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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# I. Importing the required libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script><script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window._Plotly) {require(['plotly'],function(plotly) {window._Plotly=plotly;});}</script>"
+      ],
+      "text/vnd.plotly.v1+html": [
+       "<script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script><script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window._Plotly) {require(['plotly'],function(plotly) {window._Plotly=plotly;});}</script>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Orignial Notebook: https://github.com/MITESHPUTHRANNEU/Speech-Emotion-Analyzer/blob/master/final_results_gender_test.ipynb\n",
+    "# This notebook author: Reza Chu\n",
+    "# Last Editing Date: 31st May 2019\n",
+    "\n",
+    "## Python\n",
+    "import os\n",
+    "import random\n",
+    "import sys\n",
+    "\n",
+    "\n",
+    "## Package\n",
+    "import glob \n",
+    "import keras\n",
+    "import IPython.display as ipd\n",
+    "import librosa\n",
+    "import librosa.display\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import plotly.graph_objs as go\n",
+    "import plotly.offline as py\n",
+    "import plotly.tools as tls\n",
+    "import seaborn as sns\n",
+    "import scipy.io.wavfile\n",
+    "import tensorflow as tf\n",
+    "py.init_notebook_mode(connected=True)\n",
+    "\n",
+    "\n",
+    "## Keras\n",
+    "from keras import regularizers\n",
+    "from keras.callbacks import ModelCheckpoint, LearningRateScheduler, EarlyStopping\n",
+    "from keras.callbacks import  History, ReduceLROnPlateau, CSVLogger\n",
+    "from keras.models import Model, Sequential\n",
+    "from keras.layers import Dense, Embedding, LSTM\n",
+    "from keras.layers import Input, Flatten, Dropout, Activation, BatchNormalization\n",
+    "from keras.layers import Conv1D, MaxPooling1D, AveragePooling1D\n",
+    "from keras.preprocessing import sequence\n",
+    "from keras.preprocessing.sequence import pad_sequences\n",
+    "from keras.preprocessing.text import Tokenizer\n",
+    "from keras.utils import np_utils\n",
+    "from keras.utils import to_categorical\n",
+    "\n",
+    "\n",
+    "## Sklearn\n",
+    "from sklearn.metrics import confusion_matrix\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "\n",
+    "\n",
+    "## Rest\n",
+    "from scipy.fftpack import fft\n",
+    "from scipy import signal\n",
+    "from scipy.io import wavfile\n",
+    "from tqdm import tqdm\n",
+    "\n",
+    "input_duration=3\n",
+    "# % pylab inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# II. Reading the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['Actor_01', 'Actor_02', 'Actor_03', 'Actor_04', 'Actor_05', 'Actor_06', 'Actor_07', 'Actor_08', 'Actor_09', 'Actor_10', 'Actor_11', 'Actor_12', 'Actor_13', 'Actor_14', 'Actor_15', 'Actor_16', 'Actor_17', 'Actor_18', 'Actor_19', 'Actor_20', 'Actor_21', 'Actor_22', 'Actor_23', 'Actor_24']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Data Directory\n",
+    "# Please edit according to your directory change.\n",
+    "dir_list = os.listdir('data/')\n",
+    "dir_list.sort()\n",
+    "print (dir_list)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create DataFrame for Data intel\n",
+    "data_df = pd.DataFrame(columns=['path', 'source', 'actor', 'gender',\n",
+    "                                'intensity', 'statement', 'repetition', 'emotion'])\n",
+    "count = 0\n",
+    "for i in dir_list:\n",
+    "    file_list = os.listdir('data/' + i)\n",
+    "    for f in file_list:\n",
+    "        nm = f.split('.')[0].split('-')\n",
+    "        path = 'data/' + i + '/' + f\n",
+    "        src = int(nm[1])\n",
+    "        actor = int(nm[-1])\n",
+    "        emotion = int(nm[2])\n",
+    "        \n",
+    "        if int(actor)%2 == 0:\n",
+    "            gender = \"female\"\n",
+    "        else:\n",
+    "            gender = \"male\"\n",
+    "        \n",
+    "        if nm[3] == '01':\n",
+    "            intensity = 0\n",
+    "        else:\n",
+    "            intensity = 1\n",
+    "        \n",
+    "        if nm[4] == '01':\n",
+    "            statement = 0\n",
+    "        else:\n",
+    "            statement = 1\n",
+    "        \n",
+    "        if nm[5] == '01':\n",
+    "            repeat = 0\n",
+    "        else:\n",
+    "            repeat = 1\n",
+    "            \n",
+    "        data_df.loc[count] = [path, src, actor, gender, intensity, statement, repeat, emotion]\n",
+    "        count += 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2452\n"
+     ]
+    },
+    {
+     "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>path</th>\n",
+       "      <th>source</th>\n",
+       "      <th>actor</th>\n",
+       "      <th>gender</th>\n",
+       "      <th>intensity</th>\n",
+       "      <th>statement</th>\n",
+       "      <th>repetition</th>\n",
+       "      <th>emotion</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>data/Actor_01/03-02-04-02-02-01-01.wav</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>male</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>data/Actor_01/03-01-04-02-01-02-01.wav</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>male</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>data/Actor_01/03-02-05-02-01-01-01.wav</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>male</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>data/Actor_01/03-01-04-01-01-02-01.wav</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>data/Actor_01/03-01-02-01-02-01-01.wav</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                     path source actor gender intensity  \\\n",
+       "0  data/Actor_01/03-02-04-02-02-01-01.wav      2     1   male         1   \n",
+       "1  data/Actor_01/03-01-04-02-01-02-01.wav      1     1   male         1   \n",
+       "2  data/Actor_01/03-02-05-02-01-01-01.wav      2     1   male         1   \n",
+       "3  data/Actor_01/03-01-04-01-01-02-01.wav      1     1   male         0   \n",
+       "4  data/Actor_01/03-01-02-01-02-01-01.wav      1     1   male         0   \n",
+       "\n",
+       "  statement repetition emotion  \n",
+       "0         1          0       4  \n",
+       "1         0          1       4  \n",
+       "2         0          0       5  \n",
+       "3         0          1       4  \n",
+       "4         1          0       2  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "print (len(data_df))\n",
+    "data_df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# III. Plotting the audio file's waveform and its spectrogram"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "data/Actor_10/03-01-05-01-01-02-10.wav\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(22050, array([-3.77176912e-05, -1.10931855e-04,  2.90319556e-04, ...,\n",
+       "         2.24706928e-05, -3.84291661e-06,  0.00000000e+00], dtype=float32))"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "filename = data_df.path[1021]\n",
+    "print (filename)\n",
+    "\n",
+    "samples, sample_rate = librosa.load(filename)\n",
+    "sample_rate, samples"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(88289, 22050)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(samples), sample_rate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def log_specgram(audio, sample_rate, window_size=20,\n",
+    "                 step_size=10, eps=1e-10):\n",
+    "    nperseg = int(round(window_size * sample_rate / 1e3))\n",
+    "    noverlap = int(round(step_size * sample_rate / 1e3))\n",
+    "    freqs, times, spec = signal.spectrogram(audio,\n",
+    "                                    fs=sample_rate,\n",
+    "                                    window='hann',\n",
+    "                                    nperseg=nperseg,\n",
+    "                                    noverlap=noverlap,\n",
+    "                                    detrend=False)\n",
+    "    return freqs, times, np.log(spec.T.astype(np.float32) + eps)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.24974798672541312"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sample_rate/ len(samples)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 0, 'Seconds')"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1008x576 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plotting Wave Form and Spectrogram\n",
+    "freqs, times, spectrogram = log_specgram(samples, sample_rate)\n",
+    "\n",
+    "fig = plt.figure(figsize=(14, 8))\n",
+    "ax1 = fig.add_subplot(211)\n",
+    "ax1.set_title('Raw wave of ' + filename)\n",
+    "ax1.set_ylabel('Amplitude')\n",
+    "librosa.display.waveplot(samples, sr=sample_rate)\n",
+    "\n",
+    "ax2 = fig.add_subplot(212)\n",
+    "ax2.imshow(spectrogram.T, aspect='auto', origin='lower', \n",
+    "           extent=[times.min(), times.max(), freqs.min(), freqs.max()])\n",
+    "ax2.set_yticks(freqs[::16])\n",
+    "ax2.set_xticks(times[::16])\n",
+    "ax2.set_title('Spectrogram of ' + filename)\n",
+    "ax2.set_ylabel('Freqs in Hz')\n",
+    "ax2.set_xlabel('Seconds')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mean = np.mean(spectrogram, axis=0)\n",
+    "std = np.std(spectrogram, axis=0)\n",
+    "spectrogram = (spectrogram - mean) / std"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([ 9.8497745e-05,  1.0179912e-04, -3.2110562e-05, ...,\n",
+       "         2.2454399e-03,  1.4861705e-03,  4.5785634e-04], dtype=float32),\n",
+       " array([22016, 66560]))"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Trim the silence voice\n",
+    "aa , bb = librosa.effects.trim(samples, top_db=30)\n",
+    "aa, bb"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plotting Mel Power Spectrogram\n",
+    "S = librosa.feature.melspectrogram(aa, sr=sample_rate, n_mels=128)\n",
+    "\n",
+    "# Convert to log scale (dB). We'll use the peak power (max) as reference.\n",
+    "log_S = librosa.power_to_db(S, ref=np.max)\n",
+    "\n",
+    "plt.figure(figsize=(12, 4))\n",
+    "librosa.display.specshow(log_S, sr=sample_rate, x_axis='time', y_axis='mel')\n",
+    "plt.title('Mel power spectrogram ')\n",
+    "plt.colorbar(format='%+02.0f dB')\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/reza/anaconda3/envs/dg/lib/python3.6/site-packages/scipy/signal/_arraytools.py:45: FutureWarning:\n",
+      "\n",
+      "Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plotting MFCC\n",
+    "mfcc = librosa.feature.mfcc(S=log_S, n_mfcc=13)\n",
+    "\n",
+    "# Let's pad on the first and second deltas while we're at it\n",
+    "delta2_mfcc = librosa.feature.delta(mfcc, order=2)\n",
+    "\n",
+    "plt.figure(figsize=(12, 4))\n",
+    "librosa.display.specshow(delta2_mfcc)\n",
+    "plt.ylabel('MFCC coeffs')\n",
+    "plt.xlabel('Time')\n",
+    "plt.title('MFCC')\n",
+    "plt.colorbar()\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "                <audio controls=\"controls\" >\n",
+       "                    <source 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type=\"audio/wav\" />\n",
+       "                    Your browser does not support the audio element.\n",
+       "                </audio>\n",
+       "              "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.Audio object>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Original Sound\n",
+    "ipd.Audio(samples, rate=sample_rate)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "                <audio controls=\"controls\" >\n",
+       "                    <source 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\" type=\"audio/wav\" />\n",
+       "                    Your browser does not support the audio element.\n",
+       "                </audio>\n",
+       "              "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.Audio object>"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Silence trimmed Sound by librosa.effects.trim()\n",
+    "ipd.Audio(aa, rate=sample_rate)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "                <audio controls=\"controls\" >\n",
+       "                    <source 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\" type=\"audio/wav\" />\n",
+       "                    Your browser does not support the audio element.\n",
+       "                </audio>\n",
+       "              "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.Audio object>"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Silence trimmed Sound by manuel trimming\n",
+    "samples_cut = samples[10000:-12500]\n",
+    "ipd.Audio(samples_cut, rate=sample_rate)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# IV. Defining the truth label"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2452"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 2 class: Positive & Negative\n",
+    "\n",
+    "# Positive: Calm, Happy\n",
+    "# Negative: Angry, Fearful, Sad\n",
+    "\n",
+    "label2_list = []\n",
+    "for i in range(len(data_df)):\n",
+    "    if data_df.emotion[i] == 2: # Calm\n",
+    "        lb = \"_positive\"\n",
+    "    elif data_df.emotion[i] == 3: # Happy\n",
+    "        lb = \"_positive\"\n",
+    "    elif data_df.emotion[i] == 4: # Sad\n",
+    "        lb = \"_negative\"\n",
+    "    elif data_df.emotion[i] == 5: # Angry\n",
+    "        lb = \"_negative\"\n",
+    "    elif data_df.emotion[i] == 6: # Fearful\n",
+    "        lb = \"_negative\"\n",
+    "    else:\n",
+    "        lb = \"_none\"\n",
+    "        \n",
+    "    # Add gender to the label    \n",
+    "    label2_list.append(data_df.gender[i] + lb)\n",
+    "    \n",
+    "len(label2_list)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2452"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#3 class: Positive, Neutral & Negative\n",
+    "\n",
+    "# Positive:  Happy\n",
+    "# Negative: Angry, Fearful, Sad\n",
+    "# Neutral: Calm, Neutral\n",
+    "\n",
+    "label3_list = []\n",
+    "for i in range(len(data_df)):\n",
+    "    if data_df.emotion[i] == 1: # Neutral\n",
+    "        lb = \"_neutral\"\n",
+    "    elif data_df.emotion[i] == 2: # Calm\n",
+    "        lb = \"_neutral\"\n",
+    "    elif data_df.emotion[i] == 3: # Happy\n",
+    "        lb = \"_positive\"\n",
+    "    elif data_df.emotion[i] == 4: # Sad\n",
+    "        lb = \"_negative\"\n",
+    "    elif data_df.emotion[i] == 5: # Angry\n",
+    "        lb = \"_negative\"\n",
+    "    elif data_df.emotion[i] == 6: # Fearful\n",
+    "        lb = \"_negative\"\n",
+    "    else:\n",
+    "        lb = \"_none\"\n",
+    "    \n",
+    "    # Add gender to the label  \n",
+    "    label3_list.append(data_df.gender[i] + lb)\n",
+    "    \n",
+    "len(label3_list)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2452"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 5 class: angry, calm, sad, happy & fearful\n",
+    "label5_list = []\n",
+    "for i in range(len(data_df)):\n",
+    "    if data_df.emotion[i] == 2:\n",
+    "        lb = \"_calm\"\n",
+    "    elif data_df.emotion[i] == 3:\n",
+    "        lb = \"_happy\"\n",
+    "    elif data_df.emotion[i] == 4:\n",
+    "        lb = \"_sad\"\n",
+    "    elif data_df.emotion[i] == 5:\n",
+    "        lb = \"_angry\"\n",
+    "    elif data_df.emotion[i] == 6:\n",
+    "        lb = \"_fearful\"    \n",
+    "    else:\n",
+    "        lb = \"_none\"\n",
+    "    \n",
+    "    # Add gender to the label  \n",
+    "    label5_list.append(data_df.gender[i] + lb)\n",
+    "    \n",
+    "len(label5_list)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2452"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# All class\n",
+    "\n",
+    "label8_list = []\n",
+    "for i in range(len(data_df)):\n",
+    "    if data_df.emotion[i] == 1:\n",
+    "        lb = \"_neutral\"\n",
+    "    elif data_df.emotion[i] == 2:\n",
+    "        lb = \"_calm\"\n",
+    "    elif data_df.emotion[i] == 3:\n",
+    "        lb = \"_happy\"\n",
+    "    elif data_df.emotion[i] == 4:\n",
+    "        lb = \"_sad\"\n",
+    "    elif data_df.emotion[i] == 5:\n",
+    "        lb = \"_angry\"\n",
+    "    elif data_df.emotion[i] == 6:\n",
+    "        lb = \"_fearful\"\n",
+    "    elif data_df.emotion[i] == 7:\n",
+    "        lb = \"_disgust\"\n",
+    "    elif data_df.emotion[i] == 8:\n",
+    "        lb = \"_surprised\"\n",
+    "    else:\n",
+    "        lb = \"_none\"\n",
+    "        \n",
+    "    # Add gender to the label \n",
+    "    label8_list.append(data_df.gender[i]  + lb)\n",
+    "    \n",
+    "len(label8_list)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "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>path</th>\n",
+       "      <th>source</th>\n",
+       "      <th>actor</th>\n",
+       "      <th>gender</th>\n",
+       "      <th>intensity</th>\n",
+       "      <th>statement</th>\n",
+       "      <th>repetition</th>\n",
+       "      <th>emotion</th>\n",
+       "      <th>label</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>data/Actor_01/03-02-04-02-02-01-01.wav</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>male</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>male_negative</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>data/Actor_01/03-01-04-02-01-02-01.wav</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>male</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>male_negative</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>data/Actor_01/03-02-05-02-01-01-01.wav</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>male</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>male_negative</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>data/Actor_01/03-01-04-01-01-02-01.wav</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>male_negative</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>data/Actor_01/03-01-02-01-02-01-01.wav</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>male_positive</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                     path source actor gender intensity  \\\n",
+       "0  data/Actor_01/03-02-04-02-02-01-01.wav      2     1   male         1   \n",
+       "1  data/Actor_01/03-01-04-02-01-02-01.wav      1     1   male         1   \n",
+       "2  data/Actor_01/03-02-05-02-01-01-01.wav      2     1   male         1   \n",
+       "3  data/Actor_01/03-01-04-01-01-02-01.wav      1     1   male         0   \n",
+       "4  data/Actor_01/03-01-02-01-02-01-01.wav      1     1   male         0   \n",
+       "\n",
+       "  statement repetition emotion          label  \n",
+       "0         1          0       4  male_negative  \n",
+       "1         0          1       4  male_negative  \n",
+       "2         0          0       5  male_negative  \n",
+       "3         0          1       4  male_negative  \n",
+       "4         1          0       2  male_positive  "
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Select the label set you want by commenting the unwanteds.\n",
+    "\n",
+    "data_df['label'] = label2_list\n",
+    "# data_df['label'] = label3_list\n",
+    "# data_df['label'] = label5_list\n",
+    "# data_df['label'] = label8_list\n",
+    "data_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Index(['male_negative', 'female_negative', 'male_positive', 'female_positive',\n",
+      "       'male_none', 'female_none'],\n",
+      "      dtype='object')\n"
+     ]
+    }
+   ],
+   "source": [
+    "print (data_df.label.value_counts().keys())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Plotting the emotion distribution\n",
+    "\n",
+    "def plot_emotion_dist(dist, color_code='#C2185B', title=\"Plot\"):\n",
+    "    \"\"\"\n",
+    "    To plot the data distributioin by class.\n",
+    "    Arg:\n",
+    "      dist: pandas series of label count. \n",
+    "    \"\"\"\n",
+    "    tmp_df = pd.DataFrame()\n",
+    "    tmp_df['Emotion'] = list(dist.keys())\n",
+    "    tmp_df['Count'] = list(dist)\n",
+    "    fig, ax = plt.subplots(figsize=(14, 7))\n",
+    "    ax = sns.barplot(x=\"Emotion\", y='Count', color=color_code, data=tmp_df)\n",
+    "    ax.set_title(title)\n",
+    "    ax.set_xticklabels(ax.get_xticklabels(),rotation=45)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.series.Series'>\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1008x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "a = data_df.label.value_counts()\n",
+    "plot_emotion_dist(a, \"#2962FF\", \"Emotion Distribution\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# V. Data Splitting"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Female Data Set\n",
+    "\n",
+    "## Uncomment all below to use Female set \n",
+    "\n",
+    "# data2_df = data_df.copy()\n",
+    "# data2_df = data2_df[data2_df.label != \"male_none\"]\n",
+    "# data2_df = data2_df[data2_df.label != \"female_none\"]\n",
+    "# data2_df = data2_df[data2_df.label != \"male_happy\"]\n",
+    "# data2_df = data2_df[data2_df.label != \"male_angry\"]\n",
+    "# data2_df = data2_df[data2_df.label != \"male_sad\"]\n",
+    "# data2_df = data2_df[data2_df.label != \"male_fearful\"]\n",
+    "# data2_df = data2_df[data2_df.label != \"male_calm\"]\n",
+    "# data2_df = data2_df[data2_df.label != \"male_positive\"]\n",
+    "# data2_df = data2_df[data2_df.label != \"male_negative\"].reset_index(drop=True)\n",
+    "\n",
+    "# tmp1 = data2_df[data2_df.actor == 22]\n",
+    "# tmp2 = data2_df[data2_df.actor == 24]\n",
+    "# data3_df = pd.concat([tmp1, tmp2],ignore_index=True).reset_index(drop=True)\n",
+    "# data2_df = data2_df[data2_df.actor != 22]\n",
+    "# data2_df = data2_df[data2_df.actor != 24].reset_index(drop=True)\n",
+    "# print (len(data2_df))\n",
+    "# data2_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "800\n"
+     ]
+    },
+    {
+     "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>path</th>\n",
+       "      <th>source</th>\n",
+       "      <th>actor</th>\n",
+       "      <th>gender</th>\n",
+       "      <th>intensity</th>\n",
+       "      <th>statement</th>\n",
+       "      <th>repetition</th>\n",
+       "      <th>emotion</th>\n",
+       "      <th>label</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>data/Actor_01/03-02-04-02-02-01-01.wav</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>male</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>male_negative</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>data/Actor_01/03-01-04-02-01-02-01.wav</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>male</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>male_negative</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>data/Actor_01/03-02-05-02-01-01-01.wav</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>male</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>male_negative</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>data/Actor_01/03-01-04-01-01-02-01.wav</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>male_negative</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>data/Actor_01/03-01-02-01-02-01-01.wav</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>male_positive</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                     path source actor gender intensity  \\\n",
+       "0  data/Actor_01/03-02-04-02-02-01-01.wav      2     1   male         1   \n",
+       "1  data/Actor_01/03-01-04-02-01-02-01.wav      1     1   male         1   \n",
+       "2  data/Actor_01/03-02-05-02-01-01-01.wav      2     1   male         1   \n",
+       "3  data/Actor_01/03-01-04-01-01-02-01.wav      1     1   male         0   \n",
+       "4  data/Actor_01/03-01-02-01-02-01-01.wav      1     1   male         0   \n",
+       "\n",
+       "  statement repetition emotion          label  \n",
+       "0         1          0       4  male_negative  \n",
+       "1         0          1       4  male_negative  \n",
+       "2         0          0       5  male_negative  \n",
+       "3         0          1       4  male_negative  \n",
+       "4         1          0       2  male_positive  "
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Male Data Set\n",
+    "\n",
+    "## Uncomment all below to use Male set \n",
+    "\n",
+    "data2_df = data_df.copy()\n",
+    "data2_df = data2_df[data2_df.label != \"male_none\"]\n",
+    "data2_df = data2_df[data2_df.label != \"female_none\"].reset_index(drop=True)\n",
+    "data2_df = data2_df[data2_df.label != \"female_neutral\"]\n",
+    "data2_df = data2_df[data2_df.label != \"female_happy\"]\n",
+    "data2_df = data2_df[data2_df.label != \"female_angry\"]\n",
+    "data2_df = data2_df[data2_df.label != \"female_sad\"]\n",
+    "data2_df = data2_df[data2_df.label != \"female_fearful\"]\n",
+    "data2_df = data2_df[data2_df.label != \"female_calm\"]\n",
+    "data2_df = data2_df[data2_df.label != \"female_positive\"]\n",
+    "data2_df = data2_df[data2_df.label != \"female_negative\"].reset_index(drop=True)\n",
+    "\n",
+    "tmp1 = data2_df[data2_df.actor == 21]\n",
+    "tmp2 = data2_df[data2_df.actor == 22]\n",
+    "tmp3 = data2_df[data2_df.actor == 23]\n",
+    "tmp4 = data2_df[data2_df.actor == 24]\n",
+    "data3_df = pd.concat([tmp1, tmp3],ignore_index=True).reset_index(drop=True)\n",
+    "data2_df = data2_df[data2_df.actor != 21]\n",
+    "data2_df = data2_df[data2_df.actor != 22]\n",
+    "data2_df = data2_df[data2_df.actor != 23].reset_index(drop=True)\n",
+    "data2_df = data2_df[data2_df.actor != 24].reset_index(drop=True)\n",
+    "print (len(data2_df))\n",
+    "data2_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "160\n"
+     ]
+    },
+    {
+     "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>path</th>\n",
+       "      <th>source</th>\n",
+       "      <th>actor</th>\n",
+       "      <th>gender</th>\n",
+       "      <th>intensity</th>\n",
+       "      <th>statement</th>\n",
+       "      <th>repetition</th>\n",
+       "      <th>emotion</th>\n",
+       "      <th>label</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>data/Actor_21/03-01-04-02-02-02-21.wav</td>\n",
+       "      <td>1</td>\n",
+       "      <td>21</td>\n",
+       "      <td>male</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>male_negative</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>data/Actor_21/03-02-03-02-01-01-21.wav</td>\n",
+       "      <td>2</td>\n",
+       "      <td>21</td>\n",
+       "      <td>male</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>male_positive</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>data/Actor_21/03-02-05-01-02-01-21.wav</td>\n",
+       "      <td>2</td>\n",
+       "      <td>21</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>male_negative</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>data/Actor_21/03-01-02-02-01-02-21.wav</td>\n",
+       "      <td>1</td>\n",
+       "      <td>21</td>\n",
+       "      <td>male</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>male_positive</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>data/Actor_21/03-01-02-02-02-01-21.wav</td>\n",
+       "      <td>1</td>\n",
+       "      <td>21</td>\n",
+       "      <td>male</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>male_positive</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                     path source actor gender intensity  \\\n",
+       "0  data/Actor_21/03-01-04-02-02-02-21.wav      1    21   male         1   \n",
+       "1  data/Actor_21/03-02-03-02-01-01-21.wav      2    21   male         1   \n",
+       "2  data/Actor_21/03-02-05-01-02-01-21.wav      2    21   male         0   \n",
+       "3  data/Actor_21/03-01-02-02-01-02-21.wav      1    21   male         1   \n",
+       "4  data/Actor_21/03-01-02-02-02-01-21.wav      1    21   male         1   \n",
+       "\n",
+       "  statement repetition emotion          label  \n",
+       "0         1          1       4  male_negative  \n",
+       "1         0          0       3  male_positive  \n",
+       "2         1          0       5  male_negative  \n",
+       "3         0          1       2  male_positive  \n",
+       "4         1          0       2  male_positive  "
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "print (len(data3_df))\n",
+    "data3_df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# VI. Getting the features of audio files using librosa"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 800/800 [00:31<00:00, 25.69it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "data = pd.DataFrame(columns=['feature'])\n",
+    "for i in tqdm(range(len(data2_df))):\n",
+    "    X, sample_rate = librosa.load(data2_df.path[i], res_type='kaiser_fast',duration=input_duration,sr=22050*2,offset=0.5)\n",
+    "#     X = X[10000:90000]\n",
+    "    sample_rate = np.array(sample_rate)\n",
+    "    mfccs = np.mean(librosa.feature.mfcc(y=X, sr=sample_rate, n_mfcc=13), axis=0)\n",
+    "    feature = mfccs\n",
+    "    data.loc[i] = [feature]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "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>feature</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>[-56.91284250420127, -56.48526874950596, -56.5...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>[-62.104852507780045, -62.104852507780045, -62...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>[-45.968126964359264, -45.81826183337232, -44....</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>[-67.73615146024396, -67.77801795322442, -66.7...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>[-69.67328949566406, -69.69331084873151, -69.6...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                             feature\n",
+       "0  [-56.91284250420127, -56.48526874950596, -56.5...\n",
+       "1  [-62.104852507780045, -62.104852507780045, -62...\n",
+       "2  [-45.968126964359264, -45.81826183337232, -44....\n",
+       "3  [-67.73615146024396, -67.77801795322442, -66.7...\n",
+       "4  [-69.67328949566406, -69.69331084873151, -69.6..."
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df3 = pd.DataFrame(data['feature'].values.tolist())\n",
+    "labels = data2_df.label"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "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>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "      <th>4</th>\n",
+       "      <th>5</th>\n",
+       "      <th>6</th>\n",
+       "      <th>7</th>\n",
+       "      <th>8</th>\n",
+       "      <th>9</th>\n",
+       "      <th>...</th>\n",
+       "      <th>249</th>\n",
+       "      <th>250</th>\n",
+       "      <th>251</th>\n",
+       "      <th>252</th>\n",
+       "      <th>253</th>\n",
+       "      <th>254</th>\n",
+       "      <th>255</th>\n",
+       "      <th>256</th>\n",
+       "      <th>257</th>\n",
+       "      <th>258</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-56.912843</td>\n",
+       "      <td>-56.485269</td>\n",
+       "      <td>-56.501424</td>\n",
+       "      <td>-57.221756</td>\n",
+       "      <td>-57.221756</td>\n",
+       "      <td>-57.221756</td>\n",
+       "      <td>-57.221756</td>\n",
+       "      <td>-57.221756</td>\n",
+       "      <td>-57.221756</td>\n",
+       "      <td>-57.221756</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-44.822108</td>\n",
+       "      <td>-46.451407</td>\n",
+       "      <td>-46.669580</td>\n",
+       "      <td>-46.596475</td>\n",
+       "      <td>-47.113484</td>\n",
+       "      <td>-46.768849</td>\n",
+       "      <td>-48.650488</td>\n",
+       "      <td>-52.294436</td>\n",
+       "      <td>-51.852247</td>\n",
+       "      <td>-47.143469</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-62.104853</td>\n",
+       "      <td>-62.104853</td>\n",
+       "      <td>-62.104853</td>\n",
+       "      <td>-62.037936</td>\n",
+       "      <td>-62.104853</td>\n",
+       "      <td>-62.104853</td>\n",
+       "      <td>-62.104853</td>\n",
+       "      <td>-62.104853</td>\n",
+       "      <td>-62.104853</td>\n",
+       "      <td>-62.104853</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-62.104853</td>\n",
+       "      <td>-62.104853</td>\n",
+       "      <td>-62.104853</td>\n",
+       "      <td>-62.104853</td>\n",
+       "      <td>-62.104853</td>\n",
+       "      <td>-62.104853</td>\n",
+       "      <td>-62.104853</td>\n",
+       "      <td>-62.104853</td>\n",
+       "      <td>-62.104853</td>\n",
+       "      <td>-62.104853</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-45.968127</td>\n",
+       "      <td>-45.818262</td>\n",
+       "      <td>-44.713754</td>\n",
+       "      <td>-43.853780</td>\n",
+       "      <td>-44.876142</td>\n",
+       "      <td>-45.742182</td>\n",
+       "      <td>-44.462401</td>\n",
+       "      <td>-45.629232</td>\n",
+       "      <td>-45.794369</td>\n",
+       "      <td>-44.506374</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-17.042166</td>\n",
+       "      <td>-17.049920</td>\n",
+       "      <td>-18.028540</td>\n",
+       "      <td>-16.300012</td>\n",
+       "      <td>-15.349774</td>\n",
+       "      <td>-14.373643</td>\n",
+       "      <td>-14.399101</td>\n",
+       "      <td>-13.917189</td>\n",
+       "      <td>-14.184953</td>\n",
+       "      <td>-14.288989</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>-67.736151</td>\n",
+       "      <td>-67.778018</td>\n",
+       "      <td>-66.746144</td>\n",
+       "      <td>-65.957883</td>\n",
+       "      <td>-67.736151</td>\n",
+       "      <td>-67.736151</td>\n",
+       "      <td>-67.736151</td>\n",
+       "      <td>-67.258683</td>\n",
+       "      <td>-67.736151</td>\n",
+       "      <td>-67.736151</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-67.736151</td>\n",
+       "      <td>-67.736151</td>\n",
+       "      <td>-67.736151</td>\n",
+       "      <td>-67.736151</td>\n",
+       "      <td>-67.736151</td>\n",
+       "      <td>-67.736151</td>\n",
+       "      <td>-67.736151</td>\n",
+       "      <td>-67.736151</td>\n",
+       "      <td>-67.736151</td>\n",
+       "      <td>-67.736151</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>-69.673289</td>\n",
+       "      <td>-69.693311</td>\n",
+       "      <td>-69.693311</td>\n",
+       "      <td>-69.693311</td>\n",
+       "      <td>-69.693311</td>\n",
+       "      <td>-69.693311</td>\n",
+       "      <td>-69.693311</td>\n",
+       "      <td>-69.620774</td>\n",
+       "      <td>-69.693311</td>\n",
+       "      <td>-68.906572</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-69.693311</td>\n",
+       "      <td>-69.693311</td>\n",
+       "      <td>-69.693311</td>\n",
+       "      <td>-69.693311</td>\n",
+       "      <td>-69.693311</td>\n",
+       "      <td>-69.383522</td>\n",
+       "      <td>-69.693311</td>\n",
+       "      <td>-69.693311</td>\n",
+       "      <td>-69.693311</td>\n",
+       "      <td>-69.693311</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 259 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         0          1          2          3          4          5    \\\n",
+       "0 -56.912843 -56.485269 -56.501424 -57.221756 -57.221756 -57.221756   \n",
+       "1 -62.104853 -62.104853 -62.104853 -62.037936 -62.104853 -62.104853   \n",
+       "2 -45.968127 -45.818262 -44.713754 -43.853780 -44.876142 -45.742182   \n",
