Course-2/Week-3/C2_W3_Assignment.ipynb

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   "source": [
    "# Neural Network with Two Layers\n",
    "\n",
    "Welcome to your week three programming assignment. You are ready to build a neural network with two layers and train it to solve a classification problem. \n",
    "\n",
    "**After this assignment, you will be able to:**\n",
    "\n",
    "- Implement a neural network with two layers to a classification problem\n",
    "- Implement forward propagation using matrix multiplication\n",
    "- Perform backward propagation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Table of Contents\n",
    "\n",
    "- [ 1 - Classification Problem](#1)\n",
    "- [ 2 - Neural Network Model with Two Layers](#2)\n",
    "  - [ 2.1 - Neural Network Model with Two Layers for a Single Training Example](#2.1)\n",
    "  - [ 2.2 - Neural Network Model with Two Layers for Multiple Training Examples](#2.2)\n",
    "  - [ 2.3 - Cost Function and Training](#2.3)\n",
    "  - [ 2.4 - Dataset](#2.4)\n",
    "  - [ 2.5 - Define Activation Function](#2.5)\n",
    "    - [ Exercise 1](#ex01)\n",
    "- [ 3 - Implementation of the Neural Network Model with Two Layers](#3)\n",
    "  - [ 3.1 - Defining the Neural Network Structure](#3.1)\n",
    "    - [ Exercise 2](#ex02)\n",
    "  - [ 3.2 - Initialize the Model's Parameters](#3.2)\n",
    "    - [ Exercise 3](#ex03)\n",
    "  - [ 3.3 - The Loop](#3.3)\n",
    "    - [ Exercise 4](#ex04)\n",
    "    - [ Exercise 5](#ex05)\n",
    "    - [ Exercise 6](#ex06)\n",
    "  - [ 3.4 - Integrate parts 3.1, 3.2 and 3.3 in nn_model()](#3.4)\n",
    "    - [ Exercise 7](#ex07)\n",
    "    - [ Exercise 8](#ex08)\n",
    "- [ 4 - Optional: Other Dataset](#4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Packages\n",
    "\n",
    "First, import all the packages you will need during this assignment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": [
     "graded"
    ]
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import colors\n",
    "# A function to create a dataset.\n",
    "from sklearn.datasets import make_blobs\n",
    "\n",
    "# Output of plotting commands is displayed inline within the Jupyter notebook.\n",
    "%matplotlib inline \n",
    "\n",
    "# Set a seed so that the results are consistent.\n",
    "np.random.seed(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the unit tests defined for this notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import w3_unittest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name='1'></a>\n",
    "## 1 - Classification Problem"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In one of the labs this week, you trained a neural network with a single perceptron, performing forward and backward propagation. That simple structure was enough to solve a \"linear\" classification problem - finding a straight line in a plane that would serve as a decision boundary to separate two classes.\n",
    "\n",
    "Imagine that now you have a more complicated problem: you still have two classes, but one line will not be enough to separate them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": [
     "graded"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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mhA6hgoICpKWlwdvbW33MxMQE3t7eSE1NfeZ92rVrh7S0NPXwyczMRHx8PLp16/bcz3P//n3k5eWVuBERacOHH36Ib7/9FgAwe/ZshIWFcQwRyYiZyE9+48YNFBYWwt7evsRxe3t7nDp16pn3CQoKwo0bN9ChQwdIkoSHDx9i1KhRL/zW2Jw5czB9+nStZiciKvbBBx/AxMQE48aNw5dffglJkjB37lwoFArR0YjoJYR/a6ysVCoVZs+eje+//x7p6enYsmUL4uLiMHPmzOfeJywsDLm5uerbpUuXdJiYiIzB2LFjsXjxYgDAvHnzMHHiRD4yRCQDQh8RqlatGkxNTZGTk1PieE5ODhwcHJ55n6lTp2LIkCEYMWIEAKBZs2bIz8/HyJEjMXnyZJiYPL3tLC0tYWlpqf0LICJ6zJgxY6BQKDBmzBh89dVXKCoqwldffcVHhoj0mNBHhCwsLNCqVSskJSWpjxUVFSEpKQnu7u7PvM+dO3eeGjumpqYAwL99EZFwo0ePxpIlSwAAX3/9NT7++GN2E5EeE/qIEACEhoZi6NChaN26Ndq2bYuFCxciPz8fISEhAIDg4GDUqlVL/db2/v7+WLBgAVq0aAE3NzecPXsWU6dOhb+/v3oQERGJNGrUKJiYmOC9997DggULUFRUhAULFvCRISI9JHwIBQQE4Pr16/j888+RnZ0NV1dXJCQkqJ9AffHixRKPAE2ZMgUKhQJTpkzBlStXUL16dfj7+/MnQhORXhk5ciQUCgVGjhyJhQsXQpIkfPPNNxxDRHpGIRnhY7Z5eXmws7NDbm4ubG1tRcchIgO2cuVKvPvuu5AkCePGjcO3337LMUSkoYr4+i27V40REcnJ8OHDsWLFCigUCixatAjjxo3jc4aI9AiHEBFRBXvnnXewcuVKKBQKREREYOzYsRxDRHqCQ4iISAdCQkKwevVqKBQKfP/99xgzZgyKiopExyIyehxCREQ6MnToUKxZswYKhQJLlizB6NGjOYaIBOMQIiLSoeDgYKxduxYmJib44YcfMGrUKI4hIoE4hIiIdGzw4MHqMbR8+XK89957HENEgnAIEREJMGjQIKxbtw4mJiZYsWIF3n33XY4hIgE4hIiIBAkKCsL69ethYmKCVatWYcSIERxDRDrGIUREJNDAgQOxYcMGmJqaYvXq1Rg+fDgKCwtFxyIyGhxCRESCBQQEYOPGjTA1NcWaNWvwzjvvcAwR6YjwnzVGRERA//79oVAoMHDgQKxduxaSJGH16tX8YdJEFYyPCBER6Yl+/fohKioKZmZmWLduHYYOHcpHhogqGIcQEZEe6du3LzZt2gQzMzOsX78ewcHBePjwoehYRAaLQ4iISM/07t0b0dHRMDMzw4YNGzBkyBCOIaIKwiFERKSHevXqhZiYGJibmyMyMhKDBw/mGCKqABxCRER6qmfPnti8eTPMzc0RFRWFoKAgPHjwQHQsIoPCIUREpMf8/f2xZcsWWFhYIDo6mmOISMs4hIiI9Fz37t3VYygmJgaBgYEcQ0RawiFERCQDfn5+iI2NhYWFBTZv3oyAgAAUFBSIjkUkexxCREQy0a1bN2zbtg2WlpaIjY3lGCLSAg4hIiIZ8fX1VY+hrVu3YsCAARxDROXAIUREJDM+Pj7Yvn07rKyssG3bNvTr1w/3798XHYtIljiEiIhkqEuXLuoxtGPHDo4hIg0Z9RC6d++e6AhERBrr3LkzduzYASsrK+zcuRN9+/blGCIqI6MeQoMGDeIYIiJZ8/b2xs6dO2FtbY24uDj06dOHvUZUBkY9hPbs2YPevXuzNIhI1ry8vBAXFwdra2vEx8ez14jKwKiHkLW1NRISEtCrVy+WBhHJmqenJ+Lj41GpUiX2GlEZGPUQiomJQaVKlbBr1y707NkTd+/eFR2JiEhjSqVSPYbYa0SlY9RDqEOHDvjpp5/wn//8B4mJiejRowfu3LkjOhYRkcY8PDxK9BrHENGLGfUQAoC3335bXRp79uzhGCIi2Xu813bv3s1eI3oBox9CANCxY0ckJCTglVdeQVJSEvz9/VkaRCRrj/fanj172GtEz8Eh9F8dOnRQl0ZycjK6d++O/Px80bGIiDTWoUMH7Nq1CzY2Nuw1oufgEHpM+/bt1aWRkpLC0iAi2WvXrh17jegFOISe8HhpqFQqdOvWDbdv3xYdi4hIY+7u7khMTIStrS1UKhX8/Pw4hoj+i0PoGR4vjX379nEMEZHsvfXWW+pe27t3L3uN6L84hJ7jrbfewu7du2FnZ4f9+/eja9euuHXrluhYREQac3NzU/favn372GtE4BB6obZt26pL48CBAywNIpI99hpRSRxCL9GmTRvs2bMHlStXxs8//wxfX1/k5eWJjkVEpDH2GtH/cAiVQuvWrbFnzx5UqVIFBw8eZGkQkeyx14ge4RAqpVatWqlLIzU1FT4+PsjNzRUdi4hIY+w1Ig6hMmnZsiWSkpJQtWpVHDp0iKVBRLLHXiNjxyFURi1atFCXxuHDh9GlSxfcvHlTdCwiIo2x18iYcQhpwNXVFcnJyXj11Vdx5MgRdO7cGf/++6/oWEREGnuy1ziGyFhwCGnIxcUFycnJqFatGo4ePcoxRESy93iv/fLLL+w1MgocQuXQvHlzdWmkpaXB29sb//zzj+hYREQae7zXjh49yl4jg8chVE7NmjVDSkoKqlevjvT0dJYGEckee42MCYeQFjRt2hQpKSmoUaMGjh07Bi8vL/z999+iYxERaYy9RsaCQ0hL3nzzTaSkpMDe3h4ZGRnw8vLCjRs3RMciItIYe42MAYeQFjVp0kRdGr/++itLg4hkj71Gho5DSMveeOMNqFQqODg44LfffkOnTp1w/fp10bGIiDTGXiNDphdDKCIiAs7OzrCysoKbmxuOHDnywvNv3ryJMWPGwNHREZaWlmjYsCHi4+N1lPblGjduDJVKBUdHRxw/fhydOnXCtWvXRMciItIYe40MlfAhFBUVhdDQUISHhyM9PR0uLi7w8fF57n9gBQUF6Ny5M86fP4+YmBicPn0ay5cvR61atXSc/MUaNWoElUqFmjVr4vfff2dpEJHssdfIECkkSZJEBnBzc0ObNm2wePFiAEBRURGcnJwwbtw4TJo06anzly5diq+++gqnTp2Cubm5Rp8zLy8PdnZ2yM3Nha2tbbnyv8yff/4JpVKJv/76C02aNEFycjLs7e0r9HMSEVUk9hqJUhFfv4U+IlRQUKB+I8JiJiYm8Pb2Rmpq6jPvs337dri7u2PMmDGwt7dH06ZNMXv2bBQWFuoqdpk0aNAAKpUKtWrVwokTJ+Dp6Yns7GzRsYiINMZeI0MidAjduHEDhYWFT/1Nwt7e/rn/UWVmZiImJgaFhYWIj4/H1KlT8fXXX+OLL7547ue5f/8+8vLyStx0qbg0ateujZMnT7I0iEj22GtkKIQ/R6isioqKUKNGDSxbtgytWrVCQEAAJk+ejKVLlz73PnPmzIGdnZ365uTkpMPEj9SvXx8qlQpOTk44deoUPD09cfXqVZ3nICLSFvYaGQKhQ6hatWowNTVFTk5OieM5OTlwcHB45n0cHR3RsGFDmJqaqo+98cYbyM7ORkFBwTPvExYWhtzcXPXt0qVL2ruIMqhXrx5UKhXq1KnD0iAig8BeI7kTOoQsLCzQqlUrJCUlqY8VFRUhKSkJ7u7uz7xP+/btcfbsWRQVFamPnTlzBo6OjrCwsHjmfSwtLWFra1viJkrdunWhUqnw2muv4fTp0+onHBIRyRV7jeRM+LfGQkNDsXz5cvz44484efIk3n//feTn5yMkJAQAEBwcjLCwMPX577//Pv755x+MHz8eZ86cQVxcHGbPno0xY8aIuoQye/3119WlcebMGSiVSly5ckV0LCIijbHXSK6ED6GAgADMnz8fn3/+OVxdXZGRkYGEhAT1E6gvXrxY4mFWJycn7Nq1C7/88guaN2+ODz74AOPHj3/mS+31mbOzM1QqFZydndUvRb18+bLoWEREGmOvkRwJfx8hEXT5PkIvc+HCBXh6eiIrKwv16tVDSkqKkCdzExFpC3uNKorBvY8QAa+99hpUKhXq1q2Lc+fOQalU4uLFi6JjERFpjL1GcsIhpAfq1KmjLo3MzEyWBhHJHnuN5IJDSE84OTlh7969qFevHrKysqBUKnHhwgXRsYiINMZeIzngENIjtWvXxt69e1G/fn11aZw/f150LCIijbHXSN9xCOmZWrVqQaVSoUGDBjh//jxLg4hkj71G+oxDSA8Vl0bDhg1x4cIFeHh4ICsrS3QsIiKNsddIX3EI6amaNWtCpVKhUaNGuHjxIjw8PJCZmSk6FhGRxthrpI84hPSYo6MjUlJS0LhxY1y6dAkeHh44d+6c6FhERBpjr5G+4RDSc8Wl8cYbb+Dy5ctQKpU4e/as6FhERBpjr5E+4RCSAQcHB6SkpKBJkybq0vjzzz9FxyIi0hh7jfQFh5BM2NvbIzk5GW+++SauXLkCpVKJM2fOiI5FRKQx9hrpAw4hGSkujaZNm+Kvv/6CUqnE6dOnRcciItIYe41E4xCSmRo1aiA5ORnNmjXD1atX4enpiVOnTomORUSkMfYaicQhJEPVq1dHUlISmjdvztIgIoPAXiNROIRkqrg0XFxckJ2dDaVSiZMnT4qORUSkMfYaicAhJGPVqlVDUlISXF1dkZOTA6VSiRMnToiORUSkMfYa6RqHkMy9+uqr2LNnD1q0aIFr165BqVTijz/+EB2LiEhj7DXSJQ4hA1BcGi1btsT169fh6emJ33//XXQsIiKNsddIVziEDETVqlWxe/dutGrVSl0ax48fFx2LiEhj7DXSBY2G0N27d3HlypWnjvOhS7GKS6N169a4ceMGPD098dtvv4mORUSkMfYaVbQyD6GYmBg0aNAAfn5+aN68OQ4fPqz+vSFDhmg1HJVdlSpVsHv3brRp0wZ///03OnXqhF9//VV0LCIijbHXqCKVeQh98cUXSEtLQ0ZGBlavXo3hw4djw4YNAABJkrQekMqucuXKSExMRNu2bdWlkZGRIToWEZHG2GtUUco8hB48eAB7e3sAQKtWrbBv3z788MMPmDFjBhQKhdYDkmaKS8PNzQ3//PMPOnXqhGPHjomORUSkMfYaVYQyD6EaNWqU+P5s8fdvT548ye/b6hk7OzskJibirbfewr///gsvLy+kp6eLjkVEpDH2GmlbqYfQrVu3AADr1q1DjRo1SvyehYUFNm7ciL1792o3HZWbra0tdu3aBXd3d3VppKWliY5FRKQx9hppU6mHUMeOHZGdnY3atWvDwcHhmee0b99ea8FIe4pLo3379rh58ya8vb1x9OhR0bGIiDTGXiNtKfUQatGiBdzc3J76IXgZGRno1q2b1oORdtnY2OCnn35Chw4d1KXxyy+/iI5FRKQx9hppQ6mH0OrVqzFs2DB06NABBw4cwJkzZzBgwAC0atUKpqamFZmRtMTGxgbx8fHo2LEjcnNz4e3tXeLtD4iI5Ia9RuVVpidLT58+HaGhoejcuTOaNm2KW7duITU1FTt27KiofKRlxaXx9ttvIy8vD126dMGhQ4dExyIi0hh7jcqj1EMoJycH48ePxxdffIEmTZrA3Nwcw4YNQ9u2bSsyH1WAV155BfHx8fDw8FCXRmpqquhYREQaY6+Rpko9hF5//XXs27cP0dHRSEtLw+bNmzFy5Eh89dVXFZmPKsh//vMfxMXFQalU4tatW/Dx8cHBgwdFxyIi0hh7jTRR6iG0atUqHDt2DH5+fgAAX19fpKSk4JtvvsGYMWMqLCBVnOLS6NSpk7o0fv75Z9GxiIg0xl6jsir1EBo4cOBTx1q2bImDBw8iOTlZq6FIdypVqoQdO3bAy8sLt2/fhq+vLw4cOCA6FhGRxthrVBYa/fT5xzk7O/OhR5mrVKkStm/fDm9vb3Vp7N+/X3QsIiKNsdeotMo9hIBHPxmY5K24NDp37oz8/Hx07doV+/btEx2LiEhj7DUqDa0MITIM1tbW2LZtG3x8fNSlwR+bQkRyxl6jl+EQohKsra2xdetW+Pr64s6dO+jWrRtSUlJExyIi0hh7jV6EQ4ieYmVlhdjYWHTt2hV37tyBn58fnxBPRLLGXqPn4RCiZyoujW7duuHu3bvw8/NDUlKS6FhERBpjr9GzcAjRc1laWmLLli3w8/PDvXv30L17d+zZs0d0LCIijbHX6EkcQvRClpaW2Lx5M/z9/XHv3j34+/sjMTFRdCwiIo2x1+hxHEL0UpaWloiJiUGPHj1w79499OjRA7t27RIdi4hIY+w1KsYhRKViYWGB6Oho9OzZE/fv30fPnj2RkJAgOhYRkcbYawRwCFEZWFhYYNOmTejdu7e6NOLj40XHIiLSGHuNOISoTCwsLBAVFYU+ffqgoKAAvXv3RlxcnOhYREQaY68ZNw4hKjNzc3NERkaib9++KCgoQJ8+fbBz507RsYiINMZeM14cQqQRc3NzbNy4Ef3791eXxo4dO0THIiLSGHvNOHEIkcbMzc2xfv16DBgwAA8ePEDfvn2xbds20bGIiDTGXjM+HEJULsWlMXDgQDx48AD9+/fH1q1bRcciItIYe824cAhRuZmZmWHdunUIDAxUl0ZsbKzoWEREGmOvGQ8OIdIKMzMzrF27FkFBQXj48CEGDBiAzZs3i45FRKQx9ppx0IshFBERAWdnZ1hZWcHNzQ1Hjhwp1f0iIyOhUCjQq1evig1IpVJcGoMHD8bDhw8REBCAmJgY0bGIiDTGXjN8wodQVFQUQkNDER4ejvT0dLi4uMDHxwfXrl174f3Onz+Pjz/+GB07dtRRUioNU1NTrFmzBkOGDEFhYSEGDhyI6Oho0bGIiDTGXjNswofQggUL8O677yIkJARNmjTB0qVLUalSJaxateq59yksLMSgQYMwffp01K1bV4dpqTRMTU2xevVqBAcHo7CwEIGBgYiKihIdi4hIY+w1wyV0CBUUFCAtLQ3e3t7qYyYmJvD29kZqaupz7zdjxgzUqFEDw4cPL9XnuX//PvLy8krcqGKZmppi1apVGDZsGAoLCxEUFITIyEjRsYiINMZeM0xCh9CNGzdQWFgIe3v7Esft7e2RnZ39zPscOHAAK1euxPLly0v9eebMmQM7Ozv1zcnJqVy5qXRMTU2xcuVKhISEoKioCIMGDcKGDRtExyIi0hh7zfAI/9ZYWdy6dQtDhgzB8uXLUa1atVLfLywsDLm5uerbpUuXKjAlPc7ExAQrVqzA8OHDUVRUhCFDhmD9+vWiYxERaYy9ZljMRH7yatWqwdTUFDk5OSWO5+TkwMHB4anzz507h/Pnz8Pf3199rKioCMCjZ/afPn0a9erVe+p+lpaWsLS01HJ6Ki0TExMsW7YMCoUCK1asQHBwMCRJwuDBg0VHIyLSCHvNcAh9RMjCwgKtWrVCUlKS+lhRURGSkpLg7u7+1PmNGzfG8ePHkZGRob716NEDnp6eyMjI4Le89JiJiQl++OEHjBw5EkVFRQgODsbatWtFxyIi0hh7zTAIfUQIAEJDQzF06FC0bt0abdu2xcKFC5Gfn4+QkBAAQHBwMGrVqoU5c+bAysoKTZs2LXH/ypUrA8BTx0n/mJiYYMmSJVAoFPjhhx8wbNgwSJKEoUOHio5GRKQR9pr8CR9CAQEBuH79Oj7//HNkZ2fD1dUVCQkJ6idQX7x4ESYmsnoqE72AiYkJvv/+e3V5hISEQJIkDBs2THQ0IiKNsNfkTSFJkiQ6hK7l5eXBzs4Oubm5sLW1FR3HKEmShHHjxiEiIkL9PfZ33nlHdCwiIo2x1ypeRXz95kMtJIRCocCiRYswduxYSJKE4cOHY+XKlaJjERFpjL0mTxxCJIxCocB3332HDz74AAAwYsSIMr0/FBGRvmGvyQ+HEAmlUCiwcOFCjB8/HgAwcuRILFu2THAqIiLNsdfkhUOIhFMoFPjmm28wYcIEAMB7772HpUuXCk5FRKQ59pp8cAiRXlAoFPj6668RGhoKAHj//fexZMkSwamIiDTHXpMHDiHSGwqFAvPnz8fHH38MABg9ejQiIiIEpyIi0hx7Tf9xCJFeUSgUmDdvHj799FMAwNixY7F48WLBqYiINMde028cQqR3FAoF5s6di4kTJwIAxo0bh++++05wKiIizbHX9BeHEOklhUKBOXPmICwsDAAwfvx4LFy4UGwoIqJyYK/pJw4h0lsKhQKzZs3C5MmTAQATJkzAN998IzgVEZHm2Gv6h0OI9JpCocDMmTMxZcoUAI9+SO+CBQsEpyIi0hx7Tb9wCJHeUygUmDFjBj7//HMAwEcffYT58+cLTkVEpDn2mv7gECJZUCgUmD59OsLDwwEAn3zyCebNmyc4FRGR5thr+oFDiGRl2rRpmDZtGgBg4sSJ+PLLL8UGIiIqJ/aaWBxCJDvh4eGYMWMGAGDSpEmYM2eO4EREROXDXhOHQ4hkaerUqfjiiy8AAJ999hlmzZolOBERUfmw18TgECLZmjx5MmbPng0AmDJlirpAiIjkir2mexxCJGthYWHqh5CnTp2qfmiZiEiu2Gu6xSFEsjdp0iT1kwvDw8PVTzokIpIr9prucAiRQfj000/x1VdfAYD65aiSJAlORUSkOfaabnAIkcH4+OOP1W9INmPGDJYGEckee63icQiRQfnoo4/Ub1U/c+ZMTJ06laVBRLLGXqtYHEJkcB7/IYazZs3ClClTWBpEJGvstYrDIUQG6cMPP8S3334LAJg9ezbCwsJYGkQka+y1isEhRAbrgw8+wKJFiwAAX375JSZNmsTSICJZY69pH4cQGbSxY8di8eLFAIB58+Zh4sSJLA0ikjX2mnZxCJHBGzNmDCIiIgAAX331FT755BOWBhHJGntNeziEyCiMHj0aS5YsAQB8/fXX+Pjjj1kaRCRrT/baRx99xF7TAIcQGY1Ro0bhhx9+AAAsWLAAoaGhLA0ikrXHe+2bb75hr2mAQ4iMysiRI7Fs2TIAwMKFCzFhwgSWBhHJ2pO99uGHH7LXyoBDiIzOu+++ixUrVkChUODbb7/F+PHjWRpEJGuP99p3333HXisDDiEySsOHD1eXxqJFizBu3DiWBhHJGntNMxxCZLTeeecdrFy5EgqFAhERERg7dixLg4hk7cleGzNmDIqKikTH0mscQmTUQkJCsHr1aigUCnz//fcsDSKSvcd7bcmSJey1l+AQIqM3dOhQrFmzRl0ao0ePZmkQkaw93mtLly5lr70AhxARgODgYKxduxYmJib44YcfMGrUKJYGEckae610OISI/mvw4MHq0li+fDnee+89lgYRyRp77eU4hIgeM2jQIKxbtw4mJiZYsWIF3n33XZYGEckae+3FOISInhAUFIT169fDxMQEq1atwogRI1gaRCRrT/ba8OHDUVhYKDqWXuAQInqGgQMHYsOGDTA1NcXq1atZGkQke4/32po1a9hr/8UhRPQcAQEB2Lhxo7o03nnnHZYGEclaQECAegz9+OOP7DVwCBG9UP/+/REZGQlTU1OsXbsWISEhRl8aRCRvAwYMKNFrw4YNM+pe4xAieol+/fohKioKZmZmWLduHYYOHWrUpUFE8vd4r/3f//2fUfcahxBRKfTt2xebNm2CmZkZ1q9fj+DgYDx8+FB0LCIijfXt21c9hoy51ziEiEqpd+/eiI6OhpmZGTZs2IAhQ4YYZWkQkeHo06eP0fcahxBRGfTq1QsxMTEwNzdHZGQkBg8ebHSlQUSG5cleGzRokFH1GocQURn17NkTmzdvhrm5OaKiohAUFIQHDx6IjkVEpLGePXuqx9CmTZuMqtc4hIg04O/vjy1btsDCwgLR0dFGVRpEZJh69OhhlL3GIUSkoe7du6tLIyYmBoGBgUZRGkRkuJ7stYEDBxp8r3EIEZWDn58fYmNjYWFhgc2bNyMgIAAFBQWiYxERaezxXtuyZYvB9xqHEFE5devWDdu2bYOlpSViY2MNvjSIyPAZU6/pxRCKiIiAs7MzrKys4ObmhiNHjjz33OXLl6Njx46oUqUKqlSpAm9v7xeeT6QLvr6+6tLYunUrBgwYYLClQUTG4cle69+/v0H2mvAhFBUVhdDQUISHhyM9PR0uLi7w8fHBtWvXnnm+SqVCYGAgUlJSkJqaCicnJ3Tp0gVXrlzRcXKiknx8fLB9+3ZYWVlh27Zt6NevH+7fvy86FhGRxh7vte3btxtkrykkSZJEBnBzc0ObNm2wePFiAEBRURGcnJwwbtw4TJo06aX3LywsRJUqVbB48WIEBweX6nPm5eXBzs4Oubm5sLW1LVd+oift3r0bPXr0wL1799C9e3fExMTA0tJSdCwiIo3pS69VxNdvoY8IFRQUIC0tDd7e3upjJiYm8Pb2Rmpqaqk+xp07d/DgwQNUrVr1uefcv38feXl5JW5EFaVz587YsWMHrKyssHPnTvTt29fg/gZFRMblyV7r06ePwXybTOgQunHjBgoLC2Fvb1/iuL29PbKzs0v1MSZOnIiaNWuWGFNPmjNnDuzs7NQ3JyencuUmehlvb2/s3LkT1tbWiIuLQ58+fXDv3j3RsYiINPZ4r9WrVw/m5uaiI2mF8OcIlcfcuXMRGRmJ2NhYWFlZPfe8sLAw5Obmqm+XLl3SYUoyVl5eXoiLi4O1tTXi4+PRu3dvjiEikjUvLy+kp6fj22+/hUKhEB1HK4QOoWrVqsHU1BQ5OTkljufk5MDBweGF950/fz7mzp2LxMRENG/e/IXnWlpawtbWtsSNSBc8PT0RHx+PSpUqISEhAb169eIYIiJZa9y4scGMIEDwELKwsECrVq2QlJSkPlZUVISkpCS4u7s/937z5s3DzJkzkZCQgNatW+siKpHGlEqlegzt2rULPXv2xN27d0XHIiIi6MG3xkJDQ7F8+XL8+OOPOHnyJN5//33k5+cjJCQEABAcHIywsDD1+V9++SWmTp2KVatWwdnZGdnZ2cjOzsbt27dFXQLRS3l4eOCnn37Cf/7zHyQmJnIMERHpCeFDKCAgAPPnz8fnn38OV1dXZGRkICEhQf0E6osXL+Lq1avq85csWYKCggL069cPjo6O6tv8+fNFXQJRqbz99tvqMVT8UtQ7d+6IjkVEZNSEv4+QCHwfIRLpwIED6Nq1K27fvo1OnTphx44dqFSpkuhYRER6z+DeR4jIGHXo0AEJCQl45ZVXkJycjO7duyM/P190LCIio8QhRCRA+/btsWvXLtjY2CAlJYVjiIhIEA4hIkHatWunHkMqlQrdunXjk/6JiHSMQ4hIIHd3dyQmJsLW1hb79u3jGCIi0jEOISLB3nrrLezevRt2dnbYv38/unbtilu3bomORURkFDiEiPRA27Zt1WOo+FVlHENERBWPQ4hIT7Rp0wZ79uxB5cqV8fPPP8PX1xd5eXmiYxERGTQOISI90rp1a/UYOnjwIMcQEVEF4xAi0jPFP3+vSpUqSE1NhY+PD3Jzc0XHIiIySBxCRHqoZcuWSEpKQtWqVXHo0CF06dIFN2/eFB2LiMjgcAgR6akWLVqox9CRI0c4hoiIKgCHEJEec3V1RXJyMl599VX88ssv6Ny5M/7991/RsYiIDAaHEJGec3FxQVJSEl599VUcPXqUY4iISIs4hIhkwMXFBcnJyahWrRrS0tLg7e2Nf/75R3QsIiLZ4xAikonmzZsjJSUF1atXR3p6OscQEZEWcAgRyUjTpk2RnJyM6tWr49ixY/Dy8sLff/8tOhYRkWxxCBHJTNOmTZGSkoIaNWogIyMDXl5euHHjhuhYRESyxCFEJENvvvkmUlJSYG9vj19//ZVjiIhIQxxCRDLVpEkT9Rj67bff0KlTJ1y/fl10LCIiWeEQIpKxN954AyqVCg4ODjh+/Dg6deqEa9euiY5FRCQbHEJEMte4cWOoVCo4Ojri999/5xgiIioDDiEiA9CoUSOoVCrUrFkTf/zxBzw9PZGTkyM6FhGR3uMQIjIQDRs2hEqlQq1atXDixAl4enoiOztbdCwiIr3GIURkQBo0aACVSoXatWvj5MmT8PT0xNWrV0XHIiLSWxxCRAamfv366jF06tQpjiEiohfgECIyQPXq1YNKpYKTkxNOnz4NpVKJv/76S3QsIiK9wyFEZKCKx1CdOnVw5swZKJVKXLlyRXQsIiK9wiFEZMDq1q0LlUqF1157DX/++SeUSiUuX74sOhYRkd7gECIycK+//jpUKhWcnZ1x9uxZKJVKXLp0SXQsIiK9wCFEZAScnZ3VY+jcuXMcQ0RE/8UhRGQkXnvtNezduxevv/46MjMzoVQqcfHiRdGxiIiE4hAiMiJ16tTB3r17UbduXfUYunDhguhYRETCcAgRGRknJyeoVCrUq1cPWVlZUCqVOH/+vOhYRERCcAgRGaHiMVS/fn2cP3+eY4iIjBaHEJGRql27NlQqFRo0aIALFy7Aw8MDWVlZomMREekUhxCREatVqxZSUlLQsGFDXLx4EUqlEpmZmaJjERHpDIcQkZErHkONGjVSj6Fz586JjkVEpBMcQkSEmjVrIiUlBY0bN8alS5egVCpx9uxZ0bGIiCochxARAQAcHR3VY+jy5cscQ0RkFDiEiEjNwcEBKpUKTZo0wZUrV+Dh4YE///xTdCwiogrDIUREJdjb2yM5ORlNmjTBX3/9BaVSiTNnzoiORURUITiEiOgp9vb2SElJwZtvvqkeQ6dPnxYdi4hI6ziEiOiZatSogZSUFDRr1gxXr16FUqnEqVOnRMciItIqDiEieq7q1asjKSkJzZs3R3Z2Njw9PXHy5EnRsYiItIZDiIheqHgMubi4qMfQiRMnRMciItIKDiEieqlq1aohKSkJrq6uyMnJ4RgiIoPBIUREpfLqq69iz549aNGiBa5duwalUonff/9ddCwionLhECKiUiseQy1btsT169fRqVMnjiEikjUOISIqk6pVq2L37t1o1aoVrl+/Dk9PTxw/flx0LCIijXAIEVGZFY+h1q1b48aNG/D09MRvv/0mOhYRUZnpxRCKiIiAs7MzrKys4ObmhiNHjrzw/OjoaDRu3BhWVlZo1qwZ4uPjdZRUxgoLAZUK2Ljx0a+FhaITkcxVqVIFu3fvRps2bfD333+jU6dO+PXXX0XHIiPCWiNtED6EoqKiEBoaivDwcKSnp8PFxQU+Pj64du3aM88/ePAgAgMDMXz4cBw7dgy9evVCr169+DyFF9myBXB2Bjw9gaCgR786Oz86TlQOlStXRmJiItq2baseQxkZGaJjkRFgrZG2KCRJkkQGcHNzQ5s2bbB48WIAQFFREZycnDBu3DhMmjTpqfMDAgKQn5+PnTt3qo+99dZbcHV1xdKlS0v1OfPy8mBnZ4fc3FzY2tpq50L01ZYtQL9+wJN/zArFo19jYoA+fXSfiwxKbm4ufHx8cPjwYVSpUgVJSUlo0aKF6FhkoFhrxqsivn4LfUSooKAAaWlp8Pb2Vh8zMTGBt7c3UlNTn3mf1NTUEucDgI+Pz3PPN2qFhcD48U+3BfC/Yx9+yMeTqdzs7OyQmJiIt956C//++y+8vLyQnp4uOhYZINYaaZvQIXTjxg0UFhbC3t6+xHF7e3tkZ2c/8z7Z2dllOh8A7t+/j7y8vBI3o7B/P3D58vN/X5KAS5cenUdUTra2tti1axfc3d3VYygtLU10LDIwrDXSNuHPEdKFOXPmwM7OTn1zcnISHUk3rl7V7nlEL1E8htq3b4+bN2/C29sbR48eFR2LDAhrjbRN6BCqVq0aTE1NkZOTU+J4Tk4OHBwcnnkfBweHMp0PAGFhYcjNzVXfLl26VP7wcuDoqN3ziErBxsYGP/30Ezp06KAeQ7/88ovoWGQgWGukbUKHkIWFBVq1aoWkpCT1saKiIiQlJcHd3f2Z93F3dy9xPgDs3r37uecDgKWlJWxtbUvcjELHjkDt2v97BuGTFArAyenReURaZGNjg/j4eHTs2BG5ubnw9vbG4cOHRcciA8BaI20T/q2x0NBQLF++HD/++CNOnjyJ999/H/n5+QgJCQEABAcHIywsTH3++PHjkZCQgK+//hqnTp3CtGnTcPToUYwdO1bUJegvU1Pg228f/e8nW6P4nxcufHQekZYVj6G3334beXl56NKlCw4dOiQ6Fskca420TfgQCggIwPz58/H555/D1dUVGRkZSEhIUD8h+uLFi7j62Dd727Vrhw0bNmDZsmVwcXFBTEwMtm7diqZNm4q6BP3Wp8+j15LWqlXyeO3afI0pVbhXXnkF8fHx8PDwUI8hvsKTyou1Rtok/H2ERDCq9xEqVlj46GUUV68++uZ5x478KxPpTH5+Prp37w6VSgUbGxskJCSgXbt2omORzLHWjE9FfP3mEDKWIUQk2J07d+Dv74/k5GS88sorSEhIQPv27UXHIiIZMbg3VCQi41GpUiXs2LEDXl5euH37Nnx9fXHgwAHRsYjIyHEIEZHOVKpUCdu3b4e3t7d6DO3nO98RkUAcQkSkU8VjqHPnzsjPz0fXrl2xb98+0bGIyEhxCBGRzllbW2Pbtm3w8