1. Neural Networks and Deep Learning/week 3/Programming assignment/Planar_data_classification_with_onehidden_layer_v6c.ipynb

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    "\n",
    "### <font color = \"darkblue\">Updates to Assignment</font>\n",
    "\n",
    "#### If you were working on the older version:\n",
    "* Please click on the \"Coursera\" icon in the top right to open up the folder directory.  \n",
    "* Navigate to the folder: Week 3/ Planar data classification with one hidden layer.  You can see your prior work in version 6b: \"Planar data classification with one hidden layer v6b.ipynb\"\n",
    "\n",
    "#### List of bug fixes and enhancements\n",
    "* Clarifies that the classifier will learn to classify regions as either red or blue.\n",
    "* compute_cost function fixes np.squeeze by casting it as a float.\n",
    "* compute_cost instructions clarify the purpose of np.squeeze.\n",
    "* compute_cost clarifies that \"parameters\" parameter is not needed, but is kept in the function definition until the auto-grader is also updated.\n",
    "* nn_model removes extraction of parameter values, as the entire parameter dictionary is passed to the invoked functions."
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Planar data classification with one hidden layer\n",
    "\n",
    "Welcome to your week 3 programming assignment. It's time to build your first neural network, which will have a hidden layer. You will see a big difference between this model and the one you implemented using logistic regression. \n",
    "\n",
    "**You will learn how to:**\n",
    "- Implement a 2-class classification neural network with a single hidden layer\n",
    "- Use units with a non-linear activation function, such as tanh \n",
    "- Compute the cross entropy loss \n",
    "- Implement forward and backward propagation\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1 - Packages ##\n",
    "\n",
    "Let's first import all the packages that you will need during this assignment.\n",
    "- [numpy](https://www.numpy.org/) is the fundamental package for scientific computing with Python.\n",
    "- [sklearn](http://scikit-learn.org/stable/) provides simple and efficient tools for data mining and data analysis. \n",
    "- [matplotlib](http://matplotlib.org) is a library for plotting graphs in Python.\n",
    "- testCases provides some test examples to assess the correctness of your functions\n",
    "- planar_utils provide various useful functions used in this assignment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
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   "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": 30,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "X, Y = load_planar_dataset()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualize the dataset using matplotlib. The data looks like a \"flower\" with some red (label y=0) and some blue (y=1) points. Your goal is to build a model to fit this data. In other words, we want the classifier to define regions as either red or blue."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "pycharm": {
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   "outputs": [
    {
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the data:\n",
    "plt.scatter(X[0, :], X[1, :], c=Y[0], 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": 31,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "text": [
      "The shape of X is: (2, 400)\nThe shape of Y is: (1, 400)\nI have m = 400 training examples!\n"
     ],
     "output_type": "stream"
    }
   ],
   "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",
    "  <tr>\n",
    "    <td>**shape of Y**</td>\n",
    "    <td>(1, 400) </td> \n",
    "  </tr>\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": 6,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "text": [
      "/Users/golammortuza/anaconda3/envs/python37/lib/python3.7/site-packages/sklearn/model_selection/_split.py:1978: FutureWarning: The default value of cv will change from 3 to 5 in version 0.22. Specify it explicitly to silence this warning.\n  warnings.warn(CV_WARNING, FutureWarning)\n"
     ],
     "output_type": "stream"
    }
   ],
   "source": [
    "# Train the logistic regression classifier\n",
    "clf = sklearn.linear_model.LogisticRegressionCV();\n",
    "clf.fit(X.T, Y[0].T);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can now plot the decision boundary of these models. Run the code below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "pycharm": {
     "is_executing": false
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "text": [
      "Accuracy of logistic regression: 47 % (percentage of correctly labelled datapoints)\n"
     ],
     "output_type": "stream"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the decision boundary for logistic regression\n",
    "plot_decision_boundary(lambda x: clf.predict(x), X, Y[0])\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": 8,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: layer_sizes\n",
