smartFactory_sequentialModel40_Yumin.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Setup"
      ],
      "metadata": {
        "id": "zrHSs3fQqYSV"
      },
      "id": "zrHSs3fQqYSV"
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "94682a80",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "94682a80",
        "outputId": "f402873b-86e1-49c0-88d9-a89fccf6b96d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import random\n",
        "import os\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt   \n",
        "import matplotlib.patches as mpathces \n",
        "import seaborn as sns \n",
        "\n",
        "# sklearn \n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score, classification_report, f1_score\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "# sequential model modeling \n",
        "import tensorflow as tf\n",
        "from keras import regularizers\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense,Dropout, Activation, BatchNormalization \n",
        "from keras import optimizers, metrics, callbacks\n",
        "\n",
        "# GoodleDrive mount\n",
        "from google.colab import drive   \n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 0.Data Load"
      ],
      "metadata": {
        "id": "1Eq5tN4RkxOO"
      },
      "id": "1Eq5tN4RkxOO"
    },
    {
      "cell_type": "markdown",
      "id": "wqkW_3EbQxj1",
      "metadata": {
        "id": "wqkW_3EbQxj1"
      },
      "source": [
        "# 1.Pre-processing\n",
        "1. Label Encoding: categorical values `LINE`, `PRODUCT_CODE`\n",
        "2. Missing values: fillna(0) \n",
        "3. scaling: StandardScaler"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 77,
      "id": "c66d3bf4",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c66d3bf4",
        "outputId": "7e4c9c7e-380f-4241-cae2-193461a8a9c0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Done.\n"
          ]
        }
      ],
      "source": [
        "train_df = pd.read_csv('/content/drive/MyDrive/train.csv')\n",
        "test_df = pd.read_csv('/content/drive/MyDrive/test.csv')\n",
        "submit = pd.read_csv('/content/drive/MyDrive/sample_submission.csv')\n",
        "\n",
        "train_x = train_df.drop(columns=['PRODUCT_ID', 'TIMESTAMP', 'Y_Class','Y_Quality'])\n",
        "train_y = train_df['Y_Class']\n",
        "test_x = test_df.drop(columns=['PRODUCT_ID', 'TIMESTAMP'])   \n",
        "\n",
        "# 1) qualitative to quantitative\n",
        "qual_col = ['LINE', 'PRODUCT_CODE']\n",
        "for i in qual_col:\n",
        "    le = LabelEncoder()    # TRY one-hot encoding \n",
        "    le = le.fit(train_x[i])\n",
        "    train_x[i] = le.transform(train_x[i])\n",
        "    for label in np.unique(test_x[i]): \n",
        "        if label not in le.classes_: \n",
        "            le.classes_ = np.append(le.classes_, label)\n",
        "    test_x[i] = le.transform(test_x[i]) \n",
        "\n",
        "# 2) Missing Values \n",
        "train_x = train_x.fillna(0)\n",
        "test_x = test_x.fillna(0)\n",
        "\n",
        "# 3) scaling: only `X_???` values (continuous)\n",
        "Xs = train_x.select_dtypes(include=float).iloc[:,1:].columns.tolist()\n",
        "scaler = StandardScaler().fit(train_x.loc[:, Xs])\n",
        "train_x.loc[:, Xs] = scaler.transform(train_x.loc[:, Xs])\n",
        "test_x.loc[:, Xs] = scaler.transform(test_x.loc[:, Xs])\n",
        "print('Done.')  \n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(train_x, train_y, test_size=0.3, random_state=37)  "
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential([\n",
        "        # Input layer: ReLU (decide input size according to dimension)\n",
        "        Dense(512, activation='relu', input_shape=(train_x.shape[-1],)),\n",
        "\n",
        "        # layer2: softmax  (output layer)\n",
        "        Dense(3, activation='softmax')\n",
        "        ])\n",
        "\n",
        "model.compile(\n",
        "    optimizer = optimizers.Adam(1e-4),\n",
        "    # optimizer='adam',\n",
        "                   loss='sparse_categorical_crossentropy',  # tow or more label classes (`Y_Class`: 0,1,2)\n",
        "                   metrics=['accuracy'])\n",
        "\n",
        "# EarlyStopping \n",
        "## callback: during eopch, save the parameter that provided BEST PERFORMANCE \n",
        "# callbacks = [callbacks.ModelCheckpoint('epoch.h5')]\n",
        "        \n",
        "history = model.fit(train_x, train_y,\n",
        "                    epochs=10,\n",
        "                    batch_size=54,  # batch size: 2^x && half of data size(598/2) \n",
        "                    validation_split=0.2,\n",
        "                    # callbacks=callbacks\n",
        "                    )   "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c4S8Io-q7CCA",
        "outputId": "ed5f82a5-27f6-4bc5-c427-937a3699cefa"
      },
      "id": "c4S8Io-q7CCA",
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "9/9 [==============================] - 1s 26ms/step - loss: 1.0249 - accuracy: 0.6318 - val_loss: 1.2370 - val_accuracy: 0.6167\n",
            "Epoch 2/10\n",
            "9/9 [==============================] - 0s 7ms/step - loss: 0.8132 - accuracy: 0.6632 - val_loss: 0.9532 - val_accuracy: 0.6833\n",
            "Epoch 3/10\n",
            "9/9 [==============================] - 0s 6ms/step - loss: 0.6965 - accuracy: 0.7594 - val_loss: 1.0432 - val_accuracy: 0.6250\n",
            "Epoch 4/10\n",
