Submission_of_time_series_Temperatur.ipynb

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      "source": [
        "# Submission II Time series\n",
        "\n",
        "* Nama : Irfan Dino\n",
        "* Asal Kota : Pekanbaru, Riau."
      ],
      "metadata": {
        "id": "_OzrC1WPorTr"
      }
    },
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Temp</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1981-01-01</td>\n",
              "      <td>20.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1981-01-02</td>\n",
              "      <td>17.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1981-01-03</td>\n",
              "      <td>18.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1981-01-04</td>\n",
              "      <td>14.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1981-01-05</td>\n",
              "      <td>15.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>3645</th>\n",
              "      <td>1990-12-27</td>\n",
              "      <td>14.0</td>\n",
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              "    <tr>\n",
              "      <th>3646</th>\n",
              "      <td>1990-12-28</td>\n",
              "      <td>13.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3647</th>\n",
              "      <td>1990-12-29</td>\n",
              "      <td>13.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3648</th>\n",
              "      <td>1990-12-30</td>\n",
              "      <td>15.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3649</th>\n",
              "      <td>1990-12-31</td>\n",
              "      <td>13.0</td>\n",
              "    </tr>\n",
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            "text/plain": [
              "            Date  Temp\n",
              "0     1981-01-01  20.7\n",
              "1     1981-01-02  17.9\n",
              "2     1981-01-03  18.8\n",
              "3     1981-01-04  14.6\n",
              "4     1981-01-05  15.8\n",
              "...          ...   ...\n",
              "3645  1990-12-27  14.0\n",
              "3646  1990-12-28  13.6\n",
              "3647  1990-12-29  13.5\n",
              "3648  1990-12-30  15.7\n",
              "3649  1990-12-31  13.0\n",
              "\n",
              "[3650 rows x 2 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 1
        }
      ],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from keras.layers import Dense, LSTM\n",
        "import matplotlib.pyplot as plt\n",
        "import tensorflow as tf\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "df = pd.read_csv('/content/sample_data/daily-min-temperatures.csv')\n",
        "df"
      ]
    },
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      "source": [
        "df.isnull().sum()"
      ],
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      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Date    0\n",
              "Temp    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "date = df['Date'].values\n",
        "temp  = df['Temp'].values\n",
        " \n",
        " \n",
        "plt.figure(figsize=(15,5))\n",
        "plt.plot(date, temp)\n",
        "plt.xlabel('date')\n",
        "plt.ylabel('Celcius')\n",
        "plt.title('daily temperature',\n",
        "          fontsize=20);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        },
        "id": "GGvqfS91ir85",
