Logistic+Regression+with+a+Neural+Network+mindset+v3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic Regression with a Neural Network mindset\n",
    "\n",
    "Welcome to your first (required) programming assignment! You will build a logistic regression classifier to recognize  cats. This assignment will step you through how to do this with a Neural Network mindset, and so will also hone your intuitions about deep learning.\n",
    "\n",
    "**Instructions:**\n",
    "- Do not use loops (for/while) in your code, unless the instructions explicitly ask you to do so.\n",
    "\n",
    "**You will learn to:**\n",
    "- Build the general architecture of a learning algorithm, including:\n",
    "    - Initializing parameters\n",
    "    - Calculating the cost function and its gradient\n",
    "    - Using an optimization algorithm (gradient descent) \n",
    "- Gather all three functions above into a main model function, in the right order."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1 - Packages ##\n",
    "\n",
    "First, let's run the cell below to 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",
    "- [h5py](http://www.h5py.org) is a common package to interact with a dataset that is stored on an H5 file.\n",
    "- [matplotlib](http://matplotlib.org) is a famous library to plot graphs in Python.\n",
    "- [PIL](http://www.pythonware.com/products/pil/) and [scipy](https://www.scipy.org/) are used here to test your model with your own picture at the end."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.5/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n",
      "  warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n",
      "/opt/conda/lib/python3.5/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n",
      "  warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import h5py\n",
    "import scipy\n",
    "from PIL import Image\n",
    "from scipy import ndimage\n",
    "from lr_utils import load_dataset\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## 2 - Overview of the Problem set ##\n",
    "\n",
    "**Problem Statement**: You are given a dataset (\"data.h5\") containing:\n",
    "    - a training set of m_train images labeled as cat (y=1) or non-cat (y=0)\n",
    "    - a test set of m_test images labeled as cat or non-cat\n",
    "    - each image is of shape (num_px, num_px, 3) where 3 is for the 3 channels (RGB). Thus, each image is square (height = num_px) and (width = num_px).\n",
    "\n",
    "You will build a simple image-recognition algorithm that can correctly classify pictures as cat or non-cat.\n",
    "\n",
    "Let's get more familiar with the dataset. Load the data by running the following code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Loading the data (cat/non-cat)\n",
    "train_set_x_orig, train_set_y, test_set_x_orig, test_set_y, classes = load_dataset()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We added \"_orig\" at the end of image datasets (train and test) because we are going to preprocess them. After preprocessing, we will end up with train_set_x and test_set_x (the labels train_set_y and test_set_y don't need any preprocessing).\n",
    "\n",
    "Each line of your train_set_x_orig and test_set_x_orig is an array representing an image. You can visualize an example by running the following code. Feel free also to change the `index` value and re-run to see other images. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y = [0], it's a 'non-cat' picture.\n"
     ]
    },
    {
     "data": {
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rlrdd38/ZoPaHO8vLLiq6IVu7GIzOXz3J3+rVrk79OMC86HYA8FPAVn+519/TBuhnAqat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\nHrd6fCwmuZUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6a69b47ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Example of a picture\n",
    "index = 1\n",
    "plt.imshow(train_set_x_orig[index])\n",
    "print (\"y = \" + str(train_set_y[:, index]) + \", it's a '\" + classes[np.squeeze(train_set_y[:, index])].decode(\"utf-8\") +  \"' picture.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Many software bugs in deep learning come from having matrix/vector dimensions that don't fit. If you can keep your matrix/vector dimensions straight you will go a long way toward eliminating many bugs. \n",
    "\n",
    "**Exercise:** Find the values for:\n",
    "    - m_train (number of training examples)\n",
    "    - m_test (number of test examples)\n",
    "    - num_px (= height = width of a training image)\n",
    "Remember that `train_set_x_orig` is a numpy-array of shape (m_train, num_px, num_px, 3). For instance, you can access `m_train` by writing `train_set_x_orig.shape[0]`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of training examples: m_train = 209\n",
      "Number of testing examples: m_test = 209\n",
      "Height/Width of each image: num_px = 64\n",
      "Each image is of size: (64, 64, 3)\n",
      "train_set_x shape: (209, 64, 64, 3)\n",
      "train_set_y shape: (1, 209)\n",
      "test_set_x shape: (50, 64, 64, 3)\n",
      "test_set_y shape: (1, 50)\n"
     ]
    }
   ],
   "source": [
    "### START CODE HERE ### (≈ 3 lines of code)\n",
    "m_train = len(train_set_x_orig)\n",
    "m_test = len (train_set_x_orig)\n",
    "num_px = len(train_set_x_orig[0][1])\n",
    "### END CODE HERE ###\n",
    "\n",
    "print (\"Number of training examples: m_train = \" + str(m_train))\n",
    "print (\"Number of testing examples: m_test = \" + str(m_test))\n",
    "print (\"Height/Width of each image: num_px = \" + str(num_px))\n",
    "print (\"Each image is of size: (\" + str(num_px) + \", \" + str(num_px) + \", 3)\")\n",
    "print (\"train_set_x shape: \" + str(train_set_x_orig.shape))\n",
    "print (\"train_set_y shape: \" + str(train_set_y.shape))\n",
    "print (\"test_set_x shape: \" + str(test_set_x_orig.shape))\n",
    "print (\"test_set_y shape: \" + str(test_set_y.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output for m_train, m_test and num_px**: \n",
    "<table style=\"width:15%\">\n",
    "  <tr>\n",
    "    <td>**m_train**</td>\n",
    "    <td> 209 </td> \n",
    "  </tr>\n",
    "  \n",
    "  <tr>\n",
    "    <td>**m_test**</td>\n",
    "    <td> 50 </td> \n",
    "  </tr>\n",
    "  \n",
    "  <tr>\n",
    "    <td>**num_px**</td>\n",
    "    <td> 64 </td> \n",
    "  </tr>\n",
    "  \n",
    "</table>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For convenience, you should now reshape images of shape (num_px, num_px, 3) in a numpy-array of shape (num_px $*$ num_px $*$ 3, 1). After this, our training (and test) dataset is a numpy-array where each column represents a flattened image. There should be m_train (respectively m_test) columns.\n",
    "\n",
    "**Exercise:** Reshape the training and test data sets so that images of size (num_px, num_px, 3) are flattened into single vectors of shape (num\\_px $*$ num\\_px $*$ 3, 1).\n",
    "\n",
    "A trick when you want to flatten a matrix X of shape (a,b,c,d) to a matrix X_flatten of shape (b$*$c$*$d, a) is to use: \n",
    "```python\n",
    "X_flatten = X.reshape(X.shape[0], -1).T      # X.T is the transpose of X\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train_set_x_flatten shape: (12288, 209)\n",
      "train_set_y shape: (1, 209)\n",
      "test_set_x_flatten shape: (12288, 50)\n",
      "test_set_y shape: (1, 50)\n",
      "sanity check after reshaping: [17 31 56 22 33]\n"
     ]
    }
   ],
   "source": [
    "# Reshape the training and test examples\n",
    "\n",
    "### START CODE HERE ### (≈ 2 lines of code)\n",
    "train_set_x_flatten = train_set_x_orig.reshape(train_set_x_orig.shape[0], -1).T\n",
    "test_set_x_flatten = test_set_x_orig.reshape(test_set_x_orig.shape[0], -1).T\n",
    "### END CODE HERE ###\n",
    "\n",
    "print (\"train_set_x_flatten shape: \" + str(train_set_x_flatten.shape))\n",
    "print (\"train_set_y shape: \" + str(train_set_y.shape))\n",
    "print (\"test_set_x_flatten shape: \" + str(test_set_x_flatten.shape))\n",
    "print (\"test_set_y shape: \" + str(test_set_y.shape))\n",
    "print (\"sanity check after reshaping: \" + str(train_set_x_flatten[0:5,0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**: \n",
    "\n",
    "<table style=\"width:35%\">\n",
    "  <tr>\n",
    "    <td>**train_set_x_flatten shape**</td>\n",
    "    <td> (12288, 209)</td> \n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td>**train_set_y shape**</td>\n",
    "    <td>(1, 209)</td> \n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td>**test_set_x_flatten shape**</td>\n",
    "    <td>(12288, 50)</td> \n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td>**test_set_y shape**</td>\n",
    "    <td>(1, 50)</td> \n",
    "  </tr>\n",
    "  <tr>\n",
    "  <td>**sanity check after reshaping**</td>\n",
    "  <td>[17 31 56 22 33]</td> \n",
    "  </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To represent color images, the red, green and blue channels (RGB) must be specified for each pixel, and so the pixel value is actually a vector of three numbers ranging from 0 to 255.\n",
    "\n",
    "One common preprocessing step in machine learning is to center and standardize your dataset, meaning that you substract the mean of the whole numpy array from each example, and then divide each example by the standard deviation of the whole numpy array. But for picture datasets, it is simpler and more convenient and works almost as well to just divide every row of the dataset by 255 (the maximum value of a pixel channel).\n",
    "\n",
    "<!-- During the training of your model, you're going to multiply weights and add biases to some initial inputs in order to observe neuron activations. Then you backpropogate with the gradients to train the model. But, it is extremely important for each feature to have a similar range such that our gradients don't explode. You will see that more in detail later in the lectures. !--> \n",
    "\n",
    "Let's standardize our dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "train_set_x = train_set_x_flatten/255.\n",
    "test_set_x = test_set_x_flatten/255."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='blue'>\n",
    "**What you need to remember:**\n",
    "\n",
    "Common steps for pre-processing a new dataset are:\n",
    "- Figure out the dimensions and shapes of the problem (m_train, m_test, num_px, ...)\n",
    "- Reshape the datasets such that each example is now a vector of size (num_px \\* num_px \\* 3, 1)\n",
    "- \"Standardize\" the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3 - General Architecture of the learning algorithm ##\n",
    "\n",
    "It's time to design a simple algorithm to distinguish cat images from non-cat images.\n",
    "\n",
    "You will build a Logistic Regression, using a Neural Network mindset. The following Figure explains why **Logistic Regression is actually a very simple Neural Network!**\n",
    "\n",
    "<img src=\"images/LogReg_kiank.png\" style=\"width:650px;height:400px;\">\n",
    "\n",
    "**Mathematical expression of the algorithm**:\n",
    "\n",
    "For one example $x^{(i)}$:\n",
    "$$z^{(i)} = w^T x^{(i)} + b \\tag{1}$$\n",
    "$$\\hat{y}^{(i)} = a^{(i)} = sigmoid(z^{(i)})\\tag{2}$$ \n",
    "$$ \\mathcal{L}(a^{(i)}, y^{(i)}) =  - y^{(i)}  \\log(a^{(i)}) - (1-y^{(i)} )  \\log(1-a^{(i)})\\tag{3}$$\n",
    "\n",
    "The cost is then computed by summing over all training examples:\n",
    "$$ J = \\frac{1}{m} \\sum_{i=1}^m \\mathcal{L}(a^{(i)}, y^{(i)})\\tag{6}$$\n",
    "\n",
    "**Key steps**:\n",
    "In this exercise, you will carry out the following steps: \n",
    "    - Initialize the parameters of the model\n",
    "    - Learn the parameters for the model by minimizing the cost  \n",
    "    - Use the learned parameters to make predictions (on the test set)\n",
    "    - Analyse the results and conclude"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4 - Building the parts of our algorithm ## \n",
    "\n",
    "The main steps for building a Neural Network are:\n",
    "1. Define the model structure (such as number of input features) \n",
    "2. Initialize the model's parameters\n",
    "3. Loop:\n",
    "    - Calculate current loss (forward propagation)\n",
    "    - Calculate current gradient (backward propagation)\n",
    "    - Update parameters (gradient descent)\n",
    "\n",
