NLP Case Study - Speech to Text Model Training.ipynb

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   "source": [
    "# Tensorflow Challenge: Speech to Text "
   ]
  },
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   "source": [
    "import pandas as pd\n",
    "import os\n",
    "import numpy as np\n",
    "import librosa\n",
    "import IPython.display as ipd\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.io import wavfile\n",
    "import warnings\n",
    "import tensorflow as tf\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import LSTM,GRU, Embedding, Input, Dense, Conv1D, MaxPool1D, BatchNormalization, Dropout, Bidirectional,Flatten\n",
    "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from tensorflow.keras.utils import to_categorical"
   ]
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   "source": [
    "df=pd.DataFrame(columns=[\"File\",\"Label\"])\n",
    "needed_labels=[\"yes\",\"no\",\"up\",\"left\",\"right\",\"down\",\n",
    "              \"one\",\"two\",\"three\",\"four\",\"five\",\"six\",\"seven\",\"eight\",\"nine\"]\n",
    "IND_SIZE=2000"
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    "labels=[]\n",
    "filenames=[]\n",
    "path='/kaggle/input/speech-commands-tensorflow/SpeechDataset/'\n",
    "for label in needed_labels:\n",
    "    if not os.path.isdir(path+label):\n",
    "        continue\n",
    "    for filename in os.listdir('/kaggle/input/speech-commands-tensorflow/SpeechDataset/'+label):\n",
    "        filenames.append(filename)\n",
    "        labels.append(label)\n",
    "        if len(labels)%IND_SIZE==0:\n",
    "            break"
   ]
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    "df=pd.DataFrame(data={\"filenames\":filenames,\"labels\":labels})"
   ]
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    "df=df.sample(frac=1)"
   ]
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       "                    filenames labels\n",
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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "samples, sample_rate = librosa.load(path+example_file_path, sr = 16000)\n",
    "fig = plt.figure(figsize=(14, 8))\n",
    "ax1 = fig.add_subplot(211)\n",
    "ax1.set_title('Raw wave of ' + path + example_file_path)\n",
    "ax1.set_xlabel('Time')\n",
    "ax1.set_ylabel('Amplitude')\n",
    "ax1.plot(np.linspace(0, len(samples)/sample_rate, sample_rate), samples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-07T16:20:25.387922Z",
     "iopub.status.busy": "2020-11-07T16:20:25.387073Z",
     "iopub.status.idle": "2020-11-07T16:20:25.403611Z",
     "shell.execute_reply": "2020-11-07T16:20:25.404395Z"
    },
    "papermill": {
     "duration": 0.062031,
     "end_time": "2020-11-07T16:20:25.404557",
     "exception": false,
     "start_time": "2020-11-07T16:20:25.342526",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16000\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "                <audio  controls=\"controls\" >\n",
       "                    <source 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type=\"audio/wav\" />\n",
       "                    Your browser does not support the audio element.\n",
       "                </audio>\n",
       "              "
      ],
      "text/plain": [
       "<IPython.lib.display.Audio object>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(sample_rate)\n",
    "ipd.Audio(samples, rate=sample_rate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-07T16:20:25.493530Z",
     "iopub.status.busy": "2020-11-07T16:20:25.492317Z",
     "iopub.status.idle": "2020-11-07T16:20:26.322472Z",
     "shell.execute_reply": "2020-11-07T16:20:26.323143Z"
    },
    "papermill": {
     "duration": 0.877754,
     "end_time": "2020-11-07T16:20:26.323309",
     "exception": false,
     "start_time": "2020-11-07T16:20:25.445555",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "                <audio  controls=\"controls\" >\n",
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       "              "
      ],
      "text/plain": [
       "<IPython.lib.display.Audio object>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "samples = librosa.resample(samples, sample_rate, 8000)\n",
    "ipd.Audio(samples, rate=8000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-07T16:20:26.434597Z",
     "iopub.status.busy": "2020-11-07T16:20:26.433346Z",
     "iopub.status.idle": "2020-11-07T16:20:26.754068Z",
     "shell.execute_reply": "2020-11-07T16:20:26.754934Z"
    },
    "papermill": {
     "duration": 0.390092,
     "end_time": "2020-11-07T16:20:26.755125",
     "exception": false,
     "start_time": "2020-11-07T16:20:26.365033",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(30,10))\n",
    "index=np.arange(len((df['labels'].value_counts().index)))\n",
    "plt.bar(index,list(df['labels'].value_counts()[:]))\n",
    "plt.xlabel('Commands Available')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Frequency Distribution')\n",
    "plt.xticks(index, df[\"labels\"].value_counts().index, fontsize=15, rotation=60)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-07T16:20:26.849039Z",
     "iopub.status.busy": "2020-11-07T16:20:26.847698Z",
     "iopub.status.idle": "2020-11-07T16:20:26.853931Z",
     "shell.execute_reply": "2020-11-07T16:20:26.853273Z"
    },
    "papermill": {
     "duration": 0.054809,
     "end_time": "2020-11-07T16:20:26.854066",
     "exception": false,
     "start_time": "2020-11-07T16:20:26.799257",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.9692132e-05, 4.2314532e-05, 3.9045488e-05, ..., 1.2277227e-05,\n",
       "       4.7883209e-06, 1.4199108e-05], dtype=float32)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-07T16:20:26.943323Z",
     "iopub.status.busy": "2020-11-07T16:20:26.942202Z",
     "iopub.status.idle": "2020-11-07T16:20:30.329173Z",
     "shell.execute_reply": "2020-11-07T16:20:30.328486Z"
    },
    "papermill": {
     "duration": 3.431542,
     "end_time": "2020-11-07T16:20:30.329319",
     "exception": false,
     "start_time": "2020-11-07T16:20:26.897777",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "\r\n",
      "\r\n",
      "ffmpeg is already the newest version (7:3.4.8-0ubuntu0.2).\r\n",
      "0 upgraded, 0 newly installed, 0 to remove and 95 not upgraded.\r\n"
     ]
    }
   ],
   "source": [
    "!apt install -y ffmpeg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-07T16:20:30.434343Z",
     "iopub.status.busy": "2020-11-07T16:20:30.433335Z",
     "iopub.status.idle": "2020-11-07T16:47:56.174615Z",
     "shell.execute_reply": "2020-11-07T16:47:56.173728Z"
    },
    "papermill": {
     "duration": 1645.798252,
     "end_time": "2020-11-07T16:47:56.174792",
     "exception": false,
     "start_time": "2020-11-07T16:20:30.376540",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "all_samples=[]\n",
    "all_labels=[]\n",
    "for i in range(df.shape[0]):\n",
    "    try:\n",
    "        samples, sample_rate=librosa.load(path+df.iloc[i][\"labels\"]+\"/\"+df.iloc[i][\"filenames\"])\n",
    "        re_sampled=librosa.resample(samples,sample_rate,8000)\n",
    "        if re_sampled.shape[0]==8000:\n",
    "            all_samples.append(re_sampled)\n",
    "            all_labels.append(df.iloc[i][\"labels\"])\n",
    "    except:\n",
    "        continue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-07T16:47:56.277082Z",
     "iopub.status.busy": "2020-11-07T16:47:56.276321Z",
     "iopub.status.idle": "2020-11-07T16:47:56.581700Z",
     "shell.execute_reply": "2020-11-07T16:47:56.581028Z"
    },
    "papermill": {
     "duration": 0.358275,
     "end_time": "2020-11-07T16:47:56.581867",
     "exception": false,
     "start_time": "2020-11-07T16:47:56.223592",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "all_samples=np.array(all_samples)\n",
    "all_labels=np.array(all_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-07T16:47:56.696944Z",
     "iopub.status.busy": "2020-11-07T16:47:56.695299Z",
     "iopub.status.idle": "2020-11-07T16:47:56.700223Z",
     "shell.execute_reply": "2020-11-07T16:47:56.699309Z"
    },
    "papermill": {
     "duration": 0.072363,
     "end_time": "2020-11-07T16:47:56.700415",
     "exception": false,
     "start_time": "2020-11-07T16:47:56.628052",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "six      1859\n",
      "nine     1846\n",
      "five     1839\n",
      "down     1835\n",
      "left     1834\n",
      "seven    1828\n",
      "right    1827\n",
      "three    1826\n",
      "two      1821\n",
      "four     1809\n",
      "eight    1808\n",
      "yes      1808\n",
      "no       1805\n",
      "one      1799\n",
      "up       1751\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(pd.value_counts(all_labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-07T16:47:56.816922Z",
     "iopub.status.busy": "2020-11-07T16:47:56.814667Z",
     "iopub.status.idle": "2020-11-07T16:47:56.817776Z",
     "shell.execute_reply": "2020-11-07T16:47:56.818338Z"
    },
    "papermill": {
     "duration": 0.069308,
     "end_time": "2020-11-07T16:47:56.818486",
     "exception": false,
     "start_time": "2020-11-07T16:47:56.749178",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "assert(len(needed_labels)==len(pd.value_counts(all_labels)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-07T16:47:56.919522Z",
     "iopub.status.busy": "2020-11-07T16:47:56.918364Z",
     "iopub.status.idle": "2020-11-07T16:47:57.588406Z",
     "shell.execute_reply": "2020-11-07T16:47:57.588984Z"
    },
    "papermill": {
     "duration": 0.723803,
     "end_time": "2020-11-07T16:47:57.589182",
     "exception": false,
     "start_time": "2020-11-07T16:47:56.865379",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "np.save(\"samples_np.npy\",all_samples)\n",
    "np.save(\"all_labels_np.npy\",all_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-07T16:47:57.692072Z",
     "iopub.status.busy": "2020-11-07T16:47:57.691285Z",
     "iopub.status.idle": "2020-11-07T16:48:00.048418Z",
     "shell.execute_reply": "2020-11-07T16:48:00.047148Z"
    },
    "papermill": {
     "duration": 2.409598,
     "end_time": "2020-11-07T16:48:00.048567",
     "exception": false,
     "start_time": "2020-11-07T16:47:57.638969",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#np.load('/kaggle/working/samples_np.npy')\n",
    "#np.load('/kaggle/working/all_labels_np.npy')\n",
    "all_samples=np.load('/kaggle/input/nlp-case-study-speech-to-text/samples_np.npy')\n",
    "all_labels=np.load('/kaggle/input/nlp-case-study-speech-to-text/all_labels_np.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-07T16:48:00.152843Z",
     "iopub.status.busy": "2020-11-07T16:48:00.151412Z",
     "iopub.status.idle": "2020-11-07T16:48:00.155688Z",
     "shell.execute_reply": "2020-11-07T16:48:00.156219Z"
    },
    "papermill": {
     "duration": 0.060356,
     "end_time": "2020-11-07T16:48:00.156371",
     "exception": false,
