{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "kAz1HwzbUe6S"
      },
      "outputs": [],
      "source": [
        "import tensorflow.keras\n",
        "from tensorflow.keras.layers import Conv2D, Input, MaxPool2D, RandomRotation, RandomTranslation, RandomZoom, Dropout, Flatten, Dense\n",
        "import numpy as np\n",
        "from tensorflow.keras.datasets import mnist"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Загружаем данные\n",
        "(x, y), (xt, yt) = mnist.load_data()\n",
        "y=y%2\n",
        "yt=yt%2\n",
        "\n",
        "# Преобразуем метки из цифр в one hot ветора 6 -> [0,0,0,0,0,0,1,0,0,0]\n",
        "y = tensorflow.keras.utils.to_categorical(y, 2)\n",
        "# for j in range(len(y)):\n",
        "#   for i in range (len(y[j])):\n",
        "#     if y[j][i]==1:\n",
        "#       if (i%2)==0:\n",
        "#         y[j][i]=0\n",
        "#         y[j][0]=1\n",
        "#       else:\n",
        "#         y[j][i]=0\n",
        "#         y[j][1]=1\n",
        "# #print (y)\n",
        "yt = tensorflow.keras.utils.to_categorical(yt, 2)\n",
        "# for j in range(len(yt)):\n",
        "#   for i in range (len(yt[j])):\n",
        "#     if yt[j][i]==1:\n",
        "#       if (i%2)==0:\n",
        "#         yt[j][i]=0\n",
        "#         yt[j][0]=1\n",
        "#       else:\n",
        "#         yt[j][i]=0\n",
        "#         yt[j][1]=1\n",
        "# #print (yt)"
      ],
      "metadata": {
        "id": "Nj0YzCV7UoAY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f6d16769-9048-4cf3-a806-050cbedd5436"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
            "11490434/11490434 [==============================] - 0s 0us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Основаня модель для классификации цифр\n",
        "main_model = tensorflow.keras.Sequential([\n",
        "    Input(shape=(28, 28, 1)),\n",
        "\n",
        "\n",
        "    Conv2D(16, 3, padding='same', activation='relu'), # На выходе 16 картинок по 28 на 28 пикселей\n",
        "    MaxPool2D(strides=2),                             # На выходе 16 картинок по 14 на 14 пикселей\n",
        "    Conv2D(32, 3, padding='same', activation='relu'), # На выходе 32 картинок по 14 на 14 пикселей\n",
        "    MaxPool2D(strides=2),                             # На выходе 32 картинок по 7 на 7 пикселей\n",
        "\n",
        "    Flatten(),\n",
        "    Dense(128, activation='relu'),\n",
        "    Dense(32, activation='relu'),\n",
        "    Dense(2, activation='softmax')   # <- здесь функция активации softmax, чтобы выходы были как вероятности от 0 до 1\n",
        "])"
      ],
      "metadata": {
        "id": "MVcdeyt5UtHv"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "main_model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bhgCxEHJUzJQ",
        "outputId": "ab15250b-9781-4456-d9a3-2fcdefbcd93e"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d (Conv2D)             (None, 28, 28, 16)        160       \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2D  (None, 14, 14, 16)       0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 14, 14, 32)        4640      \n",
            "                                                                 \n",
            " max_pooling2d_1 (MaxPooling  (None, 7, 7, 32)         0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 1568)              0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 128)               200832    \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 32)                4128      \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 2)                 66        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 209,826\n",
            "Trainable params: 209,826\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "main_model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "JD6xFnaHU2xX"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# validation_split=0.2 - 20% обучающей выборки будет валидационной, т.е. сеть получит для обучения 80% примеров, а 20% будет отобрано для автоматической проверки распознования\n",
        "main_model.fit(x, y, batch_size=60, epochs=3, validation_split=0.2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fJxWCtQrU7GD",
        "outputId": "71e3eaf6-dfb3-4307-b2fc-78fc5680d537"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/3\n",
            "800/800 [==============================] - 38s 45ms/step - loss: 0.1915 - accuracy: 0.9561 - val_loss: 0.0431 - val_accuracy: 0.9846\n",
            "Epoch 2/3\n",
            "800/800 [==============================] - 35s 44ms/step - loss: 0.0420 - accuracy: 0.9856 - val_loss: 0.0390 - val_accuracy: 0.9871\n",
            "Epoch 3/3\n",
            "800/800 [==============================] - 36s 45ms/step - loss: 0.0275 - accuracy: 0.9903 - val_loss: 0.0455 - val_accuracy: 0.9872\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f4557fa7c70>"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "main_model.save(\"neuro2.pd\")#создаем модель"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_LIxUdCJv6gC",
        "outputId": "21eb1aae-e5ca-4a49-b44a-76a64f26084d"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _update_step_xla while saving (showing 3 of 3). These functions will not be directly callable after loading.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "hist = main_model.history.history\n",
        "\n",
        "epoch_ind = range(1, len(hist['accuracy']) + 1)\n",
        "plt.plot(epoch_ind, hist['accuracy'], label='Точность на обучающей выборке')\n",
