pipeline_clever_valset.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "\n",
    "df = pd.read_feather('data/logs.ft')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Defined `validation_pid.lst`, containing PIDs that should be added to validation set as contain redundant malicious techniques / benign activity, that will contain similar events in training set as well:\n",
    "```\n",
    "malicious:\n",
    "8216: netstat enum\n",
    "4556: powershell enc command\n",
    "1580: whoami.exe /groups\n",
    "2204: browser_broker embedding\n",
    "1396: fodhelper (1 out of 2)\n",
    "\n",
    "valid:\n",
    "7236: edge.exe\n",
    "1532: backgroundtaskhost.exe\n",
    "2520: dllhost loads\n",
    "8636: dllhost loads\n",
    "8740: svchost.exe\n",
    "```\n",
    "\n",
    "Creating train/val sets from df."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "def preprocess_df(df):\n",
    "    fields = ['UtcTime', 'ProcessId', 'EventID', 'User', 'Image', 'ImageLoaded', 'CommandLine',             'ParentImage', 'ParentCommandLine', 'DestinationPort', 'Protocol', 'QueryName', 'TargetFilename', 'TargetObject', 'raw']\n",
    "    newdf = df[fields]\n",
    "\n",
    "    # drop all records where ProcessId in NaN (happens for WMI events, cannot classify [TODO: think how to overcome and add to dataset])\n",
    "    newdf = newdf[~newdf.ProcessId.isna()]\n",
    "\n",
    "    # drop EventID 5 - ProcessTerminated as not valuable\n",
    "    newdf.drop(newdf[newdf.EventID == '5'].index, inplace=True)\n",
    "\n",
    "    # get binary name (last part of \"Image\" after \"\\\")\n",
    "    newdf['binary'] = newdf.Image.str.split(r'\\\\').apply(lambda x: x[-1].lower())\n",
    "\n",
    "    # same with binary pathes\n",
    "    newdf['path'] = newdf.Image.str.split(r'\\\\').apply(lambda x: '\\\\'.join(x[:-1]).lower())\n",
    "\n",
    "    newdf['arguments'] = newdf.CommandLine.fillna('empty').str.split().\\\n",
    "                                                            apply(lambda x: ' '.join(x[1:]))\n",
    "\n",
    "\n",
    "    # add new features whether suspicious string are in arguments?\n",
    "    # 1. base64?\n",
    "    # will match at least 32 character long consequent string with base64 characters only\n",
    "    b64_regex = r\"[a-zA-Z0-9+\\/]{64,}={0,2}\"\n",
    "\n",
    "    # map this search as 0 and 1 using astype(int)\n",
    "    b64s = newdf['arguments'].apply(lambda x: re.search(b64_regex, x)).notnull()\n",
    "    newdf['b64'] = b64s.astype(int)\n",
    "\n",
    "    # matches if there's call for some file with extension (at the end dot) via UNC path\n",
    "    unc_regex = r\"\\\\\\\\[a-zA-Z0-9]+\\\\[a-zA-Z0-9\\\\]+\\.\"\n",
    "    uncs = newdf['arguments'][newdf['arguments'].apply(lambda x: re.search(unc_regex, x)).notnull()]\n",
    "\n",
    "    url_regex = r\"https?:\\/\\/(www\\.)?[-a-zA-Z0-9@:%._\\+~#=]{1,256}\\.[a-zA-Z0-9()]{1,6}\\b([-a-zA-Z0-9()@:%_\\+.~#?&//=]*)\"\n",
    "    urls = newdf['arguments'].apply(lambda x: re.search(url_regex, x)).notnull()\n",
    "\n",
    "    # verified pd.concat part - merges two boolean series correctly\n",
    "    newdf['unc_url'] = pd.concat([uncs, urls]).astype(int)\n",
    "\n",
    "    newdf['network'] = newdf['Protocol'].notnull().astype(int)\n",
    "\n",
    "    newdf = newdf[['ProcessId','binary','EventID','path', 'unc_url', 'b64', 'network']]\n",
    "    # treat eventID as int8\n",
    "    newdf['EventID'] = newdf['EventID'].astype('int8')\n",
    "    return newdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df = preprocess_df(df)\n",
    "\n",
    "MAX_TIMESTEPS = 128\n",
    "# number of features (except ProcessId itself)\n",
    "N = len(new_df.columns) - 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
    "from datetime import datetime\n",
    "import numpy as np\n",
    "\n",
    "def groupby_transform(dataframe, column):\n",
    "    global MAX_TIMESTEPS, N\n",
    "    total = len(dataframe.groupby(column))\n",
    "    start = datetime.now()\n",
    "    print(f\"Started at: {start}\")\n",
    "\n",
    "    print(f\"Total categories of '{column}': '{total}'\")\n",
    "    print(f\"Unique values: {dataframe[column].nunique()}\")\n",
    "\n",
    "    # Initialize Numpy Arrays with correct shape\n",
    "    train_X = np.empty(shape=(0, MAX_TIMESTEPS, N)).astype(np.int16)\n",
    "    train_y = np.empty(shape=(0,1)).astype(np.int16)\n",
    "    val_X = np.empty(shape=(0, MAX_TIMESTEPS, N)).astype(np.int16)\n",
    "    val_y = np.empty(shape=(0,1)).astype(np.int16)\n",
    "\n",
    "    # get encode object for string columns\n",
    "    binary_le = LabelEncoder().fit(['OTHER'] + list(dataframe['binary'].unique()))\n",
    "    path_le = LabelEncoder().fit(['OTHER'] + list(dataframe['path'].unique()))\n",
    "    \n",
    "    # load malicious/valid process lists\n",
    "    with open(r'data/pid_valid.lst') as f:\n",
    "        valid_lst = [x.strip() for x in f.readlines()]\n",
    "    with open(r'data/pid_malicious.lst') as f:\n",
    "        mal_lst = [x.strip() for x in f.readlines()]\n",
    "    \n",
    "    with open(r'data/validation_pid.lst') as f:\n",
    "        validation_data = f.readlines()\n",
    "\n",
    "    validation_pids = []\n",
    "    for line in validation_data:\n",
    "        if ':' in line:\n",
    "            validation_pids.append(line.split(':')[0])\n",
    "\n",
    "    try:\n",
    "        for i, (value, df) in enumerate(dataframe.groupby(column)):\n",
    "            # skip processes with less than 3 events \n",
    "            # - too little to identify malicious activity\n",
    "            if len(df) < 4:\n",
    "                continue\n",
    "            \n",
    "            if value in valid_lst:\n",
    "                temp_y = np.array([0]).reshape(1,1)\n",
    "            elif value in mal_lst:\n",
    "                temp_y = np.array([1]).reshape(1,1)\n",
    "            else:\n",
    "                print(f'Unclassified ProcessID: {value}')\n",
    "                raise Exception\n",
    "\n",
    "            # Create 3D array from \n",
    "            temp_X = np.hstack((\n",
    "                df[['EventID', 'unc_url', 'b64', 'network']].to_numpy(),\n",
    "                binary_le.transform(list(df['binary'])).reshape(-1,1),\n",
    "                path_le.transform(list(df['path'])).reshape(-1,1)\n",
    "            ))\n",
    "\n",
    "            # PADDING\n",
    "            temp_X = pad_sequences(temp_X.T, maxlen=MAX_TIMESTEPS).T\n",
    "            \n",
    "            # adding this example to actual set\n",
    "            if value in validation_pids:\n",
    "                val_X = np.concatenate((val_X, temp_X.reshape(1, MAX_TIMESTEPS, N)))\n",
    "                val_y = np.concatenate((val_y, temp_y))\n",
    "            else:\n",
    "                train_X = np.concatenate((train_X, temp_X.reshape(1, MAX_TIMESTEPS, N)))\n",
    "                train_y = np.concatenate((train_y, temp_y))\n",
    "        \n",
    "        end = datetime.now()\n",
    "        print(f\"Ended at: {end}\")\n",
    "        print(f\"Script completion time: {end - start}\")\n",
    "        return train_X, val_X, train_y, val_y\n",
    "\n",
    "    except KeyboardInterrupt:\n",
    "        end = datetime.now()\n",
    "        print(f\"Ended at:\\niteration:{i}\\ntime:{end}\")\n",
    "        print(f\"Script completion time: {end - start}\")\n",
    "        return train_X, val_X, train_y, val_y\n",
    "\n",
    "    #except Exception as ex:\n",
    "    #    print(ex)\n",
    "    #    np.save(f'X{i}.npy', X)\n",
    "    #    np.save(f'y{i}.npy', y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Started at: 2020-07-02 19:36:50.167468\nTotal categories of 'ProcessId': '93'\nUnique values: 93\nEnded at: 2020-07-02 19:36:50.282200\nScript completion time: 0:00:00.114732\n"
    }
   ],
   "source": [
    "train_X, val_X, train_y, val_y = groupby_transform(new_df, 'ProcessId')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "(70, 128, 6)\n(10, 128, 6)\n(70, 1)\n(10, 1)\n"
    }
   ],
   "source": [
    "print(train_X.shape)\n",
    "print(val_X.shape)\n",
    "print(train_y.shape)\n",
    "print(val_y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Malicious processes: 14\nValid processes: 56\nMalicious percentage: 20.0 %\n"
    }
   ],
   "source": [
    "u, c = np.unique(train_y, return_counts=True)\n",
    "pos, neg = c\n",
    "pos, neg\n",
    "print(f\"Malicious processes: {neg}\\nValid processes: {pos}\")\n",
    "print(f\"Malicious percentage: {round(neg*100/(pos+neg),4)} %\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initialization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "METRICS = [\n",
    "    tf.keras.metrics.TruePositives(name='tp'),\n",
    "    tf.keras.metrics.FalsePositives(name='fp'),\n",
    "    tf.keras.metrics.TrueNegatives(name='tn'),\n",
    "    tf.keras.metrics.FalseNegatives(name='fn'), \n",
    "    # Precision: (TP) / (TP + FP)\n",
    "    # what proportion of predicted Positives is truly Positive\n",
    "    tf.keras.metrics.Precision(name='precision'),\n",
    "    # Recall: (TP) / (TP+FN)\n",
    "    # what proportion of actual Positives is correctly classified\n",
    "    tf.keras.metrics.Recall(name='recall'),\n",
    "    tf.keras.metrics.AUC(name='auc'),\n",
    "    tf.keras.metrics.Accuracy(name='accuracy')\n",
    "    ]\n",
    "\n",
    "def model_conv_rnn_02(MAX_TIMESTEPS, OH_DIMENSION):\n",
    "    model = tf.keras.models.Sequential([\n",
    "                tf.keras.layers.Conv1D(filters=64, kernel_size=3, activation='relu', \n",
    "                                    input_shape=[MAX_TIMESTEPS, OH_DIMENSION]),\n",
    "                tf.keras.layers.MaxPooling1D(pool_size=2),\n",
    "                tf.keras.layers.LSTM(64),\n",
    "                tf.keras.layers.Dropout(0.2),\n",
    "                tf.keras.layers.Dense(32, activation='relu'),\n",
    "                tf.keras.layers.Dense(1, activation='sigmoid')\n",
    "    ])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "colors = plt.rcParams['axes.prop_cycle'].by_key()['color']\n",
    "\n",
    "def plot_loss(epochs, history, label='', n=1, val=False):\n",
    "    # Use a log scale to show the wide range of values.\n",
    "    plt.semilogy(epochs,  history['loss'],\n",
    "               color=colors[n], label='Train '+label)\n",
    "    if val:\n",
    "      plt.semilogy(epochs,  history['val_loss'],\n",
    "            color=colors[n], label='Val '+label,\n",
    "            linestyle=\"--\")\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.ylabel('Loss')\n",
    "  \n",
