docs/examples/cevae_example.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CEVAE vs. Meta-Learners Benchmark with IHDP + Synthetic Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-01T21:16:06.716322Z",
     "start_time": "2021-02-01T21:16:00.790891Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "import torch\n",
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from xgboost import XGBRegressor\n",
    "from lightgbm import LGBMRegressor\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.metrics import mean_squared_error as mse\n",
    "from scipy.stats import entropy\n",
    "import warnings\n",
    "import logging\n",
    "\n",
    "from causalml.inference.meta import BaseXRegressor, BaseRRegressor, BaseSRegressor, BaseTRegressor\n",
    "from causalml.inference.nn import CEVAE\n",
    "from causalml.propensity import ElasticNetPropensityModel\n",
    "from causalml.metrics import *\n",
    "from causalml.dataset import simulate_hidden_confounder\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "warnings.filterwarnings('ignore')\n",
    "logger = logging.getLogger('causalml')\n",
    "logger.setLevel(logging.DEBUG)\n",
    "\n",
    "plt.style.use('fivethirtyeight')\n",
    "sns.set_palette('Paired')\n",
    "plt.rcParams['figure.figsize'] = (12,8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## IHDP semi-synthetic dataset\n",
    "\n",
    "Hill introduced a semi-synthetic dataset constructed from the Infant Health\n",
    "and Development Program (IHDP). This dataset is based on a randomized experiment\n",
    "investigating the effect of home visits by specialists on future cognitive scores. The IHDP simulation is considered the de-facto standard benchmark for neural network treatment effect\n",
    "estimation methods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-01T21:16:07.130301Z",
     "start_time": "2021-02-01T21:16:06.722641Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(6723, 30)\n",
      "(672300, 30)\n"
     ]
    }
   ],
   "source": [
    "# load all ihadp data\n",
    "df = pd.DataFrame()\n",
    "for i in range(1, 10):\n",
    "    data = pd.read_csv('./data/ihdp_npci_' + str(i) + '.csv', header=None)\n",
    "    df = pd.concat([data, df])\n",
    "cols =  [\"treatment\", \"y_factual\", \"y_cfactual\", \"mu0\", \"mu1\"] + [i for i in range(25)]\n",
    "df.columns = cols\n",
    "print(df.shape)\n",
    "\n",
    "# replicate the data 100 times\n",
    "replications = 100\n",
    "df = pd.concat([df]*replications, ignore_index=True)\n",
    "print(df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-01T21:16:07.144511Z",
     "start_time": "2021-02-01T21:16:07.139182Z"
    }
   },
   "outputs": [],
   "source": [
    "# set which features are binary\n",
    "binfeats = [6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24]\n",
    "# set which features are continuous\n",
    "contfeats = [i for i in range(25) if i not in binfeats]\n",
    "\n",
    "# reorder features with binary first and continuous after\n",
    "perm = binfeats + contfeats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-01T21:16:07.366309Z",
     "start_time": "2021-02-01T21:16:07.152398Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>treatment</th>\n",
       "      <th>y_factual</th>\n",
       "      <th>y_cfactual</th>\n",
       "      <th>mu0</th>\n",
       "      <th>mu1</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>...</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "      <th>20</th>\n",
       "      <th>21</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "      <th>24</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>49.647921</td>\n",
       "      <td>34.950762</td>\n",
       "      <td>37.173291</td>\n",
       "      <td>50.383798</td>\n",
       "      <td>-0.528603</td>\n",
       "      <td>-0.343455</td>\n",
       "      <td>1.128554</td>\n",
       "      <td>0.161703</td>\n",
       "      <td>-0.316603</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>16.073412</td>\n",
       "      <td>49.435313</td>\n",
       "      <td>16.087249</td>\n",
       "      <td>49.546234</td>\n",
       "      <td>-1.736945</td>\n",
       "      <td>-1.802002</td>\n",
       "      <td>0.383828</td>\n",
       "      <td>2.244320</td>\n",
       "      <td>-0.629189</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>19.643007</td>\n",
       "      <td>48.598210</td>\n",
       "      <td>18.044855</td>\n",
       "      <td>49.661068</td>\n",
       "      <td>-0.807451</td>\n",
       "      <td>-0.202946</td>\n",
       "      <td>-0.360898</td>\n",
       "      <td>-0.879606</td>\n",
       "      <td>0.808706</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>26.368322</td>\n",
       "      <td>49.715204</td>\n",
       "      <td>24.605964</td>\n",
       "      <td>49.971196</td>\n",
       "      <td>0.390083</td>\n",
       "      <td>0.596582</td>\n",
       "      <td>-1.850350</td>\n",
       "      <td>-0.879606</td>\n",
       "      <td>-0.004017</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>20.258893</td>\n",
       "      <td>51.147418</td>\n",
       "      <td>20.612816</td>\n",
       "      <td>49.794120</td>\n",
       "      <td>-1.045229</td>\n",
       "      <td>-0.602710</td>\n",
       "      <td>0.011465</td>\n",
       "      <td>0.161703</td>\n",
       "      <td>0.683672</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   treatment  y_factual  y_cfactual        mu0        mu1         0         1  \\\n",
       "0          1  49.647921   34.950762  37.173291  50.383798 -0.528603 -0.343455   \n",
       "1          0  16.073412   49.435313  16.087249  49.546234 -1.736945 -1.802002   \n",
       "2          0  19.643007   48.598210  18.044855  49.661068 -0.807451 -0.202946   \n",
       "3          0  26.368322   49.715204  24.605964  49.971196  0.390083  0.596582   \n",
       "4          0  20.258893   51.147418  20.612816  49.794120 -1.045229 -0.602710   \n",
       "\n",
       "          2         3         4  ...  15  16  17  18  19  20  21  22  23  24  \n",
       "0  1.128554  0.161703 -0.316603  ...   1   1   1   1   0   0   0   0   0   0  \n",
       "1  0.383828  2.244320 -0.629189  ...   1   1   1   1   0   0   0   0   0   0  \n",
       "2 -0.360898 -0.879606  0.808706  ...   1   0   1   1   0   0   0   0   0   0  \n",
       "3 -1.850350 -0.879606 -0.004017  ...   1   0   1   1   0   0   0   0   0   0  \n",
       "4  0.011465  0.161703  0.683672  ...   1   1   1   1   0   0   0   0   0   0  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.reset_index(drop=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-01T21:16:34.604702Z",
     "start_time": "2021-02-01T21:16:07.370970Z"
    }
   },
   "outputs": [],
   "source": [
    "X = df[perm].values\n",
    "treatment = df['treatment'].values\n",
    "y = df['y_factual'].values\n",
    "y_cf = df['y_cfactual'].values\n",
    "tau = df.apply(lambda d: d['y_factual'] - d['y_cfactual'] if d['treatment']==1 \n",
    "               else d['y_cfactual'] - d['y_factual'], \n",
    "               axis=1)\n",
    "mu_0 = df['mu0'].values\n",
    "mu_1 = df['mu1'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-01T21:16:35.018237Z",
     "start_time": "2021-02-01T21:16:34.606960Z"
    }
   },
   "outputs": [],
   "source": [
    "# seperate for train and test\n",
    "itr, ite = train_test_split(np.arange(X.shape[0]), test_size=0.2, random_state=1)\n",
    "X_train, treatment_train, y_train, y_cf_train, tau_train, mu_0_train, mu_1_train = X[itr], treatment[itr], y[itr], y_cf[itr], tau[itr], mu_0[itr], mu_1[itr]\n",
    "X_val, treatment_val, y_val, y_cf_val, tau_val, mu_0_val, mu_1_val = X[ite], treatment[ite], y[ite], y_cf[ite], tau[ite], mu_0[ite], mu_1[ite]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CEVAE Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-01T21:16:35.024352Z",
     "start_time": "2021-02-01T21:16:35.020848Z"
    }
   },
   "outputs": [],
   "source": [
    "# cevae model settings\n",
    "outcome_dist = \"normal\"\n",
    "latent_dim = 20\n",
    "hidden_dim = 200\n",
    "num_epochs = 5\n",
    "batch_size = 1000\n",
    "learning_rate = 0.001\n",
    "learning_rate_decay = 0.01\n",
    "num_layers = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-01T21:16:35.032884Z",
     "start_time": "2021-02-01T21:16:35.029438Z"
    }
   },
   "outputs": [],
   "source": [
    "cevae = CEVAE(outcome_dist=outcome_dist,\n",
    "              latent_dim=latent_dim,\n",
    "              hidden_dim=hidden_dim,\n",
    "              num_epochs=num_epochs,\n",
    "              batch_size=batch_size,\n",
    "              learning_rate=learning_rate,\n",
    "              learning_rate_decay=learning_rate_decay,\n",
    "              num_layers=num_layers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-01T21:18:39.930593Z",
     "start_time": "2021-02-01T21:16:35.037013Z"
    },
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO \t Training with 538 minibatches per epoch\n",
      "DEBUG \t step     0 loss = 1021.35\n",
      "DEBUG \t step     1 loss = 421.484\n",
      "DEBUG \t step     2 loss = 338.296\n",
      "DEBUG \t step     3 loss = 319.514\n",
      "DEBUG \t step     4 loss = 217.484\n",
      "DEBUG \t step     5 loss = 237.474\n",
      "DEBUG \t step     6 loss = 242.367\n",
      "DEBUG \t step     7 loss = 236.713\n",
      "DEBUG \t step     8 loss = 200.399\n",
      "DEBUG \t step     9 loss = 201.788\n",
      "DEBUG \t step    10 loss = 220.049\n",
      "DEBUG \t step    11 loss = 213.79\n",
      "DEBUG \t step    12 loss = 190.921\n",
      "DEBUG \t step    13 loss = 196.359\n",
      "DEBUG \t step    14 loss = 189.747\n",
      "DEBUG \t step    15 loss = 167.321\n",
      "DEBUG \t step    16 loss = 159.207\n",
      "DEBUG \t step    17 loss = 154.599\n",
      "DEBUG \t step    18 loss = 150.961\n",
      "DEBUG \t step    19 loss = 149.938\n",
      "DEBUG \t step    20 loss = 134.768\n",
      "DEBUG \t step    21 loss = 140.833\n",
      "DEBUG \t step    22 loss = 146.769\n",
      "DEBUG \t step    23 loss = 132.524\n",
      "DEBUG \t step    24 loss = 134.194\n",
      "DEBUG \t step    25 loss = 130.618\n",
      "DEBUG \t step    26 loss = 136.787\n",
      "DEBUG \t step    27 loss = 126.727\n",
      "DEBUG \t step    28 loss = 120.942\n",
      "DEBUG \t step    29 loss = 118.619\n",
      "DEBUG \t step    30 loss = 120.946\n",
      "DEBUG \t step    31 loss = 110.782\n",
      "DEBUG \t step    32 loss = 120.907\n",
      "DEBUG \t step    33 loss = 106.87\n",
      "DEBUG \t step    34 loss = 95.3908\n",
      "DEBUG \t step    35 loss = 104.229\n",
      "DEBUG \t step    36 loss = 100.688\n",
      "DEBUG \t step    37 loss = 102.31\n",
      "DEBUG \t step    38 loss = 96.3181\n",
      "DEBUG \t step    39 loss = 92.0119\n",
      "DEBUG \t step    40 loss = 101.374\n",
      "DEBUG \t step    41 loss = 95.1874\n",
      "DEBUG \t step    42 loss = 91.693\n",
      "DEBUG \t step    43 loss = 83.7838\n",
      "DEBUG \t step    44 loss = 76.9446\n",
      "DEBUG \t step    45 loss = 77.8403\n",
      "DEBUG \t step    46 loss = 81.372\n",
      "DEBUG \t step    47 loss = 82.7198\n",
      "DEBUG \t step    48 loss = 72.8519\n",
      "DEBUG \t step    49 loss = 76.6569\n",
      "DEBUG \t step    50 loss = 75.7397\n",
      "DEBUG \t step    51 loss = 79.6319\n",
      "DEBUG \t step    52 loss = 79.2719\n",
      "DEBUG \t step    53 loss = 74.6354\n",
      "DEBUG \t step    54 loss = 68.5501\n",
      "DEBUG \t step    55 loss = 72.5121\n",
      "DEBUG \t step    56 loss = 65.3819\n",
      "DEBUG \t step    57 loss = 68.0494\n",
      "DEBUG \t step    58 loss = 69.0703\n",
      "DEBUG \t step    59 loss = 67.7917\n",
      "DEBUG \t step    60 loss = 66.9287\n",
      "DEBUG \t step    61 loss = 58.5794\n",
      "DEBUG \t step    62 loss = 59.4718\n",
      "DEBUG \t step    63 loss = 62.9541\n",
      "DEBUG \t step    64 loss = 60.0412\n",
      "DEBUG \t step    65 loss = 57.8926\n",
      "DEBUG \t step    66 loss = 57.5324\n",
      "DEBUG \t step    67 loss = 56.5494\n",
      "DEBUG \t step    68 loss = 52.2587\n",
      "DEBUG \t step    69 loss = 55.7073\n",
      "DEBUG \t step    70 loss = 54.979\n",
      "DEBUG \t step    71 loss = 55.4208\n",
      "DEBUG \t step    72 loss = 54.7927\n",
      "DEBUG \t step    73 loss = 49.0343\n",
      "DEBUG \t step    74 loss = 53.8712\n",
      "DEBUG \t step    75 loss = 50.4505\n",
      "DEBUG \t step    76 loss = 49.2015\n",
      "DEBUG \t step    77 loss = 49.1161\n",
      "DEBUG \t step    78 loss = 51.0351\n",
      "DEBUG \t step    79 loss = 47.8925\n",
      "DEBUG \t step    80 loss = 48.4682\n",
      "DEBUG \t step    81 loss = 47.0941\n",
      "DEBUG \t step    82 loss = 44.807\n",
      "DEBUG \t step    83 loss = 43.6143\n",
      "DEBUG \t step    84 loss = 48.9903\n",
      "DEBUG \t step    85 loss = 46.6454\n",
      "DEBUG \t step    86 loss = 46.2746\n",
      "DEBUG \t step    87 loss = 47.5599\n",
      "DEBUG \t step    88 loss = 45.7764\n",
      "DEBUG \t step    89 loss = 42.9916\n",
      "DEBUG \t step    90 loss = 43.2444\n",
      "DEBUG \t step    91 loss = 43.616\n",
      "DEBUG \t step    92 loss = 41.0364\n",
      "DEBUG \t step    93 loss = 40.7751\n",
      "DEBUG \t step    94 loss = 39.693\n",
      "DEBUG \t step    95 loss = 41.2092\n",
      "DEBUG \t step    96 loss = 41.3535\n",
      "DEBUG \t step    97 loss = 39.0969\n",
      "DEBUG \t step    98 loss = 39.176\n",
      "DEBUG \t step    99 loss = 41.4575\n",
      "DEBUG \t step   100 loss = 40.5371\n",
      "DEBUG \t step   101 loss = 39.4805\n",
      "DEBUG \t step   102 loss = 37.7776\n",
      "DEBUG \t step   103 loss = 36.5425\n",
      "DEBUG \t step   104 loss = 37.3177\n",
      "DEBUG \t step   105 loss = 37.9773\n",
      "DEBUG \t step   106 loss = 36.8961\n",
      "DEBUG \t step   107 loss = 36.6936\n",
      "DEBUG \t step   108 loss = 35.1503\n",
      "DEBUG \t step   109 loss = 37.8622\n",
      "DEBUG \t step   110 loss = 36.6135\n",
      "DEBUG \t step   111 loss = 34.6556\n",
      "DEBUG \t step   112 loss = 32.9034\n",
      "DEBUG \t step   113 loss = 35.928\n",
      "DEBUG \t step   114 loss = 35.6375\n",
      "DEBUG \t step   115 loss = 34.8875\n",
      "DEBUG \t step   116 loss = 32.4369\n",
      "DEBUG \t step   117 loss = 35.5889\n",
      "DEBUG \t step   118 loss = 33.3445\n",
      "DEBUG \t step   119 loss = 35.3891\n",
      "DEBUG \t step   120 loss = 32.7132\n",
      "DEBUG \t step   121 loss = 32.4759\n",
      "DEBUG \t step   122 loss = 33.143\n",
      "DEBUG \t step   123 loss = 31.3498\n",
      "DEBUG \t step   124 loss = 31.6331\n",
      "DEBUG \t step   125 loss = 33.2434\n",
      "DEBUG \t step   126 loss = 31.1028\n",
      "DEBUG \t step   127 loss = 32.8674\n",
      "DEBUG \t step   128 loss = 32.8578\n",
      "DEBUG \t step   129 loss = 32.625\n",
      "DEBUG \t step   130 loss = 31.8448\n",
      "DEBUG \t step   131 loss = 30.8554\n",
      "DEBUG \t step   132 loss = 31.9763\n",
      "DEBUG \t step   133 loss = 29.6616\n",
      "DEBUG \t step   134 loss = 30.0425\n",
      "DEBUG \t step   135 loss = 30.836\n",
      "DEBUG \t step   136 loss = 31.0736\n",
      "DEBUG \t step   137 loss = 30.8878\n",
      "DEBUG \t step   138 loss = 30.43\n",
      "DEBUG \t step   139 loss = 30.6093\n",
      "DEBUG \t step   140 loss = 30.7339\n",
      "DEBUG \t step   141 loss = 30.0207\n",
      "DEBUG \t step   142 loss = 29.3626\n",
      "DEBUG \t step   143 loss = 29.7463\n",
      "DEBUG \t step   144 loss = 29.4184\n",
      "DEBUG \t step   145 loss = 29.2421\n",
      "DEBUG \t step   146 loss = 29.7529\n",
      "DEBUG \t step   147 loss = 29.3111\n",
      "DEBUG \t step   148 loss = 28.7811\n",
      "DEBUG \t step   149 loss = 29.3185\n",
      "DEBUG \t step   150 loss = 28.3709\n",
      "DEBUG \t step   151 loss = 30.2563\n",
      "DEBUG \t step   152 loss = 29.5989\n",
      "DEBUG \t step   153 loss = 28.8563\n",
      "DEBUG \t step   154 loss = 27.3948\n",
      "DEBUG \t step   155 loss = 28.3484\n",
      "DEBUG \t step   156 loss = 29.0616\n",
      "DEBUG \t step   157 loss = 28.8883\n",
      "DEBUG \t step   158 loss = 27.0463\n",
      "DEBUG \t step   159 loss = 27.3796\n",
      "DEBUG \t step   160 loss = 29.0732\n",
      "DEBUG \t step   161 loss = 26.8263\n",
      "DEBUG \t step   162 loss = 27.2883\n",
      "DEBUG \t step   163 loss = 28.6272\n",
      "DEBUG \t step   164 loss = 26.7478\n",
      "DEBUG \t step   165 loss = 27.6244\n",
      "DEBUG \t step   166 loss = 26.3508\n",
      "DEBUG \t step   167 loss = 26.1734\n",
      "DEBUG \t step   168 loss = 26.4877\n",
      "DEBUG \t step   169 loss = 26.9542\n",
      "DEBUG \t step   170 loss = 27.5395\n",
      "DEBUG \t step   171 loss = 26.4924\n",
      "DEBUG \t step   172 loss = 26.2203\n",
      "DEBUG \t step   173 loss = 26.039\n",
      "DEBUG \t step   174 loss = 25.7883\n",
      "DEBUG \t step   175 loss = 25.7104\n",
      "DEBUG \t step   176 loss = 25.9135\n",
      "DEBUG \t step   177 loss = 25.8419\n",
      "DEBUG \t step   178 loss = 26.897\n",
      "DEBUG \t step   179 loss = 24.8235\n",
      "DEBUG \t step   180 loss = 25.8669\n",
      "DEBUG \t step   181 loss = 26.442\n",
      "DEBUG \t step   182 loss = 24.7512\n",
      "DEBUG \t step   183 loss = 25.4444\n",
      "DEBUG \t step   184 loss = 25.7225\n",
      "DEBUG \t step   185 loss = 24.9703\n",
      "DEBUG \t step   186 loss = 25.5197\n",
      "DEBUG \t step   187 loss = 25.3311\n",
      "DEBUG \t step   188 loss = 25.0711\n",
      "DEBUG \t step   189 loss = 25.5542\n",
      "DEBUG \t step   190 loss = 25.2289\n",
      "DEBUG \t step   191 loss = 24.9589\n",
      "DEBUG \t step   192 loss = 24.5436\n",
      "DEBUG \t step   193 loss = 24.4451\n",
      "DEBUG \t step   194 loss = 23.3428\n",
      "DEBUG \t step   195 loss = 24.6046\n",
      "DEBUG \t step   196 loss = 25.1871\n",
      "DEBUG \t step   197 loss = 24.1005\n",
      "DEBUG \t step   198 loss = 24.287\n",
      "DEBUG \t step   199 loss = 24.4165\n",
      "DEBUG \t step   200 loss = 24.5855\n",
      "DEBUG \t step   201 loss = 23.2874\n",
      "DEBUG \t step   202 loss = 23.8787\n",
      "DEBUG \t step   203 loss = 24.5806\n",
      "DEBUG \t step   204 loss = 24.0906\n",
      "DEBUG \t step   205 loss = 25.0818\n",
      "DEBUG \t step   206 loss = 23.9177\n",
      "DEBUG \t step   207 loss = 25.0566\n",
      "DEBUG \t step   208 loss = 23.0722\n",
      "DEBUG \t step   209 loss = 23.8822\n",
      "DEBUG \t step   210 loss = 24.3339\n",
      "DEBUG \t step   211 loss = 24.7321\n",
      "DEBUG \t step   212 loss = 22.9672\n",
      "DEBUG \t step   213 loss = 23.6966\n",
      "DEBUG \t step   214 loss = 23.0869\n",
      "DEBUG \t step   215 loss = 23.5599\n",
      "DEBUG \t step   216 loss = 23.6307\n",
      "DEBUG \t step   217 loss = 23.1928\n",
      "DEBUG \t step   218 loss = 23.9375\n",
      "DEBUG \t step   219 loss = 23.65\n",
      "DEBUG \t step   220 loss = 22.5324\n",
      "DEBUG \t step   221 loss = 23.7082\n",
      "DEBUG \t step   222 loss = 22.854\n",
      "DEBUG \t step   223 loss = 21.8886\n",
      "DEBUG \t step   224 loss = 23.4573\n",
      "DEBUG \t step   225 loss = 22.4752\n",
      "DEBUG \t step   226 loss = 22.2281\n",
      "DEBUG \t step   227 loss = 22.6597\n",
      "DEBUG \t step   228 loss = 22.8313\n",
      "DEBUG \t step   229 loss = 22.8756\n",
      "DEBUG \t step   230 loss = 22.1289\n",
      "DEBUG \t step   231 loss = 22.6235\n",
      "DEBUG \t step   232 loss = 22.0739\n",
      "DEBUG \t step   233 loss = 22.7643\n",
      "DEBUG \t step   234 loss = 21.5396\n",
      "DEBUG \t step   235 loss = 21.5537\n",
      "DEBUG \t step   236 loss = 21.8743\n",
      "DEBUG \t step   237 loss = 22.6117\n",
      "DEBUG \t step   238 loss = 22.8206\n",
      "DEBUG \t step   239 loss = 22.8641\n",
      "DEBUG \t step   240 loss = 22.5666\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "DEBUG \t step   241 loss = 22.3578\n",
      "DEBUG \t step   242 loss = 23.3638\n",
      "DEBUG \t step   243 loss = 22.1094\n",
      "DEBUG \t step   244 loss = 22.1056\n",
      "DEBUG \t step   245 loss = 22.1651\n",
      "DEBUG \t step   246 loss = 21.4072\n",
      "DEBUG \t step   247 loss = 21.4627\n",
      "DEBUG \t step   248 loss = 21.2096\n",
      "DEBUG \t step   249 loss = 21.3499\n",
      "DEBUG \t step   250 loss = 21.4386\n",
      "DEBUG \t step   251 loss = 21.3385\n",
      "DEBUG \t step   252 loss = 21.3782\n",
      "DEBUG \t step   253 loss = 20.7455\n",
      "DEBUG \t step   254 loss = 22.3244\n",
      "DEBUG \t step   255 loss = 21.1068\n",
      "DEBUG \t step   256 loss = 21.5648\n",
      "DEBUG \t step   257 loss = 21.5746\n",
      "DEBUG \t step   258 loss = 21.6169\n",
      "DEBUG \t step   259 loss = 21.2303\n",
      "DEBUG \t step   260 loss = 21.8207\n",
      "DEBUG \t step   261 loss = 21.2217\n",
      "DEBUG \t step   262 loss = 22.4259\n",
      "DEBUG \t step   263 loss = 21.2911\n",
      "DEBUG \t step   264 loss = 21.9783\n",
      "DEBUG \t step   265 loss = 120.585\n",
      "DEBUG \t step   266 loss = 22.3958\n",
      "DEBUG \t step   267 loss = 21.1204\n",
      "DEBUG \t step   268 loss = 20.3405\n",
      "DEBUG \t step   269 loss = 19.9695\n",
      "DEBUG \t step   270 loss = 21.6718\n",
      "DEBUG \t step   271 loss = 20.8654\n",
      "DEBUG \t step   272 loss = 20.4101\n",
      "DEBUG \t step   273 loss = 20.769\n",
      "DEBUG \t step   274 loss = 20.5526\n",
      "DEBUG \t step   275 loss = 20.026\n",
      "DEBUG \t step   276 loss = 20.2413\n",
      "DEBUG \t step   277 loss = 20.0747\n",
      "DEBUG \t step   278 loss = 20.6848\n",
      "DEBUG \t step   279 loss = 20.0956\n",
      "DEBUG \t step   280 loss = 20.667\n",
      "DEBUG \t step   281 loss = 19.8283\n",
      "DEBUG \t step   282 loss = 19.8651\n",
      "DEBUG \t step   283 loss = 19.4686\n",
      "DEBUG \t step   284 loss = 19.7195\n",
      "DEBUG \t step   285 loss = 20.1469\n",
      "DEBUG \t step   286 loss = 19.8956\n",
      "DEBUG \t step   287 loss = 20.3657\n",
      "DEBUG \t step   288 loss = 20.1624\n",
      "DEBUG \t step   289 loss = 20.8871\n",
      "DEBUG \t step   290 loss = 19.7327\n",
      "DEBUG \t step   291 loss = 19.3476\n",
      "DEBUG \t step   292 loss = 19.841\n",
      "DEBUG \t step   293 loss = 20.0052\n",
      "DEBUG \t step   294 loss = 19.7133\n",
      "DEBUG \t step   295 loss = 19.7911\n",
      "DEBUG \t step   296 loss = 18.6917\n",
      "DEBUG \t step   297 loss = 19.795\n",
      "DEBUG \t step   298 loss = 19.1175\n",
      "DEBUG \t step   299 loss = 20.1492\n",
      "DEBUG \t step   300 loss = 19.7831\n",
      "DEBUG \t step   301 loss = 19.7247\n",
      "DEBUG \t step   302 loss = 19.5755\n",
      "DEBUG \t step   303 loss = 19.9661\n",
      "DEBUG \t step   304 loss = 18.2884\n",
      "DEBUG \t step   305 loss = 19.6565\n",
      "DEBUG \t step   306 loss = 19.6213\n",
      "DEBUG \t step   307 loss = 19.2026\n",
      "DEBUG \t step   308 loss = 19.8699\n",
      "DEBUG \t step   309 loss = 18.7806\n",
      "DEBUG \t step   310 loss = 18.8876\n",
      "DEBUG \t step   311 loss = 19.3982\n",
      "DEBUG \t step   312 loss = 19.1813\n",
      "DEBUG \t step   313 loss = 18.9337\n",
      "DEBUG \t step   314 loss = 18.2574\n",
      "DEBUG \t step   315 loss = 19.0662\n",
      "DEBUG \t step   316 loss = 19.1584\n",
      "DEBUG \t step   317 loss = 18.1926\n",
      "DEBUG \t step   318 loss = 18.7658\n",
      "DEBUG \t step   319 loss = 18.2249\n",
      "DEBUG \t step   320 loss = 19.003\n",
      "DEBUG \t step   321 loss = 19.0593\n",
      "DEBUG \t step   322 loss = 18.9254\n",
      "DEBUG \t step   323 loss = 19.0602\n",
      "DEBUG \t step   324 loss = 18.5273\n",
      "DEBUG \t step   325 loss = 18.2321\n",
      "DEBUG \t step   326 loss = 18.354\n",
      "DEBUG \t step   327 loss = 18.2741\n",
      "DEBUG \t step   328 loss = 18.544\n",
      "DEBUG \t step   329 loss = 18.3197\n",
      "DEBUG \t step   330 loss = 18.8422\n",
      "DEBUG \t step   331 loss = 18.4199\n",
      "DEBUG \t step   332 loss = 17.7382\n",
      "DEBUG \t step   333 loss = 18.1209\n",
      "DEBUG \t step   334 loss = 18.4557\n",
      "DEBUG \t step   335 loss = 18.5937\n",
      "DEBUG \t step   336 loss = 17.7678\n",
      "DEBUG \t step   337 loss = 19.1363\n",
      "DEBUG \t step   338 loss = 18.0725\n",
      "DEBUG \t step   339 loss = 18.3309\n",
      "DEBUG \t step   340 loss = 17.9822\n",
      "DEBUG \t step   341 loss = 17.7317\n",
      "DEBUG \t step   342 loss = 18.1821\n",
      "DEBUG \t step   343 loss = 18.1704\n",
      "DEBUG \t step   344 loss = 18.0436\n",
      "DEBUG \t step   345 loss = 17.3161\n",
      "DEBUG \t step   346 loss = 17.1744\n",
      "DEBUG \t step   347 loss = 18.4531\n",
      "DEBUG \t step   348 loss = 17.097\n",
      "DEBUG \t step   349 loss = 17.2031\n",
      "DEBUG \t step   350 loss = 17.7855\n",
      "DEBUG \t step   351 loss = 17.3887\n",
      "DEBUG \t step   352 loss = 18.1904\n",
      "DEBUG \t step   353 loss = 16.9673\n",
      "DEBUG \t step   354 loss = 17.6665\n",
      "DEBUG \t step   355 loss = 17.9181\n",
      "DEBUG \t step   356 loss = 17.3892\n",
      "DEBUG \t step   357 loss = 18.6147\n",
      "DEBUG \t step   358 loss = 17.0139\n",
      "DEBUG \t step   359 loss = 17.4958\n",
      "DEBUG \t step   360 loss = 16.8143\n",
      "DEBUG \t step   361 loss = 16.8076\n",
      "DEBUG \t step   362 loss = 17.2509\n",
      "DEBUG \t step   363 loss = 16.6091\n",
      "DEBUG \t step   364 loss = 16.5105\n",
      "DEBUG \t step   365 loss = 16.8734\n",
      "DEBUG \t step   366 loss = 16.7367\n",
      "DEBUG \t step   367 loss = 16.3754\n",
      "DEBUG \t step   368 loss = 16.7072\n",
      "DEBUG \t step   369 loss = 16.6687\n",
      "DEBUG \t step   370 loss = 16.4918\n",
      "DEBUG \t step   371 loss = 17.4622\n",
      "DEBUG \t step   372 loss = 16.5902\n",
      "DEBUG \t step   373 loss = 17.0211\n",
      "DEBUG \t step   374 loss = 16.1971\n",
      "DEBUG \t step   375 loss = 17.1127\n",
      "DEBUG \t step   376 loss = 17.0151\n",
      "DEBUG \t step   377 loss = 16.5271\n",
      "DEBUG \t step   378 loss = 15.7553\n",
      "DEBUG \t step   379 loss = 17.5206\n",
      "DEBUG \t step   380 loss = 16.1141\n",
      "DEBUG \t step   381 loss = 16.0002\n",
      "DEBUG \t step   382 loss = 16.7775\n",
      "DEBUG \t step   383 loss = 16.0455\n",
      "DEBUG \t step   384 loss = 16.4851\n",
      "DEBUG \t step   385 loss = 15.9572\n",
      "DEBUG \t step   386 loss = 16.045\n",
      "DEBUG \t step   387 loss = 16.3194\n",
      "DEBUG \t step   388 loss = 16.827\n",
      "DEBUG \t step   389 loss = 16.818\n",
      "DEBUG \t step   390 loss = 16.5154\n",
      "DEBUG \t step   391 loss = 16.4575\n",
      "DEBUG \t step   392 loss = 16.3866\n",
      "DEBUG \t step   393 loss = 16.7649\n",
      "DEBUG \t step   394 loss = 16.3661\n",
      "DEBUG \t step   395 loss = 16.0388\n",
      "DEBUG \t step   396 loss = 16.3603\n",
      "DEBUG \t step   397 loss = 15.9295\n",
      "DEBUG \t step   398 loss = 16.2829\n",
      "DEBUG \t step   399 loss = 15.7255\n",
      "DEBUG \t step   400 loss = 15.9625\n",
      "DEBUG \t step   401 loss = 16.2877\n",
      "DEBUG \t step   402 loss = 15.9384\n",
      "DEBUG \t step   403 loss = 15.7691\n",
      "DEBUG \t step   404 loss = 15.3813\n",
      "DEBUG \t step   405 loss = 16.3497\n",
      "DEBUG \t step   406 loss = 15.6471\n",
      "DEBUG \t step   407 loss = 15.7245\n",
      "DEBUG \t step   408 loss = 15.5237\n",
      "DEBUG \t step   409 loss = 15.4977\n",
      "DEBUG \t step   410 loss = 15.7544\n",
      "DEBUG \t step   411 loss = 16.4454\n",
      "DEBUG \t step   412 loss = 15.8385\n",
      "DEBUG \t step   413 loss = 15.8783\n",
      "DEBUG \t step   414 loss = 14.5518\n",
      "DEBUG \t step   415 loss = 15.248\n",
      "DEBUG \t step   416 loss = 15.4766\n",
      "DEBUG \t step   417 loss = 15.1702\n",
      "DEBUG \t step   418 loss = 15.0027\n",
      "DEBUG \t step   419 loss = 14.7798\n",
      "DEBUG \t step   420 loss = 14.2242\n",
      "DEBUG \t step   421 loss = 14.7344\n",
      "DEBUG \t step   422 loss = 15.3192\n",
      "DEBUG \t step   423 loss = 14.5862\n",
      "DEBUG \t step   424 loss = 14.8549\n",
      "DEBUG \t step   425 loss = 15.1208\n",
      "DEBUG \t step   426 loss = 15.6343\n",
      "DEBUG \t step   427 loss = 14.9648\n",
      "DEBUG \t step   428 loss = 15.8638\n",
      "DEBUG \t step   429 loss = 14.7795\n",
      "DEBUG \t step   430 loss = 15.1229\n",
      "DEBUG \t step   431 loss = 14.9709\n",
      "DEBUG \t step   432 loss = 15.3807\n",
      "DEBUG \t step   433 loss = 14.2497\n",
      "DEBUG \t step   434 loss = 15.0741\n",
      "DEBUG \t step   435 loss = 13.8058\n",
      "DEBUG \t step   436 loss = 15.0915\n",
      "DEBUG \t step   437 loss = 15.2831\n",
      "DEBUG \t step   438 loss = 15.0772\n",
      "DEBUG \t step   439 loss = 15.8433\n",
      "DEBUG \t step   440 loss = 15.3281\n",
      "DEBUG \t step   441 loss = 14.7288\n",
      "DEBUG \t step   442 loss = 15.1505\n",
      "DEBUG \t step   443 loss = 15.3472\n",
      "DEBUG \t step   444 loss = 13.545\n",
      "DEBUG \t step   445 loss = 14.6441\n",
      "DEBUG \t step   446 loss = 14.0351\n",
      "DEBUG \t step   447 loss = 14.0212\n",
      "DEBUG \t step   448 loss = 14.1237\n",
      "DEBUG \t step   449 loss = 14.4073\n",
      "DEBUG \t step   450 loss = 14.4118\n",
      "DEBUG \t step   451 loss = 13.9406\n",
      "DEBUG \t step   452 loss = 15.0758\n",
      "DEBUG \t step   453 loss = 14.9103\n",
      "DEBUG \t step   454 loss = 14.3315\n",
      "DEBUG \t step   455 loss = 13.8796\n",
      "DEBUG \t step   456 loss = 13.9354\n",
      "DEBUG \t step   457 loss = 13.8283\n",
      "DEBUG \t step   458 loss = 14.8273\n",
      "DEBUG \t step   459 loss = 14.4759\n",
      "DEBUG \t step   460 loss = 14.5714\n",
      "DEBUG \t step   461 loss = 14.0121\n",
      "DEBUG \t step   462 loss = 14.393\n",
      "DEBUG \t step   463 loss = 14.4324\n",
      "DEBUG \t step   464 loss = 14.0807\n",
      "DEBUG \t step   465 loss = 14.3522\n",
      "DEBUG \t step   466 loss = 14.4154\n",
      "DEBUG \t step   467 loss = 13.1898\n",
      "DEBUG \t step   468 loss = 14.06\n",
      "DEBUG \t step   469 loss = 20.7401\n",
      "DEBUG \t step   470 loss = 14.2803\n",
      "DEBUG \t step   471 loss = 14.287\n",
      "DEBUG \t step   472 loss = 14.0215\n",
      "DEBUG \t step   473 loss = 13.4496\n",
      "DEBUG \t step   474 loss = 14.033\n",
      "DEBUG \t step   475 loss = 14.4732\n",
      "DEBUG \t step   476 loss = 13.7291\n",
      "DEBUG \t step   477 loss = 13.0513\n",
      "DEBUG \t step   478 loss = 13.6051\n",
      "DEBUG \t step   479 loss = 13.5316\n",
      "DEBUG \t step   480 loss = 13.5474\n",
      "DEBUG \t step   481 loss = 13.7794\n",
      "DEBUG \t step   482 loss = 13.8363\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "DEBUG \t step   483 loss = 13.2939\n",
      "DEBUG \t step   484 loss = 13.3987\n",
      "DEBUG \t step   485 loss = 13.4694\n",
      "DEBUG \t step   486 loss = 13.0736\n",
      "DEBUG \t step   487 loss = 12.9663\n",
      "DEBUG \t step   488 loss = 13.4017\n",
      "DEBUG \t step   489 loss = 13.1387\n",
      "DEBUG \t step   490 loss = 12.8554\n",
      "DEBUG \t step   491 loss = 13.7535\n",
      "DEBUG \t step   492 loss = 13.0516\n",
      "DEBUG \t step   493 loss = 12.9229\n",
      "DEBUG \t step   494 loss = 13.0794\n",
      "DEBUG \t step   495 loss = 12.6742\n",
      "DEBUG \t step   496 loss = 12.5159\n",
      "DEBUG \t step   497 loss = 13.8863\n",
      "DEBUG \t step   498 loss = 13.275\n",
      "DEBUG \t step   499 loss = 13.8195\n",
      "DEBUG \t step   500 loss = 14.2111\n",
      "DEBUG \t step   501 loss = 12.8113\n",
      "DEBUG \t step   502 loss = 13.5611\n",
      "DEBUG \t step   503 loss = 13.1597\n",
      "DEBUG \t step   504 loss = 12.7698\n",
      "DEBUG \t step   505 loss = 12.655\n",
      "DEBUG \t step   506 loss = 13.3424\n",
      "DEBUG \t step   507 loss = 13.0807\n",
      "DEBUG \t step   508 loss = 13.4257\n",
      "DEBUG \t step   509 loss = 12.769\n",
      "DEBUG \t step   510 loss = 13.2426\n",
      "DEBUG \t step   511 loss = 13.7624\n",
      "DEBUG \t step   512 loss = 13.4707\n",
      "DEBUG \t step   513 loss = 12.6719\n",
      "DEBUG \t step   514 loss = 12.7837\n",
      "DEBUG \t step   515 loss = 12.3574\n",
      "DEBUG \t step   516 loss = 12.4319\n",
      "DEBUG \t step   517 loss = 12.2339\n",
      "DEBUG \t step   518 loss = 12.5959\n",
      "DEBUG \t step   519 loss = 12.9824\n",
      "DEBUG \t step   520 loss = 12.7877\n",
      "DEBUG \t step   521 loss = 13.0799\n",
      "DEBUG \t step   522 loss = 12.6134\n",
      "DEBUG \t step   523 loss = 12.0151\n",
      "DEBUG \t step   524 loss = 13.6236\n",
      "DEBUG \t step   525 loss = 13.0926\n",
      "DEBUG \t step   526 loss = 12.7921\n",
      "DEBUG \t step   527 loss = 12.3066\n",
      "DEBUG \t step   528 loss = 12.657\n",
      "DEBUG \t step   529 loss = 12.1989\n",
      "DEBUG \t step   530 loss = 12.6969\n",
      "DEBUG \t step   531 loss = 12.205\n",
      "DEBUG \t step   532 loss = 12.7905\n",
      "DEBUG \t step   533 loss = 12.6645\n",
      "DEBUG \t step   534 loss = 11.9637\n",
      "DEBUG \t step   535 loss = 12.3953\n",
      "DEBUG \t step   536 loss = 12.326\n",
      "DEBUG \t step   537 loss = 12.3011\n",
      "DEBUG \t step   538 loss = 12.3628\n",
      "DEBUG \t step   539 loss = 13.1567\n",
      "DEBUG \t step   540 loss = 12.5927\n",
      "DEBUG \t step   541 loss = 12.5462\n",
      "DEBUG \t step   542 loss = 12.2117\n",
      "DEBUG \t step   543 loss = 11.9447\n",
      "DEBUG \t step   544 loss = 12.5186\n",
      "DEBUG \t step   545 loss = 11.6064\n",
      "DEBUG \t step   546 loss = 12.1038\n",
      "DEBUG \t step   547 loss = 12.4013\n",
      "DEBUG \t step   548 loss = 12.1646\n",
      "DEBUG \t step   549 loss = 11.6217\n",
      "DEBUG \t step   550 loss = 11.7608\n",
      "DEBUG \t step   551 loss = 12.044\n",
      "DEBUG \t step   552 loss = 11.5987\n",
      "DEBUG \t step   553 loss = 12.2336\n",
      "DEBUG \t step   554 loss = 11.6134\n",
      "DEBUG \t step   555 loss = 12.212\n",
      "DEBUG \t step   556 loss = 11.7942\n",
      "DEBUG \t step   557 loss = 11.8134\n",
      "DEBUG \t step   558 loss = 11.8879\n",
      "DEBUG \t step   559 loss = 11.5601\n",
      "DEBUG \t step   560 loss = 11.8819\n",
      "DEBUG \t step   561 loss = 11.2771\n",
      "DEBUG \t step   562 loss = 12.6852\n",
      "DEBUG \t step   563 loss = 11.8853\n",
      "DEBUG \t step   564 loss = 11.8232\n",
      "DEBUG \t step   565 loss = 12.2208\n",
      "DEBUG \t step   566 loss = 11.8434\n",
      "DEBUG \t step   567 loss = 10.8617\n",
      "DEBUG \t step   568 loss = 11.9089\n",
      "DEBUG \t step   569 loss = 12.8768\n",
      "DEBUG \t step   570 loss = 11.7326\n",
      "DEBUG \t step   571 loss = 11.6924\n",
      "DEBUG \t step   572 loss = 12.071\n",
      "DEBUG \t step   573 loss = 11.4507\n",
      "DEBUG \t step   574 loss = 11.9765\n",
      "DEBUG \t step   575 loss = 12.3481\n",
      "DEBUG \t step   576 loss = 10.7076\n",
      "DEBUG \t step   577 loss = 11.2173\n",
      "DEBUG \t step   578 loss = 11.6225\n",
      "DEBUG \t step   579 loss = 11.7975\n",
      "DEBUG \t step   580 loss = 11.4295\n",
      "DEBUG \t step   581 loss = 11.7824\n",
      "DEBUG \t step   582 loss = 12.1286\n",
      "DEBUG \t step   583 loss = 10.932\n",
      "DEBUG \t step   584 loss = 11.9352\n",
      "DEBUG \t step   585 loss = 11.4005\n",
      "DEBUG \t step   586 loss = 11.1264\n",
      "DEBUG \t step   587 loss = 10.3828\n",
      "DEBUG \t step   588 loss = 10.6477\n",
      "DEBUG \t step   589 loss = 11.2266\n",
      "DEBUG \t step   590 loss = 11.7988\n",
      "DEBUG \t step   591 loss = 11.1602\n",
      "DEBUG \t step   592 loss = 11.2809\n",
      "DEBUG \t step   593 loss = 11.0131\n",
      "DEBUG \t step   594 loss = 11.3859\n",
      "DEBUG \t step   595 loss = 11.1015\n",
      "DEBUG \t step   596 loss = 11.4198\n",
      "DEBUG \t step   597 loss = 10.501\n",
      "DEBUG \t step   598 loss = 11.206\n",
      "DEBUG \t step   599 loss = 11.2975\n",
      "DEBUG \t step   600 loss = 10.0333\n",
      "DEBUG \t step   601 loss = 9.98137\n",
      "DEBUG \t step   602 loss = 12.6949\n",
      "DEBUG \t step   603 loss = 11.1914\n",
      "DEBUG \t step   604 loss = 10.2179\n",
      "DEBUG \t step   605 loss = 10.8835\n",
      "DEBUG \t step   606 loss = 10.3426\n",
      "DEBUG \t step   607 loss = 10.9994\n",
      "DEBUG \t step   608 loss = 10.4913\n",
      "DEBUG \t step   609 loss = 10.5934\n",
      "DEBUG \t step   610 loss = 11.2756\n",
      "DEBUG \t step   611 loss = 10.6515\n",
      "DEBUG \t step   612 loss = 10.634\n",
      "DEBUG \t step   613 loss = 10.6894\n",
      "DEBUG \t step   614 loss = 10.4173\n",
      "DEBUG \t step   615 loss = 10.3444\n",
      "DEBUG \t step   616 loss = 16.9274\n",
      "DEBUG \t step   617 loss = 10.6686\n",
      "DEBUG \t step   618 loss = 10.6302\n",
      "DEBUG \t step   619 loss = 11.4147\n",
      "DEBUG \t step   620 loss = 10.4305\n",
      "DEBUG \t step   621 loss = 9.93963\n",
      "DEBUG \t step   622 loss = 10.2567\n",
      "DEBUG \t step   623 loss = 10.4703\n",
      "DEBUG \t step   624 loss = 10.5793\n",
      "DEBUG \t step   625 loss = 10.7117\n",
      "DEBUG \t step   626 loss = 10.6469\n",
      "DEBUG \t step   627 loss = 10.6067\n",
      "DEBUG \t step   628 loss = 10.2047\n",
      "DEBUG \t step   629 loss = 10.7753\n",
      "DEBUG \t step   630 loss = 9.84085\n",
      "DEBUG \t step   631 loss = 9.8512\n",
      "DEBUG \t step   632 loss = 9.90551\n",
      "DEBUG \t step   633 loss = 10.2306\n",
      "DEBUG \t step   634 loss = 10.4\n",
      "DEBUG \t step   635 loss = 9.96456\n",
      "DEBUG \t step   636 loss = 10.0543\n",
      "DEBUG \t step   637 loss = 10.4722\n",
      "DEBUG \t step   638 loss = 10.2624\n",
      "DEBUG \t step   639 loss = 9.8927\n",
      "DEBUG \t step   640 loss = 9.74269\n",
      "DEBUG \t step   641 loss = 10.0714\n",
      "DEBUG \t step   642 loss = 9.4886\n",
      "DEBUG \t step   643 loss = 11.2356\n",
      "DEBUG \t step   644 loss = 10.4613\n",
      "DEBUG \t step   645 loss = 9.92244\n",
      "DEBUG \t step   646 loss = 10.5003\n",
      "DEBUG \t step   647 loss = 9.28321\n",
      "DEBUG \t step   648 loss = 10.0217\n",
      "DEBUG \t step   649 loss = 9.95832\n",
      "DEBUG \t step   650 loss = 9.89816\n",
      "DEBUG \t step   651 loss = 9.97542\n",
      "DEBUG \t step   652 loss = 9.11257\n",
      "DEBUG \t step   653 loss = 9.9837\n",
      "DEBUG \t step   654 loss = 10.1827\n",
      "DEBUG \t step   655 loss = 10.101\n",
      "DEBUG \t step   656 loss = 9.23931\n",
      "DEBUG \t step   657 loss = 8.75782\n",
      "DEBUG \t step   658 loss = 9.40421\n",
      "DEBUG \t step   659 loss = 9.13174\n",
      "DEBUG \t step   660 loss = 9.68286\n",
      "DEBUG \t step   661 loss = 10.4162\n",
      "DEBUG \t step   662 loss = 8.75674\n",
      "DEBUG \t step   663 loss = 10.001\n",
      "DEBUG \t step   664 loss = 9.40904\n",
      "DEBUG \t step   665 loss = 10.1505\n",
      "DEBUG \t step   666 loss = 10.1748\n",
      "DEBUG \t step   667 loss = 10.2148\n",
      "DEBUG \t step   668 loss = 10.2481\n",
      "DEBUG \t step   669 loss = 9.96609\n",
      "DEBUG \t step   670 loss = 9.65714\n",
      "DEBUG \t step   671 loss = 9.60848\n",
      "DEBUG \t step   672 loss = 9.84922\n",
      "DEBUG \t step   673 loss = 10.0371\n",
      "DEBUG \t step   674 loss = 9.28353\n",
      "DEBUG \t step   675 loss = 9.06586\n",
      "DEBUG \t step   676 loss = 9.44504\n",
      "DEBUG \t step   677 loss = 9.66529\n",
      "DEBUG \t step   678 loss = 9.7542\n",
      "DEBUG \t step   679 loss = 9.10189\n",
      "DEBUG \t step   680 loss = 9.36793\n",
      "DEBUG \t step   681 loss = 9.47525\n",
      "DEBUG \t step   682 loss = 9.98902\n",
      "DEBUG \t step   683 loss = 9.58746\n",
      "DEBUG \t step   684 loss = 8.77309\n",
      "DEBUG \t step   685 loss = 9.58264\n",
      "DEBUG \t step   686 loss = 9.774\n",
      "DEBUG \t step   687 loss = 10.1397\n",
      "DEBUG \t step   688 loss = 10.2031\n",
      "DEBUG \t step   689 loss = 8.85642\n",
      "DEBUG \t step   690 loss = 8.65729\n",
      "DEBUG \t step   691 loss = 9.30864\n",
      "DEBUG \t step   692 loss = 9.08819\n",
      "DEBUG \t step   693 loss = 8.79863\n",
      "DEBUG \t step   694 loss = 9.54987\n",
      "DEBUG \t step   695 loss = 8.96493\n",
      "DEBUG \t step   696 loss = 8.57488\n",
      "DEBUG \t step   697 loss = 9.37986\n",
      "DEBUG \t step   698 loss = 9.12005\n",
      "DEBUG \t step   699 loss = 9.55977\n",
      "DEBUG \t step   700 loss = 9.71548\n",
      "DEBUG \t step   701 loss = 8.66767\n",
      "DEBUG \t step   702 loss = 9.24891\n",
      "DEBUG \t step   703 loss = 8.96681\n",
      "DEBUG \t step   704 loss = 8.50462\n",
      "DEBUG \t step   705 loss = 8.97093\n",
      "DEBUG \t step   706 loss = 8.42754\n",
      "DEBUG \t step   707 loss = 8.31459\n",
      "DEBUG \t step   708 loss = 8.92468\n",
      "DEBUG \t step   709 loss = 8.62381\n",
      "DEBUG \t step   710 loss = 8.99014\n",
      "DEBUG \t step   711 loss = 9.12061\n",
      "DEBUG \t step   712 loss = 9.1673\n",
      "DEBUG \t step   713 loss = 8.71748\n",
      "DEBUG \t step   714 loss = 9.10944\n",
      "DEBUG \t step   715 loss = 8.2948\n",
      "DEBUG \t step   716 loss = 9.03157\n",
      "DEBUG \t step   717 loss = 8.86918\n",
      "DEBUG \t step   718 loss = 8.4948\n",
      "DEBUG \t step   719 loss = 8.20143\n",
      "DEBUG \t step   720 loss = 9.02752\n",
      "DEBUG \t step   721 loss = 9.07482\n",
      "DEBUG \t step   722 loss = 8.47549\n",
      "DEBUG \t step   723 loss = 8.6139\n",
      "DEBUG \t step   724 loss = 8.71389\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "DEBUG \t step   725 loss = 8.71019\n",
      "DEBUG \t step   726 loss = 9.34067\n",
      "DEBUG \t step   727 loss = 8.33531\n",
      "DEBUG \t step   728 loss = 8.50657\n",
      "DEBUG \t step   729 loss = 7.92335\n",
      "DEBUG \t step   730 loss = 8.73418\n",
      "DEBUG \t step   731 loss = 7.50367\n",
      "DEBUG \t step   732 loss = 8.30074\n",
      "DEBUG \t step   733 loss = 8.10457\n",
      "DEBUG \t step   734 loss = 8.57933\n",
      "DEBUG \t step   735 loss = 8.29648\n",
      "DEBUG \t step   736 loss = 9.08495\n",
      "DEBUG \t step   737 loss = 9.19558\n",
      "DEBUG \t step   738 loss = 7.57463\n",
      "DEBUG \t step   739 loss = 8.25734\n",
      "DEBUG \t step   740 loss = 8.1562\n",
      "DEBUG \t step   741 loss = 8.13552\n",
      "DEBUG \t step   742 loss = 8.61787\n",
      "DEBUG \t step   743 loss = 7.84507\n",
      "DEBUG \t step   744 loss = 8.50339\n",
      "DEBUG \t step   745 loss = 9.99432\n",
      "DEBUG \t step   746 loss = 8.67392\n",
      "DEBUG \t step   747 loss = 7.62062\n",
      "DEBUG \t step   748 loss = 8.47083\n",
      "DEBUG \t step   749 loss = 7.59856\n",
      "DEBUG \t step   750 loss = 8.73944\n",
      "DEBUG \t step   751 loss = 7.82123\n",
      "DEBUG \t step   752 loss = 8.3673\n",
      "DEBUG \t step   753 loss = 8.05969\n",
      "DEBUG \t step   754 loss = 7.67401\n",
      "DEBUG \t step   755 loss = 8.23807\n",
      "DEBUG \t step   756 loss = 7.85361\n",
      "DEBUG \t step   757 loss = 8.29006\n",
      "DEBUG \t step   758 loss = 7.93663\n",
      "DEBUG \t step   759 loss = 7.14638\n",
      "DEBUG \t step   760 loss = 7.75548\n",
      "DEBUG \t step   761 loss = 7.23605\n",
      "DEBUG \t step   762 loss = 8.39854\n",
      "DEBUG \t step   763 loss = 8.36651\n",
      "DEBUG \t step   764 loss = 8.08217\n",
      "DEBUG \t step   765 loss = 8.51663\n",
      "DEBUG \t step   766 loss = 17.1032\n",
      "DEBUG \t step   767 loss = 8.11124\n",
      "DEBUG \t step   768 loss = 8.07747\n",
      "DEBUG \t step   769 loss = 7.82815\n",
      "DEBUG \t step   770 loss = 9.03203\n",
      "DEBUG \t step   771 loss = 8.53237\n",
      "DEBUG \t step   772 loss = 7.96279\n",
      "DEBUG \t step   773 loss = 8.05574\n",
      "DEBUG \t step   774 loss = 7.76004\n",
      "DEBUG \t step   775 loss = 7.35636\n",
      "DEBUG \t step   776 loss = 8.11715\n",
      "DEBUG \t step   777 loss = 8.26839\n",
      "DEBUG \t step   778 loss = 8.3788\n",
      "DEBUG \t step   779 loss = 8.4216\n",
      "DEBUG \t step   780 loss = 8.70143\n",
      "DEBUG \t step   781 loss = 7.68424\n",
      "DEBUG \t step   782 loss = 7.71564\n",
      "DEBUG \t step   783 loss = 8.99345\n",
      "DEBUG \t step   784 loss = 7.84072\n",
      "DEBUG \t step   785 loss = 7.97106\n",
      "DEBUG \t step   786 loss = 8.17313\n",
      "DEBUG \t step   787 loss = 8.43836\n",
      "DEBUG \t step   788 loss = 8.48604\n",
      "DEBUG \t step   789 loss = 7.89398\n",
      "DEBUG \t step   790 loss = 7.66896\n",
      "DEBUG \t step   791 loss = 7.93176\n",
      "DEBUG \t step   792 loss = 7.50743\n",
      "DEBUG \t step   793 loss = 7.35892\n",
      "DEBUG \t step   794 loss = 8.19966\n",
      "DEBUG \t step   795 loss = 8.04621\n",
      "DEBUG \t step   796 loss = 7.20783\n",
      "DEBUG \t step   797 loss = 7.82553\n",
      "DEBUG \t step   798 loss = 7.99542\n",
      "DEBUG \t step   799 loss = 7.39769\n",
      "DEBUG \t step   800 loss = 7.53701\n",
      "DEBUG \t step   801 loss = 7.24536\n",
      "DEBUG \t step   802 loss = 7.33658\n",
      "DEBUG \t step   803 loss = 7.342\n",
      "DEBUG \t step   804 loss = 7.75321\n",
      "DEBUG \t step   805 loss = 6.91357\n",
      "DEBUG \t step   806 loss = 7.52435\n",
      "DEBUG \t step   807 loss = 7.56103\n",
      "DEBUG \t step   808 loss = 7.79389\n",
      "DEBUG \t step   809 loss = 8.33436\n",
      "DEBUG \t step   810 loss = 7.46276\n",
      "DEBUG \t step   811 loss = 7.03648\n",
      "DEBUG \t step   812 loss = 7.09304\n",
      "DEBUG \t step   813 loss = 7.55697\n",
      "DEBUG \t step   814 loss = 7.74993\n",