        "plt.plot(epoch_ind, hist['val_accuracy'], label='Точность на валидационной выборке')\n",
        "plt.legend()\n",
        "\n",
        "# accuracy - точность = правильно_распознано / всего_примеров"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "h01Z3B8nU9dH",
        "outputId": "3e96c499-b1a5-425d-9c2b-39a3b26d7efa"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f4557fa4370>"
            ]
          },
          "metadata": {},
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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04qMrT84Wykkv2/bqKIpfr2L5RNJibgsY8VqM8tBoBK7dyX54O4FHd6V+kK8u1ta6ronW7QRaOSthb8WRLaKKxqSGqCoIAdy/U8roStH/k4H87LJtT25RwkW2T1x4a1avVl5wWxmEELiRnoOohDTpdgJRienIzCl+vZG53BgtnZTwcbGWbuzIqdREVYNJDdHz0qiBrJslj64U/T8zGVAXn4pbIrN6T0xpLmG2kMKqcvtUy93Oyi28Biah8MaOUYlpuJ1V/PjJi6ZSOxWOwPi4KNHIxoJTqYn0hEkN0dMU5JUyQ+jxKc4pheX5y8LcrvgpoScvuJXXrdw+kZb0B/k4XzSV+uFppBslTKU2NpKhqb0lfFyU8HYqHIVp6sCp1ETVCZMaqr2kkvwlJCq6luSXGT0cSSkhUSmaLWTpCNThhaD6dD+vAP/cyJCmUUclpiPudvFTfjIZ0MjGXJpG7e1sjRYqTqUmqu6Y1JBherwkf2YJ1W11Lcn/5BRmretZHAtHYIz541Sd5BVocDEl4+E06sIE5nJqyVOpXeqboZWT9WNTqa1gqeCML6Kahr+FqWYpVpK/hOq2OpfkL2V0pZaV5K/JhBC4cjMLZxLSpNsJXEjORJ66eB0eO0vTx24nUJjE1OdUaiKDwKSGqo8nS/KXdMNDXUrymypLv3dQLS/Jb0iEEAjeeQ7b/k4o9prSzESagVRUlddByanURIaKSQ1VjUopyf+UU0IsyV9rfHvsOrb9nQBjIxnautbTqgXTsH5dTqUmqkWY1NDzK8gtfhroyVEWXUvyW5Zw7yCW5KcnRFy/i3m/RAMApnb1woiXGuk5IiLSp3IlNStXrsQXX3yBlJQU+Pj4YPny5ejQoUOJbfPz8xESEoJNmzYhKSkJTZs2xeeff44uXbpIbdRqNWbPno3Q0FCkpKRApVJh6NChmD59uvRX1tChQ7Fp0yatbQcEBCAsLKw8XaCyyssupf7KY0mLziX5n0xYWJKfdHcrMxdjtkSiQCPQvZUj3nvRXd8hEZGe6ZzUbNu2DUFBQVizZg38/PywZMkSBAQE4NKlS7CzsyvWfvr06QgNDcW6devg5eWFffv2oXfv3jh69Chat24NAPj888+xevVqbNq0CS1atMDff/+NYcOGQalU4qOPPpK21aVLF2zYsEF6bmpqWp4+E/D0kvyPzxYqT0n+YqMsLMlPFatArcG47yKRmpELD1tzfN6nFU8zERFkQogSJjiWzs/PD+3bt8eKFSsAABqNBi4uLhg3bhyCg4OLtVepVJg2bRrGjh0rLevTpw/MzMwQGhoKAHjrrbdgb2+P9evXl9pm6NChSEtLw549e3TuJABkZGRAqVQiPT0dVlYGXo31mSX5HyYt5S7JX8KpIZbkpyoU8tsFrP0zFuZyY/z4YSd42lnqOyQiqiS6fH/rNFKTl5eHiIgITJ06VVpmZGQEf39/HDt2rMR1cnNzoVBoX/9gZmaGw4cPS89feOEFfP3117h8+TKaNGmCs2fP4vDhw1i8eLHWegcPHoSdnR3q1auH1157DfPnz0eDBg1K3W9ubq70PCMjQ5euVl/PKsmf+fDf8pbkf/KGhyzJT9VM2PlkrP0zFgDwRV8fJjREJNEpqbl9+zbUajXs7e21ltvb2+PixYslrhMQEIDFixfj5ZdfhoeHB8LDw7Fr1y6o1Y/KygcHByMjIwNeXl4wNjaGWq3GggULMGjQIKlNly5d8Pbbb8Pd3R0xMTH4v//7P3Tt2hXHjh2DsXHxKp8hISGYM2eOLt3Tv0ovyf/EDQ9Zkp9qmJhbWfjkhygAwIgX3dHN21HPERFRdVLps5+WLl2KkSNHwsvLCzKZDB4eHhg2bBi++eYbqc327duxZcsWbN26FS1atMCZM2cwfvx4qFQqBAYGAgAGDBggtff29karVq3g4eGBgwcP4vXXXy+236lTpyIoKEh6npGRARcXl0rs6TPkPyi9UFxFlOR/8joWluQnA5OdW4BRmyOQlVuADu71MaWrl75DIqJqRqekxsbGBsbGxkhNTdVanpqaCgcHhxLXsbW1xZ49e5CTk4M7d+5ApVIhODgYjRo9mno5adIkBAcHS4mLt7c3rl+/jpCQECmpeVKjRo1gY2ODq1evlpjUmJqaVt2FxEUl+TOfkrQ8d0n+x5axJD/VMkIIBO86hys3s2BnaYoVA1vzRpJEVIxO34xyuRxt27ZFeHg4evXqBaDwQuHw8HB8+OGHT11XoVDAyckJ+fn52LlzJ/r16ye9dv/+fRg9MSvG2NgYGk3pdU0SExNx584dODrqefg5KRJY92rZ2pZWkt/ysWV1G3CGENETNh69hp/P3kAdIxlWDmoDO0vWKSKi4nT+cz8oKAiBgYFo164dOnTogCVLliA7OxvDhg0DAAwZMgROTk4ICQkBAJw4cQJJSUnw9fVFUlISZs+eDY1Gg8mTJ0vb7NGjBxYsWICGDRuiRYsWOH36NBYvXozhw4cDALKysjBnzhz06dMHDg4OiImJweTJk+Hp6YmAgICKeB/Kz0pV+G+pJfkfeyisOUOISEd/X7uLBb9eAAD8X7dmaO9WX88REVF1pXNS079/f9y6dQszZ85ESkoKfH19ERYWJl08HB8frzXqkpOTg+nTpyM2NhYWFhbo1q0bNm/eDGtra6nN8uXLMWPGDIwZMwY3b96ESqXCBx98gJkzZwIoHLWJiorCpk2bkJaWBpVKhTfffBPz5s3Tf60aC3tgaiJgyhkYRBXtZmaOVGDvrVaOGNbJTd8hEVE1pnOdmpqqVtWpITIA+WoNBv33BE7G3UVjOwvsGdsJ5qa8loyottHl+5sXbxBRtbQw7CJOxt2FudwYqwe3ZUJDRM/EpIaIqp3fziVj3V9xAIAv+/rA0453XCeiZ2NSQ0TVytWbWZj0w1kAwPsvN0JXFtgjojJiUkNE1UZ2bgFGhUYgO08NP/f6mBzQVN8hEVENwqSGiKoFIQQm74zC1ZtZsLcyxYqBbVCHBfaISAf8jUFE1cI3R67h16hk1DGSYdWgNrC11HO5BiKqcZjUEJHenYy7i09/KyywN617M7R1ZYE9ItIdkxoi0qubGTkYuzUSao3Av31UGPqCm75DIqIaikkNEelNvlqDD7eexq3MXDSxt8Bnfbwh461EiKicmNQQkd58vvciTl67CwvTOlgzuC3qyllgj4jKj0kNEenFL1E38N/DjwrsNbJlgT0iej5Maoioyl29mYnJO6IAAB90boQuLR30HBERGQImNURUpbJyC/DB5gjcz1OjY6MGmPQmC+wRUcVgUkNEVUYIgck7ziLmVjYcrBRY9k5rFtgjogrD3yZEVGXWH47Db+dSYGIsw0oW2COiCsakhoiqxInYOwjZexEAML17c7R1rafniIjI0DCpIaJKl5qRg7FbT0OtEejlq8KQjq76DomIDBCTGiKqVPlqDcZuicTtrFw0tbfEp2+zwB4RVQ4mNURUqUJ+u4i/r9+DpWkdrHmXBfaIqPIwqSGiSvPT2Rv45khhgb1F/XzgbmOu54iIyJAxqSGiSnElNRPBOwsL7I1+xQNvtmCBPSKqXExqiKjCZebk44PQwgJ7L3g0wMQ3mug7JCKqBZjUEFGFEkJg0g9RiL2VDUclC+wRUdXhbxoiqlDr/opF2D+FBfZWDWoDGwsW2COiqsGkhogqzLGYO/g87BIAYOZbzdG6IQvsEVHVYVJDRBUiJT0H476LhFoj8HZrJwz+FwvsEVHVYlJDRM8tr0CDsVsjcTsrD14OlljQmwX2iKjqMakhouf26W8XEHH9HiwVdbBmcFuYyY31HRIR1UJMaojoufx4Jgkbj14DACzu5ws3FtgjIj1hUkNE5XYpJRPBO88BAMa+6oE3mtvrOSIiqs2Y1BBRuWTk5GN0aAQe5KvxoqcNgt5oqu+QiKiWY1JDRDorLLB3FrG3s6FSKrB0gC+MjXhhMBHpF5MaItLZ2j9jse+fVMiNjbBqcFs0YIE9IqoGmNQQkU6OXr2NhWEXAQAzezSHr4u1fgMiInqISQ0RlVly+gOM++40NALo08YZg/wa6jskIiIJkxoiKpO8Ag3GbInEnew8NHO0wvxeLVlgj4iqFSY1RFQmC36Nxun4tIcF9tqwwB4RVTtMaojomfacTsKmY9cBAEv6+8K1AQvsEVH1U66kZuXKlXBzc4NCoYCfnx9OnjxZatv8/HzMnTsXHh4eUCgU8PHxQVhYmFYbtVqNGTNmwN3dHWZmZvDw8MC8efMghJDaCCEwc+ZMODo6wszMDP7+/rhy5Up5wiciHVxMyUDwrigAwLjXPPF6MxbYI6LqSeekZtu2bQgKCsKsWbMQGRkJHx8fBAQE4ObNmyW2nz59OtauXYvly5cjOjoao0aNQu/evXH69Gmpzeeff47Vq1djxYoVuHDhAj7//HMsXLgQy5cvl9osXLgQy5Ytw5o1a3DixAmYm5sjICAAOTk55eg2EZVFRk4+Rm2OQE6+Bi81tsF4/yb6DomIqFQy8fhwSBn4+fmhffv2WLFiBQBAo9HAxcUF48aNQ3BwcLH2KpUK06ZNw9ixY6Vlffr0gZmZGUJDQwEAb731Fuzt7bF+/foS2wghoFKpMHHiRHzyyScAgPT0dNjb22Pjxo0YMGDAM+POyMiAUqlEeno6rKysdOkyUa2k0Qh8EBqB/dGpcLI2w8/jXkR9c7m+wyKiWkaX72+dRmry8vIQEREBf3//RxswMoK/vz+OHTtW4jq5ublQKBRay8zMzHD48GHp+QsvvIDw8HBcvnwZAHD27FkcPnwYXbt2BQDExcUhJSVFa79KpRJ+fn5P3W9GRobWg4jKbs2fMdgf/bDA3qA2TGiIqNqro0vj27dvQ61Ww95e+5y6vb09Ll68WOI6AQEBWLx4MV5++WV4eHggPDwcu3btglqtltoEBwcjIyMDXl5eMDY2hlqtxoIFCzBo0CAAQEpKirSfJ/db9NqTQkJCMGfOHF26R0QPHbl6G1/uuwQAmP3vFvBhgT0iqgEqffbT0qVL0bhxY3h5eUEul+PDDz/EsGHDYGT0aNfbt2/Hli1bsHXrVkRGRmLTpk348ssvsWnTpnLvd+rUqUhPT5ceCQkJFdEdIoN3I+1Rgb2+bZ3xTgcXfYdERFQmOo3U2NjYwNjYGKmpqVrLU1NT4eDgUOI6tra22LNnD3JycnDnzh2oVCoEBwejUaNGUptJkyYhODhYujbG29sb169fR0hICAIDA6Vtp6amwtHRUWu/vr6+Je7X1NQUpqa8Hw2RLnIL1BizJRJ3s/PQQmWFeSywR0Q1iE4jNXK5HG3btkV4eLi0TKPRIDw8HB07dnzqugqFAk5OTigoKMDOnTvRs2dP6bX79+9rjdwAgLGxMTQaDQDA3d0dDg4OWvvNyMjAiRMnnrlfIiq7+b9cwJmENFgp6mD1oLZQmLDAHhHVHDqN1ABAUFAQAgMD0a5dO3To0AFLlixBdnY2hg0bBgAYMmQInJycEBISAgA4ceIEkpKS4Ovri6SkJMyePRsajQaTJ0+WttmjRw8sWLAADRs2RIsWLXD69GksXrwYw4cPBwDIZDKMHz8e8+fPR+PGjeHu7o4ZM2ZApVKhV69eFfA2ENGuyERsPl5YYG/pgNZo2KCuniMiItKNzklN//79cevWLcycORMpKSnw9fVFWFiYdBFvfHy81qhLTk4Opk+fjtjYWFhYWKBbt27YvHkzrK2tpTbLly/HjBkzMGbMGNy8eRMqlQoffPABZs6cKbWZPHkysrOz8f777yMtLQ0vvvgiwsLCis2sIiLdXUjOwP/tPgcA+Oj1xnjVy07PERER6U7nOjU1FevUEJUs/UE+/r3iMK7fuY+Xm9hiw9D2MDbidTREVD1UWp0aIjIsGo3AxO1ncP3OfThZm2Fpf18mNERUYzGpIarFVh+KwYELNyGvY4Q1g9uiHgvsEVENxqSGqJb668otLPpfYYG9uf9uAW9npZ4jIiJ6PkxqiGqhpLQH+Ohhgb3+7VwwoENDfYdERPTcmNQQ1TK5BWqMCY3Avfv5aOlkhTk9W+g7JCKiCsGkhqiWmftzNM4mpkNpZsICe0RkUJjUENUiOyISseVEPGQyYMkAX7jUZ4E9IjIcTGqIaol/bqRj2sMCex+/3hivNmWBPSIyLExqiGqB9Pv5GB0aidwCDV5paouPXmus75CIiCockxoiA6fRCEzYfgbxd+/DuZ4ZlvT3hREL7BGRAWJSQ2TgVv5xFb9ffFRgz7ouC+wRkWFiUkNkwP68fAuLD1wGAMzv2RItnVhgj4gMF5MaIgOVeO8+Pvr+NIQA3unggn7tXfQdEhFRpWJSQ2SAcvLVGLMlEmn38+HtpMSsHiywR0SGj0kNkQGa83M0ohLTYV3XBKsHt2GBPSKqFZjUEBmY7X8n4LuThQX2lg5oDed6LLBHRLUDkxoiA3I+KR0z9pwHAEzwb4LOTWz1HBERUdVhUkNkINLu52H0lgjkFmjwmpcdPnzVU98hERFVKSY1RAZAoxGYsO0MEu4+QMP6dfFVPxbYI6Lah0kNkQFY/vtV/HHpFkzrGGH14DZQ1jXRd0hERFWOSQ1RDXfw0k0sCX9YYK9XS7RQscAeEdVOTGqIarCEu/fx8fdnIAQw0K8h+rZjgT0iqr2Y1BDVUEUF9tIf5MPHWYlZPZrrOyQiIr1iUkNUQ83+6R+cS0pHvbomWDW4LUzrsMAeEdVuTGqIaqBtp+Lx/akEyGTAsndaw8naTN8hERHpHZMaohrmXGI6Zvz4DwBg4htN8FJjFtgjIgKY1BDVKPeyCwvs5RVo4N/MDmNeYYE9IqIiTGqIagi1RmD8tjNIvPcArg3qYhEL7BERaWFSQ1RDLAu/gkOXb0FhYoTVg9pCacYCe0REj2NSQ1QD/HHxJpb9fgUAsKCXN5qrrPQcERFR9cOkhqiaS7h7H+O3FRbYG/yvhujT1lnfIRERVUtMaoiqsZx8NUaFRhQW2HOxxoy3WGCPiKg0TGqIqikhBGbsOY9/bmSgvrkcqwe1YYE9IqKnYFJDVE19fyoBP0QkwkgGLH+nNVQssEdE9FRMaoiqoajENMwqKrD3ZlN08rTRc0RERNUfkxqiauZudh5Gh0YiT62BfzN7jO7soe+QiIhqBCY1RNWIWiPw8fenkZT2AG4N6mJRPx8W2CMiKiMmNUTVyNIDl/HXlduFBfYGs8AeEZEuypXUrFy5Em5ublAoFPDz88PJkydLbZufn4+5c+fCw8MDCoUCPj4+CAsL02rj5uYGmUxW7DF27FipzSuvvFLs9VGjRpUnfKJqKfxCKpb9fhUAEPK2N5o5ssAeEZEudE5qtm3bhqCgIMyaNQuRkZHw8fFBQEAAbt68WWL76dOnY+3atVi+fDmio6MxatQo9O7dG6dPn5banDp1CsnJydJj//79AIC+fftqbWvkyJFa7RYuXKhr+ETVUvyd+5iw7QwAYEhHV/RuzQJ7RES6kgkhhC4r+Pn5oX379lixYgUAQKPRwMXFBePGjUNwcHCx9iqVCtOmTdMadenTpw/MzMwQGhpa4j7Gjx+PX375BVeuXIFMVng9wSuvvAJfX18sWbJEl3AlGRkZUCqVSE9Ph5UV/wKm6iMnX423Vx1FdHIGWje0xrb3O0Jeh2eGiYgA3b6/dfrNmZeXh4iICPj7+z/agJER/P39cezYsRLXyc3NhUKh0FpmZmaGw4cPl7qP0NBQDB8+XEpoimzZsgU2NjZo2bIlpk6divv375caa25uLjIyMrQeRNWNEALTdp9HdHIGGpjLsWpQGyY0RETlVEeXxrdv34ZarYa9vb3Wcnt7e1y8eLHEdQICArB48WK8/PLL8PDwQHh4OHbt2gW1Wl1i+z179iAtLQ1Dhw7VWj5w4EC4urpCpVIhKioKU6ZMwaVLl7Br164StxMSEoI5c+bo0j2iKrf1ZDx2Rj4qsOeoZIE9IqLy0impKY+lS5di5MiR8PLygkwmg4eHB4YNG4ZvvvmmxPbr169H165doVKptJa///770v+9vb3h6OiI119/HTExMfDwKF7HY+rUqQgKCpKeZ2RkwMXFpYJ6RfT8ziakYc5P0QCASQFeeIEF9oiInotO49w2NjYwNjZGamqq1vLU1FQ4ODiUuI6trS327NmD7OxsXL9+HRcvXoSFhQUaNWpUrO3169dx4MABjBgx4pmx+Pn5AQCuXr1a4uumpqawsrLSehBVF4UF9iKQp9bgzeb2GNW5+M8DERHpRqekRi6Xo23btggPD5eWaTQahIeHo2PHjk9dV6FQwMnJCQUFBdi5cyd69uxZrM2GDRtgZ2eH7t27PzOWM2fOAAAcHR116QKR3qk1Ah99dxo30nPgbmOOL/v5FLt+jIiIdKfz6aegoCAEBgaiXbt26NChA5YsWYLs7GwMGzYMADBkyBA4OTkhJCQEAHDixAkkJSXB19cXSUlJmD17NjQaDSZPnqy1XY1Ggw0bNiAwMBB16miHFRMTg61bt6Jbt25o0KABoqKiMGHCBLz88sto1apVeftOpBdf7b+Mw1dvw8zEGGsGt4WVggX2iIgqgs5JTf/+/XHr1i3MnDkTKSkp8PX1RVhYmHTxcHx8PIyMHg0A5eTkYPr06YiNjYWFhQW6deuGzZs3w9raWmu7Bw4cQHx8PIYPH15sn3K5HAcOHJASKBcXF/Tp0wfTp0/XNXwivToQnYoVfxSeMv2sjzeaOljqOSIiIsOhc52amop1akjfrt3ORo8Vh5GZU4ChL7hh9r9b6DskIqJqr9Lq1BBR+TzIU2NUaAQycwrQpqE1/q9bM32HRERkcJjUEFWywgJ753AxJRM2FnKsGtSWBfaIiCoBf7MSVbLQE/HYdToJxkYyLH+nDRyUimevREREOmNSQ1SJTsffw9yf/wEATA5oio4eDfQcERGR4WJSQ1RJ7mTlYsyWSOSrBbq0cMD7L7PAHhFRZWJSQ1QJ1BqBj74/jeT0HDSyMccXfVuxwB4RUSVjUkNUCRb97xKOXL1TWGDv3bawZIE9IqJKx6SGqIL9758UrDoYAwD4/D+t0MSeBfaIiKoCkxqiChR3OxsTt58FAAzr5IZ/+6iesQYREVUUJjVEFeR+XgFGh0YgM7cA7VzrscAeEVEVY1JDVAEKC+ydf1hgzxQrB7WBiTF/vIiIqhJ/6xJVgM3Hr2P3wwJ7Kwa2hr0VC+wREVU1JjVEzyky/h7m/RINAAju4oV/NWKBPSIifWBSQ/QcbmflYkxoYYG9bt4OGPGSu75DIiKqtZjUEJVTgVqDcVtPIyUjBx625lj4Hx8W2CMi0iMmNUTl9OX/LuNY7B3UlRtj7bttYWFaR98hERHVakxqiMoh7HwK1hwqLLC38D+t4GnHAntERPrGpIZIR7G3svDJD4UF9t570R1vtWKBPSKi6oBJDZEOCgvsRSIrtwDt3eohuKuXvkMiIqKHmNQQlZEQAlN3ncOl1EzYWppi5UAW2CMiqk74G5mojDYdvYYfz9yAsZEMKwe2gR0L7BERVStMaojKIOL6Xcz/9QIAYGpXL3Rwr6/niIiI6ElMaoie4VZmLsZsiUSBRqB7K0e89yIL7BERVUdMaoieokCtwbjvIpGakQtPOwt83qcVC+wREVVTTGqInuKLfZdwPPYuzOXGWDOYBfaIiKozJjVEpdh7Lhlr/4wFAHzR1weedhZ6joiIiJ6GSQ1RCWJuZWHSjigAwMiX3NHN21HPERER0bMwqSF6QnZuAUZtjkBWbgE6uNfHlC4ssEdEVBMwqSF6jBACU3ZG4crNLNhZmmLFwNaowwJ7REQ1An9bEz1mw5Fr+CUqGXWMZFg5qA3sLFlgj4iopmBSQ/TQ39fu4tPfCgvs/V+3ZmjvxgJ7REQ1CZMaIgA3M3OkAntvtXLEsE5u+g6JiIh0xKSGar18tQYfbj2Nm5m5aMwCe0RENRaTGqr1FoZdxMm4u7AwrYM177aFOQvsERHVSExqqFb77Vwy1v0VBwD44j+t4GHLAntERDUVkxqqta7ezMSkH84CAD54uRG6ssAeEVGNxqSGaqXs3AKMCo1Edp4a/2pUH5MCmuo7JCIiek5MaqjWEUJg8s4oXL2ZBXsrUyx/pw0L7BERGYBy/SZfuXIl3NzcoFAo4Ofnh5MnT5baNj8/H3PnzoWHhwcUCgV8fHwQFham1cbNzQ0ymazYY+zYsVKbnJwcjB07Fg0aNICFhQX69OmD1NTU8oRPtdz6w3H49WGBvVWD2sDW0lTfIRERUQXQOanZtm0bgoKCMGvWLERGRsLHxwcBAQG4efNmie2nT5+OtWvXYvny5YiOjsaoUaPQu3dvnD59Wmpz6tQpJCcnS4/9+/cDAPr27Su1mTBhAn7++Wf88MMPOHToEG7cuIG3335b1/CpljsZdxchey8CAKZ3b4a2riywR0RkKGRCCKHLCn5+fmjfvj1WrFgBANBoNHBxccG4ceMQHBxcrL1KpcK0adO0Rl369OkDMzMzhIaGlriP8ePH45dffsGVK1cgk8mQnp4OW1tbbN26Ff/5z38AABcvXkSzZs1w7Ngx/Otf/3pm3BkZGVAqlUhPT4eVlZUuXSYDcTMjB92XH8atzFz820eFpQN8WY+GiKia0+X7W6eRmry8PERERMDf3//