{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "### Updates to Assignment\n", "\n", "#### If you were working on the older version:\n", "* Please click on the \"Coursera\" icon in the top right to open up the folder directory. \n", "* Navigate to the folder: Week 3/ Planar data classification with one hidden layer. You can see your prior work in version 6b: \"Planar data classification with one hidden layer v6b.ipynb\"\n", "\n", "#### List of bug fixes and enhancements\n", "* Clarifies that the classifier will learn to classify regions as either red or blue.\n", "* compute_cost function fixes np.squeeze by casting it as a float.\n", "* compute_cost instructions clarify the purpose of np.squeeze.\n", "* compute_cost clarifies that \"parameters\" parameter is not needed, but is kept in the function definition until the auto-grader is also updated.\n", "* nn_model removes extraction of parameter values, as the entire parameter dictionary is passed to the invoked functions." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Planar data classification with one hidden layer\n", "\n", "Welcome to your week 3 programming assignment. It's time to build your first neural network, which will have a hidden layer. You will see a big difference between this model and the one you implemented using logistic regression. \n", "\n", "**You will learn how to:**\n", "- Implement a 2-class classification neural network with a single hidden layer\n", "- Use units with a non-linear activation function, such as tanh \n", "- Compute the cross entropy loss \n", "- Implement forward and backward propagation\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1 - Packages ##\n", "\n", "Let's first import all the packages that you will need during this assignment.\n", "- [numpy](https://www.numpy.org/) is the fundamental package for scientific computing with Python.\n", "- [sklearn](http://scikit-learn.org/stable/) provides simple and efficient tools for data mining and data analysis. \n", "- [matplotlib](http://matplotlib.org) is a library for plotting graphs in Python.\n", "- testCases provides some test examples to assess the correctness of your functions\n", "- planar_utils provide various useful functions used in this assignment" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# Package imports\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from testCases_v2 import *\n", "import sklearn\n", "import sklearn.datasets\n", "import sklearn.linear_model\n", "from planar_utils import plot_decision_boundary, sigmoid, load_planar_dataset, load_extra_datasets\n", "\n", "%matplotlib inline\n", "\n", "np.random.seed(1) # set a seed so that the results are consistent" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2 - Dataset ##\n", "\n", "First, let's get the dataset you will work on. The following code will load a \"flower\" 2-class dataset into variables `X` and `Y`." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "X, Y = load_planar_dataset()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Visualize the dataset using matplotlib. The data looks like a \"flower\" with some red (label y=0) and some blue (y=1) points. Your goal is to build a model to fit this data. In other words, we want the classifier to define regions as either red or blue." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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nZ8S/WfgZO3FwxMT9m0dUl6C3mjoU3Qnl6NX38jfS56Wbq5140a5DbHn2azY/+SVHNjf9xbfdb/2GvcLMge4Dsev0DicOIMtoOh37egwhY+GWerm3rCiMX/A6fs3C0Qf5owv0QzEZaD1tFOP+eZkjm/a5PU8xGQjt0gpJpyApMlEDOzNxxTs+J96E8EaM3AKMFkKUS5KkB1ZJkjRXCLHOC9f2UQ8UbD3gcaGyYGsytrLKWinr1SctJvQnsl9H8tfvQa10xM8VPwOBbZrT7vrzANj81Jfsfut3NKsNIQRJ7/xOm6vHMviT+5ts7NycX4IAiqKag+R+HFUa2cztdm8Q1q01l6f/zOGlWzHnFhE1sAvB7VoAYIwIcVv8I+t19H7hJuIm9EdoAl0tJXR9NBx1HpELB8f++vqj//nK7powsk6puTLSTUy0oZEVhfHzXqP/m7cT2bcj4T3b0uu5G5i89gN0fkbyN+xl99u/O7rbqBpoAnulhZQfF5M5Z31jm++R6KHdHJ9vDR9/7Nhe9WqDrFNoMa4vba8ZV+3EAbo9eJnbAivZoCNuQn8Ug97nxJsoXslakSRJATYD7YAPhRBN95fkg/AebdEH+mEvr3LeIUtED+mGPsA1pNEYyHodnW67gE63XeCyL/mb+W6zKewVZvZ/MafJiTxlZ5Qw4+ft7DV0RBsRTkBZERVBYXBSP01Jlpj42nWNYmPH6ZMp2pnCgS/mIhsctQM6fyPj5r6KYtCf4mwfjYlXHLkQQgV6SpIUCsyUJKmbEGLXicdIkjQdmA4QHx/vjdv6OEMkWWbkL0+xcOLjaHYVzWJD529E8Tcy9POHGtu8WmGvMLt2hj+KrazK7fbGIj21iBcfm4fVYnekcAeFItltDulfTUXT6ZFUFUWRueu/Y/CPaJxG1JIkMeiDe0l87Cry1iZhDA+i2cgedVr78NEweDWPXAhRLEnSUuB8YNdJ+z4FPgVH1oo37+vj9Gk2LJFL9n3Dvs/nULovg8j+nWh3/XiMoYEITSNt5ioOfD0PzabS9uqxtLliVK37PjYE8RcN5eAPi9wWqxhCAhrBIs/88vVmLGbnVEqh0yNUO63276QiJBy/ijLa2Y7Qa2DjjMZPJqJXO4LaNEeSfRnKZwN1/mVKkhQF2I46cT9gHPBanS3zUe/4x0bS62lnxyGEYOm0F8iat6G6I3ve6l3s+/Qfzl/8ZpOZYof3audRgOvwkq0ITWsyTmj/HtfKVQBF0/CvLKf1gR0Ate7DWV+U7Mtg2RUvULIvA0mW0YcEMOSzB2k5cUCj2uXj1Hjjm94cWCpJ0g5gI7BQCPGPF67roxHIXrTZyYmDI4xRuC2ZlJ+WNKJlzlSk5nrMrLFXVDnZ39iYTJ5ffjrb8Th/WPfWDWGOW2zlVcwedg+FO1IcwmSVZqoOF7D08uco2JbcaHb5qB3eyFrZIYToJYRIFEJ0E0I87w3DfDQOqb8td+sE7RVmDv6wqBEsck9wu1iP2ua6QP9aCXM1FCPOa4/e4BpnloQgLN9R5q74GenTiL1KD/2yFLXK6qLDopqt7Hz950ayykdtaRpzTx9NBlmvAw852PIpdDoaEv/YSOImDnBRbNT5m0h89IomE1YBuPDy7rTvFIXeoKDoZIwmHQZJ0HP7CmQEIZ3jGTPjORc9m4biyOb97PnwL/ezGE1QtCu1wW3ycXo0ndUrH02CNleNIfmbBdgrnX/UugAT7W84v5Gscs/w7x5nze1vk/rrcocYlBB0e+hyuj08rbFNc2LR7L2k7D+CJIHQBNHNAnngydGERVzrkEdohBdk3trd7HjtZ/JW78JSXA4edMklWW7UkI+P2uHTWvHhhBCCtXe9x8FvFmCvsoAQ6AJMxI7tzajfn22SqWjW0grMecX4x0U1uYKVlYsP8u2n67FajlfSKjqZVq3DeOaNiY1i06Ffl7HyptcdipOn+Pkr/kYmr3n/tPV3fNQPnrRWfCNyH044convoe2Vozn4g6MVWOvLRxE7tneTCleciCE4AENw00o5BChNzuKPbzc5OXEA1a6RmV5MWkohrdqEN6hNms3OmtvfqZY9OBXDvn7U58TPAnyO3IcL0tFu9r6O9mdGWWoOSy5+mpJ9mRSPmeZSvQkgyxI52aUN7sjz1u/BWlK7Zsr6YP8mOQPz4YrPkfs4JylPy2Xf57MpO5hNzNDutLt2XIMIgWmqyqzRD5FKMJb4zuisZmwm1/tqmiA2LqTe7TmZna/95LEi1h216bHqo/HxOXIfp4WluJyD3y6gYFsyoV1a0f7G8zFFNLxDqoms+RtZfMkzCLuKZrWT8fdatr/4PRds+IiAuPrpvnOMNd+sZHGX0QhAU3RImgpCc1I61OllWrUJp2XCqZt3eBPVaiNrgQe9cTdIskyzUT3rzyAfXsPnyH3UmuK96cweeg+q2YpaaUHxM7D9xe+ZsPQtInq1b2zzAEcMeNlVLzrFgG0VFirQseKu95nwZ/2VOaiqxvdzM1B1xwuAhOJoGiFpKjICyWSgZ984br5rUL3Z4dE+s2ueuBNHm0cofkYkRWLMzOebTCWvj5rxOXIftWbFta849KqPOgO1yopaZWXZVS9ycdLXTUIDPG9tkpP+SnZ8e1K69EWTFdapEofeXMGNdw7Cz8/7Dmp/Uh6aJAMnpfLJMpLdzvmVe5n88wv4BzROZo0+yJ/AVjGUHXTttSmZ9Iz47gmKtiXj1zyCNleMwhjeOOJdPk4fnyP3USuq8ooo2nXI7YiuIj2fspTDBLeNbQTLnNHsxzNEcmMTONCtP+KEEfL6lYfITy/gmfcu8vq9LWY7kqLg4sgBTZEZ/uzVjebEAYRdJbhDnIsjl/0M9H7uBlpfMpzWlwxvJOt81IWmmU/mo8kh7KrHEbdqsVLQRNqsRQ/uWt0041Cn3k5OHABJJiWtlJ1ztnr93u07R6O6K6wRgtax/jQbnuj1e54O6+//kJxl2503ShLNhnan+0NNq4jKx+nhc+Q+aoVf8wgCWka736kJVt70OsVJqQ1qkzt0JgNDPn0Qxc+I2T/Qw1ESc95f7PV7BwQauOjKHhiMx1P2ZBmMfnpuenSs1+93OtjKqzjw5TxHEdCJCEHuyp3YyiobxzAfXsHnyH3UCkmSGPrlw8hG97FltdLC9pd/aGCr3NNm2igmr33fk2QMSBIFhXVvPqFarFTmFDqFcyZd1JW7Hx1B5+4xxDQPYuiotrzw9iTiGzhD5WQqDxc4ZAzcIOsVKrKONLBFPryJL0buo9bEDOlG9JBu5CxxH5bIWbWzgS3yTHhiWyLDNpJf7EYhUQiCtdpVNrpDtdrY+PD/sf/zOSAEislAjyevoev9lyJJEom9W5DYu8WpL9SABLSIdNuEAxzrCvWdlumjfvGNyH2cFqX7Mz3ua2qpatNuGeDI4z4JWVMZPqnLGV939S1vsv/zOahVFlSzFWtxOVuf/pqk92fUxdx6RedvotPtF6CcVOCj+BvpcOtk9IFNo0+rjzPD58h9nBY1KfXFDGlaJf19hyQwZGAssqo68rhVO7JqZ5CxiKFPntniXlVuIYd+W+4Sa7ZXmtn+wvcIzf2otynQ97XpdLrjQhR/I7oAE4qfkU7/uYD+b97W2Kb5qCPeaPXWEvgWiMGhpfapEOLdul7XR9MkfuoQ9nzwJ8LuPNKV9To63z21cYzygCRJ3PrEeUxMLWTVz+sRlWaGTOlOy77tzviaJfszUUwGNItryMZWVomtrBJDiKdF1sZFVhT6v/4fej93A1W5RfjFhKHz85Xgnwt4I0ZuBx4UQmyRJCkI2CxJ0kIhRJIXrl1NRbmFzesyMFfZ6NKjOXHxod68vI9akvjoFaT8uBhrUTmazdFQWPE30nLiACJ7d2hk69zTIiGcaY9N8Mq1AhOauXXiALJRj64RQxRCCAo276c0OYvQLq08qhbq/IwEJTRrYOt81Cd1duRCiMPA4aP/XyZJ0h6gBeA1R755XTqfvLUKSZJQNQ1Zkug3pBW33D0YWW78asJ/E34x4Uzd/hk7X/+F9Flr0Af5EzW4C0U7Uvij0/U0G9mTxMeuPGcdRWDLaJqP7kX24i1ODl3xN9L13ksaTS3QfKSE+eMfoXR/JpIso6kqkb07MPafl5qkxK8P7+LVxhKSJCUAK4BuQojSk/ZNB6YDxMfH90lLS6vVNctKzTxwywysVuepvMGocN30AQwb4z2t5LJSM7IsExDYtJoTNGU2P/0lSW//Ud0mTNIp6PyNXLD+I0I6tmxk6+oHW1kly699hez5G5GNejSrnQ63TqL/W7c3miOfN/YhclbuRBydJYFjhhA/ZTCjfnm6UWxqaqiqRvLefFRVo13HKAxG9+PY8jIL+5PyMJp0dOwag053fClRUzWqqmz4+emRlYZfYqz3xhKSJAUCfwD3nezEAYQQnwKfgqNDUG2vu2GVe4dvtagsnL3XK448eW8+X3ywlrycMgTQtn0Et947hOhmQXW+9rlMZU4hu9/4FfWEkamwq9jKqtj46KeM/fOFRrTOu1Rk5rPlma9I+2sNms2OpAmEEAS0jKbv69NpOWFAo9lWebiA3NW7nJw4gGaxkTZjJZWHC/BvHtFI1tU/NpvK2uWHWLcqFYNBYcTYdvTsF+dUiZy04zAfvLEC1S6QJIeM8PW3DWDIyDZO1/r791389csOdDoZgUBRZO57YhTtOkUx69cdzJu1B5tVxWDUMfmSrky8qGuT0BjyiiOXJEmPw4n/IITwag5WZaUVu919JkBlhbXO18/JLuX1ZxZhsRz/ERzYm8/zj87lzU8uwnQKcaX01CJm/LiNg/uOEBxqYuLUrrTpEEFxYRUtW4URGHzuLiblLNuGZNDByTFjIcheVHu51IbGWlLOwR8WU7TrEGHd29D26jE1hh+qcguZmXgLtmLXhgzFu1NZeulznDfvVZoNa5wSfHN+MYpB7zZ2L1SNeWMfZuq2Tx2Ntc8xrFaVlx6fR3ZmSXUnpu2bs4huFsRNdwykQ5doiouqePulpS6dmr7+eB2xcSG0bud4yW3flMVfv27HZtOw2Y4f+7/nFzNsbDuWLzxQfQ273cqfv+zAbte48PLGlV4A72StSMAXwB4hxFt1N8mZzt2bodfvcnK0AIoikdi77iJN8/5KcvqjgUMXympRWbviEB27xjDjx23s3ZWLf4CB8yZ3YvSEjsiyxIpFyXz10Tq0o0L9pSVmPntvNZIsYTAo2G0qQ0e34/rbBpyTsXxdgMnjaERnapovsOKkVGYPuw/NasNeYUYXYGLLU18yadV7hHaKd3vO1ue/rXbiRZHNONilLxVBYeitZloe3E1cShIbH/k/Llj7YUM+SjXB7eNqTHssT88ldcZK2kwb1YBWNQyz/9hJWkqhk5abpgpyskp57emFNG8RQl5umYsTB7BZVRb8vYf/3D8UgN++24LN6vo52lWVJXP3u+joWC0qc2bsZuJFXdHrG7eTkjde0UOAa4GdkiRtO7rtCSHEHC9cm7YdIunULYY9u3Kq/xiyLOHnr2fypXXPWz50sKDaEZ+IxWxn55YsfvxiEzabihBQVmrh52+2sHLJQSKiA9i8LsOlea0QIFSBucrx4lm24AA7tmRx4eXd6TuwFYHBRlYtPciMH7dTcKSC0DA/plzajdETOjaJKdrpEDvOJVQHOGKz7a4/r4GtqR3LrnwJa/FxKV57hRkqLSy/+mUu3PyJ23NSf10OQGFULLv6jUbTOX42Vr8ADnXqRVVAMMrWxmsmrvMz0uOpa9nyxBduHbpaYSZz7vqz0pEfziphxeKDVJRZSOzTgl794lCOxqYtZht//77Lo8S6qgoy04s9XlsIyMt1vKCrqmwej7XbBJLk/iZCQHFhFVExjZty6o2slVVAvXkgSZK45/GRLJ6zl8Xz9mMx2+nRpwUXXp5IeETdW3e1iAshLaUIcZIzl2WJzetdqxhtVpXUg4WkHiys9T0Kj1TyzScb+P7zTfQbHM+mtenVL6Xiwip+/mYLZaUWpl7Ro24P08DoTAZG//Eci6c+hdAEapUFXaAfIZ1a0uv5GziyZT+7/vcbpfsyiOzXiW4PXd6oUrflGXmUHsh0leIVgpKkVCqzj+AfG+lynmZ1vJSTu/arduLV+3R6Dse3p2NRan2ZXSu6PzyN1N+XU7DJVYVS0imYIptWF6fasGTefn78chOqqqGpgnUrU4mNC+HRF8ahqYKNa9LcDsJqi04n06GzQwhuy7oMJFlCqJ4dtjtsNpXiosqz35E3BDqdzPgpXRg/5czLqj1x/oVd2HiCYz1GXb4g7tA0gWZVWbPskMs+q0Vl9szdTJjaBaPJc0zealUpLzUTHGJC18hTuWPEjunN5Wk/kfLzUqryiogZ1JXYcX1I+XkJq256w5FrLqBw+0EO/rCQ8xf9j6j+nRrFVs1qd3TBcYcso1rtbneFJbYhd/UuKoNC3Z+qqYRc4Z089TNFkiT6vTadRRc8ib3S7LRP1utof2Pj2gdQeKSCuX8lsX93HpHRgYy/sHO1Iz2Z4sJKfvxiIzbb8RmGxWwnLaWQu6//rfr3WZekO71BYdxkx3exstKKLEloJ0+xT4GmCV5/ehEDhiZw892DGm1WfVY48vokvnU4dzw4jM/fX4vdrqJpwhFKaeBKa1mWyT1cRnzrcNIPFfLTV5vZvycPo1HHsDFtsdlUVi46ePRYiQlTuzDl8sQmEXs3hgfT+Y4Lq/9dciCTFde+6vQrE6qGvdzMqpte56JdXzaGmQS1aY4xLIjKSlfBLFN0KIGtYtye1+/N25gz7F4Uuw1V75qaKul1JN7U+KGk5qN60e2hy9j5+i8gSY55sibo/+ZthHVNaFTbsjNKeP7RuVitKqpdIzWlkG2bMplyeXe3i4Wb12e4dYrHBkR1JTDIwH9fPr96Vt+5e7Mz/i1ZrSob1qTRvXcsA4Ym1Nm2M+Ff78gBevVvyftftyAzvRidXuHxu2Y1uA2qXSU41I/sjBJefHw+FrNjdGi3WZk/aw/gPPqYPXM3SBJTpzX+ivnJLL/yRY9DpeKkNCzFZRhDGz6185gU75KLnka12BCqhqTIyEY9Q7942ONoKnpAZ4Z++TApbywhI74DmqI74ZoQ0TyENh2bhnpgr2dvoP2NE8iYvQ5Zp9ByymD8m4U3tll899kGqqpsTmtKdrvGjB+3k3qwkDsfHu6Ur62qGt6scTkRSXLMxGNbHg83xcWH0ntAS7ZuyMBSvRYHINVqdm4x21k8dx89+7ZAb9A1+ADLJ5p1FFmRiW8dTmxcCM1i68/JyLKEojj/kXU6mY7dYggN82Pmz9uxnpShI4SrX7RaVOb+udtjamZjYS2toHBHSo3H5K/f20DWuNJiXF8mr/+ItteMJaJPB9pddx5TNn5M7OheNZ7X7ppxPLHqKTp1CEenkzCZdJhMOiKjA3no6TFNaqE6sFUMne+4kI7TJ9e7E8/OLGHn1myKCz03phBCsHdXrktiwDF2bM5i5k/bnLb16BOHZ0F5ZyQJdHoZWand8UaTjhHjXJuF/+e+IVxxYx9i40IIDfdj0Ig2XH9bf4ym2o13D+zJ57arfuH2q39hxo/b0DzIBtcH5/SI3GK2sW1TFhaznc7dm9V6QeKy63rzf2+tcqkmPRV6g0JM80ASe7dg4ex92E46X6+XiYkNJiTMxP6kfHQ6GVXVaHU0vAOwf09ereN+qiooKzUTFl73RV9vodncx5mrkSWqDhc0jDEeCOuawLCvHj2tc6wWO7t3H2HoBd25MNKf4qIqwsL96dAlukk58YaitLiK/72whIzUIoRwhDxCQk2Mn9KZUeM7uPQmlRXPI1u7XWPRnP1cdm3v6m0xzYMYN6kji+fsd0k9PhlFJ/P4i+ehNyikHSxg9ozdHMmrcIRhNEcBkCRLyLJEVHQgtz0wlOAQk8t1ZEVm9PkdGX1+x+ptQgj2J+WzcW2a2xTGEzn2fOYqRzZNyoEjPPDkaCorbKxblUpZqZkOnaPpktjM69+Zc9aR79iSxQevr0CSQGgCTYNR49tz1c19T/kh9h0Yj3rPYH7+ejNFBZVuHaskQe8BLenSvRnrV6dht6kMGtGGEePaYTTqmDotkYMHCli15CA7Nmeh6GSGjmrDBZd2x+SnJyerlOzMEqKbBRLX6nj3mJAQE8W17F4jSRAY5D5fW9MEu7cfJmW/o1Cp/5CEU0oPCCHq/AUzRYQQ0iGO4iT3FbmK0UBol4Q63aOhSdpxmHdfWQ4Ih9NSNcZM7MiAGxL+lU4c4M3nF5OWUuS0raTYzO/fb2X+rD08+79J1fFnSZLoOyieDas8Z5mYq2xomnAKSUy7vg9dEpuzeO4+ykstCCFIO1TkNEDS6WUSe7eg3dHQVqvW4Qwf6xhtCyGwWVV0epn83AqHIz/N7BJJkrj13sEktAvnp682o3nIajkZTRPs3HqYpx74h7ycchCOWLrRpCMuPpRHXxiH0YNEwJngVa2V2tK3b1+xaVP95d2Wl1q4/9Y/XN6gBqPCLXcPrvWChBACc5WNbz5Zz5YNmdVxa6NJR4++LbjjwWFe/yGvXX6ILz9ae8q3v8GoMPr8Dlx5o2sut7nKxitPLiAnqxSz2Y7BqCBJEg8+NZqOXV0X9DatTeOXb7eSd7iMgEAD46d05oJLup2xlkTumt3MG/Oga6WhIhPZpwOT137Q6A5QCEHWvA0kf78IYVdpc9UY4i8YhCQ7P3NFuYX7b57hMio0GnXcfPegRlvcqk/yc8tYs+wQFRVWuveKpWuP5k4ONjuzhCfunuVx5ihJMGBoArcfnWWCo1ju2YfmUJBf4facZi2Cee3DC93uO4bdpvLFB2vZsCYNvV7BbtPo0qMZdzw47JQV2HXl8/fWsHLJQa9cS6+XGT+ls9MMpLbUu9ZKU2L96lS38TirRWX+rCTiWoWi0ynENHfEwtMPFZKVUUJM8yBat4uodjKSJOHnb+A/9w8laUcOa5cfQiAYOKw13Xo2rxdnNHB4AmmHClk4e2/14o8sS7RuF8H+pDwUnYKqagwd3ZbLr3P/Rfj9+61kphdjP5q6deyl8M7Ly3j/60udUhc3rEnjs3dXVx9TUW7l7993smZZCnq9QlxCGFMu605sXO3zkGMGd2Xqts9Yffvb5K3aBZpAUmTiLxrCkP97oEk48eVXv0TG32urxb4y566n2ciejJn5vJPw1eoFe7GbLSA5p3taLHbmzdpzzjnyFYuS+fbTDWiaQLVrLFtwgIS24Tz87Njq6sXC/Ioaw39CwJYNGU7bgkNMvPbRhfzv+cXs253LiXVLBoPClTf2OaVtOr3Cf+4fyhU39iE3u4zI6ADCIxtG2bGm4a4kSy51KDVhs2msWHTwjBy5J85JR15eanEpuz9GyoECnnt4LkIThEX4YzLpOJxdiiSBahcEhRi58Y5BJPaOdXLoXXs0p2uP5vVuuyRJXHFDH86f0pl9SXn4Bxjo3L0ZOp1MeamFgiMVRMUEusQgT2TV0pRqJ34imibYsyuX7r2OF+X88vUWl9G/zaqRk10GQEZaMetXpnLnw8PoO6hVrZ8jpGNLJi55C6FpVOUVow/yQx/QNNqJZS/Y5OTEAezlZnKWbiP9z9UkXDIccDj8re/OQg11X7pfWlz3Bs5NiZLiKr79dINT6MJitpNyoICF/+xl4kVdAYitRS8AyU2NoF6v8MizY5n7VxLz/tpDeZmF2JYhTLu+92n1OA0J9SMktGG/SwOHJbBxTVr1rPwYRqOOjl2j2bXt8GnVnlg91CycKeekI+/QJRqDUefyoYNjtHBse+7hMpf9RQUOgZ027SJ4+Lmx+NXzlM0ToeH+LqO9wGBjrUS4Tl5kPRFz1fFwh92mUpDvKgR1Mpom+OStVXzyU0unFLHaIMlyk0h/O5GUn5ZUO3EB5McmkNmmC1aDibTvtnPbsN5ERgeSt2Y3ppQU5B4t0E56bklodOl+bmmub1qb7rZE22ZVWb7wQLUjD4/wJyTMREmR2c3RIMmO9SN3yIrMpIu7Menibt4yu0Ho1rM5Pfq0YPvmLKcQa/tOUdz1yHDee3U5u7YdrtW1JAm69fRuhfM5mX7YqVsMCW3D0RvOrPpRaIK0lEJ+/KLx9DPqQqdu7gtb7HaVjl2OV9IpOrnWqVU2m8ahA0e8Yl9T4mDXfuztOYTS8GjMgcEkE8JT9/9D7uFSSvamE1aUR2BJIZJ6wqBA05BVlSmXN60epXXFZlM9jiptJ83wHnl2LDq9q/tQFInQUD+uqEWo5GxCkiRuf3AYtz8wlL6D4uk9oCW33jOYB58ajdGk56FnxtB/SDz6Ez4To0lHeIS/06KmrEiY/PRcfl3N6a6nyzk5IpckiYeeGcvsGbtYtuCY9KSgssJ9iy532O0aa5ancOPtAxpFQL4uXHlTX154dC5Wy/EfptGo4/ypXQg+YUoqSRJjJnRk4T97a5Vq2RgL4/VBmytHk/rHCso1heyEjk4FPgKJqkobv32/jUsHxCHJEj3WLiCtQyKHjxYDheVn01PNJSrmpkZ8itOjpLiKDavTKCmqIjuzmL278pBliQHDErj4yh4EBBpJ7N2C37/f5nKuTifTb5BzeCmuVRhv/t9FLPh7D7u2HcZqVQkNM9F3UCuGjm7baDPZ+kSWJXr1b0mv/q6zDUmSuOOh4ezcms3KJQexWzUGDk+gz8CWbNuYxbxZSZQWm+nSoxkXXNKdiCjvxvbPyawVd3zxvmPV+XQeV5IkPv3lSmQJVi456Fi1FjBkVBuGj23X6NKVNZF7uIx/ft/J3t25hIb7M+HCLm6nu3a7xufvrWHT2jQUnVyt2ngykgRf/n6125eapmpsXJvO8oXJ2O0qg4a3ZsiothjOcEZU3wghWHblC2zYksfeDr3RdK5Ox89fz8c/TGNm95sp3Z/p1Gxa8Tcy4rsnaHXR0IY0+4xZu+IQX3ywFqEJlwIyRScTGRXAi+9egMGg8M0n61m9NKU6S0enlwkKMvL825Pd5l77aFg8Za38axx5dmYJT933z2lVQjaPC+aldy/g1acWknqwoHpR0GBUaNkqjCdeOq/JiFfVlYL8CjJSi1g8dx87tmS77J96RSIXuVFnFELw/muO+OCx2KHBqNCiZQhPvHx+k3bm8z5YxB+Ls7G5iTCGhJp47+vLqMotZNmVL5K3NgnFoANZpu/Lt9Dp9imNYPXpU1xUxUP/mVnjuonRpOPaW/szbExbhBBsXpfB4jn7qKiw0qt/HOMmdfJYr/Bvw2ZTKcirICjESEBgw38m/6r0Q3fExoXQvEUwGWnFtTpeb1C49tb+bF6XQVpKoVNmh9WikplezIY1aQwe0aaGq5w9REQFEBEVQGKfFvz12w7mztiNxaISGGTg8ut6uy1pBkjakePkxMHx+RxKLuTTd1Zxx4PDzig0ZS0ppzK7gICW0ejroTO9JEmMvnUkM1f/Dictiuv1CsOPPq9fTDgTlrxFZU4h1sJSgtq1QDGcPWGDjWvSTikRaDHb2bk1m2Fj2lYX7/Qd5D5T59+KEILZM3bz9287ETi0YHr2i+OWuwc3iTCSt1q9fQlMBvKEEE12ObomidgTkSTo0acFS+fvJ3lvvtvsF4vZzvpV544jP4YsS1w0rQcXTaudNvqmteluP59j+z59dw23PTAUi8VOTlYpIaEmQmuQFFAtVtbe+S4pPy5B1itodo2O0yfR783bvN7Y2GjSc/ejI3jv1WUI4cjOkCXQKxAVHYDNplaHz/ybhTe57JvaYK6ynXIWKssSoWH/7rCJucrG6mUpHNibT0yzQIaPbe8Ux14ybz9//brDaUC3bWMm77+6nEeeG9sYJjvhrRH518AHwLdeul69MHhka9JTC09ZNSkEbN2QgXqKclxvltierej0skMGwc1HJQRsWpfOD59vZPnCZCRZQrWrdOgSzR0PDnebSrnmP29z6LflqGYr6tHstn2fzUYx6un76nSv29+9VyyP3dSJ/3tmNnnRLdGQqDTDN++vZu6fu3nmjYn4+dcsbdCU6dqjOX/+vKNGZ67oZEac537G9W+gIL+CZx78h8oKG6oqkBWJOTOTuPeJkdVpgn/9utPFb9htGvv35JGTXUqz2ODGML0ar6RjCCFWALVvmdNIDBvTjti4EAy1cMC1ceLDx7b1lmlnLYOGt64xzVNowtHZyWLHXGXDZtPYuyuPN19YTFZGMYvn7GPtikOYq2xYCktJ+WUpapWzXrhaaSHpvRkUbE/2uv2q1caK614hPyoOISvHtEtRZYWcrFI++t9Kj2Xl3kBTVbLmb2Tv//1N3trdXs8MatM+km69POcsSzJcN70/LVqGevW+ZxPvvLyUslJr9W9eUwVWq8qHb6zAbtfQNEFJkfviL51OJjfbtR6loflXDSkNBoUnXz2fNctSWLcyFVXVOLjvyGktgEqSI34+ZFTrBqn0bOq0aR/JmAkdmftnktv9dlVzqW9WVY3UgwU8/cBsJElCkSW+0ASjBjanIiIKv2zXxVbVbOOfQXcTntiGcX+/hCkq1Cv2H168hbywZm6nFAKJHZuzeeSOPxk9vkOtBNdOh7LUHOaOvB9rURmaXUOSJUK7JDB+wWsYQrzXOuzex0bw5UfrWLn4uFaIopMICjbx1KvjiYxueG34pkJ5qZn0Q0Vu91nMdg7uy6dj1xhCwvzcOnO7XSOmHmWva0uDOXJJkqYD0wHi4xtvIUWvVxgxrn314t3u7Yf54oO1lJWY0YTAaNJRUWZ1e26z2GD6DGhJvyGtaN0uoiHNbtJccUMfbDZHp3GXghIPA0yhgf2o4Max7P4FKzKRe40moG0RiesWobc5j8w1s5WCrQdYfNHTTFr1nldst5WbEadoOWu3aSxfmEyb9pEMGtHaK/cFWDz1KSozjzg1TC7ccZC1d77LiO//67X7yIrMLXcP5oJLu7NiUTIlRVV07dGcfoPjz5msqzNl+5Ysj/tUVVR/ny+8vDs/f73ZKbyi0zt6fjZ2WAUa0JELIT4FPgVH+mFD3fdUdO3RnP99ehEF+RUOPeOUQj54fYWrpoJJx+XX9aLPQN9qvjuuuqkvJUVVbN+UBZJjAU2WZYxGhaJayvICaIqOspBwdvYfTY91C1BU57iksKkUbE2mNDmL4Ha11+fwRLPh3YnIfQfRvmYBI4vFzty/krzmyEv2ZVCanOXS9V6z2Ej9fSVDv7CiGL0bm49pHsRl13q3ovBsQlM1Dh0sBAQJbSNQFBlNrVn0qu1RedzR53egqsrG378ez1rp1a8lN989qOEeoAb+VaEVT0iSRGS0YyrbvVcsPfu2qG5IAQ4n3rVHc7cVXT4cKIrMXY+MICujmAN78gkMNtKjTws2rU3nyw9PLcvrhKxQGh7N6vFXEn9gB60O7HAaM8sGHUV7UlEtVoLaxqIznXk+r19MOG3GJJK1YxtpHRLRFMUROHZDWal7bZEzwVJYiqxTcPupCIG9yvuO/N9CeZmF9StTKSmuom3HKLr3iiVpx2E+fmsV9qNiejqdwvT7htCtV3MUWcLuxpF36hZTXQchSRKTL+7G+As6U5BfQVCw6ZT6/g2Jt9IPfwJGApGSJGUCzwghvvDGtRuaY5oKO7Zks2ZZCkLAoOEJ9Ogb1yQaHTd1WrQMdVo4GzS8NeYqG79/vw2LxY4QgpBQP4oKKmtWi5MkNJ2O9Pbd0VvNtEjbX73LVlLBkgufPnocxE8Zwqhfn0bWn9nXuWh7Cq0OZhNSmEtqhx4URzavXvQ8hixLdO7mPZGssO5t0I46FQGUhEdTEh6D3mqmta4KQ0jDyLOea+zZmcPbLy5FILBaHI0cIiL9OZJfcdJgws4Hry/nxXcmM+Xy7vzz+65qmQpJkggI1HPbA66Vu3q90iRCKSfzr6nsbAw2r0vnnz92UXCkktZtw7noyh4ktHXE1jVNsHdXDoVHKolvE058QtgprnZ2o6kaxcVmAgL0FBdV8cwDc6gy22oWej6KoaqCwQt/q/GYyP6dzrhhxVfK2OrFTgFsGzyestAoNN3RF4MEJpOO59+aREzzM/8Rn9yBacfrP7PlxR/YkjiMstBINFlB1lQUk4H7nxrjW0w/TWw2lXuu/43KSmdNpWMf+cmuTlEkxkzsyNU39yNpx2EWzt5HSVEVib1jGTuxU62URhuaf31lZ0Mze+Zu/vx5e/UoYNvmLJJ25vDg02OIiPTn1acWOtpX4fiBt+8UxX1PjKpVauTZiKzI1a2/YprrefZ/E/njh21s35RZ3bXcE1aTP0gSip8BtdLi9pgjG/ayevr/GPrZQ6dtmyk6FHOuI3NBAhLXLSKtQyI5rTpAUABdEpsz7fo+Z+TEhRDs/2IO257/lsqsI/jHRtLz6WvpcMskEh+5go2lfpTtKEKTHVN4TZbR7IL3XlnG+99cds5+H+qDDavT3Pb39DRWVVVBTnYpAF0Sm9Ml8ex9cfq+JfVAVaWVmT9td9a3EI7S9a8/WockSRQcqXRaYNmflM9PX23m+tsGNILFDU+z2GDufNjRwGHdykP88u1WCj3ka0dEB3K9ZT5bn/uGHS/94PGaB76ci2q10+/VW/FvXvusosRHrmDLU19iP/qSUDSVtgd30C+ggilzPz6Np3Jl91u/seWpr1DNjkyoyqwjbLj/YyzFFSQ+PI3tWbZqJ+6EBDu2ZPtK5WtJdkYJX3+87pT1Hyei1yu06xBVj1Y1HGeXPutZwqHkAhTF/RT/cFYp+bllLqvkNpvKysXJp+wyUnikghWLklm9LIWKcvdpkmcbA4e15u3PLqZrj+YuGtcGo8LFV/ZA1imEdj5FhyIBKT8sYmb3myk7VDuRf4Au911CxzsuRDEZ0AcHoPgZiOrfmXGzXz6Tx6mmMreQTY9+Vu3Ej2GvNLPjxe9RLVasHjrECwFVlbWXXf638+3/ra9xQV1RJE6MujnqQWRGjT83Klp9I