      "DEBUG \t step   815 loss = 7.77072\n",
      "DEBUG \t step   816 loss = 7.57071\n",
      "DEBUG \t step   817 loss = 7.87914\n",
      "DEBUG \t step   818 loss = 7.59507\n",
      "DEBUG \t step   819 loss = 7.95819\n",
      "DEBUG \t step   820 loss = 7.26536\n",
      "DEBUG \t step   821 loss = 7.76702\n",
      "DEBUG \t step   822 loss = 6.81672\n",
      "DEBUG \t step   823 loss = 7.69591\n",
      "DEBUG \t step   824 loss = 7.49277\n",
      "DEBUG \t step   825 loss = 7.71589\n",
      "DEBUG \t step   826 loss = 7.54939\n",
      "DEBUG \t step   827 loss = 7.14454\n",
      "DEBUG \t step   828 loss = 6.54073\n",
      "DEBUG \t step   829 loss = 7.31939\n",
      "DEBUG \t step   830 loss = 8.24107\n",
      "DEBUG \t step   831 loss = 7.75897\n",
      "DEBUG \t step   832 loss = 7.0123\n",
      "DEBUG \t step   833 loss = 6.6658\n",
      "DEBUG \t step   834 loss = 7.17121\n",
      "DEBUG \t step   835 loss = 7.8772\n",
      "DEBUG \t step   836 loss = 6.91091\n",
      "DEBUG \t step   837 loss = 7.24767\n",
      "DEBUG \t step   838 loss = 7.3708\n",
      "DEBUG \t step   839 loss = 6.72671\n",
      "DEBUG \t step   840 loss = 6.91319\n",
      "DEBUG \t step   841 loss = 7.38147\n",
      "DEBUG \t step   842 loss = 6.73919\n",
      "DEBUG \t step   843 loss = 7.1541\n",
      "DEBUG \t step   844 loss = 7.09714\n",
      "DEBUG \t step   845 loss = 7.6505\n",
      "DEBUG \t step   846 loss = 6.37122\n",
      "DEBUG \t step   847 loss = 7.15714\n",
      "DEBUG \t step   848 loss = 6.78871\n",
      "DEBUG \t step   849 loss = 6.43234\n",
      "DEBUG \t step   850 loss = 6.64114\n",
      "DEBUG \t step   851 loss = 6.98987\n",
      "DEBUG \t step   852 loss = 7.51277\n",
      "DEBUG \t step   853 loss = 7.34095\n",
      "DEBUG \t step   854 loss = 7.5216\n",
      "DEBUG \t step   855 loss = 6.37953\n",
      "DEBUG \t step   856 loss = 7.08232\n",
      "DEBUG \t step   857 loss = 6.96187\n",
      "DEBUG \t step   858 loss = 6.12791\n",
      "DEBUG \t step   859 loss = 6.71254\n",
      "DEBUG \t step   860 loss = 6.15329\n",
      "DEBUG \t step   861 loss = 6.74574\n",
      "DEBUG \t step   862 loss = 7.24058\n",
      "DEBUG \t step   863 loss = 6.16476\n",
      "DEBUG \t step   864 loss = 7.61778\n",
      "DEBUG \t step   865 loss = 6.35608\n",
      "DEBUG \t step   866 loss = 6.53307\n",
      "DEBUG \t step   867 loss = 6.36949\n",
      "DEBUG \t step   868 loss = 6.71838\n",
      "DEBUG \t step   869 loss = 7.3967\n",
      "DEBUG \t step   870 loss = 6.65597\n",
      "DEBUG \t step   871 loss = 6.77125\n",
      "DEBUG \t step   872 loss = 6.67395\n",
      "DEBUG \t step   873 loss = 6.40736\n",
      "DEBUG \t step   874 loss = 6.35543\n",
      "DEBUG \t step   875 loss = 6.74703\n",
      "DEBUG \t step   876 loss = 6.58434\n",
      "DEBUG \t step   877 loss = 6.62172\n",
      "DEBUG \t step   878 loss = 6.65244\n",
      "DEBUG \t step   879 loss = 6.97937\n",
      "DEBUG \t step   880 loss = 6.42221\n",
      "DEBUG \t step   881 loss = 6.84026\n",
      "DEBUG \t step   882 loss = 6.72631\n",
      "DEBUG \t step   883 loss = 6.90398\n",
      "DEBUG \t step   884 loss = 6.6266\n",
      "DEBUG \t step   885 loss = 6.51678\n",
      "DEBUG \t step   886 loss = 6.65169\n",
      "DEBUG \t step   887 loss = 6.63095\n",
      "DEBUG \t step   888 loss = 6.24306\n",
      "DEBUG \t step   889 loss = 7.46224\n",
      "DEBUG \t step   890 loss = 6.84275\n",
      "DEBUG \t step   891 loss = 6.19764\n",
      "DEBUG \t step   892 loss = 7.16809\n",
      "DEBUG \t step   893 loss = 6.57301\n",
      "DEBUG \t step   894 loss = 6.72905\n",
      "DEBUG \t step   895 loss = 7.3967\n",
      "DEBUG \t step   896 loss = 6.78504\n",
      "DEBUG \t step   897 loss = 6.52102\n",
      "DEBUG \t step   898 loss = 6.07938\n",
      "DEBUG \t step   899 loss = 5.95618\n",
      "DEBUG \t step   900 loss = 6.14126\n",
      "DEBUG \t step   901 loss = 5.67246\n",
      "DEBUG \t step   902 loss = 5.59678\n",
      "DEBUG \t step   903 loss = 6.5394\n",
      "DEBUG \t step   904 loss = 6.4651\n",
      "DEBUG \t step   905 loss = 6.64771\n",
      "DEBUG \t step   906 loss = 6.44477\n",
      "DEBUG \t step   907 loss = 5.17112\n",
      "DEBUG \t step   908 loss = 5.80493\n",
      "DEBUG \t step   909 loss = 6.36914\n",
      "DEBUG \t step   910 loss = 6.68615\n",
      "DEBUG \t step   911 loss = 5.53628\n",
      "DEBUG \t step   912 loss = 6.51742\n",
      "DEBUG \t step   913 loss = 6.95286\n",
      "DEBUG \t step   914 loss = 7.2883\n",
      "DEBUG \t step   915 loss = 6.09494\n",
      "DEBUG \t step   916 loss = 6.74383\n",
      "DEBUG \t step   917 loss = 6.3917\n",
      "DEBUG \t step   918 loss = 6.25799\n",
      "DEBUG \t step   919 loss = 6.55483\n",
      "DEBUG \t step   920 loss = 6.44743\n",
      "DEBUG \t step   921 loss = 5.77905\n",
      "DEBUG \t step   922 loss = 5.98885\n",
      "DEBUG \t step   923 loss = 5.83527\n",
      "DEBUG \t step   924 loss = 5.93447\n",
      "DEBUG \t step   925 loss = 5.9199\n",
      "DEBUG \t step   926 loss = 6.01515\n",
      "DEBUG \t step   927 loss = 6.14634\n",
      "DEBUG \t step   928 loss = 5.77208\n",
      "DEBUG \t step   929 loss = 6.78369\n",
      "DEBUG \t step   930 loss = 6.21236\n",
      "DEBUG \t step   931 loss = 5.98394\n",
      "DEBUG \t step   932 loss = 6.51115\n",
      "DEBUG \t step   933 loss = 6.44652\n",
      "DEBUG \t step   934 loss = 5.83554\n",
      "DEBUG \t step   935 loss = 6.30905\n",
      "DEBUG \t step   936 loss = 5.93238\n",
      "DEBUG \t step   937 loss = 6.50758\n",
      "DEBUG \t step   938 loss = 5.93256\n",
      "DEBUG \t step   939 loss = 6.06647\n",
      "DEBUG \t step   940 loss = 6.03391\n",
      "DEBUG \t step   941 loss = 5.51953\n",
      "DEBUG \t step   942 loss = 6.03728\n",
      "DEBUG \t step   943 loss = 6.18949\n",
      "DEBUG \t step   944 loss = 6.10855\n",
      "DEBUG \t step   945 loss = 5.92263\n",
      "DEBUG \t step   946 loss = 6.72183\n",
      "DEBUG \t step   947 loss = 6.11911\n",
      "DEBUG \t step   948 loss = 5.84314\n",
      "DEBUG \t step   949 loss = 6.02928\n",
      "DEBUG \t step   950 loss = 5.82459\n",
      "DEBUG \t step   951 loss = 5.98588\n",
      "DEBUG \t step   952 loss = 5.75092\n",
      "DEBUG \t step   953 loss = 6.19303\n",
      "DEBUG \t step   954 loss = 5.78729\n",
      "DEBUG \t step   955 loss = 5.9059\n",
      "DEBUG \t step   956 loss = 5.31694\n",
      "DEBUG \t step   957 loss = 5.71936\n",
      "DEBUG \t step   958 loss = 6.06149\n",
      "DEBUG \t step   959 loss = 4.93583\n",
      "DEBUG \t step   960 loss = 5.8746\n",
      "DEBUG \t step   961 loss = 5.81154\n",
      "DEBUG \t step   962 loss = 6.22302\n",
      "DEBUG \t step   963 loss = 4.62915\n",
      "DEBUG \t step   964 loss = 6.26837\n",
      "DEBUG \t step   965 loss = 6.9227\n",
      "DEBUG \t step   966 loss = 5.69589\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "DEBUG \t step   967 loss = 4.89925\n",
      "DEBUG \t step   968 loss = 5.95339\n",
      "DEBUG \t step   969 loss = 5.41167\n",
      "DEBUG \t step   970 loss = 5.61495\n",
      "DEBUG \t step   971 loss = 6.08719\n",
      "DEBUG \t step   972 loss = 5.70671\n",
      "DEBUG \t step   973 loss = 6.29176\n",
      "DEBUG \t step   974 loss = 5.96967\n",
      "DEBUG \t step   975 loss = 5.64207\n",
      "DEBUG \t step   976 loss = 6.11389\n",
      "DEBUG \t step   977 loss = 5.4677\n",
      "DEBUG \t step   978 loss = 5.26326\n",
      "DEBUG \t step   979 loss = 5.63665\n",
      "DEBUG \t step   980 loss = 5.47218\n",
      "DEBUG \t step   981 loss = 5.76207\n",
      "DEBUG \t step   982 loss = 5.25431\n",
      "DEBUG \t step   983 loss = 5.11318\n",
      "DEBUG \t step   984 loss = 5.23281\n",
      "DEBUG \t step   985 loss = 4.9322\n",
      "DEBUG \t step   986 loss = 5.19766\n",
      "DEBUG \t step   987 loss = 5.32089\n",
      "DEBUG \t step   988 loss = 5.56581\n",
      "DEBUG \t step   989 loss = 5.68178\n",
      "DEBUG \t step   990 loss = 4.37302\n",
      "DEBUG \t step   991 loss = 5.50948\n",
      "DEBUG \t step   992 loss = 5.3806\n",
      "DEBUG \t step   993 loss = 6.08309\n",
      "DEBUG \t step   994 loss = 5.74113\n",
      "DEBUG \t step   995 loss = 5.29156\n",
      "DEBUG \t step   996 loss = 6.09862\n",
      "DEBUG \t step   997 loss = 4.34491\n",
      "DEBUG \t step   998 loss = 4.74828\n",
      "DEBUG \t step   999 loss = 5.1352\n",
      "DEBUG \t step  1000 loss = 5.90098\n",
      "DEBUG \t step  1001 loss = 5.65187\n",
      "DEBUG \t step  1002 loss = 4.99241\n",
      "DEBUG \t step  1003 loss = 4.93651\n",
      "DEBUG \t step  1004 loss = 5.71697\n",
      "DEBUG \t step  1005 loss = 5.12284\n",
      "DEBUG \t step  1006 loss = 6.20878\n",
      "DEBUG \t step  1007 loss = 5.12986\n",
      "DEBUG \t step  1008 loss = 4.9672\n",
      "DEBUG \t step  1009 loss = 5.65217\n",
      "DEBUG \t step  1010 loss = 5.48825\n",
      "DEBUG \t step  1011 loss = 5.54487\n",
      "DEBUG \t step  1012 loss = 5.84657\n",
      "DEBUG \t step  1013 loss = 5.74514\n",
      "DEBUG \t step  1014 loss = 5.23785\n",
      "DEBUG \t step  1015 loss = 4.71362\n",
      "DEBUG \t step  1016 loss = 4.36813\n",
      "DEBUG \t step  1017 loss = 5.45256\n",
      "DEBUG \t step  1018 loss = 5.15537\n",
      "DEBUG \t step  1019 loss = 5.42831\n",
      "DEBUG \t step  1020 loss = 5.17\n",
      "DEBUG \t step  1021 loss = 4.94556\n",
      "DEBUG \t step  1022 loss = 5.84439\n",
      "DEBUG \t step  1023 loss = 5.11129\n",
      "DEBUG \t step  1024 loss = 4.68024\n",
      "DEBUG \t step  1025 loss = 4.6169\n",
      "DEBUG \t step  1026 loss = 4.95606\n",
      "DEBUG \t step  1027 loss = 4.74444\n",
      "DEBUG \t step  1028 loss = 4.27131\n",
      "DEBUG \t step  1029 loss = 4.88013\n",
      "DEBUG \t step  1030 loss = 4.77623\n",
      "DEBUG \t step  1031 loss = 5.86898\n",
      "DEBUG \t step  1032 loss = 5.16058\n",
      "DEBUG \t step  1033 loss = 4.97931\n",
      "DEBUG \t step  1034 loss = 5.05067\n",
      "DEBUG \t step  1035 loss = 5.13984\n",
      "DEBUG \t step  1036 loss = 5.39295\n",
      "DEBUG \t step  1037 loss = 4.95942\n",
      "DEBUG \t step  1038 loss = 5.33035\n",
      "DEBUG \t step  1039 loss = 4.99434\n",
      "DEBUG \t step  1040 loss = 4.98677\n",
      "DEBUG \t step  1041 loss = 4.65488\n",
      "DEBUG \t step  1042 loss = 4.61823\n",
      "DEBUG \t step  1043 loss = 4.68538\n",
      "DEBUG \t step  1044 loss = 4.55243\n",
      "DEBUG \t step  1045 loss = 4.72619\n",
      "DEBUG \t step  1046 loss = 4.88855\n",
      "DEBUG \t step  1047 loss = 4.91348\n",
      "DEBUG \t step  1048 loss = 4.14682\n",
      "DEBUG \t step  1049 loss = 5.40462\n",
      "DEBUG \t step  1050 loss = 4.9091\n",
      "DEBUG \t step  1051 loss = 4.81781\n",
      "DEBUG \t step  1052 loss = 4.87586\n",
      "DEBUG \t step  1053 loss = 5.02846\n",
      "DEBUG \t step  1054 loss = 5.07139\n",
      "DEBUG \t step  1055 loss = 4.59791\n",
      "DEBUG \t step  1056 loss = 4.63243\n",
      "DEBUG \t step  1057 loss = 5.06353\n",
      "DEBUG \t step  1058 loss = 3.85668\n",
      "DEBUG \t step  1059 loss = 5.28508\n",
      "DEBUG \t step  1060 loss = 5.2355\n",
      "DEBUG \t step  1061 loss = 4.07526\n",
      "DEBUG \t step  1062 loss = 4.13481\n",
      "DEBUG \t step  1063 loss = 5.15536\n",
      "DEBUG \t step  1064 loss = 4.30691\n",
      "DEBUG \t step  1065 loss = 4.27459\n",
      "DEBUG \t step  1066 loss = 4.41401\n",
      "DEBUG \t step  1067 loss = 4.55242\n",
      "DEBUG \t step  1068 loss = 5.11923\n",
      "DEBUG \t step  1069 loss = 4.62136\n",
      "DEBUG \t step  1070 loss = 4.88281\n",
      "DEBUG \t step  1071 loss = 6.58954\n",
      "DEBUG \t step  1072 loss = 4.35964\n",
      "DEBUG \t step  1073 loss = 4.70629\n",
      "DEBUG \t step  1074 loss = 4.33995\n",
      "DEBUG \t step  1075 loss = 4.68683\n",
      "DEBUG \t step  1076 loss = 4.2739\n",
      "DEBUG \t step  1077 loss = 3.67668\n",
      "DEBUG \t step  1078 loss = 4.68557\n",
      "DEBUG \t step  1079 loss = 4.38688\n",
      "DEBUG \t step  1080 loss = 4.37331\n",
      "DEBUG \t step  1081 loss = 4.81933\n",
      "DEBUG \t step  1082 loss = 4.4695\n",
      "DEBUG \t step  1083 loss = 4.97354\n",
      "DEBUG \t step  1084 loss = 4.51781\n",
      "DEBUG \t step  1085 loss = 4.12469\n",
      "DEBUG \t step  1086 loss = 6.42285\n",
      "DEBUG \t step  1087 loss = 5.01891\n",
      "DEBUG \t step  1088 loss = 4.62022\n",
      "DEBUG \t step  1089 loss = 4.87794\n",
      "DEBUG \t step  1090 loss = 4.91586\n",
      "DEBUG \t step  1091 loss = 4.10107\n",
      "DEBUG \t step  1092 loss = 4.64939\n",
      "DEBUG \t step  1093 loss = 5.02957\n",
      "DEBUG \t step  1094 loss = 4.41712\n",
      "DEBUG \t step  1095 loss = 4.42776\n",
      "DEBUG \t step  1096 loss = 4.28038\n",
      "DEBUG \t step  1097 loss = 4.93038\n",
      "DEBUG \t step  1098 loss = 4.39647\n",
      "DEBUG \t step  1099 loss = 4.14815\n",
      "DEBUG \t step  1100 loss = 4.47418\n",
      "DEBUG \t step  1101 loss = 4.53913\n",
      "DEBUG \t step  1102 loss = 4.18599\n",
      "DEBUG \t step  1103 loss = 4.42585\n",
      "DEBUG \t step  1104 loss = 4.52254\n",
      "DEBUG \t step  1105 loss = 3.73001\n",
      "DEBUG \t step  1106 loss = 3.80091\n",
      "DEBUG \t step  1107 loss = 4.65234\n",
      "DEBUG \t step  1108 loss = 4.22851\n",
      "DEBUG \t step  1109 loss = 3.80812\n",
      "DEBUG \t step  1110 loss = 4.85446\n",
      "DEBUG \t step  1111 loss = 3.86523\n",
      "DEBUG \t step  1112 loss = 4.18319\n",
      "DEBUG \t step  1113 loss = 4.21953\n",
      "DEBUG \t step  1114 loss = 5.04039\n",
      "DEBUG \t step  1115 loss = 4.80243\n",
      "DEBUG \t step  1116 loss = 4.30441\n",
      "DEBUG \t step  1117 loss = 5.39042\n",
      "DEBUG \t step  1118 loss = 4.25597\n",
      "DEBUG \t step  1119 loss = 5.07854\n",
      "DEBUG \t step  1120 loss = 4.12041\n",
      "DEBUG \t step  1121 loss = 3.47527\n",
      "DEBUG \t step  1122 loss = 4.13058\n",
      "DEBUG \t step  1123 loss = 3.55016\n",
      "DEBUG \t step  1124 loss = 4.84087\n",
      "DEBUG \t step  1125 loss = 4.22556\n",
      "DEBUG \t step  1126 loss = 4.61652\n",
      "DEBUG \t step  1127 loss = 4.38913\n",
      "DEBUG \t step  1128 loss = 4.1752\n",
      "DEBUG \t step  1129 loss = 4.35237\n",
      "DEBUG \t step  1130 loss = 4.11809\n",
      "DEBUG \t step  1131 loss = 4.52757\n",
      "DEBUG \t step  1132 loss = 3.64453\n",
      "DEBUG \t step  1133 loss = 3.92684\n",
      "DEBUG \t step  1134 loss = 4.419\n",
      "DEBUG \t step  1135 loss = 4.53101\n",
      "DEBUG \t step  1136 loss = 4.20247\n",
      "DEBUG \t step  1137 loss = 4.4274\n",
      "DEBUG \t step  1138 loss = 4.00318\n",
      "DEBUG \t step  1139 loss = 6.42864\n",
      "DEBUG \t step  1140 loss = 4.00687\n",
      "DEBUG \t step  1141 loss = 4.74919\n",
      "DEBUG \t step  1142 loss = 3.83376\n",
      "DEBUG \t step  1143 loss = 4.00634\n",
      "DEBUG \t step  1144 loss = 3.43185\n",
      "DEBUG \t step  1145 loss = 3.91977\n",
      "DEBUG \t step  1146 loss = 3.8136\n",
      "DEBUG \t step  1147 loss = 4.02812\n",
      "DEBUG \t step  1148 loss = 4.1181\n",
      "DEBUG \t step  1149 loss = 3.40067\n",
      "DEBUG \t step  1150 loss = 3.87853\n",
      "DEBUG \t step  1151 loss = 4.30686\n",
      "DEBUG \t step  1152 loss = 4.22774\n",
      "DEBUG \t step  1153 loss = 4.38618\n",
      "DEBUG \t step  1154 loss = 4.56262\n",
      "DEBUG \t step  1155 loss = 4.45982\n",
      "DEBUG \t step  1156 loss = 4.59891\n",
      "DEBUG \t step  1157 loss = 4.44961\n",
      "DEBUG \t step  1158 loss = 4.0087\n",
      "DEBUG \t step  1159 loss = 4.88411\n",
      "DEBUG \t step  1160 loss = 3.81384\n",
      "DEBUG \t step  1161 loss = 3.60741\n",
      "DEBUG \t step  1162 loss = 4.1445\n",
      "DEBUG \t step  1163 loss = 4.40349\n",
      "DEBUG \t step  1164 loss = 3.83159\n",
      "DEBUG \t step  1165 loss = 3.76538\n",
      "DEBUG \t step  1166 loss = 4.21465\n",
      "DEBUG \t step  1167 loss = 3.94987\n",
      "DEBUG \t step  1168 loss = 4.0818\n",
      "DEBUG \t step  1169 loss = 4.06183\n",
      "DEBUG \t step  1170 loss = 3.47987\n",
      "DEBUG \t step  1171 loss = 3.67692\n",
      "DEBUG \t step  1172 loss = 4.20745\n",
      "DEBUG \t step  1173 loss = 3.84148\n",
      "DEBUG \t step  1174 loss = 3.49437\n",
      "DEBUG \t step  1175 loss = 3.67877\n",
      "DEBUG \t step  1176 loss = 3.95581\n",
      "DEBUG \t step  1177 loss = 4.26368\n",
      "DEBUG \t step  1178 loss = 3.89446\n",
      "DEBUG \t step  1179 loss = 3.66383\n",
      "DEBUG \t step  1180 loss = 4.65264\n",
      "DEBUG \t step  1181 loss = 3.91674\n",
      "DEBUG \t step  1182 loss = 3.80197\n",
      "DEBUG \t step  1183 loss = 3.24795\n",
      "DEBUG \t step  1184 loss = 4.25066\n",
      "DEBUG \t step  1185 loss = 3.59737\n",
      "DEBUG \t step  1186 loss = 4.23543\n",
      "DEBUG \t step  1187 loss = 4.40551\n",
      "DEBUG \t step  1188 loss = 3.06393\n",
      "DEBUG \t step  1189 loss = 3.78871\n",
      "DEBUG \t step  1190 loss = 4.47356\n",
      "DEBUG \t step  1191 loss = 3.01607\n",
      "DEBUG \t step  1192 loss = 3.5921\n",
      "DEBUG \t step  1193 loss = 4.14678\n",
      "DEBUG \t step  1194 loss = 4.06156\n",
      "DEBUG \t step  1195 loss = 3.63912\n",
      "DEBUG \t step  1196 loss = 3.80904\n",
      "DEBUG \t step  1197 loss = 3.94498\n",
      "DEBUG \t step  1198 loss = 4.46766\n",
      "DEBUG \t step  1199 loss = 3.94135\n",
      "DEBUG \t step  1200 loss = 3.16809\n",
      "DEBUG \t step  1201 loss = 4.44084\n",
      "DEBUG \t step  1202 loss = 4.10566\n",
      "DEBUG \t step  1203 loss = 3.80488\n",
      "DEBUG \t step  1204 loss = 3.19777\n",
      "DEBUG \t step  1205 loss = 2.95526\n",
      "DEBUG \t step  1206 loss = 4.49641\n",
      "DEBUG \t step  1207 loss = 4.23787\n",
      "DEBUG \t step  1208 loss = 3.70975\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "DEBUG \t step  1209 loss = 3.79127\n",
      "DEBUG \t step  1210 loss = 3.59221\n",
      "DEBUG \t step  1211 loss = 3.88194\n",
      "DEBUG \t step  1212 loss = 3.40576\n",
      "DEBUG \t step  1213 loss = 3.87329\n",
      "DEBUG \t step  1214 loss = 3.49796\n",
      "DEBUG \t step  1215 loss = 3.24266\n",
      "DEBUG \t step  1216 loss = 3.73337\n",
      "DEBUG \t step  1217 loss = 3.64298\n",
      "DEBUG \t step  1218 loss = 3.20159\n",
      "DEBUG \t step  1219 loss = 2.85318\n",
      "DEBUG \t step  1220 loss = 3.73986\n",
      "DEBUG \t step  1221 loss = 3.01543\n",
      "DEBUG \t step  1222 loss = 3.32277\n",
      "DEBUG \t step  1223 loss = 2.74171\n",
      "DEBUG \t step  1224 loss = 3.70805\n",
      "DEBUG \t step  1225 loss = 3.61112\n",
      "DEBUG \t step  1226 loss = 2.88479\n",
      "DEBUG \t step  1227 loss = 3.65801\n",
      "DEBUG \t step  1228 loss = 4.02943\n",
      "DEBUG \t step  1229 loss = 2.83562\n",
      "DEBUG \t step  1230 loss = 3.24228\n",
      "DEBUG \t step  1231 loss = 3.2782\n",
      "DEBUG \t step  1232 loss = 3.59486\n",
      "DEBUG \t step  1233 loss = 3.65803\n",
      "DEBUG \t step  1234 loss = 2.6809\n",
      "DEBUG \t step  1235 loss = 3.3619\n",
      "DEBUG \t step  1236 loss = 3.39297\n",
      "DEBUG \t step  1237 loss = 3.81023\n",
      "DEBUG \t step  1238 loss = 3.22556\n",
      "DEBUG \t step  1239 loss = 3.19648\n",
      "DEBUG \t step  1240 loss = 4.0888\n",
      "DEBUG \t step  1241 loss = 3.74848\n",
      "DEBUG \t step  1242 loss = 2.87371\n",
      "DEBUG \t step  1243 loss = 2.63874\n",
      "DEBUG \t step  1244 loss = 3.5867\n",
      "DEBUG \t step  1245 loss = 2.79683\n",
      "DEBUG \t step  1246 loss = 2.68036\n",
      "DEBUG \t step  1247 loss = 3.90314\n",
      "DEBUG \t step  1248 loss = 2.79271\n",
      "DEBUG \t step  1249 loss = 3.35704\n",
      "DEBUG \t step  1250 loss = 3.22364\n",
      "DEBUG \t step  1251 loss = 4.49007\n",
      "DEBUG \t step  1252 loss = 3.48859\n",
      "DEBUG \t step  1253 loss = 3.53123\n",
      "DEBUG \t step  1254 loss = 3.95726\n",
      "DEBUG \t step  1255 loss = 3.76191\n",
      "DEBUG \t step  1256 loss = 3.16396\n",
      "DEBUG \t step  1257 loss = 3.27892\n",
      "DEBUG \t step  1258 loss = 3.61666\n",
      "DEBUG \t step  1259 loss = 2.60104\n",
      "DEBUG \t step  1260 loss = 3.61282\n",
      "DEBUG \t step  1261 loss = 3.39698\n",
      "DEBUG \t step  1262 loss = 3.25254\n",
      "DEBUG \t step  1263 loss = 3.60338\n",
      "DEBUG \t step  1264 loss = 3.24701\n",
      "DEBUG \t step  1265 loss = 2.68532\n",
      "DEBUG \t step  1266 loss = 3.48767\n",
      "DEBUG \t step  1267 loss = 3.38295\n",
      "DEBUG \t step  1268 loss = 3.05102\n",
      "DEBUG \t step  1269 loss = 2.66065\n",
      "DEBUG \t step  1270 loss = 4.91023\n",
      "DEBUG \t step  1271 loss = 3.58709\n",
      "DEBUG \t step  1272 loss = 2.62444\n",
      "DEBUG \t step  1273 loss = 3.1492\n",
      "DEBUG \t step  1274 loss = 2.40123\n",
      "DEBUG \t step  1275 loss = 3.45261\n",
      "DEBUG \t step  1276 loss = 3.09002\n",
      "DEBUG \t step  1277 loss = 3.43325\n",
      "DEBUG \t step  1278 loss = 3.65285\n",
      "DEBUG \t step  1279 loss = 5.20928\n",
      "DEBUG \t step  1280 loss = 3.18166\n",
      "DEBUG \t step  1281 loss = 2.98796\n",
      "DEBUG \t step  1282 loss = 3.51501\n",
      "DEBUG \t step  1283 loss = 3.69819\n",
      "DEBUG \t step  1284 loss = 2.9171\n",
      "DEBUG \t step  1285 loss = 3.58279\n",
      "DEBUG \t step  1286 loss = 3.22799\n",
      "DEBUG \t step  1287 loss = 2.95054\n",
      "DEBUG \t step  1288 loss = 2.73463\n",
      "DEBUG \t step  1289 loss = 2.94937\n",
      "DEBUG \t step  1290 loss = 3.66875\n",
      "DEBUG \t step  1291 loss = 5.37338\n",
      "DEBUG \t step  1292 loss = 3.4862\n",
      "DEBUG \t step  1293 loss = 3.53109\n",
      "DEBUG \t step  1294 loss = 3.13318\n",
      "DEBUG \t step  1295 loss = 3.44508\n",
      "DEBUG \t step  1296 loss = 3.03238\n",
      "DEBUG \t step  1297 loss = 3.20079\n",
      "DEBUG \t step  1298 loss = 2.97329\n",
      "DEBUG \t step  1299 loss = 2.847\n",
      "DEBUG \t step  1300 loss = 2.9055\n",
      "DEBUG \t step  1301 loss = 2.11617\n",
      "DEBUG \t step  1302 loss = 3.67571\n",
      "DEBUG \t step  1303 loss = 3.05302\n",
      "DEBUG \t step  1304 loss = 2.67335\n",
      "DEBUG \t step  1305 loss = 3.19011\n",
      "DEBUG \t step  1306 loss = 2.28169\n",
      "DEBUG \t step  1307 loss = 3.15299\n",
      "DEBUG \t step  1308 loss = 2.48567\n",
      "DEBUG \t step  1309 loss = 3.02921\n",
      "DEBUG \t step  1310 loss = 2.74102\n",
      "DEBUG \t step  1311 loss = 2.92383\n",
      "DEBUG \t step  1312 loss = 3.50952\n",
      "DEBUG \t step  1313 loss = 3.4817\n",
      "DEBUG \t step  1314 loss = 2.90958\n",
      "DEBUG \t step  1315 loss = 3.17264\n",
      "DEBUG \t step  1316 loss = 3.00095\n",
      "DEBUG \t step  1317 loss = 3.28235\n",
      "DEBUG \t step  1318 loss = 3.1123\n",
      "DEBUG \t step  1319 loss = 3.19697\n",
      "DEBUG \t step  1320 loss = 3.23534\n",
      "DEBUG \t step  1321 loss = 2.62485\n",