fFRj6G9e/eKjkVERohDiIiEsLa2xtatW+Hr64s7d+6gW7duSElJER2LiIwMhxARCWNlZYXY2Fh07doVd+7cgZ+fH5KTk0XHIiIjwiFEREJZWVlhy5Yt6NatG+7evQs/P7+nfowOEVFF4RAiIuGKx5Cfnx/u3buH7t27Y8+ePaJjEZER4BAiIr1gaWmJzZs3w9/fH/fu3YO/vz8SExNFxyIiA8chRER6w9LSEtHR0ejRowfu3buHHj16YNeuXaJjEZEB4xAiIr1SPIZ69uyJ+/fvo2fPnkhISBAdi4gMFIcQEekdCwsLbNq0Cb1791aPofj4eNGxiMgAcQgRkV6ysLBAVFQU+vTpg4KCAvTu3RtxcXGiYxGRgeEQIiK9ZW5ujsjISPTt2xcFBQXo06cPdu7cKToWERkQDiEi0mvm5ubYuHEj+vfvrx5DO3bsEB2LiAwEhxAR6T1zc3OsX78eAwYMwIMHD9C3b19s27ZNdCwiMgAcQkQkC8VjaODAgXjw4AH69++PrVu3io5FRDLHIUREsmFmZoZ169YhMDBQPYZiY2NFxyIiGeMQIiJZMTMzw9q1axEUFISHDx9iwIAB2Lx5s+hYRCRTHEJEJDvFY2jw4MF4+PAhAgICEBMTIzoWEckQhxARyZKpqSnWrFmDIUOGoLCwEAMHDkR0dLToWEQkMxxCRCRbpqamWL16NYKDg1FYWIjAwEBERUWJjkVEMmImOoAIkiQBAPLy8gQnISJtWLhwIR4+fIgNGzYgMDAQ+fn56Nevn+hYRKRlxV+3i7+Oa4NC0uZHk4nMzEzUq1dPdAwiIiLSwLlz51C3bl2tfCyjfESoatWqAICLFy/Czs5OcBrdycvLg5OTEy5dugRbW1vRcXSG183rNga8bl63McjNzUWdOnXUX8e1wSiHkInJo6dG2dnZGdX/gYrZ2tryuo0Ir9u48LqNi7Fed/HXca18LK19JCIiIiKZ4RAiIiIio2WUQ8jS0hLh4eGwtLQUHUWneN28bmPA6+Z1GwNet/au2yhfNUZEREQEGOkjQkREREQAhxAREREZMQ4hIiIiMlocQkRERGS0jGII/fPPPxg0aBBsbW1RuXJlDB8+HLdv337h+ePGjUOjRo1gbW2NOnXq4IMPPkBubq4OU2smIiICzs7OsLKygpubG44cOfLC86Ojo9G4cWNYWVmhWbNmiI+P11FS7SrLdS9fvhwdO3ZElSpVUKVKFXh7e7/035O+Kuufd7HIyEgoFAr06tWrYgNWkLJe982bNzFmzBg4OjrC0tISDRs2lOX/18t63QsXLlT3mJOTEyZMmIB79+7pKG357du3D/7+/qhZsyYUCgW2bt360vuoVCq0bNkSlpaWqF+/PtasWVPhObWtrNe9ZcsWdO7cGdWrV4etrS3c3d2xa9cu3YTVIk3+vIv9/PPPMDMzg6ura9k/sWQEfH19JRcXF+nQoUPS/v37pfr160uBgYHPPf/48eNSnz59pO3bt0tnz56VkpKSpAYNGkh9+/bVYeqyi4yMlCwsLKRVq1ZJf/zxh/Tuu+9KlStXlnJycp55/s8//yyZmppK8+bNk06cOCFNmTJFMjc3l44fP67j5OVT1usOCgqSIiIipGPHjkknT56Uhg0bJtnZ2UmXL1/WcfLyKet1F8vKypJq1aoldezYUerZs6duwmpRWa/7/v37UuvWraVu3bpJBw4ckLKysiSVSiVlZGToOHn5lPW6169fL1laWkrr16+XsrKypF27dkmOjo7ShAkTdJxcc/Hx8dLkyZOlLVu2SACk2NjYF56fmZkpVapUSQoNDZVOnDghLVq0SDI1NZUSEhJ0E1hLynrd48ePl7788kvpyJEj0pkzZ6SwsDDJ3NxcSk9P101gLSnrdRf7999/pbp160pdunSRXFxcyvx5DX4InThxQgIg/fLLL+pjP/30k6RQKKQrV66U+uNs2rRJsrCwkB48eFARMbWibdu20pgxY9T/XFhYKNWsWVOaM2fOM88fMGCA5OfnV+KYm5ub9N5771VoTm0r63U/6eHDh5KNjY30448/VlTECqHJdT98+FBq166dtGLFCmno0KGyHEJlve4lS5ZIdevWlQoKCnQVsUKU9brHjBkjderUqcSx0NBQqX379hWas6KU5gvjp59+Kr355psljgUEBEg+Pj4VmKxilWUQPK5JkybS9OnTtR9IR8py3QEBAdKUKVOk8PBwjYaQwX9rLDU1FZUrV0br1q3Vx7y9vWFiYoLDhw+X+uPk5ubC1tYWZmb6+ePZCgoKkJaWBm9vb/UxExMTeHt7IzU19Zn3SU1NLXE+APj4+Dz3fH2kyXU/6c6dO3jw4IFWf4hfRdP0umfMmIEaNWpg+PDhuoipdZpc9/bt2+Hu7o4xY8bA3t4eTZs2xezZs1FYWKir2OWmyXW3a9cOaWlp6m+fZWZmIj4+Ht26ddNJZhEModO0oaioCLdu3ZJVp2lq9erVyMzMRHh4uMYfQz+/qmtRdnY2atSoUeKYmZkZqlatiuzs7FJ9jBs3bmDmzJkYOXJkRUTUihs3bqCwsBD29vYljtvb2+PUqVPPvE92dvYzzy/tvxd9oMl1P2nixImoWbPmUwWqzzS57gMHDmDlypXIyMjQQcKKocl1Z2ZmIjk5GYMGDUJ8fDzOnj2L0aNH48GDB+UqT13S5LqDgoJw48YNdOjQAZIk4eHDhxg1ahQ+++wzXUQW4nmdlpeXh7t378La2lpQMt2aP38+bt++jQEDBoiOUqH+/PNPTJo0Cfv37y/XgxSyfURo0qRJUCgUL7yV9gvhi+Tl5cHPzw9NmjTBtGnTyh+c9MrcuXMRGRmJ2NhYWFlZiY5TYW7duoUhQ4Zg+fLlqFatmug4OlVUVIQaNWpg2bJlaNWqFQICAjB58mQsXbpUdLQKpVKpMHv2bHz//fdIT0/Hli1bEBcXh5kzZ4qORhVow4YNmD59OjZt2vTUgwCGpLCwEEFBQZg+fToaNmxYro8l20eEPvroIwwbNuyF59StWxcODg64du1aieMPHz7EP//8AwcHhxfe/9atW/D19YWNjQ1iY2Nhbm5e3tgVplq1ajA1NUVOTk6J4zk5Oc+9TgcHhzKdr480ue5i8+fPx9y5c7Fnzx40b968ImNqXVmv+9y5czh//jz8/f3Vx4qKigA8eoT09OnTqFevXsWG1gJN/rwdHR1hbm4OU1NT9bE33ngD2dnZKCgogIWFRYVm1gZNrnvq1KkYMmQIRowYAQBo1qwZ8vPzMXLkSEyePBkmJrL9e/BzPa/TbG1tjeLRoMjISIwYMQLR0dGyeoRbE7du3cLRo0dx7NgxjB07FsCjTpMkCWZmZkhMTESnTp1K9bFk+19C9erV0bhx4xfeLCws4O7ujps3byItLU193+TkZBQVFcHNze25Hz8vLw9dunSBhYUFtm/frvePFlhYWKBVq1ZISkpSHysqKkJSUhLc3d2feR93d/cS5wPA7t27n3u+PtLkugFg3rx5mDlzJhISEko8f0wuynrdjRs3xvHjx5GRkaG+9ejRA56ensjIyICTk5Mu42tMkz/v9u3b4+zZs+rhBwBnzpyBo6OjLEYQoNl137lz56mxUzwGJQP9EZOG0Gma2rhxI0JCQrBx40b4+fmJjlPhbG1tn+q0UaNGoVGjRsjIyHjh1/enlPnp1TLk6+srtWjRQjp8+LB04MABqUGDBiVePn/58mWpUaNG0uHDhyVJkqTc3FzJzc1NatasmXT27Fnp6tWr6tvDhw9FXcZLRUZGSpaWltKaNWukEydOSCNHjpQqV64sZWdnS5IkSUOGDJEmTZqkPv/nn3+WzMzMpPnz50snT56UwsPDZfvy+bJc99y5cyULCwspJiamxJ/trVu3RF2CRsp63U+S66vGynrdFy9elGxsbKSxY8dKp0+flnbu3CnVqFFD+uKLL0RdgkbKet3h4eGSjY2NtHHjRikzM1NKTEyU6tWrJw0YMEDUJZTZrVu3pGPHjknHjh2TAEgLFiyQjh07Jl24cEGSJEmaNGmSNGTIEPX5xS+f/+STT6STJ09KERERsnz5fFmve/369ZKZmZkUERFRotNu3rwp6hI0UtbrfpKmrxoziiH0999/S4GBgdIrr7wi2draSiEhISW+6GVlZUkApJSUFEmSJCklJUUC8MxbVlaWmIsopUWLFkl16tSRLCwspLZt20qHDh1S/56Hh4c0dOjQEudv2rRJatiwoWRhYSG9+eabUlxcnI4Ta0dZrvu111575p9teHi47oOXU1n/vB8n1yEkSWW/7oMHD0pubm6SpaWlVLduXWnWrFl6/Zea5ynLdT948ECaNm2aVK9ePcnKykpycnKSRo8eLf3777+6D66h53Vx8XUOHTpU8vDweOo+rq6ukoWFhVS3bl1p9erVOs9dXmW9bg8PjxeeLxea/Hk/TtMhpJAkA32MlIiIiOglZPscISIiIqLy4hAiIiIio8UhREREREaLQ4iIiIiMFocQERERGS0OISIiIjJaHEJERERktDiEiIiIyGhxCBEREZHR4hAiItnauHEjrK2tcfXqVfWxkJAQNG/eHLm5uQKTEZFc8EdsEJFsSZIEV1dXvP3221i0aBHCw8OxatUqHDp0CLVq1RIdj4hkwEx0ACIiTSkUCsyaNQv9+vWDg4MDFi1ahP3796tHUO/evaFSqeDl5YWYmBjBaYlIH/ERISKSvZYtW+KPP/5AYmIiPDw81MdVKhVu3bqFH3/8kUOIiJ6JzxEiIllLSEjAqVOnUFhYCHt7+xK/p1QqYWNjIygZEckBhxARyVZ6ejoGDBiAlStXwsvLC1OnThUdiYhkhs8RIiJZOn/+PPz8/PDZZ58hMDAQdevWhbu7O9LT09GyZUvR8YhIJviIEBHJzj///ANfX1/07NkTkyZNAgC4ubmha9eu+OyzzwSnIyI54SNCRCQ7VatWxalTp546HhcXJyANEckZXzVGRAbL29sbv/76K/Lz81G1alVER0fD3d1ddCwi0iMcQkRERGS0+BwhIiIiMlocQkRERGS0OISIiIjIaHEIERERkdHiECIiIiKjxSFERERERotDiIiIiIwWhxAREREZLQ4hIiIiMlocQkRERGS0OISIiIjIaHEIERERkdH6f9/VjiLK4ZiuAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "xmin, xmax = -0.2, 1.4\n",
    "x_line = np.arange(xmin, xmax, 0.1)\n",
    "# Data points (observations) from two classes.\n",
    "ax.scatter(0, 0, color=\"r\")\n",
    "ax.scatter(0, 1, color=\"b\")\n",
    "ax.scatter(1, 0, color=\"b\")\n",
    "ax.scatter(1, 1, color=\"r\")\n",
    "ax.set_xlim([xmin, xmax])\n",
    "ax.set_ylim([-0.1, 1.1])\n",
    "ax.set_xlabel('$x_1$')\n",
    "ax.set_ylabel('$x_2$')\n",
    "# Example of the lines which can be used as a decision boundary to separate two classes.\n",
    "ax.plot(x_line, -1 * x_line + 1.5, color=\"black\")\n",
    "ax.plot(x_line, -1 * x_line + 0.5, color=\"black\")\n",
    "plt.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This logic can appear in many applications. For example, if you train a model to predict whether you should buy a house knowing its size and the year it was built. A big new house will not be affordable, while a small old house will not be worth buying. So, you might be interested in either a big old house, or a small new house.\n",
    "\n",
    "The one perceptron neural network is not enough to solve such classification problem. Let's look at how you can adjust that model to find the solution.\n",
    "\n",
    "In the plot above, two lines can serve as a decision boundary. Your intuition might tell you that you should also increase the number of perceptrons. And that is absolutely right! You need to feed your data points (coordinates $x_1$, $x_2$) into two nodes separately and then unify them somehow with another one to make a decision. \n",
    "\n",
    "Now let's figure out the details, build and train your first multi-layer neural network!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name='2'></a>\n",
    "## 2 - Neural Network Model with Two Layers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name='2.1'></a>\n",
    "### 2.1 - Neural Network Model with Two Layers for a Single Training Example"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"images/nn_model_2_layers.png\" style=\"width:1000px;\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The input and output layers of the neural network are the same as for one perceptron model, but there is a **hidden layer** now in between them. The training examples $x^{(i)}=\\begin{bmatrix}x_1^{(i)} \\\\ x_2^{(i)}\\end{bmatrix}$ from the input layer of size $n_x = 2$ are first fed into the hidden layer of size $n_h = 2$. They are simultaneously fed into the first perceptron with weights $W_1^{[1]}=\\begin{bmatrix}w_{1,1}^{[1]} & w_{2,1}^{[1]}\\end{bmatrix}$, bias  $b_1^{[1]}$; and into the second perceptron with weights $W_2^{[1]}=\\begin{bmatrix}w_{1,2}^{[1]} & w_{2,2}^{[1]}\\end{bmatrix}$, bias $b_2^{[1]}$. The integer in the square brackets $^{[1]}$ denotes the layer number, because there are two layers now with their own parameters and outputs, which need to be distinguished. \n",
    "\n",
    "\\begin{align}\n",
    "z_1^{[1](i)} &= w_{1,1}^{[1]} x_1^{(i)} + w_{2,1}^{[1]} x_2^{(i)} + b_1^{[1]} = W_1^{[1]}x^{(i)} + b_1^{[1]},\\\\\n",
    "z_2^{[1](i)} &= w_{1,2}^{[1]} x_1^{(i)} + w_{2,2}^{[1]} x_2^{(i)} + b_2^{[1]} = W_2^{[1]}x^{(i)} + b_2^{[1]}.\\tag{1}\n",
    "\\end{align}\n",
    "\n",
    "These expressions for one training example $x^{(i)}$ can be rewritten in a matrix form :\n",
    "\n",
    "$$z^{[1](i)} = W^{[1]} x^{(i)} + b^{[1]},\\tag{2}$$\n",
    "\n",
    "where \n",
    "\n",
    "&emsp; &emsp; $z^{[1](i)} = \\begin{bmatrix}z_1^{[1](i)} \\\\ z_2^{[1](i)}\\end{bmatrix}$ is vector of size $\\left(n_h \\times 1\\right) = \\left(2 \\times 1\\right)$; \n",
    "\n",
    "&emsp; &emsp; $W^{[1]} = \\begin{bmatrix}W_1^{[1]} \\\\ W_2^{[1]}\\end{bmatrix} = \n",
