Planar+data+classification+with+one+hidden+layer+v5.ipynb

{
 "cells": [
  {
   "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](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": 36,
   "metadata": {
    "collapsed": false
   },
   "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": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X, Y = load_planar_dataset()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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       "          8.91600979e-01,   1.73201591e+00,  -2.11632466e+00,\n",
       "          3.06832921e+00]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "        -3.27807629e+00,  -2.34587605e+00,  -3.56919962e+00,\n",
       "        -3.51991588e+00,  -1.23848616e+00,  -3.47814659e+00,\n",
       "        -3.65480564e+00,  -3.82629239e+00,  -3.80826364e+00,\n",
       "        -3.56242008e+00,  -2.44646389e+00,  -2.51865217e+00,\n",
       "        -3.70032292e+00,  -2.98399234e+00,  -3.49686963e+00,\n",
       "        -4.88897726e-01,  -3.76272064e+00,   1.42253750e+00,\n",
       "        -3.69488964e+00,  -2.79391432e+00,  -8.53390853e-01,\n",
       "        -3.97467897e+00,  -3.03195393e+00,  -7.20267166e-01,\n",
       "         2.87171160e+00,  -1.53928239e+00,   2.40812143e+00,\n",
       "         8.91600979e-01,   1.73201591e+00,  -2.11632466e+00,\n",
       "         3.06832921e+00])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[1,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-4.035124003739587"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[1,:].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1]], dtype=uint8)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "?plt.scatter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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F2GLOD8Tgjx1dcccaM86Szl4SMGLCBLxKMAHCnnxpYCYH8KIeL9AS/S1zUlyTecwlnByK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0AAAg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\n7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TGyYlL0pcyZM4dRo0bdlrY3bd5I3z792Hnw3/h7dcfR3o+qmkJSM49jMBiI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\nkgEhhBDCyf0/susIAB7VMQEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa3f253e400>"
      ]
     },
     "metadata": {},
     "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": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "?np.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 400)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "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.shape[1]  # 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",
    "<table style=\"width:20%\">\n",
    "  \n",
    "  <tr>\n",
    "    <td>**shape of X**</td>\n",
    "    <td> (2, 400) </td> \n",
    "  </tr>\n",
    "  \n",
    "  <tr>\n",
    "    <td>**shape of Y**</td>\n",
    "    <td>(1, 400) </td> \n",
    "  </tr>\n",
    "  \n",
    "    <tr>\n",
    "    <td>**m**</td>\n",
    "    <td> 400 </td> \n",
    "  </tr>\n",
    "  \n",
    "</table>"
   ]
  },
  {
   "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": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "?sklearn.linear_model.LogisticRegressionCV()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "?sklearn.linear_model.LogisticRegressionCV.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.5/site-packages/sklearn/utils/validation.py:515: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    }
   ],
   "source": [
    "# Train the logistic regression classifier\n",
    "clf = sklearn.linear_model.LogisticRegressionCV();\n",
    "clf.fit(X.T, Y.T);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "?plot_decision_boundary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "?clf.predict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can now plot the decision boundary of these models. Run the code below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false,
    "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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3t2HDBubPnw8wX2u94WBlx1UNhda6B9gxcJtSqgdoHS6ZEOJICWjNDRVr8PQFuZZZ5OBk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\nkffagxwoXkV64sUopSip2ERNQy6pl/3XaB+CcS1h0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241bqaUuhF43t11CCGEEBPYTVrr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\nhBBixCRQCCGEEGLE/h/96Dt9FJOxrwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa3d532a4e0>"
