Fashion MNIST using ANN.ipynb

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  "metadata": {
    "colab": {
      "name": "Fashion MNIST using ANN.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "yiMFF7bsZAzp"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import Sequential\n",
        "from tensorflow.keras.layers import Dense, Flatten"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jow_j1iuZEJE"
      },
      "source": [
        "(X_train, y_train), (X_test, y_test) = keras.datasets.fashion_mnist.load_data()"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nTipnslEZRhX",
        "outputId": "0ba276ce-a995-4dc4-8c40-90a0d441feda"
      },
      "source": [
        "X_train.shape"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(60000, 28, 28)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H-8nXl2UZxkF",
        "outputId": "b1b66200-cc19-42a4-e572-332a75a7ce3a"
      },
      "source": [
        "X_train[0]"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
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          "metadata": {},
          "execution_count": 8
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      ]
    },
    {
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wJGSYYmQZz6y",
        "outputId": "c8a4faca-742e-42fb-99cc-de92f751693d"
      },
      "source": [
        "y_train[:5]"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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          "metadata": {},
          "execution_count": 10
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      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-AOcA0_GaGLD"
      },
      "source": [
        "def show_img(mat):\n",
        "    return plt.matshow(mat)"
      ],
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 275
        },
        "id": "xYWf6uWKaNn3",
        "outputId": "011a93ad-7486-455c-8d50-9f3708e342b7"
      },
      "source": [
        "show_img(X_train[0])"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 288x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vF77K9H-aQli"
      },
      "source": [
        "labels = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']  #Taken from Keras Docs"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "InN2ATx9Z2PJ"
      },
      "source": [
        "X_train_sc = X_train/255\n",
        "X_test_sc = X_test/255"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kGO5tUiDaugO"
      },
      "source": [
        "model = Sequential([\n",
        "                    Flatten(input_shape = (28,28)),\n",
        "                    Dense(32, activation = 'relu'),\n",
        "                    Dense(10, activation = 'sigmoid')\n",
        "])"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sqGblFXVa-Q-",
        "outputId": "7cdbb679-75ff-4115-b542-5220f314ad36"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " flatten_1 (Flatten)         (None, 784)               0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 16)                12560     \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 10)                170       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 12,730\n",
            "Trainable params: 12,730\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gmhRPxgIbAmK"
      },
      "source": [
        "model.compile(\n",
        "    optimizer = 'adam',\n",
        "    loss = 'sparse_categorical_crossentropy',\n",
        "    metrics = ['accuracy']\n",
        ")"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_yLmdMY-bVwT",
        "outputId": "fbc28198-2d46-47f2-a026-36343a72002d"
      },
      "source": [
        "model.fit(X_train_sc, y_train, epochs = 10)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.4320 - accuracy: 0.8483\n",
            "Epoch 2/10\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.4001 - accuracy: 0.8580\n",
            "Epoch 3/10\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.3807 - accuracy: 0.8655\n",
            "Epoch 4/10\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.3694 - accuracy: 0.8682\n",
            "Epoch 5/10\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.3600 - accuracy: 0.8704\n",
            "Epoch 6/10\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.3519 - accuracy: 0.8745\n",
            "Epoch 7/10\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.3445 - accuracy: 0.8754\n",
            "Epoch 8/10\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.3398 - accuracy: 0.8773\n",
            "Epoch 9/10\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.3346 - accuracy: 0.8797\n",
            "Epoch 10/10\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.3298 - accuracy: 0.8807\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fdfec2fbf10>"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YFhwY6DGbYJa",
        "outputId": "bad6f576-9865-4fc3-a857-0369b9cc821f"
      },
      "source": [
        "model.evaluate(X_test_sc, y_test)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 1s 1ms/step - loss: 0.4089 - accuracy: 0.8562\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.40893056988716125, 0.8561999797821045]"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nE_QY-noeALE"
      },
      "source": [
        "y_pred = model.predict(X_test_sc)"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zN4l3x-CeJJl",
        "outputId": "e8b0ed25-644b-4876-eff3-37c3f9b8ea32"
      },
      "source": [
        "y_pred[0]"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([3.4139246e-02, 1.7669797e-04, 4.1602463e-02, 2.3663342e-03,\n",
              "       2.8376460e-02, 9.9800742e-01, 9.1089100e-02, 9.8956859e-01,\n",
              "       4.1717839e-01, 9.9956030e-01], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1n0TCY5CfS13"
      },
      "source": [
        "y_pred_labels = [np.argmax(i) for i in y_pred]"
      ],
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "id": "cBqhP7FTeKeJ",
        "outputId": "47d169e7-0b04-4f51-96f3-6c7a996257a5"
      },
      "source": [
        "#Verifying the Results\n",
        "show_img(X_test[0])\n",
        "print('Actuals:', str(labels[np.argmax(y_pred[0])]))\n",
        "print('Predicted:', str(labels[y_test[0]]))"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Actuals: Ankle boot\n",