     "start_time": "2020-11-07T16:48:00.096015",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['four', 'five', 'five', ..., 'nine', 'down', 'right'], dtype='<U5')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-07T16:48:00.261194Z",
     "iopub.status.busy": "2020-11-07T16:48:00.260441Z",
     "iopub.status.idle": "2020-11-07T16:48:00.270191Z",
     "shell.execute_reply": "2020-11-07T16:48:00.269465Z"
    },
    "papermill": {
     "duration": 0.065785,
     "end_time": "2020-11-07T16:48:00.270317",
     "exception": false,
     "start_time": "2020-11-07T16:48:00.204532",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "time_steps=8000\n",
    "lb=LabelEncoder()\n",
    "all_samples=all_samples.reshape(-1,time_steps,1)\n",
    "all_labels_int=lb.fit_transform(all_labels)\n",
    "outcome_class=list(lb.classes_)\n",
    "all_labels_one_hot=to_categorical(all_labels_int, num_classes=len(needed_labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-07T16:48:00.372901Z",
     "iopub.status.busy": "2020-11-07T16:48:00.372102Z",
     "iopub.status.idle": "2020-11-07T16:48:00.378222Z",
     "shell.execute_reply": "2020-11-07T16:48:00.378813Z"
    },
    "papermill": {
     "duration": 0.060539,
     "end_time": "2020-11-07T16:48:00.378964",
     "exception": false,
     "start_time": "2020-11-07T16:48:00.318425",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_labels_one_hot[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-07T16:48:00.481973Z",
     "iopub.status.busy": "2020-11-07T16:48:00.481212Z",
     "iopub.status.idle": "2020-11-07T16:48:00.487690Z",
     "shell.execute_reply": "2020-11-07T16:48:00.487125Z"
    },
    "papermill": {
     "duration": 0.060395,
     "end_time": "2020-11-07T16:48:00.487828",
     "exception": false,
     "start_time": "2020-11-07T16:48:00.427433",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['down',\n",
       " 'eight',\n",
       " 'five',\n",
       " 'four',\n",
       " 'left',\n",
       " 'nine',\n",
       " 'no',\n",
       " 'one',\n",
       " 'right',\n",
       " 'seven',\n",
       " 'six',\n",
       " 'three',\n",
       " 'two',\n",
       " 'up',\n",
       " 'yes']"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outcome_class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-07T16:48:00.593418Z",
     "iopub.status.busy": "2020-11-07T16:48:00.592377Z",
     "iopub.status.idle": "2020-11-07T16:48:00.860972Z",
     "shell.execute_reply": "2020-11-07T16:48:00.860361Z"
    },
    "papermill": {
     "duration": 0.323894,
     "end_time": "2020-11-07T16:48:00.861105",
     "exception": false,
     "start_time": "2020-11-07T16:48:00.537211",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test= train_test_split(all_samples,all_labels_one_hot,test_size=0.2,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-07T16:48:00.966961Z",
     "iopub.status.busy": "2020-11-07T16:48:00.966041Z",
     "iopub.status.idle": "2020-11-07T16:48:00.970737Z",
     "shell.execute_reply": "2020-11-07T16:48:00.971303Z"
    },
    "papermill": {
     "duration": 0.060875,
     "end_time": "2020-11-07T16:48:00.971457",
     "exception": false,
     "start_time": "2020-11-07T16:48:00.910582",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Size of training set:  21790\n",
      "Size of testing set:  5448\n"
     ]
    }
   ],
   "source": [
    "print(\"Size of training set: \",X_train.shape[0])\n",
    "print(\"Size of testing set: \",X_test.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-07T16:48:01.078133Z",
     "iopub.status.busy": "2020-11-07T16:48:01.077274Z",
     "iopub.status.idle": "2020-11-07T16:48:01.082024Z",
     "shell.execute_reply": "2020-11-07T16:48:01.081445Z"
    },
    "papermill": {
     "duration": 0.06017,
     "end_time": "2020-11-07T16:48:01.082145",
     "exception": false,
     "start_time": "2020-11-07T16:48:01.021975",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "BATCH_SIZE=256\n",
    "LR=1e-3\n",
    "STEPS_PER_EPOCH=X_train.shape[0]//BATCH_SIZE\n",
    "EPOCHS=200"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-07T16:48:01.199482Z",
     "iopub.status.busy": "2020-11-07T16:48:01.198558Z",
     "iopub.status.idle": "2020-11-07T16:48:06.226199Z",
     "shell.execute_reply": "2020-11-07T16:48:06.226961Z"
    },
    "papermill": {
     "duration": 5.093986,
     "end_time": "2020-11-07T16:48:06.227136",
     "exception": false,
     "start_time": "2020-11-07T16:48:01.133150",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#Models DeepSpeech2 and Wav2letter++ (From Facebook)\n",
    "model=Sequential([\n",
    "    Input(shape=(time_steps,1)),\n",
    "    BatchNormalization(axis=-1, momentum=0.99, epsilon=1e-3, center=True, scale=True),\n",
    "    \n",
    "    Conv1D(8,13, padding='valid', activation='relu', strides=1),\n",
    "    MaxPool1D(3),\n",
    "    Dropout(0.3),\n",
    "    \n",
    "    Conv1D(16, 11, padding='valid', activation='relu', strides=1),\n",
    "    MaxPool1D(3),\n",
    "    Dropout(0.3),\n",
    "    \n",
    "    Conv1D(32, 9, padding='valid', activation='relu', strides=1),\n",
    "    MaxPool1D(3),\n",
    "    Dropout(0.3),\n",
    "    \n",
    "    BatchNormalization(axis=-1, momentum=0.99, epsilon=1e-3, center=True, scale=True),\n",
    "    \n",
    "    Bidirectional(LSTM(128, return_sequences=True)),\n",
    "    Bidirectional(LSTM(128, return_sequences=True)),\n",
    "    Bidirectional(LSTM(128, return_sequences=True)),\n",
    "    \n",
    "    BatchNormalization(axis=-1, momentum=0.99, epsilon=1e-3, center=True, scale=True),\n",
    "    Flatten(),\n",
    "    Dense(256,activation=\"relu\"),\n",
    "    Dense(len(outcome_class),activation='softmax')\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-07T16:48:06.341087Z",
     "iopub.status.busy": "2020-11-07T16:48:06.338262Z",
     "iopub.status.idle": "2020-11-07T16:48:06.359015Z",
     "shell.execute_reply": "2020-11-07T16:48:06.361592Z"
    },
    "papermill": {
     "duration": 0.082137,
     "end_time": "2020-11-07T16:48:06.361856",
     "exception": false,
     "start_time": "2020-11-07T16:48:06.279719",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "batch_normalization (BatchNo (None, 8000, 1)           4         \n",
      "_________________________________________________________________\n",
      "conv1d (Conv1D)              (None, 7988, 8)           112       \n",
      "_________________________________________________________________\n",
      "max_pooling1d (MaxPooling1D) (None, 2662, 8)           0         \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 2662, 8)           0         \n",
      "_________________________________________________________________\n",
      "conv1d_1 (Conv1D)            (None, 2652, 16)          1424      \n",
      "_________________________________________________________________\n",
      "max_pooling1d_1 (MaxPooling1 (None, 884, 16)           0         \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 884, 16)           0         \n",
      "_________________________________________________________________\n",
      "conv1d_2 (Conv1D)            (None, 876, 32)           4640      \n",
      "_________________________________________________________________\n",
      "max_pooling1d_2 (MaxPooling1 (None, 292, 32)           0         \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 292, 32)           0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_1 (Batch (None, 292, 32)           128       \n",
      "_________________________________________________________________\n",
      "bidirectional (Bidirectional (None, 292, 256)          164864    \n",
      "_________________________________________________________________\n",
      "bidirectional_1 (Bidirection (None, 292, 256)          394240    \n",
      "_________________________________________________________________\n",
      "bidirectional_2 (Bidirection (None, 292, 256)          394240    \n",
      "_________________________________________________________________\n",
      "batch_normalization_2 (Batch (None, 292, 256)          1024      \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 74752)             0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 256)               19136768  \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 15)                3855      \n",
      "=================================================================\n",
      "Total params: 20,101,299\n",
      "Trainable params: 20,100,721\n",
      "Non-trainable params: 578\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-07T16:48:06.505036Z",
     "iopub.status.busy": "2020-11-07T16:48:06.499583Z",
     "iopub.status.idle": "2020-11-07T16:48:06.514322Z",
     "shell.execute_reply": "2020-11-07T16:48:06.515049Z"
    },
    "papermill": {
     "duration": 0.088584,
     "end_time": "2020-11-07T16:48:06.515207",
     "exception": false,
     "start_time": "2020-11-07T16:48:06.426623",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "model.compile(loss='categorical_crossentropy',optimizer=Adam(LR),metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-07T16:48:06.630792Z",
     "iopub.status.busy": "2020-11-07T16:48:06.628656Z",
     "iopub.status.idle": "2020-11-07T16:48:06.632900Z",
     "shell.execute_reply": "2020-11-07T16:48:06.632243Z"
    },
    "papermill": {
     "duration": 0.064564,
     "end_time": "2020-11-07T16:48:06.633029",
     "exception": false,
     "start_time": "2020-11-07T16:48:06.568465",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#model.load_weights('/kaggle/input/nlp-case-study-speech-to-text/best_model.hdf5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-07T16:48:06.747067Z",
     "iopub.status.busy": "2020-11-07T16:48:06.745320Z",
     "iopub.status.idle": "2020-11-07T16:48:06.747831Z",
     "shell.execute_reply": "2020-11-07T16:48:06.748334Z"
    },
    "papermill": {
     "duration": 0.062644,
     "end_time": "2020-11-07T16:48:06.748593",
     "exception": false,
     "start_time": "2020-11-07T16:48:06.685949",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "checkpoint=ModelCheckpoint('best_model.hdf5',monitor='val_accuracy',verbose=1, save_best_only=True,mode=\"max\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-07T16:48:06.879110Z",
     "iopub.status.busy": "2020-11-07T16:48:06.878198Z",
     "iopub.status.idle": "2020-11-07T18:06:32.714173Z",
     "shell.execute_reply": "2020-11-07T18:06:32.713192Z"
    },
    "papermill": {
     "duration": 4705.913708,
     "end_time": "2020-11-07T18:06:32.714320",
     "exception": false,
     "start_time": "2020-11-07T16:48:06.800612",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 5.6238 - accuracy: 0.0996\n",
      "Epoch 00001: val_accuracy improved from -inf to 0.06443, saving model to best_model.hdf5\n",
      "85/85 [==============================] - 27s 314ms/step - loss: 5.6238 - accuracy: 0.0996 - val_loss: 3.0528 - val_accuracy: 0.0644\n",
