fall_detection_watch.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import Statements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-25T21:12:17.282862Z",
     "start_time": "2018-11-25T21:11:50.016938Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import scipy\n",
    "import random\n",
    "from six.moves import range\n",
    "# random.seed(5001)\n",
    "\n",
    "from sklearn.metrics import f1_score, confusion_matrix, classification_report\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.pyplot import imshow\n",
    "import pandas as pd\n",
    "from scipy.signal import decimate\n",
    "from scipy.stats import mode\n",
    "import pywt\n",
    "import seaborn as sn\n",
    "from collections import Counter\n",
    "from keras.utils import to_categorical"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "4ae02363-68a4-4fc7-88a8-0760a60b77e7"
    }
   },
   "source": [
    "# Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-25T22:05:26.597934Z",
     "start_time": "2018-11-25T22:05:26.553043Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(46752, 4)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0    29191\n",
       "1    17561\n",
       "Name: outcome, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pa = pd.read_csv('Raw_Data_90ADL.csv')\n",
    "print (pa.shape)\n",
    "pa['outcome'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-25T22:05:40.326232Z",
     "start_time": "2018-11-25T22:05:40.308655Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(46752, 4)\n"
     ]
    }
   ],
   "source": [
    "pa = pa.dropna()\n",
    "print (pa.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-22T17:43:01.230802Z",
     "start_time": "2018-11-22T17:43:00.824385Z"
    },
    "nbpresent": {
     "id": "345786db-e817-4950-94bb-2ca3a05a1758"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['gear_1' 'gear_2' 'lgwatch_1' 'lgwatch_2']\n",
      "['stand' 'sit' 'walk' 'stairsup' 'stairsdown' 'bike']\n",
      "['a' 'b' 'c' 'd' 'e' 'f' 'g' 'h' 'i']\n"
     ]
    }
   ],
   "source": [
    "acts = pa['gt'].unique()\n",
    "users = pa['User'].unique()\n",
    "devices = pa['Device'].unique()\n",
    "print (devices)\n",
    "print (acts)\n",
    "print (users)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-22T17:43:05.481725Z",
     "start_time": "2018-11-22T17:43:05.476026Z"
    },
    "nbpresent": {
     "id": "50079432-e9f9-4762-b957-b4d881f55fbc"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'stand': 0, 'sit': 1, 'walk': 2, 'stairsup': 3, 'stairsdown': 4, 'bike': 5}\n",
      "{'gear_1': 0, 'gear_2': 1, 'lgwatch_1': 2, 'lgwatch_2': 3}\n",
      "{'a': 0, 'b': 1, 'c': 2, 'd': 3, 'e': 4, 'f': 5, 'g': 6, 'h': 7, 'i': 8}\n"
     ]
    }
   ],
   "source": [
    "l = {}\n",
    "for i, act in enumerate(acts):\n",
    "    l[act] = i\n",
    "print (l)\n",
    "\n",
    "dev = {}\n",
    "for i, d in enumerate(devices):\n",
    "    dev[d] = i\n",
    "print (dev)\n",
    "\n",
    "use = {}\n",
    "for i, u in enumerate(users):\n",
    "    use[u] = i\n",
    "print (use)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> \n",
    "Train users: a, b, c, d, e, f, g, h\n",
    "\n",
    "Test user: i\n",
    "</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-22T17:43:06.365566Z",
     "start_time": "2018-11-22T17:43:06.358509Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['b', 'c', 'd', 'e', 'f', 'g', 'h', 'i'], ['c', 'd', 'e', 'f', 'g', 'h', 'i', 'a'], ['d', 'e', 'f', 'g', 'h', 'i', 'a', 'b'], ['e', 'f', 'g', 'h', 'i', 'a', 'b', 'c'], ['f', 'g', 'h', 'i', 'a', 'b', 'c', 'd'], ['g', 'h', 'i', 'a', 'b', 'c', 'd', 'e'], ['h', 'i', 'a', 'b', 'c', 'd', 'e', 'f'], ['i', 'a', 'b', 'c', 'd', 'e', 'f', 'g'], ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h']] ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i']\n"
     ]
    }
   ],
   "source": [
    "userTrain, userTest = [], []\n",
    "\n",
    "for i in range(len(users)):\n",
    "    train = []\n",
    "    \n",
    "    if i != 8:\n",
    "        train.extend(users[i+1:])\n",
    "    if i != 0:\n",
    "        train.extend(users[:i])\n",
    "    #print (users[i])\n",
    "    userTrain.append(train)\n",
    "    userTest.append(users[i])\n",
    "    \n",
    "print (userTrain, userTest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-22T17:43:12.556568Z",
     "start_time": "2018-11-22T17:43:07.317206Z"
    },
    "nbpresent": {
     "id": "3807f2a7-eddd-4a14-bd7a-fafe02779c30"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20\n",
      "d done\n",
      "44\n",
      "e done\n",
      "65\n",
      "f done\n",
      "83\n",
      "g done\n",
      "98\n",
      "h done\n",
      "110\n",
      "i done\n",
      "134\n",
      "a done\n",
      "152\n",
      "b done\n",
      "Done\n",
      "(152,) (152,) (152,)\n",
      "24\n",
      "c done\n",
      "Done\n",
      "(24,) (24,) (24,)\n"
     ]
    }
   ],
   "source": [
    "def getData(userList):\n",
    "\n",
    "    devs, acc, labels = [], [], []\n",
    "\n",
    "    for user in userList:\n",
    "        pa_user = pa[pa['User'] == user]\n",
    "        for act in acts:\n",
    "            pa_act = pa_user[pa_user['gt'] == act]\n",
    "            for device in devices:\n",
    "                pa_dev = pa_act[pa_act['Device'] == device]\n",
    "                pa_dev = pa_dev[['x', 'y','z']]\n",
    "                if str(device) == 'nexus4_1' or str(device) == 'nexus4_2':\n",
    "                    min_win = 400\n",
    "                elif str(device) == 's3_1' or str(device) == 's3_2':\n",
    "                    min_win = 300\n",
    "                elif str(device) == 's3mini_1' or str(device) == 's3mini_2':\n",
    "                    min_win = 200\n",
    "                else:\n",
    "                    min_win = 100\n",
    "                if(pa_dev.shape[0] >= min_win):\n",
    "                    acc.append(pa_dev.values)\n",
    "                    devs.append(device)\n",
    "                    labels.append(l[act])\n",
    "        print (len(labels))\n",
    "        print (f'{user} done')\n",
    "    \n",
    "    acc = np.array(acc)\n",
    "    labels = np.array(labels)\n",
    "    devs = np.array(devs)\n",
    "    print (\"Done\")\n",
    "    print(acc.shape, labels.shape, devs.shape)\n",
    "    return acc, labels, devs\n",
    "\n",
    "Xtrain1, Ytrain1, devs1 = getData(userTrain[2])\n",
    "Xtest1, Ytest1, devs2 = getData(userTest[2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "439cbde1-13d9-41b1-ab67-887ba5a49ee5"
    }
   },
   "source": [
    "# Getting the Windowed Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-22T17:43:16.626197Z",
     "start_time": "2018-11-22T17:43:16.621571Z"
    },
    "nbpresent": {
     "id": "52c75edd-6a0f-4fa5-ad0b-c29b539fbe6d"
    }
   },
   "outputs": [],
   "source": [
    "def getWindowedData(acc, labels, index, w_min):\n",
    "    \n",
    "    windowData, windowLabels = [], []\n",
    "    num_windows = acc[index].shape[0] // w_min\n",
    "    if num_windows == 0:\n",
    "        print(acc[index].shape[0], w_min)\n",
    "    k = 0\n",
    "    for _ in range(num_windows):\n",
    "        windowData.append(acc[index][k:k+w_min])\n",
    "        k += w_min\n",
    "        windowLabels.append(labels[index])\n",
    "    return windowData, windowLabels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-22T17:43:16.813418Z",
     "start_time": "2018-11-22T17:43:16.773320Z"
    },
    "nbpresent": {
     "id": "6c89d9d8-dae6-4898-ad32-40d0b7917053"
    }
   },
   "outputs": [],
   "source": [
    "# Getting 2 seconds (100 samples) of data for all devices\n",
    "\n",
    "main_train = []\n",
    "for i in range(len(Xtrain1)):\n",
    "    if str(devs1[i]) == 'nexus4_1' or str(devs1[i]) == 'nexus4_2':\n",
    "        w_min = 400\n",
    "    elif str(devs1[i]) == 's3_1' or str(devs1[i]) == 's3_2':\n",
    "        w_min = 300\n",
    "    elif str(devs1[i]) == 's3mini_1' or str(devs1[i]) == 's3mini_1':\n",
    "        w_min = 200\n",
    "    else:\n",
    "        w_min = 100\n",
    "    main_train.append((getWindowedData(Xtrain1, Ytrain1, i, w_min)))\n",
    "\n",
    "\n",
    "main_test = []\n",
    "for i in range(len(Xtest1)):\n",
    "    if str(devs2[i]) == 'nexus4_1' or str(devs2[i]) == 'nexus4_2':\n",
    "        w_min = 400\n",
    "    elif str(devs2[i]) == 's3_1' or str(devs2[i]) == 's3_2':\n",
    "        w_min = 300\n",
    "    elif str(devs2[i]) == 's3mini_1' or str(devs2[i]) == 's3mini_1':\n",
    "        w_min = 200\n",
    "    else:\n",
    "        w_min = 100\n",
    "    main_test.append((getWindowedData(Xtest1, Ytest1, i, w_min)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "b300d560-c6df-49a3-8801-3b85c227f5ad"
    }
   },
   "source": [
    "# Decimating the Windowed Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-22T17:43:17.927408Z",
     "start_time": "2018-11-22T17:43:17.914883Z"
    }
   },
   "outputs": [],
   "source": [
    "def decimateThatSignal(main, i):\n",
    "        final_decimated_signals = []\n",
    "        label = []\n",
    "        if main[i][0][0].shape[0] == 400:\n",
    "            for j in range(len(main[i][0])):\n",
    "                decimated_signal_0 = decimate(main[i][0][j][:,0], 4)\n",
    "                decimated_signal_1 = decimate(main[i][0][j][:,1], 4)\n",
    "                decimated_signal_2 = decimate(main[i][0][j][:,2], 4)\n",
    "                decimated_signal = np.dstack((decimated_signal_0, decimated_signal_1, decimated_signal_2))\n",
    "                final_decimated_signals.append(decimated_signal)\n",
    "                label.append(main[i][1][j])\n",
    "            return np.array(final_decimated_signals), np.array(label)\n",
    "        elif main[i][0][0].shape[0] == 300:\n",
    "            for j in range(len(main[i][0])):\n",
    "                decimated_signal_0 = decimate(main[i][0][j][:,0], 3)\n",
    "                decimated_signal_1 = decimate(main[i][0][j][:,1], 3)\n",
    "                decimated_signal_2 = decimate(main[i][0][j][:,2], 3)\n",
    "                decimated_signal = np.dstack((decimated_signal_0, decimated_signal_1, decimated_signal_2))\n",
    "                final_decimated_signals.append(decimated_signal)\n",
    "                label.append(main[i][1][j])\n",
    "            return np.array(final_decimated_signals), np.array(label)\n",
    "        elif main[i][0][0].shape[0] == 200:\n",
    "            for j in range(len(main[i][0])):\n",
    "                decimated_signal_0 = decimate(main[i][0][j][:,0], 2)\n",
    "                decimated_signal_1 = decimate(main[i][0][j][:,1], 2)\n",
    "                decimated_signal_2 = decimate(main[i][0][j][:,2], 2)\n",
    "                decimated_signal = np.dstack((decimated_signal_0, decimated_signal_1, decimated_signal_2))\n",
    "                final_decimated_signals.append(decimated_signal)\n",
    "                label.append(main[i][1][j])\n",
    "            return np.array(final_decimated_signals), np.array(label)\n",
    "        else:\n",
    "            return np.array(main[i][0]), np.array(main[i][1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-22T17:43:23.113291Z",
     "start_time": "2018-11-22T17:43:21.555949Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    }
   ],
   "source": [
    "Xtrain1 = decimateThatSignal(main_train, 0)[0].reshape((-1, 100, 3))\n",
    "Ytrain1 = decimateThatSignal(main_train, 0)[1]\n",
    "for i in range(1, len(main_train)):\n",
    "    print (i)\n",
    "    Xtrain1, Ytrain1 = np.vstack((Xtrain1, decimateThatSignal(main_train, i)[0].reshape((-1, 100, 3)))), np.hstack((Ytrain1, decimateThatSignal(main_train, i)[1]))\n",
    "    #mainDecimated.append(DecimateThatSignal(i).reshape((-1, 100, 3)))\n",
    "Xtrain1 = np.array(Xtrain1)\n",
    "Ytrain1 = np.array(Ytrain1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-22T17:43:23.197441Z",
     "start_time": "2018-11-22T17:43:23.115642Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "Xtest1 = decimateThatSignal(main_test, 0)[0].reshape((-1, 100, 3))\n",
    "Ytest1 = decimateThatSignal(main_test, 0)[1]\n",
    "for i in range(1, len(main_test)):\n",
    "    print (i)\n",
    "    Xtest1, Ytest1 = np.vstack((Xtest1, decimateThatSignal(main_test, i)[0].reshape((-1, 100, 3)))), np.hstack((Ytest1, decimateThatSignal(main_test, i)[1]))\n",
    "    #mainDecimated.append(DecimateThatSignal(i).reshape((-1, 100, 3)))\n",
    "Xtest1 = np.array(Xtest1)\n",
    "Ytest1 = np.array(Ytest1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-22T17:43:23.254004Z",
     "start_time": "2018-11-22T17:43:23.201165Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(25650, 100, 3) (25650,)\n",
      "(4469, 100, 3) (4469,)\n"
     ]
    }
   ],
   "source": [
    "print (Xtrain1.shape, Ytrain1.shape)\n",
    "print (Xtest1.shape, Ytest1.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "c922ca6f-84d0-4669-89dc-189928d8827a"
    }
   },
   "source": [
    "# Getting the DWTed Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-22T17:43:23.805205Z",
     "start_time": "2018-11-22T17:43:23.788154Z"
    },
    "nbpresent": {
     "id": "733c86a8-d7f0-4775-a97c-d6ed1f90a25a"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['bior1.1',\n",
       " 'bior1.3',\n",
       " 'bior1.5',\n",
       " 'bior2.2',\n",
       " 'bior2.4',\n",
       " 'bior2.6',\n",
       " 'bior2.8',\n",
       " 'bior3.1',\n",
       " 'bior3.3',\n",
       " 'bior3.5',\n",
       " 'bior3.7',\n",
       " 'bior3.9',\n",
       " 'bior4.4',\n",
       " 'bior5.5',\n",
       " 'bior6.8',\n",
       " 'cgau1',\n",
       " 'cgau2',\n",
       " 'cgau3',\n",
       " 'cgau4',\n",
       " 'cgau5',\n",
       " 'cgau6',\n",
       " 'cgau7',\n",
       " 'cgau8',\n",
       " 'cmor',\n",
       " 'coif1',\n",
       " 'coif2',\n",
       " 'coif3',\n",
       " 'coif4',\n",
