final_results_gender_test.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing the required libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/therochvoices/anaconda3/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
      "  return f(*args, **kwds)\n",
      "/home/therochvoices/anaconda3/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
      "  return f(*args, **kwds)\n",
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import librosa\n",
    "import librosa.display\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "from matplotlib.pyplot import specgram\n",
    "import keras\n",
    "from keras.preprocessing import sequence\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Embedding\n",
    "from keras.layers import LSTM\n",
    "from keras.preprocessing.text import Tokenizer\n",
    "from keras.preprocessing.sequence import pad_sequences\n",
    "from keras.utils import to_categorical\n",
    "from keras.layers import Input, Flatten, Dropout, Activation\n",
    "from keras.layers import Conv1D, MaxPooling1D, AveragePooling1D\n",
    "from keras.models import Model\n",
    "from keras.callbacks import ModelCheckpoint\n",
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras import regularizers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "mylist= os.listdir('RawData/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(mylist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1072"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(mylist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "03-02-06-01-01-02-02.wav\n"
     ]
    }
   ],
   "source": [
    "print(mylist[1000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "03\n"
     ]
    }
   ],
   "source": [
    "print(mylist[400][6:-16])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting the audio file's waveform and its spectrogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "data, sampling_rate = librosa.load('RawData/03-02-01-01-01-01-01.wav')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/therochvoices/anaconda3/lib/python3.6/site-packages/IPython/core/magics/pylab.py:160: UserWarning: pylab import has clobbered these variables: ['test', 'shuffle']\n",
      "`%matplotlib` prevents importing * from pylab and numpy\n",
      "  \"\\n`%matplotlib` prevents importing * from pylab and numpy\"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PolyCollection at 0x7ffa405c7eb8>"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ffa497445f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "% pylab inline\n",
    "import os\n",
    "import pandas as pd\n",
    "import librosa\n",
    "import glob \n",
    "\n",
    "plt.figure(figsize=(15, 5))\n",
    "librosa.display.waveplot(data, sr=sampling_rate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/therochvoices/.local/lib/python3.6/site-packages/scipy/io/wavfile.py:273: WavFileWarning: Chunk (non-data) not understood, skipping it.\n",
      "  WavFileWarning)\n",
      "/home/therochvoices/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:26: RuntimeWarning: divide by zero encountered in log\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ffa405ce5f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import scipy.io.wavfile\n",
    "import numpy as np\n",
    "import sys\n",
    "\n",
    "\n",
    "sr,x = scipy.io.wavfile.read('RawData/03-02-01-01-01-01-01.wav')\n",
    "\n",
    "## Parameters: 10ms step, 30ms window\n",
    "nstep = int(sr * 0.01)\n",
    "nwin  = int(sr * 0.03)\n",
    "nfft = nwin\n",
    "\n",
    "window = np.hamming(nwin)\n",
    "\n",
    "## will take windows x[n1:n2].  generate\n",
    "## and loop over n2 such that all frames\n",
    "## fit within the waveform\n",
    "nn = range(nwin, len(x), nstep)\n",
    "\n",
    "X = np.zeros( (len(nn), nfft//2) )\n",
    "\n",
    "for i,n in enumerate(nn):\n",
    "    xseg = x[n-nwin:n]\n",
    "    z = np.fft.fft(window * xseg, nfft)\n",
    "    X[i,:] = np.log(np.abs(z[:nfft//2]))\n",
    "\n",
    "plt.imshow(X.T, interpolation='nearest',\n",
    "    origin='lower',\n",
    "    aspect='auto')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setting the labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "feeling_list=[]\n",
    "for item in mylist:\n",
    "    if item[6:-16]=='02' and int(item[18:-4])%2==0:\n",
    "        feeling_list.append('female_calm')\n",
    "    elif item[6:-16]=='02' and int(item[18:-4])%2==1:\n",
    "        feeling_list.append('male_calm')\n",
    "    elif item[6:-16]=='03' and int(item[18:-4])%2==0:\n",
    "        feeling_list.append('female_happy')\n",
    "    elif item[6:-16]=='03' and int(item[18:-4])%2==1:\n",
    "        feeling_list.append('male_happy')\n",
    "    elif item[6:-16]=='04' and int(item[18:-4])%2==0:\n",
    "        feeling_list.append('female_sad')\n",
    "    elif item[6:-16]=='04' and int(item[18:-4])%2==1:\n",
    "        feeling_list.append('male_sad')\n",
    "    elif item[6:-16]=='05' and int(item[18:-4])%2==0:\n",
    "        feeling_list.append('female_angry')\n",
    "    elif item[6:-16]=='05' and int(item[18:-4])%2==1:\n",
    "        feeling_list.append('male_angry')\n",
    "    elif item[6:-16]=='06' and int(item[18:-4])%2==0:\n",
    "        feeling_list.append('female_fearful')\n",
    "    elif item[6:-16]=='06' and int(item[18:-4])%2==1:\n",
    "        feeling_list.append('male_fearful')\n",
    "    elif item[:1]=='a':\n",
    "        feeling_list.append('male_angry')\n",
    "    elif item[:1]=='f':\n",
    "        feeling_list.append('male_fearful')\n",
    "    elif item[:1]=='h':\n",
    "        feeling_list.append('male_happy')\n",
    "    #elif item[:1]=='n':\n",
    "        #feeling_list.append('neutral')\n",
    "    elif item[:2]=='sa':\n",
    "        feeling_list.append('male_sad')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = pd.DataFrame(feeling_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>male_calm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>female_sad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>female_happy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>female_angry</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>male_calm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>female_fearful</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>female_calm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>female_angry</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>female_angry</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>female_happy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                0\n",
       "0       male_calm\n",
       "1      female_sad\n",
       "2    female_happy\n",
       "3    female_angry\n",
       "4       male_calm\n",
       "5  female_fearful\n",
       "6     female_calm\n",
       "7    female_angry\n",
       "8    female_angry\n",
       "9    female_happy"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting the features of audio files using librosa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(columns=['feature'])\n",
    "bookmark=0\n",
    "for index,y in enumerate(mylist):\n",
    "    if mylist[index][6:-16]!='01' and mylist[index][6:-16]!='07' and mylist[index][6:-16]!='08' and mylist[index][:2]!='su' and mylist[index][:1]!='n' and mylist[index][:1]!='d':\n",
    "        X, sample_rate = librosa.load('RawData/'+y, res_type='kaiser_fast',duration=2.5,sr=22050*2,offset=0.5)\n",
    "        sample_rate = np.array(sample_rate)\n",
    "        mfccs = np.mean(librosa.feature.mfcc(y=X, \n",
    "                                            sr=sample_rate, \n",
    "                                            n_mfcc=13),\n",
    "                        axis=0)\n",
    "        feature = mfccs\n",
    "        #[float(i) for i in feature]\n",
    "        #feature1=feature[:135]\n",
    "        df.loc[bookmark] = [feature]\n",
    "        bookmark=bookmark+1        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>feature</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[-54.00648378224577, -53.859335597669386, -53....</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[-61.11610899722908, -61.182049213622435, -61....</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[-45.015563266982596, -44.629660401787724, -44...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[-50.9771850193904, -50.9771850193904, -50.977...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[-60.01485460395111, -62.686900339255686, -65....</td>\n",
       "    </tr>\n",
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       "</table>\n",
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      "text/plain": [
       "                                             feature\n",
       "0  [-54.00648378224577, -53.859335597669386, -53....\n",
       "1  [-61.11610899722908, -61.182049213622435, -61....\n",
       "2  [-45.015563266982596, -44.629660401787724, -44...\n",
       "3  [-50.9771850193904, -50.9771850193904, -50.977...\n",
       "4  [-60.01485460395111, -62.686900339255686, -65...."
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3 = pd.DataFrame(df['feature'].values.tolist())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "df3[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "newdf = pd.concat([df3,labels], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "rnewdf = newdf.rename(index=str, columns={\"0\": \"label\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
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       "      <td>-54.006484</td>\n",
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       "      <td>-24.992415</td>\n",
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       "      <td>-61.116109</td>\n",
       "      <td>-61.127331</td>\n",
       "      <td>-61.484289</td>\n",
       "      <td>-61.930793</td>\n",
       "      <td>-62.137960</td>\n",
       "      <td>-61.608501</td>\n",
       "      <td>-61.129610</td>\n",
       "      <td>...</td>\n",
       "      <td>-47.050684</td>\n",
       "      <td>-45.928923</td>\n",
       "      <td>-48.077250</td>\n",
       "      <td>-48.512450</td>\n",
       "      <td>-47.164555</td>\n",
       "      <td>-45.601555</td>\n",
       "      <td>-46.416879</td>\n",
       "      <td>-33.938513</td>\n",
       "      <td>-25.520195</td>\n",
       "      <td>female_sad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-45.015563</td>\n",
       "      <td>-44.629660</td>\n",
       "      <td>-44.085723</td>\n",
       "      <td>-42.954231</td>\n",
       "      <td>-41.694458</td>\n",
       "      <td>-42.090132</td>\n",
       "      <td>-41.863881</td>\n",
       "      <td>-41.749610</td>\n",
       "      <td>-42.341478</td>\n",
       "      <td>-42.682609</td>\n",
       "      <td>...</td>\n",
       "      <td>-30.815291</td>\n",
       "      <td>-31.671790</td>\n",
       "      <td>-33.268018</td>\n",
       "      <td>-32.475122</td>\n",
       "      <td>-31.580941</td>\n",
       "      <td>-32.787421</td>\n",
       "      <td>-30.771874</td>\n",
       "      <td>-13.566299</td>\n",
       "      <td>-7.109394</td>\n",
       "      <td>female_happy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-50.977185</td>\n",
       "      <td>-50.977185</td>\n",
       "      <td>-50.977185</td>\n",
       "      <td>-50.977185</td>\n",
       "      <td>-51.089200</td>\n",
       "      <td>-49.162177</td>\n",
       "      <td>-50.417059</td>\n",
       "      <td>-50.739059</td>\n",
       "      <td>-50.266212</td>\n",
       "      <td>-50.501419</td>\n",
       "      <td>...</td>\n",
       "      <td>-36.058478</td>\n",
       "      <td>-34.865138</td>\n",
       "      <td>-34.480951</td>\n",
       "      <td>-33.973080</td>\n",
       "      <td>-35.824079</td>\n",
       "      <td>-35.251552</td>\n",
       "      <td>-35.716202</td>\n",
       "      <td>-24.986105</td>\n",
       "      <td>-17.429731</td>\n",
       "      <td>female_angry</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-60.014855</td>\n",
       "      <td>-62.686900</td>\n",
       "      <td>-65.488106</td>\n",
       "      <td>-60.481269</td>\n",
       "      <td>-60.038140</td>\n",
       "      <td>-64.788395</td>\n",
       "      <td>-66.386250</td>\n",
       "      <td>-61.152503</td>\n",
       "      <td>-58.949653</td>\n",
       "      <td>-59.597212</td>\n",
       "      <td>...</td>\n",
       "      <td>-43.592583</td>\n",
       "      <td>-42.287832</td>\n",
       "      <td>-42.087951</td>\n",
       "      <td>-42.331040</td>\n",
       "      <td>-40.317863</td>\n",
       "      <td>-37.837947</td>\n",
       "      <td>-35.932857</td>\n",
       "      <td>-32.704999</td>\n",
       "      <td>-28.022448</td>\n",
       "      <td>male_calm</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 217 columns</p>\n",
       "</div>"
      ],
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       "         0          1          2          3          4          5    \\\n",
       "0 -54.006484 -53.859336 -53.728333 -54.429916 -55.226626 -55.285655   \n",
       "1 -61.116109 -61.182049 -61.153947 -61.116109 -61.127331 -61.484289   \n",
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       "3 -50.977185 -50.977185 -50.977185 -50.977185 -51.089200 -49.162177   \n",
       "4 -60.014855 -62.686900 -65.488106 -60.481269 -60.038140 -64.788395   \n",
       "\n",
       "         6          7          8          9        ...             207  \\\n",
       "0 -55.285655 -55.285655 -55.285655 -55.285655      ...      -25.459799   \n",
       "1 -61.930793 -62.137960 -61.608501 -61.129610      ...      -47.050684   \n",
       "2 -41.863881 -41.749610 -42.341478 -42.682609      ...      -30.815291   \n",
       "3 -50.417059 -50.739059 -50.266212 -50.501419      ...      -36.058478   \n",
       "4 -66.386250 -61.152503 -58.949653 -59.597212      ...      -43.592583   \n",
       "\n",
       "         208        209        210        211        212        213  \\\n",