    "\\begin{bmatrix}w_{1,1}^{[1]} & w_{2,1}^{[1]} \\\\ w_{1,2}^{[1]} & w_{2,2}^{[1]}\\end{bmatrix}$ is matrix of size $\\left(n_h \\times n_x\\right) = \\left(2 \\times 2\\right)$;\n",
    "\n",
    "&emsp; &emsp; $b^{[1]} = \\begin{bmatrix}b_1^{[1]} \\\\ b_2^{[1]}\\end{bmatrix}$ is vector of size $\\left(n_h \\times 1\\right) = \\left(2 \\times 1\\right)$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, the hidden layer activation function needs to be applied for each of the elements in the vector $z^{[1](i)}$. Various activation functions can be used here and in this model you will take the sigmoid function $\\sigma\\left(x\\right) = \\frac{1}{1 + e^{-x}}$. Remember that its derivative is $\\frac{d\\sigma}{dx} = \\sigma\\left(x\\right)\\left(1-\\sigma\\left(x\\right)\\right)$. The output of the hidden layer is a vector of size $\\left(n_h \\times 1\\right) = \\left(2 \\times 1\\right)$:\n",
    "\n",
    "$$a^{[1](i)} = \\sigma\\left(z^{[1](i)}\\right) = \n",
    "\\begin{bmatrix}\\sigma\\left(z_1^{[1](i)}\\right) \\\\ \\sigma\\left(z_2^{[1](i)}\\right)\\end{bmatrix}.\\tag{3}$$\n",
    "\n",
    "Then the hidden layer output gets fed into the output layer of size $n_y = 1$. This was covered in the previous lab, the only difference are: $a^{[1](i)}$ is taken instead of $x^{(i)}$ and layer notation $^{[2]}$ appears to identify all parameters and outputs:\n",
    "\n",
    "$$z^{[2](i)} = w_1^{[2]} a_1^{[1](i)} + w_2^{[2]} a_2^{[1](i)} + b^{[2]}= W^{[2]} a^{[1](i)} + b^{[2]},\\tag{4}$$\n",
    "\n",
    "&emsp; &emsp; $z^{[2](i)}$ and $b^{[2]}$ are scalars for this model, as $\\left(n_y \\times 1\\right) = \\left(1 \\times 1\\right)$; \n",
    "\n",
    "&emsp; &emsp; $W^{[2]} = \\begin{bmatrix}w_1^{[2]} & w_2^{[2]}\\end{bmatrix}$ is vector of size $\\left(n_y \\times n_h\\right) = \\left(1 \\times 2\\right)$.\n",
    "\n",
    "Finally, the same sigmoid function is used as the output layer activation function:\n",
    "\n",
    "$$a^{[2](i)} = \\sigma\\left(z^{[2](i)}\\right).\\tag{5}$$\n",
    "\n",
    "Mathematically the two layer neural network model for each training example $x^{(i)}$ can be written with the expressions $(2) - (5)$. Let's rewrite them next to each other for convenience:\n",
    "\n",
    "\\begin{align}\n",
    "z^{[1](i)} &= W^{[1]} x^{(i)} + b^{[1]},\\\\\n",
    "a^{[1](i)} &= \\sigma\\left(z^{[1](i)}\\right),\\\\\n",
    "z^{[2](i)} &= W^{[2]} a^{[1](i)} + b^{[2]},\\\\\n",
    "a^{[2](i)} &= \\sigma\\left(z^{[2](i)}\\right).\\\\\n",
    "\\tag{6}\n",
    "\\end{align}\n",
    "\n",
    "Note, that all of the parameters to be trained in the model are without $^{(i)}$ index - they are independent on the input data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, the predictions for some example $x^{(i)}$ can be made taking the output $a^{[2](i)}$ and calculating $\\hat{y}$ as: $\\hat{y} = \\begin{cases} 1 & \\mbox{if } a^{[2](i)} > 0.5, \\\\ 0 & \\mbox{otherwise }. \\end{cases}$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name='2.2'></a>\n",
    "### 2.2 - Neural Network Model with Two Layers for Multiple Training Examples"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similarly to the single perceptron model, $m$ training examples can be organised in a matrix $X$ of a shape ($2 \\times m$), putting $x^{(i)}$ into columns. Then the model $(6)$ can be rewritten in terms of matrix multiplications:\n",
    "\n",
    "\\begin{align}\n",
    "Z^{[1]} &= W^{[1]} X + b^{[1]},\\\\\n",
    "A^{[1]} &= \\sigma\\left(Z^{[1]}\\right),\\\\\n",
    "Z^{[2]} &= W^{[2]} A^{[1]} + b^{[2]},\\\\\n",
    "A^{[2]} &= \\sigma\\left(Z^{[2]}\\right),\\\\\n",
    "\\tag{7}\n",
    "\\end{align}\n",
    "\n",
    "where $b^{[1]}$ is broadcasted to the matrix of size $\\left(n_h \\times m\\right) = \\left(2 \\times m\\right)$ and $b^{[2]}$ to the vector of size $\\left(n_y \\times m\\right) = \\left(1 \\times m\\right)$. It would be a good exercise for you to have a look at the expressions $(7)$ and check that sizes of the matrices will actually match to perform required multiplications.\n",
    "\n",
    "You have derived expressions to perform forward propagation. Time to evaluate your model and train it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name='2.3'></a>\n",
    "### 2.3 - Cost Function and Training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the evaluation of this simple neural network you can use the same cost function as for the single perceptron case - log loss function. Originally initialized weights were just some random values, now you need to perform training of the model: find such set of parameters $W^{[1]}$, $b^{[1]}$, $W^{[2]}$, $b^{[2]}$, that will minimize the cost function.\n",
    "\n",
    "Like in the previous example of a single perceptron neural network, the cost function can be written as:\n",
    "\n",
    "$$\\mathcal{L}\\left(W^{[1]}, b^{[1]}, W^{[2]}, b^{[2]}\\right) = \\frac{1}{m}\\sum_{i=1}^{m} L\\left(W^{[1]}, b^{[1]}, W^{[2]}, b^{[2]}\\right) =  \\frac{1}{m}\\sum_{i=1}^{m}  \\large\\left(\\small - y^{(i)}\\log\\left(a^{[2](i)}\\right) - (1-y^{(i)})\\log\\left(1- a^{[2](i)}\\right)  \\large  \\right), \\small\\tag{8}$$\n",
    "\n",
    "where $y^{(i)} \\in \\{0,1\\}$ are the original labels and $a^{[2](i)}$ are the continuous output values of the forward propagation step (elements of array $A^{[2]}$).\n",
    "\n",
    "To minimize it, you can use gradient descent, updating the parameters with the following expressions:\n",
    "\n",
    "\\begin{align}\n",
    "W^{[1]} &= W^{[1]} - \\alpha \\frac{\\partial \\mathcal{L} }{ \\partial W^{[1]} },\\\\\n",
    "b^{[1]} &= b^{[1]} - \\alpha \\frac{\\partial \\mathcal{L} }{ \\partial b^{[1]} },\\\\\n",
    "W^{[2]} &= W^{[2]} - \\alpha \\frac{\\partial \\mathcal{L} }{ \\partial W^{[2]} },\\\\\n",
    "b^{[2]} &= b^{[2]} - \\alpha \\frac{\\partial \\mathcal{L} }{ \\partial b^{[2]} },\\\\\n",
    "\\tag{9}\n",
    "\\end{align}\n",
    "\n",
    "where $\\alpha$ is the learning rate."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "To perform training of the model you need to calculate now $\\frac{\\partial \\mathcal{L} }{ \\partial W^{[1]}}$, $\\frac{\\partial \\mathcal{L} }{ \\partial b^{[1]}}$, $\\frac{\\partial \\mathcal{L} }{ \\partial W^{[2]}}$, $\\frac{\\partial \\mathcal{L} }{ \\partial b^{[2]}}$. \n",
    "\n",
    "Let's start from the end of the neural network. You can rewrite here the corresponding expressions for $\\frac{\\partial \\mathcal{L} }{ \\partial W }$ and $\\frac{\\partial \\mathcal{L} }{ \\partial b }$ from the single perceptron neural network:\n",
    "\n",
    "\\begin{align}\n",
    "\\frac{\\partial \\mathcal{L} }{ \\partial W } &= \n",
    "\\frac{1}{m}\\left(A-Y\\right)X^T,\\\\\n",
    "\\frac{\\partial \\mathcal{L} }{ \\partial b } &= \n",
    "\\frac{1}{m}\\left(A-Y\\right)\\mathbf{1},\\\\\n",
    "\\end{align}\n",
    "\n",
    "where $\\mathbf{1}$ is just a ($m \\times 1$) vector of ones. Your one perceptron is in the second layer now, so $W$ will be exchanged with $W^{[2]}$, $b$ with $b^{[2]}$, $A$ with $A^{[2]}$, $X$ with $A^{[1]}$:\n",
    "\n",
    "\\begin{align}\n",
    "\\frac{\\partial \\mathcal{L} }{ \\partial W^{[2]} } &= \n",
    "\\frac{1}{m}\\left(A^{[2]}-Y\\right)\\left(A^{[1]}\\right)^T,\\\\\n",
    "\\frac{\\partial \\mathcal{L} }{ \\partial b^{[2]} } &= \n",
    "\\frac{1}{m}\\left(A^{[2]}-Y\\right)\\mathbf{1}.\\\\\n",
    "\\tag{10}\n",
    "\\end{align}\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now find $\\frac{\\partial \\mathcal{L} }{ \\partial W^{[1]}} = \n",
    "\\begin{bmatrix}\n",
    "\\frac{\\partial \\mathcal{L} }{ \\partial w_{1,1}^{[1]}} & \\frac{\\partial \\mathcal{L} }{ \\partial w_{2,1}^{[1]}} \\\\\n",
    "\\frac{\\partial \\mathcal{L} }{ \\partial w_{1,2}^{[1]}} & \\frac{\\partial \\mathcal{L} }{ \\partial w_{2,2}^{[1]}} \\end{bmatrix}$. It was shown in the videos that $$\\frac{\\partial \\mathcal{L} }{ \\partial w_{1,1}^{[1]}}=\\frac{1}{m}\\sum_{i=1}^{m} \\left( \n",
    "\\left(a^{[2](i)} - y^{(i)}\\right) \n",
    "w_1^{[2]} \n",
    "\\left(a_1^{[1](i)}\\left(1-a_1^{[1](i)}\\right)\\right)\n",
    "x_1^{(i)}\\right)\\tag{11}$$\n",
    "\n",
    "If you do this accurately for each of the elements $\\frac{\\partial \\mathcal{L} }{ \\partial W^{[1]}}$, you will get the following matrix:\n",
    "\n",
    "$$\\frac{\\partial \\mathcal{L} }{ \\partial W^{[1]}} = \\begin{bmatrix}\n",
    "\\frac{\\partial \\mathcal{L} }{ \\partial w_{1,1}^{[1]}} & \\frac{\\partial \\mathcal{L} }{ \\partial w_{2,1}^{[1]}} \\\\\n",
    "\\frac{\\partial \\mathcal{L} }{ \\partial w_{1,2}^{[1]}} & \\frac{\\partial \\mathcal{L} }{ \\partial w_{2,2}^{[1]}} \\end{bmatrix}$$\n",
    "$$= \\frac{1}{m}\\begin{bmatrix}\n",
    "\\sum_{i=1}^{m} \\left( \\left(a^{[2](i)} - y^{(i)}\\right) w_1^{[2]} \\left(a_1^{[1](i)}\\left(1-a_1^{[1](i)}\\right)\\right)\n",
    "x_1^{(i)}\\right) & \n",