    "You often build 1-3 separately and integrate them into one function we call `model()`.\n",
    "\n",
    "### 4.1 - Helper functions\n",
    "\n",
    "**Exercise**: Using your code from \"Python Basics\", implement `sigmoid()`. As you've seen in the figure above, you need to compute $sigmoid( w^T x + b) = \\frac{1}{1 + e^{-(w^T x + b)}}$ to make predictions. Use np.exp()."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: sigmoid\n",
    "\n",
    "def sigmoid(z):\n",
    "    \"\"\"\n",
    "    Compute the sigmoid of z\n",
    "\n",
    "    Arguments:\n",
    "    z -- A scalar or numpy array of any size.\n",
    "\n",
    "    Return:\n",
    "    s -- sigmoid(z)\n",
    "    \"\"\"\n",
    "\n",
    "    ### START CODE HERE ### (≈ 1 line of code)\n",
    "    s = 1/(1+np.exp(-z))\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    return s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sigmoid([0, 2]) = [ 0.5         0.88079708]\n"
     ]
    }
   ],
   "source": [
    "print (\"sigmoid([0, 2]) = \" + str(sigmoid(np.array([0,2]))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**: \n",
    "\n",
    "<table>\n",
    "  <tr>\n",
    "    <td>**sigmoid([0, 2])**</td>\n",
    "    <td> [ 0.5         0.88079708]</td> \n",
    "  </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2 - Initializing parameters\n",
    "\n",
    "**Exercise:** Implement parameter initialization in the cell below. You have to initialize w as a vector of zeros. If you don't know what numpy function to use, look up np.zeros() in the Numpy library's documentation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: initialize_with_zeros\n",
    "\n",
    "def initialize_with_zeros(dim):\n",
    "    \"\"\"\n",
    "    This function creates a vector of zeros of shape (dim, 1) for w and initializes b to 0.\n",
    "    \n",
    "    Argument:\n",
    "    dim -- size of the w vector we want (or number of parameters in this case)\n",
    "    \n",
    "    Returns:\n",
    "    w -- initialized vector of shape (dim, 1)\n",
    "    b -- initialized scalar (corresponds to the bias)\n",
    "    \"\"\"\n",
    "    \n",
    "    ### START CODE HERE ### (≈ 1 line of code)\n",
    "    w = np.zeros((dim, 1))\n",
    "    b = 0\n",
    "    ### END CODE HERE ###\n",
    "\n",
    "    assert(w.shape == (dim, 1))\n",
    "    assert(isinstance(b, float) or isinstance(b, int))\n",
    "    \n",
    "    return w, b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "w = [[ 0.]\n",
      " [ 0.]]\n",
      "b = 0\n"
     ]
    }
   ],
   "source": [
    "dim = 2\n",
    "w, b = initialize_with_zeros(dim)\n",
    "print (\"w = \" + str(w))\n",
    "print (\"b = \" + str(b))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**: \n",
    "\n",
    "\n",
    "<table style=\"width:15%\">\n",
    "    <tr>\n",
    "        <td>  ** w **  </td>\n",
    "        <td> [[ 0.]\n",
    " [ 0.]] </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>  ** b **  </td>\n",
    "        <td> 0 </td>\n",
    "    </tr>\n",
    "</table>\n",
    "\n",
    "For image inputs, w will be of shape (num_px $\\times$ num_px $\\times$ 3, 1)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.3 - Forward and Backward propagation\n",
    "\n",
    "Now that your parameters are initialized, you can do the \"forward\" and \"backward\" propagation steps for learning the parameters.\n",
    "\n",
    "**Exercise:** Implement a function `propagate()` that computes the cost function and its gradient.\n",
    "\n",
    "**Hints**:\n",
    "\n",
    "Forward Propagation:\n",
    "- You get X\n",
    "- You compute $A = \\sigma(w^T X + b) = (a^{(0)}, a^{(1)}, ..., a^{(m-1)}, a^{(m)})$\n",
    "- You calculate the cost function: $J = -\\frac{1}{m}\\sum_{i=1}^{m}y^{(i)}\\log(a^{(i)})+(1-y^{(i)})\\log(1-a^{(i)})$\n",
    "\n",
    "Here are the two formulas you will be using: \n",
    "\n",
    "$$ \\frac{\\partial J}{\\partial w} = \\frac{1}{m}X(A-Y)^T\\tag{7}$$\n",
    "$$ \\frac{\\partial J}{\\partial b} = \\frac{1}{m} \\sum_{i=1}^m (a^{(i)}-y^{(i)})\\tag{8}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: propagate\n",
    "\n",
    "def propagate(w, b, X, Y):\n",
    "    \"\"\"\n",
    "    Implement the cost function and its gradient for the propagation explained above\n",
    "\n",
    "    Arguments:\n",
    "    w -- weights, a numpy array of size (num_px * num_px * 3, 1)\n",
    "    b -- bias, a scalar\n",
    "    X -- data of size (num_px * num_px * 3, number of examples)\n",
    "    Y -- true \"label\" vector (containing 0 if non-cat, 1 if cat) of size (1, number of examples)\n",
    "\n",
    "    Return:\n",
    "    cost -- negative log-likelihood cost for logistic regression\n",
    "    dw -- gradient of the loss with respect to w, thus same shape as w\n",
    "    db -- gradient of the loss with respect to b, thus same shape as b\n",
    "    \n",
    "    Tips:\n",
    "    - Write your code step by step for the propagation. np.log(), np.dot()\n",
    "    \"\"\"\n",
    "    \n",
    "    m = X.shape[1]\n",
    "\n",
    "\n",
    "    # FORWARD PROPAGATION (FROM X TO COST)\n",
    "    ### START CODE HERE ### (≈ 2 lines of code)\n",
    "\n",
    "\n",
    "    A = sigmoid(np.dot(w.T,X) +b)     \n",
    "    # compute activation\n",
    "    cost = -1/m * np.sum(Y*np.log(A)+(1-Y)*(np.log(1-A)))      # compute cost\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    # BACKWARD PROPAGATION (TO FIND GRAD)\n",
    "    ### START CODE HERE ### (≈ 2 lines of code)\n",
    "    dw = 1/m*np.dot(X, np.transpose(A-Y))\n",
    "    db = 1/m*np.sum(A-Y)\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "\n",
    "\n",
    "    cost = np.squeeze(cost)\n",
    "    \n",
    "    \n",
    "    assert(dw.shape == w.shape)\n",
    "    assert(db.dtype == float)\n",
    "    assert(cost.shape == ())\n",
    "    \n",
    "    grads = {\"dw\": dw,\n",
    "             \"db\": db}\n",
    "    \n",
    "    return grads, cost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dw = [[ 0.99993216]\n",
      " [ 1.99980262]]\n",
      "db = 0.499935230625\n",
      "cost = 6.00006477319\n"
     ]
    }
   ],
   "source": [
    "w, b, X, Y = np.array([[1],[2]]), 2, np.array([[1,2],[3,4]]), np.array([[1,0]])\n",
    "grads, cost = propagate(w, b, X, Y)\n",
    "print (\"dw = \" + str(grads[\"dw\"]))\n",
    "print (\"db = \" + str(grads[\"db\"]))\n",
    "print (\"cost = \" + str(cost))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "\n",
    "<table style=\"width:50%\">\n",
    "    <tr>\n",
    "        <td>  ** dw **  </td>\n",
    "        <td> [[ 0.99993216]\n",
    " [ 1.99980262]]</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>  ** db **  </td>\n",
    "        <td> 0.499935230625 </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>  ** cost **  </td>\n",
    "        <td> 6.000064773192205</td>\n",
    "    </tr>\n",
    "\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### d) Optimization\n",
    "- You have initialized your parameters.\n",
    "- You are also able to compute a cost function and its gradient.\n",
    "- Now, you want to update the parameters using gradient descent.\n",
    "\n",
    "**Exercise:** Write down the optimization function. The goal is to learn $w$ and $b$ by minimizing the cost function $J$. For a parameter $\\theta$, the update rule is $ \\theta = \\theta - \\alpha \\text{ } d\\theta$, where $\\alpha$ is the learning rate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: optimize\n",
    "\n",
    "def optimize(w, b, X, Y, num_iterations, learning_rate, print_cost = False):\n",
    "    \"\"\"\n",
    "    This function optimizes w and b by running a gradient descent algorithm\n",
    "    \n",
    "    Arguments:\n",
    "    w -- weights, a numpy array of size (num_px * num_px * 3, 1)\n",
    "    b -- bias, a scalar\n",
    "    X -- data of shape (num_px * num_px * 3, number of examples)\n",
    "    Y -- true \"label\" vector (containing 0 if non-cat, 1 if cat), of shape (1, number of examples)\n",
    "    num_iterations -- number of iterations of the optimization loop\n",
    "    learning_rate -- learning rate of the gradient descent update rule\n",
    "    print_cost -- True to print the loss every 100 steps\n",
    "    \n",
    "    Returns:\n",
    "    params -- dictionary containing the weights w and bias b\n",
    "    grads -- dictionary containing the gradients of the weights and bias with respect to the cost function\n",
    "    costs -- list of all the costs computed during the optimization, this will be used to plot the learning curve.\n",
    "    \n",
    "    Tips:\n",
    "    You basically need to write down two steps and iterate through them:\n",
    "        1) Calculate the cost and the gradient for the current parameters. Use propagate().\n",
    "        2) Update the parameters using gradient descent rule for w and b.\n",
    "    \"\"\"\n",
    "    \n",
    "    costs = []\n",
    "    \n",
    "    for i in range(num_iterations):\n",
    "        \n",
    "        # Cost and gradient calculation (≈ 1-4 lines of code)\n",
    "        ### START CODE HERE ### \n",
    "        grads, cost = propagate(w, b, X, Y)\n",
    "        ### END CODE HERE ###\n",
    "        \n",
    "        # Retrieve derivatives from grads\n",
    "        dw = grads[\"dw\"]\n",
    "        db = grads[\"db\"]\n",
    "        \n",
    "        # update rule (≈ 2 lines of code)\n",
    "        ### START CODE HERE ###\n",
    "        w = w - learning_rate*dw\n",
    "        b = b - learning_rate*db\n",
    "        ### END CODE HERE ###\n",
    "        \n",
    "        # Record the costs\n",
    "        if i % 100 == 0:\n",
    "            costs.append(cost)\n",
    "        \n",
    "        # Print the cost every 100 training examples\n",
    "        if print_cost and i % 100 == 0:\n",
    "            print (\"Cost after iteration %i: %f\" %(i, cost))\n",
    "    \n",
    "    params = {\"w\": w,\n",
    "              \"b\": b}\n",
    "    \n",
    "    grads = {\"dw\": dw,\n",
    "             \"db\": db}\n",
    "    \n",
    "    return params, grads, costs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "w = [[ 0.1124579 ]\n",
      " [ 0.23106775]]\n",
      "b = 1.55930492484\n",
      "dw = [[ 0.90158428]\n",
      " [ 1.76250842]]\n",
      "db = 0.430462071679\n"
     ]
    }
   ],
   "source": [
    "params, grads, costs = optimize(w, b, X, Y, num_iterations= 100, learning_rate = 0.009, print_cost = False)\n",
    "\n",
    "print (\"w = \" + str(params[\"w\"]))\n",
    "print (\"b = \" + str(params[\"b\"]))\n",
    "print (\"dw = \" + str(grads[\"dw\"]))\n",
    "print (\"db = \" + str(grads[\"db\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**: \n",
    "\n",
    "<table style=\"width:40%\">\n",
    "    <tr>\n",
    "       <td> **w** </td>\n",
    "       <td>[[ 0.1124579 ]\n",
    " [ 0.23106775]] </td>\n",
    "    </tr>\n",
    "    \n",
    "    <tr>\n",
    "       <td> **b** </td>\n",
    "       <td> 1.55930492484 </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "       <td> **dw** </td>\n",
    "       <td> [[ 0.90158428]\n",
    " [ 1.76250842]] </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "       <td> **db** </td>\n",
    "       <td> 0.430462071679 </td>\n",
    "    </tr>\n",
    "\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise:** The previous function will output the learned w and b. We are able to use w and b to predict the labels for a dataset X. Implement the `predict()` function. There is two steps to computing predictions:\n",
    "\n",
    "1. Calculate $\\hat{Y} = A = \\sigma(w^T X + b)$\n",
    "\n",
    "2. Convert the entries of a into 0 (if activation <= 0.5) or 1 (if activation > 0.5), stores the predictions in a vector `Y_prediction`. If you wish, you can use an `if`/`else` statement in a `for` loop (though there is also a way to vectorize this). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: predict\n",
    "\n",
    "def predict(w, b, X):\n",
    "    '''\n",
    "    Predict whether the label is 0 or 1 using learned logistic regression parameters (w, b)\n",
    "    \n",
    "    Arguments:\n",
    "    w -- weights, a numpy array of size (num_px * num_px * 3, 1)\n",
    "    b -- bias, a scalar\n",
    "    X -- data of size (num_px * num_px * 3, number of examples)\n",
    "    \n",
    "    Returns:\n",
