06_1027_DelhiClimate(RNN) .ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "091a7c0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from scipy.interpolate import interp1d\n",
    "from scipy.stats import mode\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.metrics import classification_report, confusion_matrix, ConfusionMatrixDisplay\n",
    "\n",
    "from sklearn.metrics import confusion_matrix,classification_report,accuracy_score\n",
    "import seaborn as sns\n",
    "\n",
    "import tensorflow as tf\n",
    "import keras\n",
    "from tensorflow import stack\n",
    "from tensorflow.keras.utils import to_categorical\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, GlobalAveragePooling1D, BatchNormalization, MaxPool1D, Reshape, Activation, Flatten\n",
    "from keras.layers import Conv1D, LSTM, Dropout, Bidirectional,LayerNormalization,SimpleRNN\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "4b9b7250",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train = pd.read_csv('./data/DailyDelhiClimate/train.csv', index_col=0)\n",
    "df_test = pd.read_csv('./data/DailyDelhiClimate/test.csv', index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "e03c9f77",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['meantemp', 'humidity', 'wind_speed', 'meanpressure'], dtype='object')"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "c1131060",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, y_train = df_train[['humidity', 'wind_speed', 'meanpressure']], df_train[['meantemp']]\n",
    "X_test, y_test = df_test[['humidity', 'wind_speed', 'meanpressure']], df_test[['meantemp']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "b3756d5a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 6))\n",
    "df_train['meantemp'].plot()\n",
    "plt.title(\"Mean Temperature\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "9d158b4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 6))\n",
    "df_train[\"humidity\"].plot()\n",
    "plt.title(\"Humidity\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "75501999",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 6))\n",
    "df_train[\"wind_speed\"].plot()\n",
    "plt.title(\"wind_speed\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "a77db3e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 6))\n",
    "df_train[\"meanpressure\"].plot()\n",
    "plt.title(\"meanpressure\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "a8a81adf",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler = MinMaxScaler()\n",
    "X_train = scaler.fit_transform(np.array(X_train))\n",
    "X_test = scaler.transform(np.array(X_test))\n",
    "y_train = scaler.fit_transform(np.array(y_train))\n",
    "y_test = scaler.transform(np.array(y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "970863f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[0.8209571 , 0.        , 0.13260331],\n",
       "        [0.90759076, 0.07058266, 0.132881  ],\n",
       "        [0.84983498, 0.10974262, 0.13299381],\n",
       "        ...,\n",
       "        [0.88063806, 0.14842886, 0.13289464],\n",
       "        [0.84983498, 0.17349597, 0.13265971],\n",
       "        [1.        , 0.        , 0.1326467 ]]),\n",
       " (1462, 3))"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train, X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "c71e85be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[0.12227074],\n",
       "        [0.04279476],\n",
       "        [0.0356623 ],\n",
       "        ...,\n",
       "        [0.24745269],\n",
       "        [0.276718  ],\n",
       "        [0.12227074]]),\n",
       " (1462, 1))"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train, y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "44e2a652",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, y_train = np.array(X_train), np.array(y_train)\n",
    "X_test, y_test = np.array(X_test), np.array(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "5f779363",
   "metadata": {},
   "outputs": [],
   "source": [
    "time_step = 6\n",
    "step = 1\n",
    "\n",
    "def split_sequences(Xdata, ydata, time_steps, steps):\n",
    "    X, y = list(), list()\n",
    "    length = len(ydata)\n",
    "    for i in range(0, length, steps):\n",
    "\n",
    "        end_ix = i + time_steps\n",
    "\n",
    "        if end_ix > length:\n",
    "            break\n",
    "\n",
    "        seq_x, seq_y = Xdata[i:end_ix], ydata[end_ix-1]\n",
    "        X.append(seq_x)\n",
    "        y.append(seq_y)\n",
    "    return np.array(X), np.array(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "16c60d1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, y_train = split_sequences(X_train, y_train, time_step, step)\n",
    "X_test, y_test = split_sequences(X_test, y_test, time_step, step)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "51b82950",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[0.8209571 , 0.        , 0.13260331],\n",
       "        [0.90759076, 0.07058266, 0.132881  ],\n",
       "        [0.84983498, 0.10974262, 0.13299381],\n",
       "        [0.66886689, 0.02921206, 0.13279856],\n",
       "        [0.84790979, 0.08763619, 0.13271178],\n",
       "        [0.80132013, 0.03505448, 0.13290703]],\n",
       "\n",
       "       [[0.90759076, 0.07058266, 0.132881  ],\n",
       "        [0.84983498, 0.10974262, 0.13299381],\n",
       "        [0.66886689, 0.02921206, 0.13279856],\n",
       "        [0.84790979, 0.08763619, 0.13271178],\n",
       "        [0.80132013, 0.03505448, 0.13290703],\n",
       "        [0.75280528, 0.14921838, 0.13316737]],\n",
       "\n",
       "       [[0.84983498, 0.10974262, 0.13299381],\n",
       "        [0.66886689, 0.02921206, 0.13279856],\n",
       "        [0.84790979, 0.08763619, 0.13271178],\n",
       "        [0.80132013, 0.03505448, 0.13290703],\n",
       "        [0.75280528, 0.14921838, 0.13316737],\n",
       "        [0.58085809, 0.16918184, 0.13300001]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[0.93415842, 0.21518238, 0.13243192],\n",
       "        [0.70957096, 0.20805804, 0.13277067],\n",
       "        [0.62516502, 0.19741829, 0.1328029 ],\n",
       "        [0.63086526, 0.08403188, 0.1325901 ],\n",
       "        [0.85973597, 0.14211274, 0.13276447],\n",
       "        [0.88063806, 0.14842886, 0.13289464]],\n",
       "\n",
       "       [[0.70957096, 0.20805804, 0.13277067],\n",
       "        [0.62516502, 0.19741829, 0.1328029 ],\n",
       "        [0.63086526, 0.08403188, 0.1325901 ],\n",
       "        [0.85973597, 0.14211274, 0.13276447],\n",
       "        [0.88063806, 0.14842886, 0.13289464],\n",
       "        [0.84983498, 0.17349597, 0.13265971]],\n",
       "\n",
       "       [[0.62516502, 0.19741829, 0.1328029 ],\n",
       "        [0.63086526, 0.08403188, 0.1325901 ],\n",
       "        [0.85973597, 0.14211274, 0.13276447],\n",
       "        [0.88063806, 0.14842886, 0.13289464],\n",
       "        [0.84983498, 0.17349597, 0.13265971],\n",
       "        [1.        , 0.        , 0.1326467 ]]])"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "699ee942",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1457, 6, 3), (109, 6, 3), (1457, 1), (109, 1))"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, X_test.shape, y_train.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "7b243eb9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_3\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " simple_rnn_6 (SimpleRNN)    (None, 6, 64)             4352      \n",
      "                                                                 \n",
      " simple_rnn_7 (SimpleRNN)    (None, 32)                3104      \n",
      "                                                                 \n",
      " dropout_1 (Dropout)         (None, 32)                0         \n",
      "                                                                 \n",
      " dense_4 (Dense)             (None, 8)                 264       \n",
      "                                                                 \n",
      " dense_5 (Dense)             (None, 1)                 9         \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 7729 (30.19 KB)\n",
      "Trainable params: 7729 (30.19 KB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(SimpleRNN(64,input_shape = (X_train.shape[1:]), return_sequences=True))\n",
    "model.add(SimpleRNN(32))\n",
    "model.add(Dropout(0.3))\n",
    "model.add(Dense(8))\n",
    "model.add(Dense(1))\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "ba4e3aaa",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss='mse', optimizer='adam', metrics=['mae'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "5e6e0969",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/500\n",
      "32/32 [==============================] - 14s 112ms/step - loss: 0.2222 - mae: 0.3896 - val_loss: 0.0421 - val_mae: 0.1682\n",
      "Epoch 2/500\n",
      "32/32 [==============================] - 0s 11ms/step - loss: 0.0907 - mae: 0.2464 - val_loss: 0.0366 - val_mae: 0.1561\n",
      "Epoch 3/500\n",
      "32/32 [==============================] - 1s 19ms/step - loss: 0.0703 - mae: 0.2178 - val_loss: 0.0349 - val_mae: 0.1525\n",
      "Epoch 4/500\n",
      "32/32 [==============================] - 1s 17ms/step - loss: 0.0663 - mae: 0.2124 - val_loss: 0.0356 - val_mae: 0.1533\n",
      "Epoch 5/500\n",
      "32/32 [==============================] - 0s 10ms/step - loss: 0.0676 - mae: 0.2171 - val_loss: 0.0324 - val_mae: 0.1481\n",
      "Epoch 6/500\n",
      "32/32 [==============================] - 0s 12ms/step - loss: 0.0617 - mae: 0.2066 - val_loss: 0.0326 - val_mae: 0.1488\n",
      "Epoch 7/500\n",
      "32/32 [==============================] - 0s 11ms/step - loss: 0.0566 - mae: 0.1991 - val_loss: 0.0345 - val_mae: 0.1533\n",
      "Epoch 8/500\n",
      "32/32 [==============================] - 0s 11ms/step - loss: 0.0555 - mae: 0.1964 - val_loss: 0.0329 - val_mae: 0.1507\n",
      "Epoch 9/500\n",
      "32/32 [==============================] - 0s 12ms/step - loss: 0.0533 - mae: 0.1939 - val_loss: 0.0310 - val_mae: 0.1462\n",
      "Epoch 10/500\n",
      "32/32 [==============================] - 0s 15ms/step - loss: 0.0558 - mae: 0.1987 - val_loss: 0.0306 - val_mae: 0.1449\n",
      "Epoch 11/500\n",
      "32/32 [==============================] - 0s 12ms/step - loss: 0.0509 - mae: 0.1879 - val_loss: 0.0314 - val_mae: 0.1478\n",
      "Epoch 12/500\n",
      "32/32 [==============================] - 0s 10ms/step - loss: 0.0488 - mae: 0.1831 - val_loss: 0.0308 - val_mae: 0.1458\n",
      "Epoch 13/500\n",
      "32/32 [==============================] - 0s 9ms/step - loss: 0.0511 - mae: 0.1903 - val_loss: 0.0305 - val_mae: 0.1455\n",
      "Epoch 14/500\n",
      "32/32 [==============================] - 0s 9ms/step - loss: 0.0465 - mae: 0.1810 - val_loss: 0.0306 - val_mae: 0.1456\n",