/J6wM/f4CLEfyKeCpBsNg2bTfVY+j/jx23MmbkbWZaQJImvtHXcek/tm0k3de55bARff7yeTWvTkGQJvd7hxIeOdlTQJlwyjNXTjR7DKwBoAltxBZse/5xRPz9Vq/tKkkT/1/9DjyeupjgpDf/m4QS1rvs0e+llz7mkFx5DCEF5ai5dEpuxZX2Gy/RfUzW6JJ5bHYjqC03V2JuU53G/3qBw96MjmD1jNwf25oGAdp2iuPGOgU76/GczPkdeDyS0DUeWcJ9ahueYnSRBRmoR7Tq6Tvd2bs1m3l9JLi+Iz95bQ9sOkdXpk2czJj9HpoC5agAV5VZCw/1QTlBOVIwGpmz6hL/6/AetyvNsRGgamXPXn/b9jaGBxAzueka2n0xl9hGObNzncb9qtWGKCuHya3uTtCMHi9nu1ARk+Nh2Xm8+0FQoLTEz48dtbFyTDsCAoa24+MqeZ764KDk6hLr7WSmKxK33DKJHnxb06NOiegZ0rq09nFtP00SQJIme/eKqv6gnIisSmoc4nk6vkJNd6taRL/xnr9tFQaEJVi1N4YJLu7Hwn70smbcfi9lOYp8WTJ2WeFY6A5OfHpMHadDQTvFcU/gXfw+4k+I9aQgPTbbPVGpWtdrY/9lsDnw1D03VaHv1GDrfcSE6/1OrA2Yv3UrSO79jLiglrFsbFIMOzeI+PBI7uhfG8GCaAS+8PYlZv+4kaWcOQcEmxk/pzMBhCWdkf1OnqsrGsw/NobiwClV1DEqWL0xmx5ZsXnp3cq0VSk9EliV69G3B9k1ZLqFJk7/eqWn4uebAj3FuPlUTYOq0RLZtzHQZQet0MqHR/uS5afwsSxKxcSFur1da4r4YxW7XKC2u4t2Xl7FnV051nHDVkoNsXp/Oi29PJjzy7HPmNXFsZH7o16WsvP41hOr8GctGPe2uOw8hBLkrdnDg6/nYK6pIuHQErS4ehqxzX5auqSrzxz1Mweb91QufW/dncPD7RUxe9yE6k/sCENVqY/aweyk4YQSev24PeFjvkHQKw394ovrfUTFB3Hz34NP6DM4mrFaVvJwygkNMrF+VSlmpudqJw7HvsJk1yw8xanyHM7rHdf8ZwHMH5lBVacNisaPTy8iyxJ0PDXea1Z2r+Bx5PRHXKoxb7x3CFx+sRT62yiLBbfcPxa5q/N/bq5x1G3QysS1DPGq4JPaOJTOtyOXFYDTpCI8MYNWSFKfraZrAXGnj7993nZOZMLJOoe1VYzEEB7B02gsITUOz2NAF+hHcNpZez17Phgc+Yv/nc7BXWhAC0udtIvL9mZy/+E23I/aMWWsp2Jpc7cQB1CorZQezSfl+IR1umeRyjhCCmd1uoiz5JKGvo05cUmSnF41iMtD/rdsxhTW9ohJvc6wZw6zfdiJJDodtNOrcLkpaLI7mFmfqyMMj/Hn94wtZs/wQyXvziWoWxPCx7apTXs91fI68HhkwNIFe/VtyYE8ekgQdOkdXixRV3NKPX7/ditVqR1MF3XvHcus9gz0WtIyd1Iml8w+gllmqp496vUxM8yBU1bFIejKqKthRgyjQuUDLyYO49OD3HPxuIVW5hTQb3oO4SQMo2pHCvs9mY7FqHOgxmLwWbRCyREBFKbo3/mb8fy92uVbqzJXYy90o3FWYOfTbcreOfNf/fnV14iciy5gigjHnFRPQMopez99I++vH1+mZG4qqKhvrVhzicGYJcQlhDBiacFoa/MsXHHBpxmC3uV/bkGUIDavbwqPRpGfU+A5n/DI4m/E58nrGYFDcVuiNGNeeoaPbUnikAv8A4yl1G4JDTDz31iRm/riNLRsyUXQyQ0e1Ycpl3Vm9LAWdXnY70vEPaDp6EPWFf7Nwuj88zWlb6u8rsJttbBsykYrgUMTR7kIVgaH8sqGU9geO0KZ9pNM5FRn5Hu+hD3I/skt6d0aNtulMBq7M+QOhaUjy2TPFz0wv5uUn5mOzqdWl7r99u5WnXjuf6Ga1k21114zBE4oiM/Jf6IC9hc+RNyKKIhMVU3st4/AIf26+ezA3n7S9/5BW/PzVZpfjjUYd4yZ3qvGalRVWFs/Zx+b1GYRH+HPBZd3PCYleIQQl4dFUBYUgFOevuYrMjB+389AzY5y2lySlerxeiwn93W63ldbcdCJh2kiAs8qJA3z4xgoqKqzVqSAWsx2rxc6n76zmyVfPr9U1igora32/voPiz3mZivrE58jPAYKCTdz+4FA+/t8qRzs1VUOWJPoOjmfoKM9djIqLKnn09r8wH1V5PJRcwOb1GVx8VQ8uvDyxocyvFxIuGc68mbvQ3CkZShKpKc4NrYQQmPNL3F5LNugwRYWyZO4+/v5jFyVFVUQ3D+Kya3oR2b8ThxdvdXueLsiPfq/eWudnaWjyc8s4klvuks8nhOM7Ul5qqVWqYHhkQK26K5lMOnoP8FWw1gWfIz9H6D0gnne+vJTN69MxV9ro2rP5Kdt3ffj6imonfiIzftzOqPEdCA6pOeVOCMH6VaksnrOPqkobvQe25LzJnQkManyxocg+HWg9uCMpuRoqrlkqJy+CSZKEX/MIqg4XuBwrKQqr0uwsW7WlOg/5cGYpn7y1ikuunIqyNsmlSMkYEcylqT9iCDj7Ck6sVhXJg9KnJEnY7LULl1x8ZQ++OUXF5bFr9uhbd235fzNn13zPR40EBBoYPqYd513QuVY9GA/s9RwTXvjP3lOe//n7a/jyg3Xs35NPRloxs2fs5sn7/nHS7c7OKGHT2nTSD9Xc0lUIQVWlFU2tfdu9U3H5x7diCjJx8tDSaNRxwaXdALCVV7H27vf4LngyVTkFcJIDk416Qgd2ZenKTJdyeqtVZc7KHM6b/zqR/TshKTL6IH+63HcJ0zJ/8ZoTF0JwKLmAVUsOsn9Pntf7ep5M89hgDEb3KZphEX61XpQcOrotl1/bG/8APQajgk4vExbhh14vozcomPx0mPx03P/kKF8j8zri+/T+xdTkD8rKam6ikH6okA2r0qo1nMHREq2sxMzcP5OYcll33n1lGcl781F0MqqqERcfyoNPjXGZli+dv58ZP26notyCTq8wdmJHLrm6Z53zf/UGHU++Pom3XlxCaYkZWZaw2zUuuKw7/Qa3QgjB/PMepmBr8vHCnaMfiqRXQEBo53jiH7kO3Te7nUXQjlJRZiGwe3suWPfhadmWvC+fjNQiOndvRrPYYHKySlm97CApyQX4BxjoN6gVvQfEsXdXLp++s5qyUguKzpEbHREZwKMvjKtzlocnZEXm5jsH8dGbK7HZVIRwFN3o9DI33TnotKSCx03uxKjzO1BUUElgkAE/fwOZaUXs251HYLCRnv3ifE7cC/j0yP/F3Hndr5SXutctefyl8+jUNcbjubNn7OKPH7a5VZuLbhZE2w6RbFybhv2EvHdFkencPYaHnx1bvW3ZggP88MVGp+m3wagwYGgCt7gpkikrdbwoNq1Lx2jUMfr8Dgwf2470Q0XMnrGL7PRiWrWNYNLFXYlr5Vg8U212Vv24lu1Jhfg1C6ddC3/CUpOpysjlwFfzUCstlAeFkt88AZvBgCYr2A1GAkuLiMs5iKboWT98Cqpw78D8/fUYdYKoMCN2vYmgEBOjJ3SgR58WLk4vN6eM5x6cTUXF8YpPP3+9U4k+OLKdDEYdFeUWlxeuLEu06xjJf1+p3aLjmZKWUsjsGbvIyiihVetwJl7clbj40Hq9p4+a8aRH7hVHLknS+cC7gAJ8LoR4tabjfY68abBxTRofvL7CZXuz2GBe++jCGs9d8M8efv1mq9v89diWIeTllDk58WPo9TJv/t9FhIb7I4Tgnht/p7TYdfSv18u89dnFTqJG5aUWnrzvb8pKLdXCYwajQou4EDIOFWJXNUebNk1Dp5N58NmxRFYU8vP177O14wCH9ruiQ7HbMFiq6LN6DjqzmZROvchs09XR5s0NkdmpWPwCKAuNdOkc5IQQ1V0MjEYdI8a14+pb+p2wW3DbVT9jrnKveHg66PUKr3104VkpweDjzPHkyOscI5ckSQE+BCYAXYArJUnqUtfr+qh/+g1uxe0PDXUsTh5tmNxnYEue+9/EU557on7FiRwbTcsept+yBIunv8vMxFuYO/Vpyorcp6jpFJnszFKnbXNnJVFWZnFSj7RaVA4dLMCucbzXpixj1+CTlxcxd8LjbGvfD03RVachqjo9ZlMAyR17UxIW5XDiOp3DCbv570hsAha/APzLipHtNmS7zX1c6oRntljsLF1wgOzM45kwO7Zke8WJAyg6mfIyx2zKXmVh/1dzWf/ARxz6fQWaWrvFSB/nDt4ITvUHkoUQKQCSJP0MXAgkeeHaPuqZgUNbM2BIAuYqG3qDDp2udu/28Ah/rryuBz9+tQVNE2hIGI0K7TtHM3FqFxb8vccpfn4MW5WVovnLUOx2xK5U5ImtUd3c01JpISzUOZa+ZX2G21E+AnDz3iirUMmNcJ8NIRSFvBatHR15PIzEq5EkrEY/mqUfIDI3g/R23TnS/BTa6DgEzbZtyqzWz0k7WPOC7+kghKB5i2AKth9k9qC7qjXPk975A0NoABft/uq0mmv4OLvxRtZKCyDjhH9nHt3mhCRJ0yVJ2iRJ0qb8fM/ZEj4aHkmS8PM3uHXims1O8Z40Kk9Kyyvem07uTU8yYOXftEraQqvUJBI3LeXWKzpgMOq47NpeLpkPsmonfv8OFLtjVCohiE3Zg2x3HqVKqkpI8RGqNux02u5JEdETQgLV6nkELGSZvBatnUbSHpFl8uLaElxcQHDxEfCgM34ikiQ5faadunlec3BvoAeVTJ3M1CsS0RsU5o6836VxhbW4gtlD73V7rmqxkvLLUna88iPps9ag1TKV0EfTpsGWi4UQnwKfgiNG3lD39XHm7P9qLhsf/AS72YpmtaEPMNFh+mQSH7uS5Ve9hKWwDL0QtCo46uQlieVXv8yFmz9h1PgOmPx0/PHDdo7kVxASqKfZhk3EHNjldI/W+7ZiM5rIjWuLrKkIWSa4MI+uW1ZQut95PDBmQgey0ord9mVEaMdDKwCaRnBxATEVR9jvLq4tNARS7Zz48ZMAiM46xKGOPWvTN5q+A48XunToEk1omB/FRSfpuRxz2CfaomnIQkPS6ThxPdlgVLjx9oEMHtmG/A17sZW4L7gpP3QY85ESTJHH1TRLDmQyZ9i92KssqJUWFH8jflGhTFz1Hv7NwmvxNM7YyiqpPFxAQFxUrWR+fdQf3nDkWUDLE/4dd3SbjyaKEIIjm/ZhKSij2chEdCbXAp6MOetZd/f7ToUutrIqdr/1G8nfLMBWVuk6YhSCkj1pVGY7mgwPGt6GQcPbAA4n8lfPb1yabchC0Gn7Gtrs2UxlYCjGqgr8qsrRBfkR3CHO6djBI9qwc0s2WzZkYLepR9MTJTpVZbNHC3WESXR6ZJsNnWZnYgcdVUXhtN23lYMdeqIpR+Pg4mgsxkPRizsk1U5M5kEA/GxmOh7Yyr7O/Y637Dt2XUkCoaE36pl2fW8XCeGX37+Al59YQGZ6cfVpLU12yjLyKQlz6NAbqypombybmOLDRL/1KNv2lSArEiPGtmfwyNbVaZnlaTk12lyWetjJkS+99FlH9erRv5u9rIryKiurbnqd8+bUmJ9Qjd1sJfnreWx/6QcqDxc4VCQliU53TKHvq7cinypM5aNe8IYj3wi0lySpNQ4HfgVwlReu66MeyF21k/kTHkWtOOqgJYmOt1/A4A+cp+LbnvvGfUs1AZbCUs8jWVlymeoDBLdrQUB8NKX7MtycBAarBUNhrsMkRcYQEkD8BYOcLy1L3P7gMA4lF7BrazYGo45+Q1oRqNOYf8nz7EqvpCooFP/iAvr0bs7oDx/DXmUl6sGPCZy7jOT2PR2ZJ0ef2yMnvqAkCZ0iERERwCApALMugejBXRk7dQjL//stSWowxWFRSKpGQGUpga2b0XZsT0ZO7upWWz4g0MhL711AVaWVkmIzUdEBWAtL+avXdCxbyqrz2XUBJtpcOZohNw9migczm4/u5fkZJAjteHw2UHowm9LkbJeXr7CrHF6yFVtZpUdhsGPYqyzMHnoPRTtTEEcXnI/9rfd8+CeSItPv1ek1XsNH/VBnRy6EsEuSdBcwH0f64ZdCiN11tsyH17GWlDN31APOjRiEYN9HswhKaEb3h44rCJal1NC8WBNuFxcBjOHBBLrpdylJEqN+fdoR07XYPPbdlPQKUf06MeLH/yLr3X89W7eLcBH2umDR64xMyabsUA4hHVsSEOcY3UqyzJFiM4fadnc48dqEUk46RlZknntvKn4BlwNgKSrj97bXYC0up90Jx+kCTEz56RNCOrTkVPj5O4pjAPyiw5i6/XN2v/MHGbPWYAgLpPOdU0m4bESN1zBFhNBsVE9ylm5z2ddy8iAnx2wrrUDWKe7bD0oS9irLKR35/s9nU7w3vdqJn4hmtrHrjV+IO78/zUf2rPE6PryPV2LkQog5wBxvXMvHqdHsKpbCUoxhQR6dnTt2vvGLSzedY2x/8XsnRx7cPo78As+JR/pgfzSbHc1iQ6gakiIjG/UM/fwhj5V/4d3bcHnqT6T8tIT8jXvJW72repRoigmj76u3Eju2zxnFawGC2sQS1Ca2+t9CCL66/ydW2OIhvBbx8BPywJ2wq+xLyqNnP0eoJ/nbBahuWripFhu73/6dwR/ff9q2myJD6PPiTfR58abTOm/8/NdZctmzZPy9tjp7J+Hi4Yw4oQMRQGjXBI+hJP/mEZiiQk95r4PfL6qxVyoCFl3wXy49+D2WglL2fTabqsMFtBjfj9bTRqHza3wNnnMVX21sE0YIQeac9SR/Mx/NppIwbSTlhw6z8/Vf0Cw2JEWmwy0TaXXxMPybRxDcrmbhoYKtyR732cqc87l7PXc9i6Y+5faHKxv1dLhpAu1uGM+uN3+laEcKYYlt6P7wNMK6ta7RBn2QPx2nT6bj9MkAWEsr0Gx2TBHuW9ydKeYqG888OJucLFFzEc8xNM2jo1etNspLjy9QFu1IQa1ynVGYdUbW7a8g/5cddO3ZnLYdIk+rnP1MkHUKY2e+gK28qnptQh/oWrqvGPQMeOdO1t757vHZkCSh+BkY/PF9tbJT8tAi70SEqrHu7vfI+Gc9ms2OsKtk/LOObS98R9trxlG4LZmQji3pdNsFTi9dH3XDV6LfhCjadYjkbxdiK6+k5eSBpPy0hPQ/V2OvcFQ+SnrFMaI+qRekpNch6xRCO8czeubzBLaMdnv9rc9+zbbnv3O7Txfkx7Ul/zhtO/jTYtbc9jb2suNOTBdgIjChGZNWv4chuGlWFQoheOLuv52KcWo4GIDoIAVrxmGKQ6Ocs18AWVV54ZUxxHVzhEx2v/sHm5/4wsmZ57Row/6eg5EUBU2SMBh0dO3ZnLsfGY4kS/Xu0GvL4aVb2f7SD5QmZxGW2IaeT11LVL+aNeuPsf+LOay/78Pq76NHZNlteqakKAhVRdbrkPU6Rs98jhbjXIoUfdRAvZbony4+R+7Kjtd/Zttz36JZHaEKxc+AZrEjapGvfAxJkQls3YxL9n7jtpGBrcrCDyEXINzkDvd+6WZ6PO66Rq2pKrmrd5Px9xqsxRXEju1Nq4uGnnGX+vqmqtLKi4/NIzP91E5ckhxa2Hc+MoJ2Lfz4vOOtbB0yEVVWjo/iVZXmOam8tOoJFKMjpn1ijBzAYvRj/dhLHFkxJ6CTBO2SNtEseTem6DDiJg4g8ZErCOl46hh6U0Sz2Vkw6XHy1u4+vlh+ErJRjxACUUP+/jGMkSFccfg3X6bLaVBvJfo+6k5pchbbnv0GtcpSHcNWq6yn5cTBMa2tyikiZ8UOt/v1fkYmr/8QQ+gJI2lJosOtE0l87Eq358iKQvPhifR/4zaGfvYgbaaNarJOfPumLO66/rdaOXEQTJjahf99djHde8WSt3o3QRUldF+3EFlTj6sgSpDfojX79x+vyjSGBTFxxTuEdW+DYjJQ0Kqd27CMXUhkxLYBAebcIpK/mseMLjew+JJnsFeeYlTbBJH1Os6b+yqjfnqKVpcMdyhEnoCkyCgmQ63XbVSLlYLNB055nK2iiuzFW8hbu/u0fxP/Fnwx8kbEbrZy6Ocl7HrzV1RLDYtIp4MQlB/KgZHud0f2as/VhbMo3ptO1eECIvt3Qn8WNj84mZLiKj54Y7n7Ev4TEQKdInHXY6Po1f/4yHj/l3MQdo3MNl3QZKXaMQtZwQ588PoK3vv60uoc7rBurZm6/TMqMvOZO+cAyfNSXEJeAKpy0ktPQPrMVfze7lrGzX2ViB6eOzg1RWRFoeXkQbScPIiCrQdYc9vb1WsvzUf3ou8rtzB7yD21upbkKfXpBPb+399sfPBjR3xeCHQBJkbPeJ7ogT45pxPxOfIGRBxdUJMkCWtpBbMH3015Wu6pY46nSVhim1MeE9opntBO5057rXUrUmvUVz9GRHQAr7w/BaPJ2cGqZhsCKGgW73Zx1G5XSd6bT8eTpH0D4qLoM1pm/uJUl044kqoSlZPm1o6qnEJm9b2NsK4JjPr16VqlLDY1Inq154L1H2GrqEJSFHQmR+ipzyu3sPm/X6BWWR0zG1ly+5KTDToierf3eP3Dy7ax8cGPsZ9UlLZg/KNclvYTxtBA7z/UWYrPkdczZSnZLLv6JY5s2Hs0PUwibtIAQjvFU5aSjWp2TWOrDZIsu0wzZaOe8J5tiexz7nUjL9yZQlnKYcK6JrjNzikprnLb+OFEEns35/4nRyO7cdRtrhhF3to9CA+LkhKSk+riibRuF0HPfnFs35iJ5agzl1QVvc1Cy+QaSipUjaKdh5gz7D4uS/3xrE3PO3lG1/XeS4ga0Jm9H/1F5eECwnu3Z/+ns9EsNlSzFUmvIOt1jPj+CeQaMmF2vfmrkxM/hqaqHPppCZ1u91Qq9e/D58jrkbLUHGZ2u8nZWQtB5j/ryJq30e2iY21Q/Az0feVWlAAT25//lqqcIpAlWl82gkEfuhdLOlsxHylh4eQnKNp1CFmnoNnsNB/Vi1G/Pu2k79GxawyL5uzD4qYHqSxDYp8W3PfEKI/ZI22vGUvSh38RUphLSXi0S+aKpgnadYryaOftDwxj9dIUFs3di7nSht/GTTTfuQWD1f2iYDVCYK+ykPbHCtpeM67mY88iogd2cQp/JD40jb3/9zf56/Y40g9vn