    "    Y_prediction -- a numpy array (vector) containing all predictions (0/1) for the examples in X\n",
    "    '''\n",
    "    \n",
    "    m = X.shape[1]\n",
    "    Y_prediction = np.zeros((1,m))\n",
    "    w = w.reshape(X.shape[0], 1)\n",
    "    \n",
    "    # Compute vector \"A\" predicting the probabilities of a cat being present in the picture\n",
    "    ### START CODE HERE ### (≈ 1 line of code)\n",
    "    A = sigmoid(np.dot(w.T,X) +b)   \n",
    "    print(str(A))\n",
    "\n",
    "\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    for i in range(A.shape[1]):\n",
    "        \n",
    "        # Convert probabilities A[0,i] to actual predictions p[0,i]\n",
    "        ### START CODE HERE ### (≈ 4 lines of code)\n",
    "        \n",
    "        print(\"i:\" + str(i))\n",
    "        print(\"A: \" + str(A[0,i]))\n",
    "        if  A[0,i] >= 0.5:\n",
    "            Y_prediction[0,i] = 1\n",
    "        else:\n",
    "            Y_prediction[0,i] = 0\n",
    "        \n",
    "#         pass\n",
    "        ### END CODE HERE ###\n",
    "    \n",
    "    assert(Y_prediction.shape == (1, m))\n",
    "    \n",
    "    return Y_prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.99987661  0.99999386]]\n",
      "i:0\n",
      "A: 0.999876605424\n",
      "i:1\n",
      "A: 0.999993855825\n",
      "predictions = [[ 1.  1.]]\n"
     ]
    }
   ],
   "source": [
    "print (\"predictions = \" + str(predict(w, b, X)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**: \n",
    "\n",
    "<table style=\"width:30%\">\n",
    "    <tr>\n",
    "         <td>\n",
    "             **predictions**\n",
    "         </td>\n",
    "          <td>\n",
    "            [[ 1.  1.]]\n",
    "         </td>  \n",
    "   </tr>\n",
    "\n",
    "</table>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "<font color='blue'>\n",
    "**What to remember:**\n",
    "You've implemented several functions that:\n",
    "- Initialize (w,b)\n",
    "- Optimize the loss iteratively to learn parameters (w,b):\n",
    "    - computing the cost and its gradient \n",
    "    - updating the parameters using gradient descent\n",
    "- Use the learned (w,b) to predict the labels for a given set of examples"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5 - Merge all functions into a model ##\n",
    "\n",
    "You will now see how the overall model is structured by putting together all the building blocks (functions implemented in the previous parts) together, in the right order.\n",
    "\n",
    "**Exercise:** Implement the model function. Use the following notation:\n",
    "    - Y_prediction for your predictions on the test set\n",
    "    - Y_prediction_train for your predictions on the train set\n",
    "    - w, costs, grads for the outputs of optimize()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: model\n",
    "\n",
    "def model(X_train, Y_train, X_test, Y_test, num_iterations = 2000, learning_rate = 0.5, print_cost = False):\n",
    "    \"\"\"\n",
    "    Builds the logistic regression model by calling the function you've implemented previously\n",
    "    \n",
    "    Arguments:\n",
    "    X_train -- training set represented by a numpy array of shape (num_px * num_px * 3, m_train)\n",
    "    Y_train -- training labels represented by a numpy array (vector) of shape (1, m_train)\n",
    "    X_test -- test set represented by a numpy array of shape (num_px * num_px * 3, m_test)\n",
    "    Y_test -- test labels represented by a numpy array (vector) of shape (1, m_test)\n",
    "    num_iterations -- hyperparameter representing the number of iterations to optimize the parameters\n",
    "    learning_rate -- hyperparameter representing the learning rate used in the update rule of optimize()\n",
    "    print_cost -- Set to true to print the cost every 100 iterations\n",
    "    \n",
    "    Returns:\n",
    "    d -- dictionary containing information about the model.\n",
    "    \"\"\"\n",
    "    \n",
    "    ### START CODE HERE ###\n",
    "    \n",
    "    # initialize parameters with zeros (≈ 1 line of code)\n",
    "    print(\"train:\" + str((X_train).shape[1]))\n",
    "    w, b = initialize_with_zeros((X_train).shape[0])\n",
    "\n",
    "    # Gradient descent (≈ 1 line of code)\n",
    "\n",
    "    parameters, grads, costs = optimize(w, b, X_train, Y_train, num_iterations, learning_rate, print_cost)\n",
    "    \n",
    "    # Retrieve parameters w and b from dictionary \"parameters\"\n",
    "    w = parameters[\"w\"]\n",
    "    b = parameters[\"b\"]\n",
    "    \n",
    "    # Predict test/train set examples (≈ 2 lines of code)\n",
    "    Y_prediction_test = predict(w, b, X_test)\n",
    "    Y_prediction_train = predict(w, b, X_train)\n",
    "\n",
    "    ### END CODE HERE ###\n",
    "\n",
    "    # Print train/test Errors\n",
    "    print(\"train accuracy: {} %\".format(100 - np.mean(np.abs(Y_prediction_train - Y_train)) * 100))\n",
    "    print(\"test accuracy: {} %\".format(100 - np.mean(np.abs(Y_prediction_test - Y_test)) * 100))\n",
    "\n",
    "    \n",
    "    d = {\"costs\": costs,\n",
    "         \"Y_prediction_test\": Y_prediction_test, \n",
    "         \"Y_prediction_train\" : Y_prediction_train, \n",
    "         \"w\" : w, \n",
    "         \"b\" : b,\n",
    "         \"learning_rate\" : learning_rate,\n",
    "         \"num_iterations\": num_iterations}\n",
    "    \n",
    "    return d"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the following cell to train your model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train:209\n",
      "Cost after iteration 0: 0.693147\n",
      "Cost after iteration 100: 0.584508\n",
      "Cost after iteration 200: 0.466949\n",
      "Cost after iteration 300: 0.376007\n",
      "Cost after iteration 400: 0.331463\n",
      "Cost after iteration 500: 0.303273\n",
      "Cost after iteration 600: 0.279880\n",
      "Cost after iteration 700: 0.260042\n",
      "Cost after iteration 800: 0.242941\n",
      "Cost after iteration 900: 0.228004\n",
      "Cost after iteration 1000: 0.214820\n",
      "Cost after iteration 1100: 0.203078\n",
      "Cost after iteration 1200: 0.192544\n",
      "Cost after iteration 1300: 0.183033\n",
      "Cost after iteration 1400: 0.174399\n",
      "Cost after iteration 1500: 0.166521\n",
      "Cost after iteration 1600: 0.159305\n",
      "Cost after iteration 1700: 0.152667\n",
      "Cost after iteration 1800: 0.146542\n",
      "Cost after iteration 1900: 0.140872\n",
      "[[ 0.94366988  0.86095311  0.88896715  0.93630641  0.74075403  0.52849619\n",
      "   0.03094677  0.85707681  0.88457925  0.67279696  0.26601085  0.4823794\n",
      "   0.74741157  0.78575729  0.00978911  0.9203284   0.02453695  0.84884703\n",
      "   0.2050248   0.03703224  0.92931392  0.11930532  0.01411064  0.7832698\n",
      "   0.58188015  0.66897565  0.75119007  0.01323558  0.03402649  0.99735115\n",
      "   0.21031727  0.78123225  0.6815842   0.46647604  0.66323375  0.03424828\n",
      "   0.08031627  0.76570656  0.34760863  0.06177743  0.6987531   0.4106426\n",
      "   0.6648871   0.02776868  0.93053125  0.46395717  0.23971605  0.9771735\n",
      "   0.66202407  0.10482388]]\n",
      "i:0\n",
      "A: 0.943669883037\n",
      "i:1\n",
      "A: 0.860953109115\n",
      "i:2\n",
      "A: 0.888967146282\n",
      "i:3\n",
      "A: 0.936306405779\n",
      "i:4\n",
      "A: 0.740754025859\n",
      "i:5\n",
      "A: 0.52849618914\n",
      "i:6\n",
      "A: 0.0309467721866\n",
      "i:7\n",
      "A: 0.857076806201\n",
      "i:8\n",
      "A: 0.884579253245\n",
      "i:9\n",
      "A: 0.672796964927\n",
      "i:10\n",
      "A: 0.266010848242\n",
      "i:11\n",
      "A: 0.482379403768\n",
      "i:12\n",
      "A: 0.747411572177\n",
      "i:13\n",
      "A: 0.785757292125\n",
      "i:14\n",
      "A: 0.00978911289155\n",
      "i:15\n",
      "A: 0.920328402062\n",
      "i:16\n",
      "A: 0.0245369486329\n",
      "i:17\n",
      "A: 0.848847025937\n",
      "i:18\n",
      "A: 0.205024799895\n",
      "i:19\n",
      "A: 0.0370322425215\n",
      "i:20\n",
      "A: 0.929313919017\n",
      "i:21\n",
      "A: 0.119305321725\n",
      "i:22\n",
      "A: 0.0141106367211\n",
      "i:23\n",
      "A: 0.78326980048\n",
      "i:24\n",
      "A: 0.581880145311\n",
      "i:25\n",
      "A: 0.668975653851\n",
      "i:26\n",
      "A: 0.751190072162\n",
      "i:27\n",
      "A: 0.0132355782082\n",
      "i:28\n",
      "A: 0.0340264868717\n",
      "i:29\n",
      "A: 0.99735115101\n",
      "i:30\n",
      "A: 0.210317267806\n",
      "i:31\n",
      "A: 0.781232251961\n",
      "i:32\n",
      "A: 0.681584197469\n",
      "i:33\n",
      "A: 0.46647604129\n",
      "i:34\n",
      "A: 0.663233749481\n",
      "i:35\n",
      "A: 0.0342482819959\n",
      "i:36\n",
      "A: 0.0803162675058\n",
      "i:37\n",
      "A: 0.765706564259\n",
      "i:38\n",
      "A: 0.347608633813\n",
      "i:39\n",
      "A: 0.0617774281286\n",
      "i:40\n",
      "A: 0.698753102456\n",
      "i:41\n",
      "A: 0.410642596163\n",
      "i:42\n",
      "A: 0.664887097525\n",
      "i:43\n",
      "A: 0.027768680452\n",
      "i:44\n",
      "A: 0.93053125077\n",
      "i:45\n",
      "A: 0.463957169969\n",
      "i:46\n",
      "A: 0.239716051853\n",
      "i:47\n",
      "A: 0.977173499684\n",
      "i:48\n",
      "A: 0.662024066584\n",
      "i:49\n",
      "A: 0.104823883597\n",
      "[[  1.96533335e-01   8.97519936e-02   8.90887727e-01   2.05354859e-04\n",
      "    4.10043201e-02   1.13855541e-01   3.58425358e-02   9.20256043e-01\n",
      "    8.11815498e-02   5.09505652e-02   1.43687735e-01   7.77661312e-01\n",
      "    2.37002682e-01   9.26822611e-01   7.20256211e-01   4.54525029e-02\n",
      "    2.88164240e-02   4.96209946e-02   9.53642451e-02   9.27127783e-01\n",
      "    1.46871713e-02   4.42749993e-02   1.99658284e-01   5.10794145e-02\n",
      "    8.71854257e-01   8.54873232e-01   4.43988460e-02   8.41877286e-01\n",
      "    5.57178266e-02   7.39175253e-01   8.73390575e-02   7.61255429e-02\n",
      "    2.01282223e-01   2.02159519e-01   7.95065561e-02   3.69885691e-02\n",
      "    1.14655638e-02   5.90397260e-02   8.36880946e-01   3.33057415e-01\n",
      "    1.98548242e-02   4.46965063e-01   8.23737950e-01   4.13465923e-02\n",
      "    4.61512591e-02   1.21739845e-01   9.76716144e-02   8.07086225e-01\n",
      "    8.93389416e-03   3.73249849e-02   7.53711249e-01   2.47934596e-01\n",
      "    1.47013078e-01   3.93089594e-01   9.02530607e-01   3.94290174e-03\n",
      "    9.38300399e-01   8.14429890e-01   5.51201724e-02   9.56820776e-01\n",
      "    8.35826040e-01   7.75371183e-01   4.97406386e-02   5.05302748e-02\n",
      "    1.68276426e-01   7.39795683e-02   4.23114248e-02   1.80374321e-01\n",
      "    7.36839673e-01   2.36170561e-02   4.78407244e-02   9.72682719e-01\n",
      "    8.87430447e-02   1.40500115e-01   7.39006094e-02   5.87414480e-01\n",
      "    8.55122639e-04   3.51320419e-02   7.21341360e-02   1.59367000e-01\n",
      "    9.18793718e-02   2.76678199e-03   2.16954763e-02   8.75788002e-01\n",
      "    7.48905473e-01   2.61224310e-02   1.31264831e-01   5.58549892e-02\n",
      "    7.96470422e-01   4.31114360e-02   2.46081640e-01   9.28094796e-02\n",
      "    5.13207713e-01   9.23532733e-01   9.11010943e-01   1.56664277e-01\n",
      "    1.40529680e-01   8.72871654e-01   6.33390909e-02   2.04276699e-01\n",
      "    1.50378528e-01   5.42005811e-02   7.16869008e-01   8.93930822e-02\n",
      "    9.68748123e-01   1.16897229e-01   9.65813244e-01   7.63463753e-01\n",
      "    8.45184245e-01   7.94804824e-01   8.77046596e-01   8.92528474e-01\n",
      "    2.33698759e-02   1.08088606e-01   9.41045938e-02   5.06133571e-02\n",
      "    6.14255764e-02   8.74814031e-01   7.14021606e-03   1.49573407e-01\n",
      "    1.38752636e-02   5.75050572e-01   4.74218632e-02   2.67728414e-04\n",
      "    8.16437270e-01   5.25431990e-03   8.27320337e-01   1.63520986e-01\n",
      "    9.19597717e-01   9.11124533e-01   2.96731271e-01   1.37316359e-01\n",
      "    7.56632692e-02   9.51896490e-01   7.13340131e-01   5.62771203e-01\n",
      "    8.46803645e-01   8.81283783e-01   5.80214923e-03   3.24191787e-02\n",
      "    3.66569448e-02   4.24241240e-02   9.02746461e-01   6.95602248e-03\n",
