lstm_sin_cos.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "from tensorflow.contrib import learn\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "from lstm import lstm_model\n",
    "from data_processing import generate_data, sin_cos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "LOG_DIR = './ops_logs/sin_cos'\n",
    "TIMESTEPS = 5\n",
    "RNN_LAYERS = [{'num_units': 5}, {'num_units': 2}]\n",
    "DENSE_LAYERS = None\n",
    "TRAINING_STEPS = 10000\n",
    "PRINT_STEPS = TRAINING_STEPS / 10\n",
    "BATCH_SIZE = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Using default config.\n",
      "INFO:tensorflow:Using config: {'_model_dir': None, '_save_checkpoints_secs': 600, '_num_ps_replicas': 0, '_keep_checkpoint_max': 5, '_tf_random_seed': None, '_task_type': None, '_environment': 'local', '_is_chief': True, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7fba8c0c03d0>, '_tf_config': gpu_options {\n",
      "  per_process_gpu_memory_fraction: 1.0\n",
      "}\n",
      ", '_num_worker_replicas': 0, '_task_id': 0, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_evaluation_master': '', '_keep_checkpoint_every_n_hours': 10000, '_master': ''}\n"
     ]
    }
   ],
   "source": [
    "regressor = learn.SKCompat(learn.Estimator(\n",
    "    model_fn=lstm_model(\n",
    "        TIMESTEPS,\n",
    "        RNN_LAYERS,\n",
    "        DENSE_LAYERS\n",
    "    ),\n",
    "    model_dir=LOG_DIR\n",
    "))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tensorflow/contrib/learn/python/learn/monitors.py:267: __init__ (from tensorflow.contrib.learn.python.learn.monitors) is deprecated and will be removed after 2016-12-05.\n",
      "Instructions for updating:\n",
      "Monitors are deprecated. Please use tf.train.SessionRunHook.\n",
      "WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tensorflow/contrib/learn/python/learn/models.py:107: mean_squared_error_regressor (from tensorflow.contrib.learn.python.learn.ops.losses_ops) is deprecated and will be removed after 2016-12-01.\n",
      "Instructions for updating:\n",
      "Use `tf.contrib.losses.mean_squared_error` and explicit logits computation.\n",
      "WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tensorflow/contrib/learn/python/learn/ops/losses_ops.py:39: mean_squared_error (from tensorflow.contrib.losses.python.losses.loss_ops) is deprecated and will be removed after 2016-12-30.\n",
      "Instructions for updating:\n",
      "Use tf.losses.mean_squared_error instead.\n",
      "WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tensorflow/contrib/losses/python/losses/loss_ops.py:539: compute_weighted_loss (from tensorflow.contrib.losses.python.losses.loss_ops) is deprecated and will be removed after 2016-12-30.\n",
      "Instructions for updating:\n",
      "Use tf.losses.compute_weighted_loss instead.\n",
      "WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tensorflow/contrib/losses/python/losses/loss_ops.py:151: add_loss (from tensorflow.contrib.losses.python.losses.loss_ops) is deprecated and will be removed after 2016-12-30.\n",
      "Instructions for updating:\n",
      "Use tf.losses.add_loss instead.\n",
      "INFO:tensorflow:Create CheckpointSaverHook.\n",
      "INFO:tensorflow:Restoring parameters from ./ops_logs/sin_cos/model.ckpt-20000\n",
      "INFO:tensorflow:Saving checkpoints for 20001 into ./ops_logs/sin_cos/model.ckpt.\n",
      "INFO:tensorflow:loss = 1.5049e-05, step = 20001\n",
      "WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tensorflow/contrib/learn/python/learn/monitors.py:662: calling evaluate (from tensorflow.contrib.learn.python.learn.estimators.estimator) with y is deprecated and will be removed after 2016-12-01.\n",
      "Instructions for updating:\n",
      "Estimator is decoupled from Scikit Learn interface by moving into\n",
      "separate class SKCompat. Arguments x, y and batch_size are only\n",
      "available in the SKCompat class, Estimator will only accept input_fn.\n",
      "Example conversion:\n",
      "  est = Estimator(...) -> est = SKCompat(Estimator(...))\n",
      "WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tensorflow/contrib/learn/python/learn/monitors.py:662: calling evaluate (from tensorflow.contrib.learn.python.learn.estimators.estimator) with x is deprecated and will be removed after 2016-12-01.\n",
      "Instructions for updating:\n",
      "Estimator is decoupled from Scikit Learn interface by moving into\n",
      "separate class SKCompat. Arguments x, y and batch_size are only\n",
      "available in the SKCompat class, Estimator will only accept input_fn.\n",
      "Example conversion:\n",
      "  est = Estimator(...) -> est = SKCompat(Estimator(...))\n",
      "WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tensorflow/contrib/learn/python/learn/models.py:107: mean_squared_error_regressor (from tensorflow.contrib.learn.python.learn.ops.losses_ops) is deprecated and will be removed after 2016-12-01.\n",
      "Instructions for updating:\n",
      "Use `tf.contrib.losses.mean_squared_error` and explicit logits computation.\n",
      "WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tensorflow/contrib/learn/python/learn/ops/losses_ops.py:39: mean_squared_error (from tensorflow.contrib.losses.python.losses.loss_ops) is deprecated and will be removed after 2016-12-30.\n",
      "Instructions for updating:\n",
      "Use tf.losses.mean_squared_error instead.\n",
      "WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tensorflow/contrib/losses/python/losses/loss_ops.py:539: compute_weighted_loss (from tensorflow.contrib.losses.python.losses.loss_ops) is deprecated and will be removed after 2016-12-30.\n",
      "Instructions for updating:\n",
      "Use tf.losses.compute_weighted_loss instead.\n",
      "WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tensorflow/contrib/losses/python/losses/loss_ops.py:151: add_loss (from tensorflow.contrib.losses.python.losses.loss_ops) is deprecated and will be removed after 2016-12-30.\n",