    "\n",
    "def layer_sizes(X, Y):\n",
    "    \"\"\"\n",
    "    Arguments:\n",
    "    X -- input dataset of shape (input size, number of examples)\n",
    "    Y -- labels of shape (output size, number of examples)\n",
    "    \n",
    "    Returns:\n",
    "    n_x -- the size of the input layer\n",
    "    n_h -- the size of the hidden layer\n",
    "    n_y -- the size of the output layer\n",
    "    \"\"\"\n",
    "    ### START CODE HERE ### (≈ 3 lines of code)\n",
    "    n_x = X.shape[0] # size of input layer\n",
    "    n_h = 4\n",
    "    n_y = Y.shape[0] # size of output layer\n",
    "    ### END CODE HERE ###\n",
    "    return (n_x, n_h, n_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "text": [
      "The size of the input layer is: n_x = 5\nThe size of the hidden layer is: n_h = 4\nThe size of the output layer is: n_y = 2\n"
     ],
     "output_type": "stream"
    }
   ],
   "source": [
    "X_assess, Y_assess = layer_sizes_test_case()\n",
    "(n_x, n_h, n_y) = layer_sizes(X_assess, Y_assess)\n",
    "print(\"The size of the input layer is: n_x = \" + str(n_x))\n",
    "print(\"The size of the hidden layer is: n_h = \" + str(n_h))\n",
    "print(\"The size of the output layer is: n_y = \" + str(n_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "array([[ 1.62434536, -0.61175641, -0.52817175],\n       [-1.07296862,  0.86540763, -2.3015387 ],\n       [ 1.74481176, -0.7612069 ,  0.3190391 ],\n       [-0.24937038,  1.46210794, -2.06014071],\n       [-0.3224172 , -0.38405435,  1.13376944]])"
     },
     "metadata": {},
     "output_type": "execute_result",
     "execution_count": 10
    }
   ],
   "source": [
    "X_assess"
   ]
  },
  {
   "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",
    "    <tr>\n",
    "    <td>**n_h**</td>\n",
    "    <td> 4 </td> \n",
    "  </tr>\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": 11,
   "metadata": {
    "pycharm": {
     "is_executing": 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) * .01\n",
    "    b1 = np.zeros((n_h, 1))\n",
    "    W2 = np.random.randn(n_y, n_h) * .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": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "text": [
      "W1 = [[-0.00416758 -0.00056267]\n [-0.02136196  0.01640271]\n [-0.01793436 -0.00841747]\n [ 0.00502881 -0.01245288]]\nb1 = [[0.]\n [0.]\n [0.]\n [0.]]\nW2 = [[-0.01057952 -0.00909008  0.00551454  0.02292208]]\nb2 = [[0.]]\n"
     ],
     "output_type": "stream"
    }
   ],
   "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": 13,
   "metadata": {
    "pycharm": {
     "is_executing": 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",
    "    # Implement Forward Propagation to calculate A2 (probabilities)\n",
    "    ### START CODE HERE ### (≈ 4 lines of code)\n",
    "    Z1 = np.dot(W1, X) + b1\n",
    "    A1 = np.tanh(Z1)\n",
    "    Z2 = np.dot(W2, A1) + b2\n",
    "    A2 = sigmoid(Z2)\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    assert(A2.shape == (1, X.shape[1]))\n",
    "    \n",
    "    cache = {\"Z1\": Z1,\n",
    "             \"A1\": A1,\n",
    "             \"Z2\": Z2,\n",
    "             \"A2\": A2}\n",
    "    \n",
    "    return A2, cache"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "text": [
      "0.26281864019752443 0.09199904522700109 -1.3076660128732143 0.21287768171914198\n"
     ],
     "output_type": "stream"
    }
   ],
   "source": [
    "X_assess, parameters = forward_propagation_test_case()\n",
    "A2, cache = forward_propagation(X_assess, parameters)\n",
    "\n",
    "# Note: we use the mean here just to make sure that your output matches ours. \n",
    "print(np.mean(cache['Z1']) ,np.mean(cache['A1']),np.mean(cache['Z2']),np.mean(cache['A2']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "<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",
    "$$J = - \\frac{1}{m} \\sum\\limits_{i = 1}^{m} \\large{(} \\small y^{(i)}\\log\\left(a^{[2] (i)}\\right) + (1-y^{(i)})\\log\\left(1- a^{[2] (i)}\\right) \\large{)} \\small\\tag{13}$$\n",
    "\n",
    "**Exercise**: Implement `compute_cost()` to compute the value of the cost $J$.\n",
    "\n",
    "**Instructions**:\n",
    "- There are many ways to implement the cross-entropy loss. To help you, we give you how we would have implemented\n",
    "$- \\sum\\limits_{i=0}^{m}  y^{(i)}\\log(a^{[2](i)})$:\n",
    "```python\n",
    "logprobs = np.multiply(np.log(A2),Y)\n",
    "cost = - np.sum(logprobs)                # no need to use a for loop!\n",
    "```\n",
    "\n",
    "(you can use either `np.multiply()` and then `np.sum()` or directly `np.dot()`).  \n",
    "Note that if you use `np.multiply` followed by `np.sum` the end result will be a type `float`, whereas if you use `np.dot`, the result will be a 2D numpy array.  We can use `np.squeeze()` to remove redundant dimensions (in the case of single float, this will be reduced to a zero-dimension array). We can cast the array as a type `float` using `float()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "pycharm": {
