 TiSeLaC.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Currently the ‘memory growth’ option should be the same for all GPUs.\n",
    "# You should set the ‘memory growth’ option before initializing GPUs.\n",
    "\n",
    "import tensorflow as tf\n",
    "gpus = tf.config.experimental.list_physical_devices('GPU')\n",
    "if gpus:\n",
    "    try:\n",
    "        for gpu in gpus:\n",
    "            tf.config.experimental.set_memory_growth(gpu, True)\n",
    "    except RuntimeError as e:\n",
    "        print(e)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "toc": true
   },
   "source": [
    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#TiSeLaC-:-Time-Series-Land-Cover-Classification-Challenge\" data-toc-modified-id=\"TiSeLaC-:-Time-Series-Land-Cover-Classification-Challenge-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>TiSeLaC : Time Series Land Cover Classification Challenge</a></span><ul class=\"toc-item\"><li><span><a href=\"#Data-presentation\" data-toc-modified-id=\"Data-presentation-1.1\"><span class=\"toc-item-num\">1.1&nbsp;&nbsp;</span>Data presentation</a></span><ul class=\"toc-item\"><li><span><a href=\"#Training-classes\" data-toc-modified-id=\"Training-classes-1.1.1\"><span class=\"toc-item-num\">1.1.1&nbsp;&nbsp;</span>Training classes</a></span></li><li><span><a href=\"#Training-file\" data-toc-modified-id=\"Training-file-1.1.2\"><span class=\"toc-item-num\">1.1.2&nbsp;&nbsp;</span>Training file</a></span></li><li><span><a href=\"#Coordinates-file\" data-toc-modified-id=\"Coordinates-file-1.1.3\"><span class=\"toc-item-num\">1.1.3&nbsp;&nbsp;</span>Coordinates file</a></span></li></ul></li><li><span><a href=\"#Analysis-of-the-source-images\" data-toc-modified-id=\"Analysis-of-the-source-images-1.2\"><span class=\"toc-item-num\">1.2&nbsp;&nbsp;</span>Analysis of the source images</a></span><ul class=\"toc-item\"><li><span><a href=\"#Checking-the-classes-distribution\" data-toc-modified-id=\"Checking-the-classes-distribution-1.2.1\"><span class=\"toc-item-num\">1.2.1&nbsp;&nbsp;</span>Checking the classes distribution</a></span></li><li><span><a href=\"#Plotting-the-23-days-images-of-the-81714-pixels\" data-toc-modified-id=\"Plotting-the-23-days-images-of-the-81714-pixels-1.2.2\"><span class=\"toc-item-num\">1.2.2&nbsp;&nbsp;</span>Plotting the 23 days images of the 81714 pixels</a></span></li><li><span><a href=\"#Giving-insights-on-mean-value-of-the-10-features-over-the-23-days,-discriminated-by-class\" data-toc-modified-id=\"Giving-insights-on-mean-value-of-the-10-features-over-the-23-days,-discriminated-by-class-1.2.3\"><span class=\"toc-item-num\">1.2.3&nbsp;&nbsp;</span>Giving insights on mean value of the 10 features over the 23 days, discriminated by class</a></span></li></ul></li><li><span><a href=\"#Classifiers\" data-toc-modified-id=\"Classifiers-1.3\"><span class=\"toc-item-num\">1.3&nbsp;&nbsp;</span>Classifiers</a></span><ul class=\"toc-item\"><li><span><a href=\"#1D-CNN\" data-toc-modified-id=\"1D-CNN-1.3.1\"><span class=\"toc-item-num\">1.3.1&nbsp;&nbsp;</span>1D-CNN</a></span><ul class=\"toc-item\"><li><span><a href=\"#Setting-up-the-model\" data-toc-modified-id=\"Setting-up-the-model-1.3.1.1\"><span class=\"toc-item-num\">1.3.1.1&nbsp;&nbsp;</span>Setting up the model</a></span></li><li><span><a href=\"#Creating-the-data-generator\" data-toc-modified-id=\"Creating-the-data-generator-1.3.1.2\"><span class=\"toc-item-num\">1.3.1.2&nbsp;&nbsp;</span>Creating the data generator</a></span></li><li><span><a href=\"#Training-our-model\" data-toc-modified-id=\"Training-our-model-1.3.1.3\"><span class=\"toc-item-num\">1.3.1.3&nbsp;&nbsp;</span>Training our model</a></span></li><li><span><a href=\"#Testing-the-model\" data-toc-modified-id=\"Testing-the-model-1.3.1.4\"><span class=\"toc-item-num\">1.3.1.4&nbsp;&nbsp;</span>Testing the model</a></span></li></ul></li><li><span><a href=\"#Advanced-Classifiers\" data-toc-modified-id=\"Advanced-Classifiers-1.3.2\"><span class=\"toc-item-num\">1.3.2&nbsp;&nbsp;</span>Advanced Classifiers</a></span><ul class=\"toc-item\"><li><span><a href=\"#1.-Multi-Channel-Deep-Convolutional-Neural-Network\" data-toc-modified-id=\"1.-Multi-Channel-Deep-Convolutional-Neural-Network-1.3.2.1\"><span class=\"toc-item-num\">1.3.2.1&nbsp;&nbsp;</span>1. Multi Channel Deep Convolutional Neural Network</a></span></li><li><span><a href=\"#2.-Time-CNN\" data-toc-modified-id=\"2.-Time-CNN-1.3.2.2\"><span class=\"toc-item-num\">1.3.2.2&nbsp;&nbsp;</span>2. Time-CNN</a></span></li></ul></li><li><span><a href=\"#Merge-of-Multimodal/Unimodal-architecture\" data-toc-modified-id=\"Merge-of-Multimodal/Unimodal-architecture-1.3.3\"><span class=\"toc-item-num\">1.3.3&nbsp;&nbsp;</span>Merge of Multimodal/Unimodal architecture</a></span><ul class=\"toc-item\"><li><span><a href=\"#Setting-up-the-data-(splitting-into-training-and-validation-sets)\" data-toc-modified-id=\"Setting-up-the-data-(splitting-into-training-and-validation-sets)-1.3.3.1\"><span class=\"toc-item-num\">1.3.3.1&nbsp;&nbsp;</span>Setting up the data (splitting into training and validation sets)</a></span></li><li><span><a href=\"#Setting-up-the-model\" data-toc-modified-id=\"Setting-up-the-model-1.3.3.2\"><span class=\"toc-item-num\">1.3.3.2&nbsp;&nbsp;</span>Setting up the model</a></span></li><li><span><a href=\"#Testing-the-model\" data-toc-modified-id=\"Testing-the-model-1.3.3.3\"><span class=\"toc-item-num\">1.3.3.3&nbsp;&nbsp;</span>Testing the model</a></span></li></ul></li></ul></li></ul></li></ul></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  TiSeLaC : Time Series Land Cover Classification Challenge \n",
    "\n",
    "This challenge originating from [this website](https://sites.google.com/site/dinoienco/tiselc) challenges anyone who is interested to deal with SITS (Satellite Imagery Time Series).\n",
    "\n",
    "The challenge involves a multi-class single label classification problem where the examples to classify are pixels described by the time series of satellite images and the prediction is related to the land cover of associated to each pixel:\n",
    "<br>\n",
    "<figure>\n",
    "       <img src=\"static/img/time_series_classification.png\" alt=\"time_series_classification\" width=\"600\">\n",
    "          <figcaption>Fig.1 - The Reunion Island site (a) and the corresponding classification according to the considered Land Cover Classes (b)</figcaption>\n",
    "</figure>\n",
    "<br>\n",
    "\n",
    "\n",
    "Before going in the EDA phase, we will first detail what our dataset is made of:\n",
    "<figure>\n",
    "       <img src=\"static/img/tiselac_pres_1.png\" alt=\"tiselac_pres_1\" width=\"600\">\n",
    "          <figcaption>Fig.2 - A visual description of the dataset (b)</figcaption>\n",
    "</figure>\n",
    "\n",
    "## Data presentation\n",
    "\n",
    "### Training classes\n",
    "\n",
    "The classes found in the *training_class.txt* file are the following:\n",
    "\n",
    "\n",
    "| Class ID | Class Name              | # Instances |\n",
    "|----------|-------------------------|-------------|\n",
    "| 1        | Urban Areas             | 16000       |\n",
    "| 2        | Other built-up surfaces | 3236        |\n",
    "| 3        | Forests                 | 16000       |\n",
    "| 4        | Sparse Vegetation       | 16000       |\n",
    "| 5        | Rocks and bare soil     | 12942       |\n",
    "| 6        | Grassland               | 5681        |\n",
    "| 7        | Sugarcane crops         | 7656        |\n",
    "| 8        | Other crops             | 1600        |\n",
    "| 9        | Water                   | 2599        |\n",
    "\n",
    "where the instances designates the pixels that makes up the image (total of 81714 pixels). It is important to note that not all pixel has a class, some are left blank.\n",
    "\n",
    "### Training file\n",
    "\n",
    "The training file is in the form of a big text file where each row represents the value of the features of the given pixel at the 23 dates. As each pixel is described by 10 features (always in the following order: (Ultra Blue, Blue, Green, Red, NIR, SWIR1, SWIR2, NDVI, NDWI, BI), we have, for each row, 10*23 columns.\n",
    "\n",
    "### Coordinates file\n",
    "\n",
    "This file gives the actual coordinates of pixels associated with a class to be able to draw a mask on the initial map of The Reunion Island.\n",
    "\n",
    "## Analysis of the source images\n",
    "\n",
    "We will first explore the RGB component of the source images in the training file by redrawing its components."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# CONSTANTS & IMPORTS\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import time\n",
    "import matplotlib.pyplot as plt\n",
    "from tqdm import tqdm\n",
    "import os\n",
    "import cv2\n",
    "import seaborn as sns;sns.set()\n",
    "\n",
    "IMAGE_SIZE = (2866, 2633, 3)\n",
    "NUM_OF_DAYS = 23\n",
    "CLASSES = [\"urban areas\", \"other built-up surfaces\", \"forests\", \n",
    "           \"sparse vegetation\", \"rocks and bare soil\", \"grassland\", \n",
    "           \"sugarcane crops\", \"other crops\", \"water\"]\n",
    "FEATURES = [\n",
    "    \"ultra blue\",\"blue\",\"green\",\"red\",\"NIR\",\"SWIR1\",\"SWIR2\",\"NDVI\",\"NDWI\",\"BI\"\n",
    "]\n",
    "\n",
    "NUM_OF_CLASSES = len(CLASSES)\n",
    "NUM_OF_FEATURES = len(FEATURES)\n",
    "NUM_OF_PIXELS = 81714"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# DATA LOADING\n",
    "data = pd.read_csv(\"data/train/training.txt\", header=None)\n",
    "coord = pd.read_csv(\"data/train/coord_training.txt\", header=None)\n",
    "classes_val = pd.read_csv(\"data/train/training_class.txt\", header=None)\n",
    "\n",
    "data_test = pd.read_csv(\"data/test/test.txt\", header=None)\n",
    "coord_test = pd.read_csv(\"data/test/coord_test.txt\", header=None)\n",
    "classes_val_test = pd.read_csv(\"data/test/test_class.txt\", header=None)\n",
    "y_test = classes_val_test.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\thoma\\.conda\\envs\\tiselac\\lib\\site-packages\\tqdm\\std.py:658: FutureWarning: The Panel class is removed from pandas. Accessing it from the top-level namespace will also be removed in the next version\n",
      "  from pandas import Panel\n",
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 81714/81714 [02:08<00:00, 637.93it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Images updated\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "img = [np.zeros(IMAGE_SIZE).astype(np.uint8) for _ in range(NUM_OF_DAYS)]\n",
    "def row_to_img(row):\n",
    "    \n",
    "    idx = row.name\n",
    "    \n",
    "    coord_row = coord.iloc[idx, :]\n",
    "\n",
    "    for i in range(23):\n",
    "        b = row[i*10+1]\n",
    "        g = row[i*10+2]\n",
    "        r = row[i*10+3]\n",
    "        \n",
    "        #b = 255\n",
    "        #g = 255\n",
    "        #r = 255\n",
    "        \n",
    "        if (r > 255):\n",
    "            r = 255\n",
    "        if (b > 255):\n",
    "            b = 255\n",
    "        if (g > 255):\n",
    "            g = 255\n",
    "        \n",
    "        img[i][coord_row[0], coord_row[1], :] = [r,g,b]\n",
    "        \n",
    "tqdm.pandas()\n",
    "data.progress_apply(row_to_img, axis=1)\n",
    "print(\"Images updated\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking the classes distribution\n",
    "\n",
    "The subsampling of the pixels done by the TiSeLaC organizers had, as a first intent, the idea of balancing the classes distribution. We are then expecting to see a somewhat balanced dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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oSoACAAAAoCgBCgAAAICiBCgAAAAAihKgAAAAAChKgAIAAACgKAEKAAAAgKIEKAAAAACKEqAAAAAAKEqAAgAAAKAoAQoAAACAogQoAAAAAIoSoAAAAAAoSoACAAAAoCgBCgAAAICiBCgAAAAAihKgAAAAAChKgAIAAACgKAEKAAAAgKIEKAAAAACKEqAAAAAAKEqAAgAAAKAoAQoAAACAogQoAAAAAIoSoAAAAAAoSoACAAAAoCgBCgAAAICiBCgAAAAAihKgAAAAAChKgAIAAACgKAEKAAAAgKKKBqimpqbU19fn0UcfTZLcdddd+ehHP5pjjz02X/jCF7Jp06YkyYMPPpihQ4dmwIABGTt2bDZv3pwkefzxxzN8+PAMHDgwZ5xxRtavX58kWbt2bUaMGJFBgwZl+PDhWblyZcnTAAAAAOAVKBag7rnnnpx88slZsmRJkudi1FlnnZWvfvWruf7665Mkc+fOTZKMHj0648ePz4IFC9LW1pY5c+YkSSZOnJhhw4alsbExffr0yfTp05Mk06ZNS11dXW644YaceOKJmTx5cqnTAAAAAOAVKhag5syZkwkTJqS2tjZJcuutt6Zv377p3bt3kmTcuHE5+uij89hjj2Xjxo3p27dvkmTo0KFpbGxMc3NzFi1alAEDBmw1niQLFy7M4MGDkyT19fW55ZZb0tzcXOpUAAAAAHgFKks98Qt3JS1dujR77rlnPv/5z+eRRx7JoYcemjFjxuT3v/99ampq2ufV1NRk+fLlWbNmTaqqqlJZWbnVeJKsWLGi/ZjKyspUVVVl9erV6dmzZ6nTAQAAAOBvVCxAvVBLS0t+/etf5wc/+EH++Z//OWPHjs2MGTNy+OGHp6Kion1eW1tbKioq2v/c0gsfb3lMp047t5lrn32qdmr+puaWdO3SeaeOoQzXgr+3TZub07Wyy65eBumYa9GyqTmdu7rerwauBQDAa0eHBag3vOENOeSQQ7L//vsnSQYNGpRZs2Zl6NChW72J+KpVq1JbW5sePXpk3bp1aWlpSefOnbNy5cr22/lqa2uzatWq7Lvvvtm8eXPWr1+f6urqnVrPU081pbW17WXPr6npnmFfvmqnvgdlzL5oeFauXLerl8FupKame077/shdvQyS/L9PXFr89V1T0z3zT/1E0e/By3PM/37fv88BAHYTnTpVvORmn6KfgrelI488Mg888ECeeOKJJMkvfvGLHHzwwenVq1e6deuWxYsXJ0nmzZuXfv36pUuXLqmrq8v8+fOTJA0NDenXr1+SpH///mloaEiSzJ8/P3V1denSxf9BBQAAAHg16rAdUPvtt1+++tWv5rOf/WyeffbZvOMd78g555yTJJk6dWrGjRuXpqamHHzwwTn11FOTJBMmTMiYMWPy7W9/O/vtt18uueSSJMnIkSMzZsyYHHvssenevXumTp3aUacBAAAAwE4qHqBuvvnm9r+/733vy/ve975t5vTu3Ttz587dZrxXr16ZOXPmNuPV1dW54oor/q7rBAAAAKCMDrsFDwAAAIDXJgEKAAAAgKIEKAAAAACKEqAAAAAAKEqAAgAAAKAoAQoAAACAogQoAAAAAIoSoAAAAAAoSoACAAAAoCgBCgAAAICiBCgAAAAAihKgAAAAAChKgAIAAACgKAEKAAAAgKIEKAAAAACKEqAAAAAAKEqAAgAAAKAoAQoAAACAogQoAAAAAIoSoAAAAAAoSoACAAAAoCgBCgAAAICiBCgAAAAAihKgAAAAAChKgAIAAACgKAEKAAAAgKIEKAAAAACKEqAAAAAAKEqAAgAAAKAoAQoAAACAogQoAAAAAIoSoAAAAAAoSoACAAAAoCgBCgAAAICiBCgAAAAAihKgAAAAAChKgAIAAACgKAEKAAAAgKIEKAAAAACKEqAAAAAAKEqAAgAAAKAoAQoAAACAogQoAAAAAIoSoAAAAAAoSoACAAAAoCgBCgAAAICiBCgAAAAAihKgAAAAAChKgAIAAACgKAEKAAAAgKIEKAAAAACKEqAAAAAAKEqAAgAAAKAoAQoAAACAogQoAAAAAIoSoAAAAAAoSoACAAAAoCgBCgAAAICiBCgAAAAAihKgAAAAAChKgAIAAACgKAEKAAAAgKIEKAAAAACKEqAAAAAAKEqAAgAAAKAoAQoAAACAogQoAAAAAIoSoAAAAAAoSoACAAAAoCgBCgAAAICiBCgAAAAAihKgAAAAAChKgAIAAACgKAEKAAAAgKIEKAAAAACKEqAAAAAAKEqAAgAAAKAoAQoAAACAogQoAAAAAIoSoAAAAAAoSoACAAAAoCgBCgAAAICiBCgAAAAAihKgAAAAAChKgAIAAACgKAEKAAAAgKIEKAAAAACKEqAAAAAAKEqAAgAAAKAoAQoAAACAogQoAAAAAIoSoAAAAAAoSoACAAAAoCgBCgAAAICiBCgAAAAAihKgAAAAAChKgAIAAACgKAEKAAAAgKIEKAAAAACKEqAAAAAAKEqAAgAAAKAoAQoAAACAogQoAAAAAIoSoAAAAAAoSoACAAAAoCgBCgAAAICiBCgAAAAAihKgAAAAAChKgAIAAACgKAEKAAAAgKIEKAAAAACKEqAAAAAAKEqAAgAAAKAoAQoAAACAogQoAAAAAIoSoAAAAAAoSoACAAAAoCgBCgAAAICiBCgAAAAAihKgAAAAACiqaIBqampKfX19Hn300a3GZ82alVNOOaX98YMPPpihQ4dmwIABGTt2bDZv3pwkefzxxzN8+PAMHDgwZ5xxRtavX58kWbt2bUaMGJFBgwZl+PDhWblyZcnTAAAAAOAVKBag7rnnnpx88slZsmTJVuN//OMfM2PGjK3GRo8enfHjx2fBggVpa2vLnDlzkiQTJ07MsGHD0tjYmD59+mT69OlJkmnTpqWuri433HBDTjzxxEyePLnUaQAAAADwChULUHPmzMmECRNSW1vbPrZp06aMHz8+Z599dvvYY489lo0bN6Zv375JkqFDh6axsTHNzc1ZtGhRBgwYsNV4kixcuDCDBw9OktTX1+eWW25Jc3NzqVMBAAAA4BWoLPXEL7Yr6Rvf+EaOP/74vPGNb2wfW7FiRWpqatof19TUZPny5VmzZk2qqqpSWVm51fgLj6msrExVVVVWr16dnj17ljodAAAAAP5GxQLUC91666154okncu655+aOO+5oH29tbU1FRUX747a2tlRUVLT/uaUXPt7ymE6ddm4z1z77VO3UfF5damq67+olAIV4fb+2uN4AAK8NHRagfvrTn+bhhx/OkCFDsmHDhqxatSqjRo3K6NGjt3oT8VWrVqW2tjY9evTIunXr0tLSks6dO2flypXtt/PV1tZm1apV2XfffbN58+asX78+1dXVO7Wep55qSmtr28ue7xfkV5eVK9ft6iWwG/H6fnUp/fp2vV9d/PscAGD30KlTxUtu9in6KXhbmjJlSm644YbMmzcvkyZNSp8+fTJt2rT06tUr3bp1y+LFi5Mk8+bNS79+/dKlS5fU1dVl/vz5SZKGhob069cvSdK/f/80NDQkSebPn5+6urp06dKlo04FAAAAgJ3QYQHqpUydOjVTpkzJwIEDs2HDhpx66qlJkgkTJmTOnDk55phjcuedd2bUqFFJkpEjR+buu+/Osccem9mzZ2f8+PG7cvkAAAAAvITit+DdfPPN24wddthhOeyww9of9+7dO3Pnzt1mXq9evTJz5sxtxqurq3PFFVf8fRcKAAAAQBGvih1QAAAAAOy+BCgAAAAAihKgAAAAAChKgAIAAACgKAEKAAAAgKIEKAAAAACKEqAAAAAAKEqAAgAAAKAoAQoAAACAogQoAAAAAIoSoAAAAAAoSoACAAAAoCgBCgAAAICiBCgAAAAAihKgAAAAAChKgAIAAACgKAEKAAAAgKIEKAAAAACKEqAAAAAAKEqAAgAAAKAoAQoAAACAogQoAAAAAIoSoAAAAAAoSoACAAAAoCgBCgAAAICiBCgAAAAAihKgAAAAAChKgAIAAACgKAEKAAAAgKIEKAAAAACKEqAAAAAAKEqAAgAAAKAoAQoAAACAogQoAAAAAIoSoAAAAAAoSoACAAAAoCgBCgAAAICiBCgAAAAAihKgAAAAAChKgAIAAACgKAEKAAAAgKIEKAAAAACKEqAAAAAAKEqAAgAAAKAoAQoAAACAogQoAAAAAIoSoAAAAAAoSoACAAAAoCgBCgAAAICiBCgAAAAAihKgAAAAAChKgAIAAACgKAEKAAAAgKIEKAAAAACKEqAAAAAAKEqAAgAAAKAoAQoAAACAogQoAAAAAIoSoAAAAAAoSoACAAAAoCgBCgAAAICiBCgAAAAAihKgAAAAAChKgAIAAACgqMpdvQAAgFJe/097pGs3v+68Gmx6dnOeWfvXXb0MAGAX8RsZALDb6tqtMheMnburl0GS8yafsKuXAADsQm7BAwAAAKAoAQoAAACAogQoAAAAAIoSoAAAAAAoSoACAAAAoCgBCgAAAICiBCgAAAAAihKgAAAAAChKgAIAAACgKAEKAAAAgKIEKAAAAACKEqAAAAAAKEqAAgAAAKAoAQoAAACAogQoAAAAAIoSoAAAAAAoSoACAAAAoCgBCgAAAICiBCgAAAAAihKgAAAAAChKgAIAAACgKAEKAAAAgKIEKAAAAACKEqAAAAAAKEqAAgAAAKAoAQoAAACAogQoAAAAAIoSoAAAAAAoSoACAAAAoCgBCgAAAICiBCgAAAAAihKgAAAAAChKgAIAAACgKAEKAAAAgKIEKAAAAACKEqAAAAAAKEqAAgAAAKAoAQoAAACAogQoAAAAAIoSoAAAAAAoSoACAAAAoCgBCgAAAICiBCgAAAAAihKgAAAAAChKgAIAAACgKAEKAAAAgKIEKAAAAACKEqAAAAAAKEqAAgAAAKAoAQoAAACAogQoAAAAAIoSoAAAAAAoSoACAAAAoCgBCgAAAICiBCgAAAAAiioaoJqamlJfX59HH300SfKDH/wg9fX1GTx4cM4999xs2rQpSfLggw9m6NChGTBgQMaOHZvNmzcnSR5//PEMHz48AwcOzBlnnJH169cnSdauXZsRI0Zk0KBBGT58eFauXFnyNAAAAAB4BYoFqHvuuScnn3xylixZkiT585//nO9+97u55ppr8pOf/CStra2ZPXt2kmT06NEZP358FixYkLa2tsyZMydJMnHixAwbNiyNjY3p06dPpk+fniSZNm1a6urqcsMNN+TEE0/M5MmTS50GAAAAAK9QsQA1Z86cTJgwIbW1tUmSrl27ZsKECamqqkpFRUXe9ra35fHHH89jjz2WjRs3pm/fvkmSoUOHprGxMc3NzVm0aFEGDBiw1XiSLFy4MIMHD06S1NfX55Zbbklzc3OpUwEAAADgFags9cQv3JXUq1ev9OrVK0myevXqXHXVVZkyZUpWrFiRmpqa9nk1NTVZvnx51qxZk6qqqlRWVm41nmSrYyorK1NVVZXVq1enZ8+epU4HAAAAgL9RsQC1PcuXL8/pp5+e448/PocddlgWL16cioqK9q+3tbWloqKi/c8tvfDxlsd06rRzm7n22adq5xfPq0ZNTfddvQSgEK/v1xbX+7XF9QaA164ODVB/+tOfcvrpp+eUU07JJz/5ySTJvvvuu9WbiK9atSq1tbXp0aNH1q1bl5aWlnTu3DkrV65sv52vtrY2q1atyr777pvNmzdn/fr1qa6u3qm1PPVUU1pb2172fL8wvbqsXLluVy+B3YjX96tL6de36/3q4nq/tvjvNwDsvjp1qnjJzT5FPwVvS01NTfnUpz6VkSNHtsen5Llb87p165bFixcnSebNm5d+/fqlS5cuqaury/z585MkDQ0N6devX5Kkf//+aWhoSJLMnz8/dXV16dKlS0edCgAAAAA7ocMC1Ny5c7Nq1ap8//vfz5AhQzJkyJBceumlSZKpU6dmypQpGThwYDZs2JBTTz01STJhwoTMmTMnxxxzTO68886MGjUqSTJy5MjcfffdOfbYYzN79uyMHz++o04DAAAAgJ1U/Ba8m2++OUly2mmn5bTTTnvROb17987cuXO3Ge/Vq1dmzpy5zXh1dXWuuOKKv+s6AQAAACijw4sx7pcAACAASURBVHZAAQAAAPDaJEABAAAAUJQABQAAAEBRAhQAAAAARQlQAAAAABQlQAEAAABQlAAFAAAAQFECFAAAAABFCVAAAAAAFCVAAQAAAFCUAAUAAABAUQIUAAAAAEUJUAAAAAAUJUABAAAAUJQABQAAAEBRAhQAAAAARQlQAAAAABQlQAEAAABQlAAFAAAAQFECFAAAAABFCVAAAAAAFCVAAQAAAFCUAAUAAABAUQIUAAAAAEUJUAAAAAAUJUABAAAAUJQABQAAAEBRAhQAAAAARQlQAAAAABQlQAEAAABQlAAFAAAAQFECFAAAAABFCVAAAAAAFCVAAQAAAFCUAAUAAABAUQIUAAAAAEUJUAAAAAAUJUABAAAAUJQABQAAAEBRAhQAAAAARQlQAAAAABQlQAEAAABQlAAFAAAAQFECFAAAAABFCVAAAAAAFCVAAQAAAFCUAAUAAABAUQIUAAAAAEUJUAAAAAAUJUABAAAAUJQABQAAAEBRAhQAAAAARQlQAAAAABQlQAEAAABQlAAFAAAAQFECFAAAAABFCVAAAAAAFCVAAQAAAFCUAAUAAABAUQIUAAAAAEUJUAAAAAAUJUABAAAAUJQABQAAAEBRAhQAAAAARQlQAAAAABQlQAEAAABQlAAFAAAAQFECFAAAAABFCVAAAAAAFCVAAQAAAFCUAAUAAABAUQIUAAAAAEUJUAAAAAAUJUABAAAAUJQABQAAAEBRAhQAAAAARQlQAAAAABQlQAEAAABQlAAFAAAAQFECFAAAAABFCVAAAAAAFCVAAQAAAFCUAAUAAABAUQIUAAAAAEUJUAAAAAAUJUABAAAAUJQABQAAAEBRAhQAAAAARQlQAAAAABQlQAEAAABQlAAFAAAAQFECFAAAAABFCVAAAAAAFCVAAQAAAFCUAAUAAABAUQIUAAAAAEUJUAAAAAAUJUABAAAAUJQABQAAAEBRAhQAAAAARQlQAAAAABQlQAEAAABQlAAFAAAAQFECFAAAAABFCVAAAAAAFCVAAQAAAFCUAAUAAABAUQIUAAAAAEUJUAAAAAAUJUABAAAAUJQABQAAAEBRAhQAAAAARQlQAAAAABQlQAEAAABQlAAFAAAAQFECFAAAAABFCVAAAAAAFCVAAQAAAFCUAAUAAABAUQIUAAAAAEUVDVBNTU2pr6/Po48+miS57bbbMnjw4Hz4wx/ON7/5zfZ5Dz74YIYOHZoBAwZk7Nix2bx5c5Lk8ccfz/DhwzNw4MCcccYZWb9+fZJk7dq1GTFiRAYNGpThw4dn5cqVJU8DAAAAgFegWIC65557cvLJJ2fJkiVJko0bN+a8887L9OnTM3/+/Nx///355S9/mSQZPXp0xo8fnwULFqStrS1z5sxJkkycODHDhg1LY2Nj+vTpk+nTpydJpk2blrq6utxwww058cQTM3ny5FKnAQAAAMArVCxAzZkzJxMmTEhtbW2S5N57782b3vSm7L///qmsrMzgwYPT2NiYxx57LBs3bkzfvn2TJEOHDk1jY2Oam5uzaNGiDBgwYKvxJFm4cGEGDx6cJKmvr88tt9yS5ubmUqcCAAAAwCtQWeqJX7gracWKFampqWl/XFtbm+XLl28zXlNTk+XLl2fNmjWpqqpKZWXlVuMvfK7KyspUVVVl9erV6dmzZ6nTAQAAAOBvVCxAvVBra2sqKiraH7e1taWiomK748//uaUXPt7ymE6ddm4z1z77VO3UfF5damq67+olAIV4fb+2uN6vLa43ALx2dViA2nfffbd6s/CVK1emtrZ2m/FVq1altrY2PXr0yLp169LS0pLOnTu3z0+e2z21atWq7Lvvvtm8eXPWr1+f6urqnVrPU081pbW17WXP9wvTq8vKlet29RLYjXh9v7qUfn273q8urvdri/9+A8Duq1Onipfc7FP0U/C2dMghh+TPf/5zli5dmpaWlvz0pz9Nv3790qtXr3Tr1i2LFy9OksybNy/9+vVLly5dUldXl/nz5ydJGhoa0q9fvyRJ//7909DQkCSZP39+6urq0qVLl446FQAAAAB2QoftgOrWrVu+/vWv56yzzsqzzz6b/v37Z+DAgUmSqVOnZty4cWlqasrBBx+cU089NUkyYcKEjBkzJt/+9rez33775ZJLLkmSjBw5MmPGjMmxxx6b7t27Z+rUqR11GgAAAADspOIB6uabb27/+3vf+9785Cc/2WZO7969M3fu3G3Ge/XqlZkzZ24zXl1dnSuuuOLvu1AAAAAAiuiwW/AAAAAAeG0SoAAAAAAoSoACAAAAoCgBCgAAAICiBCgAAAAAihKgAAAAAChKgAIAAACgKAEKAAAAgKIEKAAAAACKEqAAAAAAKEqAAgAAAKAoAQoAAACAogQoAAAAAIoSoAAAAAAoSoACAAAAoCgBCgAAAICiBCgAAAAAihKgAAAAAChKgAIAAACgKAEKAAAAgKIEKAAAAACK2qkAtXz58tx5552l1gIAAADAbmiHAWr27Nn54he/mNWrV2fo0KEZO3ZsvvGNb3TE2gAAAADYDewwQM2dOzfnnntuGhsb88EPfjDXX399br311o5YGwAAAAC7gR0GqIqKirzhDW/I7bffnn//939PZWVlWltbO2JtAAAAAOwGdhigunbtmu985zv57W9/myOOOCKzZ8/OHnvs0RFrAwAAAGA3sMMANXny5CxZsiQXXnhhXv/612fx4sWZNGlSR6wNAAAAgN1A5Y4mvOUtb8nkyZOTPPcpeCeffHIOPPDA4gsDAAAAYPfgU/AAAAAAKMqn4AEAAABQlE/BAwAAAKAon4IHAAAAQFE+BQ8AAACAonbqU/CS5Bvf+EY2bNhQdFEAAAAA7D52GKBuvPHGXHbZZdmwYUPa2trS2tqap59+OnfddVdHrA8AAACAf3A7DFAXXXRRRo0alauvvjqf/vSnc+ONN2avvfbqiLUBAAAAsBvY4XtA7bHHHjnmmGPSt2/fdOvWLeeff34WLlzYAUsDAAAAYHewwwDVrVu3bNq0KQcccEAefPDBdOrUKRUVFR2xNgAAAAB2Azu8Be8DH/hARowYkQsvvDAf+9jHsnjx4uy9994dsTYAAAAAdgM7DFCf/exn8x//8R/p2bNnpk+fnkWLFqW+vr4j1gYAAADAbmC7AepnP/vZVo/vv//+JMl+++2XxYsX58Mf/nDZlQEAAACwW9hugJo5c+Z2D6qoqBCgAAAAAHhZXlaAWrZsWfbff/80NTXlL3/5S975znd2yOIAAAAA+Me3w0/BmzVrVs4888wkyZo1a3LWWWflhz/8YfGFAQAAALB72GGAuuaaa3L11VcnSfbff/80NDTkf//3f4svDAAAAIDdww4DVEtLS6qqqtofd+/ePRUVFUUXBQAAAMDuY4cB6i1veUumTp2aZcuWZdmyZbn00kvzL//yLx2wNAAAAAB2BzsMUBMnTszSpUvzkY98JCeccEKWLFmS888/vwOWBgAAAMDuYLufgve8N7zhDbn88ss7Yi0AAAAA7IZ2uAMKAAAAAF4JAQoAAACAonYYoFatWtUR6wAAAABgN7XDAPWf//mfHbEOAAAAAHZTOwxQvXr1yu9+97u0trZ2xHoAAAAA2M3s8FPw/vSnP2XYsGGprKxM165d09bWloqKivzud7/riPUBAAAA8