      "Epoch 15/500\n",
      "32/32 [==============================] - 0s 11ms/step - loss: 0.0458 - mae: 0.1787 - val_loss: 0.0305 - val_mae: 0.1452\n",
      "Epoch 16/500\n",
      "32/32 [==============================] - 0s 9ms/step - loss: 0.0473 - mae: 0.1823 - val_loss: 0.0305 - val_mae: 0.1449\n",
      "Epoch 17/500\n",
      "32/32 [==============================] - 0s 9ms/step - loss: 0.0471 - mae: 0.1821 - val_loss: 0.0310 - val_mae: 0.1453\n",
      "Epoch 18/500\n",
      "32/32 [==============================] - 0s 10ms/step - loss: 0.0449 - mae: 0.1792 - val_loss: 0.0308 - val_mae: 0.1447\n",
      "Epoch 19/500\n",
      "32/32 [==============================] - 0s 9ms/step - loss: 0.0458 - mae: 0.1806 - val_loss: 0.0312 - val_mae: 0.1450\n",
      "Epoch 20/500\n",
      "32/32 [==============================] - 0s 10ms/step - loss: 0.0455 - mae: 0.1787 - val_loss: 0.0312 - val_mae: 0.1444\n",
      "Epoch 21/500\n",
      "32/32 [==============================] - 1s 16ms/step - loss: 0.0433 - mae: 0.1756 - val_loss: 0.0313 - val_mae: 0.1452\n",
      "Epoch 22/500\n",
      "32/32 [==============================] - 0s 14ms/step - loss: 0.0429 - mae: 0.1730 - val_loss: 0.0308 - val_mae: 0.1445\n",
      "Epoch 23/500\n",
      "32/32 [==============================] - 0s 10ms/step - loss: 0.0433 - mae: 0.1757 - val_loss: 0.0307 - val_mae: 0.1443\n",
      "Epoch 24/500\n",
      "32/32 [==============================] - 0s 11ms/step - loss: 0.0422 - mae: 0.1739 - val_loss: 0.0312 - val_mae: 0.1450\n",
      "Epoch 25/500\n",
      "32/32 [==============================] - 0s 10ms/step - loss: 0.0429 - mae: 0.1753 - val_loss: 0.0306 - val_mae: 0.1443\n",
      "Epoch 26/500\n",
      "32/32 [==============================] - 0s 11ms/step - loss: 0.0414 - mae: 0.1706 - val_loss: 0.0312 - val_mae: 0.1449\n",
      "Epoch 27/500\n",
      "32/32 [==============================] - 0s 9ms/step - loss: 0.0406 - mae: 0.1697 - val_loss: 0.0312 - val_mae: 0.1450\n",
      "Epoch 28/500\n",
      "32/32 [==============================] - 0s 10ms/step - loss: 0.0408 - mae: 0.1709 - val_loss: 0.0309 - val_mae: 0.1444\n",
      "Epoch 29/500\n",
      "32/32 [==============================] - 0s 10ms/step - loss: 0.0420 - mae: 0.1728 - val_loss: 0.0315 - val_mae: 0.1454\n",
      "Epoch 30/500\n",
      "32/32 [==============================] - 0s 10ms/step - loss: 0.0404 - mae: 0.1703 - val_loss: 0.0313 - val_mae: 0.1448\n",
      "Epoch 31/500\n",
      "32/32 [==============================] - 0s 11ms/step - loss: 0.0397 - mae: 0.1695 - val_loss: 0.0305 - val_mae: 0.1440\n",
      "Epoch 32/500\n",
      "32/32 [==============================] - 1s 18ms/step - loss: 0.0396 - mae: 0.1695 - val_loss: 0.0305 - val_mae: 0.1444\n",
      "Epoch 33/500\n",
      "32/32 [==============================] - 0s 10ms/step - loss: 0.0396 - mae: 0.1683 - val_loss: 0.0316 - val_mae: 0.1451\n",
      "Epoch 34/500\n",
      "32/32 [==============================] - 0s 12ms/step - loss: 0.0394 - mae: 0.1672 - val_loss: 0.0314 - val_mae: 0.1450\n",
      "Epoch 35/500\n",
      "32/32 [==============================] - 0s 13ms/step - loss: 0.0404 - mae: 0.1711 - val_loss: 0.0316 - val_mae: 0.1453\n",
      "Epoch 36/500\n",
      "32/32 [==============================] - 0s 10ms/step - loss: 0.0387 - mae: 0.1660 - val_loss: 0.0312 - val_mae: 0.1447\n",
      "Epoch 37/500\n",
      "32/32 [==============================] - 0s 11ms/step - loss: 0.0395 - mae: 0.1682 - val_loss: 0.0314 - val_mae: 0.1450\n",
      "Epoch 38/500\n",
      "32/32 [==============================] - 0s 10ms/step - loss: 0.0389 - mae: 0.1666 - val_loss: 0.0313 - val_mae: 0.1449\n",
      "Epoch 39/500\n",
      "32/32 [==============================] - 0s 10ms/step - loss: 0.0378 - mae: 0.1650 - val_loss: 0.0306 - val_mae: 0.1443\n",
      "Epoch 40/500\n",
      "32/32 [==============================] - 0s 10ms/step - loss: 0.0391 - mae: 0.1682 - val_loss: 0.0306 - val_mae: 0.1446\n",
      "Epoch 41/500\n",
      "32/32 [==============================] - 0s 15ms/step - loss: 0.0384 - mae: 0.1664 - val_loss: 0.0307 - val_mae: 0.1446\n",
      "Epoch 42/500\n",
      "32/32 [==============================] - 0s 13ms/step - loss: 0.0384 - mae: 0.1662 - val_loss: 0.0309 - val_mae: 0.1442\n",
      "Epoch 43/500\n",
      "32/32 [==============================] - 0s 10ms/step - loss: 0.0379 - mae: 0.1658 - val_loss: 0.0309 - val_mae: 0.1445\n",
      "Epoch 44/500\n",
      "32/32 [==============================] - 0s 11ms/step - loss: 0.0387 - mae: 0.1685 - val_loss: 0.0304 - val_mae: 0.1441\n",
      "Epoch 45/500\n",
      "32/32 [==============================] - 0s 10ms/step - loss: 0.0373 - mae: 0.1639 - val_loss: 0.0308 - val_mae: 0.1443\n",
      "Epoch 46/500\n",
      "32/32 [==============================] - 0s 12ms/step - loss: 0.0367 - mae: 0.1618 - val_loss: 0.0304 - val_mae: 0.1445\n",
      "Epoch 47/500\n",
      "32/32 [==============================] - 0s 10ms/step - loss: 0.0370 - mae: 0.1628 - val_loss: 0.0306 - val_mae: 0.1443\n",
      "Epoch 48/500\n",
      "32/32 [==============================] - 0s 13ms/step - loss: 0.0374 - mae: 0.1650 - val_loss: 0.0309 - val_mae: 0.1446\n",
      "Epoch 49/500\n",
      "32/32 [==============================] - 0s 13ms/step - loss: 0.0363 - mae: 0.1620 - val_loss: 0.0303 - val_mae: 0.1442\n",
      "Epoch 50/500\n",
      "32/32 [==============================] - 0s 15ms/step - loss: 0.0367 - mae: 0.1638 - val_loss: 0.0304 - val_mae: 0.1445\n",
      "Epoch 51/500\n",
      "32/32 [==============================] - 0s 16ms/step - loss: 0.0384 - mae: 0.1671 - val_loss: 0.0302 - val_mae: 0.1441\n",
      "Epoch 52/500\n",
      "32/32 [==============================] - 0s 11ms/step - loss: 0.0369 - mae: 0.1630 - val_loss: 0.0306 - val_mae: 0.1443\n",
      "Epoch 53/500\n",
      "32/32 [==============================] - 0s 11ms/step - loss: 0.0363 - mae: 0.1602 - val_loss: 0.0303 - val_mae: 0.1443\n",
      "Epoch 54/500\n",
      "32/32 [==============================] - 0s 11ms/step - loss: 0.0355 - mae: 0.1609 - val_loss: 0.0304 - val_mae: 0.1441\n",
      "Epoch 55/500\n",
      "32/32 [==============================] - 0s 9ms/step - loss: 0.0354 - mae: 0.1594 - val_loss: 0.0303 - val_mae: 0.1441\n",
      "Epoch 56/500\n",
      "32/32 [==============================] - 0s 11ms/step - loss: 0.0360 - mae: 0.1611 - val_loss: 0.0304 - val_mae: 0.1444\n",
      "Epoch 57/500\n",
      "32/32 [==============================] - 0s 11ms/step - loss: 0.0357 - mae: 0.1607 - val_loss: 0.0304 - val_mae: 0.1441\n",
      "Epoch 58/500\n",
      "32/32 [==============================] - 0s 11ms/step - loss: 0.0359 - mae: 0.1606 - val_loss: 0.0308 - val_mae: 0.1444\n",
      "Epoch 59/500\n",
      "32/32 [==============================] - 0s 15ms/step - loss: 0.0365 - mae: 0.1624 - val_loss: 0.0305 - val_mae: 0.1442\n",
      "Epoch 60/500\n",
      "32/32 [==============================] - 0s 16ms/step - loss: 0.0357 - mae: 0.1611 - val_loss: 0.0304 - val_mae: 0.1444\n",
      "Epoch 61/500\n",
      "32/32 [==============================] - 0s 16ms/step - loss: 0.0354 - mae: 0.1602 - val_loss: 0.0301 - val_mae: 0.1442\n",
      "Epoch 62/500\n",
      "32/32 [==============================] - 1s 16ms/step - loss: 0.0355 - mae: 0.1603 - val_loss: 0.0303 - val_mae: 0.1441\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 63/500\n",
      "32/32 [==============================] - 0s 12ms/step - loss: 0.0349 - mae: 0.1589 - val_loss: 0.0301 - val_mae: 0.1445\n",
      "Epoch 64/500\n",
      "32/32 [==============================] - 1s 21ms/step - loss: 0.0350 - mae: 0.1596 - val_loss: 0.0304 - val_mae: 0.1442\n",
      "Epoch 65/500\n",
      "32/32 [==============================] - 0s 14ms/step - loss: 0.0360 - mae: 0.1612 - val_loss: 0.0302 - val_mae: 0.1441\n",
      "Epoch 66/500\n",
      "32/32 [==============================] - 0s 16ms/step - loss: 0.0349 - mae: 0.1584 - val_loss: 0.0301 - val_mae: 0.1440\n",
      "Epoch 67/500\n",
      "32/32 [==============================] - 0s 13ms/step - loss: 0.0352 - mae: 0.1601 - val_loss: 0.0302 - val_mae: 0.1441\n",
      "Epoch 68/500\n",
      "32/32 [==============================] - 1s 18ms/step - loss: 0.0351 - mae: 0.1594 - val_loss: 0.0302 - val_mae: 0.1440\n",
      "Epoch 69/500\n",
      "32/32 [==============================] - 1s 16ms/step - loss: 0.0354 - mae: 0.1595 - val_loss: 0.0300 - val_mae: 0.1440\n",
      "Epoch 70/500\n",
      "32/32 [==============================] - 0s 14ms/step - loss: 0.0348 - mae: 0.1587 - val_loss: 0.0301 - val_mae: 0.1441\n",
      "Epoch 71/500\n",
      "32/32 [==============================] - 1s 18ms/step - loss: 0.0346 - mae: 0.1572 - val_loss: 0.0301 - val_mae: 0.1442\n",
      "Epoch 72/500\n",
      "32/32 [==============================] - 1s 18ms/step - loss: 0.0348 - mae: 0.1580 - val_loss: 0.0300 - val_mae: 0.1443\n",
      "Epoch 73/500\n",
      "32/32 [==============================] - 0s 14ms/step - loss: 0.0346 - mae: 0.1576 - val_loss: 0.0300 - val_mae: 0.1441\n",
      "Epoch 74/500\n",
      "32/32 [==============================] - 0s 11ms/step - loss: 0.0351 - mae: 0.1592 - val_loss: 0.0302 - val_mae: 0.1441\n",
      "Epoch 75/500\n",
      "32/32 [==============================] - 1s 20ms/step - loss: 0.0349 - mae: 0.1593 - val_loss: 0.0302 - val_mae: 0.1442\n",
      "Epoch 76/500\n",
      "32/32 [==============================] - 1s 23ms/step - loss: 0.0345 - mae: 0.1577 - val_loss: 0.0301 - val_mae: 0.1442\n",
      "Epoch 77/500\n",
      "32/32 [==============================] - 0s 15ms/step - loss: 0.0346 - mae: 0.1583 - val_loss: 0.0303 - val_mae: 0.1442\n",
      "Epoch 78/500\n",
      "32/32 [==============================] - 1s 24ms/step - loss: 0.0344 - mae: 0.1576 - val_loss: 0.0301 - val_mae: 0.1441\n",
      "Epoch 79/500\n",
      "32/32 [==============================] - 1s 16ms/step - loss: 0.0343 - mae: 0.1576 - val_loss: 0.0300 - val_mae: 0.1443\n",
      "Epoch 80/500\n",
      "32/32 [==============================] - 1s 24ms/step - loss: 0.0336 - mae: 0.1558 - val_loss: 0.0301 - val_mae: 0.1440\n",
      "Epoch 81/500\n",
      "32/32 [==============================] - 1s 20ms/step - loss: 0.0340 - mae: 0.1563 - val_loss: 0.0300 - val_mae: 0.1441\n",
      "Epoch 82/500\n",
      "32/32 [==============================] - 1s 17ms/step - loss: 0.0340 - mae: 0.1560 - val_loss: 0.0300 - val_mae: 0.1438\n",
      "Epoch 83/500\n",
      "32/32 [==============================] - 0s 14ms/step - loss: 0.0339 - mae: 0.1567 - val_loss: 0.0300 - val_mae: 0.1440\n",
      "Epoch 84/500\n",
      "32/32 [==============================] - 0s 10ms/step - loss: 0.0337 - mae: 0.1552 - val_loss: 0.0299 - val_mae: 0.1442\n",
      "Epoch 85/500\n",
      "32/32 [==============================] - 0s 15ms/step - loss: 0.0345 - mae: 0.1580 - val_loss: 0.0299 - val_mae: 0.1441\n",
