RNN(RF).ipynb

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  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "b1082f0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "d698be6b",
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Month</th>\n",
       "      <th>Year</th>\n",
       "      <th>RF</th>\n",
       "      <th>T max</th>\n",
       "      <th>T min</th>\n",
       "      <th>Avg_tem</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2013</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31.1</td>\n",
       "      <td>25.8</td>\n",
       "      <td>28.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>2013</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31.3</td>\n",
       "      <td>27.5</td>\n",
       "      <td>29.40</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>2013</td>\n",
       "      <td>1.6</td>\n",
       "      <td>31.3</td>\n",
       "      <td>27.3</td>\n",
       "      <td>29.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>2013</td>\n",
       "      <td>5.8</td>\n",
       "      <td>30.7</td>\n",
       "      <td>25.7</td>\n",
       "      <td>28.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>2013</td>\n",
       "      <td>0.3</td>\n",
       "      <td>30.8</td>\n",
       "      <td>24.4</td>\n",
       "      <td>27.60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Date  Month  Year   RF  T max   T min  Avg_tem\n",
       "0     1     10  2013  0.0    31.1   25.8    28.45\n",
       "1     2     10  2013  0.0    31.3   27.5    29.40\n",
       "2     3     10  2013  1.6    31.3   27.3    29.30\n",
       "3     4     10  2013  5.8    30.7   25.7    28.20\n",
       "4     5     10  2013  0.3    30.8   24.4    27.60"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = pd.read_csv('NewSl.csv')\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "a3d3ed7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "    RF\n",
       "0  0.0\n",
       "1  0.0\n",
       "2  1.6\n",
       "3  5.8\n",
       "4  0.3"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_mod1 = dataset[['RF']]\n",
    "dataset_mod1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "549ae4bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       0.0\n",
       "1       0.0\n",
       "2       1.6\n",
       "3       5.8\n",
       "4       0.3\n",
       "       ... \n",
       "360    32.9\n",
       "361     0.2\n",
       "362     1.3\n",
       "363    30.6\n",
       "364    22.5\n",
       "Name: RF, Length: 365, dtype: float64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_set = dataset_mod1.iloc[0:365,0]\n",
    "\n",
    "training_set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "4974d897",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Feature Scaling \n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "sc = MinMaxScaler(feature_range=(0,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "32849bdc",
   "metadata": {},
   "outputs": [
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       "       [1.300e+00],\n",
       "       [3.060e+01],\n",
       "       [2.250e+01]])"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_set = training_set.values.reshape(len(training_set), 1)\n",
    "training_set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "9444b93d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "       [2.18871595e-01]])"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_set_scaled = sc.fit_transform(training_set)\n",
    "training_set_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "7723185d",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = []\n",
    "y_train = []\n",
    "n_future = 5 # next 4 days temperature forecast\n",
    "n_past = 100 # Past 30 days \n",
    "for i in range(0,len(training_set_scaled)-n_past-n_future+1):\n",
    "    x_train.append(training_set_scaled[i : i + n_past , 0])     \n",
    "    y_train.append(training_set_scaled[i + n_past : i + n_past + n_future , 0 ])\n",
    "x_train , y_train = np.array(x_train), np.array(y_train)\n",
    "x_train = np.reshape(x_train, (x_train.shape[0] , x_train.shape[1], 1) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "062ba1a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.preprocessing import sequence\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout, Embedding, LSTM, Bidirectional\n",
    "from keras.datasets import imdb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "eb140f5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.layers import LSTM,Dense ,Dropout\n",
    "# Fitting RNN to training set using Keras Callbacks. Read Keras callbacks docs for more info."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "d33505cd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/500\n",
      "7/7 [==============================] - 8s 282ms/step - loss: 0.0155 - acc: 0.1731 - val_loss: 0.0182 - val_acc: 0.2264\n",
      "Epoch 2/500\n",
      "7/7 [==============================] - 0s 62ms/step - loss: 0.0145 - acc: 0.1971 - val_loss: 0.0171 - val_acc: 0.1698\n",
      "Epoch 3/500\n",
      "7/7 [==============================] - 0s 69ms/step - loss: 0.0143 - acc: 0.1923 - val_loss: 0.0165 - val_acc: 0.2453\n",
      "Epoch 4/500\n",
      "7/7 [==============================] - 0s 61ms/step - loss: 0.0143 - acc: 0.2404 - val_loss: 0.0169 - val_acc: 0.1698\n",
      "Epoch 5/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0142 - acc: 0.2596 - val_loss: 0.0170 - val_acc: 0.1698\n",
      "Epoch 6/500\n",
      "7/7 [==============================] - 0s 65ms/step - loss: 0.0144 - acc: 0.2212 - val_loss: 0.0173 - val_acc: 0.1321\n",
      "Epoch 7/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0143 - acc: 0.2115 - val_loss: 0.0167 - val_acc: 0.2264\n",
      "Epoch 8/500\n",
      "7/7 [==============================] - 0s 65ms/step - loss: 0.0144 - acc: 0.2163 - val_loss: 0.0173 - val_acc: 0.1509\n",
      "Epoch 9/500\n",
      "7/7 [==============================] - 0s 61ms/step - loss: 0.0143 - acc: 0.1394 - val_loss: 0.0169 - val_acc: 0.1698\n",
      "Epoch 10/500\n",
      "7/7 [==============================] - 0s 66ms/step - loss: 0.0143 - acc: 0.1683 - val_loss: 0.0170 - val_acc: 0.1887\n",
      "Epoch 11/500\n",
      "7/7 [==============================] - 0s 63ms/step - loss: 0.0142 - acc: 0.2788 - val_loss: 0.0172 - val_acc: 0.2075\n",
      "Epoch 12/500\n",
      "7/7 [==============================] - 0s 61ms/step - loss: 0.0143 - acc: 0.2452 - val_loss: 0.0167 - val_acc: 0.1509\n",
      "Epoch 13/500\n",
      "7/7 [==============================] - 0s 62ms/step - loss: 0.0144 - acc: 0.1538 - val_loss: 0.0169 - val_acc: 0.1509\n",
      "Epoch 14/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0142 - acc: 0.1587 - val_loss: 0.0174 - val_acc: 0.1698\n",
      "Epoch 15/500\n",
      "7/7 [==============================] - 0s 62ms/step - loss: 0.0143 - acc: 0.1923 - val_loss: 0.0172 - val_acc: 0.2075\n",
      "Epoch 16/500\n",
      "7/7 [==============================] - 0s 63ms/step - loss: 0.0142 - acc: 0.2981 - val_loss: 0.0170 - val_acc: 0.1698\n",
      "Epoch 17/500\n",
      "7/7 [==============================] - 0s 63ms/step - loss: 0.0143 - acc: 0.2548 - val_loss: 0.0170 - val_acc: 0.2453\n",
      "Epoch 18/500\n",
      "7/7 [==============================] - 0s 60ms/step - loss: 0.0143 - acc: 0.3413 - val_loss: 0.0171 - val_acc: 0.2453\n",
      "Epoch 19/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0143 - acc: 0.3173 - val_loss: 0.0172 - val_acc: 0.1509\n",
      "Epoch 20/500\n",
      "7/7 [==============================] - 0s 66ms/step - loss: 0.0142 - acc: 0.2692 - val_loss: 0.0168 - val_acc: 0.1887\n",
      "Epoch 21/500\n",
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      "Epoch 22/500\n",
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      "Epoch 25/500\n",
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      "Epoch 26/500\n",
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      "Epoch 27/500\n",
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      "Epoch 28/500\n",
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      "Epoch 30/500\n",
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      "Epoch 31/500\n",
      "7/7 [==============================] - 0s 66ms/step - loss: 0.0142 - acc: 0.2308 - val_loss: 0.0169 - val_acc: 0.1887\n",
      "Epoch 32/500\n",
      "7/7 [==============================] - 0s 67ms/step - loss: 0.0143 - acc: 0.2308 - val_loss: 0.0174 - val_acc: 0.2264\n",
      "Epoch 33/500\n",
      "7/7 [==============================] - 0s 66ms/step - loss: 0.0142 - acc: 0.2548 - val_loss: 0.0170 - val_acc: 0.2264\n",
      "Epoch 34/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0143 - acc: 0.1971 - val_loss: 0.0168 - val_acc: 0.1509\n",
      "Epoch 35/500\n",
      "7/7 [==============================] - 0s 63ms/step - loss: 0.0142 - acc: 0.1538 - val_loss: 0.0171 - val_acc: 0.1887\n",
      "Epoch 36/500\n",
      "7/7 [==============================] - 0s 65ms/step - loss: 0.0142 - acc: 0.2260 - val_loss: 0.0171 - val_acc: 0.1698\n",
      "Epoch 37/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0142 - acc: 0.1875 - val_loss: 0.0168 - val_acc: 0.1698\n",
      "Epoch 38/500\n",
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      "Epoch 39/500\n",
      "7/7 [==============================] - 0s 69ms/step - loss: 0.0142 - acc: 0.2212 - val_loss: 0.0170 - val_acc: 0.1698\n",
      "Epoch 40/500\n",
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      "Epoch 41/500\n",
