Trajectory Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import random\n",
    "import pickle\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import matplotlib.mlab as mlab\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from pandas import concat\n",
    "from sklearn.model_selection import cross_val_score, train_test_split\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "from keras.models import Sequential\n",
    "from keras.layers import LSTM, Dense, Input, Embedding, Masking\n",
    "from keras.preprocessing.sequence import pad_sequences\n",
    "\n",
    "dataPath = '../PedestrianData/IntentionData'\n",
    "finalPath = '../PedestrianData/final.pickle'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def series_to_supervised(data, n_in=1, n_out=1, dropnan=True):\n",
    "    \"\"\"\n",
    "    Frame a time series as a supervised learning dataset.\n",
    "    Arguments:\n",
    "        data: Sequence of observations as a list or NumPy array.\n",
    "        n_in: Number of lag observations as input (X).\n",
    "        n_out: Number of observations as output (y).\n",
    "        dropnan: Boolean whether or not to drop rows with NaN values.\n",
    "    Returns:\n",
    "        Pandas DataFrame of series framed for supervised learning.\n",
    "    \"\"\"\n",
    "    n_vars = 1 if type(data) is list else data.shape[1]\n",
    "    df = pd.DataFrame(data)\n",
    "    cols, names = list(), list()\n",
    "    # input sequence (t-n, ... t-1)\n",
    "    for i in range(n_in, 0, -1):\n",
    "        cols.append(df.shift(i))\n",
    "        names += [('var%d(t-%d)' % (j+1, i)) for j in range(n_vars)]\n",
    "    # forecast sequence (t, t+1, ... t+n)\n",
    "    for i in range(0, n_out):\n",
    "        cols.append(df.shift(-i))\n",
    "        if i == 0:\n",
    "            names += [('var%d(t)' % (j+1)) for j in range(n_vars)]\n",
    "        else:\n",
    "            names += [('var%d(t+%d)' % (j+1, i)) for j in range(n_vars)]\n",
    "    # put it all together\n",
    "    agg = concat(cols, axis=1)\n",
    "    agg.columns = names\n",
    "    # drop rows with NaN values\n",
    "    if dropnan:\n",
    "        agg.dropna(inplace=True)\n",
    "    return agg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\rafva\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:4034: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  downcast=downcast, **kwargs)\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-44-587bb71af8b6>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     13\u001b[0m                         \u001b[1;34m\"Vel_Mag\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"Acc_Mag\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     14\u001b[0m                         \u001b[1;34m\"Vel_Ang\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"Acc_Ang\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"Gaz_Ang\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 15\u001b[1;33m                         \u001b[1;34m\"Vel_Bin\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"Acc_Bin\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"Gaz_Bin\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"Chg_Bin\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     16\u001b[0m                        ])\n\u001b[0;32m     17\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36mparser_f\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, tupleize_cols, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision)\u001b[0m\n\u001b[0;32m    700\u001b[0m                     skip_blank_lines=skip_blank_lines)\n\u001b[0;32m    701\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 702\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    703\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    704\u001b[0m     \u001b[0mparser_f\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m    427\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    428\u001b[0m     \u001b[1;31m# Create the parser.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 429\u001b[1;33m     \u001b[0mparser\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    430\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    431\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[0;32m    893\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'has_index_names'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'has_index_names'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    894\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 895\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    896\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    897\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[1;34m(self, engine)\u001b[0m\n\u001b[0;32m   1120\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_make_engine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'c'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1121\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'c'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1122\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mCParserWrapper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1123\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1124\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'python'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, src, **kwds)\u001b[0m\n\u001b[0;32m   