new.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "g9_aGJP3tvF4"
   },
   "outputs": [],
   "source": [
    "import glob\n",
    "import os\n",
    "import librosa\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import wave\n",
    "import contextlib\n",
    "import pandas as pd\n",
    "\n",
    "from sklearn.metrics import accuracy_score,classification_report,f1_score\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.pyplot import specgram\n",
    "%matplotlib inline\n",
    "plt.style.use('ggplot')\n",
    "\n",
    "plt.rcParams['font.family'] = 'serif'\n",
    "plt.rcParams['font.serif'] = 'Ubuntu'\n",
    "plt.rcParams['font.monospace'] = 'Ubuntu Mono'\n",
    "plt.rcParams['font.size'] = 12\n",
    "plt.rcParams['axes.labelsize'] = 11\n",
    "plt.rcParams['axes.labelweight'] = 'bold'\n",
    "plt.rcParams['axes.titlesize'] = 14\n",
    "plt.rcParams['xtick.labelsize'] = 10\n",
    "plt.rcParams['ytick.labelsize'] = 10\n",
    "plt.rcParams['legend.fontsize'] = 11\n",
    "plt.rcParams['figure.titlesize'] = 13"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ovHpIKfEtvGG"
   },
   "source": [
    "# Features plots - Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "CGCfJPgjtvGM"
   },
   "outputs": [],
   "source": [
    "def load_sound_files(file_paths):\n",
    "    raw_sounds = []\n",
    "    for fp in file_paths:\n",
    "        X,sr = librosa.load(fp)\n",
    "        raw_sounds.append(X)\n",
    "    return raw_sounds\n",
    "\n",
    "def plot_waves(sound_names,raw_sounds):\n",
    "    i = 1\n",
    "    fig = plt.figure(figsize=(25,60), dpi = 900)\n",
    "    for n,f in zip(sound_names,raw_sounds):\n",
    "        plt.subplot(10,1,i)\n",
    "        librosa.display.waveplot(np.array(f),sr=22050)\n",
    "        plt.title(n.title())\n",
    "        i += 1\n",
    "    plt.suptitle('Figure 1: Waveplot',x=0.5, y=0.915,fontsize=18)\n",
    "    plt.show()\n",
    "    \n",
    "def plot_specgram(sound_names,raw_sounds):\n",
    "    i = 1\n",
    "    fig = plt.figure(figsize=(25,60), dpi = 900)\n",
    "    for n,f in zip(sound_names,raw_sounds):\n",
    "        plt.subplot(10,1,i)\n",
    "        specgram(np.array(f), Fs=22050)\n",
    "        plt.title(n.title())\n",
    "        i += 1\n",
    "    plt.suptitle('Figure 2: Spectrogram',x=0.5, y=0.915,fontsize=18)\n",
    "    plt.show()\n",
    "\n",
    "def plot_log_power_specgram(sound_names,raw_sounds):\n",
    "    i = 1\n",
    "    fig = plt.figure(figsize=(25,60), dpi = 900)\n",
    "    for n,f in zip(sound_names,raw_sounds):\n",
    "        plt.subplot(10,1,i)\n",
    "        D = librosa.logamplitude(np.abs(librosa.stft(f))**2, ref_power=np.max)\n",
    "        librosa.display.specshow(D,x_axis='time' ,y_axis='log')\n",
    "        plt.title(n.title())\n",
    "        i += 1\n",
    "    plt.suptitle('Figure 3: Log power spectrogram',x=0.5, y=0.915,fontsize=18)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ndXOjzE0tvGT"
   },
   "source": [
    "# Extracting features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "CRakr1AetvGW"
   },
   "outputs": [],
   "source": [
    "def extract_feature(file_name):\n",
    "    X, sample_rate = librosa.load(file_name)\n",
    "    stft = np.abs(librosa.stft(X))\n",
    "    mfccs = np.mean(librosa.feature.mfcc(y=X, sr=sample_rate, n_mfcc=40).T,axis=0)\n",
    "    chroma = np.mean(librosa.feature.chroma_stft(S=stft, sr=sample_rate).T,axis=0)\n",
    "    mel = np.mean(librosa.feature.melspectrogram(X, sr=sample_rate).T,axis=0)\n",
    "    contrast = np.mean(librosa.feature.spectral_contrast(S=stft, sr=sample_rate).T,axis=0)\n",
    "    tonnetz = np.mean(librosa.feature.tonnetz(y=librosa.effects.harmonic(X), sr=sample_rate).T,axis=0)\n",
    "    #print(chroma.shape)\n",
    "    #print(\"chroma\")\n",
    "    #print(mfccs.shape)\n",
    "    #print(\"mfccs\")\n",
    "    #print(mel.shape)\n",
    "    #print(\"mels\")\n",
    "    #print(contrast.shape)\n",
    "    #print(\"contrast\")\n",
    "    #print(tonnetz.shape)\n",
    "    #print(\"tonnetz\")    \n",
    "    return mfccs,chroma,mel,contrast,tonnetz\n",
    "\n",
    "def parse_audio_files(parent_dir,sub_dirs,file_ext='*.wav'):\n",
    "    d=0\n",
    "    duration1 = []\n",
    "    duration2 = []\n",
    "    for label, sub_dir in enumerate(sub_dirs):\n",
    "        for fn in glob.glob(os.path.join(parent_dir,sub_dir,file_ext)):\n",
    "            with contextlib.closing(wave.open(fn,'r')) as f:\n",
    "                frames = f.getnframes()\n",
    "                rate = f.getframerate()\n",
    "                duration = frames / float(rate)\n",
    "                print(pd.Series(fn.split('_')).unique())\n",
    "                print(d,duration)\n",
    "                d=d+1\n",
    "                print(\" \")\n",
    "                #dn=0\n",
    "                if(sub_dir!='noise_set'):\n",
    "                    #if(duration<0):\n",
    "                    #    dn =dn+1\n",
    "                    duration1.append(duration)  \n",
    "                else:\n",
    "                    duration2.append(duration)\n",
    "            \n",
    "    return duration1,duration2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def parse_audio_files2(parent_dir,sub_dirs,file_ext='*.wav'):\n",
    "    features = np.empty((0,193))\n",
    "    finlabel = np.empty((0,3))\n",
    "    labells=[]\n",
    "    labells1=np.empty(0) \n",
    "    labells2=np.empty(0) \n",
    "    d=0\n",
    "    fns = []\n",
    "    labels=[]\n",
    "    label1s=[]\n",
    "    label2s=[]\n",
    "    for label, sub_dir in enumerate(sub_dirs):\n",
    "        for fn in glob.glob(os.path.join(parent_dir,sub_dir,file_ext)):\n",
    "            mfccs, chroma, mel, contrast,tonnetz = extract_feature(fn)\n",
    "            ext_features = np.hstack([mfccs,chroma,mel,contrast,tonnetz])\n",
    "          #  print(\"ext_features\")\n",
    "          #  print(ext_features.shape)\n",
    "            features = np.vstack([features,ext_features])\n",
    "          #  print(\"features\")\n",
    "          #  print(features.shape)\n",
    "            if(sub_dir!='noise_set'):\n",
    "                    d = d+1\n",
    "                    if (len(fn.split('_'))>=6):\n",
    "                        label1 = \"1\"\n",
    "                    else:\n",
    "                        label1=\"0\"\n",
    "                    label=\"1\"\n",
    "                    if(sub_dir=='TRIM_old'):\n",
    "                        if(fn.split('_')[-1]=='2.wav' ):\n",
    "                            label2=\"2\"\n",
    "                        if(fn.split('_')[3]=='Multiplepeacock'):\n",
    "                            label2 = \"4\"\n",
    "                        else:\n",
    "                            label2 =\"1\"\n",
    "                    else:\n",
    "                        if(fn.split('_')[-1]=='1.wav'):\n",
    "                            label2=\"1\"\n",
    "                        elif(fn.split('_')[-1]=='2.wav'):\n",
    "                            label2 =\"2\" \n",
    "                        elif(fn.split('_')[-1]=='3.wav'):\n",
    "                            label2 =\"3\"\n",
    "                        elif(fn.split('_')[-1]=='MI.wav' or fn.split('_')[-1]=='3or4.wav'):\n",
    "                            label2 =\"4\"                        \n",
    "                        else:\n",
    "                            label2 =\"0\"\n",
    "                            \n",
    "            else:\n",
    "                label =\"0\"\n",
    "                label1=\"1\"\n",
    "                label2 =\"0\"\n",
    "            fns.append(fn.split('_')[1:])\n",
    "            use = np.hstack([label,label1,label2])\n",
    "            finlabel = np.vstack([finlabel,use])\n",
    "            labels.append(label)\n",
    "            label1s.append(label1)\n",
    "            label2s.append(label2)\n",
    "    return(fns, np.array(features), np.array(finlabel),np.array(labels), np.array(label1s), np.array(label2s))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> #  Feed Train & Test Data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "_kg_hide-output": true,
    "colab": {},
    "colab_type": "code",
    "id": "yi23reLvtvGh"
   },
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      "['../input/data-w/data/data/noise' 'set/5-219342-A-38.wav']\n",
      "1220 5.0\n",
      " \n",
      "['../input/data-w/data/data/Chincholi/16' 'Eow' 'Peacock' '2.wav']\n",
      "1221 8.150702947845804\n",
      " \n",
      "['../input/data-w/data/data/Chincholi/13' 'Eow' 'Peacock' '3.wav']\n",
      "1222 6.621360544217687\n",
      " \n",
      "['../input/data-w/data/data/Chincholi/19' 'Eow' 'Peacock' '3.wav']\n",
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      " \n",
      "['../input/data-w/data/data/Chincholi/09' 'Eow' 'Peacock' '2.wav']\n",
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      " \n",
      "['../input/data-w/data/data/Chincholi/20' 'Eow' 'Peacock' '2.wav']\n",
      "1225 9.712222222222222\n",
      " \n",
      "['../input/data-w/data/data/Chincholi/04' 'Eow' 'Peacock' '2.wav']\n",
      "1226 6.070136054421769\n",
      " \n",
      "['../input/data-w/data/data/Chincholi/03' 'Eow' 'Peacock' '3.wav']\n",
      "1227 1.5594557823129251\n",
      " \n",
      "['../input/data-w/data/data/Chincholi/08' 'Eow' 'Peacock' '1.wav']\n",
      "1228 7.910612244897959\n",
      " \n",
      "['../input/data-w/data/data/Chincholi/15' 'Eow' 'Peacock' '2.wav']\n",
      "1229 5.351927437641724\n",
      " \n",
      "['../input/data-w/data/data/Chincholi/14' 'Eow' 'Peacock' 'MI.wav']\n",
      "1230 5.434875283446712\n",
      " \n",
      "['../input/data-w/data/data/Chincholi/07' 'Eow' 'Peacock' 'MI.wav']\n",
      "1231 11.796780045351474\n",
      " \n",
      "['../input/data-w/data/data/Chincholi/12' 'Eow' 'Peacock' '2.wav']\n",
      "1232 9.840498866213151\n",
      " \n",
      "['../input/data-w/data/data/Chincholi/06' 'Eow' 'Peacock' 'MI.wav']\n",
      "1233 11.047460317460317\n",
      " \n",
      "['../input/data-w/data/data/Chincholi/21' 'Eow' 'Peacock' '2.wav']\n",
      "1234 9.992607709750567\n",
      " \n",
      "['../input/data-w/data/data/Chincholi/11' 'Eow' 'Peacock' '1.wav']\n",
      "1235 5.384263038548752\n",
      " \n",
      "['../input/data-w/data/data/Chincholi/05' 'Eow' 'Peacock' '2.wav']\n",
      "1236 7.864285714285714\n",
      " \n",
      "['../input/data-w/data/data/Chincholi/10' 'Eow' 'Peacock' '1.wav']\n",
      "1237 7.32968253968254\n",
      " \n",
      "['../input/data-w/data/data/Chincholi/18' 'Eow' 'Peacock' '1.wav']\n",
      "1238 7.528775510204081\n",
      " \n",
      "['../input/data-w/data/data/Chincholi/01' 'Eow' 'Peacock' '2.wav']\n",
      "1239 5.617006802721089\n",
      " \n",
      "['../input/data-w/data/data/Chincholi/17' 'Eow' 'Peacock' '1.wav']\n",
      "1240 8.308571428571428\n",
      " \n",
      "['../input/data-w/data/data/Chincholi/02' 'Eow' 'Peacock' '2.wav']\n",
      "1241 4.280045351473923\n",
      " \n"
     ]
    }
   ],
   "source": [
    "parent_dir = '../input/data-w/data/data'\n",
    "\n",
    "sub_dirs = ['C_new2','TRIM_old','A_new1','trimmed_new3','noise_set','Chincholi','iid','Kaan'] #train dirs\n",
    "duration1,duration2= parse_audio_files(parent_dir,sub_dirs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "dafr = pd.DataFrame()\n",
    "dafr[\"peacock_audio_durations\"] = pd.Series(duration1)\n",
    "dafr[\"noise_durations\"] = pd.Series(duration2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style=\"whitegrid\")\n",
    "ax = sns.violinplot(data=dafr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/scipy/sparse/lil.py:512: FutureWarning: future versions will not create a writeable array from broadcast_array. Set the writable flag explicitly to avoid this warning.\n",
      "  if not i.flags.writeable or i.dtype not in (np.int32, np.int64):\n",
      "/opt/conda/lib/python3.6/site-packages/scipy/sparse/lil.py:514: FutureWarning: future versions will not create a writeable array from broadcast_array. Set the writable flag explicitly to avoid this warning.\n",
      "  if not j.flags.writeable or j.dtype not in (np.int32, np.int64):\n",
      "/opt/conda/lib/python3.6/site-packages/scipy/sparse/lil.py:512: FutureWarning: future versions will not create a writeable array from broadcast_array. Set the writable flag explicitly to avoid this warning.\n",
      "  if not i.flags.writeable or i.dtype not in (np.int32, np.int64):\n",
      "/opt/conda/lib/python3.6/site-packages/scipy/sparse/lil.py:514: FutureWarning: future versions will not create a writeable array from broadcast_array. Set the writable flag explicitly to avoid this warning.\n",
      "  if not j.flags.writeable or j.dtype not in (np.int32, np.int64):\n",
      "/opt/conda/lib/python3.6/site-packages/librosa/core/pitch.py:146: UserWarning: Trying to estimate tuning from empty frequency set.\n",
      "  warnings.warn('Trying to estimate tuning from empty frequency set.')\n"
     ]
    }
   ],
   "source": [
    "fn,features,final_labels,label,label1,label2 = parse_audio_files2(parent_dir,sub_dirs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1242, 3)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_labels.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n",
      "/opt/conda/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:516: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