       " 'coif5',\n",
       " 'coif6',\n",
       " 'coif7',\n",
       " 'coif8',\n",
       " 'coif9',\n",
       " 'coif10',\n",
       " 'coif11',\n",
       " 'coif12',\n",
       " 'coif13',\n",
       " 'coif14',\n",
       " 'coif15',\n",
       " 'coif16',\n",
       " 'coif17',\n",
       " 'db1',\n",
       " 'db2',\n",
       " 'db3',\n",
       " 'db4',\n",
       " 'db5',\n",
       " 'db6',\n",
       " 'db7',\n",
       " 'db8',\n",
       " 'db9',\n",
       " 'db10',\n",
       " 'db11',\n",
       " 'db12',\n",
       " 'db13',\n",
       " 'db14',\n",
       " 'db15',\n",
       " 'db16',\n",
       " 'db17',\n",
       " 'db18',\n",
       " 'db19',\n",
       " 'db20',\n",
       " 'db21',\n",
       " 'db22',\n",
       " 'db23',\n",
       " 'db24',\n",
       " 'db25',\n",
       " 'db26',\n",
       " 'db27',\n",
       " 'db28',\n",
       " 'db29',\n",
       " 'db30',\n",
       " 'db31',\n",
       " 'db32',\n",
       " 'db33',\n",
       " 'db34',\n",
       " 'db35',\n",
       " 'db36',\n",
       " 'db37',\n",
       " 'db38',\n",
       " 'dmey',\n",
       " 'fbsp',\n",
       " 'gaus1',\n",
       " 'gaus2',\n",
       " 'gaus3',\n",
       " 'gaus4',\n",
       " 'gaus5',\n",
       " 'gaus6',\n",
       " 'gaus7',\n",
       " 'gaus8',\n",
       " 'haar',\n",
       " 'mexh',\n",
       " 'morl',\n",
       " 'rbio1.1',\n",
       " 'rbio1.3',\n",
       " 'rbio1.5',\n",
       " 'rbio2.2',\n",
       " 'rbio2.4',\n",
       " 'rbio2.6',\n",
       " 'rbio2.8',\n",
       " 'rbio3.1',\n",
       " 'rbio3.3',\n",
       " 'rbio3.5',\n",
       " 'rbio3.7',\n",
       " 'rbio3.9',\n",
       " 'rbio4.4',\n",
       " 'rbio5.5',\n",
       " 'rbio6.8',\n",
       " 'shan',\n",
       " 'sym2',\n",
       " 'sym3',\n",
       " 'sym4',\n",
       " 'sym5',\n",
       " 'sym6',\n",
       " 'sym7',\n",
       " 'sym8',\n",
       " 'sym9',\n",
       " 'sym10',\n",
       " 'sym11',\n",
       " 'sym12',\n",
       " 'sym13',\n",
       " 'sym14',\n",
       " 'sym15',\n",
       " 'sym16',\n",
       " 'sym17',\n",
       " 'sym18',\n",
       " 'sym19',\n",
       " 'sym20']"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pywt.wavelist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-22T17:43:24.642508Z",
     "start_time": "2018-11-22T17:43:24.639612Z"
    },
    "nbpresent": {
     "id": "594027c7-3e29-4d49-bb5d-0e495b66f643"
    }
   },
   "outputs": [],
   "source": [
    "# Change window size here as and when DWT wavelet changes\n",
    "\n",
    "w_min = 50+3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-22T17:43:25.269150Z",
     "start_time": "2018-11-22T17:43:25.263699Z"
    }
   },
   "outputs": [],
   "source": [
    "def performDWT(x, Y):\n",
    "    masterX = []\n",
    "    for i in range(len(x)):\n",
    "        Xca, Xda = pywt.dwt(x[i].reshape((-1, 3))[:,0], 'db4', mode='periodic')\n",
    "        Yca, Yda = pywt.dwt(x[i].reshape((-1, 3))[:,1], 'db4', mode='periodic')\n",
    "        Zca, Zda = pywt.dwt(x[i].reshape((-1, 3))[:,2], 'db4', mode='periodic')\n",
    "        coef = np.hstack((Xca, Yca, Zca)).reshape((-1, w_min, 3))\n",
    "        masterX.append((coef, Y[i]))\n",
    "        print (i)\n",
    "    masterX = np.array(masterX)\n",
    "    return masterX"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-22T17:43:34.042522Z",
     "start_time": "2018-11-22T17:43:26.305157Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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    }
   ],
   "source": [
    "masterTrain = performDWT(Xtrain1, Ytrain1)\n",
    "masterTest = performDWT(Xtest1, Ytest1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-22T17:43:34.052316Z",
     "start_time": "2018-11-22T17:43:34.048067Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(25650, 2) (4469, 2)\n"
     ]
    }
   ],
   "source": [
    "print (masterTrain.shape, masterTest.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train-Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-22T17:43:39.844976Z",
     "start_time": "2018-11-22T17:43:39.746468Z"
    }
   },
   "outputs": [],
   "source": [
    "#for training\n",
    "Y_train = []\n",
    "X_train = np.zeros((masterTrain.shape[0], 1, w_min, 3))\n",
    "for i in range(masterTrain.shape[0]):\n",
    "    X_train[i, :, :] = masterTrain[i][0][:]\n",
    "    Y_train.append(masterTrain[i][1])\n",
    "y_train = np.array(Y_train)   \n",
    "\n",
    "\n",
    "#for test\n",
    "Y_test = []\n",
    "X_test = np.zeros((masterTest.shape[0], 1, w_min, 3))\n",
    "for i in range(masterTest.shape[0]):\n",
    "    X_test[i, :, :] = masterTest[i][0][:]\n",
    "    Y_test.append(masterTest[i][1])\n",
    "y_test = np.array(Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-22T17:43:40.087648Z",
     "start_time": "2018-11-22T17:43:40.081760Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(25650,) (4469,)\n"
     ]
    }
   ],
   "source": [
    "print (y_train.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-22T17:43:40.735696Z",
     "start_time": "2018-11-22T17:43:40.723166Z"
    }
   },
   "outputs": [],
   "source": [
    "X_train = X_train.reshape((-1, 53, 3)).astype('float32')\n",
    "X_test = X_test.reshape((-1, 53, 3)).astype('float32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-22T17:43:41.331584Z",
     "start_time": "2018-11-22T17:43:41.325902Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(25650, 53, 3) (4469, 53, 3) (25650,) (4469,)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Params Initialize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-22T17:43:42.334690Z",
     "start_time": "2018-11-22T17:43:42.328822Z"
    }
   },
   "outputs": [],
   "source": [
    "batch_size = 128\n",
    "num_classes = 6\n",
    "img_rows, img_cols = 53, 3\n",
    "learning_rate = 2e-4\n",
    "# subset_size = 2000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train-Valid-Pool Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-22T17:43:44.521278Z",
     "start_time": "2018-11-22T17:43:44.513136Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([740, 663, 717, 762, 737, 850])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.bincount(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-22T17:43:54.257904Z",
     "start_time": "2018-11-22T17:43:54.167793Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pool: (3129, 53, 3) (3129,)\n",
      "Test: (1340, 53, 3) (1340,)\n"
     ]
    }
   ],
   "source": [
    "X_test, X_pool, y_test, y_pool = train_test_split(X_test, y_test, test_size=0.7, stratify=y_test)\n",
    "\n",
    "print ('Pool:', X_pool.shape, y_pool.shape)\n",
    "print ('Test:', X_test.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Zero-Mean Normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-22T17:44:26.450280Z",
     "start_time": "2018-11-22T17:44:26.029362Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(25650, 53, 3) (3129, 53, 3) (1340, 53, 3)\n",
      "(25650,) (3129,) (1340,)\n"
     ]
    }
   ],
   "source": [
    "# X_train_All, X_test, y_train_All, y_test = train_test_split(data, labels, stratify=labels, test_size=0.2, random_state=5233)\n",
    "\n",
    "#X_train, X_test, y_train_All, y_test = train_test_split(data, labels, stratify=labels, test_size=0.2, random_state=5233)\n",
    "\n",
    "X_train_fit0 = StandardScaler().fit(X_train[:, :, 0])\n",
    "X_train0 = X_train_fit0.transform(X_train[:, :, 0])\n",
    "\n",
    "X_train_fit1 = StandardScaler().fit(X_train[:, :, 1])\n",
    "X_train1 = X_train_fit1.transform(X_train[:, :, 1])\n",
    "\n",
    "X_train_fit2 = StandardScaler().fit(X_train[:, :, 2])\n",
    "X_train2 = X_train_fit2.transform(X_train[:, :, 2])\n",
    "\n",
    "X_pool0 = X_train_fit0.transform(X_pool[:, :, 0])\n",
    "\n",
    "X_pool1 = X_train_fit1.transform(X_pool[:, :, 1])\n",
    "\n",
    "X_pool2 = X_train_fit2.transform(X_pool[:, :, 2])\n",
    "\n",
    "X_test0 = X_train_fit0.transform(X_test[:, :, 0])\n",
    "\n",
    "X_test1 = X_train_fit1.transform(X_test[:, :, 1])\n",
    "\n",
    "X_test2 = X_train_fit2.transform(X_test[:, :, 2])\n",
    "\n",
    "# print (X_train0.shape, X_test0.shape)\n",
    "\n",
    "X_train = np.dstack((X_train0, X_train1, X_train2))\n",
    "X_pool = np.dstack((X_pool0, X_pool1, X_pool2))\n",
    "X_test = np.dstack((X_test0, X_test1, X_test2))\n",
    "\n",
    "del X_train0, X_train1, X_train2, X_pool0, X_pool1, X_pool2, X_test0, X_test1, X_test2\n",
    "\n",
    "print (X_train.shape, X_pool.shape, X_test.shape)\n",
    "print (y_train.shape, y_pool.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-22T17:45:02.887500Z",
     "start_time": "2018-11-22T17:45:02.876315Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Distribution of Training Classes: [3760 3562 4764 3961 4110 5493]\n"
     ]
    }
   ],
   "source": [
    "print ('Distribution of Training Classes:', np.bincount(y_train))\n",
    "\n",
    "Y_train = to_categorical(y_train, num_classes)\n",
    "Y_test = to_categorical(y_test, num_classes)\n",
    "Y_pool = to_categorical(y_pool, num_classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-22T17:45:03.480047Z",
     "start_time": "2018-11-22T17:45:03.471797Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Distribution of Train Classes: [3760 3562 4764 3961 4110 5493]\n",
      "Distribution of Pool Classes: [518 464 502 534 516 595]\n",
      "Distribution of Test Classes: [222 199 215 228 221 255]\n"
     ]
    }
   ],
   "source": [
    "print ('Distribution of Train Classes:', np.bincount(y_train))\n",
    "print ('Distribution of Pool Classes:', np.bincount(y_pool))\n",
    "print ('Distribution of Test Classes:', np.bincount(y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-22T17:45:03.937393Z",
     "start_time": "2018-11-22T17:45:03.932762Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train: (25650, 53, 3) (25650, 6)\n",
      "Pool: (3129, 53, 3) (3129, 6)\n",
      "Test: (1340, 53, 3) (1340, 6)\n"
     ]
    }
   ],
   "source": [
    "print ('Train:', X_train.shape, Y_train.shape)\n",
    "print ('Pool:', X_pool.shape, Y_pool.shape)\n",
    "print ('Test:', X_test.shape, Y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-21T06:14:24.186633Z",
     "start_time": "2018-11-21T06:14:24.183033Z"
    }
   },
   "source": [
    "# Keras Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-22T18:00:52.806972Z",
     "start_time": "2018-11-22T18:00:52.795177Z"
    }
   },
   "outputs": [],
   "source": [
    "import keras\n",
    "from keras.models import Sequential, Model, load_model\n",
    "from keras.layers import Dense, Dropout, Input, Flatten, Reshape, concatenate, Lambda\n",
    "from keras.layers import Conv1D, Conv2D, MaxPooling1D, MaxPooling2D, BatchNormalization\n",
    "from keras.utils import to_categorical\n",
    "from keras import backend as K\n",
    "from keras.optimizers import Adam\n",
    "from keras.callbacks import ModelCheckpoint, EarlyStopping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-22T17:45:05.691105Z",
     "start_time": "2018-11-22T17:45:05.683920Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(25650, 53, 1) (3129, 53, 1) (1340, 53, 1)\n"
     ]
    }
   ],
   "source": [
    "X_train0 = X_train[:, :, 0].reshape((-1, w_min, 1))\n",
    "X_train1 = X_train[:, :, 1].reshape((-1, w_min, 1))\n",
    "X_train2 = X_train[:, :, 2].reshape((-1, w_min, 1))\n",
    "\n",
    "X_pool0 = X_pool[:, :, 0].reshape((-1, w_min, 1))\n",
    "X_pool1 = X_pool[:, :, 1].reshape((-1, w_min, 1))\n",
    "X_pool2 = X_pool[:, :, 2].reshape((-1, w_min, 1))\n",
    "\n",
    "X_test0 = X_test[:, :, 0].reshape((-1, w_min, 1))\n",
    "X_test1 = X_test[:, :, 1].reshape((-1, w_min, 1))\n",
    "X_test2 = X_test[:, :, 2].reshape((-1, w_min, 1))\n",
    "\n",
    "print (X_train0.shape, X_pool0.shape, X_test0.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-22T17:45:06.937989Z",
     "start_time": "2018-11-22T17:45:06.924729Z"
    }
   },
   "outputs": [],
   "source": [
    "def harnet():\n",
    "\n",
    "    # Model X\n",
    "    inputX = Input(shape=(X_train0.shape[1], X_train0.shape[2]))\n",
    "\n",
    "    convX1 = Conv1D(filters=8, kernel_size=2, padding='same', activation='relu')(inputX)\n",
    "    batchX1 = BatchNormalization()(convX1)\n",
    "    poolX1 = MaxPooling1D(pool_size=2, padding='same')(batchX1)\n",
    "\n",
    "    convX2 = Conv1D(filters=16, kernel_size=2, padding='same', activation='relu')(poolX1)\n",
    "    batchX2 = BatchNormalization()(convX2)\n",
    "    poolX2 = MaxPooling1D(pool_size=2, padding='same')(batchX2)\n",
    "\n",
    "    modelX = Flatten()(poolX2)\n",
    "\n",
    "    # Model Y\n",
    "    inputY = Input(shape=(X_train1.shape[1], X_train2.shape[2]))\n",
    "\n",
    "    convY1 = Conv1D(filters=8, kernel_size=5, padding='same', activation='relu')(inputY)\n",
    "    batchY1 = BatchNormalization()(convY1)\n",
    "    poolY1 = MaxPooling1D(pool_size=2, padding='same')(batchY1)\n",
    "\n",
    "    convY2 = Conv1D(filters=16, kernel_size=5, padding='same', activation='relu')(poolY1)\n",
    "    batchY2 = BatchNormalization()(convY2)\n",
    "    poolY2 = MaxPooling1D(pool_size=2, padding='same')(batchY2)\n",
    "\n",
    "    modelY = Flatten()(poolY2)\n",
    "\n",
    "    # Model Z\n",
    "    inputZ = Input(shape=(X_train2.shape[1], X_train2.shape[2]))\n",
    "\n",
    "    convZ1 = Conv1D(filters=8, kernel_size=5, padding='same', activation='relu')(inputZ)\n",
    "    batchZ1 = BatchNormalization()(convZ1)\n",
    "    poolZ1 = MaxPooling1D(pool_size=2, padding='same')(batchZ1)\n",
    "\n",
    "    convZ2 = Conv1D(filters=16, kernel_size=5, padding='same', activation='relu')(poolZ1)\n",
    "    batchZ2 = BatchNormalization()(convZ2)\n",
    "    poolZ2 = MaxPooling1D(pool_size=2, padding='same')(batchZ2)\n",
    "\n",
    "    # Merge Models X, Y, Z\n",
    "    modelZ = Flatten()(poolZ2)\n",
    "\n",
    "    merged_model = concatenate([modelX, modelY, modelZ])\n",
