Preprocess & Train.ipynb

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   "source": [
    "# !python -m pip install tensorflow\n",
    "# !python -m pip install tensorflow-macos\n",
    "# !python -m pip install tensorflow-metal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "92cae003-6e4c-4ce4-9c79-0dd022e42492",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python Platform: macOS-14.2.1-arm64-arm-64bit\n",
      "Tensor Flow Version: 2.16.2\n",
      "Keras Version: 3.5.0\n",
      "\n",
      "Python 3.11.7 (main, Dec 15 2023, 12:09:56) [Clang 14.0.6 ]\n",
      "Pandas 2.2.2\n",
      "Scikit-Learn 1.5.1\n",
      "SciPy 1.14.1\n",
      "GPU is available\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "import keras\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import sklearn as sk\n",
    "import scipy as sp\n",
    "import tensorflow as tf\n",
    "import platform\n",
    "import seaborn as sns\n",
    "\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Activation\n",
    "from keras.layers import Dense\n",
    "from keras.layers import Dropout\n",
    "from keras import regularizers\n",
    "from keras.regularizers import l1_l2\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "\n",
    "# CMP normalisation\n",
    "from bioinfokit.analys import norm\n",
    "nm = norm()\n",
    "# Z-score Standardisation\n",
    "import scipy.stats as stats\n",
    "# Pre-processing\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "print (f\"Python Platform: {platform.platform ()}\")\n",
    "print (f\"Tensor Flow Version: {tf.__version__}\")\n",
    "print(f\"Keras Version: {keras.__version__}\")\n",
    "print ()\n",
    "\n",
    "print (f\"Python {sys.version}\")\n",
    "print (f\"Pandas {pd.__version__}\")\n",
    "print (f\"Scikit-Learn {sk.__version__}\")\n",
    "print (f\"SciPy {sp.__version__}\")\n",
    "gpu = len (tf.config.list_physical_devices ('GPU'))>0\n",
    "print (\"GPU is\", \"available\" if gpu else \"NOT AVAILABLE\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "1e787c37",
   "metadata": {},
   "outputs": [
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       "      <th>ZAP70</th>\n",
       "      <th>ZBED2</th>\n",
       "      <th>ZBTB16</th>\n",
       "      <th>ZNF683</th>\n",
       "      <th>Sample</th>\n",
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       "                  ADA     ADGRE1  ADGRG3  AIM2     ALAS2      ANXA5  AOC3  \\\n",
       "Cell_Index                                                                  \n",
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       "...               ...        ...     ...   ...       ...        ...   ...   \n",
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       "\n",
       "            APOBEC3G  APOE       AQP9  ...  VPREB3     VPS28  VSIG4     XBP1  \\\n",
       "Cell_Index                             ...                                     \n",
       "724557           0.0   0.0   0.000000  ...     0.0  6.689423    0.0  0.00000   \n",
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       "\n",
       "                YBX3  ZAP70  ZBED2  ZBTB16  ZNF683             Sample  \n",
       "Cell_Index                                                             \n",
       "724557      5.038572    0.0    0.0     0.0     0.0  Adult Bone Marrow  \n",
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       "...              ...    ...    ...     ...     ...                ...  \n",
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       "869866      4.345425    0.0    0.0     0.0     0.0  Adult Bone Marrow  \n",
       "71530       4.345425    0.0    0.0     0.0     0.0  Adult Bone Marrow  \n",
       "\n",
       "[3894 rows x 398 columns]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Adult Bone Marrow Erythroid cells\n",
    "abm = pd.read_csv(\"Ery_AdultBoneMarrow_DBEC_MolsPerCell.csv\",\n",
    "                 skiprows = 7,\n",
    "                 low_memory=False)\n",
    "abm = abm[abm['ALAS2'] > 0]\n",
    "abm.set_index(['Cell_Index'], inplace=True)\n",
    "nm = norm()\n",
    "nm.cpm(df=abm)\n",
    "abm = nm.cpm_norm\n",
    "abm = abm.fillna(0)\n",
    "abm = abm.replace({0:1})\n",
    "abm = abm.map(np.log)\n",
    "abm = abm.assign(Sample='Adult Bone Marrow') # Sample name\n",
    "abm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "b00d2404",
   "metadata": {},
   "outputs": [
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       "      <th>ADA</th>\n",
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       "      <th>ADGRG3</th>\n",
       "      <th>AIM2</th>\n",
       "      <th>ALAS2</th>\n",
       "      <th>ANXA5</th>\n",
       "      <th>AOC3</th>\n",
       "      <th>APOBEC3G</th>\n",
       "      <th>APOE</th>\n",
       "      <th>AQP9</th>\n",
       "      <th>...</th>\n",
       "      <th>VPREB3</th>\n",
       "      <th>VPS28</th>\n",
       "      <th>VSIG4</th>\n",
       "      <th>XBP1</th>\n",
       "      <th>YBX3</th>\n",
       "      <th>ZAP70</th>\n",
       "      <th>ZBED2</th>\n",
       "      <th>ZBTB16</th>\n",
       "      <th>ZNF683</th>\n",
       "      <th>Sample</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cell_Index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>144770</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.666551</td>\n",
       "      <td>11.074671</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.363043</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.13921</td>\n",
       "      <td>3.995495</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Fetal Liver</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124485</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.716898</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.973403</td>\n",
       "      <td>12.327434</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.176453</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.13921</td>\n",
       "      <td>2.896883</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Fetal Liver</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>437509</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.612461</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.957577</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.13921</td>\n",
       "      <td>5.381790</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Fetal Liver</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>277780</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.410045</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.666551</td>\n",
       "      <td>11.074671</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Fetal Liver</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>308053</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.973403</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Fetal Liver</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>384930</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.919314</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>4.506321</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Fetal Liver</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>617184</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.666551</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>4.842793</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Fetal Liver</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>460329</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.359698</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>4.688642</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Fetal Liver</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>729136</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.973403</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>3.590030</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Fetal Liver</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404299</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.973403</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>3.995495</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Fetal Liver</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3752 rows × 398 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            ADA  ADGRE1     ADGRG3  AIM2     ALAS2      ANXA5  AOC3  \\\n",
       "Cell_Index                                                            \n",
       "144770      0.0     0.0   0.000000   0.0  3.666551  11.074671   0.0   \n",
       "124485      0.0     0.0  12.716898   0.0  2.973403  12.327434   0.0   \n",
       "437509      0.0     0.0   0.000000   0.0  5.612461   0.000000   0.0   \n",
       "277780      0.0     0.0  13.410045   0.0  3.666551  11.074671   0.0   \n",
       "308053      0.0     0.0   0.000000   0.0  2.973403   0.000000   0.0   \n",
       "...         ...     ...        ...   ...       ...        ...   ...   \n",
       "384930      0.0     0.0   0.000000   0.0  4.919314   0.000000   0.0   \n",
       "617184      0.0     0.0   0.000000   0.0  3.666551   0.000000   0.0   \n",
       "460329      0.0     0.0   0.000000   0.0  4.359698   0.000000   0.0   \n",
       "729136      0.0     0.0   0.000000   0.0  2.973403   0.000000   0.0   \n",
       "404299      0.0     0.0   0.000000   0.0  2.973403   0.000000   0.0   \n",
       "\n",
       "             APOBEC3G  APOE  AQP9  ...  VPREB3     VPS28  VSIG4      XBP1  \\\n",
       "Cell_Index                         ...                                      \n",
       "144770       0.000000   0.0   0.0  ...     0.0  8.363043    0.0  10.13921   \n",
       "124485      11.176453   0.0   0.0  ...     0.0  0.000000    0.0  10.13921   \n",
       "437509       0.000000   0.0   0.0  ...     0.0  7.957577    0.0  10.13921   \n",
       "277780       0.000000   0.0   0.0  ...     0.0  0.000000    0.0   0.00000   \n",
       "308053       0.000000   0.0   0.0  ...     0.0  0.000000    0.0   0.00000   \n",
       "...               ...   ...   ...  ...     ...       ...    ...       ...   \n",
       "384930       0.000000   0.0   0.0  ...     0.0  0.000000    0.0   0.00000   \n",
       "617184       0.000000   0.0   0.0  ...     0.0  0.000000    0.0   0.00000   \n",
       "460329       0.000000   0.0   0.0  ...     0.0  0.000000    0.0   0.00000   \n",
       "729136       0.000000   0.0   0.0  ...     0.0  0.000000    0.0   0.00000   \n",
       "404299       0.000000   0.0   0.0  ...     0.0  0.000000    0.0   0.00000   \n",
       "\n",
       "                YBX3  ZAP70  ZBED2  ZBTB16  ZNF683       Sample  \n",
       "Cell_Index                                                       \n",
       "144770      3.995495    0.0    0.0     0.0     0.0  Fetal Liver  \n",
       "124485      2.896883    0.0    0.0     0.0     0.0  Fetal Liver  \n",
       "437509      5.381790    0.0    0.0     0.0     0.0  Fetal Liver  \n",
       "277780      0.000000    0.0    0.0     0.0     0.0  Fetal Liver  \n",
       "308053      0.000000    0.0    0.0     0.0     0.0  Fetal Liver  \n",
       "...              ...    ...    ...     ...     ...          ...  \n",
       "384930      4.506321    0.0    0.0     0.0     0.0  Fetal Liver  \n",
       "617184      4.842793    0.0    0.0     0.0     0.0  Fetal Liver  \n",
       "460329      4.688642    0.0    0.0     0.0     0.0  Fetal Liver  \n",
       "729136      3.590030    0.0    0.0     0.0     0.0  Fetal Liver  \n",
       "404299      3.995495    0.0    0.0     0.0     0.0  Fetal Liver  \n",
       "\n",
       "[3752 rows x 398 columns]"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fetal Liver Erythroid cells\n",
    "fl = pd.read_csv(\"Ery_FetalLiver_DBEC_MolsPerCell.csv\",\n",
    "                 skiprows = 7,\n",
    "                 low_memory=False)\n",
    "fl = fl[fl['ALAS2'] > 0]\n",
    "fl.set_index(['Cell_Index'], inplace=True)\n",
    "nm = norm()\n",
    "nm.cpm(df=fl)\n",
    "fl = nm.cpm_norm\n",
    "fl = fl.fillna(0)\n",
    "fl = fl.replace({0:1})\n",
    "fl = fl.map(np.log)\n",
    "fl = fl.assign(Sample='Fetal Liver') # Sample name\n",
    "fl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "dd81b396",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ADA</th>\n",
       "      <th>ADGRE1</th>\n",
       "      <th>ADGRG3</th>\n",
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       "      <th>ZBTB16</th>\n",
       "      <th>ZNF683</th>\n",
       "      <th>Sample</th>\n",
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       "      <th>611594</th>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.480751</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Cord Blood</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>717478</th>\n",
       "      <td>12.429216</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.479686</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.929407</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.986869</td>\n",
       "      <td>7.878646</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Cord Blood</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192288</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.955109</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.622554</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.371123</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Cord Blood</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>540087</th>\n",
       "      <td>12.429216</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.946881</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.596307</td>\n",
       "      <td>7.238609</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Cord Blood</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>640132</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.344191</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.480751</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Cord Blood</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>421174</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.344191</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.564460</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Cord Blood</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62095</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.037339</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.257607</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Cord Blood</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>349834</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.749657</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.969925</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Cord Blood</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73062</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.651044</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.480751</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Cord Blood</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1683 rows × 398 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  ADA  ADGRE1  ADGRG3  AIM2     ALAS2      ANXA5  AOC3  \\\n",
       "Cell_Index                                                               \n",
       "611594       0.000000     0.0     0.0   0.0  4.749657  12.881201   0.0   \n",
       "559476       0.000000     0.0     0.0   0.0  5.953629  11.581918   0.0   \n",
       "717478      12.429216     0.0     0.0   0.0  7.479686   0.000000   0.0   \n",
       "192288       0.000000     0.0     0.0   0.0  7.955109   0.000000   0.0   \n",
       "540087      12.429216     0.0     0.0   0.0  6.946881   0.000000   0.0   \n",
       "...               ...     ...     ...   ...       ...        ...   ...   \n",
       "640132       0.000000     0.0     0.0   0.0  4.344191   0.000000   0.0   \n",
       "421174       0.000000     0.0     0.0   0.0  4.344191   0.000000   0.0   \n",
       "62095        0.000000     0.0     0.0   0.0  5.037339   0.000000   0.0   \n",
       "349834       0.000000     0.0     0.0   0.0  4.749657   0.000000   0.0   \n",
       "73062        0.000000     0.0     0.0   0.0  3.651044   0.000000   0.0   \n",
       "\n",
       "            APOBEC3G  APOE       AQP9  ...  VPREB3     VPS28  VSIG4  \\\n",
       "Cell_Index                             ...                            \n",
       "611594           0.0   0.0  13.697728  ...     0.0  0.000000    0.0   \n",
       "559476           0.0   0.0   0.000000  ...     0.0  0.000000    0.0   \n",
       "717478           0.0   0.0   0.000000  ...     0.0  7.929407    0.0   \n",
       "192288           0.0   0.0   0.000000  ...     0.0  8.622554    0.0   \n",
       "540087           0.0   0.0   0.000000  ...     0.0  0.000000    0.0   \n",
       "...              ...   ...        ...  ...     ...       ...    ...   \n",
       "640132           0.0   0.0   0.000000  ...     0.0  0.000000    0.0   \n",
       "421174           0.0   0.0   0.000000  ...     0.0  0.000000    0.0   \n",
       "62095            0.0   0.0   0.000000  ...     0.0  0.000000    0.0   \n",
       "349834           0.0   0.0   0.000000  ...     0.0  0.000000    0.0   \n",
       "73062            0.0   0.0   0.000000  ...     0.0  0.000000    0.0   \n",
       "\n",
       "                 XBP1      YBX3  ZAP70  ZBED2  ZBTB16  ZNF683      Sample  \n",
       "Cell_Index                                                                 \n",
       "611594      11.373164  6.915835    0.0    0.0     0.0     0.0  Cord Blood  \n",
       "559476       0.000000  5.480751    0.0    0.0     0.0     0.0  Cord Blood  \n",
       "717478       9.986869  7.878646    0.0    0.0     0.0     0.0  Cord Blood  \n",
       "192288       0.000000  8.371123    0.0    0.0     0.0     0.0  Cord Blood  \n",
       "540087      11.596307  7.238609    0.0    0.0     0.0     0.0  Cord Blood  \n",
       "...               ...       ...    ...    ...     ...     ...         ...  \n",
       "640132       0.000000  5.480751    0.0    0.0     0.0     0.0  Cord Blood  \n",
       "421174       0.000000  4.564460    0.0    0.0     0.0     0.0  Cord Blood  \n",
       "62095        0.000000  5.257607    0.0    0.0     0.0     0.0  Cord Blood  \n",
       "349834       0.000000  4.969925    0.0    0.0     0.0     0.0  Cord Blood  \n",
       "73062        0.000000  5.480751    0.0    0.0     0.0     0.0  Cord Blood  \n",
       "\n",
       "[1683 rows x 398 columns]"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cord Blood Erythroid cells\n",
    "cb = pd.read_csv(\"Ery_CordBlood_DBEC_MolsPerCell.csv\",\n",
    "                 skiprows = 7,\n",
    "                 low_memory=False)\n",
    "cb = cb[cb['ALAS2'] > 0]\n",
    "cb.set_index(['Cell_Index'], inplace=True)\n",
    "nm = norm()\n",
    "nm.cpm(df=cb)\n",
    "cb = nm.cpm_norm\n",
    "cb = cb.fillna(0)\n",
    "cb = cb.replace({0:1})\n",
    "cb = cb.map(np.log)\n",
    "cb = cb.assign(Sample='Cord Blood') # Sample name\n",
    "cb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "eaa32a5b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ADA</th>\n",
       "      <th>ADGRE1</th>\n",
       "      <th>ADGRG3</th>\n",
       "      <th>AIM2</th>\n",
       "      <th>ALAS2</th>\n",
       "      <th>ANXA5</th>\n",
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       "      <th>VSIG4</th>\n",
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       "      <th>ZAP70</th>\n",
       "      <th>ZBED2</th>\n",
       "      <th>ZBTB16</th>\n",
       "      <th>ZNF683</th>\n",
       "      <th>Sample</th>\n",
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       "      <th>465057</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Negative Control</td>\n",
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       "    <tr>\n",
       "      <th>124703</th>\n",
       "      <td>7.529512</td>\n",
       "      <td>0.00000</td>\n",
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       "      <td>8.534848</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.992265</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.236327</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Negative Control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>468260</th>\n",
       "      <td>7.529512</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>7.148554</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.046743</td>\n",
       "      <td>5.760353</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Negative Control</td>\n",
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       "    <tr>\n",
       "      <th>427285</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.59691</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>8.479175</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>8.593566</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.943393</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Negative Control</td>\n",
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       "    <tr>\n",
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       "      <td>7.59691</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19158</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Negative Control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>430509</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Negative Control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>509952</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.100833</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Negative Control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278959</th>\n",
       "      <td>6.836365</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.455407</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Negative Control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>376684</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.100833</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Negative Control</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3193 rows × 398 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 ADA   ADGRE1  ADGRG3  AIM2  ALAS2     ANXA5  AOC3  APOBEC3G  \\\n",