      "DEBUG \t step  1322 loss = 2.39473\n",
      "DEBUG \t step  1323 loss = 2.65671\n",
      "DEBUG \t step  1324 loss = 2.6517\n",
      "DEBUG \t step  1325 loss = 2.83837\n",
      "DEBUG \t step  1326 loss = 2.96297\n",
      "DEBUG \t step  1327 loss = 3.27864\n",
      "DEBUG \t step  1328 loss = 2.8699\n",
      "DEBUG \t step  1329 loss = 2.41302\n",
      "DEBUG \t step  1330 loss = 2.75787\n",
      "DEBUG \t step  1331 loss = 2.02633\n",
      "DEBUG \t step  1332 loss = 2.64443\n",
      "DEBUG \t step  1333 loss = 3.00131\n",
      "DEBUG \t step  1334 loss = 2.90105\n",
      "DEBUG \t step  1335 loss = 2.53407\n",
      "DEBUG \t step  1336 loss = 2.69649\n",
      "DEBUG \t step  1337 loss = 3.10092\n",
      "DEBUG \t step  1338 loss = 2.40056\n",
      "DEBUG \t step  1339 loss = 2.89754\n",
      "DEBUG \t step  1340 loss = 3.58338\n",
      "DEBUG \t step  1341 loss = 2.91623\n",
      "DEBUG \t step  1342 loss = 3.01027\n",
      "DEBUG \t step  1343 loss = 2.88131\n",
      "DEBUG \t step  1344 loss = 2.61064\n",
      "DEBUG \t step  1345 loss = 3.21264\n",
      "DEBUG \t step  1346 loss = 3.68778\n",
      "DEBUG \t step  1347 loss = 3.20522\n",
      "DEBUG \t step  1348 loss = 3.02826\n",
      "DEBUG \t step  1349 loss = 2.26471\n",
      "DEBUG \t step  1350 loss = 1.86408\n",
      "DEBUG \t step  1351 loss = 2.38076\n",
      "DEBUG \t step  1352 loss = 3.04889\n",
      "DEBUG \t step  1353 loss = 2.88127\n",
      "DEBUG \t step  1354 loss = 2.29979\n",
      "DEBUG \t step  1355 loss = 2.32288\n",
      "DEBUG \t step  1356 loss = 2.58144\n",
      "DEBUG \t step  1357 loss = 3.13952\n",
      "DEBUG \t step  1358 loss = 2.64957\n",
      "DEBUG \t step  1359 loss = 2.66308\n",
      "DEBUG \t step  1360 loss = 2.4935\n",
      "DEBUG \t step  1361 loss = 2.44679\n",
      "DEBUG \t step  1362 loss = 2.35046\n",
      "DEBUG \t step  1363 loss = 2.68055\n",
      "DEBUG \t step  1364 loss = 2.70021\n",
      "DEBUG \t step  1365 loss = 2.92847\n",
      "DEBUG \t step  1366 loss = 2.65287\n",
      "DEBUG \t step  1367 loss = 3.36018\n",
      "DEBUG \t step  1368 loss = 3.14083\n",
      "DEBUG \t step  1369 loss = 3.2839\n",
      "DEBUG \t step  1370 loss = 2.87706\n",
      "DEBUG \t step  1371 loss = 2.28323\n",
      "DEBUG \t step  1372 loss = 2.71482\n",
      "DEBUG \t step  1373 loss = 3.14818\n",
      "DEBUG \t step  1374 loss = 1.91019\n",
      "DEBUG \t step  1375 loss = 3.26189\n",
      "DEBUG \t step  1376 loss = 2.32266\n",
      "DEBUG \t step  1377 loss = 2.58565\n",
      "DEBUG \t step  1378 loss = 2.78616\n",
      "DEBUG \t step  1379 loss = 2.61887\n",
      "DEBUG \t step  1380 loss = 1.77536\n",
      "DEBUG \t step  1381 loss = 2.46593\n",
      "DEBUG \t step  1382 loss = 2.03291\n",
      "DEBUG \t step  1383 loss = 2.25107\n",
      "DEBUG \t step  1384 loss = 2.02538\n",
      "DEBUG \t step  1385 loss = 2.64462\n",
      "DEBUG \t step  1386 loss = 2.52711\n",
      "DEBUG \t step  1387 loss = 2.82251\n",
      "DEBUG \t step  1388 loss = 1.84549\n",
      "DEBUG \t step  1389 loss = 2.80308\n",
      "DEBUG \t step  1390 loss = 2.50824\n",
      "DEBUG \t step  1391 loss = 2.32621\n",
      "DEBUG \t step  1392 loss = 2.47522\n",
      "DEBUG \t step  1393 loss = 2.25115\n",
      "DEBUG \t step  1394 loss = 2.13335\n",
      "DEBUG \t step  1395 loss = 2.34713\n",
      "DEBUG \t step  1396 loss = 2.70859\n",
      "DEBUG \t step  1397 loss = 2.40365\n",
      "DEBUG \t step  1398 loss = 1.77973\n",
      "DEBUG \t step  1399 loss = 2.20398\n",
      "DEBUG \t step  1400 loss = 2.03752\n",
      "DEBUG \t step  1401 loss = 2.92017\n",
      "DEBUG \t step  1402 loss = 2.30887\n",
      "DEBUG \t step  1403 loss = 2.55533\n",
      "DEBUG \t step  1404 loss = 3.27081\n",
      "DEBUG \t step  1405 loss = 2.00323\n",
      "DEBUG \t step  1406 loss = 2.58616\n",
      "DEBUG \t step  1407 loss = 2.32837\n",
      "DEBUG \t step  1408 loss = 2.62355\n",
      "DEBUG \t step  1409 loss = 2.55319\n",
      "DEBUG \t step  1410 loss = 2.91456\n",
      "DEBUG \t step  1411 loss = 2.51186\n",
      "DEBUG \t step  1412 loss = 2.58023\n",
      "DEBUG \t step  1413 loss = 2.11317\n",
      "DEBUG \t step  1414 loss = 2.72763\n",
      "DEBUG \t step  1415 loss = 2.46438\n",
      "DEBUG \t step  1416 loss = 2.66077\n",
      "DEBUG \t step  1417 loss = 3.45261\n",
      "DEBUG \t step  1418 loss = 1.30968\n",
      "DEBUG \t step  1419 loss = 2.02033\n",
      "DEBUG \t step  1420 loss = 1.66572\n",
      "DEBUG \t step  1421 loss = 2.63344\n",
      "DEBUG \t step  1422 loss = 2.79048\n",
      "DEBUG \t step  1423 loss = 2.36907\n",
      "DEBUG \t step  1424 loss = 2.09989\n",
      "DEBUG \t step  1425 loss = 1.90149\n",
      "DEBUG \t step  1426 loss = 1.62709\n",
      "DEBUG \t step  1427 loss = 1.95195\n",
      "DEBUG \t step  1428 loss = 1.51384\n",
      "DEBUG \t step  1429 loss = 2.89507\n",
      "DEBUG \t step  1430 loss = 2.15085\n",
      "DEBUG \t step  1431 loss = 3.11155\n",
      "DEBUG \t step  1432 loss = 2.44331\n",
      "DEBUG \t step  1433 loss = 2.20407\n",
      "DEBUG \t step  1434 loss = 2.08581\n",
      "DEBUG \t step  1435 loss = 2.42461\n",
      "DEBUG \t step  1436 loss = 1.99394\n",
      "DEBUG \t step  1437 loss = 2.04695\n",
      "DEBUG \t step  1438 loss = 2.82294\n",
      "DEBUG \t step  1439 loss = 2.33058\n",
      "DEBUG \t step  1440 loss = 2.10667\n",
      "DEBUG \t step  1441 loss = 2.3715\n",
      "DEBUG \t step  1442 loss = 2.13589\n",
      "DEBUG \t step  1443 loss = 2.0997\n",
      "DEBUG \t step  1444 loss = 2.40378\n",
      "DEBUG \t step  1445 loss = 2.69322\n",
      "DEBUG \t step  1446 loss = 2.3217\n",
      "DEBUG \t step  1447 loss = 3.06968\n",
      "DEBUG \t step  1448 loss = 2.19487\n",
      "DEBUG \t step  1449 loss = 2.62741\n",
      "DEBUG \t step  1450 loss = 1.93388\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "DEBUG \t step  1451 loss = 2.23005\n",
      "DEBUG \t step  1452 loss = 2.05846\n",
      "DEBUG \t step  1453 loss = 2.37242\n",
      "DEBUG \t step  1454 loss = 1.70136\n",
      "DEBUG \t step  1455 loss = 2.47376\n",
      "DEBUG \t step  1456 loss = 2.62243\n",
      "DEBUG \t step  1457 loss = 2.22\n",
      "DEBUG \t step  1458 loss = 2.60625\n",
      "DEBUG \t step  1459 loss = 1.61209\n",
      "DEBUG \t step  1460 loss = 2.40373\n",
      "DEBUG \t step  1461 loss = 3.32855\n",
      "DEBUG \t step  1462 loss = 2.61678\n",
      "DEBUG \t step  1463 loss = 3.63504\n",
      "DEBUG \t step  1464 loss = 2.30637\n",
      "DEBUG \t step  1465 loss = 2.62554\n",
      "DEBUG \t step  1466 loss = 2.52577\n",
      "DEBUG \t step  1467 loss = 2.04929\n",
      "DEBUG \t step  1468 loss = 2.80166\n",
      "DEBUG \t step  1469 loss = 2.27281\n",
      "DEBUG \t step  1470 loss = 2.53645\n",
      "DEBUG \t step  1471 loss = 2.23338\n",
      "DEBUG \t step  1472 loss = 2.09672\n",
      "DEBUG \t step  1473 loss = 2.42459\n",
      "DEBUG \t step  1474 loss = 2.39755\n",
      "DEBUG \t step  1475 loss = 2.70626\n",
      "DEBUG \t step  1476 loss = 2.14803\n",
      "DEBUG \t step  1477 loss = 2.12395\n",
      "DEBUG \t step  1478 loss = 2.0754\n",
      "DEBUG \t step  1479 loss = 2.52702\n",
      "DEBUG \t step  1480 loss = 2.14769\n",
      "DEBUG \t step  1481 loss = 1.52042\n",
      "DEBUG \t step  1482 loss = 2.93158\n",
      "DEBUG \t step  1483 loss = 2.05924\n",
      "DEBUG \t step  1484 loss = 2.20132\n",
      "DEBUG \t step  1485 loss = 2.50342\n",
      "DEBUG \t step  1486 loss = 2.16502\n",
      "DEBUG \t step  1487 loss = 2.30084\n",
      "DEBUG \t step  1488 loss = 1.63317\n",
      "DEBUG \t step  1489 loss = 1.89554\n",
      "DEBUG \t step  1490 loss = 1.68024\n",
      "DEBUG \t step  1491 loss = 1.84459\n",
      "DEBUG \t step  1492 loss = 1.63598\n",
      "DEBUG \t step  1493 loss = 1.38678\n",
      "DEBUG \t step  1494 loss = 1.71994\n",
      "DEBUG \t step  1495 loss = 1.81303\n",
      "DEBUG \t step  1496 loss = 2.59038\n",
      "DEBUG \t step  1497 loss = 1.6169\n",
      "DEBUG \t step  1498 loss = 1.90588\n",
      "DEBUG \t step  1499 loss = 2.14643\n",
      "DEBUG \t step  1500 loss = 2.01967\n",
      "DEBUG \t step  1501 loss = 1.91788\n",
      "DEBUG \t step  1502 loss = 1.75204\n",
      "DEBUG \t step  1503 loss = 2.31053\n",
      "DEBUG \t step  1504 loss = 2.12471\n",
      "DEBUG \t step  1505 loss = 2.22645\n",
      "DEBUG \t step  1506 loss = 2.04981\n",
      "DEBUG \t step  1507 loss = 1.88154\n",
      "DEBUG \t step  1508 loss = 1.58932\n",
      "DEBUG \t step  1509 loss = 1.74206\n",
      "DEBUG \t step  1510 loss = 2.37344\n",
      "DEBUG \t step  1511 loss = 1.17495\n",
      "DEBUG \t step  1512 loss = 1.82669\n",
      "DEBUG \t step  1513 loss = 1.3465\n",
      "DEBUG \t step  1514 loss = 1.10967\n",
      "DEBUG \t step  1515 loss = 1.68837\n",
      "DEBUG \t step  1516 loss = 2.49356\n",
      "DEBUG \t step  1517 loss = 1.35455\n",
      "DEBUG \t step  1518 loss = 1.27578\n",
      "DEBUG \t step  1519 loss = 1.65972\n",
      "DEBUG \t step  1520 loss = 1.66863\n",
      "DEBUG \t step  1521 loss = 1.89212\n",
      "DEBUG \t step  1522 loss = 1.54516\n",
      "DEBUG \t step  1523 loss = 1.393\n",
      "DEBUG \t step  1524 loss = 1.88502\n",
      "DEBUG \t step  1525 loss = 2.90167\n",
      "DEBUG \t step  1526 loss = 1.52293\n",
      "DEBUG \t step  1527 loss = 1.99959\n",
      "DEBUG \t step  1528 loss = 1.23991\n",
      "DEBUG \t step  1529 loss = 2.5743\n",
      "DEBUG \t step  1530 loss = 1.36191\n",
      "DEBUG \t step  1531 loss = 1.72816\n",
      "DEBUG \t step  1532 loss = 1.58642\n",
      "DEBUG \t step  1533 loss = 1.48767\n",
      "DEBUG \t step  1534 loss = 1.89661\n",
      "DEBUG \t step  1535 loss = 2.36828\n",
      "DEBUG \t step  1536 loss = 1.07969\n",
      "DEBUG \t step  1537 loss = 1.76135\n",
      "DEBUG \t step  1538 loss = 1.71266\n",
      "DEBUG \t step  1539 loss = 1.89935\n",
      "DEBUG \t step  1540 loss = 1.46401\n",
      "DEBUG \t step  1541 loss = 0.630489\n",
      "DEBUG \t step  1542 loss = 1.97178\n",
      "DEBUG \t step  1543 loss = 1.54882\n",
      "DEBUG \t step  1544 loss = 1.59709\n",
      "DEBUG \t step  1545 loss = 1.05165\n",
      "DEBUG \t step  1546 loss = 1.80869\n",
      "DEBUG \t step  1547 loss = 2.13186\n",
      "DEBUG \t step  1548 loss = 2.48523\n",
      "DEBUG \t step  1549 loss = 1.36797\n",
      "DEBUG \t step  1550 loss = 2.11571\n",
      "DEBUG \t step  1551 loss = 1.90579\n",
      "DEBUG \t step  1552 loss = 1.53151\n",
      "DEBUG \t step  1553 loss = 1.99713\n",
      "DEBUG \t step  1554 loss = 2.22942\n",
      "DEBUG \t step  1555 loss = 2.03508\n",
      "DEBUG \t step  1556 loss = 1.91097\n",
      "DEBUG \t step  1557 loss = 1.64553\n",
      "DEBUG \t step  1558 loss = 2.31868\n",
      "DEBUG \t step  1559 loss = 1.88206\n",
      "DEBUG \t step  1560 loss = 1.84929\n",
      "DEBUG \t step  1561 loss = 1.74253\n",
      "DEBUG \t step  1562 loss = 1.55262\n",
      "DEBUG \t step  1563 loss = 1.24187\n",
      "DEBUG \t step  1564 loss = 2.21666\n",
      "DEBUG \t step  1565 loss = 1.54179\n",
      "DEBUG \t step  1566 loss = 1.18126\n",
      "DEBUG \t step  1567 loss = 1.60436\n",
      "DEBUG \t step  1568 loss = 1.62646\n",
      "DEBUG \t step  1569 loss = 1.13235\n",
      "DEBUG \t step  1570 loss = 1.73874\n",
      "DEBUG \t step  1571 loss = 2.98272\n",
      "DEBUG \t step  1572 loss = 1.97496\n",
      "DEBUG \t step  1573 loss = 1.40697\n",
      "DEBUG \t step  1574 loss = 1.75862\n",
      "DEBUG \t step  1575 loss = 2.24646\n",
      "DEBUG \t step  1576 loss = 1.71452\n",
      "DEBUG \t step  1577 loss = 2.13269\n",
      "DEBUG \t step  1578 loss = 1.87098\n",
      "DEBUG \t step  1579 loss = 0.903461\n",
      "DEBUG \t step  1580 loss = 1.25201\n",
      "DEBUG \t step  1581 loss = 1.8638\n",
      "DEBUG \t step  1582 loss = 1.8996\n",
      "DEBUG \t step  1583 loss = 1.43805\n",
      "DEBUG \t step  1584 loss = 1.15156\n",
      "DEBUG \t step  1585 loss = 1.41428\n",
      "DEBUG \t step  1586 loss = 1.13043\n",
      "DEBUG \t step  1587 loss = 0.838783\n",
      "DEBUG \t step  1588 loss = 0.782387\n",
      "DEBUG \t step  1589 loss = 1.6801\n",
      "DEBUG \t step  1590 loss = 2.16813\n",
      "DEBUG \t step  1591 loss = 2.3584\n",
      "DEBUG \t step  1592 loss = 2.03198\n",
      "DEBUG \t step  1593 loss = 1.6852\n",
      "DEBUG \t step  1594 loss = 1.6894\n",
      "DEBUG \t step  1595 loss = 2.05611\n",
      "DEBUG \t step  1596 loss = 2.04665\n",
      "DEBUG \t step  1597 loss = 1.44473\n",
      "DEBUG \t step  1598 loss = 2.35641\n",
      "DEBUG \t step  1599 loss = 1.77884\n",
      "DEBUG \t step  1600 loss = 1.29297\n",
      "DEBUG \t step  1601 loss = 1.44123\n",
      "DEBUG \t step  1602 loss = 1.03164\n",
      "DEBUG \t step  1603 loss = 1.97062\n",
      "DEBUG \t step  1604 loss = 1.84778\n",
      "DEBUG \t step  1605 loss = 1.97628\n",
      "DEBUG \t step  1606 loss = 1.80254\n",
      "DEBUG \t step  1607 loss = 1.53044\n",
      "DEBUG \t step  1608 loss = 1.69098\n",
      "DEBUG \t step  1609 loss = 1.92866\n",
      "DEBUG \t step  1610 loss = 1.70258\n",
      "DEBUG \t step  1611 loss = 1.76521\n",
      "DEBUG \t step  1612 loss = 1.52449\n",
      "DEBUG \t step  1613 loss = 1.15307\n",
      "DEBUG \t step  1614 loss = 1.88707\n",
      "DEBUG \t step  1615 loss = 1.61141\n",
      "DEBUG \t step  1616 loss = 1.23801\n",
      "DEBUG \t step  1617 loss = 1.51574\n",
      "DEBUG \t step  1618 loss = 1.26473\n",
      "DEBUG \t step  1619 loss = 1.24652\n",
      "DEBUG \t step  1620 loss = 1.06793\n",
      "DEBUG \t step  1621 loss = 1.89787\n",
      "DEBUG \t step  1622 loss = 1.49286\n",
      "DEBUG \t step  1623 loss = 0.830939\n",
      "DEBUG \t step  1624 loss = 1.66349\n",
      "DEBUG \t step  1625 loss = 1.17004\n",
      "DEBUG \t step  1626 loss = 1.24293\n",
      "DEBUG \t step  1627 loss = 1.90752\n",
      "DEBUG \t step  1628 loss = 2.46158\n",
      "DEBUG \t step  1629 loss = 1.45676\n",
      "DEBUG \t step  1630 loss = 1.70154\n",
      "DEBUG \t step  1631 loss = 1.18527\n",
      "DEBUG \t step  1632 loss = 1.32646\n",
      "DEBUG \t step  1633 loss = 1.34788\n",
      "DEBUG \t step  1634 loss = 1.57518\n",
      "DEBUG \t step  1635 loss = 1.92275\n",
      "DEBUG \t step  1636 loss = 1.85572\n",
      "DEBUG \t step  1637 loss = 1.18637\n",
      "DEBUG \t step  1638 loss = 0.775541\n",
      "DEBUG \t step  1639 loss = 1.3429\n",
      "DEBUG \t step  1640 loss = 1.74344\n",
      "DEBUG \t step  1641 loss = 1.40233\n",
      "DEBUG \t step  1642 loss = 1.9051\n",
      "DEBUG \t step  1643 loss = 1.16771\n",
      "DEBUG \t step  1644 loss = 1.1377\n",
      "DEBUG \t step  1645 loss = 1.73862\n",
      "DEBUG \t step  1646 loss = 0.958234\n",
      "DEBUG \t step  1647 loss = 1.11713\n",
      "DEBUG \t step  1648 loss = 0.944722\n",
      "DEBUG \t step  1649 loss = 3.08687\n",
      "DEBUG \t step  1650 loss = 1.27105\n",
      "DEBUG \t step  1651 loss = 0.857286\n",
      "DEBUG \t step  1652 loss = 1.52856\n",
      "DEBUG \t step  1653 loss = 1.96828\n",
      "DEBUG \t step  1654 loss = 0.92382\n",
      "DEBUG \t step  1655 loss = 2.05783\n",
      "DEBUG \t step  1656 loss = 1.16256\n",
      "DEBUG \t step  1657 loss = 1.42272\n",
      "DEBUG \t step  1658 loss = 1.07507\n",
      "DEBUG \t step  1659 loss = 1.64777\n",
      "DEBUG \t step  1660 loss = 0.919807\n",
      "DEBUG \t step  1661 loss = 0.726715\n",
      "DEBUG \t step  1662 loss = 1.57691\n",
      "DEBUG \t step  1663 loss = 1.38782\n",
      "DEBUG \t step  1664 loss = 1.26784\n",
      "DEBUG \t step  1665 loss = 1.64389\n",
      "DEBUG \t step  1666 loss = 0.984072\n",
      "DEBUG \t step  1667 loss = 1.65232\n",
      "DEBUG \t step  1668 loss = 1.8319\n",
      "DEBUG \t step  1669 loss = 1.46141\n",
      "DEBUG \t step  1670 loss = 0.989564\n",
      "DEBUG \t step  1671 loss = 1.60373\n",
      "DEBUG \t step  1672 loss = 1.79838\n",
      "DEBUG \t step  1673 loss = 1.0971\n",
      "DEBUG \t step  1674 loss = 1.6531\n",
      "DEBUG \t step  1675 loss = 0.569279\n",
      "DEBUG \t step  1676 loss = 1.1229\n",
      "DEBUG \t step  1677 loss = 2.09242\n",
      "DEBUG \t step  1678 loss = 1.25957\n",
      "DEBUG \t step  1679 loss = 1.20155\n",
      "DEBUG \t step  1680 loss = 0.445877\n",
      "DEBUG \t step  1681 loss = 1.06367\n",
      "DEBUG \t step  1682 loss = 1.53222\n",
      "DEBUG \t step  1683 loss = 1.46691\n",
      "DEBUG \t step  1684 loss = 1.33858\n",
      "DEBUG \t step  1685 loss = 1.34251\n",
      "DEBUG \t step  1686 loss = 1.41284\n",
      "DEBUG \t step  1687 loss = 1.13937\n",
      "DEBUG \t step  1688 loss = 2.37319\n",
      "DEBUG \t step  1689 loss = 0.934886\n",
      "DEBUG \t step  1690 loss = 0.989814\n",
      "DEBUG \t step  1691 loss = 1.37887\n",
      "DEBUG \t step  1692 loss = 1.40474\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "DEBUG \t step  1693 loss = 1.73022\n",
      "DEBUG \t step  1694 loss = 0.660628\n",
      "DEBUG \t step  1695 loss = 1.47228\n",
      "DEBUG \t step  1696 loss = 1.16098\n",
      "DEBUG \t step  1697 loss = 1.3503\n",
      "DEBUG \t step  1698 loss = 1.31396\n",
      "DEBUG \t step  1699 loss = 2.02182\n",
      "DEBUG \t step  1700 loss = 0.960196\n",
      "DEBUG \t step  1701 loss = 1.45575\n",
      "DEBUG \t step  1702 loss = 1.09297\n",
      "DEBUG \t step  1703 loss = 1.27731\n",
      "DEBUG \t step  1704 loss = 1.63084\n",
      "DEBUG \t step  1705 loss = 1.46701\n",
      "DEBUG \t step  1706 loss = 1.58075\n",
      "DEBUG \t step  1707 loss = 2.77646\n",
      "DEBUG \t step  1708 loss = 1.66917\n",
      "DEBUG \t step  1709 loss = 1.53974\n",
      "DEBUG \t step  1710 loss = 0.746076\n",
      "DEBUG \t step  1711 loss = 0.787667\n",
      "DEBUG \t step  1712 loss = 1.48705\n",
      "DEBUG \t step  1713 loss = 1.15223\n",
      "DEBUG \t step  1714 loss = 0.74432\n",
      "DEBUG \t step  1715 loss = 1.20326\n",
      "DEBUG \t step  1716 loss = 1.05584\n",
      "DEBUG \t step  1717 loss = 1.25595\n",
      "DEBUG \t step  1718 loss = 1.63639\n",
      "DEBUG \t step  1719 loss = 1.18738\n",
      "DEBUG \t step  1720 loss = 0.997565\n",
      "DEBUG \t step  1721 loss = 1.59334\n",
      "DEBUG \t step  1722 loss = 1.18497\n",
      "DEBUG \t step  1723 loss = 1.39869\n",
      "DEBUG \t step  1724 loss = 1.13685\n",
      "DEBUG \t step  1725 loss = 0.477479\n",
      "DEBUG \t step  1726 loss = 1.42541\n",
      "DEBUG \t step  1727 loss = 1.47176\n",
      "DEBUG \t step  1728 loss = 2.13344\n",
      "DEBUG \t step  1729 loss = 0.989916\n",
      "DEBUG \t step  1730 loss = 1.00084\n",
      "DEBUG \t step  1731 loss = 1.31844\n",
      "DEBUG \t step  1732 loss = 1.44907\n",
      "DEBUG \t step  1733 loss = 1.14411\n",
      "DEBUG \t step  1734 loss = 0.997098\n",
      "DEBUG \t step  1735 loss = 1.22144\n",
      "DEBUG \t step  1736 loss = 1.65521\n",
      "DEBUG \t step  1737 loss = 1.04064\n",
      "DEBUG \t step  1738 loss = 1.40232\n",
      "DEBUG \t step  1739 loss = 1.21052\n",
      "DEBUG \t step  1740 loss = 0.52208\n",
      "DEBUG \t step  1741 loss = 0.96464\n",
      "DEBUG \t step  1742 loss = 0.922535\n",
      "DEBUG \t step  1743 loss = 0.57069\n",
      "DEBUG \t step  1744 loss = 1.29497\n",
      "DEBUG \t step  1745 loss = 0.764636\n",
      "DEBUG \t step  1746 loss = 0.596204\n",
      "DEBUG \t step  1747 loss = 1.47739\n",
      "DEBUG \t step  1748 loss = 0.704551\n",
      "DEBUG \t step  1749 loss = 1.13051\n",
      "DEBUG \t step  1750 loss = 1.81735\n",
      "DEBUG \t step  1751 loss = 1.15569\n",
      "DEBUG \t step  1752 loss = 0.62525\n",
      "DEBUG \t step  1753 loss = -0.14409\n",
      "DEBUG \t step  1754 loss = 0.819491\n",
      "DEBUG \t step  1755 loss = 0.584971\n",
      "DEBUG \t step  1756 loss = 1.50396\n",
      "DEBUG \t step  1757 loss = 1.12784\n",
      "DEBUG \t step  1758 loss = 1.37416\n",
      "DEBUG \t step  1759 loss = 0.944302\n",
      "DEBUG \t step  1760 loss = 0.708327\n",
      "DEBUG \t step  1761 loss = 1.51183\n",
      "DEBUG \t step  1762 loss = 0.951956\n",
      "DEBUG \t step  1763 loss = 1.13992\n",
      "DEBUG \t step  1764 loss = -0.0584559\n",
      "DEBUG \t step  1765 loss = 0.941625\n",
      "DEBUG \t step  1766 loss = 1.46371\n",
      "DEBUG \t step  1767 loss = 1.36433\n",
      "DEBUG \t step  1768 loss = 0.560516\n",
      "DEBUG \t step  1769 loss = 1.35952\n",
      "DEBUG \t step  1770 loss = 1.01687\n",
      "DEBUG \t step  1771 loss = 1.21911\n",
      "DEBUG \t step  1772 loss = 1.8578\n",
      "DEBUG \t step  1773 loss = 0.774448\n",
      "DEBUG \t step  1774 loss = 1.37295\n",
      "DEBUG \t step  1775 loss = 1.18173\n",
      "DEBUG \t step  1776 loss = 1.66936\n",
      "DEBUG \t step  1777 loss = 0.860755\n",
      "DEBUG \t step  1778 loss = 1.32138\n",
      "DEBUG \t step  1779 loss = 0.898082\n",
      "DEBUG \t step  1780 loss = 1.12301\n",
      "DEBUG \t step  1781 loss = 0.960121\n",
      "DEBUG \t step  1782 loss = 1.20348\n",
      "DEBUG \t step  1783 loss = 0.758963\n",
      "DEBUG \t step  1784 loss = 0.862989\n",
      "DEBUG \t step  1785 loss = 1.21436\n",
      "DEBUG \t step  1786 loss = 0.458139\n",
      "DEBUG \t step  1787 loss = 1.46172\n",
      "DEBUG \t step  1788 loss = 0.843393\n",
      "DEBUG \t step  1789 loss = 0.533864\n",
      "DEBUG \t step  1790 loss = 0.960291\n",
      "DEBUG \t step  1791 loss = 0.630529\n",
      "DEBUG \t step  1792 loss = 1.45164\n",
      "DEBUG \t step  1793 loss = 0.664835\n",
      "DEBUG \t step  1794 loss = 0.710118\n",
      "DEBUG \t step  1795 loss = 0.719209\n",
      "DEBUG \t step  1796 loss = 0.810381\n",
      "DEBUG \t step  1797 loss = 0.138259\n",
      "DEBUG \t step  1798 loss = 1.22091\n",
      "DEBUG \t step  1799 loss = 0.446191\n",
      "DEBUG \t step  1800 loss = 1.12451\n",
      "DEBUG \t step  1801 loss = 0.847999\n",
      "DEBUG \t step  1802 loss = 1.09745\n",
      "DEBUG \t step  1803 loss = 1.45925\n",
      "DEBUG \t step  1804 loss = 0.713525\n",
      "DEBUG \t step  1805 loss = 0.953999\n",
      "DEBUG \t step  1806 loss = 1.14265\n",
      "DEBUG \t step  1807 loss = 0.244373\n",
      "DEBUG \t step  1808 loss = 1.06263\n",
      "DEBUG \t step  1809 loss = 0.771337\n",
      "DEBUG \t step  1810 loss = 1.0411\n",
      "DEBUG \t step  1811 loss = 1.37541\n",
      "DEBUG \t step  1812 loss = 1.5398\n",
      "DEBUG \t step  1813 loss = 1.04689\n",
      "DEBUG \t step  1814 loss = 1.50583\n",
      "DEBUG \t step  1815 loss = 0.278969\n",
      "DEBUG \t step  1816 loss = 0.303059\n",
      "DEBUG \t step  1817 loss = 0.843962\n",
      "DEBUG \t step  1818 loss = 0.360989\n",
      "DEBUG \t step  1819 loss = 1.42488\n",
      "DEBUG \t step  1820 loss = 0.334529\n",
      "DEBUG \t step  1821 loss = 1.15429\n",
      "DEBUG \t step  1822 loss = 0.942839\n",
      "DEBUG \t step  1823 loss = -0.0623802\n",
      "DEBUG \t step  1824 loss = 1.2242\n",
      "DEBUG \t step  1825 loss = 0.110633\n",