    "    print (K.int_shape(merged_model))\n",
    "\n",
    "    final_merge = Reshape((K.int_shape(merged_model)[1]//3, 3, 1))(merged_model)\n",
    "    print (K.int_shape(final_merge))\n",
    "\n",
    "    # Conv2D\n",
    "    conv1 = Conv2D(filters=8, kernel_size=(3, 3), padding='same')(final_merge)\n",
    "    batch1 = BatchNormalization()(conv1)\n",
    "    pool1 = MaxPooling2D(pool_size=(2, 2), padding='same')(batch1)\n",
    "\n",
    "    conv2 = Conv2D(filters=16, kernel_size=(3, 3), padding='same')(pool1)\n",
    "    batch2 = BatchNormalization()(conv2)\n",
    "    pool2 = MaxPooling2D(pool_size=(2, 2), padding='same')(batch2)\n",
    "\n",
    "    #drop2d = Lambda(lambda x: K.dropout(x, level=0.25))(pool2)\n",
    "\n",
    "    flatten = Flatten()(pool2)\n",
    "\n",
    "    # Dense\n",
    "    fc1 = Dense(16, activation='relu', kernel_initializer='glorot_normal')(flatten)\n",
    "    # fc1 = Dropout(0.25)(fc1)\n",
    "    fc1 = Lambda(lambda x: K.dropout(x, level=0.25))(fc1)\n",
    "    fc2 = Dense(8, activation='relu')(flatten)\n",
    "\n",
    "    # Output Layer - Softmax\n",
    "    output = Dense(num_classes, activation='softmax')(fc2)\n",
    "    \n",
    "    # Final Model\n",
    "    model = Model([inputX, inputY, inputZ], output)\n",
    "    \n",
    "    model.compile(loss='categorical_crossentropy', optimizer=Adam(learning_rate), metrics=['accuracy']) #beta_1=0.9, beta_2=0.999))\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-22T17:45:22.876839Z",
     "start_time": "2018-11-22T17:45:07.687672Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(None, 672)\n",
      "(None, 224, 3, 1)\n"
     ]
    }
   ],
   "source": [
    "model = harnet()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-22T17:45:22.897895Z",
     "start_time": "2018-11-22T17:45:22.879690Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            (None, 53, 1)        0                                            \n",
      "__________________________________________________________________________________________________\n",
      "input_2 (InputLayer)            (None, 53, 1)        0                                            \n",
      "__________________________________________________________________________________________________\n",
      "input_3 (InputLayer)            (None, 53, 1)        0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_1 (Conv1D)               (None, 53, 8)        24          input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_3 (Conv1D)               (None, 53, 8)        48          input_2[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_5 (Conv1D)               (None, 53, 8)        48          input_3[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_1 (BatchNor (None, 53, 8)        32          conv1d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_3 (BatchNor (None, 53, 8)        32          conv1d_3[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_5 (BatchNor (None, 53, 8)        32          conv1d_5[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_1 (MaxPooling1D)  (None, 27, 8)        0           batch_normalization_1[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_3 (MaxPooling1D)  (None, 27, 8)        0           batch_normalization_3[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_5 (MaxPooling1D)  (None, 27, 8)        0           batch_normalization_5[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_2 (Conv1D)               (None, 27, 16)       272         max_pooling1d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_4 (Conv1D)               (None, 27, 16)       656         max_pooling1d_3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_6 (Conv1D)               (None, 27, 16)       656         max_pooling1d_5[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_2 (BatchNor (None, 27, 16)       64          conv1d_2[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_4 (BatchNor (None, 27, 16)       64          conv1d_4[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_6 (BatchNor (None, 27, 16)       64          conv1d_6[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_2 (MaxPooling1D)  (None, 14, 16)       0           batch_normalization_2[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_4 (MaxPooling1D)  (None, 14, 16)       0           batch_normalization_4[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_6 (MaxPooling1D)  (None, 14, 16)       0           batch_normalization_6[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "flatten_1 (Flatten)             (None, 224)          0           max_pooling1d_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "flatten_2 (Flatten)             (None, 224)          0           max_pooling1d_4[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "flatten_3 (Flatten)             (None, 224)          0           max_pooling1d_6[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_1 (Concatenate)     (None, 672)          0           flatten_1[0][0]                  \n",
      "                                                                 flatten_2[0][0]                  \n",
      "                                                                 flatten_3[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "reshape_1 (Reshape)             (None, 224, 3, 1)    0           concatenate_1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_1 (Conv2D)               (None, 224, 3, 8)    80          reshape_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_7 (BatchNor (None, 224, 3, 8)    32          conv2d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2D)  (None, 112, 2, 8)    0           batch_normalization_7[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_2 (Conv2D)               (None, 112, 2, 16)   1168        max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_8 (BatchNor (None, 112, 2, 16)   64          conv2d_2[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2D)  (None, 56, 1, 16)    0           batch_normalization_8[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "flatten_4 (Flatten)             (None, 896)          0           max_pooling2d_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "dense_2 (Dense)                 (None, 8)            7176        flatten_4[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "dense_3 (Dense)                 (None, 6)            54          dense_2[0][0]                    \n",
      "==================================================================================================\n",
      "Total params: 10,566\n",
      "Trainable params: 10,374\n",
      "Non-trainable params: 192\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-22T17:52:30.427742Z",
     "start_time": "2018-11-22T17:45:50.968277Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 25650 samples, validate on 1340 samples\n",
      "Epoch 1/50\n",
      "25650/25650 [==============================] - 20s 784us/step - loss: 1.3887 - acc: 0.4399 - val_loss: 1.2912 - val_acc: 0.4373\n",
      "Epoch 2/50\n",
      "25650/25650 [==============================] - 7s 288us/step - loss: 0.9525 - acc: 0.6552 - val_loss: 1.2295 - val_acc: 0.4799\n",
      "Epoch 3/50\n",
      "25650/25650 [==============================] - 7s 283us/step - loss: 0.7550 - acc: 0.7312 - val_loss: 1.1989 - val_acc: 0.5157\n",
      "Epoch 4/50\n",
      "25650/25650 [==============================] - 7s 284us/step - loss: 0.6780 - acc: 0.7523 - val_loss: 1.2824 - val_acc: 0.5075\n",
      "Epoch 5/50\n",
      "25650/25650 [==============================] - 7s 288us/step - loss: 0.6325 - acc: 0.7683 - val_loss: 1.2757 - val_acc: 0.5149\n",
      "Epoch 6/50\n",
      "25650/25650 [==============================] - 7s 287us/step - loss: 0.6008 - acc: 0.7809 - val_loss: 1.2616 - val_acc: 0.5075\n",
      "Epoch 7/50\n",
      "25650/25650 [==============================] - 8s 296us/step - loss: 0.5789 - acc: 0.7867 - val_loss: 1.3388 - val_acc: 0.5037\n",
      "Epoch 8/50\n",
      "25650/25650 [==============================] - 8s 297us/step - loss: 0.5609 - acc: 0.7963 - val_loss: 1.3357 - val_acc: 0.5090\n",
      "Epoch 9/50\n",
      "25650/25650 [==============================] - 7s 288us/step - loss: 0.5482 - acc: 0.8008 - val_loss: 1.2956 - val_acc: 0.5261\n",
      "Epoch 10/50\n",
      "25650/25650 [==============================] - 7s 291us/step - loss: 0.5332 - acc: 0.8032 - val_loss: 1.2964 - val_acc: 0.5209\n",
      "Epoch 11/50\n",
      "25650/25650 [==============================] - 8s 296us/step - loss: 0.5243 - acc: 0.8062 - val_loss: 1.2905 - val_acc: 0.5284\n",
      "Epoch 12/50\n",
      "25650/25650 [==============================] - 8s 302us/step - loss: 0.5136 - acc: 0.8100 - val_loss: 1.2887 - val_acc: 0.5194\n",
      "Epoch 13/50\n",
      "25650/25650 [==============================] - 8s 302us/step - loss: 0.5034 - acc: 0.8131 - val_loss: 1.3060 - val_acc: 0.5313\n",
      "Epoch 14/50\n",
      "25650/25650 [==============================] - 7s 289us/step - loss: 0.4954 - acc: 0.8154 - val_loss: 1.3813 - val_acc: 0.5104\n",
      "Epoch 15/50\n",
      "25650/25650 [==============================] - 8s 308us/step - loss: 0.4885 - acc: 0.8175 - val_loss: 1.3578 - val_acc: 0.5239\n",
      "Epoch 16/50\n",
      "25650/25650 [==============================] - 8s 309us/step - loss: 0.4828 - acc: 0.8212 - val_loss: 1.3176 - val_acc: 0.5082\n",
      "Epoch 17/50\n",
      "25650/25650 [==============================] - 7s 288us/step - loss: 0.4759 - acc: 0.8232 - val_loss: 1.3791 - val_acc: 0.5179\n",
      "Epoch 18/50\n",
      "25650/25650 [==============================] - 8s 296us/step - loss: 0.4702 - acc: 0.8252 - val_loss: 1.5129 - val_acc: 0.4866\n",
      "Epoch 19/50\n",
      "25650/25650 [==============================] - 8s 310us/step - loss: 0.4626 - acc: 0.8275 - val_loss: 1.5428 - val_acc: 0.4985\n",
      "Epoch 20/50\n",
      "25650/25650 [==============================] - 8s 298us/step - loss: 0.4547 - acc: 0.8320 - val_loss: 1.3836 - val_acc: 0.4828\n",
      "Epoch 21/50\n",
      "25650/25650 [==============================] - 7s 281us/step - loss: 0.4527 - acc: 0.8317 - val_loss: 1.4036 - val_acc: 0.4918\n",
      "Epoch 22/50\n",
      "25650/25650 [==============================] - 8s 299us/step - loss: 0.4441 - acc: 0.8339 - val_loss: 1.4429 - val_acc: 0.4910\n",
      "Epoch 23/50\n",
      "25650/25650 [==============================] - 7s 292us/step - loss: 0.4414 - acc: 0.8370 - val_loss: 1.3666 - val_acc: 0.4776\n",
      "Epoch 24/50\n",
      "25650/25650 [==============================] - 8s 307us/step - loss: 0.4358 - acc: 0.8387 - val_loss: 1.5079 - val_acc: 0.4948\n",
      "Epoch 25/50\n",
      "25650/25650 [==============================] - 7s 288us/step - loss: 0.4326 - acc: 0.8403 - val_loss: 1.4371 - val_acc: 0.4739\n",
      "Epoch 26/50\n",
      "25650/25650 [==============================] - 7s 290us/step - loss: 0.4297 - acc: 0.8417 - val_loss: 1.4312 - val_acc: 0.4881\n",
      "Epoch 27/50\n",
      "25650/25650 [==============================] - 8s 293us/step - loss: 0.4239 - acc: 0.8428 - val_loss: 1.5525 - val_acc: 0.4776\n",
      "Epoch 28/50\n",
      "25650/25650 [==============================] - 8s 295us/step - loss: 0.4205 - acc: 0.8460 - val_loss: 1.5174 - val_acc: 0.4694\n",
      "Epoch 29/50\n",
      "25650/25650 [==============================] - 8s 295us/step - loss: 0.4160 - acc: 0.8472 - val_loss: 1.5129 - val_acc: 0.5007\n",
      "Epoch 30/50\n",
      "25650/25650 [==============================] - 8s 295us/step - loss: 0.4121 - acc: 0.8477 - val_loss: 1.4694 - val_acc: 0.4716\n",
      "Epoch 31/50\n",
      "25650/25650 [==============================] - 7s 291us/step - loss: 0.4056 - acc: 0.8500 - val_loss: 1.5415 - val_acc: 0.4672\n",
      "Epoch 32/50\n",
      "25650/25650 [==============================] - 8s 313us/step - loss: 0.4029 - acc: 0.8508 - val_loss: 1.5159 - val_acc: 0.4888\n",
      "Epoch 33/50\n",
      "25650/25650 [==============================] - 8s 307us/step - loss: 0.4012 - acc: 0.8507 - val_loss: 1.5703 - val_acc: 0.4657\n",
      "Epoch 34/50\n",
      "25650/25650 [==============================] - 8s 327us/step - loss: 0.3980 - acc: 0.8531 - val_loss: 1.6157 - val_acc: 0.4769\n",
      "Epoch 35/50\n",
      "25650/25650 [==============================] - 10s 383us/step - loss: 0.3935 - acc: 0.8544 - val_loss: 1.5870 - val_acc: 0.4978\n",
      "Epoch 36/50\n",
      "25650/25650 [==============================] - 10s 378us/step - loss: 0.3900 - acc: 0.8561 - val_loss: 1.7228 - val_acc: 0.4642\n",
      "Epoch 37/50\n",
      "25650/25650 [==============================] - 9s 353us/step - loss: 0.3879 - acc: 0.8575 - val_loss: 1.6819 - val_acc: 0.4784\n",
      "Epoch 38/50\n",
      "25650/25650 [==============================] - 8s 312us/step - loss: 0.3852 - acc: 0.8581 - val_loss: 1.6413 - val_acc: 0.4679\n",
      "Epoch 39/50\n",
      "25650/25650 [==============================] - 7s 270us/step - loss: 0.3824 - acc: 0.8585 - val_loss: 1.6031 - val_acc: 0.4612\n",
      "Epoch 40/50\n",
      "25650/25650 [==============================] - 7s 272us/step - loss: 0.3779 - acc: 0.8601 - val_loss: 1.7032 - val_acc: 0.4657\n",
      "Epoch 41/50\n",
      "25650/25650 [==============================] - 7s 259us/step - loss: 0.3749 - acc: 0.8613 - val_loss: 1.5263 - val_acc: 0.4866\n",
      "Epoch 42/50\n",
      "25650/25650 [==============================] - 7s 266us/step - loss: 0.3756 - acc: 0.8614 - val_loss: 1.6102 - val_acc: 0.4575\n",
      "Epoch 43/50\n",
      "25650/25650 [==============================] - 8s 306us/step - loss: 0.3740 - acc: 0.8616 - val_loss: 1.6702 - val_acc: 0.4746\n",
      "Epoch 44/50\n",
      "25650/25650 [==============================] - 6s 253us/step - loss: 0.3677 - acc: 0.8629 - val_loss: 1.6121 - val_acc: 0.4724\n",
      "Epoch 45/50\n",