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      "Epoch 42/500\n",
      "7/7 [==============================] - 0s 60ms/step - loss: 0.0142 - acc: 0.2500 - val_loss: 0.0176 - val_acc: 0.2453\n",
      "Epoch 43/500\n",
      "7/7 [==============================] - 0s 63ms/step - loss: 0.0142 - acc: 0.3413 - val_loss: 0.0170 - val_acc: 0.1887\n",
      "Epoch 44/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0143 - acc: 0.2837 - val_loss: 0.0173 - val_acc: 0.2642\n",
      "Epoch 45/500\n",
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      "Epoch 46/500\n",
      "7/7 [==============================] - 0s 62ms/step - loss: 0.0142 - acc: 0.2115 - val_loss: 0.0171 - val_acc: 0.1698\n",
      "Epoch 47/500\n",
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      "Epoch 48/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0141 - acc: 0.2308 - val_loss: 0.0170 - val_acc: 0.1698\n",
      "Epoch 49/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0143 - acc: 0.2163 - val_loss: 0.0171 - val_acc: 0.2264\n",
      "Epoch 50/500\n",
      "7/7 [==============================] - 0s 62ms/step - loss: 0.0142 - acc: 0.2163 - val_loss: 0.0177 - val_acc: 0.1887\n",
      "Epoch 51/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0141 - acc: 0.2885 - val_loss: 0.0171 - val_acc: 0.2075\n",
      "Epoch 52/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0142 - acc: 0.3029 - val_loss: 0.0171 - val_acc: 0.1887\n",
      "Epoch 53/500\n",
      "7/7 [==============================] - 0s 60ms/step - loss: 0.0142 - acc: 0.2500 - val_loss: 0.0177 - val_acc: 0.2264\n",
      "Epoch 54/500\n",
      "7/7 [==============================] - 0s 62ms/step - loss: 0.0142 - acc: 0.2500 - val_loss: 0.0174 - val_acc: 0.1887\n",
      "Epoch 55/500\n",
      "7/7 [==============================] - 0s 65ms/step - loss: 0.0142 - acc: 0.1875 - val_loss: 0.0175 - val_acc: 0.1321\n",
      "Epoch 56/500\n",
      "7/7 [==============================] - 0s 69ms/step - loss: 0.0141 - acc: 0.2115 - val_loss: 0.0173 - val_acc: 0.1698\n",
      "Epoch 57/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0140 - acc: 0.2115 - val_loss: 0.0185 - val_acc: 0.2453\n",
      "Epoch 58/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0140 - acc: 0.2933 - val_loss: 0.0174 - val_acc: 0.2075\n",
      "Epoch 59/500\n",
      "7/7 [==============================] - 0s 69ms/step - loss: 0.0140 - acc: 0.2933 - val_loss: 0.0181 - val_acc: 0.2642\n",
      "Epoch 60/500\n",
      "7/7 [==============================] - 0s 65ms/step - loss: 0.0140 - acc: 0.2500 - val_loss: 0.0174 - val_acc: 0.2642\n",
      "Epoch 61/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0139 - acc: 0.1971 - val_loss: 0.0174 - val_acc: 0.2453\n",
      "Epoch 62/500\n",
      "7/7 [==============================] - 0s 63ms/step - loss: 0.0140 - acc: 0.1779 - val_loss: 0.0184 - val_acc: 0.2642\n",
      "Epoch 63/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7/7 [==============================] - 0s 62ms/step - loss: 0.0139 - acc: 0.2933 - val_loss: 0.0176 - val_acc: 0.2075\n",
      "Epoch 64/500\n",
      "7/7 [==============================] - 0s 63ms/step - loss: 0.0138 - acc: 0.2981 - val_loss: 0.0173 - val_acc: 0.2075\n",
      "Epoch 65/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0139 - acc: 0.2356 - val_loss: 0.0176 - val_acc: 0.1887\n",
      "Epoch 66/500\n",
      "7/7 [==============================] - 0s 65ms/step - loss: 0.0139 - acc: 0.2692 - val_loss: 0.0179 - val_acc: 0.2642\n",
      "Epoch 67/500\n",
      "7/7 [==============================] - 0s 63ms/step - loss: 0.0138 - acc: 0.2019 - val_loss: 0.0179 - val_acc: 0.2642\n",
      "Epoch 68/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0138 - acc: 0.2548 - val_loss: 0.0176 - val_acc: 0.2264\n",
      "Epoch 69/500\n",
      "7/7 [==============================] - 0s 63ms/step - loss: 0.0138 - acc: 0.2692 - val_loss: 0.0178 - val_acc: 0.2642\n",
      "Epoch 70/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0138 - acc: 0.2404 - val_loss: 0.0179 - val_acc: 0.2453\n",
      "Epoch 71/500\n",
      "7/7 [==============================] - 0s 65ms/step - loss: 0.0138 - acc: 0.2404 - val_loss: 0.0182 - val_acc: 0.2642\n",
      "Epoch 72/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0138 - acc: 0.3317 - val_loss: 0.0177 - val_acc: 0.2642\n",
      "Epoch 73/500\n",
      "7/7 [==============================] - 0s 61ms/step - loss: 0.0137 - acc: 0.2644 - val_loss: 0.0186 - val_acc: 0.2264\n",
      "Epoch 74/500\n",
      "7/7 [==============================] - 0s 65ms/step - loss: 0.0138 - acc: 0.2788 - val_loss: 0.0177 - val_acc: 0.2642\n",
      "Epoch 75/500\n",
      "7/7 [==============================] - 0s 63ms/step - loss: 0.0138 - acc: 0.2115 - val_loss: 0.0183 - val_acc: 0.2453\n",
      "Epoch 76/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0137 - acc: 0.2163 - val_loss: 0.0187 - val_acc: 0.2642\n",
      "Epoch 77/500\n",
      "7/7 [==============================] - 0s 63ms/step - loss: 0.0135 - acc: 0.3173 - val_loss: 0.0181 - val_acc: 0.2453\n",
      "Epoch 78/500\n",
      "7/7 [==============================] - 0s 62ms/step - loss: 0.0137 - acc: 0.2404 - val_loss: 0.0177 - val_acc: 0.2453\n",
      "Epoch 79/500\n",
      "7/7 [==============================] - 0s 62ms/step - loss: 0.0136 - acc: 0.1971 - val_loss: 0.0186 - val_acc: 0.3019\n",
      "Epoch 80/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0137 - acc: 0.2885 - val_loss: 0.0181 - val_acc: 0.2830\n",
      "Epoch 81/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0135 - acc: 0.2644 - val_loss: 0.0194 - val_acc: 0.2453\n",
      "Epoch 82/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0136 - acc: 0.2981 - val_loss: 0.0183 - val_acc: 0.2830\n",
      "Epoch 83/500\n",
      "7/7 [==============================] - 0s 61ms/step - loss: 0.0136 - acc: 0.2644 - val_loss: 0.0176 - val_acc: 0.2642\n",
      "Epoch 84/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0137 - acc: 0.3269 - val_loss: 0.0188 - val_acc: 0.2830\n",
      "Epoch 85/500\n",
      "7/7 [==============================] - 0s 63ms/step - loss: 0.0135 - acc: 0.2596 - val_loss: 0.0180 - val_acc: 0.2453\n",
      "Epoch 86/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0135 - acc: 0.3317 - val_loss: 0.0183 - val_acc: 0.2642\n",
      "Epoch 87/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0136 - acc: 0.1635 - val_loss: 0.0174 - val_acc: 0.2453\n",
      "Epoch 88/500\n",
      "7/7 [==============================] - 0s 60ms/step - loss: 0.0136 - acc: 0.3365 - val_loss: 0.0194 - val_acc: 0.3019\n",
      "Epoch 89/500\n",
      "7/7 [==============================] - 0s 65ms/step - loss: 0.0134 - acc: 0.2163 - val_loss: 0.0180 - val_acc: 0.2264\n",
      "Epoch 90/500\n",
      "7/7 [==============================] - 0s 60ms/step - loss: 0.0133 - acc: 0.3317 - val_loss: 0.0195 - val_acc: 0.2075\n",
      "Epoch 91/500\n",
      "7/7 [==============================] - 0s 62ms/step - loss: 0.0134 - acc: 0.2212 - val_loss: 0.0193 - val_acc: 0.2642\n",
      "Epoch 92/500\n",
      "7/7 [==============================] - 0s 65ms/step - loss: 0.0134 - acc: 0.2788 - val_loss: 0.0169 - val_acc: 0.2642\n",
      "Epoch 93/500\n",
      "7/7 [==============================] - 0s 63ms/step - loss: 0.0136 - acc: 0.3125 - val_loss: 0.0168 - val_acc: 0.3019\n",
      "Epoch 94/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0138 - acc: 0.2404 - val_loss: 0.0170 - val_acc: 0.2642\n",
      "Epoch 95/500\n",
      "7/7 [==============================] - 0s 63ms/step - loss: 0.0137 - acc: 0.2308 - val_loss: 0.0181 - val_acc: 0.2264\n",
      "Epoch 96/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0135 - acc: 0.2740 - val_loss: 0.0180 - val_acc: 0.2830\n",
      "Epoch 97/500\n",
      "7/7 [==============================] - 0s 60ms/step - loss: 0.0133 - acc: 0.2885 - val_loss: 0.0193 - val_acc: 0.2642\n",
      "Epoch 98/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0130 - acc: 0.2788 - val_loss: 0.0185 - val_acc: 0.2642\n",
      "Epoch 99/500\n",
      "7/7 [==============================] - 0s 62ms/step - loss: 0.0129 - acc: 0.2596 - val_loss: 0.0178 - val_acc: 0.2642\n",
      "Epoch 100/500\n",
      "7/7 [==============================] - 0s 61ms/step - loss: 0.0129 - acc: 0.3173 - val_loss: 0.0174 - val_acc: 0.2642\n",
      "Epoch 101/500\n",
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      "Epoch 102/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0131 - acc: 0.2692 - val_loss: 0.0173 - val_acc: 0.2264\n",
      "Epoch 103/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0134 - acc: 0.2163 - val_loss: 0.0176 - val_acc: 0.2264\n",
      "Epoch 104/500\n",
      "7/7 [==============================] - 0s 61ms/step - loss: 0.0131 - acc: 0.3462 - val_loss: 0.0180 - val_acc: 0.3019\n",
      "Epoch 105/500\n",
      "7/7 [==============================] - 0s 63ms/step - loss: 0.0127 - acc: 0.3173 - val_loss: 0.0177 - val_acc: 0.3019\n",
      "Epoch 106/500\n",
      "7/7 [==============================] - 0s 61ms/step - loss: 0.0124 - acc: 0.2404 - val_loss: 0.0176 - val_acc: 0.2830\n",
      "Epoch 107/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0124 - acc: 0.3173 - val_loss: 0.0183 - val_acc: 0.2264\n",
      "Epoch 108/500\n",
      "7/7 [==============================] - 0s 61ms/step - loss: 0.0124 - acc: 0.2644 - val_loss: 0.0180 - val_acc: 0.3208\n",
      "Epoch 109/500\n",