1851\u001b[0m         \u001b[0mkwds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'usecols'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0musecols\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1852\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1853\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_reader\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mparsers\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTextReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1854\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munnamed_cols\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_reader\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munnamed_cols\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1855\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "files = os.listdir(dataPath)\n",
    "final = []\n",
    "for i in range(len(files)):\n",
    "    file = dataPath + \"/\" + files[i]\n",
    "    \n",
    "    # Reading trial csv to headers\n",
    "    df = pd.read_csv(file, sep=\",\", header=None, \n",
    "                 names=[\"Time\", \n",
    "                        \"Pos_x\", \"Pos_y\", \"Pos_z\", \n",
    "                        \"Gaz_x\", \"Gaz_y\", \"Gaz_z\", \n",
    "                        \"Vel_x\", \"Vel_z\",\n",
    "                        \"Acc_x\", \"Acc_z\",\n",
    "                        \"Vel_Mag\", \"Acc_Mag\",\n",
    "                        \"Vel_Ang\", \"Acc_Ang\", \"Gaz_Ang\",\n",
    "                        \"Vel_Bin\", \"Acc_Bin\", \"Gaz_Bin\", \"Chg_Bin\"          \n",
    "                       ])\n",
    "    \n",
    "    # Transforming data to shifted data (for t+1, etc)\n",
    "    data = series_to_supervised(df, 1, 2, False)\n",
    "    \n",
    "    # Sequence includes all of the features we want\n",
    "    sequence = data[[\n",
    "        'var2(t+1)', 'var4(t+1)', # Coordinate at t+1\n",
    "        'var2(t)', 'var4(t)', # Position X and Z at t\n",
    "        'var5(t)', 'var6(t)', 'var7(t)', # Gaze X Y Z\n",
    "        'var8(t)', 'var9(t)', # Velocity X and Z\n",
    "        'var10(t)', 'var11(t)', # Acceleration X and Z\n",
    "        'var12(t)', 'var13(t)', # Velocity and Acceleration Magnitudes\n",
    "        'var17(t)', 'var18(t)', 'var19(t)' # Vel, Acc, Gaz bins\n",
    "    ]]\n",
    "    \n",
    "    # Replace NaNs with 0\n",
    "    sequence.fillna(0, inplace=True)\n",
    "    \n",
    "    final.append(sequence)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>var2(t+1)</th>\n",
       "      <th>var4(t+1)</th>\n",
       "      <th>var2(t)</th>\n",
       "      <th>var4(t)</th>\n",
       "      <th>var5(t)</th>\n",
       "      <th>var6(t)</th>\n",
       "      <th>var7(t)</th>\n",
       "      <th>var8(t)</th>\n",
       "      <th>var9(t)</th>\n",
       "      <th>var10(t)</th>\n",
       "      <th>var11(t)</th>\n",
       "      <th>var12(t)</th>\n",
       "      <th>var13(t)</th>\n",
       "      <th>var17(t)</th>\n",
       "      <th>var18(t)</th>\n",
       "      <th>var19(t)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>289.9</td>\n",
       "      <td>653.2</td>\n",
       "      <td>290.0</td>\n",
       "      <td>653.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>289.9</td>\n",
       "      <td>653.2</td>\n",
       "      <td>289.9</td>\n",
       "      <td>653.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>-0.905756</td>\n",
       "      <td>0.905756</td>\n",
       "      <td>-8.203941</td>\n",
       "      <td>8.203941</td>\n",
       "      <td>1.280933</td>\n",
       "      <td>11.602124</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>289.8</td>\n",
       "      <td>653.2</td>\n",
       "      <td>289.9</td>\n",
       "      <td>653.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.074304</td>\n",
       "      <td>-8.074304</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.418791</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>289.8</td>\n",
       "      <td>653.3</td>\n",
       "      <td>289.8</td>\n",
       "      <td>653.2</td>\n",
       "      <td>0.9</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>-0.908670</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-8.256820</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.908670</td>\n",
       "      <td>8.256820</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>289.8</td>\n",
       "      <td>653.3</td>\n",
       "      <td>289.8</td>\n",
       "      <td>653.3</td>\n",
       "      <td>0.9</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.997460</td>\n",
       "      <td>9.063628</td>\n",
       "      <td>9.949274</td>\n",
       "      <td>0.997460</td>\n",
       "      <td>13.458730</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   var2(t+1)  var4(t+1)  var2(t)  var4(t)  var5(t)  var6(t)  var7(t)  \\\n",
       "0      289.9      653.2    290.0    653.1      1.0     -0.2     -0.2   \n",
       "1      289.9      653.2    289.9    653.2      1.0     -0.2     -0.2   \n",
       "2      289.8      653.2    289.9    653.2      1.0     -0.2     -0.2   \n",
       "3      289.8      653.3    289.8    653.2      0.9     -0.2     -0.3   \n",
       "4      289.8      653.3    289.8    653.3      0.9     -0.2     -0.3   \n",