      "Epoch 86/500\n",
      "32/32 [==============================] - 0s 13ms/step - loss: 0.0343 - mae: 0.1576 - val_loss: 0.0300 - val_mae: 0.1439\n",
      "Epoch 87/500\n",
      "32/32 [==============================] - 0s 14ms/step - loss: 0.0335 - mae: 0.1548 - val_loss: 0.0299 - val_mae: 0.1441\n",
      "Epoch 88/500\n",
      "32/32 [==============================] - 0s 14ms/step - loss: 0.0342 - mae: 0.1573 - val_loss: 0.0299 - val_mae: 0.1442\n",
      "Epoch 89/500\n",
      "32/32 [==============================] - 1s 16ms/step - loss: 0.0343 - mae: 0.1567 - val_loss: 0.0300 - val_mae: 0.1441\n",
      "Epoch 90/500\n",
      "32/32 [==============================] - 1s 16ms/step - loss: 0.0338 - mae: 0.1563 - val_loss: 0.0300 - val_mae: 0.1440\n",
      "Epoch 91/500\n",
      "32/32 [==============================] - 0s 14ms/step - loss: 0.0340 - mae: 0.1567 - val_loss: 0.0300 - val_mae: 0.1440\n",
      "Epoch 92/500\n",
      "32/32 [==============================] - 1s 17ms/step - loss: 0.0342 - mae: 0.1573 - val_loss: 0.0300 - val_mae: 0.1442\n",
      "Epoch 93/500\n",
      "32/32 [==============================] - 0s 11ms/step - loss: 0.0339 - mae: 0.1570 - val_loss: 0.0299 - val_mae: 0.1441\n",
      "Epoch 94/500\n",
      "32/32 [==============================] - 0s 12ms/step - loss: 0.0337 - mae: 0.1561 - val_loss: 0.0299 - val_mae: 0.1440\n",
      "Epoch 95/500\n",
      "32/32 [==============================] - 0s 12ms/step - loss: 0.0337 - mae: 0.1550 - val_loss: 0.0299 - val_mae: 0.1440\n",
      "Epoch 96/500\n",
      "32/32 [==============================] - 0s 12ms/step - loss: 0.0334 - mae: 0.1555 - val_loss: 0.0299 - val_mae: 0.1438\n",
      "Epoch 97/500\n",
      "32/32 [==============================] - 1s 17ms/step - loss: 0.0338 - mae: 0.1566 - val_loss: 0.0298 - val_mae: 0.1440\n",
      "Epoch 98/500\n",
      "32/32 [==============================] - 1s 25ms/step - loss: 0.0335 - mae: 0.1556 - val_loss: 0.0300 - val_mae: 0.1439\n",
      "Epoch 99/500\n",
      "32/32 [==============================] - 1s 22ms/step - loss: 0.0336 - mae: 0.1562 - val_loss: 0.0299 - val_mae: 0.1439\n",
      "Epoch 100/500\n",
      "32/32 [==============================] - 1s 36ms/step - loss: 0.0334 - mae: 0.1549 - val_loss: 0.0298 - val_mae: 0.1439\n",
      "Epoch 101/500\n",
      "32/32 [==============================] - 1s 21ms/step - loss: 0.0334 - mae: 0.1554 - val_loss: 0.0299 - val_mae: 0.1439\n",
      "Epoch 102/500\n",
      "32/32 [==============================] - 1s 21ms/step - loss: 0.0335 - mae: 0.1557 - val_loss: 0.0299 - val_mae: 0.1440\n",
      "Epoch 103/500\n",
      "32/32 [==============================] - 0s 16ms/step - loss: 0.0335 - mae: 0.1550 - val_loss: 0.0299 - val_mae: 0.1440\n",
      "Epoch 104/500\n",
      "32/32 [==============================] - 1s 18ms/step - loss: 0.0334 - mae: 0.1557 - val_loss: 0.0298 - val_mae: 0.1441\n",
      "Epoch 105/500\n",
      "32/32 [==============================] - 1s 18ms/step - loss: 0.0334 - mae: 0.1559 - val_loss: 0.0299 - val_mae: 0.1437\n",
      "Epoch 106/500\n",
      "32/32 [==============================] - 0s 14ms/step - loss: 0.0338 - mae: 0.1559 - val_loss: 0.0299 - val_mae: 0.1437\n",
      "Epoch 107/500\n",
      "32/32 [==============================] - 1s 22ms/step - loss: 0.0332 - mae: 0.1550 - val_loss: 0.0298 - val_mae: 0.1440\n",
      "Epoch 108/500\n",
      "32/32 [==============================] - 1s 26ms/step - loss: 0.0336 - mae: 0.1560 - val_loss: 0.0298 - val_mae: 0.1441\n",
      "Epoch 109/500\n",
      "32/32 [==============================] - 0s 13ms/step - loss: 0.0334 - mae: 0.1556 - val_loss: 0.0298 - val_mae: 0.1439\n",
      "Epoch 110/500\n",
      "32/32 [==============================] - 1s 20ms/step - loss: 0.0333 - mae: 0.1549 - val_loss: 0.0298 - val_mae: 0.1440\n",
      "Epoch 111/500\n",
      "32/32 [==============================] - 1s 21ms/step - loss: 0.0329 - mae: 0.1532 - val_loss: 0.0298 - val_mae: 0.1440\n",
      "Epoch 112/500\n",
      "32/32 [==============================] - 1s 21ms/step - loss: 0.0334 - mae: 0.1549 - val_loss: 0.0298 - val_mae: 0.1440\n",
      "Epoch 113/500\n",
      "32/32 [==============================] - 0s 14ms/step - loss: 0.0334 - mae: 0.1553 - val_loss: 0.0298 - val_mae: 0.1440\n",
      "Epoch 114/500\n",
      "32/32 [==============================] - 1s 17ms/step - loss: 0.0332 - mae: 0.1555 - val_loss: 0.0298 - val_mae: 0.1439\n",
      "Epoch 115/500\n",
      "32/32 [==============================] - 1s 16ms/step - loss: 0.0332 - mae: 0.1545 - val_loss: 0.0298 - val_mae: 0.1438\n",
      "Epoch 116/500\n",
      "32/32 [==============================] - 1s 20ms/step - loss: 0.0331 - mae: 0.1552 - val_loss: 0.0297 - val_mae: 0.1439\n",
      "Epoch 117/500\n",
      "32/32 [==============================] - 1s 16ms/step - loss: 0.0332 - mae: 0.1552 - val_loss: 0.0297 - val_mae: 0.1439\n",
      "Epoch 118/500\n",
      "32/32 [==============================] - 0s 14ms/step - loss: 0.0329 - mae: 0.1541 - val_loss: 0.0298 - val_mae: 0.1440\n",
      "Epoch 119/500\n",
      "32/32 [==============================] - 1s 20ms/step - loss: 0.0331 - mae: 0.1547 - val_loss: 0.0297 - val_mae: 0.1438\n",
      "Epoch 120/500\n",
      "32/32 [==============================] - 1s 16ms/step - loss: 0.0330 - mae: 0.1541 - val_loss: 0.0297 - val_mae: 0.1439\n",
      "Epoch 121/500\n",
      "32/32 [==============================] - 0s 14ms/step - loss: 0.0329 - mae: 0.1542 - val_loss: 0.0297 - val_mae: 0.1437\n",
      "Epoch 122/500\n",
      "32/32 [==============================] - 1s 18ms/step - loss: 0.0335 - mae: 0.1560 - val_loss: 0.0297 - val_mae: 0.1436\n",
      "Epoch 123/500\n",
      "32/32 [==============================] - 1s 21ms/step - loss: 0.0331 - mae: 0.1552 - val_loss: 0.0298 - val_mae: 0.1438\n",
      "Epoch 124/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32/32 [==============================] - 1s 16ms/step - loss: 0.0332 - mae: 0.1542 - val_loss: 0.0297 - val_mae: 0.1439\n",
      "Epoch 125/500\n",
      "32/32 [==============================] - 0s 14ms/step - loss: 0.0332 - mae: 0.1556 - val_loss: 0.0297 - val_mae: 0.1438\n",
      "Epoch 126/500\n",
      "32/32 [==============================] - 0s 15ms/step - loss: 0.0331 - mae: 0.1557 - val_loss: 0.0297 - val_mae: 0.1438\n",
      "Epoch 127/500\n",
      "32/32 [==============================] - 1s 16ms/step - loss: 0.0327 - mae: 0.1541 - val_loss: 0.0297 - val_mae: 0.1438\n",
      "Epoch 128/500\n",
      "32/32 [==============================] - 0s 11ms/step - loss: 0.0332 - mae: 0.1563 - val_loss: 0.0297 - val_mae: 0.1437\n",
      "Epoch 129/500\n",
      "32/32 [==============================] - 0s 13ms/step - loss: 0.0333 - mae: 0.1550 - val_loss: 0.0297 - val_mae: 0.1439\n",
      "Epoch 130/500\n",
      "32/32 [==============================] - 0s 13ms/step - loss: 0.0329 - mae: 0.1542 - val_loss: 0.0297 - val_mae: 0.1439\n",
      "Epoch 131/500\n",
      "32/32 [==============================] - 0s 10ms/step - loss: 0.0330 - mae: 0.1545 - val_loss: 0.0297 - val_mae: 0.1439\n",
      "Epoch 132/500\n",
      "32/32 [==============================] - 1s 16ms/step - loss: 0.0330 - mae: 0.1537 - val_loss: 0.0297 - val_mae: 0.1436\n",
      "Epoch 133/500\n",
      "32/32 [==============================] - 0s 14ms/step - loss: 0.0329 - mae: 0.1537 - val_loss: 0.0297 - val_mae: 0.1436\n",
      "Epoch 134/500\n",
      "32/32 [==============================] - 1s 22ms/step - loss: 0.0329 - mae: 0.1540 - val_loss: 0.0297 - val_mae: 0.1436\n",
      "Epoch 135/500\n",
      "32/32 [==============================] - 1s 16ms/step - loss: 0.0330 - mae: 0.1542 - val_loss: 0.0297 - val_mae: 0.1436\n",
      "Epoch 136/500\n",
      "32/32 [==============================] - 1s 22ms/step - loss: 0.0329 - mae: 0.1539 - val_loss: 0.0297 - val_mae: 0.1437\n",
      "Epoch 137/500\n",
      "32/32 [==============================] - 0s 13ms/step - loss: 0.0331 - mae: 0.1550 - val_loss: 0.0297 - val_mae: 0.1439\n",
      "Epoch 138/500\n",
      "32/32 [==============================] - 1s 18ms/step - loss: 0.0331 - mae: 0.1552 - val_loss: 0.0296 - val_mae: 0.1437\n",
      "Epoch 139/500\n",
      "32/32 [==============================] - 1s 16ms/step - loss: 0.0332 - mae: 0.1555 - val_loss: 0.0296 - val_mae: 0.1435\n",
      "Epoch 140/500\n",
      "32/32 [==============================] - 0s 13ms/step - loss: 0.0326 - mae: 0.1533 - val_loss: 0.0296 - val_mae: 0.1434\n",
      "Epoch 141/500\n",
      "32/32 [==============================] - 0s 15ms/step - loss: 0.0328 - mae: 0.1541 - val_loss: 0.0297 - val_mae: 0.1437\n",
      "Epoch 142/500\n",
      "32/32 [==============================] - 0s 12ms/step - loss: 0.0325 - mae: 0.1526 - val_loss: 0.0296 - val_mae: 0.1436\n",
      "Epoch 143/500\n",
      "32/32 [==============================] - 0s 11ms/step - loss: 0.0327 - mae: 0.1537 - val_loss: 0.0297 - val_mae: 0.1435\n",
      "Epoch 144/500\n",
      "32/32 [==============================] - 0s 10ms/step - loss: 0.0328 - mae: 0.1536 - val_loss: 0.0296 - val_mae: 0.1437\n",
      "Epoch 145/500\n",
      "32/32 [==============================] - 0s 12ms/step - loss: 0.0329 - mae: 0.1546 - val_loss: 0.0296 - val_mae: 0.1436\n",
      "Epoch 146/500\n",
      "32/32 [==============================] - 0s 11ms/step - loss: 0.0328 - mae: 0.1544 - val_loss: 0.0296 - val_mae: 0.1434\n",
      "Epoch 147/500\n",
      "32/32 [==============================] - 0s 11ms/step - loss: 0.0326 - mae: 0.1535 - val_loss: 0.0296 - val_mae: 0.1436\n",
      "Epoch 148/500\n",
      "32/32 [==============================] - 0s 15ms/step - loss: 0.0335 - mae: 0.1558 - val_loss: 0.0297 - val_mae: 0.1437\n",
      "Epoch 149/500\n",
      "32/32 [==============================] - 0s 14ms/step - loss: 0.0329 - mae: 0.1542 - val_loss: 0.0296 - val_mae: 0.1437\n",
      "Epoch 150/500\n",
      "32/32 [==============================] - 0s 14ms/step - loss: 0.0330 - mae: 0.1547 - val_loss: 0.0296 - val_mae: 0.1436\n",
      "Epoch 151/500\n",
      "32/32 [==============================] - 0s 15ms/step - loss: 0.0330 - mae: 0.1549 - val_loss: 0.0296 - val_mae: 0.1441\n",
      "Epoch 152/500\n",
      "32/32 [==============================] - 0s 11ms/step - loss: 0.0330 - mae: 0.1550 - val_loss: 0.0296 - val_mae: 0.1435\n",
      "Epoch 153/500\n",
      "32/32 [==============================] - 0s 12ms/step - loss: 0.0330 - mae: 0.1546 - val_loss: 0.0296 - val_mae: 0.1437\n",
      "Epoch 154/500\n",
      "32/32 [==============================] - 0s 12ms/step - loss: 0.0327 - mae: 0.1536 - val_loss: 0.0297 - val_mae: 0.1435\n",
      "Epoch 155/500\n",
      "32/32 [==============================] - 0s 13ms/step - loss: 0.0326 - mae: 0.1530 - val_loss: 0.0296 - val_mae: 0.1438\n",
      "Epoch 156/500\n",