      "7/7 [==============================] - 0s 62ms/step - loss: 0.0126 - acc: 0.2644 - val_loss: 0.0191 - val_acc: 0.1887\n",
      "Epoch 110/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0126 - acc: 0.2212 - val_loss: 0.0197 - val_acc: 0.2642\n",
      "Epoch 111/500\n",
      "7/7 [==============================] - 0s 61ms/step - loss: 0.0121 - acc: 0.2740 - val_loss: 0.0166 - val_acc: 0.2453\n",
      "Epoch 112/500\n",
      "7/7 [==============================] - 0s 63ms/step - loss: 0.0122 - acc: 0.2596 - val_loss: 0.0176 - val_acc: 0.2264\n",
      "Epoch 113/500\n",
      "7/7 [==============================] - 0s 63ms/step - loss: 0.0118 - acc: 0.3173 - val_loss: 0.0201 - val_acc: 0.1887\n",
      "Epoch 114/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0139 - acc: 0.2404 - val_loss: 0.0182 - val_acc: 0.2642\n",
      "Epoch 115/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0141 - acc: 0.3077 - val_loss: 0.0180 - val_acc: 0.1887\n",
      "Epoch 116/500\n",
      "7/7 [==============================] - 0s 62ms/step - loss: 0.0136 - acc: 0.3029 - val_loss: 0.0177 - val_acc: 0.1887\n",
      "Epoch 117/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0135 - acc: 0.2596 - val_loss: 0.0176 - val_acc: 0.2264\n",
      "Epoch 118/500\n",
      "7/7 [==============================] - 0s 62ms/step - loss: 0.0130 - acc: 0.2356 - val_loss: 0.0177 - val_acc: 0.2453\n",
      "Epoch 119/500\n",
      "7/7 [==============================] - 0s 62ms/step - loss: 0.0124 - acc: 0.2837 - val_loss: 0.0189 - val_acc: 0.2264\n",
      "Epoch 120/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0127 - acc: 0.3269 - val_loss: 0.0205 - val_acc: 0.2264\n",
      "Epoch 121/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0125 - acc: 0.2885 - val_loss: 0.0184 - val_acc: 0.1887\n",
      "Epoch 122/500\n",
      "7/7 [==============================] - 0s 65ms/step - loss: 0.0127 - acc: 0.2837 - val_loss: 0.0163 - val_acc: 0.2830\n",
      "Epoch 123/500\n",
      "7/7 [==============================] - 0s 63ms/step - loss: 0.0120 - acc: 0.2933 - val_loss: 0.0170 - val_acc: 0.2453\n",
      "Epoch 124/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0120 - acc: 0.2740 - val_loss: 0.0170 - val_acc: 0.2830\n",
      "Epoch 125/500\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "7/7 [==============================] - 0s 62ms/step - loss: 0.0120 - acc: 0.2500 - val_loss: 0.0160 - val_acc: 0.2830\n",
      "Epoch 126/500\n",
      "7/7 [==============================] - 0s 63ms/step - loss: 0.0117 - acc: 0.2308 - val_loss: 0.0184 - val_acc: 0.2075\n",
      "Epoch 127/500\n",
      "7/7 [==============================] - 0s 65ms/step - loss: 0.0117 - acc: 0.2596 - val_loss: 0.0188 - val_acc: 0.2075\n",
      "Epoch 128/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0112 - acc: 0.3221 - val_loss: 0.0207 - val_acc: 0.1887\n",
      "Epoch 129/500\n",
      "7/7 [==============================] - 0s 63ms/step - loss: 0.0112 - acc: 0.2452 - val_loss: 0.0190 - val_acc: 0.3396\n",
      "Epoch 130/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0126 - acc: 0.2404 - val_loss: 0.0162 - val_acc: 0.2642\n",
      "Epoch 131/500\n",
      "7/7 [==============================] - 0s 63ms/step - loss: 0.0127 - acc: 0.3317 - val_loss: 0.0161 - val_acc: 0.3019\n",
      "Epoch 132/500\n",
      "7/7 [==============================] - 0s 63ms/step - loss: 0.0120 - acc: 0.2452 - val_loss: 0.0154 - val_acc: 0.3396\n",
      "Epoch 133/500\n",
      "7/7 [==============================] - 0s 62ms/step - loss: 0.0125 - acc: 0.2596 - val_loss: 0.0156 - val_acc: 0.2264\n",
      "Epoch 134/500\n",
      "7/7 [==============================] - 0s 65ms/step - loss: 0.0128 - acc: 0.2212 - val_loss: 0.0157 - val_acc: 0.2642\n",
      "Epoch 135/500\n",
      "7/7 [==============================] - 0s 63ms/step - loss: 0.0117 - acc: 0.2548 - val_loss: 0.0164 - val_acc: 0.2642\n",
      "Epoch 136/500\n",
      "7/7 [==============================] - 0s 63ms/step - loss: 0.0114 - acc: 0.2308 - val_loss: 0.0161 - val_acc: 0.3208\n",
      "Epoch 137/500\n",
      "7/7 [==============================] - 0s 63ms/step - loss: 0.0120 - acc: 0.2548 - val_loss: 0.0168 - val_acc: 0.2830\n",
      "Epoch 138/500\n",
      "7/7 [==============================] - 0s 61ms/step - loss: 0.0122 - acc: 0.2548 - val_loss: 0.0155 - val_acc: 0.2830\n",
      "Epoch 139/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0119 - acc: 0.2933 - val_loss: 0.0158 - val_acc: 0.3019\n",
      "Epoch 140/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0109 - acc: 0.2644 - val_loss: 0.0162 - val_acc: 0.3208\n",
      "Epoch 141/500\n",
      "7/7 [==============================] - 0s 61ms/step - loss: 0.0115 - acc: 0.2837 - val_loss: 0.0173 - val_acc: 0.2642\n",
      "Epoch 142/500\n",
      "7/7 [==============================] - 0s 63ms/step - loss: 0.0112 - acc: 0.2308 - val_loss: 0.0154 - val_acc: 0.3585\n",
      "Epoch 143/500\n",
      "7/7 [==============================] - 0s 65ms/step - loss: 0.0105 - acc: 0.2981 - val_loss: 0.0166 - val_acc: 0.3585\n",
      "Epoch 144/500\n",
      "7/7 [==============================] - 0s 61ms/step - loss: 0.0112 - acc: 0.2740 - val_loss: 0.0166 - val_acc: 0.2830\n",
      "Epoch 145/500\n",
      "7/7 [==============================] - 0s 65ms/step - loss: 0.0110 - acc: 0.2981 - val_loss: 0.0156 - val_acc: 0.3774\n",
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      "Epoch 157/500\n",
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      "Epoch 160/500\n",
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      "Epoch 161/500\n",
      "7/7 [==============================] - 0s 63ms/step - loss: 0.0108 - acc: 0.2933 - val_loss: 0.0196 - val_acc: 0.2075\n",
      "Epoch 162/500\n",
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      "Epoch 166/500\n",
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      "Epoch 168/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0096 - acc: 0.2837 - val_loss: 0.0192 - val_acc: 0.2830\n",
      "Epoch 169/500\n",
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      "Epoch 170/500\n",
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      "Epoch 172/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0093 - acc: 0.3654 - val_loss: 0.0208 - val_acc: 0.2830\n",
      "Epoch 173/500\n",
      "7/7 [==============================] - 0s 63ms/step - loss: 0.0109 - acc: 0.2740 - val_loss: 0.0192 - val_acc: 0.2264\n",
      "Epoch 174/500\n",
      "7/7 [==============================] - 0s 63ms/step - loss: 0.0118 - acc: 0.2163 - val_loss: 0.0181 - val_acc: 0.1509\n",
      "Epoch 175/500\n",
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      "Epoch 176/500\n",
      "7/7 [==============================] - 0s 62ms/step - loss: 0.0117 - acc: 0.2981 - val_loss: 0.0166 - val_acc: 0.2830\n",
      "Epoch 177/500\n",
      "7/7 [==============================] - 0s 63ms/step - loss: 0.0108 - acc: 0.2452 - val_loss: 0.0189 - val_acc: 0.2075\n",
      "Epoch 178/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0104 - acc: 0.2644 - val_loss: 0.0192 - val_acc: 0.2453\n",
      "Epoch 179/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0110 - acc: 0.3221 - val_loss: 0.0170 - val_acc: 0.2830\n",
      "Epoch 180/500\n",
      "7/7 [==============================] - 0s 62ms/step - loss: 0.0096 - acc: 0.3365 - val_loss: 0.0167 - val_acc: 0.3019\n",
      "Epoch 181/500\n",
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      "Epoch 182/500\n",
      "7/7 [==============================] - 0s 61ms/step - loss: 0.0139 - acc: 0.2644 - val_loss: 0.0172 - val_acc: 0.1509\n",
      "Epoch 183/500\n",
      "7/7 [==============================] - 0s 65ms/step - loss: 0.0131 - acc: 0.2308 - val_loss: 0.0170 - val_acc: 0.3208\n",
      "Epoch 184/500\n",
      "7/7 [==============================] - 0s 62ms/step - loss: 0.0132 - acc: 0.2019 - val_loss: 0.0168 - val_acc: 0.2264\n",
      "Epoch 185/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0130 - acc: 0.2356 - val_loss: 0.0178 - val_acc: 0.2453\n",
      "Epoch 186/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0124 - acc: 0.2452 - val_loss: 0.0195 - val_acc: 0.2453\n",
      "Epoch 187/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7/7 [==============================] - 0s 63ms/step - loss: 0.0122 - acc: 0.1875 - val_loss: 0.0195 - val_acc: 0.2453\n",
      "Epoch 188/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0123 - acc: 0.2404 - val_loss: 0.0191 - val_acc: 0.2453\n",
      "Epoch 189/500\n",
      "7/7 [==============================] - 0s 61ms/step - loss: 0.0114 - acc: 0.2548 - val_loss: 0.0192 - val_acc: 0.2264\n",
      "Epoch 190/500\n",
      "7/7 [==============================] - 0s 63ms/step - loss: 0.0108 - acc: 0.3317 - val_loss: 0.0208 - val_acc: 0.2075\n",
      "Epoch 191/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0098 - acc: 0.3125 - val_loss: 0.0231 - val_acc: 0.1887\n",
      "Epoch 192/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0099 - acc: 0.2740 - val_loss: 0.0224 - val_acc: 0.2453\n",
      "Epoch 193/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0094 - acc: 0.3029 - val_loss: 0.0203 - val_acc: 0.2264\n",
      "Epoch 194/500\n",
      "7/7 [==============================] - 0s 63ms/step - loss: 0.0090 - acc: 0.3654 - val_loss: 0.0197 - val_acc: 0.3208\n",
      "Epoch 195/500\n",
      "7/7 [==============================] - 0s 65ms/step - loss: 0.0097 - acc: 0.3173 - val_loss: 0.0182 - val_acc: 0.2453\n",
      "Epoch 196/500\n",