            "9/9 [==============================] - 0s 6ms/step - loss: 0.6459 - accuracy: 0.7741 - val_loss: 0.9852 - val_accuracy: 0.6250\n",
            "Epoch 5/10\n",
            "9/9 [==============================] - 0s 6ms/step - loss: 0.6109 - accuracy: 0.7845 - val_loss: 0.9487 - val_accuracy: 0.6167\n",
            "Epoch 6/10\n",
            "9/9 [==============================] - 0s 6ms/step - loss: 0.5975 - accuracy: 0.7678 - val_loss: 1.0415 - val_accuracy: 0.6250\n",
            "Epoch 7/10\n",
            "9/9 [==============================] - 0s 6ms/step - loss: 0.5818 - accuracy: 0.7887 - val_loss: 0.9745 - val_accuracy: 0.6917\n",
            "Epoch 8/10\n",
            "9/9 [==============================] - 0s 7ms/step - loss: 0.5562 - accuracy: 0.7824 - val_loss: 1.0007 - val_accuracy: 0.6333\n",
            "Epoch 9/10\n",
            "9/9 [==============================] - 0s 7ms/step - loss: 0.5510 - accuracy: 0.7782 - val_loss: 1.0016 - val_accuracy: 0.6167\n",
            "Epoch 10/10\n",
            "9/9 [==============================] - 0s 6ms/step - loss: 0.5571 - accuracy: 0.7908 - val_loss: 0.9910 - val_accuracy: 0.6500\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 90,
      "id": "57a8de60",
      "metadata": {
        "id": "57a8de60",
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "adae2293-2faf-4558-8a86-a8a013fa8137"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "10/10 [==============================] - 0s 2ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
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            ]
          },
          "metadata": {},
          "execution_count": 90
        }
      ],
      "source": [
        "# a = pd.DataFrame(test_x)\n",
        "preds = model.predict(test_x)\n",
        "a=[]\n",
        "for i in preds: \n",
        "    i = list(i)\n",
        "    a.append(i.index(max(i)))\n",
        "a\n",
        "# preds   "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 91,
      "id": "b687c6a1",
      "metadata": {
        "id": "b687c6a1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "0fb8784a-dd42-40c6-c9c6-3fc536646573"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "submit = pd.read_csv('/content/drive/MyDrive/sample_submission.csv')\n",
        "submit['Y_Class'] = a\n",
        "\n",
        "submit.to_csv('smartFactory_submission.csv', index=False)\n",
        "sns.countplot(x=a);  "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 89,
      "id": "f5a8c473",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "f5a8c473",
        "outputId": "460fe44e-5ce0-40c9-9262-3996668cc089"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1008x288 with 2 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0AAAAEWCAYAAABPK/eBAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/NK7nSAAAACXBIWXMAAAsTAAALEwEAmpwYAABlr0lEQVR4nO3deZyW8/7H8denXUpSlpQWlELNTE04RbIdoSNZIh1kS7aUcyiyZAnROcUR5xQqzhDH70jHTmTL0ipKqEzJmlJKWufz++N7T91Ns9bcc90z9/v5eMxj7vu6r/u6Pvc9d33vz/X9fj9fc3dERERERERSQaWoAxARERERESkrSoBERERERCRlKAESEREREZGUoQRIRERERERShhIgERERERFJGUqAREREREQkZSgBkkiY2ctmdkFp7xslM8s2s+MTcFw3swNjt/9pZjcXZ98dOE8vM3ttR+Ms5LidzWxpaR9XRKQoamtKdNxy3daIlESVqAOQ8sPM1sTdrQmsBzbH7l/m7lnFPZa7n5SIfSs6d+9bGscxs6bA10BVd98UO3YWUOy/oYhIIqitiZ7aGqnolABJsbl7rdzbZpYNXOLub+Tdz8yq5P5HJyIiUhJqa6Q80uexfNEQONlpuUOczGygmf0AjDWzumb2gpktM7NfYrcbxT1nipldErvd28zeM7PhsX2/NrOTdnDfZmb2jpmtNrM3zGyUmf27gLiLE+MdZvZ+7HivmVn9uMfPM7PFZrbczAYX8v4cbmY/mFnluG3dzWxO7PZhZvaBma00s+/N7EEzq1bAscaZ2Z1x96+LPec7M7soz76nmNksM/vVzL4xsyFxD78T+73SzNaY2R9y39u453cws2lmtir2u0Nx35vCmFmr2PNXmtlcMzs17rGTzWxe7JjfmtlfY9vrx/4+K81shZm9a2b6/0skhaitUVtTWFtTjPd5DzMbG3sNv5jZxLjHupnZ7NhrWGhmXWLbtxluaGZDcv/OZtbUwlDAi81sCfBmbPt/Yn+HVbHPyCFxz9/FzP4W+3uuin3GdjGzF83s6jyvZ46Zdc/vtcrO0xcIKS37AHsATYA+hM/W2Nj9xsDvwIOFPP9w4AugPnAv8KiZ2Q7s+yTwMVAPGAKcV8g5ixPjucCFwF5ANSD3C/nBwMOx4+8bO18j8uHuHwG/AcfmOe6TsdubgQGx1/MH4DjgikLiJhZDl1g8JwDNgbxjwn8Dzgd2B04BLjez02KPdYr93t3da7n7B3mOvQfwIvBA7LX9HXjRzOrleQ3bvTdFxFwV+B/wWux5VwNZZnZQbJdHCUNcagOHEmtQgL8AS4E9gb2BGwEv6nwiUuGorVFbU1BbU9T7/ARhSOUhsWONiMVwGPA4cF3sNXQCsgs4R36OBloBJ8buv0x4n/YCZrLtcL/hQDugA+FzfD2QA4wH/py7k5mlAQ0J740kgBIgKS05wK3uvt7df3f35e7+f+6+1t1XA0MJ/0kUZLG7j3H3zYT/CBoQvugWe18zawy0B25x9w3u/h4wqaATFjPGse7+pbv/DjwDpMe2nwm84O7vuPt64ObYe1CQp4CeAGZWGzg5tg13n+HuH7r7JnfPBv6VTxz56RGL7zN3/43QCMe/vinu/qm757j7nNj5inNcCI3YV+7+RCyup4D5wJ/i9inovSnMEUAt4J7Y3+hN4AVi7w2wETjYzHZz91/cfWbc9gZAE3ff6O7vursSIJHUo7ZGbU16fgcq7H02swbASUDfWNuy0d3fjj31YuAxd3899hq+dff5xYwfYIi7/xaLD3d/zN1Xx/5eQ4A0M6tjYdTCRcA1sXNsdvepsf0mAS3MrHnsmOcBT7v7hhLEISWgBEhKyzJ3X5d7x8xqmtm/Yt28vxK6wXeP75rP44fcG+6+NnazVgn33RdYEbcN4JuCAi5mjD/E3V4bF9O+8ceONQrLCzoX4Qrc6WZWHTgdmOnui2NxtIh11f8Qi+MuwhW6omwTA7A4z+s73Mzeig0HWAX0LeZxc4+9OM+2xYQrUrkKem+KjNnd4xvw+OOeQWiwF5vZ22b2h9j2+4AFwGtmtsjMBhXvZYhIBaO2Rm1Nvn+vIt7n/Qh/s1/yeep+wMJixpufLe+NmVU2s3tiw+h+ZWtPUv3YT438zhX7TD8N/DmWKPUk9FhJgigBktKS92r8X4CDgMPdfTe2doMXNNSgNHwP7GFmNeO27VfI/jsT4/fxx46ds15BO7v7PMJ/6iex7ZAECMMb5gPNY3HcuCMxELr84z1JuKq0n7vXAf4Zd9yiek++IwwjiNcY+LYYcRV13P1s2/k7W47r7tPcvRth6MBEwtU+YlfT/uLu+wOnAtea2XE7GYuIlD9qa9TWFKSw9/kbwt9s93ye9w1wQAHH/I0wbC7XPvnsE/8azwW6EYYJ1gGaxsXwM7CukHONB3oRhiau9TzDBaV0KQGSRKlNGH+7MjbG99ZEnzB2lWs6MMTMqsV6D/5UyFN2JsZnga5mdqSFSaS3U/S/pyeBawj/Kf8nTxy/AmvMrCVweTFjeAbobWYHxxrFvPHXJlzxWhcb43xu3GPLCMMo9i/g2C8RuuPPNbMqZnY2cDBhuNrO+IhwBe96M6tqZp0Jf6MJsb9ZLzOr4+4bCe9JDoCZdTWzA2Pj71cRxrIXNgxERFKD2prtpWpbU+D77O7fE+bmPGShWEJVM8tNkB4FLjSz48yskpk1jL0/ALOBc2L7ZxKGJBYVw3pCL11NQi9bbgw5wGPA381s31hv0R9ivXXEEp4c4G+o9yfhlABJoowEdiFc8fgQeKWMztuLMLlzOXAnoUt5fQH7jmQHY3T3ucCVhIbme+AXwiT9wuSOi37T3X+O2/5XQoOxGhgTi7k4Mbwcew1vEoaHvZlnlyuA281sNXALsd6U2HPXEsZHv2+hItAReY69HOhKuKK2nDBRs2ueuEssNp75T4Srkz8DDwHnx423Pg/Ijg0d6Ev4e0KYUPoGsAb4AHjI3d/amVhEpEIYidqavFK1rRlJ4e/zeYT5pPOBn4D+sRg+JhRZGEG4wPY2W3ulbib02PwC3Ma2PWr5eZzQA/ctMC8WR7y/Ap8C04AVwDC2/S7+ONAayLeioJQe0zxiqcjM7Glgvrsn/KqgiIikJrU1UhrM7Hygj7sfGXUsFZ16gKRCMbP2ZnZArBu7C2Es7sSIwxIRkQpEbY2UttjwwiuA0VHHkgqqRB2ASCnbB/gvYZLoUuByd58VbUgiIlLBqK2RUmNmJxI+T29Q9DA7KQUaAiciIiIiIilDQ+BERERERCRllLshcPXr1/emTZtGHYaISEqbMWPGz+6+Z9RxJCO1UyIi0SusnSp3CVDTpk2ZPn161GGIiKQ0M8u7ervEqJ0SEYleYe2UhsCJiIiIiEjKUAIkIiIiIiIpQwmQiIiIiIikjHI3B0hEyr+NGzeydOlS1q1bF3UoUoQaNWrQqFEjqlatGnUoIiIipUIJkIiUuaVLl1K7dm2aNm2KmUUdjhTA3Vm+fDlLly6lWbNmUYcjIiJSKjQETkTK3Lp166hXr56SnyRnZtSrV089dSIiUmaysqBpU6hUKfzOyir9c6gHSEQioeSnfNDfSUREykpWFvTpA2vXhvuLF4f7AL16ld55UqoH6JNP4K9/BfeoIxERERERkXiDB29NfnKtXRu2l6aUSoBmz4a//Q1efz3qSEQkSsuXLyc9PZ309HT22WcfGjZsuOX+hg0bCn3u9OnT6devX5Hn6NChQ6nEOmXKFLp27VoqxxIRSVVlMaxKdt6SJSXbvqNSKgE65xzYe28YOTLqSESkJEq74apXrx6zZ89m9uzZ9O3blwEDBmy5X61aNTZt2lTgczMzM3nggQeKPMfUqVN3LkgRESkVucOqFi8Oo4Byh1UpCUo+jRuXbPuOSqkEqHp1uOIKePllmD8/6mhEpDjKquHq3bs3ffv25fDDD+f666/n448/5g9/+AMZGRl06NCBL774Ati2R2bIkCFcdNFFdO7cmf3333+bxKhWrVpb9u/cuTNnnnkmLVu2pFevXnhsHO5LL71Ey5YtadeuHf369Suyp2fFihWcdtpptGnThiOOOII5c+YA8Pbbb2/pwcrIyGD16tV8//33dOrUifT0dA499FDefffd0n3DRETKibIaViU7b+hQqFlz2201a4btpSmlEiCAvn2hWjUoxgVcEUkCZdlwLV26lKlTp/L3v/+dli1b8u677zJr1ixuv/12brzxxnyfM3/+fF599VU+/vhjbrvtNjZu3LjdPrNmzWLkyJHMmzePRYsW8f7777Nu3Touu+wyXn75ZWbMmMGyZcuKjO/WW28lIyODOXPmcNddd3H++ecDMHz4cEaNGsXs2bN599132WWXXXjyySc58cQTmT17Np988gnp6ek79d6IiJRXZTWsSnZer14wejQ0aQJm4ffo0aVbAAFSMAHaa6/wJo4fDytWRB2NiBSlLBuus846i8qVKwOwatUqzjrrLA499FAGDBjA3Llz833OKaecQvXq1alfvz577bUXP/7443b7HHbYYTRq1IhKlSqRnp5OdnY28+fPZ//999+yvk7Pnj2LjO+9997jvPPOA+DYY49l+fLl/Prrr3Ts2JFrr72WBx54gJUrV1KlShXat2/P2LFjGTJkCJ9++im1a9fe0bdFRMoBzXEpWFkNq5LS0asXZGdDTk74XdrJD6RgAgTQv3+4gjxmTNSRiEhRyrLh2nXXXbfcvvnmmznmmGP47LPP+N///lfgWjjVq1ffcrty5cr5zh8qzj47Y9CgQTzyyCP8/vvvdOzYkfnz59OpUyfeeecdGjZsSO/evXn88cdL9Zwikjw0x6VwZTWsSsqPlEyA2rSBY4+FBx+EfEariEgSiarhWrVqFQ0bNgRg3LhxpX78gw46iEWLFpGdnQ3A008/XeRzjjrqKLJi32imTJlC/fr12W233Vi4cCGtW7dm4MCBtG/fnvnz57N48WL23ntvLr30Ui655BJmzpxZ6q9BRJKD5rgUrqyGVUn5kZIJEIReoKVL4f/+L+pIRKQwUTVc119/PTfccAMZGRml3mMDsMsuu/DQQw/RpUsX2rVrR+3atalTp06hzxkyZAgzZsygTZs2DBo0iPHjxwMwcuRIDj30UNq0aUPVqlU56aSTmDJlCmlpaWRkZPD0009zzTXXlPprEJHkoDkuRSuLYVUloSGL0TJP0KqgZvYY0BX4yd0PzefxXsBAwIDVwOXu/klRx83MzPTp06fvdHw5OXDQQVCvHnz44U4fTkRK4PPPP6dVq1ZRhxG5NWvWUKtWLdydK6+8kubNmzNgwICow9pOfn8vM5vh7pkRhZTUSqudEimupk3DsLe8mjQJX/YlueQOWYzvtatZU71Spa2wdiqRPUDjgC6FPP41cLS7twbuAEYnMJbtVKoE11wDH32kBEhEojFmzBjS09M55JBDWLVqFZdddlnUIYlIOaQ5LuWLhixGL2EJkLu/AxRYZ83dp7r7L7G7HwKNEhVLQXr3hjp1YMSIsj6ziAhbFmCdN28eWVlZ1Mz7DUZEpBg0x6V80ZDF6CXLHKCLgZcLetDM+pjZdDObXpy1MoqrVi249NIwD0gfOhERESmvkm2OixQs2cpyp+J8pMgTIDM7hpAADSxoH3cf7e6Z7p655557lur5r7oqlIwcNapUDysiIiIisp1kGrKYqiXUI02AzKwN8AjQzd2XRxFDkyZw+umhq3jNmigiEBEREZFUkUxDFlN1PlJkCZCZNQb+C5zn7l9GFQeEktgrV4LWCRQRERGRREuWIYupOh8pYQmQmT0FfAAcZGZLzexiM+trZn1ju9wC1AMeMrPZZhZZzdAOHaB9e7j//vBBFJGK7ZhjjuHVV1/dZtvIkSO5/PLLC3xO586dyS1tfPLJJ7Ny5crt9hkyZAjDhw8v9NwTJ05k3rx5W+7fcsstvPHGGyWIPn9Tpkyha9euO32c8szMupjZF2a2wMwG5fP4iFh7M9vMvjSzlbHtx8Rtn21m68zstNhj48zs67jH0sv0RYmIJFCyzUcqK4msAtfT3Ru4e1V3b+Tuj7r7P939n7HHL3H3uu6eHvuJbD0Js9AL9OWX8MorUUUhImWlZ8+eTJgwYZttEyZMoGfPnsV6/ksvvcTuu+++Q+fOmwDdfvvtHH/88Tt0LNnKzCoDo4CTgIOBnmZ2cPw+7j4gt80B/kEYhYC7vxW3/VhgLfBa3FOvi2urZif8xUi5kIoTx6XiSab5SGUp8iIIyeLMM2HffVUSWyQVnHnmmbz44ots2LABgOzsbL777juOOuooLr/8cjIzMznkkEO49dZb831+06ZN+fnnnwEYOnQoLVq04Mgjj+SLL77Yss+YMWNo3749aWlpnHHGGaxdu5apU6cyadIkrrvuOtLT01m4cCG9e/fm2WefBWDy5MlkZGTQunVrLrroItavX7/lfLfeeitt27aldevWzJ8/v9DXt2LFCk477TTatGnDEUccwZw5cwB4++23SU9PJz09nYyMDFavXs33339Pp06dSE9P59BDD+Xdd9/duTc3OocBC9x9kbtvACYA3QrZvyfwVD7bzwRedve1+TwmAqTuxHGpeJJpPlJZqhJ1AMmiWrVQEe7GG+Gzz+DQQ6OOSCQ19O8Ps2eX7jHT02HkyIIf32OPPTjssMN4+eWX6datGxMmTKBHjx6YGUOHDmWPPfZg8+bNHHfcccyZM4c2bdrke5wZM2YwYcIEZs+ezaZNm2jbti3t2rUD4PTTT+fSSy8F4KabbuLRRx/l6quv5tRTT6Vr166ceeaZ2xxr3bp19O7dm8mTJ9OiRQvOP/98Hn74Yfr37w9A/fr1mTlzJg899BDDhw/nkUceKfD13XrrrWRkZDBx4kTefPNNzj//fGbPns3w4cMZNWoUHTt2ZM2aNdSoUYPRo0dz4oknMnjwYDZv3szavLNhy4+GwDdx95cCh+e3o5k1AZoBb+bz8DnA3/NsG2pmtwCTgUHuvj6fY/YB+gA0ruhjR6TQieMV/YujVDy9eqXe51Y9QHH69IEaNcJcIBGp2OKHwcUPf3vmmWdo27YtGRkZzJ07d5vhanm9++67dO/enZo1a7Lbbrtx6qmnbnnss88+46ijjqJ169ZkZWUxd+7cQuP54osvaNasGS1atADgggsu4J133tny+Omnnw5Au3btyM7OLvRY7733Hueddx4Axx57LMuXL+fXX3+lY8eOXHvttTzwwAOsXLmSKlWq0L59e8aOHcuQIUP49NNPqV27dqHHriDOAZ51983xG82sAdAaiJ8gdgPQEmgP7EEBSzYkcrkGST6pOnFcpKJQD1CcevXg/PNh/Hi46y5QGyaSeIX11CRSt27dGDBgADNnzmTt2rW0a9eOr7/+muHDhzNt2jTq1q1L7969Wbdu3Q4dv3fv3kycOJG0tDTGjRvHlClTdire6tWrA1C5cmU2bdq0Q8cYNGgQp5xyCi+99BIdO3bk1VdfpVOnTrzzzju8+OKL9O7dm2uvvZbzzz9/p2KNyLfAfnH3G8W25ecc4Mp8tvcAnnP3jbkb3P372M31ZjYW+GspxCrlXOPGYdhbfttFJPmpByiPa66B9evhX/+KOhIRSaRatWpxzDHHcNFFF23p/fn111/ZddddqVOnDj/++CMvv/xyocfo1KkTEydO5Pfff2f16tX873//2/LY6tWradCgARs3biQrbmJA7dq1Wb169XbHOuigg8jOzmbBggUAPPHEExx99NE79NqOOuqoLeecMmUK9evXZ7fddmPhwoW0bt2agQMH0r59e+bPn8/ixYvZe++9ufTSS7nkkkuYOXPmDp0zCUwDmptZMzOrRkhyJuXdycxaAnUJVUrz2m5eUKxXCDMz4DTgs9INW8qjVJ04LlJRKAHK4+CD4cQTYdQoiM2PFpEKqmfPnnzyySdbEqC0tDQyMjJo2bIl5557Lh07diz0+W3btuXss88mLS2Nk046ifbt22957I477uDwww+nY8eOtGzZcsv2c845h/vuu4+MjAwWLly4ZXuNGjUYO3YsZ511Fq1bt6ZSpUr07duXHTFkyBBmzJhBmzZtGDRoEOPHjwdCqe9DDz2UNm3aULVqVU466SSmTJmy5XU//fTTXHPNNTt0zqi5+ybgKsLwtc+BZ9x9rpndbmanxu16DjDB3T3++WbWlNCD9HaeQ2eZ2afAp0B94M4EvQQpR1J14rhIRWF52oCkl5mZ6blrcSTKK6/ASSeFhVFjw+hFpBR9/vnntGrVKuowpJjy+3uZ2Ywoly9IZmXRTokku6ysUBRiyZIwNHDoUCWIUrYKa6fUA5SPP/4RWrYMJbHLWX4oIiIiEimVCZdkpwQoH5UqhdK8s2bBe+9FHY2IiIhI+VFYmXCRZKAEqADnnQd160ZXoUqkoitvw29Tlf5OIlJSKhMuyU4JUAFq1oTLLoOJE+Hrr6OORqRiqVGjBsuXL9eX6yTn7ixfvpwaNWpEHYqIlCMFlQNXmXBJFloHqBBXXgnDh8M//gF/z7suuIjssEaNGrF06VKWLVsWdShShBo1atCoUaOowxCRcmTo0DDnJ34YnMqESzJRAlSIRo3grLPgkUdgyBDYbbeoIxKpGKpWrUqzZs2iDkNERBIgt9qbqsBJstIQuCL07w+rV8PYsVFHIiIiIlI+9OoF2dmQkxN+K/mRZKIEqAiHHQYdOsADD8DmzVFHIyIiIiIiO0MJUDH07w+LFsELL0QdiYiIiIiI7AwlQMXQvTvst19YGFVERETKXlYWNG0a1upr2lSLaorIjlMCVAxVqsDVV8Pbb4fFUUVERKTsZGWFqmKLF4N7+N2nj5IgEdkxSoCK6ZJLYNdd4f77o45EREQktQwevG1JZQj3Bw+OJh4RKd+UABVT3brQuzc89RT88EPU0YiIiKSOJUtKtl1EpDAJS4DM7DEz+8nMPivg8ZZm9oGZrTezvyYqjtLUrx9s2AAPPxx1JCIiIqmjceOSbRcRKUwie4DGAV0KeXwF0A8YnsAYSlWLFtC1a0iA1q2LOhoREZHUMHQo1Ky57baaNcN2EZGSSlgC5O7vEJKcgh7/yd2nARsTFUMi9O8Py5aFoXAiIiKSeL16wejR0KQJmIXfo0drcU0R2THlYg6QmfUxs+lmNn3ZsmWRxnLssdC6dSiJ7R5pKCIiIimjVy/IzoacnPBbyY+I7KhykQC5+2h3z3T3zD333DPSWMxCL9Cnn8Jbb0UaioiIiIiIlFC5SICSzbnnQv36MHJk1JGIiIiIiEhJKAHaATVqwOWXwwsvwFdfRR2NiIgAmFkXM/vCzBaY2aB8Hh9hZrNjP1+a2cq4xzbHPTYpbnszM/sodsynzaxaGb0cERFJkESWwX4K+AA4yMyWmtnFZtbXzPrGHt/HzJYC1wI3xfbZLVHxlLbLL4cqVeCBB6KOREREzKwyMAo4CTgY6GlmB8fv4+4D3D3d3dOBfwD/jXv499zH3P3UuO3DgBHufiDwC3BxIl+HiIgkXiKrwPV09wbuXtXdG7n7o+7+T3f/Z+zxH2Lbd3P33WO3f01UPKWtQQPo2RPGjoWVK6OORkQk5R0GLHD3Re6+AZgAdCtk/55AofU8zcyAY4FnY5vGA6ftfKgiIhIlDYHbCddcA7/9Bo8+GnUkIiIpryHwTdz9pbFt2zGzJkAz4M24zTVi1UY/NLPTYtvqASvdfVNRxxQRkfJDCdBOaNsWOnUKw+A2bSp6fxERSQrnAM+6++a4bU3cPRM4FxhpZgeU5IDJtFyDiIgUTgnQThowAJYsgYkTo45ERCSlfQvsF3e/UWxbfs4hz/A3d/829nsRMAXIAJYDu5tZlaKOmUzLNYiISOGUAO2kP/0JmjVTSWwRkYhNA5rHqrZVIyQ5k/LuZGYtgbqEIj252+qaWfXY7fpAR2CeuzvwFnBmbNcLgOcT+ipERCThlADtpMqVoV8/eP99mDYt6mhERFJTbJ7OVcCrwOfAM+4+18xuN7P4qm7nABNiyU2uVsB0M/uEkPDc4+7zYo8NBK41swWEOUGa9SkiUs7Ztm1A8svMzPTp06dHHcY2fv0VGjUKvUFZWVFHIyKSeGY2IzZnRvJIxnZKRCTVFNZOqQeoFOy2G1x8MTzzDHxb0IhzERERERGJnBKgUnL11bB5M4waFXUkIiIiIiJSECVApWT//aFbN/jXv2Dt2qijERERERGR/CgBKkUDBsCKFfDvf0cdScnl5MDo0XDTTfDNN0XvL9H78cfQ6ygiIiIixacEqBQddRRkZISS2OWptsSPP8Ipp8Bll8HQoaE3q3dvmDevyKdKBDZvhjvvhH33hbQ0eOGF8vV5ExEREYmSEqBSZAb9+8Pnn8Nrr0UdTfG8+mr4Ev3WW/Dgg5CdDVdcAf/5DxxyCJx6aijxLcnhxx+hSxe4+Wbo2hXWrw/VBzt1gqlTo45OREREJPmlTAKUlQVNm0KlSuF3ospVn3027LNP8i+Mun49/OUv4ct0/fphDaMrr4QmTeD++2HxYrj11pD8HHlk6N164YUwVE6i8eabkJ4O770HY8bAxImhl+6hh+Crr6BjR+jePSTgIiIiIpK/lEiAsrKgT5/wpd49/O7TJzFJUPXqoQfllVeS94vol19Chw7w97/D5ZeH5Kd16233qV8fhgyBJUu2JkR/+hO0aQNPPAEbN0YSekravDkko8cfD7vvDh9/DJdcEnocq1YNf8MFC+COO2DyZDj00PD40qVRRy4iIiKSfFIiARo8ePvKbGvXhu2JcNllIRG6//7EHH9HucPYsdC2bRjq9txzofdgl10Kfs6uu0K/frBwITz+ePjSff75cOCB4fX99luZhZ+Svv8+JD633w7nnZd/sgpQq1YoYLFwYfh7Pf44NG8OgwbBL7+UfdwiIiIiySolEqAlS0q2fWfttRf8+c/hS+jy5Yk5R0mtXAk9e8JFF0H79vDJJ3DaacV/ftWq4Qv4nDlhKFzjxmG+U+PGoafo558TE3cqe+21MD/r449D4jp+fEh0CrPnnjBiBHzxBZx5Jtx7LxxwANx3H/z+e9nELSIiIpLMUiIBaty4ZNtLwzXXhC+cY8Yk7hzFNXVqmDvy7LOhytsbb0CjRjt2LLNQMe7dd7fOD7rttjB36JprwlA52TmbNoXeyS5dQjI9bVqoylcSzZqFoYqzZsERR8D110OLFiGRUulsERERSWUpkQANHQo1a267rWbNsD1RWreG444LldWimi+zeXOYF9KpUyj+8N57cOONULly6Ry/Qwd4/nn47DM466wwnO6AA0JP0aefls45Us3SpXDssXDXXXDhhaH35+CDd/x4aWnw0kuhyt+++4YewDZtYNIklc4WERGR1JQSCVCvXmGRzyZNQg9Gkybhfq9eiT1v//7w7beh56WsffNNSMBuuQV69NjaE5AIhxwC48bBokVw9dVhblGbNqFM87vv6ot2cb30Uuipmzkz9N48+uj2ifuO6twZPvwwfBY3bYJu3UJlP5U4FxERkVSTsATIzB4zs5/M7LMCHjcze8DMFpjZHDNrm6hYICQ72dmhjHN2duKTH4CTTw4T0UeMKNsk4LnnwpX/6dNDYpKVBXXqJP68++0XXuuSJWHS/kcfhd6njh1Dj4NKaOdv48YwRO2UU6BhQ5gxI8whK21mcMYZocfuX/8KCeuRR4ZkSIveioiISKpIZA/QOKBLIY+fBDSP/fQBHk5gLJGoVCnMi5k2DT74IPHnW7sW+vaF00+H/fcPvT4XXBC++JalPfYIC3UuXgz/+Ad89134kt26dUjINmwo23iS2ZIlcPTRoUjBZZeFXpqDDkrsOatWDWXgv/oqDAOdMiX8bS6+OPQcioiIiFRk5gnsmjCzpsAL7n5oPo/9C5ji7k/F7n8BdHb37ws7ZmZmpk+fPj0R4SbEmjWhZ+SEE+CZZxJ3njlzQpW3efPguuvgzjuhWrXEna8kNm4Mr33YsDA3qFEjuPZauPTSoquaVWSTJoXiBps2hSGZ55wTTRw//xzmHI0aFZL2q68O5bP32COaeHbWmjVhGOG8eeGzl5MTemBzcgr/Kc4+O3OsypXD+lp77bX1Z889t96uWze8/+WFmc1w98yo40hG5a2dEhGpiAprp6qUdTBxGgLx15uXxrYVmgCVN7VqhS/6f/tb6BFp0qR0j+8eCi1cd134AvXaayHZSiZVq4Yhh+eeGxaIHTYsJEB33AFXXRW+cO+5Z9RRlp0NG0KCMWIEZGSE5PDAA6OLp379sCjuNdeEOWPDh4fqhTfcEP42ha0TFbWNG8OQvo8/Dj2tH38Mc+fu+HDLSpUK/jEr/PGi9t24EZYtgxUr8j93UQlS3vu1a5d9766IiEhFEGUP0AvAPe7+Xuz+ZGCgu2932czM+hCGydG4ceN2i8tZreUlS8KQtGuvDeuylJZly0JVrxdeCPONxo4NX4zKgw8/DInQxInhC/ZFF8Ff/hLKN1dkX38deno+/hiuvDIkGzVqRB3VtubMCdUCX3wxzEm67bYwlLJKlJdLCMn+woXbJjszZ8K6deHxevXCGleHHRZ+2rQJn63iJitllUxs3BjWB/vpp/CzbFnht3/9Nf/jVK9eeIKU97HSTmTVA1Qw9QCJiESvsHZKQ+DKSI8eoXdm6dLSGfY1eXIoN718eUiq+vUrn1eDP/88zH/597/DVfuzzw4FAdLSoo6s9P33vyHRg1Dh7Ywzoo2nKG+/DQMHhmIWrVrB3XfDqaeW3efsxx+3TXY+/hh++SU8tssu0Lbt1mTnsMNC8lwe/w0UZd26kAwVlCjlvV/Qgre77rptQtSkSZijt6OUABWsvLZTIiIVSbImQKcAVwEnA4cDD7j7YUUds7w2LB98ENbNefDBcOV/R23cGAoM3HtvmCz/1FOhdHJ5t3QpjBwZqpOtWRMWAR00KFSRK+9fatevh7/+Nfzt27eHCRNCj2B54B566W64Ab74InyGhw0L1eNK0+rVofpdfLKzZEl4rFIlOPTQbZOdQw6JvkcqWf32W/ESpRo1dq44ixKggpXXdkpEpCKJJAEys6eAzkB94EfgVqAqgLv/08wMeJBQKW4tcGF+w9/yKq8Ni3tYh+eXX2D+/B2b7LxwYSh0MG1amFc0YkS4qluR/PJLWFD1/vvDl7VWrcJ6QqecEr58V60adYQls3Bh6NWaMSOsCzVsWPIUpyiJTZvCEMshQ0JVvz/9KRROOHS7SxtF27AhFMOI792ZN29rqfj99992KFtGRsX7nFcEyZgAmVkX4H6gMvCIu9+T5/ERwDGxuzWBvdx9dzNLJ1Qi3Q3YDAx196djzxkHHA2sij2vt7vPLiyO8tpOiYhUJJH1ACVCeW5YnnoqFAL43//Cl/qSeOIJuOKKcNV7zBg488zExJgsfv8dxo+H//wH3nknfAHffXc48cSQDJ10UpgwnsyeeQYuuSRMbh83LpQCL+/WroUHHoB77gm9NuefH+YINW6c//45ObBgwbbJzqxZoVcMwjyVww7bmvC0b5/8f1cJki0BMrPKwJfACYSiOtOAnu6e7ypXZnY1kOHuF5lZC8Dd/Ssz2xeYAbRy95WxBOgFdy/2ktbluZ0SEakolAAliY0bwzyFli3hjTeK95xffw2JT1YWHHVUmCtT0JfNiurXX+H118Ok/JdeCnNDzODww0My1LVrmDOULEPl1q2DAQPgn/8MvX4TJpR+9b+oLV8ekqDcOSRXXx2Gya1fv3UI27Rp4WflyrBPzZqQmblt706TJsnzd5OSScIE6A/AEHc/MXb/BgB3v7uA/acCt7r76/k89glwZiwhGocSIBGRckcJUBK5557wRXHOnLD4ZGE++ij0GGVnw623hspcqT7vIScnVP568cXwM21a2N6wYaiEd8opcNxx0a0v9OWXoeDFJ5+E0uRDh5a/YXslsWRJ+GyOHx96ujZtCtsrVw5V2OKTnVat9PmtSJIwAToT6OLul8Tunwcc7u5X5bNvE+BDoJG7b87z2GHAeOAQd8+JJUB/ANYDk4FB7r4+n2OW62qlIiIVjRKgJLJ8eVgYtWfPUAksP5s3hyIHt9wC++4LTz4JHTuWbZzlxQ8/wMsvh2TotdfCsKxq1aBz55AMnXIKHHBA2cTy5JNw2WWhPPH48eHcqeKzz+Cxx0Lv5GGHhcIcNWtGHZUkUjlPgAYSkp+r82xvAEwBLnD3D+O2/QBUA0YDC9399sJiKe/tlIhIRVBYO1WO1h2vGOrVC/MmsrJCJaa8vv02LGR6441w+umhJ0HJT8H22QcuvBCefRZ+/jmUB7/qqtBrds01YYHRVq1CFba33grDEEvb2rWhKEWvXmEo3uzZqZX8QCiG8Pe/h0IPHToo+ZFIfAvsF3e/UWxbfs4BnorfYGa7AS8Cg3OTHwB3/96D9cBYoMhqpSIiktyUAEXgmmvCXIl//Wvb7ZMmhS/QH30UeocmTAgT/6V4qlWDY4+Fv/0tlGz+6qtQWnu//cJclWOPDRPse/QIPTT5JaAl9fnnYS7SI4+EoY1TpkCjRjt/XBEpsWlAczNrZmbVCEnOpLw7mVlLoC7wQdy2asBzwON55/rEeoCIVS49DfgsUS9ARETKhhKgCLRqFda5eeihkAj9/nvotejWLXxZnzkzLJipyeE758ADQ7L52mth6OFzz4Xk5733oHfv0Ht0+OFw++2hTHVOTsmOP358mNT/44/wyiuhLLTmuIjsPDP7k5mVqH1y902EteVeBT4HnnH3uWZ2u5mdGrfrOcAE33b8dw+gE9DbzGbHftJjj2WZ2afAp4RlHe7csVclIiLJQnOAIvLqqyEJGjwYnn8+zKEYMADuvjvMIZHEcQ+lmHMLKXz8cdjWoMHWQgrHHw+1a+f//N9+C4vZjh8PRx8d5v7su2/ZvgaRqCVyDpCZ/ZtQeOD/gMfcfX4izpMoFaWdEhEpz1QEIQm5h9XsP/8c9torfJnu0iXqqFLTTz+FHpwXXwyJ6apVoXLb0UdvLbN94IFh388+C71I8+fDzTeHH/X6SCpKdBGE2JycnsCFgBPm3zzl7qsTdc7SUlHaKRGR8kxFEJKQGdx/fxjqNmeOkp8o7bVXKEzx9NOwbFkolnDNNaEgxYAB0Lw5HHQQXHxxqHC2YkVYl+i225T8iCSKu/8KPAtMABoA3YGZsQVMRUREdph6gEQK8fXXW4fKvfUWdOoEjz8e5g+JpLIED4E7ldDzcyDwODDe3X8ys5rAPHdvmojzlpb82qmNGzeydOlS1q1bF1FUkmxq1KhBo0aNqFqRF4sTiVBh7ZSuX4sUolmzUKDiqqtCkYRK6jMVKQtnACPc/Z34je6+1swujiimnbJ06VJq165N06ZNMVW4SXnuzvLly1m6dCnNmjWLOhyRlKOvcyLFpORHpMwMAT7OvWNmu5hZUwB3nxxRTDtl3bp11KtXT8mPAGBm1KtXTz2CIhHRVzoREUk2/wHiC9Nvjm0r15T8SDx9HkSiowRIRESSTRV335B7J3a7WoTxlHvLly8nPT2d9PR09tlnHxo2bLjl/oYNGwp97vTp0+nXr1+R5+jQoUNphSsiklBKgEREJNksi1+81My6AT9HGE+Zy8qCpk3D0NumTcP9nVGvXj1mz57N7Nmz6du3LwMGDNhyv1q1amzatKnA52ZmZvLAAw8UeY6pU6fuXJAR2Lx5c9QhiEgElACJiEiy6QvcaGZLzOwbYCBwWcQxlZmsLOjTBxYvDmvGLV4c7u9sEpRX79696du3L4cffjjXX389H3/8MX/4wx/IyMigQ4cOfPHFFwBMmTKFrl27AjBkyBAuuugiOnfuzP77779NYlSrVq0t+3fu3JkzzzyTli1b0qtXL3Irzr700ku0bNmSdu3a0a9fvy3HjZednc1RRx1F27Ztadu27TaJ1bBhw2jdujVpaWkMGjQIgAULFnD88ceTlpZG27ZtWbhw4TYxA1x11VWMGzcOgKZNmzJw4EDatm3Lf/7zH8aMGUP79u1JS0vjjDPOYO3atQD8+OOPdO/enbS0NNLS0pg6dSq33HILI0eO3HLcwYMHc//99+/sn0JEypiqwImISFJx94XAEWZWK3Z/TcQhlanBgyH2HXyLtWvD9l69SvdcS5cuZerUqVSuXJlff/2Vd999lypVqvDGG29w44038n//93/bPWf+/Pm89dZbrF69moMOOojLL798u1LOs2bNYu7cuey777507NiR999/n8zMTC677DLeeecdmjVrRs+ePfONaa+99uL111+nRo0afPXVV/Ts2ZPp06fz8ssv8/zzz/PRRx9Rs2ZNVqxYAUCvXr0YNGgQ3bt3Z926deTk5PDNN98U+rrr1avHzJkzgTA88NJLLwXgpptu4tFHH+Xqq6+mX79+HH300Tz33HNs3ryZNWvWsO+++3L66afTv39/cnJymDBhAh9//HFhpxKRJFSsBMjMdgV+d/ccM2sBtARedveNCY1ORERSkpmdAhwC1MidLO7ut0caVBlZsqRk23fGWWedReXKlQFYtWoVF1xwAV999RVmxsaN+Tfxp5xyCtWrV6d69erstdde/PjjjzRq1GibfQ477LAt29LT08nOzqZWrVrsv//+W8o+9+zZk9GjR293/I0bN3LVVVcxe/ZsKleuzJdffgnAG2+8wYUXXkjNmjUB2GOPPVi9ejXffvst3bt3B8LaOsVx9tlnb7n92WefcdNNN7Fy5UrWrFnDiSeeCMCbb77J448/DkDlypWpU6cOderUoV69esyaNYsff/yRjIwM6tWrV6xzikjyKG4P0DvAUWZWF3gNmAacDZTytSgREUl1ZvZPoCZwDPAIcCZxZbErusaNw7C3/LaXtl133XXL7ZtvvpljjjmG5557juzsbDp37pzvc6pXr77lduXKlfOdP1ScfQoyYsQI9t57bz755BNycnKKndTEq1KlCjk5WwsJ5i03Hf+6e/fuzcSJE0lLS2PcuHFMmTKl0GNfcskljBs3jh9++IGLLrqoxLGJSPSKOwfI3H0tcDrwkLufRbgyJyIiUto6uPv5wC/ufhvwB6BFxDGVmaFDIdbJsUXNmmF7Iq1atYqGDRsCbJkvU5oOOuggFi1aRHZ2NgBPP/10gXE0aNCASpUq8cQTT2wpVHDCCScwduzYLXN0VqxYQe3atWnUqBETJ04EYP369axdu5YmTZowb9481q9fz8qVK5k8ueDlo1avXk2DBg3YuHEjWXETrY477jgefvhhIBRLWLVqFQDdu3fnlVdeYdq0aVt6i0SkfCl2AmRmfyD0+LwY21a5GE/qYmZfmNkCMxuUz+NNzGyymc0xsylm1ii/44iISErJvVy/1sz2BTYCDSKMp0z16gWjR0OTJmAWfo8eXfrzf/K6/vrrueGGG8jIyChRj01x7bLLLjz00EN06dKFdu3aUbt2berUqbPdfldccQXjx48nLS2N+fPnb+mt6dKlC6eeeiqZmZmkp6czfPhwAJ544gkeeOAB2rRpQ4cOHfjhhx/Yb7/96NGjB4ceeig9evQgIyOjwLjuuOMODj/8cDp27EjLli23bL///vt56623aN26Ne3atWPevHkAVKtWjWOOOYYePXpsGT4oIuWL5VZmKXQns6OBvwDvu/swM9sf6O/uBS4MYGaVgS+BE4ClhGFzPd19Xtw+/wFecPfxZnYscKG7n1dYLJmZmT59+vRivDQREUkUM5vh7pkJOvbNwD+A44BRgANj3P2WRJyvtOXXTn3++ee0atUqooiSx5o1a6hVqxbuzpVXXknz5s0ZMGBA1GGVSE5OzpYKcs2bN9+pY+lzIZI4hbVTxeoBcve33f3UWPJTCfi5sOQn5jBggbsvii1iNwHolmefg4E3Y7ffyudxERFJIbE2ZrK7r3T3/wOaAC3LS/IjhRszZgzp6ekccsghrFq1issuK1/VzefNm8eBBx7Icccdt9PJj4hEp7hV4J4krMuwmdCTs5uZ3e/u9xXytIZAfB3KpcDhefb5hDCv6H6gO1DbzOq5+/I85+8D9AFonIhZoCIikhRi1UZHARmx++uB9dFGJaVlwIAB5a7HJ97BBx/MokWLog5DRHZScecAHezuvwKnAS8DzYBCh6oV01+Bo81sFnA08C0hydqGu49290x3z9xzzz1L4bQiIpLEJpvZGZZb/1pERKQUFbcMdlUzq0pIgB50941mVtTkoW+B/eLuN4pt28LdvyP0ABFb8O4Md19ZzJhERKRiugy4FthkZusAA9zdd4s2LBERqQiK2wP0LyAb2BV4x8yaAL8W8ZxpQHMza2Zm1YBzgEnxO5hZ/dh4b4AbgMeKG7iIiFRM7l7b3Su5ezV33y12v1jJTzGqj44ws9mxny/NbGXcYxeY2Vexnwvitrczs09jx3xAPVMiIuVbsXqA3P0B4IG4TYvN7JginrPJzK4CXiWUzH7M3eea2e3AdHefBHQG7o71Jr0DXLkDr0FERCoQM+uU33Z3f6eI51UmVI3bUn3UzCbFVx919wFx+19NbK6Rme0B3ApkEqrOzYg99xfgYeBS4CPgJaALYTi4iIiUQ8XqATKzOmb2dzObHvv5G6E3qFDu/pK7t3D3A9x9aGzbLbHkB3d/1t2bx/a5JDbZVUREUtt1cT83A/8DhhTjecWpPhqvJ/BU7PaJwOvuviKW9LwOdDGzBsBu7v6hh3UjHicMBy9XjjnmGF599dVtto0cOZLLL7+8wOd07tyZ3HLeJ598MitXrtxunyFDhmxZj6cgEydO3LKGDsAtt9zCG2+8UYLoRURKV3GHwD0GrAZ6xH5+BcYmKigREUld7v6nuJ8TgEOBX4rx1PyqjzbMb8fYUO5mbF2KoaDnNozdLvSYZtYn9yLhsmXLihFq2erZsycTJkzYZtuECRPo2bNnsZ7/0ksvsfvuu+/QufMmQLfffjvHH3/8Dh0rKps3b1efSUTKseImQAe4+62xq2qL3P02YP9EBiYiIhKzFCjt1SLPAZ5191L5Zpvs1UrPPPNMXnzxRTZs2ABAdnY23333HUcddRSXX345mZmZHHLIIdx66635Pr9p06b8/PPPAAwdOpQWLVpw5JFH8sUXX2zZZ8yYMbRv3560tDTOOOMM1q5dy9SpU5k0aRLXXXcd6enpLFy4kN69e/Pss88CMHnyZDIyMmjdujUXXXQR69ev33K+W2+9lbZt29K6dWvmz5+/XUzZ2dkcddRRtG3blrZt2zJ16tQtjw0bNozWrVuTlpbGoEFhKtiCBQs4/vjjSUtLo23btixcuJApU6bQtWvXLc+76qqrGDdu3JYYBg4cuGXR0/xeH8CPP/5I9+7dSUtLIy0tjalTp3LLLbcwcuTILccdPHgw999/f4n+ZiKSOMWtAve7mR3p7u8BmFlH4PfEhSUiIqnKzP5BmIcD4UJdOjCzGE8tsvponHPYdt7pt4R5qfHPnRLb3qiYxyyW/v1h9uydOcL20tMh7vv2dvbYYw8OO+wwXn75Zbp168aECRPo0aMHZsbQoUPZY4892Lx5M8cddxxz5syhTZs2+R5nxowZTJgwgdmzZ7Np0ybatm1Lu3btADj99NO59NJLAbjpppt49NFHufrqqzn11FPp2rUrZ5555jbHWrduHb1792by5Mm0aNGC888/n4cffpj+/fsDUL9+fWbOnMlDDz3E8OHDeeSRR7Z5/l577cXrr79OjRo1+Oqrr+jZsyfTp0/n5Zdf5vnnn+ejjz6iZs2arFixAoBevXoxaNAgunfvzrp168jJyeGbb76