A9uhwHqqquu6oh1AAAAALCbelm34N13332ZM2dOevTokbvuuiu9evXqiLUBAAAAsBvYYYCaMWNGrr766jQ2Nmbjxv/P3t0HWVnf9/9/LbvLKgVvwF0h1DA1mjDeRCbiDZJZTZoRjCWJK0kNdnSc1qg1Wq3BKK4QEhOsRWlswPlpTSZD/AeNglK6ihqMSjJVJtMMija1iojOuqggoCyw7O+PjvuNUXLQ+DkLh8djhsHz2Ws57zOfucB5znVduyU/+tGPMnfu3GrMBgAAAEANqBig/v3f/z233XZb9t133xx44IFZsGBBFi9eXI3ZAAAAAKgBFQPUOw8ff8d+++2XhoaKj44CAAAAgCS78BDyESNGZNmyZamrq8vWrVtz++23ewYUAAAAALusYoC69tprc+WVV+bZZ5/NMccckzFjxuTGG2+sxmwAAAAA1ICKAerggw/OT3/607z99tvp6enJ4MGDqzEXAAAAADWiYoDavHlz5s6dm8ceeyz19fX5/Oc/nwsuuOBdz4UCAAAAgJ2p+BDy9vb2dHZ25uqrr87UqVPz3HPP5brrrqvGbAAAAADUgIpXQD399NO5//77+16feOKJOf3004sOBQAAAEDtqHgFVEtLS15//fW+12+99VYOPPDAokMBAAAAUDsqXgE1fPjwnHnmmZk4cWLq6+vz0EMP5aCDDuq7Da+9vb34kAAAAADsuSoGqFGjRmXUqFF9r91+BwAAAMAHUTFAffOb36zGHAAAAADUqIrPgAIAAACAP4UABQAAAEBRAhQAAAAARVUMUM8991zuvPPO9Pb25rLLLssXvvCF/PrXv67GbAAAAADUgIoBasaMGWlqasqyZcvS2dmZ73//+5kzZ041ZgMAAACgBlQMUN3d3fnSl76Uxx57LKeddlpOOOGEbNu2rRqzAQAAAFADKgaorVu3Zt26dVm2bFlOOumkrFu3Lt3d3dWYDQAAAIAaUDFA/fVf/3U+97nP5dhjj81hhx2WyZMn59xzz63GbAAAAADUgIZKB0yZMiVnnXVWBgz4v1Z1zz335MADDyw+GAAAAAC1oeIVUJs3b851112Xc889N+vXr8+cOXOyefPmaswGAAAAQA2oGKCuu+66DBkyJK+99lqampqyadOmTJ8+vRqzAQAAAFADKgaoVatW5fLLL09DQ0P23XffzJ49O6tWrarGbAAAAADUgIoB6p1nP72jp6fnPWsAAAAAsDMVH0J+3HHH5Z//+Z+zZcuWPProo/nZz36WE044oRqzAQAAAFADKl7K9K1vfSuDBg3KkCFDMmfOnIwePTpXXnllNWYDAAAAoAZUvAKqsbExxx9/fC6++OKsX78+Tz75ZJqamqoxGwAAAAA1oOIVUHPmzMnNN9+cJNmyZUtuvfXWzJs3r/hgAAAAANSGigHqoYceyo9//OMkyfDhw/Ozn/0sS5YsKT4YAAAAALWhYoDatm1bGhsb+143Njamrq6u6FAAAAAA1I6Kz4D6zGc+kyuuuCKTJ09OXV1dFi5cmGOOOaYaswEAAABQAypeAXXttdfmoIMOyqxZs3LDDTdk2LBhueaaa6oxGwAAAAA1oOIVUIMGDcrVV19djVkAAAAAqEEVA9RvfvOb3HTTTdmwYUN6e3v71u+7776igwEAAABQGyoGqOnTp6etrS1HHHGEh48DAAAA8IFVDFANDQ0577zzqjELAAAAADWo4kPIDz/88Dz77LPVmAUAAACAGlTxCqg1a9bkzDPPzMc+9rE0NTX1rXsGFAAAAAC7omKAuvzyy6sxBwAAAAA1qmKAOv7447N+/fq8/fbb6e3tTU9PT1588cVqzAYAAABADagYoH74wx/m1ltvTZLU19dn27ZtOeyww9yCBwAAAMAuqfgQ8kWLFuUXv/hFJkyYkAceeCCzZs3KYYcdVo3ZAAAAAKgBFQPU0KFD09LSkkMPPTTPPPNMvvKVr+S///u/qzEbAAAAADWgYoBqaGjIiy++mEMPPTRPPvlktm/fnu7u7mrMBgAAAEANqBigLrjgglx77bU55ZRTsnTp0pxyyik58cQTqzEbAAAAADWg4kPIjzjiiPz0pz9NkixcuDCrV6/OgAEVuxUAAAAAJPkjV0CtX78+69evz/nnn58NGzZk/fr16e7uzkEHHZRLL720mjMCAAAAsAfb6RVQV1xxRR5//PEkyQknnPD/vqGhIRMmTCg/GQAAAAA1YacB6vbbb0+SXH311Zk1a1bVBgIAAACgtlR8mNMPfvCDrFmzJkmybNmyzJ07Nxs3biw+GAAAAAC1oWKAmjFjRm677bb8z//8T9rb2/PSSy9l2rRp1ZgNAAAAgBpQMUCtXLky3/nOd/Lggw/mjDPOyKxZs7J27dpqzAYAAABADagYoHp7ezNgwIA8/vjjOfHEE5MkW7ZsKT4YAAAAALWhYoD6+Mc/nvPPPz8vvfRSjj/++FxxxRX51Kc+VY3ZAAAAAKgBO/0peO+YNWtWli5dmmOPPTaNjY0ZO3ZsvvKVr1RjNgAAAABqQMUroAYNGpSxY8fmzTffzFNPPZVPf/rT+d///d9qzAYAAABADah4BdQPf/jD/PjHP86wYcP61urq6vLQQw8VHQwAAACA2lAxQC1atCgPPPBADj744GrMAwAAAECNqXgL3ogRI8QnAAAAAD60ildAjRs3LjfccEP+8i//Mvvss0/f+pFHHll0MAAAAABqQ8UAdffddydJOjo6+tY8AwoAAACAXVUxQD388MPVmAMAAACAGrXTAHXbbbfl/PPPz3XXXfe+X29vb//Qb7po0aLceuutSZLW1tZ8+9vfzvLlyzNr1qx0d3fntNNOy+WXX54kWbVqVa655pps3rw5Y8eOzcyZM9PQ0JCXX345U6dOzWuvvZa/+Iu/yOzZs/Nnf/ZnH3omAAAAAMrY6UPIhwwZkiQ54IAD3vfXh/X222/n+9//fubPn59FixblySefzMMPP5xp06Zl3rx5WbJkSVauXJlHHnkkSTJ16tRMnz49999/f3p7e7NgwYIkycyZMzNlypR0dHTkqKOOyrx58z70TAAAAACUs9MroM4666wkyTe/+c2P9A17enqyY8eOvP322xk0aFC2b9+ewYMHZ9SoUTnkkEOSJJMmTUpHR0cOO+ywbNmyJWPGjEmStLW15eabb85Xv/rVPPHEE5k7d27f+t/8zd9k6tSpH+msAAAAAPzpKj4D6qM2ePDg/MM//ENOO+207LvvvjnuuOPy6quvprm5ue+YlpaWdHZ2vme9ubk5nZ2deeONNzJ48OA0NDS8ax0AAACA3U/VA9QzzzyTn//85/nFL36RIUOG5Fvf+lZeeOGF1NXV9R3T29uburq67Nix433X3/n99/3h60qGDRv8p30Q+lVz85D+HgEoxPm9d7Hfexf7DQB7r50GqH/5l3/JZZddlhUrVuTYY4/9yN7wsccey7hx4zJs2LAk/3f73O233576+vq+Y7q6utLS0pLhw4enq6urb33dunVpaWnJ0KFDs3HjxvT09KS+vr7v+A/itdc2ZceO3l0+3v8w7V66ujb29wjUEOf37qX0+W2/dy/2e+/i328AqF0DBtT90Yt9dvoQ8sWLF6ezszMzZ87Mhg0bsn79+nf9+rBGjx6d5cuX56233kpvb28efvjhHHPMMXn++eezevXq9PT0ZPHixWltbc3IkSPT1NSUFStWJPm/n57X2tqaxsbGjB07NkuWLEmSLFy4MK2trR96JgAAAADK2ekVUOPHj88pp5ySJDnhhBPe9bW6urqsWrXqQ73hZz/72Tz99NNpa2tLY2Njjj766FxyySUZP358LrnkknR3d+fkk0/OxIkTkySzZ89Oe3t7Nm3alCOPPDLnnHNOkmTGjBm56qqrcsstt2TEiBG56aabPtQ8AAAAAJS10wA1c+bMzJw5M2effXbuuOOOj/RNv/GNb+Qb3/jGu9bGjRuXe++99z3Hjh49Onfdddd71keOHJn58+d/pHMBAAAA8NGr+BDyO+64I//1X/+VRx99NNu2bctnP/vZHHfccdWYDQAAAIAasNNnQL1j0aJFufTSS7Nhw4Zs3rw5//iP/5gFCxZUYzYAAAAAakDFK6B+8pOf5M477+z7KXPnn39+/vZv/zZf+9rXig8HAAAAwJ6v4hVQO3bs6ItPSXLwwQdnwICK3wYAAAAASXYhQB1wwAF58MEH+14/+OCD2X///YsOBQAAAEDtqHgL3rXXXpu///u/z/e+973U1dWloaEhc+fOrcZsAAAAANSAigHq8MMPT0dHR1544YX09PTk0EMPTUNDxW8DAAAAgCS7EKCSpL6+Pp/4xCdKzwIAAABADfI0cQAAAACKEqAAAAAAKGqXAtSaNWuSJMuWLcvcuXOzcePGokMBAAAAUDsqBqjp06fntttuy3PPPZf29va89NJLmTZtWjVmAwAAAKAGVAxQK1euzHe+850sXbo0Z5xxRmbNmpW1a9dWYzYAAAAAakDFANXb25sBAwbk8ccfz4knnpgk2bJlS/HBAAAAAKgNFQPUxz/+8Zx//vl56aWXcvzxx+eKK67Ipz71qWrMBgAAAEANaKh0wKxZs7J06dIce+yxaWxszNixY3PGGWdUYzYAAAAAakDFK6Cuu+66fPnLX86f//mfJ0m+/vWv58orryw+GAAAAAC1YadXQM2YMSOdnZ1ZsWJFXn/99b717du3Z82aNVUZDgAA4P0cuP++aRhY8YYOqmD71u15Y8Pb/T0GsJvb6d/YkydPzu9+97s8++yzmTBhQt96fX19xowZU5XhAAAA3k/DwIb817xl/T0GSY75+1P6ewRgD7DTAHX00Ufn6KOPzkknnZThw4dXcyYAAAAAakjFa1ZfeeWVTJ06NRs2bEhvb2/f+n333Vd0MAAAAABqQ8UANX369LS1teWII45IXV1dNWYCAAAAoIZUDFANDQ0577zzqjELAAAAADVoQKUDDj/88Dz77LPVmAUAAACAGlTxCqg1a9bkzDPPzMc+9rE0NTX1rXsGFAAAAAC7omKAuvzyy6sxBwAAAAA1qmKA+uQnP1mNOQAAAACoURUD1Iknnpi6urr09vb2/RS85ubm/PKXvyw+HAAAAAB7vooB6plnnun7761bt2bx4sV5/vnniw4FAAAAQO2o+FPwft/AgQPT1taWxx9/vNQ8AAAAANSYildArV+/vu+/e3t7s3Llyrz55ptFhwIAAACgdnygZ0AlybBhw3LNNdcUHwwAAACA2vCBngEFAAAAAB9UxQC1Y8eO3H777fnlL3+Z7du3Z/z48bnwwgvT0FDxWwEAAACg8kPIb7zxxvz617/Oueeem/POOy+/+c1vcsMNN1RjNgAAAABqQMXLmB599NH8/Oc/T2NjY5LklFNOyZe+9KVMmzat+HAAAAAA7PkqXgHV29vbF5+SZODAge96DQAAAAB/TMUANXr06PzgBz/Iiy++mDVr1mTWrFn55Cc/WY3ZAAAAAKgBFQPUjBkz8uabb+ass87KV7/61bz++uu59tprqzEbAAAAADWg4jOgBg8enOuvvz5J0t3dnaampuJDAQAAAFA7dnoF1NatW/Ptb387S5cu7Vu79NJLc/XVV2f79u1VGQ4AAACAPd9OA9TNN9+cTZs25TOf+Uzf2ne/+91s2LAh//qv/1qV4QAAAADY8+00QC1btiw33nhjhg0b1rd28MEH54YbbsiDDz5YleEAAAAA2PPtNEA1NjZmn332ec/64MGDM3DgwKJDAQAAAFA7dhqgBgwYkE2bNr1nfdOmTZ4BBQAAAMAu22mA+qu/+qu0t7fnrbfe6lt766230t7enlNPPbUqwwEAAACw59tpgDr33HMzZMiQjB8/Pl/72tcyefLkjB8/Pvvtt18uvvjias4IAAAAwB6sYWdfGDBgQL73ve/lwgsvzFNPPZUBAwbk05/+dFpaWqo5HwAAAAB7uJ0GqHeMHDkyI0eOrMYsAAAAANSgnd6CBwAAAAAfBQEKAAAAgKIEKAAAAACKEqAAAAAAKEqAAgAAAKAoAQoAAACAogQoAAAAAIoSoAAAAAAoSoACAAAAoCgBCgAAAICiBCgAAAAAihKgAAAAAChKgAIAAACgKAEKAAAAgKIEKAAAAACKEqAAAAAAKEqAAgAAAKAoAQoAAACAogQoAAAAAIoSoAAAAAAoSoACAAAAoCgBCgAAAICiBCgAAAAAihKgAAAAAChKgAIAAACgKAEKAAAAgKIEKAAAAACKEqAAAAAAKEqAAgAAAKAoAQoAAACAogQoAAAAAIoSoAAAAAAoSoACAAAAoCgBCgAAAICiBCgAAAAAihKgAAAAAChKgAIAAACgKAEKAAAAgKIEKAAAAACKEqAAAAAAKEqAAgAAAKAoAQoAAACAogQoAAAAAIoSoAAAAAAoSoACAAAAoCgBCgAAAICiBCgAAAAAihKgAAAAAChKgAIAAACgKAEKAAAAgKIEKAAAAACKEqAAAAAAKEqAAgAAAKAoAQoAAACAogQoAAAAAIoSoAAAAAAoSoACAAAAoCgBCgAAAICiBCgAAAAAihKgAAAAAChKgAIAAACgKAEKAAAAgKIEKAAAAACKEqAAAAAAKEqAAgAAAKAoAQoAAACAogQoAAAAAIoSoAAAAAAoSoACAAAAoCgBCgAAAICiBCgAAAAAiuqXAPXwww+nra0tp512Wq677rokyfLlyzNp0qSceuqpmTNnTt+xq1atSltbWyZMmJBrrrkm27dvT5K8/PLLOfvsszNx4sRcdNFF2bx5c398FAAAAAAqqHqAWrNmTWbMmJF58+bl3nvvzdNPP51HHnkk06ZNy7x587JkyZKsXLkyjzzySJJk6tSpmT59eu6///709vZmwYIFSZKZM2dmypQp6ejoyFFHHZV58+ZV+6MAAAAAsAuqHqCWLl2aL37xixk+fHgaGxszZ86c7Lvvvhk1alQOOeSQNDQ0ZNKkSeno6MjatWuzZcuWjBkzJknS1taWjo6ObNu2LU888UQmTJjwrnUAAAAAdj8N1X7D1atXp7GxMRdeeGFeeeWVnHLKKTn88MPT3Nzcd0xLS0s6Ozvz6quvvmu9ubk5nZ2deeONNzJ48OA0NDS8ax0AAACA3U/VA1RPT0+efPLJzJ8/P4MGDcpFF12UffbZJ3V1dX3H9Pb2pq6uLjt27Hjf9Xd+/31/+LqSYcMG/2kfhH7V3Dykv0cACnF+713s997FfkPtcn4DlVQ9QB100EEZN25chg4dmiT5whe+kI6OjtTX1/cd09XVrQbzUAAAIABJREFUlZaWlgwfPjxdXV196+vWrUtLS0uGDh2ajRs3pqenJ/X19X3HfxCvvbYpO3b07vLx/kLdvXR1bezvEaghzu/dS+nz237vXuz33sW/33yUnN+7F+c3MGBA3R+92Kfqz4D63Oc+l8ceeyxvvvlmenp68uijj2bixIl5/vnns3r16vT09GTx4sVpbW3NyJEj09TUlBUrViRJFi1alNbW1jQ2Nmbs2LFZsmRJkmThwoVpbW2t9kcBAAAAYBdU/QqoY445Jn/3d3+XKVOmZNu2bRk/fny+/vWv59BDD80ll1yS7u7unHzyyZk4cWKSZPbs2Wlvb8+mTZty5JFH5pxzzkmSzJgxI1dddVVuueWWjBgxIjfddFO1PwoAAAAAu6DqASpJJk+enMmTJ79rbdy4cbn33nvfc+zo0aNz1113vWd95MiRmT9/frEZAQAAAPhoVP0WPAAAAAD2LgIUAAAAAEUJUAAAAAAUJUABAAAAUJQABQAAAEBRAhQAAAAARQlQAAAAABQlQAEAAABQlAAFAAAAQFECFAAAAABFCVAAAAAAFCVAAQAAAFCUAAUAAABAUQIUAAAAAEUJUAAAAAAUJUABAAAAUFRDfw8AAAAfhf33G5iBTU39PQZJtnZ3Z8ObW/t7DAB2IwIUAAA1YWBTU266+oL+HoMk/zjr/0siQAHw/7gFDwAAAICiBCgAAAAAihKgAAAAAChKgAIAAACgKAEKAAAAgKIEKAAAAACKEqAAAAAAKEqAAgAAAKAoAQoAAACAogQoAAAAAIoSoAAAAAAoSoACAAAAoCgBCgAAAICiBCgAAAAAihKgAAAAAChKgAIAAACgKAEKAAAAgKIEKAAAAACKEqAAAAAAKEqAAgAAAKAoAQoAAACAogQoAAAAAIoSoAAAAAAoSoACAAAAoCgBCgAAAICiBCgAAAAAihKgAAAAAChKgAIAAACgKAEKAAAAgKIEKAAAAACKEqAAAAAAKEqAAgAAAKAoAQoAAACAogQoAAAAAIoSoAAAAAAoSoACAAAAoCgBCgAAAICiBCgAAAAAihKgAAAAAChKgAIAAACgKAEKAAAAgKIEKAAAAACKEqAAAAAAKEqAAgAAAKAoAQoAAACAogQoAAAAAIoSoAAAAAAoSoACAAAAoCgBCgAAAICiBCgAAAAAihKgAAAAAChKgAIAAACgKAEKAAAAgKIEKAAAAACKEqAAAAAAKEqAAgAAAKAoAQoAAACAogQoAAAAAIoSoAAAAAAoSoACAAAAoCgBCgAAAICiBCgAAAAAihKgAAAAAChKgAIAAACgKAEKAAAAgKIEKAAAAACKEqAAAAAAKEqAAgAAAKAoAQoAAACAogQoAAAAAIoSoAAAAAAoSoACAAAAoCgBCgAAAICiBCgAAAAAihKgAAAAAChKgAIAAACgKAEKAAAAgKIEKAAAAACKEqAAAAAAKEqAAgAAAKAoAQoAAACAogQoAAAAAIoSoAAAAAAoSoACAAAAoCgBCgAAAICiBCgAAAAAihKgAAAAAChKgAIAAACgKAEKAAAAgKIEKAAAAACKEqAAAAAAKEqAAgAAAKAoAQoAAACAogQoAAAAAIoSoAAAAAAoSoACAAAAoCgBCgAAAICiBCgAAAAAihKgAAAAAChKgAIAAACgKAEKAAAAgKIEKAAAAACKEqAAAAAAKKpfA9Q//dM/5aqrrkqSLF++PJMmTcqpp56aOXPm9B2zatWqtLW1ZcKECbnmmmuyffv2JMnLL7+cs88+OxMnTsxFF12UzZs398tnAAAAAOCP67cA9atf/Sr33HNPkmTLli2ZNm1a5s2blyVLlmTlypV55JFHkiRTp07N9OnTc//996e3tzcLFixIksycOTNTpkxJR0dHjjrqqMybN6+/PgoAAAAAf0S/BKj169dnzpw5ufDCC5Mkv/3tbzNq1KgccsghaWhoyKRJk9LR0ZG1a9dmy5YtGTNmTJKkra0tHR0d2bZtW5544olMmDDhXesAAAAA7H4a+uNNp0+fnssvvzyvvPJKkuTVV19Nc3Nz39dbWlrS2dn5nvXm5uZ0dnbmjTfeyODBg9PQ0PCu9Q9i2LDBH8Enob80Nw/p7xGAQpzfexf7vXex33sX+713sd9AJVUPUHfeeWdGjBiRcePG5e67706S7NixI3V1dX3H9Pb2pq6ubqfr7/z++/7wdSWvvbYpO3b07vLx/kLdvXR1bezvEaghzu/dS+nz237vXuz33sV+713s997F/58DAwbU/dGLfaoeoJYsWZKurq58+ctfzoYNG/LWW29l7dq1qa+v7zumq6srLS0tGT58eLq6uvrW161bl5aWlgwdOjQbN25MT09P6uvr+44HAAAAYPdT9WdA/eQnP8nixYuzaNGiXHrppfn85z+ff/u3f8vzzz+f1atXp6enJ4sXL05ra2tGjhyZpqamrFixIkmyaNGitLa2prGxMWPHjs2SJUuSJAsXLkxra2u1PwoAAAAAu6BfngH1h5qamnL99dfnkksuSXd3d04++eRMnDgxSTJ79uy0t7dn06ZNOfLII3POOeckSWbMmJGrrroqt9xyS0aMGJGbbrqpPz8CAAAAADvRrwGqra0tbW1tSZJx48bl3nvvfc8xo0ePzl133fWe9ZEjR2b+/PnFZwQAAADgT1P1W/AAAAAA2LsIUAAAAAAUJUABAAAAUJQABQAAAEBRAhQAAAAARQlQAAAAABQlQAEAAABQlAAFAAAAQFECFAAAAABFCVAAAAAAFCVAAQAAAFCUAAUAAABAUQIUAAAAAEUJUAAAAAAUJUABAAAAUJQABQAAAEBRAhQAAAAARQlQAAAAABQlQAEAAABQlAAFAAAAQFECFAAAAABFCVAAAAAAFCVAAQAAAFCUAAUAAABAUQIUAAAAAEUJUAAAAAAUJUABAAAAUJQABQAAAEBRAhQAAAAARQlQAAAAABQlQAEAAABQlAAFAAAAQFECFAAAAABFCVAAAAAAFCVAAQAAAFCUAAUAAABAUQIUAAAAAEUJUAAAAAAUJUABAAAAUJQABQAAAEBRAhQAAAAARQlQAAAAABQlQAEAAABQlAAFAAAAQFEN/T0A7I4O3H9gGgY29fcYJNm+tTtvbNja32MAAADwJxCg4H00DGzKihv+rr/HIMmxV/5bEgEKAABgT+YWPAAAAACKEqAAAAAAKEqAAgAAAKAoAQoAAACAogQoAAAAAIoSoAAAAAAoSoACAAAAoCgBCgAAAICiBCgAAAAAihKgAAAAAChKgAIAAACgKAEKAAAAgKIEKAAAAACKEqAAAAAAKEqAAgAAAKAoAQoAAACAogQoAAAAAIoSoAAAAAAoSoACAAAAoCgBCgAAAICiBCgAAAAAihKgAAAAAChKgAIAAACgKAEKAAAAgKIEKAAAAACKEqAAAAAAKEqAAgAAAKAoAQoAAACAogQoAAAAAIoSoAAAAAAoSoACAAAAoCgBCgAAAICiBCgAAAAAihKgAAAAAChKgAIAAACgKAEKAAAAgKIEKAAAAACKEqAAAAAAKEqAAgAAAKCohv4eAAAAAOAdBx7QlIbGgf09Bkm2b9uaN9Z3fyR/lgAFAAAA7DYaGgfml4u/099jkKT1r76T5KMJUG7BAwAAAKAoAQoAAACAogQoAAAAAIoSoAAAAAAoSoACAAAAoCgBCgAAAICiBCgAAAAAihKgAAAAAChKgAIAAACgKAEKAAAAgKIEKAAAAACKEqAAAAAAKEqAAgAAAKAoAQoAAACAogQoAAAAAIoSoAAAAAAoSoACAAAAoCgBCgAAAICiBCgAAAAAihKgAAAAAChKgAIAAACgKAEKAAAAgKIEKAAAAACKEqAAAAAAKEqAAgAAAKAoAQoAAACAogQoAAAAAIoSoAAAAAAoSoACAAAAoCgBCgAAAICiBCgAAAAAihKgAAAAAChKgAIAAACgqH4JUD/60Y9y+umn5/TTT88NN9yQJFm+fHkmTZqUU089NXPmzOk7dtWqVWlra8uECRNyzTXXZPv27UmSl19+OWeffXYmTpyYiy66KJs3b+6PjwIAAABABVUPUMuXL89jjz2We+65JwsXLsxTTz2VxYsXZ9q0aZk3b16WLFmSlStX5pFHHkmSTJ06NdOnT8/999+f3t7eLFiwIEkyc+bMTJkyJR0dHTnqqKMyb968an8UAAAAAHZB1QNUc3NzrrrqqgwcODCNjY35xCc+kRdeeCGjRo3KIYcckoaGhkyaNCkdHR1Zu3ZttmzZkjFjxiRJ2tra0tHRkW3btuWJJ57IhAkT3rUOAAAAwO6n6gHq8MMP7wtKL7zwQv7jP/4jdXV1aW5u7jumpaUlnZ2defXVV9+13tzcnM7OzrzxxhsZPHhwGhoa3rUOAAAAwO6nob/e+He/+10uuOCCXHnllamvr88LL7zQ97Xe3t7U1dVlx44dqaure8/6O7//vj98XcmwYYP/pPnpX83NQ/p7BKrIfu9d7PfexX7vXez33sV+713sN9Suj+r87pcAtWLFilx66aWZNm1aTj/99Pznf/5nurq6+r7e1dWVlpaWDB8+/F3r69atS0tLS4YOHZqNGzemp6cn9fX1fcd/EK+9tik7dvTu8vH+Qt29dHVtLPrn2+/di/3eu9jvvYv93rvY772L/d67lN5v9i7O793Lrp7fAwbU/dGLfap+C94rr7ySiy++OLNnz87pp5+eJDnmmGPy/PPPZ/Xq1enp6cnixYvT2tqakSNHpqmpKStWrEiSLFq0KK2trWlsbMzYsWOzZMmSJMnChQvT2tpa7Y8CAAAAwC6o+hVQt99+e7q7u3P99df3rZ111lm5/vrrc8kll6S7uzsnn3xyJk6cmCSZPXt22tvbs2nTphx55JE555xzkiQzZszIVVddlVtuuSUjRozITTfdVO2PAgAAAMAuqHqAam9vT3t7+/t+7d57733P2ujRo3PXXXe9Z33kyJGZP3/+Rz4fAAAAAB+tqt+CBwAAAMDeRYACAAAAoCgBCgAAAICiBCgAAAAAihKgAAAAAChKgAIAAACgKAEKAAAAgKIEKAAAAACKEqAAAAAAKEqAAgAAAKAoAQoAAACAogQoAAAAAIoSoAAAAAAoSoACAAAAoCgBCgAAAICiBCgAAAAAihKgAAAAAChKgAIAAACgKAEKAAAAgKIEKAAAAACKEqAAAAAAKKqhvwcAAACAP2b//ffJwIGN/T0GSbZu3ZYNG7b09xjsgQQoAAAAdmsDBzbmxhtv7O8xSHLFFVckEaD44NyCBwAAAEBRAhQAAAAARQlQAAAAABQlQAEAAABQlAAFAAAAQFECFAAAAABFCVAAAAAAFCVAAQAAAFCUAAUAAABAUQIUAAAAAEUJUAAAAAAUJUABAAAAUJQABQAAAEBRAhQAAAAARQlQAAAAABQlQAEAAABQlAAFAAAAQFECFAAAAABFCVAAAAAAFCVAAQAAAFCUAAUAAABAUQIUAAAAAEUJUAAAAAAUJUABAAAAUJQABQAAAEBRAhQAAAAARQlQAAAAABQlQAEAAABQlAAFAAAAQFECFAAAAABFCVAAAAAAFCVAAQAAAFCUAAUAAABAUQIUAAAAAEUJUAAAAAAUJUABAAAAUJQABQAAAEBRAhQAAAAARQlQAAAAABQlQAEAAABQlAAFAAAAQFECFAAAAABFCVAAAAAAFCVAAQAAAFCUAAUAAABAUQIUAAAAAEUJUAAAAAAUJUABAAAAUJQABQAAAEBRAhQAAAAARQlQAAAAABQlQAEAAABQlAAFAAAAQFECFAAAAABFCVAAAAAAFCVAAQAAAFCUAAUAAABAUQIUAAAAAEUJUAAAAAAUJUABAAAAUJQABQAAAEBRAhQAAAAARQlQAAAAABQlQAEAAABQlAAFAAAAQFECFAAAAABFCVAAAAAAFCVAAQAAAFCUAAUAAABAUQIUAAAAAEUJUAAAAAAUJUABAAAAUJQABQAAAEBRAhQAAAAARQlQAAAAABQlQAEAAABQlAAFAAAAQFECFAAAAABFCVAAAAAAFCVAAQAAAFCUAAUAAABAUQIUAAAAAEUJUAAAAAAUJUABAAAAUJQABQAAAEBRAhQAAAAARQlQAAAAABQlQAEAAABQlAAFAAAAQFECFAAAAABFCVAAAAAAFCVAAQAAAFCUAAUAAABAUQIUAAAAAEUJUAAAAAAUJUABAAAAUJQABQAAAEBRe3SAuu+++/LFL34xp556au64447+HgcAAACA99HQ3wN8WJ2dnZkzZ07uvvvuDBw4MGeddVZOOOGEHHbYYf09GgAAAAC/Z4+9Amr58uU58cQTc8ABB2TQoEGZMGFCOjo6+nssAAAAAP7AHnsF1Kuvvprm5ua+1y0tLfntb3+7y98/YEDdB37Pgw78sw/8PZTxYfbvgxq437Di78GuqcZ+HzR4aPH3YNdUY7/3Pcj5vbuoxn7vf8Cg4u/BrqnGfu93gPN7d1GN/W4csk/x92DXVOX83m+/4u/BrqnGfjfte0Dx92DX7Op+Vzqurre3t/ejGKjabrnllnR3d+eyyy5LkixYsCArV67Md7/73X6eDAAAAIDft8fegjd8+PB0dXX1ve7q6kpLS0s/TgQAAADA+9ljA9RJJ52UX/3qV3n99dfz9ttv54EHHkhra2t/jwUAAADAH9hjnwF18MEH///t3XdcVFf6P/APAhIVFDUElyS6dk3sWQGRqAMoEWYGGBCUBVtsq4BGJWCJYokimFiwJGKMUVmNBmzBTrFEUYyxQ4yrKPpVkF4dYHh+f8xv7s7AzGg2Apbn/Xrta81w595zT3nuueeeewafffYZRo8ejcrKSnh5eaFXr14NnSzGGGOMMcYYY4wxVsMruwYUY4wxxhhjjDHGGHs1vLKv4DHGGGOMMcYYY4yxVwMPQDHGGGOMMcYYY4yxOsUDUIwxxhhjjDHGGGOsTvEAFGOMMcYYY4wxxhirUzwAxRhjjDHGGGOMMcbqFA9ANbC4uDiEhoY2dDJeqJKSEgQGBkL9Bxb37NmjcZ5bt25FUlJSQyRPg7+/v/Dvrl27vrD9/tl9rVmzBgkJCXjw4AEcHBwAAElJSfj+++9fWJpelDlz5sDR0RE///xzvR53xYoVSEtLg0KhwMKFCyEWi+Hq6oqtW7cCAB4/foyQkJB6TdOfVdd5t3v37j+1b4VCgYCAAJSXlyM7Oxtjx46FVCrFiBEjkJaWBgA4duwYduzYUSfpZXXr6tWriIyM1LtNcXExpk2bBgDIysrCxIkTX8ixtV0HHj9+DGtrazx48ADAy3MdUOfg4CCkr76ox311oaGhiIuLq9e0PMvEiRORlZWlt++ybds2JCQkAFBeY11dXeHm5gY3NzdcuXIFpaWlCAgIgEKhqM+kv7SioqIQFRX1l/ejqx4BwI0bN4RYcOfOHfj7+0MqleLTTz9FYWEhAODzzz9HVlbWX04He32o+l3r1q0T2rCbmxt69eqF77777pXodz2PuroXaEjq7XnZsmVwdXWFWCwW+ojXrl1DREREQybxtaNej/5XqnIrKytDcHAwXFxcIJPJkJiYCOD1KTcegGIv3Pr16+Ht7Q0DAwPI5XKsXLkSy5Yt09jG19cXGzduREVFRQOlUunChQsNenyV6dOnw9HRUeOz69evo6SkpIFSpNvevXtx+PBhiMXiejtmeno6njx5gu7duyMuLg4FBQU4cOAA9uzZg927d+PGjRto06YNWrdujZMnT9Zbuv6sus67S5cu/ak2tXPnTtjb26NJkyZYtWoVnJ2dceDAAQQGBmLRokUAgGHDhuHYsWPIzc2tkzSzunP79u1nllthYaEw2GhpaYno6OgXcmz16wAAVFdXY968eaisrBS2eVmuA+z5RUdHw9LSUuffc3JykJiYCEdHRxARMjIysH//fuF/vXv3RrNmzTBgwADs2rWrHlP+Zlu+fDkmTpwIIsK//vUvTJw4EQcOHED37t2xadMmAMCkSZNq9dXYm0u93xUQECC04dDQUHTo0AF+fn6vRL/rebws9wIvSlJSEiwtLWFpaYlz587h6tWrOHDgALZu3YpFixahvLwcPXv2xOPHj/H77783dHJfG3+1HqmX27fffgsjIyP8/PPP+O6777BixQpkZWW9NuXGA1B15Pz58xojoaonmQ8ePMAnn3yCUaNGYdy4cQCAe/fu4Z///CfEYjFWrlwpPDFetWoVvL294ezsDH9/f+Tk5AAA7O3tsWTJEri7u8PT0xOZmZm1jn/48GF4e3tDKpXik08+waVLlwAoR2cDAgLg7OyMtLQ0nDp1Cl5eXnB3d0dAQADy8/P1fv/777+HVCqFu7s7FixYUOu4JSUlSExMxMCBAwEAqampqK6uRnBwsMZ2jRs3xkcffYSDBw/+pXx+Xt988w1cXFwgkUgQHh4OhUKBpUuXAgBGjBghbLdgwQJIpVJIpVLcu3cPgHIWwahRo+Dh4YHx48cL+V0zL2v64osv4ObmhrFjx+L//u//hO+cP38egObTyppPum/fvo1du3Zh165diI2NrbVvfftZuHAhZDIZnJ2dsW/fvlrfPXfuHGQyGWQyGcaNG4e8vLxaT07Vn8ja2tpiwoQJcHNzw4QJE0BEGDFiBHJzc3XW0YMHD8LFxQWurq4IDQ1FZWUlSktLERISAplMBjc3N+EpTHp6Ory9vSGTyTBq1ChkZGTUSvOWLVsglUoBAJ07d8a0adPQqFEjNG3aFO+//z4ePXoEAHB3d39hN9Av2pQpUzTyLjY2FmKxGBKJBKGhoSgtLQWgmd+VlZXYtGkTPDw8IJVKERERASJCSUkJJk2aJJRjQkICzp49i8TERKxduxanT5/GwYMH4ebmBplMhqCgIMjlco30EBG2b98OV1dXAMCXX34JHx8fAMo61bx5c2HbYcOGISYmpp5yqu48fvwYfn5+kMlk8PLywuXLlwEoZ71ERETA3d0d7u7uuHnzJgBlZ0LV9h0dHXHixAkAynY2ZcoUDB8+HImJiVixYoUQF9etWwcAOuu7Og8PD1y/fh2AcjbaoEGDkJubqzPm3Lp1S9jfkiVLMHToUADKm/6pU6dCJpPB09MTZ8+eRVFREdauXYvExERs3LgRJSUlCAoKgo+PD0QiEebOnQsiwtKlS5GdnY1p06ZpxIGcnBxMnjwZEokEHh4eOHXqFABlbJg/fz78/f3h4OCAjRs31jqvmtcBANi8eTPs7OzQsmVL4bO6uA6cP38eXl5ekMlkCAkJQXl5OWbNmiW0NVVMlMvlmDt3LpydnSEWi3Ho0CGN/dy9exfDhg3D5cuXnytG7dixAyNGjIBYLIaHhwfu3LkDQFm3Vq9eDS8vL7i6ugrlffPmTXh4eMDDwwPr16/XeT7JycmQyWSQSCRCGnWVZc1zf546qOvckpKS4ObmBolEgqlTpwqx/VkzxGJiYuDs7AwAQh6MHz8eUqlUYyalWCzGtm3bNGbIvWy++uorDBs2DD4+PggICNDah9NVFrpijbZYoa4u6tG5c+dgYWEBc3Nz3LhxA02bNsWgQYMAKK9L//znPwEAnTp1wsOHD3H//v0Xnpd1TV9sV9VX9X65rlh669Yt+Pv7w9PTEyKRCDt37gSgjHuffvopXFxc8O9//xtpaWkYMWIEJBIJ/Pz88PjxY1RVVWH+/Pnw8fGBo6Mjpk6diqdPn+LBgwdwd3dHcHAwxGIxxowZg4KCAgDQ2QdXd/bsWUilUkgkEkyePBklJSWIi4uDv78/JBIJvv76a73xes6cOfD29sbQoUOxefNmAH++36VSUVGBsLAwLFq0CCYmJgBe7n5XTfV9L6CtL1yzLt29e1coSx8fH1y9ehWA7v68tj58TZs3b4a7uzsAZd9CLpejqqoK5eXlaNy4sbCdRCLBli1bXlT2vhYkEgn+85//AABmzZqFhQsXAgB+++03TJo0SWc7r1mPdLVtBwcHzJgxA87OzrUeEKqXW1paGpydndGoUSO0bNkS3bp1w+nTp4U0vvLlRqxOpKSkkJ+fn/DfISEhFBsbS5mZmdSlSxfKzMwkIqLY2Fiyt7en3NxcksvlNHLkSDp+/DhlZGRQQEAAKRQKIiIKDg6m7777joiIunTpQsePHyciouXLl9Py5cs1jq1QKGj06NGUm5tLRER79uyhyZMnExGRn58frV27loiIcnNzSSqVUkFBARER7dy5k+bOnavz+1VVVWRjY0MVFRWkUCgoNDSUHj9+rHHs48ePU1BQUK38iI2NpZCQEI3PTpw4QVOnTv2zWfunJScn04gRI6isrIwqKytpypQptGPHDiJS5qVKly5d6PDhw0REFB4eTuHh4SSXy0kikdDDhw+JiOjUqVM0ZswYItLMy5q6dOlC+/fvJyKiHTt2COfp5+dHKSkpRESUmZlJIpGIiDTrh+qztWvX6ty/vv2MGzeOKioq6NGjRzRgwADKzs6u9d0rV64QEdGmTZvo9OnTGvuoeewuXboIx1LPM1119PHjxzRgwAB69OgRERHNnj2bjh8/TpGRkfTDDz8QEVFxcTG5urrS/fv3KTQ0lA4dOkRERHFxcbR3716N9FZXV1P//v2ptLS0Vj78+uuvZGtrK9RhIiIbGxvKz8/Xmm8NTZV36enp5OTkRHl5eUREFBYWRuHh4cI2qvw+efIkBQYGUlVVFSkUCpo5cybt27eP4uLiKCwsjIiIbt68KXxXVY+IiBwcHCgnJ4eIlPX55s2bGmm5efMmeXp61kqjs7Mz9ejRg06dOiV8lpaWRm5ubi8sHxpKVFQURUdHE5Eybzdv3kxERCKRiKKiooiIKCEhgcRiMRERBQYG0u3bt4mI6OzZs8LnISEhQjx78OABubi4EBFRWVkZTZ8+nZ4+faqzvqv7/vvvhbI7c+YMTZw4UW/M8fDwoOTkZOG7qjY7Y8YMOnHiBBERZWVlkaOjIxUXF2vE3YP0j3ZlAAAakUlEQVQHD9KGDRuIiEgul5OTkxNdu3ZNo+2r/zsoKIi2bNlCRET379+ngQMH0pMnT2jt2rXk5eVFcrmccnJyqE+fPlRYWKhxXjWvA9euXaNx48aRQqEgkUgkXP+IXvx1ICUlhT766CMqKioiIqIVK1bQkiVLiEh5zXNwcKC0tDSKjo6m6dOnk0KhoOzsbHJxcSG5XE4ikYhSU1PJxcWFUlNTiYieGaOKi4tpzJgxVF5eTkREq1evpsWLFxORsm59//33RES0bds2CggIICIisVhMZ86cISKidevWacRflZCQEJowYQJVVlbS48ePaeDAgZSdna2zLGue+/PUQW3nlpOTQ/b29kI5RUdHU2BgoHA+mZmZWq/pRERSqZT++OMPIiK6dOkSBQcHU1FREeXm5pKrq6twzkRE7u7ulJaWpr0gG1hCQgKNGjWK5HI5FRQUkEgk0tqH01UW2mKNrlihut7WVT1aunSp0OeJj4+nCRMm0Jw5c8jd3Z1mzpypcb1ctmwZbd269YXmZX3QF9tVZaXeL9cVS5cuXUpnz54lImXc69OnDxEp+0TqfXoXFxdKTEwkIqKYmBgKDw+nCxcuCNdlhUJBfn5+dOTIEcrMzKSuXbvSjRs3iIgoICCAtm3bprMPrk4ul9OAAQOE6/fKlStp27ZtFBsbS0OHDqXKykoi0h+vxWIxlZSUUFFRETk5OdH169f/537X7t27hVig7mXud6nU972Arr5wzbrk6elJR48eJSKi3377jYYMGUJyuVxnf15bH15dfn4+2dnZaXwWGBhI1tbW1KNHD432XVxcTP3796fq6uo/n6GvqcjISNq+fTsRKeOrqt+3Zs0aiomJ0dnOif5bj/S1bdW1pKaa5bZq1SoKCQkRrv/29va0ceNGIno9ys2ooQfA3kStW7fGe++9J/y3g4MDWrVqBQAYPnw4Lly4ACcnJ4SEhGDPnj24e/cuLl++jLZt2wrf+fjjjwEoZ4NcvHhRY/+NGjXC+vXrkZiYiLt37+LChQto1Oi/k9169eoFALhy5QoePXqE0aNHA1C+ItGiRQud3zc0NETfvn3h5eUFR0dHjBs3rtZU/IyMDLRp0+a58uHdd98VnizUpZSUFLi6uqJJkyYAAE9PT+zbt0946qfOyckJgPJJ4MWLF5GRkYHMzEz861//ErZRfy1OlZc1vfXWW8KTIzc3N6xevfqFnc+zyGQyGBsbo02bNujXrx9+/fVXfPLJJ8LfHR0dERAQACcnJzg6OmLgwIHPXPOkd+/etT5r166d1jr622+/oV+/fkI9UK07sWHDBjx9+lSY0VVWVoY//vgDgwcPxuLFi3H69Gk4ODhAJBJpHEf11KBp06Yan6empuKzzz7DypUr0aJFC+HzNm3aIDMzE+bm5s+bZfUuNTUVIpFImA3i4+ODOXPmCH9X5bdq6rRMJgMAPH36FFZWVvD09MTXX3+NrKwsDBkyRFjDR51IJMKoUaPg5OQEZ2dndO/eXePvutrqkSNHkJaWhvHjx+Pw4cMwNzevt7Za1wYMGIDAwECkpaVh8ODB8PPzE/7m7e0NQBmPQ0NDkZeXh8jISCQlJeHIkSPC2jUqqrZvaWkJExMTjBw5EiKRCLNnz4aJiQnOnj2rtb6///77wj5cXV3h4+ODzz//HD///DOkUqnOmFNQUICHDx9i8ODBAJRxbNu2bQCUT8jv3LmDtWvXAgCqqqpqzYwVi8W4evUqtm7dijt37qCgoABlZWU620lKSorwRO/9999H7969ceXKFQCAjY0NGjdujNatW8Pc3BzFxcUaM+bU61Z5eTkWLVqENWvWaFyHVOqibrVv3x5mZmbCeaheK2rVqhUcHR1x4cIFpKamwtvbG40aNYKFhQXi4+OF70+fPh09e/bEP/7xDwB4ZowyNTXFV199hfj4eGRkZOD06dMa7U39en3s2DHk5eUhOztbmCEmk8m0znQFlLPkjIyMYGlpiT59+uDKlSs6y7LmuT9PHdR2bqdOnUKvXr2EfoqPj4/wmtaz3Lt3Tyj7vn37om/fvsLfvLy8cPLkSeG8rayskJGRgW7duj3XvuvT2bNnMXz4cDRu3BiNGzcW+gaAZh9OV1loizWGhoZaY4VKXdWje/fuwdbWFoAyNly4cAE7duxAz549sXr1aoSHhyM8PByAskxexVivL7bXpC+WhoaG4vTp0/j2229x69YtoV0B/435eXl5ePLkiRAHfH19hW3Mzc0RExODO3fuICMjQ/h+69at8cEHHwBQll9hYaHOPri633//HZaWlkI9mDVrFgDl+rEffPABjIyUt3H64rVYLEazZs0AKK9vKSkp/3O/a9euXZg/f36tPH0V+l31fS+gqy+clpYmbF9aWor79+9j2LBhAIA+ffqgRYsWwsxHbf15bX14dffv38c777wj/PePP/4IQ0NDnDlzBgUFBRg9ejR69+6NPn36wNTUFESE/Px84T70TTd48GBs3boVtra26NSpE+7cuYPc3FycOnUKa9euhZWVlc52rvKstq3tnqpmuU2ePBnLly+Hh4cH2rdvD3t7exgbGwPAa1FuPABVRwwMDDSmlquve/HWW29pbKu6gADKSmpkZITr169j1qxZGDt2rDAFT31/qk5LzeMAyoDm5eUFqVSK/v37o2vXrhqvz6iOr1Ao0K9fP3zzzTcAlK8klJaW6v3+hg0bcPnyZZw6dQoTJkzAypUrYW1trXHe6uejj6GhobA+SF2qrq6u9VlVVZXWbVVpV+VrdXU13nvvPezfvx+AMs9UryIAtctSRf1Gi4g08kRVXrrSoM3EiRORnZ0NAMKNgK79GBoaCv9W1Sd1Y8eOhUgkQlJSEiIjI3H16lVIpVKNelRVVaXxPW3nqauOGhkZaZSranpwdXU1IiMj8eGHHwJQvuLTokULGBsbo2/fvkhKSsLWrVuRnJwsdKRUap7DsWPHEBYWhlWrVsHGxqbW+Wu70X2Z1KyTRKRRjuptdMyYMcLrukVFRTA0NESzZs1w+PBhnD59GklJSdiyZUutV4jmz5+P9PR0nDx5EsHBwQgICICbm5vw95ptNTk5Gf3790ezZs3QvXt3WFlZCR3KmmX6qvroo48QHx+P5ORkHDp0CHv37hUW+q8Zhw0NDeHr6wsbGxvY2NhgwIABmD17trCNqoyMjIywZ88eXLhwAadOncLIkSOxfft2nfVdnYWFBdq3b4/z58/j3LlzWLBgAe7du6c15hgaGup8Xam6uho//PCD0PnPzs5G69atNV4H2L59O44ePQpvb2/Y2dnh1q1bel9/qvk3IhIWjVa/adZ2DVKvWxcvXkRubq7Qcc/OzsakSZOwbt06dOjQoU6uA+rxStd51KzT9+7dw9/+9jcAwLx587B+/XokJydjyJAh+OSTT/TGqEePHsHf3x9+fn4YNGgQ3n77bY28V79eq/5fPV3qMbummvHc2NhYb1mqn/vz1EFt51bzZrRmfNKnZtlXVlZiwIABwn7U29nLHKsbNWqkte8AaOaxrrLQFWu0xQqVuqpH6mViYWGBdu3aoWfPngCUgxNBQUHCtkZGRi9tmeijL7bX7Cvpi6UzZsxA8+bNIRKJ4OLiovHaqqrcjY2NNWKHXC5HdnY2bt26hbVr12L06NGQyWTIz88XjqMtZurqg6ureazi4mJhm+eJc6rzVVFd254V04Da/a6srCzk5+drDCqrvMxtWaW+7wV09YXVt9dWD/WVnZGRkdY+vPrAWM2+XUJCAkaNGgVjY2NYWFhgyJAhuHjxIvr06SMc42Uvu/rUt29fhIaG4uzZs7C2tkbr1q1x5MgRVFVVwcrKCgkJCTrbucqz2rZ6PFCpWW7FxcX47LPPhAfVkyZN0lgq5VUvt1c35S+5li1bIjMzE3K5HAUFBfj11191bnvy5EkUFRVBLpfj0KFDsLOzQ2pqKqytrTFq1Cj8/e9/R3Jy8nP/YkxGRgYMDAwwZcoU2NjY4Pjx41q/27t3b1y+fBl3794FoBxcioiI0Pn9vLw8uLi4oEuXLpg+fToGDhxYaxG0du3a4eHDh8+VzocPH6Jdu3bPte1fYWtri/j4eDx9+hRVVVWIjY0VngYaGhrq7Vh36NABhYWFwiyz2NhYjZtQXcrKyoRfAYqNjYWdnR0AZb24ffs2AAjryeiinrbo6GhhAUhLS0u9+zl8+DCICA8fPsTVq1fx0Ucfafx9xIgRKC0txdixYzF27FjcvHkTzZs3R0FBAfLy8lBRUSG8Z6yPrjras2dPXL58GU+ePAGg/PWNhIQE2NraCuspZGdnQyqV4tGjR5gxYwauXbuGkSNHYvr06cL6OyqtWrWCQqEQgvfVq1cRFhaGLVu21Bp8ApSdJPUZhi8ja2trJCYmCutA7N69W+u52NraYv/+/SgtLUVVVRWmTZuGo0ePYseOHYiKisLw4cOxcOFC5OXloaSkBIaGhlAoFKiqqsKwYcPQsmVLTJ48GW5ubrXWJqjZVvfu3Yvdu3cDUK5BlpOTgw4dOgBQrglVH221rkVERODAgQPw8PDAggULNOqaagbM8ePH0bFjR2EB5enTp2PQoEFISEjQGkdv3rwJPz8/9O/fHyEhIejYsSPu3r2rs77X5ObmhhUrVsDGxgZNmjTRGXPMzMzw/vvvC4u9qq+bZGtri3//+98AlGUnkUhQXl6uEUN++eUX+Pj4QCqVQi6XIz09XejQaouBtra2+OmnnwAAmZmZuHTpktBhfRb1uvXxxx8jMTFRiF/vvPMONm3aJNStur4OqJ9HXl4eEhISYG1tjf79++PQoUMgIuTm5sLPz09YDL1Xr14ICwvD4sWLUVZW9swYde3aNbRr1w5jx45Fz549ceLECb3X65YtW8LKygrJyckAoPeXK+Pj44V4fv36dfTs2VNnWWo792fVQW3nppo9oZoZ++OPP2qNT9q0bdtWKPvi4mJERERALpejpKQEe/fuFdbaAZRlrz6z+2ViZ2eHY8eOoaKiAiUlJUhOTtY6UKqrLLTFGl2xQqWu6pF6mfTt2xd5eXlIT08HACQmJgoDlIAy1r+sZaKPrtiu3ldS9cn0xdJffvkFQUFBcHJyEtZRqlkGZmZmsLS0xJkzZwAA+/fvx5o1a3Du3DkMHz4cnp6eaN68Oc6fP6+3/HT1wdW1b98eubm5wjls3rxZaNPq9MXrEydOoKKiAoWFhUhKSoK9vf2f7ncB/53Ro82r0O+q73sBXX1hdaampnjvvfdw7NgxAMDly5eRk5ODzp07A9Den9fWh1envi4qAHTr1k24TygrK0NKSgp69OgB4L+zuF7mmWv1zcjICL169cL27dthbW0NW1tbfPPNN8KMSX3tXFWPnqdt11Sz3I4ePSrMak9PT8fNmzeFhzmvQ7nxDKg60rlzZwwePBiurq549913aw0CqOvQoQMmTZqEoqIiiMVi2Nvbo3PnzggICIBEIgEA9OjR47l/Grpbt27o3r07hg8fDgMDA9jb22sdALOwsMCyZcswY8YMVFdXw9LSEpGRkWjevLnW77dq1Qo+Pj7w8vJCkyZN0L59e3h6emrsc8CAAVi+fDmqq6ufOTJ7/vz5Wr/8VhdEIhHS0tLg6emJqqoq2NvbC9OzHR0d4ebmpvOnrhs3bow1a9bgyy+/hFwuh6mpKVasWPHMYzZv3hwnTpzAmjVrYGlpieXLlwMAJkyYgNDQUMTGxj7z3FWd1LfffrvWT3vq28/Tp0/h6emJiooKLF68WGPRXwCYOXMmQkNDYWRkhKZNm2Lp0qUwMzPDhAkT4OXlhTZt2ghPR/VxcXHRWkctLS0xb948fPrpp6iurkafPn0gk8lQXl6OsLAwiMViKBQKBAcHo23btpgyZYow48DY2BhhYWG1jjVo0CBcvHgRgwcPxsaNG6FQKDR++jcoKAiOjo64desW2rdvX+sp/8umW7dumDx5Mvz9/VFZWYkPP/xQ+NU5dQ4ODsJioQqFAh9//DE8PDxQWlqKmTNnQiKRwNDQEMHBwWjevDns7Ozw9ddfw8zMDEFBQRg/fjxMTEzQunVr4RUL9TTk5+ejuLgYZmZmmDt3LubOnYu9e/fCxMQEX331lTBtv77aal3z9/fHrFmzEBcXB0NDQ422fOnSJfz0009o0qQJwsPDYW5uLiz4a2RkBFtbWzx9+rTWVOsPPvgAffr0gVgsRpMmTdCvXz8MGjQI1tbWWut7TUOHDsXChQuFzqy+mBMREYG5c+di9erV6Nq1q/AUdf78+ViwYIHQFiMiImBqaopevXph3bp1WLlyJcaMGYOwsDBs2rQJpqam6Nu3Lx48eIB//OMfsLKygr+/vxCnAOUsoAULFgixcenSpRrTw/V5ma4D06ZNQ1hYGCQSCRQKBaZMmYIPP/wQnTt3xtKlS4VXpb/44guYmpoK3+vfvz9sbGywevXqZ8aogQMHYufOnXBxcQERoX///vjjjz/0pisyMhJz5szB6tWr9Q7sNW3aFDKZDFVVVVi8eDFatWqlsyxr1q+AgIBn1kFt5/b2229j8eLFCAgIQGVlJaysrPDll18+T3ZDJBIhJSUFHTt2hEgkwpUrV+Du7o7q6mr4+voKsyeKiopQUlLyUr5+BwBDhgzBb7/9Bg8PD7Ro0QLvvPOO1qfWuspCW6zRFStu3LgBoO7qkYODA3bt2gVfX1+89dZbWL9+PebPn4/y8nK0adNG48YoNTUVq1at+gs51zB0xfagoCAsWbIE69atg729vbC9rlgaGBgIX19fmJiYoFu3bnj33Xe19r0jIyMRFhaGyMhItGzZEhEREcjPz8fs2bMRHx8PY2Nj9OvXT2+/XVcfXJ2JiQkiIyPx+eefo7KyEm3btkVERASOHj2qsZ2+eG1iYgJfX1+UlJRg8uTJ6NSp05/udwHKgS1tr+2/Kv2u+r4X0NUX3rBhg8Z2qroUFRUFY2NjREVFCQuFa+vPa+vDqzM3N0fbtm1x+/ZtoawXLVqE4cOHw9DQEF5eXsLAm2o5CKZp8ODBSE1NRceOHWFhYYHc3FwMGTIEgPIhvq52rl6PntW2a6pZbt7e3ggODoZEIoGRkRFWr14t9FFei3Krq8Wl2Jtr2bJlwuKMusjlcvLw8CC5XF5PqXozqC9C/TpJS0vTuvBlTV9++SUlJSXVfYJeEz/88IOw2KI+I0eOFBY0fx3VXBj7ZRUVFUVZWVlERHT06FFhIeKXEV8H3kzZ2dnk6+v7zO22bt0qLAD8Mrp06RLFxcUREVFFRQV5eHi8tAumPw8fHx/hh2V0ed7r7OvgVYqlf4W+H7N5Fu53Nay/0p8/ceKE8AMn+kybNo3S09P/p2OwF+9NKjd+BY+9cAEBAfjpp5/0ri+yfft2TJ06VePnQBnTpVu3bmjTpo3wpFibR48eIScnR3hKwZ5t1KhR+OWXX1BeXq5zmyNHjsDZ2RmtW7eux5QxbaysrDB+/Hi4u7tjx44d+Pzzzxs6STrxdeDNZGFhgaFDh+p9xby0tBTnzp2Dj49PPabsz2nfvr3wwwAymQyurq4v7Wyt5zF37lxER0fr3SY6OhqhoaH1lKKG9SrF0obC/a5Xl6OjI7Kzs5GVlaVzm6tXr+Ldd99F165d6zFlTJ83qdwMSF/vkDHGGGOMMcYYY4yxv4hnQDHGGGOMMcYYY4yxOsUDUIwxxhhjjDHGGGOsTvEAFGOMMcYYY4wxxhirU0YNnQDGGGOMsdeNQqHAtm3bcPDgQSgUClRWVkIkEmH69OlYsGABOnfujE8//bShk8kYY4wxVm94AIoxxhhj7AULCwtDYWEhfvjhB5iZmaGsrAyzZ8/GvHnzYGho2NDJY4wxxhirdzwAxRhjjDH2Aj148AAHDx7EmTNnYGpqCgBo2rQpFi1ahEuXLiEpKUnY9qeffsKPP/6IyspKFBYWYuLEifD19cWTJ08QEhKC/Px8AMDgwYMxY8YMnZ8DwJ49e7Bz505UV1fD3NwcX3zxBTp27IiLFy8iPDwc1dXVAIDJkyfD2dm5PrOEMcYYY4zXgGKMMcYYe5Fu3LiBTp06CYNPKhYWFhoDP6WlpdizZw82bdqEffv2YdWqVYiMjAQA7N69G++99x727t2LmJgY3Lt3D8XFxTo/v3DhAvbt24eYmBjs27cPEyZMQEBAAAAgKioK48aNQ1xcHJYtW4aUlJT6ywzGGGOMsf+PZ0AxxhhjjL1AjRo1EmYb6dOsWTN88803OHnyJDIyMpCeno6ysjIAwMcff4xJkybh0aNHsLOzw6xZs2BmZqbz8+TkZNy7dw8jR44U9l9UVISCggIMHz4cixcvRmJiIuzs7DBz5sw6O3fGGGOMMV0MiIgaOhGMMcYYY6+LrKwsODs7a7yCp/r8iy++QNOmTdGzZ0+4urrCx8cH3t7e6NWrFywsLODm5obff/8dgHKG1Llz55CSkoL4+HhER0ejR48eWj//+eefYWhoiODgYABAdXU1srOzYWlpCQMDA2RlZeGXX37B6dOncfnyZRw5cgQmJiYNkj+MMcYYezPxK3iMMcYYYy+QpaUlJBIJ5s6di5KSEgBASUkJwsLCYG5ujrfeegsAcP36dbRq1QpTp06Fvb29sDaUQqHAypUrsWHDBjg5OWHevHno1KkT/vjjD52f29vbIz4+HtnZ2QCAnTt3YsyYMQCAkSNHIi0tDTKZDEuWLEFRURGePHnSADnDGGOMsTcZz4BijDHGGHvBqqqqsGHDBhw7dgyGhoaoqKiAk5MTAgMDsWDBAnTu3Bm+vr747LPPcPfuXRgYGMDa2hrHjx9HTEwMzMzMEBoaiqysLDRu3Bhdu3bFokWLUFhYqPXzxo0bIyYmBjt37oSBgQFMTU2xePFidO7cGRcvXsSyZctQXV0NAwMDSKVSjBs3rqGziDHGGGNvGB6AYowxxhhjjDHGGGN1il/BY4wxxhhjjDHGGGN1igegGGOMMcYYY4wxxlid4gEoxhhjjDHGGGOMMVaneACKMcYYY4wxxhhjjNUpHoBijDHGGGOMMcYYY3WKB6AYY4wxxhhjjDHGWJ3iASjGGGOMMcYYY4wxVqd4AIoxxhhjjDHGGGOM1an/B4D2BHmIvoE8AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "val_count = classes_val.iloc[:,0].value_counts().sort_index()\n",
    "x, y = val_count.keys(), val_count.values\n",
    "plt.figure(figsize = (20,20))\n",
    "ax = sns.barplot([CLASSES[i] + f\" ({i+1})\" for i in range(len(CLASSES))], y)\n",
    "ax.set_title(\"Classes distribution of the training set\")\n",
    "ax.set_ylabel(\"Counts of instances per class\")\n",
    "ax.set_xlabel(\"Classes\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "val_count = classes_val_test.iloc[:,0].value_counts().sort_index()\n",
    "x, y = val_count.keys(), val_count.values\n",
    "plt.figure(figsize = (20,20))\n",
    "ax = sns.barplot([CLASSES[i] + f\" ({i+1})\" for i in range(len(CLASSES))], y)\n",
    "ax.set_title(\"Classes distribution of the test set\")\n",
    "ax.set_ylabel(\"Counts of instances per class\")\n",
    "ax.set_xlabel(\"Classes\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting the 23 days images of the 81714 pixels\n",
    "\n",
    "As out of the 2866*2633 pixels, only 81714 were retained, we cannot have a good overview of their position and variation in the images presented there. However, a few things are important to point out:\n",
    " - We can notice clearly the shape of the Reunion island from the original image shown above\n",
    " - Some changes in noticeable clusters of pixels can be observed, especially at the bottom right of the image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x288819af248>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (20,20))\n",
    "plt.imshow(img[13], aspect='auto')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating the gif\n",
    "import imageio\n",
    "\n",
    "# Write some Text\n",
    "\n",
    "font                   = cv2.FONT_HERSHEY_SIMPLEX\n",
    "bottomLeftCornerOfText = (1050,2600)\n",
    "fontScale              = 5\n",
    "fontColor              = (255,255,255)\n",
    "lineType               = 4\n",
    "thickness              = 10\n",
    "\n",
    "scale_percent = 65 # percent of original size\n",
    "width = int(img[0].shape[1] * scale_percent / 100)\n",
    "height = int(img[0].shape[0] * scale_percent / 100)\n",
    "dim = (width, height)\n",
    "# resize image\n",
    "\n",
    "with imageio.get_writer('day_animation.gif', mode='I') as writer:\n",
    "    for i in range(0,NUM_OF_DAYS):\n",
    "        for j in range(5):\n",
    "            img_d = np.copy(img[i])\n",
    "            cv2.putText(img_d,f'Day {i+1}', \n",
    "                bottomLeftCornerOfText, \n",
    "                font, \n",
    "                fontScale,\n",
    "                fontColor,\n",
    "                thickness,\n",
    "                lineType)\n",
    "            resized = cv2.resize(img_d, dim, interpolation = cv2.INTER_NEAREST) \n",
    "            writer.append_data(resized)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x1440 with 23 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs=plt.subplots(6, 4, figsize=(20, 20), constrained_layout=True)\n",
    "\n",
    "for i in range(0,NUM_OF_DAYS):\n",
    "    \n",
    "    x = int(i / 4)\n",
    "    y = i % 4\n",
    "    \n",
    "    axs[x, y].imshow(img[i])\n",
    "    \n",
    "    axs[x,y].set_title(f'Day n°{i+1}')\n",
    "\n",
    "fig.delaxes(axs[5,3])\n",
    "#fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Giving insights on mean value of the 10 features over the 23 days, discriminated by class\n",
    "\n",
    "In order to see if we can extract any pattern ourselves, we will plot the mean value evolution of each feature over the 23 days by calculating the mean of these values for each class, resulting in one plot per class, with 10 lines on each plots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 81714/81714 [12:48<00:00, 106.38it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculation over..\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# we start by building an array of 10 matrices of size 10*23 each\n",
    "\n",
    "list_of_per_class_features_mean = [\n",
    "    np.zeros((10,23)) for _ in range(len(CLASSES))\n",
    "]\n",
    "\n",
    "list_of_per_class_features_std = [\n",
    "    np.zeros((10,23)) for _ in range(len(CLASSES))\n",
    "]\n",
    "\n",
    "N = data.shape[0]\n",
    "\n",
    "def row_to_class(row):\n",
    "    \n",
    "    idx = row.name\n",
    "    \n",
    "    class_val = int(classes_val.iloc[idx, 0])\n",
    "    \n",
    "    # designates the feature\n",
    "    for i in range(10):\n",
    "        # designates the day\n",
    "        for j in range(23):\n",
    "            \n",
    "            list_of_per_class_features_mean[class_val-1][i,j] += row[j*10 + i] # calculating the mean value\n",
    "            \n",
    "            list_of_per_class_features_std[class_val-1][i,j] += row[j*10 + i]*row[j*10 + i]/N\n",
    "    \n",
    "data.progress_apply(row_to_class, axis=1)\n",
    "print(\"Calculation over..\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(3,3, figsize=(20,15))\n",
    "\n",
    "for idx in range(len(list_of_per_class_features_mean)):\n",
    "    \n",
    "    i, j = idx % 3, int(idx / 3)\n",
    "    \n",
    "    values = list_of_per_class_features_mean[idx]/N\n",
    "    \n",
    "    standard_devs = list_of_per_class_features_std[idx] - np.square(values)\n",
    "    \n",
    "    for idx_f, feature in enumerate(FEATURES):\n",
    "        \n",
    "        x = np.arange(1,24)\n",
    "        y = values[idx_f, :]\n",
    "        \n",
    "        e = np.sqrt(standard_devs[idx_f, :])\n",
    "        \n",
    "        axs[i, j].errorbar(x, y, label=feature)\n",
    "        \n",
    "\n",
    "    axs[i, j].set_title(f\"Class {CLASSES[idx]}\")\n",
    "    axs[i, j].set_ylabel(\"Features values\")\n",
    "    axs[i, j].set_xlabel(\"Day number\")\n",
    "    axs[i, j].legend()\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see there, a few different classes see their features be highly disturbed through the days (e.g the *water* class or the *rocks and bare soil* are the most noticeable). Oppositely, the *forests* and *crops* classes are pretty steady in their results. \n",
    "\n",
    "This apparent diversity in the variation of the features for each class is a **good sign**, it should not be too hard for an automatic classifier to be able to discriminate them from each other."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Classifiers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "##  Keras imports\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.preprocessing import sequence\n",
    "from tensorflow.keras.models import Sequential, Model\n",
    "from tensorflow.keras.layers import Dense, Dropout, BatchNormalization, Input, Concatenate, Conv1D, MaxPooling1D\n",
    "from tensorflow.keras.layers import LSTM, Flatten\n",
    "\n",
    "from tensorflow.keras.optimizers import Adam, SGD\n",
    "from tensorflow.keras.models import load_model\n",
    "from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping,ReduceLROnPlateau \n",
    "\n",
    "## Sklearn imports\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import OneHotEncoder, StandardScaler, MinMaxScaler\n",
    "\n",
    "LR = 0.001\n",
    "BATCH_SIZE = 256\n",
    "EPOCHS = 50\n",
    "VAL_SIZE = 0.1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1D-CNN\n",
    "\n",
    "For this part, we will implement the Multi-Scale Convolutional Neural Networks presented in the following paper: [Multi-Scale Convolutional Neural Networks for Time SeriesClassification](https://arxiv.org/pdf/1603.06995.pdf)\n",
    "\n",
    "The architecture looks like this:\n",
    "\n",
    "<figure>\n",
    "       <img src=\"static/img/mcnn.png\" alt=\"mcnn\" width=\"1000\">\n",
    "          <figcaption>Fig.3 - Overall architecture of MCNN</figcaption>\n",
    "</figure>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will first setup our constants and imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.layers import Conv1D, Input, Concatenate, GlobalMaxPooling1D, MaxPooling1D\n",
    "from tensorflow.keras.models import Model\n",
    "\n",
    "LR = 0.01\n",
    "BATCH_SIZE = 256\n",
    "EPOCHS = 100\n",
    "VAL_SIZE = 0.2\n",
    "DOWN_SAMPLING_FACTOR = 2\n",
    "DOWN_SAMPLING_SIZE = int(np.ceil(NUM_OF_DAYS)/DOWN_SAMPLING_FACTOR) + 1\n",
    "SMOOTH_SIZE = 4\n",
    "\n",
    "scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Formatting the X data\n",
    "data_train = data.to_numpy()\n",
    "X = data_train.reshape((NUM_OF_PIXELS, 23, NUM_OF_FEATURES)).astype(np.int64)\n",
    "coord_np = coord.to_numpy().astype(np.int32).reshape(-1, 2)\n",
    "idx = np.arange(X.shape[0])\n",
    "\n",
    "# Formatting the y data\n",
    "classes_train = classes_val.to_numpy().astype(np.uint8)\n",
    "enc = OneHotEncoder(handle_unknown='ignore', sparse=False)\n",
    "y = enc.fit_transform(classes_train).astype(np.uint8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting x and y into training and validation sets\n",
    "X_train, X_val, y_train, y_val, idx_train, idx_val = train_test_split(X, y, idx, test_size = VAL_SIZE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After splitting the image data, we take care of the pixel positions that we scale without data leak by fitting the transformer only to the training set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "coord_train = coord_np[idx_train]\n",
    "coord_train = scaler.fit_transform(coord_train)\n",
    "\n",
    "coord_val = scaler.transform(coord_np[idx_val])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Setting up the model\n",
    "\n",
    "Here, we first create three simple models to extract features that may be specific to our three different scale of time series:\n",
    " - The original time series\n",
    " - The smoothed time series\n",
    " - The down sampled time series\n",
    "\n",
    "They are then concatenated and a new Convolutional layer is added before jumping to 3 fully connected layers. This model, in the same way than the LSTM, is pretty light in parameters, making its training relatively harder, hense requiring more advanced finetuning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_base_model(input_len, fsize=6):\n",
    "    #this base model represents the convolution + pooling phase (there will be three of it)\n",
    "\n",
    "    input_seq = Input(shape=(input_len, 10))\n",
    "\n",
    "    x = Conv1D(40, fsize, padding=\"same\", activation=\"relu\")(input_seq)\n",
    "    x = MaxPooling1D()(x)\n",
    "    model = Model(inputs=input_seq, outputs=x)\n",
    "    \n",
    "    return model\n",
    "\n",
    "def get_coord_model():\n",
    "    input_c = Input(shape=(2,))\n",
    "    model = Model(inputs=input_c, outputs=input_c)\n",
    "    \n",
    "    return model\n",
    "\n",
    "#it takes the original time series and its down-sampled versions as an input, and returns the result of classification as an output\n",
    "def get_1D_CNN():\n",
    "\n",
    "    input_original = Input(shape=(23, 10))\n",
    "    input_multifrequency = Input(shape=(23 , 10))\n",
    "    input_down_sampling = Input(shape=(DOWN_SAMPLING_SIZE, 10))\n",
    "    \n",
    "    base_net_original = get_base_model(23)\n",
    "    base_net_multifrequency = get_base_model(23)\n",
    "    base_net_down_sampling = get_base_model(DOWN_SAMPLING_SIZE, fsize=3)\n",
    "    \n",
    "    embedding_original = base_net_original(input_original)\n",
    "    embedding_multifrequency = base_net_multifrequency(input_multifrequency)\n",
    "    embedding_down_sampling = base_net_down_sampling(input_down_sampling)\n",
    "    \n",
    "    merged = Concatenate(axis=1)([embedding_original, embedding_multifrequency, embedding_down_sampling])#concatenate all the outputs\n",
    "    \n",
    "    conv = Conv1D(40, 6, padding=\"same\", activation=\"relu\")(merged)\n",
    "    x = GlobalMaxPooling1D()(conv)\n",
    "    \n",
    "    coord_model = get_coord_model()\n",
    "    #concatenating the coord model and the CNN-extracted features\n",
    "    x = Concatenate(axis=-1)([x, coord_model.output])\n",
    "    x = Dense(256, activation=\"relu\")(x)\n",
    "    x = Dense(128, activation=\"relu\")(x)\n",
    "    x = Dropout(0.3)(x)\n",
    "    x = Dense(64, activation=\"relu\")(x)\n",
    "    #x = Dropout(0.3)(x)\n",
    "    x = BatchNormalization()(x)\n",
    "    x = Dense(32, activation=\"relu\")(x)\n",
    "    \n",
    "    out = Dense(NUM_OF_CLASSES, activation='softmax')(x)\n",
    "    \n",
    "    model = Model(inputs=[input_original, input_multifrequency, input_down_sampling, coord_model.input], outputs=out)\n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Creating the data generator\n",
    "\n",
    "We have to take care of the different scale of data by making sure that the generator, when taking one time series as an input, outputs three of them, one of which being the original, the two others having been through their own sets of processing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def smoothing(row):\n",
    "    # given a row of shape (23,10), smoothes all 10 feature according to the smoothing factor (mean over a SMOOTH_SIZE window)\n",
    "    \n",
    "    to_smooth = np.copy(row)\n",
    "    \n",
    "    for i in range(row.shape[1]):\n",
    "        for j in range(row.shape[0]):\n",
    "            \n",
    "            smoothing_indices = np.array([k % row.shape[0] for k in range(int(j - SMOOTH_SIZE / 2), int(j + SMOOTH_SIZE / 2 + 1))])\n",
    "            \n",
    "            acc = 0\n",
    "            for idx in smoothing_indices:\n",
    "                acc += row[idx,i]\n",
    "                \n",
    "            to_smooth[j,i] = acc / SMOOTH_SIZE\n",
    "            \n",
    "    return to_smooth\n",
    "\n",
    "def down_sampling(row):\n",
    "    new_row = []\n",
    "    \n",
    "    for j in range(0, len(row)+1, DOWN_SAMPLING_FACTOR):\n",
    "        new_row.append(row[j])\n",
    "    \n",
    "    return np.array(new_row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def data_generator(X, y, input_shape=(23,10), batch_size=64):\n",
    "    \"\"\"\n",
    "        X is a tuple (X_train, X_coord) where X_train is the pixels data and X_coord their coordinates\n",
    "    \"\"\"\n",
    "    X_train, X_coord = X\n",
    "    \n",
    "    while True:\n",
    "        \n",
    "        for i in range(0, len(X_train), batch_size):\n",
    "            \n",
    "            upper = min(i+batch_size, len(X_train)-1)\n",
    "            \n",
    "            batch = np.copy(X_train[i:upper])\n",
    "            smoothed = np.copy(X_train[i:upper])\n",
    "            down_sampled = np.zeros((len(batch),DOWN_SAMPLING_SIZE,input_shape[1]))\n",
    "            coords = np.copy(X_coord[i:upper])\n",
    "            \n",
    "            for row_idx in range(len(batch)):\n",
    "                smoothed[row_idx, :] = smoothing(X_train[row_idx, :])\n",
    "                down_sampled[row_idx, :] = down_sampling(X_train[row_idx, :])\n",
    "            \n",
    "            \n",
    "            X_batch = [\n",
    "                batch,smoothed,down_sampled, coords\n",
    "            ]\n",
    "            \n",
    "            y_batch = y[i:upper]\n",
    "                \n",
    "            yield X_batch, y_batch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Training our model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_gen = data_generator(\n",
    "    (X_train,coord_train), y_train, batch_size=BATCH_SIZE)\n",
    "val_gen = data_generator(\n",
    "    (X_val,coord_val), y_val, batch_size=BATCH_SIZE)\n",
    "train_steps = round(len(X_train) / BATCH_SIZE) + 1\n",
