Optimization algorithms comparison.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook uses my implementation of a neural net to compare the performance of different optimization algorithms, namely:\n",
    "- gradient descent (GD)\n",
    "- GD with momentum\n",
    "- RMSProp\n",
    "- Adam\n",
    "\n",
    "The network is required to perform binary classification."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import sklearn\n",
    "import sklearn.datasets\n",
    "import numpy as np\n",
    "import scipy.io\n",
    "import matplotlib.pyplot as plt\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Activation\n",
    "\n",
    "from neural_network import NeuralNetworkImpl\n",
    "from utils import plot_decision_boundary, compute_accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Qex3JaiF1eG+im9Zn63uTgnYQmsvrW3mH8D2obi8tbzmfiuwCDsxag5AEjUb1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FPu0V\nRL5KnUBpYcMZdeadMJqVB4IiqwwYFMOurQXs2JxPfZ2vQmffjCg/L2HQO//ho5NYumw2UTFBWKwS\nFqtEdEyZmdNZAAAgAElEQVQQS5fNIjI6yPB60TFBAV/HnpLGPwZ86xyAIAgSkAvMAUqA3cBlmqYd\n6bLPjcBwTdNuEAThUuACTdMuMTxhF/wYcgD/LXia23gn/qIeWT9GWLj7L0SP8Z+x1B/O54t5dyI3\n67RT1SOTfsUspvz19wEpq21lNawceLXfKkC0W4jI7EvDkUJESUTTNIJT45j75Z9wJEThbmrl3fiL\nfArRVEHg8PhZNETFoUomPU6NQEreIdKP7ddn/d28jxrQEBXHgSnn9vpZCAJYrCbjSlFNC+hB+00Q\n1FjHsH0bKV2wmHrRjsUq0dTgRJIE5FYnqiAQV3ICW3sbZreT2NJ8RFVh99nn4ww6xVQxm0WeevUC\nIqMcLFv0MolfrPbO5gVVpSR9MEn5OUiKHFDQD3QXs63zLguoEwR6/mXg/i3ElRUYbm93BJOXOZ66\nuCQETSOqvJDoimKao+No6ZtOg2b2ajf1BItVIiTURm1193UAfm3UearfaPAQJThn4RAuvXq0d/Vx\n9FAFzz+6HlXVcLsVbDYTjmALDz51LuGRDjRNo7KDpRSXGNLtqkXTNO767cdUVTT7cv9NIvMvGMKF\nV4w64zZ/W/zQlpDjgTxN0052XPxdYDHQVclpMbCs4//fB14WBEHQfswZ6P8yzCEOJv/1Nrb9+jlU\ntx6DlxxWJKuenHVW1Rse50iMImp0f7/vVVmXknBW+YZhTr6zjohh6WTevCTA+aKZ9dEf2fCzh1A9\nunm8pmpMfvVWMi6fTd2hkzQcLiA4LZ6YCYO9P5aW/Ao/8xpR0xi282saI+MoTh9MXVwKSCLFA0ZQ\n3H846UezSMk/gmCQb9DQtXDyhk7o7SMEIHNkPMePBIghC0K3neOZojU0gh3Tz4dWAXBBB9NRloGO\n61QlpTNs51oianWGkSJKuK2+OSaTWaK0qAGrVaI6LJaCuZcQXlOJqMo0RsUhm61EVpUS0mT8DnRC\nEwRMHrfxPXb89GwtjbQHh1KWOoCYsgIff2EAe1sLQ3ev8xtokluq2DN6NNT1zoEO9Pdm7MQUXbI5\nQLLVCPoC5Jt1FaoC6z/LJS0jkkGZcSx/aTtHDlXouReHmeFjkhg9IYVxk1O9lcCCIBCf1DtvDUEQ\nuPPh2Tz/2AbKihtQFE0fCAT4/OOj7N9Tyh0PziIs/Me5GvguVgAXAfM0Tbuu4/OVwARN027qss/h\njn1KOj6f6Nin2+zO/88rgE7UH84n59WPaS2qInH2GPpdMxdzsJ3a/Xl8vfh+2rsUbzmSY1i05y/Y\nYyNQXG4KVmyk6NMdOKsbaMoro6202thlSxCwRYfS/9r5jLz/SkwGOQhVVqjecQTVIxMzKRNTAC+B\nTjhrG/lP8iXGiWuLxJ7zfk6L7NutWJxtjFu/CpPH4y2U09A7ssqkdIoGjKA9OLSzyT1GgkLDrDz9\n1wu45+ZPqOmu+vQ0s3jQjVZCw3s3W7U424gtOYnZ7aQhOpH6mG5CSppGVEUxw3avQxElKlIyOD7C\n15zHYpV46OkFrPkwm20bTxpKXvc/uJ2Ewlyf0I0fHDY2z7kERetmndBhftAplzF09zoiq0/Rntsc\nwdRHxRPaUIOjpQnRbMIWYuP/sXeegVFVeRv/nXunpfcCoYVAAoTeu/QiKCqK3dW1u+5adl3b2ntb\nyypreXddd23YEakCItJDDyWBBJKQhPSeTL33vh8mGTLMTDJJwJrfl5Ap9547ZM6551+eZ8bXT/LG\n57nkZJV7P6aP6582N5kLLx/Cnb//3E1Y72zTs3cE9XU2KsrctfwNRpmHnp1Dj0T/ZSp88farm9n+\nQ45bkluSBL2SInnkhXM7fHx/+bF3AN7+p0//q/TnNc4XCnETcBNAjx49vL3kN0XEwETGvX6Hx+PR\nw5O57MQSKtKPUZaWScyYfkSkOjtv7bUNfDP+j9TlFPmXwNU0LKXVHHrlc0o2H2Duhpc9tr2STiZu\nolPi115nxl5vblEKwxQVRs8LJnB05W7y4nvTJT8Lg9UMGsgKRGYepC5poNt7bKZAdk+aT5/DO52m\n4QYdRV16ktl3mJsKphAQGh7AA0/NYu2KDH5Ym43N5kCnlwkI1BMaZmLqnGSmzuyLJEv84Z7JPPfw\nt15L/5wH9HzI4VDw564zuiCHvunbyO/dn6LufZBUlYTsgxQm9nOzXWw+eJvJ+bk1BIVyrP9wt6cl\nAd16hNO1exjbNuV4nfyFgPyUIegtZgoT+xN9MoeuuUfddP7lQCMjn7uRuRdO48E/LfNdeSMEIFAb\n+xsOjprG+NUfgwaHRp5DZUzXU97GdivTBoVy2ZMLkfQ6zlWCnPmV0yUZVNXVWdwco0lHcr9YQkNN\nTJ+TzJpvMlr+cM8g5aX1OByqRxjJblP4+tMD3P7XyR06vqqopG3J9ficVVUjP7eKooIav3cVPyZn\nYgHIB7o3+70bUOjjNflCCB0QBnjtr9Y07W3gbXDuAM7A+H7VRA7q7SEbvf/Zj6jNKnDpwPuLYrFR\nvieL4h/SiZ/srm1Tn1/K1j+8Sv6K7c6SUAEx41OZ/O69hPbxXus8/B938snvlzDk2y/RWy1u82zP\njD00BIRQ1tW9WcYcHEr6qGn0To6mtKiW2hrPHEhYhHPyj+sSylU3jOaK60ZitTowBei9xmt7943m\nzw9P56n7V3sdpxDCYzehaVBe2rLFn85upe+BbeyZNA+rKcA14ZuDQjBaGrAEeX7hheIgqqYUYdAR\nXFtFbGEuRd2TkFQVTZLo1Seaux6ciqZpPidtTQOLKZBDo6cCgqroeBqCQumZlY7RaiGoWwzDnriO\nvr+bDcCs8/uz6qtD/sXQNY3y2G5UxCRQEZPgVrZrN5g4JoW5us1HT+hJ3vEKVi09jFAcqHYHkqKS\nvHcTGcMnowidKz8g0AgNM9GtZxhPP7CaIxklrY/lDBIaHkDhCc+eF02D3GNedjFtxGZTcPgwFpJ1\nEutXH8HcYCe+SwgTpyf9bEJCZ2IBSAP6CiESgQLgMuCK017zNfA7YCtwMbC+M/5/9sh+f22bJ/8m\nFIuNki0H3RYAS1k1S4fd5N6opEHp5oMsG3MbF2d/gBBgKa0mqHsMstEZHtr8Qx4h5SVIXpKVsqLQ\n8+h+jwWgCbvNgd1Hx3NIqJG4ZlIBkuy0A2yJg3tPIiQPpWPnpbTzLzGq6ASFPZKxGQPc7vZVnR6v\n1fuqiuxw0PXwfgLjI7GW15Cyfyu9j+yjISqa4TfPYfLfTnlEpAyIJeNAsddzO8fc+KkKQWFSKoW9\nB5A6MIbrnnJPki+6ehhWs531q460eq2aENiMJop69vUaxsnJqqCirJ7I6CCEEFxy9XBmndefI4dK\nOPbOMur++xWSphH4w3KyBo6mMqYLQtXoZqvgtocW8MS9qzA32Nr9mbeXk/m+hRnjfIjOtQVjo+y0\nN19gc4Od71YfwWZV0Btkln6azl8emU5yf/9d9s4WHV4ANE1zCCFuB1YDMvBvTdMOCiEeB3ZqmvY1\n8C/gf0KILJx3/pd19Lyd+EbzNsv5iWzUu+r4mzj8+pdOsxgv2CrrWNJtEZpDcXUOD/nbVQy651L2\n7S5Arq31kD5uItDm/Q5bp5coK6n3GbIpPlmLoqh+CYI1YbcrXif/jiArDsq79PDUx8HpCYCqInDe\n9QsgsjifpMO7MNgs9Lx4PhH9e3Ji+TZM0WGk3DSf6JEpbse48oZRPHXfKuTycszGIK/ncUMIDh8o\nQdPcjUmEEFx23UgyD5ZQfLLmVNOSl1i9JoSzAcxHDF+nl6iqNBMRFcjhxUs58MISLCVVhA/oSdKM\nERwOMuGoMxNUV82Qbd+i4VSJTf3TRezcmuf8f2hh8hcCYuKDKSuu92vHojfIraqCNl2qNwxGp9x3\nRxFCsOh3w/n361vdQmJNuaomGYumsb7+/Pe88q+L/ZKqPpucES0gTdNWACtOe+zhZv+2AJeciXN1\n0jqJi6Zw+PWlqDbvuwBdkImkK2eQ/cFazxyBEPS62D0emr9yR4udwE1ib01aNnsffQ9zUQWhXQZy\nMiyy0RvX8/3dJ6Uy+/x+rF2eiaY5S+okSaAqGma77x2M3abw7uJt3PDH8T5fczrDRndj7fJMrNaW\nJa3bQkVMAkazjy5iTSO8rIj+e37AaPWslCnfdYQxL95K8vW+k4PduodyoS2Two1b2Tj7cr/GpCLY\n91UaQy90V900GGQeem4OT9y3ivzcZpVgquqK1UsOO5ElBRQk9vd5fIdDpUtCKGn3vEnmm9/gaHD+\n/ZTvPkrV4Tz0IQEoFpvT04BGY6IAIwPuuIhN76a3OFnLsqD/oHhuvnMCFeUNPP/IWurrvHdCG4wy\noyf0pGdiJNs352Iy6Zgyqy9h4Saef3Rdq4uCKUCHEIKrbhxFv9S4Fl/rL+MmJyLrJD79725KiuoI\nDjFSV+e9jNtqdpB7rOIn9wnoFIP7FTLkwavI+2ozDSfLPaQEdEEmus0dzdg3/oQu2ETG4q+R9LLz\nVkUIZix90mUt2URAXMtywqejWGwcevVzlK7bqR81A3NwqNOYpdktuGQykHr/leQRzIJLB2NusGMw\nyKxedth3srYRTXMaglx81TC/m236pMQwdFQ39qbln7FFwBIUQlh5EbUOu7MDthmSppKcvs01+dsM\nRrIHjKS0ayKaEMTUljHiZC1xXU7V/xefrOHrTw9w5FAx4ZGBDNRVU71mB8LuIKimirpw/yaLxR8c\n4dXZQz3CYqqqUdRcZrnxLl9SHMiN5i9lXXv5PK7BKDNzXj+E2UzG4q89zIYUs5WALpF0nTGCnM83\nojkUukwfzthXbycoIYb4hFCk3QVe9QclWXD97eOY0OjJEBoewOL3L6WyooGGeisH9hbx9SfpWMx2\nZJ3EjHNTWHjlUGRZYtZ5pxasT/+32ynN0Qp/eWQ6iUlRbTYiao3R43syenxPNE3DblO4+fKPvfsI\nC9FYaOBOTZWZwvwaomICiYnzVDE903QuAL9ChCQIT+1FXV4JQnZWcMSM7U/XGSPoNmc0MaOdSouj\nX7yV1LsupmjDPvShgSTMGumK36sOhYMvf8bhN77CUl7jLE9pSyOOBsEF+fQM2c2BSXNJ2r+d6Pxj\nCE2jISiUwrGT2Ph6OjpZQlU1NGDEmG5+h2n0jbXyrS0AFWX1LPvsAAf2FBIYbGDi9CQK8irJONCx\nJGTT1r64ex+M5npsQqBJEkIDoan0PriLwHrnZKtKErsnzcNqCnIlVUvCYnj0L8v50/1T6N3XmfB+\n4r5Vro7lkqI6shUHCd37E59/jF6Zezg0YopfYSAV2PL9cabPdQ8p1VRbkGXJPVkpBKqsc4asWlEg\nSewTxbkXDqBi+0Eko95jAQCoyyniokPvcs77D3iEoqbNSWHNssNej60qGhvXZZOVWUballyEEKQO\niWf8lN70S41j9nkRzJzXj4Z6GwGBep/hv2NHy1vVGgLo3ScKuT2d9n4ihMBg1NGrTxTHvGgZSRIk\nJp1a0BVF5d3F29i28Tg6vYzDodInJYY/3jv5rEpKdy4Av0LWnv83SndkuLbhABW7s0i5cb5r8q/L\nLabhZDnh/XuQdOUMj2NsuPwJ8lfu8PASaCvdMtMZPjCKb6fP4nB5PcKhnJrE7KrbZJS2Jc/vbk/F\nobp5tXqjtLiOR/68HIvZ7iynLIaT+TWMOycRxaG1S2GyiYVXDKFPv1gO7DvJjk25aGh0SQglIVhQ\n9cRijFWnmrRKuvZqTBQ3m3CEREO9nRceWYuskwgLN3nsfBRZR17yEPJ7D0CTZKcEQ/O7SR9xertD\n85r0jIoJam8/FQDZmWU8ds9K/nxDaouS5E1VQqdXZMV1CWHwiAT2pnk3UjqaUcrRjFJXf8DWjTls\n+yEHvUHm8utGMm1OsptBTxNWi52iwlrCIgKIiw9pbPTyfR2yLCgrbXDbfZ0trr1lDE8/uBq7XUVx\nqAjJ6T997a1j3XYfn72/h+0/5GC3q64czdHDJbz+/EbufXzmWRtf5wLwK6Mi/Rhlu454NGA5Gizs\nefQ9us0dzXeXPEbp9sNIBh2qzUH/2y9g5LM3Ur4ni8Jvd2GvayB/+Xavd3jhqb2oPpKPZvcvjNIQ\nGMKBUqjHioZAa+EO1lvNuzdkWdC9V0SrLktffrSPhnr3ihOr1cHm77K5+a6J5B6vaLvGfCNrVx5l\n2twU+g+K55KrnO3+pcV1PP7XFTiGT0GRJAJrq+l5dD9VkXEeIaImFEVDURRKi300nAmB2ihVoUmS\n+6TfdGGnTbQ6neS1sam60ozqIyEv65y5l5YStA6HSlWlmUNlGsG94qjOOOHUDWo6hslA8vXnuk38\nzS0TAabPTeFwerFXaQ5vjWFNCdSP3t1JbHww/VLjWLn0EBtWH8VisRMRGUjRyVrnzsahOHcGrfwZ\nCUlgCvhxpr6evSN56tXzWL3sMNmZZcR1CWHOggFuQnWqorJu5RGPfgqHQ+VoRimlxXXExAWflfF1\nLgC/MmoyTyDpZLxNa/V5xaw970HKdx9FtTtcE3zG4qUUrEmjNqsQxeYAIXxO8A2F5cz97u98d/Ej\n2BsTyEqDFTTcJgOHrOPgqKlUR8UhZBnFj0qN09HrJSRJQqeXsFoc6A0yikOle68I7nxgSqvv35N2\nwuuE5nCo1FRbuOvWYbz/0nqKHXokRUEONGH29pXwUi1TVdHAf97czpzzB1BSVEtC9zBefWYDNdVW\nCHYuTNaAYKpiuhBmAMmhobao3OMHp40hpLyI+ogYD1lsU4CeMROd5bVWq4O9afmYG+ykbcn1kC1u\nQnE4PyijuR6dptBgCPS6WNusCjs25zLntb+y647X0OfmOcNhDoX4KUMZ+dxNaJrGqqWHWP7FQWpr\nrETFBLHwyqFMmNKbgUO7kpQcTVZmaZsWX5tV4eP/7CI4xEh2ZplrsmwyhLc3/sX7qsVvQghnKOvH\nrMOPjg3myutH+XzeYnH4HLdOJ1FRVt+5AHTiH6HJ3VC9JJcATLERVKQf87BydDRYqdx/3K/jGyOC\niRufyqX5n1CWlolisRE9pj/W8hq+HnkzltJqUDUyh4ynKirOezesn0TGBHHznRMICNTz+Qf7yMos\nJSo6iAsvH0JoeIBHjPl0PDpUG9E0OLwrj8KX/oHaawhKt944dAbQJM+u4KYVpFm1TBM7NuWyL60A\nITkXFY8vsRBoQqauweoM/3Tgs/B2EfXhMUQW51MXGoE1IAgQBKlWUof0Yu2KTOK6hPDOa1sah6+1\nOOEG1VQycPs69DaLM2+kqmT3H0lhYj+P1x5OLybzYAn6AZMIGCq4ZGwYA6f2dxnAf/yfXaxbmYnN\n4iC0ogQ5v54l+UXY7TOZMrMvf354OhtWH+GrJfu9Nvr54kROlbNKrB2icE1oGj+L+vvmBATqCQzW\nU1vt+VnY7QpdzqKfcOcC8CsjcnASkYOTKN99xM1iUBdootclk8l6bw0K7Yvry4HOcj5wSkHHjDlV\nfaFLiGbBrrfYcusr5Hy7h7IuPb2av7QFq9mBJEk8+peVLl34qgoz/77nE0YUpGM/mosuwEif381i\n5LM3oQ/2/65u586TdOk1mOKERN+LVPMQi4+Fxp+KIkXWOaMSfsTvjaZG5dIW9HSa3q/qdFTEJjBw\n+zqC6qqRVAVJVdgi9OzaZvQrGQpORdChm1eis9uc61/jOpF0KA1zUCiVsZ5ew6qqYbU6sFrh/c3V\nvHyd8zUN9TbWrshEqq5h5NY1NASH4TAYCT2yj73pOxif+SaGIBMz5vVj2WcHPI7bGh2Z/JtYuzyT\nCy4d7Oba9lMihGDhFUP58N873RZpg1Fm3GSnpejZotMQ5lfIzOVP03XWSCSjHl1IAPqQQIY99jtS\n71jYqqvY6UhGPbogE3KAgZ4XTqTfbQt8vjawazQzlj7JeUc+QN9CZ66/+u46g8RbL2/CanG4vvhB\n1RWkfr8Se2YOqBqOegtH/rWS1bPu4fTm8jgvZuBNaAhOduvjMzYPtDjxtwVNkkA+VWrr65iSLJg5\nrx+TpiVhsJoRp3sneIlnqbKO4h59MFrN6O02NOHMPfg7+YPTi6E+KJSK2ASsxlOLqKwodM9Kb/X9\niqKyfZPTz6EwvxqdTqL3wTT2TZhL5rCJZA0cze5zzqMgrheb71zsep+3MsgfAyGJn51Zy9TZyVzx\n+5GEhpmQZYHRpGPW/H787pa2qd+2lZ/HEtjJGcUYEcLMr5/CUlaNpayakMR4V3ln11kjKfx2p1t/\ngNDJLhOQ5kgBBoY/fh3G8GDiJg0iLLk7/hAVH9pYpuf5BdcbZBQ/7SUry80eicFemXuRTnOpUq12\nKtOPU7wpnfhJpyQszl80iLde2YzqI7ms+WN32FFau5NvhqpoHNx3kvMuHsiJ3EpOZJehqipCVZxx\nfm/HEQJLQBCHh03CbjASWZyP1dS2+HZlTFfKuvRAaJqHUbzB0bolpc2qsG5lJnq9TFJKNLqKco4O\nGovjNJnrsi492LQpnXMUBSFJHgv2j4XDrhByFu+q28vU2clMmdUXi8WB0SAjtaHTvb10LgC/YkzR\nYZii3eOHUz5+iB13LSbrv6vRVA1DWBCD7r2c9Gc/wlZV58oP6AJNJMwdzaA/L2rzeXU6iYsuH8Kn\n7+85bUurY8iIruzfXYjVhz1ic7xVhYRWlnpNpap2B2VpmW4LwNhJiezfXcjm77xbZEpNE6sv2jB5\nN0c0eibIDjuKr4nbBycLqnnr5c2NoSUJWS/QNGd/gdf5UlGoiYilOkqAkKiKjm/VK/l07EaT2xhL\nEhIxNdTR4+h+HBHevXBPJ+94Je+9tZ2QECPhIQYaGjzDf6pOz4nuyaxbnsFH7+3xu+qrLUiyIKF7\nGIUnqp39JV5OoSgqWRkljJ7Qy+2xb7/J4NvlGTTU20kZEMvFVw+jWw//rr8Ju10hO7MMSRYkJUe3\nSa4EnOEgb45lZ4sO+wGcTTr9AM4eis2OvbYBY0QIQpJoOFlO+nMfk7dsC/qQQPrftoC+1891M5Fv\nKxvXZvHlx/uoKGsgPCKA8xcNIiBAz7uLt/lM0Or1sk8ROIAR339NSLWnkKwuJIAJb91N78umuT2u\nKip33fgFVacZlxiMMqO66dh6pN5zwvQjVu+LlNRYZs7rx9EtRyn452dUBEZyMjHZaZl4pvFRBurx\nmia/X+HMMbTWaQ2gs1kY9d1XHJyxgFp9gP86SqpCSFUZ9aGRXsNrQlE6nBt64tX5vPjIWmpqLF7H\npdNJGE065l2Yylef7Pea/A4I0POP/16CvrEW/40XNrI3Ld/1d9nkJPfIC3NJ6O7fIrBjcw7/en1b\nY5OghqyT+MNfJpM6pEv7L7YdtMUPoHMB+A1Re6yQw//8muqMPGLG9CflpvkExLZN5qE9NK/WyThQ\nxDN/+9br64aMSCAs3MSm7475TPZ1LcklKe0Hp1dtMwwRIVyav8SrmU1JUS0vP/UdZSV1yLKE4lA5\n7+KBnL9oMPu2HufDd3ZQXGkjIEBPSmocWRnF1NbY2nX3HxEVyCv/WghA+otL2PrUJ2wbf65zJ3A2\nQk7+7FI0Db0sGDKmB5dcNYy0rXl89v6e1o+Lhmgsw7Xb2qCkp6pImup1YZVlQWsRQFkWCEl4LY00\nGGXeWXIFNdUW3l28jX07873uJCRJMGZSL7b/kOP1bykgQM+df5tKv9Q4Ck5U8eifV3jclAgBw0Z1\n5w4/So5P5FTy+F9XehzDaNTx7Bvne/UUdjhUFEXFeIaT0T+2IUwnvwAK1+5i3QUPo9qduu0n1+3h\n4N8/Y96mV13le2eL5qWay7846PN1s8/rj4bGjs25WE5rFBICunQLY9Ztiwj6rgsZr36GZNChaRr6\n4ABmfvO018kfIDY+hGf+cT4FJ6qoq7HSo3ckJ45X8tBd35CXU4lBLzN1djKXXTsco0nPTZd91O7k\nr8l06is16C+X0ufqmQz+dAufbSqj1Ny+kFKL+Hm8XgVH+OO9VwNw3sUDSewTyYf/3klBnm+ZZISE\npuGa/CUJdDrZ5+6t+ZhCy4qpiYw71fWtaeh0gtbyvkaTjouuGMIXH+7zugB0aTRVCQ0zccf9U1i/\n+ggfnVY9A85qoX07871bUeHsFWsqRsg4UOw1H6FpkHnIuxz36az5JsNr4l1RVTZ8e5SLLh/qeqy2\nxsJ7b25n9458NFWja7cwrrl5NClnSJSuLXRWAf0GUBWF7696GkeDxRXjVyw2bFV1fDnoej5OuIT0\nF5a4NXKdLY55sxDE2fRVWFDNgMFdiIkPQadz/9PUG2T+8JdJTJ2VzOhnrmfRiY855/0HmLX8GS49\nsYSoYZ4+yKeT0D2clNQ4igpqeOGxteQdrwTN2S+wcV0WLzy6Dk3TCApqn0m8wSgzbU6y6/fcYxWs\nWJtHYVQ3/vTsAkZN8O59cLaRHQ5M1e7+wQOHduXp185n/DmJLi9cFz7+DlTVd2/F6VTFdEWVJITD\njl4vERYRQI/EKI//1+aEhZt4/p8XMGt+fyIiAz2kkg1GmQsuG+L2WECA3qeksl4vM3BoV6/P63QS\nJpOeZ/62hv+9vcNng5wsCQryqrBaWq6eKz5Z43Wn4bCrlJysdf2uKipP3b+a3dvzURodyvLzqnjx\n8XXk5bTs8Xw26NwB/Aao2JeNw5emjwbmkxXseew9qo+cYOI7f3E+rGnkfr6RA3//DEtJJfFThzHk\ngSsISexYPDMszESdl+YfWSe5vvQPPDWLj/69k60bj+NwqCT2ieKqG0fRreepcJUpKozu88e1awyf\nnZacBucXNe94JSuXHqKyomUnsNMRwrlADRgUz/RzU9A0jfffSeP7b49it6sIASu+OEh0O7o5m27w\n2x2pVVWE6iBqQE9efWYDNpuDsRMTGTe5Fzq9zPV/HE9UTBDffuNUYTVYGrAZAzq2U2l6rxBoQmC3\nq1RXWaiusrSof3/bPZNd4n73PzmTxS9t4tiRUmRZQpIFi64ZzvDR7pVoQ0cmeK3y0uslJk1PYtqc\nFAQtqDQAACAASURBVB67ZwUWswOr1YFOLyFLEtfcMpqnHliN2WxvUTqipsbKQ3d9gyxLTD83hUVX\nD/NanZPcP5bsI2UeuxaDUaZvs8az/XsKqaxo8KiEs9sUln6ynz/+9RzfgzkLdOYAfgOU7T7Cyil3\n46jz1KVvjmzUszDrfwQlxLDzvrc5/MZSl1+A0MnoAo2ct30xYSn+lYN6Y9N32bz35naPCTg4xMgr\n/17oSsqBcxHSVO2Ml8P94epPqKv1XIR0OgmHovotmCbJgrAwE9PmJDNgSBeSkqMRQnBw30n+/sR6\nt5BAQF0NOtVBbUjEmQ8DeUPTEKpCYF01gTYz1Qk9sDWGcoxGHV17hPHg07Ndn3f+qh18d/mTbBsy\nlbrw6LM/vmbo9BI3/HEc4yb39niuqqKB+nobsfEhZGWUsmNzDg6HxuQZSfTt55xY07bk8tYrm9FU\np42m0aQjvmsoDz49C6NJj8VsZ/OGY2RnlhEbH8zkmX359psM1iw73KZ+CYNRZs75/Vl45TDPcVaa\nue8PS90WFEkSBIcaef6fF7gqe77+NJ0vPtzrdUGPjgnipXcu8ns8vujMAfxKsZRWsf+5j8j9YhNy\ngJGUm+bR/7YFLvVFX0QOSUIXaGx1AZCMesp2HgHg4KtfuDWNaQ4Fe62Znfe9w/QvH2/3NUyY0pv8\n3CrWLs9Ap5PR0DAF6PnzQ9PcJn9w5g6En01jbSE8MsDrAqBpWpvUMmVZ4olX5hMS6l5TvvrrUxOL\nzmYlNW09oZVlKJJM+riZ1IVFnp2qoEZ0MnStLCIp5xBB4wazxn5q8gdn93JBXhUb12YxfW4KDfU2\njtQbyI/uQV2op4icN3zZa/qL0aTjxjvGk5NVTlZGKVu/z8Fg0DF8THe3nFF4ZCChYSZee+579u0q\ncN3tb1ybRY/ECB56bi6jxvekd99otmw4Rk2Nhf4D4xk6MsF142AK0DN9boqbPPaRwyVtmvzB2e+w\nelkGCy4d4hHKCo8I4OHn5/Lem9vJPFSCAAYO68K1t4x1K+uMignCYNR5FcNrzw6xo3QuAL8QLOVO\nX15LWbVL4mHXg//ixPJtzF79fIuaOJIsc84HD7JuwUOoDoebRERzNFUjIC6Cog37kPQ6z65hTaNw\n7a4OXYcQgsuuHcHcCwaQnVlGYLCB5P6xP6o13vyLBvLvxVu9Jg7bgiQJykvrPRaA5l2mqTs3EFZR\niqSpyKrC0C2ryEkZSmHPFBR9+3INLaHTS4yd2Ivr/nAlQsBrz2zAttNTftlmVdiy4ThhEQG89fdN\nADj6jfRrdyIEHZKVbjrG15+kU5hf7Qqb7NtVQLee4Tz83BwMRh2a5vyMt/6Qw/5mk38Teccref25\nDdz90HSiYoI475JBfp8/Ni6Y7MzSNofWVFWjvs7qVUyua7cw7n9yFg6HM+znrQdg5LgefPB/aR5i\nLGfKmrKtdC4AvxAOvfYFlvIat8lbabBSuu0wRRv20mWq57a0OV2nD+eC9P8j459fk7d0C7XHT7r5\nBSAEppgwYsb0x1xcifAxIesCz4w5RVh4AMPHtD+UdDqapnE4vYi9OwsICNAzbnIi8Qnezb7HTu5F\nYX4VK786jE4voakaQSFGQKO81P/4vyQJrzX1QcHOid3YUEdoRYmbE5qsKCQd2kXy8QMM+e5Nlry/\nt+VKnDbQf1Act9w9ieBgAydyKnjrlc1uCcjTUVWVt/6+6VRit6k+v5XS0hHjelB4oprCE+0fd1R0\nECdyKj0m4PzcKu68/nNsVgVFUemdHI3VbPPZNJa+5yQ11ZY26+WMmtCTLd/7J4DYHFmWWjVoaSnR\nbTTquP/JWbz69AZqaiwuC9RFvxvOoGGemktnmw4tAEKISGAJ0AvIARZpmuaRyhZCKECTqEiepmnn\nd+S8v0Xylm7xquPjqDNTsDqt1QUAICSxC6Oev5kRT13PsjG3UbE3u9mzGqpdwVpRQ8Js79K1sslA\n39/Pbe8lnDUUReWVp74j81AJVosDWRYs//Igi64Zxqz5nv62QggWXjmM2ecPIPtIGcEhBnr3jebd\nxdv4/tssv8+rKhqJfT1tGsdN7kVOdjlGc71TbkL1rJzRFIV+fSMoLvQ9QbcFIeDmuyayY3MOX3yw\nD5vN0WKnrdGoIzo2mBPeKk+a7M4ENK+jbJJSvuWuiWRnlvHSE07vXVc/moRTFtyPu+qCFhaPhvpT\nf+fZmZ5uWs2RJEFpcV2bF4BD+4tcl3k6siy8fnYGo8ycBf1bnOCbyMos5YsP95GXU0lUdCDnXzKI\nEWN7ANC9VwQvvHUBeccrsZjt9OoTdcZ7Afylo2e9D1inadqzQoj7Gn+/18vrzJqmDfXyeCd+Ygjz\nbCQBkAw69GFtix1aK2upzjjh/qAGlpJK9j7+P8a+ejvTvnicdQv+BpqGw2xDF2QiYlAiQx+6ur2X\ncNbYuDaLjIPFrpBOk8nKJ//dw7BR3Xx6qwaHGBkyIsH1e3VlyzmS5hiMMpdeO9zrF3fyjD5888VB\nGmxhSF4mfwBDWDCGsCBCwkxUlret6sgbvfpEcTi9iE/+u7vVpi29XqJ33ygio4N8lj8iBLIsmDE/\nhcSkKGprrPTtF+syMe83MI6/PTOHr5bsJze7gujYIOZfPJCE7uF89v4edu844ezlOMs1Jhq0Sys/\n73iFz4WqT0oMkdGB5B6rpKSoFlnnNJmZdV4/Fiwa7P1NzTiwt5BXn97g2lnVVlt48+VNXHT5EOZe\nkAo4b0Kam8L8VHR0AVgATGn893vABrwvAJ10kH63LaB891FXVU4TQpJIumJ6m46Vv3IHwosfqmpz\ncHzJd4x99Xa6ThvGoryPOf7JBiwlVcROGEiXqUNbzDX8VGxYfdRru7+mauzYnMe8i1L9Os7pzWfN\niY4NolefKPKOVRAdG8z8hQN9tvgHBBp47MVzef+dNIoykonLO4qsnBqfHGhk2BPXISSJuRcM8FqW\n2haMRh0zz+3Hlx/t86tjV1FUMg8VU3CiukXpDUXRGDikK4OHJ3h9vmfvSO64f4rbY0uX7CdtSx6y\nTpz1yV8IGDW+R7vkknv0iiAro9TjTl9vkBk8IsEVj7eY7VRXWYiICvTsl/DBf9/e4dEvYbMqfPHh\nPqbOTsb0I2r9tEZHF4A4TdNOAmiadlII4ctpwSSE2Ak4gGc1Tfuqg+f9zZG4aAqF3+7i2Efr0RyK\ncwLXNMYtvoPgnmewg7DZ98EYEUK/m887c8c+S9h8TmAqNquDkqJajCZdiy5QJUW12H00ORlNOq6+\ncTRDR3Xz+f7KigZKTtYS2yWEiMhAIqOD+NP9U1Dumci+J9/n0Cuf46i3YIoJZ9gT15Fy/bkAzJzX\nj5KTtWz49mirblbeMBhlhoxMYNw5ifzfP7b49Z6mPq+aakurOd/myfmsjFLWfJNBRVk9A4bEM+Pc\nfm6T786teSz/4iB2u4K9barj7WLi1CSuaadc8sz5/fl+bTbKaZIiOp3E5Bl9XL+bAvR+TdiaprFz\nax5rvsnwGdaTdRJ5xytJHvDzMaRptQ9ACLEWiPfy1IPAe5qmhTd7baWmaR7iMkKIrpqmFQohegPr\ngemapmWf/rrG194E3ATQo0ePEbm5uX5fzG+BygPHyV+Vhi7AQM+FkwmMb/s20lJaxSc9L/fw/JUM\nOlJums/Y1/54pob7o/Dlx/tY/vkBj3BGkyiY3e40k0lMimLitCTWrsigtLiO2PgQLrp8CLFdQnj8\nryuxWhweYQGDUaZ332jufWyG134Eq9XBWy9vYt+uAvR6GYddYciobtx8xwQ3wxFN01AsNopLzaRt\nzUNVVIaP6U6vJGdIpabKzFdL9rNu5RG/rlkIwYDB8Sy8cii9+0YhhOCGRR/6XMTag8Eoc91tYxl/\nTm/Wrczk4//scsX89XoJo0nPoy+e6wrBPHbPCo4d9d7pfSaRZcHkmX343c1jaKi3YTTq3AzW/SXj\nYDFvv7KJqkoziqIhyxJjJvTksutGtNky8t3F29i68bjX8s4mjEYdD78wt80Ko23ljPYBaJo2o4UT\nFQshujTe/XcBSnwco7Dx5zEhxAZgGOB1AdA07W3gbXA2grV6Bb8xIgYmEjEwsUPHMMWEM/L5m9h5\n7zuoVjuaqqILMmGKjWDoI9ecoZH+eMya35/N3x2jqtLsmgD1BhmHQ6G+7tQidzSjlKMZpa7f845X\nsvilH4iND/EZr77g0sHMPq+/a/LXNI1jR8uprbHQu08UH767i/27CnHYT1lC7ttZwH/+uZ2b7pzg\nOo4Qgm+WZvDNFwdRFBVN1Vi59BDjJidy3W1jCQ0P4