hMPXq1WPmzPDRW758eb6vr1+/fhx99NE899xzbN68mTVr1rDvvvty+umn079/f3JycpgwYQIff/xxoecSkbJT3B6gvsAoM8s2s2zgQUKZUhERkdI2HZgR+/kAGOjufy7G84qsPgpgZi2BurFj53oV+KOZ1TWzusAfgVfd/XvgVzM7Ilb97Xzg+Z14bZGJHwYXP/ztmWeeoW3btmRkZDB37txthqvl9e6779K9e3dq1qzJbrvtxqmnnrrlsc8++4yjjjqK1q1bk5WVxdy5cwuN54svvqBZs2a0aNECgAsuuIB33tla5+L0008HoF27dmRnZ2/3/I0bN3LppZfSunVrzjrrrC1xv/HGG1x44YXUrFkTCMnf6tWr+fbbb+nevTsANWrU2PJ4Yc4+++wiX9+bb765ZS5V5cqVqVOnDk2bNqVevXrMmjWL1157jYyMDOrVq1fk+USkbBS3CtwnQJqZ7Ra7/6uZ9QfmJDA2ERFJTc8C63KHp5lZZTOr6e5rC3tSMauPQkiMJsSKGuQ+d4WZ3UFIogBud/cVsdtXAOOAXQjV33aqAlxhPTWJ1K1bNwYMGMDMmTNZu3Yt7dq14+uvv2b48OFMmzaNunXr0rt3b9atW7dDx+/duzcTJ04kLS2NcePGMWXKlJ2Kt3r16kBIKjZt2rTd4yNGjGDvvffmk08+IScnhxo1apT4HFWqVCEnJ2fL/byvfdddt9Z7Kunru+SSSxg3bhw//PADF110UYljE5HEKW4PEBASH3fPXf/n2gTEIyIiMpmQbOTaBShW2bCiqo/G7g9x9+3WCHL3x9z9wNjP2Ljt09390Ngxr4pPnMqTWrVqccwxx3DRRRdt6f359ddf2XXXXalTpw4//vgjL79ceG7XqVMnJk6cyO+//87q1av53//+t+Wx1atX06BBAzZu3EhWVtaW7bVr12b16tXbHeuggw4iOzubBQsWAPDEE09w9NFHF/v1rFq1igYNGlCpUiWeeOKJLYUKTjjhBMaOHbtljs6KFSuoXbs2jRo1YuLEiQCsX7+etWvX0qRJE+bNm8f69etZuXIlkydPLvB8Bb2+4447jocffhgIxRJWrVoFQPfu3XnllVeYNm0aJ554YrFfl4gkXokSoDy0EJyIiCRCDXdfk3sndrvo8UpSpJ49e/LJJ59sSYDS0tLIyMigZcuWnHvuuXTs2LHQ57dt25azzz6btLQ0TjrpJNq3b7/lsTvuuIPDDz+cjh070rJlyy3bzznnHO677z4yMjJYuHDhlu01atRg7NixnHXWWbRu3ZpKlSrRt2/fYr+WK664gvHjx5OWlsb8+fO39NZ06dKFU089lczMTNLT07eU6X7iiSd44IEHaNOmDR06dOCHH35gv/32o0ePHhx66KH06NGDjIyMAs9X0Ou7//77eeutt2jdujXt2rXbMhSvWrVqHHPMMfTo0YPKlSsX+3WJSOLZjl7IMrMl7t64lOMpUmZmpueuSyAiItEwsxnunpmgY78PXO3uM2P32wEPuvsfEnG+0pZfO/X555/TqlVpF7KTZJaTk7Olglzz5s3z3UefC5HEKaydKnQOkJmtZmslnm0eYtvhCSIiIqWlP/AfM/uO0N7sA5xd6DNEksi8efPo2rUr3bt3LzD5EZHoFJoAuXvtsgpEREQEwN2nxSq1HRTb9IW7b4wyJpGSOPjgg1m0aFHUYUicn36Cjh1h2DCIFRiUFLYzc4BERERKnZldCezq7p+5+2dALTO7Iuq4RKT8GjkSFiyAa6+FHSx0KBWIEiAREUk2l7r7ytw77v4LcGl04ZSOclo8ThJEn4eys2oVjBoFLVvC4sXw0ENRRyRRUwIkIiLJpnJs0VEgrAMEVIswnp1Wo0YNli9fri+9AoTkZ/ny5Tu0dpGU3MMPw6+/QlYWnHACDB0KK1dGHZVEqVgLoYqIiJShV4CnzexfsfuXsZOLj0atUaNGLF26lGXLlkUdiiSJGjVq0KhRo6jDqPB+/x1GjIATT4S2bcMcoNzfd98ddXQSFSVAIiKSbAYCfYDcRWHmECrBlVtVq1alWbNmUYchknIeeywUQLjhhnA/IwPOPTfMCbrqKmjYMNLwJCIaAiciIknF3XOAj4Bs4DDgWODzKGMSkfJn40a47z7o0AE6ddq6/c47IScHbr01utgkWkqAIpKVBU2bQqVK4XdWVtQRiYhEy8xamNmtZjYf+AewBMDdj3H3B6ONTkTKm6eeCkUPbrgBts4qhGbN4IorYOxYmDcvuvgkOkqAIpCVBX36hH+U7uF3nz5KgkQk5c0n9PZ0dfcj3f0fwOaIYxKRcignB+65B1q3hlNO2f7xwYOhVq2tQ+MktSgBisDgwbB27bbb1q4N20VEUtjpwPfAW2Y2xsyOA6yI50gCff89PPpouFgnUp5MmgSff75970+u+vVh4MCw33vvlX18Ei0lQBFYsqRk20VEUoG7T3T3c4CWwFtAf2AvM3vYzP4YaXAp6rrr4JJL4Pnno45EpPjc4a67YP/94ayzCt6vf39o0CB8zpXkp5aEJkBm1sXMvjCzBWY2KJ/HG5vZW2Y2y8zmmNnJiYwnWTRuXLLtIiKpxN1/c/cn3f1PQCNgFqEynJShRYtgwoRw+4YbYNOmaOMRKa4334Rp0+D666FKIfWOa9aE226DDz+EiRPLLDxJAglLgGIL140CTgIOBnqa2cF5drsJeMbdM4BzgJRYm3fo0PCPLl7NmmG7iIhs5e6/uPtodz8u6lhSzX33QeXKMGoUzJ8fJoyLlAd33x16di64oOh9L7wQWrZUkp9qEtkDdBiwwN0XufsGYALQLc8+DuwWu10H+C6B8SSNXr1g9Gho0iSMS23SJNzv1SvqyEREROCHH0LCc8EFcPnloYzwrbfCb79FHZlI4T7+GCZPhmuvhRo1it6/SpVQLOGLL8J8N0kNiUyAGgLfxN1fGtsWbwjwZzNbCrwEXJ3fgcysj5lNN7PpFWUV7V69IDs7VCnJzlbyIyIiyWPEiLCGyvXXhwt1994bCiKMHBl1ZCKFu/tuqFsXLrus+M859dSQ5A8ZoiQ/VURdBKEnMM7dGwEnA0+Y2XYxxYY/ZLp75p577lnmQYqIiKSKX36Bhx6CHj3gwAPDto4doVs3GDYMfv452vhECjJvXpjLc9VVULt28Z9nFoZ8/vBDSP6l4ktkAvQtsF/c/UaxbfEuBp4BcPcPgBpA/QTGJCIiIoUYNQrWrIFBeUoX3X13uDp+553RxCVSlHvuCXOq+/Ur+XM7dIDTTgu9nRVksJEUIpEJ0DSguZk1M7NqhCIHk/LsswQ4DsDMWhESIH3sRESkxIqqPBrbp4eZzTOzuWb2ZGzbMWY2O+5nnZmdFntsnJl9HfdYetm9orL3229w//1w8smQlrbtY61awUUXhd6hRYuiiU+kINnZ8OSTYWH5+jt4Kf2uu5Tkp4qEJUDuvgm4CngV+JxQ7W2umd1uZqfGdvsLcKmZfQI8BfR2VyV2EREpmeJUHjWz5sANQEd3P4SwzhDu/pa7p7t7OnAssBZ4Le6p1+U+7u6zE/1aovTII2GI24035v/4bbeFSeM331y2cYkUZfhwqFQJ/vKXHT9Gq1Zw8cXw8MNK8iu6hM4BcveX3L2Fux/g7kNj225x90mx2/PcvaO7p8UaltcKP6KIiEi+ilN59FJglLv/AuDuP+VznDOBl919bUKjTUIbNoQvkUcdFeb85GfffWHAgHClfebMso1PpCA//hgquJ1/PjRqtHPHGjIkJPmDB5dKaJKkoi6CICIiUhqKU3m0BdDCzN43sw/NrEs+xzmHMCIh3tDYYt0jzKx6fievCNVKs7Jg6dKwHkphrr8e6tWDgVqaVpLEyJGwfn34bO6s3CR/wgSYMWPnjyfJSQmQiIikiipAc6AzoQrpGDPbPfdBM2sAtCYM3c51A9ASaA/sAeT7tb+8VyvdvDlMIE9Phy75pYVx6tSBm26CN96A118vk/BECrRqVZiXduaZ0KJF6RwzPsnXxIyKSQmQiIhUBMWpPLoUmOTuG939a+BLQkKUqwfwnLtvzN3g7t97sB4YSxhqV+E89xx8+WXo/TErev/LL4emTcMXxJychIcnUqCHHoJffy2657Ik6tQJ89wmT4bXNDmjQlICJCIiFUFxKo9OJPT+YGb1CUPi4qc69yTP8LdYrxBmZsBpwGelH3q03EOJ6+bN4Ywzivec6tVDpaxZs+CpvAMGRcrI2rVh3Z4uXSAjo3SP3bevkvyKTAmQiIiUe8WsPPoqsNzM5gFvEaq7LQcws6aEHqS38xw6y8w+BT4lrFNX4QrkvvZaKGgwcCBUrlz85/XsGb503nRTmH8hUtYeeyys2VOavT+5qleHoUPhk09C0Q+pWKy8VZ3OzMz06dOnRx2GiEhKM7MZ7p4ZdRzJqLy1U507w4IFoexvtWole+7rr8Mf/xiuwvfvn4joRPK3cSMceCDstx+8+27xhm6WVE4OZGbCihUwfz7UqFH655DEKaydUg+QiIhIipo6Fd5+O6ydUtLkB+CEE+D448NwuFWrSj8+kYI8+SQsWVL8eWs7olIlGDYMFi8OawNJxaEESEREJEXdfXeodnXppTt+jGHDYPlyuPfe0otLpDA5OeFz16YNnHxyYs91wgnh5847YeXKxJ5Lyo4SIBERkRT06afwwgvQrx/UqrXjx2nbFs49NwyD+zZv3T2RBHj+efj888T2/sQbNiwMgxs2LPHnkrKhBEhERCQF3XNPSHyuumrnj3XnnbBpEwwZsvPHEimMO9x1FxxwQFj7pyxkZIQkf+RIJfkVhRIgERGRFLNwYVjpvm9f2GOP/PfJygplgCtVCr+zsgo+XrNmcMUVoSrX558nImKRYPJkmD49LFZapUrZnffOO8PQu1tvLbtzSuIoARIREUkx990XvjwOGJD/41lZ0KdPmPztHn736VN4EnTTTbDrrokpSSyS6+67oUEDuOCCsj1vbpI/dizMm1e255bSpwRIREQkhXz/ffgS17s37Ltv/vsMHhwWmYy3dm3YXpD69cNaQs8/D++/X2rhimzx0Ufw5puhamH16mV//sGDw7BRJfnlnxIgERGRFPL3v4f5OtdfX/A+S5aUbHuu/v3D1fnrrw89RyKl6e67oW7d0BsZhdwkf9IkeO+9aGKQ0qEESEREJEX88gv8859w9tlhEnlBGjcu2fZcu+4Kt90W1hd6/vkdj1Mkr7lzw2eqXz+oXTu6OHKT/OuuU5JfnikBEhERSREPPghr1sCgQYXvN3Qo1Ky57baaNcP2olx4IbRsGYYJbdq047GKxBs2LCTYV18dbRw1a4Yk/8MPYeLEaGORHacESEREJAX89hvcfz907RoWkCxMr14wejQ0aRLWWWnSJNzv1avo81SpEoYqzZ8f5hqJ7Kyvv4YnnwxD3+rVizoaJfkVgRIgERGRFDBmDCxfXvwJ3L16QXZ2KP2bnV285CdXt27QoUMoGfzbbzsSrchWw4eHcux/+UvUkQRVqoR1tL74IpR+l/JHCZCIiEgFt2FD+BJ59NEhMUk0M7j33lBxbuTIxJ9PKq4ffoBHHw1lrxs2jDqarU49VUl+eaYESEREpIJ74omwgn1Zlu/t2DH0BA0bBj//XHbnlYpl5EjYuLHwqoVRMAvraf3wA4wYEXU0UlIJTYDMrIuZfWFmC8xsuymXZjbCzGbHfr40s5WJjEdERCTVbN4ckpCMDPjjH8v23HffHa6O33ln2Z5XKoaVK+Ghh+Css6B586ij2V6HDnDaaaG3c9myqKORkkhYAmRmlYFRwEnAwUBPMzs4fh93H+Du6e6eDvwD+G+i4pHCZWVB06ZhjG3TpoWv9i0iIuXHf/8LX30FN94YrlqXpVat4KKLwpfYRYvK9txS/j30EKxeXXTVwijddZeS/PIokT1AhwEL3H2Ru28AJgDdCtm/J/BUAuORAmRlhcoqixeHmvaLF4f7SoJERMo39/AFrUUL6N49mhhuuy1MGr/55mjOL+XT2rVhaNlJJ0F6etTRFKxVK7j4Ynj4YSX55UkiE6CGwDdx95fGtm3HzJoAzYA3C3i8j5lNN7Ppy9THWOoGDw7/0cRbuzZsFxGR8uvVV2H27LB6feXK0cSw774wYEAoYzxzZjQxSPnz6KNh7tiNN0YdSdGGDAlJ/k03RR2JFFeyFEE4B3jW3Tfn96C7j3b3THfP3HPPPcs4tIpvyZKSbRcRkfLh7ruhUSP485+jjeP668P6LQMHRhuHlA8bNoQCA0ceGX6SXW6S/9RTMGNG1NFIcSQyAfoW2C/ufqPYtvycg4a/RaZx45JtFxFJRkUV3ont08PM5pnZXDN7Mm775riiPJPitjczs49ix3zazKqVxWspDe+/D++8A3/9K1SLOOo6dcLV8TfegNdfjzYWSX5PPgnffFO2VQt3VnyS7x51NFKURCZA04DmscajGiHJmZR3JzNrCdQFPkhgLFKIoUOhZs1tt9WsGbaLiJQHxSm8Y2bNgRuAju5+CNA/7uHfc4vyuPupcduHASPc/UDgF+DiBL6MUnX33eEL2SWXRB1JcPnlocjOwIFhcVWR/OTkhKqFaWlh/k95UadOmOc2ebKS/PIgYQmQu28CrgJeBT4HnnH3uWZ2u5nFNy7nABPclS9HpVcvGD0amjQJFYKaNAn3S7Lqt4hIxIpTeOdSYJS7/wLg7j8VdkAzM+BY4NnYpvHAaaUZdKJ88gm8+CL07w+77hp1NEH16qFS1qxZYaiQSH4mToT580PvT1lXLdxZffsqyS8tkyfD6afD778n5vhW3vKOzMxMnz59etRhiIikNDOb4e6ZUceRy8zOBLq4+yWx++cBh7v7VXH7TAS+BDoClYEh7v5K7LFNwGxgE3CPu080s/rAh7HeH8xsP+Bldz80n/P3AfoANG7cuN3ixYsT9VKLpWdPeOGFMJezbt1IQ9lGTg5kZsIvv4QvudWrRx2RJBN3aN8eVq0Kn4+oCnfsjCefDBeQn3gi+rl35dXUqXDCCXDAATBlCuyxx44dp7B2KlmKIIiIiCRaFaA50Jmw9MIYM9s99liTWEN5LjDSzA4oyYGTqVjPggXwzDNhyFkyJT8Q1pobNgyys0PZYJF4b7wRighEWbVwZ51zTlh0+KabYP36qKMpf2bOhJNPhoYN4bXXdjz5KYoSIBERqQiKU3hnKTDJ3Te6+9eE3qDmAO7+bez3ImAKkAEsB3Y3syqFHDPp3HcfVK0aqlIloxNOgOOPD8PhVq2KOhpJJnffHSqqnXde1JHsuNwkf/HisJCrFN/nn8OJJ4b5VG+8Afvsk7hzKQESEZGKoDiFdyYSen+IDW9rASwys7pmVj1ue0dgXmxu6lvAmbHnXwA8n+DXsVO++w7GjYMLL4QGDaKOpmDDhsHy5XDvvVFHIsniww/hrbfgL38p/0MjTzgh/Nx5J6xcGXU05cOiReHCSOXKIflJdCViJUAiIlLuFbPwzqvAcjObR0hsrnP35UArYLqZfRLbfo+7z4s9ZyBwrZktAOoBj5bdqyq5v/8dNm2C666LOpLCtW0L554LI0bAt0nfpyZl4e67w3CnPn2ijqR0DBsGK1YoyS+Ob78Nyc+6dSH5ad488edUEQQRESmxZCuCkEyiaqdWrAhXTU87Df797zI/fYl9/TUcdBBccAGMGRN1NBKlzz6D1q1hyBC49daooyk9vXrBc8/BV1+FOS2yvWXLoFOnkARNnhyKYJQWFUEQERGp4P7xD/jtNxiU7xKwyadZM7jiCnjssTD2X1LXsGGhXPvVV0cdSem6807YvLliJXWlaeVK+OMfw3ypF14o3eSnKEqAREREyrk1a+CBB+BPf4JDtyvSnbxuuil88b3hhqgjkah8/XVYF6pv38RV/IpKbpI/dizMm1f0/qlkzZpQ7W3uXPjvf0MvUFlSAiRJKSsrLCZWqVL4nZUVdUQiIslrzJgwBO7GG6OOpGTq1w8lj59/Ht5/P+poJAr33Rcmvl97bdSRJMbgwVCrlpL8eOvWhaG6H30Ukt8uXco+BiVAknSyssIkyMWLw6JoixeH+0qCRES2t349DB8OnTvDEUdEHU3J9e8fKtZdf334P19Sxw8/hCGQF1wQyl9XRLlJ/qRJ8N57UUcTvY0boUePMN9n7Fg444xo4lACJEln8GBYu3bbbWvXhu0iIrKtJ54I5a/L6xXmXXeF224Lq78/n9RFxqW0jRgRvhBff33UkSSWkvxg8+aQ7P7vfzBqFJx/fnSxKAGSpLNkScm2i4ikqs2bwwTydu3CuiPl1YUXQsuWIYnbtCnqaKQsrFwJDz8cegMOPDDqaBKrZs2Q5H/wAUycGHU00XAP87yeegruuSfMjYqSEiBJOgUtfpXoRbFERMqbZ5+FBQtC4mAWdTQ7rkqVsA7M/PlhWIxUfKNGwerV5adq4c5K5STfPSxw+8gjYTTPwIFRR6QESJLQ0KHhakm8mjXDdhERCdxD0nDQQdC9e9TR7Lxu3aBDh1Ay+Lffoo5GEmntWhg5Ek45BdLSoo6mbFSpEno+vvgizHtKJbfdFoY79usHd9wRdTSBEiBJOr16wejR0KRJuKLZpEm436tX1JGJiCSPV16BTz4JV9ArVYDW3AzuvRe+/x7uvz/qaCSRHnkEfv65/M5b21Gnnpp6Sf7f/hYSoAsvDElQsvRUm5ez2VhRrbAtIiJbFbbCdqorq3bqqKNClcwFC6BatYSfrsycdhq89RYsXBgqaEnFsmFDmPPTtCm8807U0ZS9qVOhY8fQE3LTTVFHk1j/+leY93PWWWHuT+XKZXv+wtqpCnDNSEREJLW89174+etfK1byA2FY35o1cOedUUciiZCVBd98U/7WrCotHTqEJP/ee2HZsqijSZysLLj88jDM8d//LvvkpyhKgERERMqZu+8OvSOXXBJ1JKWvVSu46CJ46CH4+uuoo5HSlFu1MCMDTjwx6miic9ddYQhcRU3yJ04M5a47d4b//Cc5L9IoARIphqys0F1fqVL4rUVZRaS0Fff/mdmz4aWXwtoieQvGVBS33RYmjVf0IUKpZuLEUARg0KDkmQsShVat4OKLQxnwCRMgJyfqiErP66/D2WdDZmZY12uXXaKOKH9KgESKkJUFffqEsfbu4XefPkqCRKT0lOT/mXvugdq14coryz7OsrLvvjBgADz5JMycGXU0UhpyqxY2bw5nnBF1NNG7/faQCPXsGZKFl18u/4ukvvdeqObYsmV4PbVrRx1RwZQAiRRh8OBQsjPe2rVhu4hIaSju/zNffRWGlFxxBey+e5mFF4nrr4d69VJnnZiK7vXXYcaMsAZMss0HicI++4Tk/oknwqKwJ58MRx8N778fdWQ7ZsaMMN9nv/3gtdegbt2oIypcQhMgM+tiZl+Y2QIzy/e/MDPrYWbzzGyumT2ZyHhEdsSSJSXbLiJSUsX9f+bee6Fq1TD8raKrUycMgXv99fAj5dvdd0PDhnDeeVFHkjwqV4Y//zksADxqVLjAceSR0LVrKHFfXsydG+Z01a0Lb7wBe+8ddURFS1gCZGaVgVHAScDBQE8zOzjPPs2BG4CO7n4I0D9R8YjsqMaNS7a9LGhOkkjFUpz/Z779FsaPDwUC9tmnbOKK2uWXh//jBg6sWPMkUs0HH8CUKRWzamFpqFYt9OouWBASxfffD4Uizj03bEtmCxfCCSeECzNvvBF6gMqDRPYAHQYscPdF7r4BmAB0y7PPpcAod/8FwN1/SmA8Ijtk6NDtJxrXrBm2R0FzkkQqnuL8P/P3v4ck4Lrryja2KFWvHiplzZoVJotL+XT33WE446WXRh1Jctt11zDkc9Gi8Pv558M8ob594bvvoo5ue0uXwnHHwfr1Ifk58MCoIyq+RCZADYFv4u4vjW2L1wJoYWbvm9mHZtYlvwOZWR8zm25m05dV5KLpkpR69YLRo6FJk1C1pkmTcL9Xr2ji0ZwkkYqnqP9nli8Piwr27AnNmkUba1nr2TNcDR88OHzRkvLls8/gf/+Dfv3CF3wpWt26oVT2woVw2WXw2GNwwAFhXtyKFVFHF/z0Exx/fIjn1VfhkEOijqhkoi6CUAVoDnQGegJjzGz3vDu5+2h3z3T3zD333LNsIxQhfAnJzg5XX7Ozo0t+QHOSRAqyo/NOzSzdzD6IbZtjZmfH7T/OzL42s9mxn/RExV/Y/zP/+EdYNyQVCwJUqhTWjsnODmWDpXy55x6oVQuuuirqSMqfffaBBx8Mc4TOOguGDw8XQIYODYsFR+WXX+CPfwzfO158MVSxK28SmQB9C8SPBGwU2xZvKTDJ3Te6+9fAl4SESEQKoDlJItvbyXmna4HzY9u6ACPzXIy7zt3TYz+zE/pC8rF6NTzwQCgvW96uspaWE04IV5vvvBNWrYo6GimuRYvgqafCEK499og6mvJr//3h8cdhzhw45phQHOSAA8KFkbLuFV29OlSs+/zzsK7TUUeV7flLS5UEHnsa0NzMmhESn3OAc/PsM5HQ8zPWzOoThsQtSmBMIuXe0KFhzk/8MLhkmJOUG0/unCSItqdMUs6WeacAZpY773Re3D75zjt19y9zd3D378zsJ2BPYGXZhF640aPDFdcbbog6kmgNGwbt2oWJ9KedFnU0W+2zD6Snq7RzXuvXwx13hAVtBwyIOpqK4dBDQ9Lx4Yfh/4N+/eBvfwsLB//5z4n/DP7+e7gQM21aKMf/xz8m9nyJlLAEyN03mdlVwKtAZeAxd59rZrcD0919UuyxP5rZPGAz4Srb8kTFJFIR5CYVgweH7ufGjUPyk4xzkpQASRnKb97p4Xn2aQFgZu8T2qUh7v5K/A5mdhhQDVgYt3momd0CTAYGuft211zNrA/QB6BxKXbHrl8fih8ceywcnvfVpJi2beGCC+CRR8JPMtl99/A3Ou640FPVvHmYy5VKcnJC6ebJk8OE+HffDW3BlVeGhW2l9BxxBLz5Znifb7gBevcOJfLvvDNcHEjEZ2/jxjAMb8qU0BvVvXvpn6MsmZezZWczMzN9+vTpUYchIjGVKuW/erWZytZWZGY2w92TZuS3mZ0JdHH3S2L3zwMOd/er4vZ5AdgI9CAMy34HaO3uK2OPNwCmABe4+4dx234gJEWjgYXufnthsZRmOzVmTOhRff318MU61W3eDLNnJ8//Le5hovobb4Sf3LmYjRqFv9dxx4WfBg2ijTMR3MMQt9yE5