      "/opt/conda/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:517: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
      "/opt/conda/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:518: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
      "/opt/conda/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:519: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
      "/opt/conda/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:520: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
      "/opt/conda/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n",
      "/opt/conda/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:541: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
      "/opt/conda/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:542: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
      "/opt/conda/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:543: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
      "/opt/conda/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:544: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
      "/opt/conda/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:545: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
      "/opt/conda/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:550: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['0', '1', '2', '3', '4'], dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import MultiLabelBinarizer,LabelBinarizer\n",
    "from numpy import argmax\n",
    "from keras.utils import to_categorical\n",
    "from keras import initializers\n",
    "\n",
    "one_hot1 = LabelBinarizer()\n",
    "# One-hot encode data\n",
    "label_ = one_hot1.fit_transform(label)\n",
    "label_1 = one_hot1.fit_transform(label1)\n",
    "#print(one_hot1.classes_)print(label_)print(label_1)\n",
    "one_hot2 = MultiLabelBinarizer()\n",
    "# One-hot encode data\n",
    "label_2 = one_hot2.fit_transform(label2)\n",
    "label__2 = to_categorical(label2)\n",
    "#print(label__2)\n",
    "one_hot2.classes_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1242, 5)\n",
      "(1242, 1)\n",
      "(1242, 1)\n",
      "(1242, 5)\n"
     ]
    }
   ],
   "source": [
    "print(label__2.shape)\n",
    "print(label_1.shape)\n",
    "print(label_.shape)\n",
    "print(label_2.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "dafr_labels = pd.DataFrame()\n",
    "dafr_labels[\"file_name\"] = fn\n",
    "dafr_labels[\"peacock\"] = label_\n",
    "dafr_labels[\"disturbance\"] = label_1\n",
    "#dafr_labels[\"single_peacock\"] = [label__2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dafr_labels['peacock'].value_counts().plot(kind='bar')\n",
    "            #,'disturbance','single_peacock']].plot(kind='bar')\n",
    "#plt.xticks(np.arange(0, 51, 5)) \n",
    "plt.yticks(np.arange(0, 1200, 50)) \n",
    "plt.show()\n",
    "dafr_labels['disturbance'].value_counts().plot(kind='bar')\n",
    "plt.yticks(np.arange(0, 1200, 50)) \n",
    "plt.show()\n",
    "#dafr_labels['single_peacock'].value_counts().plot(kind='bar')\n",
    "#plt.yticks(np.arange(0, 1200, 25)) \n",
    "#plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1143\n",
       "1      99\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(label).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    1174\n",
       "0      68\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(label1).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1146\n",
       "1      48\n",
       "2      41\n",
       "4       4\n",
       "3       3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(label2).value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Aj-3YAWttvGr"
   },
   "source": [
    "# MLP keras "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Kgb0pURqtvGt",
    "outputId": "0ea78ba2-b274-4ccb-92dd-c310ad913d73"
   },
   "outputs": [],
   "source": [
    "import keras \n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Activation,Flatten, Dropout, BatchNormalization\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from keras.layers import Conv2D, MaxPooling2D\n",
    "#from keras import regularizers, optimizer\n",
    "import pandas as pd\n",
    "import random\n",
    "import tensorflow as tf\n",
    "from sklearn.metrics import confusion_matrix,precision_score,recall_score,f1_score,multilabel_confusion_matrix\n",
    "from keras.wrappers.scikit_learn import KerasClassifier\n",
    "from keras.utils import np_utils\n",
    "from keras.layers import Input\n",
    "from keras.models import Model\n",
    "from keras.optimizers import Adam\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import KFold\n",
    "from keras import backend as K\n",
    "random.seed(42)\n",
    "np.random.seed(42)\n",
    "tf.set_random_seed(42)\n",
    "\n",
    "my_init = initializers.glorot_uniform(seed=42)\n",
    "session_conf = tf.ConfigProto(intra_op_parallelism_threads=1,\n",
    "                              inter_op_parallelism_threads=1)\n",
    "sess = tf.Session(graph=tf.get_default_graph(), config=session_conf)\n",
    "K.set_session(sess)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "OYlHjiYWtvG1"
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y1_train, y1_test,y2_train,y2_test,y3_train,y3_test = train_test_split(features,\n",
    "                                            label_,label_1,label__2, test_size=0.33, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(832, 5)\n",
      "(410, 5)\n"
     ]
    }
   ],
   "source": [
    "print(y3_train.shape)\n",
    "print(y3_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "0lzGtpAMtvHE"
   },
   "source": [
    "## Multi output neural network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def recall_m(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",
    "def precision_m(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",
    "def f1_m(y_true, y_pred):\n",
    "    precision = precision_m(y_true, y_pred)\n",
    "    recall = recall_m(y_true, y_pred)\n",
    "    return 2*((precision*recall)/(precision+recall+K.epsilon()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def f1(y_true, y_pred):\n",
    "    y_pred = K.round(y_pred)\n",
    "    tp = K.sum(K.cast(y_true*y_pred, 'float'), axis=0)\n",
    "    tn = K.sum(K.cast((1-y_true)*(1-y_pred), 'float'), axis=0)\n",
    "    fp = K.sum(K.cast((1-y_true)*y_pred, 'float'), axis=0)\n",
    "    fn = K.sum(K.cast(y_true*(1-y_pred), 'float'), axis=0)\n",
    "\n",
    "    p = tp / (tp + fp + K.epsilon())\n",
    "    r = tp / (tp + fn + K.epsilon())\n",
    "\n",
    "    f1 = 2*p*r / (p+r+K.epsilon())\n",
    "    f1 = tf.where(tf.is_nan(f1), tf.zeros_like(f1), f1)\n",
    "    return K.mean(f1)\n",
    "\n",
    "def f1_loss(y_true, y_pred):\n",
    "    \n",
    "    tp = K.sum(K.cast(y_true*y_pred, 'float'), axis=0)\n",
    "    tn = K.sum(K.cast((1-y_true)*(1-y_pred), 'float'), axis=0)\n",
    "    fp = K.sum(K.cast((1-y_true)*y_pred, 'float'), axis=0)\n",
    "    fn = K.sum(K.cast(y_true*(1-y_pred), 'float'), axis=0)\n",
    "\n",
    "    p = tp / (tp + fp + K.epsilon())\n",
    "    r = tp / (tp + fn + K.epsilon())\n",
    "\n",
    "    f1 = 2*p*r / (p+r+K.epsilon())\n",
    "    f1 = tf.where(tf.is_nan(f1), tf.zeros_like(f1), f1)\n",
    "    return 1 - K.mean(f1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:5: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(activation=\"relu\", input_dim=193, units=40, kernel_initializer=\"uniform\")`\n",
      "  \"\"\"\n",
      "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:7: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(activation=\"relu\", units=20, kernel_initializer=\"uniform\")`\n",
      "  import sys\n",
      "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:8: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(activation=\"relu\", units=8, kernel_initializer=\"uniform\")`\n",
      "  \n",
      "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:10: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(activation=\"sigmoid\", units=1, kernel_initializer=\"uniform\")`\n",
      "  # Remove the CWD from sys.path while we load stuff.\n",
      "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:11: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(activation=\"sigmoid\", units=1, kernel_initializer=\"uniform\")`\n",
      "  # This is added back by InteractiveShellApp.init_path()\n",
      "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:12: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(activation=\"softmax\", units=5, kernel_initializer=\"uniform\")`\n",
      "  if sys.path[0] == '':\n"
     ]
    }
   ],
   "source": [
    "#create the model\n",
    "inn = Input(shape = (193,))\n",
    "x = Sequential()(inn)\n",
    "# Adding the input layer and the first hidden layer\n",
    "x = Dense(output_dim = 40, init = 'uniform', activation = 'relu', input_dim = 193)(x)\n",
    "# Adding the second hidden layer\n",
    "x=Dense(output_dim = 20, init = 'uniform', activation = 'relu')(x)\n",
    "x= Dense(output_dim = 8, init = 'uniform', activation = 'relu')(x)\n",
    "# Adding the output layer\n",
    "out1 =Dense(output_dim = 1, init = 'uniform', activation = 'sigmoid')(x)\n",
    "out2 =Dense(output_dim = 1, init = 'uniform', activation = 'sigmoid')(x)\n",
    "out3 =Dense(output_dim = 5, init = 'uniform', activation = 'softmax')(x)\n",
    "# Compiling Neural Network\n",
    "model = Model(inputs=[inn], outputs=[out1, out2,out3])\n",
    "#classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])\n",
    "# compile the model\n",
    "model.compile(optimizer='adam', loss=['binary_crossentropy','binary_crossentropy','categorical_crossentropy'],\n",
    "              metrics=[f1_m,f1_m,f1_m])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "class_weights = {0: 1.08377,\n",
    "                1: 25.875,\n",
    "                2: 30.3,\n",
    "                3: 414,\n",
    "                4: 310.5} # w(i) = N/n(i)\n",
    "cw1 = {0:1.,\n",
    "       1:10.} \n",
    "cw2 = {0:10.,\n",
    "       1:1.} "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 582 samples, validate on 250 samples\n",
      "Epoch 1/100\n",
      "582/582 [==============================] - 3s 5ms/step - loss: 8.9964 - dense_4_loss: 1.1681 - dense_5_loss: 0.9665 - dense_6_loss: 6.8618 - dense_4_f1_m: 0.0734 - dense_4_f1_m_1: 0.0734 - dense_4_f1_m_2: 0.0734 - dense_5_f1_m: 0.9498 - dense_5_f1_m_1: 0.9498 - dense_5_f1_m_2: 0.9498 - dense_6_f1_m: 0.0276 - dense_6_f1_m_1: 0.0276 - dense_6_f1_m_2: 0.0276 - val_loss: 10.9308 - val_dense_4_loss: 1.0396 - val_dense_5_loss: 0.9322 - val_dense_6_loss: 8.9590 - val_dense_4_f1_m: 0.0000e+00 - val_dense_4_f1_m_1: 0.0000e+00 - val_dense_4_f1_m_2: 0.0000e+00 - val_dense_5_f1_m: 0.9675 - val_dense_5_f1_m_1: 0.9675 - val_dense_5_f1_m_2: 0.9675 - val_dense_6_f1_m: 0.2719 - val_dense_6_f1_m_1: 0.2719 - val_dense_6_f1_m_2: 0.2719\n",
      "Epoch 2/100\n",
      "582/582 [==============================] - 1s 1ms/step - loss: 7.9660 - dense_4_loss: 0.9488 - dense_5_loss: 0.7037 - dense_6_loss: 6.3134 - dense_4_f1_m: 0.0000e+00 - dense_4_f1_m_1: 0.0000e+00 - dense_4_f1_m_2: 0.0000e+00 - dense_5_f1_m: 0.9728 - dense_5_f1_m_1: 0.9728 - dense_5_f1_m_2: 0.9728 - dense_6_f1_m: 0.5945 - dense_6_f1_m_1: 0.5945 - dense_6_f1_m_2: 0.5945 - val_loss: 10.5639 - val_dense_4_loss: 0.9447 - val_dense_5_loss: 0.9235 - val_dense_6_loss: 8.6956 - val_dense_4_f1_m: 0.0000e+00 - val_dense_4_f1_m_1: 0.0000e+00 - val_dense_4_f1_m_2: 0.0000e+00 - val_dense_5_f1_m: 0.9675 - val_dense_5_f1_m_1: 0.9675 - val_dense_5_f1_m_2: 0.9675 - val_dense_6_f1_m: 0.7116 - val_dense_6_f1_m_1: 0.7116 - val_dense_6_f1_m_2: 0.7116\n",
      "Epoch 3/100\n",
      "582/582 [==============================] - 1s 1ms/step - loss: 7.1545 - dense_4_loss: 0.7761 - dense_5_loss: 0.5687 - dense_6_loss: 5.8097 - dense_4_f1_m: 0.3265 - dense_4_f1_m_1: 0.3265 - dense_4_f1_m_2: 0.3265 - dense_5_f1_m: 0.9730 - dense_5_f1_m_1: 0.9730 - dense_5_f1_m_2: 0.9730 - dense_6_f1_m: 0.7652 - dense_6_f1_m_1: 0.7652 - dense_6_f1_m_2: 0.7652 - val_loss: 11.3625 - val_dense_4_loss: 1.0588 - val_dense_5_loss: 1.1054 - val_dense_6_loss: 9.1982 - val_dense_4_f1_m: 0.2987 - val_dense_4_f1_m_1: 0.2987 - val_dense_4_f1_m_2: 0.2987 - val_dense_5_f1_m: 0.9730 - val_dense_5_f1_m_1: 0.9730 - val_dense_5_f1_m_2: 0.9730 - val_dense_6_f1_m: 0.8677 - val_dense_6_f1_m_1: 0.8677 - val_dense_6_f1_m_2: 0.8677\n",
      "Epoch 4/100\n",
      "582/582 [==============================] - 1s 989us/step - loss: 6.6074 - dense_4_loss: 0.6060 - dense_5_loss: 0.4800 - dense_6_loss: 5.5213 - dense_4_f1_m: 0.4513 - dense_4_f1_m_1: 0.4513 - dense_4_f1_m_2: 0.4513 - dense_5_f1_m: 0.9681 - dense_5_f1_m_1: 0.9681 - dense_5_f1_m_2: 0.9681 - dense_6_f1_m: 0.8767 - dense_6_f1_m_1: 0.8767 - dense_6_f1_m_2: 0.8767 - val_loss: 11.8084 - val_dense_4_loss: 1.0942 - val_dense_5_loss: 1.1616 - val_dense_6_loss: 9.5527 - val_dense_4_f1_m: 0.3640 - val_dense_4_f1_m_1: 0.3640 - val_dense_4_f1_m_2: 0.3640 - val_dense_5_f1_m: 0.9746 - val_dense_5_f1_m_1: 0.9746 - val_dense_5_f1_m_2: 0.9746 - val_dense_6_f1_m: 0.8997 - val_dense_6_f1_m_1: 0.8997 - val_dense_6_f1_m_2: 0.8997\n",
      "Epoch 5/100\n",
      "582/582 [==============================] - 1s 1ms/step - loss: 6.0350 - dense_4_loss: 0.3755 - dense_5_loss: 0.3482 - dense_6_loss: 5.3112 - dense_4_f1_m: 0.4305 - dense_4_f1_m_1: 0.4305 - dense_4_f1_m_2: 0.4305 - dense_5_f1_m: 0.9701 - dense_5_f1_m_1: 0.9701 - dense_5_f1_m_2: 0.9701 - dense_6_f1_m: 0.8965 - dense_6_f1_m_1: 0.8965 - dense_6_f1_m_2: 0.8965 - val_loss: 11.7872 - val_dense_4_loss: 1.0976 - val_dense_5_loss: 1.0576 - val_dense_6_loss: 9.6321 - val_dense_4_f1_m: 0.2760 - val_dense_4_f1_m_1: 0.2760 - val_dense_4_f1_m_2: 0.2760 - val_dense_5_f1_m: 0.9104 - val_dense_5_f1_m_1: 0.9104 - val_dense_5_f1_m_2: 0.9104 - val_dense_6_f1_m: 0.8196 - val_dense_6_f1_m_1: 0.8196 - val_dense_6_f1_m_2: 0.8196\n",