      "32/32 [==============================] - 0s 11ms/step - loss: 0.0331 - mae: 0.1546 - val_loss: 0.0296 - val_mae: 0.1435\n",
      "Epoch 157/500\n",
      "32/32 [==============================] - 0s 12ms/step - loss: 0.0329 - mae: 0.1528 - val_loss: 0.0296 - val_mae: 0.1436\n",
      "Epoch 158/500\n",
      "32/32 [==============================] - 0s 12ms/step - loss: 0.0332 - mae: 0.1546 - val_loss: 0.0296 - val_mae: 0.1435\n",
      "Epoch 159/500\n",
      "32/32 [==============================] - 0s 11ms/step - loss: 0.0327 - mae: 0.1540 - val_loss: 0.0296 - val_mae: 0.1440\n",
      "Epoch 160/500\n",
      "32/32 [==============================] - 0s 15ms/step - loss: 0.0330 - mae: 0.1546 - val_loss: 0.0296 - val_mae: 0.1437\n",
      "Epoch 161/500\n",
      "32/32 [==============================] - 0s 11ms/step - loss: 0.0325 - mae: 0.1536 - val_loss: 0.0296 - val_mae: 0.1438\n",
      "Epoch 162/500\n",
      "32/32 [==============================] - 0s 12ms/step - loss: 0.0331 - mae: 0.1548 - val_loss: 0.0296 - val_mae: 0.1439\n",
      "Epoch 163/500\n",
      "32/32 [==============================] - 0s 13ms/step - loss: 0.0327 - mae: 0.1537 - val_loss: 0.0296 - val_mae: 0.1439\n",
      "Epoch 164/500\n",
      "32/32 [==============================] - 0s 13ms/step - loss: 0.0330 - mae: 0.1541 - val_loss: 0.0296 - val_mae: 0.1438\n",
      "Epoch 165/500\n",
      "32/32 [==============================] - 0s 11ms/step - loss: 0.0331 - mae: 0.1549 - val_loss: 0.0297 - val_mae: 0.1438\n",
      "Epoch 166/500\n",
      "32/32 [==============================] - 0s 14ms/step - loss: 0.0328 - mae: 0.1542 - val_loss: 0.0296 - val_mae: 0.1436\n",
      "Epoch 167/500\n",
      "32/32 [==============================] - 0s 11ms/step - loss: 0.0327 - mae: 0.1534 - val_loss: 0.0296 - val_mae: 0.1436\n",
      "Epoch 168/500\n",
      "32/32 [==============================] - 0s 11ms/step - loss: 0.0331 - mae: 0.1552 - val_loss: 0.0296 - val_mae: 0.1436\n",
      "Epoch 169/500\n",
      "32/32 [==============================] - 0s 11ms/step - loss: 0.0326 - mae: 0.1532 - val_loss: 0.0296 - val_mae: 0.1436\n",
      "Epoch 170/500\n",
      "32/32 [==============================] - 0s 11ms/step - loss: 0.0331 - mae: 0.1543 - val_loss: 0.0296 - val_mae: 0.1437\n",
      "Epoch 171/500\n",
      "32/32 [==============================] - 0s 11ms/step - loss: 0.0325 - mae: 0.1530 - val_loss: 0.0296 - val_mae: 0.1436\n",
      "Epoch 172/500\n",
      "32/32 [==============================] - 0s 12ms/step - loss: 0.0328 - mae: 0.1537 - val_loss: 0.0296 - val_mae: 0.1434\n",
      "Epoch 173/500\n",
      "32/32 [==============================] - 0s 13ms/step - loss: 0.0327 - mae: 0.1537 - val_loss: 0.0297 - val_mae: 0.1436\n",
      "Epoch 174/500\n",
      "32/32 [==============================] - 0s 14ms/step - loss: 0.0326 - mae: 0.1540 - val_loss: 0.0297 - val_mae: 0.1438\n",
      "Epoch 175/500\n",
      "32/32 [==============================] - 0s 11ms/step - loss: 0.0328 - mae: 0.1545 - val_loss: 0.0295 - val_mae: 0.1437\n",
      "Epoch 176/500\n",
      "32/32 [==============================] - 0s 12ms/step - loss: 0.0338 - mae: 0.1562 - val_loss: 0.0296 - val_mae: 0.1437\n",
      "Epoch 177/500\n",
      "32/32 [==============================] - 0s 12ms/step - loss: 0.0328 - mae: 0.1537 - val_loss: 0.0296 - val_mae: 0.1439\n",
      "Epoch 178/500\n",
      "32/32 [==============================] - 0s 12ms/step - loss: 0.0331 - mae: 0.1552 - val_loss: 0.0296 - val_mae: 0.1436\n",
      "Epoch 179/500\n",
      "32/32 [==============================] - 0s 12ms/step - loss: 0.0327 - mae: 0.1538 - val_loss: 0.0296 - val_mae: 0.1437\n",
      "Epoch 180/500\n",
      "32/32 [==============================] - 0s 12ms/step - loss: 0.0325 - mae: 0.1533 - val_loss: 0.0297 - val_mae: 0.1438\n",
      "Epoch 181/500\n",
      "32/32 [==============================] - 0s 12ms/step - loss: 0.0326 - mae: 0.1538 - val_loss: 0.0296 - val_mae: 0.1435\n",
      "Epoch 182/500\n",
      "32/32 [==============================] - 0s 14ms/step - loss: 0.0327 - mae: 0.1540 - val_loss: 0.0296 - val_mae: 0.1435\n",
      "Epoch 183/500\n",
      "32/32 [==============================] - 0s 12ms/step - loss: 0.0329 - mae: 0.1546 - val_loss: 0.0295 - val_mae: 0.1437\n",
      "Epoch 184/500\n",
      "32/32 [==============================] - 0s 11ms/step - loss: 0.0330 - mae: 0.1547 - val_loss: 0.0295 - val_mae: 0.1434\n",
      "Epoch 185/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32/32 [==============================] - 1s 17ms/step - loss: 0.0325 - mae: 0.1534 - val_loss: 0.0295 - val_mae: 0.1435\n",
      "Epoch 186/500\n",
      "32/32 [==============================] - 0s 13ms/step - loss: 0.0326 - mae: 0.1537 - val_loss: 0.0296 - val_mae: 0.1437\n",
      "Epoch 187/500\n",
      "32/32 [==============================] - 1s 18ms/step - loss: 0.0328 - mae: 0.1541 - val_loss: 0.0296 - val_mae: 0.1433\n",
      "Epoch 188/500\n",
      "32/32 [==============================] - 0s 10ms/step - loss: 0.0328 - mae: 0.1542 - val_loss: 0.0295 - val_mae: 0.1436\n",
      "Epoch 189/500\n",
      "32/32 [==============================] - 0s 13ms/step - loss: 0.0324 - mae: 0.1532 - val_loss: 0.0296 - val_mae: 0.1432\n",
      "Epoch 190/500\n",
      "32/32 [==============================] - 1s 24ms/step - loss: 0.0325 - mae: 0.1531 - val_loss: 0.0295 - val_mae: 0.1433\n",
      "Epoch 191/500\n",
      "32/32 [==============================] - 1s 18ms/step - loss: 0.0329 - mae: 0.1548 - val_loss: 0.0294 - val_mae: 0.1435\n",
      "Epoch 192/500\n",
      "32/32 [==============================] - 1s 18ms/step - loss: 0.0327 - mae: 0.1536 - val_loss: 0.0295 - val_mae: 0.1435\n",
      "Epoch 193/500\n",
      "32/32 [==============================] - 0s 16ms/step - loss: 0.0331 - mae: 0.1550 - val_loss: 0.0295 - val_mae: 0.1437\n",
      "Epoch 194/500\n",
      "32/32 [==============================] - 1s 22ms/step - loss: 0.0330 - mae: 0.1543 - val_loss: 0.0295 - val_mae: 0.1436\n",
      "Epoch 195/500\n",
      "32/32 [==============================] - 1s 27ms/step - loss: 0.0328 - mae: 0.1540 - val_loss: 0.0295 - val_mae: 0.1435\n",
      "Epoch 196/500\n",
      "32/32 [==============================] - 1s 18ms/step - loss: 0.0326 - mae: 0.1538 - val_loss: 0.0295 - val_mae: 0.1435\n",
      "Epoch 197/500\n",
      "32/32 [==============================] - 1s 22ms/step - loss: 0.0328 - mae: 0.1540 - val_loss: 0.0296 - val_mae: 0.1434\n",
      "Epoch 198/500\n",
      "32/32 [==============================] - 0s 14ms/step - loss: 0.0327 - mae: 0.1539 - val_loss: 0.0295 - val_mae: 0.1435\n",
      "Epoch 199/500\n",
      "32/32 [==============================] - 1s 19ms/step - loss: 0.0326 - mae: 0.1541 - val_loss: 0.0295 - val_mae: 0.1434\n",
      "Epoch 200/500\n",
      "32/32 [==============================] - 0s 15ms/step - loss: 0.0325 - mae: 0.1531 - val_loss: 0.0295 - val_mae: 0.1432\n",
      "Epoch 201/500\n",
      "32/32 [==============================] - 0s 14ms/step - loss: 0.0325 - mae: 0.1531 - val_loss: 0.0295 - val_mae: 0.1434\n",
      "Epoch 202/500\n",
      "32/32 [==============================] - 0s 15ms/step - loss: 0.0330 - mae: 0.1550 - val_loss: 0.0296 - val_mae: 0.1436\n",
      "Epoch 203/500\n",
      "32/32 [==============================] - 1s 20ms/step - loss: 0.0328 - mae: 0.1543 - val_loss: 0.0295 - val_mae: 0.1437\n",
      "Epoch 204/500\n",
      "32/32 [==============================] - 1s 28ms/step - loss: 0.0329 - mae: 0.1547 - val_loss: 0.0295 - val_mae: 0.1434\n",
      "Epoch 205/500\n",
      "32/32 [==============================] - 1s 16ms/step - loss: 0.0328 - mae: 0.1541 - val_loss: 0.0295 - val_mae: 0.1435\n",
      "Epoch 206/500\n",
      "32/32 [==============================] - 1s 20ms/step - loss: 0.0328 - mae: 0.1542 - val_loss: 0.0295 - val_mae: 0.1435\n",
      "Epoch 207/500\n",
      "32/32 [==============================] - 0s 16ms/step - loss: 0.0329 - mae: 0.1539 - val_loss: 0.0294 - val_mae: 0.1434\n",
      "Epoch 208/500\n",
      "32/32 [==============================] - 1s 16ms/step - loss: 0.0325 - mae: 0.1540 - val_loss: 0.0295 - val_mae: 0.1433\n",
      "Epoch 209/500\n",
      "32/32 [==============================] - 1s 18ms/step - loss: 0.0328 - mae: 0.1535 - val_loss: 0.0295 - val_mae: 0.1435\n",
      "Epoch 210/500\n",
      "32/32 [==============================] - 0s 15ms/step - loss: 0.0327 - mae: 0.1545 - val_loss: 0.0295 - val_mae: 0.1435\n",
      "Epoch 211/500\n",
      "32/32 [==============================] - 0s 15ms/step - loss: 0.0326 - mae: 0.1538 - val_loss: 0.0295 - val_mae: 0.1434\n",
      "Epoch 212/500\n",
      "32/32 [==============================] - 1s 19ms/step - loss: 0.0324 - mae: 0.1528 - val_loss: 0.0294 - val_mae: 0.1434\n",
      "Epoch 213/500\n",
      "32/32 [==============================] - 1s 17ms/step - loss: 0.0331 - mae: 0.1543 - val_loss: 0.0294 - val_mae: 0.1432\n",
      "Epoch 214/500\n",
      "32/32 [==============================] - 1s 17ms/step - loss: 0.0325 - mae: 0.1539 - val_loss: 0.0294 - val_mae: 0.1433\n",
      "Epoch 215/500\n",
      "32/32 [==============================] - 0s 15ms/step - loss: 0.0329 - mae: 0.1541 - val_loss: 0.0294 - val_mae: 0.1432\n",
      "Epoch 216/500\n",
      "32/32 [==============================] - 0s 13ms/step - loss: 0.0329 - mae: 0.1556 - val_loss: 0.0294 - val_mae: 0.1432\n",
      "Epoch 217/500\n",
      "32/32 [==============================] - 1s 20ms/step - loss: 0.0326 - mae: 0.1532 - val_loss: 0.0294 - val_mae: 0.1432\n",
      "Epoch 218/500\n",
      "32/32 [==============================] - 0s 14ms/step - loss: 0.0324 - mae: 0.1527 - val_loss: 0.0295 - val_mae: 0.1431\n",
      "Epoch 219/500\n",
      "32/32 [==============================] - 1s 20ms/step - loss: 0.0330 - mae: 0.1545 - val_loss: 0.0295 - val_mae: 0.1436\n",
      "Epoch 220/500\n",
      "32/32 [==============================] - 1s 20ms/step - loss: 0.0330 - mae: 0.1548 - val_loss: 0.0294 - val_mae: 0.1433\n",
      "Epoch 221/500\n",
      "32/32 [==============================] - 1s 27ms/step - loss: 0.0330 - mae: 0.1548 - val_loss: 0.0294 - val_mae: 0.1434\n",
      "Epoch 222/500\n",
      "32/32 [==============================] - 1s 24ms/step - loss: 0.0327 - mae: 0.1538 - val_loss: 0.0294 - val_mae: 0.1432\n",
      "Epoch 223/500\n",
      "32/32 [==============================] - 0s 12ms/step - loss: 0.0325 - mae: 0.1538 - val_loss: 0.0295 - val_mae: 0.1437\n",
      "Epoch 224/500\n",
      "32/32 [==============================] - 0s 16ms/step - loss: 0.0329 - mae: 0.1544 - val_loss: 0.0294 - val_mae: 0.1435\n",
      "Epoch 225/500\n",
      "32/32 [==============================] - 0s 15ms/step - loss: 0.0328 - mae: 0.1543 - val_loss: 0.0294 - val_mae: 0.1434\n",
      "Epoch 226/500\n",
      "32/32 [==============================] - 1s 19ms/step - loss: 0.0326 - mae: 0.1534 - val_loss: 0.0294 - val_mae: 0.1433\n",