    "val_steps = round(len(X_val) / BATCH_SIZE) + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = get_1D_CNN()\n",
    "\n",
    "opt = Adam(LR)\n",
    "model.compile(opt, loss=\"categorical_crossentropy\",\n",
    "    metrics=['categorical_accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_filename = \"tiselac-1d-cnn-{epoch:02d}-{val_loss:.2f}.h5\"\n",
    "\n",
    "callbacks = [\n",
    "    ModelCheckpoint(\n",
    "        os.path.join(\"models/\", model_filename),\n",
    "        monitor='val_loss', verbose=1, save_best_only=True, save_freq='epoch'),\n",
    "    EarlyStopping(monitor='val_loss', min_delta = 1e-3, patience = 5),\n",
    "    ReduceLROnPlateau(monitor='val_loss', factor=0.2, verbose=1,\n",
    "                              patience=3, min_lr=0.00001)\n",
    "\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "255/256 [============================>.] - ETA: 0s - loss: 0.9701 - categorical_accuracy: 0.6822\n",
      "Epoch 00001: val_loss improved from inf to 0.84626, saving model to models/tiselac-1d-cnn-01-0.85.h5\n",
      "256/256 [==============================] - 139s 541ms/step - loss: 0.9693 - categorical_accuracy: 0.6825 - val_loss: 0.8463 - val_categorical_accuracy: 0.7473\n",
      "Epoch 2/100\n",
      "255/256 [============================>.] - ETA: 0s - loss: 0.7121 - categorical_accuracy: 0.7766\n",
      "Epoch 00002: val_loss improved from 0.84626 to 0.79918, saving model to models/tiselac-1d-cnn-02-0.80.h5\n",
      "256/256 [==============================] - 132s 515ms/step - loss: 0.7123 - categorical_accuracy: 0.7766 - val_loss: 0.7992 - val_categorical_accuracy: 0.7636\n",
      "Epoch 3/100\n",
      "255/256 [============================>.] - ETA: 0s - loss: 0.6377 - categorical_accuracy: 0.8002\n",
      "Epoch 00003: val_loss improved from 0.79918 to 0.65793, saving model to models/tiselac-1d-cnn-03-0.66.h5\n",
      "256/256 [==============================] - 129s 502ms/step - loss: 0.6377 - categorical_accuracy: 0.8002 - val_loss: 0.6579 - val_categorical_accuracy: 0.7999\n",
      "Epoch 4/100\n",
      "255/256 [============================>.] - ETA: 0s - loss: 0.5908 - categorical_accuracy: 0.8155\n",
      "Epoch 00004: val_loss improved from 0.65793 to 0.64650, saving model to models/tiselac-1d-cnn-04-0.65.h5\n",
      "256/256 [==============================] - 130s 507ms/step - loss: 0.5909 - categorical_accuracy: 0.8155 - val_loss: 0.6465 - val_categorical_accuracy: 0.8042\n",
      "Epoch 5/100\n",
      "255/256 [============================>.] - ETA: 0s - loss: 0.5600 - categorical_accuracy: 0.8234\n",
      "Epoch 00005: val_loss improved from 0.64650 to 0.58749, saving model to models/tiselac-1d-cnn-05-0.59.h5\n",
      "256/256 [==============================] - 129s 503ms/step - loss: 0.5602 - categorical_accuracy: 0.8234 - val_loss: 0.5875 - val_categorical_accuracy: 0.8269\n",
      "Epoch 6/100\n",
      "255/256 [============================>.] - ETA: 0s - loss: 0.5335 - categorical_accuracy: 0.8331\n",
      "Epoch 00006: val_loss did not improve from 0.58749\n",
      "256/256 [==============================] - 125s 489ms/step - loss: 0.5337 - categorical_accuracy: 0.8331 - val_loss: 0.6075 - val_categorical_accuracy: 0.8112\n",
      "Epoch 7/100\n",
      "255/256 [============================>.] - ETA: 0s - loss: 0.5211 - categorical_accuracy: 0.8363\n",
      "Epoch 00007: val_loss improved from 0.58749 to 0.53709, saving model to models/tiselac-1d-cnn-07-0.54.h5\n",
      "256/256 [==============================] - 125s 489ms/step - loss: 0.5213 - categorical_accuracy: 0.8362 - val_loss: 0.5371 - val_categorical_accuracy: 0.8379\n",
      "Epoch 8/100\n",
      "255/256 [============================>.] - ETA: 0s - loss: 0.5025 - categorical_accuracy: 0.8419\n",
      "Epoch 00008: val_loss did not improve from 0.53709\n",
      "256/256 [==============================] - 126s 493ms/step - loss: 0.5028 - categorical_accuracy: 0.8418 - val_loss: 0.5688 - val_categorical_accuracy: 0.8266\n",
      "Epoch 9/100\n",
      "255/256 [============================>.] - ETA: 0s - loss: 0.4886 - categorical_accuracy: 0.8463\n",
      "Epoch 00009: val_loss improved from 0.53709 to 0.48403, saving model to models/tiselac-1d-cnn-09-0.48.h5\n",
      "256/256 [==============================] - 126s 492ms/step - loss: 0.4889 - categorical_accuracy: 0.8462 - val_loss: 0.4840 - val_categorical_accuracy: 0.8475\n",
      "Epoch 10/100\n",
      "255/256 [============================>.] - ETA: 0s - loss: 0.4798 - categorical_accuracy: 0.8488\n",
      "Epoch 00010: val_loss improved from 0.48403 to 0.45598, saving model to models/tiselac-1d-cnn-10-0.46.h5\n",
      "256/256 [==============================] - 126s 492ms/step - loss: 0.4800 - categorical_accuracy: 0.8487 - val_loss: 0.4560 - val_categorical_accuracy: 0.8543\n",
      "Epoch 11/100\n",
      "255/256 [============================>.] - ETA: 0s - loss: 0.4706 - categorical_accuracy: 0.8506\n",
      "Epoch 00011: val_loss did not improve from 0.45598\n",
      "256/256 [==============================] - 125s 487ms/step - loss: 0.4708 - categorical_accuracy: 0.8506 - val_loss: 0.4874 - val_categorical_accuracy: 0.8435\n",
      "Epoch 12/100\n",
      "255/256 [============================>.] - ETA: 0s - loss: 0.4601 - categorical_accuracy: 0.8552\n",
      "Epoch 00012: val_loss did not improve from 0.45598\n",
      "256/256 [==============================] - 125s 487ms/step - loss: 0.4603 - categorical_accuracy: 0.8552 - val_loss: 0.4915 - val_categorical_accuracy: 0.8476\n",
      "Epoch 13/100\n",
      "255/256 [============================>.] - ETA: 0s - loss: 0.4488 - categorical_accuracy: 0.8580\n",
      "Epoch 00013: val_loss did not improve from 0.45598\n",
      "\n",
      "Epoch 00013: ReduceLROnPlateau reducing learning rate to 0.0019999999552965165.\n",
      "256/256 [==============================] - 128s 499ms/step - loss: 0.4490 - categorical_accuracy: 0.8580 - val_loss: 0.4664 - val_categorical_accuracy: 0.8526\n",
      "Epoch 14/100\n",
      "255/256 [============================>.] - ETA: 0s - loss: 0.3980 - categorical_accuracy: 0.8733\n",
      "Epoch 00014: val_loss improved from 0.45598 to 0.42409, saving model to models/tiselac-1d-cnn-14-0.42.h5\n",
      "256/256 [==============================] - 125s 487ms/step - loss: 0.3981 - categorical_accuracy: 0.8733 - val_loss: 0.4241 - val_categorical_accuracy: 0.8645\n",
      "Epoch 15/100\n",
      "255/256 [============================>.] - ETA: 0s - loss: 0.3856 - categorical_accuracy: 0.8773\n",
      "Epoch 00015: val_loss did not improve from 0.42409\n",
      "256/256 [==============================] - 126s 492ms/step - loss: 0.3857 - categorical_accuracy: 0.8772 - val_loss: 0.4287 - val_categorical_accuracy: 0.8643\n",
      "Epoch 16/100\n",
      "255/256 [============================>.] - ETA: 0s - loss: 0.3796 - categorical_accuracy: 0.8794\n",
      "Epoch 00016: val_loss improved from 0.42409 to 0.41526, saving model to models/tiselac-1d-cnn-16-0.42.h5\n",
      "256/256 [==============================] - 126s 494ms/step - loss: 0.3797 - categorical_accuracy: 0.8794 - val_loss: 0.4153 - val_categorical_accuracy: 0.8676\n",
      "Epoch 17/100\n",
      "255/256 [============================>.] - ETA: 0s - loss: 0.3755 - categorical_accuracy: 0.8810\n",
      "Epoch 00017: val_loss did not improve from 0.41526\n",
      "256/256 [==============================] - 123s 481ms/step - loss: 0.3758 - categorical_accuracy: 0.8808 - val_loss: 0.4221 - val_categorical_accuracy: 0.8661\n",
      "Epoch 18/100\n",
      "255/256 [============================>.] - ETA: 0s - loss: 0.3727 - categorical_accuracy: 0.8815\n",
      "Epoch 00018: val_loss improved from 0.41526 to 0.41488, saving model to models/tiselac-1d-cnn-18-0.41.h5\n",
      "256/256 [==============================] - 124s 486ms/step - loss: 0.3728 - categorical_accuracy: 0.8814 - val_loss: 0.4149 - val_categorical_accuracy: 0.8687\n",
      "Epoch 19/100\n",
      "255/256 [============================>.] - ETA: 0s - loss: 0.3682 - categorical_accuracy: 0.8829\n",
      "Epoch 00019: val_loss improved from 0.41488 to 0.41438, saving model to models/tiselac-1d-cnn-19-0.41.h5\n",
      "256/256 [==============================] - 125s 490ms/step - loss: 0.3684 - categorical_accuracy: 0.8829 - val_loss: 0.4144 - val_categorical_accuracy: 0.8687\n",
      "Epoch 20/100\n",
      "255/256 [============================>.] - ETA: 0s - loss: 0.3642 - categorical_accuracy: 0.8831\n",
      "Epoch 00020: val_loss improved from 0.41438 to 0.40338, saving model to models/tiselac-1d-cnn-20-0.40.h5\n",
      "256/256 [==============================] - 126s 491ms/step - loss: 0.3643 - categorical_accuracy: 0.8832 - val_loss: 0.4034 - val_categorical_accuracy: 0.8726\n",
      "Epoch 21/100\n",
      "255/256 [============================>.] - ETA: 0s - loss: 0.3595 - categorical_accuracy: 0.8843\n",
      "Epoch 00021: val_loss did not improve from 0.40338\n",
      "256/256 [==============================] - 126s 491ms/step - loss: 0.3597 - categorical_accuracy: 0.8842 - val_loss: 0.4038 - val_categorical_accuracy: 0.8737\n",
      "Epoch 22/100\n",
      "255/256 [============================>.] - ETA: 0s - loss: 0.3558 - categorical_accuracy: 0.8862\n",
      "Epoch 00022: val_loss did not improve from 0.40338\n",
      "256/256 [==============================] - 125s 488ms/step - loss: 0.3559 - categorical_accuracy: 0.8862 - val_loss: 0.4106 - val_categorical_accuracy: 0.8706\n",
      "Epoch 23/100\n",
      "255/256 [============================>.] - ETA: 0s - loss: 0.3522 - categorical_accuracy: 0.8873\n",
      "Epoch 00023: val_loss improved from 0.40338 to 0.40018, saving model to models/tiselac-1d-cnn-23-0.40.h5\n",
      "256/256 [==============================] - 124s 486ms/step - loss: 0.3524 - categorical_accuracy: 0.8872 - val_loss: 0.4002 - val_categorical_accuracy: 0.8723\n",
      "Epoch 24/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "255/256 [============================>.] - ETA: 0s - loss: 0.3492 - categorical_accuracy: 0.8882\n",
      "Epoch 00024: val_loss did not improve from 0.40018\n",
      "256/256 [==============================] - 124s 485ms/step - loss: 0.3493 - categorical_accuracy: 0.8881 - val_loss: 0.4141 - val_categorical_accuracy: 0.8672\n",
      "Epoch 25/100\n",
      "255/256 [============================>.] - ETA: 0s - loss: 0.3472 - categorical_accuracy: 0.8879\n",
      "Epoch 00025: val_loss improved from 0.40018 to 0.39679, saving model to models/tiselac-1d-cnn-25-0.40.h5\n",
      "256/256 [==============================] - 130s 506ms/step - loss: 0.3472 - categorical_accuracy: 0.8879 - val_loss: 0.3968 - val_categorical_accuracy: 0.8763\n",
      "Epoch 26/100\n",
      "255/256 [============================>.] - ETA: 0s - loss: 0.3417 - categorical_accuracy: 0.8897\n",
      "Epoch 00026: val_loss did not improve from 0.39679\n",
      "256/256 [==============================] - 127s 495ms/step - loss: 0.3419 - categorical_accuracy: 0.8897 - val_loss: 0.4121 - val_categorical_accuracy: 0.8725\n",
      "Epoch 27/100\n",
      "255/256 [============================>.] - ETA: 0s - loss: 0.3400 - categorical_accuracy: 0.8903\n",
      "Epoch 00027: val_loss did not improve from 0.39679\n",
      "256/256 [==============================] - 127s 494ms/step - loss: 0.3401 - categorical_accuracy: 0.8903 - val_loss: 0.4029 - val_categorical_accuracy: 0.8725\n",
      "Epoch 28/100\n",
      "255/256 [============================>.] - ETA: 0s - loss: 0.3369 - categorical_accuracy: 0.8907\n",
      "Epoch 00028: val_loss improved from 0.39679 to 0.38749, saving model to models/tiselac-1d-cnn-28-0.39.h5\n",
      "256/256 [==============================] - 126s 493ms/step - loss: 0.3371 - categorical_accuracy: 0.8906 - val_loss: 0.3875 - val_categorical_accuracy: 0.8782\n",
      "Epoch 29/100\n",
      "255/256 [============================>.] - ETA: 0s - loss: 0.3337 - categorical_accuracy: 0.8915\n",
      "Epoch 00029: val_loss did not improve from 0.38749\n",
      "256/256 [==============================] - 125s 487ms/step - loss: 0.3338 - categorical_accuracy: 0.8915 - val_loss: 0.4014 - val_categorical_accuracy: 0.8716\n",
      "Epoch 30/100\n",
      "255/256 [============================>.] - ETA: 0s - loss: 0.3310 - categorical_accuracy: 0.8928\n",
      "Epoch 00030: val_loss did not improve from 0.38749\n",
      "256/256 [==============================] - 125s 487ms/step - loss: 0.3311 - categorical_accuracy: 0.8927 - val_loss: 0.3925 - val_categorical_accuracy: 0.8784\n",
      "Epoch 31/100\n",
      "255/256 [============================>.] - ETA: 0s - loss: 0.3273 - categorical_accuracy: 0.8937\n",
      "Epoch 00031: val_loss did not improve from 0.38749\n",
      "\n",
      "Epoch 00031: ReduceLROnPlateau reducing learning rate to 0.0003999999724328518.\n",
      "256/256 [==============================] - 125s 489ms/step - loss: 0.3275 - categorical_accuracy: 0.8937 - val_loss: 0.4039 - val_categorical_accuracy: 0.8728\n",
      "Epoch 32/100\n",
      "255/256 [============================>.] - ETA: 0s - loss: 0.3135 - categorical_accuracy: 0.8990\n",
      "Epoch 00032: val_loss did not improve from 0.38749\n",
      "256/256 [==============================] - 126s 493ms/step - loss: 0.3136 - categorical_accuracy: 0.8989 - val_loss: 0.3894 - val_categorical_accuracy: 0.8801\n",
      "Epoch 33/100\n",
      "255/256 [============================>.] - ETA: 0s - loss: 0.3097 - categorical_accuracy: 0.8994\n",
      "Epoch 00033: val_loss did not improve from 0.38749\n",
      "256/256 [==============================] - 128s 499ms/step - loss: 0.3097 - categorical_accuracy: 0.8993 - val_loss: 0.3918 - val_categorical_accuracy: 0.8798\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x2891d695048>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit_generator(\n",
    "    train_gen,\n",
    "    steps_per_epoch=train_steps,\n",
    "    epochs=EPOCHS,\n",
    "    validation_data=val_gen,\n",
    "    validation_steps=val_steps,\n",
    "    callbacks=callbacks\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Testing the model\n",
    "\n",
    "We will now test the model on the provided dataset and use a F1 Score metric for it (provided by Sk-Learn)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import f1_score\n",
    "\n",
    "data_test_np = data_test.to_numpy()\n",
    "\n",
    "X_test = data_test_np.reshape((data_test_np.shape[0], 23, NUM_OF_FEATURES)).astype(np.float32)\n",
    "coord_test_np = coord_test.to_numpy().astype(np.int32).reshape(-1, 2)\n",
    "coord_test_np = scaler.transform(coord_test_np)\n",
    "\n",
    "test_gen = data_generator((X_test,coord_test_np), y_test, batch_size=BATCH_SIZE)\n",
    "test_steps = round(len(X_test) / BATCH_SIZE) + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict_generator(test_gen, steps=test_steps)\n",
    "y_pred = np.argmax(y_pred, axis=1) +1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8870601785364084"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(y_test.flatten()[:17972], y_pred, average=\"weighted\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Advanced Classifiers\n",
    "\n",
    "Based on [this paper (Ismail Fawaz, H., Forestier, G., Weber, J. et al, 2019)](https://arxiv.org/pdf/1809.04356.pdf), I retained a number of the models compared.\n",
    "\n",
    "#### 1. Multi Channel Deep Convolutional Neural Network\n",
    "\n",
    "Detailed in [this paper (Zheng et al., 2014, 2016)](http://staff.ustc.edu.cn/~cheneh/paper_pdf/2016/YiZheng-FCS2016.pdf), this architecture want to exploit a presumed independence between the different features of the MTS data by applying convolution independently (in parallel) on each dimension of the input. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.layers import Flatten, AveragePooling1D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_channel_model(ts_length):\n",
    "\n",
    "    input_seq = Input(shape=(ts_length,1))\n",
    "\n",
    "    x = Conv1D(12, 12, padding=\"same\", activation=\"relu\")(input_seq)\n",
    "    x = MaxPooling1D()(x)\n",
    "    x = Conv1D(24, 8, padding='same', activation=\"relu\")(x)\n",
    "    x = MaxPooling1D()(x)\n",
    "    model = Model(inputs=input_seq, outputs=x)\n",
    "    \n",
    "    return model\n",
    "\n",
    "def get_MCDCNN():\n",
    "    \n",
    "    input_models = [get_channel_model(23) for i in range(10)]\n",
    "    \n",
    "    merged = Concatenate(axis=1)([channel.input for channel in input_models])#concatenate all the outputs\n",
    "    x = Flatten()(merged)\n",
    "    x = Dense(732, activation=\"relu\")(x)\n",
    "    x = Dropout(0.3)(x)\n",
    "    x = Dense(256, activation=\"relu\")(x)\n",
    "    x = BatchNormalization()(x)\n",
    "    x = Dense(128, activation=\"relu\")(x)\n",
    "    x = Dense(NUM_OF_CLASSES, activation=\"softmax\")(x)\n",
    "    \n",
    "    mcdcnn_model = Model([channel.input for channel in input_models], x)\n",
    "    return mcdcnn_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "LR = 0.01\n",
    "opt = SGD(LR, decay=0.0005)\n",
    "\n",
    "mcdcnn_model = get_MCDCNN()\n",
    "mcdcnn_model.compile(opt, loss=\"categorical_crossentropy\",metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Starting training..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "def data_generator_mcdcnn(X, y, input_shape=(10,23,1), batch_size=64):\n",
    "    \n",
    "    while True:\n",
    "        \n",
    "        for i in range(0, len(X), batch_size):\n",
    "            \n",
    "            upper = min(i+batch_size, len(X)-1)\n",
    "            \n",
    "            batch = np.copy(X[i:upper])\n",
    "\n",
    "            batch = batch.reshape((batch.shape[0],)+input_shape)\n",
    "            \n",
    "            y_batch = y[i:upper]\n",
    "                \n",
    "            yield [batch[:,i,:,:] for i in range(10)], y_batch\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_filename = \"tiselac-mcdcnn-{epoch:02d}-{val_loss:.2f}.h5\"\n",
    "\n",
    "callbacks = [\n",
    "    ModelCheckpoint(\n",
    "        os.path.join(\"models/\", model_filename),\n",
    "        monitor='val_loss', verbose=1, save_best_only=True, save_freq='epoch'),\n",
    "    EarlyStopping(monitor='val_loss', min_delta = 1e-4, patience = 5),\n",
    "    ReduceLROnPlateau(monitor='val_loss', factor=0.2, verbose=1,\n",
    "                              patience=3, min_lr=0.00001)\n",
    "\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_val, y_train, y_val = train_test_split(X, y, test_size = VAL_SIZE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/120\n",
      "253/256 [============================>.] - ETA: 0s - loss: 1.0860 - accuracy: 0.6550\n",
      "Epoch 00001: val_loss improved from inf to 0.80646, saving model to models/tiselac-mcdcnn-01-0.81.h5\n",
      "256/256 [==============================] - 7s 28ms/step - loss: 1.0834 - accuracy: 0.6557 - val_loss: 0.8065 - val_accuracy: 0.7477\n",
      "Epoch 2/120\n",
      "255/256 [============================>.] - ETA: 0s - loss: 0.8089 - accuracy: 0.7475\n",
      "Epoch 00002: val_loss improved from 0.80646 to 0.73327, saving model to models/tiselac-mcdcnn-02-0.73.h5\n",
      "256/256 [==============================] - 7s 28ms/step - loss: 0.8084 - accuracy: 0.7476 - val_loss: 0.7333 - val_accuracy: 0.7638\n",
      "Epoch 3/120\n",
      "255/256 [============================>.] - ETA: 0s - loss: 0.7471 - accuracy: 0.7657\n",
      "Epoch 00003: val_loss improved from 0.73327 to 0.68336, saving model to models/tiselac-mcdcnn-03-0.68.h5\n",
      "256/256 [==============================] - 7s 27ms/step - loss: 0.7470 - accuracy: 0.7657 - val_loss: 0.6834 - val_accuracy: 0.7817\n",
      "Epoch 4/120\n",
      "255/256 [============================>.] - ETA: 0s - loss: 0.7098 - accuracy: 0.7778\n",
      "Epoch 00004: val_loss improved from 0.68336 to 0.64763, saving model to models/tiselac-mcdcnn-04-0.65.h5\n",
      "256/256 [==============================] - 7s 28ms/step - loss: 0.7096 - accuracy: 0.7778 - val_loss: 0.6476 - val_accuracy: 0.7935\n",
      "Epoch 5/120\n",
      "253/256 [============================>.] - ETA: 0s - loss: 0.6828 - accuracy: 0.7868\n",
      "Epoch 00005: val_loss improved from 0.64763 to 0.62620, saving model to models/tiselac-mcdcnn-05-0.63.h5\n",
      "256/256 [==============================] - 8s 32ms/step - loss: 0.6823 - accuracy: 0.7868 - val_loss: 0.6262 - val_accuracy: 0.8005\n",
      "Epoch 6/120\n",
      "253/256 [============================>.] - ETA: 0s - loss: 0.6658 - accuracy: 0.7911\n",
      "Epoch 00006: val_loss improved from 0.62620 to 0.61206, saving model to models/tiselac-mcdcnn-06-0.61.h5\n",
      "256/256 [==============================] - 7s 28ms/step - loss: 0.6655 - accuracy: 0.7911 - val_loss: 0.6121 - val_accuracy: 0.8029\n",
      "Epoch 7/120\n",
      "253/256 [============================>.] - ETA: 0s - loss: 0.6491 - accuracy: 0.7962\n",
      "Epoch 00007: val_loss improved from 0.61206 to 0.60103, saving model to models/tiselac-mcdcnn-07-0.60.h5\n",
      "256/256 [==============================] - 7s 28ms/step - loss: 0.6485 - accuracy: 0.7963 - val_loss: 0.6010 - val_accuracy: 0.8056\n",
      "Epoch 8/120\n",
      "254/256 [============================>.] - ETA: 0s - loss: 0.6340 - accuracy: 0.8003\n",