      "Instructions for updating:\n",
      "Use tf.losses.add_loss instead.\n",
      "INFO:tensorflow:Starting evaluation at 2017-06-23-02:52:53\n",
      "INFO:tensorflow:Restoring parameters from ./ops_logs/sin_cos/model.ckpt-20001\n",
      "INFO:tensorflow:Finished evaluation at 2017-06-23-02:52:53\n",
      "INFO:tensorflow:Saving dict for global step 20001: global_step = 20001, loss = 1.52416e-05\n",
      "WARNING:tensorflow:Skipping summary for global_step, must be a float or np.float32.\n",
      "INFO:tensorflow:Validation (step 20001): loss = 1.52416e-05, global_step = 20001\n",
      "INFO:tensorflow:global_step/sec: 73.3152\n",
      "INFO:tensorflow:loss = 1.29122e-05, step = 20101 (1.365 sec)\n",
      "INFO:tensorflow:global_step/sec: 545.777\n",
      "INFO:tensorflow:loss = 1.49976e-05, step = 20201 (0.183 sec)\n",
      "INFO:tensorflow:global_step/sec: 524.477\n",
      "INFO:tensorflow:loss = 1.34932e-05, step = 20301 (0.191 sec)\n",
      "INFO:tensorflow:global_step/sec: 532.595\n",
      "INFO:tensorflow:loss = 1.86075e-05, step = 20401 (0.188 sec)\n",
      "INFO:tensorflow:global_step/sec: 549.3\n",
      "INFO:tensorflow:loss = 2.27843e-05, step = 20501 (0.182 sec)\n",
      "INFO:tensorflow:global_step/sec: 640.516\n",
      "INFO:tensorflow:loss = 1.48988e-05, step = 20601 (0.156 sec)\n",
      "INFO:tensorflow:global_step/sec: 612.078\n",
      "INFO:tensorflow:loss = 1.21389e-05, step = 20701 (0.163 sec)\n",
      "INFO:tensorflow:global_step/sec: 632.14\n",
      "INFO:tensorflow:loss = 2.0646e-05, step = 20801 (0.158 sec)\n",
      "INFO:tensorflow:global_step/sec: 609.875\n",
      "INFO:tensorflow:loss = 1.07195e-05, step = 20901 (0.164 sec)\n",
      "INFO:tensorflow:global_step/sec: 615.233\n",
      "INFO:tensorflow:loss = 1.33995e-05, step = 21001 (0.162 sec)\n",
      "INFO:tensorflow:global_step/sec: 621.852\n",
      "INFO:tensorflow:loss = 1.63667e-05, step = 21101 (0.161 sec)\n",
      "INFO:tensorflow:global_step/sec: 556.805\n",
      "INFO:tensorflow:loss = 1.12885e-05, step = 21201 (0.180 sec)\n",
      "INFO:tensorflow:global_step/sec: 645.494\n",
      "INFO:tensorflow:loss = 1.17751e-05, step = 21301 (0.155 sec)\n",
      "INFO:tensorflow:global_step/sec: 677.058\n",
      "INFO:tensorflow:loss = 1.33793e-05, step = 21401 (0.148 sec)\n",
      "INFO:tensorflow:global_step/sec: 638.435\n",
      "INFO:tensorflow:loss = 1.47959e-05, step = 21501 (0.157 sec)\n",
      "INFO:tensorflow:global_step/sec: 559.088\n",
      "INFO:tensorflow:loss = 2.15485e-05, step = 21601 (0.179 sec)\n",
      "INFO:tensorflow:global_step/sec: 541.94\n",
      "INFO:tensorflow:loss = 1.2828e-05, step = 21701 (0.184 sec)\n",
      "INFO:tensorflow:global_step/sec: 622.162\n",
      "INFO:tensorflow:loss = 1.19724e-05, step = 21801 (0.161 sec)\n",
      "INFO:tensorflow:global_step/sec: 695.091\n",
      "INFO:tensorflow:loss = 1.34749e-05, step = 21901 (0.144 sec)\n",
      "INFO:tensorflow:global_step/sec: 666.214\n",
      "INFO:tensorflow:loss = 1.42398e-05, step = 22001 (0.150 sec)\n",
      "INFO:tensorflow:global_step/sec: 666.995\n",
      "INFO:tensorflow:loss = 1.36745e-05, step = 22101 (0.150 sec)\n",
      "INFO:tensorflow:global_step/sec: 684.163\n",
      "INFO:tensorflow:loss = 1.43641e-05, step = 22201 (0.146 sec)\n",
      "INFO:tensorflow:global_step/sec: 695.749\n",
      "INFO:tensorflow:loss = 1.16507e-05, step = 22301 (0.144 sec)\n",
      "INFO:tensorflow:global_step/sec: 671.578\n",
      "INFO:tensorflow:loss = 1.13339e-05, step = 22401 (0.149 sec)\n",
      "INFO:tensorflow:global_step/sec: 687.413\n",
      "INFO:tensorflow:loss = 1.12791e-05, step = 22501 (0.145 sec)\n",
      "INFO:tensorflow:global_step/sec: 691.339\n",
      "INFO:tensorflow:loss = 1.05488e-05, step = 22601 (0.145 sec)\n",
      "INFO:tensorflow:global_step/sec: 684.73\n",
      "INFO:tensorflow:loss = 1.10253e-05, step = 22701 (0.146 sec)\n",
      "INFO:tensorflow:global_step/sec: 703.631\n",
      "INFO:tensorflow:loss = 1.04233e-05, step = 22801 (0.142 sec)\n",
      "INFO:tensorflow:global_step/sec: 686.055\n",
      "INFO:tensorflow:loss = 1.12501e-05, step = 22901 (0.146 sec)\n",
      "INFO:tensorflow:global_step/sec: 692.876\n",
      "INFO:tensorflow:loss = 1.40322e-05, step = 23001 (0.144 sec)\n",
      "INFO:tensorflow:global_step/sec: 658.531\n",
      "INFO:tensorflow:loss = 1.74308e-05, step = 23101 (0.152 sec)\n",
      "INFO:tensorflow:global_step/sec: 681.444\n",
      "INFO:tensorflow:loss = 1.16474e-05, step = 23201 (0.147 sec)\n",
      "INFO:tensorflow:global_step/sec: 689.014\n",
      "INFO:tensorflow:loss = 1.19713e-05, step = 23301 (0.145 sec)\n",
      "INFO:tensorflow:global_step/sec: 679.86\n",
      "INFO:tensorflow:loss = 1.0388e-05, step = 23401 (0.147 sec)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:global_step/sec: 667.633\n",
      "INFO:tensorflow:loss = 1.03316e-05, step = 23501 (0.150 sec)\n",
      "INFO:tensorflow:global_step/sec: 529.064\n",
      "INFO:tensorflow:loss = 1.12464e-05, step = 23601 (0.189 sec)\n",
      "INFO:tensorflow:global_step/sec: 528.753\n",
      "INFO:tensorflow:loss = 1.91092e-05, step = 23701 (0.189 sec)\n",
      "INFO:tensorflow:global_step/sec: 573.415\n",
      "INFO:tensorflow:loss = 1.60847e-05, step = 23801 (0.175 sec)\n",
      "INFO:tensorflow:global_step/sec: 556.402\n",
      "INFO:tensorflow:loss = 9.26454e-06, step = 23901 (0.180 sec)\n",
      "INFO:tensorflow:global_step/sec: 606.914\n",
      "INFO:tensorflow:loss = 2.14155e-05, step = 24001 (0.165 sec)\n",
      "INFO:tensorflow:global_step/sec: 567.901\n",
      "INFO:tensorflow:loss = 9.90086e-06, step = 24101 (0.176 sec)\n",
      "INFO:tensorflow:global_step/sec: 526.269\n",
      "INFO:tensorflow:loss = 1.08341e-05, step = 24201 (0.190 sec)\n",
      "INFO:tensorflow:global_step/sec: 619.03\n",
      "INFO:tensorflow:loss = 1.05501e-05, step = 24301 (0.161 sec)\n",