      "7/7 [==============================] - 0s 63ms/step - loss: 0.0100 - acc: 0.2933 - val_loss: 0.0185 - val_acc: 0.3208\n",
      "Epoch 197/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0089 - acc: 0.2981 - val_loss: 0.0209 - val_acc: 0.3396\n",
      "Epoch 198/500\n",
      "7/7 [==============================] - 0s 63ms/step - loss: 0.0088 - acc: 0.3077 - val_loss: 0.0209 - val_acc: 0.2075\n",
      "Epoch 199/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0089 - acc: 0.2981 - val_loss: 0.0195 - val_acc: 0.2075\n",
      "Epoch 200/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0085 - acc: 0.3269 - val_loss: 0.0215 - val_acc: 0.2830\n",
      "Epoch 201/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0093 - acc: 0.3462 - val_loss: 0.0188 - val_acc: 0.3019\n",
      "Epoch 202/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0092 - acc: 0.2981 - val_loss: 0.0217 - val_acc: 0.2453\n",
      "Epoch 203/500\n",
      "7/7 [==============================] - 0s 61ms/step - loss: 0.0089 - acc: 0.3125 - val_loss: 0.0201 - val_acc: 0.2642\n",
      "Epoch 204/500\n",
      "7/7 [==============================] - 0s 61ms/step - loss: 0.0093 - acc: 0.2981 - val_loss: 0.0210 - val_acc: 0.2830\n",
      "Epoch 205/500\n",
      "7/7 [==============================] - 0s 63ms/step - loss: 0.0085 - acc: 0.3606 - val_loss: 0.0213 - val_acc: 0.1698\n",
      "Epoch 206/500\n",
      "7/7 [==============================] - 0s 62ms/step - loss: 0.0097 - acc: 0.2596 - val_loss: 0.0177 - val_acc: 0.1887\n",
      "Epoch 207/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0103 - acc: 0.2981 - val_loss: 0.0171 - val_acc: 0.2453\n",
      "Epoch 208/500\n",
      "7/7 [==============================] - 0s 65ms/step - loss: 0.0097 - acc: 0.3269 - val_loss: 0.0191 - val_acc: 0.2830\n",
      "Epoch 209/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0089 - acc: 0.2500 - val_loss: 0.0229 - val_acc: 0.3208\n",
      "Epoch 210/500\n",
      "7/7 [==============================] - 0s 65ms/step - loss: 0.0088 - acc: 0.3558 - val_loss: 0.0182 - val_acc: 0.3208\n",
      "Epoch 211/500\n",
      "7/7 [==============================] - 0s 62ms/step - loss: 0.0091 - acc: 0.3558 - val_loss: 0.0184 - val_acc: 0.2075\n",
      "Epoch 212/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0089 - acc: 0.3654 - val_loss: 0.0184 - val_acc: 0.2264\n",
      "Epoch 213/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0086 - acc: 0.3413 - val_loss: 0.0188 - val_acc: 0.1887\n",
      "Epoch 214/500\n",
      "7/7 [==============================] - 0s 61ms/step - loss: 0.0087 - acc: 0.2692 - val_loss: 0.0180 - val_acc: 0.2075\n",
      "Epoch 215/500\n",
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      "Epoch 216/500\n",
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      "Epoch 217/500\n",
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      "Epoch 218/500\n",
      "7/7 [==============================] - 0s 61ms/step - loss: 0.0076 - acc: 0.3750 - val_loss: 0.0196 - val_acc: 0.3019\n",
      "Epoch 219/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0085 - acc: 0.3077 - val_loss: 0.0216 - val_acc: 0.3019\n",
      "Epoch 220/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0081 - acc: 0.3221 - val_loss: 0.0183 - val_acc: 0.2264\n",
      "Epoch 221/500\n",
      "7/7 [==============================] - 0s 63ms/step - loss: 0.0125 - acc: 0.2644 - val_loss: 0.0175 - val_acc: 0.2453\n",
      "Epoch 222/500\n",
      "7/7 [==============================] - 0s 63ms/step - loss: 0.0112 - acc: 0.2644 - val_loss: 0.0177 - val_acc: 0.1887\n",
      "Epoch 223/500\n",
      "7/7 [==============================] - 0s 63ms/step - loss: 0.0122 - acc: 0.2692 - val_loss: 0.0173 - val_acc: 0.1509\n",
      "Epoch 224/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0115 - acc: 0.2788 - val_loss: 0.0167 - val_acc: 0.2453\n",
      "Epoch 225/500\n",
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      "Epoch 226/500\n",
      "7/7 [==============================] - 0s 62ms/step - loss: 0.0094 - acc: 0.2981 - val_loss: 0.0187 - val_acc: 0.2642\n",
      "Epoch 227/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0087 - acc: 0.3269 - val_loss: 0.0181 - val_acc: 0.2453\n",
      "Epoch 228/500\n",
      "7/7 [==============================] - 0s 65ms/step - loss: 0.0079 - acc: 0.2740 - val_loss: 0.0196 - val_acc: 0.2264\n",
      "Epoch 229/500\n",
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      "Epoch 230/500\n",
      "7/7 [==============================] - 0s 66ms/step - loss: 0.0076 - acc: 0.3510 - val_loss: 0.0199 - val_acc: 0.3019\n",
      "Epoch 231/500\n",
      "7/7 [==============================] - 0s 63ms/step - loss: 0.0079 - acc: 0.3606 - val_loss: 0.0212 - val_acc: 0.2830\n",
      "Epoch 232/500\n",
      "7/7 [==============================] - 0s 63ms/step - loss: 0.0071 - acc: 0.4279 - val_loss: 0.0225 - val_acc: 0.2264\n",
      "Epoch 233/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0087 - acc: 0.3558 - val_loss: 0.0188 - val_acc: 0.3019\n",
      "Epoch 234/500\n",
      "7/7 [==============================] - 0s 63ms/step - loss: 0.0100 - acc: 0.3173 - val_loss: 0.0187 - val_acc: 0.2264\n",
      "Epoch 235/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0078 - acc: 0.3846 - val_loss: 0.0210 - val_acc: 0.2264\n",
      "Epoch 236/500\n",
      "7/7 [==============================] - 0s 62ms/step - loss: 0.0076 - acc: 0.3462 - val_loss: 0.0216 - val_acc: 0.1887\n",
      "Epoch 237/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0076 - acc: 0.3990 - val_loss: 0.0214 - val_acc: 0.2642\n",
      "Epoch 238/500\n",
      "7/7 [==============================] - 0s 65ms/step - loss: 0.0077 - acc: 0.3894 - val_loss: 0.0212 - val_acc: 0.2453\n",
      "Epoch 239/500\n",
      "7/7 [==============================] - 0s 63ms/step - loss: 0.0072 - acc: 0.4135 - val_loss: 0.0227 - val_acc: 0.2642\n",
      "Epoch 240/500\n",
      "7/7 [==============================] - 0s 63ms/step - loss: 0.0086 - acc: 0.3558 - val_loss: 0.0214 - val_acc: 0.2075\n",
      "Epoch 241/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0076 - acc: 0.3510 - val_loss: 0.0226 - val_acc: 0.2075\n",
      "Epoch 242/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0071 - acc: 0.4087 - val_loss: 0.0231 - val_acc: 0.2642\n",
      "Epoch 243/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0070 - acc: 0.3750 - val_loss: 0.0231 - val_acc: 0.1321\n",
      "Epoch 244/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0091 - acc: 0.3173 - val_loss: 0.0214 - val_acc: 0.2075\n",
      "Epoch 245/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0075 - acc: 0.3846 - val_loss: 0.0231 - val_acc: 0.2264\n",
      "Epoch 246/500\n",
      "7/7 [==============================] - 0s 63ms/step - loss: 0.0078 - acc: 0.3702 - val_loss: 0.0222 - val_acc: 0.1887\n",
      "Epoch 247/500\n",
      "7/7 [==============================] - 0s 63ms/step - loss: 0.0073 - acc: 0.3413 - val_loss: 0.0225 - val_acc: 0.2642\n",
      "Epoch 248/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0079 - acc: 0.3317 - val_loss: 0.0242 - val_acc: 0.1698\n",
      "Epoch 249/500\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "7/7 [==============================] - 0s 63ms/step - loss: 0.0071 - acc: 0.3894 - val_loss: 0.0206 - val_acc: 0.2264\n",
      "Epoch 250/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0076 - acc: 0.3750 - val_loss: 0.0198 - val_acc: 0.2075\n",
      "Epoch 251/500\n",
      "7/7 [==============================] - 0s 63ms/step - loss: 0.0081 - acc: 0.3221 - val_loss: 0.0184 - val_acc: 0.2453\n",
      "Epoch 252/500\n",
      "7/7 [==============================] - 0s 63ms/step - loss: 0.0098 - acc: 0.2788 - val_loss: 0.0186 - val_acc: 0.1698\n",
      "Epoch 253/500\n",
      "7/7 [==============================] - 0s 61ms/step - loss: 0.0089 - acc: 0.3317 - val_loss: 0.0189 - val_acc: 0.2264\n",
      "Epoch 254/500\n",
      "7/7 [==============================] - 0s 62ms/step - loss: 0.0086 - acc: 0.3173 - val_loss: 0.0197 - val_acc: 0.1887\n",
      "Epoch 255/500\n",
      "7/7 [==============================] - 0s 65ms/step - loss: 0.0076 - acc: 0.3462 - val_loss: 0.0192 - val_acc: 0.2075\n",
      "Epoch 256/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0073 - acc: 0.3846 - val_loss: 0.0186 - val_acc: 0.2264\n",
      "Epoch 257/500\n",
      "7/7 [==============================] - 0s 65ms/step - loss: 0.0072 - acc: 0.3173 - val_loss: 0.0219 - val_acc: 0.2453\n",
      "Epoch 258/500\n",
      "7/7 [==============================] - 0s 65ms/step - loss: 0.0076 - acc: 0.3077 - val_loss: 0.0193 - val_acc: 0.3019\n",
      "Epoch 259/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0079 - acc: 0.3221 - val_loss: 0.0225 - val_acc: 0.1509\n",
      "Epoch 260/500\n",
      "7/7 [==============================] - 0s 65ms/step - loss: 0.0071 - acc: 0.3558 - val_loss: 0.0228 - val_acc: 0.2075\n",
      "Epoch 261/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0074 - acc: 0.3894 - val_loss: 0.0233 - val_acc: 0.2642\n",
      "Epoch 262/500\n",
      "7/7 [==============================] - 0s 62ms/step - loss: 0.0072 - acc: 0.3702 - val_loss: 0.0221 - val_acc: 0.2453\n",
      "Epoch 263/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0085 - acc: 0.3125 - val_loss: 0.0192 - val_acc: 0.2830\n",
      "Epoch 264/500\n",
      "7/7 [==============================] - 0s 63ms/step - loss: 0.0109 - acc: 0.3269 - val_loss: 0.0185 - val_acc: 0.3019\n",