    "\\sum_{i=1}^{m} \\left( \\left(a^{[2](i)} - y^{(i)}\\right) w_1^{[2]} \\left(a_1^{[1](i)}\\left(1-a_1^{[1](i)}\\right)\\right)\n",
    "x_2^{(i)}\\right)  \\\\\n",
    "\\sum_{i=1}^{m} \\left( \\left(a^{[2](i)} - y^{(i)}\\right) w_2^{[2]} \\left(a_2^{[1](i)}\\left(1-a_2^{[1](i)}\\right)\\right)\n",
    "x_1^{(i)}\\right) & \n",
    "\\sum_{i=1}^{m} \\left( \\left(a^{[2](i)} - y^{(i)}\\right) w_2^{[2]} \\left(a_2^{[1](i)}\\left(1-a_2^{[1](i)}\\right)\\right)\n",
    "x_2^{(i)}\\right)\\end{bmatrix}\\tag{12}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at this, you can notice that all terms and indices somehow are very consistent, so it all can be unified into a matrix form. And that's true! $\\left(W^{[2]}\\right)^T = \\begin{bmatrix}w_1^{[2]} \\\\ w_2^{[2]}\\end{bmatrix}$ of size $\\left(n_h \\times n_y\\right) = \\left(2 \\times 1\\right)$ can be multiplied with the vector $A^{[2]} - Y$ of size $\\left(n_y \\times m\\right) = \\left(1 \\times m\\right)$, resulting in a matrix of size $\\left(n_h \\times m\\right) = \\left(2 \\times m\\right)$:\n",
    "\n",
    "$$\\left(W^{[2]}\\right)^T \\left(A^{[2]} - Y\\right)=\n",
    "\\begin{bmatrix}w_1^{[2]} \\\\ w_2^{[2]}\\end{bmatrix}\n",
    "\\begin{bmatrix}\\left(a^{[2](1)} - y^{(1)}\\right) &  \\cdots & \\left(a^{[2](m)} - y^{(m)}\\right)\\end{bmatrix}\n",
    "=\\begin{bmatrix}\n",
    "\\left(a^{[2](1)} - y^{(1)}\\right) w_1^{[2]} & \\cdots & \\left(a^{[2](m)} - y^{(m)}\\right) w_1^{[2]} \\\\\n",
    "\\left(a^{[2](1)} - y^{(1)}\\right) w_2^{[2]} & \\cdots & \\left(a^{[2](m)} - y^{(m)}\\right) w_2^{[2]} \\end{bmatrix}$$.\n",
    "\n",
    "Now taking matrix $A^{[1]}$ of the same size $\\left(n_h \\times m\\right) = \\left(2 \\times m\\right)$,\n",
    "\n",
    "$$A^{[1]}\n",
    "=\\begin{bmatrix}\n",
    "a_1^{[1](1)} & \\cdots & a_1^{[1](m)} \\\\\n",
    "a_2^{[1](1)} & \\cdots & a_2^{[1](m)} \\end{bmatrix},$$\n",
    "\n",
    "you can calculate:\n",
    "\n",
    "$$A^{[1]}\\cdot\\left(1-A^{[1]}\\right)\n",
    "=\\begin{bmatrix}\n",
    "a_1^{[1](1)}\\left(1 - a_1^{[1](1)}\\right) & \\cdots & a_1^{[1](m)}\\left(1 - a_1^{[1](m)}\\right) \\\\\n",
    "a_2^{[1](1)}\\left(1 - a_2^{[1](1)}\\right) & \\cdots & a_2^{[1](m)}\\left(1 - a_2^{[1](m)}\\right) \\end{bmatrix},$$\n",
    "\n",
    "where \"$\\cdot$\" denotes **element by element** multiplication."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the element by element multiplication,\n",
    "\n",
    "$$\\left(W^{[2]}\\right)^T \\left(A^{[2]} - Y\\right)\\cdot \\left(A^{[1]}\\cdot\\left(1-A^{[1]}\\right)\\right)=\\begin{bmatrix}\n",
    "\\left(a^{[2](1)} - y^{(1)}\\right) w_1^{[2]}\\left(a_1^{[1](1)}\\left(1 - a_1^{[1](1)}\\right)\\right) & \\cdots & \\left(a^{[2](m)} - y^{(m)}\\right) w_1^{[2]}\\left(a_1^{[1](m)}\\left(1 - a_1^{[1](m)}\\right)\\right) \\\\\n",
    "\\left(a^{[2](1)} - y^{(1)}\\right) w_2^{[2]}\\left(a_2^{[1](1)}\\left(1 - a_2^{[1](1)}\\right)\\right) & \\cdots & \\left(a^{[2](m)} - y^{(m)}\\right) w_2^{[2]} \\left(a_2^{[1](m)}\\left(1 - a_2^{[1](m)}\\right)\\right) \\end{bmatrix}.$$\n",
    "\n",
    "If you perform matrix multiplication with $X^T$ of size $\\left(m \\times n_x\\right) = \\left(m \\times 2\\right)$, you will get matrix of size $\\left(n_h \\times n_x\\right) = \\left(2 \\times 2\\right)$:\n",
    "\n",
    "$$\\left(\\left(W^{[2]}\\right)^T \\left(A^{[2]} - Y\\right)\\cdot \\left(A^{[1]}\\cdot\\left(1-A^{[1]}\\right)\\right)\\right)X^T = \n",
    "\\begin{bmatrix}\n",
    "\\left(a^{[2](1)} - y^{(1)}\\right) w_1^{[2]}\\left(a_1^{[1](1)}\\left(1 - a_1^{[1](1)}\\right)\\right) & \\cdots & \\left(a^{[2](m)} - y^{(m)}\\right) w_1^{[2]}\\left(a_1^{[1](m)}\\left(1 - a_1^{[1](m)}\\right)\\right) \\\\\n",
    "\\left(a^{[2](1)} - y^{(1)}\\right) w_2^{[2]}\\left(a_2^{[1](1)}\\left(1 - a_2^{[1](1)}\\right)\\right) & \\cdots & \\left(a^{[2](m)} - y^{(m)}\\right) w_2^{[2]} \\left(a_2^{[1](m)}\\left(1 - a_2^{[1](m)}\\right)\\right) \\end{bmatrix}\n",
    "\\begin{bmatrix}\n",
    "x_1^{(1)} & x_2^{(1)} \\\\\n",
    "\\cdots & \\cdots \\\\\n",
    "x_1^{(m)} & x_2^{(m)}\n",
    "\\end{bmatrix}$$\n",
    "$$=\\begin{bmatrix}\n",
    "\\sum_{i=1}^{m} \\left( \\left(a^{[2](i)} - y^{(i)}\\right) w_1^{[2]} \\left(a_1^{[1](i)}\\left(1 - a_1^{[1](i)}\\right) \\right)\n",
    "x_1^{(i)}\\right) & \n",
    "\\sum_{i=1}^{m} \\left( \\left(a^{[2](i)} - y^{(i)}\\right) w_1^{[2]} \\left(a_1^{[1](i)}\\left(1-a_1^{[1](i)}\\right)\\right)\n",
    "x_2^{(i)}\\right)  \\\\\n",
    "\\sum_{i=1}^{m} \\left( \\left(a^{[2](i)} - y^{(i)}\\right) w_2^{[2]} \\left(a_2^{[1](i)}\\left(1-a_2^{[1](i)}\\right)\\right)\n",
    "x_1^{(i)}\\right) & \n",
    "\\sum_{i=1}^{m} \\left( \\left(a^{[2](i)} - y^{(i)}\\right) w_2^{[2]} \\left(a_2^{[1](i)}\\left(1-a_2^{[1](i)}\\right)\\right)\n",
    "x_2^{(i)}\\right)\\end{bmatrix}$$\n",
    "\n",
    "This is exactly like in the expression $(12)$! So, $\\frac{\\partial \\mathcal{L} }{ \\partial W^{[1]}}$ can be written as a mixture of multiplications:\n",
    "\n",
    "$$\\frac{\\partial \\mathcal{L} }{ \\partial W^{[1]}} = \\frac{1}{m}\\left(\\left(W^{[2]}\\right)^T \\left(A^{[2]} - Y\\right)\\cdot \\left(A^{[1]}\\cdot\\left(1-A^{[1]}\\right)\\right)\\right)X^T\\tag{13},$$\n",
    "\n",
    "where \"$\\cdot$\" denotes element by element multiplications."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vector $\\frac{\\partial \\mathcal{L} }{ \\partial b^{[1]}}$ can be found very similarly, but the last terms in the chain rule will be equal to $1$, i.e. $\\frac{\\partial z_1^{[1](i)}}{ \\partial b_1^{[1]}} = 1$. Thus,\n",
    "\n",
    "$$\\frac{\\partial \\mathcal{L} }{ \\partial b^{[1]}} = \\frac{1}{m}\\left(\\left(W^{[2]}\\right)^T \\left(A^{[2]} - Y\\right)\\cdot \\left(A^{[1]}\\cdot\\left(1-A^{[1]}\\right)\\right)\\right)\\mathbf{1},\\tag{14}$$\n",
    "\n",
    "where $\\mathbf{1}$ is a ($m \\times 1$) vector of ones.\n",
    "\n",
    "Expressions $(10)$, $(13)$ and $(14)$ can be used for the parameters update $(9)$ performing backward propagation:\n",
    "\n",
    "\\begin{align}\n",
    "\\frac{\\partial \\mathcal{L} }{ \\partial W^{[2]} } &= \n",
    "\\frac{1}{m}\\left(A^{[2]}-Y\\right)\\left(A^{[1]}\\right)^T,\\\\\n",
    "\\frac{\\partial \\mathcal{L} }{ \\partial b^{[2]} } &= \n",
    "\\frac{1}{m}\\left(A^{[2]}-Y\\right)\\mathbf{1},\\\\\n",
    "\\frac{\\partial \\mathcal{L} }{ \\partial W^{[1]}} &= \\frac{1}{m}\\left(\\left(W^{[2]}\\right)^T \\left(A^{[2]} - Y\\right)\\cdot \\left(A^{[1]}\\cdot\\left(1-A^{[1]}\\right)\\right)\\right)X^T,\\\\\n",
    "\\frac{\\partial \\mathcal{L} }{ \\partial b^{[1]}} &= \\frac{1}{m}\\left(\\left(W^{[2]}\\right)^T \\left(A^{[2]} - Y\\right)\\cdot \\left(A^{[1]}\\cdot\\left(1-A^{[1]}\\right)\\right)\\right)\\mathbf{1},\\\\\n",
    "\\tag{15}\n",
    "\\end{align}\n",
    "\n",
    "where $\\mathbf{1}$ is a ($m \\times 1$) vector of ones."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, to understand deeply and properly how neural networks perform and get trained, **you do need knowledge of linear algebra and calculus joined together**! But do not worry! All together it is not that scary if you do it step by step accurately with understanding of maths.\n",
    "\n",
    "Time to implement this all in the code!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name='2.2'></a>\n",
    "### 2.2 - Dataset\n",
    "\n",
    "First, let's get the dataset you will work on. The following code will create $m=2000$ data points $(x_1, x_2)$ and save them in the `NumPy` array `X` of a shape $(2 \\times m)$ (in the columns of the array). The labels ($0$: blue, $1$: red) will be saved in the `NumPy` array `Y` of a shape $(1 \\times m)$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": [
     "graded"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The shape of X is: (2, 2000)\n",
      "The shape of Y is: (1, 2000)\n",
      "I have m = 2000 training examples!\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "m = 2000\n",
    "samples, labels = make_blobs(n_samples=m, \n",
    "                             centers=([2.5, 3], [6.7, 7.9], [2.1, 7.9], [7.4, 2.8]), \n",
    "                             cluster_std=1.1,\n",
    "                             random_state=0)\n",
    "labels[(labels == 0) | (labels == 1)] = 1\n",
    "labels[(labels == 2) | (labels == 3)] = 0\n",
    "X = np.transpose(samples)\n",
    "Y = labels.reshape((1, m))\n",
    "\n",
    "plt.scatter(X[0, :], X[1, :], c=Y, cmap=colors.ListedColormap(['blue', 'red']));\n",
    "\n",
    "print ('The shape of X is: ' + str(X.shape))\n",
    "print ('The shape of Y is: ' + str(Y.shape))\n",
    "print ('I have m = %d training examples!' % (m))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name='2.3'></a>\n",
    "### 2.3 - Define Activation Function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name='ex01'></a>\n",
    "### Exercise 1\n",
    "\n",