      "INFO:tensorflow:global_step/sec: 658.944\n",
      "INFO:tensorflow:loss = 1.00762e-05, step = 24401 (0.152 sec)\n",
      "INFO:tensorflow:global_step/sec: 614.36\n",
      "INFO:tensorflow:loss = 9.62359e-06, step = 24501 (0.163 sec)\n",
      "INFO:tensorflow:global_step/sec: 555.469\n",
      "INFO:tensorflow:loss = 9.66424e-06, step = 24601 (0.180 sec)\n",
      "INFO:tensorflow:global_step/sec: 681.84\n",
      "INFO:tensorflow:loss = 9.50145e-06, step = 24701 (0.147 sec)\n",
      "INFO:tensorflow:global_step/sec: 686.728\n",
      "INFO:tensorflow:loss = 8.60504e-06, step = 24801 (0.146 sec)\n",
      "INFO:tensorflow:global_step/sec: 672.639\n",
      "INFO:tensorflow:loss = 9.47409e-06, step = 24901 (0.149 sec)\n",
      "INFO:tensorflow:global_step/sec: 686.766\n",
      "INFO:tensorflow:loss = 1.0083e-05, step = 25001 (0.146 sec)\n",
      "INFO:tensorflow:global_step/sec: 669.56\n",
      "INFO:tensorflow:loss = 1.00355e-05, step = 25101 (0.149 sec)\n",
      "INFO:tensorflow:global_step/sec: 668.847\n",
      "INFO:tensorflow:loss = 8.7211e-06, step = 25201 (0.150 sec)\n",
      "INFO:tensorflow:global_step/sec: 684.379\n",
      "INFO:tensorflow:loss = 9.15974e-06, step = 25301 (0.146 sec)\n",
      "INFO:tensorflow:global_step/sec: 434.478\n",
      "INFO:tensorflow:loss = 9.37325e-06, step = 25401 (0.230 sec)\n",
      "INFO:tensorflow:global_step/sec: 556.359\n",
      "INFO:tensorflow:loss = 9.23484e-06, step = 25501 (0.180 sec)\n",
      "INFO:tensorflow:global_step/sec: 547.735\n",
      "INFO:tensorflow:loss = 8.74906e-06, step = 25601 (0.183 sec)\n",
      "INFO:tensorflow:global_step/sec: 454.517\n",
      "INFO:tensorflow:loss = 9.38872e-06, step = 25701 (0.220 sec)\n",
      "INFO:tensorflow:global_step/sec: 579.941\n",
      "INFO:tensorflow:loss = 1.17061e-05, step = 25801 (0.172 sec)\n",
      "INFO:tensorflow:global_step/sec: 624.801\n",
      "INFO:tensorflow:loss = 8.96409e-06, step = 25901 (0.160 sec)\n",
      "INFO:tensorflow:global_step/sec: 626.865\n",
      "INFO:tensorflow:loss = 1.03333e-05, step = 26001 (0.159 sec)\n",
      "INFO:tensorflow:global_step/sec: 605.334\n",
      "INFO:tensorflow:loss = 1.09606e-05, step = 26101 (0.165 sec)\n",
      "INFO:tensorflow:global_step/sec: 639.526\n",
      "INFO:tensorflow:loss = 2.6492e-05, step = 26201 (0.156 sec)\n",
      "INFO:tensorflow:global_step/sec: 639.812\n",
      "INFO:tensorflow:loss = 2.51146e-05, step = 26301 (0.156 sec)\n",
      "INFO:tensorflow:global_step/sec: 649.174\n",
      "INFO:tensorflow:loss = 1.17851e-05, step = 26401 (0.154 sec)\n",
      "INFO:tensorflow:global_step/sec: 663.011\n",
      "INFO:tensorflow:loss = 1.0458e-05, step = 26501 (0.151 sec)\n",
      "INFO:tensorflow:global_step/sec: 616.086\n",
      "INFO:tensorflow:loss = 8.73109e-06, step = 26601 (0.162 sec)\n",
      "INFO:tensorflow:global_step/sec: 689.493\n",
      "INFO:tensorflow:loss = 7.61579e-06, step = 26701 (0.145 sec)\n",
      "INFO:tensorflow:global_step/sec: 653.553\n",
      "INFO:tensorflow:loss = 7.81784e-06, step = 26801 (0.153 sec)\n",
      "INFO:tensorflow:global_step/sec: 653.104\n",
      "INFO:tensorflow:loss = 9.69246e-06, step = 26901 (0.153 sec)\n",
      "INFO:tensorflow:global_step/sec: 689.641\n",
      "INFO:tensorflow:loss = 7.85191e-06, step = 27001 (0.145 sec)\n",
      "INFO:tensorflow:global_step/sec: 642.282\n",
      "INFO:tensorflow:loss = 1.56781e-05, step = 27101 (0.156 sec)\n",
      "INFO:tensorflow:global_step/sec: 651.593\n",
      "INFO:tensorflow:loss = 1.01368e-05, step = 27201 (0.153 sec)\n",
      "INFO:tensorflow:global_step/sec: 634.619\n",
      "INFO:tensorflow:loss = 7.77003e-06, step = 27301 (0.158 sec)\n",
      "INFO:tensorflow:global_step/sec: 629.227\n",
      "INFO:tensorflow:loss = 9.28844e-06, step = 27401 (0.159 sec)\n",
      "INFO:tensorflow:global_step/sec: 654.733\n",
      "INFO:tensorflow:loss = 7.84801e-06, step = 27501 (0.153 sec)\n",
      "INFO:tensorflow:global_step/sec: 562.544\n",
      "INFO:tensorflow:loss = 1.38593e-05, step = 27601 (0.178 sec)\n",
      "INFO:tensorflow:global_step/sec: 536.055\n",
      "INFO:tensorflow:loss = 7.6528e-06, step = 27701 (0.187 sec)\n",
      "INFO:tensorflow:global_step/sec: 571.873\n",
      "INFO:tensorflow:loss = 1.84598e-05, step = 27801 (0.175 sec)\n",
      "INFO:tensorflow:global_step/sec: 577.038\n",
      "INFO:tensorflow:loss = 1.32074e-05, step = 27901 (0.175 sec)\n",
      "INFO:tensorflow:global_step/sec: 638.003\n",
      "INFO:tensorflow:loss = 9.56414e-06, step = 28001 (0.155 sec)\n",
      "INFO:tensorflow:global_step/sec: 658.605\n",
      "INFO:tensorflow:loss = 7.35068e-06, step = 28101 (0.152 sec)\n",
      "INFO:tensorflow:global_step/sec: 651.394\n",
      "INFO:tensorflow:loss = 1.13497e-05, step = 28201 (0.153 sec)\n",
      "INFO:tensorflow:global_step/sec: 655.227\n",
      "INFO:tensorflow:loss = 7.21105e-06, step = 28301 (0.153 sec)\n",
      "INFO:tensorflow:global_step/sec: 660.458\n",
      "INFO:tensorflow:loss = 1.67554e-05, step = 28401 (0.151 sec)\n",
      "INFO:tensorflow:global_step/sec: 658.645\n",
      "INFO:tensorflow:loss = 7.5815e-06, step = 28501 (0.152 sec)\n",
      "INFO:tensorflow:global_step/sec: 646.78\n",
      "INFO:tensorflow:loss = 9.92941e-06, step = 28601 (0.155 sec)\n",
      "INFO:tensorflow:global_step/sec: 660.048\n",
      "INFO:tensorflow:loss = 7.87334e-06, step = 28701 (0.152 sec)\n",
      "INFO:tensorflow:global_step/sec: 694.136\n",
      "INFO:tensorflow:loss = 7.58369e-06, step = 28801 (0.144 sec)\n",
      "INFO:tensorflow:global_step/sec: 687.536\n",
      "INFO:tensorflow:loss = 1.33303e-05, step = 28901 (0.146 sec)\n",
      "INFO:tensorflow:global_step/sec: 678.127\n",
      "INFO:tensorflow:loss = 7.71335e-06, step = 29001 (0.147 sec)\n",
      "INFO:tensorflow:global_step/sec: 655.437\n",
      "INFO:tensorflow:loss = 1.14983e-05, step = 29101 (0.153 sec)\n",
      "INFO:tensorflow:global_step/sec: 667.191\n",