      "25650/25650 [==============================] - 6s 252us/step - loss: 0.3662 - acc: 0.8653 - val_loss: 1.6322 - val_acc: 0.4881\n",
      "Epoch 46/50\n",
      "25650/25650 [==============================] - 7s 283us/step - loss: 0.3642 - acc: 0.8653 - val_loss: 1.6640 - val_acc: 0.4672\n",
      "Epoch 47/50\n",
      "25650/25650 [==============================] - 8s 306us/step - loss: 0.3611 - acc: 0.8663 - val_loss: 1.6124 - val_acc: 0.4560\n",
      "Epoch 48/50\n",
      "25650/25650 [==============================] - 8s 314us/step - loss: 0.3603 - acc: 0.8657 - val_loss: 1.6548 - val_acc: 0.4522\n",
      "Epoch 49/50\n",
      "25650/25650 [==============================] - 9s 338us/step - loss: 0.3557 - acc: 0.8679 - val_loss: 1.6866 - val_acc: 0.4687\n",
      "Epoch 50/50\n",
      "25650/25650 [==============================] - 8s 301us/step - loss: 0.3544 - acc: 0.8688 - val_loss: 1.7499 - val_acc: 0.4619\n"
     ]
    }
   ],
   "source": [
    "# save_path = './Save_Files/Watch_User_C.h5'\n",
    "# checkpoint = ModelCheckpoint(save_path, monitor='val_acc', save_best_only=True, mode='max')\n",
    "# callback = [checkpoint]\n",
    "\n",
    "# hist = model.fit([X_train0, X_train1, X_train2], Y_train, epochs=50, batch_size=128, \\\n",
    "#                  validation_data=([X_test0, X_test1, X_test2], Y_test), callbacks=callback)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-22T17:58:30.616729Z",
     "start_time": "2018-11-22T17:58:25.191812Z"
    }
   },
   "outputs": [],
   "source": [
    "model = load_model('./Save_Files/Watch_User_C.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-22T18:01:38.103454Z",
     "start_time": "2018-11-22T18:01:38.054354Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# np.save('./Save_Files/hist_Watch_User_C', hist.history)\n",
    "histC = np.load('./Save_Files/hist_Watch_User_C.npy').tolist()\n",
    "len(histC['val_loss'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-22T18:01:40.088675Z",
     "start_time": "2018-11-22T18:01:39.772274Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5313432841158625\n",
      "0.5313432841158625\n",
      "1340/1340 [==============================] - 0s 227us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[1.3059604285368278, 0.5313432832262409]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print (max(hist.history['val_acc']))\n",
    "print (max(histC['val_acc']))\n",
    "model.evaluate([X_test0, X_test1, X_test2], Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-22T18:01:47.177052Z",
     "start_time": "2018-11-22T18:01:46.929409Z"
    }
   },
   "outputs": [],
   "source": [
    "y_prob = model.predict([X_test0, X_test1, X_test2])\n",
    "y_pred = y_prob.argmax(axis=-1)\n",
    "\n",
    "y_true = np.array([np.argmax(y) for y in Y_test])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-22T18:01:47.185145Z",
     "start_time": "2018-11-22T18:01:47.180701Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1340,) (1340,)\n"
     ]
    }
   ],
   "source": [
    "print (y_true.shape, y_pred.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preprocess for Active Learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-22T18:01:47.916777Z",
     "start_time": "2018-11-22T18:01:47.905210Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3129, 53, 3) (3129, 6)\n"
     ]
    }
   ],
   "source": [
    "subset_size = X_pool.shape[0]\n",
    "\n",
    "subset_indices = np.asarray(random.sample(range(0, X_pool.shape[0]), subset_size))\n",
    "X_pool_subset = X_pool[subset_indices]\n",
    "Y_pool_subset = Y_pool[subset_indices]\n",
    "# X_pool_new = np.delete(X_pool, subset_indices, axis=0)\n",
    "# Y_pool_new = np.delete(Y_pool, subset_indices, axis=0)\n",
    "\n",
    "print (X_pool_subset.shape, Y_pool_subset.shape)\n",
    "# print (X_pool_new.shape, Y_pool_new.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-22T18:01:50.391308Z",
     "start_time": "2018-11-22T18:01:50.386151Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3129, 53, 1) (3129, 53, 1) (3129, 53, 1)\n"
     ]
    }
   ],
   "source": [
    "X_pool_subset0 = X_pool_subset[:, :, 0].reshape((-1, w_min, 1))\n",
    "X_pool_subset1 = X_pool_subset[:, :, 1].reshape((-1, w_min, 1))\n",
    "X_pool_subset2 = X_pool_subset[:, :, 2].reshape((-1, w_min, 1))\n",
    "\n",
    "print (X_pool_subset0.shape, X_pool_subset1.shape, X_pool_subset2.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from acquisition_functions import bald, varratio, maxentropy, random_acq"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# BALD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-22T18:06:26.451685Z",
     "start_time": "2018-11-22T18:06:11.291986Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3129, 53, 1)\n",
      "3129/3129 [==============================] - 1s 401us/step\n",
      "3129/3129 [==============================] - 1s 187us/step\n",
      "3129/3129 [==============================] - 1s 185us/step\n",
      "3129/3129 [==============================] - 1s 182us/step\n",
      "3129/3129 [==============================] - 1s 185us/step\n",
      "3129/3129 [==============================] - 1s 180us/step\n",
      "3129/3129 [==============================] - 1s 181us/step\n",
      "3129/3129 [==============================] - 1s 178us/step\n",
      "3129/3129 [==============================] - 1s 187us/step\n",
      "3129/3129 [==============================] - 1s 192us/step\n"
     ]
    }
   ],
   "source": [
    "n_queries = 500\n",
    "model = load_model('./Save_Files/Watch_User_C.h5')\n",
    "\n",
    "bald_uncertainty_estimates = bald([X_pool_subset0, X_pool_subset1, X_pool_subset2], \\\n",
    "                                  num_classes, model, 32, 10)\n",
    "\n",
    "bald_uncertainty_estimates = bald_uncertainty_estimates.flatten()\n",
    "pool_to_be_revealed = bald_uncertainty_estimates.argsort()[-n_queries:][::-1]\n",
    "\n",
    "X_acquired_bald = X_pool[pool_to_be_revealed]\n",
    "Y_acquired_bald = Y_pool[pool_to_be_revealed]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-22T18:07:14.478626Z",
     "start_time": "2018-11-22T18:07:14.471067Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(500, 53, 3) (500, 6)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([ 75,  75,  75, 101,  77,  97])"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print (X_acquired_bald.shape, Y_acquired_bald.shape)\n",
    "\n",
    "y_acquired_bald = Y_acquired_bald.argmax(axis=-1)\n",
    "np.bincount(y_acquired_bald)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-22T18:07:17.987984Z",
     "start_time": "2018-11-22T18:07:17.983454Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(500, 53, 1) (500, 53, 1) (500, 53, 1)\n"
     ]
    }
   ],
   "source": [
    "X_acquired0 = X_acquired_bald[:, :, 0].reshape((-1, w_min, 1))\n",
    "X_acquired1 = X_acquired_bald[:, :, 1].reshape((-1, w_min, 1))\n",
    "X_acquired2 = X_acquired_bald[:, :, 2].reshape((-1, w_min, 1))\n",
    "\n",
    "print (X_acquired0.shape, X_acquired1.shape, X_acquired2.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-22T18:08:29.179331Z",
     "start_time": "2018-11-22T18:07:19.856997Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 500 samples, validate on 1340 samples\n",
      "Epoch 1/100\n",
      "500/500 [==============================] - 4s 8ms/step - loss: 0.9994 - acc: 0.6060 - val_loss: 0.7975 - val_acc: 0.6784\n",
      "Epoch 2/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.7296 - acc: 0.6600 - val_loss: 0.7182 - val_acc: 0.7104\n",
      "Epoch 3/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.6392 - acc: 0.7200 - val_loss: 0.6776 - val_acc: 0.7388\n",
      "Epoch 4/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.5672 - acc: 0.7640 - val_loss: 0.6809 - val_acc: 0.7306\n",
      "Epoch 5/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.5618 - acc: 0.7600 - val_loss: 0.6659 - val_acc: 0.7455\n",
      "Epoch 6/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.5311 - acc: 0.7780 - val_loss: 0.6502 - val_acc: 0.7358\n",
      "Epoch 7/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.5033 - acc: 0.7980 - val_loss: 0.6480 - val_acc: 0.7448\n",
      "Epoch 8/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.4665 - acc: 0.8320 - val_loss: 0.6403 - val_acc: 0.7530\n",
      "Epoch 9/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.4474 - acc: 0.8320 - val_loss: 0.6467 - val_acc: 0.7530\n",
      "Epoch 10/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.4265 - acc: 0.8520 - val_loss: 0.6412 - val_acc: 0.7604\n",
      "Epoch 11/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.4267 - acc: 0.8400 - val_loss: 0.6441 - val_acc: 0.7507\n",
      "Epoch 12/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.4220 - acc: 0.8560 - val_loss: 0.6295 - val_acc: 0.7642\n",
      "Epoch 13/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.3948 - acc: 0.8620 - val_loss: 0.6366 - val_acc: 0.7672\n",
      "Epoch 14/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.3801 - acc: 0.8740 - val_loss: 0.6459 - val_acc: 0.7672\n",
      "Epoch 15/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.3694 - acc: 0.8740 - val_loss: 0.6245 - val_acc: 0.7664\n",
      "Epoch 16/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.3673 - acc: 0.8700 - val_loss: 0.6368 - val_acc: 0.7709\n",
      "Epoch 17/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.3474 - acc: 0.8800 - val_loss: 0.6506 - val_acc: 0.7590\n",
      "Epoch 18/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.3330 - acc: 0.8960 - val_loss: 0.6366 - val_acc: 0.7701\n",
      "Epoch 19/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.3192 - acc: 0.9100 - val_loss: 0.6400 - val_acc: 0.7754\n",
      "Epoch 20/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.3116 - acc: 0.9100 - val_loss: 0.6485 - val_acc: 0.7642\n",
      "Epoch 21/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.2948 - acc: 0.9180 - val_loss: 0.6469 - val_acc: 0.7739\n",
      "Epoch 22/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.2970 - acc: 0.9160 - val_loss: 0.6365 - val_acc: 0.7694\n",
      "Epoch 23/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.2764 - acc: 0.9340 - val_loss: 0.6400 - val_acc: 0.7799\n",
      "Epoch 24/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.2731 - acc: 0.9420 - val_loss: 0.6459 - val_acc: 0.7746\n",
      "Epoch 25/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.2591 - acc: 0.9420 - val_loss: 0.6427 - val_acc: 0.7828\n",
      "Epoch 26/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.2535 - acc: 0.9440 - val_loss: 0.6492 - val_acc: 0.7739\n",
      "Epoch 27/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.2573 - acc: 0.9380 - val_loss: 0.6530 - val_acc: 0.7724\n",
      "Epoch 28/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.2445 - acc: 0.9360 - val_loss: 0.6453 - val_acc: 0.7739\n",
      "Epoch 29/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.2439 - acc: 0.9500 - val_loss: 0.6505 - val_acc: 0.7791\n",
      "Epoch 30/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.2272 - acc: 0.9500 - val_loss: 0.6481 - val_acc: 0.7799\n",
      "Epoch 31/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.2339 - acc: 0.9380 - val_loss: 0.6573 - val_acc: 0.7664\n",
      "Epoch 32/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.2283 - acc: 0.9500 - val_loss: 0.6566 - val_acc: 0.7776\n",
      "Epoch 33/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.2048 - acc: 0.9560 - val_loss: 0.6528 - val_acc: 0.7791\n",
      "Epoch 34/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.2143 - acc: 0.9600 - val_loss: 0.6684 - val_acc: 0.7724\n",
      "Epoch 35/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.1997 - acc: 0.9660 - val_loss: 0.6552 - val_acc: 0.7784\n",
      "Epoch 36/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.1989 - acc: 0.9580 - val_loss: 0.6675 - val_acc: 0.7791\n",
      "Epoch 37/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.1930 - acc: 0.9700 - val_loss: 0.6539 - val_acc: 0.7769\n",
      "Epoch 38/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.1859 - acc: 0.9680 - val_loss: 0.6699 - val_acc: 0.7754\n",
      "Epoch 39/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.1897 - acc: 0.9620 - val_loss: 0.6584 - val_acc: 0.7836\n",
      "Epoch 40/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.1795 - acc: 0.9700 - val_loss: 0.6592 - val_acc: 0.7791\n",
      "Epoch 41/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.1740 - acc: 0.9800 - val_loss: 0.6662 - val_acc: 0.7806\n",
      "Epoch 42/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.1677 - acc: 0.9820 - val_loss: 0.6593 - val_acc: 0.7806\n",
      "Epoch 43/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.1630 - acc: 0.9780 - val_loss: 0.6761 - val_acc: 0.7761\n",
      "Epoch 44/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.1671 - acc: 0.9780 - val_loss: 0.6690 - val_acc: 0.7769\n",
      "Epoch 45/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.1556 - acc: 0.9820 - val_loss: 0.6751 - val_acc: 0.7776\n",
      "Epoch 46/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.1488 - acc: 0.9820 - val_loss: 0.6723 - val_acc: 0.7806\n",
      "Epoch 47/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.1523 - acc: 0.9860 - val_loss: 0.6848 - val_acc: 0.7776\n",
      "Epoch 48/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.1440 - acc: 0.9800 - val_loss: 0.6770 - val_acc: 0.7791\n",
      "Epoch 49/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.1408 - acc: 0.9840 - val_loss: 0.6791 - val_acc: 0.7806\n",
      "Epoch 50/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.1388 - acc: 0.9800 - val_loss: 0.6810 - val_acc: 0.7784\n",
      "Epoch 51/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.1369 - acc: 0.9840 - val_loss: 0.6828 - val_acc: 0.7769\n",