      "Epoch 6/100\n",
      "582/582 [==============================] - 1s 991us/step - loss: 5.7934 - dense_4_loss: 0.2788 - dense_5_loss: 0.2834 - dense_6_loss: 5.2311 - dense_4_f1_m: 0.4541 - dense_4_f1_m_1: 0.4541 - dense_4_f1_m_2: 0.4541 - dense_5_f1_m: 0.9497 - dense_5_f1_m_1: 0.9497 - dense_5_f1_m_2: 0.9497 - dense_6_f1_m: 0.8999 - dense_6_f1_m_1: 0.8999 - dense_6_f1_m_2: 0.8999 - val_loss: 11.8017 - val_dense_4_loss: 1.0747 - val_dense_5_loss: 1.0369 - val_dense_6_loss: 9.6900 - val_dense_4_f1_m: 0.2893 - val_dense_4_f1_m_1: 0.2893 - val_dense_4_f1_m_2: 0.2893 - val_dense_5_f1_m: 0.9118 - val_dense_5_f1_m_1: 0.9118 - val_dense_5_f1_m_2: 0.9118 - val_dense_6_f1_m: 0.8462 - val_dense_6_f1_m_1: 0.8462 - val_dense_6_f1_m_2: 0.8462\n",
      "Epoch 7/100\n",
      "582/582 [==============================] - 1s 1ms/step - loss: 5.4456 - dense_4_loss: 0.2648 - dense_5_loss: 0.2968 - dense_6_loss: 4.8841 - dense_4_f1_m: 0.4296 - dense_4_f1_m_1: 0.4296 - dense_4_f1_m_2: 0.4296 - dense_5_f1_m: 0.9417 - dense_5_f1_m_1: 0.9417 - dense_5_f1_m_2: 0.9417 - dense_6_f1_m: 0.8867 - dense_6_f1_m_1: 0.8867 - dense_6_f1_m_2: 0.8867 - val_loss: 12.1901 - val_dense_4_loss: 1.1110 - val_dense_5_loss: 1.2002 - val_dense_6_loss: 9.8789 - val_dense_4_f1_m: 0.3640 - val_dense_4_f1_m_1: 0.3640 - val_dense_4_f1_m_2: 0.3640 - val_dense_5_f1_m: 0.9636 - val_dense_5_f1_m_1: 0.9636 - val_dense_5_f1_m_2: 0.9636 - val_dense_6_f1_m: 0.9228 - val_dense_6_f1_m_1: 0.9228 - val_dense_6_f1_m_2: 0.9228\n",
      "Epoch 8/100\n",
      "582/582 [==============================] - 1s 982us/step - loss: 5.2895 - dense_4_loss: 0.2430 - dense_5_loss: 0.2987 - dense_6_loss: 4.7479 - dense_4_f1_m: 0.4427 - dense_4_f1_m_1: 0.4427 - dense_4_f1_m_2: 0.4427 - dense_5_f1_m: 0.9599 - dense_5_f1_m_1: 0.9599 - dense_5_f1_m_2: 0.9599 - dense_6_f1_m: 0.9144 - dense_6_f1_m_1: 0.9144 - dense_6_f1_m_2: 0.9144 - val_loss: 11.6341 - val_dense_4_loss: 1.0539 - val_dense_5_loss: 1.0493 - val_dense_6_loss: 9.5309 - val_dense_4_f1_m: 0.3453 - val_dense_4_f1_m_1: 0.3453 - val_dense_4_f1_m_2: 0.3453 - val_dense_5_f1_m: 0.9531 - val_dense_5_f1_m_1: 0.9531 - val_dense_5_f1_m_2: 0.9531 - val_dense_6_f1_m: 0.8719 - val_dense_6_f1_m_1: 0.8719 - val_dense_6_f1_m_2: 0.8719\n",
      "Epoch 9/100\n",
      "582/582 [==============================] - 1s 1ms/step - loss: 5.1570 - dense_4_loss: 0.2085 - dense_5_loss: 0.2816 - dense_6_loss: 4.6669 - dense_4_f1_m: 0.4868 - dense_4_f1_m_1: 0.4868 - dense_4_f1_m_2: 0.4868 - dense_5_f1_m: 0.9553 - dense_5_f1_m_1: 0.9553 - dense_5_f1_m_2: 0.9553 - dense_6_f1_m: 0.8894 - dense_6_f1_m_1: 0.8894 - dense_6_f1_m_2: 0.8894 - val_loss: 11.6439 - val_dense_4_loss: 1.0414 - val_dense_5_loss: 1.0365 - val_dense_6_loss: 9.5659 - val_dense_4_f1_m: 0.3320 - val_dense_4_f1_m_1: 0.3320 - val_dense_4_f1_m_2: 0.3320 - val_dense_5_f1_m: 0.9505 - val_dense_5_f1_m_1: 0.9505 - val_dense_5_f1_m_2: 0.9505 - val_dense_6_f1_m: 0.8853 - val_dense_6_f1_m_1: 0.8853 - val_dense_6_f1_m_2: 0.8853\n",
      "Epoch 10/100\n",
      "582/582 [==============================] - 1s 1ms/step - loss: 4.9172 - dense_4_loss: 0.1856 - dense_5_loss: 0.2436 - dense_6_loss: 4.4881 - dense_4_f1_m: 0.5830 - dense_4_f1_m_1: 0.5830 - dense_4_f1_m_2: 0.5830 - dense_5_f1_m: 0.9679 - dense_5_f1_m_1: 0.9679 - dense_5_f1_m_2: 0.9679 - dense_6_f1_m: 0.9295 - dense_6_f1_m_1: 0.9295 - dense_6_f1_m_2: 0.9295 - val_loss: 11.5792 - val_dense_4_loss: 1.0440 - val_dense_5_loss: 1.0537 - val_dense_6_loss: 9.4815 - val_dense_4_f1_m: 0.3720 - val_dense_4_f1_m_1: 0.3720 - val_dense_4_f1_m_2: 0.3720 - val_dense_5_f1_m: 0.9577 - val_dense_5_f1_m_1: 0.9577 - val_dense_5_f1_m_2: 0.9577 - val_dense_6_f1_m: 0.8970 - val_dense_6_f1_m_1: 0.8970 - val_dense_6_f1_m_2: 0.8970\n",
      "Epoch 11/100\n",
      "582/582 [==============================] - 1s 1ms/step - loss: 4.8954 - dense_4_loss: 0.1726 - dense_5_loss: 0.2438 - dense_6_loss: 4.4790 - dense_4_f1_m: 0.5004 - dense_4_f1_m_1: 0.5004 - dense_4_f1_m_2: 0.5004 - dense_5_f1_m: 0.9677 - dense_5_f1_m_1: 0.9677 - dense_5_f1_m_2: 0.9677 - dense_6_f1_m: 0.8893 - dense_6_f1_m_1: 0.8893 - dense_6_f1_m_2: 0.8893 - val_loss: 12.2599 - val_dense_4_loss: 1.2627 - val_dense_5_loss: 1.2639 - val_dense_6_loss: 9.7333 - val_dense_4_f1_m: 0.3467 - val_dense_4_f1_m_1: 0.3467 - val_dense_4_f1_m_2: 0.3467 - val_dense_5_f1_m: 0.9730 - val_dense_5_f1_m_1: 0.9730 - val_dense_5_f1_m_2: 0.9730 - val_dense_6_f1_m: 0.9375 - val_dense_6_f1_m_1: 0.9375 - val_dense_6_f1_m_2: 0.9375\n",
      "Epoch 12/100\n",
      "582/582 [==============================] - 1s 1ms/step - loss: 4.7957 - dense_4_loss: 0.1940 - dense_5_loss: 0.2522 - dense_6_loss: 4.3495 - dense_4_f1_m: 0.4642 - dense_4_f1_m_1: 0.4642 - dense_4_f1_m_2: 0.4642 - dense_5_f1_m: 0.9664 - dense_5_f1_m_1: 0.9664 - dense_5_f1_m_2: 0.9664 - dense_6_f1_m: 0.9218 - dense_6_f1_m_1: 0.9218 - dense_6_f1_m_2: 0.9218 - val_loss: 11.7322 - val_dense_4_loss: 1.0808 - val_dense_5_loss: 1.1064 - val_dense_6_loss: 9.5450 - val_dense_4_f1_m: 0.3787 - val_dense_4_f1_m_1: 0.3787 - val_dense_4_f1_m_2: 0.3787 - val_dense_5_f1_m: 0.9654 - val_dense_5_f1_m_1: 0.9654 - val_dense_5_f1_m_2: 0.9654 - val_dense_6_f1_m: 0.9009 - val_dense_6_f1_m_1: 0.9009 - val_dense_6_f1_m_2: 0.9009\n",
      "Epoch 13/100\n",
      "582/582 [==============================] - 1s 1ms/step - loss: 4.7459 - dense_4_loss: 0.1687 - dense_5_loss: 0.2477 - dense_6_loss: 4.3294 - dense_4_f1_m: 0.4557 - dense_4_f1_m_1: 0.4557 - dense_4_f1_m_2: 0.4557 - dense_5_f1_m: 0.9554 - dense_5_f1_m_1: 0.9554 - dense_5_f1_m_2: 0.9554 - dense_6_f1_m: 0.9037 - dense_6_f1_m_1: 0.9037 - dense_6_f1_m_2: 0.9037 - val_loss: 11.7016 - val_dense_4_loss: 1.1280 - val_dense_5_loss: 1.0893 - val_dense_6_loss: 9.4844 - val_dense_4_f1_m: 0.3253 - val_dense_4_f1_m_1: 0.3253 - val_dense_4_f1_m_2: 0.3253 - val_dense_5_f1_m: 0.9434 - val_dense_5_f1_m_1: 0.9434 - val_dense_5_f1_m_2: 0.9434 - val_dense_6_f1_m: 0.8828 - val_dense_6_f1_m_1: 0.8828 - val_dense_6_f1_m_2: 0.8828\n",
      "Epoch 14/100\n",
      "582/582 [==============================] - 1s 1ms/step - loss: 4.4613 - dense_4_loss: 0.1577 - dense_5_loss: 0.2215 - dense_6_loss: 4.0822 - dense_4_f1_m: 0.5819 - dense_4_f1_m_1: 0.5819 - dense_4_f1_m_2: 0.5819 - dense_5_f1_m: 0.9745 - dense_5_f1_m_1: 0.9745 - dense_5_f1_m_2: 0.9745 - dense_6_f1_m: 0.9301 - dense_6_f1_m_1: 0.9301 - dense_6_f1_m_2: 0.9301 - val_loss: 12.3151 - val_dense_4_loss: 1.1639 - val_dense_5_loss: 1.1988 - val_dense_6_loss: 9.9524 - val_dense_4_f1_m: 0.3587 - val_dense_4_f1_m_1: 0.3587 - val_dense_4_f1_m_2: 0.3587 - val_dense_5_f1_m: 0.9636 - val_dense_5_f1_m_1: 0.9636 - val_dense_5_f1_m_2: 0.9636 - val_dense_6_f1_m: 0.9039 - val_dense_6_f1_m_1: 0.9039 - val_dense_6_f1_m_2: 0.9039\n",
      "Epoch 15/100\n",
      "582/582 [==============================] - 1s 1ms/step - loss: 4.3799 - dense_4_loss: 0.1441 - dense_5_loss: 0.2122 - dense_6_loss: 4.0236 - dense_4_f1_m: 0.4437 - dense_4_f1_m_1: 0.4437 - dense_4_f1_m_2: 0.4437 - dense_5_f1_m: 0.9648 - dense_5_f1_m_1: 0.9648 - dense_5_f1_m_2: 0.9648 - dense_6_f1_m: 0.8982 - dense_6_f1_m_1: 0.8982 - dense_6_f1_m_2: 0.8982 - val_loss: 12.7316 - val_dense_4_loss: 1.2669 - val_dense_5_loss: 1.3221 - val_dense_6_loss: 10.1425 - val_dense_4_f1_m: 0.3533 - val_dense_4_f1_m_1: 0.3533 - val_dense_4_f1_m_2: 0.3533 - val_dense_5_f1_m: 0.9683 - val_dense_5_f1_m_1: 0.9683 - val_dense_5_f1_m_2: 0.9683 - val_dense_6_f1_m: 0.9304 - val_dense_6_f1_m_1: 0.9304 - val_dense_6_f1_m_2: 0.9304\n",
      "Epoch 16/100\n",
      "582/582 [==============================] - 1s 1ms/step - loss: 4.2503 - dense_4_loss: 0.1202 - dense_5_loss: 0.2325 - dense_6_loss: 3.8976 - dense_4_f1_m: 0.4970 - dense_4_f1_m_1: 0.4970 - dense_4_f1_m_2: 0.4970 - dense_5_f1_m: 0.9724 - dense_5_f1_m_1: 0.9724 - dense_5_f1_m_2: 0.9724 - dense_6_f1_m: 0.9233 - dense_6_f1_m_1: 0.9233 - dense_6_f1_m_2: 0.9233 - val_loss: 11.7428 - val_dense_4_loss: 1.1292 - val_dense_5_loss: 1.0898 - val_dense_6_loss: 9.5237 - val_dense_4_f1_m: 0.3720 - val_dense_4_f1_m_1: 0.3720 - val_dense_4_f1_m_2: 0.3720 - val_dense_5_f1_m: 0.9552 - val_dense_5_f1_m_1: 0.9552 - val_dense_5_f1_m_2: 0.9552 - val_dense_6_f1_m: 0.9133 - val_dense_6_f1_m_1: 0.9133 - val_dense_6_f1_m_2: 0.9133\n",
      "Epoch 17/100\n",
      "582/582 [==============================] - 1s 1ms/step - loss: 4.2484 - dense_4_loss: 0.1237 - dense_5_loss: 0.2114 - dense_6_loss: 3.9133 - dense_4_f1_m: 0.5452 - dense_4_f1_m_1: 0.5452 - dense_4_f1_m_2: 0.5452 - dense_5_f1_m: 0.9736 - dense_5_f1_m_1: 0.9736 - dense_5_f1_m_2: 0.9736 - dense_6_f1_m: 0.9366 - dense_6_f1_m_1: 0.9366 - dense_6_f1_m_2: 0.9366 - val_loss: 13.0621 - val_dense_4_loss: 1.2399 - val_dense_5_loss: 1.2776 - val_dense_6_loss: 10.5446 - val_dense_4_f1_m: 0.3800 - val_dense_4_f1_m_1: 0.3800 - val_dense_4_f1_m_2: 0.3800 - val_dense_5_f1_m: 0.9686 - val_dense_5_f1_m_1: 0.9686 - val_dense_5_f1_m_2: 0.9686 - val_dense_6_f1_m: 0.9433 - val_dense_6_f1_m_1: 0.9433 - val_dense_6_f1_m_2: 0.9433\n",
      "Epoch 18/100\n",
      "582/582 [==============================] - 1s 1ms/step - loss: 4.1912 - dense_4_loss: 0.1345 - dense_5_loss: 0.2132 - dense_6_loss: 3.8436 - dense_4_f1_m: 0.5587 - dense_4_f1_m_1: 0.5587 - dense_4_f1_m_2: 0.5587 - dense_5_f1_m: 0.9682 - dense_5_f1_m_1: 0.9682 - dense_5_f1_m_2: 0.9682 - dense_6_f1_m: 0.9239 - dense_6_f1_m_1: 0.9239 - dense_6_f1_m_2: 0.9239 - val_loss: 12.1604 - val_dense_4_loss: 1.1545 - val_dense_5_loss: 1.1679 - val_dense_6_loss: 9.8381 - val_dense_4_f1_m: 0.3640 - val_dense_4_f1_m_1: 0.3640 - val_dense_4_f1_m_2: 0.3640 - val_dense_5_f1_m: 0.9680 - val_dense_5_f1_m_1: 0.9680 - val_dense_5_f1_m_2: 0.9680 - val_dense_6_f1_m: 0.9111 - val_dense_6_f1_m_1: 0.9111 - val_dense_6_f1_m_2: 0.9111\n",
      "Epoch 19/100\n",
      "582/582 [==============================] - 1s 1ms/step - loss: 4.0319 - dense_4_loss: 0.1185 - dense_5_loss: 0.2076 - dense_6_loss: 3.7058 - dense_4_f1_m: 0.5143 - dense_4_f1_m_1: 0.5143 - dense_4_f1_m_2: 0.5143 - dense_5_f1_m: 0.9748 - dense_5_f1_m_1: 0.9748 - dense_5_f1_m_2: 0.9748 - dense_6_f1_m: 0.9201 - dense_6_f1_m_1: 0.9201 - dense_6_f1_m_2: 0.9201 - val_loss: 12.6194 - val_dense_4_loss: 1.3212 - val_dense_5_loss: 1.2870 - val_dense_6_loss: 10.0112 - val_dense_4_f1_m: 0.3533 - val_dense_4_f1_m_1: 0.3533 - val_dense_4_f1_m_2: 0.3533 - val_dense_5_f1_m: 0.9683 - val_dense_5_f1_m_1: 0.9683 - val_dense_5_f1_m_2: 0.9683 - val_dense_6_f1_m: 0.9414 - val_dense_6_f1_m_1: 0.9414 - val_dense_6_f1_m_2: 0.9414\n",
      "Epoch 20/100\n",
      "582/582 [==============================] - 1s 974us/step - loss: 4.1715 - dense_4_loss: 0.1895 - dense_5_loss: 0.2715 - dense_6_loss: 3.7105 - dense_4_f1_m: 0.4660 - dense_4_f1_m_1: 0.4660 - dense_4_f1_m_2: 0.4660 - dense_5_f1_m: 0.9668 - dense_5_f1_m_1: 0.9668 - dense_5_f1_m_2: 0.9668 - dense_6_f1_m: 0.9202 - dense_6_f1_m_1: 0.9202 - dense_6_f1_m_2: 0.9202 - val_loss: 12.8051 - val_dense_4_loss: 1.1970 - val_dense_5_loss: 1.1831 - val_dense_6_loss: 10.4250 - val_dense_4_f1_m: 0.4120 - val_dense_4_f1_m_1: 0.4120 - val_dense_4_f1_m_2: 0.4120 - val_dense_5_f1_m: 0.9704 - val_dense_5_f1_m_1: 0.9704 - val_dense_5_f1_m_2: 0.9704 - val_dense_6_f1_m: 0.9311 - val_dense_6_f1_m_1: 0.9311 - val_dense_6_f1_m_2: 0.9311\n",
      "Epoch 21/100\n",
      "582/582 [==============================] - 1s 1ms/step - loss: 3.7367 - dense_4_loss: 0.1009 - dense_5_loss: 0.2127 - dense_6_loss: 3.4231 - dense_4_f1_m: 0.5275 - dense_4_f1_m_1: 0.5275 - dense_4_f1_m_2: 0.5275 - dense_5_f1_m: 0.9739 - dense_5_f1_m_1: 0.9739 - dense_5_f1_m_2: 0.9739 - dense_6_f1_m: 0.9250 - dense_6_f1_m_1: 0.9250 - dense_6_f1_m_2: 0.9250 - val_loss: 13.8423 - val_dense_4_loss: 1.2567 - val_dense_5_loss: 1.2323 - val_dense_6_loss: 11.3534 - val_dense_4_f1_m: 0.4067 - val_dense_4_f1_m_1: 0.4067 - val_dense_4_f1_m_2: 0.4067 - val_dense_5_f1_m: 0.9683 - val_dense_5_f1_m_1: 0.9683 - val_dense_5_f1_m_2: 0.9683 - val_dense_6_f1_m: 0.9404 - val_dense_6_f1_m_1: 0.9404 - val_dense_6_f1_m_2: 0.9404\n",
      "Epoch 22/100\n",
      "582/582 [==============================] - 1s 1ms/step - loss: 3.6906 - dense_4_loss: 0.1050 - dense_5_loss: 0.2144 - dense_6_loss: 3.3711 - dense_4_f1_m: 0.5268 - dense_4_f1_m_1: 0.5268 - dense_4_f1_m_2: 0.5268 - dense_5_f1_m: 0.9751 - dense_5_f1_m_1: 0.9751 - dense_5_f1_m_2: 0.9751 - dense_6_f1_m: 0.9380 - dense_6_f1_m_1: 0.9380 - dense_6_f1_m_2: 0.9380 - val_loss: 16.4515 - val_dense_4_loss: 1.8031 - val_dense_5_loss: 1.6813 - val_dense_6_loss: 12.9671 - val_dense_4_f1_m: 0.3333 - val_dense_4_f1_m_1: 0.3333 - val_dense_4_f1_m_2: 0.3333 - val_dense_5_f1_m: 0.9712 - val_dense_5_f1_m_1: 0.9712 - val_dense_5_f1_m_2: 0.9712 - val_dense_6_f1_m: 0.9415 - val_dense_6_f1_m_1: 0.9415 - val_dense_6_f1_m_2: 0.9415\n",
      "Epoch 23/100\n",
      "582/582 [==============================] - 1s 1ms/step - loss: 3.7072 - dense_4_loss: 0.1422 - dense_5_loss: 0.2300 - dense_6_loss: 3.3350 - dense_4_f1_m: 0.4464 - dense_4_f1_m_1: 0.4464 - dense_4_f1_m_2: 0.4464 - dense_5_f1_m: 0.9720 - dense_5_f1_m_1: 0.9720 - dense_5_f1_m_2: 0.9720 - dense_6_f1_m: 0.9271 - dense_6_f1_m_1: 0.9271 - dense_6_f1_m_2: 0.9271 - val_loss: 12.9454 - val_dense_4_loss: 1.2176 - val_dense_5_loss: 1.1575 - val_dense_6_loss: 10.5703 - val_dense_4_f1_m: 0.3440 - val_dense_4_f1_m_1: 0.3440 - val_dense_4_f1_m_2: 0.3440 - val_dense_5_f1_m: 0.9524 - val_dense_5_f1_m_1: 0.9524 - val_dense_5_f1_m_2: 0.9524 - val_dense_6_f1_m: 0.8642 - val_dense_6_f1_m_1: 0.8642 - val_dense_6_f1_m_2: 0.8642\n",