      "INFO:tensorflow:loss = 7.34761e-06, step = 29201 (0.150 sec)\n",
      "INFO:tensorflow:global_step/sec: 692.876\n",
      "INFO:tensorflow:loss = 6.77253e-06, step = 29301 (0.144 sec)\n",
      "INFO:tensorflow:global_step/sec: 683.364\n",
      "INFO:tensorflow:loss = 7.37182e-06, step = 29401 (0.146 sec)\n",
      "INFO:tensorflow:global_step/sec: 671.578\n",
      "INFO:tensorflow:loss = 6.95666e-06, step = 29501 (0.149 sec)\n",
      "INFO:tensorflow:global_step/sec: 535.008\n",
      "INFO:tensorflow:loss = 1.12547e-05, step = 29601 (0.187 sec)\n",
      "INFO:tensorflow:global_step/sec: 595.927\n",
      "INFO:tensorflow:loss = 8.37865e-06, step = 29701 (0.168 sec)\n",
      "INFO:tensorflow:global_step/sec: 650.259\n",
      "INFO:tensorflow:loss = 7.49123e-06, step = 29801 (0.154 sec)\n",
      "INFO:tensorflow:global_step/sec: 629.612\n",
      "INFO:tensorflow:loss = 6.58023e-06, step = 29901 (0.159 sec)\n",
      "INFO:tensorflow:Saving checkpoints for 30000 into ./ops_logs/sin_cos/model.ckpt.\n",
      "INFO:tensorflow:Loss for final step: 7.93065e-06.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "SKCompat()"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X, y = generate_data(sin_cos, np.linspace(0, 100, 10000, dtype=np.float32), TIMESTEPS, seperate=False)\n",
    "# create a lstm instance and validation monitor\n",
    "validation_monitor = learn.monitors.ValidationMonitor(X['val'], y['val'],\n",
    "                                                      every_n_steps=PRINT_STEPS,\n",
    "                                                      early_stopping_rounds=1000)\n",
    "regressor.fit(X['train'], y['train'], \n",
    "              monitors=[validation_monitor], \n",
    "              batch_size=BATCH_SIZE,\n",
    "              steps=TRAINING_STEPS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tensorflow/contrib/learn/python/learn/models.py:107: mean_squared_error_regressor (from tensorflow.contrib.learn.python.learn.ops.losses_ops) is deprecated and will be removed after 2016-12-01.\n",
      "Instructions for updating:\n",
      "Use `tf.contrib.losses.mean_squared_error` and explicit logits computation.\n",
      "WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tensorflow/contrib/learn/python/learn/ops/losses_ops.py:39: mean_squared_error (from tensorflow.contrib.losses.python.losses.loss_ops) is deprecated and will be removed after 2016-12-30.\n",
      "Instructions for updating:\n",
      "Use tf.losses.mean_squared_error instead.\n",
      "WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tensorflow/contrib/losses/python/losses/loss_ops.py:539: compute_weighted_loss (from tensorflow.contrib.losses.python.losses.loss_ops) is deprecated and will be removed after 2016-12-30.\n",
      "Instructions for updating:\n",
      "Use tf.losses.compute_weighted_loss instead.\n",
      "WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tensorflow/contrib/losses/python/losses/loss_ops.py:151: add_loss (from tensorflow.contrib.losses.python.losses.loss_ops) is deprecated and will be removed after 2016-12-30.\n",
      "Instructions for updating:\n",
      "Use tf.losses.add_loss instead.\n",
      "INFO:tensorflow:Restoring parameters from ./ops_logs/sin_cos/model.ckpt-30000\n",
      "MSE: 0.000007\n"
     ]
    }
   ],
   "source": [
    "predicted = regressor.predict(X['test'])\n",
    "rmse = np.sqrt(((predicted - y['test']) ** 2).mean(axis=0))\n",
    "score = mean_squared_error(predicted, y['test'])\n",
    "print (\"MSE: %f\" % score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fba75f7f050>,\n",
       " <matplotlib.lines.Line2D at 0x7fba75fa60d0>]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEACAYAAAC3adEgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fba8c0aadd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fba75b78950>,\n",
       " <matplotlib.lines.Line2D at 0x7fba75b78b50>]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEACAYAAAC3adEgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJztnXd4lMXa/z+T0HsNvUNC70WaRkCI9CodRRSOlePBox49\n77H8zvt6kGPHLkXpHRJq6ChNpIWEkIROAOm9pez8/phEAyaQ7D67s/vsfK5rLzZPnp35Zpnde+ae\nue9bSCkxGAwGgyGdAN0CDAaDweBdGMNgMBgMhrswhsFgMBgMd2EMg8FgMBjuwhgGg8FgMNyFMQwG\ng8FguAtLDIMQYpIQ4owQIuo+93wmhEgQQuwRQjS2ol+DwWAwWI9VK4YpQJesfimEeByoIaWsBYwB\nvraoX4PBYDBYjCWGQUr5M3DpPrf0An5Mu3c7UFQIUcaKvg0Gg8FgLZ7aY6gAnMjw88m0awaDwWDw\nMszms8FgMBjuIpeH+jkJVMrwc8W0a3chhDCJmwwGg8EJpJTCqrasXDGItEdmhAMjAIQQDwGXpZRn\nMrtRSmkeUvL2229r16D7kepI5dNtn5K/U37eXPMmZ6+fve/9KakpzIyaSc3PajJw3kDOXD+j/W+4\n93HqlOTxxyW1a0uWLJGkpt7//jNnJGPHSkqWlEyaJPnXv8y4SH+Yz8gfD6ux6rjqTGALECyEOC6E\nGCmEGCOEGA0gpVwOHBFCHAS+AZ63ol+Dfbl65yq9Z/dmdvRsRjUZxf92/F9KFyx939cEBgQyuMFg\nov4SRaUilWj2bTN2nNzhIcUPZu1aaNoUWraEqCjo2RMCHvAJDAqCTz6BDRvUv/Pnw82bHpFr8GOs\nOpU0REpZXkqZV0pZWUo5RUr5jZTy2wz3vCilrCmlbCSl3GVFvwZ7cvraadpMakPFIhXZ8NQGShYo\nmaPX58+dnwmdJ/D545/TdWZXFh9Y7Cal2WfGDBgyRP37zjuQO3fOXl+/PvzyCwQGQseOcO6cW2Qa\nDIDn9hgMOSQ0NFS3BC2cvHqSDj92YETDEbz18FuA8+9F79q9qVy0Ml1ndCUpNYkn6j1hodLs8/33\n8N57sG4d1KvnfDv58sGnn4YSGQkdOsD69VCqlHU6fQ1//Yx4AuEO/5SzCCGkN+kxeJYLNy/QZnIb\nnm78NK+3e92ydqPORNF5Wmem9p5KWM0wy9rNDvPnw8svw8aNUKuWNW1KCW++CatWKeNQtKg17Rp8\nFyEE0sLNZ2MYDF7BnZQ7PDbtMR6q+BAfPPaB5e1vObGF3rN7Ezk8ksZlPZORZdMm6N8fIiOhscVd\nSgnPPw/Hj0N4uHIxGfwXYxgMtkNKycglI7mWdI15A+YRINwTXjMvZh5/i/wbO0fvJKhgkFv6SCcx\nUW0yT50KnTu7p4/kZOjSBZo1gwkT3NOHwTew2jCYADeDdibvnsyvp35lWp9pbjMKAAPqDWBEwxEM\nXzQch3S4rZ87d6BfP+VCcpdRALWBPW8eLFoEc+e6rx+D/2FWDAatxJyNIfSHUDY+tZG6peu6vb8U\nRwodfuhAWM0w3mz/plv6ePVVOHhQfWELy+ZwWbNjB3Trpv6tUsX9/Rm8D+NKMtiGpNQkmn/bnLGt\nxjKq6SiP9Xvy6kmaftuUZUOW0bx8c0vb3rhRHUvdu9ezJ4Y++AAiIlS8g9lv8D+MK8lgG/7z83+o\nXLQyTzd52qP9VihSgY86f8TTS54mKTXJsnavXoWnnoJvv/X8MdJXX4VcueDTTz3br8GemBWDQQv7\nz+3nkamPsGv0LioVrfTgF1iMlJJes3vRpGwT3n30XUvafPlluHEDJk2ypLkcc/AgPPSQcilVq6ZH\ng0EPxpVk8HlSHam0m9KOEQ1H8FyL57TpOHXtFI2+bsRPI3+idqnaLrW1axc8/jjs3w8lcxaobSn/\n+Y9yJ61Y4Zn9DYN3YHtX0sWLuhUY3M2UPVMIEAGMaT5Gq47yhcvzVvu3GLtyrEuJyBwOeO45eP99\nvUYBYNw4OH1aBdYZ7M2JKyf4bPtnbmnb6wzDe+/pVmBwJ1fvXOV/1v8