      "DEBUG \t step  1826 loss = 1.04671\n",
      "DEBUG \t step  1827 loss = 0.814721\n",
      "DEBUG \t step  1828 loss = 0.981389\n",
      "DEBUG \t step  1829 loss = 0.374465\n",
      "DEBUG \t step  1830 loss = 0.682603\n",
      "DEBUG \t step  1831 loss = 0.888044\n",
      "DEBUG \t step  1832 loss = 1.00653\n",
      "DEBUG \t step  1833 loss = -0.192628\n",
      "DEBUG \t step  1834 loss = 1.33105\n",
      "DEBUG \t step  1835 loss = -0.292317\n",
      "DEBUG \t step  1836 loss = 1.40156\n",
      "DEBUG \t step  1837 loss = 0.548849\n",
      "DEBUG \t step  1838 loss = 0.733393\n",
      "DEBUG \t step  1839 loss = 0.737875\n",
      "DEBUG \t step  1840 loss = 0.953065\n",
      "DEBUG \t step  1841 loss = 1.35565\n",
      "DEBUG \t step  1842 loss = 0.334132\n",
      "DEBUG \t step  1843 loss = 0.527886\n",
      "DEBUG \t step  1844 loss = 0.728576\n",
      "DEBUG \t step  1845 loss = 0.971659\n",
      "DEBUG \t step  1846 loss = 1.0362\n",
      "DEBUG \t step  1847 loss = 1.1995\n",
      "DEBUG \t step  1848 loss = 0.74542\n",
      "DEBUG \t step  1849 loss = 0.822038\n",
      "DEBUG \t step  1850 loss = 0.14102\n",
      "DEBUG \t step  1851 loss = 0.351881\n",
      "DEBUG \t step  1852 loss = 0.718691\n",
      "DEBUG \t step  1853 loss = 0.454031\n",
      "DEBUG \t step  1854 loss = 1.34327\n",
      "DEBUG \t step  1855 loss = 1.12586\n",
      "DEBUG \t step  1856 loss = 0.794541\n",
      "DEBUG \t step  1857 loss = 0.881259\n",
      "DEBUG \t step  1858 loss = 0.402362\n",
      "DEBUG \t step  1859 loss = 0.490797\n",
      "DEBUG \t step  1860 loss = 0.12956\n",
      "DEBUG \t step  1861 loss = 1.00601\n",
      "DEBUG \t step  1862 loss = 0.0126683\n",
      "DEBUG \t step  1863 loss = 0.367983\n",
      "DEBUG \t step  1864 loss = 0.519085\n",
      "DEBUG \t step  1865 loss = 1.5708\n",
      "DEBUG \t step  1866 loss = 1.47664\n",
      "DEBUG \t step  1867 loss = 0.891001\n",
      "DEBUG \t step  1868 loss = 1.33164\n",
      "DEBUG \t step  1869 loss = 1.43242\n",
      "DEBUG \t step  1870 loss = 1.57703\n",
      "DEBUG \t step  1871 loss = 0.409759\n",
      "DEBUG \t step  1872 loss = 0.481442\n",
      "DEBUG \t step  1873 loss = 0.433702\n",
      "DEBUG \t step  1874 loss = 0.102985\n",
      "DEBUG \t step  1875 loss = 1.07597\n",
      "DEBUG \t step  1876 loss = 0.628031\n",
      "DEBUG \t step  1877 loss = -0.0152627\n",
      "DEBUG \t step  1878 loss = 0.482545\n",
      "DEBUG \t step  1879 loss = 1.55648\n",
      "DEBUG \t step  1880 loss = 0.844998\n",
      "DEBUG \t step  1881 loss = 0.42592\n",
      "DEBUG \t step  1882 loss = -0.0152035\n",
      "DEBUG \t step  1883 loss = -0.0997669\n",
      "DEBUG \t step  1884 loss = 1.01354\n",
      "DEBUG \t step  1885 loss = 0.490207\n",
      "DEBUG \t step  1886 loss = 0.736687\n",
      "DEBUG \t step  1887 loss = 0.433603\n",
      "DEBUG \t step  1888 loss = 1.07525\n",
      "DEBUG \t step  1889 loss = 0.678383\n",
      "DEBUG \t step  1890 loss = 0.980835\n",
      "DEBUG \t step  1891 loss = 0.470526\n",
      "DEBUG \t step  1892 loss = 0.591348\n",
      "DEBUG \t step  1893 loss = 0.496179\n",
      "DEBUG \t step  1894 loss = 0.164359\n",
      "DEBUG \t step  1895 loss = 0.505431\n",
      "DEBUG \t step  1896 loss = 0.848054\n",
      "DEBUG \t step  1897 loss = 1.22015\n",
      "DEBUG \t step  1898 loss = 0.21223\n",
      "DEBUG \t step  1899 loss = 0.804585\n",
      "DEBUG \t step  1900 loss = 0.337482\n",
      "DEBUG \t step  1901 loss = 0.380753\n",
      "DEBUG \t step  1902 loss = 1.09557\n",
      "DEBUG \t step  1903 loss = 0.452767\n",
      "DEBUG \t step  1904 loss = 0.505589\n",
      "DEBUG \t step  1905 loss = 0.533463\n",
      "DEBUG \t step  1906 loss = 0.732611\n",
      "DEBUG \t step  1907 loss = 0.457369\n",
      "DEBUG \t step  1908 loss = 0.397615\n",
      "DEBUG \t step  1909 loss = 0.304795\n",
      "DEBUG \t step  1910 loss = 0.832857\n",
      "DEBUG \t step  1911 loss = 0.776005\n",
      "DEBUG \t step  1912 loss = 0.0557357\n",
      "DEBUG \t step  1913 loss = 1.06473\n",
      "DEBUG \t step  1914 loss = 0.621938\n",
      "DEBUG \t step  1915 loss = 3.8174\n",
      "DEBUG \t step  1916 loss = 0.834741\n",
      "DEBUG \t step  1917 loss = 0.432647\n",
      "DEBUG \t step  1918 loss = 1.0107\n",
      "DEBUG \t step  1919 loss = 0.887171\n",
      "DEBUG \t step  1920 loss = 0.214395\n",
      "DEBUG \t step  1921 loss = 0.27015\n",
      "DEBUG \t step  1922 loss = 0.723923\n",
      "DEBUG \t step  1923 loss = 0.0225524\n",
      "DEBUG \t step  1924 loss = 0.311126\n",
      "DEBUG \t step  1925 loss = 0.163129\n",
      "DEBUG \t step  1926 loss = 1.0852\n",
      "DEBUG \t step  1927 loss = 0.845341\n",
      "DEBUG \t step  1928 loss = 0.067302\n",
      "DEBUG \t step  1929 loss = 1.81058\n",
      "DEBUG \t step  1930 loss = 0.711902\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "DEBUG \t step  1931 loss = 0.544337\n",
      "DEBUG \t step  1932 loss = 0.729942\n",
      "DEBUG \t step  1933 loss = 0.281568\n",
      "DEBUG \t step  1934 loss = 0.746916\n",
      "DEBUG \t step  1935 loss = 0.731851\n",
      "DEBUG \t step  1936 loss = 0.861581\n",
      "DEBUG \t step  1937 loss = 0.587285\n",
      "DEBUG \t step  1938 loss = 0.375893\n",
      "DEBUG \t step  1939 loss = 0.52338\n",
      "DEBUG \t step  1940 loss = 0.0507239\n",
      "DEBUG \t step  1941 loss = 0.544204\n",
      "DEBUG \t step  1942 loss = 0.139653\n",
      "DEBUG \t step  1943 loss = 0.603852\n",
      "DEBUG \t step  1944 loss = 0.591492\n",
      "DEBUG \t step  1945 loss = 0.211932\n",
      "DEBUG \t step  1946 loss = 0.632158\n",
      "DEBUG \t step  1947 loss = 0.613739\n",
      "DEBUG \t step  1948 loss = 1.12637\n",
      "DEBUG \t step  1949 loss = 0.655486\n",
      "DEBUG \t step  1950 loss = 0.687108\n",
      "DEBUG \t step  1951 loss = 0.224532\n",
      "DEBUG \t step  1952 loss = 0.675569\n",
      "DEBUG \t step  1953 loss = 1.16836\n",
      "DEBUG \t step  1954 loss = 0.575642\n",
      "DEBUG \t step  1955 loss = 0.314398\n",
      "DEBUG \t step  1956 loss = 0.949717\n",
      "DEBUG \t step  1957 loss = 1.06026\n",
      "DEBUG \t step  1958 loss = 0.894075\n",
      "DEBUG \t step  1959 loss = 0.268737\n",
      "DEBUG \t step  1960 loss = -0.0684191\n",
      "DEBUG \t step  1961 loss = 0.301358\n",
      "DEBUG \t step  1962 loss = 0.670349\n",
      "DEBUG \t step  1963 loss = 0.631736\n",
      "DEBUG \t step  1964 loss = 1.17734\n",
      "DEBUG \t step  1965 loss = -0.0977912\n",
      "DEBUG \t step  1966 loss = 0.872278\n",
      "DEBUG \t step  1967 loss = 0.0835433\n",
      "DEBUG \t step  1968 loss = -0.0705985\n",
      "DEBUG \t step  1969 loss = 0.193565\n",
      "DEBUG \t step  1970 loss = 0.817641\n",
      "DEBUG \t step  1971 loss = 1.54214\n",
      "DEBUG \t step  1972 loss = -0.0112863\n",
      "DEBUG \t step  1973 loss = 0.170732\n",
      "DEBUG \t step  1974 loss = 0.437139\n",
      "DEBUG \t step  1975 loss = -0.0416076\n",
      "DEBUG \t step  1976 loss = 0.201051\n",
      "DEBUG \t step  1977 loss = 0.663106\n",
      "DEBUG \t step  1978 loss = 0.647153\n",
      "DEBUG \t step  1979 loss = 0.138818\n",
      "DEBUG \t step  1980 loss = 0.0719861\n",
      "DEBUG \t step  1981 loss = 1.12457\n",
      "DEBUG \t step  1982 loss = 0.123392\n",
      "DEBUG \t step  1983 loss = 0.35576\n",
      "DEBUG \t step  1984 loss = 0.187577\n",
      "DEBUG \t step  1985 loss = 0.158135\n",
      "DEBUG \t step  1986 loss = 0.172388\n",
      "DEBUG \t step  1987 loss = 0.864039\n",
      "DEBUG \t step  1988 loss = 0.522948\n",
      "DEBUG \t step  1989 loss = 0.218993\n",
      "DEBUG \t step  1990 loss = 0.958601\n",
      "DEBUG \t step  1991 loss = 0.0281422\n",
      "DEBUG \t step  1992 loss = 0.15538\n",
      "DEBUG \t step  1993 loss = 0.298106\n",
      "DEBUG \t step  1994 loss = 0.192198\n",
      "DEBUG \t step  1995 loss = -0.437914\n",
      "DEBUG \t step  1996 loss = 0.17182\n",
      "DEBUG \t step  1997 loss = 0.625345\n",
      "DEBUG \t step  1998 loss = 0.443585\n",
      "DEBUG \t step  1999 loss = -0.0372677\n",
      "DEBUG \t step  2000 loss = 0.0965499\n",
      "DEBUG \t step  2001 loss = 0.684757\n",
      "DEBUG \t step  2002 loss = 0.0434506\n",
      "DEBUG \t step  2003 loss = 0.179006\n",
      "DEBUG \t step  2004 loss = 0.585443\n",
      "DEBUG \t step  2005 loss = 0.75187\n",
      "DEBUG \t step  2006 loss = -0.19287\n",
      "DEBUG \t step  2007 loss = 0.753149\n",
      "DEBUG \t step  2008 loss = 0.524784\n",
      "DEBUG \t step  2009 loss = 0.500014\n",
      "DEBUG \t step  2010 loss = 0.68905\n",
      "DEBUG \t step  2011 loss = 0.508104\n",
      "DEBUG \t step  2012 loss = 1.12944\n",
      "DEBUG \t step  2013 loss = 0.636447\n",
      "DEBUG \t step  2014 loss = 1.07191\n",
      "DEBUG \t step  2015 loss = 0.620359\n",
      "DEBUG \t step  2016 loss = -0.0672604\n",
      "DEBUG \t step  2017 loss = 0.12611\n",
      "DEBUG \t step  2018 loss = -0.160067\n",
      "DEBUG \t step  2019 loss = 0.560006\n",
      "DEBUG \t step  2020 loss = -0.0938559\n",
      "DEBUG \t step  2021 loss = 0.2633\n",
      "DEBUG \t step  2022 loss = -0.24172\n",
      "DEBUG \t step  2023 loss = 0.23306\n",
      "DEBUG \t step  2024 loss = -0.119578\n",
      "DEBUG \t step  2025 loss = 0.304582\n",
      "DEBUG \t step  2026 loss = 0.222591\n",
      "DEBUG \t step  2027 loss = 0.47586\n",
      "DEBUG \t step  2028 loss = 0.504828\n",
      "DEBUG \t step  2029 loss = 0.422783\n",
      "DEBUG \t step  2030 loss = 0.346542\n",
      "DEBUG \t step  2031 loss = 0.22548\n",
      "DEBUG \t step  2032 loss = 0.0345138\n",
      "DEBUG \t step  2033 loss = 0.727085\n",
      "DEBUG \t step  2034 loss = 0.438053\n",
      "DEBUG \t step  2035 loss = -0.163181\n",
      "DEBUG \t step  2036 loss = 0.816675\n",
      "DEBUG \t step  2037 loss = 0.0115353\n",
      "DEBUG \t step  2038 loss = 0.768062\n",
      "DEBUG \t step  2039 loss = 0.24584\n",
      "DEBUG \t step  2040 loss = 0.290391\n",
      "DEBUG \t step  2041 loss = 0.955838\n",
      "DEBUG \t step  2042 loss = 0.185171\n",
      "DEBUG \t step  2043 loss = -0.360956\n",
      "DEBUG \t step  2044 loss = 0.12458\n",
      "DEBUG \t step  2045 loss = 0.00191054\n",
      "DEBUG \t step  2046 loss = 0.0451765\n",
      "DEBUG \t step  2047 loss = 0.215519\n",
      "DEBUG \t step  2048 loss = 0.159755\n",
      "DEBUG \t step  2049 loss = 0.917712\n",
      "DEBUG \t step  2050 loss = -0.26462\n",
      "DEBUG \t step  2051 loss = 0.310773\n",
      "DEBUG \t step  2052 loss = -0.0363671\n",
      "DEBUG \t step  2053 loss = 0.0293219\n",
      "DEBUG \t step  2054 loss = -0.00587582\n",
      "DEBUG \t step  2055 loss = 0.471752\n",
      "DEBUG \t step  2056 loss = 0.238597\n",
      "DEBUG \t step  2057 loss = 0.0422264\n",
      "DEBUG \t step  2058 loss = -0.543846\n",
      "DEBUG \t step  2059 loss = 0.777388\n",
      "DEBUG \t step  2060 loss = -0.693749\n",
      "DEBUG \t step  2061 loss = 0.0994059\n",
      "DEBUG \t step  2062 loss = -0.286047\n",
      "DEBUG \t step  2063 loss = 0.766898\n",
      "DEBUG \t step  2064 loss = -0.142116\n",
      "DEBUG \t step  2065 loss = 0.883171\n",
      "DEBUG \t step  2066 loss = 0.180947\n",
      "DEBUG \t step  2067 loss = 0.210857\n",
      "DEBUG \t step  2068 loss = 0.118777\n",
      "DEBUG \t step  2069 loss = -0.141074\n",
      "DEBUG \t step  2070 loss = 0.363284\n",
      "DEBUG \t step  2071 loss = 0.39178\n",
      "DEBUG \t step  2072 loss = 0.305299\n",
      "DEBUG \t step  2073 loss = 0.545026\n",
      "DEBUG \t step  2074 loss = -0.226126\n",
      "DEBUG \t step  2075 loss = 0.169667\n",
      "DEBUG \t step  2076 loss = -0.336501\n",
      "DEBUG \t step  2077 loss = 0.965252\n",
      "DEBUG \t step  2078 loss = -0.170774\n",
      "DEBUG \t step  2079 loss = 0.0928747\n",
      "DEBUG \t step  2080 loss = 0.134985\n",
      "DEBUG \t step  2081 loss = 0.0768925\n",
      "DEBUG \t step  2082 loss = 0.207024\n",
      "DEBUG \t step  2083 loss = -0.157205\n",
      "DEBUG \t step  2084 loss = -0.13322\n",
      "DEBUG \t step  2085 loss = 0.262412\n",
      "DEBUG \t step  2086 loss = 0.327786\n",
      "DEBUG \t step  2087 loss = -0.0993449\n",
      "DEBUG \t step  2088 loss = 0.244769\n",
      "DEBUG \t step  2089 loss = -0.0589051\n",
      "DEBUG \t step  2090 loss = 0.332496\n",
      "DEBUG \t step  2091 loss = 0.925634\n",
      "DEBUG \t step  2092 loss = -0.257988\n",
      "DEBUG \t step  2093 loss = 0.518207\n",
      "DEBUG \t step  2094 loss = 0.286856\n",
      "DEBUG \t step  2095 loss = -0.300405\n",
      "DEBUG \t step  2096 loss = -0.0130847\n",
      "DEBUG \t step  2097 loss = 0.519027\n",
      "DEBUG \t step  2098 loss = 0.318041\n",
      "DEBUG \t step  2099 loss = -0.133822\n",
      "DEBUG \t step  2100 loss = -0.076749\n",
      "DEBUG \t step  2101 loss = 0.0152595\n",
      "DEBUG \t step  2102 loss = 0.678585\n",
      "DEBUG \t step  2103 loss = -0.164601\n",
      "DEBUG \t step  2104 loss = 0.384856\n",
      "DEBUG \t step  2105 loss = 0.0680997\n",
      "DEBUG \t step  2106 loss = -0.0351076\n",
      "DEBUG \t step  2107 loss = 0.231791\n",
      "DEBUG \t step  2108 loss = -0.117496\n",
      "DEBUG \t step  2109 loss = -0.0222189\n",
      "DEBUG \t step  2110 loss = -0.0573999\n",
      "DEBUG \t step  2111 loss = 0.524485\n",
      "DEBUG \t step  2112 loss = 0.0913248\n",
      "DEBUG \t step  2113 loss = 0.280226\n",
      "DEBUG \t step  2114 loss = 0.318695\n",
      "DEBUG \t step  2115 loss = 0.039408\n",
      "DEBUG \t step  2116 loss = 0.0231956\n",
      "DEBUG \t step  2117 loss = -0.144188\n",
      "DEBUG \t step  2118 loss = -0.249522\n",
      "DEBUG \t step  2119 loss = 0.182491\n",
      "DEBUG \t step  2120 loss = -0.137275\n",
      "DEBUG \t step  2121 loss = -0.116535\n",
      "DEBUG \t step  2122 loss = -0.502473\n",
      "DEBUG \t step  2123 loss = 0.106871\n",
      "DEBUG \t step  2124 loss = 0.219624\n",
      "DEBUG \t step  2125 loss = 0.236981\n",
      "DEBUG \t step  2126 loss = 0.308991\n",
      "DEBUG \t step  2127 loss = 0.361933\n",
      "DEBUG \t step  2128 loss = -0.0891354\n",
      "DEBUG \t step  2129 loss = 0.375717\n",
      "DEBUG \t step  2130 loss = 0.458\n",
      "DEBUG \t step  2131 loss = 0.804599\n",
      "DEBUG \t step  2132 loss = -0.850078\n",
      "DEBUG \t step  2133 loss = -0.565978\n",
      "DEBUG \t step  2134 loss = 0.395504\n",
      "DEBUG \t step  2135 loss = 0.0360778\n",
      "DEBUG \t step  2136 loss = 0.262763\n",
      "DEBUG \t step  2137 loss = 0.173679\n",
      "DEBUG \t step  2138 loss = 0.245434\n",
      "DEBUG \t step  2139 loss = -0.325045\n",
      "DEBUG \t step  2140 loss = 0.197687\n",
      "DEBUG \t step  2141 loss = 0.10554\n",
      "DEBUG \t step  2142 loss = 0.629076\n",
      "DEBUG \t step  2143 loss = -0.444622\n",
      "DEBUG \t step  2144 loss = 0.29245\n",
      "DEBUG \t step  2145 loss = -0.169153\n",
      "DEBUG \t step  2146 loss = -0.122091\n",
      "DEBUG \t step  2147 loss = -0.482058\n",
      "DEBUG \t step  2148 loss = -0.145807\n",
      "DEBUG \t step  2149 loss = -0.321955\n",
      "DEBUG \t step  2150 loss = -0.204977\n",
      "DEBUG \t step  2151 loss = 0.260222\n",
      "DEBUG \t step  2152 loss = -0.0221428\n",
      "DEBUG \t step  2153 loss = -0.299182\n",
      "DEBUG \t step  2154 loss = 0.492136\n",
      "DEBUG \t step  2155 loss = -0.512058\n",
      "DEBUG \t step  2156 loss = -0.701374\n",
      "DEBUG \t step  2157 loss = 0.616286\n",
      "DEBUG \t step  2158 loss = -0.580705\n",
      "DEBUG \t step  2159 loss = 0.543072\n",
      "DEBUG \t step  2160 loss = -0.271091\n",
      "DEBUG \t step  2161 loss = -0.152006\n",
      "DEBUG \t step  2162 loss = -0.0906625\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "DEBUG \t step  2163 loss = -0.341321\n",
      "DEBUG \t step  2164 loss = -0.0973744\n",
      "DEBUG \t step  2165 loss = 0.335691\n",
      "DEBUG \t step  2166 loss = -0.513224\n",
      "DEBUG \t step  2167 loss = 0.441127\n",
      "DEBUG \t step  2168 loss = -0.195149\n",
      "DEBUG \t step  2169 loss = -0.155654\n",
      "DEBUG \t step  2170 loss = 0.146065\n",
      "DEBUG \t step  2171 loss = -0.157879\n",
      "DEBUG \t step  2172 loss = 0.427397\n",
      "DEBUG \t step  2173 loss = -0.264271\n",
      "DEBUG \t step  2174 loss = 0.255104\n",
      "DEBUG \t step  2175 loss = 0.143516\n",
      "DEBUG \t step  2176 loss = -0.144723\n",
      "DEBUG \t step  2177 loss = 0.362921\n",
      "DEBUG \t step  2178 loss = 0.085199\n",
      "DEBUG \t step  2179 loss = 0.166598\n",
      "DEBUG \t step  2180 loss = -0.529532\n",
      "DEBUG \t step  2181 loss = -0.318048\n",
      "DEBUG \t step  2182 loss = -0.0852365\n",
      "DEBUG \t step  2183 loss = -0.226952\n",
      "DEBUG \t step  2184 loss = 0.372169\n",
      "DEBUG \t step  2185 loss = 0.46677\n",
      "DEBUG \t step  2186 loss = -0.0550372\n",
      "DEBUG \t step  2187 loss = 0.123473\n",
      "DEBUG \t step  2188 loss = -0.709439\n",
      "DEBUG \t step  2189 loss = 0.627293\n",
      "DEBUG \t step  2190 loss = -0.932047\n",
      "DEBUG \t step  2191 loss = -0.0653693\n",
      "DEBUG \t step  2192 loss = 0.694153\n",
      "DEBUG \t step  2193 loss = -0.0535071\n",
      "DEBUG \t step  2194 loss = -0.691768\n",
      "DEBUG \t step  2195 loss = -0.0777673\n",
      "DEBUG \t step  2196 loss = -0.0291022\n",
      "DEBUG \t step  2197 loss = 0.0775634\n",
      "DEBUG \t step  2198 loss = -0.00225392\n",
      "DEBUG \t step  2199 loss = 0.467416\n",
      "DEBUG \t step  2200 loss = -0.0729818\n",
      "DEBUG \t step  2201 loss = -0.174586\n",
      "DEBUG \t step  2202 loss = -0.0735762\n",
      "DEBUG \t step  2203 loss = -0.291103\n",
      "DEBUG \t step  2204 loss = 0.206642\n",
      "DEBUG \t step  2205 loss = -0.35946\n",
      "DEBUG \t step  2206 loss = 0.0623758\n",
      "DEBUG \t step  2207 loss = -0.0335207\n",
      "DEBUG \t step  2208 loss = -0.322341\n",
      "DEBUG \t step  2209 loss = -0.164268\n",
      "DEBUG \t step  2210 loss = -0.298333\n",
      "DEBUG \t step  2211 loss = -0.542928\n",
      "DEBUG \t step  2212 loss = 0.818519\n",
      "DEBUG \t step  2213 loss = -0.175861\n",
      "DEBUG \t step  2214 loss = -1.18826\n",
      "DEBUG \t step  2215 loss = 0.020086\n",
      "DEBUG \t step  2216 loss = -1.07731\n",
      "DEBUG \t step  2217 loss = 0.861459\n",
      "DEBUG \t step  2218 loss = -0.30791\n",
      "DEBUG \t step  2219 loss = 12.8663\n",
      "DEBUG \t step  2220 loss = 0.110738\n",
      "DEBUG \t step  2221 loss = 0.415476\n",
      "DEBUG \t step  2222 loss = -0.0830224\n",
      "DEBUG \t step  2223 loss = 0.026601\n",
      "DEBUG \t step  2224 loss = -0.484626\n",
      "DEBUG \t step  2225 loss = -0.643493\n",
      "DEBUG \t step  2226 loss = -0.531596\n",
      "DEBUG \t step  2227 loss = -0.159798\n",
      "DEBUG \t step  2228 loss = 0.444723\n",
      "DEBUG \t step  2229 loss = -0.209576\n",
      "DEBUG \t step  2230 loss = -0.117957\n",
      "DEBUG \t step  2231 loss = 0.26718\n",
      "DEBUG \t step  2232 loss = -0.623983\n",
      "DEBUG \t step  2233 loss = -0.134441\n",
      "DEBUG \t step  2234 loss = -1.03047\n",
      "DEBUG \t step  2235 loss = 0.10526\n",
      "DEBUG \t step  2236 loss = -0.168391\n",
      "DEBUG \t step  2237 loss = -0.325326\n",
      "DEBUG \t step  2238 loss = -0.636917\n",
      "DEBUG \t step  2239 loss = -1.01447\n",
      "DEBUG \t step  2240 loss = -0.137275\n",
      "DEBUG \t step  2241 loss = -0.0928798\n",
      "DEBUG \t step  2242 loss = 0.521724\n",
      "DEBUG \t step  2243 loss = -0.726267\n",
      "DEBUG \t step  2244 loss = -0.151048\n",
      "DEBUG \t step  2245 loss = -0.0553814\n",
      "DEBUG \t step  2246 loss = -0.0806889\n",
      "DEBUG \t step  2247 loss = -0.265405\n",
      "DEBUG \t step  2248 loss = -0.605389\n",
      "DEBUG \t step  2249 loss = 0.609598\n",
      "DEBUG \t step  2250 loss = 0.201578\n",
      "DEBUG \t step  2251 loss = -0.301686\n",
      "DEBUG \t step  2252 loss = 0.254437\n",
      "DEBUG \t step  2253 loss = 0.53236\n",
      "DEBUG \t step  2254 loss = -0.405195\n",
      "DEBUG \t step  2255 loss = -0.0701203\n",
      "DEBUG \t step  2256 loss = -0.2183\n",
      "DEBUG \t step  2257 loss = -0.766243\n",
      "DEBUG \t step  2258 loss = -0.732259\n",
      "DEBUG \t step  2259 loss = -0.142207\n",
      "DEBUG \t step  2260 loss = -0.15166\n",
      "DEBUG \t step  2261 loss = -0.700015\n",
      "DEBUG \t step  2262 loss = 0.0802323\n",
      "DEBUG \t step  2263 loss = 0.313499\n",
      "DEBUG \t step  2264 loss = 0.283268\n",
      "DEBUG \t step  2265 loss = -0.458733\n",
      "DEBUG \t step  2266 loss = 0.169434\n",
      "DEBUG \t step  2267 loss = 0.0517936\n",
      "DEBUG \t step  2268 loss = -0.303608\n",
      "DEBUG \t step  2269 loss = 0.273257\n",
      "DEBUG \t step  2270 loss = -0.392904\n",
      "DEBUG \t step  2271 loss = 0.44848\n",
      "DEBUG \t step  2272 loss = -0.703877\n",
      "DEBUG \t step  2273 loss = -1.01002\n",
      "DEBUG \t step  2274 loss = 0.359133\n",
      "DEBUG \t step  2275 loss = 0.212775\n",
      "DEBUG \t step  2276 loss = -0.519192\n",
      "DEBUG \t step  2277 loss = -0.2437\n",
      "DEBUG \t step  2278 loss = -0.667431\n",
      "DEBUG \t step  2279 loss = -0.996026\n",
      "DEBUG \t step  2280 loss = 0.273185\n",
      "DEBUG \t step  2281 loss = -0.00770547\n",
      "DEBUG \t step  2282 loss = -0.162126\n",
      "DEBUG \t step  2283 loss = 0.175816\n",
      "DEBUG \t step  2284 loss = 0.0773304\n",
      "DEBUG \t step  2285 loss = -0.512412\n",
      "DEBUG \t step  2286 loss = -0.607146\n",
      "DEBUG \t step  2287 loss = 0.182539\n",
      "DEBUG \t step  2288 loss = -0.694855\n",
      "DEBUG \t step  2289 loss = 0.335107\n",
      "DEBUG \t step  2290 loss = 0.351011\n",
      "DEBUG \t step  2291 loss = -0.367074\n",
      "DEBUG \t step  2292 loss = 0.961813\n",
      "DEBUG \t step  2293 loss = 0.319814\n",
      "DEBUG \t step  2294 loss = -0.0375465\n",
      "DEBUG \t step  2295 loss = -0.685502\n",
      "DEBUG \t step  2296 loss = 0.702536\n",
      "DEBUG \t step  2297 loss = -0.0365256\n",
      "DEBUG \t step  2298 loss = 0.297325\n",
      "DEBUG \t step  2299 loss = -0.161133\n",
      "DEBUG \t step  2300 loss = -0.0621092\n",
      "DEBUG \t step  2301 loss = -0.524049\n",
      "DEBUG \t step  2302 loss = -0.428477\n",
      "DEBUG \t step  2303 loss = -0.481184\n",
      "DEBUG \t step  2304 loss = -0.582241\n",
      "DEBUG \t step  2305 loss = -0.22409\n",
      "DEBUG \t step  2306 loss = -0.0466428\n",
      "DEBUG \t step  2307 loss = -0.807201\n",
      "DEBUG \t step  2308 loss = -0.418819\n",
      "DEBUG \t step  2309 loss = -0.11762\n",
      "DEBUG \t step  2310 loss = -0.00959172\n",
      "DEBUG \t step  2311 loss = -0.00444585\n",
      "DEBUG \t step  2312 loss = 0.043913\n",
      "DEBUG \t step  2313 loss = 0.571166\n",
      "DEBUG \t step  2314 loss = -0.537292\n",
      "DEBUG \t step  2315 loss = 0.270969\n",
      "DEBUG \t step  2316 loss = -0.212546\n",