       "Cell_Index                                                                     \n",
       "465057      6.836365  0.00000     0.0   0.0    0.0  6.370823   0.0  8.401317   \n",
       "124703      7.529512  0.00000     0.0   0.0    0.0  6.370823   0.0  8.534848   \n",
       "468260      7.529512  0.00000     0.0   0.0    0.0  5.677676   0.0  7.148554   \n",
       "427285      0.000000  7.59691     0.0   0.0    0.0  7.181754   0.0  7.148554   \n",
       "738786      6.836365  7.59691     0.0   0.0    0.0  8.162583   0.0  0.000000   \n",
       "...              ...      ...     ...   ...    ...       ...   ...       ...   \n",
       "19158       0.000000  0.00000     0.0   0.0    0.0  0.000000   0.0  0.000000   \n",
       "430509      0.000000  0.00000     0.0   0.0    0.0  0.000000   0.0  0.000000   \n",
       "509952      0.000000  0.00000     0.0   0.0    0.0  0.000000   0.0  0.000000   \n",
       "278959      6.836365  0.00000     0.0   0.0    0.0  0.000000   0.0  6.455407   \n",
       "376684      0.000000  0.00000     0.0   0.0    0.0  0.000000   0.0  0.000000   \n",
       "\n",
       "            APOE      AQP9  ...  VPREB3     VPS28  VSIG4      XBP1      YBX3  \\\n",
       "Cell_Index                  ...                                                \n",
       "465057       0.0  0.000000  ...     0.0  7.090877    0.0  9.950981  0.000000   \n",
       "124703       0.0  0.000000  ...     0.0  5.992265    0.0  9.236327  0.000000   \n",
       "468260       0.0  0.000000  ...     0.0  0.000000    0.0  8.046743  5.760353   \n",
       "427285       0.0  8.479175  ...     0.0  7.601703    0.0  7.199445  8.593566   \n",
       "738786       0.0  8.191493  ...     0.0  8.071706    0.0  7.199445  7.369791   \n",
       "...          ...       ...  ...     ...       ...    ...       ...       ...   \n",
       "19158        0.0  0.000000  ...     0.0  0.000000    0.0  0.000000  0.000000   \n",
       "430509       0.0  0.000000  ...     0.0  0.000000    0.0  0.000000  0.000000   \n",
       "509952       0.0  0.000000  ...     0.0  0.000000    0.0  6.100833  0.000000   \n",
       "278959       0.0  0.000000  ...     0.0  0.000000    0.0  0.000000  0.000000   \n",
       "376684       0.0  0.000000  ...     0.0  0.000000    0.0  6.100833  0.000000   \n",
       "\n",
       "            ZAP70  ZBED2    ZBTB16  ZNF683            Sample  \n",
       "Cell_Index                                                    \n",
       "465057        0.0    0.0  0.000000     0.0  Negative Control  \n",
       "124703        0.0    0.0  0.000000     0.0  Negative Control  \n",
       "468260        0.0    0.0  0.000000     0.0  Negative Control  \n",
       "427285        0.0    0.0  7.943393     0.0  Negative Control  \n",
       "738786        0.0    0.0  0.000000     0.0  Negative Control  \n",
       "...           ...    ...       ...     ...               ...  \n",
       "19158         0.0    0.0  0.000000     0.0  Negative Control  \n",
       "430509        0.0    0.0  0.000000     0.0  Negative Control  \n",
       "509952        0.0    0.0  0.000000     0.0  Negative Control  \n",
       "278959        0.0    0.0  0.000000     0.0  Negative Control  \n",
       "376684        0.0    0.0  0.000000     0.0  Negative Control  \n",
       "\n",
       "[3193 rows x 398 columns]"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# non-Erythroid cells as Negative Control\n",
    "pbmc = pd.read_csv(\"PBMC_DBEC_MolsPerCell.csv\",\n",
    "                 skiprows = 7,\n",
    "                 low_memory=False)\n",
    "pbmc = pbmc[pbmc['ALAS2'] < 1]\n",
    "pbmc.set_index(['Cell_Index'], inplace=True)\n",
    "nm = norm()\n",
    "nm.cpm(df=pbmc)\n",
    "pbmc = nm.cpm_norm\n",
    "pbmc = pbmc.fillna(0)\n",
    "pbmc = pbmc.replace({0:1})\n",
    "pbmc = pbmc.map(np.log)\n",
    "pbmc = pbmc.assign(Sample='Negative Control') # Sample name\n",
    "pbmc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "e53d4125",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cell downsample count is 1683\n"
     ]
    }
   ],
   "source": [
    "# Max cell number for down-sampling \n",
    "def downsample(abm, fl, cb, pbmc):\n",
    "    ds = min([len(abm), len(fl), len(cb), len(pbmc)])\n",
    "    print(f'Cell downsample count is {ds}')\n",
    "    return ds\n",
    "\n",
    "cell_downsample_N = downsample(abm, fl, cb, pbmc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "0e46b66c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Down-sampling - balances the datasets in numbers\n",
    "\n",
    "# ABM NECs Ds\n",
    "abm_ds = abm.sample(n=cell_downsample_N)\n",
    "\n",
    "# FLP NECs Ds\n",
    "fl_ds = fl.sample(n=cell_downsample_N)\n",
    "\n",
    "# CB NECs Ds\n",
    "cb_ds = cb.sample(n=cell_downsample_N)\n",
    "\n",
    "# NC Ds\n",
    "nc_ds = pbmc.sample(n=cell_downsample_N)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "a3c2e00a",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>ADA</th>\n",
       "      <th>ADGRE1</th>\n",
       "      <th>ADGRG3</th>\n",
       "      <th>AIM2</th>\n",
       "      <th>ALAS2</th>\n",
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       "      <th>Sample_Adult Bone Marrow</th>\n",
       "      <th>Sample_Cord Blood</th>\n",
       "      <th>Sample_Fetal Liver</th>\n",
       "      <th>Sample_Negative Control</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>870264</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.455407</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26419</th>\n",
       "      <td>6.836365</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.593967</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.369791</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6732 rows × 401 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 ADA  ADGRE1  ADGRG3  AIM2     ALAS2     ANXA5  AOC3  \\\n",
       "Cell_Index                                                             \n",
       "857497      0.000000     0.0     0.0   0.0  5.981305  0.000000   0.0   \n",
       "789792      0.000000     0.0     0.0   0.0  5.673820  0.000000   0.0   \n",
       "259221      0.000000     0.0     0.0   0.0  3.841238  0.000000   0.0   \n",
       "274648      0.000000     0.0     0.0   0.0  5.450676  0.000000   0.0   \n",
       "592314      0.000000     0.0     0.0   0.0  6.326145  0.000000   0.0   \n",
       "...              ...     ...     ...   ...       ...       ...   ...   \n",
       "86327       0.000000     0.0     0.0   0.0  0.000000  4.984529   0.0   \n",
       "181147      0.000000     0.0     0.0   0.0  0.000000  0.000000   0.0   \n",
       "411494      0.000000     0.0     0.0   0.0  0.000000  0.000000   0.0   \n",
       "870264      0.000000     0.0     0.0   0.0  0.000000  0.000000   0.0   \n",
       "26419       6.836365     0.0     0.0   0.0  0.000000  6.593967   0.0   \n",
       "\n",
       "            APOBEC3G  APOE  AQP9  ...      XBP1      YBX3     ZAP70  ZBED2  \\\n",
       "Cell_Index                        ...                                        \n",
       "857497      0.000000   0.0   0.0  ...  0.000000  6.331340  0.000000    0.0   \n",
       "789792      0.000000   0.0   0.0  ...  0.000000  5.395247  0.000000    0.0   \n",
       "259221      0.000000   0.0   0.0  ...  0.000000  4.478956  0.000000    0.0   \n",
       "274648      0.000000   0.0   0.0  ...  0.000000  4.596739  0.000000    0.0   \n",
       "592314      0.000000   0.0   0.0  ...  0.000000  6.291335  0.000000    0.0   \n",
       "...              ...   ...   ...  ...       ...       ...       ...    ...   \n",
       "86327       0.000000   0.0   0.0  ...  6.100833  5.760353  0.000000    0.0   \n",
       "181147      0.000000   0.0   0.0  ...  0.000000  0.000000  7.885921    0.0   \n",
       "411494      0.000000   0.0   0.0  ...  0.000000  0.000000  0.000000    0.0   \n",
       "870264      6.455407   0.0   0.0  ...  0.000000  0.000000  0.000000    0.0   \n",
       "26419       0.000000   0.0   0.0  ...  0.000000  7.369791  0.000000    0.0   \n",
       "\n",
       "            ZBTB16  ZNF683  Sample_Adult Bone Marrow  Sample_Cord Blood  \\\n",
       "Cell_Index                                                                \n",
       "857497         0.0     0.0                       1.0                0.0   \n",
       "789792         0.0     0.0                       1.0                0.0   \n",
       "259221         0.0     0.0                       1.0                0.0   \n",
       "274648         0.0     0.0                       1.0                0.0   \n",
       "592314         0.0     0.0                       1.0                0.0   \n",
       "...            ...     ...                       ...                ...   \n",
       "86327          0.0     0.0                       0.0                0.0   \n",
       "181147         0.0     0.0                       0.0                0.0   \n",
       "411494         0.0     0.0                       0.0                0.0   \n",
       "870264         0.0     0.0                       0.0                0.0   \n",
       "26419          0.0     0.0                       0.0                0.0   \n",
       "\n",
       "            Sample_Fetal Liver  Sample_Negative Control  \n",
       "Cell_Index                                               \n",
       "857497                     0.0                      0.0  \n",
       "789792                     0.0                      0.0  \n",
       "259221                     0.0                      0.0  \n",
       "274648                     0.0                      0.0  \n",
       "592314                     0.0                      0.0  \n",
       "...                        ...                      ...  \n",
       "86327                      0.0                      1.0  \n",
       "181147                     0.0                      1.0  \n",
       "411494                     0.0                      1.0  \n",
       "870264                     0.0                      1.0  \n",
       "26419                      0.0                      1.0  \n",
       "\n",
       "[6732 rows x 401 columns]"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# data frame concatenation - train frame creation\n",
    "ST_combined = pd.concat([abm_ds, fl_ds, cb_ds, nc_ds])\n",
    "\n",
    "ST_combined = pd.get_dummies(ST_combined, columns=['Sample'])\n",
    "\n",
    "ST_combined = ST_combined.astype(float)\n",
    "\n",
    "tf = ST_combined\n",
    "\n",
    "tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "fc1658a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# shuffle train frame\n",
    "tf = tf.sample(frac=1, random_state=14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "c7074de4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ADA</th>\n",
       "      <th>ADGRE1</th>\n",
       "      <th>ADGRG3</th>\n",
       "      <th>AIM2</th>\n",
       "      <th>ALAS2</th>\n",
       "      <th>ANXA5</th>\n",
       "      <th>AOC3</th>\n",
       "      <th>APOBEC3G</th>\n",
       "      <th>APOE</th>\n",
       "      <th>AQP9</th>\n",
       "      <th>...</th>\n",
       "      <th>XBP1</th>\n",
       "      <th>YBX3</th>\n",
       "      <th>ZAP70</th>\n",
       "      <th>ZBED2</th>\n",
       "      <th>ZBTB16</th>\n",
       "      <th>ZNF683</th>\n",
       "      <th>Sample_Adult Bone Marrow</th>\n",
       "      <th>Sample_Cord Blood</th>\n",
       "      <th>Sample_Fetal Liver</th>\n",
       "      <th>Sample_Negative Control</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cell_Index</th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "  <tbody>\n",
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       "      <th>356989</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>242722</th>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.815106</td>\n",
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       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>129924</th>\n",
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       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>63008</th>\n",
       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 401 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            ADA  ADGRE1  ADGRG3  AIM2     ALAS2     ANXA5  AOC3  APOBEC3G  \\\n",
       "Cell_Index                                                                  \n",
       "356989      0.0     0.0     0.0   0.0  0.781403  0.000000   0.0  0.000000   \n",
       "150594      0.0     0.0     0.0   0.0  0.000000  0.538027   0.0  0.000000   \n",
       "242722      0.0     0.0     0.0   0.0  0.815106  0.000000   0.0  0.000000   \n",
       "129924      0.0     0.0     0.0   0.0  0.705516  0.000000   0.0  0.000000   \n",
       "63008       0.0     0.0     0.0   0.0  0.000000  0.511906   0.0  0.587801   \n",
       "\n",
       "            APOE  AQP9  ...      XBP1      YBX3  ZAP70  ZBED2  ZBTB16  ZNF683  \\\n",
       "Cell_Index              ...                                                     \n",
       "356989       0.0   0.0  ...  0.000000  0.662571    0.0    0.0     0.0     0.0   \n",
       "150594       0.0   0.0  ...  0.526101  0.906259    0.0    0.0     0.0     0.0   \n",
       "242722       0.0   0.0  ...  0.000000  0.447513    0.0    0.0     0.0     0.0   \n",
       "129924       0.0   0.0  ...  0.000000  0.631373    0.0    0.0     0.0     0.0   \n",
       "63008        0.0   0.0  ...  0.000000  0.826133    0.0    0.0     0.0     0.0   \n",
       "\n",
       "            Sample_Adult Bone Marrow  Sample_Cord Blood  Sample_Fetal Liver  \\\n",
       "Cell_Index                                                                    \n",
       "356989                           1.0                0.0                 0.0   \n",
       "150594                           0.0                0.0                 0.0   \n",
       "242722                           0.0                1.0                 0.0   \n",
       "129924                           0.0                0.0                 1.0   \n",
       "63008                            0.0                0.0                 0.0   \n",
       "\n",
       "            Sample_Negative Control  \n",
       "Cell_Index                           \n",
       "356989                          0.0  \n",
       "150594                          1.0  \n",
       "242722                          0.0  \n",
       "129924                          0.0  \n",
       "63008                           1.0  \n",
       "\n",
       "[5 rows x 401 columns]"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# min-max scaling / fit-transform (ft)\n",
    "tf_ft = MinMaxScaler().fit_transform(tf)\n",
    "# ndarray to pd.df\n",
    "tf = pd.DataFrame(tf_ft, columns = tf.columns, index=tf.index)\n",
    "tf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "f8ed2d54",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train-test split\n",
    "x = tf.drop(['Sample_Adult Bone Marrow', 'Sample_Cord Blood',\n",
    "             'Sample_Fetal Liver', 'Sample_Negative Control'], axis=1)\n",
    "y = tf[['Sample_Adult Bone Marrow', 'Sample_Cord Blood',\n",
    "        'Sample_Fetal Liver', 'Sample_Negative Control']]\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test = train_test_split(x,y,test_size=0.2,random_state=14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "1a486145-940c-4770-8f54-61d86e7576d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5385"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "int(len(x_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "795f3def",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input shape\n",
      "397\n",
      "Batch size\n",
      "5385\n",
      "Number of batches\n",
      "359\n"
     ]
    }
   ],
   "source": [
    "input_shape = len(x_train.T)\n",
    "print('Input shape')\n",
    "print(input_shape)\n",
    "print('Batch size')\n",
    "print(len(x_train))\n",
    "batch_size = int(len(x_train) / 15)\n",
    "print('Number of batches')\n",
    "print(batch_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9495ad5-39c5-4aed-bdcb-6016ad51d634",
   "metadata": {},
   "source": [
    "# ErythrOrigin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "799352a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1400\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.11/site-packages/keras/src/layers/core/dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m2s\u001B[0m 67ms/step - accuracy: 0.2842 - loss: 57.3140\n",
      "Epoch 2/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 31ms/step - accuracy: 0.3567 - loss: 57.0265\n",
      "Epoch 3/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 30ms/step - accuracy: 0.3723 - loss: 56.7437\n",
      "Epoch 4/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.4172 - loss: 56.4600\n",
      "Epoch 5/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.4761 - loss: 56.1873\n",
      "Epoch 6/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.5446 - loss: 55.9110\n",
      "Epoch 7/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.5568 - loss: 55.6386\n",
      "Epoch 8/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.5845 - loss: 55.3620\n",
      "Epoch 9/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.5853 - loss: 55.0859\n",
      "Epoch 10/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.5705 - loss: 54.8308\n",
      "Epoch 11/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.5851 - loss: 54.5507\n",
      "Epoch 12/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.5853 - loss: 54.2873\n",
      "Epoch 13/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.5912 - loss: 54.0427\n",
      "Epoch 14/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.6024 - loss: 53.7910\n",
      "Epoch 15/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.5788 - loss: 53.5596\n",
      "Epoch 16/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.5972 - loss: 53.3059\n",
      "Epoch 17/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.6040 - loss: 53.0702\n",
      "Epoch 18/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.5977 - loss: 52.8400\n",
      "Epoch 19/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.5990 - loss: 52.5887\n",
      "Epoch 20/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.6190 - loss: 52.3476\n",
      "Epoch 21/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.6081 - loss: 52.1277\n",
      "Epoch 22/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.6069 - loss: 51.8992\n",
      "Epoch 23/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.6084 - loss: 51.6690\n",
      "Epoch 24/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.6144 - loss: 51.4320\n",
      "Epoch 25/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.6094 - loss: 51.2130\n",
      "Epoch 26/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.6080 - loss: 50.9837\n",
      "Epoch 27/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.6051 - loss: 50.7617\n",
      "Epoch 28/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.6230 - loss: 50.5175\n",
      "Epoch 29/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.6190 - loss: 50.3005\n",
      "Epoch 30/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.6121 - loss: 50.0854\n",
      "Epoch 31/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.6247 - loss: 49.8443\n",
      "Epoch 32/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.6331 - loss: 49.6273\n",
      "Epoch 33/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.6171 - loss: 49.4045\n",
      "Epoch 34/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.6280 - loss: 49.1854\n",
      "Epoch 35/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.6270 - loss: 48.9591\n",
      "Epoch 36/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.6228 - loss: 48.7434\n",
      "Epoch 37/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.6245 - loss: 48.5295\n",
      "Epoch 38/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.6267 - loss: 48.3016\n",
      "Epoch 39/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.6323 - loss: 48.0774\n",
      "Epoch 40/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.6266 - loss: 47.8622\n",
      "Epoch 41/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.6315 - loss: 47.6504\n",
      "Epoch 42/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.6376 - loss: 47.4231\n",
      "Epoch 43/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.6323 - loss: 47.2090\n",
      "Epoch 44/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.6358 - loss: 46.9890\n",
      "Epoch 45/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.6403 - loss: 46.7843\n",
      "Epoch 46/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.6409 - loss: 46.5588\n",
      "Epoch 47/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.6470 - loss: 46.3294\n",
      "Epoch 48/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.6505 - loss: 46.1262\n",
      "Epoch 49/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.6430 - loss: 45.9104\n",
      "Epoch 50/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.6567 - loss: 45.6898\n",
      "Epoch 51/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.6434 - loss: 45.4872\n",
      "Epoch 52/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 30ms/step - accuracy: 0.6517 - loss: 45.2724\n",
      "Epoch 53/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.6513 - loss: 45.0594\n",
      "Epoch 54/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.6484 - loss: 44.8494\n",
      "Epoch 55/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.6556 - loss: 44.6408\n",
      "Epoch 56/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.6443 - loss: 44.4399\n",
      "Epoch 57/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.6480 - loss: 44.2150\n",