      "Epoch 2/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 2.5720 - accuracy: 0.1115\n",
      "Epoch 00002: val_accuracy improved from 0.06443 to 0.08076, saving model to best_model.hdf5\n",
      "85/85 [==============================] - 24s 282ms/step - loss: 2.5720 - accuracy: 0.1115 - val_loss: 2.6962 - val_accuracy: 0.0808\n",
      "Epoch 3/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 2.3796 - accuracy: 0.1653\n",
      "Epoch 00003: val_accuracy improved from 0.08076 to 0.09563, saving model to best_model.hdf5\n",
      "85/85 [==============================] - 24s 283ms/step - loss: 2.3796 - accuracy: 0.1653 - val_loss: 2.6768 - val_accuracy: 0.0956\n",
      "Epoch 4/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 2.0086 - accuracy: 0.2865\n",
      "Epoch 00004: val_accuracy improved from 0.09563 to 0.16557, saving model to best_model.hdf5\n",
      "85/85 [==============================] - 24s 283ms/step - loss: 2.0086 - accuracy: 0.2865 - val_loss: 2.4832 - val_accuracy: 0.1656\n",
      "Epoch 5/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 1.5725 - accuracy: 0.4411\n",
      "Epoch 00005: val_accuracy improved from 0.16557 to 0.27772, saving model to best_model.hdf5\n",
      "85/85 [==============================] - 24s 284ms/step - loss: 1.5725 - accuracy: 0.4411 - val_loss: 2.0232 - val_accuracy: 0.2777\n",
      "Epoch 6/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 1.2520 - accuracy: 0.5720\n",
      "Epoch 00006: val_accuracy improved from 0.27772 to 0.53634, saving model to best_model.hdf5\n",
      "85/85 [==============================] - 24s 284ms/step - loss: 1.2520 - accuracy: 0.5720 - val_loss: 1.3777 - val_accuracy: 0.5363\n",
      "Epoch 7/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 1.0158 - accuracy: 0.6672\n",
      "Epoch 00007: val_accuracy improved from 0.53634 to 0.59471, saving model to best_model.hdf5\n",
      "85/85 [==============================] - 24s 283ms/step - loss: 1.0158 - accuracy: 0.6672 - val_loss: 1.2552 - val_accuracy: 0.5947\n",
      "Epoch 8/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.7358 - accuracy: 0.7628\n",
      "Epoch 00008: val_accuracy improved from 0.59471 to 0.71751, saving model to best_model.hdf5\n",
      "85/85 [==============================] - 24s 284ms/step - loss: 0.7358 - accuracy: 0.7628 - val_loss: 0.8674 - val_accuracy: 0.7175\n",
      "Epoch 9/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.6256 - accuracy: 0.7970\n",
      "Epoch 00009: val_accuracy improved from 0.71751 to 0.79093, saving model to best_model.hdf5\n",
      "85/85 [==============================] - 24s 281ms/step - loss: 0.6256 - accuracy: 0.7970 - val_loss: 0.6784 - val_accuracy: 0.7909\n",
      "Epoch 10/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.5012 - accuracy: 0.8400\n",
      "Epoch 00010: val_accuracy did not improve from 0.79093\n",
      "85/85 [==============================] - 23s 272ms/step - loss: 0.5012 - accuracy: 0.8400 - val_loss: 0.7542 - val_accuracy: 0.7757\n",
      "Epoch 11/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.4203 - accuracy: 0.8633\n",
      "Epoch 00011: val_accuracy improved from 0.79093 to 0.82067, saving model to best_model.hdf5\n",
      "85/85 [==============================] - 24s 285ms/step - loss: 0.4203 - accuracy: 0.8633 - val_loss: 0.5823 - val_accuracy: 0.8207\n",
      "Epoch 12/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.3637 - accuracy: 0.8834\n",
      "Epoch 00012: val_accuracy improved from 0.82067 to 0.86307, saving model to best_model.hdf5\n",
      "85/85 [==============================] - 24s 286ms/step - loss: 0.3637 - accuracy: 0.8834 - val_loss: 0.4675 - val_accuracy: 0.8631\n",
      "Epoch 13/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.3360 - accuracy: 0.8902\n",
      "Epoch 00013: val_accuracy improved from 0.86307 to 0.87500, saving model to best_model.hdf5\n",
      "85/85 [==============================] - 24s 283ms/step - loss: 0.3360 - accuracy: 0.8902 - val_loss: 0.3964 - val_accuracy: 0.8750\n",
      "Epoch 14/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.3276 - accuracy: 0.8947\n",
      "Epoch 00014: val_accuracy did not improve from 0.87500\n",
      "85/85 [==============================] - 23s 273ms/step - loss: 0.3276 - accuracy: 0.8947 - val_loss: 0.4794 - val_accuracy: 0.8655\n",
      "Epoch 15/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.2790 - accuracy: 0.9087\n",
      "Epoch 00015: val_accuracy improved from 0.87500 to 0.88344, saving model to best_model.hdf5\n",
      "85/85 [==============================] - 24s 284ms/step - loss: 0.2790 - accuracy: 0.9087 - val_loss: 0.3911 - val_accuracy: 0.8834\n",
      "Epoch 16/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.2890 - accuracy: 0.9066\n",
      "Epoch 00016: val_accuracy did not improve from 0.88344\n",
      "85/85 [==============================] - 23s 275ms/step - loss: 0.2890 - accuracy: 0.9066 - val_loss: 0.4739 - val_accuracy: 0.8605\n",
      "Epoch 17/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.2530 - accuracy: 0.9176\n",
      "Epoch 00017: val_accuracy did not improve from 0.88344\n",
      "85/85 [==============================] - 23s 272ms/step - loss: 0.2530 - accuracy: 0.9176 - val_loss: 0.4294 - val_accuracy: 0.8757\n",
      "Epoch 18/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.2302 - accuracy: 0.9252\n",
      "Epoch 00018: val_accuracy improved from 0.88344 to 0.88950, saving model to best_model.hdf5\n",
      "85/85 [==============================] - 24s 283ms/step - loss: 0.2302 - accuracy: 0.9252 - val_loss: 0.3978 - val_accuracy: 0.8895\n",
      "Epoch 19/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.2368 - accuracy: 0.9233\n",
      "Epoch 00019: val_accuracy did not improve from 0.88950\n",
      "85/85 [==============================] - 23s 275ms/step - loss: 0.2368 - accuracy: 0.9233 - val_loss: 0.4201 - val_accuracy: 0.8833\n",
      "Epoch 20/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.1858 - accuracy: 0.9367\n",
      "Epoch 00020: val_accuracy improved from 0.88950 to 0.90290, saving model to best_model.hdf5\n",
      "85/85 [==============================] - 24s 285ms/step - loss: 0.1858 - accuracy: 0.9367 - val_loss: 0.3539 - val_accuracy: 0.9029\n",
      "Epoch 21/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.1764 - accuracy: 0.9425\n",
      "Epoch 00021: val_accuracy did not improve from 0.90290\n",
      "85/85 [==============================] - 23s 273ms/step - loss: 0.1764 - accuracy: 0.9425 - val_loss: 0.4379 - val_accuracy: 0.8878\n",
      "Epoch 22/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.1677 - accuracy: 0.9445\n",
      "Epoch 00022: val_accuracy did not improve from 0.90290\n",
      "85/85 [==============================] - 23s 272ms/step - loss: 0.1677 - accuracy: 0.9445 - val_loss: 0.3647 - val_accuracy: 0.9011\n",
      "Epoch 23/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.1804 - accuracy: 0.9411\n",
      "Epoch 00023: val_accuracy did not improve from 0.90290\n",
      "85/85 [==============================] - 23s 273ms/step - loss: 0.1804 - accuracy: 0.9411 - val_loss: 0.3861 - val_accuracy: 0.8987\n",
      "Epoch 24/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.1719 - accuracy: 0.9423\n",
      "Epoch 00024: val_accuracy did not improve from 0.90290\n",
      "85/85 [==============================] - 23s 274ms/step - loss: 0.1719 - accuracy: 0.9423 - val_loss: 0.3783 - val_accuracy: 0.9007\n",
      "Epoch 25/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.1493 - accuracy: 0.9500\n",
      "Epoch 00025: val_accuracy improved from 0.90290 to 0.90437, saving model to best_model.hdf5\n",
      "85/85 [==============================] - 24s 284ms/step - loss: 0.1493 - accuracy: 0.9500 - val_loss: 0.3875 - val_accuracy: 0.9044\n",
      "Epoch 26/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.1664 - accuracy: 0.9461\n",
      "Epoch 00026: val_accuracy did not improve from 0.90437\n",
      "85/85 [==============================] - 23s 274ms/step - loss: 0.1664 - accuracy: 0.9461 - val_loss: 0.4222 - val_accuracy: 0.8978\n",
      "Epoch 27/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.1299 - accuracy: 0.9583\n",
      "Epoch 00027: val_accuracy did not improve from 0.90437\n",
      "85/85 [==============================] - 23s 275ms/step - loss: 0.1299 - accuracy: 0.9583 - val_loss: 0.4037 - val_accuracy: 0.9036\n",
      "Epoch 28/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.1251 - accuracy: 0.9602\n",
      "Epoch 00028: val_accuracy did not improve from 0.90437\n",
      "85/85 [==============================] - 23s 273ms/step - loss: 0.1251 - accuracy: 0.9602 - val_loss: 0.3737 - val_accuracy: 0.9042\n",
      "Epoch 29/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.1418 - accuracy: 0.9531\n",
      "Epoch 00029: val_accuracy improved from 0.90437 to 0.91006, saving model to best_model.hdf5\n",
      "85/85 [==============================] - 24s 283ms/step - loss: 0.1418 - accuracy: 0.9531 - val_loss: 0.3450 - val_accuracy: 0.9101\n",
      "Epoch 30/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.1258 - accuracy: 0.9585\n",
      "Epoch 00030: val_accuracy did not improve from 0.91006\n",
      "85/85 [==============================] - 23s 273ms/step - loss: 0.1258 - accuracy: 0.9585 - val_loss: 0.4893 - val_accuracy: 0.8871\n",
      "Epoch 31/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.1034 - accuracy: 0.9649\n",
      "Epoch 00031: val_accuracy did not improve from 0.91006\n",
      "85/85 [==============================] - 23s 273ms/step - loss: 0.1034 - accuracy: 0.9649 - val_loss: 0.4045 - val_accuracy: 0.9027\n",
      "Epoch 32/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0902 - accuracy: 0.9712\n",
      "Epoch 00032: val_accuracy did not improve from 0.91006\n",
      "85/85 [==============================] - 23s 273ms/step - loss: 0.0902 - accuracy: 0.9712 - val_loss: 0.3974 - val_accuracy: 0.9055\n",
      "Epoch 33/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.1008 - accuracy: 0.9675\n",
      "Epoch 00033: val_accuracy did not improve from 0.91006\n",
      "85/85 [==============================] - 23s 273ms/step - loss: 0.1008 - accuracy: 0.9675 - val_loss: 0.3906 - val_accuracy: 0.9046\n",
      "Epoch 34/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.1100 - accuracy: 0.9638\n",
      "Epoch 00034: val_accuracy did not improve from 0.91006\n",
      "85/85 [==============================] - 23s 273ms/step - loss: 0.1100 - accuracy: 0.9638 - val_loss: 0.4111 - val_accuracy: 0.9055\n",
      "Epoch 35/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0901 - accuracy: 0.9707\n",
      "Epoch 00035: val_accuracy improved from 0.91006 to 0.91153, saving model to best_model.hdf5\n",
      "85/85 [==============================] - 24s 284ms/step - loss: 0.0901 - accuracy: 0.9707 - val_loss: 0.3809 - val_accuracy: 0.9115\n",