      "Epoch 24/100\n",
      "582/582 [==============================] - 1s 1ms/step - loss: 3.5219 - dense_4_loss: 0.0842 - dense_5_loss: 0.1865 - dense_6_loss: 3.2512 - dense_4_f1_m: 0.5435 - dense_4_f1_m_1: 0.5435 - dense_4_f1_m_2: 0.5435 - dense_5_f1_m: 0.9735 - dense_5_f1_m_1: 0.9735 - dense_5_f1_m_2: 0.9735 - dense_6_f1_m: 0.9293 - dense_6_f1_m_1: 0.9293 - dense_6_f1_m_2: 0.9293 - val_loss: 16.1247 - val_dense_4_loss: 1.4668 - val_dense_5_loss: 1.4042 - val_dense_6_loss: 13.2538 - val_dense_4_f1_m: 0.3533 - val_dense_4_f1_m_1: 0.3533 - val_dense_4_f1_m_2: 0.3533 - val_dense_5_f1_m: 0.9683 - val_dense_5_f1_m_1: 0.9683 - val_dense_5_f1_m_2: 0.9683 - val_dense_6_f1_m: 0.9331 - val_dense_6_f1_m_1: 0.9331 - val_dense_6_f1_m_2: 0.9331\n",
      "Epoch 25/100\n",
      "582/582 [==============================] - 1s 995us/step - loss: 6.6550 - dense_4_loss: 0.4198 - dense_5_loss: 0.4283 - dense_6_loss: 5.8069 - dense_4_f1_m: 0.4730 - dense_4_f1_m_1: 0.4730 - dense_4_f1_m_2: 0.4730 - dense_5_f1_m: 0.9381 - dense_5_f1_m_1: 0.9381 - dense_5_f1_m_2: 0.9381 - dense_6_f1_m: 0.8770 - dense_6_f1_m_1: 0.8770 - dense_6_f1_m_2: 0.8770 - val_loss: 11.7022 - val_dense_4_loss: 1.1307 - val_dense_5_loss: 1.0906 - val_dense_6_loss: 9.4809 - val_dense_4_f1_m: 0.4320 - val_dense_4_f1_m_1: 0.4320 - val_dense_4_f1_m_2: 0.4320 - val_dense_5_f1_m: 0.9771 - val_dense_5_f1_m_1: 0.9771 - val_dense_5_f1_m_2: 0.9771 - val_dense_6_f1_m: 0.9389 - val_dense_6_f1_m_1: 0.9389 - val_dense_6_f1_m_2: 0.9389\n",
      "Epoch 26/100\n",
      "582/582 [==============================] - 1s 1ms/step - loss: 4.9045 - dense_4_loss: 0.1214 - dense_5_loss: 0.1920 - dense_6_loss: 4.5911 - dense_4_f1_m: 0.5464 - dense_4_f1_m_1: 0.5464 - dense_4_f1_m_2: 0.5464 - dense_5_f1_m: 0.9714 - dense_5_f1_m_1: 0.9714 - dense_5_f1_m_2: 0.9714 - dense_6_f1_m: 0.9392 - dense_6_f1_m_1: 0.9392 - dense_6_f1_m_2: 0.9392 - val_loss: 13.0834 - val_dense_4_loss: 1.5347 - val_dense_5_loss: 1.3973 - val_dense_6_loss: 10.1514 - val_dense_4_f1_m: 0.3467 - val_dense_4_f1_m_1: 0.3467 - val_dense_4_f1_m_2: 0.3467 - val_dense_5_f1_m: 0.9686 - val_dense_5_f1_m_1: 0.9686 - val_dense_5_f1_m_2: 0.9686 - val_dense_6_f1_m: 0.9450 - val_dense_6_f1_m_1: 0.9450 - val_dense_6_f1_m_2: 0.9450\n",
      "Epoch 27/100\n",
      "582/582 [==============================] - 1s 994us/step - loss: 4.5057 - dense_4_loss: 0.1479 - dense_5_loss: 0.1817 - dense_6_loss: 4.1762 - dense_4_f1_m: 0.5613 - dense_4_f1_m_1: 0.5613 - dense_4_f1_m_2: 0.5613 - dense_5_f1_m: 0.9804 - dense_5_f1_m_1: 0.9804 - dense_5_f1_m_2: 0.9804 - dense_6_f1_m: 0.9463 - dense_6_f1_m_1: 0.9463 - dense_6_f1_m_2: 0.9463 - val_loss: 14.2628 - val_dense_4_loss: 1.3636 - val_dense_5_loss: 1.3818 - val_dense_6_loss: 11.5174 - val_dense_4_f1_m: 0.4067 - val_dense_4_f1_m_1: 0.4067 - val_dense_4_f1_m_2: 0.4067 - val_dense_5_f1_m: 0.9706 - val_dense_5_f1_m_1: 0.9706 - val_dense_5_f1_m_2: 0.9706 - val_dense_6_f1_m: 0.9439 - val_dense_6_f1_m_1: 0.9439 - val_dense_6_f1_m_2: 0.9439\n",
      "Epoch 28/100\n",
      "582/582 [==============================] - 1s 1ms/step - loss: 6.1328 - dense_4_loss: 0.3513 - dense_5_loss: 0.3541 - dense_6_loss: 5.4273 - dense_4_f1_m: 0.4924 - dense_4_f1_m_1: 0.4924 - dense_4_f1_m_2: 0.4924 - dense_5_f1_m: 0.9358 - dense_5_f1_m_1: 0.9358 - dense_5_f1_m_2: 0.9358 - dense_6_f1_m: 0.8756 - dense_6_f1_m_1: 0.8756 - dense_6_f1_m_2: 0.8756 - val_loss: 12.3396 - val_dense_4_loss: 1.1442 - val_dense_5_loss: 0.9543 - val_dense_6_loss: 10.2410 - val_dense_4_f1_m: 0.3560 - val_dense_4_f1_m_1: 0.3560 - val_dense_4_f1_m_2: 0.3560 - val_dense_5_f1_m: 0.9415 - val_dense_5_f1_m_1: 0.9415 - val_dense_5_f1_m_2: 0.9415 - val_dense_6_f1_m: 0.9046 - val_dense_6_f1_m_1: 0.9046 - val_dense_6_f1_m_2: 0.9046\n",
      "Epoch 29/100\n",
      "582/582 [==============================] - 1s 1ms/step - loss: 5.0242 - dense_4_loss: 0.1648 - dense_5_loss: 0.2338 - dense_6_loss: 4.6256 - dense_4_f1_m: 0.4815 - dense_4_f1_m_1: 0.4815 - dense_4_f1_m_2: 0.4815 - dense_5_f1_m: 0.9606 - dense_5_f1_m_1: 0.9606 - dense_5_f1_m_2: 0.9606 - dense_6_f1_m: 0.9232 - dense_6_f1_m_1: 0.9232 - dense_6_f1_m_2: 0.9232 - val_loss: 12.6872 - val_dense_4_loss: 1.2240 - val_dense_5_loss: 1.1517 - val_dense_6_loss: 10.3114 - val_dense_4_f1_m: 0.3507 - val_dense_4_f1_m_1: 0.3507 - val_dense_4_f1_m_2: 0.3507 - val_dense_5_f1_m: 0.9486 - val_dense_5_f1_m_1: 0.9486 - val_dense_5_f1_m_2: 0.9486 - val_dense_6_f1_m: 0.9107 - val_dense_6_f1_m_1: 0.9107 - val_dense_6_f1_m_2: 0.9107\n",
      "Epoch 30/100\n",
      "582/582 [==============================] - 1s 1ms/step - loss: 4.6739 - dense_4_loss: 0.1092 - dense_5_loss: 0.1920 - dense_6_loss: 4.3727 - dense_4_f1_m: 0.5326 - dense_4_f1_m_1: 0.5326 - dense_4_f1_m_2: 0.5326 - dense_5_f1_m: 0.9689 - dense_5_f1_m_1: 0.9689 - dense_5_f1_m_2: 0.9689 - dense_6_f1_m: 0.9363 - dense_6_f1_m_1: 0.9363 - dense_6_f1_m_2: 0.9363 - val_loss: 13.2979 - val_dense_4_loss: 1.3129 - val_dense_5_loss: 1.2664 - val_dense_6_loss: 10.7186 - val_dense_4_f1_m: 0.3987 - val_dense_4_f1_m_1: 0.3987 - val_dense_4_f1_m_2: 0.3987 - val_dense_5_f1_m: 0.9607 - val_dense_5_f1_m_1: 0.9607 - val_dense_5_f1_m_2: 0.9607 - val_dense_6_f1_m: 0.9245 - val_dense_6_f1_m_1: 0.9245 - val_dense_6_f1_m_2: 0.9245\n",
      "Epoch 31/100\n",
      "582/582 [==============================] - 1s 1ms/step - loss: 4.4597 - dense_4_loss: 0.0901 - dense_5_loss: 0.1906 - dense_6_loss: 4.1791 - dense_4_f1_m: 0.5521 - dense_4_f1_m_1: 0.5521 - dense_4_f1_m_2: 0.5521 - dense_5_f1_m: 0.9754 - dense_5_f1_m_1: 0.9754 - dense_5_f1_m_2: 0.9754 - dense_6_f1_m: 0.9373 - dense_6_f1_m_1: 0.9373 - dense_6_f1_m_2: 0.9373 - val_loss: 14.0564 - val_dense_4_loss: 1.4929 - val_dense_5_loss: 1.4236 - val_dense_6_loss: 11.1399 - val_dense_4_f1_m: 0.3533 - val_dense_4_f1_m_1: 0.3533 - val_dense_4_f1_m_2: 0.3533 - val_dense_5_f1_m: 0.9683 - val_dense_5_f1_m_1: 0.9683 - val_dense_5_f1_m_2: 0.9683 - val_dense_6_f1_m: 0.9443 - val_dense_6_f1_m_1: 0.9443 - val_dense_6_f1_m_2: 0.9443\n",
      "Epoch 32/100\n",
      "582/582 [==============================] - 1s 988us/step - loss: 4.4317 - dense_4_loss: 0.1346 - dense_5_loss: 0.2028 - dense_6_loss: 4.0943 - dense_4_f1_m: 0.5193 - dense_4_f1_m_1: 0.5193 - dense_4_f1_m_2: 0.5193 - dense_5_f1_m: 0.9737 - dense_5_f1_m_1: 0.9737 - dense_5_f1_m_2: 0.9737 - dense_6_f1_m: 0.9287 - dense_6_f1_m_1: 0.9287 - dense_6_f1_m_2: 0.9287 - val_loss: 14.3148 - val_dense_4_loss: 1.5226 - val_dense_5_loss: 1.4869 - val_dense_6_loss: 11.3053 - val_dense_4_f1_m: 0.3533 - val_dense_4_f1_m_1: 0.3533 - val_dense_4_f1_m_2: 0.3533 - val_dense_5_f1_m: 0.9683 - val_dense_5_f1_m_1: 0.9683 - val_dense_5_f1_m_2: 0.9683 - val_dense_6_f1_m: 0.9475 - val_dense_6_f1_m_1: 0.9475 - val_dense_6_f1_m_2: 0.9475\n",
      "Epoch 33/100\n",
      "582/582 [==============================] - 1s 1ms/step - loss: 4.1522 - dense_4_loss: 0.1072 - dense_5_loss: 0.2035 - dense_6_loss: 3.8416 - dense_4_f1_m: 0.4868 - dense_4_f1_m_1: 0.4868 - dense_4_f1_m_2: 0.4868 - dense_5_f1_m: 0.9779 - dense_5_f1_m_1: 0.9779 - dense_5_f1_m_2: 0.9779 - dense_6_f1_m: 0.9405 - dense_6_f1_m_1: 0.9405 - dense_6_f1_m_2: 0.9405 - val_loss: 14.2002 - val_dense_4_loss: 1.3897 - val_dense_5_loss: 1.3561 - val_dense_6_loss: 11.4543 - val_dense_4_f1_m: 0.4200 - val_dense_4_f1_m_1: 0.4200 - val_dense_4_f1_m_2: 0.4200 - val_dense_5_f1_m: 0.9706 - val_dense_5_f1_m_1: 0.9706 - val_dense_5_f1_m_2: 0.9706 - val_dense_6_f1_m: 0.9301 - val_dense_6_f1_m_1: 0.9301 - val_dense_6_f1_m_2: 0.9301\n",
      "Epoch 34/100\n",
      "582/582 [==============================] - 1s 989us/step - loss: 3.8588 - dense_4_loss: 0.0852 - dense_5_loss: 0.2021 - dense_6_loss: 3.5715 - dense_4_f1_m: 0.5613 - dense_4_f1_m_1: 0.5613 - dense_4_f1_m_2: 0.5613 - dense_5_f1_m: 0.9810 - dense_5_f1_m_1: 0.9810 - dense_5_f1_m_2: 0.9810 - dense_6_f1_m: 0.9411 - dense_6_f1_m_1: 0.9411 - dense_6_f1_m_2: 0.9411 - val_loss: 15.8199 - val_dense_4_loss: 1.6461 - val_dense_5_loss: 1.5671 - val_dense_6_loss: 12.6066 - val_dense_4_f1_m: 0.3533 - val_dense_4_f1_m_1: 0.3533 - val_dense_4_f1_m_2: 0.3533 - val_dense_5_f1_m: 0.9706 - val_dense_5_f1_m_1: 0.9706 - val_dense_5_f1_m_2: 0.9706 - val_dense_6_f1_m: 0.9439 - val_dense_6_f1_m_1: 0.9439 - val_dense_6_f1_m_2: 0.9439\n",
      "Epoch 35/100\n",
      "582/582 [==============================] - 1s 1ms/step - loss: 3.7638 - dense_4_loss: 0.1008 - dense_5_loss: 0.2012 - dense_6_loss: 3.4618 - dense_4_f1_m: 0.5258 - dense_4_f1_m_1: 0.5258 - dense_4_f1_m_2: 0.5258 - dense_5_f1_m: 0.9774 - dense_5_f1_m_1: 0.9774 - dense_5_f1_m_2: 0.9774 - dense_6_f1_m: 0.9355 - dense_6_f1_m_1: 0.9355 - dense_6_f1_m_2: 0.9355 - val_loss: 15.0930 - val_dense_4_loss: 1.4951 - val_dense_5_loss: 1.2890 - val_dense_6_loss: 12.3088 - val_dense_4_f1_m: 0.3400 - val_dense_4_f1_m_1: 0.3400 - val_dense_4_f1_m_2: 0.3400 - val_dense_5_f1_m: 0.9706 - val_dense_5_f1_m_1: 0.9706 - val_dense_5_f1_m_2: 0.9706 - val_dense_6_f1_m: 0.9378 - val_dense_6_f1_m_1: 0.9378 - val_dense_6_f1_m_2: 0.9378\n",
      "Epoch 36/100\n",
      "582/582 [==============================] - 1s 1ms/step - loss: 3.4630 - dense_4_loss: 0.0855 - dense_5_loss: 0.1950 - dense_6_loss: 3.1825 - dense_4_f1_m: 0.5378 - dense_4_f1_m_1: 0.5378 - dense_4_f1_m_2: 0.5378 - dense_5_f1_m: 0.9808 - dense_5_f1_m_1: 0.9808 - dense_5_f1_m_2: 0.9808 - dense_6_f1_m: 0.9497 - dense_6_f1_m_1: 0.9497 - dense_6_f1_m_2: 0.9497 - val_loss: 15.3125 - val_dense_4_loss: 1.4371 - val_dense_5_loss: 1.2467 - val_dense_6_loss: 12.6287 - val_dense_4_f1_m: 0.3400 - val_dense_4_f1_m_1: 0.3400 - val_dense_4_f1_m_2: 0.3400 - val_dense_5_f1_m: 0.9706 - val_dense_5_f1_m_1: 0.9706 - val_dense_5_f1_m_2: 0.9706 - val_dense_6_f1_m: 0.9381 - val_dense_6_f1_m_1: 0.9381 - val_dense_6_f1_m_2: 0.9381\n",
      "Epoch 37/100\n",
      "582/582 [==============================] - 1s 986us/step - loss: 3.3972 - dense_4_loss: 0.0925 - dense_5_loss: 0.2105 - dense_6_loss: 3.0942 - dense_4_f1_m: 0.5315 - dense_4_f1_m_1: 0.5315 - dense_4_f1_m_2: 0.5315 - dense_5_f1_m: 0.9750 - dense_5_f1_m_1: 0.9750 - dense_5_f1_m_2: 0.9750 - dense_6_f1_m: 0.9512 - dense_6_f1_m_1: 0.9512 - dense_6_f1_m_2: 0.9512 - val_loss: 14.0966 - val_dense_4_loss: 1.3448 - val_dense_5_loss: 1.0849 - val_dense_6_loss: 11.6670 - val_dense_4_f1_m: 0.4067 - val_dense_4_f1_m_1: 0.4067 - val_dense_4_f1_m_2: 0.4067 - val_dense_5_f1_m: 0.9659 - val_dense_5_f1_m_1: 0.9659 - val_dense_5_f1_m_2: 0.9659 - val_dense_6_f1_m: 0.9289 - val_dense_6_f1_m_1: 0.9289 - val_dense_6_f1_m_2: 0.9289\n",
      "Epoch 38/100\n",
      "582/582 [==============================] - 1s 1ms/step - loss: 2.9841 - dense_4_loss: 0.0769 - dense_5_loss: 0.1939 - dense_6_loss: 2.7134 - dense_4_f1_m: 0.5272 - dense_4_f1_m_1: 0.5272 - dense_4_f1_m_2: 0.5272 - dense_5_f1_m: 0.9770 - dense_5_f1_m_1: 0.9770 - dense_5_f1_m_2: 0.9770 - dense_6_f1_m: 0.9534 - dense_6_f1_m_1: 0.9534 - dense_6_f1_m_2: 0.9534 - val_loss: 16.2395 - val_dense_4_loss: 1.4381 - val_dense_5_loss: 1.1872 - val_dense_6_loss: 13.6141 - val_dense_4_f1_m: 0.3667 - val_dense_4_f1_m_1: 0.3667 - val_dense_4_f1_m_2: 0.3667 - val_dense_5_f1_m: 0.9683 - val_dense_5_f1_m_1: 0.9683 - val_dense_5_f1_m_2: 0.9683 - val_dense_6_f1_m: 0.9434 - val_dense_6_f1_m_1: 0.9434 - val_dense_6_f1_m_2: 0.9434\n",
      "Epoch 39/100\n",
      "582/582 [==============================] - 1s 985us/step - loss: 2.6821 - dense_4_loss: 0.0606 - dense_5_loss: 0.1859 - dense_6_loss: 2.4357 - dense_4_f1_m: 0.5636 - dense_4_f1_m_1: 0.5636 - dense_4_f1_m_2: 0.5636 - dense_5_f1_m: 0.9771 - dense_5_f1_m_1: 0.9771 - dense_5_f1_m_2: 0.9771 - dense_6_f1_m: 0.9548 - dense_6_f1_m_1: 0.9548 - dense_6_f1_m_2: 0.9548 - val_loss: 18.2467 - val_dense_4_loss: 1.8440 - val_dense_5_loss: 1.5683 - val_dense_6_loss: 14.8344 - val_dense_4_f1_m: 0.3533 - val_dense_4_f1_m_1: 0.3533 - val_dense_4_f1_m_2: 0.3533 - val_dense_5_f1_m: 0.9686 - val_dense_5_f1_m_1: 0.9686 - val_dense_5_f1_m_2: 0.9686 - val_dense_6_f1_m: 0.9429 - val_dense_6_f1_m_1: 0.9429 - val_dense_6_f1_m_2: 0.9429\n",
      "Epoch 40/100\n",
      "582/582 [==============================] - 1s 1ms/step - loss: 2.6121 - dense_4_loss: 0.1020 - dense_5_loss: 0.2047 - dense_6_loss: 2.3054 - dense_4_f1_m: 0.5302 - dense_4_f1_m_1: 0.5302 - dense_4_f1_m_2: 0.5302 - dense_5_f1_m: 0.9740 - dense_5_f1_m_1: 0.9740 - dense_5_f1_m_2: 0.9740 - dense_6_f1_m: 0.9372 - dense_6_f1_m_1: 0.9372 - dense_6_f1_m_2: 0.9372 - val_loss: 18.4330 - val_dense_4_loss: 1.5065 - val_dense_5_loss: 1.2666 - val_dense_6_loss: 15.6598 - val_dense_4_f1_m: 0.3667 - val_dense_4_f1_m_1: 0.3667 - val_dense_4_f1_m_2: 0.3667 - val_dense_5_f1_m: 0.9683 - val_dense_5_f1_m_1: 0.9683 - val_dense_5_f1_m_2: 0.9683 - val_dense_6_f1_m: 0.9455 - val_dense_6_f1_m_1: 0.9455 - val_dense_6_f1_m_2: 0.9455\n",
      "Epoch 41/100\n",
      "582/582 [==============================] - 1s 986us/step - loss: 4.1815 - dense_4_loss: 0.2107 - dense_5_loss: 0.2880 - dense_6_loss: 3.6828 - dense_4_f1_m: 0.4788 - dense_4_f1_m_1: 0.4788 - dense_4_f1_m_2: 0.4788 - dense_5_f1_m: 0.9549 - dense_5_f1_m_1: 0.9549 - dense_5_f1_m_2: 0.9549 - dense_6_f1_m: 0.8868 - dense_6_f1_m_1: 0.8868 - dense_6_f1_m_2: 0.8868 - val_loss: 13.6157 - val_dense_4_loss: 1.6477 - val_dense_5_loss: 1.2811 - val_dense_6_loss: 10.6869 - val_dense_4_f1_m: 0.3400 - val_dense_4_f1_m_1: 0.3400 - val_dense_4_f1_m_2: 0.3400 - val_dense_5_f1_m: 0.9638 - val_dense_5_f1_m_1: 0.9638 - val_dense_5_f1_m_2: 0.9638 - val_dense_6_f1_m: 0.9138 - val_dense_6_f1_m_1: 0.9138 - val_dense_6_f1_m_2: 0.9138\n",