OqbRlNwopqMA63rzxgMElNn9yUp+VTTVnCI\nsV35BF/JUE2D3n2iqa+z8dG7u9wWF7tdRVFsfPyfXfzxXmf3akvnlmUJRVERkkCvk0gd2oUDe0+i\n00moioYkC869IJXkAbEsfnGjy2nNanEgSaeSspIkCAjU0zMxkrtv/MLZXSxg7ORErrphVJvCK/1S\n4+g/KJ4dm3JRHAqKQ2XH5lzS9xby5MvzCY8M9Os4+bmVbNlwrEWZDCEgMiaQhO5nz96xPXQ0BPQ1\n8Dvg2cafS09/gRAiAmjQNM0qhIgGJgDPd/C8v0kcDRbMRRUExEeiC2w57tlQVMHOe98m96tNoEHP\nCycy8rmbXDuGAbdfSOz4VDLf+gZzUQUJc0bT55qZqHaF9BeWkL9yOwFdouj/hwuIG+9fDP2nIijY\nwON/n8f6VUdI25KLKUBPfZ3Ne4XLadisCvm5VV6fMxhlomODXXeXRQU1vPj4OqqrzEiScO4sFE/N\nebtNYcfmHK6+aRQBjc5oOdnlzvBIs0nCZlXY9kMOw8d0Z+jIbgjh1JD3ZwGw2hR69YmirKSOirIG\n6mot1FT7n8RuTkRkIHW1VrcJTG+QGTIigfiEUHZuy0OWJZfxehOqqrFv16keg6SUGHZty/O6i5o4\nNYnS4joiIgOYfm4KvZKiqK+zkZVZiskkk5QS66queeVfC8nKLMNsttMrKZJN67P5bvVRrBYHQ0cm\nkJIa5+wmbxyvAmzbeJzjR8uYMa8fScnR9EhsfWecn1fFjk25btftcKg01NlY9lk6V9/kX3hp/+7C\nFiuuTAE6jCYddz4w9WeXQ+voAvAs8IkQ4nogD7gEQAgxErhF07QbgP7AW0IIFaf43LOaph3q4Hl/\nU6gOhbS/vkXmW98gZIGmaKTcPJ9Rz9+M5CWZa69tYNmoWzEXV7pq/Y99tJ6T63Zz4aF3MYQ6K4qi\nhycT/dbdrvc1FFWwbOQtWCvrUMxWEIK8pZsZ/ti1DGyHMcyPSWCQgfkLB7qSd3ff+EWHj2mzKkiN\nnciqovLsQ2uorPBvkpVkieoqi2sB2LQ+G7uXzlOrxcGGNUcZOtKZX+jeKwKTSddiQhqcd8JPP7Ca\n2horOp2ExWxvl16Q0ahj4ZVDCQkz8fkHeziRU0VgkJ5pc1NcFS9yC016zXMECxYNYs+OE26ToSwL\nl9rl6ZNfWUkdyz5NJyuzFEkSjBjbg6tvGk1omMktTj7vooHMu+hUk9RDd33jcbdtt6vk51Xz/jtp\nSJIgJTWOOx6Y4tFd3pwDewtRvXxoiqKxe3u+3wuAXi8jyQLFywYgNNzEdbeOZfCIBL/KR39sOjQi\nTdPKNU2brmla38afFY2P72yc/NE0bYumaYM0TRvS+PNfZ2LgvyXS7nmTzLe/QTFbcdRZUMxWMt/+\nhrS/vuX19UffW421ss6t0UtzKFir6sj6z2qf59n90L8xl1Q5J38ATUNpsLL7oXcxl/z4SoUdoWs3\n701gbWXP9hNomsbB/UXU17duj9ic5hrwFrMDzUenscV8KmM6clwPAoIMrVoHqIpGRVkDdpuCuaF9\nk78kCybN6M13q4/w6tPfkZ9bRa+kSO5/chYLrxhKbY2FjWuzqK40o3pRCJVlidHNFE43bziGdNrA\nNQ0uuGyIx+R/sqCaJ+5bxdEMZzeuomikbcnl4buX42jBEMj5Xt9+vg6His2mkHGwmC8/3tficYxG\nnc8OdIPJ/3vjkeN7eA0fNnX3Dh/T/Wc5+UOnHPTPHnu9mcy3l6OcpuapNFjJfOsb7PWed6SFa3ej\nNFg8HlcarBS0IOWQ9+Um9+7gRoROpmBVWjtG/9Nx/qLBfpXtybJwliz6YNumXJZ/cZDKioY2Vekk\nD4h1O/+Isd0xeplUDEaZMRN7uX7X62Uefm4OffvHnjXNOCFBUIiBR1+Yy5YNOWQfKWvsnXDmN564\nbxWf/G8399z8Fe+/s4MP393lTJLqJNdEZjTpiIwO5OKrhpKTXU7GwWLWLs/wKClVVY3PTzOfqau1\n8vg9Kz0S1prmzCM8fu+qFl3hmhRDW8JuU9iw+miLrxk5rofXhdNgcPpD+EtEZCBX3TgKvUF2yT8Y\nTTqSkqOZPsf/4/wUdEpB/MxpKChD+FDDFLJEQ0GZh1l7ULdohCyhnSY5K2SJoG4xvk/Wwm2nt76B\nnyMOu8Ku7SfIy6lkwrTepG3JxW5VUVSVhO7hWCx2qirMLpeymLhg4rqEsGvbCa/HUxwqX3+azn1P\nzGyTVlBWRikOh+qaMIeMSCCxTxTHjpa5av71BpnYuBAmTHFXwdQbZEpO1jrDCo4zXwfRb0Ast9w9\niU3fHcNuVzwmQYvZwcovD3lcrywLwqMCsVoc9O0fg96g487ff97q7uNEbhW3XvkxiX2iueTqYaxd\nkUlDg+860RM5lXz87i6SB8SSn1tFbHwIoyf0wGhyJnjnXzyQD/4vrdXeieY7K2+EhJq48U/jeee1\nLWiahsPuVBJN7h/LjHNTWnzv6UyZ1ZcBg+PZ+v1x6uttDB7elQGDu/yoGlftoXMB+JkT2DXa6105\nOMM6gV09pXv73XI+R99dfSqU04hk0NPvVt8qHElXTCfjzWWoNvcvjuZQ6HZu++qtf0wqKxp44q+r\nqK+zYrE4MJp0yLLErX+ZRGJSJOGRgWiaRuahEooLa+iSEEbf/jGcLKj2uQCAs/Ln43fbJoKnKCqV\n5Q2uEklJlrjnkel8vzaLjWuzUBSNCeckMm1uslu5KMDyLw9SW2P1OvnrDTKaqjkridq5NmQfLccY\noCf7SKnPslFvi52iaJSV1AOwe0d+mxq9GurtHNx3kkP7T7Y6blXVWLsikx/WZ2G1KBhNOj56dycP\nPjObhO7hnDOjD5Vl9az48hCS7F2PCXCV2LbEmIm9SBkQy47NudTX2xgwKJ7kAbHtStbGxoew4NLW\nO4V/TnQuAD9z9MEB9Ll2NlnvrXGb0OUAI32vm40+2HM7HDEwkfH/vIMtt77qShKrDgfj3vgTkYM8\nNdebGPro78hftYOGgjIc9RaETkbSy4xbfCfG8B9fqrat/OsfW6msaHBNXk012R/+ayfP/3MB4CzH\n7JcaR79G+73cYxVs3diyKJjNqnDksNcKZ5+oqkZwiLvap04ve8gSe2PX1hNepYqFcO4krvj9SD7/\nYC9pW3L9duhqjiQJ0ncX4tMv0R/aufi0ZdGyWpTGnw6sAl579nueff18hBBcePlQ5iwYQH5eFetX\nHmHntjy3HYHBKHPF7/0qhSc8MpBZ53lqRv0W6FwAfgGMffV20DSy3lvjlGm2O+hzzUzGvPwHn+/p\nc81selwwkcK1u0HT6DpzhKv6xxfG8GAu2PcOxz/5noI1aQR2iSL5+nM7ZADzY2G12Dm0/6TXO9fq\nSjOF+dUkdD/VgGO1Ovj7E+s5drTML114bxOXTic1hmlUt8oXnV5i2KjurgqgtuIrdyHrJLp2D2Xj\n2ixMAToGDe9K+p5C7DbVq6etL+x2FbtdoaqNzmf+IoRzrO3pbPaJBhVl9RQV1hAVHcQXH+/jh7VZ\n2KwKKalxzL1gANs25lBbY3GFmpp0izrxTecC8AtA0usY/8+7GPncTTTklxLYLabVyRzAEBpEr4sm\ntelcstFA5JDe5C3bQs6n31OalsmQ+y73qRD6c8E5iXsXqheS8OjQ/OjfO8nOLPUthuYHYyf14vLf\nj+DvT37HiZxKZ7OTwylhfP3tY9t93Cmz+7LkvV0euj4Ou8ryLw669R6ERwQw8bzeLP/8oN9314pD\nxWCQWr12SRJt9kgAGD6mO/EJoXy7LKNdO5SWxmMx23n+0bXkZJW7xp++t5CjGSU89tI84ruemeqv\n3wqdC8AvCENoEIYBrU/8HaF4Uzpr5tyLw2IDVaMut5h1aRmMeu4m+v/hgrN67o4QFGwkNj7Ya4mg\nEE7xryZURW1MgLZ/8tfrZbr1DCc4xMTDz83l8IEi0ncXktAjnHGTE13Jv327Cli19BBVFWb6DYxj\n3kWpRMe2HE6bOjuZ1csOU1pU5/Hc6XmBqkoza5dntjkf8NbLW5g2py8n86u97oCi44IYNLQrO7fm\nIYSgrtbq12Igy4Irrx9FRFQgWzYcx9bGzuSEHuHU11qp8qLMKkkSDfV28o5Xuv/fac4w3dIl+7n5\nroltOt9vnc4FoBM3tt7+moeBvNJgZed979D3ujmtdiD/lFx321hefHwddrvqqrnXG2T69o/hH899\nT+/kaJcaY0fDEw6HwuARCWiaxqf/28OaZYfR6Z0J2k//t4c/PzSNvTvz+frTdFdsujC/mu9WH+Hi\nq4Yy94JUr45R4AwtNbSh58Bidvja/Pgev6Jis6uEhJmoqTa7LSzRsUE89cp8TAEGrr3VuZPZvOEY\n77y2uUUbyIBAp7VnVIzzJuUvD0/jyftXYW5ouamtCZ1O4unXzuNwehF/f3L9Ka8B4QyLXXPzaI5n\nlXtNXKuqM7nfSdvo7APoxIXDbKXqYI7X54QsufyCf66kpMbxyPNzGTOhJ10SQklKjnY2ce1zOoUt\n++wA9972FWXFdcR16XhS+39v7WDLhuN8uzwTu13F3GDHYnFQWd7AM39bw1cf7/coVdQ0+PR/e3ni\n3lUoiu/Z1FzfRjnNNu4ANFUjN7ucJ1+ez5zzBxAbH0K3HuFcdeMonlt8AaYA9/zFhCm9ufP+qa7O\n6NNJSY3hlX8tpG//Ux283XpG8Nq7lxAVE+RXT0OTuU7/QfE89NxcRk/oSXzXUIaOTOCvj81g3ORE\nQsNM6H3kSELDf743Jz9XWlUD/SkZOXKktnPnzp96GL8ZVLuD/4XM8+oZrAs2ce73rxA1rO9PMLK2\noyoqd/z+c2qqT2uIE9AnOYYFlw7iH89+36EYtd4gExUdRFGhZ9hJp5caRd98v//mOycwYmx3rBYH\nIWEmt9LDR/68nJzsinaPrTWEcJZA3vrntuWIlry3i2+XZ3jkJ/QGmZi4YB598VzsReXkfL4R1eag\n+/yxGBMT+Oz9PfywPhu7TUGWJVRNc/P4NRhl7n18Jn1SWuhTAcwNNu68/nOP0k+jUcf1fxzn1lTX\nEpqmuf7f4ruG/uw0ejpCW9RAOxeATtz47tLHyftqM6rd/QsW3CuOi7M/+MV8UXKyy3nmwTVeNXVk\nWfDG+5eSk1XOZ+/vIT+3CiEEFkvbJBUCAp2NSeYWmppaIjBI79whCAgJMXLlDaMYNd4prZBxoJiX\nHl/X4SSqaPJl8SLQ9sBTs9tVKbPiy4N8+v4eD5N2g1Fmcg8J7Y330DQNTVGR9Dr6XDubca//yfW3\no6oa61dmsurrw9TWWOndN4pLrh5G776ePS3eOHKohJefWt/oa6DhcKjMmt+PRdcM9+vv88ihEv75\n9x+oq3WGOkNCTdxy18SflU5/R+hcADppN5byalZMupOGglLs9RZ0gSYkvY65371E5OCkn3p4fuPP\nAhDQTDq4od7G0w+uoSCvyu/KF71BJrl/DAf3FZ2RMRsMMnc+ONXlNJZ5sJgl7+0m+0jLvritodfL\n6A0SqqohEGiaxjU3j2HCVN89IS3x7ze2+vRODq8oYeimFW6P6YJMnPPhg/Q4b3y7zucNh13h4L4i\nzGYbKalxRPgp3VxeWs/9t3/tkptuwmjS8cw/znflL37JnFE/gE5+W5iiwrgg/f8oWJVGxd5sgnrE\n0mvhpJ918tcbPXpFYDB6UdUUTv/cgNN04wODnJLSt175sc/O0uYYjDIz5vVj9PieHM1Y7Rbrb2/5\npM2m8MWHe10LQEpqHA8/P5frL/7Ar14F32hcft1IunQLxWFXSUqJ8dve0BumAL1vQ3WHZ/LaUW8h\n861vzugCoNM7ndDayvpVmV5zL4pDZf2qI1xy9bAzMbxfDJ1J4E48kGSZ7vPGMuTBK+lz9cxf3OQP\nTumFW+6eiMEoO029cd6xBwbqueF27xORJAnCI7zfSUqycHX2hoaZWHjFUBY1Nhv99bEZJPaJQgjn\nnWS/gXFezeL9oTC/2uOx7okeJnttQlE04rqE0LdfLP0Hxbdr8nfYFQpOVFFVaWbi1N5eZZZ1QqXL\n8Uyv77dVeZa0/hTk51Z5XUwdDpX83F+W4u2ZoHMH0MmvltQhXXj6tfNYt/IIJ/OrSUqJYcrMPoS2\nYPc3+/z+fPTuTo/qHVXRsNkU7n9yJv0Gujuk9u0Xy6MvnusSmDOb7dx1/ec+zyHLAlmWvMb3vYUg\nLrt2BC895p4P8HUH7g29Qe5QfHvdykw+/e8ep2CaotI3JYaZ8/ux5psMVEVFVTX0BplBPUMIX1fG\n6VclBxjp2caGxLNFrz5RHNx30qMHRK+X6PUb7Bzu3AF08qsmJi6Ey64dwV1/m8b5lwxqcfIHp6rj\n+HN6e00m2qwKrz37vc/yzab3BATouffxmYRFBLh2H81RFA27Q/UojTQYZZcJS3P6pcbx54en0ysp\nClkWhIQaOffCVMIjW5dFFgIu9TM56o2dW/P4+D+7MJudJa4Ou0rm4RJ2bs1jxJjuBAYbiI4NYtE1\nw7n9mfOIHtYHOdDoer9k1BPYNYqUG+e16/wdoa7GSn5uJVbLqST91NnJyF6UbXW6tklA/1roTAJ3\n0slpaJrGDZd8gMOLGqcpwGnt139QvJd3uqOqGjdf/pFX2WKjSUd4ZAAVpQ0gnHX5U2b15aobG5lr\nRwAAFOpJREFUR/k9WX/4rzRWL8vw+bzBINOrTxT3Pj6zXYYk5aX1PP/IWq9lrh7nMsosuHQwc+cl\nk/HmMo783wpUm4Pel09lwJ0X/6highaznXde28LenfnodDKqojL7/P4svHIoQghyj1Xw9qub3cpA\nb7pjAj17t24j+UugMwncSScdxFePlhDC64ReW2Pho3d3kbY5F1XVGDisK1f8fqTPjmMhBBOnJbHs\n03SnI5aq8sP6bPKOV3DPozM8JKJPR1VUtnzvW8U0Nj6YBYsGM3Zyot+Tf32djRO5ldRWW/j8g72U\nldS3aMzSHJtV4auP9jFlRl9S71hI6h0L/Xrf2eCNF3/g0P6TOOyq6/NfvewwpkA98y8aSM/ekTz1\n6nnUVDnlJlrbFf6a6VwAOunkNIQQ9BsQy2Ev5uwOh0pyqns83WZTeOyelVSU1btUQfftzOfIoRJ6\n9o7keFa5l+MorPjioNtiYrU4OJ5Vzpcf7+fS3w1vcYylJXVYfRiiSJLg7r9No0u3sFavFZw7ns/e\n38vqrw8h6yS/qqC8IeskMg4WM3Jcj3a9/0xQVlLH4fQij4XXZnV+3vMuTHXtsH7LE38THcoBCCEu\nEUIcFEKojUbwvl43RwiRKYTIEkLc15FzdtLJj8FVN47CFKBDbiZ9YDDKXPq74R4lpGmbc6mptrhJ\nQmsa2GwOErqHYTDKbtL7BqOOoSO7eZVwtttVNq5t2coQnKWY3nx6wVmxFBjsvxT1hjVHWfNNBna7\n2u7JH0AgnNf6E1JSVOtzx2NusGOztv/6fo10dAdwALgI8O5ODgghZOANYCaQD6QJIb7WNO1QB8/d\nSSdnjW49I3jylfNY+dVBMg+WEBUbxNwFA7zG/jMPFXvITYNTvvlkQQ0PPTuHLz7aR3ZmGWHhJs69\nKBVJEhzYW+j13L7u7JsTFh5A7z5RZGWWufUcSJIgsU8UYa3c3ZYW17Jjcy5Wi4MN3x49MxOjwK/c\nSEexWuxkHChBkgUpqXFuZa3xXUN9msoHBhlaDa391ujQp6Fp2mGgtaTVaCBL07Rjja/9GFgAdC4A\nnfysiYkL5pqbW7fCjIwOQqf3NEARwlnW2SMxkjsfmOr2XGVFg8/mria3spYoPlnrCmEIAQinHk5Q\nsJFb72655PLbbzJY8t/dqKqG6qe1pE4ngaBFFdXefaOoLG8gNj6k9QP6gcOhYjHbCQwyuOS1N63L\n4r23dziVVDXQ0LjlrokMG+00LYqMDmLwiAT27y50Uw01GGXOXzToFyNl8mPxY5SBJgDNDVfzGx/r\npJMfDZtNYfOGY7z/zg7WLDtMXY219Tf5yaRpSV7Nv/UGmWlz+lJSVOtWiggQERnIzHn9PBrGJFnQ\nIzGc+jrf4ztZUM0jdy9n9/Y8VNVpDiMJiZQBcbzw5gUtyhkUn6xhyX93Y7cpKA7/fYVlncQd900h\nJNSIKUCP0aRzhrWaXfbh9CIevns5JUW1/h3UBw67wgf/l8atV37MHdd9xp+u/ZTvVh8hJ7uc997a\ngc2qYG6wO0tTzQ4Wv/gDpcWnznnLXRMZM7EXer2E0ajDFKBnwaLBzJrfr0Pj+jXSahmoEGIt4G1f\n96CmaUsbX7MB+IumaR41m0KIS4DZmqbd0Pj71cBoTdP+6ON8NwE3AfTo0WNEbm6u/1fTSSdeKD5Z\nwyN/XoHF7BR7k2WBTi/z10dn0Kdfy+qT/rJvZwGLX/rBKcsvQLEr9BsYx5FDpSCcJaGTpiVx5fUj\n0TV20Wqaxo7NuSz9JJ3CE05BOlXVnN3LssQDT892M7Jp4tWnN7An7YTH5K3XSzz7xoIWDWeWfrKf\npUvSW5Si9kZSSjQPPDUbgIP7TpJ7vIKvl6R7VAkJAWMnJXLL3e03ZvnnSz+we/sJt8Y3g1GmZ2IE\nWUfKXV4PTcg6ibkXDOCSq9xlHCxmO7U1ViIiA1yf+W+BM1oGqmnajA6OJx9obirbDfAe/HSe723g\nbXD2AXTw3J38xrHZFB6+e7lbclNRNBTFwWvPbuCVf1/s9e69rQwZmcDr/73EWYHiUDl6uIS1KzLd\nqnw2rc/Gble44Y9OKQohBGMm9mLlUmc0tCmW73yPwuIXN/Ls6ws8znUovcjrnbskS2QcKGbitFML\ngN2u8PWn6WxYcxSrxUFYmMnn5C/LAk3Dq47RieOVrFp6iPkLBzJkRILzLt/Lx6ZpzgWivVSUN7Br\nW55Hp67NqnA8q8Jj8genjk9ZiafUhClAj+m0hH0n7vwYGZE0oK8QIhEoAC4DrvgRzttJJ2zZkO2z\nsqWh3kZOdrnfMsStodfLDB6egMOu8ObfN3n0C9hsCts25nDZtSMIDnF2y9ZUWzhxvNLrhF5eUk9J\nUa1HTN1olLGYvUtQ/7A+i/+8uR1VUUkd0oX6Oit5OVWueHiJxbsmj14vc94lA4mICuD9d9KwWjzH\nvn7VEeYvHAg4E6qyJPA2iiaZ7PZwMr8anV72atfpcKjo9bLHrsNo0vmVN+nEk46WgV4ohMgHxgHL\nhRCrGx/vKoRYAaBpmgO4HVgNHAY+0TTtYMeG3Ukn/rFz2wmfzymqdkZNy5uorbV6vVMFp1FMafGp\nSVhRPCUhmhBCeE0UT5nd16srls3q4OjhUmd8X9HYv6eQ7CPeLRSb73r0eomwCBMz5/VDcWg+8wLN\nF53hY7p7NSEzGGWmz22/pEJ0bJDP5LgpQIfBJCOajV2SBIFBBsafk9juc/6W6WgV0JfAl14eLwTO\nbfb7CmDF6a/rpJOzTXCw0edzAs7Y3b/bOUOMPqtNHHaF6NhTSdrwiAAiogIp8WIAbwrUE981lNxj\nFWzecAyb1cGIsT2Y9//t3X10VPWZwPHvc++8JMSECISEQMJLxUB4qRgMUWhRXjy8ubT2YHVZtdQe\ncLvbqtuzYtfTPcXWc2q3tbutnkPd1tpaV2tPpbYLKAi0YFdU5EVeEkAoLyEQQt4ISUgyM7/9Y4aU\nITMhYTKZSe7zOYdDZubOvc89wH24v9/vPs/dEzl04CxHD1fT1ubH7bZDiUTCL/adDKCmprrJzk2n\npcVH8W0jmbtoPAPSPIyflBMxAYgl7WWqL33/60/M5Mff+zMQHIaxbGHSlFzmLLz2ydbsYRmMGTuY\nIwfPhSUCj9dm3uJCpt8+hld+9iF7d1UglnDztDyWfnkq3hQd6rkWWgtI9Wule8/wg6c2RVy+uHjJ\nJO5eelNcjvubX+3knbVlYcNAbo/N1Fvzefix8AnSsv3B7l++tmBlTcsSXG6Lr62cySdlVaz//QHa\n2oIN0i8Nd3z9mzM5eriaw6VVpGd4OXTgLNs2H+lyfCNHX89TP1oU8bMXn3uP97b9tT12ywJviptV\nP1xA9rCM9u2MMZz4ay37dldguyzGT8zpkXo6jRdaeO77WzlcVoXLFVxeO3PuDSx9aCqWHXnQoqK8\nnpPHahkyNI0xY4c4ermndgRT6jK/+dVONvyxtD0JiMCMWZ9qn4yNh4A/wKsvfcSWtw9j28GhnJIZ\no3jwH0si1uM/faqet948wLGjNYzIz2T+4kICBr7z+PoOw1Rer4sHVhRz28zRbN30Ce+sO0h1VSMX\nm9uI8nBwGI/HZtb8AoZmX8fwkZkUFA4Nu2AGAoZ3txxhwx9KabzQyvjJOXzui5PD5iKOHDrH6me3\nUVcbrKcTj7aK1VWN1FQ3Mmz4wPY5kyu1tvj4yTN/pnRfZfsk9uCsNB5fNafLXcL6G00ASl2horye\nne+fRASKpuWTMzzj6l/qAReb26g+10jm9QNI60Z5BoDfvryLdWv2R1yVM3Z8FoMGp7Hrw5MRi9Nd\n4nJbWJZgWVawnr/Pj9sVLE3h9wfvNrKHpfPEd+aS1slw2eXqappY+dU3O3Rb83pdPP3ju8jK7r3K\nn79cvZ1tm46ETRpbljByzPV8+we9X4I6GWg1UKWukDtiILldLI7Wk1JS3QzPy7ym7/p8gaitJRsb\nWzl+tCbixd+2LTye4LzAvcuK+MzsG9i3q4LmpjbWvrGPivL6sHH+UyfqefH57Xxt5cwuxbVlw2F8\nkdoq+gNsWlfGvcu6dO2Jmd8fYNvmox1WDAUChlMn6jlz6nyvJfq+ShOAUkmqqCSPLW8d6tDA3OOx\nyclJ52xF5CduvV6b5Y9OZ9yknPbCdTdPy6PydANnKs53mOT1+wPs+qCclhZfl1pZnjxWG