     "is_executing": 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",
    "    [Note that the parameters argument is not used in this function, \n",
    "    but the auto-grader currently expects this parameter.\n",
    "    Future version of this notebook will fix both the notebook \n",
    "    and the auto-grader so that `parameters` is not needed.\n",
    "    For now, please include `parameters` in the function signature,\n",
    "    and also when invoking this function.]\n",
    "    \n",
    "    Returns:\n",
    "    cost -- cross-entropy cost given equation (13)\n",
    "    \n",
    "    \"\"\"\n",
    "    \n",
    "    m = Y.shape[1] # number of example\n",
    "\n",
    "    # Compute the cross-entropy cost\n",
    "    ### START CODE HERE ### (≈ 2 lines of code)\n",
    "    logprobs = np.dot(Y, np.log(A2.T)) + np.dot((1-Y), np.log(1 - A2).T)\n",
    "    cost = -np.sum(logprobs) / m\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    cost = float(np.squeeze(cost))  # makes sure cost is the dimension we expect. \n",
    "                                    # E.g., turns [[17]] into 17 \n",
    "    assert(isinstance(cost, float))\n",
    "    \n",
    "    return cost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "text": [
      "cost = 0.6930587610394646\n"
     ],
     "output_type": "stream"
    }
   ],
   "source": [
    "A2, Y_assess, parameters = compute_cost_test_case()\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": 17,
   "metadata": {
    "pycharm": {
     "is_executing": 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",
    "    dW2 = np.dot(dZ2, A1.T) / m\n",
    "    db2 = np.sum(dZ2, axis = 1, keepdims=True)/m\n",
    "    dZ1 = np.multiply(np.dot(W2.T, dZ2), (1 - np.power(A1, 2)))\n",
    "    dW1 = np.dot(dZ1, X.T)/m\n",
    "    db1 = np.sum(dZ1, axis=1, keepdims=True)/m\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": 18,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "text": [
      "dW1 = [[ 0.00301023 -0.00747267]\n [ 0.00257968 -0.00641288]\n [-0.00156892  0.003893  ]\n [-0.00652037  0.01618243]]\ndb1 = [[ 0.00176201]\n [ 0.00150995]\n [-0.00091736]\n [-0.00381422]]\ndW2 = [[ 0.00078841  0.01765429 -0.00084166 -0.01022527]]\ndb2 = [[-0.16655712]]\n"
     ],
     "output_type": "stream"
    }
   ],
   "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": 19,
   "metadata": {
    "pycharm": {
     "is_executing": 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": 20,
   "metadata": {
    "pycharm": {
     "is_executing": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "text": [
      "W1 = [[-0.00643025  0.01936718]\n [-0.02410458  0.03978052]\n [-0.01653973 -0.02096177]\n [ 0.01046864 -0.05990141]]\nb1 = [[-1.02420756e-06]\n [ 1.27373948e-05]\n [ 8.32996807e-07]\n [-3.20136836e-06]]\nW2 = [[-0.01041081 -0.04463285  0.01758031  0.04747113]]\nb2 = [[0.00010457]]\n"
     ],
     "output_type": "stream"
    }
   ],
   "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": 21,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: nn_model\n",
    "\n",
    "def nn_model(X, Y, n_h, num_iterations = 10000, 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\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 (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": 22,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "text": [
      "Cost after iteration 0: 0.692739\nCost after iteration 1000: 0.000218\n",
      "Cost after iteration 2000: 0.000107\n",
      "Cost after iteration 3000: 0.000071\n",
      "Cost after iteration 4000: 0.000053\nCost after iteration 5000: 0.000042\n",
      "Cost after iteration 6000: 0.000035\n",
      "Cost after iteration 7000: 0.000030\nCost after iteration 8000: 0.000026\n",
      "Cost after iteration 9000: 0.000023\n",
      "W1 = [[-0.65848169  1.21866811]\n [-0.76204273  1.39377573]\n [ 0.5792005  -1.10397703]\n [ 0.76773391 -1.41477129]]\nb1 = [[ 0.287592  ]\n [ 0.3511264 ]\n [-0.2431246 ]\n [-0.35772805]]\nW2 = [[-2.45566237 -3.27042274  2.00784958  3.36773273]]\nb2 = [[0.20459656]]\n"
     ],
     "output_type": "stream"
    }
   ],
   "source": [
    "X_assess, Y_assess = nn_model_test_case()\n",
    "parameters = nn_model(X_assess, Y_assess, 4, num_iterations=10000, print_cost=True)\n",
    "print(\"W1 = \" + str(parameters[\"W1\"]))\n",
    "print(\"b1 = \" + str(parameters[\"b1\"]))\n",
    "print(\"W2 = \" + str(parameters[\"W2\"]))\n",
    "print(\"b2 = \" + str(parameters[\"b2\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "\n",
    "<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": 23,
   "metadata": {
    "pycharm": {
     "is_executing": 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",
    "    predictions = A2 > .5\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    return predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "pycharm": {