Pn4V95tajqdnlhRYvkHg1\nkfC4cKfb+P57dXT0qaes0+UsuXOrZHuvvQa3b+tWY3Anb657k3M33JM0y+tcSSVLSjZvhpAQ3WoM\n7uCNNW9w5sYZpvSaolvK76w5vIbREaPZ/8J+8uXKl6PXXrsGwcGwfDk0aeImgU7Qp4/ab3jduswi\nBi9ix8kd9J7Tm7gX4yiUp5D99xjGj1eGYckS3WoMVnP40mFafteSfc/to1zhcrrl3EXfOX15qOJD\nvNb2tRy97u234cgR+PFHNwlzkvSN6JgYKGOqq9sKKSXtp7Tn6SZP/36iz/aG4fZtSZ06MHkymOSJ\n9mLwgsHUK12Pfz78T91S/kTsuVgemfoICS8lUDRf9rLSnT6t0mHv2uWdgWXjxqnaDV99pVuJwUoW\nxS7inY3vsGv0LgIDVNCK7Q2DlJLp0+Hrr+Gnn8zJCrsQfTaaTj924uDLBymUp5BuOZkycslIKhau\nyP/r8P+ydf9f/gKFCsF//+tmYU5y4YJyyZrjq/bBIR00+roR73d8n+7B3X+/bvtTSQCDB6tBHRmp\nW4nBKt7Z8A5/b/N3rzUKAO888g5f/volZ65nWnX2Lg4fVid/3nRPVg1LKFlSZWD9f9mzcwYfYG7M\nXArmLki3Wt3c2o9XrhgA5syBjz6CbdvMqsHX2fPbHrrO6MrBlw9SIHcB3XLuy9gVYxFC8EnYJ/e9\n79lnoVw57z9Fd/myqgOxZYt19SAMekhxpFD/y/p89vhndK5xd3ZGv1gxAAwYALduwbJlupUYXOXt\nDW/zRrs3vN4oALzR7g1+3PsjZ2+czfKeY8dg4UL46189KMxJihWDsWPhXWuCuw0amRE1g9IFS/NY\n9cfc3pfXGoaAAHXi4733VFESg2+y89ROdp7ayehmo3VLyRblCpdjUP1BfLot66RD48erFUOJEh4U\n5gJjx6pqb4cO6VZicBaHdPD+z+/zziPvIDzgQvFawwDQuzdcuaIyVhp8kw+2fMC41uNyHB+gk7+3\n+Ttf7/yaK7ev/Ol3J0/C7Nnwt79pEOYkhQvDmDHeu0lueDBLDiyhcN7CdKjWwSP9ebVhCAyEv/9d\nzdAMvsehi4dYe3gtzzR9RreUHFGteDW61urKlzu+/NPvJkyAkSMhyL0F4Czn5ZfVvt2ZB++rG7wM\nKSXjN4/n9bave2S1AF5uGACGD4eoKJXf3uBbfLT1I8Y0G0PhvIV1S8kxb7R9g0+3f8rN5Ju/X7t8\nWQWy+dJqIZ2gIBg0CD5zT2odgxvZdGwTF29dpE/tPh7r0+sNQ968ykdqatr6FudunGNW9CxeavWS\nbilOUS+oHg9VfIipe6b+fu3bb1WltAoV9OlyhVdfhW++UWk8DL7D+M3j+Xubv/8ezOYJvN4wgPKP\nrlgBR4/qVmLILhN/mciAugMoW6isbilO87fWf+Oz7Z/hkA6Sk9Vs+5VXdKtynurVoVMnZeAMvsG+\nM/vY89sehjca7tF+fcIwFC2q/LoTJ+pWYsgOt5Jv8dWvXzGuzTjdUlyifeX25M+dn1UHVzFvnooD\naNpUtyrXGDdOfY5SU3UrMWSHz7Z/xvMtnvf44Q2fMAygIjinTlW5XwzezZyYObSo0ILgksG6pbiE\nEIKxrcby6fZP+fBD39xbuJcWLVRSvaVLdSsxPIiLty4yP3a+lqPePmMYqleHNm1gxgzdSgz3Q0rJ\n5798zgstXtAtxRIG1R/EjhN7uCAO0M29WQg8xksvweef61ZheBCTd0+mR3APggp6/giczxgG+GNA\nm4A372X7ye1cvn2ZsJphuqVYQr5c+Sh3cgwV+39GgE99WrJmwACVjnv/ft1KDFmR6kjlix1f8GLL\nF7X071NDvVMnSE6GTZt0KzFkxcRfJvJCixe8ojqbFZw5A8cXPkc0s7h8+7JuOZaQJw+MHm327LyZ\nZQnLCCoYRMsKLbX071OfXiHgxRfNMthbOXP9DMsSljGy8UjdUixj8mQY8HhZutTszIwo+/gxx4yB\nWbNUbIbB+5j4y0RebKFntQA+ZhgARoyAdevg1CndSgz38v2u7xlQdwDF8xfXLcUSUlPV0c7nnoMx\nzcbwzc5v8KZsxK5Qvjx06QLTp+tWYriXA+cPEHUmiifqPaFNg88ZhsKFlY90iveUDDagfKJf7/ya\n51s8r1uKZaxaBaVKQfPmEFo1lFspt9h+crtuWZbx7LPw3Xdmz87b+G7nd4xsPJK8ufJq0+BzhgHg\nmWdg0iRwOHQrMaSz6tAqyhcuT+OyjXVLsYyvv1ZV2gACRACjm47mm53f6BVlIY8+qqKgd+zQrcSQ\nTlJqEtOipjGq6SitOnzSMDRvDkWKKJeSwTuYtHsSo5roHcxWcvw4bN6s8gul82TjJ1kUu8g2m9AB\nAWqS9d13upUY0gmPC6deUD1qlqipVYdPGgYh1ID+/nvdSgygNp3XHl7LoPqDHnyzjzBliioxW7Dg\nH9eCCgYRVjOM6VH2ccyPHKlKlJr8Sd7BpN2TeKaJ/mzEPmkYAIYOhZUr4fx53UoM06Km0adOH4rk\nLaJbiiU4HCrK/umn//y70c1G22oTulw5eOQRVWPCoJfjV47zy8lf6Funr24pvmsYiheHnj1VGmSD\nPqSUtnMjbdyoXJVNmvz5d49WfZRbybf49dSvnhfmJp591qy+vYEpu6cwuP5g8ufOr1uK7xoG+MM/\napPJm0+y5cQWpJS0rdRWtxTLmDIFnnpKuSzvRQjBiEYj+GHvDx7X5S7CwtTx76go3Ur8l1RHKpP3\nTPaaCZZPG4b27SElxZyq0En6asFTlaXczdWrEB4Ow4Zlfc+IRiOYEzOHOyl3PCfMjQQGqvigH+xj\n63yOtUfWUqpAKZqUy2SZqgGfNgxCqApvxp2kh+tJ11kYu5ARjUbolmIZ8+apY5ylS2d9T9ViValX\nuh7LEpZ5TpibGT4cZs5UEy2D55m823tWC+DjhgHUzG7OHEhK0q3E/1h8YDFtK7elTKEyuqVYRrob\n6UE82ehJW7mTateGSpVgzRrdSvyPq3eusvLgSq861efzhqF6dTWoV6zQrcT/mBY1jeENPVtZyp0k\nJKhH164Pvrd/3f5sPLqRczfOuV+Yhxgxwqy+dbBg/wIerfYoJfKX0C3ld3zeMIAZ0Do4fe00v5z8\nhZ4hPXVLsYypU9UKNHfuB99bOG9heoT0YOa+mW7X5SkGDYLly9U+i8FzTN83nWEN7rOppQFbGIYB\nA9QS+OJF3Ur8h1nRs+hduzcFchfQLcUSHA5VBGpEDrZL7OZOKlVKxTQsWKBbif+QeDWR3ad30y3Y\nu6pA2cIwFCumMkXOnatbif8wLWqa181yXGHrVhXl3LBh9l/zaNVHOXfzHPvO7HOfMA9jVt+eZda+\nWfSr08/jNZ0fhC0MA6hTFdOm6VbhH0SfjebcjXOEVg3VLcUyZs6EIUMyj13IisCAQIY1GGarFBnd\nu6t4hmPHdCvxD6bvm87wRt63T2eJYRBChAkhDggh4oUQr2fy+yeFEGeFELvSHpkkG3CNsDC1cXjw\noNUtG+5letR0hjQYQmBAoG4plpCcrI6pDh6c89cObjCY2TGzbZMiI29eeOIJU6fBE0SdieLy7cu0\nq9xOt5Q/4bJhEEIEABOBLkA9YLAQonYmt86WUjZNe0x2td97yZ0bBg5UMz+D+3BIBzP2zbDVaaQ1\na6BGDXXCLac0CGpAoTyF2Jq41Xphmhg6VFV3M7iXGVEzGNpgqFeWwbVCUUsgQUp5TEqZDMwGemVy\nn9tDYwcNUsnAbDJ580o2HdtEifwlaFCmgW4plpHuRnIGIQSD6g1i1j77fJO2aQNXrkB0tG4l9sUh\nHcyMnsmwht65T2eFYagAnMjwc2LatXvpK4TYI4SYK4SoaEG/f6J1a7h+3QxodzIneg6D6zvhc/FS\nbt6EpUuV+8RZBjcYzLz980hx2CNsOCBArb7nzNGtxL5sPLqRUgVKUbd0Xd1SMiWXh/oJB2ZKKZOF\nEKOBH4COmd34zjvv/P48NDSU0NDQbHeScUA3sM+E1mtIcaSwIHYB257ZpluKZSxdCi1bQhkXgrdr\nlqhJpaKV2HB0A52qd7JOnEYGDlSrqPfey9mGvCF7zI6ezZD6Ti5TgQ0bNrBhwwbrBN2DcHXTTAjx\nEPCOlDIs7ec3ACmlHJ/F/QHARSllsUx+J13V8+uvyqWUkGAGtNWsPrSaN9e9yY5n7ZO1sHdv6NMH\nnnzStXY+2voRMWdjmNRrkjXCNCMl1KypjoA3a6Zbjb1IcaRQ7sNy7Hh2B1WLVbWkTSEEUkrLvvGs\ncCXtAGoKIaoIIfIAg1ArhN8RQpTN8GMvYL8F/WZK+iDetctdPfgvc2PmMrDeQN0yLOPSJVi/XhkG\nV3mi3hMsjltsm4yrQqgJlnEnWc/6I+upXry6ZUbBHbhsGKSUqcCLQCQQgzp9FCuEeFcI0T3ttpeF\nENFCiN1p9z7lar9ZkT6gTUUqa0lOTWbRgUU8Uc8FZ7yXsXAhPPaYKsrjKhWLVKR+UH1WHVrlemNe\nQrphcDh0K7EXc2LmeP0Ey5JzUlLKlVLKECllLSnlf9KuvS2lXJr2/E0pZX0pZRMpZUcpZbwV/WZF\n+j6DGdDWsebwGoJLBlO5aGXdUixj3jzXNp3vZXD9wcyKts/ppPr1oVAh2GafLSXtJKcms/jAYvrX\n7a9byn3xvgO0FlC/PhQubAa0lcyJmWOr1cLFiyoNRnYyqWaXfnX6sSJhBTeSbljXqEaEMKeTrGbt\nkbU+McGypWEw7iRruZNyh/C4cAbUHaBbimUsWQKdOqkZsVWULlialhVasuKgfXLADxyoNqBTU3Ur\nsQdzY+b6xATLloYB1ICeN88MaCuIPBRJ/aD6VCiSWXiKbzJ/PvR3w2q+f93+LIi1T3rSkBAoVw42\nbdKtxPdJSk3yCTcS2NgwBAers+mbN+tW4vv4wmZZTrh8GX7+WSWMs5retXuzImEFt5JvWd+4Jvr3\nN6m4rWD1odXULV2XikXcEt9rKbY1DAD9+pkB7Sq3km+xNH4p/er20y3FMsLDVV3nwoWtbzuoYBBN\nyzUl8lCk9Y1rol8/WLTIHOZwlbn7fcONBDY3DH37qiOJJneS86w6tIom5ZpQtlDZB9/sI7jLjZRO\nvzr9mB87330deJiQEChe3BzmcIX0fbp+dXxjgmVrw1C3riq+ssM+gboeZ2HsQvrX8X6faHa5cgU2\nbIAePdzXR586fVgav9Q2wW5gVt+uEnkokgZBDXxmn87WhkEIM6BdITk1maXxS+ldu7duKZaxdKkq\nX1m0qPv6KF+4PPWD6rP2yFr3deJh0j9HZvXtHAtiF/jEpnM6tjYMYAa0K2w4uoGQUiE+M8vJDvPn\nqxrh7qZ/nf7M328fd1KDBqrmiUk1k3NSHCk+N8GyvWFo0kQdWY2K0q3E91gYu5C+tfvqlmEZ167B\nunXudSOl07dOX5bELSE5Ndn9nXkAs/p2nk3HNlGteDWvD2rLiO0NgxnQzpHqSGVx3GL61LEgw5yX\nsGwZtG2rNlLdTaWilahVohbrj653f2cewqy+nWNh7EL61Patz5HtDQOoAb1woW4VvsW2xG2ULlCa\nmiVq6pZiGe4+jXQv/evay53UvDncugUxMbqV+A4O6WDxgcX0reNbK2+/MAytWqkUy3FxupX4Dgtj\nF/rcYL4ft2/D6tWecSOl069OPxYfWGybym5C/HEE3JA9dpzcQeG8haldqrZuKTnCLwxDQIDKuW/c\nSdlDSsnCA/YyDOvXQ8OGULq05/qsVrwaFYtUZPNx+4TfG7dszlh0YJFP7tP5hWEAM6Bzwp7f9hAo\nAmkQZJ/6qOHh0LOn5/vtXbs3iw8s9nzHbqJNGzhzBg4e1K3E+5FS+uzK228