+       "3 -67.736151 -67.778018 -66.746144 -65.957883 -67.736151 -67.736151   \n",
+       "4 -69.673289 -69.693311 -69.693311 -69.693311 -69.693311 -69.693311   \n",
+       "\n",
+       "         6          7          8          9      ...            249  \\\n",
+       "0 -57.221756 -57.221756 -57.221756 -57.221756    ...     -44.822108   \n",
+       "1 -62.104853 -62.104853 -62.104853 -62.104853    ...     -62.104853   \n",
+       "2 -44.462401 -45.629232 -45.794369 -44.506374    ...     -17.042166   \n",
+       "3 -67.736151 -67.258683 -67.736151 -67.736151    ...     -67.736151   \n",
+       "4 -69.693311 -69.620774 -69.693311 -68.906572    ...     -69.693311   \n",
+       "\n",
+       "         250        251        252        253        254        255  \\\n",
+       "0 -46.451407 -46.669580 -46.596475 -47.113484 -46.768849 -48.650488   \n",
+       "1 -62.104853 -62.104853 -62.104853 -62.104853 -62.104853 -62.104853   \n",
+       "2 -17.049920 -18.028540 -16.300012 -15.349774 -14.373643 -14.399101   \n",
+       "3 -67.736151 -67.736151 -67.736151 -67.736151 -67.736151 -67.736151   \n",
+       "4 -69.693311 -69.693311 -69.693311 -69.693311 -69.383522 -69.693311   \n",
+       "\n",
+       "         256        257        258  \n",
+       "0 -52.294436 -51.852247 -47.143469  \n",
+       "1 -62.104853 -62.104853 -62.104853  \n",
+       "2 -13.917189 -14.184953 -14.288989  \n",
+       "3 -67.736151 -67.736151 -67.736151  \n",
+       "4 -69.693311 -69.693311 -69.693311  \n",
+       "\n",
+       "[5 rows x 259 columns]"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df3.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "newdf = pd.concat([df3,labels], axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "800"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rnewdf = newdf.rename(index=str, columns={\"0\": \"label\"})\n",
+    "len(rnewdf)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "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>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "      <th>4</th>\n",
+       "      <th>5</th>\n",
+       "      <th>6</th>\n",
+       "      <th>7</th>\n",
+       "      <th>8</th>\n",
+       "      <th>9</th>\n",
+       "      <th>...</th>\n",
+       "      <th>250</th>\n",
+       "      <th>251</th>\n",
+       "      <th>252</th>\n",
+       "      <th>253</th>\n",
+       "      <th>254</th>\n",
+       "      <th>255</th>\n",
+       "      <th>256</th>\n",
+       "      <th>257</th>\n",
+       "      <th>258</th>\n",
+       "      <th>label</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-56.912843</td>\n",
+       "      <td>-56.485269</td>\n",
+       "      <td>-56.501424</td>\n",
+       "      <td>-57.221756</td>\n",
+       "      <td>-57.221756</td>\n",
+       "      <td>-57.221756</td>\n",
+       "      <td>-57.221756</td>\n",
+       "      <td>-57.221756</td>\n",
+       "      <td>-57.221756</td>\n",
+       "      <td>-57.221756</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-46.451407</td>\n",
+       "      <td>-46.669580</td>\n",
+       "      <td>-46.596475</td>\n",
+       "      <td>-47.113484</td>\n",
+       "      <td>-46.768849</td>\n",
+       "      <td>-48.650488</td>\n",
+       "      <td>-52.294436</td>\n",
+       "      <td>-51.852247</td>\n",
+       "      <td>-47.143469</td>\n",
+       "      <td>male_negative</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-62.104853</td>\n",
+       "      <td>-62.104853</td>\n",
+       "      <td>-62.104853</td>\n",
+       "      <td>-62.037936</td>\n",
+       "      <td>-62.104853</td>\n",
+       "      <td>-62.104853</td>\n",
+       "      <td>-62.104853</td>\n",
+       "      <td>-62.104853</td>\n",
+       "      <td>-62.104853</td>\n",
+       "      <td>-62.104853</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-62.104853</td>\n",
+       "      <td>-62.104853</td>\n",
+       "      <td>-62.104853</td>\n",
+       "      <td>-62.104853</td>\n",
+       "      <td>-62.104853</td>\n",
+       "      <td>-62.104853</td>\n",
+       "      <td>-62.104853</td>\n",
+       "      <td>-62.104853</td>\n",
+       "      <td>-62.104853</td>\n",
+       "      <td>male_negative</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-45.968127</td>\n",
+       "      <td>-45.818262</td>\n",
+       "      <td>-44.713754</td>\n",
+       "      <td>-43.853780</td>\n",
+       "      <td>-44.876142</td>\n",
+       "      <td>-45.742182</td>\n",
+       "      <td>-44.462401</td>\n",
+       "      <td>-45.629232</td>\n",
+       "      <td>-45.794369</td>\n",
+       "      <td>-44.506374</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-17.049920</td>\n",
+       "      <td>-18.028540</td>\n",
+       "      <td>-16.300012</td>\n",
+       "      <td>-15.349774</td>\n",
+       "      <td>-14.373643</td>\n",
+       "      <td>-14.399101</td>\n",
+       "      <td>-13.917189</td>\n",
+       "      <td>-14.184953</td>\n",
+       "      <td>-14.288989</td>\n",
+       "      <td>male_negative</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>-67.736151</td>\n",
+       "      <td>-67.778018</td>\n",
+       "      <td>-66.746144</td>\n",
+       "      <td>-65.957883</td>\n",
+       "      <td>-67.736151</td>\n",
+       "      <td>-67.736151</td>\n",
+       "      <td>-67.736151</td>\n",
+       "      <td>-67.258683</td>\n",
+       "      <td>-67.736151</td>\n",
+       "      <td>-67.736151</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-67.736151</td>\n",
+       "      <td>-67.736151</td>\n",
+       "      <td>-67.736151</td>\n",
+       "      <td>-67.736151</td>\n",
+       "      <td>-67.736151</td>\n",
+       "      <td>-67.736151</td>\n",
+       "      <td>-67.736151</td>\n",
+       "      <td>-67.736151</td>\n",
+       "      <td>-67.736151</td>\n",
+       "      <td>male_negative</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>-69.673289</td>\n",
+       "      <td>-69.693311</td>\n",
+       "      <td>-69.693311</td>\n",
+       "      <td>-69.693311</td>\n",
+       "      <td>-69.693311</td>\n",
+       "      <td>-69.693311</td>\n",
+       "      <td>-69.693311</td>\n",
+       "      <td>-69.620774</td>\n",
+       "      <td>-69.693311</td>\n",
+       "      <td>-68.906572</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-69.693311</td>\n",
+       "      <td>-69.693311</td>\n",
+       "      <td>-69.693311</td>\n",
+       "      <td>-69.693311</td>\n",
+       "      <td>-69.383522</td>\n",
+       "      <td>-69.693311</td>\n",
+       "      <td>-69.693311</td>\n",
+       "      <td>-69.693311</td>\n",
+       "      <td>-69.693311</td>\n",
+       "      <td>male_positive</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>-54.181857</td>\n",
+       "      <td>-53.653303</td>\n",
+       "      <td>-52.179519</td>\n",
+       "      <td>-52.163266</td>\n",
+       "      <td>-53.387515</td>\n",
+       "      <td>-53.853849</td>\n",
+       "      <td>-52.760702</td>\n",
+       "      <td>-51.047829</td>\n",
+       "      <td>-50.953316</td>\n",
+       "      <td>-50.094384</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-52.989496</td>\n",
+       "      <td>-52.172938</td>\n",
+       "      <td>-51.714458</td>\n",
+       "      <td>-53.732064</td>\n",
+       "      <td>-54.112915</td>\n",
+       "      <td>-52.875975</td>\n",
+       "      <td>-52.818990</td>\n",
+       "      <td>-52.377234</td>\n",
+       "      <td>-53.531375</td>\n",
+       "      <td>male_positive</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>-68.049082</td>\n",
+       "      <td>-68.049082</td>\n",
+       "      <td>-68.049082</td>\n",
+       "      <td>-68.049082</td>\n",
+       "      <td>-68.049082</td>\n",
+       "      <td>-68.049082</td>\n",
+       "      <td>-68.049082</td>\n",
+       "      <td>-68.049082</td>\n",
+       "      <td>-68.049082</td>\n",
+       "      <td>-68.049082</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>male_negative</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>-61.542955</td>\n",
+       "      <td>-61.670297</td>\n",
+       "      <td>-61.663520</td>\n",
+       "      <td>-61.675594</td>\n",
+       "      <td>-59.627474</td>\n",
+       "      <td>-58.578270</td>\n",
+       "      <td>-60.384651</td>\n",
+       "      <td>-60.370816</td>\n",
+       "      <td>-59.209070</td>\n",
+       "      <td>-59.793678</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-40.469880</td>\n",
+       "      <td>-41.462666</td>\n",
+       "      <td>-40.453254</td>\n",
+       "      <td>-42.066735</td>\n",
+       "      <td>-42.704448</td>\n",
+       "      <td>-41.112258</td>\n",
+       "      <td>-41.137339</td>\n",
+       "      <td>-41.213322</td>\n",
+       "      <td>-38.552507</td>\n",
+       "      <td>male_positive</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>-55.815831</td>\n",
+       "      <td>-55.791512</td>\n",
+       "      <td>-55.661960</td>\n",
+       "      <td>-55.702223</td>\n",
+       "      <td>-55.800005</td>\n",
+       "      <td>-55.791471</td>\n",
+       "      <td>-55.815808</td>\n",
+       "      <td>-55.815831</td>\n",
+       "      <td>-55.815831</td>\n",
+       "      <td>-55.676474</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-35.430449</td>\n",
+       "      <td>-34.440064</td>\n",
+       "      <td>-34.951263</td>\n",
+       "      <td>-33.887344</td>\n",
+       "      <td>-34.126421</td>\n",
+       "      <td>-33.674242</td>\n",
+       "      <td>-34.369253</td>\n",
+       "      <td>-36.464253</td>\n",
+       "      <td>-38.092677</td>\n",
+       "      <td>male_positive</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>-63.557785</td>\n",
+       "      <td>-63.707485</td>\n",
+       "      <td>-63.975054</td>\n",
+       "      <td>-63.975054</td>\n",
+       "      <td>-63.975054</td>\n",
+       "      <td>-63.975054</td>\n",
+       "      <td>-63.975054</td>\n",
+       "      <td>-63.975054</td>\n",
+       "      <td>-63.975054</td>\n",
+       "      <td>-63.975054</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-39.142542</td>\n",
+       "      <td>-40.554657</td>\n",
+       "      <td>-40.985578</td>\n",
+       "      <td>-41.396802</td>\n",
+       "      <td>-40.635562</td>\n",
+       "      <td>-40.026052</td>\n",
+       "      <td>-40.481173</td>\n",
+       "      <td>-39.127330</td>\n",
+       "      <td>-34.881790</td>\n",
+       "      <td>male_positive</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>10 rows × 260 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           0          1          2          3          4          5  \\\n",
+       "0 -56.912843 -56.485269 -56.501424 -57.221756 -57.221756 -57.221756   \n",
+       "1 -62.104853 -62.104853 -62.104853 -62.037936 -62.104853 -62.104853   \n",
+       "2 -45.968127 -45.818262 -44.713754 -43.853780 -44.876142 -45.742182   \n",
+       "3 -67.736151 -67.778018 -66.746144 -65.957883 -67.736151 -67.736151   \n",
+       "4 -69.673289 -69.693311 -69.693311 -69.693311 -69.693311 -69.693311   \n",
+       "5 -54.181857 -53.653303 -52.179519 -52.163266 -53.387515 -53.853849   \n",
+       "6 -68.049082 -68.049082 -68.049082 -68.049082 -68.049082 -68.049082   \n",
+       "7 -61.542955 -61.670297 -61.663520 -61.675594 -59.627474 -58.578270   \n",
+       "8 -55.815831 -55.791512 -55.661960 -55.702223 -55.800005 -55.791471   \n",
+       "9 -63.557785 -63.707485 -63.975054 -63.975054 -63.975054 -63.975054   \n",
+       "\n",
+       "           6          7          8          9      ...              250  \\\n",
+       "0 -57.221756 -57.221756 -57.221756 -57.221756      ...       -46.451407   \n",
+       "1 -62.104853 -62.104853 -62.104853 -62.104853      ...       -62.104853   \n",
+       "2 -44.462401 -45.629232 -45.794369 -44.506374      ...       -17.049920   \n",
+       "3 -67.736151 -67.258683 -67.736151 -67.736151      ...       -67.736151   \n",
+       "4 -69.693311 -69.620774 -69.693311 -68.906572      ...       -69.693311   \n",
+       "5 -52.760702 -51.047829 -50.953316 -50.094384      ...       -52.989496   \n",
+       "6 -68.049082 -68.049082 -68.049082 -68.049082      ...              NaN   \n",
+       "7 -60.384651 -60.370816 -59.209070 -59.793678      ...       -40.469880   \n",
+       "8 -55.815808 -55.815831 -55.815831 -55.676474      ...       -35.430449   \n",
+       "9 -63.975054 -63.975054 -63.975054 -63.975054      ...       -39.142542   \n",
+       "\n",
+       "         251        252        253        254        255        256  \\\n",
+       "0 -46.669580 -46.596475 -47.113484 -46.768849 -48.650488 -52.294436   \n",
+       "1 -62.104853 -62.104853 -62.104853 -62.104853 -62.104853 -62.104853   \n",
+       "2 -18.028540 -16.300012 -15.349774 -14.373643 -14.399101 -13.917189   \n",
+       "3 -67.736151 -67.736151 -67.736151 -67.736151 -67.736151 -67.736151   \n",
+       "4 -69.693311 -69.693311 -69.693311 -69.383522 -69.693311 -69.693311   \n",
+       "5 -52.172938 -51.714458 -53.732064 -54.112915 -52.875975 -52.818990   \n",
+       "6        NaN        NaN        NaN        NaN        NaN        NaN   \n",
+       "7 -41.462666 -40.453254 -42.066735 -42.704448 -41.112258 -41.137339   \n",
+       "8 -34.440064 -34.951263 -33.887344 -34.126421 -33.674242 -34.369253   \n",
+       "9 -40.554657 -40.985578 -41.396802 -40.635562 -40.026052 -40.481173   \n",
+       "\n",
+       "         257        258          label  \n",
+       "0 -51.852247 -47.143469  male_negative  \n",
+       "1 -62.104853 -62.104853  male_negative  \n",
+       "2 -14.184953 -14.288989  male_negative  \n",
+       "3 -67.736151 -67.736151  male_negative  \n",
+       "4 -69.693311 -69.693311  male_positive  \n",
+       "5 -52.377234 -53.531375  male_positive  \n",
+       "6        NaN        NaN  male_negative  \n",
+       "7 -41.213322 -38.552507  male_positive  \n",
+       "8 -36.464253 -38.092677  male_positive  \n",
+       "9 -39.127330 -34.881790  male_positive  \n",
+       "\n",
+       "[10 rows x 260 columns]"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rnewdf.head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2284"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rnewdf.isnull().sum().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
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+       "      <th>4</th>\n",
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+       "      <th>256</th>\n",
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+       "      <th>258</th>\n",
+       "      <th>label</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
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+       "      <th>0</th>\n",
+       "      <td>-56.912843</td>\n",
+       "      <td>-56.485269</td>\n",
+       "      <td>-56.501424</td>\n",
+       "      <td>-57.221756</td>\n",
+       "      <td>-57.221756</td>\n",
+       "      <td>-57.221756</td>\n",
+       "      <td>-57.221756</td>\n",
+       "      <td>-57.221756</td>\n",
+       "      <td>-57.221756</td>\n",
+       "      <td>-57.221756</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-46.451407</td>\n",
+       "      <td>-46.669580</td>\n",
+       "      <td>-46.596475</td>\n",
+       "      <td>-47.113484</td>\n",
+       "      <td>-46.768849</td>\n",
+       "      <td>-48.650488</td>\n",
+       "      <td>-52.294436</td>\n",
+       "      <td>-51.852247</td>\n",
+       "      <td>-47.143469</td>\n",
+       "      <td>male_negative</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-62.104853</td>\n",
+       "      <td>-62.104853</td>\n",
+       "      <td>-62.104853</td>\n",
+       "      <td>-62.037936</td>\n",
+       "      <td>-62.104853</td>\n",
+       "      <td>-62.104853</td>\n",
+       "      <td>-62.104853</td>\n",
+       "      <td>-62.104853</td>\n",
+       "      <td>-62.104853</td>\n",
+       "      <td>-62.104853</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-62.104853</td>\n",
+       "      <td>-62.104853</td>\n",
+       "      <td>-62.104853</td>\n",
+       "      <td>-62.104853</td>\n",
+       "      <td>-62.104853</td>\n",
+       "      <td>-62.104853</td>\n",
+       "      <td>-62.104853</td>\n",
+       "      <td>-62.104853</td>\n",
+       "      <td>-62.104853</td>\n",
+       "      <td>male_negative</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-45.968127</td>\n",
+       "      <td>-45.818262</td>\n",
+       "      <td>-44.713754</td>\n",
+       "      <td>-43.853780</td>\n",
+       "      <td>-44.876142</td>\n",
+       "      <td>-45.742182</td>\n",
+       "      <td>-44.462401</td>\n",
+       "      <td>-45.629232</td>\n",
+       "      <td>-45.794369</td>\n",
+       "      <td>-44.506374</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-17.049920</td>\n",
+       "      <td>-18.028540</td>\n",
+       "      <td>-16.300012</td>\n",
+       "      <td>-15.349774</td>\n",
+       "      <td>-14.373643</td>\n",
+       "      <td>-14.399101</td>\n",
+       "      <td>-13.917189</td>\n",
+       "      <td>-14.184953</td>\n",
+       "      <td>-14.288989</td>\n",
+       "      <td>male_negative</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>-67.736151</td>\n",
+       "      <td>-67.778018</td>\n",
+       "      <td>-66.746144</td>\n",
+       "      <td>-65.957883</td>\n",
+       "      <td>-67.736151</td>\n",
+       "      <td>-67.736151</td>\n",
+       "      <td>-67.736151</td>\n",
+       "      <td>-67.258683</td>\n",
+       "      <td>-67.736151</td>\n",
+       "      <td>-67.736151</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-67.736151</td>\n",
+       "      <td>-67.736151</td>\n",
+       "      <td>-67.736151</td>\n",
+       "      <td>-67.736151</td>\n",
+       "      <td>-67.736151</td>\n",
+       "      <td>-67.736151</td>\n",
+       "      <td>-67.736151</td>\n",
+       "      <td>-67.736151</td>\n",
+       "      <td>-67.736151</td>\n",
+       "      <td>male_negative</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>-69.673289</td>\n",
+       "      <td>-69.693311</td>\n",
+       "      <td>-69.693311</td>\n",
+       "      <td>-69.693311</td>\n",
+       "      <td>-69.693311</td>\n",
+       "      <td>-69.693311</td>\n",
+       "      <td>-69.693311</td>\n",
+       "      <td>-69.620774</td>\n",
+       "      <td>-69.693311</td>\n",
+       "      <td>-68.906572</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-69.693311</td>\n",
+       "      <td>-69.693311</td>\n",
+       "      <td>-69.693311</td>\n",
+       "      <td>-69.693311</td>\n",
+       "      <td>-69.383522</td>\n",
+       "      <td>-69.693311</td>\n",
+       "      <td>-69.693311</td>\n",
+       "      <td>-69.693311</td>\n",
+       "      <td>-69.693311</td>\n",
+       "      <td>male_positive</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 260 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           0          1          2          3          4          5  \\\n",
+       "0 -56.912843 -56.485269 -56.501424 -57.221756 -57.221756 -57.221756   \n",
+       "1 -62.104853 -62.104853 -62.104853 -62.037936 -62.104853 -62.104853   \n",
+       "2 -45.968127 -45.818262 -44.713754 -43.853780 -44.876142 -45.742182   \n",
+       "3 -67.736151 -67.778018 -66.746144 -65.957883 -67.736151 -67.736151   \n",
+       "4 -69.673289 -69.693311 -69.693311 -69.693311 -69.693311 -69.693311   \n",
+       "\n",
+       "           6          7          8          9      ...              250  \\\n",
+       "0 -57.221756 -57.221756 -57.221756 -57.221756      ...       -46.451407   \n",
+       "1 -62.104853 -62.104853 -62.104853 -62.104853      ...       -62.104853   \n",
+       "2 -44.462401 -45.629232 -45.794369 -44.506374      ...       -17.049920   \n",
+       "3 -67.736151 -67.258683 -67.736151 -67.736151      ...       -67.736151   \n",
+       "4 -69.693311 -69.620774 -69.693311 -68.906572      ...       -69.693311   \n",
+       "\n",
+       "         251        252        253        254        255        256  \\\n",
+       "0 -46.669580 -46.596475 -47.113484 -46.768849 -48.650488 -52.294436   \n",
+       "1 -62.104853 -62.104853 -62.104853 -62.104853 -62.104853 -62.104853   \n",
+       "2 -18.028540 -16.300012 -15.349774 -14.373643 -14.399101 -13.917189   \n",
+       "3 -67.736151 -67.736151 -67.736151 -67.736151 -67.736151 -67.736151   \n",
+       "4 -69.693311 -69.693311 -69.693311 -69.383522 -69.693311 -69.693311   \n",
+       "\n",
+       "         257        258          label  \n",
+       "0 -51.852247 -47.143469  male_negative  \n",
+       "1 -62.104853 -62.104853  male_negative  \n",
+       "2 -14.184953 -14.288989  male_negative  \n",
+       "3 -67.736151 -67.736151  male_negative  \n",
+       "4 -69.693311 -69.693311  male_positive  \n",
+       "\n",
+       "[5 rows x 260 columns]"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rnewdf = rnewdf.fillna(0)\n",
+    "rnewdf.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# VII. Data Augmentation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_time_series(data):\n",
+    "    \"\"\"\n",
+    "    Plot the Audio Frequency.\n",
+    "    \"\"\"\n",
+    "    fig = plt.figure(figsize=(14, 8))\n",
+    "    plt.title('Raw wave ')\n",
+    "    plt.ylabel('Amplitude')\n",
+    "    plt.plot(np.linspace(0, 1, len(data)), data)\n",
+    "    plt.show()\n",
+    "\n",
+    "\n",
+    "def noise(data):\n",
+    "    \"\"\"\n",
+    "    Adding White Noise.\n",
+    "    \"\"\"\n",
+    "    # you can take any distribution from https://docs.scipy.org/doc/numpy-1.13.0/reference/routines.random.html\n",
+    "    noise_amp = 0.005*np.random.uniform()*np.amax(data)\n",
+    "    data = data.astype('float64') + noise_amp * np.random.normal(size=data.shape[0])\n",
+    "    return data\n",
+    "    \n",
+    "def shift(data):\n",
+    "    \"\"\"\n",
+    "    Random Shifting.\n",
+    "    \"\"\"\n",
+    "    s_range = int(np.random.uniform(low=-5, high = 5)*500)\n",
+    "    return np.roll(data, s_range)\n",
+    "    \n",
+    "def stretch(data, rate=0.8):\n",
+    "    \"\"\"\n",
+    "    Streching the Sound.\n",
+    "    \"\"\"\n",
+    "    data = librosa.effects.time_stretch(data, rate)\n",
+    "    return data\n",
+    "    \n",
+    "def pitch(data, sample_rate):\n",
+    "    \"\"\"\n",
+    "    Pitch Tuning.\n",
+    "    \"\"\"\n",
+    "    bins_per_octave = 12\n",
+    "    pitch_pm = 2\n",
+    "    pitch_change =  pitch_pm * 2*(np.random.uniform())   \n",
+    "    data = librosa.effects.pitch_shift(data.astype('float64'), \n",
+    "                                      sample_rate, n_steps=pitch_change, \n",
+    "                                      bins_per_octave=bins_per_octave)\n",
+    "    return data\n",
+    "    \n",
+    "def dyn_change(data):\n",
+    "    \"\"\"\n",
+    "    Random Value Change.\n",
+    "    \"\"\"\n",
+    "    dyn_change = np.random.uniform(low=1.5,high=3)\n",
+    "    return (data * dyn_change)\n",
+    "    \n",
+    "def speedNpitch(data):\n",
+    "    \"\"\"\n",
+    "    peed and Pitch Tuning.\n",
+    "    \"\"\"\n",
+    "    # you can change low and high here\n",
+    "    length_change = np.random.uniform(low=0.8, high = 1)\n",
+    "    speed_fac = 1.0  / length_change\n",
+    "    tmp = np.interp(np.arange(0,len(data),speed_fac),np.arange(0,len(data)),data)\n",
+    "    minlen = min(data.shape[0], tmp.shape[0])\n",
+    "    data *= 0\n",
+    "    data[0:minlen] = tmp[0:minlen]\n",
+    "    return data\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1008x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "                <audio controls=\"controls\" >\n",
+       "                    <source 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type=\"audio/wav\" />\n",
+       "                    Your browser does not support the audio element.\n",
+       "                </audio>\n",
+       "              "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.Audio object>"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X, sample_rate = librosa.load(data2_df.path[216], res_type='kaiser_fast',duration=4,sr=22050*2,offset=0.5)\n",
+    "plot_time_series(X)\n",
+    "ipd.Audio(X, rate=sample_rate)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1008x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "                <audio controls=\"controls\" >\n",
+       "                    <source 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\" type=\"audio/wav\" />\n",
+       "                    Your browser does not support the audio element.\n",
+       "                </audio>\n",
+       "              "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.Audio object>"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x = pitch(X, sample_rate)\n",
+    "plot_time_series(x)\n",
+    "ipd.Audio(x, rate=sample_rate)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 800/800 [00:33<00:00, 23.54it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Augmentation Method 1\n",
+    "\n",
+    "syn_data1 = pd.DataFrame(columns=['feature', 'label'])\n",
+    "for i in tqdm(range(len(data2_df))):\n",
+    "    X, sample_rate = librosa.load(data2_df.path[i], res_type='kaiser_fast',duration=input_duration,sr=22050*2,offset=0.5)\n",
+    "    if data2_df.label[i]:\n",
+    "#     if data2_df.label[i] == \"male_positive\":\n",
+    "        X = noise(X)\n",
+    "        sample_rate = np.array(sample_rate)\n",
+    "        mfccs = np.mean(librosa.feature.mfcc(y=X, sr=sample_rate, n_mfcc=13), axis=0)\n",
+    "        feature = mfccs\n",
+    "        a = random.uniform(0, 1)\n",
+    "        syn_data1.loc[i] = [feature, data2_df.label[i]]\n",
+    "        "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 800/800 [02:32<00:00,  6.17it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Augmentation Method 2\n",
+    "\n",
+    "syn_data2 = pd.DataFrame(columns=['feature', 'label'])\n",
+    "for i in tqdm(range(len(data2_df))):\n",
+    "    X, sample_rate = librosa.load(data2_df.path[i], res_type='kaiser_fast',duration=input_duration,sr=22050*2,offset=0.5)\n",
+    "    if data2_df.label[i]:\n",
+    "#     if data2_df.label[i] == \"male_positive\":\n",
+    "        X = pitch(X, sample_rate)\n",
+    "        sample_rate = np.array(sample_rate)\n",
+    "        mfccs = np.mean(librosa.feature.mfcc(y=X, sr=sample_rate, n_mfcc=13), axis=0)\n",
+    "        feature = mfccs\n",
+    "        a = random.uniform(0, 1)\n",
+    "        syn_data2.loc[i] = [feature, data2_df.label[i]]\n",
+    "        "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(800, 800)"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(syn_data1), len(syn_data2) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "syn_data1 = syn_data1.reset_index(drop=True)\n",
+    "syn_data2 = syn_data2.reset_index(drop=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "800"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df4 = pd.DataFrame(syn_data1['feature'].values.tolist())\n",
+    "labels4 = syn_data1.label\n",
+    "syndf1 = pd.concat([df4,labels4], axis=1)\n",
+    "syndf1 = syndf1.rename(index=str, columns={\"0\": \"label\"})\n",
+    "syndf1 = syndf1.fillna(0)\n",
+    "len(syndf1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
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+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
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+       "      <th>4</th>\n",
+       "      <th>5</th>\n",
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+       "      <th>254</th>\n",
+       "      <th>255</th>\n",
+       "      <th>256</th>\n",
+       "      <th>257</th>\n",
+       "      <th>258</th>\n",
+       "      <th>label</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-42.655028</td>\n",
+       "      <td>-41.631001</td>\n",
+       "      <td>-42.889592</td>\n",
+       "      <td>-45.655462</td>\n",
+       "      <td>-43.978180</td>\n",
+       "      <td>-43.158309</td>\n",
+       "      <td>-42.575014</td>\n",
+       "      <td>-43.440535</td>\n",
+       "      <td>-46.040724</td>\n",
+       "      <td>-46.640464</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-39.005377</td>\n",
+       "      <td>-38.213447</td>\n",
+       "      <td>-38.433231</td>\n",
+       "      <td>-39.303873</td>\n",
+       "      <td>-39.492250</td>\n",
+       "      <td>-40.346354</td>\n",
+       "      <td>-40.741678</td>\n",
+       "      <td>-41.220059</td>\n",
+       "      <td>-44.056411</td>\n",
+       "      <td>male_negative</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-55.219944</td>\n",
+       "      <td>-52.616484</td>\n",
+       "      <td>-52.286107</td>\n",
+       "      <td>-52.161278</td>\n",
+       "      <td>-53.126588</td>\n",
+       "      <td>-55.292935</td>\n",
+       "      <td>-54.199089</td>\n",
+       "      <td>-52.846805</td>\n",
+       "      <td>-53.711407</td>\n",
+       "      <td>-55.357601</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-52.011125</td>\n",
+       "      <td>-52.840875</td>\n",
+       "      <td>-54.944207</td>\n",
+       "      <td>-53.116663</td>\n",
+       "      <td>-52.679384</td>\n",
+       "      <td>-53.999925</td>\n",
+       "      <td>-53.172648</td>\n",
+       "      <td>-53.296122</td>\n",
+       "      <td>-54.285751</td>\n",
+       "      <td>male_negative</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-32.440129</td>\n",
+       "      <td>-31.861092</td>\n",
+       "      <td>-34.672080</td>\n",
+       "      <td>-34.073788</td>\n",
+       "      <td>-32.633722</td>\n",
+       "      <td>-33.913732</td>\n",
+       "      <td>-33.039583</td>\n",
+       "      <td>-33.686151</td>\n",
+       "      <td>-33.661578</td>\n",
+       "      <td>-31.001432</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-16.046093</td>\n",
+       "      <td>-17.121549</td>\n",
+       "      <td>-15.584639</td>\n",
+       "      <td>-14.820404</td>\n",
+       "      <td>-14.208298</td>\n",
+       "      <td>-14.230996</td>\n",
+       "      <td>-13.517215</td>\n",
+       "      <td>-13.323313</td>\n",
+       "      <td>-12.951925</td>\n",
+       "      <td>male_negative</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>-61.922411</td>\n",
+       "      <td>-62.215679</td>\n",
+       "      <td>-58.022851</td>\n",
+       "      <td>-57.290941</td>\n",
+       "      <td>-61.027303</td>\n",
+       "      <td>-59.469569</td>\n",
+       "      <td>-58.475640</td>\n",
+       "      <td>-59.944717</td>\n",
+       "      <td>-60.281107</td>\n",
+       "      <td>-60.326236</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-60.675019</td>\n",
+       "      <td>-60.296145</td>\n",
+       "      <td>-61.405229</td>\n",
+       "      <td>-62.748245</td>\n",
+       "      <td>-60.925061</td>\n",
+       "      <td>-60.605445</td>\n",
+       "      <td>-61.236013</td>\n",
+       "      <td>-60.299258</td>\n",
+       "      <td>-59.810118</td>\n",
+       "      <td>male_negative</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>-61.966295</td>\n",
+       "      <td>-59.694255</td>\n",
+       "      <td>-59.229291</td>\n",
+       "      <td>-58.562706</td>\n",
+       "      <td>-58.993120</td>\n",
+       "      <td>-60.443855</td>\n",
+       "      <td>-61.110881</td>\n",
+       "      <td>-58.887908</td>\n",
+       "      <td>-60.432024</td>\n",
+       "      <td>-61.621168</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-59.579235</td>\n",
+       "      <td>-58.850751</td>\n",
+       "      <td>-59.550176</td>\n",
+       "      <td>-58.473981</td>\n",
+       "      <td>-58.458420</td>\n",
+       "      <td>-57.595753</td>\n",
+       "      <td>-57.773556</td>\n",
+       "      <td>-59.198322</td>\n",
+       "      <td>-60.918181</td>\n",
+       "      <td>male_positive</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 260 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           0          1          2          3          4          5  \\\n",
+       "0 -42.655028 -41.631001 -42.889592 -45.655462 -43.978180 -43.158309   \n",
+       "1 -55.219944 -52.616484 -52.286107 -52.161278 -53.126588 -55.292935   \n",
+       "2 -32.440129 -31.861092 -34.672080 -34.073788 -32.633722 -33.913732   \n",
+       "3 -61.922411 -62.215679 -58.022851 -57.290941 -61.027303 -59.469569   \n",
+       "4 -61.966295 -59.694255 -59.229291 -58.562706 -58.993120 -60.443855   \n",
+       "\n",
+       "           6          7          8          9      ...              250  \\\n",
+       "0 -42.575014 -43.440535 -46.040724 -46.640464      ...       -39.005377   \n",
+       "1 -54.199089 -52.846805 -53.711407 -55.357601      ...       -52.011125   \n",
+       "2 -33.039583 -33.686151 -33.661578 -31.001432      ...       -16.046093   \n",
+       "3 -58.475640 -59.944717 -60.281107 -60.326236      ...       -60.675019   \n",
+       "4 -61.110881 -58.887908 -60.432024 -61.621168      ...       -59.579235   \n",
+       "\n",
+       "         251        252        253        254        255        256  \\\n",
+       "0 -38.213447 -38.433231 -39.303873 -39.492250 -40.346354 -40.741678   \n",
+       "1 -52.840875 -54.944207 -53.116663 -52.679384 -53.999925 -53.172648   \n",
+       "2 -17.121549 -15.584639 -14.820404 -14.208298 -14.230996 -13.517215   \n",
+       "3 -60.296145 -61.405229 -62.748245 -60.925061 -60.605445 -61.236013   \n",
+       "4 -58.850751 -59.550176 -58.473981 -58.458420 -57.595753 -57.773556   \n",
+       "\n",
+       "         257        258          label  \n",
+       "0 -41.220059 -44.056411  male_negative  \n",
+       "1 -53.296122 -54.285751  male_negative  \n",
+       "2 -13.323313 -12.951925  male_negative  \n",
+       "3 -60.299258 -59.810118  male_negative  \n",
+       "4 -59.198322 -60.918181  male_positive  \n",
+       "\n",
+       "[5 rows x 260 columns]"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "syndf1.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "800"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df4 = pd.DataFrame(syn_data2['feature'].values.tolist())\n",
+    "labels4 = syn_data2.label\n",
+    "syndf2 = pd.concat([df4,labels4], axis=1)\n",
+    "syndf2 = syndf2.rename(index=str, columns={\"0\": \"label\"})\n",
+    "syndf2 = syndf2.fillna(0)\n",
+    "len(syndf2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
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+       "\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
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+       "      <th>7</th>\n",
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+       "      <th>9</th>\n",
+       "      <th>...</th>\n",
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+       "      <th>256</th>\n",
+       "      <th>257</th>\n",
+       "      <th>258</th>\n",
+       "      <th>label</th>\n",
+       "    </tr>\n",
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+       "  <tbody>\n",
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+       "      <td>-57.455891</td>\n",
+       "      <td>-58.015750</td>\n",
+       "      <td>-58.366476</td>\n",
+       "      <td>-58.366476</td>\n",
+       "      <td>-58.366476</td>\n",
+       "      <td>-58.366476</td>\n",
+       "      <td>-58.366476</td>\n",