       "\n",
       "    var8(t)   var9(t)  var10(t)  var11(t)  var12(t)   var13(t)  var17(t)  \\\n",
       "0  0.000000  0.000000  0.000000  0.000000  0.000000   0.000000       0.0   \n",
       "1 -0.905756  0.905756 -8.203941  8.203941  1.280933  11.602124       8.0   \n",
       "2  0.000000  0.000000  8.074304 -8.074304  0.000000  11.418791       0.0   \n",
       "3 -0.908670  0.000000 -8.256820  0.000000  0.908670   8.256820       7.0   \n",
       "4  0.000000  0.997460  9.063628  9.949274  0.997460  13.458730       1.0   \n",
       "\n",
       "   var18(t)  var19(t)  \n",
       "0       0.0         3  \n",
       "1       8.0         3  \n",
       "2       4.0         3  \n",
       "3       7.0         3  \n",
       "4       1.0         3  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_data = pickle.load(open(finalPath,\"rb\"))\n",
    "final_data[1].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First trial:\n",
      "[[290.  654.  290.  ...   0.    0.    6. ]\n",
      " [289.9 654.  290.  ...   0.    0.    6. ]\n",
      " [289.9 654.  289.9 ...   7.    7.    6. ]\n",
      " ...\n",
      " [289.5 649.3 289.5 ...   5.    1.    5. ]\n",
      " [289.6 649.3 289.5 ...   0.    1.    5. ]\n",
      " [  0.    0.  289.6 ...   3.    3.    5. ]]\n",
      "Final shape:  (2333,)\n",
      "\n",
      "First row of trial:\n",
      "[ 2.90e+02  6.54e+02  2.90e+02  6.54e+02 -8.00e-01 -2.00e-01 -6.00e-01\n",
      "  0.00e+00  0.00e+00  0.00e+00  0.00e+00  0.00e+00  0.00e+00  0.00e+00\n",
      "  0.00e+00  6.00e+00]\n",
      "Trial1 shape:  (232, 16)\n",
      "Trial2 shape:  (177, 16)\n",
      "Trial100 shape:  (125, 16)\n"
     ]
    }
   ],
   "source": [
    "final = []\n",
    "for df in final_data:\n",
    "    final.append(np.array(df))\n",
    "final = np.array(final)\n",
    "\n",
    "# Final is an array of crossing trials (sequences)\n",
    "print(\"First trial:\")\n",
    "print(final[0])\n",
    "print(\"Final shape: \", final.shape)\n",
    "\n",
    "# Each trial is an array of timesteps\n",
    "print(\"\\nFirst row of trial:\")\n",
    "print(final[0][0])\n",
    "print(\"Trial1 shape: \", final[0].shape)\n",
    "print(\"Trial2 shape: \", final[1].shape)\n",
    "print(\"Trial100 shape: \", final[101].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before padding:  (2333,) object\n",
      "After padding:  (2333, 1031, 16) float64\n",
      "\n",
      "X shape:  (2333, 1031, 13)\n",
      "y shape:  (2333, 1031, 2)\n",
      "X[0] shape:  (1031, 13)\n",
      "y[0] shape:  (1031, 2)\n",
      "\n",
      "X_train shape:  (1866, 1031, 13)\n",
      "y_train shape:  (1866, 1031, 2)\n",
      "X_train[0] shape:  (1031, 13)\n",
      "y_train[0] shape:  (1031, 2)\n",
      "1031 13 2\n"
     ]
    }
   ],
   "source": [
    "from keras.preprocessing.sequence import pad_sequences\n",
    "\n",
    "# pad sequences\n",
    "padded = pad_sequences(final, dtype='float64', padding='post', value = -0.01)\n",
    "print(\"Before padding: \", final.shape, final.dtype)\n",
    "print(\"After padding: \", padded.shape, padded.dtype)\n",
    "\n",
    "X = []\n",
    "y = []\n",
    "for seq in padded:\n",
    "    X.append(seq[:, 3:])\n",
    "    y.append(seq[:, 1:3])\n",
    "X = np.array(X)\n",
    "y = np.array(y)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=42)\n",
    "\n",
    "print(\"\\nX shape: \", X.shape)\n",
    "print(\"y shape: \", y.shape)\n",
    "print(\"X[0] shape: \", X[0].shape)\n",
    "print(\"y[0] shape: \", y[0].shape)\n",
    "print(\"\\nX_train shape: \", X_train.shape)\n",
    "print(\"y_train shape: \", y_train.shape)\n",
    "print(\"X_train[0] shape: \", X_train[0].shape)\n",
    "print(\"y_train[0] shape: \", y_train[0].shape)\n",
    "\n",
    "n_steps_in, n_features, n_steps_out = X.shape[1], X.shape[2], y.shape[2]\n",
    "print(n_steps, n_features, n_outputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = y.reshape(y_train.shape[0], y_train.shape[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Error when checking target: expected dense_15 to have 2 dimensions, but got array with shape (1866, 1031, 2)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-44-285b7847be89>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[1;31m# fit network\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 9\u001b[1;33m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m50\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m32\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\keras\\engine\\training.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)\u001b[0m\n\u001b[0;32m    950\u001b[0m             \u001b[0msample_weight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    951\u001b[0m             \u001b[0mclass_weight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mclass_weight\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 952\u001b[1;33m             batch_size=batch_size)\n\u001b[0m\u001b[0;32m    953\u001b[0m         \u001b[1;31m# Prepare validation data.