      "Epoch 227/500\n",
      "32/32 [==============================] - 1s 20ms/step - loss: 0.0326 - mae: 0.1542 - val_loss: 0.0294 - val_mae: 0.1431\n",
      "Epoch 228/500\n",
      "32/32 [==============================] - 1s 16ms/step - loss: 0.0323 - mae: 0.1521 - val_loss: 0.0294 - val_mae: 0.1433\n",
      "Epoch 229/500\n",
      "32/32 [==============================] - 0s 12ms/step - loss: 0.0325 - mae: 0.1535 - val_loss: 0.0294 - val_mae: 0.1432\n",
      "Epoch 230/500\n",
      "32/32 [==============================] - 1s 16ms/step - loss: 0.0319 - mae: 0.1517 - val_loss: 0.0295 - val_mae: 0.1432\n",
      "Epoch 231/500\n",
      "32/32 [==============================] - 1s 19ms/step - loss: 0.0323 - mae: 0.1534 - val_loss: 0.0294 - val_mae: 0.1429\n",
      "Epoch 232/500\n",
      "32/32 [==============================] - 0s 13ms/step - loss: 0.0324 - mae: 0.1537 - val_loss: 0.0293 - val_mae: 0.1427\n",
      "Epoch 233/500\n",
      "32/32 [==============================] - 0s 14ms/step - loss: 0.0326 - mae: 0.1526 - val_loss: 0.0293 - val_mae: 0.1428\n",
      "Epoch 234/500\n",
      "32/32 [==============================] - 0s 15ms/step - loss: 0.0322 - mae: 0.1528 - val_loss: 0.0294 - val_mae: 0.1430\n",
      "Epoch 235/500\n",
      "32/32 [==============================] - 1s 27ms/step - loss: 0.0325 - mae: 0.1530 - val_loss: 0.0293 - val_mae: 0.1432\n",
      "Epoch 236/500\n",
      "32/32 [==============================] - 0s 13ms/step - loss: 0.0324 - mae: 0.1527 - val_loss: 0.0293 - val_mae: 0.1429\n",
      "Epoch 237/500\n",
      "32/32 [==============================] - 1s 17ms/step - loss: 0.0326 - mae: 0.1534 - val_loss: 0.0294 - val_mae: 0.1432\n",
      "Epoch 238/500\n",
      "32/32 [==============================] - 1s 27ms/step - loss: 0.0326 - mae: 0.1533 - val_loss: 0.0293 - val_mae: 0.1431\n",
      "Epoch 239/500\n",
      "32/32 [==============================] - 1s 25ms/step - loss: 0.0324 - mae: 0.1529 - val_loss: 0.0293 - val_mae: 0.1430\n",
      "Epoch 240/500\n",
      "32/32 [==============================] - 0s 15ms/step - loss: 0.0326 - mae: 0.1540 - val_loss: 0.0293 - val_mae: 0.1431\n",
      "Epoch 241/500\n",
      "32/32 [==============================] - 1s 16ms/step - loss: 0.0324 - mae: 0.1519 - val_loss: 0.0294 - val_mae: 0.1432\n",
      "Epoch 242/500\n",
      "32/32 [==============================] - 1s 16ms/step - loss: 0.0322 - mae: 0.1524 - val_loss: 0.0293 - val_mae: 0.1430\n",
      "Epoch 243/500\n",
      "32/32 [==============================] - 0s 15ms/step - loss: 0.0322 - mae: 0.1520 - val_loss: 0.0293 - val_mae: 0.1426\n",
      "Epoch 244/500\n",
      "32/32 [==============================] - 0s 12ms/step - loss: 0.0325 - mae: 0.1526 - val_loss: 0.0293 - val_mae: 0.1430\n",
      "Epoch 245/500\n",
      "32/32 [==============================] - 0s 10ms/step - loss: 0.0327 - mae: 0.1531 - val_loss: 0.0294 - val_mae: 0.1432\n",
      "Epoch 246/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32/32 [==============================] - 0s 14ms/step - loss: 0.0327 - mae: 0.1539 - val_loss: 0.0293 - val_mae: 0.1431\n",
      "Epoch 247/500\n",
      "32/32 [==============================] - 0s 16ms/step - loss: 0.0327 - mae: 0.1535 - val_loss: 0.0293 - val_mae: 0.1432\n",
      "Epoch 248/500\n",
      "32/32 [==============================] - 1s 30ms/step - loss: 0.0326 - mae: 0.1533 - val_loss: 0.0293 - val_mae: 0.1429\n",
      "Epoch 249/500\n",
      "32/32 [==============================] - 0s 15ms/step - loss: 0.0320 - mae: 0.1517 - val_loss: 0.0295 - val_mae: 0.1434\n",
      "Epoch 250/500\n",
      "32/32 [==============================] - 0s 15ms/step - loss: 0.0322 - mae: 0.1527 - val_loss: 0.0293 - val_mae: 0.1433\n",
      "Epoch 251/500\n",
      "32/32 [==============================] - 0s 14ms/step - loss: 0.0320 - mae: 0.1524 - val_loss: 0.0294 - val_mae: 0.1429\n",
      "Epoch 252/500\n",
      "32/32 [==============================] - 0s 16ms/step - loss: 0.0326 - mae: 0.1541 - val_loss: 0.0293 - val_mae: 0.1429\n",
      "Epoch 253/500\n",
      "32/32 [==============================] - 0s 15ms/step - loss: 0.0325 - mae: 0.1532 - val_loss: 0.0293 - val_mae: 0.1431\n",
      "Epoch 254/500\n",
      "32/32 [==============================] - 1s 16ms/step - loss: 0.0324 - mae: 0.1528 - val_loss: 0.0294 - val_mae: 0.1435\n",
      "Epoch 255/500\n",
      "32/32 [==============================] - 0s 12ms/step - loss: 0.0327 - mae: 0.1535 - val_loss: 0.0294 - val_mae: 0.1433\n",
      "Epoch 256/500\n",
      "32/32 [==============================] - 0s 13ms/step - loss: 0.0327 - mae: 0.1534 - val_loss: 0.0293 - val_mae: 0.1429\n",
      "Epoch 257/500\n",
      "32/32 [==============================] - 0s 14ms/step - loss: 0.0324 - mae: 0.1528 - val_loss: 0.0294 - val_mae: 0.1433\n",
      "Epoch 258/500\n",
      "32/32 [==============================] - 0s 10ms/step - loss: 0.0327 - mae: 0.1536 - val_loss: 0.0294 - val_mae: 0.1429\n",
      "Epoch 259/500\n",
      "32/32 [==============================] - 1s 27ms/step - loss: 0.0324 - mae: 0.1532 - val_loss: 0.0294 - val_mae: 0.1428\n",
      "Epoch 260/500\n",
      "32/32 [==============================] - 1s 16ms/step - loss: 0.0325 - mae: 0.1538 - val_loss: 0.0294 - val_mae: 0.1430\n",
      "Epoch 261/500\n",
      "32/32 [==============================] - 0s 16ms/step - loss: 0.0324 - mae: 0.1527 - val_loss: 0.0294 - val_mae: 0.1433\n",
      "Epoch 262/500\n",
      "32/32 [==============================] - 0s 12ms/step - loss: 0.0323 - mae: 0.1532 - val_loss: 0.0293 - val_mae: 0.1430\n",
      "Epoch 263/500\n",
      "32/32 [==============================] - 0s 13ms/step - loss: 0.0331 - mae: 0.1544 - val_loss: 0.0292 - val_mae: 0.1426\n",
      "Epoch 264/500\n",
      "32/32 [==============================] - 0s 13ms/step - loss: 0.0320 - mae: 0.1518 - val_loss: 0.0293 - val_mae: 0.1431\n",
      "Epoch 265/500\n",
      "32/32 [==============================] - 0s 13ms/step - loss: 0.0326 - mae: 0.1535 - val_loss: 0.0292 - val_mae: 0.1431\n",
      "Epoch 266/500\n",
      "32/32 [==============================] - 0s 11ms/step - loss: 0.0325 - mae: 0.1525 - val_loss: 0.0292 - val_mae: 0.1428\n",
      "Epoch 267/500\n",
      "32/32 [==============================] - 0s 9ms/step - loss: 0.0323 - mae: 0.1534 - val_loss: 0.0292 - val_mae: 0.1429\n",
      "Epoch 268/500\n",
      "32/32 [==============================] - 0s 13ms/step - loss: 0.0325 - mae: 0.1531 - val_loss: 0.0293 - val_mae: 0.1427\n",
      "Epoch 269/500\n",
      "32/32 [==============================] - 0s 12ms/step - loss: 0.0325 - mae: 0.1530 - val_loss: 0.0292 - val_mae: 0.1431\n",
      "Epoch 270/500\n",
      "32/32 [==============================] - 0s 16ms/step - loss: 0.0323 - mae: 0.1528 - val_loss: 0.0292 - val_mae: 0.1430\n",
      "Epoch 271/500\n",
      "32/32 [==============================] - 1s 19ms/step - loss: 0.0321 - mae: 0.1522 - val_loss: 0.0292 - val_mae: 0.1431\n",
      "Epoch 272/500\n",
      "32/32 [==============================] - 0s 10ms/step - loss: 0.0325 - mae: 0.1530 - val_loss: 0.0293 - val_mae: 0.1430\n",
      "Epoch 273/500\n",
      "32/32 [==============================] - 1s 17ms/step - loss: 0.0325 - mae: 0.1527 - val_loss: 0.0292 - val_mae: 0.1428\n",
      "Epoch 274/500\n",
      "32/32 [==============================] - 1s 26ms/step - loss: 0.0327 - mae: 0.1534 - val_loss: 0.0292 - val_mae: 0.1429\n",
      "Epoch 275/500\n",
      "32/32 [==============================] - 0s 13ms/step - loss: 0.0329 - mae: 0.1541 - val_loss: 0.0292 - val_mae: 0.1426\n",
      "Epoch 276/500\n",
      "32/32 [==============================] - 0s 10ms/step - loss: 0.0320 - mae: 0.1515 - val_loss: 0.0292 - val_mae: 0.1426\n",
      "Epoch 277/500\n",
      "32/32 [==============================] - 0s 11ms/step - loss: 0.0321 - mae: 0.1528 - val_loss: 0.0293 - val_mae: 0.1430\n",
      "Epoch 278/500\n",
      "32/32 [==============================] - 0s 11ms/step - loss: 0.0320 - mae: 0.1517 - val_loss: 0.0292 - val_mae: 0.1425\n",
      "Epoch 279/500\n",
      "32/32 [==============================] - 0s 11ms/step - loss: 0.0326 - mae: 0.1538 - val_loss: 0.0292 - val_mae: 0.1429\n",
      "Epoch 280/500\n",
      "32/32 [==============================] - 1s 23ms/step - loss: 0.0326 - mae: 0.1528 - val_loss: 0.0292 - val_mae: 0.1430\n",
      "Epoch 281/500\n",
      "32/32 [==============================] - 1s 22ms/step - loss: 0.0324 - mae: 0.1529 - val_loss: 0.0292 - val_mae: 0.1428\n",
      "Epoch 282/500\n",
      "32/32 [==============================] - 1s 16ms/step - loss: 0.0324 - mae: 0.1527 - val_loss: 0.0292 - val_mae: 0.1430\n",
      "Epoch 283/500\n",
      "32/32 [==============================] - 0s 12ms/step - loss: 0.0323 - mae: 0.1527 - val_loss: 0.0293 - val_mae: 0.1430\n",
      "Epoch 284/500\n",
      "32/32 [==============================] - 0s 16ms/step - loss: 0.0326 - mae: 0.1531 - val_loss: 0.0293 - val_mae: 0.1431\n",
      "Epoch 285/500\n",
      "32/32 [==============================] - 0s 16ms/step - loss: 0.0325 - mae: 0.1538 - val_loss: 0.0292 - val_mae: 0.1429\n",
      "Epoch 286/500\n",
      "32/32 [==============================] - 0s 9ms/step - loss: 0.0322 - mae: 0.1515 - val_loss: 0.0292 - val_mae: 0.1428\n",
      "Epoch 287/500\n",
      "32/32 [==============================] - 0s 12ms/step - loss: 0.0321 - mae: 0.1526 - val_loss: 0.0292 - val_mae: 0.1429\n",
      "Epoch 288/500\n",
      "32/32 [==============================] - 1s 21ms/step - loss: 0.0327 - mae: 0.1541 - val_loss: 0.0291 - val_mae: 0.1427\n",
      "Epoch 289/500\n",
      "32/32 [==============================] - 0s 14ms/step - loss: 0.0322 - mae: 0.1520 - val_loss: 0.0293 - val_mae: 0.1427\n",
      "Epoch 290/500\n",
      "32/32 [==============================] - 0s 12ms/step - loss: 0.0323 - mae: 0.1532 - val_loss: 0.0292 - val_mae: 0.1428\n",
      "Epoch 291/500\n",
      "32/32 [==============================] - 0s 10ms/step - loss: 0.0326 - mae: 0.1536 - val_loss: 0.0291 - val_mae: 0.1427\n",
      "Epoch 292/500\n",
      "32/32 [==============================] - 1s 21ms/step - loss: 0.0325 - mae: 0.1532 - val_loss: 0.0292 - val_mae: 0.1427\n",
      "Epoch 293/500\n",
      "32/32 [==============================] - 1s 16ms/step - loss: 0.0323 - mae: 0.1526 - val_loss: 0.0292 - val_mae: 0.1428\n",
      "Epoch 294/500\n",
      "32/32 [==============================] - 0s 15ms/step - loss: 0.0326 - mae: 0.1537 - val_loss: 0.0292 - val_mae: 0.1428\n",
      "Epoch 295/500\n",
      "32/32 [==============================] - 0s 10ms/step - loss: 0.0324 - mae: 0.1531 - val_loss: 0.0293 - val_mae: 0.1428\n",
      "Epoch 296/500\n",
      "32/32 [==============================] - 0s 14ms/step - loss: 0.0318 - mae: 0.1516 - val_loss: 0.0292 - val_mae: 0.1426\n",
      "Epoch 297/500\n",
