Project_LSTM_Model.ipynb

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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import io\n",
        "from google.colab import files\n",
        "from sklearn.model_selection import train_test_split\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, LSTM\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, GRU, Conv1D, Flatten\n",
        "from keras.optimizers import Adam\n",
        "from keras.wrappers.scikit_learn import KerasRegressor\n",
        "from sklearn.model_selection import GridSearchCV"
      ],
      "metadata": {
        "id": "pj07tBXGQ3-n"
      },
      "execution_count": 142,
      "outputs": []
    },
    {
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      "source": [
        "# Load the data\n",
        "uploaded = files.upload()\n",
        "df = pd.read_csv(io.BytesIO(uploaded['LSTM_Proj.csv']))\n",
        "\n",
        "\n",
        "df_clean = df.dropna(subset=['sector_performance_1week', 'sector_performance_5week',\n",
        "                             'sector_performance_20week', 'index_performance_1week',\n",
        "                             'index_performance_5week', 'index_performance_20week',\n",
        "                             'company_performance_1week', 'company_performance_5week',\n",
        "                             'company_performance_20week', 'twentyfour_hr_post_release%change'])\n",
        "\n",
        "X = df_clean[['sector_performance_1week', 'sector_performance_5week',\n",
        "              'sector_performance_20week', 'index_performance_1week',\n",
        "              'index_performance_5week', 'index_performance_20week',\n",
        "              'company_performance_1week', 'company_performance_5week',\n",
        "              'company_performance_20week']]\n",
        "\n",
        "Y = df_clean['twentyfour_hr_post_release%change']\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "id": "xFEWX7tROALY",
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      },
      "execution_count": 143,
      "outputs": [
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              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving LSTM_Proj.csv to LSTM_Proj (15).csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Normalizing data\n",
        "scaler = MinMaxScaler()\n",
        "X = scaler.fit_transform(X)\n",
        "\n",
        "y_scaler = MinMaxScaler()\n",
        "y = y_scaler.fit_transform(np.array(Y).reshape(-1, 1))\n",
        "\n",
        "# Training and testing sets\n",
        "train_size = int(0.8 * len(df))\n",
        "X_train, X_test = X[:train_size], X[train_size:]\n",
        "y_train, y_test = y[:train_size], y[train_size:]\n",
        "\n",
        "# Reshape the input data for LSTM input\n",
        "X_train = np.reshape(X_train, (X_train.shape[0], 1, X_train.shape[1]))\n",
        "X_test = np.reshape(X_test, (X_test.shape[0], 1, X_test.shape[1]))\n",
        "\n",
        "# Defining LSTM model\n",
        "model = Sequential()\n",
        "model.add(LSTM(64, input_shape=(1, X_train.shape[2])))\n",
        "model.add(Dense(32, activation='relu'))\n",
        "model.add(Dense(32, activation='relu'))\n",
        "model.add(Dense(1))  # One output neurons\n",
        "\n",
        "# Compiling the model\n",
        "model.compile(loss='mse', optimizer=Adam(learning_rate=0.001), metrics=['mean_absolute_error'])\n",
        "\n",
        "# Fit the model to the training data\n",
        "model.fit(X_train, y_train, epochs=200, batch_size=36, verbose=1)\n",
        "\n",
        "# Evaluate the model on the test data\n",
        "score = model.evaluate(X_test, y_test, batch_size=16, verbose=1)\n",
        "print('Test score:', score)\n",
        "\n",
        "# Using the model to make predictions on new data\n",
        "X_new = np.array([[0.2, 0.3, 0.4, 0.1, 0.2, 0.3, 0.05, 0.1, 0.15]])\n",
        "X_new = scaler.transform(X_new)\n",
        "X_new = np.reshape(X_new, (X_new.shape[0], 1, X_new.shape[1]))\n",
        "y_pred = model.predict(X_new)\n",
        "y_pred = y_scaler.inverse_transform(y_pred)\n",
        "print('Predicted changes in price after 1 day:', y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "38F1dDPtNgRt",
        "outputId": "c5df4621-952c-46bc-b419-cb835d854210"
      },
      "execution_count": 157,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/200\n",
            "3/3 [==============================] - 3s 7ms/step - loss: 0.3498 - mean_absolute_error: 0.5526\n",
            "Epoch 2/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.2738 - mean_absolute_error: 0.4824\n",
            "Epoch 3/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.2138 - mean_absolute_error: 0.4204\n",
            "Epoch 4/200\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.1628 - mean_absolute_error: 0.3622\n",
            "Epoch 5/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.1177 - mean_absolute_error: 0.3088\n",
            "Epoch 6/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.0835 - mean_absolute_error: 0.2568\n",
            "Epoch 7/200\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.0616 - mean_absolute_error: 0.2151\n",
            "Epoch 8/200\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.0532 - mean_absolute_error: 0.1826\n",
            "Epoch 9/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.0540 - mean_absolute_error: 0.1786\n",
            "Epoch 10/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.0540 - mean_absolute_error: 0.1782\n",
            "Epoch 11/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.0533 - mean_absolute_error: 0.1776\n",
            "Epoch 12/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.0521 - mean_absolute_error: 0.1777\n",
            "Epoch 13/200\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.0515 - mean_absolute_error: 0.1800\n",
            "Epoch 14/200\n",
            "3/3 [==============================] - 0s 9ms/step - loss: 0.0515 - mean_absolute_error: 0.1824\n",
            "Epoch 15/200\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.0515 - mean_absolute_error: 0.1830\n",
            "Epoch 16/200\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.0515 - mean_absolute_error: 0.1837\n",
            "Epoch 17/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.0514 - mean_absolute_error: 0.1839\n",
            "Epoch 18/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.0512 - mean_absolute_error: 0.1830\n",
            "Epoch 19/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.0507 - mean_absolute_error: 0.1801\n",
            "Epoch 20/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.0507 - mean_absolute_error: 0.1768\n",
            "Epoch 21/200\n",
            "3/3 [==============================] - 0s 12ms/step - loss: 0.0507 - mean_absolute_error: 0.1742\n",
            "Epoch 22/200\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.0516 - mean_absolute_error: 0.1737\n",
            "Epoch 23/200\n",
            "3/3 [==============================] - 0s 8ms/step - loss: 0.0536 - mean_absolute_error: 0.1758\n",
            "Epoch 24/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.0546 - mean_absolute_error: 0.1765\n",
            "Epoch 25/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.0551 - mean_absolute_error: 0.1770\n",
            "Epoch 26/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.0558 - mean_absolute_error: 0.1781\n",
            "Epoch 27/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.0537 - mean_absolute_error: 0.1751\n",
            "Epoch 28/200\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.0509 - mean_absolute_error: 0.1722\n",
            "Epoch 29/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.0499 - mean_absolute_error: 0.1721\n",
            "Epoch 30/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.0492 - mean_absolute_error: 0.1717\n",
            "Epoch 31/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.0489 - mean_absolute_error: 0.1714\n",
            "Epoch 32/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.0488 - mean_absolute_error: 0.1707\n",
            "Epoch 33/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.0488 - mean_absolute_error: 0.1724\n",
            "Epoch 34/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.0480 - mean_absolute_error: 0.1732\n",
            "Epoch 35/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.0489 - mean_absolute_error: 0.1793\n",
            "Epoch 36/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.0496 - mean_absolute_error: 0.1848\n",
            "Epoch 37/200\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.0499 - mean_absolute_error: 0.1861\n",
            "Epoch 38/200\n",
            "3/3 [==============================] - 0s 11ms/step - loss: 0.0489 - mean_absolute_error: 0.1824\n",
            "Epoch 39/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.0478 - mean_absolute_error: 0.1766\n",
            "Epoch 40/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.0474 - mean_absolute_error: 0.1752\n",
            "Epoch 41/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.0481 - mean_absolute_error: 0.1796\n",
            "Epoch 42/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.0482 - mean_absolute_error: 0.1808\n",
            "Epoch 43/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.0474 - mean_absolute_error: 0.1775\n",
            "Epoch 44/200\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.0466 - mean_absolute_error: 0.1735\n",
            "Epoch 45/200\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.0465 - mean_absolute_error: 0.1727\n",
            "Epoch 46/200\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.0464 - mean_absolute_error: 0.1721\n",
            "Epoch 47/200\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.0463 - mean_absolute_error: 0.1704\n",
            "Epoch 48/200\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.0458 - mean_absolute_error: 0.1689\n",
            "Epoch 49/200\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.0459 - mean_absolute_error: 0.1698\n",
            "Epoch 50/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.0456 - mean_absolute_error: 0.1686\n",
            "Epoch 51/200\n",
            "3/3 [==============================] - 0s 9ms/step - loss: 0.0455 - mean_absolute_error: 0.1672\n",
            "Epoch 52/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.0455 - mean_absolute_error: 0.1646\n",
            "Epoch 53/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.0463 - mean_absolute_error: 0.1630\n",
            "Epoch 54/200\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.0470 - mean_absolute_error: 0.1625\n",
            "Epoch 55/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.0468 - mean_absolute_error: 0.1620\n",
            "Epoch 56/200\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.0467 - mean_absolute_error: 0.1618\n",
            "Epoch 57/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.0454 - mean_absolute_error: 0.1648\n",
            "Epoch 58/200\n",
            "3/3 [==============================] - 0s 8ms/step - loss: 0.0452 - mean_absolute_error: 0.1722\n",
            "Epoch 59/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.0472 - mean_absolute_error: 0.1798\n",
            "Epoch 60/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.0476 - mean_absolute_error: 0.1820\n",
            "Epoch 61/200\n",
            "3/3 [==============================] - 0s 8ms/step - loss: 0.0468 - mean_absolute_error: 0.1788\n",
            "Epoch 62/200\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.0444 - mean_absolute_error: 0.1686\n",
            "Epoch 63/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.0444 - mean_absolute_error: 0.1613\n",
            "Epoch 64/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.0462 - mean_absolute_error: 0.1586\n",
            "Epoch 65/200\n",
            "3/3 [==============================] - 0s 8ms/step - loss: 0.0474 - mean_absolute_error: 0.1596\n",
            "Epoch 66/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.0472 - mean_absolute_error: 0.1592\n",
            "Epoch 67/200\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.0455 - mean_absolute_error: 0.1581\n",
            "Epoch 68/200\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.0425 - mean_absolute_error: 0.1571\n",
            "Epoch 69/200\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.0467 - mean_absolute_error: 0.1798\n",
            "Epoch 70/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.0553 - mean_absolute_error: 0.2045\n",
            "Epoch 71/200\n",
            "3/3 [==============================] - 0s 13ms/step - loss: 0.0574 - mean_absolute_error: 0.2097\n",
            "Epoch 72/200\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.0514 - mean_absolute_error: 0.1937\n",
            "Epoch 73/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.0461 - mean_absolute_error: 0.1740\n",
            "Epoch 74/200\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.0431 - mean_absolute_error: 0.1618\n",
            "Epoch 75/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.0427 - mean_absolute_error: 0.1590\n",
            "Epoch 76/200\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.0428 - mean_absolute_error: 0.1606\n",
            "Epoch 77/200\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.0428 - mean_absolute_error: 0.1630\n",
            "Epoch 78/200\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.0427 - mean_absolute_error: 0.1625\n",
            "Epoch 79/200\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.0425 - mean_absolute_error: 0.1630\n",
            "Epoch 80/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.0431 - mean_absolute_error: 0.1664\n",
            "Epoch 81/200\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.0428 - mean_absolute_error: 0.1643\n",
            "Epoch 82/200\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.0419 - mean_absolute_error: 0.1573\n",
            "Epoch 83/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.0430 - mean_absolute_error: 0.1555\n",
            "Epoch 84/200\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.0433 - mean_absolute_error: 0.1542\n",
            "Epoch 85/200\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.0438 - mean_absolute_error: 0.1541\n",
            "Epoch 86/200\n",
            "3/3 [==============================] - 0s 8ms/step - loss: 0.0439 - mean_absolute_error: 0.1537\n",
            "Epoch 87/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.0436 - mean_absolute_error: 0.1534\n",
            "Epoch 88/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.0428 - mean_absolute_error: 0.1533\n",
            "Epoch 89/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.0421 - mean_absolute_error: 0.1580\n",
            "Epoch 90/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.0430 - mean_absolute_error: 0.1657\n",
            "Epoch 91/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.0434 - mean_absolute_error: 0.1689\n",
            "Epoch 92/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.0424 - mean_absolute_error: 0.1628\n",
            "Epoch 93/200\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.0416 - mean_absolute_error: 0.1568\n",
            "Epoch 94/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.0415 - mean_absolute_error: 0.1555\n",
            "Epoch 95/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.0419 - mean_absolute_error: 0.1532\n",
            "Epoch 96/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.0431 - mean_absolute_error: 0.1533\n",
            "Epoch 97/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.0423 - mean_absolute_error: 0.1524\n",
            "Epoch 98/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.0418 - mean_absolute_error: 0.1602\n",
            "Epoch 99/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.0436 - mean_absolute_error: 0.1720\n",
            "Epoch 100/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.0468 - mean_absolute_error: 0.1817\n",
            "Epoch 101/200\n",
            "3/3 [==============================] - 0s 9ms/step - loss: 0.0459 - mean_absolute_error: 0.1783\n",
            "Epoch 102/200\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.0420 - mean_absolute_error: 0.1664\n",
            "Epoch 103/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.0407 - mean_absolute_error: 0.1567\n",
            "Epoch 104/200\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.0408 - mean_absolute_error: 0.1511\n",
            "Epoch 105/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.0410 - mean_absolute_error: 0.1499\n",
            "Epoch 106/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.0408 - mean_absolute_error: 0.1521\n",
            "Epoch 107/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.0406 - mean_absolute_error: 0.1531\n",
            "Epoch 108/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.0406 - mean_absolute_error: 0.1522\n",
            "Epoch 109/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.0408 - mean_absolute_error: 0.1503\n",
            "Epoch 110/200\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.0403 - mean_absolute_error: 0.1511\n",
            "Epoch 111/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.0403 - mean_absolute_error: 0.1554\n",
            "Epoch 112/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.0406 - mean_absolute_error: 0.1593\n",
            "Epoch 113/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.0410 - mean_absolute_error: 0.1622\n",
            "Epoch 114/200\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.0413 - mean_absolute_error: 0.1635\n",
            "Epoch 115/200\n",
            "3/3 [==============================] - 0s 8ms/step - loss: 0.0407 - mean_absolute_error: 0.1587\n",
            "Epoch 116/200\n",
            "3/3 [==============================] - 0s 8ms/step - loss: 0.0401 - mean_absolute_error: 0.1575\n",
            "Epoch 117/200\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.0399 - mean_absolute_error: 0.1574\n",
            "Epoch 118/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.0397 - mean_absolute_error: 0.1502\n",
            "Epoch 119/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.0420 - mean_absolute_error: 0.1481\n",
            "Epoch 120/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.0453 - mean_absolute_error: 0.1522\n",
            "Epoch 121/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.0453 - mean_absolute_error: 0.1518\n",
            "Epoch 122/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.0425 - mean_absolute_error: 0.1485\n",
            "Epoch 123/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.0408 - mean_absolute_error: 0.1481\n",
            "Epoch 124/200\n",
            "3/3 [==============================] - 0s 8ms/step - loss: 0.0401 - mean_absolute_error: 0.1520\n",
            "Epoch 125/200\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.0397 - mean_absolute_error: 0.1538\n",
            "Epoch 126/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.0397 - mean_absolute_error: 0.1544\n",
            "Epoch 127/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.0395 - mean_absolute_error: 0.1523\n",
            "Epoch 128/200\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.0397 - mean_absolute_error: 0.1495\n",
            "Epoch 129/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.0402 - mean_absolute_error: 0.1474\n",
            "Epoch 130/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.0401 - mean_absolute_error: 0.1477\n",
            "Epoch 131/200\n",
            "3/3 [==============================] - 0s 8ms/step - loss: 0.0395 - mean_absolute_error: 0.1488\n",
            "Epoch 132/200\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.0394 - mean_absolute_error: 0.1489\n",
            "Epoch 133/200\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.0392 - mean_absolute_error: 0.1502\n",
            "Epoch 134/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.0397 - mean_absolute_error: 0.1544\n",
            "Epoch 135/200\n",
            "3/3 [==============================] - 0s 8ms/step - loss: 0.0395 - mean_absolute_error: 0.1528\n",
            "Epoch 136/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.0402 - mean_absolute_error: 0.1490\n",
            "Epoch 137/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.0399 - mean_absolute_error: 0.1466\n",
            "Epoch 138/200\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.0391 - mean_absolute_error: 0.1462\n",
            "Epoch 139/200\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.0393 - mean_absolute_error: 0.1526\n",
            "Epoch 140/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.0396 - mean_absolute_error: 0.1583\n",
            "Epoch 141/200\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.0397 - mean_absolute_error: 0.1586\n",
            "Epoch 142/200\n",
            "3/3 [==============================] - 0s 9ms/step - loss: 0.0388 - mean_absolute_error: 0.1540\n",
            "Epoch 143/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.0385 - mean_absolute_error: 0.1493\n",
            "Epoch 144/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.0381 - mean_absolute_error: 0.1478\n",
            "Epoch 145/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.0383 - mean_absolute_error: 0.1497\n",
            "Epoch 146/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.0386 - mean_absolute_error: 0.1515\n",
            "Epoch 147/200\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.0383 - mean_absolute_error: 0.1509\n",
            "Epoch 148/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.0379 - mean_absolute_error: 0.1473\n",
            "Epoch 149/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.0378 - mean_absolute_error: 0.1469\n",
            "Epoch 150/200\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.0375 - mean_absolute_error: 0.1458\n",
            "Epoch 151/200\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.0376 - mean_absolute_error: 0.1431\n",
            "Epoch 152/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.0384 - mean_absolute_error: 0.1439\n",
            "Epoch 153/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.0387 - mean_absolute_error: 0.1441\n",
            "Epoch 154/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.0392 - mean_absolute_error: 0.1452\n",
            "Epoch 155/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.0380 - mean_absolute_error: 0.1433\n",
            "Epoch 156/200\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.0372 - mean_absolute_error: 0.1465\n",
            "Epoch 157/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.0375 - mean_absolute_error: 0.1497\n",
            "Epoch 158/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.0376 - mean_absolute_error: 0.1504\n",
            "Epoch 159/200\n",
            "3/3 [==============================] - 0s 8ms/step - loss: 0.0374 - mean_absolute_error: 0.1486\n",
            "Epoch 160/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.0373 - mean_absolute_error: 0.1469\n",
            "Epoch 161/200\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.0376 - mean_absolute_error: 0.1453\n",
            "Epoch 162/200\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.0397 - mean_absolute_error: 0.1470\n",
            "Epoch 163/200\n",
            "3/3 [==============================] - 0s 8ms/step - loss: 0.0414 - mean_absolute_error: 0.1476\n",
            "Epoch 164/200\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.0395 - mean_absolute_error: 0.1460\n",
            "Epoch 165/200\n",
            "3/3 [==============================] - 0s 9ms/step - loss: 0.0372 - mean_absolute_error: 0.1464\n",
            "Epoch 166/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.0373 - mean_absolute_error: 0.1497\n",
            "Epoch 167/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.0372 - mean_absolute_error: 0.1494\n",
            "Epoch 168/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.0366 - mean_absolute_error: 0.1467\n",
            "Epoch 169/200\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.0372 - mean_absolute_error: 0.1444\n",
            "Epoch 170/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.0397 - mean_absolute_error: 0.1473\n",
            "Epoch 171/200\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.0401 - mean_absolute_error: 0.1482\n",
            "Epoch 172/200\n",
            "3/3 [==============================] - 0s 11ms/step - loss: 0.0386 - mean_absolute_error: 0.1464\n",
            "Epoch 173/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.0371 - mean_absolute_error: 0.1451\n",
            "Epoch 174/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.0365 - mean_absolute_error: 0.1451\n",
            "Epoch 175/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.0365 - mean_absolute_error: 0.1453\n",
            "Epoch 176/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.0363 - mean_absolute_error: 0.1445\n",
            "Epoch 177/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.0362 - mean_absolute_error: 0.1432\n",
            "Epoch 178/200\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.0374 - mean_absolute_error: 0.1433\n",
            "Epoch 179/200\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.0406 - mean_absolute_error: 0.1467\n",
            "Epoch 180/200\n",
            "3/3 [==============================] - 0s 8ms/step - loss: 0.0416 - mean_absolute_error: 0.1481\n",
            "Epoch 181/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.0394 - mean_absolute_error: 0.1437\n",
            "Epoch 182/200\n",
            "3/3 [==============================] - 0s 10ms/step - loss: 0.0374 - mean_absolute_error: 0.1443\n",
            "Epoch 183/200\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.0368 - mean_absolute_error: 0.1480\n",
            "Epoch 184/200\n",
            "3/3 [==============================] - 0s 9ms/step - loss: 0.0381 - mean_absolute_error: 0.1536\n",
            "Epoch 185/200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.0380 - mean_absolute_error: 0.1546\n",
            "Epoch 186/200\n",
            "3/3 [==============================] - 0s 8ms/step - loss: 0.0372 - mean_absolute_error: 0.1493\n",
            "Epoch 187/200\n",
            "3/3 [==============================] - 0s 11ms/step - loss: 0.0372 - mean_absolute_error: 0.1431\n",
            "Epoch 188/200\n",
            "3/3 [==============================] - 0s 10ms/step - loss: 0.0387 - mean_absolute_error: 0.1437\n",
            "Epoch 189/200\n",
            "3/3 [==============================] - 0s 10ms/step - loss: 0.0379 - mean_absolute_error: 0.1431\n",
            "Epoch 190/200\n",
            "3/3 [==============================] - 0s 10ms/step - loss: 0.0368 - mean_absolute_error: 0.1430\n",
            "Epoch 191/200\n",
            "3/3 [==============================] - 0s 10ms/step - loss: 0.0365 - mean_absolute_error: 0.1444\n",
            "Epoch 192/200\n",
            "3/3 [==============================] - 0s 10ms/step - loss: 0.0362 - mean_absolute_error: 0.1444\n",
            "Epoch 193/200\n",
            "3/3 [==============================] - 0s 8ms/step - loss: 0.0359 - mean_absolute_error: 0.1446\n",
            "Epoch 194/200\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.0360 - mean_absolute_error: 0.1464\n",
            "Epoch 195/200\n",
            "3/3 [==============================] - 0s 8ms/step - loss: 0.0359 - mean_absolute_error: 0.1474\n",
            "Epoch 196/200\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.0354 - mean_absolute_error: 0.1440\n",
            "Epoch 197/200\n",
            "3/3 [==============================] - 0s 10ms/step - loss: 0.0366 - mean_absolute_error: 0.1413\n",
            "Epoch 198/200\n",
            "3/3 [==============================] - 0s 8ms/step - loss: 0.0372 - mean_absolute_error: 0.1422\n",
            "Epoch 199/200\n",
            "3/3 [==============================] - 0s 8ms/step - loss: 0.0361 - mean_absolute_error: 0.1415\n",
            "Epoch 200/200\n",
            "3/3 [==============================] - 0s 8ms/step - loss: 0.0359 - mean_absolute_error: 0.1418\n",
            "2/2 [==============================] - 1s 10ms/step - loss: 0.0603 - mean_absolute_error: 0.1936\n",
            "Test score: [0.0603429414331913, 0.19364336133003235]\n",
            "1/1 [==============================] - 1s 651ms/step\n",
            "Predicted changes in price after 1 day: [[7.154521]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = model.predict(X_test)\n",
        "\n",
        "y_pred = y_pred.flatten()\n",
        "y_test = y_test.flatten()\n",
        "\n",
        "plt.figure(figsize=(16, 8))\n",
        "\n",
        "plt.plot(y_test, label='Actual')\n",
        "plt.plot(y_pred, label='Predicted')\n",
        "\n",
        "plt.title('Actual vs Predicted % Price Change')\n",
        "plt.xlabel('Observations')\n",
        "plt.ylabel('% Change')\n",
        "plt.legend(loc='upper right')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 735
        },
        "id": "JIo-h9KORE3X",
        "outputId": "e6b26445-84c9-4e36-bd3d-94f0a42aa53d"
      },
      "execution_count": 158,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 24ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x800 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Function to create model, required for KerasRegressor\n",
        "def create_model(optimizer='adam', neurons=50):\n",
        "    model = Sequential()\n",
        "    model.add(LSTM(neurons, input_shape=(1, X_train.shape[2])))\n",
        "    model.add(Dense(32, activation='relu'))\n",
        "    model.add(Dense(1))\n",
        "    model.compile(loss='mean_squared_error', optimizer=optimizer)\n",
        "    return model\n",
        "\n",
        "# create model\n",
        "model = KerasRegressor(build_fn=create_model, verbose=0)\n",
        "\n",
        "# define the grid search parameters\n",
        "neurons = [50, 100, 150]\n",
        "optimizer = ['SGD', 'RMSprop', 'Adagrad', 'Adadelta', 'Adam', 'Adamax', 'Nadam']\n",
        "batch_size = [10, 20, 40, 60, 80, 100]\n",
        "epochs = [10, 50, 100]\n",
        "\n",
        "param_grid = dict(neurons=neurons, optimizer=optimizer, batch_size=batch_size, epochs=epochs)\n",
        "\n",
        "grid = GridSearchCV(estimator=model, param_grid=param_grid, n_jobs=-1, cv=3)\n",
        "grid_result = grid.fit(X_train, y_train)\n",
        "\n",
        "# summarize results\n",
        "print(\"Best: %f using %s\" % (grid_result.best_score_, grid_result.best_params_))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "2TZB7bzOb0Y6",
        "outputId": "ad61a556-dda6-4bb1-ac53-ac9bd1b056d6"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-68-44ee0f447f5c>:11: DeprecationWarning: KerasRegressor is deprecated, use Sci-Keras (https://github.com/adriangb/scikeras) instead. See https://www.adriangb.com/scikeras/stable/migration.html for help migrating.\n",
            "  model = KerasRegressor(build_fn=create_model, verbose=0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best: -0.045163 using {'batch_size': 80, 'epochs': 100, 'neurons': 150, 'optimizer': 'SGD'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.optimizers import SGD\n",
        "\n",
        "# Define the model\n",
        "model = Sequential()\n",
        "model.add(LSTM(150, input_shape=(1, X_train.shape[2])))\n",
        "model.add(Dense(32, activation='relu'))\n",
        "model.add(Dense(32, activation='relu'))\n",
        "model.add(Dense(1)) \n",
        "\n",
        "sgd = SGD(learning_rate=0.01)\n",
        "\n",
        "# Compile the model\n",
        "model.compile(loss='mse', optimizer=sgd, metrics=['mse'])\n",
        "\n",
        "# Fit the model to the training data\n",
        "model.fit(X_train, y_train, epochs=100, batch_size=80, verbose=1)\n",
        "\n",
        "# Evaluate the model on the test data\n",
        "score = model.evaluate(X_test, y_test, batch_size=16, verbose=1)\n",
        "print('Test score:', score)\n",
        "\n",
        "# Use the model to make predictions on new data\n",
        "X_new = np.array([[0.2, 0.3, 0.4, 0.1, 0.2, 0.3, 0.05, 0.1, 0.15, 0.4, 0.05]])\n",
        "X_new = scaler.transform(X_new)\n",
        "X_new = np.reshape(X_new, (X_new.shape[0], 1, X_new.shape[1]))\n",
        "y_pred = model.predict(X_new)\n",
        "y_pred = y_scaler.inverse_transform(y_pred)\n",
        "print('Predicted changes in price after 1 day:', y_pred)\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "MLPQldGoyGRT",
        "outputId": "6ca0ad4b-4d21-45a9-896d-42d8fea75a6e"
      },
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "1/1 [==============================] - 2s 2s/step - loss: 0.3754 - mse: 0.3754\n",
            "Epoch 2/100\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 0.3469 - mse: 0.3469\n",
            "Epoch 3/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 0.3212 - mse: 0.3212\n",
            "Epoch 4/100\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 0.2976 - mse: 0.2976\n",
            "Epoch 5/100\n",
            "1/1 [==============================] - 0s 16ms/step - loss: 0.2764 - mse: 0.2764\n",
            "Epoch 6/100\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 0.2574 - mse: 0.2574\n",
            "Epoch 7/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 0.2405 - mse: 0.2405\n",
            "Epoch 8/100\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 0.2257 - mse: 0.2257\n",
            "Epoch 9/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 0.2124 - mse: 0.2124\n",
            "Epoch 10/100\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 0.2002 - mse: 0.2002\n",
            "Epoch 11/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 0.1890 - mse: 0.1890\n",
            "Epoch 12/100\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 0.1787 - mse: 0.1787\n",
            "Epoch 13/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 0.1691 - mse: 0.1691\n",
            "Epoch 14/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 0.1602 - mse: 0.1602\n",
            "Epoch 15/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 0.1519 - mse: 0.1519\n",
            "Epoch 16/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 0.1442 - mse: 0.1442\n",
            "Epoch 17/100\n",
            "1/1 [==============================] - 0s 16ms/step - loss: 0.1370 - mse: 0.1370\n",
            "Epoch 18/100\n",
            "1/1 [==============================] - 0s 23ms/step - loss: 0.1303 - mse: 0.1303\n",
            "Epoch 19/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 0.1241 - mse: 0.1241\n",
            "Epoch 20/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 0.1183 - mse: 0.1183\n",
            "Epoch 21/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 0.1129 - mse: 0.1129\n",
            "Epoch 22/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 0.1079 - mse: 0.1079\n",
            "Epoch 23/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 0.1032 - mse: 0.1032\n",
            "Epoch 24/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 0.0989 - mse: 0.0989\n",
            "Epoch 25/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 0.0948 - mse: 0.0948\n",
            "Epoch 26/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 0.0911 - mse: 0.0911\n",
            "Epoch 27/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 0.0876 - mse: 0.0876\n",
            "Epoch 28/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 0.0844 - mse: 0.0844\n",
            "Epoch 29/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 0.0814 - mse: 0.0814\n",
            "Epoch 30/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 0.0786 - mse: 0.0786\n",
            "Epoch 31/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 0.0760 - mse: 0.0760\n",
            "Epoch 32/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 0.0736 - mse: 0.0736\n",
            "Epoch 33/100\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 0.0714 - mse: 0.0714\n",
            "Epoch 34/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 0.0693 - mse: 0.0693\n",
            "Epoch 35/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 0.0674 - mse: 0.0674\n",
            "Epoch 36/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 0.0657 - mse: 0.0657\n",
            "Epoch 37/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 0.0640 - mse: 0.0640\n",
            "Epoch 38/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 0.0625 - mse: 0.0625\n",
            "Epoch 39/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 0.0611 - mse: 0.0611\n",
            "Epoch 40/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 0.0598 - mse: 0.0598\n",
            "Epoch 41/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 0.0586 - mse: 0.0586\n",
            "Epoch 42/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 0.0575 - mse: 0.0575\n",
            "Epoch 43/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 0.0565 - mse: 0.0565\n",
            "Epoch 44/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 0.0555 - mse: 0.0555\n",
            "Epoch 45/100\n",
            "1/1 [==============================] - 0s 17ms/step - loss: 0.0546 - mse: 0.0546\n",
            "Epoch 46/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 0.0538 - mse: 0.0538\n",
            "Epoch 47/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 0.0530 - mse: 0.0530\n",
            "Epoch 48/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 0.0524 - mse: 0.0524\n",
            "Epoch 49/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 0.0517 - mse: 0.0517\n",
            "Epoch 50/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 0.0511 - mse: 0.0511\n",
            "Epoch 51/100\n",
            "1/1 [==============================] - 0s 18ms/step - loss: 0.0506 - mse: 0.0506\n",
            "Epoch 52/100\n",
            "1/1 [==============================] - 0s 19ms/step - loss: 0.0501 - mse: 0.0501\n",
            "Epoch 53/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 0.0496 - mse: 0.0496\n",
            "Epoch 54/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 0.0492 - mse: 0.0492\n",
            "Epoch 55/100\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 0.0488 - mse: 0.0488\n",
            "Epoch 56/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 0.0484 - mse: 0.0484\n",
            "Epoch 57/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 0.0480 - mse: 0.0480\n",
            "Epoch 58/100\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 0.0477 - mse: 0.0477\n",
            "Epoch 59/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 0.0474 - mse: 0.0474\n",
            "Epoch 60/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 0.0472 - mse: 0.0472\n",
            "Epoch 61/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 0.0469 - mse: 0.0469\n",
            "Epoch 62/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 0.0467 - mse: 0.0467\n",
            "Epoch 63/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 0.0465 - mse: 0.0465\n",
            "Epoch 64/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 0.0463 - mse: 0.0463\n",
            "Epoch 65/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 0.0461 - mse: 0.0461\n",
            "Epoch 66/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 0.0459 - mse: 0.0459\n",
            "Epoch 67/100\n",
            "1/1 [==============================] - 0s 16ms/step - loss: 0.0458 - mse: 0.0458\n",
            "Epoch 68/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 0.0456 - mse: 0.0456\n",
            "Epoch 69/100\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 0.0455 - mse: 0.0455\n",
            "Epoch 70/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 0.0454 - mse: 0.0454\n",
            "Epoch 71/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 0.0453 - mse: 0.0453\n",
            "Epoch 72/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 0.0452 - mse: 0.0452\n",
            "Epoch 73/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 0.0451 - mse: 0.0451\n",
            "Epoch 74/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 0.0450 - mse: 0.0450\n",
            "Epoch 75/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 0.0449 - mse: 0.0449\n",
            "Epoch 76/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 0.0448 - mse: 0.0448\n",
            "Epoch 77/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 0.0448 - mse: 0.0448\n",
            "Epoch 78/100\n",
            "1/1 [==============================] - 0s 16ms/step - loss: 0.0447 - mse: 0.0447\n",
            "Epoch 79/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 0.0446 - mse: 0.0446\n",
            "Epoch 80/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 0.0446 - mse: 0.0446\n",
            "Epoch 81/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 0.0445 - mse: 0.0445\n",
            "Epoch 82/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 0.0445 - mse: 0.0445\n",
            "Epoch 83/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 0.0445 - mse: 0.0445\n",
            "Epoch 84/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 0.0444 - mse: 0.0444\n",
            "Epoch 85/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 0.0444 - mse: 0.0444\n",
            "Epoch 86/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 0.0443 - mse: 0.0443\n",
            "Epoch 87/100\n",
            "1/1 [==============================] - 0s 18ms/step - loss: 0.0443 - mse: 0.0443\n",
            "Epoch 88/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 0.0443 - mse: 0.0443\n",
            "Epoch 89/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 0.0443 - mse: 0.0443\n",
            "Epoch 90/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 0.0442 - mse: 0.0442\n",
            "Epoch 91/100\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 0.0442 - mse: 0.0442\n",
            "Epoch 92/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 0.0442 - mse: 0.0442\n",
            "Epoch 93/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 0.0442 - mse: 0.0442\n",
            "Epoch 94/100\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 0.0441 - mse: 0.0441\n",
            "Epoch 95/100\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 0.0441 - mse: 0.0441\n",
            "Epoch 96/100\n",
            "1/1 [==============================] - 0s 19ms/step - loss: 0.0441 - mse: 0.0441\n",
            "Epoch 97/100\n",
            "1/1 [==============================] - 0s 18ms/step - loss: 0.0441 - mse: 0.0441\n",
            "Epoch 98/100\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 0.0441 - mse: 0.0441\n",
            "Epoch 99/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 0.0441 - mse: 0.0441\n",
            "Epoch 100/100\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 0.0441 - mse: 0.0441\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0418 - mse: 0.0418\n",
            "Test score: [0.04181037098169327, 0.04181037098169327]\n",
            "1/1 [==============================] - 0s 472ms/step\n",
            "Predicted changes in price after 1 day: [[4.64687]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def create_model():\n",
        "    model = Sequential()\n",
        "    model.add(LSTM(64, input_shape=(1, X_train.shape[2])))\n",
        "    model.add(Dense(32, activation='relu'))\n",
        "    model.add(Dense(32, activation='relu'))\n",
        "    model.add(Dense(1))  # One output neurons\n",
        "\n",
        "    # Compiling the model\n",
        "    model.compile(loss='mse', optimizer=Adam(learning_rate=0.001), metrics=['mean_absolute_error'])\n",
        "    return model\n",
        "\n",
        "model = KerasRegressor(build_fn=create_model, epochs=200, batch_size=36, verbose=1)\n",
        "\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.model_selection import KFold\n",
        "\n",
        "# define 5-fold cross validation test harness\n",
        "kfold = KFold(n_splits=5, shuffle=True)\n",
        "results = cross_val_score(model, X_train, y_train, cv=kfold)\n",
        "\n",
        "print(\"Results: %.2f (%.2f) MSE\" % (results.mean(), results.std()))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "GdfBlVvXKom6",
        "outputId": "817d443f-7e90-497b-dfc3-2c3e40d2cf1c"
      },
      "execution_count": 159,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-159-3afcf7bf6c11>:12: DeprecationWarning: KerasRegressor is deprecated, use Sci-Keras (https://github.com/adriangb/scikeras) instead. See https://www.adriangb.com/scikeras/stable/migration.html for help migrating.\n",
            "  model = KerasRegressor(build_fn=create_model, epochs=200, batch_size=36, verbose=1)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/200\n",