        "outputId": "1b2d3556-1966-4c86-e6d0-71dc00f12758"
      },
      "execution_count": 3,
      "outputs": [
        {
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\n",
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "train_date, val_date, train_temp, val_temp = train_test_split(date, temp, test_size=0.2, shuffle=False)\n",
        "print(len(train_temp))\n",
        "print(len(val_temp))"
      ],
      "metadata": {
        "id": "Ud3nEilYEzKj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fa28e038-2a2a-4db9-ae25-f689c95e8c06"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2920\n",
            "730\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "temp_train_reshape = train_temp.reshape(-1,1)\n",
        "temp_val_reshape = val_temp.reshape(-1,1)"
      ],
      "metadata": {
        "id": "Di6ZqkM1RMeA"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "min_max_scaler = MinMaxScaler(feature_range=(0,1))\n",
        "\n",
        "scaled_temp_train = min_max_scaler.fit_transform(temp_train_reshape)\n",
        "scaled_temp_val = min_max_scaler.fit_transform(temp_val_reshape)"
      ],
      "metadata": {
        "id": "R5UT6JyOGHRh"
      },
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "temp_train_fix = scaled_temp_train.reshape(2920,)\n",
        "temp_val_fix = scaled_temp_val.reshape(730,)"
      ],
      "metadata": {
        "id": "P4o02jYMTKK9"
      },
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def windowed_dataset(series, window_size, batch_size, shuffle_buffer):\n",
        "    series = tf.expand_dims(series, axis=-1)\n",
        "    ds = tf.data.Dataset.from_tensor_slices(series)\n",
        "    ds = ds.window(window_size + 1, shift=1, drop_remainder=True)\n",
        "    ds = ds.flat_map(lambda w: w.batch(window_size + 1))\n",
        "    ds = ds.shuffle(shuffle_buffer)\n",
        "    ds = ds.map(lambda w: (w[:-1], w[-1:]))\n",
        "    return ds.batch(batch_size).prefetch(1)"
      ],
      "metadata": {
        "id": "sYNirmI5jAYx"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_set = windowed_dataset(temp_train_fix, window_size=70, batch_size=100, shuffle_buffer=1000)\n",
        "validation_set = windowed_dataset(temp_val_fix, window_size=70, batch_size=100, shuffle_buffer=1000)\n",
        "model = tf.keras.models.Sequential([\n",
        "  tf.keras.layers.LSTM(32, return_sequences=True),\n",
        "  tf.keras.layers.LSTM(32),\n",
        "  tf.keras.layers.Dense(1),\n",
        "])"
      ],
      "metadata": {
        "id": "uj3ekCgyjfW8"
      },
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from re import VERBOSE\n",
        "class myCallback(tf.keras.callbacks.Callback):\n",
        "  def on_epoch_end(self, epoch, logs={}):\n",
        "    if(logs.get('mae')<0.1):\n",
        "      print(\"\\nMAE telah mencapai <10%!\")\n",
        "      self.model.stop_training = True\n",
        "callbacks = myCallback()\n",
        "\n",
        "optimizer = tf.keras.optimizers.SGD(lr=1.0000e-04, momentum=0.9)\n",
        "model.compile(loss=tf.keras.losses.Huber(),\n",
        "              optimizer=optimizer,\n",
        "              metrics=[\"mae\"])\n",
        "history = model.fit(train_set, validation_data=validation_set, epochs=100,\n",
        "                    callbacks=[callbacks], verbose=2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rHYAUTzvjvQc",