      "Epoch 265/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0089 - acc: 0.3221 - val_loss: 0.0189 - val_acc: 0.3019\n",
      "Epoch 266/500\n",
      "7/7 [==============================] - 0s 63ms/step - loss: 0.0085 - acc: 0.3269 - val_loss: 0.0207 - val_acc: 0.2264\n",
      "Epoch 267/500\n",
      "7/7 [==============================] - 0s 61ms/step - loss: 0.0076 - acc: 0.3269 - val_loss: 0.0201 - val_acc: 0.2453\n",
      "Epoch 268/500\n",
      "7/7 [==============================] - 0s 62ms/step - loss: 0.0064 - acc: 0.3365 - val_loss: 0.0198 - val_acc: 0.2075\n",
      "Epoch 269/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0059 - acc: 0.3510 - val_loss: 0.0219 - val_acc: 0.2075\n",
      "Epoch 270/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0058 - acc: 0.3221 - val_loss: 0.0233 - val_acc: 0.2075\n",
      "Epoch 271/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0083 - acc: 0.3846 - val_loss: 0.0220 - val_acc: 0.2264\n",
      "Epoch 272/500\n",
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      "Epoch 273/500\n",
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      "Epoch 274/500\n",
      "7/7 [==============================] - 0s 63ms/step - loss: 0.0091 - acc: 0.3077 - val_loss: 0.0187 - val_acc: 0.1698\n",
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      "Epoch 276/500\n",
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      "Epoch 277/500\n",
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      "Epoch 278/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0072 - acc: 0.3654 - val_loss: 0.0207 - val_acc: 0.2075\n",
      "Epoch 279/500\n",
      "7/7 [==============================] - 0s 63ms/step - loss: 0.0094 - acc: 0.3077 - val_loss: 0.0250 - val_acc: 0.2453\n",
      "Epoch 280/500\n",
      "7/7 [==============================] - 0s 65ms/step - loss: 0.0121 - acc: 0.2452 - val_loss: 0.0226 - val_acc: 0.1698\n",
      "Epoch 281/500\n",
      "7/7 [==============================] - 0s 66ms/step - loss: 0.0128 - acc: 0.2837 - val_loss: 0.0249 - val_acc: 0.1321\n",
      "Epoch 282/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0112 - acc: 0.3269 - val_loss: 0.0289 - val_acc: 0.1509\n",
      "Epoch 283/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0108 - acc: 0.4087 - val_loss: 0.0268 - val_acc: 0.1887\n",
      "Epoch 284/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0112 - acc: 0.3077 - val_loss: 0.0233 - val_acc: 0.2642\n",
      "Epoch 285/500\n",
      "7/7 [==============================] - 0s 63ms/step - loss: 0.0092 - acc: 0.2740 - val_loss: 0.0209 - val_acc: 0.2453\n",
      "Epoch 286/500\n",
      "7/7 [==============================] - 0s 61ms/step - loss: 0.0074 - acc: 0.3173 - val_loss: 0.0198 - val_acc: 0.1887\n",
      "Epoch 287/500\n",
      "7/7 [==============================] - 0s 62ms/step - loss: 0.0081 - acc: 0.3077 - val_loss: 0.0210 - val_acc: 0.2075\n",
      "Epoch 288/500\n",
      "7/7 [==============================] - 0s 66ms/step - loss: 0.0068 - acc: 0.3365 - val_loss: 0.0219 - val_acc: 0.2642\n",
      "Epoch 289/500\n",
      "7/7 [==============================] - 0s 65ms/step - loss: 0.0074 - acc: 0.3029 - val_loss: 0.0202 - val_acc: 0.1887\n",
      "Epoch 290/500\n",
      "7/7 [==============================] - 0s 66ms/step - loss: 0.0071 - acc: 0.2981 - val_loss: 0.0202 - val_acc: 0.2264\n",
      "Epoch 291/500\n",
      "7/7 [==============================] - 0s 63ms/step - loss: 0.0074 - acc: 0.2692 - val_loss: 0.0219 - val_acc: 0.2075\n",
      "Epoch 292/500\n",
      "7/7 [==============================] - 0s 63ms/step - loss: 0.0082 - acc: 0.3510 - val_loss: 0.0200 - val_acc: 0.2830\n",
      "Epoch 293/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0073 - acc: 0.3029 - val_loss: 0.0204 - val_acc: 0.2075\n",
      "Epoch 294/500\n",
      "7/7 [==============================] - 0s 63ms/step - loss: 0.0069 - acc: 0.3462 - val_loss: 0.0212 - val_acc: 0.2453\n",
      "Epoch 295/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0066 - acc: 0.3558 - val_loss: 0.0239 - val_acc: 0.2453\n",
      "Epoch 296/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0099 - acc: 0.3125 - val_loss: 0.0228 - val_acc: 0.1887\n",
      "Epoch 297/500\n",
      "7/7 [==============================] - 0s 63ms/step - loss: 0.0096 - acc: 0.3125 - val_loss: 0.0202 - val_acc: 0.2264\n",
      "Epoch 298/500\n",
      "7/7 [==============================] - 0s 66ms/step - loss: 0.0107 - acc: 0.3077 - val_loss: 0.0191 - val_acc: 0.2075\n",
      "Epoch 299/500\n",
      "7/7 [==============================] - 0s 62ms/step - loss: 0.0090 - acc: 0.2788 - val_loss: 0.0196 - val_acc: 0.1698\n",
      "Epoch 300/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0071 - acc: 0.3462 - val_loss: 0.0215 - val_acc: 0.2453\n",
      "Epoch 301/500\n",
      "7/7 [==============================] - 0s 65ms/step - loss: 0.0064 - acc: 0.4183 - val_loss: 0.0220 - val_acc: 0.2075\n",
      "Epoch 302/500\n",
      "7/7 [==============================] - 0s 61ms/step - loss: 0.0065 - acc: 0.3750 - val_loss: 0.0226 - val_acc: 0.2075\n",
      "Epoch 303/500\n",
      "7/7 [==============================] - 0s 65ms/step - loss: 0.0074 - acc: 0.3317 - val_loss: 0.0214 - val_acc: 0.1321\n",
      "Epoch 304/500\n",
      "7/7 [==============================] - 0s 63ms/step - loss: 0.0069 - acc: 0.3413 - val_loss: 0.0206 - val_acc: 0.1698\n",
      "Epoch 305/500\n",
      "7/7 [==============================] - 0s 65ms/step - loss: 0.0060 - acc: 0.4327 - val_loss: 0.0247 - val_acc: 0.2264\n",
      "Epoch 306/500\n",
      "7/7 [==============================] - 0s 63ms/step - loss: 0.0071 - acc: 0.3798 - val_loss: 0.0230 - val_acc: 0.2075\n",
      "Epoch 307/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0089 - acc: 0.2885 - val_loss: 0.0226 - val_acc: 0.1887\n",
      "Epoch 308/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0088 - acc: 0.2981 - val_loss: 0.0208 - val_acc: 0.1887\n",
      "Epoch 309/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0102 - acc: 0.2837 - val_loss: 0.0184 - val_acc: 0.2264\n",
      "Epoch 310/500\n",
      "7/7 [==============================] - 0s 62ms/step - loss: 0.0110 - acc: 0.3173 - val_loss: 0.0181 - val_acc: 0.2264\n",
      "Epoch 311/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7/7 [==============================] - 0s 65ms/step - loss: 0.0093 - acc: 0.3558 - val_loss: 0.0181 - val_acc: 0.2264\n",
      "Epoch 312/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0081 - acc: 0.4038 - val_loss: 0.0202 - val_acc: 0.2830\n",
      "Epoch 313/500\n",
      "7/7 [==============================] - 0s 65ms/step - loss: 0.0070 - acc: 0.3317 - val_loss: 0.0207 - val_acc: 0.3019\n",
      "Epoch 314/500\n",
      "7/7 [==============================] - 0s 61ms/step - loss: 0.0066 - acc: 0.3317 - val_loss: 0.0219 - val_acc: 0.2264\n",
      "Epoch 315/500\n",
      "7/7 [==============================] - 0s 63ms/step - loss: 0.0068 - acc: 0.3029 - val_loss: 0.0217 - val_acc: 0.3019\n",
      "Epoch 316/500\n",
      "7/7 [==============================] - 0s 63ms/step - loss: 0.0085 - acc: 0.3413 - val_loss: 0.0194 - val_acc: 0.3585\n",
      "Epoch 317/500\n",
      "7/7 [==============================] - 0s 63ms/step - loss: 0.0077 - acc: 0.3365 - val_loss: 0.0203 - val_acc: 0.2453\n",
      "Epoch 318/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0067 - acc: 0.3894 - val_loss: 0.0235 - val_acc: 0.1887\n",
      "Epoch 319/500\n",
      "7/7 [==============================] - 0s 63ms/step - loss: 0.0066 - acc: 0.3317 - val_loss: 0.0226 - val_acc: 0.2075\n",
      "Epoch 320/500\n",
      "7/7 [==============================] - 0s 63ms/step - loss: 0.0060 - acc: 0.3510 - val_loss: 0.0223 - val_acc: 0.2075\n",
      "Epoch 321/500\n",
      "7/7 [==============================] - 0s 65ms/step - loss: 0.0051 - acc: 0.3702 - val_loss: 0.0237 - val_acc: 0.1887\n",
      "Epoch 322/500\n",
      "7/7 [==============================] - 0s 68ms/step - loss: 0.0060 - acc: 0.3221 - val_loss: 0.0217 - val_acc: 0.1887\n",
      "Epoch 323/500\n",
      "7/7 [==============================] - 0s 71ms/step - loss: 0.0054 - acc: 0.3510 - val_loss: 0.0237 - val_acc: 0.2075\n",
      "Epoch 324/500\n",
      "7/7 [==============================] - 0s 67ms/step - loss: 0.0071 - acc: 0.3365 - val_loss: 0.0246 - val_acc: 0.2453\n",
      "Epoch 325/500\n",
      "7/7 [==============================] - 0s 67ms/step - loss: 0.0071 - acc: 0.3510 - val_loss: 0.0242 - val_acc: 0.2264\n",
      "Epoch 326/500\n",
      "7/7 [==============================] - 0s 70ms/step - loss: 0.0070 - acc: 0.3125 - val_loss: 0.0212 - val_acc: 0.2642\n",
      "Epoch 327/500\n",
      "7/7 [==============================] - 0s 67ms/step - loss: 0.0079 - acc: 0.2981 - val_loss: 0.0233 - val_acc: 0.2453\n",
      "Epoch 328/500\n",
      "7/7 [==============================] - 0s 70ms/step - loss: 0.0062 - acc: 0.3558 - val_loss: 0.0254 - val_acc: 0.1887\n",
      "Epoch 329/500\n",
      "7/7 [==============================] - 0s 67ms/step - loss: 0.0060 - acc: 0.3606 - val_loss: 0.0253 - val_acc: 0.1887\n",
      "Epoch 330/500\n",
      "7/7 [==============================] - 0s 67ms/step - loss: 0.0060 - acc: 0.3077 - val_loss: 0.0260 - val_acc: 0.2075\n",
      "Epoch 331/500\n",
      "7/7 [==============================] - 0s 66ms/step - loss: 0.0063 - acc: 0.3606 - val_loss: 0.0244 - val_acc: 0.2264\n",
      "Epoch 332/500\n",
