From 1b2bd08f2a047e10c0ec424b8d24e532e690b02b Mon Sep 17 00:00:00 2001
From: callMeIvan <evanwcall@gmail.com>
Date: Sun, 31 Mar 2019 13:47:49 -0600
Subject: [PATCH 1/2] tenserflow and keras

---
 .idea/Deep-Learning-in-Python.iml             |  14 +
 .idea/encodings.xml                           |   4 +
 .idea/libraries/R_User_Library.xml            |   6 +
 .idea/misc.xml                                |   7 +
 .idea/modules.xml                             |   8 +
 .idea/workspace.xml                           | 140 ++++
 .../Time Series Prediction-checkpoint.ipynb   | 721 ++++++++++++++++++
 Time Series Prediction.ipynb                  | 233 +++---
 __pycache__/keras.cpython-37.pyc              | Bin 0 -> 1731 bytes
 9 files changed, 1025 insertions(+), 108 deletions(-)
 create mode 100644 .idea/Deep-Learning-in-Python.iml
 create mode 100644 .idea/encodings.xml
 create mode 100644 .idea/libraries/R_User_Library.xml
 create mode 100644 .idea/misc.xml
 create mode 100644 .idea/modules.xml
 create mode 100644 .idea/workspace.xml
 create mode 100644 .ipynb_checkpoints/Time Series Prediction-checkpoint.ipynb
 create mode 100644 __pycache__/keras.cpython-37.pyc

diff --git a/.idea/Deep-Learning-in-Python.iml b/.idea/Deep-Learning-in-Python.iml
new file mode 100644
index 0000000..bba6157
--- /dev/null
+++ b/.idea/Deep-Learning-in-Python.iml
@@ -0,0 +1,14 @@
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\ No newline at end of file
diff --git a/.idea/encodings.xml b/.idea/encodings.xml
new file mode 100644
index 0000000..15a15b2
--- /dev/null
+++ b/.idea/encodings.xml
@@ -0,0 +1,4 @@
+<?xml version="1.0" encoding="UTF-8"?>
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diff --git a/.idea/libraries/R_User_Library.xml b/.idea/libraries/R_User_Library.xml
new file mode 100644
index 0000000..71f5ff7
--- /dev/null
+++ b/.idea/libraries/R_User_Library.xml
@@ -0,0 +1,6 @@
+<component name="libraryTable">
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+    <SOURCES />
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diff --git a/.idea/misc.xml b/.idea/misc.xml
new file mode 100644
index 0000000..8656114
--- /dev/null
+++ b/.idea/misc.xml
@@ -0,0 +1,7 @@
+<?xml version="1.0" encoding="UTF-8"?>
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diff --git a/.idea/modules.xml b/.idea/modules.xml
new file mode 100644
index 0000000..6ad326e
--- /dev/null
+++ b/.idea/modules.xml
@@ -0,0 +1,8 @@
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diff --git a/.idea/workspace.xml b/.idea/workspace.xml
new file mode 100644
index 0000000..768dee3
--- /dev/null
+++ b/.idea/workspace.xml
@@ -0,0 +1,140 @@
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diff --git a/.ipynb_checkpoints/Time Series Prediction-checkpoint.ipynb b/.ipynb_checkpoints/Time Series Prediction-checkpoint.ipynb
new file mode 100644
index 0000000..3d8b41e
--- /dev/null
+++ b/.ipynb_checkpoints/Time Series Prediction-checkpoint.ipynb	
@@ -0,0 +1,721 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Get the dataset and prepare it for analysis and model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Set the index to date"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Open</th>\n",
+       "      <th>High</th>\n",
+       "      <th>Low</th>\n",
+       "      <th>Change %</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Date</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2017-08-10</th>\n",
+       "      <td>64.165</td>\n",
+       "      <td>63.898</td>\n",
+       "      <td>64.175</td>\n",
+       "      <td>63.855</td>\n",
+       "      <td>0.48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-08-09</th>\n",
+       "      <td>63.860</td>\n",
+       "      <td>63.780</td>\n",
+       "      <td>63.860</td>\n",
+       "      <td>63.710</td>\n",
+       "      <td>0.26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-08-08</th>\n",
+       "      <td>63.692</td>\n",
+       "      <td>63.750</td>\n",
+       "      <td>63.785</td>\n",
+       "      <td>63.615</td>\n",
+       "      <td>-0.23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-08-07</th>\n",
+       "      <td>63.840</td>\n",
+       "      <td>63.710</td>\n",
+       "      <td>63.865</td>\n",
+       "      <td>63.648</td>\n",
+       "      <td>0.26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-08-04</th>\n",
+       "      <td>63.675</td>\n",
+       "      <td>63.670</td>\n",
+       "      <td>63.786</td>\n",
+       "      <td>63.572</td>\n",
+       "      <td>-0.05</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Price    Open    High     Low  Change %\n",
+       "Date                                                \n",
+       "2017-08-10  64.165  63.898  64.175  63.855      0.48\n",
+       "2017-08-09  63.860  63.780  63.860  63.710      0.26\n",
+       "2017-08-08  63.692  63.750  63.785  63.615     -0.23\n",
+       "2017-08-07  63.840  63.710  63.865  63.648      0.26\n",
+       "2017-08-04  63.675  63.670  63.786  63.572     -0.05"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv('USD_INR.csv')\n",
+    "df['Date'] = pd.to_datetime(df[\"Date\"])\n",
+    "df_idx = df.set_index([\"Date\"], drop=True)\n",
+    "df_idx.head(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Flip the dataframe"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_idx = df_idx.sort_index(axis=1, ascending=True)\n",
+    "df_idx = df_idx.iloc[::-1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Plot the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x2e7c14304e0>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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8ZZc9t4JJzywLS3yhMLBzSxISqm8+2n2w0NMcU5WyGnp9Wx+0NiyhGxMCm3bnkb0nz7O6zs2n9ebqk3qw5I8n+j0/JSmRn/46zrP/8YadvLDsl3CEGnQXVLKCUOJhSX7IjPcYeu+iav/OgmKnhp5ci/b3+sZeIWOC4MUvfuE/y34GYPnG3Rx//xJG3reEm19eBcDQjFRuOvVIuqU1qfQaIsLGmeOZcXY/AG599ZuQ94L58pc9bNieW6drHP4tpPSwXimn9mvrOS9j2kKW/biL/KLyNUBvffWbKnvB5Bc5NXRL6NWzV8iYOtqwPZfpr3zDba+uJmPaQiY++ZnnWNvmycydOoLMGgyCmXRMBhMGdQCcXjCh9KvHP2XMQx/V6WbsjDfW+uxntPb90HpqUqbP/gWzP+fWV76pcJ2f3CXmDlfW5JJsTS7VsoRuTB3s2J/PmIc+8nvsi9tGs+zWUxjevXWNuyQ+MLF8bczlG/13INu675CnP/ucTzdWe815yzex7Mddnv3rXvrKs102h3lteK8e9Ma1IxnZMw2AD286kUU3nOD3Ma/46fHyY84BP2fCtn35AKRYDb1a9goZUwfD/lLeQ+WLW0fTPKUBsycdzU9/HVengS9JiQmexRsmPvkZQ+9dxMOLNnCwoNhzzjF/fd+zfefra9ixP7/S6+3LK+Lml1d5Rm6WlCoLsrb4nOPdDFITJV5NLGXT2QJ0bd2EI9oEPo/LroOFfPXLHu5/+1ufudSvfuFLAJqlWL/z6lhCNyYI2jVPoU3zFFbddSpj+7ULyiCh6af38Wzn5Bbw0KLv6HfnOxQWl7LdT/I+/6nPKpSVGXi3bw18/6GKTSxnPPpJreIscNu4K7sZCuX9x6ty8/xVnPP4pzz+wQ8M8PONoUmyNblUxxK6MbVU7DU68v+uPCbo12/U0LfnS5lef3qL4V7fDN75g9OssXFXXkDXVVVmLCxv9/7vNSMB+H7HAfbm1XwyrSPbNwfg+F5plZ5TVf/xZbeO9lt+9X++ZPwjH3v2u7au/IaycVhCN6YWVJU/vbYagLvP7kfn1MYheR4R4fVrjqvynN7typs1FmRVbJs+fC6Uucs38cqXznnnZ3biqE7lzSR3vl7zRSkGd24JwLj+7as99+ELB3H9Kb18yto2T6Ghn1GfC7/Zypot+wFqdFO5PrOEbkwt3P/Oel5avgmA9KahXXhhQKeWbJw53vNz1sAOnmMv/G444HyoALzq52bjjsMG70zz6mEyuItvolyQtYWsTXvZkVt5e/zhHl68ASCgwURnD+rIdaf09Oz36+DU7qua3Avg2CMqr/2bcpbQjamFJz74AYCUpAROP6r6mmkwNfZa2KEs0Z3u1o4/WF+x3/o+t738xjG+NeM+7ZtzoTsU/6ELynvVTHhsqc90BKHkb/ELf2uDbtt3KBzhxDxL6MbUkHcTxrczTg/78xeVVJxOoFlK+bRMx9//Pqc8+CFZm/aSX1TCWLdbZcdWvmtyvnXd8Z6bt+cM7lQhkYZyObkyf/3VUT77KUkJjOqV7hlE9JsRzqI4wVr1KN7Z5FymXlu3dT9tm6f4XWz4/W+3c9lzKyIQVdVe/tKZTuBCr4muvEdRbtrt1GYnPLbU53FnDOjAn/+7lqO7tuKpSUdXuO6oXunMumgw17zg9E8/UFhM8xB1FTz8Bum3M07j3oXrmOrOc7PiT6dQWFxK66bJXDmqB+1bVFwg2lRkCd3UW6rK6Q9/TI/0Jiy+8USfY9e88CVvrNpa6WMHdmrB7EsyKz0eSilJCeQXlXLpcd08ZdV1k/zittE0bJDA13eOrfK8MwZ04LWvNrNo3Q7+b0U2U0Z2q/TcklKlh591RGsjJSmRGRP6e/a9+5x3ahWaG87xyBK6qZcOFhR7Bqz8kFM+5DyvsJj7317vk8yXTjuZ+976lnOGdGRUz/SAbv6F0ufTR5O1aa9P7xZwBjb97d3vuGBYZ7qmNmbV5n3MW76Jnm2a1miQ0253HdC3V2+tMqF7J/OqzjPhYwnd1EsLV231uYFY2eRQZU0Dj/x6cFjiCkTLxg05sXebCuVtmqdw33kDPPsn9W7DSX7Oq87VJx3BlDkryM0vrvSc7D2+fd47tLQmkWhgN0VNvVQ2C2JV1t19WrXnxKPRfZzZEY/yGsZ/uL15viNNdx2ofl5zE3pWQzf12hUndCcpMYFZS74n644xJCYIr2Vt4cwB7WnUsP4ONe+e3oS8KuZ2OXwN0cP7s5vIsIRu6qVmyQ3ILShm+jhnvpQ/ntrbc2zSiK6RCitqNEtJYl9e5VPqHixwkv1LU0eQmCAcbQk9KliTi6mX+ndsYcPJq7Bx50E++X4nOw8UkDFtIe+s2eZz/IA762PT5AYMzUiN+I1i4wg4oYtIooh8JSJvuPvdRGSZiGwQkbkiUrEjrzFRxHsNy/Xbc2nlp++5cZSNLs28ZxEAV/xrpefYocISzxzoTZPtS340qUkN/Tpgndf+fcBDqtoT2ANMCWZgxgRb2RqWhwpL2H2wkPfWbo90SDGlbOToyPve98xj08QSelQJKKGLSCdgPPC0uy/AycB895Q5wIRQBGhMsOUVOs0F3iMtja/Xrq44w+Med2rdXQfLp9j1nnLARF6gNfS/AzcDZbe2WwN7VbWso2o20NHfA0VkqoisEJEVOTmhXfDWmMp4r8Zz6t+dObb7VdEtr74b1Lklw7ul+pT9YW4Wq7L3+pSl2DqfUaXahC4iZwA7VHWld7GfU/3O5KOqs1U1U1Uz09PTaxmmMXVzptdqPDvdPtPHdG8dqXBiwtwrjmHjzPGeWRo/3rCTs2aVzw8z0J0H3USPQL4vHQecJSLjgBSgOU6NvaWINHBr6Z2ALVVcw5iI2rDDdwHiWRcN5og2TSMUTWy5aHgX/vbedz5l715/gs2AGIWqraGr6nRV7aSqGcCFwPuqejGwBDjPPW0ysCBkURoTRAniTEJlAtO6aTJDuji18TbNkhnYqYUl8yhVlzsatwAvicg9wFfAM8EJyZjgK5uhEOCKUT0iHE3seeWqqpfBM9GhRgldVT8APnC3fwQqLi1iTIgUFpeydd+hWi0WnNY0mew9zjzh1558RLBDMyYq2EhREzOmvbKKUQ98QG5+5UPSK3OgoJiLhnch644xNG5oXe1MfLKEbmLGR9853V7L5hGpiUOFJTRLbkDLxjY61MQvS+gmZiS4q/KUDQwKlKpSUFzqs0ybMfHIvnuamJHoTgC1dV8+3dMD63K4IGsz77pD/G2Yuol39j/cxJzXs7Zw3BFpAZ173UtZnu1uaTW/mWpMLLGEbmJGQbHT7TDDTcxZm/aSX1RC19aNOeexT9m2P5+OLRsxqHNLhma0oktr38WFh3ezkaEmvllCN1Hvg/U7aN4oybN4cUGxc1N0wmNLK5y7ee8hNu89xMJvtvqUv/2H42nROKnC+cbEE0voJur99tnlPvsvfvEL73+7w++5N47pRUpSIve+WT7T8ytXHcuR7ZqHNEZjooEldBNztu8vYPv+8kWJN84cX+Gcy0/oHs6QjIkK1o/LxLRnJmdGOgRjooYldBMznr10qM/+05dkMrpP2whFY0z0sYRuotrf3l3v2R7V03c+/VP6WjI3xpsldBM0+/OL2LQ7L6jXfPT97wGYfvqRPivLP3+ZzQtnzOHspqgJmgmzlvLjzoN+b1LW1un92/HhdzmeKW+DeW1j4o3V0E3Q/LjzIABLKulSWBulqnRu1bj6E40xltBN8H23PTdo1youUc8cLsaYqllCN0HXrkVK0K6180ABac2Sg3Y9Y+KZJXQTNJ1aNQKclYVqa8JjS7nupa8AZ9rbr7P3UVJa++sZU5/YTVFTIwuyNpOTW8Dvjq84ErO0VAG4af4q1m7dT9PkBmRmpCI4c5mLgCooSqk655eqcqiohPXbcumW1oSsTXvJ2rSXBVlbuCCzMwDd0wKbKteY+s4SuqmRsuloJx+bQVKi7xe8JK8FJJ5durHOzzV3xSYArjzRFnU2JhDVJnQRSQE+ApLd8+er6p0i0g14CUgFvgQmqWphKIM10aPnbW9xxQnd6Z7ehIlHd6b7rW8C0L5FClv35XPFqO7069CCDm57ekmpIu6KQwkCIkKClC9asWVvPi0bJ9G2