      "Epoch 00008: val_loss improved from 0.60103 to 0.58423, saving model to models/tiselac-mcdcnn-08-0.58.h5\n",
      "256/256 [==============================] - 7s 28ms/step - loss: 0.6337 - accuracy: 0.8003 - val_loss: 0.5842 - val_accuracy: 0.8108\n",
      "Epoch 9/120\n",
      "254/256 [============================>.] - ETA: 0s - loss: 0.6237 - accuracy: 0.8030\n",
      "Epoch 00009: val_loss improved from 0.58423 to 0.57322, saving model to models/tiselac-mcdcnn-09-0.57.h5\n",
      "256/256 [==============================] - 7s 28ms/step - loss: 0.6235 - accuracy: 0.8030 - val_loss: 0.5732 - val_accuracy: 0.8138\n",
      "Epoch 10/120\n",
      "254/256 [============================>.] - ETA: 0s - loss: 0.6118 - accuracy: 0.8073\n",
      "Epoch 00010: val_loss improved from 0.57322 to 0.56190, saving model to models/tiselac-mcdcnn-10-0.56.h5\n",
      "256/256 [==============================] - 7s 29ms/step - loss: 0.6116 - accuracy: 0.8073 - val_loss: 0.5619 - val_accuracy: 0.8175\n",
      "Epoch 11/120\n",
      "254/256 [============================>.] - ETA: 0s - loss: 0.6036 - accuracy: 0.8093\n",
      "Epoch 00011: val_loss improved from 0.56190 to 0.55338, saving model to models/tiselac-mcdcnn-11-0.55.h5\n",
      "256/256 [==============================] - 7s 28ms/step - loss: 0.6032 - accuracy: 0.8093 - val_loss: 0.5534 - val_accuracy: 0.8196\n",
      "Epoch 12/120\n",
      "254/256 [============================>.] - ETA: 0s - loss: 0.5953 - accuracy: 0.8131\n",
      "Epoch 00012: val_loss improved from 0.55338 to 0.54821, saving model to models/tiselac-mcdcnn-12-0.55.h5\n",
      "256/256 [==============================] - 7s 28ms/step - loss: 0.5951 - accuracy: 0.8132 - val_loss: 0.5482 - val_accuracy: 0.8217\n",
      "Epoch 13/120\n",
      "254/256 [============================>.] - ETA: 0s - loss: 0.5891 - accuracy: 0.8130\n",
      "Epoch 00013: val_loss improved from 0.54821 to 0.54678, saving model to models/tiselac-mcdcnn-13-0.55.h5\n",
      "256/256 [==============================] - 7s 28ms/step - loss: 0.5889 - accuracy: 0.8131 - val_loss: 0.5468 - val_accuracy: 0.8233\n",
      "Epoch 14/120\n",
      "254/256 [============================>.] - ETA: 0s - loss: 0.5802 - accuracy: 0.8176\n",
      "Epoch 00014: val_loss improved from 0.54678 to 0.53860, saving model to models/tiselac-mcdcnn-14-0.54.h5\n",
      "256/256 [==============================] - 7s 28ms/step - loss: 0.5800 - accuracy: 0.8176 - val_loss: 0.5386 - val_accuracy: 0.8261\n",
      "Epoch 15/120\n",
      "254/256 [============================>.] - ETA: 0s - loss: 0.5735 - accuracy: 0.8189\n",
      "Epoch 00015: val_loss improved from 0.53860 to 0.53068, saving model to models/tiselac-mcdcnn-15-0.53.h5\n",
      "256/256 [==============================] - 7s 28ms/step - loss: 0.5732 - accuracy: 0.8190 - val_loss: 0.5307 - val_accuracy: 0.8284\n",
      "Epoch 16/120\n",
      "255/256 [============================>.] - ETA: 0s - loss: 0.5704 - accuracy: 0.8198 ETA: 0s - los\n",
      "Epoch 00016: val_loss improved from 0.53068 to 0.52700, saving model to models/tiselac-mcdcnn-16-0.53.h5\n",
      "256/256 [==============================] - 7s 28ms/step - loss: 0.5703 - accuracy: 0.8198 - val_loss: 0.5270 - val_accuracy: 0.8304\n",
      "Epoch 17/120\n",
      "253/256 [============================>.] - ETA: 0s - loss: 0.5649 - accuracy: 0.8220\n",
      "Epoch 00017: val_loss improved from 0.52700 to 0.51996, saving model to models/tiselac-mcdcnn-17-0.52.h5\n",
      "256/256 [==============================] - 7s 28ms/step - loss: 0.5647 - accuracy: 0.8220 - val_loss: 0.5200 - val_accuracy: 0.8317\n",
      "Epoch 18/120\n",
      "254/256 [============================>.] - ETA: 0s - loss: 0.5583 - accuracy: 0.8222\n",
      "Epoch 00018: val_loss improved from 0.51996 to 0.51478, saving model to models/tiselac-mcdcnn-18-0.51.h5\n",
      "256/256 [==============================] - 7s 28ms/step - loss: 0.5578 - accuracy: 0.8223 - val_loss: 0.5148 - val_accuracy: 0.8328\n",
      "Epoch 19/120\n",
      "253/256 [============================>.] - ETA: 0s - loss: 0.5556 - accuracy: 0.8232\n",
      "Epoch 00019: val_loss improved from 0.51478 to 0.51121, saving model to models/tiselac-mcdcnn-19-0.51.h5\n",
      "256/256 [==============================] - 7s 28ms/step - loss: 0.5554 - accuracy: 0.8232 - val_loss: 0.5112 - val_accuracy: 0.8332\n",
      "Epoch 20/120\n",
      "254/256 [============================>.] - ETA: 0s - loss: 0.5506 - accuracy: 0.8265\n",
      "Epoch 00020: val_loss improved from 0.51121 to 0.50835, saving model to models/tiselac-mcdcnn-20-0.51.h5\n",
      "256/256 [==============================] - 7s 28ms/step - loss: 0.5506 - accuracy: 0.8266 - val_loss: 0.5083 - val_accuracy: 0.8345\n",
      "Epoch 21/120\n",
      "253/256 [============================>.] - ETA: 0s - loss: 0.5485 - accuracy: 0.8267\n",
      "Epoch 00021: val_loss did not improve from 0.50835\n",
      "256/256 [==============================] - 7s 28ms/step - loss: 0.5483 - accuracy: 0.8267 - val_loss: 0.5087 - val_accuracy: 0.8355\n",
      "Epoch 22/120\n",
      "255/256 [============================>.] - ETA: 0s - loss: 0.5438 - accuracy: 0.8286\n",
      "Epoch 00022: val_loss improved from 0.50835 to 0.50419, saving model to models/tiselac-mcdcnn-22-0.50.h5\n",
      "256/256 [==============================] - 7s 28ms/step - loss: 0.5439 - accuracy: 0.8285 - val_loss: 0.5042 - val_accuracy: 0.8359\n",
      "Epoch 23/120\n",
      "255/256 [============================>.] - ETA: 0s - loss: 0.5413 - accuracy: 0.8284\n",
      "Epoch 00023: val_loss improved from 0.50419 to 0.49764, saving model to models/tiselac-mcdcnn-23-0.50.h5\n",
      "256/256 [==============================] - 7s 28ms/step - loss: 0.5412 - accuracy: 0.8284 - val_loss: 0.4976 - val_accuracy: 0.8381\n",
      "Epoch 24/120\n",
      "255/256 [============================>.] - ETA: 0s - loss: 0.5374 - accuracy: 0.8294\n",
      "Epoch 00024: val_loss improved from 0.49764 to 0.49746, saving model to models/tiselac-mcdcnn-24-0.50.h5\n",
      "256/256 [==============================] - 7s 28ms/step - loss: 0.5374 - accuracy: 0.8293 - val_loss: 0.4975 - val_accuracy: 0.8370\n",
      "Epoch 25/120\n",
      "254/256 [============================>.] - ETA: 0s - loss: 0.5372 - accuracy: 0.8287\n",
      "Epoch 00025: val_loss improved from 0.49746 to 0.49323, saving model to models/tiselac-mcdcnn-25-0.49.h5\n",
      "256/256 [==============================] - 7s 28ms/step - loss: 0.5370 - accuracy: 0.8287 - val_loss: 0.4932 - val_accuracy: 0.8407\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 26/120\n",
      "255/256 [============================>.] - ETA: 0s - loss: 0.5307 - accuracy: 0.8318\n",
      "Epoch 00026: val_loss improved from 0.49323 to 0.49259, saving model to models/tiselac-mcdcnn-26-0.49.h5\n",
      "256/256 [==============================] - 7s 28ms/step - loss: 0.5305 - accuracy: 0.8318 - val_loss: 0.4926 - val_accuracy: 0.8397\n",
      "Epoch 27/120\n",
      "255/256 [============================>.] - ETA: 0s - loss: 0.5288 - accuracy: 0.8328\n",
      "Epoch 00027: val_loss improved from 0.49259 to 0.49057, saving model to models/tiselac-mcdcnn-27-0.49.h5\n",
      "256/256 [==============================] - 7s 28ms/step - loss: 0.5290 - accuracy: 0.8327 - val_loss: 0.4906 - val_accuracy: 0.8408\n",
      "Epoch 28/120\n",
      "254/256 [============================>.] - ETA: 0s - loss: 0.5251 - accuracy: 0.8335\n",
      "Epoch 00028: val_loss improved from 0.49057 to 0.48951, saving model to models/tiselac-mcdcnn-28-0.49.h5\n",
      "256/256 [==============================] - 7s 28ms/step - loss: 0.5251 - accuracy: 0.8334 - val_loss: 0.4895 - val_accuracy: 0.8403\n",
      "Epoch 29/120\n",
      "254/256 [============================>.] - ETA: 0s - loss: 0.5230 - accuracy: 0.8351\n",
      "Epoch 00029: val_loss did not improve from 0.48951\n",
      "256/256 [==============================] - 7s 28ms/step - loss: 0.5227 - accuracy: 0.8350 - val_loss: 0.4918 - val_accuracy: 0.8394\n",
      "Epoch 30/120\n",
      "255/256 [============================>.] - ETA: 0s - loss: 0.5205 - accuracy: 0.8354\n",
      "Epoch 00030: val_loss improved from 0.48951 to 0.48408, saving model to models/tiselac-mcdcnn-30-0.48.h5\n",
      "256/256 [==============================] - 7s 28ms/step - loss: 0.5206 - accuracy: 0.8353 - val_loss: 0.4841 - val_accuracy: 0.8415\n",
      "Epoch 31/120\n",
      "254/256 [============================>.] - ETA: 0s - loss: 0.5179 - accuracy: 0.8360\n",
      "Epoch 00031: val_loss improved from 0.48408 to 0.48248, saving model to models/tiselac-mcdcnn-31-0.48.h5\n",
      "256/256 [==============================] - 7s 28ms/step - loss: 0.5177 - accuracy: 0.8361 - val_loss: 0.4825 - val_accuracy: 0.8425\n",
      "Epoch 32/120\n",
      "253/256 [============================>.] - ETA: 0s - loss: 0.5167 - accuracy: 0.8352\n",
      "Epoch 00032: val_loss improved from 0.48248 to 0.47915, saving model to models/tiselac-mcdcnn-32-0.48.h5\n",
      "256/256 [==============================] - 7s 28ms/step - loss: 0.5167 - accuracy: 0.8352 - val_loss: 0.4791 - val_accuracy: 0.8440\n",
      "Epoch 33/120\n",
      "253/256 [============================>.] - ETA: 0s - loss: 0.5162 - accuracy: 0.8365\n",
      "Epoch 00033: val_loss improved from 0.47915 to 0.47896, saving model to models/tiselac-mcdcnn-33-0.48.h5\n",
      "256/256 [==============================] - 7s 28ms/step - loss: 0.5162 - accuracy: 0.8366 - val_loss: 0.4790 - val_accuracy: 0.8444\n",
      "Epoch 34/120\n",
      "254/256 [============================>.] - ETA: 0s - loss: 0.5114 - accuracy: 0.8379\n",
      "Epoch 00034: val_loss improved from 0.47896 to 0.47471, saving model to models/tiselac-mcdcnn-34-0.47.h5\n",
      "256/256 [==============================] - 7s 28ms/step - loss: 0.5111 - accuracy: 0.8380 - val_loss: 0.4747 - val_accuracy: 0.8459\n",
      "Epoch 35/120\n",
      "255/256 [============================>.] - ETA: 0s - loss: 0.5093 - accuracy: 0.8375\n",
      "Epoch 00035: val_loss did not improve from 0.47471\n",
      "256/256 [==============================] - 7s 28ms/step - loss: 0.5093 - accuracy: 0.8375 - val_loss: 0.4750 - val_accuracy: 0.8458\n",
      "Epoch 36/120\n",
      "254/256 [============================>.] - ETA: 0s - loss: 0.5087 - accuracy: 0.8391\n",
      "Epoch 00036: val_loss improved from 0.47471 to 0.47092, saving model to models/tiselac-mcdcnn-36-0.47.h5\n",
      "256/256 [==============================] - 7s 29ms/step - loss: 0.5086 - accuracy: 0.8391 - val_loss: 0.4709 - val_accuracy: 0.8456\n",
      "Epoch 37/120\n",
      "254/256 [============================>.] - ETA: 0s - loss: 0.5079 - accuracy: 0.8390\n",
      "Epoch 00037: val_loss improved from 0.47092 to 0.46949, saving model to models/tiselac-mcdcnn-37-0.47.h5\n",
      "256/256 [==============================] - 7s 28ms/step - loss: 0.5078 - accuracy: 0.8390 - val_loss: 0.4695 - val_accuracy: 0.8475\n",
      "Epoch 38/120\n",
      "255/256 [============================>.] - ETA: 0s - loss: 0.5052 - accuracy: 0.8394\n",
      "Epoch 00038: val_loss did not improve from 0.46949\n",
      "256/256 [==============================] - 7s 28ms/step - loss: 0.5053 - accuracy: 0.8393 - val_loss: 0.4700 - val_accuracy: 0.8476\n",
      "Epoch 39/120\n",
      "253/256 [============================>.] - ETA: 0s - loss: 0.5030 - accuracy: 0.8398\n",
      "Epoch 00039: val_loss improved from 0.46949 to 0.46746, saving model to models/tiselac-mcdcnn-39-0.47.h5\n",
      "256/256 [==============================] - 7s 28ms/step - loss: 0.5031 - accuracy: 0.8398 - val_loss: 0.4675 - val_accuracy: 0.8482\n",
      "Epoch 40/120\n",
      "255/256 [============================>.] - ETA: 0s - loss: 0.5010 - accuracy: 0.8414\n",
      "Epoch 00040: val_loss improved from 0.46746 to 0.46559, saving model to models/tiselac-mcdcnn-40-0.47.h5\n",
      "256/256 [==============================] - 7s 28ms/step - loss: 0.5011 - accuracy: 0.8413 - val_loss: 0.4656 - val_accuracy: 0.8494\n",
      "Epoch 41/120\n",
      "255/256 [============================>.] - ETA: 0s - loss: 0.5003 - accuracy: 0.8412\n",
      "Epoch 00041: val_loss improved from 0.46559 to 0.46448, saving model to models/tiselac-mcdcnn-41-0.46.h5\n",
      "256/256 [==============================] - 7s 28ms/step - loss: 0.5001 - accuracy: 0.8412 - val_loss: 0.4645 - val_accuracy: 0.8495\n",
      "Epoch 42/120\n",
      "254/256 [============================>.] - ETA: 0s - loss: 0.4996 - accuracy: 0.8422\n",
      "Epoch 00042: val_loss did not improve from 0.46448\n",
      "256/256 [==============================] - 7s 28ms/step - loss: 0.4994 - accuracy: 0.8423 - val_loss: 0.4645 - val_accuracy: 0.8497\n",
      "Epoch 43/120\n",
      "255/256 [============================>.] - ETA: 0s - loss: 0.4972 - accuracy: 0.8427\n",
      "Epoch 00043: val_loss improved from 0.46448 to 0.46127, saving model to models/tiselac-mcdcnn-43-0.46.h5\n",
      "256/256 [==============================] - 7s 28ms/step - loss: 0.4972 - accuracy: 0.8427 - val_loss: 0.4613 - val_accuracy: 0.8500\n",
      "Epoch 44/120\n",
      "255/256 [============================>.] - ETA: 0s - loss: 0.4957 - accuracy: 0.8428\n",
      "Epoch 00044: val_loss did not improve from 0.46127\n",
      "256/256 [==============================] - 7s 28ms/step - loss: 0.4956 - accuracy: 0.8428 - val_loss: 0.4622 - val_accuracy: 0.8498\n",
      "Epoch 45/120\n",
      "255/256 [============================>.] - ETA: 0s - loss: 0.4927 - accuracy: 0.8431\n",
      "Epoch 00045: val_loss improved from 0.46127 to 0.46116, saving model to models/tiselac-mcdcnn-45-0.46.h5\n",
      "256/256 [==============================] - 7s 29ms/step - loss: 0.4925 - accuracy: 0.8431 - val_loss: 0.4612 - val_accuracy: 0.8511\n",
      "Epoch 46/120\n",
      "254/256 [============================>.] - ETA: 0s - loss: 0.4945 - accuracy: 0.8432\n",
      "Epoch 00046: val_loss did not improve from 0.46116\n",
      "256/256 [==============================] - 7s 28ms/step - loss: 0.4946 - accuracy: 0.8432 - val_loss: 0.4614 - val_accuracy: 0.8507\n",
      "Epoch 47/120\n",
      "254/256 [============================>.] - ETA: 0s - loss: 0.4896 - accuracy: 0.8435\n",
      "Epoch 00047: val_loss improved from 0.46116 to 0.45873, saving model to models/tiselac-mcdcnn-47-0.46.h5\n",
      "256/256 [==============================] - 7s 29ms/step - loss: 0.4895 - accuracy: 0.8435 - val_loss: 0.4587 - val_accuracy: 0.8523\n",
      "Epoch 48/120\n",
      "255/256 [============================>.] - ETA: 0s - loss: 0.4893 - accuracy: 0.8439\n",
      "Epoch 00048: val_loss improved from 0.45873 to 0.45658, saving model to models/tiselac-mcdcnn-48-0.46.h5\n",
      "256/256 [==============================] - 7s 28ms/step - loss: 0.4893 - accuracy: 0.8439 - val_loss: 0.4566 - val_accuracy: 0.8531\n",
      "Epoch 49/120\n",
      "254/256 [============================>.] - ETA: 0s - loss: 0.4899 - accuracy: 0.8435\n",
      "Epoch 00049: val_loss improved from 0.45658 to 0.45639, saving model to models/tiselac-mcdcnn-49-0.46.h5\n",
      "256/256 [==============================] - 7s 28ms/step - loss: 0.4899 - accuracy: 0.8436 - val_loss: 0.4564 - val_accuracy: 0.8524\n",
      "Epoch 50/120\n",
      "255/256 [============================>.] - ETA: 0s - loss: 0.4859 - accuracy: 0.8446\n",
      "Epoch 00050: val_loss improved from 0.45639 to 0.45336, saving model to models/tiselac-mcdcnn-50-0.45.h5\n",
      "256/256 [==============================] - 7s 28ms/step - loss: 0.4860 - accuracy: 0.8445 - val_loss: 0.4534 - val_accuracy: 0.8540\n",
      "Epoch 51/120\n",
      "253/256 [============================>.] - ETA: 0s - loss: 0.4858 - accuracy: 0.8453\n",
      "Epoch 00051: val_loss did not improve from 0.45336\n",
      "256/256 [==============================] - 7s 28ms/step - loss: 0.4856 - accuracy: 0.8453 - val_loss: 0.4535 - val_accuracy: 0.8529\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 52/120\n",
      "253/256 [============================>.] - ETA: 0s - loss: 0.4858 - accuracy: 0.8458\n",
      "Epoch 00052: val_loss improved from 0.45336 to 0.45016, saving model to models/tiselac-mcdcnn-52-0.45.h5\n",
      "256/256 [==============================] - 7s 29ms/step - loss: 0.4857 - accuracy: 0.8459 - val_loss: 0.4502 - val_accuracy: 0.8537\n",
      "Epoch 53/120\n",
      "255/256 [============================>.] - ETA: 0s - loss: 0.4827 - accuracy: 0.8470\n",
      "Epoch 00053: val_loss did not improve from 0.45016\n",
      "256/256 [==============================] - 7s 28ms/step - loss: 0.4827 - accuracy: 0.8469 - val_loss: 0.4511 - val_accuracy: 0.8548\n",
      "Epoch 54/120\n",
      "254/256 [============================>.] - ETA: 0s - loss: 0.4833 - accuracy: 0.8463\n",
      "Epoch 00054: val_loss did not improve from 0.45016\n",
      "256/256 [==============================] - 7s 28ms/step - loss: 0.4833 - accuracy: 0.8463 - val_loss: 0.4523 - val_accuracy: 0.8534\n",
      "Epoch 55/120\n",
      "254/256 [============================>.] - ETA: 0s - loss: 0.4814 - accuracy: 0.8465\n",
      "Epoch 00055: val_loss improved from 0.45016 to 0.44948, saving model to models/tiselac-mcdcnn-55-0.45.h5\n",
      "256/256 [==============================] - 7s 28ms/step - loss: 0.4813 - accuracy: 0.8466 - val_loss: 0.4495 - val_accuracy: 0.8551\n",
      "Epoch 56/120\n",
      "255/256 [============================>.] - ETA: 0s - loss: 0.4803 - accuracy: 0.8475\n",
      "Epoch 00056: val_loss improved from 0.44948 to 0.44802, saving model to models/tiselac-mcdcnn-56-0.45.h5\n",
      "256/256 [==============================] - 7s 28ms/step - loss: 0.4803 - accuracy: 0.8475 - val_loss: 0.4480 - val_accuracy: 0.8552\n",
      "Epoch 57/120\n",
      "254/256 [============================>.] - ETA: 0s - loss: 0.4799 - accuracy: 0.8475\n",
      "Epoch 00057: val_loss did not improve from 0.44802\n",
      "256/256 [==============================] - 7s 28ms/step - loss: 0.4798 - accuracy: 0.8475 - val_loss: 0.4486 - val_accuracy: 0.8547\n",
      "Epoch 58/120\n",
      "255/256 [============================>.] - ETA: 0s - loss: 0.4794 - accuracy: 0.8472\n",
      "Epoch 00058: val_loss improved from 0.44802 to 0.44609, saving model to models/tiselac-mcdcnn-58-0.45.h5\n",
      "256/256 [==============================] - 7s 28ms/step - loss: 0.4796 - accuracy: 0.8472 - val_loss: 0.4461 - val_accuracy: 0.8555\n",
      "Epoch 59/120\n",
      "253/256 [============================>.] - ETA: 0s - loss: 0.4787 - accuracy: 0.8481\n",
      "Epoch 00059: val_loss improved from 0.44609 to 0.44532, saving model to models/tiselac-mcdcnn-59-0.45.h5\n",
      "256/256 [==============================] - 7s 28ms/step - loss: 0.4788 - accuracy: 0.8480 - val_loss: 0.4453 - val_accuracy: 0.8550\n",
      "Epoch 60/120\n",
      "253/256 [============================>.] - ETA: 0s - loss: 0.4792 - accuracy: 0.8481\n",
      "Epoch 00060: val_loss did not improve from 0.44532\n",
      "256/256 [==============================] - 7s 28ms/step - loss: 0.4790 - accuracy: 0.8482 - val_loss: 0.4458 - val_accuracy: 0.8552\n",
      "Epoch 61/120\n",
      "254/256 [============================>.] - ETA: 0s - loss: 0.4747 - accuracy: 0.8489\n",
      "Epoch 00061: val_loss did not improve from 0.44532\n",
      "256/256 [==============================] - 7s 28ms/step - loss: 0.4746 - accuracy: 0.8489 - val_loss: 0.4454 - val_accuracy: 0.8555\n",
      "Epoch 62/120\n",
      "254/256 [============================>.] - ETA: 0s - loss: 0.4746 - accuracy: 0.8491\n",
      "Epoch 00062: val_loss improved from 0.44532 to 0.44446, saving model to models/tiselac-mcdcnn-62-0.44.h5\n",
      "256/256 [==============================] - 7s 28ms/step - loss: 0.4745 - accuracy: 0.8491 - val_loss: 0.4445 - val_accuracy: 0.8564\n",
      "Epoch 63/120\n",
      "254/256 [============================>.] - ETA: 0s - loss: 0.4736 - accuracy: 0.8493\n",
      "Epoch 00063: val_loss improved from 0.44446 to 0.44325, saving model to models/tiselac-mcdcnn-63-0.44.h5\n",
      "256/256 [==============================] - 7s 29ms/step - loss: 0.4733 - accuracy: 0.8494 - val_loss: 0.4432 - val_accuracy: 0.8565\n",
      "Epoch 64/120\n",
      "255/256 [============================>.] - ETA: 0s - loss: 0.4747 - accuracy: 0.8499\n",
      "Epoch 00064: val_loss improved from 0.44325 to 0.44210, saving model to models/tiselac-mcdcnn-64-0.44.h5\n",
      "256/256 [==============================] - 7s 28ms/step - loss: 0.4747 - accuracy: 0.8499 - val_loss: 0.4421 - val_accuracy: 0.8564\n",
      "Epoch 65/120\n",
      "254/256 [============================>.] - ETA: 0s - loss: 0.4721 - accuracy: 0.8501\n",
      "Epoch 00065: val_loss improved from 0.44210 to 0.44188, saving model to models/tiselac-mcdcnn-65-0.44.h5\n",
      "256/256 [==============================] - 7s 28ms/step - loss: 0.4719 - accuracy: 0.8501 - val_loss: 0.4419 - val_accuracy: 0.8556\n",
      "Epoch 66/120\n",
      "254/256 [============================>.] - ETA: 0s - loss: 0.4695 - accuracy: 0.8497\n",
      "Epoch 00066: val_loss improved from 0.44188 to 0.44078, saving model to models/tiselac-mcdcnn-66-0.44.h5\n",
      "256/256 [==============================] - 7s 28ms/step - loss: 0.4695 - accuracy: 0.8496 - val_loss: 0.4408 - val_accuracy: 0.8582\n",
