From 64d9ad8823633aa8bf496b213636b5179c0c411c Mon Sep 17 00:00:00 2001
From: yiiinghuang <mac@dyn-129-236-232-36.dyn.columbia.edu>
Date: Wed, 28 Sep 2016 21:13:55 -0400
Subject: [PATCH 1/8] changed uni

changed uni
---
 notebooks/week-1/week 1 - VM test.ipynb | 42 ++++++++++++++++++++-----
 1 file changed, 35 insertions(+), 7 deletions(-)

diff --git a/notebooks/week-1/week 1 - VM test.ipynb b/notebooks/week-1/week 1 - VM test.ipynb
index 235273c..e69ac68 100644
--- a/notebooks/week-1/week 1 - VM test.ipynb	
+++ b/notebooks/week-1/week 1 - VM test.ipynb	
@@ -11,22 +11,42 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "hello world\n"
+     ]
+    }
+   ],
    "source": [
     "print \"hello world\""
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "keras successfully imported\n",
+      "theano successfully imported\n",
+      "tensorflow successfully imported\n",
+      "sklearn successfully imported\n",
+      "seaborn successfully imported\n"
+     ]
+    }
+   ],
    "source": [
     "import keras\n",
     "print \"keras successfully imported\"\n",
@@ -42,13 +62,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "uni: YH2792\n"
+     ]
+    }
+   ],
    "source": [
-    "print \"uni:\", \"fill in your uni here\""
+    "print \"uni:\", \"YH2792\""
    ]
   }
  ],

From 5a595ebf44b0a85100fe07943111f47b4b935bac Mon Sep 17 00:00:00 2001
From: yiiinghuang <mac@dyn-129-236-232-36.dyn.columbia.edu>
Date: Wed, 28 Sep 2016 21:17:50 -0400
Subject: [PATCH 2/8] assignment_2

card gamegame
---
 notebooks/week-2/04 - Lab 2 Assignment.ipynb | 145 ++++++++++++++++---
 1 file changed, 124 insertions(+), 21 deletions(-)

diff --git a/notebooks/week-2/04 - Lab 2 Assignment.ipynb b/notebooks/week-2/04 - Lab 2 Assignment.ipynb
index 5d51991..0f5dfd8 100644
--- a/notebooks/week-2/04 - Lab 2 Assignment.ipynb	
+++ b/notebooks/week-2/04 - Lab 2 Assignment.ipynb	
@@ -13,7 +13,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {
     "collapsed": true
    },
@@ -31,9 +31,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {
-    "collapsed": true
+    "collapsed": false,
+    "scrolled": true
    },
    "outputs": [],
    "source": [
@@ -50,9 +51,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "scrolled": true
    },
    "outputs": [],
    "source": [
@@ -60,12 +62,14 @@
     "    \n",
     "    # create here two local variables to store a unique ID for each player and the player's current 'pot' of money\n",
     "    # [FILL IN YOUR VARIABLES HERE]\n",
-    "    \n",
+    "    pot = 0\n",
+    "    playerID = None\n",
     "    # in the __init__() function, use the two input variables to initialize the ID and starting pot of each player\n",
     "    \n",
     "    def __init__(self, inputID, startingPot):\n",
     "        # [CREATE YOUR INITIALIZATIONS HERE]\n",
-    "        \n",
+    "        self.pot = startingPot\n",
+    "        self.playerID = inputID\n",
     "    # create a function for playing the game. This function starts by taking an input for the dealer's card\n",
     "    # and picking a random number from the 'cards' list for the player's card\n",
     "\n",
@@ -80,16 +84,20 @@
     "        \n",
     "        if playerCard < dealerCard:\n",
     "            # [INCREMENT THE PLAYER'S POT, AND RETURN A MESSAGE]\n",
+    "            self.pot -= 50\n",
+    "            print 'player ' + str(self.playerID) + ' lose: ' + 'dealer ' + str(dealerCard) + ' vs '  + 'player ' + str(playerCard)\n",
     "        else:\n",
     "            # [INCREMENT THE PLAYER'S POT, AND RETURN A MESSAGE]\n",
-    "        \n",
+    "            self.pot += 50\n",
+    "            print 'player ' + str(self.playerID) + ' win: ' + 'dealer ' + str(dealerCard) + ' vs ' + 'player ' + str(playerCard)       \n",
     "    # create an accessor function to return the current value of the player's pot\n",
     "    def returnPot(self):\n",
     "        # [FILL IN THE RETURN STATEMENT]\n",
-    "        \n",
+    "        return self.pot\n",
     "    # create an accessor function to return the player's ID\n",
     "    def returnID(self):\n",
-    "        # [FILL IN THE RETURN STATEMENT]"
+    "        # [FILL IN THE RETURN STATEMENT]\n",
+    "        return self.playerID"
    ]
   },
   {
@@ -101,7 +109,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {
     "collapsed": true
    },
@@ -111,7 +119,8 @@
     "    \n",
     "    for player in players:\n",
     "        dealerCard = random.choice(cards)\n",
-    "        #[EXECUTE THE PLAY() FUNCTION FOR EACH PLAYER USING THE DEALER CARD, AND PRINT OUT THE RESULTS]"
+    "        #[EXECUTE THE PLAY() FUNCTION FOR EACH PLAYER USING THE DEALER CARD, AND PRINT OUT THE RESULTS]\n",
+    "        player.play(dealerCard)"
    ]
   },
   {
@@ -123,7 +132,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {
     "collapsed": false
    },
@@ -132,7 +141,9 @@
     "def checkBalances(players):\n",
     "    \n",
     "    for player in players:\n",
-    "        #[PRINT OUT EACH PLAYER'S BALANCE BY USING EACH PLAYER'S ACCESSOR FUNCTIONS]"
+    "        dealerCard = random.choice(cards)\n",
+    "        #[PRINT OUT EACH PLAYER'S BALANCE BY USING EACH PLAYER'S ACCESSOR FUNCTIONS]\n",
+    "        print 'player ',player.returnID(), 'has $', player.returnPot(), ' left'"
    ]
   },
   {
@@ -144,7 +155,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {
     "collapsed": true
    },
@@ -162,7 +173,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {
     "collapsed": false
    },
@@ -181,11 +192,88 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "start game 0\n",
+      "player 0 win: dealer 0 vs player 7\n",
+      "player 1 lose: dealer 7 vs player 2\n",
+      "player 2 lose: dealer 7 vs player 6\n",
+      "player 3 lose: dealer 4 vs player 2\n",
+      "player 4 lose: dealer 5 vs player 3\n",
+      "\n",
+      "start game 1\n",
+      "player 0 lose: dealer 5 vs player 0\n",
+      "player 1 win: dealer 4 vs player 9\n",
+      "player 2 win: dealer 9 vs player 9\n",
+      "player 3 lose: dealer 2 vs player 0\n",
+      "player 4 lose: dealer 2 vs player 1\n",
+      "\n",
+      "start game 2\n",
+      "player 0 lose: dealer 8 vs player 3\n",
+      "player 1 lose: dealer 3 vs player 0\n",
+      "player 2 lose: dealer 5 vs player 0\n",