      "32/32 [==============================] - 0s 14ms/step - loss: 0.0321 - mae: 0.1520 - val_loss: 0.0291 - val_mae: 0.1426\n",
      "Epoch 298/500\n",
      "32/32 [==============================] - 0s 10ms/step - loss: 0.0319 - mae: 0.1507 - val_loss: 0.0292 - val_mae: 0.1428\n",
      "Epoch 299/500\n",
      "32/32 [==============================] - 0s 9ms/step - loss: 0.0322 - mae: 0.1525 - val_loss: 0.0292 - val_mae: 0.1426\n",
      "Epoch 300/500\n",
      "32/32 [==============================] - 0s 10ms/step - loss: 0.0322 - mae: 0.1526 - val_loss: 0.0291 - val_mae: 0.1423\n",
      "Epoch 301/500\n",
      "32/32 [==============================] - 0s 9ms/step - loss: 0.0322 - mae: 0.1521 - val_loss: 0.0291 - val_mae: 0.1424\n",
      "Epoch 302/500\n",
      "32/32 [==============================] - 0s 11ms/step - loss: 0.0318 - mae: 0.1515 - val_loss: 0.0291 - val_mae: 0.1423\n",
      "Epoch 303/500\n",
      "32/32 [==============================] - 0s 10ms/step - loss: 0.0322 - mae: 0.1523 - val_loss: 0.0291 - val_mae: 0.1424\n",
      "Epoch 304/500\n",
      "32/32 [==============================] - 0s 10ms/step - loss: 0.0322 - mae: 0.1532 - val_loss: 0.0292 - val_mae: 0.1427\n",
      "Epoch 305/500\n",
      "32/32 [==============================] - 0s 13ms/step - loss: 0.0321 - mae: 0.1525 - val_loss: 0.0292 - val_mae: 0.1426\n",
      "Epoch 306/500\n",
      "32/32 [==============================] - 0s 11ms/step - loss: 0.0324 - mae: 0.1528 - val_loss: 0.0291 - val_mae: 0.1425\n",
      "Epoch 307/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32/32 [==============================] - 0s 14ms/step - loss: 0.0322 - mae: 0.1521 - val_loss: 0.0291 - val_mae: 0.1427\n",
      "Epoch 308/500\n",
      "32/32 [==============================] - 0s 15ms/step - loss: 0.0318 - mae: 0.1513 - val_loss: 0.0291 - val_mae: 0.1426\n",
      "Epoch 309/500\n",
      "32/32 [==============================] - 0s 11ms/step - loss: 0.0324 - mae: 0.1528 - val_loss: 0.0291 - val_mae: 0.1426\n",
      "Epoch 310/500\n",
      "32/32 [==============================] - 0s 12ms/step - loss: 0.0320 - mae: 0.1524 - val_loss: 0.0291 - val_mae: 0.1424\n",
      "Epoch 311/500\n",
      "32/32 [==============================] - 0s 11ms/step - loss: 0.0324 - mae: 0.1540 - val_loss: 0.0291 - val_mae: 0.1425\n",
      "Epoch 312/500\n",
      "32/32 [==============================] - 0s 10ms/step - loss: 0.0321 - mae: 0.1519 - val_loss: 0.0291 - val_mae: 0.1423\n",
      "Epoch 313/500\n",
      "32/32 [==============================] - 0s 10ms/step - loss: 0.0324 - mae: 0.1531 - val_loss: 0.0291 - val_mae: 0.1427\n",
      "Epoch 314/500\n",
      "32/32 [==============================] - 0s 10ms/step - loss: 0.0321 - mae: 0.1524 - val_loss: 0.0291 - val_mae: 0.1425\n",
      "Epoch 315/500\n",
      "32/32 [==============================] - 0s 11ms/step - loss: 0.0324 - mae: 0.1525 - val_loss: 0.0291 - val_mae: 0.1423\n",
      "Epoch 316/500\n",
      "32/32 [==============================] - 0s 10ms/step - loss: 0.0324 - mae: 0.1525 - val_loss: 0.0291 - val_mae: 0.1427\n",
      "Epoch 317/500\n",
      "32/32 [==============================] - 0s 12ms/step - loss: 0.0324 - mae: 0.1539 - val_loss: 0.0292 - val_mae: 0.1424\n",
      "Epoch 318/500\n",
      "32/32 [==============================] - 0s 15ms/step - loss: 0.0324 - mae: 0.1532 - val_loss: 0.0291 - val_mae: 0.1424\n",
      "Epoch 319/500\n",
      "32/32 [==============================] - 1s 16ms/step - loss: 0.0320 - mae: 0.1521 - val_loss: 0.0291 - val_mae: 0.1424\n",
      "Epoch 320/500\n",
      "32/32 [==============================] - 0s 12ms/step - loss: 0.0324 - mae: 0.1530 - val_loss: 0.0291 - val_mae: 0.1424\n",
      "Epoch 321/500\n",
      "32/32 [==============================] - 0s 12ms/step - loss: 0.0324 - mae: 0.1525 - val_loss: 0.0291 - val_mae: 0.1425\n",
      "Epoch 322/500\n",
      "32/32 [==============================] - 0s 16ms/step - loss: 0.0326 - mae: 0.1537 - val_loss: 0.0291 - val_mae: 0.1424\n",
      "Epoch 323/500\n",
      "32/32 [==============================] - 0s 13ms/step - loss: 0.0323 - mae: 0.1530 - val_loss: 0.0290 - val_mae: 0.1423\n",
      "Epoch 324/500\n",
      "32/32 [==============================] - 0s 10ms/step - loss: 0.0323 - mae: 0.1533 - val_loss: 0.0290 - val_mae: 0.1420\n",
      "Epoch 325/500\n",
      "32/32 [==============================] - 0s 10ms/step - loss: 0.0323 - mae: 0.1527 - val_loss: 0.0291 - val_mae: 0.1425\n",
      "Epoch 326/500\n",
      "32/32 [==============================] - 0s 12ms/step - loss: 0.0322 - mae: 0.1519 - val_loss: 0.0291 - val_mae: 0.1423\n",
      "Epoch 327/500\n",
      "32/32 [==============================] - 0s 10ms/step - loss: 0.0322 - mae: 0.1524 - val_loss: 0.0290 - val_mae: 0.1424\n",
      "Epoch 328/500\n",
      "32/32 [==============================] - 1s 31ms/step - loss: 0.0322 - mae: 0.1525 - val_loss: 0.0290 - val_mae: 0.1421\n",
      "Epoch 329/500\n",
      "32/32 [==============================] - 1s 17ms/step - loss: 0.0313 - mae: 0.1500 - val_loss: 0.0291 - val_mae: 0.1422\n",
      "Epoch 330/500\n",
      "32/32 [==============================] - 0s 12ms/step - loss: 0.0322 - mae: 0.1526 - val_loss: 0.0291 - val_mae: 0.1425\n",
      "Epoch 331/500\n",
      "32/32 [==============================] - 0s 13ms/step - loss: 0.0322 - mae: 0.1531 - val_loss: 0.0290 - val_mae: 0.1423\n",
      "Epoch 332/500\n",
      "32/32 [==============================] - 0s 11ms/step - loss: 0.0320 - mae: 0.1529 - val_loss: 0.0290 - val_mae: 0.1423\n",
      "Epoch 333/500\n",
      "32/32 [==============================] - 1s 19ms/step - loss: 0.0323 - mae: 0.1535 - val_loss: 0.0290 - val_mae: 0.1423\n",
      "Epoch 334/500\n",
      "32/32 [==============================] - 0s 13ms/step - loss: 0.0322 - mae: 0.1524 - val_loss: 0.0290 - val_mae: 0.1420\n",
      "Epoch 335/500\n",
      "32/32 [==============================] - 1s 21ms/step - loss: 0.0320 - mae: 0.1523 - val_loss: 0.0289 - val_mae: 0.1422\n",
      "Epoch 336/500\n",
      "32/32 [==============================] - 0s 12ms/step - loss: 0.0322 - mae: 0.1526 - val_loss: 0.0290 - val_mae: 0.1424\n",
      "Epoch 337/500\n",
      "32/32 [==============================] - 0s 12ms/step - loss: 0.0321 - mae: 0.1523 - val_loss: 0.0289 - val_mae: 0.1422\n",
      "Epoch 338/500\n",
      "32/32 [==============================] - 0s 11ms/step - loss: 0.0319 - mae: 0.1515 - val_loss: 0.0290 - val_mae: 0.1421\n",
      "Epoch 339/500\n",
      "32/32 [==============================] - 0s 11ms/step - loss: 0.0322 - mae: 0.1526 - val_loss: 0.0289 - val_mae: 0.1420\n",
      "Epoch 340/500\n",
      "32/32 [==============================] - 0s 10ms/step - loss: 0.0320 - mae: 0.1520 - val_loss: 0.0289 - val_mae: 0.1421\n",
      "Epoch 341/500\n",
      "32/32 [==============================] - 0s 12ms/step - loss: 0.0319 - mae: 0.1510 - val_loss: 0.0290 - val_mae: 0.1419\n",
      "Epoch 342/500\n",
      "32/32 [==============================] - 0s 11ms/step - loss: 0.0316 - mae: 0.1506 - val_loss: 0.0290 - val_mae: 0.1423\n",
      "Epoch 343/500\n",
      "32/32 [==============================] - 0s 11ms/step - loss: 0.0322 - mae: 0.1528 - val_loss: 0.0289 - val_mae: 0.1424\n",
      "Epoch 344/500\n",
      "32/32 [==============================] - 0s 15ms/step - loss: 0.0323 - mae: 0.1525 - val_loss: 0.0289 - val_mae: 0.1421\n",
      "Epoch 345/500\n",
      "32/32 [==============================] - 1s 16ms/step - loss: 0.0322 - mae: 0.1528 - val_loss: 0.0289 - val_mae: 0.1418\n",
      "Epoch 346/500\n",
      "32/32 [==============================] - 0s 10ms/step - loss: 0.0321 - mae: 0.1522 - val_loss: 0.0289 - val_mae: 0.1419\n",
      "Epoch 347/500\n",
      "32/32 [==============================] - 0s 11ms/step - loss: 0.0320 - mae: 0.1525 - val_loss: 0.0290 - val_mae: 0.1421\n",
      "Epoch 348/500\n",
      "32/32 [==============================] - 0s 9ms/step - loss: 0.0320 - mae: 0.1528 - val_loss: 0.0290 - val_mae: 0.1423\n",
      "Epoch 349/500\n",
      "32/32 [==============================] - 0s 13ms/step - loss: 0.0320 - mae: 0.1512 - val_loss: 0.0289 - val_mae: 0.1421\n",
      "Epoch 350/500\n",
      "32/32 [==============================] - 1s 21ms/step - loss: 0.0320 - mae: 0.1520 - val_loss: 0.0289 - val_mae: 0.1419\n",
      "Epoch 351/500\n",
      "32/32 [==============================] - 0s 12ms/step - loss: 0.0318 - mae: 0.1513 - val_loss: 0.0289 - val_mae: 0.1419\n",
      "Epoch 352/500\n",
      "32/32 [==============================] - 0s 11ms/step - loss: 0.0321 - mae: 0.1517 - val_loss: 0.0289 - val_mae: 0.1420\n",
      "Epoch 353/500\n",
      "32/32 [==============================] - 1s 18ms/step - loss: 0.0322 - mae: 0.1524 - val_loss: 0.0289 - val_mae: 0.1422\n",
      "Epoch 354/500\n",
      "32/32 [==============================] - 0s 15ms/step - loss: 0.0322 - mae: 0.1523 - val_loss: 0.0289 - val_mae: 0.1417\n",
      "Epoch 355/500\n",
      "32/32 [==============================] - 0s 9ms/step - loss: 0.0312 - mae: 0.1500 - val_loss: 0.0288 - val_mae: 0.1417\n",
      "Epoch 356/500\n",
      "32/32 [==============================] - 1s 18ms/step - loss: 0.0321 - mae: 0.1524 - val_loss: 0.0289 - val_mae: 0.1418\n",
      "Epoch 357/500\n",
      "32/32 [==============================] - 0s 13ms/step - loss: 0.0319 - mae: 0.1525 - val_loss: 0.0288 - val_mae: 0.1417\n",
      "Epoch 358/500\n",
      "32/32 [==============================] - 0s 11ms/step - loss: 0.0318 - mae: 0.1516 - val_loss: 0.0289 - val_mae: 0.1419\n",
      "Epoch 359/500\n",
      "32/32 [==============================] - 0s 10ms/step - loss: 0.0318 - mae: 0.1518 - val_loss: 0.0288 - val_mae: 0.1417\n",
      "Epoch 360/500\n",
      "32/32 [==============================] - 0s 12ms/step - loss: 0.0318 - mae: 0.1514 - val_loss: 0.0288 - val_mae: 0.1418\n",
      "Epoch 361/500\n",
      "32/32 [==============================] - 0s 13ms/step - loss: 0.0318 - mae: 0.1515 - val_loss: 0.0288 - val_mae: 0.1416\n",
      "Epoch 362/500\n",
      "32/32 [==============================] - 1s 21ms/step - loss: 0.0322 - mae: 0.1527 - val_loss: 0.0288 - val_mae: 0.1416\n",
      "Epoch 363/500\n",
      "32/32 [==============================] - 0s 13ms/step - loss: 0.0321 - mae: 0.1521 - val_loss: 0.0288 - val_mae: 0.1416\n",
      "Epoch 364/500\n",
      "32/32 [==============================] - 0s 11ms/step - loss: 0.0321 - mae: 0.1524 - val_loss: 0.0288 - val_mae: 0.1420\n",
      "Epoch 365/500\n",
      "32/32 [==============================] - 0s 11ms/step - loss: 0.0321 - mae: 0.1523 - val_loss: 0.0288 - val_mae: 0.1417\n",
      "Epoch 366/500\n",
      "32/32 [==============================] - 0s 13ms/step - loss: 0.0318 - mae: 0.1520 - val_loss: 0.0288 - val_mae: 0.1414\n",
      "Epoch 367/500\n",
      "32/32 [==============================] - 0s 11ms/step - loss: 0.0323 - mae: 0.1529 - val_loss: 0.0288 - val_mae: 0.1415\n",