803Q7EOgIMP3vraTz45lEiWxHCH//43DIubPx8OOywUUDjuuNI7x+bN4YLm00+HeXh9+5besROpsHYqkUPgRCQFNG4chr3lt12kDBV33ulH7r4R+NrMcuedTjOz3YAXgcG5yQ+Au38fu7nezMYCf03UC8hr06Yw7Cszs3S/zJRnlSuHYXDJ5LDDQqW6+GRo8mSYNAnGjQv7HHzw1oTo6KPDIq/l0U8/be15mDw5FKaAkPB17Rpe47HHqsenLJnBGWeEoWmPPw5Dhmz9rN11V/h87oycnHAR5umnQy9TeUl+ihJ1FTgRKeeSbZ0kSVlb5p2aWTXCvNNJefaZSKg6Svy809j+zwGPu/uz8U+I9QBhZgacBnyWuJewrWefDV+ob7wx9YZTlUdmYR2Uvn3D/Ihly2DGjJDENmoUevO6dQvr4fzhD3DzzWE4UTKX9l6zBl56Ca69FtLSYO+9Q7L3f/8XSpOPGhV6HZYsCcnen/+s5CcqVaqERZK//BJGjAjDEQ8/HE4/HebNK/r5+XEPf/vHHguf14q0BpmGwInITsvKSp45SVI2km0IHICZnQyMZOu806Hx805jSczfCJXeNgND3X2Cmf0ZGAvMjTtcb3efbWZvEgoiGDAb6OvuhRagLY12yj1MrN+4MayjUkmXK8u99evhgw+29p58/HG4ur7LLqGSVu5V+/T06P7eGzfCRx9tjfHDD0NPZPXqcOSRW2Ns21ZFH5Ld6tUhERo+PJTQP++80DvUtGnxj3HzzWFe0TXXhGOVtwsxhbVTSoBEpEJRMla40np/kjEBShal0U69+GIYUjRuXJj4LxXPqlXw9ttbk43cq/T16m1bUGH//RP3xTMnJyTYuTG8/Xb4slypUhhqmJvwdOgQEjUpf37+OazF9OCD4e/dt29oA/beu/Dn3XsvDBwIF18cei/LW/IDSoBEJEXkLckNYTje6NFKgqB03x8lQAUrjXbqyCPhm29gwQJNIE8V33237fyapUvD9iZNtiYixx5b9BfXomRnbz3H5MlhqB7AQQdtTbo6d4a6dXfuPJJcli6F228Pw9lq1ID+/cOQtvzmoz38MFxxBZx9dmg3ymtvnxIgEUkJTZvmX5ChSZOtk3XLWjL1SJXm+6MEqGA72069+y506hQWObzqqqL3l4rHPczlyK2w9tZbsHJleKx1660JUadOULt24cf6+efw/NxKdYtiqy02aLA14TnuuDBPSSq+L7+EW24JRQ3q1oVBg8L/M7lzeZ94As4/P/RA//e/5fsCjBIgEUkJyVaSO9l6pErz/VECVLCdbadOOilMns/O3r7AiKSmzZth5sytCdF774U5RVWqhInuuUnM4YeHeTzvvbe1l2fWrHCM3XYLPTu5+7ZqVT6HNUnpmDUrXJx7+eVQuOLmm2GPPUKRi86dwzDcGjWijnLnKAESkZSQbD1AFTkeJUAF25l2atasMMH8rrvCAoci+fn9d5g6dWtCNGNGuIix666wYUNIgqpVC3N3chOezMyQMInEe/fd8H/N+++H+0ccEdYdq1Ur2rhKg9YBEpGUMHRo/j0uUZXkzl0UsbjbEy3Z3h/Z3qZN8Mc/hvH3IgXZZZetC43edRf88ksoqf3WW+Gq/fHHh3lk6kGUohx1VEiCXnoJXnklzBOqCMlPUZQAiUiFkTusLFnm3CTbIrHJ9v7I9tq3h1dfjToKKW/q1oXu3cOPSEmZwSmnhJ9UoQRIRCqUXr2S5wt9Mva4JNP7IyIiEgUtrSYikiC9eoWCB02ahCtsTZqoJLeIiEjU1AMkIpJA6nERERFJLuoBEhERERGRlKEESEREREREUoYSIBERERERSRlKgEREREREJGUoARIRERERkZRh7h51DCViZsuAfJYWLLfqAz9HHUQS0/tTOL0/BdN7U7idfX+auPuepRVMRaJ2KuXo/Smc3p/C6f0pWMLaqXKXAFU0Zjbd3TOjjiNZ6f0pnN6fgum9KZzeHykufVYKp/encHp/Cqf3p2CJfG80BE5ERERERFKGEiAREREREUkZSoCiNzrqAJKc3p/C6f0pmN6bwun9keLSZ6Vwen8Kp/encHp/Cpaw90ZzgEREREREJGWoB0hERERERFKGEiAREREREUkZSoAiYmb7mdlbZjbPzOaa2TVRx5RszKyymc0ysxeijiXZmNnuZvasmc03s8/N7A9Rx5RMzGxA7N/VZ2b2lJnViDqmKJnZY2b2k5l9FrdtDzN73cy+iv2uG2WMknzUThVN7VTB1E4VTu3Utsq6nVICFJ1NwF/c/WDgCOBKMzs44piSzTXA51EHkaTuB15x95ZAGnqftjCzhkA/INPdDwUqA+dEG1XkxgFd8mwbBEx29+bA5Nh9kXhqp4qmdqpgaqcKoHYqX+Mow3ZKCVBE3P17d58Zu72a8B9Dw2ijSh5m1gg4BXgk6liSjZnVAToBjwK4+wZ3XxlpUMmnCrCLmVUBagLfRRxPpNz9HWBFns3dgPGx2+OB08oyJkl+aqcKp3aqYGqnikXtVJyybqeUACUBM2sKZAAfRRxKMhkJXA/kRBxHMmoGLAPGxoZePGJmu0YdVLJw92+B4cAS4Htglbu/Fm1USWlvd/8+dvsHYO8og5HkpnYqXyNRO1UQtVOFUDtVbAlrp5QARczMagH/B/R391+jjicZmFlX4Cd3nxF1LEmqCtAWeNjdM4Df0PClLWJjhLsRGuB9gV3N7M/RRpXcPKyHoDURJF9qp7andqpIaqcKoXaq5Eq7nVICFCEzq0poVLLc/b9Rx5NEOgKnmlk2MAE41sz+HW1ISWUpsNTdc6/EPktoaCQ4Hvja3Ze5+0bgv0CHiGNKRj+aWQOA2O+fIo5HkpDaqQKpnSqc2qnCqZ0qnoS1U0qAImJmRhgb+7m7/z3qeJKJu9/g7o3cvSlhUuCb7q4rIzHu/gPwjZkdFNt0HDAvwpCSzRLgCDOrGft3dhyafJufScAFsdsXAM9HGIskIbVTBVM7VTi1U0VSO1U8CWunlABFpyNwHuGq0ezYz8lRByXlxtVAlpnNAdKBu6INJ3nErjg+C8wEPiX8Pzc60qAiZmZPAR8AB5nZUjO7GLgHOMHMviJcjbwnyhglKamdkp2hdqoAaqe2V9btlIUhdSIiIiIiIhWfeoBERERERCRlKAESEREREZGUoQRIRERERERShhIgERERERFJGUqAREREREQkZSgBEikhM9scVxJ2tpmV2urWZtbUzD4rreOJiEjqUTslUrgqUQcgUg797u7pUQchIiJSALVTIoVQD5BIKTGzbDO718w+NbOPzezA2PamZvammc0xs8lm1ji2fW8ze87MPon9dIgdqrKZjTGzuWb2mpntEtu/n5nNix1nQkQvU0REyim1UyKBEiCRktslz9CCs+MeW+XurYEHgZGxbf8Axrt7GyALeCC2/QHgbXdPA9oCc2PbmwOj3P0QYCVwRmz7ICAjdpy+iXlpIiJSAaidEimEuXvUMYiUK2a2xt1r5bM9GzjW3ReZWVXgB3evZ2Y/Aw3cfWNs+/fuXt/MlgGN3H193DGaAq+7e/PY/YFAVXe/08xeAdYAE4GJ7r4mwS9VRETKIbVTIoVTD5BI6fICbpfE+rjbm9k6V+8UYBThKtw0M9McPhERKSm1U5LylACJlK6z435/ELs9FTgndrsX8G7s9mTgcgAzq2xmdQo6qJlVAvZz97eAgUAdYLureyIiIkVQOyUpT5m5SMntYmaz4+6/4u65JUbrmtkcwtWxnrFtVwNjzew6YBlwYWz7NcBoM7uYcAXtcuD7As5ZGfh3rPEx4AF3X1lKr0dERCoWtVMihdAcIJFSEhtbnenuP0cdi4iISF5qp0QCDYETEREREZGUoR4gERERERFJGeoBEhERERGRlKEESEREREREUoYSIBERERERSRlKgEREREREJGUoARIRERERkZTx/8fbgv9Wx5djAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "epochs = range(1, len(history.history[\"loss\"]) + 1)\n",
        "\n",
        "fig, axes = plt.subplots(1,2,figsize=(14,4))\n",
        "ax= axes.flatten()\n",
        "a, b = ax[0], ax[1]\n",
        "\n",
        "a.plot(epochs, history.history[\"loss\"], \"bo\", label=\"Training loss\")\n",
        "a.plot(epochs, history.history[\"val_loss\"], \"b\", label=\"Validation loss\")\n",
        "a.set_title(\"Training and validation loss\")\n",
        "a.set_xlabel(\"Epochs\")\n",
        "a.set_ylabel(\"Loss\")\n",
        "a.legend()   \n",
        "\n",
        "b.plot(epochs, history.history[\"accuracy\"], \"bo\", label=\"Training accuracy\")\n",
        "b.plot(epochs, history.history[\"val_accuracy\"], \"b\", label=\"Validation accuracy\")\n",
        "b.set_title(\"Training and validation accuracy\")\n",
        "b.set_xlabel(\"Epochs\")\n",
        "b.set_ylabel(\"Accuracy\")\n",
        "b.legend();"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 4.Submit"
      ],
      "metadata": {
        "id": "UGXZF-80lBAP"
      },
      "id": "UGXZF-80lBAP"
    },
    {
      "cell_type": "markdown",
      "source": [
        "# THE END"
      ],
      "metadata": {
        "id": "20a97788"
      },
      "id": "20a97788"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "369e67a9",
      "metadata": {
        "id": "369e67a9"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": [],
      "machine_shape": "hm"
    },
    "gpuClass": "premium",
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.7"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}