3FOxecL\ncOJ4XRfPLnatrX4C0Z5pcFnU1TVrArgK7QimVJIaOy6LKdNGBMsuhHi8Ntm56Uy4aRiWHXmis7m5\njd/+ehfNTeGr9KsqG8Iqll4uEAhELGgXycgxgyL3BHZZjOrFpiopKS4GXh+56J3PFyBvZGy9lJ1A\nE4BSSUpEWPHoDJY/Mp1JU3K5sXAo9y0r4t+fmU/JjNFRl3kaA6fLz/Oj724Oe7+2uinqsYyB9Iyu\nzQHccedYXO6Olw7bZTF7fkGX9tETRIT7lhV1mFT3eG3mLizo9pyLE+kQkFJJzLKEqbfmd2iy4vG6\nWPEvM1j9w20Rn5oNBAxnKs5TfqKOEfnBOYjUAZ7gssoID1p5vHaXl05mZKbyb0/fyU//M9hWUYAh\n2dex/JHpnRaii4fi6aNwu21ef3kXlRXnychMZdHdE5i9oPcSUV+mCUCpPqi1xUfZ3sqI9XgusW2L\nupqm9gQwaUpo2OiKkR7LEj4754ZuHT9/dLCtYl1NEwYSuuRySnFeezlo1T06BKRUHxMIGFb96zo2\nri2jrTX6wn9fm5/80X8bk/emuHn4sRm4PXb7EI43xUXO8Ay+8PfhD8QZYzh+tIZ9uyu40BC9NHXm\noAGOXW/fH+gdgFJ9zN6dFZw6Wd/pNh6vzWdmfYqMgSlh7xeV5PPM84t5d8sR6mqaGDcxh6KS/LA2\nipWnG3j2u5uprW7CsgRfm5+5d43nnvunOPoJ2/5IE4BSfczeXac6beQyIM3Doi9MZP7nCiN+Pjgr\njcX3TI74WcAf4Hvf2kBtdVPYMd5Ze5DsYencPndsLKGrJKNDQEoluebmNs6cOt/+PEB6RkrUbS1L\neHzVHBaG+g53V+m+SpoaWzskmNYWH2t/p0V8+xu9A1AqSfna/Lz83x/yly1HsGwLEzDMmn8jcxaM\nY81reyLeBaSmuWMqyFZ9rhETZVqhvq75mverkpPeASiVpF5a/T7/96dgqYOWi772piyb3z7EAyuK\nO2zvcll841uzYupwNmrMIEyUBwz0war+R+8AlEpCjRda2L71WIfuV60tfjatPcjzv76Homn5/P71\njzl7uoFxE7KZs7CA1AGxPfyUP3oQNxRkBRvLXHZsj8dmyf1TOvmm6os0ASiVhKqrGrFdVocEAGAw\nNNRfZNCQNB5cMa3Hj/3Yk3fw2ksfhaps+snJzWDpV25h3ERtu9jfaAJQKgkNzkrDH6U1oiCkD4w+\nERwrj9fFAyumcf/yYlqa29izs4I9O8opP17LbbePYWBm5Po7qu/RBKBUEkq7zkvJZ0exfduxsDaP\nHq/N7IUFEYux9bTGhlaeWrmeutpmWi76cHts3nh1D489eQeFk4ddfQcq6ekksFJJ6ksPT2PG7WNw\ne2xSUlzBTl7zbmRJnNpYXul/frGDc2cb26uEtrX6aW3x85NntkZt3K76lpjuAERkCfBtYDxQbIyJ\n2L5LRI4BDYAf8HW1W41STuZy23zpqyV8cVkRdTVNDBo8oFebn3/wl+P4I9TbDwQMB/dXhjWJV31T\nrENA+4C7gZ92Yds7jDHnYjyeUo6TmuomdXjvdzOLOgchhA1Lqb4rpiEgY0ypMeZgTwWjlEoehZNz\nIlYb9fsCFEzouebvKnF6aw7AABtE5CMRWd7ZhiKyXER2iMiOqqqqXgpPKXWlpQ/dQmqqG9v+22XC\n47W558GbY37eQCWHqw4Bicg7QE6Ej540xrzZxeNMN8ZUiMhQYKOIlBljtkba0BjzAvACwNSpUzsp\neaWUiqfcvIE8/V93sf7NAxzcX8ngrDTmLS5k3AR9HqC/uGoCMMbMifUgxpiK0O9nRWQNUAxETABK\nqeQxOCuNf/jKLYkOQ8VJ3IeARCRNRNIv/QzcSXDyWCmlVALFlABE5PMiUg7cCqwVkbdD7+eKyLrQ\nZtnAuyKyB/gAWGuMeSuW4yqllIpdTMtAjTFrgDUR3q8AFoR+Pgp8OpbjKKWU6nn6JLBSSjmUJgCl\nlHIoTQBKKeVQYjrrLp1gIlIFHE/AoYcATipboefbvznpfJ10rhD5fEcaY7K68uWkTgCJIiI7nFSw\nTs+3f3PS+TrpXCH289UhIKWUcihNAEop5VCaACJ7IdEB9DI93/7NSefrpHOFGM9X5wCUUsqh9A5A\nKaUcShNAFCLyHyJSJiIfi8gaEclMdEzxJCJLRGS/iAREpF+uohCReSJyUEQ+EZEnEh1PPInIiyJy\nVkQcUXhRRPJEZIuIlIb+Hj+S6JjiSURSROQDEdkTOt9V17IfTQDRbQQmGmMmA4eAbyY4nni71N6z\nX5bpFhEbeB6YDxQC94lIYWKjiquXgHmJDqIX+YBvGGPGAyXAP/XzP98WYJYx5tPATcA8ESnp7k40\nAURhjNlgjPGFXm4HRiQynnhzQHvPYuATY8xRY0wr8BqwOMExxU2o4VJNouPoLcaY08aYnaGfG4BS\nYHhio4ofE3Qh9NId+tXtCV1NAF3zZWB9ooNQMRkOnLzsdTn9+ALhZCIyCpgCvJ/YSOJLRGwR2Q2c\nBTYaY7p9vjGVg+7rutLuUkSeJHh7+UpvxhYPPdTes6+K0N68+/9jUslNRK4Dfgc8aow5n+h44skY\n4wduCs1PrhGRicaYbs35ODoBXK3dpYg8CCwCZpt+sF62J9p79mHlQN5lr0cAFQmKRcWBiLgJXvxf\nMca8keh4eosxpk5E/kRwzqdbCUCHgKIQkXnASuDvjDFNiY5HxexDYKyIjBYRD3Av8IcEx6R6iIgI\n8HOg1BjzbKLjiTcRybq0MlFEUoE5QFl396MJILrngHRgo4jsFpHViQ4onqK19+wvQhP6/wy8TXCC\n8HVjzP7ERhU/IvIq8B5QICLlIvJQomOKs+nA/cCs0L/X3SKyINFBxdEwYIuIfEzwPzcbjTH/292d\n6JPASinlUHoHoJRSDqUJQCmlHEoTgFJKOZQmAKWUcihNAEop5VCaAJRSyqE0ASillENpAlBKKYf6\nf9M/c6keS0VqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d4b85c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_X, train_Y = sklearn.datasets.make_moons(n_samples=1000, noise=.4)\n",
    "plt.scatter(train_X[:, 0], train_X[:, 1], c=train_Y, s=40, cmap=plt.cm.Spectral);\n",
    "plt.show()\n",
    "train_X = train_X.T\n",
    "train_Y = train_Y.reshape((1, train_Y.shape[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters to train: 37\n",
      "=== iteration 0, cost: 1.4393831166385411, accuracy: 0.5\n",
      "=== iteration 10, cost: 0.9083737702014374, accuracy: 0.53125\n",
      "=== iteration 20, cost: 0.6873859880839983, accuracy: 0.453125\n",
      "=== iteration 30, cost: 0.5965464938928928, accuracy: 0.625\n",
      "=== iteration 40, cost: 0.5496586486512001, accuracy: 0.765625\n",
      "=== iteration 50, cost: 0.5239491249357613, accuracy: 0.828125\n",
      "=== iteration 60, cost: 0.5119184000795318, accuracy: 0.8125\n",
      "=== iteration 70, cost: 0.503399221539247, accuracy: 0.734375\n",
      "=== iteration 80, cost: 0.4972085018142332, accuracy: 0.875\n",
      "=== iteration 90, cost: 0.48941489424015844, accuracy: 0.8125\n",
      "=== iteration 100, cost: 0.48741966694763084, accuracy: 0.84375\n",
      "=== iteration 110, cost: 0.48573407943200164, accuracy: 0.90625\n",
      "=== iteration 120, cost: 0.47824748835122355, accuracy: 0.8125\n",
      "=== iteration 130, cost: 0.47546903556744907, accuracy: 0.796875\n",
      "=== iteration 140, cost: 0.4759866246672335, accuracy: 0.796875\n",
      "=== iteration 150, cost: 0.47006559928635716, accuracy: 0.84375\n",
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     "data": {
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V7Orqpaoiw5V3rud1Jy1ifkMN92zcQVtHD23t3WQrMnR099G+r4+KCjGjOsuy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\nzCzv/wOyHWKZBpfGuQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12718b748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Neural net (sgd) model train accuracy: 0.8420000000000003\n"
     ]
    },
    {
     "data": {
      "image/png": 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6+iWe+sF/bDudW9RrAblMGH7puzHcerVjN3GQacpxBekRi+ERG8sSrK/WyGUG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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x129334898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_nn_sgd = NeuralNetworkImpl(layer_sizes=[5, 3, 1],\n",
    "    layer_activations=['relu', 'relu', 'sigmoid'], alpha=0.001,\n",
