Two_Channel_CNN_Structural_Evolutionary_with_attention_Serine.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/faisalahm3d/Research/blob/master/Two_Channel_CNN_Structural_Evolutionary_with_attention_Serine.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "23EJJrCPzz6z",
        "outputId": "aa5aa530-cd18-4d9b-9ad3-ba1f58f4450d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "B6Yl9aaamAfO"
      },
      "outputs": [],
      "source": [
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "NsEqb4_VFrVs"
      },
      "outputs": [],
      "source": [
        "def calculate_performance(test_num, pred_y, labels):\n",
        "    tp = 0\n",
        "    fp = 0\n",
        "    tn = 0\n",
        "    fn = 0\n",
        "    for index in range(test_num):\n",
        "        if labels[index] == 1:\n",
        "            if labels[index] == pred_y[index]:\n",
        "                tp = tp + 1\n",
        "            else:\n",
        "                fn = fn + 1\n",
        "        else:\n",
        "            if labels[index] == pred_y[index]:\n",
        "                tn = tn + 1\n",
        "            else:\n",
        "                fp = fp + 1\n",
        "\n",
        "    # entering any of the else statement means that the evaluation metric is invalid\n",
        "    acc = float(tp + tn) / test_num\n",
        "    \n",
        "    if((tp + fp) != 0):\n",
        "      sensitivity = float(tp) / (tp + fn)\n",
        "    else:\n",
        "      sensitivity = 0\n",
        "\n",
        "    if((tn + fp) != 0):\n",
        "      specificity = float(tn) / (tn + fp)\n",
        "    else:\n",
        "      specificity = 0\n",
        "    \n",
        "    precision = tp / (tp + fp)\n",
        "\n",
        "    MCC = float(tp * tn - fp * fn) / (np.sqrt((tp + fp) * (tp + fn) * (tn + fp) * (tn + fn)))\n",
        "    \n",
        "    return acc, sensitivity, specificity, precision, MCC"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "jpAr3km28GvW"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "npzfile = np.load('/content/drive/MyDrive/DLPhos/normalized_balanced_pssm_spd_profile/train_balanced_S_10.npz', allow_pickle=True)\n",
        "x_train= npzfile['arr_0']\n",
        "y_train= npzfile['arr_1']\n",
        "x_test = npzfile['arr_2']\n",
        "y_test = npzfile['arr_3']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "bFJEQCfal6vz"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x_train, x_val, y_train, y_val = train_test_split(x_train, y_train, test_size=0.10, random_state=10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vz_1R7MgBy8q"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "scaler = StandardScaler()\n",
        "x_train = scaler.fit_transform(x_train)\n",
        "x_test = scaler.fit_transform(x_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UUKB9_-5Evdh",
        "outputId": "fbecea70-9dfc-451b-b27f-daa960cc276b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([0, 0, 0, ..., 1, 1, 0], dtype=int32)"
            ]
          },
          "execution_count": 77,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rqq2Gk2OMOhO"
      },
      "outputs": [],
      "source": [
        "from tensorflow.keras.utils import to_categorical as labelEncoding\n",
        "y_train = labelEncoding(y_train, dtype=int)\n",
        "y_test  = labelEncoding(y_test, dtype=int)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "Atm3ksudCRLN"
      },
      "outputs": [],
      "source": [
        "def get_pssm_spd(data):\n",
        "  row, col = data.shape\n",
        "  print (row)\n",
        "  pssm= data[:, :420]\n",
        "  spd = data[:, 420:]\n",
        "  return np.array([pssm[r,:].reshape(21,20) for r in range(row)]), np.array([spd[r, :].reshape(21,8) for r in range(row)])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jHcEzplgBPP1",
        "outputId": "b562bd7a-757e-4fa4-9405-cf820bcc3848"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(7306, 588)"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "x_test.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IjBsJQ5mBV6M",
        "outputId": "e8b7378b-c022-4f78-acbe-cee1feabfa5c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(55182, 588)"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "x_train.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bmd5Ud5ACo45",
        "outputId": "d76ea0c2-7498-409d-db65-314a8fb2f283"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "49663\n",
            "7306\n",
            "5519\n"
          ]
        }
      ],
      "source": [
        "trainX1, trainX2 = get_pssm_spd(x_train)\n",
        "testX1, testX2 = get_pssm_spd(x_test)\n",
        "valX1, valtX2 = get_pssm_spd(x_val)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EgtI4nWuDNa3",
        "outputId": "7a608e9d-c656-4242-bee8-18f0778848e1"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(46904, 21, 20)"
            ]
          },
          "execution_count": 47,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "trainX1.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "WDhM8NWsIgS5"
      },
      "outputs": [],
      "source": [
        "from keras.layers import Input, Conv1D, Dense, Activation, Dropout, MaxPooling1D, Flatten,concatenate,GlobalMaxPool1D\n",
        "from keras import Model\n",
        "from tensorflow.keras.optimizers import (RMSprop, Adam, SGD)\n",
        "from keras.models import load_model\n",
        "from tensorflow.keras.utils import plot_model\n",
        "from keras import regularizers\n",
        "from keras.regularizers import (l1, l2, l1_l2)\n",
        "from keras.callbacks import (EarlyStopping, ModelCheckpoint)\n",
        "from tensorflow.keras.optimizers import (RMSprop, Adam, SGD)\n",
        "from keras.layers import (Input, Dense, Dropout, Flatten, BatchNormalization,\n",
        "                                     Conv1D, Conv2D, MaxPooling1D, MaxPooling2D,\n",
        "                                     LSTM, GRU, Embedding, Bidirectional, Concatenate)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "y4gxIu9vLu9D"
      },
      "outputs": [],
      "source": [
        "def get_structural_model():\n",
        "    ### Head-1:\n",
        "    input1 = Input(shape=trainX2[0].shape)\n",
        "\n",
        "    x = Conv1D(filters=32, kernel_size=3, padding='same', kernel_regularizer=l2(l=0.01))(input1)\n",
        "    #x = BatchNormalization()(x)\n",
        "    x = Dropout(rate=0.5)(x)\n",
        "\n",
        "    #x = Conv1D(filters=8, kernel_size=3, padding='same', activation='relu', kernel_regularizer=l2(l=0.01))(x)\n",
        "    #x = BatchNormalization()(x)\n",
        "    #x = Dropout(rate=0.50)(x)\n",
        "\n",
        "    head1 = Flatten()(x)\n",
        "\n",
        "    z= Dense(units=256, activation='relu',)(head1)\n",
        "    z = Dropout(rate=0.5)(z)\n",
        "\n",
        "    z = Dense(units=64, activation='relu',)(z)\n",
        "    z = Dropout(rate=0.5)(z)\n",
        "\n",
        "    z = Dense(units=16, activation='relu',)(z)\n",
        "    z = Dropout(rate=0.5)(z)\n",
        "    \n",
        "    z = Dense(1, activation='sigmoid')(z)\n",
        "\n",
        "    return Model(inputs=[input1], outputs=[z])\n",
        "#end-def"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "JM1iqUntMXMs",
        "outputId": "62946276-4c25-4a79-811c-aff7babd8e3b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"model\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_1 (InputLayer)        [(None, 21, 8)]           0         \n",
            "                                                                 \n",
            " conv1d (Conv1D)             (None, 21, 32)            800       \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 21, 32)            0         \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 672)               0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 256)               172288    \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 256)               0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 64)                16448     \n",
            "                                                                 \n",
            " dropout_2 (Dropout)         (None, 64)                0         \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 16)                1040      \n",
            "                                                                 \n",
            " dropout_3 (Dropout)         (None, 16)                0         \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 1)                 17        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 190,593\n",
            "Trainable params: 190,593\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        },
        {
          "data": {
            "image/png": 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gnoFYT10TrE+hq+jWuHPnDn31zW59XhJtbW0XL14sLS2l72yIRKL4+Pj4+Pies2Tpn1wuz83NbW5uDg0NHWw5x8XFTZo0SSwWE0Ioinr69Om1a9fKysq0Wk1C0ECDpoEG+1wT2hIdHV1aWnr//v01a9YkJCSwXZ2XRX19/bx587y8vN5//306JSYmJiQkJDQ0VJWbQjpVUFBw+vTpvLw85SNn9Z9zSkpKUVHR+fPn6VHwZ86ccXZ29vf3/9vf/qbdehI00OBpIOblsS66JmJiYujB26NGjTp58qR2M1fd1q1bjYyMXFxcFM806wK6JlR08eLF6OhobdVnKMnNzU1MTGROKjYAmp+HaKC+aKWBSI+uCQ7FmLTixIkTy5Yto/Q7z8hQEhISQgg5efKk7opoa2ubPXv29evXWcwK58kgp4fzEDTB4XCys7OZDxy8LF0TQ4YW5wXFFKMAgwQCMQuovicMVGteUO1OMXrhwgWhULhz5049Hw0AQCBmQVxcXExMzNatW6urq69evVpZWenv70/PHpKens78wZKRkbF9+3bFYlpaWlBQED0vaFlZmVgsDgsLk0qlGzdurKiouH37dmdn55w5c+jZSdTKivw+sqWrq0v3BwAA/gUCsb6pOGGg6rQ1xWhgYKBEIomNjR1YNQBgwBCI9U3dCQPVos8pRgFAWxCI9U2TCQNVodMpRgFAFxCI9U2TCQP7pYspRgFA1xCI9a3fCQM1mRdUF1OMAoCuIRDrW78TBqo7L6i2phjNy8vD8DUAViAQs+Czzz5LTEyMj4+3s7MLCAgYNWqUYj5lQkh4ePisWbOWL18+ZsyYhIQEeh5OxSuz1q9f7+Dg4O3tvWDBgvr6ekJIe3u7j4+PmZmZv7+/l5fXlStXFG+7UTcrAGAFO++se8lxOJyoqKioqKhe19ra2ubn5zNTdu/erfh78uTJFRUVzLUCgaCvybzVymr+/PkSiUTFjwAAWoQrYoPH+hSjAKAhBGIAAJYhEBuwLVu2ZGVlNTU1ubu7nzp1iu3qAMAAoY/YgCUmJiYmJrJdCwDQFK6IAQBYhkAMAMAyBGIAAJYhEAMAsKyXm3UnTpzQfz2GBvrBiiF/AG/cuEFego9puF6S83BIYb5JlH47LwAA6JSytzgDGBz6dVC4+gODhj5iAACWIRADALAMgRgAgGUIxAAALEMgBgBgGQIxAADLEIgBAFiGQAwAwDIEYgAAliEQAwCwDIEYAIBlCMQAACxDIAYAYBkCMQAAyxCIAQBYhkAMAMAyBGIAAJYhEAMAsAyBGACAZQjEAAAsQyAGAGAZAjEAAMsQiAEAWIZADADAMgRiAACWIRADALAMgRgAgGUIxAAALEMgBgBgGQIxAADLEIgBAFiGQAwAwDIEYgAAlnEoimK7DgBqOHr06KFDh7q6uujFhw8fEkLc3d3pRSMjow8++GDlypWs1Q9AfQjEYGDu3LkzceJEJRv885//nDBhgt7qA6A5BGIwPGPHjr13716vq0QiUWlpqZ7rA6Ah9BGD4Vm9ejWXy+2ZzuVy16xZo//6AGgIV8RgeB48eCASiXo9dUtLS0Uikf6rBKAJXBGD4fHw8Hj11Vc5HA4zkcPhTJkyBVEYDBECMRikd99919jYmJlibGz87rvvslUfAE2gawIMUnV1tZOTk2IQGyHEyMjo6dOnw4cPZ7FWAAODK2IwSA4ODgEBAYqLYmNj45kzZyIKg4FCIAZDtXr1aubvudWrV7NYGQBNoGsCDJVEIrG3t+/o6CCEcLnc6upqa2trtisFMBC4IgZDJRQK582bZ2JiYmJismDBAkRhMFwIxGDAVq1aJZfL5XI5JpcAg4auCTBg7e3tdnZ2FEXV1taamZmxXR2AgaI0kJ2dzXb1AQDYl52drUksNdFKDTTPZAhYtmxZRESEr68v2xXRrdTUVELIpk2b2K7I/ykqKuJwOMrnYxsyXpJzzOAsW7ZMwxw06po4ceLEsmXL0LlB43A42dnZ77zzDtsV0a2QkBBCyMmTJ9muyP/p7OwkhJiYaOGSYvB7Sc4xg6N5u7wUpy8MYS9JCIahDaMmAABYhkAMAMAyBGIAAJYhEAMAsAyBmGXnz5+3srL69ttv2a4IALAGgZhlGPwHABj6w7LAwMCmpiY9FNTW1jZ79uzr16/roSwAUAuuiF8Whw4dqq6uZrsWANALBGI2Xbt2zdXVlcPh7Nu3jxCSmZlpYWFhbm5+5syZ+fPnC4XCkSNHHjt2jN44PT2dz+c7ODisW7fOycmJz+f7+fndunWLXisWi01NTR0dHenFjz76yMLCgsPh1NbWEkIiIiIiIyPLy8s5HA79es0LFy4IhcKdO3ey8LEB4F8hELNpxowZzL6C8PDwTZs2tbW1CQSC7Ozs8vJyDw+PDz/8UCaTEULEYnFYWJhUKt24cWNFRcXt27c7OzvnzJlTWVlJCElPT2c+YZmRkbF9+3bFYlpaWlBQkKenJ0VRZWVlhBC5XE4IYb7zDQDYgkA8GPn5+QmFQnt7+9DQ0NbW1sePHytWmZiYjBs3jsfjeXt7Z2ZmNjc3Z2VlDaCIwMBAiUQSGxurvVoDwAAhEA9qpqamhBD6irinKVOmmJub//rrr/qtFABoGQKxYePxeDU1NWzXAgA0gkBswGQyWWNj48iRI9muCABoBIHYgBUUFFAUNW3aNHrRxMSkr04MABjMEIgNTFdXV0NDQ2dn5507dyIiIlxdXcPCwuhVIpGovr4+NzdXJpPV1NQ8evSIuaOtre3Tp08rKiqam5tlMlleXh6GrwEMEgjEbNq3b9/UqVMJIdHR0YsWLcrMzKRfRDRhwoQHDx4cOHAgMjKSEDJv3rzS0lJ6l/b2dh8fHzMzM39/fy8vrytXrvB4PHpVeHj4rFmzli9fPmbMmISEBPplmr6+vvT4tvXr1zs4OHh7ey9YsKC+vp6VzwsAvcIjzmzasGHDhg0bmCnh4eGKv+lBxN12EQgEVVVVveZma2ubn5/PTNm9e7fi78mTJ1dUVCgW58+fL5FIBlpxANAmXBEbGPpBDAAYSvQXiO/du/enP/3plVdeEQgEJiYmVlZWXl5egYGBN27c0E8Furq6UlNT/fz8mImnT5/28PDgMJiamjo4OMycOTM5ObmhoUE/dRvCLl26FBMTwzzOq1evZm4wd+5cgUBgbGz8yiuv3L59m5VKxsfHe3t7C4VCHo8nEok++eSTlpaWbtv0ev6o4ptvvpk6dapAIHBzc1uzZs2zZ8/o9LNnzyYlJenuP+tLfuSTkpLGjh1rZmZmYWExduzY2NhY5k9AJeXqul16R2kgOztbxRwOHjzI5XL/7d/+7cKFCw0NDe3t7eXl5cePH/fz8/uv//ovTeqgovv370+fPp0QMnHixJ5rPT09raysKIqib4VduXIlLCyMw+E4OTn99NNPKhZBCMnOztZmpf9VTEwM/XzHqFGjTp48qbuClAsODg4ODlZx423btgUFBUkkEnrR09Nz2LBhhJBz584xN8vLy1u0aJGWK6qOgICAjIyMuro6iUSSnZ3N5XLnzZvH3ED5+aPE8ePH6aDQ2NhYWFjo4eExadIkmUxGr01LSwsICGhoaFAxN9XPMRz5wMDAPXv2VFdXNzc3nzhxgsvlzpkzR8Vyddcufeagyc4qBuIbN24YGxu/+eabivNP4cKFC3v37tWkDqooKipasmTJkSNHJk2apDwQM508edLIyMjBwaGxsVGVUnQdiAcJ1QPxrl27vLy82traFCmenp5Hjx41MjJydnZmHlXWw0FgYGBnZ6dikZ614/Hjx/Riv+ePErNmzRoxYkRXVxe9SM/udO3aNcUGYrHY19e351ejVyqeYzjyFEUtXryYeQRCQkIIIU+fPlWlXEo37aKEPromduzYIZfLd+3a1fPN52+99Va3u1W6MHHixNOnT69cuVIxwEAVwcHBYWFh1dXVX375pe7qNlSVlZXFxsZu376dz+cz0/38/CIiIp48eRIVFcVW3Xo6d+6csbGxYtHOzo4QIpVK6cWBnT+0yspKJycnDodDL7q4uBBCmCML4+LiioqK0tLSNKk/E448LScnh3kEnJ2dCSGK/gfl5RIdtItyOg/EHR0dly9fHjZs2Ouvv658S4qiUlJS6BltbGxs3n77bcUsCsrnhxw3bhyHwzEyMnrttdfoQ/nJJ59YWVnx+fz//u//1qTy9BDdvLw8TTJ5OaWnp1MUtXDhwp6rduzY4eXldfDgwUuXLvW674DPBEKIXC7ftm2bq6urmZnZhAkT6B9t6nry5ImZmZm7u/sA9u3Gw8ODOQ003UHs4eGhSLGxsQkICEhLS6O09K4WHPlelZaWWltbu7m5qViu1tulH5pcTqvSNXH//n1CyLRp0/rNbdu2baampocPH25sbLxz586rr75qZ2f37Nkzeu3WrVsJIZcvX25qaqqurvb397ewsOjo6KAoqrOzc9SoUa6urszfGps2bUpNTe1WxBtvvKF61wRFUXTvvouLS7+Vp9A18a88PDy8vb27JXp6ej58+JCiqOvXrxsZGY0aNaqlpYXq8QN5wGcCRVFRUVE8Hu/UqVMNDQ1btmwxMjJSvZef1traKhAIxGJxz1V9nT9KFBQUcLnc9PR0iUTyyy+/jBs37q233uq2TUxMDCGksLCw39xUOcdw5Jk6Ojqqqqr27t3L4/EOHz6sVrnabZd+ctBkZ1UC8c8//0wI+cMf/qB8M6lUamlpGRoaqkj58ccfCSHx8fH0In0SKDp9MjIyCCFlZWX0Iv0cxIkTJ+jF1tZWV1fXpqambqWoG4gpiuJwONbW1sorT0MgVmhpaeFwOEFBQd3SFeGAoij6WZUNGzZQ/xoONDkT2trazM3NFftKpVIejxceHq7WB9y6dauXl5fiNhfTwMLBp59+qrjuGTlyZGVlZbcNvvrqK0LI119/3W9W/Z5jOPLdDB8+nBAybNiwv/zlL4r/GSqWq8V26ZfOH+iwtLQk/9r50quSkpKWlpYpU6YoUqZOnWpqaqp4A0U33eaH/OMf/xgXF5eWlkZ3yR85cuTtt98WCoUaVr61tZWiKNXz0dtQPBZVVVX1O81QdXU1RVHm5uZKttmxY8e5c+cyMjKWLVvGTNfkTLh3755UKh0/fjy9yszMzNHRUa1pQnNyck6cOPHdd98JBALV91Ji69atBw8evHz58htvvFFdXf3nP//Z19f3+vXrdGcxjT5Qz58/17w4HPluKisr6fEqMTEx+/fvz8/Pd3BwULFcLbZLv3QeiEeNGsXn8+kOCiUaGxvJ71Fbwdraurm5WZVSLC0t/+M//iM5OfnHH398/fXXv/jii1OnTg24zgp0tceOHavi9mlpaXrr3WdRcHCw8g3a29sJIcpvsPD5/KysrBkzZrz//vtJSUmKdE3OhNbWVkLIp59+yrwIdXJy6ndH2vHjx1NSUgoKCkaMGKHiLsr99ttvSUlJMTExb775JiHE3d39wIEDNjY2ycnJ6enpis3oh9Hpg6YhHPluuFyuvb393Llz3d3dvby8EhMTmd9Q5eVqsV36pfObdTwe76233qqtrf3hhx96rq2vr//jH/9ICLG2tiaEdGtyteZ4FIvFXC43NTX16tWrLi4unp6eGtedXLhwgRAyf/58Fbd/Sbom+j0O9Bnc75B4X1/fzZs3l5aWJiQkKBI1ORPs7e0JId3uDaj4M2Xv3r1HjhzJz8/XYiwoLS2Vy+XMDIVCoa2tbUlJCXOzjo4O8vtB0xCOfF9EIpGxsTHzyPdbrhbbpV/6GL4WFxfH4/E2b97c1tbWbdUvv/xCj2kbP368paUl3aFMu3XrVkdHx2uvvaZiKSNHjnznnXdOnToVGxsbERGhebWfPXuWmpo6cuTI999/X/PcXioODg4cDqepqanfLRMSEsaOHVtYWKhI0eRMcHFx4fP5RUVFatWWoqjo6Oji4uLc3Nxu14MaokPYb7/9pkhpbm6ur69n9ksQQugDRfdmaghHnlZXV7dixSQCW/wAACAASURBVApmCv1PkT7yKparxXbplz4C8aRJk44ePfrLL7/4+/ufP3++qalJJpM9fPjwwIEDH3zwAZfLJYTw+fzIyMicnJwjR45IJJLi4uL169c7OTmtXbtW9YIiIyM7OzsbGhroX4JqoSiqpaWFHnhfU1OTnZ09ffp0Y2Pj3NxczfuaXzbm5uYeHh59TU7ERP9MZo7o1ORM4PP5a9asOXbsWGZmpkQikcvlVVVVdBwMDQ0dPnx4rw/y3r17d/fu3QcOHOByucyH3ffs2aPKh1WSs7u7+6xZsw4cOHD16tW2trbKykr6U3zwwQfMzegD5ePjo0pxyuHI0ywsLL777rv8/HyJRCKTyQoLC9977z0LC4vNmzerXq4W26V/mvxKVf0RZ4qiHj9+HBUV5ePjY2lpaWxsbG1tPXny5A8++OCHH36gN+jq6kpOTh49ejSXy7WxsVm8ePG9e/foVRkZGXTH+ejRo8vLy/fv308HRzc3t/v37zNLmTVr1sGDB7sVfePGjenTpyt6rBwdHf38/L7//nuKos6ePTthwgRzc3NTU1MjIyNCCD1M4vXXX4+Pj6+rq1P9aJCXpmtCleFrdE+RVCqlF3NycujOIjs7O/p+PdPHH3/MHESlyZnw4sWL6OhoV1dXExMTe3v7pUuXlpSUUBS1ePFiQsi2bdt6VrW4uLjXr0ZycjK9gZLzR3nOFEXV1tZGRESIRCIej2dpaTl9+vS//vWv3bYJDAx0dnZWPH2nhCrnGI48beHChe7u7paWljwez9PTMzQ0tLi4WMVyadptl35y0GRntQLxkIdAzFRaWmpiYtLXyE39k8vl/v7+hw4dGmw519bW8vn8PXv2qLKxKucYjrxWaL1dlMM0mKATIpEoPj4+Pj6+52Ra+ieXy3Nzc5ubm0NDQwdbznFxcZMmTRKLxdqqEo68Vmi9XZRDIAZdiYmJCQkJCQ0NVeXekU4VFBScPn06Ly9P+QBb/eeckpJSVFR0/vx5+k6JtuDIa0hH7aIE3tABOrRz587vvvtu165dn3/+OYvVmD179uzZswdbzmfOnHnx4kVBQQHzjpm24MgPmE7bpS8IxKBbc+fOnTt3Ltu1GIwWLVq0aNEi3eWPIz8wum6XXqFrwmC0tbUN4PUQus4KADSHQGwwDh06xJxQcZBkBQCaQyDWK6rv+V7FYrGpqamjoyO9+NFHH1lYWHA4nNraWkJIREREZGRkeXk5h8MRiUTp6el8Pt/BwWHdunVOTk58Pt/Pz08xOYtaWRFCLly4IBQKd+7cqeejAQA0BGK9iouLi4mJ2bp1a3V19dWrVysrK/39/enpndLT0+n3tdAyMjK2b9+uWExLSwsKCvL09KQoqqysTCwWh4WFSaXSjRs3VlRU3L59u7Ozc86cOZWVlepmRX6fmqCrq0v3BwAAeoFArD9tbW0pKSlLlixZtWqVlZWVj4/Pl19+WVtbu3///oFlaGJiQl9ce3t7Z2ZmNjc3Z2VlDSCfwMBAiUQSGxs7sGoAgIYQiPVH3fle1TJlyhRzc3O1ZoAFgEECgVh/NJxzuV88Hq+mpkYrWQGAPiEQ64/mcy4rIZPJtJUVAOgZArH+9Dvfq4mJieLlT+oqKCigKGratGmaZwUAeoZArD/9zvcqEonq6+tzc3NlMllNTc2jR4+Yu9va2j59+rSioqK5uZkOsl1dXQ0NDZ2dnXfu3ImIiHB1dQ0LCxtAVnl5eRi+BsAiBGK9+uyzzxITE+Pj4+3s7AICAkaNGlVQUGBhYUGvDQ8PnzVr1vLly8eMGZOQkEC/o8XX15celLZ+/XoHBwdvb+8FCxbU19cTQtrb2318fMzMzPz9/b28vK5cuaJ4WZm6WQEAizDXhF5xOJyoqKioqKhe19ra2ubn5zNTdu/erfh78uTJFRUVzLUCgaCvdzGoldX8+fMlEomKHwEAtA5XxAas33dEAoBBQCAGAGAZArFB2rJlS1ZWVlNTk7u7+6lTp9iuDgBoBH3EBikxMTExMZHtWgCAduCKGACAZQjEAAAsQyAGAGAZAjEAAMu0cLMuJCRE80yGhtTU1JMnT7JdC926efMmQaOz52U4x15CHIqiBrzzjRs3UlJStFgbAHUVFhYSQiZPnsx2ReCltnnzZl9f3wHvrlEgBmAd/VKoEydOsF0RgIFDHzEAAMsQiAEAWIZADADAMgRiAACWIRADALAMgRgAgGUIxAAALEMgBgBgGQIxAADLEIgBAFiGQAwAwDIEYgAAliEQAwCwDIEYAIBlCMQAACxDIAYAYBkCMQAAyxCIAQBYhkAMAMAyBGIAAJYhEAMAsAyBGACAZQjEAAAsQyAGAGAZAjEAAMsQiAEAWIZADADAMgRiAACWIRADALAMgRgAgGUIxAAALEMgBgBgmQnbFQBQj1QqffHihWKxo6ODENLQ0KBI4fF45ubmLNQMYKA4FEWxXQcANWRmZn700UdKNsjIyAgPD9dbfQA0h0AMBqampsbJyUkul/e61tjY+LfffrO3t9dzrQA0gT5iMDD29vazZ882NjbuucrY2PgPf/gDojAYHARiMDyrVq3q9ZccRVGrVq3Sf30ANISuCTA8zc3N9vb2zFt2NFNT05qaGqFQyEqtAAYMV8RgeAQCQVBQEJfLZSaamJgsWrQIURgMEQIxGKSVK1d2dnYyU+Ry+cqVK9mqD4Am0DUBBqmjo8POzq65uVmRYmlpWVtby+PxWKwVwMDgihgMkqmpaUhIiKmpKb3I5XKXLVuGKAwGCoEYDNWKFSvox+oIITKZbMWKFezWB2DA0DUBhqqrq8vR0bGmpoYQYmdn9+zZs14HFwMMfrgiBkNlZGS0YsUKU1NTLpe7cuVKRGEwXAjEYMCWL1/e0dGBfgkwdAOZfe3GjRuVlZVarwqAuiiKGjZsGCHk4cOHFRUVbFcHgLi4uPj6+qq9G6W+4OBgHdQfAMDgBQcHDyCoDnA+4uDg4JMnT2r3AxgWDoeTnZ39zjvvsF0R3QoJCSGEDOa2vnv3LiHE29ub7Yro1eBvl5cT3S4DgInhwbC9bCEYhiTcrAMAYBkCMQAAyxCIAQBYhkAMAMAyBGIAAJYhEOvV+fPnraysvv32W7YrAgCDCAKxXmGKJQDoCeOI9SowMLCpqUkPBbW1tc2ePfv69et6KAsANIQr4qHp0KFD1dXVbNcCAFSCQKw/165dc3V15XA4+/btI4RkZmZaWFiYm5ufOXNm/vz5QqFw5MiRx44dozdOT0/n8/kODg7r1q1zcnLi8/l+fn63bt2i14rFYlNTU0dHR3rxo48+srCw4HA4tbW1hJCIiIjIyMjy8nIOhyMSiQghFy5cEAqFO3fuZOFjA0B/EIj1Z8aMGcy+gvDw8E2bNrW1tQkEguzs7PLycg8Pjw8//FAmkxFCxGJxWFiYVCrduHFjRUXF7du3Ozs758yZQ897l56ezpzmIiMjY/v27YrFtLS0oKAgT09PiqLKysoIIXK5nBDS1dWltw8LAKpDIGafn5+fUCi0t7cPDQ1tbW19/PixYpWJicm4ceN4PJ63t3dmZmZzc3NWVtYAiggMDJRIJLGxsdqrNQBoDQLxIEK/CpO+Iu5pypQp5ubmv/76q34rBQA6h0BsSHg8Hv2KNgAYShCIDYZMJmtsbBw5ciTbFQEALUMgNhgFBQUURU2bNo1eNDEx6asTAwAMCwLxoNbV1dXQ0NDZ2Xnnzp2IiAhXV9ewsDB6lUgkqq+vz83NlclkNTU1jx49Yu5oa2v79OnTioqK5uZmmUyWl5eH4WsAgxYCsf7s27dv6tSphJDo6OhFixZlZmampqYSQiZMmPDgwYMDBw5ERkYSQubNm1daWkrv0t7e7uPjY2Zm5u/v7+XldeXKFR6PR68KDw+fNWvW8uXLx4wZk5CQYGZmRgjx9fWlx7etX7/ewcHB29t7wYIF9fX1rHxeAFARHnHWnw0bNmzYsIGZEh4ervibHkTcbReBQFBVVdVrbra2tvn5+cyU3bt3K/6ePHky863G8+fPl0gkA604AOgWrogHNfpBDAAY2nQViE+fPu3h4cFhMDU1dXBwmDlzZnJyckNDg47KBXZdunQpJiaG2fqrV69mbjB37lyBQGBsbPzKK6/cvn2blUrGx8d7e3sLhUIejycSiT755JOWlpZu23R1daWmpvr5+amVc1JS0tixY83MzCwsLMaOHRsbG8v8IaKk3LNnzyYlJenu/y7aZXC2y/9HqS84ODg4OFiVLT09Pa2srCiKom86XblyJSwsjMPhODk5/fTTTwMoevAghGRnZ+su/5iYGPr5jlGjRp08eVJ3BSmneltTFLVt27agoCCJREIvenp6Dhs2jBBy7tw55mZ5eXmLFi3SckXVERAQkJGRUVdXJ5FIsrOzuVzuvHnzmBvcv39/+vTphJCJEyeqlXNgYOCePXuqq6ubm5tPnDjB5XLnzJmjYrlpaWkBAQENDQ2qFIR2USvnwdkuTHoKxEwnT540MjJycHBobGwcQOk6JZVKfX19VdlS14F4kFC9rXft2uXl5dXW1qZI8fT0PHr0qJGRkbOzM7OtWf/CBwYGdnZ2KhbpWTseP35MLxYVFS1ZsuTIkSOTJk1S9wu/ePFi5hEICQkhhDx9+lSVcimKEovFvr6+Mpms34LQLmrlPAjbpRsW+oiDg4PDwsKqq6u//PJL/ZeuHGaPHJiysrLY2Njt27fz+Xxmup+fX0RExJMnT6KiotiqW0/nzp0zNjZWLNrZ2RFCpFIpvThx4sTTp0+vXLlSMUBFdTk5Ocwj4OzsTAhR/M5VXi4hJC4urqioKC0tTd1y+4J2oQ22dumJnZt19GDYvLw8Qsju3bvNzc0FAkF1dXVkZKSzs/O9e/coikpJSaHnu7GxsXn77bcVcywonx+SEKJkX3VnjwQVpaenUxS1cOHCnqt27Njh5eV18ODBS5cu9bqvkvZSPlMoIUQul2/bts3V1dXMzGzChAnZ2dkDqPyTJ0/MzMzc3d0HsK9ypaWl1tbWbm5uKpZrY2MTEBCQlpZGaelNLmiXXrHeLr0YwFW0hl0TFEXRPeUuLi704tatWwkhGzdu3Lt375IlS/73f/9327Ztpqamhw8fbmxsvHPnzquvvmpnZ/fs2TN6+7Vr11pYWNy9e7e9vb2kpGTq1KkCgUDxU0L5vitXrhw+fLiiJsnJyYSQmpoaenHp0qX07JH9IuiaYPDw8PD29u6W6Onp+fDhQ4qirl+/bmRkNGrUqJaWFqrHT2Dl7UWfG5cvX25qaqqurvb397ewsOjo6KDXRkVF8Xi8U6dONTQ0bNmyxcjISN17D62trQKBQCwW91z1xhtvqPsTmNbR0VFVVbV3714ej3f48GG1yo2JiSGEFBYWKi8C7aJWbrTB0y49sROIKYricDjW1tb033SjKjpxpFKppaVlaGioYuMff/yREBIfH08vrl27lpntTz/9RAjZvn27KvsiEKtFlbZuaWnhcDhBQUHd0hVfeIqi6GdVNmzYQP3rF77f9up2bmRkZBBCysrKKIpqa2szNzdX7CuVSnk8Xnh4uFofcOvWrV5eXoobWUwD/sIPHz6cEDJs2LC//OUvitikYrlfffUVIeTrr79WXgTaRa3caIOkXXrFzgMdra2tFEUJhcJe15aUlLS0tEyZMkWRMnXqVFNTU2b/AxNzfkh199VEamrqyZMntZ7toHLz5k3F7BZ9qa6upijK3NxcyTY7duw4d+5cRkbGsmXLmOnqthdzptB79+5JpdLx48fTq8zMzBwdHdWaJjQnJ+fEiRPfffedQCBQfa9+VVZWNjY2FhYWxsTE7N+/Pz8/38HBQcVy6cP4/PlzzauBdulmkLRLr9jpI75//z4hZOzYsb2ubWxsJIRYWloyE62trZubm/vKUDE/5AD2BQ21t7cTQpTfQuHz+VlZWRwO5/33329ra1Oka9Jera2thJBPP/1UMVb90aNHzHssyh0/fvzzzz8vKCgYNWqUiruoiMvl2tvbz5079/jx4yUlJYmJiaqXSz+qTh9SDaFduhkk7dIrdq6IL1y4QAiZP39+r2utra0JId2aXMkMkMz5IdXdVxObNm1ivq9oSKIH+ihHn6P9Dnr39fXdvHnznj17EhISXF1d6URN2sve3p4QkpqaGhER0e/G3ezdu/fixYv5+fndYo12iUQiY2PjkpIS1cvt6Oggvx9SDaFd+sJuu/SKhSviZ8+epaamjhw58v333+91g/Hjx1taWv7888+KlFu3bnV0dLz22mu9bs+cH7LffTF7pNY5ODhwOJympqZ+t0xISBg7dmxhYaEiRd22ZnJxceHz+UVFRWrVlqKo6Ojo4uLi3Nxc7X7b6+rqVqxYwUwpLS2Vy+UuLi6ql0sfRro3U0NoF9pga5de6TwQUxTV0tLS1dVFUVRNTU12dvb06dONjY1zc3P76iPm8/mRkZE5OTlHjhyRSCTFxcXr1693cnJau3atYpu+5ofsd1+1Zo/U1UEZWszNzT08PPqanIiJ/iHMHLOpSlsryW3NmjXHjh3LzMyUSCRyubyqquq3334jhISGhg4fPrzXR3Xv3r27e/fuAwcOcLlc5iP4e/bsUeXDKsnZwsLiu+++y8/Pl0gkMpmssLDwvffes7Cw2Lx5s+rl0ofRx8dHlcooh3ahDbZ26d0AbvCpcmfw7NmzEyZMMDc3NzU1NTIyIoTQwyRef/31+Pj4uro6xZZJSUn0Bb+Li4tiTElXV1dycvLo0aO5XK6Njc3ixYvpwcW0tWvXcrlcZ2dnExMToVD49ttvl5eXK9Yq37eurm7WrFl8Pt/d3f1Pf/rTxx9/TAgRiUT06Lfbt2+7ubmZmZnNmDFDMVKnVwSjJhjEYjGXy5VKpfRiTk6Op6cnIcTOzo6+I8/08ccfM4dJKWmvjIwM+ibJ6NGjy8vL9+/fT//zdnNzu3//PkVRL168iI6OdnV1NTExsbe3X7p0aUlJCUVRixcvJoRs27atZ1WLi4t7/SIkJyfTG9y4cWP69OlOTk50uqOjo5+f3/fff0+vVZIzRVELFy50d3e3tLTk8Xienp6hoaHFxcUqlksLDAx0dnamL1yUQLsYdLv0pNvhazqydu1aW1tbFitAIRD/q9LSUhMTk77GZuqfXC739/c/dOiQAeVMUVRtbS2fz9+zZ0+/W6Jd9JYzpYN26clQp8HE/JCDikgkio+Pj4+P7zldlv7J5fLc3Nzm5ubQ0FBDyZkWFxc3adIksVisrQzRLlqh9XbpyVADMQw2MTExISEhoaGhqtwd0qmCgoLTp0/n5eUpH0I7qHImhKSkpBQVFZ0/f57L5WoxW7SLhnTULt0YXiDesmVLVlZWU1OTu7v7qVOn2K4O/H87d+4Ui8W7du1itxqzZ88+evSoYkYRg8j5zJkzL168KCgosLGx0XrmaJcB02m7MBneq5ISExO7jcQektra2mbPnn39+vVBlVW/5s6dO3fuXD0UNMQsWrRo0aJFussf7TIwum4XBcO7In5JaHFCTsztCTDIIRDrEKWlCTmVz/yp7tyeFy5cEAqFO3fu1PPRAIC+IBDrUFxcXExMzNatW6urq69evVpZWenv70/PG5Kens58PDojI2P79u2KxbS0tKCgIHoeuLKyMrFYHBYWJpVKN27cWFFRcfv27c7Ozjlz5lRWVqqbFfl9wElXV5fuDwAAqASBWFfa2tpSUlKWLFmyatUqKysrHx+fL7/8sra2dv/+/QPL0MTEhL649vb2zszMbG5uzsrKGkA+gYGBEokkNjZ2YNUAAK1DINYVnU7IyZz5EwAMHQKxruh6Qk7FzJ8AYOgQiHVFpxNyMmf+BABDh0CsKzqdkJM586eGWQEA6xCIdUXrE3L2NfOnulnl5eVh+BrAoIJArEOfffZZYmJifHy8nZ1dQEDAqFGjCgoKLCws6LXh4eGzZs1avnz5mDFjEhIS6LlAfX196UFp69evd3Bw8Pb2XrBgQX19PSGkvb3dx8fHzMzM39/fy8vrypUrirfgqJsVAAwqhveIswHhcDhRUVFRUVG9rrW1tc3Pz2em7N69W/H35MmTKyoqmGsFAkFfk3yrldX8+fMlEomKHwEA9ABXxAYDM38CDFUIxAAALEMgNgCY+RNgaEMfsQF4SWb+BHhp4YoYAIBlCMQAACxDIAYAYBkCMQAAyxCIAQDYRqkvODiY7VoDAAxGwcHBAwiqHIqi1C3pxo0b9CQGAKxLTU0lhGzatIntigAQQoiLi4uvr6+6ew0kEAMMHvT7+k6cOMF2RQAGDn3EAAAsQyAGAGAZAjEAAMsQiAEAWIZADADAMgRiAACWIRADALAMgRgAgGUIxAAALEMgBgBgGQIxAADLEIgBAFiGQAwAwDIEYgAAliEQAwCwDIEYAIBlCMQAACxDIAYAYBkCMQAAyxCIAQBYhkAMAMAyBGIAAJYhEAMAsAyBGACAZQjEAAAsQyAGAGAZAjEAAMsQiAEAWIZADADAMgRiAACWIRADALAMgRgAgGUmbFcAQD23bt365z//qVh88OABIWT//v2KlIkTJ77xxhss1AxgoDgURbFdBwA1nDt3LigoyNjY2MjIiBBCn8AcDocQ0tXVJZfLv/3223//939nuZYA6kAgBgMjk8ns7OwkEkmva4VCYU1NjampqZ5rBaAJ9BGDgeFyucuXL+811CpZBTCYIRCD4Vm+fHlHR0fPdJlMtmLFCv3XB0BD6JoAw9PV1TVixIjnz593S7e3t3/27BnddwxgQHDKguExMjJavXp1ty4IU1PTsLAwRGEwRDhrwSD17J3o6OhYvnw5W/UB0AS6JsBQjR49uqysTLHo4eFRXl7OYn0ABgxXxGCoVq1axeVy6b9NTU3fe+89dusDMGC4IgZDVVZWNnr0aMXivXv3vLy8WKwPwIDhihgMlUgkmjhxIofD4XA4EydORBQGw4VADAbs3XffNTY2NjY2fvfdd9muC8DAoWsCDNjTp09dXFwoiqqsrHR2dma7OgADpI9AnJKScuPGDV2XAi+ngoICQsjMmTNZrgcMUb6+vps3b9Z1Kfromrhx48bNmzf1UBBbTp06VVVVxXYtdO7mzZuDsB1dXV3d3NzYrgWbBme7DA03b97Uz0WknuYjnjZt2smTJ/VTlv5xOJxNmza98847bFdEt0JCQgghg60d6+vrCSG2trZsV4Q1g7Ndhgb62OoBJoYHw/Yyh2AYMjBqAgCAZQjEAAAsQyAGAGAZAjEAAMsQiFlz/vx5Kyurb7/9lu2KAADLEIhZg2caAYCG4WusCQwMbGpq0kNBbW1ts2fPvn79uh7KAoABwBXx0Hfo0KHq6mq2awEAfUIgZse1a9dcXV05HM6+ffsIIZmZmRYWFubm5mfOnJk/f75QKBw5cuSxY8fojdPT0/l8voODw7p165ycnPh8vp+f361bt+i1YrHY1NTU0dGRXvzoo48sLCw4HE5tbS0hJCIiIjIysry8nMPhiEQiQsiFCxeEQuHOnTtZ+NgA0BsEYnbMmDGD2VcQHh6+adOmtrY2gUCQnZ1dXl7u4eHx4YcfymQyQohYLA4LC5NKpRs3bqyoqLh9+3ZnZ+ecOXMqKysJIenp6cynqzMyMrZv365YTEtLCwoK8vT0pCiKfrGQXC4nhHR1dentwwKAcgjEg4ufn59QKLS3tw8NDW1tbX38+LFilYmJybhx43g8nre3d2ZmZnNzc1ZW1gCKCAwMlEgksbGx2qs1AGgEgXiQot8VT18R9zRlyhRzc/Nff/1Vv5UCAJ1AIDZUPB6vpqaG7VoAgBYgEBskmUzW2Ng4cuRItisCAFqAQGyQCgoKKIqaNm0avWhiYtJXJwYADH4IxAajq6uroaGhs7Pzzp07ERERrq6uYWFh9CqRSFRfX5+bmyuTyWpqah49esTc0dbW9unTpxUVFc3NzTKZLC8vD8PXAAYVBGJ27Nu3b+rUqYSQ6OjoRYsWZWZmpqamEkImTJjw4MGDAwcOREZGEkLmzZtXWlpK79Le3u7j42NmZubv7+/l5XXlyhUej0evCg8PnzVr1vLly8eMGZOQkGBmZkYI8fX1pce3rV+/3sHBwdvbe8GCBfT7LABgUMEjzuzYsGHDhg0bmCnh4eGKv+lBxN12EQgEfb0Zz9bWNj8/n5mye/duxd+TJ0+uqKhQLM6fP18ikQy04gCgfbgiNhj0gxgAMPQgEAMAsGywBOLTp097eHhwejNq1ChCyJ49exwcHDgczpdffsl2ZfVty5YtWVlZTU1N7u7up06dYrs6qrp06VJMTAyzZVevXs3cYO7cuQKBwNjY+JVXXrl9+zYrlYyPj/f29hYKhTweTyQSffLJJy0tLd226erqSk1N9fPzUyvnpKSksWPHmpmZWVhYjB07NjY2ltkjpKTcs2fPJiUl6e4HkEG0CyFEJpMlJiaKRCJTU1Nra+vx48crethmzpzZM1BYWlrSa1k8tgNH6V5wcHBwcLAqW3p6elpZWdF/d3Z2SqXS58+fjxs3jk6hb1t98cUXuqroQBFCsrOz2a6FzqnejhRFbdu2LSgoSCKR0Iuenp7Dhg0jhJw7d465WV5e3qJFi7RcUXUEBARkZGTU1dVJJJLs7Gwulztv3jzmBvfv358+fTohZOLEiWrlHBgYuGfPnurq6ubm5hMnTnC53Dlz5qhYblpaWkBAQENDgyoFDcl2oShq8eLFY8aMuXnzpkwme/r06cKFC4uLi+lVAQEBPUPZW2+9pVjLyrHVxOANxAqKE0LFQCyVSn19ffta1AUE4m527drl5eXV1tamSPH09Dx69KiRkZGzs3NjY6MinfUvfGBgYGdnp2KRnj7p8ePH9GJRUdGSJUuOHDkyadIkdQPx4sWLmUcgJCSEEPL06VNVyqUoSiwW+/r6ymSyfgsaku1y7NgxDodz586dXte+9dZbiv8ltLVr116+fJn+m5VjP8TqdgAAIABJREFUq6HB0jWhRG5urlrbd5t+F7Px6llZWVlsbOz27dv5fD4z3c/PLyIi4smTJ1FRUWzVradz584ZGxsrFu3s7AghUqmUXpw4ceLp06dXrlypGCmoupycHOYRcHZ2JoQofiMrL5cQEhcXV1RUlJaWpm65fTGsdvniiy9effVVHx+fXtdeuHBBIBAoFisrK3/55Zc333yTXtT/sdWcAQTivvz973/39va2srLi8/k+Pj4XL14kPabf7Tkbr1wu37Ztm6urq5mZ2YQJE7Kzs0l/0wGDWtLT0ymKWrhwYc9VO3bs8PLyOnjw4KVLl3rdl6KolJQUepI5Gxubt99+WzGxUb9t1GvLquvJkydmZmbu7u4D2Fe50tJSa2trNzc3Fcu1sbEJCAhIS0ujtPRKLQNql46Ojps3b06aNEnFj/b5559v3Lixr7V6OLZaoIer7gF3TVy+fDk5OVmx2K1r4uTJk3FxcfX19XV1ddOmTRs2bBidvnTpUnr63V4Xo6KieDzeqVOnGhoatmzZYmRk9NNPP1EUtXXrVkLI5cuXm5qaqqur/f39LSwsOjo6VKk2QdcEg4eHh7e3d7dET0/Phw8fUhR1/fp1IyOjUaNGtbS0UD1+Am/bts3U1PTw4cONjY137tx59dVX7ezsnj17Rq9V3kZ9tazqWltbBQKBWCzuueqNN95Qt2uC1tHRUVVVtXfvXh6Pd/jwYbXKjYmJIYQUFhYqL2LotcvDhw8JIZMmTZo5c6ajoyOPxxs7duy+ffu6urp6blxVVeXt7S2Xy3vNSj/HVnODLhB3+z+hJBAzJSYmEkKqq6sppYG4ra3N3Nw8NDSUXpRKpTweLzw8nPr9ZFJ0n2VkZBBCysrKVKk2ArFCS0sLh8MJCgrqlq74wlMURT80uGHDBupfv/BSqdTS0lLROhRF/fjjj4SQ+Ph4elFJGylpWdVt3brVy8urW+cjbcCBePjw4YSQYcOG/eUvf+nr/3pf5X711VeEkK+//lp5EUOvXYqLiwkhc+bM+eGHH+rq6hobG//85z8TQo4cOdJz4w0bNii5b6SHY6sVg65rgnlFfOXKFRX34nK5RIVHHu7duyeVSsePH08vmpmZOTo69jqrr/LpgHtatmxZr2PvhhJVRs7R/wvNzc2VbLNjx44xY8ZkZGRcu3aNmV5SUtLS0jJlyhRFytSpU01NTRUvheqG2Uaqt2xfcnJyTpw4cfHiRWbno+YqKyurq6u/+eab//mf/5k8eXLP2xVKyqUP4/PnzzWvhmG1C90j/8orr/j5+dna2lpZWW3fvt3Kymr//v3dtnz69OnZs2cVk650o59jqxWD+hHnmTNnzpw5s6+1f/vb35KTk0tKSiQSiYoRs7W1lRDy6aeffvrpp4pEJycnjWtKIiIifH19Nc9nMKNnw1Cuvb2d/P5F6gufz8/KypoxY8b777+flJSkSG9sbCSEKEaD0qytrZubm/stV8OWPX78eEpKSkFBwYgRI1TcRUVcLtfe3n7u3Lnu7u5eXl6JiYnMe0TKy6XnDKEPqYYMq13oDeiXLtJMTU3d3NzKy8u7bZmUlPThhx92u/1I09ux1YpBHYiVePz48eLFi5csWfLVV1+NGDFi7969n3zySb972dvbE0JSU1MjIiK0Wx9fX1/mi+OGpJMnT/a7DX1+9/vTxNfXd/PmzXv27ElISHB1daUTra2tCSHdvt4qTrusScvu3bv34sWL+fn53WKNdolEImNj45KSEtXL7ejoIL8fUg0ZVrtYWlqOHj367t27zMTOzk4rKytmyrNnz7755pt79+71zEGfx1YrBl3XhIqKi4tlMll4eLiHhwefz+dwOKrs5eLiwufzi4qKdF29lxb99GNTU1O/WyYkJIwdO7awsFCRMn78eEtLy59//lmRcuvWrY6Ojtdee63f3AbWshRFRUdHFxcX5+bmajcK19XVrVixgplSWloql8tdXFxUL5c+jHQvs4YMq10IIcuWLSssLHzw4AG9KJVKHz161G00W1JS0qpVq2xtbZmJ+j+2WmGogZj+d33p0qX29vbS0lJmd1W36XeZi8bGxmvWrDl27FhmZqZEIpHL5VVVVb/99ht7n2OoMTc39/Dw6GuWOCb6hzBzvCefz4+MjMzJyTly5IhEIikuLl6/fr2Tk9PatWtVya2vlg0NDR0+fHivj+revXt39+7dBw4c4HK5zN7wPXv2qPJhleRsYWHx3Xff5efn0/1mhYWF7733noWFxebNm1Uvlz6MfY2lVYthtQshZPPmzW5ubmFhYY8fP66rq4uOjm5ra6Nv2dGeP3/+1Vdfbdq0qduO+j+22qGHG4Kq3Hn84YcfvLy86Co5OjrOnj272wb/+Z//Sf/7srCwWLJkCUVR0dHRtra21tbWISEh+/btI4R4eno+fvz49u3bbm5uZmZmM2bMePbsWbfFFy9eREdHu7q6mpiY2NvbL126tKSkJCMjg+68Hz16dHl5+f79+4VCISHEzc3t/v37/X5AglETDGKxmMvlSqVSejEnJ4ceDGNnZ0ffkWf6+OOPmcOkurq6kpOTR48ezeVybWxsFi9efO/ePXpVv23Ua8tSFLV48WJCyLZt23pWlb4735NirM6NGzemT5+u6NN0dHT08/P7/vvv6bVKcqYoauHChe7u7paWljwez9PTMzQ0VPGEbr/l0gIDA52dnXsds8U09NqFVllZuXz5chsbGx6P9/rrr+fl5THXbt68edWqVT33YuXYam6wBGKDhkDMVFpaamJi0teYWf2Ty+X+/v6HDh0yoJwpiqqtreXz+Xv27Ol3S7SLurR+bDVnqF0TMGiJRKL4+Pj4+Pie05jpn1wuz83NbW5uDg0NNZScaXFxcZMmTRKLxdrK8CVpF1Vo/dhqDoEYtC8mJiYkJCQ0NFSVu0M6VVBQcPr06by8POVDaAdVzoSQlJSUoqKi8+fP0wPkteVlaJd+6ejYashQh6/BILdz587vvvtu165dn3/+OYvVmD179uzZsw0r5zNnzrx48aKgoIB5x0xbhny7KKfTY6sJBGID0NbWNnv27OvXrw+qrPo1d+7cuXPn6qGgIWbRokWLFi3SXf4vc7vo+tgOGLomDIAWZ/LEpKAAgxACsZ5QfU8kKBaLTU1NHR0d6cWPPvrIwsKCw+HQj3h2m8kzPT2dz+c7ODisW7fOycmJz+f7+fkphlGrlRUh5MKFC0KhcOfOnXo+GgDAhECsJ3FxcTExMVu3bq2urr569WplZaW/vz8950h6ejrz8eiMjIzt27crFtPS0oKCgugJ5MrKysRicVhYmFQq3bhxY0VFxe3btzs7O+fMmVNZWaluVuT3Z167urp0fwAAoE8IxPrQ1taWkpKyZMmSVatWWVlZ+fj4fPnll7W1tT1nk1KRiYkJfXHt7e2dmZnZ3NyclZU1gHwCAwMlEklsbOzAqgEAWoFArA/qTiSolilTppibm6s15SMADCoIxPqgyUSCquDxeDU1NVrJCgD0D4FYHzSZSLBfMplMW1kBACsQiPWh34kETUxMVH8bSDcFBQUURU2bNk3zrACAFQjE+tDvRIIikai+vj43N1cmk9XU1Dx69Ii5e7eJPQkhXV1dDQ0NnZ2dd+7ciYiIcHV1VbwtRq2s8vLyMHwNgHUIxHry2WefJSYmxsfH29nZBQQEjBo1qqCgwMLCgl4bHh4+a9as5cuXjxkzJiEhgX5xgK+vLz0obf369Q4ODt7e3gsWLKivryeEtLe3+/j4mJmZ+fv7e3l5XblyRfEWHHWzAgDW4RFnPeFwOFFRUVFRUb2utbW1zc/PZ6bs3r1b8ffkyZMrKiqYawUCQV+TfKuV1fz58yUSiYofAQB0BFfEBqnfl48BgAFBIAYAYBkCsYHZsmVLVlZWU1OTu7v7qVOn2K4OAGgB+ogNTGJiYmJiItu1AABtwhUxAADLEIgBAFiGQAwAwDIEYgAAlunpZl1VVdWJEyf0UxYrbty4wXYVdI5+hGRot6MhQrvoTlVVlZ6m06J0Lzg4WB+fBABA24KDg/UQJDkURbH9SQEGjn41FK4HwaChjxgAgGUIxAAALEMgBgBgGQIxAADLEIgBAFiGQAwAwDIEYgAAliEQAwCwDIEYAIBlCMQAACxDIAYAYBkCMQAAyxCIAQBYhkAMAMAyBGIAAJYhEAMAsAyBGACAZQjEAAAsQyAGAGAZAjEAAMsQiAEAWIZADADAMgRiAACWIRADALAMgRgAgGUIxAAALEMgBgBgGQIxAADLEIgBAFiGQAwAwDIEYgAAliEQAwCwDIEYAIBlHIqi2K4DgBqOHj166NChrq4uevHhw4eEEHd3d3rRyMjogw8+WLlyJWv1A1AfAjEYmDt37kycOFHJBv/85z8nTJigt/oAaA6BGAzP2LFj79271+sqkUhUWlqq5/oAaAh9xGB4Vq9ezeVye6Zzudw1a9bovz4AGsIVMRieBw8eiESiXk/d0tJSkUik/yoBaAJXxGB4PDw8Xn31VQ6Hw0zkcDhTpkxBFAZDhEAMBundd981NjZmphgbG7/77rts1QdAE+iaAINUXV3t5OSkGMRGCDEyMnr69Onw4cNZrBXAwOCKGAySg8P/Y+/eg6I40/2Bvw1zY4DhEkCI3AREg2LUqD8hUuhxNSIHiQYVouYQz7oquiMKLkEiKyC4XgopImiZGKqiHoO3wkuJu2WUTRnRJAWuRDcqKIoSw0VgwBlkmOnfH30yp8NlGGBmemb4fv6ib28/8/T42NP99ttu4eHhmpNia2vr2bNnowqDmUIhBnO1atUq9u+5VatWcRgMwHDg0gSYK5lM5urq2tXVRQjh8/kNDQ2Ojo5cBwUwFDgjBnMlkUgWLFjA4/F4PN7ChQtRhcF8oRCDGVu5cqVKpVKpVBhcAswaLk2AGevs7HRxcaFpuqmpycbGhutwAIbIhApxj/75AAAGZTrVj8d1AL+TmJgYEhLCdRT6sX//fkLI5s2buQ7EsMrLy/Py8oqLi7kK4Pbt2xRFaR+PzUxxnlsLxuSW6yj+j2mdERcXFy9btozrQPRj6dKlhJBTp05xHYhhnTx5cvny5Rx+i7q7uwkhPJ5pnVLoBee5tWCmllsL/PrCiGKRJRhGGvSaAADgGAoxAADHUIgBADiGQgwAwDEUYtNy6dIlBweHCxcucB0IABgPCrFpMZ3+NABgNOj6Y1oiIyPb2tqMsCOFQjF37twbN24YYV8AoB3OiEeoI0eONDQ0cB0FABCCQmxSrl+/7u3tTVHUgQMHCCGFhYW2trZisfjcuXMRERESicTT0/PEiRPMyvn5+SKRyM3Nbd26dR4eHiKRKDQ09NatW8xSqVQqEAjc3d2ZyQ0bNtja2lIU1dTURAhJTExMSkqqqamhKIp52+bly5clEkl2djYHHxtgxEMhNiGzZs1iXytISEjYvHmzQqGwt7cvLi6uqanx8/Nbs2aNUqkkhEil0vj4eLlcvmnTptra2oqKiu7u7nnz5tXV1RFC8vPz2Q+LFxQUZGRkaCbz8vKioqL8/f1pmq6uriaEqFQqQgj7FXAAYDQoxGYgNDRUIpG4urrGxsa+evXq6dOnmkU8Hu+tt94SCoVBQUGFhYXt7e1FRUVD2EVkZKRMJtu+fbv+ogYAXaEQmxOBQEAIYc6Ie5s2bZpYLP7555+NGxQADBcKsUURCoWNjY1cRwEAg4NCbDmUSmVra6unpyfXgQDA4KAQW46ysjKapmfOnMlM8ni8/i5iAIBJQSE2b2q1uqWlpbu7+86dO4mJid7e3vHx8cyigICAly9flpSUKJXKxsbGJ0+esDd0dnaur6+vra1tb29XKpWlpaXovgbAFRRiE3LgwIHp06cTQlJSUqKjowsLC5n3LU2aNOnRo0eff/55UlISIWTBggUPHz5kNuns7AwODraxsQkLCwsMDLx27ZpQKGQWJSQkzJkzJy4ubty4cVlZWcy7NUNCQpj+bevXr3dzcwsKClq4cOHLly85+bwAwMAjziZk48aNGzduZM9JSEjQ/M10Iu6xib29/bNnz/pszdnZ+erVq+w5e/bs0fw9ZcqU2tpazWRERIRMJhtq4AAwLDgjNm/MgxgAYNbMqRCfOXPGz8+PYhEIBG5ubrNnz967d29LSwvXAcLArly5kpqayj6Uq1atYq8wf/58e3t7a2vrCRMmVFRUcBWnUqnMyckJCAgQCASOjo4TJ07U/ICYPXs21YudnR2zNDMzMygoSCKRCIXCgICAv/zlLx0dHcyi8+fP796923D/d5pFbrXkhxCyc+fOHomdOHEie/P+jouhc2twtMkghBQXFw+4mr+/v4ODA03TzH2qa9euxcfHUxTl4eHxww8/GD5MXcXExMTExBiu/dTUVOb5Dl9f31OnThluR9oxL3vXceX09PSoqCiZTMZM+vv7v/HGG4SQixcvslcrLS2Njo7Wc6CDtHjx4nHjxt28eVOpVNbX1y9atKiqqopZFB4e3vvf0XvvvadZWlBQ0NzcLJPJiouL+Xz+ggULNM3m5eWFh4e3tLToEoNF5lZ7frKysnokdsKECezNtRwXw+XWCEwplEEWYrZTp05ZWVm5ubm1trYaJrpBM3QhNhG6f6F37doVGBioUCg0c/z9/Y8fP25lZTV69Gj2geO8WJw4cYKiqDt37vS59L333tPUO8batWu/+eYb5u/IyMju7m7NImbEj6dPn2rmSKXSkJAQpVI5YBgWmVvt+cnKyjp69Gh/22o/LrRhcmsc5nRpQouYmJj4+PiGhoZDhw5xHQv0obq6evv27RkZGSKRiD0/NDQ0MTHx+fPnycnJXMXW28GDB6dOnRocHNzn0suXL9vb22sm6+rqfvrpp//4j/9gJi9evGhtba1Z6uLiQgiRy+WaOTt27Lh9+3ZeXp6+ojWv3A6YHy20HxdigNwajYUUYkII03+2tLSUmVSpVOnp6d7e3jY2NpMmTWL+A9Q+sCQh5J///OeMGTPEYrFEIgkODmY6EvTZFAxKfn4+TdOLFi3qvWjnzp2BgYFffPHFlStX+tyWpunc3FxmbCMnJ6f3339fM57GgAd0CMeuq6vr5s2bkydP1vGj/e1vf9u0aVN/S58/f25jYzNmzBjNHCcnp/Dw8Ly8PFpPb2Mxo9z21js//dHluOg9t8bD5en475FhXJqgaZopml5eXsxkcnKyUCg8ffp0S0vLtm3brKysmCvIaWlphJBvvvmmra2toaEhLCzM1ta2q6uLpumOjg6JRLJ7926FQvHixYslS5Y0NjZqaUo7XJpg8/PzCwoK6jHT39//8ePHNE3fuHHDysrK19e3o6OD7vXzOT09XSAQHD16tLW19c6dO1OnTnVxcXnx4gWzVMsBpYd07B4/fkwImTx58uzZs93d3YVC4fjx4w8cOKBWq3uv/OzZs6CgIJVK1WdTr169sre3l0qlPeanpqYSQiorK7VHYnm57aF3frKysjw9PR0dHfl8vq+vb3R09Pfff88s0vG46De3RmNKoQyvENM0TVGUo6MjTdMKhUIsFsfGxjLz5XK5UChMSEigf/tuaa6mFRQUEEKqq6tpmv7pp59Ir5sbWprSDoVYo6Ojg6KoqKioHvM1xYKmaeZZlY0bN9K/LxZyudzOzk6Tf5qmv//+e0JIZmYmM6nlgA7t2FVVVRFC5s2b99133zU3N7e2tn7yySeEkGPHjvVeeePGjQcPHuyvqbS0tMDAwB4XlGma/vLLLwkhX331lfZILC+3PfTOz9OnTysqKtrb21+/fl1eXj5lyhQbG5uffvqJ1vm46DG3xmQ5D3S8evWKpmmJREIIuX//vlwu13R8sbGxcXd373N8SPbAkn5+fm5ubitXrty0aVN8fLyvr++gmurt2bNnJ0+e1MNnM2Hl5eUDrtPQ0EDTtFgs1rLOzp07L168WFBQsHz5cvb8u3fvdnR0TJs2TTNn+vTpAoFA8y6SHtgHdGjHjnk0ccKECaGhocycjIyMgwcPHj58eMWKFew16+vrz58/v3fv3j7bOXv27MmTJ//xj3+wLygzmFT8+uuv2iPRhXnllq3P/Hh5eXl5eTF/z5w5s6ioaPLkyQUFBYWFhToeFz3m1pgspxA/ePCAEDJ+/HhCyKtXrwghn3766aeffqpZwcPDQ3sLNjY2V69e/eSTT7KzszMzM5ctW1ZUVDS0phg3b97s8dUfmTo7O8lvBa4/IpGoqKho1qxZq1ev3r17t2Z+a2srIUTTS5fh6OjY3t4+4H6HduyYFZh3SjEEAoGPj09NTU2PNXfv3r1mzZoet8gYX3/9dW5ubllZ2Ztvvtl7KfO4OZOWYTKv3Gpoz49GcHCwtbU1809bx+Oix9wak+XcrLt8+TIhJCIighDi6upKCNm/fz/75F+Xc7cJEyZcuHChvr4+JSWluLh43759Q26KEDJyLk1ox/zbGLCzfUhIyJYtWx4+fMjuTOro6EgI6VEadBztc2jHzs7ObuzYsffu3WPP7O7udnBwYM958eLF//zP/7CfQdf47LPPjh07dvXq1f6qTFdXF/ktLcNkXrllDJgfDbVarVarmf9mdDwuesytMVlIIX7x4sX+/fs9PT1Xr15NCPHy8hKJRLdv3x5UI/X19cxhdnV13bVr19SpU+/duze0poDNzc2Noqi2trYB18zKyho/fnxlZaVmzsSJE+3s7H788UfNnFu3bnV1db3zzjsDtjbkY7d8+fLKyspHjx4xk3K5/MmTJz16Te3evXvlypXOzs7smTRNp6SkVFVVlZSU9DjTZGNSMWrUqMEG1pt55XbA/Lz33nvsSebuX0hICDOpy3HRY26NySwLMU3THR0dzN3SxsbG4uLid99919rauqSkhLlGLBKJPv744xMnThQWFspkMpVK9ezZs19++UV7s/X19evWrfv555+7uroqKyufPHkyc+bMoTUFbGKx2M/Pr7/BidiYH9HsfqYikSgpKens2bPHjh2TyWRVVVXr16/38PBYu3atLq31d+xiY2NHjRrV32O+W7Zs8fHxiY+Pf/r0aXNzc0pKikKhYG4NMX799dcvv/xy8+bNPTa8d+/enj17Pv/8cz6fz35Od9++fezVmFRo6Q+rO/PK7YD5ef78+ddff93a2qpUKsvLy//4xz96e3uvX7+eWTrgcSF6za1RGe037IDIQL0mzp8/P2nSJLFYLBAIrKysCCFMN4kZM2ZkZmY2NzezV379+nVKSoq3tzePx3N1df3ggw/u3r1bUFDAXMsfO3ZsTU3N4cOHmcLt4+Pz4MGD2tra0NBQJycna2vrN998My0tjXkEqM+mBvw46DXBJpVK+Xy+XC5nJs+ePevv708IcXFxYe7ms23dupXdxUqtVu/du3fs2LF8Pt/JyWnx4sX3799nFmk/oHT/x27x4sWEkPT09P4Crquri4uLc3JyEgqFM2bMKC0tZS/dsmXLypUre2/F3Nnvbe/evezVIiMjR48e3Wd/ODbLy+2A+UlKSvL397e1teXxeJ6enmvWrKmvr2e3oP240PrOrdGYUii6dV8zFyjEbA8fPuTxeFqeXjUylUoVFhZ25MgR4++6qalJJBLt27dvwDWR28HSe26NxiwvTYDZCQgIyMzMzMzMZA+1xRWVSlVSUtLe3h4bG2v8ve/YsWPy5MlSqVRfDSK3GnrPrdGgEIORpKamLl26NDY2Vpc7SwZVVlZ25syZ0tJS7d1vDSE3N/f27duXLl3i8/l6bBa5JQbLrXGgEIPxZGdnS6XSXbt2cRvG3Llzjx8/7u7ubuT9njt37vXr12VlZU5OTnpvHLk1XG6NwHIe6ACzMH/+/Pnz53MdBTeio6Ojo6MN1z5yy3UUQ4czYjOmUCg0j3uaTlMAMFgoxGbsyJEjDQ0NptYUAAwWCjHH6P4HhJVKpQKBQHO5bcOGDba2thRFMY/bJyYmJiUl1dTUUBQVEBCQn58vEonc3NzWrVvn4eEhEolCQ0M1o7cMqilCyOXLlyUSSXZ2tpGzATAyoRBzbMeOHampqWlpaQ0NDd9++21dXV1YWBgzdlR+fj7zIhlGQUFBRkaGZjIvLy8qKsrf35+m6erqaqlUGh8fL5fLN23aVFtbW1FR0d3dPW/evLq6usE2RX4bu0CtVhs+AQCAQswphUKRm5u7ZMmSlStXOjg4BAcHHzp0qKmp6fDhw0NrkMfjMSfXQUFBhYWF7e3tRUVFQ2gnMjJSJpNt3759aGEAwKCgEHNpsAPCDsq0adPEYvGghogFAE6gEHNpOAPC6kIoFDY2NuqlKQAwHBRiLg1nQNgBKZVKfTUFAAaFQsylAQeE5fF4zJtphqCsrIym6ZkzZw6/KQAwKBRiLg04IGxAQMDLly9LSkqUSmVjY+OTJ0/Ymzs7O9fX19fW1ra3tzNFVq1Wt7S0dHd337lzJzEx0dvbOz4+fghNlZaWovsagNGgEHPsr3/9a05OTmZmpouLS3h4uK+vb1lZma2tLbM0ISFhzpw5cXFx48aNy8rKYl4AExISwnRKW79+vZubW1BQ0MKFC1++fEkI6ezsDA4OtrGxCQsLCwwMvHbtmuZtZoNtCgCMBmNNcIyiqOTk5OTk5D6XOjs7X716lT1nz549mr+nTJlSW1vLXmpvb9/fyxoG1VRERIRMJtPxIwDAMOGM2KIM+BJJADBBKMQAABxDIbYQ27ZtKyoqamtrGzNmzOnTp7kOBwAGAdeILUROTk5OTg7XUQDAUOCMGACAYyjEAAAcQyEGAOAYCjEAAMdM62bd/v37T506xXUU+nHz5k1CyNKlS7kOxLCY50cs/mNyArk1nP6ee+IKRdM01zH8L3zhYAgqKysJIVOmTOE6EDA/pnPaZ0KFGGAImFdAnTx5kutAAIYO14gBADiGQgwAwDEUYgAAjqEQAwBwDIUYAIBjKMQAABxDIQYA4BgKMQAAx1CIAQA4hkIMAMAxFGIAAI63dw/YAAAgAElEQVShEAMAcAyFGACAYyjEAAAcQyEGAOAYCjEAAMdQiAEAOIZCDADAMRRiAACOoRADAHAMhRgAgGMoxAAAHEMhBgDgGAoxAADHUIgBADiGQgwAwDEUYgAAjqEQAwBwDIUYAIBjKMQAABxDIQYA4BgKMQAAx3hcBwAwOHK5/PXr15rJrq4uQkhLS4tmjlAoFIvFHEQGMFQUTdNcxwAwCIWFhRs2bNCyQkFBQUJCgtHiARg+FGIwM42NjR4eHiqVqs+l1tbWv/zyi6urq5GjAhgOXCMGM+Pq6jp37lxra+vei6ytrf/whz+gCoPZQSEG87Ny5co+f8nRNL1y5UrjxwMwTLg0Aeanvb3d1dWVfcuOIRAIGhsbJRIJJ1EBDBnOiMH82NvbR0VF8fl89kwejxcdHY0qDOYIhRjM0ooVK7q7u9lzVCrVihUruIoHYDhwaQLMUldXl4uLS3t7u2aOnZ1dU1OTUCjkMCqAocEZMZglgUCwdOlSgUDATPL5/OXLl6MKg5lCIQZz9eGHHzKP1RFClErlhx9+yG08AEOGSxNgrtRqtbu7e2NjIyHExcXlxYsXfXYuBjB9OCMGc2VlZfXhhx8KBAI+n79ixQpUYTBfKMRgxuLi4rq6unBdAsydsUdfKy8vr6urM/JOwVLRNP3GG28QQh4/flxbW8t1OGAhvLy8QkJCjLpL2rhiYmKM+vEAAAYpJibGyIWRg/GIY2JiTp06Zfz9mg6KooqLi5ctW8Z1IIa1dOlSQoihj/W9e/cIIUFBQQbdi6kxTm5HJia3RoaB4cG8jbQSDBYJN+sAADiGQgwAwDEUYgAAjqEQAwBwDIUYAIBjKMRm49KlSw4ODhcuXOA6EADQMxRis0FjeCYAC4V+xGYjMjKyra3NCDtSKBRz5869ceOGEfYFAARnxNDbkSNHGhoauI4CYARBITYP169f9/b2pijqwIEDhJDCwkJbW1uxWHzu3LmIiAiJROLp6XnixAlm5fz8fJFI5Obmtm7dOg8PD5FIFBoaeuvWLWapVCoVCATu7u7M5IYNG2xtbSmKampqIoQkJiYmJSXV1NRQFBUQEEAIuXz5skQiyc7O5uBjA4wMKMTmYdasWexrBQkJCZs3b1YoFPb29sXFxTU1NX5+fmvWrFEqlYQQqVQaHx8vl8s3bdpUW1tbUVHR3d09b948Zty7/Px89jAXBQUFGRkZmsm8vLyoqCh/f3+apqurqwkhKpWKEKJWq432YQFGGhRi8xYaGiqRSFxdXWNjY1+9evX06VPNIh6P99ZbbwmFwqCgoMLCwvb29qKioiHsIjIyUiaTbd++XX9RA8DvoBBbCOY1mswZcW/Tpk0Ti8U///yzcYMCAJ2gEI8UQqGQeb0bAJgaFOIRQalUtra2enp6ch0IAPQBhXhEKCsro2l65syZzCSPx+vvIgYAGB8KscVSq9UtLS3d3d137txJTEz09vaOj49nFgUEBLx8+bKkpESpVDY2Nj558oS9obOzc319fW1tbXt7u1KpLC0tRfc1AINCITYPBw4cmD59OiEkJSUlOjq6sLBw//79hJBJkyY9evTo888/T0pKIoQsWLDg4cOHzCadnZ3BwcE2NjZhYWGBgYHXrl0TCoXMooSEhDlz5sTFxY0bNy4rK8vGxoYQEhISwvRvW79+vZubW1BQ0MKFC1++fMnJ5wUYUfCIs3nYuHHjxo0b2XMSEhI0fzOdiHtsYm9v/+zZsz5bc3Z2vnr1KnvOnj17NH9PmTKF/UbkiIgImUw21MABYGA4I7ZYzIMYAGD6TLEQnzlzxs/Pj2IRCARubm6zZ8/eu3dvS0sL1wGCQVy5ciU1NZV99FetWsVeYf78+fb29tbW1hMmTKioqOAkyMzMzKCgIIlEIhQKAwIC/vKXv3R0dGiW7ty5k/q9iRMnsjdXKpU5OTkBAQECgcDR0XHixInMj4/z58/v3r3bcP93Iremfl5CG1dMTExMTIwua/r7+zs4ONA0zdx0unbtWnx8PEVRHh4eP/zwg4HDNCxCSHFxseHaT01NZZ7v8PX1PXXqlOF2pJ3ux5qm6fT09KioKJlMxkz6+/u/8cYbhJCLFy+yVystLY2OjtZzoIMRHh5eUFDQ3Nwsk8mKi4v5fP6CBQs0S7Oysnr8+5owYQJ788WLF48bN+7mzZtKpbK+vn7RokVVVVXMory8vPDw8JaWFl3CQG5NJLf6YgaFmO3UqVNWVlZubm6tra0GiG5Y5HJ5SEiILmsauhCbCN2P9a5duwIDAxUKhWaOv7//8ePHraysRo8ezT7WnBeLyMjI7u5uzSQzasfTp0+ZyaysrKNHj/a37YkTJyiKunPnTn8rSKXSkJAQpVI5YBjIbQ+c5FaPTPHShBYxMTHx8fENDQ2HDh3iOpaeMHrk0FRXV2/fvj0jI0MkErHnh4aGJiYmPn/+PDk5mavYert48aK1tbVm0sXFhRAil8t12fbgwYNTp04NDg7ub4UdO3bcvn07Ly9v+HEykFsNvedWv8ysEBNCmM6wpaWlhJA9e/aIxWJ7e/uGhoakpKTRo0ffv3+fpunc3FxmvBsnJ6f3339fM8aC9vEhCSFath3s6JGgo/z8fJqmFy1a1HvRzp07AwMDv/jiiytXrvS5rZbjpX2kUEKISqVKT0/39va2sbGZNGlScXHxEIJ//vy5jY3NmDFjBlyzq6vr5s2bkydP1rKOk5NTeHh4Xl4erae3sSC3GnrPrZ4Z+Qx8mJcmaJpmulJ5eXkxk2lpaYSQTZs2ffbZZ0uWLPn3v/+dnp4uEAiOHj3a2tp6586dqVOnuri4vHjxgll/7dq1tra29+7d6+zsvHv37vTp0+3t7TU/f7Rvu2LFilGjRmki2bt3LyGksbGRmfzggw+Y0SMHRHBpgsXPzy8oKKjHTH9//8ePH9M0fePGDSsrK19f346ODrrXz2ftx4v5bnzzzTdtbW0NDQ1hYWG2trZdXV3M0uTkZKFQePr06ZaWlm3btllZWQ323sOrV6/s7e2lUqlmTlZWlqenp6OjI5/P9/X1jY6O/v7775lFjx8/JoRMnjx59uzZ7u7uQqFw/PjxBw4cUKvV7DZTU1MJIZWVldp3jdxynlv9Mr9CTNM0RVGOjo7M38wXQnMJTC6X29nZxcbGalb+/vvvCSGZmZnM5Nq1a9nN/vDDD4SQjIwMXbZFIR4UXY51R0cHRVFRUVE95muKBU3TzLMqGzdupH9fLAY8Xj2+GwUFBYSQ6upqmqYVCoVYLNZsK5fLhUJhQkLCoD5gWlpaYGCg5iYYTdNPnz6tqKhob29//fp1eXn5lClTbGxsfvrpJ5qmq6qqCCHz5s377rvvmpubW1tbP/nkE0LIsWPH2G1++eWXhJCvvvpK+66RW25zq3fm90DHq1evaJqWSCR9Lr17925HR8e0adM0c6ZPny4QCNjXH9jY40MOdtvh2L9//6lTp/TerEm5efOmZnSL/jQ0NNA0LRaLtayzc+fOixcvFhQULF++nD1/sMeLPVLo/fv35XK5pv+TjY2Nu7v7oIYJPXv27MmTJ//xj3/Y29trZnp5eXl5eTF/z5w5s6ioaPLkyQUFBYWFhcxjjRMmTAgNDWVWyMjIOHjw4OHDh1esWKFpgUnFr7/+qnsk/UFuDZdbvTO/a8QPHjwghIwfP77Ppa2trYQQOzs79kxHR8f29vb+GtSMDzmEbWGYOjs7CSGaZ6/7JBKJioqKKIpavXq1QqHQzB/O8Xr16hUh5NNPP9V0Sn3y5ImO94UIIV9//fXf/va3srIyX19fLasFBwdbW1sz31gPDw9CCHNHgSEQCHx8fGpqatibMI+bM2kZJuTWcLnVO/M7I758+TIhJCIios+ljo6OhJAeXxctI0Cyx4cc7LbDsXnzZvb7iizS0qVLB1yH+bcxYGf7kJCQLVu27Nu3Lysry9vbm5k5nOPl6upKCNm/f39iYuKAK/fw2Wef/f3vf7969WqPOtWbWq1Wq9VMKbSzsxs7duy9e/fYK3R3dzs4OLDndHV1kd/SMkzIreFyq3dmdkb84sWL/fv3e3p6rl69us8VJk6caGdn9+OPP2rm3Lp1q6ur65133ulzffb4kANui9Ej9c7NzY2iqLa2tgHXzMrKGj9+fGVlpWbOYI81m5eXl0gkun379qCipWk6JSWlqqqqpKSkz0rx3nvvsSeZO1QhISHM5PLlyysrKx89esRMyuXyJ0+e9OhxxaRi1KhRgwqsT8it4XKrdyZdiGma7ujoYG59NjY2FhcXv/vuu9bW1iUlJf1dIxaJRElJSWfPnj127JhMJquqqlq/fr2Hh8fatWs16/Q3PuSA2w5q9EhDJcWyiMViPz+//gYnYmN+RLP7mepyrLW09vHHH584caKwsFAmk6lUqmfPnv3yyy+EkNjY2FGjRvX5mO+9e/f27Nnz+eef8/l89rO2+/btY1Z4/vz5119/3draqlQqy8vL//jHP3p7e69fv55ZumXLFh8fn/j4+KdPnzY3N6ekpCgUCua2kgaTCi39YXWH3Bout/pn5JuDutyRPH/+/KRJk8RisUAgsLKyIoQw3SRmzJiRmZnZ3NysWXP37t3MDw0vLy/NUzdqtXrv3r1jx47l8/lOTk6LFy9mOhcz1q5dy+fzR48ezePxJBLJ+++/X1NTo1mqfdvm5uY5c+aIRKIxY8b8+c9/3rp1KyEkICCA6f1WUVHh4+NjY2Mza9YsTS+fPhH0mmCRSqV8Pl8ulzOTZ8+e9ff3J4S4uLgwd/PZtm7dyu5ipeV4FRQUMDdnxo4dW1NTc/jwYeY/bx8fnwcPHtA0/fr165SUFG9vbx6P5+rq+sEHH9y9e5em6cWLFxNC0tPTe4fK3J3vbe/evcwKSUlJ/v7+tra2PB7P09NzzZo19fX17Bbq6uri4uKcnJyEQuGMGTNKS0t77CIyMnL06NE9+l31htxynlv9MsVCbFBr1651dnbmMAAahfj3Hj58yOPxtDy9amQqlSosLOzIkSPG33VTU5NIJNq3b9+AayK3g6X33OqXSV+aMBBTH4dphAkICMjMzMzMzGQPtcUVlUpVUlLS3t4eGxtr/L3v2LFj8uTJUqlUXw0itxp6z61+jcRCDKYmNTV16dKlsbGxutxZMqiysrIzZ86UlpZq735rCLm5ubdv37506RKfz9djs8gtMVhu9WhkFeJt27YVFRW1tbWNGTPm9OnTXIcD/yc7O1sqle7atYvbMObOnXv8+HHNiCJGc+7cudevX5eVlTk5Oem9ceTWcLnVF/PrRzwcOTk5OTk5XEdhcAqFYu7cuTdu3DCppgY0f/78+fPnG2FHJig6Ojo6Otpw7SO3XEcxgJF1RjxC6HFAToztCWAEKMQmitbTgJzaR/4c7Niely9flkgk2dnZRs4GgGVDITZRO3bsSE1NTUtLa2ho+Pbbb+vq6sLCwpjxSvLz89mPRxcUFGRkZGgm8/LyoqKimHHgqqurpVJpfHy8XC7ftGlTbW1tRUVFd3f3vHnz6urqBtsU+a3DiVqtNnwCAEYQFGJTpFAocnNzlyxZsnLlSgcHh+Dg4EOHDjU1NR0+fHhoDfJ4PObkOigoqLCwsL29vaioaAjtREZGymSy7du3Dy0MAOgTCrEpMuiAnOyRPwHAFKAQmyJDD8ipGfkTAEwBCrEpMuiAnOyRPwHAFKAQmyKDDsjJHvlzmE0BgF6gEJsivQ/I2d/In4NtqrS0FN3XAPQOhdhE/fWvf83JycnMzHRxcQkPD/f19S0rK7O1tWWWJiQkzJkzJy4ubty4cVlZWcxYoCEhIUyntPXr17u5uQUFBS1cuPDly5eEkM7OzuDgYBsbm7CwsMDAwGvXrmneoDPYpgBA70bWI85mhKKo5OTk5OTkPpc6OztfvXqVPWfPnj2av6dMmVJbW8team9v398A4YNqKiIiQiaT6fgRAEBHOCMeETDyJ4ApQyEGAOAYCrGFw8ifAKYP14gt3AgZ+RPArOGMGACAYyjEAAAcQyEGAOAYCjEAAMdQiAEAuEYbV0xMDNefGABAm5iYGCMXRoqmaWN+wvLycmYQAwC92L9/PyFk8+bNXAcClsPLyyskJMSYezR2IQbQL+adeydPnuQ6EIChwzViAACOoRADAHAMhRgAgGMoxAAAHEMhBgDgGAoxAADHUIgBADiGQgwAwDEUYgAAjqEQAwBwDIUYAIBjKMQAABxDIQYA4BgKMQAAx1CIAQA4hkIMAMAxFGIAAI6hEAMAcAyFGACAYyjEAAAcQyEGAOAYCjEAAMdQiAEAOIZCDADAMRRiAACOoRADAHAMhRgAgGMoxAAAHEMhBgDgGAoxAADHUIgBADiGQgwAwDEe1wEADM6tW7f+9a9/aSYfPXpECDl8+LBmzttvv/3//t//4yAygKGiaJrmOgaAQbh48WJUVJS1tbWVlRUhhPkCUxRFCFGr1SqV6sKFC//5n//JcZQAg4FCDGZGqVS6uLjIZLI+l0okksbGRoFAYOSoAIYD14jBzPD5/Li4uD5LrZZFAKYMhRjMT1xcXFdXV+/5SqXyww8/NH48AMOESxNgftRq9Ztvvvnrr7/2mO/q6vrixQvm2jGAGcFXFsyPlZXVqlWrelyCEAgE8fHxqMJgjvCtBbPU++pEV1dXXFwcV/EADAcuTYC5Gjt2bHV1tWbSz8+vpqaGw3gAhgxnxGCuVq5cyefzmb8FAsF//dd/cRsPwJDhjBjMVXV19dixYzWT9+/fDwwM5DAegCHDGTGYq4CAgLfffpuiKIqi3n77bVRhMF8oxGDGPvroI2tra2tr648++ojrWACGDpcmwIzV19d7eXnRNF1XVzd69GiuwwEYIhMtxOXl5bm5uVxHAWagrKyMEDJ79myO4wBzsGXLlpCQEK6j6IOJXpqoq6s7ffo011Ho0+nTp589e8Z1FAZ38+bNmzdvGnOP3t7ePj4+xtwjV4yfWwtz+vTpuro6rqPom0mPR3zq1CmuQ9AbiqI2b968bNkyrgMxrKVLlxLjHriXL18SQpydnY22R64YP7cWhhkr1TSZdCEGGNBIKMFg8Uz00gQAwMiBQgwAwDEUYgAAjqEQAwBwDIXYpF26dMnBweHChQtcBwIABoRCbNJM83EbANAvdF8zaZGRkW1tbUbYkUKhmDt37o0bN4ywLwDoAWfEQAghR44caWho4DoKgBEKhdh0Xb9+3dvbm6KoAwcOEEIKCwttbW3FYvG5c+ciIiIkEomnp+eJEyeYlfPz80UikZub27p16zw8PEQiUWho6K1bt5ilUqlUIBC4u7szkxs2bLC1taUoqqmpiRCSmJiYlJRUU1NDUVRAQAAh5PLlyxKJJDs7m4OPDTDyoBCbrlmzZrGvFSQkJGzevFmhUNjb2xcXF9fU1Pj5+a1Zs0apVBJCpFJpfHy8XC7ftGlTbW1tRUVFd3f3vHnzmIfr8/Pz2U9XFxQUZGRkaCbz8vKioqL8/f1pmmZePqRSqQgharXaaB8WYCRDITY/oaGhEonE1dU1Njb21atXT58+1Szi8XhvvfWWUCgMCgoqLCxsb28vKioawi4iIyNlMtn27dv1FzUA9AuF2Iwx75Nnzoh7mzZtmlgs/vnnn40bFAAMGgqxJRMKhY2NjVxHAQADQCG2WEqlsrW11dPTk+tAAGAAKMQWq6ysjKbpmTNnMpM8Hq+/ixgAwC0UYouiVqtbWlq6u7vv3LmTmJjo7e0dHx/PLAoICHj58mVJSYlSqWxsbHzy5Al7Q2dn5/r6+tra2vb2dqVSWVpaiu5rAEaDQmy6Dhw4MH36dEJISkpKdHR0YWHh/v37CSGTJk169OjR559/npSURAhZsGDBw4cPmU06OzuDg4NtbGzCwsICAwOvXbsmFAqZRQkJCXPmzImLixs3blxWVpaNjQ0hJCQkhOnftn79ejc3t6CgoIULFzLvvAAAo8EjzqZr48aNGzduZM9JSEjQ/M10Iu6xib29fX9vxnN2dr569Sp7zp49ezR/T5kypba2VjMZEREhk8mGGjgADA7OiC0K8yAGAJgXFGIAAI6ZcSE+c+aMn58fxSIQCNzc3GbPnr13796WlhauAzSqbdu2FRUVtbW1jRkz5vTp01yHo6srV66kpqayD+WqVavYK8yfP9/e3t7a2nrChAkVFRWcBJmZmRkUFCSRSIRCYUBAwF/+8peOjg7N0p07d1K/N3HiRPbmSqUyJycnICBAIBA4OjpOnDiRuQp0/vz53bt3G+5HjFnklvSfnx46OzvHjx//6aefMpOGzp6x0SapuLhYx9j8/f0dHBxommY6DFy7di0+Pp6iKA8Pjx9++MHAYQ4CIaS4uJjrKAwuJiYmJiZGx5XT09OjoqJkMhkz6e/v/8YbbxBCLl68yF6ttLQ0Ojpaz4EORnh4eEFBQXNzs0wmKy4u5vP5CxYs0CzNysrq8W9qwoQJ7M0XL148bty4mzdvKpXK+vr6RYsWVVVVMYvy8vLCw8NbWlp0CcMic0trzQ/bli1bCCFpaWmaOYPKHm3a/wYtpxCznTp1ysrKys3NrbW11QDRDYUpfwn0SPdisWvXrsDAQIVCoZnj7+9//PhxKyur0aNHsw8c58UiMjKyu7tbM8kMn/T06VNmMisr6+jRo/1te+LECYqi7ty5098KUqk0JCREqVQOGIZF5nbA/DC+++67+fPn9yjE9GCyR5v2v0EzvjShRUxMTHx8fENDw6FDh7iOBfpQXV29ffv2jIwMkUjEnh8aGpqYmPj8+fPk5GSuYuvt4sWL1tbWmkkXFxdCiFwu12XbgwcPTp06NTg4uL8VduzYcfv27by8vOHHyTCv3A6YH0KIQqHYunVrnynSe/a4YpmFmBDCPMhQWlrKTKpUqvT0dG9vbxsbm0mTJjFn3NpH+CWE/POf/5wxY4ZYLJZIJMHBwUyPrj6bgkHJz8+naXrRokW9F+3cuTMwMPCLL764cuVKn9vSNJ2bm8sMMufk5PT+++9rBjYa8IDq5dg9f/7cxsZmzJgxA67Z1dV18+bNyZMna1nHyckpPDw8Ly+P1tNrscwot7rkhxCSlpa2YcMGV1fX3ov0nj3OcHk63r9hXpqgaZopml5eXsxkcnKyUCg8ffp0S0vLtm3brKysmCvIaWlphJBvvvmmra2toaEhLCzM1ta2q6uLpumOjg6JRLJ7926FQvHixYslS5Y0NjZqaUo7YsI/i/RIx5/Pfn5+QUFBPWb6+/s/fvyYpukbN25YWVn5+vp2dHTQvX4+p6enCwSCo0ePtra23rlzZ+rUqS4uLi9evGCWajmg9FCPHdurV6/s7e2lUqlmTlZWlqenp6OjI5/P9/X1jY6O/v7775lFjx8/JoRMnjx59uzZ7u7uQqFw/PjxBw4cUKvV7DZTU1MJIZWVldp3bXm51SU/169fX7RoEU3TzPBVPS5N0Dpnjzbtf4MWW4hpmqYoytHRkaZphUIhFotjY2OZ+XK5XCgUJiQk0L99tzRX0woKCggh1dXVNE3/9NNPpNfNDS1NaWfKXwI90qVYdHR0UBQVFRXVY76mWNA0zTw0uHHjRvr3xUIul9vZ2WnyT9P0999/TwjJzMxkJrUc0CEfO7a0tLTAwEDNTTCapp8+fVpRUdHe3v769evy8vIpU6bY2Nj89NNPNE1XVVURQubNm/fdd981Nze3trZ+8sknhJBjx46x2/zyyy8JIV999ZX2XVtebgfMj1wunzZt2rNnz+j+C7GO2aNN+9+gxV6aePXqFU3TEomEEHL//n25XK7pVGRjY+Pu7t7nQL3sEX79/Pzc3NxWrly5Y8cOTX8a3Zvqbfny5ZSl06XnXENDA03TYrFYyzo7d+4cN25cQUHB9evX2fPv3r3b0dExbdo0zZzp06cLBALNS6F6YB/Q4Rw7xtmzZ0+ePPn3v//d3t5eM9PLy2vKlCl2dnYCgWDmzJlFRUUKhYIpUszz5RMmTAgNDXV2dnZwcMjIyHBwcDh8+DC7WSYVv/76q+6R9Me8cjtgfrZt2/anP/1p9OjRWhrRY/Y4ZLGPOD948IAQMn78eELIq1evCCGffvqpphMiIcTDw0N7CzY2NlevXv3kk0+ys7MzMzOXLVtWVFQ0tKYYiYmJISEhg/8o5oQZDUO7zs5O8ts/wv6IRKKioqJZs2atXr169+7dmvmtra2EEDs7O/bKjo6O7e3tA+53OMeOEPL111/n5uaWlZW9+eabWlYLDg62trZmvn5M48yLARkCgcDHx6empoa9CTPuB5OWYTKv3GrPz/Xr16uqqnJzc7U3osfscchiC/Hly5cJIREREYQQ5jL//v37ExMTB9XIhAkTLly40NjYmJub+7e//W3ChAmxsbFDa4oQEhISwn5xnEU6derUgOsw/3IG7IofEhKyZcuWffv2ZWVleXt7MzMdHR0JIT1Kg47DLg/5a0AI+eyzz/7+979fvXq1R53qTa1Wq9VqphTa2dmNHTv23r177BW6u7sdHBzYc7q6ushvaRkm88qt9vwcOXLkm2++sbL63a/27Ozs7OzsH374QXPmrsfsccgyL028ePFi//79np6eq1evJoR4eXmJRKLbt28PqpH6+nrmK+Lq6rpr166pU6feu3dvaE0Bm5ubG0VRbW1tA66ZlZU1fvz4yspKzZyJEyfa2dn9+OOPmjm3bt3q6up65513BmxtaMeOpumUlJSqqqqSkpI+q/B7773HnmTuUGl++ixfvryysvLRo0fMpFwuf/LkSY/eWkwqRo0aNajA+mReuSVa81NUVMS+isq+Rsy+fqLH7HHIEgoxTdMdHR3MndbGxsbi4uJ3333X2tq6pKSEuUYsEok+/vjjEydOFBYWymQylUr17NmzX375RXuz9fX169at+/nnn7u6uiorK588eTJz5syhNQVsYrHYz8+vv1Hi2Jgf0ew+vCKRKCkp6ezZs8eOHZPJZFVVVWWjKKYAACAASURBVOvXr/fw8Fi7dq0urfV37GJjY0eNGtXnY7737t3bs2fP559/zufz2VfD9+3bx6zw/Pnzr7/+urW1ValUlpeX//GPf/T29l6/fj2zdMuWLT4+PvHx8U+fPm1ubk5JSVEoFMwtKQ0mFdr70urIvHJLdMuPdnrMHpeMfHNQR7r0mjh//vykSZPEYrFAIGB+v1AU5ejoOGPGjMzMzObmZvbKr1+/TklJ8fb25vF4rq6uH3zwwd27dwsKCpgr/WPHjq2pqTl8+DBTuH18fB48eFBbWxsaGurk5GRtbf3mm2+mpaUxj1f12dSAn4iY8B1bPdKxi5VUKuXz+XK5nJk8e/asv78/IcTFxYW5m8+2detWdhcrtVq9d+/esWPH8vl8JyenxYsX379/n1mk/YDS/R+7xYsXE0LS09N7h8rc2e9t7969zApJSUn+/v62trY8Hs/T03PNmjX19fXsFurq6uLi4pycnIRC4YwZM0pLS3vsIjIycvTo0T36tPVmebnVMT+M/npN6Jg92rT/DZpxITYvpvwl0CMdi8XDhw95PJ6WJ4ONTKVShYWFHTlyxPi7bmpqEolE+/btG3BN5LY33bNHm/a/QUu4NAFmJyAgIDMzMzMzkz2MGVdUKlVJSUl7eztzJ9bIduzYMXnyZKlUqq8GR1Ru9Z49rqAQAzdSU1OXLl0aGxury50lgyorKztz5kxpaan27reGkJube/v27UuXLvH5fD02O0Jya6DscQKFGDiTnZ0tlUp37drFbRhz5849fvy4u7u7kfd77ty5169fl5WVOTk56b1xi8+tQbNnfBbbj3ikUSgUc+fOvXHjhkk1NaD58+czwxuOQNHR0dHR0YZr37Jza+jsGRnOiC3EkSNHGhoaTK0pANAFCrEJofsfhFAqlQoEAs1PvA0bNtja2lIUxTwempiYmJSUVFNTQ1FUQEBAfn6+SCRyc3Nbt26dh4eHSCQKDQ3VjBgwqKYIIZcvX5ZIJNnZ2UbOBsDIgUJsQnbs2JGampqWltbQ0PDtt9/W1dWFhYUxo5nk5+ezH48uKCjIyMjQTObl5UVFRfn7+9M0XV1dLZVK4+Pj5XL5pk2bamtrKyoquru7582bV1dXN9imyG/Py6rVasMnAGCEQiE2FQqFIjc3d8mSJStXrnRwcAgODj506FBTU1OPkbp0x+PxmJProKCgwsLC9vb2oqKiIbQTGRkpk8m2b98+tDAAYEAoxKZisIMQDsq0adPEYvGghnwEAKNBITYVwxmEUBdCoZB5SBQATA0KsakYziCEA1IqlfpqCgD0DoXYVAw4CCGPx2PehjAEZWVlNE3PnDlz+E0BgN6hEJuKAQchDAgIePnyZUlJiVKpbGxsfPLkCXtzZ2fn+vr62tra9vZ2psiq1eqWlpbu7u47d+4kJiZ6e3szb7YebFOlpaXovgZgUCjEJuSvf/1rTk5OZmami4tLeHi4r69vWVmZra0tszQhIWHOnDlxcXHjxo3LyspiXkkQEhLCdEpbv369m5tbUFDQwoULX758SQjp7OwMDg62sbEJCwsLDAy8du2a5g06g20KAAwKjzibEIqikpOTk5OT+1zq7Ox89epV9pw9e/Zo/p4yZYrmDacMe3v7/gYIH1RTERERMplMx48AAEOAM2KLNeCLywDARKAQAwBwDIXYAm3btq2oqKitrW3MmDGnT5/mOhwAGACuEVugnJycnJwcrqMAAF3hjBgAgGMoxAAAHEMhBgDgGAoxAADHTPpm3cmTJ7kOQZ/Ky8u5DsHgmEdILOzAmQjk1pLRJqm4uJjrxACApSkuLua6tvWNomma6+QADB3z2iecJ4JZwzViAACOoRADAHAMhRgAgGMoxAAAHEMhBgDgGAoxAADHUIgBADiGQgwAwDEUYgAAjqEQAwBwDIUYAIBjKMQAABxDIQYA4BgKMQAAx1CIAQA4hkIMAMAxFGIAAI6hEAMAcAyFGACAYyjEAAAcQyEGAOAYCjEAAMdQiAEAOIZCDADAMRRiAACOoRADAHAMhRgAgGMoxAAAHEMhBgDgGAoxAADHUIgBADiGQgwAwDEUYgAAjlE0TXMdA8AgHD9+/MiRI2q1mpl8/PgxIWTMmDHMpJWV1X//93+vWLGCs/gABg+FGMzMnTt33n77bS0r/Otf/5o0aZLR4gEYPhRiMD/jx4+/f/9+n4sCAgIePnxo5HgAhgnXiMH8rFq1is/n957P5/M//vhj48cDMEw4Iwbz8+jRo4CAgD6/ug8fPgwICDB+SADDgTNiMD9+fn5Tp06lKIo9k6KoadOmoQqDOUIhBrP00UcfWVtbs+dYW1t/9NFHXMUDMBy4NAFmqaGhwcPDQ9OJjRBiZWVVX18/atQoDqMCGBqcEYNZcnNzCw8P15wUW1tbz549G1UYzBQKMZirVatWsX/PrVq1isNgAIYDlybAXMlkMldX166uLkIIn89vaGhwdHTkOiiAocAZMZgriUSyYMECHo/H4/EWLlyIKgzmC4UYzNjKlStVKpVKpcLgEmDWcGkCzFhnZ6eLiwtN001NTTY2NlyHAzBUtMkoLi7mOhkAMFIUFxdzXfP+D4/rbPQ0wsvx/v37CSGbN2/mOhDDKi8vz8vL08uxvn37NkVR2sdjszwj5HtiOMuXL+c6hN8xuUK8bNkyrkPg0qlTp8jISEJeXp5ePuaSJUsIITyeyX2TDWrkfE8MBIUYQJ9GWgkGi4ReEwAAHEMhBgDgGAoxAADHUIgBADiGQmwJLl265ODgcOHCBa4DAYChQCG2BDQejwQwZ+j6YwkiIyPb2tqMsCOFQjF37twbN24YYV8AIwfOiGEQjhw50tDQwHUUAJYGhdjsXb9+3dvbm6KoAwcOEEIKCwttbW3FYvG5c+ciIiIkEomnp+eJEyeYlfPz80UikZub27p16zw8PEQiUWho6K1bt5ilUqlUIBC4u7szkxs2bLC1taUoqqmpiRCSmJiYlJRUU1NDURTzjs7Lly9LJJLs7GwOPjaABUEhNnuzZs1iXytISEjYvHmzQqGwt7cvLi6uqanx8/Nbs2aNUqkkhEil0vj4eLlcvmnTptra2oqKiu7u7nnz5tXV1RFC8vPz2U/NFhQUZGRkaCbz8vKioqL8/f1pmq6uriaEqFQqQgj7xXEAMAQoxBYrNDRUIpG4urrGxsa+evXq6dOnmkU8Hu+tt94SCoVBQUGFhYXt7e1FRUVD2EVkZKRMJtu+fbv+ogYYiVCILZ9AICCEMGfEvU2bNk0sFv/888/GDQoA/g8KMRChUNjY2Mh1FAAjFwrxSKdUKltbWz09PbkOBGDkQiEe6crKymianjlzJjPJ4/H6u4gBAAaCQjwSqdXqlpaW7u7uO3fuJCYment7x8fHM4sCAgJevnxZUlKiVCobGxufPHnC3tDZ2bm+vr62tra9vV2pVJaWlqL7GsDwoRCbvQMHDkyfPp0QkpKSEh0dXVhYyLxHZ9KkSY8ePfr888+TkpIIIQsWLHj48CGzSWdnZ3BwsI2NTVhYWGBg4LVr14RCIbMoISFhzpw5cXFx48aNy8rKYt7IGRISwvRvW79+vZubW1BQ0MKFC1++fMnJ5wWwPHjE2ext3Lhx48aN7DkJCQmav5lOxD02sbe3f/bsWZ+tOTs7X716lT1nz549mr+nTJlSW1urmYyIiJDJZEMNHAD+F86IRyLmQQwAMBFmVojPnDnj5+dHsQgEAjc3t9mzZ+/du7elpYXrAMEgrly5kpqayj76q1atYq8wf/58e3t7a2vrCRMmVFRUcBWnUqnMyckJCAgQCASOjo4TJ05k/4DQ6OzsHD9+/KeffspMnj9/fvfu3Qb93xEJNHW0yWBerq7Lmv7+/g4ODjRNMzedrl27Fh8fT1GUh4fHDz/8YOAwDSsmJiYmJsZw7aempjLPd/j6+p46dcpwO9JO92NN03R6enpUVJRMJmMm/f3933jjDULIxYsX2auVlpZGR0frOdBBWrx48bhx427evKlUKuvr6xctWlRVVdV7tS1bthBC0tLSNHPy8vLCw8NbWlp03NGgvidIYG+EkOLiYv0ErQ/mXYjZTp06ZWVl5ebm1traaoDohkUul4eEhOiypqELsYnQ/Vjv2rUrMDBQoVBo5vj7+x8/ftzKymr06NHsY815HTlx4gRFUXfu3NG+2nfffTd//vwedYSmaalUGhISolQqddmX7t8TJLBPplaIzezShBYxMTHx8fENDQ2HDh3iOpaeMHrk0FRXV2/fvj0jI0MkErHnh4aGJiYmPn/+PDk5mavYejt48ODUqVODg4O1rKNQKLZu3ZqXl9d70Y4dO27fvt3noiFDAs2F5RRiQgjTGba0tJQQsmfPHrFYbG9v39DQkJSUNHr06Pv379M0nZuby4x34+Tk9P7772vGWNA+PiQhRMu2gx09EnSUn59P0/SiRYt6L9q5c2dgYOAXX3xx5cqVPrfVcry0jxRKCFGpVOnp6d7e3jY2NpMmTWLO37Xr6uq6efPm5MmTta+Wlpa2YcMGV1fX3oucnJzCw8Pz8vJo/b1vBQk0G9ydjPc0zEsTNE0zXam8vLyYybS0NELIpk2bPvvssyVLlvz73/9OT08XCARHjx5tbW29c+fO1KlTXVxcXrx4way/du1aW1vbe/fudXZ23r17d/r06fb29k+fPmWWat92xYoVo0aN0kSyd+9eQkhjYyMz+cEHHzCjRw4IlybY/Pz8goKCesz09/d//PgxTdM3btywsrLy9fXt6Oige/2y1n68mO/GN99809bW1tDQEBYWZmtr29XVxSxNTk4WCoWnT59uaWnZtm2blZXVgPceHj9+TAiZPHny7Nmz3d3dhULh+PHjDxw4oFarNetcv3590aJFNE0zI3v0+GVN03RqaiohpLKycsDM6Pg9QQL7Q0zs0oRFFWKapimKcnR0ZP5mviuaq2NyudzOzi42Nlaz8vfff08IyczMZCbXrl3LbvaHH34ghGRkZOiyLQrxoOhyrDs6OiiKioqK6jFfU0dommaeVdm4cSP9+zoy4PHq8d0oKCgghFRXV9M0rVAoxGKxZlu5XC4UChMSErRHW1VVRQiZN2/ed99919zc3Nra+sknnxBCjh07pmln2rRpz549o/uvI19++SUh5KuvvtK+L1q37wkSqIWpFWKLeqDj1atXNE1LJJI+l969e7ejo2PatGmaOdOnTxcIBOzrD2zs8SEHu+1wPHv27OTJk3pv1qSUl5cPuE5DQwNN02KxWMs6O3fuvHjxYkFBwfLly9nzB3u82COF3r9/Xy6XT5w4kVlkY2Pj7u4+4DChzKOJEyZMCA0NZeZkZGQcPHjw8OHDK1asIIRs27btT3/60+jRo7U0wnzYX3/9Vfu+dIQEmhGLKsQPHjwghIwfP77Ppa2trYQQOzs79kxHR8f29vb+GtSMDzmEbYfs5s2bPf5VjEydnZ3kt3+f/RGJREVFRbNmzVq9evXu3bs184dzvF69ekUI+fTTTzXdVAkhHh4e2rdiVmDuCjAEAoGPj09NTQ0h5Pr161VVVbm5udobYR4oZz748CGBZsSibtZdvnyZEBIREdHnUkdHR0JIj2+SlhEg2eNDDnbb4Rg5lya0Y/5RDdhLPyQkZMuWLQ8fPszKytLMHM7xYm4E7d+/nx3wgKfwdnZ2Y8eOvXfvHntmd3e3g4MDIeTIkSPffPONlZUV8zAFs4vs7GyKon788UfN+l1dXZoPPnxIoBmxnEL84sWL/fv3e3p6rl69us8VJk6caGdnxz5st27d6urqeuedd/pcnz0+5IDbYvRIvXNzc6Moqq2tbcA1s7Kyxo8fX1lZqZkz2GPN5uXlJRKJbt++PdiAly9fXllZ+ejRI2ZSLpc/efKE6YxVVFTErkrsS5zsn//Mhx01atRgd90nJNCMmGshpmm6o6ODuaPa2NhYXFz87rvvWltbl5SU9HeNWCQSJSUlnT179tixYzKZrKqqav369R4eHmvXrtWs09/4kANuO6jRIw2VFMsiFov9/Pz6G5yIjfl9bW1tzZ4z4LHW0trHH3984sSJwsJCmUymUqmePXv2yy+/EEJiY2NHjRrV3xPAW7Zs8fHxiY+Pf/r0aXNzc0pKikKhYO446Yj5sNo70uoOCTQnBvjdOUS63Ek/f/78pEmTxGKxQCCwsrIihDDdJGbMmJGZmdnc3KxZc/fu3cwvFC8vr6NHjzIz1Wr13r17x44dy+fznZycFi9ezHQuZqxdu5bP548ePZrH40kkkvfff7+mpkazVPu2zc3Nc+bMEYlEY8aM+fOf/7x161ZCSEBAANP7raKiwsfHx8bGZtasWZoOQH1Crwk2qVTK5/PlcjkzefbsWX9/f0KIi4sLc6OfbevWrezeV1qOV0FBAXNXZ+zYsTU1NYcPH2b+8/bx8Xnw4AFN069fv05JSfH29ubxeK6urh988MHdu3dpml68eDEhJD09vb+A6+rq4uLinJychELhjBkzSktL+1ytv5v+kZGRo0ePZnfY6o+O3xMksD/ExHpNmFkhNqi1a9c6OztzGACNQvx7Dx8+5PF4mv9HOadSqcLCwo4cOWKIxpuamkQi0b59+3RZWcfvCRLYH1MrxOZ6acJAzHsAJ4sTEBCQmZmZmZnZ0dHBdSxEpVKVlJS0t7fHxsYaov0dO3ZMnjxZKpXqsU0k0FygEINJS01NXbp0aWxsrC43nQyqrKzszJkzpaWl2nvmDk1ubu7t27cvXbrE5/P12zISaBZQiP/Xtm3bioqK2traxowZc/r0aa7Dgf+TnZ0tlUp37drFbRhz5849fvy4ZkQRPTp37tzr16/LysqcnJz03jhBAs2BRT3QMRw5OTk5OTlcRwF9mz9/PjPyoUWKjo6Ojo426C6QQBOHM+IRR6FQaJ4iNZ2mAEYyFOIRR4+DI2OcZQC9QCE2S7SeBkfWPgrzYMdZvnz5skQiyc7ONnI2AMwdCrFZ2rFjR2pqalpaWkNDw7fffltXVxcWFsYMOpWfn79s2TLNmgUFBRkZGZrJvLy8qKgoZkzO6upqqVQaHx8vl8s3bdpUW1tbUVHR3d09b968urq6wTZFfuv8p1arDZ8AAIuCQmx+FApFbm7ukiVLVq5c6eDgEBwcfOjQoaampsOHDw+tQR6Px5xcBwUFFRYWtre3FxUVDaGdyMhImUy2ffv2oYUBMGKhEJsfgw6OzB6FGQCMA4XY/Bh6cGTNKMwAYBwoxObHoIMjs0dhBgDjQCE2PwYdHJk9CvMwmwIAHaEQmx+9D47c3yjMg22qtLQU3dcAhgCF2Cz99a9/zcnJyczMdHFxCQ8P9/X1LSsrs7W1ZZYmJCTMmTMnLi5u3LhxWVlZzLjMISEhTKe09evXu7m5BQUFLVy48OXLl4SQzs7O4OBgGxubsLCwwMDAa9euaV50NtimAGAIMNaEWaIoKjk5OTk5uc+lzs7OV69eZc/Zs2eP5u8pU6bU1tayl9rb2/f3HodBNRURESGTyXT8CACggTNiwCjMABxDIQYA4BgK8YiGUZgBTAGuEY9oGIUZwBTgjBgAgGMoxAAAHEMhBgDgGAoxAADHTO5m3dKlS7kOgUs3b94kIyAJzPMjFv8xDWeEfE9GDoqmaa5j+F/l5eW5ublcRwFmprKykhAyZcoUrgMBM7Nly5aQkBCuo/hfJlSIAYaAeZnTyZMnuQ4EYOhwjRgAgGMoxAAAHEMhBgDgGAoxAADHUIgBADiGQgwAwDEUYgAAjqEQAwBwDIUYAIBjKMQAABxDIQYA4BgKMQAAx1CIAQA4hkIMAMAxFGIAAI6hEAMAcAyFGACAYyjEAAAcQyEGAOAYCjEAAMdQiAEAOIZCDADAMRRiAACOoRADAHAMhRgAgGMoxAAAHEMhBgDgGAoxAADHUIgBADiGQgwAwDEUYgAAjqEQAwBwjMd1AACDI5fLX79+rZns6uoihLS0tGjmCIVCsVjMQWQAQ0XRNM11DACDUFhYuGHDBi0rFBQUJCQkGC0egOFDIQYz09jY6OHhoVKp+lxqbW39yy+/uLq6GjkqgOHANWIwM66urnPnzrW2tu69yNra+g9/+AOqMJgdFGIwPytXruzzlxxN0ytXrjR+PADDhEsTYH7a29tdXV3Zt+wYAoGgsbFRIpFwEhXAkOGMGMyPvb19VFQUn89nz+TxeNHR0ajCYI5QiMEsrVixoru7mz1HpVKtWLGCq3gAhgOXJsAsdXV1ubi4tLe3a+bY2dk1NTUJhUIOowIYGpwRg1kSCARLly4VCATMJJ/PX758OaowmCkUYjBXH374IfNYHSFEqVR++OGH3MYDMGS4NAHmSq1Wu7u7NzY2EkJcXFxevHjRZ+diANOHM2IwV1ZWVh9++KFAIODz+StWrEAVBvOFQgxmLC4urqurC9clwNyZx+hr5eXldXV1XEcBJoem6TfeeIMQ8vjx49raWq7DAZPj5eUVEhLCdRQ6oM1BTEwM13kCAPMTExPDdfXSiXmcERNCYmJiTp06xXUU+nHy5Mnly5fTI+A2KUVRxcXFy5YtM9wu7t27RwgJCgoy3C64MnK+JwaydOlSrkPQldkUYoA+WWQJhpEGN+sAADiGQgwAwDEUYgAAjqEQAwBwDIUYAIBjKMRm49KlSw4ODhcuXOA6EADQMxRis4H+pACWCv2IzUZkZGRbW5sRdqRQKObOnXvjxg0j7AsACM6IobcjR440NDRwHQXACIJCbB6uX7/u7e1NUdSBAwcIIYWFhba2tmKx+Ny5cxERERKJxNPT88SJE8zK+fn5IpHIzc1t3bp1Hh4eIpEoNDT01q1bzFKpVCoQCNzd3ZnJDRs22NraUhTV1NRECElMTExKSqqpqaEoKiAggBBy+fJliUSSnZ3NwccGGBlQiM3DrFmz2NcKEhISNm/erFAo7O3ti4uLa2pq/Pz81qxZo1QqCSFSqTQ+Pl4ul2/atKm2traioqK7u3vevHnMCHb5+fnswR8KCgoyMjI0k3l5eVFRUf7+/jRNV1dXE0JUKhUhRP3/27vToCbSfX/gTyAkIUBYBiKMCsomg7LoqCMopZalNchFXFDgqnMz3nuuityIgsXgwggIHpcCiitoOeOh6qjXYlwKlxJfOIpTjsuZKeHA0esCCuIwCsoSliCB9P9F/yc3hyUkIaHTyffzit6efvrX7c/O008/rVSO28ECWBokYnYLDw8XiURubm7x8fHd3d2vX79WLeJyuZ999hmfzw8MDCwuLu7s7CwpKdFjF1FRUTKZbN++fYarNQD8EyRiM0F/RpO+Ix5q9uzZQqHw6dOn41spANAKErGl4PP59OfdAMDUIBFbBIVC0d7ePmnSJKYrAgDDQCK2CBUVFRRFzZs3j57kcrkjNWIAwPhDIjZbSqWyra2tv7+/uro6OTnZ09NTIpHQi3x9fVtbW8vKyhQKRUtLS0NDg/qGLi4uTU1N9fX1nZ2dCoWivLwc3dcAjAqJmB2OHTs2Z84cQkhaWlpMTExxcXF+fj4hJDg4+OXLl999911KSgoh5Msvv3zx4gW9SW9vb1BQkK2tbUREhL+//+3bt/l8Pr0oMTFx8eLFCQkJ06ZNy87OtrW1JYSEhYXR/du2bt0qFosDAwOXL1/e2trKyPECWBS84swOSUlJSUlJ6nMSExNVf9OdiAdt4uDg8ObNm2FLc3FxuXXrlvqcw4cPq/6eOXOm+heRIyMjZTKZvhUHgNHhjths0S9iAIDpM59EfPHiRW9vb44aHo8nFosXLVp05MiRtrY2pisIo7h582Z6err6edy4caP6CsuWLXNwcLC2tp4+ffqjR4+YqqdCocjNzfX19eXxeE5OTjNmzFD/AaHS29sbEBCwd+9eevLKlSuHDh0y6v+ObAkgIUSpVObn54eHhw9dNFJ4xyGATKLYIDY2NjY2Vps1fXx8HB0dKYqiH1Xdvn1bIpFwOBwPD49ffvnFyNXUVmlpqVEjn56eTr/fMWXKlPPnzxtvR6MihJSWlmqzZkZGRnR0tEwmoyd9fHw++eQTQsi1a9fUVysvL4+JiTF8RXWxatWqadOmPXjwQKFQNDU1rVixoqamZuhqO3fuJITs2bNHNaegoGDhwoVtbW1a7kin64RFAXz+/Pn8+fMJISEhIUOXagivrgHUPm8wzmwTsbrz589bWVmJxeL29nYj1E5nxk7EpkPLRHzw4EF/f3+5XK6a4+Pjc/bsWSsrq4kTJ6qfNcbzyLlz5zgcTnV1tebVfv7552XLlg1KxBRFSaXSsLAwhUKhzb60v05YFMCqqqrVq1efOXMmNDR0aCIeNbw6BZBFidh8miY0iI2NlUgkzc3NJ06cYLouMFhtbe2+ffsyMzMFAoH6/PDw8OTk5N9++y01NZWpug11/PjxWbNmBQUFaVhHLpfv2rWroKBg6KL9+/dXVVUNu0hv7ApgSEjIxYsX169fr+rDo27U8BojgKbAIhIxIYTuQlteXk5PDgwMZGRkeHp62traBgcH07cemseWJITcuXNn7ty5QqFQJBIFBQXRfQmGLQq0V1hYSFHUihUrhi46cOCAv7//999/f/PmzWG3pSgqLy+PHtvI2dl55cqVqvE0Rj2bepy4vr6+Bw8ehIaGal5tz54927Ztc3NzG7rI2dl54cKFBQUFlOG+t8KiAGqmTXiNEUCTwOTtuNbG2DRBURSdNCdPnkxPpqam8vn8CxcutLW17d6928rKim5B3rNnDyHkxx9/7OjoaG5ujoiIsLOz6+vroyiqq6tLJBIdOnRILpe/fft29erVLS0tGorSAE0T6ry9vQMDAwfN9PHxefXqFUVR9+7ds7KymjJlSldXFzXkl3VGRgaPxzt9+nR7e3t1dfWsWbNcXV3fvn1LL9VwNim9TtyrV68IIaGhoYsWLXJ3d+fz+QEBAceOHVMqlap17t69u2LFCoqi6JE9BjVNUBSVnp5OCKmsrNS8+t5AeAAAIABJREFUL0rr64RFAVT3xRdfDGqa0Ca8lC4BZFHTBDvSwdgTMUVRHA7HycmJoii5XC4UCuPj4+n5PT09fD4/MTGR+uPKU7W1FRUVEUJqa2spivrHP/5Bhjz60FCUBkjEKl1dXRwOJzo6etB8VR6hKIp+VyUpKYn65zzS09Njb2+vCj5FUX/7298IIVlZWfSkhrOp34mrqakhhCxduvTnn3/+8OFDe3v7N998Qwg5c+aMqpzZs2e/efOGGjkR/+UvfyGE/PWvf9W8L0q764RdAVQ3NBGPGl6a9gFkUSK2lBc6uru7KYoSiUSEkGfPnvX09MyYMYNeZGtr6+7uPuwQkepjS3p7e4vF4g0bNmzfvl0ikUyZMkWnooZau3atAQ6M5ZqbmymKEgqFGtY5cODAtWvXioqK4uLi1Oc/fvy4q6tr9uzZqjlz5szh8Xiqb5EMon429TtxdLPm9OnTVf2uMjMzjx8/fvLkyfXr1xNCdu/e/Z//+Z8TJ07UUAh9sO/evdO8Ly2xK4CajRpemmEDaCIspY34+fPnhJCAgABCSHd3NyFk7969qh7HDQ0NPT09mkuwtbW9devWggULcnJyvL294+Pj5XK5fkWBSm9vL/njX+BIBAJBSUkJh8PZtGmTXC5XzW9vbyeE2Nvbq6/s5OTU2dk56n71O3EeHh6EEPqbUjQej+fl5VVXV0cIuXv3bk1NzX/8x39oLoR+oZw+8LFjVwA10xxeFcMG0ERYyh3xjRs3CCGRkZGEEPopSn5+fnJysk6FTJ8+/erVqy0tLXl5eX/+85+nT58eHx+vX1GEkPPnz+u6CetwOBzNK9D/qEbtpR8WFrZz586jR49mZ2d7enrSM52cnAghg7KGlqN96ncN2Nvb+/n5PXnyRH1mf3+/o6MjIeTUqVM//vijldU/3dzk5OTk5OT88ssvqhvPvr4+8seBjx27AqiZ5vCqGDaAJsIi7ojfvn2bn58/adKkTZs2EUImT54sEAiqqqp0KqSpqYm+RNzc3A4ePDhr1qwnT57oVxSoiMViDofT0dEx6prZ2dkBAQGVlZWqOTNmzLC3t//1119Vcx4+fNjX1/f555+PWpreJy4uLq6ysvLly5f0ZE9PT0NDA93dqqSkRL3VT72NWP3nP32wEyZM0HXXw2JdADXTEF4VwwbQRJhhIqYoqquri37S2tLSUlpaOn/+fGtr67KyMrqNWCAQfP311+fOnSsuLpbJZAMDA2/evPn99981F9vU1LRly5anT5/29fVVVlY2NDTMmzdPv6JARSgUent7jzQ4kTr697W1tbX6nJSUlEuXLp05c0Ymk9XU1GzdutXDw2Pz5s3alDbSiYuPj58wYcJIbwDv3LnTy8tLIpG8fv36w4cPaWlpcrmcfqakJfpgNfdE1h7rAqiZNuE1bABNxbg/HtSHNk8/r1y5EhwcLBQKeTwe/fOQ7iYxd+7crKysDx8+qK/88ePHtLQ0T09PLpfr5ua2Zs2ax48fFxUV0c8B/Pz86urqTp48SSduLy+v58+f19fXh4eHOzs7W1tbf/rpp3v27Onv7x+pKM1VRa8JdVKp1MbGpqenh568dOmSj48PIcTV1ZV+0K9u165d6r2vlErlkSNH/Pz8bGxsnJ2dV61a9ezZM3qR5rNJjXziVq1aRQjJyMgYqcKNjY0JCQnOzs58Pn/u3Lnl5eXDrjZSr4moqKiJEycO6pI1LC2vE3YF8P79+/Pnz6ebgwkh7u7u4eHhd+7cUa0wani1DyCLek2wIx2wKKDaQCJW9+LFCy6Xe/r06fGp0qgGBgYiIiJOnTpljMLfv38vEAiOHj2qzcpaXicI4EhYlDfMsGkC2MXX1zcrKysrK6urq4vpupCBgYGysrLOzk76MazB7d+/PzQ0VCqVGrBMBNAMIBED89LT09euXRsfH6/NQyejqqiouHjxYnl5ueaeufrJy8urqqq6fv26jY2NYUtGANkOiRhMQk5OjlQqPXjwILPVWLJkydmzZ93d3Q1e8uXLlz9+/FhRUeHs7GzwwgkCyHKW0o/Y0sjl8iVLlty7d8+kitJs2bJl9NCRZikmJiYmJsaou0AA2Qt3xObp1KlTzc3NplYUAAwLidh0USMPUSiVSnk8nuoH4LZt2+zs7DgcDv16aHJyckpKSl1dHYfD8fX1LSwsFAgEYrF4y5YtHh4eAoEgPDxcNZ6ATkURQm7cuCESiXJycsY5GgBmDInYdO3fvz89PX3Pnj3Nzc0//fRTY2NjREQEPdZJYWHhunXrVGsWFRVlZmaqJgsKCqKjo318fCiKqq2tlUqlEomkp6dn+/bt9fX1jx496u/vX7p0aWNjo65FkT/eplUqlcYPAIClQCI2UXK5PC8vb/Xq1Rs2bHB0dAwKCjpx4sT79+9PnjypX4FcLpe+uQ4MDCwuLu7s7CwpKdGjnKioKJlMtm/fPv2qAQBDIRGbKF2HKNTJ7NmzhULhGActBABDQSI2UWMZolAbfD6ffgcXABiHRGyixjJE4agUCoWhigKAsUMiNlGjDlHI5XLpbyXooaKigqKoefPmjb0oABg7JGITNeoQhb6+vq2trWVlZQqFoqWlpaGhQX1zFxeXpqam+vr6zs5OOskqlcq2trb+/v7q6urk5GRPT0/6y9a6FlVeXo7uawCGhURsur799tvc3NysrCxXV9eFCxdOmTKloqLCzs6OXpqYmLh48eKEhIRp06ZlZ2fTHywICwujO6Vt3bpVLBYHBgYuX768tbWVENLb2xsUFGRraxsREeHv73/79m3V93V0LQoADAuvOJsuDoeTmpqampo67FIXF5dbt26pzzl8+LDq75kzZ9bX16svdXBwGGn4cJ2KioyMlMlkWh4CAGgDd8SWYtTPmgEAU5CIAQAYhkRs/nbv3l1SUtLR0TF16tQLFy4wXR0AGAxtxOYvNzc3NzeX6VoAwIhwRwwAwDAkYgAAhiERAwAwDIkYAIBhSMQAAEyj2CA2NpbpOAEA+8TGxjKdvbTCoSiK6ViN7v79+/S4BwCD5OfnE0J27NjBdEXAFE2ePDksLIzpWoyOHYkYYCT0B/d++OEHpisCoD+0EQMAMAyJGACAYUjEAAAMQyIGAGAYEjEAAMOQiAEAGIZEDADAMCRiAACGIREDADAMiRgAgGFIxAAADEMiBgBgGBIxAADDkIgBABiGRAwAwDAkYgAAhiERAwAwDIkYAIBhSMQAAAxDIgYAYBgSMQAAw5CIAQAYhkQMAMAwJGIAAIYhEQMAMAyJGACAYUjEAAAMQyIGAGAYEjEAAMOQiAEAGIZEDADAMCRiAACGcZmuAIBuHj58+Pe//101+fLlS0LIyZMnVXNCQkK++OILBmoGoC8ORVFM1wFAB9euXYuOjra2traysiKE0Bcwh8MhhCiVyoGBgatXr/7Lv/wLw7UE0AUSMbCMQqFwdXWVyWTDLhWJRC0tLTweb5xrBTAWaCMGlrGxsUlISBg21WpYBGDKkIiBfRISEvr6+obOVygU//qv/zr+9QEYIzRNAPsolcpPP/303bt3g+a7ubm9ffuWbjsGYBFcssA+VlZWGzduHNQEwePxJBIJsjCwEa5aYKWhrRN9fX0JCQlM1QdgLNA0AWzl5+dXW1urmvT29q6rq2OwPgB6wx0xsNWGDRtsbGzov3k83r/9278xWx8AveGOGNiqtrbWz89PNfns2TN/f38G6wOgN9wRA1v5+vqGhIRwOBwOhxMSEoIsDOyFRAws9tVXX1lbW1tbW3/11VdM1wVAf2iaABZramqaPHkyRVGNjY0TJ05kujoAejKhRHz//v28vDymawEsU1FRQQhZtGgRw/UAttm5c2dYWBjTtfj/TKhporGx8cKFC0zXgmEPHjx48OAB07Uwujdv3hjqXHt6enp5eRmkKBaxkOvEeC5cuNDY2Mh0Lf6PyY1HfP78eaarwKS1a9cSCwjCDz/8EBcXZ5DDbG1tJYS4uLiMvSgWsZDrxHjocVNNh8klYgCdWFoKBrNkQk0TAACWCYkYAIBhSMQAAAxDIgYAYBgSsTm4fv26o6Pj1atXma4IAOgDidgcmM5bOQCgB3RfMwdRUVEdHR3jsCO5XL5kyZJ79+6Nw74ALAfuiEEHp06dam5uZroWAOYGiZj17t696+npyeFwjh07RggpLi62s7MTCoWXL1+OjIwUiUSTJk06d+4cvXJhYaFAIBCLxVu2bPHw8BAIBOHh4Q8fPqSXSqVSHo/n7u5OT27bts3Ozo7D4bx//54QkpycnJKSUldXx+FwfH19CSE3btwQiUQ5OTkMHDaAGUEiZr0FCxaotxUkJibu2LFDLpc7ODiUlpbW1dV5e3v/6U9/UigUhBCpVCqRSHp6erZv315fX//o0aP+/v6lS5fS790XFhauW7dOVVRRUVFmZqZqsqCgIDo62sfHh6Io+htFAwMDhBClUjluBwtglpCIzVZ4eLhIJHJzc4uPj+/u7n79+rVqEZfL/eyzz/h8fmBgYHFxcWdnZ0lJiR67iIqKkslk+/btM1ytASwRErH5oz87T98RDzV79myhUPj06dPxrRQA/B8kYiB8Pr+lpYXpWgBYLiRiS6dQKNrb2ydNmsR0RQAsFxKxpauoqKAoat68efQkl8sdqREDAIwEidgSKZXKtra2/v7+6urq5ORkT09PiURCL/L19W1tbS0rK1MoFC0tLQ0NDeoburi4NDU11dfXd3Z2KhSK8vJydF8DGDskYtY7duzYnDlzCCFpaWkxMTHFxcX5+fmEkODg4JcvX3733XcpKSmEkC+//PLFixf0Jr29vUFBQba2thEREf7+/rdv3+bz+fSixMTExYsXJyQkTJs2LTs729bWlhASFhZG92/bunWrWCwODAxcvnw5/WkMABg7vOLMeklJSUlJSepzEhMTVX/TnYgHbeLg4PDmzZthS3Nxcbl165b6nMOHD6v+njlzZn19vWoyMjJSJpPpW3EA+P9wR2yJ6BcxAMBEIBEDADCMZYn44sWL3t7eHDU8Hk8sFi9atOjIkSNtbW1MV9DU7d69u6SkpKOjY+rUqYb6oP04uHnzZnp6uvrZ37hxo/oKy5Ytc3BwsLa2nj59+qNHj5iqJyFEqVTm5+eHh4cPXaRQKHJzc319fXk8npOT04wZM+h2nitXrhw6dMioP1MQQFNHmYzS0lIt6+Pj4+Po6EhRFP30//bt2xKJhMPheHh4/PLLL0aupnHFxsbGxsYyXQuj0/5cUxSVkZERHR0tk8noSR8fn08++YQQcu3aNfXVysvLY2JiDFxRHT1//nz+/PmEkJCQkKFLV61aNW3atAcPHigUiqamphUrVtTU1NCLCgoKFi5c2NbWpuWOdLpOEMChCCGlpaUGq/eYsTsRqzt//ryVlZVYLG5vbzdC7cakp6cnLCxMmzWRiAc5ePCgv7+/XC5XzfHx8Tl79qyVldXEiRPVzzXjeaSqqmr16tVnzpwJDQ0dmkfOnTvH4XCqq6tH2lwqlYaFhSkUCm32pf11ggAOy9QSMcuaJjSIjY2VSCTNzc0nTpxgui6DYRhf/dTW1u7bty8zM1MgEKjPDw8PT05O/u2331JTU5mq21AhISEXL15cv369qi+guuPHj8+aNSsoKGikzffv319VVVVQUGDAKiGAbGE+iZgQQr+VUF5eTgg5fPiwUCh0cHBobm5OSUmZOHHis2fPKIrKy8ujBx5zdnZeuXKlarAbzQP1EkI0bKvrML6gpcLCQoqiVqxYMXTRgQMH/P39v//++5s3bw67rYbzpXnIZkLIwMBARkaGp6enra1tcHAwff8+Fn19fQ8ePAgNDdWwjrOz88KFCwsKCijDffgKAWQN5m7GBxtj0wRFUXSf1smTJ9OTe/bsIYRs3779v//7v1evXv2///u/GRkZPB7v9OnT7e3t1dXVs2bNcnV1ffv2Lb3+5s2b7ezsnjx50tvb+/jx4zlz5jg4OLx+/Zpeqnnb9evXT5gwQVWTI0eOEEJaWlroyTVr1tDD+I4KTRPqvL29AwMDB8308fF59eoVRVH37t2zsrKaMmVKV1cXNeSXtebzRV8bP/74Y0dHR3Nzc0REhJ2dXV9fH700NTWVz+dfuHChra1t9+7dVlZWOj17+OKLLwb9sn716hUhJDQ0dNGiRe7u7nw+PyAg4NixY0qlUn219PR0QkhlZeWou9DyOkEAR0LQNGE8Dg4OHA6ns7NTfeaf//znpKSkixcvenl55eXlrV69esOGDY6OjkFBQSdOnHj//v3JkydVK480UK9cLh91WzCs7u7uV69e+fj4jLRCWFjYjh076uvrv/nmm0GLtDxfww7Z3NvbW1xcvGrVqjVr1jg5Oe3du9fGxka/8ZpVurq6CCFubm45OTmPHz9+9+7dypUrk5KS/ud//kd9NT8/P0JITU3NWPalggCyiFkl4u7uboqiRCLRsEsfP37c1dU1e/Zs1Zw5c+bweDz19gd16gP16rrtWFy4cIFj7uLi4kaNQ3NzM0VRQqFQwzoHDhyYNm1aUVHR3bt31efrer7Uh2x+9uxZT0/PjBkz6EW2trbu7u5jHK+ZbvScPn16eHi4i4uLo6NjZmamo6PjoLxGH+y7d+/Gsi8VBJBFzOoV5+fPnxNCAgIChl3a3t5OCLG3t1ef6eTkNOgOWp1qoF49ttXbvHnzduzYYfBiTcr9+/dHfajS29tL/vgXOBKBQFBSUrJgwYJNmzYdOnRINX8s56u7u5sQsnfv3r1796pmenh4jLqhBvTm9DMDGo/H8/LyqqurU1+NHtmDPvCxQwBZxKwS8Y0bNwghkZGRwy51cnIihAy6kjQMxas+UK+u247FpEmT1D8cZ65GTcT0P6pRe+mHhYXt3Lnz6NGj2dnZnp6e9MyxnC83NzdCSH5+fnJy8qgra8ne3t7Pz+/JkyfqM/v7+x0dHdXn9PX1kT8OfOwQQBYxn6aJt2/f5ufnT5o0adOmTcOuMGPGDHt7+19//VU15+HDh319fZ9//vmw66sP1DvqthjG1+DEYjGHw+no6Bh1zezs7ICAgMrKStUcXc+1usmTJwsEgqqqKv2qPZK4uLjKysqXL1/Skz09PQ0NDYM6Y9EHO2HCBIPsEQFkEbYmYoqiurq66GemLS0tpaWl8+fPt7a2LisrG6mNWCAQpKSkXLp06cyZMzKZrKamZuvWrR4eHps3b1atM9JAvaNuq9MwvsYKinkRCoXe3t4jjRKnjv59bW1trT5n1HOtobSvv/763LlzxcXFMplsYGDgzZs3v//+OyEkPj5+woQJ+r0BvHPnTi8vL4lE8vr16w8fPqSlpcnl8kFPyeiD1dBVVicIIJsw2WXjn2nTpenKlSvBwcFCoZDH41lZWRFCOByOk5PT3Llzs7KyPnz4oFrz0KFD9C+UyZMnnz59mp6pVCqPHDni5+dnY2Pj7Oy8atUqunMxbfPmzTY2NhMnTuRyuSKRaOXKlXV1daqlmrf98OHD4sWLBQLB1KlT/+u//mvXrl2EEF9fX7r326NHj7y8vGxtbRcsWKDqADQsdF9TJ5VKbWxsenp66MlLly7RfQBcXV2TkpIGrbxr1y713lcazldRURH9VMfPz6+uru7kyZP0f95eXl7Pnz+nKOrjx49paWmenp5cLtfNzW3NmjWPHz+mKGrVqlWEkIyMjGFre//+/fnz56saQ93d3cPDw+/cuaNaobGxMSEhwdnZmc/nz507t7y8fFAJUVFREydOHNQla1haXicI4EiIiXVfY1kiNqrNmze7uLgwWAEKififvXjxgsvlqv4fZdzAwEBERMSpU6eMUfj79+8FAsHRo0e1WVnL6wQBHImpJWK2Nk0YCbsHcDI7vr6+WVlZWVlZdDdSZg0MDJSVlXV2dsbHxxuj/P3794eGhkqlUgOWiQCyBRIxmLT09PS1a9fGx8dr89DJqCoqKi5evFheXq65Z65+8vLyqqqqrl+/bmNjY9iSEUBWQCL+/1g6UK8lyMnJkUqlBw8eZLYaS5YsOXv2rGpEEQO6fPnyx48fKyoqnJ2dDV44QQDZwKz6EY9Fbm5ubm4u07UYD3K5fMmSJffu3TOpojRbtmzZsmXLjL0XpsTExMTExBh1FwigicMdscUx4JicGN4TwCCQiFmJMtCYnJoH/9R1eM8bN26IRKKcnJxxjgYA2yERs9L+/fvT09P37NnT3Nz8008/NTY2RkRE0GOdFBYWqr8hXVRUlJmZqZosKCiIjo6mx+Ssra2VSqUSiaSnp2f79u319fWPHj3q7+9funRpY2OjrkWRP/qcKJVK4wcAwKwgEbOPwcfkHGnwT11FRUXJZLJ9+/bpVw0Ai4VEzD5GHZNTffBPABgfSMTsY+wxOVWDfwLA+EAiZh+jjsmpPvgnAIwPJGL2MeqYnOqDf46xKADQEhIx+xh8TM6RBv/Utajy8nJ0XwPQAxIxK3377be5ublZWVmurq4LFy6cMmVKRUWFnZ0dvTQxMXHx4sUJCQnTpk3Lzs6mhwMNCwujO6Vt3bpVLBYHBgYuX768tbWVENLb2xsUFGRraxsREeHv73/79m3V93V0LQoA9IBXnFmJw+GkpqampqYOu9TFxeXWrVvqcw4fPqz6e+bMmfX19epLHRwcRho+XKeiIiMjZTKZlocAACq4IwYM/gnAMCRiAACGIRFbNAz+CWAK0EZs0Sxn8E8AU4Y7YgAAhiERAwAwDIkYAIBhSMQAAAwzuYd1P/zwA9NVYBL9YoXZB+H+/fvEAg7TeCzkOrEglMkoLS1lOhgAYClKS0uZznn/h0NRFNMBAdAf/TEn3BsCq6GNGACAYUjEAAAMQyIGAGAYEjEAAMOQiAEAGIZEDADAMCRiAACGIREDADAMiRgAgGFIxAAADEMiBgBgGBIxAADDkIgBABiGRAwAwDAkYgAAhiERAwAwDIkYAIBhSMQAAAxDIgYAYBgSMQAAw5CIAQAYhkQMAMAwJGIAAIYhEQMAMAyJGACAYUjEAAAMQyIGAGAYEjEAAMOQiAEAGIZEDADAMCRiAACGIREDADAMiRgAgGEciqKYrgOADs6ePXvq1CmlUklPvnr1ihAydepUetLKyurf//3f169fz1j9AHSHRAwsU11dHRISomGFv//978HBweNWH4CxQyIG9gkICHj27Nmwi3x9fV+8eDHO9QEYI7QRA/ts3LjRxsZm6HwbG5uvv/56/OsDMEa4Iwb2efnypa+v77CX7osXL3x9fce/SgBjgTtiYB9vb+9Zs2ZxOBz1mRwOZ/bs2cjCwEZIxMBKX331lbW1tfoca2vrr776iqn6AIwFmiaAlZqbmz08PFSd2AghVlZWTU1NEyZMYLBWAPrBHTGwklgsXrhwoeqm2NraetGiRcjCwFJIxMBWGzduVP89t3HjRgYrAzAWaJoAtpLJZG5ubn19fYQQGxub5uZmJycnpisFoA/cEQNbiUSiL7/8ksvlcrnc5cuXIwsDeyERA4tt2LBhYGBgYGAAg0sAq6FpAlist7fX1dWVoqj379/b2toyXR0AfVEsVFpaynTYAMAUlZaWMp2f9MFlOm76M6d0HBcXl5ycHBYWxnRFjCs/P58QsmPHDgOWWVVVxeFwNI/HZjYs5DrRW1xcHNNV0BMrmyZ++OGHuLg4NtZ8JBwOp7S0dN26dUxXxLjWrl1LCDl//rwBy+zv7yeEcLksvqXQnoVcJ3pjb3ws4vIFM2YhKRjMG3pNAAAwDIkYAIBhSMQAAAxDIgYAYBgSMYtdv37d0dHx6tWrTFcEAMYEiZjFzKkDH4AlQ9cfFouKiuro6BiHHcnl8iVLlty7d28c9gVggXBHDKM7depUc3Mz07UAMFtIxGx19+5dT09PDodz7NgxQkhxcbGdnZ1QKLx8+XJkZKRIJJo0adK5c+folQsLCwUCgVgs3rJli4eHh0AgCA8Pf/jwIb1UKpXyeDx3d3d6ctu2bXZ2dhwO5/3794SQ5OTklJSUuro6DodDf5rzxo0bIpEoJyeHgcMGMEdIxGy1YMEC9baCxMTEHTt2yOVyBweH0tLSuro6b2/vP/3pTwqFghAilUolEklPT8/27dvr6+sfPXrU39+/dOnSxsZGQkhhYaH6W6FFRUWZmZmqyYKCgujoaB8fH4qiamtrCSEDAwOEEPXvxQHAWCARm5vw8HCRSOTm5hYfH9/d3f369WvVIi6X+9lnn/H5/MDAwOLi4s7OzpKSEj12ERUVJZPJ9u3bZ7haA1g0JGKzxePxCCH0HfFQs2fPFgqFT58+Hd9KAcAwkIgtF5/Pb2lpYboWAIBEbKkUCkV7e/ukSZOYrggAIBFbqoqKCoqi5s2bR09yudyRGjEAwNiQiC2IUqlsa2vr7++vrq5OTk729PSUSCT0Il9f39bW1rKyMoVC0dLS0tDQoL6hi4tLU1NTfX19Z2enQqEoLy9H9zUAA0IiZqtjx47NmTOHEJKWlhYTE1NcXEx/iCg4OPjly5ffffddSkoKIeTLL7988eIFvUlvb29QUJCtrW1ERIS/v//t27f5fD69KDExcfHixQkJCdOmTcvOzqY/xBkWFkb3b9u6datYLA4MDFy+fHlraysjxwtgxvCKM1slJSUlJSWpz0lMTFT9TXciHrSJg4PDmzdvhi3NxcXl1q1b6nMOHz6s+nvmzJn19fWqycjISJlMpm/FAWAw3BFbEPpFDAAwNWabiC9evOjt7c1Rw+PxxGLxokWLjhw50tbWxnQFYXQ3b95MT09XP5UbN25UX2HZsmUODg7W1tbTp09/9OgRU/UkhCiVyvz8/PDw8KGLFApFbm6ur68vj8dzcnKaMWMG/fPiypUrhw4dMt7/jmyPnrHjY1ooFiotLdWy5j4+Po6OjhRF0c+pbt++LZFIOByOh4fHL7/8YuRq6oAQUlpaarzy09PT6fc7pkyZcv78eePtSLPY2NjY2FgtV87IyIguVfxgAAALVElEQVSOjpbJZPSkj4/PJ598Qgi5du2a+mrl5eUxMTEGrqiOnj9/Pn/+fEJISEjI0KWrVq2aNm3agwcPFApFU1PTihUrampq6EUFBQULFy5sa2vTckfaXyfmET3jxcfUWEoiVnf+/HkrKyuxWNze3m6E2umDvReQTrRPxAcPHvT395fL5ao5Pj4+Z8+etbKymjhxovqJYzyVVFVVrV69+syZM6GhoUNTyblz5zgcTnV19UibS6XSsLAwhUKhzb60vE7MJnqUceJjgsy2aUKD2NhYiUTS3Nx84sQJpusCw6itrd23b19mZqZAIFCfHx4enpyc/Ntvv6WmpjJVt6FCQkIuXry4fv16VRcUdcePH581a1ZQUNBIm+/fv7+qqqqgoMBQ9TGn6BEjxMc0WWIiJoTQ/WfLy8vpyYGBgYyMDE9PT1tb2+DgYPqOW/PAkoSQO3fuzJ07VygUikSioKAguiPBsEWBTgoLCymKWrFixdBFBw4c8Pf3//7772/evDnsthRF5eXl0WMbOTs7r1y5UjWexqgn1ODnrq+v78GDB6GhoRrWcXZ2XrhwYUFBAWWg762YTfRoBo+PiWLydlxfY2yaoCiKTpqTJ0+mJ1NTU/l8/oULF9ra2nbv3m1lZUW3IO/Zs4cQ8uOPP3Z0dDQ3N0dERNjZ2fX19VEU1dXVJRKJDh06JJfL3759u3r16paWFg1FaUZY+5NKJ1o2TXh7ewcGBg6a6ePj8+rVK4qi7t27Z2VlNWXKlK6uLmrIj+uMjAwej3f69On29vbq6upZs2a5urq+ffuWXqrhhFL6njuVL774YtCP61evXhFCQkNDFy1a5O7uzufzAwICjh07plQq1VdLT08nhFRWVo66C22uE7OJnoph42OaLDQRUxTF4XCcnJwoipLL5UKhMD4+np7f09PD5/MTExOpP648VVtbUVERIaS2tpaiqH/84x9kyKMPDUVpxt4LSCfaJOKuri4OhxMdHT1oviqVUBRFv6uSlJRE/XMq6enpsbe3V8Wfoqi//e1vhJCsrCx6UsMJ1fvcqQxNJTU1NYSQpUuX/vzzzx8+fGhvb//mm28IIWfOnFFf7S9/+Qsh5K9//euouxj1OjGn6KkYMD4my0Jf6Oju7qYoSiQSEUKePXvW09MzY8YMepGtra27u/uw40OqDyzp7e0tFos3bNiwfft2iUQyZcoUnYoa6v79+wY4MNP25s2bUYcZam5upihKKBRqWOfAgQPXrl0rKiqKi4tTn//48eOurq7Zs2er5syZM4fH46m+RTKI+gkdy7kbCd3uOX36dFXHrMzMzOPHj588eXL9+vWq1eiDfffu3Vj2RTOn6KkYMD4my0IT8fPnzwkhAQEBhJDu7m5CyN69e/fu3atawcPDQ3MJtra2t27d+uabb3JycrKystatW1dSUqJfUbSCggKzfyJBCImNjdW8Qm9vL/kjhY1EIBCUlJQsWLBg06ZNhw4dUs1vb28nhNjb26uv7OTk1NnZOWrFxnLuRkJvTn9xisbj8by8vOrq6tRXo18opw98jMwpeioGjI/JstCHdTdu3CCEREZGEkLc3NwIIfn5+eq/FLS5P50+ffrVq1ebmprS0tJKS0uPHj2qd1GEtT+pdDJqFiZ//KsbtRt/WFjYzp07X7x4kZ2drZrp5ORECBmUOLQc7XMs524k9vb2fn5+T548UZ/Z39/v6OioPqevr4/8ceBjZE7RUzFgfEyWJSbit2/f5ufnT5o0adOmTYSQyZMnCwSCqqoqnQppamqi/4G5ubkdPHhw1qxZT5480a8oUCcWizkcTkdHx6hrZmdnBwQEVFZWqubMmDHD3t7+119/Vc15+PBhX1/f559/PmppRjp3cXFxlZWVL1++pCd7enoaGhoG9WajD3bChAlj352ZRY9mwPiYLPNPxBRFdXV10c+pW1paSktL58+fb21tXVZWRrcRCwSCr7/++ty5c8XFxTKZbGBg4M2bN7///rvmYpuamrZs2fL06dO+vr7KysqGhoZ58+bpVxSoEwqF3t7eIw1OpI7+iW1tba0+JyUl5dKlS2fOnJHJZDU1NVu3bvXw8Ni8ebM2pY107uLj4ydMmKDfS8A7d+708vKSSCSvX7/+8OFDWlqaXC6nH9mp0Aeroa+x9swsejQDxsd0jecvU0PRptfElStXgoODhUIhj8ezsrIihNDdJObOnZuVlfXhwwf1lT9+/JiWlubp6cnlct3c3NasWfP48eOioiL6KYGfn19dXd3JkyfpxO3l5fX8+fP6+vrw8HBnZ2dra+tPP/10z549/f39IxU16hERi2ma0Kb7mlQqtbGx6enpoScvXbrk4+NDCHF1daWf9avbtWuXegcspVJ55MgRPz8/GxsbZ2fnVatWPXv2jF6k+YRSI5+7VatWEUIyMjKGre39+/fnz5+vag91d3cPDw+/c+eOaoXGxsaEhARnZ2c+nz937tzy8vJBJURFRU2cOHFQn7ZhaXOdmFn0DB4f02S2iZhd2HsB6UTLRPzixQsul3v69OlxqJI2BgYGIiIiTp06ZYzC379/LxAIjh49qs3K2lwnZhY9g8fHNJl/0wSwjq+vb1ZWVlZWVldXF9N1IQMDA2VlZZ2dnfHx8cYof//+/aGhoVKp1FAFmln0DB4f04REDKYoPT197dq18fHx2jx3MqqKioqLFy+Wl5dr7pyrn7y8vKqqquvXr9vY2BiwWLOJnpHiY4KQiMFE5eTkSKXSgwcPMluNJUuWnD171t3d3eAlX758+ePHjxUVFc7OzgYv3AyiZ9T4mBoLfaEDWGHZsmXLli1juhbGEhMTExMTY7zy2R49Y8fHpOCO2CLI5fJhPx7BbFEAQEMitginTp1qbm42taIAgIZEzBrUyGPFSqVSHo+naonbtm2bnZ0dh8OhRzlITk5OSUmpq6vjcDi+vr6FhYUCgUAsFm/ZssXDw0MgEISHh6sGdtGpKELIjRs3RCJRTk7OOEcDwJwgEbPG/v3709PT9+zZ09zc/NNPPzU2NkZERNBDUhUWFq5bt061ZlFRUWZmpmqyoKAgOjrax8eHoqja2lqpVCqRSHp6erZv315fX//o0aP+/v6lS5c2NjbqWhT5Y1gDpVJp/AAAmC0kYnaQy+V5eXmrV6/esGGDo6NjUFDQiRMn3r9/f/LkSf0K5HK59M11YGBgcXFxZ2dnSUmJHuVERUXJZLJ9+/bpVw0AIEjEbKHrWLE6mT17tlAoNNTosQCgKyRidhjLWLHa4PP5LS0tBikKAHSFRMwOYxkrdlQKhcJQRQGAHpCI2WHUsWK5XC790Ro9VFRUUBQ1b968sRcFAHpAImaHUceK9fX1bW1tLSsrUygULS0tDQ0N6pu7uLg0NTXV19d3dnbSSVapVLa1tfX391dXVycnJ3t6ekokEj2KKi8vR/c1gDFCImaNb7/9Njc3Nysry9XVdeHChVOmTKmoqLCzs6OXJiYmLl68OCEhYdq0adnZ2fR3ZcLCwuhOaVu3bhWLxYGBgcuXL29tbSWE9Pb2BgUF2draRkRE+Pv73759W/WhM12LAoAxwlgTrMHhcFJTU1NTU4dd6uLicuvWLfU5hw8fVv09c+bM+vp69aUODg4jfcdBp6IiIyNlMpmWhwAAw8IdsYUa9fuSADBukIgBABiGRGxxdu/eXVJS0tHRMXXq1AsXLjBdHQBAG7Hlyc3Nzc3NZboWAPB/cEcMAMAwJGIAAIYhEQMAMAyJGACAYSx+WLd27Vqmq2BI+fn558+fZ7oWxvXgwQNididunFnCdWKBOBRFMV0Hnd2/fz8vL4/pWgCAydm5c2dYWBjTtdAZKxMxAIA5QRsxAADDkIgBABiGRAwAwDAkYgAAhv0/Lx/j8aagkYcAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model = get_structural_model()\n",
        "model.summary()\n",
        "plot_model(model, to_file='model-spd.png', show_shapes=True, show_layer_names=False, expand_nested=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vhk0hbxcHNSS"
      },
      "outputs": [],
      "source": [
        "# patient early stopping\n",
        "es = EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=20)\n",
        "# save the best model\n",
        "mc = ModelCheckpoint('best_model_spd.h5', monitor='val_accuracy', mode='max', verbose=1, save_best_only=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jWjzttO7RxR5",
        "outputId": "f2f845e1-18c4-431a-d5e9-339af01c6229"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/200\n",
            "1663/1669 [============================>.] - ETA: 0s - loss: 0.7428 - accuracy: 0.5876\n",
            "Epoch 1: val_accuracy improved from -inf to 0.68307, saving model to best_model_spd.h5\n",
            "1669/1669 [==============================] - 10s 6ms/step - loss: 0.7425 - accuracy: 0.5876 - val_loss: 0.6583 - val_accuracy: 0.6831\n",
            "Epoch 2/200\n",
            "1664/1669 [============================>.] - ETA: 0s - loss: 0.6451 - accuracy: 0.6449\n",
            "Epoch 2: val_accuracy improved from 0.68307 to 0.69825, saving model to best_model_spd.h5\n",
            "1669/1669 [==============================] - 9s 5ms/step - loss: 0.6451 - accuracy: 0.6450 - val_loss: 0.6039 - val_accuracy: 0.6982\n",
            "Epoch 3/200\n",
            "1667/1669 [============================>.] - ETA: 0s - loss: 0.6054 - accuracy: 0.6855\n",
            "Epoch 3: val_accuracy improved from 0.69825 to 0.72606, saving model to best_model_spd.h5\n",
            "1669/1669 [==============================] - 9s 5ms/step - loss: 0.6053 - accuracy: 0.6856 - val_loss: 0.5570 - val_accuracy: 0.7261\n",
            "Epoch 4/200\n",
            "1668/1669 [============================>.] - ETA: 0s - loss: 0.5595 - accuracy: 0.7289\n",
            "Epoch 4: val_accuracy improved from 0.72606 to 0.73972, saving model to best_model_spd.h5\n",
            "1669/1669 [==============================] - 9s 6ms/step - loss: 0.5595 - accuracy: 0.7289 - val_loss: 0.5429 - val_accuracy: 0.7397\n",
            "Epoch 5/200\n",
            "1669/1669 [==============================] - ETA: 0s - loss: 0.5244 - accuracy: 0.7575\n",
            "Epoch 5: val_accuracy improved from 0.73972 to 0.74140, saving model to best_model_spd.h5\n",
            "1669/1669 [==============================] - 9s 5ms/step - loss: 0.5244 - accuracy: 0.7575 - val_loss: 0.5232 - val_accuracy: 0.7414\n",
            "Epoch 6/200\n",
            "1668/1669 [============================>.] - ETA: 0s - loss: 0.5003 - accuracy: 0.7737\n",
            "Epoch 6: val_accuracy improved from 0.74140 to 0.78152, saving model to best_model_spd.h5\n",
            "1669/1669 [==============================] - 9s 5ms/step - loss: 0.5003 - accuracy: 0.7736 - val_loss: 0.4874 - val_accuracy: 0.7815\n",
            "Epoch 7/200\n",
            "1665/1669 [============================>.] - ETA: 0s - loss: 0.4801 - accuracy: 0.7889\n",
            "Epoch 7: val_accuracy improved from 0.78152 to 0.79467, saving model to best_model_spd.h5\n",
            "1669/1669 [==============================] - 10s 6ms/step - loss: 0.4803 - accuracy: 0.7888 - val_loss: 0.4639 - val_accuracy: 0.7947\n",
            "Epoch 8/200\n",
            "1663/1669 [============================>.] - ETA: 0s - loss: 0.4614 - accuracy: 0.8003\n",
            "Epoch 8: val_accuracy improved from 0.79467 to 0.82232, saving model to best_model_spd.h5\n",
            "1669/1669 [==============================] - 9s 5ms/step - loss: 0.4613 - accuracy: 0.8002 - val_loss: 0.4300 - val_accuracy: 0.8223\n",
            "Epoch 9/200\n",
            "1665/1669 [============================>.] - ETA: 0s - loss: 0.4449 - accuracy: 0.8103\n",
            "Epoch 9: val_accuracy did not improve from 0.82232\n",
            "1669/1669 [==============================] - 9s 5ms/step - loss: 0.4451 - accuracy: 0.8103 - val_loss: 0.4634 - val_accuracy: 0.8023\n",
            "Epoch 10/200\n",
            "1667/1669 [============================>.] - ETA: 0s - loss: 0.4364 - accuracy: 0.8185\n",
            "Epoch 10: val_accuracy did not improve from 0.82232\n",
            "1669/1669 [==============================] - 11s 7ms/step - loss: 0.4365 - accuracy: 0.8185 - val_loss: 0.4348 - val_accuracy: 0.8179\n",
            "Epoch 11/200\n",
            "1660/1669 [============================>.] - ETA: 0s - loss: 0.4221 - accuracy: 0.8279\n",
            "Epoch 11: val_accuracy did not improve from 0.82232\n",
            "1669/1669 [==============================] - 9s 6ms/step - loss: 0.4220 - accuracy: 0.8280 - val_loss: 0.4379 - val_accuracy: 0.8093\n",
            "Epoch 12/200\n",
            "1662/1669 [============================>.] - ETA: 0s - loss: 0.4168 - accuracy: 0.8284\n",
            "Epoch 12: val_accuracy did not improve from 0.82232\n",
            "1669/1669 [==============================] - 9s 6ms/step - loss: 0.4168 - accuracy: 0.8283 - val_loss: 0.4406 - val_accuracy: 0.8114\n",
            "Epoch 13/200\n",
            "1669/1669 [==============================] - ETA: 0s - loss: 0.4079 - accuracy: 0.8332\n",
            "Epoch 13: val_accuracy improved from 0.82232 to 0.84356, saving model to best_model_spd.h5\n",
            "1669/1669 [==============================] - 9s 6ms/step - loss: 0.4079 - accuracy: 0.8332 - val_loss: 0.3900 - val_accuracy: 0.8436\n",
            "Epoch 14/200\n",
            "1666/1669 [============================>.] - ETA: 0s - loss: 0.3988 - accuracy: 0.8380\n",
            "Epoch 14: val_accuracy did not improve from 0.84356\n",
            "1669/1669 [==============================] - 9s 5ms/step - loss: 0.3989 - accuracy: 0.8380 - val_loss: 0.4066 - val_accuracy: 0.8350\n",
            "Epoch 15/200\n",
            "1661/1669 [============================>.] - ETA: 0s - loss: 0.3937 - accuracy: 0.8445\n",
            "Epoch 15: val_accuracy improved from 0.84356 to 0.85941, saving model to best_model_spd.h5\n",
            "1669/1669 [==============================] - 9s 5ms/step - loss: 0.3934 - accuracy: 0.8446 - val_loss: 0.3532 - val_accuracy: 0.8594\n",
            "Epoch 16/200\n",
            "1665/1669 [============================>.] - ETA: 0s - loss: 0.3862 - accuracy: 0.8478\n",
            "Epoch 16: val_accuracy improved from 0.85941 to 0.86902, saving model to best_model_spd.h5\n",
            "1669/1669 [==============================] - 9s 5ms/step - loss: 0.3861 - accuracy: 0.8478 - val_loss: 0.3339 - val_accuracy: 0.8690\n",
            "Epoch 17/200\n",
            "1669/1669 [==============================] - ETA: 0s - loss: 0.3786 - accuracy: 0.8486\n",
            "Epoch 17: val_accuracy did not improve from 0.86902\n",
            "1669/1669 [==============================] - 9s 5ms/step - loss: 0.3786 - accuracy: 0.8486 - val_loss: 0.3557 - val_accuracy: 0.8597\n",
            "Epoch 18/200\n",
            "1666/1669 [============================>.] - ETA: 0s - loss: 0.3747 - accuracy: 0.8535\n",
            "Epoch 18: val_accuracy did not improve from 0.86902\n",
            "1669/1669 [==============================] - 9s 6ms/step - loss: 0.3747 - accuracy: 0.8534 - val_loss: 0.3546 - val_accuracy: 0.8602\n",
            "Epoch 19/200\n",
            "1667/1669 [============================>.] - ETA: 0s - loss: 0.3748 - accuracy: 0.8557\n",
            "Epoch 19: val_accuracy did not improve from 0.86902\n",
            "1669/1669 [==============================] - 9s 5ms/step - loss: 0.3747 - accuracy: 0.8557 - val_loss: 0.3508 - val_accuracy: 0.8638\n",
            "Epoch 20/200\n",
            "1663/1669 [============================>.] - ETA: 0s - loss: 0.3664 - accuracy: 0.8572\n",
            "Epoch 20: val_accuracy improved from 0.86902 to 0.89666, saving model to best_model_spd.h5\n",
            "1669/1669 [==============================] - 9s 5ms/step - loss: 0.3664 - accuracy: 0.8572 - val_loss: 0.2889 - val_accuracy: 0.8967\n",
            "Epoch 21/200\n",
            "1663/1669 [============================>.] - ETA: 0s - loss: 0.3627 - accuracy: 0.8589\n",
            "Epoch 21: val_accuracy did not improve from 0.89666\n",
            "1669/1669 [==============================] - 9s 5ms/step - loss: 0.3628 - accuracy: 0.8588 - val_loss: 0.3293 - val_accuracy: 0.8692\n",
            "Epoch 22/200\n",
            "1669/1669 [==============================] - ETA: 0s - loss: 0.3613 - accuracy: 0.8611\n",
            "Epoch 22: val_accuracy did not improve from 0.89666\n",
            "1669/1669 [==============================] - 9s 5ms/step - loss: 0.3613 - accuracy: 0.8611 - val_loss: 0.3106 - val_accuracy: 0.8865\n",
            "Epoch 23/200\n",
            "1667/1669 [============================>.] - ETA: 0s - loss: 0.3576 - accuracy: 0.8634\n",
            "Epoch 23: val_accuracy did not improve from 0.89666\n",
            "1669/1669 [==============================] - 9s 6ms/step - loss: 0.3578 - accuracy: 0.8634 - val_loss: 0.3403 - val_accuracy: 0.8678\n",
            "Epoch 24/200\n",
            "1667/1669 [============================>.] - ETA: 0s - loss: 0.3567 - accuracy: 0.8653\n",
            "Epoch 24: val_accuracy did not improve from 0.89666\n",
            "1669/1669 [==============================] - 9s 5ms/step - loss: 0.3568 - accuracy: 0.8653 - val_loss: 0.3344 - val_accuracy: 0.8700\n",
            "Epoch 25/200\n",
            "1668/1669 [============================>.] - ETA: 0s - loss: 0.3542 - accuracy: 0.8673\n",
            "Epoch 25: val_accuracy did not improve from 0.89666\n",
            "1669/1669 [==============================] - 9s 5ms/step - loss: 0.3543 - accuracy: 0.8672 - val_loss: 0.2942 - val_accuracy: 0.8930\n",
            "Epoch 26/200\n",
            "1668/1669 [============================>.] - ETA: 0s - loss: 0.3470 - accuracy: 0.8691\n",
            "Epoch 26: val_accuracy did not improve from 0.89666\n",
            "1669/1669 [==============================] - 9s 5ms/step - loss: 0.3470 - accuracy: 0.8691 - val_loss: 0.3491 - val_accuracy: 0.8648\n",
            "Epoch 27/200\n",
            "1664/1669 [============================>.] - ETA: 0s - loss: 0.3476 - accuracy: 0.8697\n",
            "Epoch 27: val_accuracy did not improve from 0.89666\n",
            "1669/1669 [==============================] - 9s 6ms/step - loss: 0.3477 - accuracy: 0.8697 - val_loss: 0.3207 - val_accuracy: 0.8749\n",
            "Epoch 28/200\n",
            "1665/1669 [============================>.] - ETA: 0s - loss: 0.3468 - accuracy: 0.8691\n",
            "Epoch 28: val_accuracy improved from 0.89666 to 0.90593, saving model to best_model_spd.h5\n",
            "1669/1669 [==============================] - 9s 5ms/step - loss: 0.3469 - accuracy: 0.8690 - val_loss: 0.2667 - val_accuracy: 0.9059\n",
            "Epoch 29/200\n",
            "1665/1669 [============================>.] - ETA: 0s - loss: 0.3400 - accuracy: 0.8739\n",
            "Epoch 29: val_accuracy improved from 0.90593 to 0.90880, saving model to best_model_spd.h5\n",
            "1669/1669 [==============================] - 9s 5ms/step - loss: 0.3400 - accuracy: 0.8739 - val_loss: 0.2535 - val_accuracy: 0.9088\n",
            "Epoch 30/200\n",
            "1666/1669 [============================>.] - ETA: 0s - loss: 0.3370 - accuracy: 0.8739\n",
            "Epoch 30: val_accuracy did not improve from 0.90880\n",
            "1669/1669 [==============================] - 9s 6ms/step - loss: 0.3370 - accuracy: 0.8739 - val_loss: 0.3335 - val_accuracy: 0.8670\n",
            "Epoch 31/200\n",
            "1666/1669 [============================>.] - ETA: 0s - loss: 0.3377 - accuracy: 0.8753\n",
            "Epoch 31: val_accuracy did not improve from 0.90880\n",
            "1669/1669 [==============================] - 9s 6ms/step - loss: 0.3379 - accuracy: 0.8753 - val_loss: 0.2889 - val_accuracy: 0.8957\n",
            "Epoch 32/200\n",
            "1662/1669 [============================>.] - ETA: 0s - loss: 0.3389 - accuracy: 0.8736\n",
            "Epoch 32: val_accuracy did not improve from 0.90880\n",
            "1669/1669 [==============================] - 9s 6ms/step - loss: 0.3391 - accuracy: 0.8734 - val_loss: 0.2964 - val_accuracy: 0.8882\n",
            "Epoch 33/200\n",
            "1667/1669 [============================>.] - ETA: 0s - loss: 0.3350 - accuracy: 0.8767\n",
            "Epoch 33: val_accuracy did not improve from 0.90880\n",
            "1669/1669 [==============================] - 9s 6ms/step - loss: 0.3350 - accuracy: 0.8767 - val_loss: 0.2979 - val_accuracy: 0.8936\n",
            "Epoch 34/200\n",
            "1665/1669 [============================>.] - ETA: 0s - loss: 0.3342 - accuracy: 0.8785\n",
            "Epoch 34: val_accuracy did not improve from 0.90880\n",
            "1669/1669 [==============================] - 9s 6ms/step - loss: 0.3342 - accuracy: 0.8786 - val_loss: 0.3481 - val_accuracy: 0.8594\n",
            "Epoch 35/200\n",
            "1669/1669 [==============================] - ETA: 0s - loss: 0.3307 - accuracy: 0.8783\n",
            "Epoch 35: val_accuracy did not improve from 0.90880\n",
            "1669/1669 [==============================] - 9s 6ms/step - loss: 0.3307 - accuracy: 0.8783 - val_loss: 0.3140 - val_accuracy: 0.8758\n",
            "Epoch 36/200\n",
            "1663/1669 [============================>.] - ETA: 0s - loss: 0.3286 - accuracy: 0.8797\n",
            "Epoch 36: val_accuracy did not improve from 0.90880\n",
            "1669/1669 [==============================] - 10s 6ms/step - loss: 0.3284 - accuracy: 0.8797 - val_loss: 0.3066 - val_accuracy: 0.8798\n",
            "Epoch 37/200\n",
            "1664/1669 [============================>.] - ETA: 0s - loss: 0.3248 - accuracy: 0.8838\n",
            "Epoch 37: val_accuracy did not improve from 0.90880\n",
            "1669/1669 [==============================] - 9s 6ms/step - loss: 0.3248 - accuracy: 0.8839 - val_loss: 0.2847 - val_accuracy: 0.8958\n",
            "Epoch 38/200\n",
            "1666/1669 [============================>.] - ETA: 0s - loss: 0.3234 - accuracy: 0.8835\n",
            "Epoch 38: val_accuracy did not improve from 0.90880\n",
            "1669/1669 [==============================] - 9s 6ms/step - loss: 0.3234 - accuracy: 0.8834 - val_loss: 0.2718 - val_accuracy: 0.9005\n",
            "Epoch 39/200\n",
            "1665/1669 [============================>.] - ETA: 0s - loss: 0.3255 - accuracy: 0.8813\n",
            "Epoch 39: val_accuracy did not improve from 0.90880\n",
            "1669/1669 [==============================] - 9s 6ms/step - loss: 0.3258 - accuracy: 0.8812 - val_loss: 0.3014 - val_accuracy: 0.8818\n",
            "Epoch 40/200\n",
            "1668/1669 [============================>.] - ETA: 0s - loss: 0.3236 - accuracy: 0.8835\n",
            "Epoch 40: val_accuracy did not improve from 0.90880\n",
            "1669/1669 [==============================] - 9s 5ms/step - loss: 0.3236 - accuracy: 0.8835 - val_loss: 0.3152 - val_accuracy: 0.8744\n",
            "Epoch 41/200\n",
            "1661/1669 [============================>.] - ETA: 0s - loss: 0.3192 - accuracy: 0.8844\n",
            "Epoch 41: val_accuracy did not improve from 0.90880\n",
            "1669/1669 [==============================] - 9s 6ms/step - loss: 0.3192 - accuracy: 0.8844 - val_loss: 0.2849 - val_accuracy: 0.8881\n",
            "Epoch 42/200\n",
            "1665/1669 [============================>.] - ETA: 0s - loss: 0.3215 - accuracy: 0.8825\n",
            "Epoch 42: val_accuracy did not improve from 0.90880\n",
            "1669/1669 [==============================] - 9s 5ms/step - loss: 0.3216 - accuracy: 0.8824 - val_loss: 0.2980 - val_accuracy: 0.8876\n",
            "Epoch 43/200\n",
            "1665/1669 [============================>.] - ETA: 0s - loss: 0.3173 - accuracy: 0.8842\n",
            "Epoch 43: val_accuracy did not improve from 0.90880\n",
            "1669/1669 [==============================] - 9s 6ms/step - loss: 0.3176 - accuracy: 0.8842 - val_loss: 0.2810 - val_accuracy: 0.8941\n",
            "Epoch 44/200\n",
            "1665/1669 [============================>.] - ETA: 0s - loss: 0.3168 - accuracy: 0.8862\n",
            "Epoch 44: val_accuracy improved from 0.90880 to 0.91015, saving model to best_model_spd.h5\n",
            "1669/1669 [==============================] - 10s 6ms/step - loss: 0.3169 - accuracy: 0.8862 - val_loss: 0.2545 - val_accuracy: 0.9101\n",
            "Epoch 45/200\n",
            "1668/1669 [============================>.] - ETA: 0s - loss: 0.3160 - accuracy: 0.8873\n",
            "Epoch 45: val_accuracy did not improve from 0.91015\n",
            "1669/1669 [==============================] - 10s 6ms/step - loss: 0.3159 - accuracy: 0.8873 - val_loss: 0.2593 - val_accuracy: 0.9071\n",
            "Epoch 46/200\n",
            "1667/1669 [============================>.] - ETA: 0s - loss: 0.3191 - accuracy: 0.8863\n",
            "Epoch 46: val_accuracy did not improve from 0.91015\n",
            "1669/1669 [==============================] - 9s 6ms/step - loss: 0.3191 - accuracy: 0.8863 - val_loss: 0.2967 - val_accuracy: 0.8845\n",
            "Epoch 47/200\n",
            "1661/1669 [============================>.] - ETA: 0s - loss: 0.3147 - accuracy: 0.8856\n",
            "Epoch 47: val_accuracy improved from 0.91015 to 0.91167, saving model to best_model_spd.h5\n",
            "1669/1669 [==============================] - 10s 6ms/step - loss: 0.3146 - accuracy: 0.8856 - val_loss: 0.2492 - val_accuracy: 0.9117\n",
            "Epoch 48/200\n",
            "1662/1669 [============================>.] - ETA: 0s - loss: 0.3141 - accuracy: 0.8860\n",
            "Epoch 48: val_accuracy did not improve from 0.91167\n",
            "1669/1669 [==============================] - 9s 6ms/step - loss: 0.3142 - accuracy: 0.8861 - val_loss: 0.2716 - val_accuracy: 0.9058\n",
            "Epoch 49/200\n",
            "1662/1669 [============================>.] - ETA: 0s - loss: 0.3139 - accuracy: 0.8859\n",
            "Epoch 49: val_accuracy did not improve from 0.91167\n",
            "1669/1669 [==============================] - 9s 5ms/step - loss: 0.3138 - accuracy: 0.8859 - val_loss: 0.3390 - val_accuracy: 0.8640\n",
            "Epoch 50/200\n",
            "1661/1669 [============================>.] - ETA: 0s - loss: 0.3143 - accuracy: 0.8862\n",
            "Epoch 50: val_accuracy did not improve from 0.91167\n",
            "1669/1669 [==============================] - 9s 6ms/step - loss: 0.3143 - accuracy: 0.8861 - val_loss: 0.2661 - val_accuracy: 0.9024\n",
            "Epoch 51/200\n",
            "1664/1669 [============================>.] - ETA: 0s - loss: 0.3144 - accuracy: 0.8870\n",
            "Epoch 51: val_accuracy did not improve from 0.91167\n",
            "1669/1669 [==============================] - 9s 5ms/step - loss: 0.3142 - accuracy: 0.8870 - val_loss: 0.2539 - val_accuracy: 0.9076\n",
            "Epoch 52/200\n",
            "1662/1669 [============================>.] - ETA: 0s - loss: 0.3100 - accuracy: 0.8900\n",
            "Epoch 52: val_accuracy did not improve from 0.91167\n",
            "1669/1669 [==============================] - 9s 5ms/step - loss: 0.3099 - accuracy: 0.8900 - val_loss: 0.2744 - val_accuracy: 0.8963\n",
            "Epoch 53/200\n",
            "1667/1669 [============================>.] - ETA: 0s - loss: 0.3110 - accuracy: 0.8891\n",
            "Epoch 53: val_accuracy did not improve from 0.91167\n",
            "1669/1669 [==============================] - 9s 6ms/step - loss: 0.3109 - accuracy: 0.8891 - val_loss: 0.2785 - val_accuracy: 0.8997\n",
            "Epoch 54/200\n",
            "1663/1669 [============================>.] - ETA: 0s - loss: 0.3084 - accuracy: 0.8901\n",
            "Epoch 54: val_accuracy did not improve from 0.91167\n",
            "1669/1669 [==============================] - 9s 5ms/step - loss: 0.3085 - accuracy: 0.8900 - val_loss: 0.2635 - val_accuracy: 0.9058\n",
            "Epoch 55/200\n",
            "1667/1669 [============================>.] - ETA: 0s - loss: 0.3119 - accuracy: 0.8881\n",
            "Epoch 55: val_accuracy did not improve from 0.91167\n",
            "1669/1669 [==============================] - 9s 6ms/step - loss: 0.3119 - accuracy: 0.8881 - val_loss: 0.2632 - val_accuracy: 0.9007\n",
            "Epoch 56/200\n",
            "1669/1669 [==============================] - ETA: 0s - loss: 0.3058 - accuracy: 0.8899\n",
            "Epoch 56: val_accuracy did not improve from 0.91167\n",
            "1669/1669 [==============================] - 9s 5ms/step - loss: 0.3058 - accuracy: 0.8899 - val_loss: 0.2575 - val_accuracy: 0.9021\n",
            "Epoch 57/200\n",
            "1668/1669 [============================>.] - ETA: 0s - loss: 0.3114 - accuracy: 0.8911\n",
            "Epoch 57: val_accuracy did not improve from 0.91167\n",
            "1669/1669 [==============================] - 9s 6ms/step - loss: 0.3114 - accuracy: 0.8912 - val_loss: 0.2404 - val_accuracy: 0.9115\n",
            "Epoch 58/200\n",
            "1669/1669 [==============================] - ETA: 0s - loss: 0.3052 - accuracy: 0.8926\n",
            "Epoch 58: val_accuracy did not improve from 0.91167\n",
            "1669/1669 [==============================] - 9s 5ms/step - loss: 0.3052 - accuracy: 0.8926 - val_loss: 0.2962 - val_accuracy: 0.8842\n",
            "Epoch 59/200\n",
            "1665/1669 [============================>.] - ETA: 0s - loss: 0.3011 - accuracy: 0.8925\n",
            "Epoch 59: val_accuracy did not improve from 0.91167\n",
            "1669/1669 [==============================] - 9s 6ms/step - loss: 0.3011 - accuracy: 0.8924 - val_loss: 0.2527 - val_accuracy: 0.9039\n",
            "Epoch 60/200\n",
            "1661/1669 [============================>.] - ETA: 0s - loss: 0.3044 - accuracy: 0.8917\n",
            "Epoch 60: val_accuracy did not improve from 0.91167\n",
            "1669/1669 [==============================] - 9s 6ms/step - loss: 0.3045 - accuracy: 0.8917 - val_loss: 0.2943 - val_accuracy: 0.8882\n",
            "Epoch 61/200\n",
            "1666/1669 [============================>.] - ETA: 0s - loss: 0.3056 - accuracy: 0.8909\n",
            "Epoch 61: val_accuracy did not improve from 0.91167\n",
            "1669/1669 [==============================] - 9s 6ms/step - loss: 0.3055 - accuracy: 0.8909 - val_loss: 0.3326 - val_accuracy: 0.8660\n",
            "Epoch 62/200\n",
            "1663/1669 [============================>.] - ETA: 0s - loss: 0.3035 - accuracy: 0.8919\n",
            "Epoch 62: val_accuracy did not improve from 0.91167\n",
            "1669/1669 [==============================] - 9s 5ms/step - loss: 0.3037 - accuracy: 0.8917 - val_loss: 0.2743 - val_accuracy: 0.8977\n",
            "Epoch 63/200\n",
            "1666/1669 [============================>.] - ETA: 0s - loss: 0.2967 - accuracy: 0.8961\n",
            "Epoch 63: val_accuracy did not improve from 0.91167\n",
            "1669/1669 [==============================] - 9s 6ms/step - loss: 0.2966 - accuracy: 0.8961 - val_loss: 0.2839 - val_accuracy: 0.8943\n",
            "Epoch 64/200\n",
            "1666/1669 [============================>.] - ETA: 0s - loss: 0.2977 - accuracy: 0.8951\n",
            "Epoch 64: val_accuracy did not improve from 0.91167\n",
            "1669/1669 [==============================] - 9s 6ms/step - loss: 0.2977 - accuracy: 0.8950 - val_loss: 0.2559 - val_accuracy: 0.9081\n",
            "Epoch 65/200\n",
            "1668/1669 [============================>.] - ETA: 0s - loss: 0.2995 - accuracy: 0.8940\n",
            "Epoch 65: val_accuracy did not improve from 0.91167\n",
            "1669/1669 [==============================] - 9s 6ms/step - loss: 0.2995 - accuracy: 0.8940 - val_loss: 0.2805 - val_accuracy: 0.8928\n",
            "Epoch 66/200\n",
            "1665/1669 [============================>.] - ETA: 0s - loss: 0.3011 - accuracy: 0.8949\n",
            "Epoch 66: val_accuracy did not improve from 0.91167\n",
            "1669/1669 [==============================] - 9s 6ms/step - loss: 0.3011 - accuracy: 0.8948 - val_loss: 0.2879 - val_accuracy: 0.8941\n",
            "Epoch 67/200\n",
            "1667/1669 [============================>.] - ETA: 0s - loss: 0.2995 - accuracy: 0.8937\n",
            "Epoch 67: val_accuracy did not improve from 0.91167\n",
            "1669/1669 [==============================] - 9s 6ms/step - loss: 0.2994 - accuracy: 0.8938 - val_loss: 0.2555 - val_accuracy: 0.9068\n",
            "Epoch 68/200\n",
            "1666/1669 [============================>.] - ETA: 0s - loss: 0.2984 - accuracy: 0.8966\n",
            "Epoch 68: val_accuracy did not improve from 0.91167\n",
            "1669/1669 [==============================] - 9s 6ms/step - loss: 0.2985 - accuracy: 0.8966 - val_loss: 0.2689 - val_accuracy: 0.8992\n",
            "Epoch 69/200\n",
            "1660/1669 [============================>.] - ETA: 0s - loss: 0.2992 - accuracy: 0.8953\n",
            "Epoch 69: val_accuracy improved from 0.91167 to 0.91436, saving model to best_model_spd.h5\n",
            "1669/1669 [==============================] - 9s 6ms/step - loss: 0.2989 - accuracy: 0.8955 - val_loss: 0.2486 - val_accuracy: 0.9144\n",
            "Epoch 70/200\n",
            "1664/1669 [============================>.] - ETA: 0s - loss: 0.2986 - accuracy: 0.8950\n",
            "Epoch 70: val_accuracy did not improve from 0.91436\n",
            "1669/1669 [==============================] - 9s 6ms/step - loss: 0.2985 - accuracy: 0.8951 - val_loss: 0.2630 - val_accuracy: 0.9056\n",
            "Epoch 71/200\n",
            "1667/1669 [============================>.] - ETA: 0s - loss: 0.2945 - accuracy: 0.8957\n",
            "Epoch 71: val_accuracy did not improve from 0.91436\n",
            "1669/1669 [==============================] - 9s 6ms/step - loss: 0.2945 - accuracy: 0.8957 - val_loss: 0.2748 - val_accuracy: 0.8955\n",
            "Epoch 72/200\n",
            "1665/1669 [============================>.] - ETA: 0s - loss: 0.2962 - accuracy: 0.8945\n",
            "Epoch 72: val_accuracy did not improve from 0.91436\n",
            "1669/1669 [==============================] - 9s 6ms/step - loss: 0.2961 - accuracy: 0.8946 - val_loss: 0.2759 - val_accuracy: 0.8970\n",
            "Epoch 73/200\n",
            "1665/1669 [============================>.] - ETA: 0s - loss: 0.2919 - accuracy: 0.8980\n",
            "Epoch 73: val_accuracy did not improve from 0.91436\n",
            "1669/1669 [==============================] - 17s 10ms/step - loss: 0.2918 - accuracy: 0.8981 - val_loss: 0.2930 - val_accuracy: 0.8889\n",
            "Epoch 74/200\n",
            "1662/1669 [============================>.] - ETA: 0s - loss: 0.2940 - accuracy: 0.8978\n",
            "Epoch 74: val_accuracy did not improve from 0.91436\n",
            "1669/1669 [==============================] - 9s 6ms/step - loss: 0.2940 - accuracy: 0.8978 - val_loss: 0.2795 - val_accuracy: 0.8970\n",
            "Epoch 75/200\n",
            "1666/1669 [============================>.] - ETA: 0s - loss: 0.2972 - accuracy: 0.8947\n",
            "Epoch 75: val_accuracy did not improve from 0.91436\n",
            "1669/1669 [==============================] - 10s 6ms/step - loss: 0.2972 - accuracy: 0.8947 - val_loss: 0.3503 - val_accuracy: 0.8576\n",
            "Epoch 76/200\n",
            "1666/1669 [============================>.] - ETA: 0s - loss: 0.2962 - accuracy: 0.8953\n",
            "Epoch 76: val_accuracy improved from 0.91436 to 0.92633, saving model to best_model_spd.h5\n",
            "1669/1669 [==============================] - 10s 6ms/step - loss: 0.2961 - accuracy: 0.8953 - val_loss: 0.2233 - val_accuracy: 0.9263\n",
            "Epoch 77/200\n",
            "1665/1669 [============================>.] - ETA: 0s - loss: 0.2956 - accuracy: 0.8949\n",
            "Epoch 77: val_accuracy did not improve from 0.92633\n",
            "1669/1669 [==============================] - 9s 6ms/step - loss: 0.2955 - accuracy: 0.8950 - val_loss: 0.3200 - val_accuracy: 0.8780\n",
            "Epoch 78/200\n",
            "1663/1669 [============================>.] - ETA: 0s - loss: 0.2896 - accuracy: 0.8987\n",
            "Epoch 78: val_accuracy did not improve from 0.92633\n",
            "1669/1669 [==============================] - 10s 6ms/step - loss: 0.2896 - accuracy: 0.8986 - val_loss: 0.2449 - val_accuracy: 0.9120\n",
            "Epoch 79/200\n",
            "1665/1669 [============================>.] - ETA: 0s - loss: 0.2948 - accuracy: 0.8983\n",
            "Epoch 79: val_accuracy did not improve from 0.92633\n",
            "1669/1669 [==============================] - 10s 6ms/step - loss: 0.2948 - accuracy: 0.8983 - val_loss: 0.3099 - val_accuracy: 0.8788\n",
            "Epoch 80/200\n",
            "1662/1669 [============================>.] - ETA: 0s - loss: 0.2944 - accuracy: 0.8972\n",
            "Epoch 80: val_accuracy did not improve from 0.92633\n",
            "1669/1669 [==============================] - 10s 6ms/step - loss: 0.2947 - accuracy: 0.8972 - val_loss: 0.2809 - val_accuracy: 0.8951\n",
            "Epoch 81/200\n",
            "1667/1669 [============================>.] - ETA: 0s - loss: 0.2933 - accuracy: 0.8973\n",
            "Epoch 81: val_accuracy did not improve from 0.92633\n",
            "1669/1669 [==============================] - 10s 6ms/step - loss: 0.2933 - accuracy: 0.8973 - val_loss: 0.2451 - val_accuracy: 0.9144\n",
            "Epoch 82/200\n",
            "1665/1669 [============================>.] - ETA: 0s - loss: 0.2905 - accuracy: 0.8983\n",
            "Epoch 82: val_accuracy did not improve from 0.92633\n",
            "1669/1669 [==============================] - 9s 6ms/step - loss: 0.2907 - accuracy: 0.8982 - val_loss: 0.2934 - val_accuracy: 0.8871\n",
            "Epoch 83/200\n",
            "1662/1669 [============================>.] - ETA: 0s - loss: 0.2924 - accuracy: 0.8984\n",
            "Epoch 83: val_accuracy did not improve from 0.92633\n",
            "1669/1669 [==============================] - 10s 6ms/step - loss: 0.2924 - accuracy: 0.8984 - val_loss: 0.2717 - val_accuracy: 0.8994\n",
            "Epoch 84/200\n",
            "1663/1669 [============================>.] - ETA: 0s - loss: 0.2912 - accuracy: 0.9001\n",
            "Epoch 84: val_accuracy did not improve from 0.92633\n",
            "1669/1669 [==============================] - 11s 7ms/step - loss: 0.2912 - accuracy: 0.9001 - val_loss: 0.2784 - val_accuracy: 0.8935\n",
            "Epoch 85/200\n",
            "1661/1669 [============================>.] - ETA: 0s - loss: 0.2896 - accuracy: 0.8997\n",
            "Epoch 85: val_accuracy did not improve from 0.92633\n",
            "1669/1669 [==============================] - 10s 6ms/step - loss: 0.2895 - accuracy: 0.8998 - val_loss: 0.2878 - val_accuracy: 0.8918\n",
            "Epoch 86/200\n",
            "1659/1669 [============================>.] - ETA: 0s - loss: 0.2951 - accuracy: 0.8983\n",
            "Epoch 86: val_accuracy did not improve from 0.92633\n",
            "1669/1669 [==============================] - 10s 6ms/step - loss: 0.2951 - accuracy: 0.8982 - val_loss: 0.2660 - val_accuracy: 0.8989\n",
            "Epoch 87/200\n",
            "1660/1669 [============================>.] - ETA: 0s - loss: 0.2927 - accuracy: 0.8983\n",
            "Epoch 87: val_accuracy did not improve from 0.92633\n",
            "1669/1669 [==============================] - 9s 6ms/step - loss: 0.2926 - accuracy: 0.8984 - val_loss: 0.2559 - val_accuracy: 0.9088\n",
            "Epoch 88/200\n",
            "1668/1669 [============================>.] - ETA: 0s - loss: 0.2886 - accuracy: 0.9000\n",
            "Epoch 88: val_accuracy did not improve from 0.92633\n",
            "1669/1669 [==============================] - 9s 6ms/step - loss: 0.2886 - accuracy: 0.9000 - val_loss: 0.2832 - val_accuracy: 0.8918\n",
            "Epoch 89/200\n",
            "1662/1669 [============================>.] - ETA: 0s - loss: 0.2886 - accuracy: 0.9006\n",
            "Epoch 89: val_accuracy did not improve from 0.92633\n",
            "1669/1669 [==============================] - 10s 6ms/step - loss: 0.2884 - accuracy: 0.9007 - val_loss: 0.2431 - val_accuracy: 0.9090\n",
            "Epoch 90/200\n",
            "1662/1669 [============================>.] - ETA: 0s - loss: 0.2882 - accuracy: 0.9007\n",
            "Epoch 90: val_accuracy did not improve from 0.92633\n",
            "1669/1669 [==============================] - 9s 6ms/step - loss: 0.2883 - accuracy: 0.9007 - val_loss: 0.2720 - val_accuracy: 0.9009\n",
            "Epoch 91/200\n",
            "1665/1669 [============================>.] - ETA: 0s - loss: 0.2870 - accuracy: 0.9007\n",
            "Epoch 91: val_accuracy did not improve from 0.92633\n",
            "1669/1669 [==============================] - 10s 6ms/step - loss: 0.2869 - accuracy: 0.9008 - val_loss: 0.2674 - val_accuracy: 0.9066\n",
            "Epoch 92/200\n",
            "1667/1669 [============================>.] - ETA: 0s - loss: 0.2882 - accuracy: 0.8991\n",
            "Epoch 92: val_accuracy did not improve from 0.92633\n",
            "1669/1669 [==============================] - 10s 6ms/step - loss: 0.2882 - accuracy: 0.8992 - val_loss: 0.2503 - val_accuracy: 0.9061\n",
            "Epoch 93/200\n",
            "1666/1669 [============================>.] - ETA: 0s - loss: 0.2876 - accuracy: 0.8987\n",
            "Epoch 93: val_accuracy did not improve from 0.92633\n",
            "1669/1669 [==============================] - 9s 6ms/step - loss: 0.2876 - accuracy: 0.8987 - val_loss: 0.2405 - val_accuracy: 0.9128\n",
            "Epoch 94/200\n",
            "1662/1669 [============================>.] - ETA: 0s - loss: 0.2889 - accuracy: 0.8998\n",
            "Epoch 94: val_accuracy did not improve from 0.92633\n",
            "1669/1669 [==============================] - 10s 6ms/step - loss: 0.2889 - accuracy: 0.8998 - val_loss: 0.2562 - val_accuracy: 0.9058\n",
            "Epoch 95/200\n",
            "1664/1669 [============================>.] - ETA: 0s - loss: 0.2865 - accuracy: 0.9004\n",
            "Epoch 95: val_accuracy did not improve from 0.92633\n",
            "1669/1669 [==============================] - 10s 6ms/step - loss: 0.2865 - accuracy: 0.9003 - val_loss: 0.2721 - val_accuracy: 0.8953\n",
            "Epoch 96/200\n",
            "1666/1669 [============================>.] - ETA: 0s - loss: 0.2858 - accuracy: 0.9008\n",
            "Epoch 96: val_accuracy did not improve from 0.92633\n",
            "1669/1669 [==============================] - 10s 6ms/step - loss: 0.2860 - accuracy: 0.9007 - val_loss: 0.2734 - val_accuracy: 0.8948\n",
            "Epoch 96: early stopping\n"
          ]
        }
      ],
      "source": [
        "# Compile Model:\n",
        "#model = Ne()\n",
        "model.compile(optimizer=Adam(learning_rate=0.0005),\n",