            "2/2 [==============================] - 2s 10ms/step - loss: 0.3721 - mean_absolute_error: 0.5704\n",
            "Epoch 2/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.3354 - mean_absolute_error: 0.5386\n",
            "Epoch 3/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.3065 - mean_absolute_error: 0.5117\n",
            "Epoch 4/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.2806 - mean_absolute_error: 0.4881\n",
            "Epoch 5/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.2542 - mean_absolute_error: 0.4637\n",
            "Epoch 6/200\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 0.2264 - mean_absolute_error: 0.4367\n",
            "Epoch 7/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.1981 - mean_absolute_error: 0.4068\n",
            "Epoch 8/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.1715 - mean_absolute_error: 0.3774\n",
            "Epoch 9/200\n",
            "2/2 [==============================] - 0s 17ms/step - loss: 0.1430 - mean_absolute_error: 0.3430\n",
            "Epoch 10/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.1156 - mean_absolute_error: 0.3080\n",
            "Epoch 11/200\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 0.0932 - mean_absolute_error: 0.2717\n",
            "Epoch 12/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0736 - mean_absolute_error: 0.2391\n",
            "Epoch 13/200\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 0.0618 - mean_absolute_error: 0.2098\n",
            "Epoch 14/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0547 - mean_absolute_error: 0.1874\n",
            "Epoch 15/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0567 - mean_absolute_error: 0.1836\n",
            "Epoch 16/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0611 - mean_absolute_error: 0.1850\n",
            "Epoch 17/200\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 0.0660 - mean_absolute_error: 0.1910\n",
            "Epoch 18/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0675 - mean_absolute_error: 0.1925\n",
            "Epoch 19/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0661 - mean_absolute_error: 0.1913\n",
            "Epoch 20/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0623 - mean_absolute_error: 0.1861\n",
            "Epoch 21/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0587 - mean_absolute_error: 0.1807\n",
            "Epoch 22/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0561 - mean_absolute_error: 0.1793\n",
            "Epoch 23/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0548 - mean_absolute_error: 0.1800\n",
            "Epoch 24/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0541 - mean_absolute_error: 0.1807\n",
            "Epoch 25/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0542 - mean_absolute_error: 0.1846\n",
            "Epoch 26/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0546 - mean_absolute_error: 0.1868\n",
            "Epoch 27/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0546 - mean_absolute_error: 0.1884\n",
            "Epoch 28/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0545 - mean_absolute_error: 0.1883\n",
            "Epoch 29/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0541 - mean_absolute_error: 0.1871\n",
            "Epoch 30/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0538 - mean_absolute_error: 0.1855\n",
            "Epoch 31/200\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 0.0530 - mean_absolute_error: 0.1824\n",
            "Epoch 32/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0527 - mean_absolute_error: 0.1800\n",
            "Epoch 33/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0524 - mean_absolute_error: 0.1778\n",
            "Epoch 34/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0524 - mean_absolute_error: 0.1763\n",
            "Epoch 35/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0523 - mean_absolute_error: 0.1750\n",
            "Epoch 36/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0522 - mean_absolute_error: 0.1742\n",
            "Epoch 37/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0522 - mean_absolute_error: 0.1742\n",
            "Epoch 38/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0520 - mean_absolute_error: 0.1738\n",
            "Epoch 39/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0517 - mean_absolute_error: 0.1734\n",
            "Epoch 40/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0515 - mean_absolute_error: 0.1737\n",
            "Epoch 41/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0511 - mean_absolute_error: 0.1741\n",
            "Epoch 42/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0512 - mean_absolute_error: 0.1749\n",
            "Epoch 43/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0511 - mean_absolute_error: 0.1762\n",
            "Epoch 44/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0509 - mean_absolute_error: 0.1764\n",
            "Epoch 45/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0508 - mean_absolute_error: 0.1765\n",
            "Epoch 46/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0506 - mean_absolute_error: 0.1762\n",
            "Epoch 47/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0504 - mean_absolute_error: 0.1752\n",
            "Epoch 48/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0503 - mean_absolute_error: 0.1745\n",
            "Epoch 49/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0500 - mean_absolute_error: 0.1730\n",
            "Epoch 50/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0499 - mean_absolute_error: 0.1723\n",
            "Epoch 51/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0498 - mean_absolute_error: 0.1719\n",
            "Epoch 52/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0496 - mean_absolute_error: 0.1714\n",
            "Epoch 53/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0494 - mean_absolute_error: 0.1713\n",
            "Epoch 54/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0493 - mean_absolute_error: 0.1713\n",
            "Epoch 55/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0492 - mean_absolute_error: 0.1718\n",
            "Epoch 56/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0490 - mean_absolute_error: 0.1716\n",
            "Epoch 57/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0488 - mean_absolute_error: 0.1713\n",
            "Epoch 58/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0487 - mean_absolute_error: 0.1712\n",
            "Epoch 59/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0485 - mean_absolute_error: 0.1706\n",
            "Epoch 60/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0484 - mean_absolute_error: 0.1708\n",
            "Epoch 61/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0483 - mean_absolute_error: 0.1707\n",
            "Epoch 62/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0481 - mean_absolute_error: 0.1700\n",
            "Epoch 63/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0481 - mean_absolute_error: 0.1691\n",
            "Epoch 64/200\n",
            "2/2 [==============================] - 0s 14ms/step - loss: 0.0477 - mean_absolute_error: 0.1681\n",
            "Epoch 65/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0476 - mean_absolute_error: 0.1677\n",
            "Epoch 66/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0474 - mean_absolute_error: 0.1671\n",
            "Epoch 67/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0472 - mean_absolute_error: 0.1667\n",
            "Epoch 68/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0472 - mean_absolute_error: 0.1668\n",
            "Epoch 69/200\n",
            "2/2 [==============================] - 0s 14ms/step - loss: 0.0470 - mean_absolute_error: 0.1666\n",
            "Epoch 70/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0470 - mean_absolute_error: 0.1660\n",
            "Epoch 71/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0467 - mean_absolute_error: 0.1657\n",
            "Epoch 72/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0465 - mean_absolute_error: 0.1653\n",
            "Epoch 73/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0464 - mean_absolute_error: 0.1651\n",
            "Epoch 74/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0462 - mean_absolute_error: 0.1647\n",
            "Epoch 75/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0462 - mean_absolute_error: 0.1644\n",
            "Epoch 76/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0461 - mean_absolute_error: 0.1641\n",
            "Epoch 77/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0459 - mean_absolute_error: 0.1635\n",
            "Epoch 78/200\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 0.0459 - mean_absolute_error: 0.1638\n",
            "Epoch 79/200\n",
            "2/2 [==============================] - 0s 14ms/step - loss: 0.0459 - mean_absolute_error: 0.1639\n",
            "Epoch 80/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0455 - mean_absolute_error: 0.1633\n",
            "Epoch 81/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0453 - mean_absolute_error: 0.1629\n",
            "Epoch 82/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0451 - mean_absolute_error: 0.1624\n",
            "Epoch 83/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0450 - mean_absolute_error: 0.1620\n",
            "Epoch 84/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0449 - mean_absolute_error: 0.1618\n",
            "Epoch 85/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0448 - mean_absolute_error: 0.1622\n",
            "Epoch 86/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0446 - mean_absolute_error: 0.1628\n",
            "Epoch 87/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0447 - mean_absolute_error: 0.1626\n",
            "Epoch 88/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0443 - mean_absolute_error: 0.1623\n",
            "Epoch 89/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0443 - mean_absolute_error: 0.1629\n",
            "Epoch 90/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0442 - mean_absolute_error: 0.1627\n",
            "Epoch 91/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0440 - mean_absolute_error: 0.1618\n",
            "Epoch 92/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0438 - mean_absolute_error: 0.1605\n",
            "Epoch 93/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0437 - mean_absolute_error: 0.1600\n",
            "Epoch 94/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0436 - mean_absolute_error: 0.1594\n",
            "Epoch 95/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0434 - mean_absolute_error: 0.1586\n",
            "Epoch 96/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0433 - mean_absolute_error: 0.1583\n",
            "Epoch 97/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0431 - mean_absolute_error: 0.1584\n",
            "Epoch 98/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0436 - mean_absolute_error: 0.1603\n",
            "Epoch 99/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0430 - mean_absolute_error: 0.1591\n",
            "Epoch 100/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0427 - mean_absolute_error: 0.1572\n",
            "Epoch 101/200\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 0.0426 - mean_absolute_error: 0.1563\n",
            "Epoch 102/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0424 - mean_absolute_error: 0.1558\n",
            "Epoch 103/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0424 - mean_absolute_error: 0.1563\n",
            "Epoch 104/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0420 - mean_absolute_error: 0.1560\n",
            "Epoch 105/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0417 - mean_absolute_error: 0.1558\n",
            "Epoch 106/200\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 0.0416 - mean_absolute_error: 0.1554\n",
            "Epoch 107/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0415 - mean_absolute_error: 0.1548\n",
            "Epoch 108/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0413 - mean_absolute_error: 0.1542\n",
            "Epoch 109/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0411 - mean_absolute_error: 0.1532\n",
            "Epoch 110/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0410 - mean_absolute_error: 0.1523\n",
            "Epoch 111/200\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 0.0409 - mean_absolute_error: 0.1514\n",
            "Epoch 112/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0407 - mean_absolute_error: 0.1512\n",
            "Epoch 113/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0406 - mean_absolute_error: 0.1515\n",
            "Epoch 114/200\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 0.0405 - mean_absolute_error: 0.1521\n",
            "Epoch 115/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0402 - mean_absolute_error: 0.1519\n",
            "Epoch 116/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0399 - mean_absolute_error: 0.1505\n",
            "Epoch 117/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0399 - mean_absolute_error: 0.1499\n",
            "Epoch 118/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0399 - mean_absolute_error: 0.1497\n",
            "Epoch 119/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0396 - mean_absolute_error: 0.1493\n",
            "Epoch 120/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0393 - mean_absolute_error: 0.1492\n",
            "Epoch 121/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0393 - mean_absolute_error: 0.1508\n",
            "Epoch 122/200\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 0.0393 - mean_absolute_error: 0.1519\n",
            "Epoch 123/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0391 - mean_absolute_error: 0.1517\n",
            "Epoch 124/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0386 - mean_absolute_error: 0.1491\n",
            "Epoch 125/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0385 - mean_absolute_error: 0.1472\n",
            "Epoch 126/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0387 - mean_absolute_error: 0.1474\n",
            "Epoch 127/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0383 - mean_absolute_error: 0.1468\n",
            "Epoch 128/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0380 - mean_absolute_error: 0.1467\n",
            "Epoch 129/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0381 - mean_absolute_error: 0.1492\n",
            "Epoch 130/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0380 - mean_absolute_error: 0.1495\n",
            "Epoch 131/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0375 - mean_absolute_error: 0.1469\n",
            "Epoch 132/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0375 - mean_absolute_error: 0.1462\n",
            "Epoch 133/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0375 - mean_absolute_error: 0.1464\n",
            "Epoch 134/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0369 - mean_absolute_error: 0.1474\n",
            "Epoch 135/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0374 - mean_absolute_error: 0.1509\n",
            "Epoch 136/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0370 - mean_absolute_error: 0.1478\n",
            "Epoch 137/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0365 - mean_absolute_error: 0.1445\n",
            "Epoch 138/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0370 - mean_absolute_error: 0.1436\n",
            "Epoch 139/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0367 - mean_absolute_error: 0.1434\n",
            "Epoch 140/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0361 - mean_absolute_error: 0.1442\n",
            "Epoch 141/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0361 - mean_absolute_error: 0.1463\n",
            "Epoch 142/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0362 - mean_absolute_error: 0.1473\n",
            "Epoch 143/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0357 - mean_absolute_error: 0.1448\n",
            "Epoch 144/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0356 - mean_absolute_error: 0.1429\n",
            "Epoch 145/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0354 - mean_absolute_error: 0.1416\n",
            "Epoch 146/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0356 - mean_absolute_error: 0.1428\n",
            "Epoch 147/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0352 - mean_absolute_error: 0.1440\n",
            "Epoch 148/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0350 - mean_absolute_error: 0.1427\n",
            "Epoch 149/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0349 - mean_absolute_error: 0.1399\n",
            "Epoch 150/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0353 - mean_absolute_error: 0.1406\n",
            "Epoch 151/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0345 - mean_absolute_error: 0.1399\n",
            "Epoch 152/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0346 - mean_absolute_error: 0.1435\n",
            "Epoch 153/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0350 - mean_absolute_error: 0.1460\n",
            "Epoch 154/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0341 - mean_absolute_error: 0.1424\n",
            "Epoch 155/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0345 - mean_absolute_error: 0.1392\n",
            "Epoch 156/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0352 - mean_absolute_error: 0.1405\n",
            "Epoch 157/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0343 - mean_absolute_error: 0.1396\n",
            "Epoch 158/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0337 - mean_absolute_error: 0.1405\n",
            "Epoch 159/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0335 - mean_absolute_error: 0.1403\n",
            "Epoch 160/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0336 - mean_absolute_error: 0.1393\n",
            "Epoch 161/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0335 - mean_absolute_error: 0.1400\n",
            "Epoch 162/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0331 - mean_absolute_error: 0.1401\n",
            "Epoch 163/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0339 - mean_absolute_error: 0.1403\n",
            "Epoch 164/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0333 - mean_absolute_error: 0.1397\n",
            "Epoch 165/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0330 - mean_absolute_error: 0.1415\n",
            "Epoch 166/200\n",
            "2/2 [==============================] - 0s 15ms/step - loss: 0.0328 - mean_absolute_error: 0.1399\n",
            "Epoch 167/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0332 - mean_absolute_error: 0.1386\n",
            "Epoch 168/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0328 - mean_absolute_error: 0.1387\n",
            "Epoch 169/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0327 - mean_absolute_error: 0.1410\n",
            "Epoch 170/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0326 - mean_absolute_error: 0.1407\n",
            "Epoch 171/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0324 - mean_absolute_error: 0.1403\n",
            "Epoch 172/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0320 - mean_absolute_error: 0.1383\n",
            "Epoch 173/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0326 - mean_absolute_error: 0.1387\n",
            "Epoch 174/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0322 - mean_absolute_error: 0.1380\n",
            "Epoch 175/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0316 - mean_absolute_error: 0.1375\n",
            "Epoch 176/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0324 - mean_absolute_error: 0.1412\n",
            "Epoch 177/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0326 - mean_absolute_error: 0.1414\n",
            "Epoch 178/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0315 - mean_absolute_error: 0.1381\n",
            "Epoch 179/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0314 - mean_absolute_error: 0.1378\n",
            "Epoch 180/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0320 - mean_absolute_error: 0.1383\n",
            "Epoch 181/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0313 - mean_absolute_error: 0.1370\n",
            "Epoch 182/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0316 - mean_absolute_error: 0.1380\n",
            "Epoch 183/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0310 - mean_absolute_error: 0.1372\n",
            "Epoch 184/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0313 - mean_absolute_error: 0.1375\n",
            "Epoch 185/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0310 - mean_absolute_error: 0.1366\n",
            "Epoch 186/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0306 - mean_absolute_error: 0.1370\n",
            "Epoch 187/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0307 - mean_absolute_error: 0.1379\n",
            "Epoch 188/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0305 - mean_absolute_error: 0.1372\n",
            "Epoch 189/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0303 - mean_absolute_error: 0.1369\n",
            "Epoch 190/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0308 - mean_absolute_error: 0.1380\n",
            "Epoch 191/200\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 0.0300 - mean_absolute_error: 0.1360\n",
            "Epoch 192/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0308 - mean_absolute_error: 0.1368\n",
            "Epoch 193/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0312 - mean_absolute_error: 0.1379\n",
            "Epoch 194/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0299 - mean_absolute_error: 0.1352\n",
            "Epoch 195/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0299 - mean_absolute_error: 0.1351\n",
            "Epoch 196/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0298 - mean_absolute_error: 0.1351\n",
            "Epoch 197/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0296 - mean_absolute_error: 0.1344\n",
            "Epoch 198/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0298 - mean_absolute_error: 0.1352\n",
            "Epoch 199/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0293 - mean_absolute_error: 0.1342\n",
            "Epoch 200/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0294 - mean_absolute_error: 0.1346\n",
            "1/1 [==============================] - 1s 778ms/step - loss: 0.0271 - mean_absolute_error: 0.1323\n",
            "Epoch 1/200\n",
            "2/2 [==============================] - 3s 12ms/step - loss: 0.3411 - mean_absolute_error: 0.5442\n",
            "Epoch 2/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.3076 - mean_absolute_error: 0.5127\n",
            "Epoch 3/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.2825 - mean_absolute_error: 0.4877\n",
            "Epoch 4/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.2576 - mean_absolute_error: 0.4633\n",
            "Epoch 5/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.2308 - mean_absolute_error: 0.4363\n",
            "Epoch 6/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.2032 - mean_absolute_error: 0.4069\n",
            "Epoch 7/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.1751 - mean_absolute_error: 0.3734\n",
            "Epoch 8/200\n",
            "2/2 [==============================] - 0s 14ms/step - loss: 0.1475 - mean_absolute_error: 0.3392\n",
            "Epoch 9/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.1228 - mean_absolute_error: 0.3131\n",
            "Epoch 10/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.1001 - mean_absolute_error: 0.2821\n",
            "Epoch 11/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0807 - mean_absolute_error: 0.2514\n",
            "Epoch 12/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0653 - mean_absolute_error: 0.2216\n",
            "Epoch 13/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0545 - mean_absolute_error: 0.1976\n",
            "Epoch 14/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0537 - mean_absolute_error: 0.1860\n",
            "Epoch 15/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0554 - mean_absolute_error: 0.1841\n",
            "Epoch 16/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0591 - mean_absolute_error: 0.1879\n",
            "Epoch 17/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0616 - mean_absolute_error: 0.1921\n",
            "Epoch 18/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0619 - mean_absolute_error: 0.1925\n",
            "Epoch 19/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0593 - mean_absolute_error: 0.1883\n",
            "Epoch 20/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0562 - mean_absolute_error: 0.1841\n",
            "Epoch 21/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0535 - mean_absolute_error: 0.1800\n",
            "Epoch 22/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0520 - mean_absolute_error: 0.1813\n",
            "Epoch 23/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0506 - mean_absolute_error: 0.1810\n",
            "Epoch 24/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0506 - mean_absolute_error: 0.1834\n",
            "Epoch 25/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0509 - mean_absolute_error: 0.1866\n",
            "Epoch 26/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0512 - mean_absolute_error: 0.1888\n",
            "Epoch 27/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0512 - mean_absolute_error: 0.1894\n",
            "Epoch 28/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0507 - mean_absolute_error: 0.1880\n",
            "Epoch 29/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0499 - mean_absolute_error: 0.1845\n",
            "Epoch 30/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0489 - mean_absolute_error: 0.1808\n",
            "Epoch 31/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0485 - mean_absolute_error: 0.1780\n",
            "Epoch 32/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0482 - mean_absolute_error: 0.1758\n",
            "Epoch 33/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0481 - mean_absolute_error: 0.1745\n",
            "Epoch 34/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0481 - mean_absolute_error: 0.1738\n",
            "Epoch 35/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0479 - mean_absolute_error: 0.1733\n",
            "Epoch 36/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0475 - mean_absolute_error: 0.1726\n",
            "Epoch 37/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0472 - mean_absolute_error: 0.1731\n",
            "Epoch 38/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0470 - mean_absolute_error: 0.1742\n",
            "Epoch 39/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0466 - mean_absolute_error: 0.1741\n",
            "Epoch 40/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0464 - mean_absolute_error: 0.1737\n",
            "Epoch 41/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0463 - mean_absolute_error: 0.1728\n",
            "Epoch 42/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0459 - mean_absolute_error: 0.1721\n",
            "Epoch 43/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0458 - mean_absolute_error: 0.1722\n",
            "Epoch 44/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0454 - mean_absolute_error: 0.1716\n",
            "Epoch 45/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0451 - mean_absolute_error: 0.1704\n",
            "Epoch 46/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0449 - mean_absolute_error: 0.1695\n",
            "Epoch 47/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0447 - mean_absolute_error: 0.1686\n",
            "Epoch 48/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0445 - mean_absolute_error: 0.1677\n",
            "Epoch 49/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0442 - mean_absolute_error: 0.1672\n",
            "Epoch 50/200\n",
            "2/2 [==============================] - 0s 14ms/step - loss: 0.0440 - mean_absolute_error: 0.1677\n",
            "Epoch 51/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0437 - mean_absolute_error: 0.1677\n",
            "Epoch 52/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0433 - mean_absolute_error: 0.1670\n",
            "Epoch 53/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0431 - mean_absolute_error: 0.1655\n",
            "Epoch 54/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0428 - mean_absolute_error: 0.1645\n",
            "Epoch 55/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0427 - mean_absolute_error: 0.1639\n",
            "Epoch 56/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0423 - mean_absolute_error: 0.1633\n",
            "Epoch 57/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0420 - mean_absolute_error: 0.1636\n",
            "Epoch 58/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0419 - mean_absolute_error: 0.1653\n",
            "Epoch 59/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0416 - mean_absolute_error: 0.1653\n",
            "Epoch 60/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0412 - mean_absolute_error: 0.1639\n",
            "Epoch 61/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0410 - mean_absolute_error: 0.1621\n",
            "Epoch 62/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0408 - mean_absolute_error: 0.1616\n",
            "Epoch 63/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0404 - mean_absolute_error: 0.1612\n",
            "Epoch 64/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0401 - mean_absolute_error: 0.1608\n",
            "Epoch 65/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0399 - mean_absolute_error: 0.1607\n",
            "Epoch 66/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0397 - mean_absolute_error: 0.1602\n",
            "Epoch 67/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0396 - mean_absolute_error: 0.1605\n",
            "Epoch 68/200\n",
            "2/2 [==============================] - 0s 14ms/step - loss: 0.0391 - mean_absolute_error: 0.1597\n",
            "Epoch 69/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0391 - mean_absolute_error: 0.1588\n",
            "Epoch 70/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0388 - mean_absolute_error: 0.1579\n",
            "Epoch 71/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0385 - mean_absolute_error: 0.1579\n",
            "Epoch 72/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0382 - mean_absolute_error: 0.1584\n",
            "Epoch 73/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0381 - mean_absolute_error: 0.1584\n",
            "Epoch 74/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0379 - mean_absolute_error: 0.1576\n",
            "Epoch 75/200\n",
            "2/2 [==============================] - 0s 18ms/step - loss: 0.0376 - mean_absolute_error: 0.1575\n",
            "Epoch 76/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0374 - mean_absolute_error: 0.1570\n",
            "Epoch 77/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0374 - mean_absolute_error: 0.1567\n",
            "Epoch 78/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0369 - mean_absolute_error: 0.1554\n",
            "Epoch 79/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0368 - mean_absolute_error: 0.1560\n",
            "Epoch 80/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0366 - mean_absolute_error: 0.1559\n",
            "Epoch 81/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0363 - mean_absolute_error: 0.1550\n",
            "Epoch 82/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0360 - mean_absolute_error: 0.1532\n",
            "Epoch 83/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0358 - mean_absolute_error: 0.1522\n",
            "Epoch 84/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0358 - mean_absolute_error: 0.1519\n",
            "Epoch 85/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0356 - mean_absolute_error: 0.1511\n",
            "Epoch 86/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0353 - mean_absolute_error: 0.1511\n",
            "Epoch 87/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0351 - mean_absolute_error: 0.1519\n",
            "Epoch 88/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0350 - mean_absolute_error: 0.1516\n",
            "Epoch 89/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0348 - mean_absolute_error: 0.1509\n",
            "Epoch 90/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0346 - mean_absolute_error: 0.1498\n",
            "Epoch 91/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0342 - mean_absolute_error: 0.1492\n",
            "Epoch 92/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0342 - mean_absolute_error: 0.1501\n",
            "Epoch 93/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0340 - mean_absolute_error: 0.1490\n",
            "Epoch 94/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0337 - mean_absolute_error: 0.1480\n",
            "Epoch 95/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0336 - mean_absolute_error: 0.1473\n",
            "Epoch 96/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0336 - mean_absolute_error: 0.1480\n",
            "Epoch 97/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0331 - mean_absolute_error: 0.1464\n",
            "Epoch 98/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0331 - mean_absolute_error: 0.1453\n",
            "Epoch 99/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0331 - mean_absolute_error: 0.1447\n",
            "Epoch 100/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0328 - mean_absolute_error: 0.1446\n",
            "Epoch 101/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0324 - mean_absolute_error: 0.1449\n",
            "Epoch 102/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0324 - mean_absolute_error: 0.1458\n",
            "Epoch 103/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0322 - mean_absolute_error: 0.1451\n",
            "Epoch 104/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0319 - mean_absolute_error: 0.1440\n",
            "Epoch 105/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0321 - mean_absolute_error: 0.1434\n",
            "Epoch 106/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0319 - mean_absolute_error: 0.1423\n",
            "Epoch 107/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0318 - mean_absolute_error: 0.1419\n",
            "Epoch 108/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0313 - mean_absolute_error: 0.1417\n",
            "Epoch 109/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0313 - mean_absolute_error: 0.1428\n",
            "Epoch 110/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0313 - mean_absolute_error: 0.1425\n",
            "Epoch 111/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0310 - mean_absolute_error: 0.1416\n",
            "Epoch 112/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0308 - mean_absolute_error: 0.1409\n",
            "Epoch 113/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0307 - mean_absolute_error: 0.1397\n",
            "Epoch 114/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0309 - mean_absolute_error: 0.1391\n",
            "Epoch 115/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0306 - mean_absolute_error: 0.1388\n",
            "Epoch 116/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0304 - mean_absolute_error: 0.1381\n",
            "Epoch 117/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0304 - mean_absolute_error: 0.1386\n",
            "Epoch 118/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0300 - mean_absolute_error: 0.1389\n",
            "Epoch 119/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0298 - mean_absolute_error: 0.1378\n",
            "Epoch 120/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0297 - mean_absolute_error: 0.1371\n",
            "Epoch 121/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0300 - mean_absolute_error: 0.1373\n",
            "Epoch 122/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0294 - mean_absolute_error: 0.1368\n",
            "Epoch 123/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0299 - mean_absolute_error: 0.1390\n",
            "Epoch 124/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0293 - mean_absolute_error: 0.1372\n",
            "Epoch 125/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0290 - mean_absolute_error: 0.1337\n",
            "Epoch 126/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0302 - mean_absolute_error: 0.1354\n",
            "Epoch 127/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0301 - mean_absolute_error: 0.1355\n",
            "Epoch 128/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0293 - mean_absolute_error: 0.1366\n",
            "Epoch 129/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0289 - mean_absolute_error: 0.1363\n",
            "Epoch 130/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0289 - mean_absolute_error: 0.1359\n",
            "Epoch 131/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0286 - mean_absolute_error: 0.1343\n",
            "Epoch 132/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0285 - mean_absolute_error: 0.1334\n",
            "Epoch 133/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0294 - mean_absolute_error: 0.1348\n",
            "Epoch 134/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0288 - mean_absolute_error: 0.1324\n",
            "Epoch 135/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0283 - mean_absolute_error: 0.1338\n",
            "Epoch 136/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0287 - mean_absolute_error: 0.1351\n",
            "Epoch 137/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0279 - mean_absolute_error: 0.1326\n",
            "Epoch 138/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0290 - mean_absolute_error: 0.1324\n",
            "Epoch 139/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0283 - mean_absolute_error: 0.1311\n",
            "Epoch 140/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0283 - mean_absolute_error: 0.1345\n",
            "Epoch 141/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0287 - mean_absolute_error: 0.1360\n",
            "Epoch 142/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0281 - mean_absolute_error: 0.1343\n",
            "Epoch 143/200\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 0.0276 - mean_absolute_error: 0.1311\n",
            "Epoch 144/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0278 - mean_absolute_error: 0.1313\n",
            "Epoch 145/200\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 0.0275 - mean_absolute_error: 0.1310\n",
            "Epoch 146/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0275 - mean_absolute_error: 0.1315\n",
            "Epoch 147/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0274 - mean_absolute_error: 0.1324\n",
            "Epoch 148/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0273 - mean_absolute_error: 0.1315\n",
            "Epoch 149/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0272 - mean_absolute_error: 0.1307\n",
            "Epoch 150/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0273 - mean_absolute_error: 0.1298\n",
            "Epoch 151/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0273 - mean_absolute_error: 0.1297\n",
            "Epoch 152/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0269 - mean_absolute_error: 0.1299\n",
            "Epoch 153/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0272 - mean_absolute_error: 0.1319\n",
            "Epoch 154/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0278 - mean_absolute_error: 0.1323\n",
            "Epoch 155/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0269 - mean_absolute_error: 0.1304\n",
            "Epoch 156/200\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 0.0273 - mean_absolute_error: 0.1321\n",
            "Epoch 157/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0265 - mean_absolute_error: 0.1289\n",
            "Epoch 158/200\n",
            "2/2 [==============================] - 0s 15ms/step - loss: 0.0271 - mean_absolute_error: 0.1283\n",
            "Epoch 159/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0272 - mean_absolute_error: 0.1284\n",
            "Epoch 160/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0264 - mean_absolute_error: 0.1281\n",
            "Epoch 161/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0273 - mean_absolute_error: 0.1320\n",
            "Epoch 162/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0272 - mean_absolute_error: 0.1319\n",
            "Epoch 163/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0264 - mean_absolute_error: 0.1282\n",
            "Epoch 164/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0265 - mean_absolute_error: 0.1274\n",
            "Epoch 165/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0262 - mean_absolute_error: 0.1280\n",
            "Epoch 166/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0263 - mean_absolute_error: 0.1284\n",
            "Epoch 167/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0263 - mean_absolute_error: 0.1278\n",
            "Epoch 168/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0261 - mean_absolute_error: 0.1273\n",
            "Epoch 169/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0260 - mean_absolute_error: 0.1266\n",
            "Epoch 170/200\n",
            "2/2 [==============================] - 0s 18ms/step - loss: 0.0259 - mean_absolute_error: 0.1264\n",
            "Epoch 171/200\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 0.0259 - mean_absolute_error: 0.1269\n",
            "Epoch 172/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0258 - mean_absolute_error: 0.1269\n",
            "Epoch 173/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0258 - mean_absolute_error: 0.1267\n",
            "Epoch 174/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0259 - mean_absolute_error: 0.1260\n",
            "Epoch 175/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0257 - mean_absolute_error: 0.1251\n",
            "Epoch 176/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0257 - mean_absolute_error: 0.1263\n",
            "Epoch 177/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0257 - mean_absolute_error: 0.1266\n",
            "Epoch 178/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0255 - mean_absolute_error: 0.1244\n",
            "Epoch 179/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0258 - mean_absolute_error: 0.1250\n",
            "Epoch 180/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0257 - mean_absolute_error: 0.1250\n",
            "Epoch 181/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0253 - mean_absolute_error: 0.1256\n",
            "Epoch 182/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0264 - mean_absolute_error: 0.1299\n",
            "Epoch 183/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0256 - mean_absolute_error: 0.1263\n",
            "Epoch 184/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0258 - mean_absolute_error: 0.1259\n",
            "Epoch 185/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0256 - mean_absolute_error: 0.1242\n",
            "Epoch 186/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0251 - mean_absolute_error: 0.1238\n",
            "Epoch 187/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0258 - mean_absolute_error: 0.1274\n",
            "Epoch 188/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0251 - mean_absolute_error: 0.1246\n",
            "Epoch 189/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0254 - mean_absolute_error: 0.1239\n",
            "Epoch 190/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0264 - mean_absolute_error: 0.1267\n",
            "Epoch 191/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0247 - mean_absolute_error: 0.1219\n",
            "Epoch 192/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0257 - mean_absolute_error: 0.1273\n",
            "Epoch 193/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0261 - mean_absolute_error: 0.1289\n",
            "Epoch 194/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0249 - mean_absolute_error: 0.1238\n",
            "Epoch 195/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0258 - mean_absolute_error: 0.1261\n",