RBoyM4O/vj2PHjpW4Tm5uLhQK7fvnmJmZ4fDhw6XuIzQ0FMOHD5e+cCIiIpCfn6+1Xy8vLzRs2PCp+83IyNB6UO2Vr9Zg7NZI3MrMRVN7S3zWx5sJDRGRgdEpqbl9+zbUajXs7e21ltvb2yMlJaXEdQICArB48WJcuXIFGo0G+/fvx65du5CcnFxi+z179iAtLQ1Dhw6VlqWkpEAul8Pa2rrM+w0JCYFSqZQeLi4uZe8oGZzP9l7EqWv3YGlaB6sHt0FdOQvsEREZmkqf8rF06VI0btwYXl5ekMvl+PDDDzFs2DAYGZW86/Xr16Nr165QqVTPtd+pU6ciPT1deiQkJDzX9qjm+iXqBtYfflhgr68PGrHAHhGRQdIpqbGxsYGxsXGxWUepqalwcHAocR1bW1vs2bMH2dnZuH79Oi5evAgLCws0atSoWNvr16/jwIEDGDFihNZyBwcH5OXlIS0trcz7NTU1hZWVldaDap8rqZmYvCMKAPBB50bo0rLkzwsREdV8OiU1crkcbdu2RXh4uLRMo9EgPDwcHTt2fOq6CoUCTk5OKCgowM6dO9GzZ89ibTZs2AA7Ozt0795da3nbtm1hYmKitd9Lly4hPj7+mful2isrtwCjQiNwP0+Njo0aYNKbLLBHRGTIdL6wICgoCIGBgWjXrh06dOiAJUuWIDs7G8OGDQMADBkyBE5OTggJCQEAnDhxAklJSfD19UVSUhJmz54NjUaDyZMna21Xo9Fgw4YNCAwMRJ062mEplUq89957CAoKQv369WFlZYVx48ahY8eOZZr5RLWPEAKTd5xFzK1sOFgpsHxgaxbYIyIycDonNf3798etW7cwc+ZMpKSkwNfXF2FhYdLFw/Hx8VrXy+Tk5GD69OmIjY2FhYUFunXrhs2bNxe76PfAgQOIj4/H8OHDS9zvV199BSMjI/Tp0we5ubkICAjAqlWrdA2faon//hWH386lwMRYhpWD2sDGggX2iIgMnc51amoq1qmpPU7E3sHA/56AWiMwt2cLDOnopu+QiIionCqtTg1RdZeakYOxW09DrRHo5avCu/9y1XdIRERURZjUkMHIV2swdkskbmcVFtj79G0W2CMiqk2Y1JDB+PS3C/j7emGBvTXvtmWBPSKiWoZJDRmEn87ewIYj1wAAi/r5wN3GXL8BERFRlWNSQzXe5dRMTHlYYG/0Kx54swUL7BER1UZMaqhGy8zJx6jNEXiQr0YnzwaY+EYTfYdERER6wqSGaiwhBCb9EIXY29lwVCqwbAAL7BER1Wb8BqAa6+s/YxH2T2GBvVWD2qABC+wREdVqTGqoRjoWcwefh10EAMzs0QKtG9bTc0RERKRvTGqoxklJz8G47yKhEcDbrZ0w2K+hvkMiIqJqgEkN1Sh5BRqM2RKB21l58HKwxILeLLBHRESFmNRQjfLpbxcQGZ8GS0UdrBncFmZyY32HRERE1QSTGqoxfjyThI1HrwEAvurnCzcW2CMioscwqaEa4VJKJoJ3ngMAjH3VA/7N7fUcERERVTdMaqjay8jJx6jQwgJ7L3raIOiNpvoOiYiIqiEmNVStFRbYO4u429lQKRVY9k5rGBvxwmAiIiqOSQ1Va2v/jMW+f1IhNzbCqsFtUd9cru+QiIiommJSQ9XW0au3sfBhgb1Z/24OXxdr/QZERETVGpMaqpaS0x9g3HenoRFAnzbOGNiBBfaIiOjpmNRQtVNYYC8Sd7Lz0MzRCvN7tWSBPSIieiYmNVTtzP81Gqfj02ClqIM1g9uwwB4REZUJkxqqVvacTsK3x64DAL7q7wvXBiywR0REZcOkhqqNC8kZCN4VBQAY95onXm/GAntERFR2TGqoWkh/kI/RoRHIydfgpcY2GO/fRN8hERFRDcOkhvROoxH45IezuHbnPpyszbBsAAvsERGR7pjUkN6t+TMG+6MfFtgb1Ab1WGCPiIjKgUkN6dWRq7fx5b5LAIA5PVvAhwX2iIionJjUkN7cSHtUYK9vW2cMaO+i75CIiKgGY1JDepFboMaYLZG4m52HFiorzGOBPSIiek5Makgv5v0SjTMJaVCamWD1oLZQmLDAHhERPR8mNVTldkUmIvR4PGQyYEl/XzRsUFffIRERkQFgUkNVKvpGBv5v9zkAwLjXGuNVLzs9R0RERIaCSQ1VmfQH+Ri9pbDAXucmtvj49cb6DomIiAwIkxqqEhqNwMTtZ3D9YYG9Jf19WWCPiIgqFJMaqhKrD8XgwIWbkNcxwprBbVlgj4iIKhyTGqp0f125hS//V1hgb17PFvB2Vuo5IiIiMkRMaqhSJaU9wEffnYYQQP92LujfvqG+QyIiIgPFpIYqTW6BGmNCI3Dvfj5aOllhTs8W+g6JiIgMWLmSmpUrV8LNzQ0KhQJ+fn44efJkqW3z8/Mxd+5ceHh4QKFQwMfHB2FhYcXaJSUlYfDgwWjQoAHMzMzg7e2Nv//+W3p96NChkMlkWo8uXbqUJ3yqInN+jsbZxHRY12WBPSIiqnx1dF1h27ZtCAoKwpo1a+Dn54clS5YgICAAly5dgp1d8Zoj06dPR2hoKNatWwcvLy/s27cPvXv3xtGjR9G6dWsAwL1799CpUye8+uqr2Lt3L2xtbXHlyhXUq1dPa1tdunTBhg0bpOempqa6hk9VZEdEIraeeFRgz6U+C+wREVHlkgkhhC4r+Pn5oX379lixYgUAQKPRwMXFBePGjUNwcHCx9iqVCtOmTcPYsWOlZX369IGZmRlCQ0MBAMHBwThy5Aj++uuvUvc7dOhQpKWlYc+ePbqEK8nIyIBSqUR6ejqsrKzKtQ0qm39upOPtVUeRW6DBeP/GGO/fRN8hERFRDaXL97dOp5/y8vIQEREBf3//RxswMoK/vz+OHTtW4jq5ublQKBRay8zMzHD48GHp+U8//YR27dqhb9++sLOzQ+vWrbFu3bpi2zp48CDs7OzQtGlTjB49Gnfu3Ck11tzcXGRkZGg9qPKl38/HqNAI5BZo8GpTW3z0GgvsERFR1dApqbl9+zbUajXs7e21ltvb2yMlJaXEdQICArB48WJcuXIFGo0G+/fvx65du5CcnCy1iY2NxerVq9G4cWPs27cPo0ePxkcffYRNmzZJbbp06YJvv/0W4eHh+Pzzz3Ho0CF07doVarW6xP2GhIRAqVRKDxcXF126SuWg0QhM2H4GCXcfwLmeGb7q7wsjFtgjIqIqotPppxs3bsDJyQlHjx5Fx44dpeWTJ0/GoUOHcOLEiWLr3Lp1CyNHjsTPP/8MmUwGDw8P+Pv745tvvsGDBw8AAHK5HO3atcPRo0el9T766COcOnWq1BGg2NhYeHh44MCBA3j99deLvZ6bm4vc3FzpeUZGBlxcXHj6qRItD7+CRfsvQ17HCLtGv4CWTqxHQ0REz6fSTj/Z2NjA2NgYqampWstTU1Ph4OBQ4jq2trbYs2cPsrOzcf36dVy8eBEWFhZo1KiR1MbR0RHNmzfXWq9Zs2aIj48vNZZGjRrBxsYGV69eLfF1U1NTWFlZaT2o8hy6fAuLD1wGAMzv1ZIJDRERVTmdkhq5XI62bdsiPDxcWqbRaBAeHq41clMShUIBJycnFBQUYOfOnejZs6f0WqdOnXDp0iWt9pcvX4arq2up20tMTMSdO3fg6OioSxeoEiTeu4+Pvy8ssPdOBxf0a8dTfUREVPV0rlMTFBSEdevWYdOmTbhw4QJGjx6N7OxsDBs2DAAwZMgQTJ06VWp/4sQJ7Nq1C7Gxsfjrr7/QpUsXaDQaTJ48WWozYcIEHD9+HJ9++imuXr2KrVu34uuvv5ZmTGVlZWHSpEk4fvw4rl27hvDwcPTs2ROenp4ICAh43veAnkNOvhpjtkQi7X4+vJ2UmNWDBfaIiEg/dK5T079/f9y6dQszZ85ESkoKfH19ERYWJl08HB8fDyOjR7lSTk4Opk+fjtjYWFhYWKBbt27YvHkzrK2tpTbt27fH7t27MXXqVMydOxfu7u5YsmQJBg0aBAAwNjZGVFQUNm3ahLS0NKhUKrz55puYN28ea9Xo2Zyf/0FUUYG9wW1YYI+IiPRG5zo1NRXr1FS87X8nYPKOKMhkwKZhHfByE1t9h0RERAam0i4UJipyPikd0/ecBwBM8G/ChIaIiPSOSQ3pLO1+HkaFRiCvQIPXvOzw4aue+g6JiIiISQ3pRqMRmLDtDBLvPUDD+nXxVT8W2CMiouqBSQ3pZPnvV/HHpVswrWOE1YPbQFnXRN8hERERAWBSQzo4eOkmloQXFthb0NsbLVQssEdERNUHkxoqk4S79/Hx92cgBDDQryH+09ZZ3yERERFpYVJDz5STr8boLRFIf5APH2clZvVo/uyViIiIqhiTGnqmWT/+g/NJGahX1wSrBreFaR0W2CMiouqHSQ091bZT8dj2dwJkMmDZO63hZG2m75CIiIhKxKSGSnUuMR0zfvwHADDxjSZ4qTEL7BERUfXFpIZKdC/7UYE9/2Z2GPMKC+wREVH1xqSGilFrBMZvO4OktAdwbVAXi1hgj4iIagAmNVTMsvArOHT5FhQmRlg9qC2UZiywR0RE1R+TGtLyx8WbWBp+BQCwoJc3mqt4R3MiIqoZmNSQJOHufYzfdgYAMPhfDdGHBfaIiKgGYVJDAAoL7I0KfVhgz8UaM95igT0iIqpZmNQQhBCYsec8/rmRgfrmcqwe1IYF9oiIqMZhUkP4/lQCfohIhJEMWP5Oa6hYYI+IiGogJjW13NmENMwqKrD3ZlN08rTRc0RERETlw6SmFrubnYcxWyKRp9bgjeb2GN3ZQ98hERERlRuTmlpKrRH4+PvTSEp7ALcGdbGonw8L7BERUY3GpKaWWnrgMv66cruwwN7gtrBSsMAeERHVbExqaqHwC6lY9vtVAEDI295o5sgCe0REVPMxqall4u/cx4SHBfaGdHRF79YssEdERIaBSU0t8iBPjQ9CI5CRU4DWDa0xvTsL7BERkeFgUlNLCCEwfc95XEjOQANzOVYNagN5HR5+IiIyHPxWqyW2nozHzshHBfYclSywR0REhoVJTS1wJiENc36KBgBMCvDCCyywR0REBohJjYG7m52HMaERyFNrENDCHqM6N9J3SERERJWCSY0BU2sEPvruNG6k58Ddxhxf9PWBTMYCe0REZJiY1Biwr/ZfxuGrt2FmYow1LLBHREQGjkmNgdofnYoVfxQW2PusjzeaOljqOSIiIqLKxaTGAF27nY2g7WcAAENfcENPXyf9BkRERFQFmNQYmAd5aowKjUBmTgHaNLTG/3Vrpu+QiIiIqgSTGgMihMC03edwMSUTNhZyrBrUlgX2iIio1uA3ngEJPRGPXaeTYGwkw/J32sBBqdB3SERERFWGSY2BOB1/D3N//gcAMDmgKTp6NNBzRERERFWLSY0BuJOVizFbIpGvFujSwgHvv8wCe0REVPuUK6lZuXIl3NzcoFAo4Ofnh5MnT5baNj8/H3PnzoWHhwcUCgV8fHwQFhZWrF1SUhIGDx6MBg0awMzMDN7e3vj777+l14UQmDlzJhwdHWFmZgZ/f39cuXKlPOEbFLVG4KPvTyM5PQeNbM3xRd9WLLBHRES1ks5JzbZt2xAUFIRZs2YhMjISPj4+CAgIwM2bN0tsP336dKxduxbLly9HdHQ0Ro0ahd69e+P06dNSm3v37qFTp04wMTHB3r17ER0djUWLFqFevXpSm4ULF2LZsmVYs2YNTpw4AXNzcwQEBCAnJ6cc3TYci/53CUeu3pEK7FmywB4REdVSMiGE0GUFPz8/tG/fHitWrAAAaDQauLi4YNy4cQgODi7WXqVSYdq0aRg7dqy0rE+fPjAzM0NoaCgAIDg4GEeOHMFff/1V4j6FEFCpVJg4cSI++eQTAEB6ejrs7e2xceNGDBgw4JlxZ2RkQKlUIj09HVZWVrp0udr63z8peH9zBABg2Tut8W8flZ4jIiIiqli6fH/rNFKTl5eHiIgI+Pv7P9qAkRH8/f1x7NixEtfJzc2FQqE9C8fMzAyHDx+Wnv/0009o164d+vbtCzs7O7Ru3Rrr1q2TXo+Li0NKSorWfpVKJfz8/J6634yMDK2HIYm7nY2J288CAIZ1cmNCQ0REtZ5OSc3t27ehVqthb2+vtdze3h4pKSklrhMQEIDFixfjypUr0Gg02L9/P3bt2oXk5GSpTWxsLFavXo3GjRtj3759GD16ND766CNs2rQJAKRt67LfkJAQKJVK6eHi4qJLV6u1+3kFGB0agczcArRzrccCe0RERADqVPYOli5dipEjR8LLywsymQweHh4YNmwYvvnmG6mNRqNBu3bt8OmnnwIAWrdujfPnz2PNmjUIDAws136nTp2KoKAg6XlGRoZBJDZCCPzfrqICe6ZYOagNTIw5ia0mU6vVyM/P13cYRER6I5fLYWT0/N9lOiU1NjY2MDY2Rmpqqtby1NRUODg4lLiOra0t9uzZg5ycHNy5cwcqlQrBwcFo1OjRtGNHR0c0b95ca71mzZph586dACBtOzU1FY6Ojlr79fX1LXG/pqamMDU11aV7NcLm49ex58wNGBvJsHJga9hbscBeTSWEQEpKCtLS0vQdChGRXhkZGcHd3R1yufy5tqNTUiOXy9G2bVuEh4ejV69eAApHWcLDw/Hhhx8+dV2FQgEnJyfk5+dj586d6Nevn/Rap06dcOnSJa32ly9fhqurKwDA3d0dDg4OCA8Pl5KYjIwMnDhxAqNHj9alCzVaxPV7mPdLNAAguIsX/BqxwF5NVpTQ2NnZoW7dupyKT0S1kkajwY0bN5CcnIyGDRs+1+9CnU8/BQUFITAwEO3atUOHDh2wZMkSZGdnY9iwYQCAIUOGwMnJCSEhIQCAEydOICkpCb6+vkhKSsLs2bOh0WgwefJkaZsTJkzACy+8gE8//RT9+vXDyZMn8fXXX+Prr78GAMhkMowfPx7z589H48aN4e7ujhkzZkClUknJlaG7nZWLsQ8L7HXzdsCIl9z1HRI9B7VaLSU0DRowOSWi2s3W1hY3btxAQUEBTEzKX5pE56Smf//+uHXrFmbOnImUlBT4+voiLCxMuog3Pj5e67xYTk4Opk+fjtjYWFhYWKBbt27YvHkzrK2tpTbt27fH7t27MXXqVMydOxfu7u5YsmQJBg0aJLWZPHkysrOz8f777yMtLQ0vvvgiwsLCis2sMkQFag3GbT2NlIwceNiaY+F/fPhXfQ1XdA1N3bp19RwJEZH+FZ12UqvVz5XU6FynpqaqyXVqPtt7EWsOxaCu3Bg/fdgJnnaW+g6JnlNOTg7i4uLg7u5eKxJzIqKnedrvxEqrU0NVL+x8CtYcigEALPxPKyY0RFQmnFFXfWg0Gmg0Gn2HUSswqanGYm9l4ZMfCgvsvfeiO95qxQJ7RFSy3bt3o3v37nBzc4OFhQVeeuklfYekNy1atMCtW7eQnp6Oxo0bIzMzs0r3f+vWLXzyySdo1aoV7OzsYGpqiv/9739VGkNtxaSmmrqfV4BRoRHIyi1Ae7d6CO7qpe+QqJaTyWRPfcyePVvfIdZaISEhGDlyJN566y38+uuvOHPmDH777Td9h6U3gYGBUKlUqF+/Prp16wZLy6ob4b558ybatGmDmJgYLFu2DEePHsWlS5fw5ptvVlkMtRmvqamGhBD4+Psz+OnsDdhamuLXcS/CjvVoDEpNvKbm8erd27Ztw8yZM7VKMVhYWMDCwkIfodVqsbGx8PHxwfHjx9GiRQt9h1NtZGRkoKCgAPXr16/S/b733nvIzc2V7m1IZcNragzYpqPX8NPZogJ7bZjQULXg4OAgPZRKJWQymdayooTm0KFD6NChA0xNTeHo6Ijg4GAUFBRI25HJZNizZ4/0fOPGjVqzIWfPnl2sqObBgwchk8m0ChXu3LkTLVq0gKmpKdzc3LBo0SKtdXJzczFlyhS4uLjA1NQUnp6eWL9+Pa5du/bUEadr166VuL9neeWVVzB+/HitZU/25dSpU3jjjTdgY2MDpVKJzp07IzIy8qnb1Wg0mDt3LpydnWFqairNOC2yb98+eHh4YMGCBbC1tYWlpSXefvttJCYmAgCuXbsGIyMj/P3331rbXbJkCVxdXaHRaEp8z93c3LBkyRLp+eLFi+Ht7Q1zc3O4uLhgzJgxyMrKkl5/8jgeP34cL774IiwtLWFvb48JEyYgLy+v1Pdrz549xWZ1PhmDRqNBSEgI3N3dYWZmBh8fH+zYsUN6/fHjZmVlhfr16+Pdd98t9pl70iuvvCIdfzMzs2Lv8dChQ0stH7JkyRK4ublJz3/55RfUq1cPLVu2hEKhgKenp9a9DIHCWcI9e/aEhYUFrKys0K9fP62itkXHY+3atXBxcUHdunXRr18/pKenlxrT3r17YWFhgb1790rLEhIS0K9fP1hbW6N+/fro2bMnrl27Vur7YAiY1FQzEdfvYv6vFwAAU7t6oYN71f6VQfohhMD9vAK9PCpysDYpKQndunVD+/btcfbsWaxevRrr16/H/PnzK2wfABAREYF+/fphwIABOHfuHGbPno0ZM2Zg48aNUpshQ4bgu+++w7Jly3DhwgWsXbsWFhYWcHFxQXJyMpKTk3Hy5EkAwMmTJ6VllXk7lczMTAQGBuLw4cM4fvw4GjdujG7duj31mo+lS5di0aJF+PLLLxEVFYWAgAD8+9//xpUrVwAUXr9x9uxZJCQkYO/evfjjjz+QmpqKXr16QQgBNzc3+Pv7Y8OGDVrb3bBhA4YOHVrm0vRGRkZYtmwZ/vnnH2zatAm///67Vr2xx8XExMDf3x+enp44ceIENm7ciO3bt2Pq1KllfKdKFhISgm+//RZr1qzBP//8gwkTJmDw4ME4dOhQie0jIiLw008/lWnbI0eORHJyMs6fP4+WLVuW+xY9t27dwtq1azFmzBhERUVh/PjxGDNmDH7++WcAhYlZz549cffuXRw6dAj79+9HbGws+vfvr7Wdq1evYvv27fj5558RFhaG06dPY8yYMSXu86+//kK/fv2wfv16dO3aFUDhheIBAQGwtLTEX3/9hSNHjsDCwgJdunTRSi4NTaXf+4nK7lZmLsZsiUSBRqB7K0e89yIL7NUWD/LVaD5zn172HT03AHXlFfOrYNWqVXBxccGKFSsgk8ng5eWFGzduYMqUKZg5cyaMjIygUCjw4MGD59rP4sWL8frrr2PGjBkAgCZNmiA6OhpffPEFhg4disuXL2P79u3Yv38//P39AUDr1ixFt17JyckBUFj4q7RbvVSk1157Tev5119/DWtraxw6dAhvvfVWiet8+eWXmDJlCgYMGAAA+Pzzz/HHH39gyZIlWLlyJTQaDYyNjbF161YpIdu6dSs8PDwQHh4Of39/jBgxAqNGjcLixYthamqKyMhInDt3Dj/++CMAwMzM7JnH5PFRFTc3N8yfPx+jRo3CqlWrirUt6te6detgYmKC5s2b44svvsCwYcMwb968ctVnys3NxaeffooDBw6gY8eOAAqP6eHDh7F27Vp07ty52DpBQUGYNGmS9Dl5mrp168LBwQEFBQWws7ODUqnUOUag8A+UoUOHSglIkyZNcObMGXz++efo0aMHwsPDce7cOcTFxUnH69tvv0WLFi1w6tQptG/fHkDhZ/Pbb7+Fk5MTAGD58uXo3r07Fi1apPVZjYyMRI8ePbBo0SKtxGjbtm3QaDT473//K42AbdiwAdbW1jh48KDBXuPDkZpqokCtwbjvIpGakQtPOwss7NOKBfaoxrlw4QI6duyo9dnt1KkTsrKypNMhLVu2xI4dO5465fjcuXPSNToWFhbSX5+P76dTp05ayzp16oQrV65ArVbjzJkzMDY2LvGLThfOzs6wtLSEu7s7Ro4cqTX8X5JVq1ZpxV10k94iqampGDlyJBo3bgylUgkrKytkZWUhPj6+xO1lZGTgxo0bJfb1woUL0nMXFxetESZXV1c4OzsjOrrwtiq9evWCsbExdu/eDaDwVNGrr74qnTZp2bIlrl69Ko1cleTAgQN4/fXX4eTkBEtLS7z77ru4c+cO7t+/L7VJT0+HhYUFFi1ahPbt22sVUevUqRPy8vJw9erVp72Fpbp69Sru37+PN954Q+s9/vbbbxETE1Os/Z49exAbG4uJEyeWaftFx87MzAybN2/Gpk2btF7/5ZdfYGFhgXr16sHHx0frpsxPevJ4vfjii9KxuHDhQrHj1bx5c1hbW2sd04YNG0oJDQB07NgRGo1G6zq2uLg4BAQEICcnB6+88orWPs+ePYurV6/C0tJSeq/q16+PnJycEt8vQ8GRmmrii32XcDz2LszlxlgzuC3MTXloahMzE2NEzw3Q276r0pIlS9CrVy+Ym5tDLpejoKCg2IWBTZs21TptcOLECQwePLjM+zAzM6uQWP/66y9YWlri2rVrGDFiBKZNm4YVK1aU2n7QoEGYNm2a9HzZsmX4888/peeBgYG4c+cOli5dCldXV5iamqJjx47PdTqgXr16pb5WlFzK5XIMGTIEGzZswNtvv42tW7di6dKlUrtu3bphwIAB8PPzg7m5OQBoJSvXrl3DW2+9hdGjR2PBggWoX78+Dh8+jPfeew95eXnSyIulpSUiIyMxZcqUUuuylPePtaLrd3799VetL3sAxW5enJ+fj8mTJ2PBggVl/iwUHbucnBxs2rQJffv2RXR0tHRh6quvvorVq1cjPz8fv/32G0aMGAFvb+9i23n8uqLHVcYfqVFRUQgODsbNmzcxfPhw/Pnnn9LpxKysLLRt2xZbtmwptp6trW2Fx1Jd8JuzGth7Lhlr/4wFAHzR1weedpxBUtvIZLIKOwWkT82aNcPOnTshhJB+iR85cgSWlpZwdnYGUPhXbEpKCuLj46FWq7Fr165iIxpyuRyenp7S86JRnsf3c+TIEa1lR44cQZMmTWBsbAxvb29oNBocOnRIOv1UHu7u7rC2toanpyf69u2LY8eOPbW9UqnUivvJmTdHjhzBqlWr0K1bNwCFF3Levn271O1ZWVlBpVLhyJEjWqNOR44cQYcOHQAAXl5eSEhIQEJCgvTX//Xr15GYmIjmzZtL64wYMQItW7bEqlWrUFBQgLffflt6TSaTYcuWLVi+fDnu3r0LAFp/+UdERECj0WDRokXSl+b27duLxWtkZARPT0+0bdsWq1atQn5+vjRac+TIEcjlcnh4eDzlHSxd8+bNYWpqivj4+GeOwK1evRoWFhZ49913y7z9