      "Epoch 52/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.1344 - acc: 0.9920 - val_loss: 0.6918 - val_acc: 0.7784\n",
      "Epoch 53/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.1346 - acc: 0.9920 - val_loss: 0.6865 - val_acc: 0.7799\n",
      "Epoch 54/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.1291 - acc: 0.9820 - val_loss: 0.6815 - val_acc: 0.7813\n",
      "Epoch 55/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.1229 - acc: 0.9960 - val_loss: 0.6882 - val_acc: 0.7769\n",
      "Epoch 56/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.1346 - acc: 0.9860 - val_loss: 0.6859 - val_acc: 0.7776\n",
      "Epoch 57/100\n",
      "500/500 [==============================] - 1s 2ms/step - loss: 0.1242 - acc: 0.9900 - val_loss: 0.7058 - val_acc: 0.7769\n",
      "Epoch 58/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.1073 - acc: 0.9980 - val_loss: 0.6995 - val_acc: 0.7784\n",
      "Epoch 59/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.1068 - acc: 0.9980 - val_loss: 0.7028 - val_acc: 0.7791\n",
      "Epoch 60/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.1028 - acc: 0.9960 - val_loss: 0.6989 - val_acc: 0.7769\n",
      "Epoch 61/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.1058 - acc: 0.9960 - val_loss: 0.7011 - val_acc: 0.7821\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 62/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.1041 - acc: 0.9940 - val_loss: 0.7036 - val_acc: 0.7776\n",
      "Epoch 63/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0973 - acc: 0.9960 - val_loss: 0.7103 - val_acc: 0.7776\n",
      "Epoch 64/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0989 - acc: 0.9940 - val_loss: 0.7174 - val_acc: 0.7784\n",
      "Epoch 65/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0936 - acc: 0.9980 - val_loss: 0.7139 - val_acc: 0.7813\n",
      "Epoch 66/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0944 - acc: 0.9980 - val_loss: 0.7120 - val_acc: 0.7799\n",
      "Epoch 67/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0944 - acc: 1.0000 - val_loss: 0.7107 - val_acc: 0.7731\n",
      "Epoch 68/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0952 - acc: 0.9960 - val_loss: 0.7212 - val_acc: 0.7776\n",
      "Epoch 69/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0873 - acc: 1.0000 - val_loss: 0.7137 - val_acc: 0.7776\n",
      "Epoch 70/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0850 - acc: 1.0000 - val_loss: 0.7230 - val_acc: 0.7731\n",
      "Epoch 71/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0810 - acc: 1.0000 - val_loss: 0.7271 - val_acc: 0.7821\n",
      "Epoch 72/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0836 - acc: 0.9960 - val_loss: 0.7286 - val_acc: 0.7806\n",
      "Epoch 73/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0859 - acc: 0.9980 - val_loss: 0.7295 - val_acc: 0.7761\n",
      "Epoch 74/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0796 - acc: 0.9960 - val_loss: 0.7291 - val_acc: 0.7769\n",
      "Epoch 75/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0820 - acc: 0.9980 - val_loss: 0.7386 - val_acc: 0.7799\n",
      "Epoch 76/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0803 - acc: 0.9980 - val_loss: 0.7346 - val_acc: 0.7754\n",
      "Epoch 77/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0738 - acc: 1.0000 - val_loss: 0.7438 - val_acc: 0.7776\n",
      "Epoch 78/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0767 - acc: 0.9960 - val_loss: 0.7406 - val_acc: 0.7791\n",
      "Epoch 79/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0754 - acc: 1.0000 - val_loss: 0.7392 - val_acc: 0.7761\n",
      "Epoch 80/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0724 - acc: 0.9980 - val_loss: 0.7449 - val_acc: 0.7731\n",
      "Epoch 81/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0688 - acc: 1.0000 - val_loss: 0.7515 - val_acc: 0.7746\n",
      "Epoch 82/100\n",
      "500/500 [==============================] - 1s 2ms/step - loss: 0.0647 - acc: 1.0000 - val_loss: 0.7539 - val_acc: 0.7701\n",
      "Epoch 83/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0651 - acc: 1.0000 - val_loss: 0.7528 - val_acc: 0.7791\n",
      "Epoch 84/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0684 - acc: 0.9960 - val_loss: 0.7615 - val_acc: 0.7769\n",
      "Epoch 85/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0692 - acc: 1.0000 - val_loss: 0.7571 - val_acc: 0.7769\n",
      "Epoch 86/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0592 - acc: 1.0000 - val_loss: 0.7655 - val_acc: 0.7754\n",
      "Epoch 87/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0568 - acc: 0.9980 - val_loss: 0.7630 - val_acc: 0.7731\n",
      "Epoch 88/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0623 - acc: 0.9980 - val_loss: 0.7723 - val_acc: 0.7776\n",
      "Epoch 89/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0591 - acc: 1.0000 - val_loss: 0.7687 - val_acc: 0.7754\n",
      "Epoch 90/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0523 - acc: 0.9980 - val_loss: 0.7747 - val_acc: 0.7769\n",
      "Epoch 91/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0569 - acc: 1.0000 - val_loss: 0.7680 - val_acc: 0.7761\n",
      "Epoch 92/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0554 - acc: 1.0000 - val_loss: 0.7768 - val_acc: 0.7776\n",
      "Epoch 93/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0534 - acc: 1.0000 - val_loss: 0.7798 - val_acc: 0.7754\n",
      "Epoch 94/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0510 - acc: 1.0000 - val_loss: 0.7812 - val_acc: 0.7769\n",
      "Epoch 95/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0574 - acc: 0.9980 - val_loss: 0.7878 - val_acc: 0.7769\n",
      "Epoch 96/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0511 - acc: 1.0000 - val_loss: 0.7878 - val_acc: 0.7791\n",
      "Epoch 97/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0505 - acc: 1.0000 - val_loss: 0.7891 - val_acc: 0.7769\n",
      "Epoch 98/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0470 - acc: 1.0000 - val_loss: 0.7925 - val_acc: 0.7806\n",
      "Epoch 99/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0463 - acc: 1.0000 - val_loss: 0.7867 - val_acc: 0.7791\n",
      "Epoch 100/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0473 - acc: 1.0000 - val_loss: 0.7913 - val_acc: 0.7776\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f201e7c6fd0>"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit([X_acquired0, X_acquired1, X_acquired2], Y_acquired_bald, epochs=100, batch_size=32, \\\n",
    "          validation_data=([X_test0, X_test1, X_test2], Y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Var Ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-22T18:25:24.324757Z",
     "start_time": "2018-11-22T18:25:06.767242Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3129/3129 [==============================] - 2s 544us/step\n",
      "3129/3129 [==============================] - 1s 230us/step\n",
      "3129/3129 [==============================] - 1s 230us/step\n",
      "3129/3129 [==============================] - 1s 222us/step\n",
      "3129/3129 [==============================] - 1s 239us/step\n",
      "3129/3129 [==============================] - 1s 232us/step\n",
      "3129/3129 [==============================] - 1s 228us/step\n",
      "3129/3129 [==============================] - 1s 236us/step\n",
      "3129/3129 [==============================] - 1s 232us/step\n",
      "3129/3129 [==============================] - 1s 224us/step\n"
     ]
    }
   ],
   "source": [
    "n_queries = 500\n",
    "model = load_model('./Save_Files/Watch_User_C.h5')\n",
    "\n",
    "var_uncertainty_estimates = varratio([X_pool_subset0, X_pool_subset1, X_pool_subset2], \\\n",
    "                                     num_classes, model, 32, 10)\n",
    "\n",
    "var_uncertainty_estimates = var_uncertainty_estimates.flatten()\n",
    "pool_to_be_revealed = var_uncertainty_estimates.argsort()[-n_queries:][::-1]\n",
    "\n",
    "X_acquired_var = X_pool[pool_to_be_revealed]\n",
    "Y_acquired_var = Y_pool[pool_to_be_revealed]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-22T18:25:24.336067Z",
     "start_time": "2018-11-22T18:25:24.327081Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(500, 53, 3) (500, 6)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([ 82,  78,  81,  79,  80, 100])"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print (X_acquired_var.shape, Y_acquired_var.shape)\n",
    "\n",
    "y_acquired_var = Y_acquired_var.argmax(axis=-1)\n",
    "np.bincount(y_acquired_var)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-22T18:28:22.345564Z",
     "start_time": "2018-11-22T18:28:22.339008Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(500, 53, 1) (500, 53, 1) (500, 53, 1)\n"
     ]
    }
   ],
   "source": [
    "X_acquired0 = X_acquired_var[:, :, 0].reshape((-1, w_min, 1))\n",
    "X_acquired1 = X_acquired_var[:, :, 1].reshape((-1, w_min, 1))\n",
    "X_acquired2 = X_acquired_var[:, :, 2].reshape((-1, w_min, 1))\n",
    "\n",
    "print (X_acquired0.shape, X_acquired1.shape, X_acquired2.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-22T18:29:57.685628Z",
     "start_time": "2018-11-22T18:28:42.021432Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 500 samples, validate on 1340 samples\n",
      "Epoch 1/100\n",
      "500/500 [==============================] - 4s 7ms/step - loss: 0.9897 - acc: 0.6020 - val_loss: 0.7745 - val_acc: 0.6754\n",
      "Epoch 2/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.6849 - acc: 0.7200 - val_loss: 0.6899 - val_acc: 0.7052\n",
      "Epoch 3/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.6098 - acc: 0.7680 - val_loss: 0.6492 - val_acc: 0.7291\n",
      "Epoch 4/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.5629 - acc: 0.7980 - val_loss: 0.6332 - val_acc: 0.7381\n",
      "Epoch 5/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.5320 - acc: 0.7960 - val_loss: 0.6206 - val_acc: 0.7478\n",
      "Epoch 6/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.4872 - acc: 0.8100 - val_loss: 0.6031 - val_acc: 0.7552\n",
      "Epoch 7/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.4706 - acc: 0.8260 - val_loss: 0.6030 - val_acc: 0.7575\n",
      "Epoch 8/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.4384 - acc: 0.8420 - val_loss: 0.5898 - val_acc: 0.7687\n",
      "Epoch 9/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.4237 - acc: 0.8440 - val_loss: 0.5899 - val_acc: 0.7612\n",
      "Epoch 10/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.4046 - acc: 0.8440 - val_loss: 0.5861 - val_acc: 0.7642\n",
      "Epoch 11/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.3981 - acc: 0.8660 - val_loss: 0.5852 - val_acc: 0.7672\n",
      "Epoch 12/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.3836 - acc: 0.8600 - val_loss: 0.5873 - val_acc: 0.7649\n",
      "Epoch 13/100\n",
      "500/500 [==============================] - 1s 2ms/step - loss: 0.3605 - acc: 0.8660 - val_loss: 0.5948 - val_acc: 0.7619\n",
      "Epoch 14/100\n",
      "500/500 [==============================] - 1s 2ms/step - loss: 0.3752 - acc: 0.8580 - val_loss: 0.5808 - val_acc: 0.7642\n",
      "Epoch 15/100\n",
      "500/500 [==============================] - 1s 2ms/step - loss: 0.3560 - acc: 0.8820 - val_loss: 0.5912 - val_acc: 0.7604\n",
      "Epoch 16/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.3379 - acc: 0.8780 - val_loss: 0.5721 - val_acc: 0.7739\n",
      "Epoch 17/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.3265 - acc: 0.8800 - val_loss: 0.5934 - val_acc: 0.7672\n",
      "Epoch 18/100\n",
      "500/500 [==============================] - 1s 2ms/step - loss: 0.3193 - acc: 0.8940 - val_loss: 0.5792 - val_acc: 0.7664\n",
      "Epoch 19/100\n",
      "500/500 [==============================] - 1s 2ms/step - loss: 0.2953 - acc: 0.9120 - val_loss: 0.5901 - val_acc: 0.7657\n",
      "Epoch 20/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.2937 - acc: 0.9140 - val_loss: 0.5876 - val_acc: 0.7619\n",
      "Epoch 21/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.2787 - acc: 0.9140 - val_loss: 0.5827 - val_acc: 0.7657\n",
      "Epoch 22/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.2865 - acc: 0.9020 - val_loss: 0.5771 - val_acc: 0.7642\n",
      "Epoch 23/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.2614 - acc: 0.9360 - val_loss: 0.5810 - val_acc: 0.7672\n",
      "Epoch 24/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.2538 - acc: 0.9340 - val_loss: 0.5817 - val_acc: 0.7672\n",
      "Epoch 25/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.2614 - acc: 0.9260 - val_loss: 0.5861 - val_acc: 0.7657\n",
      "Epoch 26/100\n",
      "500/500 [==============================] - 1s 2ms/step - loss: 0.2509 - acc: 0.9340 - val_loss: 0.5823 - val_acc: 0.7627\n",
      "Epoch 27/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.2350 - acc: 0.9360 - val_loss: 0.5837 - val_acc: 0.7619\n",
      "Epoch 28/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.2264 - acc: 0.9420 - val_loss: 0.5818 - val_acc: 0.7724\n",
      "Epoch 29/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.2411 - acc: 0.9340 - val_loss: 0.5793 - val_acc: 0.7642\n",
      "Epoch 30/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.2233 - acc: 0.9400 - val_loss: 0.5898 - val_acc: 0.7634\n",
      "Epoch 31/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.2191 - acc: 0.9440 - val_loss: 0.5891 - val_acc: 0.7642\n",
      "Epoch 32/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.2085 - acc: 0.9480 - val_loss: 0.5859 - val_acc: 0.7664\n",
      "Epoch 33/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.2120 - acc: 0.9460 - val_loss: 0.5823 - val_acc: 0.7627\n",
      "Epoch 34/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.1955 - acc: 0.9640 - val_loss: 0.5901 - val_acc: 0.7687\n",
      "Epoch 35/100\n",