      "DEBUG \t step  2317 loss = 0.112569\n",
      "DEBUG \t step  2318 loss = -0.455186\n",
      "DEBUG \t step  2319 loss = -0.424695\n",
      "DEBUG \t step  2320 loss = -0.464438\n",
      "DEBUG \t step  2321 loss = -0.473156\n",
      "DEBUG \t step  2322 loss = -0.105536\n",
      "DEBUG \t step  2323 loss = -0.198469\n",
      "DEBUG \t step  2324 loss = 0.422803\n",
      "DEBUG \t step  2325 loss = 0.887627\n",
      "DEBUG \t step  2326 loss = -0.685745\n",
      "DEBUG \t step  2327 loss = -0.656979\n",
      "DEBUG \t step  2328 loss = -1.1468\n",
      "DEBUG \t step  2329 loss = -0.416101\n",
      "DEBUG \t step  2330 loss = -0.0506251\n",
      "DEBUG \t step  2331 loss = 0.38371\n",
      "DEBUG \t step  2332 loss = -0.410896\n",
      "DEBUG \t step  2333 loss = -0.490316\n",
      "DEBUG \t step  2334 loss = -0.148082\n",
      "DEBUG \t step  2335 loss = -1.2066\n",
      "DEBUG \t step  2336 loss = -0.480291\n",
      "DEBUG \t step  2337 loss = -0.564195\n",
      "DEBUG \t step  2338 loss = -0.051699\n",
      "DEBUG \t step  2339 loss = 0.554887\n",
      "DEBUG \t step  2340 loss = 0.464537\n",
      "DEBUG \t step  2341 loss = -0.586118\n",
      "DEBUG \t step  2342 loss = -0.224842\n",
      "DEBUG \t step  2343 loss = 0.140776\n",
      "DEBUG \t step  2344 loss = 0.0989285\n",
      "DEBUG \t step  2345 loss = -0.140234\n",
      "DEBUG \t step  2346 loss = -0.220834\n",
      "DEBUG \t step  2347 loss = 0.358295\n",
      "DEBUG \t step  2348 loss = -0.935413\n",
      "DEBUG \t step  2349 loss = -0.797103\n",
      "DEBUG \t step  2350 loss = -0.370552\n",
      "DEBUG \t step  2351 loss = -0.255635\n",
      "DEBUG \t step  2352 loss = 0.0331677\n",
      "DEBUG \t step  2353 loss = -0.0654061\n",
      "DEBUG \t step  2354 loss = -0.792516\n",
      "DEBUG \t step  2355 loss = 1.00517\n",
      "DEBUG \t step  2356 loss = -0.0650678\n",
      "DEBUG \t step  2357 loss = 0.100208\n",
      "DEBUG \t step  2358 loss = 0.315501\n",
      "DEBUG \t step  2359 loss = -0.196945\n",
      "DEBUG \t step  2360 loss = -0.706372\n",
      "DEBUG \t step  2361 loss = 0.134541\n",
      "DEBUG \t step  2362 loss = -0.114532\n",
      "DEBUG \t step  2363 loss = -0.661938\n",
      "DEBUG \t step  2364 loss = -0.826783\n",
      "DEBUG \t step  2365 loss = 0.561703\n",
      "DEBUG \t step  2366 loss = -0.380749\n",
      "DEBUG \t step  2367 loss = -0.599982\n",
      "DEBUG \t step  2368 loss = -0.552984\n",
      "DEBUG \t step  2369 loss = -0.809876\n",
      "DEBUG \t step  2370 loss = -0.41806\n",
      "DEBUG \t step  2371 loss = -0.293652\n",
      "DEBUG \t step  2372 loss = 0.019794\n",
      "DEBUG \t step  2373 loss = 0.366571\n",
      "DEBUG \t step  2374 loss = -0.330331\n",
      "DEBUG \t step  2375 loss = -0.108959\n",
      "DEBUG \t step  2376 loss = 0.0823981\n",
      "DEBUG \t step  2377 loss = -0.122074\n",
      "DEBUG \t step  2378 loss = 0.104684\n",
      "DEBUG \t step  2379 loss = -0.245806\n",
      "DEBUG \t step  2380 loss = -0.458836\n",
      "DEBUG \t step  2381 loss = -0.728625\n",
      "DEBUG \t step  2382 loss = 0.366162\n",
      "DEBUG \t step  2383 loss = -0.402356\n",
      "DEBUG \t step  2384 loss = -0.915713\n",
      "DEBUG \t step  2385 loss = 0.25255\n",
      "DEBUG \t step  2386 loss = -0.596414\n",
      "DEBUG \t step  2387 loss = 0.191845\n",
      "DEBUG \t step  2388 loss = 0.173331\n",
      "DEBUG \t step  2389 loss = -0.235943\n",
      "DEBUG \t step  2390 loss = -0.578616\n",
      "DEBUG \t step  2391 loss = -0.387393\n",
      "DEBUG \t step  2392 loss = -0.509603\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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      "DEBUG \t step  2394 loss = -0.146879\n",
      "DEBUG \t step  2395 loss = -0.162622\n",
      "DEBUG \t step  2396 loss = -0.580962\n",
      "DEBUG \t step  2397 loss = -0.704767\n",
      "DEBUG \t step  2398 loss = -0.471613\n",
      "DEBUG \t step  2399 loss = -0.18096\n",
      "DEBUG \t step  2400 loss = -0.162947\n",
      "DEBUG \t step  2401 loss = 0.0571842\n",
      "DEBUG \t step  2402 loss = -0.707115\n",
      "DEBUG \t step  2403 loss = -0.812926\n",
      "DEBUG \t step  2404 loss = 0.680889\n",
      "DEBUG \t step  2405 loss = 0.158955\n",
      "DEBUG \t step  2406 loss = -0.636955\n",
      "DEBUG \t step  2407 loss = -0.821936\n",
      "DEBUG \t step  2408 loss = 0.0161349\n",
      "DEBUG \t step  2409 loss = 2.05343\n",
      "DEBUG \t step  2410 loss = -0.449846\n",
      "DEBUG \t step  2411 loss = -0.112297\n",
      "DEBUG \t step  2412 loss = 0.23516\n",
      "DEBUG \t step  2413 loss = 0.598729\n",
      "DEBUG \t step  2414 loss = -0.637791\n",
      "DEBUG \t step  2415 loss = -0.0771543\n",
      "DEBUG \t step  2416 loss = -0.720933\n",
      "DEBUG \t step  2417 loss = -0.324247\n",
      "DEBUG \t step  2418 loss = -0.615081\n",
      "DEBUG \t step  2419 loss = -0.489061\n",
      "DEBUG \t step  2420 loss = -0.81913\n",
      "DEBUG \t step  2421 loss = -0.291852\n",
      "DEBUG \t step  2422 loss = -0.279411\n",
      "DEBUG \t step  2423 loss = -0.168712\n",
      "DEBUG \t step  2424 loss = -0.823371\n",
      "DEBUG \t step  2425 loss = -0.956634\n",
      "DEBUG \t step  2426 loss = 0.283457\n",
      "DEBUG \t step  2427 loss = 0.194569\n",
      "DEBUG \t step  2428 loss = -0.838871\n",
      "DEBUG \t step  2429 loss = -0.0047413\n",
      "DEBUG \t step  2430 loss = -0.559076\n",
      "DEBUG \t step  2431 loss = -0.689148\n",
      "DEBUG \t step  2432 loss = -0.299682\n",
      "DEBUG \t step  2433 loss = -0.884385\n",
      "DEBUG \t step  2434 loss = -0.595315\n",
      "DEBUG \t step  2435 loss = -1.11435\n",
      "DEBUG \t step  2436 loss = 0.0495753\n",
      "DEBUG \t step  2437 loss = -0.0852002\n",
      "DEBUG \t step  2438 loss = -0.15404\n",
      "DEBUG \t step  2439 loss = -0.266736\n",
      "DEBUG \t step  2440 loss = 0.195932\n",
      "DEBUG \t step  2441 loss = 0.185633\n",
      "DEBUG \t step  2442 loss = -0.863258\n",
      "DEBUG \t step  2443 loss = -0.382026\n",
      "DEBUG \t step  2444 loss = 0.252158\n",
      "DEBUG \t step  2445 loss = -0.448511\n",
      "DEBUG \t step  2446 loss = -0.179625\n",
      "DEBUG \t step  2447 loss = -0.114999\n",
      "DEBUG \t step  2448 loss = -1.00638\n",
      "DEBUG \t step  2449 loss = -0.0562548\n",
      "DEBUG \t step  2450 loss = 0.120608\n",
      "DEBUG \t step  2451 loss = -0.248703\n",
      "DEBUG \t step  2452 loss = 0.580167\n",
      "DEBUG \t step  2453 loss = -0.403365\n",
      "DEBUG \t step  2454 loss = -0.427596\n",
      "DEBUG \t step  2455 loss = -0.386274\n",
      "DEBUG \t step  2456 loss = -0.0709784\n",
      "DEBUG \t step  2457 loss = -0.478124\n",
      "DEBUG \t step  2458 loss = -0.427781\n",
      "DEBUG \t step  2459 loss = 0.213299\n",
      "DEBUG \t step  2460 loss = 0.185551\n",
      "DEBUG \t step  2461 loss = -1.15001\n",
      "DEBUG \t step  2462 loss = -0.908913\n",
      "DEBUG \t step  2463 loss = -0.296839\n",
      "DEBUG \t step  2464 loss = -0.213982\n",
      "DEBUG \t step  2465 loss = -0.139768\n",
      "DEBUG \t step  2466 loss = -0.554577\n",
      "DEBUG \t step  2467 loss = -1.29373\n",
      "DEBUG \t step  2468 loss = 0.168238\n",
      "DEBUG \t step  2469 loss = 0.134877\n",
      "DEBUG \t step  2470 loss = -0.255521\n",
      "DEBUG \t step  2471 loss = -0.750256\n",
      "DEBUG \t step  2472 loss = -0.0114451\n",
      "DEBUG \t step  2473 loss = -0.410735\n",
      "DEBUG \t step  2474 loss = 0.218873\n",
      "DEBUG \t step  2475 loss = -0.141217\n",
      "DEBUG \t step  2476 loss = -0.78113\n",
      "DEBUG \t step  2477 loss = -0.143108\n",
      "DEBUG \t step  2478 loss = -0.0878578\n",
      "DEBUG \t step  2479 loss = 0.498992\n",
      "DEBUG \t step  2480 loss = -0.385873\n",
      "DEBUG \t step  2481 loss = 0.697456\n",
      "DEBUG \t step  2482 loss = -0.330902\n",
      "DEBUG \t step  2483 loss = -0.416052\n",
      "DEBUG \t step  2484 loss = -0.0582824\n",
      "DEBUG \t step  2485 loss = -0.749726\n",
      "DEBUG \t step  2486 loss = -0.705093\n",
      "DEBUG \t step  2487 loss = -0.366732\n",
      "DEBUG \t step  2488 loss = 0.0636343\n",
      "DEBUG \t step  2489 loss = -0.428274\n",
      "DEBUG \t step  2490 loss = -0.97996\n",
      "DEBUG \t step  2491 loss = -0.721423\n",
      "DEBUG \t step  2492 loss = -0.901971\n",
      "DEBUG \t step  2493 loss = -0.821726\n",
      "DEBUG \t step  2494 loss = -0.48277\n",
      "DEBUG \t step  2495 loss = 0.159761\n",
      "DEBUG \t step  2496 loss = -0.802472\n",
      "DEBUG \t step  2497 loss = -0.687559\n",
      "DEBUG \t step  2498 loss = -0.256268\n",
      "DEBUG \t step  2499 loss = -0.571636\n",
      "DEBUG \t step  2500 loss = -0.184076\n",
      "DEBUG \t step  2501 loss = -0.0532485\n",
      "DEBUG \t step  2502 loss = 0.0489593\n",
      "DEBUG \t step  2503 loss = -0.699592\n",
      "DEBUG \t step  2504 loss = -0.964232\n",
      "DEBUG \t step  2505 loss = -0.33835\n",
      "DEBUG \t step  2506 loss = -0.425566\n",
      "DEBUG \t step  2507 loss = -0.0965802\n",
      "DEBUG \t step  2508 loss = -0.745661\n",
      "DEBUG \t step  2509 loss = -0.103916\n",
      "DEBUG \t step  2510 loss = -0.489986\n",
      "DEBUG \t step  2511 loss = -1.22721\n",
      "DEBUG \t step  2512 loss = -0.573065\n",
      "DEBUG \t step  2513 loss = -0.8967\n",
      "DEBUG \t step  2514 loss = -0.714046\n",
      "DEBUG \t step  2515 loss = -0.893781\n",
      "DEBUG \t step  2516 loss = 0.465743\n",
      "DEBUG \t step  2517 loss = -0.941392\n",
      "DEBUG \t step  2518 loss = -0.858442\n",
      "DEBUG \t step  2519 loss = -0.18183\n",
      "DEBUG \t step  2520 loss = -0.380441\n",
      "DEBUG \t step  2521 loss = -0.374258\n",
      "DEBUG \t step  2522 loss = -0.682367\n",
      "DEBUG \t step  2523 loss = -0.821137\n",
      "DEBUG \t step  2524 loss = -0.445525\n",
      "DEBUG \t step  2525 loss = -0.97567\n",
      "DEBUG \t step  2526 loss = -0.547556\n",
      "DEBUG \t step  2527 loss = -0.853315\n",
      "DEBUG \t step  2528 loss = 0.114161\n",
      "DEBUG \t step  2529 loss = -0.579036\n",
      "DEBUG \t step  2530 loss = 0.0135827\n",
      "DEBUG \t step  2531 loss = -0.0582753\n",
      "DEBUG \t step  2532 loss = -0.140801\n",
      "DEBUG \t step  2533 loss = -0.182517\n",
      "DEBUG \t step  2534 loss = -0.829945\n",
      "DEBUG \t step  2535 loss = -0.0669306\n",
      "DEBUG \t step  2536 loss = -0.467228\n",
      "DEBUG \t step  2537 loss = -0.584846\n",
      "DEBUG \t step  2538 loss = -0.273549\n",
      "DEBUG \t step  2539 loss = -0.00248221\n",
      "DEBUG \t step  2540 loss = -0.345479\n",
      "DEBUG \t step  2541 loss = -0.515946\n",
      "DEBUG \t step  2542 loss = -0.103854\n",
      "DEBUG \t step  2543 loss = 0.187452\n",
      "DEBUG \t step  2544 loss = -0.154338\n",
      "DEBUG \t step  2545 loss = -0.915668\n",
      "DEBUG \t step  2546 loss = -0.75074\n",
      "DEBUG \t step  2547 loss = -0.235062\n",
      "DEBUG \t step  2548 loss = -0.615748\n",
      "DEBUG \t step  2549 loss = 0.163511\n",
      "DEBUG \t step  2550 loss = -0.558204\n",
      "DEBUG \t step  2551 loss = -0.429658\n",
      "DEBUG \t step  2552 loss = -0.527625\n",
      "DEBUG \t step  2553 loss = -0.663658\n",
      "DEBUG \t step  2554 loss = -0.866039\n",
      "DEBUG \t step  2555 loss = -0.0667327\n",
      "DEBUG \t step  2556 loss = -1.14744\n",
      "DEBUG \t step  2557 loss = -0.599862\n",
      "DEBUG \t step  2558 loss = -0.628051\n",
      "DEBUG \t step  2559 loss = -1.02429\n",
      "DEBUG \t step  2560 loss = -0.812641\n",
      "DEBUG \t step  2561 loss = -0.207669\n",
      "DEBUG \t step  2562 loss = -0.346239\n",
      "DEBUG \t step  2563 loss = -0.42864\n",
      "DEBUG \t step  2564 loss = -0.769289\n",
      "DEBUG \t step  2565 loss = -0.442619\n",
      "DEBUG \t step  2566 loss = -0.551839\n",
      "DEBUG \t step  2567 loss = -0.434892\n",
      "DEBUG \t step  2568 loss = -0.822885\n",
      "DEBUG \t step  2569 loss = -0.0774252\n",
      "DEBUG \t step  2570 loss = -0.962704\n",
      "DEBUG \t step  2571 loss = 0.382489\n",
      "DEBUG \t step  2572 loss = -0.340682\n",
      "DEBUG \t step  2573 loss = -0.42353\n",
      "DEBUG \t step  2574 loss = -0.0114898\n",
      "DEBUG \t step  2575 loss = -0.210306\n",
      "DEBUG \t step  2576 loss = -0.625316\n",
      "DEBUG \t step  2577 loss = -0.61977\n",
      "DEBUG \t step  2578 loss = -0.641895\n",
      "DEBUG \t step  2579 loss = -0.158468\n",
      "DEBUG \t step  2580 loss = -0.376173\n",
      "DEBUG \t step  2581 loss = -0.562516\n",
      "DEBUG \t step  2582 loss = -0.606728\n",
      "DEBUG \t step  2583 loss = -0.486623\n",
      "DEBUG \t step  2584 loss = -0.253736\n",
      "DEBUG \t step  2585 loss = 0.342148\n",
      "DEBUG \t step  2586 loss = -0.165116\n",
      "DEBUG \t step  2587 loss = -0.173551\n",
      "DEBUG \t step  2588 loss = -1.46536\n",
      "DEBUG \t step  2589 loss = 0.0896398\n",
      "DEBUG \t step  2590 loss = -0.545322\n",
      "DEBUG \t step  2591 loss = -0.406094\n",
      "DEBUG \t step  2592 loss = -0.918525\n",
      "DEBUG \t step  2593 loss = -0.894497\n",
      "DEBUG \t step  2594 loss = -0.578103\n",
      "DEBUG \t step  2595 loss = -0.553256\n",
      "DEBUG \t step  2596 loss = -0.593555\n",
      "DEBUG \t step  2597 loss = 0.00266581\n",
      "DEBUG \t step  2598 loss = -0.0584986\n",
      "DEBUG \t step  2599 loss = -0.607323\n",
      "DEBUG \t step  2600 loss = 0.38463\n",
      "DEBUG \t step  2601 loss = -0.481794\n",
      "DEBUG \t step  2602 loss = -0.902755\n",
      "DEBUG \t step  2603 loss = -0.823573\n",
      "DEBUG \t step  2604 loss = -0.581352\n",
      "DEBUG \t step  2605 loss = -0.546566\n",
      "DEBUG \t step  2606 loss = -1.1963\n",
      "DEBUG \t step  2607 loss = -0.66562\n",
      "DEBUG \t step  2608 loss = -0.885256\n",
      "DEBUG \t step  2609 loss = -0.510776\n",
      "DEBUG \t step  2610 loss = -0.414367\n",
      "DEBUG \t step  2611 loss = -0.63994\n",
      "DEBUG \t step  2612 loss = -0.993912\n",
      "DEBUG \t step  2613 loss = -1.01504\n",
      "DEBUG \t step  2614 loss = 0.596202\n",
      "DEBUG \t step  2615 loss = 0.482037\n",
      "DEBUG \t step  2616 loss = -0.301577\n",
      "DEBUG \t step  2617 loss = -1.49396\n",
      "DEBUG \t step  2618 loss = -0.392669\n",
      "DEBUG \t step  2619 loss = -0.324627\n",
      "DEBUG \t step  2620 loss = 0.619205\n",
      "DEBUG \t step  2621 loss = -0.269684\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "DEBUG \t step  2622 loss = -0.661252\n",
      "DEBUG \t step  2623 loss = -0.774471\n",
      "DEBUG \t step  2624 loss = -1.18561\n",
      "DEBUG \t step  2625 loss = -0.275053\n",
      "DEBUG \t step  2626 loss = -0.887767\n",
      "DEBUG \t step  2627 loss = 0.287073\n",
      "DEBUG \t step  2628 loss = -0.905378\n",
      "DEBUG \t step  2629 loss = 0.0570901\n",
      "DEBUG \t step  2630 loss = -0.351999\n",
      "DEBUG \t step  2631 loss = -0.118707\n",
      "DEBUG \t step  2632 loss = -0.671623\n",
      "DEBUG \t step  2633 loss = -0.681996\n",
      "DEBUG \t step  2634 loss = -0.521377\n",
      "DEBUG \t step  2635 loss = -0.617793\n",
      "DEBUG \t step  2636 loss = -0.603524\n",
      "DEBUG \t step  2637 loss = -0.821486\n",
      "DEBUG \t step  2638 loss = -0.356088\n",
      "DEBUG \t step  2639 loss = -0.536534\n",
      "DEBUG \t step  2640 loss = -0.747998\n",
      "DEBUG \t step  2641 loss = -0.439992\n",
      "DEBUG \t step  2642 loss = -0.0627628\n",
      "DEBUG \t step  2643 loss = 0.331022\n",
      "DEBUG \t step  2644 loss = -0.441603\n",
      "DEBUG \t step  2645 loss = -0.515788\n",
      "DEBUG \t step  2646 loss = -0.475961\n",
      "DEBUG \t step  2647 loss = -0.401744\n",
      "DEBUG \t step  2648 loss = 0.262217\n",
      "DEBUG \t step  2649 loss = -0.831643\n",
      "DEBUG \t step  2650 loss = -1.12754\n",
      "DEBUG \t step  2651 loss = -0.829439\n",
      "DEBUG \t step  2652 loss = 0.0111126\n",
      "DEBUG \t step  2653 loss = 0.0545446\n",
      "DEBUG \t step  2654 loss = -0.34779\n",
      "DEBUG \t step  2655 loss = -0.239686\n",
      "DEBUG \t step  2656 loss = -0.0659961\n",
      "DEBUG \t step  2657 loss = -0.0800167\n",
      "DEBUG \t step  2658 loss = -0.56742\n",
      "DEBUG \t step  2659 loss = -1.12966\n",
      "DEBUG \t step  2660 loss = -0.735846\n",
      "DEBUG \t step  2661 loss = -0.857747\n",
      "DEBUG \t step  2662 loss = -0.626603\n",
      "DEBUG \t step  2663 loss = 0.501296\n",
      "DEBUG \t step  2664 loss = -0.345909\n",
      "DEBUG \t step  2665 loss = -0.48826\n",
      "DEBUG \t step  2666 loss = -0.425832\n",
      "DEBUG \t step  2667 loss = -0.622227\n",
      "DEBUG \t step  2668 loss = 0.0905803\n",
      "DEBUG \t step  2669 loss = -0.934806\n",
      "DEBUG \t step  2670 loss = -0.55195\n",
      "DEBUG \t step  2671 loss = 0.285835\n",
      "DEBUG \t step  2672 loss = -0.62289\n",
      "DEBUG \t step  2673 loss = -0.438078\n",
      "DEBUG \t step  2674 loss = -0.351686\n",
      "DEBUG \t step  2675 loss = -0.476577\n",
      "DEBUG \t step  2676 loss = -0.894385\n",
      "DEBUG \t step  2677 loss = -0.258823\n",
      "DEBUG \t step  2678 loss = -0.413825\n",
      "DEBUG \t step  2679 loss = -0.737152\n",
      "DEBUG \t step  2680 loss = -0.756135\n",
      "DEBUG \t step  2681 loss = -0.475365\n",
      "DEBUG \t step  2682 loss = -0.271527\n",
      "DEBUG \t step  2683 loss = -0.628242\n",
      "DEBUG \t step  2684 loss = -1.36686\n",
      "DEBUG \t step  2685 loss = -0.608447\n",
      "DEBUG \t step  2686 loss = -0.685795\n",
      "DEBUG \t step  2687 loss = -0.240269\n",
      "DEBUG \t step  2688 loss = 0.146378\n",
      "DEBUG \t step  2689 loss = -1.10885\n"
     ]
    }
   ],
   "source": [
    "# fit\n",
    "losses = cevae.fit(X=torch.tensor(X_train, dtype=torch.float),\n",
    "                   treatment=torch.tensor(treatment_train, dtype=torch.float),\n",
    "                   y=torch.tensor(y_train, dtype=torch.float))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-01T23:41:35.070742Z",
     "start_time": "2021-02-01T21:18:39.932087Z"
    },
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO \t Evaluating 538 minibatches\n",
      "DEBUG \t batch ate = 0.62191\n",
      "DEBUG \t batch ate = 0.613137\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "DEBUG \t batch ate = 0.642462\n",
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      "INFO \t Evaluating 135 minibatches\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "DEBUG \t batch ate = 0.550225\n",
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      "DEBUG \t batch ate = 0.58916\n"
     ]
    }
   ],
   "source": [
    "# predict\n",
    "ite_train = cevae.predict(X_train)\n",
    "ite_val = cevae.predict(X_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-01T23:41:35.076150Z",
     "start_time": "2021-02-01T23:41:35.073086Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.58953923 0.5956359\n"
     ]
    }
   ],
   "source": [
    "ate_train = ite_train.mean()\n",
    "ate_val = ite_val.mean()\n",
    "print(ate_train, ate_val)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Meta Learners"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-01T23:43:21.827553Z",
     "start_time": "2021-02-01T23:41:35.077523Z"
    }
   },
   "outputs": [],
   "source": [
    "# fit propensity model\n",
    "p_model = ElasticNetPropensityModel()\n",
    "p_train = p_model.fit_predict(X_train, treatment_train)\n",
    "p_val = p_model.fit_predict(X_val, treatment_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-01T23:43:57.203494Z",
     "start_time": "2021-02-01T23:43:21.829195Z"
    }
   },
   "outputs": [],
   "source": [
    "s_learner = BaseSRegressor(LGBMRegressor())\n",
    "s_ate = s_learner.estimate_ate(X_train, treatment_train, y_train)[0]\n",
    "s_ite_train = s_learner.fit_predict(X_train, treatment_train, y_train)\n",
    "s_ite_val = s_learner.predict(X_val)\n",
    "\n",
    "t_learner = BaseTRegressor(LGBMRegressor())\n",
    "t_ate = t_learner.estimate_ate(X_train, treatment_train, y_train)[0][0]\n",
    "t_ite_train = t_learner.fit_predict(X_train, treatment_train, y_train)\n",
    "t_ite_val = t_learner.predict(X_val, treatment_val, y_val)\n",
    "\n",
    "x_learner = BaseXRegressor(LGBMRegressor())\n",
    "x_ate = x_learner.estimate_ate(X_train, treatment_train, y_train, p_train)[0][0]\n",
    "x_ite_train = x_learner.fit_predict(X_train, treatment_train, y_train, p_train)\n",
    "x_ite_val = x_learner.predict(X_val, treatment_val, y_val, p_val)\n",
    "\n",
    "r_learner = BaseRRegressor(LGBMRegressor())\n",
    "r_ate = r_learner.estimate_ate(X_train, treatment_train, y_train, p_train)[0][0]\n",
    "r_ite_train = r_learner.fit_predict(X_train, treatment_train, y_train, p_train)\n",
    "r_ite_val = r_learner.predict(X_val)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Results Comparsion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-01T23:44:17.848932Z",
     "start_time": "2021-02-01T23:43:57.205878Z"
    }
   },
   "outputs": [],
   "source": [
    "df_preds_train = pd.DataFrame([s_ite_train.ravel(),\n",
    "                               t_ite_train.ravel(),\n",
    "                               x_ite_train.ravel(),\n",
    "                               r_ite_train.ravel(),\n",
    "                               ite_train.ravel(),\n",
    "                               tau_train.ravel(),\n",
    "                               treatment_train.ravel(),\n",
    "                               y_train.ravel()],\n",
    "                               index=['S','T','X','R','CEVAE','tau','w','y']).T\n",
    "\n",
    "df_cumgain_train = get_cumgain(df_preds_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-01T23:44:20.252428Z",
     "start_time": "2021-02-01T23:44:17.850639Z"
    }
   },
   "outputs": [],
   "source": [
    "df_result_train = pd.DataFrame([s_ate, t_ate, x_ate, r_ate, ate_train, tau_train.mean()],\n",
    "                               index=['S','T','X','R','CEVAE','actual'], columns=['ATE'])\n",
    "df_result_train['MAE'] = [mean_absolute_error(t,p) for t,p in zip([s_ite_train, t_ite_train, x_ite_train, r_ite_train, ite_train],\n",
    "                                                                  [tau_train.values.reshape(-1,1)]*5 )\n",
    "                          ] + [None]\n",
    "df_result_train['AUUC'] = auuc_score(df_preds_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-01T23:44:20.261314Z",
     "start_time": "2021-02-01T23:44:20.253755Z"
    }
   },
   "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>ATE</th>\n",
       "      <th>MAE</th>\n",
       "      <th>AUUC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>S</th>\n",
       "      <td>4.690540</td>\n",
       "      <td>4.581416</td>\n",
       "      <td>0.684130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T</th>\n",
       "      <td>4.708557</td>\n",
       "      <td>4.715296</td>\n",
       "      <td>0.684878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X</th>\n",