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    954\u001b[0m         \u001b[0mdo_validation\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\keras\\engine\\training.py\u001b[0m in \u001b[0;36m_standardize_user_data\u001b[1;34m(self, x, y, sample_weight, class_weight, check_array_lengths, batch_size)\u001b[0m\n\u001b[0;32m    787\u001b[0m                 \u001b[0mfeed_output_shapes\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    788\u001b[0m                 \u001b[0mcheck_batch_axis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m  \u001b[1;31m# Don't enforce the batch size.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 789\u001b[1;33m                 exception_prefix='target')\n\u001b[0m\u001b[0;32m    790\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    791\u001b[0m             \u001b[1;31m# Generate sample-wise weight values given the `sample_weight` and\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\keras\\engine\\training_utils.py\u001b[0m in \u001b[0;36mstandardize_input_data\u001b[1;34m(data, names, shapes, check_batch_axis, exception_prefix)\u001b[0m\n\u001b[0;32m    126\u001b[0m                         \u001b[1;34m': expected '\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mnames\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m' to have '\u001b[0m \u001b[1;33m+\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    127\u001b[0m                         \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m' dimensions, but got array '\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 128\u001b[1;33m                         'with shape ' + str(data_shape))\n\u001b[0m\u001b[0;32m    129\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mcheck_batch_axis\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    130\u001b[0m                     \u001b[0mdata_shape\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdata_shape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Error when checking target: expected dense_15 to have 2 dimensions, but got array with shape (1866, 1031, 2)"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(Masking(mask_value=-0.01, input_shape=(n_steps_in, n_features)))\n",
    "model.add(LSTM(2, activation='relu', return_sequences=True))\n",
    "model.add(LSTM(2, activation='relu'))\n",
    "model.add(Dense(n_steps_out))\n",
    "model.compile(optimizer='adam', loss='mse')\n",
    "\n",
    "# fit network\n",
    "model.fit(X_train, y_train, epochs=50, batch_size=32, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def seq_generator():\n",
    "    while True:\n",
    "        for arr in final:\n",
    "            X_train = np.array(arr[:, 3:])\n",
    "            y_train = np.array(arr[:, 1:3]) \n",
    "            X_train = X_train.reshape((X_train.shape[0]), 1, X_train.shape[1])\n",
    "            y_train = y_train.reshape((y_train.shape[0]), 1, y_train.shape[1])\n",
    "            yield X_train, y_train\n",
    "\n",
    "model.fit_generator(seq_generator(), steps_per_epoch=30, epochs=50, verbose=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using arash's data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3304, 83, 2)\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mInvalidArgumentError\u001b[0m                      Traceback (most recent call last)",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\u001b[0m in \u001b[0;36mget_attr\u001b[1;34m(self, name)\u001b[0m\n\u001b[0;32m   2408\u001b[0m       \u001b[1;32mwith\u001b[0m \u001b[0mc_api_util\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtf_buffer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mbuf\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2409\u001b[1;33m         \u001b[0mc_api\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTF_OperationGetAttrValueProto\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_c_op\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbuf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2410\u001b[0m         \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mc_api\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbuf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mInvalidArgumentError\u001b[0m: Operation 'lstm_27/while/add_7' has no attr named '_XlaCompile'.",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\gradients_impl.py\u001b[0m in \u001b[0;36m_MaybeCompile\u001b[1;34m(scope, op, func, grad_fn)\u001b[0m\n\u001b[0;32m    414\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 415\u001b[1;33m       \u001b[0mxla_compile\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mop\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_attr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"_XlaCompile\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    416\u001b[0m       xla_separate_compiled_gradients = op.get_attr(\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\u001b[0m in \u001b[0;36mget_attr\u001b[1;34m(self, name)\u001b[0m\n\u001b[0;32m   2412\u001b[0m       \u001b[1;31m# Convert to ValueError for backwards compatibility.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2413\u001b[1;33m       \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2414\u001b[0m     \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mattr_value_pb2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mAttrValue\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Operation 'lstm_27/while/add_7' has no attr named '_XlaCompile'.",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-64-8cca768652e8>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     43\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     44\u001b[0m \u001b[1;31m# fit network\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 45\u001b[1;33m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minputseqtrain\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mytrain\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m50\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\keras\\engine\\training.