            "Predicted: Ankle boot\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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I5yFcuSQeJ18zL77/qv643jv1GdeXxgoLAoyMx9NCdp1cGdZ/sueKsL74jXjd/WXf3xHWx0/V/zx8M4xvixcE+Pqyj8L6jhP1x+El6cCpeD2BL07F6wWMjsavv5Hh+Pm75t76c3B+cvxlHRv9fMoXSHGykLv/SPWXm2jdv2gAbcG0YSA5QgBIjhAAkiMEgOQIASA5QgBIrjhPoJlaPU8AwNTe9m067oenHOrnSABIjhAAkiMEgOQIASA5QgBIjhAAkiMEgOQIASA5QgBIjhAAkiMEgOQIASA5QgBIjhAAkiMEgOQIASA5QgBIjhAAkiMEgOQIASA5QgBIjhAAkiMEgOSKIWBmq83sDTPbZWYfmtn91fWPmtl+M3u/+nNb69sF0Gx907jNqKQH3f1dM1sg6R0ze72qfcfdv9269gC0WjEE3H1Q0mB1+YSZ7Za0qtWNAWiPC3pPwMzWSrpR0tvVVfeZ2Q4ze8bMFje5NwBtMO0QMLP5kl6Q9IC7H5f0pKQrJa3XxJHC43Xut9nMtpvZ9hGda0LLAJppWiFgZjVNBMCz7v6iJLn7kLuPufu4pKckbZjqvu6+xd0H3H2gpv5m9Q2gSaYzOmCSnpa0292fmHT9ykk3u1PSzua3B6DVpjM68PuS/kzSB2b2fnXdw5LuNrP1klzSXkn3tKRDAC01ndGBH0ma6nvNX21+OwDajRmDQHKEAJAcIQAkRwgAyRECQHKEAJAcIQAkRwgAyRECQHKEAJAcIQAkRwgAyRECQHKEAJAcIQAkZ+7evgcz+1zSp5OuWirpUNsauHD015hu7q+be5Oa39/l7r5sqkJbQ+BXHtxsu7sPdKyBAvprTDf31829Se3tj9MBIDlCAEiu0yGwpcOPX0J/jenm/rq5N6mN/XX0PQEAndfpIwEAHUYIAMkRAkByhACQHCEAJPd/9ejb/QUidPQAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 288x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "s2v3tMhfevlQ",
        "outputId": "c920a1b0-b3ea-423b-8096-1aec9cf499c4"
      },
      "source": [
        "cm = tf.math.confusion_matrix(y_test, y_pred_labels)\n",
        "\n",
        "plt.figure(figsize = (10,6))\n",
        "sns.heatmap(cm, fmt = 'g', annot = True, xticklabels = labels, yticklabels = labels)\n",
        "plt.xticks(rotation = 45)\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('Actuals')\n",
        "plt.show()"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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sZ+lwadFXmfpdm7vta27NmxLb9nSlRETGi8hoEQkDPhORmiKyTkS2iMhsESns2m6ViAS5bvuLyEHX7XtEZL2rt2yLiFRyre+WZP0YEfFxrT8vIiNEZDNQz13vMzb2OJsitwFw/vwFdu3aS2DJAHe9fKr2/3mYalXvIneunPj6+hBU8x6Wrf4jXc9duHw1bZs3zOaEGXfhwkUA/Px88fXzw45/hCTP6IsxJrHgAsiVK5ctc4N9j+XU1Kl9H1FRBzlw4BBxcXFMnz6X9u1aWx0rTSGhYfx96rTVMdLN09rZ045j8Lw2ThenM3OLDXhU0eVSCqhvjHkDmAi8bYypDmwFBqXx3BeBL40xNYEgIFpEqgJdgAdd6+OBrq7t8wJhxpgaxpjQbHgvaSpTphQ1a9xL2PpNVrx8MhXLlWHjlh2cPnOWS5evELJuA7HHTwIwdfYCHun5KgM/+ZIzSQqC6xavCOWh5o3cHTlNDoeDiPClHD2yheXLV7M+3Pp2vpHD4SD0j/lEHQxn5Yo1RERsBuCb0Z+x78B67rqrPGO+nWBxyrTZ6VhOTcnAAA5HxyTejz5ylJI2/+XqiTy5nT3hOAbPbmNv5olF1wxjTLyIFAQKGWN+d62fAKT1W/0P4D0ReRsoY4y5BDQHagHhIhLpul/etX08MCvL30E65c2bh+nTvuONtwYl69mwSoWypXnmqUfp9eYgXnxrEJUrlsPhcNClY1sWTR3DrB+/5I6iRRg26odkz9uyYze5c+akUvkyFiVPndPpJKh2K8qUC6J20H3cc09lqyPdxOl00qBeMFXvqk+tWtWpevddALz8Yn/uqlCXPbujePSxYItT3prdjmWlbocexxbzgjldnlh0XUjHNtf4573lur7SGDMFaA9cAhaKSDNAgAnGmJqupbIx5gPXUy6nNo9LRHqJSISIRDid6YmUMb6+vsyY9h1Tp85mzpxFWb7/29UpuBXTv/+cCSM/oUD+fJQtHYh/kcL4+PjgcDh4LLgV226YM7BoeQhtW9hvaDGpM2fOsur3NbRu1cTqKKk6c+YcIavX0aLlP39bOJ1OZs6cR4cObSxMdmt2PZZTEnMkltKlSibeLxVYgpiYWAsTeSdPbGdPOo7BM9s4TTq8aB1jzBnglIhc/23eHbje63WQhN4rgMeuP0dEygP7jTFfAXOB6sBy4DERKebapoiIpNklY4wZa4wJMsYEORx5s+ItJfPd2BHs3LWPL768vYnn2eUv17yRo8dOsHz1HzzUohEnTv6d+PjykHVULPdP8zmdTpasDKWtDYcW/f2LULBgASBhXlSL5o3YvTvK4lTJFfUvQsGC+QHIlSsnTZs1YO+e/ZRP0mv40MMt2LPHXrmTsuuxnJLwiEgqVixH2bKl8fPzo3PnDsybv9TqWF7HE9vZk45j8Mw2TpMX9HR5+slRewCjRSQPsB/4j2v9cGC6iPQCFiTZvjPQXUTigFjgY2PM3yIyEFgqIg4gDugN/OmuN3GjB+vXpnu3x9iydQcR4Qk/JO+//wmLFmf8G4dZre/7n3D6zDl8fX0Y0PdFCuTPxztf/o/dew+AQGBAcQa99XLi9hGbtxNQzJ/SNpxLUKJEcX784Qt8fBw4HA5mzpzHgoXLrI6VTEBAMUaPHebqSRRmz1rIksUrWfLbNPIXyI8IbNu6i7593rc6aorsfCynJD4+nj6vD2Thgin4OByMnzCNHTv2WB0rTZMnjaJxo3r4+xfh4P4IBg8ZzrjxP1sdK1We1s6edhyD57VxutiktyozPOKUEXaX1aeMcIfbOWWElbLrlBHZKatOGeFOWX3KCKWU93L7KSNmf5K5U0Y88o6eMkIppZRSKk1uGF4Ukb4isl1EtonIVBHJJSLlRCRMRPaJyLTrJ2gXkZyu+/tcj5dNa/9adCmllFLK/rJ5Ir2IBAKvAUHGmHsBH+AJ4FPgc2NMReAUcP2SBM8Cp1zrP3dtd0tadCmllFLK/tzz7UVfILeI+AJ5gKNAM2Cm6/EJQEfX7Q6u+7geby4itxzC1KJLKaWUUvZnTKaWpKd6ci29ku/eHCHhi3iHSCi2zgAbgNNJLjsYDQS6bgcCh13Pvebavuit3oKnf3tRKaWUUipNxpixQKrn/HBdSrADUA44DcwAsvQkiFp0KaWUUsr+sv+UES2AA8aYEwAi8gvwIFBIRHxdvVmlgCOu7Y8ApUm4pKAvUBD461YvoMOLSimllLK/7J/TdQioKyJ5XHOzmgM7gJX8c6L1HiScXB3gV9d9XI+vMGmch0t7upRSSillf9l8VnljTJiIzAQ2knA5wU0kDEcuAH4WkaGuddcvMPwDMElE9gF/k/BNx1vSoksppZRSCjDGDAIG3bB6P1AnhW0vA49nZP9adCmllFLK/rzgMkBadCmllFLK/rzgsoVadCmllFLK/rSnSwH4OnysjpBheTzsAtLnZva1OkKG+T8x0uoIGVauYIDVETLkwJlYqyNkWN4cuayOkCEXrl62OoJSCbyg6NJTRiillFJKuYH2dCmllFLK/rL5lBHuoEWXUkoppWzPOHUivVJKKaVU9vOCOV1adCmllFLK/rxgeFEn0iullFJKuYH2dCmllFLK/nROl1JKKaWUG+icLqWUUkopN/CCokvndCmllFJKuYH2dCmllFLK/rzggte26ukSkXgRiRSRbSIyQ0TypLH9KhEJct0+KCL+7kmaPcaMGcahQxvZsOG3xHXVq9/N77/PISxsEWvWzCcoqIaFCVN3110ViAhfmrj8dXIXr736nNWxAJi0eiuPDp9Bp+EzeeenFVyJu8a7U1bQ4bPpdBo+k0HTfycuPqHbesHGfTw+YhaPjZjJ0yPnsjvmL0uzBwaWYOGiqURs+I3wiKW8/PJ/AHj//94gLGwRf6xbyK+/TiSgRDFLc96oR68nWbB6GgtDptPzhSeTPfbMS93Ye2IDhYsUsijdrbVu1YTt21aza0co/fv1tjpOinLmzMGKVb8Q+sd81oUv4t0BfQD4ZvRnbNm2ipC18whZO49q1apanDR1ntDOSX03dgQx0ZuJ3LTc6ijp5mltnCanM3OLDdiq6AIuGWNqGmPuBa4CL1odCEASZHtbTZo0g/btn0627uOP3+Ojj77ggQfaMmTICD7++L3sjnFb9uyJIqh2K4Jqt6LOA224ePESc+YusjoWx85cYGroNqb0eYRZbz1GvNPJ4sj9PHRfReb0e5yZb3biSlw8s8N2ARBYJD8/vBTMzDcfo1eL+/lwZoil+ePjr/Heu0MJqtWSpk0eodcL3alSpSJffD6WBx5oS726D7Fo0QrefbePpTmTqlSlAp27daRT6x60a/IkTVo25M5ypQAIKFmcBk3rcuTwUYtTpszhcPDVlx8R3K4b1Wo0pUuXjlStWsnqWDe5cuUq7R7uRoN6wTSo144WLRoRVLsmAO8P/ISG9dvRsH47tm7daXHSlHlKOyc1ceJ0Hg7uanWMdPPENk6T02RusQG7FV1JhQAVRaSJiMy/vlJERopIz1s9UUTecPWWbROR113rPhGR3km2+UBE3nLd7ici4SKyRUQGu9aVFZHdIjIR2AaUzvq3mFxo6HpOnTqdbJ0xhgIF8gNQsGB+jh49lt0xMq1Zswbs3/8nhw4dsToKAPFOw5W4a1yLd3I57hp3FMhDw6p3IiKICPeUvoNjZy4AULNscQrkyQlA9TuLJa63SmzsCSIjtwNw/vwFdu+OomTJAM6dO5+4Td68eTA26navcFc5Nm/cxuVLl4mPjyd87UZaP9wMgAFD3+CzwV/aKm9SdWrfR1TUQQ4cOERcXBzTp8+lfbvWVsdK0YULFwHw8/PFz8/Xtm2aEk9q5+tCQsP4+4bPZzvzxDZOk3FmbrEBWxZdIuILtAW23sZzawH/AR4A6gLPi8h9wDSgc5JNOwPTRKQVUAmoA9QEaolII9c2lYBvjDH3GGP+vN33kxlvvTWY//73PfbtW8d//zuQ99//1IoYGdKlcwemTZtjdQwAihfMy9ONq9Pmo6m0/PAn8uXKQf3KpRIfj4t3smDjXh5Msu662et306BKttfa6XbnnaWoUeNuwsMjARj0wVvs3rOWLl06MPTD/1mc7h97d+4jqO59FCpckFy5c9G4xYMEBBaneZvGHDt6gl3b91odMVUlAwM4HB2TeD/6yFFKlgywMFHqHA4HIWvnse/AelauWMOGiM0AvP9/b7Jm3QI+/mQAOXLksDhlyjypnT2VtrE92a3oyi0ikUAEcAj44Tb20QCYbYy5YIw5D/wCNDTGbAKKiUhJEakBnDLGHAZauZZNwEagCgnFFsCfxph1Kb2IiPQSkQgRiYiPP5/SJlmiV6/u9Os3hIoV69K//xBGjx6Wba+VFfz8/AgObsXMWfPT3tgNzl68wqrtB1nw7hMsfb8rl65eY8GGf37pf/xLKPeXK8H95Uske174vhjmhO+mz0N13B05RXnz5mHK1G/p339IYi/X4A+GU/mu+kybNpcXXuxhccJ/RO09yNivJzBuxih+nPY1O7ftIUeOHLz0+jN88cloq+N5DafTScP67bi78oPcH1SDqnffxeBBwwi6vyVNGz1C4cKFeP2NXlbHVCrr6PBilrs+p6umMeZVY8xV4BrJc+bKxP5nAI8BXUjo+QIQ4L9JXreiMeZ6sZfq2JIxZqwxJsgYE+Tjky8TkW6tW7dOzJmTMDdq1iz7TqS/rk2bpmzatJXjx09aHQWAdXuPEFgkP0Xy5cbPx0Hze8sS+WfCEO3opRs4deEyb7Wrm+w5e2L+YvCM1XzRsyWF8mbmcMsavr6+TJkymmk/z+HXuUtuevznn+fQsUMbC5KlbuZPc3mkRTeeav88Z86cZd/u/ZS6syTzVk1l5YZ5BJQsxpzlP+FfrKjVUZOJORJL6VIlE++XCixBTEyshYnSdubMOUJW/0GLFo04duwEAFevXuWnyTOpVcuenxee2M6exhvb2DidmVrswG5FV0r+BO4WkZwiUghonsb2IUBHEckjInmBR1zrIKHQeoKEwmuGa90S4BkRyQcgIoEiYpuvgh09eoxGjRKKgqZNH2TfvoPWBkpDly4dbTO0CFCicD62HDrOpavXMMYQti+G8sUK8UvYLtbuieaTrs1wOCRx+6OnzvPmxGUMfbIpZe6wx7frvv32U3bv3sfXX//T8VuhQtnE28HBLdm9J8qCZKkr4l8YgBKBAbR6uBm//DyPune3pGmtdjSt1Y7YmON0bN6Vk8et/XbojcIjIqlYsRxly5bGz8+Pzp07MG/+Uqtj3aSofxEKFkyY65krV06aNmvAnj