      ]
     },
     "metadata": {},
     "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",
    "<table style=\"width:20%\">\n",
    "  <tr>\n",
    "    <td>**Accuracy**</td>\n",
    "    <td> 47% </td> \n",
    "  </tr>\n",
    "  \n",
    "</table>\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",
    "<img src=\"images/classification_kiank.png\" style=\"width:600px;height:300px;\">\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": 38,
   "metadata": {
    "collapsed": false
   },
   "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": [
    "# 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)\n",
    "\n",
    "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",
    "<table style=\"width:20%\">\n",
    "  <tr>\n",
    "    <td>**n_x**</td>\n",
    "    <td> 5 </td> \n",
    "  </tr>\n",
    "  \n",
    "    <tr>\n",
    "    <td>**n_h**</td>\n",
    "    <td> 4 </td> \n",
    "  </tr>\n",
    "  \n",
    "    <tr>\n",
    "    <td>**n_y**</td>\n",
    "    <td> 2 </td> \n",
    "  </tr>\n",
    "  \n",
    "</table>"
   ]
  },
  {
   "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": 39,
   "metadata": {
    "collapsed": false
   },
   "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)\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": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "W1 = [[-0.41675785 -0.05626683]\n",
      " [-2.1361961   1.64027081]\n",
      " [-1.79343559 -0.84174737]\n",
      " [ 0.50288142 -1.24528809]]\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",
    "<table style=\"width:90%\">\n",
    "  <tr>\n",
    "    <td>**W1**</td>\n",
    "    <td> [[-0.00416758 -0.00056267]\n",
    " [-0.02136196  0.01640271]\n",
    " [-0.01793436 -0.00841747]\n",
    " [ 0.00502881 -0.01245288]] </td> \n",
    "  </tr>\n",
    "  \n",
    "  <tr>\n",
    "    <td>**b1**</td>\n",
    "    <td> [[ 0.]\n",
    " [ 0.]\n",
    " [ 0.]\n",
    " [ 0.]] </td> \n",
    "  </tr>\n",
    "  \n",
    "  <tr>\n",
    "    <td>**W2**</td>\n",
    "    <td> [[-0.01057952 -0.00909008  0.00551454  0.02292208]]</td> \n",
    "  </tr>\n",
    "  \n",
    "\n",
    "  <tr>\n",
    "    <td>**b2**</td>\n",
    "    <td> [[ 0.]] </td> \n",
    "  </tr>\n",
    "  \n",
    "</table>\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": 40,
   "metadata": {
    "collapsed": false
   },
   "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",
    "#     print(\"W1 shape: \", W1.shape)\n",
    "#     print(\"X shape: \", X.shape)\n",
    "#     print(\"W2 shape: \", W2.shape)\n",
    "    \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",
    "    #print(\"A1 shape: \", A1.shape)\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",
    "    cache = {\"Z1\": Z1,\n",
    "             \"A1\": A1,\n",
    "             \"Z2\": Z2,\n",
    "             \"A2\": A2}\n",
    "    \n",
    "    return A2, cache"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.262818640198 0.091999045227 -1.30766601287 0.212877681719\n"
     ]
    }
   ],
   "source": [
    "X_assess, parameters = forward_propagation_test_case()\n",
    "\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",
    "<table style=\"width:50%\">\n",
    "  <tr>\n",
    "    <td> 0.262818640198 0.091999045227 -1.30766601287 0.212877681719 </td> \n",
    "  </tr>\n",
    "</table>"
   ]
  },
  {
   "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",
    "$$J = - \\frac{1}{m} \\sum\\limits_{i = 0}^{m} \\large{(} \\small y^{(i)}\\log\\left(a^{[2] (i)}\\right) + (1-y^{(i)})\\log\\left(1- a^{[2] (i)}\\right) \\large{)} \\small\\tag{13}$$\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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "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",
    "    \n",
    "    Returns:\n",
    "    cost -- cross-entropy cost given equation (13)\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 = np.multiply(np.log(A2),Y)\n",
    "    ##+np.multiply(np.log(1-A2),1-Y)\n",
    "    #cost = -np.sum(logprobs)\n",
    "    ### END CODE HERE ###\n",
    "    #print(m)\n",