+       "      <td>-58.366476</td>\n",
+       "      <td>-58.366476</td>\n",
+       "      <td>...</td>\n",
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+       "      <td>-50.291433</td>\n",
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+       "      <td>-49.922888</td>\n",
+       "      <td>-49.717019</td>\n",
+       "      <td>-50.676514</td>\n",
+       "      <td>-53.910571</td>\n",
+       "      <td>-55.820576</td>\n",
+       "      <td>-57.067588</td>\n",
+       "      <td>male_negative</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-62.773059</td>\n",
+       "      <td>-62.773059</td>\n",
+       "      <td>-62.773059</td>\n",
+       "      <td>-62.773059</td>\n",
+       "      <td>-62.773059</td>\n",
+       "      <td>-62.773059</td>\n",
+       "      <td>-62.773059</td>\n",
+       "      <td>-62.773059</td>\n",
+       "      <td>-62.773059</td>\n",
+       "      <td>-62.773059</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-62.773059</td>\n",
+       "      <td>-62.773059</td>\n",
+       "      <td>-62.773059</td>\n",
+       "      <td>-62.773059</td>\n",
+       "      <td>-62.773059</td>\n",
+       "      <td>-62.773059</td>\n",
+       "      <td>-62.773059</td>\n",
+       "      <td>-62.773059</td>\n",
+       "      <td>-62.773059</td>\n",
+       "      <td>male_negative</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-47.605232</td>\n",
+       "      <td>-46.625293</td>\n",
+       "      <td>-47.425215</td>\n",
+       "      <td>-49.043574</td>\n",
+       "      <td>-48.964560</td>\n",
+       "      <td>-48.288764</td>\n",
+       "      <td>-48.906125</td>\n",
+       "      <td>-51.401454</td>\n",
+       "      <td>-49.835744</td>\n",
+       "      <td>-47.608633</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-21.055637</td>\n",
+       "      <td>-21.569586</td>\n",
+       "      <td>-20.796359</td>\n",
+       "      <td>-19.045929</td>\n",
+       "      <td>-18.160538</td>\n",
+       "      <td>-17.901236</td>\n",
+       "      <td>-17.885066</td>\n",
+       "      <td>-17.290123</td>\n",
+       "      <td>-19.650012</td>\n",
+       "      <td>male_negative</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>-69.802849</td>\n",
+       "      <td>-70.034258</td>\n",
+       "      <td>-69.537954</td>\n",
+       "      <td>-69.222710</td>\n",
+       "      <td>-69.773514</td>\n",
+       "      <td>-69.773514</td>\n",
+       "      <td>-69.773514</td>\n",
+       "      <td>-69.617643</td>\n",
+       "      <td>-69.773514</td>\n",
+       "      <td>-69.773514</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-69.773514</td>\n",
+       "      <td>-69.773514</td>\n",
+       "      <td>-69.773514</td>\n",
+       "      <td>-69.773514</td>\n",
+       "      <td>-69.773514</td>\n",
+       "      <td>-69.773514</td>\n",
+       "      <td>-69.773514</td>\n",
+       "      <td>-69.773514</td>\n",
+       "      <td>-69.773514</td>\n",
+       "      <td>male_negative</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>-71.245390</td>\n",
+       "      <td>-71.097453</td>\n",
+       "      <td>-71.678199</td>\n",
+       "      <td>-71.678199</td>\n",
+       "      <td>-71.678199</td>\n",
+       "      <td>-71.678199</td>\n",
+       "      <td>-71.678199</td>\n",
+       "      <td>-71.678199</td>\n",
+       "      <td>-71.678199</td>\n",
+       "      <td>-70.730889</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-71.678199</td>\n",
+       "      <td>-71.678199</td>\n",
+       "      <td>-71.678199</td>\n",
+       "      <td>-71.678199</td>\n",
+       "      <td>-71.678199</td>\n",
+       "      <td>-71.614492</td>\n",
+       "      <td>-71.678199</td>\n",
+       "      <td>-71.678199</td>\n",
+       "      <td>-71.678199</td>\n",
+       "      <td>male_positive</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 260 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           0          1          2          3          4          5  \\\n",
+       "0 -57.753215 -57.455891 -58.015750 -58.366476 -58.366476 -58.366476   \n",
+       "1 -62.773059 -62.773059 -62.773059 -62.773059 -62.773059 -62.773059   \n",
+       "2 -47.605232 -46.625293 -47.425215 -49.043574 -48.964560 -48.288764   \n",
+       "3 -69.802849 -70.034258 -69.537954 -69.222710 -69.773514 -69.773514   \n",
+       "4 -71.245390 -71.097453 -71.678199 -71.678199 -71.678199 -71.678199   \n",
+       "\n",
+       "           6          7          8          9      ...              250  \\\n",
+       "0 -58.366476 -58.366476 -58.366476 -58.366476      ...       -48.784671   \n",
+       "1 -62.773059 -62.773059 -62.773059 -62.773059      ...       -62.773059   \n",
+       "2 -48.906125 -51.401454 -49.835744 -47.608633      ...       -21.055637   \n",
+       "3 -69.773514 -69.617643 -69.773514 -69.773514      ...       -69.773514   \n",
+       "4 -71.678199 -71.678199 -71.678199 -70.730889      ...       -71.678199   \n",
+       "\n",
+       "         251        252        253        254        255        256  \\\n",
+       "0 -50.291433 -50.476648 -49.922888 -49.717019 -50.676514 -53.910571   \n",
+       "1 -62.773059 -62.773059 -62.773059 -62.773059 -62.773059 -62.773059   \n",
+       "2 -21.569586 -20.796359 -19.045929 -18.160538 -17.901236 -17.885066   \n",
+       "3 -69.773514 -69.773514 -69.773514 -69.773514 -69.773514 -69.773514   \n",
+       "4 -71.678199 -71.678199 -71.678199 -71.678199 -71.614492 -71.678199   \n",
+       "\n",
+       "         257        258          label  \n",
+       "0 -55.820576 -57.067588  male_negative  \n",
+       "1 -62.773059 -62.773059  male_negative  \n",
+       "2 -17.290123 -19.650012  male_negative  \n",
+       "3 -69.773514 -69.773514  male_negative  \n",
+       "4 -71.678199 -71.678199  male_positive  \n",
+       "\n",
+       "[5 rows x 260 columns]"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "syndf2.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\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>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "      <th>4</th>\n",
+       "      <th>5</th>\n",
+       "      <th>6</th>\n",
+       "      <th>7</th>\n",
+       "      <th>8</th>\n",
+       "      <th>9</th>\n",
+       "      <th>...</th>\n",
+       "      <th>250</th>\n",
+       "      <th>251</th>\n",
+       "      <th>252</th>\n",
+       "      <th>253</th>\n",
+       "      <th>254</th>\n",
+       "      <th>255</th>\n",
+       "      <th>256</th>\n",
+       "      <th>257</th>\n",
+       "      <th>258</th>\n",
+       "      <th>label</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-56.912843</td>\n",
+       "      <td>-56.485269</td>\n",
+       "      <td>-56.501424</td>\n",
+       "      <td>-57.221756</td>\n",
+       "      <td>-57.221756</td>\n",
+       "      <td>-57.221756</td>\n",
+       "      <td>-57.221756</td>\n",
+       "      <td>-57.221756</td>\n",
+       "      <td>-57.221756</td>\n",
+       "      <td>-57.221756</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-46.451407</td>\n",
+       "      <td>-46.669580</td>\n",
+       "      <td>-46.596475</td>\n",
+       "      <td>-47.113484</td>\n",
+       "      <td>-46.768849</td>\n",
+       "      <td>-48.650488</td>\n",
+       "      <td>-52.294436</td>\n",
+       "      <td>-51.852247</td>\n",
+       "      <td>-47.143469</td>\n",
+       "      <td>male_negative</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-62.104853</td>\n",
+       "      <td>-62.104853</td>\n",
+       "      <td>-62.104853</td>\n",
+       "      <td>-62.037936</td>\n",
+       "      <td>-62.104853</td>\n",
+       "      <td>-62.104853</td>\n",
+       "      <td>-62.104853</td>\n",
+       "      <td>-62.104853</td>\n",
+       "      <td>-62.104853</td>\n",
+       "      <td>-62.104853</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-62.104853</td>\n",
+       "      <td>-62.104853</td>\n",
+       "      <td>-62.104853</td>\n",
+       "      <td>-62.104853</td>\n",
+       "      <td>-62.104853</td>\n",
+       "      <td>-62.104853</td>\n",
+       "      <td>-62.104853</td>\n",
+       "      <td>-62.104853</td>\n",
+       "      <td>-62.104853</td>\n",
+       "      <td>male_negative</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-45.968127</td>\n",
+       "      <td>-45.818262</td>\n",
+       "      <td>-44.713754</td>\n",
+       "      <td>-43.853780</td>\n",
+       "      <td>-44.876142</td>\n",
+       "      <td>-45.742182</td>\n",
+       "      <td>-44.462401</td>\n",
+       "      <td>-45.629232</td>\n",
+       "      <td>-45.794369</td>\n",
+       "      <td>-44.506374</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-17.049920</td>\n",
+       "      <td>-18.028540</td>\n",
+       "      <td>-16.300012</td>\n",
+       "      <td>-15.349774</td>\n",
+       "      <td>-14.373643</td>\n",
+       "      <td>-14.399101</td>\n",
+       "      <td>-13.917189</td>\n",
+       "      <td>-14.184953</td>\n",
+       "      <td>-14.288989</td>\n",
+       "      <td>male_negative</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>-67.736151</td>\n",
+       "      <td>-67.778018</td>\n",
+       "      <td>-66.746144</td>\n",
+       "      <td>-65.957883</td>\n",
+       "      <td>-67.736151</td>\n",
+       "      <td>-67.736151</td>\n",
+       "      <td>-67.736151</td>\n",
+       "      <td>-67.258683</td>\n",
+       "      <td>-67.736151</td>\n",
+       "      <td>-67.736151</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-67.736151</td>\n",
+       "      <td>-67.736151</td>\n",
+       "      <td>-67.736151</td>\n",
+       "      <td>-67.736151</td>\n",
+       "      <td>-67.736151</td>\n",
+       "      <td>-67.736151</td>\n",
+       "      <td>-67.736151</td>\n",
+       "      <td>-67.736151</td>\n",
+       "      <td>-67.736151</td>\n",
+       "      <td>male_negative</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>-69.673289</td>\n",
+       "      <td>-69.693311</td>\n",
+       "      <td>-69.693311</td>\n",
+       "      <td>-69.693311</td>\n",
+       "      <td>-69.693311</td>\n",
+       "      <td>-69.693311</td>\n",
+       "      <td>-69.693311</td>\n",
+       "      <td>-69.620774</td>\n",
+       "      <td>-69.693311</td>\n",
+       "      <td>-68.906572</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-69.693311</td>\n",
+       "      <td>-69.693311</td>\n",
+       "      <td>-69.693311</td>\n",
+       "      <td>-69.693311</td>\n",
+       "      <td>-69.383522</td>\n",
+       "      <td>-69.693311</td>\n",
+       "      <td>-69.693311</td>\n",
+       "      <td>-69.693311</td>\n",
+       "      <td>-69.693311</td>\n",
+       "      <td>male_positive</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 260 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           0          1          2          3          4          5  \\\n",
+       "0 -56.912843 -56.485269 -56.501424 -57.221756 -57.221756 -57.221756   \n",
+       "1 -62.104853 -62.104853 -62.104853 -62.037936 -62.104853 -62.104853   \n",
+       "2 -45.968127 -45.818262 -44.713754 -43.853780 -44.876142 -45.742182   \n",
+       "3 -67.736151 -67.778018 -66.746144 -65.957883 -67.736151 -67.736151   \n",
+       "4 -69.673289 -69.693311 -69.693311 -69.693311 -69.693311 -69.693311   \n",
+       "\n",
+       "           6          7          8          9      ...              250  \\\n",
+       "0 -57.221756 -57.221756 -57.221756 -57.221756      ...       -46.451407   \n",
+       "1 -62.104853 -62.104853 -62.104853 -62.104853      ...       -62.104853   \n",
+       "2 -44.462401 -45.629232 -45.794369 -44.506374      ...       -17.049920   \n",
+       "3 -67.736151 -67.258683 -67.736151 -67.736151      ...       -67.736151   \n",
+       "4 -69.693311 -69.620774 -69.693311 -68.906572      ...       -69.693311   \n",
+       "\n",
+       "         251        252        253        254        255        256  \\\n",
+       "0 -46.669580 -46.596475 -47.113484 -46.768849 -48.650488 -52.294436   \n",
+       "1 -62.104853 -62.104853 -62.104853 -62.104853 -62.104853 -62.104853   \n",
+       "2 -18.028540 -16.300012 -15.349774 -14.373643 -14.399101 -13.917189   \n",
+       "3 -67.736151 -67.736151 -67.736151 -67.736151 -67.736151 -67.736151   \n",
+       "4 -69.693311 -69.693311 -69.693311 -69.383522 -69.693311 -69.693311   \n",
+       "\n",
+       "         257        258          label  \n",
+       "0 -51.852247 -47.143469  male_negative  \n",
+       "1 -62.104853 -62.104853  male_negative  \n",
+       "2 -14.184953 -14.288989  male_negative  \n",
+       "3 -67.736151 -67.736151  male_negative  \n",
+       "4 -69.693311 -69.693311  male_positive  \n",
+       "\n",
+       "[5 rows x 260 columns]"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Combining the Augmented data with original\n",
+    "combined_df = pd.concat([rnewdf, syndf1, syndf2], ignore_index=True)\n",
+    "combined_df = combined_df.fillna(0)\n",
+    "combined_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "StratifiedShuffleSplit(n_splits=1, random_state=12, test_size=0.2,\n",
+      "            train_size=None)\n"
+     ]
+    }
+   ],
+   "source": [
+    "#  Stratified Shuffle Split\n",
+    "\n",
+    "X = combined_df.drop(['label'], axis=1)\n",
+    "y = combined_df.label\n",
+    "xxx = StratifiedShuffleSplit(1, test_size=0.2, random_state=12)\n",
+    "for train_index, test_index in xxx.split(X, y):\n",
+    "    X_train, X_test = X.iloc[train_index], X.iloc[test_index]\n",
+    "    y_train, y_test = y.iloc[train_index], y.iloc[test_index]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "male_negative    1152\n",
+       "male_positive     768\n",
+       "Name: label, dtype: int64"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_train.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "male_negative    288\n",
+       "male_positive    192\n",
+       "Name: label, dtype: int64"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_test.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_train.isna().sum().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train = np.array(X_train)\n",
+    "y_train = np.array(y_train)\n",
+    "X_test = np.array(X_test)\n",
+    "y_test = np.array(y_test)\n",
+    "lb = LabelEncoder()\n",
+    "y_train = np_utils.to_categorical(lb.fit_transform(y_train))\n",
+    "y_test = np_utils.to_categorical(lb.fit_transform(y_test))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1., 0.],\n",
+       "       [0., 1.],\n",
+       "       [1., 0.],\n",
+       "       ...,\n",
+       "       [1., 0.],\n",
+       "       [0., 1.],\n",
+       "       [0., 1.]], dtype=float32)"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_train"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1920, 259)"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_train.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# VIII. Changing dimension for CNN model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_traincnn = np.expand_dims(X_train, axis=2)\n",
+    "x_testcnn = np.expand_dims(X_test, axis=2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set up Keras util functions\n",
+    "\n",
+    "from keras import backend as K\n",
+    "\n",
+    "def precision(y_true, y_pred):\n",
+    "    true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))\n",
+    "    predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))\n",
+    "    precision = true_positives / (predicted_positives + K.epsilon())\n",
+    "    return precision\n",
+    "\n",
+    "\n",
+    "def recall(y_true, y_pred):\n",
+    "    true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))\n",
+    "    possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))\n",
+    "    recall = true_positives / (possible_positives + K.epsilon())\n",
+    "    return recall\n",
+    "\n",
+    "\n",
+    "def fscore(y_true, y_pred):\n",
+    "    if K.sum(K.round(K.clip(y_true, 0, 1))) == 0:\n",
+    "        return 0\n",
+    "\n",
+    "    p = precision(y_true, y_pred)\n",
+    "    r = recall(y_true, y_pred)\n",
+    "    f_score = 2 * (p * r) / (p + r + K.epsilon())\n",
+    "    return f_score\n",
+    "\n",
+    "def get_lr_metric(optimizer):\n",
+    "    def lr(y_true, y_pred):\n",
+    "        return optimizer.lr\n",
+    "    return lr\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# New model\n",
+    "model = Sequential()\n",
+    "model.add(Conv1D(256, 8, padding='same',input_shape=(X_train.shape[1],1)))\n",
+    "model.add(Activation('relu'))\n",
+    "model.add(Conv1D(256, 8, padding='same'))\n",
+    "model.add(BatchNormalization())\n",
+    "model.add(Activation('relu'))\n",
+    "model.add(Dropout(0.25))\n",
+    "model.add(MaxPooling1D(pool_size=(8)))\n",
+    "model.add(Conv1D(128, 8, padding='same'))\n",
+    "model.add(Activation('relu'))\n",
+    "model.add(Conv1D(128, 8, padding='same'))\n",
+    "model.add(Activation('relu'))\n",
+    "model.add(Conv1D(128, 8, padding='same'))\n",
+    "model.add(Activation('relu'))\n",
+    "model.add(Conv1D(128, 8, padding='same'))\n",
+    "model.add(BatchNormalization())\n",
+    "model.add(Activation('relu'))\n",
+    "model.add(Dropout(0.25))\n",
+    "model.add(MaxPooling1D(pool_size=(8)))\n",
+    "model.add(Conv1D(64, 8, padding='same'))\n",
+    "model.add(Activation('relu'))\n",
+    "model.add(Conv1D(64, 8, padding='same'))\n",
+    "model.add(Activation('relu'))\n",
+    "model.add(Flatten())\n",
+    "# Edit according to target class no.\n",
+    "model.add(Dense(2))\n",
+    "model.add(Activation('softmax'))\n",
+    "opt = keras.optimizers.SGD(lr=0.0001, momentum=0.0, decay=0.0, nesterov=False)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Original Model\n",
+    "\n",
+    "# model = Sequential()\n",
+    "# model.add(Conv1D(256, 5,padding='same', input_shape=(X_train.shape[1],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(5))\n",
+    "# model.add(Activation('softmax'))\n",
+    "# opt = keras.optimizers.rmsprop(lr=0.00001, decay=1e-6)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "_________________________________________________________________\n",
+      "Layer (type)                 Output Shape              Param #   \n",
+      "=================================================================\n",
+      "conv1d_1 (Conv1D)            (None, 259, 256)          2304      \n",
+      "_________________________________________________________________\n",
+      "activation_1 (Activation)    (None, 259, 256)          0         \n",
+      "_________________________________________________________________\n",
+      "conv1d_2 (Conv1D)            (None, 259, 256)          524544    \n",
+      "_________________________________________________________________\n",
+      "batch_normalization_1 (Batch (None, 259, 256)          1024      \n",
+      "_________________________________________________________________\n",
+      "activation_2 (Activation)    (None, 259, 256)          0         \n",
+      "_________________________________________________________________\n",
+      "dropout_1 (Dropout)          (None, 259, 256)          0         \n",
+      "_________________________________________________________________\n",
+      "max_pooling1d_1 (MaxPooling1 (None, 32, 256)           0         \n",
+      "_________________________________________________________________\n",
+      "conv1d_3 (Conv1D)            (None, 32, 128)           262272    \n",
+      "_________________________________________________________________\n",
+      "activation_3 (Activation)    (None, 32, 128)           0         \n",
+      "_________________________________________________________________\n",
+      "conv1d_4 (Conv1D)            (None, 32, 128)           131200    \n",
+      "_________________________________________________________________\n",
+      "activation_4 (Activation)    (None, 32, 128)           0         \n",
+      "_________________________________________________________________\n",
+      "conv1d_5 (Conv1D)            (None, 32, 128)           131200    \n",
+      "_________________________________________________________________\n",
+      "activation_5 (Activation)    (None, 32, 128)           0         \n",
+      "_________________________________________________________________\n",
+      "conv1d_6 (Conv1D)            (None, 32, 128)           131200    \n",
+      "_________________________________________________________________\n",
+      "batch_normalization_2 (Batch (None, 32, 128)           512       \n",
+      "_________________________________________________________________\n",
+      "activation_6 (Activation)    (None, 32, 128)           0         \n",
+      "_________________________________________________________________\n",
+      "dropout_2 (Dropout)          (None, 32, 128)           0         \n",
+      "_________________________________________________________________\n",
+      "max_pooling1d_2 (MaxPooling1 (None, 4, 128)            0         \n",
+      "_________________________________________________________________\n",
+      "conv1d_7 (Conv1D)            (None, 4, 64)             65600     \n",
+      "_________________________________________________________________\n",
+      "activation_7 (Activation)    (None, 4, 64)             0         \n",
+      "_________________________________________________________________\n",
+      "conv1d_8 (Conv1D)            (None, 4, 64)             32832     \n",
+      "_________________________________________________________________\n",
+      "activation_8 (Activation)    (None, 4, 64)             0         \n",
+      "_________________________________________________________________\n",
+      "flatten_1 (Flatten)          (None, 256)               0         \n",
+      "_________________________________________________________________\n",
+      "dense_1 (Dense)              (None, 2)                 514       \n",
+      "_________________________________________________________________\n",
+      "activation_9 (Activation)    (None, 2)                 0         \n",
+      "=================================================================\n",
+      "Total params: 1,283,202\n",
+      "Trainable params: 1,282,434\n",
+      "Non-trainable params: 768\n",
+      "_________________________________________________________________\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Plotting Model Summary\n",
+    "\n",
+    "model.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Compile your model\n",
+    "\n",
+    "model.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['accuracy', fscore])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# IX. Removed the whole training part for avoiding unnecessary long epochs list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train on 1920 samples, validate on 480 samples\n",
+      "Epoch 1/700\n",
+      "1920/1920 [==============================] - 4s 2ms/step - loss: 0.6579 - acc: 0.5854 - fscore: 0.5854 - val_loss: 0.6631 - val_acc: 0.5500 - val_fscore: 0.5500\n",
+      "Epoch 2/700\n",
+      "1920/1920 [==============================] - 2s 788us/step - loss: 0.6414 - acc: 0.6104 - fscore: 0.6104 - val_loss: 0.6575 - val_acc: 0.5667 - val_fscore: 0.5667\n",
+      "Epoch 3/700\n",
+      "1920/1920 [==============================] - 2s 969us/step - loss: 0.6243 - acc: 0.6219 - fscore: 0.6219 - val_loss: 0.6489 - val_acc: 0.6083 - val_fscore: 0.6083\n",
+      "Epoch 4/700\n",
+      "1920/1920 [==============================] - 2s 857us/step - loss: 0.6199 - acc: 0.6333 - fscore: 0.6333 - val_loss: 0.6462 - val_acc: 0.5917 - val_fscore: 0.5917\n",
+      "Epoch 5/700\n",
+      "1920/1920 [==============================] - 2s 929us/step - loss: 0.6147 - acc: 0.6401 - fscore: 0.6401 - val_loss: 0.6524 - val_acc: 0.6042 - val_fscore: 0.6042\n",
+      "Epoch 6/700\n",
+      "1920/1920 [==============================] - 2s 854us/step - loss: 0.6074 - acc: 0.6484 - fscore: 0.6484 - val_loss: 0.6428 - val_acc: 0.6229 - val_fscore: 0.6229\n",
+      "Epoch 7/700\n",
+      "1920/1920 [==============================] - 2s 873us/step - loss: 0.6073 - acc: 0.6495 - fscore: 0.6495 - val_loss: 0.6406 - val_acc: 0.6208 - val_fscore: 0.6208\n",
+      "Epoch 8/700\n",
+      "1920/1920 [==============================] - 2s 797us/step - loss: 0.6029 - acc: 0.6609 - fscore: 0.6609 - val_loss: 0.6397 - val_acc: 0.6375 - val_fscore: 0.6375\n",
+      "Epoch 9/700\n",
+      "1920/1920 [==============================] - 2s 908us/step - loss: 0.6032 - acc: 0.6547 - fscore: 0.6547 - val_loss: 0.6389 - val_acc: 0.6312 - val_fscore: 0.6312\n",
+      "Epoch 10/700\n",
+      "1920/1920 [==============================] - 2s 827us/step - loss: 0.5912 - acc: 0.6740 - fscore: 0.6740 - val_loss: 0.6346 - val_acc: 0.6604 - val_fscore: 0.6604\n",
+      "Epoch 11/700\n",
+      "1920/1920 [==============================] - 2s 910us/step - loss: 0.5948 - acc: 0.6583 - fscore: 0.6583 - val_loss: 0.6407 - val_acc: 0.6458 - val_fscore: 0.6458\n",
+      "Epoch 12/700\n",
+      "1920/1920 [==============================] - 2s 787us/step - loss: 0.5941 - acc: 0.6656 - fscore: 0.6656 - val_loss: 0.6304 - val_acc: 0.6562 - val_fscore: 0.6562\n",
+      "Epoch 13/700\n",
+      "1920/1920 [==============================] - 2s 967us/step - loss: 0.5900 - acc: 0.6708 - fscore: 0.6708 - val_loss: 0.6337 - val_acc: 0.6604 - val_fscore: 0.6604\n",
+      "Epoch 14/700\n",
+      "1920/1920 [==============================] - 2s 1ms/step - loss: 0.5838 - acc: 0.6781 - fscore: 0.6781 - val_loss: 0.6272 - val_acc: 0.6708 - val_fscore: 0.6708\n",
+      "Epoch 15/700\n",
+      "1920/1920 [==============================] - 2s 831us/step - loss: 0.5844 - acc: 0.6734 - fscore: 0.6734 - val_loss: 0.6314 - val_acc: 0.6708 - val_fscore: 0.6708\n",
+      "Epoch 16/700\n",
+      "1920/1920 [==============================] - 2s 842us/step - loss: 0.5819 - acc: 0.6823 - fscore: 0.6823 - val_loss: 0.6325 - val_acc: 0.6583 - val_fscore: 0.6583\n",
+      "Epoch 17/700\n",
+      "1920/1920 [==============================] - 2s 814us/step - loss: 0.5817 - acc: 0.6802 - fscore: 0.6802 - val_loss: 0.6354 - val_acc: 0.6583 - val_fscore: 0.6583\n",
+      "Epoch 18/700\n",
+      "1920/1920 [==============================] - 1s 760us/step - loss: 0.5717 - acc: 0.6781 - fscore: 0.6781 - val_loss: 0.6293 - val_acc: 0.6833 - val_fscore: 0.6833\n",
+      "Epoch 19/700\n",
+      "1920/1920 [==============================] - 1s 745us/step - loss: 0.5682 - acc: 0.6990 - fscore: 0.6990 - val_loss: 0.6279 - val_acc: 0.6937 - val_fscore: 0.6937\n",
+      "Epoch 20/700\n",
+      "1920/1920 [==============================] - 1s 751us/step - loss: 0.5695 - acc: 0.6969 - fscore: 0.6969 - val_loss: 0.6258 - val_acc: 0.6875 - val_fscore: 0.6875\n",
+      "Epoch 21/700\n",
+      "1920/1920 [==============================] - 1s 746us/step - loss: 0.5646 - acc: 0.7042 - fscore: 0.7042 - val_loss: 0.6243 - val_acc: 0.6917 - val_fscore: 0.6917\n",
+      "Epoch 22/700\n",
+      "1920/1920 [==============================] - 1s 742us/step - loss: 0.5710 - acc: 0.6755 - fscore: 0.6755 - val_loss: 0.6248 - val_acc: 0.6583 - val_fscore: 0.6583\n",
+      "Epoch 23/700\n",
+      "1920/1920 [==============================] - 1s 742us/step - loss: 0.5693 - acc: 0.6833 - fscore: 0.6833 - val_loss: 0.6169 - val_acc: 0.7021 - val_fscore: 0.7021\n",
+      "Epoch 24/700\n",
+      "1920/1920 [==============================] - 1s 752us/step - loss: 0.5667 - acc: 0.6969 - fscore: 0.6969 - val_loss: 0.6211 - val_acc: 0.6875 - val_fscore: 0.6875\n",
+      "Epoch 25/700\n",
+      "1920/1920 [==============================] - 1s 739us/step - loss: 0.5595 - acc: 0.7078 - fscore: 0.7078 - val_loss: 0.6239 - val_acc: 0.6604 - val_fscore: 0.6604\n",
+      "Epoch 26/700\n",
+      "1920/1920 [==============================] - 1s 742us/step - loss: 0.5562 - acc: 0.7073 - fscore: 0.7073 - val_loss: 0.6131 - val_acc: 0.6937 - val_fscore: 0.6937\n",
+      "Epoch 27/700\n",
+      "1920/1920 [==============================] - 1s 740us/step - loss: 0.5502 - acc: 0.7125 - fscore: 0.7125 - val_loss: 0.6139 - val_acc: 0.7000 - val_fscore: 0.7000\n",
+      "Epoch 28/700\n",
+      "1920/1920 [==============================] - 1s 761us/step - loss: 0.5497 - acc: 0.7026 - fscore: 0.7026 - val_loss: 0.6087 - val_acc: 0.7021 - val_fscore: 0.7021\n",
+      "Epoch 29/700\n",
+      "1920/1920 [==============================] - 1s 759us/step - loss: 0.5512 - acc: 0.7089 - fscore: 0.7089 - val_loss: 0.6088 - val_acc: 0.6979 - val_fscore: 0.6979\n",
+      "Epoch 30/700\n",
+      "1920/1920 [==============================] - 1s 754us/step - loss: 0.5514 - acc: 0.7068 - fscore: 0.7068 - val_loss: 0.6110 - val_acc: 0.6979 - val_fscore: 0.6979\n",
+      "Epoch 31/700\n",
+      "1920/1920 [==============================] - 1s 742us/step - loss: 0.5493 - acc: 0.7047 - fscore: 0.7047 - val_loss: 0.6079 - val_acc: 0.6979 - val_fscore: 0.6979\n",
+      "Epoch 32/700\n",
+      "1920/1920 [==============================] - 1s 739us/step - loss: 0.5413 - acc: 0.7208 - fscore: 0.7208 - val_loss: 0.6042 - val_acc: 0.6979 - val_fscore: 0.6979\n",
+      "Epoch 33/700\n",
+      "1920/1920 [==============================] - 1s 748us/step - loss: 0.5446 - acc: 0.7219 - fscore: 0.7219 - val_loss: 0.6125 - val_acc: 0.7063 - val_fscore: 0.7062\n",
+      "Epoch 34/700\n",
+      "1920/1920 [==============================] - 1s 749us/step - loss: 0.5387 - acc: 0.7198 - fscore: 0.7198 - val_loss: 0.6035 - val_acc: 0.7042 - val_fscore: 0.7042\n",
+      "Epoch 35/700\n",
+      "1920/1920 [==============================] - 1s 747us/step - loss: 0.5300 - acc: 0.7286 - fscore: 0.7286 - val_loss: 0.6141 - val_acc: 0.6833 - val_fscore: 0.6833\n",
+      "Epoch 36/700\n",
+      "1920/1920 [==============================] - 1s 745us/step - loss: 0.5268 - acc: 0.7422 - fscore: 0.7422 - val_loss: 0.6004 - val_acc: 0.7188 - val_fscore: 0.7187\n",
+      "Epoch 37/700\n",
+      "1920/1920 [==============================] - 1s 740us/step - loss: 0.5353 - acc: 0.7281 - fscore: 0.7281 - val_loss: 0.6150 - val_acc: 0.6813 - val_fscore: 0.6812\n",
+      "Epoch 38/700\n",
+      "1920/1920 [==============================] - 1s 737us/step - loss: 0.5305 - acc: 0.7458 - fscore: 0.7458 - val_loss: 0.5994 - val_acc: 0.7208 - val_fscore: 0.7208\n",
+      "Epoch 39/700\n",
+      "1920/1920 [==============================] - 1s 747us/step - loss: 0.5300 - acc: 0.7281 - fscore: 0.7281 - val_loss: 0.6037 - val_acc: 0.7083 - val_fscore: 0.7083\n",
+      "Epoch 40/700\n",
+      "1920/1920 [==============================] - 1s 755us/step - loss: 0.5288 - acc: 0.7266 - fscore: 0.7266 - val_loss: 0.5945 - val_acc: 0.7104 - val_fscore: 0.7104\n",
+      "Epoch 41/700\n",
+      "1920/1920 [==============================] - 1s 734us/step - loss: 0.5213 - acc: 0.7318 - fscore: 0.7318 - val_loss: 0.5944 - val_acc: 0.7312 - val_fscore: 0.7312\n",
+      "Epoch 42/700\n",
+      "1920/1920 [==============================] - 1s 743us/step - loss: 0.5238 - acc: 0.7328 - fscore: 0.7328 - val_loss: 0.5937 - val_acc: 0.7167 - val_fscore: 0.7167\n",
+      "Epoch 43/700\n",
+      "1920/1920 [==============================] - 1s 737us/step - loss: 0.5200 - acc: 0.7380 - fscore: 0.7380 - val_loss: 0.5928 - val_acc: 0.7354 - val_fscore: 0.7354\n",
+      "Epoch 44/700\n",
+      "1920/1920 [==============================] - 1s 737us/step - loss: 0.5184 - acc: 0.7365 - fscore: 0.7365 - val_loss: 0.5929 - val_acc: 0.6958 - val_fscore: 0.6958\n",
+      "Epoch 45/700\n",
+      "1920/1920 [==============================] - 1s 750us/step - loss: 0.5160 - acc: 0.7536 - fscore: 0.7536 - val_loss: 0.5921 - val_acc: 0.7229 - val_fscore: 0.7229\n",
+      "Epoch 46/700\n",
+      "1920/1920 [==============================] - 1s 749us/step - loss: 0.5165 - acc: 0.7469 - fscore: 0.7469 - val_loss: 0.5866 - val_acc: 0.7354 - val_fscore: 0.7354\n",
+      "Epoch 47/700\n",
+      "1920/1920 [==============================] - 1s 748us/step - loss: 0.5095 - acc: 0.7542 - fscore: 0.7542 - val_loss: 0.6000 - val_acc: 0.6917 - val_fscore: 0.6917\n",
+      "Epoch 48/700\n",
+      "1920/1920 [==============================] - 1s 752us/step - loss: 0.5093 - acc: 0.7396 - fscore: 0.7396 - val_loss: 0.5859 - val_acc: 0.7188 - val_fscore: 0.7187\n",
+      "Epoch 49/700\n",
+      "1920/1920 [==============================] - 1s 751us/step - loss: 0.5021 - acc: 0.7578 - fscore: 0.7578 - val_loss: 0.5861 - val_acc: 0.7083 - val_fscore: 0.7083\n",
+      "Epoch 50/700\n",
+      "1920/1920 [==============================] - 1s 741us/step - loss: 0.5046 - acc: 0.7505 - fscore: 0.7505 - val_loss: 0.5912 - val_acc: 0.7396 - val_fscore: 0.7396\n",
+      "Epoch 51/700\n",
+      "1920/1920 [==============================] - 1s 746us/step - loss: 0.4942 - acc: 0.7578 - fscore: 0.7578 - val_loss: 0.5823 - val_acc: 0.7333 - val_fscore: 0.7333\n",
+      "Epoch 52/700\n",
+      "1920/1920 [==============================] - 1s 744us/step - loss: 0.5018 - acc: 0.7589 - fscore: 0.7589 - val_loss: 0.5857 - val_acc: 0.7250 - val_fscore: 0.7250\n",
+      "Epoch 53/700\n",
+      "1920/1920 [==============================] - 1s 754us/step - loss: 0.5061 - acc: 0.7510 - fscore: 0.7510 - val_loss: 0.5835 - val_acc: 0.7146 - val_fscore: 0.7146\n",
+      "Epoch 54/700\n",
+      "1920/1920 [==============================] - 1s 754us/step - loss: 0.4892 - acc: 0.7646 - fscore: 0.7646 - val_loss: 0.5776 - val_acc: 0.7292 - val_fscore: 0.7292\n",
+      "Epoch 55/700\n",
+      "1920/1920 [==============================] - 1s 746us/step - loss: 0.4922 - acc: 0.7682 - fscore: 0.7682 - val_loss: 0.5907 - val_acc: 0.7083 - val_fscore: 0.7083\n",
+      "Epoch 56/700\n",
+      "1920/1920 [==============================] - 1s 754us/step - loss: 0.4878 - acc: 0.7698 - fscore: 0.7698 - val_loss: 0.5792 - val_acc: 0.7333 - val_fscore: 0.7333\n",
+      "Epoch 57/700\n",
+      "1920/1920 [==============================] - 1s 743us/step - loss: 0.4822 - acc: 0.7729 - fscore: 0.7729 - val_loss: 0.5764 - val_acc: 0.7354 - val_fscore: 0.7354\n",
+      "Epoch 58/700\n",
+      "1920/1920 [==============================] - 1s 738us/step - loss: 0.4861 - acc: 0.7771 - fscore: 0.7771 - val_loss: 0.5690 - val_acc: 0.7208 - val_fscore: 0.7208\n",
+      "Epoch 59/700\n",
+      "1920/1920 [==============================] - 1s 738us/step - loss: 0.4806 - acc: 0.7672 - fscore: 0.7672 - val_loss: 0.5723 - val_acc: 0.7250 - val_fscore: 0.7250\n",
+      "Epoch 60/700\n",
+      "1920/1920 [==============================] - 1s 738us/step - loss: 0.4749 - acc: 0.7755 - fscore: 0.7755 - val_loss: 0.5855 - val_acc: 0.7000 - val_fscore: 0.7000\n",
+      "Epoch 61/700\n",
+      "1920/1920 [==============================] - 1s 749us/step - loss: 0.4746 - acc: 0.7812 - fscore: 0.7812 - val_loss: 0.5735 - val_acc: 0.7167 - val_fscore: 0.7167\n",
+      "Epoch 62/700\n",
+      "1920/1920 [==============================] - 1s 742us/step - loss: 0.4706 - acc: 0.7740 - fscore: 0.7740 - val_loss: 0.5624 - val_acc: 0.7354 - val_fscore: 0.7354\n",
+      "Epoch 63/700\n",
+      "1920/1920 [==============================] - 1s 736us/step - loss: 0.4787 - acc: 0.7760 - fscore: 0.7760 - val_loss: 0.5619 - val_acc: 0.7438 - val_fscore: 0.7437\n",