1RFC9+R+I2Dwe3ZOeOPVZFvCVPaWdP5pVt7AU9XbY/T5cx5rCITCdhMvsBEoYBb7X9RhEZD6x3rfreNbSIMWa7iOQHjhhjjrrWLRWRqsAfIgJwHugGxGfH+7mViRO/pmHDevj7F2bfvjCGDv0fL7/8DsOHf4Cvrw+XL1+hd+933B0r3fLkyU2L5o14+eW3rY6SqNqdxWhRrTxPfvELPg4HVQKL0qluVeoNGEeJQvl4+uu5ADSvVo4XWt7P2GUbOX3xMh//EgqAr4+DKX0esSx/vXpBPNW1E9u27uSPdQsB+GDQZzzdowt3VSqP0+nk0OEjvPbaAMsypmTkuGEULlyQuLhrDH77E86dzb4h+KwUHx9Pn9cHsnDBFHwcDsZPmMYOGxYuAcXvYPTYYTh8fHA4HMz+ZQFLFq9k3oLJFPUvgoiwdcsO+vZ53+qoKfKUdk5q8qRRNG5UD3//IhzcH8HgIcMZN/5nq2OlyhPb+N9APOkbL3aVK9edHteI8U6315SZcnZmX6sjZJj/EyOtjpBhJfPaa7gvLQfOeN5wSd4c1g9ZZ8SFq5etjqBs6trVI5L2VlnnwtBumfpdm3fgZLfmTYnte7qUUkoppewyRJgZWnQppZRSyv5sMhk+M7ToUkoppZT9eUFPlyd8e1EppZRSyuNpT5dSSiml7M8ml/LJDC26lFJKKWV/XjC8qEWXUkoppWzPLmeVzwwtupRSSillf17Q06UT6ZVSSiml3EB7upRSSillf17Q06VFl1JKKaXsT7+9qABcF8r2KJ6WuVDnL62OkGFnt8+wOkKGFbjncasjeIDz9R0AACAASURBVL3L165aHUEpz+QFPV06p0sppZRSyg20p0sppZRStme8oKdLiy6llFJK2Z8WXUoppZRSbqAnR1VKKaWUcgMv6OnSifRKKaWUUm6gPV1KKaWUsj8v6OnSoksppZRStmeMFl1KKaWUUtlPe7qUUkoppdzAC4ounUivlFJKKeUGHl10iUi8iESKyHYR2Swib4qIR76nUqVKsHjxz2zcuIwNG36jd+//AFC4cEHmz5/M1q2rmD9/MoUKFbA46T/GjhlO9OFINm1clriucOFCLFw4he3bQ1i4cAqFChW0MOHNxowZzuFDm9i44Z/M//14AFs2ryQifCnTp31HwYLWt/HkOUt55KX3eOTFd5k0ZwkAI374mfa93qHTywN4/cMvOXv+AgALVq7l8VfeT1xqPNyTXVF/WpY9pTa+7vU+vbhy+TBFixa2IFn6fDd2BDHRm4nctNzqKKlKqY0HDuzL/qhw1octZn3YYtq0bmphwrS1btWE7dtWs2tHKP379bY6Tpo84bi4kae1cVqM02RqsQOPLFCSuGSMqWmMuQdoCbQFBt24kYjYfhj12rV43nlnKPff34LGjTvywgtPU6VKJd5662VWrVpDtWpNWLVqDW+99bLVURNNnDSD4Hbdkq3r3683K1es4Z57GrJyxRrb/aBPmjSDdu27J1u3fEUI993fgqDardi7d7/lmfcejGbWklVM+XwQM0YNZfX6SA7FHKPefffwy7cfMeubjygTGMAP0+cD8HDT+swY+SEzRn7IR2/2IrC4P1UqlLEsf0ptDAl/WLRo0Yg/D0VbkCr9Jk6czsPBXa2OcUuptfHXX39PnQfaUOeBNixestKCZOnjcDj46suPCG7XjWo1mtKlS0eqVq1kdaxb8oTjIilPbOM0OU3mFhvw9KIrkTHmONALeEUS9BSRX0VkBbBcRPKKyI8isl5ENolIBwARuce1LlJEtohIJde2C1y9Z9tEpEt254+NPU5k5DYAzp+/wK5d+yhZsjjBwS2ZPHkWAJMnz6Jdu1bZHSXdQkPDOHXqdLJ17dq1YtLkGQBMmjyD9u1bWxEtVSllXrZsNfHx8QCErd9EYKkSVkRLdOBwDNUrVyB3rpz4+vgQdG8Vlq2JoP791fD18QGgepUKHDt56qbnLvp9HW0a13V35GRSamOAYZ8N4t33PrL9N5BCQsP4O4X8dpJaG3uKOrXvIyrqIAcOHCIuLo7p0+fSvp29Pitu5AnHRVKe2MZpcmZysQGvKboAjDH7AR+gmGvV/cBjxpjGwABghTGmDtAUGCYieYEXgS+NMTWBICAaaAPEGGNqGGPuBRa7833ceWcpata8h/DwSIoV8yc29jiQUJgVK+bvzigZ5ml5b9SzR2eWWNxDULFMKTZu283ps+e5dPkKIRGbOXby72TbzF4aQoOgajc9d8nqMNpaXHSlpF1wK2JiYtm6dafVUbzaiy/1ICJ8KWPGDLfd0H5SJQMDOBwdk3g/+shRSpYMsDCR99E2tievKrpS8Jsx5vpvq1bAOyISCawCcgF3An8A74nI20AZY8wlYCvQUkQ+FZGGxpgz7gqcN28epk4dTb9+Qzh37vxNj9u8k+Amdu/VSOrtt1/l2rV4pk6dbWmO8neW5D+PP8wLAz/jpfeHU7n8nTgc//yojv35V3x9HDzctH6y523ZFUWunDmpVLaUuyPfUu7cuejf/xUGDxlhdRSvNnbsJKpWbUDtOq2JjT3Op5++b3UkpbKUzumyGREpD8QDx12rLiR9GOjkmgNW0xhzpzFmpzFmCtAeuAQsFJFmxpg9JPSSbQWGisj/pfBavUQkQkQirl27uTi6Hb6+vkydOppp0+Ywd25C59rx4ycJCEjouAsIKMaJEyez5LWyy815/7I4Ufp07/44D7VtTo+er1odBYBHWzdm2ldDGD9sAAXy5aVMYMJfqHN/C2H1+kj+2+9FRCTZcxavXkfbJvbr5Spfvixly5YmPHwJu3evpVRgCdatW0Tx4ndYHc2rHD9+EqfTiTGGH3+cQu2gmlZHSlXMkVhKlyqZeL9UYAliYmItTOR9vLKNdU6XfYjIHcBoYKRJuXtlCfCquH5Tich9rn/LA/uNMV8Bc4HqIlISuGiMmQwMI6EAS8YYM9YYE2SMCfL1zZcl72H06M/YvXsfX331feK6BQuW0a1bJwC6devE/Pm/ZclrZZd583+je7fHAeje7XHmzVtqcaK0tWrZhDffeJFOjz3DpUuXrY4DwF+nzwJw9PhfLF+7gYea1CU0YgvjZi7kq0GvkztXzmTbO51Oloasp22jB6yIe0vbt++i9J33UblyfSpXrk/0kaPUrduWY8dOWB3Nq1z/YwegQ/s2bN++28I0txYeEUnFiuUoW7Y0fn5+dO7cgXnz7f9Z4Um8so29YE6X7b/Vl4bcruFCP+AaMAn4Xyrbfgh8AWxxnVbiABAMdAa6i0gcEAt8DNQmYc6XE4gDXsrWdwHUrx9E166d2Lp1J+vWLQRg0KBhDB/+DZMnf0OPHl04dOgI3brZ59uLkyaOpFGjevj7F2F/VDhDPhzBsGEjmTJlND3/8wSHDkXz1FPZ3nQZMnHiSBo1rIu/fxGi9q3nw6Ej6N/vFXLkzMHCBVMAWL9+I6+8+p6lOd/46GvOnD2Pr68P773cnQL58vLfbydxNe4aLwwYBkD1yhV4/9WeAGzYtpvi/kUpVaLYLfbqHim18fjx06yOlW6TJ42iseu4Prg/gsFDhjNu/M9Wx0ompTZu1KgeNarfgzGGP/+Mpvcr71gdM1Xx8fH0eX0gCxdMwcfhYPyEaezYscfqWLfkCcdFUp7YxmmxyxBhZognzbmxq9y5y3hcI8Y7462OkCE3DqV5grPbZ1gdIcMK3PO41REyJN5pkz9fM8DH4VkDDJ7Yxso9rl094tYP5lOPN8nU79rCM1ZZ/ovE03u6lFJKKfVv4AX1vxZdSimllLI9bxhe9Kx+bqWUUkr9O7lhIr2IFBKRmSKyS0R2ikg9ESkiIr+JyF7Xv4Vd24qIfCUi+1wnV7/pS3c30qJLKaWUUrZnnJlb0ulLYLExpgpQA9gJvAMsN8ZUApa77kPCpQcruZZewLdp7VyLLqWUUkr964lIQaAR8AOAMeaqMeY00AGY4NpsAtDRdbsDMNEkWAcUEpFbXkdOiy6llFJK2V/2Dy+WA04A41zXaP7edbnA4saYo65tYoHirtuBwOEkz492rUuVFl1KKaWUsr3MDi8mvZKMa+l1w0v4knAy9G+NMfeRcFWbZCe8c518/bZn9Ou3F5VSSillf5k8ZYQxZiww9habRAPRxpgw1/2ZJBRdx0SkhDHmqGv48PqlBo8ApZM8v5RrXaq0p0sppZRS/3rGmFjgsIhUdq1qDuwAfgV6uNb1IOGSgbjWP+36FmNd4EySYcgUaU+XUkoppWwvA99AzIxXgZ9EJAewH/gPCR1U00XkWeBPEi4fCLAQeAjYB1x0bXtLWnQppZRSyvbcUXQZYyKBoBQeap7CtgbonZH9a9GllFJKKdtzU09XttKiKwvExV+zOoLXu79oBasjZFjZWs9YHSHDTn3xqNURMqTAazOtjpBhuXxzWB0hQy5cvWx1BKUSGMuvV51pOpFeKaWUUsoNtKdLKaWUUranw4tKKaWUUm5gnJ4/vKhFl1JKKaVsT3u6lFJKKaXcwOhEeqWUUkoplR7a06WUUkop29PhRaWUUkopN9CJ9EoppZRSbmCM1QkyT+d0KaWUUkq5gfZ0KaWUUsr2vGF40St7ukQkQER+FpEoEdkgIgtF5K4M7qOQiLycXRnT8t3YEcREbyZy03KrImSInfMO/N/bLN4yh6krxiWue6HfM/y07Ecm//Y9X00djn/xogDkL5iPz34Yyk/LfmTcgtGUr1zOqtiJChTMz3cTPidk/XxWh82jVu0aBHdozao/fuXI39uoUfMeqyNy8O/zdJm8JnFpMOo3ftp4EICpm/7kkfEhdJoQyherdyc+54f1UbT/cTUdx69m7cETFiW/WetWTdi+bTW7doTSv19vq+OkKGfOHKxY9Quhf8xnXfgi3h3QB4BFS38mZO08QtbOY9fetfw0dbTFSVPnCe2clJ0/41LjaW2cFuOUTC124HVFl4gIMBtYZYypYIypBbwLFM/grgoBlhVdEydO5+Hgrla9fIbZOe+CaYvo07VfsnWTv/2Zri2eoVvL5whd9gfP9e0BQM/XurFn+166tniGD/p8zJtDXrUicjIffvIuK5eF0rBOMM0bPMrePfvZvXMvz3Z/jXVrI6yOB0DZIvmY1u1BpnV7kClP1SeXrw9NKxYn/PBfrIo6zrRuDzKrRwOeDioLQNRf51myO5aZTzdg1CNB/HfFDuKd1k/YcDgcfPXlRwS360a1Gk3p0qUjVatWsjrWTa5cuUq7h7vRoF4wDeq1o0WLRgTVrknbVk/QsH47GtZvR/j6Tcz7dYnVUVPkKe2clJ0/41LiiW2cFmMyt9iB1xVdQFMgzhiT+CeeMWYzECoiw0Rkm4hsFZEuACKST0SWi8hG1/oOrqd9AlQQkUgRGebuNxESGsbfp067+2Vvm53zbgrbwtlT55Ktu3D+YuLt3LlzJf5AlqtUlojQTQD8ue8QJUoHUMS/sNuy3ih/gXzUrR/ElEmzAIiLi+PsmXPs3bOfqH0HLct1K+sP/0WpgnkoWSA3MzYf5j+1y5HDN+GjpkienACsijpG68oB5PB1EFgwD6UL5WFbrPXHT53a9xEVdZADBw4RFxfH9Olzad+utdWxUnThQsIx7Ofni5+fLybJb5X8+fPRqFE9Fsz/zap4t+RJ7XydnT/jUuKJbZwW7emyp3uBDSmsfxSoCdQAWgDDRKQEcBl4xBhzPwkF2whXb9k7QJQxpqYxpl8K+1Me7qW3n2NexAzaPNqCMcN+AGDvjiiaPtQQgLtrViGgVHGKlbjDsox3linFXyf/5otvPmLp6lkM/2oIufPktixPeizZfZQ2VUoA8OfpC2w6coruU//g2elhbI89A8CJ81cIyP/P+yiWLxfHz1+xJG9SJQMDOBwdk3g/+shRSpYMsDBR6hwOByFr57HvwHpWrljDhojNiY89HNyS339fy7lz5y1MmDpPamdPpW1sT95YdKWmATDVGBNvjDkG/A7UBgT4WES2AMuAQDI+FKk80Leffk+7oMdZ/MsyHn/mUQAmjvyJfAXzM/m37+n8TCf2bNtHvNO6M/L5+vhQrcbdTPhhGq0adeLSxUu82vc5y/KkJS7eye9Rx2lZKeHDPd5pOHMljolP1KVvo8r0XxCZrEdG3T6n00nD+u24u/KD3B9Ug6p3/zNt9bHH2zFzxjwL0ymV9YyRTC12kKGiS0QKi0j17AqTRbYDtTKwfVfgDqCWMaYmcAzIldaTRKSXiESISITTeeH2kipbWDz7N5o91AhIGHb8sO8ndGv5HB+89hGFihYk5s+YNPaQfWJijnE05hibNmwBYP7cpVSrfrdledISevAEVYoVoGjehGHE4vly0bxicUSEewMK4RA4dSmOO/LlJPbcpcTnHT9/mWL5cloVO1HMkVhKlyqZeL9UYAliYmItTJS2M2fOEbL6D1q0SDiGixQtTK1a1VmyeKXFyVLnie3sabyxjY0zc4sdpFl0icgqESkgIkWAjcB3IvK/7I9221YAOUWk1/UVrkLxNNBFRHxE5A6gEbAeKAgcN8bEiUhToIzraeeA/Km9iDFmrDEmyBgT5HDkza73orJJ6XKBibcbt27AwX2HAMhXIB++fglnUunwVDCR67Ykm//lbieOnyQmOpYKFcsC0KBxXfbsjrIsT1oW7/pnaBGgSYVihB/+G4A/T10gLt5QOLcfTcoXY8nuWK5ec3LkzEUOnbrIvQGFrIqdKDwikooVy1G2bGn8/Pzo3LkD8+YvtTrWTYr6F6FgwYSPp1y5ctK0WQP27Ek4Ljp2bMPixSu5cuWqlRFvyVPa2ZN5Yxs7jWRqsYP0nKeroDHmrIg8B0w0xgxyDcXZkjHGiMgjwBci8jYJc7YOAq8D+YDNgAH6G2NiReQnYJ6IbAUigF2u/fwlImtEZBuwyN3zuiZPGkXjRvXw9y/Cwf0RDB4ynHHjf3ZnhAyxc94Pv/k/atWrSaEiBZkXMYPvRoyjfrO6lKlQGqfTEHvkGJ+8PQKAcpXKMOiLdzEY9u8+yNA3P7U4PQx4+yNGffcZfjn8OHQwmtdfHkDb4OYM/XQARf2LMGn6t2zfuosnO/VKe2fZ6FLcNcIO/cXAFv+cwqLjvaX4YOlWHpsYip+PgyGtqyEiVPDPT6u7Aug0MQQfh/BOs7vxcVj/oRgfH0+f1weycMEUfBwOxk+Yxo4de6yOdZOA4ncweuwwHD4+OBwOZv+yILFn69HHgvl8xBiLE96ap7RzUnb+jEuJJ7ZxWuwyRJgZktb8Clcx0gqYAAwwxoSLyBZjjN2HGd3GN0egTlLJZvf7V7Q6QoYdvmifc0+lV9QnLa2OkCEFXptpdYQMy5sjzdkLtnLh6mWrIyibunb1iFuroN1V2mbqd23lXYssr9rS09M1BFgChLoKrvLA3uyNpZRSSin1D7uc9iEz0iy6jDEzgBlJ7u8HOmVnKKWUUkqppLzhi8+pFl0i8jUJc59SZIx5LVsSKaWUUkrdwNt7uuxxfRGllFJKKS+QatFljJngziBKKaWUUqmxy2kfMiPNOV2uc1q9DdxNkpOGGmOaZWMupZRSSqlE3nDKiPSckf4nYCdQDhhMwjmvwrMxk1JKKaVUMsZkbrGD9BRdRY0xPwBxxpjfjTHPANrLpZRSSim3+beckT7O9e9REXkYiAGKZF8kpZRSSinvk56ia6iIFATeBL4GCgB9szWVUkoppVQS3jCnKz0nR53vunkGaJq9cZRSSimlbmaXeVmZkZ5vL44jhZOkuuZ2KeU2G0/uszpChtyRpyAnLp6xOkaGedr1DC/FhFgdIUP8y7b0uOsZ+jp8rI6QIdec8VZHuC0+jvRMs7aHeKfT7a9pl3lZmZGe4cX5SW7nAh4hYV6XUm7jaQUXoAWXG3hawQWedwFpTyu4PJUnFVxgTd5/y/DirKT3RWQqEJptiZRSSimlvFB6erpuVAkoltVBlFJKKaVS868YXhSRcySf0xVLwhnqlVJKKaXcwgvm0adreDG/O4IopZRSSqXGG3q60pwJJyLL07NOKaWUUkqlLtWeLhHJBeQB/EWkMHC9xCwABLohm1JKKaUU4P3fXnwBeB0oCWzgn6LrLDAym3MppZRSSiVy/5nBsl6qRZcx5kvgSxF51RjztRszKaWUUkolY/D8nq70nN3MKSKFrt8RkcIi8nI2ZlJKKaWUSsZpMrfYQXqKrueNMaev3zHGnAKez75ISimllFLeJz0nR/URETEm4VKTIuID5MjeWEoppZRS/3D+S4YXFwPTRKS5iDQHpgKLsjfWzURkgIhsF5EtIhIpIg9kwT5XiUhQZrfJDq1bNWH7ttXs2hFK/3693f3yt0UzZ7/vxo4gJnozkZs856wtdm3jSdPn0LHbi3To+gKTps1OXP/TjLm0e/J5OnR9gRGjfgBg/pIVdOrRO3Gp1uAhdu2Jsir6TezaxkmNGTOMQ4c2smHDb4nrJk0aRVjYIsLCFrF79xrCwtz+qyXdPOFnb8yY4Rw+tImNG5bd9NjrfXpx5fJhihYtbEGyrGGQTC12kJ6i621gBfCia9kK5M7OUDcSkXpAMHC/MaY60AI47M4M7uRwOPjqy48IbteNajWa0qVLR6pWrWR1rFvSzO4xceJ0Hg7uanWMdLNrG+/df5BZvy5m6vdfMGvCN/y+dj2HomNYv2EzK0PXMWvCKOb+NIaeT3UCILh1M2ZNGMWsCaP47/+9RWCJ4lS5q4LF7yKBXdv4RpMmzaB9+6eTrevevTcPPNCWBx5oy+zZi5g7d7FF6dLmCT97kybNoF377jetL1WqBC1aNOLPQ9EWpMo6zkwudpBm0WWMcQJhwEGgDtAM2Jm9sW5SAjhpjLniynTSGBMjIv8nIuEisk1ExoqIQGLv1Kcisl5E9ohIQ9f63CLys4jsFJHZJCkeReRbEYlw9aYNdvP7S6ZO7fuIijrIgQOHiIuLY/r0ubRv19rKSGnSzO4REhrG36dOp72hTdi1jfcfPEy1eyqTO1cufH19CKpZjWW/r2HanAU8260zOXIkzKAoWrjQTc9d+NvvtG3R2N2RU2XXNr5RaOh6Tt3i2H3ssWCmTZvrxkQZ4wk/e6GhYSm28bDPBvHuex/hmiWkLJRq0SUid4nIIBHZBXwNHAIwxjQ1xrj7PF1LgdKuAuobEbn+iTfSGFPbGHMvCQVUcJLn+Bpj6pBwrrFBrnUvAReNMVVd62ol2X6AMSYIqA40FpHq2fmGbqVkYACHo2MS70cfOUrJkgFWxUkXzaxSYtc2rli+DBs3b+f0mbNcunyZkD/CiT12goOHjrBh8zaefP51evbux9adu2967uLlv/NQyybuD50Ku7ZxRjRoUIdjx04SFXXQ6ihep11wK2JiYtm61d19JVnPG4YXbzWRfhcQAgQbY/YBiEhft6S6gTHmvIjUAhoCTUmYY/YOcE5E+pNw5vwiwHZgnutpv7j+3QCUdd1uBHzl2ucWEdmS5GU6i0gvEtqkBHA3kPTxZFzb9gIQn4I4HHkz+zaVUm5SoeydPNP1cXr1HUDuXLmoXKk8DoeD+Ph4zp49x5Sxn7Nt5x7eev+/LJ4xDlcnOlu27yJ3rlxUKl/W2jfgZTp37sD06fbt5fJUuXPnon//V2w/LJpedhkizIxbFV2PAk8AK0VkMfAzWFcqGmPigVXAKhHZSsIZ86sDQcaYwyLyAZAryVOuuP6NJ41vaYpIOeAtoLYx5pSIjL9hXynlGQuMBfDNEZilfbYxR2IpXapk4v1SgSWIiYnNypfIcppZpcTObdypXWs6uYbhvhg9noBi/hw4FE2Lxg8iIlS7uzIiwqnTZyjiGmZctMxeQ4tg7zZODx8fHzp0aEP9+g9bHcXrlC9flrJlSxMevgRIODbWrVtEgwbtOHbshMXpMs4biq5UhxeNMXOMMU8AVYCVJAzTFXPNfWrlroAAIlJZRJLODK0JXO/3Pyki+YDH0rGr1cBTrn3eS0LRBgnXk7wAnBGR4kDbLAl+m8IjIqlYsRxly5bGz8+Pzp07MG/+UisjpUkzq5TYuY3/cs19ORp7nOW/r+Ghlk1o1rAe6zduBuDgoWjirl2jcKGCADidTpasCLFd0WXnNk6PZs0asGdPFEeOeE6h6Cm2b99F6Tvvo3Ll+lSuXJ/oI0epW7etRxZc4P3DiwAYYy4AU4AprgtfP07CNxrd+VOdD/jadWb8a8A+Eob2TgPbgFggPB37+RYYJyI7SfgywAYAY8xmEdlEwpDqYWBNlr+DDIiPj6fP6wNZuGAKPg4H4ydMY8eOPVZGSpNmdo/Jk0bRuFE9/P2LcHB/BIOHDGfc+J+tjpUqO7dx3/eGcvrsWXx9fRnw5ssUyJ+PR4NbMfDjz+nY7UX8/Hz5eOCbiUOLEZHbCCjmT+nAEhYnT87ObZzUxIlf07BhPfz9C7NvXxhDh/6P8eOn0blze6ZN+9XqeGnyhJ+9iRNH0qhhXfz9ixC1bz0fDh3B+PHTrI6lkhD9NkPmZfXwolIqfS7FhFgdIcNyl2xodYQM8XX4WB0hw645462OkGE+jvScwclerlw+7Nbuo3kBT2bqd2272KmWd3el54z0SimllFKW8oYz0mvRpZRSSinb84YhJS26lFJKKWV7Xv3tRaWUUkqpfxMR8RGRTSIy33W/nIiEicg+EZkmIjlc63O67u9zPV42PfvXoksppZRStucUydSSTn1IfqnDT4HPjTEVgVPAs671zwKnXOs/d22XJi26lFJKKWV7JpNLWkSkFPAw8L3rvpBwvemZrk0mAB1dtzu47uN6vPn16z/fihZdSimllLI9ZyaXdPgC6J9k86LAaWPMNdf9aCDQdTuQhPN64nr8jGv7W9KiSymllFJeT0R6iUhEkqVXkseCgePGmA3ZmUG/vaiUUkop23Nm8jRdSa+ZnIIHgfYi8hAJ114uAHwJFBIRX1dvVingiGv7I0BpIFpEfIGCwF9pZdCeLqWUUkrZnhPJ1HIrxph3jTGljDFlgSeAFcaYriRce/r6tZ17AHNdt3913cf1+AqTjkv8aNGllFJKKdvL7on0qXgbeENE9pEwZ+sH1/ofgKKu9W8A76RnZzq8mAXaBNS0OkKGLYmNtDpChtS5o7LVETJs97loqyNkWOGc+a2OkCGedh1DgI0l77c6QobcH7PR6ggZ5onXMYx3esOpP7NXZocX08sYswpY5bq9H6iTwjaXgcczum/POzKVUkoppTyQ9nQppZRSyva8oS9Qiy6llFJK2Z5e8FoppZRSyg3cNacrO2nRpZRSSinb84bhRZ1Ir5RSSinlBtrTpZRSSinb84aeLi26lFJKKWV7Rud0KaWUUkplP2/o6dI5XUoppZRSbqA9XUoppZSyPe3psikRGSAi20Vki4hEisgDInJQRPxT2La9iKR4oUoRaSIi9bMrZ59hfZi88SdG/TYqcV25u8szfM4Ivlr0NZ/P/4K7atwFQN6C+RgwdgBfLxnJ/379H2XuKpNdsW7b3j3r2LRxGRHhS1n3x0Kr4yR6d0Q/5m+exaTlPySue+aNHsyJmM74pWMZv3Qs9Zo9AECBwgX4esYIftuzgDeGvmZV5Js4HA5WhMxhyvQxADRsXI8Vq2ezMnQu85dMpVz5Oy1OmFyPXk+yYPU0FoZMp+cLTwLw9qA+LF47i3mrfmbU+OHkL5DP4pQp+27sCGKiNxO5abnVUW5SJfR7Ki3+mkoLv6Tir/8DwKdgPspNGkLllWMoN2kIPgXyApCzQikq/DKMe3f/gv/zj1gZO0WtWzVh+7bV7NoR55dQEgAAIABJREFUSv9+va2Ok6IxY4Zz+NAmNm5Ylrhu0KC3iPh/9u47PopqC+D474QE6R3pTcD6FKQooiLSpSNIeaKCBUVEwAIWFLE3sCCK4BMQBWlSRaVYAAUEEUF6lY6AVKUm5/1xJ7iEULPZmQ3n6ycfdmc3uyfj7Oyde889d+5kfp7zNV9O/IwCBfL5GOGpRcM+Phs+LXgdVmmu0SUi1wH1gXKqehVQA9hwsuer6nhVfTWZ14kFqgKp1uiaOnIqPe589rhtbZ9qy7C3h/LwLR35rNentH2qLQDNOzRnzZI1dKz9EL279KZdz3apFVaK1Kh5GxUq1qLSdXX9DuWYSSO+4ZHbT2xXDx8wija12tGmVjtmfTsHgMMHDzPg9YH0faFfpMM8pfvb38XKFauP3X/zree4/97HuPmGRoweOYFHHn/Qx+iOV/rSkjRv3Zimte+iQdVWVK15I0VLFObHH+ZQ78bmNKjaknWr/+CBTm39DjVZn3wygnr1b/c7jJNa0+ppVtbtxKqGjwCQt30z9v+0kOU338/+nxaS98FmABzdvY/Nz/Vn+4AxfoabrJiYGN595yXqN2jNlWVupkWLxlx2WWm/wzrBkCEjadDwjuO29e7djwoVa3HNtXWYNGkqTz/VyafoTi1a9vHZSJCU/QRBmmt0AQWAHap6CEBVd6jqZu+xjiIyX0QWicilACLSRkTe824PEpF+IjIHGAE8AHTxestuDHegi39ezL7d+47fqEqmrJkAyJQ1Mzu3/QVA0dJFWfjTQgA2rt7IhYXzkSNPjnCHlCb9Nmche3fvPaPnHjxwkIVzf+fwocOpHNWZK1AwHzVrV+XTwSOPbVNVsno9GtmyZWXrlj/9Cu8EJS8uwW/zf+fggYPEx8cz96f51K5XjZnfzyY+Ph6ABb/8Tv6CwewhmDFzDn/t2u13GGcse81r2TXK9crtGjWN7DUrARC/cw8HFq6Eo0f9DC9Z11S8mtWr17F27XqOHDnCiBHjaNigtt9hnWDmzDnsSnIs7Nu3/9jtTJkzoUHpQkkiWvbx2UhI4U8QpMVG12SgiIisEJH3ReSmkMd2qGo54APgsZP8fmGgsqreCvQD3lLVsqo6I3XDdvr3HEDbp+5m4OxB3NP9bga/NgiAtUvXcF0d1+l2cZmLubDQheQucMJoqa9Ula8mDWPO7K+4957g9hQkatq2MYOnDODJXo+TNXswh7oAXnr1aXo++zoJCf+eNjo/1J3PRw1g4dLpNG/ZiHfe+tDHCI+3cukqKlS6mhw5s5MhYwZuqnE9+Qsd38Bq9t+G/DDtR58ijGIKFw15nlIT3iJXK/cFGps3B0e37wLg6PZdxOYN/sVYwUL52bBx87H7GzdtoWDB/D5GdHZ69uzKqlVzaNWyCT2ff9PvcJIV7fs4rUpzjS5V3Q+UB9oB24HhItLGe/gL799fgOIneYmRqhqfmjGeSt076vLR8wNoW6kNA54fQKc3Orug3h9J5myZeferPtRv24DVi1eTEB+UtrtT9eYmXHNtHeo3aE379m244YZr/Q7ppMZ8Mp7mlVvTplY7dv65k4eebe93SMmqVacqO3bs5LcFi4/b/kCHNrRsdh9XXVaFYZ+O5sWXn/IpwhOtXrmO/n0GM3BkXz4e3oelv6847