+      "player 3 win: dealer 1 vs player 3\n",
+      "player 4 win: dealer 3 vs player 7\n",
+      "\n",
+      "start game 3\n",
+      "player 0 lose: dealer 4 vs player 0\n",
+      "player 1 win: dealer 8 vs player 9\n",
+      "player 2 win: dealer 2 vs player 8\n",
+      "player 3 win: dealer 1 vs player 6\n",
+      "player 4 win: dealer 5 vs player 6\n",
+      "\n",
+      "start game 4\n",
+      "player 0 lose: dealer 7 vs player 1\n",
+      "player 1 lose: dealer 9 vs player 1\n",
+      "player 2 win: dealer 1 vs player 2\n",
+      "player 3 win: dealer 7 vs player 8\n",
+      "player 4 win: dealer 6 vs player 8\n",
+      "\n",
+      "start game 5\n",
+      "player 0 win: dealer 0 vs player 2\n",
+      "player 1 win: dealer 4 vs player 7\n",
+      "player 2 win: dealer 0 vs player 6\n",
+      "player 3 win: dealer 1 vs player 3\n",
+      "player 4 lose: dealer 1 vs player 0\n",
+      "\n",
+      "start game 6\n",
+      "player 0 win: dealer 4 vs player 4\n",
+      "player 1 win: dealer 2 vs player 9\n",
+      "player 2 win: dealer 1 vs player 7\n",
+      "player 3 lose: dealer 9 vs player 3\n",
+      "player 4 lose: dealer 1 vs player 0\n",
+      "\n",
+      "start game 7\n",
+      "player 0 win: dealer 4 vs player 8\n",
+      "player 1 lose: dealer 9 vs player 8\n",
+      "player 2 win: dealer 1 vs player 4\n",
+      "player 3 win: dealer 3 vs player 8\n",
+      "player 4 win: dealer 1 vs player 9\n",
+      "\n",
+      "start game 8\n",
+      "player 0 lose: dealer 5 vs player 2\n",
+      "player 1 win: dealer 8 vs player 9\n",
+      "player 2 lose: dealer 9 vs player 2\n",
+      "player 3 lose: dealer 9 vs player 6\n",
+      "player 4 win: dealer 2 vs player 9\n",
+      "\n",
+      "start game 9\n",
+      "player 0 lose: dealer 8 vs player 4\n",
+      "player 1 win: dealer 2 vs player 3\n",
+      "player 2 win: dealer 0 vs player 7\n",
+      "player 3 lose: dealer 6 vs player 0\n",
+      "player 4 lose: dealer 3 vs player 2\n"
+     ]
+    }
+   ],
    "source": [
     "for i in range(10):\n",
     "    print ''\n",
@@ -202,11 +290,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "game results:\n",
+      "player  0 has $ 400  left\n",
+      "player  1 has $ 600  left\n",
+      "player  2 has $ 700  left\n",
+      "player  3 has $ 500  left\n",
+      "player  4 has $ 500  left\n"
+     ]
+    }
+   ],
    "source": [
     "print ''\n",
     "print 'game results:'\n",
@@ -306,8 +408,9 @@
   }
  ],
  "metadata": {
+  "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python 2",
+   "display_name": "Python [default]",
    "language": "python",
    "name": "python2"
   },

From 214db54c77e95e8c316031117c1029cf0bb612cd Mon Sep 17 00:00:00 2001
From: yiiinghuang <mac@dyn-129-236-233-42.dyn.columbia.edu>
Date: Wed, 5 Oct 2016 18:04:47 -0400
Subject: [PATCH 3/8] Assignment3

Basic Artificial Neural Network
---
 .../week-3/01-basic ann-assignment 3.ipynb    | 696 ++++++++++++++++++
 1 file changed, 696 insertions(+)
 create mode 100644 notebooks/week-3/01-basic ann-assignment 3.ipynb

diff --git a/notebooks/week-3/01-basic ann-assignment 3.ipynb b/notebooks/week-3/01-basic ann-assignment 3.ipynb
new file mode 100644
index 0000000..a03bdb2
--- /dev/null
+++ b/notebooks/week-3/01-basic ann-assignment 3.ipynb	
@@ -0,0 +1,696 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Lab 3 - Basic Artificial Neural Network\n",
+    "\n",
+    "Here we will build a very rudimentary Artificial Neural Network (ANN) and use it to solve some basic classification problems. This example is implemented using only basic math and linear algebra functions, which will allow us to study how each aspect of the network works, and to gain an intuitive understanding of its functions. In future labs we will use pre-built libraries such as Keras which automate and optimize much of these functions, making the network much faster and easier to use.\n",
+    "\n",
+    "The code and MNIST test data is taken directly from [http://neuralnetworksanddeeplearning.com/](http://neuralnetworksanddeeplearning.com/) by [Michael Nielsen](http://michaelnielsen.org/). Please review the [first chapter](http://neuralnetworksanddeeplearning.com/chap1.html) of the book for a thorough explanation of the code."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import random\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "class Network(object):\n",
+    "    \n",
+    "    def __init__(self, sizes):\n",
+    "        \n",
+    "        \"\"\"The list ``sizes`` contains the number of neurons in the\n",
+    "        respective layers of the network.  For example, if the list\n",
+    "        was [2, 3, 1] then it would be a three-layer network, with the\n",
+    "        first layer containing 2 neurons, the second layer 3 neurons,\n",
+    "        and the third layer 1 neuron.  The biases and weights for the\n",
+    "        network are initialized randomly, using a Gaussian\n",
+    "        distribution with mean 0, and variance 1.  Note that the first\n",
+    "        layer is assumed to be an input layer, and by convention we\n",
+    "        won't set any biases for those neurons, since biases are only\n",
+    "        ever used in computing the outputs from later layers.\"\"\"\n",
+    "        \n",
+    "        self.num_layers = len(sizes)\n",
+    "        self.sizes = sizes\n",
+    "        self.biases = [np.random.randn(y, 1) for y in sizes[1:]]\n",
+    "        self.weights = [np.random.randn(y, x)\n",
+    "                       for x, y in zip(sizes[:-1], sizes[1:])]\n",
+    "        \n",
+    "    def feedforward (self, a):\n",
+    "        \n",
+    "        #Return the output of the network if \"a\" is input.\n",
+    "        \n",
+    "        for b, w in zip(self.biases, self.weights):\n",
+    "            a = sigmoid(np.dot(w, a)+b)\n",
+    "        return a\n",
+    "    \n",
+    "    def SGD(self, training_data, epochs, mini_batch_size, eta,\n",
+    "            test_data=None):\n",
+    "        \n",
+    "        \"\"\"Train the neural network using mini-batch stochastic\n",
+    "        gradient descent.  The \"training_data\" is a list of tuples\n",
+    "        \"(x, y)\" representing the training inputs and the desired\n",
+    "        outputs.  The other non-optional parameters are\n",