    "    \n",
    "#   print(1/m*np.dot(np.log(A2),Y.T)+np.dot(np.log(1-A2),(1-Y).T))\n",
    "          \n",
    "#   cost = -1/m*(np.dot(np.log(A2).T,Y)+np.dot(np.log(1-A2).T,(1-Y)))\n",
    "    logprobs = np.multiply(np.log(A2),Y)+np.multiply(np.log(1-A2),1-Y)\n",
    "    cost = -1/m*np.sum(logprobs)\n",
    "    cost = 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": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n",
      "cost = 0.693058761039\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",
    "<table style=\"width:20%\">\n",
    "  <tr>\n",
    "    <td>**cost**</td>\n",
    "    <td> 0.693058761... </td> \n",
    "  </tr>\n",
    "  \n",
    "</table>"
   ]
  },
  {
   "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",
    "<img src=\"images/grad_summary.png\" style=\"width:600px;height:300px;\">\n",
    "\n",
    "<!--\n",
    "$\\frac{\\partial \\mathcal{J} }{ \\partial z_{2}^{(i)} } = \\frac{1}{m} (a^{[2](i)} - y^{(i)})$\n",
    "\n",
    "$\\frac{\\partial \\mathcal{J} }{ \\partial W_2 } = \\frac{\\partial \\mathcal{J} }{ \\partial z_{2}^{(i)} } a^{[1] (i) T} $\n",
    "\n",
    "$\\frac{\\partial \\mathcal{J} }{ \\partial b_2 } = \\sum_i{\\frac{\\partial \\mathcal{J} }{ \\partial z_{2}^{(i)}}}$\n",
    "\n",
    "$\\frac{\\partial \\mathcal{J} }{ \\partial z_{1}^{(i)} } =  W_2^T \\frac{\\partial \\mathcal{J} }{ \\partial z_{2}^{(i)} } * ( 1 - a^{[1] (i) 2}) $\n",
    "\n",
    "$\\frac{\\partial \\mathcal{J} }{ \\partial W_1 } = \\frac{\\partial \\mathcal{J} }{ \\partial z_{1}^{(i)} }  X^T $\n",
    "\n",
    "$\\frac{\\partial \\mathcal{J} _i }{ \\partial b_1 } = \\sum_i{\\frac{\\partial \\mathcal{J} }{ \\partial z_{1}^{(i)}}}$\n",
    "\n",
    "- Note that $*$ denotes elementwise multiplication.\n",
    "- The notation you will use is common in deep learning coding:\n",
    "    - dW1 = $\\frac{\\partial \\mathcal{J} }{ \\partial W_1 }$\n",
    "    - db1 = $\\frac{\\partial \\mathcal{J} }{ \\partial b_1 }$\n",
    "    - dW2 = $\\frac{\\partial \\mathcal{J} }{ \\partial W_2 }$\n",
    "    - db2 = $\\frac{\\partial \\mathcal{J} }{ \\partial b_2 }$\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": 42,
   "metadata": {
    "collapsed": false
   },
   "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",
    "    W2 = parameters[\"W2\"]\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",
    "    ### 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",
    "    #print(\"dZ2\", dZ2)\n",
    "    \n",
    "    dW2 = 1/m*np.dot(dZ2,A1.T)\n",
    "    #print(\"dW2\", dW2)\n",
    "    \n",
    "    db2 = 1/m*np.sum(dZ2,axis=1,keepdims=True)\n",
    "#     print(\"db2\", db2)\n",
    "    \n",
    "#     #print(dZ2.shape+\" \"+W2.shape)\n",
    "#     print(\"gprime \",1-np.power(A1,2))\n",
    "#     print(\"A1: \", A1)\n",
    "    \n",
    "#     print(\"W2: \", W2)\n",
    "#     print(\"W2.T ,(1-np.power(A1,2): \",np.multiply(W2.T,(1-np.power(A1,2))))\n",
    "    \n",
    "    #print()\n",
    "    \n",
    "    dZ1 = np.multiply(dZ2,np.multiply(W2.T,(1-np.power(A1,2))))\n",
    "    \n",
    "    \n",
    "    #print(\"dZ1\", dZ1)\n",
    "    dW1 =1/m* np.dot(dZ1,X.T)\n",
    "    #print(\"dW1 \",dW1)\n",
    "    db1 = 1/m*np.sum(dZ1,axis=1,keepdims=True)\n",
    "    #print(\"db1\", db1)\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": 159,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "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",
    "<table style=\"width:80%\">\n",
    "  <tr>\n",
    "    <td>**dW1**</td>\n",
    "    <td> [[ 0.00301023 -0.00747267]\n",
    " [ 0.00257968 -0.00641288]\n",
    " [-0.00156892  0.003893  ]\n",
    " [-0.00652037  0.01618243]] </td> \n",
    "  </tr>\n",
    "  \n",
    "  <tr>\n",
    "    <td>**db1**</td>\n",
    "    <td>  [[ 0.00176201]\n",
    " [ 0.00150995]\n",
    " [-0.00091736]\n",
    " [-0.00381422]] </td> \n",
    "  </tr>\n",
    "  \n",
    "  <tr>\n",
    "    <td>**dW2**</td>\n",
    "    <td> [[ 0.00078841  0.01765429 -0.00084166 -0.01022527]] </td> \n",
    "  </tr>\n",
    "  \n",
    "\n",
    "  <tr>\n",
    "    <td>**db2**</td>\n",
    "    <td> [[-0.16655712]] </td> \n",
    "  </tr>\n",
    "  \n",
    "</table>  "
   ]
  },
  {
   "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",