    "Define sigmoid activation function $\\sigma\\left(z\\right) =\\frac{1}{1+e^{-z}} $."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": [
     "graded"
    ]
   },
   "outputs": [],
   "source": [
    "def sigmoid(z):\n",
    "    ### START CODE HERE ### (~ 1 line of code)\n",
    "    res = 1/(1 + np.exp(-z))\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    return res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": [
     "graded"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sigmoid(-2) = 0.11920292202211755\n",
      "sigmoid(0) = 0.5\n",
      "sigmoid(3.5) = 0.9706877692486436\n"
     ]
    }
   ],
   "source": [
    "print(\"sigmoid(-2) = \" + str(sigmoid(-2)))\n",
    "print(\"sigmoid(0) = \" + str(sigmoid(0)))\n",
    "print(\"sigmoid(3.5) = \" + str(sigmoid(3.5)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### __Expected Output__\n",
    "\n",
    "Note: the values may vary in the last decimal places.\n",
    "\n",
    "```Python\n",
    "sigmoid(-2) = 0.11920292202211755\n",
    "sigmoid(0) = 0.5\n",
    "sigmoid(3.5) = 0.9706877692486436\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[92m All tests passed\n"
     ]
    }
   ],
   "source": [
    "w3_unittest.test_sigmoid(sigmoid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name='3'></a>\n",
    "## 3 - Implementation of the Neural Network Model with Two Layers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name='3.1'></a>\n",
    "### 3.1 - Defining the Neural Network Structure"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name='ex02'></a>\n",
    "### Exercise 2\n",
    "\n",
    "Define three variables:\n",
    "- `n_x`: the size of the input layer\n",
    "- `n_h`: the size of the hidden layer (set it equal to 2 for now)\n",
    "- `n_y`: the size of the output layer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>    \n",
    "<summary>\n",
    "    <font size=\"3\" color=\"darkgreen\"><b>Hint</b></font>\n",
    "</summary>\n",
    "<p>\n",
    "<ul>\n",
    "    Use shapes of X and Y to find n_x and n_y:\n",
    "    <li>the size of the input layer n_x equals to the size of the input vectors placed in the columns of the array X,</li>\n",
    "    <li>the outpus for each of the data point will be saved in the columns of the the array Y.</li>\n",
    "</ul>\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": [
     "graded"
    ]
   },
   "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",
    "    # Size of input layer.\n",
    "    n_x = X.shape[0]\n",
    "    # Size of hidden layer.\n",
    "    n_h = 2\n",
    "    # Size of output layer.\n",
    "    n_y = Y.shape[0]\n",
    "    ### END CODE HERE ###\n",
    "    return (n_x, n_h, n_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": [
     "graded"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The size of the input layer is: n_x = 2\n",
      "The size of the hidden layer is: n_h = 2\n",
      "The size of the output layer is: n_y = 1\n"
     ]
    }
   ],
   "source": [
    "(n_x, n_h, n_y) = layer_sizes(X, Y)\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__\n",
    "\n",
    "```Python\n",
    "The size of the input layer is: n_x = 2\n",
    "The size of the hidden layer is: n_h = 2\n",
    "The size of the output layer is: n_y = 1\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[92m All tests passed\n"
     ]
    }
   ],
   "source": [
    "w3_unittest.test_layer_sizes(layer_sizes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name='3.2'></a>\n",
    "### 3.2 - Initialize the Model's Parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name='ex03'></a>\n",
    "### Exercise 3\n",
    "\n",
    "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 matrix 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 vector as zeros. \n",
    "    - Use: `np.zeros((a,b))` to initialize a matrix of shape (a,b) with zeros."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": [
     "graded"
    ]
   },
   "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",
    "    ### 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": 12,
   "metadata": {
    "tags": [
     "graded"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "W1 = [[ 0.01788628  0.0043651 ]\n",
      " [ 0.00096497 -0.01863493]]\n",
      "b1 = [[0.]\n",
      " [0.]]\n",
      "W2 = [[-0.00277388 -0.00354759]]\n",
      "b2 = [[0.]]\n"
     ]
    }
   ],
   "source": [
    "parameters = initialize_parameters(n_x, n_h, n_y)\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",
    "Note: the elements of the arrays W1 and W2 maybe be different due to random initialization. You can try to restart the kernel to get the same values.\n",
    "\n",
    "```Python\n",
    "W1 = [[ 0.01788628  0.0043651 ]\n",
    " [ 0.00096497 -0.01863493]]\n",
    "b1 = [[0.]\n",
    " [0.]]\n",
    "W2 = [[-0.00277388 -0.00354759]]\n",
    "b2 = [[0.]]\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[92m All tests passed\n"
     ]
    }
   ],
   "source": [
    "# Note: \n",
    "# Actual values are not checked here in the unit tests (due to random initialization).\n",
    "w3_unittest.test_initialize_parameters(initialize_parameters)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name='3.3'></a>\n",
    "### 3.3 - The Loop"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name='ex04'></a>\n",
    "### Exercise 4\n",
    "\n",
    "Implement `forward_propagation()`.\n",
    "\n",
    "**Instructions**:\n",
    "- Look above at the mathematical representation $(7)$ of your classifier (section [2.2](#2.2)):\n",
    "\\begin{align}\n",
    "Z^{[1]} &= W^{[1]} X + b^{[1]},\\\\\n",
    "A^{[1]} &= \\sigma\\left(Z^{[1]}\\right),\\\\\n",
    "Z^{[2]} &= W^{[2]} A^{[1]} + b^{[2]},\\\\\n",
    "A^{[2]} &= \\sigma\\left(Z^{[2]}\\right).\\\\\n",
    "\\end{align}\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 `Z1` multiplying matrices `W1`, `X` and adding vector `b1`. Then find `A1` using the `sigmoid` activation function. Perform similar computations for `Z2` and `A2`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "tags": [
     "graded"
    ]
   },
   "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 -- python dictionary containing Z1, A1, Z2, A2 \n",
    "    (that simplifies the calculations in the back propagation step)\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.\n",
    "    ### START CODE HERE ### (~ 4 lines of code)\n",
    "    Z1 = np.matmul(W1, X) + b1\n",
    "    A1 = sigmoid(Z1)\n",
    "    Z2 = np.matmul(W2, A1) + b2\n",
    "    A2 = sigmoid(Z2)\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    assert(A2.shape == (n_y, X.shape[1]))\n",
    "\n",
    "    cache = {\"Z1\": Z1,\n",
    "             \"A1\": A1,\n",
    "             \"Z2\": Z2,\n",
    "             \"A2\": A2}\n",
    "    \n",
    "    return A2, cache"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "tags": [
     "graded"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.49920157 0.49922234 0.49921223 ... 0.49921215 0.49921043 0.49920665]]\n"
     ]
    }
   ],
   "source": [
    "A2, cache = forward_propagation(X, parameters)\n",
    "\n",
    "print(A2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### __Expected Output__ \n",
    "Note: the elements of the array A2 maybe be different depending on the initial parameters. If you would like to get exactly the same output, try to restart the Kernel and rerun the notebook.\n",
    "\n",
    "```Python\n",
    "[[0.49920157 0.49922234 0.49921223 ... 0.49921215 0.49921043 0.49920665]]\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test case \"change_weights_check\". Wrong output of Z1 for X = \n",
      "[[ 5.46584646  6.71120407  7.21301753 ...  1.77559174  3.52245562\n",
      "   7.86492998]\n",
      " [ 2.91868287 10.31812597  7.79616824 ...  2.43434264  3.64044705\n",
      "   6.77517917]]\n",
      "Test for i = 1, j = 1999. \n",
      "\tExpected: \n",
      "-0.03577862954823146\n",
      "\tGot: \n",
      "-0.03577862954823145\n",
      "\u001b[92m 47  Tests passed\n",
      "\u001b[91m 1  Tests failed\n"
     ]
    }
   ],
   "source": [
    "# Note: \n",
    "# Actual values are not checked here in the unit tests (due to random initialization).\n",
    "w3_unittest.test_forward_propagation(forward_propagation)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remember, that your weights were just initialized with some random values, so the model has not been trained yet. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name='ex05'></a>\n",
    "### Exercise 5\n",
    "\n",
    "Define a cost function $(8)$ which will be used to train the model:\n",
    "\n",
    "$$\\mathcal{L}\\left(W, b\\right)  = \\frac{1}{m}\\sum_{i=1}^{m}  \\large\\left(\\small - y^{(i)}\\log\\left(a^{(i)}\\right) - (1-y^{(i)})\\log\\left(1- a^{(i)}\\right)  \\large  \\right) \\small.$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "tags": [
     "graded"
    ]