+    "        self-explanatory.  If \"test_data\" is provided then the\n",
+    "        network will be evaluated against the test data after each\n",
+    "        epoch, and partial progress printed out.  This is useful for\n",
+    "        tracking progress, but slows things down substantially.\"\"\"\n",
+    "\n",
+    "        if test_data: n_test = len(test_data)\n",
+    "        n = len(training_data)\n",
+    "        for j in xrange(epochs):\n",
+    "            random.shuffle(training_data)\n",
+    "            mini_batches = [\n",
+    "                training_data[k:k+mini_batch_size]\n",
+    "                for k in xrange(0, n, mini_batch_size)]\n",
+    "            for mini_batch in mini_batches:\n",
+    "                self.update_mini_batch(mini_batch, eta)\n",
+    "            \n",
+    "            if test_data:\n",
+    "                print \"Epoch {0}: {1} / {2}\".format(\n",
+    "                    j, self.evaluate(test_data), n_test)\n",
+    "            else:\n",
+    "                print \"Epoch {0} complete\".format(j)\n",
+    "    \n",
+    "    def update_mini_batch(self, mini_batch, eta):\n",
+    "        \n",
+    "        \"\"\"Update the network's weights and biases by applying\n",
+    "        gradient descent using backpropagation to a single mini batch.\n",
+    "        The \"mini_batch\" is a list of tuples \"(x, y)\", and \"eta\"\n",
+    "        is the learning rate.\"\"\"\n",
+    "\n",
+    "        nabla_b = [np.zeros(b.shape) for b in self.biases]\n",
+    "        nabla_w = [np.zeros(w.shape) for w in self.weights]\n",
+    "        for x, y in mini_batch:\n",
+    "            delta_nabla_b, delta_nabla_w = self.backprop(x, y)\n",
+    "            nabla_b = [nb+dnb for nb, dnb in zip(nabla_b, delta_nabla_b)]\n",
+    "            nabla_w = [nw+dnw for nw, dnw in zip(nabla_w, delta_nabla_w)]\n",
+    "        self.weights = [w-(eta/len(mini_batch))*nw \n",
+    "                        for w, nw in zip(self.weights, nabla_w)]\n",
+    "        self.biases = [b-(eta/len(mini_batch))*nb \n",
+    "                       for b, nb in zip(self.biases, nabla_b)]\n",
+    "        \n",
+    "    def backprop(self, x, y):\n",
+    "        \n",
+    "        \"\"\"Return a tuple ``(nabla_b, nabla_w)`` representing the\n",
+    "        gradient for the cost function C_x.  ``nabla_b`` and\n",
+    "        ``nabla_w`` are layer-by-layer lists of numpy arrays, similar\n",
+    "        to ``self.biases`` and ``self.weights``.\"\"\"\n",
+    "        \n",
+    "        nabla_b = [np.zeros(b.shape) for b in self.biases]\n",
+    "        nabla_w = [np.zeros(w.shape) for w in self.weights]\n",
+    "        \n",
+    "        # feedforward\n",
+    "        activation = x\n",
+    "        activations = [x] # list to store all the activations, layer by layer\n",
+    "        zs = [] # list to store all the z vectors, layer by layer\n",
+    "        for b, w in zip(self.biases, self.weights):\n",
+    "            z = np.dot(w, activation)+b\n",
+    "            zs.append(z)\n",
+    "            activation = sigmoid(z)\n",
+    "            activations.append(activation)\n",
+    "        # backward pass\n",
+    "        delta = self.cost_derivative(activations[-1], y) * \\\n",
+    "            sigmoid_prime(zs[-1])\n",
+    "        nabla_b[-1] = delta\n",
+    "        nabla_w[-1] = np.dot(delta, activations[-2].transpose())\n",
+    "        \n",
+    "        \"\"\"Note that the variable l in the loop below is used a little\n",
+    "        differently to the notation in Chapter 2 of the book.  Here,\n",
+    "        l = 1 means the last layer of neurons, l = 2 is the\n",
+    "        second-last layer, and so on.  It's a renumbering of the\n",
+    "        scheme in the book, used here to take advantage of the fact\n",
+    "        that Python can use negative indices in lists.\"\"\"\n",
+    "        \n",
+    "        for l in xrange(2, self.num_layers):\n",
+    "            z = zs[-l]\n",
+    "            sp = sigmoid_prime(z)\n",
+    "            delta = np.dot(self.weights[-l+1].transpose(), delta) * sp\n",
+    "            nabla_b[-l] = delta\n",
+    "            nabla_w[-l] = np.dot(delta, activations[-l-1].transpose())\n",
+    "        return (nabla_b, nabla_w)\n",
+    "\n",
+    "    def evaluate(self, test_data):\n",
+    "        \n",
+    "        \"\"\"Return the number of test inputs for which the neural\n",
+    "        network outputs the correct result. Note that the neural\n",
+    "        network's output is assumed to be the index of whichever\n",
+    "        neuron in the final layer has the highest activation.\"\"\"\n",
+    "        \n",
+    "        test_results = [(np.argmax(self.feedforward(x)), y)\n",
+    "                        for (x, y) in test_data]\n",
+    "        return sum(int(x == y) for (x, y) in test_results)\n",
+    "\n",
+    "    def cost_derivative(self, output_activations, y):\n",
+    "        \"\"\"Return the vector of partial derivatives \\partial C_x /\n",
+    "        \\partial a for the output activations.\"\"\"\n",
+    "        return (output_activations-y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def sigmoid(z):\n",
+    "#     The sigmoid function.\n",
+    "    return 1.0/(1.0 + np.exp(-z))\n",
+    "\n",
+    "def sigmoid_prime(z):\n",
+    "#     Derivative of the sigmoid function.\n",
+    "    return sigmoid(z)*(1-sigmoid(z))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Iris dataset example\n",
+    "\n",
+    "Now we will test our basic artificial neural network on a very simple classification problem. First we will use the [seaborn data visualization library](https://stanford.edu/~mwaskom/software/seaborn/) to load the ['iris' dataset](https://en.wikipedia.org/wiki/Iris_flower_data_set), \n",
+    "which consists of 50 samples from each of three species of Iris (Iris setosa, Iris virginica and Iris versicolor), with four features measuring the length and the width of each flower's sepals and petals. After we load the data we will vizualize it using some functions in seaborn."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "     sepal_length  sepal_width  petal_length  petal_width     species\n",