      "500/500 [==============================] - 1s 2ms/step - loss: 0.2036 - acc: 0.9500 - val_loss: 0.5903 - val_acc: 0.7687\n",
      "Epoch 36/100\n",
      "500/500 [==============================] - 1s 2ms/step - loss: 0.2018 - acc: 0.9600 - val_loss: 0.5836 - val_acc: 0.7642\n",
      "Epoch 37/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.1922 - acc: 0.9600 - val_loss: 0.6060 - val_acc: 0.7634\n",
      "Epoch 38/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.1907 - acc: 0.9540 - val_loss: 0.5863 - val_acc: 0.7627\n",
      "Epoch 39/100\n",
      "500/500 [==============================] - 1s 2ms/step - loss: 0.1794 - acc: 0.9640 - val_loss: 0.6002 - val_acc: 0.7694\n",
      "Epoch 40/100\n",
      "500/500 [==============================] - 1s 2ms/step - loss: 0.1955 - acc: 0.9520 - val_loss: 0.5965 - val_acc: 0.7619\n",
      "Epoch 41/100\n",
      "500/500 [==============================] - 1s 2ms/step - loss: 0.1663 - acc: 0.9780 - val_loss: 0.5958 - val_acc: 0.7627\n",
      "Epoch 42/100\n",
      "500/500 [==============================] - 1s 2ms/step - loss: 0.1745 - acc: 0.9640 - val_loss: 0.5948 - val_acc: 0.7619\n",
      "Epoch 43/100\n",
      "500/500 [==============================] - 1s 2ms/step - loss: 0.1656 - acc: 0.9760 - val_loss: 0.5952 - val_acc: 0.7687\n",
      "Epoch 44/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.1643 - acc: 0.9700 - val_loss: 0.6034 - val_acc: 0.7657\n",
      "Epoch 45/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.1547 - acc: 0.9820 - val_loss: 0.5963 - val_acc: 0.7664\n",
      "Epoch 46/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.1485 - acc: 0.9800 - val_loss: 0.6050 - val_acc: 0.7627\n",
      "Epoch 47/100\n",
      "500/500 [==============================] - 1s 2ms/step - loss: 0.1552 - acc: 0.9660 - val_loss: 0.6084 - val_acc: 0.7679\n",
      "Epoch 48/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.1442 - acc: 0.9760 - val_loss: 0.6019 - val_acc: 0.7634\n",
      "Epoch 49/100\n",
      "500/500 [==============================] - 1s 2ms/step - loss: 0.1357 - acc: 0.9780 - val_loss: 0.6044 - val_acc: 0.7694\n",
      "Epoch 50/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.1432 - acc: 0.9780 - val_loss: 0.6071 - val_acc: 0.7709\n",
      "Epoch 51/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.1405 - acc: 0.9800 - val_loss: 0.6100 - val_acc: 0.7597\n",
      "Epoch 52/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.1395 - acc: 0.9840 - val_loss: 0.6082 - val_acc: 0.7731\n",
      "Epoch 53/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.1223 - acc: 0.9940 - val_loss: 0.6147 - val_acc: 0.7619\n",
      "Epoch 54/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.1285 - acc: 0.9860 - val_loss: 0.6091 - val_acc: 0.7746\n",
      "Epoch 55/100\n",
      "500/500 [==============================] - 1s 2ms/step - loss: 0.1208 - acc: 0.9900 - val_loss: 0.6141 - val_acc: 0.7612\n",
      "Epoch 56/100\n",
      "500/500 [==============================] - 1s 2ms/step - loss: 0.1155 - acc: 0.9900 - val_loss: 0.6165 - val_acc: 0.7634\n",
      "Epoch 57/100\n",
      "500/500 [==============================] - 1s 2ms/step - loss: 0.1295 - acc: 0.9820 - val_loss: 0.6157 - val_acc: 0.7709\n",
      "Epoch 58/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.1162 - acc: 0.9860 - val_loss: 0.6209 - val_acc: 0.7701\n",
      "Epoch 59/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.1102 - acc: 0.9920 - val_loss: 0.6228 - val_acc: 0.7649\n",
      "Epoch 60/100\n",
      "500/500 [==============================] - 1s 2ms/step - loss: 0.1150 - acc: 0.9840 - val_loss: 0.6262 - val_acc: 0.7687\n",
      "Epoch 61/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.1125 - acc: 0.9840 - val_loss: 0.6230 - val_acc: 0.7731\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 62/100\n",
      "500/500 [==============================] - 1s 2ms/step - loss: 0.1063 - acc: 0.9900 - val_loss: 0.6232 - val_acc: 0.7619\n",
      "Epoch 63/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.1046 - acc: 0.9900 - val_loss: 0.6291 - val_acc: 0.7716\n",
      "Epoch 64/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0990 - acc: 0.9960 - val_loss: 0.6335 - val_acc: 0.7642\n",
      "Epoch 65/100\n",
      "500/500 [==============================] - 1s 2ms/step - loss: 0.0976 - acc: 0.9940 - val_loss: 0.6341 - val_acc: 0.7672\n",
      "Epoch 66/100\n",
      "500/500 [==============================] - 1s 2ms/step - loss: 0.1041 - acc: 0.9880 - val_loss: 0.6378 - val_acc: 0.7649\n",
      "Epoch 67/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.1006 - acc: 0.9920 - val_loss: 0.6389 - val_acc: 0.7739\n",
      "Epoch 68/100\n",
      "500/500 [==============================] - 1s 2ms/step - loss: 0.1016 - acc: 0.9880 - val_loss: 0.6454 - val_acc: 0.7687\n",
      "Epoch 69/100\n",
      "500/500 [==============================] - 1s 2ms/step - loss: 0.0874 - acc: 1.0000 - val_loss: 0.6375 - val_acc: 0.7649\n",
      "Epoch 70/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0898 - acc: 0.9960 - val_loss: 0.6445 - val_acc: 0.7664\n",
      "Epoch 71/100\n",
      "500/500 [==============================] - 1s 2ms/step - loss: 0.0904 - acc: 0.9920 - val_loss: 0.6405 - val_acc: 0.7679\n",
      "Epoch 72/100\n",
      "500/500 [==============================] - 1s 2ms/step - loss: 0.0818 - acc: 0.9940 - val_loss: 0.6410 - val_acc: 0.7664\n",
      "Epoch 73/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0861 - acc: 0.9980 - val_loss: 0.6440 - val_acc: 0.7701\n",
      "Epoch 74/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0754 - acc: 1.0000 - val_loss: 0.6515 - val_acc: 0.7649\n",
      "Epoch 75/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0776 - acc: 0.9980 - val_loss: 0.6491 - val_acc: 0.7664\n",
      "Epoch 76/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0779 - acc: 0.9960 - val_loss: 0.6533 - val_acc: 0.7739\n",
      "Epoch 77/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0741 - acc: 0.9980 - val_loss: 0.6477 - val_acc: 0.7731\n",
      "Epoch 78/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0711 - acc: 0.9980 - val_loss: 0.6543 - val_acc: 0.7664\n",
      "Epoch 79/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0720 - acc: 1.0000 - val_loss: 0.6607 - val_acc: 0.7701\n",
      "Epoch 80/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0686 - acc: 0.9980 - val_loss: 0.6601 - val_acc: 0.7694\n",
      "Epoch 81/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0717 - acc: 1.0000 - val_loss: 0.6590 - val_acc: 0.7731\n",
      "Epoch 82/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0654 - acc: 0.9960 - val_loss: 0.6614 - val_acc: 0.7724\n",
      "Epoch 83/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0705 - acc: 1.0000 - val_loss: 0.6618 - val_acc: 0.7694\n",
      "Epoch 84/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0677 - acc: 1.0000 - val_loss: 0.6658 - val_acc: 0.7694\n",
      "Epoch 85/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0662 - acc: 1.0000 - val_loss: 0.6662 - val_acc: 0.7724\n",
      "Epoch 86/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0659 - acc: 0.9980 - val_loss: 0.6676 - val_acc: 0.7679\n",
      "Epoch 87/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0592 - acc: 1.0000 - val_loss: 0.6663 - val_acc: 0.7739\n",
      "Epoch 88/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0562 - acc: 1.0000 - val_loss: 0.6661 - val_acc: 0.7716\n",
      "Epoch 89/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0600 - acc: 0.9980 - val_loss: 0.6686 - val_acc: 0.7694\n",
      "Epoch 90/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0595 - acc: 1.0000 - val_loss: 0.6756 - val_acc: 0.7739\n",
      "Epoch 91/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0613 - acc: 1.0000 - val_loss: 0.6752 - val_acc: 0.7664\n",
      "Epoch 92/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0508 - acc: 1.0000 - val_loss: 0.6730 - val_acc: 0.7694\n",
      "Epoch 93/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0534 - acc: 1.0000 - val_loss: 0.6800 - val_acc: 0.7694\n",
      "Epoch 94/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0549 - acc: 0.9980 - val_loss: 0.6830 - val_acc: 0.7709\n",
      "Epoch 95/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0655 - acc: 0.9940 - val_loss: 0.6817 - val_acc: 0.7694\n",
      "Epoch 96/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0465 - acc: 1.0000 - val_loss: 0.6855 - val_acc: 0.7709\n",
      "Epoch 97/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0455 - acc: 1.0000 - val_loss: 0.6874 - val_acc: 0.7746\n",
      "Epoch 98/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0496 - acc: 1.0000 - val_loss: 0.6844 - val_acc: 0.7716\n",
      "Epoch 99/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0483 - acc: 0.9980 - val_loss: 0.6952 - val_acc: 0.7687\n",
      "Epoch 100/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0445 - acc: 1.0000 - val_loss: 0.6925 - val_acc: 0.7701\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f201c0fdb70>"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit([X_acquired0, X_acquired1, X_acquired2], Y_acquired_var, epochs=100, batch_size=32, \\\n",
    "          validation_data=([X_test0, X_test1, X_test2], Y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Max Entropy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-22T18:34:17.199571Z",
     "start_time": "2018-11-22T18:33:57.197371Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3129, 53, 1)\n",
      "3129/3129 [==============================] - 2s 690us/step\n",
      "3129/3129 [==============================] - 1s 235us/step\n",
      "3129/3129 [==============================] - 1s 242us/step\n",
      "3129/3129 [==============================] - 1s 254us/step\n",
      "3129/3129 [==============================] - 1s 273us/step\n",
      "3129/3129 [==============================] - 1s 296us/step\n",
      "3129/3129 [==============================] - 1s 310us/step\n",
      "3129/3129 [==============================] - 1s 254us/step\n",
      "3129/3129 [==============================] - 1s 238us/step\n",
      "3129/3129 [==============================] - 1s 244us/step\n"
     ]
    }
   ],
   "source": [
    "n_queries = 500\n",
    "model = load_model('./Save_Files/Watch_User_C.h5')\n",
    "\n",
    "maxent_uncertainty_estimates = maxentropy([X_pool_subset0, X_pool_subset1, X_pool_subset2], \\\n",
    "                                     num_classes, model, 32, 10)\n",
    "\n",
    "maxent_uncertainty_estimates = maxent_uncertainty_estimates.flatten()\n",
    "pool_to_be_revealed = maxent_uncertainty_estimates.argsort()[-n_queries:][::-1]\n",
    "\n",
    "X_acquired_maxent = X_pool[pool_to_be_revealed]\n",
    "Y_acquired_maxent = Y_pool[pool_to_be_revealed]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-22T18:34:17.221729Z",
     "start_time": "2018-11-22T18:34:17.202629Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(500, 53, 3) (500, 6)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([ 86,  72,  87,  76,  76, 103])"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print (X_acquired_maxent.shape, Y_acquired_maxent.shape)\n",
    "\n",
    "y_acquired_maxent = Y_acquired_maxent.argmax(axis=-1)\n",
    "np.bincount(y_acquired_maxent)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-22T18:35:56.385520Z",
     "start_time": "2018-11-22T18:35:56.375355Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(500, 53, 1) (500, 53, 1) (500, 53, 1)\n"
     ]
    }
   ],
   "source": [
    "X_acquired0 = X_acquired_maxent[:, :, 0].reshape((-1, w_min, 1))\n",
    "X_acquired1 = X_acquired_maxent[:, :, 1].reshape((-1, w_min, 1))\n",
    "X_acquired2 = X_acquired_maxent[:, :, 2].reshape((-1, w_min, 1))\n",
    "\n",
    "print (X_acquired0.shape, X_acquired1.shape, X_acquired2.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-22T18:37:14.209013Z",
     "start_time": "2018-11-22T18:35:58.071942Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 500 samples, validate on 1340 samples\n",
      "Epoch 1/100\n",
      "500/500 [==============================] - 4s 8ms/step - loss: 0.9266 - acc: 0.6340 - val_loss: 0.7979 - val_acc: 0.6873\n",
      "Epoch 2/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.6551 - acc: 0.7100 - val_loss: 0.7123 - val_acc: 0.7157\n",
      "Epoch 3/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.5598 - acc: 0.7620 - val_loss: 0.6711 - val_acc: 0.7276\n",
      "Epoch 4/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.5188 - acc: 0.7940 - val_loss: 0.6689 - val_acc: 0.7388\n",
      "Epoch 5/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.4956 - acc: 0.7980 - val_loss: 0.6547 - val_acc: 0.7440\n",
      "Epoch 6/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.4326 - acc: 0.8500 - val_loss: 0.6458 - val_acc: 0.7560\n",
      "Epoch 7/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.4316 - acc: 0.8340 - val_loss: 0.6624 - val_acc: 0.7575\n",
      "Epoch 8/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.4008 - acc: 0.8460 - val_loss: 0.6411 - val_acc: 0.7567\n",
      "Epoch 9/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.3735 - acc: 0.8840 - val_loss: 0.6498 - val_acc: 0.7582\n",
      "Epoch 10/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.3619 - acc: 0.8760 - val_loss: 0.6441 - val_acc: 0.7530\n",
      "Epoch 11/100\n",
      "500/500 [==============================] - 1s 2ms/step - loss: 0.3592 - acc: 0.8780 - val_loss: 0.6514 - val_acc: 0.7619\n",
      "Epoch 12/100\n",
      "500/500 [==============================] - 1s 2ms/step - loss: 0.3413 - acc: 0.8800 - val_loss: 0.6361 - val_acc: 0.7627\n",
      "Epoch 13/100\n",
      "500/500 [==============================] - 1s 2ms/step - loss: 0.3336 - acc: 0.9020 - val_loss: 0.6397 - val_acc: 0.7642\n",
      "Epoch 14/100\n",