       "      <td>4.555315</td>\n",
       "      <td>4.549527</td>\n",
       "      <td>0.671956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R</th>\n",
       "      <td>0.714936</td>\n",
       "      <td>5.991034</td>\n",
       "      <td>0.586835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CEVAE</th>\n",
       "      <td>0.589539</td>\n",
       "      <td>6.238858</td>\n",
       "      <td>0.566627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>actual</th>\n",
       "      <td>4.755900</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             ATE       MAE      AUUC\n",
       "S       4.690540  4.581416  0.684130\n",
       "T       4.708557  4.715296  0.684878\n",
       "X       4.555315  4.549527  0.671956\n",
       "R       0.714936  5.991034  0.586835\n",
       "CEVAE   0.589539  6.238858  0.566627\n",
       "actual  4.755900       NaN       NaN"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_result_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-01T23:44:22.760162Z",
     "start_time": "2021-02-01T23:44:20.262955Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_gain(df_preds_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-01T23:44:27.616283Z",
     "start_time": "2021-02-01T23:44:22.761877Z"
    }
   },
   "outputs": [],
   "source": [
    "df_preds_val = pd.DataFrame([s_ite_val.ravel(),\n",
    "                             t_ite_val.ravel(),\n",
    "                             x_ite_val.ravel(),\n",
    "                             r_ite_val.ravel(),\n",
    "                             ite_val.ravel(),\n",
    "                             tau_val.ravel(),\n",
    "                             treatment_val.ravel(),\n",
    "                             y_val.ravel()],\n",
    "                             index=['S','T','X','R','CEVAE','tau','w','y']).T\n",
    "\n",
    "df_cumgain_val = get_cumgain(df_preds_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-01T23:44:28.162127Z",
     "start_time": "2021-02-01T23:44:27.617962Z"
    }
   },
   "outputs": [],
   "source": [
    "df_result_val = pd.DataFrame([s_ite_val.mean(), t_ite_val.mean(), x_ite_val.mean(), r_ite_val.mean(), ate_val, tau_val.mean()],\n",
    "                              index=['S','T','X','R','CEVAE','actual'], columns=['ATE'])\n",
    "df_result_val['MAE'] = [mean_absolute_error(t,p) for t,p in zip([s_ite_val, t_ite_val, x_ite_val, r_ite_val, ite_val],\n",
    "                                                                  [tau_val.values.reshape(-1,1)]*5 )\n",
    "                          ] + [None]\n",
    "df_result_val['AUUC'] = auuc_score(df_preds_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-01T23:44:28.169322Z",
     "start_time": "2021-02-01T23:44:28.163676Z"
    }
   },
   "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>ATE</th>\n",
       "      <th>MAE</th>\n",
       "      <th>AUUC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>S</th>\n",
       "      <td>4.690676</td>\n",
       "      <td>4.582191</td>\n",
       "      <td>0.683782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T</th>\n",
       "      <td>4.709923</td>\n",
       "      <td>4.717909</td>\n",
       "      <td>0.684032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X</th>\n",
       "      <td>4.560680</td>\n",
       "      <td>4.544644</td>\n",
       "      <td>0.671907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R</th>\n",
       "      <td>0.761550</td>\n",
       "      <td>5.997526</td>\n",
       "      <td>0.586110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CEVAE</th>\n",
       "      <td>0.595636</td>\n",
       "      <td>6.241192</td>\n",
       "      <td>0.566356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>actual</th>\n",
       "      <td>4.774991</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             ATE       MAE      AUUC\n",
       "S       4.690676  4.582191  0.683782\n",
       "T       4.709923  4.717909  0.684032\n",
       "X       4.560680  4.544644  0.671907\n",
       "R       0.761550  5.997526  0.586110\n",
       "CEVAE   0.595636  6.241192  0.566356\n",
       "actual  4.774991       NaN       NaN"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_result_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-01T23:44:28.889771Z",
     "start_time": "2021-02-01T23:44:28.170875Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_gain(df_preds_val)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Synthetic Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-01T23:48:04.322003Z",
     "start_time": "2021-02-01T23:46:46.214260Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO \t Training with 80 minibatches per epoch\n",
      "DEBUG \t step     0 loss = 14.0534\n",
      "DEBUG \t step     1 loss = 13.2864\n",
      "DEBUG \t step     2 loss = 13.0712\n",
      "DEBUG \t step     3 loss = 12.4646\n",
      "DEBUG \t step     4 loss = 12.0247\n",
      "DEBUG \t step     5 loss = 11.5239\n",
      "DEBUG \t step     6 loss = 11.2934\n",
      "DEBUG \t step     7 loss = 11.3141\n",
      "DEBUG \t step     8 loss = 10.8347\n",
      "DEBUG \t step     9 loss = 10.7364\n",
      "DEBUG \t step    10 loss = 10.5978\n",
      "DEBUG \t step    11 loss = 10.2533\n",
      "DEBUG \t step    12 loss = 10.131\n",
      "DEBUG \t step    13 loss = 10.0307\n",
      "DEBUG \t step    14 loss = 9.57977\n",
      "DEBUG \t step    15 loss = 9.79295\n",
      "DEBUG \t step    16 loss = 9.46927\n",
      "DEBUG \t step    17 loss = 9.57581\n",
      "DEBUG \t step    18 loss = 9.24119\n",
      "DEBUG \t step    19 loss = 9.34084\n",
      "DEBUG \t step    20 loss = 9.32529\n",
      "DEBUG \t step    21 loss = 9.40313\n",
      "DEBUG \t step    22 loss = 9.27057\n",
      "DEBUG \t step    23 loss = 9.05239\n",
      "DEBUG \t step    24 loss = 9.17952\n",
      "DEBUG \t step    25 loss = 8.93083\n",
      "DEBUG \t step    26 loss = 8.88059\n",
      "DEBUG \t step    27 loss = 9.06328\n",
      "DEBUG \t step    28 loss = 8.97881\n",
      "DEBUG \t step    29 loss = 8.7639\n",
      "DEBUG \t step    30 loss = 8.80499\n",
      "DEBUG \t step    31 loss = 8.87173\n",
      "DEBUG \t step    32 loss = 8.56747\n",
      "DEBUG \t step    33 loss = 8.61066\n",
      "DEBUG \t step    34 loss = 8.79932\n",
      "DEBUG \t step    35 loss = 8.62871\n",
      "DEBUG \t step    36 loss = 8.54852\n",
      "DEBUG \t step    37 loss = 8.38022\n",
      "DEBUG \t step    38 loss = 8.31573\n",
      "DEBUG \t step    39 loss = 8.53857\n",
      "DEBUG \t step    40 loss = 8.57149\n",
      "DEBUG \t step    41 loss = 8.25793\n",
      "DEBUG \t step    42 loss = 8.54684\n",
      "DEBUG \t step    43 loss = 8.47699\n",
      "DEBUG \t step    44 loss = 8.3233\n",
      "DEBUG \t step    45 loss = 8.40228\n",
      "DEBUG \t step    46 loss = 8.14949\n",
      "DEBUG \t step    47 loss = 8.2015\n",
      "DEBUG \t step    48 loss = 8.07472\n",
      "DEBUG \t step    49 loss = 8.16795\n",
      "DEBUG \t step    50 loss = 8.34108\n",
      "DEBUG \t step    51 loss = 8.57682\n",
      "DEBUG \t step    52 loss = 8.24426\n",
      "DEBUG \t step    53 loss = 8.33251\n",
      "DEBUG \t step    54 loss = 8.10115\n",
      "DEBUG \t step    55 loss = 8.67902\n",
      "DEBUG \t step    56 loss = 8.14677\n",
      "DEBUG \t step    57 loss = 8.1041\n",
      "DEBUG \t step    58 loss = 8.15102\n",
      "DEBUG \t step    59 loss = 8.00679\n",
      "DEBUG \t step    60 loss = 8.0271\n",
      "DEBUG \t step    61 loss = 7.96041\n",
      "DEBUG \t step    62 loss = 7.82294\n",
      "DEBUG \t step    63 loss = 8.13456\n",
      "DEBUG \t step    64 loss = 8.23367\n",
      "DEBUG \t step    65 loss = 8.1886\n",
      "DEBUG \t step    66 loss = 8.11654\n",
      "DEBUG \t step    67 loss = 8.22645\n",
      "DEBUG \t step    68 loss = 8.29743\n",
      "DEBUG \t step    69 loss = 8.24127\n",
      "DEBUG \t step    70 loss = 7.86166\n",
      "DEBUG \t step    71 loss = 8.22115\n",
      "DEBUG \t step    72 loss = 7.8913\n",
      "DEBUG \t step    73 loss = 7.96265\n",
      "DEBUG \t step    74 loss = 7.96243\n",
      "DEBUG \t step    75 loss = 7.99336\n",
      "DEBUG \t step    76 loss = 7.97742\n",
      "DEBUG \t step    77 loss = 7.90728\n",
      "DEBUG \t step    78 loss = 7.79539\n",
      "DEBUG \t step    79 loss = 8.1732\n",
      "DEBUG \t step    80 loss = 8.05217\n",
      "DEBUG \t step    81 loss = 8.34642\n",
      "DEBUG \t step    82 loss = 8.03199\n",
      "DEBUG \t step    83 loss = 7.64226\n",
      "DEBUG \t step    84 loss = 7.60438\n",
      "DEBUG \t step    85 loss = 7.5962\n",
      "DEBUG \t step    86 loss = 7.85927\n",
      "DEBUG \t step    87 loss = 7.98567\n",
      "DEBUG \t step    88 loss = 7.82793\n",
      "DEBUG \t step    89 loss = 7.90716\n",
      "DEBUG \t step    90 loss = 7.71277\n",
      "DEBUG \t step    91 loss = 7.97724\n",
      "DEBUG \t step    92 loss = 7.84886\n",
      "DEBUG \t step    93 loss = 7.88323\n",
      "DEBUG \t step    94 loss = 7.58179\n",
      "DEBUG \t step    95 loss = 7.89912\n",
      "DEBUG \t step    96 loss = 7.67735\n",
      "DEBUG \t step    97 loss = 7.84808\n",
      "DEBUG \t step    98 loss = 7.66705\n",
      "DEBUG \t step    99 loss = 7.65615\n",
      "DEBUG \t step   100 loss = 7.73811\n",
      "DEBUG \t step   101 loss = 7.64997\n",
      "DEBUG \t step   102 loss = 8.36613\n",
      "DEBUG \t step   103 loss = 7.72687\n",
      "DEBUG \t step   104 loss = 7.68498\n",
      "DEBUG \t step   105 loss = 7.50849\n",
      "DEBUG \t step   106 loss = 7.63987\n",
      "DEBUG \t step   107 loss = 7.75501\n",
      "DEBUG \t step   108 loss = 7.62423\n",
      "DEBUG \t step   109 loss = 7.66921\n",
      "DEBUG \t step   110 loss = 7.50166\n",
      "DEBUG \t step   111 loss = 7.62314\n",
      "DEBUG \t step   112 loss = 7.80907\n",
      "DEBUG \t step   113 loss = 7.65659\n",
      "DEBUG \t step   114 loss = 7.55159\n",
      "DEBUG \t step   115 loss = 7.60577\n",
      "DEBUG \t step   116 loss = 7.36759\n",
      "DEBUG \t step   117 loss = 7.43037\n",
      "DEBUG \t step   118 loss = 7.41372\n",
      "DEBUG \t step   119 loss = 7.58245\n",
      "DEBUG \t step   120 loss = 7.75382\n",
      "DEBUG \t step   121 loss = 7.75345\n",
      "DEBUG \t step   122 loss = 7.71091\n",
      "DEBUG \t step   123 loss = 7.61762\n",
      "DEBUG \t step   124 loss = 7.5415\n",
      "DEBUG \t step   125 loss = 7.70995\n",
      "DEBUG \t step   126 loss = 7.43083\n",
      "DEBUG \t step   127 loss = 7.62284\n",
      "DEBUG \t step   128 loss = 7.57494\n",
      "DEBUG \t step   129 loss = 7.43229\n",
      "DEBUG \t step   130 loss = 7.417\n",
      "DEBUG \t step   131 loss = 7.36716\n",
      "DEBUG \t step   132 loss = 7.58527\n",
      "DEBUG \t step   133 loss = 7.61684\n",
      "DEBUG \t step   134 loss = 7.55247\n",
      "DEBUG \t step   135 loss = 7.54181\n",
      "DEBUG \t step   136 loss = 7.47493\n",
      "DEBUG \t step   137 loss = 7.65583\n",
      "DEBUG \t step   138 loss = 7.33769\n",
      "DEBUG \t step   139 loss = 7.36649\n",
      "DEBUG \t step   140 loss = 7.3634\n",
      "DEBUG \t step   141 loss = 7.50731\n",
      "DEBUG \t step   142 loss = 7.60657\n",
      "DEBUG \t step   143 loss = 7.38694\n",
      "DEBUG \t step   144 loss = 7.3596\n",
      "DEBUG \t step   145 loss = 7.42744\n",
      "DEBUG \t step   146 loss = 7.46609\n",
      "DEBUG \t step   147 loss = 7.44444\n",
      "DEBUG \t step   148 loss = 7.44656\n",
      "DEBUG \t step   149 loss = 7.32834\n",
      "DEBUG \t step   150 loss = 7.63049\n",
      "DEBUG \t step   151 loss = 7.43903\n",
      "DEBUG \t step   152 loss = 7.28372\n",
      "DEBUG \t step   153 loss = 7.28897\n",
      "DEBUG \t step   154 loss = 7.3515\n",
      "DEBUG \t step   155 loss = 7.29871\n",
      "DEBUG \t step   156 loss = 7.47948\n",
      "DEBUG \t step   157 loss = 7.56888\n",
      "DEBUG \t step   158 loss = 7.50302\n",
      "DEBUG \t step   159 loss = 7.14918\n",
      "DEBUG \t step   160 loss = 7.34611\n",
      "DEBUG \t step   161 loss = 7.04855\n",
      "DEBUG \t step   162 loss = 7.38615\n",
      "DEBUG \t step   163 loss = 7.39172\n",
      "DEBUG \t step   164 loss = 7.35778\n",
      "DEBUG \t step   165 loss = 7.39445\n",
      "DEBUG \t step   166 loss = 7.41489\n",
      "DEBUG \t step   167 loss = 7.36096\n",
      "DEBUG \t step   168 loss = 7.49107\n",
      "DEBUG \t step   169 loss = 7.31799\n",
      "DEBUG \t step   170 loss = 7.34851\n",
      "DEBUG \t step   171 loss = 7.17355\n",
      "DEBUG \t step   172 loss = 7.38851\n",
      "DEBUG \t step   173 loss = 7.35425\n",
      "DEBUG \t step   174 loss = 7.39068\n",
      "DEBUG \t step   175 loss = 7.08015\n",
      "DEBUG \t step   176 loss = 7.05245\n",
      "DEBUG \t step   177 loss = 7.43696\n",
      "DEBUG \t step   178 loss = 7.32325\n",
      "DEBUG \t step   179 loss = 7.31021\n",
      "DEBUG \t step   180 loss = 7.32132\n",
      "DEBUG \t step   181 loss = 7.34862\n",
      "DEBUG \t step   182 loss = 7.2863\n",
      "DEBUG \t step   183 loss = 7.04851\n",
      "DEBUG \t step   184 loss = 7.09608\n",
      "DEBUG \t step   185 loss = 7.30419\n",
      "DEBUG \t step   186 loss = 7.57377\n",
      "DEBUG \t step   187 loss = 7.17361\n",
      "DEBUG \t step   188 loss = 7.14099\n",
      "DEBUG \t step   189 loss = 7.0449\n",
      "DEBUG \t step   190 loss = 7.33529\n",
      "DEBUG \t step   191 loss = 8.26479\n",
      "DEBUG \t step   192 loss = 7.07407\n",
      "DEBUG \t step   193 loss = 7.17149\n",
      "DEBUG \t step   194 loss = 7.18364\n",
      "DEBUG \t step   195 loss = 7.27539\n",
      "DEBUG \t step   196 loss = 7.32838\n",
      "DEBUG \t step   197 loss = 7.26303\n",
      "DEBUG \t step   198 loss = 7.17846\n",
      "DEBUG \t step   199 loss = 7.43274\n",
      "DEBUG \t step   200 loss = 7.05834\n",
      "DEBUG \t step   201 loss = 7.06987\n",
      "DEBUG \t step   202 loss = 7.23815\n",
      "DEBUG \t step   203 loss = 7.2454\n",
      "DEBUG \t step   204 loss = 7.29509\n",
      "DEBUG \t step   205 loss = 7.13663\n",
      "DEBUG \t step   206 loss = 6.96725\n",
      "DEBUG \t step   207 loss = 7.11374\n",
      "DEBUG \t step   208 loss = 6.93604\n",
      "DEBUG \t step   209 loss = 7.14596\n",
      "DEBUG \t step   210 loss = 7.12832\n",
      "DEBUG \t step   211 loss = 7.16911\n",
      "DEBUG \t step   212 loss = 6.9426\n",
      "DEBUG \t step   213 loss = 7.18095\n",
      "DEBUG \t step   214 loss = 7.06178\n",
      "DEBUG \t step   215 loss = 7.10941\n",
      "DEBUG \t step   216 loss = 7.11186\n",
      "DEBUG \t step   217 loss = 7.20186\n",
      "DEBUG \t step   218 loss = 7.27586\n",
      "DEBUG \t step   219 loss = 7.1021\n",
      "DEBUG \t step   220 loss = 6.94478\n",
      "DEBUG \t step   221 loss = 7.09795\n",
      "DEBUG \t step   222 loss = 6.88571\n",
      "DEBUG \t step   223 loss = 7.03089\n",
      "DEBUG \t step   224 loss = 7.23866\n",
      "DEBUG \t step   225 loss = 7.10442\n",
      "DEBUG \t step   226 loss = 6.95982\n",
      "DEBUG \t step   227 loss = 8.71509\n",
      "DEBUG \t step   228 loss = 6.93005\n",
      "DEBUG \t step   229 loss = 7.2101\n",
      "DEBUG \t step   230 loss = 7.23326\n",
      "DEBUG \t step   231 loss = 6.94798\n",
      "DEBUG \t step   232 loss = 6.83511\n",
      "DEBUG \t step   233 loss = 6.99621\n",
      "DEBUG \t step   234 loss = 6.79696\n",
      "DEBUG \t step   235 loss = 7.21458\n",
      "DEBUG \t step   236 loss = 6.97841\n",
      "DEBUG \t step   237 loss = 7.12467\n",
      "DEBUG \t step   238 loss = 6.98927\n",
      "DEBUG \t step   239 loss = 7.13294\n",
      "DEBUG \t step   240 loss = 7.17033\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "DEBUG \t step   241 loss = 7.09788\n",
      "DEBUG \t step   242 loss = 6.98868\n",
      "DEBUG \t step   243 loss = 7.0711\n",
      "DEBUG \t step   244 loss = 7.10628\n",
      "DEBUG \t step   245 loss = 7.12893\n",
      "DEBUG \t step   246 loss = 6.94537\n",
      "DEBUG \t step   247 loss = 6.98222\n",
      "DEBUG \t step   248 loss = 7.12801\n",
      "DEBUG \t step   249 loss = 6.94684\n",
      "DEBUG \t step   250 loss = 7.01901\n",
      "DEBUG \t step   251 loss = 7.03228\n",
      "DEBUG \t step   252 loss = 7.14612\n",
      "DEBUG \t step   253 loss = 7.04241\n",
      "DEBUG \t step   254 loss = 6.92232\n",
      "DEBUG \t step   255 loss = 7.02093\n",
      "DEBUG \t step   256 loss = 6.98689\n",
      "DEBUG \t step   257 loss = 6.97682\n",
      "DEBUG \t step   258 loss = 6.99232\n",
      "DEBUG \t step   259 loss = 7.01528\n",
      "DEBUG \t step   260 loss = 6.86835\n",
      "DEBUG \t step   261 loss = 7.00633\n",
      "DEBUG \t step   262 loss = 7.06246\n",
      "DEBUG \t step   263 loss = 6.90189\n",
      "DEBUG \t step   264 loss = 7.07629\n",
      "DEBUG \t step   265 loss = 6.88559\n",
      "DEBUG \t step   266 loss = 6.92606\n",
      "DEBUG \t step   267 loss = 6.8929\n",
      "DEBUG \t step   268 loss = 6.83142\n",
      "DEBUG \t step   269 loss = 6.73955\n",
      "DEBUG \t step   270 loss = 6.81085\n",
      "DEBUG \t step   271 loss = 6.87084\n",
      "DEBUG \t step   272 loss = 6.88125\n",
      "DEBUG \t step   273 loss = 6.94562\n",
      "DEBUG \t step   274 loss = 6.9711\n",
      "DEBUG \t step   275 loss = 7.01001\n",
      "DEBUG \t step   276 loss = 6.91986\n",
      "DEBUG \t step   277 loss = 6.92239\n",
      "DEBUG \t step   278 loss = 6.70706\n",
      "DEBUG \t step   279 loss = 6.84017\n",
      "DEBUG \t step   280 loss = 7.09178\n",
      "DEBUG \t step   281 loss = 6.7313\n",
      "DEBUG \t step   282 loss = 6.79816\n",
      "DEBUG \t step   283 loss = 6.86953\n",
      "DEBUG \t step   284 loss = 6.92598\n",
      "DEBUG \t step   285 loss = 7.0731\n",
      "DEBUG \t step   286 loss = 6.91421\n",
      "DEBUG \t step   287 loss = 6.76945\n",
      "DEBUG \t step   288 loss = 6.74834\n",
      "DEBUG \t step   289 loss = 6.84824\n",
      "DEBUG \t step   290 loss = 6.88344\n",
      "DEBUG \t step   291 loss = 6.85244\n",
      "DEBUG \t step   292 loss = 6.922\n",
      "DEBUG \t step   293 loss = 9.57555\n",
      "DEBUG \t step   294 loss = 6.83098\n",
      "DEBUG \t step   295 loss = 7.43121\n",
      "DEBUG \t step   296 loss = 6.95061\n",
      "DEBUG \t step   297 loss = 6.79967\n",
      "DEBUG \t step   298 loss = 6.7929\n",
      "DEBUG \t step   299 loss = 6.7355\n",
      "DEBUG \t step   300 loss = 7.01345\n",
      "DEBUG \t step   301 loss = 6.83328\n",
      "DEBUG \t step   302 loss = 6.62454\n",
      "DEBUG \t step   303 loss = 6.84473\n",
      "DEBUG \t step   304 loss = 9.05065\n",
      "DEBUG \t step   305 loss = 7.038\n",
      "DEBUG \t step   306 loss = 6.60419\n",
      "DEBUG \t step   307 loss = 6.80575\n",
      "DEBUG \t step   308 loss = 6.73912\n",
      "DEBUG \t step   309 loss = 6.47463\n",
      "DEBUG \t step   310 loss = 6.84484\n",
      "DEBUG \t step   311 loss = 6.73429\n",
      "DEBUG \t step   312 loss = 6.89219\n",
      "DEBUG \t step   313 loss = 7.05905\n",
      "DEBUG \t step   314 loss = 6.82365\n",
      "DEBUG \t step   315 loss = 6.72354\n",
      "DEBUG \t step   316 loss = 6.54532\n",
      "DEBUG \t step   317 loss = 6.95339\n",
      "DEBUG \t step   318 loss = 7.0503\n",
      "DEBUG \t step   319 loss = 6.78209\n",
      "DEBUG \t step   320 loss = 6.59514\n",
      "DEBUG \t step   321 loss = 6.89779\n",
      "DEBUG \t step   322 loss = 6.72151\n",
      "DEBUG \t step   323 loss = 6.90015\n",
      "DEBUG \t step   324 loss = 7.00599\n",
      "DEBUG \t step   325 loss = 6.85437\n",
      "DEBUG \t step   326 loss = 6.89033\n",
      "DEBUG \t step   327 loss = 6.7871\n",
      "DEBUG \t step   328 loss = 6.8493\n",
      "DEBUG \t step   329 loss = 6.80922\n",
      "DEBUG \t step   330 loss = 6.96322\n",
      "DEBUG \t step   331 loss = 6.84506\n",
      "DEBUG \t step   332 loss = 6.87015\n",
      "DEBUG \t step   333 loss = 6.88979\n",
      "DEBUG \t step   334 loss = 6.64982\n",
      "DEBUG \t step   335 loss = 6.86292\n",
      "DEBUG \t step   336 loss = 6.92489\n",
      "DEBUG \t step   337 loss = 6.62396\n",
      "DEBUG \t step   338 loss = 6.84564\n",
      "DEBUG \t step   339 loss = 6.62305\n",
      "DEBUG \t step   340 loss = 7.36375\n",
      "DEBUG \t step   341 loss = 6.73599\n",
      "DEBUG \t step   342 loss = 6.80353\n",
      "DEBUG \t step   343 loss = 6.96371\n",
      "DEBUG \t step   344 loss = 6.89915\n",
      "DEBUG \t step   345 loss = 6.64238\n",
      "DEBUG \t step   346 loss = 6.51934\n",
      "DEBUG \t step   347 loss = 6.78445\n",
      "DEBUG \t step   348 loss = 6.94965\n",
      "DEBUG \t step   349 loss = 6.78796\n",
      "DEBUG \t step   350 loss = 6.77106\n",
      "DEBUG \t step   351 loss = 6.7466\n",
      "DEBUG \t step   352 loss = 6.77313\n",
      "DEBUG \t step   353 loss = 6.70463\n",
      "DEBUG \t step   354 loss = 6.96683\n",
      "DEBUG \t step   355 loss = 6.73415\n",
      "DEBUG \t step   356 loss = 6.73694\n",
      "DEBUG \t step   357 loss = 6.60738\n",
      "DEBUG \t step   358 loss = 9.84151\n",
      "DEBUG \t step   359 loss = 6.84548\n",
      "DEBUG \t step   360 loss = 6.57425\n",
      "DEBUG \t step   361 loss = 6.78442\n",
      "DEBUG \t step   362 loss = 6.68523\n",
      "DEBUG \t step   363 loss = 6.93113\n",
      "DEBUG \t step   364 loss = 9.26669\n",
      "DEBUG \t step   365 loss = 6.71749\n",
      "DEBUG \t step   366 loss = 6.60656\n",
      "DEBUG \t step   367 loss = 6.7795\n",
      "DEBUG \t step   368 loss = 6.55477\n",
      "DEBUG \t step   369 loss = 6.73777\n",
      "DEBUG \t step   370 loss = 6.80791\n",
      "DEBUG \t step   371 loss = 6.75802\n",
      "DEBUG \t step   372 loss = 6.80779\n",
      "DEBUG \t step   373 loss = 6.82983\n",
      "DEBUG \t step   374 loss = 6.5821\n",
      "DEBUG \t step   375 loss = 6.81309\n",
      "DEBUG \t step   376 loss = 6.58409\n",
      "DEBUG \t step   377 loss = 6.59094\n",
      "DEBUG \t step   378 loss = 6.59232\n",
      "DEBUG \t step   379 loss = 7.0035\n",
      "DEBUG \t step   380 loss = 6.65775\n",
      "DEBUG \t step   381 loss = 6.61621\n",
      "DEBUG \t step   382 loss = 6.6329\n",
      "DEBUG \t step   383 loss = 6.63025\n",
      "DEBUG \t step   384 loss = 6.61858\n",
      "DEBUG \t step   385 loss = 6.63814\n",
      "DEBUG \t step   386 loss = 6.50298\n",
      "DEBUG \t step   387 loss = 6.62591\n",
      "DEBUG \t step   388 loss = 6.56514\n",
      "DEBUG \t step   389 loss = 6.67944\n",
      "DEBUG \t step   390 loss = 6.80612\n",
      "DEBUG \t step   391 loss = 6.61369\n",
      "DEBUG \t step   392 loss = 6.85104\n",
      "DEBUG \t step   393 loss = 6.61612\n",
      "DEBUG \t step   394 loss = 6.55337\n",
      "DEBUG \t step   395 loss = 6.76919\n",
      "DEBUG \t step   396 loss = 6.66491\n",
      "DEBUG \t step   397 loss = 6.57224\n",
      "DEBUG \t step   398 loss = 6.54065\n",
      "DEBUG \t step   399 loss = 6.73794\n",
      "INFO \t Evaluating 80 minibatches\n",
      "DEBUG \t batch ate = 0.823513\n",
      "DEBUG \t batch ate = 0.824189\n",
      "DEBUG \t batch ate = 0.820978\n",