   },
   "outputs": [],
   "source": [
    "def compute_cost(A2, Y):\n",
    "    \"\"\"\n",
    "    Computes the cost function as a log loss\n",
    "    \n",
    "    Arguments:\n",
    "    A2 -- The output of the neural network of shape (1, number of examples)\n",
    "    Y -- \"true\" labels vector of shape (1, number of examples)\n",
    "    \n",
    "    Returns:\n",
    "    cost -- log loss\n",
    "    \n",
    "    \"\"\"\n",
    "    # Number of examples.\n",
    "    m = Y.shape[1]\n",
    "    \n",
    "    ### START CODE HERE ### (~ 2 lines of code)\n",
    "    logloss = - np.multiply(np.log(A2), Y) - np.multiply(np.log(1 - A2), 1 - Y)\n",
    "    cost = 1/m * np.sum(logloss)\n",
    "    ### END CODE HERE ###\n",
    "\n",
    "    assert(isinstance(cost, float))\n",
    "    \n",
    "    return cost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "tags": [
     "graded"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cost = 0.6931477703826823\n"
     ]
    }
   ],
   "source": [
    "print(\"cost = \" + str(compute_cost(A2, Y)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### __Expected Output__ \n",
    "Note: the elements of the arrays W1 and W2 maybe be different!\n",
    "\n",
    "```Python\n",
    "cost = 0.6931477703826823\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[92m All tests passed\n"
     ]
    }
   ],
   "source": [
    "# Note: \n",
    "# Actual values are not checked here in the unit tests (due to random initialization).\n",
    "w3_unittest.test_compute_cost(compute_cost, A2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate partial derivatives as shown in $(15)$:\n",
    "\n",
    "\\begin{align}\n",
    "\\frac{\\partial \\mathcal{L} }{ \\partial W^{[2]} } &= \n",
    "\\frac{1}{m}\\left(A^{[2]}-Y\\right)\\left(A^{[1]}\\right)^T,\\\\\n",
    "\\frac{\\partial \\mathcal{L} }{ \\partial b^{[2]} } &= \n",
    "\\frac{1}{m}\\left(A^{[2]}-Y\\right)\\mathbf{1},\\\\\n",
    "\\frac{\\partial \\mathcal{L} }{ \\partial W^{[1]}} &= \\frac{1}{m}\\left(\\left(W^{[2]}\\right)^T \\left(A^{[2]} - Y\\right)\\cdot \\left(A^{[1]}\\cdot\\left(1-A^{[1]}\\right)\\right)\\right)X^T,\\\\\n",
    "\\frac{\\partial \\mathcal{L} }{ \\partial b^{[1]}} &= \\frac{1}{m}\\left(\\left(W^{[2]}\\right)^T \\left(A^{[2]} - Y\\right)\\cdot \\left(A^{[1]}\\cdot\\left(1-A^{[1]}\\right)\\right)\\right)\\mathbf{1}.\\\\\n",
    "\\end{align}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "tags": [
     "graded"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dW1 = [[-1.49856632e-05  1.67791519e-05]\n",
      " [-2.12394543e-05  2.43895135e-05]]\n",
      "db1 = [[5.11207671e-07]\n",
      " [7.06236219e-07]]\n",
      "dW2 = [[-0.00032641 -0.0002606 ]]\n",
      "db2 = [[-0.00078732]]\n"
     ]
    }
   ],
   "source": [
    "def backward_propagation(parameters, cache, X, Y):\n",
    "    \"\"\"\n",
    "    Implements the backward propagation, calculating gradients\n",
    "    \n",
    "    Arguments:\n",
    "    parameters -- python dictionary containing our parameters \n",
    "    cache -- python dictionary containing Z1, A1, Z2, A2\n",
    "    X -- input data of shape (n_x, number of examples)\n",
    "    Y -- \"true\" labels vector of shape (n_y, number of examples)\n",
    "    \n",
    "    Returns:\n",
    "    grads -- python dictionary containing gradients with respect to different parameters\n",
    "    \"\"\"\n",
    "    m = X.shape[1]\n",
    "    \n",
    "    # First, retrieve W from the dictionary \"parameters\".\n",
    "    W1 = parameters[\"W1\"]\n",
    "    W2 = parameters[\"W2\"]\n",
    "    \n",
    "    # Retrieve also A1 and A2 from dictionary \"cache\".\n",
    "    A1 = cache[\"A1\"]\n",
    "    A2 = cache[\"A2\"]\n",
    "    \n",
    "    # Backward propagation: calculate partial derivatives denoted as dW1, db1, dW2, db2 for simplicity. \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) * A1 * (1 - A1)\n",
    "    dW1 = 1/m * np.dot(dZ1, X.T)\n",
    "    db1 = 1/m * np.sum(dZ1, axis = 1, keepdims = True)\n",
    "    \n",
    "    grads = {\"dW1\": dW1,\n",
    "             \"db1\": db1,\n",
    "             \"dW2\": dW2,\n",
    "             \"db2\": db2}\n",
    "    \n",
    "    return grads\n",
    "\n",
    "grads = backward_propagation(parameters, cache, X, Y)\n",
    "\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": [
    "<a name='ex06'></a>\n",
    "### Exercise 6\n",
    "\n",
    "Implement `update_parameters()`.\n",
    "\n",
    "**Instructions**:\n",
    "- Update parameters as shown in $(9)$ (section [2.3](#2.3)):\n",
    "\\begin{align}\n",
    "W^{[1]} &= W^{[1]} - \\alpha \\frac{\\partial \\mathcal{L} }{ \\partial W^{[1]} },\\\\\n",
    "b^{[1]} &= b^{[1]} - \\alpha \\frac{\\partial \\mathcal{L} }{ \\partial b^{[1]} },\\\\\n",
    "W^{[2]} &= W^{[2]} - \\alpha \\frac{\\partial \\mathcal{L} }{ \\partial W^{[2]} },\\\\\n",
    "b^{[2]} &= b^{[2]} - \\alpha \\frac{\\partial \\mathcal{L} }{ \\partial b^{[2]} }.\\\\\n",
    "\\end{align}\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. Retrieve each derivative from the dictionary \"grads\" (which is the output of `backward_propagation()`) by using `grads[\"..\"]`.\n",
    "    3. Update parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "tags": [
     "graded"
    ]
   },
   "outputs": [],
   "source": [
    "def update_parameters(parameters, grads, learning_rate=1.2):\n",
    "    \"\"\"\n",
    "    Updates parameters using the gradient descent update rule\n",
    "    \n",
    "    Arguments:\n",
    "    parameters -- python dictionary containing parameters \n",
    "    grads -- python dictionary containing gradients\n",
    "    learning_rate -- learning rate for gradient descent\n",
    "    \n",
    "    Returns:\n",
    "    parameters -- python dictionary containing 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": 22,
   "metadata": {
    "tags": [
     "graded"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "W1 updated = [[ 0.01790427  0.00434496]\n",
      " [ 0.00099046 -0.01866419]]\n",
      "b1 updated = [[-6.13449205e-07]\n",
      " [-8.47483463e-07]]\n",
      "W2 updated = [[-0.00238219 -0.00323487]]\n",
      "b2 updated = [[0.00094478]]\n"
     ]
    }
   ],
   "source": [
    "parameters_updated = update_parameters(parameters, grads)\n",
    "\n",
    "print(\"W1 updated = \" + str(parameters_updated[\"W1\"]))\n",
    "print(\"b1 updated = \" + str(parameters_updated[\"b1\"]))\n",
    "print(\"W2 updated = \" + str(parameters_updated[\"W2\"]))\n",
    "print(\"b2 updated = \" + str(parameters_updated[\"b2\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### __Expected Output__ \n",
    "Note: the actual values can be different!\n",
    "\n",
    "```Python\n",
    "W1 updated = [[ 0.01790427  0.00434496]\n",
    " [ 0.00099046 -0.01866419]]\n",
    "b1 updated = [[-6.13449205e-07]\n",
    " [-8.47483463e-07]]\n",
    "W2 updated = [[-0.00238219 -0.00323487]]\n",
    "b2 updated = [[0.00094478]]\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[92m All tests passed\n"
     ]
    }
   ],
   "source": [
    "w3_unittest.test_update_parameters(update_parameters)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name='3.4'></a>\n",
    "### 3.4 - Integrate parts 3.1, 3.2 and 3.3 in nn_model()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name='ex07'></a>\n",
    "### Exercise 7\n",
    "\n",
    "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": 24,
   "metadata": {
    "tags": [
     "graded"
    ]
   },
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: nn_model\n",
    "\n",
    "def nn_model(X, Y, n_h, num_iterations=10, learning_rate=1.2, print_cost=False):\n",
    "    \"\"\"\n",
    "    Arguments:\n",
    "    X -- dataset of shape (n_x, number of examples)\n",
    "    Y -- labels of shape (n_y, number of examples)\n",
    "    num_iterations -- number of iterations in the loop\n",
    "    learning_rate -- learning rate parameter for gradient descent\n",
    "    print_cost -- if True, print the cost every iteration\n",
    "    \n",
    "    Returns:\n",
    "    parameters -- parameters learnt by the model. They can then be used to predict.\n",
    "    \"\"\"\n",
    "    \n",
    "    n_x = layer_sizes(X, Y)[0]\n",
    "    n_y = layer_sizes(X, Y)[2]\n",
    "    \n",
    "    # Initialize parameters.\n",
    "    ### START CODE HERE ### (~ 1 line of code)\n",
    "    parameters = initialize_parameters(n_x, n_h, n_y)\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    # Loop.\n",
    "    for i in range(0, num_iterations):\n",
    "         \n",
    "        ### START CODE HERE ### (~ 4 lines of code)\n",
    "        # Forward propagation. Inputs: \"X, parameters\". Outputs: \"A2, cache\".\n",
    "        A2, cache = forward_propagation(X, parameters)\n",
    "        \n",
    "        # Cost function. Inputs: \"A2, Y\". Outputs: \"cost\".\n",