lht3+Vujh6NZ/yor3yMMjqtataVlfU7s7bNc+S+sx6Zr7nihOcEteclLenR43VKlbqWYZ+PoX37Nn6Hc96wnq6AUtV4Vf1eVXsADwFNvYcOef/Gc/KZm3+fyXuISDsRmSci89bvX5+ygENUb1qdn776CYCZE2ceS6Q/sP8A7zz2Ng/f0pHenXuRPVd2tq7fErb3DYfNm7cCsH37TsaO+4qKFcv6HNHJ7dqxi4SEBFSV8Z99yeVlL/U7pGRdc2156txSnfmLvqX/wLe4oUolho3szxVXXsr8eW64ecwXk6h47dU+R3q8UZ+No0mN1vy34X3s2bOXtavdZ+TWlg24ueaNPNq+u88RRqejXrpB/M497P1mFhnLXMzR7buJzZsTgNi8OYnfEfyh0c2btlKkcMFj9wsXKnDs/BFNPv98DE0aByd/NVRa2cehLJE+gETkEhEJzRYsC/xxji+3D8ia3AOq2l9VK6hqhaJZwjdz7K9tf3FlpSsBKHN9GTavc93DmbNlJjbOtRNrt6rN4p9/58D+A2F735TKlCkjWbJkPna7Zo2bWLx4uc9RnVzuC3Mdu33TLTeyZvlaH6M5uRd79uKqy6pQ7spqtGvbhZnTZ9O6ZXuyZctKyVLFAah68/WsWL761C8UYbnyuEZAgUL5qVWvGhNGf8WN1a7jvofu5IE7unDwwEGfI4w+kvECYjJnPHY7y41Xc3DFH+yd+jM5m1UHIGez6uyZMsfPMM/I3HkLKFWqBMWLFyEuLo7mzRsxYeJkv8M6I6VKFj92u0H9Wixfvsq/YE4hmvfxyaSFRPq0WKcrC9BHRHIAR4FVuKHG+ufwWhOAUSLSCOgY7ryux/t05crrriRbzmwMmjOYz3p/Rp8n3qXdc/eTLl0Mhw8doc8TfQAoUqoIXXo/gqqyfsV63un6TjhDSbF8+fIyaqQryZAuNh2ffz6WyZO/9zcoz3N9u3P1dWXIkSs7Y+YN539vDuLqymUpfXlJVJWtG7fxerfex54/avZQMmfJRGz6OG6scz1dWnVl3cpzbbeHX3x8PF06dmfgkD4kJCh7du/h4Q7BGV4EeG/gG+TMmZ0jR47Ss9ur7Nu7nx6vdiN9+jgGjXofgAXzFvHs46/4HOmJPh3Sl5uqXEeePLlYt2YePZ9/k4GDPvc7LOLy5KBY/6cBkHTp2D3uB/b/MJ8Dv62kaN9u5Gpek8Ob/mR9h9cAl+tVavxbpMuSCTSBPHc3ZEXNB0kIwMVafHw8nTp3Z9KXQ0kXE8OgwcNZsmSF32Gd4JNP3qPKjZXIkycXq1f9zAsv9qJO7WpcfHFJEhISWL9+Iw91DNZnL1G07OOzEZQhwpQQtQSAFKtftF7U7cRvti7wO4Szck3eS/wO4awt37fR7xDOWs4Lku3YDay1e6JvuGR+wXJ+h3BWym2e73cIZy1dTPQN4sQnRF+T4ujhTRHtP3q1WOsUfdc+8cenvvd3pcWeLmOMMcakMVHXu5EMa3QZY4wxJvAS0kCzyxpdxhhjjAm86BuAPVH0DXwbY4wxxkQh6+kyxhhjTOBF/+CiNbqMMcYYEwXSwvCiNbqMMcYYE3hBKXCaEtboMsYYY0zgpYXZi5ZIb4wxxhgTAdbTZYwxxpjAi/5+Lmt0GWOMMSYKWCK9McYYY0wEpIWcLmt0hcHXUbZ4dDSas3253yGcF3Yf/NvvEM5K3kzZ/Q7hrEXbAtL7v3vd7xDOWpabu/odwlnLnyWn3yEEXvQ3uSyR3hhjjDEmIqynyxhjjDGBZzldxhhjjDERYDldxhhjjDEREP1NLsvpMsYYY4yJCOvpMsYYY0zgWU6XMcYYY0wEaBoYYLRGlzHGGGMCz3q6jDHGGGMiIC3MXrREemOMMcaYCLCeLmOMMcYEXvT3cwWwp0tEnhaRxSKyUEQWiMi1YXztqiIyMVyvl5pq16rK4t+ns2zJTLo+3sHvcM6IxZz6oi1eiI6Ys2XPyoDBbzHj54lMnzOB8hXL0PXpjkz7cQxTZnzB518MIF/+vH6HeVJB3cdDJs+mSfcPuPWZD+jWbzSHjhxFVekz+lsaPPkejZ9+n8+mzAFg3z8H6fjOMG579kOadP+AsTOCtaZtUPdxUtmyZeXDQb35fvZ4vps9nnIVy3DZFZcw7ptPmTrzCwYOfY8sWTP7HeY5SUBT9BMEohqMQABE5DqgN1BVVQ+JSB4gvapuDtPrVwUeU9X65/C7sap6NLnHYtMXCutOjImJYeniGdSp24qNG7cwe9YkWt/xIEuXrgzn24SVxZz6oi1eSP2Yw7Xg9TsfvMycn35h6JDRxMXFkTFTBhISEti/zy0Afs/9rbn4kpJ0e6Rnit9r+z97UvwaoVJ7H5/rgtfbdu2lzSuDGPNiezKkj+Px90dxw1WlUIW5y9bxwj2NiIkRdu79m9zZMvPRxBnsO3CILrfV4K+9f9Po6b58+9ajxMWmO+v3DveC15H47IVrweu3+r7Ez7PnM2zIaOLiYsmYMSNDvxjAi8++yeyf5tHi9iYUKVaIN19+L8XvtfGv3yUMIZ+x+4rflqLv2gHrRkY03uQEraerALBDVQ8BqOoOVd0sIutEpKeIzBeRRSJyKYCIZBaRj0XkZxH5VUQaeduLi8gM7/nzRaRy0jcSkYre75QUkfIi8oOI/CIi34hIAe8534vI2yIyD+gUqZ1wTcWrWb16HWvXrufIkSOMGDGOhg1qR+rtz4nFnPqiLV6IjpizZstCpcoVGDpkNABHjhxh7559xxpcAJkyZSRIF6ihgryP4+MTOHT4KEfjEzhw+Ah5c2RlxPfzuL9hFWJi3Pdf7myu10VE+OfgYVSVfw4dJnvmjKSLCcZXVJD3caisWbNwbeXyDDt2LB9l7959XFSqGLN/mgfA9O9nUbdBTT/DPGeawv+CIBhH9L8mA0VEZIWIvC8iN4U8tkNVywEfAI95254GvlXVa4CbgTdEJDPwJ1DTe34L4N3QN/EaYf2ARsB6oA/QTFXLAx8DL4U8Pb2qVlDVXuH+Y0+mYKH8bNj4b+fexk1bKFgwf6Te/pxYzKkv2uKF6Ii5aLHC7NzxF2+//xKTp4/mzXefJ2OmjAA80b0T836fxq231eeNl/v4HGnygrqP8+XMxl11rqP2429To0tvsma6gMr/KcnGP3fxzc+LadVzAA/2/ow/tu0EoGW1iqzZsp0aj7xFs2f70bVV7WMNM78FdR8nVaRYIf7asYve773I19+P5I13epIxU0ZWLFtN7brVAKjfqFYgYz9fBKrRpar7gfJAO2A7MFxE2ngPf+H9+wtQ3LtdC3hCRBYA3wMZgKJAHDBARBYBI4HLQ97mMqA/0EBV1wOXAP8Bpniv0x0oHPL84eH7C40xQRObLh1Xlrmcwf8bTq0qTTnwzwE6drkXgFdffIcK/6nOFyMn0rbd7T5HGl32/n2A735dzqTXHmZK7y4cOHSEibMWcvjoUdLHxTKsx33celM5enw8HoCfFq/m0iL5mdq7CyOeu59XPvua/QcO+fxXRJfY2Fj+U+YyhgwcTp2qt/HPPwfo0PkeHu34DHfe05JJ3w4nS5bMHDlyxO9Qz0lCCn+CIFCNLgBVjVfV71W1B/AQ0NR7KPHTF8+/sy4FaKqqZb2foqq6FOgCbAPKABWA9CFvsQU4CFwd8hqLQ17jSlWtFfL8v0mGiLQTkXkiMi8hIdmnnLPNm7ZSpHDBY/cLFyrA5s1bw/oe4WYxp75oixeiI+bNm7exZfM2fv1lIQATx03myqsuP+45X4ycSL2ADskEdR/PXrKWQnlykCtbZuJi01G93KX8tmoj+XJmo3r5SwGoXu5SVm78E4BxMxdQvfyliAhF8+WiUJ4crN2yw88/4Zig7uOktmze6h3LiwD40juWV69cy+1N21G3WgvGjp7EH2s3+BzpubHhxTATkUtEpHTIprLAH6f4lW+AjiIi3u8nNqSyA1tUNQG4AwjNxNwN1ANe8RLrlwN5vSR+RCRORK44Xayq2t8bdqwQExPemSBz5y2gVKkSFC9ehLi4OJo3b8SEiZPD+h7hZjGnvmiLF6Ij5u1/7mDzxq2ULFUcgBtuqsSK5aspcVGxY8+pXbcaq1au8SnCUwvqPs6fKxsL12ziwKEjqCpzlq6lRIE83Hz1Jcxdtg6Aecv/oFi+3N7zszNnyVoAdu7Zz7qtOymcNzzJ5SkV1H2c1PY/d7J501YuCjmWVy5fTe48uQCXN9fp0fsZMmiEj1Geu7TQ0xW0Ol1ZgD4ikgM4CqzCDTWebLbhC8DbwEIRiQHWes99HxgtIncCX5Okt0pVt4lIfeAr4G6gGfCuiGTH7ZO3gcVh/tvOWHx8PJ06d2fSl0NJFxPDoMHDWbJkhV/hnBGLOfVFW7wQPTE/3e0l+g54nbj0caxft5HODz5Nrz7PU7JUCRI0gY0bNtOtS8pnLqaGoO7jq0oWpmaFy2jZsz/p0sVwadH8NLupHAePHOWp/l/w6eQ5ZMoQR4827vTerkEVnvl4HE2f6YeidL6tOjmzZvL5r3CCuo+T80y3l+nz4WukTx/HH+s28OhDz9CsZUPuuqclAF9NnMrwz8b4HOX5K1AlI6JVuEtGGGPOTLhKRkRSuEtGpLZzLRnhp3CXjIiEcJWMiKRIl4y4o9itKfquHfLHF77PzAhaT5cxxhhjzAnSQu+GNbqMMcYYE3hBqSqfEtboMsYYY0zgBWUGYkoEavaiMcYYY0xaZT1dxhhjjAm8oJR9SAlrdBljjDEm8NJCTpcNLxpjjDEm8FK7Ir2IFBGR70RkiYgsFpFO3vZcIjJFRFZ6/+b0touIvCsiq0RkoYiUO917WKPLGGOMMcYVZX9UVS8HKgEdRORy4AlgmqqWBqZ59wFuAUp7P+2AD073BtboMsYYY0zgpfYyQKq6RVXne7f3AUuBQkAjYLD3tMFAY+92I+ATdWYDOUSkwKnew3K6jDHGGBN4kVxBR0SKA1cDc4B8qrrFe2grkM+7XQgIXT18o7dtCydhPV3GGGOMCbwENEU/ItJOROaF/LRL7n1EJAswGuisqntDH1PX8jvn1p/1dIWB74s5nYNomwMSI9G3lyUKY45PiK5J2TuibB3DaJQ1CtcxPPDHVL9DOGsZi9XwO4TAS+nZSVX7A/1P9RwRicM1uD5T1S+8zdtEpICqbvGGD//0tm8CioT8emFv20lZT5cxxhhjznvirpT/ByxV1d4hD40H7vJu3wWMC9l+pzeLsRKwJ2QYMlnW02WMMcaYwIvAMkDXA3cAi0RkgbftKeBVYISI3AP8ATT3HpsE1AVWAf8AbU/3BtboMsYYY0zgpXZxVFWdyckzhqon83wFOpzNe1ijyxhjjDGBF8nZi6nFGl3GGGOMCbzomuaTPEukN8YYY4yJAOvpMsYYY0zgRSCRPtVZo8sYY4wxgZfaifSRYI0uY4wxxgReWkikt5wuY4wxxpgIsJ4uY4wxxgReWhheTPM9XSISLyILROQ3EZkvIpX9julMxcTEMPfnbxg7ZrDfoZzWBRdcwKwfJ/LLvCn8tuBbejz7qN8hJav/h2+yccMCfp3/79psTW+tx4Jfp3HwwHrKlbvKx+iS9+GHb7Jh/a/M/+XfmHv0eIx5cyfz85yv+XLiZxQokO8Ur+CfwoULMnXySBb+9h2/LfiWjg/d43dIp3XxxSWZN3fysZ+dO5bxcMd7/Q7rpKJxHycK4jluyKgJNG7zMI3adGTIyPEA9B04jGrN7qbpPZ1pek9nps+ed9zvbNm2nYp1WjLw87F+hHxStWtVZfHv01m2ZCZdHz+rGp6BpCn8LwjSfKMLOKCqZVW1DPAk8IrfAZ2phzvey9JlK/0O44wcOnSIGrWaU75CTcpXqEXtWlW59ppyfod1gk+GjKR+g9bHbVu8ZDnNW9zHjBlzfIrq1IYMGUmDhncct613735UqFiLa66tw6RJU3n6qU4+RXdqR48e5fGuPbmqzM1cf0MD2rdvw2WXlfY7rFNasWI1FSrWOrZ///nnAGPHfeV3WCcVjfs4UdDOcSvX/MHoiVMY1u8NRn/0Nj/Mmsf6jW4pvTuaNWT0/95m9P/epkqlCsf93ut9P+bGa4N1vouJieHdd16ifoPWXFnmZlq0aBw1x8XJJKim6CcIzodGV6hswC4AEckiItO83q9FItIo8Uki8oyILBeRmSIyTEQei3SghQoV4JZbqvPxx8Mi/dbn7O+//wEgLi6W2Li4QCY9zpw5h127dh+3bdmyVaxYscaniE4vuZj37dt/7HamzJkI4K4GYOvWP/l1we8A7N//N8uWraRQwfw+R3XmqlW7gTVr/mD9+k1+h3JS0bqPg3iOW7N+I1deXpqMGS4gNjYdFcpewdQZs075O9NmzKZQgXyULF4kQlGemWsqXs3q1etYu3Y9R44cYcSIcTRsUNvvsFJEU/gTBOdDoyujN7y4DPgIeMHbfhBooqrlgJuBXt5K4RWBpkAZ4BagQnIvmtp69erJk0++SEJC9NTgjYmJYd7cyWzZtJBp06bz89xf/Q4pTevZsyurVs2hVcsm9Hz+Tb/DOa1ixQpTtsx/mPNz9BwXLZo3YvjwYA0ZnUo07eMgnuNKlSjK/IVL2b1nLwcOHmLG7Pls/XMHAMPGfEmTuzvR/bU+7PEuev755wAfDxvDg3e18DPsZBUslJ8NGzcfu79x0xYKRkFjPK07HxpdicOLlwJ1gE9ERHCLWr4sIguBqUAhIB9ulfFxqnpQVfcBEyIdcN26Ndj+5w7m/7oo0m+dIgkJCVSoWItiJSpQscLVXHHFJX6HlKb16PE6pUpdy7DPx9C+fRu/wzmlzJkzMWL4AB55rMdxvXRBFhcXR/36tRg1eqLfoZyRaNrHQT3HlSxWhLtbNaHd48/xQNeeXFKqBDExMbRodAtfDe3H6I/eIm/unLzx/kAA+g76nDtua0CmTBl9jvz8kICm6CcIzqvZi6o6S0TyAHmBut6/5VX1iIisAzKc6WuJSDugHUBMuuzExGQOW5yVK1egfv1a1KlTjQwZLiBbtqwMHvQud7V5OGzvkZr27NnL9z/86JI4Fy/3O5w07/PPxzBu7Ce88EJvv0NJVmxsLCOHD2DYsDGMHRvc3Kik6tS5mV9/XcSfXk9HkEXbPg7yOa5pvZo0rVcTgLcHDCF/3tzkyZXj2OPN6tWkw5MvAbBo6Qqm/PATvfsNZt/+v5GYGC5IH8d/b63nS+yhNm/aSpHCBY/dL1yoAJs3b/UxopQLSsMpJc6Hnq5jRORSIB2wE8gO/Ok1uG4GinlP+xFoICIZRCQLUD+511LV/qpaQVUrhLPBBdC9+6uUuKgCpS+uxO2tH+S7734MxMnoVPLkyUX27NkAyJAhAzWqV2H58tU+R5V2lSpZ/NjtBvVrsXz5Kv+COY0B/XuxdNkq3n6nv9+hnJUWLRpHzdBitO3jIJ/jdnr5k1u2bWfa9NnUrV6F7Tv/Ovb4tJlzKFWiKACf9HmFycMHMHn4AFo3a8B9tzcLRIMLYO68BZQqVYLixYsQFxdH8+aNmDBxst9hpYiqpugnCM6Hnq6MIrLAuy3AXaoaLyKfARNEZBEwD1gGoKpzRWQ8sBDYBiwC9vgQd1QpUCAfH//vbdKliyEmJoZRoybw5aSpp//FCBvyyXtUqXIdefLkYs3quTz/Qi92/bWbt956gbx5czFu7GB+W7iY+vVbn/7FIuSTT96jyo2VyJMnF6tX/cwLL/aiTu1qXHxxSRISEli/fiMPdXzK7zCTdX3litzRuhkLFy1h3lx3wn/mmVf56utvfY7s1DJlykiN6lV48MFufodyWtG6j4Oqy7OvsXvvPmJjY3m6czuyZc3CEy+9xfJVa0GEQvkvpMej7f0O87Ti4+Pp1Lk7k74cSrqYGAYNHs6SJSv8DitF0kJPlwSl9RckIpJFVfeLSCZgOtBOVeef7Plx6QtF3U6MtoBjRPwO4axJFMYcH6Ck5jMRfXs4+j570biP//kjeBd8p5OxWA2/QzhrRw9viujhcU3Bm1L08fl58w++H87nQ0/XuegvIpfjcrwGn6rBZYwxxpjUF5QCpylhja5kqOp//Y7BGGOMMf9KCyNz1ugyxhhjTOClhZyu82r2ojHGGGOMX6ynyxhjjDGBZ8OLxhhjjDERkBaGF63RZYwxxpjAs9mLxhhjjDERkJAGhhctkd4YY4wxJgKsp8sYY4wxgWfDi8YYY4wxEZAWhhet0WWMMcaYwLOeLgNE3wK20Sgqr3CiMeYokz42zu8Qztqho0f8DuGsxKaLvq+JaFw8+kAULtIdaVH5PZCEJdIbY4wxxkRA9F3CGGOMMea8Y8OLxhhjjDERkBaGF63RZYwxxpjASws9XZbTZYwxxhgTAdbTZYwxxpjAU03wO4QUs0aXMcYYYwIvIQ0ML1qjyxhjjDGBp5ZIb4wxxhiT+tJCT5cl0htjjDHGRID1dBljjDEm8NLC8GKq9nSJSGMRURG59Ayfv05E8iSzff9Zvu9ZPf8Ur9NGRAqG47XOVu1aVVn8+3SWLZlJ18c7+BHCWbOYU1+0xQvBj7lQoQJM+moY836Zwtx5k3nwwbbHHnvggbuY/+s05s6bzIsvPuFjlKc2oH8vNm/8jQW/TvM7lJMqXLgAX3/9OfPnT+WXX6bQoYPbz7feWpdffpnC33+vpVy5K32O8uSCfBwPGTWBxm0eplGbjgwZOR6AvgOHUa3Z3TS9pzNN7+nM9NnzAPhp3gKat3uEJm0fpnm7R5gzf6GfoZ+VBNUU/QSBpGbLUUSGAwWBb1W1xxk8fx1QQVV3JNm+X1WznMX7ntXzT/E63wOPqeq8Uz0vNn2hsO7EmJgYli6eQZ26rdi4cQuzZ02i9R0PsnTpynC+TVhZzKkv2uKF1I/5gjAseJ0/f17y57+QBQsWkyVLZmb+OIGWLdpx4YV56dq1A7feejeHDx8mb97cbN++M8XvlxoLXt94w7Xs3/83Awe+Q9mrq4f1tePCtOB1/vwXevv5d7JkycxPP02kefN2qCoJCQm8997LPPnkS8yfvyjF73Uk/mgYIv5XJD5757rg9co1f/D4870Y1u8N4mJjeaBrT559pD0TpnxPpowZaduy8XHPX7pyDblz5uDCPLlYueYP7u/ak29HfXxO7x1X4DI5p188R/lzXJai79qtu5dGNN7kpFpPl4hkAW4A7gFahmyvKiLfi8goEVkmIp+JiCT53Ywi8pWI3JfM6z4uInNFZKGI9DzF+78lIotFZJqI5PW2lRWR2d7vjhGRnCfbLiLNgArAZyKyQEQyhmXHnIFrKl7N6tXUrmuVAAAgAElEQVTrWLt2PUeOHGHEiHE0bFA7Um9/Tizm1Bdt8UJ0xLx163YWLFgMwP79f7N8+WoKFszPvffdTq9eH3D48GGAsDS4UsuMmXP4a9duv8M4pa1b/2TBgt8Bt5+XLVtFwYL5WL58FStXrvE5ulML8nG8Zv1Grry8NBkzXEBsbDoqlL2CqTNmnfT5l5W+iAvz5AKgVImiHDx0mMOHw38hkBpUNUU/QZCaw4uNgK9VdQWwU0TKhzx2NdAZuBy4CLg+5LEswARgmKoOCH1BEakFlAauAcoC5UWkSjLvnRmYp6pXAD8Aib1snwDdVPUqYNGptqvqKGAecLuqllXVA+eyE85FwUL52bBx87H7GzdtoWDB/JF6+3NiMae+aIsXoi/mokULU6bM5cydu4DSpS+i8vXX8P0PY/n6m+GUK3+V3+GlGUWLFqZs2SuYO3eB36GckSAfx6VKFGX+wqXs3rOXAwcPMWP2fLb+6QaLho35kiZ3d6L7a33Ys+/ErJspP8zi8tIXkT59ynuMzZlJzUZXK+Bz7/bn3v1EP6vqRnXlZRcAxUMeGwcMVNVPknnNWt7Pr8B84FJcIyypBGC4d/tT4AYRyQ7kUNUfvO2DgSon237Gf6UxJk3InDkTQ4d9QNeuz7Nv335i06UjZ87sVL2pMU8//TJDhvT1O8Q0IXPmTAwb1o/HH3f72aRMyWJFuLtVE9o9/hwPdO3JJaVKEBMTQ4tGt/DV0H6M/ugt8ubOyRvvDzzu91atXU/v/oN59tH2PkV+9hLQFP0EQarMXhSRXEA14EoRUSAdoCLyuPeUQyFPj08Sx49AHREZqif2Bwrwiqp+eJYhhX1vi0g7oB2ApMtOTEzmsL325k1bKVL43/z9woUKsHnz1rC9fmqwmFNftMUL0RNzbGwsQ4f2Y/jnYxk/7hsANm3eeuz2L/N+IyEhgTx5crFjx19+hhrVYmNjGTasH8OHj2XcuK/9DueMBf04blqvJk3r1QTg7QFDyJ83N3ly5Tj2eLN6Nenw5EvH7m/9cwednnmVl5/sTNFCBSIe77kKyhBhSqRWT1czYIiqFlPV4qpaBFgL3HgGv/sssAtI7rLyG+BuL18MESkkIhcm87wYLwaA/wIzVXUPsEtEEmO4A/jhZNu92/uArMkFqar9VbWCqlYIZ4MLYO68BZQqVYLixYsQFxdH8+aNmDBxcljfI9ws5tQXbfFC9MT8wQevsXz5Kvr0+d+xbRMmTKbKTZUAKFWqBOnTx1mDK4X69Xud5ctX8e67H/kdylkJ+nG808vn27JtO9Omz6Zu9Sps3/nvsTpt5hxKlSgKwN59+3nwyRfp3O4Oyl15mS/xnqu0MHsxtep0tQJeS7JttLd9+IlPP0En4GMReV1VuyZuVNXJInIZMMvLvd8PtAb+TPL7fwPXiEh377EW3va7gH4ikglYA7Q9zfZB3vYDwHWRyuuKj4+nU+fuTPpyKOliYhg0eDhLlqyIxFufM4s59UVbvBAdMV93XQX+e3tTfl+0lFmzJwHwXI/X+WTwCPr1e525c7/h8JEjtLvvUZ8jPblPh/TlpirXkSdPLtatmUfP599k4KDPT/+LEVS5cgVuv70pixYtZba3n3v0eIMLLkhP7949yZMnF198MZCFC5fQsOGdPkd7vKAfx12efY3de/cRGxvL053bkS1rFp546S2Wr1oLIhTKfyE9vGHEYWMmsWHTFvoNHk6/we7ruP+bz5E7Z45TvYUJk1QtGXG+CHfJCGPMmQlHyYhIS42SEakpXCUjIincJSMi4VxLRvgp0iUjcmYplaLv2l37V/leMiL6Pk3GGGOMOe8EJRk+JazRZYwxxpjASwsjc9boMsYYY0zgBSUZPiVSde1FY4wxxhjjWE+XMcYYYwJPLafLGGOMMSb1pYXhRWt0GWOMMSbwLJHeGGOMMSYC0sLwoiXSG2OMMcYAIlJHRJaLyCoReSLcr289XcYYY4wJvNQeXhSRdLh1n2sCG4G5IjJeVZeE6z2s0WWMMcaYwItATtc1wCpVXQMgIp8DjYCwNbpseNEYY4wxgacp/DkDhYANIfc3etvCxnq6wuDo4U2ptoimiLRT1f6p9frhFm3xgsUcCdEWL1jMkRBt8YLF7KeUfteKSDugXcim/pHeL9bTFXztTv+UQIm2eMFijoRoixcs5kiItnjBYo5aqtpfVSuE/CRtcG0CioTcL+xtCxtrdBljjDHGwFygtIiUEJH0QEtgfDjfwIYXjTHGGHPeU9WjIvIQ8A2QDvhYVReH8z2s0RV80TYOH23xgsUcCdEWL1jMkRBt8YLFnKap6iRgUmq9vqSFsvrGGGOMMUFnOV3GGGOMMRFgjS5jjDHGmAiwRpeJKBFJtZpmqSla4zapQ0Sy2DFh0jIRifH+teM8jKzRlYYkfjhEJKOIZPI7nqRERNRLIhSR2iIS1kq/qSVJ3A1FJIffMSUVLSdGEckVcvsSP2M5VyJSGhgClPU7lpMRkTQzSSr02Pam8QdKtHz2zoaI5ASyeXcDe5xHI2t0pSGqqiLSGBgHTBWRO0Qks99xJQppuNwMPAvs8zeiMxMSdz3gaQI26zdJo7CaiFzud0zJ8a6cq4nIuyLyANBNRLKd7veCRlVXAmuBJ0TkKr/jSUpEsgMVvds1g3o8nIkkx3ZboFViD4yfQi5wSwN3BrExmEI34j6fPYEh1rMbPjZ7MQ0RkcuAD4HHgTxAJ2CUqvYPPXn5SUSaAn2AB1R1vIikV9XDfsd1OiJyPTAaF/dYEblAVQ/5HVcoEekM3A40V9W1fsdzMiLyK1AMKKOqG0QkTlWP+B3X6XhfOqKqCd79V4HSQE9VXehrcCFE5GKgCVAZuAQor6p/+xtVyojIdUAP4DZV9fViLfFcKiJVgYdxVcvfBMYF7ZyQEiIyDSgPNFTV6X7Hk1b4fsVgwkNESgE9ge2qOkdVv8SdpLqLyPV+NbiSuToaD+zEnaxQ1cMiki7igZ1GMnEvAOYBzwCo6qEgxS0i1XANrhtUda2IXC0iNfyOC04YHroAmAbMBF4RkdhoaXCpkyAiuQFU9QlgCdAzCD1eiftZVVfgFum9BhgBRHVDQETK4S4mtwG+Nx69BteNQD/gY+A3oArQIpp7vJI5572LO35ai8ilPoSUJlmjK4ol+ZCsA34FcnhDCplUdRYwDNfr5Ut8SXK4agC5cTkC+URkMICqxgesARMadw0RqQ/kBFoDv4nIOBFJ52fcyZwgNwALgZdF5HWgN/CIiLSMeHAhkuzLy4A8qvqYqjbEVXwe5j1WVURq+xjqKYX8DQ8Bb4vIyyJSUlWfwX3pPuM1DnyRZD/fA+QCnsINhXcWkYLeY3mCPkyUND5VnY/rHS8JVAhI/NcBw1V1IvAQrvF9B9BEROJ8jewcJDl+mopICyBeVdsBe3AX73lF5H4RucvXYKOcDS9GqZAu7htwC3T+7Q3XPQZcDKwBfgEGAi1VdaaPsT4G1PPiKQN0BFYDs4ENqtrYr9hORUQeBRoCi4D/AE96t9/CDStVSxxqinBcoSfI9rgegDnATUADL77lQHtgo6p+GukYkxKRLrghryPAKlxsmYHBQHFAgGaqutqvGE9HRO7DfbH+F/gedzy/p6ozROQNID9wb6SHmEQkm6ru9W7fAHQFWqvqXhFpBNyMOx9k8WLsqqoHIxnjuRCRe4ECwAHgPeB+oAbwPPCLH5+9kNgaAQ8CXVR1ibftW2Ax0F9VF/kVW0qIyMO4HvNpwKXAXlVtIyK9gQuBSsCtQRpOjzbW0xWlvAZXbeADXIOgk4h8oapvAr8DTYHmuC+BmX4ln3rDnter6s24YY5/gLXekFIlILeIFAzI1esxXl7M9ap6E64X6QCuYfMP8Cju5OrL7MskvS53AwtVdZOqDlXVVqr6M1AfaIEbEvWViLTGnair4PZha+AjVd2nqrcCLwNNgtbgEpGKItJIRC4QkSy4i5nmwK24nuW1uB6uG1X1cdwXcKQbXCWBDiKSQdys2sdwx+XlAKo6DpgCZMc1yD+KkgZXJ9xiwz8CdwIPquo7uGHp3kRwRl3iuUlEyotIdREpCnyNa3Q3EZFrxM3EjcFdADeKVGzhJCIZcA3021X1Kdz+jxORbqr