MQ/v2cOIEHDmiW4n3\ns+jAIvrW6YuwSW1UKTUbhrjFuFqy1lsIDDSr7+yy/9x+7qTeoWm5prql5Bi/MQy5ckGvXirni+H+\n+OosJyt27VLptUNCPN93g6AGCAT7zu7zfOduom9f8znKDumnkXxxguU3hgGUYViyRLcK7ybufByX\nbl+iZYWWuqVYRni4+r/XgRDCdu6kRx6B+Hg4fVq3Eu9m0YFFPndMNR2/MgwdO8KePXD+vG4l3kv6\nYA4Q9hkaS5bocSOlYzfDkCcPhIVBRIRuJd7LkUtHSLyaSLvK7XRLcQr7fPqzQf78yjgsW6Zbiffi\ni8E49+PYMTh5Elq31qehTaU2nLh6gmOXj+kTYTG9esFi+9g6y1l8YDE9Q3oSGBCoW4pT+JVhAONO\nuh+JVxM5dOkQD1d5WLcUy4iIgG7d1KapLnIF5KJ7cHeWxNln4D3+uCp2dO2abiXeia8f9/Y7w9Ct\nG6xdqyI4DXezNH4pj9d8nNyBuXVLsQxdp5HupXeIvdxJRYqoVdiqVbqVeB9nb5xl35l9dKzWUbcU\np/E7w1CqlEqst9Y+GZEtIzwunJ4hXvAtahFXrsDWrdC5s24l8FiNx9h5eicXb13ULcUyzOo7c5bF\nL6Nzjc7kzZVXtxSn8TvDAGZAZ8b1pOv8fPxnwmqG6ZZiGatWqfiVQoV0K4ECuQvQoVoHlsXbZ4Or\nZ09YvhyS7ZFA1jLC48PpEezB3CtuwG8NQ3i4SsdtUEQeiqR1pdYUyVtEtxTL8BY3Ujq9Q1Swm12o\nWBGqVVN7DQbF7ZTbrDuyjq61uuqW4hJ+aRiqV4egINi+XbcS7yE8LpyewV70LeoiycmwYgV0765b\nyR90D+7OmsNruJVsnw2u3r3N6jsj646so3HZxpQsUFK3FJfwS8MAxp2UkVRHKssSltEjxLeXvxnZ\nvFnNZitW1K3kD0oWKEmTsk1Yc3iNbimWkX5s1SYZP1zGLhMsYxgMbE3cSsUiFX2qwtSD8DY3Ujp2\nC3arX19lLzYVElXthYj4CFsc4PBbw9CsmTqDbWo02GeWk47OpHkPoldILyLiI0h12GODSwgzyUpn\n1+ldFM5TmFola+mW4jJ+axgCAtQXhxnQ9jumGhur9hgaNdKt5M9UK16NcoXLsS1xm24plmEMgyIi\nzh6rBfBjwwBmQINKmnct6ZpPpgbOivTcSN6a1LJHcA/C48J1y7CMdu1U6pETJ3Qr0Ut4vH0mWH5t\nGB59VNWvPXNGtxJ9RMRH0DO4p0+mBs4Kb3UjpdMjuAcR8fbJQJcrF3Ttqt53f+X4leMkXk2kdUWN\nSbksxK8NQ968Kip26VLdSvRhNzfSb78pV9Ijj+hWkjUtKrTg4q2LHLp4SLcUy+jd27+T6kXERdC1\nVlefTZp3L35tGMC/s0Sev3mevWf28mi1R3VLsYxly6BLF5Ua2lsJEAF0D+5uq1VD584qLujKFd1K\n9BAeb68DHH5vGLp2hY0b4eZN3Uo8z/KE5XSq3ol8ufLplmIZ3u5GSsdu7qRChVT6kZUrdSvxPFfv\nXGXria10ruEFSbkswu8NQ/Hi0KIFrF6tW4nnWRK3xFaznJs3Yf16lRLa2+lUvRM7Tu7g8u3LuqVY\nhr8e5og8FEnbym0pnLewbimW4feGAdQM0982zm6n3GbN4TU+n9MlI2vXqviUEiV0K3kwBfMUpH2V\n9qw8aJ8pdvfuasXgb0n1wuN8P2nevRjDAPTooTag/Smp3voj62lYpiGlC5bWLcUyfMWNlE7P4J62\ncieVLw81a8JPP+lW4jlSHCksT1huDIMdSU+q98svupV4DrtFOzscqlqbLxmG7sHdWXlwJSmOFN1S\nLMPfVt9bTmyhctHKVCpaSbcUSzGGIQ1/GtBSSlsF4wDs2AElS0KNGrqVZJ8KRSpQtVhVNh/frFuK\nZaRnE/CXpHp2inbOiDEMafiTYdh1eheF8hQipFSIbimW4WtupHTsFgXdoIH6Nzparw5PYbcJVjrG\nMKTRogVcvAgHD+pW4n7s5kYCZRh69dKtIuf0DLHXPoMQ/jPJijsfx42kGzQp20S3FMsxhiGNgAC1\nCe0PA9pus5zDh+HcOWjZUreSnNOkbBNuJt8k7rx90vz6i2FIP41kp3Qy6RjDkAF/GNC/53SpZI+c\nLqD+z7p3V8bd1xBC2C4K+uGHISEBTp/WrcS92KX2Qmb44EfJfXTsCLt2wYULupW4j4i4CLrV6kau\ngFy6pViGr+4vpGO3KOjcuSEsTJ0Ssyt2TCeTEWMYMpA/P3TooGoF25XweHsF41y6BL/+Cp066Vbi\nPB2qdWD36d1cuGmfGYndV9/LE5bTsVpHW6WTyYgxDPdg5wF99c5VtpzYYqucLitWQGgoFCigW4nz\n5M+dnw7VOrDioH1mJGFhsGkT3LihW4l7sFtW4nuxxDAIIcKEEAeEEPFCiNcz+X0eIcRsIUSCEGKr\nEMJriwt36waRkXDnjm4l1hN5KJK2leyV08XX3Ujp2M2dVKyYOgxgxxxkd1LusPrwarrV6qZbittw\n2TAIIQKAiUAXoB4wWAhR+57bRgEXpZS1gE+AD1zt112UKQP16sGGDbqVWE9EfISt3EhJSbBqldp4\n9nW6BXcj8lAkSalJuqVYhl1X3xuObqBBUANbpZO5FytWDC2BBCnlMSllMjAbuPdEeS/gh7Tn84GO\nFvTrNuw4oFMdqSxPWE73YBt8i6axaROEhEDZsrqVuE7ZQmUJLhnMT8fsk2ioZ0975iCzY9K8e7HC\nMFQAMlZ7TUy7luk9UspU4LIQwmtzYKYbBjuF9W9L3Eb5wuWpUqyKbimWYRc3Ujp2i4KuWhXKlYNt\n23QrsQ4ppVp5h9jbMOg6s5hlRMg777zz+/PQ0FBCQ0M9IOduateGfPlgzx5oYpOgRrvNcqRUhsFO\nZVl7hvSk9+zefBL2iW2CptInWW3b6lZiDXvP7CVPYB7qlKqjVceGDRvY4EZ/txWG4SSQcTO5Ytq1\njCQClYBTQohAoIiU8mJmjWU0DLrIGNZvF8MQER/B1N5TdcuwjH37VEBbvXq6lVhHg6AGpMpU9p/b\nT70ge/xhPXvCiBEwfrxuJdaQfhpJt+G+d9L87rvvWtq+Fa6kHUBNIUQVIUQeYBBw73o4Angy7fkA\nYJ0F/bpEpGGDAAAgAElEQVQVO+0zHLp4iEu3L9G8fHPdUiwj3Y1kk4k1oKKg7XY6qVkzVQc6Pl63\nEmuw2wGOrHDZMKTtGbwIRAIxwGwpZawQ4l0hRPpO5ySglBAiAfgr8Iar/bqbtm3h2DFITNStxHUi\n4lW0c4CwT9jKkiW+mTTvQdjNMAQE2GeSderaKQ5dPES7yu10S3E7lnxTSClXSilDpJS1pJT/Sbv2\ntpRyadrzO1LKJ9J+/5CU8qgV/bqTXLmga1d7DGi7zXJOnoRDh6CdDT+foVVDiTkbw9kbZ3VLsQy7\nGIal8UsJqxlG7sDcuqW4HftMId2AHQb05duX2XFyB52q+3DOiHtYuhQef1zl5LEbeXPlpVP1TixP\nWK5bimV06AB798L587qVuIbdJlj3wxiG+9ClC2zeDFev6lbiPCsPruThKg9TME9B3VIsw27HVO/F\nbjUa8uVTCSqX+7Ctu5l8k41HNxJWM0y3FI9gDMN9KFxY7TVERupW4jx2m+Vcv66KzYfZ+PPZtVZX\n1hxew+2U27qlWEZ6yU9fZe3htTQv35zi+YvrluIRjGF4AL7sTkpOTWblwZW2inZevRpatYKiRXUr\ncR+lCpSiUZlGrD+yXrcUy+jWDdasgds+auvsFgf0IIxheAA9eqglcEqKbiU5Z/OJzVQtVpUKRe4N\nRPdd7O5GSqdnSE9bRUGXLg0NG8J6H7R1DulgacJS20c7Z8QYhgdQqRJUqaL2GnyNiDh7uZFSU9XG\ncw/7/ElZ0jOkJ+Hx4Ugb5WXx1dX3zlM7KZ6vODVL1NQtxWMYw5ANfHVA26304LZtUL68ysFjd4JL\nBlM4T2F2nd6lW4pl+GoOMn9zI4ExDNkifePMlwZ03Pk4bibfpElZm+T0wH/cSOnYzZ0UEqIOdOzc\nqVtJzvCHpHn3YgxDNmjcWBXuOXBAt5LsExEfQffg7tpzuliJXxqGePsYBvC91ffxK8c5ee0krSu2\n1i3FoxjDkA0yJtXzFey2/I2PVzl3mjXTrcRztK7YmsSriRy/cly3FMvwtc9RRFwEXWt1JTAgULcU\nj2IMQzbxpQF94eYF9vy2hw7VOuiWYhkREer/IMCPRmxgQCDdanUjIs4+wW6tW6uUJseO6VaSPewW\nB5Rd/Ohj5hqhoRATA2d9IIXNioMr6FCtA/lz59ctxTKWLPGP00j3Yjd3UmCgimnwhUnWtTvX2HJi\nC11qdNEtxeMYw5BN8uaFzp19ozCM3WY558+rXDsdvbogrHvoXKMzW09s5eodH87Lcg+9evmGYYg8\nFEnrSq0pnLewbikexxiGHOAL7qSk1CQiD0XSLbibbimWsXQpdOqkcu74G4XyFKJd5XasOrhKtxTL\neOwx2L5d7Rl5M3abYOUEYxhyQNeusG4d3LqlW0nWbDq2ieCSwZQtVFa3FMuwa+2F7GI3d1KhQtC+\nPaxcqVtJ1qQ6UlmesNwYBsODKVECmjaFtWt1K8kau0U737qljHE3+yyAckz34O4sT1hOisMH87Jk\ngbevvrclbqNc4XJUKVZFtxQtGMOQQ7x5QEspbRftvGaNqrtdsqRuJfqoWKQi1YpVY/NxH8zLkgU9\nesCKFZCcrFtJ5vizGwmMYcgxPXuqo5MOh24lfybmXAwO6aBBUAPdUiwjPNy/3Ujp2C0Kunx5qFlT\npVD3Ruw2wcopxjDkkJo1lUvp1191K/kziw8spnft3raJdnY4/ohf8Hd6hvRkSdwSk1TPAxy6eIgL\nNy/QvHxz3VK0YQyDE3hr0ZHFBxbTK8Q+0+vt26FUKahRQ7cS/TQq04ik1CQOnPehvCwPwFuT6qV/\njgKE/349+u9f7gLeONM5ceUERy4foX2V9rqlWIa/n0bKiBDCdu6kBg3UqjAmRreSu1kcp1be/owx\nDE7QsqWKgD58WLeSP1gSt4Tuwd3JFZBLtxTLMIbhbux2bFUI9f/rTavvszfOsu/MPlulk3EGYxic\nIDAQundX/m9vYUncEnqH2GeWk540r7n/unn/xCNVHiHmbAxnb/hAXpZs4m2r74i4CLrU7ELeXHl1\nS9GKMQxO4k0D+tKtS2xP3E7nGp11S7GMJUv8L2neg8ibKy+da3RmWfwy3VIs4+GH1STg9GndShSL\n4xbbaoLlLOZj5ySdOsGOHXDpkm4lsDxhOaFVQymYp6BuKZZh3EiZYzd3Uu7cEBbmHTnIriddZ+PR\njXSt1VW3FO0Yw+AkBQuqjKsrVuhWYr/NsrNnIToaOvi3mzdTutbqyroj67iV7MV5WXKIt6y+Vx1c\nRZtKbSiar6huKdoxhsEFvGFA3065TeShSFtFaS5dqhKt5fVvN2+mlMhfgqblmrL68GrdUizj8cdh\n40a4cUOvDrtNsFzBGAYX6N4dVq2CpCR9GtYdWUejMo0oXbC0PhEWY9xI96dv7b4sOrBItwzLKFZM\nnfRbrdHWJacmszxhuV9HO2fEGAYXKFtWFTjftEmfhvRoZ7tw8yasX68y2Royp3ft3kTERZCc6qWJ\nhpxA9+p747GN1CpRi/KFy+sT4UUYw+AiOgd0qiOV8LhwW0U7r1oFLVqotCOGzKlUtBLVi1dn0zGN\nMxKL6dFDuRBTU/X0b7cJlqsYw+AiOsP6t5/cTumCpalRwj45IxYuhH79dKvwfvrW6cvC2IW6ZVhG\ntWpqBb5tm+f7llIaw3APxjC4SL16KuBt3z7P9734gL3OXCclwbJl0Ns+f5Lb