+      "Epoch 64/700\n",
+      "1920/1920 [==============================] - 1s 746us/step - loss: 0.4678 - acc: 0.7802 - fscore: 0.7802 - val_loss: 0.5671 - val_acc: 0.7375 - val_fscore: 0.7375\n",
+      "Epoch 65/700\n",
+      "1920/1920 [==============================] - 1s 751us/step - loss: 0.4627 - acc: 0.7896 - fscore: 0.7896 - val_loss: 0.5640 - val_acc: 0.7417 - val_fscore: 0.7417\n",
+      "Epoch 66/700\n",
+      "1920/1920 [==============================] - 1s 743us/step - loss: 0.4588 - acc: 0.7896 - fscore: 0.7896 - val_loss: 0.5636 - val_acc: 0.7438 - val_fscore: 0.7437\n",
+      "Epoch 67/700\n",
+      "1920/1920 [==============================] - 1s 747us/step - loss: 0.4661 - acc: 0.7792 - fscore: 0.7792 - val_loss: 0.5725 - val_acc: 0.7292 - val_fscore: 0.7292\n",
+      "Epoch 68/700\n",
+      "1920/1920 [==============================] - 1s 749us/step - loss: 0.4578 - acc: 0.7891 - fscore: 0.7891 - val_loss: 0.5560 - val_acc: 0.7562 - val_fscore: 0.7562\n",
+      "Epoch 69/700\n",
+      "1920/1920 [==============================] - 1s 739us/step - loss: 0.4510 - acc: 0.7979 - fscore: 0.7979 - val_loss: 0.5597 - val_acc: 0.7458 - val_fscore: 0.7458\n",
+      "Epoch 70/700\n",
+      "1920/1920 [==============================] - 1s 758us/step - loss: 0.4488 - acc: 0.7938 - fscore: 0.7937 - val_loss: 0.5742 - val_acc: 0.7125 - val_fscore: 0.7125\n",
+      "Epoch 71/700\n",
+      "1920/1920 [==============================] - 1s 748us/step - loss: 0.4502 - acc: 0.7995 - fscore: 0.7995 - val_loss: 0.5522 - val_acc: 0.7479 - val_fscore: 0.7479\n",
+      "Epoch 72/700\n",
+      "1920/1920 [==============================] - 1s 737us/step - loss: 0.4477 - acc: 0.8052 - fscore: 0.8052 - val_loss: 0.5585 - val_acc: 0.7208 - val_fscore: 0.7208\n",
+      "Epoch 73/700\n",
+      "1920/1920 [==============================] - 1s 744us/step - loss: 0.4411 - acc: 0.8042 - fscore: 0.8042 - val_loss: 0.5470 - val_acc: 0.7521 - val_fscore: 0.7521\n",
+      "Epoch 74/700\n",
+      "1920/1920 [==============================] - 1s 738us/step - loss: 0.4420 - acc: 0.7964 - fscore: 0.7964 - val_loss: 0.5453 - val_acc: 0.7562 - val_fscore: 0.7562\n",
+      "Epoch 75/700\n",
+      "1920/1920 [==============================] - 1s 744us/step - loss: 0.4393 - acc: 0.8031 - fscore: 0.8031 - val_loss: 0.5428 - val_acc: 0.7583 - val_fscore: 0.7583\n",
+      "Epoch 76/700\n",
+      "1920/1920 [==============================] - 1s 749us/step - loss: 0.4455 - acc: 0.7859 - fscore: 0.7859 - val_loss: 0.5421 - val_acc: 0.7458 - val_fscore: 0.7458\n",
+      "Epoch 77/700\n",
+      "1920/1920 [==============================] - 1s 748us/step - loss: 0.4404 - acc: 0.8021 - fscore: 0.8021 - val_loss: 0.5659 - val_acc: 0.7104 - val_fscore: 0.7104\n",
+      "Epoch 78/700\n",
+      "1920/1920 [==============================] - 1s 747us/step - loss: 0.4425 - acc: 0.8104 - fscore: 0.8104 - val_loss: 0.5501 - val_acc: 0.7458 - val_fscore: 0.7458\n",
+      "Epoch 79/700\n",
+      "1920/1920 [==============================] - 1s 746us/step - loss: 0.4373 - acc: 0.8016 - fscore: 0.8016 - val_loss: 0.5418 - val_acc: 0.7667 - val_fscore: 0.7667\n",
+      "Epoch 80/700\n",
+      "1920/1920 [==============================] - 1s 756us/step - loss: 0.4332 - acc: 0.8120 - fscore: 0.8120 - val_loss: 0.5395 - val_acc: 0.7583 - val_fscore: 0.7583\n",
+      "Epoch 81/700\n",
+      "1920/1920 [==============================] - 1s 774us/step - loss: 0.4255 - acc: 0.8115 - fscore: 0.8115 - val_loss: 0.5503 - val_acc: 0.7583 - val_fscore: 0.7583\n",
+      "Epoch 82/700\n",
+      "1920/1920 [==============================] - 1s 742us/step - loss: 0.4301 - acc: 0.8078 - fscore: 0.8078 - val_loss: 0.5349 - val_acc: 0.7833 - val_fscore: 0.7833\n",
+      "Epoch 83/700\n",
+      "1920/1920 [==============================] - 1s 736us/step - loss: 0.4232 - acc: 0.8141 - fscore: 0.8141 - val_loss: 0.5284 - val_acc: 0.7750 - val_fscore: 0.7750\n",
+      "Epoch 84/700\n",
+      "1920/1920 [==============================] - 1s 755us/step - loss: 0.4194 - acc: 0.8187 - fscore: 0.8187 - val_loss: 0.5414 - val_acc: 0.7375 - val_fscore: 0.7375\n",
+      "Epoch 85/700\n",
+      "1920/1920 [==============================] - 1s 732us/step - loss: 0.4158 - acc: 0.8203 - fscore: 0.8203 - val_loss: 0.5379 - val_acc: 0.7562 - val_fscore: 0.7562\n",
+      "Epoch 86/700\n",
+      "1920/1920 [==============================] - 1s 741us/step - loss: 0.4149 - acc: 0.8198 - fscore: 0.8198 - val_loss: 0.5360 - val_acc: 0.7521 - val_fscore: 0.7521\n",
+      "Epoch 87/700\n",
+      "1920/1920 [==============================] - 1s 737us/step - loss: 0.4121 - acc: 0.8234 - fscore: 0.8234 - val_loss: 0.5499 - val_acc: 0.7292 - val_fscore: 0.7292\n",
+      "Epoch 88/700\n",
+      "1920/1920 [==============================] - 1s 746us/step - loss: 0.4108 - acc: 0.8276 - fscore: 0.8276 - val_loss: 0.5214 - val_acc: 0.7729 - val_fscore: 0.7729\n",
+      "Epoch 89/700\n",
+      "1920/1920 [==============================] - 1s 743us/step - loss: 0.4101 - acc: 0.8208 - fscore: 0.8208 - val_loss: 0.5134 - val_acc: 0.7729 - val_fscore: 0.7729\n",
+      "Epoch 90/700\n",
+      "1920/1920 [==============================] - 1s 743us/step - loss: 0.4006 - acc: 0.8344 - fscore: 0.8344 - val_loss: 0.5148 - val_acc: 0.7646 - val_fscore: 0.7646\n",
+      "Epoch 91/700\n",
+      "1920/1920 [==============================] - 1s 745us/step - loss: 0.4012 - acc: 0.8297 - fscore: 0.8297 - val_loss: 0.5238 - val_acc: 0.7500 - val_fscore: 0.7500\n",
+      "Epoch 92/700\n",
+      "1920/1920 [==============================] - 1s 736us/step - loss: 0.4095 - acc: 0.8250 - fscore: 0.8250 - val_loss: 0.5201 - val_acc: 0.7729 - val_fscore: 0.7729\n",
+      "Epoch 93/700\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1920/1920 [==============================] - 1s 746us/step - loss: 0.4012 - acc: 0.8297 - fscore: 0.8297 - val_loss: 0.5261 - val_acc: 0.7604 - val_fscore: 0.7604\n",
+      "Epoch 94/700\n",
+      "1920/1920 [==============================] - 1s 741us/step - loss: 0.4043 - acc: 0.8292 - fscore: 0.8292 - val_loss: 0.5111 - val_acc: 0.7792 - val_fscore: 0.7792\n",
+      "Epoch 95/700\n",
+      "1920/1920 [==============================] - 1s 741us/step - loss: 0.4024 - acc: 0.8203 - fscore: 0.8203 - val_loss: 0.5196 - val_acc: 0.7708 - val_fscore: 0.7708\n",
+      "Epoch 96/700\n",
+      "1920/1920 [==============================] - 1s 735us/step - loss: 0.3896 - acc: 0.8391 - fscore: 0.8391 - val_loss: 0.5235 - val_acc: 0.7208 - val_fscore: 0.7208\n",
+      "Epoch 97/700\n",
+      "1920/1920 [==============================] - 1s 745us/step - loss: 0.3871 - acc: 0.8406 - fscore: 0.8406 - val_loss: 0.5051 - val_acc: 0.7562 - val_fscore: 0.7562\n",
+      "Epoch 98/700\n",
+      "1920/1920 [==============================] - 1s 753us/step - loss: 0.3913 - acc: 0.8385 - fscore: 0.8385 - val_loss: 0.5026 - val_acc: 0.7875 - val_fscore: 0.7875\n",
+      "Epoch 99/700\n",
+      "1920/1920 [==============================] - 1s 735us/step - loss: 0.3835 - acc: 0.8406 - fscore: 0.8406 - val_loss: 0.5003 - val_acc: 0.7583 - val_fscore: 0.7583\n",
+      "Epoch 100/700\n",
+      "1920/1920 [==============================] - 1s 733us/step - loss: 0.3819 - acc: 0.8458 - fscore: 0.8458 - val_loss: 0.4984 - val_acc: 0.7937 - val_fscore: 0.7937\n",
+      "Epoch 101/700\n",
+      "1920/1920 [==============================] - 1s 750us/step - loss: 0.3893 - acc: 0.8281 - fscore: 0.8281 - val_loss: 0.5695 - val_acc: 0.6542 - val_fscore: 0.6542\n",
+      "Epoch 102/700\n",
+      "1920/1920 [==============================] - 1s 756us/step - loss: 0.3797 - acc: 0.8479 - fscore: 0.8479 - val_loss: 0.4926 - val_acc: 0.8083 - val_fscore: 0.8083\n",
+      "Epoch 103/700\n",
+      "1920/1920 [==============================] - 1s 742us/step - loss: 0.3706 - acc: 0.8458 - fscore: 0.8458 - val_loss: 0.4964 - val_acc: 0.7958 - val_fscore: 0.7958\n",
+      "Epoch 104/700\n",
+      "1920/1920 [==============================] - 1s 743us/step - loss: 0.3654 - acc: 0.8505 - fscore: 0.8505 - val_loss: 0.4906 - val_acc: 0.7708 - val_fscore: 0.7708\n",
+      "Epoch 105/700\n",
+      "1920/1920 [==============================] - 1s 751us/step - loss: 0.3653 - acc: 0.8510 - fscore: 0.8510 - val_loss: 0.4901 - val_acc: 0.8063 - val_fscore: 0.8062\n",
+      "Epoch 106/700\n",
+      "1920/1920 [==============================] - 1s 750us/step - loss: 0.3635 - acc: 0.8521 - fscore: 0.8521 - val_loss: 0.4955 - val_acc: 0.7625 - val_fscore: 0.7625\n",
+      "Epoch 107/700\n",
+      "1920/1920 [==============================] - 1s 746us/step - loss: 0.3630 - acc: 0.8484 - fscore: 0.8484 - val_loss: 0.4924 - val_acc: 0.8125 - val_fscore: 0.8125\n",
+      "Epoch 108/700\n",
+      "1920/1920 [==============================] - 1s 739us/step - loss: 0.3713 - acc: 0.8469 - fscore: 0.8469 - val_loss: 0.5111 - val_acc: 0.7875 - val_fscore: 0.7875\n",
+      "Epoch 109/700\n",
+      "1920/1920 [==============================] - 1s 756us/step - loss: 0.3544 - acc: 0.8573 - fscore: 0.8573 - val_loss: 0.4903 - val_acc: 0.7979 - val_fscore: 0.7979\n",
+      "Epoch 110/700\n",
+      "1920/1920 [==============================] - 1s 743us/step - loss: 0.3581 - acc: 0.8531 - fscore: 0.8531 - val_loss: 0.4904 - val_acc: 0.7917 - val_fscore: 0.7917\n",
+      "Epoch 111/700\n",
+      "1920/1920 [==============================] - 1s 740us/step - loss: 0.3508 - acc: 0.8484 - fscore: 0.8484 - val_loss: 0.4805 - val_acc: 0.8063 - val_fscore: 0.8062\n",
+      "Epoch 112/700\n",
+      "1920/1920 [==============================] - 1s 740us/step - loss: 0.3531 - acc: 0.8578 - fscore: 0.8578 - val_loss: 0.4934 - val_acc: 0.7958 - val_fscore: 0.7958\n",
+      "Epoch 113/700\n",
+      "1920/1920 [==============================] - 1s 754us/step - loss: 0.3435 - acc: 0.8672 - fscore: 0.8672 - val_loss: 0.4769 - val_acc: 0.7979 - val_fscore: 0.7979\n",
+      "Epoch 114/700\n",
+      "1920/1920 [==============================] - 1s 747us/step - loss: 0.3418 - acc: 0.8594 - fscore: 0.8594 - val_loss: 0.4828 - val_acc: 0.7833 - val_fscore: 0.7833\n",
+      "Epoch 115/700\n",
+      "1920/1920 [==============================] - 1s 734us/step - loss: 0.3504 - acc: 0.8557 - fscore: 0.8557 - val_loss: 0.4802 - val_acc: 0.7833 - val_fscore: 0.7833\n",
+      "Epoch 116/700\n",
+      "1920/1920 [==============================] - 1s 745us/step - loss: 0.3455 - acc: 0.8682 - fscore: 0.8682 - val_loss: 0.4811 - val_acc: 0.8042 - val_fscore: 0.8042\n",
+      "Epoch 117/700\n",
+      "1920/1920 [==============================] - 1s 746us/step - loss: 0.3305 - acc: 0.8693 - fscore: 0.8693 - val_loss: 0.4872 - val_acc: 0.7688 - val_fscore: 0.7687\n",
+      "Epoch 118/700\n",
+      "1920/1920 [==============================] - 1s 743us/step - loss: 0.3315 - acc: 0.8714 - fscore: 0.8714 - val_loss: 0.4723 - val_acc: 0.8208 - val_fscore: 0.8208\n",
+      "Epoch 119/700\n",
+      "1920/1920 [==============================] - 1s 746us/step - loss: 0.3404 - acc: 0.8615 - fscore: 0.8615 - val_loss: 0.4744 - val_acc: 0.8292 - val_fscore: 0.8292\n",
+      "Epoch 120/700\n",
+      "1920/1920 [==============================] - 1s 741us/step - loss: 0.3191 - acc: 0.8760 - fscore: 0.8760 - val_loss: 0.4728 - val_acc: 0.8000 - val_fscore: 0.8000\n",
+      "Epoch 121/700\n",
+      "1920/1920 [==============================] - 1s 736us/step - loss: 0.3243 - acc: 0.8755 - fscore: 0.8755 - val_loss: 0.4743 - val_acc: 0.7729 - val_fscore: 0.7729\n",
+      "Epoch 122/700\n",
+      "1920/1920 [==============================] - 1s 753us/step - loss: 0.3207 - acc: 0.8802 - fscore: 0.8802 - val_loss: 0.5061 - val_acc: 0.7583 - val_fscore: 0.7583\n",
+      "Epoch 123/700\n",
+      "1920/1920 [==============================] - 1s 739us/step - loss: 0.3293 - acc: 0.8734 - fscore: 0.8734 - val_loss: 0.4609 - val_acc: 0.8146 - val_fscore: 0.8146\n",
+      "Epoch 124/700\n",
+      "1920/1920 [==============================] - 1s 750us/step - loss: 0.3302 - acc: 0.8734 - fscore: 0.8734 - val_loss: 0.4688 - val_acc: 0.8042 - val_fscore: 0.8042\n",
+      "Epoch 125/700\n",
+      "1920/1920 [==============================] - 1s 754us/step - loss: 0.3262 - acc: 0.8677 - fscore: 0.8677 - val_loss: 0.4852 - val_acc: 0.7833 - val_fscore: 0.7833\n",
+      "Epoch 126/700\n",
+      "1920/1920 [==============================] - 1s 756us/step - loss: 0.3218 - acc: 0.8714 - fscore: 0.8714 - val_loss: 0.4821 - val_acc: 0.7854 - val_fscore: 0.7854\n",
+      "Epoch 127/700\n",
+      "1920/1920 [==============================] - 1s 750us/step - loss: 0.3105 - acc: 0.8812 - fscore: 0.8812 - val_loss: 0.5048 - val_acc: 0.7021 - val_fscore: 0.7021\n",
+      "Epoch 128/700\n",
+      "1920/1920 [==============================] - 1s 742us/step - loss: 0.3204 - acc: 0.8781 - fscore: 0.8781 - val_loss: 0.4606 - val_acc: 0.7958 - val_fscore: 0.7958\n",
+      "Epoch 129/700\n",
+      "1920/1920 [==============================] - 1s 740us/step - loss: 0.3070 - acc: 0.8802 - fscore: 0.8802 - val_loss: 0.4494 - val_acc: 0.8208 - val_fscore: 0.8208\n",
+      "Epoch 130/700\n",
+      "1920/1920 [==============================] - 1s 745us/step - loss: 0.3032 - acc: 0.8875 - fscore: 0.8875 - val_loss: 0.4709 - val_acc: 0.7562 - val_fscore: 0.7562\n",
+      "Epoch 131/700\n",
+      "1920/1920 [==============================] - 1s 745us/step - loss: 0.3053 - acc: 0.8807 - fscore: 0.8807 - val_loss: 0.4484 - val_acc: 0.8250 - val_fscore: 0.8250\n",
+      "Epoch 132/700\n",
+      "1920/1920 [==============================] - 1s 740us/step - loss: 0.3054 - acc: 0.8885 - fscore: 0.8885 - val_loss: 0.4868 - val_acc: 0.7292 - val_fscore: 0.7292\n",
+      "Epoch 133/700\n",
+      "1920/1920 [==============================] - 1s 746us/step - loss: 0.2939 - acc: 0.8927 - fscore: 0.8927 - val_loss: 0.4383 - val_acc: 0.8396 - val_fscore: 0.8396\n",
+      "Epoch 134/700\n",
+      "1920/1920 [==============================] - 1s 743us/step - loss: 0.3017 - acc: 0.8849 - fscore: 0.8849 - val_loss: 0.4603 - val_acc: 0.7937 - val_fscore: 0.7937\n",
+      "Epoch 135/700\n",
+      "1920/1920 [==============================] - 1s 746us/step - loss: 0.3037 - acc: 0.8844 - fscore: 0.8844 - val_loss: 0.4766 - val_acc: 0.7396 - val_fscore: 0.7396\n",
+      "Epoch 136/700\n",
+      "1920/1920 [==============================] - 1s 750us/step - loss: 0.2879 - acc: 0.9026 - fscore: 0.9026 - val_loss: 0.4307 - val_acc: 0.8438 - val_fscore: 0.8437\n",
+      "Epoch 137/700\n",
+      "1920/1920 [==============================] - 1s 761us/step - loss: 0.2938 - acc: 0.8875 - fscore: 0.8875 - val_loss: 0.4413 - val_acc: 0.8396 - val_fscore: 0.8396\n",
+      "Epoch 138/700\n",
+      "1920/1920 [==============================] - 1s 745us/step - loss: 0.2842 - acc: 0.8979 - fscore: 0.8979 - val_loss: 0.5078 - val_acc: 0.7063 - val_fscore: 0.7062\n",
+      "Epoch 139/700\n",
+      "1920/1920 [==============================] - 1s 728us/step - loss: 0.2867 - acc: 0.8937 - fscore: 0.8937 - val_loss: 0.4507 - val_acc: 0.8104 - val_fscore: 0.8104\n",
+      "Epoch 140/700\n",
+      "1920/1920 [==============================] - 1s 751us/step - loss: 0.2875 - acc: 0.8922 - fscore: 0.8922 - val_loss: 0.4350 - val_acc: 0.8479 - val_fscore: 0.8479\n",
+      "Epoch 141/700\n",
+      "1920/1920 [==============================] - 1s 743us/step - loss: 0.2852 - acc: 0.8964 - fscore: 0.8964 - val_loss: 0.4223 - val_acc: 0.8458 - val_fscore: 0.8458\n",
+      "Epoch 142/700\n",
+      "1920/1920 [==============================] - 1s 744us/step - loss: 0.2747 - acc: 0.9026 - fscore: 0.9026 - val_loss: 0.4568 - val_acc: 0.7875 - val_fscore: 0.7875\n",
+      "Epoch 143/700\n",
+      "1920/1920 [==============================] - 1s 745us/step - loss: 0.2845 - acc: 0.8844 - fscore: 0.8844 - val_loss: 0.4255 - val_acc: 0.8375 - val_fscore: 0.8375\n",
+      "Epoch 144/700\n",
+      "1920/1920 [==============================] - 1s 754us/step - loss: 0.2724 - acc: 0.9010 - fscore: 0.9010 - val_loss: 0.4528 - val_acc: 0.7854 - val_fscore: 0.7854\n",
+      "Epoch 145/700\n",
+      "1920/1920 [==============================] - 1s 741us/step - loss: 0.2661 - acc: 0.9021 - fscore: 0.9021 - val_loss: 0.4213 - val_acc: 0.8333 - val_fscore: 0.8333\n",
+      "Epoch 146/700\n",
+      "1920/1920 [==============================] - 1s 747us/step - loss: 0.2742 - acc: 0.9052 - fscore: 0.9052 - val_loss: 0.4201 - val_acc: 0.8313 - val_fscore: 0.8312\n",
+      "Epoch 147/700\n",
+      "1920/1920 [==============================] - 1s 737us/step - loss: 0.2631 - acc: 0.9104 - fscore: 0.9104 - val_loss: 0.4364 - val_acc: 0.8250 - val_fscore: 0.8250\n",
+      "Epoch 148/700\n",
+      "1920/1920 [==============================] - 1s 743us/step - loss: 0.2705 - acc: 0.9021 - fscore: 0.9021 - val_loss: 0.4706 - val_acc: 0.7417 - val_fscore: 0.7417\n",
+      "Epoch 149/700\n",
+      "1920/1920 [==============================] - 1s 747us/step - loss: 0.2705 - acc: 0.9005 - fscore: 0.9005 - val_loss: 0.4174 - val_acc: 0.8396 - val_fscore: 0.8396\n",
+      "Epoch 150/700\n",
+      "1920/1920 [==============================] - 1s 744us/step - loss: 0.2669 - acc: 0.9068 - fscore: 0.9068 - val_loss: 0.4106 - val_acc: 0.8542 - val_fscore: 0.8542\n",
+      "Epoch 151/700\n",
+      "1920/1920 [==============================] - 1s 743us/step - loss: 0.2647 - acc: 0.9057 - fscore: 0.9057 - val_loss: 0.4162 - val_acc: 0.8500 - val_fscore: 0.8500\n",
+      "Epoch 152/700\n",
+      "1920/1920 [==============================] - 1s 744us/step - loss: 0.2511 - acc: 0.9167 - fscore: 0.9167 - val_loss: 0.4273 - val_acc: 0.8417 - val_fscore: 0.8417\n",
+      "Epoch 153/700\n",
+      "1920/1920 [==============================] - 1s 750us/step - loss: 0.2482 - acc: 0.9109 - fscore: 0.9109 - val_loss: 0.3956 - val_acc: 0.8562 - val_fscore: 0.8562\n",
+      "Epoch 154/700\n",
+      "1920/1920 [==============================] - 1s 741us/step - loss: 0.2557 - acc: 0.9146 - fscore: 0.9146 - val_loss: 0.4123 - val_acc: 0.8208 - val_fscore: 0.8208\n",
+      "Epoch 155/700\n",
+      "1920/1920 [==============================] - 1s 738us/step - loss: 0.2479 - acc: 0.9167 - fscore: 0.9167 - val_loss: 0.4174 - val_acc: 0.8417 - val_fscore: 0.8417\n",
+      "Epoch 156/700\n",
+      "1920/1920 [==============================] - 1s 751us/step - loss: 0.2523 - acc: 0.9120 - fscore: 0.9120 - val_loss: 0.4048 - val_acc: 0.8313 - val_fscore: 0.8312\n",
+      "Epoch 157/700\n",
+      "1920/1920 [==============================] - 1s 749us/step - loss: 0.2560 - acc: 0.9073 - fscore: 0.9073 - val_loss: 0.3993 - val_acc: 0.8562 - val_fscore: 0.8562\n",
+      "Epoch 158/700\n",
+      "1920/1920 [==============================] - 1s 739us/step - loss: 0.2413 - acc: 0.9099 - fscore: 0.9099 - val_loss: 0.5574 - val_acc: 0.6750 - val_fscore: 0.6750\n",
+      "Epoch 159/700\n",
+      "1920/1920 [==============================] - 1s 745us/step - loss: 0.2583 - acc: 0.9036 - fscore: 0.9036 - val_loss: 0.3911 - val_acc: 0.8542 - val_fscore: 0.8542\n",
+      "Epoch 160/700\n",
+      "1920/1920 [==============================] - 1s 744us/step - loss: 0.2453 - acc: 0.9083 - fscore: 0.9083 - val_loss: 0.4028 - val_acc: 0.8479 - val_fscore: 0.8479\n",
+      "Epoch 161/700\n",
+      "1920/1920 [==============================] - 1s 752us/step - loss: 0.2383 - acc: 0.9125 - fscore: 0.9125 - val_loss: 0.4047 - val_acc: 0.8167 - val_fscore: 0.8167\n",
+      "Epoch 162/700\n",
+      "1920/1920 [==============================] - 1s 749us/step - loss: 0.2308 - acc: 0.9229 - fscore: 0.9229 - val_loss: 0.3961 - val_acc: 0.8333 - val_fscore: 0.8333\n",
+      "Epoch 163/700\n",
+      "1920/1920 [==============================] - 1s 743us/step - loss: 0.2320 - acc: 0.9214 - fscore: 0.9214 - val_loss: 0.3968 - val_acc: 0.8604 - val_fscore: 0.8604\n",
+      "Epoch 164/700\n",
+      "1920/1920 [==============================] - 1s 736us/step - loss: 0.2246 - acc: 0.9266 - fscore: 0.9266 - val_loss: 0.3943 - val_acc: 0.8562 - val_fscore: 0.8562\n",
+      "Epoch 165/700\n",
+      "1920/1920 [==============================] - 1s 747us/step - loss: 0.2344 - acc: 0.9203 - fscore: 0.9203 - val_loss: 0.3841 - val_acc: 0.8625 - val_fscore: 0.8625\n",
+      "Epoch 166/700\n",
+      "1920/1920 [==============================] - 1s 747us/step - loss: 0.2209 - acc: 0.9365 - fscore: 0.9365 - val_loss: 0.3974 - val_acc: 0.8521 - val_fscore: 0.8521\n",
+      "Epoch 167/700\n",
+      "1920/1920 [==============================] - 1s 739us/step - loss: 0.2305 - acc: 0.9188 - fscore: 0.9187 - val_loss: 0.3732 - val_acc: 0.8583 - val_fscore: 0.8583\n",
+      "Epoch 168/700\n",
+      "1920/1920 [==============================] - 1s 744us/step - loss: 0.2262 - acc: 0.9203 - fscore: 0.9203 - val_loss: 0.4200 - val_acc: 0.8208 - val_fscore: 0.8208\n",
+      "Epoch 169/700\n",
+      "1920/1920 [==============================] - 1s 745us/step - loss: 0.2155 - acc: 0.9292 - fscore: 0.9292 - val_loss: 0.3820 - val_acc: 0.8521 - val_fscore: 0.8521\n",
+      "Epoch 170/700\n",
+      "1920/1920 [==============================] - 1s 749us/step - loss: 0.2272 - acc: 0.9219 - fscore: 0.9219 - val_loss: 0.4455 - val_acc: 0.7667 - val_fscore: 0.7667\n",
+      "Epoch 171/700\n",
+      "1920/1920 [==============================] - 1s 739us/step - loss: 0.2170 - acc: 0.9302 - fscore: 0.9302 - val_loss: 0.3894 - val_acc: 0.8500 - val_fscore: 0.8500\n",
+      "Epoch 172/700\n",
+      "1920/1920 [==============================] - 1s 755us/step - loss: 0.2119 - acc: 0.9323 - fscore: 0.9323 - val_loss: 0.4219 - val_acc: 0.7937 - val_fscore: 0.7937\n",
+      "Epoch 173/700\n",
+      "1920/1920 [==============================] - 1s 747us/step - loss: 0.2169 - acc: 0.9281 - fscore: 0.9281 - val_loss: 0.3983 - val_acc: 0.8500 - val_fscore: 0.8500\n",
+      "Epoch 174/700\n",
+      "1920/1920 [==============================] - 1s 745us/step - loss: 0.2089 - acc: 0.9349 - fscore: 0.9349 - val_loss: 0.3631 - val_acc: 0.8750 - val_fscore: 0.8750\n",
+      "Epoch 175/700\n",
+      "1920/1920 [==============================] - 1s 738us/step - loss: 0.2003 - acc: 0.9328 - fscore: 0.9328 - val_loss: 0.3900 - val_acc: 0.8375 - val_fscore: 0.8375\n",
+      "Epoch 176/700\n",
+      "1920/1920 [==============================] - 1s 747us/step - loss: 0.2018 - acc: 0.9375 - fscore: 0.9375 - val_loss: 0.3983 - val_acc: 0.8313 - val_fscore: 0.8312\n",
+      "Epoch 177/700\n",
+      "1920/1920 [==============================] - 1s 749us/step - loss: 0.2031 - acc: 0.9375 - fscore: 0.9375 - val_loss: 0.3883 - val_acc: 0.8375 - val_fscore: 0.8375\n",
+      "Epoch 178/700\n",
+      "1920/1920 [==============================] - 1s 742us/step - loss: 0.2018 - acc: 0.9443 - fscore: 0.9443 - val_loss: 0.3725 - val_acc: 0.8604 - val_fscore: 0.8604\n",
+      "Epoch 179/700\n",
+      "1920/1920 [==============================] - 1s 748us/step - loss: 0.2049 - acc: 0.9323 - fscore: 0.9323 - val_loss: 0.3624 - val_acc: 0.8417 - val_fscore: 0.8417\n",
+      "Epoch 180/700\n",
+      "1920/1920 [==============================] - 1s 746us/step - loss: 0.2009 - acc: 0.9323 - fscore: 0.9323 - val_loss: 0.3752 - val_acc: 0.8604 - val_fscore: 0.8604\n",
+      "Epoch 181/700\n",
+      "1920/1920 [==============================] - 1s 750us/step - loss: 0.1976 - acc: 0.9370 - fscore: 0.9370 - val_loss: 0.3552 - val_acc: 0.8812 - val_fscore: 0.8812\n",
+      "Epoch 182/700\n",
+      "1920/1920 [==============================] - 1s 742us/step - loss: 0.1902 - acc: 0.9370 - fscore: 0.9370 - val_loss: 0.3878 - val_acc: 0.8333 - val_fscore: 0.8333\n",
+      "Epoch 183/700\n",
+      "1920/1920 [==============================] - 1s 755us/step - loss: 0.1964 - acc: 0.9370 - fscore: 0.9370 - val_loss: 0.3464 - val_acc: 0.8771 - val_fscore: 0.8771\n",
+      "Epoch 184/700\n",
+      "1920/1920 [==============================] - 1s 743us/step - loss: 0.1910 - acc: 0.9417 - fscore: 0.9417 - val_loss: 0.3673 - val_acc: 0.8750 - val_fscore: 0.8750\n",
+      "Epoch 185/700\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1920/1920 [==============================] - 1s 757us/step - loss: 0.1912 - acc: 0.9370 - fscore: 0.9370 - val_loss: 0.3850 - val_acc: 0.8271 - val_fscore: 0.8271\n",
+      "Epoch 186/700\n",
+      "1920/1920 [==============================] - 1s 737us/step - loss: 0.2034 - acc: 0.9323 - fscore: 0.9323 - val_loss: 0.4120 - val_acc: 0.8167 - val_fscore: 0.8167\n",
+      "Epoch 187/700\n",
+      "1920/1920 [==============================] - 1s 744us/step - loss: 0.1848 - acc: 0.9422 - fscore: 0.9422 - val_loss: 0.4090 - val_acc: 0.8125 - val_fscore: 0.8125\n",
+      "Epoch 188/700\n",
+      "1920/1920 [==============================] - 1s 749us/step - loss: 0.1847 - acc: 0.9385 - fscore: 0.9385 - val_loss: 0.4064 - val_acc: 0.8125 - val_fscore: 0.8125\n",
+      "Epoch 189/700\n",
+      "1920/1920 [==============================] - 1s 751us/step - loss: 0.1888 - acc: 0.9339 - fscore: 0.9339 - val_loss: 0.4149 - val_acc: 0.8000 - val_fscore: 0.8000\n",
+      "Epoch 190/700\n",
+      "1920/1920 [==============================] - 1s 750us/step - loss: 0.1842 - acc: 0.9437 - fscore: 0.9437 - val_loss: 0.3442 - val_acc: 0.8812 - val_fscore: 0.8812\n",
+      "Epoch 191/700\n",
+      "1920/1920 [==============================] - 1s 738us/step - loss: 0.1857 - acc: 0.9422 - fscore: 0.9422 - val_loss: 0.3879 - val_acc: 0.8500 - val_fscore: 0.8500\n",
+      "Epoch 192/700\n",
+      "1920/1920 [==============================] - 1s 735us/step - loss: 0.1831 - acc: 0.9453 - fscore: 0.9453 - val_loss: 0.3721 - val_acc: 0.8542 - val_fscore: 0.8542\n",
+      "Epoch 193/700\n",
+      "1920/1920 [==============================] - 1s 748us/step - loss: 0.1732 - acc: 0.9474 - fscore: 0.9474 - val_loss: 0.4583 - val_acc: 0.7458 - val_fscore: 0.7458\n",
+      "Epoch 194/700\n",
+      "1920/1920 [==============================] - 1s 747us/step - loss: 0.1666 - acc: 0.9490 - fscore: 0.9490 - val_loss: 0.3251 - val_acc: 0.9062 - val_fscore: 0.9062\n",
+      "Epoch 195/700\n",
+      "1920/1920 [==============================] - 1s 741us/step - loss: 0.1742 - acc: 0.9432 - fscore: 0.9432 - val_loss: 0.3771 - val_acc: 0.8500 - val_fscore: 0.8500\n",
+      "Epoch 196/700\n",
+      "1920/1920 [==============================] - 1s 751us/step - loss: 0.1659 - acc: 0.9484 - fscore: 0.9484 - val_loss: 0.4340 - val_acc: 0.7729 - val_fscore: 0.7729\n",
+      "Epoch 197/700\n",
+      "1920/1920 [==============================] - 1s 747us/step - loss: 0.1709 - acc: 0.9479 - fscore: 0.9479 - val_loss: 0.3427 - val_acc: 0.8604 - val_fscore: 0.8604\n",
+      "Epoch 198/700\n",
+      "1920/1920 [==============================] - 1s 745us/step - loss: 0.1702 - acc: 0.9474 - fscore: 0.9474 - val_loss: 0.3663 - val_acc: 0.8542 - val_fscore: 0.8542\n",
+      "Epoch 199/700\n",
+      "1920/1920 [==============================] - 1s 736us/step - loss: 0.1661 - acc: 0.9464 - fscore: 0.9464 - val_loss: 0.3924 - val_acc: 0.8208 - val_fscore: 0.8208\n",
+      "Epoch 200/700\n",
+      "1920/1920 [==============================] - 1s 750us/step - loss: 0.1666 - acc: 0.9510 - fscore: 0.9510 - val_loss: 0.3374 - val_acc: 0.8688 - val_fscore: 0.8687\n",
+      "Epoch 201/700\n",
+      "1920/1920 [==============================] - 1s 738us/step - loss: 0.1657 - acc: 0.9464 - fscore: 0.9464 - val_loss: 0.3558 - val_acc: 0.8479 - val_fscore: 0.8479\n",
+      "Epoch 202/700\n",
+      "1920/1920 [==============================] - 1s 747us/step - loss: 0.1574 - acc: 0.9479 - fscore: 0.9479 - val_loss: 0.4495 - val_acc: 0.7646 - val_fscore: 0.7646\n",
+      "Epoch 203/700\n",
+      "1920/1920 [==============================] - 1s 753us/step - loss: 0.1621 - acc: 0.9453 - fscore: 0.9453 - val_loss: 0.3599 - val_acc: 0.8354 - val_fscore: 0.8354\n",
+      "Epoch 204/700\n",
+      "1920/1920 [==============================] - 1s 740us/step - loss: 0.1529 - acc: 0.9589 - fscore: 0.9589 - val_loss: 0.3429 - val_acc: 0.8667 - val_fscore: 0.8667\n",
+      "Epoch 205/700\n",
+      "1920/1920 [==============================] - 1s 739us/step - loss: 0.1563 - acc: 0.9583 - fscore: 0.9583 - val_loss: 0.3524 - val_acc: 0.8583 - val_fscore: 0.8583\n",
+      "Epoch 206/700\n",
+      "1920/1920 [==============================] - 1s 739us/step - loss: 0.1583 - acc: 0.9516 - fscore: 0.9516 - val_loss: 0.3537 - val_acc: 0.8625 - val_fscore: 0.8625\n",
+      "Epoch 207/700\n",
+      "1920/1920 [==============================] - 1s 741us/step - loss: 0.1569 - acc: 0.9474 - fscore: 0.9474 - val_loss: 0.3336 - val_acc: 0.8708 - val_fscore: 0.8708\n",
+      "Epoch 208/700\n",
+      "1920/1920 [==============================] - 1s 746us/step - loss: 0.1514 - acc: 0.9594 - fscore: 0.9594 - val_loss: 0.3297 - val_acc: 0.8646 - val_fscore: 0.8646\n",
+      "Epoch 209/700\n",
+      "1920/1920 [==============================] - 1s 744us/step - loss: 0.1499 - acc: 0.9594 - fscore: 0.9594 - val_loss: 0.3287 - val_acc: 0.8771 - val_fscore: 0.8771\n",
+      "Epoch 210/700\n",
+      "1920/1920 [==============================] - 1s 745us/step - loss: 0.1460 - acc: 0.9620 - fscore: 0.9620 - val_loss: 0.3280 - val_acc: 0.8646 - val_fscore: 0.8646\n",
+      "Epoch 211/700\n",
+      "1920/1920 [==============================] - 1s 742us/step - loss: 0.1583 - acc: 0.9521 - fscore: 0.9521 - val_loss: 0.3180 - val_acc: 0.8854 - val_fscore: 0.8854\n",
+      "Epoch 212/700\n",
+      "1920/1920 [==============================] - 1s 740us/step - loss: 0.1450 - acc: 0.9562 - fscore: 0.9562 - val_loss: 0.3724 - val_acc: 0.8271 - val_fscore: 0.8271\n",
+      "Epoch 213/700\n",
+      "1920/1920 [==============================] - 1s 750us/step - loss: 0.1499 - acc: 0.9536 - fscore: 0.9536 - val_loss: 0.3265 - val_acc: 0.8917 - val_fscore: 0.8917\n",
+      "Epoch 214/700\n",
+      "1920/1920 [==============================] - 1s 736us/step - loss: 0.1454 - acc: 0.9562 - fscore: 0.9562 - val_loss: 0.3286 - val_acc: 0.8792 - val_fscore: 0.8792\n",
+      "Epoch 215/700\n",
+      "1920/1920 [==============================] - 1s 746us/step - loss: 0.1454 - acc: 0.9620 - fscore: 0.9620 - val_loss: 0.3082 - val_acc: 0.9021 - val_fscore: 0.9021\n",
+      "Epoch 216/700\n",
+      "1920/1920 [==============================] - 1s 749us/step - loss: 0.1518 - acc: 0.9479 - fscore: 0.9479 - val_loss: 0.3368 - val_acc: 0.8521 - val_fscore: 0.8521\n",
+      "Epoch 217/700\n",
+      "1920/1920 [==============================] - 1s 749us/step - loss: 0.1326 - acc: 0.9677 - fscore: 0.9677 - val_loss: 0.3365 - val_acc: 0.8729 - val_fscore: 0.8729\n",
+      "Epoch 218/700\n",
+      "1920/1920 [==============================] - 1s 745us/step - loss: 0.1393 - acc: 0.9589 - fscore: 0.9589 - val_loss: 0.3134 - val_acc: 0.8938 - val_fscore: 0.8937\n",
+      "Epoch 219/700\n",
+      "1920/1920 [==============================] - 1s 748us/step - loss: 0.1408 - acc: 0.9562 - fscore: 0.9562 - val_loss: 0.3206 - val_acc: 0.8750 - val_fscore: 0.8750\n",
+      "Epoch 220/700\n",
+      "1920/1920 [==============================] - 1s 753us/step - loss: 0.1252 - acc: 0.9740 - fscore: 0.9740 - val_loss: 0.3114 - val_acc: 0.8854 - val_fscore: 0.8854\n",
+      "Epoch 221/700\n",
+      "1920/1920 [==============================] - 1s 763us/step - loss: 0.1308 - acc: 0.9609 - fscore: 0.9609 - val_loss: 0.3767 - val_acc: 0.8167 - val_fscore: 0.8167\n",
+      "Epoch 222/700\n",
+      "1920/1920 [==============================] - 1s 735us/step - loss: 0.1413 - acc: 0.9568 - fscore: 0.9568 - val_loss: 0.3361 - val_acc: 0.8833 - val_fscore: 0.8833\n",
+      "Epoch 223/700\n",
+      "1920/1920 [==============================] - 1s 742us/step - loss: 0.1359 - acc: 0.9573 - fscore: 0.9573 - val_loss: 0.3147 - val_acc: 0.8604 - val_fscore: 0.8604\n",
+      "Epoch 224/700\n",
+      "1920/1920 [==============================] - 1s 749us/step - loss: 0.1235 - acc: 0.9698 - fscore: 0.9698 - val_loss: 0.3467 - val_acc: 0.8479 - val_fscore: 0.8479\n",
+      "Epoch 225/700\n",
+      "1920/1920 [==============================] - 1s 744us/step - loss: 0.1254 - acc: 0.9682 - fscore: 0.9682 - val_loss: 0.2936 - val_acc: 0.8958 - val_fscore: 0.8958\n",
+      "Epoch 226/700\n",
+      "1920/1920 [==============================] - 1s 741us/step - loss: 0.1317 - acc: 0.9609 - fscore: 0.9609 - val_loss: 0.3228 - val_acc: 0.8646 - val_fscore: 0.8646\n",
+      "Epoch 227/700\n",
+      "1920/1920 [==============================] - 1s 742us/step - loss: 0.1219 - acc: 0.9719 - fscore: 0.9719 - val_loss: 0.3116 - val_acc: 0.8708 - val_fscore: 0.8708\n",
+      "Epoch 228/700\n",
+      "1920/1920 [==============================] - 1s 740us/step - loss: 0.1284 - acc: 0.9656 - fscore: 0.9656 - val_loss: 0.3274 - val_acc: 0.8792 - val_fscore: 0.8792\n",
+      "Epoch 229/700\n",
+      "1920/1920 [==============================] - 1s 745us/step - loss: 0.1259 - acc: 0.9594 - fscore: 0.9594 - val_loss: 0.3320 - val_acc: 0.8542 - val_fscore: 0.8542\n",
+      "Epoch 230/700\n",
+      "1920/1920 [==============================] - 1s 747us/step - loss: 0.1305 - acc: 0.9635 - fscore: 0.9635 - val_loss: 0.2967 - val_acc: 0.8938 - val_fscore: 0.8937\n",
+      "Epoch 231/700\n",
+      "1920/1920 [==============================] - 2s 798us/step - loss: 0.1197 - acc: 0.9677 - fscore: 0.9677 - val_loss: 0.3232 - val_acc: 0.8771 - val_fscore: 0.8771\n",
+      "Epoch 232/700\n",
+      "1920/1920 [==============================] - 1s 765us/step - loss: 0.1251 - acc: 0.9672 - fscore: 0.9672 - val_loss: 0.3017 - val_acc: 0.8854 - val_fscore: 0.8854\n",
+      "Epoch 233/700\n",
+      "1920/1920 [==============================] - 1s 776us/step - loss: 0.1282 - acc: 0.9635 - fscore: 0.9635 - val_loss: 0.3016 - val_acc: 0.8833 - val_fscore: 0.8833\n",
+      "Epoch 234/700\n",
+      "1920/1920 [==============================] - 1s 732us/step - loss: 0.1255 - acc: 0.9646 - fscore: 0.9646 - val_loss: 0.4540 - val_acc: 0.7750 - val_fscore: 0.7750\n",
+      "Epoch 235/700\n",
+      "1920/1920 [==============================] - 1s 761us/step - loss: 0.1217 - acc: 0.9667 - fscore: 0.9667 - val_loss: 0.3006 - val_acc: 0.8812 - val_fscore: 0.8812\n",
+      "Epoch 236/700\n",
+      "1920/1920 [==============================] - 1s 744us/step - loss: 0.1285 - acc: 0.9651 - fscore: 0.9651 - val_loss: 0.3898 - val_acc: 0.8229 - val_fscore: 0.8229\n",
+      "Epoch 237/700\n",
+      "1920/1920 [==============================] - 1s 759us/step - loss: 0.1176 - acc: 0.9677 - fscore: 0.9677 - val_loss: 0.2798 - val_acc: 0.8917 - val_fscore: 0.8917\n",
+      "Epoch 238/700\n",
+      "1920/1920 [==============================] - 1s 743us/step - loss: 0.1154 - acc: 0.9672 - fscore: 0.9672 - val_loss: 0.5011 - val_acc: 0.7188 - val_fscore: 0.7187\n",
+      "Epoch 239/700\n",
+      "1920/1920 [==============================] - 1s 746us/step - loss: 0.1216 - acc: 0.9651 - fscore: 0.9651 - val_loss: 0.2875 - val_acc: 0.8917 - val_fscore: 0.8917\n",
+      "Epoch 240/700\n",
+      "1920/1920 [==============================] - 1s 761us/step - loss: 0.1150 - acc: 0.9734 - fscore: 0.9734 - val_loss: 0.3089 - val_acc: 0.8750 - val_fscore: 0.8750\n",
+      "Epoch 241/700\n",