      "    7.28528692e-01   8.04734016e-01   8.48847026e-01   1.97286016e-01\n",
      "    8.73972266e-01   8.56810568e-01   4.60108117e-01   9.98074787e-02\n",
      "    2.67726747e-02   9.16713593e-01   5.70477051e-02   2.34413956e-01\n",
      "    9.17441504e-01   1.43642340e-02   1.48384241e-02   4.18971050e-02\n",
      "    4.81257763e-03   6.74987512e-02   7.96958661e-01   7.94548221e-02\n",
      "    8.88055227e-01   1.63703299e-02   9.64896262e-01   4.74597209e-02\n",
      "    3.78354422e-02   6.75950812e-01   7.60983832e-01   8.91154251e-01\n",
      "    2.15482871e-01   1.80695199e-02   9.46591763e-01   7.71101522e-01\n",
      "    4.14565207e-02   8.02916154e-01   8.02541805e-02   6.89037478e-01\n",
      "    4.92103989e-02   4.87010785e-02   1.89987579e-02   3.71043577e-02\n",
      "    1.73595068e-03   7.71575747e-01   4.06433366e-02   6.60606392e-02\n",
      "    8.34508562e-01   2.27408842e-02   6.17839573e-02   4.56149270e-02\n",
      "    7.64947622e-01   6.19347921e-02   3.55887869e-03   1.03103435e-01\n",
      "    3.83745905e-01   8.77909931e-01   7.72818586e-02   1.79082665e-02\n",
      "    8.09911232e-01   2.02130387e-02   1.89353139e-02   1.83142729e-02\n",
      "    1.94041166e-01   2.01151983e-01   8.48224028e-02   1.61929290e-01\n",
      "    1.82858623e-01]]\n",
      "i:0\n",
      "A: 0.196533334541\n",
      "i:1\n",
      "A: 0.0897519936268\n",
      "i:2\n",
      "A: 0.890887727474\n",
      "i:3\n",
      "A: 0.000205354859433\n",
      "i:4\n",
      "A: 0.0410043201439\n",
      "i:5\n",
      "A: 0.113855540876\n",
      "i:6\n",
      "A: 0.0358425357885\n",
      "i:7\n",
      "A: 0.920256043198\n",
      "i:8\n",
      "A: 0.0811815498001\n",
      "i:9\n",
      "A: 0.0509505652208\n",
      "i:10\n",
      "A: 0.143687735203\n",
      "i:11\n",
      "A: 0.777661312361\n",
      "i:12\n",
      "A: 0.237002682094\n",
      "i:13\n",
      "A: 0.926822611105\n",
      "i:14\n",
      "A: 0.72025621109\n",
      "i:15\n",
      "A: 0.0454525029252\n",
      "i:16\n",
      "A: 0.0288164240464\n",
      "i:17\n",
      "A: 0.0496209946458\n",
      "i:18\n",
      "A: 0.0953642451386\n",
      "i:19\n",
      "A: 0.927127783428\n",
      "i:20\n",
      "A: 0.0146871712678\n",
      "i:21\n",
      "A: 0.0442749993337\n",
      "i:22\n",
      "A: 0.199658283522\n",
      "i:23\n",
      "A: 0.0510794145213\n",
      "i:24\n",
      "A: 0.871854256842\n",
      "i:25\n",
      "A: 0.854873231574\n",
      "i:26\n",
      "A: 0.04439884597\n",
      "i:27\n",
      "A: 0.841877286131\n",
      "i:28\n",
      "A: 0.0557178266403\n",
      "i:29\n",
      "A: 0.739175253329\n",
      "i:30\n",
      "A: 0.0873390574633\n",
      "i:31\n",
      "A: 0.0761255429111\n",
      "i:32\n",
      "A: 0.20128222293\n",
      "i:33\n",
      "A: 0.202159518561\n",
      "i:34\n",
      "A: 0.0795065560525\n",
      "i:35\n",
      "A: 0.0369885690797\n",
      "i:36\n",
      "A: 0.0114655638089\n",
      "i:37\n",
      "A: 0.0590397259525\n",
      "i:38\n",
      "A: 0.836880946425\n",
      "i:39\n",
      "A: 0.3330574153\n",
      "i:40\n",
      "A: 0.0198548242476\n",
      "i:41\n",
      "A: 0.446965062808\n",
      "i:42\n",
      "A: 0.823737949842\n",
      "i:43\n",
      "A: 0.0413465922547\n",
      "i:44\n",
      "A: 0.0461512591366\n",
      "i:45\n",
      "A: 0.121739845282\n",
      "i:46\n",
      "A: 0.0976716143784\n",
      "i:47\n",
      "A: 0.80708622519\n",
      "i:48\n",
      "A: 0.00893389415668\n",
      "i:49\n",
      "A: 0.0373249849496\n",
      "i:50\n",
      "A: 0.75371124924\n",
      "i:51\n",
      "A: 0.247934595648\n",
      "i:52\n",
      "A: 0.147013078493\n",
      "i:53\n",
      "A: 0.393089593865\n",
      "i:54\n",
      "A: 0.902530607222\n",
      "i:55\n",
      "A: 0.00394290174169\n",
      "i:56\n",
      "A: 0.938300399367\n",
      "i:57\n",
      "A: 0.814429890181\n",
      "i:58\n",
      "A: 0.055120172398\n",
      "i:59\n",
      "A: 0.956820776377\n",
      "i:60\n",
      "A: 0.835826040001\n",
      "i:61\n",
      "A: 0.775371182949\n",
      "i:62\n",
      "A: 0.0497406386247\n",
      "i:63\n",
      "A: 0.0505302747597\n",
      "i:64\n",
      "A: 0.168276426306\n",
      "i:65\n",
      "A: 0.0739795682923\n",
      "i:66\n",
      "A: 0.042311424831\n",
      "i:67\n",
      "A: 0.180374321018\n",
      "i:68\n",
      "A: 0.736839672605\n",
      "i:69\n",
      "A: 0.0236170561057\n",
      "i:70\n",
      "A: 0.0478407243787\n",
      "i:71\n",
      "A: 0.972682718636\n",
      "i:72\n",
      "A: 0.0887430446708\n",
      "i:73\n",
      "A: 0.140500114962\n",
      "i:74\n",
      "A: 0.0739006093891\n",
      "i:75\n",
      "A: 0.5874144802\n",
      "i:76\n",
      "A: 0.000855122638727\n",
      "i:77\n",
      "A: 0.0351320418531\n",
      "i:78\n",
      "A: 0.0721341360291\n",
      "i:79\n",
      "A: 0.159366999937\n",
      "i:80\n",
      "A: 0.0918793718381\n",
      "i:81\n",
      "A: 0.00276678199106\n",
      "i:82\n",
      "A: 0.021695476285\n",
      "i:83\n",
      "A: 0.87578800205\n",
      "i:84\n",
      "A: 0.748905473309\n",
      "i:85\n",
      "A: 0.0261224310227\n",
      "i:86\n",
      "A: 0.131264831035\n",
      "i:87\n",
      "A: 0.0558549892378\n",
      "i:88\n",
      "A: 0.796470422333\n",
      "i:89\n",
      "A: 0.0431114359531\n",
      "i:90\n",
      "A: 0.246081639848\n",
      "i:91\n",
      "A: 0.0928094795604\n",
      "i:92\n",
      "A: 0.513207713097\n",
      "i:93\n",
      "A: 0.923532732924\n",
      "i:94\n",
      "A: 0.911010943431\n",
      "i:95\n",
      "A: 0.156664276627\n",
      "i:96\n",
      "A: 0.140529679714\n",
      "i:97\n",
      "A: 0.87287165434\n",
      "i:98\n",
      "A: 0.0633390909289\n",
      "i:99\n",
      "A: 0.204276699318\n",
      "i:100\n",
      "A: 0.150378527905\n",
      "i:101\n",
      "A: 0.0542005810629\n",
      "i:102\n",
      "A: 0.716869007765\n",
      "i:103\n",
      "A: 0.0893930822448\n",
      "i:104\n",
      "A: 0.968748123497\n",
      "i:105\n",
      "A: 0.116897228581\n",
      "i:106\n",
      "A: 0.965813243983\n",
      "i:107\n",
      "A: 0.763463752556\n",
      "i:108\n",
      "A: 0.845184244949\n",
      "i:109\n",
      "A: 0.794804824113\n",
      "i:110\n",
      "A: 0.877046596005\n",
      "i:111\n",
      "A: 0.892528474405\n",
      "i:112\n",
      "A: 0.0233698758989\n",
      "i:113\n",
      "A: 0.108088605846\n",
      "i:114\n",
      "A: 0.0941045937542\n",
      "i:115\n",
      "A: 0.0506133571373\n",
      "i:116\n",
      "A: 0.0614255763597\n",
      "i:117\n",
      "A: 0.87481403098\n",
      "i:118\n",
      "A: 0.00714021605766\n",
      "i:119\n",
      "A: 0.149573406858\n",
      "i:120\n",
      "A: 0.0138752636059\n",
      "i:121\n",
      "A: 0.575050571748\n",
      "i:122\n",
      "A: 0.0474218632429\n",
      "i:123\n",
      "A: 0.000267728414106\n",
      "i:124\n",
      "A: 0.816437269769\n",
      "i:125\n",
      "A: 0.00525431989649\n",
      "i:126\n",
      "A: 0.827320336607\n",
      "i:127\n",
      "A: 0.163520986401\n",
      "i:128\n",
      "A: 0.919597717005\n",
      "i:129\n",
      "A: 0.911124533376\n",
      "i:130\n",
      "A: 0.296731270777\n",
      "i:131\n",
      "A: 0.137316359084\n",
      "i:132\n",
      "A: 0.0756632692354\n",
      "i:133\n",
      "A: 0.951896489829\n",
      "i:134\n",
      "A: 0.713340130775\n",
      "i:135\n",
      "A: 0.562771203204\n",
      "i:136\n",
      "A: 0.846803644789\n",
      "i:137\n",
      "A: 0.881283782679\n",
      "i:138\n",
      "A: 0.00580214922933\n",
      "i:139\n",
      "A: 0.0324191786817\n",
      "i:140\n",
      "A: 0.0366569447942\n",
      "i:141\n",
      "A: 0.0424241239502\n",
      "i:142\n",
      "A: 0.902746460696\n",
      "i:143\n",
      "A: 0.00695602248135\n",
      "i:144\n",
      "A: 0.728528691703\n",
      "i:145\n",
      "A: 0.804734015837\n",
      "i:146\n",
      "A: 0.848847025937\n",
      "i:147\n",
      "A: 0.197286016452\n",
      "i:148\n",
      "A: 0.873972266047\n",
      "i:149\n",
      "A: 0.856810568159\n",
      "i:150\n",
      "A: 0.460108116782\n",
      "i:151\n",
      "A: 0.0998074786541\n",
      "i:152\n",
      "A: 0.0267726747262\n",
      "i:153\n",
      "A: 0.916713593035\n",
      "i:154\n",
      "A: 0.0570477051106\n",
      "i:155\n",
      "A: 0.234413955649\n",
      "i:156\n",
      "A: 0.917441503887\n",
      "i:157\n",
      "A: 0.0143642340159\n",
      "i:158\n",
      "A: 0.0148384241275\n",
      "i:159\n",
      "A: 0.0418971050109\n",
      "i:160\n",
      "A: 0.00481257763182\n",
      "i:161\n",
      "A: 0.0674987512492\n",
      "i:162\n",
      "A: 0.796958661142\n",
      "i:163\n",
      "A: 0.079454822094\n",
      "i:164\n",
      "A: 0.888055227152\n",
      "i:165\n",
      "A: 0.0163703299375\n",
      "i:166\n",
      "A: 0.964896261509\n",
      "i:167\n",
      "A: 0.0474597208662\n",
      "i:168\n",
      "A: 0.0378354422071\n",
      "i:169\n",
      "A: 0.675950811676\n",
      "i:170\n",
      "A: 0.760983832376\n",
      "i:171\n",
      "A: 0.891154250811\n",
      "i:172\n",
      "A: 0.215482870741\n",
      "i:173\n",
      "A: 0.0180695199403\n",
      "i:174\n",
      "A: 0.946591762594\n",
      "i:175\n",
      "A: 0.771101522265\n",
      "i:176\n",
      "A: 0.0414565207084\n",
      "i:177\n",
      "A: 0.802916154466\n",
      "i:178\n",
      "A: 0.0802541805344\n",
      "i:179\n",
      "A: 0.689037478175\n",
      "i:180\n",
      "A: 0.049210398866\n",
      "i:181\n",
      "A: 0.0487010785315\n",
      "i:182\n",
      "A: 0.0189987579325\n",
      "i:183\n",
      "A: 0.0371043576623\n",
      "i:184\n",
      "A: 0.0017359506795\n",
      "i:185\n",
      "A: 0.771575747438\n",
      "i:186\n",
      "A: 0.0406433365961\n",
      "i:187\n",
      "A: 0.0660606392318\n",
      "i:188\n",
      "A: 0.834508561546\n",
      "i:189\n",
      "A: 0.0227408842473\n",
      "i:190\n",
      "A: 0.0617839572924\n",
      "i:191\n",
      "A: 0.0456149270445\n",
      "i:192\n",
      "A: 0.764947622175\n",
      "i:193\n",
      "A: 0.0619347921164\n",
      "i:194\n",
      "A: 0.00355887869246\n",
      "i:195\n",
      "A: 0.103103435065\n",
      "i:196\n",
      "A: 0.383745904649\n",
      "i:197\n",
      "A: 0.877909931299\n",
      "i:198\n",
      "A: 0.0772818585526\n",
      "i:199\n",
      "A: 0.0179082665371\n",
      "i:200\n",
      "A: 0.809911232023\n",
      "i:201\n",
      "A: 0.0202130387116\n",
      "i:202\n",
      "A: 0.0189353139367\n",
      "i:203\n",
      "A: 0.0183142728643\n",
      "i:204\n",
      "A: 0.194041165885\n",
      "i:205\n",
      "A: 0.201151983011\n",
      "i:206\n",
      "A: 0.084822402776\n",
      "i:207\n",
      "A: 0.161929290039\n",
      "i:208\n",
      "A: 0.182858623315\n",
      "train accuracy: 99.04306220095694 %\n",
      "test accuracy: 70.0 %\n"
     ]
    }
   ],
   "source": [
    "d = model(train_set_x, train_set_y, test_set_x, test_set_y, num_iterations = 2000, learning_rate = 0.005, print_cost = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**: \n",
    "\n",
    "<table style=\"width:40%\"> \n",
    "    \n",
    "    <tr>\n",
    "        <td> **Train Accuracy**  </td> \n",
    "        <td> 99.04306220095694 % </td>\n",
    "    </tr>\n",
    "\n",
    "    <tr>\n",
    "        <td>**Test Accuracy** </td> \n",
    "        <td> 70.0 % </td>\n",
    "    </tr>\n",
    "</table> \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "**Comment**: Training accuracy is close to 100%. This is a good sanity check: your model is working and has high enough capacity to fit the training data. Test error is 68%. It is actually not bad for this simple model, given the small dataset we used and that logistic regression is a linear classifier. But no worries, you'll build an even better classifier next week!\n",
    "\n",
    "Also, you see that the model is clearly overfitting the training data. Later in this specialization you will learn how to reduce overfitting, for example by using regularization. Using the code below (and changing the `index` variable) you can look at predictions on pictures of the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.5/site-packages/ipykernel/__main__.py:4: DeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y = 1, you predicted that it is a \"cat\" picture.\n"
     ]
    },