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)\u001b[0m\n\u001b[0;32m   1008\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1009\u001b[0m             \u001b[0mins\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mx\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0msample_weights\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1010\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_train_function\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1011\u001b[0m         \u001b[0mf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtrain_function\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1012\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\keras\\engine\\training.py\u001b[0m in \u001b[0;36m_make_train_function\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    507\u001b[0m                     training_updates = self.optimizer.get_updates(\n\u001b[0;32m    508\u001b[0m                         \u001b[0mparams\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_collected_trainable_weights\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 509\u001b[1;33m                         loss=self.total_loss)\n\u001b[0m\u001b[0;32m    510\u001b[0m                 updates = (self.updates +\n\u001b[0;32m    511\u001b[0m                            \u001b[0mtraining_updates\u001b[0m \u001b[1;33m+\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\keras\\legacy\\interfaces.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     89\u001b[0m                 warnings.warn('Update your `' + object_name + '` call to the ' +\n\u001b[0;32m     90\u001b[0m                               'Keras 2 API: ' + signature, stacklevel=2)\n\u001b[1;32m---> 91\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     92\u001b[0m         \u001b[0mwrapper\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_original_function\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     93\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\keras\\optimizers.py\u001b[0m in \u001b[0;36mget_updates\u001b[1;34m(self, loss, params)\u001b[0m\n\u001b[0;32m    473\u001b[0m     \u001b[1;33m@\u001b[0m\u001b[0minterfaces\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlegacy_get_updates_support\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    474\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mget_updates\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mloss\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 475\u001b[1;33m         \u001b[0mgrads\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_gradients\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    476\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdates\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mK\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate_add\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miterations\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    477\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\keras\\optimizers.py\u001b[0m in \u001b[0;36mget_gradients\u001b[1;34m(self, loss, params)\u001b[0m\n\u001b[0;32m     87\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     88\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mget_gradients\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mloss\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 89\u001b[1;33m         \u001b[0mgrads\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mK\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgradients\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     90\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mgrads\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     91\u001b[0m             raise ValueError('An operation has `None` for gradient. '\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py\u001b[0m in \u001b[0;36mgradients\u001b[1;34m(loss, variables)\u001b[0m\n\u001b[0;32m   2755\u001b[0m         \u001b[0mA\u001b[0m \u001b[0mgradients\u001b[0m \u001b[0mtensor\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2756\u001b[0m     \"\"\"\n\u001b[1;32m-> 2757\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgradients\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvariables\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolocate_gradients_with_ops\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2758\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2759\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\gradients_impl.py\u001b[0m in \u001b[0;36mgradients\u001b[1;34m(ys, xs, grad_ys, name, colocate_gradients_with_ops, gate_gradients, aggregation_method, stop_gradients, unconnected_gradients)\u001b[0m\n\u001b[0;32m    662\u001b[0m     return _GradientsHelper(ys, xs, grad_ys, name, colocate_gradients_with_ops,\n\u001b[0;32m    663\u001b[0m                             \u001b[0mgate_gradients\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maggregation_method\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstop_gradients\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 664\u001b[1;33m                             unconnected_gradients)\n\u001b[0m\u001b[0;32m    665\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    666\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\gradients_impl.py\u001b[0m in \u001b[0;36m_GradientsHelper\u001b[1;34m(ys, xs, grad_ys, name, colocate_gradients_with_ops, gate_gradients, aggregation_method, stop_gradients, unconnected_gradients, src_graph)\u001b[0m\n\u001b[0;32m    963\u001b[0m                 \u001b[1;31m# functions.