      "Epoch 58/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.6571 - loss: 44.0034\n",
      "Epoch 59/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.6579 - loss: 43.7993\n",
      "Epoch 60/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.6573 - loss: 43.5884\n",
      "Epoch 61/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.6528 - loss: 43.3783\n",
      "Epoch 62/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.6682 - loss: 43.1711\n",
      "Epoch 63/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 30ms/step - accuracy: 0.6393 - loss: 42.9757\n",
      "Epoch 64/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.6608 - loss: 42.7576\n",
      "Epoch 65/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.6597 - loss: 42.5514\n",
      "Epoch 66/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.6568 - loss: 42.3550\n",
      "Epoch 67/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 30ms/step - accuracy: 0.6609 - loss: 42.1501\n",
      "Epoch 68/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.6663 - loss: 41.9384\n",
      "Epoch 69/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.6590 - loss: 41.7444\n",
      "Epoch 70/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.6678 - loss: 41.5385\n",
      "Epoch 71/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.6673 - loss: 41.3301\n",
      "Epoch 72/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.6641 - loss: 41.1315\n",
      "Epoch 73/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.6607 - loss: 40.9297\n",
      "Epoch 74/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.6510 - loss: 40.7479\n",
      "Epoch 75/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.6657 - loss: 40.5292\n",
      "Epoch 76/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.6650 - loss: 40.3348\n",
      "Epoch 77/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.6685 - loss: 40.1333\n",
      "Epoch 78/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.6757 - loss: 39.9148\n",
      "Epoch 79/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.6572 - loss: 39.7360\n",
      "Epoch 80/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.6652 - loss: 39.5436\n",
      "Epoch 81/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.6797 - loss: 39.3307\n",
      "Epoch 82/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.6774 - loss: 39.1311\n",
      "Epoch 83/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.6715 - loss: 38.9433\n",
      "Epoch 84/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.6753 - loss: 38.7487\n",
      "Epoch 85/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.6680 - loss: 38.5596\n",
      "Epoch 86/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.6721 - loss: 38.3579\n",
      "Epoch 87/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.6728 - loss: 38.1653\n",
      "Epoch 88/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.6835 - loss: 37.9573\n",
      "Epoch 89/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.6745 - loss: 37.7824\n",
      "Epoch 90/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.6706 - loss: 37.5881\n",
      "Epoch 91/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.6906 - loss: 37.3789\n",
      "Epoch 92/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.6832 - loss: 37.1933\n",
      "Epoch 93/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.6793 - loss: 37.0137\n",
      "Epoch 94/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.6782 - loss: 36.8228\n",
      "Epoch 95/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.6778 - loss: 36.6261\n",
      "Epoch 96/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.6869 - loss: 36.4345\n",
      "Epoch 97/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.6741 - loss: 36.2542\n",
      "Epoch 98/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.6833 - loss: 36.0609\n",
      "Epoch 99/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.6836 - loss: 35.8708\n",
      "Epoch 100/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.6852 - loss: 35.6825\n",
      "Epoch 101/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.6834 - loss: 35.4952\n",
      "Epoch 102/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.6909 - loss: 35.3000\n",
      "Epoch 103/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.6773 - loss: 35.1296\n",
      "Epoch 104/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.6858 - loss: 34.9402\n",
      "Epoch 105/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.6965 - loss: 34.7463\n",
      "Epoch 106/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.6816 - loss: 34.5673\n",
      "Epoch 107/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.6934 - loss: 34.3870\n",
      "Epoch 108/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.6967 - loss: 34.1990\n",
      "Epoch 109/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.6869 - loss: 34.0194\n",
      "Epoch 110/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.7035 - loss: 33.8296\n",
      "Epoch 111/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.6874 - loss: 33.6658\n",
      "Epoch 112/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.6963 - loss: 33.4677\n",
      "Epoch 113/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.6946 - loss: 33.2868\n",
      "Epoch 114/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 30ms/step - accuracy: 0.6949 - loss: 33.1080\n",
      "Epoch 115/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.6966 - loss: 32.9205\n",
      "Epoch 116/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.6897 - loss: 32.7463\n",
      "Epoch 117/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.6913 - loss: 32.5796\n",
      "Epoch 118/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.7010 - loss: 32.3789\n",
      "Epoch 119/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.6935 - loss: 32.2111\n",
      "Epoch 120/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.6927 - loss: 32.0392\n",
      "Epoch 121/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.7091 - loss: 31.8505\n",
      "Epoch 122/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.6964 - loss: 31.6821\n",
      "Epoch 123/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.7139 - loss: 31.5011\n",
      "Epoch 124/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.6960 - loss: 31.3324\n",
      "Epoch 125/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.7081 - loss: 31.1548\n",
      "Epoch 126/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.7165 - loss: 30.9792\n",
      "Epoch 127/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.7071 - loss: 30.8043\n",
      "Epoch 128/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.7042 - loss: 30.6286\n",
      "Epoch 129/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.7076 - loss: 30.4622\n",
      "Epoch 130/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.7110 - loss: 30.2895\n",
      "Epoch 131/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.7082 - loss: 30.1184\n",
      "Epoch 132/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.7050 - loss: 29.9409\n",
      "Epoch 133/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.7068 - loss: 29.7729\n",
      "Epoch 134/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.7067 - loss: 29.6105\n",
      "Epoch 135/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.7096 - loss: 29.4379\n",
      "Epoch 136/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.7083 - loss: 29.2624\n",
      "Epoch 137/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.7088 - loss: 29.1027\n",
      "Epoch 138/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.7236 - loss: 28.9161\n",
      "Epoch 139/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.7126 - loss: 28.7603\n",
      "Epoch 140/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.7263 - loss: 28.5855\n",
      "Epoch 141/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.7083 - loss: 28.4190\n",
      "Epoch 142/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.7096 - loss: 28.2626\n",
      "Epoch 143/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.7210 - loss: 28.0889\n",
      "Epoch 144/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.7044 - loss: 27.9381\n",
      "Epoch 145/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.7328 - loss: 27.7494\n",
      "Epoch 146/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.7118 - loss: 27.6076\n",
      "Epoch 147/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.7181 - loss: 27.4321\n",
      "Epoch 148/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.7174 - loss: 27.2819\n",
      "Epoch 149/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.7259 - loss: 27.1015\n",
      "Epoch 150/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.7154 - loss: 26.9468\n",
      "Epoch 151/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.7257 - loss: 26.7883\n",
      "Epoch 152/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.7226 - loss: 26.6207\n",
      "Epoch 153/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.7282 - loss: 26.4574\n",
      "Epoch 154/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.7168 - loss: 26.3088\n",
      "Epoch 155/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.7393 - loss: 26.1357\n",
      "Epoch 156/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.7284 - loss: 25.9749\n",
      "Epoch 157/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.7240 - loss: 25.8215\n",
      "Epoch 158/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.7287 - loss: 25.6706\n",
      "Epoch 159/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.7210 - loss: 25.5124\n",
      "Epoch 160/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.7262 - loss: 25.3614\n",
      "Epoch 161/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.7325 - loss: 25.1946\n",
      "Epoch 162/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.7349 - loss: 25.0324\n",
      "Epoch 163/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.7282 - loss: 24.8826\n",
      "Epoch 164/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.7326 - loss: 24.7252\n",
      "Epoch 165/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.7344 - loss: 24.5730\n",
      "Epoch 166/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.7402 - loss: 24.4087\n",
      "Epoch 167/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.7449 - loss: 24.2522\n",
      "Epoch 168/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.7417 - loss: 24.1033\n",
      "Epoch 169/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.7446 - loss: 23.9515\n",
      "Epoch 170/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.7424 - loss: 23.8122\n",
      "Epoch 171/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.7461 - loss: 23.6489\n",
      "Epoch 172/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.7459 - loss: 23.5000\n",
      "Epoch 173/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.7428 - loss: 23.3516\n",
      "Epoch 174/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.7525 - loss: 23.1888\n",
      "Epoch 175/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.7377 - loss: 23.0575\n",
      "Epoch 176/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.7477 - loss: 22.9045\n",
      "Epoch 177/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.7454 - loss: 22.7456\n",
      "Epoch 178/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.7527 - loss: 22.5987\n",
      "Epoch 179/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.7479 - loss: 22.4552\n",
      "Epoch 180/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.7502 - loss: 22.3102\n",
      "Epoch 181/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.7535 - loss: 22.1666\n",
      "Epoch 182/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.7424 - loss: 22.0253\n",
      "Epoch 183/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.7562 - loss: 21.8656\n",
      "Epoch 184/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.7559 - loss: 21.7203\n",
      "Epoch 185/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.7470 - loss: 21.5936\n",
      "Epoch 186/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.7627 - loss: 21.4339\n",
      "Epoch 187/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.7488 - loss: 21.3084\n",
      "Epoch 188/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.7626 - loss: 21.1486\n",
      "Epoch 189/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.7545 - loss: 21.0152\n",
      "Epoch 190/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.7496 - loss: 20.8868\n",
      "Epoch 191/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.7570 - loss: 20.7327\n",
      "Epoch 192/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.7728 - loss: 20.5857\n",
      "Epoch 193/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.7524 - loss: 20.4570\n",
      "Epoch 194/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.7645 - loss: 20.3104\n",
      "Epoch 195/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.7543 - loss: 20.1744\n",
      "Epoch 196/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.7567 - loss: 20.0368\n",
      "Epoch 197/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.7647 - loss: 19.8974\n",
      "Epoch 198/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.7571 - loss: 19.7666\n",
      "Epoch 199/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.7611 - loss: 19.6135\n",
      "Epoch 200/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.7685 - loss: 19.4845\n",
      "Epoch 201/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.7561 - loss: 19.3556\n",
      "Epoch 202/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.7654 - loss: 19.2153\n",
      "Epoch 203/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.7711 - loss: 19.0636\n",
      "Epoch 204/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.7610 - loss: 18.9437\n",
      "Epoch 205/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.7626 - loss: 18.8133\n",
      "Epoch 206/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.7679 - loss: 18.6721\n",
      "Epoch 207/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.7685 - loss: 18.5450\n",
      "Epoch 208/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.7728 - loss: 18.4055\n",
      "Epoch 209/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.7616 - loss: 18.2828\n",
      "Epoch 210/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.7716 - loss: 18.1482\n",
      "Epoch 211/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.7691 - loss: 18.0183\n",
      "Epoch 212/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.7718 - loss: 17.8857\n",
      "Epoch 213/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.7771 - loss: 17.7540\n",
      "Epoch 214/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.7750 - loss: 17.6212\n",
      "Epoch 215/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.7649 - loss: 17.5046\n",
      "Epoch 216/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.7799 - loss: 17.3667\n",
      "Epoch 217/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.7767 - loss: 17.2400\n",
      "Epoch 218/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.7730 - loss: 17.1172\n",
      "Epoch 219/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.7776 - loss: 16.9851\n",
      "Epoch 220/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.7780 - loss: 16.8603\n",
      "Epoch 221/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.7814 - loss: 16.7303\n",
      "Epoch 222/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.7692 - loss: 16.6235\n",
      "Epoch 223/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.7850 - loss: 16.4872\n",
      "Epoch 224/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.7766 - loss: 16.3645\n",
      "Epoch 225/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.7705 - loss: 16.2475\n",
      "Epoch 226/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.7888 - loss: 16.1079\n",
      "Epoch 227/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.7828 - loss: 15.9914\n",
      "Epoch 228/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.7904 - loss: 15.8696\n",
      "Epoch 229/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.7820 - loss: 15.7633\n",
      "Epoch 230/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.7928 - loss: 15.6232\n",
      "Epoch 231/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.7877 - loss: 15.5052\n",
      "Epoch 232/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.7829 - loss: 15.3922\n",
      "Epoch 233/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.7912 - loss: 15.2632\n",
      "Epoch 234/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.7841 - loss: 15.1537\n",
      "Epoch 235/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.7865 - loss: 15.0355\n",
      "Epoch 236/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.7777 - loss: 14.9255\n",
      "Epoch 237/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.7905 - loss: 14.7972\n",
      "Epoch 238/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.7951 - loss: 14.6816\n",
      "Epoch 239/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.7941 - loss: 14.5616\n",
      "Epoch 240/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.7829 - loss: 14.4502\n",
      "Epoch 241/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.7911 - loss: 14.3275\n",
      "Epoch 242/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.7885 - loss: 14.2205\n",
      "Epoch 243/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.7968 - loss: 14.1029\n",
      "Epoch 244/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.7964 - loss: 13.9915\n",
      "Epoch 245/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.7905 - loss: 13.8811\n",
      "Epoch 246/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.7964 - loss: 13.7660\n",
      "Epoch 247/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.7898 - loss: 13.6580\n",
      "Epoch 248/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.7893 - loss: 13.5399\n",
      "Epoch 249/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.8104 - loss: 13.4259\n",
      "Epoch 250/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.8000 - loss: 13.3204\n",
      "Epoch 251/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.7872 - loss: 13.2189\n",
      "Epoch 252/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.7986 - loss: 13.1107\n",
      "Epoch 253/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.8043 - loss: 12.9973\n",
      "Epoch 254/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.7958 - loss: 12.8895\n",
      "Epoch 255/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.8046 - loss: 12.7723\n",
      "Epoch 256/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.7964 - loss: 12.6752\n",
      "Epoch 257/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.8008 - loss: 12.5653\n",
      "Epoch 258/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.8058 - loss: 12.4538\n",
      "Epoch 259/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.7996 - loss: 12.3514\n",
      "Epoch 260/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 30ms/step - accuracy: 0.7910 - loss: 12.2591\n",
      "Epoch 261/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.7932 - loss: 12.1411\n",
      "Epoch 262/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.8021 - loss: 12.0414\n",
      "Epoch 263/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.8013 - loss: 11.9298\n",
      "Epoch 264/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.8008 - loss: 11.8303\n",
      "Epoch 265/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.7930 - loss: 11.7395\n",
      "Epoch 266/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.8009 - loss: 11.6210\n",
      "Epoch 267/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.8001 - loss: 11.5275\n",
      "Epoch 268/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.8127 - loss: 11.4185\n",
      "Epoch 269/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.8054 - loss: 11.3224\n",
      "Epoch 270/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.8079 - loss: 11.2159\n",
      "Epoch 271/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.8065 - loss: 11.1331\n",
      "Epoch 272/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.8099 - loss: 11.0259\n",
      "Epoch 273/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.8058 - loss: 10.9291\n",
      "Epoch 274/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.8035 - loss: 10.8279\n",
      "Epoch 275/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.8150 - loss: 10.7246\n",
      "Epoch 276/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.8120 - loss: 10.6236\n",
      "Epoch 277/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.8069 - loss: 10.5351\n",
      "Epoch 278/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.8017 - loss: 10.4435\n",
      "Epoch 279/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.8087 - loss: 10.3338\n",
      "Epoch 280/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.8112 - loss: 10.2382\n",
      "Epoch 281/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.8053 - loss: 10.1569\n",
      "Epoch 282/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.8193 - loss: 10.0478\n",
      "Epoch 283/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.8112 - loss: 9.9608\n",
      "Epoch 284/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.8053 - loss: 9.8775\n",
      "Epoch 285/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.8138 - loss: 9.7693\n",
      "Epoch 286/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.8167 - loss: 9.6846\n",
      "Epoch 287/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.8174 - loss: 9.5905\n",