eQq7DhSwevM+jjsijTbNUmyWRGMCFEgNvQA4WVUPiEgS8ImIvAXcADykqi+JyJPAFOCJEMZqosxTH/0IQG5++ZJw/ztxYMCLT3gb0Kl8u1taEzIzUuscnzH1TbU3RdVxwN1Ncn8UOBmY75bPASaEJEIT9e5Z6ExV++ez+tUqmRtjgiOgXi4ikigiWcAO4D3gB2CvqpZVzbKBjpU8dqqIrBCRFTk5OcGI2URQk4aJlR4barVqYyIqoJuiqloCDBKRlsCrQB9/p1Xy2NnAbIDMzEy/55jYcbCwhON7ppGTW8AfTulFo4aJvLtmGyO6t6ZvB1tEwphIqlEvF1XdKyIfACOAliLSwK2ldwK2hCA+E0U++2EXAD/tPMgnt5zsKR/VK72yhxhjwqjaJhcRSXdr5ohII+AUYB2wBDjPPW0ysCBUQZro8PMuZ66W7D2HIhyJMcafQGro7YE5IpKI8wEwT1XfEJG1wEsicg/wFfBMCOM0UeCINs4An9vP6BvhSIwx/lSb0FV1FTDYT/mPgE1KXY+43cg9id0YE11sLhcTMHVvadvch8ZEJ0voJmBlXZTEMroxUckSuglY2eRbCZbRjYlKltCNX8UlpeQXlfiU3fn6GsCaXIyJVpbQjV8X/WMZR97+tk/Zt9uclYgK6jDfuTEmdGz6XOPXFxt3A5AxbSHDuqX6DB7KOVAQqbCMMVWwhG6q9cVPu/nip92e/bF920YwGmNMZazJxfh1Yu+Kw/mPaNOUP43vQ8vGDSMQkTGmOlZDN341bphIm2bJfHjTSTSqYoZFY0z0sBq68UsVWjZOsmRuTAyxhG78KlW1/ubGxBhL6MavUsWzBqgxJjZYQjd+qSoJls+NiSmW0I1fTg090lEYY2rCErrxS60N3ZiYYwnd+GVt6MbEHkvoxq9Sa0M3JuZYQjd+qdo0ucbEGkvoxq9SVZsm15gYY0P/jV+f/rCLRkk2StSYWFJtDV1EOovIEhFZJyJrROQ6tzxVRN4TkQ3u71ahD9eEw7zlmwA4dNgCF8aY6BZIk0sxcKOq9gFGAFeLSF9gGrBYVXsCi919EwdufnkVAC9cPjzCkRhjaqLahK6qW1X1S3c7F1gHdATOBua4p80BJoQqSBM+m3bnebaP7ZEWwUiMMTVVo5uiIpIBDAaWAW1VdSs4SR9oE+zgTPjd+uo3AFw5qkeEIzHG1FTACV1EmgIvA39Q1f01eNxUEVkhIitycnJqE6MJo4837ATgspEZkQ3EGFNjASV0EUnCSeb/UdVX3OLtItLePd4e2OHvsao6W1UzVTUzPb3iKjgmery9ehsAx/dMo02zlAhHY4ypqUB6uQjwDLBOVR/0OvQ6MNndngwsCH54Jpyu/PdKAH4zomuEIzHG1EYg/dCPAyYB34hIllt2KzATmCciU4BfgImhCdGEw968Qs/2qf3aRTASY0xtVZvQVfUTqHTQ4OjghmPC7ZQHP+T7HQc8+/ede1QEozHG1IUN/a/Hfsw54JPM+7ZvzgVDu0QwImNMXdjQ/3ro4UUbeGjRd579MX3bMuuiwSQ3sKH+xsQyS+hxrqRU2XWwgNJS2LAjl0nPfFHhnCd/czSJNleuMTHPEnocy5i2sNJjt5x2JBcM7YyAJXNj4oQl9Dj0ypfZ3DDva7/H/jVlGMf3tPEAxsQjS+hxprRUfZL5XWf2ZfKxGbacnDH1gCX0OHPCA0s8259PH027Fjbi05j6whJ6HLn9tdVk7zkEwIZ7Tycp0XqlGlOfWEKPcUUlpfS87S2fsjeuHWnJ3Jh6yN71Me6if3zus//KVcfSv2OLCEVjjIkkq6HHqL15hQy6+z3P/uIbR9EjvWkEIzLGRJol9BiUX1Tik8zX3X0ajRraKE9j6jtrcolBn7iLUAC8dd3xlsyNMYDV0INqyfod9GrbjI4tG9Xocas376OguISGiYkc1clp/1ZV8gpLSEwQikuVpEShsLiU99Zu9/QzX/mnU2jVBxLXAAAPeklEQVTdNDnof4cxJjZZQg+SLXsPcemzywHYOHO833P25RVRqsqevEKSEhPonNqYa1/8iv9+vcXnvOHdUln20+4qn2/kEWmWzI0xPiyhB4GqcuzM9z37GdMW8sB5A/jVkE6UqpKUmEBufhED7343oOtVl8wB/v274bWO1xgTnyyh19HWfYf4n39/WaH8pvmruGn+KgCGZrRi+cY9lV7jhcuHc2yPNEpKlR63vukp/+mv4wDYn1/Ma19t5pS+bWnZKIkmyfbPZoypSFQ1bE+WmZmpK1asCNvzhdpV/1nJm99s8+zPumgw17zwVUCPbZbSgPvOHcDp/dvZPCvGmCqJyEpVzazuPKvq1dKcTzf6JPOv7xhLi8ZJjD6yLUmJwhGHjd4E+PeU4fRq25T0ZsmWxI0xQWcJvYaKSkpZvXkfd76+BoArTujO9HF9PMfLuhD269CcNVv2M2NCfy4a1sXmHDfGhFy1CV1E/gmcAexQ1f5uWSowF8gANgLnq2rljcRxYOn3OxGBGW+sY93W/QDMv/IYMjNS/Z4//8pj2XmggM6pjcMZpjGmHgukhv4cMAt43qtsGrBYVWeKyDR3/5bghxd5qkq36W9WKH/yN0MqTebg1NQtmRtjwqnahK6qH4lIxmHFZwMnuttzgA+Ig4SuqhwoKOZQUQnnPPYpm/ce8jnepGEix/RozexJmSRYE4oxJsrUtg29rapuBVDVrSLSJogxhc3BgmLmLt/E3W+srfK8Jg0T+eSWk2nVpGGYIjPGmJoL+U1REZkKTAXo0qVLqJ+uSkUlpXy8IYfLnqu+6+TVJ/Xg2pN7kpJk86QYY2JDbRP6dhFp79bO2wM7KjtRVWcDs8Hph17L56ux+9/+lsc/+CGgc1ObNOSoji14ZnImDWxhCGNMjKptQn8dmAzMdH8vCFpEQfDo4g2VJvPWTRqy62Ahq+4aS/OUpDBHZowxoRNIt8UXcW6ApolINnAnTiKfJyJTgF+AiaEMsoyqVjkgZ/fBQp755EceW1KezH97bAZTRnYjuUECbZrbgsnGmPgVSC+XX1dyaHSQY6nUpz/s5LZXV/PTzoOAM8eJd2LfsT+fY2e+T3FpeYvOsG6pzLvimHCFaIwxERcTI0Uv+scyn33vfuGdUxuxabdv98L/XjPSM6+4McbUFzFxB/CJi4cAcMtpR1Y45p3MP5t+Mhtnjrdkboypl2JutsXSUiWvqMSzgs/Pu/LonNqYFo3sBqcxJj7F7WyLCQlCU3c+8OQGifTvaLVxY4yBGGlyMcYYUz1L6MYYEycsoRtjTJywhG6MMXHCEroxxsQJS+jGGBMnwtoPXURygJ9r+fA0YGcQwwmFaI/R4qu7aI8x2uOD6I8xGuPrqqrp1Z0U1oReFyKyIpCO9ZEU7TFafHUX7TFGe3wQ/TFGe3xVsSYXY4yJE5bQjTEmTsRSQp8d6QACEO0xWnx1F+0xRnt8EP0xRnt8lYqZNnRjjDFVi6UaujHGmCpYQjfGmDgRdQldqlo0NEqISNS9bt5i4TWMBdH6OopImvs7KuMDEJFE93dUxigi7dzfURlfbUVFYhKR3iJyFIBGaaO+iAwTkdsBVLU00vEcTkT6iciJEJ2vofv6/SWaPwxFZKSIPCEiV0H0vY4iMlhE3gSuh+iLD0BEjhOROcCfRCQ12mJ0X8PFwAyIztewLiL65hKRBiLyDPA68KiI3Cgind1jUfHJKSItReRR4FFgl1sWNUlJRBJE5HHgZeBWEZkhIpllxyIbHYhIcxF5DJgFZKtqabT823oTkSHAE8BKYJyIPCQigyIcFuD5N54DPAu8oKq3RTomf0SkO/A4sAToCswQkfGRjcohjoeA54E5qnp5pGMKhUi/4bsCzVS1N/A/QDpwlYg0iqJPzlnAKFUdrqqPQ9TV0FsCTYE+wMU4Hzo3ikjTKInzVmAEMNbr9YuWf1tvw4Dlqvo08DsgDyexp0U2LM//t1bAWlX9N4CIpEfhB+PRwDpVfQ64EcgCziirpEWS+3+uKfCVqj4PICI9oqHSE0xh/2NEZIiI9HJ3k4BMEUlS1XU4NfUmwLnhjsubG2Mfd/d/gQQRSRKRM0VkuoiME5GUCMbXzev5U4FjgcaqmoNTU98NXO2eG/Y3vRtfY3f3eSAHaCMi54nI/4rIhSLSJdxxeROR80XkBhE51i36EmgqIu1UdRvwPs6cHsdFOL6RbtFkYKyI3CQiS4BHgNkiUu38HiGMcYTXexlgOdBJRDqr6h5gKbAXOCdK4rsRGC4it4vIUuAB4DkROToS8YVC2BK6+yZfCDwG/EtExqjqt8Bi4DfuaV8DXwEDRaRluGKrJMbn3BizgM+AbcDvgQPAHcC1IpIa5vgyROQt4GngPyLSV1W/Bz4CbnBP2wq8AgwWkQ7hrA0fFt+/3PjWAh8D7wBXAeuBicBNItIpXLF5xZgoIncAt7hFT4nImcBBYCMwyi3/ENgHhLUJ0E98T4jI+W6CfBin/fwu4BqgGXCxiIR1bWC3GXIh8B5wvog0dQ/lA58A57v764G1QOtwVoD8xNcEQFX347y3zwWmA7/Geb+cG8kPxmAKaUI/7E3wRyBLVY8BFgCXuOUfA8e4yecgkA10Ag6FMrYAYnwN56s3OG+iO1V1jKo+CtwGDAaaRyC+Zao6Gqed8s8i0hd4DhghIt1VtRjYjvPmahTh+Ga47aoPAHep6smq+g/gdpyvv91CHd/hVLUE6A3cqKoPAn8GrsVZMH0rMMj9ICrGSUjnuI8Lywejn/juBK4WkV6qOgPoo6ofquou4EVgghtrODXB+YC+1t0+wS3PAT4HjhKRYe7fshk4TlXzoyA+VPUR4CRV/UhVC3De55k4TWwxL9Q19BTwvOkPAkVueXNgg4h0xald7gBuco8tBjoShmRZTYwtgNXum/uAqs7ySl6fAG0Iz3+CsvjKamFrAFR1Fk6774XAFpyvu/e7x1bj3J8oiHB8RwNTgSaqOqfsAW6tvR3wSxjiQ0QuEZFRXt/6tgOtRKSBqs4HfgBOwfm/lw/c457XEVge6hpwNfG9AnwDXCQioqr7vB7aA1gmbhfBMMXYXFU34wyPn4fzeg0TkY5uAv8c51v2Q27NvR/wi1cTXCTiGy4iHcrOdb/tlDkapxJZEsr4wiUkCV1ExojIe8AD7tdFxUmCPUXkK+A0nBrRXOBI4BngFHHuQn+D0/SSG4rYahhjIjBHRMaKSIKqqjh37d/B+Sq5P4zxFeO0jQ8WkYEiMhBYjVPLTcRJQp1E5FERWY0z7/y+UDUV1CC+TkBbr8edJU63sS3A7hDGJyLSXpz25sk4N4wfc5PMTuAonG8J4DRlTAJ2qOqfgb3uV/YLgadDUQOuYXyP4nxTKOs7PVpElgEnA/9wE2nQVRLjEyKSpqr5qpoHLMK5YXsygKpuU9WHgWXAP3GaU+9zz414fO7jkkXkRBFZAZwKzAzzN4jQUdWg/gBH4Pxjno3TLPEC8Ef3WG/gFa9z7wAecbczgDOBXwU7pjrGeDvwEM6H33ic2sfZYY7vRZz252ZuPG/gfPhkurH/wX1cW5wbpGdFWXzXuI87Fqdb4IQQx5fo/u4F/NvdboDTpe4ZnJ5B7+B8FW/sHp8HXO9uJwHpURbfXK/X8SzgnAi9ho96vz/c8utxKhQtcHqtgVPJaBaF8TXy+r8Y0v+HkfgJ1oubACS42xcDj3sduwznTndbnG6JD+O0AwKMBOaXPTakf2jdYxSgQYTim+LGl+7ud/c6djXwO3dbojC+a8riC8O/cQPgL8B9ODc3z8Tpc+z9N+zAaQaY7CbQC9xj/wGG1+f4AoxRcO41jPIqawr8HfgCp7moQ5TGtzzU8UX6p85NLiJyKU4b1Ay36Bvg1yKS4e4nAT+6x3Nxutn9XkSuA57C+UoU0htOQYhxMYCG6OZTAPE1wGnnfcjd/8l93FScZPqlG19IXsc6xndZWXyhJCKjcGr/rYDv3ViLgJNEZBh4+nP/GXhAnTb9d4FL3Ca2Bu7fVS/jq0GMCtyN09OmzHicb2hfA0ep6pYojS8rlPFFhTp+WjbFuUt8Hc6b9ki3/O84X8OXAv/GaQ98C+eOcx+cu89zgBGh/sSK9hhrGN9CoK17/A84NY6h9Tk+rziPByZ57T+OM1jtt8BKtywBpx16PtDZLWuH1zeK+hpfLWKcB2S4ZWcDJ9T3+KLhJxgvchf390xgrrudiFPLHenud3aTY8OI/JFRHmMN4nsOSHb3G1t8PjE2BpIpb1u9GPiru50FXOtuZwIvRuDfOKrji4UYoz2+aPipc5OLqpZ1Pfs70E1ETlXnrvs+Vf3EPXYlTpfAiHQNivYYaxBfHlDsPiZs/WajPb6y51PVAi3v8TEGp180wKVAHxF5A+dbRcibgGItvtrGGKpeSrEYX1QI8ifoFcCHXvvDcAYRvQm0i/SnVyzEaPHVOb5EnK/dbwFHuGVH4PQcGQl0tPhiO8Zojy+SP0Fbgs7tp10qIvNx7jIX4Nzw3KCqPwTlSeoo2mO0+OrOrZE1xJl+4FWcm7K7cL6Oh2zcQKCiPT6I/hijPb6ICvInZ2OckZ87gd9H+tMqFmO0+IIS4wigFKcv/JRIxxNr8cVCjNEeX6R+gj2k+Sqctqsx6syTEI2iPUaLr+6ycebbeTBKY4z2+CD6Y4z2+CIiaE0uUP6VPGgXDIFoj9HiM8bUVlATujHGmMiJq9U6jDGmPrOEbowxccISujHGxAlL6MYYEycsoZu4JSIlIpIlImtE5GtxFl2u8v+8OOuiXhSuGI0JJkvoJp4dUtVBqtoPZ96PcThrdFYlA7CEbmKSdVs0cUtEDqhqU6/97jhT+qbhrLn6L5zpksFZDehTEfkcZ/rkn3Bm33wEZ5bJE3Fm+ntMVZ8K2x9hTA1YQjdx6/CE7pbtwVnHNhcoVdV8EemJM91qpoiciLMc4Rnu+VOBNqp6j4gk48z/PlFVfwrrH2NMAEK6mrkxUahsOtUkYJaIDMKZMrlXJeePBQaIyHnufgugJ+6qTMZEE0vopt5wm1xKcNbtvBNnfcmBOPeSKlv1XXBm8XsnLEEaUwd2U9TUCyKSDjwJzFKnnbEFsNWdl2YSzhzb4DTFNPN66DvA/4hIknudXiLSBGOikNXQTTxrJCJZOM0rxTg3QR90jz0OvCwiE4ElOKtVAawCikXka5wl9R7G6fnypTsPdw4wIVx/gDE1YTdFjTEmTliTizHGxAlL6MYYEycsoRtjTJywhG6MMXHCEroxxsQJS+jGGBMnLKEbY0ycsIRujDFx4v8BgKt+wbQYX2YAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data = df_idx[['Price']]\n",
+    "data.plot(y='Price')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total data: 37 years\n",
+      "80 percent data = 1980 to 2009\n"
+     ]
+    }
+   ],
+   "source": [
+    "diff = data.index.values[-1] - data.index.values[0]\n",
+    "days = diff.astype('timedelta64[D]')\n",
+    "days = days / np.timedelta64(1, 'D')\n",
+    "years = int(days/365)\n",
+    "print(\"Total data: %d years\"%years)\n",
+    "print(\"80 percent data = 1980 to %d\"%(1980 + int(0.8*years)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Create training and testing data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x864 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "split_date = pd.Timestamp('01-01-2009')\n",
+    "\n",
+    "train = data.loc[:split_date]\n",
+    "test = data.loc[split_date:]\n",
+    "\n",
+    "ax = train.plot(figsize=(10,12))\n",
+    "test.plot(ax=ax)\n",
+    "plt.legend(['train', 'test'])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Normalize the dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "sc = MinMaxScaler()\n",
+    "train_sc = sc.fit_transform(train)\n",
+    "test_sc = sc.transform(test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:14: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
+      "  \n",
+      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:15: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
+      "  from ipykernel import kernelapp as app\n",
+      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:17: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
+      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:18: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n"
+     ]
+    }
+   ],
+   "source": [
+    "train_sc_df = pd.DataFrame(train_sc, columns=['Y'], index=train.index)\n",
+    "test_sc_df = pd.DataFrame(test_sc, columns=['Y'], index=test.index)\n",
+    "\n",
+    "for s in range(1,2):\n",
+    "    train_sc_df['X_{}'.format(s)] = train_sc_df['Y'].shift(s)\n",
+    "    test_sc_df['X_{}'.format(s)] = test_sc_df['Y'].shift(s)\n",
+    "\n",
+    "X_train = train_sc_df.dropna().drop('Y', axis=1)\n",
+    "y_train = train_sc_df.dropna().drop('X_1', axis=1)\n",
+    "\n",
+    "X_test = test_sc_df.dropna().drop('Y', axis=1)\n",
+    "y_test = test_sc_df.dropna().drop('X_1', axis=1)\n",
+    "\n",
+    "X_train = X_train.as_matrix()\n",
+    "y_train = y_train.as_matrix()\n",
+    "\n",