+      "111           6.4          2.7           5.3          1.9   virginica\n",
+      "126           6.2          2.8           4.8          1.8   virginica\n",
+      "107           7.3          2.9           6.3          1.8   virginica\n",
+      "87            6.3          2.3           4.4          1.3  versicolor\n",
+      "83            6.0          2.7           5.1          1.6  versicolor\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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R2d2DJXN0CA8JRFq8GrrY8fwli8a0dpMRZVX7zV8vy8yHqcdkUYNyofhSdX6z\nerV5bB0mUehtUW6KbF9VkAJL5uhg6ulfmFEFKWAy9aLs4Llh9z8pUAUpROHZDMomV3PpT5qvry+i\no6MRHR3tyqclJ+vq6kJ5efnNOw6Tnp6OgAD+MkXkKNYCa+0JhRzYvz08EHe4yobz+PD0x6K2D09/\nDF1ErGihBQDKqj9FiiYRM+JuE43l5OkrksF4U3QRmJ4eZfOYiWj0WAvfHPj3UDsr9uLO6Km4dP2K\nxbGSU7uQpy0StQ2dxwZyXSpqm7Ht02pRv08On0dBtg47vziLpx+4Da+/fxIAsP4Hs/nLFY1ptVfr\nUHKqTNRWcqoM8eGTbA7Fl6rzm9WrLfrvXapEbVv3VCErSbA4R82lVtHiyYBItUri/qcKBdm6YW0M\nyibXctmVp6amBp988gmuXLmCzs5O0TEfHx/85je/cdVQyMHKy8ux+/s/QGyQ7fslL3YYgU2/R1ZW\nlhNHRjS2WAuOc8aNhV4mcK9RIkxXrn9DU7tk3yvNDLsj8hTWg7X77D5m8rWcF4bPY3LzXVd3LwCg\n1Th4r+msOZDIU8jVqL6tUaZ/s8Vii9w5bKlXq2Oz495Frq/cvcTAfHCrYyMaKZcstuzcuRO/+MUv\nEBgYiOjoaCgU4rel+fj4uGIY5ESxQUokqoJHexhEY5qzg+OG79UWgqVvViJkAvcEiYC+SI30J9BF\nqRl2R+Qp5MI3xykC0dndKXnMWjCnolcFQPyJQraG4Ab4929PD1UGyj6WyNsNv16rleGS/YTgCMl2\nyVB8mTq3pV6tcURArty9xMB8cKtjIxopl3z08xtvvIGFCxfiL3/5C3bv3o2dO3eK/istLXXFMIiI\nvNpAeORQjgqFHNir/cL+9Xjtr2/hhf3rcb27BfnJ80X98pPnI378JMn2FIltSelxahTOFb/Nt3Cu\nDmn8qxORx4gJicLilOE1n4N9Z/4Xf/7uI+QkzBQdGwjdHgjkHmpZ5mJEBEaK2qTmMan57r4Zk3Gs\nUo/7ZyfgaMUV2ccSeTOp6/WV63qLGl2cMh+JYZMtalAuFH8k9WqNPfcucn3T49QS7SkQ1EEjGhvR\nSLnknS16vR6/+tWvEBQUdPPOdmhoaMCrr76KL7/8Ejdu3MDkyZOxdu1aZGRkSPY/cuQIVq5cKWrz\n8fHBV199BY1GerWW3FtXVxd27txp9+MKCgqYF0NeZ2h45NAQWkdkFUjt1a5rvYwTV8qxKDkH3b39\nn25wov4SqjONAAAgAElEQVQ7/MPEabg/5T6kaHTQtzdDUGmQEpGAMGWIxXmDlQFYlpOMzIQINLQY\nEaVWIo2fRkTkUS5dv4Lj9d8NmwvKkTUhHcfqvwUALErOQUxoFCaNjxaFbksFcvf1+SApRmN1Hhs6\n39U3tSNQ4QfjjW7MyIhCWEgArjQZsSwn2WFzIJGnkL1e14uv18frv8OM2Gk2h+LLBejbUq/W2HPv\nYq2vVDsAJMWGO/yeiMhWLvlpu/POO1FdXY27777bYedsbW3FihUrcPfdd+PNN99EeHg4zp8/j9BQ\n66uVPj4+2Lt3L1SqwbebcaHFc5WXl+PfP3oD4zS2vyXwRpMRycnJzIshrzQQHuno/chSe7VNPd24\n2FqPi631w/r27/WeMcm2GgtWBuCuDIbhEnkqQ3uT5FyQISQDgPnYj/7hny1yIOQCuW2Zx6zNd8mT\n+O44Gptu5XptSyg+MLJ6tcaeexe5vnLtzrgnIrKVSxZbnn32Wfz0pz9FYGAgZs6ciZAQy79uhoWF\n2XXO4uJiREdH45VXXjG3xcTE2PRYtVqN4GDmi3gLdUYkQiZJ70WVcv1CixNHQ+T5hu/1jgubKLlX\nW+EnfQmRy2LoNHWj5lIrDC1GaMOVSOBfmIi8glyWw6Tx0ViYmG1+p4u1nBZgcI5oaG6HcpwCvT19\nGB8SKJorbjaPcJ6hsc7e67XUNV/qnS1y7Km5NmMXymub0dDUjkiNCuk3eScr65k8nUt+WgsLCwEA\nv/rVr2TDcCsqKuw65+eff47Zs2fjRz/6EY4ePYrIyEg8/PDDePDBB60+rq+vD0uWLEFnZyeSk5Ox\nZs0a3H777XY9NxGRtxrY6z30LcjLMxcjN3EulmcuFrVrxmmQn5iLsjN7zG2LdLlQKwSL83aaulF6\n4KzooxmLclNROFfHGyciDzeQ5TB0fshPzsHHpw+Y/5K+OGU+YkLk38EmNUfcN2MyKmubMXtajDnb\nydo8wnmGSLoetUoNlmXmiz7+eXnmYsSEREle8xenLLBpwcWemmszdqFkfzVKD5w1txXO1WFZTrLk\nggvrmbyBS35Sf/Ob3zj8E4fq6urw7rvv4vHHH8fq1avxzTff4OWXX4ZCoUBBQYHkY7RaLX79618j\nMzMTXV1dKCkpwcqVK7F9+3akpaXZ/Nx6vR4Gg0HymMlkgq+vS3KHiegmWKv2k9rrve3ULkyJTLXY\nq32tcRw++vw75E4rQrdfOxS9Khw50IGkgFZETBW/e7DmUqvohgkAtu6pRFaSlm/vJdaqhxue5TBO\nEYitJ0tFWxZ2VX2KGbHTkBwovVVBao745PB5FGTrzHNFH2B1HuE843ysVfcnl60CAFMj00Rt1q75\ntmwrsqfmymubRQstQP/i6RRdBKanWy7Esp7JG7hksWXp0qUOP2dvby+mTp2KZ555BgCQmpqK6upq\nvPfee7KLLfHx8YiPjzd/nZWVhbq6OmzevBnr1q2z+bm3bduGjRs3yh6/WW4MEbkGa9V+Unu9+9st\n93WXnjqDC/VGXKg3/r1X/0c/6luMlo+XaBvoy5smYq16vqFZDgfPH7XIhgAG5xEpcnNEV3cvAOl5\nZcDAPMJ5xvlYq55BLltleNvNrvk3Y0/NNTS1S/a90ix9DtYzeQOXvgfr2rVrOH36NOrr6zFnzhyM\nHz8enZ2dUCgUdq+EC4IAnU78caE6nQ779u2z6zxTpkzBsWPH7HrM8uXLMW/ePMljq1ev5qo+kZtg\nrdpPLntBKmshUi0dTC2EW7ZrJdrk+tLYw1r1LvbMI+ZjMnNBgH//ay+EK9En89iBeYTzjPOxVr3L\nrdSqqJ8dNRepUUn0BKJk7iVYz+QNXLLY0tvbi9/97nfYsmULOjo64OPjgz//+c8YP3481qxZg9tu\nuw1r1qyx65zTpk3DuXPnRG3nzp1DdHS0XeeprKyEIFjmC1gjCILsYxQK2wOliMi5WKvWSQXPxYVN\nxIMZ+dj+3eC+7gcz8hEXNtEiRC95soCC7ATs/KLG3LcgOwHpcWqLc08UgrFqaRoaO/Uw+fZvOYoI\nFMwfzUhjG2vVM0kFawJAT18viqYW4lpnK07Ul+Niaz2WZy42H5eSEBOKotxU0baBvHviEKJUYOX3\n0tBq7EJybBhWFWZC39yBru5eaEIDETE+CGcvXjUfH36OotxUzjMOxFr1DLaG3saFTcTj05bBYGyC\nqaf/I6G1So3VWh0qISZUVJMB/r4Q1EGIjwm1uA9Ijg1D4VydRWZLmsQ9Q0JMqOy5Y4VgVNQ2MzSX\nPIJLfjJfe+01/OlPf8LPfvYz3H333Vi4cKH52Lx587B9+3a7F1see+wxrFixAn/4wx+Ql5eHkydP\nYvv27Xj55ZfNfTZs2ICGhgbzFqF33nkHsbGxSEpKQmdnJ0pKSnD48GG89dZbjvlGiYg8hFzwXO6M\nyfAxJCA3YjCHxceggTHOhP3nD1iE6C3JnoO0OA30LUYI4Uqkx6kRFORvce5VS9PQqqzExxc/Nrfd\nn5QHU89E3iQReSCpMO3Hpy1Du8koCuEsSFuI1dMfxeSwWKuBm4EKfxTO1SErSYuG5nYEBSpw4Uor\n3vlo8AMUVn4vFd3dfdj5xVlMigxBapwaWz4enGcK5+qwNDsRWUla85wUz1/EaIyRC7qXC71tNxlR\nVrXf/PWyzHy7nq+9w4SdXwwuoBTlpsBk6kXZwXMW9xhLsxORmRCBhhYjotRKpMWpoVD4St6P5M+M\ntzj3wwtT8PmxOhSXnhL1ZWguuSuX/FSWlpbi2WefxUMPPYSenh7RsUmTJqGurs7uc06ZMgWbNm3C\nq6++itdffx2xsbH45S9/iUWLFpn7GAwG1NcP7hk2mUxYt24d9Ho9xo0bh5SUFGzevBnTp0+/9W+O\niMgDyQXPTYoMwTtl1UNabwBoQuzkPtkQvXumivd1V9Q2W5y7sVMvWmgBgA9Pf4y0iGTcMSl1xN8P\nEbmWVLCmwdgk+qUNAHZW7MWd0VNt+mSTQIU/UuPUSI1T40j5FdFCCwC0tg/+4nV7qiD6JQwQh20y\n04HGKntCb2uv1okWRwGg5FQZpkam2RGQWyVq27qnCgkxYbLhtndliMNwpe4Ztu6pRELMeItz/8/e\nKhRk6yz6MjSX3JVLFluuXr1qka8yoKenB93d3bd03uzsbGRnZ8seX7t2rejrJ598Ek8++eQtPRcR\nkTeRC55rkAuqsyNET+rcJl/pYLyGNunzEpF7k5oTTD3S93O2hm0OJRWmORCWO/zfQ8mFbRKNFXZd\nr50UkCt3LyEVbit7DplAXanaZ2guuSuXLLbExcXh4MGDuPvuuy2OHTlyBElJSa4YBhGRR7J17zUA\ntN3oQGXDeejbmyCoNEiNnIzgcUEW/bThSkyKDMHtqYJ5L/SxSr1s6K09IXpSoXaKXulgvMhgjeSY\nFX4Ki/3bfIswkfuQmhMUftI1Kqg0qG6sEc1hXd3dqGw4D4OxCeGB4eg1hmC8Kgh+vr7QNxuhGW85\nbw2E5Q7/91DDwzalsiA4l5A3s+t6rdIgNnQCsiakmzNbTtSXS9as1H2HtXsJqXZ7AvTlAnWlal8I\nV0rWOgDWP40ql/y0PfbYY3jxxRfh7++P3NxcAMCVK1dw4sQJbNmyxeIdKERE1M+evddtNzqw47t9\nKDszuF0nPzEPSzMWWCy4TBSCcUeaYBFUp4sdLxl6m6CORX5insW5o4MnWIxZKugyIlDA/Ul5+PC0\n+PGTw6Mlxxzdl4nX3v3W3MY92UTuJS5sIpZnLhbNTQpfBe7TzcEnZ780tz0+bRlONpSLtio8Nm0Z\nmtuui+aDeTHz8e2eUKRO0qKythkALOaiUJUCDy9Mwf/srUJHVzeWZCfggyHHl2QnID56MAxXLpuK\ncwl5s5iQKCxOmY9dVZ+a2xanzEdMSJRFX0GpRVZUBspEfReguqkW75woMbfJ3XdMUCtxe6pWtKWv\nIDsBk6NCJO8xYoVgizFI3TMU5aYiPU4t0Z4CVZB4DEW5qYgVgi2z4goz0d5hEm1FYv2Tq7nkJ23p\n0qW4du0afv/73+MPf/gDAOD73/8+goKC8Mwzz+B73/ueK4bh1bq6ulBeXm7XY9LT0xEQEOCkERGR\nI9iz97qy4bxo0QIAys58jAytZS5Knb5NdBME9OcdZOoicMnQjtUPTMV1YxdClYE4WnEFtZeNOPx5\nEHKnDQbnHv68A3dpOpAaJ17IGRp0OTSk0tQzEWkRyWhoa0JksAYpwmTZMf9z2iRRG/dkE7mXAH8F\nFqcswJTIVFy4dhmXr1/B0UsnAQCLknPQ3duNrAkZUCqC8K+f/bvosY3GJpSdFme7fHbpU+TdXoQd\nu8+jIFtn/uXt5/80HU2tN0RhmtOSBTRe7cC7n1RhyRwdTD2Dfzm/LVGLiLD+v5TLZVNxLiFvdun6\nFRyv/85chwo/fxyv/w4zYqchOVB833DGcAll1Z+K2nZV7UN+So6oTe6+o/riVdGCKADs/KIGaXEa\nyXuMe6ZEW9Se3D2DXDsAJMWGi9qkal3f3GGR68T6J1dz2bLe448/jmXLluH48eNoaWnB+PHjMW3a\nNISEhLhqCF6tvLwcu7//A8QG2fbZ8xc7jMCm3yMrK8vJIyOikbBnP7Vepq9ULoq1PdJHyxtwtLxB\n1D4tWYsL9UZcqB943I3+55TZJz006HJo2/BFH7kxX+++ZtHGPdlE7iXAX4HkiAQY2sXBuBdb+z+c\nIDUiETdMNyweJ5ftMpDtNJDJcKHhOrp7erF4lniuS41TY9f/nsWFhuu40HBddGxoZovcPMe5hLyZ\nob0JF1vrzXU42G77fYNUjUo9Xi5XRW9n7UndM1hrH94mVetyuU6sf3Ill76HSqVSYdasWa58yjEl\nNkiJRJXl2/OIyHPZs/dakOkbGWzZbu8eabl2qf3X9pAbc4j/eABXHfpcROQc9sxTgHy2S3+20w1R\nJoNc3cvNSUMzW+TmOc4l5M0ccd8gVaNSj7f33sBZtSdV63K5Tqx/ciWnLbZ88skndvW/7777nDQS\nIiLPMTwMNyYkyiIXYXnmYsSFTbR4bGrkZMlclRRhskVf63ukU4btcU5Bepwaq5amo7GzASbf/m1E\nEYGR5rf03iq5Mav61CjI9jcH6wnqoBE/FxE51sB8pW9rxFPTHzG/uyVrQjrGB4aip68Xk0KjLeaw\n8QHhWJyYh11nxJktf/trB+6bMRnHKvUA+uekgbofHn6ZHBuGwrk6i0yItCF/sZab5ziXkDeLC5uI\nx6ctg8HYZA691So1svcNw/PUliTnIVwp3nkgd9+RHqeWrEPpvBXH1J5UEK5UrQvqIIn7GdY/uZbT\nFlt++MMf2tzXx8cHFRUVzhoKEZFHkAvDzU2ciymRqTC0N0OrUst+KkDwuCAszViADK04F0Xq04is\n7YVWBSlEOQiqIAXg24POsNP4eMjYlmUuho9P3Ii+Z6kx69STsOfQBdFe66LclBE9DxE5ltR89eht\nD6Ctqw2lFXvNbQNzWLo2BVX1l9DS5ItPPupAlFqDH8z6Ptp6riE8MBx9xhDMeUAJP18f3JYUIcpt\nkAu6XZqdiMyECDS0GM2ZLsHKwSw6a1kQRN6s3WQUbe1blpkv2S/A3x/q4BBRvku4MgTZk2cgUT35\npvcdCoUvJkWGiHLeFP4+CApyTu1ZC72Wu6fJShJY/zRqnPbTtn///pt3IiIis5uF4Q7fKy0leFyQ\nRS6KHKm90BW1zSguPWXRd8JEE0qGja3k1C5MlQjMs9fwMVfUNov+EgUAW/dUIStJ4D5rIjchNV+1\n3Lgq+gUPGJzDYAzHm/89OLdcqDfiyHeNWP+D2RZ1nTJZ/LW1oNu7Miw/YWUoucwHIm9Ve7VO9Olf\nAFByqgxTI9Msrte1V+uw+XgJhktUT7bpvqPmUite23bCoj1WCDHXnSNr72ah17bkuxC5kvRmNgeI\niYmx678BfX19+PnPf47Lly87a2hERG7JWhiuy8YgF2rnwrFZC7UkIvcgNV/JBd8a2ptHVNecE4hs\nZ8+9xEjvO1xdm5wLyNO43fuoent7sXPnTjzyyCOIjo4e7eEQEbmMvSGT9pDa4yz1VlptuBJ3TVFj\nxl2BaO2+ilD/MBw+0glBpUFs6ARkTUg37wE/UV8OjUqNv52vhL69CYJKg9RI6W1L9mCoJZH7GJ4j\nNbCdQGq+GhqqOXS+UAUEoUvThH/6pyDznHJFb8LtqQKMN7pRUdssOycBVgK91UpU1DbfdF4j8lZS\n9amVuV5HqDQ4fOEk9O2NEFQRSIlIGPF9hzZciUmRIbg9VTBnrB2r1Ls0CBfg/QG5L7e8IvX19Y32\nEIiIXC4ubKLNYbj2sLbHefgvJhMEBSbfpsd/ffupuS3/tvmYpE7HtAkZ2FU12H5/ygJUXqnB/3z3\n/mDfxDwszVgwogUXhloSuQe5HKnFKQsk5yutUoNlmfn4y4WvkRKRgLKq/YgNnYDu3m7srzlo7rdo\nag4y29Lx1genzW1ycxIgPSesKszE8Wq9Rfil3DmIvI1cfc6Pn2Vxvf7n25fjcN0xlFUPubYnz8f3\nknJGdN8xUQjGHWmCRUBurOCcT0fl/QF5Go++GjU0NODVV1/Fl19+iRs3bmDy5MlYu3YtMjIyZB9z\n+PBhrFu3DqdPn0Z0dDSeeuopFBYWunDURETSAvwVWJyywKYwXHvcbI/zUGdbzoluxgCgrPpTpEQk\niG7cAODDqn3IT8kR9z3zMTK0yTbnxkhhqCWRe7hZjpTUfAUA8WETse6rNwD0fzLR8ByX3af348kp\nk0RtcnMSID0n9PT14vmNB20+B5G3kavPuLBYi+u1r4+v9LVdkzii+446fZtooQUASg+cxT1Top1S\nh7w/IE/jsT+Zra2tWLFiBe6++268+eabCA8Px/nz5xEaKr+yefHiRTz11FNYsWIFXn31VRw6dAgv\nvPACBEHAzJkzXTh6IiJpAf4Km8NwbWVtj/PwmyG5bBa9UbpdKqOhoU26rz0Yakk0+qzlOSRHJMjO\nVze6O83/lstxud59zaJNak4aMHxO+PL4Rcl+1s5B5E3k6lPqOn69s02270juO+y5v3AU3h+QJ/HY\nxZbi4mJER0fjlVdeMbcNDdqV8u677yI2NhbPPfccACAhIQFff/01Nm/ezMUWIvJa9uxxFmT2bwtK\n6fahGQ0DIoOl+xKRZ7nVPIehj5OaIwAgxH88APEvavbkLjC7gcY6ufqUuo6HBEpv65G75ts8BtYh\nkVUeu9jy+eefY/bs2fjRj36Eo0ePIjIyEg8//DAefPBB2cecPHkS99xzj6ht1qxZWLt2rbOHS0Rk\nk7YbHahsOO/QwFm5Pc6xUUGobqwRBeulRiRiccp80VuQF6fMR6pGh2WZ+aKPk3wwIx8KBIqe6/6k\nPKQIk0c0XkA+lJOIXCcubCIen7YMBmOTOWhTq9RYzXNo7zSiw9SB+1MW4MOqfThRX46chJmizJb8\n5PkYjyhMiuz/a/vtqQLCggMB325U6M+guaPlpnXP7AbyVrZe/+Ry3lIjEi3a/X39kZ883yKzJSUi\nQfK+w8enD5WNZwbbIhKhCrRcQEmICcWqwkzomzvMAbmCOoh1SPR3HrvYUldXh3fffRePP/44Vq9e\njW+++QYvv/wyFAoFCgoKJB9jMBig0YhXcDUaDdra2tDV1YWAgABXDJ2ISFLbjQ7s+G4fys58bG5z\nROCs1B7n2Kgg7D37uWTw5dK0PKRrk/s/sSA4AqkaHRR+CqgUSixKzkF3b/8vXaoAJQKuT0RuRBG6\n/dqh6FVhfIcAhd/IFkWshXJywYXItdpNRlHmyrLMfPm+nUbsqPgYu6o+RWzoBCxKzkFoYDDSIhJx\nZ/RtuHRNj3EIweEjndj+7XH88+IMtBq7sH3/aUyaoIQhoBWfXRr8ZdBa3TO7gbyRPdc/azlvUu3G\nrhtI0eigb2+GoNIgJSIB/r7+FvcdS5LzoAwMwLvffmBuW5wyH0vT8iQXXNo7TNj5xWBuS1FuiiP/\nlxB5NLe7Ivn5+eG///u/ER8fb7Vfb28vpk6dimeeeQYAkJqaiurqarz33nuyiy2OotfrYTAYJI+Z\nTCb4+vo69fmJyDaeVquVDedFNzyAYwJnAcs9ztWNNVaDL++ImSI6Vt1Yg7ePl1icN09bhNKPBvaH\n3wDQhKSYiBHtpb5ZKCd5H0+r1bGi9mqd6N1sAFByqgxTI9Mka7Gy8Yz5XXEXW+txsbUeAPCz2U9D\naYrBH9+sATCYHdHUesP8S9qdtwfh40s7ROe7Wd0zu8H1WKvOZe/1Ty5vRao9wF+BGZOyRP3+dr7S\n4r7jg+qPLcLvd1V9inQhGXdEi+8N+gP4q0RtW/dUIStJYF0SwYmLLW+//bbNfX18fPDYY4+Zv77r\nrrtu+hhBEKDT6URtOp0O+/btk32MVqtFU5M4NKqpqQnBwcF2vatl27Zt2Lhxo+xxayG9ROQ6nlar\ncuG0jgicHe5mwZe29jf5tlu0jTQYz96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J2vV6ccoCi75xYRPR0dWB611tonpdnDJf8vls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qeSn6ta4uPjJT9YqBVfYWGhEB8fb/WBW8l1obQttc+vd+/ewieffCK6T63zktqeGudmjzFd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\nSUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f3b2b0e7550>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "import seaborn as sns; sns.set(style=\"ticks\", color_codes=True)\n",
+    "from sklearn.preprocessing import OneHotEncoder\n",
+    "from sklearn.utils import shuffle\n",
+    "\n",
+    "iris_data = sns.load_dataset(\"iris\")\n",
+    "\n",
+    "# randomly shuffle data\n",
+    "iris_data = shuffle(iris_data)\n",
+    "\n",
+    "# print first 5 data points\n",
+    "print iris_data[:5]\n",
+    "\n",
+    "# create pairplot of iris data\n",
+    "g = sns.pairplot(iris_data, hue=\"species\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next, we will prepare the data set for training in our ANN. In order to work with our functions, the data needs to be converted to numpy format, split into feature and target sets, and then recombined as separate lists within a single dataset. Finally, we split the data set into training and testing sets, and convert the targets of the training set to 'one-hot' encoding (OHE). OHE takes each piece of categorical data and converts it to a list of binary values the length of which is equal to the number of categories, and the position of the current category denoted with a '1' and '0' for all others. For example, in our dataset we have 3 possible categories: versicolor, virginica, and setosa. After applying OHE, versicolor becomes [1,0,0], virginica becomes [0,1,0], and setosa becomes [0,0,1]. OHE is a standard format for target data as it allows easy application of the cost function during training."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# convert iris data to numpy format\n",
+    "iris_array = iris_data.as_matrix()\n",
+    "\n",
+    "# split data into feature and \n",
+    "X = iris_array[:, :4].astype(float)\n",
+    "y = iris_array[:, -1]\n",
+    "\n",
+    "_, y = np.unique(y, return_inverse=True)\n",