       "0 -24.992415 -23.932245 -22.430385 -21.987297 -21.481994 -23.021132   \n",
       "1 -45.928923 -48.077250 -48.512450 -47.164555 -45.601555 -46.416879   \n",
       "2 -31.671790 -33.268018 -32.475122 -31.580941 -32.787421 -30.771874   \n",
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       "4 -42.287832 -42.087951 -42.331040 -40.317863 -37.837947 -35.932857   \n",
       "\n",
       "         214        215           0    \n",
       "0 -21.895807 -19.556590     male_calm  \n",
       "1 -33.938513 -25.520195    female_sad  \n",
       "2 -13.566299  -7.109394  female_happy  \n",
       "3 -24.986105 -17.429731  female_angry  \n",
       "4 -32.704999 -28.022448     male_calm  \n",
       "\n",
       "[5 rows x 217 columns]"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnewdf[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
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       "      <td>-18.925595</td>\n",
       "      <td>-21.786640</td>\n",
       "      <td>-20.754498</td>\n",
       "      <td>-19.611138</td>\n",
       "      <td>-18.707570</td>\n",
       "      <td>male_angry</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278</th>\n",
       "      <td>-54.890476</td>\n",
       "      <td>-55.082771</td>\n",
       "      <td>-55.491740</td>\n",
       "      <td>-55.931780</td>\n",
       "      <td>-56.456179</td>\n",
       "      <td>-56.885582</td>\n",
       "      <td>-56.170076</td>\n",
       "      <td>-55.628215</td>\n",
       "      <td>-55.790014</td>\n",
       "      <td>-56.255584</td>\n",
       "      <td>...</td>\n",
       "      <td>-24.708296</td>\n",
       "      <td>-24.062067</td>\n",
       "      <td>-25.253228</td>\n",
       "      <td>-25.955295</td>\n",
       "      <td>-24.380699</td>\n",
       "      <td>-23.710777</td>\n",
       "      <td>-24.509507</td>\n",
       "      <td>-17.463303</td>\n",
       "      <td>-10.507857</td>\n",
       "      <td>male_fearful</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>350</th>\n",
       "      <td>-52.825639</td>\n",
       "      <td>-54.354379</td>\n",
       "      <td>-55.203119</td>\n",
       "      <td>-53.219728</td>\n",
       "      <td>-52.131949</td>\n",
       "      <td>-51.888673</td>\n",
       "      <td>-52.101189</td>\n",
       "      <td>-53.579453</td>\n",
       "      <td>-51.453194</td>\n",
       "      <td>-52.490305</td>\n",
       "      <td>...</td>\n",
       "      <td>-42.661962</td>\n",
       "      <td>-43.307553</td>\n",
       "      <td>-44.278628</td>\n",
       "      <td>-42.806481</td>\n",
       "      <td>-41.552950</td>\n",
       "      <td>-41.201243</td>\n",
       "      <td>-41.038168</td>\n",
       "      <td>-19.680480</td>\n",
       "      <td>-11.289456</td>\n",
       "      <td>female_calm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>-41.760355</td>\n",
       "      <td>-40.574815</td>\n",
       "      <td>-40.468672</td>\n",
       "      <td>-39.937271</td>\n",
       "      <td>-39.798856</td>\n",
       "      <td>-37.604573</td>\n",
       "      <td>-38.886673</td>\n",
       "      <td>-38.371278</td>\n",
       "      <td>-37.976550</td>\n",
       "      <td>-35.484767</td>\n",
       "      <td>...</td>\n",
       "      <td>-23.786493</td>\n",
       "      <td>-23.645276</td>\n",
       "      <td>-25.365829</td>\n",
       "      <td>-26.623363</td>\n",
       "      <td>-27.236919</td>\n",
       "      <td>-27.016517</td>\n",
       "      <td>-27.363197</td>\n",
       "      <td>-22.081647</td>\n",
       "      <td>-18.553605</td>\n",
       "      <td>male_happy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>746</th>\n",
       "      <td>-63.686916</td>\n",
       "      <td>-63.051308</td>\n",
       "      <td>-61.875360</td>\n",
       "      <td>-58.411917</td>\n",
       "      <td>-59.979095</td>\n",
       "      <td>-63.111073</td>\n",
       "      <td>-62.112733</td>\n",
       "      <td>-62.759936</td>\n",
       "      <td>-57.220393</td>\n",
       "      <td>-55.843121</td>\n",
       "      <td>...</td>\n",
       "      <td>-37.654335</td>\n",
       "      <td>-39.993273</td>\n",
       "      <td>-40.194686</td>\n",
       "      <td>-40.652033</td>\n",
       "      <td>-38.354162</td>\n",
       "      <td>-37.340070</td>\n",
       "      <td>-34.022989</td>\n",
       "      <td>-27.791583</td>\n",
       "      <td>-22.466257</td>\n",
       "      <td>male_sad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>-45.655834</td>\n",
       "      <td>-45.655834</td>\n",
       "      <td>-45.655834</td>\n",
       "      <td>-45.655834</td>\n",
       "      <td>-45.655834</td>\n",
       "      <td>-45.655834</td>\n",
       "      <td>-45.655834</td>\n",
       "      <td>-45.655834</td>\n",
       "      <td>-45.655834</td>\n",
       "      <td>-45.655834</td>\n",
       "      <td>...</td>\n",
       "      <td>-16.183297</td>\n",
       "      <td>-13.093471</td>\n",
       "      <td>-12.076940</td>\n",
       "      <td>-10.745269</td>\n",
       "      <td>-10.060971</td>\n",
       "      <td>-11.283584</td>\n",
       "      <td>-14.897975</td>\n",
       "      <td>-9.194204</td>\n",
       "      <td>-3.097675</td>\n",
       "      <td>male_angry</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>931</th>\n",
       "      <td>-62.390479</td>\n",
       "      <td>-62.390479</td>\n",
       "      <td>-61.371265</td>\n",
       "      <td>-60.118553</td>\n",
       "      <td>-61.395121</td>\n",
       "      <td>-61.945805</td>\n",
       "      <td>-62.390479</td>\n",
       "      <td>-62.390479</td>\n",
       "      <td>-62.390479</td>\n",
       "      <td>-62.390479</td>\n",
       "      <td>...</td>\n",
       "      <td>-28.786750</td>\n",
       "      <td>-28.611832</td>\n",
       "      <td>-29.005042</td>\n",
       "      <td>-29.318282</td>\n",
       "      <td>-29.130217</td>\n",
       "      <td>-29.359694</td>\n",
       "      <td>-29.330987</td>\n",
       "      <td>-25.396057</td>\n",
       "      <td>-20.998069</td>\n",
       "      <td>male_sad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>-42.376237</td>\n",
       "      <td>-42.376237</td>\n",
       "      <td>-42.376237</td>\n",
       "      <td>-42.376237</td>\n",
       "      <td>-42.376237</td>\n",
       "      <td>-42.376237</td>\n",
       "      <td>-42.376237</td>\n",
       "      <td>-42.376237</td>\n",
       "      <td>-42.376237</td>\n",
       "      <td>-42.376237</td>\n",
       "      <td>...</td>\n",
       "      <td>-30.774719</td>\n",
       "      <td>-31.082190</td>\n",
       "      <td>-33.081206</td>\n",
       "      <td>-32.700964</td>\n",
       "      <td>-30.198979</td>\n",
       "      <td>-27.977144</td>\n",
       "      <td>-27.112395</td>\n",
       "      <td>-17.806685</td>\n",
       "      <td>-9.232742</td>\n",
       "      <td>female_angry</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>726</th>\n",
       "      <td>-44.673350</td>\n",
       "      <td>-45.382460</td>\n",
       "      <td>-47.761914</td>\n",
       "      <td>-45.743562</td>\n",
       "      <td>-46.851701</td>\n",
       "      <td>-47.458628</td>\n",
       "      <td>-47.009455</td>\n",
       "      <td>-47.929579</td>\n",
       "      <td>-45.842484</td>\n",
       "      <td>-46.259641</td>\n",
       "      <td>...</td>\n",
       "      <td>-26.619008</td>\n",
       "      <td>-27.665880</td>\n",
       "      <td>-28.242622</td>\n",
       "      <td>-26.789738</td>\n",
       "      <td>-27.741444</td>\n",
       "      <td>-28.015254</td>\n",
       "      <td>-27.476151</td>\n",
       "      <td>-25.762917</td>\n",
       "      <td>-22.156509</td>\n",
       "      <td>male_happy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>685</th>\n",
       "      <td>-54.618063</td>\n",
       "      <td>-54.440360</td>\n",
       "      <td>-52.263047</td>\n",
       "      <td>-50.440580</td>\n",
       "      <td>-53.345816</td>\n",
       "      <td>-52.922601</td>\n",
       "      <td>-51.648446</td>\n",
       "      <td>-51.949550</td>\n",
       "      <td>-51.201616</td>\n",
       "      <td>-50.583471</td>\n",
       "      <td>...</td>\n",
       "      <td>-48.692624</td>\n",
       "      <td>-48.775022</td>\n",
       "      <td>-47.790065</td>\n",
       "      <td>-47.637920</td>\n",
       "      <td>-46.495466</td>\n",
       "      <td>-46.077736</td>\n",
       "      <td>-47.451677</td>\n",
       "      <td>-43.758818</td>\n",
       "      <td>-37.026687</td>\n",
       "      <td>female_calm</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 217 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           0          1          2          3          4          5    \\\n",
       "761 -44.835060 -44.835060 -44.835060 -45.951642 -42.390272 -42.331702   \n",
       "278 -54.890476 -55.082771 -55.491740 -55.931780 -56.456179 -56.885582   \n",
       "350 -52.825639 -54.354379 -55.203119 -53.219728 -52.131949 -51.888673   \n",
       "248 -41.760355 -40.574815 -40.468672 -39.937271 -39.798856 -37.604573   \n",
       "746 -63.686916 -63.051308 -61.875360 -58.411917 -59.979095 -63.111073   \n",
       "195 -45.655834 -45.655834 -45.655834 -45.655834 -45.655834 -45.655834   \n",
       "931 -62.390479 -62.390479 -61.371265 -60.118553 -61.395121 -61.945805   \n",
       "84  -42.376237 -42.376237 -42.376237 -42.376237 -42.376237 -42.376237   \n",
       "726 -44.673350 -45.382460 -47.761914 -45.743562 -46.851701 -47.458628   \n",
       "685 -54.618063 -54.440360 -52.263047 -50.440580 -53.345816 -52.922601   \n",
       "\n",
       "           6          7          8          9        ...             207  \\\n",
       "761 -44.590021 -44.613802 -44.818724 -45.054628      ...      -19.227881   \n",
       "278 -56.170076 -55.628215 -55.790014 -56.255584      ...      -24.708296   \n",
       "350 -52.101189 -53.579453 -51.453194 -52.490305      ...      -42.661962   \n",
       "248 -38.886673 -38.371278 -37.976550 -35.484767      ...      -23.786493   \n",
       "746 -62.112733 -62.759936 -57.220393 -55.843121      ...      -37.654335   \n",
       "195 -45.655834 -45.655834 -45.655834 -45.655834      ...      -16.183297   \n",
       "931 -62.390479 -62.390479 -62.390479 -62.390479      ...      -28.786750   \n",
       "84  -42.376237 -42.376237 -42.376237 -42.376237      ...      -30.774719   \n",
       "726 -47.009455 -47.929579 -45.842484 -46.259641      ...      -26.619008   \n",
       "685 -51.648446 -51.949550 -51.201616 -50.583471      ...      -48.692624   \n",
       "\n",
       "           208        209        210        211        212        213  \\\n",
       "761 -20.324789 -18.661977 -18.019083 -18.925595 -21.786640 -20.754498   \n",
       "278 -24.062067 -25.253228 -25.955295 -24.380699 -23.710777 -24.509507   \n",
       "350 -43.307553 -44.278628 -42.806481 -41.552950 -41.201243 -41.038168   \n",
       "248 -23.645276 -25.365829 -26.623363 -27.236919 -27.016517 -27.363197   \n",
       "746 -39.993273 -40.194686 -40.652033 -38.354162 -37.340070 -34.022989   \n",
       "195 -13.093471 -12.076940 -10.745269 -10.060971 -11.283584 -14.897975   \n",
       "931 -28.611832 -29.005042 -29.318282 -29.130217 -29.359694 -29.330987   \n",
       "84  -31.082190 -33.081206 -32.700964 -30.198979 -27.977144 -27.112395   \n",
       "726 -27.665880 -28.242622 -26.789738 -27.741444 -28.015254 -27.476151   \n",
       "685 -48.775022 -47.790065 -47.637920 -46.495466 -46.077736 -47.451677   \n",
       "\n",
       "           214        215           0    \n",
       "761 -19.611138 -18.707570    male_angry  \n",
       "278 -17.463303 -10.507857  male_fearful  \n",
       "350 -19.680480 -11.289456   female_calm  \n",
       "248 -22.081647 -18.553605    male_happy  \n",
       "746 -27.791583 -22.466257      male_sad  \n",
       "195  -9.194204  -3.097675    male_angry  \n",
       "931 -25.396057 -20.998069      male_sad  \n",
       "84  -17.806685  -9.232742  female_angry  \n",
       "726 -25.762917 -22.156509    male_happy  \n",
       "685 -43.758818 -37.026687   female_calm  \n",
       "\n",
       "[10 rows x 217 columns]"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.utils import shuffle\n",
    "rnewdf = shuffle(newdf)\n",
    "rnewdf[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [],
   "source": [
    "rnewdf=rnewdf.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(960, 217)"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnewdf.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dividing the data into test and train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [],
   "source": [
    "newdf1 = np.random.rand(len(rnewdf)) < 0.8\n",
    "train = rnewdf[newdf1]\n",
    "test = rnewdf[~newdf1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
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       "      <th>8</th>\n",
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       "      <th>207</th>\n",
       "      <th>208</th>\n",
       "      <th>209</th>\n",
       "      <th>210</th>\n",
       "      <th>211</th>\n",
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       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>681</th>\n",
       "      <td>-47.559454</td>\n",
       "      <td>-46.952800</td>\n",
       "      <td>-46.029665</td>\n",
       "      <td>-45.643964</td>\n",
       "      <td>-45.393950</td>\n",
       "      <td>-46.039500</td>\n",
       "      <td>-46.395240</td>\n",
       "      <td>-47.277190</td>\n",
       "      <td>-45.923697</td>\n",
       "      <td>-44.394762</td>\n",
       "      <td>...</td>\n",
       "      <td>-38.472220</td>\n",
       "      <td>-37.000428</td>\n",
       "      <td>-36.662633</td>\n",
       "      <td>-37.369145</td>\n",
       "      <td>-38.407979</td>\n",
       "      <td>-35.355184</td>\n",
       "      <td>-34.987680</td>\n",
       "      <td>-19.146210</td>\n",