      "Epoch 36/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.1026 - accuracy: 0.9662\n",
      "Epoch 00036: val_accuracy did not improve from 0.91153\n",
      "85/85 [==============================] - 23s 273ms/step - loss: 0.1026 - accuracy: 0.9662 - val_loss: 0.3810 - val_accuracy: 0.9088\n",
      "Epoch 37/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0939 - accuracy: 0.9699\n",
      "Epoch 00037: val_accuracy did not improve from 0.91153\n",
      "85/85 [==============================] - 23s 272ms/step - loss: 0.0939 - accuracy: 0.9699 - val_loss: 0.4046 - val_accuracy: 0.9058\n",
      "Epoch 38/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0983 - accuracy: 0.9676\n",
      "Epoch 00038: val_accuracy did not improve from 0.91153\n",
      "85/85 [==============================] - 23s 273ms/step - loss: 0.0983 - accuracy: 0.9676 - val_loss: 0.4247 - val_accuracy: 0.9025\n",
      "Epoch 39/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0836 - accuracy: 0.9734\n",
      "Epoch 00039: val_accuracy improved from 0.91153 to 0.91722, saving model to best_model.hdf5\n",
      "85/85 [==============================] - 24s 284ms/step - loss: 0.0836 - accuracy: 0.9734 - val_loss: 0.3583 - val_accuracy: 0.9172\n",
      "Epoch 40/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.1267 - accuracy: 0.9630\n",
      "Epoch 00040: val_accuracy did not improve from 0.91722\n",
      "85/85 [==============================] - 23s 273ms/step - loss: 0.1267 - accuracy: 0.9630 - val_loss: 0.4653 - val_accuracy: 0.8970\n",
      "Epoch 41/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.1159 - accuracy: 0.9625\n",
      "Epoch 00041: val_accuracy did not improve from 0.91722\n",
      "85/85 [==============================] - 23s 273ms/step - loss: 0.1159 - accuracy: 0.9625 - val_loss: 0.3789 - val_accuracy: 0.9130\n",
      "Epoch 42/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0728 - accuracy: 0.9770\n",
      "Epoch 00042: val_accuracy did not improve from 0.91722\n",
      "85/85 [==============================] - 23s 274ms/step - loss: 0.0728 - accuracy: 0.9770 - val_loss: 0.3991 - val_accuracy: 0.9104\n",
      "Epoch 43/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0748 - accuracy: 0.9751\n",
      "Epoch 00043: val_accuracy did not improve from 0.91722\n",
      "85/85 [==============================] - 23s 272ms/step - loss: 0.0748 - accuracy: 0.9751 - val_loss: 0.4217 - val_accuracy: 0.9091\n",
      "Epoch 44/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.1154 - accuracy: 0.9647\n",
      "Epoch 00044: val_accuracy did not improve from 0.91722\n",
      "85/85 [==============================] - 23s 273ms/step - loss: 0.1154 - accuracy: 0.9647 - val_loss: 0.4228 - val_accuracy: 0.9022\n",
      "Epoch 45/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0983 - accuracy: 0.9686\n",
      "Epoch 00045: val_accuracy did not improve from 0.91722\n",
      "85/85 [==============================] - 23s 272ms/step - loss: 0.0983 - accuracy: 0.9686 - val_loss: 0.4089 - val_accuracy: 0.9093\n",
      "Epoch 46/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0747 - accuracy: 0.9748\n",
      "Epoch 00046: val_accuracy improved from 0.91722 to 0.91814, saving model to best_model.hdf5\n",
      "85/85 [==============================] - 24s 281ms/step - loss: 0.0747 - accuracy: 0.9748 - val_loss: 0.4134 - val_accuracy: 0.9181\n",
      "Epoch 47/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0713 - accuracy: 0.9771\n",
      "Epoch 00047: val_accuracy did not improve from 0.91814\n",
      "85/85 [==============================] - 23s 274ms/step - loss: 0.0713 - accuracy: 0.9771 - val_loss: 0.4180 - val_accuracy: 0.9137\n",
      "Epoch 48/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0752 - accuracy: 0.9765\n",
      "Epoch 00048: val_accuracy did not improve from 0.91814\n",
      "85/85 [==============================] - 23s 274ms/step - loss: 0.0752 - accuracy: 0.9765 - val_loss: 0.4098 - val_accuracy: 0.9124\n",
      "Epoch 49/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0823 - accuracy: 0.9740\n",
      "Epoch 00049: val_accuracy did not improve from 0.91814\n",
      "85/85 [==============================] - 23s 273ms/step - loss: 0.0823 - accuracy: 0.9740 - val_loss: 0.3942 - val_accuracy: 0.9102\n",
      "Epoch 50/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0812 - accuracy: 0.9731\n",
      "Epoch 00050: val_accuracy did not improve from 0.91814\n",
      "85/85 [==============================] - 23s 274ms/step - loss: 0.0812 - accuracy: 0.9731 - val_loss: 0.4641 - val_accuracy: 0.9068\n",
      "Epoch 51/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0760 - accuracy: 0.9762\n",
      "Epoch 00051: val_accuracy improved from 0.91814 to 0.91850, saving model to best_model.hdf5\n",
      "85/85 [==============================] - 24s 282ms/step - loss: 0.0760 - accuracy: 0.9762 - val_loss: 0.3798 - val_accuracy: 0.9185\n",
      "Epoch 52/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.1224 - accuracy: 0.9622\n",
      "Epoch 00052: val_accuracy did not improve from 0.91850\n",
      "85/85 [==============================] - 23s 274ms/step - loss: 0.1224 - accuracy: 0.9622 - val_loss: 0.4366 - val_accuracy: 0.8998\n",
      "Epoch 53/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.1123 - accuracy: 0.9656\n",
      "Epoch 00053: val_accuracy did not improve from 0.91850\n",
      "85/85 [==============================] - 23s 271ms/step - loss: 0.1123 - accuracy: 0.9656 - val_loss: 0.3927 - val_accuracy: 0.9146\n",
      "Epoch 54/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0690 - accuracy: 0.9787\n",
      "Epoch 00054: val_accuracy did not improve from 0.91850\n",
      "85/85 [==============================] - 23s 273ms/step - loss: 0.0690 - accuracy: 0.9787 - val_loss: 0.4222 - val_accuracy: 0.9156\n",
      "Epoch 55/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0540 - accuracy: 0.9821\n",
      "Epoch 00055: val_accuracy did not improve from 0.91850\n",
      "85/85 [==============================] - 23s 273ms/step - loss: 0.0540 - accuracy: 0.9821 - val_loss: 0.5202 - val_accuracy: 0.8946\n",
      "Epoch 56/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0747 - accuracy: 0.9767\n",
      "Epoch 00056: val_accuracy did not improve from 0.91850\n",
      "85/85 [==============================] - 23s 272ms/step - loss: 0.0747 - accuracy: 0.9767 - val_loss: 0.4525 - val_accuracy: 0.9080\n",
      "Epoch 57/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0741 - accuracy: 0.9757\n",
      "Epoch 00057: val_accuracy did not improve from 0.91850\n",
      "85/85 [==============================] - 23s 273ms/step - loss: 0.0741 - accuracy: 0.9757 - val_loss: 0.4587 - val_accuracy: 0.9106\n",
      "Epoch 58/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0604 - accuracy: 0.9805\n",
      "Epoch 00058: val_accuracy improved from 0.91850 to 0.92015, saving model to best_model.hdf5\n",
      "85/85 [==============================] - 24s 285ms/step - loss: 0.0604 - accuracy: 0.9805 - val_loss: 0.4071 - val_accuracy: 0.9202\n",
      "Epoch 59/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0437 - accuracy: 0.9844\n",
      "Epoch 00059: val_accuracy improved from 0.92015 to 0.92493, saving model to best_model.hdf5\n",
      "85/85 [==============================] - 24s 283ms/step - loss: 0.0437 - accuracy: 0.9844 - val_loss: 0.3872 - val_accuracy: 0.9249\n",
      "Epoch 60/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0457 - accuracy: 0.9857\n",
      "Epoch 00060: val_accuracy did not improve from 0.92493\n",
      "85/85 [==============================] - 23s 275ms/step - loss: 0.0457 - accuracy: 0.9857 - val_loss: 0.3975 - val_accuracy: 0.9183\n",
      "Epoch 61/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0501 - accuracy: 0.9846\n",
      "Epoch 00061: val_accuracy did not improve from 0.92493\n",
      "85/85 [==============================] - 23s 273ms/step - loss: 0.0501 - accuracy: 0.9846 - val_loss: 0.4403 - val_accuracy: 0.9117\n",
      "Epoch 62/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0698 - accuracy: 0.9797\n",
      "Epoch 00062: val_accuracy did not improve from 0.92493\n",
      "85/85 [==============================] - 23s 273ms/step - loss: 0.0698 - accuracy: 0.9797 - val_loss: 0.4063 - val_accuracy: 0.9242\n",
      "Epoch 63/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0490 - accuracy: 0.9839\n",
      "Epoch 00063: val_accuracy did not improve from 0.92493\n",
      "85/85 [==============================] - 23s 274ms/step - loss: 0.0490 - accuracy: 0.9839 - val_loss: 0.3847 - val_accuracy: 0.9224\n",
      "Epoch 64/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0684 - accuracy: 0.9791\n",
      "Epoch 00064: val_accuracy did not improve from 0.92493\n",
      "85/85 [==============================] - 23s 273ms/step - loss: 0.0684 - accuracy: 0.9791 - val_loss: 0.4627 - val_accuracy: 0.9069\n",
      "Epoch 65/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0657 - accuracy: 0.9797\n",
      "Epoch 00065: val_accuracy did not improve from 0.92493\n",
      "85/85 [==============================] - 23s 272ms/step - loss: 0.0657 - accuracy: 0.9797 - val_loss: 0.3742 - val_accuracy: 0.9224\n",
      "Epoch 66/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0542 - accuracy: 0.9828\n",
      "Epoch 00066: val_accuracy did not improve from 0.92493\n",
      "85/85 [==============================] - 23s 273ms/step - loss: 0.0542 - accuracy: 0.9828 - val_loss: 0.4820 - val_accuracy: 0.9134\n",
      "Epoch 67/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0656 - accuracy: 0.9784\n",
      "Epoch 00067: val_accuracy did not improve from 0.92493\n",
      "85/85 [==============================] - 23s 273ms/step - loss: 0.0656 - accuracy: 0.9784 - val_loss: 0.4862 - val_accuracy: 0.9068\n",
      "Epoch 68/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0585 - accuracy: 0.9815\n",
      "Epoch 00068: val_accuracy did not improve from 0.92493\n",
      "85/85 [==============================] - 23s 271ms/step - loss: 0.0585 - accuracy: 0.9815 - val_loss: 0.4524 - val_accuracy: 0.9150\n",
      "Epoch 69/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0635 - accuracy: 0.9796\n",
      "Epoch 00069: val_accuracy did not improve from 0.92493\n",
      "85/85 [==============================] - 23s 272ms/step - loss: 0.0635 - accuracy: 0.9796 - val_loss: 0.3816 - val_accuracy: 0.9214\n",
      "Epoch 70/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0586 - accuracy: 0.9817\n",
      "Epoch 00070: val_accuracy did not improve from 0.92493\n",
      "85/85 [==============================] - 23s 274ms/step - loss: 0.0586 - accuracy: 0.9817 - val_loss: 0.4216 - val_accuracy: 0.9189\n",
      "Epoch 71/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0453 - accuracy: 0.9848\n",
      "Epoch 00071: val_accuracy did not improve from 0.92493\n",