      "500/500 [==============================] - 1s 2ms/step - loss: 0.3444 - acc: 0.8800 - val_loss: 0.6452 - val_acc: 0.7649\n",
      "Epoch 15/100\n",
      "500/500 [==============================] - 1s 2ms/step - loss: 0.3241 - acc: 0.8920 - val_loss: 0.6310 - val_acc: 0.7694\n",
      "Epoch 16/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.3049 - acc: 0.9020 - val_loss: 0.6404 - val_acc: 0.7657\n",
      "Epoch 17/100\n",
      "500/500 [==============================] - 1s 2ms/step - loss: 0.2972 - acc: 0.9040 - val_loss: 0.6401 - val_acc: 0.7672\n",
      "Epoch 18/100\n",
      "500/500 [==============================] - 1s 2ms/step - loss: 0.2702 - acc: 0.9140 - val_loss: 0.6439 - val_acc: 0.7694\n",
      "Epoch 19/100\n",
      "500/500 [==============================] - 1s 2ms/step - loss: 0.2774 - acc: 0.9160 - val_loss: 0.6529 - val_acc: 0.7657\n",
      "Epoch 20/100\n",
      "500/500 [==============================] - 1s 2ms/step - loss: 0.2640 - acc: 0.9260 - val_loss: 0.6363 - val_acc: 0.7724\n",
      "Epoch 21/100\n",
      "500/500 [==============================] - 1s 2ms/step - loss: 0.2638 - acc: 0.9360 - val_loss: 0.6405 - val_acc: 0.7701\n",
      "Epoch 22/100\n",
      "500/500 [==============================] - 1s 2ms/step - loss: 0.2402 - acc: 0.9380 - val_loss: 0.6595 - val_acc: 0.7664\n",
      "Epoch 23/100\n",
      "500/500 [==============================] - 1s 2ms/step - loss: 0.2369 - acc: 0.9220 - val_loss: 0.6392 - val_acc: 0.7716\n",
      "Epoch 24/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.2328 - acc: 0.9380 - val_loss: 0.6622 - val_acc: 0.7612\n",
      "Epoch 25/100\n",
      "500/500 [==============================] - 1s 2ms/step - loss: 0.2182 - acc: 0.9420 - val_loss: 0.6484 - val_acc: 0.7739\n",
      "Epoch 26/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.2067 - acc: 0.9560 - val_loss: 0.6494 - val_acc: 0.7687\n",
      "Epoch 27/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.2047 - acc: 0.9660 - val_loss: 0.6425 - val_acc: 0.7754\n",
      "Epoch 28/100\n",
      "500/500 [==============================] - 1s 2ms/step - loss: 0.2041 - acc: 0.9560 - val_loss: 0.6457 - val_acc: 0.7716\n",
      "Epoch 29/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.1940 - acc: 0.9660 - val_loss: 0.6501 - val_acc: 0.7679\n",
      "Epoch 30/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.1934 - acc: 0.9580 - val_loss: 0.6525 - val_acc: 0.7724\n",
      "Epoch 31/100\n",
      "500/500 [==============================] - 1s 2ms/step - loss: 0.1877 - acc: 0.9540 - val_loss: 0.6657 - val_acc: 0.7672\n",
      "Epoch 32/100\n",
      "500/500 [==============================] - 1s 2ms/step - loss: 0.1740 - acc: 0.9540 - val_loss: 0.6589 - val_acc: 0.7791\n",
      "Epoch 33/100\n",
      "500/500 [==============================] - 1s 2ms/step - loss: 0.1746 - acc: 0.9740 - val_loss: 0.6548 - val_acc: 0.7701\n",
      "Epoch 34/100\n",
      "500/500 [==============================] - 1s 2ms/step - loss: 0.1704 - acc: 0.9620 - val_loss: 0.6638 - val_acc: 0.7731\n",
      "Epoch 35/100\n",
      "500/500 [==============================] - 1s 2ms/step - loss: 0.1625 - acc: 0.9780 - val_loss: 0.6528 - val_acc: 0.7761\n",
      "Epoch 36/100\n",
      "500/500 [==============================] - 1s 2ms/step - loss: 0.1571 - acc: 0.9760 - val_loss: 0.6642 - val_acc: 0.7754\n",
      "Epoch 37/100\n",
      "500/500 [==============================] - 1s 2ms/step - loss: 0.1550 - acc: 0.9720 - val_loss: 0.6622 - val_acc: 0.7746\n",
      "Epoch 38/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.1486 - acc: 0.9800 - val_loss: 0.6650 - val_acc: 0.7746\n",
      "Epoch 39/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.1629 - acc: 0.9720 - val_loss: 0.6731 - val_acc: 0.7754\n",
      "Epoch 40/100\n",
      "500/500 [==============================] - 1s 2ms/step - loss: 0.1403 - acc: 0.9800 - val_loss: 0.6600 - val_acc: 0.7791\n",
      "Epoch 41/100\n",
      "500/500 [==============================] - 1s 2ms/step - loss: 0.1432 - acc: 0.9780 - val_loss: 0.6751 - val_acc: 0.7739\n",
      "Epoch 42/100\n",
      "500/500 [==============================] - 1s 2ms/step - loss: 0.1292 - acc: 0.9840 - val_loss: 0.6800 - val_acc: 0.7761\n",
      "Epoch 43/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.1299 - acc: 0.9880 - val_loss: 0.6734 - val_acc: 0.7821\n",
      "Epoch 44/100\n",
      "500/500 [==============================] - 1s 2ms/step - loss: 0.1245 - acc: 0.9860 - val_loss: 0.6863 - val_acc: 0.7701\n",
      "Epoch 45/100\n",
      "500/500 [==============================] - 1s 2ms/step - loss: 0.1219 - acc: 0.9860 - val_loss: 0.6788 - val_acc: 0.7776\n",
      "Epoch 46/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.1224 - acc: 0.9920 - val_loss: 0.6746 - val_acc: 0.7776\n",
      "Epoch 47/100\n",
      "500/500 [==============================] - 1s 2ms/step - loss: 0.1105 - acc: 0.9920 - val_loss: 0.6928 - val_acc: 0.7679\n",
      "Epoch 48/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.1103 - acc: 0.9900 - val_loss: 0.6776 - val_acc: 0.7813\n",
      "Epoch 49/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.1074 - acc: 0.9940 - val_loss: 0.6938 - val_acc: 0.7694\n",
      "Epoch 50/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.1030 - acc: 0.9900 - val_loss: 0.6828 - val_acc: 0.7791\n",
      "Epoch 51/100\n",
      "500/500 [==============================] - 1s 2ms/step - loss: 0.1019 - acc: 0.9960 - val_loss: 0.6850 - val_acc: 0.7799\n",
      "Epoch 52/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.1028 - acc: 0.9860 - val_loss: 0.6869 - val_acc: 0.7799\n",
      "Epoch 53/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0947 - acc: 0.9940 - val_loss: 0.6927 - val_acc: 0.7784\n",
      "Epoch 54/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0936 - acc: 0.9920 - val_loss: 0.6903 - val_acc: 0.7828\n",
      "Epoch 55/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.1059 - acc: 0.9840 - val_loss: 0.7044 - val_acc: 0.7746\n",
      "Epoch 56/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0871 - acc: 0.9940 - val_loss: 0.6899 - val_acc: 0.7813\n",
      "Epoch 57/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.1172 - acc: 0.9760 - val_loss: 0.7086 - val_acc: 0.7761\n",
      "Epoch 58/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0913 - acc: 0.9980 - val_loss: 0.6915 - val_acc: 0.7791\n",
      "Epoch 59/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0852 - acc: 0.9920 - val_loss: 0.6983 - val_acc: 0.7776\n",
      "Epoch 60/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0850 - acc: 0.9960 - val_loss: 0.7078 - val_acc: 0.7746\n",
      "Epoch 61/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0789 - acc: 0.9960 - val_loss: 0.7151 - val_acc: 0.7731\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 62/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0828 - acc: 0.9980 - val_loss: 0.7081 - val_acc: 0.7813\n",
      "Epoch 63/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0800 - acc: 0.9940 - val_loss: 0.7045 - val_acc: 0.7806\n",
      "Epoch 64/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0700 - acc: 1.0000 - val_loss: 0.7187 - val_acc: 0.7761\n",
      "Epoch 65/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0767 - acc: 0.9980 - val_loss: 0.7170 - val_acc: 0.7761\n",
      "Epoch 66/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0657 - acc: 1.0000 - val_loss: 0.7242 - val_acc: 0.7784\n",
      "Epoch 67/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0695 - acc: 0.9940 - val_loss: 0.7356 - val_acc: 0.7746\n",
      "Epoch 68/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0674 - acc: 0.9960 - val_loss: 0.7162 - val_acc: 0.7813\n",
      "Epoch 69/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0659 - acc: 0.9960 - val_loss: 0.7167 - val_acc: 0.7821\n",
      "Epoch 70/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0625 - acc: 0.9940 - val_loss: 0.7236 - val_acc: 0.7821\n",
      "Epoch 71/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0698 - acc: 0.9940 - val_loss: 0.7263 - val_acc: 0.7836\n",
      "Epoch 72/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0575 - acc: 0.9980 - val_loss: 0.7232 - val_acc: 0.7836\n",
      "Epoch 73/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0577 - acc: 0.9980 - val_loss: 0.7256 - val_acc: 0.7836\n",
      "Epoch 74/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0539 - acc: 1.0000 - val_loss: 0.7206 - val_acc: 0.7843\n",
      "Epoch 75/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0599 - acc: 0.9980 - val_loss: 0.7211 - val_acc: 0.7791\n",
      "Epoch 76/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0573 - acc: 0.9980 - val_loss: 0.7307 - val_acc: 0.7806\n",
      "Epoch 77/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0548 - acc: 0.9980 - val_loss: 0.7368 - val_acc: 0.7769\n",
      "Epoch 78/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0536 - acc: 1.0000 - val_loss: 0.7356 - val_acc: 0.7821\n",
      "Epoch 79/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0496 - acc: 1.0000 - val_loss: 0.7315 - val_acc: 0.7821\n",
      "Epoch 80/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0510 - acc: 1.0000 - val_loss: 0.7451 - val_acc: 0.7701\n",
      "Epoch 81/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0472 - acc: 1.0000 - val_loss: 0.7329 - val_acc: 0.7813\n",
      "Epoch 82/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0514 - acc: 0.9980 - val_loss: 0.7418 - val_acc: 0.7791\n",
      "Epoch 83/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0459 - acc: 1.0000 - val_loss: 0.7376 - val_acc: 0.7851\n",
      "Epoch 84/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0438 - acc: 1.0000 - val_loss: 0.7434 - val_acc: 0.7769\n",
      "Epoch 85/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0448 - acc: 1.0000 - val_loss: 0.7607 - val_acc: 0.7739\n",
      "Epoch 86/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0467 - acc: 1.0000 - val_loss: 0.7616 - val_acc: 0.7806\n",
      "Epoch 87/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0401 - acc: 1.0000 - val_loss: 0.7555 - val_acc: 0.7843\n",
      "Epoch 88/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0405 - acc: 1.0000 - val_loss: 0.7595 - val_acc: 0.7761\n",
      "Epoch 89/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0365 - acc: 1.0000 - val_loss: 0.7599 - val_acc: 0.7731\n",
      "Epoch 90/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0394 - acc: 1.0000 - val_loss: 0.7583 - val_acc: 0.7761\n",
      "Epoch 91/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0369 - acc: 0.9980 - val_loss: 0.7611 - val_acc: 0.7791\n",
      "Epoch 92/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0410 - acc: 1.0000 - val_loss: 0.7612 - val_acc: 0.7776\n",
      "Epoch 93/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0372 - acc: 1.0000 - val_loss: 0.7699 - val_acc: 0.7761\n",
      "Epoch 94/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0349 - acc: 1.0000 - val_loss: 0.7658 - val_acc: 0.7761\n",
      "Epoch 95/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0371 - acc: 1.0000 - val_loss: 0.7597 - val_acc: 0.7828\n",
      "Epoch 96/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0321 - acc: 1.0000 - val_loss: 0.7739 - val_acc: 0.7813\n",
      "Epoch 97/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0334 - acc: 1.0000 - val_loss: 0.7731 - val_acc: 0.7828\n",
      "Epoch 98/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0327 - acc: 1.0000 - val_loss: 0.7743 - val_acc: 0.7799\n",
      "Epoch 99/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0323 - acc: 1.0000 - val_loss: 0.7764 - val_acc: 0.7776\n",
      "Epoch 100/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0378 - acc: 1.0000 - val_loss: 0.7834 - val_acc: 0.7828\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f200ca6cd68>"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit([X_acquired0, X_acquired1, X_acquired2], Y_acquired_maxent, epochs=100, batch_size=32, \\\n",
    "          validation_data=([X_test0, X_test1, X_test2], Y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Acquisitions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-22T18:40:05.129818Z",
     "start_time": "2018-11-22T18:39:53.212342Z"
    }
   },
   "outputs": [],
   "source": [
    "n_queries = 500\n",
    "model = load_model('./Save_Files/Watch_User_C.h5')\n",
    "\n",
    "rand_uncertainty_estimates = random_acq([X_pool_subset0, X_pool_subset1, X_pool_subset2], \\\n",
    "                                     num_classes, model, 32, 10)\n",
    "\n",
    "rand_uncertainty_estimates = rand_uncertainty_estimates.flatten()\n",
    "pool_to_be_revealed = rand_uncertainty_estimates.argsort()[-n_queries:][::-1]\n",
    "\n",
    "X_acquired_rand = X_pool[pool_to_be_revealed]\n",
    "Y_acquired_rand = Y_pool[pool_to_be_revealed]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-22T18:40:37.670574Z",
     "start_time": "2018-11-22T18:40:37.663705Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(500, 53, 3) (500, 6)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([83, 71, 86, 95, 77, 88])"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print (X_acquired_rand.shape, Y_acquired_rand.shape)\n",
    "\n",
    "y_acquired_rand = Y_acquired_rand.argmax(axis=-1)\n",
    "np.bincount(y_acquired_rand)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-22T18:40:44.924605Z",
     "start_time": "2018-11-22T18:40:44.916994Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(500, 53, 1) (500, 53, 1) (500, 53, 1)\n"
     ]
    }
   ],
   "source": [
    "X_acquired0 = X_acquired_rand[:, :, 0].reshape((-1, w_min, 1))\n",
    "X_acquired1 = X_acquired_rand[:, :, 1].reshape((-1, w_min, 1))\n",
    "X_acquired2 = X_acquired_rand[:, :, 2].reshape((-1, w_min, 1))\n",
    "\n",
    "print (X_acquired0.shape, X_acquired1.shape, X_acquired2.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-22T18:42:04.621366Z",