     "is_executing": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "text": [
      "predictions mean = 0.6666666666666666\n"
     ],
     "output_type": "stream"
    }
   ],
   "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": 25,
   "metadata": {
    "pycharm": {
     "is_executing": false
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "text": [
      "Cost after iteration 0: 0.693048\n",
      "Cost after iteration 1000: 0.288083\n",
      "Cost after iteration 2000: 0.254385\n",
      "Cost after iteration 3000: 0.233864\n",
      "Cost after iteration 4000: 0.226792\n",
      "Cost after iteration 5000: 0.222644\n",
      "Cost after iteration 6000: 0.219731\n",
      "Cost after iteration 7000: 0.217504\n",
      "Cost after iteration 8000: 0.219463\n",
      "Cost after iteration 9000: 0.218560\n"
     ],
     "output_type": "stream"
    },
    {
     "data": {
      "text/plain": "Text(0.5, 1.0, 'Decision Boundary for hidden layer size 4')"
     },
     "metadata": {},
     "output_type": "execute_result",
     "execution_count": 25
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Build a model with a n_h-dimensional hidden layer\n",
    "parameters = nn_model(X, Y, 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[0])\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": 26,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "text": [
      "Accuracy: 90%\n"
     ],
     "output_type": "stream"
    }
   ],
   "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": {
    "pycharm": {
     "is_executing": true
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "text": [
      "Accuracy for 1 hidden units: 67.5 %\n"
     ],
     "output_type": "stream"
    }
   ],
   "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[0])\n",
    "    predictions = predict(parameters, X)\n",
    "    accuracy = float((np.dot(Y,predictions.T) + np.dot(1-Y,1-predictions.T))/float(Y.size)*100)\n",
    "    print (\"Accuracy for {} hidden units: {} %\".format(n_h, accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Interpretation**:\n",
    "- The larger models (with more hidden units) are able to fit the training set better, until eventually the largest models overfit the data. \n",
    "- The best hidden layer size seems to be around n_h = 5. Indeed, a value around here seems to  fits the data well without also incurring noticeable overfitting.\n",
    "- You will also learn later about regularization, which lets you use very large models (such as n_h = 50) without much overfitting. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Optional questions**:\n",
    "\n",
    "**Note**: Remember to submit the assignment by clicking the blue \"Submit Assignment\" button at the upper-right. \n",
    "\n",
    "Some optional/ungraded questions that you can explore if you wish: \n",
    "- What happens when you change the tanh activation for a sigmoid activation or a ReLU activation?\n",
    "- Play with the learning_rate. What happens?\n",
    "- What if we change the dataset? (See part 5 below!)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<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": {},
   "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": 28,
   "metadata": {
    "pycharm": {
     "is_executing": false
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Datasets\n",
    "noisy_circles, noisy_moons, blobs, gaussian_quantiles, no_structure = load_extra_datasets()\n",
    "\n",
    "datasets = {\"noisy_circles\": noisy_circles,\n",
    "            \"noisy_moons\": noisy_moons,\n",
    "            \"blobs\": blobs,\n",
    "            \"gaussian_quantiles\": gaussian_quantiles}\n",
    "\n",
    "### START CODE HERE ### (choose your dataset)\n",
    "dataset = \"noisy_moons\"\n",
    "### END CODE HERE ###\n",
    "\n",
    "X, Y = datasets[dataset]\n",
    "X, Y = X.T, Y.reshape(1, Y.shape[0])\n",
    "\n",
    "# make blobs binary\n",
    "if dataset == \"blobs\":\n",
    "    Y = Y%2\n",
    "\n",
    "# Visualize the data\n",
    "plt.scatter(X[0, :], X[1, :], c=Y[0], s=40, cmap=plt.cm.Spectral);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Congrats on finishing this Programming Assignment!\n",
    "\n",
    "Reference:\n",
    "- http://scs.ryerson.ca/~aharley/neural-networks/\n",
    "- http://cs231n.github.io/neural-networks-case-study/"
   ]
  }
 ],
 "metadata": {
  "coursera": {
   "course_slug": "neural-networks-deep-learning",
   "graded_item_id": "wRuwL",
   "launcher_item_id": "NI888"
  },
  "kernelspec": {
   "display_name": "PyCharm (vsm)",
   "language": "python",
   "name": "pycharm-7ed7eb04"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.5"
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  "pycharm": {
   "stem_cell": {
    "cell_type": "raw",
    "source": [],
    "metadata": {
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 },
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}