+    "X_test = X_test.as_matrix()\n",
+    "y_test = y_test.as_matrix()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train size: (7451 x 1)\n",
+      "Test size: (2245 x 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('Train size: (%d x %d)'%(X_train.shape[0], X_train.shape[1]))\n",
+    "print('Test size: (%d x %d)'%(X_test.shape[0], X_test.shape[1]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setup baseline model of SVM Regressor"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.svm import SVR\n",
+    "regressor = SVR(kernel='rbf')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
+      "  y = column_or_1d(y, warn=True)\n",
+      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
+      "  \"avoid this warning.\", FutureWarning)\n"
+     ]
+    }
+   ],
+   "source": [
+    "regressor.fit(X_train, y_train)\n",
+    "y_pred = regressor.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x2e7c7b1da90>]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(y_test)\n",
+    "plt.plot(y_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "R-squared is: -0.963381\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import r2_score\n",
+    "\n",
+    "def adj_r2_score(r2, n, k):\n",
+    "    return 1-((1-r2)*((n-1)/(n-k-1)))\n",
+    "\n",
+    "r2_test = r2_score(y_test, y_pred)\n",
+    "print(\"R-squared is: %f\"%r2_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Build a Neural Network"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import tensorflow as tf\n",
+    "from tensorflow.keras.models import Sequential\n",
+    "from tensorflow.keras.layers import Dense\n",
+    "from tensorflow.keras.optimizers import Adam\n",
+    "import tensorflow.keras.backend as K"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1 hidden layer with 1 neuron"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:From C:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\resource_variable_ops.py:435: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Colocations handled automatically by placer.\n",
+      "WARNING:tensorflow:From C:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\keras\\utils\\losses_utils.py:170: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Use tf.cast instead.\n",
+      "WARNING:tensorflow:From C:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Use tf.cast instead.\n",
+      "Epoch 1/20\n",
+      "7451/7451 [==============================] - 1s 72us/sample - loss: 0.0034\n",
+      "Epoch 2/20\n",
+      "7451/7451 [==============================] - 0s 38us/sample - loss: 0.0025\n",
+      "Epoch 3/20\n",
+      "7451/7451 [==============================] - 0s 39us/sample - loss: 0.0024\n",
+      "Epoch 4/20\n",
+      "7451/7451 [==============================] - 0s 37us/sample - loss: 0.0024\n",
+      "Epoch 5/20\n",
+      "7451/7451 [==============================] - 0s 38us/sample - loss: 0.0024\n",
+      "Epoch 6/20\n",
+      "7451/7451 [==============================] - 0s 37us/sample - loss: 0.0024\n",
+      "Epoch 7/20\n",
+      "7451/7451 [==============================] - 0s 36us/sample - loss: 0.0024\n",
+      "Epoch 8/20\n",
+      "7451/7451 [==============================] - 0s 36us/sample - loss: 0.0024\n",
+      "Epoch 9/20\n",
+      "7451/7451 [==============================] - 0s 37us/sample - loss: 0.0024\n",
+      "Epoch 10/20\n",
+      "7451/7451 [==============================] - 0s 37us/sample - loss: 0.0024\n",
+      "Epoch 11/20\n",
+      "7451/7451 [==============================] - 0s 36us/sample - loss: 0.0024\n",
+      "Epoch 12/20\n",
+      "7451/7451 [==============================] - 0s 37us/sample - loss: 0.0024\n",
+      "Epoch 13/20\n",
+      "7451/7451 [==============================] - 0s 38us/sample - loss: 0.0024\n",
+      "Epoch 14/20\n",
+      "7451/7451 [==============================] - 0s 36us/sample - loss: 0.0024\n",
+      "Epoch 15/20\n",
+      "7451/7451 [==============================] - 0s 37us/sample - loss: 0.0024\n",
+      "Epoch 16/20\n",
+      "7451/7451 [==============================] - 0s 37us/sample - loss: 0.0024\n",
+      "Epoch 17/20\n",
+      "7451/7451 [==============================] - 0s 38us/sample - loss: 0.0024\n",
+      "Epoch 18/20\n",
+      "7451/7451 [==============================] - 0s 37us/sample - loss: 0.0024\n",
+      "Epoch 19/20\n",
+      "7451/7451 [==============================] - 0s 38us/sample - loss: 0.0024\n",
+      "Epoch 20/20\n",
+      "7451/7451 [==============================] - 0s 38us/sample - loss: 0.0024\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<tensorflow.python.keras.callbacks.History at 0x2e7cbd87588>"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "K.clear_session()\n",
+    "model = Sequential()\n",
+    "model.add(Dense(1, input_shape=(X_test.shape[1],), activation='tanh', kernel_initializer='lecun_uniform'))\n",
+    "model.compile(optimizer=Adam(lr=0.001), loss='mean_squared_error')\n",
+    "model.fit(X_train, y_train, batch_size=16, epochs=20, verbose=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "R-Squared: -1.148897\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "y_pred = model.predict(X_test)\n",
+    "plt.plot(y_test)\n",
+    "plt.plot(y_pred)\n",
+    "print('R-Squared: %f'%(r2_score(y_test, y_pred)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2 Hidden Layers with 50 neurons each and ReLU activation function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/20\n",
+      "7451/7451 [==============================] - 1s 103us/sample - loss: 0.0332\n",
+      "Epoch 2/20\n",
+      "7451/7451 [==============================] - 0s 50us/sample - loss: 1.2320e-05\n",
+      "Epoch 3/20\n",
+      "7451/7451 [==============================] - 0s 49us/sample - loss: 1.0278e-05\n",
+      "Epoch 4/20\n",
+      "7451/7451 [==============================] - 0s 48us/sample - loss: 9.9471e-06\n",
+      "Epoch 5/20\n",
+      "7451/7451 [==============================] - 0s 47us/sample - loss: 9.8869e-06\n",
+      "Epoch 6/20\n",
+      "7451/7451 [==============================] - 0s 48us/sample - loss: 9.6331e-06\n",
+      "Epoch 7/20\n",
+      "7451/7451 [==============================] - 0s 48us/sample - loss: 1.0694e-05\n",
+      "Epoch 8/20\n",
+      "7451/7451 [==============================] - 0s 48us/sample - loss: 1.1236e-05\n",
+      "Epoch 9/20\n",
+      "7451/7451 [==============================] - 0s 47us/sample - loss: 1.2764e-05\n",
+      "Epoch 10/20\n",
+      "7451/7451 [==============================] - 0s 49us/sample - loss: 1.2035e-05\n",
+      "Epoch 11/20\n",
+      "7451/7451 [==============================] - 0s 47us/sample - loss: 1.3691e-05\n",
+      "Epoch 12/20\n",
+      "7451/7451 [==============================] - 0s 47us/sample - loss: 1.3934e-05\n",
+      "Epoch 13/20\n",
+      "7451/7451 [==============================] - 0s 48us/sample - loss: 1.4127e-05\n",
+      "Epoch 14/20\n",
+      "7451/7451 [==============================] - 0s 49us/sample - loss: 2.8624e-05\n",
+      "Epoch 15/20\n",
+      "7451/7451 [==============================] - 0s 50us/sample - loss: 1.5024e-05\n",
+      "Epoch 16/20\n",
+      "7451/7451 [==============================] - 0s 47us/sample - loss: 2.1077e-05\n",
+      "Epoch 17/20\n",
+      "7451/7451 [==============================] - 0s 48us/sample - loss: 1.8178e-05\n",
+      "Epoch 18/20\n",
+      "7451/7451 [==============================] - 0s 48us/sample - loss: 2.2788e-05\n",
+      "Epoch 19/20\n",
+      "7451/7451 [==============================] - 0s 49us/sample - loss: 2.3774e-05\n",
+      "Epoch 20/20\n",
+      "7451/7451 [==============================] - 0s 47us/sample - loss: 3.1842e-05\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<tensorflow.python.keras.callbacks.History at 0x2e7cdfa9978>"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "K.clear_session()\n",
+    "model = Sequential()\n",
+    "model.add(Dense(50, input_shape=(X_test.shape[1],), activation='relu', kernel_initializer='lecun_uniform'))\n",
+    "model.add(Dense(50, input_shape=(X_test.shape[1],), activation='relu'))\n",
+    "model.add(Dense(1))\n",
+    "model.compile(optimizer=Adam(lr=0.001), loss='mean_squared_error')\n",
+    "model.fit(X_train, y_train, batch_size=16, epochs=20, verbose=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "R-Squared: 0.996083\n"
+     ]
+    },
+    {
+     "data": {
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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "y_pred = model.predict(X_test)\n",
+    "plt.plot(y_test)\n",
+    "plt.plot(y_pred)\n",
+    "print('R-Squared: %f'%(r2_score(y_test, y_pred)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "## Try to predict the difference between consecutive days\n",
+    "### This is also known as introducing stationarity in the dataset.\n",
+    "### A better test of the model is to predict stationarized data where the mean, standard deviation, autocorrelation are constant over time.\n",
+    "### Because stock price data, currency exchange etc. are generated using a completely stochastic random walk process. Being able to predict future outcomes of a stochastic process is by definition not possible, and if someone claims to do this, one should be a bit skeptical [Vegard Flovik, PhD]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Time Series Prediction.ipynb b/Time Series Prediction.ipynb
index 97a812f..3d8b41e 100644
--- a/Time Series Prediction.ipynb	
+++ b/Time Series Prediction.ipynb	
@@ -3,9 +3,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import numpy as np\n",
@@ -37,18 +35,18 @@
      "data": {
       "text/html": [
        "<div>\n",
-       "<style>\n",