      "Epoch 288/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.8268 - loss: 9.4892\n",
      "Epoch 289/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.8192 - loss: 9.4137\n",
      "Epoch 290/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.8097 - loss: 9.3269\n",
      "Epoch 291/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.8218 - loss: 9.2217\n",
      "Epoch 292/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.8114 - loss: 9.1479\n",
      "Epoch 293/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.8158 - loss: 9.0534\n",
      "Epoch 294/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.8207 - loss: 8.9698\n",
      "Epoch 295/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.8252 - loss: 8.8692\n",
      "Epoch 296/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.8195 - loss: 8.7834\n",
      "Epoch 297/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.8156 - loss: 8.7091\n",
      "Epoch 298/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.8339 - loss: 8.6058\n",
      "Epoch 299/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.8110 - loss: 8.5417\n",
      "Epoch 300/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.8256 - loss: 8.4475\n",
      "Epoch 301/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.8259 - loss: 8.3558\n",
      "Epoch 302/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.8198 - loss: 8.2843\n",
      "Epoch 303/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.8209 - loss: 8.1863\n",
      "Epoch 304/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.8226 - loss: 8.1127\n",
      "Epoch 305/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.8168 - loss: 8.0376\n",
      "Epoch 306/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.8252 - loss: 7.9495\n",
      "Epoch 307/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.8229 - loss: 7.8573\n",
      "Epoch 308/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.8234 - loss: 7.7863\n",
      "Epoch 309/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.8171 - loss: 7.7076\n",
      "Epoch 310/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.8204 - loss: 7.6258\n",
      "Epoch 311/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.8180 - loss: 7.5430\n",
      "Epoch 312/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.8194 - loss: 7.4761\n",
      "Epoch 313/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.8240 - loss: 7.3930\n",
      "Epoch 314/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.8253 - loss: 7.3126\n",
      "Epoch 315/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.8181 - loss: 7.2352\n",
      "Epoch 316/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.8291 - loss: 7.1581\n",
      "Epoch 317/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.8285 - loss: 7.0706\n",
      "Epoch 318/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 30ms/step - accuracy: 0.8220 - loss: 7.0046\n",
      "Epoch 319/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.8323 - loss: 6.9214\n",
      "Epoch 320/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.8270 - loss: 6.8419\n",
      "Epoch 321/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 30ms/step - accuracy: 0.8327 - loss: 6.7772\n",
      "Epoch 322/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.8317 - loss: 6.7010\n",
      "Epoch 323/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.8277 - loss: 6.6292\n",
      "Epoch 324/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.8265 - loss: 6.5557\n",
      "Epoch 325/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.8296 - loss: 6.4807\n",
      "Epoch 326/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.8338 - loss: 6.4084\n",
      "Epoch 327/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.8304 - loss: 6.3362\n",
      "Epoch 328/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.8337 - loss: 6.2690\n",
      "Epoch 329/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.8358 - loss: 6.1898\n",
      "Epoch 330/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.8402 - loss: 6.1223\n",
      "Epoch 331/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.8395 - loss: 6.0530\n",
      "Epoch 332/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.8306 - loss: 5.9836\n",
      "Epoch 333/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.8428 - loss: 5.9061\n",
      "Epoch 334/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.8379 - loss: 5.8503\n",
      "Epoch 335/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.8412 - loss: 5.7722\n",
      "Epoch 336/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.8432 - loss: 5.7105\n",
      "Epoch 337/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.8396 - loss: 5.6419\n",
      "Epoch 338/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.8372 - loss: 5.5798\n",
      "Epoch 339/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.8410 - loss: 5.5091\n",
      "Epoch 340/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.8446 - loss: 5.4376\n",
      "Epoch 341/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.8437 - loss: 5.3770\n",
      "Epoch 342/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.8371 - loss: 5.3179\n",
      "Epoch 343/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.8492 - loss: 5.2468\n",
      "Epoch 344/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.8444 - loss: 5.1844\n",
      "Epoch 345/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.8417 - loss: 5.1217\n",
      "Epoch 346/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.8334 - loss: 5.0720\n",
      "Epoch 347/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.8329 - loss: 5.0066\n",
      "Epoch 348/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.8376 - loss: 4.9360\n",
      "Epoch 349/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 30ms/step - accuracy: 0.8445 - loss: 4.8691\n",
      "Epoch 350/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.8400 - loss: 4.8204\n",
      "Epoch 351/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.8454 - loss: 4.7558\n",
      "Epoch 352/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.8430 - loss: 4.6951\n",
      "Epoch 353/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.8463 - loss: 4.6366\n",
      "Epoch 354/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.8471 - loss: 4.5716\n",
      "Epoch 355/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.8443 - loss: 4.5178\n",
      "Epoch 356/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.8550 - loss: 4.4556\n",
      "Epoch 357/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 30ms/step - accuracy: 0.8454 - loss: 4.4042\n",
      "Epoch 358/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.8493 - loss: 4.3401\n",
      "Epoch 359/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.8486 - loss: 4.2829\n",
      "Epoch 360/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.8534 - loss: 4.2244\n",
      "Epoch 361/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.8538 - loss: 4.1691\n",
      "Epoch 362/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.8454 - loss: 4.1202\n",
      "Epoch 363/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.8538 - loss: 4.0640\n",
      "Epoch 364/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.8519 - loss: 4.0119\n",
      "Epoch 365/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.8571 - loss: 3.9634\n",
      "Epoch 366/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.8546 - loss: 3.9093\n",
      "Epoch 367/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.8499 - loss: 3.8558\n",
      "Epoch 368/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.8541 - loss: 3.8060\n",
      "Epoch 369/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.8536 - loss: 3.7486\n",
      "Epoch 370/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.8514 - loss: 3.7053\n",
      "Epoch 371/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.8513 - loss: 3.6512\n",
      "Epoch 372/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.8502 - loss: 3.6049\n",
      "Epoch 373/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.8526 - loss: 3.5477\n",
      "Epoch 374/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.8583 - loss: 3.4954\n",
      "Epoch 375/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.8583 - loss: 3.4438\n",
      "Epoch 376/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.8464 - loss: 3.4075\n",
      "Epoch 377/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.8582 - loss: 3.3481\n",
      "Epoch 378/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.8516 - loss: 3.3094\n",
      "Epoch 379/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.8585 - loss: 3.2533\n",
      "Epoch 380/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.8470 - loss: 3.2205\n",
      "Epoch 381/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.8624 - loss: 3.1654\n",
      "Epoch 382/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.8575 - loss: 3.1158\n",
      "Epoch 383/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.8688 - loss: 3.0715\n",
      "Epoch 384/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.8509 - loss: 3.0402\n",
      "Epoch 385/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.8559 - loss: 2.9895\n",
      "Epoch 386/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.8604 - loss: 2.9482\n",
      "Epoch 387/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.8609 - loss: 2.8951\n",
      "Epoch 388/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.8638 - loss: 2.8568\n",
      "Epoch 389/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.8569 - loss: 2.8194\n",
      "Epoch 390/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.8583 - loss: 2.7757\n",
      "Epoch 391/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.8651 - loss: 2.7248\n",
      "Epoch 392/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.8758 - loss: 2.6789\n",
      "Epoch 393/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.8748 - loss: 2.6367\n",
      "Epoch 394/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.8785 - loss: 2.5907\n",
      "Epoch 395/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.8661 - loss: 2.5630\n",
      "Epoch 396/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.8677 - loss: 2.5243\n",
      "Epoch 397/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.8651 - loss: 2.4850\n",
      "Epoch 398/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.8722 - loss: 2.4464\n",
      "Epoch 399/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.8727 - loss: 2.4066\n",
      "Epoch 400/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.8673 - loss: 2.3769\n",
      "Epoch 401/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.8663 - loss: 2.3347\n",
      "Epoch 402/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.8650 - loss: 2.2997\n",
      "Epoch 403/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.8703 - loss: 2.2602\n",
      "Epoch 404/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.8698 - loss: 2.2341\n",
      "Epoch 405/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.8748 - loss: 2.1947\n",
      "Epoch 406/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 30ms/step - accuracy: 0.8711 - loss: 2.1569\n",
      "Epoch 407/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.8753 - loss: 2.1192\n",
      "Epoch 408/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.8679 - loss: 2.0951\n",
      "Epoch 409/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.8734 - loss: 2.0597\n",
      "Epoch 410/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.8668 - loss: 2.0214\n",
      "Epoch 411/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.8732 - loss: 1.9904\n",
      "Epoch 412/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.8676 - loss: 1.9655\n",
      "Epoch 413/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.8777 - loss: 1.9335\n",
      "Epoch 414/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.8745 - loss: 1.8954\n",
      "Epoch 415/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.8738 - loss: 1.8661\n",
      "Epoch 416/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.8831 - loss: 1.8277\n",
      "Epoch 417/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.8814 - loss: 1.8048\n",
      "Epoch 418/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.8835 - loss: 1.7791\n",
      "Epoch 419/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.8820 - loss: 1.7497\n",
      "Epoch 420/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.8828 - loss: 1.7127\n",
      "Epoch 421/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.8724 - loss: 1.6983\n",
      "Epoch 422/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.8800 - loss: 1.6666\n",
      "Epoch 423/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.8780 - loss: 1.6435\n",
      "Epoch 424/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.8774 - loss: 1.6127\n",
      "Epoch 425/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.8861 - loss: 1.5773\n",
      "Epoch 426/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.8823 - loss: 1.5584\n",
      "Epoch 427/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.8761 - loss: 1.5401\n",
      "Epoch 428/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.8854 - loss: 1.5030\n",
      "Epoch 429/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.8801 - loss: 1.4907\n",
      "Epoch 430/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.8782 - loss: 1.4712\n",
      "Epoch 431/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.8876 - loss: 1.4302\n",
      "Epoch 432/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.8870 - loss: 1.4150\n",
      "Epoch 433/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.8936 - loss: 1.3861\n",
      "Epoch 434/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 30ms/step - accuracy: 0.8863 - loss: 1.3639\n",
      "Epoch 435/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.8838 - loss: 1.3540\n",
      "Epoch 436/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.8871 - loss: 1.3282\n",
      "Epoch 437/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.8974 - loss: 1.2954\n",
      "Epoch 438/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.8856 - loss: 1.2876\n",
      "Epoch 439/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.8883 - loss: 1.2655\n",
      "Epoch 440/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.8888 - loss: 1.2460\n",
      "Epoch 441/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.8940 - loss: 1.2289\n",
      "Epoch 442/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.8834 - loss: 1.2177\n",
      "Epoch 443/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.8832 - loss: 1.1931\n",
      "Epoch 444/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.8967 - loss: 1.1673\n",
      "Epoch 445/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.8880 - loss: 1.1561\n",
      "Epoch 446/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.8929 - loss: 1.1404\n",
      "Epoch 447/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.8873 - loss: 1.1313\n",
      "Epoch 448/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.8917 - loss: 1.1084\n",
      "Epoch 449/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.8953 - loss: 1.0915\n",
      "Epoch 450/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.8903 - loss: 1.0819\n",
      "Epoch 451/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.8857 - loss: 1.0733\n",
      "Epoch 452/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.8935 - loss: 1.0515\n",
      "Epoch 453/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.8912 - loss: 1.0410\n",
      "Epoch 454/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.8935 - loss: 1.0239\n",
      "Epoch 455/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.8975 - loss: 1.0066\n",
      "Epoch 456/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.8958 - loss: 0.9944\n",
      "Epoch 457/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.8888 - loss: 0.9868\n",
      "Epoch 458/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.8963 - loss: 0.9741\n",
      "Epoch 459/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.8969 - loss: 0.9650\n",
      "Epoch 460/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.8915 - loss: 0.9534\n",
      "Epoch 461/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.8932 - loss: 0.9444\n",
      "Epoch 462/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9057 - loss: 0.9227\n",
      "Epoch 463/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.8980 - loss: 0.9239\n",
      "Epoch 464/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.8987 - loss: 0.9091\n",
      "Epoch 465/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.8996 - loss: 0.9085\n",
      "Epoch 466/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9054 - loss: 0.8980\n",
      "Epoch 467/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.8969 - loss: 0.8969\n",
      "Epoch 468/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.8993 - loss: 0.8818\n",
      "Epoch 469/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9048 - loss: 0.8722\n",
      "Epoch 470/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 30ms/step - accuracy: 0.8972 - loss: 0.8741\n",
      "Epoch 471/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9005 - loss: 0.8685\n",
      "Epoch 472/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.8993 - loss: 0.8721\n",
      "Epoch 473/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.8978 - loss: 0.8547\n",
      "Epoch 474/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.8995 - loss: 0.8557\n",
      "Epoch 475/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9049 - loss: 0.8534\n",
      "Epoch 476/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9120 - loss: 0.8372\n",
      "Epoch 477/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9075 - loss: 0.8442\n",
      "Epoch 478/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.8940 - loss: 0.8408\n",
      "Epoch 479/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9030 - loss: 0.8345\n",
      "Epoch 480/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.8985 - loss: 0.8263\n",
      "Epoch 481/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9066 - loss: 0.8282\n",
      "Epoch 482/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9084 - loss: 0.8186\n",
      "Epoch 483/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9069 - loss: 0.8175\n",
      "Epoch 484/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9025 - loss: 0.8171\n",
      "Epoch 485/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9092 - loss: 0.8078\n",
      "Epoch 486/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9028 - loss: 0.8183\n",
      "Epoch 487/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9052 - loss: 0.8108\n",
      "Epoch 488/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 26ms/step - accuracy: 0.9061 - loss: 0.8094\n",
      "Epoch 489/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 30ms/step - accuracy: 0.9004 - loss: 0.8092\n",
      "Epoch 490/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9029 - loss: 0.8055\n",
      "Epoch 491/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9107 - loss: 0.7965\n",
      "Epoch 492/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9148 - loss: 0.7949\n",
      "Epoch 493/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9073 - loss: 0.7962\n",
      "Epoch 494/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9037 - loss: 0.7954\n",
      "Epoch 495/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9125 - loss: 0.7807\n",
      "Epoch 496/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9097 - loss: 0.7858\n",
      "Epoch 497/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9073 - loss: 0.7858\n",
      "Epoch 498/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9105 - loss: 0.7804\n",
      "Epoch 499/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9106 - loss: 0.7754\n",
      "Epoch 500/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9108 - loss: 0.7682\n",
      "Epoch 501/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9161 - loss: 0.7648\n",
      "Epoch 502/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9084 - loss: 0.7639\n",
      "Epoch 503/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9131 - loss: 0.7628\n",
      "Epoch 504/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9176 - loss: 0.7630\n",
      "Epoch 505/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9099 - loss: 0.7645\n",
      "Epoch 506/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9099 - loss: 0.7667\n",
      "Epoch 507/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9152 - loss: 0.7514\n",
      "Epoch 508/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9108 - loss: 0.7564\n",
      "Epoch 509/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9161 - loss: 0.7538\n",
      "Epoch 510/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9205 - loss: 0.7458\n",
      "Epoch 511/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9133 - loss: 0.7440\n",
      "Epoch 512/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9162 - loss: 0.7412\n",
      "Epoch 513/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9101 - loss: 0.7448\n",
      "Epoch 514/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9205 - loss: 0.7343\n",
      "Epoch 515/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9200 - loss: 0.7324\n",
      "Epoch 516/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9146 - loss: 0.7359\n",
      "Epoch 517/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9244 - loss: 0.7253\n",
      "Epoch 518/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9136 - loss: 0.7323\n",
      "Epoch 519/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9170 - loss: 0.7303\n",