      "Epoch 67/120\n",
      "253/256 [============================>.] - ETA: 0s - loss: 0.4703 - accuracy: 0.8498\n",
      "Epoch 00067: val_loss improved from 0.44078 to 0.44019, saving model to models/tiselac-mcdcnn-67-0.44.h5\n",
      "256/256 [==============================] - 7s 28ms/step - loss: 0.4699 - accuracy: 0.8499 - val_loss: 0.4402 - val_accuracy: 0.8576\n",
      "Epoch 68/120\n",
      "254/256 [============================>.] - ETA: 0s - loss: 0.4674 - accuracy: 0.8518\n",
      "Epoch 00068: val_loss improved from 0.44019 to 0.43837, saving model to models/tiselac-mcdcnn-68-0.44.h5\n",
      "256/256 [==============================] - 7s 28ms/step - loss: 0.4673 - accuracy: 0.8518 - val_loss: 0.4384 - val_accuracy: 0.8585\n",
      "Epoch 69/120\n",
      "255/256 [============================>.] - ETA: 0s - loss: 0.4715 - accuracy: 0.8498\n",
      "Epoch 00069: val_loss did not improve from 0.43837\n",
      "256/256 [==============================] - 7s 28ms/step - loss: 0.4714 - accuracy: 0.8498 - val_loss: 0.4385 - val_accuracy: 0.8584\n",
      "Epoch 70/120\n",
      "253/256 [============================>.] - ETA: 0s - loss: 0.4706 - accuracy: 0.8494\n",
      "Epoch 00070: val_loss improved from 0.43837 to 0.43777, saving model to models/tiselac-mcdcnn-70-0.44.h5\n",
      "256/256 [==============================] - 7s 28ms/step - loss: 0.4706 - accuracy: 0.8493 - val_loss: 0.4378 - val_accuracy: 0.8581\n",
      "Epoch 71/120\n",
      "254/256 [============================>.] - ETA: 0s - loss: 0.4672 - accuracy: 0.8515\n",
      "Epoch 00071: val_loss improved from 0.43777 to 0.43619, saving model to models/tiselac-mcdcnn-71-0.44.h5\n",
      "256/256 [==============================] - 7s 28ms/step - loss: 0.4671 - accuracy: 0.8514 - val_loss: 0.4362 - val_accuracy: 0.8588\n",
      "Epoch 72/120\n",
      "253/256 [============================>.] - ETA: 0s - loss: 0.4664 - accuracy: 0.8514\n",
      "Epoch 00072: val_loss improved from 0.43619 to 0.43513, saving model to models/tiselac-mcdcnn-72-0.44.h5\n",
      "256/256 [==============================] - 7s 28ms/step - loss: 0.4663 - accuracy: 0.8514 - val_loss: 0.4351 - val_accuracy: 0.8588\n",
      "Epoch 73/120\n",
      "253/256 [============================>.] - ETA: 0s - loss: 0.4639 - accuracy: 0.8523\n",
      "Epoch 00073: val_loss improved from 0.43513 to 0.43461, saving model to models/tiselac-mcdcnn-73-0.43.h5\n",
      "256/256 [==============================] - 7s 28ms/step - loss: 0.4639 - accuracy: 0.8522 - val_loss: 0.4346 - val_accuracy: 0.8599\n",
      "Epoch 74/120\n",
      "255/256 [============================>.] - ETA: 0s - loss: 0.4649 - accuracy: 0.8525\n",
      "Epoch 00074: val_loss did not improve from 0.43461\n",
      "256/256 [==============================] - 7s 28ms/step - loss: 0.4649 - accuracy: 0.8524 - val_loss: 0.4360 - val_accuracy: 0.8589\n",
      "Epoch 75/120\n",
      "254/256 [============================>.] - ETA: 0s - loss: 0.4632 - accuracy: 0.8521\n",
      "Epoch 00075: val_loss improved from 0.43461 to 0.43368, saving model to models/tiselac-mcdcnn-75-0.43.h5\n",
      "256/256 [==============================] - 7s 28ms/step - loss: 0.4633 - accuracy: 0.8521 - val_loss: 0.4337 - val_accuracy: 0.8596\n",
      "Epoch 76/120\n",
      "255/256 [============================>.] - ETA: 0s - loss: 0.4652 - accuracy: 0.8521\n",
      "Epoch 00076: val_loss did not improve from 0.43368\n",
      "256/256 [==============================] - 7s 28ms/step - loss: 0.4651 - accuracy: 0.8521 - val_loss: 0.4349 - val_accuracy: 0.8588\n",
      "Epoch 77/120\n",
      "254/256 [============================>.] - ETA: 0s - loss: 0.4628 - accuracy: 0.8524\n",
      "Epoch 00077: val_loss did not improve from 0.43368\n",
      "256/256 [==============================] - 7s 28ms/step - loss: 0.4625 - accuracy: 0.8525 - val_loss: 0.4350 - val_accuracy: 0.8596\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 78/120\n",
      "255/256 [============================>.] - ETA: 0s - loss: 0.4633 - accuracy: 0.8523\n",
      "Epoch 00078: val_loss did not improve from 0.43368\n",
      "\n",
      "Epoch 00078: ReduceLROnPlateau reducing learning rate to 0.0019999999552965165.\n",
      "256/256 [==============================] - 7s 28ms/step - loss: 0.4632 - accuracy: 0.8523 - val_loss: 0.4349 - val_accuracy: 0.8599\n",
      "Epoch 79/120\n",
      "255/256 [============================>.] - ETA: 0s - loss: 0.4599 - accuracy: 0.8525\n",
      "Epoch 00079: val_loss improved from 0.43368 to 0.43293, saving model to models/tiselac-mcdcnn-79-0.43.h5\n",
      "256/256 [==============================] - 7s 28ms/step - loss: 0.4598 - accuracy: 0.8525 - val_loss: 0.4329 - val_accuracy: 0.8601\n",
      "Epoch 80/120\n",
      "253/256 [============================>.] - ETA: 0s - loss: 0.4607 - accuracy: 0.8528\n",
      "Epoch 00080: val_loss improved from 0.43293 to 0.43159, saving model to models/tiselac-mcdcnn-80-0.43.h5\n",
      "256/256 [==============================] - 7s 28ms/step - loss: 0.4605 - accuracy: 0.8527 - val_loss: 0.4316 - val_accuracy: 0.8606\n",
      "Epoch 81/120\n",
      "254/256 [============================>.] - ETA: 0s - loss: 0.4577 - accuracy: 0.8533\n",
      "Epoch 00081: val_loss improved from 0.43159 to 0.43039, saving model to models/tiselac-mcdcnn-81-0.43.h5\n",
      "256/256 [==============================] - 7s 28ms/step - loss: 0.4575 - accuracy: 0.8533 - val_loss: 0.4304 - val_accuracy: 0.8605\n",
      "Epoch 82/120\n",
      "254/256 [============================>.] - ETA: 0s - loss: 0.4595 - accuracy: 0.8525\n",
      "Epoch 00082: val_loss improved from 0.43039 to 0.42988, saving model to models/tiselac-mcdcnn-82-0.43.h5\n",
      "256/256 [==============================] - 7s 28ms/step - loss: 0.4595 - accuracy: 0.8525 - val_loss: 0.4299 - val_accuracy: 0.8609\n",
      "Epoch 83/120\n",
      "254/256 [============================>.] - ETA: 0s - loss: 0.4598 - accuracy: 0.8536\n",
      "Epoch 00083: val_loss did not improve from 0.42988\n",
      "256/256 [==============================] - 7s 28ms/step - loss: 0.4598 - accuracy: 0.8536 - val_loss: 0.4303 - val_accuracy: 0.8605\n",
      "Epoch 84/120\n",
      "255/256 [============================>.] - ETA: 0s - loss: 0.4598 - accuracy: 0.8532\n",
      "Epoch 00084: val_loss improved from 0.42988 to 0.42911, saving model to models/tiselac-mcdcnn-84-0.43.h5\n",
      "256/256 [==============================] - 7s 28ms/step - loss: 0.4598 - accuracy: 0.8532 - val_loss: 0.4291 - val_accuracy: 0.8619\n",
      "Epoch 85/120\n",
      "254/256 [============================>.] - ETA: 0s - loss: 0.4569 - accuracy: 0.8545\n",
      "Epoch 00085: val_loss did not improve from 0.42911\n",
      "256/256 [==============================] - 7s 28ms/step - loss: 0.4564 - accuracy: 0.8546 - val_loss: 0.4296 - val_accuracy: 0.8616\n",
      "Epoch 86/120\n",
      "255/256 [============================>.] - ETA: 0s - loss: 0.4567 - accuracy: 0.8551\n",
      "Epoch 00086: val_loss did not improve from 0.42911\n",
      "256/256 [==============================] - 7s 28ms/step - loss: 0.4566 - accuracy: 0.8551 - val_loss: 0.4307 - val_accuracy: 0.8608\n",
      "Epoch 87/120\n",
      "254/256 [============================>.] - ETA: 0s - loss: 0.4598 - accuracy: 0.8526\n",
      "Epoch 00087: val_loss improved from 0.42911 to 0.42882, saving model to models/tiselac-mcdcnn-87-0.43.h5\n",
      "256/256 [==============================] - 7s 28ms/step - loss: 0.4597 - accuracy: 0.8527 - val_loss: 0.4288 - val_accuracy: 0.8617\n",
      "Epoch 88/120\n",
      "255/256 [============================>.] - ETA: 0s - loss: 0.4583 - accuracy: 0.8530\n",
      "Epoch 00088: val_loss improved from 0.42882 to 0.42844, saving model to models/tiselac-mcdcnn-88-0.43.h5\n",
      "256/256 [==============================] - 7s 28ms/step - loss: 0.4582 - accuracy: 0.8529 - val_loss: 0.4284 - val_accuracy: 0.8615\n",
      "Epoch 89/120\n",
      "254/256 [============================>.] - ETA: 0s - loss: 0.4591 - accuracy: 0.8530\n",
      "Epoch 00089: val_loss did not improve from 0.42844\n",
      "256/256 [==============================] - 7s 28ms/step - loss: 0.4591 - accuracy: 0.8530 - val_loss: 0.4288 - val_accuracy: 0.8612\n",
      "Epoch 90/120\n",
      "255/256 [============================>.] - ETA: 0s - loss: 0.4589 - accuracy: 0.8538\n",
      "Epoch 00090: val_loss did not improve from 0.42844\n",
      "256/256 [==============================] - 7s 28ms/step - loss: 0.4588 - accuracy: 0.8538 - val_loss: 0.4293 - val_accuracy: 0.8611\n",
      "Epoch 91/120\n",
      "253/256 [============================>.] - ETA: 0s - loss: 0.4568 - accuracy: 0.8547\n",
      "Epoch 00091: val_loss improved from 0.42844 to 0.42826, saving model to models/tiselac-mcdcnn-91-0.43.h5\n",
      "256/256 [==============================] - 7s 28ms/step - loss: 0.4569 - accuracy: 0.8546 - val_loss: 0.4283 - val_accuracy: 0.8614\n",
      "Epoch 92/120\n",
      "255/256 [============================>.] - ETA: 0s - loss: 0.4590 - accuracy: 0.8538\n",
      "Epoch 00092: val_loss did not improve from 0.42826\n",
      "256/256 [==============================] - 7s 28ms/step - loss: 0.4589 - accuracy: 0.8538 - val_loss: 0.4301 - val_accuracy: 0.8609\n",
      "Epoch 93/120\n",
      "254/256 [============================>.] - ETA: 0s - loss: 0.4566 - accuracy: 0.8530\n",
      "Epoch 00093: val_loss did not improve from 0.42826\n",
      "256/256 [==============================] - 7s 29ms/step - loss: 0.4565 - accuracy: 0.8529 - val_loss: 0.4300 - val_accuracy: 0.8612\n",
      "Epoch 94/120\n",
      "255/256 [============================>.] - ETA: 0s - loss: 0.4558 - accuracy: 0.8551\n",
      "Epoch 00094: val_loss did not improve from 0.42826\n",
      "\n",
      "Epoch 00094: ReduceLROnPlateau reducing learning rate to 0.0003999999724328518.\n",
      "256/256 [==============================] - 7s 28ms/step - loss: 0.4559 - accuracy: 0.8550 - val_loss: 0.4298 - val_accuracy: 0.8613\n",
      "Epoch 95/120\n",
      "253/256 [============================>.] - ETA: 0s - loss: 0.4557 - accuracy: 0.8547\n",
      "Epoch 00095: val_loss did not improve from 0.42826\n",
      "256/256 [==============================] - 7s 28ms/step - loss: 0.4559 - accuracy: 0.8546 - val_loss: 0.4302 - val_accuracy: 0.8609\n",
      "Epoch 96/120\n",
      "253/256 [============================>.] - ETA: 0s - loss: 0.4568 - accuracy: 0.8541\n",
      "Epoch 00096: val_loss did not improve from 0.42826\n",
      "256/256 [==============================] - 7s 28ms/step - loss: 0.4566 - accuracy: 0.8541 - val_loss: 0.4296 - val_accuracy: 0.8609\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x28941ccffc8>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BATCH_SIZE = 256\n",
    "EPOCHS = 120\n",
    "\n",
    "train_gen = data_generator_mcdcnn(X_train, y_train, batch_size=BATCH_SIZE)\n",
    "val_gen = data_generator_mcdcnn(X_val, y_val, batch_size=BATCH_SIZE)\n",
    "train_steps = round(len(X_train) / BATCH_SIZE) + 1\n",
    "val_steps = round(len(X_val) / BATCH_SIZE) + 1\n",
    "\n",
    "mcdcnn_model.fit_generator(\n",
    "    train_gen,\n",
    "    steps_per_epoch=train_steps,\n",
    "    epochs=EPOCHS,\n",
    "    validation_data=val_gen,\n",
    "    validation_steps=val_steps,\n",
    "    callbacks=callbacks\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Testing the model\n",
    "\n",
    "We will now test the model on the provided dataset and use a F1 Score metric for it (provided by Sk-Learn)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import f1_score\n",
    "\n",
    "data_test_np = data_test.to_numpy()\n",
    "\n",
    "X_test = data_test_np.reshape((data_test_np.shape[0], 23, NUM_OF_FEATURES)).astype(np.float32)\n",
    "\n",
    "test_gen = data_generator_mcdcnn(X_test, y_test, batch_size=BATCH_SIZE)\n",
    "test_steps = round(len(X_test) / BATCH_SIZE) + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = mcdcnn_model.predict_generator(test_gen, steps=test_steps)\n",
    "y_pred = np.argmax(y_pred, axis=1) +1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_gen = data_generator_mcdcnn(X_test, y_test, batch_size=BATCH_SIZE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8687671316110633"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(y_test.flatten()[:-1], y_pred[:17972], average=\"weighted\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. Time-CNN\n",
    "\n",
    "Seen as an improvement of the MCDCNN method, by training multivariate time series jointly for better feature extraction, this architecture has been developed in [this paper (Zhao B, Lu H, Chen S, Liu J, Wu D , 2017)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7870510).\n",
    "\n",
    "Multiple unique characteristics are notable:\n",
    " - A MSE loss used with a sigmoid output layer\n",
    " - Use of Average pooling instead of Max Pooling\n",
    " - No Pooling layer after the last convolution layer before the feature extracted classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_time_cnn(input_shape=(23,10)):\n",
    "    \n",
    "    input_l = Input(input_shape)\n",
    "    x = Conv1D(24, 8, activation=\"relu\")(input_l)\n",
    "    x = AveragePooling1D(pool_size=3)(x)\n",
    "    x = Conv1D(24, 4, activation=\"relu\")(x)\n",
    "    x = Flatten()(x)\n",
    "    x = Dense(128, activation=\"relu\")(x)\n",
    "    x = Dropout(0.3)(x)\n",
    "    x = Dense(64, activation=\"relu\")(x)\n",
    "    x = BatchNormalization()(x)\n",
    "    x = Dense(32, activation=\"relu\")(x)\n",
    "    x = Dense(NUM_OF_CLASSES, activation='sigmoid')(x)\n",
    "    \n",
    "    model = Model(input_l, x)\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "LR = 0.001\n",
    "opt = Adam(LR, decay=0)\n",
    "\n",
    "time_cnn = get_time_cnn()\n",
    "time_cnn.compile(opt, loss=\"mse\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_filename = \"tiselac-time-cnn-{epoch:02d}-{val_loss:.2f}.h5\"\n",
    "\n",
    "callbacks = [\n",
    "    ModelCheckpoint(\n",
    "        os.path.join(\"models/\", model_filename),\n",
    "        monitor='val_loss', verbose=1, save_best_only=True, save_freq='epoch'),\n",
    "    EarlyStopping(monitor='val_loss', min_delta = 1e-4, patience = 5),\n",
    "    ReduceLROnPlateau(monitor='val_loss', factor=0.2, verbose=1,\n",
    "                              patience=3, min_lr=0.00001)\n",
    "\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 65371 samples, validate on 16343 samples\n",
      "Epoch 1/2000\n",
      "64512/65371 [============================>.] - ETA: 0s - loss: 0.0570\n",
      "Epoch 00001: val_loss improved from inf to 0.03589, saving model to models/tiselac-time-cnn-01-0.04.h5\n",
      "65371/65371 [==============================] - 4s 54us/sample - loss: 0.0567 - val_loss: 0.0359\n",
      "Epoch 2/2000\n",
      "65024/65371 [============================>.] - ETA: 0s - loss: 0.0359\n",
      "Epoch 00002: val_loss improved from 0.03589 to 0.03257, saving model to models/tiselac-time-cnn-02-0.03.h5\n",
      "65371/65371 [==============================] - 2s 32us/sample - loss: 0.0359 - val_loss: 0.0326\n",
      "Epoch 3/2000\n",
      "65024/65371 [============================>.] - ETA: 0s - loss: 0.0327\n",
      "Epoch 00003: val_loss improved from 0.03257 to 0.03102, saving model to models/tiselac-time-cnn-03-0.03.h5\n",
      "65371/65371 [==============================] - 2s 33us/sample - loss: 0.0327 - val_loss: 0.0310\n",
      "Epoch 4/2000\n",
      "64256/65371 [============================>.] - ETA: 0s - loss: 0.0309\n",
      "Epoch 00004: val_loss improved from 0.03102 to 0.02984, saving model to models/tiselac-time-cnn-04-0.03.h5\n",
      "65371/65371 [==============================] - 2s 32us/sample - loss: 0.0309 - val_loss: 0.0298\n",
      "Epoch 5/2000\n",
      "63616/65371 [============================>.] - ETA: 0s - loss: 0.0295\n",
      "Epoch 00005: val_loss improved from 0.02984 to 0.02868, saving model to models/tiselac-time-cnn-05-0.03.h5\n",
      "65371/65371 [==============================] - 2s 33us/sample - loss: 0.0295 - val_loss: 0.0287\n",
      "Epoch 6/2000\n",
      "63744/65371 [============================>.] - ETA: 0s - loss: 0.0287\n",
      "Epoch 00006: val_loss did not improve from 0.02868\n",
      "65371/65371 [==============================] - 2s 32us/sample - loss: 0.0287 - val_loss: 0.0305\n",
      "Epoch 7/2000\n",
      "64384/65371 [============================>.] - ETA: 0s - loss: 0.0278\n",
      "Epoch 00007: val_loss improved from 0.02868 to 0.02738, saving model to models/tiselac-time-cnn-07-0.03.h5\n",
      "65371/65371 [==============================] - 2s 33us/sample - loss: 0.0278 - val_loss: 0.0274\n",
      "Epoch 8/2000\n",
      "63872/65371 [============================>.] - ETA: 0s - loss: 0.0272\n",
      "Epoch 00008: val_loss did not improve from 0.02738\n",
      "65371/65371 [==============================] - 2s 32us/sample - loss: 0.0272 - val_loss: 0.0286\n",
      "Epoch 9/2000\n",
      "64640/65371 [============================>.] - ETA: 0s - loss: 0.0265\n",
      "Epoch 00009: val_loss did not improve from 0.02738\n",
      "65371/65371 [==============================] - 2s 32us/sample - loss: 0.0265 - val_loss: 0.0300\n",
      "Epoch 10/2000\n",
      "64640/65371 [============================>.] - ETA: 0s - loss: 0.0262\n",
      "Epoch 00010: val_loss did not improve from 0.02738\n",
      "\n",
      "Epoch 00010: ReduceLROnPlateau reducing learning rate to 0.00020000000949949026.\n",
      "65371/65371 [==============================] - 2s 32us/sample - loss: 0.0262 - val_loss: 0.0274\n",
      "Epoch 11/2000\n",
      "64512/65371 [============================>.] - ETA: 0s - loss: 0.0243\n",
      "Epoch 00011: val_loss improved from 0.02738 to 0.02412, saving model to models/tiselac-time-cnn-11-0.02.h5\n",
      "65371/65371 [==============================] - 2s 33us/sample - loss: 0.0242 - val_loss: 0.0241\n",
      "Epoch 12/2000\n",
      "64384/65371 [============================>.] - ETA: 0s - loss: 0.0239\n",
      "Epoch 00012: val_loss improved from 0.02412 to 0.02396, saving model to models/tiselac-time-cnn-12-0.02.h5\n",
      "65371/65371 [==============================] - 2s 34us/sample - loss: 0.0239 - val_loss: 0.0240\n",
      "Epoch 13/2000\n",
      "64128/65371 [============================>.] - ETA: 0s - loss: 0.0237\n",
      "Epoch 00013: val_loss improved from 0.02396 to 0.02348, saving model to models/tiselac-time-cnn-13-0.02.h5\n",
      "65371/65371 [==============================] - 2s 34us/sample - loss: 0.0236 - val_loss: 0.0235\n",
      "Epoch 14/2000\n",
      "64768/65371 [============================>.] - ETA: 0s - loss: 0.0234\n",
      "Epoch 00014: val_loss did not improve from 0.02348\n",
      "65371/65371 [==============================] - 2s 33us/sample - loss: 0.0234 - val_loss: 0.0245\n",
      "Epoch 15/2000\n",
      "65152/65371 [============================>.] - ETA: 0s - loss: 0.0233\n",
      "Epoch 00015: val_loss did not improve from 0.02348\n",
      "65371/65371 [==============================] - 2s 33us/sample - loss: 0.0233 - val_loss: 0.0243\n",
      "Epoch 16/2000\n",
      "63616/65371 [============================>.] - ETA: 0s - loss: 0.0233\n",
      "Epoch 00016: val_loss improved from 0.02348 to 0.02328, saving model to models/tiselac-time-cnn-16-0.02.h5\n",
      "65371/65371 [==============================] - 2s 33us/sample - loss: 0.0233 - val_loss: 0.0233\n",
      "Epoch 17/2000\n",
      "65280/65371 [============================>.] - ETA: 0s - loss: 0.0231\n",
      "Epoch 00017: val_loss improved from 0.02328 to 0.02321, saving model to models/tiselac-time-cnn-17-0.02.h5\n",
      "65371/65371 [==============================] - 2s 33us/sample - loss: 0.0231 - val_loss: 0.0232\n",
      "Epoch 18/2000\n",
      "64128/65371 [============================>.] - ETA: 0s - loss: 0.0230\n",
      "Epoch 00018: val_loss did not improve from 0.02321\n",
      "65371/65371 [==============================] - 2s 33us/sample - loss: 0.0230 - val_loss: 0.0232\n",
      "Epoch 19/2000\n",
      "64128/65371 [============================>.] - ETA: 0s - loss: 0.0229\n",
      "Epoch 00019: val_loss did not improve from 0.02321\n",
      "\n",
      "Epoch 00019: ReduceLROnPlateau reducing learning rate to 4.0000001899898055e-05.\n",
      "65371/65371 [==============================] - 2s 33us/sample - loss: 0.0229 - val_loss: 0.0233\n",
      "Epoch 20/2000\n",
      "64384/65371 [============================>.] - ETA: 0s - loss: 0.0223\n",
      "Epoch 00020: val_loss improved from 0.02321 to 0.02256, saving model to models/tiselac-time-cnn-20-0.02.h5\n",
      "65371/65371 [==============================] - 2s 33us/sample - loss: 0.0223 - val_loss: 0.0226\n",
      "Epoch 21/2000\n",
      "64512/65371 [============================>.] - ETA: 0s - loss: 0.0222\n",
      "Epoch 00021: val_loss improved from 0.02256 to 0.02247, saving model to models/tiselac-time-cnn-21-0.02.h5\n",
      "65371/65371 [==============================] - 2s 33us/sample - loss: 0.0223 - val_loss: 0.0225\n",
      "Epoch 22/2000\n",
      "64640/65371 [============================>.] - ETA: 0s - loss: 0.0222\n",