    "    plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "BATCH_SIZE = 10\n",
    "SHUFFLE_BUFFER = 100\n",
    "\n",
    "train_ds = tf.data.Dataset.from_tensor_slices((train_X, train_y)).\\\n",
    "                    shuffle(SHUFFLE_BUFFER).batch(BATCH_SIZE).prefetch(1)\n",
    "val_ds = tf.data.Dataset.from_tensor_slices((val_X, val_y)).\\\n",
    "                    shuffle(SHUFFLE_BUFFER).batch(BATCH_SIZE).prefetch(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training without `validation_data`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "tf.keras.backend.clear_session()\n",
    "np.random.seed(42)\n",
    "tf.random.set_seed(42)\n",
    "\n",
    "OPT = tf.keras.optimizers.Adam(learning_rate=0.005)\n",
    "model3 = model_conv_rnn_02(MAX_TIMESTEPS, N)\n",
    "model3.compile(optimizer=OPT, loss='binary_crossentropy', metrics=METRICS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "h = model3.fit(train_ds, epochs=300, verbose=0)#, validation_data=val_ds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
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OYK/H+48GsMGxWKIA7gBwvlJqrVLqHNdft882n0tEN/X29vo5vHgQpRca/fphF3JxTt2Bdj1npiPINjNNx1YKbtmMeK+XCi0yucZbbIGp1piNcsdsmkVshLwptmXzPQD/DGDM60Wl1J0A/gjgDiK6GMClAN7ndWwa5gLYam13Ofs8IaJpRPQTACuJ6Atp2nSvUuqKlpaWLJpRIo78SOq2PdNnj4cxmYiln3FRC0GsmGJTDW40S2yq1o1mjbMBeKyNDOoU8qRoYkNE5wDoVko9n+k4pdQ3AYwA+DGA85RS2XyryeuUGT5rj1LqSqXUEqXUN7L4nPKkaRZwzn/y+oxDzf4l7wK61wFdz5vMosE9wH8s5DI4XuixMNnGeiYiZmejVYEbLcWyqVaxcaU+hxoL/70QphzFtGxOAHAeEb0Ndm+dSkS3uQ8ioncAWA7gLgBfzvIzugDMs7Y7AWzPqbWVyupLgX/tAeas4O2GDqBzNbvR/udU41breZ2fTne95n2eSbFsysiNlqvw2QM5q9aN5o7ZNPC1xsvgYUGoWIomNkqpLyilOpVSCwF8AMDDSqkP2ccQ0UoAPwVwPoBLALQT0fVZfMyzAJYS0SIiCjqfc09BLqCSqAuarLXWBcD0A81rfU5Jm/1OKZvBNKGrWLGy0ezU5zLorPIdZzMVLBv3OJug1EcT8qfU42zqAVyolNqolBoD8BEA4wp8EdHtAJ4CcBARdRHRZQCglIoD+CSAB8AZb79RSr06aa0vJ/R4nLYFwMJ3mCrRusrA/i28HNzt/f4pY9nk60azLJuqjdm4xtkEG3gpYiPkQYZ0p8KhlHoUwKMe+59wbcfAlo77uIsynPt+APfn3chKJ2JZNk0zgX94HPjP5aZ+mi7SOdjj/f7JEJuyiNnkWa5mSmSjebjRABlrI+RFqS0boVAk3Wjzzb6mWUCfE8LSbrSBdG40S2zGPJMHc0OLTaCh+rLRqtWN5jWoE5AkASEvRGyqhYbpvGxfbPY1zTbTECQtm3RutGHv9XzRAhNuKS83Ws4xG9uNVq2WjTtmI240IX9EbKqFRScBF/6M4zWaptnsRotHecI1qmE3mtdYGztFuZBJAlpgwi1l5kbLNRvNtmyqdYoB9zgbZ7ZOERshD0RsqoWaWuDQvwVqrFvaPJsLa+56BYACZizjJ3qvqQhsa8YuWROP5tepahELNZWXZZNrcH9KxWz0FAPashE3mpA7IjbVTJMzVcGrd/HyoLN46eVKS2fZXN8B3Pbe3NsQHwFqg0AgUl6pzwWxbKZIzEYsG6EAiNhUM02zePnK73iw5/xjedsrI822OtwZaZseyb0N8VGgLgzUhcpkPpt8a6NNgZjNuPlsHLGRbDQhD0RsqpmOg9mq6NsGLDgeaJzJ+70y0jKJTTZsehR4+c7U82qxKadstLF4+jpxmZhSlo0Ts6mLACBxowl5IWJTzTTNBI6/itfnrAQanVkWvMQmZsVstBvNfnL3G7d5+r+Bx79ptrVlU1tmYgPkZplMxSkGamqcYpwiNkLuTMqgTqGEnHItz/C5/L3se68JsKXjJsWycToVu3Pp3wm0pC2obYgOukrUDLNVUzaWjSUwiSiX+smGlKrPVepGc4sNwN8dmRpayAMRm2qnNgCsvsRsN8/xFpvYsDNvSR9bNi/dwXEezf7N/sQmNpxqPZRdzMZqWy5xmynlRrPFRiwbIT9EbKYaLZ1AbxrLJtLGYvPczcD2F4G2heb1fZs57jMRsWGXZTPCQhOoL3yRz1zI1402NoXcaHqcDSDTDAh5IzGbqUbzXKCva/z++ChQP43Xt784/nVdyHMiYkPjLZtAhCscxAZLLzgpbrQ8LZuqd6NZ00UFG7PLRhtLeH+Pqg2l0lflEFIQsZlqNM/haQfc9c9iw6aYJ8AutAErRbo3C7FJif84MRvtkktXCHSySLFschhrM6XdaFmIzT1XATedAvR6PNhUExsfAr5zMNC/q9QtKXtEbKYaLZ0cq3DPaxMf4RTXxacA7/oyVxuwKwn4nYM+NsSdsBYzHbMpG7GxM+xyEIupIDbucTaAkyCQhdisceZJHNpTuHaVI71d/HsaELGZCInZTDWanSB/3zYz6BNgCyQQBi76FW9vf8G8Vhf276/XKdSJUaAmYmI25SA2+94GRnvNdk6WzRSK2ZAVswk2+Hej9aw36yO96Y+rBrRbuNBTc1QhYtlMNXRG2X3XAFufAR76KrB7g7FsNKEWs94ww1+sJRE3HbhOEoiP8nm12KSb4qDYKAXceASw7l6zL5+YTU3dFIjZWN1DfTswvNffQNjN1jRV1SA2u98Enr7J+7XkMAGprjARYtlMNdoWcmbYjjXAze/mfUN7jWWjCTeb9cYOfz8m++kuKToFiNkM9AB/+iJw9ndN6ZRs8RKGfLLR6iJToBCnJTYNM/iejuw3s8Kmw7aAhj2KvlYSsRHgh6t5fdXfp/5GAPMQVurElwpALJupRrgF+OwbwDXrgOM+ya6SHWtMbEUTssSmYYY/N4FdhSDFsgnzjzTUnJvY/OXbwMu/Bl68Lfv3arzG+OQzzqYuVMVTDHiITbL6hI/7Z39XKtmy2bMRuPk0s+1VtTwmbjS/iNhMRUJNnJV2+teAE64Gdq7lH0ug3hwT1m404pRoO2YTHUwVFo2dUJC0bJyYDcDWTS5io0UwH1eFV8XpfLLRApHqd6PZ42yysUyjA1yeCMRioxTHyyqNF34O7HoNWHAib2cSG6/v5vB+4LeXAoNVniThExGbqU7nUU4HqoCl7zH7tRstUO8M6LOe3H79YY75uHFbNok4nzvgxIJyFRs9n0o+T4+2ZaPFK5c5bZJutPAUSBCwxtloy8adxehFdJC/M+EWdru9+SBw4wq2FCaTP30J+Mt3c3//aD+7DFd8kLe9xCaTG63rWa64vu253NtQRYjYTHU6HX/0gWcA844y+7UbLdjAghMbNMHhfW8BvVvHn8sWm8So+XEmLZvp/twwbgoxeZdd1UBbcPlYNlNCbFwxG8Df/YsO8T0Lt7Bls2MNAAXsfavgTc3Ik98HHvpK7u8f7WfR1HGaWCbLxvXdHOk1U7L7HTZQ5UiCwFSncQZPJz3v2NT92rIJ1nPHMeZkmtWF+Mfj1fHb++KjpoPXlkTjDGDLU9m3Uafg5iM2trAEGzizKp+YTaCKxcZrnE19uzOtuB/LZsAp+lrHne7uN3i/7nwrhdEBdjnr76+nZeN8J20X8qbHgP89z7jfpMwPABEbAeDppN0kLZvGVMuiLgSM9AERjx+Q243mtmzal/Agv/5dPP2BX7QLrFCWDRz3UD6pz3Vh77hVNeA1zqamFqif7i91Xcf/gvXOE/5O3j+ws/BtLSaj/UBwArHR3wHbjbbzZV5u/qvzmogN4NONRkRLiCjkrJ9CRFcRUetE7xMqGJ0gEKg3bic9fUBi1Dt+4k59ToqNE7PpdNx02fqw4wUQG1tYdLvySn0OT63UZ8B/zC06aNxow/t4nApgRGeyyXVqi2j/xJaNlxtNuxyT55ExOID/mM3vACSI6AAANwNYBOBXRWuVUHpCLjcawD8s7X/2+gHZYuNl2cw+nOfT6cpRbPa9xdk9w/uyez+QmiCg13OO2RDPgDqVUp8BHm/lx7KJDrFFHG4Bel43LqZSiU2uVStGByaO2Xi50ZTreyFiA8C/2IwppeIA/hbA95RSnwEwu3jNEkpO2MuNNsBTEADe2TdpEwScH2sgAsxazlk62aDFYfcbnN2z9Zns3g+4pj1w1nOxTFSCYxG1U6yCAMBP7G6xScRNau/+LcBfv+fEbOqBsOX8CNRPbszGvjc5i00/f/99WTZD4/dpxI0GwL/YxIjoIgAfAXCfsy9QnCYJZUFdiMdKBCzL5tazgXuv5vWEk9rc22Uq+6ZYNlErQSBk9s9dzaXn/ZQ9SZ7L5QbJpeRNyrQHlhvNXf16IsbijtiEvDufasBrnA3AU4v3buGJ9TTP3QL8YCXf75+dDfz5yzymJthgxIZqucDrZFVG3vRYqvWcSwYkwKKZ4kbzcMdFPdxobgtIxAaAf7G5BMBxAL6mlHqLiBYByGM4t1ARtHRysc6AIzbxYWDzk+b12CCLzz2f4m376c62bAJWzbW2BY6FlEU66DixyaHT8uooEjHgq23A76/wf56xhBMsb6/8Uizp8BpnAwBHXwHMPw7447XmYaH7NU4CGOy25jxSbBHAOebA04GOgzhBIFtxz4V7r+aafxo/GXRuEnF+eEoRG8tyf/NB4LoWM1W27UZzJ45I6jMAn2KjlHpNKXWVUup2ImoD0KSUuqHIbRNKzSX3Ayd/3lg2QGq6b