      "85/85 [==============================] - 23s 271ms/step - loss: 0.0453 - accuracy: 0.9848 - val_loss: 0.4152 - val_accuracy: 0.9161\n",
      "Epoch 72/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0555 - accuracy: 0.9840\n",
      "Epoch 00072: val_accuracy did not improve from 0.92493\n",
      "85/85 [==============================] - 23s 272ms/step - loss: 0.0555 - accuracy: 0.9840 - val_loss: 0.4301 - val_accuracy: 0.9170\n",
      "Epoch 73/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0614 - accuracy: 0.9809\n",
      "Epoch 00073: val_accuracy did not improve from 0.92493\n",
      "85/85 [==============================] - 23s 274ms/step - loss: 0.0614 - accuracy: 0.9809 - val_loss: 0.5460 - val_accuracy: 0.9053\n",
      "Epoch 74/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0691 - accuracy: 0.9807\n",
      "Epoch 00074: val_accuracy did not improve from 0.92493\n",
      "85/85 [==============================] - 23s 272ms/step - loss: 0.0691 - accuracy: 0.9807 - val_loss: 0.3995 - val_accuracy: 0.9242\n",
      "Epoch 75/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0587 - accuracy: 0.9828\n",
      "Epoch 00075: val_accuracy did not improve from 0.92493\n",
      "85/85 [==============================] - 23s 275ms/step - loss: 0.0587 - accuracy: 0.9828 - val_loss: 0.5634 - val_accuracy: 0.9022\n",
      "Epoch 76/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0774 - accuracy: 0.9765\n",
      "Epoch 00076: val_accuracy did not improve from 0.92493\n",
      "85/85 [==============================] - 23s 274ms/step - loss: 0.0774 - accuracy: 0.9765 - val_loss: 0.4390 - val_accuracy: 0.9139\n",
      "Epoch 77/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0484 - accuracy: 0.9854\n",
      "Epoch 00077: val_accuracy did not improve from 0.92493\n",
      "85/85 [==============================] - 23s 272ms/step - loss: 0.0484 - accuracy: 0.9854 - val_loss: 0.4568 - val_accuracy: 0.9213\n",
      "Epoch 78/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0570 - accuracy: 0.9840\n",
      "Epoch 00078: val_accuracy did not improve from 0.92493\n",
      "85/85 [==============================] - 23s 274ms/step - loss: 0.0570 - accuracy: 0.9840 - val_loss: 0.4821 - val_accuracy: 0.9099\n",
      "Epoch 79/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0761 - accuracy: 0.9776\n",
      "Epoch 00079: val_accuracy did not improve from 0.92493\n",
      "85/85 [==============================] - 23s 273ms/step - loss: 0.0761 - accuracy: 0.9776 - val_loss: 0.5155 - val_accuracy: 0.9051\n",
      "Epoch 80/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0615 - accuracy: 0.9818\n",
      "Epoch 00080: val_accuracy did not improve from 0.92493\n",
      "85/85 [==============================] - 23s 272ms/step - loss: 0.0615 - accuracy: 0.9818 - val_loss: 0.4369 - val_accuracy: 0.9183\n",
      "Epoch 81/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0384 - accuracy: 0.9884\n",
      "Epoch 00081: val_accuracy improved from 0.92493 to 0.92713, saving model to best_model.hdf5\n",
      "85/85 [==============================] - 24s 281ms/step - loss: 0.0384 - accuracy: 0.9884 - val_loss: 0.3920 - val_accuracy: 0.9271\n",
      "Epoch 82/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0336 - accuracy: 0.9895\n",
      "Epoch 00082: val_accuracy improved from 0.92713 to 0.93080, saving model to best_model.hdf5\n",
      "85/85 [==============================] - 24s 286ms/step - loss: 0.0336 - accuracy: 0.9895 - val_loss: 0.3652 - val_accuracy: 0.9308\n",
      "Epoch 83/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0295 - accuracy: 0.9913\n",
      "Epoch 00083: val_accuracy did not improve from 0.93080\n",
      "85/85 [==============================] - 23s 275ms/step - loss: 0.0295 - accuracy: 0.9913 - val_loss: 0.4449 - val_accuracy: 0.9174\n",
      "Epoch 84/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0426 - accuracy: 0.9883\n",
      "Epoch 00084: val_accuracy did not improve from 0.93080\n",
      "85/85 [==============================] - 23s 273ms/step - loss: 0.0426 - accuracy: 0.9883 - val_loss: 0.6589 - val_accuracy: 0.8935\n",
      "Epoch 85/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0884 - accuracy: 0.9753\n",
      "Epoch 00085: val_accuracy did not improve from 0.93080\n",
      "85/85 [==============================] - 23s 273ms/step - loss: 0.0884 - accuracy: 0.9753 - val_loss: 0.4468 - val_accuracy: 0.9178\n",
      "Epoch 86/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0507 - accuracy: 0.9859\n",
      "Epoch 00086: val_accuracy did not improve from 0.93080\n",
      "85/85 [==============================] - 23s 275ms/step - loss: 0.0507 - accuracy: 0.9859 - val_loss: 0.4187 - val_accuracy: 0.9246\n",
      "Epoch 87/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0805 - accuracy: 0.9782\n",
      "Epoch 00087: val_accuracy did not improve from 0.93080\n",
      "85/85 [==============================] - 23s 273ms/step - loss: 0.0805 - accuracy: 0.9782 - val_loss: 0.4159 - val_accuracy: 0.9183\n",
      "Epoch 88/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0659 - accuracy: 0.9802\n",
      "Epoch 00088: val_accuracy did not improve from 0.93080\n",
      "85/85 [==============================] - 23s 275ms/step - loss: 0.0659 - accuracy: 0.9802 - val_loss: 0.4536 - val_accuracy: 0.9183\n",
      "Epoch 89/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0636 - accuracy: 0.9815\n",
      "Epoch 00089: val_accuracy did not improve from 0.93080\n",
      "85/85 [==============================] - 23s 273ms/step - loss: 0.0636 - accuracy: 0.9815 - val_loss: 0.4130 - val_accuracy: 0.9236\n",
      "Epoch 90/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0450 - accuracy: 0.9870\n",
      "Epoch 00090: val_accuracy did not improve from 0.93080\n",
      "85/85 [==============================] - 23s 273ms/step - loss: 0.0450 - accuracy: 0.9870 - val_loss: 0.4090 - val_accuracy: 0.9262\n",
      "Epoch 91/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0292 - accuracy: 0.9909\n",
      "Epoch 00091: val_accuracy did not improve from 0.93080\n",
      "85/85 [==============================] - 23s 273ms/step - loss: 0.0292 - accuracy: 0.9909 - val_loss: 0.3968 - val_accuracy: 0.9277\n",
      "Epoch 92/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0340 - accuracy: 0.9898\n",
      "Epoch 00092: val_accuracy did not improve from 0.93080\n",
      "85/85 [==============================] - 23s 275ms/step - loss: 0.0340 - accuracy: 0.9898 - val_loss: 0.4199 - val_accuracy: 0.9273\n",
      "Epoch 93/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0848 - accuracy: 0.9763\n",
      "Epoch 00093: val_accuracy did not improve from 0.93080\n",
      "85/85 [==============================] - 23s 271ms/step - loss: 0.0848 - accuracy: 0.9763 - val_loss: 0.4043 - val_accuracy: 0.9161\n",
      "Epoch 94/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0394 - accuracy: 0.9881\n",
      "Epoch 00094: val_accuracy did not improve from 0.93080\n",
      "85/85 [==============================] - 23s 273ms/step - loss: 0.0394 - accuracy: 0.9881 - val_loss: 0.3896 - val_accuracy: 0.9264\n",
      "Epoch 95/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0309 - accuracy: 0.9912\n",
      "Epoch 00095: val_accuracy improved from 0.93080 to 0.93245, saving model to best_model.hdf5\n",
      "85/85 [==============================] - 24s 281ms/step - loss: 0.0309 - accuracy: 0.9912 - val_loss: 0.3911 - val_accuracy: 0.9325\n",
      "Epoch 96/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0243 - accuracy: 0.9927\n",
      "Epoch 00096: val_accuracy did not improve from 0.93245\n",
      "85/85 [==============================] - 23s 271ms/step - loss: 0.0243 - accuracy: 0.9927 - val_loss: 0.3892 - val_accuracy: 0.9279\n",
      "Epoch 97/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0440 - accuracy: 0.9866\n",
      "Epoch 00097: val_accuracy did not improve from 0.93245\n",
      "85/85 [==============================] - 23s 273ms/step - loss: 0.0440 - accuracy: 0.9866 - val_loss: 0.5395 - val_accuracy: 0.9093\n",
      "Epoch 98/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0404 - accuracy: 0.9876\n",
      "Epoch 00098: val_accuracy did not improve from 0.93245\n",
      "85/85 [==============================] - 23s 272ms/step - loss: 0.0404 - accuracy: 0.9876 - val_loss: 0.4593 - val_accuracy: 0.9146\n",
      "Epoch 99/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0432 - accuracy: 0.9866\n",
      "Epoch 00099: val_accuracy did not improve from 0.93245\n",
      "85/85 [==============================] - 23s 271ms/step - loss: 0.0432 - accuracy: 0.9866 - val_loss: 0.4609 - val_accuracy: 0.9216\n",
      "Epoch 100/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0490 - accuracy: 0.9858\n",
      "Epoch 00100: val_accuracy did not improve from 0.93245\n",
      "85/85 [==============================] - 23s 273ms/step - loss: 0.0490 - accuracy: 0.9858 - val_loss: 0.4587 - val_accuracy: 0.9249\n",
      "Epoch 101/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0337 - accuracy: 0.9901\n",
      "Epoch 00101: val_accuracy did not improve from 0.93245\n",
      "85/85 [==============================] - 23s 275ms/step - loss: 0.0337 - accuracy: 0.9901 - val_loss: 0.4570 - val_accuracy: 0.9251\n",
      "Epoch 102/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0298 - accuracy: 0.9909\n",
      "Epoch 00102: val_accuracy did not improve from 0.93245\n",
      "85/85 [==============================] - 23s 272ms/step - loss: 0.0298 - accuracy: 0.9909 - val_loss: 0.4578 - val_accuracy: 0.9251\n",
      "Epoch 103/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0303 - accuracy: 0.9904\n",
      "Epoch 00103: val_accuracy did not improve from 0.93245\n",
      "85/85 [==============================] - 23s 272ms/step - loss: 0.0303 - accuracy: 0.9904 - val_loss: 0.4477 - val_accuracy: 0.9233\n",
      "Epoch 104/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0249 - accuracy: 0.9928\n",
      "Epoch 00104: val_accuracy did not improve from 0.93245\n",
      "85/85 [==============================] - 23s 271ms/step - loss: 0.0249 - accuracy: 0.9928 - val_loss: 0.4017 - val_accuracy: 0.9286\n",
      "Epoch 105/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0295 - accuracy: 0.9918\n",
      "Epoch 00105: val_accuracy did not improve from 0.93245\n",
      "85/85 [==============================] - 23s 271ms/step - loss: 0.0295 - accuracy: 0.9918 - val_loss: 0.4713 - val_accuracy: 0.9242\n",
      "Epoch 106/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0535 - accuracy: 0.9838\n",
      "Epoch 00106: val_accuracy did not improve from 0.93245\n",
      "85/85 [==============================] - 23s 271ms/step - loss: 0.0535 - accuracy: 0.9838 - val_loss: 0.4458 - val_accuracy: 0.9209\n",
      "Epoch 107/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.1320 - accuracy: 0.9648\n",