      "7/7 [==============================] - 0s 71ms/step - loss: 0.0070 - acc: 0.3221 - val_loss: 0.0224 - val_acc: 0.2642\n",
      "Epoch 333/500\n",
      "7/7 [==============================] - 0s 70ms/step - loss: 0.0070 - acc: 0.2885 - val_loss: 0.0234 - val_acc: 0.2075\n",
      "Epoch 334/500\n",
      "7/7 [==============================] - 0s 68ms/step - loss: 0.0053 - acc: 0.3846 - val_loss: 0.0248 - val_acc: 0.2264\n",
      "Epoch 335/500\n",
      "7/7 [==============================] - 0s 69ms/step - loss: 0.0062 - acc: 0.3942 - val_loss: 0.0269 - val_acc: 0.1887\n",
      "Epoch 336/500\n",
      "7/7 [==============================] - 0s 69ms/step - loss: 0.0053 - acc: 0.3846 - val_loss: 0.0245 - val_acc: 0.2453\n",
      "Epoch 337/500\n",
      "7/7 [==============================] - 0s 69ms/step - loss: 0.0077 - acc: 0.2837 - val_loss: 0.0234 - val_acc: 0.1887\n",
      "Epoch 338/500\n",
      "7/7 [==============================] - 0s 69ms/step - loss: 0.0058 - acc: 0.3221 - val_loss: 0.0254 - val_acc: 0.2075\n",
      "Epoch 339/500\n",
      "7/7 [==============================] - 0s 62ms/step - loss: 0.0055 - acc: 0.3654 - val_loss: 0.0256 - val_acc: 0.2075\n",
      "Epoch 340/500\n",
      "7/7 [==============================] - 0s 73ms/step - loss: 0.0054 - acc: 0.3750 - val_loss: 0.0274 - val_acc: 0.2075\n",
      "Epoch 341/500\n",
      "7/7 [==============================] - 0s 66ms/step - loss: 0.0059 - acc: 0.3413 - val_loss: 0.0250 - val_acc: 0.2264\n",
      "Epoch 342/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0080 - acc: 0.3606 - val_loss: 0.0221 - val_acc: 0.2075\n",
      "Epoch 343/500\n",
      "7/7 [==============================] - 0s 62ms/step - loss: 0.0079 - acc: 0.3413 - val_loss: 0.0249 - val_acc: 0.1509\n",
      "Epoch 344/500\n",
      "7/7 [==============================] - 0s 65ms/step - loss: 0.0076 - acc: 0.3462 - val_loss: 0.0263 - val_acc: 0.2075\n",
      "Epoch 345/500\n",
      "7/7 [==============================] - 0s 62ms/step - loss: 0.0097 - acc: 0.3269 - val_loss: 0.0221 - val_acc: 0.1887\n",
      "Epoch 346/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0076 - acc: 0.2981 - val_loss: 0.0201 - val_acc: 0.2075\n",
      "Epoch 347/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0057 - acc: 0.3750 - val_loss: 0.0220 - val_acc: 0.2642\n",
      "Epoch 348/500\n",
      "7/7 [==============================] - 0s 65ms/step - loss: 0.0083 - acc: 0.3365 - val_loss: 0.0235 - val_acc: 0.2264\n",
      "Epoch 349/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0101 - acc: 0.2692 - val_loss: 0.0242 - val_acc: 0.1698\n",
      "Epoch 350/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0126 - acc: 0.2548 - val_loss: 0.0209 - val_acc: 0.1887\n",
      "Epoch 351/500\n",
      "7/7 [==============================] - 0s 65ms/step - loss: 0.0086 - acc: 0.3462 - val_loss: 0.0220 - val_acc: 0.2830\n",
      "Epoch 352/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0080 - acc: 0.3365 - val_loss: 0.0235 - val_acc: 0.2264\n",
      "Epoch 353/500\n",
      "7/7 [==============================] - 0s 63ms/step - loss: 0.0064 - acc: 0.3702 - val_loss: 0.0241 - val_acc: 0.2453\n",
      "Epoch 354/500\n",
      "7/7 [==============================] - 0s 63ms/step - loss: 0.0067 - acc: 0.3269 - val_loss: 0.0232 - val_acc: 0.2453\n",
      "Epoch 355/500\n",
      "7/7 [==============================] - 0s 65ms/step - loss: 0.0060 - acc: 0.3462 - val_loss: 0.0219 - val_acc: 0.2264\n",
      "Epoch 356/500\n",
      "7/7 [==============================] - 0s 65ms/step - loss: 0.0074 - acc: 0.3269 - val_loss: 0.0232 - val_acc: 0.2075\n",
      "Epoch 357/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0058 - acc: 0.3990 - val_loss: 0.0238 - val_acc: 0.2075\n",
      "Epoch 358/500\n",
      "7/7 [==============================] - 0s 65ms/step - loss: 0.0056 - acc: 0.3269 - val_loss: 0.0256 - val_acc: 0.1887\n",
      "Epoch 359/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0065 - acc: 0.3462 - val_loss: 0.0263 - val_acc: 0.1698\n",
      "Epoch 360/500\n",
      "7/7 [==============================] - 0s 63ms/step - loss: 0.0058 - acc: 0.3125 - val_loss: 0.0271 - val_acc: 0.2075\n",
      "Epoch 361/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0063 - acc: 0.3894 - val_loss: 0.0290 - val_acc: 0.1887\n",
      "Epoch 362/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0081 - acc: 0.3558 - val_loss: 0.0207 - val_acc: 0.1887\n",
      "Epoch 363/500\n",
      "7/7 [==============================] - 0s 63ms/step - loss: 0.0085 - acc: 0.2837 - val_loss: 0.0201 - val_acc: 0.2264\n",
      "Epoch 364/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0079 - acc: 0.3173 - val_loss: 0.0207 - val_acc: 0.2075\n",
      "Epoch 365/500\n",
      "7/7 [==============================] - 0s 63ms/step - loss: 0.0069 - acc: 0.3942 - val_loss: 0.0215 - val_acc: 0.2075\n",
      "Epoch 366/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0067 - acc: 0.3846 - val_loss: 0.0213 - val_acc: 0.1887\n",
      "Epoch 367/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0080 - acc: 0.3510 - val_loss: 0.0227 - val_acc: 0.2075\n",
      "Epoch 368/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0102 - acc: 0.2788 - val_loss: 0.0225 - val_acc: 0.0943\n",
      "Epoch 369/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0076 - acc: 0.3558 - val_loss: 0.0206 - val_acc: 0.2642\n",
      "Epoch 370/500\n",
      "7/7 [==============================] - 0s 63ms/step - loss: 0.0065 - acc: 0.3750 - val_loss: 0.0241 - val_acc: 0.2075\n",
      "Epoch 371/500\n",
      "7/7 [==============================] - 0s 63ms/step - loss: 0.0064 - acc: 0.3798 - val_loss: 0.0254 - val_acc: 0.2642\n",
      "Epoch 372/500\n",
      "7/7 [==============================] - 0s 65ms/step - loss: 0.0050 - acc: 0.4183 - val_loss: 0.0227 - val_acc: 0.2453\n",
      "Epoch 373/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0059 - acc: 0.3413 - val_loss: 0.0243 - val_acc: 0.2075\n",
      "Epoch 374/500\n",
      "7/7 [==============================] - 0s 65ms/step - loss: 0.0059 - acc: 0.4135 - val_loss: 0.0257 - val_acc: 0.2453\n",
      "Epoch 375/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0054 - acc: 0.3365 - val_loss: 0.0247 - val_acc: 0.2075\n",
      "Epoch 376/500\n",
      "7/7 [==============================] - 0s 62ms/step - loss: 0.0053 - acc: 0.3942 - val_loss: 0.0246 - val_acc: 0.2075\n",
      "Epoch 377/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0063 - acc: 0.3317 - val_loss: 0.0231 - val_acc: 0.2264\n",
      "Epoch 378/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0071 - acc: 0.3317 - val_loss: 0.0244 - val_acc: 0.2075\n",
      "Epoch 379/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0065 - acc: 0.3077 - val_loss: 0.0252 - val_acc: 0.1887\n",
      "Epoch 380/500\n",
      "7/7 [==============================] - 0s 65ms/step - loss: 0.0055 - acc: 0.3558 - val_loss: 0.0244 - val_acc: 0.2075\n",
      "Epoch 381/500\n",
      "7/7 [==============================] - 0s 63ms/step - loss: 0.0059 - acc: 0.3654 - val_loss: 0.0271 - val_acc: 0.2075\n",
      "Epoch 382/500\n",
      "7/7 [==============================] - 0s 65ms/step - loss: 0.0085 - acc: 0.3365 - val_loss: 0.0246 - val_acc: 0.1887\n",
      "Epoch 383/500\n",
      "7/7 [==============================] - 0s 63ms/step - loss: 0.0082 - acc: 0.3413 - val_loss: 0.0229 - val_acc: 0.1698\n",
      "Epoch 384/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0061 - acc: 0.4087 - val_loss: 0.0233 - val_acc: 0.2075\n",
      "Epoch 385/500\n",
      "7/7 [==============================] - 0s 65ms/step - loss: 0.0082 - acc: 0.3606 - val_loss: 0.0216 - val_acc: 0.1698\n",
      "Epoch 386/500\n",
      "7/7 [==============================] - 0s 63ms/step - loss: 0.0059 - acc: 0.3462 - val_loss: 0.0218 - val_acc: 0.2075\n",
      "Epoch 387/500\n",
      "7/7 [==============================] - 0s 66ms/step - loss: 0.0058 - acc: 0.3125 - val_loss: 0.0258 - val_acc: 0.1887\n",
      "Epoch 388/500\n",
      "7/7 [==============================] - 0s 63ms/step - loss: 0.0055 - acc: 0.3942 - val_loss: 0.0267 - val_acc: 0.1887\n",
      "Epoch 389/500\n",
      "7/7 [==============================] - 0s 65ms/step - loss: 0.0051 - acc: 0.3654 - val_loss: 0.0263 - val_acc: 0.1887\n",
      "Epoch 390/500\n",
      "7/7 [==============================] - 0s 63ms/step - loss: 0.0050 - acc: 0.3702 - val_loss: 0.0272 - val_acc: 0.2075\n",
      "Epoch 391/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0053 - acc: 0.3846 - val_loss: 0.0281 - val_acc: 0.1887\n",
      "Epoch 392/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0050 - acc: 0.3942 - val_loss: 0.0274 - val_acc: 0.1698\n",
      "Epoch 393/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0055 - acc: 0.3462 - val_loss: 0.0269 - val_acc: 0.2075\n",
      "Epoch 394/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0049 - acc: 0.4038 - val_loss: 0.0287 - val_acc: 0.2075\n",
      "Epoch 395/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0058 - acc: 0.3654 - val_loss: 0.0279 - val_acc: 0.2264\n",
      "Epoch 396/500\n",
      "7/7 [==============================] - 0s 65ms/step - loss: 0.0059 - acc: 0.3942 - val_loss: 0.0290 - val_acc: 0.2453\n",
      "Epoch 397/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0073 - acc: 0.3125 - val_loss: 0.0259 - val_acc: 0.1698\n",
      "Epoch 398/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0071 - acc: 0.3558 - val_loss: 0.0229 - val_acc: 0.2075\n",