x4/drFmz8OWXX+LkyZPS58fc3Fx6vVmzZvjss89w9uzZYtvx8vLCkSNHtK7JOXz4sHQsmjVrVux4RUdHIy0tTet4xcfH48aNG1CpCuuTHT9+HEZGRmjatKnU5uWXX0ZISAjS09PRsmVLLF26FBMmTAAAtGnTBtu2bYOdnV21n/FbkXj6Sc9ibmVh0o4oAMDIl9zRzdtRzxERld+YMWOQkJCAcePG4eLFi/jxxx8xa9YsBAUFaV2QamxsDHd3d3h6esLOzk7n/UycOBHh4eGYN28eLl++jE2bNmHFihX45JNPABRe8xEYGIjhw4djz549iIuLw8GDB0v8In6a3Nxc5OTk4OLFi9i7dy9atmypc6yPa9y4MTZv3owLFy7gxIkTGDRo0DNHEiZNmoTPP/8c27Ztw6VLlxAcHIwzZ87g448/BgC88cYbaNasGQYOHIi///4bf//9NwYOHAhfX1+ta3iaNWuGf/3rX5gyZQreeeedEvdbv359eHp6wtPTE3XqPEqyPT09kZ+fj+XLlyM2NhabN2/GmjVrSo15xIgRyMjIwMiRIxEdHY2wsDBMmjQJH374odb1NGq1Gjk5OcjJyZFORxY9z8nJgRACBQUFUKvVsLS0xCeffIIJEyZg06ZNiImJQWRkJJYvX17sVNHChQuxaNEinUZH7t+/j5SUFFy/fh2LFy9GnTp1tBJUjUaDnJwcZGZmYtu2bbhz506Jn4cJEyZg48aNWLVqFa5cuYKVK1di06ZN0kXV/v7+8Pb2xqBBgxAZGYmTJ09iyJAh6Ny5M9q1aydtR6FQIDAwEGfPnsVff/2Fjz76CP369dO6nqZolE6pVOLrr7/G9OnTpQvIBw0aBBsbG/Ts2RN//fWX9DPw0UcfFfsjwaCIWiI9PV0AEOnp6foORZKVky/8Fx0UrlN+EX3XHBX5BWp9h0RV5MGDByI6Olo8ePBA36GUy4YNG4RSqSzxtYMHD4r27dsLuVwuHBwcxJQpU0R+fn6ZtzVr1izh4+Oj1eaPP/4QAMS9e/ekZTt27BDNmzcXJiYmomHDhuKLL77QWufBgwdiwoQJwtHRUcjlcuHp6Sm++eYbrTZxcXECgIiLiytxf0UPGxsbMXDgQHH37t1S+9G5c2fx8ccfay17si+RkZGiXbt2QqFQiMaNG4sffvhBuLq6iq+++qrU7arVajF79mzh5OQkTExMhI+Pj9i7d69Wm5iYGNG9e3dRt25dYWFhIXr37i0SExOLbWv9+vUCgDh58mSp+yvyZFyLFy8Wjo6OwszMTAQEBIhvv/1W65g8eRwPHz4s/Pz8hFwuF3Z2dmLChAkiNzdX6/16/D1+2mPDhg1CCCE0Go1YsmSJaNq0qTAxMRG2trYiICBAHDp0SAjx6Li99dZbWn0BIHbv3l1qXx+PRS6XixYtWoht27ZJrwcGBkqv16lTR3h6eooVK1YIIYT46quvhKurq9b2li5dKtzd3YWJiYnw9PQU69at03r9+vXr4t///rcwNzcXlpaWom/fviIlJUV6vehzs2rVKqFSqYRCoRD/+c9/tD5/gYGBomfPnlrbHT58uHjxxReFWl34XZKcnCyGDBkibGxshKmpqWjUqJEYOXJktfoeLPK034m6fH+z+J6eCCEw7rvT+CUqGXaWpvjloxdhZ8l6NLVFTSy+RzXfvHnz8MMPPyAqKkrfoZTZ+PHj4evri6FDh+o7lCoze/Zs7NmzB2fOnNF3KFWGxfdquA1HruGXqGTUMZJh1aA2TGiIqNJkZWXh/PnzWLFiBcaNG6fvcHRiYmICY+OqvZidaq6af2ViDXTq2l18+lvh1L3/69YM7dxYYI+IKs+HH36I7777Dr169cLw4cP1HY5OvvjiC32HQDUITz9VsZuZOXhr2WHczMxFDx8Vlg3wZT2aWoinn4iIHuHppxooX63Bh1tP42ZmLhrbWeCzt72Z0BAREVUQJjVVaGHYRZyMuwsL0zpY8y4L7BEREVUkJjVV5NeoZKz7Kw4A8GXfVvCwZYE9IiKiisSkpgpcvZmJyTsKK09+8HIjdGnJAntEREQVjUlNJcvKLcCo0Ehk56nxr0b1MSmg6bNXIiIiIp0xqalEQghM2RGFqzezYG9liuXvtEEdY77lRERUdYpuQVEb8Bu2Eq0/HIdfzz0qsGdrafrslYiIqMZavnw55s2bBwBYuXKldD+yqrR79250794dbm5usLCwwEsvvVTlMegLk5pKcjLuLkL2XgQATO/eDG1dWWCPajaZTPbUx+zZs/UdIpHe9evXD5s2bYKJiQnmz5+P9957r0r3HxISgpEjR+Ktt97Cr7/+ijNnzuC3336r0hj0iXOKK8HNjByM3RoJtUagp68KgS+46TskoueWnJws/X/btm2YOXMmLl26JC2zsOCMPiJ7e3tcvnwZKSkpsLOz07rbeWWLjY3Fp59+iuPHj6NFixZVtt/qhCM1FSxfrcHYrZG4lZmLpvaWCGGBPTIQDg4O0kOpVEImk2ktK0pqDh06hA4dOsDU1BSOjo4IDg5GQUGBtB2ZTIY9e/ZIzzdu3Ahra2vp+ezZs+Hr66u174MHD0ImkyEtLU1atnPnTrRo0QKmpqZwc3PDokWLtNbJzc3FlClT4OLiAlNTU3h6emL9+vW4du3aU0ecrl27VuL+nuWVV17B+PHjtZY92ZdTp07hjTfegI2NDZRKJTp37ozIyMinbnfo0KFSbHK5HF5eXti8ebP0ekxMDHr27Al7e3tYWFigffv2OHDgQLHtzJ49u1hfe/XqJb3u5uaGJUuWSM/Dw8OLtQEeHYvHH48fPwCYMmUKmjRpgrp166JRo0aYMWOG1nUdZTnGRcfp8Zs6zpgxAzKZTCvO+Ph49OzZExYWFrCyskK/fv2Qmpqq076e/AyWtv9nfbaLPgNGRkZQqVSIiYmBiYlJsf2X9n4aGRnBzs4O7733HnJyckqN43HW1tbYuHEjAGDfvn3w8PDAggULYGtrC0tLS7z99ttITEzUWmf16tXw8PCAXC5H06ZNtT5PQOHP6OrVq9G1a1eYmZmhUaNG2LFjR6nvTW5uLvz9/eHv74/c3Fyp3X//+180a9YMCoUCXl5eWLVqVanvQ0VhUlPBPtt7Eaeu3YOlaR2sHtwGdeUcDKMyEALIy9bPowLvlJKUlIRu3bqhffv2OHv2LFavXo3169dj/vz5FbYPAIiIiEC/fv0wYMAAnDt3DrNnz8aMGTOkX+4AMGTIEHz33XdYtmwZLly4gLVr18LCwgIuLi5ITk5GcnIyTp48CQA4efKktMzFxaVCY31cZmYmAgMDcfjwYRw/fhyNGzdGt27dkJmZ+dT1unTpguTkZFy5cgU9evTAsGHDkJWVBaDwZpXdunVDeHg4Tp8+jS5duqBHjx6Ij4/X2oYQAi1atJD62a9fv1L3p9FoMHHixKeOvl26dAnJyclaCUYRS0tLbNy4EdHR0Vi6dCnWrVuHr7766ql9fJbExEQsWbIEZmZmWnH27NkTd+/exaFDh7B//37Exsaif//+z7WvkpTnsz1p0qQy3wbl0qVLSEpKQmhoKLZt24YNGzboHOOtW7dw9uxZJCQkYO/evfjjjz+QmpqKXr16oeiOSLt378bHH3+MiRMn4vz58/jggw8wbNgw/PHHH1rbmjFjBvr06YOzZ89i0KBBGDBgAC5cuFBsn2q1GgMGDEBWVhb27NkDU9PCa0e3bNmCmTNnYsGCBbhw4QI+/fRTzJgxA5s2bdK5X7rgN24F+iXqBtYfLiyw90VfHzRigT0qq/z7wKcq/ez7/24AcvMK2dSqVavg4uKCFStWQCaTwcvLCzdu3MCUKVMwc+ZMGBkZQaFQ4MGDB8+1n8WLF+P111/HjBkzAABNmjRBdHQ0vvjiCwwdOhSXL1/G9u3bsX//fvj7+wMAGjVqJK3v4OAAANJfw7a2ttKyyvTaa69pPf/6669hbW2NQ4cO4a233ip1PVNTUzg4OEAIAZVKBXNzc+nO1T4+PvDx8ZHazps3D7t378ZPP/2EDz/8UFqen58PMzMzqZ9mZmZaf1U/btOmTcjNzUXPnj2l5KlI0TpOTk4wNzeHUqkstv706dOl/7u5ueGTTz7B999/j8mTJ5fax2eZNm0a+vfvrzUKFR4ejnPnziEuLk5KRr/99lu0aNECp06dQvv27cu9vyeV5bP9uD/++ANHjx7FiBEjiiUMJbGzs4O1tTWys7Mhl8tLfF+fRaPRwNjYGFu3bpXej61bt8LDwwPh4eHw9/fHl19+iaFDh2LMmDEAgKCgIBw/fhxffvklXn31VWlbffv2xYgRIwAUfqb279+P5cuXa422CCEwbNgwXL16FYcOHdJKgmfNmoVFixbh7bffBgC4u7sjOjoaa9euRWBgoM59KyuO1FSQK6mZmLwjCgAwqrMHurSs/F+QRNXNhQsX0LFjR61Trp06dUJWVpY0BN6yZUvs2LHjqdNMz507BwsLC+nRtWvXYvvp1KmT1rJOnTrhypUrUKvVOHPmDIyNjdG5c+fn6o+zszMsLS3h7u6OkSNHIj09/antV61apRX3p59+qvV6amoqRo4cicaNG0OpVMLKygpZWVnFRlWe9Msvv8DCwgKmpqbSX7tFIxZZWVn45JNP0KxZM1hbW8PCwgIXLlwots2MjAyYmz87eb1//z6mT5+OhQsXlng9yJ07d1CnTh3UrVu31G1s27YNnTp1kk5LTp8+/Zl9fJrIyEjs3r1bmlVU5MKFC3BxcdEaXWvevDmsra21RhWe9XkCgPT0dK02T16TUpbPdhEhBCZOnIhZs2aVOTlxdnaGubm5NHr3zjvvaL3+wgsvwNLSEi4uLujfv3+xfRZ58v1wdXWFs7MzoqOjpX6U9LPz5ChMx44diz1/ss2kSZOwefNmtG/fHvXrP5oMk52djZiYGLz33nta7+n8+fMRExNTpvejvDhSUwEyc/LxQWgE7uep0bFRA3zyZhN9h0Q1jUndwhETfe27Ci1ZsgS9evWCubk55HI5CgoKig3RN23aFD/99JP0/MSJExg8eHCZ9/H4KYrn8ddff8HS0hLXrl3DiBEjMG3aNKxYsaLU9oMGDcK0adOk58uWLcOff/4pPQ8MDMSdO3ewdOlSuLq6wtTUFB07dkReXt5T43j11VexevVq5OfnY+/evRgyZAiioqKkUZD9+/fjyy+/hKenJ8zMzPCf//yn2DZv3LgBlerZo4FffPEFmjZtih49emDnzp3FXo+NjYWrq2up1woeO3YMgwYNwpw5cxAQEAClUonvv/++2DVPupg4cSI++eQTODqWrxp7WT5PlpaWWtc3JSUl4ZVXXinX/r799ltkZ2dj1KhRWLBgQZnWKfqsxcXF4f3338fixYsxceJE6fVt27ahWbNmSElJwUcffYRRo0bhl19+0dpGvXr1St1+ZVzbeeHCBezduxdvv/02+vfvj4CAAACQRvfWrVsHPz8/rXWKRhgrC5Oa5ySEwOQdUYi9lQ0HKwWWD2zNAnukO5mswk4B6VOzZs2wc+dOCCGkX6JHjhyBpaUlnJ2dART+VZiSkoL4+Hio1Wrs2rWr2IiGXC6Hp6en9PzJv0qbNWuGI0eOaC07cuQImjRpAmNjY3h7e0Oj0eDQoUPS6afycHd3h7W1NTw9PdG3b18cO3bsqe2VSqVW3I//9VoU46pVq9CtWzcAQEJCAm7fvv3MOMzNzaXtNmvWDJ9++ikOHDiAESNG4MiRIxg6dCh69+4NoPAL5dq1a1rrazQaREZGYuzYsU/dT3JyMlavXo1Dhw6V2ubQoUNPrXty9OhRuLq6aiV3169ff1YXS/XTTz/h8uXL+PXXX4u91qxZMyQkJCAhIUEanYiOjkZaWhqaN28utXvW5wkAjIyMtNo8OUpVls82UDjSVZT8mpiYlLmfj3/W+vTpg927d2slNS4uLvD09ISnpyfee+89hISEFNuGl5dXsffj+vXrSExMlN6Pop+dx08BHTlyROv9AoDjx49jyJAhWs9bt26t1Wbz5s147bXXMG/ePIwcORLnz5+HlZUV7O3toVKpEBsbi0GDBpX5PagI/PZ9TnvPp2Dv+RSYGMuwanAb2FiwwB7VXmPGjEFCQgLGjRuHixcv4scff8SsWbMQFBSkdc2BsbEx3N3d4enpCTs7O533M3HiRISHh2PevHm4fPkyNm3ahBUrVkiFztzc3BAYGIjhw4djz549iIuLw8GDB7F9+3ad9pObm4ucnBxcvHgRe/fuRcuWLXWO9XGNGzfG5s2bceHCBZw4cQKDBg0q06hSbm4uUlJSkJiYiP/+97+4e/cuvLy8pG3u2rULZ86cwdmzZzFw4EBoNBpp3YSEBIwcORI3b9585gW0K1euRO/evYt9eQFAXl4edu7cid9//x09e/ZESkoKUlJSkJ6eDiEEbt26JcUTHx+P77//HjExMVi2bBl2795dbHtCCOTk5EiPopGlJ6/zWbhwIebPn1/i6S5/f394e3tj0KBBiIyMxMmTJzFkyBB07twZ7dq1e8a7qpuyfraLrmF5ctbYs9y8eRMpKSk4ceIEfv75Z+n4FsnLy0NOTg6uX7+OHTt2lPhZfOONN9CsWTMMHDgQf//9N/7++28MHDgQvr6+0vVckyZNwsaNG7F69WpcuXIFixcvxq5du4oVCfzhhx/wzTff4PLly5g1axZOnjypdY0W8ChpnzBhAlxcXBAUFCS9NmfOHISEhGDZsmW4fPkyzp07hw0bNmDx4sU6vS86E7VEenq6ACDS09MrdLtqtUZ8tf+S+PZoXIVulwzbgwcPRHR0tHjw4IG+QymXDRs2CKVSWeJrBw8eFO3btxdyuVw4ODiIKVOmiPz8/DJva9asWcLHx0erzR9//CEAiHv37knLduzYIZo3by5MTExEw4YNxRdffKG1zoMHD8SECROEo6OjkMvlwtPTU3zzzTdabeLi4gQAERcXV+L+ih42NjZi4MCB4u7du6X2o3PnzuLjjz/WWvZkXyIjI0W7du2EQqEQjRs3Fj/88INwdXUVX331VanbDQwMlOKoU6eO8PT0FCtWrNDqw6uvvirMzMyEi4uLWLFihVYsEydOFC+//LL466+/im23Z8+e0nNXV1dhZmYmEhISSmzz5Hvy5MPV1VVab9KkSaJBgwbCwsJC9O/fX3z11VfFjvHTtlXULwDCx8dHqNVqrTgff7+uX78u/v3vfwtzc3NhaWkp+vbtK1JSUko9Bo/3pejzVNLnuWj/p0+flpY967PduXNnIZPJxKlTp566/5JiefKzdufOHa04ih7W1tbirbfeEteuXRNCCKFUKsWGDRuk7cXExIju3buLunXrCgsLC9G7d2+RmJiotc9Vq1aJRo0aCRMTE9GkSRPx7bffar0OQKxcuVK88cYbwtTUVLi5uYlt27Y99b25dOmSMDMzE/v27ZOWbdmyRfj6+gq5XC7q1asnXn75ZbFr164S34en/U7U5ftb9rADBi8jIwNKpRLp6emwsrLSdzhUy+Xk5CAuLg7u7u5lnvJJpE8HDx7E7NmzcfDgwWKvpaWlwdfXt9hpr/KwtrbWqT4QVTyZTIbdu3frPNr0PJ72O1GX72+efiIiomeSy+XFrhEqYmRkBFtb2wrZD5N8eh5MaoiI6JleeOEF7Nq1q8TXrKyscOrUqQrZT0pKSoVsh2onzn4iIiIiSU2+KoUjNURERGQQypXUrFy5Em5ublAoFPDz85Pun1KS/Px8zJ07Fx4eHlAoFPDx8UFYWJhWm5JutPbkdLZXXnmlWJtRo0aVJ3wiIiIyQDqfftq2bRuCgoKwZs0a+Pn5YcmSJQgICMClS5dKrDcxffp0hIaGYt26dfDy8sK+ffvQu3dvHD16VKsWQosWLbTu6VFSee6RI0di7ty50vOnlekmqgkerydCRFRbVdQpL52TmsWLF2PkyJEYNmwYAGDNmjX49ddf8c033yA4OLhY+82bN2PatGlSBc3Ro0fjwIEDWLRoEUJDQx8FUqfOM28oV7du3Sq56RxRZZPL5TAyMsKNGzdga2sLuVxeKWXMiYiqO/GweKNMJtOpCnNJdEpq8vLyEBERgalTp0rLjIyM4O/vX2r58Nzc3GJT9MzMzHD48GGtZVeuXIFKpYJCoUDHjh0REhKChg0barXZsmULQkND4eDggB49emDGjBkcraEaycjICO7u7khOTsaNG3q65xMRUTUhk8ng7Oz83PeG0impuX37NtRqNezt7bWW29vb4+LFiyWuExAQgMWLF+Pll1+Wbn++a9cuqNVqqY2fnx82btyIpk2bIjk5GXPmzMFLL72E8+fPw9LSEgAwcOBAuLq6QqVSISoqClOmTMGlS5dKnWKYm5urVW47IyNDl64SVTq5XI6GDRuioKBA6+eBiKi2MTExqZCbXVb6lO6lS5di5MiR8PLygkwmg4eHB4YNG4ZvvvlGavP4beBbtWoFPz8/uLq6Yvv27XjvvfcAAO+//77UxtvbG46Ojnj99dcRExMDDw+PYvsNCQnBnDlzKrFnRM+vaLj1eYdciYhIx9lPNjY2MDY2Rmpqqtby1NTUUq91sbW1xZ49e5CdnY3r16/j4sWLsLCwQKNGjUrdj7W1NZo0aYKrV6+W2qbodualtZk6dSrS09OlR0JCwrO6R0RERDWYTkmNXC5H27ZtER4eLi3TaDQIDw9Hx44dn7quQqGAk5MTCgoKsHPnTvTs2bPUtllZWYiJiYGjo2Opbc6cOQMApbYxNTWFlZWV1oOIiIgMl86nn4KCghAYGIh27dqhQ4cOWLJkCbKzs6XZUEOGDIGTkxNCQkIAACdOnEBSUhJ8fX2RlJSE2bNnQ6PRYPLkydI2P/nkE/To0QOurq64ceMGZs2aBWNjY7zzzjsAgJiYGGzduhXdunVDgwYNEBUVhQkTJuDll19Gq1atKuJ9ICIiohpO56Smf//+uHXrFmbOnImUlBT4+voiLCxMung4Pj4eRkaPBoBycnIwffp0xMbGwsLCAt26dcPmzZthbW0ttUlMTMQ777yDO3fuwNbWFi+++CKOHz8u3SBNLpfjwIEDUgLl4uKCPn36YPr06WWOu2gOPC8YJiIiqjmKvrfLUstGJmryTR50kJiYCBcXF32HQUREROWQkJAAZ2fnp7apNUmNRqPBjRs3YGlpWeFFzjIyMuDi4oKEhASDvHbH0PsHGH4f2b+az9D7yP7VfJXVRyEEMjMzoVKptM4ElaTW3KXbyMjomRne8zL0C5INvX+A4feR/av5DL2P7F/NVxl9VCqVZWrHu3QTERGRQWBSQ0RERAaBSU0FMDU1xaxZs2BqaqrvUCqFofcPMPw+sn81n6H3kf2r+apDH2vNhcJERERk2DhSQ0RERAaBSQ0REREZBCY1REREZBCY1BAREZFBYFLzhD///BM9evSASqWCTCbDnj17nrnOwYMH0aZNG5iamsLT0xMbN24s1mblypVwc3ODQqGAn58fTp48WfHBl4Gu/du1axfeeOMN2NrawsrKCh07dsS+ffu02syePRsymUzr4eXlVYm9eDpd+3jw4MFi8ctkMqSkpGi1q6nHcOjQoSX2r0WLFlKb6nQMQ0JC0L59e1haWsLOzg69evXCpUuXnrneDz/8AC8vLygUCnh7e+O3337Tel0IgZkzZ8LR0RFmZmbw9/fHlStXKqsbpSpP/9atW4eXXnoJ9erVQ7169eDv71/s81fSce7SpUtldqVU5enjxo0bi8WvUCi02tTkY/jKK6+U+HPYvXt3qU11OoarV69Gq1atpEJ6HTt2xN69e5+6TnX4GWRS84Ts7Gz4+Phg5cqVZWofFxeH7t2749VXX8WZM2cwfvx4jBgxQuuLf9u2bQgKCsKsWbMQGRkJHx8fBAQE4ObNm5XVjVLp2r8///wTb7zxBn777TdERETg1VdfRY8ePXD69Gmtdi1atEBycrL0OHz4cGWEXya69rHIpUuXtPpgZ2cnvVaTj+HSpUu1+pWQkID69eujb9++Wu2qyzE8dOgQxo4di+PHj2P//v3Iz8/Hm2++iezs7FLXOXr0KN555x289957OH36NHr16oVevXrh/PnzUpuFCxdi2bJlWLNmDU6cOAFzc3MEBAQgJyenKrolKU//Dh48iHfeeQd//PEHjh07BhcXF7z55ptISkrSatelSxetY/jdd99VdndKVJ4+AoWVaB+P//r161qv1+RjuGvXLq2+nT9/HsbGxsV+DqvLMXR2dsZnn32GiIgI/P3333jttdfQs2dP/PPPPyW2rzY/g4JKBUDs3r37qW0mT54sWrRoobWsf//+IiAgQHreoUMHMXbsWOm5Wq0WKpVKhISEVGi8uipL/0rSvHlzMWfOHOn5rFmzhI+PT8UFVoHK0sc//vhDABD37t0rtY0hHcPdu3cLmUwmrl27Ji2rzsfw5s2bAoA4dOhQqW369esnunfvrrXMz89PfPDBB0IIITQajXBwcBBffPGF9HpaWpowNTUV3333XeUEXkZl6d+TCgoKhKWlpdi0aZO0LDAwUPTs2bMSInx+Zenjhg0bhFKpLPV1QzuGX331lbC0tBRZWVnSsup8DIUQol69euK///1via9Vl59BjtQ8p2PHjsHf319rWUBAAI4dOwYAyMvLQ0REhFYbIyMj+Pv7S21qEo1Gg8zMTNSvX19r+ZUrV6BSqdCoUSMMGjQI8fHxeoqw/Hx9feHo6Ig33ngDR44ckZYb2jFcv349/P394erqqrW8uh7D9PR0ACj2mXvcs34O4+LikJKSotVGqVTCz89P78ewLP170v3795Gfn19snYMHD8LOzg5NmzbF6NGjcefOnQqNtbzK2sesrCy4urrCxcWl2KiAoR3D9evXY8CAATA3N9daXh2PoVqtxvfff4/s7Gx07NixxDbV5WeQSc1zSklJgb29vdYye3t7ZGRk4MGDB7h9+zbUanWJbZ68ZqMm+PLLL5GVlYV+/fpJy/z8/LBx40aEhYVh9erViIuLw0svvYTMzEw9Rlp2jo6OWLNmDXbu3ImdO3fCxcUFr7zyCiIjIwHAoI7hjRs3sHfvXowYMUJreXU9hhqNBuPHj0enTp3QsmXLUtuV9nNYdHyK/q1ux7Cs/XvSlClToFKptL4gunTpgm+//Rbh4eH4/PPPcejQIXTt2hVqtboyQi+zsvaxadOm+Oabb/Djjz8iNDQUGo0GL7zwAhITEwEY1jE8efIkzp8/X