        "              loss='binary_crossentropy',\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "# Run Model:\n",
        "history = model.fit(x=trainX2, y= y_train, validation_data=(testX2, y_test), batch_size=32, epochs=200, verbose=1, callbacks=[es,mc])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "8xfJE549bNiK",
        "outputId": "2b1fef93-d496-4c7e-cefd-fe422b79a80d"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from matplotlib import pyplot as plt\n",
        "#plot training and validation accuracy\n",
        "plt.plot(history.history['accuracy'])\n",
        "plt.plot(history.history['val_accuracy'])\n",
        "#plt.plot(history.history['val_accuracy'])\n",
        "plt.title('model accuracy')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train','validation'], loc='lower right')\n",
        "plt.savefig('accuracy_curve_spd_balanced_smote.png')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "MvJeh0eGbQvR",
        "outputId": "eee04791-d1ec-4401-e5aa-ff5882b50a0a"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "#plot training loss\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.plot()\n",
        "plt.title('model loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train','validation'], loc='upper right')\n",
        "plt.savefig('loss_curve_spd_balanced_smote.png')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TFOaZT4bIr5g"
      },
      "outputs": [],
      "source": [
        "# load the saved model\n",
        "saved_model = load_model('best_model_spd.h5')\n",
        "# evaluate the model\n",
        "#_, train_acc = saved_model.evaluate(trainX, trainy, verbose=0)\n",
        "#_, test_acc = saved_model.evaluate(testX, testy, verbose=0)\n",
        "#print('Train: %.3f, Test: %.3f' % (train_acc, test_acc))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PYA8Z_PVGBIE",
        "outputId": "2bcde9f8-55a0-457e-c95e-1bf9210fff4e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Result Summary:\n",
            "------------------------------\n",
            "accuracy:  0.9263317599460553, sensitivity:  0.9779808890735355, specificity:  0.8910638297872341 ,precision:  0.8597516435354273 ,MCC :  0.8560035242805009\n"
          ]
        }
      ],
      "source": [
        "probabilities = saved_model.predict(testX2)\n",
        "predicted_classes = probabilities >= 0.5\n",
        "predicted_classes = predicted_classes.astype(int)\n",
        "acc, sensitivity, specificity, precision, MCC = calculate_performance(len(y_test),predicted_classes , y_test)\n",
        "\n",
        "print('Result Summary:')\n",
        "print('------------------------------')\n",
        "print('accuracy: ',acc, end =\", \")\n",
        "print('sensitivity: ',sensitivity, end =\", \")\n",
        "print('specificity: ',specificity, end =\" ,\")\n",
        "print('precision: ',precision, end =\" ,\")\n",
        "print('MCC : ',MCC)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KMuwk9qCtGBG"
      },
      "outputs": [],
      "source": [
        "def get_evolutionary_model():\n",
        "    ### Head-1:\n",
        "    input1 = Input(shape=trainX1[0].shape)\n",
        "\n",
        "    x = Conv1D(filters=32, kernel_size=3, padding='same', kernel_regularizer=l2(l=0.01))(input1)\n",
        "    #x = BatchNormalization()(x)\n",
        "    x = Dropout(rate=0.5)(x)\n",
        "\n",
        "    #x = Conv1D(filters=8, kernel_size=3, padding='same', activation='relu', kernel_regularizer=l2(l=0.01))(x)\n",
        "    #x = BatchNormalization()(x)\n",
        "    #x = Dropout(rate=0.50)(x)\n",
        "\n",
        "    head1 = Flatten()(x)\n",
        "\n",
        "    z= Dense(units=256, activation='relu',)(head1)\n",
        "    z = Dropout(rate=0.5)(z)\n",
        "\n",
        "    z = Dense(units=64, activation='relu',)(z)\n",
        "    z = Dropout(rate=0.5)(z)\n",
        "\n",
        "    z = Dense(units=16, activation='relu',)(z)\n",
        "    z = Dropout(rate=0.5)(z)\n",
        "    \n",
        "    z = Dense(1, activation='sigmoid')(z)\n",
        "\n",
        "    return Model(inputs=[input1], outputs=[z])\n",
        "#end-def"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "JjGhHGLqtRC4",
        "outputId": "e5ec5cec-da87-4fb1-eb01-cda942a6a1a8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"model_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_2 (InputLayer)        [(None, 21, 20)]          0         \n",
            "                                                                 \n",
            " conv1d_1 (Conv1D)           (None, 21, 32)            1952      \n",
            "                                                                 \n",
            " dropout_4 (Dropout)         (None, 21, 32)            0         \n",
            "                                                                 \n",
            " flatten_1 (Flatten)         (None, 672)               0         \n",
            "                                                                 \n",
            " dense_4 (Dense)             (None, 256)               172288    \n",
            "                                                                 \n",
            " dropout_5 (Dropout)         (None, 256)               0         \n",
            "                                                                 \n",
            " dense_5 (Dense)             (None, 64)                16448     \n",
            "                                                                 \n",
            " dropout_6 (Dropout)         (None, 64)                0         \n",
            "                                                                 \n",
            " dense_6 (Dense)             (None, 16)                1040      \n",
            "                                                                 \n",
            " dropout_7 (Dropout)         (None, 16)                0         \n",
            "                                                                 \n",
            " dense_7 (Dense)             (None, 1)                 17        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 191,745\n",
            "Trainable params: 191,745\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model = get_evolutionary_model()\n",
        "model.summary()\n",
        "plot_model(model, to_file='model-pssm.png', show_shapes=True, show_layer_names=False, expand_nested=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D5eJVjnktguG",
        "outputId": "8cf36d5e-8407-4ca3-e8f7-c9b48a777840"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/200\n",
            "1667/1669 [============================>.] - ETA: 0s - loss: 0.6864 - accuracy: 0.6340\n",
            "Epoch 1: val_accuracy improved from -inf to 0.66976, saving model to best_model_pssm.h5\n",
            "1669/1669 [==============================] - 10s 6ms/step - loss: 0.6863 - accuracy: 0.6341 - val_loss: 0.6238 - val_accuracy: 0.6698\n",
            "Epoch 2/200\n",
            "1663/1669 [============================>.] - ETA: 0s - loss: 0.5619 - accuracy: 0.7353\n",
            "Epoch 2: val_accuracy improved from 0.66976 to 0.73517, saving model to best_model_pssm.h5\n",
            "1669/1669 [==============================] - 9s 6ms/step - loss: 0.5616 - accuracy: 0.7356 - val_loss: 0.5432 - val_accuracy: 0.7352\n",
            "Epoch 3/200\n",
            "1665/1669 [============================>.] - ETA: 0s - loss: 0.4866 - accuracy: 0.7913\n",
            "Epoch 3: val_accuracy did not improve from 0.73517\n",
            "1669/1669 [==============================] - 11s 7ms/step - loss: 0.4863 - accuracy: 0.7915 - val_loss: 0.6430 - val_accuracy: 0.6962\n",
            "Epoch 4/200\n",
            "1662/1669 [============================>.] - ETA: 0s - loss: 0.4347 - accuracy: 0.8210\n",
            "Epoch 4: val_accuracy did not improve from 0.73517\n",
            "1669/1669 [==============================] - 10s 6ms/step - loss: 0.4347 - accuracy: 0.8209 - val_loss: 0.7315 - val_accuracy: 0.6853\n",
            "Epoch 5/200\n",
            "1662/1669 [============================>.] - ETA: 0s - loss: 0.4063 - accuracy: 0.8377\n",
            "Epoch 5: val_accuracy improved from 0.73517 to 0.74073, saving model to best_model_pssm.h5\n",
            "1669/1669 [==============================] - 11s 7ms/step - loss: 0.4062 - accuracy: 0.8377 - val_loss: 0.6351 - val_accuracy: 0.7407\n",
            "Epoch 6/200\n",
            "1664/1669 [============================>.] - ETA: 0s - loss: 0.3921 - accuracy: 0.8478\n",
            "Epoch 6: val_accuracy did not improve from 0.74073\n",
            "1669/1669 [==============================] - 12s 7ms/step - loss: 0.3920 - accuracy: 0.8478 - val_loss: 0.7047 - val_accuracy: 0.7107\n",
            "Epoch 7/200\n",
            "1662/1669 [============================>.] - ETA: 0s - loss: 0.3708 - accuracy: 0.8592\n",
            "Epoch 7: val_accuracy improved from 0.74073 to 0.74208, saving model to best_model_pssm.h5\n",
            "1669/1669 [==============================] - 10s 6ms/step - loss: 0.3707 - accuracy: 0.8593 - val_loss: 0.6381 - val_accuracy: 0.7421\n",
            "Epoch 8/200\n",
            "1664/1669 [============================>.] - ETA: 0s - loss: 0.3636 - accuracy: 0.8656\n",
            "Epoch 8: val_accuracy did not improve from 0.74208\n",
            "1669/1669 [==============================] - 11s 6ms/step - loss: 0.3640 - accuracy: 0.8654 - val_loss: 0.6429 - val_accuracy: 0.7372\n",
            "Epoch 9/200\n",
            "1660/1669 [============================>.] - ETA: 0s - loss: 0.3502 - accuracy: 0.8704\n",
            "Epoch 9: val_accuracy improved from 0.74208 to 0.76686, saving model to best_model_pssm.h5\n",
            "1669/1669 [==============================] - 13s 8ms/step - loss: 0.3501 - accuracy: 0.8706 - val_loss: 0.5884 - val_accuracy: 0.7669\n",
            "Epoch 10/200\n",
            "1661/1669 [============================>.] - ETA: 0s - loss: 0.3473 - accuracy: 0.8727\n",
            "Epoch 10: val_accuracy improved from 0.76686 to 0.78557, saving model to best_model_pssm.h5\n",
            "1669/1669 [==============================] - 9s 5ms/step - loss: 0.3474 - accuracy: 0.8727 - val_loss: 0.5300 - val_accuracy: 0.7856\n",
            "Epoch 11/200\n",
            "1664/1669 [============================>.] - ETA: 0s - loss: 0.3414 - accuracy: 0.8756\n",
            "Epoch 11: val_accuracy improved from 0.78557 to 0.81945, saving model to best_model_pssm.h5\n",
            "1669/1669 [==============================] - 10s 6ms/step - loss: 0.3415 - accuracy: 0.8757 - val_loss: 0.4623 - val_accuracy: 0.8195\n",
            "Epoch 12/200\n",
            "1669/1669 [==============================] - ETA: 0s - loss: 0.3240 - accuracy: 0.8820\n",
            "Epoch 12: val_accuracy did not improve from 0.81945\n",
            "1669/1669 [==============================] - 10s 6ms/step - loss: 0.3240 - accuracy: 0.8820 - val_loss: 0.6082 - val_accuracy: 0.7623\n",
            "Epoch 13/200\n",
            "1663/1669 [============================>.] - ETA: 0s - loss: 0.3241 - accuracy: 0.8810\n",
            "Epoch 13: val_accuracy did not improve from 0.81945\n",
            "1669/1669 [==============================] - 12s 7ms/step - loss: 0.3244 - accuracy: 0.8809 - val_loss: 0.5592 - val_accuracy: 0.7782\n",
            "Epoch 14/200\n",
            "1668/1669 [============================>.] - ETA: 0s - loss: 0.3222 - accuracy: 0.8842\n",
            "Epoch 14: val_accuracy did not improve from 0.81945\n",
            "1669/1669 [==============================] - 9s 5ms/step - loss: 0.3222 - accuracy: 0.8842 - val_loss: 0.6052 - val_accuracy: 0.7611\n",
            "Epoch 15/200\n",
            "1661/1669 [============================>.] - ETA: 0s - loss: 0.3175 - accuracy: 0.8844\n",
            "Epoch 15: val_accuracy did not improve from 0.81945\n",
            "1669/1669 [==============================] - 9s 6ms/step - loss: 0.3173 - accuracy: 0.8845 - val_loss: 0.6977 - val_accuracy: 0.7254\n",
            "Epoch 16/200\n",
            "1665/1669 [============================>.] - ETA: 0s - loss: 0.3105 - accuracy: 0.8903\n",
            "Epoch 16: val_accuracy did not improve from 0.81945\n",
            "1669/1669 [==============================] - 9s 6ms/step - loss: 0.3105 - accuracy: 0.8903 - val_loss: 0.5506 - val_accuracy: 0.7803\n",
            "Epoch 17/200\n",
            "1666/1669 [============================>.] - ETA: 0s - loss: 0.3049 - accuracy: 0.8921\n",
            "Epoch 17: val_accuracy did not improve from 0.81945\n",
            "1669/1669 [==============================] - 9s 6ms/step - loss: 0.3050 - accuracy: 0.8921 - val_loss: 0.5789 - val_accuracy: 0.7726\n",
            "Epoch 18/200\n",
            "1669/1669 [==============================] - ETA: 0s - loss: 0.3038 - accuracy: 0.8942\n",
            "Epoch 18: val_accuracy did not improve from 0.81945\n",
            "1669/1669 [==============================] - 10s 6ms/step - loss: 0.3038 - accuracy: 0.8942 - val_loss: 0.5438 - val_accuracy: 0.7900\n",
            "Epoch 19/200\n",
            "1665/1669 [============================>.] - ETA: 0s - loss: 0.2989 - accuracy: 0.8946\n",
            "Epoch 19: val_accuracy did not improve from 0.81945\n",
            "1669/1669 [==============================] - 10s 6ms/step - loss: 0.2989 - accuracy: 0.8946 - val_loss: 0.5053 - val_accuracy: 0.8073\n",
            "Epoch 20/200\n",
            "1661/1669 [============================>.] - ETA: 0s - loss: 0.2944 - accuracy: 0.8968\n",
            "Epoch 20: val_accuracy did not improve from 0.81945\n",
            "1669/1669 [==============================] - 9s 6ms/step - loss: 0.2942 - accuracy: 0.8970 - val_loss: 0.4881 - val_accuracy: 0.8129\n",
            "Epoch 21/200\n",
            "1661/1669 [============================>.] - ETA: 0s - loss: 0.2895 - accuracy: 0.8996\n",
            "Epoch 21: val_accuracy did not improve from 0.81945\n",
            "1669/1669 [==============================] - 10s 6ms/step - loss: 0.2894 - accuracy: 0.8996 - val_loss: 0.5634 - val_accuracy: 0.7847\n",
            "Epoch 22/200\n",
            "1664/1669 [============================>.] - ETA: 0s - loss: 0.2874 - accuracy: 0.9010\n",
            "Epoch 22: val_accuracy improved from 0.81945 to 0.85722, saving model to best_model_pssm.h5\n",
            "1669/1669 [==============================] - 10s 6ms/step - loss: 0.2873 - accuracy: 0.9009 - val_loss: 0.3795 - val_accuracy: 0.8572\n",
            "Epoch 23/200\n",
            "1665/1669 [============================>.] - ETA: 0s - loss: 0.2919 - accuracy: 0.8995\n",
            "Epoch 23: val_accuracy did not improve from 0.85722\n",
            "1669/1669 [==============================] - 10s 6ms/step - loss: 0.2920 - accuracy: 0.8996 - val_loss: 0.4972 - val_accuracy: 0.8006\n",
            "Epoch 24/200\n",
            "1662/1669 [============================>.] - ETA: 0s - loss: 0.2870 - accuracy: 0.9020\n",
            "Epoch 24: val_accuracy did not improve from 0.85722\n",
            "1669/1669 [==============================] - 10s 6ms/step - loss: 0.2871 - accuracy: 0.9019 - val_loss: 0.4603 - val_accuracy: 0.8228\n",
            "Epoch 25/200\n",
            "1667/1669 [============================>.] - ETA: 0s - loss: 0.2866 - accuracy: 0.9017\n",
            "Epoch 25: val_accuracy did not improve from 0.85722\n",
            "1669/1669 [==============================] - 10s 6ms/step - loss: 0.2868 - accuracy: 0.9016 - val_loss: 0.5669 - val_accuracy: 0.7744\n",
            "Epoch 26/200\n",
            "1669/1669 [==============================] - ETA: 0s - loss: 0.2806 - accuracy: 0.9059\n",
            "Epoch 26: val_accuracy did not improve from 0.85722\n",
            "1669/1669 [==============================] - 10s 6ms/step - loss: 0.2806 - accuracy: 0.9059 - val_loss: 0.4530 - val_accuracy: 0.8281\n",
            "Epoch 27/200\n",
            "1664/1669 [============================>.] - ETA: 0s - loss: 0.2807 - accuracy: 0.9068\n",
            "Epoch 27: val_accuracy did not improve from 0.85722\n",
            "1669/1669 [==============================] - 10s 6ms/step - loss: 0.2804 - accuracy: 0.9069 - val_loss: 0.4011 - val_accuracy: 0.8479\n",
            "Epoch 28/200\n",
            "1662/1669 [============================>.] - ETA: 0s - loss: 0.2777 - accuracy: 0.9048\n",
            "Epoch 28: val_accuracy did not improve from 0.85722\n",
            "1669/1669 [==============================] - 10s 6ms/step - loss: 0.2775 - accuracy: 0.9049 - val_loss: 0.5099 - val_accuracy: 0.8006\n",
            "Epoch 29/200\n",
            "1660/1669 [============================>.] - ETA: 0s - loss: 0.2752 - accuracy: 0.9082\n",
            "Epoch 29: val_accuracy did not improve from 0.85722\n",
            "1669/1669 [==============================] - 9s 6ms/step - loss: 0.2752 - accuracy: 0.9081 - val_loss: 0.6489 - val_accuracy: 0.7466\n",
            "Epoch 30/200\n",
            "1666/1669 [============================>.] - ETA: 0s - loss: 0.2741 - accuracy: 0.9077\n",
            "Epoch 30: val_accuracy did not improve from 0.85722\n",
            "1669/1669 [==============================] - 9s 6ms/step - loss: 0.2741 - accuracy: 0.9076 - val_loss: 0.4443 - val_accuracy: 0.8282\n",
            "Epoch 31/200\n",
            "1668/1669 [============================>.] - ETA: 0s - loss: 0.2711 - accuracy: 0.9070\n",
            "Epoch 31: val_accuracy did not improve from 0.85722\n",
            "1669/1669 [==============================] - 10s 6ms/step - loss: 0.2710 - accuracy: 0.9070 - val_loss: 0.5435 - val_accuracy: 0.7916\n",
            "Epoch 32/200\n",
            "1668/1669 [============================>.] - ETA: 0s - loss: 0.2712 - accuracy: 0.9085\n",
            "Epoch 32: val_accuracy did not improve from 0.85722\n",
            "1669/1669 [==============================] - 9s 6ms/step - loss: 0.2712 - accuracy: 0.9085 - val_loss: 0.4409 - val_accuracy: 0.8297\n",
            "Epoch 33/200\n",
            "1663/1669 [============================>.] - ETA: 0s - loss: 0.2661 - accuracy: 0.9110\n",
            "Epoch 33: val_accuracy did not improve from 0.85722\n",
            "1669/1669 [==============================] - 11s 7ms/step - loss: 0.2663 - accuracy: 0.9109 - val_loss: 0.5040 - val_accuracy: 0.8045\n",
            "Epoch 34/200\n",
            "1664/1669 [============================>.] - ETA: 0s - loss: 0.2682 - accuracy: 0.9106\n",
            "Epoch 34: val_accuracy did not improve from 0.85722\n",
            "1669/1669 [==============================] - 9s 6ms/step - loss: 0.2682 - accuracy: 0.9106 - val_loss: 0.5151 - val_accuracy: 0.7948\n",
            "Epoch 35/200\n",
            "1669/1669 [==============================] - ETA: 0s - loss: 0.2700 - accuracy: 0.9102\n",
            "Epoch 35: val_accuracy did not improve from 0.85722\n",
            "1669/1669 [==============================] - 9s 6ms/step - loss: 0.2700 - accuracy: 0.9102 - val_loss: 0.5546 - val_accuracy: 0.7911\n",
            "Epoch 36/200\n",
            "1669/1669 [==============================] - ETA: 0s - loss: 0.2663 - accuracy: 0.9120\n",
            "Epoch 36: val_accuracy did not improve from 0.85722\n",
            "1669/1669 [==============================] - 9s 6ms/step - loss: 0.2663 - accuracy: 0.9120 - val_loss: 0.4277 - val_accuracy: 0.8365\n",
            "Epoch 37/200\n",
            "1660/1669 [============================>.] - ETA: 0s - loss: 0.2666 - accuracy: 0.9120\n",
            "Epoch 37: val_accuracy did not improve from 0.85722\n",
            "1669/1669 [==============================] - 10s 6ms/step - loss: 0.2665 - accuracy: 0.9121 - val_loss: 0.4117 - val_accuracy: 0.8469\n",
            "Epoch 38/200\n",
            "1666/1669 [============================>.] - ETA: 0s - loss: 0.2670 - accuracy: 0.9108\n",
            "Epoch 38: val_accuracy did not improve from 0.85722\n",
            "1669/1669 [==============================] - 10s 6ms/step - loss: 0.2670 - accuracy: 0.9109 - val_loss: 0.5574 - val_accuracy: 0.7802\n",
            "Epoch 39/200\n",
            "1661/1669 [============================>.] - ETA: 0s - loss: 0.2639 - accuracy: 0.9115\n",
            "Epoch 39: val_accuracy did not improve from 0.85722\n",
            "1669/1669 [==============================] - 10s 6ms/step - loss: 0.2637 - accuracy: 0.9116 - val_loss: 0.4201 - val_accuracy: 0.8461\n",
            "Epoch 40/200\n",
            "1669/1669 [==============================] - ETA: 0s - loss: 0.2590 - accuracy: 0.9132\n",
            "Epoch 40: val_accuracy did not improve from 0.85722\n",
            "1669/1669 [==============================] - 10s 6ms/step - loss: 0.2590 - accuracy: 0.9132 - val_loss: 0.4123 - val_accuracy: 0.8560\n",
            "Epoch 41/200\n",
            "1664/1669 [============================>.] - ETA: 0s - loss: 0.2595 - accuracy: 0.9152\n",
            "Epoch 41: val_accuracy did not improve from 0.85722\n",
            "1669/1669 [==============================] - 10s 6ms/step - loss: 0.2596 - accuracy: 0.9153 - val_loss: 0.4583 - val_accuracy: 0.8223\n",
            "Epoch 42/200\n",
            "1664/1669 [============================>.] - ETA: 0s - loss: 0.2563 - accuracy: 0.9161\n",
            "Epoch 42: val_accuracy did not improve from 0.85722\n",
            "1669/1669 [==============================] - 10s 6ms/step - loss: 0.2564 - accuracy: 0.9160 - val_loss: 0.5268 - val_accuracy: 0.8070\n",
            "Epoch 42: early stopping\n"
          ]
        }