      "Epoch 399/500\n",
      "7/7 [==============================] - 0s 65ms/step - loss: 0.0065 - acc: 0.3798 - val_loss: 0.0231 - val_acc: 0.1887\n",
      "Epoch 400/500\n",
      "7/7 [==============================] - 0s 65ms/step - loss: 0.0071 - acc: 0.3654 - val_loss: 0.0250 - val_acc: 0.1698\n",
      "Epoch 401/500\n",
      "7/7 [==============================] - 0s 63ms/step - loss: 0.0058 - acc: 0.3750 - val_loss: 0.0254 - val_acc: 0.2075\n",
      "Epoch 402/500\n",
      "7/7 [==============================] - 0s 66ms/step - loss: 0.0051 - acc: 0.3846 - val_loss: 0.0256 - val_acc: 0.2075\n",
      "Epoch 403/500\n",
      "7/7 [==============================] - 0s 65ms/step - loss: 0.0050 - acc: 0.3846 - val_loss: 0.0276 - val_acc: 0.1887\n",
      "Epoch 404/500\n",
      "7/7 [==============================] - 0s 65ms/step - loss: 0.0079 - acc: 0.3750 - val_loss: 0.0217 - val_acc: 0.2264\n",
      "Epoch 405/500\n",
      "7/7 [==============================] - 0s 66ms/step - loss: 0.0087 - acc: 0.3221 - val_loss: 0.0224 - val_acc: 0.1698\n",
      "Epoch 406/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0095 - acc: 0.2452 - val_loss: 0.0208 - val_acc: 0.1698\n",
      "Epoch 407/500\n",
      "7/7 [==============================] - 0s 68ms/step - loss: 0.0097 - acc: 0.2885 - val_loss: 0.0202 - val_acc: 0.1698\n",
      "Epoch 408/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0079 - acc: 0.3750 - val_loss: 0.0207 - val_acc: 0.1887\n",
      "Epoch 409/500\n",
      "7/7 [==============================] - 0s 71ms/step - loss: 0.0071 - acc: 0.3894 - val_loss: 0.0220 - val_acc: 0.2075\n",
      "Epoch 410/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0056 - acc: 0.3558 - val_loss: 0.0237 - val_acc: 0.2264\n",
      "Epoch 411/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0066 - acc: 0.3173 - val_loss: 0.0237 - val_acc: 0.1887\n",
      "Epoch 412/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0058 - acc: 0.3846 - val_loss: 0.0239 - val_acc: 0.2075\n",
      "Epoch 413/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0053 - acc: 0.3702 - val_loss: 0.0242 - val_acc: 0.2075\n",
      "Epoch 414/500\n",
      "7/7 [==============================] - 0s 65ms/step - loss: 0.0055 - acc: 0.3798 - val_loss: 0.0259 - val_acc: 0.1887\n",
      "Epoch 415/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0050 - acc: 0.4327 - val_loss: 0.0257 - val_acc: 0.2075\n",
      "Epoch 416/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0049 - acc: 0.4327 - val_loss: 0.0258 - val_acc: 0.1887\n",
      "Epoch 417/500\n",
      "7/7 [==============================] - 0s 65ms/step - loss: 0.0046 - acc: 0.3990 - val_loss: 0.0273 - val_acc: 0.1887\n",
      "Epoch 418/500\n",
      "7/7 [==============================] - 0s 65ms/step - loss: 0.0056 - acc: 0.3894 - val_loss: 0.0250 - val_acc: 0.2075\n",
      "Epoch 419/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0052 - acc: 0.3990 - val_loss: 0.0256 - val_acc: 0.1698\n",
      "Epoch 420/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0053 - acc: 0.4135 - val_loss: 0.0233 - val_acc: 0.2075\n",
      "Epoch 421/500\n",
      "7/7 [==============================] - 0s 65ms/step - loss: 0.0052 - acc: 0.4231 - val_loss: 0.0267 - val_acc: 0.1887\n",
      "Epoch 422/500\n",
      "7/7 [==============================] - 0s 62ms/step - loss: 0.0064 - acc: 0.3750 - val_loss: 0.0241 - val_acc: 0.1698\n",
      "Epoch 423/500\n",
      "7/7 [==============================] - 0s 63ms/step - loss: 0.0061 - acc: 0.3462 - val_loss: 0.0243 - val_acc: 0.1887\n",
      "Epoch 424/500\n",
      "7/7 [==============================] - 0s 65ms/step - loss: 0.0048 - acc: 0.3942 - val_loss: 0.0247 - val_acc: 0.1321\n",
      "Epoch 425/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0062 - acc: 0.3365 - val_loss: 0.0246 - val_acc: 0.1887\n",
      "Epoch 426/500\n",
      "7/7 [==============================] - 0s 62ms/step - loss: 0.0065 - acc: 0.3462 - val_loss: 0.0228 - val_acc: 0.2075\n",
      "Epoch 427/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0051 - acc: 0.3894 - val_loss: 0.0245 - val_acc: 0.1698\n",
      "Epoch 428/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0051 - acc: 0.4279 - val_loss: 0.0224 - val_acc: 0.1698\n",
      "Epoch 429/500\n",
      "7/7 [==============================] - 0s 65ms/step - loss: 0.0054 - acc: 0.3606 - val_loss: 0.0245 - val_acc: 0.2075\n",
      "Epoch 430/500\n",
      "7/7 [==============================] - 0s 65ms/step - loss: 0.0053 - acc: 0.3606 - val_loss: 0.0250 - val_acc: 0.1887\n",
      "Epoch 431/500\n",
      "7/7 [==============================] - 0s 63ms/step - loss: 0.0041 - acc: 0.3942 - val_loss: 0.0262 - val_acc: 0.2075\n",
      "Epoch 432/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0051 - acc: 0.4087 - val_loss: 0.0236 - val_acc: 0.1887\n",
      "Epoch 433/500\n",
      "7/7 [==============================] - 0s 65ms/step - loss: 0.0060 - acc: 0.3221 - val_loss: 0.0273 - val_acc: 0.2075\n",
      "Epoch 434/500\n",
      "7/7 [==============================] - 0s 63ms/step - loss: 0.0077 - acc: 0.3269 - val_loss: 0.0254 - val_acc: 0.1132\n",
      "Epoch 435/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7/7 [==============================] - 0s 68ms/step - loss: 0.0058 - acc: 0.3173 - val_loss: 0.0235 - val_acc: 0.2264\n",
      "Epoch 436/500\n",
      "7/7 [==============================] - 0s 65ms/step - loss: 0.0072 - acc: 0.3798 - val_loss: 0.0250 - val_acc: 0.2075\n",
      "Epoch 437/500\n",
      "7/7 [==============================] - 0s 63ms/step - loss: 0.0055 - acc: 0.4038 - val_loss: 0.0243 - val_acc: 0.2075\n",
      "Epoch 438/500\n",
      "7/7 [==============================] - 0s 65ms/step - loss: 0.0063 - acc: 0.3942 - val_loss: 0.0229 - val_acc: 0.1887\n",
      "Epoch 439/500\n",
      "7/7 [==============================] - 0s 66ms/step - loss: 0.0063 - acc: 0.3317 - val_loss: 0.0251 - val_acc: 0.1698\n",
      "Epoch 440/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0055 - acc: 0.3173 - val_loss: 0.0277 - val_acc: 0.1887\n",
      "Epoch 441/500\n",
      "7/7 [==============================] - 0s 63ms/step - loss: 0.0054 - acc: 0.3798 - val_loss: 0.0236 - val_acc: 0.2075\n",
      "Epoch 442/500\n",
      "7/7 [==============================] - 0s 63ms/step - loss: 0.0056 - acc: 0.4231 - val_loss: 0.0226 - val_acc: 0.2075\n",
      "Epoch 443/500\n",
      "7/7 [==============================] - 0s 63ms/step - loss: 0.0057 - acc: 0.3510 - val_loss: 0.0246 - val_acc: 0.1698\n",
      "Epoch 444/500\n",
      "7/7 [==============================] - 0s 62ms/step - loss: 0.0054 - acc: 0.3654 - val_loss: 0.0268 - val_acc: 0.1698\n",
      "Epoch 445/500\n",
      "7/7 [==============================] - 0s 72ms/step - loss: 0.0046 - acc: 0.3654 - val_loss: 0.0272 - val_acc: 0.1887\n",
      "Epoch 446/500\n",
      "7/7 [==============================] - 0s 63ms/step - loss: 0.0053 - acc: 0.3558 - val_loss: 0.0272 - val_acc: 0.2075\n",
      "Epoch 447/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0045 - acc: 0.3606 - val_loss: 0.0292 - val_acc: 0.2264\n",
      "Epoch 448/500\n",
      "7/7 [==============================] - 0s 66ms/step - loss: 0.0049 - acc: 0.3558 - val_loss: 0.0260 - val_acc: 0.1698\n",
      "Epoch 449/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0045 - acc: 0.4038 - val_loss: 0.0289 - val_acc: 0.1698\n",
      "Epoch 450/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0044 - acc: 0.4231 - val_loss: 0.0292 - val_acc: 0.1887\n",
      "Epoch 451/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0045 - acc: 0.3702 - val_loss: 0.0268 - val_acc: 0.1887\n",
      "Epoch 452/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0041 - acc: 0.3606 - val_loss: 0.0269 - val_acc: 0.1887\n",
      "Epoch 453/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0050 - acc: 0.3750 - val_loss: 0.0259 - val_acc: 0.2075\n",
      "Epoch 454/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0039 - acc: 0.4231 - val_loss: 0.0277 - val_acc: 0.1887\n",
      "Epoch 455/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0049 - acc: 0.3702 - val_loss: 0.0291 - val_acc: 0.2453\n",
      "Epoch 456/500\n",
      "7/7 [==============================] - 0s 63ms/step - loss: 0.0060 - acc: 0.3365 - val_loss: 0.0262 - val_acc: 0.2453\n",
      "Epoch 457/500\n",
      "7/7 [==============================] - 0s 63ms/step - loss: 0.0071 - acc: 0.3413 - val_loss: 0.0301 - val_acc: 0.1887\n",
      "Epoch 458/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0073 - acc: 0.3029 - val_loss: 0.0274 - val_acc: 0.1509\n",
      "Epoch 459/500\n",
      "7/7 [==============================] - 0s 63ms/step - loss: 0.0074 - acc: 0.3654 - val_loss: 0.0243 - val_acc: 0.2453\n",
      "Epoch 460/500\n",
      "7/7 [==============================] - 0s 65ms/step - loss: 0.0098 - acc: 0.3702 - val_loss: 0.0251 - val_acc: 0.1509\n",
      "Epoch 461/500\n",
      "7/7 [==============================] - 0s 66ms/step - loss: 0.0077 - acc: 0.3269 - val_loss: 0.0203 - val_acc: 0.1698\n",
      "Epoch 462/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0079 - acc: 0.2981 - val_loss: 0.0211 - val_acc: 0.2075\n",
      "Epoch 463/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0057 - acc: 0.3702 - val_loss: 0.0244 - val_acc: 0.1887\n",
      "Epoch 464/500\n",
      "7/7 [==============================] - 0s 65ms/step - loss: 0.0064 - acc: 0.3510 - val_loss: 0.0253 - val_acc: 0.1887\n",
      "Epoch 465/500\n",