+      "1920/1920 [==============================] - 1s 740us/step - loss: 0.1106 - acc: 0.9661 - fscore: 0.9661 - val_loss: 0.3509 - val_acc: 0.8542 - val_fscore: 0.8542\n",
+      "Epoch 242/700\n",
+      "1920/1920 [==============================] - 1s 738us/step - loss: 0.1084 - acc: 0.9755 - fscore: 0.9755 - val_loss: 0.3409 - val_acc: 0.8375 - val_fscore: 0.8375\n",
+      "Epoch 243/700\n",
+      "1920/1920 [==============================] - 1s 744us/step - loss: 0.1065 - acc: 0.9714 - fscore: 0.9714 - val_loss: 0.2783 - val_acc: 0.9021 - val_fscore: 0.9021\n",
+      "Epoch 244/700\n",
+      "1920/1920 [==============================] - 1s 738us/step - loss: 0.1056 - acc: 0.9734 - fscore: 0.9734 - val_loss: 0.2799 - val_acc: 0.8979 - val_fscore: 0.8979\n",
+      "Epoch 245/700\n",
+      "1920/1920 [==============================] - 1s 745us/step - loss: 0.1094 - acc: 0.9651 - fscore: 0.9651 - val_loss: 0.3279 - val_acc: 0.8646 - val_fscore: 0.8646\n",
+      "Epoch 246/700\n",
+      "1920/1920 [==============================] - 1s 756us/step - loss: 0.1048 - acc: 0.9786 - fscore: 0.9786 - val_loss: 0.2959 - val_acc: 0.8896 - val_fscore: 0.8896\n",
+      "Epoch 247/700\n",
+      "1920/1920 [==============================] - 1s 744us/step - loss: 0.0976 - acc: 0.9760 - fscore: 0.9760 - val_loss: 0.3213 - val_acc: 0.8583 - val_fscore: 0.8583\n",
+      "Epoch 248/700\n",
+      "1920/1920 [==============================] - 1s 737us/step - loss: 0.1006 - acc: 0.9766 - fscore: 0.9766 - val_loss: 0.3716 - val_acc: 0.8271 - val_fscore: 0.8271\n",
+      "Epoch 249/700\n",
+      "1920/1920 [==============================] - 1s 751us/step - loss: 0.1022 - acc: 0.9708 - fscore: 0.9708 - val_loss: 0.2810 - val_acc: 0.8854 - val_fscore: 0.8854\n",
+      "Epoch 250/700\n",
+      "1920/1920 [==============================] - 1s 745us/step - loss: 0.1108 - acc: 0.9661 - fscore: 0.9661 - val_loss: 0.2793 - val_acc: 0.8875 - val_fscore: 0.8875\n",
+      "Epoch 251/700\n",
+      "1920/1920 [==============================] - 1s 743us/step - loss: 0.1015 - acc: 0.9719 - fscore: 0.9719 - val_loss: 0.2759 - val_acc: 0.8958 - val_fscore: 0.8958\n",
+      "Epoch 252/700\n",
+      "1920/1920 [==============================] - 1s 747us/step - loss: 0.1030 - acc: 0.9698 - fscore: 0.9698 - val_loss: 0.3052 - val_acc: 0.8812 - val_fscore: 0.8812\n",
+      "Epoch 253/700\n",
+      "1920/1920 [==============================] - 1s 753us/step - loss: 0.0930 - acc: 0.9760 - fscore: 0.9760 - val_loss: 0.3047 - val_acc: 0.8667 - val_fscore: 0.8667\n",
+      "Epoch 254/700\n",
+      "1920/1920 [==============================] - 1s 747us/step - loss: 0.1039 - acc: 0.9755 - fscore: 0.9755 - val_loss: 0.3010 - val_acc: 0.8688 - val_fscore: 0.8687\n",
+      "Epoch 255/700\n",
+      "1920/1920 [==============================] - 1s 742us/step - loss: 0.0923 - acc: 0.9776 - fscore: 0.9776 - val_loss: 0.2905 - val_acc: 0.8833 - val_fscore: 0.8833\n",
+      "Epoch 256/700\n",
+      "1920/1920 [==============================] - 1s 765us/step - loss: 0.1031 - acc: 0.9703 - fscore: 0.9703 - val_loss: 0.2693 - val_acc: 0.9062 - val_fscore: 0.9062\n",
+      "Epoch 257/700\n",
+      "1920/1920 [==============================] - 1s 753us/step - loss: 0.0949 - acc: 0.9745 - fscore: 0.9745 - val_loss: 0.2884 - val_acc: 0.8896 - val_fscore: 0.8896\n",
+      "Epoch 258/700\n",
+      "1920/1920 [==============================] - 1s 742us/step - loss: 0.1006 - acc: 0.9719 - fscore: 0.9719 - val_loss: 0.2626 - val_acc: 0.9083 - val_fscore: 0.9083\n",
+      "Epoch 259/700\n",
+      "1920/1920 [==============================] - 1s 747us/step - loss: 0.0962 - acc: 0.9755 - fscore: 0.9755 - val_loss: 0.2673 - val_acc: 0.9083 - val_fscore: 0.9083\n",
+      "Epoch 260/700\n",
+      "1920/1920 [==============================] - 1s 743us/step - loss: 0.0938 - acc: 0.9776 - fscore: 0.9776 - val_loss: 0.2766 - val_acc: 0.8979 - val_fscore: 0.8979\n",
+      "Epoch 261/700\n",
+      "1920/1920 [==============================] - 1s 754us/step - loss: 0.0952 - acc: 0.9776 - fscore: 0.9776 - val_loss: 0.3224 - val_acc: 0.8625 - val_fscore: 0.8625\n",
+      "Epoch 262/700\n",
+      "1920/1920 [==============================] - 1s 739us/step - loss: 0.0855 - acc: 0.9766 - fscore: 0.9766 - val_loss: 0.4306 - val_acc: 0.7917 - val_fscore: 0.7917\n",
+      "Epoch 263/700\n",
+      "1920/1920 [==============================] - 1s 745us/step - loss: 0.1003 - acc: 0.9729 - fscore: 0.9729 - val_loss: 0.2713 - val_acc: 0.9021 - val_fscore: 0.9021\n",
+      "Epoch 264/700\n",
+      "1920/1920 [==============================] - 1s 746us/step - loss: 0.0864 - acc: 0.9802 - fscore: 0.9802 - val_loss: 0.3459 - val_acc: 0.8417 - val_fscore: 0.8417\n",
+      "Epoch 265/700\n",
+      "1920/1920 [==============================] - 1s 761us/step - loss: 0.0944 - acc: 0.9776 - fscore: 0.9776 - val_loss: 0.2625 - val_acc: 0.9021 - val_fscore: 0.9021\n",
+      "Epoch 266/700\n",
+      "1920/1920 [==============================] - 1s 742us/step - loss: 0.0825 - acc: 0.9766 - fscore: 0.9766 - val_loss: 0.2525 - val_acc: 0.9125 - val_fscore: 0.9125\n",
+      "Epoch 267/700\n",
+      "1920/1920 [==============================] - 1s 741us/step - loss: 0.0913 - acc: 0.9760 - fscore: 0.9760 - val_loss: 0.2630 - val_acc: 0.8958 - val_fscore: 0.8958\n",
+      "Epoch 268/700\n",
+      "1920/1920 [==============================] - 1s 747us/step - loss: 0.0881 - acc: 0.9813 - fscore: 0.9812 - val_loss: 0.2527 - val_acc: 0.9062 - val_fscore: 0.9062\n",
+      "Epoch 269/700\n",
+      "1920/1920 [==============================] - 1s 752us/step - loss: 0.0781 - acc: 0.9844 - fscore: 0.9844 - val_loss: 0.2552 - val_acc: 0.8979 - val_fscore: 0.8979\n",
+      "Epoch 270/700\n",
+      "1920/1920 [==============================] - 1s 742us/step - loss: 0.0813 - acc: 0.9818 - fscore: 0.9818 - val_loss: 0.2452 - val_acc: 0.9104 - val_fscore: 0.9104\n",
+      "Epoch 271/700\n",
+      "1920/1920 [==============================] - 1s 745us/step - loss: 0.0813 - acc: 0.9807 - fscore: 0.9807 - val_loss: 0.3540 - val_acc: 0.8250 - val_fscore: 0.8250\n",
+      "Epoch 272/700\n",
+      "1920/1920 [==============================] - 1s 737us/step - loss: 0.0873 - acc: 0.9771 - fscore: 0.9771 - val_loss: 0.2673 - val_acc: 0.9125 - val_fscore: 0.9125\n",
+      "Epoch 273/700\n",
+      "1920/1920 [==============================] - 1s 751us/step - loss: 0.0800 - acc: 0.9813 - fscore: 0.9812 - val_loss: 0.2902 - val_acc: 0.8854 - val_fscore: 0.8854\n",
+      "Epoch 274/700\n",
+      "1920/1920 [==============================] - 1s 746us/step - loss: 0.0828 - acc: 0.9776 - fscore: 0.9776 - val_loss: 0.3144 - val_acc: 0.8542 - val_fscore: 0.8542\n",
+      "Epoch 275/700\n",
+      "1920/1920 [==============================] - 1s 750us/step - loss: 0.0847 - acc: 0.9792 - fscore: 0.9792 - val_loss: 0.2687 - val_acc: 0.9021 - val_fscore: 0.9021\n",
+      "Epoch 276/700\n",
+      "1920/1920 [==============================] - 1s 747us/step - loss: 0.0811 - acc: 0.9807 - fscore: 0.9807 - val_loss: 0.2468 - val_acc: 0.9146 - val_fscore: 0.9146\n",
+      "Epoch 277/700\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1920/1920 [==============================] - 1s 751us/step - loss: 0.0833 - acc: 0.9755 - fscore: 0.9755 - val_loss: 0.2919 - val_acc: 0.8729 - val_fscore: 0.8729\n",
+      "Epoch 278/700\n",
+      "1920/1920 [==============================] - 1s 742us/step - loss: 0.0842 - acc: 0.9807 - fscore: 0.9807 - val_loss: 0.2311 - val_acc: 0.9104 - val_fscore: 0.9104\n",
+      "Epoch 279/700\n",
+      "1920/1920 [==============================] - 1s 737us/step - loss: 0.0851 - acc: 0.9786 - fscore: 0.9786 - val_loss: 0.2428 - val_acc: 0.9146 - val_fscore: 0.9146\n",
+      "Epoch 280/700\n",
+      "1920/1920 [==============================] - 1s 756us/step - loss: 0.0746 - acc: 0.9854 - fscore: 0.9854 - val_loss: 0.3275 - val_acc: 0.8479 - val_fscore: 0.8479\n",
+      "Epoch 281/700\n",
+      "1920/1920 [==============================] - 1s 736us/step - loss: 0.0860 - acc: 0.9807 - fscore: 0.9807 - val_loss: 0.2651 - val_acc: 0.9021 - val_fscore: 0.9021\n",
+      "Epoch 282/700\n",
+      "1920/1920 [==============================] - 1s 745us/step - loss: 0.0717 - acc: 0.9802 - fscore: 0.9802 - val_loss: 0.2453 - val_acc: 0.9083 - val_fscore: 0.9083\n",
+      "Epoch 283/700\n",
+      "1920/1920 [==============================] - 1s 742us/step - loss: 0.0697 - acc: 0.9849 - fscore: 0.9849 - val_loss: 0.2927 - val_acc: 0.8917 - val_fscore: 0.8917\n",
+      "Epoch 284/700\n",
+      "1920/1920 [==============================] - 1s 739us/step - loss: 0.0814 - acc: 0.9760 - fscore: 0.9760 - val_loss: 0.2758 - val_acc: 0.8812 - val_fscore: 0.8812\n",
+      "Epoch 285/700\n",
+      "1920/1920 [==============================] - 1s 746us/step - loss: 0.0722 - acc: 0.9828 - fscore: 0.9828 - val_loss: 0.2680 - val_acc: 0.8917 - val_fscore: 0.8917\n",
+      "Epoch 286/700\n",
+      "1920/1920 [==============================] - 1s 744us/step - loss: 0.0684 - acc: 0.9859 - fscore: 0.9859 - val_loss: 0.2884 - val_acc: 0.8792 - val_fscore: 0.8792\n",
+      "Epoch 287/700\n",
+      "1920/1920 [==============================] - 1s 748us/step - loss: 0.0721 - acc: 0.9833 - fscore: 0.9833 - val_loss: 0.3021 - val_acc: 0.8625 - val_fscore: 0.8625\n",
+      "Epoch 288/700\n",
+      "1920/1920 [==============================] - 1s 744us/step - loss: 0.0751 - acc: 0.9813 - fscore: 0.9812 - val_loss: 0.2462 - val_acc: 0.9021 - val_fscore: 0.9021\n",
+      "Epoch 289/700\n",
+      "1920/1920 [==============================] - 1s 736us/step - loss: 0.0736 - acc: 0.9813 - fscore: 0.9812 - val_loss: 0.2697 - val_acc: 0.8896 - val_fscore: 0.8896\n",
+      "Epoch 290/700\n",
+      "1920/1920 [==============================] - 1s 745us/step - loss: 0.0745 - acc: 0.9802 - fscore: 0.9802 - val_loss: 0.2459 - val_acc: 0.9062 - val_fscore: 0.9062\n",
+      "Epoch 291/700\n",
+      "1920/1920 [==============================] - 1s 744us/step - loss: 0.0632 - acc: 0.9870 - fscore: 0.9870 - val_loss: 0.2516 - val_acc: 0.8958 - val_fscore: 0.8958\n",
+      "Epoch 292/700\n",
+      "1920/1920 [==============================] - 1s 744us/step - loss: 0.0733 - acc: 0.9818 - fscore: 0.9818 - val_loss: 0.2481 - val_acc: 0.9042 - val_fscore: 0.9042\n",
+      "Epoch 293/700\n",
+      "1920/1920 [==============================] - 1s 747us/step - loss: 0.0720 - acc: 0.9833 - fscore: 0.9833 - val_loss: 0.2617 - val_acc: 0.9021 - val_fscore: 0.9021\n",
+      "Epoch 294/700\n",
+      "1920/1920 [==============================] - 1s 753us/step - loss: 0.0701 - acc: 0.9802 - fscore: 0.9802 - val_loss: 0.2452 - val_acc: 0.9042 - val_fscore: 0.9042\n",
+      "Epoch 295/700\n",
+      "1920/1920 [==============================] - 1s 744us/step - loss: 0.0700 - acc: 0.9833 - fscore: 0.9833 - val_loss: 0.2683 - val_acc: 0.8875 - val_fscore: 0.8875\n",
+      "Epoch 296/700\n",
+      "1920/1920 [==============================] - 1s 741us/step - loss: 0.0626 - acc: 0.9870 - fscore: 0.9870 - val_loss: 0.2498 - val_acc: 0.8938 - val_fscore: 0.8937\n",
+      "Epoch 297/700\n",
+      "1920/1920 [==============================] - 1s 750us/step - loss: 0.0676 - acc: 0.9880 - fscore: 0.9880 - val_loss: 0.3457 - val_acc: 0.8458 - val_fscore: 0.8458\n",
+      "Epoch 298/700\n",
+      "1920/1920 [==============================] - 1s 743us/step - loss: 0.0734 - acc: 0.9833 - fscore: 0.9833 - val_loss: 0.2199 - val_acc: 0.9229 - val_fscore: 0.9229\n",
+      "Epoch 299/700\n",
+      "1920/1920 [==============================] - 1s 756us/step - loss: 0.0694 - acc: 0.9849 - fscore: 0.9849 - val_loss: 0.2453 - val_acc: 0.9062 - val_fscore: 0.9062\n",
+      "Epoch 300/700\n",
+      "1920/1920 [==============================] - 1s 740us/step - loss: 0.0658 - acc: 0.9818 - fscore: 0.9818 - val_loss: 0.2396 - val_acc: 0.9062 - val_fscore: 0.9062\n",
+      "Epoch 301/700\n",
+      "1920/1920 [==============================] - 1s 749us/step - loss: 0.0746 - acc: 0.9792 - fscore: 0.9792 - val_loss: 0.2384 - val_acc: 0.9104 - val_fscore: 0.9104\n",
+      "Epoch 302/700\n",
+      "1920/1920 [==============================] - 1s 749us/step - loss: 0.0696 - acc: 0.9844 - fscore: 0.9844 - val_loss: 0.2363 - val_acc: 0.9104 - val_fscore: 0.9104\n",
+      "Epoch 303/700\n",
+      "1920/1920 [==============================] - 1s 771us/step - loss: 0.0659 - acc: 0.9839 - fscore: 0.9839 - val_loss: 0.2257 - val_acc: 0.9271 - val_fscore: 0.9271\n",
+      "Epoch 304/700\n",
+      "1920/1920 [==============================] - 1s 780us/step - loss: 0.0618 - acc: 0.9875 - fscore: 0.9875 - val_loss: 0.3145 - val_acc: 0.8562 - val_fscore: 0.8562\n",
+      "Epoch 305/700\n",
+      "1920/1920 [==============================] - 1s 751us/step - loss: 0.0669 - acc: 0.9844 - fscore: 0.9844 - val_loss: 0.3326 - val_acc: 0.8479 - val_fscore: 0.8479\n",
+      "Epoch 306/700\n",
+      "1920/1920 [==============================] - 1s 754us/step - loss: 0.0650 - acc: 0.9875 - fscore: 0.9875 - val_loss: 0.2784 - val_acc: 0.8812 - val_fscore: 0.8812\n",
+      "Epoch 307/700\n",
+      "1920/1920 [==============================] - 1s 745us/step - loss: 0.0638 - acc: 0.9854 - fscore: 0.9854 - val_loss: 0.2480 - val_acc: 0.9042 - val_fscore: 0.9042\n",
+      "Epoch 308/700\n",
+      "1920/1920 [==============================] - 1s 741us/step - loss: 0.0652 - acc: 0.9854 - fscore: 0.9854 - val_loss: 0.2309 - val_acc: 0.9208 - val_fscore: 0.9208\n",
+      "Epoch 309/700\n",
+      "1920/1920 [==============================] - 1s 740us/step - loss: 0.0604 - acc: 0.9854 - fscore: 0.9854 - val_loss: 0.2228 - val_acc: 0.9104 - val_fscore: 0.9104\n",
+      "Epoch 310/700\n",
+      "1920/1920 [==============================] - 1s 744us/step - loss: 0.0525 - acc: 0.9927 - fscore: 0.9927 - val_loss: 0.3536 - val_acc: 0.8333 - val_fscore: 0.8333\n",
+      "Epoch 311/700\n",
+      "1920/1920 [==============================] - 1s 746us/step - loss: 0.0606 - acc: 0.9870 - fscore: 0.9870 - val_loss: 0.2490 - val_acc: 0.9021 - val_fscore: 0.9021\n",
+      "Epoch 312/700\n",
+      "1920/1920 [==============================] - 1s 756us/step - loss: 0.0589 - acc: 0.9896 - fscore: 0.9896 - val_loss: 0.2199 - val_acc: 0.9208 - val_fscore: 0.9208\n",
+      "Epoch 313/700\n",
+      "1920/1920 [==============================] - 1s 744us/step - loss: 0.0626 - acc: 0.9839 - fscore: 0.9839 - val_loss: 0.4081 - val_acc: 0.8167 - val_fscore: 0.8167\n",
+      "Epoch 314/700\n",
+      "1920/1920 [==============================] - 1s 752us/step - loss: 0.0630 - acc: 0.9870 - fscore: 0.9870 - val_loss: 0.2391 - val_acc: 0.8979 - val_fscore: 0.8979\n",
+      "Epoch 315/700\n",
+      "1920/1920 [==============================] - 1s 734us/step - loss: 0.0631 - acc: 0.9859 - fscore: 0.9859 - val_loss: 0.2256 - val_acc: 0.9167 - val_fscore: 0.9167\n",
+      "Epoch 316/700\n",
+      "1920/1920 [==============================] - 1s 745us/step - loss: 0.0625 - acc: 0.9844 - fscore: 0.9844 - val_loss: 0.3006 - val_acc: 0.8708 - val_fscore: 0.8708\n",
+      "Epoch 317/700\n",
+      "1920/1920 [==============================] - 1s 752us/step - loss: 0.0582 - acc: 0.9844 - fscore: 0.9844 - val_loss: 0.2668 - val_acc: 0.8958 - val_fscore: 0.8958\n",
+      "Epoch 318/700\n",
+      "1920/1920 [==============================] - 1s 753us/step - loss: 0.0626 - acc: 0.9833 - fscore: 0.9833 - val_loss: 0.2410 - val_acc: 0.9021 - val_fscore: 0.9021\n",
+      "Epoch 319/700\n",
+      "1920/1920 [==============================] - 1s 741us/step - loss: 0.0565 - acc: 0.9875 - fscore: 0.9875 - val_loss: 0.2315 - val_acc: 0.9104 - val_fscore: 0.9104\n",
+      "Epoch 320/700\n",
+      "1920/1920 [==============================] - 1s 735us/step - loss: 0.0599 - acc: 0.9849 - fscore: 0.9849 - val_loss: 0.3590 - val_acc: 0.8333 - val_fscore: 0.8333\n",
+      "Epoch 321/700\n",
+      "1920/1920 [==============================] - 1s 747us/step - loss: 0.0564 - acc: 0.9854 - fscore: 0.9854 - val_loss: 0.2147 - val_acc: 0.9146 - val_fscore: 0.9146\n",
+      "Epoch 322/700\n",
+      "1920/1920 [==============================] - 1s 731us/step - loss: 0.0568 - acc: 0.9854 - fscore: 0.9854 - val_loss: 0.2190 - val_acc: 0.9125 - val_fscore: 0.9125\n",
+      "Epoch 323/700\n",
+      "1920/1920 [==============================] - 1s 745us/step - loss: 0.0522 - acc: 0.9885 - fscore: 0.9885 - val_loss: 0.2646 - val_acc: 0.8792 - val_fscore: 0.8792\n",
+      "Epoch 324/700\n",
+      "1920/1920 [==============================] - 1s 738us/step - loss: 0.0549 - acc: 0.9885 - fscore: 0.9885 - val_loss: 0.2148 - val_acc: 0.9146 - val_fscore: 0.9146\n",
+      "Epoch 325/700\n",
+      "1920/1920 [==============================] - 1s 743us/step - loss: 0.0586 - acc: 0.9844 - fscore: 0.9844 - val_loss: 0.2809 - val_acc: 0.8646 - val_fscore: 0.8646\n",
+      "Epoch 326/700\n",
+      "1920/1920 [==============================] - 1s 746us/step - loss: 0.0557 - acc: 0.9870 - fscore: 0.9870 - val_loss: 0.2224 - val_acc: 0.9062 - val_fscore: 0.9062\n",
+      "Epoch 327/700\n",
+      "1920/1920 [==============================] - 1s 746us/step - loss: 0.0522 - acc: 0.9885 - fscore: 0.9885 - val_loss: 0.2159 - val_acc: 0.9229 - val_fscore: 0.9229\n",
+      "Epoch 328/700\n",
+      "1920/1920 [==============================] - 1s 754us/step - loss: 0.0581 - acc: 0.9849 - fscore: 0.9849 - val_loss: 0.2522 - val_acc: 0.9000 - val_fscore: 0.9000\n",
+      "Epoch 329/700\n",
+      "1920/1920 [==============================] - 1s 747us/step - loss: 0.0457 - acc: 0.9932 - fscore: 0.9932 - val_loss: 0.2447 - val_acc: 0.9083 - val_fscore: 0.9083\n",
+      "Epoch 330/700\n",
+      "1920/1920 [==============================] - 1s 740us/step - loss: 0.0494 - acc: 0.9911 - fscore: 0.9911 - val_loss: 0.2140 - val_acc: 0.9250 - val_fscore: 0.9250\n",
+      "Epoch 331/700\n",
+      "1920/1920 [==============================] - 1s 752us/step - loss: 0.0500 - acc: 0.9875 - fscore: 0.9875 - val_loss: 0.2204 - val_acc: 0.9167 - val_fscore: 0.9167\n",
+      "Epoch 332/700\n",
+      "1920/1920 [==============================] - 1s 731us/step - loss: 0.0492 - acc: 0.9880 - fscore: 0.9880 - val_loss: 0.2049 - val_acc: 0.9208 - val_fscore: 0.9208\n",
+      "Epoch 333/700\n",
+      "1920/1920 [==============================] - 1s 737us/step - loss: 0.0540 - acc: 0.9885 - fscore: 0.9885 - val_loss: 0.2243 - val_acc: 0.9229 - val_fscore: 0.9229\n",
+      "Epoch 334/700\n",
+      "1920/1920 [==============================] - 1s 733us/step - loss: 0.0460 - acc: 0.9901 - fscore: 0.9901 - val_loss: 0.2040 - val_acc: 0.9208 - val_fscore: 0.9208\n",
+      "Epoch 335/700\n",
+      "1920/1920 [==============================] - 1s 747us/step - loss: 0.0484 - acc: 0.9885 - fscore: 0.9885 - val_loss: 0.2727 - val_acc: 0.8812 - val_fscore: 0.8812\n",
+      "Epoch 336/700\n",
+      "1920/1920 [==============================] - 1s 741us/step - loss: 0.0486 - acc: 0.9896 - fscore: 0.9896 - val_loss: 0.2289 - val_acc: 0.9104 - val_fscore: 0.9104\n",
+      "Epoch 337/700\n",
+      "1920/1920 [==============================] - 1s 746us/step - loss: 0.0468 - acc: 0.9901 - fscore: 0.9901 - val_loss: 0.2370 - val_acc: 0.9250 - val_fscore: 0.9250\n",
+      "Epoch 338/700\n",
+      "1920/1920 [==============================] - 1s 755us/step - loss: 0.0462 - acc: 0.9922 - fscore: 0.9922 - val_loss: 0.2008 - val_acc: 0.9250 - val_fscore: 0.9250\n",
+      "Epoch 339/700\n",
+      "1920/1920 [==============================] - 1s 754us/step - loss: 0.0494 - acc: 0.9917 - fscore: 0.9917 - val_loss: 0.2132 - val_acc: 0.9229 - val_fscore: 0.9229\n",
+      "Epoch 340/700\n",
+      "1920/1920 [==============================] - 1s 778us/step - loss: 0.0481 - acc: 0.9901 - fscore: 0.9901 - val_loss: 0.2747 - val_acc: 0.8792 - val_fscore: 0.8792\n",
+      "Epoch 341/700\n",
+      "1920/1920 [==============================] - 2s 910us/step - loss: 0.0498 - acc: 0.9917 - fscore: 0.9917 - val_loss: 0.2704 - val_acc: 0.8938 - val_fscore: 0.8937\n",
+      "Epoch 342/700\n",
+      "1920/1920 [==============================] - 2s 898us/step - loss: 0.0451 - acc: 0.9917 - fscore: 0.9917 - val_loss: 0.2243 - val_acc: 0.9062 - val_fscore: 0.9062\n",
+      "Epoch 343/700\n",
+      "1920/1920 [==============================] - 2s 841us/step - loss: 0.0438 - acc: 0.9911 - fscore: 0.9911 - val_loss: 0.2642 - val_acc: 0.8833 - val_fscore: 0.8833\n",
+      "Epoch 344/700\n",
+      "1920/1920 [==============================] - 2s 905us/step - loss: 0.0460 - acc: 0.9922 - fscore: 0.9922 - val_loss: 0.2191 - val_acc: 0.9125 - val_fscore: 0.9125\n",
+      "Epoch 345/700\n",
+      "1920/1920 [==============================] - 2s 872us/step - loss: 0.0439 - acc: 0.9901 - fscore: 0.9901 - val_loss: 0.2291 - val_acc: 0.9083 - val_fscore: 0.9083\n",
+      "Epoch 346/700\n",
+      "1920/1920 [==============================] - 2s 924us/step - loss: 0.0536 - acc: 0.9859 - fscore: 0.9859 - val_loss: 0.2202 - val_acc: 0.9146 - val_fscore: 0.9146\n",
+      "Epoch 347/700\n",
+      "1920/1920 [==============================] - 2s 872us/step - loss: 0.0421 - acc: 0.9938 - fscore: 0.9937 - val_loss: 0.2182 - val_acc: 0.9271 - val_fscore: 0.9271\n",
+      "Epoch 348/700\n",
+      "1920/1920 [==============================] - 2s 935us/step - loss: 0.0440 - acc: 0.9932 - fscore: 0.9932 - val_loss: 0.2249 - val_acc: 0.9208 - val_fscore: 0.9208\n",
+      "Epoch 349/700\n",
+      "1920/1920 [==============================] - 2s 895us/step - loss: 0.0472 - acc: 0.9896 - fscore: 0.9896 - val_loss: 0.2159 - val_acc: 0.9146 - val_fscore: 0.9146\n",
+      "Epoch 350/700\n",
+      "1920/1920 [==============================] - 2s 880us/step - loss: 0.0436 - acc: 0.9922 - fscore: 0.9922 - val_loss: 0.2008 - val_acc: 0.9229 - val_fscore: 0.9229\n",
+      "Epoch 351/700\n",
+      "1920/1920 [==============================] - 2s 821us/step - loss: 0.0449 - acc: 0.9911 - fscore: 0.9911 - val_loss: 0.2048 - val_acc: 0.9375 - val_fscore: 0.9375\n",
+      "Epoch 352/700\n",
+      "1920/1920 [==============================] - 2s 1ms/step - loss: 0.0438 - acc: 0.9932 - fscore: 0.9932 - val_loss: 0.2041 - val_acc: 0.9313 - val_fscore: 0.9312\n",
+      "Epoch 353/700\n",
+      "1920/1920 [==============================] - 2s 1ms/step - loss: 0.0501 - acc: 0.9885 - fscore: 0.9885 - val_loss: 0.2156 - val_acc: 0.9146 - val_fscore: 0.9146\n",
+      "Epoch 354/700\n",
+      "1920/1920 [==============================] - 2s 1ms/step - loss: 0.0399 - acc: 0.9922 - fscore: 0.9922 - val_loss: 0.2045 - val_acc: 0.9208 - val_fscore: 0.9208\n",
+      "Epoch 355/700\n",
+      "1920/1920 [==============================] - 2s 874us/step - loss: 0.0452 - acc: 0.9906 - fscore: 0.9906 - val_loss: 0.2050 - val_acc: 0.9250 - val_fscore: 0.9250\n",
+      "Epoch 356/700\n",
+      "1920/1920 [==============================] - 2s 887us/step - loss: 0.0388 - acc: 0.9932 - fscore: 0.9932 - val_loss: 0.1947 - val_acc: 0.9313 - val_fscore: 0.9312\n",
+      "Epoch 357/700\n",
+      "1920/1920 [==============================] - 2s 906us/step - loss: 0.0447 - acc: 0.9911 - fscore: 0.9911 - val_loss: 0.2120 - val_acc: 0.9250 - val_fscore: 0.9250\n",
+      "Epoch 358/700\n",
+      "1920/1920 [==============================] - 2s 904us/step - loss: 0.0471 - acc: 0.9901 - fscore: 0.9901 - val_loss: 0.2502 - val_acc: 0.9062 - val_fscore: 0.9062\n",
+      "Epoch 359/700\n",
+      "1920/1920 [==============================] - 2s 853us/step - loss: 0.0421 - acc: 0.9922 - fscore: 0.9922 - val_loss: 0.2028 - val_acc: 0.9271 - val_fscore: 0.9271\n",
+      "Epoch 360/700\n",
+      "1920/1920 [==============================] - 2s 810us/step - loss: 0.0406 - acc: 0.9922 - fscore: 0.9922 - val_loss: 0.3207 - val_acc: 0.8625 - val_fscore: 0.8625\n",
+      "Epoch 361/700\n",
+      "1920/1920 [==============================] - 2s 912us/step - loss: 0.0405 - acc: 0.9911 - fscore: 0.9911 - val_loss: 0.2302 - val_acc: 0.9083 - val_fscore: 0.9083\n",
+      "Epoch 362/700\n",
+      "1920/1920 [==============================] - 2s 930us/step - loss: 0.0394 - acc: 0.9927 - fscore: 0.9927 - val_loss: 0.2208 - val_acc: 0.9167 - val_fscore: 0.9167\n",
+      "Epoch 363/700\n",
+      "1920/1920 [==============================] - 2s 882us/step - loss: 0.0413 - acc: 0.9927 - fscore: 0.9927 - val_loss: 0.2245 - val_acc: 0.9229 - val_fscore: 0.9229\n",
+      "Epoch 364/700\n",
+      "1920/1920 [==============================] - 2s 922us/step - loss: 0.0412 - acc: 0.9896 - fscore: 0.9896 - val_loss: 0.2662 - val_acc: 0.8854 - val_fscore: 0.8854\n",
+      "Epoch 365/700\n",
+      "1920/1920 [==============================] - 2s 1ms/step - loss: 0.0442 - acc: 0.9906 - fscore: 0.9906 - val_loss: 0.2051 - val_acc: 0.9146 - val_fscore: 0.9146\n",
+      "Epoch 366/700\n",
+      "1920/1920 [==============================] - 2s 920us/step - loss: 0.0410 - acc: 0.9932 - fscore: 0.9932 - val_loss: 0.2231 - val_acc: 0.9167 - val_fscore: 0.9167\n",
+      "Epoch 367/700\n",
+      "1920/1920 [==============================] - 2s 906us/step - loss: 0.0439 - acc: 0.9896 - fscore: 0.9896 - val_loss: 0.2042 - val_acc: 0.9167 - val_fscore: 0.9167\n",
+      "Epoch 368/700\n",
+      "1920/1920 [==============================] - 2s 1ms/step - loss: 0.0433 - acc: 0.9885 - fscore: 0.9885 - val_loss: 0.2628 - val_acc: 0.8896 - val_fscore: 0.8896\n",
+      "Epoch 369/700\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1920/1920 [==============================] - 2s 1ms/step - loss: 0.0405 - acc: 0.9906 - fscore: 0.9906 - val_loss: 0.2146 - val_acc: 0.9125 - val_fscore: 0.9125\n",
+      "Epoch 370/700\n",
+      "1920/1920 [==============================] - 2s 930us/step - loss: 0.0376 - acc: 0.9948 - fscore: 0.9948 - val_loss: 0.2122 - val_acc: 0.9271 - val_fscore: 0.9271\n",
+      "Epoch 371/700\n",
+      "1920/1920 [==============================] - 2s 894us/step - loss: 0.0385 - acc: 0.9927 - fscore: 0.9927 - val_loss: 0.3235 - val_acc: 0.8625 - val_fscore: 0.8625\n",
+      "Epoch 372/700\n",
+      "1920/1920 [==============================] - 2s 828us/step - loss: 0.0423 - acc: 0.9896 - fscore: 0.9896 - val_loss: 0.3636 - val_acc: 0.8250 - val_fscore: 0.8250\n",
+      "Epoch 373/700\n",
+      "1920/1920 [==============================] - 2s 925us/step - loss: 0.0393 - acc: 0.9938 - fscore: 0.9937 - val_loss: 0.2442 - val_acc: 0.9021 - val_fscore: 0.9021\n",
+      "Epoch 374/700\n",
+      "1920/1920 [==============================] - 1s 778us/step - loss: 0.0406 - acc: 0.9927 - fscore: 0.9927 - val_loss: 0.2083 - val_acc: 0.9167 - val_fscore: 0.9167\n",
+      "Epoch 375/700\n",
+      "1920/1920 [==============================] - 1s 767us/step - loss: 0.0387 - acc: 0.9917 - fscore: 0.9917 - val_loss: 0.1847 - val_acc: 0.9292 - val_fscore: 0.9292\n",
+      "Epoch 376/700\n",
+      "1920/1920 [==============================] - 1s 751us/step - loss: 0.0403 - acc: 0.9917 - fscore: 0.9917 - val_loss: 0.2187 - val_acc: 0.9187 - val_fscore: 0.9187\n",
+      "Epoch 377/700\n",
+      "1920/1920 [==============================] - 1s 755us/step - loss: 0.0394 - acc: 0.9932 - fscore: 0.9932 - val_loss: 0.2168 - val_acc: 0.9167 - val_fscore: 0.9167\n",
+      "Epoch 378/700\n",
+      "1920/1920 [==============================] - 2s 842us/step - loss: 0.0345 - acc: 0.9948 - fscore: 0.9948 - val_loss: 0.2387 - val_acc: 0.9042 - val_fscore: 0.9042\n",
+      "Epoch 379/700\n",
+      "1920/1920 [==============================] - 2s 824us/step - loss: 0.0423 - acc: 0.9911 - fscore: 0.9911 - val_loss: 0.2110 - val_acc: 0.9271 - val_fscore: 0.9271\n",
+      "Epoch 380/700\n",
+      "1920/1920 [==============================] - 2s 827us/step - loss: 0.0390 - acc: 0.9932 - fscore: 0.9932 - val_loss: 0.2150 - val_acc: 0.9208 - val_fscore: 0.9208\n",
+      "Epoch 381/700\n",
+      "1920/1920 [==============================] - 1s 756us/step - loss: 0.0444 - acc: 0.9891 - fscore: 0.9891 - val_loss: 0.2310 - val_acc: 0.9000 - val_fscore: 0.9000\n",
+      "Epoch 382/700\n",
+      "1920/1920 [==============================] - 2s 868us/step - loss: 0.0423 - acc: 0.9911 - fscore: 0.9911 - val_loss: 0.2036 - val_acc: 0.9229 - val_fscore: 0.9229\n",
+      "Epoch 383/700\n",
+      "1920/1920 [==============================] - 2s 794us/step - loss: 0.0360 - acc: 0.9953 - fscore: 0.9953 - val_loss: 0.2003 - val_acc: 0.9250 - val_fscore: 0.9250\n",
+      "Epoch 384/700\n",
+      "1920/1920 [==============================] - 2s 849us/step - loss: 0.0408 - acc: 0.9891 - fscore: 0.9891 - val_loss: 0.2555 - val_acc: 0.8896 - val_fscore: 0.8896\n",
+      "Epoch 385/700\n",
+      "1920/1920 [==============================] - 2s 891us/step - loss: 0.0393 - acc: 0.9906 - fscore: 0.9906 - val_loss: 0.1950 - val_acc: 0.9292 - val_fscore: 0.9292\n",
+      "Epoch 386/700\n",
+      "1920/1920 [==============================] - 1s 777us/step - loss: 0.0360 - acc: 0.9938 - fscore: 0.9937 - val_loss: 0.2409 - val_acc: 0.8938 - val_fscore: 0.8937\n",
+      "Epoch 387/700\n",
+      "1920/1920 [==============================] - 2s 880us/step - loss: 0.0356 - acc: 0.9943 - fscore: 0.9943 - val_loss: 0.2305 - val_acc: 0.9104 - val_fscore: 0.9104\n",
+      "Epoch 388/700\n",
+      "1920/1920 [==============================] - 2s 896us/step - loss: 0.0431 - acc: 0.9885 - fscore: 0.9885 - val_loss: 0.1908 - val_acc: 0.9250 - val_fscore: 0.9250\n",
+      "Epoch 389/700\n",
+      "1920/1920 [==============================] - 2s 856us/step - loss: 0.0394 - acc: 0.9896 - fscore: 0.9896 - val_loss: 0.2108 - val_acc: 0.9146 - val_fscore: 0.9146\n",
+      "Epoch 390/700\n",
+      "1920/1920 [==============================] - 2s 832us/step - loss: 0.0315 - acc: 0.9958 - fscore: 0.9958 - val_loss: 0.1997 - val_acc: 0.9229 - val_fscore: 0.9229\n",
+      "Epoch 391/700\n",
+      "1920/1920 [==============================] - 2s 820us/step - loss: 0.0325 - acc: 0.9943 - fscore: 0.9943 - val_loss: 0.1895 - val_acc: 0.9250 - val_fscore: 0.9250\n",
+      "Epoch 392/700\n",
+      "1920/1920 [==============================] - 2s 879us/step - loss: 0.0356 - acc: 0.9932 - fscore: 0.9932 - val_loss: 0.2654 - val_acc: 0.8917 - val_fscore: 0.8917\n",
+      "Epoch 393/700\n",
+      "1920/1920 [==============================] - 2s 974us/step - loss: 0.0363 - acc: 0.9927 - fscore: 0.9927 - val_loss: 0.1852 - val_acc: 0.9333 - val_fscore: 0.9333\n",
+      "Epoch 394/700\n",
+      "1920/1920 [==============================] - 2s 915us/step - loss: 0.0378 - acc: 0.9927 - fscore: 0.9927 - val_loss: 0.2197 - val_acc: 0.9229 - val_fscore: 0.9229\n",
+      "Epoch 395/700\n",
+      "1920/1920 [==============================] - 2s 915us/step - loss: 0.0349 - acc: 0.9938 - fscore: 0.9937 - val_loss: 0.1999 - val_acc: 0.9250 - val_fscore: 0.9250\n",
+      "Epoch 396/700\n",
+      "1920/1920 [==============================] - 2s 859us/step - loss: 0.0394 - acc: 0.9906 - fscore: 0.9906 - val_loss: 0.1875 - val_acc: 0.9313 - val_fscore: 0.9312\n",
+      "Epoch 397/700\n",
+      "1920/1920 [==============================] - 2s 826us/step - loss: 0.0377 - acc: 0.9922 - fscore: 0.9922 - val_loss: 0.2411 - val_acc: 0.8896 - val_fscore: 0.8896\n",
+      "Epoch 398/700\n",
+      "1920/1920 [==============================] - 2s 869us/step - loss: 0.0370 - acc: 0.9917 - fscore: 0.9917 - val_loss: 0.2240 - val_acc: 0.9125 - val_fscore: 0.9125\n",
+      "Epoch 399/700\n",
+      "1920/1920 [==============================] - 2s 1ms/step - loss: 0.0367 - acc: 0.9932 - fscore: 0.9932 - val_loss: 0.2393 - val_acc: 0.8979 - val_fscore: 0.8979\n",
+      "Epoch 400/700\n",
+      "1920/1920 [==============================] - 2s 899us/step - loss: 0.0310 - acc: 0.9958 - fscore: 0.9958 - val_loss: 0.1908 - val_acc: 0.9354 - val_fscore: 0.9354\n",
+      "Epoch 401/700\n",
+      "1920/1920 [==============================] - 2s 882us/step - loss: 0.0341 - acc: 0.9917 - fscore: 0.9917 - val_loss: 0.2950 - val_acc: 0.8625 - val_fscore: 0.8625\n",
+      "Epoch 402/700\n",
+      "1920/1920 [==============================] - 2s 964us/step - loss: 0.0327 - acc: 0.9943 - fscore: 0.9943 - val_loss: 0.1961 - val_acc: 0.9229 - val_fscore: 0.9229\n",
+      "Epoch 403/700\n",
+      "1920/1920 [==============================] - 2s 943us/step - loss: 0.0338 - acc: 0.9948 - fscore: 0.9948 - val_loss: 0.1896 - val_acc: 0.9250 - val_fscore: 0.9250\n",
+      "Epoch 404/700\n",
+      "1920/1920 [==============================] - 2s 843us/step - loss: 0.0302 - acc: 0.9927 - fscore: 0.9927 - val_loss: 0.2039 - val_acc: 0.9250 - val_fscore: 0.9250\n",
+      "Epoch 405/700\n",
+      "1920/1920 [==============================] - 2s 1ms/step - loss: 0.0350 - acc: 0.9896 - fscore: 0.9896 - val_loss: 0.1895 - val_acc: 0.9250 - val_fscore: 0.9250\n",
+      "Epoch 406/700\n",
+      "1920/1920 [==============================] - 2s 923us/step - loss: 0.0342 - acc: 0.9911 - fscore: 0.9911 - val_loss: 0.1969 - val_acc: 0.9292 - val_fscore: 0.9292\n",
+      "Epoch 407/700\n",
+      "1920/1920 [==============================] - 2s 873us/step - loss: 0.0334 - acc: 0.9932 - fscore: 0.9932 - val_loss: 0.2078 - val_acc: 0.9125 - val_fscore: 0.9125\n",
+      "Epoch 408/700\n",
+      "1920/1920 [==============================] - 2s 1ms/step - loss: 0.0272 - acc: 0.9964 - fscore: 0.9964 - val_loss: 0.1930 - val_acc: 0.9333 - val_fscore: 0.9333\n",
+      "Epoch 409/700\n",
+      "1920/1920 [==============================] - 2s 943us/step - loss: 0.0309 - acc: 0.9953 - fscore: 0.9953 - val_loss: 0.1908 - val_acc: 0.9208 - val_fscore: 0.9208\n",
+      "Epoch 410/700\n",
+      "1920/1920 [==============================] - 2s 904us/step - loss: 0.0287 - acc: 0.9979 - fscore: 0.9979 - val_loss: 0.2176 - val_acc: 0.9083 - val_fscore: 0.9083\n",
+      "Epoch 411/700\n",
+      "1920/1920 [==============================] - 2s 864us/step - loss: 0.0327 - acc: 0.9927 - fscore: 0.9927 - val_loss: 0.1746 - val_acc: 0.9292 - val_fscore: 0.9292\n",
+      "Epoch 412/700\n",
+      "1920/1920 [==============================] - 2s 820us/step - loss: 0.0282 - acc: 0.9953 - fscore: 0.9953 - val_loss: 0.1853 - val_acc: 0.9375 - val_fscore: 0.9375\n",
+      "Epoch 413/700\n",
+      "1920/1920 [==============================] - 2s 921us/step - loss: 0.0301 - acc: 0.9964 - fscore: 0.9964 - val_loss: 0.1822 - val_acc: 0.9354 - val_fscore: 0.9354\n",
+      "Epoch 414/700\n",
+      "1920/1920 [==============================] - 2s 946us/step - loss: 0.0302 - acc: 0.9953 - fscore: 0.9953 - val_loss: 0.1836 - val_acc: 0.9313 - val_fscore: 0.9312\n",
+      "Epoch 415/700\n",
+      "1920/1920 [==============================] - 2s 917us/step - loss: 0.0331 - acc: 0.9932 - fscore: 0.9932 - val_loss: 0.2026 - val_acc: 0.9167 - val_fscore: 0.9167\n",
+      "Epoch 416/700\n",
+      "1920/1920 [==============================] - 2s 943us/step - loss: 0.0331 - acc: 0.9938 - fscore: 0.9937 - val_loss: 0.2442 - val_acc: 0.9021 - val_fscore: 0.9021\n",
+      "Epoch 417/700\n",
+      "1920/1920 [==============================] - 2s 893us/step - loss: 0.0320 - acc: 0.9943 - fscore: 0.9943 - val_loss: 0.1864 - val_acc: 0.9313 - val_fscore: 0.9312\n",
+      "Epoch 418/700\n",
