.3426 - accuracy: 0.4974 - val_loss: 1.4807 - val_accuracy: 0.4931\n",
      "Epoch 35/40\n",
      "36/36 [==============================] - ETA: 0s - loss: 1.3110 - accuracy: 0.5269\n",
      "Epoch 00035: val_accuracy did not improve from 0.49306\n",
      "36/36 [==============================] - 3s 76ms/step - loss: 1.3110 - accuracy: 0.5269 - val_loss: 1.4582 - val_accuracy: 0.4792\n",
      "Epoch 36/40\n",
      "36/36 [==============================] - ETA: 0s - loss: 1.2871 - accuracy: 0.5260\n",
      "Epoch 00036: val_accuracy did not improve from 0.49306\n",
      "36/36 [==============================] - 3s 77ms/step - loss: 1.2871 - accuracy: 0.5260 - val_loss: 1.4909 - val_accuracy: 0.4792\n",
      "Epoch 37/40\n",
      "36/36 [==============================] - ETA: 0s - loss: 1.2621 - accuracy: 0.5217\n",
      "Epoch 00037: val_accuracy did not improve from 0.49306\n",
      "36/36 [==============================] - 2s 66ms/step - loss: 1.2621 - accuracy: 0.5217 - val_loss: 1.4748 - val_accuracy: 0.4792\n",
      "Epoch 38/40\n",
      "36/36 [==============================] - ETA: 0s - loss: 1.2690 - accuracy: 0.5269\n",
      "Epoch 00038: val_accuracy did not improve from 0.49306\n",
      "36/36 [==============================] - 2s 58ms/step - loss: 1.2690 - accuracy: 0.5269 - val_loss: 1.4835 - val_accuracy: 0.4722\n",
      "Epoch 39/40\n",
      "36/36 [==============================] - ETA: 0s - loss: 1.2522 - accuracy: 0.5304\n",
      "Epoch 00039: val_accuracy improved from 0.49306 to 0.50000, saving model to best_initial_model.hdf5\n",
      "36/36 [==============================] - 2s 62ms/step - loss: 1.2522 - accuracy: 0.5304 - val_loss: 1.4685 - val_accuracy: 0.5000\n",
      "Epoch 40/40\n",
      "36/36 [==============================] - ETA: 0s - loss: 1.2588 - accuracy: 0.5278\n",
      "Epoch 00040: val_accuracy did not improve from 0.50000\n",
      "36/36 [==============================] - 2s 59ms/step - loss: 1.2588 - accuracy: 0.5278 - val_loss: 1.4897 - val_accuracy: 0.4792\n"
     ]
    }
   ],
   "source": [
    "import tensorflow.keras as keras\n",
    "\n",
    "# FIT MODEL AND USE CHECKPOINT TO SAVE BEST MODEL\n",
    "checkpoint = ModelCheckpoint(\"best_initial_model.hdf5\", monitor='val_accuracy', verbose=1,\n",
    "    save_best_only=True, mode='max', period=1, save_weights_only=True)\n",
    "\n",
    "model_history=model.fit(X_train, y_train,batch_size=32, epochs=40, validation_data=(X_test, y_test),callbacks=[checkpoint])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# PLOT MODEL HISTORY OF ACCURACY AND LOSS OVER EPOCHS\n",
    "plt.plot(model_history.history['accuracy'])\n",
    "plt.plot(model_history.history['val_accuracy'])\n",
    "plt.title('Model Accuracy')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Train', 'Test'], loc='upper left')\n",
    "plt.savefig('Initial_Model_Accuracy.png')\n",
    "plt.show()\n",
    "# summarize history for loss\n",
    "plt.plot(model_history.history['loss'])\n",
    "plt.plot(model_history.history['val_loss'])\n",
    "plt.title('Model Loss')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.savefig('Initial_Model_loss.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Post-Model Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9/9 [==============================] - 0s 15ms/step - loss: 1.4897 - accuracy: 0.4792\n",
      "Loss of the model is -  1.4897209405899048\n",
      "9/9 [==============================] - 0s 13ms/step - loss: 1.4897 - accuracy: 0.4792\n",
      "Accuracy of the model is -  47.91666567325592 %\n"
     ]
    }
   ],
   "source": [
    "# PRINT LOSS AND ACCURACY PERCENTAGE ON TEST SET\n",
    "print(\"Loss of the model is - \" , model.evaluate(X_test,y_test)[0])\n",
    "print(\"Accuracy of the model is - \" , model.evaluate(X_test,y_test)[1]*100 , \"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Actual Values</th>\n",
       "      <th>Predicted Values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>sad</td>\n",
       "      <td>sad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>surprise</td>\n",
       "      <td>surprise</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>neutral</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>sad</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>fear</td>\n",
       "      <td>surprise</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>sad</td>\n",
       "      <td>sad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>disgust</td>\n",
       "      <td>disgust</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>angry</td>\n",
       "      <td>angry</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>surprise</td>\n",
       "      <td>angry</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>angry</td>\n",
       "      <td>angry</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Actual Values Predicted Values\n",
       "140  sad           sad            \n",
       "141  surprise      surprise       \n",
       "142  neutral       neutral        \n",
       "143  sad           neutral        \n",
       "144  fear          surprise       \n",
       "145  sad           sad            \n",
       "146  disgust       disgust        \n",
       "147  angry         angry          \n",
       "148  surprise      angry          \n",
       "149  angry         angry          "
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# PREDICTIONS\n",
    "predictions = model.predict(X_test)\n",
    "predictions=predictions.argmax(axis=1)\n",
    "predictions = predictions.astype(int).flatten()\n",
    "predictions = (lb.inverse_transform((predictions)))\n",
    "predictions = pd.DataFrame({'Predicted Values': predictions})\n",
    "\n",
    "# ACTUAL LABELS\n",
    "actual=y_test.argmax(axis=1)\n",
    "actual = actual.astype(int).flatten()\n",
    "actual = (lb.inverse_transform((actual)))\n",
    "actual = pd.DataFrame({'Actual Values': actual})\n",
    "\n",
    "# COMBINE BOTH \n",
    "finaldf = actual.join(predictions)\n",
