MultimodalNetworkTraining.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multimodal Classification Training\n",
    "\n",
    "This notebook creates the Multimodal Network Architecture and trains it for grasp testset1. After training the network weights will be stored in the folder `../Deep-Multi-Sensory-Object-Categorization_dataset/grasp_testset1_logs`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dependencies\n",
    "\n",
    "`Python 3.5.4` is used for development and following packages are required to run the code provided in the notebook:\n",
    "\n",
    "`pip install googledrivedownloader`<br>\n",
    "`pip install matplotlib`<br>\n",
    "`pip install tensorflow-gpu`<br>\n",
    "`pip install keras`<br>\n",
    "`pip install numpy`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle, os, csv, time, shutil\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "from google_drive_downloader import GoogleDriveDownloader as gdd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_image(image, title):\n",
    "    \"\"\"Print the image\n",
    "\n",
    "    :param image: image pixels in list\n",
    "    :param title: title as string to be printed on top of the image\n",
    "    \"\"\"\n",
    "    plt.imshow(image)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    plt.show()\n",
    "\n",
    "def time_taken(start, end):\n",
    "    \"\"\"Human readable time between `start` and `end`\n",
    "\n",
    "    :param start: time.time()\n",
    "    :param end: time.time()\n",
    "    :returns: day:hour:minute:second\n",
    "    \"\"\"\n",
    "    time = end-start\n",
    "    day = time // (24 * 3600)\n",
    "    time = time % (24 * 3600)\n",
    "    hour = time // 3600\n",
    "    time %= 3600\n",
    "    minutes = time // 60\n",
    "    time %= 60\n",
    "    seconds = time\n",
    "    day_hour_min_sec = str('%02d' % int(day))+\":\"+str('%02d' % int(hour))+\":\"+str('%02d' % int(minutes))+\":\"+str('%02d' % int(seconds))\n",
    "    \n",
    "    return day_hour_min_sec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Downloading `category_object_labels.bin`.\n",
    "This contains two dictionaries that stores category labels and object labels\n",
    "\"\"\"\n",
    "\n",
    "gdd.download_file_from_google_drive(file_id='15xPAbso4-uLk8PcRi9cb70r1r3FaU6ux',\n",
    "                                    dest_path='../Deep-Multi-Sensory-Object-Categorization_dataset/category_object_labels.bin',\n",
    "                                    unzip=False)\n",
    "\n",
    "bin_file = open(\"../Deep-Multi-Sensory-Object-Categorization_dataset/category_object_labels.bin\", \"rb\")\n",
    "\n",
    "category_labels = pickle.load(bin_file)\n",
    "object_labels = pickle.load(bin_file)\n",
    "\n",
    "bin_file.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Video Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Downloading testset1 for video of grasp interaction and reading it.\n",
    "\"\"\"\n",
    "\n",
    "db_file_name = \"grasp_120x90_testset1.bin\"\n",
    "gdd.download_file_from_google_drive(file_id='1o8FvoQskCS9rE8Bh4VkUUNMtJgKmDe67',\n",
    "                                    dest_path='../Deep-Multi-Sensory-Object-Categorization_dataset/'+db_file_name,\n",
    "                                    unzip=False)\n",
    "\n",
    "bin_file = open(\"../Deep-Multi-Sensory-Object-Categorization_dataset/\"+db_file_name, \"rb\")\n",
    "\n",
    "video_frames_5d_train = pickle.load(bin_file)\n",
    "category_label_train = pickle.load(bin_file)\n",
    "object_label_train = pickle.load(bin_file)\n",
    "category_label_train_one_hot = pickle.load(bin_file)\n",
    "object_label_train_one_hot = pickle.load(bin_file)\n",
    "\n",
    "video_frames_5d_test = pickle.load(bin_file)\n",
    "category_label_test = pickle.load(bin_file)\n",
    "object_label_test = pickle.load(bin_file)\n",
    "category_label_test_one_hot = pickle.load(bin_file)\n",
    "object_label_test_one_hot = pickle.load(bin_file)\n",
    "\n",
    "bin_file.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "batch, frames, height, width, channel:  (400, 20, 90, 120, 3)\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Category:  egg\n",
      "Object:  egg_rough_styrofoam\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "Display a frame of the video dataset\n",
    "\"\"\"\n",
    "\n",
    "batch_no = 188\n",
    "frame_no = 10\n",
    "\n",
    "print(\"batch, frames, height, width, channel: \", video_frames_5d_train.shape)\n",
    "\n",
    "print_image(video_frames_5d_train[batch_no][frame_no], db_file_name)\n",
    "\n",
    "print(\"Category: \", list(category_labels.keys())[list(category_labels.values()).index(category_label_train[batch_no])])\n",
    "print(\"Object: \", list(object_labels.keys())[list(object_labels.values()).index(object_label_train[batch_no])])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sound Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Downloading testset1 for sound of grasp interaction and reading it.\n",
    "\"\"\"\n",
    "\n",
    "db_file_name = \"grasp_60Freq_50TimePerSec_testset1.bin\"\n",
    "gdd.download_file_from_google_drive(file_id='1WChpWIY53Ww1Z7NWjJ1YfQOim5goMD9N',\n",
    "                                    dest_path='../Deep-Multi-Sensory-Object-Categorization_dataset/'+db_file_name,\n",
    "                                    unzip=False)\n",
    "\n",
    "bin_file = open(\"../Deep-Multi-Sensory-Object-Categorization_dataset/\"+db_file_name, \"rb\")\n",
    "\n",
    "audio_frames_2d_train = pickle.load(bin_file)\n",
    "category_label_train = pickle.load(bin_file)\n",
    "object_label_train = pickle.load(bin_file)\n",
    "category_label_train_one_hot = pickle.load(bin_file)\n",
    "object_label_train_one_hot = pickle.load(bin_file)\n",
    "\n",
    "audio_frames_2d_test = pickle.load(bin_file)\n",
    "category_label_test = pickle.load(bin_file)\n",
    "object_label_test = pickle.load(bin_file)\n",
    "category_label_test_one_hot = pickle.load(bin_file)\n",
    "object_label_test_one_hot = pickle.load(bin_file)\n",
    "\n",
    "bin_file.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "batch, frames, freq, channel: (400, 100, 60, 2)\n"
     ]
    },
    {
     "data": {
      "image/png": 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6yHnpKCrICaK8zB9zk0vtttG4dFQ6KNM2F+6wvF/+qe6S8w6cOtQ+JnX/49K2IZ+VcnIeGhuV8Re8KEBTVxNT8/Jhea98eOfg57Sf087eMookdlbE+c+nRWrJKfvEjSbOQ6zT2UAXMQINt0j5616+Uaadgy0NM6nJUik3Lzif/aLBACIzVADMUBkMfQ1jVAIzVD1GkI1QuG4Ry01hCpmUd0Y/cGYPAKBREW82hRpU6SIwB4U51BKMJz+pzmeNfWoOaCyUPtgbCQGVjPjq24yppf7qQJ30oUYcZEreQbt0jfSfPyWvYUU6DqvKMmqeQVBWxj1y9ykAQDQxpOcTB3zpOul38nk+PmxfIDJL6SllZuyCRoXNREVPCWsTwuaK+3YDAOJAmFqgbAmDfmGiNi7ji5SQVa4XBlRw/aek/3B2rn0Ma3xXXBaHezCgiwIZ/T6Giu22rZyyz6yMKZwQUcvWmaQ45uaCATTNRwXADJXB0LdgsE39FGaoeow4JlQrWTz1gIgg3j1zqL1v/7j4Xm68VmIMTleFkZSbwioen5Yl91aciB9w/eqmlrq8nN8pDv2NXN2tbZR1Fc7KvpHHxE8T1iM91n/t9aawlvIhYRNBS1mevgbDnvFkT8r469cKe2Hn23FD0LSh+oQPI0jPq96bhiFEQ0KBwjkJYajs9T6qxqAcl58o6udQljckrCZzcr7d1rHD2ricvFnsjMloM8Ex78sijZJ3TIqH5CJSWcbImXSiretHqKwLbvUNXLrQJhoSBiKzUwDMUBkMfQuJTDcAZqh6DyZEjRA7MxIg+IwJnyZYjYQZHMiJ36SuWcMnl+Sp36zJ+/SiZyQuL6+dhKwP/doBXUUreyaRPyv/N4aUFcXy1A+acrvXR+XgyoQ/xq0MOibVygUd29PlRFLyXnGI1UfUp6NsIjuvKTQzynzmvZON3GqcspUopyubA8KkHIsCfBJ1akmOIb1epOOPRr3vyyVgD07KufIzcj14TlifC07l0PdP9c4Vu1jHELhjIu9PLD4m5451H1erHcduKpNqgxCtmMF19cEMlcHQpxBnuhkqwAxV7xEDXAsxkRJGdWPuVHvXfVXJhD9a2QkAyOvSXRhosrDek0EiLcalw7gYqcCRApWK6QiK0k2tYWEGpBoujkk1C+ff9K7/ZkETmpXhOKWFoOUnH5VdypiWlXlod5l5oX25WdlfOJNgG1VlR84/VI863qcrvv8oox1OC+NMuVU+jWWKJvwS59ATyqBU0iZ9WlOAlmR1MVVU/1PNS0VwRVlRppMRsvqf4gRLIl0Z3K7UGcDFUZmhAjYmRWwwGLYJMdOG/tYDEX2IiKaJ6P7EtjEi+iwRPaqvo2v1sZ0wRtVrMCGoBRgJ5Yn8pLSv9nWyKat6H5uSmqLfq7Ivz9klr58q3wwAaAyev+o3MCmvoQZZ8TFdjUt4W+vjyszymmg8JV+vY1LZRdlffMJHa8/dKH6a0v7OVTN377dZDoCgKYwjN+X0pzSyWwX1Urowll32vh4Xs+Q0qlLnVE5GfVcDp7wOTmNY/mdlMRR2PkeDyen2/1nVzcJuTVxeKnW0jWblunPdM6p2QnQgvi4nZRPVauhG8rjtQo8Z1R9Dqj7/SWLbWyEl2N9DRG/V92/p1Ql7CWNUBkOfgkGIEGzob92+mP8eUvswiZcD+LD+/2FIDcy+hDEqg6GPsZFpnWKCiO5JvL9dBR3Xwi5XUYqZzxDRzosZ41bADFWvEQNBjfCpuacCAJ6+x5dAPFUXF8DCSVk6/0cSzaPDIxquoKk1qYa/OQdPaLc6GwwbTjtd3mcToQDpsiYHNzud2kPHNKBRAzRbiall/pyGLmjg5/BxmfK0CprikvVP64GTqvWkXv+2VpVOPwemVMZ4aQWxN1UBJZfkOyTTr6CR0LvSKWQwIVNkdgnLGiKQ1KOChh84xF26V2tN3aJFcbiH2bWqv28/GIRGt7jY6phl5ls2czzbCTNUBkOfQgI+N9U7M0VEe5RN7QEwve4R2wQzVJsBAvKhOKz/dNE/5D53+gYAQHanMJNmJE/Lb52Uqu+5AS2ecMjHJ7RO6TK7Eg/WlJlUdYUpga6uO5blWNfyIcm7cbIsyfCHSDXZQ3WUBw1N3B3W9JW699Y7JubapE8JE2RlM/H1N0q7Ac8C0hoo6ZKQQ5V5gTrZkfVFWrKz0jaek1QZqgvrcmEFnAjIDEZdQrSGUSg7itZgUk7b3cEFc/YzNjk84S4ArwbwHn39+Gae7FJghspg6FMwEyLuDaMioj8F8FyIL2sSwDshBupOInotgBMAfqInJ9sEmKHqNQiI09xOj/mnc9e2d81MScBirihP/YGsMKhcWp70wzlZJp9aHmwf43xThTld3teKMvURYSgtr8uHrLppQq0F6IJEm5oOk3HpMIl4TBf0mJ+RMbQTgWusr57FhDVpE+grmipop0GVKWVfLoxBDlJ2pf4nOHG6xvkCdKTBoI45kYrUxS5oM074vpQNBaqzzhfhb9pKEbyLRdwjRsXMP73Kruf35ASbDDNUBkOfQpzp9hMFzFD1HgwETcKD87sAeD8UAKAhNL52TmhQXRnVU3acAQBMV1WAbtHTpAFX7VjJRG5WaBJdq6tyOc9eUlrEyyUwO19UZlmZjqu2nE4+peX/VFkaV3cLM8lPKdtIzDycNAtcIKaKygWjsgIXat1A1xcABHllPFGn5DHcil6YTAHSyjoulyhcfcUrXtSgUPVfUZ+v4F0MtsCZftnADJXB0MeILCkZgBmqniNoAYWzhDMDspq157qZ9r7UkLKUE8KYFisSV3V3VdhAo6wMZcl/LU5OeO4mJ9miCbVKWpKrf65G3/z1jm3J9ms+KStsZ75PGJsr/gD49JoorZWYqypEp+J6jWHPakamNWF5Qnxo6VlJW3HxTs1BXdlLrBQ6VtT2JSlbikZdgQg/fucDizWlxa0QOqG7ZIJwOC7XjrVtNO9F9a4UuMh0gxkqg6GvEfdo1e9yhxkqg6FPIUnJZqgAM1Q9B8WiGRWMyjQv6Uwn1Z1qjnUFHkZyM07slNSOyrBPcQlOS0iDUzFYOKL96ewtTCT+V3ZIPwOaOlPaJ8csXSeBk/lprbt3xi/Lz1+fbY8b8FpT1A4e9WN1ZdMzNVkocKqdpNpYhdMymNRMohbgvMRMuOlb6wapbUgaNBqW/ViaO6RNZkiCObFTixqeOr/yS7y0fTpRWwUGobnxFJorGmaoDIY+BTN6FvB5ucMMVY8RZYGlJ8XYNyEBiY0Eo0qldIl+UEIMWnUN2qxohRZV1axMeW3wsUVhK+W9qmSpsksNJR3JeMBQ/cnlPbKxMSLHVnbKze6c6PUxz9hGjwqjSS9pcGVKfxihq6qc0D/fL3UJWevtoR2YqaxrWB3mA76yjAvSdNpSznmeXtA0ogkf3Jo8F+A1q+IV0mLi2vbrRW0+qGcBn5c7zFAZDH0KhjEqBzNUvUbAiHMxlmvq+yEfCuD+HxwQplBLC7OpLUrb+XPCLnJT/mtxicXOaZRW14yrmEyJSACH1oAGdg5qSkqY0lfZXx/yT+nCGWkTVIXlRaMSphDlOtU7AaC1c0jbCoMK57wvSk7gBn1+hRZW1hVWu9JvUn4sqYqMwTGooKrjjlaQjYlX2HYFwpzpAjNUBkOfgrExPfSrAWaoeoygQRg4kcIiy2pdYU/pvDYuGXk4L8xqRlcD68eL57Wt7lImVeqsv5dyBYhLnr00hl06jLxvjrr3TsJFticXkpYPCptL1bSGnjK0dl2/RJWYVPdCm6bFuJSalEuxOXd+8GVcln3hsrAl1mDO1LJf9aOGBnw6n5SrJKNBo90yLVc6pFyW/UQBM1QGQx/DCpA6mKHqMZhEmiU9IZSnmPeBTsNZ+f/EnKR/tFrCRJ68R4QVnwiFoZSP+vp16ZK0yZ1T2RWVUIk1sTjhAmuzIbfaF3SL61Fnu2Q/TV2Ny58T1pKd19W6RF0/J+8SnNa0IE04jmek4gsVNN+n4avcdCMuCHMLp2VVtFXwksLRsPjHgkAZVH0DMiyBk5G58nxWDItMdzBDZTD0MYxRCcxQ9RgcAM1Bxs5h8U2FCcozlpXYoRMQRhWfEgbysBb/aNbl66CED8lFnrvIdJeM7B60SV9rU0OSOOWE8zQWSlfWqqr6O/KYZx+tnDKpWfUPafyUK7rAaf9ED0rqGJvQ8WvMFakUMe+TE5BrBwBLnSuDrQFZ6eQ90kc4X2nvi3eKjy4ckg8Sl1WCeC3f1BXIpByYyRiVwq4CACI6QERfIKKHiOgBInqTbr9sKskarjyIMz3c0N+VDjNUghaANzPzjQCeDeD1RHQTfCXZIwA+r+8Nhi2CaKZv5O9Kh039IMUXAbhCjMtE9BCAfZBKss/VZh8G8EWsV/I6AOIMo9aUS1tv+nSVG0cluXaooNro6nzmpj4Rl6VtZtHfeNl5mca5cAHnsnBaU81BOr+takyFVZd2I9sbRXkfJx7AzrGeWhYHeGWvdJyqSaM4MfVLL2h8wqgL/JRpl0tncVM+F3ogjTqd3U6j3WmoUyIVJrWk00J1xrfTblb3zbdrDILPDzK93CHO9N75qIjoxQA+ACAE8EFmfk/POt9kmKHqAhEdAvBdAO7GBivJEtFtAG4DgHDUZoeG3qFXkelEFAL4XQAvBDAJ4GtEdBczP9iTE2wyzFAlQESDAP4SwK8w8xLRxp5mWjr7dgDIHjjA1CKUVbWzvuzZxeIuWX5PaYBnZkyYVduJXu8M2ASA/Ky0rY0qu9AhxRlV4iz6MQ6clW21cWnrqtKktKJMqubq/fljXJIwafBmrI73KKspNInwBFaFzXZ1mGaXIueCOs7372of4zTT44o4zVPnVOPchTAkdNGpIuwq0raBC3dYC656Ml95TvUeR6bfCuAoMx8DACK6AzJjuCwM1ZU/ud0giCgNMVIfYeaP6eYprSCLfq8ka7gyESPY0B+kXt89ib/burraB+Bk4v2kbrssYIwKAAl1+kMADzHz+xK7LriSLEVAZpEAFZNzlWcA4NtnRSalrukqcaRsZk610jWcoD6WqCxzvzCa0WlhMeeeIgwlrHX6rACgtE/O5WoBBg3pp6oMy6XQhA3ff2WXMrW0MJvsgrAkF1WRmfcBq445QV+dDnobu0ToLhrItDelBkWyhlyIwdHj8l4r18S7xvzxcWeGtWNha+KKDk8AmvGGucQsM9+yxv6VqNll49gzQyV4DoBXAfg2Ed2r296Oy6iSrOHKg0z9ejbpmQRwIPF+P4DTvep8s2GGCgAzfwkrP3GAC6wkSzGQLgN5XdkLd3uWUCmr9MtZeWWVJOaCBldqpGeq7IeycETo0chj8n7HN8WBlZoWf1Bzj09BOf294tNxFZJLB+V16JgK5mmScrPgb37Hvqo7nSSxPmRdab2qZ0dplQiOSjKGbkE7WpQg11QzIV+sdfdYKyMHR7RydEtXARPBoW0/mb53CxOs50nKvbht4YimG6mv6kqrRtPDyPSvAThCRIcBnALwCgA/06vONxtmqAyGPkUvwxOYuUVEbwDwGUh4woeY+YGedL4FMEPVY1AEpJcY1YZQlQMjC+19S7oCNh1KqkixIKygmJPXqQXZ3qz5r6UCYTqV3bItPyP9BnUv4evgkpFd9eRAVxHrIxpXVTvvkDb7ckwqrcUdKruVarGPA0vvlhSZkMUXxXmVhqkKW6odGNU+PYvMKHMKlPlUDwoDdNLHnPLsjjVuKqO+Kh6QVVKaEZZECQZHe3frQcoAXQKzxm251UYkWFjbx6ZI7ZNCEzwoTJSWzpfkaZ3pLCzh/HLdfW0OeptCw8yfAvCpnnW4hTBDZTD0MUwzXWCGqsfgQGKbnrbrFADgyQNT7X3DKVnFOq5loO45d7Dj2L1jEvg0tegF9OKWxmPtFr9Pq6greGPCIKLEqiIiF6XtkpFlX31MXgunOxkW4Csxu9gqVX1BK+uE83z3rXEVslMWU90t7CI7L7SsWXTyxT42KqjLZ4mzcquV9gkLyw24qsoJqeO8jDMsiS8szssx6ZqypaIvelG/JrFaCCCtq5MpLTzBQ9KWE3FaqWUNUHPVmncIy6uPy+dIL/r+Iz13TkuCsfrl3MpmeEakbpIrk5TR1dxj4QLvAAAgAElEQVTl3pTyklW/Kz+PbyMwQ2Uw9ClMitjDDJXB0MewqZ/ADFWPEWeB0qEY/3mv+CxrCWfoNSm53E8UHgYA3Du/HwBw7LRMJw7ungMAVBd9IOXIlEtxkSlI7LSmdCo4VPQqmNW6TD1qc1pfLy1tnQpIS2c2tQk/3YoGxXGdnddxkp5HfeipkndGU1fib1OnaukldYIvypiaRX9buUDShlZ/dlPNhqqb5ms+lMH1H9RlKsmaxsNZHdOgvy6tguyrjchrXttmcnLuxohOmUf81Ck3J1NXlzbknP7NAQ2UTfl0p9qoJmVnxGmfOytTv+o+uYgFDaUIF334BjQZOy6pU/4Swyl7nZR8OcMMlcHQxzDhPIEZql4jxeCxBor61M5xIuBT9UqWWZ7CL9klYSz/q3QrAODElDiIg2X/tThmk5/WxGL13c5nhB3MFxMpJBk5F6lEy8hDMoaMyrwsXuuc7f4QzqpUS0aOqWsFZpec3Br0jCT7JRlv/NQj8n5Jjk0vap3CnSpbk/htpU8JS0zNCxsKmioRo9V0knUD00taW/ChR+WYXSJWwU3ZHi7n223zVXHSt2sgdieQKztzCqaAl7IJtYahY3tuDEHdX8t0WRmVqxitr9lzGiKhlXZ4PKGWoaEQYVEXQxZxSWAmtMxQATBDZTD0NWzqJzBD1WMEQYzCYB1NfaLvSfnAzPsawjz+rvxkeZ29HgBQ0Dp/7qZcavqnaOmgUiq9XwcmNVVEqyBTnGAMg9JPOCxP/YUROZbmhcFlZ7VdJSHzsqASM0omqjs6k53rJc+oBjXBuFVUX5j6cXKdMZEonE5oprtAzFTnMrtjKEkEKinDTrpFfXqUlfMlBfnK12k6j8ZT5LR6TlvrPXRyNb7/xphWwNGAWmcDHGvMzXj2W9drl5uT8TeH1e/nhjbUGXYBAKRJ5VRQ5nepjApmqBzMUBkMfQwzVAIzVD1G3ApQningH2oi9TMU+FSLL5dvBgB8be4aAMBUqTMNZmxAHFBLcz7w0Mm5NEeEKbjE4nC0MyEYAAKVliloSk5RE6Nnqprcm3KrW94vRE5qJlI/TdTt60n8u0+r5WhgJ6tfqD4hDMKxmDiRFhNqKotLOA4b6kdzfqGqX/ULKppWo0GW8Vhn5ehg0SsKusTqNqOa08+j4w0ajp3546OsBoOq/9Ct9jnZGycWKP87zWeto5jrrFTtkBQWhAabxuUNyNNsABZH5WGGymDoY1gclcAMVa8REVKLKRytSfxNJfZxNscr4wCA2YowplhF0dIpYUvDGWVfTX9z5qeVpSgbcOJ6jaqrAZiIiTqn8jE7lSUpvQhLTq5XXxLfuhPTawvlLWjslQurSqzK1fZovT1dEczPdYrspUvKjhK/LVbfFGn6SlhudvQbzibq/ikjaccjuVW5pvqdEoUgMsvCZFzajWNQqVmJYYr2SXpMKpE7HNbizuvgTndOL0LkP6srsNEer+4LlfWhKh1Tsiq0ftaohyk0rY0L513RMENlMPQxbOonMENlMPQpzEflYYaq1wgZrdEWbsidkbfkna2Hs5Jx74L4HpuX1Jmbx2V9P3I3ZcLZnap2lmd3ap252UzHfsA7jue+S5fHXc2+hgZ+qjRWKzG1zLiZl4ZTpCvqWFY1hTjt2zrnc7okHeePqQdbgx+dekB7Cgdfoy/Ssu/BuOhRuXSZ+KyvlxHs0przGpYQTsmAWVNSaMwHVxYel4G3RuWzuiDO6OGj8nrtMwEA2SV//bOzOl1zU9VlmaqlHxalCxcGAQCZqFOLPdBppysv7xYSOqZ5m1BbkM1QATBDZTD0NcyZLjBD1WtEhHAhhfc++iIAwI8dvLe9q6b5MKWmOIt3DAhT2JcT5nDvgiQpp2a8qmZhxi3n63K8BjC2lDRlEg/02phbUtfXwKl2ar1AZV/JYxpDsq94UhhEY1ATjNVZ7ZbyASA/Jawi85gwQNZleCpqmIUykubu4fYx6RMSZRoqW2rzm1PaR4KFRKeEhbKroqzndlrpVPaBpOGE6lGNy4UIyloTUPe7hOMolwiVKDc6+gnScvtH5+Y6zpcEx9y9oev95hVyYTYflYMtKRgMfQtCFAcb+ruksxD9BBE9QEQxEd3Ste9tRHSUiB4mohdd0okuAcaoegyKgbAOLCxJgu4DpT3tfTWVannwhG7T9Bf31HQVlFu7vHQLB+qv0ZV5V/XYSbdUJ/wTN3SHKZNywZyubarq2nkW0BzoTLpt5anjPMnwBOcHaqe2KJOKRzXR+Jwww9SC9/XEc1oVRn06pCwsUn1ySq9wC2qtPsppkrOqaHLTX5dI+808Iu+ToQsAkDsuLCk94YNGXZUc5/PqPl83WeoHbJGP6n4A/wrAHyQ3EtFNkGo1NwPYC+BzRHQ989aXpTZDZTD0KbYq14+ZHwIA6lagkJLvdzBzHcDjRHQUUhr+y5s+qC6Yoeo1GAirhAY7DXL/mG6Q873oildZLv/JeVkJy2jgZzrvgwjLuzT4Ub+prK7ckcZWxt6dhcKU+rPaaTHaNu5qm7ghg6b6rVT0rj7kVhM15aXuxx+UlLU4Xw678ygjUTkWqnp24/hYXNb0l7JPgwHg/WlYwR8Ur05xXF0/VyWGUp23Mk+KvytV8ywsntdVRF2J5GjLicGFgS/IBTZBRPck3t/OzLdf4gj2AfhK4v22lYE3Q2Uw9DEuYNVvzZLuRPQ5ALtX2PUOZv74aoetsG1bysCboeoxiIEwkVUxkfX+kMNaWK92QKjN/afEV9Woy/vKvC7lJVhGQxfQHKNyviRXjy/wOb3tGKj0ohOEk+2unp9jVFEmeYy8Zk+Iz6c5KLFdqaqKwJUTH8YxHE0w5qo6vVyNOzrfqduWPKlWO3coVXBxSSvhUmrntY+dPdeT/rYDrM70nvTF/IKLOKxvysDbqp/B0Mdg3tjfJuEuAK8goqyWgj8C4KubdrY1YIyqx+AAaA4yWFf0vjHnH0i/eODvAQBnC0KTHoAwqnRGo52dX+tRL7k7/oD6joZV3G1emM7MU10Rg4RkS0sliB8RxlOYkWPnrxcKNXJUKxqP+a+9cFZ9OHq352bE91PZq9LBw75tSgsmZGY0Dqmmq3In5CFLmkzMCz7ROJqf77g+m1VpeDVmdrmxqG5sxaofEf1LAP8DwA4AnySie5n5Rcz8ABHdCeBBAC0Ar9+OFT/ADJXB0LcQtrQlq35/BeCvVtn3bgDv3vRBrAMzVAZDH8Mi0wVmqHoMDiS9JVa9qLmKn8Y90RBH9T+ePgwAiLS2XaskbVLzckwr56dzy/u1tPi8hh6owmRrwFWlSSzva2Bnea/0Syo8ldVj03MyDYqyhfYxzUFpU3+Kprios96VWm8UvRszO6ua7GckLcalv8ROm6l+vupoN+INtDF4bKL/6bKCGSqDoU/BoLa44tUOM1S9RsCIihHSWhFmIOOX9788dy0AYFkZVJjSlBmoBrnek3HGP0ZbBWU4U7LN6ZU7DfXM/Pk63y4soVHUY9WfXNstTCqZqOtmFtkFVet02ulazYVaCemT4yJTE2ngZOCSkV3KS2YDjnKjCBcEu1oCM1QGQ79ii5zplwPMUCVARCGAewCcYuYf1tiROwCMAfgGgFcxc2OtPgACRYRmWaIr5xL+oOlFYSCOVOQL0pULhYw0pCGoJBhPF/OvjuuNG7rKKf6Zm5vpDAZ1jMpll1T0607IuCOlQaLFczKKOOfq/GlqzZJnhNGUMKp2crBWmGl/ciecl9QRj/s8TaXfYZQKgAV8duNNAB5KvH8vgPcz8xEA8wBeuy2jMly1YKYN/V3pMEalIKL9AH4IEjPyb0lSyZ8H4Ge0yYcBvAvA763VT9AA8pMhMkviO6rs9VnDjd3CNDLqv3KyLtmsbK9kdJXuVOJr0XvQ1bFrDKus8Iyyo0TT4cfFz+QqGFd3dlY9Tms+cLsaMoABTYhojgg7Ku0XVpSf6awwAwA5rZQczcv4nbywgwu6DBJMK+5OQnYJ0earWhcMII6vfCO0EZih8vhtAL8GwAkYjQNYYGYX8rxq5jgR3QbgNgBIDY2u1MRguHAwOiuoXsUwQwWAiH4YwDQzf52Inus2r9B0RRqgchq3A0B+9wEOWkDpOedXy33aPpEeOV0SobnFb0hcVXPEZQ9rbFSChKRLnXX28tOuuIO8j3J+mKU9wqTyc26YToJY3jn25Yo8AL74gVvlc+J7qfL5jCpacBozuroY6mqlkwrWCsfRYqJWXxfC4SHta3HVNgYPI54CM1SC5wB4GRG9FEAOwBCEYY0QUUpZ1bZljhuuYpihAmDOdAAAM7+Nmfcz8yGI9OrfMvMrAXwBwI9rs1cDWE23x2DYBGzMkW7OdMNbANxBRL8B4JsA/nC9AygG0suMclOeAcXh6nltSlVRGWjslOlVbkwCJBt1+To49F+Lm3pVdsvNmNVpnXOqJ/Wo8lqOPUp3HhzqEIqnpPHCdd7B7zTRm6rsmVnSajQjGe0/Ef6QVbVRF9CpUz+oEz12VWLWmK/E5fOvh2ENGKMCYIbqPDDzFwF8Uf8/BtGINhi2Hoy2XNDVDjNUPQaHQHOIMDAkrCMV+oDH2aos7zcbctmHdon6Z6hhCo5RVXcmNKZU7bNVUCe6E6zU+7eViLl0FWWag1pZRhlV4ZyMIVUV5hNW/deem5Yk4TijqqAtTesZFNbVEXCalm1uEw1oSo4mGierxKyGjbQxJGGGCjBDZTD0N2zqB8AMVc8Rp4Dqzhi37pRQhKMLE+197p5zip6FrLCLmTlZso+X1Hc04h1PrQVlMQ2XHtMZwlAf8ed2AZ6OfYVVeV84KY0bo0K/hk76FJf0WQkTiIuSKB0NqK9qQau8FBJlblyVmby0pYKmB2mlYcMmwAwVADNUBkP/wgI+2zBD1WNQDKTKAY4tjgMACmnPXp4+PgkAuDfYDwA4uyBB8NGysJZUSQM2z/qbM1VVoTytYOx8Uk1fALiNdMm9akCmErPw3DIAIFcRBseTZ9vH1G69vqOPKCMeqKCpQn2JJ3p7tU9fQ3Qh0C2WiNwzWMCnwOKoDIZ+Rkwb+7sEENFvEtF3iOg+IvorIhpJ7HsbER0looeJ6EWX/HkuEsaoegwOgCjPuG5EclyOL4219x3Mii9ncUh8PJWmMKkFvdGaKpNSZe8Xys2SvmoqjX5jLVU4zk/7czu54pJKERfPyPv64Qkdm6bUDHt55OagsKDCaYlvqh0e0H41paaSkGzpwlqpMobegLaGUX0WwNuYuUVE7wXwNgBvIaKbIAHQNwPYC+BzRHT9dlSiMUZlMPQr+AL+LuU0zH+TSL7/CiRdDABeDuAOZq4z8+MAjmKb4gqNUfUYQQvIzBNaGoCUTsRRnagLu5qvy2rZzLSs9qWy0ibIyCsfSKz6VaVt5riwo9qoK9wg+5NxTq4wg6uI3FJp4vqIrhzqimGr4L1LroiD89mGjbijTZTzbbOqwOfkXCit7+v6GTle8ZoYLhZ0Ic70CSK6J/H+dk2Wv1C8BsCf6f/7IIbLYVUFkc2GGSqDoZ+xcbY0y8y3rLaTiD4HYPcKu97BzB/XNu+AFBr9iDvskkbUQ5ihMhj6GT0iqcz8grX2E9GrAfwwgOczt9caJwEcSDTbNgURM1Q9BrWA/CzjXE3VMBPljh5Z2gkAWKxLjEE4J1Oy1qiGE2hVmlzep5kEGtgZZbSNzrJSKneVdLa2NP7S1fyra7+BpuFkF85/GLqUmTgrt0JGq9HELkyhkfildCUhOyXPWBObLT2mx9iiOCoiejEkAf/7mTkppHYXgI8S0fsgzvQjAL666QNaAWaoDIY+xhat+v0OgCyAz4oCN77CzK9j5geI6E4AD0KmhK/fjhU/wAzV5iAGlusiiVKuZ87b3YyEmbTr9zX1qanhA5VzvnLNvmNyXywelmMGTwvDiTSoM13xd3Kz4FJopI1LUk5pKk1uXtNvSv5eawxr1RntxlVTDpblwcpnfPyDU/Js656rcz0Ykuo6kaXS9B5bYKiY+bo19r0bUkdgW2HhCQaDoe9hjKrH4BRQHycMZ87314zlhKV8Z0p8VaHW7wt3yfZWS31AGc94SvuEXTntdOerKu+X9wOT3ofhQg2cNIxDZtml4ehzKamDnpVthUl1hmkTaukYDu71n+3YCTmPJiMbg9p8bNHUr+9hhspg6FcwLjk95kqBGaoeI04D5QMJH1DLB0yeXBoGANTnVCZlQP1NM8JQuKCBnglG5BZ9OCX/DE6K/MrCk8X3VfMqMsidkzZhQx/DemxDhfQKM666sp/xp9Vf5Vb/2p9jSMbkKiYDADeUJTp5F+ryHGyPn/XKhjEqAGaoDIa+hk39BGaoeg0GwmqAMwuSHlMr+1W//KCwIWjKTGpYGFR0RhhWalwSgFvTPmm4JeFYiGQRESOPuiIP8holFhWdJEyq5ET2dEjO9bUo52sWPctLLyuL05U8TglLcgnMrt4f4GvycbWrQIOmzpAWf2CVJjb0AGaoAJihMhj6G2aoAJihMhj6FsQ29XMwQ9VjBC0ge45QnlApzrp3OGdHJQQgMyavsXrKl4YllWZkQIItlzT9BvBKCM2iTK+qE/KVOX0qF9QJANlFrWYzLFO7lAsG1Ren2hk0E05wduoJTnlBq9Ho9jjnb5HUiCwGRMclTCHIaQqNKn6SU1ewqV/vYKt+AMxQGQx9DWNUAjNUPUacBqq7YwyOShBnkLjTdg6KqPnUsgieL52R192HpFifq0YTJCIFBk/K8dWaOrlDZUDqD3cOdABoFqSNU/90icuxhjaU9ouzO1VLhByMije+sKR1CBfFA88ZZUdp73jnaq3js9IBDQZ99Jicp1zuvhyGS4UZKgBmqAyG/oX5qNowQ9VjUAvIngtQHhf/zZF9Pqm31BBGs7QklCcoCntZKLkASrkrg7r3S2SWJTygskuYTXlvp9xLkJA0T5edOqeeT11dow/J9tF7ZCw84Msrt4azOm6Ve8mr9MyQbA+rXm007kqZiU+cWvkiGHoHM1QAzFAZDH0NMnVnAGaoeg5iYTmptFCeUtNHZJ5bEooTl3UpL61VYha1zYCwl8ZYIgVnUPxOzt/kEo+bmhaTWfKPXJewHOu3GrQ6j2nskypI2aNTfrwDOwAALa1M41b7XH2/sJzwZ8Wdj3db3TNsFcxQGQz9DJv6ATBD1XNQBGQWGTWVIA5X8oYqnQ8X5PI7AT12MTNZz/dT6q9yArGhLrzVR+U18C4kNJ3enpNq0eRmJ+9S2S3MLT3ryyyHJWFFUUH2tQrK9lxKTehjrgJNq+kuNkNd1WkMPYI509swQ2Uw9DPMUAEwQ9VzBE3G4JkI80vCTA4enm/vK6RFJuXRugrnnZPVt9QBjVRXRpVMSi4elwTguZuELrkYqWZR7uDamF8hbLMrV2whpTFYE9KmeFKo0NJN7YrdGP7CYwCAdEHGUnv6HhlTWfxkcaKuH63CmNrJyMaoeo8tMFRE9OuQYqMxgGkAP8fMp0kE1D8A4KUAKrr9G5s/ovNhUsQGQ5+CIKt+G/m7RPwmMz+VmZ8O4BMA/pNufwmk8swRALcB+L1LPtNFwgyVwdCvYJ+YvN7fJZ2GeSnxdgCex70cwJ+w4CsARohoz6Wd7eJgUz8FEY0A+CCAp0C+qNcAeBhS3voQgOMAfpKZ51fpwnWEOE3I75DpXKmZbe96xuhJAEAhJVPAb9F+2VGXr8EFhy4O+4DM6e/eBQAIWnLvpCULB4WzztntT51d0IDPXOfzx6XZuKDR7Lx/BMfz8nGoJnPK3IyUnSedPsapRF+BnizuVPK8kNQZc7xfILbIR0VE7wbwswAWAfyAbt4H4GSimSvpfmZrRuVhjMrjAwA+zcxPBvA0AA8BeCuAzzPzEQCf1/cGw9aBN/gHTBDRPYm/25LdENHniOj+Ff5eDgDM/A5mPgAp5/4Gd9gqI9pyGKMCQERDAP4FgJ8DAGZuAGjol/hcbfZhAF+EVJRdFUxAK0doteQZ4CrOAMDzJ74DADgWitD5UFEc5ctlYVDHpscBAFFCZ31MAy6Lk5Ir4wIx6yPy1YWJBOM4lPtq6ITW9dM6f86ZnpuTtrmFhGrngX3S77iELMRZObdT+OTQ36tpVfiM5lcmleGQKoBGvn9LVL40XMC0bpaZb1lt53ol3RP4KIBPAngn+qikuzEqwbUAZgD8ERF9k4g+SEQDAHYx8xkA0NedKx1MRLe5J1mrbj9MQw+xcUZ10SCiI4m3LwPwHf3/LgA/S4JnA1h0v4ethjEqQQrAMwD8MjPfTUQfwAVM85j5dgC3A8Dg6AFOVxjN8vkVksdS4mAqpjpTT2IVsovV4cQVz6icJEtY13ABZThupSfJeDIaUuD8Wc289OPq+o08IgwuSoQcRCNS5bgxJqwuaKj+uQtxSPTfrkKjCMfFnxUvyedqM6lo9Wo0vMY+Qxd4y3L93kNEN0DCE54A8Drd/ilIaMJRSHjCz2/JaFaAGSrBJIBJZr5b3/8FxFBNEdEeZj6jqx3Tq/ZgMGwGtqak+4+tsp0BvH7zR7A+zFABYOazRHSSiG5g5ocBPB/Ag/r3agDv0dePr9sZARwAVFZ2VPSrW01lTOlAWIXzTbnUmXRe/FDNqmc8rZxWRt6jQZWuzp82CRr+Ts4syLmWrhE2F2W1FuAZOV/6gSek/++5rn1MNCCBqa5+n0tKhrIyJBgVAmF+ToKYBiXJmsqS37MhfxRviy/2soWl0AjMUHn8MoCPEFEGwDEIzQ0A3ElErwVwAsBPbOP4DFcjzFABMEPVBjPfC2ClVZPnX0g/rTww87QAgbqhMnu9P+re8kEAwImyZBTzlDCT4uFFAMBAVnxA003PqMp75CsipVLcTjiW1x1HE8J2adm59CR5n12QV+ezgsYw1UcSPiqtmlw4I/6r+pjGfVHneQCAr5fxh3Pik4omxa/KzU7flaFH6IGj/EqBGSqDoU9BsKmfgxkqg6GPYYZKYIaqxxA9KkJ5WOZMtbO+Rt/ifklTaUbqVF+SaVezKV/D2RkJFSg+lE50KC+udHuoM8lIs2xmnubbprTSem62s83Ck3T6GB/SvryDPD8rU8fUcVH9bAxLfF9zUMYYJpz1wbKIYfGCpIZRWtNhdOoX7pIws2jKFkd7BjNUAMxQGQz9DTNUAMxQ9RwUiY55rSJsafypM+19Y2lZvv9WWerhNUYkmo8XxYEd5FQzfdizpMETqim1U1iQqzrTFPKFqJhgPE0Ncyhpqsw5bavVlCs75Ot2FZUBgNTR3jq4U8fvwhT0/A94dsSzUoUmrkg4gtOhaquBliwqv6cwhc82zFAZDP0MM1QAzFBtGhzz2V9caG+ba4q/amlZ1Do5p2v/msCcGRYHVH3CxwRE0+IrcuqdLjyhVdDUmkTxYpd0XB8RhuPkXVx4QtvPVfOMilMuJaezbbqkY6hU/ViWkrJFQFCQz0GhjNESkHsPK5clMENlMPQxbOonMEPVaxAQp6mtV/7wrBdcGMwJY2pXcdHUGVcx2WWXcOLurOyV/wcm5X2zKMdEwy3dnvBnnZLVt6XDsq05pPIu6iZLq2RMnFj1IxeP+q1HAADhdz8ZAJDSto0je9ttg2ldTnTCeWFCtc/Qe1jAZxtmqAyGfoYZKgBmqHqOsM4YeayJ0jWarlLzjOcHD4rMz6khqQLz6JwI6A1khFE1NL5q+myhfUxmXthPYVpYTDVSJ5XewHFCTSZoysbMshzTGNL36loafVD+KV8z2D4mNynbggmRbImacp5oUOv8JSRhMl0SxNGC+t8s0XhTYJHpHmaoDIY+hlvkuNphhqrH4JBQHw5BukoXs/cHRSqoGigdcqt/+/adBQA8MS/JyunFhPCqHl4bk23pirKkWfnqogSjOvtsiWuq7JeTBw05JlKpmNJhYVJOZhgAqgdEgjg9ntfzSIduZTA3m1hW7C7MYExqc2E+qjbMUBkMfQyb+gnMUBkM/QwzVADMUPUcHEj1l9agTJ0G8l6r6WxNqrQcW5BqMxRIm8nFYQBASx3lYT1Rpl0Pd0qfLojT3cCZRAymS6sJahpAuqD9qe56lJY+konGLuCzPpLWfZ2a6VRvttuSq0Jzbm7ti2DoGYxRCawKjcHQz9iCKjQORPTviIiJaELfExH9dyI6SkT3EdEzenOmC4cxql6D5C8YFyo0PlBp75qvifN8YVkc145tzc+qQ1s10ynr77zsvJPalBeXmuNuzrDu2zoJGIoDPVbTYarCklx6TIdqZ+D6lX1ZdZ47LXWnkw4AVFTK5hjVKpWTDT3C1lWhAREdAPBCiOS2w0sAHNG/ZwH4PX3dchijMhj6FC6OaiN/PcD7AfwaOvnZywH8CQu+AmBEqzFtOYxR9Rqx+IDyBaE32dBrmofqkxrRCsmBu8PqKqCnsQY05h+j0YKK7GltvsKUsLDqhKjiLR9OVqHplIJxLKwxqAxL5V048a3Xh2Xf2P3LckhL2jhGFWd8wGfQFY4Q5GUM7WRkciVyzLHSM2z8Wk4Q0T2J97drvcl1QUQvA3CKmb9FRMld+wCcTLyf1G1bXoTUDJXB0MfoVUl3IvocgN0r7HoHgLcD+MGVDlth27Y8hcxQ9RgcyApd6Yz4c8IRL/Pigj/nF0XuJZNVn1ReKxxn5JXm8u1jMosq56Krf7UJYTpL18Xt8znkZlQgb4/eS4EK6M1oJWatUtNeOQSQWVYGlZd+WwMpbXv+PRpqReT256lUOt5TRhhhR0VlY1cXjx46ypn5BSttJ6J/BuAwAMem9gP4BhHdCmFQBxLN9wM43ZsRXRjMR2Uw9DEo3tjfxYKZv83MO5n5EDMfghinZzDzWQB3AfhZXf17NoBFZt7yaR9gjKrniDPA8jVAakRYRT3yl3jfgLCrRyORfqlOymofK6OKtEJyvpSIo9LVuFi7Ke+UNjlVCA592UBkljReSmOu6pKRg5QSn/yssrCEOmTm/p0AABLOSURBVEtKVwTDUmdtvigvjaiZENmr1zvagFyCtIyfm5Za02tss3DepwC8FMBRABVIUd5tgRkqg6Ffwdhyo6+syv3PAF6/pQNYBWaoNgEUE1JpYRnpwMcY5UPxSYUp9QvprvatGCp7Svub08m4uAh1FzcVNjTKPMmotKhDq6DywpG8Dp0QppOd1RXD3dn2MVFOE6XLWgor7QKr3JgSInsDWvrLFXdw5bLq7oOs//in7sRmw5qwyHSBGSqDoZ9hhgqAGSqDoW9hwnkeZqh6jKAJDJxmzB2UOdvQHq/nVI0kBGBAg0EXJ7TCjIYRjAxL4ORcPNI+JjcjX1G+LNMqp/Q5tV/DCXwkAwbOOme6vHfO9cKkTNVaRRlTs+AXe52eVTHWZOS2bjtp/97znh7WFJoZEWF3AZ+Rc7J3OddXgqsFaFO/DYDZhPMUZqgMhn6G2SkAZqh6jjgNVPYQMgVxnC81cu19wwPCrnKqkb7QVNXOQXFy7ylKGkt5zDu70yVhMZUdqtapFWSy87I/N+cd2LGTbBmTu7t0WJhNdlH6aOapPUYHF8rgko/DmUUAQG3HLgCdaqDRY8c7P+yuHfK6sKgD2EBycmQJzBcCm/oJzFAZDP0KBmBTPwBmqHoOioDMIlBWEbzlhmdH51Ii87JcFZaVyoufplERivPQpKZizfhjHPtxYnfNARXQU+10TiSRurAGp9fuxfWEdQ0dlWToqOApVZSVcTb2inhf9mHJkCg8IYp81f1F/+G6Ynp4sjNIOShK23h5Gash7g4aNawNs1MAzFAZDH0Nm/oJzFApiOhXAfwC5Bn2bUi6wB4AdwAYA/ANAK9i5saqnUCliAeAoUFhL7mUX91abMgSXWVJGNXAsLSpQZN5NWm5495UwpRRmRe3Stdq+5sSssLtVTeXlAxt4/xQ6t864aWE69dIPb/ABW3qSp5japl5z4ACDfh0si7cUD0Zx+qaXrZ4VVh6zQXBVv0ElpQMgIj2AXgjgFuY+SkAQgCvAPBeAO9n5iMA5gG8dvtGabjqsFEZ4qvAlhmj8kgByBNRE0ABIg72PAA/o/s/DOBdEDnWVcEh0BiJcUhX8JJ1/c4sSXEElLWKclYLKqSEzYSh+JIqg/5ridPSJlOSNi1NeSnvdSJ5vn8nBeMeP9TSiskqnBeOi+8rddavFDb1XPmSsCHOS5tY/VipWe9vYk2ZcWkwlFNfmqbzxLVEDUDDJUMCPq8CK7QBGKMCwMynAPwWRC/6DIBFAF8HsMDMbu7m1A3PAxHdRkT3ENE9kVO7NBh6gXiDf1c4zFABIKJRiD70YQB7AQxAhO27seLjjZlvZ+ZbmPmW0CXuGgw9ADFv6O9Kh039BC8A8DgzzwAAEX0MwD+HiNmnlFVtSN2QGAhahJTqo0+XB9v7lhfFmc66lBNPSrhCc1SmXUFOp4AF74BvFBPRmQBaWQ3qHJf+08t+6ueCQDGu72ck/YXDzlCGxv4x358GfJb3y1iK35HpW9BQjaklP/WLFiVkIShIWwrlOUfdqTSG3uAq8T9tBMaoBCcAPJuICiR6rM8H8CCALwD4cW3zagAf36bxGa5KSK7fRv6udBijAsDMdxPRX0BCEFoAvgngdgCfBHAHEf2GbvvD9TsDwhrh5IIkFrt0GQCYmBB2slwQBlLLS6zBrl2SgrJ3UF5nq56FzYbCwpxDvD7aWWmmIx1G2VaroFWaJ+V9YUoYWqoqLGn5Gp/WkylJ28EHJNGYB+V81FBWF/qk5HBEk6U1Dea88ARD73EVTOs2AjNUCmZ+J4B3dm0+BuDWbRiOwbClBUj7HWaoegyKgFQZKFdk6X5pOVFRJissJY6FgezYKT6f4az4hRyTmq/4Y9o3qj5YneKnq3ZcOL2CvnpeFUQ1PSZ7TvoPF0TuJVsc9wNm15/KvCxIpRnnxwoGfTpP+PhZ6b8kK5vBiKTdxPPzMGwStoBREdG7APwigBnd9HZm/pTuexskfjAC8EZm/symD2gFmKEyGPoZWzfzez8z/1ZyAxHdBAl8vhmyGv45IrqeeQ3BsU2CGaoegwMRs4s0KTlueB9PpIGdrbpc9rlIQhmWK7pqphrnUdMfo24nqNw6wpp7lR3psr+TnVa6q+fXKjiGJecLauLQyk1V28e4ffGQrOQFU5JeE7iKyYkE5lBTcNhJtaj4nYngbR4o3ta538sB3MHMdQCPE9FRiCvky1s9EFv1Mxj6FYytDPh8AxHdR0Qf0rhCYPWS7lsOY1Q9BqcZtT0thK5KcdHHFuWzkuMyXxZHE50R/09dV+lYfUuOEQHeR+X8T2lNTm4MO6lg76Nq6mIh1TW+SX1hpb2aOjMm5y1+xyclh1pDsDkmjCqjCdPhnEbYjxT8h1Pm5KrPRM431b3qZytVPQHhgoI5J4jonsT725n59nZfa5d0/z0Avw4xjb8O4L8BeA2spLvBYNgQNm6oZpn5ltW7WbmkezeI6H8C+IS+7ZuS7maoeo0AoHwEVjaza9hHdjdjYTpLKpgXXitsK+OSkac0/SYh/5uf7bpRdVd1n7Kblv8KnXbR0FHxcaWqKranvitXITkuZPxwS/WOY52vqjUkzMqtBgJAdM4zMcAnJ7tYq/MqKRsuHVuz6rcnUar9XwK4X/+/C8BHieh9EGf6EQBf3fQBrQAzVAZDv8L5qDYf/5WInq5nPA7g3wAAMz9ARHdCsjRaAF6/HSt+gBkqg6GvsRWrfsz8qjX2vRvAuzd9EOvADFWPEVQJg/dlsXyjxBMMZb1GU12nafMZmbYN5mWqlA7lIZXaKzdluexTXBav02mVqnfGWoVm5H7VkZrxN3J5t7Sp7NWy7xVpO/qwtIn121640eugR2nRyBq/T4JPIw3wjNr1/BKhEjkZl9OdaoclaLhCuEOr0iSmgNHSEgwXC7aFCYUZKoOhX8EwQ6UwQ9VjUCxO7KAUnrcvq/rpQwVhJMtVYS/jg5LaktGA0HJC0T9Q0tLWSFc/uKuCnKp7RhWq0marqOkwWjewMaiVa+pyjEtEBrxiaKQOdpd209JqyskqN7mMhlXoj4dcwrJ7jV2y8pqy8oYLgeX6ATBDZTD0Na4GUbyNwAzVZkGJSCPyzMpVmWm0ZFtlXpKPAw3wJGVS0YIPHyhq0rHzL4Xq/nHpMk7+BfDBoU6Yz+UdNItafUbr+w0cL7WPaY5o+o7zScWdInsu0FQ6EAZIrU4JGCeg55KVLUyhhzBDBcAMlcHQv2AGIpv7AWaoeg6KgXQZoJ3ih6pH/hLPlSWYcmlacl2oKoykFMj2gtb5y5/2x+RnNRh0p7CWgq7ylfapkN6IZ1RhTVf7qupnyitT02Tn/KysRFLZr0TymLC6lFahCZqdP4zMOd+WBqRtO/CTHYXT86Q2cDs5n5cxhY3BrhMAM1QGQ3/DDBUAM1Q9R5QD5p8MpHQF7+TMaHtfrKtw1NAVtaL4enbvkeTe2XmJb6Kbfcmt5Yak1Th5l/kbOgUv8jPJBGaVdcloAvOSK/AnL7VxkWyJMr64Q3pJmFRKRfVaY51VdKKBhMzLtKb+DEqbaFnSg9rFHg7tl891fNJ/5kqlo79wTK5HdzqOYQUw2j7Dqx1mqAyGvgX76fVVDjNUBkO/gmHOdIUZqs1AANTnxPGcnvfhCfFeVSoYloDIXF6mXSF1hic0F71O+aDqT7mwhMK0vF8+qIGaiSo0LuAzVe4s9x5ptEMcqopCygdxckqnh2dnpe0eSamJNfDTVa4BgHhhUT+fHqP+k7hW1/PKYkC8RsCnC2HoNZwj/4pTGzUfFQAzVAZDf8MMFQAzVD0HRUB2jtAa7Ky/BwCjYxJo2dIgUFc5OSgKy0ilhb0E04n0G71PM8syBXAaUy7VpbzPs6PBSWVmiXMCPg2nrdeYvPddaEFOgzlj54iXg1zFZMA7xiklNC7Q8vVxWVlSXZjUSqyGXLCopuH0PCjUpfFcUYzKkpIdzFAZDP0KBrC9xR36BmaoegyKJZSAc8JE6jv8E/HWCamLd2xxAgCwPCn+oMXhXEcfqUpCB12aoKhsqbxLmEN9THXWU77/zKJKwqTVd6SpOVmVNg9amh4T+v7jrBwT75SwgbCifrN5TYcZ8GNzUjMuZYY0TAHlLr9TUkNdGUGgbd0xcUnYZZD3NQxdMvPF+JlcgvQVxz+MUQEwQ2Uw9DEshcbBDFWPEeUZi/+sCWgC8uBunwBcaYl/JhXIzTd+SKjO0r1SubixV9hMppgI4mzpyp0W+KuPyytF0qZwxvuz6uPqv8q48sfyMnhGAzV1Ba+0368qzh+RW2D/J2VFL2x1psUk2ZcL9MRuEciLHnms47NzRVb9gqzv34nsOd+UQ5tJHbnGf9ZjEijKy8u4UGwofedyAwNscVQAzFAZDP0Ni0wHYIaq56AmITOVQnNUnoQ37phq7zs8cA6AZ1S1rKyeZW9RaeKMrIQ9UtnfPiZ/Wn0vLgZKCU66JH4iV+cPAFJaoy+tK471MY252i/nKWpmi2NnADBxv/qFshqHpL6e1k5hT5yoiJPNi7+KJ6VgSTgkDjQnN0xjI9IwGSvlZIvHhpFEfFKqLgVHT/iN0SXUDUiM84rCFvmoiOiXAbwBUsThk8z8a7r9bQBeCyAC8EZm/syWDKgLZqgMhn4F85as+hHRD0DKtz+VmetEtFO33wTgFQBuhpTL+hwRXb8dlWjMUPUYQQMoHgcqNWEmZ68bau+7bmAGANDS+n47cuK/mqkKe3l8RnxV1EyumsmLi0BPaY5vpItxzYEE41nQxhqZ7ion19SvNXDGyb2cf5+1RsVnFJY6o8rjRBQ7RuSzsCYUu9io9iqfvjpfFeB9RzQnPjB2PzyVLe5IWqbOhOsLAV+pTuetYVS/BOA9zFyXU/K0bn85gDt0++NEdBTArQC+vBWDSuLi7wyDwbDJYHAUbegPWtI98XfbBZzoegDfR0R3E9HfEdEzdfs+ACcT7SZ125bDGJXB0K+4MJmXNUu6E9HnAOxeYdc7IHZgFMCzATwTwJ1EdC18LkP3qLYcZqh6DGJJWQl1BjU55fWoThRFB2q2KnOys2WZSs0syPtWXb+OZBCnW6l3wpjKgaOstuHE1E9zht2tFFY7S7mHWrEmrPupX3WXTN/itKb8VF2peK1y00pME920TadoLg0mGNQ5ZlOOTU7nKC1hCa2pGe1jDffGWvvWw6U44vsZPQpPYOYXrLaPiH4JwMeYmQF8lYhiABMQBnUg0XQ/gNM9GdAFwqZ+BkOfggFwzBv6u0T8NYDnAQARXQ8gA2AWwF0AXkFEWSI6DOAIgK9e6skuBsaoeoyWKny2hjQNpOoDMo8vCaNargmLKT0uS/bhbnE+P+NaWap/eHZn+5j016VNS0MKyvs1PabgnrT+WdMouuBQee+q0jg25hKZ62P+a68Ny7agpZVk0qqLrgwru+iZSlodu+HeXZ2feY+wRm5qxedw659/nNVg2nof1RQ8fonH85YJ530IwIeI6H4ADQCvVnb1ABHdCeBBSNjC67djxQ8wQ2Uw9DV4C6a0zNwA8K9X2fduAO/e9EGsA2JLeuwpiGgGQBlCnS8XTODyGe/lNNZrmHnHxR5MRJ+GfN6NYJaZX3yx5+p3mKHaBBDRPWutwPQbLqfxXk5jNfQO5kw3GAx9DzNUBoOh72GGanNw+3YP4AJxOY33chqroUcwH5XBYOh7GKMyGAx9DzNUBoOh72GGqscgohcT0cNEdJSI3rrd40mCiA4Q0ReI6CEieoCI3qTbx4jos0T0qL6OrtfXVoGIQiL6JhF9Qt8f1iz/R4noz4gos14fhssfZqh6CCIKAfwugJcAuAnAT6v4WL+gBeDNzHwjJFP+9Tq+twL4PDMfAfB5fd8veBOAhxLv3wvg/TrWeYj6pOEKhxmq3uJWAEeZ+ZimJdwBER/rCzDzGWb+hv6/DDEA+yBj/LA2+zCAH92eEXaCiPYD+CEAH9T3BEme/Qtt0jdjNWwuzFD1Fn0jNLYeiOgQgO8CcDeAXcx8BhBjBmDn6kduKX4bwK8BcJm54wAWmNkV/uvb62voLcxQ9RZ9IzS2FohoEMBfAvgVZl7a7vGsBCL6YQDTzPz15OYVmvbd9TX0Hqae0Fv0jdDYaiCiNMRIfYSZP6abp4hoDzOfIaI9AKZX72HL8BwALyOilwLIARiCMKwRIkopq+q762vYHBij6i2+BuCIrkxlIBU87trmMbWhPp4/BPAQM78vsesuAK/W/18N4ONbPbZuMPPbmHk/Mx+CXMe/ZeZXAvgCgB/XZn0xVsPmwwxVD6FP+TcA+AzEUX0nMz+wvaPqwHMAvArA84joXv17KYD3AHghET0K4IX6vl/xFgD/ViuijEMMr+EKh6XQGAyGvocxKoPB0PcwQ2UwGPoeZqgMBkPfwwyVwWDoe5ihMhgMfQ8zVAaDoe9hhspgMPQ9/i8LOYhdVVUwlgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Category:  egg\n",
      "Object:  egg_rough_styrofoam\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "Display a frame of the sound dataset\n",
    "\"\"\"\n",
    "\n",
    "batch_no = 188\n",
    "\n",
    "print(\"batch, frames, freq, channel:\", audio_frames_2d_train.shape)\n",
    "\n",
    "print_image(audio_frames_2d_train[batch_no, :, :, 0], db_file_name+\" Spec\")\n",
    "print_image(audio_frames_2d_train[batch_no, :, :, 1], db_file_name+\" Delta\")\n",
    "\n",
    "print(\"Category: \", list(category_labels.keys())[list(category_labels.values()).index(category_label_train[batch_no])])\n",
    "print(\"Object: \", list(object_labels.keys())[list(object_labels.values()).index(object_label_train[batch_no])])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Haptic Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Downloading testset1 for haptic of grasp interaction and reading it.\n",
    "\"\"\"\n",
    "\n",
    "db_file_name = \"grasp_Freq_50TimePerSec_7channels_testset1.bin\"\n",
    "gdd.download_file_from_google_drive(file_id='16g4Cp67SHqpVSUyOryPEH2HCTZhMcCeB',\n",
    "                                    dest_path='../Deep-Multi-Sensory-Object-Categorization_dataset/'+db_file_name,\n",
    "                                    unzip=False)\n",
    "\n",