       "      <td>-13.405656</td>\n",
       "      <td>female_fearful</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>-63.390224</td>\n",
       "      <td>-63.390224</td>\n",
       "      <td>-63.390224</td>\n",
       "      <td>-63.390224</td>\n",
       "      <td>-63.390224</td>\n",
       "      <td>-63.390224</td>\n",
       "      <td>-63.390224</td>\n",
       "      <td>-63.390224</td>\n",
       "      <td>-63.390224</td>\n",
       "      <td>-63.390224</td>\n",
       "      <td>...</td>\n",
       "      <td>-35.848092</td>\n",
       "      <td>-36.053178</td>\n",
       "      <td>-36.651360</td>\n",
       "      <td>-35.653360</td>\n",
       "      <td>-36.421265</td>\n",
       "      <td>-36.802328</td>\n",
       "      <td>-36.183128</td>\n",
       "      <td>-30.295624</td>\n",
       "      <td>-24.850874</td>\n",
       "      <td>male_calm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>800</th>\n",
       "      <td>-52.693499</td>\n",
       "      <td>-54.248310</td>\n",
       "      <td>-54.010196</td>\n",
       "      <td>-53.867435</td>\n",
       "      <td>-55.288423</td>\n",
       "      <td>-53.951777</td>\n",
       "      <td>-52.008044</td>\n",
       "      <td>-48.862990</td>\n",
       "      <td>-51.029948</td>\n",
       "      <td>-51.563314</td>\n",
       "      <td>...</td>\n",
       "      <td>-47.290767</td>\n",
       "      <td>-46.900155</td>\n",
       "      <td>-45.724512</td>\n",
       "      <td>-43.331517</td>\n",
       "      <td>-46.376735</td>\n",
       "      <td>-47.129054</td>\n",
       "      <td>-47.289186</td>\n",
       "      <td>-46.629222</td>\n",
       "      <td>-48.141014</td>\n",
       "      <td>female_calm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>290</th>\n",
       "      <td>-48.954499</td>\n",
       "      <td>-48.828615</td>\n",
       "      <td>-49.681607</td>\n",
       "      <td>-52.032167</td>\n",
       "      <td>-48.399128</td>\n",
       "      <td>-47.975902</td>\n",
       "      <td>-49.650617</td>\n",
       "      <td>-51.260967</td>\n",
       "      <td>-49.758039</td>\n",
       "      <td>-50.975235</td>\n",
       "      <td>...</td>\n",
       "      <td>-23.577653</td>\n",
       "      <td>-24.113671</td>\n",
       "      <td>-22.895823</td>\n",
       "      <td>-22.712570</td>\n",
       "      <td>-24.839601</td>\n",
       "      <td>-27.322341</td>\n",
       "      <td>-28.557564</td>\n",
       "      <td>-28.592688</td>\n",
       "      <td>-25.435740</td>\n",
       "      <td>male_calm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>553</th>\n",
       "      <td>-38.169292</td>\n",
       "      <td>-38.604812</td>\n",
       "      <td>-39.648455</td>\n",
       "      <td>-38.935264</td>\n",
       "      <td>-36.714463</td>\n",
       "      <td>-37.764206</td>\n",
       "      <td>-38.196970</td>\n",
       "      <td>-37.231801</td>\n",
       "      <td>-37.168429</td>\n",
       "      <td>-37.498526</td>\n",
       "      <td>...</td>\n",
       "      <td>-42.489171</td>\n",
       "      <td>-42.223035</td>\n",
       "      <td>-41.850117</td>\n",
       "      <td>-42.947111</td>\n",
       "      <td>-43.057692</td>\n",
       "      <td>-42.099055</td>\n",
       "      <td>-41.735188</td>\n",
       "      <td>-26.506845</td>\n",
       "      <td>-17.708895</td>\n",
       "      <td>female_fearful</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>287</th>\n",
       "      <td>-53.781658</td>\n",
       "      <td>-53.352600</td>\n",
       "      <td>-53.589690</td>\n",
       "      <td>-54.727440</td>\n",
       "      <td>-55.393060</td>\n",
       "      <td>-55.169562</td>\n",
       "      <td>-53.168506</td>\n",
       "      <td>-53.740698</td>\n",
       "      <td>-53.981647</td>\n",
       "      <td>-50.686010</td>\n",
       "      <td>...</td>\n",
       "      <td>-43.582494</td>\n",
       "      <td>-43.651902</td>\n",
       "      <td>-42.388076</td>\n",
       "      <td>-41.603793</td>\n",
       "      <td>-41.811917</td>\n",
       "      <td>-41.113073</td>\n",
       "      <td>-42.656904</td>\n",
       "      <td>-35.905604</td>\n",
       "      <td>-29.330324</td>\n",
       "      <td>female_fearful</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>318</th>\n",
       "      <td>-47.141751</td>\n",
       "      <td>-47.141751</td>\n",
       "      <td>-47.141751</td>\n",
       "      <td>-47.141751</td>\n",
       "      <td>-47.141751</td>\n",
       "      <td>-47.141751</td>\n",
       "      <td>-47.141751</td>\n",
       "      <td>-47.141751</td>\n",
       "      <td>-47.141751</td>\n",
       "      <td>-47.141751</td>\n",
       "      <td>...</td>\n",
       "      <td>-39.181118</td>\n",
       "      <td>-39.666826</td>\n",
       "      <td>-39.918825</td>\n",
       "      <td>-39.222033</td>\n",
       "      <td>-39.457464</td>\n",
       "      <td>-38.702862</td>\n",
       "      <td>-38.153943</td>\n",
       "      <td>-25.546979</td>\n",
       "      <td>-17.472005</td>\n",
       "      <td>female_happy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>-57.643262</td>\n",
       "      <td>-57.643262</td>\n",
       "      <td>-57.643589</td>\n",
       "      <td>-57.643262</td>\n",
       "      <td>-57.643262</td>\n",
       "      <td>-57.643262</td>\n",
       "      <td>-57.643262</td>\n",
       "      <td>-57.643262</td>\n",
       "      <td>-57.643262</td>\n",
       "      <td>-57.643262</td>\n",
       "      <td>...</td>\n",
       "      <td>-46.717207</td>\n",
       "      <td>-45.317096</td>\n",
       "      <td>-42.927891</td>\n",
       "      <td>-44.548107</td>\n",
       "      <td>-45.088133</td>\n",
       "      <td>-43.467389</td>\n",
       "      <td>-43.025462</td>\n",
       "      <td>-28.498161</td>\n",
       "      <td>-20.792921</td>\n",
       "      <td>female_calm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>764</th>\n",
       "      <td>-63.929677</td>\n",
       "      <td>-63.929677</td>\n",
       "      <td>-63.929677</td>\n",
       "      <td>-63.929677</td>\n",
       "      <td>-63.929677</td>\n",
       "      <td>-63.929677</td>\n",
       "      <td>-63.929677</td>\n",
       "      <td>-63.929677</td>\n",
       "      <td>-63.929677</td>\n",
       "      <td>-63.929677</td>\n",
       "      <td>...</td>\n",
       "      <td>-32.173178</td>\n",
       "      <td>-32.802381</td>\n",
       "      <td>-32.499949</td>\n",
       "      <td>-28.504604</td>\n",
       "      <td>-27.440622</td>\n",
       "      <td>-30.175324</td>\n",
       "      <td>-31.456388</td>\n",
       "      <td>-26.781961</td>\n",
       "      <td>-23.549286</td>\n",
       "      <td>male_happy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>707</th>\n",
       "      <td>-55.036996</td>\n",
       "      <td>-53.557774</td>\n",
       "      <td>-52.267947</td>\n",
       "      <td>-49.682951</td>\n",
       "      <td>-53.053395</td>\n",
       "      <td>-54.894633</td>\n",
       "      <td>-53.467181</td>\n",
       "      <td>-51.356150</td>\n",
       "      <td>-53.419991</td>\n",
       "      <td>-54.236260</td>\n",
       "      <td>...</td>\n",
       "      <td>-32.057033</td>\n",
       "      <td>-34.574110</td>\n",
       "      <td>-35.825892</td>\n",
       "      <td>-34.997460</td>\n",
       "      <td>-33.130019</td>\n",
       "      <td>-33.358526</td>\n",
       "      <td>-33.725561</td>\n",
       "      <td>-30.821615</td>\n",
       "      <td>-26.456878</td>\n",
       "      <td>male_calm</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 217 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           0          1          2          3          4          5    \\\n",
       "681 -47.559454 -46.952800 -46.029665 -45.643964 -45.393950 -46.039500   \n",
       "77  -63.390224 -63.390224 -63.390224 -63.390224 -63.390224 -63.390224   \n",
       "800 -52.693499 -54.248310 -54.010196 -53.867435 -55.288423 -53.951777   \n",
       "290 -48.954499 -48.828615 -49.681607 -52.032167 -48.399128 -47.975902   \n",
       "553 -38.169292 -38.604812 -39.648455 -38.935264 -36.714463 -37.764206   \n",
       "287 -53.781658 -53.352600 -53.589690 -54.727440 -55.393060 -55.169562   \n",
       "318 -47.141751 -47.141751 -47.141751 -47.141751 -47.141751 -47.141751   \n",
       "111 -57.643262 -57.643262 -57.643589 -57.643262 -57.643262 -57.643262   \n",
       "764 -63.929677 -63.929677 -63.929677 -63.929677 -63.929677 -63.929677   \n",
       "707 -55.036996 -53.557774 -52.267947 -49.682951 -53.053395 -54.894633   \n",
       "\n",
       "           6          7          8          9         ...              207  \\\n",
       "681 -46.395240 -47.277190 -45.923697 -44.394762       ...       -38.472220   \n",
       "77  -63.390224 -63.390224 -63.390224 -63.390224       ...       -35.848092   \n",
       "800 -52.008044 -48.862990 -51.029948 -51.563314       ...       -47.290767   \n",
       "290 -49.650617 -51.260967 -49.758039 -50.975235       ...       -23.577653   \n",
       "553 -38.196970 -37.231801 -37.168429 -37.498526       ...       -42.489171   \n",
       "287 -53.168506 -53.740698 -53.981647 -50.686010       ...       -43.582494   \n",
       "318 -47.141751 -47.141751 -47.141751 -47.141751       ...       -39.181118   \n",
       "111 -57.643262 -57.643262 -57.643262 -57.643262       ...       -46.717207   \n",
       "764 -63.929677 -63.929677 -63.929677 -63.929677       ...       -32.173178   \n",
       "707 -53.467181 -51.356150 -53.419991 -54.236260       ...       -32.057033   \n",
       "\n",
       "           208        209        210        211        212        213  \\\n",
       "681 -37.000428 -36.662633 -37.369145 -38.407979 -35.355184 -34.987680   \n",
       "77  -36.053178 -36.651360 -35.653360 -36.421265 -36.802328 -36.183128   \n",
       "800 -46.900155 -45.724512 -43.331517 -46.376735 -47.129054 -47.289186   \n",
       "290 -24.113671 -22.895823 -22.712570 -24.839601 -27.322341 -28.557564   \n",
       "553 -42.223035 -41.850117 -42.947111 -43.057692 -42.099055 -41.735188   \n",
       "287 -43.651902 -42.388076 -41.603793 -41.811917 -41.113073 -42.656904   \n",
       "318 -39.666826 -39.918825 -39.222033 -39.457464 -38.702862 -38.153943   \n",
       "111 -45.317096 -42.927891 -44.548107 -45.088133 -43.467389 -43.025462   \n",
       "764 -32.802381 -32.499949 -28.504604 -27.440622 -30.175324 -31.456388   \n",
       "707 -34.574110 -35.825892 -34.997460 -33.130019 -33.358526 -33.725561   \n",
       "\n",
       "           214        215             0    \n",
       "681 -19.146210 -13.405656  female_fearful  \n",
       "77  -30.295624 -24.850874       male_calm  \n",
       "800 -46.629222 -48.141014     female_calm  \n",
       "290 -28.592688 -25.435740       male_calm  \n",
       "553 -26.506845 -17.708895  female_fearful  \n",
       "287 -35.905604 -29.330324  female_fearful  \n",
       "318 -25.546979 -17.472005    female_happy  \n",
       "111 -28.498161 -20.792921     female_calm  \n",
       "764 -26.781961 -23.549286      male_happy  \n",
       "707 -30.821615 -26.456878       male_calm  \n",
       "\n",
       "[10 rows x 217 columns]"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[250:260]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainfeatures = train.iloc[:, :-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainlabel = train.iloc[:, -1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "testfeatures = test.iloc[:, :-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [],
   "source": [
    "testlabel = test.iloc[:, -1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/therochvoices/anaconda3/lib/python3.6/site-packages/sklearn/preprocessing/label.py:111: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/home/therochvoices/anaconda3/lib/python3.6/site-packages/sklearn/preprocessing/label.py:128: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    }
   ],
   "source": [
    "from keras.utils import np_utils\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "X_train = np.array(trainfeatures)\n",
    "y_train = np.array(trainlabel)\n",
    "X_test = np.array(testfeatures)\n",
    "y_test = np.array(testlabel)\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.transform(y_test))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['female_angry', 'female_calm', 'female_fearful', 'female_happy',\n",
       "       'female_sad', 'male_angry', 'male_calm', 'male_fearful',\n",
       "       'male_happy', 'male_sad'], dtype=object)"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lb.classes_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 1., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       ...,\n",
       "       [0., 0., 1., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 1., 0., 0.]])"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(786, 216)"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Changing dimension for CNN model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "x_traincnn =np.expand_dims(X_train, axis=2)\n",
    "x_testcnn= np.expand_dims(X_test, axis=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "\n",
    "model.add(Conv1D(256, 5,padding='same',\n",
    "                 input_shape=(216,1)))\n",
    "model.add(Activation('relu'))\n",
    "model.add(Conv1D(128, 5,padding='same'))\n",
    "model.add(Activation('relu'))\n",
    "model.add(Dropout(0.1))\n",
    "model.add(MaxPooling1D(pool_size=(8)))\n",
    "model.add(Conv1D(128, 5,padding='same',))\n",
    "model.add(Activation('relu'))\n",
    "#model.add(Conv1D(128, 5,padding='same',))\n",
    "#model.add(Activation('relu'))\n",
    "#model.add(Conv1D(128, 5,padding='same',))\n",
    "#model.add(Activation('relu'))\n",
    "#model.add(Dropout(0.2))\n",
    "model.add(Conv1D(128, 5,padding='same',))\n",
    "model.add(Activation('relu'))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(10))\n",
    "model.add(Activation('softmax'))\n",