            "Epoch 196/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0258 - mean_absolute_error: 0.1258\n",
            "Epoch 197/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0248 - mean_absolute_error: 0.1234\n",
            "Epoch 198/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0249 - mean_absolute_error: 0.1239\n",
            "Epoch 199/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0246 - mean_absolute_error: 0.1227\n",
            "Epoch 200/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0245 - mean_absolute_error: 0.1221\n",
            "1/1 [==============================] - 1s 539ms/step - loss: 0.0603 - mean_absolute_error: 0.1726\n",
            "Epoch 1/200\n",
            "2/2 [==============================] - 2s 19ms/step - loss: 0.3480 - mean_absolute_error: 0.5497\n",
            "Epoch 2/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.3209 - mean_absolute_error: 0.5259\n",
            "Epoch 3/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.2964 - mean_absolute_error: 0.5027\n",
            "Epoch 4/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.2716 - mean_absolute_error: 0.4779\n",
            "Epoch 5/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.2425 - mean_absolute_error: 0.4491\n",
            "Epoch 6/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.2131 - mean_absolute_error: 0.4206\n",
            "Epoch 7/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.1846 - mean_absolute_error: 0.3870\n",
            "Epoch 8/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.1573 - mean_absolute_error: 0.3549\n",
            "Epoch 9/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.1305 - mean_absolute_error: 0.3236\n",
            "Epoch 10/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.1083 - mean_absolute_error: 0.2985\n",
            "Epoch 11/200\n",
            "2/2 [==============================] - 0s 15ms/step - loss: 0.0871 - mean_absolute_error: 0.2711\n",
            "Epoch 12/200\n",
            "2/2 [==============================] - 0s 16ms/step - loss: 0.0718 - mean_absolute_error: 0.2441\n",
            "Epoch 13/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0603 - mean_absolute_error: 0.2175\n",
            "Epoch 14/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0547 - mean_absolute_error: 0.1961\n",
            "Epoch 15/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0542 - mean_absolute_error: 0.1833\n",
            "Epoch 16/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0565 - mean_absolute_error: 0.1788\n",
            "Epoch 17/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0606 - mean_absolute_error: 0.1805\n",
            "Epoch 18/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0623 - mean_absolute_error: 0.1822\n",
            "Epoch 19/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0616 - mean_absolute_error: 0.1814\n",
            "Epoch 20/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0593 - mean_absolute_error: 0.1781\n",
            "Epoch 21/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0562 - mean_absolute_error: 0.1771\n",
            "Epoch 22/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0533 - mean_absolute_error: 0.1775\n",
            "Epoch 23/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0529 - mean_absolute_error: 0.1813\n",
            "Epoch 24/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0520 - mean_absolute_error: 0.1836\n",
            "Epoch 25/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0519 - mean_absolute_error: 0.1876\n",
            "Epoch 26/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0523 - mean_absolute_error: 0.1896\n",
            "Epoch 27/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0522 - mean_absolute_error: 0.1905\n",
            "Epoch 28/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0521 - mean_absolute_error: 0.1904\n",
            "Epoch 29/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0518 - mean_absolute_error: 0.1896\n",
            "Epoch 30/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0515 - mean_absolute_error: 0.1883\n",
            "Epoch 31/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0510 - mean_absolute_error: 0.1867\n",
            "Epoch 32/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0507 - mean_absolute_error: 0.1848\n",
            "Epoch 33/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0504 - mean_absolute_error: 0.1825\n",
            "Epoch 34/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0502 - mean_absolute_error: 0.1808\n",
            "Epoch 35/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0499 - mean_absolute_error: 0.1789\n",
            "Epoch 36/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0498 - mean_absolute_error: 0.1775\n",
            "Epoch 37/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0497 - mean_absolute_error: 0.1762\n",
            "Epoch 38/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0496 - mean_absolute_error: 0.1752\n",
            "Epoch 39/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0495 - mean_absolute_error: 0.1747\n",
            "Epoch 40/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0493 - mean_absolute_error: 0.1744\n",
            "Epoch 41/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0490 - mean_absolute_error: 0.1743\n",
            "Epoch 42/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0488 - mean_absolute_error: 0.1747\n",
            "Epoch 43/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0485 - mean_absolute_error: 0.1753\n",
            "Epoch 44/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0483 - mean_absolute_error: 0.1763\n",
            "Epoch 45/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0483 - mean_absolute_error: 0.1776\n",
            "Epoch 46/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0482 - mean_absolute_error: 0.1782\n",
            "Epoch 47/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0481 - mean_absolute_error: 0.1786\n",
            "Epoch 48/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0479 - mean_absolute_error: 0.1785\n",
            "Epoch 49/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0478 - mean_absolute_error: 0.1779\n",
            "Epoch 50/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0475 - mean_absolute_error: 0.1773\n",
            "Epoch 51/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0473 - mean_absolute_error: 0.1766\n",
            "Epoch 52/200\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 0.0472 - mean_absolute_error: 0.1752\n",
            "Epoch 53/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0469 - mean_absolute_error: 0.1736\n",
            "Epoch 54/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0467 - mean_absolute_error: 0.1727\n",
            "Epoch 55/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0468 - mean_absolute_error: 0.1715\n",
            "Epoch 56/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0463 - mean_absolute_error: 0.1700\n",
            "Epoch 57/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0461 - mean_absolute_error: 0.1699\n",
            "Epoch 58/200\n",
            "2/2 [==============================] - 0s 16ms/step - loss: 0.0460 - mean_absolute_error: 0.1699\n",
            "Epoch 59/200\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 0.0457 - mean_absolute_error: 0.1696\n",
            "Epoch 60/200\n",
            "2/2 [==============================] - 0s 17ms/step - loss: 0.0455 - mean_absolute_error: 0.1696\n",
            "Epoch 61/200\n",
            "2/2 [==============================] - 0s 15ms/step - loss: 0.0452 - mean_absolute_error: 0.1698\n",
            "Epoch 62/200\n",
            "2/2 [==============================] - 0s 15ms/step - loss: 0.0450 - mean_absolute_error: 0.1700\n",
            "Epoch 63/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0448 - mean_absolute_error: 0.1699\n",
            "Epoch 64/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0445 - mean_absolute_error: 0.1693\n",
            "Epoch 65/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0443 - mean_absolute_error: 0.1676\n",
            "Epoch 66/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0441 - mean_absolute_error: 0.1658\n",
            "Epoch 67/200\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 0.0440 - mean_absolute_error: 0.1650\n",
            "Epoch 68/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0437 - mean_absolute_error: 0.1639\n",
            "Epoch 69/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0436 - mean_absolute_error: 0.1630\n",
            "Epoch 70/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0433 - mean_absolute_error: 0.1632\n",
            "Epoch 71/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0428 - mean_absolute_error: 0.1639\n",
            "Epoch 72/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0428 - mean_absolute_error: 0.1654\n",
            "Epoch 73/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0425 - mean_absolute_error: 0.1658\n",
            "Epoch 74/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0423 - mean_absolute_error: 0.1652\n",
            "Epoch 75/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0420 - mean_absolute_error: 0.1639\n",
            "Epoch 76/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0419 - mean_absolute_error: 0.1635\n",
            "Epoch 77/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0414 - mean_absolute_error: 0.1625\n",
            "Epoch 78/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0411 - mean_absolute_error: 0.1609\n",
            "Epoch 79/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0409 - mean_absolute_error: 0.1592\n",
            "Epoch 80/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0408 - mean_absolute_error: 0.1577\n",
            "Epoch 81/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0405 - mean_absolute_error: 0.1573\n",
            "Epoch 82/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0402 - mean_absolute_error: 0.1580\n",
            "Epoch 83/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0400 - mean_absolute_error: 0.1586\n",
            "Epoch 84/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0400 - mean_absolute_error: 0.1597\n",
            "Epoch 85/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0396 - mean_absolute_error: 0.1595\n",
            "Epoch 86/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0392 - mean_absolute_error: 0.1573\n",
            "Epoch 87/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0389 - mean_absolute_error: 0.1558\n",
            "Epoch 88/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0388 - mean_absolute_error: 0.1554\n",
            "Epoch 89/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0385 - mean_absolute_error: 0.1544\n",
            "Epoch 90/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0381 - mean_absolute_error: 0.1532\n",
            "Epoch 91/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0380 - mean_absolute_error: 0.1528\n",
            "Epoch 92/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0378 - mean_absolute_error: 0.1521\n",
            "Epoch 93/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0376 - mean_absolute_error: 0.1511\n",
            "Epoch 94/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0375 - mean_absolute_error: 0.1509\n",
            "Epoch 95/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0371 - mean_absolute_error: 0.1514\n",
            "Epoch 96/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0375 - mean_absolute_error: 0.1537\n",
            "Epoch 97/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0374 - mean_absolute_error: 0.1526\n",
            "Epoch 98/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0366 - mean_absolute_error: 0.1500\n",
            "Epoch 99/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0364 - mean_absolute_error: 0.1483\n",
            "Epoch 100/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0361 - mean_absolute_error: 0.1469\n",
            "Epoch 101/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0360 - mean_absolute_error: 0.1466\n",
            "Epoch 102/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0358 - mean_absolute_error: 0.1464\n",
            "Epoch 103/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0356 - mean_absolute_error: 0.1459\n",
            "Epoch 104/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0354 - mean_absolute_error: 0.1461\n",
            "Epoch 105/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0353 - mean_absolute_error: 0.1457\n",
            "Epoch 106/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0351 - mean_absolute_error: 0.1448\n",
            "Epoch 107/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0350 - mean_absolute_error: 0.1445\n",
            "Epoch 108/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0348 - mean_absolute_error: 0.1439\n",
            "Epoch 109/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0346 - mean_absolute_error: 0.1428\n",
            "Epoch 110/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0345 - mean_absolute_error: 0.1413\n",
            "Epoch 111/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0346 - mean_absolute_error: 0.1407\n",
            "Epoch 112/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0345 - mean_absolute_error: 0.1417\n",
            "Epoch 113/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0343 - mean_absolute_error: 0.1436\n",
            "Epoch 114/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0340 - mean_absolute_error: 0.1424\n",
            "Epoch 115/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0337 - mean_absolute_error: 0.1397\n",
            "Epoch 116/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0336 - mean_absolute_error: 0.1384\n",
            "Epoch 117/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0336 - mean_absolute_error: 0.1377\n",
            "Epoch 118/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0334 - mean_absolute_error: 0.1375\n",
            "Epoch 119/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0331 - mean_absolute_error: 0.1385\n",
            "Epoch 120/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0332 - mean_absolute_error: 0.1397\n",
            "Epoch 121/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0329 - mean_absolute_error: 0.1391\n",
            "Epoch 122/200\n",
            "2/2 [==============================] - 0s 14ms/step - loss: 0.0332 - mean_absolute_error: 0.1382\n",
            "Epoch 123/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0328 - mean_absolute_error: 0.1365\n",
            "Epoch 124/200\n",
            "2/2 [==============================] - 0s 15ms/step - loss: 0.0328 - mean_absolute_error: 0.1368\n",
            "Epoch 125/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0326 - mean_absolute_error: 0.1382\n",
            "Epoch 126/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0324 - mean_absolute_error: 0.1386\n",
            "Epoch 127/200\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 0.0321 - mean_absolute_error: 0.1369\n",
            "Epoch 128/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0319 - mean_absolute_error: 0.1346\n",
            "Epoch 129/200\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 0.0325 - mean_absolute_error: 0.1335\n",
            "Epoch 130/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0326 - mean_absolute_error: 0.1330\n",
            "Epoch 131/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0318 - mean_absolute_error: 0.1335\n",
            "Epoch 132/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0314 - mean_absolute_error: 0.1350\n",
            "Epoch 133/200\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 0.0317 - mean_absolute_error: 0.1360\n",
            "Epoch 134/200\n",
            "2/2 [==============================] - 0s 15ms/step - loss: 0.0315 - mean_absolute_error: 0.1355\n",
            "Epoch 135/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0311 - mean_absolute_error: 0.1325\n",
            "Epoch 136/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0310 - mean_absolute_error: 0.1310\n",
            "Epoch 137/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0306 - mean_absolute_error: 0.1312\n",
            "Epoch 138/200\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 0.0305 - mean_absolute_error: 0.1316\n",
            "Epoch 139/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0309 - mean_absolute_error: 0.1335\n",
            "Epoch 140/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0305 - mean_absolute_error: 0.1322\n",
            "Epoch 141/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0300 - mean_absolute_error: 0.1298\n",
            "Epoch 142/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0307 - mean_absolute_error: 0.1295\n",
            "Epoch 143/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0300 - mean_absolute_error: 0.1275\n",
            "Epoch 144/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0295 - mean_absolute_error: 0.1283\n",
            "Epoch 145/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0298 - mean_absolute_error: 0.1314\n",
            "Epoch 146/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0302 - mean_absolute_error: 0.1336\n",
            "Epoch 147/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0296 - mean_absolute_error: 0.1301\n",
            "Epoch 148/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0293 - mean_absolute_error: 0.1261\n",
            "Epoch 149/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0293 - mean_absolute_error: 0.1256\n",
            "Epoch 150/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0290 - mean_absolute_error: 0.1258\n",
            "Epoch 151/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0285 - mean_absolute_error: 0.1255\n",
            "Epoch 152/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0287 - mean_absolute_error: 0.1274\n",
            "Epoch 153/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0289 - mean_absolute_error: 0.1282\n",
            "Epoch 154/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0285 - mean_absolute_error: 0.1251\n",
            "Epoch 155/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0281 - mean_absolute_error: 0.1236\n",
            "Epoch 156/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0282 - mean_absolute_error: 0.1233\n",
            "Epoch 157/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0281 - mean_absolute_error: 0.1239\n",
            "Epoch 158/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0276 - mean_absolute_error: 0.1236\n",
            "Epoch 159/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0274 - mean_absolute_error: 0.1240\n",
            "Epoch 160/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0274 - mean_absolute_error: 0.1234\n",
            "Epoch 161/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0271 - mean_absolute_error: 0.1208\n",
            "Epoch 162/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0273 - mean_absolute_error: 0.1206\n",
            "Epoch 163/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0269 - mean_absolute_error: 0.1206\n",
            "Epoch 164/200\n",
            "2/2 [==============================] - 0s 14ms/step - loss: 0.0271 - mean_absolute_error: 0.1228\n",
            "Epoch 165/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0267 - mean_absolute_error: 0.1223\n",
            "Epoch 166/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0263 - mean_absolute_error: 0.1210\n",
            "Epoch 167/200\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 0.0261 - mean_absolute_error: 0.1197\n",
            "Epoch 168/200\n",
            "2/2 [==============================] - 0s 14ms/step - loss: 0.0262 - mean_absolute_error: 0.1189\n",
            "Epoch 169/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0266 - mean_absolute_error: 0.1201\n",
            "Epoch 170/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0260 - mean_absolute_error: 0.1199\n",
            "Epoch 171/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0257 - mean_absolute_error: 0.1186\n",
            "Epoch 172/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0254 - mean_absolute_error: 0.1171\n",
            "Epoch 173/200\n",
            "2/2 [==============================] - 0s 15ms/step - loss: 0.0251 - mean_absolute_error: 0.1171\n",
            "Epoch 174/200\n",
            "2/2 [==============================] - 0s 16ms/step - loss: 0.0249 - mean_absolute_error: 0.1176\n",
            "Epoch 175/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0250 - mean_absolute_error: 0.1183\n",
            "Epoch 176/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0248 - mean_absolute_error: 0.1175\n",
            "Epoch 177/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0246 - mean_absolute_error: 0.1165\n",
            "Epoch 178/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0245 - mean_absolute_error: 0.1155\n",
            "Epoch 179/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0242 - mean_absolute_error: 0.1148\n",
            "Epoch 180/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0243 - mean_absolute_error: 0.1151\n",
            "Epoch 181/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0239 - mean_absolute_error: 0.1142\n",
            "Epoch 182/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0240 - mean_absolute_error: 0.1138\n",
            "Epoch 183/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0237 - mean_absolute_error: 0.1135\n",
            "Epoch 184/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0235 - mean_absolute_error: 0.1133\n",
            "Epoch 185/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0234 - mean_absolute_error: 0.1140\n",
            "Epoch 186/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0235 - mean_absolute_error: 0.1151\n",
            "Epoch 187/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0231 - mean_absolute_error: 0.1132\n",
            "Epoch 188/200\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 0.0232 - mean_absolute_error: 0.1126\n",
            "Epoch 189/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0232 - mean_absolute_error: 0.1123\n",
            "Epoch 190/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0228 - mean_absolute_error: 0.1113\n",
            "Epoch 191/200\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 0.0231 - mean_absolute_error: 0.1139\n",
            "Epoch 192/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0224 - mean_absolute_error: 0.1110\n",
            "Epoch 193/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0223 - mean_absolute_error: 0.1102\n",
            "Epoch 194/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0223 - mean_absolute_error: 0.1108\n",
            "Epoch 195/200\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 0.0218 - mean_absolute_error: 0.1098\n",
            "Epoch 196/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0218 - mean_absolute_error: 0.1105\n",
            "Epoch 197/200\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 0.0218 - mean_absolute_error: 0.1115\n",
            "Epoch 198/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0216 - mean_absolute_error: 0.1102\n",
            "Epoch 199/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0213 - mean_absolute_error: 0.1076\n",
            "Epoch 200/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0215 - mean_absolute_error: 0.1091\n",
            "1/1 [==============================] - 1s 508ms/step - loss: 0.0459 - mean_absolute_error: 0.1792\n",
            "Epoch 1/200\n",
            "2/2 [==============================] - 2s 15ms/step - loss: 0.3231 - mean_absolute_error: 0.5280\n",
            "Epoch 2/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.2851 - mean_absolute_error: 0.4942\n",
            "Epoch 3/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.2519 - mean_absolute_error: 0.4605\n",
            "Epoch 4/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.2219 - mean_absolute_error: 0.4289\n",
            "Epoch 5/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.1908 - mean_absolute_error: 0.3945\n",
            "Epoch 6/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.1625 - mean_absolute_error: 0.3585\n",
            "Epoch 7/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.1357 - mean_absolute_error: 0.3264\n",
            "Epoch 8/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.1099 - mean_absolute_error: 0.2941\n",
            "Epoch 9/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0887 - mean_absolute_error: 0.2638\n",
            "Epoch 10/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0709 - mean_absolute_error: 0.2316\n",
            "Epoch 11/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0591 - mean_absolute_error: 0.2070\n",
            "Epoch 12/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0528 - mean_absolute_error: 0.1866\n",
            "Epoch 13/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0510 - mean_absolute_error: 0.1784\n",
            "Epoch 14/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0536 - mean_absolute_error: 0.1780\n",
            "Epoch 15/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0568 - mean_absolute_error: 0.1840\n",
            "Epoch 16/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0588 - mean_absolute_error: 0.1871\n",
            "Epoch 17/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0582 - mean_absolute_error: 0.1860\n",
            "Epoch 18/200\n",
            "2/2 [==============================] - 0s 14ms/step - loss: 0.0560 - mean_absolute_error: 0.1824\n",
            "Epoch 19/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0532 - mean_absolute_error: 0.1776\n",
            "Epoch 20/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0508 - mean_absolute_error: 0.1740\n",
            "Epoch 21/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0496 - mean_absolute_error: 0.1746\n",
            "Epoch 22/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0488 - mean_absolute_error: 0.1754\n",
            "Epoch 23/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0490 - mean_absolute_error: 0.1769\n",
            "Epoch 24/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0493 - mean_absolute_error: 0.1795\n",
            "Epoch 25/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0492 - mean_absolute_error: 0.1802\n",
            "Epoch 26/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0491 - mean_absolute_error: 0.1800\n",
            "Epoch 27/200\n",
            "2/2 [==============================] - 0s 15ms/step - loss: 0.0489 - mean_absolute_error: 0.1789\n",
            "Epoch 28/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0483 - mean_absolute_error: 0.1767\n",
            "Epoch 29/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0482 - mean_absolute_error: 0.1747\n",
            "Epoch 30/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0476 - mean_absolute_error: 0.1733\n",
            "Epoch 31/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0478 - mean_absolute_error: 0.1724\n",
            "Epoch 32/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0474 - mean_absolute_error: 0.1717\n",
            "Epoch 33/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0472 - mean_absolute_error: 0.1710\n",
            "Epoch 34/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0471 - mean_absolute_error: 0.1708\n",
            "Epoch 35/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0468 - mean_absolute_error: 0.1703\n",
            "Epoch 36/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0466 - mean_absolute_error: 0.1694\n",
            "Epoch 37/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0464 - mean_absolute_error: 0.1690\n",
            "Epoch 38/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0461 - mean_absolute_error: 0.1685\n",
            "Epoch 39/200\n",
            "2/2 [==============================] - 0s 17ms/step - loss: 0.0459 - mean_absolute_error: 0.1684\n",
            "Epoch 40/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0457 - mean_absolute_error: 0.1681\n",
            "Epoch 41/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0455 - mean_absolute_error: 0.1679\n",
            "Epoch 42/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0454 - mean_absolute_error: 0.1677\n",
            "Epoch 43/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0452 - mean_absolute_error: 0.1674\n",
            "Epoch 44/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0451 - mean_absolute_error: 0.1677\n",
            "Epoch 45/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0449 - mean_absolute_error: 0.1670\n",
            "Epoch 46/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0447 - mean_absolute_error: 0.1663\n",
            "Epoch 47/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0445 - mean_absolute_error: 0.1658\n",
            "Epoch 48/200\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 0.0444 - mean_absolute_error: 0.1650\n",
            "Epoch 49/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0442 - mean_absolute_error: 0.1643\n",
            "Epoch 50/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0441 - mean_absolute_error: 0.1638\n",
            "Epoch 51/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0441 - mean_absolute_error: 0.1639\n",
            "Epoch 52/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0438 - mean_absolute_error: 0.1634\n",
            "Epoch 53/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0436 - mean_absolute_error: 0.1631\n",
            "Epoch 54/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0435 - mean_absolute_error: 0.1634\n",
            "Epoch 55/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0434 - mean_absolute_error: 0.1631\n",
            "Epoch 56/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0433 - mean_absolute_error: 0.1626\n",
            "Epoch 57/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0431 - mean_absolute_error: 0.1623\n",
            "Epoch 58/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0429 - mean_absolute_error: 0.1615\n",
            "Epoch 59/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0428 - mean_absolute_error: 0.1611\n",
            "Epoch 60/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0430 - mean_absolute_error: 0.1607\n",
            "Epoch 61/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0426 - mean_absolute_error: 0.1605\n",
            "Epoch 62/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0424 - mean_absolute_error: 0.1608\n",
            "Epoch 63/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0423 - mean_absolute_error: 0.1609\n",
            "Epoch 64/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0422 - mean_absolute_error: 0.1610\n",
            "Epoch 65/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0422 - mean_absolute_error: 0.1614\n",
            "Epoch 66/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0420 - mean_absolute_error: 0.1606\n",
            "Epoch 67/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0418 - mean_absolute_error: 0.1589\n",
            "Epoch 68/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0417 - mean_absolute_error: 0.1583\n",
            "Epoch 69/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0415 - mean_absolute_error: 0.1577\n",
            "Epoch 70/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0414 - mean_absolute_error: 0.1589\n",
            "Epoch 71/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0412 - mean_absolute_error: 0.1593\n",
            "Epoch 72/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0411 - mean_absolute_error: 0.1593\n",
            "Epoch 73/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0410 - mean_absolute_error: 0.1580\n",
            "Epoch 74/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0407 - mean_absolute_error: 0.1559\n",
            "Epoch 75/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0406 - mean_absolute_error: 0.1554\n",
            "Epoch 76/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0405 - mean_absolute_error: 0.1548\n",
            "Epoch 77/200\n",
            "2/2 [==============================] - 0s 14ms/step - loss: 0.0402 - mean_absolute_error: 0.1548\n",
            "Epoch 78/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0405 - mean_absolute_error: 0.1569\n",
            "Epoch 79/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0401 - mean_absolute_error: 0.1569\n",
            "Epoch 80/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0401 - mean_absolute_error: 0.1559\n",
            "Epoch 81/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0398 - mean_absolute_error: 0.1541\n",
            "Epoch 82/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0397 - mean_absolute_error: 0.1535\n",
            "Epoch 83/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0395 - mean_absolute_error: 0.1533\n",
            "Epoch 84/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0396 - mean_absolute_error: 0.1537\n",
            "Epoch 85/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0393 - mean_absolute_error: 0.1544\n",
            "Epoch 86/200\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 0.0392 - mean_absolute_error: 0.1537\n",
            "Epoch 87/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0393 - mean_absolute_error: 0.1525\n",
            "Epoch 88/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0390 - mean_absolute_error: 0.1520\n",
            "Epoch 89/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0389 - mean_absolute_error: 0.1532\n",
            "Epoch 90/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0389 - mean_absolute_error: 0.1546\n",
            "Epoch 91/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0389 - mean_absolute_error: 0.1546\n",
            "Epoch 92/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0387 - mean_absolute_error: 0.1534\n",
            "Epoch 93/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0386 - mean_absolute_error: 0.1517\n",
            "Epoch 94/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0386 - mean_absolute_error: 0.1506\n",
            "Epoch 95/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0386 - mean_absolute_error: 0.1505\n",
            "Epoch 96/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0384 - mean_absolute_error: 0.1503\n",
            "Epoch 97/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0384 - mean_absolute_error: 0.1507\n",
            "Epoch 98/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0383 - mean_absolute_error: 0.1505\n",
            "Epoch 99/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0382 - mean_absolute_error: 0.1497\n",
            "Epoch 100/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0381 - mean_absolute_error: 0.1494\n",
            "Epoch 101/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0380 - mean_absolute_error: 0.1491\n",
            "Epoch 102/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0379 - mean_absolute_error: 0.1492\n",
            "Epoch 103/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0382 - mean_absolute_error: 0.1509\n",
            "Epoch 104/200\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 0.0379 - mean_absolute_error: 0.1507\n",
            "Epoch 105/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0381 - mean_absolute_error: 0.1491\n",
            "Epoch 106/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0380 - mean_absolute_error: 0.1483\n",
            "Epoch 107/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0375 - mean_absolute_error: 0.1478\n",
            "Epoch 108/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0377 - mean_absolute_error: 0.1505\n",
            "Epoch 109/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0378 - mean_absolute_error: 0.1514\n",
            "Epoch 110/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0377 - mean_absolute_error: 0.1508\n",
            "Epoch 111/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0378 - mean_absolute_error: 0.1486\n",
            "Epoch 112/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0372 - mean_absolute_error: 0.1472\n",
            "Epoch 113/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0373 - mean_absolute_error: 0.1483\n",
            "Epoch 114/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0371 - mean_absolute_error: 0.1486\n",
            "Epoch 115/200\n",
            "2/2 [==============================] - 0s 15ms/step - loss: 0.0370 - mean_absolute_error: 0.1486\n",
            "Epoch 116/200\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 0.0371 - mean_absolute_error: 0.1480\n",
            "Epoch 117/200\n",
            "2/2 [==============================] - 0s 14ms/step - loss: 0.0369 - mean_absolute_error: 0.1475\n",
            "Epoch 118/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0368 - mean_absolute_error: 0.1475\n",
            "Epoch 119/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0369 - mean_absolute_error: 0.1477\n",
            "Epoch 120/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0367 - mean_absolute_error: 0.1467\n",
            "Epoch 121/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0365 - mean_absolute_error: 0.1465\n",
            "Epoch 122/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0366 - mean_absolute_error: 0.1460\n",
            "Epoch 123/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0365 - mean_absolute_error: 0.1466\n",
            "Epoch 124/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0363 - mean_absolute_error: 0.1462\n",
            "Epoch 125/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0362 - mean_absolute_error: 0.1456\n",
            "Epoch 126/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0361 - mean_absolute_error: 0.1454\n",
            "Epoch 127/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0360 - mean_absolute_error: 0.1452\n",
            "Epoch 128/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0361 - mean_absolute_error: 0.1453\n",
            "Epoch 129/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0359 - mean_absolute_error: 0.1450\n",
            "Epoch 130/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0359 - mean_absolute_error: 0.1446\n",
            "Epoch 131/200\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 0.0358 - mean_absolute_error: 0.1444\n",
            "Epoch 132/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0358 - mean_absolute_error: 0.1445\n",
            "Epoch 133/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0363 - mean_absolute_error: 0.1443\n",
            "Epoch 134/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0355 - mean_absolute_error: 0.1441\n",
            "Epoch 135/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0356 - mean_absolute_error: 0.1449\n",
            "Epoch 136/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0354 - mean_absolute_error: 0.1445\n",
            "Epoch 137/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0354 - mean_absolute_error: 0.1440\n",
            "Epoch 138/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0353 - mean_absolute_error: 0.1436\n",
            "Epoch 139/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0350 - mean_absolute_error: 0.1433\n",
            "Epoch 140/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0350 - mean_absolute_error: 0.1428\n",
            "Epoch 141/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0350 - mean_absolute_error: 0.1423\n",
            "Epoch 142/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0348 - mean_absolute_error: 0.1420\n",
            "Epoch 143/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0349 - mean_absolute_error: 0.1421\n",
            "Epoch 144/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0348 - mean_absolute_error: 0.1418\n",
            "Epoch 145/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0349 - mean_absolute_error: 0.1427\n",
            "Epoch 146/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0345 - mean_absolute_error: 0.1420\n",
            "Epoch 147/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0344 - mean_absolute_error: 0.1419\n",
            "Epoch 148/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0343 - mean_absolute_error: 0.1423\n",
            "Epoch 149/200\n",
            "2/2 [==============================] - 0s 16ms/step - loss: 0.0342 - mean_absolute_error: 0.1418\n",
            "Epoch 150/200\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 0.0341 - mean_absolute_error: 0.1410\n",
            "Epoch 151/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0340 - mean_absolute_error: 0.1401\n",
            "Epoch 152/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0342 - mean_absolute_error: 0.1397\n",
            "Epoch 153/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0342 - mean_absolute_error: 0.1405\n",
            "Epoch 154/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0339 - mean_absolute_error: 0.1404\n",
            "Epoch 155/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0338 - mean_absolute_error: 0.1394\n",
            "Epoch 156/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0336 - mean_absolute_error: 0.1390\n",
            "Epoch 157/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0337 - mean_absolute_error: 0.1389\n",
            "Epoch 158/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0334 - mean_absolute_error: 0.1388\n",
            "Epoch 159/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0332 - mean_absolute_error: 0.1388\n",
            "Epoch 160/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0336 - mean_absolute_error: 0.1404\n",
            "Epoch 161/200\n",
            "2/2 [==============================] - 0s 14ms/step - loss: 0.0335 - mean_absolute_error: 0.1400\n",
            "Epoch 162/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0331 - mean_absolute_error: 0.1387\n",
            "Epoch 163/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0330 - mean_absolute_error: 0.1382\n",
            "Epoch 164/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0329 - mean_absolute_error: 0.1381\n",
            "Epoch 165/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0329 - mean_absolute_error: 0.1382\n",
            "Epoch 166/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0326 - mean_absolute_error: 0.1370\n",
            "Epoch 167/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0325 - mean_absolute_error: 0.1369\n",
            "Epoch 168/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0325 - mean_absolute_error: 0.1369\n",
            "Epoch 169/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0324 - mean_absolute_error: 0.1359\n",
            "Epoch 170/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0325 - mean_absolute_error: 0.1353\n",
            "Epoch 171/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0319 - mean_absolute_error: 0.1337\n",
            "Epoch 172/200\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 0.0323 - mean_absolute_error: 0.1353\n",
            "Epoch 173/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0319 - mean_absolute_error: 0.1341\n",
            "Epoch 174/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0320 - mean_absolute_error: 0.1327\n",
            "Epoch 175/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0324 - mean_absolute_error: 0.1345\n",
            "Epoch 176/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0317 - mean_absolute_error: 0.1331\n",
            "Epoch 177/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0314 - mean_absolute_error: 0.1321\n",
            "Epoch 178/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0315 - mean_absolute_error: 0.1334\n",
            "Epoch 179/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0318 - mean_absolute_error: 0.1340\n",
            "Epoch 180/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0312 - mean_absolute_error: 0.1321\n",
            "Epoch 181/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0316 - mean_absolute_error: 0.1345\n",
            "Epoch 182/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0311 - mean_absolute_error: 0.1337\n",
            "Epoch 183/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0308 - mean_absolute_error: 0.1314\n",
            "Epoch 184/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0310 - mean_absolute_error: 0.1318\n",
            "Epoch 185/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0308 - mean_absolute_error: 0.1319\n",