    "        cost = compute_cost(A2, Y)\n",
    "        \n",
    "        # Backpropagation. Inputs: \"parameters, cache, X, Y\". Outputs: \"grads\".\n",
    "        grads = backward_propagation(parameters, cache, X, Y)\n",
    "        \n",
    "        # Gradient descent parameter update. Inputs: \"parameters, grads, learning_rate\". Outputs: \"parameters\".\n",
    "        parameters = update_parameters(parameters, grads, learning_rate)\n",
    "        ### END CODE HERE ###\n",
    "        \n",
    "        # Print the cost every iteration.\n",
    "        if print_cost:\n",
    "            print (\"Cost after iteration %i: %f\" %(i, cost))\n",
    "\n",
    "    return parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "tags": [
     "graded"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Cost after iteration 2999: 0.189669\n",
      "W1 = [[ 2.08801776 -1.89503493]\n",
      " [ 2.46515642 -2.07480543]]\n",
      "b1 = [[-4.84976773]\n",
      " [ 6.30623075]]\n",
      "W2 = [[-7.20236323  7.07357254]]\n",
      "b2 = [[-3.45260284]]\n"
     ]
    }
   ],
   "source": [
    "parameters = nn_model(X, Y, n_h=2, num_iterations=3000, learning_rate=1.2, 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\"]))\n",
    "\n",
    "W1 = parameters[\"W1\"]\n",
    "b1 = parameters[\"b1\"]\n",
    "W2 = parameters[\"W2\"]\n",
    "b2 = parameters[\"b2\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### __Expected Output__ \n",
    "Note: the actual values can be different!\n",
    "\n",
    "```Python\n",
    "Cost after iteration 0: 0.693148\n",
    "Cost after iteration 1: 0.693147\n",
    "Cost after iteration 2: 0.693147\n",
    "Cost after iteration 3: 0.693147\n",
    "Cost after iteration 4: 0.693147\n",
    "Cost after iteration 5: 0.693147\n",
    "...\n",
    "Cost after iteration 2995: 0.209524\n",
    "Cost after iteration 2996: 0.208025\n",
    "Cost after iteration 2997: 0.210427\n",
    "Cost after iteration 2998: 0.208929\n",
    "Cost after iteration 2999: 0.211306\n",
    "W1 = [[ 2.14274251 -1.93155541]\n",
    " [ 2.20268789 -2.1131799 ]]\n",
    "b1 = [[-4.83079243]\n",
    " [ 6.2845223 ]]\n",
    "W2 = [[-7.21370685  7.0898022 ]]\n",
    "b2 = [[-3.48755239]]\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[92m All tests passed\n"
     ]
    }
   ],
   "source": [
    "# Note: \n",
    "# Actual values are not checked here in the unit tests (due to random initialization).\n",
    "w3_unittest.test_nn_model(nn_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The final model parameters can be used to find the boundary line and for making predictions. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name='ex08'></a>\n",
    "### Exercise 8\n",
    "\n",
    "Computes probabilities using forward propagation, and make classification to 0/1 using 0.5 as the threshold."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "tags": [
     "graded"
    ]
   },
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: predict\n",
    "\n",
    "def predict(X, parameters):\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 (blue: 0 / red: 1)\n",
    "    \"\"\"\n",
    "    \n",
    "    ### START CODE HERE ### (в‰€ 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": 28,
   "metadata": {
    "tags": [
     "graded"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coordinates (in the columns):\n",
      "[[2 8 2 8]\n",
      " [2 8 8 2]]\n",
      "Predictions:\n",
      "[[ True  True False False]]\n"
     ]
    }
   ],
   "source": [
    "X_pred = np.array([[2, 8, 2, 8], [2, 8, 8, 2]])\n",
    "Y_pred = predict(X_pred, parameters)\n",
    "\n",
    "print(f\"Coordinates (in the columns):\\n{X_pred}\")\n",
    "print(f\"Predictions:\\n{Y_pred}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### __Expected Output__ \n",
    "\n",
    "```Python\n",
    "Coordinates (in the columns):\n",
    "[[2 8 2 8]\n",
    " [2 8 8 2]]\n",
    "Predictions:\n",
    "[[ True  True False False]]\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[92m All tests passed\n"
     ]
    }
   ],
   "source": [
    "w3_unittest.test_predict(predict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's visualize the boundary line. Do not worry if you don't understand the function `plot_decision_boundary` line by line - it simply makes prediction for some points on the plane and plots them as a contour plot (just two colors - blue and red)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "tags": [
     "graded"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Decision Boundary for hidden layer size 2')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_decision_boundary(predict, parameters, X, Y):\n",
    "    # Define bounds of the domain.\n",
    "    min1, max1 = X[0, :].min()-1, X[0, :].max()+1\n",
    "    min2, max2 = X[1, :].min()-1, X[1, :].max()+1\n",
    "    # Define the x and y scale.\n",
    "    x1grid = np.arange(min1, max1, 0.1)\n",
    "    x2grid = np.arange(min2, max2, 0.1)\n",
    "    # Create all of the lines and rows of the grid.\n",
    "    xx, yy = np.meshgrid(x1grid, x2grid)\n",
    "    # Flatten each grid to a vector.\n",
    "    r1, r2 = xx.flatten(), yy.flatten()\n",
    "    r1, r2 = r1.reshape((1, len(r1))), r2.reshape((1, len(r2)))\n",
    "    # Vertical stack vectors to create x1,x2 input for the model.\n",
    "    grid = np.vstack((r1,r2))\n",
    "    # Make predictions for the grid.\n",
    "    predictions = predict(grid, parameters)\n",
    "    # Reshape the predictions back into a grid.\n",
    "    zz = predictions.reshape(xx.shape)\n",
    "    # Plot the grid of x, y and z values as a surface.\n",
    "    plt.contourf(xx, yy, zz, cmap=plt.cm.Spectral.reversed())\n",
    "    plt.scatter(X[0, :], X[1, :], c=Y, cmap=colors.ListedColormap(['blue', 'red']));\n",
    "\n",
    "# Plot the decision boundary.\n",
    "plot_decision_boundary(predict, parameters, X, Y)\n",
    "plt.title(\"Decision Boundary for hidden layer size \" + str(n_h))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's great, you can see that more complicated classification problems can be solved with two layer neural network!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name='4'></a>\n",
    "## 4 - Optional: Other Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Build a slightly different dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "tags": [
     "graded"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_samples = 2000\n",
    "samples, labels = make_blobs(n_samples=n_samples, \n",
    "                             centers=([2.5, 3], [6.7, 7.9], [2.1, 7.9], [7.4, 2.8]), \n",
    "                             cluster_std=1.1,\n",
    "                             random_state=0)\n",
    "labels[(labels == 0)] = 0\n",
    "labels[(labels == 1)] = 1\n",
    "labels[(labels == 2) | (labels == 3)] = 1\n",
    "X_2 = np.transpose(samples)\n",
    "Y_2 = labels.reshape((1,n_samples))\n",
    "\n",
    "plt.scatter(X_2[0, :], X_2[1, :], c=Y_2, cmap=colors.ListedColormap(['blue', 'red']));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that when building your neural network, a number of the nodes in the hidden layer could be taken as a parameter. Try to change this parameter and investigate the results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "tags": [
     "graded"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Decision Boundary')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# parameters_2 = nn_model(X_2, Y_2, n_h=1, num_iterations=3000, learning_rate=1.2, print_cost=False)\n",
    "parameters_2 = nn_model(X_2, Y_2, n_h=2, num_iterations=3000, learning_rate=1.2, print_cost=False)\n",
    "# parameters_2 = nn_model(X_2, Y_2, n_h=15, num_iterations=3000, learning_rate=1.2, print_cost=False)\n",
    "\n",
    "# This function will call predict function \n",
    "plot_decision_boundary(predict, parameters_2, X_2, Y_2)\n",
    "plt.title(\"Decision Boundary\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see that there are some misclassified points - real-world datasets are usually linearly inseparable, and there will be a small percentage of errors. More than that, you do not want to build a model that fits too closely, almost exactly to a particular set of data - it may fail to predict future observations. This problem is known as **overfitting**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Congrats on finishing this programming assignment!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "graded"
    ]
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "name": "C1_W1_Assignment_Solution.ipynb",
   "provenance": []
  },
  "coursera": {
   "schema_names": [
    "AI4MC1-1"
   ]
  },
  "grader_version": "1",
  "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": "3.8.8"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}