    "opt = keras.optimizers.rmsprop(lr=0.00001, decay=1e-6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv1d_1 (Conv1D)            (None, 216, 256)          1536      \n",
      "_________________________________________________________________\n",
      "activation_1 (Activation)    (None, 216, 256)          0         \n",
      "_________________________________________________________________\n",
      "conv1d_2 (Conv1D)            (None, 216, 128)          163968    \n",
      "_________________________________________________________________\n",
      "activation_2 (Activation)    (None, 216, 128)          0         \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 216, 128)          0         \n",
      "_________________________________________________________________\n",
      "max_pooling1d_1 (MaxPooling1 (None, 27, 128)           0         \n",
      "_________________________________________________________________\n",
      "conv1d_3 (Conv1D)            (None, 27, 128)           82048     \n",
      "_________________________________________________________________\n",
      "activation_3 (Activation)    (None, 27, 128)           0         \n",
      "_________________________________________________________________\n",
      "conv1d_4 (Conv1D)            (None, 27, 128)           82048     \n",
      "_________________________________________________________________\n",
      "activation_4 (Activation)    (None, 27, 128)           0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 3456)              0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 10)                34570     \n",
      "_________________________________________________________________\n",
      "activation_5 (Activation)    (None, 10)                0         \n",
      "=================================================================\n",
      "Total params: 364,170\n",
      "Trainable params: 364,170\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss='categorical_crossentropy', optimizer=opt,metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Removed the whole training part for avoiding unnecessary long epochs list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 782 samples, validate on 178 samples\n",
      "Epoch 1/700\n",
      "782/782 [==============================] - 5s 7ms/step - loss: 2.2910 - acc: 0.1228 - val_loss: 2.2304 - val_acc: 0.1180\n",
      "Epoch 2/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 2.2050 - acc: 0.1432 - val_loss: 2.1708 - val_acc: 0.1966\n",
      "Epoch 3/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 2.1579 - acc: 0.1918 - val_loss: 2.1508 - val_acc: 0.1517\n",
      "Epoch 4/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 2.1244 - acc: 0.1957 - val_loss: 2.1119 - val_acc: 0.2135\n",
      "Epoch 5/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 2.0937 - acc: 0.2097 - val_loss: 2.0901 - val_acc: 0.2247\n",
      "Epoch 6/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 2.0616 - acc: 0.2379 - val_loss: 2.0575 - val_acc: 0.2472\n",
      "Epoch 7/700\n",
      "782/782 [==============================] - 6s 8ms/step - loss: 2.0299 - acc: 0.2506 - val_loss: 2.0325 - val_acc: 0.2472\n",
      "Epoch 8/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 2.0041 - acc: 0.2545 - val_loss: 2.0179 - val_acc: 0.2528\n",
      "Epoch 9/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 1.9807 - acc: 0.2775 - val_loss: 2.0050 - val_acc: 0.2584\n",
      "Epoch 10/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 1.9485 - acc: 0.3133 - val_loss: 1.9718 - val_acc: 0.2303\n",
      "Epoch 11/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 1.9371 - acc: 0.2801 - val_loss: 1.9425 - val_acc: 0.2809\n",
      "Epoch 12/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 1.9097 - acc: 0.2954 - val_loss: 1.9261 - val_acc: 0.3258\n",
      "Epoch 13/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 1.8821 - acc: 0.3210 - val_loss: 1.9133 - val_acc: 0.3202\n",
      "Epoch 14/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 1.8632 - acc: 0.3107 - val_loss: 1.8888 - val_acc: 0.3202\n",
      "Epoch 15/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 1.8432 - acc: 0.3120 - val_loss: 1.8831 - val_acc: 0.3090\n",
      "Epoch 16/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 1.8216 - acc: 0.3274 - val_loss: 1.8479 - val_acc: 0.3371\n",
      "Epoch 17/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 1.8025 - acc: 0.3299 - val_loss: 1.8376 - val_acc: 0.3034\n",
      "Epoch 18/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 1.7844 - acc: 0.3325 - val_loss: 1.8062 - val_acc: 0.3539\n",
      "Epoch 19/700\n",
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      "Epoch 51/700\n",
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      "Epoch 52/700\n",
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      "Epoch 53/700\n",
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      "Epoch 56/700\n",
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      "Epoch 57/700\n",
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      "Epoch 58/700\n",
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      "Epoch 59/700\n",
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      "Epoch 60/700\n",
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      "Epoch 61/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 1.3190 - acc: 0.4859 - val_loss: 1.4035 - val_acc: 0.4382\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 62/700\n",
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      "Epoch 67/700\n",
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      "Epoch 76/700\n",
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      "Epoch 77/700\n",
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      "Epoch 78/700\n",
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      "Epoch 82/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 1.2050 - acc: 0.5179 - val_loss: 1.2825 - val_acc: 0.4663\n",
      "Epoch 83/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 1.2102 - acc: 0.5217 - val_loss: 1.2988 - val_acc: 0.4888\n",
      "Epoch 84/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 1.2044 - acc: 0.5307 - val_loss: 1.3419 - val_acc: 0.4382\n",
      "Epoch 85/700\n",
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      "Epoch 119/700\n",
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      "Epoch 122/700\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 123/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 1.0766 - acc: 0.6074 - val_loss: 1.2256 - val_acc: 0.5169\n",
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      "Epoch 127/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 1.0742 - acc: 0.5895 - val_loss: 1.2192 - val_acc: 0.5000\n",
      "Epoch 128/700\n",
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      "Epoch 129/700\n",
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      "Epoch 142/700\n",
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      "Epoch 143/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 1.0269 - acc: 0.6317 - val_loss: 1.2501 - val_acc: 0.4831\n",
      "Epoch 144/700\n",
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      "Epoch 233/700\n",
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      "Epoch 234/700\n",
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      "Epoch 235/700\n",
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      "Epoch 236/700\n",
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      "Epoch 237/700\n",
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      "Epoch 238/700\n",
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      "Epoch 239/700\n",
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      "Epoch 240/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.8721 - acc: 0.6829 - val_loss: 1.1396 - val_acc: 0.5225\n",
      "Epoch 241/700\n",
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      "Epoch 242/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.8623 - acc: 0.6611 - val_loss: 1.2343 - val_acc: 0.5393\n",
      "Epoch 243/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.8707 - acc: 0.6752 - val_loss: 1.1726 - val_acc: 0.5281\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 244/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.8579 - acc: 0.6739 - val_loss: 1.1922 - val_acc: 0.5337\n",
      "Epoch 245/700\n",
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      "Epoch 247/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.8554 - acc: 0.6893 - val_loss: 1.1613 - val_acc: 0.5393\n",
      "Epoch 248/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.8668 - acc: 0.6547 - val_loss: 1.1622 - val_acc: 0.5000\n",
      "Epoch 249/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.8529 - acc: 0.6905 - val_loss: 1.1431 - val_acc: 0.5281\n",
      "Epoch 250/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.8575 - acc: 0.6829 - val_loss: 1.1339 - val_acc: 0.5000\n",
      "Epoch 251/700\n",
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      "Epoch 252/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.8568 - acc: 0.6816 - val_loss: 1.1970 - val_acc: 0.5000\n",
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      "Epoch 255/700\n",
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      "Epoch 256/700\n",
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      "Epoch 257/700\n",
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      "Epoch 258/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.8391 - acc: 0.6829 - val_loss: 1.1544 - val_acc: 0.5506\n",
      "Epoch 259/700\n",
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      "Epoch 262/700\n",
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      "Epoch 263/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.8322 - acc: 0.7059 - val_loss: 1.1389 - val_acc: 0.5281\n",
      "Epoch 264/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.8304 - acc: 0.6867 - val_loss: 1.1377 - val_acc: 0.5000\n",
      "Epoch 265/700\n",
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      "Epoch 266/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.8195 - acc: 0.7059 - val_loss: 1.1744 - val_acc: 0.4888\n",
      "Epoch 267/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.8267 - acc: 0.6944 - val_loss: 1.1580 - val_acc: 0.5337\n",
      "Epoch 268/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.8272 - acc: 0.6867 - val_loss: 1.1308 - val_acc: 0.5449\n",
      "Epoch 269/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.8244 - acc: 0.6957 - val_loss: 1.1305 - val_acc: 0.5169\n",
      "Epoch 270/700\n",
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      "Epoch 271/700\n",
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      "Epoch 351/700\n",
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      "Epoch 354/700\n",
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      "Epoch 355/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.6942 - acc: 0.7558 - val_loss: 1.2048 - val_acc: 0.5000\n",
      "Epoch 356/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.7059 - acc: 0.7315 - val_loss: 1.1536 - val_acc: 0.5506\n",
      "Epoch 357/700\n",
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      "Epoch 358/700\n",
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      "Epoch 359/700\n",
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      "Epoch 360/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.7010 - acc: 0.7455 - val_loss: 1.1358 - val_acc: 0.5337\n",
      "Epoch 361/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.6883 - acc: 0.7545 - val_loss: 1.1710 - val_acc: 0.5056\n",
      "Epoch 362/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.6980 - acc: 0.7455 - val_loss: 1.1539 - val_acc: 0.5169\n",
      "Epoch 363/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.6897 - acc: 0.7621 - val_loss: 1.2081 - val_acc: 0.5337\n",
      "Epoch 364/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.6918 - acc: 0.7596 - val_loss: 1.1485 - val_acc: 0.5281\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 365/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.6754 - acc: 0.7570 - val_loss: 1.1571 - val_acc: 0.5393\n",
      "Epoch 366/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.6907 - acc: 0.7609 - val_loss: 1.1491 - val_acc: 0.5506\n",
      "Epoch 367/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.6865 - acc: 0.7698 - val_loss: 1.1411 - val_acc: 0.5281\n",
      "Epoch 368/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.6773 - acc: 0.7634 - val_loss: 1.1472 - val_acc: 0.5225\n",
      "Epoch 369/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.6860 - acc: 0.7558 - val_loss: 1.1442 - val_acc: 0.5056\n",
      "Epoch 370/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.6810 - acc: 0.7609 - val_loss: 1.1727 - val_acc: 0.5393\n",
      "Epoch 371/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.6757 - acc: 0.7417 - val_loss: 1.1613 - val_acc: 0.5337\n",
      "Epoch 372/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.6719 - acc: 0.7685 - val_loss: 1.1627 - val_acc: 0.5618\n",
      "Epoch 373/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.6770 - acc: 0.7609 - val_loss: 1.1645 - val_acc: 0.5393\n",
      "Epoch 374/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.6802 - acc: 0.7468 - val_loss: 1.1565 - val_acc: 0.5281\n",
      "Epoch 375/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.6744 - acc: 0.7647 - val_loss: 1.1845 - val_acc: 0.5169\n",
      "Epoch 376/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.6706 - acc: 0.7621 - val_loss: 1.1766 - val_acc: 0.5337\n",
      "Epoch 377/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.6640 - acc: 0.7685 - val_loss: 1.1643 - val_acc: 0.5112\n",
      "Epoch 378/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.6775 - acc: 0.7519 - val_loss: 1.1490 - val_acc: 0.5506\n",