    "bin_file = open(\"../Deep-Multi-Sensory-Object-Categorization_dataset/\"+db_file_name, \"rb\")\n",
    "\n",
    "haptic_frames_2d_train = pickle.load(bin_file)\n",
    "category_label_train = pickle.load(bin_file)\n",
    "object_label_train = pickle.load(bin_file)\n",
    "category_label_train_one_hot = pickle.load(bin_file)\n",
    "object_label_train_one_hot = pickle.load(bin_file)\n",
    "\n",
    "haptic_frames_2d_test = pickle.load(bin_file)\n",
    "category_label_test = pickle.load(bin_file)\n",
    "object_label_test = pickle.load(bin_file)\n",
    "category_label_test_one_hot = pickle.load(bin_file)\n",
    "object_label_test_one_hot = pickle.load(bin_file)\n",
    "\n",
    "bin_file.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "batch, frames, channel:  (400, 100, 7)\n",
      "Category:  egg\n",
      "Object:  egg_rough_styrofoam\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "Printing shape of the haptic dataset\n",
    "\"\"\"\n",
    "\n",
    "batch_no = 188\n",
    "\n",
    "print(\"batch, frames, channel: \", haptic_frames_2d_train.shape)\n",
    "\n",
    "print(\"Category: \", list(category_labels.keys())[list(category_labels.values()).index(category_label_train[batch_no])])\n",
    "print(\"Object: \", list(object_labels.keys())[list(object_labels.values()).index(object_label_train[batch_no])])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building the Multimodal Network Architecture\n",
    "\n",
    "<img src=\"pics/Multimodal.png\" alt=\"drawing\" width=\"400px\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multimodal Network Hyper-parameters\n",
    "\n",
    "This network was trained for 300 epochs using Adam optimization with learning rate 1 x $10^{-4}$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Y:  Tensor(\"LabelData:0\", shape=(?, 20), dtype=float32)\n",
      "video_X:  Tensor(\"InputData:0\", shape=(?, ?, 90, 120, 3), dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "# Network hyper-parameters\n",
    "batch = 5\n",
    "training_epochs = 300\n",
    "display_step = 1\n",
    "\n",
    "if (db_file_name.split(\".\")[0].split(\"_\")[0]) == 'low':\n",
    "    behavior = \"low_drop\"\n",
    "    testset = db_file_name.split(\".\")[0].split(\"_\")[5]\n",
    "elif (db_file_name.split(\".\")[0].split(\"_\")[0]) == 'lift':\n",
    "    behavior = \"lift_slow\"\n",
    "    testset = db_file_name.split(\".\")[0].split(\"_\")[5]\n",
    "else:\n",
    "    behavior = db_file_name.split(\".\")[0].split(\"_\")[0]\n",
    "    testset = db_file_name.split(\".\")[0].split(\"_\")[4]\n",
    "folder_name = behavior+'_'+testset\n",
    "model_path = \"../Deep-Multi-Sensory-Object-Categorization_dataset/\"+folder_name+\"_logs/model.ckpt\"\n",
    "logs_path = \"../Deep-Multi-Sensory-Object-Categorization_dataset/\"+folder_name+\"_logs/\"\n",
    "\n",
    "num_classes = category_label_train_one_hot.shape[1]\n",
    "\n",
    "Y = tf.placeholder('float', [None, num_classes], name='LabelData')\n",
    "print(\"Y: \", Y)\n",
    "\n",
    "# Video\n",
    "video_frames = video_time_step_size = video_frames_5d_train.shape[1]\n",
    "video_height = video_frames_5d_train.shape[2]\n",
    "video_width = video_frames_5d_train.shape[3]\n",
    "video_channel = video_frames_5d_train.shape[4]\n",
    "\n",
    "video_X = tf.placeholder('float', [None, None, video_height, video_width, video_channel], name='InputData')\n",
    "print(\"video_X: \", video_X)\n",
    "\n",
    "# Audio\n",
    "audio_frames = audio_time_step_size = audio_frames_2d_train.shape[1]\n",
    "audio_freq_height = audio_frames_2d_train.shape[2]\n",
    "audio_channel = audio_frames_2d_train.shape[3]\n",
    "\n",
    "audio_keep_prob = tf.placeholder_with_default(1.0, shape=(), name='audio_keep')\n",
    "\n",
    "# Haptic\n",
    "haptic_frames = haptic_time_step_size = haptic_frames_2d_train.shape[1]\n",
    "haptic_channel = haptic_frames_2d_train.shape[2]\n",
    "\n",
    "haptic_keep_prob = tf.placeholder_with_default(1.0, shape=(), name='haptic_keep')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "gdd.download_file_from_google_drive(file_id='1NPs3CMhbZ-C6CpbEWX4kJAkhzHe-gNbG',\n",
    "                                    dest_path='./TTRNN.py',\n",
    "                                    unzip=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from TTRNN import TT_GRU, TT_LSTM\n",
    "from keras.regularizers import l2\n",
    "from keras.layers import Dense\n",
    "\n",
    "tt_input_shape = [2, 90, 90, 2]\n",
    "tt_output_shape = [6, 6, 6, 6]\n",
    "tt_ranks = [1, 4, 4, 4, 1]\n",
    "alpha = 1e-2\n",
    "\n",
    "\"\"\"\n",
    "Functions used to define models\n",
    "\"\"\"\n",
    "\n",
    "haptic_skip_2nd_maxpool = [\"grasp\", \"hold\", \"low\"]\n",
    "\n",
    "def model(video_data_placeholder):\n",
    "    with tf.name_scope(\"Model\"):\n",
    "        # Video\n",
    "        XR = tf.reshape(video_data_placeholder, [batch, video_frames, video_height*video_width*video_channel])\n",
    "        rnn_layer = TT_GRU(tt_input_shape=tt_input_shape, tt_output_shape=tt_output_shape,\n",
    "                               tt_ranks=tt_ranks,\n",
    "                                return_sequences=False,\n",
    "                                dropout=0.25, recurrent_dropout=0.25, activation='tanh')\n",
    "        h = rnn_layer(XR)\n",
    "        video_logits = Dense(output_dim=num_classes, activation='relu', kernel_regularizer=l2(alpha))(h)\n",
    "        \n",
    "        # Audio\n",
    "        audio_data_placeholder = tf.placeholder('float', [None, audio_frames, audio_freq_height, audio_channel], name='audio_InputData')\n",
    "        \n",
    "        net = tf.layers.conv2d(inputs=audio_data_placeholder, filters=20, kernel_size=[57, 6], strides=[1, 1], padding=\"same\", activation=tf.nn.relu)\n",
    "        net = tf.layers.max_pooling2d(inputs=net, pool_size=[4, 4], strides=[4, 4])\n",
    "        net = tf.layers.dropout(inputs=net, rate=audio_keep_prob)\n",
    "        net = tf.layers.conv2d(inputs=net, filters=40, kernel_size=[1, 3], strides=[1, 1], padding=\"same\", activation=tf.nn.relu)\n",
    "        if (db_file_name.split(\"_\")[0]) == \"hold\":\n",
    "            net = tf.layers.max_pooling2d(inputs=net, pool_size=[1, 4], strides=[1, 4])\n",
    "        else:\n",
    "            net = tf.layers.max_pooling2d(inputs=net, pool_size=[4, 4], strides=[4, 4])\n",
    "        net = tf.layers.flatten(net)\n",
    "        # Dense Layer\n",
    "        net = tf.layers.dense(inputs=net, units=256, activation=tf.nn.relu)\n",
    "        net = tf.layers.dropout(inputs=net, rate=audio_keep_prob)\n",
    "        net = tf.layers.dense(inputs=net, units=256, activation=tf.nn.relu)\n",
    "        net = tf.layers.dropout(inputs=net, rate=audio_keep_prob)\n",
    "        audio_logits = tf.layers.dense(inputs=net, units=num_classes, activation=tf.nn.relu)\n",
    "        \n",
    "        # Haptic\n",
    "        haptic_data_placeholder = tf.placeholder('float', [None, haptic_frames, haptic_channel], name='haptic_InputData')\n",
    "        net = tf.reshape(haptic_data_placeholder, [-1, haptic_frames, haptic_channel, 1])\n",
    "        net = tf.layers.conv2d(inputs=net, filters=32, kernel_size=[20, 5], padding=\"same\", activation=tf.nn.relu)\n",
    "        net = tf.layers.max_pooling2d(inputs=net, pool_size=[10, 1], strides=2)\n",
    "        net = tf.layers.conv2d(inputs=net, filters=64, kernel_size=[1, 3], padding=\"same\", activation=tf.nn.relu)\n",
    "        if (db_file_name.split(\"_\")[0]) in haptic_skip_2nd_maxpool:\n",
    "            net = tf.layers.max_pooling2d(inputs=net, pool_size=[1, 1], strides=[1, 2])\n",
    "        else:\n",
    "            net = tf.layers.max_pooling2d(inputs=net, pool_size=[10, 1], strides=2)\n",
    "                \n",
    "        net = tf.layers.flatten(net)\n",
    "        # Dense Layer\n",
    "        net = tf.layers.dense(inputs=net, units=1024, activation=tf.nn.relu)\n",
    "        net = tf.layers.dropout(inputs=net, rate=haptic_keep_prob)\n",
    "        haptic_logits = tf.layers.dense(inputs=net, units=num_classes, activation=tf.nn.relu)\n",
    "        \n",
    "        # Concatenate \n",
    "        logits = tf.concat([video_logits, audio_logits, haptic_logits], axis=1)\n",
    "        logits = tf.nn.relu(logits)\n",
    "        logits = tf.layers.dense(inputs=logits, units=num_classes)\n",
    "        \n",
    "    return logits\n",
    "\n",
    "def loss(prediction, label_placeholder):\n",
    "    with tf.name_scope('Loss'):\n",
    "        cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=prediction, labels=label_placeholder))\n",
    "        # Create a summary to monitor cost tensor\n",
    "        cost_scalar = tf.summary.scalar(\"loss\", cost)\n",
    "    return cost, cost_scalar\n",
    "\n",
    "def training(prediction, label_placeholder):\n",
    "    with tf.name_scope('Optimizer'):\n",
    "        optimizer = tf.train.AdamOptimizer(learning_rate=0.0001)\n",
    "        train_op = optimizer.minimize(cost)\n",
    "    return train_op\n",
    "\n",
    "def evaluate(prediction, Y):\n",
    "    with tf.name_scope('Accuracy'):\n",
    "        # Test model\n",
    "        correct_prediction = tf.equal(tf.argmax(prediction, 1), tf.argmax(Y, 1))\n",
    "        # Calculate accuracy\n",
    "        accuracy = tf.reduce_mean(tf.cast(correct_prediction, 'float'))\n",
    "        # Create a summary to monitor accuracy tensor\n",
    "        accuracy_scalar = tf.summary.scalar(\"accuracy\", accuracy)\n",
    "    return accuracy, accuracy_scalar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Compression factor = 17472 / 125971200 = 0.00013869836915104405\n",
      "Compressrion factor = 0.00013869836915104405\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\anaconda2\\envs\\py3_5\\lib\\site-packages\\ipykernel_launcher.py:25: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(activation=\"relu\", kernel_regularizer=<keras.reg..., units=20)`\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model_dict:  {'Loss': <tf.Tensor 'Loss/Mean:0' shape=() dtype=float32>, 'Accuracy': <tf.Tensor 'Accuracy/Mean:0' shape=() dtype=float32>, 'Model': <tf.Tensor 'Model/dense_6/BiasAdd:0' shape=(5, 20) dtype=float32>, 'Optimizer': <tf.Operation 'Optimizer/Adam' type=NoOp>, 'Loss_scalar': <tf.Tensor 'Loss/loss:0' shape=() dtype=string>, 'Accuracy_scalar': <tf.Tensor 'Accuracy/accuracy:0' shape=() dtype=string>}\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "Creating the Neural Network\n",