            "Epoch 186/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0306 - mean_absolute_error: 0.1309\n",
            "Epoch 187/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0304 - mean_absolute_error: 0.1310\n",
            "Epoch 188/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0304 - mean_absolute_error: 0.1316\n",
            "Epoch 189/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0302 - mean_absolute_error: 0.1300\n",
            "Epoch 190/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0300 - mean_absolute_error: 0.1293\n",
            "Epoch 191/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0299 - mean_absolute_error: 0.1295\n",
            "Epoch 192/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0298 - mean_absolute_error: 0.1294\n",
            "Epoch 193/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0297 - mean_absolute_error: 0.1287\n",
            "Epoch 194/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0302 - mean_absolute_error: 0.1307\n",
            "Epoch 195/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0294 - mean_absolute_error: 0.1283\n",
            "Epoch 196/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0294 - mean_absolute_error: 0.1286\n",
            "Epoch 197/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0293 - mean_absolute_error: 0.1286\n",
            "Epoch 198/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0291 - mean_absolute_error: 0.1281\n",
            "Epoch 199/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0292 - mean_absolute_error: 0.1283\n",
            "Epoch 200/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0289 - mean_absolute_error: 0.1268\n",
            "1/1 [==============================] - 1s 858ms/step - loss: 0.0394 - mean_absolute_error: 0.1632\n",
            "Epoch 1/200\n",
            "2/2 [==============================] - 3s 11ms/step - loss: 0.4029 - mean_absolute_error: 0.6011\n",
            "Epoch 2/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.3748 - mean_absolute_error: 0.5775\n",
            "Epoch 3/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.3568 - mean_absolute_error: 0.5624\n",
            "Epoch 4/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.3422 - mean_absolute_error: 0.5495\n",
            "Epoch 5/200\n",
            "2/2 [==============================] - 0s 15ms/step - loss: 0.3259 - mean_absolute_error: 0.5350\n",
            "Epoch 6/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.3085 - mean_absolute_error: 0.5186\n",
            "Epoch 7/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.2889 - mean_absolute_error: 0.5002\n",
            "Epoch 8/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.2676 - mean_absolute_error: 0.4795\n",
            "Epoch 9/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.2443 - mean_absolute_error: 0.4562\n",
            "Epoch 10/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.2204 - mean_absolute_error: 0.4311\n",
            "Epoch 11/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.1960 - mean_absolute_error: 0.4041\n",
            "Epoch 12/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.1723 - mean_absolute_error: 0.3752\n",
            "Epoch 13/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.1473 - mean_absolute_error: 0.3473\n",
            "Epoch 14/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.1242 - mean_absolute_error: 0.3190\n",
            "Epoch 15/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.1015 - mean_absolute_error: 0.2895\n",
            "Epoch 16/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0824 - mean_absolute_error: 0.2578\n",
            "Epoch 17/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0671 - mean_absolute_error: 0.2281\n",
            "Epoch 18/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0566 - mean_absolute_error: 0.2001\n",
            "Epoch 19/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0505 - mean_absolute_error: 0.1771\n",
            "Epoch 20/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0543 - mean_absolute_error: 0.1719\n",
            "Epoch 21/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0558 - mean_absolute_error: 0.1718\n",
            "Epoch 22/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0582 - mean_absolute_error: 0.1754\n",
            "Epoch 23/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0580 - mean_absolute_error: 0.1747\n",
            "Epoch 24/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0553 - mean_absolute_error: 0.1713\n",
            "Epoch 25/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0524 - mean_absolute_error: 0.1657\n",
            "Epoch 26/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0509 - mean_absolute_error: 0.1664\n",
            "Epoch 27/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0495 - mean_absolute_error: 0.1712\n",
            "Epoch 28/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0489 - mean_absolute_error: 0.1738\n",
            "Epoch 29/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0494 - mean_absolute_error: 0.1769\n",
            "Epoch 30/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0495 - mean_absolute_error: 0.1779\n",
            "Epoch 31/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0493 - mean_absolute_error: 0.1777\n",
            "Epoch 32/200\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 0.0491 - mean_absolute_error: 0.1766\n",
            "Epoch 33/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0481 - mean_absolute_error: 0.1728\n",
            "Epoch 34/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0476 - mean_absolute_error: 0.1706\n",
            "Epoch 35/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0472 - mean_absolute_error: 0.1680\n",
            "Epoch 36/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0472 - mean_absolute_error: 0.1653\n",
            "Epoch 37/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0468 - mean_absolute_error: 0.1633\n",
            "Epoch 38/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0467 - mean_absolute_error: 0.1621\n",
            "Epoch 39/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0466 - mean_absolute_error: 0.1608\n",
            "Epoch 40/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0463 - mean_absolute_error: 0.1603\n",
            "Epoch 41/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0461 - mean_absolute_error: 0.1609\n",
            "Epoch 42/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0456 - mean_absolute_error: 0.1616\n",
            "Epoch 43/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0453 - mean_absolute_error: 0.1625\n",
            "Epoch 44/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0452 - mean_absolute_error: 0.1633\n",
            "Epoch 45/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0450 - mean_absolute_error: 0.1641\n",
            "Epoch 46/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0447 - mean_absolute_error: 0.1637\n",
            "Epoch 47/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0445 - mean_absolute_error: 0.1624\n",
            "Epoch 48/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0442 - mean_absolute_error: 0.1609\n",
            "Epoch 49/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0438 - mean_absolute_error: 0.1600\n",
            "Epoch 50/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0436 - mean_absolute_error: 0.1598\n",
            "Epoch 51/200\n",
            "2/2 [==============================] - 0s 14ms/step - loss: 0.0433 - mean_absolute_error: 0.1590\n",
            "Epoch 52/200\n",
            "2/2 [==============================] - 0s 16ms/step - loss: 0.0430 - mean_absolute_error: 0.1580\n",
            "Epoch 53/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0427 - mean_absolute_error: 0.1570\n",
            "Epoch 54/200\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 0.0425 - mean_absolute_error: 0.1561\n",
            "Epoch 55/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0422 - mean_absolute_error: 0.1559\n",
            "Epoch 56/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0419 - mean_absolute_error: 0.1556\n",
            "Epoch 57/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0416 - mean_absolute_error: 0.1547\n",
            "Epoch 58/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0414 - mean_absolute_error: 0.1535\n",
            "Epoch 59/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0411 - mean_absolute_error: 0.1525\n",
            "Epoch 60/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0408 - mean_absolute_error: 0.1519\n",
            "Epoch 61/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0405 - mean_absolute_error: 0.1514\n",
            "Epoch 62/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0402 - mean_absolute_error: 0.1513\n",
            "Epoch 63/200\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 0.0399 - mean_absolute_error: 0.1505\n",
            "Epoch 64/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0395 - mean_absolute_error: 0.1495\n",
            "Epoch 65/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0392 - mean_absolute_error: 0.1488\n",
            "Epoch 66/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0390 - mean_absolute_error: 0.1482\n",
            "Epoch 67/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0385 - mean_absolute_error: 0.1470\n",
            "Epoch 68/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0383 - mean_absolute_error: 0.1472\n",
            "Epoch 69/200\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 0.0379 - mean_absolute_error: 0.1469\n",
            "Epoch 70/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0378 - mean_absolute_error: 0.1457\n",
            "Epoch 71/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0373 - mean_absolute_error: 0.1448\n",
            "Epoch 72/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0371 - mean_absolute_error: 0.1444\n",
            "Epoch 73/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0372 - mean_absolute_error: 0.1449\n",
            "Epoch 74/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0365 - mean_absolute_error: 0.1437\n",
            "Epoch 75/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0362 - mean_absolute_error: 0.1416\n",
            "Epoch 76/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0365 - mean_absolute_error: 0.1409\n",
            "Epoch 77/200\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 0.0358 - mean_absolute_error: 0.1399\n",
            "Epoch 78/200\n",
            "2/2 [==============================] - 0s 14ms/step - loss: 0.0355 - mean_absolute_error: 0.1414\n",
            "Epoch 79/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0356 - mean_absolute_error: 0.1424\n",
            "Epoch 80/200\n",
            "2/2 [==============================] - 0s 17ms/step - loss: 0.0352 - mean_absolute_error: 0.1415\n",
            "Epoch 81/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0349 - mean_absolute_error: 0.1396\n",
            "Epoch 82/200\n",
            "2/2 [==============================] - 0s 14ms/step - loss: 0.0344 - mean_absolute_error: 0.1377\n",
            "Epoch 83/200\n",
            "2/2 [==============================] - 0s 15ms/step - loss: 0.0345 - mean_absolute_error: 0.1364\n",
            "Epoch 84/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0343 - mean_absolute_error: 0.1355\n",
            "Epoch 85/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0338 - mean_absolute_error: 0.1352\n",
            "Epoch 86/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0334 - mean_absolute_error: 0.1357\n",
            "Epoch 87/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0332 - mean_absolute_error: 0.1360\n",
            "Epoch 88/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0329 - mean_absolute_error: 0.1353\n",
            "Epoch 89/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0325 - mean_absolute_error: 0.1342\n",
            "Epoch 90/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0327 - mean_absolute_error: 0.1346\n",
            "Epoch 91/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0319 - mean_absolute_error: 0.1328\n",
            "Epoch 92/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0324 - mean_absolute_error: 0.1326\n",
            "Epoch 93/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0319 - mean_absolute_error: 0.1318\n",
            "Epoch 94/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0312 - mean_absolute_error: 0.1319\n",
            "Epoch 95/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0314 - mean_absolute_error: 0.1320\n",
            "Epoch 96/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0318 - mean_absolute_error: 0.1327\n",
            "Epoch 97/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0307 - mean_absolute_error: 0.1302\n",
            "Epoch 98/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0303 - mean_absolute_error: 0.1290\n",
            "Epoch 99/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0305 - mean_absolute_error: 0.1295\n",
            "Epoch 100/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0301 - mean_absolute_error: 0.1284\n",
            "Epoch 101/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0300 - mean_absolute_error: 0.1287\n",
            "Epoch 102/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0300 - mean_absolute_error: 0.1281\n",
            "Epoch 103/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0291 - mean_absolute_error: 0.1270\n",
            "Epoch 104/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0293 - mean_absolute_error: 0.1267\n",
            "Epoch 105/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0294 - mean_absolute_error: 0.1270\n",
            "Epoch 106/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0285 - mean_absolute_error: 0.1256\n",
            "Epoch 107/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0286 - mean_absolute_error: 0.1253\n",
            "Epoch 108/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0291 - mean_absolute_error: 0.1258\n",
            "Epoch 109/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0282 - mean_absolute_error: 0.1236\n",
            "Epoch 110/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0278 - mean_absolute_error: 0.1234\n",
            "Epoch 111/200\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 0.0276 - mean_absolute_error: 0.1232\n",
            "Epoch 112/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0280 - mean_absolute_error: 0.1239\n",
            "Epoch 113/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0272 - mean_absolute_error: 0.1223\n",
            "Epoch 114/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0288 - mean_absolute_error: 0.1235\n",
            "Epoch 115/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0283 - mean_absolute_error: 0.1254\n",
            "Epoch 116/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0266 - mean_absolute_error: 0.1207\n",
            "Epoch 117/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0272 - mean_absolute_error: 0.1214\n",
            "Epoch 118/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0273 - mean_absolute_error: 0.1213\n",
            "Epoch 119/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0262 - mean_absolute_error: 0.1193\n",
            "Epoch 120/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0262 - mean_absolute_error: 0.1193\n",
            "Epoch 121/200\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 0.0263 - mean_absolute_error: 0.1196\n",
            "Epoch 122/200\n",
            "2/2 [==============================] - 0s 14ms/step - loss: 0.0263 - mean_absolute_error: 0.1197\n",
            "Epoch 123/200\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 0.0260 - mean_absolute_error: 0.1183\n",
            "Epoch 124/200\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 0.0257 - mean_absolute_error: 0.1169\n",
            "Epoch 125/200\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 0.0255 - mean_absolute_error: 0.1170\n",
            "Epoch 126/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0253 - mean_absolute_error: 0.1173\n",
            "Epoch 127/200\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 0.0254 - mean_absolute_error: 0.1171\n",
            "Epoch 128/200\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 0.0254 - mean_absolute_error: 0.1157\n",
            "Epoch 129/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0253 - mean_absolute_error: 0.1157\n",
            "Epoch 130/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0247 - mean_absolute_error: 0.1145\n",
            "Epoch 131/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0255 - mean_absolute_error: 0.1173\n",
            "Epoch 132/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0251 - mean_absolute_error: 0.1147\n",
            "Epoch 133/200\n",
            "2/2 [==============================] - 0s 14ms/step - loss: 0.0245 - mean_absolute_error: 0.1133\n",
            "Epoch 134/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0245 - mean_absolute_error: 0.1130\n",
            "Epoch 135/200\n",
            "2/2 [==============================] - 0s 19ms/step - loss: 0.0252 - mean_absolute_error: 0.1150\n",
            "Epoch 136/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0241 - mean_absolute_error: 0.1124\n",
            "Epoch 137/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0241 - mean_absolute_error: 0.1122\n",
            "Epoch 138/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0244 - mean_absolute_error: 0.1127\n",
            "Epoch 139/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0242 - mean_absolute_error: 0.1133\n",
            "Epoch 140/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0239 - mean_absolute_error: 0.1120\n",
            "Epoch 141/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0235 - mean_absolute_error: 0.1109\n",
            "Epoch 142/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0243 - mean_absolute_error: 0.1117\n",
            "Epoch 143/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0232 - mean_absolute_error: 0.1088\n",
            "Epoch 144/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0243 - mean_absolute_error: 0.1137\n",
            "Epoch 145/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0241 - mean_absolute_error: 0.1120\n",
            "Epoch 146/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0239 - mean_absolute_error: 0.1079\n",
            "Epoch 147/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0262 - mean_absolute_error: 0.1203\n",
            "Epoch 148/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0232 - mean_absolute_error: 0.1080\n",
            "Epoch 149/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0242 - mean_absolute_error: 0.1122\n",
            "Epoch 150/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0250 - mean_absolute_error: 0.1139\n",
            "Epoch 151/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0236 - mean_absolute_error: 0.1070\n",
            "Epoch 152/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0239 - mean_absolute_error: 0.1122\n",
            "Epoch 153/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0231 - mean_absolute_error: 0.1091\n",
            "Epoch 154/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0223 - mean_absolute_error: 0.1063\n",
            "Epoch 155/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0234 - mean_absolute_error: 0.1089\n",
            "Epoch 156/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0229 - mean_absolute_error: 0.1066\n",
            "Epoch 157/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0220 - mean_absolute_error: 0.1063\n",
            "Epoch 158/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0231 - mean_absolute_error: 0.1102\n",
            "Epoch 159/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0228 - mean_absolute_error: 0.1090\n",
            "Epoch 160/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0220 - mean_absolute_error: 0.1061\n",
            "Epoch 161/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0225 - mean_absolute_error: 0.1065\n",
            "Epoch 162/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0226 - mean_absolute_error: 0.1055\n",
            "Epoch 163/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0219 - mean_absolute_error: 0.1053\n",
            "Epoch 164/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0221 - mean_absolute_error: 0.1061\n",
            "Epoch 165/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0218 - mean_absolute_error: 0.1058\n",
            "Epoch 166/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0217 - mean_absolute_error: 0.1048\n",
            "Epoch 167/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0219 - mean_absolute_error: 0.1040\n",
            "Epoch 168/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0216 - mean_absolute_error: 0.1034\n",
            "Epoch 169/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0219 - mean_absolute_error: 0.1051\n",
            "Epoch 170/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0216 - mean_absolute_error: 0.1054\n",
            "Epoch 171/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0214 - mean_absolute_error: 0.1030\n",
            "Epoch 172/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0217 - mean_absolute_error: 0.1039\n",
            "Epoch 173/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0212 - mean_absolute_error: 0.1021\n",
            "Epoch 174/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0220 - mean_absolute_error: 0.1064\n",
            "Epoch 175/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0215 - mean_absolute_error: 0.1053\n",
            "Epoch 176/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0223 - mean_absolute_error: 0.1036\n",
            "Epoch 177/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0213 - mean_absolute_error: 0.1025\n",
            "Epoch 178/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0213 - mean_absolute_error: 0.1033\n",
            "Epoch 179/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0217 - mean_absolute_error: 0.1048\n",
            "Epoch 180/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0209 - mean_absolute_error: 0.1033\n",
            "Epoch 181/200\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 0.0207 - mean_absolute_error: 0.1017\n",
            "Epoch 182/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0208 - mean_absolute_error: 0.1014\n",
            "Epoch 183/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0212 - mean_absolute_error: 0.1028\n",
            "Epoch 184/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0212 - mean_absolute_error: 0.1052\n",
            "Epoch 185/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0214 - mean_absolute_error: 0.1061\n",
            "Epoch 186/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0221 - mean_absolute_error: 0.1075\n",
            "Epoch 187/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0207 - mean_absolute_error: 0.1010\n",
            "Epoch 188/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0202 - mean_absolute_error: 0.0999\n",
            "Epoch 189/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0211 - mean_absolute_error: 0.1040\n",
            "Epoch 190/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0207 - mean_absolute_error: 0.1036\n",
            "Epoch 191/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0202 - mean_absolute_error: 0.1000\n",
            "Epoch 192/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0206 - mean_absolute_error: 0.1011\n",
            "Epoch 193/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0199 - mean_absolute_error: 0.1003\n",
            "Epoch 194/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0203 - mean_absolute_error: 0.1019\n",
            "Epoch 195/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0208 - mean_absolute_error: 0.1032\n",
            "Epoch 196/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0204 - mean_absolute_error: 0.1020\n",
            "Epoch 197/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0201 - mean_absolute_error: 0.1001\n",
            "Epoch 198/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0198 - mean_absolute_error: 0.0998\n",
            "Epoch 199/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0199 - mean_absolute_error: 0.1007\n",
            "Epoch 200/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0198 - mean_absolute_error: 0.1009\n",
            "1/1 [==============================] - 1s 685ms/step - loss: 0.1148 - mean_absolute_error: 0.2814\n",
            "Results: -0.06 (0.03) MSE\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import KFold\n",
        "\n",
        "# define 5-fold cross validation test harness\n",
        "kfold = KFold(n_splits=5, shuffle=True)\n",
        "fold_no = 1\n",
        "for train, test in kfold.split(X_train, y_train):\n",
        "    model = create_model()\n",
        "    model.fit(X_train[train], y_train[train], epochs=200, batch_size=36, verbose=1)\n",
        "    \n",
        "    # Make predictions on the test data\n",
        "    y_pred = model.predict(X_train[test])\n",
        "    \n",
        "    # Inverse scale the predicted data\n",
        "    y_pred = y_scaler.inverse_transform(y_pred)\n",
        "    \n",
        "    # Inverse scale the actual data\n",
        "    y_actual = y_scaler.inverse_transform(y_train[test])\n",
        "    \n",
        "    # Reshape for plotting\n",
        "    y_actual = np.reshape(y_actual, y_actual.shape[0])\n",
        "    y_pred = np.reshape(y_pred, y_pred.shape[0])\n",
        "    \n",
        "    # Plot the actual vs predicted data\n",
        "    plt.figure(figsize=(16, 8))\n",
        "\n",
        "    plt.plot(y_actual, label='Actual')\n",
        "    plt.plot(y_pred, label='Predicted')\n",
        "\n",
        "    plt.title('Actual vs Predicted % Price Change - Fold {}'.format(fold_no))\n",
        "    plt.xlabel('Observations')\n",
        "    plt.ylabel('% Change')\n",
        "    plt.legend(loc='upper right')\n",
        "    plt.show()\n",
        "\n",
        "    fold_no += 1\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36207
        },
        "id": "SDEGJYB8LJIa",
        "outputId": "39bc9739-10ba-45d8-db17-532a4a3de04b"
      },
      "execution_count": 161,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/200\n",
            "2/2 [==============================] - 2s 15ms/step - loss: 0.3360 - mean_absolute_error: 0.5440\n",
            "Epoch 2/200\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 0.3000 - mean_absolute_error: 0.5099\n",
            "Epoch 3/200\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 0.2741 - mean_absolute_error: 0.4848\n",
            "Epoch 4/200\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 0.2504 - mean_absolute_error: 0.4601\n",
            "Epoch 5/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.2262 - mean_absolute_error: 0.4348\n",
            "Epoch 6/200\n",
            "2/2 [==============================] - 0s 16ms/step - loss: 0.2007 - mean_absolute_error: 0.4082\n",
            "Epoch 7/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.1748 - mean_absolute_error: 0.3765\n",
            "Epoch 8/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.1485 - mean_absolute_error: 0.3440\n",
            "Epoch 9/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.1246 - mean_absolute_error: 0.3156\n",
            "Epoch 10/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.1026 - mean_absolute_error: 0.2891\n",
            "Epoch 11/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0845 - mean_absolute_error: 0.2650\n",
            "Epoch 12/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0683 - mean_absolute_error: 0.2367\n",
            "Epoch 13/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0561 - mean_absolute_error: 0.2119\n",
            "Epoch 14/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0505 - mean_absolute_error: 0.1910\n",
            "Epoch 15/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0480 - mean_absolute_error: 0.1743\n",
            "Epoch 16/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0491 - mean_absolute_error: 0.1683\n",
            "Epoch 17/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0530 - mean_absolute_error: 0.1710\n",
            "Epoch 18/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0546 - mean_absolute_error: 0.1722\n",
            "Epoch 19/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0542 - mean_absolute_error: 0.1716\n",
            "Epoch 20/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0524 - mean_absolute_error: 0.1697\n",
            "Epoch 21/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0497 - mean_absolute_error: 0.1667\n",
            "Epoch 22/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0473 - mean_absolute_error: 0.1667\n",
            "Epoch 23/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0463 - mean_absolute_error: 0.1689\n",
            "Epoch 24/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0457 - mean_absolute_error: 0.1725\n",
            "Epoch 25/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0459 - mean_absolute_error: 0.1761\n",
            "Epoch 26/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0460 - mean_absolute_error: 0.1782\n",
            "Epoch 27/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0458 - mean_absolute_error: 0.1784\n",
            "Epoch 28/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0454 - mean_absolute_error: 0.1765\n",
            "Epoch 29/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0447 - mean_absolute_error: 0.1742\n",
            "Epoch 30/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0445 - mean_absolute_error: 0.1715\n",
            "Epoch 31/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0441 - mean_absolute_error: 0.1685\n",
            "Epoch 32/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0437 - mean_absolute_error: 0.1666\n",
            "Epoch 33/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0436 - mean_absolute_error: 0.1657\n",
            "Epoch 34/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0433 - mean_absolute_error: 0.1651\n",
            "Epoch 35/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0430 - mean_absolute_error: 0.1651\n",
            "Epoch 36/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0427 - mean_absolute_error: 0.1658\n",
            "Epoch 37/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0428 - mean_absolute_error: 0.1671\n",
            "Epoch 38/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0425 - mean_absolute_error: 0.1678\n",
            "Epoch 39/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0424 - mean_absolute_error: 0.1674\n",
            "Epoch 40/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0421 - mean_absolute_error: 0.1662\n",
            "Epoch 41/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0418 - mean_absolute_error: 0.1653\n",
            "Epoch 42/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0416 - mean_absolute_error: 0.1650\n",
            "Epoch 43/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0416 - mean_absolute_error: 0.1651\n",
            "Epoch 44/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0412 - mean_absolute_error: 0.1644\n",
            "Epoch 45/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0411 - mean_absolute_error: 0.1630\n",
            "Epoch 46/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0409 - mean_absolute_error: 0.1617\n",
            "Epoch 47/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0407 - mean_absolute_error: 0.1606\n",
            "Epoch 48/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0404 - mean_absolute_error: 0.1593\n",
            "Epoch 49/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0403 - mean_absolute_error: 0.1588\n",
            "Epoch 50/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0403 - mean_absolute_error: 0.1586\n",
            "Epoch 51/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0400 - mean_absolute_error: 0.1587\n",
            "Epoch 52/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0399 - mean_absolute_error: 0.1597\n",
            "Epoch 53/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0395 - mean_absolute_error: 0.1599\n",
            "Epoch 54/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0395 - mean_absolute_error: 0.1599\n",
            "Epoch 55/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0392 - mean_absolute_error: 0.1594\n",
            "Epoch 56/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0391 - mean_absolute_error: 0.1587\n",
            "Epoch 57/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0388 - mean_absolute_error: 0.1572\n",
            "Epoch 58/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0387 - mean_absolute_error: 0.1556\n",
            "Epoch 59/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0387 - mean_absolute_error: 0.1538\n",
            "Epoch 60/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0388 - mean_absolute_error: 0.1531\n",
            "Epoch 61/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0383 - mean_absolute_error: 0.1529\n",
            "Epoch 62/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0380 - mean_absolute_error: 0.1545\n",
            "Epoch 63/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0378 - mean_absolute_error: 0.1569\n",
            "Epoch 64/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0379 - mean_absolute_error: 0.1589\n",
            "Epoch 65/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0378 - mean_absolute_error: 0.1592\n",
            "Epoch 66/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0377 - mean_absolute_error: 0.1581\n",
            "Epoch 67/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0371 - mean_absolute_error: 0.1550\n",
            "Epoch 68/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0369 - mean_absolute_error: 0.1532\n",
            "Epoch 69/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0369 - mean_absolute_error: 0.1519\n",
            "Epoch 70/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0369 - mean_absolute_error: 0.1516\n",
            "Epoch 71/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0365 - mean_absolute_error: 0.1511\n",
            "Epoch 72/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0362 - mean_absolute_error: 0.1518\n",
            "Epoch 73/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0363 - mean_absolute_error: 0.1537\n",
            "Epoch 74/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0361 - mean_absolute_error: 0.1542\n",
            "Epoch 75/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0359 - mean_absolute_error: 0.1535\n",
            "Epoch 76/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0355 - mean_absolute_error: 0.1515\n",
            "Epoch 77/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0354 - mean_absolute_error: 0.1495\n",
            "Epoch 78/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0356 - mean_absolute_error: 0.1485\n",
            "Epoch 79/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0358 - mean_absolute_error: 0.1469\n",
            "Epoch 80/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0354 - mean_absolute_error: 0.1466\n",
            "Epoch 81/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0355 - mean_absolute_error: 0.1494\n",
            "Epoch 82/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0347 - mean_absolute_error: 0.1496\n",
            "Epoch 83/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0347 - mean_absolute_error: 0.1502\n",
            "Epoch 84/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0343 - mean_absolute_error: 0.1491\n",
            "Epoch 85/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0344 - mean_absolute_error: 0.1481\n",
            "Epoch 86/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0342 - mean_absolute_error: 0.1464\n",
            "Epoch 87/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0339 - mean_absolute_error: 0.1461\n",
            "Epoch 88/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0339 - mean_absolute_error: 0.1476\n",
            "Epoch 89/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0341 - mean_absolute_error: 0.1491\n",
            "Epoch 90/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0337 - mean_absolute_error: 0.1475\n",
            "Epoch 91/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0332 - mean_absolute_error: 0.1450\n",
            "Epoch 92/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0331 - mean_absolute_error: 0.1437\n",
            "Epoch 93/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0331 - mean_absolute_error: 0.1428\n",
            "Epoch 94/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0330 - mean_absolute_error: 0.1427\n",
            "Epoch 95/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0326 - mean_absolute_error: 0.1427\n",
            "Epoch 96/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0330 - mean_absolute_error: 0.1459\n",
            "Epoch 97/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0326 - mean_absolute_error: 0.1456\n",
            "Epoch 98/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0321 - mean_absolute_error: 0.1432\n",
            "Epoch 99/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0319 - mean_absolute_error: 0.1415\n",
            "Epoch 100/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0320 - mean_absolute_error: 0.1402\n",
            "Epoch 101/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0321 - mean_absolute_error: 0.1404\n",
            "Epoch 102/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0316 - mean_absolute_error: 0.1396\n",
            "Epoch 103/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0315 - mean_absolute_error: 0.1408\n",
            "Epoch 104/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0313 - mean_absolute_error: 0.1416\n",
            "Epoch 105/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0313 - mean_absolute_error: 0.1418\n",
            "Epoch 106/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0310 - mean_absolute_error: 0.1411\n",
            "Epoch 107/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0307 - mean_absolute_error: 0.1391\n",
            "Epoch 108/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0306 - mean_absolute_error: 0.1381\n",
            "Epoch 109/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0306 - mean_absolute_error: 0.1372\n",
            "Epoch 110/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0303 - mean_absolute_error: 0.1366\n",
            "Epoch 111/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0301 - mean_absolute_error: 0.1372\n",
            "Epoch 112/200\n",
            "2/2 [==============================] - 0s 14ms/step - loss: 0.0301 - mean_absolute_error: 0.1385\n",
            "Epoch 113/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0299 - mean_absolute_error: 0.1379\n",
            "Epoch 114/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0299 - mean_absolute_error: 0.1365\n",
            "Epoch 115/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0297 - mean_absolute_error: 0.1355\n",
            "Epoch 116/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0293 - mean_absolute_error: 0.1355\n",
            "Epoch 117/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0294 - mean_absolute_error: 0.1366\n",
            "Epoch 118/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0293 - mean_absolute_error: 0.1351\n",
            "Epoch 119/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0288 - mean_absolute_error: 0.1334\n",
            "Epoch 120/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0287 - mean_absolute_error: 0.1326\n",
            "Epoch 121/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0288 - mean_absolute_error: 0.1332\n",
            "Epoch 122/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0283 - mean_absolute_error: 0.1322\n",
            "Epoch 123/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0283 - mean_absolute_error: 0.1323\n",
            "Epoch 124/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0281 - mean_absolute_error: 0.1313\n",
            "Epoch 125/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0279 - mean_absolute_error: 0.1302\n",
            "Epoch 126/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0279 - mean_absolute_error: 0.1316\n",
            "Epoch 127/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0276 - mean_absolute_error: 0.1307\n",
            "Epoch 128/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0282 - mean_absolute_error: 0.1319\n",
            "Epoch 129/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0272 - mean_absolute_error: 0.1283\n",
            "Epoch 130/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0270 - mean_absolute_error: 0.1286\n",
            "Epoch 131/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0270 - mean_absolute_error: 0.1288\n",
            "Epoch 132/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0269 - mean_absolute_error: 0.1278\n",
            "Epoch 133/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0266 - mean_absolute_error: 0.1272\n",
            "Epoch 134/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0265 - mean_absolute_error: 0.1267\n",
            "Epoch 135/200\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 0.0263 - mean_absolute_error: 0.1259\n",
            "Epoch 136/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0262 - mean_absolute_error: 0.1259\n",
            "Epoch 137/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0263 - mean_absolute_error: 0.1263\n",
            "Epoch 138/200\n",
            "2/2 [==============================] - 0s 16ms/step - loss: 0.0262 - mean_absolute_error: 0.1265\n",
            "Epoch 139/200\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 0.0256 - mean_absolute_error: 0.1236\n",
            "Epoch 140/200\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 0.0261 - mean_absolute_error: 0.1243\n",
            "Epoch 141/200\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 0.0258 - mean_absolute_error: 0.1235\n",
            "Epoch 142/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0252 - mean_absolute_error: 0.1235\n",
            "Epoch 143/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0259 - mean_absolute_error: 0.1257\n",
            "Epoch 144/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0256 - mean_absolute_error: 0.1247\n",
            "Epoch 145/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0253 - mean_absolute_error: 0.1231\n",
            "Epoch 146/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0249 - mean_absolute_error: 0.1214\n",
            "Epoch 147/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0247 - mean_absolute_error: 0.1222\n",
            "Epoch 148/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0245 - mean_absolute_error: 0.1218\n",
            "Epoch 149/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0243 - mean_absolute_error: 0.1203\n",
            "Epoch 150/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0244 - mean_absolute_error: 0.1198\n",
            "Epoch 151/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0243 - mean_absolute_error: 0.1191\n",
            "Epoch 152/200\n",
            "2/2 [==============================] - 0s 15ms/step - loss: 0.0239 - mean_absolute_error: 0.1188\n",
            "Epoch 153/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0239 - mean_absolute_error: 0.1195\n",
            "Epoch 154/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0237 - mean_absolute_error: 0.1185\n",
            "Epoch 155/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0235 - mean_absolute_error: 0.1171\n",
            "Epoch 156/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0235 - mean_absolute_error: 0.1169\n",
            "Epoch 157/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0232 - mean_absolute_error: 0.1173\n",
            "Epoch 158/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0232 - mean_absolute_error: 0.1187\n",
            "Epoch 159/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0240 - mean_absolute_error: 0.1187\n",
            "Epoch 160/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0230 - mean_absolute_error: 0.1179\n",
            "Epoch 161/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0230 - mean_absolute_error: 0.1181\n",
            "Epoch 162/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0226 - mean_absolute_error: 0.1154\n",