      "Epoch 00022: val_loss improved from 0.02247 to 0.02242, saving model to models/tiselac-time-cnn-22-0.02.h5\n",
      "65371/65371 [==============================] - 2s 33us/sample - loss: 0.0223 - val_loss: 0.0224\n",
      "Epoch 23/2000\n",
      "64768/65371 [============================>.] - ETA: 0s - loss: 0.0222\n",
      "Epoch 00023: val_loss improved from 0.02242 to 0.02240, saving model to models/tiselac-time-cnn-23-0.02.h5\n",
      "65371/65371 [==============================] - 2s 33us/sample - loss: 0.0222 - val_loss: 0.0224\n",
      "Epoch 24/2000\n",
      "64256/65371 [============================>.] - ETA: 0s - loss: 0.0223\n",
      "Epoch 00024: val_loss did not improve from 0.02240\n",
      "65371/65371 [==============================] - 2s 33us/sample - loss: 0.0223 - val_loss: 0.0224\n",
      "Epoch 25/2000\n",
      "63872/65371 [============================>.] - ETA: 0s - loss: 0.0221\n",
      "Epoch 00025: val_loss improved from 0.02240 to 0.02237, saving model to models/tiselac-time-cnn-25-0.02.h5\n",
      "\n",
      "Epoch 00025: ReduceLROnPlateau reducing learning rate to 1e-05.\n",
      "65371/65371 [==============================] - 3s 52us/sample - loss: 0.0221 - val_loss: 0.0224\n",
      "Epoch 26/2000\n",
      "63616/65371 [============================>.] - ETA: 0s - loss: 0.0220\n",
      "Epoch 00026: val_loss improved from 0.02237 to 0.02231, saving model to models/tiselac-time-cnn-26-0.02.h5\n",
      "65371/65371 [==============================] - 2s 34us/sample - loss: 0.0220 - val_loss: 0.0223\n",
      "Epoch 27/2000\n",
      "65024/65371 [============================>.] - ETA: 0s - loss: 0.0220\n",
      "Epoch 00027: val_loss improved from 0.02231 to 0.02230, saving model to models/tiselac-time-cnn-27-0.02.h5\n",
      "65371/65371 [==============================] - 2s 33us/sample - loss: 0.0220 - val_loss: 0.0223\n",
      "Epoch 28/2000\n",
      "64256/65371 [============================>.] - ETA: 0s - loss: 0.0221\n",
      "Epoch 00028: val_loss did not improve from 0.02230\n",
      "65371/65371 [==============================] - 2s 33us/sample - loss: 0.0220 - val_loss: 0.0223\n",
      "Epoch 29/2000\n",
      "64512/65371 [============================>.] - ETA: 0s - loss: 0.0220\n",
      "Epoch 00029: val_loss did not improve from 0.02230\n",
      "65371/65371 [==============================] - 2s 33us/sample - loss: 0.0220 - val_loss: 0.0223\n",
      "Epoch 30/2000\n",
      "65280/65371 [============================>.] - ETA: 0s - loss: 0.0219\n",
      "Epoch 00030: val_loss did not improve from 0.02230\n",
      "65371/65371 [==============================] - 2s 33us/sample - loss: 0.0219 - val_loss: 0.0223\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 31/2000\n",
      "65152/65371 [============================>.] - ETA: 0s - loss: 0.0220\n",
      "Epoch 00031: val_loss improved from 0.02230 to 0.02227, saving model to models/tiselac-time-cnn-31-0.02.h5\n",
      "65371/65371 [==============================] - 2s 35us/sample - loss: 0.0220 - val_loss: 0.0223\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x28978652b88>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BATCH_SIZE = 128\n",
    "EPOCHS = 2000\n",
    "\n",
    "time_cnn.fit(\n",
    "    X_train, y_train,\n",
    "    epochs=EPOCHS,\n",
    "    batch_size=BATCH_SIZE,\n",
    "    validation_data=(X_val, y_val),\n",
    "    callbacks=callbacks\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Testing the model\n",
    "\n",
    "We will now test the model on the provided dataset and use a F1 Score metric for it (provided by Sk-Learn)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import f1_score\n",
    "\n",
    "data_test_np = data_test.to_numpy()\n",
    "\n",
    "X_test = data_test_np.reshape((data_test_np.shape[0], 23, NUM_OF_FEATURES)).astype(np.float32)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = time_cnn.predict(X_test)\n",
    "y_pred = np.argmax(y_pred, axis=1) +1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8731067419980513"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(y_test.flatten(), y_pred, average=\"weighted\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Merge of Multimodal/Unimodal architecture\n",
    "\n",
    "We will first work with Deep Learning oriented solution and try implementing an LSTM on this data. Let's setup our working environment with the adequate hyperparameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "LR = 0.001\n",
    "BATCH_SIZE = 256\n",
    "EPOCHS = 50\n",
    "VAL_SIZE = 0.1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Setting up the data (splitting into training and validation sets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler_coord = StandardScaler()\n",
    "\n",
    "# Formatting the X data\n",
    "data_train = data.to_numpy()\n",
    "#data_train = minmax_scaler.fit_transform(data_train)\n",
    "\n",
    "data_coord = coord.to_numpy().astype(np.int32).reshape(-1, 2)\n",
    "data_coord = scaler_coord.fit_transform(data_coord)\n",
    "\n",
    "\n",
    "X = data_train.reshape((NUM_OF_PIXELS, 23, NUM_OF_FEATURES)).astype(np.int32)\n",
    "\n",
    "\n",
    "# Formatting the y data\n",
    "classes_train = classes_val.to_numpy().astype(np.uint8)\n",
    "enc = OneHotEncoder(handle_unknown='ignore', sparse=False)\n",
    "y = enc.fit_transform(classes_train).astype(np.uint8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting x and y into training and validation sets\n",
    "indices = np.arange(X.shape[0])\n",
    "X_train, X_val, y_train, y_val, idx_train, idx_val = train_test_split(X, y, indices, test_size = VAL_SIZE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_formatted = [\n",
    "    X_train, \n",
    "    #X_train\n",
    "    data_coord[idx_train]\n",
    "]\n",
    "X_train_formatted.extend([np.expand_dims(X_train[:,:,i], axis=2) for i in range(10)])\n",
    "\n",
    "X_val_formatted = [\n",
    "    X_val, \n",
    "    #X_val\n",
    "    data_coord[idx_val]\n",
    "]\n",
    "X_val_formatted.extend([np.expand_dims(X_val[:,:,i], axis=2) for i in range(10)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Setting up the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_filename = \"tiselac-mult-uni-{epoch:02d}-{val_loss:.2f}.h5\"\n",
    "\n",
    "callbacks = [\n",
    "    ModelCheckpoint(\n",
    "        os.path.join(\"models/\", model_filename),\n",
    "        monitor='val_loss', verbose=1, save_best_only=True, save_freq='epoch'),\n",
    "    #EarlyStopping(monitor='val_loss', min_delta = 1e-3, patience = 6),\n",
    "    ReduceLROnPlateau(monitor='val_loss', factor=0.2, verbose=1,\n",
    "                              patience=3, min_lr=0.00001)\n",
    "\n",
    "\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"static/img/tiselac_paper_ibm.png\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_multivariate_model():\n",
    "    \"\"\"\n",
    "        Treats the data of each band as a multimodal time serie\n",
    "    \"\"\"\n",
    "    input_lstm = Input((23, 10))\n",
    "    #lstm = LSTM(512, input_shape=(23, 10))(input_lstm)\n",
    "    \n",
    "    conv = Conv1D(32, 3, padding=\"same\", activation=\"relu\")(input_lstm)\n",
    "    conv = Conv1D(32, 3, padding=\"same\", activation=\"relu\")(conv)\n",
    "    conv = Conv1D(32, 3, padding=\"same\", activation=\"relu\")(conv)\n",
    "    flat = Flatten()(conv)\n",
    "    \n",
    "    return Model(inputs=input_lstm,outputs=flat)\n",
    "\n",
    "def get_coordinates_model():\n",
    "    \"\"\"\n",
    "        Treats the pixel coordinates on the image\n",
    "    \"\"\"\n",
    "    input_seq = Input(shape=(2,))\n",
    "\n",
    "    #x = Conv1D(32, 3, padding=\"same\", activation=\"relu\")(input_seq)\n",
    "    #x = MaxPooling1D()(x)\n",
    "    #x = Conv1D(32, 3, padding=\"same\", activation=\"relu\")(x)\n",
    "    \n",
    "    return Model(inputs=input_seq, outputs=input_seq)\n",
    "\n",
    "def get_univariate_models():\n",
    "    \"\"\"\n",
    "        Treats each band independently then concatenate models in one single\n",
    "    \"\"\"\n",
    "    # Models built for each band\n",
    "    inputs = [Input((23,1)) for _ in range(10)]\n",
    "    convs1 = [Conv1D(8,3,padding=\"same\",activation=\"relu\")(inputs[i]) for i in range(10)]    \n",
    "    pools = [MaxPooling1D()(convs1[i]) for i in range(10)]\n",
    "    convs2 = [Conv1D(4,3,padding=\"same\",activation=\"relu\")(pools[i]) for i in range(10)]\n",
    "    pools = [MaxPooling1D()(convs2[i]) for i in range(10)]\n",
    "    flatt = [Flatten()(pools[i]) for i in range(10)]\n",
    "    \n",
    "    \n",
    "    # First concatenate: each band at the first convolutional level\n",
    "    conca1 = Concatenate()(convs1)\n",
    "    conv1 = Conv1D(8,10,padding=\"same\",activation=\"relu\")(conca1)\n",
    "    flatt1 = Flatten()(conv1)\n",
    "    flatt.append(flatt1)\n",
    "    \n",
    "    # Second concatenate: each band at the second convolutional level\n",
    "    conca2 = Concatenate()(convs2)\n",
    "    conv2 = Conv1D(4,10,padding=\"same\",activation=\"relu\")(conca2)\n",
    "    flatt2 = Flatten()(conv2)\n",
    "    flatt.append(flatt2)\n",
    "    \n",
    "    # Third and last concatenate after every flatten, adding the process of the first and second concats\n",
    "    conca3 = Concatenate()(flatt)\n",
    "    \n",
    "    return Model(inputs=inputs, outputs=conca3)\n",
    "    \n",
    "# Merging inputs from the time series and the pixel position\n",
    "mult_model = get_multivariate_model()\n",
    "coord_model = get_coordinates_model()\n",
    "univ_models = get_univariate_models()\n",
    "\n",
    "\n",
    "merged = Concatenate(axis=-1)([\n",
    "    mult_model.output, \n",
    "    coord_model.output, \n",
    "    univ_models.output])\n",
    "\n",
    "x = Dense(256, activation=\"relu\")(merged)\n",
    "x = Dense(128, activation=\"relu\")(x)\n",
    "x = Dropout(0.3)(x)\n",
    "x = Dense(64, activation=\"relu\")(x)\n",
    "x = Dropout(0.3)(x)\n",
    "x = Dense(32, activation=\"relu\")(x)\n",
    "out = Dense(NUM_OF_CLASSES, activation='softmax')(x)\n",
    "\n",
    "model = Model(inputs=[\n",
    "    mult_model.input, \n",
    "    coord_model.input, \n",
    "    univ_models.input], outputs=[out])\n",
    "\n",
    "opt = Adam(LR)\n",
    "\n",
    "model.compile(opt, loss=\"categorical_crossentropy\",\n",
    "    metrics=['categorical_accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 73542 samples, validate on 8172 samples\n",
      "Epoch 1/50\n",
      "73216/73542 [============================>.] - ETA: 0s - loss: 1.9781 - categorical_accuracy: 0.5942\n",
      "Epoch 00001: val_loss improved from inf to 0.79496, saving model to models/tiselac-lstm-01-0.79.h5\n",
      "73542/73542 [==============================] - 8s 106us/sample - loss: 1.9741 - categorical_accuracy: 0.5946 - val_loss: 0.7950 - val_categorical_accuracy: 0.7521\n",
      "Epoch 2/50\n",
      "72960/73542 [============================>.] - ETA: 0s - loss: 0.8818 - categorical_accuracy: 0.7344\n",
      "Epoch 00002: val_loss improved from 0.79496 to 0.69212, saving model to models/tiselac-lstm-02-0.69.h5\n",
      "73542/73542 [==============================] - 3s 41us/sample - loss: 0.8808 - categorical_accuracy: 0.7348 - val_loss: 0.6921 - val_categorical_accuracy: 0.7912\n",
      "Epoch 3/50\n",
      "73216/73542 [============================>.] - ETA: 0s - loss: 0.7433 - categorical_accuracy: 0.7783\n",
      "Epoch 00003: val_loss improved from 0.69212 to 0.58370, saving model to models/tiselac-lstm-03-0.58.h5\n",
      "73542/73542 [==============================] - 3s 43us/sample - loss: 0.7430 - categorical_accuracy: 0.7785 - val_loss: 0.5837 - val_categorical_accuracy: 0.8235\n",
      "Epoch 4/50\n",
      "72192/73542 [============================>.] - ETA: 0s - loss: 0.6631 - categorical_accuracy: 0.8026\n",
      "Epoch 00004: val_loss improved from 0.58370 to 0.55157, saving model to models/tiselac-lstm-04-0.55.h5\n",
      "73542/73542 [==============================] - 3s 42us/sample - loss: 0.6631 - categorical_accuracy: 0.8026 - val_loss: 0.5516 - val_categorical_accuracy: 0.8310\n",
      "Epoch 5/50\n",
      "72448/73542 [============================>.] - ETA: 0s - loss: 0.6023 - categorical_accuracy: 0.8209\n",
      "Epoch 00005: val_loss improved from 0.55157 to 0.54354, saving model to models/tiselac-lstm-05-0.54.h5\n",
      "73542/73542 [==============================] - 3s 43us/sample - loss: 0.6019 - categorical_accuracy: 0.8209 - val_loss: 0.5435 - val_categorical_accuracy: 0.8325\n",
      "Epoch 6/50\n",
      "72192/73542 [============================>.] - ETA: 0s - loss: 0.5447 - categorical_accuracy: 0.8385\n",
      "Epoch 00006: val_loss improved from 0.54354 to 0.52418, saving model to models/tiselac-lstm-06-0.52.h5\n",
      "73542/73542 [==============================] - 3s 42us/sample - loss: 0.5455 - categorical_accuracy: 0.8382 - val_loss: 0.5242 - val_categorical_accuracy: 0.8500\n",
      "Epoch 7/50\n",
      "73472/73542 [============================>.] - ETA: 0s - loss: 0.5109 - categorical_accuracy: 0.8463\n",
      "Epoch 00007: val_loss improved from 0.52418 to 0.46064, saving model to models/tiselac-lstm-07-0.46.h5\n",
      "73542/73542 [==============================] - 3s 43us/sample - loss: 0.5108 - categorical_accuracy: 0.8464 - val_loss: 0.4606 - val_categorical_accuracy: 0.8655\n",
      "Epoch 8/50\n",
      "72960/73542 [============================>.] - ETA: 0s - loss: 0.4770 - categorical_accuracy: 0.8581 ETA: 0s - loss: 0.4788 - cate\n",
      "Epoch 00008: val_loss did not improve from 0.46064\n",
      "73542/73542 [==============================] - 3s 41us/sample - loss: 0.4770 - categorical_accuracy: 0.8582 - val_loss: 0.4785 - val_categorical_accuracy: 0.8486\n",
      "Epoch 9/50\n",
      "72704/73542 [============================>.] - ETA: 0s - loss: 0.4532 - categorical_accuracy: 0.8632\n",
      "Epoch 00009: val_loss improved from 0.46064 to 0.43217, saving model to models/tiselac-lstm-09-0.43.h5\n",
      "73542/73542 [==============================] - 4s 52us/sample - loss: 0.4530 - categorical_accuracy: 0.8633 - val_loss: 0.4322 - val_categorical_accuracy: 0.8642\n",
      "Epoch 10/50\n",
      "72704/73542 [============================>.] - ETA: 0s - loss: 0.4306 - categorical_accuracy: 0.8693\n",
      "Epoch 00010: val_loss improved from 0.43217 to 0.40136, saving model to models/tiselac-lstm-10-0.40.h5\n",
      "73542/73542 [==============================] - 4s 54us/sample - loss: 0.4303 - categorical_accuracy: 0.8694 - val_loss: 0.4014 - val_categorical_accuracy: 0.8697\n",
      "Epoch 11/50\n",
      "72960/73542 [============================>.] - ETA: 0s - loss: 0.4062 - categorical_accuracy: 0.8763\n",
      "Epoch 00011: val_loss improved from 0.40136 to 0.39100, saving model to models/tiselac-lstm-11-0.39.h5\n",
      "73542/73542 [==============================] - 4s 50us/sample - loss: 0.4058 - categorical_accuracy: 0.8765 - val_loss: 0.3910 - val_categorical_accuracy: 0.8747\n",
      "Epoch 12/50\n",
      "73472/73542 [============================>.] - ETA: 0s - loss: 0.3885 - categorical_accuracy: 0.8813\n",
      "Epoch 00012: val_loss improved from 0.39100 to 0.37096, saving model to models/tiselac-lstm-12-0.37.h5\n",
      "73542/73542 [==============================] - 3s 44us/sample - loss: 0.3886 - categorical_accuracy: 0.8812 - val_loss: 0.3710 - val_categorical_accuracy: 0.8841\n",
      "Epoch 13/50\n",
      "73472/73542 [============================>.] - ETA: 0s - loss: 0.3713 - categorical_accuracy: 0.8866 ETA: 0s - loss: 0.370\n",
      "Epoch 00013: val_loss improved from 0.37096 to 0.36854, saving model to models/tiselac-lstm-13-0.37.h5\n",
      "73542/73542 [==============================] - 3s 43us/sample - loss: 0.3712 - categorical_accuracy: 0.8867 - val_loss: 0.3685 - val_categorical_accuracy: 0.8835\n",
      "Epoch 14/50\n",
      "72704/73542 [============================>.] - ETA: 0s - loss: 0.3493 - categorical_accuracy: 0.8932\n",
      "Epoch 00014: val_loss improved from 0.36854 to 0.35564, saving model to models/tiselac-lstm-14-0.36.h5\n",
      "73542/73542 [==============================] - 3s 43us/sample - loss: 0.3492 - categorical_accuracy: 0.8932 - val_loss: 0.3556 - val_categorical_accuracy: 0.8842\n",
      "Epoch 15/50\n",
      "72704/73542 [============================>.] - ETA: 0s - loss: 0.3346 - categorical_accuracy: 0.8972\n",
      "Epoch 00015: val_loss improved from 0.35564 to 0.34797, saving model to models/tiselac-lstm-15-0.35.h5\n",
      "73542/73542 [==============================] - 3s 44us/sample - loss: 0.3342 - categorical_accuracy: 0.8973 - val_loss: 0.3480 - val_categorical_accuracy: 0.8891\n",
      "Epoch 16/50\n",
      "72448/73542 [============================>.] - ETA: 0s - loss: 0.3189 - categorical_accuracy: 0.9017\n",
      "Epoch 00016: val_loss improved from 0.34797 to 0.34168, saving model to models/tiselac-lstm-16-0.34.h5\n",
      "73542/73542 [==============================] - 3s 46us/sample - loss: 0.3188 - categorical_accuracy: 0.9017 - val_loss: 0.3417 - val_categorical_accuracy: 0.8897\n",
      "Epoch 17/50\n",
      "72704/73542 [============================>.] - ETA: 0s - loss: 0.3097 - categorical_accuracy: 0.9036\n",
      "Epoch 00017: val_loss improved from 0.34168 to 0.32338, saving model to models/tiselac-lstm-17-0.32.h5\n",
      "73542/73542 [==============================] - 3s 45us/sample - loss: 0.3092 - categorical_accuracy: 0.9038 - val_loss: 0.3234 - val_categorical_accuracy: 0.8992\n",
      "Epoch 18/50\n",
      "73216/73542 [============================>.] - ETA: 0s - loss: 0.2874 - categorical_accuracy: 0.9099\n",
      "Epoch 00018: val_loss improved from 0.32338 to 0.32321, saving model to models/tiselac-lstm-18-0.32.h5\n",
      "73542/73542 [==============================] - 3s 44us/sample - loss: 0.2872 - categorical_accuracy: 0.9099 - val_loss: 0.3232 - val_categorical_accuracy: 0.8964\n",
      "Epoch 19/50\n",
      "72960/73542 [============================>.] - ETA: 0s - loss: 0.2837 - categorical_accuracy: 0.9117\n",
      "Epoch 00019: val_loss improved from 0.32321 to 0.31063, saving model to models/tiselac-lstm-19-0.31.h5\n",
      "73542/73542 [==============================] - 3s 44us/sample - loss: 0.2835 - categorical_accuracy: 0.9117 - val_loss: 0.3106 - val_categorical_accuracy: 0.9021\n",
      "Epoch 20/50\n",
      "72448/73542 [============================>.] - ETA: 0s - loss: 0.2675 - categorical_accuracy: 0.9166\n",
      "Epoch 00020: val_loss did not improve from 0.31063\n",
      "73542/73542 [==============================] - 3s 42us/sample - loss: 0.2670 - categorical_accuracy: 0.9167 - val_loss: 0.3279 - val_categorical_accuracy: 0.9010\n",
      "Epoch 21/50\n",
      "73216/73542 [============================>.] - ETA: 0s - loss: 0.2632 - categorical_accuracy: 0.9187\n",
      "Epoch 00021: val_loss improved from 0.31063 to 0.30411, saving model to models/tiselac-lstm-21-0.30.h5\n",
      "73542/73542 [==============================] - 3s 44us/sample - loss: 0.2629 - categorical_accuracy: 0.9188 - val_loss: 0.3041 - val_categorical_accuracy: 0.9049\n",
      "Epoch 22/50\n",
      "73472/73542 [============================>.] - ETA: 0s - loss: 0.2439 - categorical_accuracy: 0.9239\n",
      "Epoch 00022: val_loss did not improve from 0.30411\n",
      "73542/73542 [==============================] - 3s 42us/sample - loss: 0.2438 - categorical_accuracy: 0.9239 - val_loss: 0.3054 - val_categorical_accuracy: 0.9071\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 23/50\n",
      "73216/73542 [============================>.] - ETA: 0s - loss: 0.2336 - categorical_accuracy: 0.9269\n",
      "Epoch 00023: val_loss improved from 0.30411 to 0.30352, saving model to models/tiselac-lstm-23-0.30.h5\n",
      "73542/73542 [==============================] - 3s 45us/sample - loss: 0.2337 - categorical_accuracy: 0.9269 - val_loss: 0.3035 - val_categorical_accuracy: 0.9092\n",
      "Epoch 24/50\n",
      "72960/73542 [============================>.] - ETA: 0s - loss: 0.2303 - categorical_accuracy: 0.9279\n",
      "Epoch 00024: val_loss did not improve from 0.30352\n",
      "73542/73542 [==============================] - 3s 42us/sample - loss: 0.2302 - categorical_accuracy: 0.9280 - val_loss: 0.3063 - val_categorical_accuracy: 0.9039\n",
      "Epoch 25/50\n",
      "73216/73542 [============================>.] - ETA: 0s - loss: 0.2156 - categorical_accuracy: 0.9316\n",
      "Epoch 00025: val_loss improved from 0.30352 to 0.28555, saving model to models/tiselac-lstm-25-0.29.h5\n",
      "73542/73542 [==============================] - 3s 45us/sample - loss: 0.2157 - categorical_accuracy: 0.9316 - val_loss: 0.2856 - val_categorical_accuracy: 0.9123\n",
      "Epoch 26/50\n",
      "72448/73542 [============================>.] - ETA: 0s - loss: 0.2075 - categorical_accuracy: 0.9352\n",
      "Epoch 00026: val_loss did not improve from 0.28555\n",
      "73542/73542 [==============================] - 3s 43us/sample - loss: 0.2080 - categorical_accuracy: 0.9351 - val_loss: 0.2901 - val_categorical_accuracy: 0.9158\n",
      "Epoch 27/50\n",
      "72960/73542 [============================>.] - ETA: 0s - loss: 0.2013 - categorical_accuracy: 0.9367\n",
      "Epoch 00027: val_loss did not improve from 0.28555\n",