        "outputId": "7d102c20-8c4a-4164-8123-c5715a91e175"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/gradient_descent.py:102: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
            "  super(SGD, self).__init__(name, **kwargs)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "29/29 - 10s - loss: 0.0624 - mae: 0.3266 - val_loss: 0.0764 - val_mae: 0.3584 - 10s/epoch - 361ms/step\n",
            "Epoch 2/100\n",
            "29/29 - 3s - loss: 0.0493 - mae: 0.2849 - val_loss: 0.0610 - val_mae: 0.3147 - 3s/epoch - 113ms/step\n",
            "Epoch 3/100\n",
            "29/29 - 3s - loss: 0.0382 - mae: 0.2448 - val_loss: 0.0493 - val_mae: 0.2771 - 3s/epoch - 116ms/step\n",
            "Epoch 4/100\n",
            "29/29 - 3s - loss: 0.0299 - mae: 0.2114 - val_loss: 0.0403 - val_mae: 0.2456 - 3s/epoch - 119ms/step\n",
            "Epoch 5/100\n",
            "29/29 - 3s - loss: 0.0239 - mae: 0.1843 - val_loss: 0.0335 - val_mae: 0.2194 - 3s/epoch - 113ms/step\n",
            "Epoch 6/100\n",
            "29/29 - 3s - loss: 0.0194 - mae: 0.1628 - val_loss: 0.0284 - val_mae: 0.1984 - 3s/epoch - 113ms/step\n",
            "Epoch 7/100\n",
            "29/29 - 3s - loss: 0.0162 - mae: 0.1465 - val_loss: 0.0246 - val_mae: 0.1818 - 3s/epoch - 112ms/step\n",
            "Epoch 8/100\n",
            "29/29 - 3s - loss: 0.0139 - mae: 0.1341 - val_loss: 0.0217 - val_mae: 0.1683 - 3s/epoch - 108ms/step\n",
            "Epoch 9/100\n",
            "29/29 - 3s - loss: 0.0123 - mae: 0.1246 - val_loss: 0.0195 - val_mae: 0.1578 - 3s/epoch - 108ms/step\n",
            "Epoch 10/100\n",
            "29/29 - 3s - loss: 0.0111 - mae: 0.1176 - val_loss: 0.0178 - val_mae: 0.1497 - 3s/epoch - 106ms/step\n",
            "Epoch 11/100\n",
            "29/29 - 3s - loss: 0.0102 - mae: 0.1125 - val_loss: 0.0165 - val_mae: 0.1436 - 3s/epoch - 106ms/step\n",
            "Epoch 12/100\n",
            "29/29 - 3s - loss: 0.0096 - mae: 0.1089 - val_loss: 0.0156 - val_mae: 0.1391 - 3s/epoch - 105ms/step\n",
            "Epoch 13/100\n",
            "29/29 - 3s - loss: 0.0092 - mae: 0.1062 - val_loss: 0.0148 - val_mae: 0.1355 - 3s/epoch - 105ms/step\n",
            "Epoch 14/100\n",
            "29/29 - 3s - loss: 0.0089 - mae: 0.1044 - val_loss: 0.0142 - val_mae: 0.1327 - 3s/epoch - 104ms/step\n",
            "Epoch 15/100\n",
            "29/29 - 3s - loss: 0.0087 - mae: 0.1031 - val_loss: 0.0138 - val_mae: 0.1304 - 3s/epoch - 104ms/step\n",
            "Epoch 16/100\n",
            "29/29 - 3s - loss: 0.0085 - mae: 0.1022 - val_loss: 0.0134 - val_mae: 0.1287 - 3s/epoch - 102ms/step\n",
            "Epoch 17/100\n",
            "29/29 - 3s - loss: 0.0084 - mae: 0.1016 - val_loss: 0.0131 - val_mae: 0.1274 - 3s/epoch - 102ms/step\n",
            "Epoch 18/100\n",
            "29/29 - 3s - loss: 0.0084 - mae: 0.1011 - val_loss: 0.0129 - val_mae: 0.1263 - 3s/epoch - 104ms/step\n",
            "Epoch 19/100\n",
            "29/29 - 3s - loss: 0.0083 - mae: 0.1008 - val_loss: 0.0127 - val_mae: 0.1254 - 3s/epoch - 104ms/step\n",
            "Epoch 20/100\n",
            "29/29 - 3s - loss: 0.0083 - mae: 0.1006 - val_loss: 0.0126 - val_mae: 0.1246 - 3s/epoch - 104ms/step\n",
            "Epoch 21/100\n",
            "29/29 - 3s - loss: 0.0082 - mae: 0.1005 - val_loss: 0.0125 - val_mae: 0.1240 - 3s/epoch - 104ms/step\n",
            "Epoch 22/100\n",
            "29/29 - 3s - loss: 0.0082 - mae: 0.1003 - val_loss: 0.0124 - val_mae: 0.1235 - 3s/epoch - 108ms/step\n",