      "Epoch 520/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9155 - loss: 0.7238\n",
      "Epoch 521/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9156 - loss: 0.7251\n",
      "Epoch 522/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9154 - loss: 0.7257\n",
      "Epoch 523/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9105 - loss: 0.7266\n",
      "Epoch 524/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9194 - loss: 0.7158\n",
      "Epoch 525/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9144 - loss: 0.7261\n",
      "Epoch 526/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9246 - loss: 0.7115\n",
      "Epoch 527/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9156 - loss: 0.7149\n",
      "Epoch 528/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9196 - loss: 0.7133\n",
      "Epoch 529/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9221 - loss: 0.7115\n",
      "Epoch 530/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9222 - loss: 0.7084\n",
      "Epoch 531/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9188 - loss: 0.7008\n",
      "Epoch 532/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9224 - loss: 0.6966\n",
      "Epoch 533/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9213 - loss: 0.7037\n",
      "Epoch 534/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9224 - loss: 0.6993\n",
      "Epoch 535/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9282 - loss: 0.6942\n",
      "Epoch 536/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9170 - loss: 0.6996\n",
      "Epoch 537/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9255 - loss: 0.6897\n",
      "Epoch 538/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9288 - loss: 0.6861\n",
      "Epoch 539/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9249 - loss: 0.6919\n",
      "Epoch 540/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9227 - loss: 0.6883\n",
      "Epoch 541/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9236 - loss: 0.6866\n",
      "Epoch 542/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 30ms/step - accuracy: 0.9226 - loss: 0.6816\n",
      "Epoch 543/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9297 - loss: 0.6775\n",
      "Epoch 544/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9300 - loss: 0.6721\n",
      "Epoch 545/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9292 - loss: 0.6832\n",
      "Epoch 546/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9268 - loss: 0.6820\n",
      "Epoch 547/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9270 - loss: 0.6780\n",
      "Epoch 548/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9256 - loss: 0.6785\n",
      "Epoch 549/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9219 - loss: 0.6737\n",
      "Epoch 550/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9275 - loss: 0.6751\n",
      "Epoch 551/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9265 - loss: 0.6678\n",
      "Epoch 552/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9272 - loss: 0.6666\n",
      "Epoch 553/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9176 - loss: 0.6782\n",
      "Epoch 554/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9253 - loss: 0.6640\n",
      "Epoch 555/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9280 - loss: 0.6600\n",
      "Epoch 556/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9296 - loss: 0.6531\n",
      "Epoch 557/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9299 - loss: 0.6571\n",
      "Epoch 558/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9334 - loss: 0.6503\n",
      "Epoch 559/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9304 - loss: 0.6543\n",
      "Epoch 560/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 30ms/step - accuracy: 0.9317 - loss: 0.6479\n",
      "Epoch 561/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9362 - loss: 0.6447\n",
      "Epoch 562/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9288 - loss: 0.6504\n",
      "Epoch 563/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9297 - loss: 0.6497\n",
      "Epoch 564/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9375 - loss: 0.6419\n",
      "Epoch 565/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9265 - loss: 0.6550\n",
      "Epoch 566/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9334 - loss: 0.6376\n",
      "Epoch 567/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9333 - loss: 0.6414\n",
      "Epoch 568/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9356 - loss: 0.6326\n",
      "Epoch 569/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9322 - loss: 0.6424\n",
      "Epoch 570/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9292 - loss: 0.6396\n",
      "Epoch 571/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9311 - loss: 0.6465\n",
      "Epoch 572/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9315 - loss: 0.6376\n",
      "Epoch 573/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9327 - loss: 0.6337\n",
      "Epoch 574/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9373 - loss: 0.6310\n",
      "Epoch 575/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9395 - loss: 0.6196\n",
      "Epoch 576/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9328 - loss: 0.6343\n",
      "Epoch 577/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9412 - loss: 0.6217\n",
      "Epoch 578/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9389 - loss: 0.6205\n",
      "Epoch 579/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9371 - loss: 0.6263\n",
      "Epoch 580/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9404 - loss: 0.6209\n",
      "Epoch 581/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9392 - loss: 0.6210\n",
      "Epoch 582/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9376 - loss: 0.6226\n",
      "Epoch 583/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9350 - loss: 0.6199\n",
      "Epoch 584/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9413 - loss: 0.6062\n",
      "Epoch 585/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9372 - loss: 0.6139\n",
      "Epoch 586/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 26ms/step - accuracy: 0.9374 - loss: 0.6104\n",
      "Epoch 587/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9411 - loss: 0.6122\n",
      "Epoch 588/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9405 - loss: 0.6059\n",
      "Epoch 589/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9397 - loss: 0.6099\n",
      "Epoch 590/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9371 - loss: 0.6083\n",
      "Epoch 591/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9398 - loss: 0.6055\n",
      "Epoch 592/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9346 - loss: 0.6104\n",
      "Epoch 593/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9386 - loss: 0.6033\n",
      "Epoch 594/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 30ms/step - accuracy: 0.9423 - loss: 0.5999\n",
      "Epoch 595/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9364 - loss: 0.6044\n",
      "Epoch 596/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9411 - loss: 0.5993\n",
      "Epoch 597/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9416 - loss: 0.6044\n",
      "Epoch 598/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9393 - loss: 0.6004\n",
      "Epoch 599/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9448 - loss: 0.5928\n",
      "Epoch 600/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9438 - loss: 0.5991\n",
      "Epoch 601/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9340 - loss: 0.6019\n",
      "Epoch 602/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9434 - loss: 0.5921\n",
      "Epoch 603/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9449 - loss: 0.5906\n",
      "Epoch 604/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9456 - loss: 0.5865\n",
      "Epoch 605/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9396 - loss: 0.5882\n",
      "Epoch 606/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9466 - loss: 0.5858\n",
      "Epoch 607/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9420 - loss: 0.5884\n",
      "Epoch 608/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9390 - loss: 0.5853\n",
      "Epoch 609/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9394 - loss: 0.5863\n",
      "Epoch 610/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9407 - loss: 0.5810\n",
      "Epoch 611/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9404 - loss: 0.5798\n",
      "Epoch 612/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9448 - loss: 0.5788\n",
      "Epoch 613/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9446 - loss: 0.5806\n",
      "Epoch 614/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9415 - loss: 0.5835\n",
      "Epoch 615/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9417 - loss: 0.5817\n",
      "Epoch 616/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9463 - loss: 0.5722\n",
      "Epoch 617/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9486 - loss: 0.5688\n",
      "Epoch 618/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9412 - loss: 0.5793\n",
      "Epoch 619/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9475 - loss: 0.5721\n",
      "Epoch 620/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9440 - loss: 0.5707\n",
      "Epoch 621/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 30ms/step - accuracy: 0.9417 - loss: 0.5713\n",
      "Epoch 622/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9441 - loss: 0.5630\n",
      "Epoch 623/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9397 - loss: 0.5727\n",
      "Epoch 624/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9401 - loss: 0.5775\n",
      "Epoch 625/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9426 - loss: 0.5705\n",
      "Epoch 626/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9471 - loss: 0.5646\n",
      "Epoch 627/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9449 - loss: 0.5621\n",
      "Epoch 628/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9480 - loss: 0.5621\n",
      "Epoch 629/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9412 - loss: 0.5667\n",
      "Epoch 630/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9447 - loss: 0.5670\n",
      "Epoch 631/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9499 - loss: 0.5630\n",
      "Epoch 632/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9494 - loss: 0.5551\n",
      "Epoch 633/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9539 - loss: 0.5516\n",
      "Epoch 634/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9507 - loss: 0.5564\n",
      "Epoch 635/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9533 - loss: 0.5526\n",
      "Epoch 636/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9500 - loss: 0.5511\n",
      "Epoch 637/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9463 - loss: 0.5545\n",
      "Epoch 638/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9442 - loss: 0.5617\n",
      "Epoch 639/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9485 - loss: 0.5526\n",
      "Epoch 640/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9483 - loss: 0.5489\n",
      "Epoch 641/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9457 - loss: 0.5522\n",
      "Epoch 642/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9431 - loss: 0.5487\n",
      "Epoch 643/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9548 - loss: 0.5469\n",
      "Epoch 644/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9509 - loss: 0.5411\n",
      "Epoch 645/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9512 - loss: 0.5406\n",
      "Epoch 646/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9553 - loss: 0.5386\n",
      "Epoch 647/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9510 - loss: 0.5420\n",
      "Epoch 648/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9481 - loss: 0.5414\n",
      "Epoch 649/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9581 - loss: 0.5344\n",
      "Epoch 650/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 26ms/step - accuracy: 0.9471 - loss: 0.5467\n",
      "Epoch 651/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9513 - loss: 0.5366\n",
      "Epoch 652/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9561 - loss: 0.5278\n",
      "Epoch 653/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9522 - loss: 0.5429\n",
      "Epoch 654/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9544 - loss: 0.5376\n",
      "Epoch 655/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 30ms/step - accuracy: 0.9500 - loss: 0.5329\n",
      "Epoch 656/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9501 - loss: 0.5393\n",
      "Epoch 657/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9549 - loss: 0.5263\n",
      "Epoch 658/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9528 - loss: 0.5314\n",
      "Epoch 659/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9492 - loss: 0.5336\n",
      "Epoch 660/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9482 - loss: 0.5309\n",
      "Epoch 661/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9531 - loss: 0.5351\n",
      "Epoch 662/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9526 - loss: 0.5295\n",
      "Epoch 663/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9501 - loss: 0.5314\n",
      "Epoch 664/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9527 - loss: 0.5247\n",
      "Epoch 665/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9535 - loss: 0.5226\n",
      "Epoch 666/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9557 - loss: 0.5225\n",
      "Epoch 667/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 30ms/step - accuracy: 0.9559 - loss: 0.5224\n",
      "Epoch 668/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9546 - loss: 0.5222\n",
      "Epoch 669/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9575 - loss: 0.5156\n",
      "Epoch 670/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9584 - loss: 0.5146\n",
      "Epoch 671/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9518 - loss: 0.5201\n",
      "Epoch 672/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9510 - loss: 0.5236\n",
      "Epoch 673/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9563 - loss: 0.5184\n",
      "Epoch 674/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9564 - loss: 0.5105\n",
      "Epoch 675/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9577 - loss: 0.5151\n",
      "Epoch 676/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9541 - loss: 0.5201\n",
      "Epoch 677/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9545 - loss: 0.5134\n",
      "Epoch 678/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9535 - loss: 0.5144\n",
      "Epoch 679/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9566 - loss: 0.5138\n",
      "Epoch 680/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9518 - loss: 0.5115\n",
      "Epoch 681/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9525 - loss: 0.5124\n",
      "Epoch 682/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9526 - loss: 0.5135\n",
      "Epoch 683/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9542 - loss: 0.5155\n",
      "Epoch 684/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9559 - loss: 0.5126\n",
      "Epoch 685/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9558 - loss: 0.5077\n",
      "Epoch 686/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9574 - loss: 0.5064\n",
      "Epoch 687/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9542 - loss: 0.5002\n",
      "Epoch 688/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9493 - loss: 0.5150\n",
      "Epoch 689/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9547 - loss: 0.5028\n",
      "Epoch 690/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9497 - loss: 0.5092\n",
      "Epoch 691/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9584 - loss: 0.5070\n",
      "Epoch 692/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9581 - loss: 0.5040\n",
      "Epoch 693/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9585 - loss: 0.4996\n",
      "Epoch 694/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9589 - loss: 0.5005\n",
      "Epoch 695/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9585 - loss: 0.4967\n",
      "Epoch 696/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9584 - loss: 0.5005\n",
      "Epoch 697/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9556 - loss: 0.4955\n",
      "Epoch 698/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9588 - loss: 0.4956\n",
      "Epoch 699/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9536 - loss: 0.4984\n",
      "Epoch 700/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9594 - loss: 0.4914\n",
      "Epoch 701/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9516 - loss: 0.5038\n",
      "Epoch 702/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9599 - loss: 0.4938\n",
      "Epoch 703/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9580 - loss: 0.4946\n",
      "Epoch 704/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9616 - loss: 0.4901\n",
      "Epoch 705/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9578 - loss: 0.4909\n",
      "Epoch 706/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9574 - loss: 0.4874\n",
      "Epoch 707/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9600 - loss: 0.4919\n",
      "Epoch 708/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9555 - loss: 0.4955\n",
      "Epoch 709/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 26ms/step - accuracy: 0.9596 - loss: 0.4861\n",
      "Epoch 710/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9602 - loss: 0.4878\n",
      "Epoch 711/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9596 - loss: 0.4888\n",
      "Epoch 712/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9626 - loss: 0.4817\n",
      "Epoch 713/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9624 - loss: 0.4841\n",
      "Epoch 714/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9604 - loss: 0.4921\n",
      "Epoch 715/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9615 - loss: 0.4867\n",
      "Epoch 716/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9579 - loss: 0.4803\n",
      "Epoch 717/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9644 - loss: 0.4828\n",
      "Epoch 718/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9609 - loss: 0.4844\n",
      "Epoch 719/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9615 - loss: 0.4811\n",
      "Epoch 720/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9627 - loss: 0.4806\n",
      "Epoch 721/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9607 - loss: 0.4770\n",
      "Epoch 722/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9652 - loss: 0.4742\n",
      "Epoch 723/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9629 - loss: 0.4799\n",
      "Epoch 724/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 33ms/step - accuracy: 0.9652 - loss: 0.4766\n",
      "Epoch 725/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 31ms/step - accuracy: 0.9574 - loss: 0.4782\n",
      "Epoch 726/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 32ms/step - accuracy: 0.9636 - loss: 0.4712\n",
      "Epoch 727/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 34ms/step - accuracy: 0.9627 - loss: 0.4754\n",
      "Epoch 728/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9611 - loss: 0.4722\n",
      "Epoch 729/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9607 - loss: 0.4722\n",
      "Epoch 730/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9571 - loss: 0.4768\n",
      "Epoch 731/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9624 - loss: 0.4713\n",
      "Epoch 732/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9637 - loss: 0.4657\n",
      "Epoch 733/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 30ms/step - accuracy: 0.9667 - loss: 0.4710\n",
      "Epoch 734/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9725 - loss: 0.4629\n",
      "Epoch 735/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9644 - loss: 0.4684\n",
      "Epoch 736/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9657 - loss: 0.4600\n",
      "Epoch 737/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9657 - loss: 0.4666\n",
      "Epoch 738/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9596 - loss: 0.4702\n",
      "Epoch 739/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9612 - loss: 0.4731\n",
      "Epoch 740/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9650 - loss: 0.4666\n",
      "Epoch 741/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9607 - loss: 0.4705\n",
      "Epoch 742/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9609 - loss: 0.4674\n",
      "Epoch 743/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9630 - loss: 0.4608\n",
      "Epoch 744/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9637 - loss: 0.4613\n",
      "Epoch 745/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9594 - loss: 0.4651\n",
      "Epoch 746/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9639 - loss: 0.4638\n",
      "Epoch 747/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9642 - loss: 0.4629\n",
      "Epoch 748/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9652 - loss: 0.4596\n",
      "Epoch 749/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9695 - loss: 0.4532\n",
      "Epoch 750/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9650 - loss: 0.4586\n",
      "Epoch 751/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9673 - loss: 0.4594\n",