      "Epoch 00107: val_accuracy did not improve from 0.93245\n",
      "85/85 [==============================] - 23s 272ms/step - loss: 0.1320 - accuracy: 0.9648 - val_loss: 0.4419 - val_accuracy: 0.9073\n",
      "Epoch 108/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0449 - accuracy: 0.9861\n",
      "Epoch 00108: val_accuracy did not improve from 0.93245\n",
      "85/85 [==============================] - 23s 274ms/step - loss: 0.0449 - accuracy: 0.9861 - val_loss: 0.4118 - val_accuracy: 0.9284\n",
      "Epoch 109/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0243 - accuracy: 0.9924\n",
      "Epoch 00109: val_accuracy did not improve from 0.93245\n",
      "85/85 [==============================] - 23s 274ms/step - loss: 0.0243 - accuracy: 0.9924 - val_loss: 0.3965 - val_accuracy: 0.9257\n",
      "Epoch 110/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0304 - accuracy: 0.9896\n",
      "Epoch 00110: val_accuracy did not improve from 0.93245\n",
      "85/85 [==============================] - 23s 271ms/step - loss: 0.0304 - accuracy: 0.9896 - val_loss: 0.4162 - val_accuracy: 0.9269\n",
      "Epoch 111/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0216 - accuracy: 0.9931\n",
      "Epoch 00111: val_accuracy did not improve from 0.93245\n",
      "85/85 [==============================] - 23s 272ms/step - loss: 0.0216 - accuracy: 0.9931 - val_loss: 0.4286 - val_accuracy: 0.9258\n",
      "Epoch 112/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0262 - accuracy: 0.9918\n",
      "Epoch 00112: val_accuracy did not improve from 0.93245\n",
      "85/85 [==============================] - 23s 272ms/step - loss: 0.0262 - accuracy: 0.9918 - val_loss: 0.4558 - val_accuracy: 0.9231\n",
      "Epoch 113/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0224 - accuracy: 0.9933\n",
      "Epoch 00113: val_accuracy improved from 0.93245 to 0.93447, saving model to best_model.hdf5\n",
      "85/85 [==============================] - 24s 281ms/step - loss: 0.0224 - accuracy: 0.9933 - val_loss: 0.3868 - val_accuracy: 0.9345\n",
      "Epoch 114/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0242 - accuracy: 0.9923\n",
      "Epoch 00114: val_accuracy did not improve from 0.93447\n",
      "85/85 [==============================] - 23s 274ms/step - loss: 0.0242 - accuracy: 0.9923 - val_loss: 0.4660 - val_accuracy: 0.9242\n",
      "Epoch 115/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0412 - accuracy: 0.9883\n",
      "Epoch 00115: val_accuracy did not improve from 0.93447\n",
      "85/85 [==============================] - 23s 272ms/step - loss: 0.0412 - accuracy: 0.9883 - val_loss: 0.4321 - val_accuracy: 0.9235\n",
      "Epoch 116/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0350 - accuracy: 0.9893\n",
      "Epoch 00116: val_accuracy did not improve from 0.93447\n",
      "85/85 [==============================] - 23s 273ms/step - loss: 0.0350 - accuracy: 0.9893 - val_loss: 0.5348 - val_accuracy: 0.9159\n",
      "Epoch 117/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0365 - accuracy: 0.9889\n",
      "Epoch 00117: val_accuracy did not improve from 0.93447\n",
      "85/85 [==============================] - 23s 273ms/step - loss: 0.0365 - accuracy: 0.9889 - val_loss: 0.4641 - val_accuracy: 0.9258\n",
      "Epoch 118/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0330 - accuracy: 0.9900\n",
      "Epoch 00118: val_accuracy did not improve from 0.93447\n",
      "85/85 [==============================] - 23s 274ms/step - loss: 0.0330 - accuracy: 0.9900 - val_loss: 0.5007 - val_accuracy: 0.9225\n",
      "Epoch 119/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0266 - accuracy: 0.9923\n",
      "Epoch 00119: val_accuracy did not improve from 0.93447\n",
      "85/85 [==============================] - 23s 272ms/step - loss: 0.0266 - accuracy: 0.9923 - val_loss: 0.4276 - val_accuracy: 0.9284\n",
      "Epoch 120/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0404 - accuracy: 0.9883\n",
      "Epoch 00120: val_accuracy did not improve from 0.93447\n",
      "85/85 [==============================] - 23s 270ms/step - loss: 0.0404 - accuracy: 0.9883 - val_loss: 0.4512 - val_accuracy: 0.9222\n",
      "Epoch 121/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0451 - accuracy: 0.9862\n",
      "Epoch 00121: val_accuracy did not improve from 0.93447\n",
      "85/85 [==============================] - 23s 272ms/step - loss: 0.0451 - accuracy: 0.9862 - val_loss: 0.5199 - val_accuracy: 0.9165\n",
      "Epoch 122/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0537 - accuracy: 0.9840\n",
      "Epoch 00122: val_accuracy did not improve from 0.93447\n",
      "85/85 [==============================] - 23s 273ms/step - loss: 0.0537 - accuracy: 0.9840 - val_loss: 0.5324 - val_accuracy: 0.9161\n",
      "Epoch 123/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0382 - accuracy: 0.9886\n",
      "Epoch 00123: val_accuracy did not improve from 0.93447\n",
      "85/85 [==============================] - 23s 271ms/step - loss: 0.0382 - accuracy: 0.9886 - val_loss: 0.4557 - val_accuracy: 0.9273\n",
      "Epoch 124/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0366 - accuracy: 0.9896\n",
      "Epoch 00124: val_accuracy did not improve from 0.93447\n",
      "85/85 [==============================] - 23s 271ms/step - loss: 0.0366 - accuracy: 0.9896 - val_loss: 0.4464 - val_accuracy: 0.9255\n",
      "Epoch 125/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0540 - accuracy: 0.9855\n",
      "Epoch 00125: val_accuracy did not improve from 0.93447\n",
      "85/85 [==============================] - 23s 273ms/step - loss: 0.0540 - accuracy: 0.9855 - val_loss: 0.5530 - val_accuracy: 0.9119\n",
      "Epoch 126/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0428 - accuracy: 0.9881\n",
      "Epoch 00126: val_accuracy did not improve from 0.93447\n",
      "85/85 [==============================] - 23s 272ms/step - loss: 0.0428 - accuracy: 0.9881 - val_loss: 0.4316 - val_accuracy: 0.9295\n",
      "Epoch 127/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0481 - accuracy: 0.9860\n",
      "Epoch 00127: val_accuracy did not improve from 0.93447\n",
      "85/85 [==============================] - 23s 273ms/step - loss: 0.0481 - accuracy: 0.9860 - val_loss: 0.5012 - val_accuracy: 0.9187\n",
      "Epoch 128/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0563 - accuracy: 0.9840\n",
      "Epoch 00128: val_accuracy did not improve from 0.93447\n",
      "85/85 [==============================] - 23s 270ms/step - loss: 0.0563 - accuracy: 0.9840 - val_loss: 0.4839 - val_accuracy: 0.9191\n",
      "Epoch 129/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0445 - accuracy: 0.9877\n",
      "Epoch 00129: val_accuracy did not improve from 0.93447\n",
      "85/85 [==============================] - 23s 275ms/step - loss: 0.0445 - accuracy: 0.9877 - val_loss: 0.4616 - val_accuracy: 0.9249\n",
      "Epoch 130/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0239 - accuracy: 0.9926\n",
      "Epoch 00130: val_accuracy did not improve from 0.93447\n",
      "85/85 [==============================] - 23s 273ms/step - loss: 0.0239 - accuracy: 0.9926 - val_loss: 0.4242 - val_accuracy: 0.9304\n",
      "Epoch 131/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0271 - accuracy: 0.9922\n",
      "Epoch 00131: val_accuracy did not improve from 0.93447\n",
      "85/85 [==============================] - 23s 271ms/step - loss: 0.0271 - accuracy: 0.9922 - val_loss: 0.5446 - val_accuracy: 0.9198\n",
      "Epoch 132/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0438 - accuracy: 0.9884\n",
      "Epoch 00132: val_accuracy did not improve from 0.93447\n",
      "85/85 [==============================] - 23s 275ms/step - loss: 0.0438 - accuracy: 0.9884 - val_loss: 0.4464 - val_accuracy: 0.9244\n",
      "Epoch 133/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0257 - accuracy: 0.9910\n",
      "Epoch 00133: val_accuracy did not improve from 0.93447\n",
      "85/85 [==============================] - 23s 272ms/step - loss: 0.0257 - accuracy: 0.9910 - val_loss: 0.4185 - val_accuracy: 0.9279\n",
      "Epoch 134/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0213 - accuracy: 0.9929\n",
      "Epoch 00134: val_accuracy did not improve from 0.93447\n",
      "85/85 [==============================] - 23s 270ms/step - loss: 0.0213 - accuracy: 0.9929 - val_loss: 0.4673 - val_accuracy: 0.9280\n",
      "Epoch 135/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0214 - accuracy: 0.9939\n",
      "Epoch 00135: val_accuracy did not improve from 0.93447\n",
      "85/85 [==============================] - 23s 273ms/step - loss: 0.0214 - accuracy: 0.9939 - val_loss: 0.4772 - val_accuracy: 0.9323\n",
      "Epoch 136/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0334 - accuracy: 0.9910\n",
      "Epoch 00136: val_accuracy did not improve from 0.93447\n",
      "85/85 [==============================] - 23s 272ms/step - loss: 0.0334 - accuracy: 0.9910 - val_loss: 0.4986 - val_accuracy: 0.9203\n",
      "Epoch 137/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0253 - accuracy: 0.9922\n",
      "Epoch 00137: val_accuracy did not improve from 0.93447\n",
      "85/85 [==============================] - 23s 273ms/step - loss: 0.0253 - accuracy: 0.9922 - val_loss: 0.4754 - val_accuracy: 0.9249\n",
      "Epoch 138/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0202 - accuracy: 0.9944\n",
      "Epoch 00138: val_accuracy did not improve from 0.93447\n",
      "85/85 [==============================] - 23s 273ms/step - loss: 0.0202 - accuracy: 0.9944 - val_loss: 0.4346 - val_accuracy: 0.9308\n",
      "Epoch 139/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0203 - accuracy: 0.9942\n",
      "Epoch 00139: val_accuracy did not improve from 0.93447\n",
      "85/85 [==============================] - 23s 273ms/step - loss: 0.0203 - accuracy: 0.9942 - val_loss: 0.5173 - val_accuracy: 0.9236\n",
      "Epoch 140/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0335 - accuracy: 0.9897\n",
      "Epoch 00140: val_accuracy did not improve from 0.93447\n",
      "85/85 [==============================] - 23s 270ms/step - loss: 0.0335 - accuracy: 0.9897 - val_loss: 0.5035 - val_accuracy: 0.9211\n",
      "Epoch 141/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0290 - accuracy: 0.9910\n",
      "Epoch 00141: val_accuracy did not improve from 0.93447\n",
      "85/85 [==============================] - 23s 273ms/step - loss: 0.0290 - accuracy: 0.9910 - val_loss: 0.5361 - val_accuracy: 0.9244\n",
      "Epoch 142/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0219 - accuracy: 0.9935\n",
      "Epoch 00142: val_accuracy did not improve from 0.93447\n",
      "85/85 [==============================] - 23s 272ms/step - loss: 0.0219 - accuracy: 0.9935 - val_loss: 0.5019 - val_accuracy: 0.9240\n",