            "Epoch 23/100\n",
            "29/29 - 3s - loss: 0.0082 - mae: 0.1002 - val_loss: 0.0123 - val_mae: 0.1231 - 3s/epoch - 116ms/step\n",
            "Epoch 24/100\n",
            "29/29 - 3s - loss: 0.0082 - mae: 0.1002 - val_loss: 0.0122 - val_mae: 0.1228 - 3s/epoch - 105ms/step\n",
            "Epoch 25/100\n",
            "29/29 - 3s - loss: 0.0082 - mae: 0.1001 - val_loss: 0.0121 - val_mae: 0.1224 - 3s/epoch - 105ms/step\n",
            "Epoch 26/100\n",
            "29/29 - 3s - loss: 0.0082 - mae: 0.1001 - val_loss: 0.0121 - val_mae: 0.1222 - 3s/epoch - 107ms/step\n",
            "Epoch 27/100\n",
            "29/29 - 3s - loss: 0.0082 - mae: 0.1000 - val_loss: 0.0120 - val_mae: 0.1220 - 3s/epoch - 104ms/step\n",
            "Epoch 28/100\n",
            "\n",
            "MAE telah mencapai <10%!\n",
            "29/29 - 3s - loss: 0.0082 - mae: 0.1000 - val_loss: 0.0120 - val_mae: 0.1218 - 3s/epoch - 107ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "loss=history.history['loss']\n",
        "mae=history.history['mae']\n",
        "val_loss=history.history['val_loss']\n",
        "val_mae=history.history['val_mae']\n",
        "\n",
        "plt.plot(range(len(mae)), mae, 'g', label='Training accuracy', marker='o')\n",
        "plt.plot(range(len(mae)), val_mae, 'r', label= 'Validation accuracy', marker='o')\n",
        "plt.xlabel('epochs')\n",
        "plt.ylabel('MAE')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "Bqki7vp_3XfB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 278
        },
        "outputId": "f63fda7d-7189-405a-a030-9282c1b8cf40"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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btWdlZxWURYdHF14L/9NPoV8/+Pe/naGvxgQBW6yv9snLyysYAeVt5ddqs1hfMEhZmFIoOYCzWXvKwpTfCnr3hsaNrZnJGFNt+WtZ8FrdSb0zc2fp5eHhcM01MHs2HDvmTKIzxphqpGPHjmzfvt3n163VdxBtYtqUrXzIEDhyxGluMiZI1JTmY+MbFfn7UKsTRJk3a+/VC045xZqZTNCIjIxk//79liQM4CSH/fv3l3tobq1uYsrviE5ZmMKOzB2EhYQx7UoPm7WHhcG118Kbb0JWVvEhgcZUM61atSIjI4O9e/cGOhRTTURGRtKqVatynVOrRzG5m7ZyGrd9eBsrR6+k66ldi1dYtMjpsH7vPRg8uBKRGmNM9WGjmMpgcMfBhIeE89batzxXuOQSaNbMmpmMMbWGJQiXxlGN6de+H2+ve5vcPA9L7YaFQefOMGeOz5ZENsaY6swShJvhnYaz+/BuvtzxZfGDqanw1Ve/rXXpgyWRjTGmOrME4eaqDldRr049z81MKSnFl9vIynLKjTGmBrIE4SY6PJpBZw1izsY5nMgpkgx2ep5U57XcGGOCnCWIIpLjkzl0/BCfbCuyymQbz5PqvJYbY0yQswRRRO92vWka3bTwqq7gtyWRjTGmurIEUURYSBhDzxnKgs0L+PXEr78dcF8SOd899/hkSWRjjKmOLEF4kNw5mRO5J3h/4/tFDiRDejocPQoxMZ534TLGmBrCEoQH57c8n3aN2nmfNBcd7SSLOXPg4MGqDc4YY6qIJQgPRIThnYaz8IeF/HzkZ8+Vbr0Vjh+3eRDGmBrLEoQXw+OHk6d5vLPuHc8VEhOha1eYPt2ZOGeMMTWMXxOEiPQVkc0isk1EHvRw/HYRWSsiq0TkaxHp6HbsIdd5m0XENztwl8PZTc8msUUib63z0swEMGoUrFkDK1dWXWDGGFNF/JYgRCQUeBHoB3QEhrknAJe3VDVeVbsAzwLPuc7tCFwPnAP0BV5yXa9KDY8fzje7vmHr/q2eKwwb5uwwN3161QZmjDFVwJ93EOcB21R1u6qeBGYDA90rqKrbOFLqAvltNQOB2ap6QlV/ALa5rlelru90PYLw9rq3PVeIiXF2m3vrLWfHOWOMqUH8mSBaAj+6vc5wlRUiIneKyPc4dxB3lfPc0SKSJiJp/tgYpVWDVlwadympa1O978x1661OcnjvPZ+/vzHGBFLAO6lV9UVVPR34C/BIOc+dpqpJqprUtGlTv8Q3vNNwtuzfwrc/feu5Qo8ecNZZ8Oqrfnl/Y4wJFH8miF1Aa7fXrVxl3swGrq7guX5T6kZCIs5dxLJlsGFD1QZnjDF+5M8EsQJoLyJtRaQOTqfzfPcKItLe7WV/IL83eD5wvYhEiEhboD3wjR9j9apRVCOuaH+F942EAG64AcLD7S7CGFOj+C1BqGoOMAb4DNgIvKuq60VkgogMcFUbIyLrRWQVcC8wwnXueuBdYAPwKXCnqnr5dfa/5PhkfjryE0t3LPVcoVkzGDgQ3nij+J4RxhgTpMRr52uQSUpK0rS0NL9c+1j2MZpNbsaQjkP418B/ea702WfQty+8844zsskYY4KAiKxU1SRPxwLeSR0MosKjuObsa5i7cS7Hc457rtSnj7PSqzUzGWNqCEsQZZQcn0zmiUw+3vqx5wohIXDzzfD55/DDD1UbnDHG+IEliDL6Xdvf0axuM++jmQBuuskZ1fTaa1UXmDHG+IkliDIKCwkjsXkiczfOJeTxEOKmxhXfda51a6cf4vXXIScnMIEaY4yPWIIoo9S1qQWjmBRlR+YORi8YXTxJjBoFu3Y5ndbGGBPELEGUUcrCFI7nFu6gzsrOImVhSuGKV17pDHu1zmpjTJCzBFFGOzN3lq08PBxGjoQFC+Cnn/wfmDHG+IkliDJqE9Om7OW33AK5uTBzpp+jMsYY/7EEUUYTe08kOjy6UFl0eDQTe08sXvnMM50F/B591Bn+GhdnW5MaY4KOJYgySo5PZtpV04iNiS0oe7DHgyTHJxevnJoK27c7I5lUYccOGD3akoQxJqhYgiiH5Phk0semc+gvh4gMi2TP0T2eK6akwMmThcuyspxyY4wJEpYgKiAmMoarz7qat9e9zcnck8Ur7PTcoe213BhjqiFLEBV0Y+cbOXDsgOelN9p47tD2Wm6MMdWQJYgK6nN6H5rXbc7M1R5GKk2cCNGFO7SJinLKjTEmSFiCqKCwkDCS45P5aMtH7MvaV/hgcjJMm+as7irilA0Y4JQbY0yQsARRCSO6jCA7L5t31r1T/GByMqSnQ14eXH45LFkCx70sFW6MMdWQJYhK6Ny8MwnNEzw3M7l78EHYs8cmzhljgooliEq6MeFGVuxewca9G71X6tkTzjsPJk1yZlgbY0wQsARRScPjhxMqocxaM8t7JRHnLuL772Hu3KoLzhhjKsESRCW1qNeC35/xe2atmUWe5nmvOHAgdOgATz/tzK42xphqzhKED9zY+UYyfs1g8Q+LvVcKCYEHHoDvvnO2JTXGmGrOEoQPDOgwgJiIGN5Y80bJFZOToWVL5y7CGGOqOUsQPhAVHsWQc4Ywd8Ncjpw84r1iRATcey8sXgzffFN1ARpjTAVYgvCRGxNu5Gj2Ud7f+H7JFUeNgkaN4JlnqiYwY4ypIEsQPtKjdQ/aNWrHG6tLaWaqXx/GjIEPPoBNm6omOGOMqQBLED4iItzY+UYW/bCIHzN/LLnyn/4EkZHOvAhjjKmmLEH40A0JN6Aob655s+SKTZvCrbfCrFmQkVE1wRljTDlZgvChdo3acVGbi3hjzRtoaXMd7r3XWadpypSqCc4YY8rJEoSPjUgYwaZ9m0jbnVZyxbg4GDYMXnkFDhyoktiMMaY8/JogRKSviGwWkW0i8qCH4/eKyAYRWSMiC0Uk1u1Yroiscj3m+zNOX7qu43VEhEaUvoAfOBPnjh6Fl17yf2DGGFNOfksQIhIKvAj0AzoCw0SkY5Fq3wFJqtoZmAM863bsmKp2cT0G+CtOXyt1O1J38fFw5ZXw/PPOntXGGFON+PMO4jxgm6puV9WTwGxgoHsFVV2sqvm/jMuBVn6Mp8qMSBjhfTvSoh58EPbtg9de839gxhhTDv5MEC0B9/GeGa4yb24BPnF7HSkiaSKyXESu9nSCiIx21Unbu3dv5SP2kfztSEudEwHQowdcdBFMngzZ2f4PzhhjyqhadFKLyB+AJMB9YkCsqiYBw4GpInJ60fNUdZqqJqlqUtOmTaso2tKFhYTRtUVXPtj0ASGPhxA3NY7UtaneTzj/fNixA+rUcTqvU0uoa4wxVcSfCWIX0NrtdStXWSEichmQAgxQ1RP55aq6y/XndmAJkOjHWH0qdW0qi3c4K7sqyo7MHYxeMNpzkkhNhZdf/u31jh0werQlCWNMwPkzQawA2otIWxGpA1wPFBqNJCKJwCs4yeEXt/JGIhLhen4K0APY4MdYfSplYQrHcwrvP52VnUXKwhQPlVOKd1BnZTnlxhgTQGH+urCq5ojIGOAzIBR4TVXXi8gEIE1V5+M0KdUD3hMRgJ2uEUtnA6+ISB5OEntaVYMmQezM3Fn28p2e63otN8aYKuK3BAGgqh8DHxcpe8zt+WVezlsGxPszNn9qE9OGHZk7PJYXL2zjNCt5KjfGmACqFp3UNc3E3hOJDo8uVBYVFsXE3hM9VJ4I0dHFyx8sNq/QGGOqlCUIP0iOT2baVdOIjYlFEAAua3sZyfHJHionw7RpEBsLInDqqc6fG4KmRc0YU0NJSYvKiUgDVf3Vy7E2qlptGsqTkpI0La2U9Y8C5Np3r2XxD4v58Z4fqVunbukn/PGPMH06rFsHHTr4P0BjTK0lIitdUwqKKe0OYonbRRYWOTavknHVGvdccA8Hjx9k1ppZZTth/Hin2emBB/walzHGlKS0BCFuzxuXcMyUoEfrHiSdlsTU5VPJ07zST2jWDB5+GObPh0WL/B+gMcZ4UFqCUC/PPb02XogI91xwD5v3b+bTbZ+W7aS773b6Jf78Z8jN9W+AxhjjQWkJoplrSe4/uz3Pf1191rYIAtd1vI7T6p/GlOVl3CAoMhKeeQZWrYI3yrCmkzHG+FhpCWI6UB9nMlv+8/zXr/o3tJolPDScMeeO4YvtX7B2z9qynTRkCFxwgTOr+uhR/wZojDFFlDiKqcQTRc5V1RU+jqfCqvMopnwHjh2g1XOtGB4/nFcHlDG//ve/cOGFMG6c03ltjDE+VJlRTEUv1FFEnhCRbcDLpZ5gCmkc1ZgRCSN4c82b/HL0l9JPAOjeHYYOhWefhV3F1jo0xhi/KTVBiEiciDwkImuAWcAdwGXeMo4p2dgLxnIi9wT/TPtn2U966imno/qRR/wXmDHGFFFighCR/wIf4azZdK2qdgMOq2p6FcRWI3U4pQNXtL+Cl1a8xImcE6WfANC2LYwdCzNnwrff+jdAY4xxKe0OYg9Op3Rzfhu1ZMNbK2ns+WPZc3QPb697u+wnPfwwNGniDHutYL+RMcaUR4kJQlWvxllVdSUwXkR+ABqJyHlVEVxNdVm7y+jUrBNTlk+hzIMEYmJgwgRYssSZQGeMMX5Wah+Eqmaq6uuqejlwAfAYMEVEfizlVOOFiDD2/LGs2bOGJelLyn7iqFFw9tlw//1w8qTf4jPGGCjnKCZV3aOqf1fVHsBFfoqpVkjunEzT6KZlnzgHEBYGkyfD1q3QogWEhNge1sYYvylxwyARKa0tY4APY6lVIsMiuSPpDp748gm27t9K+ybty3biwYNOYjh40Hmdv4c1OEuHG2OMj5S23Pde4EfgbeB/FFmgT1WX+jW6cgiGiXJF/XzkZ2KnxjKq6yj+ccU/ynZSXJznHehiYyE93ZfhGWNqgcpMlGsBPAx0Ap4H+gD7VHVpdUoOwapFvRYM6zSM11e9zsFjB8t2ku1hbYypIqWNYspV1U9VdQROB/U2YImIjKmS6GqBey64h6zsLF79toxLb3jbq9r2sDbG+FhZZlJHiMg1wJvAncALwAf+Dqy2SGiRQK+4Xvz9m7+Tk5dT+gme9rAODXXKjTHGh0qbSf0G8F+gK/C4qp6rqk+oqi0K5EOJLRL58dcfqfNEHeKmxpG6toRRSUX3sG7UyFmG48iRqgvYGFMrlNZJnQfkrzPtXlEAVdUGfoytXIKxkxogdW0qoxeMJis7q6AsOjyaaVdNIzm+DKOS8vKgb1/4+mv47jvbw9oYUy4V7qRW1RBVre96NHB71K9OySGYpSxMKZQcALKys0hZmFK2C4SEwIwZEBUFf/gDZGf7PkhjTK1Urolyxvd2ZnoefeSt3KPTToNXXoG0NGc5DmOM8QFLEAHWJsbz6CNv5V4NHgwjR8Jf/wrLllU+MGNMrWcJIsAm9p5IdHjhUUmCMKFXBe4Enn/e6bz+wx/g8GEfRWiMqa0sQQRYcnwy066aRmxMLILQNLopinL0ZAX2oG7QAGbNcmZa332374M1xtQqFd6TuroJ1lFMRakqPWf2ZPO+zWy7axv16tQr/0UeecSZFzFnDlx7re+DNMbUGD7bk7oCb9xXRDaLyDYRedDD8XtFZIOIrBGRhSIS63ZshIhsdT1G+DPO6kREePayZ9lzdA9/W/a3il1k3DhISnIW8du927cBGmNqDb8lCBEJBV4E+gEdgWEi0rFIte+AJFXtDMwBnnWd2xgYB5wPnAeME5FG/oq1ujm/1fkM7jiYScsm8fORn8t/gfBwePNNOHbM6bjOy/N5jMaYms+fdxDnAdtUdbuqngRmAwPdK6jqYlXNnwSwHGjlev574HNVPaCqB4HPgb5+jLXa+evv/sqJ3BNMWFrBYasdOsBzz8Hnn8Mpp9jeEcaYcvNngmiJs1R4vgxXmTe3AJ+U51wRGS0iaSKStnfv3kqGW720b9Ke0V1HM23lNLbs31Kxi9Sv76zTdPCgs491/t4RliSMMWVQLUYxicgfgCRgUnnOU9VpqpqkqklNmzb1T3AB9NiljxEVHsXDCx+u2AVSUpx1mtxlZTnlxhhTCn8miF1Aa7fXrVxlhYjIZUAKMEBVT5Tn3Jqueb3m3H/h/czdOJflGcvLfwHbO8IYUwn+TBArgPYi0lZE6gDXA4W2MBWRRNSAA+0AABm8SURBVOAVnOTwi9uhz4DLRaSRq3P6cldZrXNv93tpXrc5D3z+AOUekuxtj4jWrT2XG2OMG78lCFXNAcbg/LBvBN5V1fUiMkFE8veyngTUA94TkVX5e2Cr6gHgCZwkswKY4CqrderVqcf4nuP5audXfLjlw/Kd7GnvCICORQeTGWNMcTZRLghk52bT6eVOhEooa+5YQ1hIWNlPTk11+hx27nTuHM4+Gz77zBnhdM89/gvaGBMUAjZRzvhGeGg4T/V+io37NjJj1YzynZycDOnpzlyIHTvgo4+c2dX33gvvvOOPcI0xNYQliCAx6KxBdG/VnXFLxhXbP6JcQkOdSXQXXww33ghLlvgsRmNMzWIJIkiICJP6TGL34d1MXT61cheLjIR58+D00+Hqq2HdOt8EaYypUSxBBJEebXowsMNAnlj6BK2ntCbk8ZDS97D2pnFj+PRTpxO7Xz/IyPB9wMaYoGYJIsj0aN2D47nHyfg1A0XZkbmD0QtGVyxJtGkDn3wCmZlOkjh0yPcBG2OCliWIIPPiiheLlZVrD+uiEhLggw9g82a48EJnwyFbt8kYgyWIoOOTPayL6t0bbr0VNm50hsPauk3GGCxBBB2f7WFd1McfFy+zdZuMqdUsQQQZT3tYR4ZFMrH3xMpd2NZtMsYUYQkiyBTdw1oQWjdozfXnXF+5C9u6TcaYIixBBKHk+GTSx6aTNy6PN695k60HtjJ52eTKXdTbuk0NG8LRo5W7tjEmKFmCCHLDOg3juo7X8ejiR1mzZ03FL5ScDNOmOaOYRJw/b7nFmUTXpw8cqJVrJRpTq9lifTXAvqx9xL8cT7O6zfjm1m+ICIvw3cXffx+GDYP27Z1F/lqWtCmgMSbY2GJ9Ndwp0afw6lWvsmbPGsYvGe/bi19zjTPjeudOZ57E5s2+vb4xptqyBFFD9D+zP6O6juLZZc/yn53/8e3Fe/VyFvU7dgwuughq6Z2aMbWNJYga5G+X/43YmFhGzBvBkZNHfHvxrl3h66+hbl0nYSxc6NvrG2OqHUsQNUj9iPrMvHom2w9u5/7/u9/3b3DmmfCf/zgd2FdcAXff7SzJYUtzGFMjWYKoYS6OvZj7LryPf678J59s/cT3b9CyJXz5pZMkXnjBWZLDluYwpkayBFEDTeg1gU7NOnHL/Fs4cMwPw1MbN4bjx4uX29IcxtQoliBqoMiwSGYNmsW+rH3c+fGd/nkTb/tH2NIcxtQYliBqqC4tujC+53hmr5tN02ebVm5zIU+8Lc0RE+Psf22MCXqWIGqw1g1aEyIh7Du2r/KbCxXlaWmO0FBn06HLL4dduyr/HsaYgLIEUYM9uvhR8rTwv+YrtbmQO09Lc8ycCdOnw3//C/HxMHdu5d/HGBMwliBqML9sLuQuORnS050mpfR05/Wtt8J338Hpp8Pgwc56Tkd8PCfDGFMlLEHUYN42EWod4+clvM88E5Ytc0Y0vf46dOkCjz9ucyaMCTKWIGowT5sLAVzY6kL/v3l4ODz5JCxdCgcPwvjxNmfCmCBjCaIGK7q5UJuYNlzQ8gJmr5/NG6vfqJogLr7Y8z4TNmfCmGrPlvuuZU7knKD/W/1Zkr6EBcMW0K99P/+/aUiIc+dQlIgNiTUmwGy5b1MgIiyC94e+T+fmnRn83mD+l/E//7+ptzkTqnDzzTYk1phqyq8JQkT6ishmEdkmIg96OH6JiHwrIjkiMrjIsVwRWeV6zPdnnLVNg4gGfJL8CS3qtaD/W/3ZvM/Pezx4mjMRFeUs+Jea6mxGlJICv/7q3ziMMeXitwQhIqHAi0A/oCMwTEQ6Fqm2ExgJvOXhEsdUtYvrMcBfcdZWzes157M/fEZoSCi/f/P37D68239v5mnOxPTp8NFHsGkTXH01/PWvcMYZ8OKL8MYbNuLJmGrAb30QItIdGK+qv3e9fghAVZ/yUHcG8KGqznErO6Kq9cr6ftYHUTErd6+k58yetG3Yli9v+pKGkQ0DE0haGtx/v7MxkUjhPovoaCfBJCcHJjZjarBA9UG0BH50e53hKiurSBFJE5HlInK1b0Mz+bqd1o33h7zPpn2buODVC2gzpY3v120qi6QkWLQImjYt3qFtI56MCYjq3Ekd68pqw4GpInJ60QoiMtqVRNL27t1b9RHWEH1O78OorqPYvH8zP/76o+/XbSorEdi3z/OxHTvg+++rLhZjjF8TxC7AfcpuK1dZmajqLtef24ElQKKHOtNUNUlVk5o2bVq5aGu5j7Z+VKzMZ+s2lYe3EU/g9FH07Qv//jfk5FRdTMbUUv5MECuA9iLSVkTqANcDZRqNJCKNRCTC9fwUoAewwW+RGv+v21RWnkY8RUfD3//uzMZeu9bp1G7b1pmp/dJL1qFtjJ+E+evCqpojImOAz4BQ4DVVXS8iE4A0VZ0vIucCHwCNgKtE5HFVPQc4G3hFRPJwktjTqmoJwo/axLRhR+aOYuWn1T+tagPJ74hOSXE2H2rTxkka7uULFjiJ4dFHC5+bv4SH+3WMMRVmM6kNAKlrUxm9