3LKdNnyVPcSBGql5ZTyBP82fI68Htn67DceR9wenbgsEdgqhqrcIrfVj7JeR+LjV9PlnosB6HDTBKzF5ej8jdVZFecSS6c9T+HWZDSbvboq8fCSray75wOGfv5Q9XHde8USGxdMZlqJU7MLWZG4dnp/Ro5rX2MKoBCgVlnomLqeTYMmODJQZBmEQJIlpk7rXmNLMlmWGDamLcPGOGLeRbt6sGDiASqzrDX31APs5VWUHji35YlMUaH0fPLa0zonalAXSvZluBSx6YL8iOzb8bSupdnsmI+UYAwPOid/J76sFS9jLa3AUlQGQNb8jTUeK7npSakY9ExY+haT135AQHw0ukA/dEH+yHodrS4dweUZP9PzyWuOX0OSMIadm1/Oisx88tcmodmcR9mq2UrKj4uxn6DpIssSj794HuMmdyIoxIifn57+Q1rx2ocXMnp8B+RTCGQd+mUplZn5+BUXEVB+QtaLJCEE/P3Hbgry3SsNuiOsW2suT/2J8QscjZlrQhfkR0gXz/rmms1e7w2XmyKJj16JclK4SdIr+EWHEX/hkFOeL4RAU1W2PPs134dcwK/xV/B9yAWsveu9c06+1zciryVC08hasIlDvyxD0snETRxA2aHDVKbnETWoKxE92rL6trfJX5sEEoR0bIlirDlNzxgZgr28yrHYebTKrsd/ryEw3rGgdtmhHzmycR/W4nIi+3f61y3uVOUUIht0bkW4AGwl5ehMx8v5jSaH4uC063uf9r2OFV4VRzajMijUZcHTYrYxe8YurvvPgFpfU5JlYsf0JnZMb45s3s/2l38g/a81TsUykiJjCA6g1VRXx5QxZz1r73iHiow8FKOBDrdOou9r06sXFc91gtu1YMKyt1h39/vkr9+DrCjEXzSUQe/f7TbF0VpSzqbHPif5+4WO2ayEyyKrUDX2fvQXpQcyCUxoRmlyFs2GdafT7VPwizkzaYimgK8gqBYITWPJpc+SvXDzUaeLY+FSkUHV0AX6oVZaHKOmEz5PyaDzWBghKTJX5PxO+sxVpM9agzEihI7/mexLqzoBW1klP8Vc4taRm6JCuOLw724Ln06Xisx8fu9wHZrZSmqHHqR27Ok2LzwmNojXP5pap3tlzd/I6tvexpxbRIXJn8L+gzH27UrXvi0ZPrYdAYGOEeih35az7IoXnMMyskTs2D6Mn/danWw4G9HsqqNC1sPfW1NVZvW5jeI9aQg3fWTdISkyQtWQjXp0/kYmr/mgyRdreSoI8o3Ia0HazFXHnThQ3VHgaOzOXl7l9jyhaQS2bubI6z4RSWLMXy9iigihwy2T6HDLpHqy/OxGH+RP1wcuI+ndP5xSNBV/I71fvsUrThxg83+/QLMelY+1WpBVO5rOdTYVFFj3kXCL8f24LOUH1s1P4ssvt6OqGuqufHbtPsKv325l4LAELr6qJ6tuet01tq4JDi/eQvrfa8lfv4eseRuwFpcTMyKRHk9cQ3Dbc1e7pKbsFoCseRspSzlcaycOVMfeNYsNq9XO2jvf5fxFb9bJzsbC58hrwYGv559ZrrddwxQZyug/nmPnGz9TnpZH81E96fHfq902c/DhSu8XbsQvJowdr/xIVV4Rga1i6P3izbS9crTX7pHxz7rqKXh09iFSurjqf8h2G130ZV65n2rX+ObbnU49TYUQCAFrlh9i87p0ussmAnH9zglVY8nFTzstAJal5pD2+0omrXmfsK4JXrHxbCN/XZLHAVWtEIKc5dvR7OopXxpNEZ8jrwXiDLuSS4pMaOd4Inq2Y+QPT3rZqnOfQ8kFLF90gEpLJL1+ep1+g+PR6b3/I5MNx38GBquFrpuWsrvvSCQhEJKEkCRi0/ajZBXCS9NquFLtOLA3v8ZEFotF5UD3gfRaM8/tfhcpYk1gK69i48Of1LrTz7mGf2wkir/Ro859rZAkjzr7TZ06zU0lSbpMkqTdkiRpkiSdszJmba8eiy7g9HsSykY9XR+4tB4sOveZPWM3Lz8xn2ULklm/KpWvPl7Hc4/MxXKGBVQ10e7accgnLExH5GUxeP4vdNy+hva7NtB/yZ+0272xVg2Xa8up/EVJREyteoJWIwQ5y7bXwaKzm9ZXjKpTqE1SZFqc1/esFfCqa5BxF3AxsMILtjRZWl8+ksi+HT06c1mvQxdgwq95BLoAE7ogP4wRwYz88UnCE88uLY2mwJG8cmb+tB2rVa3uiWkx2zmcVcq8WXu8fr8eT12L2qMrhxL7c6hDDyoDgtGpdqKzU2mefgC/qnIUPyNtr/VOwU67TlGndNKyXDv97xNR/iXZLO4whgUx7p+XMYQFOQrFji5Wy0Y9slFPaPfWBHeIo9Ulwxj00b0o/sbqmZjib8IYEXxWF9PVKbQihNgDNBmt5fpC1usYv+B1Dv22nIM/LELWKUT260T+xr1UpucRM6w73R68nID4aIqT0tCsNsK6tzkrY21NgS0bMsCNq7NZVVYvTeHCyxO9di9NE3z+6Wa2xPfBZlWREGR07EHr5B203LMNcGiwB7dvQec7LvTKPfV6hen3DuaTt1e56LMAKIpE/yEJDBx3J9tf+gFzXhH+LSKpyilE85QFZdDR7obxXrHvbKXZ8ESuzPmdw8u2YSurdAy8NEHUwC4Yw4Kcjo2bMIB9n/5DWUo20YO70u768W719YUQqBYbilHfpP2cV9IPJUlaBjwkhKhVTmFTST8sz8hj0yOfkvHPWgD8W0SiGPSEdkug20OXE9n73NMsORuYP2sPv363xa2SYXSzQN745CKv3Wvp/P1888l6l5i1ToYL9FkEVJSScMlwWl852uv524ezSvjt+21sWZ/hSHnWBCaTjuBQE0+/PoGgYOcZ4KbHPyPp/ZkucWDJoCOiR1vOX/I/J90TIQSHl2wle+Em9CGBtL1qDIGtnEW/fLhHCEHSezPY/tL3WIvKMYQF0uOJa+hy78WN6tDPuLGEJEmLgGZudv1XCPHX0WOWcQpHLknSdGA6QHx8fJ+0NPeqcA2FpbCUGZ1vxFJY6rp4JIHiZ2T4N4+RcMnwxjHwLKAqrwhbaSWBrZt5NbaYe7iM/977t4sIlk4vM+mirlx8VU+v3eu2K3+iqsrNKFcIxo1syTX3j/LavTxRXmphzfIUjuRX0LZDJH0GtHS7qCuEYPfbv7Pj1Z+wHCnBEBZIsxE96XzHFJqP6e3kYFSrjYUTHyd/w17s5VXIBh2SLDPoo3tpf8P5bu0oPZhNyb4MgtvFnpVqjN5k+0vfs+PVn5yy1XT+Rro/duVpSw0cQ7XasBwpwRgRfMaV2PXaIehsHJFvf+UHtr/wvceqQQBDWBBX5v7hC5GcRGX2EZZd/TL565KQdQqKn4EB795F2yvHnNZ1Sour2Lg2HYvFTreescQnhFXv++OHrcybtceRoifAYFSIiAzgmTcmVHefrys52aU8esdf7ncKQUeKeOKveykpruJIXgUxzYIIDG4aaaNC02pc3Nv9zu9sfvJLl9G7YjJwacoPTg2ubRVVLL3sOXKWbUc26tGsNqIGdmHMzOebbDu/uiI0jb0fz2L3uzOwFpYSMyyR3i/eRFjXBFSLlR+jLnabzqgLNHFl3kx0JgPJ3y5g2wvfUZGZT1DrZvR+4Sa3Az+haWx74Tt2/e83R6NyWaLT7VPo8/Itp+1bfAVBJ5G9aEuNThxA2O0U7jjoC7GcgKaqzBl+H+VpuQhVQ7PYsFeYWX3r//CLDiN2TO3K49euOMQXH6xFAlRVMPPH7fQdFM+t9w5BliUuuboXXXvEsnT+firKrfQZ2JLBI9vUKFx1uhQVVB4rznWLccdu3n9tOds2ZqJTJFRNMHB4a264fSA6XePKFJ0qQ2Pfp7Pdp+JJEml/rKDznVOrN635z9vkLNuOarZW/yby1uxmxXWvMvbPF7xpdpNh1U1vkPr7iupOTemz1pC9eDOTVr9/dNHY0wBXoiItl/S/1zhE8Y5+xiV7M1hx/atYSyrocNMEpzO2Pv8tu9/8zakr1J6P/kI1Wxn43t1eeZ66ph9eJElSJjAImC1J0nyvWNUABMRFOQTva0Bo4pwUo6oL2Qs2UZVf7BKOUistbHvum1pdY/aMXXzy1ipsVhWrVUVVNaxWlc3r0lmzPKX6uE7dYrj9wWE89MwYRo3v4FUnDhDXKtRjPrekqeS2aM2WNanY7Rpmi4rNprF26UF++qrx13dOhWp2n08tVBV71fEBjK2sktQ/VrgMajSLjaz5GzEfqU3bvLOLkgOZHPp1mXO7PSGwV1jY/Pjn+EWHonmoENVsdvQhAWx7/luXF6VaaWHTo5+inVB3olqs7H7rN5fWfmqlhf2fz8FaWnshtpqokyMXQswUQsQJIYxCiBghRJNdNs9etJkFEx9jZuItrL37PRIuHX7KdC1TVCihNajSnUsITSPll6XMH/8Ic0bez95P/nZSFzxGyb5MNIv7zImS/ZmnvM+Cf/by67db3e6zWFQWzd53eobXgcAgIzHNg9yUwmu03beZ/PDmaCfpkts1WDZvn1NVZlOk1UXDnAqdjiEpCnET+lf/23ykxOP0XjHoqcotqjcbG4u8VbvcKo8iBLmrdmIICaTVxcNc/INs1NNq6hDM+cUeFzztlRYqswuq/12VW+S2OxI4CtE8aa6fLud0aMVutpK/LonUP1Zw4Kt51W/Q4qRU9n48i+B2LSg/lINs1DkWNY5+3oq/EVmnMOr3Z5p0ypG3EEKwdNoLZM3bUL24c2TTPvZ/PpuJq95zytYI6RiHYtRVa5OcSEiHuFPe57fvttR4TFWll3qX1oKkHTkUFlS5CGRJQuAfG4WkqiC7WXS0qZSXWQiP8G8oU0+bxMeu5NAvS7EUlFX3g9UFmGhz5WinMv6AuCj3Tg3Hyz2oTXOnf2ct2MSRTfsJiIsk4dIR6APPvn6vhrBAj6Ep/dE1gSGfPoCttILDi7Y4xO9sKs1H92LI5w9hK630OGIXmuakUmqKCvUoQaxZ7Y7IgBc4Zx35wR8Wseb2d0CSsJdVOu88+oYs3Z+J4m+k5QWDiZ8yGGtRGUW7Uwnp2JK2V491yT09V8lZvt3JiYNj6le8N53kr+fT6bYLqrfHntcXU1Qo9kqLU3hF8TfS85nra7xPeZnFbd50NRJODZHrmzXLU7C4mV0IRaGqc2dEpodpr6YRHHL6lb4NiSkqlAu3f8ae92eS9udqDKGBdL7jQhIuG+F0nKzX0fPp69j69FdObdV0/ia6PTytuv2cpbicuSPuo+xQDvZKMzo/I+vv+5DxC98gql/NeutNjbgJ/d33AvA3VtcKmPNLsBSUoQmBZLWjD/Kjw62T0Af4oQ/wI3pwV3JW7kScoJUvG/XEXzAIfdDxF7zOz0iHWyax/4s5TqEYxWSg1SXDMIYHe+WZzsnGEkc27WPV9P85tL5PduInoVZaSJ+5kmYjetDptikMev8eutx10VnvxIUQHPxhEX/2ms5PzS9l0dSnKNxx0O2xaTNWuu2NqFZaOPjDIqdtsqIwccU7RA/t7pD/DDBhjAxhyKcP1LjQKYQgO6PmeKtBrzBhasPJ+FaWey73l/xMxGYdRLY7HyPbbbQrSG30xc7aYIoIodezNzB122dMXPY2rS8f6XaG2fX+S+n31h34x0Y4zosJo8+rt9DzqeNpdhse/IiSfZmOTA5NYK8wYyutZM7w+6jMK2ywZ/IGitHAuDmvoA8JQBfkh+JnRPEz0mJ8P7o9dDmqxcqcofdwZMNehNWOZrFhOVLK8qtfIm9dEgAjf3qSsK4J6AJM6IP80PkbiezTgSGfPYjQNAq2JbP1hW9ZedPrBLSKJuGS4chGPfpgfxSTnlYXD2XIpw967ZnOmRG5ZlfJ37AXgN3v/oFWVfspuqTTkb1wM+2uHYemqliLyzGEBDZa2mFpcha28irCurV2saFwZwp7P/qLwm0HCekUT/fHriTUjYby5ie+YM8HM6tH2Rl/ryVr/kYC4qKoSM/DFB1Kt4en0eXui5ANekd4wc0U0F1zDP/YSCYufavWeeSqqvH2i0tI2plb43M//foEQkIbbqquKJ7DZoGRQfQIqmB9+j6y4jsiJAlJ04g/sJPY9D3MHnYvY2Y+jykypMHsrS8kSaLT9Ml0mj4ZTVVd/pZCCFJ+XOI2nKZZbPzT/04uPfj9WaVTEj2wC1dk/0bm7HWYj5QSPaQr4d3bAHBoxkqsZZUuzc3VKivbX/qBcX+/hCkqlCmbP+HIxn2UJmcR2qUVET3bkfbnKlZPfwvLCYvEssmAzmRgwpL/IRv1BMbHeP17c0448sy561l+zSsOlULhyIs9LSTHwsPON39hx8s/Yq+yIOt1dLn7Ino9f0ODfUGL96Sx5NJnKU/LRVZkJEVh0Ef30uYKh2Tr/i/nsvbOd9Esjh9U/vo9JH8zn/Y3TWDIZw9Wj7aq8opIevcP50wEIdAsNsoOZgNQmXWEzU98TkVaLm2uHsPeT2a5rMLrAkx0uHmiR3v9osPwiw7zuP8Y3326gZ1bD3vcL0kw/b4htEw49bW8ibUG7Wq7XWP8Py8ReNvbpPz2M1ZZT2F0LFltu5LVujMhxfmUXPQiV618owEtrn/cfteFcGm3dyJVecVkzt1AZdYRkt6fibW4nNgxven1zHUEtYkl+buFbH32ayrS8wiIi6Lns9fT/vrGz4vQ+RlJuHSEy/aSfRnuJXGFoDgptfqfkiQR1b8TUUdb+eWu2c3ya152+R1pZitWs5W1d77LhVs+9eozHOOsd+SlB7NZctlzdZKvFKpGyb50dr3+a3WakGaxsfvdP7CVVzLwXe/ketaEvdLMnBH3YSkoAyGqGw2vuuVNAuJjCO3SinV3vVftxE8k+dv5xAztVl2xl7dmd40t0o6hVlrY+/EsEp+4ii53X0zS+zMc52gCXYCJ2LG9Sbjc9Yt+OlRV2Vi64ID7nRL06d+S6fcPwWSquS1efZDQNpzd2w+7SAHo9DKt20egD/JnxPdPkDlnPSmxnchq3am64US+sSWLtFgSF++m25iuDW57TQghvLpIL8kyUQM6O9oYukGz2tj69NeUHsisngEe/H4hB79fhM7fiL3KUr0uVZ6Wy+pb36Qyu4Aej1/lNRu9iaTI7meokkRolwSP521/4bsa/VDxnnQqDxfg3zzCS5Yep+kH+k7B3o//8riC7A5JkatV5WSDDsXPyNAvHmb3W7+7z/X8bA7WknKv2uyO1N+P5vKe9OVRKy3seOVHshd4zl0Wdo1db/1W/W9DaKDneoaTkI16inak0PeVW5i4/B263HMxHW+7gDF/vsDoGc+f8WykvMxCTlYpW9ale7ZFQPvO0Y3ixAFGj++Azk34TKdTGD3eUQQm7CrlFkFmmy7OXYNkGVXR8ceMvQ1lbo0IIVgydx/33vQ7N1z0Pfff/AcrFiV77foD37vbY92FYjJQvDvVufmK4Ghuttm1b6Zdcyyuesh1b0yK96Sx4+Uf3aYMyiY9Pf57tedzTxitu0OSpHpr+nz2j8gPZDmtHNeEpFMQCEdHdZ2C0AQDP7yLZsO6e3wZyAYdZSmHiejV3uN1NbtKwZYDWMsryVu5k8NLtxEQF0WXey6unnad8jkOZmMvd9+FqDgplezFW2r8EpjzjsfkYoZ1R+dvxHaKhV5wFDj4xThCGpF9OhDZp25VrJUVVj57dw3bN2ehaVqNDRQAuvZwJ+PTMISG+/PES+fx6buryckuBaBZ82Cm3zeE0HBH5oGs12Fp3w5ZU1FPfqlJEum5Z9A5qh745/ddzPp9Z3VWUGFBJd99toGqSivjp9R9ATmyTwcGfXQfa+9817kUVpIco3+dDKchFS9UjYxZa2l9+cg623YilYcLSP52ARXpeUQP6UbCJcNOq6hv2wvfuZ/JStD9oWmEdo6nYFsyAXFRLnHu4A4tqcjI93ht/9gIr6UbnsxZ78ijB3cle9Fm1JMWN6WjokOKQY/QNIRdc+Rz2jU0jn8R19/7AXHnfe3x+qrFRvGedPZ+PAvF30S768Y5leynz1rDyhtfR7PanEckkkTan6vo/7/b6fSfC9xc2ZmwrgnoAv1cY3OSYzqa/P1CRA2OPGbo8em9rCiMnf0y88c9jGZX0ax2hKo50gVPbA6tyIR0iKtxuni6vP3SUg7uy0dVazcliI1r3MXCVm3CeendCygpdnzu7hZbO18/lu1L3BduGE2N/xOyWlX+/mOXS2qn1aIy8+cdjJnYyStZNp2mT0bYVTY9+imSoiBUFb9m4XScPoltL3x/2terzD5SZ5tOJGvBJpZc/DTaUemI5O8WsvWZr5m89oNaLy7mrU1yWeQEQJLJXriJXW/8gmzQoVlstLxwMMO+fMShfw70fOpa8tbudhteUfyMDP3qkXqrSznrQysdbpno+CBPnPZJEvpAPy5O+pqBH9xDv9f/Q0iXePeOUAjS/lpDh5snoPg5CyLJJgOGkADW3PYW+z+fw54PZjJn2H1sffZrAIp2p7LsqhexFpW59vQUArXSwob7P6pVaCZ+6hCMYYGu+a0C0ESNWTiKv5Fez93otC2ydwemZf7KkE8fpO8rt3LegtdpMaE/ip8BfZA/ukA/gtu3YOysl05pW23JTC8mNbmg1k5ckkA6hUxCfVCRlc+yq1/ku6BJfBc8mZU3vIbBavaYMTP6ngkY/F3FsvR6meFj29W3uackP7fs5JqmajRVo6jAO2XgAJ3vuJArc/9g3OyXmbTmfS7Z/y2dbr/QbcZTTUiKTKgX+4uqVhtLpz2PvdJSvY5kL6+iIj2PjQ99UuvrBLSIdLtdVmSObDmAarZiK61EtdjImLWWFTe8Vn1Ms+GJDP3iYYyRwSj+RiRFRhfoR5urxzB1+2c0G+Y9Hf2TafzhRB0xRYQwee0HrLntbXKWO1pdxQxPZPAn9xPcNpagNs05vHQbFel5bs9XzTasRWX0e/M2ytNyHY14j34p9QEmrGWVx52oJlCrLOx841daXz6SpHf/cLv4eCKyQcfhJdtoddHQGhehFIOeSavfZ+UNr5G7aidIErJO8dz0WZaQZJmYYd0Z8NYdbpvu6vyMtJl2XIY1dmRPSvZlULj9IAHx0UQN6Oy1EULqwQL+/Hk7ai3boUky9O4fj+KhqrC+sBSV8Xff2zEfKakuaDr442IOL9vGRbu/dNLzPoZOJ/Pwi+N549nFaJrAbtfQ6WRatQnn4it7NKj97ggOMWG3u//cVU0QGORdxUadv4mYId2q/60P9GPMzOdZfNHTjkYMtUg88GseTvPRvbxmU86y7W5fJprNzqHfljPs60drdZ3uj1zB8qtedKmrcJe1o5qtZPy9lqrcQvxiHGqSbaaNIuHS4ZQeyEIf5EdAi/oJpZzMWe/IAYLbteD8RW+iHs1zVQyORSnVamPhpCfIX7/HY4dtXYCRZiN6UJZymMNLtzp9GSyFpW4X6jSbnZRfl1GyL8NVy9wNZWk5zB52L3lrd6MYDbS9Ziz93viPi0RoQFwU5y96E2tJOarZypLLnydv5U6312x37TiGfVW7L+eJhHRsSYibvPO68Nt3W1nw955a648YTTqCgo1cf1v/Ux/sZRxCRZVOfzdhV7EUlHLw+0Uew2BtO0Tx7peXsHl9BiVFZtp2jKR9p6gmIeEQFGyiW89Ydm3NdnLoOr1M7wEtvSb7WxOxY/swLetX0mauInvhZlL/WIGs1zkUMm12hKY5tEs0QXD7Foz58wWvpvUekyFwR00hyZOJnzKYxP9ew7bnv0XW65Akx2KutaTCbXcmxWSgPC2v2pGDI7QZ2in+9B6gjpwTjvwYxxz4MfZ88KfHmBU44laR/ToRM6w7y695ySXO7inbQqgamtXuSMlav8dj+y0Azaay5QRdaLXKQvK3CziyaR9TNn7sVvPBEBIIIdBy0kAKNu1zsUsX6EeL8/p5vGdDkpZSeFpOfOykjnToHO2xeUJ941hPcf0+2CvMHF68pcb1DKNJz+ARberTvDPmP/cN4Z2Xl3LoQAGKTka1a7TvHMXNdw5qMBsMwQG0v3487a8fz8D37iJ91hpUs424Cf0xhgdRsC0ZU2QIoZ29L0TXbEQP9wkLkkTs2NpJKwNkL9nK3o/+QlJkNJsNY2Qoo355moUTH3P7O1fNVoLaNndzpYblnHLkJ7P/cw+azIAxPIhuD15O1wcvQ5Ik8lYn1Wp0DaD4GWh10VD8m0ew7//+cfsHlg06JEUhvHc7jqx1bhisWWyUHsgie/EWWoxz0YivpuP0yez5YCbm3OLqqZ1s1BOYEEOrS4bVytb6Zs3yFGy1TP9s3zmKa29t+FH4MYp2p3Jkk3t1RUmnENAyuoEt8h7+AQaeeGk82Rkl5BwupXmLYJq3aLyFZGN4sEsnovqMERuCA+j3xn/Y+Mj/OQY+QlSnF/d/+45aXaPkQCaLL3zSKZxZlXWEhRMeo/NdU0l69w+nkIviZ6TNlaMwRTR+de9Zv9hZE3YPC4SyQUfi41eR+PhV1aP4YzoTLkiSkxyoLsBE68tGEtWvEwFxUUxc8Q6R/Ts5ctMVmeBOLYm/aAjdHp7GRbu+oCrziNtVcLXKwpGNNUu2GkMDmbLpEzrcOglTTBj+sRF0ve8SJq9+32X20VjYbadOMVQUGf8APbfcPbhhjHKDpbicOcPvxVrsfuFZ1uvoOH1yA1vlfWJbhtC7f8tGdeKNRec7LmT8vNeInzqEiD4d6HLPxVy064tat61Lem8Gqps1L81uRxfkR+LjV6EP8kfxd2izdLhlIoM/vt/bj3FGnNMj8lZTh7D3o1kuCxWSotDifOeRYbeHLmfl9a+6LC7qAk30fv5G0v9a7ShZv2US8VOOO6TwHm25YN2H2MqrkGSpOhXpGP4tIilPc9UYUfyM+HtYIT8Rv+gwBn1wD4M+uOeUxzYGfQfFs3LJQSxm589Yr5eJaxWKTqfQsWsM4yZ3IjSs8SRPD367wPEjdffSUWSGfvGQ19cOfDQ8MUO7EzO0+xmdW7TzkNt4ulplpXh3KiO+e4JuD0/DnFeMMSK4WhmyKVAnRy5J0hvABYAVOAjcKIQo9oJdXiHx8as49OsyLIVl1dkl7jSZARIuHkbRzhR2vvazYwQuHGGMcX+/RNSAznS995Ia7+VJl7nbw9NYcfXLLlWjkiKfE42dO3WLoX2nKPbszKlOO9QbFJq3COaJl8Zj8HJXnzOlcEeKxzBbZO/21Xo2Pv69RPRq78gjP2ngp/gZCe/pSDNVDPp6K+qpC3X9lS0EHhdC2CVJeg14HDj9VIp6wi86jKnbP2f3uzPImLUGQ2gAne+c6qLJfIxez1xP57umkrtqF4Zgf2KGJdZZAbHVhUNI/O/VbH/hu+oXhOJnYOzfL52Vovwns3v7YfYl5aKdUNIsNI3r/zOgyThxgLDurVH8jS7OXNIphNdQtVsb0metYeOj/0fp/iyMEcF0vf9Suj8yrUmpAaqqxvbNWaQfKiIyKoB+g+MxNpI0QlOly70Xs/+L2dhPdOSShGLU0+HG8z2f2ASQPHWvOO0LSdJFwKVCCM9iBEfp27ev2LSp6fc99CaWojLyVu9CHxxA9JCuTepHfqZomuDem36ntNg11z2+dRgvvN10Ys6WojJ+b3uNS4xcF2BiyqZPzjissvvHZfz2yhwKwmIwmCuJS9lDhLmYdteex+CP7/OC5XWnrNTMi4/Np7ioEnOVHaNJh6LIPP7iOOJbh5/6Av8i8tYlsfKG16hIz0MIQWjnVgz/7nG3dRqNgSRJm4UQLhkS3nTkfwO/CCHc1upKkjQdmA4QHx/fJy0tzSv39dF4pKcW8eJj81zi4wCKTua9ry71ejFKXSjadYjlV71EaXIWyBKmyBCGffUIzUedWWFKQX4Fj934EzZJQSg6EBqyqtJ671YSsg5wWeqPTvnFjcX7ry9n6/oMl4rbiKgA/vfpRU0iF76pUZl9BEmn1EqmuSHx5MhPOfeVJGkR4E7Z6L9CiL+OHvNfwA784Ok6QohPgU/BMSKvpd0+mjDyKcrrm5qDCOvWmqk7PqciMx/NaiOwdfM62fjL15uwyno4VgsgyWg6mUOdexNXcpjC7Sm0OK9xHbndrrF1Q6Zb2YTyMgvph4po1abxXzZNDf/YUyciNCVO6ciFEGNr2i9J0g3AZGCM8Nbw3sdZQYuWIQQEGlxG5JIECW3CCQis/4rCM8Fbi1XbN2cfd+InIGkaR4IjPae0NiCaqiE8dXGXJaqqTkOy0EeTpU555JIknQ88AkwRQpxaM9XHOYUkSdzx0DCMJh06veOrZDAo+AcYuOWexssZbyjkGnRiApuFEdatdQNa4x6DUUeLePc55TarSmR0oMcu7z7OHuqaVvABYAQWHp2irhNC3FZnq3ycNbTvFM1rH13I8gUHyM4soXXbCIaNbdckYuPa0UrdmhxuXRg4LIHliw6g2k9yhIrCtG+bTt7/oOGtyUjd6rLdbtd45LaZBAYbufrmfgwYmtDwxvnwCnVy5EKIxtfw9NHohIX7M/WKxlcBPEbu4TK+/b/17N6RgwR069mc6/7Tn6iYIK/e59JrepK0M4eigkosZjuKDLIsM/2BYUQ0Af2NY2zZkOlxn6oKSorMfPLWKowmHT37xjWgZT68hdeyVk6Hf2P6oY+GobzUwqN3/klFubVaOkCSJQIDDbz20YUEBHp3pmCzqWxak07SzhzCwv0YNqYdUTGBXr1HXXng1hkU5J9akzwk1MR7X1/WABb5OFM8Za2c01orPv59LF2wH4tFddJ/EZrAYrZ7tYflMfR6hahmDsd9OLOE5H35HrXBG4s2HSI9Np44kZJic5Oz3UftaDqldz581AGbTWX9qlQW/L0HmxtJXatVZV9SHhOmerfj/R8/bGXeLMc9hYDtW7KZP2sPT7x0XpOpbJ16eXe2b8p0aQXnjrJSM2FH+5X6OHtoGt80Hz7qgMVi58XH5pGbXYbF4l4bXlEkYpp7N0aenVnC3L+cXxwWs52MtCLee3UZh7NKsdk0evWL46IrEqsbOjc0ca3CeOyFcXz/2UZSDhTUeGxTWKT2cfr4Qis+znoWzdnH4axSj04cHJWmo8/v4HH/mbB5XXp1ZsyJ2G0aO7ce5kheBSVFVaxcnMxT98+mtNh9l6qGoG2HKJ55YyLPvjnRY5jFYFTQN0KzDx91x+fIfZw2aSmFrF+VSnpqUWObAsCapSluwyngaHfm56/nzoeGE9M82Ls3Fh6bSDmhqoLKSivz/95z6oPrmdbtImiZ4Fp2LsuOLBwfZye+0IqPWlNRbuHN5xaTmV6MLEtomqBV63Duf3J041ZxehhhyjIMGdmG66b3r5e2cr0GtOSv33aiqaeOPdttGts2ZXHZtbVvO1ZfPPzsWD54fTkH9x9BUWRUVWPChV04b3LnxjbNxxnic+Q+as0nb68m/VCRU2bDoeQCPn9vDfc+MbLR7Bo4LIEZGdudpHQBFJ3C+Rd2qbfeoHHxoYyd2JElc/djsdodEsWK5FbXBJpO/Dk4xMQTL42nIL+C4qIqYluG4Ofnk7Q9m/E5ch+1oqzUTNKOwy7paXa7xo4tWVSUW7yeo10brBY7G1aluZSZSzKcN7kTsXH12/Lsihv60KNPC5YvTKay0kqX7s34/YdtLqEeo1HHuMmd6tWW0yUiKoCIqIDGNsOHF/A5ch+1oqzUgk6RsdtcF/cUnUx5mbVRHPmqpQfJOVzq0jdUp1Po1rNhqis7d29G5+7HBUJDw/35/P011eEnoQn6DW5Fr74tGsQeH/8+fI7cRzXlZRbm/pnEprXp6A0Ko8a3Z+R57VEU2VGt6CEWrShSo43s1q5IdZsfbbOqbFybTpfEhi+VHzgsgW49mrNmeQrLFhwg53AZG9aksn1zJlfd3JfBI9o0uE0+zm18WSs+AMdC5lP3/8O8WUnkZJeSkVrEz19v5p2XliKEQK9XuPjKHhiMzvFmg1Hhkqt6otM1zlfJU7qcJDmUGBuLgCADyxclk5NdhmrXsFpUykotfPXROnZsyWo0u3ycm/gcuQ8AFv6zl7ISs1PoxGpxVEPu3ZULwPgpXbjhtgFExQSiKBLRzYK46c6BjJ3UeLHfEePaYTS5Tiz1BoVBwxtPRnZfUh75ueWoJ+WZWy0qM37a3khWOVNeZmHL+gx2b3dd+/BxduELrfgAYNO6DGxu4t8Ws50dW7KqY8BDRrVlyKi2DW2eR/oNbsXG1Wns2JKNxWpHwjFKP29yJxLaNl5jh8OZJR4bOuRklTawNa7888cu/vx5B8rRmZSiSNz7+Ejad47GYrFjNOpO2QHKR9PB58h9AODn5/6roOhk/PybZqcfcHS5ufOR4ezbncemtWno9I6ReGO3L4tpHuTREUY3a1x1xO2bs5j16w5sNhWb7fj6wuvPLMRg1GOusqLoFDp0jmbC1C507dHc59SbOHVy5JIkvQBcCGhAHnCDECLbG4b5aFhGT+hIWkqRS5m7LEsMGp7QOEbVEkmS6NQthk7dYhrblGo6dWtGaLg/eTllTvntBqPC1GkO7XaL2cb+PfnodDLtO0c32DrD3D+TsLhZILbbBXa7FQDNqrJ7+2H27s4lLj6Ux18c16Rf6P926vrNeUMIkSiE6An8Azxdd5N8NAYDhibQd1A8BqOCLEvo9DJ6vcLVN/f1ekOGfwOyLPH4i+No3zkKvV7GZNLh56/nqpv60ntAS5YvPMBd1//Gh2+s4J2Xl3H39b+xe/vhBrGtqODU2uTHUO0aWenF/Pz15nq0yEdd8VpjCUmSHgfihRC3n+pYX2OJpktaSiE7NmehNyr0G9SqyRaMCCFI+XExu/73K+a8YmKGJdLzmesI7RTf2Ka5UFxYSXm5lWbNg9DpFZL35vPaMwtd0iYNRoU3Pp5a7yqJX3yw9rS12RWdzOe/XuULsTQy9dZYQpKklyRJygCuxjciP6tJP1TIysXJZGWWEBbuT0iYX2ObhBDCrZbJhoc+Zs1tb1O47SCV2QWk/racv/vfQeHOlEawsmZCw/2Jiw+tlgqY50EzXWiCVUsP1rs9A4a1Ou1zVLvGF++vqQdrfHiDUzpySZIWSZK0y81/FwIIIf4rhGgJ/ADcVcN1pkuStEmSpE35+fneewIfXuHrT9bxzINzWDh7H2uXH+KL99fy/CNza5SGrU8sxeWsvPF1vvWfwDeG8fzd/w7y1zvUAyuy8tn38d/YK8zVxwtNw15excaHPmkUe0+HI7nlLpWoADabRn5u7cMeZ4rQcJuyeSrWr04jO6OkHizyUVdO+dcUQoyt5bV+AOYAz3i4zqfAp+AIrdTWQB/1z7f/t56l8w44bbNY7GRnFDN35m7iWoWxaulBNFUweGRr+g1uhVJPnenB4ZTnjrqfkj0ZaFYbAEc27WPemIeYvO4DinYdQtIrYHY9N3fVznqzy1t07BpNRmqRS+620aSjQ+foer9/i5YhaB6EvWpCCMGu7dnEtqxf/Rofp0+dfo2SJLU/4Z8XAnvrZo6Phib3cBlLFxxwu89m05g9czefvruarRsy2b45iy8/XMf/nl/sUujiTbIXbaHs4OFqJ34Mu9nCthe/xxASgOShO4IuoPHDQadi/JQu6A2Kk+SBokgEBRnpN+T0wx6nS3hkAL0HtHTYcAIGo8JVN/X1KEmsyDImn0pik6Suw6pXj4ZZdgDnAfd6wSYfDcj2TZk1dkewWlQs5uPhFYvZTvK+I2xam+51W/Jyylg6fz8r5u2lVDKQ0aYLBzv3piC6BQIJNEH+uj00H9MbSXEtv1dMBjrcMtHrdnmb8Ah/nn5tAl0TmyPJEopOpu+gVjz9xoQGkxWYfu9gRo5rj8GooNPJBIeYuPqWfoyf0tljuqkQgj4DWjaIfT5OjzrlkQshLvGWIT4aB1mRkRXJRcu7JixmO2uWH2LA0ASv2CCE4KcvN7Fk/gEkCVSbQB05FUloCFkhq7UN//ISeq6eR0DLKBSDnrF/v8TCiY8hNIFmsSEbdET07UjPp6/zik31TWzLEB55bmy1/K6nGUZ9odMrXHNrP664sQ8Wsw0/fwOyLFFeZmHVEvcLxlMu694oCpc+To2vsvNfTu/+cfz81ennCHszDW3z+gyWLUh2zuSQJITkGJ1qOj0VQaGkd+vD+Y9eAEDM4K5My/qVtBmrMOcVETWoK9GDujS4Q6wrjW2vTiejO8E5r11xyONL/eD+Iw1llo/TxOfI/+WERwZw2bU9+f37bdhsqttsipMxmnQMHXVqKda1yw8x67edFBZUENsylEuv7knXHq6ysgv/2XvK7Bih6Mhv25mWkwdVb9MH+NHu2nGnNvgsoiIzn4KtB/BvEUlEr/YN7ujzc8uxeuh/mp9b3qC2+Kg9Pkfug/FTutAlsTkrFiWzeO4+j63KwOHEu/eKpUV8KDN/3k5FmYVuvWJJ7BWLfEImyz9/7OKvX3dUF72k7D/COy8t5bYHhtJnoHPRTlmpm/QTNwj9uft11ewqq295k0O/LEU2GhCqSmCrGMbNfZXAlt7LZCnIr2Dvrlz8AvR07xXrIgPctkMkRpPOaV0EHDOwdp2ivGaHD+9y7v4yfJwWLRPCuPqWfiyas8/jMWERftx812CKCip48r5/0FSBqmqsWHyQ+IQwHnl+HAaDgrnKxl+/7HAZ2VmtKj98sYneA1o6jTR79IkjN7usRilVSZbo3vvc7bCz/eUfOPT7clSLDdXiyNYp2ZfBgvMf46JdXzh9Xru3H+b377eSnVFCWIQ/Uy7vfspmFXa7ygevrWDHliwUnYwsy0gS9B0Yz8EDRzDoFUaOb8/gEa0JCjZit6lOL3S9QWHiRV3r5+F91BmfHrkPJ8IiPKfvXX1zP+Jbh/Ht/23EZlWrUxAtZjupKYUs+DsJgMy04mp51JMpLqqissLqtO38Czvj519zWpufn57Lr+11Oo9yVpH03gzUSovTNqFqVKTnUrDleHro5nXpvPPSUlIOFGA22/+/vXuPjrK8Ezj+/b3vzOR+I8GEXAiw3IyQhIuA10LFC5AligdaPHJqu7Zb10U91XKK7FrXYy8ue6TdxepyXFps6dlKu11tOSuIovVS7oRyFEHEQEy4BJJAyG0yM8/+MSEQZobEOslc8vv8Rd6ZvO9vngm/eeZ5fu/zcLz2HD//2Tb+sKFn/XxTYxv1J5sxxnD0SAMPLtnA3p2f4fUa3B1e2ts6aWvt5J03P6Gu5izVRxr49dpd/Mczf+KJZ+YwaXoRti2IwOhxOaz44e3kDtM1d6KV9shVD5WLynhpzQ48nT1700Nykpl63XDe2vwxEiRHd7q9vL3lEyrunkhqegLeEL1rEXAl9Pyzy8hM4qHlM/nRis1BJ9pE4Il/ncNVefGZSIwxuBubgz4mtk1r3WmYMhZjDOtf3BX4TafDy6sb9nNrxXjONrXzwrPvcKy6EcsSkpOdtLf7E3dv3B1eDh04RW1NE0uXfQmf14fPELHdn1Tf6Tukerh59t8wb0EJTqdFQqIDp8uieFQW//zjOxAR/4RoiKoGT1eCyctPZ1hhekBli8Nhce11xUG3Z3O57IAEf4HtsEhOid8bUUSEjLHB67N97k6GlI8GoLXFTVNTW9Dn2Q6LTw7W8/T3XuPTw2fwdPq3l2tqbO9TEr+go91D1S7/VnSWbWkSjxHaI1c9iAgLFpczp7KEmuom0jMSyStI7368dFIBL7+0N+D3/De1XJzEfHj5LH70T5tpPtuOzxgEobA4k4q7r+FXL+7k0AcnGZKTQuVXJjJydA4FwzNDxpSRkUR6RmJYX2e0ufbfvs3WRU/hbbs4vGInJzBiwU3dk52uBAehiljaWjtZ+dQbCNKnyqNQbFtIDLHJiIpeYVvG9vPQZWxj2/r/2snbmw93lww6nRapaQk8taqiR8L1+QwH9p/g9KnzFBZnYdvCDx/fFLCpwex541jyzWls3XSIX6/d1WN5V5fL5sFlN1M+tXBgXlwE1Wzcxs7vvsDZgzW4MtMoeeguylbci+W4+A3mP1e9y473jvbbHptOl83Tqypoamzj5Zf2UHO0kbT0RObeWcItc8dFvO59sAu1jK1+9KqQzp1tZ//eOixLKJ1c0L0Gx6zbx1B9uIFPPq7HsiwmlA/j/qXXkZrWs9dsWdKjbvyJ72wMujPNlo0HKZtSyKzbx5I9NIVXXt5P/YlmCoszufOrZQOykFQ0KJo3g6J5MzDGBE2YdZ+dZeSYbI58fIaG0y10dvoIZ0fM4bRYtGQSZ0638JMfbO0eiz9T38JvXtrD8bpmlnzz2rBdT4WPJnIV1KZXP2TDL6uwHf6E4vUa7ntgOiNHZ3ctb+sfK/fi5cN9x/nd+iq+9u0Z3b9/tqmNHe8dxd3hYUJ5PrnD0jhW3RDyeht+uYfSyfmUTi6gNI7LDPvi8iTu8/pY89P32bXtGCL+D0ixBKfTCnnzzqWcTov8okyOHgnd/llDkhg1JgePx8fPf/bnoBOqb20+xPyFE8jIjP6FyQYbTeQqwOGD9fx2fVXX5rwXj697fjujxmbT3u7psdBWR4eXd944wty7JjA0N5X33z7C2ue2AeD1+vjf3/yF8ikFV1ycq+FMaz+9mtj3xmuH2L39WMBmFL2Ncoj4b+D6+0du5M3XDl3hidDY0Mbu7TXs3l4T8mlOp82nh88MimGuWKNT0irAlo0Hg+5g4/H6+PhAfdCEbNnCwQ9O0tjQytrnttHp9tLp9uLrqluu2l1L1hW2MBs+IiucLyGuvP7HjwK2hQP/wlcXvjEFY4x/nuK9t46QV5CObYd4bh9HZ3w+E/eTzrFKe+QqQFNja9DKB5/X4HTaeINsvSYCySkudrx7NGhicHd4Scp1II0SUL7ocFrc+ZXScIUfd1rOu0M+Vj61kP176rAdFh3tnoA6fHeHNyxLDov46/1Hjs7+wudS4ac9chVg4uSCoOtiJyQ6mDg5P2BDAvD31k4eP8eJunN4PMHHbb0ewzOr5zOsMB0Rf6lbemYiDzx6E+OuyQ3764gX4yfkBh1GEWDB4jL+fd1CHnl8VsgNIS7ncFokJjpC3n17Kdu2SExykJWdzGPf/7JWrUQp7ZGrADNvHcPmVw/g8bZ3bwlmOywys5L41kPXs3rlnzh04BQ+r0EsodPtXzXxt+urAP9k3eXVFA6HxaTpReTmp/Pj1ZWcb+6go91DVnay7szei7vvKWf/3jrcHZ7ub0quBJvSyQUUFmfhdnt587VDNJ/ruPKJujgcFsuenM3JE8384vntAQtkXeBy2dx862imTC9i/IQ8fZ+iWFh65CLyqIgYEckJx/lUZKWkuviXZ+dxw8xRJKe4SE1LYNZtY/j+yjkkpbj47pOzWf70bSy4p4wLmaXT7cXT6cPTVRJ36Xis7bBITnUxt7Kk+1hqWgLZQ1M0OfRBflEGT66cy+RpRaSkusi5KoUFi8v4h8duAmDd89vYsyP0JOXlfF5DVnYy028cQeHQBGwCa9Jt2yIrO5nFX59KSekwfZ+i3BfukYtIEf5t3sK/95eKmMysJO5fej33Lw3++KgxOZw+1YLtsOjs7JkIjPEPwwzJSaGj3cPk6UXMW3AN6Vq29lfLL8rgoeUzA463trjZ/m51wHtwJT5jSE5x8dHq3zP8F2txZQ6jdsQ4WtOy8Dhd2A6LaTcM5977p+kt+jEiHEMrq4BlwCthOJeKIa0t7itsESf84Kd/O6DxDEZNjW1BP0zhYnnipaNcDqd/vRtpa2P38hcx7W5yWz4lt/ZT/xMsi6L51zP7O/cOQPQqXL7Qx62IVAK1xph9fXjut0Rkl4jsqq+v/yKXVVFi/DW5mCAdQRG4emLewAc0CGUPTemex7ic02lTPGoIrgSbpGQnLpfN2Kuv4r4HpnN8axVWsI06fD7qNu3o56hVuPXaIxeRLUCw/5UrgMfxD6v0yhizBlgD/rVWPkeMKkrlFaQz/cZidrx/tLvOWSwhIcFm4ZLyyAY3SCQkOJhdMZ4tG3vWmrsSbG6ruJqFSyZxrLqRk3XnKBieSX5hBgCWy+EvewnCcmgNRKzp9R0zxswOdlxEJgIjgX1dJUmFwB4RmWaMORHWKFXU+rul1zN6/FA2/+Ejzp/voGRiHnctLiMvP733X1ZhsfDeSTidNpte+RCv12A7LOZUXs38Rf7a/OEjsgJuuMqfPSXocsSWy8HIxbMGJG4VPmFb/VBEqoGpxphet9rW1Q+VCj+Px0dri5uUVBe23fuoac3GbWxd9BTG58PX0YkjNYnk/Gwq/ryahKz43MQj1unqh0rFOYfD+ly30BfNm8HdB9dxeN0mWmpPk3dzKcULbsJ2xe8mHvEqbIncGDMiXOdSSg2MlMKhlK3QCpVYp0WiSikV4zSRK6VUjNNErpRSMU4TuVJKxThN5EopFePCVkf+uS4qUg8c7afT5wC91rIPctpGfaPt1Dtto74JVzsVG2OGXn4wIom8P4nIrmAF8+oibaO+0XbqnbZR3/R3O+nQilJKxThN5EopFePiMZGviXQAMUDbqG+0nXqnbdQ3/dpOcTdGrpRSg0089siVUmpQ0USulFIxLq4TuYg8KiJGRHIiHUu0EZGVIvKRiPxFRH4vIpmRjilaiMgdInJQRA6LyPciHU80EpEiEdkqIh+KyAci8nCkY4pWImKLyF4R+WN/XSNuE7mIFOHfhu5YpGOJUq8DE4wxpcAhYHmE44kKImIDzwFzgBJgsYiURDaqqOQBHjXGlAAzgAe1nUJ6GDjQnxeI20QOrAKWATqbG4QxZrMxxtP14zb8W/UpmAYcNsYcMca4gf8GKiMcU9Qxxhw3xuzp+ncz/kRVENmooo+IFALzgBf78zpxmchFpBKoNcbsi3QsMeIbwP9FOogoUQDUXPLzZ2iCuiIRGQFMArZHOJRo9BP8HUpff14kZrd6E5EtQF6Qh1YAj+MfVhnUrtRGxphXup6zAv/X5PUDGZuKDyKSCvwOeMQYcy7S8UQTEakAThljdovIzP68VswmcmPM7GDHRWQiMBLYJyLgHzLYIyLTjDEnBjDEiAvVRheIyH1ABXCL0RsKLqgFii75ubDrmLqMiDjxJ/H1xpj/iXQ8UegGYL6IzAUSgXQR+ZUxJux768X9DUEiUg1MNcboCm2XEJE7gGeBLxlj6iMdT7QQEQf+yd9b8CfwncA9xpgPIhpYlBF/L2kd0GCMeSTC4US9rh75Y8aYiv44f1yOkas+WQ2kAa+LSJWIvBDpgKJB1wTwPwKb8E/gvaxJPKgbgCXAl7v+fqq6ep4qAuK+R66UUvFOe+RKKRXjNJErpVSM00SulFIxThO5UkrFOE3kSikV4zSRK6VUjNNErpRSMe7/AUZyE7iW1FnMAAAAAElFTkSuQmCC\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Visualize the data:\n", "plt.scatter(X[0, :], X[1, :], c=Y, s=40, cmap=plt.cm.Spectral);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You have:\n", " - a numpy-array (matrix) X that contains your features (x1, x2)\n", " - a numpy-array (vector) Y that contains your labels (red:0, blue:1).\n", "\n", "Lets first get a better sense of what our data is like. \n", "\n", "**Exercise**: How many training examples do you have? In addition, what is the `shape` of the variables `X` and `Y`? \n", "\n", "**Hint**: How do you get the shape of a numpy array? [(help)](https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.shape.html)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The shape of X is: (2, 400)\n", "The shape of Y is: (1, 400)\n", "I have m = 400 training examples!\n" ] } ], "source": [ "### START CODE HERE ### (≈ 3 lines of code)\n", "shape_X = X.shape\n", "shape_Y = Y.shape\n", "m = (X.size)/shape_X[0] # training set size\n", "### END CODE HERE ###\n", "\n", "print ('The shape of X is: ' + str(shape_X))\n", "print ('The shape of Y is: ' + str(shape_Y))\n", "print ('I have m = %d training examples!' % (m))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Expected Output**:\n", " \n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
**shape of X** (2, 400)
**shape of Y**(1, 400)
**m** 400
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3 - Simple Logistic Regression\n", "\n", "Before building a full neural network, lets first see how logistic regression performs on this problem. You can use sklearn's built-in functions to do that. Run the code below to train a logistic regression classifier on the dataset." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\abdur\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:63: 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", " return f(*args, **kwargs)\n" ] } ], "source": [ "# Train the logistic regression classifier\n", "clf = sklearn.linear_model.LogisticRegressionCV();\n", "clf.fit(X.T, Y.T);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can now plot the decision boundary of these models. Run the code below." ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy of logistic regression: 47 % (percentage of correctly labelled datapoints)\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Plot the decision boundary for logistic regression\n", "plot_decision_boundary(lambda x: clf.predict(x), X, Y)\n", "plt.title(\"Logistic Regression\")\n", "\n", "# Print accuracy\n", "LR_predictions = clf.predict(X.T)\n", "print ('Accuracy of logistic regression: %d ' % float((np.dot(Y,LR_predictions) + np.dot(1-Y,1-LR_predictions))/float(Y.size)*100) +\n", " '% ' + \"(percentage of correctly labelled datapoints)\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Expected Output**:\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", "
**Accuracy** 47%
\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Interpretation**: The dataset is not linearly separable, so logistic regression doesn't perform well. Hopefully a neural network will do better. Let's try this now! " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 4 - Neural Network model\n", "\n", "Logistic regression did not work well on the \"flower dataset\". You are going to train a Neural Network with a single hidden layer.\n", "\n", "**Here is our model**:\n", "\n", "\n", "**Mathematically**:\n", "\n", "For one example $x^{(i)}$:\n", "$$z^{[1] (i)} = W^{[1]} x^{(i)} + b^{[1]}\\tag{1}$$ \n", "$$a^{[1] (i)} = \\tanh(z^{[1] (i)})\\tag{2}$$\n", "$$z^{[2] (i)} = W^{[2]} a^{[1] (i)} + b^{[2]}\\tag{3}$$\n", "$$\\hat{y}^{(i)} = a^{[2] (i)} = \\sigma(z^{ [2] (i)})\\tag{4}$$\n", "$$y^{(i)}_{prediction} = \\begin{cases} 1 & \\mbox{if } a^{[2](i)} > 0.5 \\\\ 0 & \\mbox{otherwise } \\end{cases}\\tag{5}$$\n", "\n", "Given the predictions on all the examples, you can also compute the cost $J$ as follows: \n", "$$J = - \\frac{1}{m} \\sum\\limits_{i = 0}^{m} \\large\\left(\\small y^{(i)}\\log\\left(a^{[2] (i)}\\right) + (1-y^{(i)})\\log\\left(1- a^{[2] (i)}\\right) \\large \\right) \\small \\tag{6}$$\n", "\n", "**Reminder**: The general methodology to build a Neural Network is to:\n", " 1. Define the neural network structure ( # of input units, # of hidden units, etc). \n", " 2. Initialize the model's parameters\n", " 3. Loop:\n", " - Implement forward propagation\n", " - Compute loss\n", " - Implement backward propagation to get the gradients\n", " - Update parameters (gradient descent)\n", "\n", "You often build helper functions to compute steps 1-3 and then merge them into one function we call `nn_model()`. Once you've built `nn_model()` and learnt the right parameters, you can make predictions on new data." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4.1 - Defining the neural network structure ####\n", "\n", "**Exercise**: Define three variables:\n", " - n_x: the size of the input layer\n", " - n_h: the size of the hidden layer (set this to 4) \n", " - n_y: the size of the output layer\n", "\n", "**Hint**: Use shapes of X and Y to find n_x and n_y. Also, hard code the hidden layer size to be 4." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "# GRADED FUNCTION: layer_sizes\n", "\n", "def layer_sizes(X, Y):\n", " \"\"\"\n", " Arguments:\n", " X -- input dataset of shape (input size, number of examples)\n", " Y -- labels of shape (output size, number of examples)\n", " \n", " Returns:\n", " n_x -- the size of the input layer\n", " n_h -- the size of the hidden layer\n", " n_y -- the size of the output layer\n", " \"\"\"\n", " ### START CODE HERE ### (≈ 3 lines of code)\n", " n_x = X.shape[0] # size of input layer\n", " n_h = 4\n", " n_y = Y.shape[0] # size of output layer\n", " ### END CODE HERE ###\n", " return (n_x, n_h, n_y)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The size of the input layer is: n_x = 5\n", "The size of the hidden layer is: n_h = 4\n", "The size of the output layer is: n_y = 2\n" ] } ], "source": [ "X_assess, Y_assess = layer_sizes_test_case()\n", "(n_x, n_h, n_y) = layer_sizes(X_assess, Y_assess)\n", "print(\"The size of the input layer is: n_x = \" + str(n_x))\n", "print(\"The size of the hidden layer is: n_h = \" + str(n_h))\n", "print(\"The size of the output layer is: n_y = \" + str(n_y))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Expected Output** (these are not the sizes you will use for your network, they are just used to assess the function you've just coded).\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