      "Epoch 143/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0218 - accuracy: 0.9941\n",
      "Epoch 00143: val_accuracy did not improve from 0.93447\n",
      "85/85 [==============================] - 23s 273ms/step - loss: 0.0218 - accuracy: 0.9941 - val_loss: 0.5122 - val_accuracy: 0.9225\n",
      "Epoch 144/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0241 - accuracy: 0.9928\n",
      "Epoch 00144: val_accuracy did not improve from 0.93447\n",
      "85/85 [==============================] - 23s 275ms/step - loss: 0.0241 - accuracy: 0.9928 - val_loss: 0.5407 - val_accuracy: 0.9192\n",
      "Epoch 145/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0312 - accuracy: 0.9911\n",
      "Epoch 00145: val_accuracy did not improve from 0.93447\n",
      "85/85 [==============================] - 23s 272ms/step - loss: 0.0312 - accuracy: 0.9911 - val_loss: 0.4788 - val_accuracy: 0.9268\n",
      "Epoch 146/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0291 - accuracy: 0.9922\n",
      "Epoch 00146: val_accuracy did not improve from 0.93447\n",
      "85/85 [==============================] - 23s 273ms/step - loss: 0.0291 - accuracy: 0.9922 - val_loss: 0.5321 - val_accuracy: 0.9227\n",
      "Epoch 147/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0375 - accuracy: 0.9894\n",
      "Epoch 00147: val_accuracy did not improve from 0.93447\n",
      "85/85 [==============================] - 23s 273ms/step - loss: 0.0375 - accuracy: 0.9894 - val_loss: 0.4832 - val_accuracy: 0.9273\n",
      "Epoch 148/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0256 - accuracy: 0.9920\n",
      "Epoch 00148: val_accuracy did not improve from 0.93447\n",
      "85/85 [==============================] - 23s 271ms/step - loss: 0.0256 - accuracy: 0.9920 - val_loss: 0.4617 - val_accuracy: 0.9290\n",
      "Epoch 149/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0338 - accuracy: 0.9907\n",
      "Epoch 00149: val_accuracy did not improve from 0.93447\n",
      "85/85 [==============================] - 23s 273ms/step - loss: 0.0338 - accuracy: 0.9907 - val_loss: 0.4952 - val_accuracy: 0.9279\n",
      "Epoch 150/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0414 - accuracy: 0.9884\n",
      "Epoch 00150: val_accuracy did not improve from 0.93447\n",
      "85/85 [==============================] - 23s 274ms/step - loss: 0.0414 - accuracy: 0.9884 - val_loss: 0.7145 - val_accuracy: 0.9036\n",
      "Epoch 151/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0458 - accuracy: 0.9877\n",
      "Epoch 00151: val_accuracy did not improve from 0.93447\n",
      "85/85 [==============================] - 23s 272ms/step - loss: 0.0458 - accuracy: 0.9877 - val_loss: 0.4844 - val_accuracy: 0.9211\n",
      "Epoch 152/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0284 - accuracy: 0.9915\n",
      "Epoch 00152: val_accuracy did not improve from 0.93447\n",
      "85/85 [==============================] - 23s 273ms/step - loss: 0.0284 - accuracy: 0.9915 - val_loss: 0.4398 - val_accuracy: 0.9343\n",
      "Epoch 153/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0397 - accuracy: 0.9896\n",
      "Epoch 00153: val_accuracy did not improve from 0.93447\n",
      "85/85 [==============================] - 23s 273ms/step - loss: 0.0397 - accuracy: 0.9896 - val_loss: 0.6966 - val_accuracy: 0.8954\n",
      "Epoch 154/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0466 - accuracy: 0.9866\n",
      "Epoch 00154: val_accuracy did not improve from 0.93447\n",
      "85/85 [==============================] - 23s 269ms/step - loss: 0.0466 - accuracy: 0.9866 - val_loss: 0.4473 - val_accuracy: 0.9328\n",
      "Epoch 155/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0257 - accuracy: 0.9924\n",
      "Epoch 00155: val_accuracy did not improve from 0.93447\n",
      "85/85 [==============================] - 23s 274ms/step - loss: 0.0257 - accuracy: 0.9924 - val_loss: 0.4660 - val_accuracy: 0.9293\n",
      "Epoch 156/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0167 - accuracy: 0.9951\n",
      "Epoch 00156: val_accuracy did not improve from 0.93447\n",
      "85/85 [==============================] - 23s 274ms/step - loss: 0.0167 - accuracy: 0.9951 - val_loss: 0.4796 - val_accuracy: 0.9253\n",
      "Epoch 157/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0191 - accuracy: 0.9942\n",
      "Epoch 00157: val_accuracy did not improve from 0.93447\n",
      "85/85 [==============================] - 23s 272ms/step - loss: 0.0191 - accuracy: 0.9942 - val_loss: 0.5431 - val_accuracy: 0.9187\n",
      "Epoch 158/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0256 - accuracy: 0.9930\n",
      "Epoch 00158: val_accuracy did not improve from 0.93447\n",
      "85/85 [==============================] - 23s 274ms/step - loss: 0.0256 - accuracy: 0.9930 - val_loss: 0.5007 - val_accuracy: 0.9251\n",
      "Epoch 159/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0262 - accuracy: 0.9929\n",
      "Epoch 00159: val_accuracy did not improve from 0.93447\n",
      "85/85 [==============================] - 23s 273ms/step - loss: 0.0262 - accuracy: 0.9929 - val_loss: 0.5042 - val_accuracy: 0.9246\n",
      "Epoch 160/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0210 - accuracy: 0.9939\n",
      "Epoch 00160: val_accuracy did not improve from 0.93447\n",
      "85/85 [==============================] - 23s 271ms/step - loss: 0.0210 - accuracy: 0.9939 - val_loss: 0.4680 - val_accuracy: 0.9277\n",
      "Epoch 161/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0220 - accuracy: 0.9935\n",
      "Epoch 00161: val_accuracy did not improve from 0.93447\n",
      "85/85 [==============================] - 23s 273ms/step - loss: 0.0220 - accuracy: 0.9935 - val_loss: 0.4874 - val_accuracy: 0.9279\n",
      "Epoch 162/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0217 - accuracy: 0.9937\n",
      "Epoch 00162: val_accuracy did not improve from 0.93447\n",
      "85/85 [==============================] - 23s 274ms/step - loss: 0.0217 - accuracy: 0.9937 - val_loss: 0.6655 - val_accuracy: 0.9075\n",
      "Epoch 163/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0307 - accuracy: 0.9919\n",
      "Epoch 00163: val_accuracy did not improve from 0.93447\n",
      "85/85 [==============================] - 23s 270ms/step - loss: 0.0307 - accuracy: 0.9919 - val_loss: 0.5243 - val_accuracy: 0.9229\n",
      "Epoch 164/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0199 - accuracy: 0.9939\n",
      "Epoch 00164: val_accuracy did not improve from 0.93447\n",
      "85/85 [==============================] - 23s 273ms/step - loss: 0.0199 - accuracy: 0.9939 - val_loss: 0.5001 - val_accuracy: 0.9290\n",
      "Epoch 165/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0146 - accuracy: 0.9958\n",
      "Epoch 00165: val_accuracy did not improve from 0.93447\n",
      "85/85 [==============================] - 23s 273ms/step - loss: 0.0146 - accuracy: 0.9958 - val_loss: 0.4703 - val_accuracy: 0.9268\n",
      "Epoch 166/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0186 - accuracy: 0.9943\n",
      "Epoch 00166: val_accuracy did not improve from 0.93447\n",
      "85/85 [==============================] - 23s 270ms/step - loss: 0.0186 - accuracy: 0.9943 - val_loss: 0.4598 - val_accuracy: 0.9297\n",
      "Epoch 167/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0118 - accuracy: 0.9962\n",
      "Epoch 00167: val_accuracy did not improve from 0.93447\n",
      "85/85 [==============================] - 23s 274ms/step - loss: 0.0118 - accuracy: 0.9962 - val_loss: 0.4452 - val_accuracy: 0.9319\n",
      "Epoch 168/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0158 - accuracy: 0.9952\n",
      "Epoch 00168: val_accuracy did not improve from 0.93447\n",
      "85/85 [==============================] - 23s 274ms/step - loss: 0.0158 - accuracy: 0.9952 - val_loss: 0.6690 - val_accuracy: 0.9181\n",
      "Epoch 169/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0344 - accuracy: 0.9909\n",
      "Epoch 00169: val_accuracy did not improve from 0.93447\n",
      "85/85 [==============================] - 23s 270ms/step - loss: 0.0344 - accuracy: 0.9909 - val_loss: 0.7337 - val_accuracy: 0.9047\n",
      "Epoch 170/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0490 - accuracy: 0.9883\n",
      "Epoch 00170: val_accuracy did not improve from 0.93447\n",
      "85/85 [==============================] - 23s 273ms/step - loss: 0.0490 - accuracy: 0.9883 - val_loss: 0.5473 - val_accuracy: 0.9124\n",
      "Epoch 171/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0270 - accuracy: 0.9923\n",
      "Epoch 00171: val_accuracy did not improve from 0.93447\n",
      "85/85 [==============================] - 23s 274ms/step - loss: 0.0270 - accuracy: 0.9923 - val_loss: 0.5135 - val_accuracy: 0.9249\n",
      "Epoch 172/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0386 - accuracy: 0.9889\n",
      "Epoch 00172: val_accuracy did not improve from 0.93447\n",
      "85/85 [==============================] - 23s 270ms/step - loss: 0.0386 - accuracy: 0.9889 - val_loss: 0.5735 - val_accuracy: 0.9165\n",
      "Epoch 173/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0176 - accuracy: 0.9941\n",
      "Epoch 00173: val_accuracy did not improve from 0.93447\n",
      "85/85 [==============================] - 23s 275ms/step - loss: 0.0176 - accuracy: 0.9941 - val_loss: 0.4960 - val_accuracy: 0.9258\n",
      "Epoch 174/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0176 - accuracy: 0.9943\n",
      "Epoch 00174: val_accuracy did not improve from 0.93447\n",
      "85/85 [==============================] - 23s 275ms/step - loss: 0.0176 - accuracy: 0.9943 - val_loss: 0.5166 - val_accuracy: 0.9249\n",
      "Epoch 175/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0442 - accuracy: 0.9882\n",
      "Epoch 00175: val_accuracy did not improve from 0.93447\n",
      "85/85 [==============================] - 23s 271ms/step - loss: 0.0442 - accuracy: 0.9882 - val_loss: 0.5736 - val_accuracy: 0.9169\n",
      "Epoch 176/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0403 - accuracy: 0.9882\n",
      "Epoch 00176: val_accuracy did not improve from 0.93447\n",
      "85/85 [==============================] - 23s 273ms/step - loss: 0.0403 - accuracy: 0.9882 - val_loss: 0.5241 - val_accuracy: 0.9229\n",
      "Epoch 177/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0486 - accuracy: 0.9864\n",
      "Epoch 00177: val_accuracy did not improve from 0.93447\n",
      "85/85 [==============================] - 23s 274ms/step - loss: 0.0486 - accuracy: 0.9864 - val_loss: 0.4645 - val_accuracy: 0.9236\n",
      "Epoch 178/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0248 - accuracy: 0.9930\n",
      "Epoch 00178: val_accuracy did not improve from 0.93447\n",
      "85/85 [==============================] - 23s 272ms/step - loss: 0.0248 - accuracy: 0.9930 - val_loss: 0.5342 - val_accuracy: 0.9238\n",