      "Epoch 42/100\n",
      "582/582 [==============================] - 1s 1ms/step - loss: 3.3715 - dense_4_loss: 0.1650 - dense_5_loss: 0.2263 - dense_6_loss: 2.9803 - dense_4_f1_m: 0.4347 - dense_4_f1_m_1: 0.4347 - dense_4_f1_m_2: 0.4347 - dense_5_f1_m: 0.9742 - dense_5_f1_m_1: 0.9742 - dense_5_f1_m_2: 0.9742 - dense_6_f1_m: 0.9220 - dense_6_f1_m_1: 0.9220 - dense_6_f1_m_2: 0.9220 - val_loss: 15.7093 - val_dense_4_loss: 1.3241 - val_dense_5_loss: 0.9764 - val_dense_6_loss: 13.4088 - val_dense_4_f1_m: 0.3320 - val_dense_4_f1_m_1: 0.3320 - val_dense_4_f1_m_2: 0.3320 - val_dense_5_f1_m: 0.9527 - val_dense_5_f1_m_1: 0.9527 - val_dense_5_f1_m_2: 0.9527 - val_dense_6_f1_m: 0.8625 - val_dense_6_f1_m_1: 0.8625 - val_dense_6_f1_m_2: 0.8625\n",
      "Epoch 43/100\n",
      "582/582 [==============================] - 1s 1ms/step - loss: 2.8411 - dense_4_loss: 0.0886 - dense_5_loss: 0.2013 - dense_6_loss: 2.5511 - dense_4_f1_m: 0.5464 - dense_4_f1_m_1: 0.5464 - dense_4_f1_m_2: 0.5464 - dense_5_f1_m: 0.9689 - dense_5_f1_m_1: 0.9689 - dense_5_f1_m_2: 0.9689 - dense_6_f1_m: 0.9319 - dense_6_f1_m_1: 0.9319 - dense_6_f1_m_2: 0.9319 - val_loss: 15.8528 - val_dense_4_loss: 1.5153 - val_dense_5_loss: 1.2110 - val_dense_6_loss: 13.1265 - val_dense_4_f1_m: 0.4200 - val_dense_4_f1_m_1: 0.4200 - val_dense_4_f1_m_2: 0.4200 - val_dense_5_f1_m: 0.9683 - val_dense_5_f1_m_1: 0.9683 - val_dense_5_f1_m_2: 0.9683 - val_dense_6_f1_m: 0.9349 - val_dense_6_f1_m_1: 0.9349 - val_dense_6_f1_m_2: 0.9349\n",
      "Epoch 44/100\n",
      "582/582 [==============================] - 1s 1ms/step - loss: 3.1589 - dense_4_loss: 0.1197 - dense_5_loss: 0.2282 - dense_6_loss: 2.8110 - dense_4_f1_m: 0.5086 - dense_4_f1_m_1: 0.5086 - dense_4_f1_m_2: 0.5086 - dense_5_f1_m: 0.9696 - dense_5_f1_m_1: 0.9696 - dense_5_f1_m_2: 0.9696 - dense_6_f1_m: 0.9349 - dense_6_f1_m_1: 0.9349 - dense_6_f1_m_2: 0.9349 - val_loss: 14.4407 - val_dense_4_loss: 1.2993 - val_dense_5_loss: 0.8764 - val_dense_6_loss: 12.2650 - val_dense_4_f1_m: 0.3053 - val_dense_4_f1_m_1: 0.3053 - val_dense_4_f1_m_2: 0.3053 - val_dense_5_f1_m: 0.9134 - val_dense_5_f1_m_1: 0.9134 - val_dense_5_f1_m_2: 0.9134 - val_dense_6_f1_m: 0.8393 - val_dense_6_f1_m_1: 0.8393 - val_dense_6_f1_m_2: 0.8393\n",
      "Epoch 45/100\n",
      "582/582 [==============================] - 1s 1ms/step - loss: 3.0195 - dense_4_loss: 0.1428 - dense_5_loss: 0.2307 - dense_6_loss: 2.6460 - dense_4_f1_m: 0.5372 - dense_4_f1_m_1: 0.5372 - dense_4_f1_m_2: 0.5372 - dense_5_f1_m: 0.9668 - dense_5_f1_m_1: 0.9668 - dense_5_f1_m_2: 0.9668 - dense_6_f1_m: 0.9199 - dense_6_f1_m_1: 0.9199 - dense_6_f1_m_2: 0.9199 - val_loss: 16.7670 - val_dense_4_loss: 1.7471 - val_dense_5_loss: 1.2971 - val_dense_6_loss: 13.7228 - val_dense_4_f1_m: 0.3000 - val_dense_4_f1_m_1: 0.3000 - val_dense_4_f1_m_2: 0.3000 - val_dense_5_f1_m: 0.9511 - val_dense_5_f1_m_1: 0.9511 - val_dense_5_f1_m_2: 0.9511 - val_dense_6_f1_m: 0.9007 - val_dense_6_f1_m_1: 0.9007 - val_dense_6_f1_m_2: 0.9007\n",
      "Epoch 46/100\n",
      "582/582 [==============================] - 1s 985us/step - loss: 2.5087 - dense_4_loss: 0.0784 - dense_5_loss: 0.1991 - dense_6_loss: 2.2311 - dense_4_f1_m: 0.5074 - dense_4_f1_m_1: 0.5074 - dense_4_f1_m_2: 0.5074 - dense_5_f1_m: 0.9740 - dense_5_f1_m_1: 0.9740 - dense_5_f1_m_2: 0.9740 - dense_6_f1_m: 0.9453 - dense_6_f1_m_1: 0.9453 - dense_6_f1_m_2: 0.9453 - val_loss: 15.9117 - val_dense_4_loss: 1.4869 - val_dense_5_loss: 0.9777 - val_dense_6_loss: 13.4471 - val_dense_4_f1_m: 0.3440 - val_dense_4_f1_m_1: 0.3440 - val_dense_4_f1_m_2: 0.3440 - val_dense_5_f1_m: 0.9509 - val_dense_5_f1_m_1: 0.9509 - val_dense_5_f1_m_2: 0.9509 - val_dense_6_f1_m: 0.8934 - val_dense_6_f1_m_1: 0.8934 - val_dense_6_f1_m_2: 0.8934\n",
      "Epoch 47/100\n",
      "582/582 [==============================] - 1s 1ms/step - loss: 2.3114 - dense_4_loss: 0.0658 - dense_5_loss: 0.1848 - dense_6_loss: 2.0608 - dense_4_f1_m: 0.5611 - dense_4_f1_m_1: 0.5611 - dense_4_f1_m_2: 0.5611 - dense_5_f1_m: 0.9743 - dense_5_f1_m_1: 0.9743 - dense_5_f1_m_2: 0.9743 - dense_6_f1_m: 0.9524 - dense_6_f1_m_1: 0.9524 - dense_6_f1_m_2: 0.9524 - val_loss: 16.5870 - val_dense_4_loss: 1.5196 - val_dense_5_loss: 1.0692 - val_dense_6_loss: 13.9981 - val_dense_4_f1_m: 0.3453 - val_dense_4_f1_m_1: 0.3453 - val_dense_4_f1_m_2: 0.3453 - val_dense_5_f1_m: 0.9586 - val_dense_5_f1_m_1: 0.9586 - val_dense_5_f1_m_2: 0.9586 - val_dense_6_f1_m: 0.9128 - val_dense_6_f1_m_1: 0.9128 - val_dense_6_f1_m_2: 0.9128\n",
      "Epoch 48/100\n",
      "582/582 [==============================] - 1s 998us/step - loss: 2.1845 - dense_4_loss: 0.0621 - dense_5_loss: 0.1832 - dense_6_loss: 1.9392 - dense_4_f1_m: 0.5092 - dense_4_f1_m_1: 0.5092 - dense_4_f1_m_2: 0.5092 - dense_5_f1_m: 0.9757 - dense_5_f1_m_1: 0.9757 - dense_5_f1_m_2: 0.9757 - dense_6_f1_m: 0.9529 - dense_6_f1_m_1: 0.9529 - dense_6_f1_m_2: 0.9529 - val_loss: 16.3944 - val_dense_4_loss: 1.4958 - val_dense_5_loss: 1.0633 - val_dense_6_loss: 13.8352 - val_dense_4_f1_m: 0.3787 - val_dense_4_f1_m_1: 0.3787 - val_dense_4_f1_m_2: 0.3787 - val_dense_5_f1_m: 0.9607 - val_dense_5_f1_m_1: 0.9607 - val_dense_5_f1_m_2: 0.9607 - val_dense_6_f1_m: 0.9128 - val_dense_6_f1_m_1: 0.9128 - val_dense_6_f1_m_2: 0.9128\n",
      "Epoch 49/100\n",
      "582/582 [==============================] - 1s 1ms/step - loss: 2.3876 - dense_4_loss: 0.0567 - dense_5_loss: 0.1798 - dense_6_loss: 2.1511 - dense_4_f1_m: 0.6071 - dense_4_f1_m_1: 0.6071 - dense_4_f1_m_2: 0.6071 - dense_5_f1_m: 0.9762 - dense_5_f1_m_1: 0.9762 - dense_5_f1_m_2: 0.9762 - dense_6_f1_m: 0.9463 - dense_6_f1_m_1: 0.9463 - dense_6_f1_m_2: 0.9463 - val_loss: 16.1328 - val_dense_4_loss: 1.3845 - val_dense_5_loss: 0.8635 - val_dense_6_loss: 13.8847 - val_dense_4_f1_m: 0.3387 - val_dense_4_f1_m_1: 0.3387 - val_dense_4_f1_m_2: 0.3387 - val_dense_5_f1_m: 0.9382 - val_dense_5_f1_m_1: 0.9382 - val_dense_5_f1_m_2: 0.9382 - val_dense_6_f1_m: 0.8785 - val_dense_6_f1_m_1: 0.8785 - val_dense_6_f1_m_2: 0.8785\n",
      "Epoch 50/100\n",
      "582/582 [==============================] - 1s 1ms/step - loss: 2.7681 - dense_4_loss: 0.1541 - dense_5_loss: 0.2393 - dense_6_loss: 2.3747 - dense_4_f1_m: 0.5577 - dense_4_f1_m_1: 0.5577 - dense_4_f1_m_2: 0.5577 - dense_5_f1_m: 0.9711 - dense_5_f1_m_1: 0.9711 - dense_5_f1_m_2: 0.9711 - dense_6_f1_m: 0.9361 - dense_6_f1_m_1: 0.9361 - dense_6_f1_m_2: 0.9361 - val_loss: 16.1342 - val_dense_4_loss: 1.3537 - val_dense_5_loss: 0.8551 - val_dense_6_loss: 13.9254 - val_dense_4_f1_m: 0.3387 - val_dense_4_f1_m_1: 0.3387 - val_dense_4_f1_m_2: 0.3387 - val_dense_5_f1_m: 0.9427 - val_dense_5_f1_m_1: 0.9427 - val_dense_5_f1_m_2: 0.9427 - val_dense_6_f1_m: 0.8717 - val_dense_6_f1_m_1: 0.8717 - val_dense_6_f1_m_2: 0.8717\n",
      "Epoch 51/100\n",
      "582/582 [==============================] - 1s 1ms/step - loss: 2.2963 - dense_4_loss: 0.0916 - dense_5_loss: 0.2115 - dense_6_loss: 1.9931 - dense_4_f1_m: 0.5109 - dense_4_f1_m_1: 0.5109 - dense_4_f1_m_2: 0.5109 - dense_5_f1_m: 0.9719 - dense_5_f1_m_1: 0.9719 - dense_5_f1_m_2: 0.9719 - dense_6_f1_m: 0.9398 - dense_6_f1_m_1: 0.9398 - dense_6_f1_m_2: 0.9398 - val_loss: 16.8428 - val_dense_4_loss: 1.4183 - val_dense_5_loss: 0.9045 - val_dense_6_loss: 14.5200 - val_dense_4_f1_m: 0.3787 - val_dense_4_f1_m_1: 0.3787 - val_dense_4_f1_m_2: 0.3787 - val_dense_5_f1_m: 0.9625 - val_dense_5_f1_m_1: 0.9625 - val_dense_5_f1_m_2: 0.9625 - val_dense_6_f1_m: 0.9194 - val_dense_6_f1_m_1: 0.9194 - val_dense_6_f1_m_2: 0.9194\n",
      "Epoch 52/100\n",
      "582/582 [==============================] - 1s 1ms/step - loss: 2.0992 - dense_4_loss: 0.0764 - dense_5_loss: 0.1928 - dense_6_loss: 1.8300 - dense_4_f1_m: 0.5017 - dense_4_f1_m_1: 0.5017 - dense_4_f1_m_2: 0.5017 - dense_5_f1_m: 0.9743 - dense_5_f1_m_1: 0.9743 - dense_5_f1_m_2: 0.9743 - dense_6_f1_m: 0.9457 - dense_6_f1_m_1: 0.9457 - dense_6_f1_m_2: 0.9457 - val_loss: 16.7729 - val_dense_4_loss: 1.3085 - val_dense_5_loss: 0.7798 - val_dense_6_loss: 14.6846 - val_dense_4_f1_m: 0.3653 - val_dense_4_f1_m_1: 0.3653 - val_dense_4_f1_m_2: 0.3653 - val_dense_5_f1_m: 0.9500 - val_dense_5_f1_m_1: 0.9500 - val_dense_5_f1_m_2: 0.9500 - val_dense_6_f1_m: 0.8973 - val_dense_6_f1_m_1: 0.8973 - val_dense_6_f1_m_2: 0.8973\n",
      "Epoch 53/100\n",
      "582/582 [==============================] - 1s 992us/step - loss: 2.1185 - dense_4_loss: 0.0858 - dense_5_loss: 0.2045 - dense_6_loss: 1.8283 - dense_4_f1_m: 0.5464 - dense_4_f1_m_1: 0.5464 - dense_4_f1_m_2: 0.5464 - dense_5_f1_m: 0.9717 - dense_5_f1_m_1: 0.9717 - dense_5_f1_m_2: 0.9717 - dense_6_f1_m: 0.9384 - dense_6_f1_m_1: 0.9384 - dense_6_f1_m_2: 0.9384 - val_loss: 17.8253 - val_dense_4_loss: 1.4804 - val_dense_5_loss: 0.9486 - val_dense_6_loss: 15.3963 - val_dense_4_f1_m: 0.3707 - val_dense_4_f1_m_1: 0.3707 - val_dense_4_f1_m_2: 0.3707 - val_dense_5_f1_m: 0.9625 - val_dense_5_f1_m_1: 0.9625 - val_dense_5_f1_m_2: 0.9625 - val_dense_6_f1_m: 0.9095 - val_dense_6_f1_m_1: 0.9095 - val_dense_6_f1_m_2: 0.9095\n",
      "Epoch 54/100\n",
      "582/582 [==============================] - 1s 1ms/step - loss: 2.2682 - dense_4_loss: 0.0794 - dense_5_loss: 0.1949 - dense_6_loss: 1.9939 - dense_4_f1_m: 0.5164 - dense_4_f1_m_1: 0.5164 - dense_4_f1_m_2: 0.5164 - dense_5_f1_m: 0.9709 - dense_5_f1_m_1: 0.9709 - dense_5_f1_m_2: 0.9709 - dense_6_f1_m: 0.9325 - dense_6_f1_m_1: 0.9325 - dense_6_f1_m_2: 0.9325 - val_loss: 17.0435 - val_dense_4_loss: 1.7464 - val_dense_5_loss: 1.2421 - val_dense_6_loss: 14.0550 - val_dense_4_f1_m: 0.3387 - val_dense_4_f1_m_1: 0.3387 - val_dense_4_f1_m_2: 0.3387 - val_dense_5_f1_m: 0.9536 - val_dense_5_f1_m_1: 0.9536 - val_dense_5_f1_m_2: 0.9536 - val_dense_6_f1_m: 0.9211 - val_dense_6_f1_m_1: 0.9211 - val_dense_6_f1_m_2: 0.9211\n",
      "Epoch 55/100\n",
      "582/582 [==============================] - 1s 985us/step - loss: 2.2923 - dense_4_loss: 0.1003 - dense_5_loss: 0.2217 - dense_6_loss: 1.9702 - dense_4_f1_m: 0.5106 - dense_4_f1_m_1: 0.5106 - dense_4_f1_m_2: 0.5106 - dense_5_f1_m: 0.9732 - dense_5_f1_m_1: 0.9732 - dense_5_f1_m_2: 0.9732 - dense_6_f1_m: 0.9409 - dense_6_f1_m_1: 0.9409 - dense_6_f1_m_2: 0.9409 - val_loss: 17.0231 - val_dense_4_loss: 1.4587 - val_dense_5_loss: 0.9686 - val_dense_6_loss: 14.5958 - val_dense_4_f1_m: 0.3653 - val_dense_4_f1_m_1: 0.3653 - val_dense_4_f1_m_2: 0.3653 - val_dense_5_f1_m: 0.9547 - val_dense_5_f1_m_1: 0.9547 - val_dense_5_f1_m_2: 0.9547 - val_dense_6_f1_m: 0.9029 - val_dense_6_f1_m_1: 0.9029 - val_dense_6_f1_m_2: 0.9029\n",
      "Epoch 56/100\n",
      "582/582 [==============================] - 1s 1ms/step - loss: 2.1065 - dense_4_loss: 0.0978 - dense_5_loss: 0.1956 - dense_6_loss: 1.8131 - dense_4_f1_m: 0.5727 - dense_4_f1_m_1: 0.5727 - dense_4_f1_m_2: 0.5727 - dense_5_f1_m: 0.9800 - dense_5_f1_m_1: 0.9800 - dense_5_f1_m_2: 0.9800 - dense_6_f1_m: 0.9550 - dense_6_f1_m_1: 0.9550 - dense_6_f1_m_2: 0.9550 - val_loss: 18.3231 - val_dense_4_loss: 1.4227 - val_dense_5_loss: 0.9316 - val_dense_6_loss: 15.9689 - val_dense_4_f1_m: 0.3707 - val_dense_4_f1_m_1: 0.3707 - val_dense_4_f1_m_2: 0.3707 - val_dense_5_f1_m: 0.9604 - val_dense_5_f1_m_1: 0.9604 - val_dense_5_f1_m_2: 0.9604 - val_dense_6_f1_m: 0.8989 - val_dense_6_f1_m_1: 0.8989 - val_dense_6_f1_m_2: 0.8989\n",
      "Epoch 57/100\n",
      "582/582 [==============================] - 1s 987us/step - loss: 2.0281 - dense_4_loss: 0.0773 - dense_5_loss: 0.1879 - dense_6_loss: 1.7628 - dense_4_f1_m: 0.5268 - dense_4_f1_m_1: 0.5268 - dense_4_f1_m_2: 0.5268 - dense_5_f1_m: 0.9773 - dense_5_f1_m_1: 0.9773 - dense_5_f1_m_2: 0.9773 - dense_6_f1_m: 0.9406 - dense_6_f1_m_1: 0.9406 - dense_6_f1_m_2: 0.9406 - val_loss: 18.4913 - val_dense_4_loss: 1.5006 - val_dense_5_loss: 1.0507 - val_dense_6_loss: 15.9400 - val_dense_4_f1_m: 0.3787 - val_dense_4_f1_m_1: 0.3787 - val_dense_4_f1_m_2: 0.3787 - val_dense_5_f1_m: 0.9651 - val_dense_5_f1_m_1: 0.9651 - val_dense_5_f1_m_2: 0.9651 - val_dense_6_f1_m: 0.9299 - val_dense_6_f1_m_1: 0.9299 - val_dense_6_f1_m_2: 0.9299\n",
      "Epoch 58/100\n",
      "582/582 [==============================] - 1s 1ms/step - loss: 2.1875 - dense_4_loss: 0.0603 - dense_5_loss: 0.1726 - dense_6_loss: 1.9546 - dense_4_f1_m: 0.5382 - dense_4_f1_m_1: 0.5382 - dense_4_f1_m_2: 0.5382 - dense_5_f1_m: 0.9776 - dense_5_f1_m_1: 0.9776 - dense_5_f1_m_2: 0.9776 - dense_6_f1_m: 0.9480 - dense_6_f1_m_1: 0.9480 - dense_6_f1_m_2: 0.9480 - val_loss: 17.5831 - val_dense_4_loss: 1.4992 - val_dense_5_loss: 1.0207 - val_dense_6_loss: 15.0631 - val_dense_4_f1_m: 0.3707 - val_dense_4_f1_m_1: 0.3707 - val_dense_4_f1_m_2: 0.3707 - val_dense_5_f1_m: 0.9580 - val_dense_5_f1_m_1: 0.9580 - val_dense_5_f1_m_2: 0.9580 - val_dense_6_f1_m: 0.9069 - val_dense_6_f1_m_1: 0.9069 - val_dense_6_f1_m_2: 0.9069\n",
      "Epoch 59/100\n",
      "582/582 [==============================] - 1s 1ms/step - loss: 2.0256 - dense_4_loss: 0.0639 - dense_5_loss: 0.1841 - dense_6_loss: 1.7776 - dense_4_f1_m: 0.5727 - dense_4_f1_m_1: 0.5727 - dense_4_f1_m_2: 0.5727 - dense_5_f1_m: 0.9758 - dense_5_f1_m_1: 0.9758 - dense_5_f1_m_2: 0.9758 - dense_6_f1_m: 0.9533 - dense_6_f1_m_1: 0.9533 - dense_6_f1_m_2: 0.9533 - val_loss: 18.5418 - val_dense_4_loss: 1.4620 - val_dense_5_loss: 0.9007 - val_dense_6_loss: 16.1790 - val_dense_4_f1_m: 0.3573 - val_dense_4_f1_m_1: 0.3573 - val_dense_4_f1_m_2: 0.3573 - val_dense_5_f1_m: 0.9429 - val_dense_5_f1_m_1: 0.9429 - val_dense_5_f1_m_2: 0.9429 - val_dense_6_f1_m: 0.8895 - val_dense_6_f1_m_1: 0.8895 - val_dense_6_f1_m_2: 0.8895\n",
      "Epoch 60/100\n",