    "finaldf[140:150]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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SGZJuOucCY8zsGTM73KxqfMS2a9mA3To0Z8y3C0JHCW7B/Pm0blPSkGqVm8v8+fpyGPfx2+yx/yGhYwSj/aLEwnlzaNi4Kff/7XqGXziQB/9vBGtWrwodKwjtF+srLCzkD6efyMlH9WH3PXvRZafqV8Usa+6c2Xw3dQrdqvu2MEv2lpBEG5nuPgzYAXgAOAP4zsxuNLNya+RmNtjMxprZ2FGjRiWYtET9OjV58vJDuOzB0SxflR8kg2S2gvx8Jn3+CT327RM6imSAoqJCZk6bykFHHMt1f3+MrerU4eVnHwkdSzJATk4O9zzyDI8//yZTv57MjB++Cx0pqJUrVzJ86MVceMnl1G/QIHQcqQSJdwJwdwfmxbcCoCnwbzO7pZzXjnL3nu7ec/Dg5DuL18wxnvzzITz94TReGD0j8eVnola5ucybO2/d4wXz55ObmxswUXhfjxtN246dadSkWegowWi/KNG0eSuatmjF9l26A9Bz34OYOW1q4FRhaL8oX4OGjdh19z0ZO/rT0FGCKSjIZ/jlF3HIYUfSu8+hoeOEZzWSvSUk6T6ZQ8zsC+AW4BNgZ3c/D9gDOC7JLKm49/zeTJ2Vx10vTg4dJWPs1H1nfvxxBrNm/UT+2rW8/uorHNDnoNCxgvrio7fZo3f1PVQO2i9Ka9KsOc1btmLurJkAfD1hLFtv1yFwqjC0X5TIW7qEX5b/DMCaNasZN2Y0bdu1DxsqEHfn5uuH065DR04acHroOFKJkj67vClwrLvPLD3R3YvM7KiEs2zSPl1zGdBnBybNWMLoO/oDcPXjY9iqVg53DNqHFo3r8J9hhzFx+mL6Xfd64LTJqVmzJldcNZzzBg+iqKiQY/ofR6dOO4SOFcya1av4ZsIYTj7vstBRgtJ+sb4B517KyFuHU1BQQMvWWzPoor+EjhSE9osSSxYv4vYbhlFYVIQXFdH7oL7ste8BoWMFMWnCeN587SU6dtqBswdE9aVz/jCEXvtWz9E5gKy94o9FR68TWJBZDvCVu3fZwll43f73pzNSlbTq+UEArC4IHCQD1In/RHpzysKwQTJA364tAe0XULJffDYtL2iOTLB3pyaA9gso2S+mL6qew0mV1qFFHQDmLdN5Bq0b1wII3sKre8yoZBpjsVX/HZzIOid2uNzdC4GpZrZdUssUERERkTBCHC7/ysw+B1YUT3T3fgnnEBEREckMWToYe9KNzOrZMUlERESkmkm0kenuHyS5PBEREZGMl6Un/iTayDSz5UDZzq3LgLHAn9z9hyTziIiIiEjlSPpw+Z3ALOAJorO5Tga2B8YBDwIHJpxHREREJKgqcpXtCku6p2k/dx/p7svd/Wd3HwUc5u5PE50UJCIiIiJZIOlK5kozOxH4d/z4eKB40LJEx4gSERERyQSqZKbHAGAgsACYH98/1czqAhcknEVEREREKknSZ5f/ABy9kac/TjKLiIiISEbIzkJm4meXtwTOAdqXXra7n5VkDhERERGpXEn3yXwB+Ah4GyhMeNkiIiIiGSdb+2Qm3cis5+6XJ7xMEREREUlY0o3Ml83sCHd/NeHlioiIiGSkbK1kJn12+RCihuYqM/vZzJab2c8JZxARERGRSpb02eUNzawZsANQJ8lli4iIiGSibK1kJn12+SCiaua2wJdAL+BT4OAkc4iIiIhI5QpxuHxPYKa79wF6AMsSziAiIiIilSzpE39Wu/tqM8PMtnL3b8xsx4QziIiIiGQMHS5Pj1lm1gT4L/CWmS0FZiacQUREREQqWdIn/vSP715jZu8BjYHXk8wgIiIiklGys5CZeCVzHXf/INSyRURERKRyJX3ij4iIiIiUEp+rkthtM1namtl7Zva1mX1lZkPi6c3M7C0z+y7+2XRz66VGpoiIiIgUKwD+5O7diIaaPN/MugFDgXfcfQfgnfjxJpm7V2rSNKoyQUVERKTKCN4jsump/0q0jbP08QEpr7OZvQDcHd8OdPe5ZtYGeN/dNzlCkCqZIiIiItWImQ02s7GlboM38rr2RGOa/w/Idfe58VPzgNzNLSfYiT9bYnVB6ATh1Yn/x+r2uCBskAywavzdAMxblh84SXitG9cCIG9VYeAk4TWpmwNoW0DJtpgyd0XgJOF1bVMfgM+m5YUNkgH27tQE0O9UKPmdGlrS42S6+yhg1KZeY2YNgOeAi9z959IZ3d3NbLPVV1UyRURERGQdM6tF1MD8l7v/J548Pz5MTvxzwebmo0amiIiISEAZdna5AQ8AU9z9jlJPvQicHt8/HXhhc+uVIYViEREREckA+wIDgUlm9mU87UrgJuAZMzub6GqNJ25uRmpkioiIiIQU/Pz2Eu7+MRtPdHBF5qXD5SIiIiKSdqpkioiIiASU9NnlSVElU0RERETSTpVMERERkYBUyRQRERERSZEamSIiIiKSdjpcLiIiIhKQDpeLiIiIiKRIlUwRERGRkLKzkKlKpoiIiIiknyqZIiIiIgGpT6aIiIiISIpUyRQREREJSJVMEREREZEUqZIpIiIiEpAqmSIiIiIiKVIlU0RERCQgVTJFRERERFKkSqaIiIhISNlZyFQlU0RERETST43MFH3y0Yf0O/Iwjjr8UB64b1ToOInaNrcJr4/6I+Oeu4ov/n0V559y4HrPDxl4EKvG303zJvXDBAxkwfy5DDnvTE47qR+nn/Q7/v3UY6EjBXP91VdxeJ/9OOW4fqGjBKdtsaHCwkIuHnQKNwz9Y+goQa34ZTl33ziUoeeeyBXnnsS0KZNCRwqmOv9OLY+ZJXpLihqZKSgsLOTGEddxz7338/yLr/D6qy/z/bRpoWMlpqCwiKF3/IfdjxvBAafdxrkn9aZLx9ZA1AA9uFdXfpy7JHDK5OXk1OT8IZfx6NMv8s8Hn+D5Z59ixg/fh44VxFH9+nPnPfpFAdoW5Xn5uSfZtl2H0DGCe2LUHey8x97cNPIZrr/7cdq0bR86UhDV/XdqdaJGZgomT5pI27bt2LZtW2rVrs3hRxzJ+++9EzpWYuYt+pkvv5kFwC8r1/DN9Hls3bIJALdcehxX/d9/cfeACcNo3qIlnbt0A6Be/fq069CRhQvnB04VRo89etKoUePQMTKCtsX6Fi2Yz9jRH3HokceEjhLUyhW/MHXyeHr3jSrcNWvVon6DhoFThVHdf6dWJ4mf+GNmuwDtSy/b3f+TdI6KWDB/Pq3btF73uFVuLpMmTgyYKJzt2jRjtx23ZczkGRx14M7MWZDHpG9nh44V3Nw5s/lu6hS67bRL6CgiGeWBu2/j9HOHsGrlytBRglo4bw4NGzfl/r9dz0/Tv6N9py4MOPcStqpTN3S0xOl36oY0hFEamNmDwIPAccDR8e2oTbx+sJmNNbOxo0bp8FNo9evW5snbBnHZbc9RUFjIn886jOv++UroWMGtXLmS4UMv5sJLLqd+gwah44hkjDGffkjjps3otGO30FGCKyoqZOa0qRx0xLFc9/fH2KpOHV5+9pHQsUQqVdKVzF7unvK3jbuPAopbl766oHJCbU6r3FzmzZ237vGC+fPJzc0NEyaQmjVr8ORt5/D0a2N54d0J7NRpa9pt05zPn74CgG1aNeGzJy5n/4G3Mn/x8sBpk1NQkM/wyy/ikMOOpHefQ0PHEcko30yewJhPPuCL0R+Tv3YtK1eu4G83XMXFw0aEjpa4ps1b0bRFK7bv0h2AnvsexCvPPho4VRj6nbqhbK1kJt3I/MzMurn71wkv91fZqfvO/PjjDGbN+oncVrm8/uor/PXW20PHStS9Vw9g6vR53PX4uwB8NW0O7Q6+Yt3z37xyLfsOuIXFeStCRUycu3Pz9cNp16EjJw04PXQckYwzcPCFDBx8IQCTxo/lhacfrZYNTIAmzZrTvGUr5s6aSZtt2/H1hLFsvV31PBlKv1Orj6QbmY8SNTTnAWuIhh91d8/ojmw1a9bkiquGc97gQRQVFXJM/+Po1GmH0LESs89uHRlw1F5M+nY2o58aCsDVd7/IGx9Xqb8V0m7ShPG8+dpLdOy0A2cPOA6Ac/4whF779g6cLHnDhl7KuLGfk5eXx1F9+zD4vAvo1/+40LGC0LaQjRlw7qWMvHU4BQUFtGy9NYMu+kvoSEFU99+p5crOQiaW5FnBZjYNuASYBBQVT3f3mSm8Pdjh8kxSJ/6zoG6PC8IGyQCrxt8NwLxl+YGThNe6cS0A8lYVBk4SXpO6OYC2BZRsiylzq88Rho3p2iYax/ezaXlhg2SAvTs1AUC/U9f9Tg3exGt7wQuJDtHy092/S2Sdk65kLnT3FxNepoiIiEjGUp/M9BhvZk8ALxEdLgcyfwgjEREREamYpBuZdYkal31LTXNAjUwRERGpllTJTAN3PzPJ5YmIiIhIGIk0Ms3s70QVy3K5+x+TyCEiIiKSaVTJ/HXGJrQcEREREckAiTQy3V3XzhIREREphyqZaWBmLYHLgW5AneLp7n5QkjlEREREpHLVSHh5/wKmAB2Aa4EZwJiEM4iIiIhkDkv4lpCkG5nN3f0BIN/dP3D3swBVMUVERESyTNLjZBZf/2+umR0JzAGaJZxBRERERCpZ0o3MG8ysMfAn4O9AI+CihDOIiIiIZIxsPfEn6cPlJwDm7pPdvQ9wKNA/4QwiIiIiUsmSrmTu4u55xQ/cfYmZ9Ug4g4iIiEjGUCUzTcszs6bFD8ysGck3dEVERESkkiXdwLsd+MzMno0fnwCMSDiDiIiISMbI0kJmso1Md3/UzMZSMmzRse7+dZIZRERERKTyJX6oOm5UqmEpIiIigvpkioiIiIikTCfdiIiIiASUpYVMVTJFREREJP1UyRQREREJSH0yRURERERSpEqmiIiISEBZWsjE3D10hlRVmaAiIiJSZQRv4nUZ+kaibZxvbjoskXVWJVNEREQkoBo1grdzK0WVamTOW5YfOkJwrRvXAiBvVWHgJOE1qZsDQN0eFwROEt6q8XcDsLogcJAMUCf+Vpsyd0XYIBmga5v6gPYLKNkvpi9aHTZIBujQog6gbQEl20Iqh078EREREZG0q1KVTBEREZFsk60n/qiSKSIiIiJpp0qmiIiISEAajF1EREREJEWqZIqIiIgElKWFTFUyRURERCT9VMkUERERCUh9MkVEREREUqRKpoiIiEhAqmSKiIiIiKRIlUwRERGRgLK0kKlKpoiIiIiknyqZIiIiIgGpT6aIiIiISIpUyRQREREJKEsLmapkioiIiEj6qZEpIiIiImmnw+UiIiIiAenEHxERERGRFKmSKSIiIhJQlhYyVckUERERkfRTJVNEREQkIPXJFBERERFJkSqZKVgwfy4jrrmSpUsWYxhH9z+e408eGDpWMNdffRWffPgBTZs148nnXgwdJ1Hb5jbh/utPo1XzhrjDg899wj+efH/d80MGHsRNlxzLtn0uZ3HeinBBA/jkow+5+aYRFBUW0f+4Ezj7nMGhIwVVWFjIpeeeSvMWLRl2012h4wSj/SKyds0aLj3/TPLz8yksKGD/PocycNAfQscKQttiQ1layFQjMxU5OTU5f8hldO7SjZUrVnDOaSfS8zf70L7j9qGjBXFUv/6ccPIArh02NHSUxBUUFjH0jv/w5TezaFBvKz594nLe+d83fPPDPLbNbcLBvbry49wloWMmrrCwkBtHXMfI+x4iNzeX3590PAf2OYjtO3UKHS2Yl597km3bdWDVil9CRwlG+0WJWrVrc/Nd91O3Xj0KCvL503ln0LPXfnTtvkvoaInTtsh8ZvYgcBSwwN27l5p+IXA+UAi84u5/3tR8dLg8Bc1btKRzl24A1Ktfn3YdOrJw4fzAqcLpsUdPGjVqHDpGEPMW/cyX38wC4JeVa/hm+jy2btkEgFsuPY6r/u+/uHvAhGFMnjSRtm3bsW3bttSqXZvDjziS9997J3SsYBYtmM/Y0R9x6JHHhI4SlPaLEmZG3Xr1ACgoKKCgoCBrq1ebo22xITNL9JaCh4HDy2TsA/wO2NXddwJu29xMEm1kmlmHVKZlsrlzZvPd1Cl020l/cVV327Vpxm47bsuYyTM46sCdmbMgj0nfzg4dK4gF8+fTuk3rdY9b5eYyf371/UPsgbtv4/Rzh2BWvf+O136xvsLCQv5w+omcfFQfdt+zF12q8e8RbYvM5u4fAmUPy50H3OTua+LXLNjcfJL+BnyunGn/3tiLzWywmY01s7GjRo2qxFipWblyJcOHXsyFl1xO/QYNQseRgOrXrc2Ttw3istueo6CwkD+fdRjX/fOV0LEkA4z59EMaN21Gpx27hY4iGSYnJ4d7HnmGx59/k6lfT2bGD9+FjhSMtsX6zJK+lbSv4lsqnaU7A/ub2f/M7AMz23Nzb0ikT6aZdQF2Ahqb2bGlnmoE1NnY+9x9FFDcuvR5y/IrL+RmFBTkM/zyizjksCPp3efQYDkkvJo1a/Dkbefw9GtjeeHdCezUaWvabdOcz5++AoBtWjXhsycuZ/+BtzJ/8fLAaZPRKjeXeXPnrXu8YP58cnNzAyYK55vJExjzyQd8Mfpj8teuZeXKFfzthqu4eNiI0NESp/2ifA0aNmLX3fdk7OhPad9xh9BxgtK2CKNM+ypVNYFmQC9gT+AZM+vom+gjltSJPzsSdSBtAhxdavpy4JyEMmwxd+fm64fTrkNHThpweug4Eti9Vw9g6vR53PX4uwB8NW0O7Q6+Yt3z37xyLfsOuKVanV2+U/ed+fHHGcya9RO5rXJ5/dVX+Outt4eOFcTAwRcycPCFAEwaP5YXnn60WjYwQftFaXlLl1CzZk0aNGzEmjWrGTdmNCeeemboWEFoW2yoioyTOQv4T9yo/NzMioAWwMKNvSGRRqa7vwC8YGZ7u/tnSSwznSZNGM+br71Ex047cPaA4wA45w9D6LVv78DJwhg29FLGjf2cvLw8jurbh8HnXUC//seFjpWIfXbryICj9mLSt7MZ/VR0dv3Vd7/IGx9/HThZWDVr1uSKq4Zz3uBBFBUVckz/4+jUSVWJ6k77RYklixdx+w3DKCwqwouK6H1QX/ba94DQsYLQtqiy/gv0Ad4zs85AbWDRpt5gSZ4Ja2a3ADcAq4DXgV2Ai9398RTeHvRweaZo3bgWAHmrCgMnCa9J3RwA6va4IHCS8FaNvxuA1QWBg2SAOvGfzlPmVp9K8sZ0bVMf0H4BJfvF9EWrwwbJAB1aRL3UtC3WbYvgZcR9bvkw0WFJPv1z702us5k9CRxIVKmcD1wNPAY8COwGrAUudfd3NzWfpMfJ7Ovufzaz/sAM4FjgQyCVRqaIiIiIVDJ3P2UjT51akfkkfXZ5rfjnkcCz7r4s4eWLiIiISAKSrmS+ZGbfEB0uP8/MWgKq14uIiEi1VUVO/KmwRCuZ7j4U2Afo6e75wAqi0eNFREREJIskWsk0s9NK3S/91KNJ5hARERHJFFlayEz8cHnp0eHrAAcD41AjU0RERCSrJNrIdPcLSz82sybAU0lmEBEREckk6pNZOVYAHQJnEBEREZE0S7pP5ktA8YCjNYBuwDNJZhARERHJJNlayUy6T+Ztpe4XADPdfVbCGURERESkkiXdJ/ODJJcnIiIikumytJCZ+OHy5ZQcLi+2DBgL/Mndf0gyj4iIiIhUjqQPl98JzAKeILog/cnA9kTDGD1IdDF2ERERkWojW/tkJn12eT93H+nuy939Z3cfBRzm7k8DTRPOIiIiIiKVJOlG5kozO9HMasS3Eym5dnnZw+giIiIiWc8s2VtSkm5kDgAGAgvi20DgVDOrC1yQcBYRERERqSRJn13+A3D0Rp7+OMksIiIiIplAfTLTwMxuMbNGZlbLzN4xs4VmdmqSGURERESk8iV9uLyvu/8MHAXMADoBlyWcQUREREQqWdJDGBUv70jgWXdflq0lYhEREZFUZGtTKOlG5stm9g2wCjjPzFpScna5iIiIiGSJpE/8GWpmtwDL3L3QzFYAv0syg4iIiEgmqZGlpcxEGplmdpC7v2tmx5aaVvol/0kih4iIiIgkI6lKZm/gXaLhi5zokpKlf6qRKSIiItVSlhYyE2tkLjezS4DJlDQuQVf5EREREclKSTUyG8Q/dwT2BF4gamgeDXye6kxaN66V/mRVVJO6OaEjZIxV4+8OHSFj1En6VL4M1rVN/dARMob2ixIdWtQJHSFjaFtkjmwdaSeRrx53vxbAzD4Ednf35fHja4BXksggIiIiIslJ+u/bXGBtqcdr42kpmbcsP+2Bqpriau70RRr5qfiv8LxVhYGThFdc2T7/+SmBk4T3j/5dAXhzysLAScLr27UloM8IlHxGPpuWFzZIBti7UxNA2wJKtkVoNbKzkJl4I/NR4HMzez5+fAzwcMIZRERERKSSJT1O5ggzew3YP550pruPTzKDiIiISCZRn8w0cfdxwLiklysiIiIiydE5hyIiIiIBZWkhkxqhA4iIiIhI9lElU0RERCQgIztLmapkioiIiEjaqZEpIiIiImmnw+UiIiIiAWXrYOyqZIqIiIhI2qmSKSIiIhJQtg7GrkqmiIiIiKSdKpkiIiIiAWVpIXPLK5lm1snM6qQzjIiIiIhkh5QamWZ2o5mdHt83M3sL+BaYa2Z7VWZAERERkWxWwyzRW2LrleLrBgBT4/u/BXYDegGPAjelP5aIiIiIVGWp9snMBWbF948AnnH3z81sCTC2UpKJiIiIVAPVvU/mYqBdfL8v8E58vyZk6QU3RURERGSLpVrJfA54wsy+BZoBb8TTdwOmVUIuERERkWohW8fJTLWReQkwE9gO+LO7r4intwH+WRnBRERERKTqSqmR6e4FwO3lTP9b2hOJiIiIVCNZWsjceCPTzHZPdSbuPi49cUREREQkG2yqkjkWcDZ/Yo8DOWlLJCIiIlKNJDl2ZZI21cjskFgKEREREckqG21kuvvMJINksgXz5zLimitZumQxhnF0/+M5/uSBoWMFsXbNGi49/0zy8/MpLChg/z6HMnDQH0LHCub6q6/ikw8/oGmzZjz53Iuh4yTq1N3b0L11A5avKWDEO9MB6N+9Fd1bN6CwyFm4Ip/Hx81hVX5R4KTJmj/7Rx66dfi6x4vnz+GIUwbRp9+JAVOFUZ0/H+VZ8ctyHrprBLNm/oBhnH3RMDp13Tl0rCC0LaqHVM8ux8x2Bs4FtgfOcve5ZnYMMNPdx1dSvoyQk1OT84dcRucu3Vi5YgXnnHYiPX+zD+07bh86WuJq1a7NzXfdT9169SgoyOdP551Bz1770bX7LqGjBXFUv/6ccPIArh02NHSUxI2emccH3y/ltJ5t1k2bsmAFL3y1gCKH3+3Ukr6dm/PCVwsDpkxe7jbbMfTOhwEoKixk2Nn92bVX77ChAqnOn4/yPDHqDnbeY28uuPImCvLzWbNmdehIwWhbrC87D5anfu3yvsAYYBvgIKBu/NT2wNWVEy1zNG/Rks5dugFQr3592nXoyMKF8wOnCsPMqFuvHgAFBQUUFBRk7VlxqeixR08aNWocOkYQ0xavYkV+4XrTvlmwgiKP7s9YspqmdWsFSJY5pk78ghatt6FZq9ahowRRnT8fZa1c8QtTJ4+nd99+ANSsVYv6DRoGThWGtkX1kWol83rgEne/x8yWl5r+PvCnzb3ZzHKAr9y9S8UjZpa5c2bz3dQpdNupelbuAAoLC7nwrFOYM/tHjj72JLpU420hG7d3u8Z8Mfvn0DGCGvfx2+yx/yGhY0gGWDhvDg0bN+X+v13PT9O/o32nLgw49xK2qlN382/OMtoWG8rWwdhTvaxkd+DVcqYvIboC0Ca5eyEw1cy2q0A2zGywmY01s7GjRo2qyFsrxcqVKxk+9GIuvORy6jdoEDpOMDk5OdzzyDM8/vybTP16MjN++C50JMkwh3VuTqHDmJ+qbyOzID+fSZ9/Qo99+4SOIhmgqKiQmdOmctARx3Ld3x9jqzp1ePnZR0LHCkLbovpItZK5hOhQ+Ywy03cHZqU4j6bAV2b2OVB8xSDcvd/G3uDuo4Di1qXPW5af4qLSr6Agn+GXX8Qhhx1J7z6HBsuRSRo0bMSuu+/J2NGf0r7jDqHjSIbotV1jurdpwF0f/xg6SlBfjxtN246dadRks3+HSzXQtHkrmrZoxfZdugPQc9+DeOXZRwOnCkPbYkM1srOQmXIj8wngVjM7kWhczJpmdgBwG/BQivP4yxbkywjuzs3XD6ddh46cNOD00HGCylu6hJo1a9KgYSPWrFnNuDGjOfHUM0PHkgzRrVV9DtmhOXd+NJP8Qg8dJ6gvPnqbPXrrULlEmjRrTvOWrZg7ayZttm3H1xPGsvV21XOkQG2L6iPVRuYw4GGi65cb8HX88wlgRCozcPcPtiBfRpg0YTxvvvYSHTvtwNkDjgPgnD8Mode+1e+M0SWLF3H7DcMoLCrCi4rofVBf9tr3gNCxghk29FLGjf2cvLw8jurbh8HnXUC//seFjpWIM3tuzQ4t69Ogdg43HN6JV6Ys5LDOLahZw7hw36hnzPSlq3jqy3mBkyZvzepVfDNhDCefd1noKEFV589HeQaceykjbx1OQUEBLVtvzaCLqmzt5VfTtlhftvbJNPfUqw1mtj3Qg6gv53h3T7kznpn1Av4OdAVqE10laIW7N0pxFkEPl2eK1o2js3WnL6rewz0AdGhRB4C8VYWbeWX2a1I3uujW+c9PCZwkvH/07wrAm1Oq19BJ5enbtSWgzwiUfEY+m5YXNkgG2LtTE0DbAtZti+AtvFMfn5DooZ/HT901kXVOeZxMAHf/3szmx/d/qeCy7gZOBp4FegKnAZ0rOA8RERGRrJKlhcyUzy7HzC4ysx+BZcAyM/vJzC62CtR43X0akOPuhe7+EHB4xSOLiIiISKZLqZJpZrcAg4Fbgc/iyXsDw4E2wJ9TmM1KM6sNfBnPby4VaOSKiIiIZKNs7ZOZ6uHyQcAgd/93qWnvmtlUYCSpNTIHEjUqLwAuBtoC1bcHuIiIiEgWq0ifzIkbmZZSNdLdZ5pZXaCNu19bgeWKiIiIZK1sHScz1cPVjwLnlzP9POCxVGZgZkcDXwKvx493M7MXU1y+iIiIiFQhG61kmtldZV53qpkdBoyOp+0FbA38K8VlXQP8huh657j7l2am0VdFRESkWquOfTJ3LvP4i/hnu/jnvPjWJcVl5bv7sjIbsnpfEkREREQkS220kenufdK8rK/M7PdAjpntAPwR+DTNyxARERGRDFDpQwiZWXGfze+BnYA1wJPAz8BFlb18ERERkUxmCd+SkvLZ5WbWBzgF2I7ospDruPtBm3jrHma2NXAS0Ae4vdRz9QBdH1FEREQky6Q6GPsZwL3A88CBwAtEl4TsADy+mbffC7wDdATGlp4tUZ/MjhUJLCIiIpJNamTpiT+pHi6/FLjA3U8B8oEr3L0HUQNzk9cwd/e73L0r8KC7dyx16+DuamCKiIiIZKFUG5kdgbfj+2uABvH9u4EzUpmBu59XoWQiIiIi1YBZsrekpNrIXAw0jO/PBrrH95sDddMdSkRERESqtlRP/PkI6AtMAp4B7jKzQ4GDgbcqKZuIiIhI1quOg7GXdgFQJ77/V6AA2JeowXl9JeQSERERkSospUamuy8pdb8IuBnAzBoC+wOvVko6ERERkSyXpYXMXz0Ye0fgpXQEEREREZHskfJg7CIiIiKSftV9nEwRERERkZSpkikiIiISUJYWMjfdyDSzYzfz/vbpiyIiIiIioZnZg8BRwAJ37x5PuxU4GlgLfA+c6e55m5rP5iqZ/04hi6fwGhEREREpRwaOk/kw0VUdHy017S2iy4oXmNnNwBXA5ZuaiblXmTZilQkqIiIiVUbwFt75z09JtI3zj/5dN7vOZtYeeLm4klnmuf7A8e4+YFPzUJ9MERERkYCSPgvbzAYDg0tNGuXuoyowi7OApzf3oirVyJy3LD90hOBaN64FQN6qwsBJwmtSNwfQtoCSbTF+5vLAScLr0a4hAMfcPzZwkvD+O6gnoM8I6PuitOJtcceHPwROEt4lvTuGjhBE3KCsSKNyHTO7iujKj//a3GurVCNTRERERMIwszOITgg62FPob6lGpoiIiEhAGXjizwbM7HDgz8AB7r4ylfdoMHYRERERWcfMngQ+A3Y0s1lmdjbR2eYNgbfM7Eszu3dz81ElU0RERCSgGhlWyHT3U8qZ/EBF57PRRqaZTSLFYYPcfZeKLlhEREREstemKpmpDMQuIiIiIr9CplUy02WjjUx3vzbJICIiIiKSPdQnU0RERCSgqnB2+ZZIuZFpZmcCpwDbAbVLP+fu1XM0UxEREREpV0pDGJnZZcDtwBdAe+C/wGSgGfBgJWUTERERyXo1LNlbYuuV4uvOAQa7+xVAPnC3u/cjani2q6xwIiIiIlI1pdrI3Bb4PL6/CmgU338SOC7doURERESqC7Nkb0lJtZE5D2gR358J7B3f70SKY2mKiIiISPWR6ok/7wL9gHFEI77/zcxOBHYHnqmkbCIiIiJZr0Y1P7t8MHHV093vNbOlwL7Ac8DISsomIiIiIlVUSo1Mdy8Ciko9fhp4urJCiYiIiFQXqfZdrGpSamSa2e6bet7dx6UnjoiIiIhkg1QPl48lOsGndKeB0if85KQtkYiIiIhUeak2MjuUeVwL6AFcBVyR1kQiIiIi1UiWnveTcp/MmeVMnmZmy4CrgdfSmkpEREREqrSUr12+EdOB3dKQQ0RERKRaqtZDGJlZs7KTgDbANcDUNGcSERERkSou1UrmIja8so8BPwEnpTWRiIiISDWSpYXMlBuZfco8LgIWAtPcvSC9kURERESkqku1kTkd+MndN7hOuZlt5+4/pjdWZlkwfy4jrrmSpUsWYxhH9z+e408eGDpWMNdffRWffPgBTZs148nnXgwdJyhtixIXDDyaunXrUaNGDjk5Odz4j8dCR0rMBfu3p+d2jVm2qoAh//lqved+t3MuZ+7VloGPfcnyNdXrb3J9PkpU923x/sN3MHPi59Rt2IQTr70XgNUrlvP2yL+yfPF8GjbP5dBzr2Cr+g0DJw2jRpZWMlMdZH460LLsRDNrHj+X1XJyanL+kMt49OkX+eeDT/D8s08x44fvQ8cK5qh+/bnznlGhY2QEbYv1/eXWkdx87xPVqoEJ8O53i7ju9e82mN6ifi1226YRC5avCZAqPH0+SlT3bdF5n0M5YsgN60378rVn2Kbrbpwy4gG26bob4197JlA6qSypNjKNDftkAjQAVqcvTmZq3qIlnbt0A6Be/fq069CRhQvnB04VTo89etKoUePQMTKCtoUAfD3vF34pp0p5Vq+2PPL5rACJMoM+HyWq+7bYuvPO1ClTpZzx5Wd03vsQADrvfQgzvvwsRLSMUMMs0VtSNnm43Mzuiu868FczW1nq6RzgN8CXqS7MzC4EHnf3pRXMmTHmzpnNd1On0G2nXUJHEckohnHjFedjGAcfeSyHHHls6EhB/Wa7Jixekc+MJatCRxHJSKt+zqN+k2jwmnqNm7Lq57ywgSTtNtcnc+f4pwFdgbWlnlsLjANuq8DycoExZjYOeBB4o7x+nsXMbDAwGGDkyJH0O+nMCiwq/VauXMnwoRdz4SWXU79Bg6BZRDLNtX+7n2YtWrFs6RJGXHE+27RtT9dddg8dK4jaOTU4frfWXPPahofQRWRDZpa9p1inIFtXfZONTHfvA2BmDwFD3P3nX7Mwdx9mZn8B+gJnAneb2TPAA+6+QSdHdx8FFHdi8XnL8n/N4n+VgoJ8hl9+EYccdiS9+xwaLIdIpmrWohUAjZs2Y899DmTa1K+qbSOzTaOtaNVwK+48Nupm07x+be7o35XLXphC3qrqdfKPyMbUbdSEFXlLqN+kGSvyllC3YfXtTpCtUu2TeQXQqOxEM9vWzHIrssC4cjkvvhUATYF/m9ktFZlPktydm68fTrsOHTlpwOmh44hknNWrVrFq5Yp19yeO+x9t228fOFU4M5eu4ox/TWDw05MY/PQkFq9YyyXPq4EpUlq7XXvx7WdvA/DtZ2/Tfre9AycKp4Yle0tKqkMYPQ48DdxXZvphRIOx901lJmY2BDiNaHD3+4HL3D3fzGoA3wF/TjFPoiZNGM+br71Ex047cPaA4wA45w9D6LVv78DJwhg29FLGjf2cvLw8jurbh8HnXUC//seFjhWEtkVkWd5ibr/2MgCKCgvZt89h7LbnPoFTJeeSPh3o3qYhjerU5P5TduGpL+bw9reLQscKTp+PEtV9W7w96ibmfjuR1b/8zOOXnUrPfgPp8dsTeWvkjXzz8Rs0bN6KQ869MnRMSTPbRJfIkheZ5QF7ufvUMtM7A6PdvexlJzc2n2uBB919ZjnPdXX3KZt4e9DD5ZmideNaAOStKgycJLwmdXMAbQso2RbjZy4PnCS8Hu2iM1iPuX9s4CTh/XdQT0CfEdD3RWnF2+KOD38InCS8S3p3hOi8k6BufOf7zTfG0ujKg7dPZJ1TrWTWBLYqZ3qdjUwvl7tfbWa7m9nviM5Y/8Tdx8XPbaqBKSIiIiJVSKp9Mv8HnFfO9POBMakuLD7p5xGgOdACeMjMhqX6fhERERGpGlKtZF4FvGtmuwDvxtMOAnoAh1RgeacCu7r7agAzu4lonM0bNvUmERERkWxVrS8r6e6jgb2JLiF5bHybDuzt7p9WYHlziA6xF9sKmF2B94uIiIhIFZBqJRN3n0BUiVyPmR3i7m+nOJtlwFdm9hZRn8xDgc+Lryzk7n9MNY+IiIhINsjWSmbKjczSzGwbosHUzwLaEV1iMhXPx7di72/J8kVEREQks6XcyDSzHOB3wCCiCuRE4F7g2VTn4e6PmFltoAtRJXOqu6/dzNtEREREspZl6XUlN9vINLMdiRqWpwErgCeIGpkD3f3riizMzI4ARgLfE41L1cHMznX31yoaXEREREQy1yYbmWb2EdAdeA440d0/iKdfvoXLuwPo4+7T4vlsD7wCqJEpIiIi1VJ17ZO5N/APYJS7f5WG5S0vbmDGfgB0iRIRERGRLLO5RuaeRIfKPzazGcCjwJO/YnljzexV4BmiPpknAGPM7FgAd//Pr5i3iIiISJWTpV0yNz1OpruPd/fzgTZEh7r7AT/F7zvSzJpWcHl1gPnAAcCBwEKgLnA0cFQF5yUiIiIiGSqls8vjK/Q8BjxmZp2IqpsXAzeY2bvu/tsU53PmFicVERERyUI1srSUWeFxMuM+lUPN7Cqi6uNZqb7XzOoAZwM7UerKP+6e8jxEREREJPOldFnJ8rh7obu/4O6/q8DbHgNaA4cBHwDbohN/REREpBqrYcneEluv5BYFQCd3/wuwwt0fAY4E9ko4g4iIiIhUsi26rOSvkB//zDOz7sA8oFXCGUREREQyRpZ2yUy8kTkqPiN9GPAi0AD4S8IZRERERKSSJd3IfAw4DmgPPBJPy004g4iIiIhUsqQbmS8Ay4AvgDUJL1tEREQk49QgO4+XJ93I3NbdD094mSIiIiKSsKQbmZ+a2c7uPinh5YqIiIhkJJ348yuY2SSia5XXBM40sx+IDpcb4O6+SxI5RERERCQZSVUydV1yERERkXIkOUB6kszdQ2dIVZUJKiIiIlVG8CbevZ/NSLSN8//2bp/IOifdJ1NERERESqmRpZ0yq1Qjc3VB6ATh1Yn/x8bP1CXfe7RrCGi/gJL9QtuiZFvMW5a/6RdWA60b1wLg/OenBE4S3j/6dwX03Qn67iytTpVqBVU92rwiIiIiAWVpIZMaoQOIiIiISPZRJVNEREQkoGztk6lKpoiIiIiknSqZIiIiIgFlaSFTlUwRERERST9VMkVEREQCytaKX7aul4iIiIgEpEamiIiIiKSdDpeLiIiIBGRZeuaPKpkiIiIiknaqZIqIiIgElJ11TFUyRURERKQSqJIpIiIiEpAuKykiIiIikiJVMkVEREQCys46piqZIiIiIlIJVMkUERERCShLu2SqkikiIiIi6adKpoiIiEhAuuKPiIiIiEiKVMkUERERCShbK37Zul4iIiIiEpAqmSIiIiIBqU+miIiIiEiKVMlM0ScffcjNN42gqLCI/sedwNnnDA4dKZgLBh5N3br1qFEjh5ycHG78x2OhIwWj/aKEtkVkwfy5jLjmSpYuWYxhHN3/eI4/eWDoWIk5dfc2dG/dgOVrChjxznQA+ndvRffWDSgschauyOfxcXNYlV8UOGny9N1ZQt8X1YMamSkoLCzkxhHXMfK+h8jNzeX3Jx3PgX0OYvtOnUJHC+Yvt46kUeMmoWMEpf2ihLZFiZycmpw/5DI6d+nGyhUrOOe0E+n5m31o33H70NESMXpmHh98v5TTerZZN23KghW88NUCihx+t1NL+nZuzgtfLQyYMhx9d+r7ojyZdrDczC4GBgEOTALOdPfVFZ2PDpenYPKkibRt245t27alVu3aHH7Ekbz/3juhY0lg2i9KaFuUaN6iJZ27dAOgXv36tOvQkYUL5wdOlZxpi1exIr9wvWnfLFhBkUf3ZyxZTdO6tQIkk0yh74vMZmbbAH8Eerp7dyAHOHlL5pVIJdPMlhO1hjd4CnB3b5REji21YP58Wrdpve5xq9xcJk2cGDBRWIZx4xXnYxgHH3kshxx5bOhIQWi/KKFtUb65c2bz3dQpdNtpl9BRMsbe7RrzxeyfQ8cIQt+dEX1fbCgDT/ypCdQ1s3ygHjBnS2dS6dy94Za8z8wGA4MBRo4cyWlnqc9GJrj2b/fTrEUrli1dwogrzmebtu3pusvuoWOJZJSVK1cyfOjFXHjJ5dRv0CB0nIxwWOfmFDqM+al6NjL13SmZonT7KjbK3UcBuPtsM7sN+BFYBbzp7m9uyXKC9Mk0s1ZAneLH7v5jea+LV3hU8cPVBQmEK0er3FzmzZ237vGC+fPJzc0NEyYDNGvRCoDGTZux5z4HMm3qV9Xyi1L7RQlti/UVFOQz/PKLOOSwI+nd59DQcTJCr+0a071NA+76uNyv+2pB350RfV9sKOm+i2XaV+sxs6bA74AOQB7wrJmd6u6PV3Q5ia6XmfUzs++A6cAHwAzgtSQzbImduu/Mjz/OYNasn8hfu5bXX32FA/ocFDpWEKtXrWLVyhXr7k8c9z/atq8eJzSUpf2ihLZFCXfn5uuH065DR04acHroOBmhW6v6HLJDc0Z+Nov8wvJ6TmU/fXeW0PdFxjsEmO7uC909H/gPsM+WzCjpSub1QC/gbXfvYWZ9gFMTzlBhNWvW5IqrhnPe4EEUFRVyTP/j6NRph9CxgliWt5jbr70MgKLCQvbtcxi77blF+16Vp/2ihLZFiUkTxvPmay/RsdMOnD3gOADO+cMQeu3bO3CyZJzZc2t2aFmfBrVzuOHwTrwyZSGHdW5BzRrGhftuB8D0pat46st5m5lTdtF3Zwl9X2wow/pk/gj0MrN6RIfLDwbGbsmMzD25vyrNbKy79zSzCUAPdy8yswnuvmsKbw92uDyT1In/LBg/c3nYIBmgR7uoq6/2i5L9QtuiZFvMW5YfNkgGaN04Oov7/OenBE4S3j/6dwX03Qn67iwt/r4I3sJ7fuK8REv8/Xdpvcl1NrNrgZOAAmA8MMjd11R0OUlXMvPMrAHwIfAvM1sArEg4g4iIiEjGCN7KLcPdrwau/rXzSbqv6e+AlcDFwOvA98DRCWcQERERkUqWWCXTzHKAl929D1AEPJLUskVEREQyVWZ1yUyfxCqZ7l4IFJlZ46SWKSIiIiJhJN0n8xdgkpm9Ram+mO7+x4RziIiIiGSEGhnXKzM9km5k/ie+lVY9B00TERERyWJJNzKbuPv/lZ5gZkMSziAiIiKSMdQnMz3Ku/zFGQlnEBEREZFKlkgl08xOAX4PdDCzF0s91RBYkkQGEREREUlOUofLPwXmAi2A20tNXw5MTCiDiIiISMYxnfiz5dx9JjAT2DuJ5YmIiIhIWIme+GNmyyk5m7w2UAtY4e6NkswhIiIikimy9cSfRBuZ7t6w+L6ZGdFlJnslmUFEREREKl/SZ5ev45H/AoeFyiAiIiISWg0s0VtSkj5cfmyphzWAnsDqJDOIiIiISOVLejD2o0vdLwBmEB0yFxEREamW1CczDdz9zCSXJyIiIiJhJNon08w6m9k7ZjY5fryLmQ1LMoOIiIhIJjFL9paUpE/8uQ+4AsgHcPeJwMkJZxARERGRSpZ0n8x67