    "    epochs=10000, optimization_algorithm='sgd')\n",
    "model_nn_sgd.train(train_X, train_Y)\n",
    "\n",
    "accuracy = compute_accuracy(model_nn_sgd, train_X, train_Y)\n",
    "print(\"Neural net (sgd) model train accuracy: {}\".format(accuracy))\n",
    "plot_decision_boundary(model_nn_sgd, train_X, train_Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== iteration 0, cost: 1.2162503663135766, accuracy: 0.5625\n",
      "=== iteration 1000, cost: 0.33750567949396715, accuracy: 0.859375\n",
      "=== iteration 2000, cost: 0.3043807689677153, accuracy: 0.875\n",
      "=== iteration 3000, cost: 0.3283309505065512, accuracy: 0.859375\n",
      "=== iteration 4000, cost: 0.35922307018833005, accuracy: 0.859375\n",
      "=== iteration 5000, cost: 0.37058402494678505, accuracy: 0.890625\n",
      "=== iteration 6000, cost: 0.3080489487265856, accuracy: 0.90625\n",
      "=== iteration 7000, cost: 0.39969824229406664, accuracy: 0.828125\n",
      "=== iteration 8000, cost: 0.45000208242940226, accuracy: 0.78125\n",
      "=== iteration 9000, cost: 0.30014233813375346, accuracy: 0.859375\n"
     ]
    },
    {
     "data": {
      "image/png": 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06zwFr9NukadQkAiJ+F3LzlPIq4epgU4/elu8qyKvEEvlFqWbGVjjRqG1AaNw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\n7UBkyR5fPwAGALK5POYzOQTc5cJHykb8sjqOsJx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JmJQ+oTCI7I2pNSXH1HZpLv12YGdE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\ntk4CSbUiAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c26b144a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Neural net (momentum) model train accuracy: 0.8620000000000003\n"
     ]
    },
    {
     "data": {
      "image/png": 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Uix7VtMfEtEmhUD9wkPugxmDQawxTcvxUhO1Nh2rVr3sYmwhOF81tBxt11/F3YaFBOBoe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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c2e1a4550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_nn_mom = NeuralNetworkImpl(layer_sizes=[5, 3, 1],\n",
    "    layer_activations=['relu', 'relu', 'sigmoid'], alpha=0.001,\n",
    "    epochs=10000, mini_batch_size=64, optimization_algorithm='momentum')\n",
    "model_nn_mom.train(train_X, train_Y)\n",
    "\n",
    "accuracy = compute_accuracy(model_nn_mom, train_X, train_Y)\n",
    "print(\"Neural net (momentum) model train accuracy: {}\".format(accuracy))\n",
    "plot_decision_boundary(model_nn_mom, train_X, train_Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== iteration 0, cost: 1.2162503663135766, accuracy: 0.5625\n",
      "=== iteration 1000, cost: 0.25408785204626333, accuracy: 0.890625\n",
      "=== iteration 2000, cost: 0.2628591787372659, accuracy: 0.90625\n",
      "=== iteration 3000, cost: 0.21712561451578388, accuracy: 0.921875\n",
      "=== iteration 4000, cost: 0.2893028893630178, accuracy: 0.859375\n",
      "=== iteration 5000, cost: 0.3003167790621004, accuracy: 0.890625\n",
      "=== iteration 6000, cost: 0.21788447560580135, accuracy: 0.953125\n",
      "=== iteration 7000, cost: 0.3130555982579666, accuracy: 0.84375\n",
      "=== iteration 8000, cost: 0.41464503989961493, accuracy: 0.828125\n",
      "=== iteration 9000, cost: 0.31873264678472624, accuracy: 0.875\n"
     ]
    },
    {
     "data": {
      "image/png": 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OOfnPSRS6WRTcozdan/sorscUCqa21sa6hblEwXhSlTEFGZxOZguIaKIQC/rm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\nX9nptrabpC5JuyX9JC/PkbQtx3x3Hqm3UiRNk7RR0h9yzt9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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c2e505748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Neural net (rmsprop) model train accuracy: 0.8800000000000003\n"
     ]
    },
    {
     "data": {
      "image/png": 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IkPj2PFBtFyno6ZMg8NJUTUPFqO2drxbVyR0iPvGgExiEBwwhBBNTqm/fYu/x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\npZRSHZoQlFJKAfD/AYNdkDxCsLEpAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c345dd780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_nn_rmsprop = NeuralNetworkImpl(layer_sizes=[5, 3, 1],\n",
    "    layer_activations=['relu', 'relu', 'sigmoid'], alpha=0.001,\n",
    "    epochs=10000, mini_batch_size=64, optimization_algorithm='rmsprop')\n",
    "model_nn_rmsprop.train(train_X, train_Y)\n",
    "\n",
    "accuracy = compute_accuracy(model_nn_rmsprop, train_X, train_Y)\n",
    "print(\"Neural net (rmsprop) model train accuracy: {}\".format(accuracy))\n",
    "plot_decision_boundary(model_nn_rmsprop, train_X, train_Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters to train: 37\n",
      "=== iteration 0, cost: 1.4053196931163985, accuracy: 0.5\n",
      "=== iteration 10, cost: 0.6084992777577702, accuracy: 0.625\n",
      "=== iteration 20, cost: 0.49316718354962846, accuracy: 0.8125\n",
      "=== iteration 30, cost: 0.4743010309031762, accuracy: 0.859375\n",
      "=== iteration 40, cost: 0.46183205553787854, accuracy: 0.859375\n",
      "=== iteration 50, cost: 0.4530006036117175, accuracy: 0.828125\n",
      "=== iteration 60, cost: 0.4434257306894511, accuracy: 0.828125\n",
      "=== iteration 70, cost: 0.43594382044463464, accuracy: 0.75\n",
      "=== iteration 80, cost: 0.42897237071539396, accuracy: 0.875\n",
      "=== iteration 90, cost: 0.42178723101437304, accuracy: 0.84375\n",