      "582/582 [==============================] - 1s 1ms/step - loss: 2.1878 - dense_4_loss: 0.1148 - dense_5_loss: 0.2204 - dense_6_loss: 1.8527 - dense_4_f1_m: 0.4947 - dense_4_f1_m_1: 0.4947 - dense_4_f1_m_2: 0.4947 - dense_5_f1_m: 0.9708 - dense_5_f1_m_1: 0.9708 - dense_5_f1_m_2: 0.9708 - dense_6_f1_m: 0.9355 - dense_6_f1_m_1: 0.9355 - dense_6_f1_m_2: 0.9355 - val_loss: 19.6776 - val_dense_4_loss: 1.4807 - val_dense_5_loss: 1.1246 - val_dense_6_loss: 17.0724 - val_dense_4_f1_m: 0.3653 - val_dense_4_f1_m_1: 0.3653 - val_dense_4_f1_m_2: 0.3653 - val_dense_5_f1_m: 0.9630 - val_dense_5_f1_m_1: 0.9630 - val_dense_5_f1_m_2: 0.9630 - val_dense_6_f1_m: 0.9074 - val_dense_6_f1_m_1: 0.9074 - val_dense_6_f1_m_2: 0.9074\n",
      "Epoch 61/100\n",
      "582/582 [==============================] - 1s 1ms/step - loss: 2.5593 - dense_4_loss: 0.1050 - dense_5_loss: 0.2087 - dense_6_loss: 2.2456 - dense_4_f1_m: 0.4971 - dense_4_f1_m_1: 0.4971 - dense_4_f1_m_2: 0.4971 - dense_5_f1_m: 0.9667 - dense_5_f1_m_1: 0.9667 - dense_5_f1_m_2: 0.9667 - dense_6_f1_m: 0.9280 - dense_6_f1_m_1: 0.9280 - dense_6_f1_m_2: 0.9280 - val_loss: 17.2433 - val_dense_4_loss: 1.7702 - val_dense_5_loss: 1.2912 - val_dense_6_loss: 14.1820 - val_dense_4_f1_m: 0.3200 - val_dense_4_f1_m_1: 0.3200 - val_dense_4_f1_m_2: 0.3200 - val_dense_5_f1_m: 0.9633 - val_dense_5_f1_m_1: 0.9633 - val_dense_5_f1_m_2: 0.9633 - val_dense_6_f1_m: 0.9295 - val_dense_6_f1_m_1: 0.9295 - val_dense_6_f1_m_2: 0.9295\n",
      "Epoch 62/100\n",
      "582/582 [==============================] - 1s 977us/step - loss: 2.1127 - dense_4_loss: 0.0872 - dense_5_loss: 0.1878 - dense_6_loss: 1.8377 - dense_4_f1_m: 0.4914 - dense_4_f1_m_1: 0.4914 - dense_4_f1_m_2: 0.4914 - dense_5_f1_m: 0.9774 - dense_5_f1_m_1: 0.9774 - dense_5_f1_m_2: 0.9774 - dense_6_f1_m: 0.9482 - dense_6_f1_m_1: 0.9482 - dense_6_f1_m_2: 0.9482 - val_loss: 19.0022 - val_dense_4_loss: 1.7143 - val_dense_5_loss: 1.2186 - val_dense_6_loss: 16.0692 - val_dense_4_f1_m: 0.3600 - val_dense_4_f1_m_1: 0.3600 - val_dense_4_f1_m_2: 0.3600 - val_dense_5_f1_m: 0.9562 - val_dense_5_f1_m_1: 0.9562 - val_dense_5_f1_m_2: 0.9562 - val_dense_6_f1_m: 0.9246 - val_dense_6_f1_m_1: 0.9246 - val_dense_6_f1_m_2: 0.9246\n",
      "Epoch 63/100\n",
      "582/582 [==============================] - 1s 1ms/step - loss: 2.0139 - dense_4_loss: 0.0638 - dense_5_loss: 0.1725 - dense_6_loss: 1.7776 - dense_4_f1_m: 0.5441 - dense_4_f1_m_1: 0.5441 - dense_4_f1_m_2: 0.5441 - dense_5_f1_m: 0.9785 - dense_5_f1_m_1: 0.9785 - dense_5_f1_m_2: 0.9785 - dense_6_f1_m: 0.9567 - dense_6_f1_m_1: 0.9567 - dense_6_f1_m_2: 0.9567 - val_loss: 20.0732 - val_dense_4_loss: 1.5218 - val_dense_5_loss: 1.1120 - val_dense_6_loss: 17.4394 - val_dense_4_f1_m: 0.3520 - val_dense_4_f1_m_1: 0.3520 - val_dense_4_f1_m_2: 0.3520 - val_dense_5_f1_m: 0.9483 - val_dense_5_f1_m_1: 0.9483 - val_dense_5_f1_m_2: 0.9483 - val_dense_6_f1_m: 0.8960 - val_dense_6_f1_m_1: 0.8960 - val_dense_6_f1_m_2: 0.8960\n",
      "Epoch 64/100\n",
      "582/582 [==============================] - 1s 1ms/step - loss: 1.7990 - dense_4_loss: 0.0572 - dense_5_loss: 0.1805 - dense_6_loss: 1.5614 - dense_4_f1_m: 0.5384 - dense_4_f1_m_1: 0.5384 - dense_4_f1_m_2: 0.5384 - dense_5_f1_m: 0.9777 - dense_5_f1_m_1: 0.9777 - dense_5_f1_m_2: 0.9777 - dense_6_f1_m: 0.9569 - dense_6_f1_m_1: 0.9569 - dense_6_f1_m_2: 0.9569 - val_loss: 20.7203 - val_dense_4_loss: 1.5292 - val_dense_5_loss: 1.1495 - val_dense_6_loss: 18.0417 - val_dense_4_f1_m: 0.3573 - val_dense_4_f1_m_1: 0.3573 - val_dense_4_f1_m_2: 0.3573 - val_dense_5_f1_m: 0.9453 - val_dense_5_f1_m_1: 0.9453 - val_dense_5_f1_m_2: 0.9453 - val_dense_6_f1_m: 0.8939 - val_dense_6_f1_m_1: 0.8939 - val_dense_6_f1_m_2: 0.8939\n",
      "Epoch 65/100\n",
      "582/582 [==============================] - 1s 1ms/step - loss: 2.3173 - dense_4_loss: 0.0772 - dense_5_loss: 0.1827 - dense_6_loss: 2.0574 - dense_4_f1_m: 0.5178 - dense_4_f1_m_1: 0.5178 - dense_4_f1_m_2: 0.5178 - dense_5_f1_m: 0.9714 - dense_5_f1_m_1: 0.9714 - dense_5_f1_m_2: 0.9714 - dense_6_f1_m: 0.9341 - dense_6_f1_m_1: 0.9341 - dense_6_f1_m_2: 0.9341 - val_loss: 19.7396 - val_dense_4_loss: 1.8850 - val_dense_5_loss: 1.4405 - val_dense_6_loss: 16.4141 - val_dense_4_f1_m: 0.3333 - val_dense_4_f1_m_1: 0.3333 - val_dense_4_f1_m_2: 0.3333 - val_dense_5_f1_m: 0.9656 - val_dense_5_f1_m_1: 0.9656 - val_dense_5_f1_m_2: 0.9656 - val_dense_6_f1_m: 0.9314 - val_dense_6_f1_m_1: 0.9314 - val_dense_6_f1_m_2: 0.9314\n",
      "Epoch 66/100\n",
      "582/582 [==============================] - 1s 1ms/step - loss: 1.9333 - dense_4_loss: 0.0817 - dense_5_loss: 0.1954 - dense_6_loss: 1.6562 - dense_4_f1_m: 0.5000 - dense_4_f1_m_1: 0.5000 - dense_4_f1_m_2: 0.5000 - dense_5_f1_m: 0.9738 - dense_5_f1_m_1: 0.9738 - dense_5_f1_m_2: 0.9738 - dense_6_f1_m: 0.9497 - dense_6_f1_m_1: 0.9497 - dense_6_f1_m_2: 0.9497 - val_loss: 21.2127 - val_dense_4_loss: 1.5831 - val_dense_5_loss: 1.2094 - val_dense_6_loss: 18.4202 - val_dense_4_f1_m: 0.3867 - val_dense_4_f1_m_1: 0.3867 - val_dense_4_f1_m_2: 0.3867 - val_dense_5_f1_m: 0.9628 - val_dense_5_f1_m_1: 0.9628 - val_dense_5_f1_m_2: 0.9628 - val_dense_6_f1_m: 0.9097 - val_dense_6_f1_m_1: 0.9097 - val_dense_6_f1_m_2: 0.9097\n",
      "Epoch 67/100\n",
      "582/582 [==============================] - 1s 1ms/step - loss: 1.7522 - dense_4_loss: 0.0593 - dense_5_loss: 0.1867 - dense_6_loss: 1.5062 - dense_4_f1_m: 0.5475 - dense_4_f1_m_1: 0.5475 - dense_4_f1_m_2: 0.5475 - dense_5_f1_m: 0.9774 - dense_5_f1_m_1: 0.9774 - dense_5_f1_m_2: 0.9774 - dense_6_f1_m: 0.9560 - dense_6_f1_m_1: 0.9560 - dense_6_f1_m_2: 0.9560 - val_loss: 21.5795 - val_dense_4_loss: 1.6369 - val_dense_5_loss: 1.1914 - val_dense_6_loss: 18.7513 - val_dense_4_f1_m: 0.3520 - val_dense_4_f1_m_1: 0.3520 - val_dense_4_f1_m_2: 0.3520 - val_dense_5_f1_m: 0.9509 - val_dense_5_f1_m_1: 0.9509 - val_dense_5_f1_m_2: 0.9509 - val_dense_6_f1_m: 0.9017 - val_dense_6_f1_m_1: 0.9017 - val_dense_6_f1_m_2: 0.9017\n",
      "Epoch 68/100\n",
      "582/582 [==============================] - 1s 1ms/step - loss: 1.6478 - dense_4_loss: 0.0441 - dense_5_loss: 0.1713 - dense_6_loss: 1.4325 - dense_4_f1_m: 0.4971 - dense_4_f1_m_1: 0.4971 - dense_4_f1_m_2: 0.4971 - dense_5_f1_m: 0.9791 - dense_5_f1_m_1: 0.9791 - dense_5_f1_m_2: 0.9791 - dense_6_f1_m: 0.9682 - dense_6_f1_m_1: 0.9682 - dense_6_f1_m_2: 0.9682 - val_loss: 21.9003 - val_dense_4_loss: 1.6811 - val_dense_5_loss: 1.2651 - val_dense_6_loss: 18.9541 - val_dense_4_f1_m: 0.3733 - val_dense_4_f1_m_1: 0.3733 - val_dense_4_f1_m_2: 0.3733 - val_dense_5_f1_m: 0.9628 - val_dense_5_f1_m_1: 0.9628 - val_dense_5_f1_m_2: 0.9628 - val_dense_6_f1_m: 0.9194 - val_dense_6_f1_m_1: 0.9194 - val_dense_6_f1_m_2: 0.9194\n",
      "Epoch 69/100\n",
      "582/582 [==============================] - 1s 1ms/step - loss: 1.6358 - dense_4_loss: 0.0390 - dense_5_loss: 0.1639 - dense_6_loss: 1.4329 - dense_4_f1_m: 0.5785 - dense_4_f1_m_1: 0.5785 - dense_4_f1_m_2: 0.5785 - dense_5_f1_m: 0.9799 - dense_5_f1_m_1: 0.9799 - dense_5_f1_m_2: 0.9799 - dense_6_f1_m: 0.9655 - dense_6_f1_m_1: 0.9655 - dense_6_f1_m_2: 0.9655 - val_loss: 23.0567 - val_dense_4_loss: 1.8253 - val_dense_5_loss: 1.3629 - val_dense_6_loss: 19.8685 - val_dense_4_f1_m: 0.3200 - val_dense_4_f1_m_1: 0.3200 - val_dense_4_f1_m_2: 0.3200 - val_dense_5_f1_m: 0.9609 - val_dense_5_f1_m_1: 0.9609 - val_dense_5_f1_m_2: 0.9609 - val_dense_6_f1_m: 0.9255 - val_dense_6_f1_m_1: 0.9255 - val_dense_6_f1_m_2: 0.9255\n",
      "Epoch 70/100\n",
      "582/582 [==============================] - 1s 1ms/step - loss: 1.5557 - dense_4_loss: 0.0388 - dense_5_loss: 0.1720 - dense_6_loss: 1.3449 - dense_4_f1_m: 0.5040 - dense_4_f1_m_1: 0.5040 - dense_4_f1_m_2: 0.5040 - dense_5_f1_m: 0.9780 - dense_5_f1_m_1: 0.9780 - dense_5_f1_m_2: 0.9780 - dense_6_f1_m: 0.9673 - dense_6_f1_m_1: 0.9673 - dense_6_f1_m_2: 0.9673 - val_loss: 24.3291 - val_dense_4_loss: 1.9282 - val_dense_5_loss: 1.3855 - val_dense_6_loss: 21.0154 - val_dense_4_f1_m: 0.3200 - val_dense_4_f1_m_1: 0.3200 - val_dense_4_f1_m_2: 0.3200 - val_dense_5_f1_m: 0.9609 - val_dense_5_f1_m_1: 0.9609 - val_dense_5_f1_m_2: 0.9609 - val_dense_6_f1_m: 0.9276 - val_dense_6_f1_m_1: 0.9276 - val_dense_6_f1_m_2: 0.9276\n",
      "Epoch 71/100\n",
      "582/582 [==============================] - 1s 1ms/step - loss: 1.6206 - dense_4_loss: 0.0440 - dense_5_loss: 0.1642 - dense_6_loss: 1.4123 - dense_4_f1_m: 0.5979 - dense_4_f1_m_1: 0.5979 - dense_4_f1_m_2: 0.5979 - dense_5_f1_m: 0.9800 - dense_5_f1_m_1: 0.9800 - dense_5_f1_m_2: 0.9800 - dense_6_f1_m: 0.9636 - dense_6_f1_m_1: 0.9636 - dense_6_f1_m_2: 0.9636 - val_loss: 22.8029 - val_dense_4_loss: 1.8385 - val_dense_5_loss: 1.3712 - val_dense_6_loss: 19.5932 - val_dense_4_f1_m: 0.3333 - val_dense_4_f1_m_1: 0.3333 - val_dense_4_f1_m_2: 0.3333 - val_dense_5_f1_m: 0.9609 - val_dense_5_f1_m_1: 0.9609 - val_dense_5_f1_m_2: 0.9609 - val_dense_6_f1_m: 0.9219 - val_dense_6_f1_m_1: 0.9219 - val_dense_6_f1_m_2: 0.9219\n",
      "Epoch 72/100\n",
      "582/582 [==============================] - 1s 993us/step - loss: 1.5808 - dense_4_loss: 0.0424 - dense_5_loss: 0.1644 - dense_6_loss: 1.3740 - dense_4_f1_m: 0.5349 - dense_4_f1_m_1: 0.5349 - dense_4_f1_m_2: 0.5349 - dense_5_f1_m: 0.9792 - dense_5_f1_m_1: 0.9792 - dense_5_f1_m_2: 0.9792 - dense_6_f1_m: 0.9682 - dense_6_f1_m_1: 0.9682 - dense_6_f1_m_2: 0.9682 - val_loss: 24.7269 - val_dense_4_loss: 1.8052 - val_dense_5_loss: 1.3499 - val_dense_6_loss: 21.5718 - val_dense_4_f1_m: 0.3867 - val_dense_4_f1_m_1: 0.3867 - val_dense_4_f1_m_2: 0.3867 - val_dense_5_f1_m: 0.9630 - val_dense_5_f1_m_1: 0.9630 - val_dense_5_f1_m_2: 0.9630 - val_dense_6_f1_m: 0.9257 - val_dense_6_f1_m_1: 0.9257 - val_dense_6_f1_m_2: 0.9257\n",
      "Epoch 73/100\n",
      "582/582 [==============================] - 1s 1ms/step - loss: 1.5233 - dense_4_loss: 0.0417 - dense_5_loss: 0.1781 - dense_6_loss: 1.3034 - dense_4_f1_m: 0.5727 - dense_4_f1_m_1: 0.5727 - dense_4_f1_m_2: 0.5727 - dense_5_f1_m: 0.9799 - dense_5_f1_m_1: 0.9799 - dense_5_f1_m_2: 0.9799 - dense_6_f1_m: 0.9699 - dense_6_f1_m_1: 0.9699 - dense_6_f1_m_2: 0.9699 - val_loss: 24.9414 - val_dense_4_loss: 1.7867 - val_dense_5_loss: 1.2846 - val_dense_6_loss: 21.8701 - val_dense_4_f1_m: 0.3867 - val_dense_4_f1_m_1: 0.3867 - val_dense_4_f1_m_2: 0.3867 - val_dense_5_f1_m: 0.9607 - val_dense_5_f1_m_1: 0.9607 - val_dense_5_f1_m_2: 0.9607 - val_dense_6_f1_m: 0.9156 - val_dense_6_f1_m_1: 0.9156 - val_dense_6_f1_m_2: 0.9156\n",
      "Epoch 74/100\n",
      "582/582 [==============================] - 1s 983us/step - loss: 1.9522 - dense_4_loss: 0.1333 - dense_5_loss: 0.2024 - dense_6_loss: 1.6164 - dense_4_f1_m: 0.4970 - dense_4_f1_m_1: 0.4970 - dense_4_f1_m_2: 0.4970 - dense_5_f1_m: 0.9721 - dense_5_f1_m_1: 0.9721 - dense_5_f1_m_2: 0.9721 - dense_6_f1_m: 0.9431 - dense_6_f1_m_1: 0.9431 - dense_6_f1_m_2: 0.9431 - val_loss: 24.5071 - val_dense_4_loss: 1.6248 - val_dense_5_loss: 1.2114 - val_dense_6_loss: 21.6709 - val_dense_4_f1_m: 0.3573 - val_dense_4_f1_m_1: 0.3573 - val_dense_4_f1_m_2: 0.3573 - val_dense_5_f1_m: 0.9510 - val_dense_5_f1_m_1: 0.9510 - val_dense_5_f1_m_2: 0.9510 - val_dense_6_f1_m: 0.8834 - val_dense_6_f1_m_1: 0.8834 - val_dense_6_f1_m_2: 0.8834\n",
      "Epoch 75/100\n",
      "582/582 [==============================] - 1s 1ms/step - loss: 1.6607 - dense_4_loss: 0.0808 - dense_5_loss: 0.2007 - dense_6_loss: 1.3793 - dense_4_f1_m: 0.5143 - dense_4_f1_m_1: 0.5143 - dense_4_f1_m_2: 0.5143 - dense_5_f1_m: 0.9766 - dense_5_f1_m_1: 0.9766 - dense_5_f1_m_2: 0.9766 - dense_6_f1_m: 0.9535 - dense_6_f1_m_1: 0.9535 - dense_6_f1_m_2: 0.9535 - val_loss: 25.0205 - val_dense_4_loss: 1.7993 - val_dense_5_loss: 1.3102 - val_dense_6_loss: 21.9110 - val_dense_4_f1_m: 0.3600 - val_dense_4_f1_m_1: 0.3600 - val_dense_4_f1_m_2: 0.3600 - val_dense_5_f1_m: 0.9630 - val_dense_5_f1_m_1: 0.9630 - val_dense_5_f1_m_2: 0.9630 - val_dense_6_f1_m: 0.9099 - val_dense_6_f1_m_1: 0.9099 - val_dense_6_f1_m_2: 0.9099\n",
      "Epoch 76/100\n",
      "582/582 [==============================] - 1s 959us/step - loss: 1.6354 - dense_4_loss: 0.0469 - dense_5_loss: 0.1748 - dense_6_loss: 1.4138 - dense_4_f1_m: 0.5785 - dense_4_f1_m_1: 0.5785 - dense_4_f1_m_2: 0.5785 - dense_5_f1_m: 0.9775 - dense_5_f1_m_1: 0.9775 - dense_5_f1_m_2: 0.9775 - dense_6_f1_m: 0.9622 - dense_6_f1_m_1: 0.9622 - dense_6_f1_m_2: 0.9622 - val_loss: 23.5433 - val_dense_4_loss: 1.6832 - val_dense_5_loss: 1.1876 - val_dense_6_loss: 20.6724 - val_dense_4_f1_m: 0.3533 - val_dense_4_f1_m_1: 0.3533 - val_dense_4_f1_m_2: 0.3533 - val_dense_5_f1_m: 0.9479 - val_dense_5_f1_m_1: 0.9479 - val_dense_5_f1_m_2: 0.9479 - val_dense_6_f1_m: 0.8992 - val_dense_6_f1_m_1: 0.8992 - val_dense_6_f1_m_2: 0.8992\n",
      "Epoch 77/100\n",
      "582/582 [==============================] - 1s 1ms/step - loss: 1.4636 - dense_4_loss: 0.0441 - dense_5_loss: 0.1784 - dense_6_loss: 1.2411 - dense_4_f1_m: 0.6037 - dense_4_f1_m_1: 0.6037 - dense_4_f1_m_2: 0.6037 - dense_5_f1_m: 0.9787 - dense_5_f1_m_1: 0.9787 - dense_5_f1_m_2: 0.9787 - dense_6_f1_m: 0.9691 - dense_6_f1_m_1: 0.9691 - dense_6_f1_m_2: 0.9691 - val_loss: 24.2266 - val_dense_4_loss: 1.6763 - val_dense_5_loss: 1.2157 - val_dense_6_loss: 21.3347 - val_dense_4_f1_m: 0.3600 - val_dense_4_f1_m_1: 0.3600 - val_dense_4_f1_m_2: 0.3600 - val_dense_5_f1_m: 0.9506 - val_dense_5_f1_m_1: 0.9506 - val_dense_5_f1_m_2: 0.9506 - val_dense_6_f1_m: 0.9059 - val_dense_6_f1_m_1: 0.9059 - val_dense_6_f1_m_2: 0.9059\n",
      "Epoch 78/100\n",
      "582/582 [==============================] - 1s 970us/step - loss: 1.4529 - dense_4_loss: 0.0538 - dense_5_loss: 0.1955 - dense_6_loss: 1.2037 - dense_4_f1_m: 0.5092 - dense_4_f1_m_1: 0.5092 - dense_4_f1_m_2: 0.5092 - dense_5_f1_m: 0.9741 - dense_5_f1_m_1: 0.9741 - dense_5_f1_m_2: 0.9741 - dense_6_f1_m: 0.9638 - dense_6_f1_m_1: 0.9638 - dense_6_f1_m_2: 0.9638 - val_loss: 25.0067 - val_dense_4_loss: 1.7549 - val_dense_5_loss: 1.2342 - val_dense_6_loss: 22.0176 - val_dense_4_f1_m: 0.3587 - val_dense_4_f1_m_1: 0.3587 - val_dense_4_f1_m_2: 0.3587 - val_dense_5_f1_m: 0.9509 - val_dense_5_f1_m_1: 0.9509 - val_dense_5_f1_m_2: 0.9509 - val_dense_6_f1_m: 0.9057 - val_dense_6_f1_m_1: 0.9057 - val_dense_6_f1_m_2: 0.9057\n",
      "Epoch 79/100\n",
      "582/582 [==============================] - 1s 1ms/step - loss: 1.4736 - dense_4_loss: 0.0360 - dense_5_loss: 0.1750 - dense_6_loss: 1.2627 - dense_4_f1_m: 0.6037 - dense_4_f1_m_1: 0.6037 - dense_4_f1_m_2: 0.6037 - dense_5_f1_m: 0.9778 - dense_5_f1_m_1: 0.9778 - dense_5_f1_m_2: 0.9778 - dense_6_f1_m: 0.9646 - dense_6_f1_m_1: 0.9646 - dense_6_f1_m_2: 0.9646 - val_loss: 26.2141 - val_dense_4_loss: 1.8882 - val_dense_5_loss: 1.3360 - val_dense_6_loss: 22.9899 - val_dense_4_f1_m: 0.3467 - val_dense_4_f1_m_1: 0.3467 - val_dense_4_f1_m_2: 0.3467 - val_dense_5_f1_m: 0.9630 - val_dense_5_f1_m_1: 0.9630 - val_dense_5_f1_m_2: 0.9630 - val_dense_6_f1_m: 0.9257 - val_dense_6_f1_m_1: 0.9257 - val_dense_6_f1_m_2: 0.9257\n",
      "Epoch 80/100\n",
      "582/582 [==============================] - 1s 1ms/step - loss: 1.4195 - dense_4_loss: 0.0446 - dense_5_loss: 0.1715 - dense_6_loss: 1.2033 - dense_4_f1_m: 0.5865 - dense_4_f1_m_1: 0.5865 - dense_4_f1_m_2: 0.5865 - dense_5_f1_m: 0.9799 - dense_5_f1_m_1: 0.9799 - dense_5_f1_m_2: 0.9799 - dense_6_f1_m: 0.9699 - dense_6_f1_m_1: 0.9699 - dense_6_f1_m_2: 0.9699 - val_loss: 26.9513 - val_dense_4_loss: 1.9363 - val_dense_5_loss: 1.4086 - val_dense_6_loss: 23.6065 - val_dense_4_f1_m: 0.3200 - val_dense_4_f1_m_1: 0.3200 - val_dense_4_f1_m_2: 0.3200 - val_dense_5_f1_m: 0.9609 - val_dense_5_f1_m_1: 0.9609 - val_dense_5_f1_m_2: 0.9609 - val_dense_6_f1_m: 0.9280 - val_dense_6_f1_m_1: 0.9280 - val_dense_6_f1_m_2: 0.9280\n",
      "Epoch 81/100\n",
      "582/582 [==============================] - 1s 993us/step - loss: 1.3942 - dense_4_loss: 0.0326 - dense_5_loss: 0.1803 - dense_6_loss: 1.1813 - dense_4_f1_m: 0.4834 - dense_4_f1_m_1: 0.4834 - dense_4_f1_m_2: 0.4834 - dense_5_f1_m: 0.9779 - dense_5_f1_m_1: 0.9779 - dense_5_f1_m_2: 0.9779 - dense_6_f1_m: 0.9691 - dense_6_f1_m_1: 0.9691 - dense_6_f1_m_2: 0.9691 - val_loss: 27.4483 - val_dense_4_loss: 1.8680 - val_dense_5_loss: 1.2853 - val_dense_6_loss: 24.2950 - val_dense_4_f1_m: 0.3867 - val_dense_4_f1_m_1: 0.3867 - val_dense_4_f1_m_2: 0.3867 - val_dense_5_f1_m: 0.9651 - val_dense_5_f1_m_1: 0.9651 - val_dense_5_f1_m_2: 0.9651 - val_dense_6_f1_m: 0.9217 - val_dense_6_f1_m_1: 0.9217 - val_dense_6_f1_m_2: 0.9217\n",
      "Epoch 82/100\n",
      "582/582 [==============================] - 1s 1ms/step - loss: 1.6317 - dense_4_loss: 0.1136 - dense_5_loss: 0.1990 - dense_6_loss: 1.3191 - dense_4_f1_m: 0.4496 - dense_4_f1_m_1: 0.4496 - dense_4_f1_m_2: 0.4496 - dense_5_f1_m: 0.9733 - dense_5_f1_m_1: 0.9733 - dense_5_f1_m_2: 0.9733 - dense_6_f1_m: 0.9493 - dense_6_f1_m_1: 0.9493 - dense_6_f1_m_2: 0.9493 - val_loss: 26.9954 - val_dense_4_loss: 1.7909 - val_dense_5_loss: 1.2215 - val_dense_6_loss: 23.9829 - val_dense_4_f1_m: 0.3507 - val_dense_4_f1_m_1: 0.3507 - val_dense_4_f1_m_2: 0.3507 - val_dense_5_f1_m: 0.9361 - val_dense_5_f1_m_1: 0.9361 - val_dense_5_f1_m_2: 0.9361 - val_dense_6_f1_m: 0.8640 - val_dense_6_f1_m_1: 0.8640 - val_dense_6_f1_m_2: 0.8640\n",
      "Epoch 83/100\n",
      "582/582 [==============================] - 1s 959us/step - loss: 1.4444 - dense_4_loss: 0.0527 - dense_5_loss: 0.1930 - dense_6_loss: 1.1987 - dense_4_f1_m: 0.5074 - dense_4_f1_m_1: 0.5074 - dense_4_f1_m_2: 0.5074 - dense_5_f1_m: 0.9770 - dense_5_f1_m_1: 0.9770 - dense_5_f1_m_2: 0.9770 - dense_6_f1_m: 0.9645 - dense_6_f1_m_1: 0.9645 - dense_6_f1_m_2: 0.9645 - val_loss: 27.5680 - val_dense_4_loss: 1.8378 - val_dense_5_loss: 1.2706 - val_dense_6_loss: 24.4595 - val_dense_4_f1_m: 0.3867 - val_dense_4_f1_m_1: 0.3867 - val_dense_4_f1_m_2: 0.3867 - val_dense_5_f1_m: 0.9630 - val_dense_5_f1_m_1: 0.9630 - val_dense_5_f1_m_2: 0.9630 - val_dense_6_f1_m: 0.9120 - val_dense_6_f1_m_1: 0.9120 - val_dense_6_f1_m_2: 0.9120\n",
      "Epoch 84/100\n",
      "582/582 [==============================] - 1s 1ms/step - loss: 1.5449 - dense_4_loss: 0.0391 - dense_5_loss: 0.1806 - dense_6_loss: 1.3251 - dense_4_f1_m: 0.5531 - dense_4_f1_m_1: 0.5531 - dense_4_f1_m_2: 0.5531 - dense_5_f1_m: 0.9765 - dense_5_f1_m_1: 0.9765 - dense_5_f1_m_2: 0.9765 - dense_6_f1_m: 0.9638 - dense_6_f1_m_1: 0.9638 - dense_6_f1_m_2: 0.9638 - val_loss: 26.7779 - val_dense_4_loss: 1.9367 - val_dense_5_loss: 1.3369 - val_dense_6_loss: 23.5043 - val_dense_4_f1_m: 0.3733 - val_dense_4_f1_m_1: 0.3733 - val_dense_4_f1_m_2: 0.3733 - val_dense_5_f1_m: 0.9628 - val_dense_5_f1_m_1: 0.9628 - val_dense_5_f1_m_2: 0.9628 - val_dense_6_f1_m: 0.9259 - val_dense_6_f1_m_1: 0.9259 - val_dense_6_f1_m_2: 0.9259\n",
      "Epoch 85/100\n",
      "582/582 [==============================] - 1s 1000us/step - loss: 1.4097 - dense_4_loss: 0.0395 - dense_5_loss: 0.1734 - dense_6_loss: 1.1967 - dense_4_f1_m: 0.5556 - dense_4_f1_m_1: 0.5556 - dense_4_f1_m_2: 0.5556 - dense_5_f1_m: 0.9790 - dense_5_f1_m_1: 0.9790 - dense_5_f1_m_2: 0.9790 - dense_6_f1_m: 0.9690 - dense_6_f1_m_1: 0.9690 - dense_6_f1_m_2: 0.9690 - val_loss: 29.2658 - val_dense_4_loss: 1.8284 - val_dense_5_loss: 1.2247 - val_dense_6_loss: 26.2128 - val_dense_4_f1_m: 0.3720 - val_dense_4_f1_m_1: 0.3720 - val_dense_4_f1_m_2: 0.3720 - val_dense_5_f1_m: 0.9554 - val_dense_5_f1_m_1: 0.9554 - val_dense_5_f1_m_2: 0.9554 - val_dense_6_f1_m: 0.9040 - val_dense_6_f1_m_1: 0.9040 - val_dense_6_f1_m_2: 0.9040\n",
      "Epoch 86/100\n",
      "582/582 [==============================] - 1s 1ms/step - loss: 1.9681 - dense_4_loss: 0.1058 - dense_5_loss: 0.2250 - dense_6_loss: 1.6373 - dense_4_f1_m: 0.5338 - dense_4_f1_m_1: 0.5338 - dense_4_f1_m_2: 0.5338 - dense_5_f1_m: 0.9741 - dense_5_f1_m_1: 0.9741 - dense_5_f1_m_2: 0.9741 - dense_6_f1_m: 0.9587 - dense_6_f1_m_1: 0.9587 - dense_6_f1_m_2: 0.9587 - val_loss: 31.2910 - val_dense_4_loss: 2.3072 - val_dense_5_loss: 1.5095 - val_dense_6_loss: 27.4743 - val_dense_4_f1_m: 0.3600 - val_dense_4_f1_m_1: 0.3600 - val_dense_4_f1_m_2: 0.3600 - val_dense_5_f1_m: 0.9615 - val_dense_5_f1_m_1: 0.9615 - val_dense_5_f1_m_2: 0.9615 - val_dense_6_f1_m: 0.9257 - val_dense_6_f1_m_1: 0.9257 - val_dense_6_f1_m_2: 0.9257\n",
      "Epoch 87/100\n",
      "582/582 [==============================] - 1s 1ms/step - loss: 1.7485 - dense_4_loss: 0.0694 - dense_5_loss: 0.1841 - dense_6_loss: 1.4950 - dense_4_f1_m: 0.4742 - dense_4_f1_m_1: 0.4742 - dense_4_f1_m_2: 0.4742 - dense_5_f1_m: 0.9744 - dense_5_f1_m_1: 0.9744 - dense_5_f1_m_2: 0.9744 - dense_6_f1_m: 0.9441 - dense_6_f1_m_1: 0.9441 - dense_6_f1_m_2: 0.9441 - val_loss: 29.8517 - val_dense_4_loss: 2.0276 - val_dense_5_loss: 1.3829 - val_dense_6_loss: 26.4412 - val_dense_4_f1_m: 0.3867 - val_dense_4_f1_m_1: 0.3867 - val_dense_4_f1_m_2: 0.3867 - val_dense_5_f1_m: 0.9630 - val_dense_5_f1_m_1: 0.9630 - val_dense_5_f1_m_2: 0.9630 - val_dense_6_f1_m: 0.9160 - val_dense_6_f1_m_1: 0.9160 - val_dense_6_f1_m_2: 0.9160\n",
      "Epoch 88/100\n",
      "582/582 [==============================] - 1s 980us/step - loss: 1.5259 - dense_4_loss: 0.0423 - dense_5_loss: 0.1787 - dense_6_loss: 1.3049 - dense_4_f1_m: 0.5922 - dense_4_f1_m_1: 0.5922 - dense_4_f1_m_2: 0.5922 - dense_5_f1_m: 0.9769 - dense_5_f1_m_1: 0.9769 - dense_5_f1_m_2: 0.9769 - dense_6_f1_m: 0.9682 - dense_6_f1_m_1: 0.9682 - dense_6_f1_m_2: 0.9682 - val_loss: 29.3936 - val_dense_4_loss: 1.7819 - val_dense_5_loss: 1.2034 - val_dense_6_loss: 26.4083 - val_dense_4_f1_m: 0.3733 - val_dense_4_f1_m_1: 0.3733 - val_dense_4_f1_m_2: 0.3733 - val_dense_5_f1_m: 0.9630 - val_dense_5_f1_m_1: 0.9630 - val_dense_5_f1_m_2: 0.9630 - val_dense_6_f1_m: 0.9095 - val_dense_6_f1_m_1: 0.9095 - val_dense_6_f1_m_2: 0.9095\n",
      "Epoch 89/100\n",
      "582/582 [==============================] - 1s 1ms/step - loss: 1.3826 - dense_4_loss: 0.0475 - dense_5_loss: 0.1958 - dense_6_loss: 1.1392 - dense_4_f1_m: 0.5416 - dense_4_f1_m_1: 0.5416 - dense_4_f1_m_2: 0.5416 - dense_5_f1_m: 0.9774 - dense_5_f1_m_1: 0.9774 - dense_5_f1_m_2: 0.9774 - dense_6_f1_m: 0.9708 - dense_6_f1_m_1: 0.9708 - dense_6_f1_m_2: 0.9708 - val_loss: 27.9616 - val_dense_4_loss: 1.6227 - val_dense_5_loss: 1.1273 - val_dense_6_loss: 25.2116 - val_dense_4_f1_m: 0.3453 - val_dense_4_f1_m_1: 0.3453 - val_dense_4_f1_m_2: 0.3453 - val_dense_5_f1_m: 0.9476 - val_dense_5_f1_m_1: 0.9476 - val_dense_5_f1_m_2: 0.9476 - val_dense_6_f1_m: 0.8979 - val_dense_6_f1_m_1: 0.8979 - val_dense_6_f1_m_2: 0.8979\n",
      "Epoch 90/100\n",
      "582/582 [==============================] - 1s 994us/step - loss: 1.2889 - dense_4_loss: 0.0463 - dense_5_loss: 0.1869 - dense_6_loss: 1.0557 - dense_4_f1_m: 0.5565 - dense_4_f1_m_1: 0.5565 - dense_4_f1_m_2: 0.5565 - dense_5_f1_m: 0.9734 - dense_5_f1_m_1: 0.9734 - dense_5_f1_m_2: 0.9734 - dense_6_f1_m: 0.9691 - dense_6_f1_m_1: 0.9691 - dense_6_f1_m_2: 0.9691 - val_loss: 28.3110 - val_dense_4_loss: 1.7644 - val_dense_5_loss: 1.1939 - val_dense_6_loss: 25.3527 - val_dense_4_f1_m: 0.3640 - val_dense_4_f1_m_1: 0.3640 - val_dense_4_f1_m_2: 0.3640 - val_dense_5_f1_m: 0.9528 - val_dense_5_f1_m_1: 0.9528 - val_dense_5_f1_m_2: 0.9528 - val_dense_6_f1_m: 0.9120 - val_dense_6_f1_m_1: 0.9120 - val_dense_6_f1_m_2: 0.9120\n",
      "Epoch 91/100\n",
      "582/582 [==============================] - 1s 1ms/step - loss: 1.4825 - dense_4_loss: 0.0320 - dense_5_loss: 0.1831 - dense_6_loss: 1.2674 - dense_4_f1_m: 0.5670 - dense_4_f1_m_1: 0.5670 - dense_4_f1_m_2: 0.5670 - dense_5_f1_m: 0.9783 - dense_5_f1_m_1: 0.9783 - dense_5_f1_m_2: 0.9783 - dense_6_f1_m: 0.9656 - dense_6_f1_m_1: 0.9656 - dense_6_f1_m_2: 0.9656 - val_loss: 30.0553 - val_dense_4_loss: 1.9025 - val_dense_5_loss: 1.2645 - val_dense_6_loss: 26.8882 - val_dense_4_f1_m: 0.3733 - val_dense_4_f1_m_1: 0.3733 - val_dense_4_f1_m_2: 0.3733 - val_dense_5_f1_m: 0.9607 - val_dense_5_f1_m_1: 0.9607 - val_dense_5_f1_m_2: 0.9607 - val_dense_6_f1_m: 0.9219 - val_dense_6_f1_m_1: 0.9219 - val_dense_6_f1_m_2: 0.9219\n",
      "Epoch 92/100\n",
      "582/582 [==============================] - 1s 993us/step - loss: 1.2605 - dense_4_loss: 0.0301 - dense_5_loss: 0.1689 - dense_6_loss: 1.0615 - dense_4_f1_m: 0.5430 - dense_4_f1_m_1: 0.5430 - dense_4_f1_m_2: 0.5430 - dense_5_f1_m: 0.9786 - dense_5_f1_m_1: 0.9786 - dense_5_f1_m_2: 0.9786 - dense_6_f1_m: 0.9742 - dense_6_f1_m_1: 0.9742 - dense_6_f1_m_2: 0.9742 - val_loss: 29.9757 - val_dense_4_loss: 1.8662 - val_dense_5_loss: 1.2496 - val_dense_6_loss: 26.8598 - val_dense_4_f1_m: 0.3733 - val_dense_4_f1_m_1: 0.3733 - val_dense_4_f1_m_2: 0.3733 - val_dense_5_f1_m: 0.9607 - val_dense_5_f1_m_1: 0.9607 - val_dense_5_f1_m_2: 0.9607 - val_dense_6_f1_m: 0.9200 - val_dense_6_f1_m_1: 0.9200 - val_dense_6_f1_m_2: 0.9200\n",
      "Epoch 93/100\n",
      "582/582 [==============================] - 1s 1ms/step - loss: 1.1773 - dense_4_loss: 0.0260 - dense_5_loss: 0.1732 - dense_6_loss: 0.9780 - dense_4_f1_m: 0.5670 - dense_4_f1_m_1: 0.5670 - dense_4_f1_m_2: 0.5670 - dense_5_f1_m: 0.9810 - dense_5_f1_m_1: 0.9810 - dense_5_f1_m_2: 0.9810 - dense_6_f1_m: 0.9759 - dense_6_f1_m_1: 0.9759 - dense_6_f1_m_2: 0.9759 - val_loss: 30.9647 - val_dense_4_loss: 1.9269 - val_dense_5_loss: 1.2622 - val_dense_6_loss: 27.7756 - val_dense_4_f1_m: 0.3733 - val_dense_4_f1_m_1: 0.3733 - val_dense_4_f1_m_2: 0.3733 - val_dense_5_f1_m: 0.9607 - val_dense_5_f1_m_1: 0.9607 - val_dense_5_f1_m_2: 0.9607 - val_dense_6_f1_m: 0.9240 - val_dense_6_f1_m_1: 0.9240 - val_dense_6_f1_m_2: 0.9240\n",
      "Epoch 94/100\n",
      "582/582 [==============================] - 1s 1ms/step - loss: 1.2045 - dense_4_loss: 0.0228 - dense_5_loss: 0.1707 - dense_6_loss: 1.0109 - dense_4_f1_m: 0.5178 - dense_4_f1_m_1: 0.5178 - dense_4_f1_m_2: 0.5178 - dense_5_f1_m: 0.9791 - dense_5_f1_m_1: 0.9791 - dense_5_f1_m_2: 0.9791 - dense_6_f1_m: 0.9777 - dense_6_f1_m_1: 0.9777 - dense_6_f1_m_2: 0.9777 - val_loss: 30.5023 - val_dense_4_loss: 1.7988 - val_dense_5_loss: 1.1890 - val_dense_6_loss: 27.5145 - val_dense_4_f1_m: 0.3867 - val_dense_4_f1_m_1: 0.3867 - val_dense_4_f1_m_2: 0.3867 - val_dense_5_f1_m: 0.9580 - val_dense_5_f1_m_1: 0.9580 - val_dense_5_f1_m_2: 0.9580 - val_dense_6_f1_m: 0.9160 - val_dense_6_f1_m_1: 0.9160 - val_dense_6_f1_m_2: 0.9160\n",
      "Epoch 95/100\n",
      "582/582 [==============================] - 1s 996us/step - loss: 1.4780 - dense_4_loss: 0.0407 - dense_5_loss: 0.1928 - dense_6_loss: 1.2445 - dense_4_f1_m: 0.5704 - dense_4_f1_m_1: 0.5704 - dense_4_f1_m_2: 0.5704 - dense_5_f1_m: 0.9765 - dense_5_f1_m_1: 0.9765 - dense_5_f1_m_2: 0.9765 - dense_6_f1_m: 0.9639 - dense_6_f1_m_1: 0.9639 - dense_6_f1_m_2: 0.9639 - val_loss: 31.8401 - val_dense_4_loss: 2.0387 - val_dense_5_loss: 1.3100 - val_dense_6_loss: 28.4914 - val_dense_4_f1_m: 0.3733 - val_dense_4_f1_m_1: 0.3733 - val_dense_4_f1_m_2: 0.3733 - val_dense_5_f1_m: 0.9607 - val_dense_5_f1_m_1: 0.9607 - val_dense_5_f1_m_2: 0.9607 - val_dense_6_f1_m: 0.9160 - val_dense_6_f1_m_1: 0.9160 - val_dense_6_f1_m_2: 0.9160\n",
      "Epoch 96/100\n",