-       "    .dataframe thead tr:only-child th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: left;\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",
@@ -146,9 +144,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "df_idx = df_idx.sort_index(axis=1, ascending=True)\n",
@@ -170,7 +166,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x11737ee50>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x2e7c14304e0>"
       ]
      },
      "execution_count": 4,
@@ -179,12 +175,14 @@
     },
     {
      "data": {
-      "image/png": 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Lk8xTkhL45I6R4QzJGFOHgFq5iEiiiCwFdgAzVXURkK2q29xDtgPVTgkjIpNE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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x116991a10>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -230,12 +228,14 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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iTAEAgOTEapla+3ZwP9lgtfK12NcqOiVfUwEgTAEAgORYMVqmnD78S3LPWvxE7GsTvpXc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\nxZSIiIhIFP4fstD31No8O5gAAAAASUVORK5CYII=\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1175dc850>"
+       "<Figure size 720x864 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -261,9 +261,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from sklearn.preprocessing import MinMaxScaler\n",
@@ -275,10 +273,21 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:14: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
+      "  \n",
+      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:15: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
+      "  from ipykernel import kernelapp as app\n",
+      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:17: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
+      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:18: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n"
+     ]
+    }
+   ],
    "source": [
     "train_sc_df = pd.DataFrame(train_sc, columns=['Y'], index=train.index)\n",
     "test_sc_df = pd.DataFrame(test_sc, columns=['Y'], index=test.index)\n",
@@ -329,9 +338,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from sklearn.svm import SVR\n",
@@ -347,8 +354,10 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/neelabhpant/anaconda/lib/python2.7/site-packages/sklearn/utils/validation.py:526: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
-      "  y = column_or_1d(y, warn=True)\n"
+      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
+      "  y = column_or_1d(y, warn=True)\n",
+      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
+      "  \"avoid this warning.\", FutureWarning)\n"
      ]
     }
    ],
@@ -365,7 +374,7 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x119948810>]"
+       "[<matplotlib.lines.Line2D at 0x2e7c7b1da90>]"
       ]
      },
      "execution_count": 12,
@@ -374,12 +383,14 @@
     },
     {
      "data": {
-      "image/png": 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D6N8pjakjuvLcDxvd7ScN71L3ixCEFkJI4a61Pi/E9l1Aj4iNSGizLNlm3A/D\nDRoaPyAbgDG927O/rIqPFu+otX8gE0uH1AT+MKmfe3J2yV3HM/rvX1Pj1LSXyVQhihGzjBBRHDVO\nHvlyNQUHwg/z96W2tAN2erRPoU92KrlbCql0hPZ1H9I1w2v9lhMHsfDO47zSGGSmxHPmaKOr/O7g\n7nUYtSC0LES4CxFl0db9PPXdBm7675LQnW3sL6vfw2DT3gMALNjsCcWYeeXhAft2TE/knlOGuNcH\ndkkP2O/OU4bw5PkHc0jv9vUakyC0BES4CxHFZfnYXVLhLokXDrd/GDC7Rdi4fNevntiXcf06BO03\nsmc793JaEDfHtMQ4Th7RTXLKCFGNCHchojis6M/lecWc/vQvbNl3IKz9wu3ny0GdjQmnusaYZQ7r\nk1Vrf3ux69QE8WEXWi8i3IWI4uvBsi9M23uZleclp0NKnc53x1RjZlm9qwSApLjAHjEu+nf02PO7\nNKCQhyC0dES4CxHFd2LTnr+lNvZbRbDtAUzh4JoMfXO+cYNMSqhduMfEKMb3N142WZIUTGjFyHup\nEFEqHTVe61+t3MVRB4WORq60Apg6Bsj+WBu+AjqU5g7w8mWHus04gtBaEeEuRJTKam+hqQhvUrLK\nErY3nTCwTufr7JOewGWDr4342Bh3XhpBaK2IcBcigtaa7YXlfmaZovLQmaC/WL7LnbY3pY6TnPYM\nkn07phInQlsQALG5CxHijXlbmPDwd+Ta/M17d0ihMAz/9cdnr633ee2l+O6cOqSWnoLQthDhLkSE\nJdtNBaPfrBQCndIT6ZWV4i56URujrWChL/40oc7ntfuip4SYTBWEtoQIdyEiuAKCXBGjc289lsS4\nGKrDSAtQUFpFelIcg7pkhOwbCJd8T5Xc64LgptUI9z0llWzYU9rcw2izZCZ7MjfGxShiYxTxsTE4\nwqhv+sWKXZRX1YTsFwxtVdlLFs1dENy0GlVn3IPf4HBqNv7jJCmw0AykJnoEa4ylSsfHxnjVNw1G\nSkIsQ7vVT2v3PY4gCIZWo7m7wt5n/LgxRE+hMbC7QLoqJH2+fCeb9h7w8333JT42xi9jY32QoCRB\n8NBqhLuLT5fWntNbaByqbEFBLhONS2vPL64Mut+BSgdF5dU4Qyv4QXn50kM585AeJIYRwCQIbYVW\nJ9yX50kBqObAnlMmxZrYPHZQJwB2F1cE3e/4f88BYOavW4P2CcXRgzrxr7NG1nt/QWiNtBrh3skW\ntq51A9SL1UD/AAAgAElEQVRAoV7Yg5dctu9bTxoMeDxo7ByodPDoV2vI218OwPAedcspIwhC7bSa\nCdUkW33MZXlFjOjRrpbeQqSxC3dX4FKP9skA5Jf4m2Wmv7GQH9ftda/PuGhMI49QENoWrUZzd9hs\nvqc++TMv/bSpGUfT9jhQ6QlWGmk9WBPjYlDKkxTMzk/rPYI9PlbVOWGYIAi102qEe5VPlr8XxGum\nSSkqr2ZQl3RunjKQR8829m+lFIlxMVQECGTKshWffvjMEU02TkFoK7Qa4V5eVUNXW/EFsbo3LXtL\nK+mamcQ1k/p7ea0kxcdSEUBzt8ciHNxTapUKQqRpFcJda01ZdQ2TBnryhu8sCu6hIUSenUUVdG2X\n7NeeGBcTULjb2+zzJYIgRIaQwl0p9ZJSKl8pFbCCsTI8oZRar5RaqpQaHflh1k6lw4nWkGELgRea\njiqHk4IDVXRO9y9bZzR3f7NMd9uDIC5WIooFIdKEo7m/AkypZfuJwADr7yrgmYYPq2648pJkJHkL\n9+oaJzuLypt6OG2OUmsyNTPZ3/kqKc7fLLNk236qa5wM6JTGHyb1o4NElgpCxAkp3LXWc4CCWrpM\nA17ThnlAO6VU10gNMBzKLOFh19wHdUlnwO2fM+7Bb/l4iUStNiYlFaYgR3qS/5uTRrM8r8i9vmZX\nCdOe+pkNew7QJTOJv00Z5JW2VxCEyBAJm3t3YJttfbvV1mSUVxnNMSPJozmu3lXiXp6/cV9TDqfN\nUWLlbE9P8tfc1+4uZUdRBTVWfoFymxZv93MXBCGyNOmEqlLqKqVUrlIqd8+ePRE77utztwCmRNvm\nh6b6bW9I3hIhNI99bSoppQXIpz6qp/F5d2nv9sLUt544qAlGJwhtk0gI9zygp229h9Xmh9Z6htZ6\njNZ6TMeOHQN1qRevuoV7YK8LSUfQOFRU1zBr2U6WWlWY+nfyL059yRG9AZizdo97HxfHDu7cBKMU\nhLZJJIT7x8DFltfM4UCR1npnBI5bZ1zFGmZd712uzZW/RIgsL/60iWveXITD6aRjeiKdMvy9ZSZb\nAtz13diLcvTukNI0AxWENkg4rpAzgbnAQKXUdqXU5Uqp6Uqp6VaXWcBGYD3wPHBNo402BC7NfUi3\nDK/c3j+u28s9H69ormG1Or5fk091jZMt+0xCsP1l1V6Fqu24Apo+WWqe9y77/E0nDCQ+yD6CIDSc\nkInDtNbnhdiugWsjNqIGkBLvuZwrJ/Tln1+sdq+/8stm7jl1aHMMq1WRu7mAS19ewNEDO9KjvUfz\nTogLLKhdhTuWbNvPhIe/ZerwbgCcN7ZX4w9WENowUa862e3pSQmey5k+sS+r7/d2z6+RmdUG4wpI\n+m7NHl6ft8XdHh8kEMnu5ritoJxnf9gABPasEQQhckS9cLfX6ExN8AgMpZRfWHtZlYNQ5O0vD1kW\nri0TTChvKwg+rzF1uH/Yg5hkBKFxidr/sJ/W7WXSI9+5A2iuO6Y/qQFc8W46YaB72ekfBe9FjVNz\n5EPf8se3fovoWFsTziCeR+UB8se4GD8gu7GGIwhCEKJSuBeVV3Phi/PZvK/MHQgTLB/4FRP60Cc7\nFYAaH8H02dKdLN2+372+w/Kq+Xrl7sYYdqugPqatpPio/JkJQlQTlYbP85+f517+0zuLAZN9MBCJ\ncbFcdmQOd320wkvrLCqv5tq3FgGw+aGpLN62n9Oe+rkRR906cAQR7g+fETwne1yM+W6mDu/KQZ3T\nObSPpPgVhMYmKoX7ih3+RbBri1OKsSb1nDbB9NhXa7z6XPvmIq/1z5ft5MQAtuK2jusevnPV4RzW\ntwM5t3wGwBmH9Ai6j2uytbrGyQ2TBzT+IBsDpxNenAy7lsGIs0HFwvYFgILi7fDHXHjnQijdDeld\noaIY0KCdULwDuo6E6jJw1oCuMcfTTijZCZ2GgNMBNVWgFCS3N9uyDzL7VJZAZk8YdDL0PAzyV8KS\nmRAbDwlpUFkMhVugcDMkZUJKFtRUm/M6q825YmIhJs7sExNn1qsOQGIGZPWBLXNNG0BsAsQlQVwC\nFO+EpAwzbhUDeblw6JXQaZDp1220Oef2X6FkF8SnQPYASOsM6V0gMb0Zv7QWSPl+yF8FPQ6F2MYV\nv1En3INFmwayt7uItQpDuMwy936ywh3VClBwoIoOaQlewU5/eHNRwFQGbR2X5u5K0zuyRyZLthe5\n73EgXC6Tw7pHaRFspxNeOh7yFpr1397w7/Mv20MrJRva50BMDOxdZ4RvwUbo0N8IxNh4IygBKvZD\n2T7I6Aqx7YxQL9sHO36Djd9DWheIT4aVH8HcJ4MMUJnjdRkOjgrzAKo6YM6XkGqEudNh+6sxwn/X\ncqiphI3fGWHe92jzcHFUmgdNZSkcyDfjTUw3Dy6ABc+Hd986DIDpP0G8Lbht6buw6DXoORYm3mIe\nINHM5p/h3YutB3APKNjg2dZxEOxZDQNOMPeweAfsXmHu+aFXwtR/NerQok647y+r9msb3DWDk0cE\n17JdcseluL/882av7e8v2i5pZ8PEZXOPtUwtb115eK2TqWCE+mfXj2dQl4xGH1+jsOAFS0sHznkT\n0PDVnXDiw9B1BDzqmbTn1jxItKVh0Br2rDGabl2ocRhN2uVKuvF7eG2aWR7/Zzj4IsjoZjTB9C6m\nva7ZNcsKYP8W82DI6AGpHULvs2cN/PwEFOdBv2PMm4HW0HmYGYeugZ/+Das+gX3r4IEu8Jc1sHct\nzDwXqkrNcTb/CAtfgSkPwYDjIbkdVJXBvvXmntaGo8o8BFVM8z0cyvfDcxNg/1ZPm1LmgbpvvVnf\nZwn6Lb+Yh263g6HvRPOAHdf4oUFRJ9w37i11L4erWQcyy9j5+2erOG6ICZMfm5PFr5tNhmOttaSj\n9cGtuVtPzNTEuFrfmlwM7VYPrX3tV/D5zXD2q8as0Vys+MB89jsGBp9slgefErhvok9+HaXqLtjB\n/5W97yT480pISDFmGxfx/tWvwiYly/zVhY4D4bSnau9zzhtG63+wO6Dh0YO8t5/xInx5O5Tugvev\nNG2JGeYhUWVlc71+sTEXudDavDnMPNe81diJTYCLPjQPuwUvwNG3m/tUVmD2m/ukMXF1G2XeHH76\nN/SfDOu/Nm8Xg06C7blQXQ5XfGPeuALhqDIP2bK95gG3f6t5qE2+BwYc5+lXUWwePK7fgutNKd4/\nPUdjEnXCvbg8tK+6L26zjFMH9fZweci8fsVYBt7xBQD/mLWKm6cMEp9sGy7X00AZICNCjQNWfQSp\nHWHlh1C4CebPgIk3wZrP4bDpHg21sqTxbbrrv4Gtvxg7+kUf1N43uY6Csq5kNmkm7YaRmAaTboXv\nH/S0Df0dHHc/tOsJw86AD/8Aa2ZBRZExXdl5YhT8dZ0xD2X2gK/uCG6WqqmCV07yrAc1X9lY/7X5\n3LcOfv6Pp33Hb5DWEdZ8AaPO8/y+qivgAZ9Ed73Gwe+/8D92ks8bakysZz6jCYk64X4gjEAkX9ya\nu9bufCjBSIyL5cnzD+aPb/3G8z9uon+nNM45VELlXRSXG+HeaCUN5zwCPzzk3bb4DfMH8MUtRoiW\nW/VjfjcDRp7jf5zdK+GZccbm22V4/cZSdQDeON0sX/Z56P4DTwrdpy0x4a8w8lyjQXccZLRpF0rB\n7541y1vmwq/PQVY/OPZOeHa8mTdwzWMMPhVWf+rZ9+o50L6PmbwFWPwmfHJ98HF0GACjL4av74R2\nvc3E78AT4Zg7oLzQzIsoZR42P//bKBFOB3x+U+Dj9TsWjroJeoyp/71pAqJOuJ88ohtjemfVScjH\nxHiE+8Y93sJ90Z3HMfp+8xR3paw9rI/H9ljpCBH51MZwldRrNM19wzeh+5TbCoN9cBUMOdXbPLH5\nZ48mN/dpM4G1/D3jWXL1HOjQz9N3e67x9sgeAKX5xgvGZXte9Yn57DvJ20QQjJMfC92nLREbZyaW\n2+fU3q/3OPPn4uKP4WHb/V71sfk87j448gb//Q+5xJhdUrONzfvNs8xb3XkzzXfr0pqPDPIA6DnW\nmG9yX/J858G4e3/d5zaaiagT7gBdMutmu4pVLrMM7C6u8NrWPsWjgV59VF/AOyDKlcVQMJRW1pAQ\nGxM0UViDibdpdwOONxNxhZvNelKmeYXvPMxs27nEPAxKdkKW+e4o2OT9ir7kLc9yVSn832i4bafR\nIrfOg5dOMNsGnezRDi+dZezaH1xt7LnnvV37mGMTjGkgLnAgnVBHUrLgniIoyoOFL5u3OTDug8Gw\nPxwu/F/dz6kUnDsT/tXfrP9tszHFzH3S/L4GTTV2+igR7BClwr2uuLxlPlmyg2qnE6XgqAEdufTI\nHK8J0362YhMfXXsk0576mYIDVU093BaLo8bpTvzVKJQVwKYfYMQ5cNozZlLqiYPNtps2GM3Mzrqv\njXB//2q44mujfT0xKvR5vvib8Vz49M+eNvtrv/3hcOLDoSctb1wNDqkZEHEyuxvTyaTbjCtpdv/G\nPV9aR/NdJmVaE9fACQ807jkbkTYh3DMt+/CT3613t736+7F+/QbYhPvInu3o1zGVnUWR/6fduKeU\nYx79gc9vmMDgrtHjHrizqCJ0J4C1X0L3MeG51tl5xDKX9D7C8yp93tuw7it/wQ7GOwJMAI3TCffZ\nvEj+vBK2zoX3Lje2395HwJaf4cdHjZ/1otf8j2d3Y3Mx+pLQ467rdQp1Iyam8QW7i4zWE7jYJoT7\nCKuOZyjSk7wnCbNSExpFc//K8sz58Lc8L+FeXlWDUvhls2wpVITwZweMC9xbZ0NGdzNpdexdRhMK\nxdb5xncZjCeFi06DgrsSdhriWV4y07OcPdBofcPPNH8u+kyEFR96Ak16jDUTbaPON28JSpnxL34T\nZt8Lpz8X3C1OEFo4beKXG2ryb8KAbC4e19uvPSk+lkqHk5KKar5YvjNiqYBdcwDbCsu82ofe/QWT\nH/shIudoDFyTy/Z5Cj9cUYzFecbneNbN4R18zSzPcrjujUqZSTaAj6wCYGOvhmvnB+4fGwd/XOBZ\nP+EfMPoi72ChxDQ47Gq4fUdwX3ZBiALahObuy7MXjvZaf/3ywwL2S4yLoeCAk+tm/sb3a/bQJzuV\n7/46qcHnd/ndz1q2i+/X5DNpYCe2FZTh1LC9sBxHjZO42Bh+WreXGq2ZeFDkiok3BJfm/p9zD/bf\n6KyB+c/5a9mlu0IfeP82+Plxszzt6boN6vBr4Ou7POuHXFL7pFdMLNywxEQN9qxlgk4Qopw2obn7\nMqRreNGSCXExVDqcLN1eBMCmvR43yhU7inj7163Bdq0Vu+y59OUFVFTXMOHh79xt7/+WR0mFSWt8\nyUu/1uscjYFLcw+YgXP9N/DlrfD67zxtWf2Mv3ko7EEnB19Qt0HF2t4iDrkMOodRSrF9jjHFCEIr\npvUI928fgHsyYefSkF27tw8vZDshNoYqh9PL7r6twJhSpj7xE7e8v4z1+SXubS/8uJGjbEI6GFsL\nvM0xe0oqvdZ37q9g+D1fudd/snLWNzcus1RioDkB7WOyuuoH6H+sSTzlDGHOyrceAHfuq9/AOlpv\nCxJEJAhuWo9wn/Ow+XxugskB4cOzFx4CwF+OO6jWDIZ2UhLjKKmopns7z8NgwsPfcd4MTz75yY/N\nwVFjNNq/f7aKrQVlQTNXuvAV5r9s8Bbe/5691mvdlbO+uXHVTw1YfKO80Hu9y3ATeQgmgCgQVWWw\n7H+waY5Zr28K1PGWS2Nz5p8RhBZG6xHu3Wx24A+u9mRks5gyrAubH5rKdceGn0+8e7tkCsuqvVIB\nA8zd6K1h/v2zVV7roaJafbf/7b1ltfbfW1pZ6/amwq25xwXQ3MsKvNdjYuFcK2XA9gX+/VfPgn90\nNa6KYPKV15eR58JdhZDeOXRfQWgjtB7hHmeLWl3xPjzZ8MmycM0hr/yy2Wv9wVmrWGbZ6QMRTPjP\nvnEio2xumxlWMequdYzIbSzKq2rR3As3eZbH/dF8ts+BzsPNhKkvvjnRr1vYsMGJy6IgeBHWf4RS\naopSao1Sar1S6pYA29srpT5QSi1VSv2qlBoW+aGGoHS3qQrjwtcGXA/sE59HD/T3WPn2LxMBOHlE\nVz5ZssPd/urcLZzy5E9Bj1sVxKWyf6c0umQYQX5Evw4suft4wAQP1ad2aaRxZYT0jQcAYNt86HOU\nCRu3R/WldzHh277Y3R1Pf0FC9wUhwoQU7kqpWOAp4ERgCHCeUmqIT7fbgMVa6xHAxcB/aGpK95gS\nZHZm32smWVfPCrxPCE4d2c29fNmR/omj+nZMo1N6IvnFlVw38ze/7cGodDjJSIrjG+vhAB73yB7W\nZG+7lHiUUpw+2qR5/XVTgf+BmpBnvt/Ag5+vJi5GkZrgY5bR2mTW6xKgyEJcIuxc7KmDWOOAx0fA\nUitfS6ehcNDxjTt4QWiDhKO5jwXWa603aq2rgLeBaT59hgDfAmitVwM5SqmmM4DmvmSS/Kd18nhO\nAPxkZen7/G/1Ouw5h3rswGlJgSf70pPi2FJQexphX6ocTg7NyaJfR0+6g9MPNkLclahs1jLjH377\nSYMBWL3Lv25sU/LPL1YDMKJHpncBk+IdJpOeo8LkPPelwjJPLXjBfJbuNtV/XFzzS3gRrIIg1Ilw\nhHt3wG403W612VkCnA6glBoL9AaCV0yONK4EUGmdTHTiNJ9KMUVbTRrYOqKUok92KgAjumdy/zSP\nD/W7V5ssdBnJ8ewu9p/wjI1RQb1mKh1OEn3s1g+ebnKOO619jrICl9qnJBCjCJgGQWvNwi2FVPnY\n8A9UOsi55TP+t3B7WNcZCnvaga6ZyXBgn3Fv3DoPHhsMH1s29v7H+u98mhWU5IpAtQc1hSp+IQhC\nvYlUhOpDwH+UUouBZcBvgJ9hWSl1FXAVQK9ejVAAw2XHPfhCkyrWXmHllZNMGk97ibIw+ObGiShl\nBP1F43K4aFyO13Zf+/P3f53E8z9u5M35W3nll80BzTlVDicJPtWd4qz1W08aTEpCLLdaGntMjKJ9\nSgL7Agj3uRv3cf7z8znrkB48cpbHDfBHayL4H7NWccbo7g0uFfj9mj3u5ZS4GnjESq/rSsFaUQQo\n77cmF+2s73nDt8ZENsgqU5czwZStEwShUQhHc88D7H5qPaw2N1rrYq31ZVrrURibe0dgo++BtNYz\ntNZjtNZjOnZsYEh9dbnxka6xFcxO7eRZPvp2uOJb70IBxZ5Jz6Asnuml5cfEqFqFo91lfv0DJ5KT\nner2eLn3k5VuH3h3n/xStheWud0JffPeZKUmcO+0YV7Jw3q0T2bNrhJ8Of95k0PFlYjMxfQ3jOdJ\nwYEqbni74T7yruMBjOpsK0js5eKog4f9p9ksdK7UuqfPaPC4BEEITjjCfQEwQCnVRymVAJwLfGzv\noJRqZ20DuAKYo7VuHCNxTbWZvHugi/GRnnmeae9xqHfC/rhE6HGIKZzrwuGTsvbDa02xXDCBT0ve\ngQ+ne+fzDoFdOLu079NHeyxS/W//nOIKzwNo8mM/4NS4i0rPuflofr6ldg12TE4Wy/OK/B4ULu2/\nXUo8r8/djNOp2V/mreF/vGQHy/OCu2WGg0tm33nyEM4b3aXuBzjnDf+2QPZ5QRAiRkjhrrV2AH8E\nvgRWAe9qrVcopaYrpaZb3QYDy5VSazBeNQFqYUWIlR/Bk7baha5Ct8GCYLL6wIXvm+Uaq6pSab7R\n+he/AbPvMRVXFjxvSra5+PL2sIaTnWZc+M48xCPQfSNg37Ns33bh3CXT7JeVmuAVARuIvh1TqXQ4\n2VvqEdxVDidV1vG27Cvjzo9WcMM7ixl139d++5/8fz9RXRM8sOqL5buYtzFw6P/yvCK3o8vl4/sQ\n4wySAnmin4esB/t3M/leU/c0iiraCEI0EpbNXWs9C5jl0/asbXkucFBkhxaEhLTA7bUlnHIll3Ja\nGvS/fKJUXz/N5Pq2M/dJU6k9RHCMq3rTEf28Czasvn8Kczfu47KXF7gnQ6tsArZzRviBSVkp5qWo\nsKzKXWJwxhz/ikh2X/sLDuvFm/M9ic3ezd1Gcnys11sFmFw5LrPL5oem+h1zw55SAG450bKn11jC\n/fTnYcTZsPJj45U04S/BLyCtsylqfdx9JsWuIAiNTvSl/K2wmRjOecN4beSMD1ypx0WMdZl2+7yd\nrXPNH8CQaebtAOCja01ATkpW0ENfMLYXXTKSmDy4k1d7UnwsRw80bSt3GAtVtcPjPVMX4d4+1RLu\ntknVvP21V0W6f9ow4mIUr841boe3f7AcgBOHdSXZ5qduz0Z5oNJB3v5y9pdVM7aPueZdVvWlCw+3\n8t1XWrZ/V63TIaeav9qIiYG/baq9jyAIESX6Yrb7HOVZHnQyDD2tdsEOEOPS3B0m2Kk2zn4Nfm9l\nZFzyFsy6qfZDxyiOG9K51knXb1bnA1BZ43Eg6hFmZkrwPAjW7vZMqva33hgO6xP4wRMTo7jrFP/0\nt79t8yT48i0+ct7z8zj+33M4+7m5bjfOXcUVpCXGeeYWiq259IxuCILQcok+4Z7RFe4qMBXsw7Xb\nurINbl8Aebne2+4p8gTRDLFis3rZIl231N0/PhCllQ7KKu3CPSXsfXM6pJAQG+OVwKyovBqlCFiD\n1ZW2IDbGE+Hq4ub/eVIir9jhPee91JYPp7jczE/sKqpwm4IAmPeM+UyTJF2C0JKJPuEOJuNgQvjC\n0S28f/gnzDzXLJ/1Klz5rVl2mXoOv8azz51W0rDUhrls/ufcUQAMu/tLJv3rewD+NL6z8fle+t+w\njqGUomN6oteEalFZFemJcVwxoQ8XHNbLLdDBUxAc4NGzRnLdMf157w/Gk2h7Ybnbe+f0p38BvCeD\nXYy87yvW55eys6jCne8GrWHzj2a5gfdFEITGJTqFe13J6uvfNvQ06H6Id5t9PTbeVPYpzjN5xz+9\nEfasqfOp+2b7TwBP6W5FtL5/BThs0a1f3g4vTfGsa+3OyZK3v5wPfsvj3QXb+GbVbnYXV5KVmkCP\n9ik88LvhZCbH07djqt+5lFL85fiBHNI7i9G9jP/9DTN/49o3F7n7DO6awRPnHcyATmm8eYXnrWXy\nYz+wu9imuVfZ0izE2fzdBUFocUTfhGokGOKTGuemDcYeH+uT7TA12xSh+O4ByH3R/N1jafllBbDp\nBxhqKyvn8hm0mYuSfZNsAVkptmfq3zuZY37yJ1j4smlb/h4MOwMeGwKVxXCbJ2bs5vc8ZpWBnb0L\nSc+6foI7fUEg/u/80Rz50Ld8t8Z73uHSI3KIjVGcOrKbX+54L83dNZk64a9BzyEIQsugbWjuvpzw\noPd6arZJTetLSjZopymm7OLdS0y90DdOh/9eCi/aMhrmvgj3tvMqXBEoF3tWwRLvhpLdHsEO8L/f\nG7NNyQ6oKoVZN9E+xT/N7prd3lGrSfGxpCQEf153C5IX3u6X3yHVXyN3a+4u4d5pcNBzCILQMmg7\nwv26RTD2KjjrFcj0zXsWhBTLd32Hx4TByg+NYN9hpfjdNt8IY4B5luu/rXBFqk96gfPG9iKuxCeh\n16MhQgR+ncFrvz/Mr/mYQZ0CdA5OII+eT68b79fnuYsO4S/HecaU4bLhV1oTsIn+k7iCILQs2o5w\n79APTnrE24wSiqrS8Potf88U5t63zqw/fwy8eop78+wbJ3LbSYPY9OBJJvtjVZAUwVMf886J3s+T\nZXF4lyQuHmd8zQd1MeaYET3qnyr3L8cdxK+3Hcuw7v7HOGFoF69yhO63D1edVEnRKwgtnrYj3OtD\nJ1tNkrFX+W932d87DTWFue1smuO2wffvlMZVR/XzaM4u4e4bsn/o5TDqfM/6Re97TEiL3+TeU4cy\n6/oJXDnBTBC7Uh/UhaX3HM9rvx/LdccOoFOIQKpFdx7H0xeM5tCcLPj2AXjzTLNBapUKQotHhHtt\n2P3dB5zgpUm786UMPhX2rA68f3Fe4PaqA9B5GBx9q6fNVXfU18Vw3DUm22XeQpRSDOmWwemju/P8\nxWM4f2wv2PBd4GyXH18Hqz71a85Iinfnig9FVmoCJw23EnzNedizoV3vsPYXBKH5aJveMnXhL2tM\nINOAydD7CNi7xiQg62KViU3v4l2vdeqjkL/KVB56dgLcvNE72KrGAeX7IcHHbXGSJegPOgHa94Ez\nX/Rsy+rj5YaplImKZduvJi9OZk/483JP/6/vgkWvmb/z3zXHbAhrv/Qsq1hJ+iUIUYBo7qFI72Lc\nEsEETnU7GHoeCvFW+oCOAz19b9sBh14Bx9xh1ssLjPdM/ipPnydGwZafPIVFLnwPRpwDiZY/fGI6\n3LDY2+c+Zzxsz4UDe73H9s195rNoG8x5xHjplBV4Fyl562zIfZkG8Y4t2delnzXsWIIgNAki3BtK\nhi2606WNJ7eHOJs9e/5znuUiq2JhVj/z2X9y6MIVfSYCGnbbtPPyQk+0KMC3f4eH+5g/Xz79k4mG\n3eefSRKnE54/Fl4/PfC5C7dAjeX7fuc+75z5giC0WES4N5Q+E0wys+k+OWju2A235kFcMix61X+/\nDv3CP4erVF2BLbNi3sLAfV1c/LF3ScH3r4D/G+0v4N843eTb2fAN7F1vio3Pn+EJyHK9dUx72pOj\nRxCEFo8I94aSkAqXfOKxwdtJTANHuQmEqjpgSgO66OyfsTEoGd3Nm4A96VlVmfkMVoe0z1GmZqwv\nX93hWa6pho2elL88eYgpNv75TZ43jL2WrX9Q+NWpBEFofkS4NzZHXG8+Hx1sSgO6yBkfuH8g4hKg\n6yjYOMfTVrLTfLpSAQy0Cd+/bfFMel4zz7u27JpZkG9593z4h+DnfHw4rPkc9qw1+9exsLggCM2L\n0rXkImlMxowZo3Nzc0N3jHYqiuEhnxKAF30QXOMOxj0BAodi4uGOfCPIlTK+6POegVu3eXu0uL7j\n7x80mTF9OfIGGHQKfHQNTPybqU3rIiXbpBu41N+tUhCEpkcptVBrPSZUP9HcG5vEdP+2pHZ1P06g\nzOL2GxAAAAVfSURBVJbOalPlyCXIj7kdbtvu76roEv5H32bK3fly7D3GA+iPC2D4md7byvZCdtNU\nUBQEIXKIcG9slDLuky46D6+bvd2FqzpUQykv8F4/8WH/OrGXfAIjbZGyZYGLZwuC0HIR94em4LLP\njXdLryNCFtwOSlpHk/zsg+mmGPgnN0DfSXU/zrkz4e3z4IL3oMchgW3pfY4yf11HwBe3wMSb6zdm\nQRCaDbG5RyNOp7Gfj74Y2vUM3V8QhFZDuDZ30dyjkZgYY18XBEEIQlg2AqXUFKXUGqXUeqXULQG2\nZyqlPlFKLVFKrVBKXRb5oQqCIAjhElK4K6VigaeAE4EhwHlKqSE+3a4FVmqtRwKTgEeVUlJkUxAE\noZkIR3MfC6zXWm/UWlcBbwM+RUjRQLoyCcvTgALAEdGRCoIgCGETjnDvDmyzrW+32uw8CQwGdgDL\ngBu01k7fAymlrlJK5Sqlcvfs2eO7WRAEQYgQkfJzPwFYDHQDRgFPKqX8Cm1qrWdorcdorcd07Bhe\nwQhBEASh7oQj3PMAu79dD6vNzmXA+9qwHtgEDIrMEAVBEIS6Eo5wXwAMUEr1sSZJzwU+9umzFTgW\nQCnVGRgIbIzkQAVBEITwCennrrV2KKX+CHwJxAIvaa1XKKWmW9ufBe4HXlFKLQMU8Det9d6gBxUE\nQRAalWaLUFVK7QG21HP3bEAeHh7kfngj98MbuR/eRPv96K21Djlp2WzCvSEopXLDCb9tK8j98Ebu\nhzdyP7xpK/dDskIKgiC0QkS4C4IgtEKiVbjPaO4BtDDkfngj98MbuR/etIn7EZU2d0EQBKF2olVz\nFwRBEGoh6oR7qPTDrRGl1Gal1DKl1GKlVK7VlqWU+loptc76bG/rf6t1f9YopU5ovpFHBqXUS0qp\nfKXUcltbna9fKXWIdR/XK6WesBLdRR1B7sc9Sqk86zeyWCl1km1ba78fPZVS3ymlVlopx2+w2tvs\nbwQArXXU/GGCqDYAfYEEYAkwpLnH1QTXvRnI9ml7GLjFWr4F+Ke1PMS6L4lAH+t+xTb3NTTw+o8C\nRgPLG3L9wK/A4ZhAu8+BE5v72iJ4P+4B/hqgb1u4H12B0dZyOrDWuu42+xvRWked5h5O+uG2wjTg\nVWv5VeA0W/vbWutKrfUmYD3mvkUtWus5mDTSdup0/UqprkCG1nqeNv/Fr9n2iSqC3I9gtIX7sVNr\nvchaLgFWYTLXttnfCESfWSac9MOtEQ3MVkotVEpdZbV11lrvtJZ3AZ2t5bZyj+p6/d2tZd/21sR1\nSqmlltnGZYJoU/dDKZUDHAzMp43/RqJNuLdVxmutR2GqYV2rlDrKvtHSMtqs21Nbv36LZzDmylHA\nTuDR5h1O06OUSgPeA/6ktS62b2uLv5FoE+7hpB9udWit86zPfOADjJllt/UaifWZb3VvK/eortef\nZy37trcKtNa7tdY12hTJeR6PKa5N3A+lVDxGsL+ptX7fam7Tv5FoE+7hpB9uVSilUpVS6a5l4Hhg\nOea6L7G6XQJ8ZC1/DJyrlEpUSvUBBmAmiVobdbp+6/W8WCl1uOUBcbFtn6jHJcQsfof5jUAbuB/W\n+F8EVmmtH7Ntatu/keae0a3rH3ASZjZ8A3B7c4+nCa63L2ZmfwmwwnXNQAfgG2AdMBvIsu1zu3V/\n1hDFs/2265mJMTVUY+ygl9fn+oExGKG3AVMaUjX3tUXwfryOKXG5FCO8urah+zEeY3JZiqkIt9iS\nE232N6K1lghVQRCE1ki0mWUEQRCEMBDhLgiC0AoR4S4IgtAKEeEuCILQChHhLgiC0AoR4S4IgtAK\nEeEuCILQChHhLgiC0Ar5fwQRB3mM8HJwAAAAAElFTkSuQmCC\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x119948890>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -420,22 +431,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 17,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Using TensorFlow backend.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "from keras.models import Sequential\n",
-    "from keras.layers import Dense\n",
-    "from keras.optimizers import Adam\n",
-    "import keras.backend as K"
+    "import tensorflow as tf\n",
+    "from tensorflow.keras.models import Sequential\n",
+    "from tensorflow.keras.layers import Dense\n",
+    "from tensorflow.keras.optimizers import Adam\n",
+    "import tensorflow.keras.backend as K"
    ]
   },
   {
@@ -447,7 +451,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 18,
    "metadata": {
     "scrolled": true
    },
@@ -456,55 +460,64 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "WARNING:tensorflow:From C:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\resource_variable_ops.py:435: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Colocations handled automatically by placer.\n",
+      "WARNING:tensorflow:From C:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\keras\\utils\\losses_utils.py:170: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Use tf.cast instead.\n",
+      "WARNING:tensorflow:From C:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Use tf.cast instead.\n",
       "Epoch 1/20\n",
-      "7451/7451 [==============================] - 0s - loss: 0.0025     \n",
+      "7451/7451 [==============================] - 1s 72us/sample - loss: 0.0034\n",
       "Epoch 2/20\n",
-      "7451/7451 [==============================] - 0s - loss: 0.0024     \n",
+      "7451/7451 [==============================] - 0s 38us/sample - loss: 0.0025\n",
       "Epoch 3/20\n",
-      "7451/7451 [==============================] - 0s - loss: 0.0024     \n",
+      "7451/7451 [==============================] - 0s 39us/sample - loss: 0.0024\n",
       "Epoch 4/20\n",
-      "7451/7451 [==============================] - 0s - loss: 0.0024     \n",