      "=== iteration 100, cost: 0.4200510041398407, accuracy: 0.859375\n",
      "=== iteration 110, cost: 0.4171099687777492, accuracy: 0.9375\n",
      "=== iteration 120, cost: 0.41069422909649045, accuracy: 0.8125\n",
      "=== iteration 130, cost: 0.40936179065112305, accuracy: 0.8125\n",
      "=== iteration 140, cost: 0.408792261669376, accuracy: 0.8125\n",
      "=== iteration 150, cost: 0.4039778363557236, accuracy: 0.84375\n",
      "=== iteration 160, cost: 0.40333678096298214, accuracy: 0.84375\n",
      "=== iteration 170, cost: 0.40225733308922607, accuracy: 0.84375\n",
      "=== iteration 180, cost: 0.40263186206034085, accuracy: 0.828125\n",
      "=== iteration 190, cost: 0.39807598444743014, accuracy: 0.90625\n",
      "=== iteration 200, cost: 0.3969064872944076, accuracy: 0.796875\n",
      "=== iteration 210, cost: 0.39551221881418197, accuracy: 0.84375\n",
      "=== iteration 220, cost: 0.39695462043361296, accuracy: 0.84375\n",
      "=== iteration 230, cost: 0.39309877565523516, accuracy: 0.875\n",
      "=== iteration 240, cost: 0.38984606691290413, accuracy: 0.8125\n",
      "=== iteration 250, cost: 0.3945201276458443, accuracy: 0.890625\n",
      "=== iteration 260, cost: 0.38824420838474016, accuracy: 0.921875\n",
      "=== iteration 270, cost: 0.3880432435680229, accuracy: 0.859375\n",
      "=== iteration 280, cost: 0.38510058567473393, accuracy: 0.8125\n",
      "=== iteration 290, cost: 0.3784131236793174, accuracy: 0.875\n",
      "=== iteration 300, cost: 0.3691521221527249, accuracy: 0.8125\n",
      "=== iteration 310, cost: 0.3657236803496844, accuracy: 0.859375\n",
      "=== iteration 320, cost: 0.3605078214171096, accuracy: 0.84375\n",
      "=== iteration 330, cost: 0.3554803745187356, accuracy: 0.765625\n",
      "=== iteration 340, cost: 0.35160873798973197, accuracy: 0.953125\n",
      "=== iteration 350, cost: 0.34397691866072205, accuracy: 0.890625\n",
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      "=== iteration 380, cost: 0.3309483489252827, accuracy: 0.890625\n",
      "=== iteration 390, cost: 0.33009161291562267, accuracy: 0.90625\n",
      "=== iteration 400, cost: 0.3241419757214448, accuracy: 0.875\n",
      "=== iteration 410, cost: 0.3224327923930944, accuracy: 0.859375\n",
      "=== iteration 420, cost: 0.31749610070836215, accuracy: 0.84375\n",
      "=== iteration 430, cost: 0.3171927765478239, accuracy: 0.703125\n",
      "=== iteration 440, cost: 0.31439766740475494, accuracy: 0.875\n",
      "=== iteration 450, cost: 0.31122541212221666, accuracy: 0.84375\n",
      "=== iteration 460, cost: 0.310385887023099, accuracy: 0.875\n",
      "=== iteration 470, cost: 0.30876020455539177, accuracy: 0.84375\n",
      "=== iteration 480, cost: 0.30541352850912096, accuracy: 0.890625\n",
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      "=== iteration 670, cost: 0.2930137750554933, accuracy: 0.796875\n",
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      "=== iteration 850, cost: 0.29129560396656284, accuracy: 0.84375\n",
      "=== iteration 860, cost: 0.2938437032172331, accuracy: 0.859375\n",
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      "=== iteration 1020, cost: 0.2908335412668285, accuracy: 0.9375\n",
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     ]
    },
    {
     "data": {
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n8JPHtjAUUFVRRt/As7vXcut98tw61u/qZtaMKrYkrfzs7wUyvQGNNVXc9VTH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\nqQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1271a6400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Neural net (adam) model train accuracy: 0.8650000000000003\n"
     ]
    },
    {
     "data": {
      "image/png": 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dm9SqPtl0kOrpORGcWqVtN3GzFbbtCFKDJgODFqYpWF+tkk0f7MZVK5LEgEmh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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1292f9c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_nn_adam = NeuralNetworkImpl(layer_sizes=[5, 3, 1],\n",
    "    layer_activations=['relu', 'relu', 'sigmoid'], alpha=0.001,\n",
    "    epochs=10000, mini_batch_size=64, optimization_algorithm='adam')\n",
    "model_nn_adam.train(train_X, train_Y)\n",
    "\n",
    "accuracy = compute_accuracy(model_nn_adam, train_X, train_Y)\n",
    "print(\"Neural net (adam) model train accuracy: {}\".format(accuracy))\n",
    "plot_decision_boundary(model_nn_adam, train_X, train_Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  64/1000 [>.............................] - ETA: 0s\n",
      "Keras NN loss/accuracy:\n",
      "loss: 0.30106549882888795\n",
      "acc: 0.8740000009536744\n"
     ]
    }
   ],
   "source": [
    "# Compare the results with a Keras model using Adam\n",
    "model = Sequential()\n",
    "model.add(Dense(5, input_dim=2))\n",
    "model.add(Activation('relu'))\n",
    "model.add(Dense(3))\n",
    "model.add(Activation('relu'))\n",
    "model.add(Dense(1))\n",
    "model.add(Activation('sigmoid'))\n",
    "model.compile(optimizer='adam',\n",
    "              loss='binary_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "model.fit(train_X.T, np.squeeze(train_Y), epochs=10000, batch_size=64, verbose=0)\n",
    "score = model.evaluate(train_X.T, np.squeeze(train_Y), batch_size=64)\n",
    "print(\"\\nKeras NN loss/accuracy:\")\n",
    "for label, value in zip(model.metrics_names, score):\n",
    "    print(\"{}: {}\".format(label, value))"
   ]
  },
  {
   "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}