**n_x** 5
**n_h** 4
**n_y** 2
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4.2 - Initialize the model's parameters ####\n", "\n", "**Exercise**: Implement the function `initialize_parameters()`.\n", "\n", "**Instructions**:\n", "- Make sure your parameters' sizes are right. Refer to the neural network figure above if needed.\n", "- You will initialize the weights matrices with random values. \n", " - Use: `np.random.randn(a,b) * 0.01` to randomly initialize a matrix of shape (a,b).\n", "- You will initialize the bias vectors as zeros. \n", " - Use: `np.zeros((a,b))` to initialize a matrix of shape (a,b) with zeros." ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "# GRADED FUNCTION: initialize_parameters\n", "\n", "def initialize_parameters(n_x, n_h, n_y):\n", " \"\"\"\n", " Argument:\n", " n_x -- size of the input layer\n", " n_h -- size of the hidden layer\n", " n_y -- size of the output layer\n", " \n", " Returns:\n", " params -- python dictionary containing your parameters:\n", " W1 -- weight matrix of shape (n_h, n_x)\n", " b1 -- bias vector of shape (n_h, 1)\n", " W2 -- weight matrix of shape (n_y, n_h)\n", " b2 -- bias vector of shape (n_y, 1)\n", " \"\"\"\n", " \n", " np.random.seed(2) # we set up a seed so that your output matches ours although the initialization is random.\n", " \n", " ### START CODE HERE ### (≈ 4 lines of code)\n", " W1 = np.random.randn(n_h,n_x) * 0.01\n", " b1 = np.zeros((n_h,1))\n", " W2 = np.random.randn(n_y,n_h) * 0.01\n", " b2 = np.zeros((n_y,1))\n", " ### END CODE HERE ###\n", " \n", " assert (W1.shape == (n_h, n_x))\n", " assert (b1.shape == (n_h, 1))\n", " assert (W2.shape == (n_y, n_h))\n", " assert (b2.shape == (n_y, 1))\n", " \n", " parameters = {\"W1\": W1,\n", " \"b1\": b1,\n", " \"W2\": W2,\n", " \"b2\": b2}\n", " \n", " return parameters" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "W1 = [[-0.00416758 -0.00056267]\n", " [-0.02136196 0.01640271]\n", " [-0.01793436 -0.00841747]\n", " [ 0.00502881 -0.01245288]]\n", "b1 = [[0.]\n", " [0.]\n", " [0.]\n", " [0.]]\n", "W2 = [[-0.01057952 -0.00909008 0.00551454 0.02292208]]\n", "b2 = [[0.]]\n" ] } ], "source": [ "n_x, n_h, n_y = initialize_parameters_test_case()\n", "\n", "parameters = initialize_parameters(n_x, n_h, n_y)\n", "print(\"W1 = \" + str(parameters[\"W1\"]))\n", "print(\"b1 = \" + str(parameters[\"b1\"]))\n", "print(\"W2 = \" + str(parameters[\"W2\"]))\n", "print(\"b2 = \" + str(parameters[\"b2\"]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Expected Output**:\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", " \n", " \n", " \n", " \n", "
**W1** [[-0.00416758 -0.00056267]\n", " [-0.02136196 0.01640271]\n", " [-0.01793436 -0.00841747]\n", " [ 0.00502881 -0.01245288]]
**b1** [[ 0.]\n", " [ 0.]\n", " [ 0.]\n", " [ 0.]]
**W2** [[-0.01057952 -0.00909008 0.00551454 0.02292208]]
**b2** [[ 0.]]
\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4.3 - The Loop ####\n", "\n", "**Question**: Implement `forward_propagation()`.\n", "\n", "**Instructions**:\n", "- Look above at the mathematical representation of your classifier.\n", "- You can use the function `sigmoid()`. It is built-in (imported) in the notebook.\n", "- You can use the function `np.tanh()`. It is part of the numpy library.\n", "- The steps you have to implement are:\n", " 1. Retrieve each parameter from the dictionary \"parameters\" (which is the output of `initialize_parameters()`) by using `parameters[\"..\"]`.\n", " 2. Implement Forward Propagation. Compute $Z^{[1]}, A^{[1]}, Z^{[2]}$ and $A^{[2]}$ (the vector of all your predictions on all the examples in the training set).\n", "- Values needed in the backpropagation are stored in \"`cache`\". The `cache` will be given as an input to the backpropagation function." ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "# GRADED FUNCTION: forward_propagation\n", "\n", "def forward_propagation(X, parameters):\n", " \"\"\"\n", " Argument:\n", " X -- input data of size (n_x, m)\n", " parameters -- python dictionary containing your parameters (output of initialization function)\n", " \n", " Returns:\n", " A2 -- The sigmoid output of the second activation\n", " cache -- a dictionary containing \"Z1\", \"A1\", \"Z2\" and \"A2\"\n", " \"\"\"\n", " # Retrieve each parameter from the dictionary \"parameters\"\n", " ### START CODE HERE ### (≈ 4 lines of code)\n", " W1 = parameters[\"W1\"]\n", " b1 = parameters[\"b1\"]\n", " W2 = parameters[\"W2\"]\n", " b2 = parameters[\"b2\"]\n", " ### END CODE HERE ###\n", " \n", " # Implement Forward Propagation to calculate A2 (probabilities)\n", " ### START CODE HERE ### (≈ 4 lines of code)\n", " Z1 = np.dot(W1,X) + b1\n", " A1 = np.tanh(Z1)\n", " Z2 = np.dot(W2,A1) + b2\n", " A2 = sigmoid(Z2)\n", " ### END CODE HERE ###\n", " \n", " assert(A2.shape == (1, X.shape[1]))\n", " \n", " # Values needed in the backpropagation are stored in \"cache\". This will be given as an input to the backpropagation\n", " cache = {\"Z1\": Z1,\n", " \"A1\": A1,\n", " \"Z2\": Z2,\n", " \"A2\": A2}\n", " \n", " return A2, cache" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.26281864019752443 0.09199904522700109 -1.3076660128732143 0.21287768171914198\n" ] } ], "source": [ "X_assess, parameters = forward_propagation_test_case()\n", "A2, cache = forward_propagation(X_assess, parameters)\n", "\n", "# Note: we use the mean here just to make sure that your output matches ours. \n", "print(np.mean(cache['Z1']) ,np.mean(cache['A1']),np.mean(cache['Z2']),np.mean(cache['A2']))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Expected Output**:\n", "\n", " \n", " \n", " \n", "
0.262818640198 0.091999045227 -1.30766601287 0.212877681719
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that you have computed $A^{[2]}$ (in the Python variable \"`A2`\"), which contains $a^{[2](i)}$ for every example, you can compute the cost function as follows:\n", "\n", "$$J = - \\frac{1}{m} \\sum\\limits_{i = 1}^{m} \\large{(} \\small y^{(i)}\\log\\left(a^{[2] (i)}\\right) + (1-y^{(i)})\\log\\left(1- a^{[2] (i)}\\right) \\large{)} \\small\\tag{13}$$\n", "\n", "**Exercise**: Implement `compute_cost()` to compute the value of the cost $J$.\n", "\n", "**Instructions**:\n", "- There are many ways to implement the cross-entropy loss. To help you, we give you how we would have implemented\n", "$- \\sum\\limits_{i=0}^{m} y^{(i)}\\log(a^{[2](i)})$:\n", "```python\n", "logprobs = np.multiply(np.log(A2),Y)\n", "cost = - np.sum(logprobs) # no need to use a for loop!\n", "```\n", "\n", "(you can use either `np.multiply()` and then `np.sum()` or directly `np.dot()`). \n", "Note that if you use `np.multiply` followed by `np.sum` the end result will be a type `float`, whereas if you use `np.dot`, the result will be a 2D numpy array. We can use `np.squeeze()` to remove redundant dimensions (in the case of single float, this will be reduced to a zero-dimension array). We can cast the array as a type `float` using `float()`." ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [], "source": [ "# GRADED FUNCTION: compute_cost\n", "\n", "def compute_cost(A2, Y, parameters):\n", " \"\"\"\n", " Computes the cross-entropy cost given in equation (13)\n", " \n", " Arguments:\n", " A2 -- The sigmoid output of the second activation, of shape (1, number of examples)\n", " Y -- \"true\" labels vector of shape (1, number of examples)\n", " parameters -- python dictionary containing your parameters W1, b1, W2 and b2\n", " [Note that the parameters argument is not used in this function, \n", " but the auto-grader currently expects this parameter.\n", " Future version of this notebook will fix both the notebook \n", " and the auto-grader so that `parameters` is not needed.\n", " For now, please include `parameters` in the function signature,\n", " and also when invoking this function.]\n", " \n", " Returns:\n", " cost -- cross-entropy cost given equation (13)\n", " \n", " \"\"\"\n", " \n", " m = Y.shape[1] # number of example\n", "\n", " # Compute the cross-entropy cost\n", " ### START CODE HERE ### (≈ 2 lines of code)\n", " logprobs = logprobs = np.multiply(Y ,np.log(A2)) + np.multiply((1-Y), np.log(1-A2))\n", " cost = (-1/m) * np.sum(logprobs)\n", " ### END CODE HERE ###\n", " \n", " cost = float(np.squeeze(cost)) # makes sure cost is the dimension we expect. \n", " # E.g., turns [[17]] into 17 \n", " assert(isinstance(cost, float))\n", " \n", " return cost" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "cost = 0.6930587610394646\n" ] } ], "source": [ "A2, Y_assess, parameters = compute_cost_test_case()\n", "\n", "print(\"cost = \" + str(compute_cost(A2, Y_assess, parameters)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Expected Output**:\n", "\n", " \n", " \n", " \n", " \n", " \n", "