    {
     "data": {
      "image/png": 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zhgYDhm2nkwyY5vXTt9XVIXvsp/LeebSIvU/suKwX7JcV63o5gKgAwXijbFs1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cVkuoAxEOtnBWwdyCE96Vv018DUfkM0pqfUeWFj4uzXiAVFv5Ox8LdsFAdN2Nt1AZxiShgjZ69A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\n8FnOcpaz3KGcQfgsZznLWe5Q/hVcokhRgy2SdQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6a79034e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Example of a picture that was wrongly classified.\n",
    "index = 1\n",
    "plt.imshow(test_set_x[:,index].reshape((num_px, num_px, 3)))\n",
    "print (\"y = \" + str(test_set_y[0,index]) + \", you predicted that it is a \\\"\" + classes[d[\"Y_prediction_test\"][0,index]].decode(\"utf-8\") +  \"\\\" picture.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's also plot the cost function and the gradients."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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4VENhIeyyizqZioiI1JWCRzUNGwYTJsDHH8ddiYiISP5S8KimY4+FDTcMfT1ERESkdhQ8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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6a6a738dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot learning curve (with costs)\n",
    "costs = np.squeeze(d['costs'])\n",
    "plt.plot(costs)\n",
    "plt.ylabel('cost')\n",
    "plt.xlabel('iterations (per hundreds)')\n",
    "plt.title(\"Learning rate =\" + str(d[\"learning_rate\"]))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "**Interpretation**:\n",
    "You can see the cost decreasing. It shows that the parameters are being learned. However, you see that you could train the model even more on the training set. Try to increase the number of iterations in the cell above and rerun the cells. You might see that the training set accuracy goes up, but the test set accuracy goes down. This is called overfitting. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6 - Further analysis (optional/ungraded exercise) ##\n",
    "\n",
    "Congratulations on building your first image classification model. Let's analyze it further, and examine possible choices for the learning rate $\\alpha$. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Choice of learning rate ####\n",
    "\n",
    "**Reminder**:\n",
    "In order for Gradient Descent to work you must choose the learning rate wisely. The learning rate $\\alpha$  determines how rapidly we update the parameters. If the learning rate is too large we may \"overshoot\" the optimal value. Similarly, if it is too small we will need too many iterations to converge to the best values. That's why it is crucial to use a well-tuned learning rate.\n",
    "\n",
    "Let's compare the learning curve of our model with several choices of learning rates. Run the cell below. This should take about 1 minute. Feel free also to try different values than the three we have initialized the `learning_rates` variable to contain, and see what happens. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "learning rate is: 0.01\n",
      "train:209\n",
      "[[ 0.97125943  0.9155338   0.92079132  0.96358044  0.78924234  0.60411297\n",
      "   0.01179527  0.89814048  0.91522859  0.70264065  0.19380387  0.49537355\n",
      "   0.7927164   0.85423431  0.00298587  0.96199699  0.01234735  0.9107653\n",
      "   0.13661137  0.01424336  0.96894735  0.1033746   0.00579297  0.86081326\n",
      "   0.53811196  0.64950178  0.83272843  0.00426307  0.0131452   0.99947804\n",
      "   0.11468372  0.82182442  0.69611733  0.4991522   0.67231401  0.01728165\n",
      "   0.04136099  0.80069693  0.26832359  0.03958566  0.74731239  0.32116434\n",
      "   0.71871197  0.01205725  0.96879962  0.62310364  0.17737126  0.98960523\n",
      "   0.74697265  0.07284605]]\n",
      "i:0\n",
      "A: 0.971259432897\n",
      "i:1\n",
      "A: 0.915533803164\n",
      "i:2\n",
      "A: 0.920791322583\n",
      "i:3\n",
      "A: 0.963580438044\n",
      "i:4\n",
      "A: 0.789242340058\n",
      "i:5\n",
      "A: 0.604112973286\n",
      "i:6\n",
      "A: 0.0117952701474\n",
      "i:7\n",
      "A: 0.898140483902\n",
      "i:8\n",
      "A: 0.915228587661\n",
      "i:9\n",
      "A: 0.702640652181\n",
      "i:10\n",
      "A: 0.193803872038\n",
      "i:11\n",
      "A: 0.49537354738\n",
      "i:12\n",
      "A: 0.792716401525\n",
      "i:13\n",
      "A: 0.854234311104\n",
      "i:14\n",
      "A: 0.00298586811586\n",
      "i:15\n",
      "A: 0.961996990004\n",
      "i:16\n",
      "A: 0.0123473506018\n",
      "i:17\n",
      "A: 0.910765300344\n",
      "i:18\n",
      "A: 0.136611374737\n",
      "i:19\n",
      "A: 0.0142433589108\n",
      "i:20\n",
      "A: 0.968947351329\n",
      "i:21\n",
      "A: 0.103374604214\n",
      "i:22\n",
      "A: 0.00579296654057\n",
      "i:23\n",
      "A: 0.860813263253\n",
      "i:24\n",
      "A: 0.538111961824\n",
      "i:25\n",
      "A: 0.64950178046\n",
      "i:26\n",
      "A: 0.832728425431\n",
      "i:27\n",
      "A: 0.00426306908855\n",
      "i:28\n",
      "A: 0.0131452045175\n",
      "i:29\n",
      "A: 0.999478040806\n",
      "i:30\n",
      "A: 0.114683722241\n",
      "i:31\n",
      "A: 0.82182442149\n",
      "i:32\n",
      "A: 0.696117331469\n",
      "i:33\n",
      "A: 0.499152201814\n",
      "i:34\n",
      "A: 0.672314012143\n",
      "i:35\n",
      "A: 0.0172816461337\n",
      "i:36\n",
      "A: 0.0413609885942\n",
      "i:37\n",
      "A: 0.800696929572\n",
      "i:38\n",
      "A: 0.268323585456\n",
      "i:39\n",
      "A: 0.0395856591359\n",
      "i:40\n",
      "A: 0.747312393792\n",
      "i:41\n",
      "A: 0.321164344101\n",
      "i:42\n",
      "A: 0.718711969053\n",
      "i:43\n",
      "A: 0.0120572513215\n",
      "i:44\n",
      "A: 0.968799624867\n",
      "i:45\n",
      "A: 0.623103644575\n",
      "i:46\n",
      "A: 0.177371258308\n",
      "i:47\n",
      "A: 0.989605232896\n",
      "i:48\n",
      "A: 0.746972654513\n",
      "i:49\n",
      "A: 0.0728460530202\n",
      "[[  1.47839653e-01   5.78008189e-02   9.42385025e-01   4.14849242e-05\n",
      "    2.27209941e-02   7.29254667e-02   2.23704495e-02   9.49717864e-01\n",
      "    5.41724297e-02   2.92729896e-02   6.82412299e-02   8.33370210e-01\n",
      "    1.71420615e-01   9.66879883e-01   8.11537151e-01   2.44343486e-02\n",
      "    7.87634096e-03   2.64027273e-02   5.60720048e-02   9.53130353e-01\n",
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      "    6.58017001e-01   9.54697511e-01   9.62775471e-01   1.05372217e-01\n",
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      "    1.78308410e-03   2.57658926e-02   8.63787547e-01   3.44218953e-02\n",
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      "    1.48599255e-01   4.17198955e-03   9.81043189e-01   8.22764984e-01\n",
      "    1.92120395e-02   8.58870443e-01   5.37478572e-02   7.84878423e-01\n",
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      "    1.26009269e-01   1.46124056e-01   4.51019669e-02   1.45139959e-01\n",
      "    1.45971589e-01]]\n",
      "i:0\n",
      "A: 0.147839653497\n",
      "i:1\n",
      "A: 0.0578008189201\n",
      "i:2\n",
      "A: 0.942385024882\n",
      "i:3\n",
      "A: 4.1484924216e-05\n",
      "i:4\n",
      "A: 0.022720994056\n",
      "i:5\n",
      "A: 0.0729254667489\n",
      "i:6\n",
      "A: 0.0223704495276\n",
      "i:7\n",
      "A: 0.949717863643\n",
      "i:8\n",
      "A: 0.0541724297265\n",
      "i:9\n",
      "A: 0.0292729895593\n",
      "i:10\n",
      "A: 0.0682412299001\n",
      "i:11\n",
      "A: 0.833370210144\n",
      "i:12\n",
      "A: 0.171420615388\n",
      "i:13\n",
      "A: 0.966879883354\n",
      "i:14\n",
      "A: 0.811537151305\n",
      "i:15\n",
      "A: 0.0244343486161\n",
      "i:16\n",
      "A: 0.00787634096236\n",
      "i:17\n",
      "A: 0.0264027273298\n",
      "i:18\n",
      "A: 0.0560720047801\n",
      "i:19\n",
      "A: 0.953130353083\n",
      "i:20\n",
      "A: 0.00530865327065\n",
      "i:21\n",
      "A: 0.0311020746724\n",
      "i:22\n",
      "A: 0.143606492578\n",
      "i:23\n",
      "A: 0.0192650471603\n",
      "i:24\n",
      "A: 0.9301327983\n",
      "i:25\n",
      "A: 0.895291210891\n",
      "i:26\n",
      "A: 0.0272790550776\n",
      "i:27\n",
      "A: 0.901480141535\n",
      "i:28\n",
      "A: 0.0273987902513\n",
      "i:29\n",
      "A: 0.809041583094\n",
      "i:30\n",
      "A: 0.0664266070059\n",
      "i:31\n",
      "A: 0.050047973005\n",
      "i:32\n",
      "A: 0.129245158465\n",
      "i:33\n",
      "A: 0.140274640081\n",
      "i:34\n",
      "A: 0.0648179131548\n",
      "i:35\n",
      "A: 0.0135261337\n",
      "i:36\n",
      "A: 0.00477693626028\n",
      "i:37\n",
      "A: 0.0265922708558\n",
      "i:38\n",
      "A: 0.889771229884\n",
      "i:39\n",
      "A: 0.264826222156\n",
      "i:40\n",
      "A: 0.0122921585922\n",
      "i:41\n",
      "A: 0.603229152515\n",
      "i:42\n",
      "A: 0.881822076384\n",
      "i:43\n",
      "A: 0.0135079742732\n",
      "i:44\n",
      "A: 0.0249595285717\n",
      "i:45\n",
      "A: 0.068896112912\n",
      "i:46\n",
      "A: 0.0586046928733\n",
      "i:47\n",
      "A: 0.868932415466\n",
      "i:48\n",
      "A: 0.00514520332187\n",
      "i:49\n",
      "A: 0.012109984483\n",
      "i:50\n",
      "A: 0.823403970205\n",
      "i:51\n",
      "A: 0.170985647426\n",
      "i:52\n",
      "A: 0.094997756331\n",
      "i:53\n",
      "A: 0.304227659618\n",
      "i:54\n",
      "A: 0.948091297867\n",
      "i:55\n",
      "A: 0.000809204742424\n",
      "i:56\n",
      "A: 0.966640038357\n",
      "i:57\n",
      "A: 0.878319465701\n",
      "i:58\n",
      "A: 0.031728488072\n",
      "i:59\n",
      "A: 0.976165699796\n",
      "i:60\n",
      "A: 0.881584697193\n",
      "i:61\n",
      "A: 0.848145722206\n",
      "i:62\n",
      "A: 0.0270795161295\n",
      "i:63\n",
      "A: 0.0228390395479\n",
      "i:64\n",
      "A: 0.105295676019\n",
      "i:65\n",
      "A: 0.044516529171\n",
      "i:66\n",
      "A: 0.0122858875619\n",
      "i:67\n",
      "A: 0.135813814148\n",
      "i:68\n",
      "A: 0.825867436874\n",
      "i:69\n",
      "A: 0.00921552659361\n",
      "i:70\n",
      "A: 0.024935383134\n",
      "i:71\n",
      "A: 0.988067069902\n",
      "i:72\n",
      "A: 0.0578381494764\n",
      "i:73\n",
      "A: 0.0857292849777\n",
      "i:74\n",
      "A: 0.0410128551211\n",
      "i:75\n",
      "A: 0.57050795563\n",
      "i:76\n",
      "A: 0.000211603230112\n",
      "i:77\n",
      "A: 0.0152264722723\n",
      "i:78\n",
      "A: 0.0618390723226\n",
      "i:79\n",
      "A: 0.139187810143\n",
      "i:80\n",
      "A: 0.066899374931\n",
      "i:81\n",
      "A: 0.00041428178709\n",
      "i:82\n",
      "A: 0.0123347660867\n",
      "i:83\n",
      "A: 0.924789061664\n",
      "i:84\n",
      "A: 0.816880995054\n",
      "i:85\n",
      "A: 0.00929503652717\n",
      "i:86\n",
      "A: 0.082377089186\n",
      "i:87\n",
      "A: 0.0275905819842\n",
      "i:88\n",
      "A: 0.852215780853\n",
      "i:89\n",
      "A: 0.0236580782532\n",
      "i:90\n",
      "A: 0.175344552433\n",
      "i:91\n",
      "A: 0.0615499363326\n",
      "i:92\n",
      "A: 0.658017000559\n",
      "i:93\n",
      "A: 0.954697510916\n",
      "i:94\n",
      "A: 0.962775470987\n",
      "i:95\n",
      "A: 0.105372216671\n",
      "i:96\n",
      "A: 0.0937239410454\n",
      "i:97\n",
      "A: 0.92906226552\n",
      "i:98\n",
      "A: 0.0268654455075\n",
      "i:99\n",
      "A: 0.144668290165\n",
      "i:100\n",
      "A: 0.0915662945175\n",
      "i:101\n",
      "A: 0.0289260930778\n",
      "i:102\n",
      "A: 0.80260313254\n",
      "i:103\n",
      "A: 0.0611847788494\n",
      "i:104\n",
      "A: 0.987937140507\n",
      "i:105\n",
      "A: 0.0584677166564\n",
      "i:106\n",
      "A: 0.987171184287\n",
      "i:107\n",
      "A: 0.837167547681\n",
      "i:108\n",
      "A: 0.894717386412\n",
      "i:109\n",
      "A: 0.858260204223\n",
      "i:110\n",
      "A: 0.936232297946\n",
      "i:111\n",
      "A: 0.933067877936\n",
      "i:112\n",
      "A: 0.00877279898226\n",
      "i:113\n",
      "A: 0.0588387681518\n",
      "i:114\n",
      "A: 0.0509517612126\n",
      "i:115\n",
      "A: 0.0240626782043\n",
      "i:116\n",
      "A: 0.0387480259541\n",
      "i:117\n",