      "Epoch 379/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.6717 - acc: 0.7494 - val_loss: 1.1387 - val_acc: 0.5337\n",
      "Epoch 380/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.6690 - acc: 0.7545 - val_loss: 1.1719 - val_acc: 0.5169\n",
      "Epoch 381/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.6621 - acc: 0.7519 - val_loss: 1.1268 - val_acc: 0.5618\n",
      "Epoch 382/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.6560 - acc: 0.7698 - val_loss: 1.2188 - val_acc: 0.5674\n",
      "Epoch 383/700\n",
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      "Epoch 384/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.6577 - acc: 0.7801 - val_loss: 1.2449 - val_acc: 0.5112\n",
      "Epoch 385/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.6636 - acc: 0.7737 - val_loss: 1.1860 - val_acc: 0.5281\n",
      "Epoch 386/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.6609 - acc: 0.7762 - val_loss: 1.1480 - val_acc: 0.5730\n",
      "Epoch 387/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.6532 - acc: 0.7711 - val_loss: 1.1763 - val_acc: 0.5281\n",
      "Epoch 388/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.6590 - acc: 0.7647 - val_loss: 1.1593 - val_acc: 0.5449\n",
      "Epoch 389/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.6443 - acc: 0.7583 - val_loss: 1.1515 - val_acc: 0.5506\n",
      "Epoch 390/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.6568 - acc: 0.7621 - val_loss: 1.1701 - val_acc: 0.5281\n",
      "Epoch 391/700\n",
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      "Epoch 392/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.6465 - acc: 0.7724 - val_loss: 1.1498 - val_acc: 0.5225\n",
      "Epoch 393/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.6556 - acc: 0.7673 - val_loss: 1.1561 - val_acc: 0.5730\n",
      "Epoch 394/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.6418 - acc: 0.7711 - val_loss: 1.1636 - val_acc: 0.5449\n",
      "Epoch 395/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.6501 - acc: 0.7685 - val_loss: 1.1220 - val_acc: 0.5449\n",
      "Epoch 396/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.6474 - acc: 0.7788 - val_loss: 1.1642 - val_acc: 0.5225\n",
      "Epoch 397/700\n",
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      "Epoch 419/700\n",
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      "Epoch 425/700\n",
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      "Epoch 426/700\n",
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      "Epoch 427/700\n",
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      "Epoch 428/700\n",
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      "Epoch 430/700\n",
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      "Epoch 431/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.5912 - acc: 0.8018 - val_loss: 1.1490 - val_acc: 0.5618\n",
      "Epoch 432/700\n",
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      "Epoch 435/700\n",
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      "Epoch 439/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.5869 - acc: 0.7826 - val_loss: 1.1473 - val_acc: 0.5393\n",
      "Epoch 440/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.5873 - acc: 0.7877 - val_loss: 1.1793 - val_acc: 0.5281\n",
      "Epoch 441/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.5726 - acc: 0.8056 - val_loss: 1.2000 - val_acc: 0.5674\n",
      "Epoch 442/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.5798 - acc: 0.7813 - val_loss: 1.1798 - val_acc: 0.5506\n",
      "Epoch 443/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.5815 - acc: 0.7992 - val_loss: 1.1484 - val_acc: 0.5618\n",
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      "Epoch 445/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.5723 - acc: 0.8043 - val_loss: 1.2405 - val_acc: 0.5281\n",
      "Epoch 446/700\n",
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      "Epoch 447/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.5868 - acc: 0.7916 - val_loss: 1.1915 - val_acc: 0.5393\n",
      "Epoch 448/700\n",
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      "Epoch 449/700\n",
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      "Epoch 450/700\n",
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      "Epoch 454/700\n",
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      "Epoch 455/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.5571 - acc: 0.7928 - val_loss: 1.2047 - val_acc: 0.5225\n",
      "Epoch 456/700\n",
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      "Epoch 457/700\n",
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      "Epoch 458/700\n",
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      "Epoch 459/700\n",
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      "Epoch 460/700\n",
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      "Epoch 461/700\n",
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      "Epoch 465/700\n",
      "782/782 [==============================] - 6s 8ms/step - loss: 0.5490 - acc: 0.8043 - val_loss: 1.1741 - val_acc: 0.5281\n",
      "Epoch 466/700\n",
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      "Epoch 471/700\n",
      "782/782 [==============================] - 6s 8ms/step - loss: 0.5519 - acc: 0.8043 - val_loss: 1.1922 - val_acc: 0.5787\n",
      "Epoch 472/700\n",
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      "Epoch 473/700\n",
      "782/782 [==============================] - 8s 10ms/step - loss: 0.5307 - acc: 0.8171 - val_loss: 1.2075 - val_acc: 0.5562\n",
      "Epoch 474/700\n",
      "782/782 [==============================] - 7s 9ms/step - loss: 0.5328 - acc: 0.8159 - val_loss: 1.2011 - val_acc: 0.5169\n",
      "Epoch 475/700\n",
      "782/782 [==============================] - 7s 9ms/step - loss: 0.5298 - acc: 0.8171 - val_loss: 1.2031 - val_acc: 0.5562\n",
      "Epoch 476/700\n",
      "782/782 [==============================] - 6s 8ms/step - loss: 0.5310 - acc: 0.8056 - val_loss: 1.2091 - val_acc: 0.5562\n",
      "Epoch 477/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.5350 - acc: 0.8184 - val_loss: 1.1945 - val_acc: 0.5393\n",
      "Epoch 478/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.5342 - acc: 0.8223 - val_loss: 1.1690 - val_acc: 0.5506\n",
      "Epoch 479/700\n",
      "782/782 [==============================] - 6s 8ms/step - loss: 0.5210 - acc: 0.8312 - val_loss: 1.2062 - val_acc: 0.5562\n",
      "Epoch 480/700\n",
      "782/782 [==============================] - 6s 8ms/step - loss: 0.5272 - acc: 0.8261 - val_loss: 1.1782 - val_acc: 0.5169\n",
      "Epoch 481/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.5262 - acc: 0.8312 - val_loss: 1.2189 - val_acc: 0.5225\n",
      "Epoch 482/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.5256 - acc: 0.8133 - val_loss: 1.2159 - val_acc: 0.5337\n",
      "Epoch 483/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.5208 - acc: 0.8235 - val_loss: 1.1986 - val_acc: 0.5449\n",
      "Epoch 484/700\n",
      "782/782 [==============================] - 6s 8ms/step - loss: 0.5259 - acc: 0.8146 - val_loss: 1.1916 - val_acc: 0.5393\n",
      "Epoch 485/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.5128 - acc: 0.8338 - val_loss: 1.2105 - val_acc: 0.5393\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 486/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.5140 - acc: 0.8107 - val_loss: 1.2039 - val_acc: 0.5618\n",
      "Epoch 487/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.5269 - acc: 0.8274 - val_loss: 1.2629 - val_acc: 0.5225\n",
      "Epoch 488/700\n",
      "782/782 [==============================] - 6s 8ms/step - loss: 0.5157 - acc: 0.8248 - val_loss: 1.1811 - val_acc: 0.5618\n",
      "Epoch 489/700\n",
      "782/782 [==============================] - 7s 9ms/step - loss: 0.5152 - acc: 0.8299 - val_loss: 1.2605 - val_acc: 0.5169\n",
      "Epoch 490/700\n",
      "782/782 [==============================] - 8s 10ms/step - loss: 0.5191 - acc: 0.8146 - val_loss: 1.2015 - val_acc: 0.5393\n",
      "Epoch 491/700\n",
      "782/782 [==============================] - 7s 9ms/step - loss: 0.5063 - acc: 0.8402 - val_loss: 1.2465 - val_acc: 0.5337\n",
      "Epoch 492/700\n",
      "782/782 [==============================] - 6s 8ms/step - loss: 0.5119 - acc: 0.8274 - val_loss: 1.2010 - val_acc: 0.5337\n",
      "Epoch 493/700\n",
      "782/782 [==============================] - 6s 8ms/step - loss: 0.5097 - acc: 0.8312 - val_loss: 1.2038 - val_acc: 0.5225\n",
      "Epoch 494/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.5109 - acc: 0.8312 - val_loss: 1.2301 - val_acc: 0.5449\n",
      "Epoch 495/700\n",
      "782/782 [==============================] - 6s 8ms/step - loss: 0.5013 - acc: 0.8376 - val_loss: 1.2168 - val_acc: 0.5449\n",
      "Epoch 496/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.5036 - acc: 0.8261 - val_loss: 1.2195 - val_acc: 0.5281\n",
      "Epoch 497/700\n",
      "782/782 [==============================] - 7s 8ms/step - loss: 0.5059 - acc: 0.8235 - val_loss: 1.1830 - val_acc: 0.5393\n",
      "Epoch 498/700\n",
      "782/782 [==============================] - 6s 8ms/step - loss: 0.4990 - acc: 0.8286 - val_loss: 1.2598 - val_acc: 0.5618\n",
      "Epoch 499/700\n",
      "782/782 [==============================] - 6s 8ms/step - loss: 0.4911 - acc: 0.8274 - val_loss: 1.2041 - val_acc: 0.5112\n",
      "Epoch 500/700\n",
      "782/782 [==============================] - 7s 9ms/step - loss: 0.5034 - acc: 0.8286 - val_loss: 1.2346 - val_acc: 0.5506\n",
      "Epoch 501/700\n",
      "782/782 [==============================] - 7s 8ms/step - loss: 0.5001 - acc: 0.8312 - val_loss: 1.2184 - val_acc: 0.5393\n",
      "Epoch 502/700\n",
      "782/782 [==============================] - 6s 8ms/step - loss: 0.5013 - acc: 0.8210 - val_loss: 1.2121 - val_acc: 0.5618\n",
      "Epoch 503/700\n",
      "782/782 [==============================] - 6s 8ms/step - loss: 0.4859 - acc: 0.8363 - val_loss: 1.2395 - val_acc: 0.5281\n",
      "Epoch 504/700\n",
      "782/782 [==============================] - 7s 9ms/step - loss: 0.4914 - acc: 0.8402 - val_loss: 1.2132 - val_acc: 0.5056\n",
      "Epoch 505/700\n",
      "782/782 [==============================] - 7s 9ms/step - loss: 0.4976 - acc: 0.8274 - val_loss: 1.2532 - val_acc: 0.5393\n",
      "Epoch 506/700\n",
      "782/782 [==============================] - 6s 8ms/step - loss: 0.4965 - acc: 0.8350 - val_loss: 1.2905 - val_acc: 0.5393\n",
      "Epoch 507/700\n",
      "782/782 [==============================] - 6s 8ms/step - loss: 0.4864 - acc: 0.8325 - val_loss: 1.2231 - val_acc: 0.5112\n",
      "Epoch 508/700\n",
      "782/782 [==============================] - 6s 8ms/step - loss: 0.4942 - acc: 0.8286 - val_loss: 1.2101 - val_acc: 0.5449\n",
      "Epoch 509/700\n",
      "782/782 [==============================] - 8s 10ms/step - loss: 0.4925 - acc: 0.8376 - val_loss: 1.2356 - val_acc: 0.5393\n",
      "Epoch 510/700\n",
      "782/782 [==============================] - 8s 10ms/step - loss: 0.4786 - acc: 0.8517 - val_loss: 1.2139 - val_acc: 0.5281\n",
      "Epoch 511/700\n",
      "782/782 [==============================] - 8s 10ms/step - loss: 0.4863 - acc: 0.8261 - val_loss: 1.2460 - val_acc: 0.5674\n",
      "Epoch 512/700\n",
      "782/782 [==============================] - 7s 9ms/step - loss: 0.4950 - acc: 0.8312 - val_loss: 1.2587 - val_acc: 0.5112\n",
      "Epoch 513/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.4952 - acc: 0.8286 - val_loss: 1.2115 - val_acc: 0.5730\n",
      "Epoch 514/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.4805 - acc: 0.8363 - val_loss: 1.2292 - val_acc: 0.5169\n",
      "Epoch 515/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.4813 - acc: 0.8376 - val_loss: 1.2067 - val_acc: 0.5618\n",
      "Epoch 516/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.4730 - acc: 0.8427 - val_loss: 1.2976 - val_acc: 0.5281\n",
      "Epoch 517/700\n",
      "782/782 [==============================] - 5s 7ms/step - loss: 0.4848 - acc: 0.8299 - val_loss: 1.2101 - val_acc: 0.5506\n",
      "Epoch 518/700\n",
      "782/782 [==============================] - 5s 7ms/step - loss: 0.4795 - acc: 0.8363 - val_loss: 1.2305 - val_acc: 0.5337\n",
      "Epoch 519/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.4722 - acc: 0.8465 - val_loss: 1.2704 - val_acc: 0.5281\n",
      "Epoch 520/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.4740 - acc: 0.8465 - val_loss: 1.2203 - val_acc: 0.5562\n",
      "Epoch 521/700\n",
      "782/782 [==============================] - 5s 7ms/step - loss: 0.4705 - acc: 0.8453 - val_loss: 1.2288 - val_acc: 0.5449\n",
      "Epoch 522/700\n",
      "782/782 [==============================] - 6s 8ms/step - loss: 0.4698 - acc: 0.8465 - val_loss: 1.2591 - val_acc: 0.5506\n",
      "Epoch 523/700\n",
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      "Epoch 540/700\n",
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      "Epoch 545/700\n",
      "782/782 [==============================] - 8s 10ms/step - loss: 0.4371 - acc: 0.8542 - val_loss: 1.2451 - val_acc: 0.5506\n",
      "Epoch 546/700\n",
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      "Epoch 548/700\n",
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      "Epoch 549/700\n",
      "782/782 [==============================] - 5s 7ms/step - loss: 0.4362 - acc: 0.8606 - val_loss: 1.3144 - val_acc: 0.5225\n",
      "Epoch 550/700\n",
      "782/782 [==============================] - 5s 7ms/step - loss: 0.4414 - acc: 0.8593 - val_loss: 1.2683 - val_acc: 0.5506\n",
      "Epoch 551/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.4269 - acc: 0.8606 - val_loss: 1.2693 - val_acc: 0.5169\n",
      "Epoch 552/700\n",
      "782/782 [==============================] - 5s 7ms/step - loss: 0.4346 - acc: 0.8568 - val_loss: 1.2758 - val_acc: 0.5449\n",
      "Epoch 553/700\n",
      "782/782 [==============================] - 5s 7ms/step - loss: 0.4270 - acc: 0.8683 - val_loss: 1.2581 - val_acc: 0.5562\n",