    "<img src=\"images/sgd.gif\" style=\"width:400;height:400;\"> <img src=\"images/sgd_bad.gif\" style=\"width:400;height:400;\">\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: update_parameters\n",
    "\n",
    "def update_parameters(parameters, grads, learning_rate = 1.2):\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": 22,
   "metadata": {
    "collapsed": false,
    "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)\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",
    "<table style=\"width:80%\">\n",
    "  <tr>\n",
    "    <td>**W1**</td>\n",
    "    <td> [[-0.00643025  0.01936718]\n",
    " [-0.02410458  0.03978052]\n",
    " [-0.01653973 -0.02096177]\n",
    " [ 0.01046864 -0.05990141]]</td> \n",
    "  </tr>\n",
    "  \n",
    "  <tr>\n",
    "    <td>**b1**</td>\n",
    "    <td> [[ -1.02420756e-06]\n",
    " [  1.27373948e-05]\n",
    " [  8.32996807e-07]\n",
    " [ -3.20136836e-06]]</td> \n",
    "  </tr>\n",
    "  \n",
    "  <tr>\n",
    "    <td>**W2**</td>\n",
    "    <td> [[-0.01041081 -0.04463285  0.01758031  0.04747113]] </td> \n",
    "  </tr>\n",
    "  \n",
    "\n",
    "  <tr>\n",
    "    <td>**b2**</td>\n",
    "    <td> [[ 0.00010457]] </td> \n",
    "  </tr>\n",
    "  \n",
    "</table>  "
   ]
  },
  {
   "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": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: nn_model\n",
    "\n",
    "def nn_model(X, Y, n_h, num_iterations = 1000, print_cost=False):\n",
    "    \"\"\"\n",
    "    Arguments:\n",
    "    X -- dataset of shape (2, number of examples)\n",
    "    Y -- labels of shape (1, number of examples)\n",
    "    n_h -- size of the hidden layer\n",
    "    num_iterations -- Number of iterations in gradient descent loop\n",
    "    print_cost -- if True, print the cost every 1000 iterations\n",
    "    \n",
    "    Returns:\n",
    "    parameters -- parameters learnt by the model. They can then be used to predict.\n",
    "    \"\"\"\n",
    "    \n",
    "    np.random.seed(3)\n",
    "    n_x = layer_sizes(X, Y)[0]\n",
    "    n_y = layer_sizes(X, Y)[2]\n",
    "    \n",
    "    # Initialize parameters, then retrieve W1, b1, W2, b2. Inputs: \"n_x, n_h, n_y\". Outputs = \"W1, b1, W2, b2, parameters\".\n",
    "    ### START CODE HERE ### (≈ 5 lines of code)\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",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    # Loop (gradient descent)\n",
    "\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, parameters\". Outputs: \"cost\".\n",
    "        cost = compute_cost(A2,Y,parameters)\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\". Outputs: \"parameters\".\n",
    "        parameters = update_parameters(parameters,grads)\n",
    "        \n",
    "        ### END CODE HERE ###\n",
    "        \n",
    "        # Print the cost every 1000 iterations\n",
    "        if print_cost and i % 1000 == 0:\n",
    "            print (\"Cost after iteration %i: %f\" %(i, cost))\n",
    "\n",
    "    return parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cost after iteration 0: 0.677453\n",
      "Cost after iteration 1000: 0.000285\n",
      "Cost after iteration 2000: 0.000141\n",
      "Cost after iteration 3000: 0.000093\n",
      "Cost after iteration 4000: 0.000070\n",
      "Cost after iteration 5000: 0.000056\n",
      "Cost after iteration 6000: 0.000046\n",
      "Cost after iteration 7000: 0.000040\n",
      "Cost after iteration 8000: 0.000035\n",
      "Cost after iteration 9000: 0.000031\n",
      "Cost after iteration 10000: 0.000028\n",
      "Cost after iteration 11000: 0.000025\n",
      "Cost after iteration 12000: 0.000023\n",
      "Cost after iteration 13000: 0.000021\n",
      "Cost after iteration 14000: 0.000020\n",
      "Cost after iteration 15000: 0.000018\n",
      "Cost after iteration 16000: 0.000017\n",
      "Cost after iteration 17000: 0.000016\n",
      "Cost after iteration 18000: 0.000015\n",
      "Cost after iteration 19000: 0.000015\n",
      "Cost after iteration 20000: 0.000014\n",
      "Cost after iteration 21000: 0.000013\n",
      "Cost after iteration 22000: 0.000013\n",
      "Cost after iteration 23000: 0.000012\n",
      "Cost after iteration 24000: 0.000012\n",
