{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "LateProblem3.ipynb",
      "version": "0.3.2",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "metadata": {
        "id": "udR2W23BEPLG",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Цель вычислительного эксперимента\n",
        "Решить задачу выбора алгоритма оптимизации на наборе данных MNIST с использованием нейронных сетей простой структуры. Выбор произвести между алгоритмами: SGD, Nesterov Momentum, Adam; по значениям скорости сходимости, значения оптимума и вида траектории. В качестве структурного параметра используется количество нейронов"
      ]
    },
    {
      "metadata": {
        "id": "xOKzmertE0p9",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "##Описание выборки\n",
        "Выборка состоит из 70000 изображений рукописного написания цифр размера 28×28 пикселей."
      ]
    },
    {
      "metadata": {
        "id": "Rwld-h5ZFE6g",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "##Блок загрузки и предобработки выборки"
      ]
    },
    {
      "metadata": {
        "id": "tHVHDKfoaLXt",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Подключение необходимых библиотек"
      ]
    },
    {
      "metadata": {
        "id": "6Bs1WT9VC60x",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "0cf78c92-3052-4330-b375-f9e8b7efb1f7"
      },
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from keras.datasets import mnist\n",
        "from keras.utils import np_utils"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "1x1VYv5h8myw",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Загрузка датасета"
      ]
    },
    {
      "metadata": {
        "id": "B6grzzdXD7XD",
        "colab_type": "code",
        "outputId": "df0dec44-2199-4066-9609-afaaa3d9909e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 373
        }
      },
      "cell_type": "code",
      "source": [
        "(X_train, y_train), (X_test, y_test) = mnist.load_data()\n",
        "data = X_train\n",
        "labels = y_train\n",
        "\n",
        "fig = plt.figure(figsize=(8, 6))\n",
        "\n",
        "for i in range(15):\n",
        "    ax = fig.add_subplot(3, 5, i + 1, xticks=[], yticks=[])\n",
        "    ax.imshow(X_train[i])"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://s3.amazonaws.com/img-datasets/mnist.npz\n",
            "11493376/11490434 [==============================] - 2s 0us/step\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAcwAAAFBCAYAAAD69Z+AAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTAAAIjBgAgAQgQETAIAI/w/50ANmaIQ3TwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7ff4dd26c5f8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "CBkB5mwba89Z",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Изображение 28×28 пикселей представляется в виде массива размера 784"
      ]
    },
    {
      "metadata": {
        "id": "zpSwkDntEGnt",
        "colab_type": "code",
        "outputId": "015f1cdc-5602-48bb-fdbc-bab5cb2f8026",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 231
        }
      },
      "cell_type": "code",
      "source": [
        "num_pixels = X_train.shape[1] * X_train.shape[2]\n",
        "data = data.reshape(X_train.shape[0], num_pixels)\n",
        "X_train = X_train.reshape(X_train.shape[0], num_pixels).astype('float32')\n",
        "X_test = X_test.reshape(X_test.shape[0], num_pixels).astype('float32')\n",
        "\n",
        "data = pd.DataFrame(data=data,    # values\n",
        "                    index=np.arange(data.shape[0]),    # 1st column as index\n",
        "                    columns=np.arange(data.shape[1]))  # 1st row as the column names\n",
        "data.head()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "      <th>8</th>\n",
              "      <th>9</th>\n",
              "      <th>...</th>\n",
              "      <th>774</th>\n",
              "      <th>775</th>\n",
              "      <th>776</th>\n",
              "      <th>777</th>\n",
              "      <th>778</th>\n",
              "      <th>779</th>\n",
              "      <th>780</th>\n",
              "      <th>781</th>\n",
              "      <th>782</th>\n",
              "      <th>783</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 784 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   0    1    2    3    4    5    6    7    8    9   ...   774  775  776  777  \\\n",
              "0    0    0    0    0    0    0    0    0    0    0 ...     0    0    0    0   \n",
              "1    0    0    0    0    0    0    0    0    0    0 ...     0    0    0    0   \n",
              "2    0    0    0    0    0    0    0    0    0    0 ...     0    0    0    0   \n",
              "3    0    0    0    0    0    0    0    0    0    0 ...     0    0    0    0   \n",
              "4    0    0    0    0    0    0    0    0    0    0 ...     0    0    0    0   \n",
              "\n",
              "   778  779  780  781  782  783  \n",
              "0    0    0    0    0    0    0  \n",
              "1    0    0    0    0    0    0  \n",
              "2    0    0    0    0    0    0  \n",
              "3    0    0    0    0    0    0  \n",
              "4    0    0    0    0    0    0  \n",
              "\n",
              "[5 rows x 784 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "metadata": {
        "id": "13LCxNj2cGIx",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Нормализация объектов выборки: из 0-255 в 0-1"
      ]
    },
    {
      "metadata": {
        "id": "ivRw1cmjKrh7",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "X_train = X_train/255\n",
        "X_test = X_test/255"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "ws530Jg8dFy0",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Классы (10 цифр) представляются в виде бинарных матриц размера 2×2"
      ]
    },
    {
      "metadata": {
        "id": "6rPOiptbL4aF",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "y_train = np_utils.to_categorical(y_train)\n",
        "y_test = np_utils.to_categorical(y_test)\n",
        "num_classes = y_test.shape[1]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "pVyVefKJHkbC",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "##Графики анализа состава выборки"
      ]
    },
    {
      "metadata": {
        "id": "54ZsP8ydHuoO",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "###Анализ выбросов, гистограмма\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "metadata": {
        "id": "zqfgA9UFh_nF",
        "colab_type": "code",
        "outputId": "220e719f-0c77-498d-f157-c3fee6128ab0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 401
        }
      },
      "cell_type": "code",
      "source": [
        "sns.countplot(labels)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/categorical.py:1428: FutureWarning: remove_na is deprecated and is a private function. Do not use.\n",
            "  stat_data = remove_na(group_data)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ff4a961b208>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfgAAAFLCAYAAAA6dp6kAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAM9P8AE/iDp4YPJlgAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7ff4a8ee5fd0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "RKO9jyckqpqh",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Выбросов не наблюдается"
      ]
    },
    {
      "metadata": {
        "id": "5gHM_cPrICD0",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "###Анализ пропусков, статистика"
      ]
    },
    {
      "metadata": {
        "id": "ssjPGtXPp1YA",
        "colab_type": "code",
        "outputId": "63a1365b-088e-440e-c9ed-ac0ff1df4167",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        }
      },
      "cell_type": "code",
      "source": [
        "data.isnull().any().describe()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count       784\n",
              "unique        1\n",
              "top       False\n",
              "freq        784\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "metadata": {
        "id": "MdmPqoKrqWc9",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "В выборке нет пропусков или испорченных данных"
      ]
    },
    {
      "metadata": {
        "id": "S7Tb0oQIIUVi",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "###Анализ мультикорелляции признаков, кор. матрица"
      ]
    },
    {
      "metadata": {
        "id": "jM3Ax3XQvjZN",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#corr_matrix = data.corr()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "A4ZGSRkUvnNZ",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Не удалось получить наглядную корелляционную матрицу для 784 признаков"
      ]
    },
    {
      "metadata": {
        "id": "AKH1MtbLIcaq",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "##Список моделей\n",
        "Строится нейронная сеть простой стуктуры"
      ]
    },
    {
      "metadata": {
        "id": "NVC9F-DjIsUU",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "##Список функций ошибки, критериев качества\n",
        "Критерии качества, используемые в задаче:\n",
        "\n",
        "*   скорость сходимости\n",
        "*   значение оптимума\n",
        "*   вид траектории\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "metadata": {
        "id": "OP5DRSokI0FM",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "##Способ разбиения выборки на обучение-контроль\n",
        "Используется разбиение, предлагаемое функцией load_data() модуля mnist библиотеки keras. А именно, 60000  и 10000 объектов на обучение и контроль соответсвенно."
      ]
    },
    {
      "metadata": {
        "id": "swGINpuqI_FV",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "##Анализ выбранной модели на разбиении обучение-контроль"
      ]
    },
    {
      "metadata": {
        "id": "mazuDJ829nYB",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from keras.models import Sequential\n",
        "from keras.layers import Dense\n",
        "from keras.layers import Dropout\n",
        "from keras.preprocessing.image import ImageDataGenerator"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Jy4PvoW-KCw9",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def baseline_model(optimizer='adam', param=num_pixels):\n",
        "\t\n",
        "\tmodel = Sequential()\n",
        "\tmodel.add(Dense(param, input_dim=num_pixels, kernel_initializer='normal', activation='relu'))\n",
        "\tmodel.add(Dense(num_classes, kernel_initializer='normal', activation='softmax'))\n",
        "\t\n",
        "\tmodel.compile(loss='categorical_crossentropy', optimizer=optimizer, metrics=['accuracy'])\n",
        "\treturn model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "ONSJ-ENDKECS",
        "colab_type": "code",
        "outputId": "3dec03a0-5f85-4f28-8b56-1bc6f7538f04",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1161
        }
      },
      "cell_type": "code",
      "source": [
        "optimizers = ['adam', 'SGD', 'nadam']\n",
        "accuracy = list()\n",
        "history = dict()\n",
        "\n",
        "for optimizer in optimizers:\n",
        "    model = baseline_model(optimizer)\n",
        "    history[optimizer] = model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=10, batch_size=200, verbose=1)\n",
        "    scores = model.evaluate(X_test, y_test, verbose=0)\n",
        "    accuracy.append(scores[1])\n",
        "    print(\"Baseline Error: %.2f%%\" % (100-scores[1]*100))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/10\n",
            "60000/60000 [==============================] - 8s 127us/step - loss: 0.2807 - acc: 0.9189 - val_loss: 0.1363 - val_acc: 0.9605\n",
            "Epoch 2/10\n",
            "60000/60000 [==============================] - 7s 123us/step - loss: 0.1109 - acc: 0.9678 - val_loss: 0.0933 - val_acc: 0.9713\n",
            "Epoch 3/10\n",
            "60000/60000 [==============================] - 7s 123us/step - loss: 0.0701 - acc: 0.9799 - val_loss: 0.0811 - val_acc: 0.9747\n",
            "Epoch 4/10\n",
            "60000/60000 [==============================] - 7s 123us/step - loss: 0.0494 - acc: 0.9859 - val_loss: 0.0701 - val_acc: 0.9779\n",
            "Epoch 5/10\n",
            "60000/60000 [==============================] - 7s 122us/step - loss: 0.0363 - acc: 0.9899 - val_loss: 0.0649 - val_acc: 0.9792\n",
            "Epoch 6/10\n",
            "60000/60000 [==============================] - 7s 122us/step - loss: 0.0256 - acc: 0.9933 - val_loss: 0.0637 - val_acc: 0.9796\n",
            "Epoch 7/10\n",
            "60000/60000 [==============================] - 7s 122us/step - loss: 0.0185 - acc: 0.9956 - val_loss: 0.0564 - val_acc: 0.9819\n",
            "Epoch 8/10\n",
            "60000/60000 [==============================] - 7s 122us/step - loss: 0.0141 - acc: 0.9968 - val_loss: 0.0629 - val_acc: 0.9796\n",
            "Epoch 9/10\n",
            "60000/60000 [==============================] - 7s 121us/step - loss: 0.0101 - acc: 0.9980 - val_loss: 0.0608 - val_acc: 0.9818\n",
            "Epoch 10/10\n",
            "60000/60000 [==============================] - 7s 121us/step - loss: 0.0078 - acc: 0.9985 - val_loss: 0.0551 - val_acc: 0.9834\n",
            "Baseline Error: 1.66%\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/10\n",
            "60000/60000 [==============================] - 6s 107us/step - loss: 1.2237 - acc: 0.7211 - val_loss: 0.7076 - val_acc: 0.8509\n",
            "Epoch 2/10\n",
            "60000/60000 [==============================] - 6s 102us/step - loss: 0.6059 - acc: 0.8603 - val_loss: 0.4961 - val_acc: 0.8814\n",
            "Epoch 3/10\n",
            "60000/60000 [==============================] - 6s 103us/step - loss: 0.4753 - acc: 0.8806 - val_loss: 0.4186 - val_acc: 0.8938\n",
            "Epoch 4/10\n",
            "60000/60000 [==============================] - 6s 102us/step - loss: 0.4165 - acc: 0.8913 - val_loss: 0.3767 - val_acc: 0.9009\n",
            "Epoch 5/10\n",
            "60000/60000 [==============================] - 6s 101us/step - loss: 0.3814 - acc: 0.8984 - val_loss: 0.3501 - val_acc: 0.9059\n",
            "Epoch 6/10\n",
            "60000/60000 [==============================] - 6s 104us/step - loss: 0.3573 - acc: 0.9031 - val_loss: 0.3306 - val_acc: 0.9104\n",
            "Epoch 7/10\n",
            "60000/60000 [==============================] - 6s 105us/step - loss: 0.3392 - acc: 0.9069 - val_loss: 0.3157 - val_acc: 0.9141\n",
            "Epoch 8/10\n",
            "60000/60000 [==============================] - 6s 103us/step - loss: 0.3246 - acc: 0.9108 - val_loss: 0.3040 - val_acc: 0.9166\n",
            "Epoch 9/10\n",
            "60000/60000 [==============================] - 6s 102us/step - loss: 0.3128 - acc: 0.9134 - val_loss: 0.2939 - val_acc: 0.9191\n",
            "Epoch 10/10\n",
            "60000/60000 [==============================] - 6s 103us/step - loss: 0.3025 - acc: 0.9163 - val_loss: 0.2849 - val_acc: 0.9215\n",
            "Baseline Error: 7.85%\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/10\n",
            "60000/60000 [==============================] - 8s 130us/step - loss: 0.2381 - acc: 0.9296 - val_loss: 0.1090 - val_acc: 0.9672\n",
            "Epoch 2/10\n",
            "60000/60000 [==============================] - 7s 124us/step - loss: 0.0834 - acc: 0.9748 - val_loss: 0.0771 - val_acc: 0.9770\n",
            "Epoch 3/10\n",
            "60000/60000 [==============================] - 7s 121us/step - loss: 0.0511 - acc: 0.9844 - val_loss: 0.0702 - val_acc: 0.9791\n",
            "Epoch 4/10\n",
            "60000/60000 [==============================] - 7s 123us/step - loss: 0.0331 - acc: 0.9902 - val_loss: 0.0633 - val_acc: 0.9815\n",
            "Epoch 5/10\n",
            "60000/60000 [==============================] - 7s 123us/step - loss: 0.0226 - acc: 0.9931 - val_loss: 0.0570 - val_acc: 0.9822\n",
            "Epoch 6/10\n",
            "60000/60000 [==============================] - 7s 123us/step - loss: 0.0150 - acc: 0.9960 - val_loss: 0.0656 - val_acc: 0.9807\n",
            "Epoch 7/10\n",
            "60000/60000 [==============================] - 7s 123us/step - loss: 0.0124 - acc: 0.9964 - val_loss: 0.0637 - val_acc: 0.9813\n",
            "Epoch 8/10\n",
            "60000/60000 [==============================] - 7s 124us/step - loss: 0.0095 - acc: 0.9975 - val_loss: 0.0672 - val_acc: 0.9808\n",
            "Epoch 9/10\n",
            "60000/60000 [==============================] - 7s 123us/step - loss: 0.0096 - acc: 0.9974 - val_loss: 0.0669 - val_acc: 0.9809\n",
            "Epoch 10/10\n",
            "60000/60000 [==============================] - 7s 123us/step - loss: 0.0099 - acc: 0.9967 - val_loss: 0.0767 - val_acc: 0.9801\n",
            "Baseline Error: 1.99%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "GGfAn57jniC3",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "По значению оптимума (Baseline Error) лучшим является алгоритм ADAM"
      ]
    },
    {
      "metadata": {
        "id": "bHcqrVwun1QA",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "###Графики скорости сходимости функции ошибки (зависимости функции ошибки от номера итерации оптимизационного алгоритма) со ст. откл."
      ]
    },
    {
      "metadata": {
        "id": "KcNducD0O9Hk",
        "colab_type": "code",
        "outputId": "90f677ad-ed4c-45c4-a7ff-d029a6a9daa0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 2173
        }
      },
      "cell_type": "code",
      "source": [
        "for optimizer in optimizers:\n",
        "    # summarize history for accuracy\n",
        "    t_train_acc = np.array(history[optimizer].history['acc'])\n",
        "    std_train_acc = t_train_acc.std()\n",
        "    t_test_acc = np.array(history[optimizer].history['val_acc'])\n",
        "    std_test_acc = t_test_acc.std()\n",
        "    plt.plot(t_train_acc)\n",
        "    plt.fill_between(np.arange(t_train_acc.shape[0]), t_train_acc+std_train_acc,\n",
        "                     t_train_acc-std_train_acc, facecolor='blue', alpha=0.5)\n",
        "    plt.plot(t_test_acc)\n",
        "    plt.fill_between(np.arange(t_test_acc.shape[0]), t_test_acc+std_test_acc,\n",
        "                     t_test_acc-std_test_acc, facecolor='green', alpha=0.5)\n",
        "    plt.title('{} accuracy'.format(optimizer))\n",
        "    plt.ylabel('accuracy')\n",
        "    plt.xlabel('epoch')\n",
        "    plt.legend(['train', 'test'], loc='upper left')\n",
        "    plt.show()\n",
        "    \n",
        "    t_train_loss = np.array(history[optimizer].history['loss'])\n",
        "    std_train_loss = t_train_loss.std()\n",
        "    t_test_loss = np.array(history[optimizer].history['val_loss'])\n",
        "    std_test_loss = t_test_loss.std()\n",
        "    plt.plot(t_train_loss)\n",
        "    plt.fill_between(np.arange(t_train_loss.shape[0]), t_train_loss+std_train_loss,\n",
        "                     t_train_loss-std_train_loss, facecolor='blue', alpha=0.5)\n",
        "    plt.plot(t_test_loss)\n",
        "    plt.fill_between(np.arange(t_test_loss.shape[0]), t_test_loss+std_test_loss,\n",
        "                     t_test_loss-std_test_loss, facecolor='green', alpha=0.5)\n",
        "    plt.title('{} loss'.format(optimizer))\n",
        "    plt.ylabel('loss')\n",
        "    plt.xlabel('epoch')\n",
        "    plt.legend(['train', 'test'], loc='upper left')\n",
        "    plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfUAAAFnCAYAAAC/5tBZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\noQ4AgEX8P4JFLuFFQFqfAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fa1b2db5828>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAe8AAAFnCAYAAACPasF4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n8AYAwGb+AuzWSULCEj7bAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fa1b2dac518>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfUAAAFnCAYAAAC/5tBZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nlbqIiIhJ/AvT2E3hVBQVNAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fa1b2c8b470>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAe8AAAFnCAYAAACPasF4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAAIABJREFUeJzs3XmcZFV9///XuffWXtX7MksP+zLD\nDIMaJJJBQcIoLvmiRgVFMTHfEI2JosZvhDwMrii/RB+JJEYFwxeBCEomiNsMqIwQlu+wBGZ6hmH2\nvfe9utZ77/n9UdXVe091163q7unP8/GoR1fdqjp1+vbyvufcc89RWmuNEEIIIRYNY74rIIQQQojZ\nkfAWQgghFhkJbyGEEGKRkfAWQgghFhkJbyGEEGKRkfAWQgghFhlrvisghChNa2sr//AP/0BHRwda\na2pqavjc5z7HxRdfDEA2m+W73/0umzdvxnVdbNtmzZo1fO5zn2PVqlUAfPjDH+bgwYNEo1GSySTN\nzc1cf/31XHPNNVN+5vnnn8/vfvc7li1bVrHvUwgxSsJbiEVMa83HPvYxvvrVr3LFFVcA8Oijj/KJ\nT3yCrVu3EgqF+PznP08ikeCBBx4gFouRyWT4zne+w5/8yZ/wq1/9Cr/fD8DnPve5Qli3trZy8803\n097ezl/8xV/M17cnhJiGdJsLsYj19fXR1dXFRRddVNj2lre8hZ/+9KeEQiH27t3L1q1buf3224nF\nYgD4/X5uuukmHnnkkUJwT7Ru3TruuOMOvvvd7zI0NDRjHX74wx/y9re/nauvvpqPf/zj9Pb2ArBt\n2zbe/e538/a3v523ve1t/OpXv5pxuxCieBLeQixitbW1XHjhhdxwww385Cc/4ejRowCF7uxt27bx\nmte8hqqqqknvjUQiM5Z9xhlnsHz5cl566aVpX/PSSy/xgx/8gHvvvZfNmzezYsUKvvnNbwJw++23\nc/PNN/PLX/6Sf/u3f+PXv/71jNuFEMWT8BZiEVNKcffdd7Nx40Z++MMfctVVV/GOd7yDRx99FIDB\nwUHq6uoKr9+7dy9XX301V199NZdddhm/+MUvZiw/Go3O2PLeunUrb33rW6mvrwfgfe97H0899RQA\n9fX1PPzww+zfv58zzjijEOrTbRdCFE/CW4hFLhaL8clPfpKf/exnPPXUU1xzzTV85jOfYf/+/dTV\n1dHZ2Vl47bnnnsvmzZvZvHkz55xzDul0esayjx8/XgjmqfT29o5r1VdVVdHT0wPAbbfdRigU4k//\n9E95y1vewubNm2fcLoQonoS3EItYe3s7zz//fOFxQ0MDN954I+eddx579+7l0ksv5eWXXx4X4MV6\n/vnnSafTrF+/ftrXNDQ00N/fX3jc399PQ0ND4bkvfOELPPHEE/z93/89N998M8PDw9NuF0IUT8Jb\niEWsra2NT3ziE7S2tha2bd++nRMnTnDhhRdy2mmn8a53vYtPf/rTdHd3A5DJZLjvvvt48cUXaWlp\nmbLc3bt383d/93fcdNNNhEKhaT//iiuu4LHHHqOvrw+ABx54gMsvv5xsNsuHP/zhwkHD2rVrsSwL\n13Wn3G4Y8q9IiNlQsiSoEIvbo48+yve//32GhoZwXZeGhgY++clPcumllwLgui533XUXP/3pT3Ec\nh0wmwwUXXMCNN95YaFWPvc47lUoRi8X4sz/7M971rndN+Zljr/O+//77uf/++3FdlzVr1vDFL36R\n6upqfvrTn/K9730PAMMw+PM//3OuueaaabcLIYon4S2EEEIsMtJXJYQQQiwyEt5CCCHEIiPhLYQQ\nQiwyEt5CCCHEIiPhLYQQQiwyi2ZVsa6umRdHmK3a2jB9fQlPyxRTk31dGbKfK0P2c2XIfs5pbIxN\nuX3Jtrwty5zvKiwZsq8rQ/ZzZch+rgzZzzNbsuEthBBCLFYS3kIIIcQiI+EthBBCLDIS3kIIIcQi\nI+EthBBCLDIS3kIIIcQiI+EthBBCLDIS3iXauvU3Rb3un//5m5w4cbzMtRFCCLEUSHiXoK3tBL/+\n9ZaiXvupT32WFStWlrlGQgghloJFMz3qQvStb93OK6/s5I1vfD1vecvbaGs7wT/903f4+te/TFdX\nJ8lkko9+9EY2bHgjf/VXN/KZz/wfHn/8NwwPxzly5DDHjx/jk5/8LJdeumG+vxUhhBCLyCkT3j/+\n7T6e291Z9OtNU+E4esbXvH51E++/8pxpn//ABz7Mpk0/5swzz+bIkUN85zt30dfXyyWXvIG3ve2d\nHD9+jC984fNs2PDGce/r7OzgH//x2zz77NP89Kf/KeEthBBiVk6Z8J4tPXNuz9qaNWsBiMWqeOWV\nnTzyyCaUMhgcHJj02vXrXwNAU1MT8Xjc24oIIYQ45Z0y4f3+K8+ZsZU80aFDMU47bQjDo7P+Pp8P\ngMce28zg4CD/+q93MTg4yP/+3x+e9FrTHJ1wX3t9FCGEEOKUt2QHrMXjcOCAKqkMwzBwHGfctv7+\nfpYvX4FhGPzud78lm82W9BlCCCHEREs2vAG2by9tybnTTz+TV1/dzfDwaNf3FVdcydNPP8mnPvVx\nQqEQTU1N3H33naVWVQghhChQepH023Z1DXlaXmtrjM2b03zsYxmiUU+LFhM0NsY8//mJyWQ/V4bs\n58qQ/ZzT2BibcvuSbnm7LrS2yoLvQgghFpclHd4AO3YYno88F0IIIcppyYd3X5/iyJHSBq4JIYQQ\nlbTkwxtgxw7pOhdCCLF4SHgDe/YYJJPzXQshhBCiOBLegG3DK6/IrhBCCLE4SGLlbd9uzmngWrFL\ngo546aUX6evrnf0HCSGEEHkS3nmdnYqOjtkNXJvNkqAjfvGLRyS8hRBClKSsc5vv2bOHv/zLv+RP\n/uRP+NCHPjTla775zW/y0ksvce+995azKkXZvt1g2TLn5C/MG1kS9N///fscOLCPoaEhHMfhpps+\nxznnnMt99/1ffve7xzEMgw0b3siaNRfw5JNbOXjwAF/96v/HsmXLyvjdCCGEOFWVLbwTiQRf+cpX\nuPTSS6d9zb59+3juuecKi3qUYtO+n/M/nTuKfn02o0hcOL6f/LgLTzwFKt8Af23ThbznnHdOW8bI\nkqCGYfD7v/8H/NEfvYuDBw/wz//8j/zTP32HBx64j4cf3oxpmjz88H/y+te/gXPOOY/PfOb/SHAL\nIYSYs7KFt9/v58477+TOO6ef1/sb3/gGn/70p/mXf/mXclVjVrQGOws+/+zet2PHdvr7+9iy5ZcA\npNMpAK644g+56aa/ZOPGq3nLW672urpCCCGWqLKFt2VZWNb0xW/atIlLLrmElStXevJ57znnnTO2\nkidqbY3xy1+mJ21ftcrlAx+wZ/XZPp/Fpz/9OdatWz9u+9/8zc0cPnyI3/72Mf76r/+C73//nlmV\nK4QQQkxlXtbz7u/vZ9OmTdx99910dHQU9Z7a2jCW5e1kKpFIYNK23t5ct3lDw8nfX1cXxTQVr33t\n7/H880/z5jdvYN++fTz55JO8973v5Z577uGv/uqvuPjiC9m1azuhkCIQ8FFVFZx2svlT1VL7fueL\n7OfKkP1cGbKfpzcv4f3ss8/S29vL9ddfTyaT4ciRI9x2223ccsst076nry/hcS1iDA9PbnkDPP64\nwxVXnHzgWnV1Mzt2tFJf30RHRzvve9+1uK7LTTf9DakUnDjRwbve9W5CoTDr1q0nmzVZu/YiPvGJ\nv+LrX/8mZ511tsff08IkqwNVhuznypD9XBmyn3OmO4Ap+5Kgd9xxB7W1tdOONj927Bg333zzSUeb\nl2NJ0Km6zQHCYc3HP57FlFlTPSF/hJUh+7kyZD9XhuznnOnCu2wt79bWVm6//XaOHz+OZVls2bKF\nK6+8kpaWFjZu3Fiuj/VEIqHYv9/gvPPc+a6KEEIIMUnZwnvdunVFXbvd0tKyIK7xnmj7dglvIYQQ\nC5PMsDaNgwcNhqTHRgghxAIk4T0NraG1VU56CyGEWHgkvGewfbsxp8VKhBBCiHKS8J7BwIDi8OHZ\nLVYihBBClJuE90ns2CFd50IIIRYWCe+T2LPHIOH1/DBCCCFECSS8T8JxYNcu2U1CCCEWDkmlImzf\nbsrANSGEEAuGhHcRursV7e0ycE0IIcTCIOFdpO3bZVcJIYRYGCSRivTKKyaZzHzXQgghhJDwLlom\nA6++KrtLCCHE/JM0moXt2+WabyGEEPNPwnsWjh9XdHfLwDUhhBDzS8J7lnbskF0mhBBifkkSzVJr\nq4njzHcthBBCLGUS3rOUTMK+fbLbhBBCzB9JoTmQa76FEELMJ0mhOTh0yGBwcL5rIYQQYqmS8J4D\nrWWpUCGEEPNHwnuOduwwcN35roUQQoilSMJ7jgYHFYcPyzXfQgghKk/CuwTSdS6EEGI+SHiXYO9e\ng+Hh+a6FEEKIpUbCuwSOA7t2yS4UQghRWZI8Jdq+3UTr+a6FEEKIpUTCu0Q9PYoTJ2TgmhBCiMpZ\nkuHd1jPMj556grSOe1KeLBUqhBCikpZkeA8lsrT19dGrD3pS3quvGqTTnhQlhBBCnFRZw3vPnj1c\nddVV3HfffZOee/bZZ3n/+9/Pddddx80334xbwRlPzmmppjocZpB2HG2XXF4mkwtwIYQQohLKljiJ\nRIKvfOUrXHrppVM+//d///d8+9vf5oEHHmB4eJgnn3yyXFWZxFCKdatOQ+MwRJsnZUrXuRBCiEop\nW3j7/X7uvPNOmpqapnx+06ZNLFu2DIC6ujr6+vrKVZUpXdByGgD9+pgn5Z04oejqkoFrQgghys8q\nW8GWhWVNX3w0GgWgs7OTp556ik996lMzlldbG8ayvGvdxkJQbTYz4HSgginCZnXJZR45EuCCCzyo\n3CmosTE231VYEmQ/V4bs58qQ/Ty9soV3MXp6evjYxz7GrbfeSm1t7Yyv7etLePzpMaLuCgbooD1x\nkGZjTcklPvUUXHRRhhmOWZakxsYYXV1D812NU57s58qQ/VwZsp9zpjuAmbdRVvF4nD//8z/npptu\n4rLLLpuXOkRpxMTPACdwtVNyealUbspUIYQQopzmLWm+8Y1v8JGPfIQ3velN81UFlDKoZiUuWeJ0\nelLmjh0S3kIIIcqrbB28ra2t3H777Rw/fhzLstiyZQtXXnklLS0tXHbZZTz88MMcPnyYhx56CIB3\nvvOdXHvtteWqzrRqVAu9+iD9+hhVannJ5R06ZNDfDzU1HlROCCGEmELZwnvdunXce++90z7f2tpa\nro+eFb+KENK1JOghoxP4VbjkMltbTS67rPRueCGEEGIq0sdLrvUNMKCPe1Lejh0GFZxzRgghxBIj\n4Q3EWIaBxQDH0Lr01B0aUhw6JNd8CyGEKA8Jb8BQJlUsxybNMN2elCkzrgkhhCgXCe+8ka7zfo+6\nzvftM4h7s2iZEEIIMY6Ed15QVROgijid2Lr0JcJcF3btkt0rhBDCe5IuY9SolYBmAK8Grplo7UlR\nQgghRIGE9xhVrEBhMKCPoT1I3Z4exfHjMnBNCCGEtyS8xzCVjxjLyJAgiTernMnANSGEEF6T8J6g\nWq0EvFsq9NVXDVIpT4oSQgghAAnvScLU4SPMEO04Oltyedks7N4tu1kIIYR3JFUmUEpRo1rQuAzS\n5kmZ0nUuhBDCSxLeU6hmBaAY8KjrvL1d0dEhA9eEEEJ4Q8J7CpYKEqWRFIOk9KAnZba2yq4WQgjh\nDUmUaVQXZlzzpvW9c6dJtvRT6EIIIYSE93SiNGARYJATuLr05T1TKdi7V3a3EEKI0kmaTEMpg2pW\n4mIzRIcnZW7fLrtbCCFE6SRNZjByzbdXA9eOHDHo82buFyGEEEuYhPcM/CpCmDoS9JLRw56UuWOH\nXDYmhBCiNBLeJzEycG3Ao6VCW1sNXNeTooQQQixREt4nEaMZA4t+jqN16akbjysOHpRrvoUQQsyd\nhPdJGMqkihU4pInT7UmZMuOaEEKIUkh4F6Gm0HXuzcC1/fsN4nFPihJCCLEESXgXIaiqCFJFnC6y\nuvQlwlwXWlul9S2EEGJuJLyLlBu4phnEm4FrO3YYaO1JUUIIIZYYCe8iVbEchUG/Po72IHX7+hRH\nj8rANSGEELMn4V0kU/mIsYwsCRL0elKmDFwTQggxFxLes+D1wLU9ewxSpZ9CF0IIscRIeM9CiFr8\nhBmiA0eXvkSYbcMrr8iPQAghxOyUNTn27NnDVVddxX333Tfpuaeffpr3vve9XHvttfzrv/5rOavh\nGaUU1WoVGpdBTnhSpnSdCyGEmK2yhXcikeArX/kKl1566ZTPf/WrX+WOO+7gRz/6EU899RT79u0r\nV1U8Vc0KQNGvj3kycK2jQ9HRIQPXhBBCFK9s4e33+7nzzjtpamqa9NzRo0eprq5m+fLlGIbB5Zdf\nzjPPPFOuqnjKUgGiNJFmiDSDnpQpS4UKIYSYjbKlhmVZBIPBKZ/r6uqirq6u8Liuro6urq5yVcVz\nIwPX+j0auPbKKybZ0k+hCyGEWCKs+a5AsWprw1iWt+eHI5HAnN4X1ivpGN7JoG7jzPB6DFX6buzq\nCnDRRSUXs2A1NsbmuwpLguznypD9XBmyn6c3L+Hd1NREd/foIh8dHR1Tdq+P1deX8LgWMYaH03N+\nd5VeSQ/7aR8+QrVaWXJtnnjCZcUKu+RyFqLGxhhdXUPzXY1TnuznypD9XBmyn3OmO4CZl5OtLS0t\nxONxjh07hm3bPP7442zYsGE+qjJnI4HtVdf5kSMGvd7M/SKEEOIUV7aWd2trK7fffjvHjx/Hsiy2\nbNnClVdeSUtLCxs3buSLX/win/3sZwF4+9vfzplnnlmuqpSFX4UJ63oS9JDWcQIqWnKZO3aYXH65\n40HthBBCnMrKFt7r1q3j3nvvnfb517/+9Tz44IPl+viKqFEtJHQPA/o4Ter8kstrbTW47DIHUy79\nFkIIMQO5RqkEUZow8DHAcbR2Sy5veFhx4ID8SIQQQsxMkqIEhjKpZgUOGeJ4c6mbXPMthBDiZJZs\nUnjVNV3t8TXfBw4YDMkASyGEEDNYsuG9erU35QRVjCDVDNNFVpe+RJjWsHOnnPQWQggxvSUb3k1N\nsGJF6XOTw5ilQjnuSXnbtxt4MG26EEKIU9SSDW+A9eu9uSwrxnIUJgMeLVbS3684ckQWKxFCCDG1\nJR3eq1e7+P2ll2MqiyqWkSVJAm9mWpGlQoUQQkxnSYe33w+rV3vT+h4duHbUk/L27jVIJj0pSggh\nxClmSYc3wPr1pV+fDRCiBj8R4nTg6EzJ5dk27Nq15H88QgghprDk02H5ck1DQ+nnqZVS1KgWNJoB\nTnhQs1zXuQxcE0IIMdGSD2+lvBu4VsVKQHk2cK2rS9HRIQPXhBBCjLfkwxvgggtcTyZtsZSfGE2k\niZNioPQCkRnXhBBCTCbJAITDcO653pz79nrGtVdeMcmUfgpdCCHEKUTCO+/CC73pOo/QgEWQIdpw\ntV1yeek0vPqq/JiEEEKMklTIO+MMTXW1NwPXqlmJi8Mg7R7UDHbskB+TEEKIUZIKeUrBhRd603Ve\nmC7Vo67zY8cMenpk4JoQQogcCe8x1q1zUB5kpE+FiNBAkn7SOl56gcjANSGEEKMkEcaoqoIzz/R2\n4JpXre+dOw0cb07LCyGEWOQkvCfwasa1KE2Y+BjgOFqXXmYiodi/X35cQgghJLwnOftsl3C49IFr\nhjKoYiUOWYbo9KBm8NvfmuzeLcuFCiHEUifhPYFpwtq1C3Pg2uCg4pFHLO67zydLhgohxBIm4T0F\nr7rOAypKiBqG6SarvVsirK1N8cADPh56yKKzU0JcCCGWGgnvKdTXa1paPB64xnFPyhvrwAGDe+7x\n8ctfWgwOel68EEKIBUrCexpeXfNdxTIMTPo9WqxkIq2htdXgrrv8bN1qyhrgQgixBEh4T+P8810C\ngdLLMZRFjOXYpEjQU3qB07Bt2LbN5M47/WzbZmCXPjOrEEKIBUrCexp+P6xZ482F1TUeL1Yyk1QK\ntm61uOsuH62tBq43HQhCCCEWkCUb3ik7ddLXeNV1HqSaAFGG6MDWlVkibHBQ8ctfWtxzj48DB5Rc\nXiaEEKeQJRvezxx9hufa/9+M56GXLdM0Nnq0WIlqATSDZRi4NpOuLsVDD/l48EGL9nYZmS6EEKeC\nJRveAI8f+Q2/PfIY7jQzoCkF69d703VezQoUin59vCwD107myBGDH/7Qx89+ZtHXV/GPF0II4SGr\nnIXfdtttvPzyyyiluOWWW1i/fn3hufvvv59HHnkEwzBYt24df/d3f1fOqkzrhY7nGcoM8Y6z/hc+\n0zfp+QsucPnd7yh5AJip/ER1M0O0k6KfELWlFThHr7xisGePn9e8xuENb3CIROalGkIIIUpQtpb3\ntm3bOHz4MA8++CBf+9rX+NrXvlZ4Lh6P84Mf/ID777+fH/3oR+zfv5+XXnqpXFU5qT19r/Lgq/9B\nIpuY9FwoBOee6+2Ma5UYuDYTx4EXXjC56y4/zzxjkqnMaXghhBAeKVt4P/PMM1x11VUAnH322QwM\nDBCP55bH9Pl8+Hw+EokEtm2TTCaprq4uV1WKciJ+nPtfuYe+VO+k57zqOg9Tj48Qg7Tj6Pm/liud\nhiefNLnrLh8vvywj04UQYrGYdbd5JpOhp6eH5cuXz/i67u5u1q5dW3hcV1dHV1cX0WiUQCDAJz7x\nCa666ioCgQDveMc7OPPMM2csr7Y2jGWZs63u9OIQiYy/kDtDgoePPMgHL/wgLVUthe0NDfDUU3hy\nrrgxfTonMrvJ+Ltp8J9eeoEe0Br++79h9274wz+E1avxZF3zsRobY94WKKYk+7kyZD9Xhuzn6RUV\n3t/73vcIh8O8973v5Y//+I+JRCJs2LCBm266qegPGjtIKx6P873vfY/NmzcTjUb5yEc+wu7du1m9\nevW07+/rm9ylXarh4fTkbaT5zlPf551nX8O5tecVtp95psmxY6UfPIR0M7CbjvRBQtllJZfnpeFh\n+Pd/h5UrNZdfbtPS4s3AusbGGF1dQ56UJaYn+7kyZD9XhuznnOkOYIrqNn/88cf50Ic+xObNm3nz\nm9/MT37yE1588cUZ39PU1ER3d3fhcWdnJ42NjQDs37+fVatWUVdXh9/v5+KLL6a1tbXY76Xssm6W\nh/f9Jy92PF/Ytm6d40lr1KdCRGgkxQBpvTB/MY8fV/zHf/j4r/+y6O6Wy8uEEGKhKSq8LctCKcUT\nTzxROI/tnuQE6YYNG9iyZQsAO3fupKmpiWg0CsDKlSvZv38/qVRuopTW1lbOOOOMuX4PZaG15teH\nH2Xr0d+itSYWg7PO8mrg2kpg/geunczevQZ33+1jyxaT/HAFIYQQC0BR3eaxWIwbb7yR9vZ2Xvva\n1/L444+jTtIMfd3rXsfatWu57rrrUEpx6623smnTJmKxGBs3buTP/uzPuOGGGzBNk9e+9rVcfPHF\nnnxDXtvW9ixDmUHeduY7Wb/eYP/+0sf4RWnCxM8AJ2jU52OohXu5vdbw8ssmu3aZXHyxwyWXOJ7M\n+S6EEGLulC5ixpBEIsHTTz/N6173Ourq6nj66ac544wzWLFiRSXqCOD5uY/W+PP8ctejRb9+Vew0\n/uisP+aeu6oYHi69K7nTfZVeDrJCXUSVmnnw30ISCsEf/IHNa17jYhY5BEDOXVWG7OfKkP1cGbKf\nc0o6593b20ttbS11dXX8+Mc/5uc//znJJbb25NGhIzy4517OXDP5UrK5qF4kXecTJZPwm99Y/OAH\nPnbtMmTOdCGEmAdFhffNN9+Mz+dj165d/OQnP+Gtb30rX/3qV8tdtwWnJ9nNzuDdxFVbyWUFVJQQ\ntSToIaO9H0lfbv39ip//3OLee30cOiSD2oQQopKKCm+lFOvXr+exxx7j+uuv5/LLL5+X+bkXAm3F\nOVp/D31qf8lljcy4NqAru1iJl9rbFT/+sY+f/MSio0NCXAghKqGo8E4kEmzfvp0tW7bwpje9iUwm\nw+DgYLnrtmDVN6V5xXqADuN/SionRjMGFgPMz2IlXjp4MLfwyS9+YTEwMN+1EUKIU1tR4f3Rj36U\nL3zhC1x77bXU1dVxxx138M53vrPcdVuwGho0ps9ln/Vzjphb0cwteA1lUcVybFIM033yNyxwWsPO\nnQZ33eXn8cdNltiwCCGEqJiiRpuP6O/vRylFVVXVSS8V89p8jzaf6MABg7b8+thNzkWc7bwDg9nP\nwJbUAxzWzxClmRbjtXOuz0IUDMLrX+/whjeEgSHPp1wV48no3MqQ/VwZsp9zphttXtR13i+88AJ/\n+7d/y/DwMK7rUltbyz/8wz9w4YUXelrJxaS5WRfCu9N8mYwa5Hz7fVjM7iLoIFUEiBGnE1unsdSp\ncxF1KpVb+OTFF8F1/axa5dLS4rJqlaaxUWMs3MvbhRBiQSsqvL/1rW/xne98h/POy831vWvXLr72\nta9x//33l7VyC1kkoolGID6ce9xvHKTVuoc19nUEqCq6HKUU1bTQqV9hgBPUM/MCLYtVMgl79hjs\n2ZNL7GAQVq50aWnRnHaaS1OTLvq6cSGEWOqKCm/DMArBDXDBBRdgyn9amptd4gdGm4/DRgc7fHez\nxr6OiG4uupxqltPFqwzoY9RxRsVPScyHVAr27zfYvx/AxO+HFStyrfJVq1yWLdNYs17zTgghloai\nw3vLli1s2LABgCeeeELCm9zAtYOHGLcOdloN0mrdw/n2+6jRxbWiTeUnppsZpI0k/YSpLU+FF7BM\nBg4dMjh0CMDEskbDvKXFZcUKjc83z5UUQogFoqjw/tKXvsRXvvIVvvCFL6CU4qKLLuLLX/5yuetW\nVmk7U3IZlgX1dZquCStv2SrNK9aPOMf5Ixrd4sYFVKsWBnUb/fooYbX0wnsi24YjRwyOHAEwMU1Y\ntkwXzpuvXKlljnUhxJI142jzD37wg4Uu3IkvU0pV9Jy3l6MOX+3dx7df+j4xn59af6ikbuqBAUXr\nzulHXp1uv5mV7gYUM3+G1poD+gls0pyj3oypTp1mZiQSmHLt9FIYBjQ15cJ81apcmIdCnn7EoiOj\ncytD9nNlyH7OmdNo85tuuqkslZlvK2PLqQ1V0ZccxHY1DcEwxhwDvLpaEwpCMjX184etx0k7A5zl\nvA01w2X1IwPXuvVeBmmjltOeZq4IAAAgAElEQVTmVJ+lwnVzs7u1t5s895yJUtDYONIyz3W1RyLz\nXUshhCiPGcP7kksuqVQ9Kirqi/DuNVdy/8s/J+lk6UzGaQxFMOe4NGdTk+bwkenDv918kYwa4jz7\nPZj4p31dNSvpZi8D+hi1SsJ7NrSGzk5FZ6fJCy/kttXXj4b5aae55JeTF0KIRW/JjucNWH6agxG6\n0wkSdpb2RJymUASfMfuBeE1NLkeOmjOusNVr7KXV+iFr7OvwM3WK+FSQiG5kmC5SepCgKv6SMzFZ\nT4+ip8fkpZdyj2trdaFVftppLtXV81s/IYSYqyUb3pDrqm4IhOlXKQazadqTcZqCEQLm7HaL3w91\ntZqe3pm73uNGW+5SsuwHCNMw5WtqVAvDuosBfYygumBW9RAz6+tT9PUpduzI9bBUVelCq7ylxaW2\nFpkFTgixKCzp8IZcgNcGQliGQW86SUcyTkMwQtia3YCxpqaThzdASvWzw/d/WWO/nyo9uWs8SiMm\nAQY4QaM+H0PJJXnlMjio2LVLsWtXLswjEV24NK2xUVNbq4lEJNCFEAvPkg/vETFfAFMZdKeG6UoN\nUxcIEfMVfy1Sba3G789dr3wytkqy07qfc51raHDHt66VMqjWK+jlIEN0UM2K2X4rYo6GhxW7dyt2\n7x4d++D35362U93C4XmsrBBiSZPwHiNs+WgORelMDdObTmK7LjX+YFGXkikFTY2aY8eLa6a5ymaP\nuYkMgyx3f3/cpWQ1qoVefZABfYxqJeE9nzIZ6OhQU65VHgyOBntdnaamJve1tlauQRdClJeE9wQB\n02JZKEpncpjBbBpbuzQEwkUFeHOzy7HjxXdza6U5aD1GyhngTGdj4VIyv4oQ0rUk6CWjE/iVNPEW\nolQK2toUbW2TfzfC4ZEgn9xyl5nihBClkvCegs8wWRaO0pUcJmFn6XCLu5QsGITqKs3A4OxOkraZ\n28ioQc6134VJ7j97jVpFUvcxoI/RqM47SQlioUkkFImE4tixyc/FYuPDPNdqh5oamc9dCFEc+Vcx\nDVMZNIWi9KQSJJwsHck4TcEo1knWsWxunn14A/QYu8la97HavhYfYWI004HFAMdp0Oeg5ngNulh4\nhoYUQ0MqP/XrKKVyk/5MdX69uhpZQlUIUSDhPQNDKRqCYfoyKYayadqTQzQFI/hnuJSsvl5jHczN\nzT1bg8ax3Ej07HWEVB1VegX9HCFONzGa5v6NiEVBa+jvV/T3Kw4eHP+caeZa5jU1k8+xx2IyIl6I\npUbC+ySUUtQFQlhK0ZdJ0Z6M0xiMEJrmUjLDgMYGTVv73P6bJlVP/lKya6lRLfTrIwzoY8SUhPdS\n5jgjk85M/r2yrFywt7SA65pEo/n15qMQjWqi0dzIeFkIUIhTh4R3kar8QUzDoDuVoDM1TH0gRHSa\nS8mam+ce3gBZNcxO617Os99DwKkiThe2TmGp4JzLFKcu24bubkUyCcPDUye0UhAKjQ90CXkhFi8J\n71mIWH7MkEFXcpie/KVk1VNcShaJaKIRiA/P/bMclWW39WNW6MvB1QxwgnrOKvE7EEuV1iOD6HJz\nwE9nqpCPRCiEvYS8EAuDhPcsBU2LZeHcpWQD2TS21tQHJi8r2tzsEj9Q2ggjrTQnfE9Sl34d/foY\ndZxZ0vKlQpyMlyEfieS2ScgL4T0J7znwGWbuWvDUMMN2Bke7NAYj45YVbWjQHDoEjlvaZ2nlkDa6\nwW1gmG6iNJZWoBAemGvIjwZ8bls4rAkGc68JBmVEvRDFKmt433bbbbz88ssopbjllltYv3594bm2\ntjY+85nPkM1mueCCC/jyl79czqp4zjQMmkNRulPDJB07PxJ99FIyy8qNPO/sKr2lnLK6CWQaOKye\noM44nXr3fPxMvUC7EAtJsSEPuaAPBHKBHgpBMJj7GgqNfh0J+rHbpWUvlqKyhfe2bds4fPgwDz74\nIPv37+eWW27hwQcfLDz/jW98g49+9KNs3LiRL33pS5w4cYIVKxbXVKCGUjQGI/Smk8TtTCHA/fn/\nJs3N3oS3rYZwVArDDXHQ9ygHzc3EdAv17mrq3NUEqSn5M4SYb1rnZq1LpUb+Zor72wkExgd9MHjy\nAwCfTy6vE4tb2cL7mWee4aqrrgLg7LPPZmBggHg8TjQaxXVdXnjhBb71rW8BcOutt5arGmVXuJTM\nMOjPpArXggctH1VVmnAIEslSPwRSZhcRexWx7DkMW0cZNHK3gzxG1F1GvbuGOnf1tEuNCnGqSqch\nnVYMDECxgW9Z41vy4fDJDwACAQl8sXCULby7u7tZu3Zt4XFdXR1dXV1Eo1F6e3uJRCJ8/etfZ+fO\nnVx88cV89rOfLVdVyk4pRbU/iKUMutMJOlLD1AfCRH1+mpo0hw6X/hefNjvxuTH8bg01mbWkjR4S\n1jFcI0PcaCdutHOYxwnrRurd86lzVxPRy8YteCKEyLHtkZnuoNjAN4xckDc2Qjbrw+/PhXwgkFuI\nJnfThZ4Av3+0VyAQyK1QJ+EvvFKxAWta63H3Ozo6uOGGG1i5ciU33ngjW7du5Yorrpj2/bW1YSzL\nw5NbcYhEvF36KUKASCbA0f5+etIJTJ/B6aeHaWtTjPn25yztP4htxwikVxBw6/Fnasn6usn429GG\nA4BNHx08SwfPEqSWRtbQwBqqaJnXIPd6X4upyX4uv+5ugNnPuTByTj8YJB/6o/eLfbzU5r5vbJSx\nPdMp269CU1MT3bnfcgA6OztpbMyNlK6trWXFihWcdtppAFx66aXs3bt3xvDu60t4Xsfh4bTnZQI0\nB6N0puJ0xuMkrAzRaISeXq+G0faR9PXhd+sI2yvxZ5uwsnWkrHaSZgeo0eHtGboYpIv9PIFfx6hz\nz6feXU21Pr2wglklRCKBsu1rMUr2c2WUsp/j8dI+27JyrftgkHzLvrjWfyCQu+/3L54Bfo2NMbq6\nhua7GvNuugOYsoX3hg0buOOOO7juuuvYuXMnTU1NRKPR3IdaFqtWreLQoUOcccYZ7Ny5k3e84x3l\nqsqUTKN8v8F+02RZKEZnKk7czhBboenti6G1Ry1fBRmzl4zRR9BpImQvJ2y3ELSbSFjHSZvdk3oC\nM2qIdvN52s3n8ekwde551LmrqdFnYsgVg0IsCrYNtq0YLkwANfv/KaaZG7AXCOSWp82Fei7cZ9o+\n3WssS04HzAeltRcdulP7x3/8R55//nmUUtx6663s2rWLWCzGxo0bOXz4MJ///OfRWnPeeefxxS9+\nEWOGizy9PgKrrQ/xw20P8ErPTk/LHcvVmq7UMCnHJps06TpYhWt73+JV2iRoLyPkNKMwsVWShHWM\nrNF/0r9tSweodc+lzl1NrT4bE7/n9ZMWYWXIfq4M2c/jGQb5UB9t3Y+97/frfOAXt33kSgBpeedM\n1/Iua3h7yesfYmNjjM7OQZ44tpX/1/aMp2WPpbWmJ51k2M5gpw26D1Vhp8vT6je0j5C9koDTgEKR\nVUMkfEexjeLmaTW0Ra0+m3p3DbXuuVhzOK83FflnVxmynytD9nN5KZUL8NraAJlMCssaaeHrQkt/\nJOynes7v1/nXjNz0mPuLbyIgCe8Jxh7V/U/HC/z6yKOUa1dorelJpBjWaRxb0XMoRiYx9apkXjDd\nIGG7Bb9bC0Da6CVhHcc1UkWXobRBjT6TOnc1de75+InMuT7yz64yZD9XhuznyijXfh4Ndj1twI99\nPPaAYKRXYeJrR54zTe9PIUh4TzCxS2Zf315+tv9hsm7W088Z69VDWQL1w6Ch92iU5EB5RwZbbpRw\ndhU+HUWjSZtdJKwTaDW771FpRUyvosFdQ517PgGqZ/V++WdXGbKfK0P2c2Usxv387nfbnHtuiXNi\nTzBdeC+yDoTyOaf2XK5bfT1h39xbmCdTH/XTfTCG1lB3WpxoQ6mzt8zMNuIM+l9hyLcXV6UIOk3U\npi8klF0BuvgfvVaaQeMIB6wtPO//NtutH3DMeIokvWWsvRBCiOlIeI+xPLqC69d8mLpgXVnKr6vT\nOCk/XfurcW1FzYoE1cuHgTJ2fijImP30+1uJW4fQOISdldSm1xO0m2AOI+CHjBMctn7Li/5/5SXr\n+xwxf8ew6kCX8/sQQghRIOE9QW2wjg+uuYGV0RbPyzYMaGrUZFMWnfuqyaZMYo0p6k6Pgypz8ClI\nW130BXaQsI6hMIjYp1OTWYffqZ3z8cOw0cFR8wle8n2f//F9h8PmbxhSxyXIhRCijCS8pxD2hXn/\n+R/gvNrzPS+7qSkXak7WpHN/Fem4Rbg6Q+NZgximt+dKpqRcklYbfYHtJM0ODO0nlj2H6swaLKe0\n2YySqpdj5tNs9/07L/i+zQFzC/3qIDaL67yVEEIsdDI7xzR8po//dc672Xr0Nzzf/pxn5UYimlgU\nhuKgHYOug1XUtcQJ12ZoPGeA7oNVOJnyT4GklU3Cd4SU2UHYbiHg1lGdXU3G6SdhHcMxSjsfn1aD\ntJnbaDO3sRcLwxclrBsJ66b8rZGQbsBgkUz3JIQQC4iE9wwMZXDlaRuJ+avYevS3nl1K1tzsMhTP\nd3poRe/RKHY2QVVTiqZzBug+GCObLN+lZGO5Rpq4fz9Jt52I3YLfrcGXqSZtdpO0TuCqjCefk1L9\npFQ/vewtbFPaIKQb8qGeC/aIbiJAjSyoIoQQM5DwLsLrl/0+Vf5qfnHgEWzXLrm8hgbNwUPgOCNb\nFIPtEZysSc2KYRrPHqT3cIzUkPeznU3HMYYZ9L2Kz63OTbXqNBJw6kmZHSStNrRyTl7ILGnlklCd\nJOgct93UfsK6YVwrPayb8BGRUBdCCCS8i3Z+3Woivgib9j5Eyi6tS9k0oaFe09E5PoiGe4I4WYO6\n04aoP2OI/uMRhnu9meWsKAqy5gADxgABt55QdiUhZzkBp5Gk1UbK7Cj/wDrAURmG1AmGODFuu0+H\nJwR6LtQtZCUtIcTSIuE9Cy2xVVy/5gYe2vMAA+mBkspqbp4c3gCpQT9d+6toOHOI2pZhTL/DYHuY\nuSxAMGcK0mYPaaOXoNNMyF5OxF6VW/jEd5yM0VPR6ozIqgQD6hADHBq3Pahr5Hy6EGJJkfCepfpQ\nPddf8BE27fkJ7cNtcy4nFtOEQ5CYohGfTfro3FdNw5mDVDWlsHwuvceic7omuyRKk7LaSZtdhOzl\nBJ1mYtmzsNUyEtZRssbgvIT4RHI+XQix1Eh4z0HUF+W61dfzs/0Ps79/35zLaW52OXho6qv1nIxJ\n175q6s8YIlybwfQN0n04hnYqf3WfVg4J3zFSVieh7EoCbj1V2fPJGoMMW0dxDO/XWi+VnE8XQpzK\nJLznyG/6efe57+XXh7fwUuf/zKmMxkbN4cPgTnMa2XUMug5UUXdanHB1hqazBxloC5OK+yrfCgdc\nlWHYf5CU255f+KSGmsxa0kZPfuGThX89d7Hn04O6Gr+uJkBVWZZJFUKIUkh4l8BQBhtPv5oqfzVP\nHNs66/f7fLkpU7t7Zghireg9HMVZniDWmKLhzCFcR5Ec9JEc8JMe8qMrHOSOkWTIvxfLiRGxVxFw\n6/FnakmZXSStE2hV+oj8SpvufDqApYP4dRUBYrmvugo/VQR07rGfKhk0J4SoKAnvEimleMOKPyDm\nr2LzoV/guLO7pKqp6SThnfsUBtoiJAf8hKozhKozRGpzN9fNDXJLDvpJDfrQbuW61W1ziAFjF363\njrC9kpDTTMBpIGW1kTQ7QFVgxrgKsFUKW6UmdcGPZelAPtCr8oEem3C/WgJeCOEZCW+PrG1YR9Qf\n5eG9/0naKb77uKZGE/BDuoi5UDIJH5mEj4G2ML6QQ6g6Tag6Q7gmd9MupOK5Fnlq0I9bifPjCjJm\nLxmjj6DTSMhekbtO3G4iYZ0gbXaVvw4LgK3S2HSRUNN/vyMB79f5YM+H+tiwNwnIuXchxElJeHvo\n9Koz+OCaG3hoz4MMZQaLeo9SucvGjhydzT9sRTZpkU1aDLaHsQJOoUUeqsoSqsqi9TDpuEVyMEBy\nwIdrl/myKaVJWZ2kzW6C9nJCTjNR+wyCTjOO7sVlAFslKnKd+EJVTMCb2l/okg/oavzjuupz9y2C\nEvBCLHFKezXnZ5l1dQ15Wl5jY8zzMkcMZQZ5aM+P6UpM3806VjoNL7xgerIOl+nPB3lVhkBk9Nxz\netgiOZDrXq/E3OlK+wjbKwg4jYWg0ThkjTi2MUTWGMJWw0s6zOdqbMCPPRdfHaojmzDx6QgWYQn5\nMolEAgwPL/zBmYvdYtzP7363zbnnenu6sLFx6gWjpOVdBjF/FR9c82F+um8ThwYOnvT1gUCu+7yv\nv/R/tE7GJN4VIt4VwvQ5BKtyLfJAxCYQsalZkSCTNHNBPuDHTpfnV0CrLMO+wySs44RUDSobxnJj\n+N1q/G517jW42CqeC3IjTtaInzLnycvJURmSdJNU3eO2+7HI+EYP2JQ28BHG0mF8hPHpMBaj931E\nsHQo/ziCRUgmthFikZDwLpOAGeCPz30/Ww79itbu7Sd9fXOzN+E9lpM1Ge4JMdwTwjBdgvkWeTCa\nxb8sSfWyJNmUWWiRZ5MmXs+6opWN7esnQy5olLbwuVEsN4bPjWHpGD6nCpyRME+MtsyNIbSE+Zxp\n5ZIhTkbFi36PpYP4iODToXy4h/ERKrTmfTqCj1B+ewSTyiygI4QYT8K7jEzD5G1nvoOqQBVPH//v\nGV9bV6fxWZAt01VWrmOQ6A2S6A2iDJdgVZZQdYZgLENVc5Kq5iR2xii0yDMJi3JMn6aVTcbsJ2P2\nA6C0ieVGc0HuxrB0BJ8TJeQsR6NxVILsuDD3foEUMcpWKWxSJIv80Zvalw/18JgWfHja8JeufCG8\nIeFdZkopLlv5Jqr8VTx6aDOunrolqVRu0pYTbeX/x6Zdg2R/gGR/AKU0gViGcHWGYFWWWGOKWGMK\nJ6sKLfJ03Ee55kHVyiFrDpA183PFa2NCyzyC5UQIOcsAxrXMs8bQorym/FTiqCwOA6RVcXP9T+zK\ntwhh6SAWAcyRrwSxdBCTAFbhfhATvwS/EHkS3hWyvvE1RP0xHtn3X2Scqa8La26uTHiPpbUiNRgg\nNRgApQlG8y3yqgzRhjTRhjSOrXLXkg/4yz+7m3LJmoNkzUGSAFrlWuY6H+ZuBMsJE3SaAXBUclzL\n3FXZ8tVNlGwuXfkjlFa5YCeIpQOFcM+Ffj7sR+7nDwRG7udeG0BR+emFhSgHCe8KOqv6bD6w+kM8\ntOfHDGcn//MKhzVVMRgszyD4k9OK1JA/v464JhCx85egpYnU5W6uA6mhfJAP+dFumQ82lMY2h7AZ\nGg1zHSl0tfvcGEGniaDTBICjUvmR7LmBcK5KL4jFU0TptNLYJLFJzvlnmgv9qVv2udZ/YMrwHzk4\nkAF9YqGQ8K6w5sgyPnTBDTy058f0JLsnP9/sMji0EFoHivSwj/Swj/4TYfxhm1B+5Pq4SWGGfCQH\nAiSHfJVZNEVpbBXHNuKkaAcNpg4XgtxyYwSdRqARAIfMuG52V6UkzJew3LX26aK7+ScytZ8QERyf\ngal9mAQw8WNoPyZjbtqPUbgfwMSXex1+LB0oPCcHA2KuJLznQXWgpnAp2ZHBw+Oeq6/XHDgIzoIa\nl6VGZ3drD+MLjpkUpjpLqDqL1pDOz+6WHPTj2hU6AFHgqASOkSBFRz7MQ4Ug97kxAm49AbceAJds\noYs9awzhqLm34sTS46gMGVwyyvbk98bQ5oQDgNEDgpEDAGvSwUEg/5xvzMHByMGAT8YFLBES3vMk\nZIV473nX8quDP+eVnl2F7aYJjQ2a9o6F+geoyKYssimLwY787G5Vua71YCxLMJalRg+TGTMpTEV/\nzVTuPLhjJIHOfJgHC0GeC/M6Am4dAC72uJa5oxIS5qJiXOXgkiCLN793Sqt8qI8cAPjG9ADkwj33\nfP5r4Xlf4eDBwMofTPgKBwhGfrscGCwcEt7zyDIs3nnWNcT8VWxre7awvalpIYf3eHbaZKgrxFB+\nUpiR2d38EZtA1KZmZQIna5BJmtgpMx/8Jtm0WZllTVXuPLhjpEjTBRoMHRhtmesYfrcWv1sLgIuD\nnW+Rj7zPUSk03rS0hCgnrTQOGRzyg2I9/J0dOTAohL/2Y2KNCf/xQT/+YMGff37s+0dfn7uSwJSD\ng1koa3jfdtttvPzyyyiluOWWW1i/fv2k13zzm9/kpZde4t577y1nVRYspRRXrLqSKn8VvznyGFpr\nYjFNOASJ5HzXbnacrEm8O0S8O4RhubkJYaoy+EMOoaosVI2OBNc6F/zZ1OjNTlnYGYOypqQCV6VJ\nG2nS+YljDO3Pt8xzg+D8bg1QM+5tLjauSucCXaVwjDRu/r5cey6WgrEHBlnw/M90tNcgF+ghQtgW\nhQMAA2vcAYCBWegdGDkgUFj53gRrmvecOj0IZQvvbdu2cfjwYR588EH279/PLbfcwoMPPjjuNfv2\n7eO5557D55NZml7XfDFV/mp+tv9hsm6W5maXg4cWwsC1uXFtg+HeIMO9Qfw+C9vNYAUdfEEHX9DO\nf83dxr3PZXwLPX/ftRXlCnVXZciYPWTMHiA3C5ypQ5hu7tpjUwcxdABTh7B0JPemMdV2yeLkg901\nUoX7jkrJdK9CFGlir4HLEBmjPPM4GNoad/og99ia4iBg5MDAyv1fmHAQYOiR0wy5bRk3ChWadbBs\n4f3MM89w1VVXAXD22WczMDBAPB4nGo0WXvONb3yDT3/60/zLv/xLuaqxqJxTey7Xrv4gm/Y+RGPj\nMIcPg3uKrNvhOgaZYYPM8NhfbI3pc/EFnXywj4a6Pzw+1B1b5UN9fLCXY/1yrWxslRvUNv4Jcl2E\n7kiYBws3S4fx6ShMyGqXTKGl7qhUvrWezgf7KfLDFWKRcZWdG+9CvnvTo3bBG5PvYS3nelPYSZQt\nvLu7u1m7dm3hcV1dHV1dXYXw3rRpE5dccgkrV64sqrza2jCW5e1lFdOt1jKfGhtXs2rZX3Lf9vs4\nsaKXzuIWJlvw/L7pf9XcFGRSMDp1jcb0O1gBGytgY+a/jpxHh9GVhpysgZ22sNMWTir31c5YZTyf\n7gIJNAlsoNAu0KC0H8MNYriBcbfC/O1jaDRaZXGNNK6Ryn/N3bTKzDnYZ9rPwjuynytjse3n6upw\nxXKlYntm7Mqj/f39bNq0ibvvvpuOjo6i3t/Xl/C0PuVcErR0Pq5ZdS2dxx/i2PG2+a5Myfw+i8xs\nJ23PAsMWY39FldKTWui+oE0gmiEQHRP9Guy0Ma6FXpHz6dgwMmrYzN8AtMLUAYxCSz3XajfcIJYT\nA2f8H7tGjzm/PnpuPddyz0z7LcxpP4tZk/1cGYtxPw8MJMqyfPVUyhbeTU1NdHePTkLS2dlJY2Nu\n4oxnn32W3t5err/+ejKZDEeOHOG2227jlltuKVd1Fp2wL8zHL/0Ae1t/wdH0q/NdnQVBa0U2aZFN\njv+1VaY7LsxH74+fhnY+zqfnKqhz4UuKSZO3aqMQ5oVz627uvl/XTCpK444J9NFz666Ryh21CCGW\nhLKF94YNG7jjjju47rrr2LlzJ01NTYUu86uvvpqrr74agGPHjnHzzTdLcE/Bb/m4bv27ue/Z39Jm\nbpvv6ixYusTz6a6txoV5NmViZ8zyhzqAcnOXpTH50gKlzfHn1t1goQVv6dCk1+u0xiWLVhlclc3f\nMriMua+yctmbEKeAsoX36173OtauXct1112HUopbb72VTZs2EYvF2LhxY7k+9pSz/kLNWU+/haCu\n5qD12HxXZxFROFkTJ2vCuF4sjRVwC2E+EuxTnU/XOndOfdwtM3rfzpY34LVycrPHMeGUkSY/GjYf\n5u7I/Nt+cH2YOoylpx/Ip3FxsSXkhVjElNaLo6+tHOcRFu457/EeesjiwAGDPrWPDvNF+tR+3EW0\nFOaiOHel9Lhud9PnjrupaUJsyoAfc7MzRn6q2PKnYGE/a1CYGDo/S5b2oRi9X9iOb8ZVtiTkp7Yo\nfp9PAYtxP//1Fe/hqtd4O9q84ue8hXfWr3c5cMCgVp9DrX0ODhn6jH30qN30GXtx1NRLjIpZmOZ8\nev5JDMvF8ruTQn3k5g/bcw74kZtnCahA40zbHT/m2yoq5EtvyeceL7WQF6KcJLwXgbPPdgmHNYlE\n7j+fiZ8G9wIauADXselXB+gxdtNr7MFWi2xatkVB4domGXumSxU1hqWxfGNa7f4FHPC5b2v+Q54s\nWtm4ykYz/iu4EvZCTEPCexEwTVi3zmXbtsnhYWBRp8+jzjkP13EYVIfpMV6l19hNRk1eM1yUi8K1\nFRnbYPoczAV8LswdrClC3h+yUZFp3q1zg+ucrImdmRzsJqAcN7/G+uIN+dHi3EmBrvOTa+gJ9wvb\ncCTwxZIg4b1IXHjh1OE9loFJjT6LGucsznLeypA6VgjylOqvUE3F9HIB79rGNN3zMDHgTZ87GvL5\nmy9o4w9P/ynaBddROHbufPvo/dxnu7bCccY8djwK+zmEvMpPSznuq7ZQY7YZ2o9BGIoYnaPR6AmB\nPrFVP3mbI7PdiUVHwnuRqK/XtLS4HDtW3HSgCoMqfRpVzmmc4VzFsGqjx9hNj/EqSdV98gLEPJlt\nwI+GvM8PGA6mpTHM3Dl6I3TyRVO0zoX9lME+RdA7tlHaDHZjQr5o+RH244I9f3/c1zH3LYKoIus5\ntuWeG4Tn5Fv62fxzTj7ssygXlNbSyhfzSsJ7EXnTmxw2b1b09s7uP4ZCEdUriDorON25kgRd+SDf\nzbDRXqbaivIZG/CjW6ccnas0puVimLlBd4alMc3c16keWwE97Xn5sXJhr3AdA2dCsE8MetdWpXfl\nK9DYOLO5yiLfwh/bqh8b8hNb/LmFJ/yji89MJzNSfC7ANU4h3HNfHVw19XatnDEHA9LiF3Mn4b2I\ntLRo/vRPs2zfbvDUU2ZhANtshWkk7Dayyn0jKfryg912M2gc87jGYt7pkevdi35D0UFvmDrXhV9E\nZ5B2yQX5FC187ShcN262HrgAABgFSURBVHcgoB2Ve03+VlLo51v4ucBMn/z1uW8fMKbtwlfawlI+\ntKvyBwNmfjKd4rv2x3+cm6+jjVsI++kOBuwJzzvS+l/CJLwXGdOE177W5YILXJ5/3uS550wyJVwp\nFqSWle6lrHQvJc0gvcar9Bi7GVSH0dIqWIJGgtOguLzTKAMMyz1p0JtW8V35hdI1aHdMmI8E+xRh\nnzsIMCa9dlbppgBcXDKgMkxV02mvPx5p6ecDffSrhTHh8djnjXzvgEUAVcRAvvEfObH174zp/p+4\nfSTw3THbcgcPMrJ/8ZHwXqQCAdiwweGiixyeecbk5ZdN3BKXjg5QxXL39Sx3X0+WBL3GHnqMVxhQ\nB3FV8f9wxVKicq3qjIlDkav+qdw5+dy5eY0y89365shjjWHkXlN4bGosv4syiuvWH8t1ckvSTnkQ\n4Bi4rpqixT96EFD0+f2Rln4prWGtxoS6NekgYOTxVAcDhvbnt83tw0cPAtzJ90+yjSm3zXEfiKJI\neC9y0Shs3Ojwe7/n8OSTFq++6s361j7CNLuvodl9DTap3KQwxm761T4cVXQfrBCT6dx18+6cJs/S\nKGNC0I8JezXpIGDMgcEszumP+8T86P2RsFfawHZAO7kDF9fNde+PdP/r/G2k23/s87n5LGeogBo7\nWr7Irv7xu2ea1v/Yr0b+NcYU23L3c7PvmbM+DTC+Ku6YFv+EcJ+wjUnbHAxHYbjZwnbpHRhPwvsU\nUVcH11xjc+KE4ne/Mzl61JsQB7AI/v/t3X9sVXf9x/Hn59zblv64K23XljW4wVg2hME2HJsOwpal\nm0aXJaKu/eJ++IfGxfjVmbFoyDKWL0LGlswljKiJkuhErWF1+oe6aTJGZUXidKA4FsCMtZDRFkp/\n3NveH+ee7x/n/uwPWtr7g3vv65Hc9Py4vf308OP1/nzO55xDY/RmGqM3YxPmojnFecu9u1vEjGfs\n54jMzA1CO8plnMdPFRvm90TTioBkIRDr7aftixUFXgdveQRjQfkcWx8/DZAI/QkBHw/8xLZJ+5m0\nPy3RMtH7Tx4qwJoy3KffFisKYttIHUW43FGBEEycOpgId6Kx0E8tCtwXJrk/vUCIppwyiL83+Xmx\nyqdgKLyLTEuLQ3t7hP/+1/DWW14GBjL7t9FDGQ3Ochrs5URtmyHzAeet97hgvU/YZPaZ6yKZFxvm\nj85yiH8Sh/JyLxE7nAh4Y8XC3nILAHcZd3tsVGDy/th6WRRrnnX2dL3/ZPiTNiKQfKVsdyZvc8UD\nb35tdBsK7rUvk0N+8jYPHstLNErKdiulILBikwmteY8QJJvnxEI8vRhwjDsnYOrgTy8eLo7k7lbV\nejBJEYtG4dgxi7/+1cPISHZLSocow+bDxE1hgmY4sa8QHzBQiHSccyPzxzmlAPCQDPoJ4T9lsZDY\nRnqBkIF/7qmjBGnD/1FSwj6+nUnFQXTiNmdCcTDDKYRZH+fECIGVdkoAx5pQICSDP1kMxN474Xvd\nIiG+fnkH86X/Xc9V1XMdm5lMDyYpQZbl3plt+fIo77zj4fBhD+NZGuU2WNQ6S6i1l7DUvp9RcyZx\nv3Wboez8UJGikNLrzUhN4A7/Tl0AkAx/k76eXhRM2G7AWxZNfEZGWjltceBuM3iwbWdSwZAoClJG\nC1KLAydqiKbsm9dYeGK0IBb+E0YBDB5Sg//e5avxVZVl5PjMROFdAsrK4JOftFm92ubQIQ///KcH\nO4uTxw0Gn7MYn72YJXYrFeUO/eEeAqafgOnHb84RMP16iIpIVri92qhtwAYyPr80vRCw0sI/ZZuZ\nomCYooiIb/d6nAnFQWYangz+ySEfX49eYt+U3zexkIi6kxGXLb4Lk4lhj1nQsHkJGhqCri4v771n\nkYs//erqCvz+9JmzDg5hRgmYPvymLxbsfYyZAc1mnyMNm+eGjnO2uSFeUeEhbEdiPf+UeQQTRwxM\n6vIl9k1Yn+9cg6lUWTX83/qnqPRWZuwzNWwuCbW18MADEdaudWemf/BBFv4Wz8BgKMdHueNjobMs\nsd0hyjiDiTAPmH78Vh/jnNdNY0RKQqxXG/Fgh7P5b949vZAcKZhd6F9qX0tdI15Lw+aSZc3NDg89\nFOGDD9yZ6efO5f86CYNFJQ1UOg00OMvdjbb74IgxM5A27B4w/QSNzqeLyFy4pxcc22TsNOL/3NNK\nmZWbWFV4C0uWOFx3XZj33nNnpl+8mP8Qn8jCS7WziGpnEY2sSmyPMJ7oocd7637Tp/PpIlLUFN4C\ngDGwYkWUG2+M8u67Ft3dXsYKIP+8LHAffepcm9im8+kiUuwU3pLG64Xbb49y880hDh/28M47HsIF\nlneXfz79QuwOTCIihUHhLVNasMB9fvhtt9kcPOjhX//y5GRmejZdzvn0oBkiyBC2yd0dk0REZkvh\nLZfk88FnPmNz++1RDhzwcPJk7memZ9t059MBIgQJmiFCjBAywwTN8ITlYSJmDg+QEBGZB4W3zMrV\nVzts3Biht9edmX7mzJU3qS0bvFTgdZqopmna+ydHCBJiOBboI4SIBbsZJhgLej3ARUQySeEtl2Xx\nYodNm8KcOGHR1eXh/PnSCPFL8VKBl0aqnMYZAn6EkBlKBHzIjCR68kEzpIAXkVlTeMtlMwZuvDHK\nDTdEOXrU4uBBD36/QvxS3ICvoMq5etqAtwkRjPXgQ2YktjySGLYPmmFdAicigMJb5sGy4NZbo6xY\nEeXvf3cffBLS/K4581BOFVfPGPDxIHeH5d2AD5lhDEH8zjBh/LokTqTIKbxl3srL4a67bG65xaa7\n28O773qI6sqrrPBQnpgxPzHgqysq8IfdyXM2YSIECBs/YcYIG39sPUCYAJHY1+S6evQihSSr4b1j\nxw6OHDmCMYYtW7awevXqxL5Dhw7x4osvYlkWS5cuZfv27VjZuFO85Ex1NbS22nziEzZdXV6OH9ef\nZ754KMNDLRVOrbthhsv8othEGIuFuZ+IGYuFu9/djp+wCcTe4ydMQNfGi+RR1sL78OHDnD59mo6O\nDk6dOsWWLVvo6OhI7H/mmWf4+c9/zqJFi/jWt75FV1cXd999d7aaIzlUVwcPPug++KSry8PAQL5b\nJDOx8FBODeVOjbthhrB3cIgwPm1vPtnrD8QKAQ3li2RS1sK7u7ub1tZWAJYtW8bQ0BCjo6PU1Lj/\nOXR2diaW6+vrGRwczFZTJE+uucZ98MlVV8G774bp6bHo7bX46KPMPQhA8sNgKKOSMirdIXyYMfCn\nGsq3GXeLABN0l427bse2xZf1RDmRdFkL74GBAVauXJlYr6+vp7+/PxHY8a99fX0cPHiQb3/729lq\niuRZRQVcf73D9dfbgE04DGfOGHp7LXp7DWfPWkT0eOSid7lD+XEODjYhbIJETCzYCWLHgj7CeGw5\nvj84KfyjRtWiFJecTVhzpri35vnz53n88cfZunUrdXV1l/z+uroqvF5PRts03UPOJfMmHuuWFli7\n1l2ORODMGTh92n319KBZ63NUXV2R7yZkyYI5f6eDQ5RIIuhTXzbBKbdPfEVJry7LyzTXNxcK7TjX\n1lblLFeydmSampoYSDnZ2dfXR2NjY2J9dHSUr33tazzxxBOsX79+xs8bHAxktH2NjT76+0cy+pky\ntdkc66oq+PjH3Zdtw7lzJjbM7vbQg7oD6Yyqqyvw+3WgplcOlOPlqsv+j88Nf7dHX17tMBIYIUoY\n24QSowK2CRElhE04sW7jbosk9rnv1WmAmZWXeQmFC2tIbmgokPFcma4YyFp4r1u3jl27dtHe3s6x\nY8doampKDJUDPPfcczz22GNs2LAhW02QAuXxQEuLQ0uLzZ13QjQK/f2Gnh43yHt6rIJ4XKkUDwsv\n5XiBaqqpwOPEiqQ5ZHB8JCAe7G7IB2OhHyKaWI/vC08oDkIp++KfocmApSZr4b1mzRpWrlxJe3s7\nxhi2bt1KZ2cnPp+P9evX89prr3H69Gn27dsHwAMPPEBbW1u2miMFzLKgudmhudnh9tujOA4MDBh6\ne93eeU+P0R3epGAYTOz8fxlQPbkAmFNBEE2GeqwAiBBMGx2IjwpETdj9Gnu5RUFsOW2/+35dEnhl\nyuoJhc2bN6etL1++PLH873//O5s/WoqYMdDY6NDY6HDbbW6YDw5Cb6/Fhx+6Q+3DwwpzKR0GCy8L\n8MbnBqQWAPMYoXdi4wRu+M8u7FOLA7eQSFkmEtsfSny/ioO5KazZACJTMAbq66G+Psrq1e5/BEND\nxILcDfPBQYW5yOUysXECi0qg0t2YgZGC5Le61xJMFfYVZYbRcCCx7r4i2Ca57L4mjiZEiJpIsjiI\nnaIotnkGCm8pSrW1sGpVlFWr3DAfGSExAa6nx9LT0ESuAG5x4MXCy8TioJoKKpzgvIqDuPgIwtRF\nQEohMKkIiBcVkUnbkp8TSawbcndXSYW3lASfD1asiLJiBYCN30+iV97TY9Hfb5jiakYRKQLJEQQP\nTHVqIW6e/wdcWx0BcnMaQOEtJam6Gm66KcpNNwHYjI25N46J3wVuYMAQ1gReEblCKbxFgMpKuOEG\nhxtucO8C5zgwOgoXLrjny1NfFy/q9q4ikl8Kb5EpGOMOtft8Dtddlz6WFo3C8LAb7BcvuoEeXx4a\nMnocqohkncJb5DJZFixcCAsXOkw8SWbbcPGi4eLFyb12Xb4mIpmi8BbJII8HGhocGhpg2bL0YA+H\nmdBTJ7Gsm8yIyOVQeIvkSFlZ8uYyEwWDbrDHh99Te+26FayITKTwFrkCVFQkbwE70dgYkybNxV96\nYItIaVJ4i1zhKiuhstKhpSU92B0H/P7kULxlVdDba+P3G0ZHDaOj4Pfr+nWRYqTwFilQxkBNDdTU\nOCxe7NDYCP396dewRaMQCBALdGKhngx2hbxIYVJ4ixQxy0oGfHMzTHcLqelC3u+fHPgKeZH8U3iL\nyLxCfqpefSCg691FsknhLSKzNteQTx2iV8iLzJ/CW0QybrYh7zgQCrkz6sfHDYEAjI0Zxsfdr9Nt\n133npdQpvEUkb4xxL5OrqIBkwM98Uj0chvFxt+ceD/ixsWTABwKTC4Dx8Wz+JiK5pfAWkYJTVua+\nfL7ZB75tM23QX6oA0AQ9uRIpvEWkJHg8yaF818yp7DjEevDJcK+srOCjjyIEg264B4MQCsWX3Rvn\nuC9DJJLd30lKl8JbRGQaxsRvkgPxsG9shJaW2c2yi0RIhHooxJQBHwy6hUEoRGI5XhAEg+r5y9QU\n3iIiWeL1Tuztw2x6/Il3OkwK+vSwV++/VCm8RUSuUMbAggXuKz30Z18ARCJukIfDbpiHw+4M/1Ao\nfTkUmvo98e3xZdue+WdK9im8RUSKmNfrvlxzKwBS2XY82JMFgFscmEnbJ78nWQS4RYEu+5srhbeI\niMyaxzN5HoBrbsVANMqk3n0oBD6fOzEwEnHXIxE36OOvUMik7XO/xve77y3mGwApvEVEJG8sa+pT\nA42NUF8/9/R1HHeUIBn46eF/6fXUwiA5epD6vnzPJVB4i4hI0TEmecpg8ijBVOuXJxplUsAn7zuQ\nfQpvERGRy2RZUF7uvly5vabPyulPExERkXlTeIuIiBSYrIb3jh07aGtro729naNHj6bte/vtt/ni\nF79IW1sbu3fvzmYzREREikrWwvvw4cOcPn2ajo4Otm/fzvbt29P2f//732fXrl386le/4uDBg5w8\neTJbTRERESkqWQvv7u5uWltbAVi2bBlDQ0OMjo4C0NPTQ21tLddccw2WZXH33XfT3d2draaIiIgU\nlazNNh8YGGDlypWJ9fr6evr7+6mpqaG/v5/6+vq0fT09PZf8vLq6KrxeT0bb2Njoy+jnyfR0rHND\nxzk3dJxzQ8d5ejm7VMyZ56NxBgcDGWqJq7HRR3//SEY/U6amY50bOs65oeOcGzrOrukKmKwNmzc1\nNTEwMJBY7+vro7Gxccp9586do6mpKVtNERERKSpZC+9169bx+uuvA3Ds2DGampqoqakBYPHixYyO\njtLb20skEuHNN99k3bp12WqKiIhIUcnasPmaNWtYuXIl7e3tGGPYunUrnZ2d+Hw+7rvvPp599lme\nfPJJAD772c+ydOnSbDVFRESkqBhnviejcyTT5z50PiV3dKxzQ8c5N3Scc0PH2ZXzc94iIiKSHQXT\n8xYRERGXet4iIiIFRuEtIiJSYBTeIiIiBUbhLSIiUmAU3iIiIgVG4S0iIlJgSjK8d+zYQVtbG+3t\n7Rw9ejTfzSlazz//PG1tbXzhC1/gjTfeyHdzitr4+Ditra10dnbmuylF7fe//z0PPvggGzduZP/+\n/fluTlHy+/1885vf5JFHHqG9vZ2urq58N+mKlLOnil0pDh8+zOnTp+no6ODUqVNs2bKFjo6OfDer\n6Bw6dIgTJ07Q0dHB4OAgn//857n//vvz3ayi9cMf/pDa2tp8N6OoDQ4Osnv3bl599VUCgQC7du3i\nnnvuyXezis5vf/tbli5dypNPPsm5c+d47LHH+NOf/pTvZl1xSi68u7u7aW1tBWDZsmUMDQ0xOjqa\neGiKZMbatWtZvXo1AFdddRVjY2PYto3Hk9lnsgucOnWKkydPKkiyrLu7m0996lPU1NRQU1PDtm3b\n8t2kolRXV8f7778PwPDwMHV1dXlu0ZWp5IbNBwYG0v4y1NfX09/fn8cWFSePx0NVVRUA+/btY8OG\nDQruLNm5cyff+9738t2Motfb28v4+DiPP/44mzZtoru7O99NKkqf+9znOHv2LPfddx8PP/ww3/3u\nd/PdpCtSyfW8J9LdYbPrL3/5C/v27WPPnj35bkpReu2117j11lv52Mc+lu+mlISLFy/y8ssvc/bs\nWR599FHefPNNjDH5blZR+d3vfkdLSws//elPOX78OFu2bNFcjimUXHg3NTUxMDCQWO/r66OxsTGP\nLSpeXV1d/OhHP+InP/kJPt/UT8aR+dm/fz89PT3s37+fjz76iPLychYtWsRdd92V76YVnYaGBm67\n7Ta8Xi/XXnst1dXVXLhwgYaGhnw3raj84x//YP369QAsX76cvr4+nXKbQskNm69bt47XX38dgGPH\njtHU1KTz3VkwMjLC888/z49//GMWLlyY7+YUrZdeeolXX32V3/zmN3zpS1/iG9/4hoI7S9avX8+h\nQ4eIRqMMDg4SCAR0PjYLrrvuOo4cOQLAmTNnqK6uVnBPoeR63mvWrGHlypW0t7djjGHr1q35blJR\n+sMf/sDg4CBPPPFEYtvOnTtpaWnJY6tE5q65uZlPf/rTPPTQQwA8/fTTWFbJ9X+yrq2tjS1btvDw\nww8TiUR49tln892kK5IeCSoiIlJgVDaKiIgUGIW3iIhIgVF4i4iIFBiFt4iISIFReIuIiBQYhbeI\nzFtnZyebN2/OdzNESobCW0REpMCU3E1aRErZK6+8wh//+Eds2+b666/nq1/9Kl//+tfZsGEDx48f\nB+AHP/gBzc3N7N+/n927d7NgwQIqKyvZtm0bzc3NHDlyhB07dlBWVkZtbS07d+4EYHR0lM2bN3Pq\n1ClaWlp4+eWXdd9vkSxRz1ukRBw9epQ///nP7N27l46ODnw+H2+//TY9PT1s3LiRX/7yl9xxxx3s\n2bOHsbExnn76aXbt2sUrr7zChg0beOmllwB46qmn2LZtG7/4xS9Yu3Ytb731FgAnT55k27ZtdHZ2\ncuLECY4dO5bPX1ekqKnnLVIi/va3v/Hhhx/y6KOPAhAIBDh37hwLFy7k5ptvBtzbB//sZz/jgw8+\noKGhgUWLFgFwxx138Otf/5oLFy4wPDzMjTfeCMBXvvIVwD3nvWrVKiorKwH3VqIjIyM5/g1FSofC\nW6RElJeXc++99/LMM88ktvX29rJx48bEuuM4GGMmDXenbp/ujsoTHx6hOy+LZI+GzUVKxJo1azhw\n4AB+vx+AvXv30t/fz9DQEP/5z38A93GMN910E0uWLOH8+fOcPXsWgO7ubm655Rbq6upYuHAhR48e\nBWDPnj3s3bs3P7+QSAlTz1ukRKxatYovf/nLPPLII1RUVNDU1MSdd95Jc3MznZ2dPPfccziOw4sv\nvsiCBQvYvn073/nOdygvL6eqqort27cD8MILL7Bjxw68Xi8+n48XXniBN954I8+/nUhp0VPFREpY\nb28vmzZt4sCBA/luiohcBg2bi4iIFBj1vEVERAqMet4iIiIFRuEtIiJSYBTeIiIiBUbhLSIiUmAU\n3iIiIgVG4S0iIlJg/h+ZtBOreuCdgwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fa1b2588eb8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfUAAAFnCAYAAAC/5tBZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ndpYwGQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fa1b2532c50>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfsAAAFnCAYAAAChL+DqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fa1b26f4630>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "KTsGixAEODKk",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "По скорости сходимости также выигрывает ADAM"