      "Epoch 179/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0198 - accuracy: 0.9939\n",
      "Epoch 00179: val_accuracy did not improve from 0.93447\n",
      "85/85 [==============================] - 23s 272ms/step - loss: 0.0198 - accuracy: 0.9939 - val_loss: 0.4443 - val_accuracy: 0.9269\n",
      "Epoch 180/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0241 - accuracy: 0.9928\n",
      "Epoch 00180: val_accuracy did not improve from 0.93447\n",
      "85/85 [==============================] - 23s 271ms/step - loss: 0.0241 - accuracy: 0.9928 - val_loss: 0.5175 - val_accuracy: 0.9249\n",
      "Epoch 181/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0191 - accuracy: 0.9943\n",
      "Epoch 00181: val_accuracy did not improve from 0.93447\n",
      "85/85 [==============================] - 23s 269ms/step - loss: 0.0191 - accuracy: 0.9943 - val_loss: 0.4766 - val_accuracy: 0.9297\n",
      "Epoch 182/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0247 - accuracy: 0.9920\n",
      "Epoch 00182: val_accuracy did not improve from 0.93447\n",
      "85/85 [==============================] - 23s 273ms/step - loss: 0.0247 - accuracy: 0.9920 - val_loss: 0.5139 - val_accuracy: 0.9258\n",
      "Epoch 183/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0182 - accuracy: 0.9946\n",
      "Epoch 00183: val_accuracy did not improve from 0.93447\n",
      "85/85 [==============================] - 23s 275ms/step - loss: 0.0182 - accuracy: 0.9946 - val_loss: 0.4593 - val_accuracy: 0.9323\n",
      "Epoch 184/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0125 - accuracy: 0.9961\n",
      "Epoch 00184: val_accuracy did not improve from 0.93447\n",
      "85/85 [==============================] - 23s 268ms/step - loss: 0.0125 - accuracy: 0.9961 - val_loss: 0.4392 - val_accuracy: 0.9330\n",
      "Epoch 185/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0145 - accuracy: 0.9956\n",
      "Epoch 00185: val_accuracy did not improve from 0.93447\n",
      "85/85 [==============================] - 23s 273ms/step - loss: 0.0145 - accuracy: 0.9956 - val_loss: 0.4728 - val_accuracy: 0.9277\n",
      "Epoch 186/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0115 - accuracy: 0.9962\n",
      "Epoch 00186: val_accuracy did not improve from 0.93447\n",
      "85/85 [==============================] - 23s 275ms/step - loss: 0.0115 - accuracy: 0.9962 - val_loss: 0.5039 - val_accuracy: 0.9269\n",
      "Epoch 187/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0143 - accuracy: 0.9958\n",
      "Epoch 00187: val_accuracy did not improve from 0.93447\n",
      "85/85 [==============================] - 23s 269ms/step - loss: 0.0143 - accuracy: 0.9958 - val_loss: 0.4867 - val_accuracy: 0.9280\n",
      "Epoch 188/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0179 - accuracy: 0.9950\n",
      "Epoch 00188: val_accuracy did not improve from 0.93447\n",
      "85/85 [==============================] - 23s 273ms/step - loss: 0.0179 - accuracy: 0.9950 - val_loss: 0.5490 - val_accuracy: 0.9286\n",
      "Epoch 189/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0147 - accuracy: 0.9957\n",
      "Epoch 00189: val_accuracy did not improve from 0.93447\n",
      "85/85 [==============================] - 23s 274ms/step - loss: 0.0147 - accuracy: 0.9957 - val_loss: 0.5392 - val_accuracy: 0.9249\n",
      "Epoch 190/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0165 - accuracy: 0.9952\n",
      "Epoch 00190: val_accuracy did not improve from 0.93447\n",
      "85/85 [==============================] - 23s 272ms/step - loss: 0.0165 - accuracy: 0.9952 - val_loss: 0.5621 - val_accuracy: 0.9218\n",
      "Epoch 191/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0388 - accuracy: 0.9899\n",
      "Epoch 00191: val_accuracy did not improve from 0.93447\n",
      "85/85 [==============================] - 23s 274ms/step - loss: 0.0388 - accuracy: 0.9899 - val_loss: 0.5601 - val_accuracy: 0.9240\n",
      "Epoch 192/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0264 - accuracy: 0.9924\n",
      "Epoch 00192: val_accuracy did not improve from 0.93447\n",
      "85/85 [==============================] - 23s 274ms/step - loss: 0.0264 - accuracy: 0.9924 - val_loss: 0.5116 - val_accuracy: 0.9304\n",
      "Epoch 193/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0234 - accuracy: 0.9938\n",
      "Epoch 00193: val_accuracy did not improve from 0.93447\n",
      "85/85 [==============================] - 23s 270ms/step - loss: 0.0234 - accuracy: 0.9938 - val_loss: 0.4806 - val_accuracy: 0.9275\n",
      "Epoch 194/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0355 - accuracy: 0.9904\n",
      "Epoch 00194: val_accuracy did not improve from 0.93447\n",
      "85/85 [==============================] - 23s 273ms/step - loss: 0.0355 - accuracy: 0.9904 - val_loss: 0.5181 - val_accuracy: 0.9269\n",
      "Epoch 195/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0289 - accuracy: 0.9919\n",
      "Epoch 00195: val_accuracy did not improve from 0.93447\n",
      "85/85 [==============================] - 23s 273ms/step - loss: 0.0289 - accuracy: 0.9919 - val_loss: 0.4928 - val_accuracy: 0.9319\n",
      "Epoch 196/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0176 - accuracy: 0.9949\n",
      "Epoch 00196: val_accuracy did not improve from 0.93447\n",
      "85/85 [==============================] - 23s 272ms/step - loss: 0.0176 - accuracy: 0.9949 - val_loss: 0.5214 - val_accuracy: 0.9253\n",
      "Epoch 197/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0199 - accuracy: 0.9941\n",
      "Epoch 00197: val_accuracy did not improve from 0.93447\n",
      "85/85 [==============================] - 23s 273ms/step - loss: 0.0199 - accuracy: 0.9941 - val_loss: 0.5402 - val_accuracy: 0.9269\n",
      "Epoch 198/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0134 - accuracy: 0.9962\n",
      "Epoch 00198: val_accuracy did not improve from 0.93447\n",
      "85/85 [==============================] - 23s 274ms/step - loss: 0.0134 - accuracy: 0.9962 - val_loss: 0.4975 - val_accuracy: 0.9310\n",
      "Epoch 199/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0234 - accuracy: 0.9935\n",
      "Epoch 00199: val_accuracy did not improve from 0.93447\n",
      "85/85 [==============================] - 23s 270ms/step - loss: 0.0234 - accuracy: 0.9935 - val_loss: 0.4982 - val_accuracy: 0.9290\n",
      "Epoch 200/200\n",
      "85/85 [==============================] - ETA: 0s - loss: 0.0752 - accuracy: 0.9802\n",
      "Epoch 00200: val_accuracy did not improve from 0.93447\n",
      "85/85 [==============================] - 23s 272ms/step - loss: 0.0752 - accuracy: 0.9802 - val_loss: 0.5977 - val_accuracy: 0.9130\n"
     ]
    }
   ],
   "source": [
    "history=model.fit(X_train,y_train,validation_data=(X_test,y_test),callbacks=[checkpoint],epochs=EPOCHS,batch_size=BATCH_SIZE,steps_per_epoch=STEPS_PER_EPOCH)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-07T18:06:47.067527Z",
     "iopub.status.busy": "2020-11-07T18:06:47.065652Z",
     "iopub.status.idle": "2020-11-07T18:06:47.068298Z",
     "shell.execute_reply": "2020-11-07T18:06:47.068853Z"
    },
    "papermill": {
     "duration": 7.008333,
     "end_time": "2020-11-07T18:06:47.069000",
     "exception": false,
     "start_time": "2020-11-07T18:06:40.060667",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def plot(history):\n",
    "    acc=history.history['accuracy']\n",
    "    val_acc=history.history['val_accuracy']\n",
    "    loss=history.history['loss']\n",
    "    val_loss=history.history['val_loss']\n",
    "    epochs=range(len(acc))\n",
    "    plt.plot(epochs,acc)\n",
    "    plt.plot(epochs,val_acc)\n",
    "    plt.title('Training accuracy and Validation accuracy')\n",
    "    plt.figure()\n",
    "    plt.plot(epochs,loss)\n",
    "    plt.plot(epochs,val_loss)\n",
    "    plt.title('Training and validation loss')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-07T18:07:03.280186Z",
     "iopub.status.busy": "2020-11-07T18:07:03.279136Z",
     "iopub.status.idle": "2020-11-07T18:07:03.669683Z",
     "shell.execute_reply": "2020-11-07T18:07:03.670351Z"
    },
    "papermill": {
     "duration": 7.755667,
     "end_time": "2020-11-07T18:07:03.670523",
     "exception": false,
     "start_time": "2020-11-07T18:06:55.914856",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-07T18:07:18.057652Z",
     "iopub.status.busy": "2020-11-07T18:07:18.056064Z",
     "iopub.status.idle": "2020-11-07T18:07:18.058438Z",
     "shell.execute_reply": "2020-11-07T18:07:18.058999Z"
    },
    "papermill": {
     "duration": 7.081863,
     "end_time": "2020-11-07T18:07:18.059148",
     "exception": false,
     "start_time": "2020-11-07T18:07:10.977285",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def predict(audio_s):\n",
    "    probabilty=model.predict(audio_s.reshape(1,8000,1))\n",
    "    index=np.argmax(probabilty[0])\n",
    "    return outcome_class[index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-07T18:07:33.125187Z",
     "iopub.status.busy": "2020-11-07T18:07:33.124041Z",
     "iopub.status.idle": "2020-11-07T18:07:35.391416Z",
     "shell.execute_reply": "2020-11-07T18:07:35.390791Z"
    },
    "papermill": {
     "duration": 9.536638,
     "end_time": "2020-11-07T18:07:35.391547",
     "exception": false,
     "start_time": "2020-11-07T18:07:25.854909",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Audio: eight\n",
      "Prediction from the model:  eight\n"
     ]
    }
   ],
   "source": [
    "sample_rate=16000\n",
    "rnd_i=np.random.randint(X_test.shape[0])\n",
    "samples=X_test[rnd_i].ravel()\n",
    "print(\"Audio:\",outcome_class[np.argmax(y_test[rnd_i])])\n",
    "print(\"Prediction from the model: \",predict(samples))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-07T18:07:50.443862Z",
     "iopub.status.busy": "2020-11-07T18:07:50.442592Z",
     "iopub.status.idle": "2020-11-07T18:07:50.447970Z",
     "shell.execute_reply": "2020-11-07T18:07:50.447402Z"
    },
    "papermill": {
     "duration": 7.947143,
     "end_time": "2020-11-07T18:07:50.448093",
     "exception": false,
     "start_time": "2020-11-07T18:07:42.500950",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "                <audio  controls=\"controls\" >\n",
       "                    <source 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type=\"audio/wav\" />\n",
       "                    Your browser does not support the audio element.\n",
       "                </audio>\n",
       "              "
      ],
      "text/plain": [
       "<IPython.lib.display.Audio object>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ipd.Audio(samples, rate=8000)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  },
  "papermill": {
   "duration": 6471.043772,
   "end_time": "2020-11-07T18:08:00.398326",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2020-11-07T16:20:09.354554",
   "version": "2.1.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}