      "Cost after iteration 25000: 0.000011\n",
      "Cost after iteration 26000: 0.000011\n",
      "Cost after iteration 27000: 0.000010\n",
      "Cost after iteration 28000: 0.000010\n",
      "Cost after iteration 29000: 0.000010\n",
      "Cost after iteration 30000: 0.000009\n",
      "Cost after iteration 31000: 0.000009\n",
      "Cost after iteration 32000: 0.000009\n",
      "Cost after iteration 33000: 0.000008\n",
      "Cost after iteration 34000: 0.000008\n",
      "Cost after iteration 35000: 0.000008\n",
      "Cost after iteration 36000: 0.000008\n",
      "Cost after iteration 37000: 0.000007\n",
      "Cost after iteration 38000: 0.000007\n",
      "Cost after iteration 39000: 0.000007\n",
      "Cost after iteration 40000: 0.000007\n",
      "Cost after iteration 41000: 0.000007\n",
      "Cost after iteration 42000: 0.000007\n",
      "Cost after iteration 43000: 0.000006\n",
      "Cost after iteration 44000: 0.000006\n",
      "Cost after iteration 45000: 0.000006\n",
      "Cost after iteration 46000: 0.000006\n",
      "Cost after iteration 47000: 0.000006\n",
      "Cost after iteration 48000: 0.000006\n",
      "Cost after iteration 49000: 0.000006\n",
      "Cost after iteration 50000: 0.000006\n",
      "Cost after iteration 51000: 0.000005\n",
      "Cost after iteration 52000: 0.000005\n",
      "Cost after iteration 53000: 0.000005\n",
      "Cost after iteration 54000: 0.000005\n",
      "Cost after iteration 55000: 0.000005\n",
      "Cost after iteration 56000: 0.000005\n",
      "Cost after iteration 57000: 0.000005\n",
      "Cost after iteration 58000: 0.000005\n",
      "Cost after iteration 59000: 0.000005\n",
      "Cost after iteration 60000: 0.000005\n",
      "Cost after iteration 61000: 0.000005\n",
      "Cost after iteration 62000: 0.000004\n",
      "Cost after iteration 63000: 0.000004\n",
      "Cost after iteration 64000: 0.000004\n",
      "Cost after iteration 65000: 0.000004\n",
      "Cost after iteration 66000: 0.000004\n",
      "Cost after iteration 67000: 0.000004\n",
      "Cost after iteration 68000: 0.000004\n",
      "Cost after iteration 69000: 0.000004\n",
      "Cost after iteration 70000: 0.000004\n",
      "Cost after iteration 71000: 0.000004\n",
      "Cost after iteration 72000: 0.000004\n",
      "Cost after iteration 73000: 0.000004\n",
      "Cost after iteration 74000: 0.000004\n",
      "Cost after iteration 75000: 0.000004\n",
      "Cost after iteration 76000: 0.000004\n",
      "Cost after iteration 77000: 0.000004\n",
      "Cost after iteration 78000: 0.000004\n",
      "Cost after iteration 79000: 0.000003\n",
      "Cost after iteration 80000: 0.000003\n",
      "Cost after iteration 81000: 0.000003\n",
      "Cost after iteration 82000: 0.000003\n",
      "Cost after iteration 83000: 0.000003\n",
      "Cost after iteration 84000: 0.000003\n",
      "Cost after iteration 85000: 0.000003\n",
      "Cost after iteration 86000: 0.000003\n",
      "Cost after iteration 87000: 0.000003\n",
      "Cost after iteration 88000: 0.000003\n",
      "Cost after iteration 89000: 0.000003\n",
      "Cost after iteration 90000: 0.000003\n",
      "Cost after iteration 91000: 0.000003\n",
      "Cost after iteration 92000: 0.000003\n",
      "Cost after iteration 93000: 0.000003\n",
      "Cost after iteration 94000: 0.000003\n",
      "Cost after iteration 95000: 0.000003\n",
      "Cost after iteration 96000: 0.000003\n",
      "Cost after iteration 97000: 0.000003\n",
      "Cost after iteration 98000: 0.000003\n",
      "Cost after iteration 99000: 0.000003\n",
      "W1 = [[-0.80108391  1.34180249]\n",
      " [-2.15538533  1.83349334]\n",
      " [-2.0060948  -0.53002498]\n",
      " [ 0.82044257 -1.75901828]]\n",
      "b1 = [[ 0.23699542]\n",
      " [ 0.02321883]\n",
      " [ 0.31091013]\n",
      " [-0.33888735]]\n",
      "W2 = [[-3.03941032 -4.92304824 -0.52207239  4.80403082]]\n",
      "b2 = [[ 0.44544469]]\n"
     ]
    }
   ],
   "source": [
    "X_assess, Y_assess = nn_model_test_case()\n",
    "parameters = nn_model(X_assess, Y_assess, 4, num_iterations=100000, 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",
    "<table style=\"width:90%\">\n",
    "\n",
    "<tr> \n",
    "    <td> \n",