      "7/7 [==============================] - 0s 63ms/step - loss: 0.0050 - acc: 0.3846 - val_loss: 0.0247 - val_acc: 0.1698\n",
      "Epoch 466/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0051 - acc: 0.3990 - val_loss: 0.0246 - val_acc: 0.1698\n",
      "Epoch 467/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0049 - acc: 0.3750 - val_loss: 0.0246 - val_acc: 0.1698\n",
      "Epoch 468/500\n",
      "7/7 [==============================] - 0s 63ms/step - loss: 0.0046 - acc: 0.3846 - val_loss: 0.0264 - val_acc: 0.1509\n",
      "Epoch 469/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0048 - acc: 0.4038 - val_loss: 0.0252 - val_acc: 0.2075\n",
      "Epoch 470/500\n",
      "7/7 [==============================] - 0s 63ms/step - loss: 0.0050 - acc: 0.4375 - val_loss: 0.0262 - val_acc: 0.1321\n",
      "Epoch 471/500\n",
      "7/7 [==============================] - 0s 63ms/step - loss: 0.0044 - acc: 0.4615 - val_loss: 0.0276 - val_acc: 0.1321\n",
      "Epoch 472/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0041 - acc: 0.3942 - val_loss: 0.0287 - val_acc: 0.1698\n",
      "Epoch 473/500\n",
      "7/7 [==============================] - 0s 65ms/step - loss: 0.0050 - acc: 0.3654 - val_loss: 0.0253 - val_acc: 0.2264\n",
      "Epoch 474/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0055 - acc: 0.3413 - val_loss: 0.0273 - val_acc: 0.1887\n",
      "Epoch 475/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0048 - acc: 0.3702 - val_loss: 0.0262 - val_acc: 0.1887\n",
      "Epoch 476/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0039 - acc: 0.4135 - val_loss: 0.0256 - val_acc: 0.1887\n",
      "Epoch 477/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0044 - acc: 0.4087 - val_loss: 0.0264 - val_acc: 0.1132\n",
      "Epoch 478/500\n",
      "7/7 [==============================] - 0s 65ms/step - loss: 0.0045 - acc: 0.3798 - val_loss: 0.0255 - val_acc: 0.1132\n",
      "Epoch 479/500\n",
      "7/7 [==============================] - 0s 66ms/step - loss: 0.0045 - acc: 0.3894 - val_loss: 0.0247 - val_acc: 0.1321\n",
      "Epoch 480/500\n",
      "7/7 [==============================] - 0s 65ms/step - loss: 0.0053 - acc: 0.3942 - val_loss: 0.0255 - val_acc: 0.1887\n",
      "Epoch 481/500\n",
      "7/7 [==============================] - 0s 66ms/step - loss: 0.0055 - acc: 0.4087 - val_loss: 0.0264 - val_acc: 0.2075\n",
      "Epoch 482/500\n",
      "7/7 [==============================] - 0s 65ms/step - loss: 0.0044 - acc: 0.3846 - val_loss: 0.0255 - val_acc: 0.1887\n",
      "Epoch 483/500\n",
      "7/7 [==============================] - 0s 65ms/step - loss: 0.0048 - acc: 0.3365 - val_loss: 0.0278 - val_acc: 0.1321\n",
      "Epoch 484/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0044 - acc: 0.3846 - val_loss: 0.0246 - val_acc: 0.2264\n",
      "Epoch 485/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0060 - acc: 0.4375 - val_loss: 0.0247 - val_acc: 0.2264\n",
      "Epoch 486/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0046 - acc: 0.3942 - val_loss: 0.0261 - val_acc: 0.1132\n",
      "Epoch 487/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0050 - acc: 0.3702 - val_loss: 0.0240 - val_acc: 0.1509\n",
      "Epoch 488/500\n",
      "7/7 [==============================] - 0s 63ms/step - loss: 0.0069 - acc: 0.3269 - val_loss: 0.0238 - val_acc: 0.1887\n",
      "Epoch 489/500\n",
      "7/7 [==============================] - 0s 65ms/step - loss: 0.0057 - acc: 0.3317 - val_loss: 0.0255 - val_acc: 0.2075\n",
      "Epoch 490/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0048 - acc: 0.3702 - val_loss: 0.0268 - val_acc: 0.2075\n",
      "Epoch 491/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0058 - acc: 0.3269 - val_loss: 0.0265 - val_acc: 0.2075\n",
      "Epoch 492/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0050 - acc: 0.3942 - val_loss: 0.0262 - val_acc: 0.2453\n",
      "Epoch 493/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0047 - acc: 0.3702 - val_loss: 0.0282 - val_acc: 0.2075\n",
      "Epoch 494/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0059 - acc: 0.3558 - val_loss: 0.0243 - val_acc: 0.1887\n",
      "Epoch 495/500\n",
      "7/7 [==============================] - 0s 63ms/step - loss: 0.0051 - acc: 0.3462 - val_loss: 0.0258 - val_acc: 0.1887\n",
      "Epoch 496/500\n",
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0043 - acc: 0.3558 - val_loss: 0.0263 - val_acc: 0.0943\n",
      "Epoch 497/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7/7 [==============================] - 0s 64ms/step - loss: 0.0051 - acc: 0.4135 - val_loss: 0.0232 - val_acc: 0.2075\n",
      "Epoch 498/500\n",
      "7/7 [==============================] - 0s 63ms/step - loss: 0.0046 - acc: 0.3221 - val_loss: 0.0255 - val_acc: 0.2075\n",
      "Epoch 499/500\n",
      "7/7 [==============================] - 0s 65ms/step - loss: 0.0042 - acc: 0.4087 - val_loss: 0.0277 - val_acc: 0.1887\n",
      "Epoch 500/500\n",
      "7/7 [==============================] - 0s 65ms/step - loss: 0.0049 - acc: 0.4279 - val_loss: 0.0254 - val_acc: 0.2453\n",
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     ]
    }
   ],
   "source": [
    "regressor = Sequential()\n",
    "regressor.add(Bidirectional(LSTM(units=30, return_sequences=True), input_shape = (x_train.shape[1],1) ) )\n",
    "regressor.add(Dropout(0.2))\n",
    "regressor.add(LSTM(units= 30 , return_sequences=True))\n",
    "regressor.add(Dropout(0.2))\n",
    "regressor.add(LSTM(units= 30 , return_sequences=True))\n",
    "regressor.add(Dropout(0.2))\n",
    "regressor.add(LSTM(units= 30))\n",
    "regressor.add(Dropout(0.2))\n",
    "regressor.add(Dense(units = n_future,activation='linear'))\n",
    "regressor.compile(optimizer='adam', loss='mean_squared_error',metrics=['acc'])\n",
    "history = regressor.fit(x_train, y_train, epochs=500,batch_size=32,validation_split=0.20 )\n",
    "\n",
    "print(history.history['loss'])\n",
    "print(history.history['acc'])\n",
    "print(history.history['val_loss'])\n",
    "print(history.history['val_acc'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "ce2df6d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>7.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     RF\n",
       "0   0.0\n",
       "1   0.0\n",
       "2   1.6\n",
       "3   5.8\n",
       "4   0.3\n",
       "..  ...\n",
       "95  0.0\n",
       "96  0.0\n",
       "97  7.4\n",
       "98  0.0\n",
       "99  0.0\n",
       "\n",
       "[100 rows x 1 columns]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read test dataset\n",
    "testdataset = pd.read_csv('NewSl.csv')\n",
    "#get only the temperature column\n",
    "testdataset = testdataset.iloc[0:100,3:4]\n",
    "testdataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "8f880ff9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Sevindu\\anaconda3\\Lib\\site-packages\\sklearn\\base.py:457: UserWarning: X has feature names, but MinMaxScaler was fitted without feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "real_temperature = pd.read_csv('NewSl.csv')\n",
    "real_temperature = real_temperature.iloc[100:105,3:4]\n",
    "\n",
    "real_temperature = real_temperature.values.reshape(len(real_temperature), 1)\n",
    "testing = sc.transform(testdataset)\n",
    "testing = np.array(testing)\n",
    "testing = np.reshape(testing,(testing.shape[1],testing.shape[0],1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "0dec6214",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[58.1],\n",
       "       [ 0. ],\n",
       "       [ 0. ],\n",
       "       [ 3.8],\n",
       "       [ 0. ]])"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "real_temperature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "f17f11dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 1s 1s/step\n"
     ]
    }
   ],
   "source": [
    "predicted_temperature = regressor.predict(testing)\n",
    "predicted_temperature = sc.inverse_transform(predicted_temperature)\n",
    "predicted_temperature = np.reshape(predicted_temperature,(predicted_temperature.shape[1],predicted_temperature.shape[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "641df09d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[59.53018  ],\n",
       "       [ 2.730783 ],\n",
       "       [ 1.9943453],\n",
       "       [ 2.7888517],\n",
       "       [ 1.0274433]], dtype=float32)"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predicted_temperature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "7f153fba",
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "9c54bda3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pyplot.plot(history.history['loss'])\n",
    "pyplot.plot(history.history['val_loss'])\n",
    "pyplot.title('model train vs validation loss')\n",
    "pyplot.ylabel('loss')\n",
    "pyplot.xlabel('epoch')\n",
    "pyplot.legend(['train', 'validation'], loc='upper right')\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "4089d7ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "RT = real_temperature\n",
    "PT = predicted_temperature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "4558c33e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3.62560749])"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_MSE= (    (   ((PT[0]-RT[0])**2) + ((PT[1]-RT[1])**2) + ((PT[2]-RT[2])**2) + ((PT[3]-RT[3])**2)    )/4 ) \n",
    "\n",
    "model_MSE"
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   "outputs": [],
   "source": []
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