+znsLodw3PnzsHCwgKmpqYYNWoUdu/ejebNm5fYtrr8DNaau3TT89u6dSvmzJmDH3/8Uet6k65du0r/b9WqFfz8/ODq6ort27fjvffe00eoOmnatCmaNm0qPX/hhRcQExODr776Cps3b9ZjZBVv06ZNsLa2Rq9evbSWV9djOHbsWJw/f16v12hVpvL077PPPsP333+PgwcPal1IO2DAAOn/3t7eaNWqFTw8PHDw4EG8/vrrFRq3Lsrax44dO2qNArzwwgto1qwZ1q5di3nz5lV2mOVWnmO4fv16eHt7o0OHDlrLq9sxbNq0Kc6cOYP09HTs2LEDgYGBOHToUKmJTXXAkZrn5ODggNTUVK1lqampsLKygpmZGWxsbGBsbFxiGwcHh6oM9bl8//33GDFiBLZv315siPFJ1tbWaNKkCa5evVpF0VW8Dh06SPEbyjEUQuCbb77Bu+++C7lc/tS21eEYfvjhh/jll1/wxx9/wNnZ+altS/s5LDo+Rf9Wp2OoS/+KfPnll/jss8/wv//9D61atXpq20aNGsHGxqbGHMMnmZiYoHXr1lL8hnIMs7Oz8f3335fpjwV9H0O5XA5PT0+0bdsWISEh8PHxwdKlS0tsW11+BpnUPKeOHTsiPDxca9n+/fulvzjkcjnatm2r1Uaj0SA8PLzUc5PVzXfffYdhw4bhu+++05p+WJqsrCzExMTA0dGxCqKrHGfOnJHiN4RjCBTO2Lh69WqZfpnq8xgKIfDhhx9i9+7d+P333+Hu7v7MdZ71c+ju7g4HBwetNhkZGThx4kSVH8Py9A8onDkyb948hIWFoV27ds9sn5iYiDt37tSYY/gktVqNc+fOSfEbwjEECqc95+bmYvDgwc9sq89jWBKNRoPc3NwSX6s2P4MVdsmxgcjMzBSnT58Wp0+fFgDE4sWLxenTp8X169eFEEIEBweLd999V2ofGxsr6tatKyZNmiQuXLggVq5cKYyNjUVYWJjU5vvvvxempqZi48aNIjo6Wrz//vvC2tpapKSkVPv+bdmyRdSpU0esXLlSJCcnS4+0tDSpzcSJE8XBgwdFXFycOHLkiPD39xc2Njbi5s2bVd4/IXTv41dffSX27Nkjrly5Is6dOyc+/vhjYWRkJA4cOCC1qcnHsMjgwYOFn59fidusTsdw9OjRQqlUioMHD2p95u7fvy+1effdd0VwcLD0/MiRI6JOnTriyy+/FBcuXBCzZs0SJiYm4ty5c1Kbzz77TFhbW4sff/xRREVFiZ49ewp3d3fx4MGDat+/zz77TMjlcrFjxw6tdTIzM4UQhZ+JTz75RBw7dkzExcWJAwcOiDZt2ojGjRuLnJycKu1fefs4Z84csW/fPhETEyMiIiLEgAEDhEKhEP/884/UpiYfwyIvvvii6N+/f7Hl1e0YBgcHi0OHDom4uDgRFRUlgoODhUwmE//73/+EENX3Z5BJzROKpvc++QgMDBRCFE6569y5c7F1fH19hVwuF40aNRIbNmwott3ly5eLhg0bCrlcLjp06CCOHz9e+Z0pga7969y581PbC1E4hd3R0VHI5XLh5OQk+vfvL65evVq1HXuMrn38/PPPhYeHh1AoFKJ+/frilVdeEb///nux7dbUYyhE4dRJMzMz8fXXX5e4zep0DEvqGwCtn6vOnTtrfQaFEGL79u2iSZMmQi6XixYtWohff/1V63WNRiNmzJgh7O3thampqXj99dfFpUuXqqBH2srTP1dX1xLXmTVrlhBCiPv374s333xT2NraChMTE+Hq6ipGjhypl6RbiPL1cfz48dLPl729vejWrZuIjIzU2m5NPoZCCHHx4kUBQEoMHlfdjuHw4cOFq6urkMvlwtbWVrz++utacVfXn0GZEEJU0KAPERERkd7wmhoiIiIyCExqiIiIyCAwqSEiIiKDwKSGiIiIDAKTGiIiIjIITGqIiIjIIDCpISIiIoPApIaIiIgMApMaIiIiMghMaoiIiMggMKkhIiIig8CkhoiIiAzC/wNYTpriZM3+8AAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(epoch_ind, hist['loss'], label='Ошибка (loss) на обучающей выборке')\n",
        "plt.plot(epoch_ind, hist['val_loss'], label='Ошибка (loss) на валидационной выборке')\n",
        "plt.legend()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "x4nnSFa7V1uT",
        "outputId": "edf2d826-bc6b-4b5b-9ba7-ec4d26e5545c"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f4555eaf430>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Проверяем работу модели на тестовой выборке\n",
        "main_model.evaluate(xt, yt)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P4VBThhOV3Up",
        "outputId": "cdbbf5cf-cfa0-4551-8c38-2ed82601105a"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 6s 19ms/step - loss: 0.0371 - accuracy: 0.9886\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.03712015226483345, 0.9886000156402588]"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "n = 10\n",
        "\n",
        "plt.figure(figsize=(2*n, 2*2))\n",
        "\n",
        "for i in range (n):\n",
        "  ax = plt.subplot(2, n, i+1)#С помощью метода subplot() библиотеки matplotlib создается subplot, который располагается на i+n+1 позиции (где i - номер текущего изображения, n - количество изображений в ряду).\n",
        "  plt.imshow(xt[i].reshape(28, 28), cmap='gray')#с помощью метода imshow() отображается декодированное изображение, которое хранится в переменной decoded_imgs[i].reshape(28, 28)\n",
        "  a=np.argmax(main_model.predict(xt[i:i+1]))\n",
        "  print('Предсказанное значение: ', a)\n",
        "  print('Реальное значение:', np.argmax(yt[i]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 716
        },
        "id": "rQSpv9X2V67k",
        "outputId": "96116048-b3a0-4da7-c946-968a788bd8a5"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 121ms/step\n",
            "Предсказанное значение:  1\n",
            "Реальное значение: 1\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "Предсказанное значение:  0\n",
            "Реальное значение: 0\n",
            "1/1 [==============================] - 0s 28ms/step\n",
            "Предсказанное значение:  1\n",
            "Реальное значение: 1\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "Предсказанное значение:  0\n",
            "Реальное значение: 0\n",
            "1/1 [==============================] - 0s 27ms/step\n",
            "Предсказанное значение:  0\n",
            "Реальное значение: 0\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "Предсказанное значение:  1\n",
            "Реальное значение: 1\n",
            "1/1 [==============================] - 0s 27ms/step\n",
            "Предсказанное значение:  0\n",
            "Реальное значение: 0\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "Предсказанное значение:  1\n",
            "Реальное значение: 1\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "Предсказанное значение:  0\n",
            "Реальное значение: 1\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "Предсказанное значение:  1\n",
            "Реальное значение: 1\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x400 with 10 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.utils import plot_model\n",
        "\n",
        "# Основная модель\n",
        "plot_model(main_model, dpi=60, show_shapes=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 616
        },
        "id": "YSptjQdPm3Lm",
        "outputId": "3629783b-5f78-44e8-8800-5ec8ef3f4e08"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    }
  ]
}