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    964\u001b[0m                 in_grads = _MaybeCompile(grad_scope, op, func_call,\n\u001b[1;32m--> 965\u001b[1;33m                                          lambda: grad_fn(op, *out_grads))\n\u001b[0m\u001b[0;32m    966\u001b[0m               \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    967\u001b[0m                 \u001b[1;31m# For function call ops, we add a 'SymbolicGradient'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\gradients_impl.py\u001b[0m in \u001b[0;36m_MaybeCompile\u001b[1;34m(scope, op, func, grad_fn)\u001b[0m\n\u001b[0;32m    418\u001b[0m       \u001b[0mxla_scope\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mop\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_attr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"_XlaScope\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdecode\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    419\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 420\u001b[1;33m       \u001b[1;32mreturn\u001b[0m \u001b[0mgrad_fn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m# Exit early\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    421\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    422\u001b[0m   \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mxla_compile\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\gradients_impl.py\u001b[0m in \u001b[0;36m<lambda>\u001b[1;34m()\u001b[0m\n\u001b[0;32m    963\u001b[0m                 \u001b[1;31m# functions.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    964\u001b[0m                 in_grads = _MaybeCompile(grad_scope, op, func_call,\n\u001b[1;32m--> 965\u001b[1;33m                                          lambda: grad_fn(op, *out_grads))\n\u001b[0m\u001b[0;32m    966\u001b[0m               \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    967\u001b[0m                 \u001b[1;31m# For function call ops, we add a 'SymbolicGradient'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\math_grad.py\u001b[0m in \u001b[0;36m_AddGrad\u001b[1;34m(op, grad)\u001b[0m\n\u001b[0;32m    907\u001b[0m   \u001b[0msy\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0marray_ops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    908\u001b[0m   \u001b[0mrx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mry\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgen_array_ops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbroadcast_gradient_args\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msy\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 909\u001b[1;33m   return (array_ops.reshape(math_ops.reduce_sum(grad, rx), sx),\n\u001b[0m\u001b[0;32m    910\u001b[0m           array_ops.reshape(math_ops.reduce_sum(grad, ry), sy))\n\u001b[0;32m    911\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\gen_array_ops.py\u001b[0m in \u001b[0;36mreshape\u001b[1;34m(tensor, shape, name)\u001b[0m\n\u001b[0;32m   7176\u001b[0m   \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   7177\u001b[0m     _, _, _op = _op_def_lib._apply_op_helper(\n\u001b[1;32m-> 7178\u001b[1;33m         \"Reshape\", tensor=tensor, shape=shape, name=name)\n\u001b[0m\u001b[0;32m   7179\u001b[0m   \u001b[1;32mexcept\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mTypeError\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   7180\u001b[0m     result = _dispatch.dispatch(\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\op_def_library.py\u001b[0m in \u001b[0;36m_apply_op_helper\u001b[1;34m(self, op_type_name, name, **keywords)\u001b[0m\n\u001b[0;32m    786\u001b[0m         op = g.create_op(op_type_name, inputs, output_types, name=scope,\n\u001b[0;32m    787\u001b[0m                          \u001b[0minput_types\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0minput_types\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mattrs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mattr_protos\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 788\u001b[1;33m                          op_def=op_def)\n\u001b[0m\u001b[0;32m    789\u001b[0m       \u001b[1;32mreturn\u001b[0m \u001b[0moutput_structure\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mop_def\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_stateful\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mop\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    790\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\util\\deprecation.py\u001b[0m in \u001b[0;36mnew_func\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    505\u001b[0m                 \u001b[1;34m'in a future version'\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mdate\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32melse\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;34m'after %s'\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mdate\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    506\u001b[0m                 instructions)\n\u001b[1;32m--> 507\u001b[1;33m       \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    508\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    509\u001b[0m     doc = _add_deprecated_arg_notice_to_docstring(\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\u001b[0m in \u001b[0;36mcreate_op\u001b[1;34m(***failed resolving arguments***)\u001b[0m\n\u001b[0;32m   3298\u001b[0m           \u001b[0minput_types\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0minput_types\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3299\u001b[0m           \u001b[0moriginal_op\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_default_original_op\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3300\u001b[1;33m           op_def=op_def)\n\u001b[0m\u001b[0;32m   3301\u001b[0m       \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_create_op_helper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mret\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcompute_device\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcompute_device\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3302\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mret\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, node_def, g, inputs, output_types, control_inputs, input_types, original_op, op_def)\u001b[0m\n\u001b[0;32m   1821\u001b[0m           op_def, inputs, node_def.attr)\n\u001b[0;32m   1822\u001b[0m       self._c_op = _create_c_op(self._graph, node_def, grouped_inputs,\n\u001b[1;32m-> 1823\u001b[1;33m                                 control_input_ops)\n\u001b[0m\u001b[0;32m   1824\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1825\u001b[0m     \u001b[1;31m# Initialize self._outputs.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\u001b[0m in \u001b[0;36m_create_c_op\u001b[1;34m(graph, node_def, inputs, control_inputs)\u001b[0m\n\u001b[0;32m   1657\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1658\u001b[0m   \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1659\u001b[1;33m     \u001b[0mc_op\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mc_api\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTF_FinishOperation\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mop_desc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1660\u001b[0m   \u001b[1;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mInvalidArgumentError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1661\u001b[0m     \u001b[1;31m# Convert to ValueError for backwards compatibility.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "arash_data = pickle.load(open('C:/Users/WorldViz/PedestrianData/cleaneddata\\seqdata',\"rb\"))\n",
    "X = arash_data[0]\n",
    "Z = arash_data[1]\n",
    "Y = arash_data[5]\n",
    "\n",
    "X = pad_sequences(X,dtype='float32',value=-0.01)\n",
    "Z = pad_sequences(Z,dtype='float32',value=-0.01)\n",
    "Y = pad_sequences(Y,dtype='float32',value=-0.01)\n",
    "\n",
    "inputseq=[]\n",
    "for i in range(X.shape[0]):\n",
    "    mrg_input=np.transpose(np.vstack((X[i],Z[i])))\n",
    "    inputseq.append(mrg_input)\n",
    "\n",
    "inputseq=np.array(inputseq)\n",
    "\n",
    "print(inputseq.shape)\n",
    "\n",
    "# seperate validation data and test set\n",
    "tst=0.2                 #% if test data\n",
    "#val=0.2                 #% if valid data\n",
    "tstsize = int(np.floor(len(X) * tst))\n",
    "#valsize = int(np.floor(len(X) * val))\n",
    "\n",
    "tstlabel = random.sample(range(0, (inputseq.shape[0]-1)), len(range(tstsize)))\n",
    "inputseqTEST=inputseq[tstlabel]\n",
    "ytest=Y[tstlabel]\n",
    "\n",
    "trnlabel=[i for i in range(inputseq.shape[0]) if i not in tstlabel]\n",
    "inputseqtrain = inputseq[trnlabel]          #Excluding test set\n",
    "ytrain = Y[trnlabel]\n",
    "\n",
    "n_features = inputseq.shape[2]\n",
    "n_steps_in=inputseq.shape[1]\n",
    "n_steps_out=ytrain.shape[1]\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Masking(mask_value=-0.01, input_shape=(n_steps_in, n_features)))\n",
    "model.add(LSTM(2, activation='relu', return_sequences=True))\n",
    "model.add(LSTM(2, activation='relu'))\n",
    "model.add(Dense(n_steps_out))\n",
    "model.compile(optimizer='adam', loss='mse')\n",
    "\n",
    "# fit network\n",
    "model.fit(inputseqtrain, ytrain, epochs=50, batch_size=1, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((3304,), (3304,), (3304,))"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arash_data = pickle.load(open('C:/Users/WorldViz/PedestrianData/cleaneddata\\seqdata',\"rb\"))\n",
    "X = arash_data[0]\n",
    "Z = arash_data[1]\n",
    "Y = arash_data[5]\n",
    "\n",
    "X.shape, Z.shape, Y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((3304, 83), (3304, 83), (3304, 147))"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = pad_sequences(X,dtype='float32',value=-0.01)\n",
    "Z = pad_sequences(Z,dtype='float32',value=-0.01)\n",
    "Y = pad_sequences(Y,dtype='float32',value=-0.01)\n",
    "\n",
    "X.shape, Z.shape, Y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}