      "Epoch 368/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32/32 [==============================] - 0s 16ms/step - loss: 0.0323 - mae: 0.1529 - val_loss: 0.0289 - val_mae: 0.1417\n",
      "Epoch 369/500\n",
      "32/32 [==============================] - 0s 15ms/step - loss: 0.0323 - mae: 0.1524 - val_loss: 0.0287 - val_mae: 0.1418\n",
      "Epoch 370/500\n",
      "32/32 [==============================] - 1s 17ms/step - loss: 0.0319 - mae: 0.1519 - val_loss: 0.0288 - val_mae: 0.1419\n",
      "Epoch 371/500\n",
      "32/32 [==============================] - 0s 13ms/step - loss: 0.0321 - mae: 0.1520 - val_loss: 0.0288 - val_mae: 0.1415\n",
      "Epoch 372/500\n",
      "32/32 [==============================] - 0s 12ms/step - loss: 0.0322 - mae: 0.1519 - val_loss: 0.0287 - val_mae: 0.1416\n",
      "Epoch 373/500\n",
      "32/32 [==============================] - 1s 17ms/step - loss: 0.0315 - mae: 0.1510 - val_loss: 0.0287 - val_mae: 0.1415\n",
      "Epoch 374/500\n",
      "32/32 [==============================] - 1s 17ms/step - loss: 0.0316 - mae: 0.1510 - val_loss: 0.0288 - val_mae: 0.1416\n",
      "Epoch 375/500\n",
      "32/32 [==============================] - 0s 14ms/step - loss: 0.0325 - mae: 0.1531 - val_loss: 0.0287 - val_mae: 0.1415\n",
      "Epoch 376/500\n",
      "32/32 [==============================] - 1s 19ms/step - loss: 0.0319 - mae: 0.1516 - val_loss: 0.0287 - val_mae: 0.1412\n",
      "Epoch 377/500\n",
      "32/32 [==============================] - 1s 27ms/step - loss: 0.0319 - mae: 0.1509 - val_loss: 0.0288 - val_mae: 0.1413\n",
      "Epoch 378/500\n",
      "32/32 [==============================] - 1s 20ms/step - loss: 0.0319 - mae: 0.1514 - val_loss: 0.0287 - val_mae: 0.1413\n",
      "Epoch 379/500\n",
      "32/32 [==============================] - 0s 14ms/step - loss: 0.0320 - mae: 0.1519 - val_loss: 0.0287 - val_mae: 0.1413\n",
      "Epoch 380/500\n",
      "32/32 [==============================] - 0s 12ms/step - loss: 0.0319 - mae: 0.1517 - val_loss: 0.0287 - val_mae: 0.1413\n",
      "Epoch 381/500\n",
      "32/32 [==============================] - 0s 9ms/step - loss: 0.0318 - mae: 0.1510 - val_loss: 0.0287 - val_mae: 0.1416\n",
      "Epoch 382/500\n",
      "32/32 [==============================] - 0s 15ms/step - loss: 0.0318 - mae: 0.1517 - val_loss: 0.0287 - val_mae: 0.1415\n",
      "Epoch 383/500\n",
      "32/32 [==============================] - 0s 14ms/step - loss: 0.0316 - mae: 0.1514 - val_loss: 0.0286 - val_mae: 0.1412\n",
      "Epoch 384/500\n",
      "32/32 [==============================] - 0s 11ms/step - loss: 0.0322 - mae: 0.1523 - val_loss: 0.0287 - val_mae: 0.1414\n",
      "Epoch 385/500\n",
      "32/32 [==============================] - 0s 13ms/step - loss: 0.0316 - mae: 0.1509 - val_loss: 0.0287 - val_mae: 0.1415\n",
      "Epoch 386/500\n",
      "32/32 [==============================] - 1s 41ms/step - loss: 0.0316 - mae: 0.1514 - val_loss: 0.0287 - val_mae: 0.1412\n",
      "Epoch 387/500\n",
      "32/32 [==============================] - 0s 13ms/step - loss: 0.0313 - mae: 0.1499 - val_loss: 0.0286 - val_mae: 0.1409\n",
      "Epoch 388/500\n",
      "32/32 [==============================] - 0s 13ms/step - loss: 0.0320 - mae: 0.1523 - val_loss: 0.0286 - val_mae: 0.1411\n",
      "Epoch 389/500\n",
      "32/32 [==============================] - 1s 16ms/step - loss: 0.0317 - mae: 0.1508 - val_loss: 0.0286 - val_mae: 0.1414\n",
      "Epoch 390/500\n",
      "32/32 [==============================] - 0s 11ms/step - loss: 0.0315 - mae: 0.1502 - val_loss: 0.0286 - val_mae: 0.1411\n",
      "Epoch 391/500\n",
      "32/32 [==============================] - 0s 13ms/step - loss: 0.0318 - mae: 0.1513 - val_loss: 0.0286 - val_mae: 0.1409\n",
      "Epoch 392/500\n",
      "32/32 [==============================] - 0s 11ms/step - loss: 0.0318 - mae: 0.1515 - val_loss: 0.0286 - val_mae: 0.1413\n",
      "Epoch 393/500\n",
      "32/32 [==============================] - 0s 12ms/step - loss: 0.0317 - mae: 0.1511 - val_loss: 0.0286 - val_mae: 0.1413\n",
      "Epoch 394/500\n",
      "32/32 [==============================] - 1s 17ms/step - loss: 0.0319 - mae: 0.1512 - val_loss: 0.0286 - val_mae: 0.1409\n",
      "Epoch 395/500\n",
      "32/32 [==============================] - 0s 12ms/step - loss: 0.0317 - mae: 0.1514 - val_loss: 0.0286 - val_mae: 0.1413\n",
      "Epoch 396/500\n",
      "32/32 [==============================] - 0s 12ms/step - loss: 0.0320 - mae: 0.1520 - val_loss: 0.0285 - val_mae: 0.1412\n",
      "Epoch 397/500\n",
      "32/32 [==============================] - 1s 17ms/step - loss: 0.0321 - mae: 0.1523 - val_loss: 0.0286 - val_mae: 0.1408\n",
      "Epoch 398/500\n",
      "32/32 [==============================] - 0s 14ms/step - loss: 0.0318 - mae: 0.1515 - val_loss: 0.0285 - val_mae: 0.1411\n",
      "Epoch 399/500\n",
      "32/32 [==============================] - 0s 14ms/step - loss: 0.0316 - mae: 0.1500 - val_loss: 0.0286 - val_mae: 0.1410\n",
      "Epoch 400/500\n",
      "32/32 [==============================] - 1s 16ms/step - loss: 0.0314 - mae: 0.1503 - val_loss: 0.0285 - val_mae: 0.1408\n",
      "Epoch 401/500\n",
      "32/32 [==============================] - 0s 11ms/step - loss: 0.0318 - mae: 0.1504 - val_loss: 0.0286 - val_mae: 0.1411\n",
      "Epoch 402/500\n",
      "32/32 [==============================] - 0s 15ms/step - loss: 0.0318 - mae: 0.1508 - val_loss: 0.0285 - val_mae: 0.1412\n",
      "Epoch 403/500\n",
      "32/32 [==============================] - 0s 15ms/step - loss: 0.0315 - mae: 0.1508 - val_loss: 0.0285 - val_mae: 0.1410\n",
      "Epoch 404/500\n",
      "32/32 [==============================] - 0s 11ms/step - loss: 0.0317 - mae: 0.1507 - val_loss: 0.0285 - val_mae: 0.1409\n",
      "Epoch 405/500\n",
      "32/32 [==============================] - 0s 14ms/step - loss: 0.0312 - mae: 0.1498 - val_loss: 0.0285 - val_mae: 0.1408\n",
      "Epoch 406/500\n",
      "32/32 [==============================] - 0s 12ms/step - loss: 0.0317 - mae: 0.1509 - val_loss: 0.0285 - val_mae: 0.1410\n",
      "Epoch 407/500\n",
      "32/32 [==============================] - 0s 12ms/step - loss: 0.0319 - mae: 0.1513 - val_loss: 0.0285 - val_mae: 0.1410\n",
      "Epoch 408/500\n",
      "32/32 [==============================] - 0s 14ms/step - loss: 0.0314 - mae: 0.1507 - val_loss: 0.0285 - val_mae: 0.1410\n",
      "Epoch 409/500\n",
      "32/32 [==============================] - 0s 13ms/step - loss: 0.0319 - mae: 0.1514 - val_loss: 0.0284 - val_mae: 0.1407\n",
      "Epoch 410/500\n",
      "32/32 [==============================] - 0s 14ms/step - loss: 0.0317 - mae: 0.1508 - val_loss: 0.0284 - val_mae: 0.1408\n",
      "Epoch 411/500\n",
      "32/32 [==============================] - 0s 14ms/step - loss: 0.0312 - mae: 0.1497 - val_loss: 0.0284 - val_mae: 0.1404\n",
      "Epoch 412/500\n",
      "32/32 [==============================] - 0s 15ms/step - loss: 0.0316 - mae: 0.1506 - val_loss: 0.0284 - val_mae: 0.1408\n",
      "Epoch 413/500\n",
      "32/32 [==============================] - 0s 13ms/step - loss: 0.0319 - mae: 0.1514 - val_loss: 0.0285 - val_mae: 0.1407\n",
      "Epoch 414/500\n",
      "32/32 [==============================] - 0s 10ms/step - loss: 0.0317 - mae: 0.1518 - val_loss: 0.0285 - val_mae: 0.1405\n",
      "Epoch 415/500\n",
      "32/32 [==============================] - 0s 12ms/step - loss: 0.0319 - mae: 0.1518 - val_loss: 0.0284 - val_mae: 0.1407\n",
      "Epoch 416/500\n",
      "32/32 [==============================] - 0s 10ms/step - loss: 0.0315 - mae: 0.1501 - val_loss: 0.0284 - val_mae: 0.1403\n",
      "Epoch 417/500\n",
      "32/32 [==============================] - 0s 12ms/step - loss: 0.0318 - mae: 0.1512 - val_loss: 0.0284 - val_mae: 0.1406\n",
      "Epoch 418/500\n",
      "32/32 [==============================] - 0s 12ms/step - loss: 0.0312 - mae: 0.1500 - val_loss: 0.0284 - val_mae: 0.1406\n",
      "Epoch 419/500\n",
      "32/32 [==============================] - 0s 13ms/step - loss: 0.0318 - mae: 0.1516 - val_loss: 0.0283 - val_mae: 0.1407\n",
      "Epoch 420/500\n",
      "32/32 [==============================] - 0s 12ms/step - loss: 0.0315 - mae: 0.1509 - val_loss: 0.0284 - val_mae: 0.1407\n",
      "Epoch 421/500\n",
      "32/32 [==============================] - 1s 21ms/step - loss: 0.0317 - mae: 0.1504 - val_loss: 0.0284 - val_mae: 0.1405\n",
      "Epoch 422/500\n",
      "32/32 [==============================] - 0s 12ms/step - loss: 0.0320 - mae: 0.1515 - val_loss: 0.0284 - val_mae: 0.1406\n",
      "Epoch 423/500\n",
      "32/32 [==============================] - 0s 13ms/step - loss: 0.0315 - mae: 0.1502 - val_loss: 0.0284 - val_mae: 0.1407\n",
      "Epoch 424/500\n",
      "32/32 [==============================] - 0s 11ms/step - loss: 0.0319 - mae: 0.1516 - val_loss: 0.0284 - val_mae: 0.1403\n",
      "Epoch 425/500\n",
      "32/32 [==============================] - 0s 11ms/step - loss: 0.0313 - mae: 0.1502 - val_loss: 0.0284 - val_mae: 0.1405\n",
      "Epoch 426/500\n",
      "32/32 [==============================] - 0s 11ms/step - loss: 0.0315 - mae: 0.1502 - val_loss: 0.0283 - val_mae: 0.1404\n",
      "Epoch 427/500\n",
      "32/32 [==============================] - 0s 11ms/step - loss: 0.0316 - mae: 0.1510 - val_loss: 0.0283 - val_mae: 0.1402\n",
      "Epoch 428/500\n",
      "32/32 [==============================] - 0s 11ms/step - loss: 0.0318 - mae: 0.1507 - val_loss: 0.0283 - val_mae: 0.1403\n",
      "Epoch 429/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32/32 [==============================] - 0s 11ms/step - loss: 0.0319 - mae: 0.1515 - val_loss: 0.0283 - val_mae: 0.1403\n",
      "Epoch 430/500\n",
      "32/32 [==============================] - 0s 16ms/step - loss: 0.0312 - mae: 0.1491 - val_loss: 0.0283 - val_mae: 0.1402\n",
      "Epoch 431/500\n",
      "32/32 [==============================] - 0s 15ms/step - loss: 0.0313 - mae: 0.1499 - val_loss: 0.0283 - val_mae: 0.1402\n",
      "Epoch 432/500\n",
      "32/32 [==============================] - 0s 12ms/step - loss: 0.0313 - mae: 0.1501 - val_loss: 0.0283 - val_mae: 0.1400\n",
      "Epoch 433/500\n",
      "32/32 [==============================] - 0s 14ms/step - loss: 0.0320 - mae: 0.1517 - val_loss: 0.0283 - val_mae: 0.1402\n",
      "Epoch 434/500\n",
      "32/32 [==============================] - 0s 11ms/step - loss: 0.0316 - mae: 0.1505 - val_loss: 0.0283 - val_mae: 0.1401\n",
      "Epoch 435/500\n",
      "32/32 [==============================] - 0s 13ms/step - loss: 0.0313 - mae: 0.1498 - val_loss: 0.0283 - val_mae: 0.1400\n",
      "Epoch 436/500\n",
      "32/32 [==============================] - 0s 13ms/step - loss: 0.0320 - mae: 0.1519 - val_loss: 0.0283 - val_mae: 0.1401\n",
      "Epoch 437/500\n",
      "32/32 [==============================] - 0s 12ms/step - loss: 0.0315 - mae: 0.1504 - val_loss: 0.0282 - val_mae: 0.1399\n",