      "Epoch 554/700\n",
      "782/782 [==============================] - 5s 7ms/step - loss: 0.4262 - acc: 0.8581 - val_loss: 1.2547 - val_acc: 0.5618\n",
      "Epoch 555/700\n",
      "782/782 [==============================] - 5s 7ms/step - loss: 0.4323 - acc: 0.8581 - val_loss: 1.2572 - val_acc: 0.5281\n",
      "Epoch 556/700\n",
      "782/782 [==============================] - 5s 7ms/step - loss: 0.4260 - acc: 0.8632 - val_loss: 1.3013 - val_acc: 0.5337\n",
      "Epoch 557/700\n",
      "782/782 [==============================] - 5s 7ms/step - loss: 0.4339 - acc: 0.8619 - val_loss: 1.2633 - val_acc: 0.5393\n",
      "Epoch 558/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.4135 - acc: 0.8747 - val_loss: 1.3015 - val_acc: 0.5618\n",
      "Epoch 559/700\n",
      "782/782 [==============================] - 7s 9ms/step - loss: 0.4172 - acc: 0.8670 - val_loss: 1.2458 - val_acc: 0.5449\n",
      "Epoch 560/700\n",
      "782/782 [==============================] - 6s 8ms/step - loss: 0.4165 - acc: 0.8555 - val_loss: 1.2440 - val_acc: 0.5506\n",
      "Epoch 561/700\n",
      "782/782 [==============================] - 6s 8ms/step - loss: 0.4296 - acc: 0.8593 - val_loss: 1.2516 - val_acc: 0.5281\n",
      "Epoch 562/700\n",
      "782/782 [==============================] - 6s 8ms/step - loss: 0.4168 - acc: 0.8721 - val_loss: 1.2601 - val_acc: 0.5225\n",
      "Epoch 563/700\n",
      "782/782 [==============================] - 5s 7ms/step - loss: 0.4085 - acc: 0.8811 - val_loss: 1.2781 - val_acc: 0.5393\n",
      "Epoch 564/700\n",
      "782/782 [==============================] - 5s 7ms/step - loss: 0.4168 - acc: 0.8683 - val_loss: 1.2853 - val_acc: 0.5506\n",
      "Epoch 565/700\n",
      "782/782 [==============================] - 5s 7ms/step - loss: 0.4094 - acc: 0.8606 - val_loss: 1.2768 - val_acc: 0.5393\n",
      "Epoch 566/700\n",
      "782/782 [==============================] - 5s 7ms/step - loss: 0.4149 - acc: 0.8632 - val_loss: 1.2401 - val_acc: 0.5562\n",
      "Epoch 567/700\n",
      "782/782 [==============================] - 5s 7ms/step - loss: 0.4122 - acc: 0.8657 - val_loss: 1.2479 - val_acc: 0.5506\n",
      "Epoch 568/700\n",
      "782/782 [==============================] - 5s 7ms/step - loss: 0.4040 - acc: 0.8798 - val_loss: 1.3038 - val_acc: 0.5225\n",
      "Epoch 569/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.4131 - acc: 0.8772 - val_loss: 1.2654 - val_acc: 0.5449\n",
      "Epoch 570/700\n",
      "782/782 [==============================] - 5s 7ms/step - loss: 0.3952 - acc: 0.8811 - val_loss: 1.2723 - val_acc: 0.5393\n",
      "Epoch 571/700\n",
      "782/782 [==============================] - 5s 7ms/step - loss: 0.4106 - acc: 0.8708 - val_loss: 1.2382 - val_acc: 0.5506\n",
      "Epoch 572/700\n",
      "782/782 [==============================] - 5s 7ms/step - loss: 0.4019 - acc: 0.8619 - val_loss: 1.2574 - val_acc: 0.5281\n",
      "Epoch 573/700\n",
      "782/782 [==============================] - 5s 7ms/step - loss: 0.3980 - acc: 0.8862 - val_loss: 1.2525 - val_acc: 0.5506\n",
      "Epoch 574/700\n",
      "782/782 [==============================] - 5s 7ms/step - loss: 0.4001 - acc: 0.8836 - val_loss: 1.2710 - val_acc: 0.5393\n",
      "Epoch 575/700\n",
      "782/782 [==============================] - 6s 8ms/step - loss: 0.3980 - acc: 0.8760 - val_loss: 1.2455 - val_acc: 0.5562\n",
      "Epoch 576/700\n",
      "782/782 [==============================] - 6s 8ms/step - loss: 0.4042 - acc: 0.8632 - val_loss: 1.2605 - val_acc: 0.5618\n",
      "Epoch 577/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.3999 - acc: 0.8836 - val_loss: 1.2834 - val_acc: 0.5506\n",
      "Epoch 578/700\n",
      "782/782 [==============================] - 5s 7ms/step - loss: 0.3890 - acc: 0.8811 - val_loss: 1.2625 - val_acc: 0.5449\n",
      "Epoch 579/700\n",
      "782/782 [==============================] - 5s 7ms/step - loss: 0.3878 - acc: 0.8900 - val_loss: 1.2814 - val_acc: 0.5562\n",
      "Epoch 580/700\n",
      "782/782 [==============================] - 5s 7ms/step - loss: 0.4000 - acc: 0.8683 - val_loss: 1.2911 - val_acc: 0.5506\n",
      "Epoch 581/700\n",
      "782/782 [==============================] - 5s 7ms/step - loss: 0.4022 - acc: 0.8849 - val_loss: 1.2638 - val_acc: 0.5449\n",
      "Epoch 582/700\n",
      "782/782 [==============================] - 5s 7ms/step - loss: 0.3915 - acc: 0.8836 - val_loss: 1.3126 - val_acc: 0.5169\n",
      "Epoch 583/700\n",
      "782/782 [==============================] - 5s 7ms/step - loss: 0.3828 - acc: 0.8862 - val_loss: 1.2910 - val_acc: 0.5169\n",
      "Epoch 584/700\n",
      "782/782 [==============================] - 5s 7ms/step - loss: 0.3927 - acc: 0.8708 - val_loss: 1.3134 - val_acc: 0.5506\n",
      "Epoch 585/700\n",
      "782/782 [==============================] - 5s 7ms/step - loss: 0.3911 - acc: 0.8734 - val_loss: 1.2995 - val_acc: 0.5506\n",
      "Epoch 586/700\n",
      "782/782 [==============================] - 5s 7ms/step - loss: 0.3909 - acc: 0.8708 - val_loss: 1.2531 - val_acc: 0.5337\n",
      "Epoch 587/700\n",
      "782/782 [==============================] - 5s 7ms/step - loss: 0.3833 - acc: 0.8811 - val_loss: 1.3061 - val_acc: 0.5281\n",
      "Epoch 588/700\n",
      "782/782 [==============================] - 5s 7ms/step - loss: 0.3900 - acc: 0.8798 - val_loss: 1.2690 - val_acc: 0.5449\n",
      "Epoch 589/700\n",
      "782/782 [==============================] - 5s 7ms/step - loss: 0.3801 - acc: 0.8926 - val_loss: 1.2605 - val_acc: 0.5618\n",
      "Epoch 590/700\n",
      "782/782 [==============================] - 6s 8ms/step - loss: 0.3793 - acc: 0.8939 - val_loss: 1.3065 - val_acc: 0.5449\n",
      "Epoch 591/700\n",
      "782/782 [==============================] - 6s 8ms/step - loss: 0.3819 - acc: 0.8798 - val_loss: 1.2712 - val_acc: 0.5506\n",
      "Epoch 592/700\n",
      "782/782 [==============================] - 6s 8ms/step - loss: 0.3864 - acc: 0.8862 - val_loss: 1.3053 - val_acc: 0.5618\n",
      "Epoch 593/700\n",
      "782/782 [==============================] - 7s 9ms/step - loss: 0.3713 - acc: 0.8824 - val_loss: 1.3041 - val_acc: 0.5449\n",
      "Epoch 594/700\n",
      "782/782 [==============================] - 6s 8ms/step - loss: 0.3750 - acc: 0.8811 - val_loss: 1.2934 - val_acc: 0.5506\n",
      "Epoch 595/700\n",
      "782/782 [==============================] - 6s 8ms/step - loss: 0.3776 - acc: 0.8811 - val_loss: 1.3109 - val_acc: 0.5562\n",
      "Epoch 596/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.3731 - acc: 0.8849 - val_loss: 1.2785 - val_acc: 0.5393\n",
      "Epoch 597/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.3673 - acc: 0.8798 - val_loss: 1.2768 - val_acc: 0.5506\n",
      "Epoch 598/700\n",
      "782/782 [==============================] - 6s 8ms/step - loss: 0.3694 - acc: 0.8913 - val_loss: 1.3155 - val_acc: 0.5562\n",
      "Epoch 599/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.3745 - acc: 0.8824 - val_loss: 1.2760 - val_acc: 0.5843\n",
      "Epoch 600/700\n",
      "782/782 [==============================] - 6s 8ms/step - loss: 0.3690 - acc: 0.8836 - val_loss: 1.3113 - val_acc: 0.5562\n",
      "Epoch 601/700\n",
      "782/782 [==============================] - 7s 9ms/step - loss: 0.3715 - acc: 0.8900 - val_loss: 1.3036 - val_acc: 0.5618\n",
      "Epoch 602/700\n",
      "782/782 [==============================] - 7s 9ms/step - loss: 0.3723 - acc: 0.8760 - val_loss: 1.2720 - val_acc: 0.5618\n",
      "Epoch 603/700\n",
      "782/782 [==============================] - 8s 10ms/step - loss: 0.3666 - acc: 0.8875 - val_loss: 1.3028 - val_acc: 0.5618\n",
      "Epoch 604/700\n",
      "782/782 [==============================] - 8s 10ms/step - loss: 0.3646 - acc: 0.8939 - val_loss: 1.3019 - val_acc: 0.5506\n",
      "Epoch 605/700\n",
      "782/782 [==============================] - 6s 8ms/step - loss: 0.3570 - acc: 0.8964 - val_loss: 1.2642 - val_acc: 0.5112\n",
      "Epoch 606/700\n",
      "782/782 [==============================] - 8s 10ms/step - loss: 0.3659 - acc: 0.8913 - val_loss: 1.2988 - val_acc: 0.5449\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 607/700\n",
      "782/782 [==============================] - 7s 9ms/step - loss: 0.3540 - acc: 0.9003 - val_loss: 1.3494 - val_acc: 0.5730\n",
      "Epoch 608/700\n",
      "782/782 [==============================] - 7s 9ms/step - loss: 0.3575 - acc: 0.8939 - val_loss: 1.3055 - val_acc: 0.5449\n",
      "Epoch 609/700\n",
      "782/782 [==============================] - 8s 10ms/step - loss: 0.3528 - acc: 0.8977 - val_loss: 1.2986 - val_acc: 0.5730\n",
      "Epoch 610/700\n",
      "782/782 [==============================] - 8s 10ms/step - loss: 0.3542 - acc: 0.9105 - val_loss: 1.3516 - val_acc: 0.5281\n",
      "Epoch 611/700\n",
      "782/782 [==============================] - 8s 10ms/step - loss: 0.3583 - acc: 0.8887 - val_loss: 1.3132 - val_acc: 0.5562\n",
      "Epoch 612/700\n",
      "782/782 [==============================] - 7s 9ms/step - loss: 0.3506 - acc: 0.9003 - val_loss: 1.3754 - val_acc: 0.5506\n",
      "Epoch 613/700\n",
      "782/782 [==============================] - 7s 9ms/step - loss: 0.3613 - acc: 0.8990 - val_loss: 1.3218 - val_acc: 0.5562\n",
      "Epoch 614/700\n",
      "782/782 [==============================] - 8s 10ms/step - loss: 0.3407 - acc: 0.8977 - val_loss: 1.2871 - val_acc: 0.5506\n",
      "Epoch 615/700\n",
      "782/782 [==============================] - 8s 10ms/step - loss: 0.3468 - acc: 0.8990 - val_loss: 1.3438 - val_acc: 0.5562\n",
      "Epoch 616/700\n",
      "782/782 [==============================] - 6s 8ms/step - loss: 0.3611 - acc: 0.8951 - val_loss: 1.3178 - val_acc: 0.5393\n",
      "Epoch 617/700\n",
      "782/782 [==============================] - 6s 8ms/step - loss: 0.3418 - acc: 0.9003 - val_loss: 1.3197 - val_acc: 0.5449\n",
      "Epoch 618/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.3538 - acc: 0.8887 - val_loss: 1.2817 - val_acc: 0.5449\n",
      "Epoch 619/700\n",
      "782/782 [==============================] - 6s 8ms/step - loss: 0.3404 - acc: 0.9028 - val_loss: 1.3471 - val_acc: 0.5337\n",
      "Epoch 620/700\n",
      "782/782 [==============================] - 6s 8ms/step - loss: 0.3403 - acc: 0.9028 - val_loss: 1.2878 - val_acc: 0.5562\n",
      "Epoch 621/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.3487 - acc: 0.9054 - val_loss: 1.3043 - val_acc: 0.5506\n",
      "Epoch 622/700\n",
      "782/782 [==============================] - 6s 8ms/step - loss: 0.3371 - acc: 0.9041 - val_loss: 1.2972 - val_acc: 0.5506\n",
      "Epoch 623/700\n",
      "782/782 [==============================] - 6s 8ms/step - loss: 0.3460 - acc: 0.9015 - val_loss: 1.2964 - val_acc: 0.5393\n",
      "Epoch 624/700\n",
      "782/782 [==============================] - 6s 8ms/step - loss: 0.3386 - acc: 0.9028 - val_loss: 1.3105 - val_acc: 0.5112\n",
      "Epoch 625/700\n",
      "782/782 [==============================] - 6s 8ms/step - loss: 0.3330 - acc: 0.9143 - val_loss: 1.3046 - val_acc: 0.5449\n",
      "Epoch 626/700\n",
      "782/782 [==============================] - 6s 8ms/step - loss: 0.3446 - acc: 0.8990 - val_loss: 1.3137 - val_acc: 0.5618\n",
      "Epoch 627/700\n",
      "782/782 [==============================] - 6s 8ms/step - loss: 0.3286 - acc: 0.9066 - val_loss: 1.2977 - val_acc: 0.5449\n",
      "Epoch 628/700\n",
      "782/782 [==============================] - 6s 8ms/step - loss: 0.3400 - acc: 0.9041 - val_loss: 1.3041 - val_acc: 0.5449\n",
      "Epoch 629/700\n",
      "782/782 [==============================] - 7s 8ms/step - loss: 0.3305 - acc: 0.9169 - val_loss: 1.3143 - val_acc: 0.5787\n",
      "Epoch 630/700\n",
      "782/782 [==============================] - 7s 9ms/step - loss: 0.3328 - acc: 0.9041 - val_loss: 1.3344 - val_acc: 0.5281\n",
      "Epoch 631/700\n",
      "782/782 [==============================] - 7s 9ms/step - loss: 0.3212 - acc: 0.9105 - val_loss: 1.3572 - val_acc: 0.5169\n",
      "Epoch 632/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.3267 - acc: 0.9066 - val_loss: 1.3206 - val_acc: 0.5393\n",
      "Epoch 633/700\n",
      "782/782 [==============================] - 5s 7ms/step - loss: 0.3299 - acc: 0.8926 - val_loss: 1.3126 - val_acc: 0.5618\n",
      "Epoch 634/700\n",
      "782/782 [==============================] - 5s 7ms/step - loss: 0.3117 - acc: 0.9207 - val_loss: 1.3493 - val_acc: 0.5449\n",
      "Epoch 635/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.3180 - acc: 0.9220 - val_loss: 1.3960 - val_acc: 0.5337\n",
      "Epoch 636/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.3180 - acc: 0.9156 - val_loss: 1.3150 - val_acc: 0.5562\n",
      "Epoch 637/700\n",
      "782/782 [==============================] - 6s 8ms/step - loss: 0.3218 - acc: 0.9143 - val_loss: 1.3484 - val_acc: 0.5449\n",
      "Epoch 638/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.3265 - acc: 0.9041 - val_loss: 1.3359 - val_acc: 0.5449\n",
      "Epoch 639/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.3245 - acc: 0.9015 - val_loss: 1.3247 - val_acc: 0.5562\n",
      "Epoch 640/700\n",
      "782/782 [==============================] - 5s 7ms/step - loss: 0.3073 - acc: 0.9194 - val_loss: 1.3223 - val_acc: 0.5674\n",
      "Epoch 641/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.3194 - acc: 0.9041 - val_loss: 1.3526 - val_acc: 0.5449\n",
      "Epoch 642/700\n",
      "782/782 [==============================] - 5s 7ms/step - loss: 0.3186 - acc: 0.9041 - val_loss: 1.3439 - val_acc: 0.5393\n",
      "Epoch 643/700\n",
      "782/782 [==============================] - 5s 7ms/step - loss: 0.3040 - acc: 0.9169 - val_loss: 1.3347 - val_acc: 0.5337\n",
      "Epoch 644/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.3131 - acc: 0.9118 - val_loss: 1.3760 - val_acc: 0.5225\n",
      "Epoch 645/700\n",
      "782/782 [==============================] - 5s 7ms/step - loss: 0.3130 - acc: 0.9130 - val_loss: 1.3899 - val_acc: 0.5337\n",