v65rd+MLkg4g4iIiEjGyNYr/iRdyVxkZtsTD8huZscTXW5SRERERLJI0pXM84FRQBczmw1MBwYknEFEREQkY9TIzkJm4o3M2cBDwHtAM+Bn4HTguoRziIiIiEglSrqR+QKQB4wD5iS8bBEREZGMk619Ms3dk1uY2WR3776Fb08uqIiIiFQXwVt4736zONE2zkFdmieyzkmf+POpme2c8DJFREREJGFJHy7fDzjDzKYDa4j+enB33yWVN6/WYEfUif/H/j1BJ+Ufv2sbAN6csjBwkvD6dm0JQN6qwsBJwmtSNweAKXNXBE4SXtc29QFtCyjZFn95/bvAScK7/vAdAJi+aHXgJOF1aFEndARAl5VMl98mvDwRERERCSDpa5fPTHJ5IiIiIpkuW0/8SbpPpoiIiIhUA0kfLhcRERGRUrJ1MHZVMkVEREQk7VTJFBEREQlIfTJFRERERFKkSqaIiIhIQNk6TqYqmSIiIiKSdqpkioiIiASUpYVMVTJFREREJP1UyRQREREJqEaWdspUJVNERERE0k6VTBEREZGAsrOOqUqmiIiIiFQCNTJFREREJO10uFxEREQkpCw9Xq5KpoiIiIiknSqZIiIiIgFZlpYyVckUERERkbRTJVNEREQkoCwdi12VTBERERFJP1UyRURERALK0kKmKpkiIiIikn6qZKbok48+5OabRlBUWET/407g7HMGh44UzCcvP8vYd18Bg9ZtO3LsHy6nVu2tQsdK3PzZP/LQrcPXPV48fw5HnDKIPv1ODJgqjOuvvopPPvyAps2a8eRzL4aOkxEKCwu59NxTad6iJcNuuit0nKCq67b4/Ik7mfvVGLZq0JjDr7gHgJ/Gf8xXrz/Bz/N/4pBL7qDZdjsETpm8tWvWcOn5Z5Kfn09hQQH79zmUgYP+EDpWWFlaylQlMwWFhYXcOOI67rn3fp5/8RVef/Vlvp82LXSsIJYtWchnrz3HH24ayZDbH6aoqIhJn74bOlYQudtsx9A7H2bonQ/z59sfoNZWddi1V+/QsYI4ql9/7rxnVOgYGeXl555k23YdQsfICNV1W3T4zSH0/n/XrjetcZt27HPWlbTcfqdAqcKrVbs2N991P/985FnueeQZxv7vE6ZMnhg6llQCNTJTMHnSRNq2bce2bdtSq3ZtDj/iSN5/753QsYIpKiokf+0aCgsLyF+7moZNW4SOFNzUiV/QovU2NGvVOnSUIHrs0ZNGjRqHjpExFi2Yz9jRH3HokceEjhJcdd4WLTt1p3a9hutNa9S6LY1ytw2UKDOYGXXr1QOgoKCAgoKCrD27OlWW8L+k6HB5ChbMn0/rNiWNh1a5uUyaWD3/6mrcrCX7HX0St553IjVrb8UOu+7JDrvuGTpWcOM+fps99j8kdAzJEA/cfRunnzuEVStXho4SnLaFlKewsJALzzqFObN/5OhjT6LLTruEjiSVIJFKpplNMrOJG7tt4n2DzWysmY0dNUqH4jLBql+WM2XMJ1z6j6cYOvI51q5exZcfvhk6VlAF+flM+vwTeuzbJ3QUyQBjPv2Qxk2b0WnHbqGjBKdtIRuTk5PDPY88w+PPv8nUrycz44fvQkcKyizZW1KSqmQeFf88P/75WPxzwKbe5O6jgOLWpa8uqIRkKWiVm8u8ufPWPV4wfz65ublhwgQ2bdIXNG3VhvqNmgCw0169mfntV+zWu2/YYAF9PW40bTt2plGTZqGjSAb4ZvIExnzyAV+M/pj8tWtZuXIFf7vhKi4eNiJ0tMRpW8jmNGjYiF1335Oxoz+lfcfqdxJUtkukkenuMwHM7FB371HqqaFmNg4YmkSOLbVT95358ccZzJr1E7mtcnn91Vf46623h44VRJMWrfjpu69Zu2Y1tWpvxfeTxrHN9juGjhXUFx+9zR69dahcIgMHX8jAwRcCMGn8WF54+tFq26jStpDy5C1dQs2aNWnQsBFr1qxm3JjRnHjqmaFjBZWtXVKT7pNpZravu38SP9iHKnDyUc2aNbniquGcN3gQRUWFHNP/ODp1qp5/cbXdoRs79TqAf1x+DjVycti6/Q7sechRm39jllqzehXfTBjDyeddFjpKUMOGXsq4sZ+Tl5fHUX37MPi8C+jX/7jQsUSC+uyRW1g4bRJrfvmZl4afzk6/HUDteg0Y/9xI1vyyjI9GXkuTbTtwwHnXh46aqCWLF3H7DcMoLCrCi4rofVBf9tr3gNCxpBKYuye3MLM9gAeBxkQN96XAWe4+LoW3BztcnknqxH8W/HvC3LBBMsDxu7YB4M0pCwMnCa9v15YA5K0qDJwkvCZ1cwCYMndF4CThdW1TH9C2gJJt8ZfXq3ffP4DrD4+KJNMXrQ6cJLwOLepABhQSx834ObnGGLB7+0aJrHOilUx3/wLY1cwax4+XJbl8ERERkYwTvJlbORIfwsjMjgR2AupYfIqTu1+XdA4RERERqTyJNjLN7F6gHtAHuB84Hvg8yQwiIiIimSTJAdKTlPRJN/u4+2nAUne/Ftgb6JxwBhERERGpZEkfLi/uZbzSzLYGlgBtEs4gIiIikjGy9bKaSTcyXzKzJsCtwDjAgfsSziAiIiIilSzpRuY3QKG7P2dm3YDdgf8mnEFEREQkY2RpITPxPpl/cfflZrYfcBDRyT//TDiDiIiIiFSypBuZxSNFHwnc5+6vALUTziAiIiKSOSzhW0KSbmTONrORwEnAq2a2VYAMIiIiIlLJkm7gnQi8ARzm7nlAM6B6X/RZREREqjVL+F9Skr6s5ErgP6UezwV0EW4RERGRLJP4ZSVFREREpES2jpOp/pAiIiIiknaqZIqIiIgElKWFTFUyRURERCT9VMkUERERCSlLS5mqZIqIiIhI2qmRKSIiIiJpp8PlIiIiIgElOUB6klTJFBEREZG0UyVTREREJCANxi4iIiIi1YKZ5ZjZeDN7eUvnoUqmiIiISEAZWsgcAkwBGm3pDMzd0xenclWZoCIiIlJlBG/jTZmzItE2Ttet629ync1sW+ARYARwibsftSXL0eFyERERkZAs2ZuZDTazsaVug8skuhP4M1D0a1arSh0uz1tVGDpCcE3q5gAwb1l+4CThtW5cC4Dpi1YHThJehxZ1AJgyd0XgJOF1bVMfgM+m5YUNkgH27tQE0Hcn6LuztOLvzqan/itwkvCWPj4gdIQg3H0UMKq858zsKGCBu39hZgf+muVUqUamiIiISLbJsHEy9wX6mdkRQB2gkZk97u6nVnRGOlwuIiIiIgC4+xXuvq27twdOBt7dkgYmqJIpIiIiElS2jpOpRqaIiIiIbMDd3wfe39L3q5EpIiIiElCWFjLVJ1NERERE0k+VTBEREZGQsrSUqUqmiIiIiKSdGpkiIiIiknY6XC4iIiISUIYNxp42qmSKiIiISNqpkikiIiISULYOxq5KpoiIiIiknSqZIiIiIgFlaSFTlUwRERERST9VMkVERERCytJSpiqZIiIiIpJ2qmSKiIiIBKRxMkVEREREUqRKpoiIiEhAGidTRERERCRFqmSKiIiIBJSlhUxVMkVEREQk/VTJFBEREQkpS0uZamSm6Pqrr+KTDz+gabNmPPnci6HjBLNg/lxGXHMlS5csxjCO7n88x588MHSsINauWcOl559Jfn4+hQUF7N/nUAYO+kPoWEEVFhZy6bmn0rxFS4bddFfoOMGs+GU5D901glkzf8Awzr5oGJ267hw6VuL0vVmiun93btOsHv/8f3vTsnFd3J1H3pvGyDem0qR+bR68YD+2a1mfHxeu4My/f8yylWtDx5U0USMzRUf1688JJw/g2mFDQ0cJKienJucPuYzOXbqxcsUKzjntRHr+Zh/ad9w+dLTE1apdm5vvup+69epRUJDPn847g5699qNr911CRwvm5eeeZNt2HVi14pfQUYJ6YtQd7LzH3lxw5U0U5OezZs3q0JGC0Pdmier+3VlQVMSwJ8YxccZSGtSpyXvX/5b3J83l972358Ov53HnS19z0dHduPjoblzz9Jeh40qaqE9minrs0ZNGjRqHjhFc8xYt6dylGwD16tenXYeOLFw4P3CqMMyMuvXqAVBQUEBBQUHWDkORikUL5jN29EcceuQxoaMEtXLFL0ydPJ7effsBULNWLeo3aBg4VRj63ixR3b875+etZuKMpQD8srqAb+cso02zevx2j2158qMfAHjyox84omfbkDGDsYT/JUWVTNlic+fM5rupU+i2U/Wt3BUWFnLhWacwZ/aPHH3sSXSpxtvigbtv4/Rzh7Bq5crQUYJaOG8ODRs35f6/Xc9P07+jfacuDDj3EraqUzd0NMkQ1f27s22L+uzSrhlffL+IVo3qMD8vqvTPz1tNq0Z1AqeTdEq0kmlmuWb2gJm9Fj/uZmZnb+L1g81srJmNHTVqVHJBZbNWrlzJ8KEXc+Ell1O/QYPQcYLJycnhnkee4fHn32Tq15OZ8cN3oSMFMebTD2nctBmdduwWOkpwRUWFzJw2lYOOOJbr/v4YW9Wpw8vPPhI6lmSI6v7dWX+rmjw6ZH+uePwLlq8q2OB5xwOkCs8s2VtSkj5c/jDwBrB1/Phb4KKNvdjdR7l7T3fvOXjw4MpPJykpKMhn+OUXcchhR9K7z6Gh42SEBg0bsevuezJ29KehowTxzeQJjPnkA8456Uhuv+4KJo4fy99uuCp0rCCaNm9F0xat2L5LdwB67nsQM6dNDZxKMkF1/+6smWM8MmR/nv10Bi+P/QmABT+vJrdJVL3MbVKHhT+vCRlR0izpRmYLd38GKAJw9wKgMOEM8iu4OzdfP5x2HTpy0oDTQ8cJKm/pEn5Z/jMAa9asZtyY0bRt1z5sqEAGDr6QB/79Ovc9/Qp/Gv5XdunRk4uHjQgdK4gmzZrTvGUr5s6aCcDXE8ay9XYdAqeS0PTdCX8f1Itv5/zMPa99s27a6+Nmccr+HQE4Zf+OvPbFrFDxgrKEb0lJuk/mCjNrDlE93Mx6AcsSzrBFhg29lHFjPycvL4+j+vZh8HkX0K//caFjJW7ShPG8+dpLdOy0A2cPiNb/nD8Mode+vQMnS96SxYu4/YZhFBYV4UVF9D6oL3vte0DoWJIBBpx7KSNvHU5BQQEtW2/NoIv+EjpSEPreLFHdvzt7dW7Jyft35Ksfl/LhiN8CcP0zE/jbS1/x0IX7c+oB2/PTomgII8ke5p5c/wcz2x34O9AdmAy0BI5394kpvN3zVqno2aRuDgDzluUHThJe68a1AJi+qHoOD1NahxbR4aYpc1cEThJe1zb1AfhsWl7YIBlg705NANB3p747Syv+7mx66r8CJwlv6eMDIAOGQp+1dE2inVG3bbpVIuucaCXT3ceZ2QHAjkT/qVPdXZ94ERERkSyT9NnlJwB13f0r4Bjg6bi6KSIiIlJNZWevzKRP/PmLuy83s/2Ag4EHgH8mnEFEREREKlnSjczijkFHAve5+ytA7YQziIiIiGQMjZOZHrPNbCRwEvCqmW0VIIOIiIiIVLKkG3gnEg3Gfpi75wHNgMsSziAiIiKSMbKzR2ZCZ5ebWSN3/xmoA7wfT2sGrAHGJpFBRERERJKT1BBGTwBHAV8QDcReuiHtQMeEcoiIiIhklCT7SSYpkUamux9lZgYc4O4/JrFMEREREQknsT6ZHl1a6JWkliciIiIi4SR94s84M9sz4WWKiIiIZCxL+F9SEr2sJLAXMMDMZgIriPpmurvvknAOEREREalESTcyD0t4eSIiIiKZTSf+/HruPjO+Vvl+RGeVf+Lu45LMICIiIiKVL9E+mWY2HHgEaA60AB4ys2FJZhARERHJJBqMPT0GALu6+2oAM7sJ+BK4IeEcIiIiIlKJkm5kziG66s/q+PFWwOyEM4iIiIhkDA3Gnh7LgK/M7C2iPpmHAp+b2V0A7v7HhPOIiIiISCVIupH5fHwr9n7CyxcRERHJKEmOXZmkxBqZZpYD9HX3AUktU0RERETCSKyR6e6FZtbOzGq7+9qklisiIiKS0bKzkJn44fIfgE/M7EWiK/4A4O53JJxDRERERCpR0o3M7+NbDaBhwssWERERyThZWshM/Io/1ya5PBEREREJI9FGppm