      "A: 0.935343372838\n",
      "i:118\n",
      "A: 0.00235202640323\n",
      "i:119\n",
      "A: 0.0883972091645\n",
      "i:120\n",
      "A: 0.00449639007017\n",
      "i:121\n",
      "A: 0.664404295566\n",
      "i:122\n",
      "A: 0.0176677024108\n",
      "i:123\n",
      "A: 2.75426443657e-05\n",
      "i:124\n",
      "A: 0.871728804698\n",
      "i:125\n",
      "A: 0.00243292079406\n",
      "i:126\n",
      "A: 0.892351131278\n",
      "i:127\n",
      "A: 0.0950411301854\n",
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      "A: 0.966495009825\n",
      "i:129\n",
      "A: 0.927285472133\n",
      "i:130\n",
      "A: 0.266413778776\n",
      "i:131\n",
      "A: 0.0870883113155\n",
      "i:132\n",
      "A: 0.0540743543435\n",
      "i:133\n",
      "A: 0.975155426634\n",
      "i:134\n",
      "A: 0.802323751084\n",
      "i:135\n",
      "A: 0.692965781415\n",
      "i:136\n",
      "A: 0.906287457672\n",
      "i:137\n",
      "A: 0.939900204167\n",
      "i:138\n",
      "A: 0.00164790717248\n",
      "i:139\n",
      "A: 0.0191364331112\n",
      "i:140\n",
      "A: 0.0166925680469\n",
      "i:141\n",
      "A: 0.0146846279643\n",
      "i:142\n",
      "A: 0.939237709549\n",
      "i:143\n",
      "A: 0.00257925925449\n",
      "i:144\n",
      "A: 0.819134438464\n",
      "i:145\n",
      "A: 0.854311894674\n",
      "i:146\n",
      "A: 0.910765300344\n",
      "i:147\n",
      "A: 0.12045201549\n",
      "i:148\n",
      "A: 0.910603559834\n",
      "i:149\n",
      "A: 0.911977136973\n",
      "i:150\n",
      "A: 0.372174949686\n",
      "i:151\n",
      "A: 0.0613527931902\n",
      "i:152\n",
      "A: 0.0130882744121\n",
      "i:153\n",
      "A: 0.955225820951\n",
      "i:154\n",
      "A: 0.0430680816876\n",
      "i:155\n",
      "A: 0.13797015814\n",
      "i:156\n",
      "A: 0.960868956371\n",
      "i:157\n",
      "A: 0.0086770503144\n",
      "i:158\n",
      "A: 0.00595741914136\n",
      "i:159\n",
      "A: 0.0219466774612\n",
      "i:160\n",
      "A: 0.00178308409561\n",
      "i:161\n",
      "A: 0.0257658925996\n",
      "i:162\n",
      "A: 0.863787546608\n",
      "i:163\n",
      "A: 0.0344218953284\n",
      "i:164\n",
      "A: 0.934152346707\n",
      "i:165\n",
      "A: 0.00935483279535\n",
      "i:166\n",
      "A: 0.990908018163\n",
      "i:167\n",
      "A: 0.0117832721007\n",
      "i:168\n",
      "A: 0.0267756872812\n",
      "i:169\n",
      "A: 0.774546159375\n",
      "i:170\n",
      "A: 0.843831857863\n",
      "i:171\n",
      "A: 0.938847462931\n",
      "i:172\n",
      "A: 0.148599255375\n",
      "i:173\n",
      "A: 0.00417198954971\n",
      "i:174\n",
      "A: 0.981043188671\n",
      "i:175\n",
      "A: 0.822764984252\n",
      "i:176\n",
      "A: 0.0192120394752\n",
      "i:177\n",
      "A: 0.858870443214\n",
      "i:178\n",
      "A: 0.0537478571971\n",
      "i:179\n",
      "A: 0.784878422888\n",
      "i:180\n",
      "A: 0.0356080495392\n",
      "i:181\n",
      "A: 0.0280545015298\n",
      "i:182\n",
      "A: 0.0109777937328\n",
      "i:183\n",
      "A: 0.0130396160238\n",
      "i:184\n",
      "A: 0.000381067986805\n",
      "i:185\n",
      "A: 0.851025983434\n",
      "i:186\n",
      "A: 0.0244476492652\n",
      "i:187\n",
      "A: 0.0457657709574\n",
      "i:188\n",
      "A: 0.881871553413\n",
      "i:189\n",
      "A: 0.0106481928548\n",
      "i:190\n",
      "A: 0.0284032917866\n",
      "i:191\n",
      "A: 0.0196773462256\n",
      "i:192\n",
      "A: 0.85457718027\n",
      "i:193\n",
      "A: 0.0301055581885\n",
      "i:194\n",
      "A: 0.00133843957767\n",
      "i:195\n",
      "A: 0.0704152763371\n",
      "i:196\n",
      "A: 0.308344786338\n",
      "i:197\n",
      "A: 0.92516762964\n",
      "i:198\n",
      "A: 0.0453183034076\n",
      "i:199\n",
      "A: 0.00931980529967\n",
      "i:200\n",
      "A: 0.869872444493\n",
      "i:201\n",
      "A: 0.00461339721822\n",
      "i:202\n",
      "A: 0.00486286963779\n",
      "i:203\n",
      "A: 0.00732772400337\n",
      "i:204\n",
      "A: 0.126009269317\n",
      "i:205\n",
      "A: 0.146124055945\n",
      "i:206\n",
      "A: 0.045101966898\n",
      "i:207\n",
      "A: 0.145139959404\n",
      "i:208\n",
      "A: 0.145971589212\n",
      "train accuracy: 99.52153110047847 %\n",
      "test accuracy: 68.0 %\n",
      "\n",
      "-------------------------------------------------------\n",
      "\n",
      "learning rate is: 0.001\n",
      "train:209\n",
      "[[ 0.74458179  0.63302701  0.70621076  0.7037801   0.5322598   0.43784581\n",
      "   0.1843739   0.71778574  0.73717649  0.59122536  0.39837511  0.44491784\n",
      "   0.63244572  0.53976962  0.09938522  0.7227688   0.12316033  0.58301417\n",
      "   0.28145733  0.16609522  0.61461919  0.14166416  0.0865388   0.4251847\n",
      "   0.67719513  0.61251308  0.46730808  0.11854922  0.21041046  0.8906756\n",
      "   0.42313203  0.56013238  0.60322016  0.37148913  0.57460259  0.11968291\n",
      "   0.24088599  0.65905854  0.4782032   0.14862075  0.4992436   0.61682528\n",
      "   0.4795275   0.16260336  0.70722369  0.23929218  0.36719514  0.87223907\n",
      "   0.45484261  0.19029187]]\n",
      "i:0\n",
      "A: 0.744581785539\n",
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      "A: 0.633027013643\n",
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      "A: 0.706210759765\n",
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      "A: 0.703780102818\n",
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      "A: 0.532259801361\n",
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      "A: 0.437845809606\n",
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      "A: 0.184373898905\n",
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      "A: 0.717785737262\n",
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      "A: 0.737176492721\n",
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      "A: 0.591225355634\n",
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      "A: 0.398375110721\n",
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      "A: 0.444917838949\n",
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      "A: 0.63244571704\n",
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      "A: 0.539769622665\n",
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      "A: 0.0993852167781\n",
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      "A: 0.722768798762\n",
      "i:16\n",
      "A: 0.12316032912\n",
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      "A: 0.583014167122\n",
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      "A: 0.281457325667\n",
      "i:19\n",
      "A: 0.166095217436\n",
      "i:20\n",
      "A: 0.614619188093\n",
      "i:21\n",
      "A: 0.141664156307\n",
      "i:22\n",
      "A: 0.0865387995852\n",
      "i:23\n",
      "A: 0.425184696224\n",
      "i:24\n",
      "A: 0.677195126714\n",
      "i:25\n",
      "A: 0.612513080304\n",
      "i:26\n",
      "A: 0.467308077974\n",
      "i:27\n",
      "A: 0.118549216984\n",
      "i:28\n",
      "A: 0.210410457255\n",
      "i:29\n",
      "A: 0.890675604139\n",
      "i:30\n",
      "A: 0.423132027822\n",
      "i:31\n",
      "A: 0.560132379868\n",
      "i:32\n",
      "A: 0.603220164275\n",
      "i:33\n",
      "A: 0.371489126215\n",
      "i:34\n",
      "A: 0.57460258973\n",
      "i:35\n",
      "A: 0.119682914663\n",
      "i:36\n",
      "A: 0.240885992578\n",
      "i:37\n",
      "A: 0.659058539454\n",
      "i:38\n",
      "A: 0.478203199906\n",
      "i:39\n",
      "A: 0.148620747063\n",
      "i:40\n",
      "A: 0.499243595267\n",
      "i:41\n",
      "A: 0.616825284802\n",
      "i:42\n",
      "A: 0.47952749539\n",
      "i:43\n",
      "A: 0.162603359073\n",
      "i:44\n",
      "A: 0.707223690416\n",
      "i:45\n",
      "A: 0.239292179578\n",
      "i:46\n",
      "A: 0.367195137229\n",
      "i:47\n",
      "A: 0.872239071749\n",
      "i:48\n",
      "A: 0.454842607004\n",
      "i:49\n",
      "A: 0.190291868848\n",
      "[[ 0.34403391  0.18575705  0.63392388  0.00949352  0.185803    0.30979007\n",
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      "   0.34504767  0.39618939  0.25822558  0.24550552  0.30677401]]\n",
      "i:0\n",
      "A: 0.344033908273\n",
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      "A: 0.185757047816\n",
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      "A: 0.329414106248\n",
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      "A: 0.131021790715\n",
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      "A: 0.302093789045\n",
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      "A: 0.220188589693\n",
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      "A: 0.604674710386\n",
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      "A: 0.630896306073\n",
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      "A: 0.137868408767\n",
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      "A: 0.521626662004\n",
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      "A: 0.22291450364\n",
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      "A: 0.418073110779\n",
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      "A: 0.209283863607\n",
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      "A: 0.126199786804\n",
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      "A: 0.247636058752\n",
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      "A: 0.0821710625911\n",
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      "A: 0.205706268386\n",
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      "A: 0.616683089968\n",
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      "A: 0.473416935084\n",
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      "A: 0.075785261128\n",
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      "A: 0.202722178457\n",
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      "A: 0.636945144894\n",
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      "A: 0.173327250061\n",
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      "A: 0.127747778235\n",
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      "A: 0.389872507958\n",
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      "A: 0.257161020275\n",
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      "A: 0.575892320297\n",
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      "A: 0.0366072862471\n",
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      "A: 0.236271917545\n",
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      "A: 0.505854602978\n",
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      "A: 0.448518810812\n",
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      "A: 0.268820282562\n",
      "i:53\n",
      "A: 0.545064409614\n",
      "i:54\n",
      "A: 0.634277479762\n",
      "i:55\n",
      "A: 0.0759306496245\n",
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      "A: 0.793891279554\n",
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      "A: 0.558487765091\n",
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      "A: 0.203998272929\n",
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      "A: 0.829503110603\n",
      "i:60\n",
      "A: 0.675515155615\n",
      "i:61\n",
      "A: 0.49340245556\n",
      "i:62\n",
      "A: 0.128250171068\n",
      "i:63\n",
      "A: 0.194837070659\n",
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      "A: 0.304058425019\n",
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      "A: 0.252390643679\n",
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      "A: 0.238493286724\n",
      "i:67\n",
      "A: 0.283067421675\n",
      "i:68\n",
      "A: 0.395622062641\n",
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      "A: 0.13380169939\n",
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      "A: 0.259834522156\n",
      "i:73\n",
      "A: 0.433479966943\n",
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      "A: 0.258697452143\n",
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      "A: 0.114571440171\n",
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      "A: 0.0971182935426\n",
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      "A: 0.114034788554\n",