+    "y = y.reshape(-1,1)\n",
+    "\n",
+    "# create on-hot encoding function\n",
+    "enc = OneHotEncoder()\n",
+    "enc.fit(y)\n",
+    "\n",
+    "# combine feature and target data\n",
+    "data = []\n",
+    "for i in range(X.shape[0]):\n",
+    "    data.append(tuple([X[i].reshape(-1,1), y[i][0]]))\n",
+    "\n",
+    "# split data into training and test sets\n",
+    "trainingSplit = int(.8 * len(data))\n",
+    "training_data = data[:trainingSplit]\n",
+    "test_data = data[trainingSplit:]\n",
+    "\n",
+    "# convert training targets to on-hot encoding\n",
+    "training_data = [[_x, enc.transform(_y.reshape(-1,1)).toarray().reshape(-1,1)] for _x, _y in training_data]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 0: 7 / 30\n",
+      "Epoch 1: 7 / 30\n",
+      "Epoch 2: 7 / 30\n",
+      "Epoch 3: 10 / 30\n",
+      "Epoch 4: 18 / 30\n",
+      "Epoch 5: 26 / 30\n",
+      "Epoch 6: 26 / 30\n",
+      "Epoch 7: 25 / 30\n",
+      "Epoch 8: 26 / 30\n",
+      "Epoch 9: 26 / 30\n",
+      "Epoch 10: 26 / 30\n",
+      "Epoch 11: 26 / 30\n",
+      "Epoch 12: 26 / 30\n",
+      "Epoch 13: 29 / 30\n",
+      "Epoch 14: 29 / 30\n",
+      "Epoch 15: 28 / 30\n",
+      "Epoch 16: 30 / 30\n",
+      "Epoch 17: 29 / 30\n",
+      "Epoch 18: 30 / 30\n",
+      "Epoch 19: 30 / 30\n",
+      "Epoch 20: 30 / 30\n"
+     ]
+    }
+   ],
+   "source": [
+    "net = Network([4, 25, 3])\n",
+    "net.SGD(training_data, 21, 10, .1, test_data=test_data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### MNIST dataset example\n",
+    "\n",
+    "Next, we will test our ANN on another, slightly more difficult classification problem. The data set we'll be using is called MNIST, which contains tens of thousands of scanned images of handwritten digits, classified according to the digit type from 0-9. The name MNIST comes from the fact that it is a Modified (M) version of a dataset originally developed by the United States' National Institute of Standards and Technology (NIST). This is a very popular dataset used to measure the effectiveness of Machine Learning models for image recongnition. This time we don't have to do as much data management since the data is already provided in the right format [here](https://github.com/mnielsen/neural-networks-and-deep-learning/tree/master/data). \n",
+    "\n",
+    "We will get into more details about working with images and proper data formats for image data in later labs, but you can already use this data to test the effectiveness of our network. With the default settings you should be able to get a classification accuracy of 95% in the test set."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import mnist_loader\n",
+    "\n",
+    "training_data, validation_data, test_data = mnist_loader.load_data_wrapper()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can use the matplotlib library to visualize one of the training images. In the data set, the pixel values of each 28x28 pixel image is encoded in a straight list of 784 numbers, so before we visualize it we have to use numpy's reshape function to convert it back t"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW0AAAFrCAYAAAAaWNg/AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nFxseuJ4AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f3b25433690>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pylab as plt\n",
+    "\n",
+    "img = training_data[0][0][:,0].reshape((28,28))\n",
+    "\n",
+    "fig = plt.figure()\n",
+    "plt.imshow(img, interpolation='nearest', vmin = 0, vmax = 1, cmap=plt.cm.gray)\n",
+    "plt.axis('off')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "net = Network([784, 30, 10])\n",
+    "net.SGD(training_data, 30, 10, 3.0, test_data=test_data)\n",
+    "\n",
+    "plt.plot(results)\n",
+    "plt.ylabel('accuracy (%)')\n",
+    "plt.ylim([0,100.0])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 0 : 16 / 36 - 44.44 % acc\n",
+      "Epoch 1 : 16 / 36 - 44.44 % acc\n",
+      "Epoch 2 : 16 / 36 - 44.44 % acc\n",
+      "Epoch 3 : 16 / 36 - 44.44 % acc\n",
+      "Epoch 4 : 16 / 36 - 44.44 % acc\n",
+      "Epoch 5 : 16 / 36 - 44.44 % acc\n",
+      "Epoch 6 : 16 / 36 - 44.44 % acc\n",
+      "Epoch 7 : 16 / 36 - 44.44 % acc\n",
+      "Epoch 8 : 16 / 36 - 44.44 % acc\n",
+      "Epoch 9 : 16 / 36 - 44.44 % acc\n",
+      "Epoch 10 : 16 / 36 - 44.44 % acc\n",
+      "Epoch 11 : 30 / 36 - 83.33 % acc\n",
+      "Epoch 12 : 29 / 36 - 80.56 % acc\n",
+      "Epoch 13 : 29 / 36 - 80.56 % acc\n",
+      "Epoch 14 : 30 / 36 - 83.33 % acc\n",
+      "Epoch 15 : 29 / 36 - 80.56 % acc\n",
+      "Epoch 16 : 29 / 36 - 80.56 % acc\n",
+      "Epoch 17 : 30 / 36 - 83.33 % acc\n",
+      "Epoch 18 : 31 / 36 - 86.11 % acc\n",
+      "Epoch 19 : 29 / 36 - 80.56 % acc\n",
+      "Epoch 20 : 29 / 36 - 80.56 % acc\n",
+      "Epoch 21 : 31 / 36 - 86.11 % acc\n",
+      "Epoch 22 : 31 / 36 - 86.11 % acc\n",
+      "Epoch 23 : 32 / 36 - 88.89 % acc\n",
+      "Epoch 24 : 30 / 36 - 83.33 % acc\n",
+      "Epoch 25 : 33 / 36 - 91.67 % acc\n",
+      "Epoch 26 : 31 / 36 - 86.11 % acc\n",
+      "Epoch 27 : 33 / 36 - 91.67 % acc\n",
+      "Epoch 28 : 31 / 36 - 86.11 % acc\n",
+      "Epoch 29 : 32 / 36 - 88.89 % acc\n",
+      "Epoch 30 : 32 / 36 - 88.89 % acc\n",
+      "Epoch 31 : 34 / 36 - 94.44 % acc\n",
+      "Epoch 32 : 32 / 36 - 88.89 % acc\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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Vv62mPt6l0gVlFA9hGQAAN0q+kK7XPvpZ73y8TamXshzd5NhnuxfYzjnAz1sTH46Wr7eX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\nAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fa72857fb10>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "import random\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns; sns.set(style=\"ticks\", color_codes=True)\n",
+    "from sklearn.preprocessing import OneHotEncoder\n",
+    "from sklearn.utils import shuffle\n",
+    "\n",
+    "class Network(object):\n",
+    "    \n",
+    "    def __init__(self, sizes):\n",
+    "        self.num_layers = len(sizes)\n",
+    "        self.sizes = sizes\n",
+    "        self.biases = [np.random.randn(y, 1) for y in sizes[1:]]\n",
+    "        self.weights = [np.random.randn(y, x) for x, y in zip(sizes[:-1], sizes[1:])]\n",