9RzR00Xrc/aAkc4iIiIhkimwdJ9PcN2jzVd7CzPYo9bAOcBxQ4O5/TuHtyQUVERGR6iJ4E2/xioJE2zjN69dMZJ2TPlz+RZlJn5jZ50lmEBEREZHKl/Th8malHtYAegKNk8wgIiIikkk0GHt6fEF02NuAfGAGcHbCGURERESkktVIeHmXA7u5ewfgMaKxMlcmnEFEREQkY5gle0tK0o3MYe7+s5ntBxwE3A/8M+EMIiIiIlLJkm5kFsY/jwTuc/dXgNoJZxARERGRSpZ0I3O2mY0ETgJeNbOtAmQQERERkUqW9DiZ9YDDgUnu/p2ZtQF2dvc3U3i7xskUERGRdAt+anfeqsJE2zhN6uYkss6JNjJ/pSoTVERERKoMNTIrSdJDGImIiIhIKdk6Tqb6Q4qIiIhI2qmSKSIiIhJQkmNXJkmVTBERERFJO1UyRURERALK0kKmKpkiIiIikn6qZIqIiIiElKWlTFUyRURERCTt1MgUERERkbTT4XIRERGRgDQYu4iIiIhIilTJFBEREQlIg7GLiIiIiKRIlUwRERGRgLK0kKlKpoiIiIiknyqZIiIiIiFlaSlTlUwRERERSTtVMkVEREQC0jiZIiIiIpL1zOxwM5tqZtPMbOgWz8fd05mrMlWZoCIiIlJlBC8jri5Ito1Tp+bG19nMcoBvgUOBWcAY4BR3/7qiy1ElU0RERESK/QaY5u4/uPta4Cngd1syo6rUyLRMuJnZuaEzZMpN20LbQttC20LbQdsiC7ZFcHVqYknezGywmY0tdRtcKs42wE+lHs+Kp1VYVWpkZorBm39JtaFtUULbooS2RQlti4i2QwltixLaFoG4+yh371nqNqoylqNGpoiIiIgUmw20LfV423hahamRKSIiIiLFxgA7mFkHM6sNnAy8uCUz0jiZFVcpJeUqStuihLZFCW2LEtoWEW2HEtoWJbQtMpC7F5jZBcAbQA7woLt/tSXzqkpDGImIiIhIFaHD5SIiIiKSdmpkioiIiEjaqZEpW8TMzjCzu0PnSDczu8bMLjWz68zskASWd4yZdavs5aSLmf3RzKaY2b9CZ0mKmbU3s8mhc1R18Xb8/Ra+95d058k02s/WZ2avmlmT0Dnk11EjM43MTCdSZQl3H+7ubyewqGOAKtPIBP4AHOruA7Z0BvqcVFvtgXIbmdonsl+q/8cWqeHuR7h7XiXHkkpWrRuZZvZfM/vCzL4qHu3ezH4xsxFmNsHMRptZbjx9+/jxJDO7ofgvazM70Mw+MrMXga/jCthFpZYxwsyGhFi/LWFmp5nZxHj9HzOzo83sf2Y23szeLt4eZd7zsJn9M94+P8Tb5MG44vVwgNWoEDO7ysy+NbOPgR3jaQ+b2fHx/ZvM7Ot4u9wWT9vU/vByqXnfbWZnlDcfM9sH6AfcamZfmtn2ya55xZjZvUBH4LV4mz1oZp/H+8bv4te0jz8P4+LbPvH09T4nAVdjS+WY2X3xd8WbZlbXzM4xszHxZ+U5M6sH6/ade+OraHxrZkfF088wsxfM7H0z+87Mro6nZ/R3Rvx/OqWc9d/ezF6Pv0M/MrMu8evXfXbix8VVyJuA/eN9/eJ4e7xoZu8C75hZAzN7J95vJhXvU1WNmdU3s1fi/WKymZ1kZsPjfWWymY0yM4tfu0f8ugnA+YGjp2Qj6zfDzFrEz/c0s/fj+9dY9HvkE+CxTXwG2pvZVDN7FJgMtC2eZ3nLi9+zh5l9EO9/b5hZmzBbRDbJ3avtDWgW/6xLtGM3Bxw4Op5+CzAsvv8y0QXiAf4f8Et8/0BgBdAhftweGBffrwF8DzQPva4pbo+dgG+BFsXbB2hKySgEg4Db4/tnAHfH9x8murapEV3f9Gdg53j9vwB2C71um1jnPYBJQD2gETANuDRep+PjfWJqqW3QJIX94eVS87873lYbm8/DwPGht0MFttcMoAVwI3Bq8brE+039eDvWiafvAIwttV3WfU6q0i3+TBcU78fAM8CppT/XwA3AhaX+T1+P9/8diC7JVifeD+bG+0Lxd07PTP/O2MT6vwPsEE/bC3i31PofX+r9G/tsnBFvm+Lv4ZpAo/h+i/izaKXnURVuwHHAfaUeNy5ex/jxY5T8jpkI9I7v3wpMDp1/C9dvBiW/N3oC78f3ryH6HVC31P/5xj4DRUCvUvOdEe8H5S2vFvAp0DKedhLRMDvBt49u69+qdSUT+GP8F+RootHtdwDWEjUgIPpwtI/v7w08G99/osx8Pnf36QDuPgNYbGY9gL7AeHdfXFkrkGYHAc+6+yIAd19CNNL/G2Y2CbiMqCFanpc8+rRPAua7+yR3LwK+omQbZqL9gefdfaW7/8yGA84uA1YDD5jZscDKePqm9ofybGw+VVVfYKiZfQm8T9SI2o7oy/++eH95lvW7Aqz7nFRB0939y/h+8fdC97iCNwkYwPqfjWfcvcjdvwN+ALrE099y98Xuvgr4D7BfFfnOKG/99wGejfeBkcCWVJLeir9nIPoj9UYzmwi8TXSt5A2OnFQBk4BDzexmM9vf3ZcBfSw6IjSJ6Ht2J4v6GzZx9w/j9z0WKG9Flbd+m/JivL8X2+AzEE+f6e6jU1zejkB34K14/xtG9LtKMky17QdjZgcChwB7u/vKuLxfB8iPG0sAhaS2jVaUeXw/0V9srYEH0xA3pL8Dd7j7i/E2u2Yjr1sT/ywqdb/4cZXdzzwalPY3wMFElc0LiH5JbEwB63dDqbOF88l0Bhzn7lPXm2h2DTAf2JVoO6wu9XTZz0lVUnqfLiSqwjwMHOPuEyzqEnFgqdeUHYDYNzM9078zyq5/LpDn7ruV89p1nwEzqwHU3sR8S+8TA4CWwB7unm9mM4g/P1WJu39rZrsDRwA3mNk7RIfCe7r7T/FnpMqtV7GNrF/p772y61b2c7+xz0C53w8bWd7zwFfuvvcWroYkpDpXMhsDS+MGZheg12ZeP5qobA/RJZY25XngcGBPohHzq4p3gRPMrDmAmTUj2k7F1yw9PVSwSvQhcEzcx6whcHTpJ82sAdDY3V8FLiZqPMHG94eZQDcz2yquVBy8mfksBxqmfa0q3xvAhaX6lvWIpzcG5sZV7IFEV4vIVg2BuWZWi6iBVNoJZlbDon62HYm6SkBUkWlmZnWJTvr6JJ5e1b4zfgamm9kJsO5kjeJ9egZRNxSI+hzXiu9vbl9vDCyIG5h9gHZpT50AM9saWOnujxMdAt89fmpR/D1wPIBHJ7XkmVlxJW+LT6ZL0kbWbwYl/+fHbeStxTb2GajI8qYCLc1s7/g1tcxsY0fZJKAqW2FKg9eB/2dmU4h22PLK9KVdBDxuZlfF793oIQJ3X2tm7xH9pV+YpryVzt2/MrMRwAdmVgiMJ6pcPmtmS4kaoR0CRkw7dx9nZk8DE4AFRNdsLa0h8IKZ1SGq3l0ST7+IcvaHuFLxDFFfo+lE23BT83mK6PDyH4n6sX2f/rWsFNcDdwIT42rVdOAo4B7gOTM7jWi7VOXq5eb8BfgfsDD+WboB9SPwOVE/3//n7qvj9vjnwHNEh/Yed/exUGW/MwYA/zSzYUQNyaeIPkf3Ee3rE1h/H5gIFMbTHwaWlpnfv4CX4kPKY4FvKn0NKsfORCfzFQH5wHlEjanJwDzW/445E3jQzBx4M+GcW6q89atL1BXoeqLuM5uywWfAzNpXZHnx5+V44C4za0zUlrmTqHuWZBBdVjJFFp05usrd3cxOJjrpo9yzH+NfuuOAE+I+WZJlKrI/SPVi0YgKL7v7v8tMP4PokOkF5bxH3xmS9Tb1GZDsVJ0rmRW1B3B3fHgwDzirvBdZNLD2y0Qnk+iXRfZKaX8Q2Rx9Z4hItlIlU0RERETSrjqf+CMiIiIilUSNTBERERFJOzUyRURERCTt1MgUkUpjZsfHw7MUPz7DSq5lnXSWl+MzvytzGW6lrtu9hfMIto1ERNJJjUyRasbMHo4bQ25m+Wb2g5ndZmb1E1j800SDk6fEzGaY2aWVmKf0sg6Mt0mLJJYnIpLtNISRSPX0NtEVeWoRXb/9fqA+0cDK6zGzmkChp2Eoivh6xas2+0IREanyVMkUqZ7WuPs8d//J3Z8gutrKMRBdf9zMJseHbb8num51fTNrbGajzGyBmS03sw/MrGfpmZrZaWY208xWmtnLRNe4Lv38BoeCzewIM/ufma0ys8Vm9pKZ1TGz94kuLXhrceW11Hv2iZe/0sxmm9k/zaxRqefrxRXbX8xsvpld+Ws3mJntaWZvmtkiM/vZzD4uvqxdGa3N7JU420wzO7XMfLYxs6fMbGl8e8XMdtjEctua2QtmtiSe5zfxBQBERDKaGpkiAlF1sVapxx2A3wMnEF1nfQ3wCrAN0eUjexBd9/1dM2sDYGZ7EV0ucBSwG/AScN2mFmpmhwMvAm8RDXDfB/iA6LvpWGBWPI828Q0z25noEnwvxtmOjZf3YKlZ3wYcSnQd5YPjvL1T3hrlawg8RlT5/Q3wJfCqmTUv87pr42y7EW2LR4sb4/GVot4DVgMHAHsDc4G34+fKcw9Qj2jb7ER0SdO8X7kuIiKVTofLRao5M/sNUYPynVKTawMD3X1+/JqDiBpNLeND3gB/MbOjiQ673wIMAd5x9xHx89+a2Z7A2ZtY/F+Af7v7sFLTJsY/V5pZIbDc3eeVev4y4Gl3v73UOpwHjDezVsDKeJlnufsb8fNnEjVYt5i7v1v6sZldSNSI/S3weKmn/uPuI+P7I8ysD1HD8FTgZKJr159Z3P3AzM4FFhA13p8pZ9HtgOfcfUL8ePqvWQ8RkaSokSlSPR0eH7auSVTBfAG4sNTzs4obmLE9iKppC6Mraa5TB9g+vt+VqHpZ2mdsupHZg6j6WRF7AJ3M7KRS04pDbU/UyKwdLxsAd//FzCZVcDnriRuw1xNVFHOBHKAusF2Zl35WzuMjS2XvACwvsx3rUbIdy/o/4N646vsO0eUnv9jC1RARSYwamSLV04fAYCAfmOPu+WWeX1HmcQ1gPtGh4rJ+Tn+8TapBdKLS38p5bjbQuZKW+whR4/JiYAZRF4J3iBq0qapBdJi9vD6VS8p7g7s/YGZvAEcAhwCfmtlf3f2aCixXRCRxamSKVE8r3X1aBV4/jqiBVeTuP2zkNVOAXmWmlX1c1niiPpP3beT5tUQVw7JZdtpY/vhkpfx42T/E0+oD3YHvN5NnU/YD/ujur8TzzCXuJ1pGL9bvH9qLaNsUZz8FWOTueaku2N1nEfXvHGVmlxN1TbimgvlFRBKlRqaIpOJt4BPgBTP7M/AN0Bo4HHjb3T8C7iKqsl0B/Bs4EOi/mfmOAF4ys2nAE0SHvfsCI919JVHFcH8ze5zojPhFwM3AaDO7FxgJLAe6AEe7+7nxofEHgJvNbCEwBxjOho3VjeluZnllpk0EvgVONbP/EQ33dAtRI7isY81sDPA+cDxRI3qv+Ll/AZcSbcfhwI9AW+B3wL3u/l3ZmZnZ/wGvxctvRLTNv05xXUREgtHZ5SKyWfFJKkcA7xJVHacSnaSyI1EjDncfTdT/8jyiRtmxbKba5u6vEjVEf0tU1fyAqM9jUfyS4USNsO+BhfF7JhKdKd4+fv0E4K9Eh/OLXUp0Fvfz8c/JRF0EUvFenKX0rR5wFtAA+AJ4iqhaOaOc919DdELQRKJtcaa7j4mzr4yz/wA8S9RYfwRoCizdSJ4awN+JGpZvxet5eorrIiISjKVhfGURERERkfWokikiIiIiaadGpoiIiIiknRqZIiIiIpJ2amSKiIiISNqpkSkiIiIiaadGpoiIiIiknRqZIiIiIpJ2amSKiIiISNr9f18KwHD4JErmAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# CREATE CONFUSION MATRIX OF ACTUAL VS. PREDICTION \n",
    "cm = confusion_matrix(actual, predictions)\n",
    "plt.figure(figsize = (12, 10))\n",
    "cm = pd.DataFrame(cm , index = [i for i in lb.classes_] , columns = [i for i in lb.classes_])\n",