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      "A: 0.641580724838\n",
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      "A: 0.564922638048\n",
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      "A: 0.142492091663\n",
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      "A: 0.26621214556\n",
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      "A: 0.235620869348\n",
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      "A: 0.633475386115\n",
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      "A: 0.197188378977\n",
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      "A: 0.416652927157\n",
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      "A: 0.256091397188\n",
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      "A: 0.205112255205\n",
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      "A: 0.758542852204\n",
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      "A: 0.627000960097\n",
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      "A: 0.224373520787\n",
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      "A: 0.339055616215\n",
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      "A: 0.852875240892\n",
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      "A: 0.58689284883\n",
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      "A: 0.731606581998\n",
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      "A: 0.159747050705\n",
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      "A: 0.117897940596\n",
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      "A: 0.416154952527\n",
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      "A: 0.28828188178\n",
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      "A: 0.0154956543668\n",
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      "A: 0.576572081641\n",
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      "A: 0.523420543806\n",
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      "A: 0.582634465126\n",
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      "A: 0.331128640558\n",
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      "A: 0.138663439611\n",
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      "A: 0.779603902279\n",
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      "A: 0.435420105159\n",
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      "A: 0.39592429401\n",
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      "A: 0.62711359303\n",
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      "A: 0.112636772604\n",
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      "A: 0.698754513362\n",
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      "A: 0.134604206824\n",
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      "A: 0.598436035593\n",
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      "A: 0.360358764822\n",
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      "A: 0.385363208645\n",
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      "A: 0.158658916918\n",
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      "A: 0.191230389339\n",
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      "A: 0.13818573105\n",
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      "A: 0.69657105139\n",
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      "A: 0.103537192466\n",
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      "A: 0.152296961809\n",
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      "A: 0.0378871461979\n",
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      "A: 0.328539452976\n",
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      "A: 0.47339083095\n",
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      "A: 0.626313006873\n",
      "i:198\n",
      "A: 0.227397551696\n",
      "i:199\n",
      "A: 0.0902158010095\n",
      "i:200\n",
      "A: 0.567823314837\n",
      "i:201\n",
      "A: 0.175077914482\n",
      "i:202\n",
      "A: 0.202523087207\n",
      "i:203\n",
      "A: 0.0950001123729\n",
      "i:204\n",
      "A: 0.345047669353\n",
      "i:205\n",
      "A: 0.396189387759\n",
      "i:206\n",
      "A: 0.258225581366\n",
      "i:207\n",
      "A: 0.245505519212\n",
      "i:208\n",
      "A: 0.306774009739\n",
      "train accuracy: 88.99521531100478 %\n",
      "test accuracy: 64.0 %\n",
      "\n",
      "-------------------------------------------------------\n",
      "\n",
      "learning rate is: 0.0001\n",
      "train:209\n",
      "[[ 0.45098635  0.48539489  0.40959087  0.44864257  0.32818894  0.43729766\n",
      "   0.28884626  0.46438078  0.45494399  0.45491705  0.36938309  0.41863679\n",
      "   0.45816519  0.5031755   0.2842568   0.45155065  0.30672371  0.37824086\n",
      "   0.26505548  0.27737934  0.40677576  0.28781555  0.24304775  0.38397796\n",
      "   0.50642581  0.47047843  0.35358916  0.31561491  0.39430714  0.4603235\n",
      "   0.37998879  0.3764821   0.32056264  0.38693085  0.40764828  0.23150119\n",
      "   0.311659    0.44981144  0.43152263  0.26276732  0.37785575  0.48883282\n",
      "   0.37790798  0.30969512  0.47842906  0.32857529  0.34457076  0.60547775\n",
      "   0.40733226  0.28828383]]\n",
      "i:0\n",
      "A: 0.450986345757\n",
      "i:1\n",
      "A: 0.485394889552\n",
      "i:2\n",
      "A: 0.40959086939\n",
      "i:3\n",
      "A: 0.448642568819\n",
      "i:4\n",
      "A: 0.328188942399\n",
      "i:5\n",
      "A: 0.437297661473\n",
      "i:6\n",
      "A: 0.288846261186\n",
      "i:7\n",
      "A: 0.464380781632\n",
      "i:8\n",
      "A: 0.454943990532\n",
      "i:9\n",
      "A: 0.454917046244\n",
      "i:10\n",
      "A: 0.369383089376\n",
      "i:11\n",
      "A: 0.418636792454\n",
      "i:12\n",
      "A: 0.458165188518\n",
      "i:13\n",
      "A: 0.503175503219\n",
      "i:14\n",
      "A: 0.284256803779\n",
      "i:15\n",
      "A: 0.451550652141\n",
      "i:16\n",
      "A: 0.306723707318\n",
      "i:17\n",
      "A: 0.37824086145\n",
      "i:18\n",
      "A: 0.265055482636\n",
      "i:19\n",
      "A: 0.277379344866\n",
      "i:20\n",
      "A: 0.406775762793\n",
      "i:21\n",
      "A: 0.287815548196\n",
      "i:22\n",
      "A: 0.243047750591\n",
      "i:23\n",
      "A: 0.383977957178\n",
      "i:24\n",
      "A: 0.50642581329\n",
      "i:25\n",
      "A: 0.47047842855\n",
      "i:26\n",
      "A: 0.353589159703\n",
      "i:27\n",
      "A: 0.315614908861\n",
      "i:28\n",
      "A: 0.394307138398\n",
      "i:29\n",
      "A: 0.460323497585\n",
      "i:30\n",
      "A: 0.379988789686\n",
      "i:31\n",
      "A: 0.376482100089\n",
      "i:32\n",
      "A: 0.32056264356\n",
      "i:33\n",
      "A: 0.386930852322\n",
      "i:34\n",
      "A: 0.407648283281\n",
      "i:35\n",
      "A: 0.231501193193\n",
      "i:36\n",
      "A: 0.311659003272\n",
      "i:37\n",
      "A: 0.449811443022\n",
      "i:38\n",
      "A: 0.431522631819\n",
      "i:39\n",
      "A: 0.262767315821\n",
      "i:40\n",
      "A: 0.377855746123\n",
      "i:41\n",
      "A: 0.48883282174\n",
      "i:42\n",
      "A: 0.377907980799\n",
      "i:43\n",
      "A: 0.309695116657\n",
      "i:44\n",
      "A: 0.478429062292\n",
      "i:45\n",
      "A: 0.328575286813\n",
      "i:46\n",
      "A: 0.344570758463\n",
      "i:47\n",
      "A: 0.605477754323\n",
      "i:48\n",
      "A: 0.40733225822\n",
      "i:49\n",
      "A: 0.288283831816\n",
      "[[ 0.4225819   0.31692389  0.42964509  0.14896683  0.28783033  0.38652698\n",
      "   0.29492571  0.44991522  0.31988018  0.32391139  0.39318147  0.34804173\n",
      "   0.40099138  0.31694856  0.28102266  0.3231201   0.25486297  0.18485428\n",
      "   0.31900054  0.52941528  0.25568417  0.27297382  0.29762542  0.35834172\n",
      "   0.38912252  0.4552143   0.2555983   0.34830216  0.29078565  0.27432926\n",
      "   0.31094887  0.44330557  0.47172673  0.39765449  0.22386371  0.46108148\n",
      "   0.27055987  0.31333951  0.49901097  0.439851    0.23953174  0.29809115\n",
      "   0.42197081  0.28385499  0.2465556   0.40478121  0.35487343  0.45521241\n",
      "   0.1451398   0.37485678  0.36671611  0.37909623  0.30298036  0.40151709\n",
      "   0.40460677  0.24757226  0.50122617  0.38917296  0.3687779   0.50666786\n",
      "   0.52492017  0.37864634  0.24031899  0.30627306  0.35114005  0.37398054\n",
      "   0.43104844  0.31851314  0.37029232  0.29232461  0.37616632  0.52373453\n",
      "   0.32507684  0.48381803  0.39170698  0.38646363  0.17397111  0.31623794\n",
      "   0.24714356  0.35235176  0.17699762  0.33409149  0.3249697   0.49338319\n",
      "   0.413497    0.25575577  0.31724095  0.36456893  0.45018347  0.34698807\n",
      "   0.38500817  0.46053573  0.23091555  0.47268593  0.43291929  0.26828088\n",
      "   0.30258929  0.4164904   0.24453098  0.27111067  0.39272427  0.3043153\n",
      "   0.27984468  0.39085873  0.52409691  0.34966557  0.55950717  0.37739831\n",
      "   0.43697184  0.33557455  0.38439647  0.48607992  0.30731772  0.31028645\n",
      "   0.30037443  0.29400226  0.42714997  0.42909528  0.30432821  0.5227302\n",
      "   0.32144602  0.4066469   0.43683178  0.17768611  0.4354696   0.18662466\n",
      "   0.39558874  0.48819809  0.35425959  0.57279833  0.35098941  0.33429763\n",
      "   0.31014998  0.49175402  0.44154511  0.31203466  0.38776576  0.38352489\n",
      "   0.29611222  0.36104647  0.33824864  0.37198268  0.52831432  0.25732676\n",
      "   0.36743298  0.44902822  0.37824086  0.36790056  0.41246464  0.37833397\n",
      "   0.3418507   0.30119593  0.2592477   0.46753926  0.26777792  0.43134603\n",
      "   0.32491304  0.19700069  0.25937972  0.33143626  0.19820128  0.35468513\n",
      "   0.36334932  0.51823778  0.37235121  0.27650473  0.47271147  0.44760504\n",
      "   0.33240186  0.29967323  0.41157608  0.47817969  0.39048545  0.28309008\n",
      "   0.46350184  0.41099669  0.34508275  0.4323286   0.35065016  0.33976266\n",
      "   0.25459527  0.29233107  0.26976618  0.30004182  0.18212017  0.34254174\n",
      "   0.26213135  0.2674514   0.45817075  0.24356149  0.25227369  0.34588203\n",
      "   0.3783451   0.32762257  0.19831251  0.51451759  0.3792938   0.41417054\n",
      "   0.34795587  0.25521854  0.42313521  0.32557428  0.38342989  0.21943589\n",
      "   0.34909483  0.39399177  0.36128874  0.38042346  0.38929593]]\n",
      "i:0\n",
      "A: 0.42258189747\n",
      "i:1\n",
      "A: 0.316923894004\n",
      "i:2\n",
      "A: 0.429645087465\n",
      "i:3\n",
      "A: 0.148966828904\n",
      "i:4\n",
      "A: 0.287830330599\n",
      "i:5\n",
      "A: 0.386526976956\n",
      "i:6\n",
      "A: 0.29492570887\n",
      "i:7\n",
      "A: 0.449915222906\n",
      "i:8\n",
      "A: 0.31988017833\n",
      "i:9\n",
      "A: 0.32391138955\n",
      "i:10\n",
      "A: 0.39318146918\n",
      "i:11\n",
      "A: 0.348041731588\n",
      "i:12\n",
      "A: 0.40099137678\n",
      "i:13\n",
      "A: 0.316948559169\n",
      "i:14\n",
      "A: 0.281022656757\n",
      "i:15\n",
      "A: 0.323120096169\n",
      "i:16\n",
      "A: 0.254862969572\n",
      "i:17\n",
      "A: 0.184854283198\n",
      "i:18\n",
      "A: 0.319000540877\n",
      "i:19\n",
      "A: 0.529415284088\n",
      "i:20\n",
      "A: 0.255684173361\n",
      "i:21\n",
      "A: 0.272973815876\n",
      "i:22\n",
      "A: 0.297625420902\n",
      "i:23\n",
      "A: 0.358341716754\n",
      "i:24\n",
      "A: 0.389122515251\n",
      "i:25\n",
      "A: 0.455214304369\n",
      "i:26\n",
      "A: 0.255598298547\n",
      "i:27\n",
      "A: 0.348302160697\n",
      "i:28\n",
      "A: 0.29078565167\n",
      "i:29\n",
      "A: 0.274329259948\n",
      "i:30\n",
      "A: 0.310948871071\n",
      "i:31\n",
      "A: 0.44330556857\n",
      "i:32\n",
      "A: 0.471726733395\n",
      "i:33\n",
      "A: 0.397654490956\n",
      "i:34\n",
      "A: 0.22386370904\n",
      "i:35\n",
      "A: 0.461081481037\n",
      "i:36\n",
      "A: 0.270559874058\n",
      "i:37\n",
      "A: 0.313339507496\n",
      "i:38\n",
      "A: 0.499010971307\n",
      "i:39\n",
      "A: 0.439851000118\n",
      "i:40\n",
      "A: 0.239531739597\n",
      "i:41\n",
      "A: 0.298091151002\n",
      "i:42\n",
      "A: 0.421970809468\n",
      "i:43\n",
      "A: 0.283854991124\n",
      "i:44\n",
      "A: 0.246555602535\n",
      "i:45\n",