      "Epoch 438/500\n",
      "32/32 [==============================] - 0s 15ms/step - loss: 0.0314 - mae: 0.1497 - val_loss: 0.0282 - val_mae: 0.1398\n",
      "Epoch 439/500\n",
      "32/32 [==============================] - 1s 19ms/step - loss: 0.0313 - mae: 0.1495 - val_loss: 0.0282 - val_mae: 0.1398\n",
      "Epoch 440/500\n",
      "32/32 [==============================] - 0s 12ms/step - loss: 0.0315 - mae: 0.1501 - val_loss: 0.0282 - val_mae: 0.1398\n",
      "Epoch 441/500\n",
      "32/32 [==============================] - 0s 14ms/step - loss: 0.0317 - mae: 0.1507 - val_loss: 0.0282 - val_mae: 0.1396\n",
      "Epoch 442/500\n",
      "32/32 [==============================] - 0s 12ms/step - loss: 0.0311 - mae: 0.1495 - val_loss: 0.0283 - val_mae: 0.1403\n",
      "Epoch 443/500\n",
      "32/32 [==============================] - 0s 10ms/step - loss: 0.0314 - mae: 0.1506 - val_loss: 0.0282 - val_mae: 0.1395\n",
      "Epoch 444/500\n",
      "32/32 [==============================] - 0s 15ms/step - loss: 0.0312 - mae: 0.1497 - val_loss: 0.0282 - val_mae: 0.1398\n",
      "Epoch 445/500\n",
      "32/32 [==============================] - 0s 16ms/step - loss: 0.0314 - mae: 0.1504 - val_loss: 0.0281 - val_mae: 0.1394\n",
      "Epoch 446/500\n",
      "32/32 [==============================] - 0s 11ms/step - loss: 0.0315 - mae: 0.1500 - val_loss: 0.0282 - val_mae: 0.1396\n",
      "Epoch 447/500\n",
      "32/32 [==============================] - 0s 15ms/step - loss: 0.0315 - mae: 0.1504 - val_loss: 0.0282 - val_mae: 0.1396\n",
      "Epoch 448/500\n",
      "32/32 [==============================] - 0s 14ms/step - loss: 0.0309 - mae: 0.1490 - val_loss: 0.0282 - val_mae: 0.1396\n",
      "Epoch 449/500\n",
      "32/32 [==============================] - 0s 11ms/step - loss: 0.0312 - mae: 0.1497 - val_loss: 0.0282 - val_mae: 0.1395\n",
      "Epoch 450/500\n",
      "32/32 [==============================] - 0s 12ms/step - loss: 0.0312 - mae: 0.1491 - val_loss: 0.0281 - val_mae: 0.1396\n",
      "Epoch 451/500\n",
      "32/32 [==============================] - 0s 11ms/step - loss: 0.0314 - mae: 0.1496 - val_loss: 0.0281 - val_mae: 0.1399\n",
      "Epoch 452/500\n",
      "32/32 [==============================] - 0s 12ms/step - loss: 0.0315 - mae: 0.1496 - val_loss: 0.0281 - val_mae: 0.1395\n",
      "Epoch 453/500\n",
      "32/32 [==============================] - 0s 10ms/step - loss: 0.0314 - mae: 0.1503 - val_loss: 0.0281 - val_mae: 0.1396\n",
      "Epoch 454/500\n",
      "32/32 [==============================] - 0s 12ms/step - loss: 0.0312 - mae: 0.1498 - val_loss: 0.0281 - val_mae: 0.1394\n",
      "Epoch 455/500\n",
      "32/32 [==============================] - 0s 10ms/step - loss: 0.0312 - mae: 0.1493 - val_loss: 0.0281 - val_mae: 0.1393\n",
      "Epoch 456/500\n",
      "32/32 [==============================] - 0s 10ms/step - loss: 0.0317 - mae: 0.1511 - val_loss: 0.0281 - val_mae: 0.1394\n",
      "Epoch 457/500\n",
      "32/32 [==============================] - 0s 13ms/step - loss: 0.0314 - mae: 0.1495 - val_loss: 0.0281 - val_mae: 0.1394\n",
      "Epoch 458/500\n",
      "32/32 [==============================] - 1s 16ms/step - loss: 0.0320 - mae: 0.1510 - val_loss: 0.0281 - val_mae: 0.1395\n",
      "Epoch 459/500\n",
      "32/32 [==============================] - 0s 10ms/step - loss: 0.0315 - mae: 0.1500 - val_loss: 0.0281 - val_mae: 0.1392\n",
      "Epoch 460/500\n",
      "32/32 [==============================] - 0s 12ms/step - loss: 0.0313 - mae: 0.1497 - val_loss: 0.0281 - val_mae: 0.1396\n",
      "Epoch 461/500\n",
      "32/32 [==============================] - 0s 11ms/step - loss: 0.0315 - mae: 0.1504 - val_loss: 0.0281 - val_mae: 0.1394\n",
      "Epoch 462/500\n",
      "32/32 [==============================] - 0s 14ms/step - loss: 0.0314 - mae: 0.1501 - val_loss: 0.0281 - val_mae: 0.1394\n",
      "Epoch 463/500\n",
      "32/32 [==============================] - 1s 16ms/step - loss: 0.0312 - mae: 0.1493 - val_loss: 0.0281 - val_mae: 0.1393\n",
      "Epoch 464/500\n",
      "32/32 [==============================] - 0s 13ms/step - loss: 0.0309 - mae: 0.1486 - val_loss: 0.0280 - val_mae: 0.1390\n",
      "Epoch 465/500\n",
      "32/32 [==============================] - 0s 11ms/step - loss: 0.0313 - mae: 0.1503 - val_loss: 0.0280 - val_mae: 0.1393\n",
      "Epoch 466/500\n",
      "32/32 [==============================] - 1s 16ms/step - loss: 0.0307 - mae: 0.1484 - val_loss: 0.0280 - val_mae: 0.1390\n",
      "Epoch 467/500\n",
      "32/32 [==============================] - 1s 18ms/step - loss: 0.0312 - mae: 0.1493 - val_loss: 0.0279 - val_mae: 0.1390\n",
      "Epoch 468/500\n",
      "32/32 [==============================] - 0s 11ms/step - loss: 0.0313 - mae: 0.1493 - val_loss: 0.0279 - val_mae: 0.1389\n",
      "Epoch 469/500\n",
      "32/32 [==============================] - 0s 15ms/step - loss: 0.0314 - mae: 0.1495 - val_loss: 0.0280 - val_mae: 0.1394\n",
      "Epoch 470/500\n",
      "32/32 [==============================] - 0s 14ms/step - loss: 0.0315 - mae: 0.1510 - val_loss: 0.0280 - val_mae: 0.1396\n",
      "Epoch 471/500\n",
      "32/32 [==============================] - 0s 12ms/step - loss: 0.0315 - mae: 0.1500 - val_loss: 0.0279 - val_mae: 0.1388\n",
      "Epoch 472/500\n",
      "32/32 [==============================] - 0s 12ms/step - loss: 0.0313 - mae: 0.1493 - val_loss: 0.0280 - val_mae: 0.1395\n",
      "Epoch 473/500\n",
      "32/32 [==============================] - 0s 11ms/step - loss: 0.0314 - mae: 0.1494 - val_loss: 0.0279 - val_mae: 0.1388\n",
      "Epoch 474/500\n",
      "32/32 [==============================] - 0s 11ms/step - loss: 0.0316 - mae: 0.1501 - val_loss: 0.0280 - val_mae: 0.1393\n",
      "Epoch 475/500\n",
      "32/32 [==============================] - 1s 18ms/step - loss: 0.0313 - mae: 0.1498 - val_loss: 0.0279 - val_mae: 0.1388\n",
      "Epoch 476/500\n",
      "32/32 [==============================] - 0s 13ms/step - loss: 0.0311 - mae: 0.1489 - val_loss: 0.0279 - val_mae: 0.1391\n",
      "Epoch 477/500\n",
      "32/32 [==============================] - 0s 11ms/step - loss: 0.0315 - mae: 0.1504 - val_loss: 0.0279 - val_mae: 0.1392\n",
      "Epoch 478/500\n",
      "32/32 [==============================] - 0s 13ms/step - loss: 0.0313 - mae: 0.1494 - val_loss: 0.0279 - val_mae: 0.1386\n",
      "Epoch 479/500\n",
      "32/32 [==============================] - 0s 12ms/step - loss: 0.0313 - mae: 0.1492 - val_loss: 0.0279 - val_mae: 0.1391\n",
      "Epoch 480/500\n",
      "32/32 [==============================] - 0s 12ms/step - loss: 0.0313 - mae: 0.1501 - val_loss: 0.0279 - val_mae: 0.1391\n",
      "Epoch 481/500\n",
      "32/32 [==============================] - 0s 15ms/step - loss: 0.0310 - mae: 0.1489 - val_loss: 0.0280 - val_mae: 0.1390\n",
      "Epoch 482/500\n",
      "32/32 [==============================] - 0s 13ms/step - loss: 0.0314 - mae: 0.1499 - val_loss: 0.0279 - val_mae: 0.1389\n",
      "Epoch 483/500\n",
      "32/32 [==============================] - 0s 11ms/step - loss: 0.0310 - mae: 0.1490 - val_loss: 0.0280 - val_mae: 0.1392\n",
      "Epoch 484/500\n",
      "32/32 [==============================] - 1s 18ms/step - loss: 0.0320 - mae: 0.1514 - val_loss: 0.0280 - val_mae: 0.1397\n",
      "Epoch 485/500\n",
      "32/32 [==============================] - 0s 14ms/step - loss: 0.0310 - mae: 0.1485 - val_loss: 0.0280 - val_mae: 0.1388\n",
      "Epoch 486/500\n",
      "32/32 [==============================] - 0s 11ms/step - loss: 0.0319 - mae: 0.1512 - val_loss: 0.0279 - val_mae: 0.1389\n",
      "Epoch 487/500\n",
      "32/32 [==============================] - 0s 12ms/step - loss: 0.0315 - mae: 0.1502 - val_loss: 0.0279 - val_mae: 0.1392\n",
      "Epoch 488/500\n",
      "32/32 [==============================] - 0s 12ms/step - loss: 0.0314 - mae: 0.1496 - val_loss: 0.0279 - val_mae: 0.1389\n",
      "Epoch 489/500\n",
      "32/32 [==============================] - 0s 10ms/step - loss: 0.0308 - mae: 0.1485 - val_loss: 0.0279 - val_mae: 0.1386\n",
      "Epoch 490/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32/32 [==============================] - 0s 14ms/step - loss: 0.0310 - mae: 0.1486 - val_loss: 0.0279 - val_mae: 0.1391\n",
      "Epoch 491/500\n",
      "32/32 [==============================] - 0s 12ms/step - loss: 0.0311 - mae: 0.1497 - val_loss: 0.0279 - val_mae: 0.1386\n",
      "Epoch 492/500\n",
      "32/32 [==============================] - 0s 11ms/step - loss: 0.0306 - mae: 0.1475 - val_loss: 0.0279 - val_mae: 0.1388\n",
      "Epoch 493/500\n",
      "32/32 [==============================] - 1s 20ms/step - loss: 0.0309 - mae: 0.1485 - val_loss: 0.0279 - val_mae: 0.1384\n",
      "Epoch 494/500\n",
      "32/32 [==============================] - 1s 21ms/step - loss: 0.0311 - mae: 0.1490 - val_loss: 0.0279 - val_mae: 0.1388\n",
      "Epoch 495/500\n",
      "32/32 [==============================] - 0s 12ms/step - loss: 0.0310 - mae: 0.1495 - val_loss: 0.0279 - val_mae: 0.1386\n",
      "Epoch 496/500\n",
      "32/32 [==============================] - 0s 11ms/step - loss: 0.0309 - mae: 0.1487 - val_loss: 0.0279 - val_mae: 0.1390\n",
      "Epoch 497/500\n",
      "32/32 [==============================] - 0s 12ms/step - loss: 0.0315 - mae: 0.1501 - val_loss: 0.0278 - val_mae: 0.1383\n",
      "Epoch 498/500\n",
      "32/32 [==============================] - 0s 11ms/step - loss: 0.0315 - mae: 0.1497 - val_loss: 0.0278 - val_mae: 0.1384\n",
      "Epoch 499/500\n",
      "32/32 [==============================] - 0s 11ms/step - loss: 0.0307 - mae: 0.1484 - val_loss: 0.0278 - val_mae: 0.1383\n",
      "Epoch 500/500\n",
      "32/32 [==============================] - 0s 10ms/step - loss: 0.0309 - mae: 0.1487 - val_loss: 0.0278 - val_mae: 0.1384\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(X_train, y_train, epochs=500, batch_size=32, validation_split=0.3, shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "b80580f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "loss = history.history['loss']\n",
    "val_loss = history.history['val_loss']\n",
    "\n",
    "x = np.arange(0, len(history.history['loss']))\n",
    "\n",
    "plt.plot(x, loss, 'y', label='loss')\n",
    "plt.plot(x, val_loss, 'r', label='val loss')\n",
    "plt.title('Losses')\n",
    "plt.xlabel('epochs')\n",
    "plt.ylabel('loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "72d0ae3f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 2s 8ms/step\n"
     ]
    }
   ],
   "source": [
    "y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "cc002e8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(y_test, color='red', label='real tempmean')\n",
    "plt.plot(y_pred, color='blue', label='predicted tempmean')\n",
    "plt.title('tempmean prediction')\n",
    "plt.xlabel('time')\n",
    "plt.ylabel('tempmean')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}