+      "1920/1920 [==============================] - 2s 900us/step - loss: 0.0328 - acc: 0.9938 - fscore: 0.9937 - val_loss: 0.1787 - val_acc: 0.9292 - val_fscore: 0.9292\n",
+      "Epoch 419/700\n",
+      "1920/1920 [==============================] - 2s 886us/step - loss: 0.0292 - acc: 0.9938 - fscore: 0.9937 - val_loss: 0.2125 - val_acc: 0.9292 - val_fscore: 0.9292\n",
+      "Epoch 420/700\n",
+      "1920/1920 [==============================] - 2s 922us/step - loss: 0.0273 - acc: 0.9964 - fscore: 0.9964 - val_loss: 0.2179 - val_acc: 0.9083 - val_fscore: 0.9083\n",
+      "Epoch 421/700\n",
+      "1920/1920 [==============================] - 2s 928us/step - loss: 0.0279 - acc: 0.9964 - fscore: 0.9964 - val_loss: 0.2249 - val_acc: 0.8979 - val_fscore: 0.8979\n",
+      "Epoch 422/700\n",
+      "1920/1920 [==============================] - 2s 910us/step - loss: 0.0307 - acc: 0.9964 - fscore: 0.9964 - val_loss: 0.2110 - val_acc: 0.9333 - val_fscore: 0.9333\n",
+      "Epoch 423/700\n",
+      "1920/1920 [==============================] - 2s 878us/step - loss: 0.0315 - acc: 0.9938 - fscore: 0.9937 - val_loss: 0.2113 - val_acc: 0.9125 - val_fscore: 0.9125\n",
+      "Epoch 424/700\n",
+      "1920/1920 [==============================] - 2s 983us/step - loss: 0.0318 - acc: 0.9911 - fscore: 0.9911 - val_loss: 0.1834 - val_acc: 0.9250 - val_fscore: 0.9250\n",
+      "Epoch 425/700\n",
+      "1920/1920 [==============================] - 2s 869us/step - loss: 0.0256 - acc: 0.9974 - fscore: 0.9974 - val_loss: 0.1825 - val_acc: 0.9208 - val_fscore: 0.9208\n",
+      "Epoch 426/700\n",
+      "1920/1920 [==============================] - 2s 899us/step - loss: 0.0223 - acc: 0.9984 - fscore: 0.9984 - val_loss: 0.1784 - val_acc: 0.9375 - val_fscore: 0.9375\n",
+      "Epoch 427/700\n",
+      "1920/1920 [==============================] - 2s 963us/step - loss: 0.0282 - acc: 0.9953 - fscore: 0.9953 - val_loss: 0.1747 - val_acc: 0.9333 - val_fscore: 0.9333\n",
+      "Epoch 428/700\n",
+      "1920/1920 [==============================] - 2s 866us/step - loss: 0.0285 - acc: 0.9948 - fscore: 0.9948 - val_loss: 0.1886 - val_acc: 0.9167 - val_fscore: 0.9167\n",
+      "Epoch 429/700\n",
+      "1920/1920 [==============================] - 2s 897us/step - loss: 0.0274 - acc: 0.9964 - fscore: 0.9964 - val_loss: 0.1954 - val_acc: 0.9250 - val_fscore: 0.9250\n",
+      "Epoch 430/700\n",
+      "1920/1920 [==============================] - 2s 805us/step - loss: 0.0300 - acc: 0.9943 - fscore: 0.9943 - val_loss: 0.1731 - val_acc: 0.9375 - val_fscore: 0.9375\n",
+      "Epoch 431/700\n",
+      "1920/1920 [==============================] - 1s 751us/step - loss: 0.0277 - acc: 0.9969 - fscore: 0.9969 - val_loss: 0.2051 - val_acc: 0.9208 - val_fscore: 0.9208\n",
+      "Epoch 432/700\n",
+      "1920/1920 [==============================] - 1s 753us/step - loss: 0.0278 - acc: 0.9964 - fscore: 0.9964 - val_loss: 0.1874 - val_acc: 0.9292 - val_fscore: 0.9292\n",
+      "Epoch 433/700\n",
+      "1920/1920 [==============================] - 1s 760us/step - loss: 0.0244 - acc: 0.9974 - fscore: 0.9974 - val_loss: 0.2439 - val_acc: 0.8938 - val_fscore: 0.8937\n",
+      "Epoch 434/700\n",
+      "1920/1920 [==============================] - 1s 753us/step - loss: 0.0313 - acc: 0.9938 - fscore: 0.9937 - val_loss: 0.1787 - val_acc: 0.9354 - val_fscore: 0.9354\n",
+      "Epoch 435/700\n",
+      "1920/1920 [==============================] - 1s 737us/step - loss: 0.0259 - acc: 0.9953 - fscore: 0.9953 - val_loss: 0.1895 - val_acc: 0.9229 - val_fscore: 0.9229\n",
+      "Epoch 436/700\n",
+      "1920/1920 [==============================] - 1s 754us/step - loss: 0.0225 - acc: 0.9979 - fscore: 0.9979 - val_loss: 0.1766 - val_acc: 0.9229 - val_fscore: 0.9229\n",
+      "Epoch 437/700\n",
+      "1920/1920 [==============================] - 1s 742us/step - loss: 0.0258 - acc: 0.9948 - fscore: 0.9948 - val_loss: 0.1644 - val_acc: 0.9375 - val_fscore: 0.9375\n",
+      "Epoch 438/700\n",
+      "1920/1920 [==============================] - 1s 758us/step - loss: 0.0268 - acc: 0.9948 - fscore: 0.9948 - val_loss: 0.1900 - val_acc: 0.9313 - val_fscore: 0.9312\n",
+      "Epoch 439/700\n",
+      "1920/1920 [==============================] - 1s 742us/step - loss: 0.0226 - acc: 0.9990 - fscore: 0.9990 - val_loss: 0.1797 - val_acc: 0.9375 - val_fscore: 0.9375\n",
+      "Epoch 440/700\n",
+      "1920/1920 [==============================] - 1s 746us/step - loss: 0.0288 - acc: 0.9958 - fscore: 0.9958 - val_loss: 0.1804 - val_acc: 0.9375 - val_fscore: 0.9375\n",
+      "Epoch 441/700\n",
+      "1920/1920 [==============================] - 1s 745us/step - loss: 0.0225 - acc: 0.9974 - fscore: 0.9974 - val_loss: 0.1826 - val_acc: 0.9333 - val_fscore: 0.9333\n",
+      "Epoch 442/700\n",
+      "1920/1920 [==============================] - 1s 757us/step - loss: 0.0300 - acc: 0.9932 - fscore: 0.9932 - val_loss: 0.2074 - val_acc: 0.9167 - val_fscore: 0.9167\n",
+      "Epoch 443/700\n",
+      "1920/1920 [==============================] - 1s 748us/step - loss: 0.0316 - acc: 0.9927 - fscore: 0.9927 - val_loss: 0.1854 - val_acc: 0.9313 - val_fscore: 0.9312\n",
+      "Epoch 444/700\n",
+      "1920/1920 [==============================] - 1s 741us/step - loss: 0.0300 - acc: 0.9927 - fscore: 0.9927 - val_loss: 0.2286 - val_acc: 0.9208 - val_fscore: 0.9208\n",
+      "Epoch 445/700\n",
+      "1920/1920 [==============================] - 1s 745us/step - loss: 0.0259 - acc: 0.9932 - fscore: 0.9932 - val_loss: 0.2136 - val_acc: 0.9104 - val_fscore: 0.9104\n",
+      "Epoch 446/700\n",
+      "1920/1920 [==============================] - 1s 749us/step - loss: 0.0266 - acc: 0.9948 - fscore: 0.9948 - val_loss: 0.2086 - val_acc: 0.9104 - val_fscore: 0.9104\n",
+      "Epoch 447/700\n",
+      "1920/1920 [==============================] - 1s 759us/step - loss: 0.0310 - acc: 0.9922 - fscore: 0.9922 - val_loss: 0.1771 - val_acc: 0.9333 - val_fscore: 0.9333\n",
+      "Epoch 448/700\n",
+      "1920/1920 [==============================] - 1s 763us/step - loss: 0.0240 - acc: 0.9948 - fscore: 0.9948 - val_loss: 0.2011 - val_acc: 0.9208 - val_fscore: 0.9208\n",
+      "Epoch 449/700\n",
+      "1920/1920 [==============================] - 1s 749us/step - loss: 0.0251 - acc: 0.9953 - fscore: 0.9953 - val_loss: 0.1975 - val_acc: 0.9313 - val_fscore: 0.9312\n",
+      "Epoch 450/700\n",
+      "1920/1920 [==============================] - 2s 932us/step - loss: 0.0289 - acc: 0.9948 - fscore: 0.9948 - val_loss: 0.2411 - val_acc: 0.9146 - val_fscore: 0.9146\n",
+      "Epoch 451/700\n",
+      "1920/1920 [==============================] - 2s 845us/step - loss: 0.0250 - acc: 0.9969 - fscore: 0.9969 - val_loss: 0.2021 - val_acc: 0.9208 - val_fscore: 0.9208\n",
+      "Epoch 452/700\n",
+      "1920/1920 [==============================] - 2s 1ms/step - loss: 0.0268 - acc: 0.9958 - fscore: 0.9958 - val_loss: 0.1962 - val_acc: 0.9208 - val_fscore: 0.9208\n",
+      "Epoch 453/700\n",
+      "1920/1920 [==============================] - 2s 846us/step - loss: 0.0259 - acc: 0.9953 - fscore: 0.9953 - val_loss: 0.1851 - val_acc: 0.9271 - val_fscore: 0.9271\n",
+      "Epoch 454/700\n",
+      "1920/1920 [==============================] - 2s 866us/step - loss: 0.0250 - acc: 0.9964 - fscore: 0.9964 - val_loss: 0.2023 - val_acc: 0.9271 - val_fscore: 0.9271\n",
+      "Epoch 455/700\n",
+      "1920/1920 [==============================] - 2s 794us/step - loss: 0.0238 - acc: 0.9958 - fscore: 0.9958 - val_loss: 0.1921 - val_acc: 0.9250 - val_fscore: 0.9250\n",
+      "Epoch 456/700\n",
+      "1920/1920 [==============================] - 2s 942us/step - loss: 0.0268 - acc: 0.9932 - fscore: 0.9932 - val_loss: 0.1818 - val_acc: 0.9250 - val_fscore: 0.9250\n",
+      "Epoch 457/700\n",
+      "1920/1920 [==============================] - 2s 884us/step - loss: 0.0208 - acc: 0.9974 - fscore: 0.9974 - val_loss: 0.1933 - val_acc: 0.9229 - val_fscore: 0.9229\n",
+      "Epoch 458/700\n",
+      "1920/1920 [==============================] - 2s 889us/step - loss: 0.0255 - acc: 0.9948 - fscore: 0.9948 - val_loss: 0.2695 - val_acc: 0.8792 - val_fscore: 0.8792\n",
+      "Epoch 459/700\n",
+      "1920/1920 [==============================] - 2s 816us/step - loss: 0.0265 - acc: 0.9938 - fscore: 0.9937 - val_loss: 0.2221 - val_acc: 0.9125 - val_fscore: 0.9125\n",
+      "Epoch 460/700\n",
+      "1920/1920 [==============================] - 2s 920us/step - loss: 0.0268 - acc: 0.9958 - fscore: 0.9958 - val_loss: 0.1845 - val_acc: 0.9250 - val_fscore: 0.9250\n",
+      "Epoch 461/700\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1920/1920 [==============================] - 2s 828us/step - loss: 0.0236 - acc: 0.9974 - fscore: 0.9974 - val_loss: 0.1788 - val_acc: 0.9271 - val_fscore: 0.9271\n",
+      "Epoch 462/700\n",
+      "1920/1920 [==============================] - 2s 828us/step - loss: 0.0260 - acc: 0.9958 - fscore: 0.9958 - val_loss: 0.2460 - val_acc: 0.8979 - val_fscore: 0.8979\n",
+      "Epoch 463/700\n",
+      "1920/1920 [==============================] - 1s 757us/step - loss: 0.0224 - acc: 0.9979 - fscore: 0.9979 - val_loss: 0.1673 - val_acc: 0.9333 - val_fscore: 0.9333\n",
+      "Epoch 464/700\n",
+      "1920/1920 [==============================] - 2s 902us/step - loss: 0.0222 - acc: 0.9974 - fscore: 0.9974 - val_loss: 0.1954 - val_acc: 0.9208 - val_fscore: 0.9208\n",
+      "Epoch 465/700\n",
+      "1920/1920 [==============================] - 2s 816us/step - loss: 0.0231 - acc: 0.9969 - fscore: 0.9969 - val_loss: 0.1839 - val_acc: 0.9250 - val_fscore: 0.9250\n",
+      "Epoch 466/700\n",
+      "1920/1920 [==============================] - 2s 858us/step - loss: 0.0253 - acc: 0.9943 - fscore: 0.9943 - val_loss: 0.1803 - val_acc: 0.9354 - val_fscore: 0.9354\n",
+      "Epoch 467/700\n",
+      "1920/1920 [==============================] - 2s 908us/step - loss: 0.0212 - acc: 0.9974 - fscore: 0.9974 - val_loss: 0.1765 - val_acc: 0.9375 - val_fscore: 0.9375\n",
+      "Epoch 468/700\n",
+      "1920/1920 [==============================] - 2s 860us/step - loss: 0.0253 - acc: 0.9948 - fscore: 0.9948 - val_loss: 0.1811 - val_acc: 0.9354 - val_fscore: 0.9354\n",
+      "Epoch 469/700\n",
+      "1920/1920 [==============================] - 1s 775us/step - loss: 0.0243 - acc: 0.9953 - fscore: 0.9953 - val_loss: 0.1951 - val_acc: 0.9313 - val_fscore: 0.9312\n",
+      "Epoch 470/700\n",
+      "1920/1920 [==============================] - 2s 854us/step - loss: 0.0265 - acc: 0.9964 - fscore: 0.9964 - val_loss: 0.1795 - val_acc: 0.9354 - val_fscore: 0.9354\n",
+      "Epoch 471/700\n",
+      "1920/1920 [==============================] - 2s 888us/step - loss: 0.0274 - acc: 0.9943 - fscore: 0.9943 - val_loss: 0.1752 - val_acc: 0.9375 - val_fscore: 0.9375\n",
+      "Epoch 472/700\n",
+      "1920/1920 [==============================] - 2s 975us/step - loss: 0.0226 - acc: 0.9958 - fscore: 0.9958 - val_loss: 0.1719 - val_acc: 0.9250 - val_fscore: 0.9250\n",
+      "Epoch 473/700\n",
+      "1920/1920 [==============================] - 2s 837us/step - loss: 0.0264 - acc: 0.9948 - fscore: 0.9948 - val_loss: 0.2229 - val_acc: 0.9083 - val_fscore: 0.9083\n",
+      "Epoch 474/700\n",
+      "1920/1920 [==============================] - 2s 918us/step - loss: 0.0236 - acc: 0.9958 - fscore: 0.9958 - val_loss: 0.1772 - val_acc: 0.9313 - val_fscore: 0.9312\n",
+      "Epoch 475/700\n",
+      "1920/1920 [==============================] - 2s 902us/step - loss: 0.0257 - acc: 0.9948 - fscore: 0.9948 - val_loss: 0.1735 - val_acc: 0.9396 - val_fscore: 0.9396\n",
+      "Epoch 476/700\n",
+      "1920/1920 [==============================] - 2s 845us/step - loss: 0.0251 - acc: 0.9943 - fscore: 0.9943 - val_loss: 0.1927 - val_acc: 0.9333 - val_fscore: 0.9333\n",
+      "Epoch 477/700\n",
+      "1920/1920 [==============================] - 2s 1ms/step - loss: 0.0204 - acc: 0.9969 - fscore: 0.9969 - val_loss: 0.1793 - val_acc: 0.9313 - val_fscore: 0.9312\n",
+      "Epoch 478/700\n",
+      "1920/1920 [==============================] - 2s 824us/step - loss: 0.0234 - acc: 0.9948 - fscore: 0.9948 - val_loss: 0.1815 - val_acc: 0.9229 - val_fscore: 0.9229\n",
+      "Epoch 479/700\n",
+      "1920/1920 [==============================] - 2s 862us/step - loss: 0.0250 - acc: 0.9958 - fscore: 0.9958 - val_loss: 0.1957 - val_acc: 0.9250 - val_fscore: 0.9250\n",
+      "Epoch 480/700\n",
+      "1920/1920 [==============================] - 2s 936us/step - loss: 0.0228 - acc: 0.9974 - fscore: 0.9974 - val_loss: 0.1934 - val_acc: 0.9250 - val_fscore: 0.9250\n",
+      "Epoch 481/700\n",
+      "1920/1920 [==============================] - 2s 849us/step - loss: 0.0240 - acc: 0.9938 - fscore: 0.9937 - val_loss: 0.1764 - val_acc: 0.9271 - val_fscore: 0.9271\n",
+      "Epoch 482/700\n",
+      "1920/1920 [==============================] - 2s 873us/step - loss: 0.0265 - acc: 0.9958 - fscore: 0.9958 - val_loss: 0.1823 - val_acc: 0.9187 - val_fscore: 0.9187\n",
+      "Epoch 483/700\n",
+      "1920/1920 [==============================] - 2s 982us/step - loss: 0.0210 - acc: 0.9984 - fscore: 0.9984 - val_loss: 0.2128 - val_acc: 0.9062 - val_fscore: 0.9062\n",
+      "Epoch 484/700\n",
+      "1920/1920 [==============================] - 2s 941us/step - loss: 0.0195 - acc: 0.9974 - fscore: 0.9974 - val_loss: 0.1734 - val_acc: 0.9313 - val_fscore: 0.9312\n",
+      "Epoch 485/700\n",
+      "1920/1920 [==============================] - 2s 841us/step - loss: 0.0215 - acc: 0.9979 - fscore: 0.9979 - val_loss: 0.1901 - val_acc: 0.9375 - val_fscore: 0.9375\n",
+      "Epoch 486/700\n",
+      "1920/1920 [==============================] - 2s 844us/step - loss: 0.0243 - acc: 0.9948 - fscore: 0.9948 - val_loss: 0.2073 - val_acc: 0.9250 - val_fscore: 0.9250\n",
+      "Epoch 487/700\n",
+      "1920/1920 [==============================] - 2s 868us/step - loss: 0.0228 - acc: 0.9953 - fscore: 0.9953 - val_loss: 0.1779 - val_acc: 0.9250 - val_fscore: 0.9250\n",
+      "Epoch 488/700\n",
+      "1920/1920 [==============================] - 2s 877us/step - loss: 0.0205 - acc: 0.9969 - fscore: 0.9969 - val_loss: 0.1900 - val_acc: 0.9250 - val_fscore: 0.9250\n",
+      "Epoch 489/700\n",
+      "1920/1920 [==============================] - 2s 857us/step - loss: 0.0245 - acc: 0.9958 - fscore: 0.9958 - val_loss: 0.1722 - val_acc: 0.9354 - val_fscore: 0.9354\n",
+      "Epoch 490/700\n",
+      "1920/1920 [==============================] - 2s 820us/step - loss: 0.0197 - acc: 0.9974 - fscore: 0.9974 - val_loss: 0.2019 - val_acc: 0.9208 - val_fscore: 0.9208\n",
+      "Epoch 491/700\n",
+      "1920/1920 [==============================] - 2s 889us/step - loss: 0.0216 - acc: 0.9969 - fscore: 0.9969 - val_loss: 0.1731 - val_acc: 0.9375 - val_fscore: 0.9375\n",
+      "Epoch 492/700\n",
+      "1920/1920 [==============================] - 2s 884us/step - loss: 0.0201 - acc: 0.9974 - fscore: 0.9974 - val_loss: 0.1692 - val_acc: 0.9333 - val_fscore: 0.9333\n",
+      "Epoch 493/700\n",
+      "1920/1920 [==============================] - 2s 821us/step - loss: 0.0200 - acc: 0.9969 - fscore: 0.9969 - val_loss: 0.2021 - val_acc: 0.9250 - val_fscore: 0.9250\n",
+      "Epoch 494/700\n",
+      "1920/1920 [==============================] - 2s 878us/step - loss: 0.0245 - acc: 0.9948 - fscore: 0.9948 - val_loss: 0.1854 - val_acc: 0.9292 - val_fscore: 0.9292\n",
+      "Epoch 495/700\n",
+      "1920/1920 [==============================] - 2s 916us/step - loss: 0.0217 - acc: 0.9984 - fscore: 0.9984 - val_loss: 0.1913 - val_acc: 0.9229 - val_fscore: 0.9229\n",
+      "Epoch 496/700\n",
+      "1920/1920 [==============================] - 2s 959us/step - loss: 0.0196 - acc: 0.9979 - fscore: 0.9979 - val_loss: 0.2076 - val_acc: 0.9208 - val_fscore: 0.9208\n",
+      "Epoch 497/700\n",
+      "1920/1920 [==============================] - 2s 836us/step - loss: 0.0184 - acc: 0.9979 - fscore: 0.9979 - val_loss: 0.1650 - val_acc: 0.9396 - val_fscore: 0.9396\n",
+      "Epoch 498/700\n",
+      "1920/1920 [==============================] - 2s 799us/step - loss: 0.0233 - acc: 0.9958 - fscore: 0.9958 - val_loss: 0.1814 - val_acc: 0.9292 - val_fscore: 0.9292\n",
+      "Epoch 499/700\n",
+      "1920/1920 [==============================] - 2s 789us/step - loss: 0.0223 - acc: 0.9964 - fscore: 0.9964 - val_loss: 0.1721 - val_acc: 0.9396 - val_fscore: 0.9396\n",
+      "Epoch 500/700\n",
+      "1920/1920 [==============================] - 2s 894us/step - loss: 0.0227 - acc: 0.9948 - fscore: 0.9948 - val_loss: 0.2286 - val_acc: 0.9167 - val_fscore: 0.9167\n",
+      "Epoch 501/700\n",
+      "1920/1920 [==============================] - 2s 938us/step - loss: 0.0194 - acc: 0.9984 - fscore: 0.9984 - val_loss: 0.1785 - val_acc: 0.9375 - val_fscore: 0.9375\n",
+      "Epoch 502/700\n",
+      "1920/1920 [==============================] - 2s 866us/step - loss: 0.0208 - acc: 0.9958 - fscore: 0.9958 - val_loss: 0.2671 - val_acc: 0.8917 - val_fscore: 0.8917\n",
+      "Epoch 503/700\n",
+      "1920/1920 [==============================] - 2s 832us/step - loss: 0.0220 - acc: 0.9958 - fscore: 0.9958 - val_loss: 0.2051 - val_acc: 0.9187 - val_fscore: 0.9187\n",
+      "Epoch 504/700\n",
+      "1920/1920 [==============================] - 2s 859us/step - loss: 0.0228 - acc: 0.9953 - fscore: 0.9953 - val_loss: 0.1734 - val_acc: 0.9292 - val_fscore: 0.9292\n",
+      "Epoch 505/700\n",
+      "1920/1920 [==============================] - 2s 927us/step - loss: 0.0168 - acc: 0.9979 - fscore: 0.9979 - val_loss: 0.1872 - val_acc: 0.9250 - val_fscore: 0.9250\n",
+      "Epoch 506/700\n",
+      "1920/1920 [==============================] - 2s 994us/step - loss: 0.0210 - acc: 0.9964 - fscore: 0.9964 - val_loss: 0.1821 - val_acc: 0.9292 - val_fscore: 0.9292\n",
+      "Epoch 507/700\n",
+      "1920/1920 [==============================] - 2s 912us/step - loss: 0.0250 - acc: 0.9958 - fscore: 0.9958 - val_loss: 0.1871 - val_acc: 0.9271 - val_fscore: 0.9271\n",
+      "Epoch 508/700\n",
+      "1920/1920 [==============================] - 2s 914us/step - loss: 0.0225 - acc: 0.9953 - fscore: 0.9953 - val_loss: 0.2437 - val_acc: 0.9000 - val_fscore: 0.9000\n",
+      "Epoch 509/700\n",
+      "1920/1920 [==============================] - 2s 886us/step - loss: 0.0234 - acc: 0.9969 - fscore: 0.9969 - val_loss: 0.1651 - val_acc: 0.9417 - val_fscore: 0.9417\n",
+      "Epoch 510/700\n",
+      "1920/1920 [==============================] - 2s 796us/step - loss: 0.0206 - acc: 0.9979 - fscore: 0.9979 - val_loss: 0.1663 - val_acc: 0.9354 - val_fscore: 0.9354\n",
+      "Epoch 511/700\n",
+      "1920/1920 [==============================] - 2s 868us/step - loss: 0.0194 - acc: 0.9974 - fscore: 0.9974 - val_loss: 0.1706 - val_acc: 0.9437 - val_fscore: 0.9437\n",
+      "Epoch 512/700\n",
+      "1920/1920 [==============================] - 2s 965us/step - loss: 0.0186 - acc: 0.9964 - fscore: 0.9964 - val_loss: 0.2321 - val_acc: 0.9125 - val_fscore: 0.9125\n",
+      "Epoch 513/700\n",
+      "1920/1920 [==============================] - 2s 897us/step - loss: 0.0207 - acc: 0.9969 - fscore: 0.9969 - val_loss: 0.1915 - val_acc: 0.9313 - val_fscore: 0.9312\n",
+      "Epoch 514/700\n",
+      "1920/1920 [==============================] - 2s 910us/step - loss: 0.0245 - acc: 0.9969 - fscore: 0.9969 - val_loss: 0.1772 - val_acc: 0.9313 - val_fscore: 0.9312\n",
+      "Epoch 515/700\n",
+      "1920/1920 [==============================] - 2s 831us/step - loss: 0.0203 - acc: 0.9964 - fscore: 0.9964 - val_loss: 0.1746 - val_acc: 0.9354 - val_fscore: 0.9354\n",
+      "Epoch 516/700\n",
+      "1920/1920 [==============================] - 2s 808us/step - loss: 0.0198 - acc: 0.9979 - fscore: 0.9979 - val_loss: 0.1888 - val_acc: 0.9250 - val_fscore: 0.9250\n",
+      "Epoch 517/700\n",
+      "1920/1920 [==============================] - 2s 936us/step - loss: 0.0187 - acc: 0.9984 - fscore: 0.9984 - val_loss: 0.1811 - val_acc: 0.9333 - val_fscore: 0.9333\n",
+      "Epoch 518/700\n",
+      "1920/1920 [==============================] - 2s 958us/step - loss: 0.0210 - acc: 0.9948 - fscore: 0.9948 - val_loss: 0.1739 - val_acc: 0.9417 - val_fscore: 0.9417\n",
+      "Epoch 519/700\n",
+      "1920/1920 [==============================] - 2s 906us/step - loss: 0.0255 - acc: 0.9922 - fscore: 0.9922 - val_loss: 0.1740 - val_acc: 0.9396 - val_fscore: 0.9396\n",
+      "Epoch 520/700\n",
+      "1920/1920 [==============================] - 2s 909us/step - loss: 0.0161 - acc: 0.9974 - fscore: 0.9974 - val_loss: 0.2149 - val_acc: 0.9104 - val_fscore: 0.9104\n",
+      "Epoch 521/700\n",
+      "1920/1920 [==============================] - 2s 870us/step - loss: 0.0192 - acc: 0.9969 - fscore: 0.9969 - val_loss: 0.1808 - val_acc: 0.9250 - val_fscore: 0.9250\n",
+      "Epoch 522/700\n",
+      "1920/1920 [==============================] - 2s 954us/step - loss: 0.0193 - acc: 0.9984 - fscore: 0.9984 - val_loss: 0.1877 - val_acc: 0.9208 - val_fscore: 0.9208\n",
+      "Epoch 523/700\n",
+      "1920/1920 [==============================] - 2s 889us/step - loss: 0.0198 - acc: 0.9974 - fscore: 0.9974 - val_loss: 0.1720 - val_acc: 0.9333 - val_fscore: 0.9333\n",
+      "Epoch 524/700\n",
+      "1920/1920 [==============================] - 2s 942us/step - loss: 0.0194 - acc: 0.9984 - fscore: 0.9984 - val_loss: 0.1627 - val_acc: 0.9417 - val_fscore: 0.9417\n",
+      "Epoch 525/700\n",
+      "1920/1920 [==============================] - 2s 909us/step - loss: 0.0212 - acc: 0.9969 - fscore: 0.9969 - val_loss: 0.1662 - val_acc: 0.9396 - val_fscore: 0.9396\n",
+      "Epoch 526/700\n",
+      "1920/1920 [==============================] - 1s 764us/step - loss: 0.0195 - acc: 0.9953 - fscore: 0.9953 - val_loss: 0.2001 - val_acc: 0.9167 - val_fscore: 0.9167\n",
+      "Epoch 527/700\n",
+      "1920/1920 [==============================] - 1s 768us/step - loss: 0.0169 - acc: 0.9984 - fscore: 0.9984 - val_loss: 0.1844 - val_acc: 0.9208 - val_fscore: 0.9208\n",
+      "Epoch 528/700\n",
+      "1920/1920 [==============================] - 2s 934us/step - loss: 0.0205 - acc: 0.9964 - fscore: 0.9964 - val_loss: 0.1668 - val_acc: 0.9333 - val_fscore: 0.9333\n",
+      "Epoch 529/700\n",
+      "1920/1920 [==============================] - 2s 953us/step - loss: 0.0208 - acc: 0.9953 - fscore: 0.9953 - val_loss: 0.1734 - val_acc: 0.9271 - val_fscore: 0.9271\n",
+      "Epoch 530/700\n",
+      "1920/1920 [==============================] - 2s 866us/step - loss: 0.0153 - acc: 0.9979 - fscore: 0.9979 - val_loss: 0.2207 - val_acc: 0.9083 - val_fscore: 0.9083\n",
+      "Epoch 531/700\n",
+      "1920/1920 [==============================] - 2s 864us/step - loss: 0.0176 - acc: 0.9984 - fscore: 0.9984 - val_loss: 0.1990 - val_acc: 0.9208 - val_fscore: 0.9208\n",
+      "Epoch 532/700\n",
+      "1920/1920 [==============================] - 2s 904us/step - loss: 0.0215 - acc: 0.9953 - fscore: 0.9953 - val_loss: 0.1713 - val_acc: 0.9313 - val_fscore: 0.9312\n",
+      "Epoch 533/700\n",
+      "1920/1920 [==============================] - 2s 862us/step - loss: 0.0190 - acc: 0.9958 - fscore: 0.9958 - val_loss: 0.1706 - val_acc: 0.9313 - val_fscore: 0.9312\n",
+      "Epoch 534/700\n",
+      "1920/1920 [==============================] - 2s 866us/step - loss: 0.0204 - acc: 0.9958 - fscore: 0.9958 - val_loss: 0.1770 - val_acc: 0.9333 - val_fscore: 0.9333\n",
+      "Epoch 535/700\n",
+      "1920/1920 [==============================] - 2s 935us/step - loss: 0.0181 - acc: 0.9969 - fscore: 0.9969 - val_loss: 0.1720 - val_acc: 0.9313 - val_fscore: 0.9312\n",
+      "Epoch 536/700\n",
+      "1920/1920 [==============================] - 2s 910us/step - loss: 0.0173 - acc: 0.9974 - fscore: 0.9974 - val_loss: 0.1913 - val_acc: 0.9208 - val_fscore: 0.9208\n",
+      "Epoch 537/700\n",
+      "1920/1920 [==============================] - 2s 828us/step - loss: 0.0205 - acc: 0.9953 - fscore: 0.9953 - val_loss: 0.1697 - val_acc: 0.9354 - val_fscore: 0.9354\n",
+      "Epoch 538/700\n",
+      "1920/1920 [==============================] - 1s 761us/step - loss: 0.0210 - acc: 0.9958 - fscore: 0.9958 - val_loss: 0.1743 - val_acc: 0.9354 - val_fscore: 0.9354\n",
+      "Epoch 539/700\n",
+      "1920/1920 [==============================] - 2s 782us/step - loss: 0.0170 - acc: 0.9964 - fscore: 0.9964 - val_loss: 0.1954 - val_acc: 0.9271 - val_fscore: 0.9271\n",
+      "Epoch 540/700\n",
+      "1920/1920 [==============================] - 2s 812us/step - loss: 0.0198 - acc: 0.9974 - fscore: 0.9974 - val_loss: 0.1862 - val_acc: 0.9292 - val_fscore: 0.9292\n",
+      "Epoch 541/700\n",
+      "1920/1920 [==============================] - 2s 861us/step - loss: 0.0167 - acc: 0.9979 - fscore: 0.9979 - val_loss: 0.1715 - val_acc: 0.9292 - val_fscore: 0.9292\n",
+      "Epoch 542/700\n",
+      "1920/1920 [==============================] - 2s 782us/step - loss: 0.0150 - acc: 0.9990 - fscore: 0.9990 - val_loss: 0.1728 - val_acc: 0.9354 - val_fscore: 0.9354\n",
+      "Epoch 543/700\n",
+      "1920/1920 [==============================] - 2s 995us/step - loss: 0.0189 - acc: 0.9974 - fscore: 0.9974 - val_loss: 0.1698 - val_acc: 0.9354 - val_fscore: 0.9354\n",
+      "Epoch 544/700\n",
+      "1920/1920 [==============================] - 2s 817us/step - loss: 0.0202 - acc: 0.9948 - fscore: 0.9948 - val_loss: 0.1756 - val_acc: 0.9333 - val_fscore: 0.9333\n",
+      "Epoch 545/700\n",
+      "1920/1920 [==============================] - 2s 853us/step - loss: 0.0178 - acc: 0.9964 - fscore: 0.9964 - val_loss: 0.2046 - val_acc: 0.9146 - val_fscore: 0.9146\n",
+      "Epoch 546/700\n",
+      "1920/1920 [==============================] - 2s 840us/step - loss: 0.0195 - acc: 0.9958 - fscore: 0.9958 - val_loss: 0.1619 - val_acc: 0.9437 - val_fscore: 0.9437\n",
+      "Epoch 547/700\n",
+      "1920/1920 [==============================] - 2s 832us/step - loss: 0.0170 - acc: 0.9974 - fscore: 0.9974 - val_loss: 0.1669 - val_acc: 0.9354 - val_fscore: 0.9354\n",
+      "Epoch 548/700\n",
+      "1920/1920 [==============================] - 2s 945us/step - loss: 0.0169 - acc: 0.9979 - fscore: 0.9979 - val_loss: 0.1582 - val_acc: 0.9396 - val_fscore: 0.9396\n",
+      "Epoch 549/700\n",
+      "1920/1920 [==============================] - 2s 900us/step - loss: 0.0190 - acc: 0.9979 - fscore: 0.9979 - val_loss: 0.1896 - val_acc: 0.9333 - val_fscore: 0.9333\n",
+      "Epoch 550/700\n",
+      "1920/1920 [==============================] - 2s 840us/step - loss: 0.0186 - acc: 0.9979 - fscore: 0.9979 - val_loss: 0.1666 - val_acc: 0.9417 - val_fscore: 0.9417\n",
+      "Epoch 551/700\n",
+      "1920/1920 [==============================] - 2s 926us/step - loss: 0.0179 - acc: 0.9958 - fscore: 0.9958 - val_loss: 0.1713 - val_acc: 0.9375 - val_fscore: 0.9375\n",
+      "Epoch 552/700\n",
+      "1920/1920 [==============================] - 2s 981us/step - loss: 0.0195 - acc: 0.9969 - fscore: 0.9969 - val_loss: 0.1973 - val_acc: 0.9187 - val_fscore: 0.9187\n",
+      "Epoch 553/700\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1920/1920 [==============================] - 2s 904us/step - loss: 0.0155 - acc: 0.9969 - fscore: 0.9969 - val_loss: 0.1666 - val_acc: 0.9417 - val_fscore: 0.9417\n",
+      "Epoch 554/700\n",
+      "1920/1920 [==============================] - 2s 787us/step - loss: 0.0140 - acc: 0.9990 - fscore: 0.9990 - val_loss: 0.1720 - val_acc: 0.9333 - val_fscore: 0.9333\n",
+      "Epoch 555/700\n",
+      "1920/1920 [==============================] - 2s 846us/step - loss: 0.0159 - acc: 0.9969 - fscore: 0.9969 - val_loss: 0.1860 - val_acc: 0.9354 - val_fscore: 0.9354\n",
+      "Epoch 556/700\n",
+      "1920/1920 [==============================] - 2s 936us/step - loss: 0.0157 - acc: 0.9979 - fscore: 0.9979 - val_loss: 0.1595 - val_acc: 0.9417 - val_fscore: 0.9417\n",
+      "Epoch 557/700\n",
+      "1920/1920 [==============================] - 2s 858us/step - loss: 0.0168 - acc: 0.9979 - fscore: 0.9979 - val_loss: 0.1647 - val_acc: 0.9375 - val_fscore: 0.9375\n",
+      "Epoch 558/700\n",
+      "1920/1920 [==============================] - 2s 938us/step - loss: 0.0156 - acc: 0.9974 - fscore: 0.9974 - val_loss: 0.1881 - val_acc: 0.9187 - val_fscore: 0.9187\n",
+      "Epoch 559/700\n",
+      "1920/1920 [==============================] - 2s 1ms/step - loss: 0.0174 - acc: 0.9990 - fscore: 0.9990 - val_loss: 0.1673 - val_acc: 0.9396 - val_fscore: 0.9396\n",
+      "Epoch 560/700\n",
+      "1920/1920 [==============================] - 2s 922us/step - loss: 0.0189 - acc: 0.9979 - fscore: 0.9979 - val_loss: 0.1787 - val_acc: 0.9333 - val_fscore: 0.9333\n",
+      "Epoch 561/700\n",
+      "1920/1920 [==============================] - 2s 859us/step - loss: 0.0221 - acc: 0.9964 - fscore: 0.9964 - val_loss: 0.1884 - val_acc: 0.9313 - val_fscore: 0.9312\n",
+      "Epoch 562/700\n",
+      "1920/1920 [==============================] - 2s 1ms/step - loss: 0.0165 - acc: 0.9979 - fscore: 0.9979 - val_loss: 0.1785 - val_acc: 0.9458 - val_fscore: 0.9458\n",
+      "Epoch 563/700\n",
+      "1920/1920 [==============================] - 2s 820us/step - loss: 0.0189 - acc: 0.9964 - fscore: 0.9964 - val_loss: 0.1605 - val_acc: 0.9458 - val_fscore: 0.9458\n",
+      "Epoch 564/700\n",
+      "1920/1920 [==============================] - 2s 830us/step - loss: 0.0204 - acc: 0.9953 - fscore: 0.9953 - val_loss: 0.1687 - val_acc: 0.9354 - val_fscore: 0.9354\n",
+      "Epoch 565/700\n",
+      "1920/1920 [==============================] - 2s 881us/step - loss: 0.0181 - acc: 0.9979 - fscore: 0.9979 - val_loss: 0.1641 - val_acc: 0.9313 - val_fscore: 0.9312\n",
+      "Epoch 566/700\n",
+      "1920/1920 [==============================] - 2s 866us/step - loss: 0.0185 - acc: 0.9958 - fscore: 0.9958 - val_loss: 0.1669 - val_acc: 0.9396 - val_fscore: 0.9396\n",
+      "Epoch 567/700\n",
+      "1920/1920 [==============================] - 2s 863us/step - loss: 0.0195 - acc: 0.9948 - fscore: 0.9948 - val_loss: 0.1651 - val_acc: 0.9292 - val_fscore: 0.9292\n",
+      "Epoch 568/700\n",
+      "1920/1920 [==============================] - 2s 803us/step - loss: 0.0185 - acc: 0.9953 - fscore: 0.9953 - val_loss: 0.1617 - val_acc: 0.9313 - val_fscore: 0.9312\n",
+      "Epoch 569/700\n",
+      "1920/1920 [==============================] - 2s 834us/step - loss: 0.0167 - acc: 0.9974 - fscore: 0.9974 - val_loss: 0.1657 - val_acc: 0.9396 - val_fscore: 0.9396\n",
+      "Epoch 570/700\n",
+      "1920/1920 [==============================] - 2s 877us/step - loss: 0.0174 - acc: 0.9953 - fscore: 0.9953 - val_loss: 0.1670 - val_acc: 0.9313 - val_fscore: 0.9312\n",
+      "Epoch 571/700\n",
+      "1920/1920 [==============================] - 2s 981us/step - loss: 0.0184 - acc: 0.9958 - fscore: 0.9958 - val_loss: 0.1816 - val_acc: 0.9333 - val_fscore: 0.9333\n",
+      "Epoch 572/700\n",
+      "1920/1920 [==============================] - 2s 1ms/step - loss: 0.0137 - acc: 0.9995 - fscore: 0.9995 - val_loss: 0.1691 - val_acc: 0.9313 - val_fscore: 0.9312\n",
+      "Epoch 573/700\n",
+      "1920/1920 [==============================] - 2s 872us/step - loss: 0.0174 - acc: 0.9953 - fscore: 0.9953 - val_loss: 0.1924 - val_acc: 0.9187 - val_fscore: 0.9187\n",
+      "Epoch 574/700\n",
+      "1920/1920 [==============================] - 2s 951us/step - loss: 0.0160 - acc: 0.9969 - fscore: 0.9969 - val_loss: 0.1698 - val_acc: 0.9333 - val_fscore: 0.9333\n",
+      "Epoch 575/700\n",
+      "1920/1920 [==============================] - 2s 889us/step - loss: 0.0196 - acc: 0.9969 - fscore: 0.9969 - val_loss: 0.1562 - val_acc: 0.9396 - val_fscore: 0.9396\n",
+      "Epoch 576/700\n",
+      "1920/1920 [==============================] - 2s 879us/step - loss: 0.0174 - acc: 0.9974 - fscore: 0.9974 - val_loss: 0.1697 - val_acc: 0.9271 - val_fscore: 0.9271\n",
+      "Epoch 577/700\n",
+      "1920/1920 [==============================] - 2s 955us/step - loss: 0.0183 - acc: 0.9969 - fscore: 0.9969 - val_loss: 0.1712 - val_acc: 0.9396 - val_fscore: 0.9396\n",
+      "Epoch 578/700\n",
+      "1920/1920 [==============================] - 2s 965us/step - loss: 0.0176 - acc: 0.9958 - fscore: 0.9958 - val_loss: 0.1983 - val_acc: 0.9146 - val_fscore: 0.9146\n",
+      "Epoch 579/700\n",
+      "1920/1920 [==============================] - 2s 941us/step - loss: 0.0217 - acc: 0.9953 - fscore: 0.9953 - val_loss: 0.1624 - val_acc: 0.9313 - val_fscore: 0.9312\n",
+      "Epoch 580/700\n",
+      "1920/1920 [==============================] - 2s 977us/step - loss: 0.0144 - acc: 0.9990 - fscore: 0.9990 - val_loss: 0.1581 - val_acc: 0.9375 - val_fscore: 0.9375\n",
+      "Epoch 581/700\n",
+      "1920/1920 [==============================] - 2s 788us/step - loss: 0.0213 - acc: 0.9943 - fscore: 0.9943 - val_loss: 0.1633 - val_acc: 0.9396 - val_fscore: 0.9396\n",
+      "Epoch 582/700\n",
+      "1920/1920 [==============================] - 1s 752us/step - loss: 0.0172 - acc: 0.9953 - fscore: 0.9953 - val_loss: 0.1650 - val_acc: 0.9417 - val_fscore: 0.9417\n",
+      "Epoch 583/700\n",
+      "1920/1920 [==============================] - 2s 856us/step - loss: 0.0140 - acc: 0.9984 - fscore: 0.9984 - val_loss: 0.1675 - val_acc: 0.9396 - val_fscore: 0.9396\n",
+      "Epoch 584/700\n",
+      "1920/1920 [==============================] - 2s 816us/step - loss: 0.0171 - acc: 0.9984 - fscore: 0.9984 - val_loss: 0.1712 - val_acc: 0.9417 - val_fscore: 0.9417\n",
+      "Epoch 585/700\n",
+      "1920/1920 [==============================] - 2s 884us/step - loss: 0.0183 - acc: 0.9958 - fscore: 0.9958 - val_loss: 0.1688 - val_acc: 0.9292 - val_fscore: 0.9292\n",
+      "Epoch 586/700\n",
+      "1920/1920 [==============================] - 2s 791us/step - loss: 0.0162 - acc: 0.9979 - fscore: 0.9979 - val_loss: 0.1619 - val_acc: 0.9354 - val_fscore: 0.9354\n",
+      "Epoch 587/700\n",
+      "1920/1920 [==============================] - 2s 796us/step - loss: 0.0162 - acc: 0.9969 - fscore: 0.9969 - val_loss: 0.1558 - val_acc: 0.9437 - val_fscore: 0.9437\n",
+      "Epoch 588/700\n",
+      "1920/1920 [==============================] - 2s 809us/step - loss: 0.0168 - acc: 0.9964 - fscore: 0.9964 - val_loss: 0.1593 - val_acc: 0.9354 - val_fscore: 0.9354\n",
+      "Epoch 589/700\n",
+      "1920/1920 [==============================] - 2s 802us/step - loss: 0.0166 - acc: 0.9964 - fscore: 0.9964 - val_loss: 0.1655 - val_acc: 0.9375 - val_fscore: 0.9375\n",
+      "Epoch 590/700\n",
+      "1920/1920 [==============================] - 2s 796us/step - loss: 0.0176 - acc: 0.9984 - fscore: 0.9984 - val_loss: 0.1647 - val_acc: 0.9313 - val_fscore: 0.9312\n",
+      "Epoch 591/700\n",
+      "1920/1920 [==============================] - 2s 813us/step - loss: 0.0200 - acc: 0.9948 - fscore: 0.9948 - val_loss: 0.1645 - val_acc: 0.9271 - val_fscore: 0.9271\n",
+      "Epoch 592/700\n",
+      "1920/1920 [==============================] - 2s 871us/step - loss: 0.0163 - acc: 0.9990 - fscore: 0.9990 - val_loss: 0.1836 - val_acc: 0.9250 - val_fscore: 0.9250\n",
+      "Epoch 593/700\n",
+      "1920/1920 [==============================] - 2s 818us/step - loss: 0.0170 - acc: 0.9958 - fscore: 0.9958 - val_loss: 0.1832 - val_acc: 0.9229 - val_fscore: 0.9229\n",
+      "Epoch 594/700\n",
+      "1920/1920 [==============================] - 1s 765us/step - loss: 0.0158 - acc: 0.9974 - fscore: 0.9974 - val_loss: 0.1759 - val_acc: 0.9292 - val_fscore: 0.9292\n",
+      "Epoch 595/700\n",
+      "1920/1920 [==============================] - 1s 760us/step - loss: 0.0192 - acc: 0.9964 - fscore: 0.9964 - val_loss: 0.1596 - val_acc: 0.9417 - val_fscore: 0.9417\n",