      "A: 0.404781207008\n",
      "i:46\n",
      "A: 0.354873425828\n",
      "i:47\n",
      "A: 0.455212410774\n",
      "i:48\n",
      "A: 0.145139804171\n",
      "i:49\n",
      "A: 0.374856776041\n",
      "i:50\n",
      "A: 0.3667161051\n",
      "i:51\n",
      "A: 0.379096233867\n",
      "i:52\n",
      "A: 0.302980363163\n",
      "i:53\n",
      "A: 0.401517086308\n",
      "i:54\n",
      "A: 0.404606774573\n",
      "i:55\n",
      "A: 0.247572260132\n",
      "i:56\n",
      "A: 0.501226174494\n",
      "i:57\n",
      "A: 0.389172955554\n",
      "i:58\n",
      "A: 0.36877789709\n",
      "i:59\n",
      "A: 0.506667859351\n",
      "i:60\n",
      "A: 0.524920168616\n",
      "i:61\n",
      "A: 0.378646343957\n",
      "i:62\n",
      "A: 0.240318988307\n",
      "i:63\n",
      "A: 0.306273062609\n",
      "i:64\n",
      "A: 0.351140048918\n",
      "i:65\n",
      "A: 0.373980542766\n",
      "i:66\n",
      "A: 0.431048441776\n",
      "i:67\n",
      "A: 0.318513142507\n",
      "i:68\n",
      "A: 0.370292320247\n",
      "i:69\n",
      "A: 0.292324613582\n",
      "i:70\n",
      "A: 0.376166323118\n",
      "i:71\n",
      "A: 0.523734526778\n",
      "i:72\n",
      "A: 0.325076842917\n",
      "i:73\n",
      "A: 0.483818032994\n",
      "i:74\n",
      "A: 0.391706980565\n",
      "i:75\n",
      "A: 0.386463629589\n",
      "i:76\n",
      "A: 0.173971112867\n",
      "i:77\n",
      "A: 0.316237944257\n",
      "i:78\n",
      "A: 0.247143563158\n",
      "i:79\n",
      "A: 0.35235175821\n",
      "i:80\n",
      "A: 0.176997620373\n",
      "i:81\n",
      "A: 0.334091490203\n",
      "i:82\n",
      "A: 0.324969697698\n",
      "i:83\n",
      "A: 0.493383190053\n",
      "i:84\n",
      "A: 0.413496998026\n",
      "i:85\n",
      "A: 0.255755767779\n",
      "i:86\n",
      "A: 0.317240953105\n",
      "i:87\n",
      "A: 0.364568930332\n",
      "i:88\n",
      "A: 0.450183465543\n",
      "i:89\n",
      "A: 0.34698806804\n",
      "i:90\n",
      "A: 0.385008173989\n",
      "i:91\n",
      "A: 0.460535727792\n",
      "i:92\n",
      "A: 0.230915550525\n",
      "i:93\n",
      "A: 0.472685927216\n",
      "i:94\n",
      "A: 0.432919290408\n",
      "i:95\n",
      "A: 0.268280877509\n",
      "i:96\n",
      "A: 0.302589292619\n",
      "i:97\n",
      "A: 0.416490398189\n",
      "i:98\n",
      "A: 0.244530984826\n",
      "i:99\n",
      "A: 0.27111067151\n",
      "i:100\n",
      "A: 0.392724268694\n",
      "i:101\n",
      "A: 0.304315299788\n",
      "i:102\n",
      "A: 0.279844677628\n",
      "i:103\n",
      "A: 0.390858726012\n",
      "i:104\n",
      "A: 0.524096907376\n",
      "i:105\n",
      "A: 0.349665565024\n",
      "i:106\n",
      "A: 0.559507167278\n",
      "i:107\n",
      "A: 0.377398313242\n",
      "i:108\n",
      "A: 0.43697183889\n",
      "i:109\n",
      "A: 0.33557454531\n",
      "i:110\n",
      "A: 0.384396471606\n",
      "i:111\n",
      "A: 0.486079924808\n",
      "i:112\n",
      "A: 0.307317717674\n",
      "i:113\n",
      "A: 0.310286447798\n",
      "i:114\n",
      "A: 0.300374433646\n",
      "i:115\n",
      "A: 0.294002263483\n",
      "i:116\n",
      "A: 0.427149971881\n",
      "i:117\n",
      "A: 0.429095284536\n",
      "i:118\n",
      "A: 0.304328205675\n",
      "i:119\n",
      "A: 0.522730198272\n",
      "i:120\n",
      "A: 0.32144601624\n",
      "i:121\n",
      "A: 0.406646899063\n",
      "i:122\n",
      "A: 0.436831776014\n",
      "i:123\n",
      "A: 0.177686108122\n",
      "i:124\n",
      "A: 0.43546960329\n",
      "i:125\n",
      "A: 0.186624661474\n",
      "i:126\n",
      "A: 0.395588738863\n",
      "i:127\n",
      "A: 0.488198087565\n",
      "i:128\n",
      "A: 0.354259593762\n",
      "i:129\n",
      "A: 0.572798327366\n",
      "i:130\n",
      "A: 0.350989405432\n",
      "i:131\n",
      "A: 0.334297633556\n",
      "i:132\n",
      "A: 0.310149982513\n",
      "i:133\n",
      "A: 0.491754020861\n",
      "i:134\n",
      "A: 0.441545109139\n",
      "i:135\n",
      "A: 0.312034656581\n",
      "i:136\n",
      "A: 0.387765763944\n",
      "i:137\n",
      "A: 0.383524891263\n",
      "i:138\n",
      "A: 0.296112216693\n",
      "i:139\n",
      "A: 0.361046469432\n",
      "i:140\n",
      "A: 0.338248643614\n",
      "i:141\n",
      "A: 0.37198267831\n",
      "i:142\n",
      "A: 0.528314316722\n",
      "i:143\n",
      "A: 0.257326756379\n",
      "i:144\n",
      "A: 0.36743298051\n",
      "i:145\n",
      "A: 0.449028222646\n",
      "i:146\n",
      "A: 0.37824086145\n",
      "i:147\n",
      "A: 0.367900562694\n",
      "i:148\n",
      "A: 0.412464636257\n",
      "i:149\n",
      "A: 0.378333965577\n",
      "i:150\n",
      "A: 0.341850697009\n",
      "i:151\n",
      "A: 0.301195930463\n",
      "i:152\n",
      "A: 0.259247704929\n",
      "i:153\n",
      "A: 0.467539258736\n",
      "i:154\n",
      "A: 0.267777917249\n",
      "i:155\n",
      "A: 0.431346032664\n",
      "i:156\n",
      "A: 0.324913038004\n",
      "i:157\n",
      "A: 0.197000689917\n",
      "i:158\n",
      "A: 0.259379720624\n",
      "i:159\n",
      "A: 0.331436257866\n",
      "i:160\n",
      "A: 0.198201279529\n",
      "i:161\n",
      "A: 0.354685126178\n",
      "i:162\n",
      "A: 0.363349324117\n",
      "i:163\n",
      "A: 0.518237782935\n",
      "i:164\n",
      "A: 0.372351209862\n",
      "i:165\n",
      "A: 0.276504731961\n",
      "i:166\n",
      "A: 0.472711467148\n",
      "i:167\n",
      "A: 0.447605039361\n",
      "i:168\n",
      "A: 0.332401857502\n",
      "i:169\n",
      "A: 0.299673233258\n",
      "i:170\n",
      "A: 0.411576076525\n",
      "i:171\n",
      "A: 0.478179688506\n",
      "i:172\n",
      "A: 0.390485447601\n",
      "i:173\n",
      "A: 0.283090082901\n",
      "i:174\n",
      "A: 0.463501843215\n",
      "i:175\n",
      "A: 0.410996689275\n",
      "i:176\n",
      "A: 0.345082753326\n",
      "i:177\n",
      "A: 0.432328600135\n",
      "i:178\n",
      "A: 0.350650158017\n",
      "i:179\n",
      "A: 0.339762655902\n",
      "i:180\n",
      "A: 0.254595268199\n",
      "i:181\n",
      "A: 0.292331066963\n",
      "i:182\n",
      "A: 0.269766176201\n",
      "i:183\n",
      "A: 0.300041824332\n",
      "i:184\n",
      "A: 0.182120165597\n",
      "i:185\n",
      "A: 0.342541737964\n",
      "i:186\n",
      "A: 0.262131347731\n",
      "i:187\n",
      "A: 0.267451401952\n",
      "i:188\n",
      "A: 0.458170747492\n",
      "i:189\n",
      "A: 0.243561492789\n",
      "i:190\n",
      "A: 0.252273691333\n",
      "i:191\n",
      "A: 0.345882027853\n",
      "i:192\n",
      "A: 0.378345103581\n",
      "i:193\n",
      "A: 0.327622567641\n",
      "i:194\n",
      "A: 0.198312513055\n",
      "i:195\n",
      "A: 0.514517592727\n",
      "i:196\n",
      "A: 0.379293799079\n",
      "i:197\n",
      "A: 0.414170537268\n",
      "i:198\n",
      "A: 0.347955874971\n",
      "i:199\n",
      "A: 0.255218536558\n",
      "i:200\n",
      "A: 0.423135208849\n",
      "i:201\n",
      "A: 0.325574284267\n",
      "i:202\n",
      "A: 0.383429888553\n",
      "i:203\n",
      "A: 0.219435894257\n",
      "i:204\n",
      "A: 0.349094827086\n",
      "i:205\n",
      "A: 0.393991770788\n",
      "i:206\n",
      "A: 0.361288736363\n",
      "i:207\n",
      "A: 0.380423462072\n",
      "i:208\n",
      "A: 0.389295926241\n",
      "train accuracy: 68.42105263157895 %\n",
      "test accuracy: 36.0 %\n",
      "\n",
      "-------------------------------------------------------\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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XSrVYYMIEyMoCHx/HnlvULZdyLrEveZ9Tr9G2QVsCvAIuX/EyMjIy8PX1LVZuLUtPTy/z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\ngfJVTDdDAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6a69f7bb00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learning_rates = [0.01, 0.001, 0.0001]\n",
    "models = {}\n",
    "for i in learning_rates:\n",
    "    print (\"learning rate is: \" + str(i))\n",
    "    models[str(i)] = model(train_set_x, train_set_y, test_set_x, test_set_y, num_iterations = 1500, learning_rate = i, print_cost = False)\n",
    "    print ('\\n' + \"-------------------------------------------------------\" + '\\n')\n",
    "\n",
    "for i in learning_rates:\n",
    "    plt.plot(np.squeeze(models[str(i)][\"costs\"]), label= str(models[str(i)][\"learning_rate\"]))\n",
    "\n",
    "plt.ylabel('cost')\n",
    "plt.xlabel('iterations')\n",
    "\n",
    "legend = plt.legend(loc='upper center', shadow=True)\n",
    "frame = legend.get_frame()\n",
    "frame.set_facecolor('0.90')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Interpretation**: \n",
    "- Different learning rates give different costs and thus different predictions results.\n",
    "- If the learning rate is too large (0.01), the cost may oscillate up and down. It may even diverge (though in this example, using 0.01 still eventually ends up at a good value for the cost). \n",
    "- A lower cost doesn't mean a better model. You have to check if there is possibly overfitting. It happens when the training accuracy is a lot higher than the test accuracy.\n",
    "- In deep learning, we usually recommend that you: \n",
    "    - Choose the learning rate that better minimizes the cost function.\n",
    "    - If your model overfits, use other techniques to reduce overfitting. (We'll talk about this in later videos.) \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## 7 - Test with your own image (optional/ungraded exercise) ##\n",
    "\n",
    "Congratulations on finishing this assignment. You can use your own image and see the output of your model. To do that:\n",
    "    1. Click on \"File\" in the upper bar of this notebook, then click \"Open\" to go on your Coursera Hub.\n",
    "    2. Add your image to this Jupyter Notebook's directory, in the \"images\" folder\n",
    "    3. Change your image's name in the following code\n",
    "    4. Run the code and check if the algorithm is right (1 = cat, 0 = non-cat)!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  1.40047715e-107]]\n",
      "i:0\n",
      "A: 1.40047715356e-107\n",
      "y = 0.0, your algorithm predicts a \"non-cat\" picture.\n"
     ]
    }
   ],
   "source": [
    "## START CODE HERE ## (PUT YOUR IMAGE NAME) \n",
    "my_image = \"my_image.jpg\"   # change this to the name of your image file \n",
    "## END CODE HERE ##\n",
    "\n",
    "# We preprocess the image to fit your algorithm.\n",
    "fname = \"images/\" + my_image\n",
    "image = np.array(ndimage.imread(fname, flatten=False))\n",
    "my_image = scipy.misc.imresize(image, size=(num_px,num_px)).reshape((1, num_px*num_px*3)).T\n",
    "my_predicted_image = predict(d[\"w\"], d[\"b\"], my_image)\n",
    "\n",
    "plt.imshow(image)\n",
    "print(\"y = \" + str(np.squeeze(my_predicted_image)) + \", your algorithm predicts a \\\"\" + classes[int(np.squeeze(my_predicted_image)),].decode(\"utf-8\") +  \"\\\" picture.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='blue'>\n",
    "**What to remember from this assignment:**\n",
    "1. Preprocessing the dataset is important.\n",
    "2. You implemented each function separately: initialize(), propagate(), optimize(). Then you built a model().\n",
    "3. Tuning the learning rate (which is an example of a \"hyperparameter\") can make a big difference to the algorithm. You will see more examples of this later in this course!"
   ]
  },
  {
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   "source": [
    "Finally, if you'd like, we invite you to try different things on this Notebook. Make sure you submit before trying anything. Once you submit, things you can play with include:\n",
    "    - Play with the learning rate and the number of iterations\n",
    "    - Try different initialization methods and compare the results\n",
    "    - Test other preprocessings (center the data, or divide each row by its standard deviation)"
   ]
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   "source": [
    "Bibliography:\n",
    "- http://www.wildml.com/2015/09/implementing-a-neural-network-from-scratch/\n",
    "- https://stats.stackexchange.com/questions/211436/why-do-we-normalize-images-by-subtracting-the-datasets-image-mean-and-not-the-c"
   ]
  }
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