      "Epoch 646/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.3156 - acc: 0.9092 - val_loss: 1.3964 - val_acc: 0.5337\n",
      "Epoch 647/700\n",
      "782/782 [==============================] - 6s 8ms/step - loss: 0.3152 - acc: 0.9118 - val_loss: 1.3327 - val_acc: 0.5169\n",
      "Epoch 648/700\n",
      "782/782 [==============================] - 6s 8ms/step - loss: 0.3112 - acc: 0.9143 - val_loss: 1.3454 - val_acc: 0.5674\n",
      "Epoch 649/700\n",
      "782/782 [==============================] - 6s 8ms/step - loss: 0.3118 - acc: 0.9182 - val_loss: 1.3812 - val_acc: 0.5393\n",
      "Epoch 650/700\n",
      "782/782 [==============================] - 6s 8ms/step - loss: 0.3032 - acc: 0.9143 - val_loss: 1.3768 - val_acc: 0.5562\n",
      "Epoch 651/700\n",
      "782/782 [==============================] - 6s 8ms/step - loss: 0.3048 - acc: 0.9105 - val_loss: 1.3739 - val_acc: 0.5899\n",
      "Epoch 652/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.3117 - acc: 0.9054 - val_loss: 1.3512 - val_acc: 0.5618\n",
      "Epoch 653/700\n",
      "782/782 [==============================] - 5s 7ms/step - loss: 0.3077 - acc: 0.9169 - val_loss: 1.3648 - val_acc: 0.5506\n",
      "Epoch 654/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.3129 - acc: 0.9079 - val_loss: 1.3662 - val_acc: 0.5618\n",
      "Epoch 655/700\n",
      "782/782 [==============================] - 5s 7ms/step - loss: 0.2977 - acc: 0.9207 - val_loss: 1.3470 - val_acc: 0.5393\n",
      "Epoch 656/700\n",
      "782/782 [==============================] - 5s 7ms/step - loss: 0.3078 - acc: 0.9194 - val_loss: 1.3686 - val_acc: 0.5449\n",
      "Epoch 657/700\n",
      "782/782 [==============================] - 5s 7ms/step - loss: 0.2963 - acc: 0.9156 - val_loss: 1.3276 - val_acc: 0.5449\n",
      "Epoch 658/700\n",
      "782/782 [==============================] - 5s 7ms/step - loss: 0.2932 - acc: 0.9361 - val_loss: 1.4348 - val_acc: 0.5169\n",
      "Epoch 659/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.2993 - acc: 0.9233 - val_loss: 1.3569 - val_acc: 0.5393\n",
      "Epoch 660/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.2972 - acc: 0.9182 - val_loss: 1.4005 - val_acc: 0.5674\n",
      "Epoch 661/700\n",
      "782/782 [==============================] - 5s 7ms/step - loss: 0.2976 - acc: 0.9169 - val_loss: 1.3446 - val_acc: 0.5281\n",
      "Epoch 662/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.2943 - acc: 0.9207 - val_loss: 1.4009 - val_acc: 0.5562\n",
      "Epoch 663/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.2907 - acc: 0.9246 - val_loss: 1.3660 - val_acc: 0.5730\n",
      "Epoch 664/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.2996 - acc: 0.9041 - val_loss: 1.3810 - val_acc: 0.5506\n",
      "Epoch 665/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.2910 - acc: 0.9322 - val_loss: 1.3956 - val_acc: 0.5393\n",
      "Epoch 666/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.2862 - acc: 0.9284 - val_loss: 1.3757 - val_acc: 0.5674\n",
      "Epoch 667/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.2818 - acc: 0.9348 - val_loss: 1.3863 - val_acc: 0.5393\n",
      "Epoch 668/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.2970 - acc: 0.9220 - val_loss: 1.3497 - val_acc: 0.5393\n",
      "Epoch 669/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.2738 - acc: 0.9284 - val_loss: 1.4036 - val_acc: 0.5112\n",
      "Epoch 670/700\n",
      "782/782 [==============================] - 5s 7ms/step - loss: 0.2851 - acc: 0.9207 - val_loss: 1.3974 - val_acc: 0.5562\n",
      "Epoch 671/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.2820 - acc: 0.9246 - val_loss: 1.3723 - val_acc: 0.5674\n",
      "Epoch 672/700\n",
      "782/782 [==============================] - 5s 7ms/step - loss: 0.2884 - acc: 0.9220 - val_loss: 1.3999 - val_acc: 0.5449\n",
      "Epoch 673/700\n",
      "782/782 [==============================] - 5s 7ms/step - loss: 0.2796 - acc: 0.9309 - val_loss: 1.4043 - val_acc: 0.5169\n",
      "Epoch 674/700\n",
      "782/782 [==============================] - 5s 7ms/step - loss: 0.2855 - acc: 0.9169 - val_loss: 1.3864 - val_acc: 0.5506\n",
      "Epoch 675/700\n",
      "782/782 [==============================] - 5s 7ms/step - loss: 0.2731 - acc: 0.9271 - val_loss: 1.3886 - val_acc: 0.5674\n",
      "Epoch 676/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.2768 - acc: 0.9373 - val_loss: 1.4040 - val_acc: 0.5393\n",
      "Epoch 677/700\n",
      "782/782 [==============================] - 5s 7ms/step - loss: 0.2764 - acc: 0.9271 - val_loss: 1.4891 - val_acc: 0.5618\n",
      "Epoch 678/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.2756 - acc: 0.9361 - val_loss: 1.3879 - val_acc: 0.5618\n",
      "Epoch 679/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.2759 - acc: 0.9309 - val_loss: 1.3783 - val_acc: 0.5618\n",
      "Epoch 680/700\n",
      "782/782 [==============================] - 5s 7ms/step - loss: 0.2758 - acc: 0.9246 - val_loss: 1.3692 - val_acc: 0.5618\n",
      "Epoch 681/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.2718 - acc: 0.9373 - val_loss: 1.4637 - val_acc: 0.5281\n",
      "Epoch 682/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.2771 - acc: 0.9194 - val_loss: 1.4044 - val_acc: 0.5449\n",
      "Epoch 683/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.2673 - acc: 0.9284 - val_loss: 1.4129 - val_acc: 0.5225\n",
      "Epoch 684/700\n",
      "782/782 [==============================] - 5s 7ms/step - loss: 0.2746 - acc: 0.9258 - val_loss: 1.4048 - val_acc: 0.5449\n",
      "Epoch 685/700\n",
      "782/782 [==============================] - 5s 7ms/step - loss: 0.2680 - acc: 0.9233 - val_loss: 1.3728 - val_acc: 0.5506\n",
      "Epoch 686/700\n",
      "782/782 [==============================] - 5s 7ms/step - loss: 0.2602 - acc: 0.9412 - val_loss: 1.3808 - val_acc: 0.5562\n",
      "Epoch 687/700\n",
      "782/782 [==============================] - 5s 7ms/step - loss: 0.2638 - acc: 0.9284 - val_loss: 1.4059 - val_acc: 0.5618\n",
      "Epoch 688/700\n",
      "782/782 [==============================] - 6s 7ms/step - loss: 0.2695 - acc: 0.9297 - val_loss: 1.4065 - val_acc: 0.5618\n",
      "Epoch 689/700\n",
      "782/782 [==============================] - 6s 8ms/step - loss: 0.2718 - acc: 0.9271 - val_loss: 1.3701 - val_acc: 0.5562\n",
      "Epoch 690/700\n",
      "782/782 [==============================] - 7s 8ms/step - loss: 0.2632 - acc: 0.9425 - val_loss: 1.3670 - val_acc: 0.5618\n",
      "Epoch 691/700\n",
      "782/782 [==============================] - 6s 8ms/step - loss: 0.2614 - acc: 0.9335 - val_loss: 1.3858 - val_acc: 0.5562\n",
      "Epoch 692/700\n",
      "782/782 [==============================] - 7s 8ms/step - loss: 0.2661 - acc: 0.9309 - val_loss: 1.3839 - val_acc: 0.5449\n",
      "Epoch 693/700\n",
      "782/782 [==============================] - 6s 8ms/step - loss: 0.2618 - acc: 0.9322 - val_loss: 1.4065 - val_acc: 0.5562\n",
      "Epoch 694/700\n",
      "782/782 [==============================] - 6s 8ms/step - loss: 0.2530 - acc: 0.9361 - val_loss: 1.4202 - val_acc: 0.5393\n",
      "Epoch 695/700\n",
      "782/782 [==============================] - 6s 8ms/step - loss: 0.2568 - acc: 0.9399 - val_loss: 1.3993 - val_acc: 0.5562\n",
      "Epoch 696/700\n",
      "782/782 [==============================] - 6s 8ms/step - loss: 0.2505 - acc: 0.9476 - val_loss: 1.3961 - val_acc: 0.5562\n",
      "Epoch 697/700\n",
      "782/782 [==============================] - 6s 8ms/step - loss: 0.2578 - acc: 0.9373 - val_loss: 1.4030 - val_acc: 0.5506\n",
      "Epoch 698/700\n",
      "782/782 [==============================] - 6s 8ms/step - loss: 0.2580 - acc: 0.9373 - val_loss: 1.4179 - val_acc: 0.5337\n",
      "Epoch 699/700\n",
      "782/782 [==============================] - 6s 8ms/step - loss: 0.2482 - acc: 0.9373 - val_loss: 1.4029 - val_acc: 0.5562\n",
      "Epoch 700/700\n",
      "782/782 [==============================] - 6s 8ms/step - loss: 0.2576 - acc: 0.9309 - val_loss: 1.4237 - val_acc: 0.5393\n"
     ]
    }
   ],
   "source": [
    "cnnhistory=model.fit(x_traincnn, y_train, batch_size=16, epochs=700, validation_data=(x_testcnn, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd32c357160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(cnnhistory.history['loss'])\n",
    "plt.plot(cnnhistory.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.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Saving the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved trained model at /home/therochvoices/Documents/CoreProject/saved_models/Emotion_Voice_Detection_Model.h5 \n"
     ]
    }
   ],
   "source": [
    "model_name = 'Emotion_Voice_Detection_Model.h5'\n",
    "save_dir = os.path.join(os.getcwd(), 'saved_models')\n",
    "# Save model and weights\n",
    "if not os.path.isdir(save_dir):\n",
    "    os.makedirs(save_dir)\n",
    "model_path = os.path.join(save_dir, model_name)\n",
    "model.save(model_path)\n",
    "print('Saved trained model at %s ' % model_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "model_json = model.to_json()\n",
    "with open(\"model.json\", \"w\") as json_file:\n",
    "    json_file.write(model_json)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "opt = keras.optimizers.rmsprop(lr=0.00001, decay=1e-6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded model from disk\n",
      "acc: 92.22%\n"
     ]
    }
   ],
   "source": [
    "# loading json and creating model\n",
    "from keras.models import model_from_json\n",
    "json_file = open('model.json', 'r')\n",
    "loaded_model_json = json_file.read()\n",
    "json_file.close()\n",
    "loaded_model = model_from_json(loaded_model_json)\n",
    "# load weights into new model\n",
    "loaded_model.load_weights(\"saved_models/Emotion_Voice_Detection_Model.h5\")\n",
    "print(\"Loaded model from disk\")\n",
    " \n",
    "# evaluate loaded model on test data\n",
    "loaded_model.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['accuracy'])\n",
    "score = loaded_model.evaluate(x_testcnn, y_test, verbose=0)\n",
    "print(\"%s: %.2f%%\" % (loaded_model.metrics_names[1], score[1]*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predicting emotions on the test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "180/180 [==============================] - 0s 1ms/step\n"
     ]
    }
   ],
   "source": [
    "preds = loaded_model.predict(x_testcnn, \n",
    "                         batch_size=32, \n",
    "                         verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.38203287e-03, 1.35085583e-02, 7.40133524e-02, ...,\n",
       "        2.39207154e-10, 3.58950381e-08, 4.22595585e-05],\n",
       "       [2.17852539e-06, 1.32769917e-09, 2.21676375e-11, ...,\n",
       "        3.78589774e-03, 1.04146649e-03, 1.02831014e-04],\n",
       "       [1.60865649e-03, 8.21916480e-03, 3.86192836e-02, ...,\n",
       "        2.38939230e-10, 2.13196785e-08, 3.44450086e-06],\n",
       "       ...,\n",
       "       [3.57850216e-09, 5.93126867e-11, 5.25050489e-11, ...,\n",
       "        9.76681173e-01, 2.02290453e-02, 5.38529770e-04],\n",
       "       [2.55696442e-08, 2.55026459e-14, 3.60264769e-11, ...,\n",
       "        1.01650665e-02, 6.09874666e-01, 1.57322162e-07],\n",
       "       [2.07617887e-07, 5.93166988e-06, 1.78839432e-09, ...,\n",
       "        1.04578608e-03, 1.18149899e-01, 8.10888946e-01]], dtype=float32)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "preds1=preds.argmax(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 5, 4, 8, 3, 6, 7, 6, 4, 1, 5, 7, 5, 2, 2, 6, 7, 2, 6, 9, 1, 2,\n",
       "       5, 1, 6, 3, 3, 9, 1, 8, 1, 6, 4, 6, 6, 5, 4, 7, 5, 3, 6, 8, 0, 3,\n",
       "       2, 6, 4, 1, 7, 0, 5, 2, 9, 9, 1, 3, 7, 5, 4, 4, 6, 4, 8, 9, 2, 8,\n",
       "       8, 3, 9, 3, 9, 3, 1, 5, 3, 7, 8, 5, 2, 6, 8, 8, 4, 4, 4, 1, 5, 7,\n",
       "       3, 8, 0, 7, 7, 6, 6, 2, 8, 0, 8, 5, 1, 4, 5, 1, 3, 4, 5, 1, 0, 2,\n",
       "       3, 6, 2, 7, 9, 7, 1, 4, 2, 3, 3, 9, 8, 5, 7, 0, 5, 3, 5, 0, 1, 4,\n",
       "       9, 2, 4, 0, 6, 4, 3, 4, 0, 6, 5, 9, 4, 5, 1, 4, 8, 5, 2, 9, 3, 9,\n",
       "       2, 5, 9, 1, 6, 9, 8, 1, 8, 2, 7, 9, 1, 7, 5, 8, 9, 6, 9, 4, 9, 1,\n",
       "       6, 7, 8, 9])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preds1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "abc = preds1.astype(int).flatten()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/therochvoices/anaconda3/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    }
   ],
   "source": [
    "predictions = (lb.inverse_transform((abc)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "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",
       "\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>predictedvalues</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>female_sad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>male_angry</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>female_sad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>male_happy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>female_happy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>male_calm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>male_fearful</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>male_calm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>female_sad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>female_calm</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  predictedvalues\n",
       "0      female_sad\n",
       "1      male_angry\n",
       "2      female_sad\n",
       "3      male_happy\n",
       "4    female_happy\n",
       "5       male_calm\n",
       "6    male_fearful\n",
       "7       male_calm\n",
       "8      female_sad\n",
       "9     female_calm"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preddf = pd.DataFrame({'predictedvalues': predictions})\n",
    "preddf[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/therochvoices/anaconda3/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    }