            "Epoch 163/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0228 - mean_absolute_error: 0.1145\n",
            "Epoch 164/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0226 - mean_absolute_error: 0.1139\n",
            "Epoch 165/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0224 - mean_absolute_error: 0.1148\n",
            "Epoch 166/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0223 - mean_absolute_error: 0.1153\n",
            "Epoch 167/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0220 - mean_absolute_error: 0.1141\n",
            "Epoch 168/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0223 - mean_absolute_error: 0.1134\n",
            "Epoch 169/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0221 - mean_absolute_error: 0.1135\n",
            "Epoch 170/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0222 - mean_absolute_error: 0.1144\n",
            "Epoch 171/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0227 - mean_absolute_error: 0.1149\n",
            "Epoch 172/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0222 - mean_absolute_error: 0.1129\n",
            "Epoch 173/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0216 - mean_absolute_error: 0.1114\n",
            "Epoch 174/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0216 - mean_absolute_error: 0.1109\n",
            "Epoch 175/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0214 - mean_absolute_error: 0.1106\n",
            "Epoch 176/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0214 - mean_absolute_error: 0.1103\n",
            "Epoch 177/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0219 - mean_absolute_error: 0.1139\n",
            "Epoch 178/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0213 - mean_absolute_error: 0.1121\n",
            "Epoch 179/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0221 - mean_absolute_error: 0.1120\n",
            "Epoch 180/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0219 - mean_absolute_error: 0.1115\n",
            "Epoch 181/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0212 - mean_absolute_error: 0.1114\n",
            "Epoch 182/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0217 - mean_absolute_error: 0.1143\n",
            "Epoch 183/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0211 - mean_absolute_error: 0.1080\n",
            "Epoch 184/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0214 - mean_absolute_error: 0.1089\n",
            "Epoch 185/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0209 - mean_absolute_error: 0.1068\n",
            "Epoch 186/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0209 - mean_absolute_error: 0.1097\n",
            "Epoch 187/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0208 - mean_absolute_error: 0.1101\n",
            "Epoch 188/200\n",
            "2/2 [==============================] - 0s 17ms/step - loss: 0.0202 - mean_absolute_error: 0.1068\n",
            "Epoch 189/200\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 0.0212 - mean_absolute_error: 0.1089\n",
            "Epoch 190/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0205 - mean_absolute_error: 0.1075\n",
            "Epoch 191/200\n",
            "2/2 [==============================] - 0s 16ms/step - loss: 0.0211 - mean_absolute_error: 0.1117\n",
            "Epoch 192/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0212 - mean_absolute_error: 0.1098\n",
            "Epoch 193/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0204 - mean_absolute_error: 0.1059\n",
            "Epoch 194/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0200 - mean_absolute_error: 0.1047\n",
            "Epoch 195/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0202 - mean_absolute_error: 0.1086\n",
            "Epoch 196/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0206 - mean_absolute_error: 0.1101\n",
            "Epoch 197/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0195 - mean_absolute_error: 0.1051\n",
            "Epoch 198/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0215 - mean_absolute_error: 0.1104\n",
            "Epoch 199/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0204 - mean_absolute_error: 0.1075\n",
            "Epoch 200/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0202 - mean_absolute_error: 0.1089\n",
            "1/1 [==============================] - 1s 621ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x800 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/200\n",
            "2/2 [==============================] - 3s 11ms/step - loss: 0.3416 - mean_absolute_error: 0.5399\n",
            "Epoch 2/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.3129 - mean_absolute_error: 0.5141\n",
            "Epoch 3/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.2871 - mean_absolute_error: 0.4899\n",
            "Epoch 4/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.2596 - mean_absolute_error: 0.4633\n",
            "Epoch 5/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.2320 - mean_absolute_error: 0.4363\n",
            "Epoch 6/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.2055 - mean_absolute_error: 0.4079\n",
            "Epoch 7/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.1780 - mean_absolute_error: 0.3760\n",
            "Epoch 8/200\n",
            "2/2 [==============================] - 0s 15ms/step - loss: 0.1522 - mean_absolute_error: 0.3474\n",
            "Epoch 9/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.1276 - mean_absolute_error: 0.3211\n",
            "Epoch 10/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.1037 - mean_absolute_error: 0.2894\n",
            "Epoch 11/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0842 - mean_absolute_error: 0.2570\n",
            "Epoch 12/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0709 - mean_absolute_error: 0.2305\n",
            "Epoch 13/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0611 - mean_absolute_error: 0.2058\n",
            "Epoch 14/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0582 - mean_absolute_error: 0.1947\n",
            "Epoch 15/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0593 - mean_absolute_error: 0.1886\n",
            "Epoch 16/200\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 0.0643 - mean_absolute_error: 0.1948\n",
            "Epoch 17/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0675 - mean_absolute_error: 0.1991\n",
            "Epoch 18/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0674 - mean_absolute_error: 0.1988\n",
            "Epoch 19/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0646 - mean_absolute_error: 0.1957\n",
            "Epoch 20/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0607 - mean_absolute_error: 0.1896\n",
            "Epoch 21/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0580 - mean_absolute_error: 0.1858\n",
            "Epoch 22/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0564 - mean_absolute_error: 0.1865\n",
            "Epoch 23/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0569 - mean_absolute_error: 0.1922\n",
            "Epoch 24/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0572 - mean_absolute_error: 0.1956\n",
            "Epoch 25/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0574 - mean_absolute_error: 0.1972\n",
            "Epoch 26/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0575 - mean_absolute_error: 0.1981\n",
            "Epoch 27/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0574 - mean_absolute_error: 0.1977\n",
            "Epoch 28/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0569 - mean_absolute_error: 0.1962\n",
            "Epoch 29/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0563 - mean_absolute_error: 0.1942\n",
            "Epoch 30/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0558 - mean_absolute_error: 0.1919\n",
            "Epoch 31/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0552 - mean_absolute_error: 0.1894\n",
            "Epoch 32/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0550 - mean_absolute_error: 0.1871\n",
            "Epoch 33/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0548 - mean_absolute_error: 0.1855\n",
            "Epoch 34/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0551 - mean_absolute_error: 0.1841\n",
            "Epoch 35/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0548 - mean_absolute_error: 0.1829\n",
            "Epoch 36/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0546 - mean_absolute_error: 0.1826\n",
            "Epoch 37/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0544 - mean_absolute_error: 0.1826\n",
            "Epoch 38/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0541 - mean_absolute_error: 0.1828\n",
            "Epoch 39/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0540 - mean_absolute_error: 0.1836\n",
            "Epoch 40/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0537 - mean_absolute_error: 0.1840\n",
            "Epoch 41/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0536 - mean_absolute_error: 0.1844\n",
            "Epoch 42/200\n",
            "2/2 [==============================] - 0s 16ms/step - loss: 0.0535 - mean_absolute_error: 0.1848\n",
            "Epoch 43/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0534 - mean_absolute_error: 0.1847\n",
            "Epoch 44/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0532 - mean_absolute_error: 0.1843\n",
            "Epoch 45/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0530 - mean_absolute_error: 0.1836\n",
            "Epoch 46/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0529 - mean_absolute_error: 0.1828\n",
            "Epoch 47/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0527 - mean_absolute_error: 0.1817\n",
            "Epoch 48/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0526 - mean_absolute_error: 0.1812\n",
            "Epoch 49/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0524 - mean_absolute_error: 0.1805\n",
            "Epoch 50/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0523 - mean_absolute_error: 0.1794\n",
            "Epoch 51/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0521 - mean_absolute_error: 0.1792\n",
            "Epoch 52/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0521 - mean_absolute_error: 0.1791\n",
            "Epoch 53/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0518 - mean_absolute_error: 0.1784\n",
            "Epoch 54/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0517 - mean_absolute_error: 0.1779\n",
            "Epoch 55/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0515 - mean_absolute_error: 0.1773\n",
            "Epoch 56/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0514 - mean_absolute_error: 0.1768\n",
            "Epoch 57/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0512 - mean_absolute_error: 0.1766\n",
            "Epoch 58/200\n",
            "2/2 [==============================] - 0s 15ms/step - loss: 0.0510 - mean_absolute_error: 0.1765\n",
            "Epoch 59/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0509 - mean_absolute_error: 0.1769\n",
            "Epoch 60/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0508 - mean_absolute_error: 0.1773\n",
            "Epoch 61/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0506 - mean_absolute_error: 0.1771\n",
            "Epoch 62/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0505 - mean_absolute_error: 0.1768\n",
            "Epoch 63/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0503 - mean_absolute_error: 0.1759\n",
            "Epoch 64/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0504 - mean_absolute_error: 0.1750\n",
            "Epoch 65/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0499 - mean_absolute_error: 0.1737\n",
            "Epoch 66/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0498 - mean_absolute_error: 0.1743\n",
            "Epoch 67/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0497 - mean_absolute_error: 0.1747\n",
            "Epoch 68/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0495 - mean_absolute_error: 0.1740\n",
            "Epoch 69/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0492 - mean_absolute_error: 0.1725\n",
            "Epoch 70/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0491 - mean_absolute_error: 0.1709\n",
            "Epoch 71/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0491 - mean_absolute_error: 0.1701\n",
            "Epoch 72/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0489 - mean_absolute_error: 0.1693\n",
            "Epoch 73/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0488 - mean_absolute_error: 0.1689\n",
            "Epoch 74/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0485 - mean_absolute_error: 0.1686\n",
            "Epoch 75/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0484 - mean_absolute_error: 0.1689\n",
            "Epoch 76/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0481 - mean_absolute_error: 0.1692\n",
            "Epoch 77/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0480 - mean_absolute_error: 0.1693\n",
            "Epoch 78/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0479 - mean_absolute_error: 0.1695\n",
            "Epoch 79/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0478 - mean_absolute_error: 0.1696\n",
            "Epoch 80/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0479 - mean_absolute_error: 0.1700\n",
            "Epoch 81/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0478 - mean_absolute_error: 0.1689\n",
            "Epoch 82/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0473 - mean_absolute_error: 0.1672\n",
            "Epoch 83/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0471 - mean_absolute_error: 0.1666\n",
            "Epoch 84/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0470 - mean_absolute_error: 0.1662\n",
            "Epoch 85/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0470 - mean_absolute_error: 0.1657\n",
            "Epoch 86/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0467 - mean_absolute_error: 0.1656\n",
            "Epoch 87/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0470 - mean_absolute_error: 0.1666\n",
            "Epoch 88/200\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 0.0466 - mean_absolute_error: 0.1668\n",
            "Epoch 89/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0464 - mean_absolute_error: 0.1656\n",
            "Epoch 90/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0465 - mean_absolute_error: 0.1652\n",
            "Epoch 91/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0463 - mean_absolute_error: 0.1647\n",
            "Epoch 92/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0460 - mean_absolute_error: 0.1643\n",
            "Epoch 93/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0458 - mean_absolute_error: 0.1642\n",
            "Epoch 94/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0460 - mean_absolute_error: 0.1646\n",
            "Epoch 95/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0456 - mean_absolute_error: 0.1641\n",
            "Epoch 96/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0454 - mean_absolute_error: 0.1636\n",
            "Epoch 97/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0453 - mean_absolute_error: 0.1630\n",
            "Epoch 98/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0453 - mean_absolute_error: 0.1627\n",
            "Epoch 99/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0452 - mean_absolute_error: 0.1626\n",
            "Epoch 100/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0450 - mean_absolute_error: 0.1623\n",
            "Epoch 101/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0451 - mean_absolute_error: 0.1630\n",
            "Epoch 102/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0447 - mean_absolute_error: 0.1623\n",
            "Epoch 103/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0446 - mean_absolute_error: 0.1620\n",
            "Epoch 104/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0445 - mean_absolute_error: 0.1617\n",
            "Epoch 105/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0444 - mean_absolute_error: 0.1615\n",
            "Epoch 106/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0443 - mean_absolute_error: 0.1612\n",
            "Epoch 107/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0442 - mean_absolute_error: 0.1612\n",
            "Epoch 108/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0440 - mean_absolute_error: 0.1612\n",
            "Epoch 109/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0440 - mean_absolute_error: 0.1610\n",
            "Epoch 110/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0437 - mean_absolute_error: 0.1604\n",
            "Epoch 111/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0438 - mean_absolute_error: 0.1605\n",
            "Epoch 112/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0434 - mean_absolute_error: 0.1602\n",
            "Epoch 113/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0434 - mean_absolute_error: 0.1607\n",
            "Epoch 114/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0438 - mean_absolute_error: 0.1620\n",
            "Epoch 115/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0434 - mean_absolute_error: 0.1616\n",
            "Epoch 116/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0432 - mean_absolute_error: 0.1598\n",
            "Epoch 117/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0429 - mean_absolute_error: 0.1589\n",
            "Epoch 118/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0428 - mean_absolute_error: 0.1585\n",
            "Epoch 119/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0426 - mean_absolute_error: 0.1580\n",
            "Epoch 120/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0425 - mean_absolute_error: 0.1580\n",
            "Epoch 121/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0423 - mean_absolute_error: 0.1582\n",
            "Epoch 122/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0423 - mean_absolute_error: 0.1581\n",
            "Epoch 123/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0422 - mean_absolute_error: 0.1583\n",
            "Epoch 124/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0419 - mean_absolute_error: 0.1573\n",
            "Epoch 125/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0418 - mean_absolute_error: 0.1562\n",
            "Epoch 126/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0421 - mean_absolute_error: 0.1564\n",
            "Epoch 127/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0421 - mean_absolute_error: 0.1566\n",
            "Epoch 128/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0413 - mean_absolute_error: 0.1551\n",
            "Epoch 129/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0417 - mean_absolute_error: 0.1572\n",
            "Epoch 130/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0420 - mean_absolute_error: 0.1595\n",
            "Epoch 131/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0419 - mean_absolute_error: 0.1589\n",
            "Epoch 132/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0413 - mean_absolute_error: 0.1566\n",
            "Epoch 133/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0409 - mean_absolute_error: 0.1549\n",
            "Epoch 134/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0408 - mean_absolute_error: 0.1544\n",
            "Epoch 135/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0406 - mean_absolute_error: 0.1542\n",
            "Epoch 136/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0405 - mean_absolute_error: 0.1541\n",
            "Epoch 137/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0403 - mean_absolute_error: 0.1541\n",
            "Epoch 138/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0402 - mean_absolute_error: 0.1547\n",
            "Epoch 139/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0402 - mean_absolute_error: 0.1552\n",
            "Epoch 140/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0401 - mean_absolute_error: 0.1544\n",
            "Epoch 141/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0396 - mean_absolute_error: 0.1532\n",
            "Epoch 142/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0395 - mean_absolute_error: 0.1526\n",
            "Epoch 143/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0394 - mean_absolute_error: 0.1520\n",
            "Epoch 144/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0393 - mean_absolute_error: 0.1516\n",
            "Epoch 145/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0389 - mean_absolute_error: 0.1511\n",
            "Epoch 146/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0385 - mean_absolute_error: 0.1511\n",
            "Epoch 147/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0383 - mean_absolute_error: 0.1518\n",
            "Epoch 148/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0391 - mean_absolute_error: 0.1542\n",
            "Epoch 149/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0380 - mean_absolute_error: 0.1518\n",
            "Epoch 150/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0384 - mean_absolute_error: 0.1492\n",
            "Epoch 151/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0387 - mean_absolute_error: 0.1492\n",
            "Epoch 152/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0378 - mean_absolute_error: 0.1483\n",
            "Epoch 153/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0371 - mean_absolute_error: 0.1490\n",
            "Epoch 154/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0379 - mean_absolute_error: 0.1521\n",
            "Epoch 155/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0378 - mean_absolute_error: 0.1527\n",
            "Epoch 156/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0370 - mean_absolute_error: 0.1498\n",
            "Epoch 157/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0370 - mean_absolute_error: 0.1475\n",
            "Epoch 158/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0370 - mean_absolute_error: 0.1474\n",
            "Epoch 159/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0365 - mean_absolute_error: 0.1472\n",
            "Epoch 160/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0363 - mean_absolute_error: 0.1477\n",
            "Epoch 161/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0369 - mean_absolute_error: 0.1493\n",
            "Epoch 162/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0364 - mean_absolute_error: 0.1490\n",
            "Epoch 163/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0359 - mean_absolute_error: 0.1467\n",
            "Epoch 164/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0359 - mean_absolute_error: 0.1458\n",
            "Epoch 165/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0360 - mean_absolute_error: 0.1455\n",
            "Epoch 166/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0357 - mean_absolute_error: 0.1456\n",
            "Epoch 167/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0357 - mean_absolute_error: 0.1460\n",
            "Epoch 168/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0357 - mean_absolute_error: 0.1463\n",
            "Epoch 169/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0358 - mean_absolute_error: 0.1468\n",
            "Epoch 170/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0351 - mean_absolute_error: 0.1455\n",
            "Epoch 171/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0350 - mean_absolute_error: 0.1449\n",
            "Epoch 172/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0353 - mean_absolute_error: 0.1455\n",
            "Epoch 173/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0354 - mean_absolute_error: 0.1453\n",
            "Epoch 174/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0347 - mean_absolute_error: 0.1433\n",
            "Epoch 175/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0345 - mean_absolute_error: 0.1436\n",
            "Epoch 176/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0349 - mean_absolute_error: 0.1453\n",
            "Epoch 177/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0344 - mean_absolute_error: 0.1437\n",
            "Epoch 178/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0341 - mean_absolute_error: 0.1423\n",
            "Epoch 179/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0340 - mean_absolute_error: 0.1418\n",
            "Epoch 180/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0340 - mean_absolute_error: 0.1419\n",
            "Epoch 181/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0338 - mean_absolute_error: 0.1416\n",
            "Epoch 182/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0338 - mean_absolute_error: 0.1423\n",
            "Epoch 183/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0337 - mean_absolute_error: 0.1414\n",
            "Epoch 184/200\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 0.0338 - mean_absolute_error: 0.1405\n",
            "Epoch 185/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0337 - mean_absolute_error: 0.1404\n",
            "Epoch 186/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0332 - mean_absolute_error: 0.1400\n",
            "Epoch 187/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0334 - mean_absolute_error: 0.1408\n",
            "Epoch 188/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0334 - mean_absolute_error: 0.1412\n",
            "Epoch 189/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0329 - mean_absolute_error: 0.1395\n",
            "Epoch 190/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0329 - mean_absolute_error: 0.1393\n",
            "Epoch 191/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0338 - mean_absolute_error: 0.1408\n",
            "Epoch 192/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0330 - mean_absolute_error: 0.1393\n",
            "Epoch 193/200\n",
            "2/2 [==============================] - 0s 14ms/step - loss: 0.0325 - mean_absolute_error: 0.1384\n",
            "Epoch 194/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0333 - mean_absolute_error: 0.1409\n",
            "Epoch 195/200\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 0.0326 - mean_absolute_error: 0.1399\n",
            "Epoch 196/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0322 - mean_absolute_error: 0.1378\n",
            "Epoch 197/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0328 - mean_absolute_error: 0.1387\n",
            "Epoch 198/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0325 - mean_absolute_error: 0.1384\n",
            "Epoch 199/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0321 - mean_absolute_error: 0.1380\n",
            "Epoch 200/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0319 - mean_absolute_error: 0.1371\n",
            "1/1 [==============================] - 0s 464ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x800 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/200\n",
            "2/2 [==============================] - 3s 13ms/step - loss: 0.4020 - mean_absolute_error: 0.6004\n",
            "Epoch 2/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.3712 - mean_absolute_error: 0.5748\n",
            "Epoch 3/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.3439 - mean_absolute_error: 0.5515\n",
            "Epoch 4/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.3223 - mean_absolute_error: 0.5323\n",
            "Epoch 5/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.3038 - mean_absolute_error: 0.5154\n",
            "Epoch 6/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.2825 - mean_absolute_error: 0.4944\n",
            "Epoch 7/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.2571 - mean_absolute_error: 0.4693\n",
            "Epoch 8/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.2299 - mean_absolute_error: 0.4399\n",
            "Epoch 9/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.2026 - mean_absolute_error: 0.4098\n",
            "Epoch 10/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.1750 - mean_absolute_error: 0.3781\n",
            "Epoch 11/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.1474 - mean_absolute_error: 0.3457\n",
            "Epoch 12/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.1202 - mean_absolute_error: 0.3129\n",
            "Epoch 13/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0967 - mean_absolute_error: 0.2785\n",
            "Epoch 14/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0764 - mean_absolute_error: 0.2443\n",
            "Epoch 15/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0608 - mean_absolute_error: 0.2130\n",
            "Epoch 16/200\n",
            "2/2 [==============================] - 0s 17ms/step - loss: 0.0524 - mean_absolute_error: 0.1889\n",
            "Epoch 17/200\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 0.0502 - mean_absolute_error: 0.1761\n",
            "Epoch 18/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0540 - mean_absolute_error: 0.1750\n",
            "Epoch 19/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0562 - mean_absolute_error: 0.1796\n",
            "Epoch 20/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0587 - mean_absolute_error: 0.1821\n",
            "Epoch 21/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0584 - mean_absolute_error: 0.1816\n",
            "Epoch 22/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0558 - mean_absolute_error: 0.1773\n",
            "Epoch 23/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0527 - mean_absolute_error: 0.1740\n",
            "Epoch 24/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0505 - mean_absolute_error: 0.1702\n",
            "Epoch 25/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0487 - mean_absolute_error: 0.1695\n",
            "Epoch 26/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0488 - mean_absolute_error: 0.1739\n",
            "Epoch 27/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0485 - mean_absolute_error: 0.1756\n",
            "Epoch 28/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0487 - mean_absolute_error: 0.1769\n",
            "Epoch 29/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0490 - mean_absolute_error: 0.1784\n",
            "Epoch 30/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0491 - mean_absolute_error: 0.1789\n",
            "Epoch 31/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0488 - mean_absolute_error: 0.1780\n",
            "Epoch 32/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0484 - mean_absolute_error: 0.1761\n",
            "Epoch 33/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0480 - mean_absolute_error: 0.1740\n",
            "Epoch 34/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0475 - mean_absolute_error: 0.1721\n",
            "Epoch 35/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0473 - mean_absolute_error: 0.1704\n",
            "Epoch 36/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0471 - mean_absolute_error: 0.1690\n",
            "Epoch 37/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0472 - mean_absolute_error: 0.1679\n",
            "Epoch 38/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0471 - mean_absolute_error: 0.1671\n",
            "Epoch 39/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0470 - mean_absolute_error: 0.1665\n",
            "Epoch 40/200\n",
            "2/2 [==============================] - 0s 24ms/step - loss: 0.0469 - mean_absolute_error: 0.1659\n",
            "Epoch 41/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0468 - mean_absolute_error: 0.1656\n",
            "Epoch 42/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0467 - mean_absolute_error: 0.1656\n",
            "Epoch 43/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0465 - mean_absolute_error: 0.1655\n",
            "Epoch 44/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0464 - mean_absolute_error: 0.1655\n",
            "Epoch 45/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0462 - mean_absolute_error: 0.1658\n",
            "Epoch 46/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0461 - mean_absolute_error: 0.1663\n",
            "Epoch 47/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0461 - mean_absolute_error: 0.1672\n",
            "Epoch 48/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0459 - mean_absolute_error: 0.1675\n",
            "Epoch 49/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0459 - mean_absolute_error: 0.1676\n",
            "Epoch 50/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0458 - mean_absolute_error: 0.1674\n",
            "Epoch 51/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0456 - mean_absolute_error: 0.1669\n",
            "Epoch 52/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0455 - mean_absolute_error: 0.1663\n",
            "Epoch 53/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0454 - mean_absolute_error: 0.1654\n",
            "Epoch 54/200\n",
            "2/2 [==============================] - 0s 14ms/step - loss: 0.0453 - mean_absolute_error: 0.1646\n",
            "Epoch 55/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0453 - mean_absolute_error: 0.1637\n",
            "Epoch 56/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0452 - mean_absolute_error: 0.1632\n",
            "Epoch 57/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0450 - mean_absolute_error: 0.1632\n",
            "Epoch 58/200\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 0.0449 - mean_absolute_error: 0.1635\n",
            "Epoch 59/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0448 - mean_absolute_error: 0.1639\n",
            "Epoch 60/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0449 - mean_absolute_error: 0.1646\n",
            "Epoch 61/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0447 - mean_absolute_error: 0.1643\n",
            "Epoch 62/200\n",
            "2/2 [==============================] - 0s 14ms/step - loss: 0.0446 - mean_absolute_error: 0.1638\n",
            "Epoch 63/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0445 - mean_absolute_error: 0.1628\n",
            "Epoch 64/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0443 - mean_absolute_error: 0.1620\n",
            "Epoch 65/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0443 - mean_absolute_error: 0.1613\n",
            "Epoch 66/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0443 - mean_absolute_error: 0.1605\n",
            "Epoch 67/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0442 - mean_absolute_error: 0.1601\n",
            "Epoch 68/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0440 - mean_absolute_error: 0.1603\n",
            "Epoch 69/200\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 0.0439 - mean_absolute_error: 0.1608\n",
            "Epoch 70/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0438 - mean_absolute_error: 0.1612\n",
            "Epoch 71/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0437 - mean_absolute_error: 0.1612\n",
            "Epoch 72/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0436 - mean_absolute_error: 0.1609\n",
            "Epoch 73/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0435 - mean_absolute_error: 0.1606\n",
            "Epoch 74/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0434 - mean_absolute_error: 0.1602\n",
            "Epoch 75/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0433 - mean_absolute_error: 0.1597\n",
            "Epoch 76/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0432 - mean_absolute_error: 0.1591\n",
            "Epoch 77/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0432 - mean_absolute_error: 0.1584\n",
            "Epoch 78/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0431 - mean_absolute_error: 0.1577\n",
            "Epoch 79/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0430 - mean_absolute_error: 0.1575\n",
            "Epoch 80/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0429 - mean_absolute_error: 0.1574\n",
            "Epoch 81/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0428 - mean_absolute_error: 0.1572\n",
            "Epoch 82/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0427 - mean_absolute_error: 0.1570\n",
            "Epoch 83/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0426 - mean_absolute_error: 0.1569\n",
            "Epoch 84/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0425 - mean_absolute_error: 0.1568\n",
            "Epoch 85/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0424 - mean_absolute_error: 0.1569\n",
            "Epoch 86/200\n",
            "2/2 [==============================] - 0s 15ms/step - loss: 0.0423 - mean_absolute_error: 0.1570\n",
            "Epoch 87/200\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 0.0423 - mean_absolute_error: 0.1570\n",
            "Epoch 88/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0422 - mean_absolute_error: 0.1570\n",
            "Epoch 89/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0421 - mean_absolute_error: 0.1568\n",
            "Epoch 90/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0421 - mean_absolute_error: 0.1561\n",
            "Epoch 91/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0419 - mean_absolute_error: 0.1550\n",
            "Epoch 92/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0419 - mean_absolute_error: 0.1548\n",
            "Epoch 93/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0418 - mean_absolute_error: 0.1543\n",
            "Epoch 94/200\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 0.0416 - mean_absolute_error: 0.1535\n",
            "Epoch 95/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0415 - mean_absolute_error: 0.1532\n",
            "Epoch 96/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0414 - mean_absolute_error: 0.1529\n",
            "Epoch 97/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0414 - mean_absolute_error: 0.1529\n",
            "Epoch 98/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0412 - mean_absolute_error: 0.1528\n",
            "Epoch 99/200\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 0.0411 - mean_absolute_error: 0.1527\n",
            "Epoch 100/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0411 - mean_absolute_error: 0.1530\n",
            "Epoch 101/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0409 - mean_absolute_error: 0.1524\n",
            "Epoch 102/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0411 - mean_absolute_error: 0.1521\n",
            "Epoch 103/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0407 - mean_absolute_error: 0.1514\n",
            "Epoch 104/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0406 - mean_absolute_error: 0.1515\n",
            "Epoch 105/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0405 - mean_absolute_error: 0.1516\n",
            "Epoch 106/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0405 - mean_absolute_error: 0.1515\n",
            "Epoch 107/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0405 - mean_absolute_error: 0.1515\n",
            "Epoch 108/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0403 - mean_absolute_error: 0.1509\n",
            "Epoch 109/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0401 - mean_absolute_error: 0.1504\n",
            "Epoch 110/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0400 - mean_absolute_error: 0.1501\n",
            "Epoch 111/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0399 - mean_absolute_error: 0.1498\n",
            "Epoch 112/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0398 - mean_absolute_error: 0.1495\n",
            "Epoch 113/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0397 - mean_absolute_error: 0.1493\n",
            "Epoch 114/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0396 - mean_absolute_error: 0.1490\n",
            "Epoch 115/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0395 - mean_absolute_error: 0.1487\n",
            "Epoch 116/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0396 - mean_absolute_error: 0.1491\n",
            "Epoch 117/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0395 - mean_absolute_error: 0.1492\n",
            "Epoch 118/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0393 - mean_absolute_error: 0.1490\n",
            "Epoch 119/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0392 - mean_absolute_error: 0.1482\n",
            "Epoch 120/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0389 - mean_absolute_error: 0.1476\n",
            "Epoch 121/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0388 - mean_absolute_error: 0.1469\n",
            "Epoch 122/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0387 - mean_absolute_error: 0.1465\n",
            "Epoch 123/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0385 - mean_absolute_error: 0.1457\n",
            "Epoch 124/200\n",
            "2/2 [==============================] - 0s 16ms/step - loss: 0.0384 - mean_absolute_error: 0.1452\n",
            "Epoch 125/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0384 - mean_absolute_error: 0.1448\n",
            "Epoch 126/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0382 - mean_absolute_error: 0.1446\n",
            "Epoch 127/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0381 - mean_absolute_error: 0.1445\n",
            "Epoch 128/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0379 - mean_absolute_error: 0.1447\n",
            "Epoch 129/200\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 0.0380 - mean_absolute_error: 0.1452\n",
            "Epoch 130/200\n",
            "2/2 [==============================] - 0s 18ms/step - loss: 0.0378 - mean_absolute_error: 0.1457\n",
            "Epoch 131/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0376 - mean_absolute_error: 0.1445\n",
            "Epoch 132/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0374 - mean_absolute_error: 0.1436\n",
            "Epoch 133/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0374 - mean_absolute_error: 0.1430\n",
            "Epoch 134/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0373 - mean_absolute_error: 0.1429\n",
            "Epoch 135/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0371 - mean_absolute_error: 0.1425\n",
            "Epoch 136/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0369 - mean_absolute_error: 0.1426\n",
            "Epoch 137/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0367 - mean_absolute_error: 0.1428\n",
            "Epoch 138/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0368 - mean_absolute_error: 0.1443\n",
            "Epoch 139/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0369 - mean_absolute_error: 0.1446\n",
            "Epoch 140/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0366 - mean_absolute_error: 0.1442\n",
            "Epoch 141/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0364 - mean_absolute_error: 0.1435\n",
            "Epoch 142/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0361 - mean_absolute_error: 0.1418\n",
            "Epoch 143/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0360 - mean_absolute_error: 0.1409\n",
            "Epoch 144/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0358 - mean_absolute_error: 0.1404\n",
            "Epoch 145/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0358 - mean_absolute_error: 0.1402\n",
            "Epoch 146/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0356 - mean_absolute_error: 0.1399\n",
            "Epoch 147/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0355 - mean_absolute_error: 0.1401\n",
            "Epoch 148/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0352 - mean_absolute_error: 0.1404\n",
            "Epoch 149/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0351 - mean_absolute_error: 0.1404\n",
            "Epoch 150/200\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 0.0349 - mean_absolute_error: 0.1396\n",
            "Epoch 151/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0347 - mean_absolute_error: 0.1385\n",
            "Epoch 152/200\n",
            "2/2 [==============================] - 0s 16ms/step - loss: 0.0348 - mean_absolute_error: 0.1378\n",
            "Epoch 153/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0351 - mean_absolute_error: 0.1383\n",
            "Epoch 154/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0342 - mean_absolute_error: 0.1374\n",