      ]
    },
    {
      "metadata": {
        "id": "hzALma1UdSPA",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "###График зависимости функции ошибки от значения структурного параметра со ст. откл.\n",
        "В качестве структурных параметров выбраны число нейронов во входном слое и размер пачки входящих объектов (batch_size). Рассмотрим 5 значений первого параметра от 10 до 784 и также 5 для второго: от 10 до 300. Для увеличения сходимости уменьшено количество раундов, что негативно влияет на точность"
      ]
    },
    {
      "metadata": {
        "id": "3isRIHqPeYQF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 13017
        },
        "outputId": "4a852ef5-b85e-4e9f-b2c7-fa8d604f106b"
      },
      "cell_type": "code",
      "source": [
        "optimizers = ['adam', 'SGD', 'nadam']\n",
        "params = np.linspace(10, 784, num=5, dtype='Int32')\n",
        "batch_size_list = np.linspace(10, 300, num=5, dtype='Int32')\n",
        "\n",
        "accuracy = {optimizer: list() for optimizer in optimizers}\n",
        "\n",
        "for optimizer in optimizers:\n",
        "    for param in params:\n",
        "        for batch_size in batch_size_list:\n",
        "            model = baseline_model(optimizer, param)\n",
        "            model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=4, batch_size=batch_size, verbose=1)\n",
        "            scores = model.evaluate(X_test, y_test, verbose=0)\n",
        "            accuracy[optimizer].append(scores[1])\n",
        "            print(\"Baseline Error: %.2f%%\" % (100-scores[1]*100))"
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/4\n",
            "60000/60000 [==============================] - 23s 386us/step - loss: 0.4331 - acc: 0.8773 - val_loss: 0.2931 - val_acc: 0.9171\n",
            "Epoch 2/4\n",
            "60000/60000 [==============================] - 17s 284us/step - loss: 0.2833 - acc: 0.9178 - val_loss: 0.2640 - val_acc: 0.9252\n",
            "Epoch 3/4\n",
            "60000/60000 [==============================] - 17s 284us/step - loss: 0.2579 - acc: 0.9260 - val_loss: 0.2509 - val_acc: 0.9267\n",
            "Epoch 4/4\n",
            "60000/60000 [==============================] - 17s 290us/step - loss: 0.2439 - acc: 0.9301 - val_loss: 0.2516 - val_acc: 0.9274\n",
            "Baseline Error: 7.26%\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/4\n",
            "60000/60000 [==============================] - 9s 154us/step - loss: 0.7801 - acc: 0.7707 - val_loss: 0.3635 - val_acc: 0.8945\n",
            "Epoch 2/4\n",
            "60000/60000 [==============================] - 3s 45us/step - loss: 0.3317 - acc: 0.9053 - val_loss: 0.2964 - val_acc: 0.9168\n",
            "Epoch 3/4\n",
            "60000/60000 [==============================] - 3s 43us/step - loss: 0.2906 - acc: 0.9177 - val_loss: 0.2716 - val_acc: 0.9243\n",
            "Epoch 4/4\n",
            "60000/60000 [==============================] - 3s 45us/step - loss: 0.2723 - acc: 0.9230 - val_loss: 0.2608 - val_acc: 0.9275\n",
            "Baseline Error: 7.25%\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/4\n",
            "60000/60000 [==============================] - 8s 138us/step - loss: 0.9423 - acc: 0.7576 - val_loss: 0.4112 - val_acc: 0.8921\n",
            "Epoch 2/4\n",
            "60000/60000 [==============================] - 2s 29us/step - loss: 0.3684 - acc: 0.8981 - val_loss: 0.3180 - val_acc: 0.9103\n",
            "Epoch 3/4\n",
            "60000/60000 [==============================] - 2s 27us/step - loss: 0.3147 - acc: 0.9115 - val_loss: 0.2938 - val_acc: 0.9198\n",
            "Epoch 4/4\n",
            "60000/60000 [==============================] - 2s 29us/step - loss: 0.2926 - acc: 0.9169 - val_loss: 0.2793 - val_acc: 0.9212\n",
            "Baseline Error: 7.88%\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/4\n",
            "60000/60000 [==============================] - 8s 137us/step - loss: 1.1739 - acc: 0.6716 - val_loss: 0.5077 - val_acc: 0.8716\n",
            "Epoch 2/4\n",
            "60000/60000 [==============================] - 1s 24us/step - loss: 0.4186 - acc: 0.8869 - val_loss: 0.3495 - val_acc: 0.9040\n",
            "Epoch 3/4\n",
            "60000/60000 [==============================] - 1s 21us/step - loss: 0.3372 - acc: 0.9043 - val_loss: 0.3083 - val_acc: 0.9122\n",
            "Epoch 4/4\n",
            "60000/60000 [==============================] - 1s 22us/step - loss: 0.3066 - acc: 0.9134 - val_loss: 0.2893 - val_acc: 0.9210\n",
            "Baseline Error: 7.90%\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/4\n",
            "60000/60000 [==============================] - 8s 136us/step - loss: 1.3171 - acc: 0.6323 - val_loss: 0.5691 - val_acc: 0.8595\n",
            "Epoch 2/4\n",
            "60000/60000 [==============================] - 1s 19us/step - loss: 0.4606 - acc: 0.8778 - val_loss: 0.3735 - val_acc: 0.9005\n",
            "Epoch 3/4\n",
            "60000/60000 [==============================] - 1s 20us/step - loss: 0.3587 - acc: 0.8994 - val_loss: 0.3233 - val_acc: 0.9112\n",
            "Epoch 4/4\n",
            "60000/60000 [==============================] - 1s 20us/step - loss: 0.3214 - acc: 0.9087 - val_loss: 0.2985 - val_acc: 0.9162\n",
            "Baseline Error: 8.38%\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/4\n",
            "60000/60000 [==============================] - 49s 816us/step - loss: 0.2068 - acc: 0.9389 - val_loss: 0.1101 - val_acc: 0.9659\n",
            "Epoch 2/4\n",
            "60000/60000 [==============================] - 42s 703us/step - loss: 0.0862 - acc: 0.9734 - val_loss: 0.0797 - val_acc: 0.9742\n",
            "Epoch 3/4\n",
            "60000/60000 [==============================] - 42s 697us/step - loss: 0.0590 - acc: 0.9818 - val_loss: 0.0711 - val_acc: 0.9791\n",
            "Epoch 4/4\n",
            "60000/60000 [==============================] - 47s 782us/step - loss: 0.0447 - acc: 0.9851 - val_loss: 0.0685 - val_acc: 0.9785\n",
            "Baseline Error: 2.15%\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/4\n",
            "60000/60000 [==============================] - 14s 236us/step - loss: 0.3243 - acc: 0.9093 - val_loss: 0.1665 - val_acc: 0.9509\n",
            "Epoch 2/4\n",
            "60000/60000 [==============================] - 7s 108us/step - loss: 0.1395 - acc: 0.9601 - val_loss: 0.1169 - val_acc: 0.9638\n",
            "Epoch 3/4\n",
            "60000/60000 [==============================] - 7s 110us/step - loss: 0.0957 - acc: 0.9719 - val_loss: 0.0983 - val_acc: 0.9693\n",
            "Epoch 4/4\n",
            "60000/60000 [==============================] - 7s 112us/step - loss: 0.0706 - acc: 0.9792 - val_loss: 0.0786 - val_acc: 0.9757\n",
            "Baseline Error: 2.43%\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/4\n",
            "60000/60000 [==============================] - 12s 204us/step - loss: 0.3785 - acc: 0.8989 - val_loss: 0.1931 - val_acc: 0.9439\n",
            "Epoch 2/4\n",
            "60000/60000 [==============================] - 4s 68us/step - loss: 0.1630 - acc: 0.9529 - val_loss: 0.1403 - val_acc: 0.9588\n",
            "Epoch 3/4\n",
            "60000/60000 [==============================] - 4s 71us/step - loss: 0.1129 - acc: 0.9674 - val_loss: 0.1090 - val_acc: 0.9673\n",
            "Epoch 4/4\n",
            "60000/60000 [==============================] - 4s 72us/step - loss: 0.0863 - acc: 0.9751 - val_loss: 0.0911 - val_acc: 0.9720\n",
            "Baseline Error: 2.80%\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/4\n",
            "60000/60000 [==============================] - 11s 187us/step - loss: 0.4326 - acc: 0.8868 - val_loss: 0.2241 - val_acc: 0.9355\n",
            "Epoch 2/4\n",
            "60000/60000 [==============================] - 3s 55us/step - loss: 0.1862 - acc: 0.9475 - val_loss: 0.1531 - val_acc: 0.9560\n",
            "Epoch 3/4\n",
            "60000/60000 [==============================] - 3s 54us/step - loss: 0.1332 - acc: 0.9619 - val_loss: 0.1218 - val_acc: 0.9654\n",
            "Epoch 4/4\n",
            "60000/60000 [==============================] - 3s 53us/step - loss: 0.1026 - acc: 0.9707 - val_loss: 0.0999 - val_acc: 0.9694\n",
            "Baseline Error: 3.06%\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/4\n",
            "60000/60000 [==============================] - 11s 177us/step - loss: 0.4911 - acc: 0.8735 - val_loss: 0.2450 - val_acc: 0.9295\n",
            "Epoch 2/4\n",
            "60000/60000 [==============================] - 3s 45us/step - loss: 0.2110 - acc: 0.9403 - val_loss: 0.1749 - val_acc: 0.9498\n",
            "Epoch 3/4\n",
            "60000/60000 [==============================] - 3s 47us/step - loss: 0.1555 - acc: 0.9560 - val_loss: 0.1387 - val_acc: 0.9591\n",
            "Epoch 4/4\n",
            "60000/60000 [==============================] - 3s 45us/step - loss: 0.1224 - acc: 0.9652 - val_loss: 0.1171 - val_acc: 0.9643\n",
            "Baseline Error: 3.57%\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/4\n",
            "60000/60000 [==============================] - 67s 1ms/step - loss: 0.1875 - acc: 0.9439 - val_loss: 0.0988 - val_acc: 0.9680\n",
            "Epoch 2/4\n",
            "60000/60000 [==============================] - 59s 981us/step - loss: 0.0787 - acc: 0.9757 - val_loss: 0.0832 - val_acc: 0.9744\n",
            "Epoch 3/4\n",
            "60000/60000 [==============================] - 60s 1ms/step - loss: 0.0551 - acc: 0.9825 - val_loss: 0.0670 - val_acc: 0.9790\n",
            "Epoch 4/4\n",
            "60000/60000 [==============================] - 61s 1ms/step - loss: 0.0411 - acc: 0.9862 - val_loss: 0.0743 - val_acc: 0.9777\n",
            "Baseline Error: 2.23%\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/4\n",
            "60000/60000 [==============================] - 17s 282us/step - loss: 0.2667 - acc: 0.9237 - val_loss: 0.1323 - val_acc: 0.9592\n",
            "Epoch 2/4\n",
            "60000/60000 [==============================] - 9s 151us/step - loss: 0.1055 - acc: 0.9694 - val_loss: 0.0907 - val_acc: 0.9718\n",
            "Epoch 3/4\n",
            "60000/60000 [==============================] - 9s 150us/step - loss: 0.0690 - acc: 0.9796 - val_loss: 0.0752 - val_acc: 0.9771\n",
            "Epoch 4/4\n",
            "60000/60000 [==============================] - 9s 150us/step - loss: 0.0479 - acc: 0.9857 - val_loss: 0.0780 - val_acc: 0.9764\n",
            "Baseline Error: 2.36%\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/4\n",
            "60000/60000 [==============================] - 14s 225us/step - loss: 0.3111 - acc: 0.9146 - val_loss: 0.1514 - val_acc: 0.9557\n",
            "Epoch 2/4\n",
            "60000/60000 [==============================] - 6s 96us/step - loss: 0.1281 - acc: 0.9628 - val_loss: 0.1081 - val_acc: 0.9681\n",
            "Epoch 3/4\n",
            "60000/60000 [==============================] - 6s 96us/step - loss: 0.0852 - acc: 0.9754 - val_loss: 0.0863 - val_acc: 0.9739\n",
            "Epoch 4/4\n",
            "60000/60000 [==============================] - 6s 97us/step - loss: 0.0628 - acc: 0.9820 - val_loss: 0.0761 - val_acc: 0.9764\n",
            "Baseline Error: 2.36%\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/4\n",
            "60000/60000 [==============================] - 13s 210us/step - loss: 0.3505 - acc: 0.9048 - val_loss: 0.1795 - val_acc: 0.9491\n",
            "Epoch 2/4\n",
            "60000/60000 [==============================] - 5s 78us/step - loss: 0.1473 - acc: 0.9580 - val_loss: 0.1236 - val_acc: 0.9636\n",
            "Epoch 3/4\n",
            "60000/60000 [==============================] - 5s 79us/step - loss: 0.0995 - acc: 0.9720 - val_loss: 0.1008 - val_acc: 0.9699\n",
            "Epoch 4/4\n",
            "60000/60000 [==============================] - 5s 78us/step - loss: 0.0744 - acc: 0.9788 - val_loss: 0.0874 - val_acc: 0.9736\n",
            "Baseline Error: 2.64%\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/4\n",
            "60000/60000 [==============================] - 12s 203us/step - loss: 0.3998 - acc: 0.8903 - val_loss: 0.1914 - val_acc: 0.9457\n",
            "Epoch 2/4\n",
            "60000/60000 [==============================] - 4s 69us/step - loss: 0.1638 - acc: 0.9533 - val_loss: 0.1298 - val_acc: 0.9623\n",
            "Epoch 3/4\n",
            "60000/60000 [==============================] - 4s 69us/step - loss: 0.1119 - acc: 0.9679 - val_loss: 0.1010 - val_acc: 0.9699\n",
            "Epoch 4/4\n",
            "60000/60000 [==============================] - 4s 72us/step - loss: 0.0839 - acc: 0.9765 - val_loss: 0.0921 - val_acc: 0.9721\n",
            "Baseline Error: 2.79%\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/4\n",
            "60000/60000 [==============================] - 80s 1ms/step - loss: 0.1791 - acc: 0.9457 - val_loss: 0.1100 - val_acc: 0.9649\n",
            "Epoch 2/4\n",
            "60000/60000 [==============================] - 67s 1ms/step - loss: 0.0795 - acc: 0.9758 - val_loss: 0.0785 - val_acc: 0.9766\n",
            "Epoch 3/4\n",
            "60000/60000 [==============================] - 75s 1ms/step - loss: 0.0572 - acc: 0.9823 - val_loss: 0.0914 - val_acc: 0.9771\n",
            "Epoch 4/4\n",
            "60000/60000 [==============================] - 74s 1ms/step - loss: 0.0409 - acc: 0.9866 - val_loss: 0.0881 - val_acc: 0.9784\n",
            "Baseline Error: 2.16%\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/4\n",
            "60000/60000 [==============================] - 21s 343us/step - loss: 0.2368 - acc: 0.9309 - val_loss: 0.1202 - val_acc: 0.9632\n",
            "Epoch 2/4\n",
            "60000/60000 [==============================] - 12s 202us/step - loss: 0.0919 - acc: 0.9730 - val_loss: 0.0842 - val_acc: 0.9736\n",
            "Epoch 3/4\n",
            "60000/60000 [==============================] - 12s 204us/step - loss: 0.0582 - acc: 0.9827 - val_loss: 0.0697 - val_acc: 0.9785\n",
            "Epoch 4/4\n",
            "60000/60000 [==============================] - 11s 191us/step - loss: 0.0396 - acc: 0.9884 - val_loss: 0.0633 - val_acc: 0.9797\n",
            "Baseline Error: 2.03%\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/4\n",
            "60000/60000 [==============================] - 16s 264us/step - loss: 0.2796 - acc: 0.9221 - val_loss: 0.1381 - val_acc: 0.9607\n",
            "Epoch 2/4\n",
            "60000/60000 [==============================] - 7s 124us/step - loss: 0.1142 - acc: 0.9669 - val_loss: 0.0976 - val_acc: 0.9695\n",
            "Epoch 3/4\n",
            "60000/60000 [==============================] - 8s 128us/step - loss: 0.0730 - acc: 0.9785 - val_loss: 0.0810 - val_acc: 0.9752\n",
            "Epoch 4/4\n",
            "60000/60000 [==============================] - 8s 128us/step - loss: 0.0517 - acc: 0.9848 - val_loss: 0.0722 - val_acc: 0.9762\n",
            "Baseline Error: 2.38%\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/4\n",
            "60000/60000 [==============================] - 14s 229us/step - loss: 0.3068 - acc: 0.9148 - val_loss: 0.1511 - val_acc: 0.9558\n",
            "Epoch 2/4\n",
            "60000/60000 [==============================] - 6s 98us/step - loss: 0.1246 - acc: 0.9640 - val_loss: 0.1077 - val_acc: 0.9675\n",
            "Epoch 3/4\n",
            "60000/60000 [==============================] - 6s 102us/step - loss: 0.0824 - acc: 0.9765 - val_loss: 0.0807 - val_acc: 0.9747\n",
            "Epoch 4/4\n",
            "60000/60000 [==============================] - 6s 104us/step - loss: 0.0594 - acc: 0.9830 - val_loss: 0.0706 - val_acc: 0.9776\n",
            "Baseline Error: 2.24%\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/4\n",
            "60000/60000 [==============================] - 13s 225us/step - loss: 0.3437 - acc: 0.9048 - val_loss: 0.1792 - val_acc: 0.9487\n",
            "Epoch 2/4\n",
            "60000/60000 [==============================] - 5s 90us/step - loss: 0.1399 - acc: 0.9605 - val_loss: 0.1157 - val_acc: 0.9651\n",
            "Epoch 3/4\n",
            "60000/60000 [==============================] - 5s 89us/step - loss: 0.0928 - acc: 0.9738 - val_loss: 0.0869 - val_acc: 0.9732\n",
            "Epoch 4/4\n",
            "60000/60000 [==============================] - 5s 86us/step - loss: 0.0680 - acc: 0.9810 - val_loss: 0.0785 - val_acc: 0.9749\n",
            "Baseline Error: 2.51%\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/4\n",
            "60000/60000 [==============================] - 94s 2ms/step - loss: 0.1783 - acc: 0.9469 - val_loss: 0.0961 - val_acc: 0.9682\n",
            "Epoch 2/4\n",
            "60000/60000 [==============================] - 81s 1ms/step - loss: 0.0784 - acc: 0.9758 - val_loss: 0.0896 - val_acc: 0.9722\n",
            "Epoch 3/4\n",
            "60000/60000 [==============================] - 81s 1ms/step - loss: 0.0539 - acc: 0.9831 - val_loss: 0.0904 - val_acc: 0.9755\n",
            "Epoch 4/4\n",
            "60000/60000 [==============================] - 80s 1ms/step - loss: 0.0425 - acc: 0.9868 - val_loss: 0.0996 - val_acc: 0.9755\n",
            "Baseline Error: 2.45%\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/4\n",
            "60000/60000 [==============================] - 21s 347us/step - loss: 0.2222 - acc: 0.9362 - val_loss: 0.1035 - val_acc: 0.9687\n",
            "Epoch 2/4\n",
            "60000/60000 [==============================] - 13s 216us/step - loss: 0.0851 - acc: 0.9743 - val_loss: 0.0805 - val_acc: 0.9760\n",
            "Epoch 3/4\n",
            "60000/60000 [==============================] - 13s 213us/step - loss: 0.0532 - acc: 0.9833 - val_loss: 0.0681 - val_acc: 0.9788\n",
            "Epoch 4/4\n",
            "60000/60000 [==============================] - 13s 215us/step - loss: 0.0364 - acc: 0.9891 - val_loss: 0.0736 - val_acc: 0.9766\n",
            "Baseline Error: 2.34%\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/4\n",
            "60000/60000 [==============================] - 16s 274us/step - loss: 0.2594 - acc: 0.9266 - val_loss: 0.1370 - val_acc: 0.9588\n",
            "Epoch 2/4\n",
            "60000/60000 [==============================] - 9s 142us/step - loss: 0.1022 - acc: 0.9703 - val_loss: 0.0942 - val_acc: 0.9718\n",
            "Epoch 3/4\n",
            "60000/60000 [==============================] - 9s 142us/step - loss: 0.0651 - acc: 0.9809 - val_loss: 0.0743 - val_acc: 0.9766\n",
            "Epoch 4/4\n",
            "60000/60000 [==============================] - 9s 143us/step - loss: 0.0450 - acc: 0.9870 - val_loss: 0.0737 - val_acc: 0.9766\n",
            "Baseline Error: 2.34%\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/4\n",
            "60000/60000 [==============================] - 15s 251us/step - loss: 0.2875 - acc: 0.9178 - val_loss: 0.1454 - val_acc: 0.9578\n",
            "Epoch 2/4\n",
            "60000/60000 [==============================] - 7s 117us/step - loss: 0.1160 - acc: 0.9660 - val_loss: 0.1019 - val_acc: 0.9699\n",
            "Epoch 3/4\n",
            "60000/60000 [==============================] - 7s 121us/step - loss: 0.0747 - acc: 0.9786 - val_loss: 0.0871 - val_acc: 0.9743\n",
            "Epoch 4/4\n",
            "60000/60000 [==============================] - 8s 128us/step - loss: 0.0543 - acc: 0.9842 - val_loss: 0.0672 - val_acc: 0.9787\n",
            "Baseline Error: 2.13%\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/4\n",
            "60000/60000 [==============================] - 14s 237us/step - loss: 0.3102 - acc: 0.9140 - val_loss: 0.1537 - val_acc: 0.9556\n",
            "Epoch 2/4\n",
            "60000/60000 [==============================] - 6s 101us/step - loss: 0.1269 - acc: 0.9635 - val_loss: 0.1056 - val_acc: 0.9684\n",
            "Epoch 3/4\n",
            "60000/60000 [==============================] - 6s 100us/step - loss: 0.0831 - acc: 0.9762 - val_loss: 0.0923 - val_acc: 0.9721\n",
            "Epoch 4/4\n",
            "60000/60000 [==============================] - 6s 101us/step - loss: 0.0609 - acc: 0.9822 - val_loss: 0.0762 - val_acc: 0.9765\n",
            "Baseline Error: 2.35%\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/4\n",
            "60000/60000 [==============================] - 27s 447us/step - loss: 0.7449 - acc: 0.7762 - val_loss: 0.3359 - val_acc: 0.9052\n",
            "Epoch 2/4\n",
            "60000/60000 [==============================] - 19s 319us/step - loss: 0.3321 - acc: 0.9058 - val_loss: 0.2944 - val_acc: 0.9168\n",
            "Epoch 3/4\n",
            "60000/60000 [==============================] - 19s 311us/step - loss: 0.2992 - acc: 0.9151 - val_loss: 0.2800 - val_acc: 0.9199\n",
            "Epoch 4/4\n",
            "60000/60000 [==============================] - 19s 311us/step - loss: 0.2802 - acc: 0.9210 - val_loss: 0.2654 - val_acc: 0.9257\n",
            "Baseline Error: 7.43%\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/4\n",