**cost** 0.693058761...
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using the cache computed during forward propagation, you can now implement backward propagation.\n", "\n", "**Question**: Implement the function `backward_propagation()`.\n", "\n", "**Instructions**:\n", "Backpropagation is usually the hardest (most mathematical) part in deep learning. To help you, here again is the slide from the lecture on backpropagation. You'll want to use the six equations on the right of this slide, since you are building a vectorized implementation. \n", "\n", "\n", "\n", "\n", "\n", "- Tips:\n", " - To compute dZ1 you'll need to compute $g^{[1]'}(Z^{[1]})$. Since $g^{[1]}(.)$ is the tanh activation function, if $a = g^{[1]}(z)$ then $g^{[1]'}(z) = 1-a^2$. So you can compute \n", " $g^{[1]'}(Z^{[1]})$ using `(1 - np.power(A1, 2))`." ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [], "source": [ "# GRADED FUNCTION: backward_propagation\n", "\n", "def backward_propagation(parameters, cache, X, Y):\n", " \"\"\"\n", " Implement the backward propagation using the instructions above.\n", " \n", " Arguments:\n", " parameters -- python dictionary containing our parameters \n", " cache -- a dictionary containing \"Z1\", \"A1\", \"Z2\" and \"A2\".\n", " X -- input data of shape (2, number of examples)\n", " Y -- \"true\" labels vector of shape (1, number of examples)\n", " \n", " Returns:\n", " grads -- python dictionary containing your gradients with respect to different parameters\n", " \"\"\"\n", " m = X.shape[1]\n", " \n", " # First, retrieve W1 and W2 from the dictionary \"parameters\".\n", " ### START CODE HERE ### (≈ 2 lines of code)\n", " W1 = parameters[\"W1\"]\n", " b1 = parameters[\"b1\"]\n", " W2 = parameters[\"W2\"]\n", " b2 = parameters[\"b2\"]\n", " ### END CODE HERE ###\n", " \n", " # Retrieve also A1 and A2 from dictionary \"cache\".\n", " ### START CODE HERE ### (≈ 2 lines of code)\n", " A1 = cache[\"A1\"]\n", " A2 = cache[\"A2\"]\n", " Z1 = cache[\"Z1\"]\n", " Z2 = cache[\"Z2\"]\n", " ### END CODE HERE ###\n", " \n", " # Backward propagation: calculate dW1, db1, dW2, db2. \n", " ### START CODE HERE ### (≈ 6 lines of code, corresponding to 6 equations on slide above)\n", " dZ2 = A2 - Y\n", " dW2 = (1/m) * np.dot(dZ2,A1.T)\n", " db2 = (1/m) *(np.sum(dZ2,axis=1,keepdims=True))\n", " dZ1 = np.dot(W2.T,dZ2) * (1 - np.power(A1,2))\n", " dW1 = (1/m) *(np.dot(dZ1,X.T))\n", " db1 = (1/m) *(np.sum(dZ1, axis=1, keepdims=True))\n", " ### END CODE HERE ###\n", " \n", " grads = {\"dW1\": dW1,\n", " \"db1\": db1,\n", " \"dW2\": dW2,\n", " \"db2\": db2}\n", " \n", " return grads" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "dW1 = [[ 0.00301023 -0.00747267]\n", " [ 0.00257968 -0.00641288]\n", " [-0.00156892 0.003893 ]\n", " [-0.00652037 0.01618243]]\n", "db1 = [[ 0.00176201]\n", " [ 0.00150995]\n", " [-0.00091736]\n", " [-0.00381422]]\n", "dW2 = [[ 0.00078841 0.01765429 -0.00084166 -0.01022527]]\n", "db2 = [[-0.16655712]]\n" ] } ], "source": [ "parameters, cache, X_assess, Y_assess = backward_propagation_test_case()\n", "\n", "grads = backward_propagation(parameters, cache, X_assess, Y_assess)\n", "print (\"dW1 = \"+ str(grads[\"dW1\"]))\n", "print (\"db1 = \"+ str(grads[\"db1\"]))\n", "print (\"dW2 = \"+ str(grads[\"dW2\"]))\n", "print (\"db2 = \"+ str(grads[\"db2\"]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Expected output**:\n", "\n", "\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", " \n", " \n", " \n", " \n", "
**dW1** [[ 0.00301023 -0.00747267]\n", " [ 0.00257968 -0.00641288]\n", " [-0.00156892 0.003893 ]\n", " [-0.00652037 0.01618243]]
**db1** [[ 0.00176201]\n", " [ 0.00150995]\n", " [-0.00091736]\n", " [-0.00381422]]
**dW2** [[ 0.00078841 0.01765429 -0.00084166 -0.01022527]]
**db2** [[-0.16655712]]
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Question**: Implement the update rule. Use gradient descent. You have to use (dW1, db1, dW2, db2) in order to update (W1, b1, W2, b2).\n", "\n", "**General gradient descent rule**: $ \\theta = \\theta - \\alpha \\frac{\\partial J }{ \\partial \\theta }$ where $\\alpha$ is the learning rate and $\\theta$ represents a parameter.\n", "\n", "**Illustration**: The gradient descent algorithm with a good learning rate (converging) and a bad learning rate (diverging). Images courtesy of Adam Harley.\n", "\n", " \n", "\n" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [], "source": [ "# GRADED FUNCTION: update_parameters\n", "\n", "def update_parameters(parameters, grads, learning_rate):\n", " \"\"\"\n", " Updates parameters using the gradient descent update rule given above\n", " \n", " Arguments:\n", " parameters -- python dictionary containing your parameters \n", " grads -- python dictionary containing your gradients \n", " \n", " Returns:\n", " parameters -- python dictionary containing your updated parameters \n", " \"\"\"\n", " # Retrieve each parameter from the dictionary \"parameters\"\n", " ### START CODE HERE ### (≈ 4 lines of code)\n", " W1 = parameters[\"W1\"]\n", " b1 = parameters[\"b1\"]\n", " W2 = parameters[\"W2\"]\n", " b2 = parameters[\"b2\"]\n", " ### END CODE HERE ###\n", " \n", " # Retrieve each gradient from the dictionary \"grads\"\n", " ### START CODE HERE ### (≈ 4 lines of code)\n", " dW1 = grads[\"dW1\"]\n", " db1 = grads[\"db1\"]\n", " dW2 = grads[\"dW2\"]\n", " db2 = grads[\"db2\"]\n", " ## END CODE HERE ###\n", " \n", " # Update rule for each parameter\n", " ### START CODE HERE ### (≈ 4 lines of code)\n", " W1 = W1 - learning_rate * dW1\n", " b1 = b1 - learning_rate * db1\n", " W2 = W2 - learning_rate * dW2\n", " b2 = b2 - learning_rate * db2\n", " ### END CODE HERE ###\n", " \n", " parameters = {\"W1\": W1,\n", " \"b1\": b1,\n", " \"W2\": W2,\n", " \"b2\": b2}\n", " \n", " return parameters" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "W1 = [[-0.00643025 0.01936718]\n", " [-0.02410458 0.03978052]\n", " [-0.01653973 -0.02096177]\n", " [ 0.01046864 -0.05990141]]\n", "b1 = [[-1.02420756e-06]\n", " [ 1.27373948e-05]\n", " [ 8.32996807e-07]\n", " [-3.20136836e-06]]\n", "W2 = [[-0.01041081 -0.04463285 0.01758031 0.04747113]]\n", "b2 = [[0.00010457]]\n" ] } ], "source": [ "parameters, grads = update_parameters_test_case()\n", "parameters = update_parameters(parameters, grads,1.2)\n", "\n", "print(\"W1 = \" + str(parameters[\"W1\"]))\n", "print(\"b1 = \" + str(parameters[\"b1\"]))\n", "print(\"W2 = \" + str(parameters[\"W2\"]))\n", "print(\"b2 = \" + str(parameters[\"b2\"]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Expected Output**:\n", "\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", " \n", " \n", " \n", " \n", "
**W1** [[-0.00643025 0.01936718]\n", " [-0.02410458 0.03978052]\n", " [-0.01653973 -0.02096177]\n", " [ 0.01046864 -0.05990141]]
**b1** [[ -1.02420756e-06]\n", " [ 1.27373948e-05]\n", " [ 8.32996807e-07]\n", " [ -3.20136836e-06]]
**W2** [[-0.01041081 -0.04463285 0.01758031 0.04747113]]
**b2** [[ 0.00010457]]
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4.4 - Integrate parts 4.1, 4.2 and 4.3 in nn_model() ####\n", "\n", "**Question**: Build your neural network model in `nn_model()`.\n", "\n", "**Instructions**: The neural network model has to use the previous functions in the right order." ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [], "source": [ "# NN_model\n", "def nn_model(X, Y, n_h, learning_rate, num_iterations = 10000, print_cost=False):\n", " n_x = layer_sizes(X, Y)[0]\n", " n_y = layer_sizes(X, Y)[2]\n", " \n", " # Initialize parameters\n", " parameters = initialize_parameters(n_x, n_h, n_y)\n", " W1 = parameters[\"W1\"]\n", " b1 = parameters[\"b1\"]\n", " W2 = parameters[\"W2\"]\n", " b2 = parameters[\"b2\"]\n", " \n", " # Loop (gradient descent)\n", " for i in range(0, num_iterations):\n", " # Forward propagation. Inputs: \"X, parameters\". Outputs: \"A2, cache\"\n", " A2, cache = forward_propagation(X, parameters)\n", " # Cost function. Inputs: \"A2, Y, parameters\". Outputs: \"cost\"\n", " cost = compute_cost(A2, Y, parameters)\n", " # Backpropagation. Inputs: \"parameters, cache, X, Y\". Outputs: \"grads\"\n", " grads = backward_propagation(parameters, cache, X, Y)\n", " # Update rule for each parameter\n", " parameters = update_parameters(parameters, grads, learning_rate)\n", " # If print_cost=True, Print the cost every 1000 iterations\n", " if print_cost and i % 1000 == 0:\n", " print (\"Cost after iteration %i: %f\" %(i, cost))\n", " # Returns parameters learnt by the model. They can then be used to predict output\n", " return parameters\n" ] }, { "cell_type": "code", "execution_count": 72, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Cost after iteration 0: 0.692739\n", "Cost after iteration 1000: 0.000257\n", "Cost after iteration 2000: 0.000127\n", "Cost after iteration 3000: 0.000084\n", "Cost after iteration 4000: 0.000063\n", "Cost after iteration 5000: 0.000050\n", "Cost after iteration 6000: 0.000042\n", "Cost after iteration 7000: 0.000036\n", "Cost after iteration 8000: 0.000031\n", "Cost after iteration 9000: 0.000028\n", "W1 = [[-0.65400312 1.21068652]\n", " [-0.75688005 1.38443617]\n", " [ 0.57449374 -1.0957478 ]\n", " [ 0.76242342 -1.40517716]]\n", "b1 = [[ 0.2841426 ]\n", " [ 0.34699428]\n", " [-0.23981061]\n", " [-0.35351855]]\n", "W2 = [[-2.42329584 -3.22274999 1.97978376 3.31771228]]\n", "b2 = [[0.20282644]]\n" ] } ], "source": [ "X_assess, Y_assess = nn_model_test_case()\n", "parameters = nn_model(X_assess, Y_assess, 4, 1.02,num_iterations=10000, print_cost=True)\n", "print(\"W1 = \" + str(parameters[\"W1\"]))\n", "print(\"b1 = \" + str(parameters[\"b1\"]))\n", "print(\"W2 = \" + str(parameters[\"W2\"]))\n", "print(\"b2 = \" + str(parameters[\"b2\"]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Expected Output**:\n", "\n", "\n", "\n", " \n", " \n", " \n", "\n", "\n", " \n", " \n", " \n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", " \n", " \n", " \n", " \n", "
\n", " **cost after iteration 0**\n", " \n", " 0.692739\n", "
\n", "
$\\vdots$
\n", "
\n", "
$\\vdots$
\n", "
**W1** [[-0.65848169 1.21866811]\n", " [-0.76204273 1.39377573]\n", " [ 0.5792005 -1.10397703]\n", " [ 0.76773391 -1.41477129]]
**b1** [[ 0.287592 ]\n", " [ 0.3511264 ]\n", " [-0.2431246 ]\n", " [-0.35772805]]
**W2** [[-2.45566237 -3.27042274 2.00784958 3.36773273]]
**b2** [[ 0.20459656]]
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4.5 Predictions\n", "\n", "**Question**: Use your model to predict by building predict().\n", "Use forward propagation to predict results.\n", "\n", "**Reminder**: predictions = $y_{prediction} = \\mathbb 1 \\text{{activation > 0.5}} = \\begin{cases}\n", " 1 & \\text{if}\\ activation > 0.5 \\\\\n", " 0 & \\text{otherwise}\n", " \\end{cases}$ \n", " \n", "As an example, if you would like to set the entries of a matrix X to 0 and 1 based on a threshold you would do: ```X_new = (X > threshold)```" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [], "source": [ "# GRADED FUNCTION: predict\n", "\n", "def predict(parameters, X):\n", " \"\"\"\n", " Using the learned parameters, predicts a class for each example in X\n", " \n", " Arguments:\n", " parameters -- python dictionary containing your parameters \n", " X -- input data of size (n_x, m)\n", " \n", " Returns\n", " predictions -- vector of predictions of our model (red: 0 / blue: 1)\n", " \"\"\"\n", " \n", " # Computes probabilities using forward propagation, and classifies to 0/1 using 0.5 as the threshold.\n", " ### START CODE HERE ### (≈ 2 lines of code)\n", " A2, cache = forward_propagation(X, parameters)\n", " predictions = (A2 > 0.5)\n", " ### END CODE HERE ###\n", " \n", " return predictions" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "predictions mean = 0.6666666666666666\n" ] } ], "source": [ "parameters, X_assess = predict_test_case()\n", "\n", "predictions = predict(parameters, X_assess)\n", "print(\"predictions mean = \" + str(np.mean(predictions)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Expected Output**: \n", "\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", "
**predictions mean** 0.666666666667
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is time to run the model and see how it performs on a planar dataset. Run the following code to test your model with a single hidden layer of $n_h$ hidden units." ] }, { "cell_type": "code", "execution_count": 66, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Cost after iteration 0: 0.692996\n", "Cost after iteration 1000: 0.085130\n", "Cost after iteration 2000: 0.035754\n", "Cost after iteration 3000: 0.032405\n", "Cost after iteration 4000: 0.030820\n", "Cost after iteration 5000: 0.029759\n", "Cost after iteration 6000: 0.028942\n", "Cost after iteration 7000: 0.028241\n", "Cost after iteration 8000: 0.027497\n", "Cost after iteration 9000: 0.026682\n" ] }, { "data": { "text/plain": [ "Text(0.5, 1.0, 'Decision Boundary for hidden layer size 4')" ] }, "execution_count": 66, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Build a model with a n_h-dimensional hidden layer\n", "parameters = nn_model(X, Y, 4, 1.2 , num_iterations = 10000, print_cost=True)\n", "\n", "# Plot the decision boundary\n", "plot_decision_boundary(lambda x: predict(parameters, x.T), X, Y)\n", "plt.title(\"Decision Boundary for hidden layer size \" + str(4))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Expected Output**:\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", "
**Cost after iteration 9000** 0.218607
\n" ] }, { "cell_type": "code", "execution_count": 67, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy: 99%\n" ] } ], "source": [ "# Print accuracy\n", "predictions = predict(parameters, X)\n", "print ('Accuracy: %d' % float((np.dot(Y,predictions.T) + np.dot(1-Y,1-predictions.T))/float(Y.size)*100) + '%')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Expected Output**: \n", "\n", "\n", " \n", " \n", " \n", " \n", "
**Accuracy** 90%
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Accuracy is really high compared to Logistic Regression. The model has learnt the leaf patterns of the flower! Neural networks are able to learn even highly non-linear decision boundaries, unlike logistic regression. \n", "\n", "Now, let's try out several hidden layer sizes." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4.6 - Tuning hidden layer size (optional/ungraded exercise) ###\n", "\n", "Run the following code. It may take 1-2 minutes. You will observe different behaviors of the model for various hidden layer sizes." ] }, { "cell_type": "code", "execution_count": 58, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy for 1 hidden units: 67.5 %\n", "Accuracy for 2 hidden units: 67.25 %\n", "Accuracy for 3 hidden units: 90.75 %\n", "Accuracy for 4 hidden units: 90.5 %\n", "Accuracy for 5 hidden units: 91.25 %\n", "Accuracy for 20 hidden units: 90.0 %\n", "Accuracy for 50 hidden units: 90.75 %\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# This may take about 2 minutes to run\n", "\n", "plt.figure(figsize=(16, 32))\n", "hidden_layer_sizes = [1, 2, 3, 4, 5, 20, 50]\n", "for i, n_h in enumerate(hidden_layer_sizes):\n", " plt.subplot(5, 2, i+1)\n", " plt.title('Hidden Layer of size %d' % n_h)\n", " parameters = nn_model(X, Y, n_h,1.2, num_iterations = 5000)\n", " plot_decision_boundary(lambda x: predict(parameters, x.T), X, Y)\n", " predictions = predict(parameters, X)\n", " accuracy = float((np.dot(Y,predictions.T) + np.dot(1-Y,1-predictions.T))/float(Y.size)*100)\n", " print (\"Accuracy for {} hidden units: {} %\".format(n_h, accuracy))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Interpretation**:\n", "- The larger models (with more hidden units) are able to fit the training set better, until eventually the largest models overfit the data. \n", "- The best hidden layer size seems to be around n_h = 5. Indeed, a value around here seems to fits the data well without also incurring noticeable overfitting.\n", "- You will also learn later about regularization, which lets you use very large models (such as n_h = 50) without much overfitting. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Optional questions**:\n", "\n", "**Note**: Remember to submit the assignment by clicking the blue \"Submit Assignment\" button at the upper-right. \n", "\n", "Some optional/ungraded questions that you can explore if you wish: \n", "- What happens when you change the tanh activation for a sigmoid activation or a ReLU activation?\n", "- Play with the learning_rate. What happens?\n", "- What if we change the dataset? (See part 5 below!)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "**You've learnt to:**\n", "- Build a complete neural network with a hidden layer\n", "- Make a good use of a non-linear unit\n", "- Implemented forward propagation and backpropagation, and trained a neural network\n", "- See the impact of varying the hidden layer size, including overfitting." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nice work! " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 5) Performance on other datasets" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you want, you can rerun the whole notebook (minus the dataset part) for each of the following datasets." ] }, { "cell_type": "code", "execution_count": 70, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Datasets\n", "noisy_circles, noisy_moons, blobs, gaussian_quantiles, no_structure = load_extra_datasets()\n", "\n", "datasets = {\"noisy_circles\": noisy_circles,\n", " \"noisy_moons\": noisy_moons,\n", " \"blobs\": blobs,\n", " \"gaussian_quantiles\": gaussian_quantiles}\n", "\n", "### START CODE HERE ### (choose your dataset)\n", "dataset = \"noisy_moons\"\n", "### END CODE HERE ###\n", "\n", "X, Y = datasets[dataset]\n", "X, Y = X.T, Y.reshape(1, Y.shape[0])\n", "\n", "# make blobs binary\n", "if dataset == \"blobs\":\n", " Y = Y%2\n", "\n", "# Visualize the data\n", "plt.scatter(X[0, :], X[1, :], c=Y, s=40, cmap=plt.cm.Spectral);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Congrats on finishing this Programming Assignment!\n", "\n", "Reference:\n", "- http://scs.ryerson.ca/~aharley/neural-networks/\n", "- http://cs231n.github.io/neural-networks-case-study/" ] } ], "metadata": { "coursera": { "course_slug": "neural-networks-deep-learning", "graded_item_id": "wRuwL", "launcher_item_id": "NI888" }, "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": "3.8.5" }, "varInspector": { "cols": { "lenName": 16, "lenType": 16, "lenVar": 40 }, "kernels_config": { "python": { "delete_cmd_postfix": "", "delete_cmd_prefix": "del ", "library": "var_list.py", "varRefreshCmd": "print(var_dic_list())" }, "r": { "delete_cmd_postfix": ") ", "delete_cmd_prefix": "rm(", "library": "var_list.r", "varRefreshCmd": "cat(var_dic_list()) " } }, "types_to_exclude": [ "module", "function", "builtin_function_or_method", "instance", "_Feature" ], "window_display": false } }, "nbformat": 4, "nbformat_minor": 2 }