    "\"\"\"\n",
    "\n",
    "model_dict = {}\n",
    "\n",
    "prediction = model(video_X)\n",
    "model_dict[\"Model\"] = prediction\n",
    "\n",
    "cost, cost_scalar = loss(prediction, Y)\n",
    "model_dict[\"Loss\"] = cost\n",
    "model_dict[\"Loss_scalar\"] = cost_scalar\n",
    "\n",
    "train_op = training(prediction, Y)\n",
    "model_dict[\"Optimizer\"] = train_op\n",
    "\n",
    "eval_op, accuracy_scalar = evaluate(prediction, Y)\n",
    "model_dict[\"Accuracy\"] = eval_op\n",
    "model_dict[\"Accuracy_scalar\"] = accuracy_scalar\n",
    "\n",
    "print(\"model_dict: \", model_dict)\n",
    "\n",
    "# Initializing the variables\n",
    "init = tf.global_variables_initializer()\n",
    "\n",
    "# 'Saver' op to save and restore all the variables\n",
    "saver = tf.train.Saver(max_to_keep=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "if os.path.exists(logs_path):\n",
    "    shutil.rmtree(logs_path)\n",
    "    os.makedirs(logs_path)\n",
    "else:\n",
    "    os.makedirs(logs_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Writing 'Time', 'Epoch', 'Cost', 'Accuracy' in CSV file\n",
    "\"\"\"\n",
    "\n",
    "epoch_cost_accuracy = []\n",
    "epoch_cost_accuracy.append(\"Time\")\n",
    "epoch_cost_accuracy.append(\"Epoch\")\n",
    "epoch_cost_accuracy.append(\"Cost\")\n",
    "epoch_cost_accuracy.append(\"Accuracy\")\n",
    "\n",
    "with open(logs_path+folder_name+\"_data.csv\",'w') as f:\n",
    "    writer = csv.writer(f, lineterminator=\"\\n\")\n",
    "    writer.writerow(epoch_cost_accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0001 , Time:  00:00:01:15\n",
      "Cost - 3.0752564787864 --> Accuracy - 0.13000000193715097\n",
      "Epoch: 0002 , Time:  00:00:02:05\n",
      "Cost - 2.79694705903530 --> Accuracy - 0.2000000037252903\n",
      "Epoch: 0003 , Time:  00:00:02:41\n",
      "Cost - 2.64181688129901 --> Accuracy - 0.3400000050663949\n",
      "Epoch: 0004 , Time:  00:00:03:14\n",
      "Cost - 2.46025109738111 --> Accuracy - 0.33000000417232517\n",
      "Epoch: 0005 , Time:  00:00:03:48\n",
      "Cost - 2.2491524234414 --> Accuracy - 0.4600000068545342\n",
      "Epoch: 0006 , Time:  00:00:04:21\n",
      "Cost - 1.98098690211772 --> Accuracy - 0.4800000056624412\n",
      "Epoch: 0007 , Time:  00:00:04:54\n",
      "Cost - 1.7446918681263 --> Accuracy - 0.5100000068545342\n",
      "Epoch: 0008 , Time:  00:00:05:27\n",
      "Cost - 1.4566049672663 --> Accuracy - 0.5600000083446502\n",
      "Epoch: 0009 , Time:  00:00:06:00\n",
      "Cost - 1.17326496243476 --> Accuracy - 0.6000000067055226\n",
      "Epoch: 0010 , Time:  00:00:06:33\n",
      "Cost - 0.94075651466846 --> Accuracy - 0.6300000086426736\n",
      "Epoch: 0011 , Time:  00:00:07:08\n",
      "Cost - 0.75952215865254 --> Accuracy - 0.6500000081956387\n",
      "Epoch: 0012 , Time:  00:00:07:43\n",
      "Cost - 0.56583484020084 --> Accuracy - 0.6700000107288362\n",
      "Epoch: 0013 , Time:  00:00:08:18\n",
      "Cost - 0.415574312116950 --> Accuracy - 0.7100000075995924\n",
      "Epoch: 0014 , Time:  00:00:08:53\n",
      "Cost - 0.319629728607833 --> Accuracy - 0.7100000090897084\n",
      "Epoch: 0015 , Time:  00:00:09:28\n",
      "Cost - 0.242725019063800 --> Accuracy - 0.7200000055134297\n",
      "Epoch: 0016 , Time:  00:00:10:02\n",
      "Cost - 0.16486104473005 --> Accuracy - 0.6900000073015692\n",
      "Epoch: 0017 , Time:  00:00:10:38\n",
      "Cost - 0.109935081750154 --> Accuracy - 0.7700000084936621\n",
      "Epoch: 0018 , Time:  00:00:11:13\n",
      "Cost - 0.077777471556328 --> Accuracy - 0.7600000068545343\n",
      "Epoch: 0019 , Time:  00:00:11:47\n",
      "Cost - 0.053588836337439 --> Accuracy - 0.7600000068545343\n",
      "Epoch: 0020 , Time:  00:00:12:20\n",
      "Cost - 0.039395683910697 --> Accuracy - 0.790000006556511\n",
      "Epoch: 0021 , Time:  00:00:12:52\n",
      "Cost - 0.0312106742989271 --> Accuracy - 0.770000007748604\n",
      "Epoch: 0022 , Time:  00:00:13:24\n",
      "Cost - 0.023761359043419 --> Accuracy - 0.7800000071525575\n",
      "Epoch: 0023 , Time:  00:00:13:57\n",
      "Cost - 0.0189834716031327 --> Accuracy - 0.7800000071525575\n",
      "Epoch: 0024 , Time:  00:00:14:30\n",
      "Cost - 0.0159334571071667 --> Accuracy - 0.790000006556511\n",
      "Epoch: 0025 , Time:  00:00:15:03\n",
      "Cost - 0.013312451285310 --> Accuracy - 0.7800000071525576\n",
      "Epoch: 0026 , Time:  00:00:15:35\n",
      "Cost - 0.011278313907678 --> Accuracy - 0.7800000071525576\n",
      "Epoch: 0027 , Time:  00:00:16:07\n",
      "Cost - 0.0095281092042569 --> Accuracy - 0.770000007748604\n",
      "Epoch: 0028 , Time:  00:00:16:40\n",
      "Cost - 0.0081684559088898 --> Accuracy - 0.7700000062584879\n",
      "Epoch: 0029 , Time:  00:00:17:13\n",
      "Cost - 0.00714215668122051 --> Accuracy - 0.7700000062584879\n",
      "Epoch: 0030 , Time:  00:00:17:46\n",
      "Cost - 0.0060617511073360 --> Accuracy - 0.7600000068545343\n",
      "Epoch: 0031 , Time:  00:00:18:18\n",
      "Cost - 0.0053217951819533 --> Accuracy - 0.7700000062584879\n",
      "Epoch: 0032 , Time:  00:00:18:51\n",
      "Cost - 0.0046141810118 --> Accuracy - 0.7700000062584879\n",
      "Epoch: 0033 , Time:  00:00:19:24\n",
      "Cost - 0.0040945139480754 --> Accuracy - 0.7700000062584879\n",
      "Epoch: 0034 , Time:  00:00:19:56\n",
      "Cost - 0.00365347410552203 --> Accuracy - 0.7600000068545343\n",
      "Epoch: 0035 , Time:  00:00:20:29\n",
      "Cost - 0.0032681500961189 --> Accuracy - 0.7700000062584879\n",
      "Epoch: 0036 , Time:  00:00:21:01\n",
      "Cost - 0.00291465930204140 --> Accuracy - 0.7700000062584879\n",
      "Epoch: 0037 , Time:  00:00:21:33\n",
      "Cost - 0.0026583958417177 --> Accuracy - 0.7600000068545343\n",
      "Epoch: 0038 , Time:  00:00:22:06\n",
      "Cost - 0.00238575488983769 --> Accuracy - 0.7700000062584879\n",
      "Epoch: 0039 , Time:  00:00:22:39\n",
      "Cost - 0.0021795173197460 --> Accuracy - 0.7700000062584879\n",
      "Epoch: 0040 , Time:  00:00:23:11\n",
      "Cost - 0.0019646309512609 --> Accuracy - 0.7600000068545343\n",
      "Epoch: 0041 , Time:  00:00:23:44\n",
      "Cost - 0.00178963583166478 --> Accuracy - 0.7700000062584879\n",
      "Epoch: 0042 , Time:  00:00:24:16\n",
      "Cost - 0.00162339471644372 --> Accuracy - 0.7700000062584879\n",
      "Epoch: 0043 , Time:  00:00:24:49\n",
      "Cost - 0.00147433504826 --> Accuracy - 0.7700000062584879\n",
      "Epoch: 0044 , Time:  00:00:25:21\n",
      "Cost - 0.00134257537611119 --> Accuracy - 0.7700000062584879\n",
      "Epoch: 0045 , Time:  00:00:25:55\n",
      "Cost - 0.00121466716664144 --> Accuracy - 0.7700000062584879\n",
      "Epoch: 0046 , Time:  00:00:26:28\n",
      "Cost - 0.00110886939983174 --> Accuracy - 0.7700000062584879\n",
      "Epoch: 0047 , Time:  00:00:27:01\n",
      "Cost - 0.00101429419955820 --> Accuracy - 0.7700000062584879\n",
      "Epoch: 0048 , Time:  00:00:27:37\n",
      "Cost - 0.00091930328817397 --> Accuracy - 0.7700000062584879\n",
      "Epoch: 0049 , Time:  00:00:28:13\n",
      "Cost - 0.00084740818201680 --> Accuracy - 0.7700000062584879\n",
      "Epoch: 0050 , Time:  00:00:28:49\n",
      "Cost - 0.00077934651799296 --> Accuracy - 0.7700000062584879\n",
      "Epoch: 0051 , Time:  00:00:29:24\n",
      "Cost - 0.00071939725567062 --> Accuracy - 0.7700000062584879\n",
      "Epoch: 0052 , Time:  00:00:30:00\n",
      "Cost - 0.00066273062293475 --> Accuracy - 0.7700000062584879\n",
      "Epoch: 0053 , Time:  00:00:30:36\n",
      "Cost - 0.00061448882206605 --> Accuracy - 0.7700000062584879\n",
      "Epoch: 0054 , Time:  00:00:31:11\n",
      "Cost - 0.00056852013767638 --> Accuracy - 0.7700000062584879\n",
      "Epoch: 0055 , Time:  00:00:31:47\n",
      "Cost - 0.00052859948627883 --> Accuracy - 0.7700000062584879\n",
      "Epoch: 0056 , Time:  00:00:32:23\n",
      "Cost - 0.00049221576373383 --> Accuracy - 0.7700000062584879\n",
      "Epoch: 0057 , Time:  00:00:33:01\n",
      "Cost - 0.000458199150125437 --> Accuracy - 0.7700000062584879\n",
      "Epoch: 0058 , Time:  00:00:33:39\n",
      "Cost - 0.00042731592156997 --> Accuracy - 0.7600000053644183\n",
      "Epoch: 0059 , Time:  00:00:34:17\n",
      "Cost - 0.000395927619865688 --> Accuracy - 0.7600000053644183\n",
      "Epoch: 0060 , Time:  00:00:34:52\n",
      "Cost - 0.000370369435404427 --> Accuracy - 0.7600000053644183\n",
      "Epoch: 0061 , Time:  00:00:35:29\n",
      "Cost - 0.000340089478322624 --> Accuracy - 0.7600000053644183\n",
      "Epoch: 0062 , Time:  00:00:36:05\n",
      "Cost - 0.0003140207261822 --> Accuracy - 0.7600000053644183\n",
      "Epoch: 0063 , Time:  00:00:36:43\n",
      "Cost - 0.00028823445682064 --> Accuracy - 0.7500000059604647\n",
      "Epoch: 0064 , Time:  00:00:37:20\n",
      "Cost - 0.000264798196076299 --> Accuracy - 0.7700000062584879\n",
      "Epoch: 0065 , Time:  00:00:37:58\n",
      "Cost - 0.00024560222905165 --> Accuracy - 0.7500000059604647\n",
      "Epoch: 0066 , Time:  00:00:38:35\n",
      "Cost - 0.000227063436159369 --> Accuracy - 0.7500000059604647\n",
      "Epoch: 0067 , Time:  00:00:39:12\n",
      "Cost - 0.000211848199251107 --> Accuracy - 0.7500000059604647\n",
      "Epoch: 0068 , Time:  00:00:39:49\n",
      "Cost - 0.000196504681298392 --> Accuracy - 0.7500000059604647\n",
      "Epoch: 0069 , Time:  00:00:40:26\n",
      "Cost - 0.00018400988519715 --> Accuracy - 0.7500000059604647\n",
      "Epoch: 0070 , Time:  00:00:41:04\n",
      "Cost - 0.000172220900140018 --> Accuracy - 0.7500000059604647\n",
      "Epoch: 0071 , Time:  00:00:41:41\n",
      "Cost - 0.00015993801589502 --> Accuracy - 0.7500000059604647\n",
      "Epoch: 0072 , Time:  00:00:42:18\n",
      "Cost - 0.000149696931794096 --> Accuracy - 0.7600000068545344\n",
      "Epoch: 0073 , Time:  00:00:42:56\n",
      "Cost - 0.000139403841330931 --> Accuracy - 0.770000007748604\n",
      "Epoch: 0074 , Time:  00:00:43:33\n",
      "Cost - 0.000130700386580429 --> Accuracy - 0.7500000059604647\n",
      "Epoch: 0075 , Time:  00:00:44:10\n",
      "Cost - 0.00012196247180327 --> Accuracy - 0.7600000068545343\n",
      "Epoch: 0076 , Time:  00:00:44:47\n",
      "Cost - 0.000113900093356278 --> Accuracy - 0.770000007748604\n",
      "Epoch: 0077 , Time:  00:00:45:25\n",
      "Cost - 0.00010691110919651 --> Accuracy - 0.7400000058114531\n",
      "Epoch: 0078 , Time:  00:00:46:02\n",
      "Cost - 0.000100466847175084 --> Accuracy - 0.7400000058114531\n",
      "Epoch: 0079 , Time:  00:00:46:39\n",
      "Cost - 9.446797789678381e- --> Accuracy - 0.740000005811453\n",
      "Epoch: 0080 , Time:  00:00:47:16\n",
      "Cost - 8.839743195494517e- --> Accuracy - 0.7500000067055227\n",
      "Epoch: 0081 , Time:  00:00:47:53\n",
      "Cost - 8.292155455364992e- --> Accuracy - 0.740000005811453\n",
      "Epoch: 0082 , Time:  00:00:48:32\n",
      "Cost - 7.827581589481265e- --> Accuracy - 0.740000005811453\n",
      "Epoch: 0083 , Time:  00:00:49:15\n",
      "Cost - 7.336104323485415e- --> Accuracy - 0.740000005811453\n",
      "Epoch: 0084 , Time:  00:00:49:54\n",
      "Cost - 6.93261760261521e- --> Accuracy - 0.740000005811453\n",
      "Epoch: 0085 , Time:  00:00:50:32\n",
      "Cost - 6.55985110142865e- --> Accuracy - 0.740000005811453\n",
      "Epoch: 0086 , Time:  00:00:51:09\n",
      "Cost - 6.195971913030008e- --> Accuracy - 0.740000005811453\n",
      "Epoch: 0087 , Time:  00:00:51:46\n",
      "Cost - 5.827682865628959e- --> Accuracy - 0.7500000067055227\n",
      "Epoch: 0088 , Time:  00:00:52:22\n",
      "Cost - 5.520929227031956e- --> Accuracy - 0.7500000067055227\n",
      "Epoch: 0089 , Time:  00:00:53:00\n",
      "Cost - 5.2288248355125675e- --> Accuracy - 0.7500000067055227\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0090 , Time:  00:00:53:37\n",
      "Cost - 4.961499898854525e- --> Accuracy - 0.740000005811453\n",
      "Epoch: 0091 , Time:  00:00:54:14\n",
      "Cost - 4.709975895593743e- --> Accuracy - 0.740000005811453\n",
      "Epoch: 0092 , Time:  00:00:54:52\n",
      "Cost - 4.435591838500841e- --> Accuracy - 0.7500000067055227\n",
      "Epoch: 0093 , Time:  00:00:55:28\n",
      "Cost - 4.1953238837777466e- --> Accuracy - 0.740000005811453\n",
      "Epoch: 0094 , Time:  00:00:56:05\n",
      "Cost - 3.963128372106439e- --> Accuracy - 0.740000005811453\n",
      "Epoch: 0095 , Time:  00:00:56:42\n",
      "Cost - 3.756643476435783e- --> Accuracy - 0.7500000067055227\n",
      "Epoch: 0096 , Time:  00:00:57:19\n",
      "Cost - 3.570849146399268e- --> Accuracy - 0.740000005811453\n",
      "Epoch: 0097 , Time:  00:00:57:55\n",
      "Cost - 3.379297855872211e- --> Accuracy - 0.7300000064074994\n",
      "Epoch: 0098 , Time:  00:00:58:32\n",
      "Cost - 3.2089095952869684e- --> Accuracy - 0.740000005811453\n",
      "Epoch: 0099 , Time:  00:00:59:08\n",
      "Cost - 3.0415517619530872e- --> Accuracy - 0.740000005811453\n",
      "Epoch: 0100 , Time:  00:00:59:46\n",
      "Cost - 2.8928195069966024e- --> Accuracy - 0.740000005811453\n",
      "Epoch: 0101 , Time:  00:01:00:22\n",
      "Cost - 2.760716603233959e- --> Accuracy - 0.740000005811453\n",
      "Epoch: 0102 , Time:  00:01:00:58\n",
      "Cost - 2.6170766267341596e- --> Accuracy - 0.7300000064074994\n",
      "Epoch: 0103 , Time:  00:01:01:34\n",
      "Cost - 2.4911762028523296e- --> Accuracy - 0.7300000064074994\n",
      "Epoch: 0104 , Time:  00:01:02:11\n",
      "Cost - 2.366045519011095e- --> Accuracy - 0.7300000064074994\n",
      "Epoch: 0105 , Time:  00:01:02:46\n",
      "Cost - 2.2559907120012205e- --> Accuracy - 0.7300000064074994\n",
      "Epoch: 0106 , Time:  00:01:03:23\n",
      "Cost - 2.152970930637821e- --> Accuracy - 0.7300000064074994\n",
      "Epoch: 0107 , Time:  00:01:03:55\n",
      "Cost - 2.052066636224481e- --> Accuracy - 0.7300000064074994\n",
      "Epoch: 0108 , Time:  00:01:04:29\n",
      "Cost - 1.9489567898745007e- --> Accuracy - 0.7300000064074994\n",
      "Epoch: 0109 , Time:  00:01:05:01\n",
      "Cost - 1.869926577455772e- --> Accuracy - 0.7300000064074994\n",
      "Epoch: 0110 , Time:  00:01:05:33\n",
      "Cost - 1.781834141638683e- --> Accuracy - 0.7300000064074994\n",
      "Epoch: 0111 , Time:  00:01:06:06\n",
      "Cost - 1.693000423301782e- --> Accuracy - 0.7300000064074994\n",
      "Epoch: 0112 , Time:  00:01:06:39\n",
      "Cost - 1.6176339124740482e- --> Accuracy - 0.740000005811453\n",
      "Epoch: 0113 , Time:  00:01:07:12\n",
      "Cost - 1.534191533210106e- --> Accuracy - 0.7300000064074994\n",
      "Epoch: 0114 , Time:  00:01:07:44\n",
      "Cost - 1.4669294199620711e- --> Accuracy - 0.740000005811453\n",
      "Epoch: 0115 , Time:  00:01:08:18\n",
      "Cost - 1.4039301402135603e- --> Accuracy - 0.740000005811453\n",
      "Epoch: 0116 , Time:  00:01:08:50\n",
      "Cost - 1.3421824152715087e- --> Accuracy - 0.740000005811453\n",
      "Epoch: 0117 , Time:  00:01:09:24\n",
      "Cost - 1.2845461716892712e- --> Accuracy - 0.740000005811453\n",
      "Epoch: 0118 , Time:  00:01:09:56\n",
      "Cost - 1.2310830533124315e- --> Accuracy - 0.740000005811453\n",
      "Epoch: 0119 , Time:  00:01:10:30\n",
      "Cost - 1.1771725098697063e- --> Accuracy - 0.740000005811453\n",
      "Epoch: 0120 , Time:  00:01:11:03\n",
      "Cost - 1.1290433482713524e- --> Accuracy - 0.740000005811453\n",
      "Epoch: 0121 , Time:  00:01:11:36\n",
      "Cost - 1.0796919397648716e- --> Accuracy - 0.740000005811453\n",
      "Epoch: 0122 , Time:  00:01:12:09\n",
      "Cost - 1.0369262608378452e- --> Accuracy - 0.7300000064074994\n",
      "Epoch: 0123 , Time:  00:01:12:43\n",
      "Cost - 9.90645370677612e- --> Accuracy - 0.740000005811453\n",
      "Epoch: 0124 , Time:  00:01:13:20\n",
      "Cost - 9.480588718702168e- --> Accuracy - 0.7500000052154066\n",
      "Epoch: 0125 , Time:  00:01:13:57\n",
      "Cost - 9.117304611550028e- --> Accuracy - 0.7300000064074994\n",
      "Epoch: 0126 , Time:  00:01:14:35\n",
      "Cost - 8.750446917105138e- --> Accuracy - 0.7300000064074994\n",
      "Epoch: 0127 , Time:  00:01:15:12\n",
      "Cost - 8.37167029885677e- --> Accuracy - 0.7300000064074994\n",
      "Epoch: 0128 , Time:  00:01:15:49\n",
      "Cost - 8.001832910053961e- --> Accuracy - 0.7300000064074994\n",
      "Epoch: 0129 , Time:  00:01:16:27\n",
      "Cost - 7.692191996966357e- --> Accuracy - 0.7300000064074994\n",
      "Epoch: 0130 , Time:  00:01:17:01\n",
      "Cost - 7.380764063213975e- --> Accuracy - 0.7300000064074994\n",
      "Epoch: 0131 , Time:  00:01:17:34\n",
      "Cost - 7.04638852653261e- --> Accuracy - 0.7300000064074994\n",
      "Epoch: 0132 , Time:  00:01:18:06\n",
      "Cost - 6.7564183694912565e- --> Accuracy - 0.7300000064074994\n",
      "Epoch: 0133 , Time:  00:01:18:39\n",
      "Cost - 6.47479183157884e- --> Accuracy - 0.740000005811453\n",
      "Epoch: 0134 , Time:  00:01:19:12\n",
      "Cost - 6.2152171381058e- --> Accuracy - 0.740000005811453\n",
      "Epoch: 0135 , Time:  00:01:19:44\n",
      "Cost - 5.9654799926534e- --> Accuracy - 0.7300000064074994\n",
      "Epoch: 0136 , Time:  00:01:20:17\n",
      "Cost - 5.727957568524291e- --> Accuracy - 0.740000005811453\n",
      "Epoch: 0137 , Time:  00:01:20:50\n",
      "Cost - 5.505637304281663e- --> Accuracy - 0.7300000064074994\n",
      "Epoch: 0138 , Time:  00:01:21:22\n",
      "Cost - 5.273180818221591e- --> Accuracy - 0.7300000064074994\n",
      "Epoch: 0139 , Time:  00:01:21:55\n",
      "Cost - 5.060695946212947e- --> Accuracy - 0.740000005811453\n",
      "Epoch: 0140 , Time:  00:01:22:28\n",
      "Cost - 4.837775422572577e- --> Accuracy - 0.7300000064074994\n",
      "Epoch: 0141 , Time:  00:01:23:01\n",
      "Cost - 4.669395578105195e- --> Accuracy - 0.7300000064074994\n",
      "Epoch: 0142 , Time:  00:01:23:33\n",
      "Cost - 4.464657208558266e- --> Accuracy - 0.7300000064074994\n",
      "Epoch: 0143 , Time:  00:01:24:08\n",
      "Cost - 4.3037252908106885e- --> Accuracy - 0.7300000064074994\n",
      "Epoch: 0144 , Time:  00:01:24:40\n",
      "Cost - 4.113291655016838e- --> Accuracy - 0.7300000064074994\n",
      "Epoch: 0145 , Time:  00:01:25:13\n",
      "Cost - 3.978884016930805e- --> Accuracy - 0.740000005811453\n",
      "Epoch: 0146 , Time:  00:01:25:46\n",
      "Cost - 3.8036484966141853e- --> Accuracy - 0.740000005811453\n",
      "Epoch: 0147 , Time:  00:01:26:18\n",
      "Cost - 3.645995840173555e- --> Accuracy - 0.7500000067055226\n",
      "Epoch: 0148 , Time:  00:01:26:51\n",
      "Cost - 3.517847650869042e- --> Accuracy - 0.7500000067055226\n",
      "Epoch: 0149 , Time:  00:01:27:23\n",
      "Cost - 3.3733083043330224e- --> Accuracy - 0.7400000073015691\n",
      "Epoch: 0150 , Time:  00:01:27:55\n",
      "Cost - 3.2430730570354173e- --> Accuracy - 0.7500000067055226\n",
      "Epoch: 0151 , Time:  00:01:28:28\n",
      "Cost - 3.1202881423553206e- --> Accuracy - 0.7400000073015691\n",
      "Epoch: 0152 , Time:  00:01:29:00\n",
      "Cost - 2.977239127233134e- --> Accuracy - 0.7500000067055226\n",
      "Epoch: 0153 , Time:  00:01:29:32\n",
      "Cost - 2.871739014409514e- --> Accuracy - 0.7400000073015691\n",
      "Epoch: 0154 , Time:  00:01:30:05\n",
      "Cost - 2.7567036369191547e- --> Accuracy - 0.7300000078976155\n",
      "Epoch: 0155 , Time:  00:01:30:37\n",
      "Cost - 2.6467336482483006e- --> Accuracy - 0.740000007301569\n",
      "Epoch: 0156 , Time:  00:01:31:10\n",
      "Cost - 2.544214076749539e- --> Accuracy - 0.740000007301569\n",
      "Epoch: 0157 , Time:  00:01:31:43\n",
      "Cost - 2.4533181232300182e- --> Accuracy - 0.740000007301569\n",
      "Epoch: 0158 , Time:  00:01:32:15\n",
      "Cost - 2.3567591696860277e- --> Accuracy - 0.740000007301569\n",
      "Epoch: 0159 , Time:  00:01:32:47\n",
      "Cost - 2.250365907485729e- --> Accuracy - 0.740000007301569\n",
      "Epoch: 0160 , Time:  00:01:33:20\n",
      "Cost - 2.181820437385795e- --> Accuracy - 0.740000007301569\n",
      "Epoch: 0161 , Time:  00:01:33:52\n",
      "Cost - 2.08406996193844e- --> Accuracy - 0.740000007301569\n",
      "Epoch: 0162 , Time:  00:01:34:24\n",
      "Cost - 2.0044976920985384e- --> Accuracy - 0.740000007301569\n",
      "Epoch: 0163 , Time:  00:01:34:57\n",
      "Cost - 1.917774009996265e- --> Accuracy - 0.740000007301569\n",
      "Epoch: 0164 , Time:  00:01:35:29\n",
      "Cost - 1.8408844049133683e- --> Accuracy - 0.740000007301569\n",
      "Epoch: 0165 , Time:  00:01:36:01\n",
      "Cost - 1.7806838851441847e- --> Accuracy - 0.740000007301569\n",
      "Epoch: 0166 , Time:  00:01:36:34\n",
      "Cost - 1.702900350153413e- --> Accuracy - 0.740000007301569\n",
      "Epoch: 0167 , Time:  00:01:37:07\n",
      "Cost - 1.6248189535161829e- --> Accuracy - 0.740000007301569\n",
      "Epoch: 0168 , Time:  00:01:37:39\n",
      "Cost - 1.5664066300047354e- --> Accuracy - 0.740000007301569\n",
      "Epoch: 0169 , Time:  00:01:38:12\n",
      "Cost - 1.5029278980449588e- --> Accuracy - 0.740000007301569\n",
      "Epoch: 0170 , Time:  00:01:38:44\n",
      "Cost - 1.4412373761985004e- --> Accuracy - 0.740000007301569\n",
      "Epoch: 0171 , Time:  00:01:39:16\n",
      "Cost - 1.383123153786414e- --> Accuracy - 0.740000007301569\n",
      "Epoch: 0172 , Time:  00:01:39:49\n",
      "Cost - 1.3318634135117687e- --> Accuracy - 0.740000007301569\n",
      "Epoch: 0173 , Time:  00:01:40:21\n",
      "Cost - 1.284775825816098e- --> Accuracy - 0.740000007301569\n",
      "Epoch: 0174 , Time:  00:01:40:54\n",
      "Cost - 1.2290456892216637e- --> Accuracy - 0.740000007301569\n",
      "Epoch: 0175 , Time:  00:01:41:26\n",
      "Cost - 1.1756997270850887e- --> Accuracy - 0.740000007301569\n",
      "Epoch: 0176 , Time:  00:01:41:59\n",
      "Cost - 1.1336785618709657e- --> Accuracy - 0.740000007301569\n",
      "Epoch: 0177 , Time:  00:01:42:31\n",
      "Cost - 1.0949356306966255e- --> Accuracy - 0.740000007301569\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0178 , Time:  00:01:43:04\n",
      "Cost - 1.0514244031867292e- --> Accuracy - 0.740000007301569\n",
      "Epoch: 0179 , Time:  00:01:43:37\n",
      "Cost - 1.0040389231136262e- --> Accuracy - 0.740000007301569\n",
      "Epoch: 0180 , Time:  00:01:44:09\n",
      "Cost - 9.724485042994494e- --> Accuracy - 0.740000007301569\n",
      "Epoch: 0181 , Time:  00:01:44:42\n",
      "Cost - 9.295333041947628e- --> Accuracy - 0.740000007301569\n",
      "Epoch: 0182 , Time:  00:01:45:14\n",
      "Cost - 9.00923065927373e- --> Accuracy - 0.740000007301569\n",
      "Epoch: 0183 , Time:  00:01:45:47\n",
      "Cost - 8.589019627081032e- --> Accuracy - 0.740000007301569\n",
      "Epoch: 0184 , Time:  00:01:46:20\n",
      "Cost - 8.282056654707047e- --> Accuracy - 0.740000007301569\n",
      "Epoch: 0185 , Time:  00:01:46:52\n",
      "Cost - 7.942310869424318e- --> Accuracy - 0.740000007301569\n",
      "Epoch: 0186 , Time:  00:01:47:24\n",
      "Cost - 7.614485449281005e- --> Accuracy - 0.740000007301569\n",
      "Epoch: 0187 , Time:  00:01:47:57\n",
      "Cost - 7.519118730314744e- --> Accuracy - 0.740000007301569\n",
      "Epoch: 0188 , Time:  00:01:48:29\n",
      "Cost - 6.994599289100738e- --> Accuracy - 0.740000007301569\n",
      "Epoch: 0189 , Time:  00:01:49:02\n",
      "Cost - 6.729358839052679e- --> Accuracy - 0.740000007301569\n",
      "Epoch: 0190 , Time:  00:01:49:35\n",
      "Cost - 6.410474266971278e- --> Accuracy - 0.740000007301569\n",
      "Epoch: 0191 , Time:  00:01:50:07\n",
      "Cost - 6.264443150882928e- --> Accuracy - 0.7300000078976155\n",
      "Epoch: 0192 , Time:  00:01:50:41\n",
      "Cost - 5.978341562240529e- --> Accuracy - 0.740000007301569\n",
      "Epoch: 0193 , Time:  00:01:51:14\n",
      "Cost - 5.674358483531708e- --> Accuracy - 0.740000007301569\n",
      "Epoch: 0194 , Time:  00:01:51:47\n",
      "Cost - 5.453821424694638e- --> Accuracy - 0.740000007301569\n",
      "Epoch: 0195 , Time:  00:01:52:20\n",
      "Cost - 5.20646238211242e- --> Accuracy - 0.740000007301569\n",
      "Epoch: 0196 , Time:  00:01:52:53\n",
      "Cost - 5.015727667867508e- --> Accuracy - 0.7300000078976155\n",
      "Epoch: 0197 , Time:  00:01:53:26\n",
      "Cost - 4.813072371767647e- --> Accuracy - 0.7300000078976155\n",
      "Epoch: 0198 , Time:  00:01:53:59\n",
      "Cost - 4.628298043485302e- --> Accuracy - 0.7300000078976155\n",
      "Epoch: 0199 , Time:  00:01:54:31\n",
      "Cost - 4.4405435808414493e- --> Accuracy - 0.7300000078976155\n",
      "Epoch: 0200 , Time:  00:01:55:04\n",
      "Cost - 4.2140462346296205e- --> Accuracy - 0.7300000078976155\n",
      "Epoch: 0201 , Time:  00:01:55:37\n",
      "Cost - 4.0858963243906024e- --> Accuracy - 0.7300000078976155\n",
      "Epoch: 0202 , Time:  00:01:56:09\n",
      "Cost - 3.9637070017306534e- --> Accuracy - 0.7200000070035458\n",
      "Epoch: 0203 , Time:  00:01:56:42\n",
      "Cost - 3.832576904727603e- --> Accuracy - 0.7300000078976155\n",
      "Epoch: 0204 , Time:  00:01:57:15\n",
      "Cost - 3.6418422411088624e- --> Accuracy - 0.7300000078976155\n",
      "Epoch: 0205 , Time:  00:01:57:48\n",
      "Cost - 3.4868702574186723e- --> Accuracy - 0.7300000078976155\n",
      "Epoch: 0206 , Time:  00:01:58:21\n",
      "Cost - 3.37660180615984e- --> Accuracy - 0.7300000078976155\n",
      "Epoch: 0207 , Time:  00:01:58:53\n",
      "Cost - 3.248451879045434e- --> Accuracy - 0.7300000078976155\n",
      "Epoch: 0208 , Time:  00:01:59:26\n",
      "Cost - 3.120301979464557e- --> Accuracy - 0.7300000078976155\n",
      "Epoch: 0209 , Time:  00:01:59:58\n",
      "Cost - 2.9861915216145e- --> Accuracy - 0.7300000078976155\n",
      "Epoch: 0210 , Time:  00:02:00:31\n",
      "Cost - 2.869962665741354e- --> Accuracy - 0.7300000078976155\n",
      "Epoch: 0211 , Time:  00:02:01:04\n",
      "Cost - 2.771615037033825e- --> Accuracy - 0.7300000078976155\n",
      "Epoch: 0212 , Time:  00:02:01:37\n",
      "Cost - 2.64644536596137e- --> Accuracy - 0.7300000078976155\n",
      "Epoch: 0213 , Time:  00:02:02:10\n",
      "Cost - 2.542137358396701e- --> Accuracy - 0.7300000078976155\n",
      "Epoch: 0214 , Time:  00:02:02:42\n",
      "Cost - 2.458690809170605e- --> Accuracy - 0.7300000078976155\n",
      "Epoch: 0215 , Time:  00:02:03:15\n",
      "Cost - 2.360343228424711e- --> Accuracy - 0.7300000078976155\n",
      "Epoch: 0216 , Time:  00:02:03:47\n",
      "Cost - 2.27689678755638e- --> Accuracy - 0.7300000078976155\n",
      "Epoch: 0217 , Time:  00:02:04:19\n",
      "Cost - 2.1994107766154503e- --> Accuracy - 0.7300000078976155\n",
      "Epoch: 0218 , Time:  00:02:04:52\n",
      "Cost - 2.163648045616639e- --> Accuracy - 0.7400000073015691\n",
      "Epoch: 0219 , Time:  00:02:05:24\n",
      "Cost - 2.0116562469141285e- --> Accuracy - 0.7300000078976155\n",
      "Epoch: 0220 , Time:  00:02:05:57\n",
      "Cost - 1.949071395390689e- --> Accuracy - 0.7300000078976155\n",
      "Epoch: 0221 , Time:  00:02:06:29\n",
      "Cost - 1.8388028752980292e- --> Accuracy - 0.7400000073015691\n",
      "Epoch: 0222 , Time:  00:02:07:02\n",
      "Cost - 1.7940993961573774e- --> Accuracy - 0.7300000078976155\n",
      "Epoch: 0223 , Time:  00:02:07:34\n",
      "Cost - 1.725554068077173e- --> Accuracy - 0.720000007748604\n",
      "Epoch: 0224 , Time:  00:02:08:06\n",
      "Cost - 1.6391273596205736e- --> Accuracy - 0.7300000071525575\n",
      "Epoch: 0225 , Time:  00:02:08:39\n",
      "Cost - 1.579522761030461e- --> Accuracy - 0.7100000068545342\n",
      "Epoch: 0226 , Time:  00:02:09:11\n",
      "Cost - 1.5050169901442696e- --> Accuracy - 0.720000007748604\n",
      "Epoch: 0227 , Time:  00:02:09:45\n",
      "Cost - 1.4513728263665366e- --> Accuracy - 0.720000007748604\n",
      "Epoch: 0228 , Time:  00:02:10:17\n",
      "Cost - 1.3560054004102535e- --> Accuracy - 0.720000007748604\n",
      "Epoch: 0229 , Time:  00:02:10:49\n",
      "Cost - 1.3381240302479113e- --> Accuracy - 0.720000007748604\n",
      "Epoch: 0230 , Time:  00:02:11:22\n",
      "Cost - 1.2665984754356423e- --> Accuracy - 0.720000007748604\n",
      "Epoch: 0231 , Time:  00:02:11:56\n",
      "Cost - 1.2278554666345083e- --> Accuracy - 0.720000007748604\n",
      "Epoch: 0232 , Time:  00:02:12:28\n",
      "Cost - 1.165270594238876e- --> Accuracy - 0.720000007748604\n",
      "Epoch: 0233 , Time:  00:02:13:01\n",
      "Cost - 1.1116264411192844e- --> Accuracy - 0.720000007748604\n",
      "Epoch: 0234 , Time:  00:02:13:34\n",
      "Cost - 1.0788438795650275e- --> Accuracy - 0.720000007748604\n",
      "Epoch: 0235 , Time:  00:02:14:07\n",
      "Cost - 1.0460613606433352e- --> Accuracy - 0.7100000068545342\n",
      "Epoch: 0236 , Time:  00:02:14:40\n",
      "Cost - 9.983776565469783e- --> Accuracy - 0.720000007748604\n",
      "Epoch: 0237 , Time:  00:02:15:12\n",
      "Cost - 9.864567189765686e- --> Accuracy - 0.720000007748604\n",
      "Epoch: 0238 , Time:  00:02:15:44\n",
      "Cost - 9.387730099952305e- --> Accuracy - 0.720000007748604\n",
      "Epoch: 0239 , Time:  00:02:16:16\n",
      "Cost - 8.970497571425534e- --> Accuracy - 0.720000007748604\n",
      "Epoch: 0240 , Time:  00:02:16:49\n",
      "Cost - 8.851288488820328e- --> Accuracy - 0.720000007748604\n",
      "Epoch: 0241 , Time:  00:02:17:22\n",
      "Cost - 8.076428255066043e- --> Accuracy - 0.720000007748604\n",
      "Epoch: 0242 , Time:  00:02:17:54\n",
      "Cost - 8.136032882966048e- --> Accuracy - 0.720000007748604\n",
      "Epoch: 0243 , Time:  00:02:18:26\n",
      "Cost - 7.748602812718274e- --> Accuracy - 0.720000007748604\n",
      "Epoch: 0244 , Time:  00:02:18:59\n",
      "Cost - 6.943940302761572e- --> Accuracy - 0.720000007748604\n",
      "Epoch: 0245 , Time:  00:02:19:31\n",
      "Cost - 6.973742392446523e- --> Accuracy - 0.720000007748604\n",
      "Epoch: 0246 , Time:  00:02:20:03\n",
      "Cost - 6.586312508716217e- --> Accuracy - 0.7300000071525575\n",
      "Epoch: 0247 , Time:  00:02:20:36\n",
      "Cost - 6.228684612530343e- --> Accuracy - 0.7300000086426737\n",
      "Epoch: 0248 , Time:  00:02:21:08\n",
      "Cost - 6.616114731627928e- --> Accuracy - 0.7300000086426737\n",
      "Epoch: 0249 , Time:  00:02:21:41\n",
      "Cost - 5.990266194189073e- --> Accuracy - 0.7300000086426737\n",
      "Epoch: 0250 , Time:  00:02:22:13\n",
      "Cost - 5.632638293562306e- --> Accuracy - 0.7200000092387201\n",
      "Epoch: 0251 , Time:  00:02:22:46\n",
      "Cost - 5.6922427882355424e- --> Accuracy - 0.7100000083446504\n",
      "Epoch: 0252 , Time:  00:02:23:19\n",
      "Cost - 5.5134290288805216e- --> Accuracy - 0.7200000092387201\n",
      "Epoch: 0253 , Time:  00:02:23:51\n",
      "Cost - 5.453824378776062e- --> Accuracy - 0.7200000092387201\n",
      "Epoch: 0254 , Time:  00:02:24:23\n",
      "Cost - 4.976987315608027e- --> Accuracy - 0.7200000092387201\n",