            "60000/60000 [==============================] - 11s 180us/step - loss: 2.0483 - acc: 0.3589 - val_loss: 1.4207 - val_acc: 0.6214\n",
            "Epoch 2/4\n",
            "60000/60000 [==============================] - 3s 46us/step - loss: 0.9538 - acc: 0.7568 - val_loss: 0.6679 - val_acc: 0.8211\n",
            "Epoch 3/4\n",
            "60000/60000 [==============================] - 3s 46us/step - loss: 0.5902 - acc: 0.8390 - val_loss: 0.4993 - val_acc: 0.8618\n",
            "Epoch 4/4\n",
            "60000/60000 [==============================] - 3s 46us/step - loss: 0.4820 - acc: 0.8666 - val_loss: 0.4306 - val_acc: 0.8789\n",
            "Baseline Error: 12.11%\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/4\n",
            "60000/60000 [==============================] - 10s 160us/step - loss: 2.2133 - acc: 0.3070 - val_loss: 2.0148 - val_acc: 0.4531\n",
            "Epoch 2/4\n",
            "60000/60000 [==============================] - 2s 29us/step - loss: 1.6063 - acc: 0.5901 - val_loss: 1.1771 - val_acc: 0.7147\n",
            "Epoch 3/4\n",
            "60000/60000 [==============================] - 2s 28us/step - loss: 0.9604 - acc: 0.7667 - val_loss: 0.7671 - val_acc: 0.8056\n",
            "Epoch 4/4\n",
            "60000/60000 [==============================] - 2s 28us/step - loss: 0.6945 - acc: 0.8205 - val_loss: 0.6000 - val_acc: 0.8418\n",
            "Baseline Error: 15.82%\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/4\n",
            "60000/60000 [==============================] - 9s 153us/step - loss: 2.2483 - acc: 0.2104 - val_loss: 2.1607 - val_acc: 0.3068\n",
            "Epoch 2/4\n",
            "60000/60000 [==============================] - 1s 21us/step - loss: 1.9651 - acc: 0.4548 - val_loss: 1.7020 - val_acc: 0.5883\n",
            "Epoch 3/4\n",
            "60000/60000 [==============================] - 1s 21us/step - loss: 1.4368 - acc: 0.6455 - val_loss: 1.1791 - val_acc: 0.6990\n",
            "Epoch 4/4\n",
            "60000/60000 [==============================] - 1s 21us/step - loss: 1.0430 - acc: 0.7207 - val_loss: 0.9024 - val_acc: 0.7502\n",
            "Baseline Error: 24.98%\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/4\n",
            "60000/60000 [==============================] - 9s 151us/step - loss: 2.2762 - acc: 0.2483 - val_loss: 2.2399 - val_acc: 0.3064\n",
            "Epoch 2/4\n",
            "60000/60000 [==============================] - 1s 18us/step - loss: 2.1581 - acc: 0.3300 - val_loss: 2.0351 - val_acc: 0.3888\n",
            "Epoch 3/4\n",
            "60000/60000 [==============================] - 1s 18us/step - loss: 1.8489 - acc: 0.4911 - val_loss: 1.6163 - val_acc: 0.5949\n",
            "Epoch 4/4\n",
            "60000/60000 [==============================] - 1s 18us/step - loss: 1.4073 - acc: 0.6479 - val_loss: 1.1947 - val_acc: 0.6944\n",
            "Baseline Error: 30.56%\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/4\n",
            "60000/60000 [==============================] - 37s 610us/step - loss: 0.4730 - acc: 0.8741 - val_loss: 0.2759 - val_acc: 0.9201\n",
            "Epoch 2/4\n",
            "60000/60000 [==============================] - 28s 475us/step - loss: 0.2501 - acc: 0.9283 - val_loss: 0.2158 - val_acc: 0.9377\n",
            "Epoch 3/4\n",
            "60000/60000 [==============================] - 29s 482us/step - loss: 0.1986 - acc: 0.9436 - val_loss: 0.1800 - val_acc: 0.9492\n",
            "Epoch 4/4\n",
            "60000/60000 [==============================] - 29s 487us/step - loss: 0.1642 - acc: 0.9538 - val_loss: 0.1520 - val_acc: 0.9549\n",
            "Baseline Error: 4.51%\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/4\n",
            "60000/60000 [==============================] - 12s 207us/step - loss: 1.2018 - acc: 0.7278 - val_loss: 0.6155 - val_acc: 0.8580\n",
            "Epoch 2/4\n",
            "60000/60000 [==============================] - 4s 72us/step - loss: 0.5195 - acc: 0.8684 - val_loss: 0.4282 - val_acc: 0.8887\n",
            "Epoch 3/4\n",
            "60000/60000 [==============================] - 4s 71us/step - loss: 0.4112 - acc: 0.8885 - val_loss: 0.3673 - val_acc: 0.9005\n",
            "Epoch 4/4\n",
            "60000/60000 [==============================] - 4s 71us/step - loss: 0.3651 - acc: 0.8987 - val_loss: 0.3342 - val_acc: 0.9075\n",
            "Baseline Error: 9.25%\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/4\n",
            "60000/60000 [==============================] - 11s 181us/step - loss: 1.5699 - acc: 0.6484 - val_loss: 0.9508 - val_acc: 0.8095\n",
            "Epoch 2/4\n",
            "60000/60000 [==============================] - 3s 48us/step - loss: 0.7515 - acc: 0.8337 - val_loss: 0.5880 - val_acc: 0.8598\n",
            "Epoch 3/4\n",
            "60000/60000 [==============================] - 3s 47us/step - loss: 0.5430 - acc: 0.8654 - val_loss: 0.4703 - val_acc: 0.8803\n",
            "Epoch 4/4\n",
            "60000/60000 [==============================] - 3s 47us/step - loss: 0.4605 - acc: 0.8799 - val_loss: 0.4142 - val_acc: 0.8894\n",
            "Baseline Error: 11.06%\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/4\n",
            "60000/60000 [==============================] - 10s 173us/step - loss: 1.8062 - acc: 0.5692 - val_loss: 1.2749 - val_acc: 0.7718\n",
            "Epoch 2/4\n",
            "60000/60000 [==============================] - 2s 39us/step - loss: 0.9885 - acc: 0.8012 - val_loss: 0.7568 - val_acc: 0.8355\n",
            "Epoch 3/4\n",
            "60000/60000 [==============================] - 2s 39us/step - loss: 0.6785 - acc: 0.8436 - val_loss: 0.5797 - val_acc: 0.8627\n",
            "Epoch 4/4\n",
            "60000/60000 [==============================] - 2s 41us/step - loss: 0.5553 - acc: 0.8628 - val_loss: 0.4951 - val_acc: 0.8786\n",
            "Baseline Error: 12.14%\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/4\n",
            "60000/60000 [==============================] - 10s 174us/step - loss: 1.8976 - acc: 0.5493 - val_loss: 1.4810 - val_acc: 0.7203\n",
            "Epoch 2/4\n",
            "60000/60000 [==============================] - 2s 35us/step - loss: 1.1966 - acc: 0.7625 - val_loss: 0.9387 - val_acc: 0.8108\n",
            "Epoch 3/4\n",
            "60000/60000 [==============================] - 2s 35us/step - loss: 0.8285 - acc: 0.8200 - val_loss: 0.7006 - val_acc: 0.8455\n",
            "Epoch 4/4\n",
            "60000/60000 [==============================] - 2s 35us/step - loss: 0.6594 - acc: 0.8460 - val_loss: 0.5819 - val_acc: 0.8645\n",
            "Baseline Error: 13.55%\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/4\n",
            "60000/60000 [==============================] - 42s 700us/step - loss: 0.4361 - acc: 0.8842 - val_loss: 0.2604 - val_acc: 0.9275\n",
            "Epoch 2/4\n",
            "60000/60000 [==============================] - 34s 564us/step - loss: 0.2389 - acc: 0.9332 - val_loss: 0.2084 - val_acc: 0.9399\n",
            "Epoch 3/4\n",
            "60000/60000 [==============================] - 35s 583us/step - loss: 0.1870 - acc: 0.9465 - val_loss: 0.1680 - val_acc: 0.9501\n",
            "Epoch 4/4\n",
            "60000/60000 [==============================] - 34s 560us/step - loss: 0.1538 - acc: 0.9572 - val_loss: 0.1440 - val_acc: 0.9576\n",
            "Baseline Error: 4.24%\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/4\n",
            "60000/60000 [==============================] - 14s 233us/step - loss: 0.9956 - acc: 0.7832 - val_loss: 0.5180 - val_acc: 0.8754\n",
            "Epoch 2/4\n",
            "60000/60000 [==============================] - 6s 95us/step - loss: 0.4596 - acc: 0.8825 - val_loss: 0.3858 - val_acc: 0.8982\n",
            "Epoch 3/4\n",
            "60000/60000 [==============================] - 6s 94us/step - loss: 0.3786 - acc: 0.8978 - val_loss: 0.3391 - val_acc: 0.9090\n",
            "Epoch 4/4\n",
            "60000/60000 [==============================] - 6s 94us/step - loss: 0.3407 - acc: 0.9061 - val_loss: 0.3117 - val_acc: 0.9150\n",
            "Baseline Error: 8.50%\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/4\n",
            "60000/60000 [==============================] - 12s 206us/step - loss: 1.3704 - acc: 0.6865 - val_loss: 0.7692 - val_acc: 0.8376\n",
            "Epoch 2/4\n",
            "60000/60000 [==============================] - 4s 64us/step - loss: 0.6388 - acc: 0.8530 - val_loss: 0.5129 - val_acc: 0.8788\n",
            "Epoch 3/4\n",
            "60000/60000 [==============================] - 4s 67us/step - loss: 0.4867 - acc: 0.8764 - val_loss: 0.4276 - val_acc: 0.8903\n",
            "Epoch 4/4\n",
            "60000/60000 [==============================] - 4s 69us/step - loss: 0.4232 - acc: 0.8880 - val_loss: 0.3827 - val_acc: 0.8991\n",
            "Baseline Error: 10.09%\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/4\n",
            "60000/60000 [==============================] - 12s 201us/step - loss: 1.5895 - acc: 0.6203 - val_loss: 1.0070 - val_acc: 0.8115\n",
            "Epoch 2/4\n",
            "60000/60000 [==============================] - 4s 60us/step - loss: 0.8087 - acc: 0.8293 - val_loss: 0.6373 - val_acc: 0.8570\n",
            "Epoch 3/4\n",
            "60000/60000 [==============================] - 4s 61us/step - loss: 0.5886 - acc: 0.8606 - val_loss: 0.5098 - val_acc: 0.8757\n",
            "Epoch 4/4\n",
            "60000/60000 [==============================] - 4s 69us/step - loss: 0.4965 - acc: 0.8755 - val_loss: 0.4444 - val_acc: 0.8890\n",
            "Baseline Error: 11.10%\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/4\n",
            "60000/60000 [==============================] - 12s 194us/step - loss: 1.6596 - acc: 0.6183 - val_loss: 1.1494 - val_acc: 0.7901\n",
            "Epoch 2/4\n",
            "60000/60000 [==============================] - 3s 54us/step - loss: 0.9375 - acc: 0.8097 - val_loss: 0.7378 - val_acc: 0.8410\n",
            "Epoch 3/4\n",
            "60000/60000 [==============================] - 3s 53us/step - loss: 0.6768 - acc: 0.8472 - val_loss: 0.5804 - val_acc: 0.8625\n",
            "Epoch 4/4\n",
            "60000/60000 [==============================] - 3s 52us/step - loss: 0.5619 - acc: 0.8647 - val_loss: 0.5007 - val_acc: 0.8739\n",
            "Baseline Error: 12.61%\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/4\n",
            "60000/60000 [==============================] - 49s 824us/step - loss: 0.4041 - acc: 0.8911 - val_loss: 0.2546 - val_acc: 0.9261\n",
            "Epoch 2/4\n",
            "60000/60000 [==============================] - 39s 651us/step - loss: 0.2243 - acc: 0.9367 - val_loss: 0.1867 - val_acc: 0.9473\n",
            "Epoch 3/4\n",
            "60000/60000 [==============================] - 40s 662us/step - loss: 0.1740 - acc: 0.9509 - val_loss: 0.1512 - val_acc: 0.9564\n",
            "Epoch 4/4\n",
            "60000/60000 [==============================] - 38s 632us/step - loss: 0.1427 - acc: 0.9603 - val_loss: 0.1352 - val_acc: 0.9618\n",
            "Baseline Error: 3.82%\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/4\n",
            "60000/60000 [==============================] - 15s 256us/step - loss: 0.9168 - acc: 0.7867 - val_loss: 0.4854 - val_acc: 0.8772\n",
            "Epoch 2/4\n",
            "60000/60000 [==============================] - 7s 117us/step - loss: 0.4394 - acc: 0.8861 - val_loss: 0.3687 - val_acc: 0.9014\n",
            "Epoch 3/4\n",
            "60000/60000 [==============================] - 7s 117us/step - loss: 0.3650 - acc: 0.8999 - val_loss: 0.3246 - val_acc: 0.9124\n",
            "Epoch 4/4\n",
            "60000/60000 [==============================] - 7s 119us/step - loss: 0.3286 - acc: 0.9090 - val_loss: 0.2989 - val_acc: 0.9185\n",
            "Baseline Error: 8.15%\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/4\n",
            "60000/60000 [==============================] - 14s 227us/step - loss: 1.1911 - acc: 0.7372 - val_loss: 0.6688 - val_acc: 0.8570\n",
            "Epoch 2/4\n",
            "60000/60000 [==============================] - 5s 83us/step - loss: 0.5728 - acc: 0.8644 - val_loss: 0.4712 - val_acc: 0.8878\n",
            "Epoch 3/4\n",
            "60000/60000 [==============================] - 5s 82us/step - loss: 0.4528 - acc: 0.8833 - val_loss: 0.4012 - val_acc: 0.8991\n",
            "Epoch 4/4\n",
            "60000/60000 [==============================] - 5s 83us/step - loss: 0.3997 - acc: 0.8947 - val_loss: 0.3619 - val_acc: 0.9056\n",
            "Baseline Error: 9.44%\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/4\n",
            "60000/60000 [==============================] - 13s 215us/step - loss: 1.3830 - acc: 0.6863 - val_loss: 0.8429 - val_acc: 0.8345\n",
            "Epoch 2/4\n",
            "60000/60000 [==============================] - 4s 71us/step - loss: 0.7013 - acc: 0.8483 - val_loss: 0.5659 - val_acc: 0.8729\n",
            "Epoch 3/4\n",
            "60000/60000 [==============================] - 4s 72us/step - loss: 0.5331 - acc: 0.8718 - val_loss: 0.4647 - val_acc: 0.8901\n",
            "Epoch 4/4\n",
            "60000/60000 [==============================] - 4s 71us/step - loss: 0.4590 - acc: 0.8834 - val_loss: 0.4128 - val_acc: 0.8984\n",
            "Baseline Error: 10.16%\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/4\n",
            "60000/60000 [==============================] - 13s 209us/step - loss: 1.5265 - acc: 0.6353 - val_loss: 0.9959 - val_acc: 0.8093\n",
            "Epoch 2/4\n",
            "60000/60000 [==============================] - 4s 67us/step - loss: 0.8244 - acc: 0.8248 - val_loss: 0.6609 - val_acc: 0.8580\n",
            "Epoch 3/4\n",
            "60000/60000 [==============================] - 4s 70us/step - loss: 0.6152 - acc: 0.8560 - val_loss: 0.5338 - val_acc: 0.8775\n",
            "Epoch 4/4\n",
            "60000/60000 [==============================] - 4s 73us/step - loss: 0.5214 - acc: 0.8708 - val_loss: 0.4673 - val_acc: 0.8858\n",
            "Baseline Error: 11.42%\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/4\n",
            "60000/60000 [==============================] - 51s 849us/step - loss: 0.3826 - acc: 0.8969 - val_loss: 0.2421 - val_acc: 0.9329\n",
            "Epoch 2/4\n",
            "60000/60000 [==============================] - 42s 695us/step - loss: 0.2148 - acc: 0.9396 - val_loss: 0.1840 - val_acc: 0.9463\n",
            "Epoch 3/4\n",
            "60000/60000 [==============================] - 41s 683us/step - loss: 0.1660 - acc: 0.9534 - val_loss: 0.1475 - val_acc: 0.9575\n",
            "Epoch 4/4\n",
            "60000/60000 [==============================] - 41s 688us/step - loss: 0.1353 - acc: 0.9629 - val_loss: 0.1271 - val_acc: 0.9624\n",
            "Baseline Error: 3.76%\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/4\n",
            "60000/60000 [==============================] - 16s 275us/step - loss: 0.8185 - acc: 0.8140 - val_loss: 0.4506 - val_acc: 0.8859\n",
            "Epoch 2/4\n",
            "60000/60000 [==============================] - 8s 130us/step - loss: 0.4145 - acc: 0.8920 - val_loss: 0.3525 - val_acc: 0.9057\n",
            "Epoch 3/4\n",
            "60000/60000 [==============================] - 8s 134us/step - loss: 0.3493 - acc: 0.9050 - val_loss: 0.3129 - val_acc: 0.9161\n",
            "Epoch 4/4\n",
            "60000/60000 [==============================] - 8s 133us/step - loss: 0.3159 - acc: 0.9130 - val_loss: 0.2889 - val_acc: 0.9202\n",
            "Baseline Error: 7.98%\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/4\n",
            "60000/60000 [==============================] - 15s 243us/step - loss: 1.1257 - acc: 0.7412 - val_loss: 0.6232 - val_acc: 0.8633\n",
            "Epoch 2/4\n",
            "60000/60000 [==============================] - 6s 97us/step - loss: 0.5397 - acc: 0.8712 - val_loss: 0.4467 - val_acc: 0.8886\n",
            "Epoch 3/4\n",
            "60000/60000 [==============================] - 6s 97us/step - loss: 0.4311 - acc: 0.8893 - val_loss: 0.3831 - val_acc: 0.9012\n",
            "Epoch 4/4\n",
            "60000/60000 [==============================] - 6s 98us/step - loss: 0.3813 - acc: 0.8992 - val_loss: 0.3472 - val_acc: 0.9082\n",
            "Baseline Error: 9.18%\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/4\n",
            "60000/60000 [==============================] - 14s 228us/step - loss: 1.3008 - acc: 0.7005 - val_loss: 0.7672 - val_acc: 0.8409\n",
            "Epoch 2/4\n",
            "60000/60000 [==============================] - 5s 85us/step - loss: 0.6495 - acc: 0.8529 - val_loss: 0.5308 - val_acc: 0.8718\n",
            "Epoch 3/4\n",
            "60000/60000 [==============================] - 5s 86us/step - loss: 0.5039 - acc: 0.8752 - val_loss: 0.4438 - val_acc: 0.8877\n",
            "Epoch 4/4\n",
            "60000/60000 [==============================] - 5s 85us/step - loss: 0.4385 - acc: 0.8867 - val_loss: 0.3969 - val_acc: 0.8968\n",
            "Baseline Error: 10.32%\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/4\n",
            "60000/60000 [==============================] - 13s 223us/step - loss: 1.4149 - acc: 0.6758 - val_loss: 0.8792 - val_acc: 0.8368\n",
            "Epoch 2/4\n",
            "60000/60000 [==============================] - 5s 79us/step - loss: 0.7384 - acc: 0.8443 - val_loss: 0.5992 - val_acc: 0.8644\n",
            "Epoch 3/4\n",
            "60000/60000 [==============================] - 5s 81us/step - loss: 0.5654 - acc: 0.8663 - val_loss: 0.4933 - val_acc: 0.8809\n",
            "Epoch 4/4\n",
            "60000/60000 [==============================] - 5s 86us/step - loss: 0.4871 - acc: 0.8781 - val_loss: 0.4376 - val_acc: 0.8893\n",
            "Baseline Error: 11.07%\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/4\n",
            "60000/60000 [==============================] - 29s 489us/step - loss: 0.3736 - acc: 0.8925 - val_loss: 0.2648 - val_acc: 0.9240\n",
            "Epoch 2/4\n",
            "60000/60000 [==============================] - 21s 347us/step - loss: 0.2644 - acc: 0.9236 - val_loss: 0.2666 - val_acc: 0.9235\n",
            "Epoch 3/4\n",
            "60000/60000 [==============================] - 21s 346us/step - loss: 0.2421 - acc: 0.9314 - val_loss: 0.2299 - val_acc: 0.9340\n",
            "Epoch 4/4\n",
            "60000/60000 [==============================] - 20s 342us/step - loss: 0.2279 - acc: 0.9347 - val_loss: 0.2341 - val_acc: 0.9321\n",
            "Baseline Error: 6.79%\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/4\n",
            "60000/60000 [==============================] - 12s 196us/step - loss: 0.5524 - acc: 0.8484 - val_loss: 0.3139 - val_acc: 0.9099\n",
            "Epoch 2/4\n",
            "60000/60000 [==============================] - 3s 50us/step - loss: 0.2944 - acc: 0.9163 - val_loss: 0.2770 - val_acc: 0.9198\n",
            "Epoch 3/4\n",
            "60000/60000 [==============================] - 3s 49us/step - loss: 0.2629 - acc: 0.9251 - val_loss: 0.2533 - val_acc: 0.9289\n",
            "Epoch 4/4\n",
            "60000/60000 [==============================] - 3s 50us/step - loss: 0.2442 - acc: 0.9308 - val_loss: 0.2399 - val_acc: 0.9320\n",
            "Baseline Error: 6.80%\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/4\n",
            "60000/60000 [==============================] - 11s 177us/step - loss: 0.6573 - acc: 0.8281 - val_loss: 0.3469 - val_acc: 0.9002\n",
            "Epoch 2/4\n",
            "60000/60000 [==============================] - 2s 30us/step - loss: 0.3137 - acc: 0.9113 - val_loss: 0.2901 - val_acc: 0.9167\n",
            "Epoch 3/4\n",
            "60000/60000 [==============================] - 2s 30us/step - loss: 0.2853 - acc: 0.9191 - val_loss: 0.2700 - val_acc: 0.9238\n",
            "Epoch 4/4\n",
            "60000/60000 [==============================] - 2s 30us/step - loss: 0.2701 - acc: 0.9237 - val_loss: 0.2632 - val_acc: 0.9273\n",
            "Baseline Error: 7.27%\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/4\n",
            "60000/60000 [==============================] - 10s 171us/step - loss: 0.8036 - acc: 0.7882 - val_loss: 0.3553 - val_acc: 0.9003\n",
            "Epoch 2/4\n",
            "60000/60000 [==============================] - 1s 23us/step - loss: 0.3337 - acc: 0.9057 - val_loss: 0.3035 - val_acc: 0.9122\n",
            "Epoch 3/4\n",
            "60000/60000 [==============================] - 1s 22us/step - loss: 0.2981 - acc: 0.9157 - val_loss: 0.2802 - val_acc: 0.9225\n",
            "Epoch 4/4\n",
            "60000/60000 [==============================] - 1s 22us/step - loss: 0.2799 - acc: 0.9205 - val_loss: 0.2710 - val_acc: 0.9225\n",
            "Baseline Error: 7.75%\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/4\n",
            "60000/60000 [==============================] - 10s 169us/step - loss: 0.8963 - acc: 0.7717 - val_loss: 0.3851 - val_acc: 0.9000\n",
            "Epoch 2/4\n",
            "60000/60000 [==============================] - 1s 19us/step - loss: 0.3433 - acc: 0.9050 - val_loss: 0.3023 - val_acc: 0.9151\n",
            "Epoch 3/4\n",
            "60000/60000 [==============================] - 1s 19us/step - loss: 0.2977 - acc: 0.9155 - val_loss: 0.2828 - val_acc: 0.9210\n",
            "Epoch 4/4\n",
            "60000/60000 [==============================] - 1s 19us/step - loss: 0.2792 - acc: 0.9209 - val_loss: 0.2726 - val_acc: 0.9240\n",
            "Baseline Error: 7.60%\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/4\n",
            "60000/60000 [==============================] - 49s 811us/step - loss: 0.2012 - acc: 0.9400 - val_loss: 0.1274 - val_acc: 0.9637\n",
            "Epoch 2/4\n",
            "60000/60000 [==============================] - 40s 663us/step - loss: 0.0987 - acc: 0.9700 - val_loss: 0.1090 - val_acc: 0.9656\n",
            "Epoch 3/4\n",
            "60000/60000 [==============================] - 40s 663us/step - loss: 0.0759 - acc: 0.9771 - val_loss: 0.1171 - val_acc: 0.9667\n",
            "Epoch 4/4\n",
            "60000/60000 [==============================] - 41s 688us/step - loss: 0.0625 - acc: 0.9809 - val_loss: 0.1045 - val_acc: 0.9741\n",
            "Baseline Error: 2.59%\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/4\n",
            "60000/60000 [==============================] - 15s 248us/step - loss: 0.2596 - acc: 0.9270 - val_loss: 0.1361 - val_acc: 0.9578\n",
            "Epoch 2/4\n",
            "60000/60000 [==============================] - 6s 95us/step - loss: 0.1022 - acc: 0.9699 - val_loss: 0.0990 - val_acc: 0.9703\n",
            "Epoch 3/4\n",
            "60000/60000 [==============================] - 6s 102us/step - loss: 0.0671 - acc: 0.9794 - val_loss: 0.0826 - val_acc: 0.9743\n",
            "Epoch 4/4\n",
            "60000/60000 [==============================] - 6s 94us/step - loss: 0.0487 - acc: 0.9845 - val_loss: 0.0668 - val_acc: 0.9797\n",
            "Baseline Error: 2.03%\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/4\n",
            "60000/60000 [==============================] - 13s 216us/step - loss: 0.3070 - acc: 0.9154 - val_loss: 0.2444 - val_acc: 0.9265\n",
            "Epoch 2/4\n",
            "60000/60000 [==============================] - 4s 61us/step - loss: 0.1198 - acc: 0.9654 - val_loss: 0.2742 - val_acc: 0.9157\n",
            "Epoch 3/4\n",
            "60000/60000 [==============================] - 4s 60us/step - loss: 0.0799 - acc: 0.9766 - val_loss: 0.0848 - val_acc: 0.9729\n",
            "Epoch 4/4\n",
            "60000/60000 [==============================] - 4s 61us/step - loss: 0.0573 - acc: 0.9830 - val_loss: 0.1068 - val_acc: 0.9641\n",
            "Baseline Error: 3.59%\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/4\n",
            "60000/60000 [==============================] - 12s 205us/step - loss: 0.3420 - acc: 0.9063 - val_loss: 0.1802 - val_acc: 0.9461\n",
            "Epoch 2/4\n",
            "60000/60000 [==============================] - 3s 46us/step - loss: 0.1381 - acc: 0.9593 - val_loss: 0.1167 - val_acc: 0.9653\n",