      "582/582 [==============================] - 1s 1ms/step - loss: 1.4585 - dense_4_loss: 0.0430 - dense_5_loss: 0.1696 - dense_6_loss: 1.2458 - dense_4_f1_m: 0.5441 - dense_4_f1_m_1: 0.5441 - dense_4_f1_m_2: 0.5441 - dense_5_f1_m: 0.9786 - dense_5_f1_m_1: 0.9786 - dense_5_f1_m_2: 0.9786 - dense_6_f1_m: 0.9656 - dense_6_f1_m_1: 0.9656 - dense_6_f1_m_2: 0.9656 - val_loss: 32.0317 - val_dense_4_loss: 2.0079 - val_dense_5_loss: 1.3691 - val_dense_6_loss: 28.6547 - val_dense_4_f1_m: 0.3867 - val_dense_4_f1_m_1: 0.3867 - val_dense_4_f1_m_2: 0.3867 - val_dense_5_f1_m: 0.9677 - val_dense_5_f1_m_1: 0.9677 - val_dense_5_f1_m_2: 0.9677 - val_dense_6_f1_m: 0.9219 - val_dense_6_f1_m_1: 0.9219 - val_dense_6_f1_m_2: 0.9219\n",
      "Epoch 97/100\n",
      "582/582 [==============================] - 1s 991us/step - loss: 1.1731 - dense_4_loss: 0.0315 - dense_5_loss: 0.1761 - dense_6_loss: 0.9654 - dense_4_f1_m: 0.5407 - dense_4_f1_m_1: 0.5407 - dense_4_f1_m_2: 0.5407 - dense_5_f1_m: 0.9797 - dense_5_f1_m_1: 0.9797 - dense_5_f1_m_2: 0.9797 - dense_6_f1_m: 0.9759 - dense_6_f1_m_1: 0.9759 - dense_6_f1_m_2: 0.9759 - val_loss: 36.7667 - val_dense_4_loss: 2.3698 - val_dense_5_loss: 1.4212 - val_dense_6_loss: 32.9757 - val_dense_4_f1_m: 0.3400 - val_dense_4_f1_m_1: 0.3400 - val_dense_4_f1_m_2: 0.3400 - val_dense_5_f1_m: 0.9659 - val_dense_5_f1_m_1: 0.9659 - val_dense_5_f1_m_2: 0.9659 - val_dense_6_f1_m: 0.9240 - val_dense_6_f1_m_1: 0.9240 - val_dense_6_f1_m_2: 0.9240\n",
      "Epoch 98/100\n",
      "582/582 [==============================] - 1s 2ms/step - loss: 1.1436 - dense_4_loss: 0.0289 - dense_5_loss: 0.1880 - dense_6_loss: 0.9266 - dense_4_f1_m: 0.5269 - dense_4_f1_m_1: 0.5269 - dense_4_f1_m_2: 0.5269 - dense_5_f1_m: 0.9787 - dense_5_f1_m_1: 0.9787 - dense_5_f1_m_2: 0.9787 - dense_6_f1_m: 0.9759 - dense_6_f1_m_1: 0.9759 - dense_6_f1_m_2: 0.9759 - val_loss: 33.4749 - val_dense_4_loss: 1.9525 - val_dense_5_loss: 1.2889 - val_dense_6_loss: 30.2335 - val_dense_4_f1_m: 0.3733 - val_dense_4_f1_m_1: 0.3733 - val_dense_4_f1_m_2: 0.3733 - val_dense_5_f1_m: 0.9628 - val_dense_5_f1_m_1: 0.9628 - val_dense_5_f1_m_2: 0.9628 - val_dense_6_f1_m: 0.9240 - val_dense_6_f1_m_1: 0.9240 - val_dense_6_f1_m_2: 0.9240\n",
      "Epoch 99/100\n",
      "582/582 [==============================] - 1s 1ms/step - loss: 1.0748 - dense_4_loss: 0.0213 - dense_5_loss: 0.1737 - dense_6_loss: 0.8798 - dense_4_f1_m: 0.5785 - dense_4_f1_m_1: 0.5785 - dense_4_f1_m_2: 0.5785 - dense_5_f1_m: 0.9813 - dense_5_f1_m_1: 0.9813 - dense_5_f1_m_2: 0.9813 - dense_6_f1_m: 0.9811 - dense_6_f1_m_1: 0.9811 - dense_6_f1_m_2: 0.9811 - val_loss: 34.9572 - val_dense_4_loss: 1.9361 - val_dense_5_loss: 1.2557 - val_dense_6_loss: 31.7654 - val_dense_4_f1_m: 0.3733 - val_dense_4_f1_m_1: 0.3733 - val_dense_4_f1_m_2: 0.3733 - val_dense_5_f1_m: 0.9604 - val_dense_5_f1_m_1: 0.9604 - val_dense_5_f1_m_2: 0.9604 - val_dense_6_f1_m: 0.9120 - val_dense_6_f1_m_1: 0.9120 - val_dense_6_f1_m_2: 0.9120\n",
      "Epoch 100/100\n",
      "582/582 [==============================] - 1s 1ms/step - loss: 1.1797 - dense_4_loss: 0.0515 - dense_5_loss: 0.1944 - dense_6_loss: 0.9337 - dense_4_f1_m: 0.5716 - dense_4_f1_m_1: 0.5716 - dense_4_f1_m_2: 0.5716 - dense_5_f1_m: 0.9771 - dense_5_f1_m_1: 0.9771 - dense_5_f1_m_2: 0.9771 - dense_6_f1_m: 0.9742 - dense_6_f1_m_1: 0.9742 - dense_6_f1_m_2: 0.9742 - val_loss: 34.8425 - val_dense_4_loss: 1.9241 - val_dense_5_loss: 1.2946 - val_dense_6_loss: 31.6238 - val_dense_4_f1_m: 0.3587 - val_dense_4_f1_m_1: 0.3587 - val_dense_4_f1_m_2: 0.3587 - val_dense_5_f1_m: 0.9580 - val_dense_5_f1_m_1: 0.9580 - val_dense_5_f1_m_2: 0.9580 - val_dense_6_f1_m: 0.9080 - val_dense_6_f1_m_1: 0.9080 - val_dense_6_f1_m_2: 0.9080\n"
     ]
    }
   ],
   "source": [
    "# fit the model\n",
    "history = model.fit(X_train, [y1_train,y2_train,y3_train],\n",
    "                    validation_split=0.3, epochs=100,batch_size = 10, verbose=1,class_weight=[cw1,cw2,class_weights])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "410/410 [==============================] - 0s 245us/step\n"
     ]
    }
   ],
   "source": [
    "# evaluate the model\n",
    "f1score = model.evaluate(X_test, [y1_test,y2_test,y3_test])\n",
    "#classifier =KerasClassifier(build_fn=making_model(193), epochs=100, batch_size=10, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#f1score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "yy1,yy2,yy3 = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "shape = yy3.shape\n",
    "for i in range(0, shape[0]):\n",
    "    for j in range(0, shape[1]):\n",
    "        if yy3[i, j] >= 0.5:\n",
    "            yy3[i, j] = 1\n",
    "        else:\n",
    "            yy3[i, j] = 0\n",
    "for i in range(0, shape[0]):\n",
    "        if yy2[i, 0] >= 0.5:\n",
    "            yy2[i, 0] = 1\n",
    "        else:\n",
    "            yy2[i, 0] = 0\n",
    "for i in range(0, shape[0]):\n",
    "        if yy1[i, 0] >= 0.5:\n",
    "            yy1[i, 0] = 1\n",
    "        else:\n",
    "            yy1[i, 0] = 0     \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "res_pred=np.empty(shape=(len(yy3),1),dtype=int)\n",
    "res_ex = np.empty(shape=(len(yy3),1),dtype=int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(yy3)):\n",
    "    res_pred[i] = argmax(yy3[i])\n",
    "    res_ex[i] = argmax(y3_test[i])\n",
    "    #print(argmax(yy3[i]))\n",
    "    #print(y3_test[i])\n",
    "    #print(\"--------------------------------------------\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[361  14]\n",
      " [  7  28]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      0.96      0.97       375\n",
      "           1       0.67      0.80      0.73        35\n",
      "\n",
      "    accuracy                           0.95       410\n",
      "   macro avg       0.82      0.88      0.85       410\n",
      "weighted avg       0.95      0.95      0.95       410\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y1_test,yy1))\n",
    "print(classification_report(y1_test,yy1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 18   6]\n",
      " [ 24 362]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.43      0.75      0.55        24\n",
      "           1       0.98      0.94      0.96       386\n",
      "\n",
      "    accuracy                           0.93       410\n",
      "   macro avg       0.71      0.84      0.75       410\n",
      "weighted avg       0.95      0.93      0.94       410\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y2_test,yy2))\n",
    "print(classification_report(y2_test,yy2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[360,   9,   3,   1,   4],\n",
       "       [  2,   5,   9,   0,   0],\n",
       "       [  3,   5,   6,   0,   0],\n",
       "       [  1,   0,   0,   0,   0],\n",
       "       [  1,   0,   1,   0,   0]])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(res_ex,res_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            (None, 193)          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "sequential_1 (Sequential)       (None, 193)          0           input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dense_1 (Dense)                 (None, 40)           7760        sequential_1[1][0]               \n",
      "__________________________________________________________________________________________________\n",
      "dense_2 (Dense)                 (None, 20)           820         dense_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dense_3 (Dense)                 (None, 8)            168         dense_2[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dense_4 (Dense)                 (None, 1)            9           dense_3[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dense_5 (Dense)                 (None, 1)            9           dense_3[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dense_6 (Dense)                 (None, 5)            45          dense_3[0][0]                    \n",
      "==================================================================================================\n",
      "Total params: 8,811\n",
      "Trainable params: 8,811\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9134171886335537\n",
      "0.9230264469574282\n",
      "0.9048780487804878\n"
     ]
    }
   ],
   "source": [
    "print(f1_score(res_ex,res_pred,average='weighted'))\n",
    "print(precision_score(res_ex,res_pred,average='weighted'))\n",
    "print(recall_score(res_ex,res_pred,average='weighted'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9048780487804878\n",
      "0.9048780487804878\n",
      "0.9048780487804878\n"
     ]
    }
   ],
   "source": [
    "print(f1_score(res_ex,res_pred,average='micro'))\n",
    "print(precision_score(res_ex,res_pred,average='micro'))\n",
    "print(recall_score(res_ex,res_pred,average='micro'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9048780487804878"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(res_ex,res_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.32341851696690405\n",
      "0.31197475978775274\n",
      "0.3391957180750284\n"
     ]
    }
   ],
   "source": [
    "print(f1_score(res_ex,res_pred,average='macro'))\n",
    "print(precision_score(res_ex,res_pred,average='macro'))\n",
    "print(recall_score(res_ex,res_pred,average='macro'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'y_train' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-39-01642ea2411b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      9\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclassification_report\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mroc_auc_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfit_predict_balanced_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'y_train' is not defined"
     ]
    }
   ],
   "source": [
    "from imblearn.keras import BalancedBatchGenerator\n",
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "def fit_predict_balanced_model(X_train, y_train, X_test, y_test):\n",
    "    model = classifier\n",
    "    training_generator = BalancedBatchGenerator(X_train, y_train)\n",
    "    model.fit_generator(generator=training_generator,epochs=100,verbose=1)\n",
    "    y_pred = model.predict_proba(X_test)\n",
    "    print(classification_report(y_test, y_pred))\n",
    "    return roc_auc_score(y_test, y_pred)\n",
    "res = fit_predict_balanced_model(X_train, y_train, X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'classifier' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-40-06dc4502b72e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mclassifier\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'classifier' is not defined"
     ]
    }
   ],
   "source": [
    "classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<keras.engine.training.Model at 0x7f209c406438>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "7qAVm6BttvHG",
    "outputId": "5305c979-f605-4363-8849-d9f53d765ae4"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/sklearn/externals/six.py:31: DeprecationWarning: The module is deprecated in version 0.21 and will be removed in version 0.23 since we've dropped support for Python 2.7. Please rely on the official version of six (https://pypi.org/project/six/).\n",
      "  \"(https://pypi.org/project/six/).\", DeprecationWarning)\n",
      "/opt/conda/lib/python3.6/site-packages/sklearn/externals/joblib/__init__.py:15: DeprecationWarning: sklearn.externals.joblib is deprecated in 0.21 and will be removed in 0.23. Please import this functionality directly from joblib, which can be installed with: pip install joblib. If this warning is raised when loading pickled models, you may need to re-serialize those models with scikit-learn 0.21+.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'y_train' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-42-7b566509347f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0meli5\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msklearn\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mPermutationImportance\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mhistory\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mvalidation_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mnb_epoch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m100\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0;31m#history=classifier.fit(X,y,validation_data=(X_test, y_test), batch_size = 10, nb_epoch = 100)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;31m#acc = []\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'y_train' is not defined"
     ]
    }
   ],
   "source": [
    "# Fitting our model \n",
    "import eli5\n",
    "from eli5.sklearn import PermutationImportance\n",
    "\n",
    "history=model.fit(X_train, y_train,validation_data=(X_test, y_test),nb_epoch = 100)\n",
    "#history=classifier.fit(X,y,validation_data=(X_test, y_test), batch_size = 10, nb_epoch = 100)\n",
    "#acc = []\n",
    "# Predicting the Test set results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "4iXz1qF3tvJY",
    "outputId": "d094e5d8-49bb-4bc0-99bb-cdc04a39bda9"
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'y_test' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-43-324ce4a10142>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# F1 score\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mf1_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'y_test' is not defined"
     ]
    }
   ],
   "source": [
    "# F1 score\n",
    "f1_score(y_test,y_pred)"
   ]
  }
 ],
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   "name": "Copy of Final_MLP_run1.ipynb",
   "provenance": [],
   "toc_visible": true,
   "version": "0.3.2"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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