    "        **cost after iteration 0**\n",
    "    </td>\n",
    "    <td> \n",
    "        0.692739\n",
    "    </td>\n",
    "</tr>\n",
    "\n",
    "<tr> \n",
    "    <td> \n",
    "        <center> $\\vdots$ </center>\n",
    "    </td>\n",
    "    <td> \n",
    "        <center> $\\vdots$ </center>\n",
    "    </td>\n",
    "</tr>\n",
    "\n",
    "  <tr>\n",
    "    <td>**W1**</td>\n",
    "    <td> [[-0.65848169  1.21866811]\n",
    " [-0.76204273  1.39377573]\n",
    " [ 0.5792005  -1.10397703]\n",
    " [ 0.76773391 -1.41477129]]</td> \n",
    "  </tr>\n",
    "  \n",
    "  <tr>\n",
    "    <td>**b1**</td>\n",
    "    <td> [[ 0.287592  ]\n",
    " [ 0.3511264 ]\n",
    " [-0.2431246 ]\n",
    " [-0.35772805]] </td> \n",
    "  </tr>\n",
    "  \n",
    "  <tr>\n",
    "    <td>**W2**</td>\n",
    "    <td> [[-2.45566237 -3.27042274  2.00784958  3.36773273]] </td> \n",
    "  </tr>\n",
    "  \n",
    "\n",
    "  <tr>\n",
    "    <td>**b2**</td>\n",
    "    <td> [[ 0.20459656]] </td> \n",
    "  </tr>\n",
    "  \n",
    "</table>  "
   ]
  },
  {
   "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": 45,
   "metadata": {
    "collapsed": false
   },
   "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",
    "    \n",
    "    \n",
    "    predictions = np.array([1 if A2[:,i]>0.5 else 0 for i in range(A2.shape[1]) ])\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    return predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predictions mean = 0.666666666667\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",
    "<table style=\"width:40%\">\n",
    "  <tr>\n",
    "    <td>**predictions mean**</td>\n",
    "    <td> 0.666666666667 </td> \n",
    "  </tr>\n",
    "  \n",
    "</table>"
   ]
  },
  {
   "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": 34,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cost after iteration 0: 0.690502\n",
      "Cost after iteration 1000: 0.279346\n",
      "Cost after iteration 2000: 0.267881\n",
      "Cost after iteration 3000: 0.261285\n",
      "Cost after iteration 4000: 0.256953\n",
      "Cost after iteration 5000: 0.253940\n",
      "Cost after iteration 6000: 0.251748\n",
      "Cost after iteration 7000: 0.250092\n",
      "Cost after iteration 8000: 0.248804\n",
      "Cost after iteration 9000: 0.247776\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fc210bbe828>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8LcteCit9HcXg0wnFKLFMV4P4nEmEJXHj+HZsJnblJEjMnXew25pruCnvK0wI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\nokxBEARBEK6YSCgEQRAEQbhiIqEQBEEQBOGKiYRCEARBEIQrJhIKYRRJk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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc210b56320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Build a model with a n_h-dimensional hidden layer\n",
    "parameters = nn_model(X, Y, n_h = 4, 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",
    "<table style=\"width:40%\">\n",
    "  <tr>\n",
    "    <td>**Cost after iteration 9000**</td>\n",
    "    <td> 0.218607 </td> \n",
    "  </tr>\n",
    "  \n",
    "</table>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 91%\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",
    "<table style=\"width:15%\">\n",
    "  <tr>\n",
    "    <td>**Accuracy**</td>\n",
    "    <td> 90% </td> \n",
    "  </tr>\n",
    "</table>"
   ]
  },
  {
   "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": null,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "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, 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 noticable 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 but 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": [
    "<font color='blue'>\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": {
    "collapsed": true
   },
   "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": null,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "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/"
   ]
  }
 ],
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  "coursera": {
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   "graded_item_id": "wRuwL",
   "launcher_item_id": "NI888"
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