      ],
      "source": [
        "# patient early stopping\n",
        "es = EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=20)\n",
        "# save the best model\n",
        "mc = ModelCheckpoint('best_model_pssm.h5', monitor='val_accuracy', mode='max', verbose=1, save_best_only=True)\n",
        "# Compile Model:\n",
        "#model = Ne()\n",
        "model.compile(optimizer=Adam(learning_rate=0.0005),\n",
        "              loss='binary_crossentropy',\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "# Run Model:\n",
        "history = model.fit(x=trainX1, y= y_train, validation_data=(testX1, y_test), batch_size=32, epochs=200, verbose=1, callbacks=[es,mc])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "MRCmkyVWtzNi",
        "outputId": "f24f0a40-f68c-48e5-821e-cc6a1115b6a8"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from matplotlib import pyplot as plt\n",
        "#plot training and validation accuracy\n",
        "plt.plot(history.history['accuracy'])\n",
        "plt.plot(history.history['val_accuracy'])\n",
        "#plt.plot(history.history['val_accuracy'])\n",
        "plt.title('model accuracy')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train','validation'], loc='lower right')\n",
        "plt.savefig('accuracy_curve_pssm_balanced_smote.png')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "BT9viZ3xt6QF",
        "outputId": "731ae4d1-98a8-480e-ac28-6aecdf764e51"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "#plot training loss\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.plot()\n",
        "plt.title('model loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train','validation'], loc='upper right')\n",
        "plt.savefig('loss_curve_pssm_balanced_smote.png')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Iu_QXTI4uCbE",
        "outputId": "fb7d75da-d701-4783-e065-1149b6ff193f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Result Summary:\n",
            "------------------------------\n",
            "accuracy:  0.8572151045178692, sensitivity:  0.9825508932280848, specificity:  0.7716312056737589 ,precision:  0.7460567823343849 ,MCC :  0.7424246064772515\n"
          ]
        }
      ],
      "source": [
        "saved_model = load_model('best_model_pssm.h5')\n",
        "probabilities = saved_model.predict(testX1)\n",
        "predicted_classes = probabilities >= 0.5\n",
        "predicted_classes = predicted_classes.astype(int)\n",
        "acc, sensitivity, specificity, precision, MCC = calculate_performance(len(y_test),predicted_classes , y_test)\n",
        "\n",
        "print('Result Summary:')\n",
        "print('------------------------------')\n",
        "print('accuracy: ',acc, end =\", \")\n",
        "print('sensitivity: ',sensitivity, end =\", \")\n",
        "print('specificity: ',specificity, end =\" ,\")\n",
        "print('precision: ',precision, end =\" ,\")\n",
        "print('MCC : ',MCC)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "In8JxYIqJwMq",
        "outputId": "0449c120-af11-4050-dbaa-fd926384bb9d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Collecting rotation-forest\n",
            "  Downloading rotation_forest-1.0.tar.gz (6.3 kB)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from rotation-forest) (1.21.6)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from rotation-forest) (1.4.1)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.7/dist-packages (from rotation-forest) (1.0.2)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->rotation-forest) (3.1.0)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->rotation-forest) (1.1.0)\n",
            "Building wheels for collected packages: rotation-forest\n",
            "  Building wheel for rotation-forest (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for rotation-forest: filename=rotation_forest-1.0-py3-none-any.whl size=7002 sha256=0cd469f237899cb321ef9c5875d9f851cd83ac839726786caf08e71e9f010a63\n",
            "  Stored in directory: /root/.cache/pip/wheels/1a/d7/d5/e657bdbc7feafd9f8808611ad808bcffbcb50c71dd93b9e8e4\n",
            "Successfully built rotation-forest\n",
            "Installing collected packages: rotation-forest\n",
            "Successfully installed rotation-forest-1.0\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-2)]: Using backend SequentialBackend with 1 concurrent workers.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "building tree 1 of 100\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-2)]: Done   1 out of   1 | elapsed:   28.1s remaining:    0.0s\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "building tree 2 of 100\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-2)]: Done   2 out of   2 | elapsed:   56.5s remaining:    0.0s\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "building tree 3 of 100\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-2)]: Done   3 out of   3 | elapsed:  1.4min remaining:    0.0s\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "building tree 4 of 100\n",
            "building tree 5 of 100\n",
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            "building tree 72 of 100\n",
            "building tree 73 of 100\n",
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            "building tree 80 of 100\n",
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            "building tree 92 of 100\n",
            "building tree 93 of 100\n",
            "building tree 94 of 100\n",
            "building tree 95 of 100\n",
            "building tree 96 of 100\n",
            "building tree 97 of 100\n",
            "building tree 98 of 100\n",
            "building tree 99 of 100\n",
            "building tree 100 of 100\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-2)]: Done 100 out of 100 | elapsed: 47.0min finished\n",
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.1s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.1s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    2.5s finished\n"
          ]
        }
      ],
      "source": [
        "!pip install rotation-forest\n",
        "from sklearn.ensemble import AdaBoostClassifier, RandomForestClassifier,GradientBoostingClassifier\n",
        "from rotation_forest import RotationForestClassifier\n",
        "#ml_model = RandomForestClassifier(n_estimators=100, bootstrap=False, random_state=47, verbose=1)\n",
        "ml_model = RotationForestClassifier(n_estimators=100, random_state=47, verbose=4, n_jobs=-2)\n",
        "ml_model.fit(x_train[:, 420: ],y_train )\n",
        "y_predict = ml_model.predict(x_test[:,420:])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SmQrABzgMv0x",
        "outputId": "eeb43dfd-4d50-4b0a-f4c7-8e80eb6d370e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([1, 1, 0, ..., 1, 1, 1], dtype=int32)"
            ]
          },
          "execution_count": 29,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_predict"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2lS1H_e8B8rY"
      },
      "outputs": [],
      "source": [
        "model.save(\"model_pssm_conv1_train_balanced.h5\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.layers import Layer, InputSpec\n",
        "def squeeze_excite_block(self, x):\n",
        "\t# store the input\n",
        "\tshortcut = x\n",
        "\t# calculate the number of filters the input has\n",
        "\tfilters = x.shape[-1]\n",
        "\t# the squeeze operation reduces the input dimensionality\n",
        "\t# here we do a global average pooling across the filters, which\n",
        "\t# reduces the input to a 1D vector\n",
        "\tx = GlobalAveragePooling2D(keepdims=True)(x)\n",
        "\t# reduce the number of filters (1 x 1 x C/r)\n",
        "\tx = Dense(filters // self.ratio, activation=\"relu\",\n",
        "\t\tkernel_initializer=\"he_normal\", use_bias=False)(x)\n",
        "\t\n",
        "\t# the excitation operation restores the input dimensionality\n",
        "\tx = Dense(filters, activation=\"sigmoid\",\n",
        "\t\tkernel_initializer=\"he_normal\", use_bias=False)(x)\n",
        "\t\n",
        "\t# multiply the attention weights with the original input\n",
        "\tx = Multiply()([shortcut, x])\n",
        "\t# return the output of the SE block\n",
        "\treturn x"
      ],
      "metadata": {
        "id": "5jWSWQ5e7ua2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "JCvioq2DJC_m"
      },
      "outputs": [],
      "source": [
        "from tensorflow.keras.layers import Layer, InputSpec\n",
        "class Position_Embedding(Layer):\n",
        "\n",
        "    def __init__(self, size=None, mode='concat', **kwargs):\n",
        "\n",
        "        self.size = size\n",
        "\n",
        "        self.mode = mode\n",
        "\n",
        "        super(Position_Embedding, self).__init__(**kwargs)\n",
        "\n",
        "    def get_config(self):\n",
        "        config = super().get_config()\n",
        "        config.update({\n",
        "            \"arg1\": self.size,\n",
        "            \"arg2\": self.mode,\n",
        "        })\n",
        "        return config\n",
        "\n",
        "    def call(self, x):\n",
        "\n",
        "        if (self.size == None) or (self.mode == 'concat'):\n",
        "            self.size = int(x.shape[-1])\n",
        "\n",
        "        position_j = 1. / K.pow(10000., 2 * K.arange(self.size / 2, dtype='float32') / self.size)\n",
        "\n",
        "        position_j = K.expand_dims(position_j, 0)\n",
        "\n",
        "        position_i = K.cumsum(K.ones_like(x[:, :, 0]), 1) - 1\n",
        "\n",
        "        position_i = K.expand_dims(position_i, 2)\n",
        "\n",
        "        position_ij = K.dot(position_i, position_j)\n",
        "\n",
        "        position_ij = K.concatenate([K.cos(position_ij), K.sin(position_ij)], 2)\n",
        "\n",
        "        if self.mode == 'sum':\n",
        "\n",
        "            return position_ij + x\n",
        "\n",
        "        elif self.mode == 'concat':\n",
        "\n",
        "            return K.concatenate([position_ij, x], 2)\n",
        "\n",
        "    def compute_output_shape(self, input_shape):\n",
        "\n",
        "        if self.mode == 'sum':\n",
        "\n",
        "            return input_shape\n",
        "\n",
        "        elif self.mode == 'concat':\n",
        "\n",
        "            return (input_shape[0], input_shape[1], input_shape[2] + self.size)\n",
        "\n",
        "\n",
        "class Self_Attention(Layer):\n",
        "    def __init__(self, output_dim, **kwargs):\n",
        "        self.output_dim = output_dim\n",
        "        super(Self_Attention, self).__init__(**kwargs)\n",
        "    \n",
        "    def get_config(self):\n",
        "        config = super().get_config()\n",
        "        config.update({\n",
        "            \"arg1\": self.output_dim,\n",
        "        })\n",
        "        return config\n",
        "\n",
        "    def build(self, input_shape):\n",
        "        self.kernel = self.add_weight(name='kernel',\n",
        "                                      shape=(3, input_shape[2], self.output_dim),\n",
        "                                      initializer='uniform',\n",
        "                                      trainable=True)\n",
        "\n",
        "        super(Self_Attention, self).build(input_shape)\n",
        "\n",
        "    def call(self, x):\n",
        "        WQ = K.dot(x, self.kernel[0])\n",
        "        WK = K.dot(x, self.kernel[1])\n",
        "        WV = K.dot(x, self.kernel[2])\n",
        "        QK = K.batch_dot(WQ, K.permute_dimensions(WK, [0, 2, 1]))\n",
        "        QK = QK / (self.output_dim ** 0.5)\n",
        "        QK = K.softmax(QK)\n",
        "        V = K.batch_dot(QK, WV)\n",
        "        return V\n",
        "\n",
        "    def compute_output_shape(self, input_shape):\n",
        "        return (input_shape[0], input_shape[1], self.output_dim)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "id": "eU_tt9EmyAQH"
      },
      "outputs": [],
      "source": [
        "self_number = 128\n",
        "weight_decay=0.0001\n",
        "def get_combined_model():\n",
        "    ### Head-1:\n",
        "    input1 = Input(shape=trainX1[0].shape)\n",
        "\n",
        "    x = Conv1D(filters=64, kernel_size=5, padding='same', kernel_regularizer=l2(l=0.01))(input1)\n",
        "    x = BatchNormalization()(x)\n",
        "    x = Dropout(rate=0.50)(x)\n",
        "    x = Position_Embedding()(x)\n",
        "    x = Self_Attention(self_number)(x)\n",
        "\n",
        "    x = Activation('relu')(x)\n",
        "\n",
        "    x = Dropout(rate = 0.5)(x)\n",
        "\n",
        "    #x = Conv1D(filters=32, kernel_size=3, padding='same', activation='relu', kernel_regularizer=l2(l=0.01))(x)\n",
        "    #x = BatchNormalization()(x)\n",
        "    #x = Dropout(rate=0.50)(x)\n",
        "\n",
        "    head1 = Flatten()(x)\n",
        "\n",
        "    #output = Dense(units=128, activation='relu',)(head1)\n",
        "    #output = BatchNormalization()(output)\n",
        "    #output = Dropout(rate=0.5)(output)\n",
        "\n",
        "    #output = Dense(units=64, activation='relu',)(head1)\n",
        "    #output = BatchNormalization()(output)\n",
        "    #output = Dropout(rate=0.5)(output)\n",
        "\n",
        "\n",
        "    #output = Dense(units=32, activation='relu',)(output)\n",
        "    #output = BatchNormalization()(output)\n",
        "    #output = Dropout(rate=0.5)(output)\n",
        "    \n",
        "    #output = Dense(units=8, activation='relu',)(output)\n",
        "    #output = BatchNormalization()(output)\n",
        "    #output = Dropout(rate=0.25)(output)\n",
        "\n",
        "    x = Model(inputs=[input1], outputs=[head1])\n",
        "\n",
        "\n",
        "        ### Head-1:\n",
        "    input2 = Input(shape=trainX2[0].shape)\n",
        "\n",
        "    y = Conv1D(filters=64, kernel_size=5, padding='same', kernel_regularizer=l2(l=0.01))(input2)\n",
        "    y = BatchNormalization()(y)\n",
        "    y = Dropout(rate=0.5)(y)\n",
        "    y = Position_Embedding()(y)\n",
        "    y = Self_Attention(self_number)(y)\n",
        "\n",
        "    y = Activation('relu')(y)\n",
        "\n",
        "    y = Dropout(rate = 0.5)(y)\n",
        "\n",
        "    #y = Conv1D(filters=16, kernel_size=3, padding='same', activation='relu', kernel_regularizer=l2(l=0.01))(y)\n",
        "    #y = BatchNormalization()(y)\n",
        "    #y = Dropout(rate=0.50)(y)\n",
        "\n",
        "    head2 = Flatten()(y)\n",
        "    #output2 = Dense(units=128, activation='relu',)(head2)\n",
        "    #output2 = BatchNormalization()(output2)\n",
        "    #output2 = Dropout(rate=0.5)(output2)\n",
        "\n",
        "    #output2 = Dense(units=64, activation='relu',)(head2)\n",
        "    #output2 = BatchNormalization()(output2)\n",
        "    #output2 = Dropout(rate=0.5)(output2)\n",
        "\n",
        "\n",
        "    #output2 = Dense(units=32, activation='relu',)(output2)\n",
        "    #output2 = BatchNormalization()(output2)\n",
        "    #output2 = Dropout(rate=0.5)(output2)\n",
        "    \n",
        "    #output2 = Dense(units=8, activation='relu',)(output2)\n",
        "    #output2 = BatchNormalization()(output2)\n",
        "    #output2 = Dropout(rate=0.5)(output2)\n",
        "\n",
        "\n",
        "    y = Model(inputs=[input2], outputs=[head2])\n",
        "\n",
        "    combined = concatenate([x.output, y.output])\n",
        "\n",
        "    z= Dense(units=2048, activation='relu', kernel_regularizer=l2(weight_decay),\n",
        "              bias_regularizer=l2(weight_decay))(combined)\n",
        "    #output2 = BatchNormalization()(output2)\n",
        "    z = Dropout(rate=0.5)(z)\n",
        "\n",
        "    z= Dense(units=1024, activation='relu',kernel_regularizer=l2(weight_decay),\n",
        "              bias_regularizer=l2(weight_decay))(combined)\n",
        "    #output2 = BatchNormalization()(output2)\n",
        "    z = Dropout(rate=0.5)(z)\n",
        "\n",
        "    z= Dense(units=512, activation='relu',kernel_regularizer=l2(weight_decay),\n",
        "              bias_regularizer=l2(weight_decay))(combined)\n",
        "    #output2 = BatchNormalization()(output2)\n",
        "    z = Dropout(rate=0.5)(z)\n",
        "\n",
        "    z= Dense(units=256, activation='relu',kernel_regularizer=l2(weight_decay),\n",
        "              bias_regularizer=l2(weight_decay))(z)\n",
        "    #output2 = BatchNormalization()(output2)\n",
        "    z = Dropout(rate=0.5)(z)\n",
        "\n",
        "    \n",
        "    z= Dense(units=128, activation='relu',kernel_regularizer=l2(weight_decay),\n",
        "              bias_regularizer=l2(weight_decay))(z)\n",
        "    #output2 = BatchNormalization()(output2)\n",
        "    z = Dropout(rate=0.5)(z)\n",
        "\n",
        "    z = Dense(units=64, activation='relu',kernel_regularizer=l2(weight_decay),\n",
        "              bias_regularizer=l2(weight_decay))(z)\n",
        "    #output2 = BatchNormalization()(output2)\n",
        "    z = Dropout(rate=0.5)(z)\n",
        "\n",
        "\n",
        "    z = Dense(units=32, activation='relu',kernel_regularizer=l2(weight_decay),\n",
        "              bias_regularizer=l2(weight_decay))(z)\n",
        "    #output2 = BatchNormalization()(output2)\n",
        "    z = Dropout(rate=0.5)(z)\n",
        "    \n",
        "    z = Dense(units=16, activation='relu',kernel_regularizer=l2(weight_decay),\n",
        "              bias_regularizer=l2(weight_decay))(z)\n",
        "    #output2 = BatchNormalization()(output2)\n",
        "    z = Dropout(rate=0.5)(z)\n",
        "    #z = Dense(8, activation='relu')(z)\n",
        "    #z = BatchNormalization()(z)\n",
        "    #z = Dropout(rate=0.5)(z)\n",
        "    z = Dense(1, activation='sigmoid')(z)\n",
        "    \n",
        "    # our model will accept the inputs of the two branches and\n",
        "    # then output a single value\n",
        "    return Model(inputs=[x.input, y.input], outputs=z)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Hz99lSDW1Pa0",
        "outputId": "1e6ea5e8-0f8a-481d-a08e-33b0c0e39d74"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_9\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " input_5 (InputLayer)           [(None, 21, 20)]     0           []                               \n",
            "                                                                                                  \n",
            " input_6 (InputLayer)           [(None, 21, 8)]      0           []                               \n",
            "                                                                                                  \n",
            " conv1d_4 (Conv1D)              (None, 21, 64)       6464        ['input_5[0][0]']                \n",
            "                                                                                                  \n",
            " conv1d_5 (Conv1D)              (None, 21, 64)       2624        ['input_6[0][0]']                \n",
            "                                                                                                  \n",
            " batch_normalization_4 (BatchNo  (None, 21, 64)      256         ['conv1d_4[0][0]']               \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " batch_normalization_5 (BatchNo  (None, 21, 64)      256         ['conv1d_5[0][0]']               \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " dropout_20 (Dropout)           (None, 21, 64)       0           ['batch_normalization_4[0][0]']  \n",
            "                                                                                                  \n",
            " dropout_22 (Dropout)           (None, 21, 64)       0           ['batch_normalization_5[0][0]']  \n",
            "                                                                                                  \n",
            " position__embedding_4 (Positio  (None, 21, 128)     0           ['dropout_20[0][0]']             \n",
            " n_Embedding)                                                                                     \n",
            "                                                                                                  \n",
            " position__embedding_5 (Positio  (None, 21, 128)     0           ['dropout_22[0][0]']             \n",
            " n_Embedding)                                                                                     \n",
            "                                                                                                  \n",
            " self__attention_4 (Self_Attent  (None, 21, 128)     49152       ['position__embedding_4[0][0]']  \n",
            " ion)                                                                                             \n",
            "                                                                                                  \n",
            " self__attention_5 (Self_Attent  (None, 21, 128)     49152       ['position__embedding_5[0][0]']  \n",
            " ion)                                                                                             \n",
            "                                                                                                  \n",
            " activation_2 (Activation)      (None, 21, 128)      0           ['self__attention_4[0][0]']      \n",