    "ax = sns.heatmap(cm, linecolor='white', cmap='Blues', linewidth=1, annot=True, fmt='')\n",
    "bottom, top = ax.get_ylim()\n",
    "ax.set_ylim(bottom + 0.5, top - 0.5)\n",
    "plt.title('Confusion Matrix', size=20)\n",
    "plt.xlabel('Predicted Labels', size=14)\n",
    "plt.ylabel('Actual Labels', size=14)\n",
    "plt.savefig('Initial_Model_Confusion_Matrix.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "       angry       0.75      0.54      0.63        39\n",
      "        calm       0.55      0.63      0.59        38\n",
      "     disgust       0.53      0.65      0.59        37\n",
      "        fear       0.52      0.32      0.39        38\n",
      "       happy       0.32      0.40      0.35        35\n",
      "     neutral       0.52      0.50      0.51        24\n",
      "         sad       0.30      0.29      0.29        38\n",
      "    surprise       0.45      0.51      0.48        39\n",
      "\n",
      "    accuracy                           0.48       288\n",
      "   macro avg       0.49      0.48      0.48       288\n",
      "weighted avg       0.49      0.48      0.48       288\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(actual, predictions, target_names = ['angry','calm','disgust','fear','happy','neutral','sad','surprise']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hyperparameter Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1152, 259)\n",
      "(288, 259)\n"
     ]
    }
   ],
   "source": [
    "# TRAIN TEST SPLIT DATA\n",
    "train,test = train_test_split(df_combined, test_size=0.2, random_state=0,\n",
    "                               stratify=df_combined[['gender','actor']])\n",
    "\n",
    "X_train = train.iloc[:, 3:]\n",
    "y_train = train.iloc[:,:2].drop(columns=['gender'])\n",
    "print(X_train.shape)\n",
    "\n",
    "X_test = test.iloc[:,3:]\n",
    "y_test = test.iloc[:,:2].drop(columns=['gender'])\n",
    "print(X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# NORMALIZE DATA\n",
    "mean = np.mean(X_train, axis=0)\n",
    "std = np.std(X_train, axis=0)\n",
    "X_train = (X_train - mean)/std\n",
    "X_test = (X_test - mean)/std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TURN DATA INTO ARRAYS FOR KERAS\n",
    "X_train = np.array(X_train)\n",
    "y_train = np.array(y_train)\n",
    "X_test = np.array(X_test)\n",
    "y_test = np.array(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# RESHAPE TO INCLUDE 3D TENSOR\n",
    "X_train = X_train[:,:,np.newaxis]\n",
    "X_test = X_test[:,:,np.newaxis]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.utils import np_utils, to_categorical\n",
    "\n",
    "lb = LabelEncoder()\n",
    "\n",
    "y_train = np_utils.to_categorical(lb.fit_transform(y_train))\n",
    "y_test = np_utils.to_categorical(lb.fit_transform(y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "# CREATE FUNCTION FOR KERAS CLASSIFIER\n",
    "opt = keras.optimizers.Adam(lr=0.0001)\n",
    "def make_classifier(optimizer=opt):\n",
    "    #BUILD CNN MODEL\n",
    "    model = Sequential()\n",
    "    model.add(layers.Conv1D(64, kernel_size=(10), activation='relu', input_shape=(X_train.shape[1],1)))\n",
    "    model.add(layers.Conv1D(128, kernel_size=(10),activation='relu',kernel_regularizer=l2(0.01), bias_regularizer=l2(0.01)))\n",
    "    model.add(layers.MaxPooling1D(pool_size=(8)))\n",
    "    model.add(layers.Dropout(0.4))\n",
    "    model.add(layers.Conv1D(128, kernel_size=(10),activation='relu'))\n",
    "    model.add(layers.MaxPooling1D(pool_size=(8)))\n",
    "    model.add(layers.Dropout(0.4))\n",
    "    model.add(layers.Flatten())\n",
    "    model.add(layers.Dense(256, activation='relu'))\n",
    "    model.add(layers.Dropout(0.4))\n",
    "    model.add(layers.Dense(8, activation='sigmoid'))\n",
    "    opt = keras.optimizers.Adam(lr=0.0001)\n",
    "    model.compile(loss='categorical_crossentropy', optimizer=opt,metrics=['accuracy'])\n",
    "    return model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_trainHot=np.argmax(y_train, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 921 samples\n",
      "921/921 [==============================] - 3s 3ms/sample - loss: 2.8954 - acc: 0.1281\n",
      "Train on 921 samples\n",
      "921/921 [==============================] - 3s 4ms/sample - loss: 2.8745 - acc: 0.1585\n",
      "Train on 922 samples\n",
      "922/922 [==============================] - 3s 3ms/sample - loss: 2.8747 - acc: 0.1616\n",
      "Train on 922 samples\n",
      "922/922 [==============================] - 3s 3ms/sample - loss: 2.8631 - acc: 0.1996\n",
      "Train on 922 samples\n",
      "922/922 [==============================] - 3s 3ms/sample - loss: 2.8904 - acc: 0.1453\n",
      "Train on 921 samples\n",
      "921/921 [==============================] - 3s 4ms/sample - loss: 2.8858 - acc: 0.1477\n",
      "Train on 921 samples\n",
      "921/921 [==============================] - 2s 2ms/sample - loss: 2.8690 - acc: 0.1737\n",
      "Train on 922 samples\n",
      "922/922 [==============================] - 2s 2ms/sample - loss: 2.8760 - acc: 0.1475\n",
      "Train on 922 samples\n",
      "922/922 [==============================] - 2s 2ms/sample - loss: 2.8883 - acc: 0.1475\n",
      "Train on 922 samples\n",
      "922/922 [==============================] - 2s 2ms/sample - loss: 2.8672 - acc: 0.1584\n",
      "Train on 921 samples\n",
      "921/921 [==============================] - 2s 2ms/sample - loss: 2.8693 - acc: 0.1824\n",
      "Train on 921 samples\n",
      "921/921 [==============================] - 3s 4ms/sample - loss: 2.8727 - acc: 0.1618\n",
      "Train on 922 samples\n",
      "922/922 [==============================] - 4s 5ms/sample - loss: 2.8862 - acc: 0.1388\n",
      "Train on 922 samples\n",
      "922/922 [==============================] - 3s 4ms/sample - loss: 2.8821 - acc: 0.1811\n",
      "Train on 922 samples\n",
      "922/922 [==============================] - 3s 4ms/sample - loss: 2.8747 - acc: 0.1768\n",