6FtH7TM4pBem+XUS\nXSU/d57eSaE8hahdqrbnO/dSjGFwESH0JNWz4yxn3TqoU0elZDbcn+CSwZTIX4Ltidt1S7EMXW5Z\nu32OrMAYBgvQMaD3n9vP7ZTbNC3X1LMdu5EFC6BvX90qfIf0VYNdaNZMpUGJj/dsv8Yw/BljGCyg\nXTs4dAhOnvRcn/P3z6dfnX62qb2QmqpWXcYwZJ/0fQa71GgICFCb0J7MQZZwIYGLty7SskJLz3Xq\nAxjDYAG5c6sgHU8O6AWxC+hft7/nOnQzP/0EFSuqTUhD9mhUphEO6WDfWQ0bXG7C09lW5++fT986\nff269kJmmHfDIvr0Ua4QTxB3Po7zN8/TulJrz3ToARYuNKuFnCKEsN3ppA4dYO9eOHfOM/3Nj51v\nqwmWVRjDYBFdu8Ivv8D58+7va0HsAlvNchwOc0zVWfrU7mMrw5AvH3TpAosXu7+vw5cOk3g1kfaV\n7VPcyirs8c3iBRQo4LkBbTc30i+/QOHC6kSSIWe0rtSaczfPkXAhQbcUyxgwAObNc38/C/YvoE/t\nPgQGBLq/Mx/DGAYL8cSAPnzpMCeunLDVLMesFpwnQATQO6S3rU4nde2q6n27e/Vt3EhZYwyDhXTt\nqiI3L1xwXx92m+VIaY6pukrfOn1ZEOuhDS4PULAgdO7s3tX3scvHOHzpMKFVQ93XiQ9jDIOFFCyo\n0kW7c0DbbZYTFaX2GJo00a3EdwmtGsrhS4c5dvmYbimW4e7V94LYBfQO6W2rGulWYgyDxbhzQB+/\ncpxDFw/ZapYzf75aLdgkHEMLuQNz06d2H+bGzNUtxTK6dXPv6nv+fntNsKzGGAaL6dYNtm6Fixet\nb3th7EJ6hvQkd2Bu6xvXgJQwZw488YRuJb7PwHoDmbvfPobBnavvxKuJxF2Io0M1UyIwK4xhsJhC\nhVQ9aHcE6dhtlrNnDyQnqyItBtd4pOojv68o7cKAAWpFaTV2m2C5A2MY3IA73Eknr55k/7n9dKzW\n0dqGNTJnDgwcaNxIVpArIBf96vSznTtpyxbrV9/z9s+jfx37TLDcgTEMbqB7d9i8GS5dsq7NuTFz\n6VW7F3lz2aMQcrobaeBA3UrswxP1nrCVO8kdq+9T104RfTaaTtU7WdeoDXHJMAghigshIoUQcUKI\nVUKIolnclyqE2CWE2C2E8EAImF4KFYKOHa0d0LOiZzG4/mDrGtTMjh0qx1TjxrqV2If2ldtz5voZ\n4i94OD2pG7F69T03Zi69QuwzwXIXrq4Y3gDWSClDgHXAP7K474aUsqmUsomU0i/y2/bvb92APnTx\nEEcvH7XVZtns2TBokHEjWUlgQCD96/ZnTvQc3VIsI331bZU7aVb0LIY0GGJNYzbGVcPQC/gh7fkP\nQFZf+n738e/RQw1oK47bzY6ezYC6A2xz5trhgLlzjRvJHdjRndS5szUJKu04wXIXrhqGICnlGQAp\n5W9AUBb35RVC/CKE2CKEsE/l+vtQuDCEhVmzapgVPYvBDezjRtqyBYoVU2VRDdbSplIbLt++zP5z\n+3VLsYwhQ2DmTNfbmRU9y1YTLHfywHdICLEaKJPxEiCBf2Zye1YVQ6pIKU8LIaoB64QQUVLKI5nd\n+MLfX6B0wdIAhIaGEhoa+iCJXsvQoTBhAvzlL863EX02mit3rtCmUhvrhGnGbDq7jwARwIC6A5gT\nPYd3H31XtxxL6NoVnnkGEhNVzQ5nkFIyK3oW33b/1lpxmtiwYQMzZmxg1y7lnbAcKaXTDyAWKJP2\nvCwQm43XTAH6ZvE7+dbat6RduHNHypIlpTx61Pk23lzzpnx11avWidJMSoqUZcpIGRenW4l92Xpi\nqwz5PEQ6HA7dUixj1CgpJ0xw/vV7f9srq3xcRaY6Uq0TpZmXX5by3XfVc/VV7vx3+b0PV11J4cBT\nac+fBP50DkcIUUwIkSfteSmgDZDlOnfGvhm2KVWYJ4/ahJ41y7nXSymZHTPbVm6kDRugfHkIDtat\nxL60qtCKZEcyO0/v1C3FMoYOdc2dNHPfTAbVH2SbGiYpKWqfbtAg97Tv6rs0HnhMCBEHdAT+AyCE\naCaESF+z1QF+FULsBtYC70spD2TVYMHcBdlyYouLsrwHV/yjO07tIFdALpqUtU+GuWnTYPhw3Srs\njRCCYQ2GMT1qum4plvHww3DmDMTG5vy1UkpmR8+21XHvNWugcmX3TbBcMgxSyotSyk5SyhApZWcp\n5eW06zullKPTnm+VUjaU6qhqIynl1Pu1ObTBUFsN6Hbt4PJl2OdEWd5Z+2YxqN4ghE3OdN68qWI7\nBtvn8+m1DGs4jFnRs0hxpOiWYgmBgWrcODPJ2pq4lQK5C9CwTEPrhWnC3RMsr1tXDWkwhHn755GU\nmqRbiiUEBDg3oFMcKcyOmW2rM9eLF8NDD0HZsrqV2J9aJWtRvXh1Vh9arVuKZaSvvnPqaZ65byZD\nGgyxzQTr2jVYutR9biTwQsNQpVgV6gXVY0XCCt1SLCPdP+pwZP81kYciqVK0CiGlQtwnzMMYN5Jn\nGdZgGNOipumWYRlNmqh9u+3bs/+apNQk5sbMtZUbacECeOQRKFXKfX14nWEANaCn77OPO6lhQyhS\nRAW8ZZcf9v7Ak42edJ8oD/Pbbyq/fm+/iHv3DgbWH8jyhOVcu3NNtxRLEEKtGmbMyP5rViSsIKRU\nCDVK1HCfMA8zbRqMGOHePrzSMPSv25/IQ5FcuX1FtxTLGDpU/Ydmh0u3LrHq4CoG1rfPYf9Zs6BX\nLyhQQLcS/6FUgVI8XOVhFsYu1C3FMoYOVXEwSdn0NE/dO5WnGj3lVk2e5MQJla6+e3f39uOVhqF4\n/uJ0rNbRVnVshw9XueVv3nzwvXNj5vJYjccokb+E+4V5CONG0sPwhsNt5U6qXh3q1lU+9gdx7sY5\n1h9Zz4B6A9wvzEPMmAH9+kG+fO7txysNA6gBPXXPVN0yLKNCBbXxujAbkze7uZFiYuDsWfDhIHaf\npXtwd3ad3kXi1UTdUixj5EiYMuXB982KnkWPkB4UyVvE/aI8gJSem2B5rWHoFtyNuAtxtkohnJ0B\nHX8hnsOXDtOlRhfPiPIAP/6oXACBgbqV+B/5c+enf93+/Lj3R91SLKN/f/j5Zzh9+v73Td1jLzfS\n7t3K49C2rfv78lrDkCcwD8MbDmfy7sm6pVhGz56wdy8cyTRLlOLHvT8ypMEQ25QdTE5WhmHkSN1K\n/Jdnmj7D5N2TccgcHIvzYgoWhL59779nt/e3vVy4dYFHqz3qOWFuZtIkePJJdQTe3XitYQAY1WQU\nP+z9wTZBOnnzqlMVP/yQ+e8d0sG0qGmMaOTmIwceZNkyqFkTatfWrcR/aVG+Bflz52fTsU26pVjG\n00+r1XdWMQ0/7P2BEQ1H2CYFxs2bqobJ0097pj+vftfqlK5DtWLVWJ6wXLcUyxg5EqZOzTymYe3h\ntZTIX4LGZe1T1uz771VmTIM+hBCMajKK73d9r1uKZbRpoz5DmcU0JKcmM3PfTFtNsBYsgFatVBoM\nT+DVhgHUqmHS7km6ZVhGkyZQvDisX//n332761tGNx3teVFuIjFR1V7ob+qua2dYw2EsjV/K5duX\ndUuxBCHgqadgciae5qXxS6lVsha1StbyuC534ekJltcbhifqPcGmY5s4fe0BO00+xMiRfx7Qv13/\njTWH1zC04VA9otzA1Kmq7kLBgrqVGEoVKEWXml2Yuc+CijdewogRmR8B/2bnN4xpNkaPKDcQHw9x\nce6PXciI1xuGwnkL07d2X1udqhg6VPneM5b9nLpnKv3q9LPN0TqHQ22WGTeS92C31XeFCuqEzuzZ\nf1w7fOkwO0/vpH9d+yxTJ01SRjBPHs/16fWGAWBUUzWg7VKnoWRJFQWcfnTVIR18t+s7Rjezjxtp\n/XpVvrNpU91KDOl0qt6JCzcvsOv0Lt1SLOO55+Crr/74+bud3zGi4Qjy5XJzBJiHSE5Wh1VGjfJs\nvz5hGFpXbE3eXHlZe2StbimWkT6gHQ616VwkbxFalG+hW5ZlfPONWi3YJKGlLQgQAYxuNpovd3yp\nW4pldOmiVt47dqiEeZP3TLbVBCs8XNVcCPFwLk2fMAxCCF5s8SITf5moW4pltGoFRYtCZOQfm852\nSQt88qQqJGJSYHgfzzR9hgWxC7h466JuKZYQGAhjxsCXX8Ki2EXUK13PVhmJJ06EF17wfL8+YRgA\nhjYcyk/Hf+Lo5aO6pViCEPD88/Dxd/bbdP7mGxWvUcQe2yW2IqhgEN2DuzNldzZySvgITz+tan1M\n3G6vTefoaLXp3KeP5/v2GcNQKE8hRjQcwde/fq1bimUMHgybbn1FtyqDbLPpnJQE332nZ5ZjyB4v\ntHiBL3/90jaR0KVLQ7veB9hzMoY+dTR8i7qJL75QqyFPbjqn4zOGAeD5Fs8zafckbiXf0i3FEnLl\nvUNAi28ouO9l3VIsY8ECqFcP6tTRrcSQFa0qtKJ4vuKsPLhStxTLyN3uM/JEjSGX0PAt6gYuX1an\nrUZr2i7xKcNQq2QtmpdvzpyYObqlWMLs6Nk0Kd+IJZPqcPu2bjXWMHEivPiibhWG+yGE4IUWL9hm\nz+7irYusPz+LconPsWqVbjXW8MMPEBYG5crp6d+nDAPw+ya0rx9dlVLy6fZPeavDX2nWDKbboGDd\nzp2qkIgnA3EMzjGo/iB2nNpBwoUE3VJc5rud39EzpCevPV+ODz/UrcZ1HA7lRtI5wfI5wxBWM4wr\nd67w0/GfdEtxiZ+O/8SN5Bt0qdmFcePgww9zVhPaG/nvf2HsWMiVS7cSw4PInzs/o5uO5uNtH+uW\n4hLJqclM3DGRVx56hUGDIDZWVTjzZcLD1YnFNm30afA5wxAYEMirrV/lg80f6JbiEp9u/5SXW75M\ngAjg0Uchf35YsUK3Kuc5cgRWr4Znn9WtxJBdXmr1ErOjZ3PuxjndUpxm/v751CxRk8ZlG5MnD7z0\nEnz0kW5VrjFhArz2mt4YIJ8zDABPNn6Snad3En02WrcUp4g7H8dPx37iycaqSpsQMG6cmnH7Kh9/\nrALazBFV36FsobIMqDvAZ/capJR8vO1j/trqr79fGzNGlf08eVKjMBfYvBl++03Vm9CJTxqGfLny\n8VLLl/jvFt/8Jh2/eTwvtnyRQnkK/X7tiSfg0CHlp/c1LlxQeyQv2+dwld8wrs04vvr1K24k3dAt\nJcesPbKWa0nX6B78x6ZW8eIwbBh8/rlGYS7wwQfw6qv6qx36pGEAeK75c0TER/hcLdvjV46z+MBi\nXmx5985S7tzKPz9+vCZhLvDllyoIp3x53UoMOSW4ZDDtq7T3yUqJ/970b95s9yaBAXd/i77yioql\nuXRJkzAniY2FbdtUOnHd+KxhKJ6/OE81eoqPtvqWQ/G/W/7LM02foUT+En/63ZgxsHEjxMRoEOYk\n16+rI6qvvqpbicFZXmvzGh9u/ZDk1GTdUrLNz8d/5viV4wxuMPhPv6tWTZXR/fRTDcJc4IMPVGBo\n/vy6lYDwpmOfQgiZEz0nr56k4dcNiXk+hrKFyrpRmTWcvXGW2hNrE/N8DOUKZ35Aefx4VfQ7Yyph\nb2b8eNi1C+bYI7TEb+k8rTMD6g7g2Wa+cXrg8RmP06d2nywT5h08CA89pP4tVszD4pwgXW9CgnKH\n5RQhBFJKy7arfdowALyy8hUc0sGnj3v/9OD11a9z9c5Vvur+VZb3XL8O1avDhg1Qt67ntDnDtWtQ\no4ZvaDXcn60ntjJowSDiX4wnb668uuXcl19P/UqfOX04+NLB+2p98kk1Pv/1Lw+Kc5KnnlIrnbff\ndu71xjDcw2/Xf6PuF3WJei6KikUqukmZ65y+dpr6X9Vn71/2PlDn++/Dvn0w08