      "73542/73542 [==============================] - 3s 43us/sample - loss: 0.2012 - categorical_accuracy: 0.9366 - val_loss: 0.2883 - val_categorical_accuracy: 0.9150\n",
      "Epoch 28/50\n",
      "72704/73542 [============================>.] - ETA: 0s - loss: 0.1926 - categorical_accuracy: 0.9397\n",
      "Epoch 00028: val_loss did not improve from 0.28555\n",
      "\n",
      "Epoch 00028: ReduceLROnPlateau reducing learning rate to 0.00020000000949949026.\n",
      "73542/73542 [==============================] - 3s 43us/sample - loss: 0.1927 - categorical_accuracy: 0.9397 - val_loss: 0.3009 - val_categorical_accuracy: 0.9158\n",
      "Epoch 29/50\n",
      "72448/73542 [============================>.] - ETA: 0s - loss: 0.1386 - categorical_accuracy: 0.9561\n",
      "Epoch 00029: val_loss improved from 0.28555 to 0.26413, saving model to models/tiselac-lstm-29-0.26.h5\n",
      "73542/73542 [==============================] - 3s 44us/sample - loss: 0.1384 - categorical_accuracy: 0.9561 - val_loss: 0.2641 - val_categorical_accuracy: 0.9300\n",
      "Epoch 30/50\n",
      "73216/73542 [============================>.] - ETA: 0s - loss: 0.1210 - categorical_accuracy: 0.9615\n",
      "Epoch 00030: val_loss did not improve from 0.26413\n",
      "73542/73542 [==============================] - 3s 43us/sample - loss: 0.1210 - categorical_accuracy: 0.9615 - val_loss: 0.2706 - val_categorical_accuracy: 0.9294\n",
      "Epoch 31/50\n",
      "72704/73542 [============================>.] - ETA: 0s - loss: 0.1126 - categorical_accuracy: 0.9645\n",
      "Epoch 00031: val_loss did not improve from 0.26413\n",
      "73542/73542 [==============================] - 3s 43us/sample - loss: 0.1127 - categorical_accuracy: 0.9645 - val_loss: 0.2761 - val_categorical_accuracy: 0.9288\n",
      "Epoch 32/50\n",
      "72448/73542 [============================>.] - ETA: 0s - loss: 0.1083 - categorical_accuracy: 0.9657\n",
      "Epoch 00032: val_loss did not improve from 0.26413\n",
      "\n",
      "Epoch 00032: ReduceLROnPlateau reducing learning rate to 4.0000001899898055e-05.\n",
      "73542/73542 [==============================] - 3s 43us/sample - loss: 0.1084 - categorical_accuracy: 0.9657 - val_loss: 0.2826 - val_categorical_accuracy: 0.9288\n",
      "Epoch 33/50\n",
      "72704/73542 [============================>.] - ETA: 0s - loss: 0.0948 - categorical_accuracy: 0.9699\n",
      "Epoch 00033: val_loss did not improve from 0.26413\n",
      "73542/73542 [==============================] - 3s 42us/sample - loss: 0.0950 - categorical_accuracy: 0.9699 - val_loss: 0.2846 - val_categorical_accuracy: 0.9307\n",
      "Epoch 34/50\n",
      "73472/73542 [============================>.] - ETA: 0s - loss: 0.0904 - categorical_accuracy: 0.9715\n",
      "Epoch 00034: val_loss did not improve from 0.26413\n",
      "73542/73542 [==============================] - 3s 43us/sample - loss: 0.0904 - categorical_accuracy: 0.9715 - val_loss: 0.2887 - val_categorical_accuracy: 0.9318\n",
      "Epoch 35/50\n",
      "72960/73542 [============================>.] - ETA: 0s - loss: 0.0897 - categorical_accuracy: 0.9715\n",
      "Epoch 00035: val_loss did not improve from 0.26413\n",
      "\n",
      "Epoch 00035: ReduceLROnPlateau reducing learning rate to 1e-05.\n",
      "73542/73542 [==============================] - 3s 43us/sample - loss: 0.0896 - categorical_accuracy: 0.9715 - val_loss: 0.2891 - val_categorical_accuracy: 0.9322\n",
      "Epoch 36/50\n",
      "73216/73542 [============================>.] - ETA: 0s - loss: 0.0869 - categorical_accuracy: 0.9728\n",
      "Epoch 00036: val_loss did not improve from 0.26413\n",
      "73542/73542 [==============================] - 3s 43us/sample - loss: 0.0869 - categorical_accuracy: 0.9728 - val_loss: 0.2897 - val_categorical_accuracy: 0.9317\n",
      "Epoch 37/50\n",
      "72448/73542 [============================>.] - ETA: 0s - loss: 0.0852 - categorical_accuracy: 0.9730\n",
      "Epoch 00037: val_loss did not improve from 0.26413\n",
      "73542/73542 [==============================] - 3s 43us/sample - loss: 0.0849 - categorical_accuracy: 0.9731 - val_loss: 0.2899 - val_categorical_accuracy: 0.9337\n",
      "Epoch 38/50\n",
      "72960/73542 [============================>.] - ETA: 0s - loss: 0.0850 - categorical_accuracy: 0.9729\n",
      "Epoch 00038: val_loss did not improve from 0.26413\n",
      "73542/73542 [==============================] - 3s 43us/sample - loss: 0.0849 - categorical_accuracy: 0.9728 - val_loss: 0.2921 - val_categorical_accuracy: 0.9333\n",
      "Epoch 39/50\n",
      "73216/73542 [============================>.] - ETA: 0s - loss: 0.0846 - categorical_accuracy: 0.9735\n",
      "Epoch 00039: val_loss did not improve from 0.26413\n",
      "73542/73542 [==============================] - 3s 43us/sample - loss: 0.0845 - categorical_accuracy: 0.9735 - val_loss: 0.2950 - val_categorical_accuracy: 0.9328\n",
      "Epoch 40/50\n",
      "72704/73542 [============================>.] - ETA: 0s - loss: 0.0837 - categorical_accuracy: 0.9735\n",
      "Epoch 00040: val_loss did not improve from 0.26413\n",
      "73542/73542 [==============================] - 3s 43us/sample - loss: 0.0837 - categorical_accuracy: 0.9734 - val_loss: 0.2955 - val_categorical_accuracy: 0.9333\n",
      "Epoch 41/50\n",
      "73472/73542 [============================>.] - ETA: 0s - loss: 0.0834 - categorical_accuracy: 0.9740\n",
      "Epoch 00041: val_loss did not improve from 0.26413\n",
      "73542/73542 [==============================] - 3s 43us/sample - loss: 0.0834 - categorical_accuracy: 0.9740 - val_loss: 0.2953 - val_categorical_accuracy: 0.9328\n",
      "Epoch 42/50\n",
      "72448/73542 [============================>.] - ETA: 0s - loss: 0.0836 - categorical_accuracy: 0.9736\n",
      "Epoch 00042: val_loss did not improve from 0.26413\n",
      "73542/73542 [==============================] - 3s 43us/sample - loss: 0.0835 - categorical_accuracy: 0.9736 - val_loss: 0.2972 - val_categorical_accuracy: 0.9327\n",
      "Epoch 43/50\n",
      "72704/73542 [============================>.] - ETA: 0s - loss: 0.0831 - categorical_accuracy: 0.9736\n",
      "Epoch 00043: val_loss did not improve from 0.26413\n",
      "73542/73542 [==============================] - 3s 43us/sample - loss: 0.0831 - categorical_accuracy: 0.9735 - val_loss: 0.2984 - val_categorical_accuracy: 0.9326\n",
      "Epoch 44/50\n",
      "72192/73542 [============================>.] - ETA: 0s - loss: 0.0831 - categorical_accuracy: 0.9734\n",
      "Epoch 00044: val_loss did not improve from 0.26413\n",
      "73542/73542 [==============================] - 3s 43us/sample - loss: 0.0827 - categorical_accuracy: 0.9736 - val_loss: 0.2989 - val_categorical_accuracy: 0.9318\n",
      "Epoch 45/50\n",
      "72960/73542 [============================>.] - ETA: 0s - loss: 0.0813 - categorical_accuracy: 0.9745\n",
      "Epoch 00045: val_loss did not improve from 0.26413\n",
      "73542/73542 [==============================] - 3s 43us/sample - loss: 0.0812 - categorical_accuracy: 0.9745 - val_loss: 0.2996 - val_categorical_accuracy: 0.9329\n",
      "Epoch 46/50\n",
      "72192/73542 [============================>.] - ETA: 0s - loss: 0.0809 - categorical_accuracy: 0.9746\n",
      "Epoch 00046: val_loss did not improve from 0.26413\n",
      "73542/73542 [==============================] - 3s 43us/sample - loss: 0.0809 - categorical_accuracy: 0.9746 - val_loss: 0.3011 - val_categorical_accuracy: 0.9334\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 47/50\n",
      "72960/73542 [============================>.] - ETA: 0s - loss: 0.0802 - categorical_accuracy: 0.9749\n",
      "Epoch 00047: val_loss did not improve from 0.26413\n",
      "73542/73542 [==============================] - 3s 43us/sample - loss: 0.0802 - categorical_accuracy: 0.9749 - val_loss: 0.3013 - val_categorical_accuracy: 0.9322\n",
      "Epoch 48/50\n",
      "72960/73542 [============================>.] - ETA: 0s - loss: 0.0797 - categorical_accuracy: 0.9746\n",
      "Epoch 00048: val_loss did not improve from 0.26413\n",
      "73542/73542 [==============================] - 3s 43us/sample - loss: 0.0797 - categorical_accuracy: 0.9746 - val_loss: 0.3024 - val_categorical_accuracy: 0.9326\n",
      "Epoch 49/50\n",
      "72960/73542 [============================>.] - ETA: 0s - loss: 0.0797 - categorical_accuracy: 0.9754\n",
      "Epoch 00049: val_loss did not improve from 0.26413\n",
      "73542/73542 [==============================] - 3s 44us/sample - loss: 0.0799 - categorical_accuracy: 0.9754 - val_loss: 0.3034 - val_categorical_accuracy: 0.9328\n",
      "Epoch 50/50\n",
      "73216/73542 [============================>.] - ETA: 0s - loss: 0.0799 - categorical_accuracy: 0.9749\n",
      "Epoch 00050: val_loss did not improve from 0.26413\n",
      "73542/73542 [==============================] - 3s 43us/sample - loss: 0.0800 - categorical_accuracy: 0.9748 - val_loss: 0.3041 - val_categorical_accuracy: 0.9321\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x288f272ec88>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(\n",
    "    X_train_formatted, y_train, batch_size=BATCH_SIZE, epochs=EPOCHS, verbose=1, \n",
    "    callbacks=callbacks, \n",
    "    validation_data=(X_val_formatted, y_val))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Testing the model\n",
    "\n",
    "We will now test the model on the provided dataset and use a F1 Score metric for it (provided by Sk-Learn)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import f1_score, confusion_matrix\n",
    "\n",
    "data_test_np = data_test.to_numpy()\n",
    "#data_test_np = data_test_np.transform(minmax_scaler)\n",
    "\n",
    "X_test = data_test_np.reshape((data_test_np.shape[0], 23, NUM_OF_FEATURES)).astype(np.float32)\n",
    "coord_test_np = coord_test.to_numpy().astype(np.int32).reshape(-1, 2)\n",
    "coord_test_np = scaler_coord.transform(coord_test_np)\n",
    "\n",
    "univariate_bands = [np.expand_dims(X_test[:,:,i], axis=2) for i in range(10)]\n",
    "\n",
    "X_test = [\n",
    "    X_test, \n",
    "    #X_test\n",
    "    coord_test_np\n",
    "]\n",
    "X_test.extend(univariate_bands)\n",
    "\n",
    "y_pred = model.predict(X_test)\n",
    "y_pred = np.argmax(y_pred, axis=1) +1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We evaluate the F1 score to compare with other teams result in the context of the TiSeLaC challenge."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9338552920515625"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(y_test.flatten(), y_pred, average=\"weighted\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To have better insights on which classes are often misclassified, we will plot a confusion matrix using seaborn and sklearn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "m = confusion_matrix(y_test.flatten(), y_pred)\n",
    "m_pd = pd.DataFrame(m)\n",
    "classes_as_dict = {i: CLASSES[i] for i in range(9)}\n",
    "m_pd = m_pd.rename(columns=classes_as_dict, index=classes_as_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x289d6e27c08>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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t+OxrPtd/e31w3effPHCGHhvbQY0TRklOaWOPKbp89qJ+25qoPROWqO6CQXLaHbpwPFVr24/3yHPOjvq+NlyTJ43WLt7XArA4m9N53VmE1/n++++1ZcsW+fr66rHHHlOxYsVua2f+AZG39Tj8u1gL11o4K9c6+N4AYGUfFahhdgRcpUPKOrMjAJaUmZF9r6jV7Z5bzxi7k008aO11VgzP+Zg2bZp69eqlEydOKCkpSV26dNHChQs9kQ0AAAAAAFjIuXPn1KBBAyUlXVmUedOmTWrYsKGio6P17rvvuu63f/9+NWvWTHXq1NGgQYOUmZkpSTp69KhatWqlunXrqkuXLjp//rzhPg2Li3nz5mnhwoXq37+/Bg4cqPnz52vatGm3+xwBAAAAALjjOOTMtres2r17t55//nkdPHhQkpSenq6BAwdq8uTJWr58ufbt26f1669cprpv374aMmSIVq5cKafTqXnz5kmShg0bppYtWyo+Pl4PPvigJk+ebLhfw+IiT5488vP731IYwcHBypmTlZsBAAAAAPAm8+bNc61/KUl79uxRkSJFFBUVJT8/PzVs2FDx8fFKTk5Wenq6ypYtK0lq1qyZ4uPjdfnyZW3btk116tS5ZtyI4eKcRYsW1bPPPqv69evLz89Pq1evVkhIiCZOnChJ6tat220/aQAAAAAAYK4zZ87ozJkz142HhoYqNDTUtT1y5MhrPv7bb7+pQIECru2wsDCdOHHiuvECBQroxIkTSk1NVUhIiGtyxJ/jRgyLi8jISEVGRiojI0MZGRl67LHHjB4CAAAAAADuEJ988olrcsLVunXrpu7du9/0cQ6HQzbb/y434HQ6ZbPZbjr+5/+v9tftGzEsLrp166ZTp05p9+7dstvtKlu2rO666y7DTwwAAAAAAKyvbdu2atq06XXjV8+2uJGCBQsqJSXFtZ2SkqKwsLDrxk+ePKmwsDDly5dPZ8+eld1ul6+vr+v+RgzXuNi4caMaN26sRYsW6fPPP1ejRo20bh2XfwIAAAAAeA9nNr6FhoaqcOHC192MiosyZcrowIEDOnTokOx2u5YtW6Zq1aopMjJSgYGB2rFjhyRpyZIlqlatmvz9/VWxYkUtX75ckrR48WJVq1bN8GtvOOPi3Xff1ezZsxUVFSVJOnLkiLp166YaNbiWOAAAAAAA3iowMFCjR49W9+7ddenSJVWvXl1169aVJI0dO1axsbE6d+6cSpUqpZiYGEnS0KFD1b9/f02ZMkWFChXSuHHjDPdjWFxkZma6SgtJioqKksPhuN3nBQAAAAAA7mBr1651/btKlSpaunTpdfcpWbKkFixYcN14ZGSkZs6c+bf2Z3iqSEREhGbMmKFz587p3LlzmjFjhiIjI//WTgAAAAAAAG6HYXExcuRI7dq1S08++aRq1aqlnTt3avjw4Z7IBgAAAACAJTjkzLY3qzM8VSQuLk7jx4/3RBYAAAAAAIBrGM64WLdunZxO6zcwAAAAAAAg+zGccZEnTx7VrVtXpUqVUmBgoGt81KhRbg0GAAAAAABgWFw0bdrUEzkAAAAAALAsrq1pHsNTRWrXrq0LFy6oadOmevTRR3X48GHXdVkBAAAAAADcybC46NOnj3777TdJUs6cOeVwOPTaa6+5PRgAAAAAAIBhcXH06FH16tVLkhQSEqJevXrp8OHDbg8GAAAAAABgWFzYbDYlJia6tn/55Rf5+RkujQEAAAAAQLbhzMb/WZ1hA9GvXz+1b99e4eHhkqTU1FSNGTPG7cEAAAAAAAAMi4tHH31U69at048//ig/Pz8VLVpUAQEBnsgGAAAAAAC8XJbO+QgICNCDDz7o7iwAAAAAAADXMFzjAgAAAAAAwCyssgkAAAAAgAGH2QG8mOGMi7S0NMXGxiomJkanT5/WgAEDlJaW5olsAAAAAADAyxkWF4MHD1bp0qV1+vRp5ciRQ2FhYerbt68nsgEAAAAAAC9nWFwkJSXp2WeflY+PjwICAtSrVy8dP37cE9kAAAAAAICXM1zjwtfXV2fPnpXNZpMkHTx4UD4+rOkJAAAAAPAeTjnNjuC1DIuLHj16qE2bNjp27Ji6du2qXbt26c033/RENgAAAAAA4OUMi4vHH39cpUqV0p49e2S32zV8+HDdddddnsgGAAAAAAC8nGFxcebMGa1YsUKnT5+W0+nU/v37JUndunVzezgAAAAAAODdDIuLnj17KleuXCpRooRrnQsAAAAAALyJw+wAXsywuDh58qSmT5/uiSwAAAAAAADXMLw8yAMPPKAffvjBE1kAAAAAAACuYTjj4qefflLTpk2VP39+BQYGyul0ymazKSEhwRP5AAAAAACAFzMsLiZOnOiJHAAAAAAAWJbD6TQ7gtcyLC4KFCig9evX6/z585Iku92upKQk9ezZ0+3hAAAAAACAdzMsLnr37q20tDQdPnxYFStW1JYtW1S+fHlPZAMAAAAAAF7OcHHOxMRExcXFqXbt2urQoYM+++wzJScneyIbAAAAAADwcobFRf78+WWz2XTvvfcqMTFRUVFRunz5sieyAQAAAAAAL2d4qkiJEiX0xhtv6Pnnn1efPn3022+/ycmiJAAAAAAAL8JvweYxnHHx+uuvq169eipevLh69OihlJQUvfPOO57IBgAAAAAAvJxhcXHy5EmtW7dO0pXZF6dPn1a+fPncHgwAAAAAAMCwuOjTp4+ioqIkSeHh4apUqZJee+01twcDAAAAAAAwLC7S0tL03HPPSZICAgL0zDPPKDU11e3BAAAAAACwCoec2fZmdYbFRVBQkNavX+/a3rRpk4KDg90aCgAAAAAAQMrCVUWGDx+uPn366LXXXpPNZlPBggU1ZswYT2QDAAAAAABezrC42LVrl5YtW6bU1FT5+/srJCTEE7kAAAAAAACMTxWZNWuWJClv3ryUFgAAAAAAr+TMxv9ZneGMi4IFCyomJkZlypRRYGCga7xbt25uDQYAAAAAAGBYXJQtW9YTOQAAAAAAAK5jWFz8dWaF0+lUUlKS2wIBAAAAAAD8ybC4mDt3rt566y1dvHjRNVa4cGGtXr3arcEAAAAAALAKh9kBvJjh4pxTp07VkiVL9NRTT2n16tWKjY3VQw895IlsAAAAAADAyxkWF/nz51dUVJTuv/9+/fjjj2rVqpUSExM9kQ0AAAAAAHg5w+IiODhYmzdv1v33369169YpJSVF6enpnsgGAAAAAAC8nOEaF7GxsVqwYIH69++vBQsWqG7duurevftt7cz6V4cFAACwpg4p68yOgKsE+QWYHQF/SM/MMDsCADezOZ1Oj/UJfgGRntoVcMewmR0ALpSrAICsoriwDooLa8nMSDY7gts8XaSx2RHcZv6hJWZHuCXDGRfR0dGy2+2ubZvNpqCgIBUtWlT9+vVTZCRlBAAAAAAAcA/D4qJatWoqXLiwWrRoIUlaunSp9u7dq5o1a2rQoEGaMWOGuzMCAAAAAAAvZbg4544dO9SuXTuFhIQoJCRELVu2VGJiomrXrq20tDRPZAQAAAAAAF7KsLjw8fHRxo0bXdsbN25UQECATp48qczMTLeGAwAAAADACpzZ+D+rM1yc88cff1T//v2VnHxlkZW7775bo0ePVnx8vCIiItS0adMs74zFOYHrsTindVj/RzYAwCpYnNM6WJzTWrLz4pwtijQyO4LbLDi01OwIt5Tlq4qkpaXJ19dXISEht70zigvgehQX1kFxAQDIKooL66C4sBaKizuT1YsLw8U527RpI5vt+l+t4uLi3BIIAAAAAADgT4bFRffu3V3/zszMVEJCgkJDQ90aCgAAAAAAK3GYHcCLGRYXDz/88DXbjz76qJ5++mn17NnTbaEAAAAAAACkLBQXR48edf3b6XTq559/1unTp90aCgAAAAAAQMpCcdG6dWvXv202m/LmzavY2Fi3hgIAAAAAAJCyUFysXbvWEzkAAAAAALCsLF6QE27gY3SHPXv2aPr06crIyFD79u1VuXJlbdiwwRPZAAAAAACAlzMsLkaMGKHixYtr5cqVCgwM1KJFi/Tee+95IhsAAAAAAPByhsWFw+HQ448/rq+++kp16tRRRESE7Ha7J7IBAAAAAAAvZ1hcBAcH6+OPP9aWLVtUo0YNxcXFKWfOnJ7IBgAAAAAAvJxhcTF27FhduHBBEyZMUO7cuXXixAm98847nsgGAAAAAIAlOOTMtjerszk9uDSqX0Ckp3YF3DFsZgeAi/V/ZAMArCLIL8DsCPhDemaG2RFwlcyMZLMjuE3juxuYHcFtlhxeZnaEWzKccQEAAAAAAGAWigsAAAAAAGBZfmYHAAAAAADA6hxmB/BizLgAAAAAAACWRXEBAAAAAAAsi+ICAAAAAABYFmtcAAAAAABgwCmn2RG8FjMuAAAAAACAZVFcAAAAAAAAy6K4AAAAAAAAlsUaFwAAAAAAGHCwxoVpmHEBAAAAAAAsi+ICAAAAAABYFsUFAAAAAACwLIoLAAAAAABgWSzOCQAAAACAAaeTxTnNwowLAAAAAABgWRQXAAAAAADAsiguAAAAAACAZbHGBQAAAAAABhxmB/BizLgAAAAAAACWRXEBAAAAAAAsi+ICAAAAAABYFmtcAAAAAABgwCmn2RG8FjMuAAAAAACAZVFcAAAAAAAAy6K4AAAAAAAAlsUaFwAAAAAAGHCwxoVpmHEBAAAAAAAsi+ICAAAAAABYFsUFAAAAAACwLIoLAAAAAABgWSzOCQAAAACAAaeTxTnNwowLAAAAAABgWRQXAAAAAADAsiguAAAAAACAZVFcZMHH08ard69O143Pn/eh3hs/woREeKpeLX27Y7W+27dBcz6bqly5QsyO5FVatmymHdtXa/u2VdqwfokqlvdQXgAAIABJREFUlH9IQUFB+vCDd7RzZ4J27VqrDz94R0FBQWZH9Sp8X1gHx8Iarn799vHx0Ttjh2nf3vX64fv/quNLbUxO570aNaqj1N8TzY6R7Uz9YKx69HzpH32Ou+7Kp0WLp2v7jlXaui1ejzxS3vWxZ59rom82L9emzV9qzdoFKle+9D+NjL+42e8csA6HnNn2ZnUUF7dQsmRxrV45T82b1b/uY31e7aKqjz1iQircdVc+ffThOD3zbEeVerCaDhw4pDdHDjQ7lte4775iGj0qVvUbtFLFStF6c9R7mjfvIw0Y0EN+fn4qX/5JlS//pIKCg9SvXzez43oNvi+sg2Nhvhu9fnd8qY3uK3GvypStqcqP1lePHh1UqWJZE1N6p+LF79XbowfLZrOZHSXbuP/+Yvpy+adq0rTeP/5c494drk1fb1PFCtF68cVemvnpJAUHB6lEiaIa+eYANWnSVo9Wrq+335qo2Z9N+RfSQ7r17xwArjAsLk6fPq1NmzZJkqZOnaoePXro8OHDbg9mBV06t9O06bO1YOGya8arV6uiOtE19MGHM01K5t1q166u7dt36+efD0iS3p8ap5bPNzU5lfe4dOmSOnXuq+PHf5Mk7dixWwULFtDGjZv15qj35HQ65XA4tGvXPhW5u7DJab0H3xfWwbEw341ev5s0rqsZcfNkt9t1+nSa5s1bopYtm5mY0vsEBwcpbsYE9XltmNlRspWOnWI0Y8Zcfb5ouWvM399fo9+K1X83faFvNi/X+1PHXDfz6/2pY9SqdXPXtq+vr+rWq6kZ0+dIkvbu2a9ffj6o2tHVdenSJb3ctb9OHE+RJO38dq/CwwvI39/fA88w+7vZ7xwA/sewuHj11Ve1f/9+bdq0SfHx8apZs6YGDRrkiWym6/lKrObMWXzNWKFC4Ro3brjatO0mu91uUjLvFlU4QkeSjrq2k5KOKXfuUKZie8ihQ0lasSLBtT12zFB9sWy11qzZoJ9++lWSdPfdkerRvQMvwB7E94V1cCzMd6PX78JREUo6cu1xKVy4kKejebUpk9/WBx/O0t69+82Okq282nuo5s9beu1Yn87KzLSr6qMNVaXyUzp2/ISGv/HaLT9P/rvyysfHRydPnnKNJScfV2REIR0+nKyV8etc46NGx2r5lwm6fPnyv/tkvNSNfmYBuJaf0R3S0tL04osv6o033lDTpk3VpEkTxcXFeSKb5fj5+enTmZPUp8/rrr82w/N8fHxueA1liiTPypEjWNOmjVdU4QjVb9DKNV6+XGnNnz9Nk6fM0PLla0xM6F34vrAOjoU1/fW42Gw22e0OExN5l86d2iozM1MzPpmrIkWYjedudevVUu7coapZq6okKcDfXykpv0uS1q3/XIGBASpcOELVn6iil7u11+Zvdujttyde97PLZpPsjv/97MqRI1hTPxiryMKF1LRxW889IcAinHfAWhDZlWFx4XA4tG/fPq1Zs0azZs3S/v37vfbNV8UKZXTvvUU0ZsxQSVLB8ALy9fVVUFCgOnXua3I673H4SLIefricazsysqBOnUrVhQsXTUzlXaKiIrT480+0/4ef9GTtp5Weni5JeuaZRvrPhDf5y4EJ+L6wDo6FNR05nKxCEeGu7YiIcCUnHTMxkXdpG/O0gnMEa/u2VQoI8FdwcJC2b1ulho3a6NixE2bHy3Z8fX30Wt9hWr1qvSQpZ84cCgoKlCTVqH7l1LX3p47Rxo2b9emshX88xlc2m0158+ZWamqapCszjZOTj0uSCheO0PwFHykx8Wc9Vfd5padf8vTTAuDFDE8V6du3r95++221b99eUVFRGjp0qAYMGOCJbJazecsO3VuskipWilbFStH64MOZmjd/KaWFh61evV6PPFxexYvfK0nq1LGNln6xyuRU3iMkJKfWrF6gzxcvV+vWXV2lRf36tfXuuDf01FMtKS1MwPeFdXAsrGnpFyv1Qrvn5Ovrq9y5Q/XMM421ZGm82bG8RpXHGqhsuVqqWClaDRu10cWL6apYKZrSwk3WrNmgTp1j5O/vL5vNpomTRun14bc+VcRut2tl/Fq90P55SVKpB0uqZMkS2rhhs0JCcmrFys+0dGm82rXtQWkBwOMMZ1xUqVJFJUqU0LfffquEhAR9+OGHyp07tyeyATeUkvK7OrzUW3PnfKCAAH/9+sshtWvf0+xYXqNr1xdUpEhhNWlcT00a/28F8xw5cshms2nq1LGusU2btqlHT+9YE8dsfF9YB8fCmt6fGqeiRe/RtztWK8A/QB9+NFMbNm42OxbgFm+N+o/eHDVIm75ZJl9fX+3Z870G9h95zX06d7r+D2+9XhmiiZNHa+u2eDmdTnXo0FtnzpzVq3266O67I9WwUR01bFTHdf8GT7XSqVOn3f58AMDmvNGJuFdZsmSJxowZo/Lly8tut2vPnj0aMWKEqlev/rd35hcQedtBgeyKC8JZB2ctAgCyKsgvwOwI+EN6ZobZEXCVzIxksyO4TbXIWmZHcJsNyQnGdzKR4YyLKVOmaOHChQoPv3JeaHJysjp37nxbxQUAAAAAAMDfYbjGRc6cOVWgQAHXdmRkJNdsBgAAAAAAHmE446J06dJ66aWX1Lx5c/n6+mrFihUKCwvT4sVXFt9r0qSJ20MCAAAAAADvZFhcXLp0SWFhYdq4caMkKTg4WMHBwdqyZYskigsAAAAAAOA+hsXFqFGjdPnyZR04cEB2u10lSpSQn5/hwwAAAAAAyDZYyN08hg3Evn371KNHD+XJk0cOh0MnT57UpEmTVKZMGU/kAwAAAAAAXsywuBgxYoTeffddV1Gxa9cuvfHGG1qwYIHbwwEAAAAAAO9meFWRCxcuXDO7omzZsrp06ZJbQwEAAAAAAEhZKC5y586tNWvWuLbXrFmjPHnyuDUUAAAAAABW4pAz296szuZ0Om+Z8uDBg+rbt68OHz4sSYqKitKYMWN07733/u2d+QVE3l5KIBuzmR0ALtb/kQ0AsIogvwCzI+AP6ZkZZkfAVTIzks2O4DaPRdY0O4LbfJ281uwIt2S4xsXmzZs1f/58XbhwQQ6HQyEhIZ7IBQAAAAAAYHyqyKxZsyRJOXLkoLQAAAAAAAAeZTjjomDBgoqJiVGZMmUUGBjoGu/WrZtbgwEAAAAAYBV3wloQ2ZVhcVG2bFlP5AAAAAAAALiOYXHRuXNnrV+/XrVq1dKpU6e0du1aNW/e3BPZAAAAAACAlzNc42Lw4MFatWqVa3vLli0aOnSoW0MBAAAAAABIWZhxsW/fPn3xxReSpHz58mnMmDFq2LCh24MBAAAAAGAVTidrXJjFcMaFw+HQb7/95tr+/fff5eNj+DAAAAAAAIB/LEtrXDRt2lQVKlSQJO3evVuDBg1yezAAAAAAAACbMwvzXU6cOKFdu3bJz89PpUuXVlhY2G3tzC8g8rYeB2RnNrMDwIXJfwCArAryCzA7Av6QnplhdgRcJTMj2ewIblM54gmzI7jN5qNfmR3hlgxnXEhSeHi46tSp4+4sAAAAAAAA18hScQEAAAAAgDdzMD/XNIarbP7www+eyAEAAAAAAHAdw+KiV69ensgBAAAAAABwHcNTRYoXL66JEyeqTJkyCgoKco1XqlTJrcEAAAAAAAAMi4vTp09ry5Yt2rJli2vMZrMpLi7OrcEAAAAAALAKJ2tcmMawuJg5c6YncgAAAAAAAItr06aNTp06JT+/K3XC8OHDdf78eY0aNUqXLl1SvXr1XEtO7N+/X4MGDdL58+dVsWJFDRs2zPW4v8NwjYvk5GS98MILio6OVkpKimJiYpSUlPS3dwQAAAAAAO5cTqdTBw8e1JIlS1y3+++/XwMHDtTkyZO1fPly7du3T+vXr5ck9e3bV0OGDNHKlSvldDo1b96829qvYXExZMgQvfjii8qRI4fuuusuNWjQQP369butnQEAAAAAgDvTr7/+Kklq3769GjVqpFmzZmnPnj0qUqSIoqKi5Ofnp4YNGyo+Pl7JyclKT09X2bJlJUnNmjVTfHz8be3XsLhITU1V1apVJV1Z2+KZZ57RuXPnbmtnAAAAAADciZxOZ7a9nTlzRklJSdfdzpw5c83X4MyZM6pSpYomTZqkGTNmaM6cOTp69KgKFCjguk9YWJhOnDih33777ZrxAgUK6MSJE7f1tTc8uSQoKEjHjx+XzWaTJG3fvl0BAQG3tTMAAAAAAGAtn3zyiSZOnHjdeLdu3dS9e3fXdrly5VSuXDnXdosWLTRhwgRVqFDBNeZ0OmWz2eRwOFw9wtXjt8OwuBgwYIA6deqkw4cPq3HjxkpLS9P48eNva2cAAAAAAMBa2rZtq6ZNm143Hhoaes329u3bdfnyZVWpUkXSlTIiMjJSKSkprvukpKQoLCxMBQsWvGb85MmTCgsLu618hsVF6dKltWDBAh08eFB2u11FixZlxgUAAAAAANlEaGjodSXFjZw9e1YTJkzQnDlzdPnyZX3++ecaNmyYXnnlFR06dEiFCxfWsmXL1Lx5c0VGRiowMFA7duxQhQoVtGTJElWrVu228hkWF0eOHNGcOXOUmpoqp/N/160dNWrUbe0QAAAAAIA7jUNO4ztlczVq1NDu3bvVpEkTORwOtWzZUuXKldPo0aPVvXt3Xbp0SdWrV1fdunUlSWPHjlVsbKzOnTunUqVKKSYm5rb2a3Ne3UbcQJMmTVSlShWVKFHimvNRbjSNxIhfQOTfTwhkc7d3lhfcgZciAEBWBfkxA9kq0jMzzI6Aq2RmJJsdwW3KF6pqdgS3+fbYf82OcEuGMy6cTieXPwUAAAAAAKYwvBxquXLltHr1ajkcDk/kAQAAAAAAcLnpjIuSJUvKZrPJ6XRqzpw5rtNE/ryEyf79+z0WEgAAAAAAeKebFhc//PDDTR+UkcF5ZAAAAAAA72GwPCTcyPBUkWefffaabYfDoebNm7stEAAAAAAAwJ9uOuMiJiZGW7dulXTltBHXA/z8VLNmTfcnAwAAAAAAXu+mxUVcXJwkacSIEYqNjfVYIAAAAAAAgD8ZXg514MCBmj17tjZv3qzMzExVrlxZrVu3lo+P4VkmAAAAAABkCw6xxoVZDIuLMWPG6NChQ2revLmcTqcWLVqkw4cPMws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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figs, axs = plt.subplots(figsize=(20,20))\n",
    "sns.heatmap(m_pd, ax=axs, annot=True)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": true,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}