      "Epoch: 0255 , Time:  00:02:24:56\n",
      "Cost - 5.275010455108029e- --> Accuracy - 0.7100000083446504\n",
      "Epoch: 0256 , Time:  00:02:25:28\n",
      "Cost - 4.947184946146876e- --> Accuracy - 0.7100000083446504\n",
      "Epoch: 0257 , Time:  00:02:26:01\n",
      "Cost - 5.0365919879169476e- --> Accuracy - 0.7200000092387201\n",
      "Epoch: 0258 , Time:  00:02:26:33\n",
      "Cost - 4.738568781803563e- --> Accuracy - 0.7200000092387201\n",
      "Epoch: 0259 , Time:  00:02:27:05\n",
      "Cost - 4.8279757747238224e- --> Accuracy - 0.7200000092387201\n",
      "Epoch: 0260 , Time:  00:02:27:38\n",
      "Cost - 4.7087665011602545e- --> Accuracy - 0.7100000083446504\n",
      "Epoch: 0261 , Time:  00:02:28:10\n",
      "Cost - 4.202127037444825e- --> Accuracy - 0.7100000083446504\n",
      "Epoch: 0262 , Time:  00:02:28:42\n",
      "Cost - 4.380940916703934e- --> Accuracy - 0.7200000092387201\n",
      "Epoch: 0263 , Time:  00:02:29:15\n",
      "Cost - 4.321336350976425e- --> Accuracy - 0.7200000092387201\n",
      "Epoch: 0264 , Time:  00:02:29:47\n",
      "Cost - 4.261731687549285e- --> Accuracy - 0.720000007748604\n",
      "Epoch: 0265 , Time:  00:02:30:20\n",
      "Cost - 4.142522436190177e- --> Accuracy - 0.720000007748604\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0266 , Time:  00:02:30:52\n",
      "Cost - 4.202127086294637e- --> Accuracy - 0.720000007748604\n",
      "Epoch: 0267 , Time:  00:02:31:24\n",
      "Cost - 4.05311551432419e- --> Accuracy - 0.7300000086426737\n",
      "Epoch: 0268 , Time:  00:02:31:57\n",
      "Cost - 3.904103857976793e- --> Accuracy - 0.7500000081956388\n",
      "Epoch: 0269 , Time:  00:02:32:29\n",
      "Cost - 4.142522480599096e- --> Accuracy - 0.720000007748604\n",
      "Epoch: 0270 , Time:  00:02:33:01\n",
      "Cost - 3.5762783889836666e- --> Accuracy - 0.7300000071525575\n",
      "Epoch: 0271 , Time:  00:02:33:33\n",
      "Cost - 3.963708525844821e- --> Accuracy - 0.7500000074505808\n",
      "Epoch: 0272 , Time:  00:02:34:07\n",
      "Cost - 3.6954876625472365e- --> Accuracy - 0.7500000074505808\n",
      "Epoch: 0273 , Time:  00:02:34:40\n",
      "Cost - 1.54610279727722 --> Accuracy - 0.5000000044703483\n",
      "Epoch: 0274 , Time:  00:02:35:13\n",
      "Cost - 0.64192570829764 --> Accuracy - 0.6800000071525575\n",
      "Epoch: 0275 , Time:  00:02:35:45\n",
      "Cost - 0.337871836288832 --> Accuracy - 0.67000000923872\n",
      "Epoch: 0276 , Time:  00:02:36:18\n",
      "Cost - 0.223869414528598 --> Accuracy - 0.720000007748604\n",
      "Epoch: 0277 , Time:  00:02:36:50\n",
      "Cost - 0.105357054431806 --> Accuracy - 0.7500000059604647\n",
      "Epoch: 0278 , Time:  00:02:37:23\n",
      "Cost - 0.101395746661000 --> Accuracy - 0.670000007748604\n",
      "Epoch: 0279 , Time:  00:02:37:55\n",
      "Cost - 0.097423122823238 --> Accuracy - 0.6600000075995923\n",
      "Epoch: 0280 , Time:  00:02:38:27\n",
      "Cost - 0.0175846356709371 --> Accuracy - 0.7500000074505807\n",
      "Epoch: 0281 , Time:  00:02:39:00\n",
      "Cost - 0.0068702906937687 --> Accuracy - 0.7500000081956388\n",
      "Epoch: 0282 , Time:  00:02:39:32\n",
      "Cost - 0.0049237527149671 --> Accuracy - 0.7500000081956388\n",
      "Epoch: 0283 , Time:  00:02:40:04\n",
      "Cost - 0.0038570959288335 --> Accuracy - 0.7500000081956388\n",
      "Epoch: 0284 , Time:  00:02:40:37\n",
      "Cost - 0.00322620818114955 --> Accuracy - 0.7500000081956388\n",
      "Epoch: 0285 , Time:  00:02:41:09\n",
      "Cost - 0.0027521143034391 --> Accuracy - 0.7400000080466271\n",
      "Epoch: 0286 , Time:  00:02:41:42\n",
      "Cost - 0.00236792324503767 --> Accuracy - 0.7600000075995924\n",
      "Epoch: 0287 , Time:  00:02:42:14\n",
      "Cost - 0.00207227941064047 --> Accuracy - 0.7500000074505807\n",
      "Epoch: 0288 , Time:  00:02:42:47\n",
      "Cost - 0.00179332585976226 --> Accuracy - 0.7500000074505807\n",
      "Epoch: 0289 , Time:  00:02:43:19\n",
      "Cost - 0.00156610106605512 --> Accuracy - 0.7600000068545343\n",
      "Epoch: 0290 , Time:  00:02:43:51\n",
      "Cost - 0.0013590703958470 --> Accuracy - 0.7600000068545343\n",
      "Epoch: 0291 , Time:  00:02:44:24\n",
      "Cost - 0.0011929140950087 --> Accuracy - 0.7600000068545343\n",
      "Epoch: 0292 , Time:  00:02:44:56\n",
      "Cost - 0.00105460159065842 --> Accuracy - 0.7600000068545343\n",
      "Epoch: 0293 , Time:  00:02:45:28\n",
      "Cost - 0.00094321383649003 --> Accuracy - 0.7600000068545343\n",
      "Epoch: 0294 , Time:  00:02:46:00\n",
      "Cost - 0.00085334415089164 --> Accuracy - 0.7600000068545343\n",
      "Epoch: 0295 , Time:  00:02:46:33\n",
      "Cost - 0.0007634006487933 --> Accuracy - 0.7600000068545343\n",
      "Epoch: 0296 , Time:  00:02:47:05\n",
      "Cost - 0.00068431545096245 --> Accuracy - 0.7700000062584879\n",
      "Epoch: 0297 , Time:  00:02:47:38\n",
      "Cost - 0.00061747455492877 --> Accuracy - 0.7800000056624414\n",
      "Epoch: 0298 , Time:  00:02:48:11\n",
      "Cost - 0.00055588677605555 --> Accuracy - 0.7800000056624414\n",
      "Epoch: 0299 , Time:  00:02:48:43\n",
      "Cost - 0.00050199371426060 --> Accuracy - 0.7800000056624414\n",
      "Epoch: 0300 , Time:  00:02:49:16\n",
      "Cost - 0.00045564728879980 --> Accuracy - 0.7800000056624414\n",
      "Optimization Finished!\n",
      "Time taken: day, hour, minutes, seconds-> 00:02:49:16\n"
     ]
    }
   ],
   "source": [
    "# Start Training\n",
    "\n",
    "start_time = time.time()\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    # Initialize variables\n",
    "    sess.run(init)\n",
    "    \n",
    "    # op to write logs to Tensorboard\n",
    "    summary_writer = tf.summary.FileWriter(logs_path, graph=tf.get_default_graph())\n",
    "    \n",
    "    # Training cycle\n",
    "    for epoch in range(training_epochs):\n",
    "        avg_cost_list = 0.0\n",
    "        total_batch = int(len(video_frames_5d_train)/batch)\n",
    "        \n",
    "        # Shuffle data\n",
    "        shuffle_indices = np.arange(len(video_frames_5d_train))\n",
    "        np.random.shuffle(shuffle_indices)\n",
    "        \n",
    "        i = 0\n",
    "        # Loop over all batches\n",
    "        for start, end in zip(range(0, len(video_frames_5d_train), batch), range(batch, len(video_frames_5d_train)+1, batch)):\n",
    "            video_input_data, label_data = video_frames_5d_train[shuffle_indices][start:end], category_label_train_one_hot[shuffle_indices][start:end]\n",
    "\n",
    "            audio_input_data = audio_frames_2d_train[shuffle_indices][start:end]\n",
    "            audio_X = tf.get_default_graph().get_tensor_by_name(\"Model/audio_InputData:0\")\n",
    "\n",
    "            haptic_input_data = haptic_frames_2d_train[shuffle_indices][start:end]\n",
    "            haptic_X = tf.get_default_graph().get_tensor_by_name(\"Model/haptic_InputData:0\")\n",
    "\n",
    "            _, new_cost, cost_scalar = sess.run([model_dict[\"Optimizer\"], model_dict[\"Loss\"], model_dict[\"Loss_scalar\"]], feed_dict={video_X: video_input_data, audio_X: audio_input_data, haptic_X: haptic_input_data, Y: label_data, audio_keep_prob: 0.5, haptic_keep_prob: 0.5})\n",
    "            # Compute average loss\n",
    "            avg_cost_list += new_cost/total_batch\n",
    "\n",
    "            summary_writer.add_summary(cost_scalar, epoch * total_batch + i)\n",
    "            i += 1\n",
    "        save_path = saver.save(sess, model_path, epoch)\n",
    "         \n",
    "        # Calculate Accuracy\n",
    "        avg_accuracy_list = 0.0\n",
    "        total_batch = int(len(video_frames_5d_test)/batch)\n",
    "        i = 0\n",
    "        for start, end in zip(range(0, len(video_frames_5d_test), batch), range(batch, len(video_frames_5d_test)+1, batch)):\n",
    "            video_input_data, label_data = video_frames_5d_test[start:end], category_label_test_one_hot[start:end]\n",
    "\n",
    "            audio_input_data = audio_frames_2d_test[start:end]\n",
    "            audio_X = tf.get_default_graph().get_tensor_by_name(\"Model/audio_InputData:0\")\n",
    "\n",
    "            haptic_input_data = haptic_frames_2d_test[start:end]\n",
    "            haptic_X = tf.get_default_graph().get_tensor_by_name(\"Model/haptic_InputData:0\")\n",
    "\n",
    "            accuracy, accuracy_scalar = sess.run([model_dict[\"Accuracy\"], model_dict[\"Accuracy_scalar\"]], feed_dict={video_X: video_input_data, audio_X: audio_input_data, haptic_X: haptic_input_data, Y: label_data, audio_keep_prob: 1.0, haptic_keep_prob: 1.0})\n",
    "            # Compute average accuracy\n",
    "            avg_accuracy_list += accuracy/total_batch\n",
    "            summary_writer.add_summary(accuracy_scalar, epoch * total_batch + i)\n",
    "            i += 1\n",
    "        \n",
    "        # Printing current epoch accuracy\n",
    "        epoch_cost_accuracy = []\n",
    "        epoch_cost_accuracy.append(time_taken(start_time, time.time()))\n",
    "        # Display logs per epoch step\n",
    "        if epoch % display_step == 0:\n",
    "            print(\"Epoch:\", '%04d' % (epoch+1), \", Time: \", time_taken(start_time, time.time()))\n",
    "            a_string = \"Cost - \"\n",
    "            epoch_cost_accuracy.append(epoch+1)\n",
    "            \n",
    "            a_string += str(avg_cost_list)\n",
    "            epoch_cost_accuracy.append(str(avg_cost_list))\n",
    "            \n",
    "            a_string = a_string[0:-2]+\" --> Accuracy - \"\n",
    "            a_string += str(avg_accuracy_list)\n",
    "            epoch_cost_accuracy.append(str(avg_accuracy_list))\n",
    "            \n",
    "            print(a_string)\n",
    "        \n",
    "        # Writing current epoch data\n",
    "        with open(logs_path+folder_name+\"_data.csv\", 'a') as f: # append to the file created\n",
    "            writer = csv.writer(f, lineterminator=\"\\n\")\n",
    "            writer.writerow(epoch_cost_accuracy)\n",
    "    \n",
    "    print(\"Optimization Finished!\")\n",
    "    end_time = time.time()\n",
    "    print(\"Time taken: day, hour, minutes, seconds->\", time_taken(start_time, end_time))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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