   ],
   "source": [
    "actual=y_test.argmax(axis=1)\n",
    "abc123 = actual.astype(int).flatten()\n",
    "actualvalues = (lb.inverse_transform((abc123)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>actualvalues</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>female_sad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>male_angry</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>female_sad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>male_happy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>female_happy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>male_calm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>male_fearful</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>male_calm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>female_sad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>female_calm</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   actualvalues\n",
       "0    female_sad\n",
       "1    male_angry\n",
       "2    female_sad\n",
       "3    male_happy\n",
       "4  female_happy\n",
       "5     male_calm\n",
       "6  male_fearful\n",
       "7     male_calm\n",
       "8    female_sad\n",
       "9   female_calm"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "actualdf = pd.DataFrame({'actualvalues': actualvalues})\n",
    "actualdf[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "finaldf = actualdf.join(preddf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Actual v/s Predicted emotions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>actualvalues</th>\n",
       "      <th>predictedvalues</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>male_sad</td>\n",
       "      <td>male_sad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>male_calm</td>\n",
       "      <td>male_calm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>male_sad</td>\n",
       "      <td>male_sad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>female_sad</td>\n",
       "      <td>female_sad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>male_sad</td>\n",
       "      <td>male_sad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>female_calm</td>\n",
       "      <td>female_calm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>male_calm</td>\n",
       "      <td>male_calm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>male_fearful</td>\n",
       "      <td>male_fearful</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>male_angry</td>\n",
       "      <td>male_happy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>male_sad</td>\n",
       "      <td>male_sad</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     actualvalues predictedvalues\n",
       "170      male_sad        male_sad\n",
       "171     male_calm       male_calm\n",
       "172      male_sad        male_sad\n",
       "173    female_sad      female_sad\n",
       "174      male_sad        male_sad\n",
       "175   female_calm     female_calm\n",
       "176     male_calm       male_calm\n",
       "177  male_fearful    male_fearful\n",
       "178    male_angry      male_happy\n",
       "179      male_sad        male_sad"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finaldf[170:220]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "matrix = confusion_matrix(actualdf, preddf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 9,  0,  0,  2,  0,  0,  0,  0,  0,  0],\n",
       "       [ 0, 17,  0,  1,  2,  0,  0,  0,  0,  0],\n",
       "       [ 0,  1, 15,  0,  0,  0,  0,  0,  0,  0],\n",
       "       [ 0,  1,  1, 15,  0,  0,  0,  0,  0,  0],\n",
       "       [ 0,  0,  0,  0, 20,  0,  0,  0,  0,  0],\n",
       "       [ 0,  0,  0,  0,  0, 22,  0,  1,  2,  0],\n",
       "       [ 0,  0,  0,  0,  0,  0, 20,  0,  1,  1],\n",
       "       [ 0,  0,  0,  0,  0,  0,  0, 15,  0,  0],\n",
       "       [ 0,  0,  0,  0,  0,  0,  0,  0, 14,  0],\n",
       "       [ 0,  0,  0,  0,  0,  0,  0,  0,  1, 19]])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb0c4b3f860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "ax = sns.heatmap(matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>predictedvalues</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>actualvalues</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female_angry</th>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>female_calm</th>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>female_fearful</th>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>female_happy</th>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>female_sad</th>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male_angry</th>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male_calm</th>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male_fearful</th>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male_happy</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male_sad</th>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                predictedvalues\n",
       "actualvalues                   \n",
       "female_angry                 21\n",
       "female_calm                  20\n",
       "female_fearful               19\n",
       "female_happy                 17\n",
       "female_sad                   20\n",
       "male_angry                   55\n",
       "male_calm                    25\n",
       "male_fearful                 55\n",
       "male_happy                   49\n",
       "male_sad                     38"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finaldf.groupby('actualvalues').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>actualvalues</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>predictedvalues</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female_angry</th>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>female_calm</th>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>female_fearful</th>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>female_happy</th>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>female_sad</th>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male_angry</th>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male_calm</th>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male_fearful</th>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male_happy</th>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male_sad</th>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 actualvalues\n",
       "predictedvalues              \n",
       "female_angry               25\n",
       "female_calm                22\n",
       "female_fearful             19\n",
       "female_happy                9\n",
       "female_sad                 25\n",
       "male_angry                 51\n",
       "male_calm                  29\n",
       "male_fearful               51\n",
       "male_happy                 50\n",
       "male_sad                   38"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finaldf.groupby('predictedvalues').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "finaldf.to_csv('Predictions.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# DEMO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [],
   "source": [
    "mylist = os.listdir('testfiles/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [],
   "source": [
    "feeling_list=[]\n",
    "for item in mylist:\n",
    "    if item[6:-16]=='02' and int(item[18:-4])%2==0:\n",
    "        feeling_list.append('female_calm')\n",
    "    elif item[6:-16]=='02' and int(item[18:-4])%2==1:\n",
    "        feeling_list.append('male_calm')\n",
    "    elif item[6:-16]=='03' and int(item[18:-4])%2==0:\n",
    "        feeling_list.append('female_happy')\n",
    "    elif item[6:-16]=='03' and int(item[18:-4])%2==1:\n",
    "        feeling_list.append('male_happy')\n",
    "    elif item[6:-16]=='04' and int(item[18:-4])%2==0:\n",
    "        feeling_list.append('female_sad')\n",
    "    elif item[6:-16]=='04' and int(item[18:-4])%2==1:\n",
    "        feeling_list.append('male_sad')\n",
    "    elif item[6:-16]=='05' and int(item[18:-4])%2==0:\n",
    "        feeling_list.append('female_angry')\n",
    "    elif item[6:-16]=='05' and int(item[18:-4])%2==1:\n",
    "        feeling_list.append('male_angry')\n",
    "    elif item[6:-16]=='06' and int(item[18:-4])%2==0:\n",
    "        feeling_list.append('female_fearful')\n",
    "    elif item[6:-16]=='06' and int(item[18:-4])%2==1:\n",
    "        feeling_list.append('male_fearful')\n",
    "    elif item[:1]=='a':\n",
    "        feeling_list.append('male_angry')\n",
    "    elif item[:1]=='f':\n",
    "        feeling_list.append('male_fearful')\n",
    "    elif item[:1]=='h':\n",
    "        feeling_list.append('male_happy')\n",
    "    #elif item[:1]=='n':\n",
    "        #feeling_list.append('neutral')\n",
    "    elif item[:2]=='sa':\n",
    "        feeling_list.append('male_sad')\n",
    "labels = pd.DataFrame(feeling_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(columns=['feature'])\n",
    "bookmark=0\n",
    "for index,y in enumerate(mylist):\n",
    "    if mylist[index][6:-16]!='01' and mylist[index][6:-16]!='07' and mylist[index][6:-16]!='08' and mylist[index][:2]!='su' and mylist[index][:1]!='n' and mylist[index][:1]!='d':\n",
    "        X, sample_rate = librosa.load('RawData/'+y, res_type='kaiser_fast',duration=2.5,sr=22050*2,offset=0.5)\n",
    "        sample_rate = np.array(sample_rate)\n",
    "        mfccs = np.mean(librosa.feature.mfcc(y=X, \n",
    "                                            sr=sample_rate, \n",
    "                                            n_mfcc=13),\n",
    "                        axis=0)\n",
    "        feature = mfccs\n",
    "        #[float(i) for i in feature]\n",
    "        #feature1=feature[:135]\n",
    "        df.loc[bookmark] = [feature]\n",
    "        bookmark=bookmark+1   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded model from disk\n",
      "\r",
      "5/5 [==============================] - 0s 6ms/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/therochvoices/anaconda3/lib/python3.6/site-packages/sklearn/preprocessing/label.py:128: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/home/therochvoices/anaconda3/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    }
   ],
   "source": [
    "df3 = pd.DataFrame(df['feature'].values.tolist())\n",
    "newdf = pd.concat([df3,labels], axis=1)\n",
    "rnewdf = newdf.rename(index=str, columns={\"0\": \"label\"})\n",
    "\n",
    "rnewdf=rnewdf.fillna(0)\n",
    "\n",
    "testfeatures = rnewdf.iloc[:, :-1]\n",
    "testlabel = rnewdf.iloc[:, -1:]\n",
    "\n",
    "\n",
    "\n",
    "X_test = np.array(testfeatures)\n",
    "y_test = np.array(testlabel)\n",
    "\n",
    "y_test = np_utils.to_categorical(lb.transform(y_test))\n",
    "\n",
    "x_testcnn= np.expand_dims(X_test, axis=2)\n",
    "\n",
    "opt = keras.optimizers.rmsprop(lr=0.00001, decay=1e-6)\n",
    "\n",
    "# loading json and creating model\n",
    "from keras.models import model_from_json\n",
    "json_file = open('model.json', 'r')\n",
    "loaded_model_json = json_file.read()\n",
    "json_file.close()\n",
    "loaded_model = model_from_json(loaded_model_json)\n",
    "# load weights into new model\n",
    "loaded_model.load_weights(\"saved_models/Emotion_Voice_Detection_Model.h5\")\n",
    "print(\"Loaded model from disk\")\n",
    " \n",
    "# evaluate loaded model on test data\n",
    "loaded_model.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['accuracy'])\n",
    "\n",
    "\n",
    "preds = loaded_model.predict(x_testcnn, \n",
    "                         batch_size=32, \n",
    "                         verbose=1)\n",
    "\n",
    "preds1=preds.argmax(axis=1)\n",
    "abc = preds1.astype(int).flatten()\n",
    "#predictions = (lb.inverse_transform((abc)))\n",
    "#preddf = pd.DataFrame({'predictedvalues': predictions})\n",
    "predictions = (lb.inverse_transform((abc)))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/therochvoices/anaconda3/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    }
   ],
   "source": [
    "preddf = pd.DataFrame({'predictedvalues': predictions})\n",
    "actual=y_test.argmax(axis=1)\n",
    "abc123 = actual.astype(int).flatten()\n",
    "actualvalues = (lb.inverse_transform((abc123)))\n",
    "actualdf = pd.DataFrame({'actualvalues': actualvalues})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [],
   "source": [
    "finaldf = actualdf.join(preddf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['female_angry', 'female_calm', 'female_fearful', 'female_happy',\n",
       "       'female_sad', 'male_angry', 'male_calm', 'male_fearful',\n",
       "       'male_happy', 'male_sad'], dtype=object)"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lb.classes_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "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>actualvalues</th>\n",
       "      <th>predictedvalues</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>male_fearful</td>\n",
       "      <td>male_sad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>female_angry</td>\n",
       "      <td>female_angry</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>female_sad</td>\n",
       "      <td>female_calm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>male_sad</td>\n",
       "      <td>male_sad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>female_happy</td>\n",
       "      <td>female_happy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   actualvalues predictedvalues\n",
       "0  male_fearful        male_sad\n",
       "1  female_angry    female_angry\n",
       "2    female_sad     female_calm\n",
       "3      male_sad        male_sad\n",
       "4  female_happy    female_happy"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finaldf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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