+    "        \n",
+    "    def feedforward (self, a):\n",
+    "        \"\"\"Return the output of the network if \"a\" is input. The np.dot() \n",
+    "        function computes the matrix multiplication between the weight and input\n",
+    "        matrices for each set of layers. When used with numpy arrays, the '+'\n",
+    "        operator performs matrix addition.\"\"\"\n",
+    "        \n",
+    "        for b, w in zip(self.biases, self.weights):\n",
+    "            a = sigmoid(np.dot(w, a)+b)\n",
+    "        return a\n",
+    "    \n",
+    "    def SGD(self, training_data, epochs, mini_batch_size, eta, test_data=None):\n",
+    "        \n",
+    "        \"\"\"Train the neural network using mini-batch stochastic\n",
+    "        gradient descent.  The \"training_data\" is a list of tuples\n",
+    "        \"(x, y)\" representing the training inputs and the desired\n",
+    "        outputs.  The other non-optional parameters specify the number \n",
+    "        of epochs, size of each mini-batch, and the learning rate.  \n",
+    "        If \"test_data\" is provided then the network will be evaluated \n",
+    "        against the test data after each epoch, and partial progress \n",
+    "        printed out.  This is useful for tracking progress, but slows\n",
+    "        things down substantially.\"\"\"\n",
+    "        \n",
+    "        # create an empty array to store the accuracy results from each epoch\n",
+    "        results = []\n",
+    "\n",
+    "        n = len(training_data)\n",
+    "        \n",
+    "        if test_data: \n",
+    "            n_test = len(test_data)\n",
+    "            \n",
+    "        # this is the code for one training step, done once for each epoch\n",
+    "        for j in xrange(epochs):\n",
+    "            \n",
+    "            # before each epoch, the data is randomly shuffled\n",
+    "            random.shuffle(training_data)\n",
+    "            \n",
+    "            # training data is broken up into individual mini-batches\n",
+    "            # \n",
+    "            mini_batches = [ training_data[k:k+mini_batch_size] \n",
+    "                            for k in xrange(0, n, mini_batch_size) ]\n",
+    "            \n",
+    "            # then each mini-batch is used to update the parameters of the \n",
+    "            # network using backpropagation and the specified learning rate\n",
+    "            for mini_batch in mini_batches:\n",
+    "                self.update_mini_batch(mini_batch, eta)\n",
+    "            \n",
+    "            # if a test data set is provided, the accuracy results \n",
+    "            # are displayed and stored in the 'results' array\n",
+    "            if test_data:\n",
+    "                num_correct = self.evaluate(test_data)\n",
+    "                accuracy = \"%.2f\" % (100 * (float(num_correct) / n_test))\n",
+    "                print \"Epoch\", j, \":\", num_correct, \"/\", n_test, \"-\", accuracy, \"% acc\"\n",
+    "                results.append(accuracy)\n",
+    "            else:\n",
+    "                print \"Epoch\", j, \"complete\"\n",
+    "                \n",
+    "        return results\n",
+    "    \n",
+    "    def update_mini_batch(self, mini_batch, eta):\n",
+    "        \n",
+    "        \"\"\"Update the network's weights and biases by applying\n",
+    "        gradient descent using backpropagation to a single mini batch.\n",
+    "        The \"mini_batch\" is a list of tuples \"(x, y)\", and \"eta\"\n",
+    "        is the learning rate.\"\"\"\n",
+    "        \n",
+    "\n",
+    "        nabla_b = [np.zeros(b.shape) for b in self.biases] #bias matrices for all layers\n",
+    "        nabla_w = [np.zeros(w.shape) for w in self.weights] #weight matrices for all layers\n",
+    "        for x, y in mini_batch:\n",
+    "            delta_nabla_b, delta_nabla_w = self.backprop(x, y)\n",
+    "            nabla_b = [nb+dnb for nb, dnb in zip(nabla_b, delta_nabla_b)]\n",
+    "            nabla_w = [nw+dnw for nw, dnw in zip(nabla_w, delta_nabla_w)]\n",
+    "        self.weights = [w-(eta/len(mini_batch))*nw \n",
+    "                        for w, nw in zip(self.weights, nabla_w)]\n",
+    "        self.biases = [b-(eta/len(mini_batch))*nb \n",
+    "                       for b, nb in zip(self.biases, nabla_b)]\n",
+    "        \n",
+    "    def backprop(self, x, y):\n",
+    "        \n",
+    "        \"\"\"Return a tuple ``(nabla_b, nabla_w)`` representing the\n",
+    "        gradient for the cost function C_x.  ``nabla_b`` and\n",
+    "        ``nabla_w`` are layer-by-layer lists of numpy arrays, similar\n",
+    "        to ``self.biases`` and ``self.weights``.\"\"\"\n",
+    "        \n",
+    "        nabla_b = [np.zeros(b.shape) for b in self.biases]\n",
+    "        nabla_w = [np.zeros(w.shape) for w in self.weights]\n",
+    "        \n",
+    "        # feedforward\n",
+    "        activation = x\n",
+    "        activations = [x] # list to store all the activations, layer by layer, including the first layer of inputs\n",
+    "        zs = [] # list to store all the z vectors, layer by layer\n",
+    "        for b, w in zip(self.biases, self.weights):\n",
+    "            z = np.dot(w, activation)+b\n",
+    "            zs.append(z)\n",
+    "            activation = sigmoid(z)\n",
+    "            activations.append(activation)\n",
+    "        # backward pass\n",
+    "        #list[-i] is the ith item counting from back\n",
+    "        delta = self.cost_derivative(activations[-1], y) * \\\n",
+    "            sigmoid_prime(zs[-1])\n",
+    "        nabla_b[-1] = delta\n",
+    "        nabla_w[-1] = np.dot(delta, activations[-2].transpose())\n",
+    "        \n",
+    "        \"\"\"Note that the variable l in the loop below is used a little\n",
+    "        differently to the notation in Chapter 2 of the book.  Here,\n",
+    "        l = 1 means the last layer of neurons, l = 2 is the\n",
+    "        second-last layer, and so on.  It's a renumbering of the\n",
+    "        scheme in the book, used here to take advantage of the fact\n",
+    "        that Python can use negative indices in lists.\"\"\"\n",
+    "        \n",
+    "        for l in xrange(2, self.num_layers):\n",