            "Epoch 155/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0340 - mean_absolute_error: 0.1374\n",
            "Epoch 156/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0339 - mean_absolute_error: 0.1370\n",
            "Epoch 157/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0337 - mean_absolute_error: 0.1367\n",
            "Epoch 158/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0335 - mean_absolute_error: 0.1369\n",
            "Epoch 159/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0334 - mean_absolute_error: 0.1372\n",
            "Epoch 160/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0332 - mean_absolute_error: 0.1371\n",
            "Epoch 161/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0331 - mean_absolute_error: 0.1371\n",
            "Epoch 162/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0328 - mean_absolute_error: 0.1363\n",
            "Epoch 163/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0328 - mean_absolute_error: 0.1346\n",
            "Epoch 164/200\n",
            "2/2 [==============================] - 0s 14ms/step - loss: 0.0328 - mean_absolute_error: 0.1341\n",
            "Epoch 165/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0324 - mean_absolute_error: 0.1333\n",
            "Epoch 166/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0321 - mean_absolute_error: 0.1328\n",
            "Epoch 167/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0319 - mean_absolute_error: 0.1333\n",
            "Epoch 168/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0328 - mean_absolute_error: 0.1376\n",
            "Epoch 169/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0323 - mean_absolute_error: 0.1375\n",
            "Epoch 170/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0313 - mean_absolute_error: 0.1319\n",
            "Epoch 171/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0315 - mean_absolute_error: 0.1300\n",
            "Epoch 172/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0323 - mean_absolute_error: 0.1308\n",
            "Epoch 173/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0316 - mean_absolute_error: 0.1293\n",
            "Epoch 174/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0302 - mean_absolute_error: 0.1290\n",
            "Epoch 175/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0318 - mean_absolute_error: 0.1368\n",
            "Epoch 176/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0321 - mean_absolute_error: 0.1411\n",
            "Epoch 177/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0308 - mean_absolute_error: 0.1358\n",
            "Epoch 178/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0299 - mean_absolute_error: 0.1294\n",
            "Epoch 179/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0306 - mean_absolute_error: 0.1285\n",
            "Epoch 180/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0306 - mean_absolute_error: 0.1280\n",
            "Epoch 181/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0297 - mean_absolute_error: 0.1279\n",
            "Epoch 182/200\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 0.0291 - mean_absolute_error: 0.1285\n",
            "Epoch 183/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0290 - mean_absolute_error: 0.1299\n",
            "Epoch 184/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0291 - mean_absolute_error: 0.1303\n",
            "Epoch 185/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0287 - mean_absolute_error: 0.1294\n",
            "Epoch 186/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0284 - mean_absolute_error: 0.1284\n",
            "Epoch 187/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0282 - mean_absolute_error: 0.1267\n",
            "Epoch 188/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0284 - mean_absolute_error: 0.1258\n",
            "Epoch 189/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0284 - mean_absolute_error: 0.1267\n",
            "Epoch 190/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0276 - mean_absolute_error: 0.1260\n",
            "Epoch 191/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0274 - mean_absolute_error: 0.1262\n",
            "Epoch 192/200\n",
            "2/2 [==============================] - 0s 16ms/step - loss: 0.0273 - mean_absolute_error: 0.1256\n",
            "Epoch 193/200\n",
            "2/2 [==============================] - 0s 17ms/step - loss: 0.0270 - mean_absolute_error: 0.1255\n",
            "Epoch 194/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0268 - mean_absolute_error: 0.1249\n",
            "Epoch 195/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0266 - mean_absolute_error: 0.1237\n",
            "Epoch 196/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0264 - mean_absolute_error: 0.1230\n",
            "Epoch 197/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0263 - mean_absolute_error: 0.1233\n",
            "Epoch 198/200\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 0.0260 - mean_absolute_error: 0.1234\n",
            "Epoch 199/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0259 - mean_absolute_error: 0.1225\n",
            "Epoch 200/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0257 - mean_absolute_error: 0.1220\n",
            "1/1 [==============================] - 0s 420ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x800 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/200\n",
            "2/2 [==============================] - 2s 12ms/step - loss: 0.3465 - mean_absolute_error: 0.5478\n",
            "Epoch 2/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.3131 - mean_absolute_error: 0.5175\n",
            "Epoch 3/200\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 0.2809 - mean_absolute_error: 0.4882\n",
            "Epoch 4/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.2497 - mean_absolute_error: 0.4575\n",
            "Epoch 5/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.2195 - mean_absolute_error: 0.4269\n",
            "Epoch 6/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.1904 - mean_absolute_error: 0.3928\n",
            "Epoch 7/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.1630 - mean_absolute_error: 0.3605\n",
            "Epoch 8/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.1352 - mean_absolute_error: 0.3279\n",
            "Epoch 9/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.1117 - mean_absolute_error: 0.3002\n",
            "Epoch 10/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0897 - mean_absolute_error: 0.2672\n",
            "Epoch 11/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0737 - mean_absolute_error: 0.2392\n",
            "Epoch 12/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0617 - mean_absolute_error: 0.2153\n",
            "Epoch 13/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0561 - mean_absolute_error: 0.1949\n",
            "Epoch 14/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0566 - mean_absolute_error: 0.1862\n",
            "Epoch 15/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0605 - mean_absolute_error: 0.1862\n",
            "Epoch 16/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0636 - mean_absolute_error: 0.1897\n",
            "Epoch 17/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0657 - mean_absolute_error: 0.1926\n",
            "Epoch 18/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0655 - mean_absolute_error: 0.1921\n",
            "Epoch 19/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0626 - mean_absolute_error: 0.1877\n",
            "Epoch 20/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0595 - mean_absolute_error: 0.1838\n",
            "Epoch 21/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0573 - mean_absolute_error: 0.1822\n",
            "Epoch 22/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0549 - mean_absolute_error: 0.1823\n",
            "Epoch 23/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0534 - mean_absolute_error: 0.1833\n",
            "Epoch 24/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0544 - mean_absolute_error: 0.1884\n",
            "Epoch 25/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0544 - mean_absolute_error: 0.1911\n",
            "Epoch 26/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0548 - mean_absolute_error: 0.1942\n",
            "Epoch 27/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0547 - mean_absolute_error: 0.1951\n",
            "Epoch 28/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0546 - mean_absolute_error: 0.1944\n",
            "Epoch 29/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0540 - mean_absolute_error: 0.1926\n",
            "Epoch 30/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0534 - mean_absolute_error: 0.1905\n",
            "Epoch 31/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0529 - mean_absolute_error: 0.1879\n",
            "Epoch 32/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0524 - mean_absolute_error: 0.1853\n",
            "Epoch 33/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0520 - mean_absolute_error: 0.1827\n",
            "Epoch 34/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0519 - mean_absolute_error: 0.1813\n",
            "Epoch 35/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0517 - mean_absolute_error: 0.1799\n",
            "Epoch 36/200\n",
            "2/2 [==============================] - 0s 15ms/step - loss: 0.0517 - mean_absolute_error: 0.1789\n",
            "Epoch 37/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0516 - mean_absolute_error: 0.1780\n",
            "Epoch 38/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0514 - mean_absolute_error: 0.1775\n",
            "Epoch 39/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0512 - mean_absolute_error: 0.1772\n",
            "Epoch 40/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0510 - mean_absolute_error: 0.1771\n",
            "Epoch 41/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0507 - mean_absolute_error: 0.1775\n",
            "Epoch 42/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0506 - mean_absolute_error: 0.1781\n",
            "Epoch 43/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0502 - mean_absolute_error: 0.1781\n",
            "Epoch 44/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0501 - mean_absolute_error: 0.1782\n",
            "Epoch 45/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0500 - mean_absolute_error: 0.1783\n",
            "Epoch 46/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0498 - mean_absolute_error: 0.1780\n",
            "Epoch 47/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0496 - mean_absolute_error: 0.1774\n",
            "Epoch 48/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0494 - mean_absolute_error: 0.1768\n",
            "Epoch 49/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0492 - mean_absolute_error: 0.1766\n",
            "Epoch 50/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0490 - mean_absolute_error: 0.1761\n",
            "Epoch 51/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0489 - mean_absolute_error: 0.1755\n",
            "Epoch 52/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0487 - mean_absolute_error: 0.1750\n",
            "Epoch 53/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0485 - mean_absolute_error: 0.1744\n",
            "Epoch 54/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0484 - mean_absolute_error: 0.1735\n",
            "Epoch 55/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0483 - mean_absolute_error: 0.1730\n",
            "Epoch 56/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0481 - mean_absolute_error: 0.1723\n",
            "Epoch 57/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0480 - mean_absolute_error: 0.1714\n",
            "Epoch 58/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0478 - mean_absolute_error: 0.1709\n",
            "Epoch 59/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0477 - mean_absolute_error: 0.1707\n",
            "Epoch 60/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0475 - mean_absolute_error: 0.1709\n",
            "Epoch 61/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0472 - mean_absolute_error: 0.1711\n",
            "Epoch 62/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0472 - mean_absolute_error: 0.1719\n",
            "Epoch 63/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0469 - mean_absolute_error: 0.1720\n",
            "Epoch 64/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0468 - mean_absolute_error: 0.1719\n",
            "Epoch 65/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0466 - mean_absolute_error: 0.1717\n",
            "Epoch 66/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0465 - mean_absolute_error: 0.1716\n",
            "Epoch 67/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0463 - mean_absolute_error: 0.1711\n",
            "Epoch 68/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0462 - mean_absolute_error: 0.1705\n",
            "Epoch 69/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0460 - mean_absolute_error: 0.1701\n",
            "Epoch 70/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0459 - mean_absolute_error: 0.1697\n",
            "Epoch 71/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0457 - mean_absolute_error: 0.1695\n",
            "Epoch 72/200\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 0.0455 - mean_absolute_error: 0.1692\n",
            "Epoch 73/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0457 - mean_absolute_error: 0.1694\n",
            "Epoch 74/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0452 - mean_absolute_error: 0.1680\n",
            "Epoch 75/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0451 - mean_absolute_error: 0.1683\n",
            "Epoch 76/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0449 - mean_absolute_error: 0.1687\n",
            "Epoch 77/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0448 - mean_absolute_error: 0.1693\n",
            "Epoch 78/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0448 - mean_absolute_error: 0.1696\n",
            "Epoch 79/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0444 - mean_absolute_error: 0.1689\n",
            "Epoch 80/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0443 - mean_absolute_error: 0.1679\n",
            "Epoch 81/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0441 - mean_absolute_error: 0.1668\n",
            "Epoch 82/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0439 - mean_absolute_error: 0.1659\n",
            "Epoch 83/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0439 - mean_absolute_error: 0.1653\n",
            "Epoch 84/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0437 - mean_absolute_error: 0.1646\n",
            "Epoch 85/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0436 - mean_absolute_error: 0.1644\n",
            "Epoch 86/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0434 - mean_absolute_error: 0.1643\n",
            "Epoch 87/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0434 - mean_absolute_error: 0.1650\n",
            "Epoch 88/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0431 - mean_absolute_error: 0.1650\n",
            "Epoch 89/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0429 - mean_absolute_error: 0.1648\n",
            "Epoch 90/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0428 - mean_absolute_error: 0.1655\n",
            "Epoch 91/200\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 0.0428 - mean_absolute_error: 0.1659\n",
            "Epoch 92/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0426 - mean_absolute_error: 0.1654\n",
            "Epoch 93/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0424 - mean_absolute_error: 0.1644\n",
            "Epoch 94/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0422 - mean_absolute_error: 0.1636\n",
            "Epoch 95/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0422 - mean_absolute_error: 0.1629\n",
            "Epoch 96/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0421 - mean_absolute_error: 0.1623\n",
            "Epoch 97/200\n",
            "2/2 [==============================] - 0s 14ms/step - loss: 0.0419 - mean_absolute_error: 0.1619\n",
            "Epoch 98/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0419 - mean_absolute_error: 0.1624\n",
            "Epoch 99/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0416 - mean_absolute_error: 0.1623\n",
            "Epoch 100/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0415 - mean_absolute_error: 0.1620\n",
            "Epoch 101/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0414 - mean_absolute_error: 0.1618\n",
            "Epoch 102/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0414 - mean_absolute_error: 0.1619\n",
            "Epoch 103/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0412 - mean_absolute_error: 0.1615\n",
            "Epoch 104/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0411 - mean_absolute_error: 0.1609\n",
            "Epoch 105/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0410 - mean_absolute_error: 0.1602\n",
            "Epoch 106/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0409 - mean_absolute_error: 0.1596\n",
            "Epoch 107/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0407 - mean_absolute_error: 0.1592\n",
            "Epoch 108/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0407 - mean_absolute_error: 0.1593\n",
            "Epoch 109/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0406 - mean_absolute_error: 0.1590\n",
            "Epoch 110/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0405 - mean_absolute_error: 0.1587\n",
            "Epoch 111/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0404 - mean_absolute_error: 0.1585\n",
            "Epoch 112/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0402 - mean_absolute_error: 0.1583\n",
            "Epoch 113/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0401 - mean_absolute_error: 0.1585\n",
            "Epoch 114/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0402 - mean_absolute_error: 0.1592\n",
            "Epoch 115/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0401 - mean_absolute_error: 0.1593\n",
            "Epoch 116/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0401 - mean_absolute_error: 0.1592\n",
            "Epoch 117/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0398 - mean_absolute_error: 0.1576\n",
            "Epoch 118/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0399 - mean_absolute_error: 0.1575\n",
            "Epoch 119/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0396 - mean_absolute_error: 0.1571\n",
            "Epoch 120/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0395 - mean_absolute_error: 0.1571\n",
            "Epoch 121/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0394 - mean_absolute_error: 0.1573\n",
            "Epoch 122/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0393 - mean_absolute_error: 0.1570\n",
            "Epoch 123/200\n",
            "2/2 [==============================] - 0s 16ms/step - loss: 0.0393 - mean_absolute_error: 0.1569\n",
            "Epoch 124/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0393 - mean_absolute_error: 0.1565\n",
            "Epoch 125/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0390 - mean_absolute_error: 0.1562\n",
            "Epoch 126/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0391 - mean_absolute_error: 0.1570\n",
            "Epoch 127/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0390 - mean_absolute_error: 0.1570\n",
            "Epoch 128/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0389 - mean_absolute_error: 0.1567\n",
            "Epoch 129/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0388 - mean_absolute_error: 0.1562\n",
            "Epoch 130/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0389 - mean_absolute_error: 0.1563\n",
            "Epoch 131/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0386 - mean_absolute_error: 0.1555\n",
            "Epoch 132/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0385 - mean_absolute_error: 0.1555\n",
            "Epoch 133/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0384 - mean_absolute_error: 0.1554\n",
            "Epoch 134/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0384 - mean_absolute_error: 0.1555\n",
            "Epoch 135/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0383 - mean_absolute_error: 0.1551\n",
            "Epoch 136/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0382 - mean_absolute_error: 0.1552\n",
            "Epoch 137/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0381 - mean_absolute_error: 0.1550\n",
            "Epoch 138/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0380 - mean_absolute_error: 0.1549\n",
            "Epoch 139/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0382 - mean_absolute_error: 0.1556\n",
            "Epoch 140/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0378 - mean_absolute_error: 0.1545\n",
            "Epoch 141/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0378 - mean_absolute_error: 0.1548\n",
            "Epoch 142/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0377 - mean_absolute_error: 0.1550\n",
            "Epoch 143/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0381 - mean_absolute_error: 0.1557\n",
            "Epoch 144/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0376 - mean_absolute_error: 0.1545\n",
            "Epoch 145/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0374 - mean_absolute_error: 0.1544\n",
            "Epoch 146/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0374 - mean_absolute_error: 0.1543\n",
            "Epoch 147/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0375 - mean_absolute_error: 0.1544\n",
            "Epoch 148/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0373 - mean_absolute_error: 0.1539\n",
            "Epoch 149/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0374 - mean_absolute_error: 0.1543\n",
            "Epoch 150/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0372 - mean_absolute_error: 0.1536\n",
            "Epoch 151/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0372 - mean_absolute_error: 0.1535\n",
            "Epoch 152/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0370 - mean_absolute_error: 0.1533\n",
            "Epoch 153/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0369 - mean_absolute_error: 0.1537\n",
            "Epoch 154/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0369 - mean_absolute_error: 0.1541\n",
            "Epoch 155/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0371 - mean_absolute_error: 0.1547\n",
            "Epoch 156/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0371 - mean_absolute_error: 0.1546\n",
            "Epoch 157/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0368 - mean_absolute_error: 0.1539\n",
            "Epoch 158/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0365 - mean_absolute_error: 0.1527\n",
            "Epoch 159/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0365 - mean_absolute_error: 0.1521\n",
            "Epoch 160/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0368 - mean_absolute_error: 0.1524\n",
            "Epoch 161/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0369 - mean_absolute_error: 0.1524\n",
            "Epoch 162/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0366 - mean_absolute_error: 0.1521\n",
            "Epoch 163/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0364 - mean_absolute_error: 0.1522\n",
            "Epoch 164/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0362 - mean_absolute_error: 0.1528\n",
            "Epoch 165/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0366 - mean_absolute_error: 0.1538\n",
            "Epoch 166/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0363 - mean_absolute_error: 0.1532\n",
            "Epoch 167/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0360 - mean_absolute_error: 0.1523\n",
            "Epoch 168/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0361 - mean_absolute_error: 0.1512\n",
            "Epoch 169/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0363 - mean_absolute_error: 0.1517\n",
            "Epoch 170/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0364 - mean_absolute_error: 0.1518\n",
            "Epoch 171/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0360 - mean_absolute_error: 0.1512\n",
            "Epoch 172/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0361 - mean_absolute_error: 0.1533\n",
            "Epoch 173/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0360 - mean_absolute_error: 0.1527\n",
            "Epoch 174/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0357 - mean_absolute_error: 0.1521\n",
            "Epoch 175/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0353 - mean_absolute_error: 0.1506\n",
            "Epoch 176/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0359 - mean_absolute_error: 0.1502\n",
            "Epoch 177/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0363 - mean_absolute_error: 0.1507\n",
            "Epoch 178/200\n",
            "2/2 [==============================] - 0s 14ms/step - loss: 0.0357 - mean_absolute_error: 0.1505\n",
            "Epoch 179/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0351 - mean_absolute_error: 0.1500\n",
            "Epoch 180/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0359 - mean_absolute_error: 0.1535\n",
            "Epoch 181/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0360 - mean_absolute_error: 0.1531\n",
            "Epoch 182/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0353 - mean_absolute_error: 0.1516\n",
            "Epoch 183/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0349 - mean_absolute_error: 0.1490\n",
            "Epoch 184/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0355 - mean_absolute_error: 0.1491\n",
            "Epoch 185/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0358 - mean_absolute_error: 0.1493\n",
            "Epoch 186/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0351 - mean_absolute_error: 0.1491\n",
            "Epoch 187/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0351 - mean_absolute_error: 0.1506\n",
            "Epoch 188/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0356 - mean_absolute_error: 0.1521\n",
            "Epoch 189/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0350 - mean_absolute_error: 0.1509\n",
            "Epoch 190/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0345 - mean_absolute_error: 0.1484\n",
            "Epoch 191/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0347 - mean_absolute_error: 0.1479\n",
            "Epoch 192/200\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 0.0354 - mean_absolute_error: 0.1481\n",
            "Epoch 193/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0349 - mean_absolute_error: 0.1475\n",
            "Epoch 194/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0341 - mean_absolute_error: 0.1470\n",
            "Epoch 195/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0348 - mean_absolute_error: 0.1502\n",
            "Epoch 196/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0352 - mean_absolute_error: 0.1513\n",
            "Epoch 197/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0345 - mean_absolute_error: 0.1491\n",
            "Epoch 198/200\n",
            "2/2 [==============================] - 0s 14ms/step - loss: 0.0345 - mean_absolute_error: 0.1474\n",
            "Epoch 199/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0343 - mean_absolute_error: 0.1463\n",
            "Epoch 200/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0344 - mean_absolute_error: 0.1462\n",
            "1/1 [==============================] - 1s 764ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x800 with 1 Axes>"
            ],
            "image/png": 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Ufyxk//GaqampFBYWnrAkHjjhWHWfRydTUlJCZmZmlWOhoaHYbLZT3gece4mOGDHihONHjhyhoKCATp06nXBbly5dcDgcHDx4kNjY2BNuP9vn+b59+3jhhRd4/fXX8fPz+9PzRUREmhoVJkVERJqIH374gcOHDzN37lzmzp17wu0ffvhhZWHybJ2qM89ut1cpLsyePZvJkyczduxYHnroIcLCwrDZbDz77LOVBaEzdfXVV3PjjTeyadMmevTowSeffMLw4cMr91EEGDJkCHv27OGzzz7j22+/5a233uKll15i5syZ/OUvf/nTxzhVAeSP/tiN53A4sFgsfPXVVyctsjSEwoWZGV966SXc3NyYOnUqBw8eZNWqVSQlJdG2bVuef/552rVrx6FDh2jZsuVpr9OyZcsa/X5qyuFwAPDggw8yatSok55zsmJehc6dOwOwZcuWag9G+bMiHcDKlSu57LLLGDJkCG+88QYRERG4u7vzzjvv8NFHH1X7mkb5/qhn4myeR6tXr67s6q5Q8TxwNdOmTSMqKophw4ZV7i155MgRwNndvG/fPlq3bo3VqoVsIiLSNKkwKSIi0kR8+OGHhIWF8frrr59w28KFC/n000+ZOXMm3t7etG/fnoSEhNNe73TLgps1a0ZWVtYJx/fv31+ly2j+/Pm0a9eOhQsXVrneH4fXnImxY8dy2223VS7n3rlzJw8//PAJ5wUHB3PjjTdy4403kpeXx5AhQ3jssceqVZisqfbt22MYBtHR0XTs2PGU57Vp0wZwdp1VTPwG5zTxpKQk4uPjT3nfip9vTX9/9ZHxZA4fPsxTTz3FvHnzcHNzq1y2XbFkuOJ/k5OT/7QwWZsqljYnJCScsthZ8TN3d3evVkH0jy699FKeffZZZs+eXasTmxcsWICXlxfffPMNnp6elcffeeedGl0vLCwMLy8vdu/efcJtfzxW3efRycTHx/Pdd99VORYeHn7mgcuFhobi4+PDjh07Trht+/btWK3WUw6xOdvn+YEDB9i9e/dJuyvvuOMOwLnEvrYHAYmIiLgKfTQnIiLSBBQWFrJw4UIuueQSJkyYcMLX1KlTyc3NrZwOPH78eDZv3synn356wrUquqcqlsaerADZvn17fvnlF0pKSiqPLV68mIMHD1Y5r6KT6viOrDVr1vDzzz/X+HsNCgpi1KhRfPLJJ8ydOxcPDw/Gjh1b5ZyMjIwqf/bz8yMmJqbKUte6MG7cOGw2G48//vgJXWiGYVTm6tOnD6GhocycObPKz/Ddd9896c/7eKGhoQwZMoT//e9/HDhw4ITHqHCq3199ZDyZv//97wwZMoTRo0cDzinr4CwcAWzbtg04uwJVTfTq1Yvo6GhefvnlE76vip9PWFgYw4YNY9asWRw+fPiEa6SlpZ32MQYMGMDo0aN56623WLRo0Qm3l5SU8OCDD55xdpvNhsViqbJ/7L59+076GNW93ogRI1i0aFGV/T53797NV199VeXc6j6PTqZZs2aMGDGiypeXl1eNMlfkvuCCC/jss8+qTMQ+evQoH330EYMGDapcTv9HZ/s8f+qpp/j000+rfFVsM/HXv/6VTz/99KTbDIiIiDQV6pgUERFpAj7//HNyc3O57LLLTnr7OeecQ2hoKB9++CFXXXUVDz30EPPnz+eKK67gpptuonfv3mRmZvL5558zc+ZM4uPjad++PUFBQcycORN/f398fX3p378/0dHR/OUvf2H+/PmMHj2aK6+8kj179jB79uwqgzUALrnkEhYuXMjll1/OxRdfTFJSEjNnzqRr167k5eXV+Pu96qqruO6663jjjTcYNWrUCd1IXbt2ZdiwYfTu3Zvg4GDWrVvH/PnzmTp1ao0fszrat2/PU089xcMPP8y+ffsYO3Ys/v7+JCUl8emnn3Lrrbfy4IMP4u7uzlNPPcVtt93G+eefz1VXXUVSUhLvvPNOtfa1e+WVVxg0aBC9evXi1ltvJTo6mn379rFkyRI2bdoEQO/evQH4xz/+wdVXX427uzuXXnppvWU83tq1a/n444/57bffKo+1bduWPn36MHnyZG6++Wbeeust+vfvX9nBVl+sViszZszg0ksvpUePHtx4441ERESwfft2EhMT+eabbwB4/fXXGTRoEN26deOWW26hXbt2HD16lJ9//plDhw6xefPm0z7O+++/zwUXXMC4ceO49NJLGT58OL6+vuzatYu5c+dy+PBhXnzxxTPKfvHFF/Pvf/+b0aNHM3HiRFJTU3n99deJiYmp8rM+E4899hjffvstAwcOZMqUKdjtdl577TXi4uIqn1tQ/ed6fXnqqaf47rvvGDRoEHfccQdubm7MmjWL4uJinn/++VPe72yf54MGDTrhWMXfR3379j3hQxMREZEmp77HgIuIiEj9u/TSSw0vLy8jPz//lOdMnjzZcHd3N9LT0w3DMIyMjAxj6tSpRlRUlOHh4WG0bNnSmDRpUuXthmEYn332mdG1a1fDzc3NAIx33nmn8rbp06cbUVFRhqenpzFw4EBj3bp1xtChQ42hQ4dWnuNwOIxnnnnGaNOmjeHp6Wn07NnTWLx4sTFp0iSjTZs2VfIBxqOPPlqt7zcnJ8fw9vY2AGP27Nkn3P7UU08Z/fr1M4KCggxvb2+jc+fOxtNPP22UlJSc9ro//vijARjz5s077XmPPvqoARhpaWknvX3BggXGoEGDDF9fX8PX19fo3Lmzceeddxo7duyoct4bb7xhREdHG56enkafPn2MFStWnPAzTEpKOuFnbxiGkZCQYFx++eVGUFCQ4eXlZXTq1Ml45JFHqpzz5JNPGlFRUYbVajUAIykpqU4yno7D4TD69+9v3H///Sfctnv3bmPIkCGGn5+fMWTIEGPPnj1/ej3AuPPOO097zul+jxW3/fjjj1WOr1q1yhg5cqTh7+9v+Pr6Gt27dzdeffXVKufs2bPHuOGGG4zw8HDD3d3diIqKMi655BJj/vz5f5rbMAyjoKDAePHFF42+ffsafn5+hoeHh9GhQwfjrrvuMnbv3l153qRJkwxfX98T7l/xvDve22+/bXTo0MHw9PQ0OnfubLzzzjsnPe9UP7c2bdoYkyZNqnJs6dKlRs+ePQ0PDw+jffv2xltvvWU88MADhpeX1wn3r+7z6GxU97/LDRs2GKNGjTL8/PwMHx8f47zzzjNWr1590mv98fd/ts/zmuQVERFpCiyGUYPdrEVERERERMqNHTuWxMREdu3aZXYUERERcSHaY1JERERERKqtsLCwyp937drFl19+ybBhw8wJJCIiIi5LHZMiIiIiIlJtERERTJ48mXbt2rF//35mzJhBcXExGzdupEOHDmbHExERERei4TciIiIiIlJto0ePZs6cORw5cgRPT08GDBjAM888o6KkiIiInDF1TIqIiIiIiIiIiEi90x6TIiIiIiIiIiIiUu9UmBQREREREREREZF6pz0m/8DhcJCSkoK/vz8Wi8XsOCIiIiIiIiIiIi7FMAxyc3OJjIzEaj11X6QKk3+QkpJCq1atzI4hIiIiIiIiIiLi0g4ePEjLli1PebsKk3/g7+8POH9wAQEBJqcRERERERERERFxLTk5ObRq1aqyznYqKkz+QcXy7YCAABUmRUREREREREREaujPtknU8BsRERERERERERGpdypMioiIiIiIiIiISL1TYVJERERERERERETqnfaYFBERERERERGRemMYBmVlZdjtdrOjSA3ZbDbc3Nz+dA/JP6PCpIiIiIiIiIiI1IuSkhIOHz5MQUGB2VHkLPn4+BAREYGHh0eNr6HCpIiIiIiIiIiI1DmHw0FSUhI2m43IyEg8PDzOuuNO6p9hGJSUlJCWlkZSUhIdOnTAaq3ZbpEqTIqIiIiIiIiISJ0rKSnB4XDQqlUrfHx8zI4jZ8Hb2xt3d3f2799PSUkJXl5eNbqOht+IiIiIiIiIiEi9qWl3nTQstfF71DNBRERERERERERE6p0KkyIiIiIiIiIiIlLvVJgUERERERERERFxURaLhUWLFpkdo0ZUmBQREREREREREamGn3/+GZvNxsUXX3xG92vbti0vv/xy3YRyYSpMioiIiIiIiIiIVMPbb7/NXXfdxYoVK0hJSTE7jstTYVJERERERERERExhGAYFJWWmfBmGcUZZ8/Ly+Pjjj5kyZQoXX3wx7777bpXbv/jiC/r27YuXlxchISFcfvnlAAwbNoz9+/dz3333YbFYsFgsADz22GP06NGjyjVefvll2rZtW/nnX3/9lZEjRxISEkJgYCBDhw5lw4YNZ/xzbqjczA4gIiIiIiIiIiJNU2Gpna7TvjHlsbc+MQofj+qXxj755BM6d+5Mp06duO6667j33nt5+OGHsVgsLFmyhMsvv5x//OMfvP/++5SUlPDll18CsHDhQuLj47n11lu55ZZbzihjbm4ukyZN4tVXX8UwDKZPn85FF13Erl278Pf3P6NrNUQqTIqIiIiIiIiIiPyJt99+m+uuuw6A0aNHk52dzfLlyxk2bBhPP/00V199NY8//njl+fHx8QAEBwdjs9nw9/cnPDz8jB7z/PPPr/LnN998k6CgIJYvX84ll1xylt+R+VSYFBERERERERERU3i729j6xCjTHru6duzYwdq1a/n0008BcHNz46qrruLtt99m2LBhbNq06Yy7Iavj6NGj/POf/2TZsmWkpqZit9spKCjgwIEDtf5YZlBhUkRERERERERETGGxWM5oObVZ3n77bcrKyoiMjKw8ZhgGnp6evPbaa3h7e5/xNa1W6wn7XJaWllb586RJk8jIyOA///kPbdq0wdPTkwEDBlBSUlKzb6SB0fAbERERERERERGRUygrK+P9999n+vTpbNq0qfJr8+bNREZGMmfOHLp3787SpUtPeQ0PDw/sdnuVY6GhoRw5cqRKcXLTpk1Vzvnpp5+4++67ueiii4iNjcXT05P09PRa/f7M1PBL0iIiIiIiIiIiIiZZvHgxx44d4+abbyYwMLDKbePHj+ftt9/mhRdeYPjw4bRv356rr76asrIyvvzyS/72t78B0LZtW1asWMHVV1+Np6cnISEhDBs2jLS0NJ5//nkmTJjA119/zVdffUVAQEDl9Tt06MAHH3xAnz59yMnJ4aGHHqpRd2ZDpY5JERERERERERGRU3j77bcZMWLECUVJcBYm161bR3BwMPPmzePzzz+nR48enH/++axdu7byvCeeeIJ9+/bRvn17QkNDAejSpQtvvPEGr7/+OvHx8axdu5YHH3zwhMc+duwYvXr14vrrr+fuu+8mLCysbr/hemQx/riYvYnLyckhMDCQ7OzsKhVqERERERERERGpuaKiIpKSkoiOjsbLy8vsOHKWTvf7rG59zWU6Jp999ln69u2Lv78/YWFhjB07lh07dlQ5p6ioiDvvvJPmzZvj5+fH+PHjOXr0qEmJRURERERERERE5FRcpjC5fPly7rzzTn755Re+++47SktLueCCC8jPz68857777uOLL75g3rx5LF++nJSUFMaNG2diahEREREREdeRX1xGQnK22TFERKSJcJnhN19//XWVP7/77ruEhYWxfv16hgwZQnZ2Nm+//TYfffQR559/PgDvvPMOXbp04ZdffuGcc84xI7aIiIiIiIjLuP+TTXyTeJSP/tKfc2NCzI4jIiKNnMt0TP5RdrbzU7zg4GAA1q9fT2lpKSNGjKg8p3PnzrRu3Zqff/75lNcpLi4mJyenypeIiIiIiEhTczi7kG+3OrfC+mlPuslpRESkKXDJwqTD4eDee+9l4MCBxMXFAXDkyBE8PDwICgqqcm6LFi04cuTIKa/17LPPEhgYWPnVqlWruowuIiIiIiLSIC3ckEzFaNSEZDVsiIhI3XPJwuSdd95JQkICc+fOPetrPfzww2RnZ1d+HTx4sBYSioiIiIiIuA7DMFiw/lDlnxOSszEqqpQiIiJ1xGX2mKwwdepUFi9ezIoVK2jZsmXl8fDwcEpKSsjKyqrSNXn06FHCw8NPeT1PT088PT3rMrKIiIiIiEiDtuFAFnvT8/F2t1Fid5CRX8LRnGLCA73MjiYiIo2Yy3RMGobB1KlT+fTTT/nhhx+Ijo6ucnvv3r1xd3dn6dKllcd27NjBgQMHGDBgQH3HFRERERERcRnzy7slL+oWQYcwPwBN5xYRkTrnMh2Td955Jx999BGfffYZ/v7+lftGBgYG4u3tTWBgIDfffDP3338/wcHBBAQEcNdddzFgwABN5BYRERERETmFolI7izenADCht3NV2vYjuSSkZDOiawszo4mISCPnMh2TM2bMIDs7m2HDhhEREVH59fHHH1ee89JLL3HJJZcwfvx4hgwZQnh4OAsXLjQxtYiIiIiISMP2TeIRcovLaNnMm/7RwcRFBQAagCMiYobJkyczduzYyj8PGzaMe++9t95zLFu2DIvFQlZWVp0+jssUJg3DOOnX5MmTK8/x8vLi9ddfJzMzk/z8fBYuXHja/SVFRERERESauopl3ON7tcRqtRAXFQhAYoqWcouIVJg8eTIWiwWLxYKHhwcxMTE88cQTlJWV1enjLly4kCeffLJa59ZXMbE2ucxSbhEREREREaldKVmFrNqdDjgLkwBdIgKwWOBwdhHpecWE+GlYqIgIwOjRo3nnnXcoLi7myy+/5M4778Td3Z2HH364ynklJSV4eHjUymMGBwfXynUaKpfpmBQREREREZHa9enGZAwD+kcH07q5DwB+nm5Eh/gCkJii5dwiUscMA0ryzfkyjDOK6unpSXh4OG3atGHKlCmMGDGCzz//vHL59dNPP01kZCSdOnUC4ODBg1x55ZUEBQURHBzMmDFj2LdvX+X17HY7999/P0FBQTRv3py//vWvGH/I9Mel3MXFxfztb3+jVatWeHp6EhMTw9tvv82+ffs477zzAGjWrBkWi6VylbHD4eDZZ58lOjoab29v4uPjmT9/fpXH+fLLL+nYsSPe3t6cd955VXLWJXVMioiIiIiINEGGYVQu464YelMhNjKQvWn5JCRnM7RjqBnxRKSpKC2AZyLNeez/SwEP3xrf3dvbm4yMDACWLl1KQEAA3333HQClpaWMGjWKAQMGsHLlStzc3HjqqacYPXo0v/32Gx4eHkyfPp13332X//3vf3Tp0oXp06fz6aefcv7555/yMW+44QZ+/vlnXnnlFeLj40lKSiI9PZ1WrVqxYMECxo8fz44dOwgICMDb2xuAZ599ltmzZzNz5kw6dOjAihUruO666wgNDWXo0KEcPHiQcePGceedd3Lrrbeybt06HnjggRr/XM6ECpMiIiIiIiJN0IYDx0hKz8fHw8ZF3SKq3BYXGcAXm1O0z6SIyEkYhsHSpUv55ptvuOuuu0hLS8PX15e33nqrcgn37NmzcTgcvPXWW1gsFgDeeecdgoKCWLZsGRdccAEvv/wyDz/8MOPGjQNg5syZfPPNN6d83J07d/LJJ5/w3XffMWLECADatWtXeXvFsu+wsDCCgoIAZ4flM888w/fff8+AAQMq77Nq1SpmzZrF0KFDmTFjBu3bt2f69OkAdOrUiS1btvDcc8/V4k/t5FSYFBERERERaYIquiUv6haBr2fVt4YVA3A0mVtE6py7j7Nz0azHPgOLFy/Gz8+P0tJSHA4HEydO5LHHHuPOO++kW7duVfaV3Lx5M7t378bf37/KNYqKitizZw/Z2dkcPnyY/v37V97m5uZGnz59TljOXWHTpk3YbDaGDh1a7cy7d++moKCAkSNHVjleUlJCz549Adi2bVuVHEBlEbOuqTApIiIiIiLSxBSW2Pli82HgxGXcALGRAQAcyCwgu7CUQG/3es0nIk2IxXJWy6nr03nnnceMGTPw8PAgMjISN7ffy2q+vlW/h7y8PHr37s2HH354wnVCQ2u2RUbF0uwzkZeXB8CSJUuIioqqcpunp/nDzTT8RkREREREpIn5JvEIecVltAr2pl/bEye+Bvl40LKZ8w3wVg3AEREBnMXHmJgYWrduXaUoeTK9evVi165dhIWFERMTU+UrMDCQwMBAIiIiWLNmTeV9ysrKWL9+/Smv2a1bNxwOB8uXLz/p7RUdm3a7vfJY165d8fT05MCBAyfkaNWqFQBdunRh7dq1Va71yy+/nP6HUUtUmBQREREREWliKpZxj+/VEqvVctJz4iKdy7m1z6SIyJm79tprCQkJYcyYMaxcuZKkpCSWLVvG3XffzaFDzr+D77nnHv71r3+xaNEitm/fzh133EFWVtYpr9m2bVsmTZrETTfdxKJFiyqv+cknnwDQpk0bLBYLixcvJi0tjby8PPz9/XnwwQe57777eO+999izZw8bNmzg1Vdf5b333gPg9ttvZ9euXTz00EPs2LGDjz76iHfffbeuf0SACpMiIiIiIiJNSnJWIT/tSQechclTiYtyLudOSFZhUkTkTPn4+LBixQpat27NuHHj6NKlCzfffDNFRUUEBDj/fn3ggQe4/vrrmTRpEgMGDMDf35/LL7/8tNedMWMGEyZM4I477qBz587ccsst5OfnAxAVFcXjjz/O3//+d1q0aMHUqVMBePLJJ3nkkUd49tln6dKlC6NHj2bJkiVER0cD0Lp1axYsWMCiRYuIj49n5syZPPPMM3X40/mdxTjVjppNVE5ODoGBgWRnZ1c+UURERERERBqL137YxYvf7uScdsHMvfXUww1+3JHKje/8SkyYH9/fX/1BCyIip1JUVERSUhLR0dF4eXmZHUfO0ul+n9Wtr6ljUkREREREpIkwDKNyGfcVvVud9tyKpdx70vIoKCmr82wiItL0qDApIiIiIiLSRKzff4x9GQX4eti4sFv4ac8N9fekRYAnhgHbDmsAjoiI1D4VJkVERERERJqIeeuc3ZIXdYvAx+P0E2Xh967JhGQVJkVEpPapMCkiIiIiItIEFJSUsWTLYQAm9D710JvjxUZVFCY1AEdERGqfCpMiIiIiIiJNwDeJR8grLqN1sA992wZX6z5xkeWTuVPUMSkitUdzmBuH2vg9qjApIiIiIiLSBFQMvRnfqyVWq6Va94kr75jcdTSXolJ7nWUTkabB3d0dgIKCApOTSG2o+D1W/F5r4s83FRERERERERGXduhYAav3ZAAwvndUte8XEehFsK8Hmfkl7DyaS/eWQXWUUESaApvNRlBQEKmpqQD4+PhgsVTvgxJpOAzDoKCggNTUVIKCgrDZbDW+lgqTIiIiIiIijdynG5IxDDi3fXNaNvOp9v0sFguxkQGs3JVOQnKOCpMictbCw8MBKouT4rqCgoIqf581pcKkiIiIiIhII2YYBvM3OJdxV3fozfFiIwOdhckUDcARkbNnsViIiIggLCyM0tJSs+NIDbm7u59Vp2QFFSZFREREREQasV/3HWN/RgG+HjZGx515Z0tclHMATqImc4tILbLZbLVS2BLXpuE3IiIiIiIijdj89QcBuLh7BD4eZ96bEhfpHICz7UgupXZHrWYTEZGmTYVJERERERGRRqqgpIwlvx0GYELvVjW6RutgH/w93Sgpc7A7Na8244mISBOnwqSIiIiIiEgj9XXCEfJL7LRp7kPfts1qdA2r1ULXyPLl3Ck5tRlPRESaOBUmRUREREREGqn568uH3vRqicViqfF14qKcy7kTtM+kiIjUIhUmRUREREREGqFDxwpYvScDiwXG1WAa9/EqB+BoMreIiNQiFSZFREREREQaoYUbkgE4t31zooK8z+paFQNwElNycDiMs84mIiICKkyKiIiIiIg0Og6H8fsy7rPslgRoF+qHl7uVghI7SRn5Z309ERERUGFSRERERESk0fl1XyYHMgvw83RjVGz4WV/PZrXQNcK5nFv7TIqISG1RYVJERERERKSRqeiWvLhbBD4ebrVyzYoBOJrMLSIitUWFSRERERERkUYkv7iMJVsOAzChz9kv465Qsc+kOiZFRKS2qDApIiIiIiLSiHydcISCEjttm/vQp02zWrtubNTvS7kNQwNwRETk7KkwKSIiIiIi0ogcP/TGYrHU2nU7hPnjYbOSU1TGoWOFtXZdERFpulSYFBERERERaSQOZhbw894MLBa4vFftLeMG8HCz0incH9BybhERqR0qTIqIiIiIiDQSCzY4uyUHtg8hKsi71q8fG1m+nDtFhUkRETl7KkyKiIiIiIg0Ag6HUVmYnNC7drslK8RGVQzA0WRuERE5eypMioiIiIiINAJr92VyMLMQP083RsWG18ljxEVqAI6IiNQeFSZFREREREQagYqhN5d0j8Dbw1Ynj9ElIgCb1UJGfglHc4rr5DFERKTpUGFSRERERETExeUXl/HllsMAXNGnbpZxA3i524gJ9QM0AEdERM6eCpMiIiIiIiIu7quEIxSU2IkO8aVX62Z1+lixUc7l3Ikp2mdSRETOjgqTIiIiIiIiLm7euoOAc+iNxWKp08eKiywfgKPJ3CIicpZUmBQREREREXFhBzIKWJOUicUCl/eMqvPHiyufzJ2opdwiInKWVJgUERERERFxYQs2OIfeDIoJITLIu84fr2v5ZO6U7CIy8jQAR0REak6FSRERERERERflcBiVhckJvetu6M3x/DzdaBfiC2ifSREROTsqTIqIiIiIiLioNUmZHDpWiL+nGxd0Da+3x42N0j6TIiJy9lSYFBERERERcVHz1zu7JS+Jj8Tbw1ZvjxtXvpw7MVkdkyIiUnMqTIqIiIiIiLig/OIyvko4DNTfMu4KceqYFBGRWqDCpIiIiIiIiAtasuUwBSV22oX40qt1UL0+dmx5x+T+jAKyC0vr9bFFRKTxUGFSRERERETEBVUs4x7fuyUWi6VeHzvIx4OWzZwTwLdqAI6IiNSQCpMiIiIiIiIuZn9GPmuTMrFYYFyvKFMyxEU6l3Mnajm3iIjUkAqTIiIiIiIiLmbBhmQABsWEEBHobUqGiuXcCckqTIqISM2oMCkiIiIiIuJCHA6DBeXLuK/o08q0HL8PwNFSbhERqRkVJkVERERERFzIL0kZJGcV4u/lxgVdW5iWIzbK2TG5Jy2PgpIy03KIiIjrUmFSRERERETEhVQMvbk0PhIvd5tpOcL8vQjz98QwYNthdU2KiMiZU2FSRERERETEReQVl/HVliMATOjd0uQ0xy3nTlZhUkREzpwKkyIiIiIiIi7iy98OU1hqp12oLz1bBZkdh7jyATiazC0iIjWhwqSIiIiIiIiLqFjGPaF3SywWi8lpIFYdkyIichZUmBQREREREXEB+9LzWbsvE6sFxvU0fxk3/L6Ue+fRXIrL7CanERERV6PCpIiIiIiIiAtYuMHZLTm4QyjhgV4mp3GKDPSimY87ZQ6DnUfyzI4jIiIuRoVJERERERGRBs7hMFiwIRloGENvKlgslt8H4GifSREROUMqTIqIiIiIiDRwv+zNIDmrEH8vN0Z2bWF2nCpiIyv2mVRhUkREzowKkyIiIiIiIg3cvPKhN5fFR+LlbjM5TVVxUc7J3AkpGoAjIiJnRoVJERERERGRBiy3qJSvEg4DDWsZd4W48o7JbYdzKLU7TE4jIiKuRIVJERERERGRBuzLLYcpKnXQPtSXHq2CzI5zgtbBPvh7ulFS5mBPmgbgiIhI9akwKSIiIiIi0oDNL1/GPaF3KywWi8lpTmS1WugaWb6cO1nLuUVEpPpUmBQREREREWmg9qXn8+u+Y1gtMK5XlNlxTkkDcEREpCZUmBQREREREWmgFmxwdksO6RhKiwAvk9OcWsUAnMQUFSZFRKT6VJgUERERERFpgBwOgwWVy7gb3tCb48VFOTsmE1NycDgMk9OIiIirUGFSRERERESkAVq9J4OU7CICvNwY0aWF2XFOq12IL17uVgpK7CRl5JsdR0REXIQKkyIiIiIiIg3Q/PUHAbisRyRe7jaT05yem81Kl4iKAThazi0iItWjwqSIiIiIiEgDk1NUyteJRwDnNG5XEBf5+3JuERGR6lBhUkREREREpIH58rfDFJU6iAnzI75loNlxqqViAI46JkVEpLpUmBQREREREWlg5pcPvbmid0ssFovJaaon9riOScPQABwREflzKkyKiIiIiIg0IEnp+azbfwyrBS7vGWV2nGrr2MIfd5uF7MJSDh0rNDuOiIi4ABUmRUREREREGpCKoTdDO4YSFuBlcprq83Cz0incH4DEFC3nFhGRP6fCpIiIiIiISANhdxgs3JAMuM7Qm+NVDMBJSNYAHBER+XMqTIqIiIiIiDQQq/ekczi7iEBvd4Z3CTM7zhmLjSovTKpjUkREqkGFSRERERERkQaiYujNZfGReLnbTE5z5uIif5/MrQE4IiLyZ1SYFBERERERaQByikr5OuEIAFf0aWlymprpEhGAzWohPa+E1Nxis+OIiEgDp8KkiIiIiIhIA7Dkt8MUlzno2MKPbuVLol2Nl7uNmFA/wNk1KSIicjoqTIqIiIiIiDQAFcu4J/RuicViMTlNzcVGVSzn1gAcERE5PRUmRURERERETLYnLY/1+49hs1oY2yPK7DhnJTZSA3BERKR6VJgUEREREREx2YLybsmhHUMJC/AyOc3ZqRiAk6il3CIi8idUmBQRERERETGR3WGwcEMy4FzG7eq6lhcmU7KLyMjTABwRETk1FSZFRERERERM9NPudI7kFBHk487wLmFmxzlr/l7uRIf4ApCYon0mRUTk1FSYFBERERERMVHF0Jsx8ZF4utlMTlM7Ysu7JrXPpIiInI4KkyIiIiIiIibJLizlm8QjAEzo3crkNLUnLso5ACdRk7lFROQ0XKowuWLFCi699FIiIyOxWCwsWrSoyu2TJ0/GYrFU+Ro9erQ5YUVERERERP7Ekt8OU1zmoFMLf+KiAsyOU2viNJlbRESqwaUKk/n5+cTHx/P666+f8pzRo0dz+PDhyq85c+bUY0IREREREZHqm7f+IOAcemOxWExOU3sqlnLvzyggp6jU5DQiItJQuZkd4ExceOGFXHjhhac9x9PTk/Dw8HpKJCIiIiIiUjO7U/PYeCALm9XCmJ6RZsepVc18PYgK8iY5q5CtKTmc06652ZFERKQBcqmOyepYtmwZYWFhdOrUiSlTppCRkXHa84uLi8nJyanyJSIiIiIiUtcWbHAOvRnWMZQwfy+T09S+iqXpCclazi0iIifXqAqTo0eP5v3332fp0qU899xzLF++nAsvvBC73X7K+zz77LMEBgZWfrVq1Xg2nBYRERERkYbJ7jBYWF6YnNC7pclp6kbFPpOJKWr+EBGRk3Oppdx/5uqrr678/926daN79+60b9+eZcuWMXz48JPe5+GHH+b++++v/HNOTo6KkyIiIiIiUqdW7U7naE4xzXzcGd6lhdlx6kTFZG51TIqIyKk0qo7JP2rXrh0hISHs3r37lOd4enoSEBBQ5UtERERERKQuzV/v7JYc0yMKD7fG+bYstnwp9560PApKykxOIyIiDVHjfAUsd+jQITIyMoiIiDA7ioiIiIiICADZhaV8k3gEaLzLuAHC/L0I8/fEYcC2w7lmxxERkQbIpQqTeXl5bNq0iU2bNgGQlJTEpk2bOHDgAHl5eTz00EP88ssv7Nu3j6VLlzJmzBhiYmIYNWqUucFFRERERETKfbE5hZIyB53D/YmNbNwrtiqWcyemaDm3iIicyKUKk+vWraNnz5707NkTgPvvv5+ePXsybdo0bDYbv/32G5dddhkdO3bk5ptvpnfv3qxcuRJPT0+Tk4uIiIiIiDhVLOOe0LslFovF5DR1q6Lwqn0mRUTkZFxq+M2wYcMwDOOUt3/zzTf1mEZEREREROTM7E7NZdPBLGxWC2N6RJkdp87FRlYMwNFkbhEROZFLdUyKiIiIiIi4svnrkwE4r1Moof6Nf2VXXPkAnJ1Hcykus5ucRkREGhoVJkVEREREROqB3WHw6caKZdytTE5TP6KCvAnycafMYbDzSJ7ZcUREpIFRYVJERKQW5ReX8ciiBL4tn7YqIiJSYeWuNI7mFNPMx53zO4eZHadeWCwW4iqWc2sAjoiI/IEKkyIiIrVo1oq9fPDLfu78aAMbDhwzO46IiDQgFUNvxvSIwsOt6bwVi43SABwRETm5pvNqKCIiUseyCkr436okAErtBnfM3kBabrHJqUREpCHILijl261HAec07qbk945JDcAREZGqVJgUERGpJf9duZe84jI6h/vTPtSXIzlF3DVnA2V2h9nRRETEZJ//lkJJmYPO4f7ERgaYHadexUU5C5PbD+foNVFERKpQYVJERKQWZOaX8O5P+wC4f2RHZl3fB18PG7/szeS5r7ebG05ERExXsYx7Qu+WWCwWk9PUrzbBPvh5ulFc5mBPWr7ZcUREpAFRYVJERKQWvLliL/klduKiAhjZtQUxYX68eEU8AP9dmcTi31JMTigiImbZdTSXzQezcLNaGNszyuw49c5qtdA1UvtMiojIiVSYFBEROUvpecW8t3ofAPeN6FjZCXNhtwhuG9oOgL/O/42dR3PNiigiIiaav8HZLXle5zBC/DxNTmMOTeYWEZGTUWFSRETkLM1avofCUjvxLQM5v3NYldseuqAT57ZvTkGJnds+WE9OUalJKUVExAxldgefbkgGmt7Qm+PFlU/mTkzWABwREfmdCpMiIiJnITW3iA9+2Q/AfSM7nrBvmJvNyqvX9CQy0Iuk9Hwe+GQzDodhRlQRETHByt3ppOYWE+zrwXmdwv78Do1UxQCcxJRsvQ6KiEglFSZFRETOwsxleykqddCzdRBDO4ae9Jzmfp7MuK43HjYr3209yozle+o5pYiImGX+Oucy7jE9IvFwa7pvv9qF+OLlbiW/xM6+DA3AERERp6b7yigiInKWjuYUMXuNs1vy/pN0Sx4vvlUQj4+JBeDFb3ewYmdavWQUERHzZBWU8N3Wo0DTXsYNzhUEXSLKB+CkaDm3iIg4qTApIiJSQ2/8uJuSMgd92zZjUEzIn55/Tb/WXNWnFYYBd8/dyMHMgnpIKSIiZvlicwoldgddIgKILR/+0pRVDMBJ1GRuEREpp8KkiIhIDaRkFTJn7UHg5HtLnsrjY2Lp3jKQrIJSpny4nqJSe13GFBERE81f71zGfUUT75asEBtZ0TGpwqSIiDipMCkiIlIDbyzbTYndQf/oYM5t/+fdkhW83G3MuK43wb4eJCTn8MiiBAxDQwBERBqbnUdz2XwoGzerhTE9Is2O0yBUDMBJSM7Ra5+IiAAqTIqIiJyxQ8cK+PjX37slz1RUkDevXtMTqwXmrT/ER2sP1HZEEREx2YLybsnzO4fR3M/T5DQNQ4cWfrjbLGQXlnLoWKHZcUREpAFQYVJEROQMvf7jbkrtBgNjmnNOu+Y1usbAmBAeGtUZgMc+T2TjgWO1GVFERExUZnewcGMyoKE3x/N0s9GxhT8AiVrOLSIiqDApIiJyRg5mFjBvnbML5r4RZ94tebzbh7ZjdGw4pXaDKbM3kJ5XXBsRRUTEZCt2pZGWW0xzXw/O6xxmdpwGpWIATkKyJnOLiIgKkyIiImfk1R92UeYwGNwhhD5tg8/qWhaLhReu6E77UF+O5BQx9aMNlNkdtZRURETMUjH0ZkyPKNxtest1vLgoDcAREZHf6VVSRESkmval57Ngg3NpXk32ljwZfy93Zl3fG18PG7/szeS5r7fXynVFRMQcx/JL+H5rKqBl3CcTWzkAJ1sDcERERIVJERGR6nrlh13YHQbndQqlV+tmtXbdmDB/XrgiHoD/rkxi8W8ptXZtERGpX1/8lkKJ3UFsZABdIwPMjtPgdAkPwGqB9LwSUnO1hYmISFOnwqSIiEg17EnLY9HG2u2WPN5F3SK4bUg7AP46/zd2Hs2t9ccQEZG6V7GMW92SJ+ftYSMmzA9wdk2KiEjTpsKkiIhINby6dBcOA0Z0CaN7y6A6eYyHRnViQLvmFJTYuf2D9eQUldbJ44iISN3YcSSX3w5l426zMKZHlNlxGqyKATiJKRqAIyLS1KkwKSIi8id2p+by2Wbn8up7z3IS9+m42ay8OrEnEYFe7E3P58FPNuNwaP8tERFXMX/9QQDO7xxGsK+HyWkaruP3mRQRkaZNhUkREZE/8fL3uzAMGBXbgrjyN1N1JcTPkxnX9cbDZuXbrUeZsXxPnT6eiIjUjlK7g083Oj/EmtC7lclpGra48r031TEpIiIqTIqIiJzGjiO5LNlyGKjbbsnj9WgVxONjYgGY/u0OVu5Kq5fHFRGRmluxM430vGKa+3owrFOo2XEatIqhQMlZhWTml5icRkREzKTCpIiIyGn8Z+lODAMu6hZOl4j6m656Tb/WXNWnFQ4D7p6zkUPHCurtsUVE5MxVDL0Z2zMKd5veZp2Ov5c70SG+ACSmaDm3iEhTpldMERGRU9iaksOXW45gscA9w+unW/J4j4+JpXvLQI4VlDJl9gaKSu31nkFERP7csfwSvt92FNA07uqKLe+aTEjWcm4RkaZMhUkREZFTePn7nQBc0j2STuH+9f74Xu423ri2F8183NmSnM20zxIwDA3DERFpaD7fnEKp3SAuKqBeu+tdWcWezQnqmBQRadJUmBQRETmJhORsvt16FKsF7hnewbQcLZv58Oo1vbBa4JN1h5iz9qBpWURE5OQqlnFP6KVuyeqq6JhM1GRuEZEmTYVJERGRk6jolrwsPpKYMD9TswzqEMKDozoB8OjnCWw8cMzUPCIi8rvtR3LYkpyNu83CZT2izI7jMmIjnR2T+zIKyCkqNTmNiIiYRYVJERGRP9h8MIvvt6VitcDdJnZLHm/K0PaMim1Bqd1gyuwNpOcVmx1JRESA+euc3ZLDO7cg2NfD5DSuI9jXg6ggb8C5p7OIiDRNKkyKiIj8wUvl3ZKX92xJu1BzuyUrWCwWXrwinnahvhzJKWLqRxsoszvMjiUi0qSV2h0s2pQMaOhNTfw+AEfLuUVEmioVJkVERI6zfv8xlu1Iw2a1cPfwGLPjVOHv5c6s63rj42Hjl72ZPP/NDrMjiYg0act3pJGeV0KInwdDO4WaHcflVAzASVTHpIhIk6XCpIiIyHEq9pYc3yuKNs19TU5zog4t/HnxingA3lyxlyW/HTY5kYhI01Ux9ObynlG42/TW6kzFRaljUkSkqdOrp4iISLlf92Wyclc6blYLd53fMPaWPJmLukVw25B2ADw0fzO7juaanEhEpOnJzC9h6fajAIzXMu4aiSsfgLMnLY+CkjKT04iIiBlUmBQRESn30nfObskr+rSiVbCPyWlO76FRnRjQrjkFJXZu+2A9uZpoKiJSrz7flEyp3aBbVCCdwwPMjuOSwgK8CPX3xGHAtsP6kE1EpClSYVJERAT4ZW8Gq/dk4G6zMPX8hrW35Mm42ay8OrEnEYFe7E3P58F5mzEMw+xYIiJNxrzyZdwaenN24soH4CSmaDm3iEhTpMKkiIg0eYZh8O/ybsmr+rYiKsjb5ETVE+LnyYzreuNhs/JN4lFmLN9jdiQRkSZha0oOiSk5uNssXBYfaXYcl1Y5ACdZA3BERJoiFSZFRKTJ+3lPBmuTMvGwWbnzvIbfLXm8Hq2CeOyyWABe/GYHq3alm5xIRKTxW7DB2S05oksLmvl6mJzGtcWW7zOZoI5JEZEmSYVJERFp0o7vlpzYvzURga7RLXm8a/q14so+LXEYcNecDRw6VmB2JBGRRqvU7mDRxmRAy7hrQ8Vk7p1Hcykus5ucRkRE6psKkyIi0qSt3JXOuv3H8HSzMmVYe7Pj1IjFYuGJMXF0iwrkWEEpU2ZvoKhUb+5EROrCsh1pZOSXEOLnydCOoWbHcXlRQd4E+bhTajfYdTTP7DgiIlLPVJgUEZEmyzAMXvre2S15bf82tAjwMjlRzXm525hxXS+a+bizJTmbaZ8laBiOiEgdmL/+IADjekXhZtPbqbNlsViIq1jOnazl3CIiTY1eSUVEpMlatjONjQey8HK3cvuwdmbHOWstm/nwyjU9sVrgk3WHmLP2oNmRREQalYy8YpZuSwVgfC8t464tseXLubXPpIhI06PCpIiINEmGYfBS+d6SNwxoS5i/63ZLHm9wh1AeHNUJgMc+T2TTwSxzA4mINCKfbUqhzGHQvWUgncL9zY7TaFQOwNFkbhGRJkeFSRERaZKWbkvlt0PZ+HjYuG2I63dLHm/K0PaMim1Bid3BlNnrSc8rNjuSiEijMH+9cxq3ht7UrrhIZ8fktsM5lNkdJqcREZH6pMKkiIg0OcfvLXnDgLY09/M0OVHtslgsvHhFPO1CfTmcXcRdH23UGz0RkbOUmJLN1sM5eNisXBYfaXacRqVtc198PWwUlznYk5ZvdhwREalHKkyKiEiT8+3WoySm5ODrYePWRtYtWcHfy51Z1/XGx8PGz3szeOGbHWZHEhFxaQvWJwMwsmsLgnw8TE7TuFitluOWc2ufSRGRpkSFSRERaVIcjt/3lrxxYDTBvo33zWWHFv68MCEegFkr9vLllsMmJxIRcU0lZQ4WbXIWJrWMu25oAI6ISNOkwqSIiDQpXyceYfuRXPw93fjL4Giz49S5i7tHVHaFPjRvM7tTc01OJCLiepbtSCUzv4RQf08GdwgxO06jFFfeMZmoATgiIk2KCpMiItJkOBwGL5fvLXnjoOgmsxTvr6M6MaBdc/JL7Nz6wXpyi0rNjiQi4lLmlQ+9GdczCjeb3kLVhbio8sJkSjYOh2FyGhERqS96VRURkSZjyZbD7Dyah7+XGzcPavzdkhXcbFZendiTiEAv9qbl8+C8zRiG3vSJiFRHel4xP25PBWC8lnHXmfahvni6WckvsbMvQwNwRESaChUmRUSkSbAf1y15y+B2BHq7m5yofoX4efLGtb3wsFn5JvEoM5bvMTuSiIhL+GxTCmUOg/iWgXRs4W92nEbLzWalS0TFPpNazi0i0lSoMCkiIk3CF5tT2JOWT6C3OzcObGt2HFP0bN2MRy/rCsCL3+xg1a50kxOJiDR888uXcWvoTd2LKx+Ak6jJ3CIiTYYKkyIi0uiV2R28snQXALcOaYe/V9PqljzexH6tubJPSxwG3DVnA4eOFZgdSUSkwUpMyWbb4Rw8bFYui48yO06jVzkARx2TIiJNhgqTIiLS6H22KYW96fk083Fn0rltzY5jKovFwhNj4ugWFcixglKmzN5AUand7FgiIg1SRbfkyNgWBPo03Q+16kvFAJyElGzthSwi0kSoMCkiIo1aqd3BKz84uyVvG9oeP083kxOZz8vdxozretHMx50tydk8+lmi2ZFERBqckjIHn21KAbSMu750aOGHu81CVkEpyVmFZscREZF6oMKkiIg0ap9uSGZ/RgHNfT24YUAbs+M0GC2b+fDKNT2xWuDjdQeZs/aA2ZFERBqUH7ankplfQpi/J4NjQsyO0yR4utkqBwwlJGs5t4hIU6DCpIiINFrHd0vePrQ9Ph7qljze4A6hPHBBJwAe/SyRTQezzA0kItKAVCzjvrxXFG42vW2qL7/vM6kBOCIiTYFeYUVEpNGav/4Qh44VEuLnyXXnqFvyZO4Y1p4LuragxO7gjtnrycgrNjuSiIjp0nKL+XFHKgATemkZd32qmMydoMncIiJNggqTIiLSKBWX2Xnth92As/jm7WEzOVHDZLFYmH5lPO1CfEnJLuKuORspszvMjiUiYqrPNiVjdxjEtwqiQ/nSYqkfXSMrBuBoKbeISFOgwqSIiDRKn6w7RHJWIS0CPJnYv7XZcRo0fy93Zl3fGx8PG6v3ZPDCtzvMjiQiYhrDMCqXcV+hoTf1rkuEP1aLs2s1NafI7DgiIlLHVJgUEZFGp6jUzhs/Orsl7zwvBi93dUv+mQ4t/HlhQjwAs5bv5asth01OJCJijsSUHLYfycXDzcql3SPNjtPk+Hi40T7UD4AE7TMpItLoqTApIiKNzse/HuRwdhERgV5c1beV2XFcxsXdI7hlcDQAD87bzO7UXJMTiYjUv4puyQu6tiDQx93kNE1TXFT5cm5N5hYRafRUmBQRkUalqNTO68d1S3q6qVvyTPxtdGfOaRdMfomdWz9YT25RqdmRRETqTUmZg882JQMwQcu4TRMbqQE4IiJNhQqTIiLSqHy45gCpucVEBXlzZR91S54pN5uV1yb2IiLQi71p+Tw07zcMwzA7lohIvfhh+1GOFZTSIsCTwR1CzY7TZFV0TCZqAI6ISKOnwqSIiDQahSV2ZizbA8Bd58fg4aaXuZoI8fPkjWt74WGz8nXiEWYu32t2JBGRelGxjPvyni2xWS0mp2m6upZ3TCZnFZKZX2JyGhERqUt6xyYiIo3G7F/2k55XTKtgb8ZrCd5Z6dm6GY9e1hWAF77Zzk+7001OJCJSt9Jyi/lxRxqgZdxmC/Byp21zHwASNQBHRKRRU2FSREQahfziMmYur+iW7IC7TS9xZ2tiv9Zc0bslDgPumrOR5KxCsyOJiNSZzzYlY3cY9GwdREyYn9lxmrxYDcAREWkS9K5NREQahfd/3k9GfgltmvswrmeU2XEaBYvFwpNj44iLCiAzv4Qps9dTVGo3O5aISK0zDIN565zLuNUt2TDERZYXJtUxKSLSqKkwKSIiLi+vuIw3Vzi7Je8Z3gE3dUvWGi93GzOu7U2Qjzu/Hcrmsc8TzY4kIlLrElNy2HE0Fw83K5d0jzQ7jgBxUc59JrdqAI6ISKOmd24iIuLy3lu9j2MFpbQL8eWyeL2hrG2tgn145eqeWCww99eDzF17wOxIIiK1at66gwCMig0n0Nvd5DQCEFveMZmUnk9uUanJaUREpK6oMCkiIi4tp6iUN1c4p0bfM0LdknVlSMdQHrygEwDTPktk88EscwOJiNSS4jI7n21OAbSMuyEJ9vUgKsgbUNekiEhjpndvIiLi0t5ZtY/swlJiwvy0/K6OTRnanpFdW1BidzBl9noy8orNjiQictZ+2JZKVkEpLQI8GRQTYnYcOU5spHM5d4IKkyIijZYKkyIi4rKyC0t5a5WzW/LeER2wWS0mJ2rcrFYL06+Mp12ILynZRdw1ZyNldofZsUREzsr89c6hN+N6tdTrSAMTVz6ZOzFZA3BERBorFSZFRMRlvb0qidyiMjq18OeiuAiz4zQJAV7uzLq+Nz4eNlbvyeCFb3eYHUlEpMZSc4tYtjMN0DLuhuj3jkkVJkVEGisVJkVExCVlFZTwv1VJgLNb0qoul3rToYU/L0yIB2DW8r18teWwyYlERGrms40p2B0GvVoH0T7Uz+w48gcVHZO7U/MoLLGbnEZEROqCCpMiIuKS/rtyL3nFZXSJCGBUbLjZcZqci7tHcMvgaAAenLeZ3am5JicSETkzhmFULuOe0LuVyWnkZML8PQnx88RhwLYj2mdSRKQxUmFSRERcTmZ+Ce/+tA+A+9QtaZq/je7MOe2CyS+xc9sH68krLjM7kohItW1JzmbH0Vw83axc3F3bgTREFouFuCjncm7tMyki0jipMCkiIi7nzRV7yS+xExcVwMiuLcyO02S52ay8NrEX4QFe7EnL56F5mzEMw+xYIiLVUtEtOSo2nEBvd5PTyKnERTqXcyckq2NSRKQxUmFSRERcSnpeMe+t3gfAfSM6YrGoW9JMIX6evHFdL9xtFr5KOMKsFXvNjiQi8qeKy+x8tikF0NCbhq6iY1IDcEREGicVJkVExKXMWr6HwlI78S0DOb9zmNlxBOjVuhmPXhoLwPNfb+en3ekmJxIROb2l21LJLiwlItCLgTEhZseR04gt75jceTSX4jINwBERaWxUmBQREZeRmlvEB7/sB+C+keqWbEiu7d+aCb1b4jDgrjkbSc4qNDuSiMgpVSzjHtcrCpv2KW7QWjbzJtDbnVK7wa6jeWbHERGRWqbCpIiIuIyZy/ZSVOqgZ+sghnYMNTuOHMdisfDU2DjiogLIzC/hjtnrKSpVZ4uINDypOUUs35kGwPheWsbd0B0/ACdBA3BERBodFSZFRMQlHM0pYvYaZ7fk/eqWbJC83G3MuLY3QT7ubD6UzeNfJJodSUTkBIs2JWN3GPRu04x2oX5mx5FqqByAo30mRUQaHZcqTK5YsYJLL72UyMhILBYLixYtqnK7YRhMmzaNiIgIvL29GTFiBLt27TInrIiI1Ko3ftxNSZmDvm2bMUj7gTVYrYJ9eOXqnlgsMGftQeauPWB2JBGRSoZhMG+dcxm3ht64jtgoTeYWEWmsXKowmZ+fT3x8PK+//vpJb3/++ed55ZVXmDlzJmvWrMHX15dRo0ZRVFRUz0lFRKQ2Hc4uZM7ag4D2lnQFQzqG8uAFnQCY9lkimw9mmRtIRKTcb4ey2ZWah6eblYu7R5gdR6opLtK5lHvb4RzK7A6T04iISG1yqcLkhRdeyFNPPcXll19+wm2GYfDyyy/zz3/+kzFjxtC9e3fef/99UlJSTuisFBER1/L6j7spsTvoHx3Mue3VLekKpgxtz8iuLSixO5gyez0ZecVmRxIRqRx6MzounAAvd5PTSHW1be6Lr4eN4jIHe9PzzY4jIiK1yKUKk6eTlJTEkSNHGDFiROWxwMBA+vfvz88//3zK+xUXF5OTk1PlS0REGo5Dxwr4+NffuyXFNVitFqZfGU+7EF9Ssou4e+5GdbmIiKmKSu18vjkFgCt6tzI5jZwJq9VCbMU+kxqAIyLSqDSawuSRI0cAaNGiRZXjLVq0qLztZJ599lkCAwMrv1q10j9SREQaktd/3E2p3WBgTHPOadfc7DhyBgK83Jl5fW98PGz8tDuDF7/daXYkEWnClm5LJbuwlMhALwa01+uJq4mtnMytRhIRkcak0RQma+rhhx8mOzu78uvgwYNmRxIRkXIHMwsqhxTcN0Ldkq6oYwt/np/QHYCZy/fwdcJhkxOJSFM1f73z3/njerXEZtVexa5Gk7lFRBqnRlOYDA8PB+Do0aNVjh89erTytpPx9PQkICCgypeIiDQMr/6wizKHweAOIfRpG2x2HKmhS7pH8pdB0QA88MlmdqfmmZxIRJqaozlFLN+ZBsB4TeN2SRUdk1tTcnA4DJPTiIhIbWk0hcno6GjCw8NZunRp5bGcnBzWrFnDgAEDTEwmIiI1sS89nwUbkgHtLdkY/P3CzvSPDia/xM5tH6wjr7jM7Egi0oR8ujEZhwF92jQjOsTX7DhSAzGhfni6WckrLmN/ZoHZcUREpJa4VGEyLy+PTZs2sWnTJsA58GbTpk0cOHAAi8XCvffey1NPPcXnn3/Oli1buOGGG4iMjGTs2LGm5hYRkTP3yg+7sDsMzusUSq/WzcyOI2fJzWbltYm9CA/wYk9aPg/N24xhqONFROqeYRiV07gnqFvSZbnZrHSOqNhnUsu5RUQaC5cqTK5bt46ePXvSs2dPAO6//3569uzJtGnTAPjrX//KXXfdxa233krfvn3Jy8vj66+/xsvLy8zYIiJyhvam5bFoo7olG5tQf0/euK4X7jYLXyUc4c0Ve82OJEBxmZ2E5Gx2p+aaHUWkTmw+lM3u1Dy83K1c3D3C7DhyFuIiywuT2mdSRKTRcDM7wJkYNmzYabsrLBYLTzzxBE888UQ9phIRkdr2ytJdOAwY0SWM7i2DzI4jtahX62Y8emks/1yUwHNfb6dbVCDnxoSYHavJKCq1s+1wDgkpOSQmZ7MlOZudR3MptRu4WS18e98Q2oX6mR1TpFZVDL25MC4Cfy93k9PI2YiLcg7ASdRkbhGRRsOlCpMiItL47U7N5bPNKQDcq0ncjdK1/Vuz6WAW89cfYuqcjXxx1yCigrzNjtXo5BWXOYuQ5QXIxOQcdqflYT/F0Igyh8GKnWkqTEqjUlRq5/NNztcULeN2fcdP5jYMA4tF09VFRFydCpMiItKgvPz9LgwDRsW2qOyMkMbFYrHw1Ng4th/JISE5hztmr+fj2wbg5W4zO5rLyi4sJTHFWXzckpxNQko2Sen5nGyhSXNfD+KiAomLCiAuMpC4qEA+25TMi9/uZO2+TCYPjK7/b0Ckjny/7Sg5RWVEBnoxoF1zs+PIWeoY7oeb1UJWQSnJWYW0bOZjdiQRETlLKkyKiEiDseNILku2HAbULdnYebnbmHFtby59bRWbD2Xz+BeJPDuuu9mxXEJGXjGJKc4CZGJKNgnJORw4xYTa8AAv4qICiI0MpFuUswjZIsDzhC6j/uUFm7VJmepCkkZl3jrn0JvxvVtitep57eo83Wx0bOHP1sPOD7ZUmBQRcX0qTIqISIPxn6U7MQy4qFs4Xconb0rj1SrYh1eu7smkd9YyZ+1BerQK4qq+rc2O1aCk5hSRkJLNlkM5JKRkk5icTUp20UnPbdnMm7jIQLq1DCQ20lmMDPX3rNbjdG8ZiIeblfS8Evam59Ney7mlETiSXcTKXWkAjO+lZdyNRVxUAFsP55CYks3ouHCz44iIyFlSYVJERBqErSk5fLnlCBYL3DNc3ZJNxZCOoTwwsiMvfruTRz5LpEtEQJMceGQYBinZRWw5VNEFmU1CSg5pucUnPT86xJfYyIDKLsjYyACCfDxq/PiebjZ6tgpiTVIma5MyVZiURuHTjck4DOjbthltQ3zNjiO1JC4qkE/WHSIhWZO5RUQaAxUmRUSkQXj5+50AXNI9kk7h/iankfp0x7AYNh3M5vttR5kyewNf3DWIYN+aF9kaOsMwOJBZ4NwLMjmnshB5rKD0hHOtFmgf6ke3qEBiowKJiwyga2RAnUwW7h8dXFmYvKafOlfFtRmGUTmNW0NvGpfYygE4mswtItIYqDApIiKmS0jO5tutR7Fa4J7hHcyOI/XMarXw76viGfPaTySl53P3nI28d1M/bI1gPzi7wyApPY+E5JzyLshsElNyyC0qO+FcN6uFDi386RYVUN4FGUiXCH98POrnn2v9opsDu1mblFkvjydSlzYdzGJPWj7e7jYu7h5pdhypRV0i/LFaIC23mNScIsICvMyOJCIiZ0GFySZm59FcokN8cbdZzY4iIlKpolvysvhIYsK0hLQpCvByZ+Z1vRn7+k+s2p3Oi9/u4G+jO5sd64yU2R3sSs0jIdlZfExIzmbr4RwKSuwnnOvhZqVLuH95F6RzQnbHFv6mTibv1SYIN6uF5KxCDh0r0FAJcWnz1zuH3lwYF46fp97yNCY+Hm60D/VjV2oeiSk5KkyKiLg4vUo3IWm5xVw162daNvNh+pXxdGyhpZIiYr7NB7P4flsqVgvcrW7JJq1TuD/PT+jOXXM2MmPZHuJbBjXYwQbFZXZ2Hskj4bj9ILcfzqG4zHHCud7uNrpGBhAXGVBZiOzQwq/BfUjo4+FGXFQgmw5msTYpU4VJcVlFpXY+35wCaBl3YxUXFVj5QdB5ncPMjiMiImdBhckmZE9aHg4DtiRnc8krq7j/go7cMrhdo1gqJyKu66XybsnLe7aknQZuNHmXxkey+WAWb61K4sF5m4kJ8zO9i7awxM62Izkklu8JmZCSzc6juZTajRPO9fN0IzbSuRQ7LiqAuMhA2oX6ucxrbf/o4MrC5DhNMRYX9e3Wo+QWlREV5M057ZqbHUfqQGxkAJ9uTCYhRQNwRERcnQqTTcg57Zrz7X1DeHjhFn7Ynsq/vtrON4lHmH5FvIoBImKKDQeOsWxHGjarhbuHx5gdRxqIv1/YmS3J2axJyuT22etZdOfAeluKmVdcxtaU3/eDTEjOZneq84O9Pwr0di8fShNQvhw7kDbBPlhdpAh5Mv2ig5m1Yq/2mRSXVrGMe3yvKJf+71FOrXIATrIG4IiIuDoVJpuYFgFevD2pD/PWH+LJL7ay8UAWF/5nJX8d3Zkbz22rf7yJSL166Ttnt+T4XlG0ae5rchppKNxsVl6b2ItLX13F7tQ8Hpq3mTeu7YXFUruvUdkFpc6J2CnZlcNpkjLyMU5ShAzx83B2QZbvBxkbGUjLZt61nslsfdoGY7HA3vR8UnOLCPPX3m3iWo5kF7FqVxoA47WMu9HqGhkAQHJWIcfyS2jm62FyIhERqSkVJpsgi8XClX1aMTAmhL8v+I2Vu9J5cvFWvkk8wosT4mndXHtKiUjd+3VfJit3peNmtXDX+dpbUqoK9ffkjet6cdWsn/kq4QhvrtjLbUPb1/h6GXnFJFR0QpZ3Qx7MLDzpuRGBXsRG/r4Uu1vLQML8PRtdEfJkAr3d6RIewNbDOfyadIyLu0eYHUnkjCzceAiH4ez+1QdejVegtzttmvuwP6OAxJQcBnUIMTuSiIjUkAqTTVhUkDfv39SPj9Ye4Okl21iblMno/6zg4Qs7c23/NuqeFJE6VdEteUWfVrQK1gcicqJerZsx7dJYHlmUwHNfb6dbVCDnxpz+zadhGKTmFpcXIHPYkpxNYko2h7OLTnp+q2DvymXYcVGBxEYGEOLnWRffjsvoFx3M1sM5rE3KUGFSXIphGJXLuDX0pvGLiwxkf0YBCSnZKkyKiLgwFSabOIvFwrX92zCkQygPztvMmqRMHvkska8Tj/D8hHiigrzNjigijdAvezNYvScDd5uFqedrb0k5tev6t2bTgSwWbDjE1DkbWXzXICLLX5sMwyA5q7ByGXbFkuz0vOKTXqtdiC+xUYF0K++EjI0MJNDHvT6/HZfQPzqYd1fvY432mRQXs/FgFnvT8vF2t3FRNxXVG7vYqACWbDlMQrIG4IiIuDIVJgWAVsE+zLnlHN77eR/Pfb2dn3ZnMOqlFTxySReu7NOqSSxfE5H6YRgG/y7vlryqbyt9ACKnZbFYePryOLYfySExJYfbPljPuTHNSSyfjp1VUHrCfawWiAnzq9IJ2SXCH38vFSGro290MAA7juaSVVBCkI/2bhPXUNEteWG38HobmCXmiSsfgJOYogE4IiKuTK/YUslqtXDjwGiGdQrjwXmbWb//GH9bsIWvEo7wr3HdCQ/UBvgicvZ+3pPB2qRMPGxW7jxP3ZLy57zcbcy8rjeXvLqKLcnZbDmuO8bNaqFjC3+6RZUPpYkKpEt4AN4eNhMTu7YQP0/ah/qyJy2fdfuOMaJrC7MjifypolI7X2xOAbSMu6mILR+Ak5SeT25RqT58EhFxUSpMygmiQ3z55LYBvL1qLy9+u5NlO9K44KXlPHZZLJf3jFL3pIjU2PHdkhP7tyYiUN2SUj2tgn148/revPbjbloH+1ROyO4Y7oenm4qQta1fdHP2pOWzdl+mCpPiEr5JPEJuURlRQd6cE93c7DhSD5r7eRIZ6EVKdhFbU3Lo306/dxERV6TCpJyUzWrh1iHtOa+8e3LzoWzu/2QzXyUc4ZnLuxHq37QHA4hIzazclc66/cfwdLMyZVjNJyxL09S/XXO98awn/aODmbP2gPaZFJdRsYx7fO+WGuDYhMRGBZKSXUSCCpMiIi7LanYAadg6tPBnwZRzeWhUJ9xtFr7bepQLXlpeuVRGRKS6DMPgpe+d3ZLX9m9DiwBtDyHSUPUr32cyITmb/OIyk9OInN7h7EJW7U4HYEIvLeNuSir3mdQAHBERl6XCpPwpt/J94D6fOoiuEQEcKyjlrjkbufPDDWTml5gdT0RcxLKdaWw8kIWXu5Xbh7UzO46InEZkkDctm3ljdxhsOHDM7Dgip7VwQzKG4ez0bd3cx+w4Uo/iopz7TCakqDApIuKqVJiUausSEcCiOwdyz/AOuFktLNlymAteWs43iUfMjiYiDZxhGLxUvrfkDQPaEuavbkmRhq6ia3KtlnNLA2YYBgvKl3Fr6E3TExfl7JjcnZpHYYnd5DQiIlITKkzKGfFws3LfyI4sunMgHVv4kZ5Xwm0frOfeuRvJLig1O56INFBLt6Xy26FsfDxs3DZE3ZIirqB/eWFS+0xKQ7bhQBZ70/Px8bBxUbcIs+NIPQvz9yTEzxOHAduO5JgdR0Sk2krtDnan5podo0FQYVJqJC4qkC/uGsSUYe2xWmDRphRGvrScH7enmh1NRBqY4/eWvGFAW5r7aXiWiCvoVz7ZeNPBLIpK1YkkDdP89QcBuDAuAl9PzfVsaiwWS+Vy7sQUFSZFxDVkF5Qy+Z21TJj5M/sz8s2OYzoVJqXGPN1s/G10Z+ZPOZd2ob6k5hZz47u/8tf5m8kpUvekiDh9u/UoiSk5+HrYuFXdkiIuo21zH0L9PSkpc/DbIe3fJg1PYYmdxZsPA1rG3ZRpAI6IuJK9aXlc/sZP/LQ7g5IyBwcyC8yOZDoVJuWs9WrdjC/vHsxfBkVjscAn6w4x+qUVrNyVZnY0ETGZw/H73pI3Dowm2NfD5EQiUl0Wi+W4fSYzTE4jcqJvtx4ht7iMls28K7cekKZHA3BExFWs3p3O5W+sZm96PpGBXsy//VwGdwg1O5bpVJiUWuHlbuOfl3Tl41sH0Ka5DynZRVz/9lr+8ekW8ovLzI4nIib5OvEI24/k4u/pxl8GR5sdR0TOkPaZlIZs/nFDb6xWi8lpxCyx5R2TO47kUlLmMDmNiMjJfbTmADf8by3ZhaX0bB3EoqkD6RoZYHasBkGFSalV/aKD+eqewUwa0AaAD9ccYPR/VvDLXnVaiDQ1DofBy+V7S944KJogH3VLiriaio7J9fuPUWbXG35pOFKyClm1Ox2A8b20jLspa9nMmwAvN0rtBjuPapCEiDQsZXYHj3+RyP99uoUyh8GYHpHMueUcwvy9zI7WYKgwKbXOx8ONx8fE8dFf+hMV5M3BzEKufvMXHv8ikcISbZ4v0lQs2XKYnUfz8Pdy4+ZB6pYUcUUdw/wJ9HanoMSuwRLSoCz57TCG4ezqbRXsY3YcMZFzAE75PpNazi0iDUhOUSl/eX8d7/y0D4AHRnbk5at64OVug+Jc2PaFuQEbCBUmpc6cGxPC1/cO5pp+rQB456d9XPTKStbv13IwkcbOfly35C2D2xHo7W5yIhGpCavVQt+2FftM6vVbGo6V5d2SF8SGm5xEGoKKwmRCsj5AEZGG4WBmAePfWM2yHWl4uVt5fWIv7hreAUtBJvzwNLwUCx9fD2k7zY5qOhUmpU75e7nz7LjuvHdTP8IDvEhKz+eKmT/z7JfbKCpV96RIY/XF5hT2pOUT6O3OjQPbmh1HRM6C9pmUhqa4zM6v5c/HQTEhJqeRhiA2UgNwRKTh+HVfJmNe/4ldqXm0CPDkk9sGcHEbB3z9MLwcByueh6JsaB4DBelmxzWdCpNSL4Z2DOWb+4YwvldLHAbMWrGXS15dxeaDWWZHE5FaVmZ38MrSXQDcOqQd/l7qlhRxZRX7TP66LxOHwzA5jQhsPJBFYamdED9POrbwMzuONAAVHZPbDudoP1wRMdX89YeY+N9fyMwvIS4qgMUTI+m+/p/wn3j45Q0oLYCIHnDl+3DnGmhzrtmRTafCpNSbQG93pl8Zz39v6EOInye7U/MYN2M1L36zQxP0RBqRzzalsDc9n2Y+7kw6t63ZcUTkLMVGBuDjYSO7sJSdqRosIeZbXb6Me2BMcywWTeMWiG7ui6+HjaJSB3vT882OIyJNkMNh8K+vtvPgvM2U2g3+EpPHorC3CH1vIGz8AByl0GYQXLcQbl0GXceA1WZ27AZBhUmpdyO7tuC7+4ZwWXwkdofBaz/u5rLXVmmzapFGoNTu4JUfnN2Stw1tj5+nm8mJRORsudms9G7TDNA+k9IwVEzjHthey7jFyWq10LViOXey3lOISP3KLy7j9tnrmbl8D30t2/kh/DX+eehW3LYtAsMBHUfDTd/CjUsgZjjoQ7UqVJgUUzTz9eCVa3oy49peBPt6sP1ILmNe+4lXlu6iVMsvRFzWpxuS2Z9RQHNfD24Y0MbsOOYoK4HtSyA72ewkIrVG+0xKQ5FbVMrmQ87C07kxzU1OIw1JbKQG4IhI/UvJKmTCjNWUbP+G+R6PM8/zCdplrQaLFeImwO0/wcSPoXV/s6M2WGplEVNd2C2CvtHB/PPTBL5OPMK/v9vJd1uPMv3KeDq28Dc7noicgeO7JacMa4+PRxN7iTEM2PoZLH0cMveCfwTc8iMERJidTOSs9Yt2FoDWJmViGIaWz4pp1uzNxO4waNvch5bNfMyOIw1I5WRurcISkXqyaX8GH7//Gi+WLiDWY7/zoM0DekyEgfdAcDtzA7oIdUyK6UL8PJlxXS/+c3UPAr3d2ZKczSWvrGLGsj3Ytcm+iMuYv/4Qh44VEuLnybX9m1i35P6f4e2RMG+SsygJkHsY5k6E0kJzs4nUgu4tA/Fws5KWW8y+jAKz40gT9tMe5zLuczWNW/4gLsq5lHtrSo4GdYlI3SorYdNnrxD4v3N51v5vYq37cbj7wICpcM9vcOl/VJQ8AypMSoNgsVgY0yOK7+4bwvDOYZTYHTz39XYmzFzNnrQ8s+OJyJ8oLrPz2g+7AbhjWHu8PZrIRs5pO2HORHhnNBz6Fdx9YdjDcPsq8G4GKRvgszud3ZQiLszL3UaPVkEArE3KMDeMNGk/le8vOUiFSfmDmFA/PN2s5BWXsT9TH6CISB0oycf4+XVyn4+lx8ZHiLYcId/qT/Ggv2G9LxFGPa3VUjWgwqQ0KGEBXrw1qQ8vTOiOv6cbGw9kcdF/VvLWyr365FOkAftk3SGSswppEeDJxP6tzY5T93KPwuL74I1zYMcSsNig941w9wYY9ncI7wZXfgBWN0hYACtfNDuxyFnTPpNittTcInYezcNigQHttL+kVOVms9I5QgNwRKQOFB6D5c9jvBSH5Zv/w78klaNGEN+3uhuvv27Dc8T/gU+w2SldlgqT0uBYLBau6NOKb+4bwuAOIRSXOXhqyTaufvMX9mfkmx1PRP6gqNTOGz86uyXvPC8GL/dG3C1ZnAfL/gWv9IR1/wPDDp0uhjt+hktfBv/w38+NHgwXlRckf3gKtn5uSmSR2tKvvDCpydxiltW7nd26sZEBNPP1MDmNNERxFZO5tc+kiNSG3KPw3TR4KQ5+fBpLYSb7HC34R9lfWHHhUkbc/CQ2L83GOFtNbDKBuJLIIG/ev6kfc9Ye5OklW1m7L5PRL6/k/y7qzLX922C1auN9kYbg418Pcji7iIhAL67q28rsOHXDXgYbP4Blz0LeUeexqN4w8kloO/DU9+tzI6Rug7Wz4NPboFlbiOheL5FFaluv1s2wWS0cOlZIclYhUUHeZkeSJqZiGffA9lrGLSdXMQBna4omc4vIWchMgtWvwMYPwV4MwG5LG14pvpSfPAfx+uR+nKPO/VqjwqQ0aBaLhYn9WzO4QwgPzd/ML3szeeSzRL5KOMLzE7prGqOIyYpK7bx+XLekp1sj65Y0DNjxFXz/KKTvdB5rFg0jHoWuY6E6k4lHPeO8794fYc41cMsP4N+iTmOL1AVfTzfiogLZfDCLX5MyieoZZXYkaUIMw/i9MKn9JeUU4iLLJ3MnZ2MYBpbqvE6LiFQ4uhVWveTcismwA3AsuCf/lzaSr0rjaR/qx4JJfWkb4mty0MZFS7nFJbQK9uGjv5zDY5d2xcvdyuo9GYx+eSVz1x7A0FAJEdN8uOYAqbnFRAV5c2WfRtYteWgdvHMRzL3GWVj0DoYLn4c710Ls5dUrSgLY3OCKd6B5DOQcgo+vhdKius0uUke0z6SYZV9GASnZRXjYrPRtq3285OQ6hvvhZrVwrKCUlGy91opINR1a5xxoOWMAbPkEDDtG++Es6vEmPVMe5KvSHgzuEMrCOwaqKFkHVJgUl2G1Wpg8MJqv7xlCnzbNyCsu4+8LtzD5nV85nF1odjyRJqewxM6MZXsAuOv8GDzcGslLSuZe+GQSvDUcDqwGNy8YdD/cswn63wZuNdjXzLsZXPMxeAU6p3d/cY8mdYtL6te2Yp9JTeaW+lXRLdmzdRDeHjZw2OHQerCXmpxMGhJPNxsdWjj3e9MAnKbD7jB4/+d9lX9PiFSLYcCeH+G9S53/7t+xBLBA1zGU3PwDD3hO495f/AALNwxowzuT+xLo7W526kbprN5FFhXpUyipf21DfPn4tgH846IueLhZWb4zjQteWsGC9YfUPSlSj2b/sp/0vGJaBXszvndLs+OcvfwM+Opv8Fo/2LoIsECP6+CuDc6l216BZ3f9kBi44j3nBO/f5sJP/6mN1CL1qm/bYCwW2JOWT3pesdlxpAmpKDgMigmBkgL4+Dp463yYf5PJyaShqRiAk6jCZJPx0doDTPsskWvfWsPtH6wnOUtNK3IaDgdsWwz/PR8+GAtJK8Dq5vx3/51rybjov0xcXMzCDcnYrBaeGBPLE2PicLM1kiaMBuiMf7IOh4Mnn3ySqKgo/Pz82Lt3LwCPPPIIb7/9dq0HFDkZm9XCLUPa8eXdg4hvGUhuURkPzNvMLe+vJzVXBXORupZfXMbM5RXdkh1wd+UX6tJCWDkdXukBa2aCoxRiRsLtq2Ds6xBYi/votT8PLnzO+f+/fwy2f1l71xapB4E+7nQq70b6Vcu5pZ7YHQY/73V26Q5uaYX3L4Md5X9/bvscdn5rYjppaCoG4CRoAE6T4HAYvPNTUuWfv048wvDpy3j9x90Ul9lNTCYNjr0UNs91Ltf++FpI2QBu3tDvNrh7E4x9nR32CMa8/hPr9h/D38uNd2/syw0D2pqdvNE743eSTz31FO+++y7PP/88Hh6/L2eLi4vjrbfeqtVwIn8mJsyfBVPO5aFRnXC3Wfh+21EueGkFn29OUfekSB16/+f9ZOSX0Ka5D+NcdQCGw+6ctPdqb1j6BBTnQHh3uOEzuG4+hMfVzeP2uwX63AwYsPAWOJpYN48jUke0z6TUt60pOWQVlNLJM5P4b650bonhFQSdL3Ge8NVDzg+ZRIC4KGfHpJZyNw3Ld6WxNy0ff083FkwZQL+2wRSVOnjhmx1c+PJKVu3S8u4mr7QQ1v4XXukFn94GadvBMxAGPwD3boGLnoegVvy4PZXxM1Zz6FghbZr78OkdAxncIdTs9E3CGRcm33//fd58802uvfZabLbfp6/Gx8ezffv2Wg0nUh1uNit3nhfDF3cNIjYygKyCUu6es5E7P9pAhpaZub7SIsg+pP34GpC84jLeXOHslrxneAfXW9ZgGLDre5g5GD67A3KSIbA1jPsv3Loc2g2r+wwXPgfRQ6AkD+ZcDfn6R7O4jn7RzQFYq8Kk1JOf9qQTa0niE7dHsGTuhsBWcPO3cPks8I+EY/tg1ctmx5QGoktEABYLpOYWk5qjlVSN3f9WObslr+rbit5tgvn4tnP495XxhPh5sDc9n+veXsOdH27QTIKmqCjHOWH75e7w5YOQfQB8Q2H4o3DfFhg+DfxCMQyDt1bu5eb3fiWvuIz+0cEsumMgMWF+Zn8HTcYZv5tMTk4mJibmhOMOh4PSUm0+LebpHB7AojsHOgslVgtfbjnCBS+t4OuEI2ZHk5rIOQxLn4SXusJLsTBjIKx5EwqPmZ2syXtv9T6OFZTSLsSXy+IjzY5zZlI2wftj4MPxkJro3Dfygqdg6q/Q/Uqw1lOR1ebu3G+yWTRkHXDulVamD1LENfSNbgbAtiM5ZBfq335S97ITvuZjjycJtB+DFnFw83cQ2gk8/WD0M86TVr3kHF4mTZ6PhxvtQ50FhUQt527Udh7NZeWudKwWmHRuWwAsFgvjerVk6QPDmHxuW6wWWLLlMMOnL2fW8j2UlDnMDS11Lz+9/H1knHPrpPxU5wdaF73o7JAcfH/l3vElZQ7+79MtPLVkGw4Dru7big9u7k8z3xoMu5QaO+N3YF27dmXlypUnHJ8/fz49e/aslVAiNeVus3LfyI4sunMgnVr4k5Ffwu2z13Pv3I1kFZSYHU+q49B6WPAXeDkOVr4IBeWTX1MTnUu1pneGhbfB/tXqojRBTlEpb65wvvG7Z4QLdUtmHYCFt8KbQyFpOdg8YMBU534y594F7l71n8knGCZ+DJ4BcOBnWHy/ntPiEsL8vWgX4othwPr96pqUulW64SPuT30EP0sRBVED4cYvISDi9xO6jnV2utuL4cu/6u9RAX4fgKPl3I1bxd6So2LDaRXsU+W2QG93Hrssli/uGkTvNs0oKLHz7FfbueiVlazeo5UqjVL2Iecgy5fK30cWZ0NIRxg7E+7e6NxOyd278vSsghJu+N8a5qw9iMUC/7y4C8+O64aHm4u8v2lE3M70DtOmTWPSpEkkJyfjcDhYuHAhO3bs4P3332fx4sV1kVHkjMVFBfL5XQP5z/e7mLl8D4s2pbB6Twb/Gt+N8zu3MDue/JG91Ll5/S8z4dDa34+3HgD9b4e2gyBhAax/z1mg/G2u8yukI/SaBPHXgG9z8/I3Ie+s2kd2YSkxYX5c0t0FuiULjzkH26yZBfbyDye6XQnn/xOatTE3Gzg7fia8Ax9dAZtmQ1gXOHeq2alE/lS/6GD2puezJilTr6tSNwwDVr2E+9LHwQJfWwYx6saF4OZZ9TyLxdkF88YA2P0dbF8MXS41J7M0GHFRgSzalEJCigqTjVVmfgkLNyQDcNOg6FOeFxsZyLzbBrBgwyH+9dV2dqfmMfG/a7gsPpJ/XNyFFgEmfDgttSt9l3M7j9/mgqPMeSyyp3MPyU4Xn3RF1J60PG5+91f2ZRTg62HjlWt6MryL/j1jFotRgwkhK1eu5IknnmDz5s3k5eXRq1cvpk2bxgUXXFAXGetVTk4OgYGBZGdnExAQYHYcqQUbDxzjgXmb2ZuWD8AVvVvyyKVdCfByNzmZUJAJ69+FX99y7vMHYHWHuPFwzu3OF5TjGQYkr4f170DCQigtcB63eTg3wO89CdoOqb/luE1MdmEpg577gdyiMl6b2LNhFybLimHtm7DiRSjKch6LHgIjnzjxedUQ/PwGfPMwWKxwzcfQ0fVfT6VxW7jhEPd/spmerYP49I6BZseRxsZhd3a9/PpfAGaWXcKO2Pt56Zrep77P0iecH0QFtISpa8HDt57CSkP0854MrvnvL0QFefPT3883O47Ugdd/3M0L3+ygW1Qgn08diMVi+dP7ZBeU8uK3O5i9Zj+GAb4eNu4b2ZFJ57bF3VVWAcnvUjbBqn/D1s+B8rJW28HOgmS7Yc4Prk5i1a507vhwPTlFZUQFefP25D50Dlftpy5Ut75Wo8JkY6bCZONUVGpn+rc7eGtVEoYBEYFePDe+O0M6asqWKVK3wZqZsPljKCvfiNo3FPrc5JxW7F+NT6uKciBhvrOL8vCm3483i4ZeN0CPa6t3Ham2f3+3k1eW7qJTC3++umcwVuuf/wOw3jkczu7aH55wLt8GCOvqLEjGjDjlP1BMZxjwxd2w4X3n0u6bv4OwzmanEjmlQ8cKGPTcj7hZLfz22AX4eJzxIhyRkystdG7psn0xYOFtv1t4Mn0YL0zozhV9Wp36fiUF8Hp/53CDQffBiMfqK7E0QNmFpcQ//i0AGx8Zqf3iGpmSMgeDnvuB1NxiXr6qB2N7Rp3R/bccyuaRzxLYdDALgE4t/HliTCz922kFVoNnGM4tvVZOhz1Lfz/e6SIYdD+06nvau3/wy34e+zwRu8OgV+sg3ryhDyF+nqe9j9ScCpM1pMJk4/brvkwenLeZ/RnOTruJ/Vvzfxd1wc9Tb6jqnMPhXGL1yxuwd9nvx8O7wTl3QOy4Kvv8lZQ5OJBZQFJ6PrlFpfRq3Yw2zX1O/mno4c3OAuVvn0BJrvOY1Q06jobek6H9+WC11em319hlFZQw6LkfySsuY8a1vbiwW8Sf36m+7V0O3z3ifD6Ac1Lr+f9wLvV3hd9/WQl8MBb2/wTN2sItPzr3oRRpoAb+6weSswr58C/9GRgTYnYcaQwKMmHONXDwF7B5UHDJG8R94oPDgNV/P5/IIO/T33/7Epg70bn6YspqCO1YP7mlQRr6wo/szyhg9s39GdRBf0c1Jos2JnPvx5sI8/dk1d/Or9GegA6HwSfrDvLc19s5VuAc5HZ5zygevqgzYf5a3t3gGAbs+hZW/tv5GgHOlUZxE5wfRrXoetq7l9kdPLVkG++u3gc4f9fPjuuGl7sLvEdwYdWtr51xNaZZs2YnLQxYLBa8vLyIiYlh8uTJ3HjjjWd6aZE617dtMF/dM5jnvtrOez/v56M1B1ixM40XJsQzoL0+IasTxbmwaY6zQzJzj/OYxQqdL8bR73aOBPUiKaOAveuPkpSWz970PJLS8zmYWYDjDx+bRAV5MzCmOQNjQhjQvvnv/2iIiIdL/g0XPAmJnzqLlIfWOrstti92TmHreT30vA4Cz+wTVXH678q95BWX0SUigFGx4WbHqepoInz3qLPwDeDhD4Pvg/5TwMPn9PdtSNw84MoP4L/D4Ng++OQGuG6h87hIA9QvOphPNyazJilThUk5e1kHYPYESN8BnoFwzUf8VNABh7GO6BDfPy9KgrNjpsMo2PUNfPkg3PBZw+2UlzoXFxnI/owCElKyVZhsRAzD4H/lQ29uGNCmxoNKrFYLV/drzajYcJ7/Zgdzfz3ApxuT+X7rUe6/oCPXn9PGdYY8NmYOu/P93aqX4egW5zGbh/N93bl3Q/Cp9xetkFNUytSPNrJiZxoAD43qxB3D2ldr+b/UjzPumHzppZd4+umnufDCC+nXrx8Aa9eu5euvv+a+++4jKSmJDz74gFdffZVbbrmlTkLXJXVMNh2r96Tz1/m/ceiYcynx5HPb8rfRnfH20KcmteLYPljzJmz8AIpzAChx82dz6GV87nkJv2b5sS8jn6JSxykv4ethIzrUFy83G5sPZVFqr/rXVacW/pwb05yB7UPo3y4Y/+P3DT26FTa8B5vn/r7HoMUKMSOde1F2GAU2dcpWR2Z+CYOf+4H8EjtvXt+bCxpKYTInBX58GjZ9BIbD2SXb52YY+lfwdeE3IEe3wtsjoSTP2fF7yct6Yy0N0py1B3h44RbOaRfM3FsHmB1HXNmRLc6iZN4RCIiCa+dDi6489nki767ex3XntOapsd2qd63MJHjjHCgrggn/c+5bLU3SG8t28/zXO7ikewSvTexldhypJev2ZTJh5s94uln5+eHhBNfSMv1NB7OY9lkCvx1yDkzqEhHAk2Ni6dNWq1dMUVYMm+c4C5LHnIVoPPycW38NuBP8q/d+ZH9GPje/t47dqXl4uVt56coeDXPlVyNVZ0u5x48fz8iRI7n99turHJ81axbffvstCxYs4NVXX+XNN99ky5YtNUtvIhUmm5a84jKeXrKNOWude9G1be7Di1fE6wWoBopK7exLzyN72zJCE/9H24wVWHEWHfc4InjXPooF9iEUUHVphJvVQuvmPrQL8SU6xJd2oX7O/w3xJdTfs/KTrIKSMn7dd4zVu9P5aU86iSk5HP+3l81qIb5lIANjQji3fQi92gTh6WaD0iLnxO/178H+Vb/fwS/c+Ulbr+udy2bllP711XZmLt9DXFQAX0wdZP6ni0XZ8NN/nANjKvYo7ToWhk+D5u1NjVZrdnwNc64GDLjweeh/m9mJRE6wJy2P4dOX4+lm5bfHLnD+nStypvYug7nXObdiCevqLEqWr24Y+e/l7ErNO/MtRJY9B8ueAf8ImPorePrXTXZp0FbsTOOG/62lXYgvPzw4zOw4UkumzF7PVwlHuKZfK54d171Wr213GMxZe4AXvtlBdqFzefeE3i35+4WdtQ9hfSnOcw5H/fk1yD3sPObdzLkSqt8tZ7TN0Zq9Gdw+ez3HCkoJD/DirUl9iIsKrJvcclJ1Vpj08/Nj06ZNxMTEVDm+e/duevToQV5eHnv27KF79+7k5+fXLL2JVJhsmpbvTONv83/jSE4RFgvcMrgd94/sqD0n/sDuMEjJKmRPmnO5dcXXodRj9Mlbyo22b+hq3V95/gp7N/5nH81yRzzhgT5Elxcfo0N8aV9egGzZzLtGyySO5Zfw894Mftqdzuo9GSSlV/37xsvdSt+2wQyMCWFg+xC6RgZgy9zt7KLcNAcK0n8/ud15zi7KThdr2ewfpOcVM/i5HykstfP2pD4M72LiQKGyEudE9uXPQUGG81jrATDyyT/d6Nol/fQf+G6as9P32vkQM9zsRCJVGIZB36e/Jz2vhHm3D6CvPtSTM/XbPFg0BRyl0GYQXP0heAcBcDSniP7PLMVicQ4vCfI5g9fn0iJn1+SxJBgwFUY9XTf5pUHLyCum91PfA7DlsQuqrqwRl3Qws4ChL/yIw4Bv7xtCxxblHzqUFkLGbueHEAEtz3pVVEZeMc9/vYOP1x0EIMDLjYdGdWJi/zbYGuLwx8agIBPWvunc/qvwmPOYfyScOxV6TQJPvzO63CfrDvKPT7dQajfo3jKQ/97QhxYB2ju0vtXZHpPBwcF88cUX3HfffVWOf/HFFwQHO/9Bmp+fj7+/PpkU1zG0Yyjf3DeEJxdvZf76Q7y5Yi9Ltx1l+pU96NEqyOx49cowDDLyS0hKz2dvWh570/NJSnMWIPdnFFBi/33pdSjHuM7te16yLSXE3blcuxhP1geNYm+76whq242/hvjxRohPrU9sbebrwUXdIriovIMiOavQWaTcnc6q3Rmk5xWzclc6K3c5C5BBPu4MaNecc2NuYdCkB2ibtgzLhvdg74+/f/mEQI+Jzhe/kJjTPXyTMWv5HgpL7cS3DOT8zmHmhDAM2PoZLH0cMvc6jzXvACMfd+4nZnYHZ105925I3Q6bP4J5N8ItSyGkg9mpRCpZLBb6RQfz5ZYjrE3KVGFSqs8wYPWrzoFl4Ox6v3xWlSF4q/c4X7/jIgPPrCgJzutc9CJ8OB5+meF8bW8RW0vhxVU09/MkItCLw9lFbDucS79o/R3l6t5fnUSYkcGElll03LkdViQ49xrP2OXc1gecw6+atYHgdid+BbUG258XqJv7efLchO5c2bcV0z5LIDElh0c+S+TjdQd5ckwcPVs3q+PvtAnJPeLsjlz3jnMbI3D+rgbeC/FXg9uZdaraHQbPfb2dN1c43zNc3C2CF6+I13ZtDdwZd0z+97//ZcqUKVx00UWVe0z++uuvfPnll8ycOZObb76Z6dOns3btWj7++OM6CV2X1DEpS7cd5e8Lt5CWW4zNauH2oe24e3iHRrdELb+4rErX497yLsi96fnkFpWd8n4eblZGBv4/e/cdHnWZtXH8O5PeG6GEkoQiLXSpAbti772L3XXdte3qq2tdV9e2u+6ua0Wxd8WOYif03kuAJISEkt7bzLx/PEkApSQwM7+Zyf25Li4nIZnfQUIyc895ztnKZbavGF39I0Eu87GOmBTsY67DNuoKyzcJu1wuNuyoIiu7iKzsIuZuKqGqfs8/U0pcOBP6duKEbrVkVnxJ1Op3zWyrFqkTTRflwNP3eKLUkeyorOOIx3+grtHJq1eN5qj+FgSTuXPgm3th60LzdlRnOPpuGHF5x5gR2lQP006DLfMgsQ9cM9Pyf18SgJxO+PkJM1T+uAfbNRLh1azNPPDZao44LJnXpozxYJESMJxOmPF/MO9/5u1xN8EJj4B9z9MTt7+3jA8X53PDkX2466QBB3etdy+FNZ9Brwlw1ZeB+0KW7NM10xYyc8127jt1EFMmHnhJhviQxlrYuRa2rYTtK2kqXElV3lLiqdr7x4fHm89x1O/7Pm1BEN+zOajss2domZC61xDM4XTx5rxcnpixrvU50oWje/KnEwe4bb5lh1Sy2ZwOWvomOBrM+7oMMQssB50J9vY/966ub+IP7yxl5prtANxybD/+eGw/7OpytYzHjnIDZGVl8Z///Id169YB0L9/f37/+98zYcKEg6/YRyiYFDDHhB/4bBXTlxYAMKBrDE+eN8zvZlI0OpxsKanZFT7uFkBur9j3D22bzWzA3v3IdXpiGIPKfyZp5VRsW+bu+uCeY2HcjTDgNJ8NipocTpZvLW/upixicW7ZHp2fAP2TI7gyeR3H1X5Np20/Y2t51TUiAYZeaELKzgMtqN46D322mqlZmxnRK56Pbpzg3dmSO9fDzAdg3Rfm7ZAoyLzFHMlr51EOv1e1E148Gsq3QPqRcOmHbXq1X6RNHI0w/XewvPnF5JAoOOnvZgZvG/7Nry6o4ORnfiEqNIhl95+gDaayf4118PH1sPoT8/YJf4UJv//Nh7lcLiY89j2F5XW8fvUYJvVLPrjrlW2B/46Bxho48zkYftHB1y5+6Z8z1/PPmRs4e2R3nj5/uNXlyN64XGah4XYTQJog8lddkLtxYMfeeQC2LoOhS4b51TUDoruY+6osMCdsijea/5ZsMiFYyaZds8n3ygZxPc2W5193WiaksbM+iMe+WsuHi/MBcxrrT5MHcMHonjre3R7bV8Gsf8DKD3f9/fYcB5Nuh37HH/QLSFvLarn61QWs3VZJaLCdJ84dyhnDu7uxcDkYHg0mA5mCSdndVysKufeTlRRXNxBst3HzMX353dF9CfGhJ14ul4vtFfVsKmqe+7hzVwiZV1KDw7nvf+JJUaG75j4mR9G7UzS9k6PolRi5a75mTQksfg3mvwgV5gcx9hDIOBvG3gDd/W/LYW2DgwU5JWRtLGJ2djErC8r3WKTTw17CTfFzObXpW2Ibtu/2G2NMQDn4LAiN8n7hXrS9oo5Jj/9AQ5Pz0J4UtlfldvjpMbOsyOUwr2yPvByOuqvN2/cC0rYV8PJkaKyG0dfCKU9aXZEEgoYaeP9K2DDDbLXvMhgKl5nfG3SG2Qh/gA5dh9PFiIe+oaKuiU9vzmRoj3hPVy3+qrYU3rkEcrPM44iznoMh5+71QzftrOKYp34iNNjO8vtPOLSZ3788bUaBRCXDzQtbZ1hKxzBz9XaueW0h/bvEMOPWI6wuR37VBcn2Vea/LTMFfy0yCbpk4Oo8mEeXBJNV2ZWLTzuBSyYcxGgbl8scG24NK3/1q2EfnZgtYlIgsTc7Q1P4bEs4CyoSyHF1JTalH/ecNVo//w5ky3zz/Xj9V7ve1/c4E0imHlqD2+K8Uq57bSFFVQ10ig7jhctHMVLH7X2CR4NJp9NJdnY2O3bswOnc81WMI47w72/4Cibl14qr6rn3k5V8tdIc883oHstT5w2nf1fvzlEtr21s7nysYvNOEzy2dELWNDj2+XkRIUG7BY+7bb5OiiIucj9dVzvXmeHDS9/e9epiZBIcfjWMvjqgQqKymgbmbCxuDSo3NS/SsePkCPtyLg7+gWPtiwhq3jLuCovFNuQ8E1J2G2Zl6R5z//SVTJuTy+i0BN67frznuyXrq8x8maxnTPgGZhnRcfdDcn/PXttfrPkc3r3E3D7lKRh9jbX1iH+rLYO3LoAtcyE4As5/zSxYmv0MfP9XcDaZJ2FnPw/p+39sd/WrC/hu7Q7uPWUg10zq7Z36xb+U58Mb58LONRAWCxe8Ab2P3OeHvz4nh79MX8X43km8fd24Q7t2UwM8lwlF6/XCTge0rbyOcY9+h90Gqx48UXPmvKWdXZDYgszjvb11QdpsrQFzbHgwc//vWLfPrsflguqiX4WVzR2XxZugvny/n77DFU9NdC+6pQ8irHPfPbstw/3rxJ1buVyw8XvTIZnzS/M7bebFz0m3ueV51PSlW7nzg+U0NDkZ2C2Wl644nO7xEYd8v+IeHgsm586dy8UXX0xubi6//lSbzYbDse+AxB8omJS9cblcfLa8kPumr6SsppHQIDt/OK4f1x/R263H1uqbHOQV17BxZ/WuELI5fCyqatjn5wXZbfRK3HPrde/mMLJrbHjbQyWnEzZ+ZwbFb/xu1/u7DIFxN0DGuR1i3mJByyKdjWbr947KepIp5bygn7kg6AdS7TtaP7YueShhY67CNuRcCA+M7xmF5bUc+fiPNDicvHXtWCb06eS5izmaYMlr8MOjUN38/7X7KLNpOy3Tc9f1V788Bd89ZB7AX/bxfp/Yi+xT5TZ44xzzZDE8Di5+D3rtFv4ULIEPrzEbTrGZMQpH3wvBe5+l9fxPG3n0q7UcP6gLL15+uHf+DOI/tq82X2+VBRDdFS79ALoO2e+n3PD6Ir5etY07TjiMm49xw9KvTT/Ba6eDzQ7X/gApww/9PsUvuFwuRj8yk6KqBj6+aYKWlnjCQXZBtoaPXQZD8oD9Ljm5+MW5zN5YzPVH9ubuk7w8WsnlMn+WvXRZOos3Ya8t3v/nRybtZRFPH3NkPFDnhjudsPZz87i1cKl5nz3YLLPJ/KNbljk6nS7+OXM9z3yfDcBxA7vwrwuHExXmm6PFOiqPBZPDhw/nsMMO48EHH6Rbt26/CTzi4vz7FQEFk7I/Oyrq+L+PVzBzjQlQhveM58nzhtG3c9tn3jmdLgrKa3dbOtPS/VjF1tJa9nPyms4xYa0dj713O4LdMyGS0OBDCEjrq2DZ2zDvefNKJgA2GHCKOa6dNrHDDox3uVxktyzS2VjMvI07yWhczkVB3zPZvoBQm3kxpt4WQWHPk4nJvJqkwyb49f+vez9ZwRtz8xibnsi714/3zEVcLlj3Fcy833SxACSkmw7JQWf69f8/j3K54KPrYMV7Zsj7td+3a1GJCCWb4PWzoDTHdKJc+pF5YvhrDdXw9d2weJp5u9swOOflvT6ZWJJXylnPziY+MoTF9x6vIfOyS84sePti023Uqb8JJeN77fdTdh8P4NYg6YMpZqZZ98Ph6m9/s2xHAtcVU+fz0/qdPHxmBpeNS7W6HP/l5i7ItmqZZRxkt/HLn44mxde64WrLWLFyKZ9+P4vQilzSbNvIiCiib/BOQmp37v9zw+P3vj08sTdEdfK/x8OORljxvumQbHl8HxwBo66ECTdDXA+3XKa2wcHt7y/lyxXmROP1R/TmTycO0KxPH+SxYDIqKoply5bRt2/fQy7SFymYlANxuVx8uHgrD362isq6JsKC7dw5uT9XZaa3fjN0uVyU1jSyuaiKTTt3BZCbi6rJKa6mvmkvP7ybRYcF0zt5t87H5hAyrVMU0e5+Bag0F+a/AItf33VEISwWRlwGY641r+TJHpocTlZsLWf2xmKWrc2md8FnnGf7jj72wtaP2WRPZW3KWYSPuphRA3oTF+E/i0ryS2s4+skfaXS4eOe6cYzrneSBiyyEb/4CebPN2xGJZobkqKv22ZElu2msg1dPhq2LoNNh5gm2ZqZJW2xbAa+fbbqTE9Lgsk8O/H1+zWfw6e9Nt0hwBJz4qHmCsduTpUaHk6EPfENto4MZfzzC66NOxEet/MgsunE0mMUGF73dpu6g5fllnP6fLGLCglly3/HuO5lSUQj/GQ0NlXDaM2Yci3QIT8xYy39/2MiFo3vy2DlDrS7HP3ihC7Kt7nx/Ge8vyufUod34z8W+O9u+0eHk1awc/jlzPdUNDuw2mDK6E38YGUJMdd5vF/FUFuz/DkNj9r6IJ7G3GanlS6FlY615Pjn7GbOsESAsDsZeZ5pcotx3+mp7RR3XvraQ5fnlhATZeOSsIZx/eE+33b+4V1vztXanHGPHjiU7Oztgg0mRA7HZbJw7qgeZfZP484cr+Hn9Tv76xRq+WrmN1KTI1gCyvLZxn/cREmQjNWm3I9ctcx87RdEpOtSz8/xcLsibA3OfhbVf7HqFM7G3+cEx/GII05PKfQkOsjOiV4Lp4Di6L3WNx7Nwcwlzl86k28b3mFD3C72dufTO/yd1W/7Ll86xLEg8nYQBRzCxXzIjUxMObYi/h/33h2waHS4y+ya5P5Qs3miOIbdsYw0Oh3E3wcQ/duz5O+0VEg4XvgUvHmNejf5gijmKG6SjK7IfubPhrQvNi1Bdhpjt7jFdDvx5A08zHWaf3ACbfoTP/wgbvoXT/w1R5ntESJCdUakJzMouYv7mYgWTAnOehRn/B7jM19DZL0JI27qcZmUXATCuT5J7t7zHdoOj7zZ1zXzA1BWoxyhlDxkp5jHGyoL9zwnskCzqgmyroqp6pi81Ad6Uib7dMBESZOfaI3pz2rAUHvlyDZ8tK+Cl+UV8vCqUu08+nHMmnrHnc7yGGnN64TdHxDebcK+hErYtN79+c7HI5pByL8FlTIr3OsLrymHBS2YMWHVzd2hUZxj/Ozh8itvHXK3IL+ea1xawvaKehMgQnr/scMak6/t4IGh3x+THH3/Mvffey5133smQIUMICdmzE2joUP9+FUodk9IeLpeLdxZs4a+fr6Z6LwtousdH7DH3sWUBTff4CPc+2G6LpnpzhGnu//b8Adf7KBh7I/Q7Qcea3KC8ZCdbf36VxLVv07VuY+v7s50pvOM4ms9tR9InLZXMvp3I7NOJjO5xPnPsYEuJ6ZZscrr44IbxHJ7mph/01cXw8+Ow4GVwNgI2GH4JHP1/ENfdPdfoiAqWwtQTzXKqcTeZTjaRvVn3Nbx/BTTVQa/xcNE77e+ydTph7n9h5oPm33F0F7NVuc8xADzz3Qae/na9z3e0iIc5nfDtX8wyMzDLZk76O9jb/oLcpS/NY1Z2EQ+cNogrM90cRDia4PkjYMcqGHkFnP6Me+9ffNKWkhomPf4DIUE2Vj144qGNP/JnPtQF2Vb/mrmBf8xcz/Ce8XzyO/+aPZ6VXcR901eycadZ6jg6LYGHzshgYLc2ZAyNdVCWu/ft4WV5ew+NWwSF/Sqw3O12XM92fT/ep6qdpsllwUtQX2HeF98LMv9gHuO38YWo9vhqRSG3vreUukYnfTtHM/WK0fRKinT7dcS9PHaU276X4MJms+FyubT8RjqsLSU1vLMgj8jQ4NYQMi0pyjc2/1Vuh4VTYeHLu17JCg6HoReYDskug6ytL1C5XLB1ETVzXyZ0zccEO8xm8wZXEDOco3nbcQxznIOICQ9lXO8kJvbrxIQ+neiTHOX5Ddj78KcPlvHewnwm9evE61ePPfQ7bKiBef+DWf/c9aCl7/Fw3AN7n2kn7bfqExM4gY4myt4tfRum/w5cDjjsRDj3FQg9hAfyhcvNYpyidebt8TfDsfcxN6+KC1+YS+eYMOb937GWfR8TCzXVwyc3wcoPzNvHPWCWHLTja6Gu0cGwB7+hvsnJzNuOoG9nD3Tf5s6BV04EbHDNTOihhU2BzuVyMexBM7f0899PJKN7gJ/S8PEuyLaqb3KQ+dgPFFXV8++LRnDasBTLajlYDU1OpmZt5l8zN1Db6CDIbuPy8ancevxhxIYf5KinpgbTUbm30LI0B5xN+/5cewgkpDYv3+m9Z3gZ3wuCDlBTWR7M/jcsfs282AkmrJ54G2ScfeDPPwgul4v//pDNk9+YmZVHHJbMfy4ecfD//8SrPBZM5ubm7vf3U1P9e6CwgkkJGAVLYd5zpkvS0bzROybFzI4cdaWOL3lTXQWs/ADXomnYWjbTAXl04Z3Go3jfcSQ7iQegS2xYazdlZt9OdI3zzhb0nKJqjn36JxxOFx/dNIGRh7JswOkwy5S+f2TX/JyuQ+GEh02HrrjXj3+HH/9mHmxePl3bzGWXOc/CjLvN7aEXwhn/cc+ThoYa0xW34CXzdpcM6s94niH/3UKDw8mPdxxFWqeoQ7+O+I+6cnj3Utj8s9m8esZ/zfbVdpqdXcTFL83zfMD98Y2w7C2z1OnaH9zTQSQ+rWWr89/PGcIFo/e/gMmv+GEXZFt9sCifO95fRre4cH7+09GEePu0mRsVlNXy1y9Wty5rSY4J456TB3LG8BT3fp9zNEFF/q6gsnj30HLzrueEe2MLMuHk3mZaOhth9n/M8sWW4LP7KJh0Oxx2ksdO3dU1Orjrw+V80nyc/8oJadx7ykDvnzyUg+axYDLQKZgUv+ZognVfwNzndi0WAegxGsbdCANP98grWdIOhctg0TRY/p6ZHQM4bEEsDhvH89UT+b5xCE52/bDtnRzFxL6mm3J87yTiIj3z93fbe0v5aPFWju6fzCtXjTm4O3G5IPs7+PY+c0wOIK4XHPsXyDhXowI8xeUycyZXfWQWCV37vRZXdXQuF3z/V/jlSfP2uJvghEfc/29w3demG7OmCILDeSliCn/dmcnj5wzj/NEaRN9hVBTCm+eaMCQ0Gi54vfWIf3u1LCk5e0R3nr5guHvr3F3VTvjPKBOonvykedFWAtrfvlzDCz9v4rJxqTx8ph+e2giQLsi2crlcnPLMLFYXVvDnEwdw41F9rC7JLX5ev5P7P13F5iJzvHtMeiIPn5HhndnMTof5GtrbTMuSTWY0UFukHwmTbjP/9eDX0s7Keq5/fSGL88oIstt48PTBXDrOv5vgOiKPB5OrV68mLy+PhoY9U/fTTz/9YO7OZyiYFL9UW2o2oc1/YdcmNHswDD7LzI/sMcra+uS3Gqph1ccmpMyf3/ruuqgUFiacwis1E/mhMATnbt+h7TYY0j2OCc0dlYenuWeRzqadVRz39E84XfDpzZkM7RHf/jspWGoCyc0/mbfD4+CIO818sRDvdH12aA01ZlN3wRJIHghXf+P2gePiJ5wO+OJ2WPSKefuYv5iOBk89eajcDtNvguyZAHznGMHPAx/gwYuP8sz1xLfsWGtCyfItJvC45H3ThXiQzvhvFsu2lPHkecM4d1QPNxa6F/NfhC/vMJtjf78Qojt79npiqelLt/KHd5Yyolc8H9/k4ycLArgLsq3mbirmwhfmEhESxJy7jyE+MtTqktymvsnBS79s5t/fb6Cu0UmQ3caUzDT+cNxhRIdZtMjQ5YLKbXsJLTea4LKhGgacYo5se+F55dptFVz96kK2ltUSGx7Ms5eMYmI/9232Fu/xWDC5adMmzjrrLFasWNE6WxJobUHWjEkRL9q53hzXXvY2NNaY90Ukmi1oo6+GWP+bxdIhbV9tZrUsexvqysz7bHYaex/Lii5n8llNBj9vLGsdnt0iNNjO4akJ5uh3304MOchFOn98ZwmfLC3guIGdeemK0e375LI8+O5hc7QDICgUxlxnghCNC/CuigJ44Wio2gb9JsNFb+t4YkfTVA8fXQurpwM2OPVp8/PA05xOmP8Czm/+gt3ZQAlxJF7yMvQ73vPXFuvkzoG3LzBdh0l9zab3hLSDvrvy2kZGPPQNThfMufsYusW5f3nCHpwOePFoc5Jh2EVmmZMErI07qzj2qZ8ID7Gz6sETfWPxYAfrgmyP615byDert3PpuF789cwhVpfjEfmlNTz8+WpmrNoOmHFO954yiFOHdvOtOc0ulzkC7qWg+7s127nl7SVUNzhI7xTFS1ccTp/kaK9cW9zPY8HkaaedRlBQEC+99BLp6enMnz+f4uJibr/9dp588kkmTZp0yMVbScGk+DyXCzZ+Z7ZrN3eoANB5MIy7AYac55FNaOIFjXWw5jNY9Crkztr1/uiuMOISdva7gF+KopiVXcTs7GK2VdTt8ekx4cGM651EZh+zTKdPcvQBH9hk76jk+H/8jMtF+wbC15bCL0/BvOd3zasZcj4cc68Zqi3W2LoIXjnZDCSfcIuZ6ykdQ30VvHsJbPrRzBs950XTNe9FNVuWs+XFi+lvb+7cH3M9HP+gfiYFotWfmiVIjnozLuaidyEq6ZDucsaqbVz/+iJ6J0fx/e1HuafOA8lfCC8dB7jgqq8gdYJ3rite53S6yHhgBjUNDr699Qj6dfHC0dm9yZ0Dqz/pkF2QbZVbXM1RT/6IywUzbzuSvp0DO5T6Yd0OHvh0FbnFpslkQp8kHjpjsGeWf/kwl8vFS79s5m9frcHlgvG9k/jfpSMDqlu2I/JYMNmpUye+//57hg4dSlxcHPPnz6d///58//333H777SxZsuSQi7eSgknxWQ3VsOwd0yFZtL75nTbof5LZrp1+RMC9WtqhFWXD4mmw9C0zv61F76Nh1BW4+p/MptJGsrKLyMouYs7GYirq9tzC1zkmrLWbMrNv0l67T25+azGfLy9k8uAuPH9ZGzaTNtbBghfh5yd3dXemHwHHPwQpIw7hDyxus+ID+PBqc/uMZ2HEJdbWI55XXQxvnWeC6ZAouPCNg57xd6jO+ff3nLr9Oa4KnmHe0XkQnPOSeVItgWHeC/DVnwAX9D8Zznn50Da9N7t/+kqmzcn1/gzAT28xP287D4Lrf9Ys7gB27v9mszC3lH9cMIyzRnh4VMDe5MyCaaeDa7cThh2oC7KtHvxsFa9k5XBU/2RePdi5536mrtHBCz9v4r8/ZFPf5CTYbuPqSencckw/oqw63u1FDU1O/vLJSt5daF7YvGhMTx46I8OvFx6J0dZ8rd1f5Q6Hg5gYk9536tSJgoIC+vfvT2pqKuvWrTv4ikVk78q2mNmRi6eZ41IAoTEw4lIYe53ZlCaBp1Nf0+12zF/MQqNF02DTD62/bJGd6DP8IvqMvJLLxx+Ow+liVUF5azflgpwSdlTW8/GSrXy8ZCsAvTtFMaFvEhP7dmJc7yS2V9TzxYpCAP543GH7r8fphJUfmGPb5XnmfZ0HmUCy73Ed9sGzTxpyrplN9fMT8PkfIakP9BpndVXiKeX58PpZ5gWriES45ANL5wqPSO/Kg1uvwNbvOK7c8QTsWG1GDBz/oOmg1BIs/+VywXcPwqx/mLdHXWUWxwS550nzrGzzIlxmXy/PETvuAXNaYcdqcwpgws3evb54TUb3OBbmlrJyawVnefu11MrtZlGdy2FeOBpyXofqgmyryrpG3l+YD8CUzI6zyC88JIhbju3HmcO789Dnq5i5ZgfP/7SJT5cW8JdTB3FSRlffOt7tRqXVDdzwxiLmbS7BboN7ThnElMy0gP3zyt61+5FERkYGy5YtIz09nbFjx/L4448TGhrKCy+8QO/eCkhE3MLlgry5MO9/sObzXa+sJqTD2Oth+CVabNFRBIea45iDz4LSHLPkaMkbZo7g7H+bX6kTCRp1BUMHns7QHn256ai+1DU6WJxbStbGIrKyi1meX8amomo2FVXzxtw8bDaIiwjB5YKTh3RlYLf9fD1t+gm+/YuZwwUQkwLH3GNmcmmGoW866v9MOLnmM3jnErjuB4jvZXVV4m4715tQsiIfYrvDZR+bzhsLjUlP5KVZm3mjuD9X3jgbPr0Z1n8NX98FG76FM5+FmK6W1igHoakBPv09LH/HvH3MvTDpDre9KLWtvI6NO6ux28zxPa+KTDTh5Ge3wI+PQsbZmtEdoAanmMc6K7eWe/fCjiZzkqFqu1lQd8EbEBrl3Rr8xHsL86mqb6Jf52gmdcBlJ72SInnpitHMXL2dBz5bRX5pLTe9uZhJ/Trx4OmD6R1gsxazd1Rx9bQF5BbXEB0WzL8vGsHRA7SIrCNq91HuGTNmUF1dzdlnn012djannnoq69evJykpiXfffZdjjrHm6JC76Ci3WKqpAVZ9ZOZHFi7d9f70I2DcTdDvBAVBYh7gbphhuiizv901ID083oSFo66AzgP3+JTy2kbmbSpm9sZiZmUXkb2jCjDPKb/+wxH077qXOTbbV8G395trgOnUnXSr2fTuhmN74mEN1TB1MmxbYY6GTZkBYYH1gLZD27rYbEOuKYakfiaUjO9pdVWUVjcw4mHzPWPhvcfRKSoUFr4MM+4xs08jk+D0/8CAky2uVNqsvhLevcx07NuC4PR/u31ExIeL8rn9/WUM6xHH9JsnuvW+28TphKknQP4CyDgHzp3q/RrE49YUVnDSv34hJiyYZfefgN1bC3BmPgiznobQaLjuR+jUzzvX9TMOp4ujnvyBLSW1PHr2EC4a07FfUK1rdPDsjxt57qeNNDQ5CQ2yc+0R6dx8dD8iQv3/+eDP63fyu7cWU1nXRI+ECF6+YvTen4+IX/PYjMm9KSkpISEhISDabRVMiiWqdsLCqebJW5XZzEZwOAw938yP1Gwu2ZfyfFjyJix5Hcq37Hp/jzEmoBx81l5fld9eUcfcTcUkx4Qxoc+vXpEu3wo//A2Wvgm4wB4Mh18NR/4Jojreq9d+rWwLvHgMVO+AAafC+a/rKG0g2PQTvHMxNFSZ2a6XfOBT/zYn/+Nn1m2v5LlLR3JiRjfzzh1rzbKU7SvM24dPgRMe0Yscvq5yuwnAty0380vPn+aRbeu3vbeUjxZv5caj+vDnEwe4/f7bpHAZvHCUebHv8unQ+yhr6hCPaXQ4GXz/DBqanPx4x1GkdfJC1+L6GfDW+eb2uVNN8C179fXKbdzwxiISIkOYc/exhIf4f/jmDjlF1dz/6Sp+Wr8TgO7xEdx32iBOGNTFb/OX1+bk8OBnq3E4XRyemsDzl40iKVojDQJRW/M1tzw7SUxM9Nt/FCKWKlwOn9wE/xgEP/7NhJIx3cxcwVtXm64EhZKyP3E94Kg/wx+WmXBiwKkmSMyfD9N/B08NgM9vhYKle3xal9hwzhjefc9Qsq4cvnsI/j0Klr4BuGDQmfC7+XDy4z4VfEgbxfeEC9+EoFBY+zn88FerK5JDtfpTExQ1VJlu+is+87l/m2PSEwGYt7lk1zs7D4Brv4PxzfP7Fk6FF47cNSJCfE/RBnj5OBNKRiXDlZ97JJR0uVzMzi4GYKK350vurtswGH2Nuf3lneYUiwSUkCA7A5s7slYWeOE4d2kufHSduT3mOoWSBzA1azMAF4/tpVByN2mdonj1qtE8f9kousdHsLWslutfX8RVry4gt7ja6vLapcnh5L7pK7lv+iocThdnj+zOm9eOVSgp7Q8mq6ur+ctf/sKECRPo27cvvXv33uOXiByA02Hmvr1yCjw/yXSlORqg+yiz2fKPK+CIOyDKyzOWxL/Zg8wTxgvfNKH2sfebmaT1FbsCgOePNLfrKvb83KYGM/D/mRHwy1PQVAu9xsPVM013TFIfa/5M4h49x5ijs2D+fpe/Z209cvAWTYP3rzA/MwaeZl6MCPO9Y08tweT83YNJMAseJj8Cl30C0V3Nwp4Xj4WsZ8xRWvEdW+bDy8dDWZ5Zsnf1N9B9pEcutXFnNdsq6ggNtjMqNcEj12izo++BqM7ma3POf6ytRTxicPc4AFZurTjARx6ipnrz/bquzDzGP0EvDO7Pyq3lzN9cQrDdxmXj0qwux+fYbDYmD+7Kt7cdwe+O7kNIkI0f1+3k+H/8zNPfrqeu0XHgO7FYeW0jV726gNfm5GKzwZ9PHMBT5w0jLFghtBzE8ptrrrmGn376icsuu4xu3bqpU1KkrWrLzNKS+c+bB/pgOtsGnWFm9vUcbWl5EkBiusCk2yDzj5Dzswkz1n5u5pZ+vhRm3AsZZ8HIK6Fiq9myWrLJfG5SP7M9t//J2rQdSIZdADvXmG260282QUOPw62uStrK5TJ/d989aN4eeQWc+g+fnTncEkyuLqygoq6R2PCQPT+gz9Fw42yzbGTt52a5VvZMOOs5LR3xBWu/MNuDm+pMoHLxex7typ290WzjHp2WYH2XVEQ8nPAwfHw9/PyE2ZzsA7NbxX0yUkwwucrTHZMz7oGCJRCRAOe9qs3bBzB1lumWPHVoN7rGhVtcje+KDA3mzskDOGdkD+7/dBW/bCjime828PGSfB44bTDHDuxidYl7lVNUzdXTFrBxZzURIUH888LhTB6sRXiyS7tnTMbHx/PFF1+QmZnpqZospRmT4nZF2SaMXPImNDa320ckwKirzJGhuO7W1icdQ3UxLHsbFk8znSC/FtUZjr4bRlwOQe1+zUr8gdMJ714C6740f9/X/WBGAYhvczpNcNfSvTXxNjj2Pp9/4eCoJ34gp7iGV64cve8Nmy6X+Z709d3QWGN+Np72DAw63bvFyi4Lp8IXt5s5i/0mw3mveHx78HWvLeSb1du5c3J/fnd0X49eq01cLnj1FMjNMuNRLnzT6orEjZbnl3H6f7JIiAxh8V+O90yTzYoPzBZuMJ3tHhiBEEh2VNSR+ffvaXS4+PTmTIb2iLe6JL/gcrn4auU2Hv58NYXldQAcN7Az9582mJ6JvjO/ee6mYm54YxFlNY10iwvnxcsPJ6O5c1kCn8dmTCYkJJCYmHhIxYkEPJcLNn4Pb54H/xkF818woWTyQDjtX+ao7XH3K5QU74lKggk3m3mRV30FQy80C5ZCouDIu+CWJWYZhULJwGW3w9kvQOfBZhnO2xeZzd3iuxxNZlZsSyh5wl/Nzw4fDyVhH3Mmf81mg1FXwvU/Q7fhUFsK711munrrq7xSpzRzueD7v5qZxC4njLgMLnzL46Gkw+liziYzXzLTyvmSu7PZ4OQnzQbytZ/Dhm+trkjc6LAuMQTbbZTWNFLQHOa41c518Okt5vakOxRKtsEbc3NpdJglKAol285ms3HykG7MvO1IbjiyD8F2GzPX7OC4p3/ime82+MTx7ncX5HHpS/Moq2lkWM94pv8uU6Gk7FW7g8mHH36Y++67j5qaGk/UI+LfGmpMt8Gz4+D1s2DDN4ANDjvRbHi8aY55EqYtpGIVmw1SJ8DZz8OfNsOfN5tOybBoqysTbwiLgYvehshOZqHFxzdotp+vaqw1Id2yt0xAcub/YMLvra6qzcakmznJ8zcXH/iDO/WDq7+FibcCNljyOjx/BGxd5NkixXA0mjD45yfM20feZZbveeGFqhVby6msayImPJghvvRktcsgGHejuf3lndDogQBLLBEeEkS/Ls0LcLa6+Th3QzW8d7lpRkg/Ao7+P/fefwCqa3Twxjwz4mrKxHSLq/FPUWHB3HXSAL7+4yTG906ivsnJ09+uZ/I/f+aHdTssqcnhdPHIF6v584craHK6OHVoN969bhydY3VMX/auTY84RowYsUebe3Z2Nl26dCEtLY2QkD3nBi1evNi9FbbDAw88wIMPPrjH+/r378/atWstqkgCWmOdmc9XUWD+u22FWWRTW2p+PzQahl8CY6/X8hDxTQrIO6aEVLjgDZh2Gqz5FH56TE+efE1duelozc2CoDAzn2zAyVZX1S5jmzsml+eXU9vgICL0ALMDg0PhuAegz7Fmvl/JRnj5BPO1mflHn52n6ffqq8yCjuyZYLOb2aWjrvTa5bOyzXzJ8b2TCLL7WCfwUXfByg+hdDNk/dO8LQEhIyWWNYUVrNpa7r45dy4XfPZH2LnWLPc652V932qD6Uu3UlLdQPf4CE4Y5JvzEf1F384xvHXtWD5bXshfP19NbnENV72ygMmDu/CXUwfRI8E7j/ur6pv4w9tL+G6tCUX/cGw//nhcP+0mkf1qUzB55plnergM9xk8eDAzZ85sfTs4WMcS5SA01e8KHcu3QkX+brebf9XsowskPhXG3gAjLoFwH3r1X0SkRep4OO2f5pjwT3+H5P6QcY7VVQlA1Q5442zzYldYrOlwTZtodVXt1iMhgm5x4RSW17Ekr5QJbT2mmz4JbswyT/BXfwLfPQTZ38FZz2sJibtV7TAjZwqXQnCECcD7n+jVElqCyYn9fOQY9+7CYswW+Q+mwC9Pw9DzzeIw8XsZ3eN4f1E+KwvcuJl70Suw4j3T4X7eKxC9j9m60srlcjF1Vg4AV05IIzio3Yc55VdsNhunD0vhmAGd+dfM9UzNymHGqu38tH4nvz+mH9dMSvfoFuz80hqumbaQtdsqCQu288R5wzh9mJbayYG1KbW7//77PV2H2wQHB9O1qzY8yX401Td3ORbsChl3DxzLt0JNUdvuKzjCzImMTYG4nmaTcf+T9AqpiPi+EZfCjjVmfuEnN0FCOnQfaXVVHVtpjhkDUrIJopLh0g+h2zCrqzooNpuNMemJTF9awLzNJW0PJmHXFtulb5ljtLlZ8FwmnPpPyDjbUyV3LMUbTQBemgORSWbzdo/DvVpCXaODhbnmlMmEPj4YTAIMPhsWvwabfoSv/mz+P6nrx+9ldDcLGNx2lLtgifn6ADMHOHWCe+43wM3eWMy67ZVEhgZx/mi98ORO0WHB3HPKIM4d1ZO/TF/J/M0lPDFjHR8uyufBMwYzqV+y26+5KLeE619fRFFVA8kxYbx4+eEM7xnv9utIYGpzO2FpaSlvvPEGV1xxxW+26ZSXl/Paa6/t9fe8bcOGDaSkpBAeHs748eN59NFH6dWr1z4/vr6+nvr6+ta3Kyrc+MqZeF9TA1QW/La7cffb1Tvbdl/B4RDbEjr2+NXtFPN2RIIeoIqI/zr+IbOlfcM38M7FcO0PENvN6qo6pu2rTShZtQ3ie8Fln/j9GJCWYHL+/hbg7IvNZk4e9BoHH11r5k1+cJVZRHLy46abTQ5O/iJ46zxz8iM+FS79CDp5fxv2wpxSGpqcdI0Np0+yZ5fsHLSWRTjPjjffJ9d+AQNPtboqOUQDusZis8GOynp2VNbROeYQ5t7VlsJ7V4CjAfqfAhNucV+hAW7qrM0AnDeqB3ERIQf4aDkY/bvG8O5145i+tIC/frGGTUXVXPbyfE4Z0o17Tx1It7gIt1zn4yX5/PmDFTQ4nAzqFstLVxxOSrx77ls6hjYHk//5z39Yvnw5v//9bwevx8XF8csvv1BRUcE999zj1gLbY+zYsbz66qv079+fwsJCHnzwQSZNmsTKlSuJidn7A9hHH330N3MpxUc5Gg/c6VjdxgG/QWHNnY7ddwscu0Nsj13ho0JHEQl09iAzB+vl481crHcuhqu+hBA9mPSqvHkmKKorh86DTFAUAAFxy5zJxXkmgAoNPohjekl9YMoM+Olx+OVJswwobzac/RL0HO3mijuA9TPg/SuhscZsQr/kfcuOnGZtNKdTJvRN8u3ZY536mcVTs56Gr++CPkd7fFu5eFZUWDC9O0WxcWc1qwoq6Nz/IINJpxM+vhHKck3If+azeu7QRpt2VvHd2h3YbHBlppbeeJLNZuPMEd05ZmBn/vHteqbNzuGLFYX8sG4HtxzbjymZ6Qf38xlwOl089e06/vvDRgBOGNSFf1wwnKgwjdOT9rG5XC5XWz5w+PDhPPXUUxx77LF7/f3vvvuOO+64gyVLlri1wENRVlZGamoqTz/9NFdfffVeP2ZvHZM9e/akvLzc8u7PDsXRCJXbmgPG/L2EjwVQtR1ow5drUNiujsaWY9ax3XfrdOwBkYl64CAi0qJkE7x4jOn8yDjHhJX6HukdG76Fdy+DplroMQYuftf8jAoALpeLUX+dSUl1Ax/eOJ5RqYf458qdDR9dB+VbzBy3I/8Mk273yvbogLBoGnx+K7gcZsnQ+a9BWLRl5Zzxn1ksyy/n6fOHcfbIHpbV0SYN1fDfseZrb9LtcOx9Vlckh+gP7yxh+tIC7jjhMG4+pt/B3cmsf8LM+81zj6u/gZTh7iwxoN03fSWvzcnluIGdeekKvcjkTasLKrhv+srWURp9O0fz0OmD2zdyBahpaOL295bx1cptANx4VB/uPKE/dl9bZCaWqqioIC4u7oD5WpsfyW3cuJF+/fb9Tbtfv35s3LixfVV6WHx8PIcddhjZ2dn7/JiwsDDCwsK8WFUH5GgyR9P2tUSmosCEkm0KHUN3BY37OmYdmaQn1CIi7ZHYG85/HV4/02yhTR4IR95pdVWBb8UHZgO1swn6HmeCogDqxLLZbIxJS+TrVduYt7nk0IPJ1Alwwyz44nZY+QH8+DfY+D2c/YLZNi9753KZJVc/PmreHnYxnP4MBFl3dLK8ppHlzfP9Mtv5ZNgSoVFw4mPw7iWQ9QwMu8h0UorfykiJY/rSAlZuPcgxXjlZZjkXwEl/VyjZDuU1jby/MB+AKeqW9LpBKbG8d/14PlqylUe/XEP2jioufmkepw1L4Z6TB9I17sAdxNvK67jmtQWs3FpBSJCNR88eyrmjfPwFJvFpbQ4mg4KCKCgo2Oe8xoKCAux239qkVVVVxcaNG7nsssusLiVwOZpMJ+O+jlZXFJhQ0uU88H3ZQ/bS6dhjz9uRSeBjX2ciIgEhfRKc8hR89gf44a9mU/eg062uKnDNewG++hPggoxz4cz/QXCo1VW53Zh0E0zO31zCTUe54Q4j4uHcl+GwyfD5bbBlLjw30XztDj3fDRcIMI4m+OJWs8AFYNIdcMy9lr+AO2dTMS4X9EmOokvsIcz386YBp0C/E8ysyS/vMHNg9UK43xrcsgCn4CAW4FRuNzNvXQ4YeiGMutK9xQW4dxfmUdvoYEDXGMb3SbK6nA7Jbrdx7qgeHD+oC09/s47X5+by2bICvl+znVuPP4wrJqQRso8t6cvzy7hm2kJ2VNaTGBXK85eNYnRaYJz0EOu0OZgcMWIEn3zyCePGjdvr73/88ceMGDHCbYUdjDvuuIPTTjuN1NRUCgoKuP/++wkKCuKiiy6ytC6/5XSY0HFfS2RaOh1djgPflz3EzMv6TafjbsesIzspdBQRsdKoK82m7nnPmU6+hFS/3Qrts1wu+PEx+Okx8/aY6+DEvwfsz78xzXMmF+aU4nC6CHLXEa+h50PPMeZo95Z5ZkHOhm9MQBke555r+LuGavhgCqz/Gmx2OPkJGH2N1VUBkJVt5ktO9IduyRY2m+mM2/ST2dK96mNtifdjg1PM94n80lrKahqIj2zjC0OOJvjwavMcKXkgnPq0Aup2aHI4mTY7F4ApE9N9e75sBxAXEcKDZ2Rw3uFme/eSvDL++sUa3l+Yz0NnDGZs7z2D4y9XFHLbe0upa3RyWJdoXr5iND0TIy2qXgJJm4PJm2++mQsvvJAePXpw4403EhQUBIDD4eDZZ5/lH//4B2+99ZbHCm2L/Px8LrroIoqLi0lOTmbixInMnTuX5ORkS+vySU4HVO3Yf6djZWEbQ8dgiEnZbYFMym5LZJoXykQlB+yTLhGRgHLCI2ZT98bv4e2LzKbumC5WVxUYnE7TJbngRfP2Uf8HR/4poJ/UDuwWS0xYMJX1TawprCCjuxtDw4Q0uPJL+OUpc1R5xftmkdDZL0DqePddxx9VF8FbF8DWhRAcbubG+tA26V2Lb/womAQz9mLSbeZY/Iz/g37Ha0O8n4qLCKFXYiR5JTWsKqho+0iBH/8GOb9ASFTAjd/whhmrtrO1rJakqFBOH5ZidTnSLKN7HB/eMIEPFuXz2NdrWbe9kgtemMtZI7pz98kDSI4O4z/fZ/PUt+sBOKp/Mv++aAQx4dqmLu7R5mDynHPO4U9/+hO33HIL99xzD7179wZg06ZNVFVVceedd3Luued6rNC2eOeddyy9vs/buR4+/b0JHysLzUyrA7EFNQeNKXvpdGw+Zh2VbDa7ioiI/wsKhnNfgZeOg+INZqbaFZ9DiJ8ct/RVTQ3wyY1mNiI207025lqrq/K4ILuNw9MS+GHdTuZtLnFvMAnm6/WoP5tNyR9eY7bjvnqyObJ85J8snaNomZLN8MY5ULIRIhLgoneh11irq2pVWF7Lpp3V2G0wrrcfHuPM/CMsextKc0z38+RHrK5IDlJG91jySmpYubW8bcHk+hnmhRAwc1qTD/NsgQFoatZmAC4Zl0p4iJ4/+hK73cb5o3tywuAuPDFjHW/Nz+PjJVuZuXo7Q3vGkZVdDJi5oPecMtB9JyBEaEcwCfDII49wxhln8Oabb5KdnY3L5eLII4/k4osvZsyYMZ6qUdwlONTMYmphC4KYbrt1Nu7lmHV0F4WOIiIdTUS82Q794jGQvwA+uwXOej6gO/s8qqEa3rscsmeaUwZnPQ9DrH0x15vGpCfxw7qdzN9czNUTPbTooOcYsxjnqz/Dsrfg58dN1+85L5out46iYAm8eR5U74S4XnDphz4XnrQ8uR3SI564CD8MjkPC4aQn4K3zYO7/YPgl0GWQ1VXJQRicEseXK7axsqANC3DK8szoCDAjODrQ93B3WbqljEW5pYQG2bl03N73Voj14iNDeeSsIZzffLx7eX45WdnFBNttPHRGBheP1d+duF+7gkmAMWPGKIT0VzEpcN6ru45ZR3cxnQYiIiK/ltTH/Mx44xxY/i50HggTb7W6Kv9TU2KO1ObPh5BIs/2833FWV+VVLXMm528uweVyeW6mWHgsnPU/8//3s1vNMebnJsFJj8PwiwM/WN8w0wTgjdXQdQhc8gHEdLW6qt/YNV/SD7slWxx2Agw4FdZ+bhbhXPlF4H99BaCWDu5VWw+wAKepHt67AurKIGUknPBXzxcXgKbOMt2Spw1LoXOMTmH4umE94/n4pkzeXbCFr1dt44YjezOhj5+N3xC/oaF/HUlwKAw+C3qONh2SCiVFRGR/+hxtlj0AzHwQ1n5pbT3+pqIAXjnZhJLh8XD59A4XSgIM6R5HeIid0ppGsndUef6CGefAjVmQmgkNVTD9JrNBt7bU89e2ytK34O0LTCjZ+ygze9MHQ0mXy9UaTGb6+xPcEx+F4AjIzTIv3ojfGZxiNnNvKqqmsq5x3x844x4oWGy+j58/DYLDvFNgACksr+XLFYUAXJWZZm0x0mZBdhsXj+3Fa1PGKJQUj1IwKSIiIvs25lo4/GrAZWb4bVtpdUX+oXgjvDwZdq4xY1OmfG2OG3dAocF2RvZKAGDe5hLvXDS+J1zxGRx7nzk+v+pj+F8mbP7FO9f3FpcLfn7SzC91NsGQ8+Hi9033qA/auLOKHZX1hAXbGZmaYHU5hya+Fxx5p7n9zb1QW2ZpOdJ+naLD6BZnOvfWFFbu/YNWfLBrYdnZL5q/d2m31+fk0uR0MTY90f2zhkXE7ymYFBERkf076e+QfoTpxnr7IqjaaXVFvq1wGbx8ApTnmfmGU2aYo/Ad2O7Hub3GHgSTboervzF/DxVbYdppMPMBs4zI3zkd8MXt8P3D5u3MP5r5pcGhlpa1P7M2mG7J0WmJgbH4YvzvIamfmen5g5bg+KPBKSYkW7m349w718Gnt5jbk243R/il3WobHLw1Pw+AKZ6aMywifk3BpIiIiOxfUAicN82EO+V58N5lZuaW/NbmX+CVU6CmCLoONaFkQqrVVVnu13Mmvar7KLj+FxhxGeCCWf+AqSdAUbZ363CnxlozT3Lhy4DNzNE8/kGw+/ZD+6yNZvFNmzYg+4PgUDjlSXN7wUtQsNTScqT9Mrqb7uKVBb8KJluWljVWQ9okOOr/LKguMHy0JJ+ymkZ6JUZy3MAuVpcjIj7okB69FBUV8cUXX/Dpp59SWFjorppERETE10QmwkXvQlgc5M2Bz28zx0hll7VfmGVBDZWQOhGu/ByiO1tdlU8Y0TOBkCAb2yrq2FJS6/0CwqLhjP/A+a+ZOXEFS+D5SbBomv99HdeUwLTTzeKVoDAz827s9VZXdUBNDidzW4NJP15882u9j4LBZ4PLaRbhOJ1WVyTtkJHSsgBnt83cLhd8fivsXAvRXeGclzWb/yA5na7WpTdXTkgjyK4lUSLyWwcdTH744Yf07duXBx98kPvvv58+ffrwyiuvuLM2ERER8SXJh8G5U8Fmh6VvwJz/Wl2R71jyBrx7KTjqof8pcOmHEK45Wi0iQoMY2iMegHmbi60rZNAZcOPs5tEENfDZLebvrcaLR8wPRWmuGROQP998fV3+ifkz+YEVW8uprG8iNjy49fhswJj8CIRGQ/4C871R/EbLvMMNOyqpbXCYdy561Sw0sgWZn3kx6vI7WL9kF7FxZzXRYcGcd3gPq8sRER/V5mCyqmrPLYoPPvgg8+fPZ/78+SxZsoT333+fe+65x+0FioiIiA/pdxxM/pu5/e1fYP031tbjC7Kegem/Mx1Twy8xXXkh4VZX5XMsmTO5N3Hd4bLpcPxDYA8xnYf/mwCbfrS2rgMpXA4vHw/FGyC2hxkTkDrB6qrarGUb94Q+nQKvayo2BY6629z+9n7/CbqFLrFhJEWF4nTB2m0Vppv6qz+Z3zz2PkjLtLZAP9fSLXn+4T2JCQ+xuBoR8VVtDiZHjRrF9OnTW98ODg5mx44drW9v376d0FDfHbYtIiIibjL2Bhh5hQniPpgCO9ZYXZE1XC4TQnz7F/P2hN/DGf/Vkb99aA0mc3wgtLHbIfMPcM1Ms7ykshBeO8NsV/bF+akbf4BXToaq7dB5MFzzrd8tVMrKDsBj3Lsbez10HgS1JfDdg1ZXI21ks9kY3NI1mZsP710BjgbofzJMuMXi6vxb9o5Kflq/E7sNrspMs7ocEfFhbQ4mZ8yYwQsvvMBZZ51FQUEB//rXv7jgggvo2rUrnTp14q677uLZZ5/1ZK0iIiLiC2w2OPlJM0exoRLevhCqLTyeawVHE3z6e8j6p3n7uAfhhL+a/zeyV6NSE7DbILe4hm3ldVaXY6QMh+t/hsOnmLdn/xteOtZs4/UVy96FN881/9bSJsGUr0yHnh+pbXCwKLcUCKDFN78WFAKnPGVuL5oG+YusrUfaLCMlFnCRseAuKMuF+FQ481mfXybl66Zm5QBw/KAu9EyMtLYYEfFpbf5um5aWxhdffMH555/PkUceydKlS8nOzubbb79l5syZ5OXlcfLJJ3uyVhEREfEVwaHmyHJ8KpTmmO2lTQ1WV+UdjXXw/hWw5HUzb/O0Z2DiH62uyufFhocwKMVswPWJrskWoZFw6j/gwrcgIhG2rYDnjzBblq1cjONywax/wsfXgbPJLFjx09mlC3NLaHA46RYXTnqnKKvL8ZzUCTDsIsAFX9wKTofVFUkbZHSP47qgzxlUMQuCQs1CqYgEq8vya6XVDXy0OB+AKZnpFlcjIr6u3S8DXXTRRSxYsIBly5Zx1FFH4XQ6GT58OOHhmqUkIiLSoUQlwcXvQmgM5M4yG2n9bcNxe9VVmO61tZ+bJ7DnTYNRV1hdld8Yk2aO8c63cgHOvgw4BW6aA32OgaY6+OJ2ePsiqC7yfi1OB3z1Z5h5v3l7/M1mM3BwmPdrcYNZzfMlM/t2whboXcXHPwRhcVC4DBZOtboaaYNRrtX8KfhdAJpOeAxSRlhckf97e0EedY1OBqfEto7xEBHZl3YFk19++SVPPfUUCxcu5KWXXuLxxx/nkksu4c4776S2ttZTNYqIiIiv6jwQzn0ZsMHiaTDveasr8pzqIph2GuT8YjbwXvIBDDrd6qr8is8swNmXmK5wyYcw+VETPK//Cp4dDxtmeq+Gxjp4/0qY3/xvafLfzNZnPz5WOjvQ50vuLrozHHOvuf39w1C109p6ZP+qdtD5m5sItjn5yDGRdT3Osboiv9focPLa7FzAdEsG/IsRInLI2vwI5/bbb+eqq65iwYIFXH/99Tz88MMceeSRLF68mPDwcEaMGMFXX33lyVpFRETEFx022XQJAcy4G7K/s7YeTyjbAlMnQ+FSiEyCKz6D3kdaXZXfGZ1mjkeu315FSbWPHv2322H8TXDtD5A8AKp3wJvnwFd3mdDQk2pL4fWzYM2nJhg9dyqM/51nr+lhZTUNrCwoByCzT4DOl/y10VdD16FQVw7f3md1NbIvTgd8MAVb1Ta2BKdyT+MUVhVUWl2V3/tyRSHbKupIjgnj1GHdrC5HRPxAm4PJV199lS+//JJ33nmHBQsW8PrrrwMQGhrKww8/zEcffcTf/vY3jxUqIiIiPmzC72H4JWZT9/tXwc71VlfkPjvWwssnQHE2xPaAKTOg+0irq/JLSdFh9OscDcACX5ozuTddM+C6H2HMdebtef+DF4+B7as9c72yLTD1RMibDWGxZp5khv93b83ZWIzLBf06R9M5toOMfrIHwSlPm9vL3oLcOdbWI3v3w99MB3xIFF8MeIxawltDdDk4LpeLqbM2A3DZuFTCgoMsrkhE/EGbg8moqCg2bzbfZLZs2fKbmZKDBg3il19+cW91IiIi4h9sNrNApOc4qC+Hty+AGh8PntoifyG8ciJUFkCn/nD1N9Cpn9VV+TWfP869u5AIOPkJuPh9iEqGHavghaPMyAJ3zlPdthJePh52roWYFJjyNaQf4b77t1DWxl3zJTuUnqNh5OXm9he3g6PJ2npkTxu+hV+eNLdPf4ZufYcBsHKrgslDsTivjGX55YQG27l4bC+ryxERP9HmYPLRRx/l8ssvJyUlhSOPPJKHH37Yk3WJiIiIvwkOgwvegLieULLJzMlzNFpd1cHL/g6mnW6O13Y/3IRFcd2trsrv+VUw2eKwE+DG2dDvBHDUw1d/MkuQqnYc+n1v/hleOQkqC83R8Wu+hS6DD/1+fURW63zJDhZMAhz7gNnuvGPVrpmhYr2yPPjoWnN79LUw5FwGp5ht96sLK3A4A3yJmwdNzTKNTGcOT6FTtH8u6xIR72tzMHnJJZewZcsWpk+fTk5ODmeccYYn6xIRERF/FJ0MF70DIVGw+Sf4+m6rKzo4Kz+Cty6AxmrofTRcPh0itVnUHVqCyVUF5VTW+VFwHd0ZLn4PTnoCgsIge6ZZjLN+xsHf54oP4I1zoL4Cek1oDr97uK9mi20tq2VzUTV2G4zt3QH//UQlwXEPmNs/PAoVhZaWI0BTvXnRrLYUUkaaxVJAeqcoIkODqGt0smlnlbU1+qmtZbV8vXIbAFMmpltcjYj4k3at90tKSmL06NHEx8d7qBwRERHxe10z4JwXARsseBEWvGR1Re2z4CX4YAo4G2HwWXDxuxAWbXVVAaNbXAS9EiNxumBRbqnV5bSPzQZjr4Prf4IuGVBTBG+dD1/cAY217buv2f+BD68GRwMMOgMu+9h01wWQrGxzjHtYz3hiw0MsrsYiIy43HdcNlfDNvVZXI9/cC1sXQXg8nPeq6fQHguw2BnWLBdCcyYP02uwcHE4XmX2TGNA11upyRMSPtCuYFBEREWmTAafAsc3baL/8E2z60dJy2sTlgp+eMPPgcMHhU+Ccl1ufuIr7+OVx7t11HgjXfAfjmjdmL3gRnj8SCpcf+HOdTvj6/+Cbe8zbY66Hc1+BkMBbDDO7OZjsMNu498Zuh1OeApsdVn4Am36yuqKOa+WHMP8Fc/vsFyAhdY/fzuhujnOv3Frh7cr8XnV9E2/PzwNgSqa6JUWkfRRMioiIiGdMvBWGXgAuB7x3BRRvtLqifXM6zbHzH/5q3j7iT2arrl0bRT3B74NJMEHiiX+DSz+C6C5QtA5eOtZ0Qjqde/+cpnr4cArM/a95+/iH4KS/B+TXmcvlImtjB54vubuU4XD41eb2l3dAU4Ol5XRIO9fDp7eY25Nuh8Mm/+ZDBqc0d0xqAU67fbQ4n4q6JtI7RXF0/85WlyMifkbBpIiIiHiGzQanPWOOMdaVmZmNtWVWV/Vbjkb45AaY9z/z9omPwTH3mPrFI8Y2B5