     "start_time": "2018-11-22T18:40:48.572852Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 500 samples, validate on 1340 samples\n",
      "Epoch 1/100\n",
      "500/500 [==============================] - 4s 9ms/step - loss: 1.0226 - acc: 0.6180 - val_loss: 0.7602 - val_acc: 0.6746\n",
      "Epoch 2/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.7174 - acc: 0.7020 - val_loss: 0.7462 - val_acc: 0.6985\n",
      "Epoch 3/100\n",
      "500/500 [==============================] - 1s 2ms/step - loss: 0.6547 - acc: 0.7380 - val_loss: 0.6605 - val_acc: 0.7261\n",
      "Epoch 4/100\n",
      "500/500 [==============================] - 1s 2ms/step - loss: 0.5876 - acc: 0.7520 - val_loss: 0.6600 - val_acc: 0.7269\n",
      "Epoch 5/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.5529 - acc: 0.7660 - val_loss: 0.6252 - val_acc: 0.7567\n",
      "Epoch 6/100\n",
      "500/500 [==============================] - 1s 2ms/step - loss: 0.5172 - acc: 0.7880 - val_loss: 0.6255 - val_acc: 0.7440\n",
      "Epoch 7/100\n",
      "500/500 [==============================] - 1s 2ms/step - loss: 0.5126 - acc: 0.7780 - val_loss: 0.6214 - val_acc: 0.7351\n",
      "Epoch 8/100\n",
      "500/500 [==============================] - 1s 2ms/step - loss: 0.4871 - acc: 0.8080 - val_loss: 0.6029 - val_acc: 0.7642\n",
      "Epoch 9/100\n",
      "500/500 [==============================] - 1s 2ms/step - loss: 0.4534 - acc: 0.8200 - val_loss: 0.6117 - val_acc: 0.7515\n",
      "Epoch 10/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.4444 - acc: 0.8420 - val_loss: 0.5972 - val_acc: 0.7634\n",
      "Epoch 11/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.4345 - acc: 0.8500 - val_loss: 0.5929 - val_acc: 0.7575\n",
      "Epoch 12/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.4252 - acc: 0.8420 - val_loss: 0.5953 - val_acc: 0.7612\n",
      "Epoch 13/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.4211 - acc: 0.8240 - val_loss: 0.5897 - val_acc: 0.7657\n",
      "Epoch 14/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.3810 - acc: 0.8620 - val_loss: 0.5782 - val_acc: 0.7687\n",
      "Epoch 15/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.3801 - acc: 0.8640 - val_loss: 0.5835 - val_acc: 0.7657\n",
      "Epoch 16/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.3685 - acc: 0.8720 - val_loss: 0.5831 - val_acc: 0.7634\n",
      "Epoch 17/100\n",
      "500/500 [==============================] - 1s 2ms/step - loss: 0.3583 - acc: 0.8820 - val_loss: 0.5802 - val_acc: 0.7679\n",
      "Epoch 18/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.3571 - acc: 0.8800 - val_loss: 0.5746 - val_acc: 0.7642\n",
      "Epoch 19/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.3647 - acc: 0.8660 - val_loss: 0.5763 - val_acc: 0.7664\n",
      "Epoch 20/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.3317 - acc: 0.8920 - val_loss: 0.5818 - val_acc: 0.7582\n",
      "Epoch 21/100\n",
      "500/500 [==============================] - 1s 2ms/step - loss: 0.3319 - acc: 0.8900 - val_loss: 0.5681 - val_acc: 0.7724\n",
      "Epoch 22/100\n",
      "500/500 [==============================] - 1s 2ms/step - loss: 0.3281 - acc: 0.8940 - val_loss: 0.5790 - val_acc: 0.7672\n",
      "Epoch 23/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.3226 - acc: 0.8940 - val_loss: 0.5694 - val_acc: 0.7657\n",
      "Epoch 24/100\n",
      "500/500 [==============================] - 1s 2ms/step - loss: 0.2959 - acc: 0.9060 - val_loss: 0.5693 - val_acc: 0.7679\n",
      "Epoch 25/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.2976 - acc: 0.9080 - val_loss: 0.5670 - val_acc: 0.7694\n",
      "Epoch 26/100\n",
      "500/500 [==============================] - 1s 2ms/step - loss: 0.2815 - acc: 0.9080 - val_loss: 0.5794 - val_acc: 0.7664\n",
      "Epoch 27/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.2849 - acc: 0.9100 - val_loss: 0.5695 - val_acc: 0.7672\n",
      "Epoch 28/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.2846 - acc: 0.9220 - val_loss: 0.5687 - val_acc: 0.7664\n",
      "Epoch 29/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.2730 - acc: 0.9140 - val_loss: 0.5696 - val_acc: 0.7709\n",
      "Epoch 30/100\n",
      "500/500 [==============================] - 1s 2ms/step - loss: 0.2653 - acc: 0.9260 - val_loss: 0.5610 - val_acc: 0.7687\n",
      "Epoch 31/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.2576 - acc: 0.9280 - val_loss: 0.5726 - val_acc: 0.7649\n",
      "Epoch 32/100\n",
      "500/500 [==============================] - 1s 2ms/step - loss: 0.2630 - acc: 0.9180 - val_loss: 0.5716 - val_acc: 0.7657\n",
      "Epoch 33/100\n",
      "500/500 [==============================] - 1s 2ms/step - loss: 0.2462 - acc: 0.9300 - val_loss: 0.5738 - val_acc: 0.7709\n",
      "Epoch 34/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.2456 - acc: 0.9320 - val_loss: 0.5696 - val_acc: 0.7687\n",
      "Epoch 35/100\n",
      "500/500 [==============================] - 1s 2ms/step - loss: 0.2451 - acc: 0.9280 - val_loss: 0.5773 - val_acc: 0.7694\n",
      "Epoch 36/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.2439 - acc: 0.9240 - val_loss: 0.5796 - val_acc: 0.7649\n",
      "Epoch 37/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.2214 - acc: 0.9400 - val_loss: 0.5751 - val_acc: 0.7679\n",
      "Epoch 38/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.2192 - acc: 0.9440 - val_loss: 0.5747 - val_acc: 0.7687\n",
      "Epoch 39/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.2164 - acc: 0.9440 - val_loss: 0.5795 - val_acc: 0.7701\n",
      "Epoch 40/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.2039 - acc: 0.9520 - val_loss: 0.5732 - val_acc: 0.7709\n",
      "Epoch 41/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.2115 - acc: 0.9460 - val_loss: 0.5907 - val_acc: 0.7701\n",
      "Epoch 42/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.2188 - acc: 0.9460 - val_loss: 0.5851 - val_acc: 0.7716\n",
      "Epoch 43/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.1987 - acc: 0.9500 - val_loss: 0.5734 - val_acc: 0.7687\n",
      "Epoch 44/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.1973 - acc: 0.9520 - val_loss: 0.5854 - val_acc: 0.7657\n",
      "Epoch 45/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.1953 - acc: 0.9480 - val_loss: 0.5777 - val_acc: 0.7716\n",
      "Epoch 46/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.1829 - acc: 0.9660 - val_loss: 0.5864 - val_acc: 0.7709\n",
      "Epoch 47/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.1847 - acc: 0.9500 - val_loss: 0.5796 - val_acc: 0.7694\n",
      "Epoch 48/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.1802 - acc: 0.9760 - val_loss: 0.5917 - val_acc: 0.7687\n",
      "Epoch 49/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.1770 - acc: 0.9620 - val_loss: 0.5896 - val_acc: 0.7709\n",
      "Epoch 50/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.1851 - acc: 0.9520 - val_loss: 0.5911 - val_acc: 0.7694\n",
      "Epoch 51/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.1777 - acc: 0.9640 - val_loss: 0.5964 - val_acc: 0.7694\n",
      "Epoch 52/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.1601 - acc: 0.9640 - val_loss: 0.5961 - val_acc: 0.7724\n",
      "Epoch 53/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.1658 - acc: 0.9700 - val_loss: 0.5916 - val_acc: 0.7716\n",
      "Epoch 54/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.1640 - acc: 0.9660 - val_loss: 0.5934 - val_acc: 0.7664\n",
      "Epoch 55/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.1596 - acc: 0.9660 - val_loss: 0.5960 - val_acc: 0.7709\n",
      "Epoch 56/100\n",
      "500/500 [==============================] - 1s 2ms/step - loss: 0.1524 - acc: 0.9740 - val_loss: 0.6118 - val_acc: 0.7687\n",
      "Epoch 57/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.1453 - acc: 0.9740 - val_loss: 0.5988 - val_acc: 0.7701\n",
      "Epoch 58/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.1590 - acc: 0.9720 - val_loss: 0.6069 - val_acc: 0.7716\n",
      "Epoch 59/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.1418 - acc: 0.9740 - val_loss: 0.6167 - val_acc: 0.7664\n",
      "Epoch 60/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.1412 - acc: 0.9760 - val_loss: 0.6173 - val_acc: 0.7687\n",
      "Epoch 61/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.1295 - acc: 0.9840 - val_loss: 0.6142 - val_acc: 0.7701\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 62/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.1347 - acc: 0.9820 - val_loss: 0.6193 - val_acc: 0.7672\n",
      "Epoch 63/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.1298 - acc: 0.9820 - val_loss: 0.6128 - val_acc: 0.7709\n",
      "Epoch 64/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.1314 - acc: 0.9840 - val_loss: 0.6308 - val_acc: 0.7664\n",
      "Epoch 65/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.1307 - acc: 0.9780 - val_loss: 0.6188 - val_acc: 0.7739\n",
      "Epoch 66/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.1199 - acc: 0.9880 - val_loss: 0.6177 - val_acc: 0.7687\n",
      "Epoch 67/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.1128 - acc: 0.9940 - val_loss: 0.6289 - val_acc: 0.7687\n",
      "Epoch 68/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.1272 - acc: 0.9760 - val_loss: 0.6270 - val_acc: 0.7709\n",
      "Epoch 69/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.1183 - acc: 0.9900 - val_loss: 0.6300 - val_acc: 0.7694\n",
      "Epoch 70/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.1172 - acc: 0.9780 - val_loss: 0.6271 - val_acc: 0.7731\n",
      "Epoch 71/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.1130 - acc: 0.9920 - val_loss: 0.6364 - val_acc: 0.7709\n",
      "Epoch 72/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.1071 - acc: 0.9880 - val_loss: 0.6380 - val_acc: 0.7701\n",
      "Epoch 73/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.1077 - acc: 0.9900 - val_loss: 0.6369 - val_acc: 0.7701\n",
      "Epoch 74/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0998 - acc: 0.9880 - val_loss: 0.6312 - val_acc: 0.7731\n",
      "Epoch 75/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.1070 - acc: 0.9900 - val_loss: 0.6491 - val_acc: 0.7679\n",
      "Epoch 76/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0994 - acc: 0.9920 - val_loss: 0.6436 - val_acc: 0.7701\n",
      "Epoch 77/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0981 - acc: 0.9860 - val_loss: 0.6476 - val_acc: 0.7694\n",
      "Epoch 78/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0949 - acc: 0.9960 - val_loss: 0.6544 - val_acc: 0.7694\n",
      "Epoch 79/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0922 - acc: 0.9940 - val_loss: 0.6562 - val_acc: 0.7739\n",
      "Epoch 80/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.1008 - acc: 0.9980 - val_loss: 0.6515 - val_acc: 0.7716\n",
      "Epoch 81/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0895 - acc: 0.9940 - val_loss: 0.6572 - val_acc: 0.7709\n",
      "Epoch 82/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0858 - acc: 0.9980 - val_loss: 0.6541 - val_acc: 0.7694\n",
      "Epoch 83/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0842 - acc: 0.9940 - val_loss: 0.6639 - val_acc: 0.7687\n",
      "Epoch 84/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0773 - acc: 0.9980 - val_loss: 0.6628 - val_acc: 0.7739\n",
      "Epoch 85/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0780 - acc: 0.9940 - val_loss: 0.6730 - val_acc: 0.7672\n",
      "Epoch 86/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0806 - acc: 0.9980 - val_loss: 0.6668 - val_acc: 0.7701\n",
      "Epoch 87/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0703 - acc: 0.9980 - val_loss: 0.6732 - val_acc: 0.7694\n",
      "Epoch 88/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0725 - acc: 0.9960 - val_loss: 0.6765 - val_acc: 0.7716\n",
      "Epoch 89/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0699 - acc: 0.9980 - val_loss: 0.6792 - val_acc: 0.7694\n",
      "Epoch 90/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0783 - acc: 0.9980 - val_loss: 0.6921 - val_acc: 0.7649\n",
      "Epoch 91/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0780 - acc: 0.9940 - val_loss: 0.6800 - val_acc: 0.7694\n",
      "Epoch 92/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0730 - acc: 0.9960 - val_loss: 0.6855 - val_acc: 0.7746\n",
      "Epoch 93/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0675 - acc: 1.0000 - val_loss: 0.6888 - val_acc: 0.7687\n",
      "Epoch 94/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0640 - acc: 1.0000 - val_loss: 0.6938 - val_acc: 0.7716\n",
      "Epoch 95/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0662 - acc: 0.9980 - val_loss: 0.6878 - val_acc: 0.7739\n",
      "Epoch 96/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0661 - acc: 0.9980 - val_loss: 0.6999 - val_acc: 0.7694\n",
      "Epoch 97/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0594 - acc: 1.0000 - val_loss: 0.7041 - val_acc: 0.7679\n",
      "Epoch 98/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0610 - acc: 0.9980 - val_loss: 0.7087 - val_acc: 0.7731\n",
      "Epoch 99/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0591 - acc: 1.0000 - val_loss: 0.7175 - val_acc: 0.7679\n",
      "Epoch 100/100\n",
      "500/500 [==============================] - 1s 1ms/step - loss: 0.0574 - acc: 0.9960 - val_loss: 0.7134 - val_acc: 0.7731\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f20114581d0>"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit([X_acquired0, X_acquired1, X_acquired2], Y_acquired_rand, epochs=100, batch_size=32, \\\n",
    "          validation_data=([X_test0, X_test1, X_test2], Y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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