+      "Epoch 596/700\n",
+      "1920/1920 [==============================] - 1s 746us/step - loss: 0.0140 - acc: 0.9979 - fscore: 0.9979 - val_loss: 0.1650 - val_acc: 0.9396 - val_fscore: 0.9396\n",
+      "Epoch 597/700\n",
+      "1920/1920 [==============================] - 1s 758us/step - loss: 0.0151 - acc: 0.9979 - fscore: 0.9979 - val_loss: 0.1591 - val_acc: 0.9333 - val_fscore: 0.9333\n",
+      "Epoch 598/700\n",
+      "1920/1920 [==============================] - 1s 753us/step - loss: 0.0154 - acc: 0.9974 - fscore: 0.9974 - val_loss: 0.1972 - val_acc: 0.9229 - val_fscore: 0.9229\n",
+      "Epoch 599/700\n",
+      "1920/1920 [==============================] - 1s 740us/step - loss: 0.0161 - acc: 0.9979 - fscore: 0.9979 - val_loss: 0.1556 - val_acc: 0.9458 - val_fscore: 0.9458\n",
+      "Epoch 600/700\n",
+      "1920/1920 [==============================] - 1s 742us/step - loss: 0.0162 - acc: 0.9984 - fscore: 0.9984 - val_loss: 0.1554 - val_acc: 0.9437 - val_fscore: 0.9437\n",
+      "Epoch 601/700\n",
+      "1920/1920 [==============================] - 1s 737us/step - loss: 0.0134 - acc: 0.9990 - fscore: 0.9990 - val_loss: 0.1535 - val_acc: 0.9375 - val_fscore: 0.9375\n",
+      "Epoch 602/700\n",
+      "1920/1920 [==============================] - 1s 733us/step - loss: 0.0146 - acc: 0.9990 - fscore: 0.9990 - val_loss: 0.1634 - val_acc: 0.9375 - val_fscore: 0.9375\n",
+      "Epoch 603/700\n",
+      "1920/1920 [==============================] - 1s 748us/step - loss: 0.0141 - acc: 0.9974 - fscore: 0.9974 - val_loss: 0.1659 - val_acc: 0.9292 - val_fscore: 0.9292\n",
+      "Epoch 604/700\n",
+      "1920/1920 [==============================] - 1s 756us/step - loss: 0.0132 - acc: 0.9979 - fscore: 0.9979 - val_loss: 0.1584 - val_acc: 0.9396 - val_fscore: 0.9396\n",
+      "Epoch 605/700\n",
+      "1920/1920 [==============================] - 1s 747us/step - loss: 0.0148 - acc: 0.9984 - fscore: 0.9984 - val_loss: 0.1737 - val_acc: 0.9375 - val_fscore: 0.9375\n",
+      "Epoch 606/700\n",
+      "1920/1920 [==============================] - 1s 746us/step - loss: 0.0168 - acc: 0.9990 - fscore: 0.9990 - val_loss: 0.1598 - val_acc: 0.9375 - val_fscore: 0.9375\n",
+      "Epoch 607/700\n",
+      "1920/1920 [==============================] - 1s 761us/step - loss: 0.0177 - acc: 0.9969 - fscore: 0.9969 - val_loss: 0.1665 - val_acc: 0.9354 - val_fscore: 0.9354\n",
+      "Epoch 608/700\n",
+      "1920/1920 [==============================] - 1s 752us/step - loss: 0.0153 - acc: 0.9974 - fscore: 0.9974 - val_loss: 0.1493 - val_acc: 0.9396 - val_fscore: 0.9396\n",
+      "Epoch 609/700\n",
+      "1920/1920 [==============================] - 1s 760us/step - loss: 0.0123 - acc: 0.9984 - fscore: 0.9984 - val_loss: 0.1527 - val_acc: 0.9375 - val_fscore: 0.9375\n",
+      "Epoch 610/700\n",
+      "1920/1920 [==============================] - 1s 756us/step - loss: 0.0166 - acc: 0.9974 - fscore: 0.9974 - val_loss: 0.1553 - val_acc: 0.9437 - val_fscore: 0.9437\n",
+      "Epoch 611/700\n",
+      "1920/1920 [==============================] - 1s 747us/step - loss: 0.0173 - acc: 0.9953 - fscore: 0.9953 - val_loss: 0.1559 - val_acc: 0.9458 - val_fscore: 0.9458\n",
+      "Epoch 612/700\n",
+      "1920/1920 [==============================] - 1s 750us/step - loss: 0.0164 - acc: 0.9964 - fscore: 0.9964 - val_loss: 0.1610 - val_acc: 0.9375 - val_fscore: 0.9375\n",
+      "Epoch 613/700\n",
+      "1920/1920 [==============================] - 1s 760us/step - loss: 0.0140 - acc: 0.9995 - fscore: 0.9995 - val_loss: 0.1547 - val_acc: 0.9375 - val_fscore: 0.9375\n",
+      "Epoch 614/700\n",
+      "1920/1920 [==============================] - 1s 744us/step - loss: 0.0186 - acc: 0.9953 - fscore: 0.9953 - val_loss: 0.1876 - val_acc: 0.9250 - val_fscore: 0.9250\n",
+      "Epoch 615/700\n",
+      "1920/1920 [==============================] - 1s 743us/step - loss: 0.0174 - acc: 0.9974 - fscore: 0.9974 - val_loss: 0.1585 - val_acc: 0.9354 - val_fscore: 0.9354\n",
+      "Epoch 616/700\n",
+      "1920/1920 [==============================] - 1s 750us/step - loss: 0.0191 - acc: 0.9958 - fscore: 0.9958 - val_loss: 0.1593 - val_acc: 0.9333 - val_fscore: 0.9333\n",
+      "Epoch 617/700\n",
+      "1920/1920 [==============================] - 1s 747us/step - loss: 0.0178 - acc: 0.9964 - fscore: 0.9964 - val_loss: 0.1563 - val_acc: 0.9354 - val_fscore: 0.9354\n",
+      "Epoch 618/700\n",
+      "1920/1920 [==============================] - 1s 742us/step - loss: 0.0147 - acc: 0.9979 - fscore: 0.9979 - val_loss: 0.1674 - val_acc: 0.9333 - val_fscore: 0.9333\n",
+      "Epoch 619/700\n",
+      "1920/1920 [==============================] - 1s 744us/step - loss: 0.0119 - acc: 1.0000 - fscore: 1.0000 - val_loss: 0.1718 - val_acc: 0.9292 - val_fscore: 0.9292\n",
+      "Epoch 620/700\n",
+      "1920/1920 [==============================] - 1s 745us/step - loss: 0.0141 - acc: 0.9990 - fscore: 0.9990 - val_loss: 0.1764 - val_acc: 0.9292 - val_fscore: 0.9292\n",
+      "Epoch 621/700\n",
+      "1920/1920 [==============================] - 1s 742us/step - loss: 0.0187 - acc: 0.9964 - fscore: 0.9964 - val_loss: 0.1610 - val_acc: 0.9354 - val_fscore: 0.9354\n",
+      "Epoch 622/700\n",
+      "1920/1920 [==============================] - 1s 742us/step - loss: 0.0140 - acc: 0.9979 - fscore: 0.9979 - val_loss: 0.1595 - val_acc: 0.9396 - val_fscore: 0.9396\n",
+      "Epoch 623/700\n",
+      "1920/1920 [==============================] - 1s 750us/step - loss: 0.0140 - acc: 0.9984 - fscore: 0.9984 - val_loss: 0.1692 - val_acc: 0.9333 - val_fscore: 0.9333\n",
+      "Epoch 624/700\n",
+      "1920/1920 [==============================] - 1s 745us/step - loss: 0.0176 - acc: 0.9953 - fscore: 0.9953 - val_loss: 0.1613 - val_acc: 0.9417 - val_fscore: 0.9417\n",
+      "Epoch 625/700\n",
+      "1920/1920 [==============================] - 1s 750us/step - loss: 0.0156 - acc: 0.9979 - fscore: 0.9979 - val_loss: 0.1705 - val_acc: 0.9292 - val_fscore: 0.9292\n",
+      "Epoch 626/700\n",
+      "1920/1920 [==============================] - 1s 730us/step - loss: 0.0168 - acc: 0.9964 - fscore: 0.9964 - val_loss: 0.1724 - val_acc: 0.9271 - val_fscore: 0.9271\n",
+      "Epoch 627/700\n",
+      "1920/1920 [==============================] - 1s 750us/step - loss: 0.0110 - acc: 1.0000 - fscore: 1.0000 - val_loss: 0.1787 - val_acc: 0.9354 - val_fscore: 0.9354\n",
+      "Epoch 628/700\n",
+      "1920/1920 [==============================] - 1s 768us/step - loss: 0.0156 - acc: 0.9984 - fscore: 0.9984 - val_loss: 0.1633 - val_acc: 0.9313 - val_fscore: 0.9312\n",
+      "Epoch 629/700\n",
+      "1920/1920 [==============================] - 1s 754us/step - loss: 0.0173 - acc: 0.9958 - fscore: 0.9958 - val_loss: 0.1616 - val_acc: 0.9375 - val_fscore: 0.9375\n",
+      "Epoch 630/700\n",
+      "1920/1920 [==============================] - 1s 756us/step - loss: 0.0130 - acc: 0.9979 - fscore: 0.9979 - val_loss: 0.1710 - val_acc: 0.9292 - val_fscore: 0.9292\n",
+      "Epoch 631/700\n",
+      "1920/1920 [==============================] - 1s 748us/step - loss: 0.0131 - acc: 0.9990 - fscore: 0.9990 - val_loss: 0.1702 - val_acc: 0.9396 - val_fscore: 0.9396\n",
+      "Epoch 632/700\n",
+      "1920/1920 [==============================] - 1s 737us/step - loss: 0.0159 - acc: 0.9995 - fscore: 0.9995 - val_loss: 0.1923 - val_acc: 0.9250 - val_fscore: 0.9250\n",
+      "Epoch 633/700\n",
+      "1920/1920 [==============================] - 1s 760us/step - loss: 0.0152 - acc: 0.9984 - fscore: 0.9984 - val_loss: 0.1683 - val_acc: 0.9354 - val_fscore: 0.9354\n",
+      "Epoch 634/700\n",
+      "1920/1920 [==============================] - 1s 747us/step - loss: 0.0140 - acc: 0.9974 - fscore: 0.9974 - val_loss: 0.1582 - val_acc: 0.9333 - val_fscore: 0.9333\n",
+      "Epoch 635/700\n",
+      "1920/1920 [==============================] - 1s 752us/step - loss: 0.0175 - acc: 0.9958 - fscore: 0.9958 - val_loss: 0.1693 - val_acc: 0.9313 - val_fscore: 0.9312\n",
+      "Epoch 636/700\n",
+      "1920/1920 [==============================] - 1s 749us/step - loss: 0.0152 - acc: 0.9974 - fscore: 0.9974 - val_loss: 0.1676 - val_acc: 0.9292 - val_fscore: 0.9292\n",
+      "Epoch 637/700\n",
+      "1920/1920 [==============================] - 1s 745us/step - loss: 0.0144 - acc: 0.9979 - fscore: 0.9979 - val_loss: 0.1564 - val_acc: 0.9396 - val_fscore: 0.9396\n",
+      "Epoch 638/700\n",
+      "1920/1920 [==============================] - 1s 740us/step - loss: 0.0158 - acc: 0.9969 - fscore: 0.9969 - val_loss: 0.1703 - val_acc: 0.9271 - val_fscore: 0.9271\n",
+      "Epoch 639/700\n",
+      "1920/1920 [==============================] - 1s 742us/step - loss: 0.0203 - acc: 0.9948 - fscore: 0.9948 - val_loss: 0.1837 - val_acc: 0.9229 - val_fscore: 0.9229\n",
+      "Epoch 640/700\n",
+      "1920/1920 [==============================] - 1s 748us/step - loss: 0.0183 - acc: 0.9969 - fscore: 0.9969 - val_loss: 0.1878 - val_acc: 0.9229 - val_fscore: 0.9229\n",
+      "Epoch 641/700\n",
+      "1920/1920 [==============================] - 1s 744us/step - loss: 0.0123 - acc: 0.9995 - fscore: 0.9995 - val_loss: 0.1595 - val_acc: 0.9313 - val_fscore: 0.9312\n",
+      "Epoch 642/700\n",
+      "1920/1920 [==============================] - 1s 748us/step - loss: 0.0166 - acc: 0.9974 - fscore: 0.9974 - val_loss: 0.1551 - val_acc: 0.9354 - val_fscore: 0.9354\n",
+      "Epoch 643/700\n",
+      "1920/1920 [==============================] - 1s 741us/step - loss: 0.0170 - acc: 0.9979 - fscore: 0.9979 - val_loss: 0.1536 - val_acc: 0.9396 - val_fscore: 0.9396\n",
+      "Epoch 644/700\n",
+      "1920/1920 [==============================] - 1s 739us/step - loss: 0.0158 - acc: 0.9984 - fscore: 0.9984 - val_loss: 0.1704 - val_acc: 0.9354 - val_fscore: 0.9354\n",
+      "Epoch 645/700\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1920/1920 [==============================] - 1s 740us/step - loss: 0.0154 - acc: 0.9974 - fscore: 0.9974 - val_loss: 0.1622 - val_acc: 0.9354 - val_fscore: 0.9354\n",
+      "Epoch 646/700\n",
+      "1920/1920 [==============================] - 1s 752us/step - loss: 0.0147 - acc: 0.9974 - fscore: 0.9974 - val_loss: 0.1627 - val_acc: 0.9375 - val_fscore: 0.9375\n",
+      "Epoch 647/700\n",
+      "1920/1920 [==============================] - 1s 760us/step - loss: 0.0132 - acc: 0.9990 - fscore: 0.9990 - val_loss: 0.1646 - val_acc: 0.9292 - val_fscore: 0.9292\n",
+      "Epoch 648/700\n",
+      "1920/1920 [==============================] - 1s 736us/step - loss: 0.0152 - acc: 0.9984 - fscore: 0.9984 - val_loss: 0.1587 - val_acc: 0.9313 - val_fscore: 0.9312\n",
+      "Epoch 649/700\n",
+      "1920/1920 [==============================] - 1s 742us/step - loss: 0.0150 - acc: 0.9979 - fscore: 0.9979 - val_loss: 0.1669 - val_acc: 0.9292 - val_fscore: 0.9292\n",
+      "Epoch 650/700\n",
+      "1920/1920 [==============================] - 1s 742us/step - loss: 0.0145 - acc: 0.9979 - fscore: 0.9979 - val_loss: 0.1705 - val_acc: 0.9271 - val_fscore: 0.9271\n",
+      "Epoch 651/700\n",
+      "1920/1920 [==============================] - 1s 740us/step - loss: 0.0146 - acc: 0.9984 - fscore: 0.9984 - val_loss: 0.1611 - val_acc: 0.9396 - val_fscore: 0.9396\n",
+      "Epoch 652/700\n",
+      "1920/1920 [==============================] - 1s 745us/step - loss: 0.0178 - acc: 0.9969 - fscore: 0.9969 - val_loss: 0.1627 - val_acc: 0.9375 - val_fscore: 0.9375\n",
+      "Epoch 653/700\n",
+      "1920/1920 [==============================] - 1s 744us/step - loss: 0.0139 - acc: 0.9984 - fscore: 0.9984 - val_loss: 0.1810 - val_acc: 0.9313 - val_fscore: 0.9312\n",
+      "Epoch 654/700\n",
+      "1920/1920 [==============================] - 1s 751us/step - loss: 0.0134 - acc: 0.9984 - fscore: 0.9984 - val_loss: 0.1667 - val_acc: 0.9271 - val_fscore: 0.9271\n",
+      "Epoch 655/700\n",
+      "1920/1920 [==============================] - 1s 742us/step - loss: 0.0181 - acc: 0.9964 - fscore: 0.9964 - val_loss: 0.1552 - val_acc: 0.9375 - val_fscore: 0.9375\n",
+      "Epoch 656/700\n",
+      "1920/1920 [==============================] - 1s 751us/step - loss: 0.0139 - acc: 0.9990 - fscore: 0.9990 - val_loss: 0.1580 - val_acc: 0.9396 - val_fscore: 0.9396\n",
+      "Epoch 657/700\n",
+      "1920/1920 [==============================] - 1s 748us/step - loss: 0.0141 - acc: 0.9974 - fscore: 0.9974 - val_loss: 0.1596 - val_acc: 0.9354 - val_fscore: 0.9354\n",
+      "Epoch 658/700\n",
+      "1920/1920 [==============================] - 1s 744us/step - loss: 0.0140 - acc: 0.9979 - fscore: 0.9979 - val_loss: 0.1654 - val_acc: 0.9333 - val_fscore: 0.9333\n",
+      "Epoch 659/700\n",
+      "1920/1920 [==============================] - 1s 752us/step - loss: 0.0176 - acc: 0.9974 - fscore: 0.9974 - val_loss: 0.2037 - val_acc: 0.9167 - val_fscore: 0.9167\n",
+      "Epoch 660/700\n",
+      "1920/1920 [==============================] - 1s 747us/step - loss: 0.0173 - acc: 0.9974 - fscore: 0.9974 - val_loss: 0.1602 - val_acc: 0.9375 - val_fscore: 0.9375\n",
+      "Epoch 661/700\n",
+      "1920/1920 [==============================] - 1s 754us/step - loss: 0.0135 - acc: 0.9995 - fscore: 0.9995 - val_loss: 0.1644 - val_acc: 0.9354 - val_fscore: 0.9354\n",
+      "Epoch 662/700\n",
+      "1920/1920 [==============================] - 1s 749us/step - loss: 0.0132 - acc: 0.9990 - fscore: 0.9990 - val_loss: 0.1723 - val_acc: 0.9333 - val_fscore: 0.9333\n",
+      "Epoch 663/700\n",
+      "1920/1920 [==============================] - 1s 753us/step - loss: 0.0146 - acc: 0.9984 - fscore: 0.9984 - val_loss: 0.1652 - val_acc: 0.9271 - val_fscore: 0.9271\n",
+      "Epoch 664/700\n",
+      "1920/1920 [==============================] - 1s 757us/step - loss: 0.0159 - acc: 0.9969 - fscore: 0.9969 - val_loss: 0.1615 - val_acc: 0.9333 - val_fscore: 0.9333\n",
+      "Epoch 665/700\n",
+      "1920/1920 [==============================] - 1s 752us/step - loss: 0.0108 - acc: 0.9995 - fscore: 0.9995 - val_loss: 0.1674 - val_acc: 0.9292 - val_fscore: 0.9292\n",
+      "Epoch 666/700\n",
+      "1920/1920 [==============================] - 1s 766us/step - loss: 0.0158 - acc: 0.9969 - fscore: 0.9969 - val_loss: 0.1685 - val_acc: 0.9313 - val_fscore: 0.9312\n",
+      "Epoch 667/700\n",
+      "1920/1920 [==============================] - 1s 773us/step - loss: 0.0140 - acc: 0.9979 - fscore: 0.9979 - val_loss: 0.1705 - val_acc: 0.9271 - val_fscore: 0.9271\n",
+      "Epoch 668/700\n",
+      "1920/1920 [==============================] - 1s 749us/step - loss: 0.0149 - acc: 0.9974 - fscore: 0.9974 - val_loss: 0.1636 - val_acc: 0.9354 - val_fscore: 0.9354\n",
+      "Epoch 669/700\n",
+      "1920/1920 [==============================] - 1s 742us/step - loss: 0.0163 - acc: 0.9969 - fscore: 0.9969 - val_loss: 0.1807 - val_acc: 0.9333 - val_fscore: 0.9333\n",
+      "Epoch 670/700\n",
+      "1920/1920 [==============================] - 1s 749us/step - loss: 0.0132 - acc: 0.9979 - fscore: 0.9979 - val_loss: 0.1576 - val_acc: 0.9333 - val_fscore: 0.9333\n",
+      "Epoch 671/700\n",
+      "1920/1920 [==============================] - 1s 737us/step - loss: 0.0158 - acc: 0.9969 - fscore: 0.9969 - val_loss: 0.1685 - val_acc: 0.9375 - val_fscore: 0.9375\n",
+      "Epoch 672/700\n",
+      "1920/1920 [==============================] - 1s 756us/step - loss: 0.0140 - acc: 0.9979 - fscore: 0.9979 - val_loss: 0.1572 - val_acc: 0.9375 - val_fscore: 0.9375\n",
+      "Epoch 673/700\n",
+      "1920/1920 [==============================] - 1s 742us/step - loss: 0.0143 - acc: 0.9969 - fscore: 0.9969 - val_loss: 0.2204 - val_acc: 0.9083 - val_fscore: 0.9083\n",
+      "Epoch 674/700\n",
+      "1920/1920 [==============================] - 1s 740us/step - loss: 0.0140 - acc: 0.9974 - fscore: 0.9974 - val_loss: 0.1616 - val_acc: 0.9333 - val_fscore: 0.9333\n",
+      "Epoch 675/700\n",
+      "1920/1920 [==============================] - 1s 745us/step - loss: 0.0144 - acc: 0.9979 - fscore: 0.9979 - val_loss: 0.1511 - val_acc: 0.9354 - val_fscore: 0.9354\n",
+      "Epoch 676/700\n",
+      "1920/1920 [==============================] - 1s 753us/step - loss: 0.0118 - acc: 0.9979 - fscore: 0.9979 - val_loss: 0.1534 - val_acc: 0.9354 - val_fscore: 0.9354\n",
+      "Epoch 677/700\n",
+      "1920/1920 [==============================] - 1s 749us/step - loss: 0.0157 - acc: 0.9974 - fscore: 0.9974 - val_loss: 0.1629 - val_acc: 0.9333 - val_fscore: 0.9333\n",
+      "Epoch 678/700\n",
+      "1920/1920 [==============================] - 1s 735us/step - loss: 0.0159 - acc: 0.9979 - fscore: 0.9979 - val_loss: 0.1596 - val_acc: 0.9333 - val_fscore: 0.9333\n",
+      "Epoch 679/700\n",
+      "1920/1920 [==============================] - 1s 736us/step - loss: 0.0149 - acc: 0.9974 - fscore: 0.9974 - val_loss: 0.1703 - val_acc: 0.9333 - val_fscore: 0.9333\n",
+      "Epoch 680/700\n",
+      "1920/1920 [==============================] - 1s 744us/step - loss: 0.0153 - acc: 0.9969 - fscore: 0.9969 - val_loss: 0.1574 - val_acc: 0.9354 - val_fscore: 0.9354\n",
+      "Epoch 681/700\n",
+      "1920/1920 [==============================] - 1s 747us/step - loss: 0.0123 - acc: 0.9984 - fscore: 0.9984 - val_loss: 0.1588 - val_acc: 0.9292 - val_fscore: 0.9292\n",
+      "Epoch 682/700\n",
+      "1920/1920 [==============================] - 1s 739us/step - loss: 0.0137 - acc: 0.9984 - fscore: 0.9984 - val_loss: 0.1612 - val_acc: 0.9313 - val_fscore: 0.9312\n",
+      "Epoch 683/700\n",
+      "1920/1920 [==============================] - 1s 752us/step - loss: 0.0114 - acc: 0.9990 - fscore: 0.9990 - val_loss: 0.1688 - val_acc: 0.9333 - val_fscore: 0.9333\n",
+      "Epoch 684/700\n",
+      "1920/1920 [==============================] - 1s 745us/step - loss: 0.0131 - acc: 0.9979 - fscore: 0.9979 - val_loss: 0.1562 - val_acc: 0.9333 - val_fscore: 0.9333\n",
+      "Epoch 685/700\n",
+      "1920/1920 [==============================] - 1s 745us/step - loss: 0.0114 - acc: 1.0000 - fscore: 1.0000 - val_loss: 0.1614 - val_acc: 0.9333 - val_fscore: 0.9333\n",
+      "Epoch 686/700\n",
+      "1920/1920 [==============================] - 1s 756us/step - loss: 0.0139 - acc: 0.9979 - fscore: 0.9979 - val_loss: 0.1661 - val_acc: 0.9333 - val_fscore: 0.9333\n",
+      "Epoch 687/700\n",
+      "1920/1920 [==============================] - 1s 743us/step - loss: 0.0142 - acc: 0.9979 - fscore: 0.9979 - val_loss: 0.1656 - val_acc: 0.9333 - val_fscore: 0.9333\n",
+      "Epoch 688/700\n",
+      "1920/1920 [==============================] - 1s 744us/step - loss: 0.0157 - acc: 0.9974 - fscore: 0.9974 - val_loss: 0.1566 - val_acc: 0.9354 - val_fscore: 0.9354\n",
+      "Epoch 689/700\n",
+      "1920/1920 [==============================] - 1s 746us/step - loss: 0.0142 - acc: 0.9969 - fscore: 0.9969 - val_loss: 0.1561 - val_acc: 0.9333 - val_fscore: 0.9333\n",
+      "Epoch 690/700\n",
+      "1920/1920 [==============================] - 1s 748us/step - loss: 0.0162 - acc: 0.9964 - fscore: 0.9964 - val_loss: 0.1738 - val_acc: 0.9313 - val_fscore: 0.9312\n",
+      "Epoch 691/700\n",
+      "1920/1920 [==============================] - 1s 756us/step - loss: 0.0121 - acc: 0.9995 - fscore: 0.9995 - val_loss: 0.1724 - val_acc: 0.9250 - val_fscore: 0.9250\n",
+      "Epoch 692/700\n",
+      "1920/1920 [==============================] - 1s 751us/step - loss: 0.0161 - acc: 0.9958 - fscore: 0.9958 - val_loss: 0.1905 - val_acc: 0.9250 - val_fscore: 0.9250\n",
+      "Epoch 693/700\n",
+      "1920/1920 [==============================] - 1s 748us/step - loss: 0.0130 - acc: 0.9984 - fscore: 0.9984 - val_loss: 0.1507 - val_acc: 0.9437 - val_fscore: 0.9437\n",
+      "Epoch 694/700\n",
+      "1920/1920 [==============================] - 1s 749us/step - loss: 0.0142 - acc: 0.9984 - fscore: 0.9984 - val_loss: 0.1756 - val_acc: 0.9250 - val_fscore: 0.9250\n",
+      "Epoch 695/700\n",
+      "1920/1920 [==============================] - 1s 754us/step - loss: 0.0153 - acc: 0.9964 - fscore: 0.9964 - val_loss: 0.1514 - val_acc: 0.9437 - val_fscore: 0.9437\n",
+      "Epoch 696/700\n",
+      "1920/1920 [==============================] - 1s 744us/step - loss: 0.0186 - acc: 0.9953 - fscore: 0.9953 - val_loss: 0.1622 - val_acc: 0.9354 - val_fscore: 0.9354\n",
+      "Epoch 697/700\n",
+      "1920/1920 [==============================] - 1s 744us/step - loss: 0.0149 - acc: 0.9969 - fscore: 0.9969 - val_loss: 0.1683 - val_acc: 0.9313 - val_fscore: 0.9312\n",
+      "Epoch 698/700\n",
+      "1920/1920 [==============================] - 1s 743us/step - loss: 0.0123 - acc: 0.9990 - fscore: 0.9990 - val_loss: 0.1550 - val_acc: 0.9354 - val_fscore: 0.9354\n",
+      "Epoch 699/700\n",
+      "1920/1920 [==============================] - 1s 742us/step - loss: 0.0147 - acc: 0.9969 - fscore: 0.9969 - val_loss: 0.1605 - val_acc: 0.9396 - val_fscore: 0.9396\n",
+      "Epoch 700/700\n",
+      "1920/1920 [==============================] - 1s 752us/step - loss: 0.0144 - acc: 0.9974 - fscore: 0.9974 - val_loss: 0.1565 - val_acc: 0.9354 - val_fscore: 0.9354\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Model Training\n",
+    "\n",
+    "lr_reduce = ReduceLROnPlateau(monitor='val_loss', factor=0.9, patience=20, min_lr=0.000001)\n",
+    "# Please change the model name accordingly.\n",
+    "mcp_save = ModelCheckpoint('model/aug_noiseNshift_2class2_np.h5', save_best_only=True, monitor='val_loss', mode='min')\n",
+    "cnnhistory=model.fit(x_traincnn, y_train, batch_size=16, epochs=700,\n",
+    "                     validation_data=(x_testcnn, y_test), callbacks=[mcp_save, lr_reduce])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plotting the Train Valid Loss Graph\n",
+    "\n",
+    "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": 73,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Saving the model.json\n",
+    "\n",
+    "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": 74,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loaded model from disk\n",
+      "acc: 93.96%\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",
+    "\n",
+    "# load weights into new model\n",
+    "loaded_model.load_weights(\"model/aug_noiseNshift_2class2_np.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": [
+    "# X. Predicting emotions on the test data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "160"
+      ]
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(data3_df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 160/160 [00:05<00:00, 26.86it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_test = pd.DataFrame(columns=['feature'])\n",
+    "for i in tqdm(range(len(data3_df))):\n",
+    "    X, sample_rate = librosa.load(data3_df.path[i], res_type='kaiser_fast',duration=input_duration,sr=22050*2,offset=0.5)\n",
+    "#     X = X[10000:90000]\n",
+    "    sample_rate = np.array(sample_rate)\n",
+    "    mfccs = np.mean(librosa.feature.mfcc(y=X, sr=sample_rate, n_mfcc=13), axis=0)\n",
+    "    feature = mfccs\n",
+    "    data_test.loc[i] = [feature]\n",
+    "    \n",
+    "test_valid = pd.DataFrame(data_test['feature'].values.tolist())\n",
+    "test_valid = np.array(test_valid)\n",
+    "test_valid_lb = np.array(data3_df.label)\n",
+    "lb = LabelEncoder()\n",
+    "test_valid_lb = np_utils.to_categorical(lb.fit_transform(test_valid_lb))\n",
+    "test_valid = np.expand_dims(test_valid, axis=2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "160/160 [==============================] - 0s 978us/step\n"
+     ]
+    }
+   ],
+   "source": [
+    "preds = loaded_model.predict(test_valid, \n",
+    "                         batch_size=16, \n",
+    "                         verbose=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[9.02437747e-01, 9.75623056e-02],\n",
+       "       [7.74625828e-03, 9.92253721e-01],\n",
+       "       [9.83769000e-01, 1.62310582e-02],\n",
+       "       [1.44755363e-01, 8.55244637e-01],\n",
+       "       [2.51911819e-01, 7.48088181e-01],\n",
+       "       [9.93456066e-01, 6.54396275e-03],\n",
+       "       [2.94017226e-01, 7.05982745e-01],\n",
+       "       [9.54675138e-01, 4.53248359e-02],\n",
+       "       [1.00791091e-02, 9.89920855e-01],\n",
+       "       [2.56100714e-01, 7.43899286e-01],\n",
+       "       [2.40712896e-01, 7.59287059e-01],\n",
+       "       [2.41282061e-02, 9.75871861e-01],\n",
+       "       [8.25557590e-01, 1.74442455e-01],\n",
+       "       [9.92534518e-01, 7.46552832e-03],\n",
+       "       [2.59766299e-02, 9.74023402e-01],\n",
+       "       [4.62669402e-01, 5.37330568e-01],\n",
+       "       [1.32880300e-01, 8.67119730e-01],\n",
+       "       [1.68901440e-02, 9.83109832e-01],\n",
+       "       [6.21189028e-02, 9.37881052e-01],\n",
+       "       [7.54640818e-01, 2.45359138e-01],\n",
+       "       [6.66550770e-02, 9.33344901e-01],\n",
+       "       [9.95067477e-01, 4.93251253e-03],\n",
+       "       [9.22456663e-03, 9.90775406e-01],\n",
+       "       [9.98589694e-01, 1.41029467e-03],\n",
+       "       [9.72774267e-01, 2.72257533e-02],\n",
+       "       [9.96585250e-01, 3.41471424e-03],\n",
+       "       [9.98866677e-01, 1.13334495e-03],\n",
+       "       [3.91824208e-02, 9.60817575e-01],\n",
+       "       [2.71135896e-01, 7.28864133e-01],\n",
+       "       [2.17471155e-03, 9.97825265e-01],\n",
+       "       [2.01057852e-03, 9.97989416e-01],\n",
+       "       [2.63491631e-01, 7.36508369e-01],\n",
+       "       [9.63920414e-01, 3.60795967e-02],\n",
+       "       [9.99323606e-01, 6.76442811e-04],\n",
+       "       [1.17144198e-03, 9.98828590e-01],\n",
+       "       [6.55439556e-01, 3.44560415e-01],\n",
+       "       [4.92029279e-01, 5.07970691e-01],\n",
+       "       [6.76786602e-01, 3.23213398e-01],\n",
+       "       [9.99866247e-01, 1.33676338e-04],\n",
+       "       [1.38100892e-01, 8.61899078e-01],\n",
+       "       [5.42253971e-01, 4.57745999e-01],\n",
+       "       [1.30410060e-01, 8.69589925e-01],\n",
+       "       [9.60772753e-01, 3.92271802e-02],\n",
+       "       [1.37905322e-03, 9.98620987e-01],\n",
+       "       [4.07006502e-01, 5.92993498e-01],\n",
+       "       [7.39706039e-01, 2.60293961e-01],\n",
+       "       [9.54863012e-01, 4.51369770e-02],\n",
+       "       [8.32664371e-01, 1.67335674e-01],\n",
+       "       [9.96675014e-01, 3.32494872e-03],\n",
+       "       [9.96560395e-01, 3.43957590e-03],\n",
+       "       [9.35723841e-01, 6.42761886e-02],\n",
+       "       [8.96258175e-01, 1.03741840e-01],\n",
+       "       [4.08396184e-01, 5.91603816e-01],\n",
+       "       [4.95112896e-01, 5.04887164e-01],\n",
+       "       [8.40549886e-01, 1.59450084e-01],\n",
+       "       [9.54625487e-01, 4.53744866e-02],\n",
+       "       [8.18508506e-01, 1.81491494e-01],\n",
+       "       [2.63310736e-03, 9.97366965e-01],\n",
+       "       [7.24176526e-01, 2.75823474e-01],\n",
+       "       [5.07210661e-03, 9.94927883e-01],\n",
+       "       [9.59553003e-01, 4.04469892e-02],\n",
+       "       [9.96902406e-01, 3.09751555e-03],\n",
+       "       [6.86468789e-03, 9.93135333e-01],\n",
+       "       [9.98571754e-01, 1.42818841e-03],\n",
+       "       [9.90100682e-01, 9.89936758e-03],\n",
+       "       [4.60998744e-01, 5.39001226e-01],\n",
+       "       [9.19513822e-01, 8.04862007e-02],\n",
+       "       [6.90514818e-02, 9.30948555e-01],\n",
+       "       [2.89750755e-01, 7.10249305e-01],\n",
+       "       [7.89580524e-01, 2.10419446e-01],\n",
+       "       [5.38489580e-01, 4.61510420e-01],\n",
+       "       [3.65078673e-02, 9.63492155e-01],\n",
+       "       [6.23798251e-01, 3.76201749e-01],\n",
+       "       [6.03777647e-01, 3.96222353e-01],\n",
+       "       [9.52673972e-01, 4.73259613e-02],\n",
+       "       [9.78594363e-01, 2.14056652e-02],\n",
+       "       [7.38765121e-01, 2.61234879e-01],\n",
+       "       [3.54052752e-01, 6.45947218e-01],\n",
+       "       [9.99942422e-01, 5.75190315e-05],\n",
+       "       [2.15125054e-01, 7.84874916e-01],\n",
+       "       [6.80405676e-01, 3.19594324e-01],\n",
+       "       [9.95777011e-01, 4.22295276e-03],\n",
+       "       [1.82327572e-02, 9.81767237e-01],\n",
+       "       [9.74136293e-01, 2.58637499e-02],\n",
+       "       [3.83876175e-01, 6.16123796e-01],\n",
+       "       [9.76525307e-01, 2.34747324e-02],\n",
+       "       [8.98704052e-01, 1.01295911e-01],\n",
+       "       [4.10448879e-01, 5.89551151e-01],\n",
+       "       [9.91157413e-01, 8.84265266e-03],\n",
+       "       [9.24599767e-01, 7.54002035e-02],\n",
+       "       [8.51717412e-01, 1.48282617e-01],\n",
+       "       [2.23906506e-02, 9.77609396e-01],\n",
+       "       [9.69508708e-01, 3.04913148e-02],\n",
+       "       [8.83970037e-02, 9.11602974e-01],\n",
+       "       [7.33813420e-02, 9.26618636e-01],\n",
+       "       [9.80508506e-01, 1.94915663e-02],\n",
+       "       [2.05766410e-01, 7.94233620e-01],\n",
+       "       [2.05802973e-02, 9.79419649e-01],\n",
+       "       [9.59737539e-01, 4.02624235e-02],\n",
+       "       [9.51587021e-01, 4.84129153e-02],\n",
+       "       [5.28562486e-01, 4.71437544e-01],\n",
+       "       [9.82225418e-01, 1.77745447e-02],\n",
+       "       [9.95358407e-01, 4.64161718e-03],\n",
+       "       [9.89413083e-01, 1.05869733e-02],\n",
+       "       [6.85702622e-01, 3.14297408e-01],\n",
+       "       [8.60162556e-01, 1.39837429e-01],\n",
+       "       [3.32062006e-01, 6.67937994e-01],\n",
+       "       [9.98952985e-01, 1.04695698e-03],\n",
+       "       [9.90052521e-01, 9.94743872e-03],\n",
+       "       [8.75887930e-01, 1.24112099e-01],\n",
+       "       [9.98890698e-01, 1.10929064e-03],\n",
+       "       [3.77380043e-01, 6.22619927e-01],\n",
+       "       [6.48253024e-01, 3.51746947e-01],\n",
+       "       [5.44818938e-01, 4.55181092e-01],\n",
+       "       [5.73064148e-01, 4.26935881e-01],\n",
+       "       [7.36568689e-01, 2.63431311e-01],\n",
+       "       [3.47799331e-01, 6.52200699e-01],\n",
+       "       [9.89478886e-01, 1.05211008e-02],\n",
+       "       [9.97661710e-01, 2.33832630e-03],\n",
+       "       [7.46137619e-01, 2.53862381e-01],\n",
+       "       [2.36266796e-02, 9.76373315e-01],\n",
+       "       [1.15855306e-01, 8.84144723e-01],\n",
+       "       [9.99604762e-01, 3.95292853e-04],\n",
+       "       [9.39756632e-01, 6.02433421e-02],\n",
+       "       [7.26442039e-01, 2.73557991e-01],\n",
+       "       [1.28157854e-01, 8.71842146e-01],\n",
+       "       [4.05701637e-01, 5.94298363e-01],\n",
+       "       [9.77908909e-01, 2.20910683e-02],\n",
+       "       [9.88749385e-01, 1.12506049e-02],\n",
+       "       [7.43776143e-01, 2.56223828e-01],\n",
+       "       [1.01428568e-01, 8.98571432e-01],\n",
+       "       [9.03055489e-01, 9.69445482e-02],\n",
+       "       [9.83866274e-01, 1.61337797e-02],\n",
+       "       [9.58156884e-01, 4.18431237e-02],\n",
+       "       [2.67699510e-01, 7.32300520e-01],\n",
+       "       [9.94916558e-01, 5.08340029e-03],\n",
+       "       [8.69181156e-02, 9.13081825e-01],\n",
+       "       [5.18520474e-01, 4.81479526e-01],\n",
+       "       [3.31227435e-03, 9.96687710e-01],\n",
+       "       [5.69702148e-01, 4.30297822e-01],\n",
+       "       [4.06953655e-02, 9.59304690e-01],\n",
+       "       [9.95598912e-01, 4.40103794e-03],\n",
+       "       [1.00790085e-02, 9.89920974e-01],\n",
+       "       [9.12350789e-03, 9.90876496e-01],\n",
+       "       [5.08049317e-02, 9.49195027e-01],\n",
+       "       [9.45671916e-01, 5.43280914e-02],\n",
+       "       [8.66154790e-01, 1.33845195e-01],\n",
+       "       [7.69116342e-01, 2.30883643e-01],\n",
+       "       [1.85288593e-01, 8.14711452e-01],\n",
+       "       [9.93687332e-01, 6.31270185e-03],\n",
+       "       [9.08513129e-01, 9.14868787e-02],\n",
+       "       [8.36249471e-01, 1.63750529e-01],\n",
+       "       [1.42808214e-01, 8.57191741e-01],\n",
+       "       [5.33238649e-01, 4.66761321e-01],\n",
+       "       [8.97382438e-01, 1.02617584e-01],\n",
+       "       [9.78488445e-01, 2.15114877e-02],\n",
+       "       [9.95081544e-01, 4.91840811e-03],\n",
+       "       [4.72754538e-01, 5.27245462e-01],\n",
+       "       [6.17820263e-01, 3.82179677e-01],\n",
+       "       [5.73170483e-01, 4.26829487e-01]], dtype=float32)"
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "preds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "preds1=preds.argmax(axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0,\n",
+       "       1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1,\n",
+       "       1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1,\n",
+       "       0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1,\n",
+       "       0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,\n",
+       "       0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0,\n",
+       "       0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0,\n",
+       "       0, 0, 0, 1, 0, 0])"
+      ]
+     },
+     "execution_count": 80,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "preds1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "abc = preds1.astype(int).flatten()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "predictions = (lb.inverse_transform((abc)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "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>predictedvalues</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>male_negative</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>male_positive</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>male_negative</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>male_positive</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>male_positive</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>male_negative</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>male_positive</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>male_negative</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>male_positive</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>male_positive</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  predictedvalues\n",
+       "0   male_negative\n",
+       "1   male_positive\n",
+       "2   male_negative\n",
+       "3   male_positive\n",
+       "4   male_positive\n",
+       "5   male_negative\n",
+       "6   male_positive\n",
+       "7   male_negative\n",
+       "8   male_positive\n",
+       "9   male_positive"
+      ]
+     },
+     "execution_count": 83,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "preddf = pd.DataFrame({'predictedvalues': predictions})\n",
+    "preddf[:10]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "actual=test_valid_lb.argmax(axis=1)\n",
+    "abc123 = actual.astype(int).flatten()\n",
+    "actualvalues = (lb.inverse_transform((abc123)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "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>male_negative</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>male_positive</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>male_negative</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>male_positive</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>male_positive</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>male_negative</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>male_positive</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>male_positive</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>male_positive</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>male_positive</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    actualvalues\n",