      "DEBUG \t batch ate = 0.822631\n",
      "DEBUG \t batch ate = 0.823555\n",
      "DEBUG \t batch ate = 0.822441\n",
      "DEBUG \t batch ate = 0.823683\n",
      "DEBUG \t batch ate = 0.822339\n",
      "DEBUG \t batch ate = 0.823964\n",
      "DEBUG \t batch ate = 0.823921\n",
      "DEBUG \t batch ate = 0.825266\n",
      "DEBUG \t batch ate = 0.822931\n",
      "DEBUG \t batch ate = 0.823049\n",
      "DEBUG \t batch ate = 0.824161\n",
      "DEBUG \t batch ate = 0.821918\n",
      "DEBUG \t batch ate = 0.824303\n",
      "DEBUG \t batch ate = 0.823845\n",
      "DEBUG \t batch ate = 0.822578\n",
      "DEBUG \t batch ate = 0.825122\n",
      "DEBUG \t batch ate = 0.823321\n",
      "DEBUG \t batch ate = 0.823198\n",
      "DEBUG \t batch ate = 0.823159\n",
      "DEBUG \t batch ate = 0.823571\n",
      "DEBUG \t batch ate = 0.822972\n",
      "DEBUG \t batch ate = 0.82311\n",
      "DEBUG \t batch ate = 0.821233\n",
      "DEBUG \t batch ate = 0.824326\n",
      "DEBUG \t batch ate = 0.823645\n",
      "DEBUG \t batch ate = 0.8233\n",
      "DEBUG \t batch ate = 0.821567\n",
      "DEBUG \t batch ate = 0.820404\n",
      "DEBUG \t batch ate = 0.821521\n",
      "DEBUG \t batch ate = 0.82027\n",
      "DEBUG \t batch ate = 0.824084\n",
      "DEBUG \t batch ate = 0.824593\n",
      "DEBUG \t batch ate = 0.823614\n",
      "DEBUG \t batch ate = 0.820698\n",
      "DEBUG \t batch ate = 0.824454\n",
      "DEBUG \t batch ate = 0.819246\n",
      "DEBUG \t batch ate = 0.823614\n",
      "DEBUG \t batch ate = 0.822471\n",
      "DEBUG \t batch ate = 0.822809\n",
      "DEBUG \t batch ate = 0.82155\n",
      "DEBUG \t batch ate = 0.822985\n",
      "DEBUG \t batch ate = 0.821966\n",
      "DEBUG \t batch ate = 0.822152\n",
      "DEBUG \t batch ate = 0.824818\n",
      "DEBUG \t batch ate = 0.821926\n",
      "DEBUG \t batch ate = 0.821183\n",
      "DEBUG \t batch ate = 0.821644\n",
      "DEBUG \t batch ate = 0.823652\n",
      "DEBUG \t batch ate = 0.822925\n",
      "DEBUG \t batch ate = 0.822612\n",
      "DEBUG \t batch ate = 0.824216\n",
      "DEBUG \t batch ate = 0.824456\n",
      "DEBUG \t batch ate = 0.822995\n",
      "DEBUG \t batch ate = 0.823972\n",
      "DEBUG \t batch ate = 0.821021\n",
      "DEBUG \t batch ate = 0.822201\n",
      "DEBUG \t batch ate = 0.821493\n",
      "DEBUG \t batch ate = 0.823859\n",
      "DEBUG \t batch ate = 0.819778\n",
      "DEBUG \t batch ate = 0.822789\n",
      "DEBUG \t batch ate = 0.825457\n",
      "DEBUG \t batch ate = 0.824181\n",
      "DEBUG \t batch ate = 0.821647\n",
      "DEBUG \t batch ate = 0.82509\n",
      "DEBUG \t batch ate = 0.821287\n",
      "DEBUG \t batch ate = 0.824007\n",
      "DEBUG \t batch ate = 0.821076\n",
      "DEBUG \t batch ate = 0.823777\n",
      "DEBUG \t batch ate = 0.822884\n",
      "DEBUG \t batch ate = 0.824057\n",
      "DEBUG \t batch ate = 0.820844\n",
      "DEBUG \t batch ate = 0.821426\n",
      "DEBUG \t batch ate = 0.82413\n",
      "DEBUG \t batch ate = 0.822516\n",
      "DEBUG \t batch ate = 0.823242\n",
      "DEBUG \t batch ate = 0.820823\n",
      "DEBUG \t batch ate = 0.822049\n",
      "INFO \t Evaluating 20 minibatches\n",
      "DEBUG \t batch ate = 0.823355\n",
      "DEBUG \t batch ate = 0.826493\n",
      "DEBUG \t batch ate = 0.825423\n",
      "DEBUG \t batch ate = 0.825241\n",
      "DEBUG \t batch ate = 0.823623\n",
      "DEBUG \t batch ate = 0.823627\n",
      "DEBUG \t batch ate = 0.821589\n",
      "DEBUG \t batch ate = 0.824463\n",
      "DEBUG \t batch ate = 0.821071\n",
      "DEBUG \t batch ate = 0.820596\n",
      "DEBUG \t batch ate = 0.823198\n",
      "DEBUG \t batch ate = 0.820816\n",
      "DEBUG \t batch ate = 0.823484\n",
      "DEBUG \t batch ate = 0.823282\n",
      "DEBUG \t batch ate = 0.825439\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "DEBUG \t batch ate = 0.822407\n",
      "DEBUG \t batch ate = 0.822365\n",
      "DEBUG \t batch ate = 0.825534\n",
      "DEBUG \t batch ate = 0.822151\n",
      "DEBUG \t batch ate = 0.823306\n"
     ]
    }
   ],
   "source": [
    "y, X, w, tau, b, e = simulate_hidden_confounder(n=100000, p=5, sigma=1.0, adj=0.)\n",
    "\n",
    "X_train, X_val, y_train, y_val, w_train, w_val, tau_train, tau_val, b_train, b_val, e_train, e_val = \\\n",
    "    train_test_split(X, y, w, tau, b, e, test_size=0.2, random_state=123, shuffle=True)\n",
    "\n",
    "preds_dict_train = {}\n",
    "preds_dict_valid = {}\n",
    "\n",
    "preds_dict_train['Actuals'] = tau_train\n",
    "preds_dict_valid['Actuals'] = tau_val\n",
    "\n",
    "preds_dict_train['generated_data'] = {\n",
    "    'y': y_train,\n",
    "    'X': X_train,\n",
    "    'w': w_train,\n",
    "    'tau': tau_train,\n",
    "    'b': b_train,\n",
    "    'e': e_train}\n",
    "preds_dict_valid['generated_data'] = {\n",
    "    'y': y_val,\n",
    "    'X': X_val,\n",
    "    'w': w_val,\n",
    "    'tau': tau_val,\n",
    "    'b': b_val,\n",
    "    'e': e_val}\n",
    "\n",
    "# Predict p_hat because e would not be directly observed in real-life\n",
    "p_model = ElasticNetPropensityModel()\n",
    "p_hat_train = p_model.fit_predict(X_train, w_train)\n",
    "p_hat_val = p_model.fit_predict(X_val, w_val)\n",
    "\n",
    "for base_learner, label_l in zip([BaseSRegressor, BaseTRegressor, BaseXRegressor, BaseRRegressor],\n",
    "                                 ['S', 'T', 'X', 'R']):\n",
    "    for model, label_m in zip([LinearRegression, XGBRegressor], ['LR', 'XGB']):\n",
    "        # RLearner will need to fit on the p_hat\n",
    "        if label_l != 'R':\n",
    "            learner = base_learner(model())\n",
    "            # fit the model on training data only\n",
    "            learner.fit(X=X_train, treatment=w_train, y=y_train)\n",
    "            try:\n",
    "                preds_dict_train['{} Learner ({})'.format(\n",
    "                    label_l, label_m)] = learner.predict(X=X_train, p=p_hat_train).flatten()\n",
    "                preds_dict_valid['{} Learner ({})'.format(\n",
    "                    label_l, label_m)] = learner.predict(X=X_val, p=p_hat_val).flatten()\n",
    "            except TypeError:\n",
    "                preds_dict_train['{} Learner ({})'.format(\n",
    "                    label_l, label_m)] = learner.predict(X=X_train, treatment=w_train, y=y_train).flatten()\n",
    "                preds_dict_valid['{} Learner ({})'.format(\n",
    "                    label_l, label_m)] = learner.predict(X=X_val, treatment=w_val, y=y_val).flatten()\n",
    "        else:\n",
    "            learner = base_learner(model())\n",
    "            learner.fit(X=X_train, p=p_hat_train, treatment=w_train, y=y_train)\n",
    "            preds_dict_train['{} Learner ({})'.format(\n",
    "                label_l, label_m)] = learner.predict(X=X_train).flatten()\n",
    "            preds_dict_valid['{} Learner ({})'.format(\n",
    "                label_l, label_m)] = learner.predict(X=X_val).flatten()\n",
    "\n",
    "# cevae model settings\n",
    "outcome_dist = \"normal\"\n",
    "latent_dim = 20\n",
    "hidden_dim = 200\n",
    "num_epochs = 5\n",
    "batch_size = 1000\n",
    "learning_rate = 1e-3\n",
    "learning_rate_decay = 0.1\n",
    "num_layers = 3\n",
    "num_samples = 10\n",
    "\n",
    "cevae = CEVAE(outcome_dist=outcome_dist,\n",
    "              latent_dim=latent_dim,\n",
    "              hidden_dim=hidden_dim,\n",
    "              num_epochs=num_epochs,\n",
    "              batch_size=batch_size,\n",
    "              learning_rate=learning_rate,\n",
    "              learning_rate_decay=learning_rate_decay,\n",
    "              num_layers=num_layers,\n",
    "              num_samples=num_samples)\n",
    "\n",
    "# fit\n",
    "losses = cevae.fit(X=torch.tensor(X_train, dtype=torch.float),\n",
    "                   treatment=torch.tensor(w_train, dtype=torch.float),\n",
    "                   y=torch.tensor(y_train, dtype=torch.float))\n",
    "\n",
    "preds_dict_train['CEVAE'] = cevae.predict(X_train).flatten()\n",
    "preds_dict_valid['CEVAE'] = cevae.predict(X_val).flatten()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-01T23:48:04.460479Z",
     "start_time": "2021-02-01T23:48:04.323693Z"
    }
   },
   "outputs": [],
   "source": [
    "actuals_train = preds_dict_train['Actuals']\n",
    "actuals_validation = preds_dict_valid['Actuals']\n",
    "\n",
    "synthetic_summary_train = pd.DataFrame({label: [preds.mean(), mse(preds, actuals_train)] for label, preds\n",
    "                                        in preds_dict_train.items() if 'generated' not in label.lower()},\n",
    "                                       index=['ATE', 'MSE']).T\n",
    "synthetic_summary_train['Abs % Error of ATE'] = np.abs(\n",
    "    (synthetic_summary_train['ATE']/synthetic_summary_train.loc['Actuals', 'ATE']) - 1)\n",
    "\n",
    "synthetic_summary_validation = pd.DataFrame({label: [preds.mean(), mse(preds, actuals_validation)]\n",
    "                                             for label, preds in preds_dict_valid.items()\n",
    "                                             if 'generated' not in label.lower()},\n",
    "                                            index=['ATE', 'MSE']).T\n",
    "synthetic_summary_validation['Abs % Error of ATE'] = np.abs(\n",
    "    (synthetic_summary_validation['ATE']/synthetic_summary_validation.loc['Actuals', 'ATE']) - 1)\n",
    "\n",
    "# calculate kl divergence for training\n",
    "for label in synthetic_summary_train.index:\n",
    "    stacked_values = np.hstack((preds_dict_train[label], actuals_train))\n",
    "    stacked_low = np.percentile(stacked_values, 0.1)\n",
    "    stacked_high = np.percentile(stacked_values, 99.9)\n",
    "    bins = np.linspace(stacked_low, stacked_high, 100)\n",
    "\n",
    "    distr = np.histogram(preds_dict_train[label], bins=bins)[0]\n",
    "    distr = np.clip(distr/distr.sum(), 0.001, 0.999)\n",
    "    true_distr = np.histogram(actuals_train, bins=bins)[0]\n",
    "    true_distr = np.clip(true_distr/true_distr.sum(), 0.001, 0.999)\n",
    "\n",
    "    kl = entropy(distr, true_distr)\n",
    "    synthetic_summary_train.loc[label, 'KL Divergence'] = kl\n",
    "\n",
    "# calculate kl divergence for validation\n",
    "for label in synthetic_summary_validation.index:\n",
    "    stacked_values = np.hstack((preds_dict_valid[label], actuals_validation))\n",
    "    stacked_low = np.percentile(stacked_values, 0.1)\n",
    "    stacked_high = np.percentile(stacked_values, 99.9)\n",
    "    bins = np.linspace(stacked_low, stacked_high, 100)\n",
    "\n",
    "    distr = np.histogram(preds_dict_valid[label], bins=bins)[0]\n",
    "    distr = np.clip(distr/distr.sum(), 0.001, 0.999)\n",
    "    true_distr = np.histogram(actuals_validation, bins=bins)[0]\n",
    "    true_distr = np.clip(true_distr/true_distr.sum(), 0.001, 0.999)\n",
    "\n",
    "    kl = entropy(distr, true_distr)\n",
    "    synthetic_summary_validation.loc[label, 'KL Divergence'] = kl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-01T23:48:07.625291Z",
     "start_time": "2021-02-01T23:48:04.462870Z"
    }
   },
   "outputs": [],
   "source": [
    "df_preds_train = pd.DataFrame([preds_dict_train['S Learner (LR)'].ravel(),\n",
    "                               preds_dict_train['S Learner (XGB)'].ravel(),\n",
    "                               preds_dict_train['T Learner (LR)'].ravel(),\n",
    "                               preds_dict_train['T Learner (XGB)'].ravel(),\n",
    "                               preds_dict_train['X Learner (LR)'].ravel(),\n",
    "                               preds_dict_train['X Learner (XGB)'].ravel(),\n",
    "                               preds_dict_train['R Learner (LR)'].ravel(),\n",
    "                               preds_dict_train['R Learner (XGB)'].ravel(),                               \n",
    "                               preds_dict_train['CEVAE'].ravel(),\n",
    "                               preds_dict_train['generated_data']['tau'].ravel(),\n",
    "                               preds_dict_train['generated_data']['w'].ravel(),\n",
    "                               preds_dict_train['generated_data']['y'].ravel()],\n",
    "                              index=['S Learner (LR)','S Learner (XGB)',\n",
    "                                     'T Learner (LR)','T Learner (XGB)',\n",
    "                                     'X Learner (LR)','X Learner (XGB)',\n",
    "                                     'R Learner (LR)','R Learner (XGB)',\n",
    "                                     'CEVAE','tau','w','y']).T\n",
    "\n",
    "synthetic_summary_train['AUUC'] = auuc_score(df_preds_train).iloc[:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-01T23:48:08.381588Z",
     "start_time": "2021-02-01T23:48:07.627371Z"
    }
   },
   "outputs": [],
   "source": [
    "df_preds_validation = pd.DataFrame([preds_dict_valid['S Learner (LR)'].ravel(),\n",
    "                               preds_dict_valid['S Learner (XGB)'].ravel(),\n",
    "                               preds_dict_valid['T Learner (LR)'].ravel(),\n",
    "                               preds_dict_valid['T Learner (XGB)'].ravel(),\n",
    "                               preds_dict_valid['X Learner (LR)'].ravel(),\n",
    "                               preds_dict_valid['X Learner (XGB)'].ravel(),\n",
    "                               preds_dict_valid['R Learner (LR)'].ravel(),\n",
    "                               preds_dict_valid['R Learner (XGB)'].ravel(),                               \n",
    "                               preds_dict_valid['CEVAE'].ravel(),\n",
    "                               preds_dict_valid['generated_data']['tau'].ravel(),\n",
    "                               preds_dict_valid['generated_data']['w'].ravel(),\n",
    "                               preds_dict_valid['generated_data']['y'].ravel()],\n",
    "                              index=['S Learner (LR)','S Learner (XGB)',\n",
    "                                     'T Learner (LR)','T Learner (XGB)',\n",
    "                                     'X Learner (LR)','X Learner (XGB)',\n",
    "                                     'R Learner (LR)','R Learner (XGB)',\n",
    "                                     'CEVAE','tau','w','y']).T\n",
    "\n",
    "synthetic_summary_validation['AUUC'] = auuc_score(df_preds_validation).iloc[:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-01T23:48:08.392392Z",
     "start_time": "2021-02-01T23:48:08.383180Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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>ATE</th>\n",
       "      <th>MSE</th>\n",
       "      <th>Abs % Error of ATE</th>\n",
       "      <th>KL Divergence</th>\n",
       "      <th>AUUC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Actuals</th>\n",
       "      <td>0.726115</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S Learner (LR)</th>\n",
       "      <td>0.832336</td>\n",
       "      <td>0.062462</td>\n",
       "      <td>0.146287</td>\n",
       "      <td>6.278413</td>\n",
       "      <td>0.499991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S Learner (XGB)</th>\n",
       "      <td>0.807743</td>\n",
       "      <td>0.039735</td>\n",
       "      <td>0.112417</td>\n",
       "      <td>2.551297</td>\n",
       "      <td>0.554885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T Learner (LR)</th>\n",
       "      <td>0.833364</td>\n",
       "      <td>0.059665</td>\n",
       "      <td>0.147703</td>\n",
       "      <td>3.312696</td>\n",
       "      <td>0.523272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T Learner (XGB)</th>\n",
       "      <td>0.803592</td>\n",
       "      <td>0.040524</td>\n",
       "      <td>0.106701</td>\n",
       "      <td>2.565715</td>\n",
       "      <td>0.553197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X Learner (LR)</th>\n",
       "      <td>0.833364</td>\n",
       "      <td>0.059665</td>\n",
       "      <td>0.147703</td>\n",
       "      <td>3.312696</td>\n",
       "      <td>0.523272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X Learner (XGB)</th>\n",
       "      <td>0.803349</td>\n",
       "      <td>0.038580</td>\n",
       "      <td>0.106367</td>\n",
       "      <td>2.500947</td>\n",
       "      <td>0.555391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R Learner (LR)</th>\n",
       "      <td>0.833845</td>\n",
       "      <td>0.060239</td>\n",
       "      <td>0.148365</td>\n",
       "      <td>3.511157</td>\n",
       "      <td>0.523214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R Learner (XGB)</th>\n",
       "      <td>0.735442</td>\n",
       "      <td>0.046848</td>\n",
       "      <td>0.012845</td>\n",
       "      <td>2.836128</td>\n",
       "      <td>0.539213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CEVAE</th>\n",
       "      <td>0.822853</td>\n",
       "      <td>0.058177</td>\n",
       "      <td>0.133227</td>\n",
       "      <td>3.157059</td>\n",
       "      <td>0.519150</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      ATE       MSE  Abs % Error of ATE  KL Divergence  \\\n",
       "Actuals          0.726115  0.000000            0.000000       0.000000   \n",
       "S Learner (LR)   0.832336  0.062462            0.146287       6.278413   \n",
       "S Learner (XGB)  0.807743  0.039735            0.112417       2.551297   \n",
       "T Learner (LR)   0.833364  0.059665            0.147703       3.312696   \n",
       "T Learner (XGB)  0.803592  0.040524            0.106701       2.565715   \n",
       "X Learner (LR)   0.833364  0.059665            0.147703       3.312696   \n",
       "X Learner (XGB)  0.803349  0.038580            0.106367       2.500947   \n",
       "R Learner (LR)   0.833845  0.060239            0.148365       3.511157   \n",
       "R Learner (XGB)  0.735442  0.046848            0.012845       2.836128   \n",
       "CEVAE            0.822853  0.058177            0.133227       3.157059   \n",
       "\n",
       "                     AUUC  \n",
       "Actuals               NaN  \n",
       "S Learner (LR)   0.499991  \n",
       "S Learner (XGB)  0.554885  \n",
       "T Learner (LR)   0.523272  \n",
       "T Learner (XGB)  0.553197  \n",
       "X Learner (LR)   0.523272  \n",
       "X Learner (XGB)  0.555391  \n",
       "R Learner (LR)   0.523214  \n",
       "R Learner (XGB)  0.539213  \n",
       "CEVAE            0.519150  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "synthetic_summary_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-01T23:48:08.401366Z",
     "start_time": "2021-02-01T23:48:08.393987Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>ATE</th>\n",
       "      <th>MSE</th>\n",
       "      <th>Abs % Error of ATE</th>\n",
       "      <th>KL Divergence</th>\n",
       "      <th>AUUC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Actuals</th>\n",
       "      <td>0.728371</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S Learner (LR)</th>\n",
       "      <td>0.832336</td>\n",
       "      <td>0.061983</td>\n",
       "      <td>0.142736</td>\n",
       "      <td>6.278413</td>\n",
       "      <td>0.499967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S Learner (XGB)</th>\n",
       "      <td>0.808844</td>\n",
       "      <td>0.040638</td>\n",
       "      <td>0.110483</td>\n",
       "      <td>2.548714</td>\n",
       "      <td>0.553011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T Learner (LR)</th>\n",
       "      <td>0.833805</td>\n",
       "      <td>0.059305</td>\n",
       "      <td>0.144753</td>\n",
       "      <td>3.316884</td>\n",
       "      <td>0.522972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T Learner (XGB)</th>\n",
       "      <td>0.803766</td>\n",
       "      <td>0.042424</td>\n",
       "      <td>0.103512</td>\n",
       "      <td>2.561688</td>\n",
       "      <td>0.549279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X Learner (LR)</th>\n",
       "      <td>0.833805</td>\n",
       "      <td>0.059305</td>\n",
       "      <td>0.144753</td>\n",
       "      <td>3.316884</td>\n",
       "      <td>0.522972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X Learner (XGB)</th>\n",
       "      <td>0.803530</td>\n",
       "      <td>0.039699</td>\n",
       "      <td>0.103187</td>\n",
       "      <td>2.489822</td>\n",
       "      <td>0.553039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R Learner (LR)</th>\n",
       "      <td>0.834179</td>\n",
       "      <td>0.059851</td>\n",
       "      <td>0.145266</td>\n",
       "      <td>3.512746</td>\n",
       "      <td>0.522887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R Learner (XGB)</th>\n",
       "      <td>0.736147</td>\n",
       "      <td>0.046685</td>\n",
       "      <td>0.010675</td>\n",
       "      <td>2.747596</td>\n",
       "      <td>0.536579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CEVAE</th>\n",
       "      <td>0.823373</td>\n",
       "      <td>0.057690</td>\n",
       "      <td>0.130430</td>\n",
       "      <td>3.152161</td>\n",
       "      <td>0.519573</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      ATE       MSE  Abs % Error of ATE  KL Divergence  \\\n",
       "Actuals          0.728371  0.000000            0.000000       0.000000   \n",
       "S Learner (LR)   0.832336  0.061983            0.142736       6.278413   \n",
       "S Learner (XGB)  0.808844  0.040638            0.110483       2.548714   \n",
       "T Learner (LR)   0.833805  0.059305            0.144753       3.316884   \n",
       "T Learner (XGB)  0.803766  0.042424            0.103512       2.561688   \n",
       "X Learner (LR)   0.833805  0.059305            0.144753       3.316884   \n",
       "X Learner (XGB)  0.803530  0.039699            0.103187       2.489822   \n",
       "R Learner (LR)   0.834179  0.059851            0.145266       3.512746   \n",
       "R Learner (XGB)  0.736147  0.046685            0.010675       2.747596   \n",
       "CEVAE            0.823373  0.057690            0.130430       3.152161   \n",
       "\n",
       "                     AUUC  \n",
       "Actuals               NaN  \n",
       "S Learner (LR)   0.499967  \n",
       "S Learner (XGB)  0.553011  \n",
       "T Learner (LR)   0.522972  \n",
       "T Learner (XGB)  0.549279  \n",
       "X Learner (LR)   0.522972  \n",
       "X Learner (XGB)  0.553039  \n",
       "R Learner (LR)   0.522887  \n",
       "R Learner (XGB)  0.536579  \n",
       "CEVAE            0.519573  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "synthetic_summary_validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-01T23:48:09.079086Z",
     "start_time": "2021-02-01T23:48:08.402848Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_gain(df_preds_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-01T23:48:09.487505Z",
     "start_time": "2021-02-01T23:48:09.083225Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_gain(df_preds_validation)"
   ]
  }
 ],
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