6CPAiUNkaXCkTqFlY5sx5jajWwIuqOtzb9rWIvK+qD4qb8psH2CsRTFQO7YXxHAH2icg7uOGBpl4+1K3AWFW9MRJxnQ0RyQtsATaKyMe43oGGXj7PrcBU4CE/JyaISB6gMdAG2OoNIxbADTEfwDXG7lTVZT7ElvQYWAjcLiIdgCuAgsB8EfkCaKGqoyId4xkqDXQGjqrqlyLyNO5LtZ6q1gAQkdXALSLyi6ru8CHGBNyF16XAftzkmReAyiKyXVVXe7F/BfQOakK9iMSETFCIwfV+1sRd4GwA+npD+q+JyN/A9gjFlTiicAvwDvAJMBTXcz/Ki/FdXE9tG9zxfU1i+kEkYjxXyXxOY3DD0pcAq7x8209xfyN+nEvSIuvpihIikt67mkJEiuMaVLuB0A/N/bghG3C9B0dwX8wRyTFIMuzVwsvh2QLsxU1BfsBrcN2LK73gS67ZqYhIWVxvQeI+Kw10VNUj4qasPwdkinSDK5mewN3AMlzpjcG4YZeKwFXAfFzjNuJDHEmOgeze0NdCYCNwLdBPVffgvrgKAkGseRYDoKpDcb1ET4rIbepm2R4BiolIJRGpCywF+qrqPxGOMTFpfi0QD9yGG/4W4CXgaqChuJIGqGpCUBtc4OIDl/uJqw9VBLfvK+F6QQ8B94lITVV9T1U3RCguFZErcOfTW4C53kOfAjlV9TVcD2IdoChu6POTaGpwich1Xs9+LPAK8K6IVPaeWhgo6fWkBmo0IlpZTlcUEDdb6grcFUgpoDZwPW6Y43WghqouFlf/6gXcSWq7uAKJOVU1IleFIfF2AB4AGqnqGhGpCVTHfenOwuVJtVDVxZGM60yISEXcTKlWQAZc3saFwGagKq4cQ0TjTnKCbARkwjWsMuKOh1/UzVi8H3dV2lJVj0YyxqS8PL4bcD0WbwA/4PZpcdzs1XJA+0h9eZ4Lb/i2GnAQNzuto6qOEVdj7A7c/r9TVX+PcFwnlH8RkSK4YbgiwKtAety5YQrwod/Hw8mIm3yQ2cuLywpMUdVKIlId6A88oaojxSVvP4HrZVyTyjGVxOWexnvDs4npBnmBd1S1goh0w51r66jqt+Lqzb0BvBvE89rJiEhH3HGzBFfG5WVc42sArt5jVVyu5RK/YkxzVNV+AvyDO3l2wOVovYfLi+oR8ngn3NX2u7gPTj1ve0wEY5SQ21fich0Kefdvwn1xVcTlOjQDSvq9X5P5Gwok7jPckNJw3MnnIlwv0m1ACZ9jbA/8hOvRPILLOYN/hzYWAZf7FFt5XImCHN7/759xPRbNcY3YNrhGdydgAvAfv/+fn+bvKekdx0W9+61wvRzNvPsXAhf6ENdFIbc74ybKPAvkw/UcPwv09Y7bUkBBv/flKf6WWO+Y/jbkWE48bjJ454o1wCDv/8UVEYjpYlxObC/vs/ZAyGP3Au97t6viJlNUCnk8zu99epZ/a2lvv+bHXcjVwF3MlcZd4F8OFPE7zrT2YzldAaduXB3cB6APbqguvZfDM1pV3xGRebhpvQNV9VfvSjhiM3vU+wR71uBOVs94cV8G7MBNrx4RqZjOhohcizuhqtdDMwrXCCumLrk7Va+sTxFXaA/XhUAt3BBHU2A6bvYnuCvw/+B6DyN+RSoidXBX/X1wPYJ5geXqZtSNEJHduHyYet7x+r4GrDZXMr1HG4AVQFER2aKqw8TVKhokIvtV9WsfYswNfCUiQ4DJuOPgQ9yFzgBcY7wvrjjyQ7hZioHs4QJQ1aMiMgI3PPqEiLwNTFM36zKdqo4Skd9wtbmOquqfqRmPuMr9nwFPquoEcRNAsopIWVVdgJsRXMvLT70ZaKeqsxOPnaAd00kl5s2FHOvxwFZV3eo9ZaqIjAduUtWP/Is0bbPhxSjgjaVPAsar6gfiZvaUwJ149+K+cPtHsqGVTIy1gadU9SYRaYAbPhqjqgtFpAcuD6pbckMjPsQa2piJw9WLKgW0xX2BjcV9gc1Q1YcCEON/cT0ATb04iwAN1OWZPYrLL9npxxesiNwEfAT8V1XnetvKAl1wvQJzvG0fAx+rm0nr+zEQKsm+Lg4cUdVN4uqd7QM+VTd82wjXOG+vqhsjHGN9XIrBXNzsvW1AH1WdKG7iRwfcjL4OuIkUMepPYv9pJf3/7w3Ntcb1hlbAHc+FgK24/d9BI5AjJa7cxnRVTVzoeSGwCXcBNh+4D9fYuh74WVW/Su2Ywilxv4ubfbnVu6AfC+xR1bu857yEu47u7muwaZg1ugJI3NTdOFXdJyJFVXW9uNIL9+G6vQ8C9+C6wpsCbdVVoI9kjElPnDHAGOCwqt4Wsv2/uCvvVhqw2S/ialxdj/uS+lRVfxBX2b0QbpgmcQhvl48x3oYbkmuDS/C/ATdte6/32DO4mZXrfIrvEVzuyzviqssfFbf2XzfcRJ0d3s8zuCvoiDZWTidJg+sR3OdqDS4HrS9uZmAsbmLFpbgJCqsiHGN9XHL8C17vTyFcvs2vqnqf95zcuJynIrgp/4FM5E6yv6vjzmWrVHWb93lsgZshOBm339NpBEuJiJul2Bd3DExX1efFVZn/HZcb1yu5vyXIvP0sqjpVXL28NrhUhG24Hur/4Wq4zcQN6TZX1eU+hZvmWaMrgLwP/sXALtyMpCq4WYqPALO8ru9MQFYgh6ou9+sE4CWYblLVv8VVZx8KZFXVuuJmLz6HK2sRqGKBItIEN9OoJe4qOz3uS+xT7/FLcA1I39YwFJfU3xv4XFX7ekOMQ3EzATPger3aaIQTub3YEq+a++CulLt7PbLiDWHkxOXrlPR+pbcGOMHYG2LuiGscXoAbZhqhrkTB1bje5J8i2QDw4sqPq9rfVVXnikhm77N2I65x0ktV3/Oem4sA93CFEjdJ4U5cft+dQHXv4vIBXHHOJ1T1R59iqw58A6TXf2dV3oM71/Y65S8HkIi0wh3PnXHH8Vu43v0uuIuJNrgL+sO475dAXRynORqAxDL7Of4H10PwHa5rvVnI9qq44YXyAYmxGC4Rsy1u+BBcY2AWMNG7n9nvWL04qgC3hNzvCjzt3U6HO/F8kfh3+BRjadwU+Wq4YpYFcDO4vsMtDo23/QbcLMWiAdiv1XB1y8qHHBex3u2HcBcP6f2O8xTxC26m2kLcOnrpvO3FcEO67/gcX05cr8+V3mfrOVwC93BcXt8G3ILbvu/Ls/ibauCKnmb2PofrcSVQLvIevw+fE7iBurgeOHAXN8uAWn7vu3P4OxI7Vm7FFXYe4t2Pw01YGAVU9DvO8+nH6nQFhNdLkOgK3Kyk6cDVIlJMXIHT74HXgPoikjmZl4lYjOrq/vyBG/pqDTTzrsIPAhOBLCJSSINTGygf8Im4pG9wFeWvFZGrVDVeVQfhuthLnuwFUpOI1MN9kXbD9W4uxK1T+TzuOLhXRP6jqntUdaaqTlHV9X7EmsQc3LBECxEp7x0XR8VN9LgbOKCuvlVgJDmOVVV/w00CKIU7JuK8Y7sFcJWI5Evy+Yyk3bhelzdxyycVx+U8vYubsPIOUN3L6wqkZPbdfNwwVjNcQ6YorsdrmYgUUdUB6nMpEVWdBDwkIv/ghnI7q+pkP2M6V16v9Be4Mie3i8hNqnpE3USXw7haXCZCbHgxYLzhug9wH5CtuKGFbbgv4gpAZVxRyVdUdbNPMbb14tiGW4Vegbdxyf7ZceUh7tQI1wdLjoiUB7Koy9e6Fffl9SDuSrsbrpfrJ1wPTQ+gdqTj9hqCzwHdVPUHb9tzuN63W3B1rdrjJk+8ogHLt/ByjO7B1WKbh8uRa4brpY340OeZEpFmuArcc1T1N3F1zpriGrpz1E1UiFX/a55lwfV0FQHGqbfUkIh8AgwBpmpAT+RJcrguwQ1/LvXudwd2qRs6vw9XyLmTRjhn7lS8ocZsqjrG71jOVWKjV1VVRG7HleB4Dtd79yIuJ3SlbwGeZ6zR5TMvZ6MIbpjuItxQzRhV7eI9ngU37LEX1+XdFjiY+OUcgfgSc3cS/70PN7PvDVzj724gcSX6hriK6K9qQNbn8pKjb8VNA5/hJZ+/ituP63GVpBvhllB50ev1iGR8uXCJ5g3VzUTL4PUWIiI9cYsrX4UbeqyNGx7YetIX9ImIZMTV6qqBW4XgO1Vd4W9UxxORTOpVjheRzrgG1lTcsP3nqvqhuNUS7sP1bMzyLdjT8I7jJ3BlQgLTSDkZ73NYDzc5ZS+ueHJrXI7RIdywekNV3eJbkKcQRUnzycbpTXRS7xx+G65XfTCu5mMQeszPH36Pb57vP7gp6JcAGb37L+KKnRYNeU5GXM9SWe++hP6byvEVD7mdBZeMWTdkW2Nc93tm737EirKeJu7Qgq0dcJW5q3j3mwOrccm7ifvXz1yuerjZRLm9+xeEPPY9cHXS7fZzTvv4bdzM1GtxdePAzQidj8udu9/bdicByJc7yd9RwPsMLibgBWZDYq4JTPJuvwh8493OhSs6+ypwpd9xRvtPknPeLd55LvS7JXGiS+Ljl/gd8/n4Yz1dAeDNSnsV14vxnYi8hrv6bqLJDCFG6qrLm6reGze0cQ+uRy4XkE9VG3vPyYebDfOgqu5O7ZjOlpcjssGbFdUUN+1+uog0xU2Vbq4ByNXwZqy+B1RQ1V1eXtERERkHPKMB6TmMRiElF3qo6livVy4Xrq7V47jeue64IdE+qvqhb8Gehhd7NVzx2cD3cMGxpX7K4nryKwL1vWP7Og1wb2K0SPp94PUqNsalTdyMmzA0NfG5cEJBaxNBlkjvA/Ek3ldXaXkj0EpEblDVbsA0YIqXL3OcCDW4auPWbmuAW16kMa746f1ADhEZKK5ExM24mV7pUzumsyUiVwGviEhjVe2Hm534jIjcqKqjcT0avlSbT0pdocWHgHkiktP7UroTt0RH4IYTo4U3fP8ocG9IgysBd9V/GTBZXc7WetyXVKBzd1T1gKp+GdQGV9Kkee/+UdxwYjncWoVHvGHcN0Qkp4+TFNKK3OCGEMXVc7xGVavgzhu7gG9FJC6xcWYNLn/ZMkARJiIX6L+JsJVxS6bMUtVnxS1Bc7f32XhKXLX04riqyJGMsRauBtBMXIOqPW5trstw6zvWx305fYKb8XWPpvISHaeT3BWcumr4vwK1RSRBXTV/BXqJSGdVHe9XvMlR1a/E1S+aLiLv4yZT+L5vo9whXB7RQXFFh7vhSm4cxfV2VfC+qKrgemBsX6dA4udP3KoZVwHrcDMt38U1vB70escb4wom+1Z4ONp557y8wFoRaaWq40XkL2C7iAzFlRupr65uXnNc6ZltPoZssET6iBKRHLglZh7A9TKOx00DX4+razXe6xquCHygqtN9iLE6bvbkc7helvS4+kAlcPlFU9SrKi6ubEU6dVOPfRU6y8zrpbtQVYd49x/GDW+MUNWvReRu3IyvQCaQesNhX+ByuQJbUDQaeF9Mj+DWrbwClzg/E5c3WRd3QfMPMDOovUfRIMkkhRuAV3DrQiZOArkTuA6XOJ8bGKwBm2gRbUImN7UA3scVSp4gIq/iLizaqeoSEbkLl7tYR1UjegFvTmSNrggTkW643JE1uByTZeKmql+JG+oYLyJdga/9yOMRVwU9TlV/EldRviWuBEAO3MlyJvC9ujpGgSAiNXGzKH/DLVIMbgbax6o60nvOx7gvgGc0CtZMC/0SMykjJy+5MBi3nuloP+OLduJqzNXEpSNUwZVkeV3/XReyI66x1TmoFzrRTlzZmRG4mdhLcRfNhYDtuIkjze0CLhgspytCQoa/XgP64XKlLvIeHokrhtlIRJqo6ut+JU6r6lyvwZVYT2cobnbfX8CfuFkv13v5XL7zTjYv4fJxMuNKQOzHldm4w+tWB/gW1yD7xY84z5Y1uMJHVfer6ixVHRHS4LoN1xBY4G900c3rlX0ZVyJkM67+XQ5cCgLqat69g+vR7+XlFtn3TgqE7j8RaSUij6rq17gLz3FAKVV9ANfbOAa3Eoc1uALCcroiIKQb+AbcOnX/E5E8wGsisk1VfxGRUbhCnYHocldvzTF16zoOxVXnPoK7ivpOA7CgrrgaV5OARl63elHc1fYFuOUtAJ4XkUa4pV6aWM7O+U1ECuCO5ftwNa4iupZiWpJkkkLiupAbRORBYIiIPKSq76nqTm/IK0ZVj/gbdXQTkTLAqyLSTN1qH4Vwdf5Qtxh6AjBORO7VKC7ompbZFUcEeA2uW3CJ5zm8ba/hcqc+FJFrVfUvYEAQr0jUVUAfBRwE3tOAFDD09lkD3Ekomzd0EQ8U9CbpjMQN5c4AGqtVXTZuWZ2VuIZ6YKvlR4mkkxQeF5HvccOJG4Bu4gr8oqp/aRQsxB106oo3HwWGexOtciZ5/Atcsep3RSSL9SoGj+V0RYBX9mEScJ+q/uxdrWTDnfxvxeVAVAb2J/YwBZF4taP8jiMpr0H7Lm6NuoLA7ap6ILGH0d/ojEmbTjNJoQGuB6YxrofZ9yXBopm3r2MSRxhEZDRu3cTV3r9LcY1gcEO8/6jqAT9iNadmja5UICKX4yosD/fuZweewc2Syosrs5AAjFTVj0SkhKqu9S3gNEBEagCTgfyq+qeELKdjjEkdp5ikEPh1IaNF6MWjiBRKnIEoIh/ihsk/xH23ZMPl3z5lExaCy3K6wkzcgtWfAX0St6nqHhFZAlwMjFXVb7wZi+WBj6zBlXKqOtWbRfWdiNxsuVvGpD5V3Q/M8n6AY5MUrgDWWoMrZZI0uB4CbheRX4D3VfV+ETkKFFHVht5z0qvqYR9DNqdhPV1hJCKXABOBUar6pLctozfUFQckqGq8iFyLuzp5wpt1YsLES5rvAVTAW+DV5zqBiTwAAATQSURBVJCMOS8kM0nBcubCREQa42qdvYSbpXgUt0j7LBGZiBtibIr7Tg9sioqxRlfYeEOKg3GJutOAGar6o/fYRcCzwNO4WldPAsO8mlyWdxRmIpLFuwI3xkSIROG6kNFARC7F1eAaqqqvikhOoBMuiX6kqs4UkYKazDq9Jnis0RUG3slmEvARrqfrUVwl97HAPFztlJ9U9SXv+flVdas1uIwxxoQSkXyqui3kfkHcjMSmuCrzs0QkK+5CXoFnLX81elijK0wSG1Le7UuA23E5c/OAFar6u1dw1Lp+jTHGnMDr1VqCKyi7VFX7e9sz4JaPqw687DW8sgAZrBRHdLEaHmES0uCK8epaDcGNu5fh39pc1uAyxhhzMvtxq2tsBZqJyBARaYBb4/Zt4GtcUe1K3koL1uCKMtboCrOQSu4rcQ2vDEBDbxzeGGOMSZaqbgR+BsrhFmT/EjcxYZKIVMCtL/sebqF2E4VseDGViUhpONYIM8YYY04QslxcetzqJZ2BS3HryE4BLsQ1tp5W1T3+RWpSwhpdxhhjTAB4lefjcMW0L8LVcnxCVcd6NSC3q+ouP2M0KWONLmOMMSZAvMlYPwB9VfUFv+Mx4WM5XcYYY0yAeJOxngDSiUgmv+Mx4WONLmOMMSZ4ZuMS6k0aYsOLxhhjTACJSCZV/cfvOEz4WKPLGGOMMSYCbHjRGGOMMSYCrNFljDHGGBMB1ugyxhhjjIkAa3QZY1KdiMSLyAIR+V1ERqZkGryIDBKRZt7tj0Tk8lM8t6qIVD6H91gnInnONUZjjEmONbqMMZFwQFXLqup/gMPAA6EPikjsubyoqt6rqktO8ZSqwFk3uowxJjVYo8sYE2kzgFJeL9QMERkPLBGRdCLyhojMFZGFInI/uKVRROQ9EVkuIlNxa9DhPfa9txAwIlJHROaLyG8iMk1EiuMad128XrYbRSSviIz23mOuiFzv/W5uEZks8v/27t81iiAM4/j3EUTEXxgQsREsVAwiKgqpAoII0UobwVYIWMR/QbQWbBVjY6HYaCGCSWERI4qFKJg0FgELGyEgKtrIa3FzGEJsQrIE8v1Ud+/OzM1u9dzuLJOZJONAur0kktaDZf27lKTlaHe0RoDnrXQcOFxVc0lGgW9VdTLJJuBVkkngGHAQGAR2A7P0NgFeOO4u4C4w3MYaqKr5JLeBH1V1s7V7ANyqqukke4EJ4BBwDZiuqhtJzgGXV/VCSFqXDF2SurA5yfv2+SVwj95jv7dVNdfqZ4Aj/fVawA5gPzAMPKyqP8CXJC+WGH8ImOqPVVXz/5nHaWCwt68wANuTbG2/caH1fZbETYUlrThDl6Qu/KqqowsLLfj8XFgCxqpqYlG7sys4jw3AUFX9XmIukrSqXNMlaa2YAK4k2QiQ5ECSLcAUcLGt+doDnFqi7xtgOMm+1neg1b8D2xa0mwTG+l+S9IPgFHCp1UaAnSt2VpLUGLokrRXj9NZrvUvyEbhD7278E+BTO3YfeL24Y1V9BUaBx0k+AI/aoafA+f5CeuAqcKIt1J/l31uU1+mFthl6jxk/r9I5SlrH3HtRkiSpA97pkiRJ6oChS5IkqQOGLkmSpA4YuiRJkjpg6JIkSeqAoUuSJKkDhi5JkqQOGLokSZI68BecpXz5i6EBHQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mcT8EJpifnsj"
      },
      "source": [
        "### Model 2"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k8uxCL7FgUoy"
      },
      "source": [
        "model2 = Sequential([\n",
        "                    Flatten(input_shape = (28,28)),\n",
        "                    Dense(64, activation = 'relu'),\n",
        "                    Dense(32, activation = 'relu'),\n",
        "                    Dense(16, activation = 'relu'),\n",
        "                    Dense(10, activation = 'sigmoid')\n",
        "])"
      ],
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OQCADPZygYWv",
        "outputId": "a0211204-cff6-4b42-b7c1-3fa147c0ff20"
      },
      "source": [
        "model2.summary()"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " flatten_3 (Flatten)         (None, 784)               0         \n",
            "                                                                 \n",
            " dense_5 (Dense)             (None, 64)                50240     \n",
            "                                                                 \n",
            " dense_6 (Dense)             (None, 32)                2080      \n",
            "                                                                 \n",
            " dense_7 (Dense)             (None, 16)                528       \n",
            "                                                                 \n",
            " dense_8 (Dense)             (None, 10)                170       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 53,018\n",
            "Trainable params: 53,018\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tak4tXABgZj6"
      },
      "source": [
        "model2.compile(\n",
        "    optimizer = 'adam',\n",
        "    loss = 'sparse_categorical_crossentropy',\n",
        "    metrics = ['accuracy']\n",
        ")"
      ],
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nvcrWgH9gh0w",
        "outputId": "f9a23974-01c4-4b0f-8e8a-fa5f41b33485"
      },
      "source": [
        "model2.fit(X_train_sc, y_train, epochs = 10)"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.5550 - accuracy: 0.8034\n",
            "Epoch 2/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.3961 - accuracy: 0.8564\n",
            "Epoch 3/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.3610 - accuracy: 0.8674\n",
            "Epoch 4/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.3349 - accuracy: 0.8751\n",
            "Epoch 5/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.3161 - accuracy: 0.8838\n",
            "Epoch 6/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.3035 - accuracy: 0.8875\n",
            "Epoch 7/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2903 - accuracy: 0.8923\n",
            "Epoch 8/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2815 - accuracy: 0.8954\n",
            "Epoch 9/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2716 - accuracy: 0.8988\n",
            "Epoch 10/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2624 - accuracy: 0.9018\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fdfef3ddb90>"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h2HMJvLigny9",
        "outputId": "2caff428-8d47-4344-f79c-c404f523e9d5"
      },
      "source": [
        "model2.evaluate(X_test_sc, y_test)"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 1s 2ms/step - loss: 0.3474 - accuracy: 0.8741\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.3473844826221466, 0.8741000294685364]"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qhVI4_Z0hNgB"
      },
      "source": [
        "y_pred_2 = model2.predict(X_test_sc)"
      ],
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XSJGEKgfhXAL"
      },
      "source": [
        "y_pred_2_labels = [np.argmax(i) for i in y_pred_2]"
      ],
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "D290X7W5hfnk",
        "outputId": "8fe316c9-57c4-4205-c820-3e56618808a2"
      },
      "source": [
        "cm2 = tf.math.confusion_matrix(y_test, y_pred_2_labels)\n",
        "\n",
        "plt.figure(figsize = (10,6))\n",
        "sns.heatmap(cm2, fmt = 'g', annot = True, xticklabels = labels, yticklabels = labels)\n",
        "plt.xticks(rotation = 45)\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('Actuals')\n",
        "plt.show()"
      ],
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zb5EIm0viPTv"
      },
      "source": [
        "# A lot of Shirts were Predicted as Tshirt/top\n",
        "# Also, a lot of Pullovers were predicted as Coat\n",
        "\n",
        "# Both the cases make sense as Tshirt and Shirt may look smae from front except for buttons\n",
        "# Also, few small size coats looks same as Pullovers, with minimal attention to buttons."
      ],
      "execution_count": 77,
      "outputs": []
    }
  ]
}