            "Epoch 3/4\n",
            "60000/60000 [==============================] - 3s 46us/step - loss: 0.0923 - acc: 0.9724 - val_loss: 0.0964 - val_acc: 0.9702\n",
            "Epoch 4/4\n",
            "60000/60000 [==============================] - 3s 47us/step - loss: 0.0681 - acc: 0.9802 - val_loss: 0.0802 - val_acc: 0.9753\n",
            "Baseline Error: 2.47%\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/4\n",
            "60000/60000 [==============================] - 12s 198us/step - loss: 0.3808 - acc: 0.8987 - val_loss: 0.1921 - val_acc: 0.9449\n",
            "Epoch 2/4\n",
            "60000/60000 [==============================] - 2s 39us/step - loss: 0.1543 - acc: 0.9568 - val_loss: 0.1214 - val_acc: 0.9645\n",
            "Epoch 3/4\n",
            "60000/60000 [==============================] - 2s 39us/step - loss: 0.1046 - acc: 0.9698 - val_loss: 0.0978 - val_acc: 0.9704\n",
            "Epoch 4/4\n",
            "60000/60000 [==============================] - 2s 39us/step - loss: 0.0774 - acc: 0.9776 - val_loss: 0.0841 - val_acc: 0.9736\n",
            "Baseline Error: 2.64%\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/4\n",
            "60000/60000 [==============================] - 60s 1ms/step - loss: 0.1972 - acc: 0.9413 - val_loss: 0.1006 - val_acc: 0.9671\n",
            "Epoch 2/4\n",
            "60000/60000 [==============================] - 51s 847us/step - loss: 0.1019 - acc: 0.9706 - val_loss: 0.1091 - val_acc: 0.9695\n",
            "Epoch 3/4\n",
            "60000/60000 [==============================] - 50s 841us/step - loss: 0.0806 - acc: 0.9777 - val_loss: 0.0998 - val_acc: 0.9740\n",
            "Epoch 4/4\n",
            "60000/60000 [==============================] - 52s 859us/step - loss: 0.0679 - acc: 0.9813 - val_loss: 0.1343 - val_acc: 0.9703\n",
            "Baseline Error: 2.97%\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/4\n",
            "60000/60000 [==============================] - 18s 295us/step - loss: 0.2146 - acc: 0.9368 - val_loss: 0.1127 - val_acc: 0.9651\n",
            "Epoch 2/4\n",
            "60000/60000 [==============================] - 8s 127us/step - loss: 0.0816 - acc: 0.9751 - val_loss: 0.0768 - val_acc: 0.9767\n",
            "Epoch 3/4\n",
            "60000/60000 [==============================] - 8s 129us/step - loss: 0.0512 - acc: 0.9840 - val_loss: 0.0762 - val_acc: 0.9761\n",
            "Epoch 4/4\n",
            "60000/60000 [==============================] - 8s 129us/step - loss: 0.0369 - acc: 0.9883 - val_loss: 0.0723 - val_acc: 0.9770\n",
            "Baseline Error: 2.30%\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/4\n",
            "60000/60000 [==============================] - 15s 246us/step - loss: 0.2558 - acc: 0.9274 - val_loss: 0.1567 - val_acc: 0.9514\n",
            "Epoch 2/4\n",
            "60000/60000 [==============================] - 5s 84us/step - loss: 0.0937 - acc: 0.9717 - val_loss: 0.1704 - val_acc: 0.9461\n",
            "Epoch 3/4\n",
            "60000/60000 [==============================] - 5s 85us/step - loss: 0.0599 - acc: 0.9816 - val_loss: 0.0703 - val_acc: 0.9775\n",
            "Epoch 4/4\n",
            "60000/60000 [==============================] - 5s 85us/step - loss: 0.0416 - acc: 0.9873 - val_loss: 0.0927 - val_acc: 0.9708\n",
            "Baseline Error: 2.92%\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/4\n",
            "60000/60000 [==============================] - 14s 229us/step - loss: 0.2871 - acc: 0.9192 - val_loss: 0.1440 - val_acc: 0.9568\n",
            "Epoch 2/4\n",
            "60000/60000 [==============================] - 4s 67us/step - loss: 0.1059 - acc: 0.9692 - val_loss: 0.0948 - val_acc: 0.9721\n",
            "Epoch 3/4\n",
            "60000/60000 [==============================] - 4s 68us/step - loss: 0.0679 - acc: 0.9798 - val_loss: 0.0812 - val_acc: 0.9739\n",
            "Epoch 4/4\n",
            "60000/60000 [==============================] - 4s 71us/step - loss: 0.0466 - acc: 0.9860 - val_loss: 0.0781 - val_acc: 0.9741\n",
            "Baseline Error: 2.59%\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/4\n",
            "60000/60000 [==============================] - 14s 226us/step - loss: 0.3149 - acc: 0.9122 - val_loss: 0.1422 - val_acc: 0.9579\n",
            "Epoch 2/4\n",
            "60000/60000 [==============================] - 4s 62us/step - loss: 0.1161 - acc: 0.9662 - val_loss: 0.0923 - val_acc: 0.9707\n",
            "Epoch 3/4\n",
            "60000/60000 [==============================] - 4s 61us/step - loss: 0.0745 - acc: 0.9784 - val_loss: 0.0749 - val_acc: 0.9760\n",
            "Epoch 4/4\n",
            "60000/60000 [==============================] - 4s 62us/step - loss: 0.0528 - acc: 0.9845 - val_loss: 0.0697 - val_acc: 0.9786\n",
            "Baseline Error: 2.14%\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/4\n",
            "60000/60000 [==============================] - 75s 1ms/step - loss: 0.1978 - acc: 0.9417 - val_loss: 0.1218 - val_acc: 0.9649\n",
            "Epoch 2/4\n",
            "60000/60000 [==============================] - 66s 1ms/step - loss: 0.1048 - acc: 0.9702 - val_loss: 0.1175 - val_acc: 0.9686\n",
            "Epoch 3/4\n",
            "60000/60000 [==============================] - 68s 1ms/step - loss: 0.0829 - acc: 0.9772 - val_loss: 0.1125 - val_acc: 0.9722\n",
            "Epoch 4/4\n",
            "60000/60000 [==============================] - 68s 1ms/step - loss: 0.0721 - acc: 0.9813 - val_loss: 0.1184 - val_acc: 0.9752\n",
            "Baseline Error: 2.48%\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/4\n",
            "60000/60000 [==============================] - 21s 344us/step - loss: 0.1988 - acc: 0.9419 - val_loss: 0.1033 - val_acc: 0.9688\n",
            "Epoch 2/4\n",
            "60000/60000 [==============================] - 11s 175us/step - loss: 0.0755 - acc: 0.9764 - val_loss: 0.0765 - val_acc: 0.9745\n",
            "Epoch 3/4\n",
            "60000/60000 [==============================] - 11s 175us/step - loss: 0.0473 - acc: 0.9848 - val_loss: 0.0704 - val_acc: 0.9789\n",
            "Epoch 4/4\n",
            "60000/60000 [==============================] - 10s 172us/step - loss: 0.0343 - acc: 0.9893 - val_loss: 0.0747 - val_acc: 0.9785\n",
            "Baseline Error: 2.15%\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/4\n",
            "60000/60000 [==============================] - 17s 279us/step - loss: 0.2283 - acc: 0.9344 - val_loss: 0.1351 - val_acc: 0.9570\n",
            "Epoch 2/4\n",
            "60000/60000 [==============================] - 7s 114us/step - loss: 0.0819 - acc: 0.9757 - val_loss: 0.0915 - val_acc: 0.9719\n",
            "Epoch 3/4\n",
            "60000/60000 [==============================] - 7s 114us/step - loss: 0.0501 - acc: 0.9851 - val_loss: 0.0795 - val_acc: 0.9751\n",
            "Epoch 4/4\n",
            "60000/60000 [==============================] - 7s 114us/step - loss: 0.0346 - acc: 0.9890 - val_loss: 0.0682 - val_acc: 0.9782\n",
            "Baseline Error: 2.18%\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/4\n",
            "60000/60000 [==============================] - 15s 258us/step - loss: 0.2626 - acc: 0.9245 - val_loss: 0.1599 - val_acc: 0.9513\n",
            "Epoch 2/4\n",
            "60000/60000 [==============================] - 5s 90us/step - loss: 0.0920 - acc: 0.9731 - val_loss: 0.1157 - val_acc: 0.9633\n",
            "Epoch 3/4\n",
            "60000/60000 [==============================] - 5s 91us/step - loss: 0.0571 - acc: 0.9837 - val_loss: 0.0753 - val_acc: 0.9762\n",
            "Epoch 4/4\n",
            "60000/60000 [==============================] - 5s 90us/step - loss: 0.0382 - acc: 0.9884 - val_loss: 0.0659 - val_acc: 0.9784\n",
            "Baseline Error: 2.16%\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/4\n",
            "60000/60000 [==============================] - 15s 248us/step - loss: 0.2908 - acc: 0.9173 - val_loss: 0.1326 - val_acc: 0.9619\n",
            "Epoch 2/4\n",
            "60000/60000 [==============================] - 5s 79us/step - loss: 0.1055 - acc: 0.9691 - val_loss: 0.0955 - val_acc: 0.9719\n",
            "Epoch 3/4\n",
            "60000/60000 [==============================] - 5s 80us/step - loss: 0.0663 - acc: 0.9803 - val_loss: 0.0745 - val_acc: 0.9775\n",
            "Epoch 4/4\n",
            "60000/60000 [==============================] - 5s 78us/step - loss: 0.0449 - acc: 0.9871 - val_loss: 0.0682 - val_acc: 0.9791\n",
            "Baseline Error: 2.09%\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/4\n",
            "60000/60000 [==============================] - 93s 2ms/step - loss: 0.2078 - acc: 0.9396 - val_loss: 0.1312 - val_acc: 0.9614\n",
            "Epoch 2/4\n",
            "60000/60000 [==============================] - 80s 1ms/step - loss: 0.1117 - acc: 0.9693 - val_loss: 0.1066 - val_acc: 0.9744\n",
            "Epoch 3/4\n",
            "60000/60000 [==============================] - 83s 1ms/step - loss: 0.0918 - acc: 0.9763 - val_loss: 0.1412 - val_acc: 0.9683\n",
            "Epoch 4/4\n",
            "60000/60000 [==============================] - 81s 1ms/step - loss: 0.0772 - acc: 0.9811 - val_loss: 0.1036 - val_acc: 0.9786\n",
            "Baseline Error: 2.14%\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/4\n",
            "60000/60000 [==============================] - 23s 377us/step - loss: 0.1911 - acc: 0.9436 - val_loss: 0.1009 - val_acc: 0.9700\n",
            "Epoch 2/4\n",
            "60000/60000 [==============================] - 13s 218us/step - loss: 0.0714 - acc: 0.9781 - val_loss: 0.0743 - val_acc: 0.9764\n",
            "Epoch 3/4\n",
            "60000/60000 [==============================] - 13s 209us/step - loss: 0.0460 - acc: 0.9856 - val_loss: 0.0714 - val_acc: 0.9790\n",
            "Epoch 4/4\n",
            "60000/60000 [==============================] - 13s 211us/step - loss: 0.0320 - acc: 0.9897 - val_loss: 0.0769 - val_acc: 0.9783\n",
            "Baseline Error: 2.17%\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/4\n",
            "60000/60000 [==============================] - 19s 311us/step - loss: 0.2164 - acc: 0.9369 - val_loss: 0.3326 - val_acc: 0.9018\n",
            "Epoch 2/4\n",
            "60000/60000 [==============================] - 9s 152us/step - loss: 0.0780 - acc: 0.9765 - val_loss: 0.0884 - val_acc: 0.9720\n",
            "Epoch 3/4\n",
            "60000/60000 [==============================] - 9s 154us/step - loss: 0.0480 - acc: 0.9850 - val_loss: 0.0822 - val_acc: 0.9738\n",
            "Epoch 4/4\n",
            "60000/60000 [==============================] - 9s 154us/step - loss: 0.0325 - acc: 0.9900 - val_loss: 0.0693 - val_acc: 0.9796\n",
            "Baseline Error: 2.04%\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/4\n",
            "60000/60000 [==============================] - 18s 299us/step - loss: 0.2427 - acc: 0.9291 - val_loss: 0.2001 - val_acc: 0.9368\n",
            "Epoch 2/4\n",
            "60000/60000 [==============================] - 7s 124us/step - loss: 0.0851 - acc: 0.9748 - val_loss: 0.0984 - val_acc: 0.9698\n",
            "Epoch 3/4\n",
            "60000/60000 [==============================] - 7s 124us/step - loss: 0.0524 - acc: 0.9845 - val_loss: 0.0868 - val_acc: 0.9733\n",
            "Epoch 4/4\n",
            "60000/60000 [==============================] - 7s 123us/step - loss: 0.0344 - acc: 0.9897 - val_loss: 0.0639 - val_acc: 0.9802\n",
            "Baseline Error: 1.98%\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/4\n",
            "60000/60000 [==============================] - 17s 285us/step - loss: 0.2732 - acc: 0.9209 - val_loss: 0.1196 - val_acc: 0.9642\n",
            "Epoch 2/4\n",
            "60000/60000 [==============================] - 6s 106us/step - loss: 0.0936 - acc: 0.9725 - val_loss: 0.0807 - val_acc: 0.9750\n",
            "Epoch 3/4\n",
            "60000/60000 [==============================] - 6s 104us/step - loss: 0.0592 - acc: 0.9821 - val_loss: 0.0699 - val_acc: 0.9784\n",
            "Epoch 4/4\n",
            "60000/60000 [==============================] - 6s 105us/step - loss: 0.0387 - acc: 0.9888 - val_loss: 0.0787 - val_acc: 0.9738\n",
            "Baseline Error: 2.62%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "igk-i7_16yiT",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "####Зависимость точности от двух параметров"
      ]
    },
    {
      "metadata": {
        "id": "zmR3kAM26q0Z",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1247
        },
        "outputId": "380bc9a5-9f5a-4f6f-b244-c0d65e195cdc"
      },
      "cell_type": "code",
      "source": [
        "from mpl_toolkits.mplot3d import Axes3D\n",
        "\n",
        "for optimizer in optimizers:\n",
        "    acc = np.array(accuracy[optimizer]).reshape((5, 5))\n",
        "    fig = plt.figure()\n",
        "    ax = Axes3D(fig)\n",
        "    X = param\n",
        "    Y = batch_size_list\n",
        "    X, Y = np.meshgrid(X, Y)\n",
        "    Z = acc\n",
        "    \n",
        "    ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap='CMRmap')"
      ],
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAk4AAAGaCAYAAAD5HsxRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nnDQ4iYiIiMTpv2Uyj5RwLW5YAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7ff48a2b68d0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAk4AAAGaCAYAAAD5HsxRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nREREIqTBSURERCRC/wd8g8obhUPChwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7ff47b7662b0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAk4AAAGaCAYAAAD5HsxRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAAIABJREFUeJzsvXuwJGd55vl8X17qem7dp9UcCdC1\n1QihwUJCXIymhZDBYCy0lieYnUARgO1BRICwkGx5A4eX1ThGExC2NlgCq2MRmDXjCf/hDcdAiBnP\nmBXIAhvUko0RUuuCWmqhS3ef7j6nTl2zMr/9IysrM6syqzIrL1/WqfcXoVCfW32Zdcun3vf5npcJ\nIQQIgiAIgiCIqXDZB0AQBEEQBDEvkHAiCIIgCIKICAkngiAIgiCIiJBwIgiCIAiCiAgJJ4IgCIIg\niIiQcCIIgiAIgoiIOumHJ0828joOgiAIgiCIQrBv31Loz6jiRBAEQRAEERESTgRBEARBEBEh4UQQ\nBEEQBBEREk4EQRAEQRARIeFEEARBEAQRERJOBEEQBEEQESHhRBAEQRAEERESTgRBEARBEBEh4UQQ\nBEEQBBEREk4EQRAEQRARIeFEEARBEAQRERJOBEEQBEEQESHhRBAEQRAEERESTgRBEARBEBEh4UQQ\nBEEQBBEREk4EQRAEQRARIeFEEARBEAQRERJOBEEQBEEQESHhRBAEQRAEERESTgRBEARBEBEh4UQQ\nBEEQBBEREk4EQRAEQRARIeFEEARBEAQRERJOBEEQBEEQEVFlHwBBEOkihAUhLAAcjDEwxmQfEkEQ\nxK6BhBNB7BIYs/8TAjCMPhhjEAIAHPHk/keCiiAIYjZIOBHEnOMIJj5ovJsmRkSRgLAVFIQQoYKK\nMU5iiiAIYgoknAhiThkVTNH+ZrKgGvwWCSqCIIgQSDgRxJwxTTApCocQCkzTjHGb44LKEVXBgor8\nUwRBLCYknAhiTlAUBiHERMFUqZTAOYcQAorC0e+b6Pf7MAz7//2+CcuyIq03LopcQeW2+wCnKkX+\nKYIgFgESTgRRcBizRdPKShVbW82xnzuCSVE42u0eOp0uDMMAY4CqqlBVBaqqolSqQFVVMIZAQSVc\nJTTleIIElekTVCsrdWxvt0DtPoIgdhsknAiioHhbckGaY1Qw9XqG7+fO7jrD6APoDr/PORsKKk1T\nUamUoKoKhBCBgir68boHWamUsbW1MzgO8k8RBLF7IOFEEAUj3MNkCwxF4SiXS1BVjk6nh50dY+w2\nJmFZAr2eMSa0FIUPBVWppKFWq0BVba+ULahMGIYtpqb5p7zVKzKkEwSxmyDhRBAFYZLp2xYZQK1W\nhqoq6HR6aDbjCaZpmKYF0+yh2/V/32712e0+uzqlJvZPAeGG9HH/lG1GJ/8UQRBFgIQTQUgmyi65\nclkHYHuTms1OjkeHYbUJ6A2/xxjzCapR/xRgt+vS8U/ZHqpJ+VPBf0sQBJE+JJwIQhJRBZNTYdI0\nFd1uulWmWRFCTPRPra0tZ+afmtTu03UNliVgmoLafQRBZAIJJ4LImWmCiXOOSkX3tOTsClO1Wo66\nQjoHOgOOfwoAtrd3ht/3+6f0Mf+U453q9/swzdnbfZWKjl7PgGF0yD9FEEQmkHAiiJyYVTA5RG13\nFZHo/qmaBP8UBXoSBBEdEk4EkTHxBJORu4cpC4RwBw5PYhb/lLc6lY5/Knx+HxnSCYIYhYQTQWTE\nIgomFwFbeMSvkk3zT/nzp1QIYQ0FlaJw8DjD+0ADkQmCiAcJJ4JImSiCqVzWoWkKut3ogilqFWe3\nEiV/inMF1aqCer2aun9qWv6UE5tAgoogdjcknAgiJaYLJoZyuTQUTFtbcStMs1dxdjNe/5Sq2vdt\np9MdtvpUVUGlUh4IKz4QVOn7p/bv34uTJ8/AsuzHifxTBLE7IeFEEAlhDFhaKqPT6Q4umn6SCyZi\nFlz/lEuQf0rT1OHvJ/NPeUUtDUQmiN0KCSeCmBFvhUlVnYuhe6ElwVQ8kvinvIIqiKA2apSByBTo\nSRDzBQkngohJUEvO8R8B44Jpe7uTii9p3jxO83Ss0eb3Tc6figPN7yOI+YWEE0FExBFMzn/jP2eo\nVkvQNA3dbi81wTSP7Jbznpw/5fqnNE0BAOzduzLwTqXnnwJIUBFEkSDhRBBTmCaYOGfgnKFer6DT\n6WF7eycj4UDm8KIQ5J96zWvWsb3dTN0/BUwP9FxZWcLW1g4NRCaIHCDhRBAhRBFMdqyABiEEms12\nrPlrcfG2AyfBORtcUGUKrMUUeXH9U96dfUnm95XLOra2pg9EJkFFEMkh4UQQI0RpyVUquqcl10S1\nWo4kaojdS5hQncU/NSqopuVPeYUQBXoSRLaQcCKIAVEEU7msQ9c19Hq2YHIvlk6FhSCiEcc/xRiH\nafbHBJUTfxGlukj+KYJIBxJOxMITTzAZI4IpP/wXOGK3Ei1/Svf5pwCgUiln4p8a/BYFehLEABJO\nxMKSpmCK6j9aFBbt/sg6emFS/pSmaVhZWYKmqahWS1CUdP1TgyMIHYhstxedx5wEFbH7IeFELBzZ\nVJioVbfYyDHCO/4pQGB7e2f4/VH/VL1egaLM5p/yElSdqtVqaLU66PUM8k8RCwEJJ2JhyLIlt2gV\nluKyeA9C0PMuTf/U9PWDq1MA+aeI3QkJJ2LX4yR8F93DNA2nFUJMo3iPXbZEj56Y7J+yBVW5XIKq\nKsPft/On+sO/TcM/5f8Z+aeI+YKEE7FrYQxQFIbl5Sq2t5sBP09TMOXRqpsnQUCty3nB759y8edP\naahWy4H+Kc7jZYZF808BNBCZKCoknIhdx2hLbvS9NosKE7XqFhuZc/myWjtq/pSqqtizZ2VYkUrH\nPwXQQGSiqJBwInYN0TxMGnRdL3RLLox5E2fzdKzJkXmyXmO68HwvG0b9U6q6iq2txuDf4/6p0Z19\ncfxTwOT8Kc45VleXcOZMg/xTRG6QcCLmnkmCyfm0Wi7rKJV0GEY2gkkI10tF7J4hv/GQc9JOxUlR\ngLW1ZZw5swPTzO9YnPVNM55/anR23yz+Kef2yZBO5AkJJ2JumV5hAkolHYDt12g0mrE+6cZDwB6u\nmiVxfEOLNydukWGsj/X1NQBcQhU1/LkW7p/iQ0E1yT81LX8qqE1JgZ5E1pBwIuYOxvw75YJ+Xiq5\nFSYAaLW64784h0R9b1cUjn4/ur+ESIY8j5PA8nIJqlqGEH1YVht5G/NnOXfLstDrWRP9U+WyDlW1\n86fC/FNRh1nHCfR0qlNkSCfCIOFEzA1RK0ylkgbD6A8rTLquZX5sefiPolycdF1FuVwCIALzeQyj\nP1e+LiIcTeNYW1sC5xosqzMQARxWznrZFhbpPKei5E9Vq2WoqpM/ZUJROKrVcur+KRqITIRBwoko\nPNErTBoMw0Sj0fK9eTqiJlu9IHf7vaapqFRKsCwLzWYHnU4HQljDi42qqkN/iRAYCCm/qCLmh6Ul\nHdVqHYCAabakX8yz1uJh+VPlso5arQpFSd8/5TLZP6WqKvp9S/pjQOQHCSeisEyrMAEYmL6DBZOL\nI2qye3fPZ8fbuDjTNBXlsu3jarU6vouLEAiZb8YH2TwqSiUNtVoFqqr4LjTOhceauXwhR0jKu27l\n4ynjHNizpw5VLfuqTDKJ2i5LGyHEoEJlotFwc9q8/ild1wYVKhWWlWx+HzAuqBSFY3m5ilOnzlCg\n5wJBwokoHNMqTEBUwWSTT8Upe7zizN7yXQIAdDpdGEb0C0CYv8Q16yoDMaWCsaD06P7U+1JmdIKM\nxzmPcy2XVaysLAFgMM322AVZCBOW1cEiBY8Giba0/FNx1p9uSCf/1G6ChBNRGNIWTC7ZV5zyqrBw\nzlCvV8A5R7vdHdutlATnAtLp+Nfzp0fb1SnTtAatvtnCDncj2Qk2gdXVCkqlGoQwIETfV2USwoIQ\nHQjRGxzD4rytx/lAFNc/FSV/alK1jfxTu5fFeYURhSW+YGrHaiHlZdzOcg1F4dB1DYwxtNsd9Hrp\nCaZJhKdHK9A0+4JTqZSgaTXfxcZpl8T1lhB+VJVhbW0ZnKuwrM6gSmG/WOyqRgdCyNsxKruSm4Yx\nPc78PtcfaAspxnjs84/jnyJBVUxIOBHS4JyBMWfgZ/DvlEo6ymUN/X58wbQb4JyjUtGHIsRuQ+Qj\nmiZhmiZM0wTQG37Pe7Eplezq1NJSbZDl43xy7wdeqIhx6nUNtdrSIGagO7xw2oKpByE6GBUN+YsY\n2Xlh2firouRP6boGTdOgKBzr62sBFapk/qnpgor8U7Ig4UTkjlNhWlur4ezZncDfKZU0lMt6KoJp\n3IOQPmmvwTlDuVyCpinodHpoNjsolTTwAseTey82lUoJjUYThtEf8ZbYn9xdb0nfJ6qyCyjNmvQE\nBGPCZwB3Lo62YDIGgqkYHyDkV5zyXX/UP2VXWzU0m60Jz/HZ5/cB0wM9l5fraDRag5/bQ5FJUGUL\nCSciN6K05NIUTPMIYwyVig5NU9HtGtja6kz/o4IT7i1Rh+2+UqkCTVPHWiGOh6ropHUBL5WUgQFc\nwLLanracMQi3LNbrIc0Mp1nXl9kKdtbPwj81bV2HcrmERmMHQtgbBGggcvaQcCIyJ65g2tlpp2o0\nzqfilMzj5GTS6LqGbrc3mKeX7PZkM+0YnCpTUFSCu5XcMaObY0Geu0tUC6ysVFAuV2FZ9v1h+2ec\nNPBo4lGGhpDtcSqCcApjmn9K08L9U1Hzp7yinfxT+UDCiciMaYJJCKBc1lAqZSOY8mW2XXWMYSCY\ndPR6kwcQy9ziH5dZL2bTohL8n9zZWLtvHlEUhj17lsC5MvQyCWHCNNsAin1O8oULck9K968f//y9\nLe122/1++IcGK9Q/NdvIGRJUSSHhRKRO1AoTY3a7JmvBVFTB4e4U7E8UTC5y08llEvbJXdOcqARn\nd589NNYwDF+Qp21kLx7VqoqlpSUI0RtU0AQsqwUhjKl/G8TibWBkkNm+ZIyl5ssLz59Shs/xUf+U\n87y2P3ym758a/BYZ0kcg4USkRtyWnGUJtFqdHAzB2QuOOOLMuQ/iZVERowgxHpWwurqEbteAZVlD\nMaWqKjjPdm5f/AuJwNpaDbquD1tzThbTPCG/4iR7/dmrq1FxdrB2u/7nhqoq0HXdk47urcKm45+y\nGRdUjHFwnv0M0KJCwolITDzBZA0rTEtL1VzMpUWpOCU1vhflPIqOZVnodnu+C014Lo/wVaaSzO2L\negHVdY7V1SUAJiyr54kWmD9kVx7k7+qTJ9z6fROMGTBNHWfObA+PJ7p/avoEAC/+lPrF/rBHwolI\nhGLP1Ay9oOu6hkpFh2la0jxM9otcXhyBrtsldsuSdx8sOpNyedydfTrq9YovKiGduX0uS0slVKuV\ngWByogXSuwjJMYcvcsVJ/vr+gebp+aeIcEg4ETMRRTCVy/pEsZDHbjcHGR+MnVaRZVloNjuF9dks\nMnZ1ykK3Gza3T/XN7fNWpuIY0p3hvJwDptkcpH3Pv4D2e2Fkrb/YwinK+rP4p7xCatQ/ldb79pe+\n9Cd4/PGfgjGGz3zmDlx22eXDnz300IP4xje+Bk3TcMMN78XNN38YrVYLf/zH/zsajW0YhoGPfex3\n8La3vSOVY4kDCSciFnEEU7M5vbqSh6ARwm0l5oH9JqQDAFqtTmqf4PIUmkmZ97aiO7fPG5XgzO0b\n/9RuWRY4Z4Em3UpFwdJSHUJ0BjvlshRMMu50ucJBJvMinMKY5J9y2n2Of+p73/se7rvvPlx88SV4\n/esvwEUXHcBFF12C1dXVmdZ+7LEjePHF4zh8+Os4duw53HPP3Th8+OsAbKF3771fxP33fxMrKyu4\n887bcO211+Ghhx7E619/Pm699VM4deokbrvtVvzlX/71zOc/KySciEikLZiAfFpog5VgJ+pmz9JS\nFQDQ6XRhGIteYZpj5RTApLl9lUoJ5XIJlUoZmuaYdPvg3ATnDKZ5FlGzmOLiXDeFAPIuasoWDgC1\nCrNYP2jg96WXvhGf+MQncezYc3jqqafx3e/+HX7+82eg6yVcdNHFuOWWj+Gqq94aeY0jR36Ma6+9\nDgBwwQUXotHYRrO5g1qtjq2ts6jX61hbWwMAXHXVW/HIIz/CysoqnnnmGQDA9vY2VlZmE21JIeFE\nTGS6YHL9O3HbUXlVJbIfwKugUrErTJ1Ob8xHs5gsjnnUDufsQ1UVnD3bAABoGsfqag1C9AZVJj/2\nxc6OHxBCwLIUWJYySH/2/semfm9pqQrLAprN8XWyZpHN2e760pYH5+nFIUxDVVVcfvkVuPzyK/DB\nDyrgXIUQAidPnsCzzz6Dc889L9btbW5u4uDBNwy/Xl1dw+bmJmq1OlZX19BqtXD8+AvY2DgXjz56\nBFde+RZ85CMfxQMPfBsf/vBNaDQa+MIX/s+0TzMSJJyIQOIIptnbUXlWnNJfR1E4KpUSOOfodLoD\nbwCJpsXDgqJ0wJiFatWEopRRLpfQ6/0CgG3+9golv6hUoOsXgvOyb8RMvC3kHELIqm4WYeSKtOVz\niSOYvL584XjOOftxzjn7E9+W9zwYY/jc5z6Pe+65G/V6HRsb50II4L//9wewf/9r8Kd/+n/h6aef\nwn/6T/8B99//F4nXjgsJJ8JHrabBskRo1SQdwWQzrxUnzjkqFR2qag/gdYzFlYoj0LJ5I4vncdpd\nbbKioihNlMsnwfkeMFZHtboMIZrodp+b+reWpaDVWocQHahqf+grCdpCbguq4s3tk19xIuGS9+ih\ntLyW6+vr2NzcHH596tQprK+vD7++8sqr8JWvfBUAcN99X8bGxgb+6Z8exdve9nYAwIEDl+LUqZMw\nTROK80k/J4o7ap3IFUWx/+OcQVHGnxa6rmJ5uQZd19BqdbCz0078Jj5PZmfAvm+q1TKWliro901s\nbTXHdmMRiwFjBiqVl1CtvgzO+4BoQ1VX0O+/CMN4cerf9/s6ms1zYFkahMBg+3gXjUYTp09v4cSJ\n09jcPItm0877KpU0rKwsYf/+vdi7dxUrK0uo1SoolTRwLrPqUoSKE60/j1xzzdvx4IN/BwA4evRJ\nrK+vo1qtDX9+xx234cyZ02i323j44e/j6qvfhvPOex1+9rOfAgBeeeVlVCrV3EUTQBWnhWe0JTf6\nGkyzwiSLpAKNMYZKRYemqeh2DWxtBYcVOpWtOX0fIyJhQdfPQNfPgjH7gbasDvrGCTAeTUQbRhnt\n9h5M+9wabW5fBZqmDYzpJV9lKm7A4SzIf77LFW6ysUXzfJ7/FVe8GQcPXoZbb/04GGP47GfvwgMP\nfAu1Wh2HDr0bN954E26//VNgDLjllo9hdXUVH/rQb+Cee+7Gpz7172Gaffze7/1vUo6diQn3+smT\njTyPhciRMA+T/QmWo983UanoEAJot7uZCCZnrXa7O/2XE2AnRevY2YlnnmWMDQbwasMk6knvUcvL\nVezsdDIrnXPOUa+Xsb3dmvq79hwreX6rlZUldLtddDr5jhBZXV1Gu90Z216dBm5bzr1fLauHXvcY\nwF6DSuWcqbfR7dbR7a4gzVbqykodvV4fpmn6tpErigrLssbafWkGsNbrVQghpBjTAWD//r149dXN\n6b+YAYwx7Nu3hhMnTktZHwDW1pbRbLbHxHXWcK7PVbdgFvbtWwr9GVWcFoxppm/OOXRdg6oqaLWy\nEUwO+Xmc4lWcGMNAMOno9YyIA3jzOJ/FHfIrE8YMlMsnoap+wWpZPfT7r6LXfQbl6rkTb0MIoNtd\nQa9XRxaPYdDcPsAfcGjP7asNPhj506LTnNu3KMg2prvHQI9b3pBwWhCmCSb7jVUHwGCaZuzqzGwU\nTwiUyzpKJQ2G0Y8smIgwivXYxme8LTf8idWFaW6h234S057HdtV2D/r9akbHGd6uCgo4DJ9nFn9u\nnwxzsnft3T7gd/oxkHCSAQmnXU5UwSQE0Gp1wRiDrucz9bpIu+qcAbyGYaLRaCXIRsnuhOYrjVvm\nm3nStQVUtYlS6ZSvLedgWV1YVhPdzpMQYnJL0LI42u29MM1SwmMKJ67PKGyemaLwoXcqaG6fM2LG\nG5Ug8/ko219VBNEi6xh2e5tuGiScdilRBJM7FsRtyamqktubYX676sIrAo5g6vdNNBrtRJ+e7fOZ\n+c+JAsBYD+XyqbG2nINldSBEB73uMVim6wENetiduAHLyvqDSDpPOtO0YJo9dEcsh06rzx6EXIGm\n2ZcNw+iDD2YZqaqae4aZ/d6x2Dva8gzAJFxIOO0y4gimoLEg8xYREIWgSo13t2DYEGJikXDacmdC\nXzuW1YYQXRjGq+gbL4/81L9Drt/X0W7vhRDZb5XOumXkjN8Axuf2LS1VoaoqVla0wdw+01OZGh8O\nmy6yW3XyhVMRjmERIeG0S0gqmBzybAfJqDg5JtlZRsRMXYXM4T7mQ39Pbss5OKLJNHfQ6zw78Raj\nxg3MM87cvn7f8u2e9EcllAcVbBbQ7jMTX/CpVSeLuXhhZwoJpzlnumCyzZ9A1Dlq83VxjoIjaJwB\nvNnlUWV7382Tx0nW9STO/TOtLedgWS0I0YOweui2n8Ck9lCvV0Ons4o8X0MyK8Sj4sWtTnl/hw3b\nfe7uPn9UgiOq4nyQkS1cFn39RYaE05ySvmCy2W0VJ1W1J9cDkyttuwnOGWq1MizLyiS7p+hMv5ZM\nb8vZtyMgRNsWTcJCp3M03AzOGITYh07H3pmaN7IuoFEu3l4zuhfHjG5Xw0uo11VwzmGa/spUWFSC\nbOEgu+I1z+GX8w4JpzljmmByhAJjQLsdXTA55OlxylKkKYqCSkUfBmzWauXMRZPsipA3sLPTsf0o\nlUoJmlYLaZdknyxdLKK15QBHNLUghJ2LZJvBt0N+mwNiA8AeAM1Uj3g345rRvVEJGLb6guf2uc9f\n2dVX+cJNjjF8t3lgZ4GE0xzAORvmyGQlmOSQfmtLUTgqlRI45+h0uuj18rwfsm9zhr1p2flTbmCn\nLZIM39952yXVqt0uMU1rcDFyP92nl8tTnDfYqG05wBZNltUCYN9/hnEiwAw+vGWA/yq48tr0DjYm\nMi/gaa/tzO2z37+8ZnQOTVMGO/t01GqVYfbUykrdlz+VV65UEYQTVZzkQMKp4CgKUK+X0e32An05\n8ymYbNKs0HDOUanoUFUFnU5vbPiuU0nL8o0mj4rT6PH786eaoZ9Aw9ol42ZeFYzBF4Lo/Dvmkcb8\n/ayI1pZzsEVTE4B9P9lm8GdCfpsB7H1g7EAhwhBlkFfxwbIsdLuW73VdLpdQLuvodg1omjIQU/bz\n1ztiJqu5fbKFi+z1FxkSTgXF35Ibb59lKZjyEBlpwTlDuVyCptmCqdkMHsC720grTiHIzOtsNVdV\nFbquoVq1P91PCkIsHtHbcsO/GBFNwjImm8HZr4Dxg+kcbgLkem1kVrvs3X2dThcdz8veff4q0DT3\n+TtaXU3q/ZOZmu6sL+e+L04lWRYknApGkIfJrmTY37AFkz1gMasKk1M5KbJuYoyhUtGhaSq6XQNb\nW5MFUz7nlM+OxKx3BzpbzUfnngUFITrek9ExHTL9Xox1Uam8DFWNPjbIFk07AMzB1xY6k5LB2Q1g\n/LIUjjYN5AVBynyfCBMOYc9fRVGG7b5KpQxN80Yl+KurUQSJ7A+XZA6XBwmngjDJ9C2EgKJw1OuV\nXLw7rkE8+xdl3OqW1wDd7fYG8+QirYSsLzB5mN2BaLsksziO4CBEPhBTCsplHapahaJwWJYYbi2P\nczFKhgXT/AU07dVY5y+ENag0uSK0130+3AzOrgfjlyc71BSR69WVKdriCQdnbh8QbW6ft7IaFrMg\nO0cq74rvbgxIngUSTpKJsktO09RBSy5Ps3OeOTTTP7UyhoFgcg3Qcd40Ze94mxXbu1WCotiCWVF4\nRhlUs2F7T/xjOhgDlpfrADAQVN6LUd/nP0knJsFtywkRb7eVLZp2ALjH0TdOoG+8FPwH7DowfsX4\ntyU/uWTmZsmsOCUlytw+5/k7OrdPUeQ+5rIrXosMCSdJRBFM5bK9nd75tJ6XaMpz5pr9wp/8qdXe\nMabBMPqxBZNnJWQtBoUQw9ldSRn3btltByeTqsgIgWHFqdVyW6je3J60YhLs3XInY7Xl3OMcF02m\nuYNuWDI4+9dg/M0Tbk/WRWx+qj5pk9Xa4XP71GG7T1VVLC+rWFqqjQR5zrKZIj6y7/tFhoRTzkwT\nTN78Iaclp+saVDW/8Q1FyXLy7xhrJSpLz0vFabQVOerdmgf/WRjBuT2jMQnloZHX3RVliyq/Edcc\n7JY7O9PjKoQ5aM+5t2mbwZ/0fc890HeB8SvjL5QD8/p8SIoMc7bzfAS60DQVjUYTpmkNn8PezRSj\nc/vSjfqwP1z1+/meP2MMjO3eUUJRIeGUE7MIJpd8+8r5iwz/Yo5g6vdNNBrtVN5s8riwJL3fRrOY\n5v/T5PQ7I1pMgrvN3P7dHrrd76NU2jPTUdmiaQfeKo0QYpAM3h3/A/YOMH7VTGvtduRnSElZ2rO+\ngGVZ6PWswM0UYXP7xjdTxD8RWQGYBAmnzJkumLyBjb2xFx/gbWflRX7reduCaW2xD1lJug8ljKhZ\nTDbZm9zTIdnxhcUkKAoHY/8AIV6GndQd86gCRBPgJINvjf8BexsYvyb2OnkiW7zIQnZ21rT7PcxQ\n7mymcIJoFcU/ty+q/49adfIg4ZQRaQgmh7wrQPnOq8OwRWNZFprNTqxBn3HWyf6c4gnOWYRi9POQ\nb1ZO+/62LBPAP4DzlyFE/BsXoj9oz/kvNn3jZLAZnL0VYG+LdNt2plDsQ5p7ZAsXmR8gZhEutlc1\nKCrB7/9T1drA3+qvTHnn9skSTkX9AJonJJxSJqpgUhSOdnuyYHLIewtoXus5n7yE4JllErnkYQ6P\nJha8g4ezP+/dggXOHwHnYaNPJhMmmkyziW5QMji7ym7RRX4dMAR6o3JA1oVMvrdKdqsuvfOf5P8b\n3d0nhEC/bw6rVna1it5D8oSEU0pkIZgc8hdOdlskK7zCwTRN9HpG5i/8IpjDXR9bduGluxMLnB8B\n5yERAVOwRdPO+PeHyeB+wcP4lRD45bn5ZL2Iu/mA4rfqkhLm/3Oy05aXa9B1bXBdUcYqU3nO7Vs0\nSDglJIpgsj8p2C25nZ3ogsnHrfsVAAAgAElEQVQh/4t+NtUZrwG+3e7CMPqDsTHzcYGaRpjAHc1i\nSh4rsTvur2gIcP4oOP/FyPejXRCEMAaVptHvB5vBS6Wrsbz6a8MYkKznnc0zRTFnLxpOdhpQw9bW\nzlAceZP9azU72R8YnTtpDnYFzsoivfeEQ8JpBmyTqp1VM1kwuUNnnRyeWZj3Vp3fz5VniKeLjMTb\nsCymJOSZsSUfMag0vTjbX4eIJgAwus+Pm8HZm9A13olTp86OmHjDtpi7MQnyxsvIEy/OHE1ZyDbF\nyxZto8cQnOwfPHfSNM2x3X3pbsbZ3ZBwiomiAJWKDiGErx/t/nxUMM3f0Nm0Klx2pcW9L7yTzd21\nirvbbVamZTERkxGiD+AoFOUZcD7bxcmyehCiFfizvnEShjFSwWJvtEepDJ6LYSZe73gOb0yCPRbJ\nhGWJYYUqP+S16naLxyj+2sUTTkFMmjvpPJfH5/b1fflTWZznl770J3j88Z+CMYbPfOYOXHaZO8Lo\noYcexDe+8TVomoYbbngvbr75w/j2t/8G/+2/PTD8naNHn8D/+B8PpX5cUSHhFBF/S278U38egimv\nN4qkYma80hJ+X9h+qpmXikyaqd7T1lherqLXS5JyvjuI8xwSogngcTD2IjjvgHMdjM329mRZXQgR\nnCQeaAZnbwDYeyIdr/OpvuN5SnPOsLy8BCEESiUNtZr9qX70IuTdEZUWMj9zyPYYLXJietLH3a1O\nhYfR2rv7VPzkJ/+MP/uzP8NFF12MCy64CBdeeACve93roaqzvT4fe+wIXnzxOA4f/jqOHXsO99xz\nNw4f/joAuw15771fxP33fxMrKyu4887bcO211+GDH7wJH/zgTcO//+53/2eyOyAhJJymEORh8l6E\nR6sqWVWYoowmSZNZXpiMMVQqOjRNRbdrRKy0CADzn0TrZDEBSJxyPokimNyjEEV8C3ESDI+DsRNg\n3Bz+PmPZiCYhAszg7FKA/UqiNGTLskMQDcNAu+22Sbyek3K56tsR5XinDMNMGL8h8wK+uLPaiuDv\nSvs9JsyMvr6+H+973/vx3HM/x3e/+z/xzDP34eTJEzj//Atw8cUH8KEP3Yw3vWl8fmMYR478GNde\nex0A4IILLkSjsY1mcwe1Wh1bW2dRr9extrYGALjqqrfikUd+hA984NeHf//nf/5V/NEf/YfkJ5wA\nEk4TYCz4ImULJ4ZarZxbS865EOXxRhE3cHO0NWVXWqKulY8QyGodbxZTs9lFtapnnOabdxhqmggI\n8RwYngJjZ8B9Q1Id0aQmEE0dCBH8OhRCoNt+asQMfgnA3pfZCIlgzwkfeqfK5RLqdRWcc5imvzKV\nVYskTWRXnOS26mSfe36iUVU1vPOd78I73/kuMMbBuYZ2u43nnnsWzzzzNHRdi3V7m5ubOHjwDcOv\nV1fXsLm5iVqtjtXVNbRaLRw//gI2Ns7Fo48ewZVXvmX4u0888TjOOWc/9u5dT+38ZoGEU0w454PZ\ncQra7W5uHqZ8QymjteoYw0AwJRkTkpcQSHedoCwmO8JhXkVNNghhAHgCjB0DYzuedun4/ZSVaAIA\no/cCTPOs5zsXA/xXUxNNUS/izo4o7/BYb16Ppjl5PdHSpGX7fGQiv+K0GMJpZGUAQKVSwRvf+Ca8\n8Y1vSnyL3vNgjOFzn/s87rnnbtTrdWxsnOt7fn/rW3+D97//g4nXTAoJpwl4HzBvS85+E0Og2Tm7\nYynG4F0He66aBsNI5uWZt4qTu0NQThbTPLTqhGig2/0xLOs4OO96nrfhIiWZaGqPxQp46RunYPS8\nO/MuAPj7wZgy03pp422RtD1dRn+atN/A6wgpu7op7wJe9KpYViyucErO+vo6Njc3h1+fOnUK6+tu\nBenKK6/CV77yVQDAffd9GRsbG8OfPfbYEdx+++/nd7AhzL+5JGM4Z6hWy1haqsA0LWxtNdHtGrl/\n2sp391n4WqWShpUVexxAo9FCq9VN9ALO77ySvclwzlGrlVGrVQbVtdaYaCqSqOGc5Rxh8QogvguG\nvwLn34ZlPQugF7FymUQ0tSaKJstsodt52vOd8wH+axmIpvTva9O0q1M7Oy2cPbuNkyfP4OTJM9jZ\nacE0Tei6hqWlKhRFwfr6KlZWllCrVaDrWuYbIQDZFR8pyw7hXK5wkbF+Wutdc83b8eCDfwcAOHr0\nSayvr6NarQ1/fscdt+HMmdNot9t4+OHv4+qr7bFHp06dRKVShabFaw1mAVWcJsA5w9JSdczoLGML\nfd7z40ZxzM/9volGoz13ibT2/Rf/DoyzQzCftmMxPE5CWACeAWPPgLGtkaT5OP64pKJpPBLEPcY+\nOj4z+OsA/sGZ15tOPv5D7/ZyVVWwsrKEra3GsNVn7+qzYxIcv5Q3yHN3ILviIr/ilK2XMnTlxLdw\nxRVvxsGDl+HWWz8Oxhg++9m78MAD30KtVsehQ+/GjTfehNtv/xQYA2655WNYXV0FYFem1tbiD/bO\nAiYmPPonTzbyPJZCErTjkjFgebmOra3xMQ5ZUamUBv6IfNqDq6t1nD274zM/t9vd1EPSFIWjWi2j\n0QjO3EkL24yrY2cneMfVKKOG904n/ALtxbnfsqJaLaHftyKN7On1eqm+uduVnZ+BsefBWCuxP2gW\n0WSfjwnL6qNSuWTi73XbP/P4ml4L8BvBWDafVldXl9BudwOz3bJEVVWsrNSwubk19jMn/NAN8lR9\nozm8omqW50m5XEKppOX6PujAOcfevSs4efJM7msDQLVagaKwzN+3wqhUStA0Ddvb+d339qYoDZwX\no8WdNfv2LYX+jCpOUwiq9MhoyciYV7e8XBvsFusk3DI9eZ18PE7R7z/Hv1XELCYZz71O5wQq5e+B\n8X4kv1IU4oomIcQgGNN5Hk5+TPxm8HMB/uuZiSaZTDKHh4cf2pUpOyahNHNMgvxdbXLWBpxWmbz1\nGeO5vy/Zr3351e4iQMJpjsjjgqlpKsplHYwBrVY3B/NzMVpPgNuONAxz5iwm2TuNsuDpJ5/Fm99i\nIr3HSYksmoSwBoIpeqWzb2x6zOCvAfiHwJge/zB3Kc64GG9MgmNEH41J8M7pG49JWOwASjmtMhtZ\nHqvd+P42CyScZiTPXCV3vewMn97t9Z1OF5VKKZfZRUWoOHnbkTs77UTnnc/zId83r2eOnsGb3zL9\n96KhgPPplR8hzIFgind/2mbwpwZf7Qf4TTmJJjkXlLTeg0zTgmnGi0lw1lYUnvucM/u1LHtXmzyf\nJ2OMZstJhITTjMgRTum/OSuKgkpFB+cc7bZbYSqX5QuarAnKYio6Mob8vvRCWj6OyaLJ8S8JYWKW\ni6LfDL5vIJpKsx7sDBSnpZsG4TEJCjRNQaVShqqq2LNnZSwmwQnzzPb4Mr35iciveFHFSSYknKYQ\nVhGZdZdW2scxK24eEUen00WvN7q1fncN3/Xef1lmMTnrFMgWlQjDMHH2TBqCMlw0jfuX4uMmg3fA\n2D4I9r+AsfLMtxcXWY+5jHVN0/Y/KYoCxvrY2WmBMTY0oeu6NhiArMA0zbFE9DR25C6qcCnK+osO\nCacZyfuTf1pCZnS23qRdertINw3HyDhjctrtcbGY0kqQ6f1w0DQVuq4m3oZ+5Ecvwki8USxYNNn+\nJRNJBJOD0TsO0zwDRdkHrv4m+v1F8TQVw2c0GpPg4G311WoVaJp9yUkakyBbOCz6+osOCacZcebV\n5bleEuEUL48o/ry6IuOcO2P2/LC8xuRkxaSngbf92G53USppqNcrw23obisl2jy0R/7hJZhmkovz\nuGiy/Usm4hi+J2GbwY8DWMPy6i1oNhmAfBPdiWCceX0dz0uOcz6MRyiVdN/zM2pMguyqruwhv3LM\n4bvjepAGJJxmREY8wCzLMcZQqejQNHUsyHP6enmNeMnGLzaaxQRkPyYn61Zd2CYBu5JYgqK4XjVv\njhNjGOyaCjb6Ol4Wd4yHzdNPnEGtMuvzwBVN9nE4O+TSu3PcZPBVgN8MzusAmqndftGRPatulrab\nZVno9cazyMJiEpxWnzcmQXbFRfauOnkBmARAwmkqYa/N/IVTvPVGRYOdRxR3vRkOdAayEBtBWUzl\nch7tm3xbdV5hbFcSvRcj9ziEwASjr7+VIgSGYurES128/vxZjswWTa7hO/0KkGsGrwP8N8BYberf\nZIXcoavyxp6kecpxYhLsNq9AtVqOXD1NE/nCjVp1MiHhNCN5BxFGFU6MYSCY9MFMtdkCHPOejZfW\nBWBSFlMe7zN5Pi9sceh9nOPfhmP0HW2laJoCRVHQbSowzbiiRwFjCizLQBr+pTBsM7g2EE3hKb+7\nGbktq+xFW1hMQr1eHfqngqqnTtsvqy37xYhDkBGASQAknGbG9jgVa0ayU2UxjOIlXk8iDbERLYsp\n3wiJLBDC/hS+slIbiMNm6iV7e7SPhX9+9HkwKDD7cR4c+4IyaYZcWpimAfDfBGPLma9VXORdzGSJ\nNiEELMuCYQjs7LhRGU5MgqqqqFTK0DQls5gE2a2yeX8fm3dIOM1Ikbbrp5F4PUqewjDJfRkniymf\nqIDsTPWapqJSsduNUYI6k57rP/7ATt/uRxJOHPZ5z5bBFAdh9SFwIcAvB2Mrma5VdOzHWNYFtBg7\n+hyc6inQ8/1etJiEfqz3TdnCRe7jTpBwmhEZwmnURO0Ipn7fRKPRTiUfxV2r2HEEWWYxJSGL+80Z\nhAwAvZ4BxvJJaj76+CYAYPLIMrstZ1eZ0jfeC2EBwoAQffv2O2eA0u1gSrlQz0/Zu7xkIN+YPn3x\n6TEJ6pi3z1uZKmIorlw/HQGQcJpKuDk8/56vc1G258mlMyIkfK38hGGctbw5VHGzmNx15uMKxzlD\npVLynaudz5RPJfCV4x0AOoIsTvasOWXwJm6mIprsi0HfNpJbxuA2PYv3moD+W2BKfsGW0ZH1vCpW\n1Se/tYEk5+3GJLjmqagxCbYRPfk5zIrsahdBwmlmLCv/0RcAUK9XYVkWms3O1OnlycgvxylKlSZu\nDpUs0hCc7o5IFZ2OIeVchRBoN7jdfOs7Qo0NBBMfnmMS0SSECYg+hOjZYkkYCL0Yml17WK+6f6a1\ndiuLWOmySV88hMUkOK0+b0wCY8Dq6rKnQpWdEX0UGcJJxqinIkPCaUbyrMjYFSYdjDF0Ot3Ms4iA\nvFt14SJtNFYhag5V4CoF8qWF4Y9RaEn7ZPn0k6fAnLcHATCmDVpyLnFEkxBi0HIzhmIpcgCmZaJe\nfhf0lXf4AjxpyKlc5Fac8gugdGI8nJgEReFYW1tBu90ZVv/r9Ro454GtvrTvIznhlwC16lxIOEUg\nSETkcRH2Gp87nS50Xc9tJ0e+rbpgkTa+3b74H61nFZy6rqJSKQ38apMM/vlUAn/498fdLxhii6ah\nJ8kjlmamuw/t0rvQb3U8eT41cD66Y6oYPrc8kd8uk4NMc7Rzn3e7vWGwrvN9p9Vnb+QIjkkwjH4i\nP6rsHX0ECadCoigKKhUdnLsp0ACgafmHUua0GrxiwG96T2+7fRHLzaqqoFotwbJEJL9aXo/LE/9y\navjvXs+/4Khocg3cxlAspea7aQmwpVsGEQnjeT5BO6ZWVpZgGEak0R1psZgtM9kVp2Ktbaecjwt4\nb0xCtVoetPrGRX9UI7q8DKeCvXlKhIRTQtJ8w3R3inF0OkHG5zznx+XvcYqWxZSUrM8p2v1W1F2B\nDr94oQXAHpfS63oTyE1YVssWSEMDd0Zeu3YTqN0V+uOgHVN79qyg1eoMR8z4R3ek96l/HDkXFbk7\n2+TuqpNFXOEyOSZB9Yl+x4juFVWjHxxlicai2xzyhIRTAmyDePInsXenWKfTC/Uw5Ts/Lr+KE2MM\nuq7CssTULKYk5HFO09bwmtzb7d6YEbUoNM+6UsA07Oe3ZXVgGieQyy6u7g5QuQ2MK9N/dwQ7oyd4\ndIem+T/1e4VU8u3ncnwnQsjyesndoTrP/qokMQmKosyFbWE3Q8IpAUm3t8fdKZbv/LjsPU5O1UVR\nOPp9M4fdY3lW7PzYo3BKiU3ucR6XWR+/48+fARPa8OtOvwvLsmAaJ5HLhdJoA9pHwJT0xqi4ozvc\nT/2cs+FFytl+zrk9YsYwvGKqv4BtuOnIrjjJ9HZltXaUmARd18A5g6ZpPsGfdUuaKk4uJJwiEFZF\nmFVceAezdrtG5ItoEce8zMJoFhNgt1WyJp+K0/hzwk12n49ROA9//wXf1z2xg36vkc8bp2UAuB5M\nOz/7pazxT/1Oi8+tTtkG32lJ07JExOKKl8VZezQmoV6vDitWozEJdkva/zyl3afpQ8IpAXEvxKNb\n6+MOZpURupkmYRU2TVMLZ9pOA8ezZZrpJ7tnyeP/dML3dc9qwI4OiN82i4WwgN6lYLVrsl1n0iEI\nd/t5u+1+P7iFIoaf9AG7gpr/Yzw/ga5psqiC0VnfntXXn9iStnf1ubtP/TEJVEVNAgmnBEStONlt\nGh26nmxrfd67wkZHvMxKtCym7E8sr4gFzhmWlqoAps/Pi0seVbPjx5pwjOEA0LN2AFHN/iFqV8GW\nbsx4kdkIa6E4u/o451hdXUq0W2oW5EYCLE7VpyhrT1s/qCU9GpNQrZagKHFjEnbhJ9sEkHBKQJQL\nsRNomEabJu8AxzSG4voDHYPPv4gxAbPAOYeuq2CMo9XqFG6nXFQaZ9zHyBQGTHQBVLJdtNUF6rdl\nu0bKeCMSqtUKNjfPQggERiSMju1I04+yqDvbFnHUDBA/AHPWmIROp4Nnn30W+/dvoFRKZ8zRl770\nJ3j88Z+CMYbPfOYOXHbZ5cOfPfTQg/jGN74GTdNwww3vxc03fxgA8Ld/+x385//8/0BRFPz2b9+K\nd77zXakcSxJIOEUgfF5duJBxfS3TAg3jHUe+wml287s/i2ny+ed1Xll5xLyetX7fhGWZGYqmbA3u\nJ0/sAKY+/Npu0yHbq3N7B6jdOddtaIdJu6Wc7eeOH8WyRArBiPIEhGyvnkzBKLPtnlYA5rSYhLNn\nz+Duu/8PvPDC8zjvvPNw8cWX4pJLDuCSS+z/7927Hmu9xx47ghdfPI7Dh7+OY8eewz333I3Dh78O\nwP4Qcu+9X8T9938TKysruPPO23DttdehVCrha1/7v/G1r/0FWq027r//MAmneUcIW/178QuGtH0t\nMioz8RacLYtpfpvtTrq541nTNA2qOr8G/oe/97zv657YGfwro8eo1wQqnwDjpWxuPyemVWadVt+o\nH8W5SNmf+G2vn9vmczN9Zl03K/IceVI0ZJ97lhUvr/Cv1Zbx1a9+Hb1eDy+88AKeffbneOaZp/CP\n//hDPPPM0/h3/+4WfOQjH41820eO/BjXXnsdAOCCCy5Eo7GNZnMHtVodW1tnUa/Xsba2BgC46qq3\n4pFHfoRSqYSrr74G1WoN1WoNd931uQzOOj4knBLgrTjlEd5ov1jyrjhF+13veJi4vp68MqPSXMdb\nUfS3ILN9jLK+r37y6Ku+r3tWhsKp3wH4b4Ap8T65TmKeilaOH8X7iX88IqHqmYEWFJEg84QXs1Um\nc9yLvX6+56/rOg4cuBQHD7pttVnW39zcxMGDbxh+vbq6hs3NTdRqdayurqHVauH48RewsXEuHn30\nCK688i0AgG63g7vuuh2NRgMf//i/x9VXy9s84kDCKQFCCCgKx/JyDZZlodnsDEqf2a0np1UXTjoJ\n2HmdV3JR4+xWyTbdXB7P/7wB521BCAHDqTil/UZtmYD5NrDqG6b/bkzmuRISHJEwbu51IhLsto02\n2OE3njKdFTKrLrLH28gXbnLXd44hKd5zYIzhc5/7PO65527U63VsbJw7fIy3trbwH//jF/Hqq6/g\n05/+BP76r78tva1PwikCQc9R5wLKGEOz2c5054z3OPLdVRe+3mgW0/h4mHjr5EGS+09R7JlywOSK\nWvaPUbYVra1TrhDsixYEMhKG3Q2w+nXZ3LYUsvMahZl7nbl8nHNfREIeOT72c1xexUlme1+2cIlr\nDk+H5O856+vr2NzcHH596tQprK+71eYrr7wKX/nKVwEA9933ZWxsbKDb7eKKK/4VVFXFeee9FtVq\nDWfPnsHa2p7Ex5OE+TVjSEJVFSwtVVEu274We0tn9qJJDuOVIM4ZqtUylpYq6PdNbG01E4kmIM9K\nWnzRwTlDrVZGrVZGt9tDo9GS+nhnKcy2znZgGZ4YgqG/Cemq2xYDq/+v6d1eAZBRBen3TQhhjyk6\nc2YbJ06cxubmFtptO+6jUilhz54VnHPOHuzZs4Klpdog2yeNPC6Z4mGxKz5pmcPz5ppr3o4HH/w7\nAMDRo09ifX0d1Wpt+PM77rgNZ86cRrvdxsMPfx9XX/02XHPN23HkyI9hWRa2ts6i3W5hZWVV1ikM\noYpTRBRFQaWi+1pSnHOUStr0P06RtLKVoq3l/jtaFlOxiSM63PNV0ekYkcfB5N1OnUy84/jBQ8d8\nx+76m1Kk1QLqf5D+7S4w3vcCNyIh+kBZ7/DjqO8rizpc2F5ffqtsHrniijfj4MHLcOutHwdjDJ/9\n7F144IFvoVar49Chd+PGG2/C7bd/CowBt9zyMayu2gLpuuveg0984qMAgNtv/71CTM8g4RQBxoBa\nrYROp+errsi4SKaRrRR9LTEUENOymOaBqMLJ2SnnhpVmf2xF4J8fGTGGi4b7RRp3QqcBVH8349dM\nUURrcQiPSFCHOT52RMJoKKK9sy94Z/Di5igtWvhmmte5T37y076vDxy4dPjvQ4eux6FD14/9zU03\n3Yybbro5lfXTgoRTBIQAtrZaYxddOcIp2WDhODjbpQ2jn1oWVRj5CMLJrTpd11CpOFESzcKWw7N6\nzj339Dac7r0l+ugLb5Ut4X1htIDSR8GUerLbiUQxH7csSHIhdXbn+SMSvKGIlcCIBMPoL/jIE3nn\nLsffRIxCwmnuyF6sOdEKgG1KjdqmSoYjarIXZ6NomjLYKScS75TLQ0xn9cZ5+mQfgB1+6fM3YXDf\nzXrDZg/A+8DU1yY5vEJTnPZsMoJCEb0RCeWyHZGgKBxCCCwv13xm9DzYzTlKUdbO+wOd/dzeHc/v\ntCDhlJA8W2fOelkxmsXkvGHmgYyKkz9KoQvD2K0m/+l02gbMrjtsedzfNOMDIyzAeCNY7S2Jjq/o\nyLuQZv/eExSRoOsa6vUq+n1zEJFgj+wwTdOXhJ7maBk/1KrLe13ChYRTQvI0a3vXS5OwLCZNUwMr\nNFmQRwvSEWecs+Huona7N+b9SGMNmdhDpUsolTTfVnanKhBUUfvh3z8PxlzT5WjFaearc3sZbOnX\nZvtbIgJyvEbO2JFWy1+NVlVlWJ2q16tQVSX1iATZrTqZLPK5FwkSThEJuyDm6Tnyr5ecaVlMxdoh\nlhzG7DeepaUaut1eRi3I7NPdJ1Xn3ETzPjY3t2FZfd+MtKWlmm+Qp3Mhe/THL/tuZzijzllzhuNU\nuhaspd+a4S+JqBTt5emMlul0/KNlHDFVqZSgaf7noHdeXxTkhm/K9leRcCoCJJwSkn+ad/I3S3so\nbQmapqDTCRcQ9lr5nFvW92OppKNctqMjstwZmE/FadwPZvu0yrAsazgjUQgByxLodg10u/4kakdM\n2WM9Knj+WbfCZFhtCIxcxOLeX+0drLzuj3HmTHuG8yPiIG9WXbSFndEyUSMSRsXU6DoyR57IFi6y\nzOG76QN0GpBwSkgRx6CEET+LKW8TYvq36RjdTdNEo9HC8nJtV31i87ZZo84IDNqe/uqLbQC2v22s\nTReX7g6U+qfAeQnA7hdOcnOFZLXqkp1zWESCLaaUgRE9OCKBcy5ZOElZerh+UXf7LhIknBKS/xgU\nMVMA2CxZTHmKwrQHGHuN7lnPEPSTT6uOc4ZSya4aJvVpGYYJo6MNj3q0TTdcNAr9DqB+GFyVOxIh\nX2RmGskSbdmcs9M+brfDIxI0za4cO1EpTkxCHq/xRRvwO1g15/WKDwmniIQ9V4veqnM8L3Y2Ubws\npjxFYVpruRUYPtgpl88WaYc87jPGgHq9il4vnQT3R/7xRTDP9CUjoOIU6b3a6qOkHgIql+2qqh4x\nTp6CbTQioV6vAgAMwxh69+p1FZzzYTZVVhEJslt1stcnbEg4JaSorTrHiGlZ1szZRPmeW7JKzahv\ny+vp8a2Sc3xEmui6Oqyi2Y/p5E/YUR+7R/7hF8N/W8KEIVoBvzXlDhMCinkB9LXroWnqYEcmw+rq\ncuYDZxcZmdvTZbbLTNMM9O45bT5vRII7WsYVU7Meu2zh4uxmJORCwikhs7bOEqw48YI4msU0LwOI\nk1RqnBEp0X1b2bZW0hab3sd0Z6eNWq2c6pv300+cGf47qNoERBCabQ3W0s3Y2bFFl6oqWFlZQrvd\nGYj4MjRNAWNsJB4hnxZLlsyrEJ9XwsSLN3rDiyOm7OpUsogE2cKJc4Z+X0YAJuGFhFNC8vc4BX8/\nLItpXpiluuVuvTdj+Layv9Cl9cbq5E0piuJrO6b9nDvxUg/DxPCQwb4Tz6nVBup3Bf5odOCsP4Xa\nabHMvjV90ZF1UZNbcYr3+nUiEvyjZbwRCa6oH43pGH0eyhZOZA4vBiScIlIcj5N/vWlZTGmtV6S+\nurcN6Wy9j072FaekOAGWuq6i0zEyHXljmhZ6LcU1hoftqAu7uzoNoHrH2GsgTNwFpVCPbk2v1SpQ\nFGUoovIe6REfec+nIr0u8yKN96NpEQmlkoZ63XkemoXY0ecc4yI+5kWDhFNCZAmnqFlMydfLpxUR\npeWZRhsyjwphkvvMG2C5vd2a8CaZzkn8yz+9AgZl+HWsilOvCZR+C0ypJDqGoK3pjGFYEQj2q4Tn\n/BDZI9drk41QnRyRoELTFOi6DkXh0HV97HmYx/1Bu+qKAQmnhMhI1+acYXnZTr/e3u7kNN8t2xfr\nJEHjVNUURUGnk35VLX3i32dBAZahty5EJPEX5Xd+9IPjw3/3RQcWwk31PswewD8Ipm6EHSWSvOEK\ngYl+ldGcH6ci4HyoyOQrkfQAACAASURBVBNZFhC5CdryfF15r+1GJAC1mgXG7NmWTuZUrVaBpqkQ\nAj7PVBb+PVkBmIQfEk4JyTNd28liArJNv/aSn4dr/EJrV9V0aJqaWlUtD6Ebp+I0S4Blmhx9/PTw\n32HVJmCk4iQsoH8FWPVfZXlogTh+FW/Oj3c+Gucca2srEMIaM6FnWRGQdy2TtbDM7Cr5Q3bdiAQX\nzvkwb2rUvzfadk6yPnmc5EPCKQZBIiLqp/8kjGYx5Zl+nVdFbfS+dXbK9XrGQCRmfgi54hWFaQ8a\njsMrL3YwNIZPSgz33v/tNbClX830uOLgnY+m6xq2thoQQgxbLHZoor8isBviEWRudpJbcZIrnMIE\nuGVZ6HatCREJ/tEys0Qk5H3uu21eaVqQcEpIlk+ssCymPLOI8qw4Mcag6xoqFXunXKPRTP3TVT5v\nBJNbda4o7GFrqxn/1lN6TIQQaDf45MRw53edf7RMsKWPJl88YxzzrxOaCDgVgfFhs96qlGH0Z2iv\nyKq+yBUQi5eWHl+4xI9I6PvM6KPCnszhxYCEUwGZboLOc6db9iNEAIBzBapqm5RnDewsCmHCxgmw\ntCuHSURhOo/JU0+eBBu8BQhhhQRfDla0BNDeAWrBsQNBFO2Dql0RmBSPoKNerw4TqCkeYTIyxcu8\ntwnjRCR4xZQcCvZCLgAknFIgrS37UbOY3LlueXicsq3QeEekOJW1LMk7dwsYD7Asiij84UOuMbwn\nmpj0fNo8JVBb/xQYj/aWMS8fiifHIyjQdX97xe+b6g9+X875zlPlJeXVJXucsrntoIiEUWEPAPv3\n7w3MPcviPpGx2WIeIOEUg7CLbtLWWdwspjwv/lmt5YQ7Oudsmhaq1XL6C42RfQXNEZthAZb5Mfk8\nn/iXU8N/GxOM4QDww395O86/fCWVoyo6k+IRnIuYNx7BNE1wboutfOMRip1HlhXyd/Tlt7hX2DsV\nqc3NM57nohK4u9T5PxnJs4GEUwq4VZl4T9JZs5jyNOylvRZjDOWyDl1X0e264Y6c5/PJJi/RWSpp\nUFUlkwDLtM7hpRfaAOxdmj0xwd8kDNx443uSLzjHeOMR2p6iqKoqKJV0aJqGpaVa4AUsyWy0SSxq\nxWnR1w57LiqKY0KfFJEw3xsiigIJpxSIKy7cZGhtpiymed3pUCrpKJc19Hrj4Y4yYw/SxBFM0wMs\nk5L8HJpb8BjDwytOlUoX1WrckMt8vHGj5P2ysH0qPZTLJZw+vQXAewFTPRcw4Wnz2W2WeR7Wuqjz\n+YognMJwIhI6ns9pzoYIVVUGEQm1gBFHkyMS5vFakzUknFIgjpBxsphs8TBbFlO+rbrkQ4z9puhW\nSPk4nwttVvedN8Cy3++j18umymCT/Haff+4MmLCrTabowfTsPhvlkkvWE6+XJ3lf10arze4FLNj4\na7f57LfeZPEIMlt1ctaWLdhkCqdZwi/dDRHu97wRCaMePvu9y8Djjz+Offv2Y2lpKZVj/9KX/gSP\nP/5TMMbwmc/cgcsuu3z4s4ceehDf+MbXoGkabrjhvbj55g/j0UcfwR/90R/gggsuAgBcfPEluP32\n30/lWNKAhFMKRLkYj2YxJek959uqm11oxDFF5yUG014nKMCyVsvWq5XGOfzwoReG/54UQwAA11//\nS8kWWwCmXc+Cjb9uNcAbjzBt0KyDfK+PlJUh09clu+KUhmdpWkSCaZq4776v4Kc//SmWlpZxySUH\ncODAQRw4cBCXXnoQ+/e/Jtb157HHjuDFF4/j8OGv49ix53DPPXfj8OGvA7CF3b33fhH33/9NrKys\n4M47b8O1114HAPilX3oL/viPv5D4fLOAhFMMwl4vk4RMWBZTsuMotsfJu1NOjil6EgKMJaugAZMD\nLGXs3IvLT//5xPDfk4IvhejjxhvfHfv25+E+kE1YNcBp85VKRY5HkFVxkptjJHfMTbbn7kYkAF/4\nwp/Csiy8/PLLePbZn+Opp47iv/7X/xdPP/0Ubrjhvfj0pz8b+XaPHPnxUAxdcMGFaDS20WzuoFar\nY2vrLOr1OtbW1gAAV131VjzyyI/wmteEjXIqBiScUiBIXKQxkDZ8Pbtsmw/RW2ijZndvgm5RSOOC\nPj3AUo6/Jw4vHmtiaAyf4G8qlzuo16s5HRURvKMvuLVimhYYA6rV8lBU5XVRX8QIBnf9Ynqc0oZz\njvPOey1e//qL8O533zD8ftxj2NzcxMGDbxh+vbq6hs3NTdRqdayurqHVauH48RewsXEuHn30CK68\n8i14zWs2cOzYc7jrrtuxvb2Nj3/8d/DWt749tXNLCgmnFPD6gKJmMSVcsVCtOtvsrkPXdXS7PWxt\nzbaLLK08rCmrYFZRk16AZTLSqDg2TovhbRkiPL38kkv2JlqHSE5Ya6VSKQ/9Una0h91mGTWhZ5Xv\nI0NAyK44yUTGuQe9zyR97/GeA2MMn/vc53HPPXejXq9jY+NcCAG87nWvx8c+9ju4/vpfwUsv/QKf\n/vQn8Fd/9TfQNC3R2mlBwikFnApQrVYe5BJlO3vMDcDMnmkXace7Ze8iSz54OOtPlLNUnOIGWBa9\nTfXqKw3AssP0DNGEQPj5XHfdm/M6rLlF1sXcskyYpoXtbbdi6B/lYeekWZYYM6HPa76PbI+RTNHG\n+XwO+F1fX8fm5ubw61OnTmF93d1wcuWVV+ErX/kqAOC++76MjY0N7Nt3Dt7znvcCAM4777XYu3cv\nTp48gXPPPS/fgw8hudljgQh6zdjz1dRh+N3WVjPzga1FCMDUNBXLy3ZuTaPRRqvVTfymYv95cRSH\nI4ar1TI6nR4ajdauyED5wfefH/57mr/pQx+K728i8mLcZ9Tvm2i3u2g0mjh9egsnTpzGmTPb6HR6\ng+dzBevra9i3bw2rq8uo16solfTYO2dlVpxkMcuutjRJyxyeN9dc83Y8+ODfAQCOHn0S6+vrqFZr\nw5/fccdtOHPmNNrtNh5++Pu4+uq34W//9jv4y7/8CwDA5uYpnD59Gvv2nSPl+IOgitOMeLOYnLJ4\nXp6efHOc/Gtl690SmQvCKPed+9iqMwVYFj1n6yePeozhE/xNpVIHy8v1GVcpvs9rUQjK9wmKR2AM\nvqrUpLBEmc/vRa04yVk/+eN8xRVvxsGDl+HWWz8Oxhg++9m78MAD30KtVsehQ+/GjTfehNtv/xQY\nA2655WNYXV3Fu971r/H5z/8h/v7vvwfDMHDnnX9QmDYdQMJpJkazmDhnOY0Lsck7jgBgg7EwJShK\n1jvl5F5s/a3HLAMsZydqtlbYBoIXft6A89KfFEVw8cVrMx0fkQ9J2tphc9G8bb6lpfB4hEUUL/KF\nEy/k+1EUPvnJT/u+PnDg0uG/Dx26HocOXe/7ebVawxe+cG8uxzYLJJxiwBiwslIby2Ky21n5XfDz\nXo8xYGmpMhgLk613S1bFyRtg2Wi05zrV2UvQm/3Wpn1upjBgeqazj5LE3yTP5yUnrXw3ZBpZlkC3\na/gq5+7AYyceoQJFUQAAy8u1gaCanDydJvLjAOSsDchvFRIuJJxiIAQCqxB5XyTyEBiAu+0ewMD4\nne16eQtCIDjAMilppK0nxWmpcl73GYNPndyGZWh2W2ZCm06IPm666frQnxNedm+KdlA8AucM6+tr\n6PfNseTp0SGzaR+f3DgAeWvb6+frcSq65UAmJJxiYlnjoiXvJ1jW6+m6hkpFh2HY2+6Xl2s5fdLK\nfhFH5E4KsCw6k4Q652zQUlXQbnfRarWhqsqwYvDoj18dPncmGcN1vYOVlXTGLeTPonwql3VRsysf\nrZbf++d4pjTNnovmpFB7q1JJ4xFmGaaeFvJbdVRxKgoknIghYSnn+eQr5VW5s89lebk2IcAy4QqS\n2lSO967bdQ3toxWDhx/8+fD3yd8039jPseJUumz/Ux/ttvs9R7RrmopyuZpCPILsVt2iCSeqOAVB\nwikl8hIXWaAoHNVqCUBwu8oRAnm0BbKtpNnCkDGG7e0sAyyz3lHmv33nvAzDnGpof+6ZBgDbZNqb\nEHz5/vdfg3PO2VvAUR+El6J7q5wxHqMDjx3fVK1WgaapEAJjbb6gHX2yk7tlxgHkfX1hjFGrLgQS\nTjEJqybkJS7c9ZILNaetY4d2dtHrBRs8XUEzf6IQ8EcoNJtt1GrVTN8A86g4MeYIXns3Z9Q5iGdO\nGgBK6Is2BIJFkBAm3vved+DkydO+UR+1WgWcK8PKgjehOvgYF+NNV645XMKqCc/X2dEHjA88tqve\nZWiaAsbYSDyCKX1XndwBw3I9VoQLCaeUyFtcOEbqWV5IjLHBiJSoOUX5eLjSrjiN+n2cCIXdcD1X\nFAX1emWi4B2l1ezB7NrG8J4Ib9PpehtrayuBoz5G56bFFVO7E5nmcBkX0vTP1x147I9HcNt8JdTr\nKhSFwzStkcHH+TzXFnlHH+GHhFNKyDGIx/87fwZVtJyiPF+wadyF3gBLr98nP7Jr1ZXLOsplHZZl\nYWurFetv/+HhF8CYvdtvUvDlhReuhP4sWEy5xmDvLishhGfL+qKJqd1LXhU2yxrf0be8XINl2ULL\nEe6KonhEu2tCTxvGANNcLGP6olSN40LCKSWKvrPOP6C2FatVlde52esk28ZfhADLLFp1jnHfNE20\nWp2ZUnQf+/HLw39P2lF37bVXxLpdx58yKqZsj9TolnW3KkViKhmLOGiXMQbTNNBudz3f8+7oc5LQ\nlYG/yv9cS7qjTwg5+W7z6p/drZBwiknYc7eowklVFVSrJViWiOyDGV8rn/ZWknV2a4ClkzPFmGvc\nV1Vlpvvp2aNbABgs0UdftAN/RwgTN9/8nmQHDfd14t2y7r/Ajef/eP0s84Q8j9MiMi4ggoQ74A48\ndlp9qqrCsiyfCT1OPIJswUg76ooDCaeUyD8Ec/J63mDHdruXqHSdnyiM3+KaJcAy6x2QaQR5ZpEz\ndepVA4A+sdqkaW3s2bOaeK0gwi5wzsVttFowOjeN8CNLsMkVENHP2dnR561OeeMR6vXqsKU8akIP\n+uAl87wpNbxYkHBKiaJUnDhng/A5JeVgxzxaddHFZ7EDLJO9wTntRmcWYrL3S9vI2+v10e+oYJjs\nb7rgguUki81EmJjyD6ElMTWOnNaRTNtLUvEyLR6hWnXjEUazpmRXnGRGIRB+SDilRP5jNvzVGdcQ\nraHb7WFrKz1DdF4jXqKKT2cUzKwBlnlHR0TF21ad1G6cpaJ15Ee/AMPAGD7R3/SmWLc7iST3s3PB\n8oYpThJTcoXU/EZ1zMbuallNj0coQdNq4JyDc4Zez4Bh2M8708xzR1++IpmM4eGQcIpJcTxO7ic/\nvyG6mfobSxFmrwGjBvckAZaO6MyyVRf99zlnqFbL4JxHbDdGP25HuPz4h78Yfi9sRp0QFn7jN5L7\nm7IiSEx5TcGVShmKwrG2tmxX2DwVgyyR1zJbrHWdtfMgKB5h795VtFrdQVVfR71eHYlFyG7DA5nD\niwUJp5TI3+MkoKoqlpdrME0zU0N0fubwYPHpDbCc1eDuXyfr84nm1fLGJnQ6Pd9U+rR55okzAIC+\n1YaFYCGhaS3s27cnxVWzFajA+JiP9fU1NBpNcK4MxVTQDqvd0eaTVemSWWGT66/q9QxflYkxNqiE\nKoEDj9OqhpLHqViQcEqJPCtOqqqgVNLBGNBsRjNEJyPrESKDVUYETViAZQorIcvziSLM3EHKs8Um\nxH2qnXi5h2nG8PPPz9/flAX9vjnYsu5+z7vDyn5O2QNoR30s83RtktVJkVtxKlabcHQWpP176ccj\nyPE4UasuDBJOKZGHcOLcninHOYdh9ME5yyUHJ79qmi1osg6wlDWEF3CrZ0Iki4eIg2la6LWUqcbw\nX/7lN8Y+lqIR9riG7bAa3a4+b2KKdtXluXa0854Wj6Cq8eMRbOG0OyJWdgMknGYg6MKbpXDy7iBz\nWjqqqqBc1jNZb5T8AjDt+9VJm84qwDL7N97xila61bN4FbOfPPYyGBQA4aNWhLBSyW+aJ6KKKcsa\nzZkarRQsYstMDkWrOMXBeb4B3h197vPNHXgsfM81w5C7o48Yh4RTSqSR3ROEs4Os2+35tqbn2RrM\n6ty8OAGWjDFsb7cy/nSV7X03KqydMTdyxr8AP/rhiwAAS5gwRPCYFlVtYf/+9TwPq5BMElP+SoEr\nplSVS9kqvpg5TrtLQJimCdMcj0fw7x5VwbntpVIUZVihSur1nETem53mDRJOKZH2ln13p5wZuFMu\n33ZTdm9UowGW9Xol85J0XvedswvQfgzTq57FPf6jj58GABgiPLrh/POXkh7WGLJiH9JeL6hS4BVT\npZIOzvlwZ6t3Xlq2F3m6sOVFnoLNiUfw7uhbW1uGYRgQAsN4BMbYmAmdRhjlAwmnguGYVy3LmuKB\nybPilP5acgMsszWHK4od3VAq6ansAkzKKy92AOi73t+UJ14xZVkWGGPodLqeylTV52Hx+ljSugDb\nolTOrLpF89vIzn1jjKHbNUbmQbJhm69U8scjjJrQZ1w1nYPfhZBwmoGwT/xJRnkoih1+CCBSlo+9\nRp6tuvRuL2mAZVKyqjjZYtBObQeARiO4LZac6I+9EAKdBreN4RP8TUXOb5oXwipTTtvFFlMKLGt0\nxEfWlal0oRZhMdYP3tHHhtXQoHgEb+bUpNNhjFGrbgIknFJkFuHkNQ13Ol30etFMw3m26tKqOEUJ\nsMx6jlxWeH1MW1sdrK7WM1srzmN/9ImTYIOXeVjFSVVb2NjYl9bhecg+x6kITHq+Bo/48BrQ/WIq\nzvBZeRc2OY+pfT/nvqxn7WIJpyC8xnIv3tT9SsXdQZpla/lLX/oTPP74T8EYw2c+cwcuu+zy4c8e\neuhBfOMbX4Omabjhhvfi5ps/PPxZt9vBLbd8GB/96G/jAx/49dSOJ01IOKVI3Flr5bI+CD+UYxrO\nC3+AZWfimII8fDFpth6dNyI7hLQ1FINFEYA/fOgFAEBfdGEhuB36utdlJ/KIcYINwa6YcobPWpYY\nC+0MqjrkjdzE8sWsOCUNwAxO3VfGWsu/+7ufgWVZOHDgUlx00aU4ePAyrK3FD8V97LEjePHF4zh8\n+Os4duw53HPP3Th8+OsA7FT2e+/9Iu6//5tYWVnBnXfehmuvvQ7nnLMfAPDnf34/lpdXZj7XPCDh\nlCLxZq1piYe4FuXiHMZsW/Dno0rhmNoZYxHHpMjhZz85CQDoWcFtOgB45zsvy+twiBBmEVOy2imy\n3nPsc11M4ZRFAGZQa/m3f/sTeOKJn+Gpp47iv/yXb+Lo0SdRqVRw4MBBXHfd9ZErQEeO/BjXXnsd\nAOCCCy5Eo7GNZnMHtVodW1tnUa/Xsba2BgC46qq34pFHfoQPfODX8fzzx3Ds2HN4xzt+OdVzTRsS\nTjMw67w6Jy3ablW1Er8Q8ty1FEekJQmwzKMFmWT2XlRTe9aPTdQL5ksvtAFooYnhQlj4zd+8IcUj\n80M2idmZJqYURcHq6jKEsHxVqXnzTEVncVt1eXHOOftxzjn7cejQu6EodlDvK6+8jKeeehKKokS+\nnc3NTRw8+Ibh16ura9jc3EStVsfq6hparRaOH38BGxvn4tFHj+DKK98CAPjyl+/F7bf/Pr7znW+n\nfm5pQsIpRSbNWnOm3qe5y8pdL/sXdFQh4B84HH8LfpHzQ5xzS1opTIOo92tzCxMTwxWlhXPPPSfF\nI3NZgOsMgHxbV14xpesatrYaEEJMDFFMW0zJqzjJbtVJWVq6KX5j41xsbJyb6Ha8x88Yw+c+93nc\nc8/dqNfr2Ng4F0IA3/nOt3H55Vfg3HPPS3rYmUPCKUVGgyK9GUV2qyrddk6+ImNyC80OsLTFYZYD\nh9MgblXLFr5lWJYV49zktxyfe3YTTGh2RSIkw+l1r6vlfFS7EZlCws396XTc3B9F4VPFVL/flxLc\nOSvyR70sUpsw2XVlfX0dm5ubw69PnTqF9XU3YPfKK6/CV77yVQDAffd9GRsbG/j+9x/ESy/9Aj/4\nwd/j5MkT0DQN+/adg7e+9W2JjiULSDiliBOC6Xh7VFXJPKMov511wWv5Ayy7ib0++YjBaNv5vbMB\n4/qY8g0o9WNfDC089OCzAJzgy+A33ne8g/xN6VCskSuTxJSqho33sP8/TUzJ9DgtYtUnqTFcBtdc\n83bcf/9h3HTTzTh69Emsr6+jWnU/pN1xx234wz/8PMrlCh5++Pv4t//2I7jhhvcNf37//YexsXFu\nIUUTQMJpJiZ5nHRdy228Rr5ZTn5Bk2WAZfYep8lreD1azmzAohHUOrUveAKWJcCYwE8fc4zh4f6m\nf/NvbgDnzHMbACAK3TIlbOI+PI6YAvxiyh3vEU1MydxVt4jm8CyM4VlzxRVvxsGDl+HWWz8Oxhg+\n+9m78MAD30KtVsehQ+/GjTfehNtv/xQYA2655WNYXV2VfcixIOGUEqWSNkz8zmo47Siy5tVlGWCZ\njxgMf2wcA3/SIcPZPzb+VqBliUGCtTv65/hzTQBqqDFcUVo4/3y/d4Fz+9i95z0asRD/GImsSPo2\nEzTeI1xM2Xk/jHHMuLciIYsZgClj7TTeuz75yU/7vj5w4NLhvw8duh6HDl0f+re/9VufSLx+lpBw\nSoiu24M/TdNEq9UdvsnkQb7tIDE0uU8KsEy8Si676sbfGJysKSHSNfBnjdOWC5qV2Dhjn0NYFMFr\nXxvsbxrd5p6umNp9yBvHkY2HbpqY4pwNdvNhLLQzS2+jbPEiy7e5KDv65gkSTjPiDXVsNu1QR1VV\ncvW15HXRssdGKBBieoBlcvKtUsyWNTWdrN/nhLAN+ZZlBQrYV19pAJYOU/RgelozXt75zjcEfj+I\naWJKCHj+7URXUBxBluS7m88VU5VKCadPbw2eg05lqjz40JidmJI5L27xPE70wp0ECacZYMyeUN3p\n9HwX2rw/fSfJI4qCV1T0+xb6/X7Goim/HCcnuT07P1o2zwXHx9RstofxCIxxmKY7OqHfN/HQ//cc\ngEn+JoGbb042ny4ogNERU7quQ1H4mJjanRQ/sDVthLAToLtdf2WKc+4TU6pqX2JGE9BnEVPyd9VJ\nWXouPU67HRJOMyAEsL3dGrvA5y+cshpW65ijNXS7PTSbncEFOvtzEwI+s3IW2OGBHKbJc/OjpYFl\nCQhhARDo9Xro9ewL1v/P3pvHyVHX+f+vuvqezExmEggIhCPHZBKiOQiQEAFZziBBUFxdEdiworDc\niiKXHIuoCCiwZDnCAsqCi8pXPJafXIIgCokJkEMQkDMhM5mje/quqt8fn/l0VXVX9VnHpzOf5+PB\ng0xPT9enr/q86/1+vV9vOthTlsmU9HhcxqZXdgCAo75JFMew994fc32NkiSVprQnk2MQBKEsgKrM\nTLnLzhqcVRJsd5v9cWsFU9FoBJMmNRdMBV2qm4jH5tjDAycXCSLj5PbxrAaWY2XGZa4eygHvSnWS\nJCIWiwAgJ/h02ruuRzeD2vJuucpjVQ72fGPzEADJUd+0xx5x10/IsVgEkUgYmUwWmYzhdm2XmaLP\niazf7WDKbyGtr4djgMaecL3BlCCgwmfKrDUMOuszUQKnnTs77A48cHIRvzUd7g6rNQws7cTR5Fje\nt9LYCbdbhVgnhKEoRMdULKpIJGKuHqOS1gPA8oCpkZdleFCFrouOxpfLl89FdzcR+NLyHv1/oyfp\nUEhBPB5FsahieHi0rrICySoaT4iU+ADvgilvCaY9P5jXxA0zyHqCKUWRIAhCKZCSJAma5o4GsVGC\nDpxYNhSeiPDAqc1p9dxZr4GlX5uXXXdYK5h1TCMjJMNEMiDuHcOOVoNoc1mu0ccZHkpDLygo6Gno\nqDzh6rqO449fjh07RiCKYqnMF40STYqua5ZAqlgs2gYGoiiWynKpVLolYb3xnuw8wZTXBLmRe4F9\nMCWUTDtlWS4F6dUyU14QtDi8WOTicJbggVOTOG2MjQzDbX0NzWc1zAaW9Zg8+ptNa/1A5KqV2ERU\nDlRm11+IZplI0NQczz3zDwiC4FimE8U09t13j/HjacjnNYuBKW09J/YTIciyBFXVLIFUKKTYluXc\npJ5gihp20t8Fw8QSh/v5OmuajlyugFyuAFmWkMsVkM/nS8EUySTHLZkpqp1yM5gKPuPk37HtSusc\nKzxwchm/A6dmPuA0C9PIsNp2yTjRDJogCI5jUvwJAhsLzmrpmBph3V8+BODcUbfbbpGqf2+0nhu3\n0WCKiM+jpftJkoRIJFwKqrymPJgybBGIxqpcB7OzZqaCGj8SdACh67olmKKIolDymYpEwujocDeY\nmqjjXjj28MDJZbzQ6FQ/Vv33D4XIFRoxsCzPwrBDM6+flyNgmqGR98bO9bsV3np9FICIgkNH3YEH\nzmr4MXUdCIdDEEURo6OpkuZEUUiZLxIJQZIkqKpqsUXw2r4CoBqrGFRVxfBwstTFZ87Ilmem3PyO\nBidaZvP76xXkPXPq5tORzxcs3/tqwZS5o6+eYMoNXVez8MCJPXjg5DJua3RqH6v2wcxmnc0aWPqZ\ncWoU2gnYSAaNBaq5frfC0EdFaLqIom7fNXjSSc6jDuxw6pZTVRoYGZoUqpeiAl9JEiv0Um6VUARB\nKA2rHRvLWDbN6oad1mCqHbNSQS036E28kUPbBVOCIJQE6OZgqlhULbqp8nMkC5k2v4/JcYYHTk3i\n9Dlm6STspiu2XxqnRo5DNDgRaJrWcAbN6/MQ8aOy/52bZbly0mM5qHkZeW3E9veCMIaZM6fX9VjN\ndMuR4MjYdAQBkCQZiiIhFFLGy2nEsJNuVsWi2nDXUCQSRiwWQTabx9DQaF1/s3ONkgmqVNfelgC6\nXiuYCo03PFiDqSAJxjmcUw0eOLlMMCaY1i+WnYFl68fx63nVPo4oiojFaCegvY6p3uN4d0LSAVRG\nTnREiltluXJeeO4dCILoaHw5bVp1fRNAu+WiEEWp5W45w+6gCIBkqyoNO6XSVb85M2UXqMmyhHg8\nBkDHyEiy5eyVwEdRWgAAIABJREFUO8FUUOLwII4ZnBDeq+9rPcEUAEydOrlmZsoLuHM4e/DAyWWC\nMcE0rgKrGVi2eCT40YlWbblGQFhfJ2Ct4/h59dyKvUAjvPziBwCcheFLlsyo+vfWspy9B1Sr2Bl2\nmoOpSCQEWSY+W+ZgSlFIoDU2lrG0rLtN43P5/M/CBJX5CTbj5N+xzcGUKIro6ZExMDBcCqbCYfvM\nlBfBlP+lOpYzrWzAAyeX8dsEk2ZOFEWsamDZ8lF8K9XZB56hkIJolAaE7I9Joa+Xl2U5O97YPAxd\ndx614qRvaqYs5ybmYCqTIbdRcW84HEI0Sh3fdYRCCkRRbNqwsxmc5vJFIuHx4ba65XbvL56CyvwE\nm3EK4tg0cKmVmXIKpvzqOOX4Bw+cmqSaxkmSvHfYNq+DtmFXM7BsV6iwXdfdDQiNzc2bE7Gm6ZAk\nCeGwPG4g6Y/z78C2Aop6ETrsPgdjmD17H8stbpbl3IYMYiYl2ZGRJIpFtSXDTjehM/loyVDXdciy\nVCMz1f6dfEFmnIiuK4jAqfr53imYokE/NYmlnXzG/2ufq7m+iU144OQyfpXqaPu9JInI5QqemRCa\n8bO85aaw3W9IN08OgOZrq34+V0QxKyOvD9v+ftq0sOVnP8pyzULXlk5nkc0an+1mDTvdvKCIxaKI\nROxLhv7P5fOXYLvLghtt08hzrhVM0eyuKEo1g6kg9E3t9pkMAh44uYwfJ0NiYBlCPl+o+8rFHajO\nyfsvckdHDLlcwRVhux1evE/lrt/l4yOqt+oXUSg03l1m5i9/ehcCREd90wEHEH2ToihIJIIry1XD\nGKmhYmhotK4Nq5phpyxLCIdjrgRT5nEf9a4N8Gou38RyKweCC9q87OaTZQmK4hxMEW3kxHqf2wEe\nOLmMl1ogq4HlGDRNHy/T+Wu46dX3mD4/AEgmM20z2LJeHZNdqz7d3EOhEGKx+rvL7PjzC+8DAPK6\n/aiVk0/+FDo64pAk9spyoigiHo+6tjb7YKo5w05RFBCPk+DLjbW5MZevXbIvOwNedvM5NUnQYEpR\nFEiSiMmTOy2mnd5eLPOMUy144NQCdkGSF5mMagaWwRhuunsSkSQRsRgR/6ZSmfGRHt6enN0KcFvp\nltN1mE6c1lZ9atAnyzFTS7+xwdudyF/fNARNL6KoZ2yONoalSz+BTCaLZJKtslw0GkY0GkEmk/N0\nbc0YdooiabrIZr1dW6PBVFAEG7D5f1zj2P4cvDyYopmoVCptm5nyL5jimOGBk8u4GThRnY8sS0in\n7XU+fuok3Be4CuNDOomOKZ+nz89b4TY9RitXVmbXbzexuwqtJoimJ8xisYiPPsghbxs0kfl0rJXl\nZFlGIhGDpmkYHk4GkmF0MuykGxT9vCuKXCrTNWPY2QxOwZQoiujoiI8Hgcb3ZGfu5iNPa+fyj6oH\nKg6vzEyhFPATY9koZFmyBFI8mPIOHji5jBsnr0YMLHWdaijaCzpoOJcrYGTE+vz8OEc1m3Hy216A\nHLO6IDocDkEQBOTTEvKafZlu4cJ9mAmajFEpCsbG0oHPFSwnHFYsflH1GXb6E0zFYlFEo4ZoXpbF\niszUzjmXL+iRJ4Ec2lEcbs1W0/vWCqbUknbKiXZsWAgCHji5TKtfsEYNLNst40RKImGoqvOgYX+e\nU+MZJ69dvxuhXMOz9i/vQYDkONj3lFOOhCSJrvt7NUokEkIsFkU2m8fw8EiAre2V0GHBhULBIv6u\nz7CTnErNwVShUHRts5dlCYlEHJpWKegvz0xVm8vXOkF6Kfl+2PFjB1cibSTbVSuYUhQZsVhkPJhS\nyzJT7ukdf/SjG/Haa69CEAScd95F6OvrL/3u2Wefxn//9z1QFAVHHHEkTjrpFGSzWVx33VUYGtqB\nXC6H005bhaVLD3FtPV7AA6cWqJa1aPTKTFGkJg0s/QycmtcFSRLRighCK2NS3KOR5+LVMF43+dNz\n7wJwcgxPY/HiuZBlCXROnFkQ7UemxPA9giujUtyEir+JMH2sLvF3NcNOWZYQiYSRSNSvT3NCEEiW\nKRQKNZSdq2+UDIAGhxwH6x/FfvDC2rHtgikApUCKBlM33fRDPP/885g9ezb23nsGZs+eg333nYFw\nOOzwyPasW/cy3nvvXaxevQZvv/0Wrr/+aqxevQYAufC86abv4+67H0BnZycuvvhcHHLIoXjllfWY\nPbsPX/zil7F164c4//yzeeA0EaEnono+8DSgIHPXGjewNF9Vek0zmSDqN6UoMjKZfF0nflbSxUGU\n5ZpBFEW8vmkYRT0LDZWb/tSpcknYXFl2kiEIRqaEBlRubRR04/djVEoz0GHBbgjTiX+XteW8FcNO\ncwZseLh++wMn3JjL186WAK0cO6gOX1H0xsepPJg65ZQvYsmSg/D663/Da69twqOP/gLvvfcO9thj\nLyxadADOOef8uh735Zf/gkMOORQAMH363kgmRzE2lkI8nsDIyDASiQS6u7sBAAsXLsZLL/0Zxx57\nfOnvt23bhqlTp7r3RD2CB04eUE/3mTmgaGXumpf2BzZHQyNBGi075vNFjIzUvyn58ZyqBWflARMD\nMZwj0WgE0WgYH7yTdtQ3LV68b+nf9uJzI1NCmhFi0HW9ZTdup9IXC5DSVwyapnsqTG/GsFPTVITD\n4XH7g/oyYM3SaDAVFEFeSE2EoC0cDmPWrD7MmtWHT3/6sxAEAblcFm+88QaGh4fqfpzBwUHMmjW7\n9HNXVzcGBwcRjyfQ1dWNdDqNd999B9Om7Ya1a1/GJz6xoHTfs846Ax99tA3f+97Nrj43L+CBkwfU\nypiYDSyJjsm7Y7lJvQENKTtGoKqao46pxpEQlJeIX8N4W0VRSEdasUiMIjNJwdH4cuXKQ6s+llOm\nhHoeNerGTca4kJldyWQq8LKsGRYyYNUMO8PhEBQlWrpfOEz8pvycd+YUTJHAOoKxMaNz02+NZRAE\nHTgFdexwOIL+/rktPYZ57YIg4NvfvgrXX381EokEpk3bzbL33XHHPXj99S245prLce+9DzJRdXCC\nB04t4PR51jT7AMPOwLL1Nfh74hJF5zl8oigiFqNlx+Z1TH5lnMzPpZ3KcvF41GLGuPHVrRAgOwz2\nTeMTn+hr+DiapiGX0yyZUEmSLIJoOwNJkmWsHJXCAixnwAByQQUIGB4ehapqTRt2uo0gAPF4rKSz\nKhQKvs7lI2uYmMFLEMdu5b3r7e3F4OBg6eeBgQH09vaWfv7EJxbi9tvvAgDcccetmDZtGjZv3oTu\n7m7sssuumDFjFlRVxfDwELq7Jzf/JDzGv2m0E4jyE4csS+joiCEcDiGVymJsLOta3drPUp3TsQSB\nmBh2dETHuwHTLV0d+xkMapo+3i2nQdc1poOmaDSCrq6OUpaJlnBe+MM70HUNBT1d8TdTprh3baSq\n6rg9RhrDw0kMDg4jlUpDVVWEwyF0dXWUMo2SJCIcVqoG2n4hiiImTYojFosimRxDKpVmKmiKxSLo\n7OwYt+YwhPOqqiKbzSOVqny9FUVGR0ccPT1d6OzsQDweHc9Ouft6K4qMrq5JEAQS0JWPDBFFEaIo\njmfMJEgS+bcoWrNWbuizgvVSCuTQgT7vZjjggAPx9NNPAAC2bNmM3t5exGLx0u8vuuhcDA3tQCaT\nwR//+AcsWrQE69evxf/8z08AADt2DCKdTqOzsyuQ9dcLzzh5AN346zGwdOtY/lBZQjPrmEZH2dqQ\nqqHrtDxCppazHDCZy3J2epyNrwyMZ5sqn8PChft4ujZN06AoMiRJxOhoCsVi0dUxMq1idiVnbZAx\ntRggV9j1mZM6GXaS11sZH8FEOifNBqmN6mQMry25oTEzXg05Ds4/KtiOPq/E4V4xb958zJrVh7PO\nOgOCIODCCy/Bb37zK8TjCXzyk4fh059eiQsuOAeCAHzpS6ejq6sLK1eehOuvvwZf+9oq5HI5XHjh\nJUxccFVD0Kt8IrZvtxebcgjkpFV5O/F1kSBJEnK5PLJZb3UUXV0JDA/b61vchBiryRgby44LW4l9\nQiaTc7W9nGYqMhlvSj2apkMU9ZIAl3Y6mV24WdDl2JXl7Pjn4x7BtoHtGFXfqfjdHXd8EYsW9dv8\nVevQoCSbzSGddjZpNY+RoUFVq236tTCLv1OpNFNzD4nOKoZQyBsDUKNzUiq93o0YdoZCChKJ2Hh2\n0d6JvhWaGXIci0UgSVIg44J6erowMpIM5JzQ29uFoaGkLyVZggBJCvl0LLaZMqXD8Xc849QCdud5\nmoFRVa0uA0u38OOKjJ7U4nFyEstkvMmiAd6UH806Jl3XkU4bm4LViTs2LoY260mKvnoP0W65etrk\nx0bsB/vqetqToKnRjrRWxsg0iln8nUqx50rutsWAHdbX2zoDkQrQE4lKw05VVRGLkUHLJHvozWbd\nzJDjoIw3AWPsSRC0W6luosADJ5cwG1hmMnlIkr+DIf04sYRCJLDIZKqPgWkVL7yparl+23U6mQfA\n0hKINUvifsmpVlmunLf+vgOCrth21LmpbwLcHZVSzxgZaydfsaJMVQ4NSvJ59sTfZpPNZHLMVafm\neqhlQ0EzOsQwsQBFUUpZKj9ex2rBlCQJpYsI8+/8kihMNHE4pzY8cGoROwNLWpLwi0YMN5vB3A2o\nqprnLdxuCt5bcf2u1JOUX7UTJ+zyEl8z70O9Zbly/vjM2yjqOWioDGIWLJje8DqcIGaZUeRy3o1K\nsW/TN3eWRSBJoq2/FLE/EJmzPwAMk81stnWTTTfRNOLVFYmEoesYz4DBMRPYiqdXM9Ah4LFYBOl0\nFvl8HpIkWjJTXs7lM69jogROLFsAsAQPnFpAEAQkEtEKA0u/na+96qyTJBGxWAQAkEploOt6KVjw\nltZfPy/sBapftVs3mkb0Uo2U5cp59a/bHf2bTjhheUOPZYd1VErKR60FQVVpy70RrBvicwXxeLRk\nEpjLFca7vHQmNE2SJCKRIB1FrI2ZAczC+awlm9OoYacXmkBRFNHREQdgLQc3OpfPrXNxEHFTkPP5\nONXhgVML6LqO4eGxiqCFBDJ+Bk5uT0AnV3qKQnRM+XyxdLsfT6uVk4Xfrt925pHGRmP475Rfsauq\n1nBZzo53306h4KBvOuCAeU0/LxaMIp0gZS4dkUgIxaKKsbFMKYD1Y4xMPcRiEWb9rGhAR85ftT93\n1Qw7rZpAd4IpGtDV+9pVdz+3BlONniuDTMDwMh278MDJA/weButm4ERczZVxTxmrjsmvTFqzx2HF\n9dvYaMxZEqteirbb5vM0W9ncCTI1pNtmnHp7pabfK5aNIgVBGB96a9VZqSp8GSNTC1kmwXAjFgN+\nQgO6VoPh2mXVxg07jQxd62Nw3JjLRx9nopTpxo/q8/HaEx44eUA7luoUheiYVFVtckxKcLSD6zfV\nS0WjEYRC1Fm92JJeausHo9BVGXm9srz38Y/v1fAajVEpYiAC5loY4u98zY40t8fI1MIpoGOFZjyj\nGsW+rGpcMESj9ho1VdUazjI1QzPB1MQLnDj1wAMnD/A/cGr+eFTcLgitjUlxk3qfTzsN43Uqyznp\npUiXkwxNc97Yn3vm7XG38MqT6wknHNLQ+gydVdYz/6xmcWv2XbNjZGrpuszdfF5ZDLQCdRUPwp6h\nHsNOURSh6yTQ1TQNoij6plGrFUyFw+G6MlNe4LcNgt/Pr53hgVOL+DnyxHkNjX/giY4pBEWRkcnk\n6z6het3BR45R+zVlpSxXC9qGTrrlMigUnF/nRvVSr6z7CHnNTt+UwUEHfbyu9VkDOvZKS14HdDRL\n4lRWrcySEM0U2eCDtRioBX1vC4UiMyVXw/i0CEEQSs7kmqaVbCjicbNhpxHA+hlMkbKhkX0l3Xz+\nzOUzr4O17yOHwAMnj6CbPwPnqgrMY1JGRhrr4vLneTn7OJntBVjHPO6j2Tb0anqpf/w9ZTvYt6en\ncuxFOY0EdEFAS0uaprWsd2kUuyxJ+RgZqlErFotIpzPQtOAztZRmx6X4hSSR2Z3EQNUI1hs17CwU\nvBH8U+sN4og/VlpP+XfK6yHH5AKVrU5MDoEHTh7hR2bGfKx6ZvsQk04yhLV5HRM9KfibcWoHHRNF\nUWTE4zFoWvPdctWgG/vQQNFWGH7AAfuhu7vTcaQJq75CgLsmm25BTCGJDYUkFcYDOhW5XA6iKCIS\noeJzb8fI1EOlDszXw9eEZhBridNrGXZGImEkEu6+5sRehmQQ67He8Goun/nx/fZwEgS2Z8SxAg+c\nPML4ovgTOFX7UoqiiFiMmnS2pmMK4kSsacSXh3UdkzmLMzaW8XTTHx5KQy0AKirLV0cdtQQjI8nS\nFTvVS+m6Vkr/sziOxA+TzVYwLAYytvMnzWNkzK95q2Nk6sG86bNYNnTKMjWCk+DfDcNOOp+v1YsJ\nURTQyCiZaudtLg5nFx44tYjT59pPoZ2TJkgQSHYhFJIrTDqbP5Z/z6sV12+/MQ+89SOL8+xTb9tm\nm3Q9i2XLFlhGmpg7vuhnIBaLoKMjbtvh5Ddm8beXM9KapV6LAS/GyNSDubTEWgYRcM8CwY5ar3mt\n7klzwOnFZ6+ZuXz0/CqKXOPEKjxw8gg/TTDtghmzjml0NO3alYsfgZOm6VBVDd3dCYuBYRClj1p4\nXZZzYt1LW231TZMnWz931ixOpUDYEEIrZfP4jE3Gy5M3Ld2waBTpRtnQeQZi9TEy9QSw5oAzCFf3\nWpgtEPwUp9dr2Emy2OTznkqN+Raw1xNMaRrJ0hUKBV8vVnlXXX3wwMkj/MySmL9Y5CqLtNAmkxnX\nN3IvuwjNOqbh4dGG2/P9xM+ynB1vvz5qm3GaP39PACh1BQHVN1Uj41Epyo1EQp5pd6xZHH/F3/Vg\n1Qq5WzY0XnP7MTI0gFVV60WD+TWiOjUW7SMAc5Yp7Uqmu1XMwZRZPJ/N5iCKRtapUSsKtzAHU2Q9\ncQiCgLGxNCSJ6I78mMvHqQ8eOHlEEKW6eJxMOM9kch520jh3vLWCnb2AfXs+uVpXFBmRSNhytU51\nJF5vwn6X5ewYGiigYJNxWrHiYMRiUUQioaayOGZRbiZDbrPT7jQbwLIo/jZDN1FRlHwdGEyzfPT9\ncuoqU1UVoihB1zVGy5rBZJnqhVg0xJHP5zE0NFrx+3oNO72CZogzmRwyGTK5gTb+GB5TgJ9DjjmV\n8MCpRVjQOIXDCkRRRC5XxNhYtvYftIDbGadGu+XKfXfMreLWOWXul/iMspz/LfJm0mM55HJF6LAe\nX5KKWLnyU6TjzsVNq7qOpPo8PjOsi7+NLE4OmUywWiG7rjKaxaHB0qRJCV/GyNQLDdhZbDwgF5ax\ncYuGMccLSzvNGT2/mMvZqlo0XbC1frFWb0dfo0OOG1xFE38zMeGBk0fUaxHQCqEQGZNSKJAvmR+D\nWN0KCN1y/Ta3itNyk9slvqDLcuX88Q//sC3T9fQIGBvL+OLbU81fyig3CaVyhyyTgJbFLIl5RtrI\nSDIQgXw1qnWkeTlGpl7aI8tEjECbsWig2cDKcrbh69WKYaeikI6+XC7fVAa7vlEyQDNDjjn28MDJ\nI7zUAkkS0TEBQCqVgapqCIUS3hysDF0nX8xW8Nr1u3aJrzxD4nzVyEJZrpy1f/7QNnCaNWuXQM0O\n6cZhLjfRcR+apkMUBXR0JMo29eAyJEBti4GgoVkcp440r8bI1Es8HkUoFGKy7AoY66uWZWoUazaw\nfsPO8kYLcxbMbQsJt4Ycc+zhgZNHePFhJG3lYciyhHTaSx1TNXQAzUVONMsUhBuu/WgNGYpCrxpp\niU8df1318TlVwZbl7HhjyzDyeuWolRUrDg5gNfbQsmZ5C7+beqlW18fyqBlZltHREWuq7NrKGJlG\n12dkcVh7/SR0dMR9W189hp3mRgtN0xEKyU1nwZqhVjDF2FvINDxwcgG77JLbgVMkEkI4THx47HRM\nfjmVN/O8WHX9Lk/Bi6IARVFKGwtA3tdYLBJ4Fx9FEAQMDeRQ1K2fgVAIOPzwJQGtyqCW+NteL1Ut\nG+iuIJd1cTr5vMUQCrm7vtpjZIwLh1pWFO2SZQp6fU6GnUYWVkUoROaFWl93/2xXyLlcACBy1/AG\n4IGTR7gVOJErxDBUVa06JsWv2XiNliDbxfUbIOJlWpZLp0lgQkXQTpt6oVD0LRtFughlpMYyFb+b\nMkUOPN3erPi7WoakXC/Vir+UlxYDbkC1LoVCwfMsiZ02kJSbSBbWbowMoCMSCTOdZaJaKxbXR7Vq\nqqphaGiktD6iU6OGnQpkmWRhDbd5bxznyeGNwIlTPzxw8ohWNU6SJI7rmOobk+LXiJd6A8J2cv2u\n1i1nL4KmJT4FsVjUcqVOT3ZunrRlWUI8HgOg47e/ehW5op2+aaprx2uUej2jGsFOL2Xodso39eod\nZUFZDNQLyYLFoCiSq1qcRiHlpoJl4DMVn0ciEciyBMAqtvZyjEwjUK0aix19gKGVtNOqEZ1aNcNO\n90X/5HsiwgicOI3AAyePaDbjJAgColGSvs1k8nWfBFgR+LFalrOj2W45uy4besVonpdl3liaOckR\nTVsE4bAhDv7TH9+1FYYfccSChh/fDQxxtbfO39X8pRRFLl2pl28uVEvFgsWAHeEwyYJls3kMDbG3\nPnIBF0WhUMToaAq6rtccI1Mo+GccSbI47Hb0iaKIjo44dF1vSCtp735eWdJWVbWU+a5P9K9D12mG\niWeZmoUHTh7RTCATDocQiSjI54sYGWnsJOplF5/1OPbPyy17Ab9ws1tO1+26+Kr7HNUq8Rllr4Jl\nQ3hj81DFqJVoVMKnPuWvvokFcXU1vRQpy0UBUNNIAeFwyHMDw3oxZ8FYtGiw+h6lLVkwL8fINIKX\nM/DcgH6H3bqoaFT0XygU8e6776C3dyokSTJlmWimidMsPHBygVaDFkPHpFXVMdVYhS8ZJ7sZfF7b\nC7iJXyaWtUt8Vt0OvWKkWTCngbc7BtPQYb1tjz06IEmSJ8+jHPO4ilQqYynrsICqqgiHldKGn8vl\na+qlCoWir5mKSCSEWCzKbBbMcNcu1N3x5TRGxvp5rz5Gpl6q+VqxgCAI6OggI1O89gWrZtiZTqdx\n0UUXYtu2bZg5cxZmzZqNWbP6MXt2Hz72sT089xncmeGBk4fU6nQTRaJjEsX6dEy1juXHVYRZs9Ru\nZblYjOhIgjKxrFXiUxTD+8XOU0hVNQwPV5bpdtlF8XTdFHoFTcpKleMqgsacBTNn6Zz0UnRsTyJR\nv16qFUjZhmrB2DPatAbFrWutnI0j7b2OaEBVLYilQ6FZzTKFQkTgb24w8Rv6usuygvvvfxDJZAqv\nv74FGzduwh/+8CT+679uQyqVxKWXXoXlyw8NZI3tDg+cPMSp000QSIdUKCQjm827MgTTLhPkDUYw\n2C7dcnSURjabY0pHQkt8uk6Ckny+gEwmO15uqizxPf/sm0jnKwOnadOinq7TC/G3mzRqMWDvuVNb\nL9XKhQ0tDXutBWsWa8ehN75CtbyOolEq+q8cIyOKxN2daIXYzDLF41HIssxM6ZWW5jo6OrFgwRIs\nWGCU84eGhhCPxwNbW7vDAycPset0C4cVRCIh5PNFjI6OuXaC8qt7TVXJZt/VZczJImWm4D2OymFl\ntpwTZnG6WUdSLNrrGP78/HsV+qZYTMGBB/Z7tka/xN/NQq/wW51/14y/VD1WFNayEnufQbezTI3i\n5HVUPkYGIN+LfL4AURShaeyca8yDg4eHWcjE6tB1qmOyL8d1d3f7uqKdDR44uUA9g36JR0cYmqYj\nmcy4fgL1ejae2fWbiqnN/iPhMAkAgvI4MkPKcmQzYGG2nB00A5HJ1Ban01LTxlc+QlFPW343aZKK\n4447CrIsuxrEsiD+rgbJQMQgiqJnV/jV3eZr66VYFy+bs0wslV7pGJlCQUVHh4xisYh0OjueFfR+\njEwjeDHSpRXIR08CtxnwFh44eQgNZuJxBZIkIZPxbkyKV6W6ajomO/8ReoVe7nFk3tC9FOJay3Ls\nbAYUWSYBSTNZsIHByucTjWaQTKYtLeKRSMy0sTTW1WTNQKSZ2AzKoe8xEVf7qyNx0u2Y9VIEAZpG\nTGuLRbYCd0EgHX2yzKavFWC8x+WZTi/HyDQCNdssFlkxA+U2A37CAycPIeJv4sdkNybFTbwo1TXj\n+m10eRhjTGhbfuVsMvc8X8wBCdvC2+ZGaei6ju3bRypuVxSSrXJuEa+/m4x18bckSeNCbnbKXuW6\nHZqByOVypXE9shwPzOeoHHNpkyW9H4VmEgVBqPkeuzlGphFYM9vkNgP+wwMnDwiFFESjZCJ8JpNz\nRfxdCzcNMN10/bbTMEiSVNIwRCIRiGJ5m3L9JzfWZ48BRvt5Ntu8DmfTq9uQylgH+yYSYcRizhuw\nfTeZMVKDBiGqqo77vOhMBp0AEItFEYmEmC17md207TIQVr1U2PfRPTTLJEls+kYBZpuGLDKZxvV0\nzYyRaSQLLknEzFLTdEbMNnmWKSh44OQC9PsjSdL4mBQglcpAUfybH+ZGqc4vewGqHaGeL7XGadBy\nUznWslxlNiZoaIYEQMvdaM8/+w7ymjVw6ukRsXBh/Y7hdiM16MDRYlGFIAjo6ppUph0J1jDSyWKA\nFeotbbaql2oFo0U+37LZqxcQM1BvfI+cxsjQ8w3Nguu6ZpIUVI6RoecaVgJ368gUHjT5DQ+cXICc\nPMPjpmOGjkmWvRVsW2ktO6RpWmCu307jNKjwnHaemVPtZLo4q2U5kiExj0pplbffGoQG68k8lXoH\nRxzx+aYez8iQVAYkhnbEMC40exz5YRjZDlqrVsXV9eilWvGXaocsEy0P+6lXs++gtB8jY87GslMe\nBvicuWDhgZMr6FBVrULH5J+3UvMGmKy6ftsJz4nHTnTcJVuHKJKgyi/heT3QzbRQKLiaIdm2bbji\ntpGRf2DGjBkNPY5ZGOwUkJTr1Ow3dMNrh2an3ILObyMWA954CrWCOSBxU1xd21+q/rlwhl6t9ZFC\nXkDctUnwVW58AAAgAElEQVRXJAveYHYaQWq2qaoaBEFAd/ekCr2UvxdttW0GOP7AAycX0HXYOj37\n5a1E19BIkNZOrt+AtSw3MkJKVobztlwaruuF8LwejPZ4wZNOpfc/2GH5OZEII1OmeaqFobVqbDN1\n2tCNjCDd0Fsr8ZlfQ9YzJH4FJE7+UmaNoLmbTFVVhEIKMwGJHcZg4xzSaTaDOhIYV76GXo2RqQW3\nGWALHji5hN28OjcF27WPX1+Q1m7DeEm3XBSaVilcth+uaxbhtiY8rxevTSLffGMAQyPWjNPuu8cx\nMtJT199btVbulDadrChoe7hdia/aax+kxUA9mLu9gg5IKjWCgCQRf6N4PFbKcsbjUV/Lq7Voh9Jh\nLT1Y42NkWs2EcwE4i/DAyUP8DJzqgdWynB3NdsuVi3CtwvNQ3cLzevDLJPLF599Focz4Mp/fipkz\nZ9b8Wz+70Srbw60lvsrXnmworFkMlGMEdc11e3mNIAiIxSIQRdLCr6qqYwelubyqqu7P43OCdYG6\nIADxeAyK0tjIlNpjZCKQZcl2jEyt157bDLALD5w8xO/AyWmosNn1ux0wynL5lrvl6hWem80ia7WG\nO41K8Yp33hmAeWwPALz11jp8/vNfc/wbc3t8UN1otUp8HR1ks9c04hKtKHLgXXxmyIy+OAB2gzon\ncbVTNxkt8RnlVW9L2+QCiHxXWM0yybKMjg6zlURrj1fvGBnzLMRUimQxw+EIgPKgicMaPHDyELvy\nnR/Ho1/8dtMxETfemG1Zzk2qlZnsWsPN6Xaz1sqvK+d33xuw/ByPh/G3v23CEUf8U8V9/Q7qGoV0\nb2oIhSIoFIoYGzNczytLfN6VV2tBhcHpdMZWvxg01tJhfd8VOsrE7CtXTS/Vqvu2dXAwe1kmwOh+\n9XpkitNrT887v/jFI7jjjjuw9957Y+bM2Zg9uw99ffMwffrekGXvt+lsNovrrrsKQ0M7kMvlcNpp\nq7DffjNwzTVXQNM09PT04vLLr0YoFMLjj/8WDz/8IARBwAknnIgVK1Z6vj7WEPQql6LbtzcmPp3I\niCL5r5yurgSGhysn2ntBR0cM6XQWqqpZynKsw6KJpSE8N3Q7AAlGs9kc8vmCbzqXE477Ed7ftrX0\n86xZk/HSS3dhy5bNlvuZg7p0mj2dUL0WA+byKn0Pytvyvdrk6CgNTVORSqWZm9EHmI0i3deDUb0U\nDaZkWSq7iKBlJufXxfw+k5EzbAXvAB2+HIeqkvc5aP0XQIKXN998C6+++io2b96ITZs2Yvv2j9Df\nPw8/+MGPPA2gnnjicWzd+iG++MUvY+vWD3H++Wdj//3n48ADl+Lww4/A6tW3YerUXXD00cfhjDO+\niDvvvA+KImPVqlNx2213YtKkTs/WFhRTpnQ4/o5nnHYqdOg6oGnBt+XXi5tlOTehwvNCoYh4XBx3\ngc9C13UoioyOjvj41bm3wvOtH4ziw23WjjpRHEZPz+TSz4b4m13nb1pSIqM+qnseOZdXy0sdqsW0\nsNVAlpqBsjJKo5xmskyNYg5QzQJoGsBGo1SrZm9HQUrEceYGB5uh2URWzCypzUA4HENf3zz09c0r\n/SaZTGLr1g88zzp96lNHlv69bds2TJ06FevWvYyLL/4WAGDp0kPw4IP3Y88990JfXz8SiQQAYN68\n+diwYT2WLVvu6fpYgwdOHuOkO3IbTdNRKKiYNCnGlPOzE36V5VrBvNmbR6UYwnM6G8tJeN66v9Ff\n170HDdaT+zvvrMe0absBMDZ7djYBK25ZDNiVOsqHvDbbQcmCHqwWQQrU7bpX7ewo6OuWy+WZLW92\ndMTHGxG8a+ZohFoC8I6ODnR0zPJtPWeddQY++mgbvve9m3H++V9DKBQCAHR3T8bg4CAGBwfR1dVV\nuj+5fcDp4XZaeODkMeW6Iy+grt9jY2mMjaUd9TqGUaR/3TTlsFiWK6feUSnm2Vh2mRE3/I3efvsj\ny8+xWAibN/8ZF1xwAbq7JzG92UejYUSj3m32Tl18ToFseTeT+bNISofsfRbJZh8D4F2WqRnMOkFz\nd2mhUBgXW8cteimaFQxKYE/Lm15ZhjQOmzYDd9xxD15/fQuuueZymGUeTucXFs87fsADJ5dw+vwY\nnXXuf8CchvHaDXelRpFkWrtc6uigwZQfJ2RzWa7ZYbde4saolGqZkVrCczvefGur5ec99khg0yYN\nxx57NJPib4DqR0g20c9uNKcSn53ztqZpkGUJ+XyRyc8iYHxf2NnsK4nHowiFysXV5RlZaTx7K0MQ\nzB5HtfVSreLlHLxmYXHO3ObNm9Dd3Y1ddtkVM2bMgqqqiEbjyOWyCIcj2L79I/T29qK3txeDg4Ol\nvxsY2I7+/nlVHnnnhAdOHuOFJUGj3XJORpGKYu5kqn8zb5R2KMt5NSoFqBbISiXHc13XLHYINMv1\n9ze3WR4rHB5DKBRGf/88JoMmP32j6qHceZuM+oiXZh/KsoTu7i7PTVIboR1sEIidRNzUwl/5epkz\nss3opVqFekexZKrKqs3A+vVrsXXrVpx33kXYsWMQ6XQaS5YcjKeffhJHHXUsnnnmSSxZcjD6++fi\nhhuuRTKZhCRJ2LBhPc4996Kgl+87vKvOJUg3SuXt8XgE+XzRlU3OS3uB8i4yN8aXtENZztDgiEil\ngusAMrfky7IMSRIxOJDEAUu+Zdk4Ozs3YevW1/Haa68Gsk4nzDqhsbEMkyl8Y1xKHul0pnS7tYtP\nKglxaSDrZ3mbljfbIcvk1nfarJeiGapWhP9mh/JUaowJ7yjy2aHjUtgKmgAgl8vi+uuvwUcfbUMu\nl8Ppp5+J2bPn4Nprr0A+n8euu07DpZdeCVmW8dRTv8dPf3o/BEHAySefgiOPPCbo5XtCta46Hji5\nhFPgFItFUCyqLZ9ggrAXMHu8yLIMURRRLnx2+viYy3KZTIbJUgjtrmHREVoQgHXr3sGqVXeUbovF\nQvjb3/4TM2fOxvPP/5GJNu96LQaChJZrRFEYD45rb6TmEp+xmXtnFkmzTLquj9sgsJllSiT8aeEv\nP/fUq5cyd/WNjWVsHtl/rKU57gDeLnA7Ah+ornFq/nGDNLGsnIlljNAwp9nNJSZBAPNlOb9GpbSC\noih4551By20f+1gHNm0qoq9vNuLxqCuDdVuhEYuBoGh2Bl79w3WLlsxIMwEP62abgDGP0S+rBrt5\nfNX1UkWEQoqN3ipIiM0Aq1kmTvPwwMljmtU4sTiM126ERqXrs1C6MpckaTxTxkZgYnXVzjDaRUXW\nKEkS1q59y/I7WR4BIGLhwkUYGUmO30ZLTI0Lz5tfozsWA15CMjjudqM5b+bWAa/1lvjMWSZWtUxm\no8gguzer6aVCIQWTJhFfIU3TxuciSq7qpZpZL58zt/PCAyeP0XUdop2leBXaaRivqmpQFAGhkIJs\nNo9sNjs+SkCuGC5q2CH4v9EGMSqlUczZkWRyDFs2f2D5/YcfvgpBEHDkkcaoFaMlvz7heauvv9cW\nA25gZHC81QlZN3MCLfHZdbCaS3zsmTBWQrNMrK5R13XIsoRQSEEyOYZ8vmDrL+W2UWqNVTFpM8Bx\nFx44eUwjGSezvUA7QLvldN1altM0a0s4zUpRfx1Jkip8pbwql7VDR595jTTzUCyqeOtto6MuElHw\n2mvPIxqNYfr06Y6PZd9BaWQFI5GwjV6k9usflMVAI7CwxlolPmrUSd6nfOnCiqXXU5KIUSR5Hdks\nZRtr1CyZsGpzKKlRqlf+UizaDHC8gQdOLmI31LeeQb/tNozX3C2XTqctnkV2qKoGVc3X5bjtVlZK\nEATEYlGEQgrSaTavmKv5RtGTO2WPPTqwZUsePT1TGz5OtdeflJiqO56z7k4OsJ0doSW+WEyEIAgl\n8Xd5ic86vieYsUntkAmjWc9611hplFpbL9Xo688F4BMLHjh5TK2Mk6bp0DSNGR1TLWg5qXwMSSM4\nO26Tk1kkEitlpZq5KrSOSmHTVbuWb5QgCJAksZQhU5QkAAG77757y8euVmJSFMPxnHwuBaiqyqyW\nSZaJ0D9oDU41jEyYZsngWF9/oRTMVpb4Wi+x1l6jWW/FZpbJcFFHSxlFO72U8frTErdUp78UF4BP\nRHjg5DFOgZOT6zerOJXl3MI+xU58dehVea05cGZBMKsbPRFWRyGKEpLJsariVVk2AqcPP3wNgID9\n9/fGpddcYjJbDORyeQgCKYv4ITyvF5qtc9NPyAvqzYRpmr1JLQ1m7Uus7pSYDO8odrv66MWQV9o6\n+9dfLAVT9GLi5z//OV566SX09c3BjBmzMH36vpBlLgCfaPDAyWPKA6edvSznJuVT2u2yInQjoRkr\nlo0DDWF1DplMbYG6LEvI5YoIh2Vs3Pg8BEHEsmXLPF2j2SSy3GKA2FGQYNYL4Xm9mM02Wc0omj2P\nms2E0RJfZYm1vMTUXImPDr1l2aHcMLMUfdcoGiVu47a5c/fHyMgINmxYjwceeAAffbQNM2bMQl/f\nHMyduz8OPfRTrk+KsOP222/B+vV/haqq+NKXTsNzz/0BW7ZswqRJnQCAL3zhVBx88DI8/vhv8fDD\nD0IQBJxwwolYsWKl52ubCPDAyUWqaZxYtBeoBR2M2UpZzk3shLc0GKGbRSwWgaLIlivzoLETf9f3\ndyT1v+eek/C3v2UhimEcdthhnqzRbDHgNNiY2FEULDYOVuF5qKUSay3awWwTMMbOuO15VL3EREp8\nkmR2/HcOZtthDh4dmcJSJ+zkyb04/vjP4PjjTwYgIJVKYfPmjdi06TU899wzWLz4QCQSCU/XsHbt\nS3jzzb9j9eo1GBkZxumnfxELFy7GV75yDpYuPaR0v0wmgzVr7sSdd94HRZGxatWpWL78sFJwxWke\nHjh5DM04RSKKr6MbWsHrspwblJe86CZKNxLiK1VpElkoFH27sjaLv5vZRGWZWNErSgoAEI/H0NHh\n7GbbLK1YDNQWnpu9jQyzyEahmyjLZptuZJkapXaJzxrMqqqGUEiBIAgMZ5nIZ11RZIZK7vY2A4lE\nAosWHYBFiw7wbSXz538CfX3948fvQDabhaZVvkYbN76Kvr7+UiA3b958bNiwHsuWLfdtrTsrPHDy\nEJplGhkZQyhkFn0Gs5HXwlqWY7mrho5KqSx52W0ktB05FLKaRJrtENze46j4O59vfmgwDZy2bt0I\nAOju7nZ1jc1mwqpRS3gei9l56zg7npsNQdnZRCvxKsvUDOUlPgAlnVQkEi59Fmm5M8guvnJoGTaf\nLzITILNmMyBJEqLRKADgsccexUEHHQxRlPDIIw/joYd+gu7ublxwwSUYHBxEV1dX6e+6uydjcHAg\nqGXvVPDAyQPKy3LFooZisdZGXrQEU37DWlnODqJtikHT1IY2etqOTEsS1TqYam3ktTAPDU4mW9vo\nZVlEKCRj48Y/AhCx3377Nf1Y5fhpMWBXYjV765DvQOUcRBJ8Rpkq1ZQjy8RZu1hkt6tPFAVEo5Hx\nLNMoVFWzfAeC1KuZocOD2RmZwrbNwLPPPo3HHnsUN910GzZv3ojOzk7MmDEL999/L+65ZzXmzp1v\nuT+Ln812hQdOLqLr9bl+O23kleUlYyPxKitFOkZiIJkxe21L0Ji1LWNjGVcGJtuVNxTFOjqm0Q4y\nt121ZVnCnntOwuuvZwBIWLBgQcuPaRZWB7nRV3rrGMLzWCwMWY4DAPL5AjRNLzUCsATd6Fnu6jO6\n0ayz+qp1kdmV+Gh20IvzkHmsCztif7ZtBl588QXcd989uPHGH5fKhZRly5bjxhu/i0MP/RQGB415\nlwMD29Hf701X7kSDB04uouvU9Zt88UnXXO2rFKfyEpnDFEIsRrpnjEDKHYPIdijLGaNSvNW2GHPI\nCHajS6jotjwrRbUtxKfHPd0I6Z5Kjq9HxOGHH970Y9HuJDKnjz1hNRWey7IISZIwNpYZ/7nScd4L\n4XkjtENXnyAI6Oggmc96L4jK9WqAYQlCsuPRlo0iyzEMN/3t2K0GeToSWMwyAUAqlcLtt9+Cm2++\nvST0/va3v46vfe087L77x7Bu3cvYe+990d8/FzfccC2SySQkScKGDetx7rkXBbz6nQMeOLmK+cpE\nG/8C0pOKXncgBVTOILO24scsrfj1js2gtENZzpoJ81+gbj+6hIzOoKMb6OgMQRCQzZIsk5ubqCxL\n2LZt0/i/ZSxatLCpxzFbDLBa8nIaeFu+kdNgtpmhum7QDlkmczdaOt3a+11pCVJuFNlciY9Nw832\nmDP3xBOPY3h4GJdf/s3Sbccd92lceeWliEQiiEajuPTSKxEOR3DWWefgwgvPgSAIOOOMMz3v+Jso\nCHqVM/327Uk/17KTY2SizP9uJJgqh14N0vR6rbEl5mAklcowW5aLxSLMj/ig4m+aeaKaHWMTMQa6\nNsuqVavxf/93LbLZMXR29uCNN15v6O+p3oqO+WDx/QZaG5divqCgG3q9wvNGMGeZxsYyzGaZiOeR\nhFRqzLfSpnkWpaJINUt8LFohkLeTBk3sZZk4/jNlinMHM884+UZ52pcET61lpSoNIp3GlkgSOaGx\nXJYLh0kmrJVONK8x+x3Zib+tOhGSlVJVo6xRKBTrfl5TpijIZscACNhtt90aWictgbC0OZVjbt9v\nNuvQqPC80feAWErEEAopTImWyzGyTP5nFWuX+AzNoCSJ0HUwpKdsjywThy144BQYdvVz3aKRalwr\nZR1bIghkA6UtyGQTMAwiC4UiEycvSRIRjzsHI6xg2CA4i78rfY2E0kYeiYRL/liG07NzaaNY/HD8\nXyL6+vrqWqMXFgNeQLv6vGjftxeeG6NL6n0PrFomNkvaRKsYg6KwZddQflFH5QF0fZ2dHdB1rcwW\nxN+1s2YzwGkfeODEFLWyUvUHUkZZznp1Z82IhCGKzV+NuwEt07RDZqRRGwSAip6tA42dupfMvlKa\npuP9918FQDbHAw9cUvU4ZrNNloS25QTR1Wd+Dyjm8pJdB1koRH7HopCeYniF5TE0xKZ2zVw+LM8y\n1XoP3Cqz2sGyzQCHfXjgxDSNZ6VGR0fxs589hC9/+VRIklRRlnPKiFRejbuj03FCURQkEuQKlB1x\nqBVz56GbYuBqbtuRSAiyHIOuA5JE33sRRx11pOPj0dcyaIuBarA2LsWpvERE5+SCQ9d1RCJhyLLM\nlOu/+bWsNSg6SEiQHEc+b18+tH8P7M1qzf5erX2+2bYZ4LQHPHBqO+yzUqqq4Ve/+iXuvvu/8E//\ndCR0XahLy1Ttatys0zHMCVtrQaZO0Ky2xVNoJ5ofnYdmt22alRJFEZJEvp6KomDevP6KLkpdB9MW\nAxRzZoS07we9okrIWKRQacxHoVB08Fdzxyi1WczBCCvO2nbE4zGEQnLDurByjzurLUh4/KJCb6rE\nZ2SZuACc0xo8cGp7BGzevAk33vhdyLKMG2/8MWbMmAHA7BRLs1L1PWK1rFQtT6NqGAaR7DpBm8Xf\nQWpGNE2DJBEvma6uTgwODpcEtyQrEocgkPcqm3XXBsEtzGUaljMj1pKXEYxUG9/jhvC8EQyReuPB\niJ9QJ3XD46q1x7O3BaElPqMJxjAMtivxcQE4x1144LQT8MAD9+LEEz+Lo48+DqJod2JoXisFOGWl\n7J227ea/ybI8Llh21yDSbVjrRFMUBYCA6dOnA8C49okMaVVVFel0ppQVKff28tpxvhaGdxS7QXIz\nJa96heduip4rDTdbejjPoHpFr+f1GRd2xm3mEp8kCTjhhBOw2267Yc6cfsyc2Yc5c/oxefIUz9ZU\njdtvvwXr1/8VqqriS186DbNnz8E111wBTdPQ09OLyy+/GqFQCI8//ls8/PCDEAQBJ5xwIlasWBnI\nejm14YHTTsC1136vxj3stFKtGXQ6O21b578BpDzHsicTDexaaYv3AllWIAgi5s8nYxKcAjuzELy6\n47zqedbHnLFjp+W8Etq+n8u1VvJqXHjeWEDL4vy2ciRJHB+ZogWmsSsv8f3nf/4XNm/ehI0bN+Ln\nP/9fXHvtd5BIJDBnzlwcddSxWLr0EF/WtXbtS3jzzb9j9eo1GBkZxumnfxGLFh2Az3zmczj88COw\nevVt+PWv/x+OPvo4rFlzJ+688z4oioxVq07F8uWHlZzBOWzBA6cJS3lmSi8r7TWelTKn1GnGoVhU\noao6YrEo4vGor5t4LbwSf7uFosgABHzyk4eiq2tSXRm7Ssd5Q6cTj4fK5iC6O3+MGhu6NavPC8yj\nZ7wqxVb3NTICWvP3oFx4XlnyYiOYL4eW31m6MNJ1oLOzG0uWLMWSJSRA0jQN7733Dl577VWHrLw3\nzJ//CfT19QMAEokOZLNZrFv3Mi6++FsAgKVLD8GDD96PPffcC319/SVn73nz5mPDhvVYtmy5b2vl\n1A8PnDjjuGPQKUlSqSupPONgt4kHNXvMT/F3sygKyTitXHk80unmhhtX0+lY5485b+K1oOMzghqP\nUy9BmkQ6jy6pFJ4LAnFDZzGYp4iiiI4O8j1nqfzuZDMgiiL23HM69txzuq/rkSQJ0WgUAPDYY4/i\noIMOxosv/gmhUAgA0N09GYODgxgcHERXV1fp78jtA76ulVM/PHDiONCYFUI6nca9967B7NmzcMwx\nx9lefVYbZlw5e8ydYcblsCL+rkUopEAUBUQiYWSzBVczDuVlDftNvL6xJazpwuwwi9RZec/tvgvh\nsIJYjIxEAnQkEvFAPdacoBcdbL3nbNsMPPvs03jssUdx00234fOfP7F0u9P7ycL7zHGGB06cBqjM\nSum6hieeeAK33nozli1bhoULlyCXy6FerZSXw4zLaadNXpYlbN++A1OnTvX8JFpf95hV/A/oiMep\nQzk7urBywmHSMceySB2wF1b7ITxvBEEQ0NERgyiKTGUWWbcZePHFF3Dffffgxht/jEQigWg0hlwu\ni3A4gu3bP0Jvby96e3sxODhY+puBge3o758X4Ko51eCBE6dptm7dihtuuBY7dgziO9+5Hvvv/3G4\nMTamcvaYuQ0/VnOYcTk0CGNN/F1OeSfajh07MGPGfoGsxa57jPrpJBJxiKIATdOhaQUoihKIp1E1\nWMwy2SFJEjo6YrbC6mYcz93UrJkxypw5pNOsBKDs2wykUincfvstuPnm20tC70WLDsDTTz+Jo446\nFs888ySWLDkY/f1zccMN1yKZTEKSJGzYsB7nnntRwKvnOMEDJ07T/O1vW3DQQUvxmc98DrJMP0ru\njY2hOA8zNm8ehkaHljRYF39TiEYoBsDaiaaqGhYtWhTs4sbRdRIQh8MhFItFpFLp8fdBsvU0atUo\ntRXawQoBMDKgjQiraw/UbV2zZsZs2cBSANouc+aeeOJxDA8P4/LLv1m67bLLvoPvfvcaPProz7Hr\nrtNwzDErIMsyzjrrHFx44TkQBAFnnHFmSSjOYQ9Br3Jm27496edaOBMGekXcnEFnOeaRJTQrQm4X\nUCgUMTaWYbYtvlr58KSTTsYVV1yO+fPnB7Q6gmG+WD0ANZeW6PtBjVK9HN9DEUWhlA1LJtPMvue0\nfV/TdKRSY65nQM2aNUWRStYg5oC2nteG+kfl8wWMjWVcXWMrWEtzHI43TJnS4fg7nnHiBEClFUJr\nBp3GyJJ83sje5POF0iZlHRvjxsyr1rAODrYvH+66667Yf//9A1idAZmDRzbPWt2HjY7vcVPwTLNM\nmUwOmUy25cfzimayTI1ip1kjhrWG+7/xPliztBQW/aPI8mhGmwdNnODggROHAdwx6KQCW7vsjXVs\nTBiyHG9qbEyrkOwN2ZRqlQ8PPfRQCM2m4VqElGhiUBSppc2zsaHShlanXsxZJpYNN6llg67rgbTv\nG4a11veBlLuN90FVtdIIE5b8o5xsBjicIOClOk6bUC44N7JS69atxa23/hg333wLOju76i59SJJo\nKSuJooByXyk3Nw46E61QIKWPWo+dTCbR0eGcLvYKoxMtj3Ta+xKNOSslyzIkSbTVrFWusz2yTNQY\nlOVuToAE9JFICMWiClEUfBOeV4d9AThn54SX6jg7AZWi86GhQfz4xzfjlVfW4+KLv4FEonP8xF7v\n2BiSDbFehRNdSCvDjCtWbrIYaCR743fQRLI3pN3cTyFwZVYKps6xyjZ8VdUQjYYhCAJTbfHlsGoS\nWY41G2YtG9sLz612CF5lpVi3GeBMXHjGidOWPP747/CjH92IY45ZgTPO+Ldxd14dlZmpxjr4yqHD\njGl5qdowYzvMHV7pNMtZkRBiMXazN7STMhIJlTo43fL38oJ2yTIZ68wgm62tuaomPHevAYBnmTjB\nwzNOnJ2OHTsGcNNNt2HGjJmmW923Qqg+zNhw2TaXlTRNK7MYaIesCNvrBEgQCggYHh6Fqmql96Hc\n38sr1/l6oM707GfDiDZMEISGsmHVhOckQ0iE56pqtUOoN6htF5sBzsSGZ5w4Ewz3s1Jml22qlQKA\nfL6ATCYX+DBjJ+iA1nbJitQaHmz4e0njWRF/ZyEamit2hxwD3mvDzMJz+n7UE9RyATiHJaplnHjg\nxBCPPfZL/O53vyn9vGXLJsya1YdsNotIJAIAOOecCzB7dh9++tP78NRTvwdAzNIOOmhZQKtud5xF\n542evKnFgKqqyOcLgW3gtaBu1cRHKM2s9sacDUsmx5pap7mkpCjlsxBVV4Jaq3/UGLNZJrObOlmn\nfxm58qA2n8/hq1/9KqZPn445c/oxa9Yc7L77HhAEybc1lfPmm2/gm9+8CKec8gWcdNIpuO66q7Bl\ny6aS4/cXvnAqDj54GR5//Ld4+OEHIQgCTjjhRKxYsTKwNXO8g5fq2oQVK1aWvoTr1r2MJ5/8Pd56\n6++49NIrsM8+xuiNDz54H7///eNYvXoNUqkUzj57FQ444CBIUnAnnfalPisEwNmg08liIGdKOpQP\nMw6qrEQtG7z0EXIDtzRCdq7zxizEkKnU2lznWLt09lE/rlwuH4ibuqZpyOXylu/EV77yVbzyygY8\n+8sXQGgAABhESURBVOyzuO2225DLZTFnzlz098/DsmWfxH77zfBtfZlMBjfd9H0sXHiA5favfOUc\nLF16iOV+a9bciTvvvA+KImPVqlOxfPlhpeCKMzHggROj3HvvXbjiimtw5ZWXVvxu7dqXcOCBB0NR\nFHR3d2PXXafh7bffwr77BjPXbOejfoPO55//I2699cdYs+ZeRCIxxw4ju2HGNBNCswBeip3N2bDy\nmWgsQTu8AG/8juxnIZL3oZGRJUb2RmTaP0oQMO7HJSOZHGOobKxj5szZmDlzDk466QsAyGDb1157\nFa+99gpefvnPvgZOiqLgBz+4BQ888N9V77dx46vo6+svjUOZN28+NmxYj2XLlvuxTA4j8MCJQTZt\neg1Tp+6Cnp5eAMBdd63GyMgw9tprOs477yLs2DGIrq7u0v27u7sxODjAAyfPqMxKDQxsww9/+H38\n4x9v4ZvfvBShUBS6Xr8VArkC15DLlQ8zNoudjRb8Ro0hzVAXaJbn9QHBaa5oUEuPae4cs2sAoBlG\n1mfhybKMjo4YCoXiuJll0CsiONkM9PZOwSc/eRg++cnDfF8Tfb/LeeSRh/HQQz9Bd3c3LrjgEgwO\nDqKrq6v0++7uyRgcHPBzqRwG4IETg/zqV7/EMcesAAB89rP/jP32m4Hdd/8YfvCD6/HIIz+ruD8r\nJ8SJwu9+92vceutNWLnyZFx55XUIh8MgGanWtFJGWYlQ7zBjJ+isMWPjZPODErSrdjl2nWO0vBeL\nka4xXSebbTQaaXmQrhfEYlGEw6wFy+1lM3DUUceis7MTM2bMwv3334t77lmNuXOtcyNZ/U5xvIUH\nTgyybt3LuOCCbwCA5epr6dJD8MQT/x8WLFiEd975R+n27ds/Qm9vr+/rnKgkk0n86Eersc8++5pu\nrWWFQP7fSCBl6EKqG0Oa9Tmqqlom2qdSaWZmjdlhDDmuz0coKERRRDQaKbmpW20pIjaDdP0Z4VMO\nFf6rqsZUsNyONgOLFhl6p2XLluPGG7+LQw/9FAYHB0u3DwxsR3//vCCWxwmQ9vgETyAGBrYjGo1B\nURTouo7zzvsakknS3bhu3cvYZ599sWDBYrzwwnMoFAoYGNiO7du3Y/r0fQJe+cThs5/9fFnQZIf5\nyloq/afrEnRdGP8PZYFVdegw40wmi9HRFHbsGMHoaAqFQhGyLKGjI46eni5MntwJSZKQSqUD8TKq\nB0mS0NXVAUWRMTycZDZoolqmeDyK0dFUaQSNrpOsVDqdwchICoODw+MaIhWKImPSpAQmT+7EpEkJ\nxGKRknmql0SjYXR2JpDJkBIie0FTe2SaKN/+9tfx/vvvASDn3r333hf9/XOxefNGJJNJpNNpbNiw\nHvPnfyLglXL8hmecGGNgYADd3ZMBkJP2pz99Is4776uIRqPo7Z2CM874CiKRCI4/fiXOPvtMCIKA\niy/+JkSx+ROSnQ3CL37xW1x55aUYHR3BlClTcdVV1yEUCnEbhJbwwqCTjCvJ5wvjhptANpstZUjc\nGhvjJjTLxHpnH+1Ey+fzGBoarXl/Z7PUyhE+7rlsU9sGdkqdBjp0vT2yTJs3b8Ktt96ErVs/hCzL\neOqpJ3DyyafgyisvRSQSQTQaxaWXXolwOIKzzjoHF154DgSBnAOpUJwzceA+ThwL1AYhGo2gp6cX\np5zyRaxZcyeWLDkIXV3duOyySyw2CPff/zC3QXCVxg06a411MTs7m8fGmIMpP5ITVv+oMaZGpJix\ndqKlXe1EM78XxCxVLAVStNzaSKaIvvesmZhyM0tOu1PNx4ntywCO79x771047bR/xR//+CyOPPIY\nAMDpp5+JOXPmOtogcNyEXp1Lpv9EU4nPKO99+OGH+MY3LsZTTz2JkZGU4yw8VVWRzeaRSqUxNDSK\noaHR0iYbi0XQ3d2Frq5JSCRiCIdDkCT3TwuxWKRURhodTTEbNCmKjK6uTug6MDQ06nr7vvm9GB4e\nxdDQSMn/KRIJo7t7Erq7yXsRiYQdL0oEQcCkSQlEImGMjCQZCpp0OHXNcTg7C7xUxylhtkEYHBzE\nL3/5CP7ylxcxffreOP/8r3MbhECovGJX1QL+938fwn33rcFpp52OxYsPRLGoOhp0lkP1OZXzxuRS\n51j5MONmBeblYmVWAyZiMRBDKKQglRrzTVBPxf3m40mSWMoORiLhitlvVPzP2uDodhSAczjNwAMn\nTgmzDUI+n8fixUtw+uln4oYbrsWvfvXLivszoj2dULz33ru4+urLEQqFcMcda7DHHnvC32HG9Tts\nt4tLudW2YSTwzzXVrZV3UxK38yhEUYSm6ZAkCZFIuCWPL7ewZpk4nJ0bHjhxSphtEKZO3QVz5+4P\nAFi8+ECsXfsS5szp5zYIAfPuu//AihUnYMWKE0wNAfWNjWkkkLLLStk5bNvNfWsXl3LAMAcltg2s\n+B1ZoS9fOBxGPp/H2FimlJWyenyZzVLddZ6vvjb6+eNBE2diwAMnDgCrDQIALFy4CGvXvoQFCxZh\ny5ZN2HPPvbBgwWI89NBP8K//+hWMjAxzG4QAqL+LsXJsTOsGnfYO2+a5b5qmQxQFZDI5ZLNZZoMm\nat/AujkoYA7ujBKiU1aKBlKyHCvNQ2zVed4JLgDnTFR44MQBYLVBAIBVq76Kq6++DHfddQcmT56M\n005bhWg06qoNAlBphbBx42uYM6ffsq5jj12BU089Az/60Y147bVXIQgCzjvvIvT19ds9JMcWLww6\njawUzTJpmoZslvhKdXVN8nzzboZYLIpIhGSZ2HHVroS+psVi7eCOenwRny9ym+E8LyESsZuHqDZp\nXdA+NgMcjhdwOwIOM1ArhIsuuqR020UXnYtvfONSfPDB+3jwwfvxve/djLfffgvXX381Vq9eE+Bq\nd0Yat0IASEYkHLYPRMybt6LIng8zroa5hJhKpZnOMlF9mNvBHckQGtYUgH251QmjNMc75jg7N9yO\ngNMWUCsEyl/+8iL22GNP7LLLrnj55b/gkEMOBQBMn743kslRjI2lAlrpzkr9VggA8MorG/D1r1+I\nYpFomew2eDo2Zmwsg+HhJAYHh5FOZ6BpOiKRELq6SPt9R0cckUgYsuyNJ1gsFsGkSQmk0xmmXLXL\nkSQRXV0dkGXZ8TVthWKxWHIW37FjBMPDSeRyeYiiiHg8ip6eLnR2diAej2JwcAAffPDB+GtlthmQ\nEFTQ9Oabb+BznzsBjzzyEABg27atOOecf8PXvrYKl1/+TeTzpHT5+OO/xapVp+LMM7+Mxx6rbGzh\ncFqBl+o4TGC2QqD87Gf/g/POuwgAMDg4iFmzZpd+19XVjcHBQcTj3LXXO+y0KzrS6TRWr74Nzzzz\nNL7xjUuQzeYbKvE5l5SaG2ZcDbMdAutC9UgkjFgs4msXoqZpyOc124HGzz//HNasWYNCoYA5c/ox\nZ85czJ07H7Nnz0EsFvNlfWYymQxuuun7WLjQmCF3992r8ZnPfA6HH34EVq++Db/+9f/D0UcfhzVr\n7sSdd94HRZGxatWpWL78MEya1On7mjk7JzzjxGECsxUCQDr2stkMdt/9Y7b3Z3kD3JlZt24tTj31\nC0gmU/jv/34QBx20HNWyUvVgZKXSGB5OYseOYaTTRFheaQoZqtupPhqNMDm7rRxRFDFpUgLhcKiU\nAQqSYlFFJpPD8cevxCOPPIp77rkPRx11HEZGRnHnnbfj058+Eqef/gV89NE2X9elKAp+8INbLJ28\n69a9jGXLlgMgQ9BfeulFbNz4Kvr6+pFIJBAORzBv3nxs2LDe17Vydm54xonDBGYrBAB44YU/YsGC\nRaWfe3t7y6aSD3ArhAB4/PHf4vzzL8bSpYeYbq1thQCgAYPOyqyU1RQyYhlVQoXONDCSJDK7TdN0\npk03AWNkSiaTRSbDivu31Wagt3dXHHbYrjjssCMAEI+3f/zjbUye3OPrmmjXoJlMJoNQKAQA6O6e\njMHBQQwODqKrq6t0H3L7gK9r5ezc8MCJEzjlVggAsGnTRsvmfMABB+Luu1dj5cqTsGXLZvT29iIW\niwex3AnNJZdcVse9Kq0Q3BpmbLTfC6WSknmArq7rkCSRuUCkHEEQkEjEIEkiRkZSrgz7dYtaNgOh\nUAgzZsz0eVW1ccoosppp5LQvPHDiBE65FQIADA5ab5s3bz5mzerDWWedAUEQcOGFl5Q/TMOUWyFs\n2bIJ3/72VXjwwQegKAp6e6fg29++CoqicCuElvDGoNM8qkSSRCQScQiCgEKhiEgkjGg0UmEKycIe\nGgopSCRiyGbzSCbHgl6OCR26ThsE2kPFEY3GkMtlEQ5HSoa8ldnp7ejvnxfgKjk7G9yOgMOBYYXw\n7LNP44EHfoZEIoEbbrgOCxcuQk9PL7dC8JxyG4T6s1JUVJ1OZy3Dbo2xMdQKgWSlzIGUqjbjY9Qc\nggDE4zEoioxkcowJTytKO9kM3H33anR1deGkk07BDTdch49//BM46qhjcfPN38e++87AkUcejVNP\n/Tzuuut+SJKEM874F9x1131IJHgjCad+qtkR8IwThwNihXDFFddg/fq1SKWSSCQSSKWS6OzscrRC\n4B19btK4Qef777+P3/zmV7joooswPJysMHOsPsxYMQ0ztnbweYEsy+joiJmcyj05TBO0T5Zp8+ZN\nuPXWm7B164eQZRlPPfUErrzyWlx33VV49NGfY9ddp+GYY1ZAlmWcddY5uPDCcyAIAs4440weNHFc\nhQdOnAmP2Qrh/PO/jtNP/yISiQ7MnDkTixcvwZNP/p5bIfiOvRWCruvQNBW/+MUjWLPmLpx55r9h\ndDRlc197jGHGJDPlxjDjWsRihkEoS/PwrFomtoMmAJg9uw+33vpfFbfffPPtFbcddtgRJTE7h+M2\nPHDiTHioFYKmabjllh/grrvuw2677Y4rrvgWnnvumYr7c7FpUAj48MMP8d3vXoNMJoPbbrsTe+21\nV0ui88aHGdc/NoZ4SBGnctbm4fE5cxxO8/DAiTPhoVYIw8ND0HW95B21cOFibN68iVshMMR3vnMZ\nli5djn/+53+paE0n0GHGjY2NMVN9mHEMslx7bEw0GkE0GvbVzLI++Jw5DqdVeODEmdCYrRA6O7uQ\nTCYxNDSE7u5ubN68ER//+ALsvvvHuBUCI9xxxz017lFLK9V4IGUeZkyhgvNwOIREIlYaZlwsqgiH\nFSY9pMhrQMel8CwTh9MsPHDiTGjMVgiSJOHCC7+BSy65AKFQCNOm7YYjjjgKsiy7boXA8Yv6rBCA\n+g06ASMrRbVSoigiFiPdfZqmQ1EEdHQkLCW+4Ep17SMA53DaAW5HwOEEgJ2H1A9/eCt+/OOboCgK\n9t//4/jKV84GAPz0p/fhqad+D4B0CB100LKAVr2z0rwVAkBKedRDKpUaK1kcUNG5ohDNFPGeMkp8\nfphetpPNAIfDEtXsCHjgxOEEDPWQ2rDhr7jqquuw99774Prrr8aKFSegp6cXl112CVavXoNUKoWz\nz16F++9/uO55bZxmoZ101Q061617GUuWHABJUpDJZKs+Ih0bQ/VSoihCVYuWDj73slI8y8ThtEK1\nwIl/ozicgLn33rtw2mn/isHBAey99z4AgAMOOAh//vOfsHbtSzjwwIOhKAq6u7ux667T8PbbbwW8\n4okADTik0n9kkLEIXRcwMjKC73znSvzwhzdi+/aBmkETQMbGGMOMRzE0RIcZo6VhxuWQQct0/fwU\nz+G4Df9WcTgBYvaQmjZtN/z1r2uh6zpeeulFDA3twI4dg+jq6i7dv7u7mw8sDQQje/OnP72IL3/5\nS+junow777wXkyZNhq4LZbqp2tBhxplMFqOjKezYMYLR0RQKheK4YWYckyd3YdKkBGKxCBRFhlBD\niGXYDPCgicPxCi4O53AChHpIAcC3vnU5brnlRoiiiBkzZmJsrHKOGUNWQBOS//3f/8GDDz6Ayy77\nDhYuXFz229atEOodZlwsFvHXv66HosjYY4+9IIoCUzYDa9e+hCuu+CamTycZ1H333Q9f+MKpuOaa\nK6BpGnp6enH55VcjFAoFvFIOp3F44MThBAj1kAKAffbZD7fc8p8AgF/+8hEkk0n09PTgnXf+Ubo/\nHWTKCYZDDjkUxxyzwsE13n0rhPJhxgAdGyPhT396AY899iuMjIxg9uw5mDt3f8ybtz/mzJnLhKv9\nxz++ANde+73Sz//xH9/BZz7zORx++BFYvfo2/PrX/w8nnnhygCvkcJoj+EsTDmeCYvaQAsjG8sYb\nr0NVVfzf//0GS5cuw4IFi/HCC8+hUChgYGA7tm/fXrqK5/jPLrvs2kBQYhZnSyDXqVQrJZTKe42W\n+FRVRTabx7/8y5fw4IM/w09+8hBOPPFk5HI53Hvv3Vi58hicfvoXkEyy1dyzbt3LWLZsOQBg6dJD\n8NJLLwa8Ig6nOXjGicMJCLOHFACsWHEC/uM/rgIAHHHE0dhnn/0AAMcfvxJnn30mBEHAxRd/E6LY\n2vVOOp3GtddeiWRyFIVCAaeffiYmT+7BjTd+F4IA7LvvDFx88bcAcCsE93EnK2XWMnV3T8Ehhxxa\nGkRdLBbx/vvvIR4P1qT17bffwiWXXIDR0VGcccaZyGQypdJcd/dkixs/h9NOcDsCDmeC8cgjD2H7\n9u0466xzMDCwHeeeexZ6enrxta+di76+flx11bdx1FHHYq+9pnMrhECotEIADINOqwCcTbZv/wgb\nNvwVhx/+T/jgg/fx7//+FWQyGfz2t08CAN57711ce+2VdTjBczjBwO0IOBxOic7OLoyMjAAARkdH\nMWlSJz788AP09fUDoGWUP3MrhMCotEIAxJIVAutBEwBMmTIVn/rUkRAEAbvv/jH09PQgmRxFLkds\nG7hWj9POsP3t43A4rnPEEUdh27atOOWUlTjnnH/D2Wefh44O4+qKlFEGuBUCM5Rrpdg/bT/++G/x\n05/eDwDjn6UdOPbY4/H00yTj9MwzT2LJkoODXCKH0zRc48ThTDD+7/9+g1122RU//OGP8frrf8Ol\nl34diYShh3Gq3nMrBE69LFu2HFdddRmee+4ZFAoFXHzxNzFjxmxce+0VePTRn2PXXaeVbDg4nHaD\nB04czgTjlVfWY8mSAwEAM2bMRD6fxciI0e4+MLAdvb1T0Ns7hVshcJoiFovje9+7qeL2m2++PYDV\ncDjuwn7Ol8PhuMruu++BjRtfBQBs3fohotE49tprOtav/ysAWkY5iFshcDgcjg28q47DmWCk02lc\nf/3VGBraAVUtYtWqr2Ly5B58//v/AV3XMGfOXPz7v18IgDhlP/747yAIAs4886tYtOgAV45fboew\nePGScVPER/HYY78v3ZfbIXA4nCCo1lXHAycOh+MrdnYIRx99HBKJDtx99x349a+fAAB88MH73A6B\nw+EEArcj4HA4zFBuh9DZ2YWTTz4Fn/nMZy3343YIHA6HRXjgxOFwfKXSDuF8xGKVLtfcDoHD4bAI\nD5w4HI6vUDuEhx76JW655T9x003fq/1H4HYIHA6HDXjgxOFwfKXcDmFgYDtUVa24X2/vFOzYYcwz\n43YIHA6HBXjgxOFwfKXSDiFmK/jmdggcDodFeFcdh8PxFTs7hD/84Sn8/e9v4JVX1mPevPlYtmw5\nPv/5f/HEDoGuodwS4f9v745Z2ggDMI4/FzIEbOBqbolTHOwQXFSUIAiBIoJEcM3QwcHFoUIcmqUI\nAZEIBmsRyeTgkE8gOGaXEAQFM9UPkBsalYNMHYTU4jW8g3Li+/9tgRy82R6Oy/9SKU+1WlWxWEzJ\nZFLb2ztKJBJWJBEOD/d1fX0lx3G0ubk1eG8hYCtyBADwRFgSYXQ0pY2Nr8pmJ3V09EPp9Jhyufl3\nn0Rot1tqNE61t3eg29tf2t2tqF4/ifpYQKTIEQDAE2FJhGq1pmx2UpLkuq56vd9WJBFarQstLOQl\nSZnMuO7uenp4uI/2UMAbxnACYJ2wJMLIyAdJUhAEOj8/Uz7/2Yokgu/7cl138Nl1P8r3/SFXAHZj\nOAGwzv+SCEEQqFwuqVj8okxm/Nl1NiQRhjy9AUAMJwAWCksi9Pt9lctbWlxc0vLyiiQ7kgie5/1z\nh6nb7b673wi8JIYTAOuEJREajVNNTU2rUFgdfM+GJMLcXE7N5uP7ATudG3meF1pyB/CIf9UBsE5Y\nEqFS+a50ekzxeFySNDMzq7W19VdLIrwlx8c/dXnZluM4KpW+aWLiU9RHAiJFjgAAAMAQOQIAAIAX\nwHACAAAwxHACAAAwxHACAAAwNPThcAAAAPzFHScAAABDDCcAAABDDCcAAABDDCcAAABDDCcAAABD\nDCcAAABDfwC4K9BsAHvwMwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7ff47b71f390>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "ck_CiKkT64Jo",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Из графиков видно, что параметр batch_size не оказывает влияние на точность. В дальнейшем будет использоваться только один параметр (число нейронов)"
      ]
    },
    {
      "metadata": {
        "id": "HuhgpXzY0T8s",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "####Зависимость точности от одного параметра (число нейронов)"
      ]
    },
    {
      "metadata": {
        "id": "O1GwBql1kpqQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1095
        },
        "outputId": "ae80e271-8970-4a49-ff7b-7d1e2bb52155"
      },
      "cell_type": "code",
      "source": [
        "for optimizer in optimizers:\n",
        "    x = np.array(params)\n",
        "    acc = np.array(accuracy[optimizer])\n",
        "    std_acc = acc.std()\n",
        "    \n",
        "    plt.plot(x, acc)\n",
        "    plt.fill_between(x, acc+std_acc,\n",
        "                     acc-std_acc, facecolor='blue', alpha=0.3)\n",
        "    plt.title('{} accuracy'.format(optimizer))\n",
        "    plt.ylabel('accuracy')\n",
        "    plt.xlabel('param')\n",
        "    plt.show()"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfUAAAFnCAYAAAC/5tBZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7ff49c0b74a8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfUAAAFnCAYAAAC/5tBZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nzBR2PUMAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7ff49c125cf8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfUAAAFnCAYAAAC/5tBZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n1EVERAxCRV1ERMQgVNRFREQM4i9CBtwKXMw5bAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7ff49bf0b780>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "gl7_Rl5w1CMu",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Графики показывают, что точность начинает убывать для значения параметра <300 для всех трех алгоритмов. Также видно, что SGD убывает быстрее всего. Высокое стандартное отклонение не позволяет выявить отличия у ADAM и Nesterov Momentum"
      ]
    },
    {
      "metadata": {
        "id": "mFFNMk2H1euB",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### График зависимости значения функции ошибки от значения структурного параметра для лучшего алгорима\n",
        "Здесь исследуется зависимость точности алгоритма ADAM от значения параметра. Рассмотрим 10 значений числа нейронов: от 1 до 50"
      ]
    },
    {
      "metadata": {
        "id": "uU8B4W0U0518",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 3137
        },
        "outputId": "2394ab2a-831d-4307-f02b-3437c6c2693c"
      },
      "cell_type": "code",
      "source": [
        "params = np.linspace(1, 50, num=10, dtype='Int32')\n",
        "optimizer = 'adam'\n",
        "accuracy = list()\n",
        "\n",
        "for param in params:\n",
        "        model = baseline_model(optimizer, param)\n",
        "        model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=8, batch_size=1000, verbose=1)\n",
        "        scores = model.evaluate(X_test, y_test, verbose=0)\n",
        "        accuracy.append(scores[1])\n",
        "        print(\"Baseline Error: %.2f%%\" % (100-scores[1]*100))"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/8\n",
            "60000/60000 [==============================] - 6s 101us/step - loss: 2.2658 - acc: 0.1377 - val_loss: 2.2056 - val_acc: 0.1742\n",
            "Epoch 2/8\n",
            "60000/60000 [==============================] - 0s 7us/step - loss: 2.1382 - acc: 0.1855 - val_loss: 2.0781 - val_acc: 0.1872\n",
            "Epoch 3/8\n",
            "60000/60000 [==============================] - 0s 6us/step - loss: 2.0399 - acc: 0.1979 - val_loss: 2.0086 - val_acc: 0.2037\n",
            "Epoch 4/8\n",
            "60000/60000 [==============================] - 0s 7us/step - loss: 1.9852 - acc: 0.2024 - val_loss: 1.9668 - val_acc: 0.2095\n",
            "Epoch 5/8\n",
            "60000/60000 [==============================] - 0s 6us/step - loss: 1.9488 - acc: 0.2064 - val_loss: 1.9364 - val_acc: 0.2122\n",
            "Epoch 6/8\n",
            "60000/60000 [==============================] - 0s 6us/step - loss: 1.9214 - acc: 0.2104 - val_loss: 1.9122 - val_acc: 0.2141\n",
            "Epoch 7/8\n",
            "60000/60000 [==============================] - 0s 6us/step - loss: 1.8993 - acc: 0.2133 - val_loss: 1.8931 - val_acc: 0.2153\n",
            "Epoch 8/8\n",
            "60000/60000 [==============================] - 0s 7us/step - loss: 1.8811 - acc: 0.2172 - val_loss: 1.8767 - val_acc: 0.2196\n",
            "Baseline Error: 78.04%\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/8\n",
            "60000/60000 [==============================] - 6s 105us/step - loss: 2.1540 - acc: 0.3435 - val_loss: 1.8747 - val_acc: 0.5415\n",
            "Epoch 2/8\n",
            "60000/60000 [==============================] - 1s 9us/step - loss: 1.5259 - acc: 0.6302 - val_loss: 1.1882 - val_acc: 0.7021\n",
            "Epoch 3/8\n",
            "60000/60000 [==============================] - 1s 10us/step - loss: 0.9920 - acc: 0.7429 - val_loss: 0.8158 - val_acc: 0.7930\n",
            "Epoch 4/8\n",
            "60000/60000 [==============================] - 1s 10us/step - loss: 0.7381 - acc: 0.8014 - val_loss: 0.6466 - val_acc: 0.8224\n",
            "Epoch 5/8\n",
            "60000/60000 [==============================] - 1s 10us/step - loss: 0.6145 - acc: 0.8271 - val_loss: 0.5563 - val_acc: 0.8407\n",
            "Epoch 6/8\n",
            "60000/60000 [==============================] - 1s 9us/step - loss: 0.5447 - acc: 0.8432 - val_loss: 0.5038 - val_acc: 0.8571\n",
            "Epoch 7/8\n",
            "60000/60000 [==============================] - 1s 10us/step - loss: 0.5005 - acc: 0.8548 - val_loss: 0.4689 - val_acc: 0.8638\n",
            "Epoch 8/8\n",
            "60000/60000 [==============================] - 1s 10us/step - loss: 0.4692 - acc: 0.8645 - val_loss: 0.4438 - val_acc: 0.8747\n",
            "Baseline Error: 12.53%\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/8\n",
            "60000/60000 [==============================] - 6s 105us/step - loss: 2.0059 - acc: 0.3940 - val_loss: 1.5664 - val_acc: 0.5935\n",
            "Epoch 2/8\n",
            "60000/60000 [==============================] - 1s 10us/step - loss: 1.1838 - acc: 0.7068 - val_loss: 0.8478 - val_acc: 0.8129\n",
            "Epoch 3/8\n",
            "60000/60000 [==============================] - 1s 10us/step - loss: 0.7113 - acc: 0.8242 - val_loss: 0.5694 - val_acc: 0.8513\n",
            "Epoch 4/8\n",
            "60000/60000 [==============================] - 1s 10us/step - loss: 0.5263 - acc: 0.8587 - val_loss: 0.4531 - val_acc: 0.8793\n",
            "Epoch 5/8\n",
            "60000/60000 [==============================] - 1s 11us/step - loss: 0.4390 - acc: 0.8809 - val_loss: 0.3926 - val_acc: 0.8931\n",
            "Epoch 6/8\n",
            "60000/60000 [==============================] - 1s 10us/step - loss: 0.3896 - acc: 0.8936 - val_loss: 0.3559 - val_acc: 0.9000\n",
            "Epoch 7/8\n",
            "60000/60000 [==============================] - 1s 10us/step - loss: 0.3586 - acc: 0.9005 - val_loss: 0.3325 - val_acc: 0.9045\n",
            "Epoch 8/8\n",
            "60000/60000 [==============================] - 1s 10us/step - loss: 0.3364 - acc: 0.9061 - val_loss: 0.3144 - val_acc: 0.9110\n",
            "Baseline Error: 8.90%\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/8\n",
            "60000/60000 [==============================] - 6s 107us/step - loss: 1.8853 - acc: 0.4440 - val_loss: 1.3057 - val_acc: 0.7730\n",
            "Epoch 2/8\n",
            "60000/60000 [==============================] - 1s 10us/step - loss: 0.9154 - acc: 0.8164 - val_loss: 0.6358 - val_acc: 0.8567\n",
            "Epoch 3/8\n",
            "60000/60000 [==============================] - 1s 10us/step - loss: 0.5479 - acc: 0.8645 - val_loss: 0.4570 - val_acc: 0.8809\n",
            "Epoch 4/8\n",
            "60000/60000 [==============================] - 1s 10us/step - loss: 0.4296 - acc: 0.8868 - val_loss: 0.3809 - val_acc: 0.8993\n",
            "Epoch 5/8\n",
            "60000/60000 [==============================] - 1s 11us/step - loss: 0.3728 - acc: 0.8986 - val_loss: 0.3411 - val_acc: 0.9081\n",
            "Epoch 6/8\n",
            "60000/60000 [==============================] - 1s 10us/step - loss: 0.3358 - acc: 0.9070 - val_loss: 0.3092 - val_acc: 0.9154\n",
            "Epoch 7/8\n",
            "60000/60000 [==============================] - 1s 10us/step - loss: 0.3066 - acc: 0.9150 - val_loss: 0.2886 - val_acc: 0.9198\n",
            "Epoch 8/8\n",
            "60000/60000 [==============================] - 1s 10us/step - loss: 0.2874 - acc: 0.9201 - val_loss: 0.2750 - val_acc: 0.9210\n",
            "Baseline Error: 7.90%\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/8\n",
            "60000/60000 [==============================] - 7s 110us/step - loss: 1.7951 - acc: 0.5693 - val_loss: 1.0951 - val_acc: 0.8020\n",
            "Epoch 2/8\n",
            "60000/60000 [==============================] - 1s 11us/step - loss: 0.7571 - acc: 0.8337 - val_loss: 0.5294 - val_acc: 0.8723\n",
            "Epoch 3/8\n",
            "60000/60000 [==============================] - 1s 11us/step - loss: 0.4704 - acc: 0.8800 - val_loss: 0.3961 - val_acc: 0.8967\n",
            "Epoch 4/8\n",
            "60000/60000 [==============================] - 1s 11us/step - loss: 0.3814 - acc: 0.8976 - val_loss: 0.3403 - val_acc: 0.9072\n",
            "Epoch 5/8\n",
            "60000/60000 [==============================] - 1s 11us/step - loss: 0.3386 - acc: 0.9069 - val_loss: 0.3094 - val_acc: 0.9141\n",
            "Epoch 6/8\n",
            "60000/60000 [==============================] - 1s 11us/step - loss: 0.3129 - acc: 0.9130 - val_loss: 0.2913 - val_acc: 0.9181\n",
            "Epoch 7/8\n",
            "60000/60000 [==============================] - 1s 11us/step - loss: 0.2952 - acc: 0.9171 - val_loss: 0.2790 - val_acc: 0.9211\n",
            "Epoch 8/8\n",
            "60000/60000 [==============================] - 1s 11us/step - loss: 0.2822 - acc: 0.9206 - val_loss: 0.2674 - val_acc: 0.9241\n",
            "Baseline Error: 7.59%\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/8\n",
            "60000/60000 [==============================] - 7s 109us/step - loss: 1.6464 - acc: 0.6102 - val_loss: 0.9119 - val_acc: 0.8294\n",
            "Epoch 2/8\n",
            "60000/60000 [==============================] - 1s 11us/step - loss: 0.6420 - acc: 0.8560 - val_loss: 0.4626 - val_acc: 0.8895\n",
            "Epoch 3/8\n",
            "60000/60000 [==============================] - 1s 11us/step - loss: 0.4202 - acc: 0.8898 - val_loss: 0.3634 - val_acc: 0.9035\n",
            "Epoch 4/8\n",
            "60000/60000 [==============================] - 1s 11us/step - loss: 0.3534 - acc: 0.9034 - val_loss: 0.3219 - val_acc: 0.9121\n",
            "Epoch 5/8\n",
            "60000/60000 [==============================] - 1s 11us/step - loss: 0.3199 - acc: 0.9107 - val_loss: 0.2976 - val_acc: 0.9169\n",
            "Epoch 6/8\n",
            "60000/60000 [==============================] - 1s 11us/step - loss: 0.2973 - acc: 0.9163 - val_loss: 0.2825 - val_acc: 0.9201\n",
            "Epoch 7/8\n",
            "60000/60000 [==============================] - 1s 11us/step - loss: 0.2805 - acc: 0.9217 - val_loss: 0.2691 - val_acc: 0.9229\n",
            "Epoch 8/8\n",
            "60000/60000 [==============================] - 1s 11us/step - loss: 0.2676 - acc: 0.9245 - val_loss: 0.2587 - val_acc: 0.9262\n",
            "Baseline Error: 7.38%\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/8\n",
            "60000/60000 [==============================] - 7s 111us/step - loss: 1.6308 - acc: 0.6217 - val_loss: 0.8581 - val_acc: 0.8375\n",
            "Epoch 2/8\n",
            "60000/60000 [==============================] - 1s 11us/step - loss: 0.6020 - acc: 0.8612 - val_loss: 0.4327 - val_acc: 0.8906\n",
            "Epoch 3/8\n",
            "60000/60000 [==============================] - 1s 11us/step - loss: 0.3985 - acc: 0.8941 - val_loss: 0.3469 - val_acc: 0.9053\n",
            "Epoch 4/8\n",
            "60000/60000 [==============================] - 1s 11us/step - loss: 0.3400 - acc: 0.9061 - val_loss: 0.3127 - val_acc: 0.9116\n",
            "Epoch 5/8\n",
            "60000/60000 [==============================] - 1s 12us/step - loss: 0.3088 - acc: 0.9136 - val_loss: 0.2898 - val_acc: 0.9183\n",
            "Epoch 6/8\n",
            "60000/60000 [==============================] - 1s 12us/step - loss: 0.2866 - acc: 0.9196 - val_loss: 0.2716 - val_acc: 0.9213\n",
            "Epoch 7/8\n",
            "60000/60000 [==============================] - 1s 11us/step - loss: 0.2698 - acc: 0.9243 - val_loss: 0.2583 - val_acc: 0.9269\n",
            "Epoch 8/8\n",
            "60000/60000 [==============================] - 1s 11us/step - loss: 0.2553 - acc: 0.9284 - val_loss: 0.2466 - val_acc: 0.9297\n",
            "Baseline Error: 7.03%\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/8\n",
            "60000/60000 [==============================] - 7s 113us/step - loss: 1.5121 - acc: 0.6585 - val_loss: 0.7585 - val_acc: 0.8388\n",
            "Epoch 2/8\n",
            "60000/60000 [==============================] - 1s 12us/step - loss: 0.5594 - acc: 0.8644 - val_loss: 0.4194 - val_acc: 0.8925\n",
            "Epoch 3/8\n",
            "60000/60000 [==============================] - 1s 12us/step - loss: 0.3874 - acc: 0.8961 - val_loss: 0.3387 - val_acc: 0.9073\n",
            "Epoch 4/8\n",
            "60000/60000 [==============================] - 1s 13us/step - loss: 0.3288 - acc: 0.9092 - val_loss: 0.3010 - val_acc: 0.9161\n",
            "Epoch 5/8\n",
            "60000/60000 [==============================] - 1s 12us/step - loss: 0.2959 - acc: 0.9177 - val_loss: 0.2761 - val_acc: 0.9227\n",
            "Epoch 6/8\n",
            "60000/60000 [==============================] - 1s 12us/step - loss: 0.2721 - acc: 0.9237 - val_loss: 0.2584 - val_acc: 0.9274\n",
            "Epoch 7/8\n",
            "60000/60000 [==============================] - 1s 12us/step - loss: 0.2540 - acc: 0.9291 - val_loss: 0.2441 - val_acc: 0.9314\n",
            "Epoch 8/8\n",
            "60000/60000 [==============================] - 1s 13us/step - loss: 0.2392 - acc: 0.9329 - val_loss: 0.2323 - val_acc: 0.9325\n",
            "Baseline Error: 6.75%\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/8\n",
            "60000/60000 [==============================] - 7s 114us/step - loss: 1.4577 - acc: 0.6863 - val_loss: 0.6938 - val_acc: 0.8577\n",
            "Epoch 2/8\n",
            "60000/60000 [==============================] - 1s 13us/step - loss: 0.5215 - acc: 0.8724 - val_loss: 0.3952 - val_acc: 0.8998\n",
            "Epoch 3/8\n",
            "60000/60000 [==============================] - 1s 13us/step - loss: 0.3690 - acc: 0.9004 - val_loss: 0.3220 - val_acc: 0.9122\n",
            "Epoch 4/8\n",
            "60000/60000 [==============================] - 1s 13us/step - loss: 0.3133 - acc: 0.9140 - val_loss: 0.2850 - val_acc: 0.9205\n",
            "Epoch 5/8\n",
            "60000/60000 [==============================] - 1s 13us/step - loss: 0.2805 - acc: 0.9224 - val_loss: 0.2601 - val_acc: 0.9263\n",
            "Epoch 6/8\n",
            "60000/60000 [==============================] - 1s 12us/step - loss: 0.2575 - acc: 0.9280 - val_loss: 0.2420 - val_acc: 0.9303\n",
            "Epoch 7/8\n",
            "60000/60000 [==============================] - 1s 13us/step - loss: 0.2400 - acc: 0.9327 - val_loss: 0.2297 - val_acc: 0.9341\n",
            "Epoch 8/8\n",
            "60000/60000 [==============================] - 1s 13us/step - loss: 0.2261 - acc: 0.9362 - val_loss: 0.2163 - val_acc: 0.9374\n",
            "Baseline Error: 6.26%\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/8\n",
            "60000/60000 [==============================] - 7s 116us/step - loss: 1.4326 - acc: 0.6756 - val_loss: 0.6596 - val_acc: 0.8498\n",
            "Epoch 2/8\n",
            "60000/60000 [==============================] - 1s 13us/step - loss: 0.4957 - acc: 0.8753 - val_loss: 0.3837 - val_acc: 0.8980\n",
            "Epoch 3/8\n",
            "60000/60000 [==============================] - 1s 13us/step - loss: 0.3595 - acc: 0.9017 - val_loss: 0.3182 - val_acc: 0.9114\n",
            "Epoch 4/8\n",
            "60000/60000 [==============================] - 1s 13us/step - loss: 0.3124 - acc: 0.9129 - val_loss: 0.2873 - val_acc: 0.9194\n",
            "Epoch 5/8\n",
            "60000/60000 [==============================] - 1s 13us/step - loss: 0.2840 - acc: 0.9206 - val_loss: 0.2670 - val_acc: 0.9244\n",
            "Epoch 6/8\n",
            "60000/60000 [==============================] - 1s 13us/step - loss: 0.2637 - acc: 0.9263 - val_loss: 0.2484 - val_acc: 0.9298\n",
            "Epoch 7/8\n",
            "60000/60000 [==============================] - 1s 13us/step - loss: 0.2466 - acc: 0.9311 - val_loss: 0.2350 - val_acc: 0.9331\n",
            "Epoch 8/8\n",
            "60000/60000 [==============================] - 1s 12us/step - loss: 0.2308 - acc: 0.9357 - val_loss: 0.2229 - val_acc: 0.9362\n",
            "Baseline Error: 6.38%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "wTZMVGuq4v6v",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 377
        },
        "outputId": "e1eb84f3-2356-4fc0-8f23-cca63445a030"
      },
      "cell_type": "code",
      "source": [
        "x = np.array(params)\n",
        "acc = np.array(accuracy)\n",
        "std_acc = acc.std()\n",
        "\n",
        "plt.plot(x, acc)\n",
        "plt.fill_between(x, acc+std_acc,\n",
        "                 acc-std_acc, facecolor='blue', alpha=0.3)\n",
        "plt.title('{} accuracy'.format(optimizer))\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('param')\n",
        "plt.show()"
      ],
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAe8AAAFnCAYAAACPasF4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7ff48a349278>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "ELGwswCG514C",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Видно, что можно выбрать значение параметра 10 без дначительной потери качества модели"
      ]
    },
    {
      "metadata": {
        "id": "EX6t_h4nodIz",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "##Вывод\n",
        "Для данной задачи среди предложенных алгоритмов оптимизации лучшим является ADAM"
      ]
    }
  ]
}