+      "7451/7451 [==============================] - 0s 37us/sample - loss: 0.0024\n",
       "Epoch 5/20\n",
-      "7451/7451 [==============================] - 0s - loss: 0.0024     \n",
+      "7451/7451 [==============================] - 0s 38us/sample - loss: 0.0024\n",
       "Epoch 6/20\n",
-      "7451/7451 [==============================] - 0s - loss: 0.0024     \n",
+      "7451/7451 [==============================] - 0s 37us/sample - loss: 0.0024\n",
       "Epoch 7/20\n",
-      "7451/7451 [==============================] - 0s - loss: 0.0024     \n",
+      "7451/7451 [==============================] - 0s 36us/sample - loss: 0.0024\n",
       "Epoch 8/20\n",
-      "7451/7451 [==============================] - 0s - loss: 0.0024     \n",
+      "7451/7451 [==============================] - 0s 36us/sample - loss: 0.0024\n",
       "Epoch 9/20\n",
-      "7451/7451 [==============================] - 0s - loss: 0.0024     \n",
+      "7451/7451 [==============================] - 0s 37us/sample - loss: 0.0024\n",
       "Epoch 10/20\n",
-      "7451/7451 [==============================] - 0s - loss: 0.0024     \n",
+      "7451/7451 [==============================] - 0s 37us/sample - loss: 0.0024\n",
       "Epoch 11/20\n",
-      "7451/7451 [==============================] - 0s - loss: 0.0024     \n",
+      "7451/7451 [==============================] - 0s 36us/sample - loss: 0.0024\n",
       "Epoch 12/20\n",
-      "7451/7451 [==============================] - 0s - loss: 0.0024     \n",
+      "7451/7451 [==============================] - 0s 37us/sample - loss: 0.0024\n",
       "Epoch 13/20\n",
-      "7451/7451 [==============================] - 0s - loss: 0.0024     \n",
+      "7451/7451 [==============================] - 0s 38us/sample - loss: 0.0024\n",
       "Epoch 14/20\n",
-      "7451/7451 [==============================] - 0s - loss: 0.0024     \n",
+      "7451/7451 [==============================] - 0s 36us/sample - loss: 0.0024\n",
       "Epoch 15/20\n",
-      "7451/7451 [==============================] - 0s - loss: 0.0024     \n",
+      "7451/7451 [==============================] - 0s 37us/sample - loss: 0.0024\n",
       "Epoch 16/20\n",
-      "7451/7451 [==============================] - 0s - loss: 0.0024     \n",
+      "7451/7451 [==============================] - 0s 37us/sample - loss: 0.0024\n",
       "Epoch 17/20\n",
-      "7451/7451 [==============================] - 0s - loss: 0.0024     \n",
+      "7451/7451 [==============================] - 0s 38us/sample - loss: 0.0024\n",
       "Epoch 18/20\n",
-      "7451/7451 [==============================] - 0s - loss: 0.0024     \n",
+      "7451/7451 [==============================] - 0s 37us/sample - loss: 0.0024\n",
       "Epoch 19/20\n",
-      "7451/7451 [==============================] - 0s - loss: 0.0024     \n",
+      "7451/7451 [==============================] - 0s 38us/sample - loss: 0.0024\n",
       "Epoch 20/20\n",
-      "7451/7451 [==============================] - 0s - loss: 0.0024     \n"
+      "7451/7451 [==============================] - 0s 38us/sample - loss: 0.0024\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "<keras.callbacks.History at 0x124527290>"
+       "<tensorflow.python.keras.callbacks.History at 0x2e7cbd87588>"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -519,24 +532,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "R-Squared: -13.593283\n"
+      "R-Squared: -1.148897\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10e09d0d0>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -566,51 +581,51 @@
      "output_type": "stream",
      "text": [
       "Epoch 1/20\n",
-      "7451/7451 [==============================] - 1s - loss: 0.0118     \n",
+      "7451/7451 [==============================] - 1s 103us/sample - loss: 0.0332\n",
       "Epoch 2/20\n",
-      "7451/7451 [==============================] - 0s - loss: 1.4968e-05     \n",
+      "7451/7451 [==============================] - 0s 50us/sample - loss: 1.2320e-05\n",
       "Epoch 3/20\n",
-      "7451/7451 [==============================] - 0s - loss: 9.7235e-06     \n",
+      "7451/7451 [==============================] - 0s 49us/sample - loss: 1.0278e-05\n",
       "Epoch 4/20\n",
-      "7451/7451 [==============================] - 1s - loss: 9.0429e-06     \n",
+      "7451/7451 [==============================] - 0s 48us/sample - loss: 9.9471e-06\n",
       "Epoch 5/20\n",
-      "7451/7451 [==============================] - 1s - loss: 1.0248e-05     \n",
+      "7451/7451 [==============================] - 0s 47us/sample - loss: 9.8869e-06\n",
       "Epoch 6/20\n",
-      "7451/7451 [==============================] - 1s - loss: 1.0652e-05     \n",
+      "7451/7451 [==============================] - 0s 48us/sample - loss: 9.6331e-06\n",
       "Epoch 7/20\n",
-      "7451/7451 [==============================] - 0s - loss: 1.0775e-05     \n",
+      "7451/7451 [==============================] - 0s 48us/sample - loss: 1.0694e-05\n",
       "Epoch 8/20\n",
-      "7451/7451 [==============================] - 0s - loss: 1.1454e-05     \n",
+      "7451/7451 [==============================] - 0s 48us/sample - loss: 1.1236e-05\n",
       "Epoch 9/20\n",
-      "7451/7451 [==============================] - 0s - loss: 1.1335e-05     \n",
+      "7451/7451 [==============================] - 0s 47us/sample - loss: 1.2764e-05\n",
       "Epoch 10/20\n",
-      "7451/7451 [==============================] - 1s - loss: 1.1369e-05     \n",
+      "7451/7451 [==============================] - 0s 49us/sample - loss: 1.2035e-05\n",
       "Epoch 11/20\n",
-      "7451/7451 [==============================] - 0s - loss: 1.0755e-05     \n",
+      "7451/7451 [==============================] - 0s 47us/sample - loss: 1.3691e-05\n",
       "Epoch 12/20\n",
-      "7451/7451 [==============================] - 1s - loss: 1.1995e-05     \n",
+      "7451/7451 [==============================] - 0s 47us/sample - loss: 1.3934e-05\n",
       "Epoch 13/20\n",
-      "7451/7451 [==============================] - 1s - loss: 1.4330e-05     \n",
+      "7451/7451 [==============================] - 0s 48us/sample - loss: 1.4127e-05\n",
       "Epoch 14/20\n",
-      "7451/7451 [==============================] - 1s - loss: 1.4510e-05     \n",
+      "7451/7451 [==============================] - 0s 49us/sample - loss: 2.8624e-05\n",
       "Epoch 15/20\n",
-      "7451/7451 [==============================] - 1s - loss: 1.6119e-05     \n",
+      "7451/7451 [==============================] - 0s 50us/sample - loss: 1.5024e-05\n",
       "Epoch 16/20\n",
-      "7451/7451 [==============================] - 1s - loss: 1.6437e-05     \n",
+      "7451/7451 [==============================] - 0s 47us/sample - loss: 2.1077e-05\n",
       "Epoch 17/20\n",
-      "7451/7451 [==============================] - 0s - loss: 2.2215e-05     \n",
+      "7451/7451 [==============================] - 0s 48us/sample - loss: 1.8178e-05\n",
       "Epoch 18/20\n",
-      "7451/7451 [==============================] - 0s - loss: 1.5089e-05     \n",
+      "7451/7451 [==============================] - 0s 48us/sample - loss: 2.2788e-05\n",
       "Epoch 19/20\n",
-      "7451/7451 [==============================] - 1s - loss: 1.7636e-05     \n",
+      "7451/7451 [==============================] - 0s 49us/sample - loss: 2.3774e-05\n",
       "Epoch 20/20\n",
-      "7451/7451 [==============================] - 0s - loss: 1.7340e-05     \n"
+      "7451/7451 [==============================] - 0s 47us/sample - loss: 3.1842e-05\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "<keras.callbacks.History at 0x124c3d4d0>"
+       "<tensorflow.python.keras.callbacks.History at 0x2e7cdfa9978>"
       ]
      },
      "execution_count": 20,
@@ -637,17 +652,19 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "R-Squared: 0.998666\n"
+      "R-Squared: 0.996083\n"
      ]
     },
     {
      "data": {
-      "image/png": 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aQtPCNEwi2rEWgHGHn576ewvRDR2m/BUi1Zru2os8owmX/VCzwAiw9pGz2Nu7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\n3rBmHkM2vQGAZ+EsilUz2Jw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hmiHbXyfHeJ0RIKXVHhZuKmnFFrWsbSXVMctX7Syn2u0L5tpx4sat2kA6hniTlnUzf5lNsd9PN0paJR+NBHchmkH76/Sz10n7O+yDhVz26uyUzBx498c/kT98HMc8Pimu/ChbS6o4c9R0cqjmWLUqUKq5yjKVmT8v47q357FxRxEFjmFca/0Bi72exaYzUfs+kfunPxravmcx3DQhuHuLdTz9urb8bGYJ7kI0g/ZFBm2/NzJILttu/lle6Uq9kTTfLjFzqu+pdHPck5MbrT850J++3Hkznzn+TmdKGWH9iGdsbzLK9h8WbirhgOKZwWcQuSr2XX7GGbET7qzzDMGWA7f8ABe9Ah3zzZm4t04F4DbrOC4+KsZKUwkmwV2I5tCRwf2Z8Usj9h1W80esvCb1gns4l7fxZwWqzkIWC513cKt1PABuzD7k8AWvlS+133PC2LLMTJbDZpkBfb/BcPA50PsYOPq6UL1eg811YA0rqnJ3izer0eCulHpHKVWolIo5t1opdZFSaolSapFSqkApNTTxzRQiNRl1HqB+tTBy2r3DasGCL+WDezwMBdSTs74YM0eMLXw4qLeN3LnX6nG4GdBvmwod+8auk9XR7Lr7/MbYxxMonjv30cDZDRyfAhyptT4KuAl4KwHtEiIt2FRk0LbVmch0tp7JOufvce9eS11aaz6YszFpXTZDjaU8YP2Eh6wfESto+/2aOev2BB+Svjd7I/Mdd8W81uWW6YyyvcKlA8KGPnbs1xLNTm8VZtcWJRtb/KUaDe5a6+lAcQPHK3RoOZoc6vvVLkQG6mCP/Ha3qMg7+dP1bAD2ri2IOnfm2t08MnY5j3+7ouUa2IAP7U9xl/VrbreOY5r9vqjjYxZs5po35/L9sp0A+Hf/Qje1t97rXWKZxZANr5s7pz4EN45vkXantf5nmV/DHrC2lIT0uSulLlFKrQLGYd6911fvtkDXTUFRUVEiXlqIpOpYJ7gPs3wTWcFqjvX+9qcNEUvyAXgCY8J3lkXnIWlpddPP5hu7ourUDnt8f84mKlzeYA6dCBe8AAefG11+6nDI7ZaQtmaU331mjvNvnyYPVLXWX2mtDwEuBh5voN4bWushWushXbt2TcRLC5FUVh25CMO11h8o2Bj6Q9cTGOvtUB6q6qzWY7eYY6R37G394F472aghb85YD8Cc9Xv405ifyabO2OzL34FjboCrP44sv2J0YhopmiWho2UCXTgHKqW6JPK6QqSqAd7V5kavIcGyZ75fFdz2BoK7HU/UajxKwRFqHVt3Fbb6Mm2xRsd4vJFt8Ph0ID+KZvn2Us7LXR06OLI0tLC1UnD79NCxAee0QItFUzU7uCulDlKBMVJKqcGAHdjT3OsKkQ5KVWAloRu+DZYt2BiakVqF2S3TnkqWbiuNONfjquZrxyO8bnueuz/+qeUbG8bljf5lUlNTFbHfT+2gwHkHN1gmsLvCze2eD+u/YM8jzYA/shRsMdIOiFYXz1DIMcAc4GCl1Fal1M1KqWFKqWGBKpcBy5RSi4BXgKt03c5FITLUHtWJImvPYG7vWsWVbvx+TWGlGUT/YvucRz6aGuzrXrq1NBhMjzdWMWVVIYWt2PdeFmNGqquqkq0lVfzurbls3lNFD2V2L91iHU+WvzJU8coPWquZohkaXWZPa31NI8efAZ5JWIuESCNK+/CryPwipxk/U+k6DafNwOdxBX/K+qpdTFq5i1lrd/P+nE0c3t7F2YBNmb8Anp+0hqcvO6LF21xc6eaT+Vu4WXdCHXQGO/IO5+hFj1JRWcYrBbuYtXYPT4xbwdHKHL7ZW+3mC/vI0AUOlm6XdCAzVIVoBgM/Wpk/Rmv95rqa79qfxeX14/Fq7ITukB3Kw+0fLOT9OWZe75LSsuCx7hTzyYItrdL3fudHC3lr5gYcuLE5ssjONtf1XLR+B7bAjNqJK3bxoO2/wXMONrYy3TcIf69jwdLyGQ1F80lwF6IZjLA7972EFj9es70Et8+PIyy4H6tWR5zrUKFjPzj+AsChj36fsLb5/ZoFGyOnqFS4vMxdb5Y58eDMysaeZSaxemPyMj6eF72wda2DOlkxpD89bUhwF6IZlPahMYN7+F36m//9grIaDwcbW4Jl99k+x4E5Yqaf2kF7Qv3YOcrFMXWCf3ONWbCZK16bw3MTVzN1VSEAj45dFmxrtnJhz+1Ir27mwtVO3GRTw6u2URyotrFLd4i43n6lP8OW+Qlto2g5jfa5CyHqF94tEz4O3KncnDVqOnNthfiVBSOQYOxWyzhW6T68ZX8Ot47sq//Q/hSHukajtY5K0rUvNhebD2xf+sHsO9/49Hl8+dM27Hi4wWL+hWDL6wrZZhBvpyo5Xf3EOZYFdFcldI81G9XX8nnIRWLInbsQzaC0Hx3olslvHwrITtz4/BobPhZ0uTRYfr/tM96yPweAXUX2r2cpN90pxuNLzGCzHHvse7e7rV/xV9sYcye7S3Am6QWWudgDuXEGG4FcOL/+S+TJD6xLSNtEy5PgLsQ+8vs1fp8XIzDT1OoLDWX8lbEcMBOJDe7XDc95L9R/odrJQMA8591xzR6t69MFW/jDO5FdJtl2C0ONpVgDAXvQyAnY8HKrbWJYpc6QYwb3yywzeM7+WuSFux8emoHa50TIkfmJ6UKCuxD7aNveapT2Y9QuAu0OTQK61Tqe3qoIG15sdge2Q2PkX6l16AURu29P/4X1RRVNasuDXyzhxzVFETljdqyYxYf2p3jT9hzXWSZRXuPlBsv3ZOmwyUo5XcCeXf+F/T7oexK06wW/ebhJbRLJJcFdiH1018c/YVF+PP5Ad0yvYyKOz3TcY3a97FwKzg4xrhBw4G9gYKjrZsyUBVzx2pz66zdgd0WoT3zlJnMx5tMsi3nC9i4KPyNsdfLAdDrA/HrSPbEv2G4/yOoA962AfFmqIZ1IcBdiH5RWeViytRQbXrq2D6yHec3HcP3Y6Mp71oI1bLHoI66KXGPT2R6ueJeCQSMByFE17Ankoflk/mb+/k3DKYHDJ4T/LZA++L3ZG9FEPpTNJWwG7OXvwoMbQmPWrXWGONampu1zQoOvLVKXBHch9sEnC8zx4J0pw8gLpLZ1tofex0VXvvbTyP3f/iO03X7/4GaHLj0AIsbGD/9yKe/M2tBgW9w+P5dbfmSj81rs/mq01jz29XKy6mRxXOq8JbRz+KWQ3Sm0f+TVoe0rRsNVH8K9S8GIHNEj0ocMhRRiHyzeuhcHbvKNXfj8YUG07h0whLo+atmy4PhhUFMKv74/WJyTbfZ9/9n6BbPzfgucF1dbXF4/t1vMxGWqeCPLtx8OwKnG4tgnONo13MYB54DVAR36xPX6IjVJcBciTq9OW0ely8vZh/dg/NKdPG41syRaqsNmgRoGHHYRrAjrnrHU+TGzOs074jP/HlHcraMZdM+0LOTMqoXA/wWONDw00uXx09/YBoB35wo+mmfmp7neOim68inDzRzssfxpEayfJlkdM4QEdyHiVJun/eWpa8mjit9bJ5sHTh1e/0l3Ry+vV19Xh6XOqBWtNXdaxnKFZRrbSk6jV8ecmOeFD5180f4y+fN/xTDL17Hb8+u/RPb/h+vUz/wnMoL0uQuxD3qrsGUine0jDx50pvn1xu+hS/9Q+dD7zLHi9ek6IGK32uPjQdt/6WfsYtOO3fWe5vKEJkOt8fcCNMNtn0RXfGxv/YFdZBy5cxdiH9zXZQGUB3bseZEHj74ODjkv8oElwBmPNXzRsOGSNdj4eN5mah+BFpWUAH0BWFtYwdeLtnHPGeYvg58376W2x3yAsY0O1Bkjf98qcLYzV0wSbYYEdyHi9Kn9bxxnrOZ5z+WcWf5l6IAjN7KiUtGBPR5KwZUfsOir59nfvZYnxq3klkD397+/LeCik44CYNSkNYxbuoOJK3ZRVu1he2kNl4V1ky9w3hXaufBlaNez6W0RaU+6ZYSI03GGmbXxPtvnkQdyErjY+2EXssTXN3JMOjBIrWdnqVm2epf5J8OqneVsL60BND6t2NLbnOlqC6QbAMy/IkSbJMFdiDiET+sHmJIdWI3or9sTPhb8hEPzcShPMD0wQBdVxpWvm7NWB/eJnO1qxYdFaTr0GRh5od//T7pi2jAJ7kLEoW4yr81lfipULthjj2BpDmeuGbxHWD8Klp1iLMZqmIG6R8Uqljlv5mC1GScuJgz4BoC83Fw4f1ToQl5Jz9uWSZ+7EHGYsrIwYkqRFR/eFro36lP+MxA5Tv0UyxKGHtQZgP57Z5BLNRMcgSGYtYsnGVYYchN07g/vnQ+9j22R9on0IHfuQsShsDyyD9yOF49uoR+fQVdE7gdSGgzd/i4AVf567sn2BHKt9/s1jCyFnM4t0z6RFiS4CxGHv9VJ3pVv7MSnWyjvSq8hoe1hM6GvOTb+rMK3AXDVt5jHb0a0THtEWpLgLkQcwtdHBTjeWEVWnREtCVM7+ib/19BjEJz6EACb/N3414TVlJWW4NNhD0pPuse8U8/q2DLtEWlJ+tyFiMOhnYCqyLL2qjJm3WazWM1gXcuWxbbcw9lSpnh56loes9ZQqbJpV7vA9lEy3FFEazS4K6XeAc4HCrXWh8c4/jtCGY4qgDu01vWkoxMiPf258/yo4N6a2rkLGWoUApBLNdm57eHa8aCMqLQFQkB83TKjgbMbOL4BOEVrfQTwOPBGAtolRErJ37tvKyMlSpXD7Ko5UG3jCut0rN4q2O8o6HlEUtslUlejwV1rPR0obuD4bK11SWB3LtA7QW0TImWs39v0RasTaUNPc9LUFMcDZkHN3iS2RqSDRD9QvRn4LsHXFCLpJvoDI1jumA03tv63uL9ucrI6i2oLUVfCHqgqpU7DDO71rqKrlLoNuA2gTx9Z5UWkjxy7Ya6ZkdUJug9stH6ilXrq/KhePrrV2yDSS0Lu3JVSRwBvARdprffUV09r/YbWeojWekjXrglMtiRECzvGvtXcqF1QukMfOPHuVnv9cm/YmPqjfx+9upMQdTT7O0Qp1Qf4Evi91npN85skRGqZuHwn57rGmztG4Efm3qWt2oYenTtAYAIqZz/Vqq8t0lM8QyHHAKcCXZRSW4HHABuA1vo14FGgM/AfZWag82qth8S+mhDp5x/jV3K8zqa9qgJHXuMntIBfH9oL5gd2ktQGkV4aDe5a62saOX4LBBeMESLj1Hj8/Og/kmNsm+iV4PS+8ZLEvaKpJP2AEI0oLyvhQsscPF5f45Vbit/beB0hwkhwF6IRf7R+BUC+sSt5jeh3avJeW6QleeQuRCMGdfSGFsNOFsOABzeY6QaEiIMEdyEaUbsCUtLty6Lbos2S2wAhGuHQLZTaV4gWJMFdiEZsdfRPdhOEaDIJ7iJl7a5wsXJHWbKbgV/Xs/KREClMgrtIWZf+ZzbnvDAj2c0IDUPM7ZHcdgjRBBLcRcraXGyujrG2MLlDVWpcLnPj7gVJbYcQTSHBXaSsI3s4OUhtZc76epcTaBUutxsfFnC2S2o7hGgKCe4iZd3ve4vJjgfxlBUmrQ0llW4qq2vwy/hykWbkO1akrL4uM8lo792zktaGD+duoqsqpcgvybpEepHgLlLWSufRAHSuWJ20NhiGoit7IW+/pLVBiH0hM1RFynIpOwA2X1XS2lBcVsHJlqVo3S1pbRBiX8idu0g5YxdtY/GWvfi9HrPA7+WtGeu54rXZrd4W2x7zrwZVmbx+fyH2hdy5i5Ti9vq555NFADybZ077V9rPE+NWAuD3a4xWzPXiqNltbvT5Vau9phCJIHfuIqXc9kFBcNvjcQNQXB7qlllXVAGYY9/v+eRnPD5/i7anU/VGc+PK91v0dYRINAnuIqVMW13EJcYMLjWmYw/0tZdV1XCdZRIbnddy3qgpTF1dyNVvzGXsou0s21Za77V2lTUv4VeNx0dlyU7c2gK5sqC7SC8S3EVKceBmlP1Vnre/Zo5SASz4GWb9BoAeqpgb313A7grzrt5fT9qX5yeu5vh/TGHGL0X73JbVO8vJpoZKsvb5GkIkiwR3kVKetL0T3N5fmQ8xz7XMp7cy+77PNeYBcKDaxkbntfiLfol5nRd/WAvA8u37nnhsS0kVB6gd+3y+EMkkwV2klKPU2uD2AcbOqOOLKp0uAAAgAElEQVTDbZ9wp2UsUxwPADD9q9frvdZQYyl5ln1fe7S8xsvJlqV0VBX7fA0hkkWCu0gpnjgGcD1o+29wu75xM3flTOFD+1OcuvZpACpcXhZualqOGr13a5PqC5FKJLiLlOJq4ujcDjHuqpds3csDvrcB6LZ1Ai6vj7s++onLXp1DWY0n/otXRP/lIES6kOAuUorN7mxS/Zus30eVXfjyLBb5DwBge88zOPjh7/lxjflgtbwmsptm5Y6yegO+rzowEucP3zapTUKkAgnuIqVUaUfE/nJ/3zhOiu5uKdM5AEwr7gxAR8oYYf2QOWtCD0h3V7g454UZPPTlUgBemPwL01abD3G11qzcEOiWkYWpRRpqNLgrpd5RShUqpZbVc/wQpdQcpZRLKXV/4pso2pIKHXnnXm8f/KVvUZh7CABeb+Sd92C1hpMtZsAu3FvB4Wo9r9n/za3W8fz4v7eD9XaWmuPgxy0xA/6oyWu44V1zQY4FG0vwVAdG2jgkj7tIP/HcuY8Gzm7geDHwJ+BfiWiQaOO0n53OA4K7DsIC928eMb9e8joccQUrel0BgMftClYpLK9hjP2J4L5defjW8TDHG6vMfULdMi6vj+sskzjTKMDl9QEaA3PGa1G5iw4E+vOd7RP5DoVoFY0+vdJaT1dK5TdwvBAoVEqdl8B2iTbKpl34DAcccwP0Pwv7x38BQF/8GuqIq+Dk0B+HymJmjfS6QzNRz31hBgUqFMBvtEyIuP5z9teo8fwDp83C5GVbecL2LgD9HzuWSfYH8WEwetbhOCwwwvaxeZKswCTSUKv2uSulblNKFSilCoqK9n3moMhMbq8fq9+Fz+KEC16AQ84L3mmrboeCEfntalhtAHg9oTv32pmrtdqp6HTBFS4vlS4vH89YGSzz+DT9jW0cYmxh5Dcr+H7GnIS9LyGSoVWDu9b6Da31EK31kK5dJVeHiLRoy16cuHFmZQfLHCrQLWPPjT7BavbPT10eGo8+SK1v9HVcheu555NFEV0+ilACskPUZsqKAw9WL3mjSe9BiFQho2VEyqh0e3Hgwe4MBffc2hmm9uyo+obV7JYZPcNMQaC15p82c8ZqTfsD632d8tLdzFxbxJv254JlG5zXBbe/dwynp9oDgOpc/3WESGUS3EXKcFdXsr8qRGd3DpZlGz5zw54TVd9vmN0y3VUJAOOX7sSFGfCdnfev93VGfjaXHt5tHGnUf5f/H/uL5kZWxya9ByFSRTxDIccAc4CDlVJblVI3K6WGKaWGBY73UEptBe4DHg7UkSdQosleGjuTXFWDp9uRocLaVLu26ODuDYwHeNP+PAD/LdgSOujsUO/rOPBwt3VsfI2S4C7SVDyjZa5p5PhOoHfCWiTarJqaKnAQGVBvGAeb54Il+lv16AO6wVxze0+Fi3lrtnGUc51ZcO6zcN7z8OwB0Lk/XPtfViyczmGz72WkdTTT/UdEN8CeB+7yyDIZBinSlCyzJxLK6/Pz3KQ13DK0H51zHY2fEKav2gVAtw5hD0879DH/xdAhN3Q37/HpYIpgAHIDC1o/XATKAIuV/Q6ugtnQz9hFP2NSqG5WR8jrCXfOgZF1grlhadJ7ECJVSJ+7SKiFm0p4ddo6Hvx8SZPOK6/x8HbtA86tC+I7KadbxOtOdjwYXcdqD971d8iLMeIG4OoxZmAH8y+FAN2+/n57IVKdBHeRUBZD0YM9lJRXsrUkeox5fR75X1h2iyE3xXdS+17M8g1khb8vFa44sj1mRfbD+6w5cMds6HtiqDB/KJz6VwBU54Pia4cQKUiCu0gov9fNXOcfuaHwaYY+M5Xte6vjOm/97srQToc4koUF9Db2cJixifZ7FoUK67vjzurI3v6XBXcLj/0LdB8YXe/o30G/U+D8UXG3Q4hUI8FdJJTXZd6tX2gxuzniXaQ6vyrszl3VtwRHtL7KzLneY9vEUOGwGfXWzz738dB2Tj0PS9v3hj98DZ36xd0OIVKNBHeRUD536E59mv3P+Eq2NFA75LCaRY1XasAP683X3XLsiAaHL9qz8oLb7drXP1xSiHQnwV0klMcVCu75xi6qCj6K6zyfJ747/LpWX2aOeqnETEVgiTGTNULYZCglwxxFBpPgLhIq/M4dzD74eHTP3bdRud07mwtpPGL7EICOHRoJ2OFDG7sevE+vKUQ6kHHuIqH8de7A86oa75bZWVrD3vIK87vR2rRl9hzOrIj9LOWtp2aYO+bAirH1P3gVIgPInbtIiM17qvj92/Mor6yMKM92NZ7aefiXS/DXfiuecGeTXtfRvgfV2h4qaLdf4yd1PwxOe6hJD26FSDcS3EVCPDdpNTN+2c38X3ZElHt9utFzlfZzmzUweah2taU4GRYL1YQF9wG/bdL5QmQqCe4iIfLs8EfLlxjVkYtV5/jLGj33An4M7RhN/5asXdBjb58zm3yuEJlK+txFQhxTOYNLbJ+z1dMFAr0dJZbO5PnLGz4RWLdle7NeO1eZ/fw1fU9t1nWEyCQZc+c+dXUho2dtSHYz2qxsh5kkrLfabRYMvp4luUNxaFcDZ5k8gZTt3DG7WW2wOvMaryREG5Exwf3Gdxcw8psVlFbHkWNEJJzHEjlqhaH34VU2lG589MoJllXmRpd9G5r4o89M32vPluAuRK2MCe75agfXWqYw8uvlyW5Km+T31QniVgdrdruw46W8puFfuD38ZqrfWDnb42G3mA9tc3MkuAtRK2P63L+xP0yequbsNScARyW7OW2O9taZYWpx4MGCQ3nYWu4iz2mLed6W4irKtYNfso+k/z6+9lG98mAbGPv4y0GITJQxd+55ypwZ+bpnRJJb0jZpb52+dWd7rssz88VUbiio97xf/3MqOdSwqWLfvxWzaicyyZJ4QgRlTHCv1dcoxO9vfGy1SLC6wd1ipVP1RgBcG+dHVS+udHPLewU4cHO4sZGjHDui6sTtopfhjL9BzyMbrytEG5FxwR3g51Vrk92EtscXPSrGc+XHAJR7o2eC/uGd+UxeuYvHrO8B0MW7c99fu91+MPRemXEqRJiMCe6zjcHB7Xs/mMGr09YlsTVtj88dHdytA87Apa3kVm6OOrZ+RxEv2l7iWuvU1mieEG1OxgR3S9iQu5ss3/PRbAnurSlWyl5ldVBCO+yukqhjIx1jggt6AHDtpy3ZPCHanIwZXmD4vcGZkTdaJ3CgtwQ4K6ltaku0qyK0075PcNOl7Bi+6MC/H4WRBf3l/0qIRMqI4K61xqI9weAOcLI/+iGeaDmqppRKS3tyLngaDjk/WO5RdowY/fElOje4XTH4dnKlv1yIhGq0W0Yp9Y5SqlAptaye40op9aJSaq1SaolSanCsei3J49M4cVPkzG/tlxaA1+cn11tMjb0jHHUtONsFj3mUI2Zwt1lD9xW+fqe1SjuFaEvi6XMfDZzdwPFzgP6Bf7cBrza/WU1T7fbhxEVx3oDI3N6iVVS6fPRQxdRkdY865lF2LP7o4G6E3annGpIyQohEazS4a62nA8UNVLkIeF+b5gIdlFI9E9XAeFR7fGQrF8qWQ5YKLev2wGeLyR8+jhXbG087K/ZductDN7UXT4zg7jUcWMP63HeV1ZA/fBxVNaEyiz++pfiEEPFLxGiZXkD4WmpbA2WtptrjIwsXhiNyceTPFm7FjoeP521szea0ORUuL+2oRMdYcHp3jcJVU43W5sSyrSVV9GQP7aikUHeAI6+FQ85r7SYLkfESEdxjPQmLOUVUKXWbUqpAKVVQVNT48mvx2lJcZQb3sJXtAV6zjWKN8w8M3fVhwl5LRJuztohcajCyooO7Cxu9VSFl1eZQVeXzMMf5R06zLMZmAJe8CrasqPOEEM2TiOC+FQhfabg3EHP1Ba31G1rrIVrrIV27dk3AS5tuemc2duVD2SPv3M+2LADg2OJvE/ZaItqocT9hKI09Jzq4X2CZSztVzbiZ5ugljyu0xmpHvbfV2ihEW5OI4P41cH1g1MwJQKnWuhmJQpruWGM1ABZHDowsZU6XyyIraH9rNqfNWLiphNP+NY1rO/8CQI8OOVF1yg+9GoCupUsA8NZUB4/5Oh7YCq0Uom2KZyjkGGAOcLBSaqtS6mal1DCl1LBAlfHAemAt8CbQtOXrE2CM/UkALE4zuJxA5KhNj7uGrSVVrd2sjPdZwRa8e9YzpHQiACo3+oGq7fhbADh0+5cA1NSE7twtpz/cCq0Uom2KZ7TMNVrrnlprm9a6t9b6ba31a1rr1wLHtdb6Lq31gVrrQVrr+vO7tjCrwwzu6op3I8rzqGLoM5LDJBEqXV6ueWMu64oqyK9aygzHnxlobDIPHnxuVH3b/scAMLWoHeU1HmqqQsGdroe0RpOFaJMyJrcMgDUrMOux+0D27n9GsDxHubjJ8l2SWpVZZq/bw+L12zjjuank1Zi9bz1VYKSsJXqOgcViUKNtDDQ2MmjkRKYtCcv50/2w1miyEG1S2gf38NztVmsouHQ4+NcR9R61fUBNeUPD9UU8Otg1K5w38Q/r2/y0ITTiyY8CwxLzHKfyMNhYy+8sk3EVrTfr3zihVdorRFuV9sHd7Qs9LM1VYTMhjx8G13/NZN/RwSJPZXR2wrq+W7qDnaXRia6EyanMIY3XWKfyuC3U/eXR1kbzqT9pe4cX7a8AxBw2KYRInLQN7qMmrSF/+DgKy0IB3eIMWyDZ5oQDTuE31iXBIl3T8ExVr8/PHR/9xGWvzk54ezOF3x9KrZwd9svUoepPIbCt+6nRhY520WVCiIRJy+C+bW81L0xZw7FqFXPX7aJMZ+M1nDDgt1F11fmjgts6LDABvDJ1LZNW7Arur9pZztu2Z7mi4oOWa3ya8/l8TT5nU79rogtlvVMhWlRaBvfTn5vGe7Zn+Mzxd5aNNYP3+j6XxewWUIOuCG77wwJTYVkNL09YzD3vzwRg2upCzn9pJqdbfuZe65ct/A7Sl/Y1PcmX35EXXVhnwpkQIrHSMrgf5VvGKRazu+Vayw+0U1V4jXqyQdqz+eEYs59X+0PB/V8TV7PEcSs/O24D4P5PFvJn62fB458v3NpCrU9vfp+38Up1aIfZv+6r/XZzSH+7EC0t7YK7XjeNT+xPBPcPMcycZXn+8nrPUYFRHP5AcH9s7DIWL5yNTflwKC97Klz8MXsC91i/Cp7z1GfTW6L5aa/2r5/CQ66DEbsoU4FAPWxmvef06GammvBZsmBkKTwUvaaqECKx0i64V9UznLG3vaHgbr7N2jv3T+b8Qj+1M3h84s+/0FvtjjhnofOO5jY1I/kD3TLV