3SIrQz95Oi2bGKumA0ANDpZaNlYJ+7YSr6WTa3THu0SfPnX/s8zFnfEZhpXkK5GV5oaG+9CA9h1eNiFHDPTU4vrOIzbNRqoB9oW8frxVwFNs/l/NxlTDgz2pGa+5fJ90XGWUJN5WLIfWJ78get4+7vvEhuxbAD4z0Z7lIiaiagdwEsAbiWiPIbmChVB0ywOkAbrvV+PDjpjbvTT3TAHzgHvmA1g1djKwjpxu6tyeVLVHcXxVwFXPMLrOksqG7Qbrb6dt4f2Zn+Ocmcs4S02ANBxMC97Xudl/3ZevuQS7GADsPy9wD+9ASw4znrIKHKSQGyEhcIWmFxiNlpsgo3mYcm2WPSstsnjPSybA88EZh4mYuPg143WopTqA/B3AG5VSh0J4LQJ3lN2ENFiIrqZiH5b6rZUFIEG7/2xwdQBnrFh46ePj3jHbJKdTjZiUwjLxmnLsR8HZh7KcQQtNuGW9O9zkxQbp7OpxsnB1FjqGBubpNg4AzX7nKD/239JPS7YANTUmPFUehbYkb7CttXNkDNFs50Blsv3Rbu+0sVsbLEJNKRa9bFhThP/4B2O21jEBvAvNnVENBvA+2ASBDJCRGEieoaIXiKiV4ko57oRRHQLEXUT0Sser51BROuJaAMRXZvpPEqpTUqpy3Jtx5QlmEZsooNO/MX5YccGTccd9xhnA+ToRhvlJ8RzbwQOOS/HmI0jWNqFVhsEdjuTfDXO8n6PF1NGbNJ0DQ3TeHBnz+vsQtRWQ3QAmHGo6Zjd35lQEy+LHb/wsmJyuUejlhutppZT9m33mH19egoOHcey54YKNhrLf4rjV2y+CuABABuVUs8S0WIAE/kgRgGcqpQ6AsAKAGcQUUpNFCKaQURNrn0HeJzrZwDOcO8koloAPwJwJoBlAC4iomVEdBgR3ef6m+F+v+AT2zKxGd7vWDDDHFAdHeAfYW3IuzYawJZPTSBLy2aUf/RHfpSrVecTs6lz3HyhJqsaQND/eZIJAlpsdmfflnInk9gAbN30rHfuoZVVOG2xGdA4TmycVHq32Ax0c5FOe3bPfHBXWHZbHX7R8bygM39SXdg7fR5gK0aNmX2xIRPnCTaIZePgN0HgTqXU4Uqpjzvbm5RS753gPUoppW3ZgPPnznc9GcDdRBQGACK6HMD3Pc71OAAv5/jRADY47YkCuAPA+UqptUqpc1x/vnooIjqXiG7q7a3geTgKjTsrSWMLRnSA643Vt7O4pFg2VsympobjNm7ByJSlFB81gtA4gzuCbEvFaDeatmyWnWe1PYvOaKpbNgAw42BOX3/0G6n725dwsVUgvWXjfsrfs5EH6+4a57TIDbf4N0zLrbO3EwQAHtiZUo7JiiPWO9ecnNtmxAwEFbFJ4jdBoJOI7nJcWbuI6HdE1OnjfbVEtAZAN4AHlVJP268rpe4E8EcAdxDRxQAuBbvq/DIXgF1+uMvZl64904joJwBWEtEXvI5RSt2rlLqipSULP/5UQ/vz3WIztBeIOGKT8CjEqWmckRoofvzbnILsNUIb4HNpkcjFDQdYbjRneNiRHzWvZfPkO+YM6oxUcYKAGuOHgnSc8Glg7pFmMrsmZ3x3+2Iz7407zqctBLdlo1OGRws0yn7QLTYduXX2STdaOsvG+q5qgd2/GXjw33haBe1GCzXxsblMZ1Fl+HWj3QrgHgBzwJ35vc6+jCilEkqpFQA6ARxNRMs9jvkmgBEAPwZwnmUN+cHrkTvtaEGl1B6l1JVKqSVKqW+kO05IQ+t8XuoO3y4/MmpZNrUhnpDtjT9ybJtUP34AACAASURBVMTdcTXOTBWqh/+dlyNpxq3ER40rLlex0YKlrbRZh/HEcdMOyK4z0pZNXZBdQ1PRsmmdx8FvzZyVvJy2hOMXwHjLJtgAgFhs9m8BbjmTB3nq/33fNuC/T+LJyvLBbdnUT89RbHSCgOP+qwunxmxsK6d5Di9vfg/wxI3A1qdT3WiAlKyBf7HpUErdqpSKO38/A9Dh90OUUvsBPArvuMs7ACwHcBeAL/s9p0MXgHnWdieA7VmeQ/DDtVuATzzDk0k1OQF1WzBGevkv0s4dcdez7Grxqj9mu9FsayZdp2CLTTLlOMtOPh4dH3t691eBZefz5/qtaJCIstgA7Eob2sPlc56b8NmrcphIbABOBDn1S8C0pcCMQ3ifbdm4xYaIO+7RAeDhrwFbngRev8+4MLevAXa8ZCom58o4y2Z6bjEb7e5LG7OxvrdL3sXfe/u7Pk5sxJXmV2x2E9GHHLdYLRF9CEDGXzsRdejK0EQUAadKv+46ZiWAnwI4H1yloJ2Irs+i/c8CWEpEi4goCOADYAtMKDThFv4BtcxzLBxKtWz0ZGrasslE40zOGkrEgc1PmP26U3B3/ImoOWekjZfZFuNMjJoxQDaBeq535rco5+iAedrVYvPcLTwrZLWQaZyNzUmfBT71HHDY+3jwb9Ns41LT/yObUBNbDN2O9RJuMU/8fc4zYr7TNrjFRk9pnk15JADYs4m/GzpW6I7ZxIY5++78HwHzjgZWX5r6/mRWniNWIja+xUbHUnYC2AHgArA4ZGI2gEeI6GWwKDyolHKnTdcDuFAptVEpNQauvbbZfSIiuh3AUwAOIqIuIroMAJzioJ8EZ8qtA/AbpdSrPq9JyIULbgVO/7pTWNESG12qJNJu4iIAi5Oblnn89Ny/na0fTXQQePGXwFdaU2MhdoKAFpt0Lrd0eFk2QPZPnqN9JmisxWZ4Pz8Jp4s5VRJ9Ozhgn26cjRczDgbe+S9svRzxAeD9txl3mk2okf9/WmxGes0DRp9TXy/fenNDuznbEQBAjiWsshOxPRu5qsSKi80+L8umYTqw8kN83SdcDbzzX83relqOpNhI+rOvydOUUlsAnGfvI6JPA/hehve8DGDlBOd9wrUdA1s67uMuynCO+wHcn+lzhAIy3clMDzakBvm12NS3mbTRM24Ajrp8/Dla55n32BZKdNBM49vbZVxm8VHzpBhqAUA5WjYetWN1pxAbAtA+8XmiAyZoXD+NO059jsEec22VynedQZtNc3J7f7gFOORc79dCTTw+R6ec21mF+n6mE4VEHPj3acDZ3wGO+lj6zx/s4YGUezawJW5bFukqYbh57hZ2lZ70ObOvLpwaD4oNmwcfgKukv+OfgEe/zg9SAdd4I7FsfFs2XlxTsFYIlUew3hRsBFItG+0SaV/C9bTctC5w3rM11YKJDpo4kG252C6wmhozKVc2xEe93XvJzsDDr//CL4CX70zdN9pvWTbtjmXjtCXXUvbliB83WraEmswU0QBXE3B3wuksG12L7bFvpT//2Bh/96YfxNuBSG4B+p71XDjUnk22LjTesnFnWdbUmAoa7sGtIjZ5iU2awRfClEA/MWqSlo0VKG1f5P3e5rnmPcP7eOpdIFVgtAgpxeezXWCRtuwrLidi3oM3k5aNR2fwzE3ACz9P3ZciNtPYItLztFST2BTD7eP+zox6iE06y0bHBL3cc5rBHv6uzHCsszpLbLJJEtjzJmcp2gQirpjNSGo1c422dvT3Kjm+SMQmH7HJMuImVBV6UGOggZ+CbctGo1Ol3QTCXCKmdwunS7c44rPFGoY17IiNV321SGuObrQsYzbRwdQnbT1Q1RYbgGMPgD+xGRsbH8QuR0aKMKg5mUYcYet2pDezZZOIAb/6AM9No79f7imXbXTcp8PJjgtEzHgfv519fJQ/yy024yyb4fGWDWBcv243mkwzkFlsiKifiPo8/vrBY26Eqcq8Y3hJxJZJYpT93KEm44JJV+YG4NjG/i1swbQ444O3/j/zuk5tdo/8BxzLJls32kQJAh5PvtGB1KfZ5KhyKxvNxo/YPHcz8K0luVWcrnS0SLfOY1foSN94i8P+fw/sAt74A7DlKWCfkzekB1B6oSfx6zgQALncaD7FZt/b7B6evjR1f10kVQgnsmzqJPXZTUaxUUo1KaWaPf6alFK+kguEKmXB8by0A+aRdhafq18Crnwi/XsBtnr2b2XRSLrVHFdJTQAYcsREj/x311crZOozwG60l+4A3nrcvDbqEht3vSy32PiZEVKXZXntbn/tnmxyTQzwgxablk4Wm9G+8bEUu0PXrtL4iLFsxhLpz6/FpmUe36NAvUkK8NvZ64eAaUtS99e5ZmZNZ9kk3WiO2GRrWVUxRYgCClOCeUeb9WTn67gQWucDs8YVi0ildT774Yd280DAujALQqCBB40m3WgexTwjbTmkPo+msWx0ZzTEszs+9V+8PTbGApQiNlbZeSA3y0ZXmH7rMf9tn0zCHuNjCoWdxRdq9k4QsP/f2pWnXVtA5tTo3i7+/kTa+LMCYfPd9Buz2bOBl14xm/iIE0OMc0ZdxpiN81ptHVs5UkFAxEbIEe0eaJjBrgfAuNb80L7EpMBG2oyFEWnjP50gkHBNDaCPGd6X3RTDiWgay8Z68hzsMROz6YSBeAaxcbt0MonNlqeB295rOtC3/8rlTbIpAjoZFHPmUT12J9LGojbaN/76bTFJis2If7Fp6TTVCgIN5nvlt7Pv28bp9e45jupC7F7r3Wq+E5ksG/u1YIOIDURshHz4/GbgU8+b2lCnXef/vdMPNOv17Ua8Im385DsuQcASikgr//CzyZiayLLp38GCpF1hyTl6bLFxxWzCLSY+pasipGPLk8CGP/OASYDjXFufNq6fcmGsiGKjrdFIm4nZuDvhmIfYRAdN6nOmgbO9XSbZ5PSvA++4JnNMzgt35qNm6Xv4nt1yhnno8JONBkjlZwcRGyF3Iq38hPqRe4CrXjRuND/YAdiIJTb1bWb8CuA9TUGyZE0WrrR0lo0O5GrrbGAXu0p05xAfMRaUjtloy6am1rSl4yAOYu8bVwCD0aLVtw1oWwT8jeOuc89XX2oScbZQP/Zw4c/dtpCXs1c4ddIcsamxwr/2/0OL09AeLinkft3NQLdxUy49DehcnX2APhH3Hvw7+wjg3V/h+6fvccaYjfVaqEnEBiI2QiFonc9FGLPBFqZxbrR2DzdaMPV4ILskgXiaBIGaGv5sLTaJUacTtKwmLXhuNxpg4jYnfY7PdfsHvD8/WZZlu4knAOVX4mYsxtNmdx5Z+HOvuJhF7OCznNiQYiGx05m9LBv7Pmf6f8UGTVxIU1PLouDXjTUWSxU/G11xXFtZXpZN+xIAlJpoEWyQ1GeI2AjlwDg3Wjt3NGOJNONstNhkMZdMIpY+FTtQb9xbAD8h20+i48TG6tC02Cw4ATjm41y+xivuod04Q3vYHZOc177MxCYRtWqLFRgiI2J2oU57pH7cI0HAtmAzWTbRIW8BCGYxW2ci5m3ZAOZea9enl2XTeSTwuY2mrJP+fLFsRGyEMsB2o0XanR+1YpfWS7fzfvuHrcvY+0k11qRLfQac7Dfr6XmgO3UyL+0CG+0Hj9+wyufrzKqaWp46AfAetGnHfkKNxn1XdmKTxo1UaOysN20xgFItFy0y2p0Wak5v2YwlTDajm0AWnf1YLL3YarHRlo2X2AA8O6iNiA0AERuhlJz3A/4BR1pT3Wi6037yB0Zs7KKHyQnUrPl0MqFU+gQBgN1GNgO7Ut0uttiEmlIng5u9gv35E7XLLocTbLDcaGUWs8nkRiokdraXrjTR0pnGsnGWkdbU1/duMtamtlzSWTZZxWzSXH/SstFutDRiM+7zJWYDiNgIpWTV3wP/vImtgqAlNjOdMTov/5qXH38ydZBdqIktA92pv/2EGfzpxVgcgEo/z47+PG35DPakik3cJTY2J38O+Kgzc0bSsvGwuGxRCTZabrTR8ceWCl2HLp0FWEjsbMQ5q4C/vxs45Dy2XBIx4M5LuHIAAIxqsWk3lo1SwE9P5SnFAeOm9KrsHKzPMmaTxrKJtAIgUxanzkPYvAg2yBQDELERyoWAFbNpX8yWztAeoPOo8ZYHEfv5B3Zxhd6fnQXc9nfpz+2VPm2jB6C2L+GxIAO7vN1oUQ+xsUm69zymrLZTb0N2zKaMLBs9On8y3GgtnTzLJ8Bp7ItP4U45Pszzybz6+/Exufp2fl0pU21bj/hPWjYeYqPnHfJDppiNzj7M2rIRNxogYiOUC8nU53b+Uc9YxtuzDvc+vtERGx2sffsvqaVmbLwGhtrMPMw55wwWjHRutKG9poS8Z5scy8bTjWaJTbDBuHvKKRtNj7GZDDcaAJzrTIc127nHuvPWKeZutCs1ETWDPPc7aciZxMaehnwixuKZEyTqp5l5bdLFbNwEG7nNmazvKYCIjVAe2G40AJh1WOrSTeNMoH9Xqsuqe533sRNZNo0dQHMn12hr6AAG93iLTe9WUzTU8xoauGPx5UZzhK+cEgS80syLycITgX/bZ+6xdkulGxyrK4rHhs2UA/u3OOOitBvNI0GgcSaLjZ+KE4lY+pgNkFqiyCs+5IXOXvSaxmIKIWIjlAfaYtA/Zt0BzZ7AsrGfWPW8Mm6SlaMzdKIf+h1w2pfZLz/Sm+pGiw9zR9W7LbPYAI5l5PEUbXc05ZqNlnDKB02GG01jJ1toyyat2DgPIvERU7R1tI+z1TIlCDTO5EGhflLlM8VsAPP9jLSZAaQTkZxmYIDF8boWYNOj/t5bRUjlZqE8OOxCoGm2cUUd8QHuOOas8j6+aSZ3Mr1d7B5r6AD6d3ofG5/AjQaYCbfCLZzlFB3gUjRqjJ+kB7u5I5pIbLQIunFbNjU1LH7llI022W40N1qA06W0a7GJDRs3GsDrE7nRAL4vmaYoACZO/daDkReckCqUmbCrGKx3ZrB//f84TjWFEMtGKA8ircAh55jtYAOw4oOcDOCFTjPe9Sp3Jk2z0ouNdg+lc6PZhFvNpF71TscUG04tX5+Jxg5+Mt+zEdi51uy3EwR0JeK6cHllo+nBqJPlRnPjtmyO/UfgUCvxw7ZsercaUdy/xQTgPcUmi1T5RDSz2OqHg4UnTnwujZ6JNjoIbHue19uXpD++ShGxESqTpNisZasmo9h4TMCWjnALDyYc7TdPxHaMwI9l0/M68INVwH+fzE/KSqUmCIRssSkjyyYZs5lEN5pNMmbjuCHf+S/W1OLkpB7DsWy2Gqt3/xYjAl6pz0mx8ZEkMJYhG02/DnCWpF+Sls0A0PUsrycm8SHjwS8DN50yeZ+XBhEboTLRnf5Ir2PZzOaYzdgY8Ni3uAaZJp6FZRNp5fjK8D7jn4/bls0EYrPyw8BRlwMzDuU4wWC3Y71Ys6jrJ91AuMyy0ZyYTancaEnLZjeSlRp0xlcgklrip3crx/WCjSw8Gd1o2Vg2E2SjnXEDcOY3gblZ1I7T1RJ6XjdtmMz7/sT3gO0vTt7npUHERqhMph9k3FENHSaGs+NF4JHrgVfvMsdma9kA7AZrnsvjbrQbLdg0fp4TN3NWAGd/G3jXl3i7f4fpCLXI6CfdurBJEBjaC9z3mdLGcJJutBJbNgPdJq6ls/bqwib4P9LL97p5Nrs9R/syi02okYXLt2WTQWyb5wDH/EN6964XrQt4ue5es68UFm2JXbYiNkJlUlsHzFnJ6w0dbNkAwFt/4aXdsXhNLZ0OnRUXH2bXXCDCT6H7t/JcKX47mSYnU6l/p+kI9b4UN5ojNg99FXjuFuCV3/s7PwDseKmwHchkpz67sWM29v8IYBHR69rKrJ/OA2TtSdjSpSM3zvBp2UyQjZYL4Wa2rjY/afaVwqL1M5NsERGxESqXjoN4GWwwHbke2Gn/sPykPmtsy0WLTXSAJzpLN8DUCy1+/TtMR3jg6cCBZxq3TiBiLBldCNSPIAJsCd30TmDNL/23aSKSbrQSWzbDe02lBv3/CFiWjY6fNXQ4YtPPgh6oz5BQ4lNsJorZ5Er7Enarhlt5SoVSWDZ+B7YWCREboXLRY3HUmOncNz7EyxTLxmOagnRErAoBTbO4A9z6DI8aP+A0/21r6GAXXJ/lRltwPPDBO0xnVhcybdODSION48/lxUA3d16FnOkz6UYrUczGTksOui2byHjLpqHDmV7aEpt06CzDiZgoZpMrurbf9KWTH6vTpaBKLDYyzkaoXFZczFMLr76UO/BIm7EQ7KfYbLKs7HI0jY5l0+NUJlhyqv+21dQ6VQ52GuvF3RnWRUwHqAeR6hkpJ0IPUCyka6TUbrRIG4tMdGC8ZVNniY0e0NnguNH2b3HmsskgNqEmYM+bE7dhophNrkxz5reZfiDf88kczBtp46SXQbFsBCE3agPACVc5I/JDwOHvN6/ZnXA8hwQBgJMOdDr1nJU8hiYbmmY5CQJpxoDYT7i6VL7fTkgXlvSaOydXSu1GIzLZfkmxsSybgNuymZ7qRvNKe9bo4yYiESuO2CbFZmlqrG4y0OOTxI0mCAVixcW8jLTxD+uJG3m++EQWCQIRl2WjZ1w89/vZt6dptsuycQWv7XE2ugS932KNQ8WwbErsRgOM2CTdaDpmEzExnf7tLAihZv4b6ZvYjeYlNo9/C/if03gcFOBUvVbFEdvZR/DDzrxjUmN1k4GOY4kbTRAKxOzDgWvWcdrzA/8CPPhvwPY1wFxn8J+fJ9a6MB8XqOcn6fffBoA4zTZbmmYB6/8PePE23nYXibQrCGRr2RTDjTZW4goCgKnQ4GXZ1AbMdAH107kTDTWx5TjaP0HMppn/t/GoGW/18PW83PgQx+OKKbat84AvdPFnZ7Js1t0HdK42CS+FQF+XuNEEoYA0z7GmGQbw2t1c6wzwZ9kQcdxG/9ib5+QmNADQ4dRbe+OPvHRbNjqtGjAxm0SWlk02U2NPhO6USuVGA4xlU1PLSztmQ2SqBuhkAi1KA90TuNGcgZW2daOnsXjiRl6OFfn6tcils2ziUeA3HwZe/EVhP1dfl7jRBKHA2GKjEsDa3/G63yf2cEvqOXLl6MuBz7xmtse50UJmMjDdIfiO2ThiExss3MRc5eBG0yWCdOKEbdkAZuS+3k6Kza7MJf/1cc/dDDx3q/MZzrw5e9/m5WQNak1n2cSHncKvBY7n6AcYcaMJQoHRM2bC8VWP9rLQ+B2QefLnTVA1H4h4IOiVfwVe+d34idfqItwR6Aw6wP8gTbtc/uBu73lcsqXYT/Z+0Akaw/t5mU5s9GyZWkRiQybF1wt93CNf4+XqS0yShe6MJ6tcj7ZoX/gFMP84ExfUIqPvQ6HQU0ekm5RukhDLRqg+tCvmnO+aTsZPJprm8AuBpVmMqZmIWYcBp103Xux0dlXfNrPPr9gMucSmEJQ69RkwVSEOPpuXdoIAYOJv2s1mT9PdlMEadU/nHR00yRn6uifNsgkBvVuAez4J/PBIYOcrvD/ZnkKLjXN9Ja7DJ5aNUH2EGoHrHDfMY9/iJzo/RTgnG/cgRSC71OeW+dxpFSpJoBSTp7lpnQ98cVdqTTTAiE3DdODMbwGLTuJtHYsBgPbF6c9rHwcYqybUbDr3ybLs6lzuvrce45TomEv8CoW+vhJXGBfLRqhudI2tUj6tp0N3pE/+0Ozz29EM7zXlegolNqWePE0TCBsrMNTMpV50MB8AjrnCTHZnWyy5iE3TbMuymSSx1Rat3ZbvHGxKDxXastH3NRF10rtdKOW9v8CI2AjVTbCMxUY/rW/+K3DKv3DH58eyGRvjOI8eKOhnumM/lIMbzU1dELjqBeCgM71ft8WmbZH3Me7jAGBQi80srp1nJ2kUW2zdlk1vF9/DXU4ySTHcaOS4Hb2+Xw98EfhqO3+vioiIjVDduMuelBOLTgZWfAj46P3AKZ/nTt7PoM7RXs5aapnL2wXLRisDN1q2pMRsMqSou8VGWzbNc3g5Fp+8mI3bstFzL+k2ZZsg8KcvAY990/s1pfja9Jw6XnGb//ej3D43SyRmI1Q3uSQITBbNs4G/+ZHZ9lvGRI/JCbdwBlahxGYsBoBM8L0SsAuX1mR4dg5E+Ole157r38FLLVCJaGliNs2dpiSSTvrINmbz1mM8V9LJ/5y6f+crZiBnqJmt4Uxxm0S0qA9lYtkI1U2y7EkZuYbSURf019Ekq1iHOeU5ndjc9fHs5scpVl2wYuJXGHW1AU1vF4uPHhyaiFqWXbFTnx3LJtTC5ZGSYqNTsS0LY9drqZmHXiTipv6ezWM3APde7XxWBssmeZ7iWjYiNkJ1k0wQKEPLxo1fy0Y/nbrFpnebESKlgLV3Auv/4P/zE0Way6XYLD0dOP0bEx8XtpIE+rZx6Rv9JJ+ITaJl44hNfZtz/5yqBlow7E7/5+cAT05Qly8RNXMm2UQHgWEnK1Nfu9uysR9URGwEIQ8qybKpDfkbZ5Ni2TRyhzGWAP7rOOCZn/JrsWHuPHU8wA9jsdJnouXCxb8BjvvHiY+zM9J6u4D6dmPJxUcnt4IAYKZUcJMcZJpga8ce9OvFWMzMmWQTGzYDOZODX10PM3vfGv+5RULERqhuyjlm48aeTC02DPzh894dTbKKtLZsBlhwRnuBvRv5NV3uxR4wOhGVatn4xXajDXRzzEt/LxLRyZtiQWchRtqN5W2j26En1JtoMGYi5u1KjQ0BcCpah9JYNvtEbAShMCSz0SrAsrHFZt19wNM/4crVbrxiNvrJVte/SorNdlNCfyIqMWaTDUd9DDjkXF4f7OHvhhbXRGzyasMl3WjtmS2bZCXwCQZjphUb631pLZtNqecpIiI2QnVTzuNs3NSFeMwHYGqdda8bf5xXzEZ3NnqGUi02iVETeNbcfhFw3zXjz1upbjS/HHYBsPoyXlcJR2yc78VkZqMlLZsJ3GhabCa0bKJ8n90DM+33pYvZ2JazWDaCkAeVlCBQGzIJAtpSsZ88NV4xm3Fis98c73alrb+fqx8DwPYXgZ1reb3a3WhA6pw3KWITK0HMpt27gGqycKbjRvNj2QDjrRs7jpPOsrGtGbFsBCEPKs6N5jxd6o5jaM/4kd1akOpCJmZju9GUMpYNkD5JYGyMU2Pv/xxvJ6KVYQHmgz2gMtRsudFGJ7fqM8BuNK+YTdKycYL7E1k22iJzJwmkuNHSWDZ2QopYNoWDiBYT0c1E9NtSt0WYJIKVliDgdCw6OAykBnGB1Gmmk240/RQ8wp1UitikSRLo28ZzuezZyE+1+7dUtxsNSB1QGWqyUp+jk2fZNM8Flr4HWPgO8/20GYuxtanvmxaIvu2p9xXgB4uE6wFF70+xbNKMs7EFpsgVBIomNkQ0j4geIaJ1RPQqEV2dx7luIaJuInrF47UziGg9EW0gomsznUcptUkpdVmu7RAqkFAFpT7bCQJ2x7H7jdTjkm60ELvREqOpndBAt3GjUa2Z+8VN1zOcwTbYDfzq/cDOlwszL045k2LZWG60gR5gz5u8XvSYTRi4+E5g5rI0brQY8JMTgXs+xdtaIP77JOCG+WZKAiA1TmOLSyKKZCYakD5mk+JGq1zLJg7gn5RShwA4FsAniGiZfQARzSCiJte+AzzO9TMAZ7h3ElEtgB8BOBPAMgAXEdEyIjqMiO5z/c0ozGUJFUWwgmI2dWFTFNKevliPIB/cza8lEwQiprOyp4ce2MXiE6gHpi0Buq3ZQm3e/LNZ3/gQl+0/69uFu55yZFzMxhGW33/MTA89mXErLzea2x0WH+HvgK7u/VSaKuH2wE73OfTvIJNlU6kxG6XUDqXUC856P4B1AOa6DjsZwN1EFAYAIrocwLjhskqpxwF41Ww4GsAGx2KJArgDwPlKqbVKqXNcf77mRCWic4nopt7e3okPFsqfci7E6aY2xAU2x+Js2egn7JH9XNLkOwcDGx92LBviTlGLzaD19dZiE24B5h8LbHnKxH3sDmXDg6mff+jfAbOWF+3yyoK6NJaNzWS6Er0smxHXjJqxYaBnvfV6L/DL93EBTtv1ZZesibksmLqQk4DitmyqLGZDRAsBrATwtL1fKXUngD8CuIOILgZwKYD3ZXHquQC2WttdGC9odjumEdFPAKwkoi94HaOUulcpdUVLS0sWzRDKllAzd6ILTih1SyZGC2J8lGMwTbMBquH01L5tTkWAbdyRBCJc7ytp2ewy5xno5mmVwy3A/OO5c+p+jTusfZvNce55cPQsmdVMwBWz8RKbybRskjFFqx3KlcIcHwF6Xuf15rn83eh+jf/sh4doBrGpDbD7zisbTVt7RbZsii7hRNQI4HcAPq2UGjcJtlLqm0R0B4AfA1iilBpwH5Pp9B770o5gU0rtAXBlFucXKp2aGuDCW0vdCn+kiM0gd4bhVhYbHZOJjfDr+li3G60mkGrZLDiO9295Crj/s0j+ZDoOAXqcMTz10zjNdkaKl7s6qanl/9FYzMlG87JsJlNsnPvXMAPo6/I+JjbMYhNo4AnzhvebJBAvN9pAz/iU+ZoAu13HWTZRdrHFhirbsiGiAFhofqmU8iw/S0TvALAcwF0AvpzlR3QBmGdtdwLIohiUIJQRycyoUY7ZhBq5KvDwfuNaiQ/zk67OqrItm0A90DjTSRBwxKZ1AT8Nv3qX8yHOs9jhF5rPnX4QMGdFZSRRFAL9JF8Olo2O2TRmCCmrBCcFdBzEbY4O8MPBSF+qNaLdaPf/Ew/ctakNels28aj5DlWq2BARAbgZwDql1HfTHLMSwE8BnA/gEgDtRHR9Fh/zLIClRLSIiIIAPgDgnvxaLgglQicxxEfYsgk28Cjz4X2pYy7iI+Mtm8EeR2xmWJZNK7va5h8HbH4i9bNaFwBL3gUccyXwN/8F/N1Nk3ON5UCyxH+Tt7CQl8OkSIRbABDQOi/zcTvXAtMPZLfbYA8L0Eivy41mjbVypzHX1jlVxdNYNkDlZM6HOwAADntJREFUJggAOAHAhwGcSkRrnL+zXMfUA7hQKbVRKTUG4CMANrtPRES3A3gKwEFE1EVElwGAUioO4JMAHgAnIPxGKfVq8S5JEIqIO2YTbBzvRtOWjY496I5iYBcLT+NMR2ycmA1gXGnuz/rw74Ez/wNoXwS0LSzqpZUVdbbYlNiai7QBf383123LxGgv0DST77EuMTPa50oQcMQm6hGJqA3ydXtloyUtmwqN2Sil/grvmIp9zBOu7RjY0nEfd5F7n/Xa/QDuz7GZglA+uGM2wUbuJPa9ZdxosRH+c1s2Y3FHbGYAm5/kzqlxJr82/3iPz4qM3zdVsIVajWU+djJYfDLQ/frEx0XagBorJT42lJrirBMEvIpy1gb5ut3zJSViHLMDKteNJghClug07d4uK2bT5m3ZuGM2er1xJgsNAExbzMuOg4H66amfZQ9unGoEIiw0NbXlUzEh6MSR7HFAQGqyglfhzkGryGrSsvEQmxrHjebOUkuMTpplI2IjCOXCvGM53fmZm6yYTSsLTUo2mmXZhFqM8OiYjabdEZuaGuCS+4GDzzGv1U1hsamLGGEnMq60he/wN+NnMQg4Hb49wRsANHSYdS+xGdpt1qMZxCatZRN1LD0Sy0YQpgx1QQ7Yv/UY++KDjmWjxtjaASzLxhGLmhpg+lJeDzYa1xkAtC0y6x0HcWwm+VlTWGwC4dSJ1LTYLH23vxk/i0GokQWneY7Z1zgTmH+M2Q63jq84MGiLzQAP3k0RGyeSURtIY9k4cxjVBkVsBGFKcfj7zboeZwMA+528GT3OxnaDdRzMy2C9EZv6aWwV2QQsl9tUFpul70m18nRGmtuFNZnUhYB/fAo4yirdeOVfgaOvMNuRtvEVB2zLJjbkZJvZNdGcJJGaQHrLpi7kiE2FJggIgpADzbPNoEOd+gwAvU6hjNiQkyBgi81BvFQKaHTcLtqFZmN3VFM5ZnPsx1O3a13JFqWibQGw/QWzXRdOvc+RtvFVH3TMJtjISSRuF1qwkeN/NTXelk3cmVaiNiCWjSBMOeas4GUgYsRGEx9JdaMBxrLp28Yj0YE0YmM9uU/lbDQ32o1WSstGYycEBCKpbcoUs2mey/Xx3GnPgbB1fWksm9oAH1OpUwwIgpAjC5xU5dEBoGlW6msxL7FxLJv9W9mnv+BEYMmp489rd1SVUJh0stButFJbNoARhpo6U89Mbwcb0sdsWubyYE63ZVMXMdenLRvluNn0XDi14kYThKnJyddyJ3HYhfw02jDDVHVODuq0xKZtEdAyD3iPU3zjkv/zPq/9lBwQyyaJ7uDLQmy0MERSl5G21MKrwUa2YrRl09LJFcEHXMXtA2ErJhUGoEycZizB25PkRhOxEYRyI1gPvNMqSj77CDMdQNQpmGhbNrV1wGfGzSvocV6no6oJ8BgTgakrIzdaijBYS+1O1VWidZkbbdk0d/LSPatrIGJcc1q4YsMsNnp6gdqAxGwEQQDP6KjRM3Dmkk2m3WhTORPNi7KybJy2eFk2QKplE24xMZoWR2z2OmJz9neAD97puNG0mDr3XcdttLhoy2ZoL7D12cJfk4OIjSCUOwedzcv66WaUeE5io0epi9ikUFYJAo6zSd+j2gDPaaTFpi7Ex4QazVTPAMdsADO1wJJ3AQe+h+95nUvAdEaajtHUOeNs3v4LcMvpqVOMF/LSinJWQRAKx/xjgE+/Ahz5EbMvlwC/fiqWTLRUkgkCZSA2duYYwHGauogZb6XjNqEmM4YG4JgdYMRGW7HHXwW852vOOTNZNs7nqgTQV5xZWiRmIwiVQOu81KC+Lp6YDXpQp2SipZLs4MvQjQZwzE6nwwMctwk2mvFBgJnVNSk2zrXMXWWOcVs2cR2zCaZOtdC/A5hxSP7X4kLERhAqBbsDcqdE+0F3QOJGS6U26BSqLIPJ42pdbjQAuPQPqces/iiPo1r/R7OvLsxu1sFuAOSdbTjOsnHcaHqcjaZvRz5XkBYRG0GoFOwOKNPMjmnf77iJJEEgldpgeSQHAN6WjZuTPsfLzU/ykmq5QkDjTBabYKP3BHDjYjbajRZKFZv+4rjRJGYjCJWC3QE15mDZ1NSw4IjYpFIXKg8XGmDSlP1Yn7pCtHaBTT8g83snykbTFMmyEbERhErB7kRydYUF6mVAp5ujLwdO/1qpW8FkUxRUJwhoq+SQc3nprp+mSWvZuNxo/eJGE4SpTSGyyIINkiDgZu6R/FcO2KVlJiLssmyWnp75+EyWDVmDfCUbTRCmOMk6WYHMx2Vi9SVA64LCtEcoPO7U50zY0wcAPPZm+QWp429s0o6zCaVOLy2WjSBMcXRnYY+vyJYTP1OYtgjFocYZxOnHjRZyudEA4IKb0x/vtmziVrma0X5eb1sE7HvbmVQtj4caD0RsBKFS0J1FuidXofKpqQEuuAWYu3riY5NuNJ/deNKy8XCj6bI3847m8472A/Xt/tvt5+MLejZBEIpHIs7LfCwbofw59G/9HedOEJgIXfom7nKj1QZ5OgsAOPofgM7ixK8kG00QKoU2J9Zy3CdL2w6hPHCnPk+ELn0TGwH2bQZev9d5v2XZFNiasRHLRhAqhcYZwHXFKZIoVCDuBAE/BMJs2fzpX4F1ltjoQaBFtJpFbARBECqRQMSZ0TOLMjt1ER6H8+aDZl9tALj4tyw+7mnIC4iIjSAIQiVCxK60bLLGAmFj0WjqQjy1uJ5evEhIzEYQBKFSCbdkJzY6I82uGp6NZZQHIjaCIAiVSttCnl7ALzp9vuNgs69mchxc4kYTBEGoVN7/i9RSMxOhy+BMO4DXNz7kXSG6CIjYCIIgVCqhpuyO16Vqpi8Fzvp20aaA9kLcaIIgCFOFvm28nLaUJ4tr7Ji0jxaxEQRBmCroIpvTl076R4vYCIIgTDVKUPlbYjaCIAhThcv+DOxY4794ZwERsREEQZgqzDuK/0qAuNEEQRCEoiNiIwiCIBQdERtBEASh6IjYCIIgCEVHxEYQBEEoOiI2giAIQtERsREEQRCKjoiNIAiCUHRIKVXqNpQlRNQDYHOOb58OYHcBm1NK5FrKE7mW8qNargPI71oWKKXGVfgUsSkCRPScUmp1qdtRCORayhO5lvKjWq4DKM61iBtNEARBKDoiNoIgCELREbEpDjeVugEFRK6lPJFrKT+q5TqAIlyLxGwEQRCEoiOWjSAIglB0RGwEQRCEoiNiU0CI6AwiWk9EG4jo2lK3J1uI6G0iWktEa4joOWdfOxE9SERvOsu2UrfTCyK6hYi6iegVa1/athPRF5z7tJ6ITi9Nq71Jcy3XEdE2596sIaKzrNfK+VrmEdEjRLSOiF4loqud/RV3bzJcS8XdGyIKE9EzRPSScy1fcfYX774opeSvAH8AagFsBLAYQBDASwCWlbpdWV7D2wCmu/Z9E8C1zvq1AP6j1O1M0/aTAKwC8MpEbQewzLk/IQCLnPtWW+prmOBargPwWY9jy/1aZgNY5aw3AXjDaXPF3ZsM11Jx9wYAAWh01gMAngZwbDHvi1g2heNoABuUUpuUUlEAdwA4v8RtKgTnA/i5s/5zAH9TwrakRSn1OIC9rt3p2n4+gDuUUqNKqbcAbADfv7IgzbWko9yvZYdS6gVnvR/AOgBzUYH3JsO1pKOcr0UppQaczYDzp1DE+yJiUzjmAthqbXch8xexHFEA/kREzxPRFc6+mUqpHQD/2ADMKFnrsidd2yv1Xn2SiF523GzavVEx10JECwGsBD9FV/S9cV0LUIH3hohqiWgNgG4ADyqlinpfRGwKB3nsq7S88hOUUqsAnAngE0R0UqkbVCQq8V79GMASACsA7ADwHWd/RVwLETUC+B2ATyul+jId6rGvrK7H41oq8t4opRJKqRUAOgEcTUTLMxye97WI2BSOLgDzrO1OANtL1JacUEptd5bdAO4Cm8m7iGg2ADjL7tK1MGvStb3i7pVSapfTOYwB+CmMC6Psr4WIAuDO+ZdKqd87uyvy3nhdSyXfGwBQSu0H8CiAM1DE+yJiUzieBbCUiBYRURDABwDcU+I2+YaIGoioSa8DeA+AV8DX8BHnsI8AuLs0LcyJdG2/B8AHiChERIsALAXwTAna5xvdATj8LfjeAGV+LUREAG4GsE4p9V3rpYq7N+mupRLvDRF1EFGrsx4BcBqA11HM+1LqrIhq+gNwFjhDZSOAL5a6PVm2fTE42+QlAK/q9gOYBuAhAG86y/ZStzVN+28HuzBi4KewyzK1HcAXnfu0HsCZpW6/j2v5BYC1AF52fvizK+RaTgS7W14GsMb5O6sS702Ga6m4ewPgcAAvOm1+BcC/OfuLdl+kXI0gCIJQdMSNJgiCIBQdERtBEASh6IjYCIIgCEVHxEYQBEEoOiI2giAIQtERsRGEEkFECatS8BoqYKVwIlpoV40WhFJTV+oGCMIUZlhxuRBBqHrEshGEMoN4XqH/cOYbeYaIDnD2LyCih5yCjw8R0Xxn/0wiusuZm+QlIjreOVUtEf3Uma/kT85IcUEoCSI2glA6Ii432vut1/qUUkcD+CGA7zn7fgjgf5VShwP4JYDvO/u/D+AxpdQR4HlwXnX2LwXwI6XUoQD2A3hvka9HENIiFQQEoUQQ0YBSqtFj/9sATlVKbXIKP+5USk0jot3gUigxZ/8OpdR0IuoB0KmUGrXOsRBcNn6ps/15AAGl1PXFvzJBGI9YNoJQnqg06+mO8WLUWk9AYrRCCRGxEYTy5P3W8iln/UlwNXEAuBjAX531hwB8HEhOiNU8WY0UBL/Ik44glI6IM1Oi5o9KKZ3+HCKip8EPhBc5+64CcAsRfQ5AD4BLnP1XA7iJiC4DWzAfB1eNFoSyQWI2glBmODGb1Uqp3aVuiyAUCnGjCYIgCEVHLBtBEASh6IhlIwiCIBQdERtBEASh6IjYCIIgCEVHxEYQBEEoOiI2giAIQtH5/0zIZqgWZvh3AAAAAElFTkSuQmCC\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plot_loss(h.epoch, h.history, val=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "1/1 [==============================] - 1s 768ms/step - loss: 0.7081 - tp: 1.0000 - fp: 0.0000e+00 - tn: 5.0000 - fn: 4.0000 - precision: 1.0000 - recall: 0.2000 - auc: 1.0000 - accuracy: 0.0000e+00\n"
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "[0.7081496119499207, 1.0, 0.0, 5.0, 4.0, 1.0, 0.2, 1.0, 0.0]"
     },
     "metadata": {},
     "execution_count": 14
    }
   ],
   "source": [
    "model3.evaluate(val_ds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "7/7 [==============================] - 0s 7ms/step - loss: 0.1541 - tp: 10.0000 - fp: 0.0000e+00 - tn: 56.0000 - fn: 4.0000 - precision: 1.0000 - recall: 0.7143 - auc: 0.9732 - accuracy: 0.0000e+00\n"
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "[0.15407080416168487, 10.0, 0.0, 56.0, 4.0, 1.0, 0.71428573, 0.97321427, 0.0]"
     },
     "metadata": {},
     "execution_count": 15
    }
   ],
   "source": [
    "model3.evaluate(train_ds)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training with `validation_data`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "tf.keras.backend.clear_session()\n",
    "np.random.seed(42)\n",
    "tf.random.set_seed(42)\n",
    "\n",
    "OPT = tf.keras.optimizers.Adam(learning_rate=0.005)\n",
    "model3 = model_conv_rnn_02(MAX_TIMESTEPS, N)\n",
    "model3.compile(optimizer=OPT, loss='binary_crossentropy', metrics=METRICS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "h = model3.fit(train_ds, epochs=500, verbose=0, validation_data=val_ds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plot_loss(h.epoch, h.history, val=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "1/1 [==============================] - 0s 12ms/step - loss: 0.6753 - tp: 1.0000 - fp: 0.0000e+00 - tn: 5.0000 - fn: 4.0000 - precision: 1.0000 - recall: 0.2000 - auc: 0.9600 - accuracy: 0.0000e+00\n"
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "[0.6753285527229309, 1.0, 0.0, 5.0, 4.0, 1.0, 0.2, 0.96000004, 0.0]"
     },
     "metadata": {},
     "execution_count": 19
    }
   ],
   "source": [
    "model3.evaluate(val_ds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "7/7 [==============================] - 0s 7ms/step - loss: 0.2043 - tp: 10.0000 - fp: 2.0000 - tn: 54.0000 - fn: 4.0000 - precision: 0.8333 - recall: 0.7143 - auc: 0.9579 - accuracy: 0.0000e+00\n"
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "[0.20427651570311614,\n 10.0,\n 2.0,\n 54.0,\n 4.0,\n 0.8333333,\n 0.71428573,\n 0.95790815,\n 0.0]"
     },
     "metadata": {},
     "execution_count": 20
    }
   ],
   "source": [
    "model3.evaluate(train_ds)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Trying Early Stopping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "es_callback = [\n",
    "    tf.keras.callbacks.EarlyStopping(\n",
    "        # Stop training when `val_loss` is no longer improving\n",
    "        monitor=\"val_loss\",\n",
    "        # \"no longer improving\" being defined as \"no better than 1e-2 less\"\n",
    "        min_delta=0.05,\n",
    "        # \"no longer improving\" being further defined as \"for at least 2 epochs\"\n",
    "        patience=400,\n",
    "        verbose=1,\n",
    "        restore_best_weights=True\n",
    "    )\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "tf.keras.backend.clear_session()\n",
    "np.random.seed(51)\n",
    "tf.random.set_seed(51)\n",
    "\n",
    "OPT = tf.keras.optimizers.Adam(learning_rate=0.005)\n",
    "model3 = model_conv_rnn_02(MAX_TIMESTEPS, N)\n",
    "model3.compile(optimizer=OPT, loss='binary_crossentropy', metrics=METRICS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Restoring model weights from the end of the best epoch.\nEpoch 00569: early stopping\n"
    }
   ],
   "source": [