uLbb3/PkRFwaxZ\nupUYrKDrjK50q9WNF1p6d6Kr7jO707lGZ15udf/TDvHxqpDPoUPefVouXefBgyp+wRmMYciE11a/\nxvWk63zZzXvzzL+w7AXy5crHh10eHJrpCzPxdI0bN5q8SHbh11O/0mt2Lw6+dJD8ub3A0Z0JPx37\nieGLhhP3Yly2VjbDh0OtWt69ahg+XNVb+Oc/nW/DGIZMOH/zPCETQ9g5eidVi1W1XpiLHL50mBbf\nteDACwcoXbB0tl7z4YfqSzc83M3inOR//geOHoVp03QrMVhJr9m9CK0SyiutX9Et5U9IKWk7uS1/\naf4XRjQaka3XHD4MLVrA/v1QpoybBTpBVBQ89pjaW3BlVWMMQxa8vf5tEi4mMLOf9/lfRiwaQfXi\n1Xkn9J1sv+bOHahdW5X/DA11mzSnSEyERo3UJnnlyrrVGKxk35l9dPyxIwdePJDpyTmdLDmwhH+u\n/yd7xuz50xHV+/HKK3D79t0lQL0BKaFzZ1Xm19W8SFYbBqSUXvNQcpzj+p3rssKHFeTm45udbsMd\nbE/cLsv9t5y8cvtKjl87a5aUzZpJmZrqBmEuMGKElP/4h24VBncxJmKMHLtirG4Zd5GUkiTrflFX\nRsRF5Pi1589LWaqUlLGxbhDmAsuWSRkSImVSkuttpX13WvZd7LNxDPdSME9B3u/4Pn9d+VevKUDi\nkA5eXvEy/9fx/5wqxDNwoIqA9KZN6F27VDnSN97QrcTgLt579D2mR00n7nycbim/M/GXiZQvXJ5u\ntbrl+LUlS6rcQ6+95gZhTpKSomJ/JkxQwa3ehm0MA6jyn0IIpkd5Rw7raXunIZHZ9ofeixAqB9Hr\nr8OVKxaLcwKHQ6W9eOcd7z7lYXCNoIJBvNHuDcZFjtMtBVAn+v73p//l88c/d7ou+ksvwYEDEBFh\nsTgn+fJLVWvBa1PUW7n8cPWBC66kdLae2CrL/becvHDzgsttucLlW5dluf+Wk9sTt7vc1rPPSvn8\n8xaIcpFvv5WyVSspU1J0KzG4m9vJt2XI5yFywf4FuqXIYQuHyddXv+5yO2vWSFmlipTXr7uuyRVO\nnJCyZEkp9++3rk0sdiXZZvM5Iy+veJmrd64ytfdU10U5yeiI0QgE3/T4xuW2Ll5U5TKXLIGWLS0Q\n5wRnzkCDBiq1dqNGejQYPMvPx39m4PyBRD8XTfH8ToTjWsD6I+sZsXgEsS/E3pV00lmGDVMz9QkT\nLBDnJP36Qf368O671rVpNp+zwbU712SVj6vIlQkrLWkvp6w+tFpW+qiSvHzrsmVtTp8uZaNGUt65\nY1mT2cbhkPKJJ6R89VXP923Qy/NLn5ejlozS0vfV21dl1U+qyqVxSy1r88wZKUuXlnLnTsuazBEL\nF0pZq5aUt25Z2y4Wrxi0G4O7xFhkGKSUcmXCSlnl4yqWfjlnh/TBvDx+uaXtOhxS9ugh5euur6hz\nzPTpUtapI+XNm57v26CXK7evyEofVZKrDq7yeN+jw0fLkYtHWt7ujBlS1q4t5Y0bljd9X06dkrJM\nGSk3u+HgpNWGwZaupHSeW/ocl25fYla/WU5vWuUEKSXDFg2jQK4CfNfzO8vbP3sWGjdWp5Q8Fdtw\n7JgKEFq1Cpo08UyfBu9i3ZF1DFs4jN1jdlOmkGeixJbGL+X5Zc+z77l9FM3nZJ6I+zBkiDqt5Km6\nDVJC167qs/Tee9a3b1xJOeBm0k3Z8KuG8ttfv7W03az4bud3sv6X9eWNJPdNRZYvl7JSJbUkdje3\nb0vZpo2U48e7vy+Dd/PPtf+Uj/34mEx1uD+o5vDFwzJoQpD8+djPbuvj0iUpK1dWrh1PMGGClC1b\nWhOzkBkYV1LOiD0XK0t9UMqS00H3Y+epnbLUB6Vk7Dn3R9G89ZaU7du7f79h9Ggpe/f2vgA7g+dJ\nTk2W7Se3l2+tfcut/dxKviWbfdNMfrz1Y7f2I6WUO3aowLfoaPf2s2qVlGXLSnnsmPv6MIbBCZYc\nWCLL/becPHLpiFvaP3rpqCz/YXmPHe1LTVX7DWPGqL0Hd/Dll2pf4epV97Rv8D3OXj8rq31STf6w\n5we3tJ/qSJUD5g6Qg+YPkg53Dex7+PFHKWvUUNHR7iA+XsqgICk3bnRP++kYw+Akn2z9RNb9oq48\nd+Ocpe1evHlR1vuinvxoy0eWtvsgrlyRsmFDKf/nf6xve+5cKcuVkzIhwfq2Db7N/rP7ZdCEIBl5\nMNLSdh0Oh3xp+UsydGqovJVs8ZGdB/CPf6jUM5ctPqdy4oSUVatK+f331rabGcYwOInD4ZBvrH5D\nNviygTxz3RoH/bkb52TjrxvLcavGeWyGk5EzZ9Tpiv/7P+vaXLxYzXD27LGuTYO9+OnYT7L0B6Xl\nisEez+AAAAh4SURBVIQVlrTncDjk66tflw2/aujxU4SqfylfeEHKdu2kvHbNmjYTE9Vnc8IEa9p7\nEF5lGID+QDSQCjS9z31hwAEgHnj9Pve54S37A4fDIf+17l+yzsQ68vDFwy61dfjiYVnvi3ry9dWv\nazEK6Zw8qRJx/e1vru8FTJqkfKE7dlijzWBfNh/fLEt/UFrOjZ7rUjtJKUlyTMQY2fzb5vL8DTf5\nc7JBaqraU2vaVB0rdYXYWBVh/Z//WCItW3ibYQgBagHrsjIMqHxMB4EqQG5gD1A7i3vd8qbdy2fb\nPpNlJpRx+mx25MFIWWZCGfnJ1k/cZhTWr1+f7XsvXJDykUek7NZNyrNnc97XrVsq5Ub16lLGxeX8\n9e4mJ++F3fGm92LXqV2yysdV5D/W/EMmpeT8uM2pq6dk+8ntZdj0MKeyD1v9XjgcUv773+rUn7N7\nAnPnqgC6KVMslfZArDYMLiXRk1LGSSkTgPudn20JJEgpj0kpk4HZQC9X+nWVl1q9xOz+sxkVPopn\nwp/h/M3z2XrduRvnGB0xmqfDn2ZG3xmMfWis2+IjNmzYkO17S5RQGU/r11fpKmbMUAnvssPatdCs\nmYqR2LULgoOd0+tOcvJe2B1vei+alGvCL8/+wu7fdtPiuxZsT9yerdelOlL5due3NPq6ER2rdWTZ\nkGVOZR+2+r0QAt56C77+WmU2fuUVlY4mOyQmwtChKuvw8uWqhrMv44nsqhWAExl+Tky7ppXQqqHE\nPB9DgdwFCP48mLErxvLLyV/+lLJbSsmOkzt4ZeUrhEwMIU9gHqKfi6Zj9Y6alGdOnjzwn//A/Pkq\naKdxY1WY5HwmNu/KFZg3Dx59FJ55Bv79b5g71/l6swb/JahgEMuHLGdc63H0n9efsOlhLIpdxI2k\nG3+6N/FqIp9v/5zaX9RmWtQ0Vg9fzduhbxMgvCvJc9eusHcv3LqlimW99pqqwS7vib1NSYFt22D0\naGjYEKpVU69r3lyPbivJ9aAbhBCrgYzhjgKQwFtSSi9JYuscRfIW4bPHP+P1tq/z5Y4veWrxU5y+\nfprgksEUzVuUq3euEnchjtIFSjOg7gD2/mUvlYpW0i37vrRpA1u3qhXE5MkqZXfp0lCxosr7/ttv\nqiRnmzbwl79Anz7KqBgMziKEYHij4TxR7wmmR03nix1fMGzRMKoWq0r5wuVJdaRy9PJRrty5Qpca\nXZjcczLtKrfzSDYCZwkKUiuHceNg0iTo0QOuXlWGolAhNbmKi1Ofq8GDVenQsmV1q7YOS1JiCCHW\nA+OklLsy+d1DwDtSyrC0n99A+cPGZ3Kv9+TnMBgMBh9CWpgS44ErhhyQlagdQE0hRBXgNDAIGJzZ\njVb+YQaDwWBwDpece0KI3kKIE8BDwFIhxIq06+WEEEsBpJSpwItAJBADzJZSxrom22AwGAzuwquy\nqxoMBoNBP15zHEAIESaEOCCEiBdCvK5bj7sRQlQUQqwTQsQIIfYJIV5Ou15cCBEphIgTQqwSQhTN\n8JrPhBAJQog9QojG+tRbjxAiQAixSwgRnvZzVSHEtrTxMEsIkSvteh4hxOy092GrEKKyXuXWI4Qo\nKoSYJ4SITRsfrfx4XLwihIgWQkQJIWak/f/7xdgQQkwSQpwRQkRluJbjcSCEeDLtvYoTQmSrAL1X\nGAYhRAAwEegC1AMGCyFq61XldlKAv0kp6wGtgRfS/uY3gDVSyhBU4OA/AIQQjwM1pJS1gDHA13pk\nu42xwP4MP48HPpRSBgOXgVFp10cBF9Peh0+ADzyq0jN8CiyXUtYBGqGyBvjduBBClAdeQgXPNkTt\niQ7Gf8bGFNR3YkZyNA6EEMWBfwEtgFbA2xmNSZZYGS3n7AO1R7Eiw89vcJ/UGXZ8AIuBTqgvgTJp\n18oCsWnPvwYGZrg/Nv0+X38AFYHVQCgQnnbtHBBw7/gAVgKt0p4HAud067f4vSgCHMrkuj+Oi/LA\nMaA4yiiEA48BZ/1lbKAyRkQ5Ow5Qh32+ynD9q4z3ZfXwihUDXhoE5ymEEFWBxsA21H/6GQAp5W/8\nEUNy73t0Evu8Rx8Df0fFxyCEKAlckvL3aMOM4+H390Gqgw2XhRAlPCvXrVQDzgshpqS51r4VQhTA\nD8eFlPIU8CFwHPV3XQF2AZf9dGwABGVzHKS/L06ND28xDH6LEKIQMB8YK6W8TtqXYwZsfTpACNEN\nOCOl3MPdR56ze3TZbkeccwFNgS+klE2BG6gVtF+NCwAhRDFU+pwqqNVDQVRCzmw34Q5dXkZW48Cl\nv91bDMNJIONGUcW0a7YmbdNsPjBNSrkk7fIZIUSZtN+XRS2bQb0fGcOu7fIetQV6CiEOA7OADigf\ne9G0vSe4+2/9/X0QQgQCRaSU2cxo4xMkAieklL+m/bwAZSj8bVyAcq0ellJeTFsBLEKNl2J+OjYg\n5+PAqe9WbzEMvwfBCSHyoPxi4Zo1eYLJwH4p5acZroUDT6U9fwpYkuH6CPg9mvxy+pLSl5FSviml\nrCylrI76f18npRwGrAcGpN32JHe/D0+mPR+A2oCzDWn/pyeEEOnpDDui4n/8alykcRx4SAiRT6j8\nGenvhT+NDcHds/+cjoNVwGNpJ92Ko/ZoVj2wV92bKxk2RcKAOCABeEO3Hg/8vW1RdSz2ALtRvtMw\noASwJu29iASKZXjNRFQK873cp/6Frz6AR/hj87kasB1Vw2MOkDvtel5gbto42QZU1a3bDe9DI9Rk\naQ+wECjqr+MCeBu1kRoF/IBK3e8XYwOYCZwC7qCM5EjURnyOxgHKgCSkvV8jstO3CXAzGAwGw114\niyvJYDAYDF6CMQwGg8FguAtjGAwGg8FwF8YwGAwGg+EujGEwGAwGw10Yw2AwGAyGuzCGwWAwGAx3\nYQyDwWAwGO7i/wOyZhJYTrYkDgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fba8b87a810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(y['test'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fba75abc050>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEACAYAAAC3adEgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fba75b81a10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_predicted = plt.plot(predicted, label='predicted')\n",
    "plot_test = plt.plot(y['test'], label='test')\n",
    "plt.legend(handles=plot_predicted+plot_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}