      "Train on 921 samples\n",
      "921/921 [==============================] - 3s 4ms/sample - loss: 2.8844 - acc: 0.1672\n",
      "Train on 921 samples\n",
      "921/921 [==============================] - 3s 4ms/sample - loss: 2.8717 - acc: 0.1911\n",
      "Train on 922 samples\n",
      "922/922 [==============================] - 3s 3ms/sample - loss: 2.8865 - acc: 0.1475\n",
      "Train on 922 samples\n",
      "922/922 [==============================] - 4s 4ms/sample - loss: 2.8580 - acc: 0.1941\n",
      "Train on 922 samples\n",
      "922/922 [==============================] - 3s 3ms/sample - loss: 2.8693 - acc: 0.1855\n",
      "Train on 921 samples\n",
      "921/921 [==============================] - 2s 2ms/sample - loss: 2.8704 - acc: 0.1737\n",
      "Train on 921 samples\n",
      "921/921 [==============================] - 2s 2ms/sample - loss: 2.8727 - acc: 0.1564\n",
      "Train on 922 samples\n",
      "922/922 [==============================] - 2s 2ms/sample - loss: 2.8479 - acc: 0.1898\n",
      "Train on 922 samples\n",
      "922/922 [==============================] - 2s 2ms/sample - loss: 2.8734 - acc: 0.1627\n",
      "Train on 922 samples\n",
      "922/922 [==============================] - 2s 2ms/sample - loss: 2.8747 - acc: 0.1703\n",
      "Train on 921 samples\n",
      "921/921 [==============================] - 2s 2ms/sample - loss: 2.8697 - acc: 0.1835\n",
      "Train on 921 samples\n",
      "921/921 [==============================] - 2s 2ms/sample - loss: 2.8819 - acc: 0.1542\n",
      "Train on 922 samples\n",
      "922/922 [==============================] - 2s 2ms/sample - loss: 2.8663 - acc: 0.1800\n",
      "Train on 922 samples\n",
      "922/922 [==============================] - 2s 2ms/sample - loss: 2.8763 - acc: 0.1692\n",
      "Train on 922 samples\n",
      "922/922 [==============================] - 2s 2ms/sample - loss: 2.8789 - acc: 0.1453\n",
      "Train on 921 samples\n",
      "921/921 [==============================] - 3s 3ms/sample - loss: 2.8781 - acc: 0.1574\n",
      "Train on 921 samples\n",
      "921/921 [==============================] - 2s 2ms/sample - loss: 2.8819 - acc: 0.1737\n",
      "Train on 922 samples\n",
      "922/922 [==============================] - 2s 2ms/sample - loss: 2.8633 - acc: 0.1931\n",
      "Train on 922 samples\n",
      "922/922 [==============================] - 2s 2ms/sample - loss: 2.8784 - acc: 0.1898\n",
      "Train on 922 samples\n",
      "922/922 [==============================] - 2s 2ms/sample - loss: 2.8885 - acc: 0.1443\n",
      "Train on 921 samples\n",
      "921/921 [==============================] - 2s 2ms/sample - loss: 2.8735 - acc: 0.1683\n",
      "Train on 921 samples\n",
      "921/921 [==============================] - 3s 3ms/sample - loss: 2.8718 - acc: 0.1422\n",
      "Train on 922 samples\n",
      "922/922 [==============================] - 3s 4ms/sample - loss: 2.8681 - acc: 0.1649\n",
      "Train on 922 samples\n",
      "922/922 [==============================] - 4s 4ms/sample - loss: 2.8689 - acc: 0.1779\n",
      "Train on 922 samples\n",
      "922/922 [==============================] - 3s 4ms/sample - loss: 2.8707 - acc: 0.1540\n",
      "Train on 921 samples\n",
      "921/921 [==============================] - 4s 4ms/sample - loss: 2.8748 - acc: 0.2020\n",
      "Train on 921 samples\n",
      "921/921 [==============================] - 3s 3ms/sample - loss: 2.8816 - acc: 0.1629\n",
      "Train on 922 samples\n",
      "922/922 [==============================] - 4s 4ms/sample - loss: 2.8669 - acc: 0.1811\n",
      "Train on 922 samples\n",
      "922/922 [==============================] - 4s 4ms/sample - loss: 2.8766 - acc: 0.1453\n",
      "Train on 922 samples\n",
      "922/922 [==============================] - 4s 4ms/sample - loss: 2.8763 - acc: 0.1659\n",
      "Train on 921 samples\n",
      "921/921 [==============================] - 4s 5ms/sample - loss: 2.8707 - acc: 0.1357 1s - loss: 2.8941\n",
      "Train on 921 samples\n",
      "921/921 [==============================] - 4s 4ms/sample - loss: 2.8820 - acc: 0.1618\n",
      "Train on 922 samples\n",
      "922/922 [==============================] - 3s 3ms/sample - loss: 2.8816 - acc: 0.1453\n",
      "Train on 922 samples\n",
      "922/922 [==============================] - 4s 4ms/sample - loss: 2.8764 - acc: 0.1757\n",
      "Train on 922 samples\n",
      "922/922 [==============================] - 3s 4ms/sample - loss: 2.8959 - acc: 0.1377\n",
      "Train on 921 samples\n",
      "921/921 [==============================] - 4s 4ms/sample - loss: 2.8822 - acc: 0.1618\n",
      "Train on 921 samples\n",
      "921/921 [==============================] - 3s 4ms/sample - loss: 2.8781 - acc: 0.1770\n",
      "Train on 922 samples\n",
      "922/922 [==============================] - 3s 4ms/sample - loss: 2.8686 - acc: 0.1800 1s - l\n",
      "Train on 922 samples\n",
      "922/922 [==============================] - 3s 3ms/sample - loss: 2.8795 - acc: 0.1659\n",
      "Train on 922 samples\n",
      "922/922 [==============================] - 2s 2ms/sample - loss: 2.8819 - acc: 0.1584\n",
      "Train on 921 samples\n"
     ]
    }
   ],
   "source": [
    "# GRID SEARCH PARAMETERS TO FIND BEST VALUES\n",
    "classifier = KerasClassifier(build_fn = make_classifier)\n",
    "params = {\n",
    "    'batch_size': [30, 32, 34],\n",
    "    'nb_epoch': [25, 50, 75],\n",
    "    'optimizer':['adam','SGD']}\n",
    "\n",
    "grid_search = GridSearchCV(estimator=classifier,\n",
    "                           param_grid=params,\n",
    "                           scoring='accuracy',\n",
    "                           cv=5)\n",
    "\n",
    "grid_search = grid_search.fit(X_train,y_trainHot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'batch_size': 32, 'nb_epoch': 75, 'optimizer': 'SGD'}"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3090382081686429"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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