YDRZ2VmFyhtFNmLRiEV0adElQJGVoGVL2O1heG7Tpk6yiIqq+piMCTI2k9qUqui6TbExsTzZ60miw6O56LWLmL+5Gs5V/Oknz+V790KTJjBoEMyYUbjjOzXVmqSMKSO7gzAl2n14NwNnD2Tl7pVM6jOJe7vfi4gEOixHXJxzp1BUs2Zw3XVOZ3ZGhpMMLroIWrWC99+H48d/q2tzLEwtZ3cQpsJOq38aS0cu5dqO13Lf5/dx24e3kZ2bHeiwHN46tJ97Dv7xD6cPIy3N6bc4eBDeeqtwcgCbY2FMCSxBmFJFh0fzzuB3SLk4henfTqdval8OHDsQ6LA8L+HhfjcgAt26wYQJsGaN89qTHTvgj3+E2bMLLxxozVGmlrMmJlMus1bP4tYFtxLXMI7RXUfz92/+zs7MnbSJacPE3hNJjq/GTTXemqQiI50Njg4fdl6ffrrTAb58OZw8+Vs9a44yNZA1MRmfuSHhBhbeuJDdv+7mvs/vY0fmjsDNvC4vb01Sr74KBw7AypUwZQp07gxff104OYDTHDV2rLPAYG5u4WN2t2FqILuDMBXS8rmWHleAjY2JJX1setUHVFapqd7nWLjztslRvuhoJ5EkJsKJE8X7N+xuwwQJu4MwPvfTYc9DTKt85nV5JSdDerqzk116uvcfcG9Lfpx6qjN0dtQoiIhwEsNrr3nu/L77bqeZ6tChwsfsbsMECUsQpkLaxHj+Aa1Xpx5HTx6t4mj8wFtz1KRJMGIETJ3qLE1+8KD3zu/9+6F7d2jUCJo3h0svhd/9Dm66yekLUf1t9ndJScISigkQSxCmQib2nkh0eOEf0DAJ4/DJw8S/HM8X278IUGQ+UtoIqXwi3u82TjsN5s+HZ5+Fq65y7lqWLoXsIsOEs7KcrVcHD4Z77nH6QebMgW++cTZQGj267AnFkonxJVWtEY9u3bqpqVpvrnlTY6fEqowXjZ0Sq2+ueVOXpi/VM/9+pjIeHTlvpO7P2h/oMP3vzTdVo6NVnZ9w5xEd7ZQXJVK4nvvj7LNV69b1ftz9EROj+tJLqnPnqn79terWraqvvlr2OPLjjo11YoqN9V6vovVNUMBZG8/j72rAf9h99bAEUX0cyz6mD3/xsIZNCNNmk5rpO+ve0TdXF08mNUpZfzxjYz3/4MfGOsfz8lT371ddtUp1/vyyJYvSHvXrqz75pOrLL6u+957qokWqTz2lGhVVvmTiz+RjAsYShAmIVT+t0m6vdFPGo6GPhyrjKXhET4yueUmiLMr7Q+stobRpo7prl+q336p+8onqjBm+SSYREapXXqn6hz+ojhmj+sgjqpMnqzZu7Ll+69aqOTmV+4zlSSb+vOuppUnNEoQJmOzcbG34dMNCySH/ETslNtDhBUZ5f7TK+mNb0t3J8eNOQlm92rmDKKmpq2tX1XbtnKQQElK2xBIdrdq8ueoZZ6iGh3uu06CB6rhxqs88o/r3v6v+61+qd97pJCX3epGRqlOnqu7Zo/rrr6rZ2eX/b1He+rU4qZWUIGwehPG7kMdDUIr/PROEvHF5AYgoyJR17kZqqtOBneW2ZLu3+RjeZpXHxjrDf/OpwpEjcPbZhZchydeokTOc9/Dh3x5vv12RT1mysDBncqKn36uICLjkEudP98d77zmxe4r5ySehTp3fHn/6k+ftbk89Fb74wqkTHu78OX++M5jg2LHf6nn771ye76S89ct7bS9KmgdhCcL4XdzUOI+bEQnCvd3v5d7u91b9xkQ1lT+SSXnrl5R8tm935oxkZcHRo87OgN5+g/7xD+dHOP/x1FPeP/cFFzgTFt0fGRne6/tLVBSEhv72OHTIGb1WVHg4nHOOk/jcH8uXF59TA85/60GDCl979mzPCbBoki+FJQgTUJ42I4oMi6Tbqd1YnrGc0JBQbupyEw/0eIB2jdqRujaVlIUpwbPGU7AqazIpb31/3MmUt25J9Vu1cpZVOXnyt8fvfud5f5GmTZ1ElV8vOxtuv714vXz33efc6eQ/XnzRe90BA5y91d0fX37pvX67doWv7W0/FBHPScmLkhJEwPsOfPWwPojqzdOQWFXV7w98r7cvuF3rPFFHQx4P0e6vdtfIJyOtQzvYlbVt3N/9BP64dmkj0Spa19/X9gLrpDbV3e5fd+t9n92nMl6sQ7u2CbYO32BMaiWwBGGChrcEIeNF8/LyAh2eMY5gS2olKClBWB+EqVa8dWgDnNH4DK4/53qu73Q95zQ7B8D6K4ypJOukNkHDU4d2VFgUyfHJpGems+iHReRpHp2adeKsJmfx4dYPOZ7z26iP6PBopl01zZKEMWVUUoIIq+pgjClJ/g+7t7uCPUf2MGfDHGavn82cjXOKnZ+VnUXKwhRLEMb4gN1BmKDlbQIewOQ+k+kZ15MuLboQGhJaUG5NUsYUZncQpkZqE9PGY39FWEgY931+HwANIhpwcZuL6RnXk2M5x3j666cLmq/yt0kFLEkY44HtB2GClqc9KaLDo5lx9Qx23buLt655i2GdhrHtwDbu//x+Hlv8WKG+DXCapB5e+LDH66euTSVuahwhj4cQNzWueu+3bYwfWBOTCWplbTL6+cjPnPa307w2SZ3X8jw6N+tM5+adSWiRwKZ9m7jns3sKJRTrADc1kY1iMgbvQ2jr16nPuS3PZfXPq9l/bH+J1zi13qlsHrOZ+hH1C5Vb34YJVtYHYQxOk1TRIbTR4dG8fOXLJMcno6r8dOQn1uxZQ7/Ufh6v8dORn2jwdAOaRDUhrmEccQ3jOJZ9jM+3f052nrOVaGl9G+VNJpZ8TKDYHYSpVcr6Y+vtbuOUqFO478L7SD+UTnpmOumH0tm0b5PH9wqREM5scibN6jajWd1mNI1uyp4je1iwZUFBMgFnnscLfV/g5q43EyKFuwU9zQspqamrPMnEn4nKklrwCFgTk4j0BZ4HQoFXVfXpIscvAaYCnYHrVXWO27ERwCOul0+q6syS3ssShPGl8vwwlzTcdnDHwew9updfjv7CL0d/KbEJSxAaRDSgYWRDYiJjaBjZkBW7VnAs51ixuo2jGvNC3xeoW6cudcPrUrdOXZamL2XClxPKNHGwIomnrPVrQ1ILxpi9CUiCEJFQYAvQB8gAVgDDVHWDW504oAFwHzA/P0GISGMgDUgCFFgJdFPVg97ezxKE8bXK3m3ExsSSPja9UFlJyeSxSx7j0PFDZJ7I5NDxQxw6foilO5b65LM0iGhAZFgkEaERRIZFkn4ovdBdTL6osCgGnjWQ8JBw5xHq/Dlz9UwOnzxcrH7DyIaMv3Q8YSFhhIaEEhYSxoNfPOgxETaNbsqMq2cQKqGESAihIaEs+mERk5dN5kTuiYJ6kWGRPHrJo1x55pVOPVf9D7d8yCOLHymUAKPCopjUZxLXdryWEAkhREIQhBAJYe6Gudz16V2FEmxUWBQv9X+J4fHDEQQRQRDeXvc2t314m88TYHVKxN4EKkF0B8ar6u9drx8CUNViu36IyAzgQ7cEMQzoqaq3uV6/AixRVa9bVVmCMIFSnv9Ry5NMSqrfsn5LFt64kKPZRzl68ihZ2Vn0S+3nNfncff7dHM85zoncExzPOc7sdbO9fp72jduTnZdNdm52wZ8Hj3v9t1mNFx4SXpBIRIQTOSe87pAYExlTKPEcPH6QPC2+N0OIhNC8bvNC1wX46fBP5GpusfphIWHExsQWqr/94HZy8nKK1fX2d8mbQHVStwR+dHudAZxfiXNbFq0kIqOB0QBt2rSpWJTGVFJpy4O489ZRPrH3RI/X9lb/mT7P0OGUDoXqeps4GBsTy9S+UwuV/ffH/3qtu+VPW4qVe0tUrRu0ZvXtq8nJyyl4dP9Xd3YdLr49aYt6LZg3dB55mkeu5pKbl0uvmb28/tjOGTKH3Lxc8jSPPM1j+PvDi9XL99IVL6FoQV1VZexnY73Wf7LXkyiuVUtRxi0Z57Xun7v/uVDdScsmeaynKDd0vqGgHsCLKzxvGJSnefRv37/QdQFeX/W6x/o5eTmc3+r8QnW37C/+PQHszNzp9bOUV1CPYlLVacA0cO4gAhyOqcWS45PLdFtfnmRS3vrlST6+SlRPXfYUjaIaFar7TJ9nPNadfPlkzm9V+N+I3pJam5g2XHP2NYXKHlr4kNekdse5dxQrn7J8itf6KZekFCp77bvXvNZ96rLCjR7vrn/Xa90X+r1QqOzDLR96rTt9wPRi5Yt+WOS1fuo1hSdqekvybWJ8949lf86k3gW0dnvdylXm73ONqdaS45NJH5tO3rg80seml5pYylo/OT6ZaVdNc5oiEGJjYr22R5enrj+v7W02vLekVta6/rx2MMZcYd42iqjsA+fuZDvQFqgDrAbO8VJ3BjDY7XVj4AegkevxA9C4pPezDYOMCU7etqOtbF1/XjsYY/aGQG0YJCJX4AxjDQVeU9WJIjLBFdB8ETkX+MCVBI4DP6vqOa5zbwbyF8mZqKqeG+dcrJPaGGPKz5baMMYY41FJCcJWczXGGOORJQhjjDEeWYIwxhjjkSUIY4wxHtWYTmoR2QsUnzVSdqcA+3wUTnVkny/41fTPaJ8vMGJVtamnAzUmQVSWiKR568mvCezzBb+a/hnt81U/1sRkjDHGI0sQxhhjPLIE8ZtpgQ7Az+zzBb+a/hnt81Uz1gdhjDHGI7uDMMYY45ElCGOMMR7V+gQhIn1FZLOIbBORBwMdjz+ISLqIrBWRVSIS9CsaishrIvKLiKxzK2ssIp+LyFbXn41KukZ15+UzjheRXa7vcZVrteSgJCKtRWSxiGwQkfUicrervEZ8jyV8vqD6Dmt1H4SIhAJbgD4425quAIap6oaABuZjIpIOJKlqdZykU24icglwBHhDVTu5yp4FDqjq065E30hV/xLIOCvDy2ccDxxR1cmBjM0XRORU4FRV/VZE6gMrgauBkdSA77GEzzeEIPoOa/sdxHnANlXdrqongdnAwADHZEqhql8CB4oUDwRmup7PxPmfMWh5+Yw1hqr+pKrfup4fBjbi7DtfI77HEj5fUKntCaIl8KPb6wyC8EssAwX+T0RWisjoQAfjJ81V9SfX85+B5oEMxo/GiMgaVxNUUDa/FCUicUAi8D9q4PdY5PNBEH2HtT1B1BYXqWpXoB9wp6v5osZybaNYE9tOXwZOB7oAPwF/C2w4lSci9YC5wFhV/dX9WE34Hj18vqD6Dmt7gtgFtHZ73cpVVqOo6i7Xn7/gbPF6XmAj8os9rnbf/PbfXwIcj8+p6h5VzVXVPGA6Qf49ikg4zo9nqqq+7yquMd+jp88XbN9hbU8QK4D2ItJWROoA1wPzAxyTT4lIXVcnGSJSF7gcWFfyWUFpPjDC9XwE8O8AxuIX+T+cLoMI4u9RRAT4F7BRVZ9zO1Qjvkdvny/YvsNaPYoJwDXMbCoQCrymqhMDHJJPiUg7nLsGgDDgrWD/jCLyNtATZ/nkPcA4YB7wLtAGZ9n3IaoatJ28Xj5jT5ymCQXSgdvc2uuDiohcBHwFrAXyXMUP47TTB/33WMLnG0YQfYe1PkEYY4zxrLY3MRljjPHCEoQxxhiPLEEYY4zxyBKEMcYYjyxBGGOM8cgShDEBJCI9ReTDQMdhjCeWIIwxxnhkCcKYMhCRP4jIN641/F8RkVAROSIiU1zr/S8Ukaauul1EZLlrQbYP8hdkE5EzROQLEVktIt+KyOmuy9cTkTkisklEUl2zcBGRp137CawRkaBYHtrULJYgjCmFiJwNDAV6qGoXIBdIBuoCaap6DrAUZ7YzwBvAX1S1M85M2vzyVOBFVU0ALsRZrA2clT7HAh2BdkAPEWmCsxTDOa7rPOnfT2lMcZYgjCldb6AbsEJEVrlet8NZQuEdV503gYtEJAZoqKpLXeUzgUtc62G1VNUPAFT1uKpmuep8o6oZrgXcVgFxQCZwHPiXiFwD5Nc1pspYgjCmdALMVNUurkcHVR3voV5F16054fY8FwhT1RyclT7nAFcCn1bw2sZUmCUIY0q3EBgsIs2gYN/kWJz/fwa76gwHvlbVTOCgiFzsKr8BWOraVSxDRK52XSNCRKK9vaFrH4EYVf0YuAdI8McHM6YkYYEOwJjqTlU3iMgjOLvyhQDZwJ3AUeA817FfcPopwFmm+p+uBLAduMlVfgPwiohMcF3juhLetj7wbxGJxLmDudfHH8uYUtlqrsZUkIgcUdV6gY7DGH+xJiZjjDEe2R2EMcYYj+wOwhhjjEeWIIwxxnhkCcIYY4xHliCMMcZ4ZAnCGGOMR/8P0eYZVMWaDdwAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(range(len(loss)), loss, 'g', label='Training accuracy', marker='o')\n",
        "plt.plot(range(len(loss)), val_loss, 'r', label= 'Validation accuracy', marker='o')\n",
        "plt.xlabel('epochs')\n",
        "plt.ylabel('loss model')\n",
        "plt.legend()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "3saRd2CeO952",
        "outputId": "a543dc93-e83e-445e-fc53-10ce02af3e59"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fb48a9317d0>"
            ]
          },
          "metadata": {},
          "execution_count": 61
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}