    "h = model3.fit(train_ds, epochs=1000, validation_data=val_ds, callbacks=es_callback, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "1/1 [==============================] - 0s 14ms/step - loss: 0.4425 - tp: 4.0000 - fp: 1.0000 - tn: 4.0000 - fn: 1.0000 - precision: 0.8000 - recall: 0.8000 - auc: 0.9200 - accuracy: 0.0000e+00\n"
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "[0.4424969553947449, 4.0, 1.0, 4.0, 1.0, 0.8, 0.8, 0.92, 0.0]"
     },
     "metadata": {},
     "execution_count": 34
    }
   ],
   "source": [
    "model3.evaluate(val_ds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plot_loss(h.epoch, h.history, label='with early stopping',val=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adding LSTM regularization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "def model_conv_rnn_02(MAX_TIMESTEPS, OH_DIMENSION):\n",
    "    model = tf.keras.models.Sequential([\n",
    "                tf.keras.layers.Conv1D(filters=64, kernel_size=3, activation='relu', \n",
    "                                    input_shape=[MAX_TIMESTEPS, OH_DIMENSION]),\n",
    "                tf.keras.layers.MaxPooling1D(pool_size=2),\n",
    "                tf.keras.layers.LSTM(64, dropout=0.2, recurrent_dropout=0.2),\n",
    "                tf.keras.layers.Dropout(0.2),\n",
    "                tf.keras.layers.Dense(32, activation='relu'),\n",
    "                tf.keras.layers.Dense(1, activation='sigmoid')\n",
    "    ])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "tf.keras.backend.clear_session()\n",
    "np.random.seed(51)\n",
    "tf.random.set_seed(51)\n",
    "\n",
    "OPT = tf.keras.optimizers.Adam(learning_rate=0.005)\n",
    "model3 = model_conv_rnn_02(MAX_TIMESTEPS, N)\n",
    "model3.compile(optimizer=OPT, loss='binary_crossentropy', metrics=METRICS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Restoring model weights from the end of the best epoch.\nEpoch 00827: early stopping\n"
    }
   ],
   "source": [
    "h = model3.fit(train_ds, epochs=1000, validation_data=val_ds, callbacks=es_callback, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "1/1 [==============================] - 0s 12ms/step - loss: 0.4407 - tp: 2.0000 - fp: 0.0000e+00 - tn: 5.0000 - fn: 3.0000 - precision: 1.0000 - recall: 0.4000 - auc: 1.0000 - accuracy: 0.0000e+00\n"
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "[0.44067540764808655, 2.0, 0.0, 5.0, 3.0, 1.0, 0.4, 1.0, 0.0]"
     },
     "metadata": {},
     "execution_count": 41
    }
   ],
   "source": [
    "model3.evaluate(val_ds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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99.475979 \r\nL 130.338834 100.21453 \r\nL 130.707313 100.565423 \r\nL 131.075792 93.994642 \r\nL 131.444271 96.063212 \r\nL 131.81275 93.990289 \r\nL 132.181229 94.679297 \r\nL 132.549708 103.758 \r\nL 132.918187 107.446388 \r\nL 133.286666 98.646062 \r\nL 133.655145 102.066186 \r\nL 134.023624 111.800521 \r\nL 134.392103 114.987573 \r\nL 134.760582 119.917818 \r\nL 135.497539 81.328 \r\nL 135.866018 77.936787 \r\nL 136.234497 81.299039 \r\nL 136.602976 79.41237 \r\nL 136.971455 98.321251 \r\nL 137.339934 109.90032 \r\nL 138.076892 85.265937 \r\nL 138.81385 104.832887 \r\nL 139.182329 97.973619 \r\nL 139.550808 108.559965 \r\nL 139.919287 110.85253 \r\nL 140.287766 100.230034 \r\nL 140.656245 96.45236 \r\nL 141.024724 93.903068 \r\nL 141.393203 82.870683 \r\nL 141.761682 87.222654 \r\nL 142.130161 86.443728 \r\nL 142.49864 94.672089 \r\nL 142.867119 93.002271 \r\nL 143.235598 99.5541 \r\nL 143.604077 100.388443 \r\nL 143.972556 98.024475 \r\nL 144.341035 108.346873 \r\nL 144.709514 106.074519 \r\nL 145.077993 116.851404 \r\nL 146.18343 105.378563 \r\nL 146.551909 108.449483 \r\nL 146.920388 132.133986 \r\nL 147.288867 134.895812 \r\nL 147.657346 123.540546 \r\nL 148.025825 105.828376 \r\nL 148.394304 108.957967 \r\nL 148.762783 107.145284 \r\nL 149.131262 107.837504 \r\nL 149.499741 100.089286 \r\nL 149.86822 83.44699 \r\nL 150.236699 110.086835 \r\nL 150.605178 102.781336 \r\nL 150.973657 111.269726 \r\nL 151.342136 115.678785 \r\nL 151.710615 105.199039 \r\nL 152.079094 112.917729 \r\nL 152.816051 89.862241 \r\nL 153.18453 99.827091 \r\nL 153.553009 120.636494 \r\nL 153.921488 82.393769 \r\nL 154.289967 120.62199 \r\nL 154.658446 111.329718 \r\nL 155.026925 119.515883 \r\nL 155.395404 114.064908 \r\nL 156.132362 125.586301 \r\nL 156.500841 103.034021 \r\nL 156.86932 116.168205 \r\nL 157.237799 106.622177 \r\nL 157.606278 114.441561 \r\nL 157.974757 102.252766 \r\nL 158.343236 94.589303 \r\nL 158.711715 120.128919 \r\nL 159.080194 106.36864 \r\nL 159.448673 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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plot_loss(h.epoch, h.history, val=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interesting, that lower `val_loss` does not obligatory result into better predictions...\n",
    "\n",
    "It appears that on probabalistic level calculated `binary_crossentropy` is lower for validation set, but actual predictions tend to be more incorrect than in previous model with higher `val_loss`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.5-final"
  },
  "orig_nbformat": 2,
  "kernelspec": {
   "name": "python37564bita866c8c9cf084ebd97979ec2adc3c734",
   "display_name": "Python 3.7.5 64-bit"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}