+       "0  male_negative\n",
+       "1  male_positive\n",
+       "2  male_negative\n",
+       "3  male_positive\n",
+       "4  male_positive\n",
+       "5  male_negative\n",
+       "6  male_positive\n",
+       "7  male_positive\n",
+       "8  male_positive\n",
+       "9  male_positive"
+      ]
+     },
+     "execution_count": 85,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "actualdf = pd.DataFrame({'actualvalues': actualvalues})\n",
+    "actualdf[:10]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "finaldf = actualdf.join(preddf)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Actual v/s Predicted emotions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>actualvalues</th>\n",
+       "      <th>predictedvalues</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>male_negative</td>\n",
+       "      <td>male_positive</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>male_positive</td>\n",
+       "      <td>male_negative</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>male_positive</td>\n",
+       "      <td>male_positive</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>male_negative</td>\n",
+       "      <td>male_negative</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>male_negative</td>\n",
+       "      <td>male_negative</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>male_negative</td>\n",
+       "      <td>male_negative</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>male_negative</td>\n",
+       "      <td>male_negative</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>male_negative</td>\n",
+       "      <td>male_positive</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>male_negative</td>\n",
+       "      <td>male_positive</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>male_positive</td>\n",
+       "      <td>male_positive</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>male_negative</td>\n",
+       "      <td>male_positive</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <td>male_positive</td>\n",
+       "      <td>male_positive</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <td>male_negative</td>\n",
+       "      <td>male_negative</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33</th>\n",
+       "      <td>male_negative</td>\n",
+       "      <td>male_negative</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34</th>\n",
+       "      <td>male_positive</td>\n",
+       "      <td>male_positive</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35</th>\n",
+       "      <td>male_negative</td>\n",
+       "      <td>male_negative</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36</th>\n",
+       "      <td>male_negative</td>\n",
+       "      <td>male_positive</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>37</th>\n",
+       "      <td>male_positive</td>\n",
+       "      <td>male_negative</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38</th>\n",
+       "      <td>male_negative</td>\n",
+       "      <td>male_negative</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>39</th>\n",
+       "      <td>male_negative</td>\n",
+       "      <td>male_positive</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     actualvalues predictedvalues\n",
+       "20  male_negative   male_positive\n",
+       "21  male_positive   male_negative\n",
+       "22  male_positive   male_positive\n",
+       "23  male_negative   male_negative\n",
+       "24  male_negative   male_negative\n",
+       "25  male_negative   male_negative\n",
+       "26  male_negative   male_negative\n",
+       "27  male_negative   male_positive\n",
+       "28  male_negative   male_positive\n",
+       "29  male_positive   male_positive\n",
+       "30  male_negative   male_positive\n",
+       "31  male_positive   male_positive\n",
+       "32  male_negative   male_negative\n",
+       "33  male_negative   male_negative\n",
+       "34  male_positive   male_positive\n",
+       "35  male_negative   male_negative\n",
+       "36  male_negative   male_positive\n",
+       "37  male_positive   male_negative\n",
+       "38  male_negative   male_negative\n",
+       "39  male_negative   male_positive"
+      ]
+     },
+     "execution_count": 87,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "finaldf[20:40]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "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>predictedvalues</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>actualvalues</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>male_negative</th>\n",
+       "      <td>96</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>male_positive</th>\n",
+       "      <td>64</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               predictedvalues\n",
+       "actualvalues                  \n",
+       "male_negative               96\n",
+       "male_positive               64"
+      ]
+     },
+     "execution_count": 88,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "finaldf.groupby('actualvalues').count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "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",
+       "    <tr>\n",
+       "      <th>predictedvalues</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>male_negative</th>\n",
+       "      <td>97</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>male_positive</th>\n",
+       "      <td>63</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 actualvalues\n",
+       "predictedvalues              \n",
+       "male_negative              97\n",
+       "male_positive              63"
+      ]
+     },
+     "execution_count": 89,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "finaldf.groupby('predictedvalues').count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "finaldf.to_csv('Predictions.csv', index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def print_confusion_matrix(confusion_matrix, class_names, figsize = (10,7), fontsize=14):\n",
+    "    \"\"\"Prints a confusion matrix, as returned by sklearn.metrics.confusion_matrix, as a heatmap.\n",
+    "    \n",
+    "    Arguments\n",
+    "    ---------\n",
+    "    confusion_matrix: numpy.ndarray\n",
+    "        The numpy.ndarray object returned from a call to sklearn.metrics.confusion_matrix. \n",
+    "        Similarly constructed ndarrays can also be used.\n",
+    "    class_names: list\n",
+    "        An ordered list of class names, in the order they index the given confusion matrix.\n",
+    "    figsize: tuple\n",
+    "        A 2-long tuple, the first value determining the horizontal size of the ouputted figure,\n",
+    "        the second determining the vertical size. Defaults to (10,7).\n",
+    "    fontsize: int\n",
+    "        Font size for axes labels. Defaults to 14.\n",
+    "        \n",
+    "    Returns\n",
+    "    -------\n",
+    "    matplotlib.figure.Figure\n",
+    "        The resulting confusion matrix figure\n",
+    "    \"\"\"\n",
+    "    df_cm = pd.DataFrame(\n",
+    "        confusion_matrix, index=class_names, columns=class_names, \n",
+    "    )\n",
+    "    fig = plt.figure(figsize=figsize)\n",
+    "    try:\n",
+    "        heatmap = sns.heatmap(df_cm, annot=True, fmt=\"d\")\n",
+    "    except ValueError:\n",
+    "        raise ValueError(\"Confusion matrix values must be integers.\")\n",
+    "        \n",
+    "    heatmap.yaxis.set_ticklabels(heatmap.yaxis.get_ticklabels(), rotation=0, ha='right', fontsize=fontsize)\n",
+    "    heatmap.xaxis.set_ticklabels(heatmap.xaxis.get_ticklabels(), rotation=45, ha='right', fontsize=fontsize)\n",
+    "    plt.ylabel('True label')\n",
+    "    plt.xlabel('Predicted label')\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "66.875"
+      ]
+     },
+     "execution_count": 92,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import accuracy_score\n",
+    "y_true = finaldf.actualvalues\n",
+    "y_pred = finaldf.predictedvalues\n",
+    "accuracy_score(y_true, y_pred)*100"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "65.40328831953"
+      ]
+     },
+     "execution_count": 93,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import f1_score\n",
+    "f1_score(y_true, y_pred, average='macro') *100"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[70, 26],\n",
+       "       [27, 37]])"
+      ]
+     },
+     "execution_count": 94,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import confusion_matrix\n",
+    "c = confusion_matrix(y_true, y_pred)\n",
+    "c"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x504 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Visualize Confusion Matrix \n",
+    "\n",
+    "# class_names = ['male_angry', 'male_calm', 'male_fearful', 'male_happy', 'male_sad']\n",
+    "# class_names = ['female_angry', 'female_calm', 'female_fearful', 'female_happy', 'female_sad']\n",
+    "# class_names = ['male_negative', 'male_neutral', 'male_positive']\n",
+    "class_names = ['male_negative', 'male_positive']\n",
+    "# class_names = ['female_angry', 'female_calm', 'female_fearful', 'female_happy', 'female_sad', 'male_angry', 'male_calm', 'male_fearful', 'male_happy', 'male_sad']\n",
+    "\n",
+    "\n",
+    "print_confusion_matrix(c, class_names)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/cnn_emotion_recognition.py b/cnn_emotion_recognition.py
new file mode 100644
index 0000000..4a589c1
--- /dev/null
+++ b/cnn_emotion_recognition.py
@@ -0,0 +1,837 @@
+# -*- coding: utf-8 -*-
+"""CNN_emotion_recognition.ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/15v1F7IVoNmRL8ZLN2IcAzgp0pRY3yifV
+
+# I. Importing the required libraries
+"""
+
+# Orignial Notebook: https://github.com/MITESHPUTHRANNEU/Speech-Emotion-Analyzer/blob/master/final_results_gender_test.ipynb
+# This notebook author: Reza Chu
+# Last Editing Date: 31st May 2019
+
+## Python
+import os
+import random
+import sys
+
+
+## Package
+import glob 
+import keras
+import IPython.display as ipd
+import librosa
+import librosa.display
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import plotly.graph_objs as go
+import plotly.offline as py
+import plotly.tools as tls
+import seaborn as sns
+import scipy.io.wavfile
+import tensorflow as tf
+py.init_notebook_mode(connected=True)
+
+
+## Keras
+from keras import regularizers
+from keras.callbacks import ModelCheckpoint, LearningRateScheduler, EarlyStopping
+from keras.callbacks import  History, ReduceLROnPlateau, CSVLogger
+from keras.models import Model, Sequential
+from keras.layers import Dense, Embedding, LSTM
+from keras.layers import Input, Flatten, Dropout, Activation, BatchNormalization
+from keras.layers import Conv1D, MaxPooling1D, AveragePooling1D
+from keras.preprocessing import sequence
+from keras.preprocessing.sequence import pad_sequences
+from keras.preprocessing.text import Tokenizer
+from keras.utils import np_utils
+from keras.utils import to_categorical
+
+
+## Sklearn
+from sklearn.metrics import confusion_matrix
+from sklearn.preprocessing import LabelEncoder
+
+
+## Rest
+from scipy.fftpack import fft
+from scipy import signal
+from scipy.io import wavfile
+from tqdm import tqdm
+
+input_duration=3
+# % pylab inline
+
+"""# II. Reading the data"""
+
+# Data Directory
+# Please edit according to your directory change.
+dir_list = os.listdir('data/')
+dir_list.sort()
+print (dir_list)
+
+# Create DataFrame for Data intel
+data_df = pd.DataFrame(columns=['path', 'source', 'actor', 'gender',
+                                'intensity', 'statement', 'repetition', 'emotion'])
+count = 0
+for i in dir_list:
+    file_list = os.listdir('data/' + i)
+    for f in file_list:
+        nm = f.split('.')[0].split('-')
+        path = 'data/' + i + '/' + f
+        src = int(nm[1])
+        actor = int(nm[-1])
+        emotion = int(nm[2])
+        
+        if int(actor)%2 == 0:
+            gender = "female"
+        else:
+            gender = "male"
+        
+        if nm[3] == '01':
+            intensity = 0
+        else:
+            intensity = 1
+        
+        if nm[4] == '01':
+            statement = 0
+        else:
+            statement = 1
+        
+        if nm[5] == '01':
+            repeat = 0
+        else:
+            repeat = 1
+            
+        data_df.loc[count] = [path, src, actor, gender, intensity, statement, repeat, emotion]
+        count += 1
+
+print (len(data_df))
+data_df.head()
+
+"""# III. Plotting the audio file's waveform and its spectrogram"""
+
+filename = data_df.path[1021]
+print (filename)
+
+samples, sample_rate = librosa.load(filename)
+sample_rate, samples
+
+len(samples), sample_rate
+
+def log_specgram(audio, sample_rate, window_size=20,
+                 step_size=10, eps=1e-10):
+    nperseg = int(round(window_size * sample_rate / 1e3))
+    noverlap = int(round(step_size * sample_rate / 1e3))
+    freqs, times, spec = signal.spectrogram(audio,
+                                    fs=sample_rate,
+                                    window='hann',
+                                    nperseg=nperseg,
+                                    noverlap=noverlap,
+                                    detrend=False)
+    return freqs, times, np.log(spec.T.astype(np.float32) + eps)
+
+sample_rate/ len(samples)
+
+# Plotting Wave Form and Spectrogram
+freqs, times, spectrogram = log_specgram(samples, sample_rate)
+
+fig = plt.figure(figsize=(14, 8))
+ax1 = fig.add_subplot(211)
+ax1.set_title('Raw wave of ' + filename)
+ax1.set_ylabel('Amplitude')
+librosa.display.waveplot(samples, sr=sample_rate)
+
+ax2 = fig.add_subplot(212)
+ax2.imshow(spectrogram.T, aspect='auto', origin='lower', 
+           extent=[times.min(), times.max(), freqs.min(), freqs.max()])
+ax2.set_yticks(freqs[::16])
+ax2.set_xticks(times[::16])
+ax2.set_title('Spectrogram of ' + filename)
+ax2.set_ylabel('Freqs in Hz')
+ax2.set_xlabel('Seconds')
+
+mean = np.mean(spectrogram, axis=0)
+std = np.std(spectrogram, axis=0)
+spectrogram = (spectrogram - mean) / std
+
+# Trim the silence voice
+aa , bb = librosa.effects.trim(samples, top_db=30)
+aa, bb
+
+# Plotting Mel Power Spectrogram
+S = librosa.feature.melspectrogram(aa, sr=sample_rate, n_mels=128)
+
+# Convert to log scale (dB). We'll use the peak power (max) as reference.
+log_S = librosa.power_to_db(S, ref=np.max)
+
+plt.figure(figsize=(12, 4))
+librosa.display.specshow(log_S, sr=sample_rate, x_axis='time', y_axis='mel')
+plt.title('Mel power spectrogram ')
+plt.colorbar(format='%+02.0f dB')
+plt.tight_layout()
+
+# Plotting MFCC
+mfcc = librosa.feature.mfcc(S=log_S, n_mfcc=13)
+
+# Let's pad on the first and second deltas while we're at it
+delta2_mfcc = librosa.feature.delta(mfcc, order=2)
+
+plt.figure(figsize=(12, 4))
+librosa.display.specshow(delta2_mfcc)
+plt.ylabel('MFCC coeffs')
+plt.xlabel('Time')
+plt.title('MFCC')
+plt.colorbar()
+plt.tight_layout()
+
+# Original Sound
+ipd.Audio(samples, rate=sample_rate)
+
+# Silence trimmed Sound by librosa.effects.trim()
+ipd.Audio(aa, rate=sample_rate)
+
+# Silence trimmed Sound by manuel trimming
+samples_cut = samples[10000:-12500]
+ipd.Audio(samples_cut, rate=sample_rate)
+
+"""# IV. Defining the truth label"""
+
+# 2 class: Positive & Negative
+
+# Positive: Calm, Happy
+# Negative: Angry, Fearful, Sad
+
+label2_list = []
+for i in range(len(data_df)):
+    if data_df.emotion[i] == 2: # Calm
+        lb = "_positive"
+    elif data_df.emotion[i] == 3: # Happy
+        lb = "_positive"
+    elif data_df.emotion[i] == 4: # Sad
+        lb = "_negative"
+    elif data_df.emotion[i] == 5: # Angry
+        lb = "_negative"
+    elif data_df.emotion[i] == 6: # Fearful
+        lb = "_negative"
+    else:
+        lb = "_none"
+        
+    # Add gender to the label    
+    label2_list.append(data_df.gender[i] + lb)
+    
+len(label2_list)
+
+#3 class: Positive, Neutral & Negative
+
+# Positive:  Happy
+# Negative: Angry, Fearful, Sad
+# Neutral: Calm, Neutral
+
+label3_list = []
+for i in range(len(data_df)):
+    if data_df.emotion[i] == 1: # Neutral
+        lb = "_neutral"
+    elif data_df.emotion[i] == 2: # Calm
+        lb = "_neutral"
+    elif data_df.emotion[i] == 3: # Happy
+        lb = "_positive"
+    elif data_df.emotion[i] == 4: # Sad
+        lb = "_negative"
+    elif data_df.emotion[i] == 5: # Angry
+        lb = "_negative"
+    elif data_df.emotion[i] == 6: # Fearful
+        lb = "_negative"
+    else:
+        lb = "_none"
+    
+    # Add gender to the label  
+    label3_list.append(data_df.gender[i] + lb)
+    
+len(label3_list)
+
+# 5 class: angry, calm, sad, happy & fearful
+label5_list = []
+for i in range(len(data_df)):
+    if data_df.emotion[i] == 2:
+        lb = "_calm"
+    elif data_df.emotion[i] == 3:
+        lb = "_happy"
+    elif data_df.emotion[i] == 4:
+        lb = "_sad"
+    elif data_df.emotion[i] == 5:
+        lb = "_angry"
+    elif data_df.emotion[i] == 6:
+        lb = "_fearful"    
+    else:
+        lb = "_none"
+    
+    # Add gender to the label  
+    label5_list.append(data_df.gender[i] + lb)
+    
+len(label5_list)
+
+# All class
+
+label8_list = []
+for i in range(len(data_df)):
+    if data_df.emotion[i] == 1:
+        lb = "_neutral"
+    elif data_df.emotion[i] == 2:
+        lb = "_calm"
+    elif data_df.emotion[i] == 3:
+        lb = "_happy"
+    elif data_df.emotion[i] == 4:
+        lb = "_sad"
+    elif data_df.emotion[i] == 5:
+        lb = "_angry"
+    elif data_df.emotion[i] == 6:
+        lb = "_fearful"
+    elif data_df.emotion[i] == 7:
+        lb = "_disgust"
+    elif data_df.emotion[i] == 8:
+        lb = "_surprised"
+    else:
+        lb = "_none"
+        
+    # Add gender to the label 
+    label8_list.append(data_df.gender[i]  + lb)
+    
+len(label8_list)
+
+# Select the label set you want by commenting the unwanteds.
+
+data_df['label'] = label2_list
+# data_df['label'] = label3_list
+# data_df['label'] = label5_list
+# data_df['label'] = label8_list
+data_df.head()
+
+print (data_df.label.value_counts().keys())
+
+# Plotting the emotion distribution
+
+def plot_emotion_dist(dist, color_code='#C2185B', title="Plot"):
+    """
+    To plot the data distributioin by class.
+    Arg:
+      dist: pandas series of label count. 
+    """
+    tmp_df = pd.DataFrame()
+    tmp_df['Emotion'] = list(dist.keys())
+    tmp_df['Count'] = list(dist)
+    fig, ax = plt.subplots(figsize=(14, 7))
+    ax = sns.barplot(x="Emotion", y='Count', color=color_code, data=tmp_df)
+    ax.set_title(title)
+    ax.set_xticklabels(ax.get_xticklabels(),rotation=45)
+
+a = data_df.label.value_counts()
+plot_emotion_dist(a, "#2962FF", "Emotion Distribution")
+
+"""# V. Data Splitting"""
+
+# Female Data Set
+
+## Uncomment all below to use Female set 
+
+# data2_df = data_df.copy()
+# data2_df = data2_df[data2_df.label != "male_none"]
+# data2_df = data2_df[data2_df.label != "female_none"]
+# data2_df = data2_df[data2_df.label != "male_happy"]
+# data2_df = data2_df[data2_df.label != "male_angry"]
+# data2_df = data2_df[data2_df.label != "male_sad"]
+# data2_df = data2_df[data2_df.label != "male_fearful"]
+# data2_df = data2_df[data2_df.label != "male_calm"]
+# data2_df = data2_df[data2_df.label != "male_positive"]
+# data2_df = data2_df[data2_df.label != "male_negative"].reset_index(drop=True)
+
+# tmp1 = data2_df[data2_df.actor == 22]
+# tmp2 = data2_df[data2_df.actor == 24]
+# data3_df = pd.concat([tmp1, tmp2],ignore_index=True).reset_index(drop=True)
+# data2_df = data2_df[data2_df.actor != 22]
+# data2_df = data2_df[data2_df.actor != 24].reset_index(drop=True)
+# print (len(data2_df))
+# data2_df.head()
+
+# Male Data Set
+
+## Uncomment all below to use Male set 
+
+data2_df = data_df.copy()
+data2_df = data2_df[data2_df.label != "male_none"]
+data2_df = data2_df[data2_df.label != "female_none"].reset_index(drop=True)
+data2_df = data2_df[data2_df.label != "female_neutral"]
+data2_df = data2_df[data2_df.label != "female_happy"]
+data2_df = data2_df[data2_df.label != "female_angry"]
+data2_df = data2_df[data2_df.label != "female_sad"]
+data2_df = data2_df[data2_df.label != "female_fearful"]
+data2_df = data2_df[data2_df.label != "female_calm"]
+data2_df = data2_df[data2_df.label != "female_positive"]
+data2_df = data2_df[data2_df.label != "female_negative"].reset_index(drop=True)
+
+tmp1 = data2_df[data2_df.actor == 21]
+tmp2 = data2_df[data2_df.actor == 22]
+tmp3 = data2_df[data2_df.actor == 23]
+tmp4 = data2_df[data2_df.actor == 24]
+data3_df = pd.concat([tmp1, tmp3],ignore_index=True).reset_index(drop=True)
+data2_df = data2_df[data2_df.actor != 21]
+data2_df = data2_df[data2_df.actor != 22]
+data2_df = data2_df[data2_df.actor != 23].reset_index(drop=True)
+data2_df = data2_df[data2_df.actor != 24].reset_index(drop=True)
+print (len(data2_df))
+data2_df.head()
+
+print (len(data3_df))
+data3_df.head()
+
+"""# VI. Getting the features of audio files using librosa"""
+
+data = pd.DataFrame(columns=['feature'])
+for i in tqdm(range(len(data2_df))):
+    X, sample_rate = librosa.load(data2_df.path[i], res_type='kaiser_fast',duration=input_duration,sr=22050*2,offset=0.5)
+#     X = X[10000:90000]
+    sample_rate = np.array(sample_rate)
+    mfccs = np.mean(librosa.feature.mfcc(y=X, sr=sample_rate, n_mfcc=13), axis=0)
+    feature = mfccs
+    data.loc[i] = [feature]
+
+data.head()
+
+df3 = pd.DataFrame(data['feature'].values.tolist())
+labels = data2_df.label
+
+df3.head()
+
+newdf = pd.concat([df3,labels], axis=1)
+
+rnewdf = newdf.rename(index=str, columns={"0": "label"})
+len(rnewdf)
+
+rnewdf.head(10)
+
+rnewdf.isnull().sum().sum()
+
+rnewdf = rnewdf.fillna(0)
+rnewdf.head()
+
+"""# VII. Data Augmentation"""
+
+def plot_time_series(data):
+    """
+    Plot the Audio Frequency.
+    """
+    fig = plt.figure(figsize=(14, 8))
+    plt.title('Raw wave ')
+    plt.ylabel('Amplitude')
+    plt.plot(np.linspace(0, 1, len(data)), data)
+    plt.show()
+
+
+def noise(data):
+    """
+    Adding White Noise.
+    """
+    # you can take any distribution from https://docs.scipy.org/doc/numpy-1.13.0/reference/routines.random.html
+    noise_amp = 0.005*np.random.uniform()*np.amax(data)
+    data = data.astype('float64') + noise_amp * np.random.normal(size=data.shape[0])
+    return data
+    
+def shift(data):
+    """
+    Random Shifting.
+    """
+    s_range = int(np.random.uniform(low=-5, high = 5)*500)
+    return np.roll(data, s_range)
+    
+def stretch(data, rate=0.8):
+    """
+    Streching the Sound.
+    """
+    data = librosa.effects.time_stretch(data, rate)
+    return data
+    
+def pitch(data, sample_rate):
+    """
+    Pitch Tuning.
+    """
+    bins_per_octave = 12
+    pitch_pm = 2
+    pitch_change =  pitch_pm * 2*(np.random.uniform())   
+    data = librosa.effects.pitch_shift(data.astype('float64'), 
+                                      sample_rate, n_steps=pitch_change, 
+                                      bins_per_octave=bins_per_octave)
+    return data
+    
+def dyn_change(data):
+    """
+    Random Value Change.
+    """
+    dyn_change = np.random.uniform(low=1.5,high=3)
+    return (data * dyn_change)
+    
+def speedNpitch(data):
+    """
+    peed and Pitch Tuning.
+    """
+    # you can change low and high here
+    length_change = np.random.uniform(low=0.8, high = 1)
+    speed_fac = 1.0  / length_change
+    tmp = np.interp(np.arange(0,len(data),speed_fac),np.arange(0,len(data)),data)
+    minlen = min(data.shape[0], tmp.shape[0])
+    data *= 0
+    data[0:minlen] = tmp[0:minlen]
+    return data
+
+X, sample_rate = librosa.load(data2_df.path[216], res_type='kaiser_fast',duration=4,sr=22050*2,offset=0.5)
+plot_time_series(X)
+ipd.Audio(X, rate=sample_rate)
+
+x = pitch(X, sample_rate)
+plot_time_series(x)
+ipd.Audio(x, rate=sample_rate)
+
+# Augmentation Method 1
+
+syn_data1 = pd.DataFrame(columns=['feature', 'label'])
+for i in tqdm(range(len(data2_df))):
+    X, sample_rate = librosa.load(data2_df.path[i], res_type='kaiser_fast',duration=input_duration,sr=22050*2,offset=0.5)
+    if data2_df.label[i]:
+#     if data2_df.label[i] == "male_positive":
+        X = noise(X)
+        sample_rate = np.array(sample_rate)
+        mfccs = np.mean(librosa.feature.mfcc(y=X, sr=sample_rate, n_mfcc=13), axis=0)
+        feature = mfccs
+        a = random.uniform(0, 1)
+        syn_data1.loc[i] = [feature, data2_df.label[i]]
+
+# Augmentation Method 2
+
+syn_data2 = pd.DataFrame(columns=['feature', 'label'])
+for i in tqdm(range(len(data2_df))):
+    X, sample_rate = librosa.load(data2_df.path[i], res_type='kaiser_fast',duration=input_duration,sr=22050*2,offset=0.5)
+    if data2_df.label[i]:
+#     if data2_df.label[i] == "male_positive":
+        X = pitch(X, sample_rate)
+        sample_rate = np.array(sample_rate)
+        mfccs = np.mean(librosa.feature.mfcc(y=X, sr=sample_rate, n_mfcc=13), axis=0)
+        feature = mfccs
+        a = random.uniform(0, 1)
+        syn_data2.loc[i] = [feature, data2_df.label[i]]
+
+len(syn_data1), len(syn_data2)
+
+syn_data1 = syn_data1.reset_index(drop=True)
+syn_data2 = syn_data2.reset_index(drop=True)
+
+df4 = pd.DataFrame(syn_data1['feature'].values.tolist())
+labels4 = syn_data1.label
+syndf1 = pd.concat([df4,labels4], axis=1)
+syndf1 = syndf1.rename(index=str, columns={"0": "label"})
+syndf1 = syndf1.fillna(0)
+len(syndf1)
+
+syndf1.head()
+
+df4 = pd.DataFrame(syn_data2['feature'].values.tolist())
+labels4 = syn_data2.label
+syndf2 = pd.concat([df4,labels4], axis=1)
+syndf2 = syndf2.rename(index=str, columns={"0": "label"})
+syndf2 = syndf2.fillna(0)
+len(syndf2)
+
+syndf2.head()
+
+# Combining the Augmented data with original
+combined_df = pd.concat([rnewdf, syndf1, syndf2], ignore_index=True)
+combined_df = combined_df.fillna(0)
+combined_df.head()
+
+#  Stratified Shuffle Split
+
+X = combined_df.drop(['label'], axis=1)
+y = combined_df.label
+xxx = StratifiedShuffleSplit(1, test_size=0.2, random_state=12)
+for train_index, test_index in xxx.split(X, y):
+    X_train, X_test = X.iloc[train_index], X.iloc[test_index]
+    y_train, y_test = y.iloc[train_index], y.iloc[test_index]
+
+y_train.value_counts()
+
+y_test.value_counts()
+
+X_train.isna().sum().sum()
+
+X_train = np.array(X_train)
+y_train = np.array(y_train)
+X_test = np.array(X_test)
+y_test = np.array(y_test)
+lb = LabelEncoder()
+y_train = np_utils.to_categorical(lb.fit_transform(y_train))
+y_test = np_utils.to_categorical(lb.fit_transform(y_test))
+
+y_train
+
+X_train.shape
+
+"""# VIII. Changing dimension for CNN model"""
+
+x_traincnn = np.expand_dims(X_train, axis=2)
+x_testcnn = np.expand_dims(X_test, axis=2)
+
+# Set up Keras util functions
+
+from keras import backend as K
+
+def precision(y_true, y_pred):
+    true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
+    predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))
+    precision = true_positives / (predicted_positives + K.epsilon())
+    return precision
+
+
+def recall(y_true, y_pred):
+    true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
+    possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))
+    recall = true_positives / (possible_positives + K.epsilon())
+    return recall
+
+
+def fscore(y_true, y_pred):
+    if K.sum(K.round(K.clip(y_true, 0, 1))) == 0:
+        return 0
+
+    p = precision(y_true, y_pred)
+    r = recall(y_true, y_pred)
+    f_score = 2 * (p * r) / (p + r + K.epsilon())
+    return f_score
+
+def get_lr_metric(optimizer):
+    def lr(y_true, y_pred):
+        return optimizer.lr
+    return lr
+
+# New model
+model = Sequential()
+model.add(Conv1D(256, 8, padding='same',input_shape=(X_train.shape[1],1)))
+model.add(Activation('relu'))
+model.add(Conv1D(256, 8, padding='same'))
+model.add(BatchNormalization())
+model.add(Activation('relu'))
+model.add(Dropout(0.25))
+model.add(MaxPooling1D(pool_size=(8)))
+model.add(Conv1D(128, 8, padding='same'))
+model.add(Activation('relu'))
+model.add(Conv1D(128, 8, padding='same'))
+model.add(Activation('relu'))
+model.add(Conv1D(128, 8, padding='same'))
+model.add(Activation('relu'))
+model.add(Conv1D(128, 8, padding='same'))
+model.add(BatchNormalization())
+model.add(Activation('relu'))
+model.add(Dropout(0.25))
+model.add(MaxPooling1D(pool_size=(8)))
+model.add(Conv1D(64, 8, padding='same'))
+model.add(Activation('relu'))
+model.add(Conv1D(64, 8, padding='same'))
+model.add(Activation('relu'))
+model.add(Flatten())
+# Edit according to target class no.
+model.add(Dense(2))
+model.add(Activation('softmax'))
+opt = keras.optimizers.SGD(lr=0.0001, momentum=0.0, decay=0.0, nesterov=False)
+
+# Original Model
+
+# model = Sequential()
+# model.add(Conv1D(256, 5,padding='same', input_shape=(X_train.shape[1],1)))
+# model.add(Activation('relu'))
+# model.add(Conv1D(128, 5,padding='same'))
+# model.add(Activation('relu'))
+# model.add(Dropout(0.1))
+# model.add(MaxPooling1D(pool_size=(8)))
+# model.add(Conv1D(128, 5,padding='same',))
+# model.add(Activation('relu'))
+# model.add(Conv1D(128, 5,padding='same',))
+# model.add(Activation('relu'))
+# model.add(Conv1D(128, 5,padding='same',))
+# model.add(Activation('relu'))
+# model.add(Dropout(0.2))
+# model.add(Conv1D(128, 5,padding='same',))
+# model.add(Activation('relu'))
+# model.add(Flatten())
+# model.add(Dense(5))
+# model.add(Activation('softmax'))
+# opt = keras.optimizers.rmsprop(lr=0.00001, decay=1e-6)
+
+# Plotting Model Summary
+
+model.summary()
+
+# Compile your model
+
+model.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['accuracy', fscore])
+
+"""# IX. Removed the whole training part for avoiding unnecessary long epochs list"""
+
+# Model Training
+
+lr_reduce = ReduceLROnPlateau(monitor='val_loss', factor=0.9, patience=20, min_lr=0.000001)
+# Please change the model name accordingly.
+mcp_save = ModelCheckpoint('model/aug_noiseNshift_2class2_np.h5', save_best_only=True, monitor='val_loss', mode='min')
+cnnhistory=model.fit(x_traincnn, y_train, batch_size=16, epochs=700,
+                     validation_data=(x_testcnn, y_test), callbacks=[mcp_save, lr_reduce])
+
+# Plotting the Train Valid Loss Graph
+
+plt.plot(cnnhistory.history['loss'])
+plt.plot(cnnhistory.history['val_loss'])
+plt.title('model loss')
+plt.ylabel('loss')
+plt.xlabel('epoch')
+plt.legend(['train', 'test'], loc='upper left')
+plt.show()
+
+"""## Saving the model"""
+
+# Saving the model.json
+
+import json
+model_json = model.to_json()
+with open("model.json", "w") as json_file:
+    json_file.write(model_json)
+
+"""## Loading the model"""
+
+# loading json and creating model
+from keras.models import model_from_json
+json_file = open('model.json', 'r')
+loaded_model_json = json_file.read()
+json_file.close()
+loaded_model = model_from_json(loaded_model_json)
+
+# load weights into new model
+loaded_model.load_weights("model/aug_noiseNshift_2class2_np.h5")
+print("Loaded model from disk")
+ 
+# evaluate loaded model on test data
+loaded_model.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['accuracy'])
+score = loaded_model.evaluate(x_testcnn, y_test, verbose=0)
+print("%s: %.2f%%" % (loaded_model.metrics_names[1], score[1]*100))
+
+"""# X. Predicting emotions on the test data"""
+
+len(data3_df)
+
+data_test = pd.DataFrame(columns=['feature'])
+for i in tqdm(range(len(data3_df))):
+    X, sample_rate = librosa.load(data3_df.path[i], res_type='kaiser_fast',duration=input_duration,sr=22050*2,offset=0.5)
+#     X = X[10000:90000]
+    sample_rate = np.array(sample_rate)
+    mfccs = np.mean(librosa.feature.mfcc(y=X, sr=sample_rate, n_mfcc=13), axis=0)
+    feature = mfccs
+    data_test.loc[i] = [feature]
+    
+test_valid = pd.DataFrame(data_test['feature'].values.tolist())
+test_valid = np.array(test_valid)
+test_valid_lb = np.array(data3_df.label)
+lb = LabelEncoder()
+test_valid_lb = np_utils.to_categorical(lb.fit_transform(test_valid_lb))
+test_valid = np.expand_dims(test_valid, axis=2)
+
+preds = loaded_model.predict(test_valid, 
+                         batch_size=16, 
+                         verbose=1)
+
+preds
+
+preds1=preds.argmax(axis=1)
+
+preds1
+
+abc = preds1.astype(int).flatten()
+
+predictions = (lb.inverse_transform((abc)))
+
+preddf = pd.DataFrame({'predictedvalues': predictions})
+preddf[:10]
+
+actual=test_valid_lb.argmax(axis=1)
+abc123 = actual.astype(int).flatten()
+actualvalues = (lb.inverse_transform((abc123)))
+
+actualdf = pd.DataFrame({'actualvalues': actualvalues})
+actualdf[:10]
+
+finaldf = actualdf.join(preddf)
+
+"""## Actual v/s Predicted emotions"""
+
+finaldf[20:40]
+
+finaldf.groupby('actualvalues').count()
+
+finaldf.groupby('predictedvalues').count()
+
+finaldf.to_csv('Predictions.csv', index=False)
+
+def print_confusion_matrix(confusion_matrix, class_names, figsize = (10,7), fontsize=14):
+    """Prints a confusion matrix, as returned by sklearn.metrics.confusion_matrix, as a heatmap.
+    
+    Arguments
+    ---------
+    confusion_matrix: numpy.ndarray
+        The numpy.ndarray object returned from a call to sklearn.metrics.confusion_matrix. 
+        Similarly constructed ndarrays can also be used.
+    class_names: list
+        An ordered list of class names, in the order they index the given confusion matrix.
+    figsize: tuple
+        A 2-long tuple, the first value determining the horizontal size of the ouputted figure,
+        the second determining the vertical size. Defaults to (10,7).
+    fontsize: int
+        Font size for axes labels. Defaults to 14.
+        
+    Returns
+    -------
+    matplotlib.figure.Figure
+        The resulting confusion matrix figure
+    """
+    df_cm = pd.DataFrame(
+        confusion_matrix, index=class_names, columns=class_names, 
+    )
+    fig = plt.figure(figsize=figsize)
+    try:
+        heatmap = sns.heatmap(df_cm, annot=True, fmt="d")
+    except ValueError:
+        raise ValueError("Confusion matrix values must be integers.")
+        
+    heatmap.yaxis.set_ticklabels(heatmap.yaxis.get_ticklabels(), rotation=0, ha='right', fontsize=fontsize)
+    heatmap.xaxis.set_ticklabels(heatmap.xaxis.get_ticklabels(), rotation=45, ha='right', fontsize=fontsize)
+    plt.ylabel('True label')
+    plt.xlabel('Predicted label')
+
+from sklearn.metrics import accuracy_score
+y_true = finaldf.actualvalues
+y_pred = finaldf.predictedvalues
+accuracy_score(y_true, y_pred)*100
+
+from sklearn.metrics import f1_score
+f1_score(y_true, y_pred, average='macro') *100
+
+from sklearn.metrics import confusion_matrix
+c = confusion_matrix(y_true, y_pred)
+c
+
+# Visualize Confusion Matrix 
+
+# class_names = ['male_angry', 'male_calm', 'male_fearful', 'male_happy', 'male_sad']
+# class_names = ['female_angry', 'female_calm', 'female_fearful', 'female_happy', 'female_sad']
+# class_names = ['male_negative', 'male_neutral', 'male_positive']
+class_names = ['male_negative', 'male_positive']
+# class_names = ['female_angry', 'female_calm', 'female_fearful', 'female_happy', 'female_sad', 'male_angry', 'male_calm', 'male_fearful', 'male_happy', 'male_sad']
+
+
+print_confusion_matrix(c, class_names)
\ No newline at end of file
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000..477e90a
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,13 @@
+glob2==0.6
+ipython==7.1.1
+keras==2.2.2
+librosa==0.6.3
+matplotlib==3.0.2
+numpy==1.15.4
+pandas==0.23.4
+plotly==3.4.2
+scipy==1.1.0
+seaborn==0.9.0
+sklearn==0.20.1
+tensorflow==1.9.0
+tqdm==4.28.1
\ No newline at end of file