      "Epoch 752/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9679 - loss: 0.4560\n",
      "Epoch 753/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 26ms/step - accuracy: 0.9644 - loss: 0.4575\n",
      "Epoch 754/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9642 - loss: 0.4565\n",
      "Epoch 755/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9676 - loss: 0.4557\n",
      "Epoch 756/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9666 - loss: 0.4526\n",
      "Epoch 757/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9616 - loss: 0.4634\n",
      "Epoch 758/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9643 - loss: 0.4583\n",
      "Epoch 759/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9689 - loss: 0.4471\n",
      "Epoch 760/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9640 - loss: 0.4545\n",
      "Epoch 761/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9655 - loss: 0.4550\n",
      "Epoch 762/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9683 - loss: 0.4480\n",
      "Epoch 763/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9612 - loss: 0.4555\n",
      "Epoch 764/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9674 - loss: 0.4474\n",
      "Epoch 765/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9670 - loss: 0.4527\n",
      "Epoch 766/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9681 - loss: 0.4471\n",
      "Epoch 767/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9655 - loss: 0.4490\n",
      "Epoch 768/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9665 - loss: 0.4478\n",
      "Epoch 769/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9681 - loss: 0.4483\n",
      "Epoch 770/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9692 - loss: 0.4454\n",
      "Epoch 771/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9700 - loss: 0.4409\n",
      "Epoch 772/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9667 - loss: 0.4444\n",
      "Epoch 773/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9626 - loss: 0.4471\n",
      "Epoch 774/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9692 - loss: 0.4427\n",
      "Epoch 775/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 26ms/step - accuracy: 0.9663 - loss: 0.4435\n",
      "Epoch 776/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9683 - loss: 0.4380\n",
      "Epoch 777/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9668 - loss: 0.4434\n",
      "Epoch 778/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9678 - loss: 0.4389\n",
      "Epoch 779/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9674 - loss: 0.4399\n",
      "Epoch 780/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9667 - loss: 0.4391\n",
      "Epoch 781/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9699 - loss: 0.4353\n",
      "Epoch 782/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 26ms/step - accuracy: 0.9691 - loss: 0.4389\n",
      "Epoch 783/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9732 - loss: 0.4358\n",
      "Epoch 784/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9693 - loss: 0.4340\n",
      "Epoch 785/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9701 - loss: 0.4356\n",
      "Epoch 786/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9638 - loss: 0.4396\n",
      "Epoch 787/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9676 - loss: 0.4343\n",
      "Epoch 788/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9783 - loss: 0.4278\n",
      "Epoch 789/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9700 - loss: 0.4331\n",
      "Epoch 790/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9664 - loss: 0.4373\n",
      "Epoch 791/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9701 - loss: 0.4339\n",
      "Epoch 792/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9639 - loss: 0.4330\n",
      "Epoch 793/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9688 - loss: 0.4272\n",
      "Epoch 794/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9721 - loss: 0.4309\n",
      "Epoch 795/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9724 - loss: 0.4298\n",
      "Epoch 796/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9699 - loss: 0.4251\n",
      "Epoch 797/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9711 - loss: 0.4255\n",
      "Epoch 798/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9721 - loss: 0.4279\n",
      "Epoch 799/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9709 - loss: 0.4263\n",
      "Epoch 800/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9704 - loss: 0.4286\n",
      "Epoch 801/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9717 - loss: 0.4219\n",
      "Epoch 802/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9722 - loss: 0.4238\n",
      "Epoch 803/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 30ms/step - accuracy: 0.9702 - loss: 0.4237\n",
      "Epoch 804/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9706 - loss: 0.4193\n",
      "Epoch 805/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9716 - loss: 0.4221\n",
      "Epoch 806/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9699 - loss: 0.4240\n",
      "Epoch 807/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9710 - loss: 0.4220\n",
      "Epoch 808/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9722 - loss: 0.4238\n",
      "Epoch 809/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9750 - loss: 0.4210\n",
      "Epoch 810/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9680 - loss: 0.4222\n",
      "Epoch 811/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9712 - loss: 0.4208\n",
      "Epoch 812/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 31ms/step - accuracy: 0.9726 - loss: 0.4204\n",
      "Epoch 813/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 34ms/step - accuracy: 0.9699 - loss: 0.4242\n",
      "Epoch 814/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 31ms/step - accuracy: 0.9696 - loss: 0.4198\n",
      "Epoch 815/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 33ms/step - accuracy: 0.9688 - loss: 0.4222\n",
      "Epoch 816/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 32ms/step - accuracy: 0.9730 - loss: 0.4174\n",
      "Epoch 817/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 34ms/step - accuracy: 0.9710 - loss: 0.4166\n",
      "Epoch 818/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 32ms/step - accuracy: 0.9728 - loss: 0.4140\n",
      "Epoch 819/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 42ms/step - accuracy: 0.9697 - loss: 0.4172\n",
      "Epoch 820/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 36ms/step - accuracy: 0.9711 - loss: 0.4146\n",
      "Epoch 821/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 43ms/step - accuracy: 0.9707 - loss: 0.4150\n",
      "Epoch 822/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 39ms/step - accuracy: 0.9759 - loss: 0.4121\n",
      "Epoch 823/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 35ms/step - accuracy: 0.9735 - loss: 0.4165\n",
      "Epoch 824/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 36ms/step - accuracy: 0.9692 - loss: 0.4124\n",
      "Epoch 825/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 47ms/step - accuracy: 0.9716 - loss: 0.4154\n",
      "Epoch 826/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 37ms/step - accuracy: 0.9734 - loss: 0.4096\n",
      "Epoch 827/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 41ms/step - accuracy: 0.9715 - loss: 0.4108\n",
      "Epoch 828/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9721 - loss: 0.4078\n",
      "Epoch 829/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 35ms/step - accuracy: 0.9729 - loss: 0.4110\n",
      "Epoch 830/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 32ms/step - accuracy: 0.9694 - loss: 0.4157\n",
      "Epoch 831/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 45ms/step - accuracy: 0.9718 - loss: 0.4122\n",
      "Epoch 832/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 43ms/step - accuracy: 0.9677 - loss: 0.4170\n",
      "Epoch 833/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 43ms/step - accuracy: 0.9740 - loss: 0.4103\n",
      "Epoch 834/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 43ms/step - accuracy: 0.9700 - loss: 0.4096\n",
      "Epoch 835/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 37ms/step - accuracy: 0.9736 - loss: 0.4085\n",
      "Epoch 836/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 46ms/step - accuracy: 0.9699 - loss: 0.4129\n",
      "Epoch 837/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 31ms/step - accuracy: 0.9730 - loss: 0.4063\n",
      "Epoch 838/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 49ms/step - accuracy: 0.9727 - loss: 0.4081\n",
      "Epoch 839/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 44ms/step - accuracy: 0.9722 - loss: 0.4079\n",
      "Epoch 840/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 50ms/step - accuracy: 0.9720 - loss: 0.4115\n",
      "Epoch 841/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9750 - loss: 0.4028\n",
      "Epoch 842/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 48ms/step - accuracy: 0.9755 - loss: 0.4034\n",
      "Epoch 843/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 41ms/step - accuracy: 0.9714 - loss: 0.4078\n",
      "Epoch 844/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 42ms/step - accuracy: 0.9752 - loss: 0.4026\n",
      "Epoch 845/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9741 - loss: 0.4029\n",
      "Epoch 846/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 33ms/step - accuracy: 0.9752 - loss: 0.4008\n",
      "Epoch 847/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 31ms/step - accuracy: 0.9736 - loss: 0.3996\n",
      "Epoch 848/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9706 - loss: 0.4069\n",
      "Epoch 849/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9736 - loss: 0.4031\n",
      "Epoch 850/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9753 - loss: 0.3965\n",
      "Epoch 851/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9714 - loss: 0.4004\n",
      "Epoch 852/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9731 - loss: 0.4009\n",
      "Epoch 853/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9752 - loss: 0.3935\n",
      "Epoch 854/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9755 - loss: 0.3987\n",
      "Epoch 855/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9792 - loss: 0.3954\n",
      "Epoch 856/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9757 - loss: 0.3927\n",
      "Epoch 857/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9740 - loss: 0.3985\n",
      "Epoch 858/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9744 - loss: 0.3990\n",
      "Epoch 859/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9713 - loss: 0.4014\n",
      "Epoch 860/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9744 - loss: 0.3921\n",
      "Epoch 861/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 31ms/step - accuracy: 0.9733 - loss: 0.3989\n",
      "Epoch 862/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9767 - loss: 0.3897\n",
      "Epoch 863/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9749 - loss: 0.3930\n",
      "Epoch 864/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9766 - loss: 0.3915\n",
      "Epoch 865/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9745 - loss: 0.3934\n",
      "Epoch 866/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9754 - loss: 0.3952\n",
      "Epoch 867/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9776 - loss: 0.3846\n",
      "Epoch 868/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9733 - loss: 0.3904\n",
      "Epoch 869/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9714 - loss: 0.3947\n",
      "Epoch 870/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9730 - loss: 0.3928\n",
      "Epoch 871/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9720 - loss: 0.3938\n",
      "Epoch 872/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9733 - loss: 0.3890\n",
      "Epoch 873/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9772 - loss: 0.3902\n",
      "Epoch 874/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9726 - loss: 0.3908\n",
      "Epoch 875/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9761 - loss: 0.3906\n",
      "Epoch 876/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9723 - loss: 0.3922\n",
      "Epoch 877/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9744 - loss: 0.3878\n",
      "Epoch 878/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9746 - loss: 0.3911\n",
      "Epoch 879/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9763 - loss: 0.3849\n",
      "Epoch 880/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9776 - loss: 0.3821\n",
      "Epoch 881/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9806 - loss: 0.3806\n",
      "Epoch 882/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9767 - loss: 0.3828\n",
      "Epoch 883/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 30ms/step - accuracy: 0.9799 - loss: 0.3839\n",
      "Epoch 884/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 30ms/step - accuracy: 0.9752 - loss: 0.3814\n",
      "Epoch 885/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9805 - loss: 0.3799\n",
      "Epoch 886/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9755 - loss: 0.3871\n",
      "Epoch 887/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9758 - loss: 0.3848\n",
      "Epoch 888/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 26ms/step - accuracy: 0.9806 - loss: 0.3795\n",
      "Epoch 889/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9791 - loss: 0.3816\n",
      "Epoch 890/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 26ms/step - accuracy: 0.9760 - loss: 0.3849\n",
      "Epoch 891/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9746 - loss: 0.3844\n",
      "Epoch 892/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9772 - loss: 0.3794\n",
      "Epoch 893/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9750 - loss: 0.3837\n",
      "Epoch 894/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9779 - loss: 0.3804\n",
      "Epoch 895/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9775 - loss: 0.3779\n",
      "Epoch 896/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9775 - loss: 0.3793\n",
      "Epoch 897/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9791 - loss: 0.3748\n",
      "Epoch 898/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9740 - loss: 0.3833\n",
      "Epoch 899/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9748 - loss: 0.3806\n",
      "Epoch 900/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9784 - loss: 0.3777\n",
      "Epoch 901/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 26ms/step - accuracy: 0.9800 - loss: 0.3723\n",
      "Epoch 902/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9752 - loss: 0.3800\n",
      "Epoch 903/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9766 - loss: 0.3783\n",
      "Epoch 904/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9755 - loss: 0.3812\n",
      "Epoch 905/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9789 - loss: 0.3785\n",
      "Epoch 906/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9768 - loss: 0.3769\n",
      "Epoch 907/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9751 - loss: 0.3783\n",
      "Epoch 908/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9792 - loss: 0.3703\n",
      "Epoch 909/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9769 - loss: 0.3714\n",
      "Epoch 910/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9782 - loss: 0.3704\n",
      "Epoch 911/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 25ms/step - accuracy: 0.9763 - loss: 0.3733\n",
      "Epoch 912/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9760 - loss: 0.3758\n",
      "Epoch 913/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9768 - loss: 0.3724\n",
      "Epoch 914/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9748 - loss: 0.3754\n",
      "Epoch 915/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9773 - loss: 0.3726\n",
      "Epoch 916/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9741 - loss: 0.3765\n",
      "Epoch 917/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9794 - loss: 0.3688\n",
      "Epoch 918/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 30ms/step - accuracy: 0.9769 - loss: 0.3713\n",
      "Epoch 919/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9730 - loss: 0.3723\n",
      "Epoch 920/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9818 - loss: 0.3646\n",
      "Epoch 921/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9762 - loss: 0.3691\n",
      "Epoch 922/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9734 - loss: 0.3726\n",
      "Epoch 923/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9803 - loss: 0.3663\n",
      "Epoch 924/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9796 - loss: 0.3631\n",
      "Epoch 925/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 26ms/step - accuracy: 0.9785 - loss: 0.3667\n",
      "Epoch 926/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 26ms/step - accuracy: 0.9756 - loss: 0.3674\n",
      "Epoch 927/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9778 - loss: 0.3703\n",
      "Epoch 928/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9768 - loss: 0.3688\n",
      "Epoch 929/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9782 - loss: 0.3654\n",
      "Epoch 930/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9760 - loss: 0.3691\n",
      "Epoch 931/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9826 - loss: 0.3598\n",
      "Epoch 932/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9795 - loss: 0.3640\n",
      "Epoch 933/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9785 - loss: 0.3681\n",
      "Epoch 934/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9783 - loss: 0.3669\n",
      "Epoch 935/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9790 - loss: 0.3642\n",
      "Epoch 936/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9776 - loss: 0.3655\n",
      "Epoch 937/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9790 - loss: 0.3613\n",
      "Epoch 938/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9755 - loss: 0.3652\n",
      "Epoch 939/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9788 - loss: 0.3597\n",
      "Epoch 940/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9774 - loss: 0.3663\n",
      "Epoch 941/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9793 - loss: 0.3633\n",
      "Epoch 942/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9810 - loss: 0.3567\n",
      "Epoch 943/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9779 - loss: 0.3597\n",
      "Epoch 944/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 26ms/step - accuracy: 0.9793 - loss: 0.3609\n",
      "Epoch 945/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9775 - loss: 0.3597\n",
      "Epoch 946/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9797 - loss: 0.3552\n",
      "Epoch 947/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9835 - loss: 0.3556\n",
      "Epoch 948/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9754 - loss: 0.3618\n",
      "Epoch 949/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9790 - loss: 0.3573\n",
      "Epoch 950/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9833 - loss: 0.3534\n",
      "Epoch 951/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9785 - loss: 0.3582\n",
      "Epoch 952/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9795 - loss: 0.3585\n",
      "Epoch 953/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9762 - loss: 0.3601\n",
      "Epoch 954/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9785 - loss: 0.3588\n",
      "Epoch 955/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9796 - loss: 0.3572\n",
      "Epoch 956/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9756 - loss: 0.3628\n",
      "Epoch 957/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9788 - loss: 0.3573\n",
      "Epoch 958/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 26ms/step - accuracy: 0.9816 - loss: 0.3548\n",
      "Epoch 959/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9811 - loss: 0.3551\n",
      "Epoch 960/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9797 - loss: 0.3536\n",
      "Epoch 961/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9830 - loss: 0.3499\n",
      "Epoch 962/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9823 - loss: 0.3506\n",
      "Epoch 963/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9819 - loss: 0.3480\n",
      "Epoch 964/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9802 - loss: 0.3516\n",
      "Epoch 965/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9789 - loss: 0.3543\n",
      "Epoch 966/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9802 - loss: 0.3532\n",
      "Epoch 967/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9788 - loss: 0.3512\n",
      "Epoch 968/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 26ms/step - accuracy: 0.9810 - loss: 0.3497\n",
      "Epoch 969/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9813 - loss: 0.3494\n",
      "Epoch 970/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9820 - loss: 0.3452\n",
      "Epoch 971/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9825 - loss: 0.3504\n",
      "Epoch 972/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9808 - loss: 0.3477\n",
      "Epoch 973/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9801 - loss: 0.3466\n",