            "                                                                                                  \n",
            " activation_3 (Activation)      (None, 21, 128)      0           ['self__attention_5[0][0]']      \n",
            "                                                                                                  \n",
            " dropout_21 (Dropout)           (None, 21, 128)      0           ['activation_2[0][0]']           \n",
            "                                                                                                  \n",
            " dropout_23 (Dropout)           (None, 21, 128)      0           ['activation_3[0][0]']           \n",
            "                                                                                                  \n",
            " flatten_4 (Flatten)            (None, 2688)         0           ['dropout_21[0][0]']             \n",
            "                                                                                                  \n",
            " flatten_5 (Flatten)            (None, 2688)         0           ['dropout_23[0][0]']             \n",
            "                                                                                                  \n",
            " concatenate_2 (Concatenate)    (None, 5376)         0           ['flatten_4[0][0]',              \n",
            "                                                                  'flatten_5[0][0]']              \n",
            "                                                                                                  \n",
            " dense_18 (Dense)               (None, 512)          2753024     ['concatenate_2[0][0]']          \n",
            "                                                                                                  \n",
            " dropout_26 (Dropout)           (None, 512)          0           ['dense_18[0][0]']               \n",
            "                                                                                                  \n",
            " dense_19 (Dense)               (None, 256)          131328      ['dropout_26[0][0]']             \n",
            "                                                                                                  \n",
            " dropout_27 (Dropout)           (None, 256)          0           ['dense_19[0][0]']               \n",
            "                                                                                                  \n",
            " dense_20 (Dense)               (None, 128)          32896       ['dropout_27[0][0]']             \n",
            "                                                                                                  \n",
            " dropout_28 (Dropout)           (None, 128)          0           ['dense_20[0][0]']               \n",
            "                                                                                                  \n",
            " dense_21 (Dense)               (None, 64)           8256        ['dropout_28[0][0]']             \n",
            "                                                                                                  \n",
            " dropout_29 (Dropout)           (None, 64)           0           ['dense_21[0][0]']               \n",
            "                                                                                                  \n",
            " dense_22 (Dense)               (None, 32)           2080        ['dropout_29[0][0]']             \n",
            "                                                                                                  \n",
            " dropout_30 (Dropout)           (None, 32)           0           ['dense_22[0][0]']               \n",
            "                                                                                                  \n",
            " dense_23 (Dense)               (None, 16)           528         ['dropout_30[0][0]']             \n",
            "                                                                                                  \n",
            " dropout_31 (Dropout)           (None, 16)           0           ['dense_23[0][0]']               \n",
            "                                                                                                  \n",
            " dense_24 (Dense)               (None, 1)            17          ['dropout_31[0][0]']             \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 3,036,033\n",
            "Trainable params: 3,035,777\n",
            "Non-trainable params: 256\n",
            "__________________________________________________________________________________________________\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<IPython.core.display.Image object>"
            ],
            "image/png": 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\n"
          },
          "metadata": {},
          "execution_count": 36
        }
      ],
      "source": [
        "import tensorflow.keras.backend as K\n",
        "model = get_combined_model()\n",
        "model.summary()\n",
        "plot_model(model, to_file='model-combined.png', show_shapes=True, show_layer_names=False, expand_nested=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "id": "cqSY7ni3eCnn"
      },
      "outputs": [],
      "source": [
        "# patient early stopping\n",
        "es = EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=10)\n",
        "# save the best model\n",
        "mc = ModelCheckpoint('best_model_combined.h5', monitor='val_accuracy', mode='max', verbose=1, save_best_only=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2pEZa0Pd1y3m",
        "outputId": "95839c4b-9549-4727-fe3b-58537c29ff94"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "1550/1552 [============================>.] - ETA: 0s - loss: 0.8357 - accuracy: 0.6249\n",
            "Epoch 1: val_accuracy improved from -inf to 0.71317, saving model to best_model_combined.h5\n",
            "1552/1552 [==============================] - 16s 9ms/step - loss: 0.8357 - accuracy: 0.6249 - val_loss: 0.7216 - val_accuracy: 0.7132\n",
            "Epoch 2/100\n",
            "1551/1552 [============================>.] - ETA: 0s - loss: 0.6902 - accuracy: 0.7245\n",
            "Epoch 2: val_accuracy did not improve from 0.71317\n",
            "1552/1552 [==============================] - 12s 8ms/step - loss: 0.6902 - accuracy: 0.7245 - val_loss: 0.7179 - val_accuracy: 0.6849\n",
            "Epoch 3/100\n",
            "1551/1552 [============================>.] - ETA: 0s - loss: 0.6419 - accuracy: 0.7498\n",
            "Epoch 3: val_accuracy improved from 0.71317 to 0.72767, saving model to best_model_combined.h5\n",
            "1552/1552 [==============================] - 14s 9ms/step - loss: 0.6418 - accuracy: 0.7498 - val_loss: 0.6831 - val_accuracy: 0.7277\n",
            "Epoch 4/100\n",
            "1550/1552 [============================>.] - ETA: 0s - loss: 0.6092 - accuracy: 0.7693\n",
            "Epoch 4: val_accuracy improved from 0.72767 to 0.74977, saving model to best_model_combined.h5\n",
            "1552/1552 [==============================] - 13s 8ms/step - loss: 0.6091 - accuracy: 0.7694 - val_loss: 0.6528 - val_accuracy: 0.7498\n",
            "Epoch 5/100\n",
            "1547/1552 [============================>.] - ETA: 0s - loss: 0.5917 - accuracy: 0.7851\n",
            "Epoch 5: val_accuracy improved from 0.74977 to 0.76209, saving model to best_model_combined.h5\n",
            "1552/1552 [==============================] - 13s 8ms/step - loss: 0.5918 - accuracy: 0.7852 - val_loss: 0.6476 - val_accuracy: 0.7621\n",
            "Epoch 6/100\n",
            "1547/1552 [============================>.] - ETA: 0s - loss: 0.5704 - accuracy: 0.7958\n",
            "Epoch 6: val_accuracy did not improve from 0.76209\n",
            "1552/1552 [==============================] - 12s 8ms/step - loss: 0.5704 - accuracy: 0.7957 - val_loss: 0.9792 - val_accuracy: 0.6362\n",
            "Epoch 7/100\n",
            "1549/1552 [============================>.] - ETA: 0s - loss: 0.5605 - accuracy: 0.8001\n",
            "Epoch 7: val_accuracy did not improve from 0.76209\n",
            "1552/1552 [==============================] - 12s 8ms/step - loss: 0.5604 - accuracy: 0.8001 - val_loss: 0.8023 - val_accuracy: 0.7146\n",
            "Epoch 8/100\n",
            "1549/1552 [============================>.] - ETA: 0s - loss: 0.5467 - accuracy: 0.8143\n",
            "Epoch 8: val_accuracy did not improve from 0.76209\n",
            "1552/1552 [==============================] - 14s 9ms/step - loss: 0.5467 - accuracy: 0.8142 - val_loss: 0.9904 - val_accuracy: 0.6626\n",
            "Epoch 9/100\n",
            "1552/1552 [==============================] - ETA: 0s - loss: 0.5407 - accuracy: 0.8202\n",
            "Epoch 9: val_accuracy did not improve from 0.76209\n",
            "1552/1552 [==============================] - 13s 9ms/step - loss: 0.5407 - accuracy: 0.8202 - val_loss: 1.4274 - val_accuracy: 0.5816\n",
            "Epoch 10/100\n",
            "1550/1552 [============================>.] - ETA: 0s - loss: 0.5355 - accuracy: 0.8210\n",
            "Epoch 10: val_accuracy did not improve from 0.76209\n",
            "1552/1552 [==============================] - 12s 8ms/step - loss: 0.5355 - accuracy: 0.8210 - val_loss: 1.8004 - val_accuracy: 0.5541\n",
            "Epoch 11/100\n",
            "1552/1552 [==============================] - ETA: 0s - loss: 0.5413 - accuracy: 0.8162\n",
            "Epoch 11: val_accuracy did not improve from 0.76209\n",
            "1552/1552 [==============================] - 12s 8ms/step - loss: 0.5413 - accuracy: 0.8162 - val_loss: 2.1777 - val_accuracy: 0.5363\n",
            "Epoch 12/100\n",
            "1546/1552 [============================>.] - ETA: 0s - loss: 0.5302 - accuracy: 0.8261\n",
            "Epoch 12: val_accuracy did not improve from 0.76209\n",
            "1552/1552 [==============================] - 13s 8ms/step - loss: 0.5302 - accuracy: 0.8260 - val_loss: 1.4290 - val_accuracy: 0.5994\n",
            "Epoch 13/100\n",
            "1549/1552 [============================>.] - ETA: 0s - loss: 0.5334 - accuracy: 0.8215\n",
            "Epoch 13: val_accuracy did not improve from 0.76209\n",
            "1552/1552 [==============================] - 13s 8ms/step - loss: 0.5332 - accuracy: 0.8215 - val_loss: 1.7503 - val_accuracy: 0.5691\n",
            "Epoch 14/100\n",
            "1548/1552 [============================>.] - ETA: 0s - loss: 0.5292 - accuracy: 0.8233\n",
            "Epoch 14: val_accuracy did not improve from 0.76209\n",
            "1552/1552 [==============================] - 14s 9ms/step - loss: 0.5292 - accuracy: 0.8233 - val_loss: 2.0625 - val_accuracy: 0.5461\n",
            "Epoch 15/100\n",
            "1548/1552 [============================>.] - ETA: 0s - loss: 0.5286 - accuracy: 0.8254\n",
            "Epoch 15: val_accuracy did not improve from 0.76209\n",
            "1552/1552 [==============================] - 12s 8ms/step - loss: 0.5287 - accuracy: 0.8252 - val_loss: 1.6987 - val_accuracy: 0.5494\n",
            "Epoch 15: early stopping\n"
          ]
        }
      ],
      "source": [
        "# Compile Model:\n",
        "from tensorflow.keras.optimizers import (RMSprop, Adam, SGD)\n",
        "model.compile(optimizer=Adam(learning_rate=0.0005), loss='binary_crossentropy',metrics=['accuracy'])\n",
        "\n",
        "# Run Model:\n",
        "#history = model.fit(x=trainX1, y= y_train, validation_data=(testX1, y_test), batch_size=16, epochs=100, verbose=1, callbacks=[])\n",
        "history=model.fit(x=[trainX1, trainX2], y=y_train, validation_data=([valX1, valtX2], y_val), epochs=100, batch_size=32, callbacks=[es,mc], verbose = 1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "CHi526HxxhQj",
        "outputId": "913baeb1-cd1f-463d-c0c6-8d3f477c0e20"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "from matplotlib import pyplot as plt\n",
        "#plot training and validation accuracy\n",
        "plt.plot(history.history['accuracy'])\n",
        "plt.plot(history.history['val_accuracy'])\n",
        "#plt.plot(history.history['val_accuracy'])\n",
        "plt.title('model accuracy')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train','validation'], loc='lower left')\n",
        "plt.savefig('accuracy_curve_combined.png')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "X6D-4eU9xvFZ",
        "outputId": "f5e63bd3-deef-4aff-8840-e6a2b099a91b"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "#plot training loss\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.plot()\n",
        "plt.title('model loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train','validation'], loc='upper right')\n",
        "plt.savefig('loss_curve_combined.png')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1oE6dAEFvSAf"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "epIzkUORELub"
      },
      "outputs": [],
      "source": [
        "probabilities = model.predict([testX1,testX2])\n",
        "predicted_classes = probabilities >= 0.5\n",
        "predicted_classes = predicted_classes.astype(int)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "87pb_BaEJvTt",
        "outputId": "b0a2c406-7e54-4a72-d313-5d3449c4b4c4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "229/229 [==============================] - 1s 4ms/step - loss: 1.1209 - accuracy: 0.3430\n"
          ]
        }
      ],
      "source": [
        "res = model.evaluate([testX1,testX2], y_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BP0wWv-iKFj9",
        "outputId": "21e6320a-5990-410e-c7aa-de5e743cf56e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.5982548594474792, 0.8894059658050537]"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "res"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ayRPqOWBJcTm",
        "outputId": "269fc259-d179-407e-8400-c9b970e31251"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Result Summary:\n",
            "------------------------------\n",
            "accuracy 0.34300574869969885, MCC 0.06992976420796893, sensitivity 0.8259803921568627,precision 0.06652191077773391,specificity 0.3144389678167585\n"
          ]
        }
      ],
      "source": [
        "acc,  sensitivity, specificity, precision, MCC = calculate_performance(len(y_test), predicted_classes, y_test)\n",
        "print('Result Summary:')\n",
        "print('------------------------------')\n",
        "print('accuracy',acc, end =\", \")\n",
        "print('MCC',MCC, end =\", \")\n",
        "print('sensitivity',sensitivity, end =\",\")\n",
        "print('precision',precision, end =\",\")\n",
        "print('specificity',specificity)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 397
        },
        "id": "5YhCDKCa7BA5",
        "outputId": "ae6a6a3c-88db-4415-f072-44c21a5272fe"
      },
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-34-4bb1374b79d8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodels\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mload_model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mload_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/content/best_model_combined.h5'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mprobabilities\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mvalX1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mvaltX2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mpredicted_classes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mprobabilities\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0;36m0.5\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mpredicted_classes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpredicted_classes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     65\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pylint: disable=broad-except\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m       \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 67\u001b[0;31m       \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfiltered_tb\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     68\u001b[0m     \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     69\u001b[0m       \u001b[0;32mdel\u001b[0m \u001b[0mfiltered_tb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/utils/generic_utils.py\u001b[0m in \u001b[0;36mclass_and_config_for_serialized_keras_object\u001b[0;34m(config, module_objects, custom_objects, printable_module_name)\u001b[0m\n\u001b[1;32m    561\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0mcls\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    562\u001b[0m     raise ValueError(\n\u001b[0;32m--> 563\u001b[0;31m         \u001b[0;34mf'Unknown {printable_module_name}: {class_name}. Please ensure this '\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    564\u001b[0m         \u001b[0;34m'object is passed to the `custom_objects` argument. See '\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    565\u001b[0m         \u001b[0;34m'https://www.tensorflow.org/guide/keras/save_and_serialize'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Unknown layer: Position_Embedding. Please ensure this object is passed to the `custom_objects` argument. See https://www.tensorflow.org/guide/keras/save_and_serialize#registering_the_custom_object for details."
          ]
        }
      ],
      "source": [
        "from tensorflow.keras.models import load_model\n",
        "model = load_model('/content/best_model_combined.h5')\n",
        "probabilities = model.predict([valX1,valtX2])\n",
        "predicted_classes = probabilities >= 0.5\n",
        "predicted_classes = predicted_classes.astype(int)\n",
        "res = model.evaluate([testX1,testX2], y_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6_2ZZ8dP7i0Z",
        "outputId": "cf6ababf-8c56-4e72-bc7e-83b32f0962f3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Result Summary:\n",
            "------------------------------\n",
            "accuracy 0.9780757383583982, MCC 0.9569411737911745, sensitivity 0.9989308624376336,precision 0.9596028757274906,specificity 0.9565057132325838\n"
          ]
        }
      ],
      "source": [
        "acc,  sensitivity, specificity, precision, MCC = calculate_performance(len(y_val), predicted_classes, y_val)\n",
        "print('Result Summary:')\n",
        "print('------------------------------')\n",
        "print('accuracy',acc, end =\", \")\n",
        "print('MCC',MCC, end =\", \")\n",
        "print('sensitivity',sensitivity, end =\",\")\n",
        "print('precision',precision, end =\",\")\n",
        "print('specificity',specificity)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Outputs of the 8 layers, which include conv2D and max pooling layers\n",
        "layer_outputs = model.layers[13]\n",
        "activation_model = Model(inputs = model.input, outputs = layer_outputs.output)\n",
        "activations = activation_model.predict([valX1, valtX2])"
      ],
      "metadata": {
        "id": "NTEjmPFGCtI5"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "activations[0].shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IVTOkzQMKCq9",
        "outputId": "fb1642a2-f60f-48c3-c82a-15c0872be711"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2688,)"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.manifold import TSNE\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        " \n",
        "# We want to get TSNE embedding with 2 dimensions\n",
        "n_components = 2\n",
        "tsne = TSNE(n_components)\n",
        "tsne_result = tsne.fit_transform(activations)\n",
        "tsne_result.shape\n",
        "# (1000, 2)\n",
        "# Two dimensions for each of our images\n",
        " \n",
        "# Plot the result of our TSNE with the label color coded\n",
        "# A lot of the stuff here is about making the plot look pretty and not TSNE\n",
        "tsne_result_df = pd.DataFrame({'tsne_1': tsne_result[:,0], 'tsne_2': tsne_result[:,1], 'label': y_val})\n",
        "fig, ax = plt.subplots(1)\n",
        "sns.scatterplot(x='tsne_1', y='tsne_2', hue='label', data=tsne_result_df, ax=ax,s=120)\n",
        "lim = (tsne_result.min()-5, tsne_result.max()+5)\n",
        "ax.set_xlim(lim)\n",
        "ax.set_ylim(lim)\n",
        "ax.set_aspect('equal')\n",
        "ax.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        },
        "id": "cyAEJmZEKEQB",
        "outputId": "24bafe91-c4b6-4fb5-c87d-af21c114ab58"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/manifold/_t_sne.py:783: FutureWarning: The default initialization in TSNE will change from 'random' to 'pca' in 1.2.\n",
            "  FutureWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/manifold/_t_sne.py:793: FutureWarning: The default learning rate in TSNE will change from 200.0 to 'auto' in 1.2.\n",
            "  FutureWarning,\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f24c0554690>"
            ]
          },
          "metadata": {},
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
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          "metadata": {
            "needs_background": "light"
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      ]
    }
  ],
  "metadata": {
    "colab": {
      "name": "Two Channel CNN-Structural Evolutionary with attention Serine.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyPe+AqAiTRiclVF2azrIW+O",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
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  "nbformat_minor": 0
}