PL8suoa3RYXM0h6nusWYzT/2RzLPube+CNs387T7C2zMyTXPUx2EPg7Bch8w8B+3W2YUcVOyvrCQ+xMzI13upyrHfMvWaze9H6XcG0eEdDNbx3OTRUQdokOOr/9vphLR2TqwsqcGoBTps5nS6mZuUAcFVmGnZ7YH5PExHPUTApIiIinhMSDhe+BbHdoXiDmd3oaLK6ql0aauCdS2D5u2ALgrNegHE3Wl1VwOuVGEmX2DAaHS6W5JVZXc6hi+pkvs5P/QcER8CmH+B/E2DN5+b3y7eazds5v0BoDFzyPgw939qaPaxlvuTotETCggOvI7TdIuLh+IfN7Z8eh7ItlpbTYbhc8PltsHON6Ww+52UICt7rh/btHE1osJ3K+ibySmq8XKj/+nH9DjYXVRMTHsw5IwNneZeIeI+CSREREfGsmC5w0dsQEgkbv/OdBRC1ZfD6WbBhBgSHmxqHXWB1VR2CzWZjTHoS4OfHuXdns5m5pNf/DF2HQm0JvHsJfHwDvHw87FhtgpGrvoQ+R1tdrce1BJMd/hj37oZdCL3GQ2MNzLjb6mo6hsXTYPk75oWnc18xP4/2ISTIzoCuMYAW4LTH1Fk5AFw0phdRYXsPfUVE9kfBpIiIiHhet2Fw1nPm9rz/waJXLS2Hym3w6imwZS6ExcFln+x15ph4TuucyZxiiytxs+TDzGKczD8ANlj2NlRshaR+cPW30G2o1RV6XJPDydxNJnDu0Itvfs1ma16EEwRrPoMNM62uKLAVLDXL18AsY0vLPOCnDE4xx7lXFWgBTlus21bJrOwi7Da4fHzqgT9BRGQvFEyKiIiIdww6A45u3kT8xe2QM8uaOko2wdTJsH3lrg621PHW1NKBtcyZXJRbSkPTPpbF+KvgULPY5vLpkNgH+hwDV3/zmy3AgWpZfjlV9U3ER4YwqHmhiDTrMhjG3mBuf3kHNNZZW0+gqi0zcyUd9XDYSTDhljZ9WkZ3LcBpj6mzNgNwUkY3eiREWlyNiPgrBZMiIiLiPUfcCYPPBmcTvHsZlGz27vW3rYCXJ0NpDiSkwZSvoWuGd2sQAPomR5MQGUJdozNwj032PhJuWQyXfQyRiVZX4zWzm49xj++dRJAWYfzWUXdBdFco3QxZ/7K6msDjcsEnN0FZLsT3grP+B/a2Pe3N2K1j0uXSApz9Ka6q5+OlWwGYMjHN2mJExK8pmBQRERHvsdngzGchZYSZwff2hVDnpSNzuXPglVOgegd0yYApMyCxt3euLb9ht9sYndZ8nDtQ5kwKALM0X3L/wmNh8iPm9qynvf8CTaCb/W9Y9wUEhcL5r0FEQps/tX/XGILsNkqqGygsVzfr/rw1L4+GJifDesQxslfb/x+LiPyagkkRERHxrpAIs8E4uivsXAsfXgNOh2evuX4GvH4m1Jeb5RNXfgExXT17TTmg1jmTCiYDRk1DU+umdQWT+5FxDqQfAU118NWfTZefHLrc2TDzAXP7xMfMi2DtEB4SRL/O0YCOc+9PQ5OT1+bmAjBlYjo2mzqjReTgKZgUERER74tNgYveMtuwN8yAmfd77lrL3oG3LzIBQL/JcOlHEBHvuetJm41t3sy9IKcEh1PBTCBYkFNKg8NJ9/gI0pI0c26fbDY4+Smwh5jvgeu+tLoi/1e1A96/ClwOGHI+HD7loO4mo7s5zr1SC3D26fPlBeysrKdLbBgnD+lmdTki4ucUTIqIiIg1uo8yx7rBHL1b8qb7rzH3f/Dx9eaJ6tAL4MI3IVRhia8Y2C2G6LBgKuuaWLtNIUAgaJkvOaFPkrqoDiT5MJhws7n91V3QUGNtPf7M6YAPr4aqbZA8AE79hwl/D0JG88KmVeqY3CuXy8XLzUtvLh+fRkiQIgUROTT6LiIiIiLWyTgHjviTuf3ZHyBvrnvu1+WC7x6Gr+8yb4+9Ec58DoJC3HP/4hbBQXZGpZrZZDrOHRha5ktO7Kdj3G1yxJ0Q1xPK8+CXp6yuxn/9+Chs/hlCosxcybDog76rXR2TCib3ZkFOKasKKggLtnPxmF5WlyMiAUDBpIiIiFjrqLth4OngbIR3LoHS3EO7P6cDPr8VfnnSvH3MvXDio23eyirepTmTgaO0uoHVhabzdXyfJIur8ROhUeb7E8DsZ6Ao29p6/NGGb+HnJ8zt05+B5P6HdHcDu8Vis8H2inp2VGoBzq9Nbe6WPHtkDxKiQi2uRkQCgR6hi4iIiLXsdjjrOeg6FGqKzDzI+qqDu6+mevjgKlj0CmCDU542HUk6UuqzxvXeFUy6tADEr83ZVIzLBf27xNA5JtzqcvzHgFOh7/HgaIAv79AinPYo2wIfXWtuj74Ghpx7yHcZFRZM705RAKzSnMk9bCmp4ZvV2wCYkplmbTEiEjAUTIqIiIj1QqPgorchqjPsWAUfXQdOZ/vuo74K3jofVk83CyXOnQqjr/ZMveI2Q7rHExZsp7i6gY07q60uRw5ByzHuCX3VLdkuNhuc/DgEhcGmH2D1J1ZX5B+aGuD9K6C21Gzfnvw3t911y3FuzZnc06uzc3C64IjDkunXJcbqckQkQCiYFBEREd8Q1wMufMs8OV/3BXz/cNs/t7oYXjsdNv1oZoxd8h5knO2xUsV9QoPtjOylOZOBoGXxTWYfzZdst8TeMPGP5vbX/wf1lZaW4xe+uRe2LoLweDhvGgSHue2uM1Ka50xuVcdki8q6Rt5dsAVQt6SIuJeCSREREfEdPUfD6f82t2c9DcvePfDnlOfDKyeaJ6gRCXDFZ9DnGM/WKW61a85kscWVyMHKL60hp7iGILuNsc3H86WdJt4K8alQWQA//d3qanzbyo9g/vPm9lnPQ0KqW+9+cHezmVsLcHb5YFE+VfVN9EmO4oh+yVaXIyIBRMGkiIiI+JZhF5gn6ACf/h62LNj3x+5cDy9PhqL1ENsdpsyAHqO8U6e4zdjmYHKe5kz6rdnZJlQe1iOOmPAQi6vxUyERcHLzEpe5/4Mda6ytx1cVbTA/GwAm3gb9T3T7JQY3d0zml9ZSVtPg9vv3Nw6ni1dn5wAwZWI6drvmNouI+yiYFBEREd9zzH3Q/xRw1MM7F5uuyF/buth0SlbkQ1I/E0oe4jZWscaIXgkE220UlteRX1prdTlyEFrmS07sq2Pch+SwyeZ7n7MJvrhdi3B+raEa3r0MGqogbRIcfY9HLhMXEULPxAhAC3AAvluzndziGuIiQjh7RA+ryxGRAKNgUkRERHyP3Q5nPw+dB0P1DrOpu2G3xSibfoJpp0FNMXQbDlO+hvielpUrhyYiNIihPUyHkuZM+h+Xy8XsjS2LbxRMHrKTHoPgCMjNguXvWV2N73C5TFi7cw1Ed4FzXoagYI9dbtecSR3nnpq1GYCLx/YiIjTI4mpEJNAomBQRERHfFBYDF78DkZ1g23L4+AazqXv1p/DmuaZjJv0IuPJziFIY4u/GpJtNzgom/c+67ZUUVTUQERLEiF7xVpfj/+J7wRF3mNvf3Au1ZZaW4zMWvwbL3gabHc6dCjFdPHq51s3cHbxjclVBOXM3lRBst3H5ePfO8hQRAQWTIiIi4svie8EFb4A9BNZ8Cm+dD+9fAY4GGHgaXPy+CTDF77XMmZyfo2DS32Q1z5ccnZ5IWLC6qdxiwu8hqa/pGP/hb1ZXY73CZfDlneb2sfdB2kSPX3JwihbgALySlQPAyUO60S0uwtpiRCQgKZgUERER35Y6Hk77l7md/S24nDDycjhvGoSEW1ubuM2otARsNthcVM2Oijqry5F2mN06XzLJ4koCSHAYnPykub3gRShcbm09Vqotg/cuNzOHDzsRJvzBK5dtWYCzuaiaqvomr1zT1+yorOPTpQWAWXojIuIJCiZFRETE9424BI64E+zBZgvrac+AXZ1ZgSQ2PIRB3UyHkrom/Uejw8ncTaZjckIfjVRwqz5Hw+CzzIsxX9xuRll0NC4XTP8dlOaYDvoz/2dmEHtBckwYXWPDcblgTWHHPM795tw8GhxORvaKZ3jPeKvLEZEApWBSRERE/MMx98LdW+G4+8Fms7oa8YAxLce5NWfSbyzPL6O6wUFC5K5gWdxo8t8gNBry58PSN62uxvvm/AfWfg5BoaZLPjLRq5fP6N58nLsDLsCpa3Tw5rxcQN2SIuJZCiZFRETEf+jodkAbq2DS78zasKtb0m7XCwZuF5sCR91lbs+8H2o60L+N3Dnw7f3m9omPQfeRXi9hcOtm7o7XMfnZsgKKqhpIiQvnxMFdrS5HRAKYgkkRERER8Qmj00wwuXZbJWU1DRZXI22RtdHMl5yg+ZKeM/YGSB4INcXw3UNWV+MdVTvhg6vA5YAh58HhUywpY9dm7o7VMelyuXh51mYArpiQRnCQYgMR8Rx9hxERERERn5AUHUbfztEALMgptbgaOZCahiaW5Jm/p4l9NV/SY4JC4JTmRTiLXoWtiywtx+OcDvjwaqgshE794dR/Wja+o+Uo94YdVdQ1OiypwQpzNhWzdlslESFBXDi6l9XliEiAUzApIiIiIj5j15zJYosrkQOZv7mERoeL7vER9EqMtLqcwJY2EYZeALjg89tMeBeofnwMNv8EIVFwwesQFm1ZKV1jw0mKCsXhdLF2W6VldXjb1Fk5AJw7qgdxkSHWFiMiAU/BpIiIiIj4DM2Z9B9Z2eYY98S+nbBpIZXnHf8whMVC4VJY9IrV1XjGhpnw8+Pm9mn/guT+lpZjs9kY3L1lzmTHOM6dU1TNd2u3A3BlZpq1xYhIh6BgUkRERER8RsucyZUFFVTVN1lcjexPVnbz4hvNl/SOmC5wzL3m9ncPmTmMgaRsC3x0rbl9+NUw9Dxr62mWkWKOc3eUOZOvzs7B5YJjBnSmT7J13aoi0nEomBQRERERn5ESH0HPxAgcTheLczVn0lcVV9WzutBsKp7QR/Mlvebwq6HrEKgrN1u6A0VTA7x/JdSWQMoIOPFRqytqldG942zmLq9t5L2FWwCYkplucTUi0lEomBQRERERnzImzXTg6Ti375qzyXRLDugaQ3JMmMXVdCBBwXDK0+b20jchb6619bjLt3+BrQshPB7OmwbBvvM1lZFigsl12yppaHJaXI1nvb9wCzUNDg7rEk2mOqFFxEsUTIqIiIiIT9GcSd/Xeoxb3ZLe13MMjLjM3P7idnD4+ciDVR/DvOfM7bOeh4RUa+v5lZ6JEcSEB9PgcLJhR+AuwGlyOHklKwcw3ZKaGysi3qJgUkRERER8Sstm7qVbyqhrDODtw36sdfFNP3VVWeK4ByEiAbavhAUvWl3NwSvaANNvNrcn3gr9T7S2nr2w2WwMbpkzGcDHub9dvZ2tZbUkRoVy5ojuVpcjIh2IgkkRERER8SmpSZF0jgmjweFk2ZYyq8uRX9lSUkNeSQ3Bdhtj0hVMWiIqCY5tnjH5/SNQuc3aeg5GQw28dzk0VEHqRDj6Xqsr2qeW49wrA3gBztSszQBcMrYX4SFBFlcjIh2JgkkRERER8Sk2m621a1LHuX1PS7fk8J7xRIcFW1xNBzbyCug+Choq4RvfDfX2yuUyx9B3rIaoznDuy2Z+po/atQAnMIPJ5fllLMgpJSTIxqXjfOsovYgEPgWTIiIiIuJzWudM5iiY9DVZG5vnS/bVfElL2e1wylOADVa8D5t/trqitlvyOix7C2x2OHcqxHS1uqL9yuhujnKvKazE4XRZXI37tcyWPHVoCl1iw60tRkQ6HAWTIiIiIuJzWo4IL8otpdER2Jtw/YnT6WJ2y3xJBZPWSxkBo682t7+4A5oarK2nLQqXm1oBjvkLpE+ytp42SO8UTURIELWNDjYXVVldjlttr6jjs2UFgFl6IyLibQomRURERMTn9OscTXxkCDUNDlYVBO7CCX+zbnslxdUNRIQEMbxnvNXlCMAx90JkJyhaB3Oftbqa/astM3MlHfVw2ImQ+UerK2qTILuNQc0LcFYG2AKc1+fk0uR0MSYtkSE94qwuR0Q6IAWTIiIiIuJz7HYbo9Na5kwWW1yNtGiZLzm2dyKhwXoq4RMiEuD4h8ztn/4O5fnW1rMvLhdM/x2Uboa4XnDm/8xxdD+R0RpMBs6cybpGB2/OywVgysQ0a4sRkQ7Lf34SiIiIiEiHMlYLcHxOSzCZ2UfHuH3KsIug5zhorIGv77a6mr2b819Y+zkEhcL50yAy0eqK2mVw98DbzP3Jkq2U1jTSIyGC4wf59pxPEQlcCiZFRERExCftvpnbGYALJ/xNo8PJvOaQOFPzJX1LyyIcWxCs+RSyZ1pd0Z5y58C395nbJz4K3UdaW89ByEgxweSqrRUB8f3I5XIxNWszAFdOSCPIbrO4IhHpqBRMioiIiIhPGtQtlqjQICrqmli3vdLqcjq8pVvKqGlwkBgVyoCuMVaXI7/WNQPGXm9uf3knNNZZW0+Lqp3wwVXgckDGuXD41VZXdFD6dYkmNMhOZX0TW0prrC7nkM3KLmL99iqiQoM4f3RPq8sRkQ5MwaSIiIiI+KTgIDuj0nSc21e0HOMe3ycJu7qrfNNRd0N0VyjZBLOfsboacDrgo2ugshA69YfT/gU2//zaCQmyM6CbCeQDYQHO1FmmW/K8w3sSGx5icTUi0pEpmBQRERERn6U5k76jJZicqGPcvis8FiY/Ym7/8hSU5lhaDj/9HTb9CCGRcP5rEBZtbT2HaHBKYMyZ3Lizih/W7cRmg6sy06wuR0Q6OAWTIiIiIuKzWuZMzttcgsvl/3Pd/FV1fRNL8soALb7xeRnnQNokaKqDr+6yro7smfDT4+b2ac9A5wHW1eImGd0DYzP3q1k5ABw3sAupSVHWFiMiHZ6CSRERERHxWUN7xBEabKeoqp7NRdVWl9Nhzd9cQpPTRc/ECHolRVpdjuyPzWYW4dhDYP1XsO4r79dQng8fXgu44PApMPQ879fgAa0LcAoq/PaFkrKaBj5YlA/AlMx0i6sREVEwKSIiIiI+LCw4iBE94wEd57ZSyzFudUv6ieT+MP535vZXf4IGLy5raWqA96+E2hLoNhwmP+q9a3tY/64xBNltlFQ3UFjuI8uF2umdBVuobXQwsFss43onWl2OiIiCSRERERHxbWN3O84t1pjVEkxqvqT/OPJPENsDyvJg1tPeu+6390H+AgiPg/OnQUi4967tYeEhQfTrbOZk+uNx7kaHk2mzcwCYkpmGzU8XEYlIYFEwKSIiIiI+bUx6EqCOSasUVdWzdlslABP6JFlcjbRZaBSc2NytmPUvKN7o+Wuu+hjm/c/cPut5SEjz/DW9bNcCHP/bzD1j1TYKy+voFB3K6cNTrC5HRARQMCkiIiIiPm5kajzBdhtby2rJL/XikVQBYM7GYgAGdoslKTrM4mqkXQaeBn2OBUcDfHkHeHIuYlE2TP+9uZ35R+h/kueuZaGWBTir/LBj8uVZmwG4dFwqYcFBFlcjImIomBQRERERnxYZGkxGd9OlpK5J79s1X1Ldkn7HZoOTn4CgUNj4Paye7pnrNNTAe5dDQyWkZsIxf/HMdXxAy/eilQX+FUwuzitlSV4ZoUF2LhmbanU5IiKtFEyKiIiIiM9rmTOpYNL7sjZqvqRfS+pjOhgBvr4b6qvcf40v74AdqyCqM5w7FYKC3X8NHzGwWyw2G2yvqGdnZb3V5bTZK1k5AJw+PIXkGHU+i4jvUDApIiIiIj5vjIJJS+QV17ClpJZgu63170D80KTbID4VKgvgp7+7974Xvw5L3wSb3YSSMV3de/8+JjosmPROUQCs8pOuycLyWr5cUQjAlMx0i6sREdmTgkkRERER8XmHpyZis8Gmomp2VNZZXU6H0dItOaJXPFFhgdsFF/BCIuCkx83tuc/CjjXuud/C5aZbEuCYeyF9knvu18dlNC/AWeUnC3Cmzc7F4XQxvncSg1JirS5HRGQPCiZFRERExOfFRYYwoKt5Qr1gc6nF1XQcs7J1jDtg9D8R+p8Mzib4wg2LcOrKzVzJpjroNxkyb3VPnX6gZQHOSj9YgFPT0MTb8/MAmDJR3ZIi4nsUTIqIiIiIX9g1Z7LY4ko6BqfT1bqRW8FkgDjxMQiOgNxZsOL9g78flws+uQlKN0NcLzjrObB3nKeWLR2T/rAA56PFWymvbSQ1KZJjBnS2uhwRkd/oOD89RERERMSvtcw4nKc5k16xZlsFJdUNRIUGMbxnvNXliDskpMIRt5vbM+4xXY8HY+6zsPZzs+37/FchsmPNHx3cHExuKamlvKbR4mr2zel08UrWZgCunJBGkN1mcUUiIr+lYFJERERE/MLoNBN+rNteSVlNg8XVBL7Z2aZbckx6IiFBetoQMCbcAkl9oXoH/PBo+z8/by58e5+5Pflv0H2Ue+vzA3GRIfRMjAB8ewHOTxt2snFnNTFhwZx3eE+ryxER2Ss9whARERERv5AcE0bv5ChcLliYozmTnqb5kgEqOAxOfsLcnv88bFvR9s+tLoL3rzJzKjPOhdHXeKZGP+APx7mnzjLdkheM7km0lleJiI9SMCkiIiIifqN1zmSOjnN7UkOTk/nNR+YVTAagPsfAoDPB5YQvbgen88Cf43TAh9dAZQF0OgxO+xfYOu7R4IzuzcHkVt/czL1+eyW/bCjCboMrJqRZXY6IyD4pmBQRERERv6E5k96xdEsZtY0OkqJC6d8lxupyxBMm/w1ComDLPFj21oE//qfHYdMPEBIJ578GYdGer9GHDU5p3sztox2Tr2TlAHDCoK70TIy0thgRkf1QMCkiIiIifmNMehIAK7eWU13fZHE1gavlGPeEvp2wa2FGYIrrDkf92dz+9j6o2U/Ynz0Tfvq7uX3av6DzQM/X5+NaFuBsLqqmyse+F5VUN/DR4nwArp6UbnE1IiL7p2BSRERERPxG9/gIusdH4HC6WJynOZOeMrtlvmSfJIsrEY8adxMkD4CaYvj+4b1/THk+fHgt4ILDp8DQ871aoq9Kjgmja2w4LhesKfSt49xvz8+jvsnJkO5xHJ6aYHU5IiL7FZDB5H//+1/S0tIIDw9n7NixzJ8/3+qSRERERMRNWudM6ji3R1TVN7F0Sxmg+ZIBLygETn7S3F74CmxdtOfvNzWYZTe1JdBtGEw+iC3eAaz1OPdW3znO3dDk5LU5OQBMmZiGrQPPARUR/xBwweS7777Lbbfdxv3338/ixYsZNmwYkydPZseOHVaXJiIiIiJuoDmTnjV/czFNThe9EiM1m64jSJ8EQ84HXM2LcBy7fm/m/ZA/H8LjzFzJkHDLyvRFg31wAc5XKwvZXlFP55gwThmSYnU5IiIHFHDB5NNPP821117LVVddxaBBg3juueeIjIxk6tSpVpcmIiIiIm7QEkwu3VJGXaPjAB8t7TVrQzGgbskO5YS/QlgsFCyBRa+a9636BOY+a26f+RwkpFlUnO/KaO6YXOUjC3BcLhcvz9oMwOXjUwkNDrin+yISgALqO1VDQwOLFi3iuOOOa32f3W7nuOOOY86cOXv9nPr6eioqKvb4JSIiIiK+K71TFJ2iw2hocrI83zcCgUAye2PzfMm+mi/ZYcR0gaPvMbe/ewjy5sH0m83bmX+AASdbV5sPy2jumNywo8onXiRZlFvK8vxywoLtXDSml9XliIi0SUAFk0VFRTgcDrp06bLH+7t06cK2bdv2+jmPPvoocXFxrb969uzpjVJFRERE5CDZbLbd5kwWW1xNYNlZWc/abZUATOijjskOZfQ10HUI1JXBKydBQyWkZsIx91ldmc/qFhdOYlQoDqeLdc3/bqw0Nct0S541ojtJ0WEWVyMi0jYBFUwejLvvvpvy8vLWX1u2bLG6JBERERE5AM2Z9IyWbslB3WJJjAq1uBrxqqBgOOVpc9vlgKjOcO5U837ZK5vNtmsBjsXHufNLa/h6pWnGuSoz3dJaRETaI6CCyU6dOhEUFMT27dv3eP/27dvp2rXrXj8nLCyM2NjYPX6JiIiIiG9rCSYX5ZbS5HBaXE3gmJ1tOlAn9lO3ZIfUcwyMv9nMmzx3KsTs/TmU7JLhIwtwps3OwemCSf060b9rjKW1iIi0R0AFk6GhoYwaNYrvvvuu9X1Op5PvvvuO8ePHW1iZiIiIiLhT/y4xxIYHU9PgYFWBZoS7g8vlYla26Zic0EfzJTusyY/AXXlmW7ccUEaKCSatXIBTVd/EOwvMyb8p6pYUET8TUMEkwG233caLL77ItGnTWLNmDTfeeCPV1dVcddVVVpcmIiIiIm5it9tauybn6zi3W+SV1LC1rJaQoF3/b6WDstmsrsBvZHQ3J+7WFlbSaFH39oeL8qmsa6J3pyiOPCzZkhpERA5WwAWTF1xwAU8++ST33Xcfw4cPZ+nSpXz99de/WYgjIiIiIv5Ncybdq6VbckSvBCJDNVdQpC16JUYSEx5Mg8PJhu1VXr++0+nilealN1dlpmG3K1QWEf8ScMEkwM0330xubi719fXMmzePsWPHWl2SiIiIiLjZmHRz3HhBTglOp8viavxfy3zJTG3jFmkzqxfg/LBuBznFNcSGB3POqB5ev76IyKEKyGBSRERERALf4JRYIkODKK9tZP2OSqvL8WtOp6t1I/fEfpovKdIerXMmt3o/mHx5lumWvGhsL3U6i4hfUjApIiIiIn4pJMjOqNQEQHMmD9XqwgpKaxqJCg1iaI94q8sR8Sutm7m9vIhrTWEFszcWE2S3cfn4NK9eW0TEXRRMioiIiIjfGpOmOZPukNU8X3Jc7yRCgvQUQaQ9WhbgrC6owOHFsRItsyVPzOhK9/gIr11XRMSd9KhDRERERPzW7pu5XS7NmTxYWRvNfMkJfTVfUqS90jtFExESRG2jg81F3lmAU1RVzydLCwC4emK6V64pIuIJCiZFRERExG8N6xlPaJCdnZX15BTXWF2OX6pvcjB/swkmJyqYFGm3ILuNQS0LcLZ65zj3m3PzaGhyMrxnPCN7JXjlmiIinqBgUkRERET8VnhIEMN7xgO0hmvSPkvyyqhrdNIpOozDukRbXY6IX2rdzO2FBTj1TQ5en5sLwBR1S4qIn1MwKSIiIiJ+reU4t+ZMHpzZzfMlJ/RJwmazWVyNiH9q2cy9ssDzweTnywopqqqna2w4J2V09fj1REQ8ScGkiIiIiPi13edMSvvNag4mdYxb5OANbl6As2prBU4PLsBxuVxMbV56c/mEVC2rEhG/p+9iIiIiIuLXRqYmEGS3kV9ay9ayWqvL8SuVdY0syzcdXhP6JllcjYj/6tc5htAgO5X1TWwp9dy823mbS1hVUEF4iJ2Lx/Ty2HVERLxFwaSIiIiI+LXosGAymue7LVDXZLvM21SCw+kiLSmSHgmRVpcj4rdCg+307xoDeHYBztRZplvynJE9iI8M9dh1RES8RcGkiIiIiPg9zZk8OFkbm+dL6hi3yCHLaDnO7aE5k3nFNXy7ZjsAV2WmeeQaIiLepmBSRERERPzemHRzDFmbudsnS/MlRdxmcOsCHM90TL46OweXC448LJm+nWM8cg0REW9TMCkiIiIifm90WgIAG3dWU1RVb3E1/mFHZR3rt1dhs8H43povKXKoMrqbYHLV1nJcLvcuwKmsa+S9hVsAuHpiulvvW0TESgomRURERMTvxUeGMqB5vpvmTLbN7GzTXTo4JZaEKM2qEzlUA7rGEGS3UVzdwLaKOrfe93sL86mqb6Jv52gm9VOHs4gEDgWTIiIiIhIQNGeyfVqOcWf2Ucgh4g7hIUH06xwNuHcBjsPp4tXZZunNlMx0bDab2+5bRMRqCiZFREREJCC0BJPzFUwekMvlag0mtfhGxH1a50xudd8CnJlrtrOlpJb4yBDOGtHdbfcrIuILFEyKiIiISEAYk2aCyTXbKiivbbS4Gt+WU1xDQXkdoUH21vmcInLoPLGZ++VZplvykrG9iAgNctv9ioj4AgWTIiIiIhIQOseGk94pCpcLFuWqa3J/WrolR/SKJzI02OJqRAJHywIcdx3lXrm1nPmbSwi227hsXJpb7lNExJcomBQRERGRgNHSNak5k/vXEkxO1DFuEbca2C0Wmw22VdSxs7L+kO9vapbpljxlaDe6xoUf8v2JiPgaBZMiIiIiEjA0Z/LAHE4XczaZjdyaLyniXtFhwaR3igIO/Tj3jso6PltWAJilNyIigUjBpIiIiIgEjJZgckV+OTUNTRZX45tWF1RQVtNIdFgww3rEWV2OSMBpWYCzquDQjnO/MSeXRoeLw1MTGNYz3g2ViYj4HgWTIiIiIhIweiREkBIXTpPTxZK8MqvL8UlZG80x7nG9EwkO0tMBEXfLSDELcA5lM3ddo4M35uUBMGWiuiVFJHDpkYiIiIiIBAybzdbaNak5k3vXMl8yU8e4RTyidQHOIRzl/nRpASXVDXSPj+CEQV3cVZqIiM9RMCkiIiIiAWVMehIA8zcXW1yJ76lrdLAgxwS2CiZFPGNwc8fklpJaymsa2/35LperdenNFRNS1dksIgFN3+FEREREJKC0dEwuySujvslhcTW+ZXFeKXWNTpJjwujXOdrqckQCUnxkKD0SIoCDW4Aze2Mxa7dVEhkaxAWje7m7PBERn6JgUkREREQCSp/kKJKiQqlvcrIi/9C24gaa2dmmizSzTxI2m83iakQCV0bKwR/nnjrLdEueN6oHcREhbq1LRMTXKJgUERERkYCiOZP71rL4ZoKOcYt4VEZ3c5y7vZu5NxdV893aHQBcmamlNyIS+BRMioiIiEjAaQkm5yuYbFVR18iyLWWA5kuKeNrglgU47dzM/WrzbMljB3QmvVOU2+sSEfE1CiZFREREJOC0BJOLcktpcjgtrsY3zNtUgtMF6Z2i6B4fYXU5IgGt5Sj3pqJqquub2vQ55bWNvL8oH4CrJ6pbUkQ6BgWTIiIiIhJwBnSNJSY8mKr6JtYUVlpdjk/IyjbHuDP7JllciUjgS44Jo0tsGC4XrCls23HudxfkUdPgYEDXGMb30b9TEekYFEyKiIiISMAJstsYndYyZ7LY4mp8Q2sw2UfHuEW8oXUBThuOczc5nEybnQvAlMx0LacSkQ5DwaSIiIiIBCTNmdxle0UdG3ZUYbOhTiwRL2mdM9mGBTjfrN7O1rJakqJCOX14iqdLExHxGQomRURERCQgtQSTC3JKcDpdFldjrdnN27gzUuKIjwy1uBqRjiEjxWzmbkvH5NRZZunNJeNSCQ8J8mhdIiK+RMGkiIiIiASkjJQ4IkKCKK1pJHtnldXlWGrWBnOcXdu4Rbwno7ljcsOOKuoaHfv8uGVbyliYW0pIkI1Lx/XyVnkiIj5BwaSIiIiIBKTQYDsjU+MBmNeBj3O7XK7WjkktvhHxnm5x4SRGheJwuli3bd9LuKZmmW7J04al0Dkm3FvliYj4BAWTIiIiIhKwxqSZIK4jz5ncXFRNYXkdoUF2Dk9NtLockQ7DZrMxuOU4d8Hej3NvK6/ji+WFgFl6IyLS0SiYFBEREZGAtWsBTjEuV8ecM9myjXtUagIRoZpdJ+JNg1s3c+99Ac7rc3NocroYm57YevRbRKQjUTApIiIiIgFrRK94QoJsbK+oJ6+kxupyLJGV3TJfUse4Rbwto7vpmFy1l47J2gYHb87LA2DKRHVLikjHpGBSRERERAJWeEgQw3rEAx1zzqTDuft8SS2+EfG2jOaOybWFlTQ6nHv83sdLtlJW00jPxAiOG9jFivJERCynYFJEREREAtqu49wdL5hcVVBORV0TMWHBDNExURGv65UYSUxYMA0OJxu2V7W+3+VytS69uXJCOkF2m1UliohYSsGkiIiIiAS0jhxMzmqeLzmuTxLBQXroL+JtdruNQXtZgPPLhiKyd1QRHRbM+Yf3sKo8ERHL6dGJiIiIiAS0UakJ2G2QV1JDYXmt1eV41eyW+ZJ9NF9SxCotS21Wbd0VTL48y3RLnn94T2LCQyypS0TEFyiYFBEREZGAFhMe0roZtyN1TdY1OliQY/68E/tpvqSIVXYtwDGbubN3VPLT+p3YbHDlhDQLKxMRsZ6CSREREREJeB3xOPfi3FLqm5x0jgmjT3K01eWIdFgtC3BWF1bgcLp4JSsHgOMHdqFXUqSFlYmIWE/BpIiIiIgEvI4YTGbtto3bZtNiDRGr9E6OJjzETk2Dg6VbSvlwcT4AUyamW1yZiIj1FEyKiIiISMAbnWaCyQ07qiiuqre4Gu+Y1TJfsq+OcYtYKchuY1A3c5z7vumrqGt0MjgllrHNL5iIiHRkCiZFREREJOAlRoVyWBdznHlBTqnF1XheeW0jK/LLAMjsq8U3IlZrXYDTPGdySma6OplFRFAwKSIiIiIdREc6zj13UzFOF/ROjqJbXITV5Yh0eC1zJgE6RYdx6rBuFlYjIuI7FEyKiIiISIcwJt10Ds7PKba4Es+bnd08X7KPjnGL+ILBzZu5AS4bl0pYcJCF1YiI+A4FkyIiIiLSIYxpnjO5uqCCirpGi6vxrFnZuxbfiIj1+nWOoXNMGLHhwVwyrpfV5YiI+IxgqwsQEREREfGGrnHhpCZFkltcw6LcUo7u39nqkjxiW3kdG3dWY7fB+N6aLyniC0KD7Xz2+4k0OV10ig6zuhwREZ+hjkkRERER6TBauiYDec5kVnO35JDuccRFhlhcjYi06BIbTvd4zXwVEdmdgkkRERER6TA6wgKcrI0mmJygY9wiIiLi4xRMioiIiEiHMbZ5Ac7y/DJqGxwWV+N+LpertWNyooJJERER8XEKJkVERESkw+iZGEHX2HAaHS6WbCm1uhy327izmu0V9YQG2xmVmmB1OSIiIiL7pWBSRERERDoMm83G2N6Be5x7dvMx7sNTEwgPCbK4GhEREZH9UzApIiIiIh1KIM+ZnLXBBJOZOsYtIiIifkDBpIiIiIh0KGObg8nFeaU0NDktrsZ9HE4XczYVAwomRURExD8omBQRERGRDqVPcjSJUaHUNTpZsbXc6nLcZsXWcirrmogJD2ZI9ziryxERERE5IAWTIiIiItKh2Gw2xqQF3nHulm3c43snEWS3WVyNiIiIyIEpmBQRERGRDmfXnMliiytxn5ZgcmI/HeMWERER/6BgUkREREQ6nJZgcmFOKQ6ny+JqDl1do4OFuaUATOijYFJERET8g4JJEREREelwBnaLJSYsmMr6JtYUVlhdziFbmGMW+XSNDadPcpTV5YiIiIi0iYJJEREREelwguw2Dk9LAAJjzmTWRnOMe0LfJGw2zZcUERER/6BgUkREREQ6pDHpSUBgBJOzm+dLZuoYt4iIiPgRBZMiIiIi0iG1LsDJKcHl8t85k+U1jSzfWg5AZl8FkyIiIuI/FEyKiIiISIc0pHsc4SF2Sqob2LizyupyDtqcTcW4XNAnOYquceFWlyMiIiLSZgomRURERKRDCg22M7KXmTM5z4+Pc2c1H+OeqG5JERER8TMKJkVERESkw2o9zu3PwWTr4hsFkyIiIuJfFEyKiIiISIfVEkzO2+SfcyYLy2vZtLMauw3G9U6yuhwRERGRdlEwKSIiIiId1oieCYQE2dhWUUd+aa3V5bRbVnYxAEN6xBMXEWJxNSIiIiLto2BSRERERDqsiNAghvaIB/xzzuSu+ZLqlhQRERH/o2BSRERERDq0XXMmiy2upH1cLldrMJnZR/MlRURExP8omBQRERGRDs1fF+Bk76hiR2U9YcF2RqYmWF2OiIiISLspmBQRERGRDm1UagJ2G+QU17C9os7qctqspVtydFoi4SFBFlcjIiIi0n4KJkVERESkQ4sND2FQSizgX12TWRvN0fMJmi8pIiIifkrBpIiIiIh0eGPSTLjnL8Fkk8PJ3OZgcmJfzZcUERER/6RgUkREREQ6PH+bM7liazmV9U3EhgczOCXO6nJEREREDoqCSRERERHp8EanmeUx67ZXUlrdYHE1B9YyX3JCn04E2W0WVyMiIiJycBRMioiIiEiHlxQdRr/O0QAsyPH9rsmsbHOMO1PzJUVERMSPKZgUEREREcF/jnPXNjhYlFsKQKbmS4qIiIgfUzApIiIiIsJuwaSPd0wuzC2hweGkW1w46Z2irC5HRERE5KApmBQRERERYVcwuXJrOVX1TRZXs2+zmudLZvbthM2m+ZIiIiLivxRMioiIiIgA3eIi6JUYidMFC324a3K25kuKiIhIgFAwKSIiIiLSzNfnTJbVNLCyoBwwG7lFRERE/JmCSRERERGRZr4eTM7ZWIzLBf06R9MlNtzqckREREQOSUAFk2lpadhstj1+PfbYY1aXJSIiIiJ+YmxzMLksv4y6RofF1fxW1sZd8yVFRERE/F2w1QW420MPPcS1117b+nZMTIyF1YiIiIiIP+mVGEmX2DC2V9SzJK+M8X18a45jVut8SQWTIiIi4v8CqmMSTBDZtWvX1l9RUVFWlyQiIiIifsJmszEm3YSRvnace2tZLZuLqrHbYGzvRKvLERERETlkARdMPvbYYyQlJTFixAieeOIJmpqa9vvx9fX1VFRU7PFLRERERDqu1jmTOcUWV7KnrGxzjHtYz3hiw0MsrkZERETk0AXUUe5bbrmFkSNHkpiYyOzZs7n77rspLCzk6aef3ufnPProozz44INerFJEREREfFnLnMlFuaU0NDkJDfaN1/JnNweTmdrGLSIiIgHCNx5l7cddd931m4U2v/61du1aAG677TaOOuoohg4dyg033MBTTz3Fv//9b+rr6/d5/3fffTfl5eWtv7Zs2eKtP5qIiIiI+KC+ydEkRIZQ1+hkZUG51eUA4HK5yNqo+ZIiIiISWHy+Y/L222/nyiuv3O/H9O7de6/vHzt2LE1NTeTk5NC/f/+9fkxYWBhhYWGHWqaIiIiIBAi73cbotES+Wb2d+ZtLGNkrweqS2LCjip2V9YSH2BmZGm91OSIiIiJu4fPBZHJyMsnJyQf1uUuXLsVut9O5c2c3VyUiIiIigWxM+q5g8oYj+1hdDrM2mGPco9MSCQsOsrgaEREREffw+WCyrebMmcO8efM4+uijiYmJYc6cOdx6661ceumlJCRY/yq3iIiIiPiPsc2buRfklOBwugiy2yytZ/bG5vmSOsYtIiIiASRggsmwsDDeeecdHnjgAerr60lPT+fWW2/ltttus7o0EREREfEzA7vFEB0WTGVdE2u3VTA4Jc6yWpocTuZuKgG0+EZEREQCS8AEkyNHjmTu3LlWlyEiIiIiASA4yM6o1AR+Wr+T+ZtLLA0ml+WXU1XfRHxkCINSYi2rQ0RERMTdfH4rt4iIiIiIFcakJwIwf3OJpXXMzjbHuMf3TrL8SLmIiIiIOymYFBERERHZi7G7BZMul8uyOmZla76kiIiIBCYFkyIiIiIiezGkRxxhwXaKqxvYuLPakhpqGppYklcGKJgUERGRwKNgUkRERERkL8KCgxjRKx6w7jj3gpxSGhxOusdHkJYUaUkNIiIiIp6iYFJEREREZB/GpCcBMH9zsSXXb5kvOaFPEjab5kuKiIhIYFEwKSIiIiKyDy1zJudZNGdS8yVFREQkkCmYFBERERHZhxG94gm22ygsryO/tNar1y6tbmB1YQUAE/omefXaIiIiIt6gYFJEREREZB8iQ4MZ0iMO8P6cyTmbinG54LAu0XSOCffqtUVERES8QcGkiIiIiMh+jGk+zu3tYFLHuEVERCTQKZgUEREREdmPljmT83O8G0y2LL7J7KNgUkRERAKTgkkRERERkf0YlZqIzQabi6rZUVHnlWvml9aQU1xDkN3G2N6JXrmmiIiIiLcpmBQRERER2Y+4iBAGdo0FvNc1OTu7GIBhPeKICQ/xyjVFREREvE3BpIiIiIjIAXh7zmTLfMmJmi8pIiIiAUzBpIiIiIjIAYz1YjDpcrmYvdEEkxMUTIqIiEgAUzApIiIiInIAo5uDybXbKimrafDotdZtr6SoqoGIkCBG9Ir36LVERERErKRgUkRERETkADpFh9EnOQqABTmlHr1WVvN8ydHpiYQFB3n0WiIiIiJWUjApIiIiItIGY9KTAJi/udij15ndPF8ys0+SR68jIiIiYjUFkyIiIiIibeCNOZONDidzN5ngM1PzJUVERCTAKZgUEREREWmDls3cKwsqqKpv8sg1lueXUd3gICEyhEHdYj1yDRERERFfoWBSRERERKQNUuIj6JEQgcPpYnGuZ+ZMztpguiUn9OmE3W7zyDVEREREfIWCSRERERGRNhrj4ePcWRvNfMkJfTVfUkRERAKfgkkRERERkTby5JzJmoYmluSZTsyJmi8pIiIiHYCCSRERERGRNmrZzL10Sxl1jQ633vf8zSU0Olx0j4+gV2KkW+9bRERExBcpmBQRERERaaO0pEiSY8JocDhZtqXMrfedlW2OcU/s2wmbTfMlRUREJPApmBQRERERaSObzeaxOZNZ2c2LbzRfUkRERDoIBZMiIiIiIu3QOmcyx33BZHFVPasLKwCzkVtERESkI1AwKSIiIiLSDi0dk4tyS2l0ON1yn3M2mW7JAV1jSI4Jc8t9ioiIiPg6BZMiIiIiIu1wWOcY4iJCqGlwsKqgwi332XqMW92SIiIi0oEomBQRERERaQe73cbotJY5k8Vuuc/WxTf9NF9SREREOg4FkyIiIiIi7TTWjQtwtpTUkFdSQ7Ddxph0BZMiIiLScSiYFBERERFpp903czudrkO6r5ZuyeE944kOCz7k2kRERET8hYJJEREREZF2GpwSS2RoEBV1TazbXnlI95W1sXm+ZF/NlxQREZGORcGkiIiIiEg7BQfZGZWaABzacW6n08XslvmSCiZFRESkg1EwKSIiIiJyENwxZ3Ld9kqKqxuICAlieM94N1UmIiIi4h8UTIqIiIiIHISWRTXzNpfgch3cnMmW+ZJj0hMJDdZDcxEREelY9OhHREREROQgDO0RR2iwnaKqejYXVR/UfWTpGLeIiIh0YAomRUREREQOQvhux68P5jh3o8PJvObPm9A3yZ2liYiIiPgFBZMiIiIiIgfpUOZMLt1SRk2Dg8SoUAZ2jXV3aSIiIiI+T8GkiIiIiMhBGtMcTM47iGCy5Rj3+D5J2O02t9YlIiIi4g8UTIqIiIiIHKSRvRIIstvYWlZLfmlNuz5X8yVFRESko1MwKSIiIiJykKLCgsnoHgfAgpy2d01W1zexJK8MgMw+CiZFRESkY1IwKSIiIiJyCA5mzuT8zSU0OV30TIygV1Kkp0oTERER8WkKJkVEREREDsGYtPbPmWw5xq1uSREREenIFEyKiIiIiByC0WmJ2GywaWc1Oyvr2/Q5s1qCSc2XFBERkQ5MwaSIiIiIyCGIiwyhf5cYoG1zJouq6lm7rRKACX2SPFqbiIiIiC9TMCkiIiIicojaM2dy9sZiAAZ0jSEpOsyjdYmIiIj4MgWTIiIiIiKHaEy66Xxsy5zJ2c3HuCfqGLeIiIh0cAomRUREREQO0ej0BADWbqugvKZxvx+btVHzJUVERERAwaSIiIiIyCHrHBNO705RuFywMHffXZN5xTVsKakl2G5jTPPxbxEREZGOSsGkiIiIiIgbjGnDnMmWbskRveKJCgv2Sl0iIiIivkrBpIiIiIiIG7QEk/ubMzkrW8e4RURERFoomBQRERERcYOWYHLl1nKq65t+8/tOp4s5zRu5FUyKiIiIKJgUEREREXGLHgmRdI+PoMnpYkle2W9+f822CkqqG4gKDWJ4z3iv1yciIiLiaxRMioiIiIi4ya45k8W/+b3Z2cWtHxMSpIfhIiIiInpEJCIiIiLiJvubM6n5kiIiIiJ7UjApIiIiIuImLcHkki1l1Dc5Wt/f0ORs3datYFJERETEUDApIiIiIuImvTtF0Sk6lIYmJ8vzy1vfv3RLGbWNDpKiQunfJcbCCkVERER8h4JJcbDBPAAAFdhJREFUERERERE3sdlsu82Z3HWcu+UY94S+nbDbbZbUJiIiIuJrFEyKiIiIiLjRmLTfzpmc3TJfsk+SJTWJiIiI+KJgqwsQEREREQkkY9JN+Lgop4Qmh5O6JidLt5QBmi8pIiIisjsFkyIiIiIibtS/awyx4cFU1DWxurCCoqp6mpwueiVG0jMx0uryRERERHyGjnKLiIiIiLhRkN3G6LRdcyZnbSgG1C0pIiIi8msKJkVERERE3KxlAc68zSXM3tg8X7Kv5kuKiIiI7E5HuUVERERE3KwlmJydXUR1gwOA8b0VTIqIiIjsTh2TIiIiIiJultE9joiQoNZQclC3WJKiwyyuSkRERMS3KJgUEREREXGzkCA7o1ITWt/WMW4RERGR31IwKSIiIiLiAS3HuUGLb0RERET2RsGkiIiIiIgHtASTIUG2PUJKERERETG0/EZERERExAPGpCVyzcR00pOjiAzVw24RERGRX9MjJBERERERD7Dbbdx76iCryxARERHxWTrKLSIiIiIiIiIiIl6nYFJERERERERERES8TsGkiIiIiIiIiIiIeJ2CSREREREREREREfE6BZMiIiIiIiIiIiLidQomRURERERERERExOsUTIqIiIiIiIiIiIjXKZgUERERERERERERr1MwKSIiIiIiIiIiIl6nYFJERERERERERES8TsGkiIiIiIiIiIiIeJ2CSREREREREREREfE6BZMiIiIiIiIiIiLidQomRURERERERERExOsUTIqIiIiIiIiIiIjX+U0w+cgjjzBhwgQiIyOJj4/f68fk5eVxyimnEBkZSefOnbnzzjtpamrybqEiIiIiIiIiIiJyQMFWF9BWDQ0NnHfeeYwfP56XX375N7/vcDg45ZRT6Nq1K7Nnz6awsJDLL7+ckJAQ/va3v1lQsYiIiIiIiIiIiOyLzeVyuawuoj1effVV/vjHP1JWVrbH+7/66itOPfVUCgoK6NKlCwDPPfccf/7zn9m5cyehoaFtuv+Kigri4uIoLy8nNjbW3eWLiIiIiIiIiIgEtLbma35zlPtA5syZw5AhQ1pDSYDJkydTUVHBqlWr9vl59fX1VFRU7PFLREREREREREREPCtggslt27btEUoCrW9v27Ztn5/36KOPEhcX1/qrZ8+eHq1TRERERERERERELA4m77rrLmw2235/rV271qM13H333ZSXl7f+2rJli0evJyIiIiIiIiIiIhYvv7n99tu58sor9/sxvXv3btN9de3alfnz5+/xvu3bt7f+3r6EhYURFhbWpmuIiIiIiIiIiIiIe1gaTCYnJ5OcnOyW+xo/fjyPPPIIO3bsoHPnzgB8++23xMbGMmjQILdcQ0RERERERERERNzD0mCyPfLy8igpKSEvLw+Hw8HSpUsB6Nu3L9HR0ZxwwgkMGjSIyy67jMcff5xt27Zx77338rvf/U4dkSIiIiIiIiIiIj7G5nK5XFYX0RZXXnkl06ZN+837f/jhB4466igAcnNzufHGG/nxxx+Jioriiiuu4LHHHiM4uO35a1vXmYuIiIiIiIiIiMhvtTVf85tg0lvKy8uJj49ny5YtCiZFRERERERERETaqaKigp49e1JWVkZcXNw+P85vjnJ7S2VlJQA9e/a0uBIRERERERERERH/VVlZud9gUh2Tv+J0OikoKCAmJgabzWZ1OW7XklirI1T8hb5mxR/p61b8jb5mxR/p61b8jb5mxd/oa1YOhcvlorKykpSUFOx2+z4/Th2Tv2K32+nRo4fVZXhcbGysvrGIX9HXrPgjfd2Kv9HXrPgjfd2Kv9HXrPgbfc3Kwdpfp2SLfUeWIiIiIiIiIiIiIh6iYFJERERERERERES8TsFkBxMWFsb9999PWFiY1aWItIm+ZsUf6etW/I2+ZsUf6ev2/9u796Coq/+P468VXd0IJUhdF+WSiVe8EMIo1ldLIzPLGRV11ls22iQqqDTSBXXyljpeQzQvo82I11HMtFQkxSIvKJJaBJgaTqlU3vKWtOzvD6f9ffcrKjW5H5PnY2b/2HMOn32x+xlmz5tzPh/823DO4t+GcxaewM1vAAAAAAAAAHgcKyYBAAAAAAAAeByFSQAAAAAAAAAeR2ESAAAAAAAAgMdRmAQAAAAAAADgcRQmK5EFCxYoODhYNWrUUFRUlA4cOGB0JOCOpk2bprZt28rHx0d16tRRjx49VFBQYHQsoMLef/99mUwmJSQkGB0FuKsff/xR/fv3l7+/vywWi8LCwnTw4EGjYwHlcjgcSk5OVkhIiCwWixo2bKhJkyaJ+3niQbJnzx51795dNptNJpNJmzZtcut3Op0aP3686tWrJ4vFos6dO6uoqMiYsIDufs6WlpZq3LhxCgsLk7e3t2w2mwYOHKiffvrJuMB4qFCYrCTWrl2rMWPGaMKECcrNzVWrVq0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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/200\n",
            "2/2 [==============================] - 3s 10ms/step - loss: 0.3888 - mean_absolute_error: 0.5837\n",
            "Epoch 2/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.3608 - mean_absolute_error: 0.5593\n",
            "Epoch 3/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.3356 - mean_absolute_error: 0.5367\n",
            "Epoch 4/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.3083 - mean_absolute_error: 0.5118\n",
            "Epoch 5/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.2780 - mean_absolute_error: 0.4856\n",
            "Epoch 6/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.2500 - mean_absolute_error: 0.4583\n",
            "Epoch 7/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.2199 - mean_absolute_error: 0.4286\n",
            "Epoch 8/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.1920 - mean_absolute_error: 0.3979\n",
            "Epoch 9/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.1620 - mean_absolute_error: 0.3639\n",
            "Epoch 10/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.1364 - mean_absolute_error: 0.3328\n",
            "Epoch 11/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.1115 - mean_absolute_error: 0.3024\n",
            "Epoch 12/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0900 - mean_absolute_error: 0.2688\n",
            "Epoch 13/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0719 - mean_absolute_error: 0.2380\n",
            "Epoch 14/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0615 - mean_absolute_error: 0.2103\n",
            "Epoch 15/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0562 - mean_absolute_error: 0.1885\n",
            "Epoch 16/200\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 0.0557 - mean_absolute_error: 0.1792\n",
            "Epoch 17/200\n",
            "2/2 [==============================] - 0s 14ms/step - loss: 0.0590 - mean_absolute_error: 0.1789\n",
            "Epoch 18/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0641 - mean_absolute_error: 0.1847\n",
            "Epoch 19/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0658 - mean_absolute_error: 0.1870\n",
            "Epoch 20/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0643 - mean_absolute_error: 0.1852\n",
            "Epoch 21/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0608 - mean_absolute_error: 0.1799\n",
            "Epoch 22/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0579 - mean_absolute_error: 0.1766\n",
            "Epoch 23/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0543 - mean_absolute_error: 0.1746\n",
            "Epoch 24/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0538 - mean_absolute_error: 0.1783\n",
            "Epoch 25/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0531 - mean_absolute_error: 0.1809\n",
            "Epoch 26/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0531 - mean_absolute_error: 0.1843\n",
            "Epoch 27/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0533 - mean_absolute_error: 0.1867\n",
            "Epoch 28/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0535 - mean_absolute_error: 0.1881\n",
            "Epoch 29/200\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 0.0534 - mean_absolute_error: 0.1882\n",
            "Epoch 30/200\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 0.0529 - mean_absolute_error: 0.1868\n",
            "Epoch 31/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0526 - mean_absolute_error: 0.1849\n",
            "Epoch 32/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0519 - mean_absolute_error: 0.1817\n",
            "Epoch 33/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0513 - mean_absolute_error: 0.1786\n",
            "Epoch 34/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0510 - mean_absolute_error: 0.1764\n",
            "Epoch 35/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0510 - mean_absolute_error: 0.1743\n",
            "Epoch 36/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0510 - mean_absolute_error: 0.1730\n",
            "Epoch 37/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0508 - mean_absolute_error: 0.1717\n",
            "Epoch 38/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0506 - mean_absolute_error: 0.1716\n",
            "Epoch 39/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0504 - mean_absolute_error: 0.1718\n",
            "Epoch 40/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0502 - mean_absolute_error: 0.1724\n",
            "Epoch 41/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0499 - mean_absolute_error: 0.1729\n",
            "Epoch 42/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0497 - mean_absolute_error: 0.1730\n",
            "Epoch 43/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0496 - mean_absolute_error: 0.1730\n",
            "Epoch 44/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0495 - mean_absolute_error: 0.1727\n",
            "Epoch 45/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0492 - mean_absolute_error: 0.1721\n",
            "Epoch 46/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0490 - mean_absolute_error: 0.1723\n",
            "Epoch 47/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0489 - mean_absolute_error: 0.1724\n",
            "Epoch 48/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0487 - mean_absolute_error: 0.1717\n",
            "Epoch 49/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0486 - mean_absolute_error: 0.1711\n",
            "Epoch 50/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0484 - mean_absolute_error: 0.1702\n",
            "Epoch 51/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0483 - mean_absolute_error: 0.1695\n",
            "Epoch 52/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0481 - mean_absolute_error: 0.1687\n",
            "Epoch 53/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0479 - mean_absolute_error: 0.1686\n",
            "Epoch 54/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0479 - mean_absolute_error: 0.1697\n",
            "Epoch 55/200\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 0.0476 - mean_absolute_error: 0.1698\n",
            "Epoch 56/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0476 - mean_absolute_error: 0.1698\n",
            "Epoch 57/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0474 - mean_absolute_error: 0.1691\n",
            "Epoch 58/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0472 - mean_absolute_error: 0.1678\n",
            "Epoch 59/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0471 - mean_absolute_error: 0.1671\n",
            "Epoch 60/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0469 - mean_absolute_error: 0.1669\n",
            "Epoch 61/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0467 - mean_absolute_error: 0.1667\n",
            "Epoch 62/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0466 - mean_absolute_error: 0.1666\n",
            "Epoch 63/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0465 - mean_absolute_error: 0.1669\n",
            "Epoch 64/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0463 - mean_absolute_error: 0.1668\n",
            "Epoch 65/200\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 0.0462 - mean_absolute_error: 0.1665\n",
            "Epoch 66/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0461 - mean_absolute_error: 0.1666\n",
            "Epoch 67/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0460 - mean_absolute_error: 0.1671\n",
            "Epoch 68/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0459 - mean_absolute_error: 0.1663\n",
            "Epoch 69/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0456 - mean_absolute_error: 0.1657\n",
            "Epoch 70/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0454 - mean_absolute_error: 0.1654\n",
            "Epoch 71/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0454 - mean_absolute_error: 0.1656\n",
            "Epoch 72/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0454 - mean_absolute_error: 0.1648\n",
            "Epoch 73/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0451 - mean_absolute_error: 0.1641\n",
            "Epoch 74/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0453 - mean_absolute_error: 0.1653\n",
            "Epoch 75/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0449 - mean_absolute_error: 0.1643\n",
            "Epoch 76/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0447 - mean_absolute_error: 0.1636\n",
            "Epoch 77/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0447 - mean_absolute_error: 0.1634\n",
            "Epoch 78/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0448 - mean_absolute_error: 0.1622\n",
            "Epoch 79/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0445 - mean_absolute_error: 0.1612\n",
            "Epoch 80/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0444 - mean_absolute_error: 0.1610\n",
            "Epoch 81/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0444 - mean_absolute_error: 0.1617\n",
            "Epoch 82/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0441 - mean_absolute_error: 0.1622\n",
            "Epoch 83/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0440 - mean_absolute_error: 0.1623\n",
            "Epoch 84/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0439 - mean_absolute_error: 0.1626\n",
            "Epoch 85/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0442 - mean_absolute_error: 0.1640\n",
            "Epoch 86/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0439 - mean_absolute_error: 0.1634\n",
            "Epoch 87/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0438 - mean_absolute_error: 0.1620\n",
            "Epoch 88/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0437 - mean_absolute_error: 0.1605\n",
            "Epoch 89/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0436 - mean_absolute_error: 0.1595\n",
            "Epoch 90/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0434 - mean_absolute_error: 0.1595\n",
            "Epoch 91/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0432 - mean_absolute_error: 0.1603\n",
            "Epoch 92/200\n",
            "2/2 [==============================] - 0s 15ms/step - loss: 0.0432 - mean_absolute_error: 0.1607\n",
            "Epoch 93/200\n",
            "2/2 [==============================] - 0s 14ms/step - loss: 0.0431 - mean_absolute_error: 0.1611\n",
            "Epoch 94/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0431 - mean_absolute_error: 0.1609\n",
            "Epoch 95/200\n",
            "2/2 [==============================] - 0s 14ms/step - loss: 0.0429 - mean_absolute_error: 0.1606\n",
            "Epoch 96/200\n",
            "2/2 [==============================] - 0s 16ms/step - loss: 0.0429 - mean_absolute_error: 0.1595\n",
            "Epoch 97/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0429 - mean_absolute_error: 0.1595\n",
            "Epoch 98/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0426 - mean_absolute_error: 0.1585\n",
            "Epoch 99/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0428 - mean_absolute_error: 0.1576\n",
            "Epoch 100/200\n",
            "2/2 [==============================] - 0s 14ms/step - loss: 0.0425 - mean_absolute_error: 0.1575\n",
            "Epoch 101/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0424 - mean_absolute_error: 0.1584\n",
            "Epoch 102/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0423 - mean_absolute_error: 0.1588\n",
            "Epoch 103/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0422 - mean_absolute_error: 0.1585\n",
            "Epoch 104/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0421 - mean_absolute_error: 0.1581\n",
            "Epoch 105/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0420 - mean_absolute_error: 0.1576\n",
            "Epoch 106/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0419 - mean_absolute_error: 0.1568\n",
            "Epoch 107/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0419 - mean_absolute_error: 0.1571\n",
            "Epoch 108/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0417 - mean_absolute_error: 0.1569\n",
            "Epoch 109/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0416 - mean_absolute_error: 0.1565\n",
            "Epoch 110/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0415 - mean_absolute_error: 0.1566\n",
            "Epoch 111/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0415 - mean_absolute_error: 0.1565\n",
            "Epoch 112/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0413 - mean_absolute_error: 0.1559\n",
            "Epoch 113/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0413 - mean_absolute_error: 0.1564\n",
            "Epoch 114/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0411 - mean_absolute_error: 0.1557\n",
            "Epoch 115/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0412 - mean_absolute_error: 0.1551\n",
            "Epoch 116/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0410 - mean_absolute_error: 0.1548\n",
            "Epoch 117/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0409 - mean_absolute_error: 0.1551\n",
            "Epoch 118/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0407 - mean_absolute_error: 0.1546\n",
            "Epoch 119/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0406 - mean_absolute_error: 0.1538\n",
            "Epoch 120/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0405 - mean_absolute_error: 0.1533\n",
            "Epoch 121/200\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 0.0404 - mean_absolute_error: 0.1533\n",
            "Epoch 122/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0403 - mean_absolute_error: 0.1538\n",
            "Epoch 123/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0403 - mean_absolute_error: 0.1541\n",
            "Epoch 124/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0401 - mean_absolute_error: 0.1537\n",
            "Epoch 125/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0402 - mean_absolute_error: 0.1543\n",
            "Epoch 126/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0403 - mean_absolute_error: 0.1532\n",
            "Epoch 127/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0398 - mean_absolute_error: 0.1517\n",
            "Epoch 128/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0402 - mean_absolute_error: 0.1521\n",
            "Epoch 129/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0398 - mean_absolute_error: 0.1527\n",
            "Epoch 130/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0397 - mean_absolute_error: 0.1542\n",
            "Epoch 131/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0396 - mean_absolute_error: 0.1539\n",
            "Epoch 132/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0392 - mean_absolute_error: 0.1517\n",
            "Epoch 133/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0393 - mean_absolute_error: 0.1505\n",
            "Epoch 134/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0390 - mean_absolute_error: 0.1492\n",
            "Epoch 135/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0390 - mean_absolute_error: 0.1493\n",
            "Epoch 136/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0386 - mean_absolute_error: 0.1491\n",
            "Epoch 137/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0386 - mean_absolute_error: 0.1508\n",
            "Epoch 138/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0389 - mean_absolute_error: 0.1518\n",
            "Epoch 139/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0387 - mean_absolute_error: 0.1513\n",
            "Epoch 140/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0384 - mean_absolute_error: 0.1490\n",
            "Epoch 141/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0382 - mean_absolute_error: 0.1472\n",
            "Epoch 142/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0380 - mean_absolute_error: 0.1471\n",
            "Epoch 143/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0378 - mean_absolute_error: 0.1470\n",
            "Epoch 144/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0377 - mean_absolute_error: 0.1471\n",
            "Epoch 145/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0376 - mean_absolute_error: 0.1472\n",
            "Epoch 146/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0383 - mean_absolute_error: 0.1472\n",
            "Epoch 147/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0373 - mean_absolute_error: 0.1456\n",
            "Epoch 148/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0377 - mean_absolute_error: 0.1477\n",
            "Epoch 149/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0372 - mean_absolute_error: 0.1467\n",
            "Epoch 150/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0369 - mean_absolute_error: 0.1447\n",
            "Epoch 151/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0378 - mean_absolute_error: 0.1437\n",
            "Epoch 152/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0371 - mean_absolute_error: 0.1427\n",
            "Epoch 153/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0367 - mean_absolute_error: 0.1444\n",
            "Epoch 154/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0369 - mean_absolute_error: 0.1464\n",
            "Epoch 155/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0371 - mean_absolute_error: 0.1472\n",
            "Epoch 156/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0366 - mean_absolute_error: 0.1447\n",
            "Epoch 157/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0363 - mean_absolute_error: 0.1421\n",
            "Epoch 158/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0366 - mean_absolute_error: 0.1413\n",
            "Epoch 159/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0367 - mean_absolute_error: 0.1423\n",
            "Epoch 160/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0359 - mean_absolute_error: 0.1423\n",
            "Epoch 161/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0359 - mean_absolute_error: 0.1432\n",
            "Epoch 162/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0357 - mean_absolute_error: 0.1424\n",
            "Epoch 163/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0355 - mean_absolute_error: 0.1408\n",
            "Epoch 164/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0355 - mean_absolute_error: 0.1402\n",
            "Epoch 165/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0354 - mean_absolute_error: 0.1400\n",
            "Epoch 166/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0352 - mean_absolute_error: 0.1399\n",
            "Epoch 167/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0351 - mean_absolute_error: 0.1398\n",
            "Epoch 168/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0358 - mean_absolute_error: 0.1416\n",
            "Epoch 169/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0347 - mean_absolute_error: 0.1395\n",
            "Epoch 170/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0350 - mean_absolute_error: 0.1388\n",
            "Epoch 171/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0352 - mean_absolute_error: 0.1385\n",
            "Epoch 172/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0350 - mean_absolute_error: 0.1386\n",
            "Epoch 173/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0347 - mean_absolute_error: 0.1390\n",
            "Epoch 174/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0345 - mean_absolute_error: 0.1389\n",
            "Epoch 175/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0343 - mean_absolute_error: 0.1378\n",
            "Epoch 176/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0345 - mean_absolute_error: 0.1368\n",
            "Epoch 177/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0343 - mean_absolute_error: 0.1371\n",
            "Epoch 178/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0340 - mean_absolute_error: 0.1367\n",
            "Epoch 179/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0340 - mean_absolute_error: 0.1370\n",
            "Epoch 180/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0339 - mean_absolute_error: 0.1368\n",
            "Epoch 181/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0336 - mean_absolute_error: 0.1356\n",
            "Epoch 182/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0341 - mean_absolute_error: 0.1361\n",
            "Epoch 183/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0337 - mean_absolute_error: 0.1350\n",
            "Epoch 184/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0338 - mean_absolute_error: 0.1357\n",
            "Epoch 185/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0345 - mean_absolute_error: 0.1372\n",
            "Epoch 186/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0330 - mean_absolute_error: 0.1338\n",
            "Epoch 187/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0341 - mean_absolute_error: 0.1361\n",
            "Epoch 188/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0340 - mean_absolute_error: 0.1358\n",
            "Epoch 189/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0326 - mean_absolute_error: 0.1335\n",
            "Epoch 190/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0334 - mean_absolute_error: 0.1365\n",
            "Epoch 191/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0341 - mean_absolute_error: 0.1374\n",
            "Epoch 192/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0325 - mean_absolute_error: 0.1325\n",
            "Epoch 193/200\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0333 - mean_absolute_error: 0.1344\n",
            "Epoch 194/200\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.0328 - mean_absolute_error: 0.1326\n",
            "Epoch 195/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0322 - mean_absolute_error: 0.1316\n",
            "Epoch 196/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0333 - mean_absolute_error: 0.1344\n",
            "Epoch 197/200\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0330 - mean_absolute_error: 0.1332\n",
            "Epoch 198/200\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.0328 - mean_absolute_error: 0.1332\n",
            "Epoch 199/200\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 0.0322 - mean_absolute_error: 0.1320\n",
            "Epoch 200/200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.0319 - mean_absolute_error: 0.1304\n",
            "1/1 [==============================] - 1s 594ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x800 with 1 Axes>"
            ],
            "image/png": 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}