      "Epoch 974/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 26ms/step - accuracy: 0.9821 - loss: 0.3459\n",
      "Epoch 975/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9838 - loss: 0.3443\n",
      "Epoch 976/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9782 - loss: 0.3536\n",
      "Epoch 977/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 26ms/step - accuracy: 0.9795 - loss: 0.3497\n",
      "Epoch 978/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9777 - loss: 0.3510\n",
      "Epoch 979/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9837 - loss: 0.3446\n",
      "Epoch 980/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9797 - loss: 0.3437\n",
      "Epoch 981/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9806 - loss: 0.3458\n",
      "Epoch 982/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9805 - loss: 0.3511\n",
      "Epoch 983/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9800 - loss: 0.3451\n",
      "Epoch 984/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9839 - loss: 0.3421\n",
      "Epoch 985/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9823 - loss: 0.3427\n",
      "Epoch 986/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 26ms/step - accuracy: 0.9800 - loss: 0.3482\n",
      "Epoch 987/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9810 - loss: 0.3417\n",
      "Epoch 988/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9798 - loss: 0.3444\n",
      "Epoch 989/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9817 - loss: 0.3440\n",
      "Epoch 990/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9848 - loss: 0.3404\n",
      "Epoch 991/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9812 - loss: 0.3444\n",
      "Epoch 992/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9828 - loss: 0.3412\n",
      "Epoch 993/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9805 - loss: 0.3445\n",
      "Epoch 994/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9789 - loss: 0.3446\n",
      "Epoch 995/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9817 - loss: 0.3447\n",
      "Epoch 996/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9811 - loss: 0.3414\n",
      "Epoch 997/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9829 - loss: 0.3398\n",
      "Epoch 998/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9855 - loss: 0.3374\n",
      "Epoch 999/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9825 - loss: 0.3348\n",
      "Epoch 1000/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9859 - loss: 0.3315\n",
      "Epoch 1001/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9834 - loss: 0.3353\n",
      "Epoch 1002/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9809 - loss: 0.3420\n",
      "Epoch 1003/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9842 - loss: 0.3360\n",
      "Epoch 1004/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9841 - loss: 0.3349\n",
      "Epoch 1005/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9819 - loss: 0.3423\n",
      "Epoch 1006/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 30ms/step - accuracy: 0.9785 - loss: 0.3375\n",
      "Epoch 1007/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9798 - loss: 0.3396\n",
      "Epoch 1008/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9816 - loss: 0.3353\n",
      "Epoch 1009/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9830 - loss: 0.3387\n",
      "Epoch 1010/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9832 - loss: 0.3348\n",
      "Epoch 1011/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9853 - loss: 0.3340\n",
      "Epoch 1012/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9839 - loss: 0.3302\n",
      "Epoch 1013/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9847 - loss: 0.3328\n",
      "Epoch 1014/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9824 - loss: 0.3364\n",
      "Epoch 1015/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9857 - loss: 0.3301\n",
      "Epoch 1016/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9822 - loss: 0.3401\n",
      "Epoch 1017/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9845 - loss: 0.3350\n",
      "Epoch 1018/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9849 - loss: 0.3320\n",
      "Epoch 1019/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9860 - loss: 0.3313\n",
      "Epoch 1020/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9810 - loss: 0.3336\n",
      "Epoch 1021/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9807 - loss: 0.3334\n",
      "Epoch 1022/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9801 - loss: 0.3359\n",
      "Epoch 1023/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9850 - loss: 0.3300\n",
      "Epoch 1024/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9795 - loss: 0.3322\n",
      "Epoch 1025/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9841 - loss: 0.3299\n",
      "Epoch 1026/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9837 - loss: 0.3285\n",
      "Epoch 1027/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9858 - loss: 0.3260\n",
      "Epoch 1028/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9828 - loss: 0.3299\n",
      "Epoch 1029/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9842 - loss: 0.3269\n",
      "Epoch 1030/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9846 - loss: 0.3273\n",
      "Epoch 1031/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9825 - loss: 0.3350\n",
      "Epoch 1032/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9848 - loss: 0.3282\n",
      "Epoch 1033/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9827 - loss: 0.3312\n",
      "Epoch 1034/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9816 - loss: 0.3338\n",
      "Epoch 1035/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9811 - loss: 0.3338\n",
      "Epoch 1036/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9850 - loss: 0.3232\n",
      "Epoch 1037/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9823 - loss: 0.3252\n",
      "Epoch 1038/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9850 - loss: 0.3257\n",
      "Epoch 1039/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9857 - loss: 0.3284\n",
      "Epoch 1040/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9842 - loss: 0.3266\n",
      "Epoch 1041/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9837 - loss: 0.3228\n",
      "Epoch 1042/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9861 - loss: 0.3211\n",
      "Epoch 1043/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9822 - loss: 0.3264\n",
      "Epoch 1044/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 26ms/step - accuracy: 0.9829 - loss: 0.3200\n",
      "Epoch 1045/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9839 - loss: 0.3223\n",
      "Epoch 1046/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 30ms/step - accuracy: 0.9814 - loss: 0.3234\n",
      "Epoch 1047/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9829 - loss: 0.3255\n",
      "Epoch 1048/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9836 - loss: 0.3228\n",
      "Epoch 1049/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9834 - loss: 0.3215\n",
      "Epoch 1050/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9842 - loss: 0.3213\n",
      "Epoch 1051/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9877 - loss: 0.3184\n",
      "Epoch 1052/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9812 - loss: 0.3273\n",
      "Epoch 1053/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9848 - loss: 0.3195\n",
      "Epoch 1054/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9868 - loss: 0.3214\n",
      "Epoch 1055/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9870 - loss: 0.3210\n",
      "Epoch 1056/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9810 - loss: 0.3271\n",
      "Epoch 1057/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 26ms/step - accuracy: 0.9841 - loss: 0.3207\n",
      "Epoch 1058/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9844 - loss: 0.3219\n",
      "Epoch 1059/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9846 - loss: 0.3228\n",
      "Epoch 1060/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9860 - loss: 0.3148\n",
      "Epoch 1061/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9867 - loss: 0.3170\n",
      "Epoch 1062/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9772 - loss: 0.3267\n",
      "Epoch 1063/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 26ms/step - accuracy: 0.9843 - loss: 0.3209\n",
      "Epoch 1064/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9848 - loss: 0.3180\n",
      "Epoch 1065/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9847 - loss: 0.3195\n",
      "Epoch 1066/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9796 - loss: 0.3224\n",
      "Epoch 1067/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 33ms/step - accuracy: 0.9851 - loss: 0.3182\n",
      "Epoch 1068/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 32ms/step - accuracy: 0.9837 - loss: 0.3195\n",
      "Epoch 1069/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 34ms/step - accuracy: 0.9873 - loss: 0.3140\n",
      "Epoch 1070/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 32ms/step - accuracy: 0.9836 - loss: 0.3168\n",
      "Epoch 1071/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 31ms/step - accuracy: 0.9836 - loss: 0.3205\n",
      "Epoch 1072/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 32ms/step - accuracy: 0.9826 - loss: 0.3214\n",
      "Epoch 1073/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 31ms/step - accuracy: 0.9868 - loss: 0.3146\n",
      "Epoch 1074/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 30ms/step - accuracy: 0.9841 - loss: 0.3159\n",
      "Epoch 1075/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9828 - loss: 0.3147\n",
      "Epoch 1076/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9814 - loss: 0.3248\n",
      "Epoch 1077/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9843 - loss: 0.3181\n",
      "Epoch 1078/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9871 - loss: 0.3114\n",
      "Epoch 1079/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9837 - loss: 0.3186\n",
      "Epoch 1080/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9838 - loss: 0.3137\n",
      "Epoch 1081/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9856 - loss: 0.3141\n",
      "Epoch 1082/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9827 - loss: 0.3151\n",
      "Epoch 1083/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9836 - loss: 0.3217\n",
      "Epoch 1084/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9843 - loss: 0.3154\n",
      "Epoch 1085/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9842 - loss: 0.3109\n",
      "Epoch 1086/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 30ms/step - accuracy: 0.9845 - loss: 0.3105\n",
      "Epoch 1087/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9830 - loss: 0.3126\n",
      "Epoch 1088/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9860 - loss: 0.3126\n",
      "Epoch 1089/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9867 - loss: 0.3105\n",
      "Epoch 1090/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9840 - loss: 0.3139\n",
      "Epoch 1091/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9802 - loss: 0.3171\n",
      "Epoch 1092/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9825 - loss: 0.3151\n",
      "Epoch 1093/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9864 - loss: 0.3108\n",
      "Epoch 1094/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 31ms/step - accuracy: 0.9849 - loss: 0.3109\n",
      "Epoch 1095/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 30ms/step - accuracy: 0.9844 - loss: 0.3112\n",
      "Epoch 1096/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9866 - loss: 0.3106\n",
      "Epoch 1097/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 30ms/step - accuracy: 0.9882 - loss: 0.3067\n",
      "Epoch 1098/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9845 - loss: 0.3094\n",
      "Epoch 1099/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9860 - loss: 0.3092\n",
      "Epoch 1100/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 30ms/step - accuracy: 0.9837 - loss: 0.3135\n",
      "Epoch 1101/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9849 - loss: 0.3098\n",
      "Epoch 1102/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9850 - loss: 0.3050\n",
      "Epoch 1103/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9841 - loss: 0.3113\n",
      "Epoch 1104/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9860 - loss: 0.3075\n",
      "Epoch 1105/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9862 - loss: 0.3079\n",
      "Epoch 1106/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9832 - loss: 0.3144\n",
      "Epoch 1107/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9838 - loss: 0.3085\n",
      "Epoch 1108/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9863 - loss: 0.3046\n",
      "Epoch 1109/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9852 - loss: 0.3085\n",
      "Epoch 1110/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 30ms/step - accuracy: 0.9871 - loss: 0.3044\n",
      "Epoch 1111/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9859 - loss: 0.3028\n",
      "Epoch 1112/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9849 - loss: 0.3076\n",
      "Epoch 1113/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9855 - loss: 0.3092\n",
      "Epoch 1114/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9810 - loss: 0.3122\n",
      "Epoch 1115/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9857 - loss: 0.3060\n",
      "Epoch 1116/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9839 - loss: 0.3041\n",
      "Epoch 1117/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9868 - loss: 0.3006\n",
      "Epoch 1118/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9847 - loss: 0.3087\n",
      "Epoch 1119/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9869 - loss: 0.3042\n",
      "Epoch 1120/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9864 - loss: 0.3061\n",
      "Epoch 1121/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9836 - loss: 0.3083\n",
      "Epoch 1122/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9840 - loss: 0.3031\n",
      "Epoch 1123/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9841 - loss: 0.3015\n",
      "Epoch 1124/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 30ms/step - accuracy: 0.9857 - loss: 0.3054\n",
      "Epoch 1125/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 32ms/step - accuracy: 0.9866 - loss: 0.3033\n",
      "Epoch 1126/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9839 - loss: 0.3046\n",
      "Epoch 1127/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9831 - loss: 0.3045\n",
      "Epoch 1128/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9873 - loss: 0.3031\n",
      "Epoch 1129/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9864 - loss: 0.3065\n",
      "Epoch 1130/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9878 - loss: 0.2957\n",
      "Epoch 1131/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9836 - loss: 0.3047\n",
      "Epoch 1132/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9875 - loss: 0.2963\n",
      "Epoch 1133/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 30ms/step - accuracy: 0.9870 - loss: 0.3009\n",
      "Epoch 1134/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9876 - loss: 0.2973\n",
      "Epoch 1135/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 30ms/step - accuracy: 0.9866 - loss: 0.2965\n",
      "Epoch 1136/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9872 - loss: 0.2998\n",
      "Epoch 1137/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9840 - loss: 0.3022\n",
      "Epoch 1138/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9874 - loss: 0.2977\n",
      "Epoch 1139/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9875 - loss: 0.2911\n",
      "Epoch 1140/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9857 - loss: 0.3006\n",
      "Epoch 1141/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9859 - loss: 0.2988\n",
      "Epoch 1142/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9855 - loss: 0.2975\n",
      "Epoch 1143/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9871 - loss: 0.3001\n",
      "Epoch 1144/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9869 - loss: 0.2952\n",
      "Epoch 1145/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9856 - loss: 0.2998\n",
      "Epoch 1146/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9867 - loss: 0.2998\n",
      "Epoch 1147/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9889 - loss: 0.2928\n",
      "Epoch 1148/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9860 - loss: 0.3013\n",
      "Epoch 1149/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9882 - loss: 0.2939\n",
      "Epoch 1150/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9849 - loss: 0.2953\n",
      "Epoch 1151/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9845 - loss: 0.2994\n",
      "Epoch 1152/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9885 - loss: 0.2944\n",
      "Epoch 1153/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9859 - loss: 0.2963\n",
      "Epoch 1154/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9850 - loss: 0.3001\n",
      "Epoch 1155/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9864 - loss: 0.2994\n",
      "Epoch 1156/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9868 - loss: 0.2956\n",
      "Epoch 1157/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9882 - loss: 0.2940\n",
      "Epoch 1158/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9884 - loss: 0.2905\n",
      "Epoch 1159/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 26ms/step - accuracy: 0.9884 - loss: 0.2896\n",
      "Epoch 1160/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9871 - loss: 0.2889\n",
      "Epoch 1161/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9868 - loss: 0.2941\n",
      "Epoch 1162/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9876 - loss: 0.2958\n",
      "Epoch 1163/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9887 - loss: 0.2914\n",
      "Epoch 1164/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9877 - loss: 0.2934\n",
      "Epoch 1165/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9879 - loss: 0.2927\n",
      "Epoch 1166/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9864 - loss: 0.2901\n",
      "Epoch 1167/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9849 - loss: 0.2948\n",
      "Epoch 1168/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9883 - loss: 0.2920\n",
      "Epoch 1169/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9866 - loss: 0.2907\n",
      "Epoch 1170/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9873 - loss: 0.2920\n",
      "Epoch 1171/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 30ms/step - accuracy: 0.9856 - loss: 0.2917\n",
      "Epoch 1172/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9864 - loss: 0.2937\n",
      "Epoch 1173/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9890 - loss: 0.2894\n",
      "Epoch 1174/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 26ms/step - accuracy: 0.9869 - loss: 0.2916\n",
      "Epoch 1175/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9881 - loss: 0.2883\n",
      "Epoch 1176/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9861 - loss: 0.2920\n",
      "Epoch 1177/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9855 - loss: 0.2903\n",
      "Epoch 1178/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9856 - loss: 0.2902\n",
      "Epoch 1179/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9864 - loss: 0.2883\n",
      "Epoch 1180/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 26ms/step - accuracy: 0.9878 - loss: 0.2891\n",
      "Epoch 1181/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9890 - loss: 0.2872\n",
      "Epoch 1182/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9882 - loss: 0.2868\n",
      "Epoch 1183/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9880 - loss: 0.2910\n",
      "Epoch 1184/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9867 - loss: 0.2891\n",
      "Epoch 1185/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9885 - loss: 0.2876\n",
      "Epoch 1186/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9893 - loss: 0.2812\n",
      "Epoch 1187/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9883 - loss: 0.2842\n",
      "Epoch 1188/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 30ms/step - accuracy: 0.9902 - loss: 0.2829\n",
      "Epoch 1189/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9848 - loss: 0.2877\n",
      "Epoch 1190/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9834 - loss: 0.2907\n",
      "Epoch 1191/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9874 - loss: 0.2827\n",
      "Epoch 1192/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9855 - loss: 0.2951\n",
      "Epoch 1193/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 30ms/step - accuracy: 0.9866 - loss: 0.2890\n",
      "Epoch 1194/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9889 - loss: 0.2835\n",
      "Epoch 1195/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9875 - loss: 0.2871\n",
      "Epoch 1196/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9868 - loss: 0.2829\n",
      "Epoch 1197/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9887 - loss: 0.2861\n",
      "Epoch 1198/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9877 - loss: 0.2834\n",
      "Epoch 1199/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9892 - loss: 0.2847\n",
      "Epoch 1200/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9888 - loss: 0.2793\n",
      "Epoch 1201/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9900 - loss: 0.2802\n",
      "Epoch 1202/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9889 - loss: 0.2822\n",
      "Epoch 1203/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9851 - loss: 0.2883\n",
      "Epoch 1204/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9887 - loss: 0.2854\n",
      "Epoch 1205/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9844 - loss: 0.2866\n",
      "Epoch 1206/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9899 - loss: 0.2816\n",
      "Epoch 1207/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9849 - loss: 0.2837\n",
      "Epoch 1208/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9886 - loss: 0.2809\n",
      "Epoch 1209/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9884 - loss: 0.2791\n",
      "Epoch 1210/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9884 - loss: 0.2832\n",
      "Epoch 1211/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9855 - loss: 0.2862\n",
      "Epoch 1212/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9881 - loss: 0.2821\n",
      "Epoch 1213/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9858 - loss: 0.2859\n",
      "Epoch 1214/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9884 - loss: 0.2801\n",
      "Epoch 1215/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9889 - loss: 0.2797\n",
      "Epoch 1216/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9865 - loss: 0.2833\n",
      "Epoch 1217/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9872 - loss: 0.2816\n",
      "Epoch 1218/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9903 - loss: 0.2782\n",
      "Epoch 1219/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9906 - loss: 0.2742\n",
      "Epoch 1220/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9874 - loss: 0.2841\n",
      "Epoch 1221/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9871 - loss: 0.2819\n",
      "Epoch 1222/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9865 - loss: 0.2815\n",
      "Epoch 1223/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9876 - loss: 0.2812\n",
      "Epoch 1224/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9879 - loss: 0.2772\n",
      "Epoch 1225/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9876 - loss: 0.2779\n",
      "Epoch 1226/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9881 - loss: 0.2785\n",
      "Epoch 1227/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9889 - loss: 0.2788\n",
      "Epoch 1228/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9872 - loss: 0.2806\n",
      "Epoch 1229/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9893 - loss: 0.2789\n",
      "Epoch 1230/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9890 - loss: 0.2737\n",
      "Epoch 1231/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9875 - loss: 0.2776\n",
      "Epoch 1232/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9903 - loss: 0.2764\n",
      "Epoch 1233/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9901 - loss: 0.2740\n",
      "Epoch 1234/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 26ms/step - accuracy: 0.9879 - loss: 0.2810\n",
      "Epoch 1235/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9899 - loss: 0.2785\n",
      "Epoch 1236/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9873 - loss: 0.2786\n",
      "Epoch 1237/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9898 - loss: 0.2757\n",
      "Epoch 1238/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 26ms/step - accuracy: 0.9888 - loss: 0.2743\n",
      "Epoch 1239/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9879 - loss: 0.2757\n",
      "Epoch 1240/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9897 - loss: 0.2742\n",
      "Epoch 1241/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9877 - loss: 0.2775\n",
      "Epoch 1242/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9870 - loss: 0.2761\n",
      "Epoch 1243/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9895 - loss: 0.2749\n",
      "Epoch 1244/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9870 - loss: 0.2824\n",
      "Epoch 1245/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9881 - loss: 0.2762\n",
      "Epoch 1246/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9898 - loss: 0.2729\n",
      "Epoch 1247/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9886 - loss: 0.2749\n",
      "Epoch 1248/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9883 - loss: 0.2729\n",
      "Epoch 1249/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9878 - loss: 0.2765\n",
      "Epoch 1250/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9897 - loss: 0.2736\n",
      "Epoch 1251/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9891 - loss: 0.2736\n",
      "Epoch 1252/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9887 - loss: 0.2737\n",
      "Epoch 1253/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9887 - loss: 0.2754\n",
      "Epoch 1254/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9872 - loss: 0.2754\n",
      "Epoch 1255/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9873 - loss: 0.2774\n",
      "Epoch 1256/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9865 - loss: 0.2731\n",
      "Epoch 1257/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9869 - loss: 0.2769\n",
      "Epoch 1258/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9874 - loss: 0.2724\n",
      "Epoch 1259/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9885 - loss: 0.2714\n",
      "Epoch 1260/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9883 - loss: 0.2696\n",
      "Epoch 1261/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9904 - loss: 0.2718\n",
      "Epoch 1262/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9898 - loss: 0.2715\n",
      "Epoch 1263/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9863 - loss: 0.2713\n",
      "Epoch 1264/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9896 - loss: 0.2682\n",
      "Epoch 1265/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9876 - loss: 0.2734\n",
      "Epoch 1266/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9874 - loss: 0.2712\n",
      "Epoch 1267/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9891 - loss: 0.2682\n",
      "Epoch 1268/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9896 - loss: 0.2677\n",
      "Epoch 1269/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9885 - loss: 0.2682\n",
      "Epoch 1270/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9884 - loss: 0.2703\n",
      "Epoch 1271/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9890 - loss: 0.2653\n",
      "Epoch 1272/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9909 - loss: 0.2659\n",
      "Epoch 1273/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9896 - loss: 0.2665\n",
      "Epoch 1274/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 30ms/step - accuracy: 0.9898 - loss: 0.2667\n",
      "Epoch 1275/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 30ms/step - accuracy: 0.9910 - loss: 0.2680\n",
      "Epoch 1276/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9905 - loss: 0.2661\n",
      "Epoch 1277/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 26ms/step - accuracy: 0.9887 - loss: 0.2698\n",
      "Epoch 1278/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9909 - loss: 0.2715\n",
      "Epoch 1279/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9857 - loss: 0.2729\n",
      "Epoch 1280/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9887 - loss: 0.2673\n",
      "Epoch 1281/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9894 - loss: 0.2696\n",
      "Epoch 1282/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9904 - loss: 0.2643\n",
      "Epoch 1283/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9876 - loss: 0.2687\n",
      "Epoch 1284/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9898 - loss: 0.2673\n",
      "Epoch 1285/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9898 - loss: 0.2644\n",
      "Epoch 1286/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9893 - loss: 0.2691\n",
      "Epoch 1287/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9872 - loss: 0.2668\n",
      "Epoch 1288/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9895 - loss: 0.2666\n",
      "Epoch 1289/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9892 - loss: 0.2658\n",
      "Epoch 1290/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9888 - loss: 0.2662\n",
      "Epoch 1291/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9882 - loss: 0.2682\n",
      "Epoch 1292/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9911 - loss: 0.2613\n",
      "Epoch 1293/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9882 - loss: 0.2652\n",
      "Epoch 1294/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 26ms/step - accuracy: 0.9888 - loss: 0.2668\n",
      "Epoch 1295/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9885 - loss: 0.2671\n",
      "Epoch 1296/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9899 - loss: 0.2638\n",
      "Epoch 1297/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9887 - loss: 0.2626\n",
      "Epoch 1298/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 30ms/step - accuracy: 0.9857 - loss: 0.2691\n",
      "Epoch 1299/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9847 - loss: 0.2696\n",
      "Epoch 1300/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9902 - loss: 0.2655\n",
      "Epoch 1301/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9865 - loss: 0.2693\n",
      "Epoch 1302/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9950 - loss: 0.2562\n",
      "Epoch 1303/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9893 - loss: 0.2598\n",
      "Epoch 1304/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9923 - loss: 0.2579\n",
      "Epoch 1305/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9875 - loss: 0.2675\n",
      "Epoch 1306/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9901 - loss: 0.2633\n",
      "Epoch 1307/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9880 - loss: 0.2638\n",
      "Epoch 1308/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9889 - loss: 0.2617\n",
      "Epoch 1309/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9890 - loss: 0.2612\n",
      "Epoch 1310/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9905 - loss: 0.2580\n",
      "Epoch 1311/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9905 - loss: 0.2605\n",
      "Epoch 1312/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9886 - loss: 0.2597\n",
      "Epoch 1313/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9877 - loss: 0.2659\n",
      "Epoch 1314/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9900 - loss: 0.2595\n",
      "Epoch 1315/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9873 - loss: 0.2648\n",
      "Epoch 1316/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9893 - loss: 0.2634\n",
      "Epoch 1317/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9892 - loss: 0.2598\n",
      "Epoch 1318/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9909 - loss: 0.2588\n",
      "Epoch 1319/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9928 - loss: 0.2550\n",
      "Epoch 1320/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9901 - loss: 0.2599\n",
      "Epoch 1321/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9909 - loss: 0.2621\n",
      "Epoch 1322/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9925 - loss: 0.2588\n",
      "Epoch 1323/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9915 - loss: 0.2590\n",
      "Epoch 1324/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9919 - loss: 0.2584\n",
      "Epoch 1325/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9864 - loss: 0.2642\n",
      "Epoch 1326/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9901 - loss: 0.2598\n",
      "Epoch 1327/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9869 - loss: 0.2618\n",
      "Epoch 1328/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9900 - loss: 0.2635\n",
      "Epoch 1329/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9881 - loss: 0.2633\n",
      "Epoch 1330/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9892 - loss: 0.2665\n",
      "Epoch 1331/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9890 - loss: 0.2627\n",
      "Epoch 1332/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9902 - loss: 0.2555\n",
      "Epoch 1333/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9889 - loss: 0.2575\n",
      "Epoch 1334/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9910 - loss: 0.2580\n",
      "Epoch 1335/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9923 - loss: 0.2533\n",
      "Epoch 1336/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9892 - loss: 0.2606\n",
      "Epoch 1337/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9915 - loss: 0.2566\n",
      "Epoch 1338/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9903 - loss: 0.2556\n",
      "Epoch 1339/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9909 - loss: 0.2539\n",
      "Epoch 1340/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9909 - loss: 0.2546\n",
      "Epoch 1341/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9922 - loss: 0.2548\n",
      "Epoch 1342/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9922 - loss: 0.2537\n",
      "Epoch 1343/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9885 - loss: 0.2609\n",
      "Epoch 1344/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9927 - loss: 0.2549\n",
      "Epoch 1345/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9878 - loss: 0.2619\n",
      "Epoch 1346/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9915 - loss: 0.2552\n",
      "Epoch 1347/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9917 - loss: 0.2549\n",
      "Epoch 1348/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9903 - loss: 0.2521\n",
      "Epoch 1349/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9896 - loss: 0.2551\n",
      "Epoch 1350/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9901 - loss: 0.2540\n",
      "Epoch 1351/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9909 - loss: 0.2524\n",
      "Epoch 1352/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9908 - loss: 0.2507\n",
      "Epoch 1353/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9917 - loss: 0.2492\n",
      "Epoch 1354/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9902 - loss: 0.2554\n",
      "Epoch 1355/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9923 - loss: 0.2526\n",
      "Epoch 1356/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9909 - loss: 0.2530\n",
      "Epoch 1357/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9908 - loss: 0.2548\n",
      "Epoch 1358/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 30ms/step - accuracy: 0.9909 - loss: 0.2506\n",
      "Epoch 1359/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 30ms/step - accuracy: 0.9904 - loss: 0.2500\n",
      "Epoch 1360/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9900 - loss: 0.2530\n",
      "Epoch 1361/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9903 - loss: 0.2521\n",
      "Epoch 1362/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9924 - loss: 0.2518\n",
      "Epoch 1363/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9912 - loss: 0.2526\n",
      "Epoch 1364/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9898 - loss: 0.2521\n",
      "Epoch 1365/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9898 - loss: 0.2550\n",
      "Epoch 1366/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9903 - loss: 0.2509\n",
      "Epoch 1367/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9929 - loss: 0.2488\n",
      "Epoch 1368/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9906 - loss: 0.2529\n",
      "Epoch 1369/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9905 - loss: 0.2499\n",
      "Epoch 1370/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9905 - loss: 0.2516\n",
      "Epoch 1371/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9907 - loss: 0.2508\n",
      "Epoch 1372/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9907 - loss: 0.2531\n",
      "Epoch 1373/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9911 - loss: 0.2516\n",
      "Epoch 1374/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9926 - loss: 0.2470\n",
      "Epoch 1375/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9905 - loss: 0.2479\n",
      "Epoch 1376/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 30ms/step - accuracy: 0.9902 - loss: 0.2531\n",
      "Epoch 1377/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9887 - loss: 0.2525\n",
      "Epoch 1378/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9915 - loss: 0.2502\n",
      "Epoch 1379/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9928 - loss: 0.2491\n",
      "Epoch 1380/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9933 - loss: 0.2461\n",
      "Epoch 1381/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9916 - loss: 0.2437\n",
      "Epoch 1382/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9883 - loss: 0.2520\n",
      "Epoch 1383/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9900 - loss: 0.2486\n",
      "Epoch 1384/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9892 - loss: 0.2472\n",
      "Epoch 1385/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9925 - loss: 0.2475\n",
      "Epoch 1386/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9903 - loss: 0.2482\n",
      "Epoch 1387/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9911 - loss: 0.2494\n",
      "Epoch 1388/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 29ms/step - accuracy: 0.9907 - loss: 0.2493\n",
      "Epoch 1389/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9927 - loss: 0.2537\n",
      "Epoch 1390/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9898 - loss: 0.2603\n",
      "Epoch 1391/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9901 - loss: 0.2548\n",
      "Epoch 1392/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9917 - loss: 0.2470\n",
      "Epoch 1393/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9915 - loss: 0.2496\n",
      "Epoch 1394/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9929 - loss: 0.2459\n",
      "Epoch 1395/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9886 - loss: 0.2508\n",
      "Epoch 1396/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9927 - loss: 0.2436\n",
      "Epoch 1397/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step - accuracy: 0.9920 - loss: 0.2468\n",
      "Epoch 1398/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 26ms/step - accuracy: 0.9889 - loss: 0.2492\n",
      "Epoch 1399/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 28ms/step - accuracy: 0.9901 - loss: 0.2469\n",
      "Epoch 1400/1400\n",
      "\u001B[1m15/15\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 26ms/step - accuracy: 0.9901 - loss: 0.2449\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.history.History at 0x40188df50>"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Model\n",
    "model = Sequential([\n",
    "                    Dense(input_shape*2, \n",
    "                          input_shape=(input_shape,), \n",
    "                          activation='relu', \n",
    "                          kernel_regularizer=l1_l2(l1=0.005, l2=0.0001),\n",
    "                         ),\n",
    "                    Dense(input_shape*4, \n",
    "                          input_shape=(input_shape,), \n",
    "                          activation='relu',\n",
    "                          ),\n",
    "                    Dropout(0.5),\n",
    "                    Dense(input_shape*8, \n",
    "                          input_shape=(input_shape,), \n",
    "                          activation='relu',\n",
    "                          ),\n",
    "                    Dropout(0.5),\n",
    "                    Dense(4, activation='softmax')\n",
    "                    ])\n",
    "\n",
    "# Optimizer\n",
    "optimizer = Adam(learning_rate=0.00001)\n",
    "\n",
    "# Model compilation\n",
    "model.compile(optimizer=optimizer,\n",
    "              loss='categorical_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "# Fit model\n",
    "model.fit(x_train, y_train, epochs=batch_size*4, batch_size=batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "8409d068-942c-472f-9bdc-662fa48ea877",
   "metadata": {},
   "outputs": [],
   "source": "model.save('./ErythroOrigin.keras')"
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "da85cce7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001B[1m43/43\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 2ms/step\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 3900x3000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Confusion matrix\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "plt.figure(figsize=(6.5, 5), dpi = 600) \n",
    "\n",
    "cm_labels = ['ABM','FL','CB','NC']\n",
    "ax= plt.subplot()\n",
    "\n",
    "y_pred = model.predict(x_test)\n",
    "cm = confusion_matrix(np.asarray(y_pred).argmax(axis=1), np.asarray(y_test).argmax(axis=1))\n",
    "sns.heatmap(cm, annot=True, fmt='g', ax=ax); #annot=True to annotate cells, ftm='g' to disable scientific notation\n",
    "\n",
    "# labels, title and ticks\n",
    "ax.set_xlabel('Predicted');ax.set_ylabel('Observed');\n",
    "ax.set_title('Confusion Matrix');\n",
    "ax.xaxis.set_ticklabels(cm_labels); ax.yaxis.set_ticklabels(cm_labels);"
   ]
  }
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