3Y4Gm5PcdoHgnt253nP6728mCbWppvfXCyH2Tdots7e9QtM/Rrk66c/1n6TMO3cduOtc7bwh4vDs78ZwRs/uUFH3RFFX7Z27YZjfOsYV78LyryCvgRT+9hwYeClqyI2t0UQhBGl4517tDlvY4YirYeAlcMds6Htiveeo2uDu90VMeqr1kv1llu6sjir/Yu6aYB5yYapxm8Mf7XabWdB7CPz2yYbHuCsFV7wL/U5uhRYKISANg7unKqz75dxn4YrR0H1gg+coS6jPfVd57AlKl1sCfex/CKUHHjV2Ft8uadUElymvqspMwOZ0Sg52IVJZ2gX3w/fLDe3Y4htOV/tAVfv9rNwReyJT7YNZ8odSdPbrAMx03It127x9b2wG8lWXApCV1ynJLRFCNCTtgrvjiMtDO5b4HhmEHqh6KS2OXAFKdxlQp7Ki64DjgruHbRi9T+3MVDoQ3G0xFuYQQqSOtAvuWO3mcLqRpXGfYgT63L1eH1WFGyKOqbsXsEObd6EVHQIpaDv2Cx63VEuysXDTl28EQEn6ACFSWvoF931gWM07/Ce+XcYP83+OOu6vXQb20AvMr0oFH/45Xbuj6jeXx+fntR/X4fKm19DAwvIacnRg0RN79KpLQojU0SaCe34Xc/r7XdaxHK42moWnDIdhswDopfYAkN17UOika83ZqtssvRLenk8WbOHp71bx5vT1Cb92S6qo8fJEbSZICe5CpLQ2Edy79TAD9FHGOv5s+8IsPOVB6HF4RD2jS9gIepuThc4TaOfdk/D2+Cr2sNF5Lf02fxFR/sXCrRGJzFKNyxP2l0acD7OFEMnRJoK70X6/GIUxFpbo1C9it8rSjlx/OVNXFZI/fBxz1ycm0Ld3m7Nj+63/GE9YPvqHPivgT+/XP40/2dzusGGk9SzMIYRIDW0iuDfqhLvMyVC2yLHbPosTm3bz0Oc/c6rxM9e/MSMhL1c7NHM/tZtjHp+E1poXp/zCV/ZHWem8iS3FZr/2Fa/N5qrX5yTkNRPBUyOLjAuRLtpkcK866ubIgrP/YU6GqsNv2LBqD5fUfMlo+7Oscf4heOz5wGIh4SpdXjbvaTwAerxmZsUOqpLzPBMoq/by2qQlwYWmJy/ewIrtZRRvWkbWph/wxZhVmwwel3nnvunEx5PcEiFEY9pkcM/uflB8FS12bHg4SIXS/74zYz1en5/3pvzM+cYcRs8KDa28/p35nPzs1EYvW1S8N7j9lO1t1u+uYIXzpmDZwXPu57wXf2SK4wFG2//JP8avjK+9LczrNlM0WGwyO1WIVNcmgzuDLm+8DmC1O3Hg4YDsUD6bF8fN56AR3/GS7SVetr/EjHEfUFplZkos37yEF2wv4/c2vKBFbuXGiP15GyLH0hdX+3jO9lpwf0NBaiwmHQzudmeSWyKEaEzbCe6XmCkF6HQA5HaL6xSr3bxD3V0VWqBikfN2/ml9nRONFQC8bX+Om95bAMCLtpe5yDIb985VDV63U03kQiAzJ3wWsX++ZR6XWkIPVt9hZFztbWk+V21wlzt3IVJd2wnuR14NI3bBXQviPmXeHjOInWn5KaL8SuuPEbnJz9j2HwBsmL8E/vjJogaHNBp+d8T+ZZb0WBjEE7hztzkkuAuR6uIK7kqps5VSq5VSa5VSw2Mc76uUmqKUWqKUmqaU6p34piaAzRl3PhqALHt8v/vusH7DS1N+wYI5rLFjSWht1lgMX2Rwv8RiTqbizMfZZj8g5jmpMJvVVWMG96wsGeMuRKprNHopMxn6K8A5wGHANUqpw+pU+xfwvtb6CODvwFOJbmgynJQfWtjZ4+gYXeG854Kbz01aE0xj8E/bmxEPSOuqe+ceesE/sbPjMUBgvdF7FgcPzV1bFPucVvLkuBV8M9/sbnJI0jAhUl48t6bHAWu11uu11m7gE+CiOnUOA6YEtqfGOJ6WBuWHVhfafejvoyscewtrjX7MV4PY6LyWA4yd0XViCAb300ZEHdulzV8i2/ydoWM+NUdcD0D1mmlNa3yCfT1jIUONZQAopwR3IVJdPMG9F7AlbH9roCzcYuCywPYlQJ5Sqv5FNdPFwEuDm90dse+2Kywd6OyLnVxM+2KPmrH4Peyy9TZTINRRaDd7tPoY5p267bynAeiwK/Zkpl92lUetLlXl9pI/fBxjF22LeU5T1Xh8zHPezY3WwKiddjFm/AohUko8wT3W+ml1Z9XcD5yilPoZOAXYBnjrnqSUuk0pVaCUKigqSm43Q1zC+ueN/mfB/seHjnXoA4Dbmks3tbfumQB8My/2+HSrduE1HJGFF74MwKVHdo9sgiOHQjphqY5OfbB4y17OHDWdx75eHlG+emc5ZxoFvPV142Pu41GwsSS47cMSNZNXCJF64nm6uBXYP2y/N7A9vILWejtwKYBSKhe4TGsdlXBda/0G8AbAkCFDUmPaZWPuXgi/TICDTof9j4Mt88DvhwNOBcBlySFPha2/2vMoCis9dCtbzjfjv+WCEwehwtYXLa32YPjc+GrXIK014GwA2vUOPM44OXRXX2FpB1XRwf3qN2bxgu1lflwyFC4OJUG75D+z2eh8Hr9P8crUE7jrtDgnbdXjurfnsjEwtN1la4c8ThUi9cVz574A6K+U6qeUsgNXA1+HV1BKdVFK1V7rIeCdxDYzibocBCfeZW478uCgM2DAWeaiIYDLErbs35l/h9t/pPO5jwDwpvUZ+j00PmKR7SP/NhHlc6Pr3rk7A4tf9DwC7lsFp/01eEhndcZeXRS1WPdxvsVcZJnN8/5/smiL+deDy+tjuPVjAAyl+XbiRGo8zRtpc5stNIkqS9ZOFSItNBrctdZe4G5gArAS+FRrvVwp9Xel1IWBaqcCq5VSa4DuwJMt1N6UU53bJ7Qz2Mw9Y+l7YrDodGMhnxaYjyz8fs3Fxkx+ZVmBYQsE90vegP0GgzUs2LfraS4YElDT6VAOZT17yiqDZVprRjo+Du5f/MosPpi7ifNfnMkwa2iR7+8cD3HII99H9cuH27yniqJyV8xjG3dXcoYKrSOrzn223usIIVJHXAO5tdbjtdYDtNYHaq2fDJQ9qrX+OrD9uda6f6DOLVrr2JEiA+mcrqGdrA6RXzFnsOas+w4At8/Pv+3mhCedHVhg+sir4LaG+8Zruh2BXfmo2hGa+fr14u1M9xwS3P/G/lc+GPsdZYWbo85/z/Y0G4rKItILB9uvNSc/O5Vjn5wc87VXbi9liFpj7tyzGA49v8G2CiFSQ9uZodpCzjjKnHTk63Fk5IHBoQySPXebk5TcYcHVyOsR92tY25uDk6qLQ486Zq/axh+sk4L7g4yNTHT8H/Ocd0edf4plCX9/4RX6j/gOb50Af+PoBdxm+YbHrO+htQ7+q1VduA5DBfY75sfdZiFEcklwb6bs/QYCYDn6d5EHLnwRHi1hvb8HO3aZ49/d3lBgtXeMf/m+rPZmLpyyksJg2SmOhvPXcNF/InaftL3NNPuf2bi7PFimtWbbmp/5q20MN1onMGnJJvo9NJ5zXgjlra8pNvPg+E//W9ztFUIknwT35uqwPwzfDMfdFn3MMLArL+dbzD7r8G6Rjp26xP0SfXqbvwhKd4fy1Xh0IyshDbwYbvsxuNtb7Sbf2MWqgmnBspIqD5McoVE5//lkLOcbc/h10RhcHnMkq690h/lWDj477vYKIZJPgnsiONtHPAAN11uZE5xen7aGcUt2BMttHeJPv+Ns3w03VvLKVgfLPNUVAGzte3Hsk+w5sN9RUcULZk8JLv5x/2eLI469bX+Wl+0vMcL2Mf+bXoDfrzEqAm1uQjeSECL5JLi3kq8nTOTFcQWhgv5nxn+yxcYmelG9J5Qq2F9lTizqNeCY6Pr3hU2euvKDiEN/s73H1t3mFITVqyInP3VWoS6bTybPZeQ3y7FXF+JWDvMXmBAibcSfIlHsk3VX/sCBn/6GcY5QHpnSAZfTvp47/frs9OXRTpUGl/a702mmFlBHXAXuShhyEzw3wKycFZbk7LAL4bK3zTvv0ecB8OdR7/KTHsBKxwMA+OztsLjLIl7vK8dj3D5vLyfYiyi3d6FzE9srhEguuXNvYfb20d0Zlv5nNPk6BxnbGWys5WbLeM40CsjzluDTCnK6wmkPQV53GHSlWblueoBBl0P+UFy/+gtgpih+3/YUWcrMl2M56Ddw00TY/3hqbp0VPO11+yg6+/fgyopvcRMhROqQO/cW1rt79IPT7E49Y9RsWE9lLsX3iO3DYFmVdpBthP1+vvhVOH9UvddwnP5X3LP+zZmWhZEHLn4V7Nlw80ScdWbBHm+sYnPOOU1urxAiueTOvYUpqyOqzHDkxqjZsJoDzooqy1Z15opZrNDQtS1WysiJLreHZYuJ0f3iljt3IdKOBPdW4O/QN7RjdUK3Q5t8DeflryekLdqwRxac+6/oStd9AQeeHtztlpUeOd6EECES3FuBcd0XkP9rGL4FHt5lDlNsquxOMPTPQCifTXGXIU2+TFcdlnv+xu/huFujKx10Bvz+y+BuO4d8mwiRbqTPvTV06Q83fNt4vcac/hh0P5ysdvvBu+fQKXcfMjQ+uAG+fwgufCmY2bJe9yyB7x6Es5/et/YKIZJG1U0j21qGDBmiCwoKGq8oovn9MO0pGHy9OUNWCNFmKKUWaq0b/bNd7tzTkWHAb6LXXxVCiFrSmSqEEBlIgrsQQmQgCe5CCJGBJLgLIUQGkuAuhBAZSIK7EEJkIAnuQgiRgSS4CyFEBkraDFWlVBGwaR9P7wLsbrRW2yGfRyT5PCLJ5xEp3T+Pvlrrro1VSlpwbw6lVEE802/bCvk8IsnnEUk+j0ht5fOQbhkhhMhAEtyFECIDpWtwfyPZDUgx8nlEks8jknwekdrE55GWfe5CCCEalq537kIIIRqQdsFdKXW2Umq1UmqtUmp4stvTWpRSG5VSS5VSi5RSBYGyTkqpSUqpXwJfOwbKlVLqxcBntEQpNTi5rW8+pdQ7SqlCpdSysLImv3+l1B8C9X9RSv0hGe+luer5LEYqpbYFvj8WKaXODTv2UOCzWK2U+m1YeUb8LCml9ldKTVVKrVRKLVdK3RMob5PfH0Fa67T5B1iAdcABgB1YDByW7Ha10nvfCHSpU/ZPYHhgezjwTGD7XOA7QAEnAPOS3f4EvP+TgcHAsn19/0AnYH3ga8fAdsdkv7cEfRYjgftj1D0s8HPiAPoFfn4smfSzBPQEBge284A1gffdJr8/av+l2537ccBarfV6rbUb+AS4KMltSqaLgPcC2+8BF4eVv69Nc4EOSqmeyWhgomitpwPFdYqb+v5/C0zSWhdrrUuAScDZLd/6xKrns6jPRcAnWmuX1noDsBbz5yhjfpa01ju01j8FtsuBlUAv2uj3R610C+69gC1h+1sDZW2BBiYqpRYqpW4LlHXXWu8A8xsc6BYobyufU1Pff6Z/LncHuhneqe2CoI19FkqpfOBoYB5t/Psj3YK7ilHWVob7nKS1HgycA9yllDq5gbpt+XOC+t9/Jn8urwIHAkcBO4DnAuVt5rNQSuUCXwD3aq3LGqoaoyzjPpN0C+5bgf3D9nsD25PUllaltd4e+FoIfIX5Z/Wu2u6WwNfCQPW28jk19f1n7Oeitd6ltfZprf3Am5jfH9BGPgullA0zsH+ktbgnYZsAAAE0SURBVP4yUNymvz/SLbgvAPorpfoppezA1cDXSW5Ti1NK5Sil8mq3gbOAZZjvvfaJ/h+AsYHtr4HrA6MCTgBKa/88zTBNff8TgLOUUh0D3RZnBcrSXp1nKpdgfn+A+VlcrZRyKKX6Af2B+WTQz5JSSgFvAyu11s+HHWrb3x/JfqLb1H+YT7rXYD7pH5Hs9rTSez4AczTDYmB57fsGOgNTgF8CXzsFyhXwSuAzWgoMSfZ7SMBnMAazu8GDeYd18768f+AmzIeKa4Ebk/2+EvhZfBB4r0swg1fPsPojAp/FauCcsPKM+FkChmJ2nywBFgX+ndtWvz9q/8kMVSGEyEDp1i0jhBAiDhLchRAiA0lwF0KIDCTBXQghMpAEdyGEyEAS3IUQIgNJcBdCiAwkwV0IITLQ/wObmUl/qYiDhAAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x124d61d90>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -682,21 +699,21 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 2",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "python2"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 2
+    "version": 3
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.13"
+   "pygments_lexer": "ipython3",
+   "version": "3.7.1"
   }
  },
  "nbformat": 4,
diff --git a/__pycache__/keras.cpython-37.pyc b/__pycache__/keras.cpython-37.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..41dd67cfbca844dc7431edcd2d2939a2927a1afd
GIT binary patch
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literal 0
HcmV?d00001


From 490536b6032e195f2944f2a8c71fe194b11bf946 Mon Sep 17 00:00:00 2001
From: callMeIvan <evanwcall@gmail.com>
Date: Sun, 31 Mar 2019 13:49:53 -0600
Subject: [PATCH 2/2] added .gitignore

---
 .gitignore                         |   1 +
 .idea/Deep-Learning-in-Python.iml  |  14 ---
 .idea/encodings.xml                |   4 -
 .idea/libraries/R_User_Library.xml |   6 --
 .idea/misc.xml                     |   7 --
 .idea/modules.xml                  |   8 --
 .idea/vcs.xml                      |   6 --
 .idea/workspace.xml                | 140 -----------------------------
 8 files changed, 1 insertion(+), 185 deletions(-)
 create mode 100644 .gitignore
 delete mode 100644 .idea/Deep-Learning-in-Python.iml
 delete mode 100644 .idea/encodings.xml
 delete mode 100644 .idea/libraries/R_User_Library.xml
 delete mode 100644 .idea/misc.xml
 delete mode 100644 .idea/modules.xml
 delete mode 100644 .idea/vcs.xml
 delete mode 100644 .idea/workspace.xml

diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..a09c56d
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1 @@
+/.idea
diff --git a/.idea/Deep-Learning-in-Python.iml b/.idea/Deep-Learning-in-Python.iml
deleted file mode 100644
index bba6157..0000000
--- a/.idea/Deep-Learning-in-Python.iml
+++ /dev/null
@@ -1,14 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<module type="PYTHON_MODULE" version="4">
-  <component name="NewModuleRootManager">
-    <content url="file://$MODULE_DIR$" />
-    <orderEntry type="inheritedJdk" />
-    <orderEntry type="sourceFolder" forTests="false" />
-    <orderEntry type="library" name="R User Library" level="project" />
-    <orderEntry type="library" name="R Skeletons" level="application" />
-  </component>
-  <component name="TestRunnerService">
-    <option name="projectConfiguration" value="pytest" />
-    <option name="PROJECT_TEST_RUNNER" value="pytest" />
-  </component>
-</module>
\ No newline at end of file
diff --git a/.idea/encodings.xml b/.idea/encodings.xml
deleted file mode 100644
index 15a15b2..0000000
--- a/.idea/encodings.xml
+++ /dev/null
@@ -1,4 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<project version="4">
-  <component name="Encoding" addBOMForNewFiles="with NO BOM" />
-</project>
\ No newline at end of file
diff --git a/.idea/libraries/R_User_Library.xml b/.idea/libraries/R_User_Library.xml
deleted file mode 100644
index 71f5ff7..0000000
--- a/.idea/libraries/R_User_Library.xml
+++ /dev/null
@@ -1,6 +0,0 @@
-<component name="libraryTable">
-  <library name="R User Library">
-    <CLASSES />
-    <SOURCES />
-  </library>
-</component>
\ No newline at end of file
diff --git a/.idea/misc.xml b/.idea/misc.xml
deleted file mode 100644
index 8656114..0000000
--- a/.idea/misc.xml
+++ /dev/null
@@ -1,7 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<project version="4">
-  <component name="JavaScriptSettings">
-    <option name="languageLevel" value="ES6" />
-  </component>
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