+    "            z = zs[-l]\n",
+    "            sp = sigmoid_prime(z)\n",
+    "            delta = np.dot(self.weights[-l+1].transpose(), delta) * sp\n",
+    "            nabla_b[-l] = delta\n",
+    "            nabla_w[-l] = np.dot(delta, activations[-l-1].transpose())\n",
+    "        return (nabla_b, nabla_w)\n",
+    "\n",
+    "    def evaluate(self, test_data):\n",
+    "        \n",
+    "        \"\"\"Return the number of test inputs for which the neural\n",
+    "        network outputs the correct result. Note that the neural\n",
+    "        network's output is assumed to be the index of whichever\n",
+    "        neuron in the final layer has the highest activation.\n",
+    "        \n",
+    "        Numpy's argmax() function returns the position of the \n",
+    "        largest element in an array. We first create a list of \n",
+    "        predicted value and target value pairs, and then count \n",
+    "        the number of times those values match to get the total \n",
+    "        number correct.\"\"\"\n",
+    "        \n",
+    "        test_results = [(np.argmax(self.feedforward(x)), y)\n",
+    "                        for (x, y) in test_data]\n",
+    "        \n",
+    "        return sum(int(x == y) for (x, y) in test_results)\n",
+    "\n",
+    "    def cost_derivative(self, output_activations, y):\n",
+    "        \"\"\"Return the vector of partial derivatives \\partial C_x /\n",
+    "        \\partial a for the output activations.\"\"\"\n",
+    "        return (output_activations-y)\n",
+    "def sigmoid(z):\n",
+    "#     The sigmoid activation function.\n",
+    "    return 1.0/(1.0 + np.exp(-z))\n",
+    "\n",
+    "def sigmoid_prime(z):\n",
+    "#     Derivative of the sigmoid function.\n",
+    "    return sigmoid(z)*(1-sigmoid(z))\n",
+    "\n",
+    "wine_data = np.loadtxt(open(\"./data/wine.csv\",\"rb\"),delimiter=\",\")\n",
+    "\n",
+    "wine_data = shuffle(wine_data)\n",
+    "\n",
+    "X = wine_data[:,1:]\n",
+    "y = wine_data[:, 0]\n",
+    "\n",
+    "# normalize the data per feature by dividing by the maximum value in each column\n",
+    "X = X / X.max(axis=0)\n",
+    "\n",
+    "# convert the textual category data to integer using numpy's unique() function\n",
+    "_, y = np.unique(y, return_inverse=True)\n",
+    "\n",
+    "# convert the list of targets to a vertical matrix with the dimensions [1 x number of samples]\n",
+    "# this is necessary for later computation\n",
+    "y = y.reshape(-1,1)\n",
+    "\n",
+    "# combine feature and target data into a new python array\n",
+    "data = []\n",
+    "for i in range(X.shape[0]):\n",
+    "    data.append(tuple([X[i].reshape(-1,1), y[i][0]]))\n",
+    "    \n",
+    "# split data into training and test sets\n",
+    "trainingSplit = int(.8 * len(data))\n",
+    "training_data = data[:trainingSplit]\n",
+    "test_data = data[trainingSplit:]\n",
+    "\n",
+    "# create an instance of the one-hot encoding function from the sci-kit learn library\n",
+    "enc = OneHotEncoder()\n",
+    "\n",
+    "# use the function to figure out how many categories exist in the data\n",
+    "enc.fit(y)\n",
+    "\n",
+    "# convert only the target data in the training set to one-hot encoding\n",
+    "training_data = [[_x, enc.transform(_y.reshape(-1,1)).toarray().reshape(-1,1)] for _x, _y in training_data]\n",
+    "\n",
+    "# define the network\n",
+    "net = Network([13, 32, 3])\n",
+    "\n",
+    "# train the network using SGD, and output the results\n",
+    "results = net.SGD(training_data, 33, 11, 0.2, test_data=test_data)\n",
+    "\n",
+    "# visualize the results\n",
+    "plt.plot(results)\n",
+    "plt.ylabel('accuracy (%)')\n",
+    "plt.ylim([0,100.0])\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python [default]",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}

From 594bbba32a77349789088ac1370c4371be3724c5 Mon Sep 17 00:00:00 2001
From: yiiinghuang <mac@dyn-129-236-232-139.dyn.columbia.edu>
Date: Tue, 18 Oct 2016 21:15:17 -0400
Subject: [PATCH 4/8] Assignment4_01

---
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diff --git a/notebooks/week-4/Assignment4_01-tensorflow ANN for regression.ipynb b/notebooks/week-4/Assignment4_01-tensorflow ANN for regression.ipynb
new file mode 100644
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+++ b/notebooks/week-4/Assignment4_01-tensorflow ANN for regression.ipynb	
@@ -0,0 +1,729 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Lab 4 - Tensorflow ANN for regression\n",
+    "\n",
+    "In this lab we will use Tensorflow to build an Artificial Neuron Network (ANN) for a regression task.\n",
+    "\n",
+    "As opposed to the low-level implementation from the previous week, here we will use Tensorflow to automate many of the computation tasks in the neural network. [Tensorflow](https://www.tensorflow.org/) is a higher-level open-source machine learning library [released by Google last year](https://googleblog.blogspot.com/2015/11/tensorflow-smarter-machine-learning-for.html) which is made specifically to optimize and speed up the development and training of neural networks. \n",
+    "\n",
+    "At its core, Tensorflow is very similar to numpy and other numerical computation libraries. Like numpy, it's main function is to do very fast computation on multi-dimensional datasets (such as computing the dot product between a vector of input values and a matrix of values representing the weights in a fully connected network). While numpy refers to such multi-dimensional data sets as 'arrays', Tensorflow calls them 'tensors', but fundamentally they are the same thing. The two main advantages of Tensorflow over custom low-level solutions are:\n",
+    "\n",
+    "- While it has a Python interface, much of the low-level computation is implemented in C/C++, making it run much faster than a native Python solution.\n",
+    "- Many common aspects of neural networks such as computation of various losses and a variety of modern optimization techniques are implemented as built in methods, reducing their implementation to a single line of code. This also helps in development and testing of various solutions, as you can easily swap in and try various solutions without having to write all the code by hand.\n",
+    "\n",
+    "You can get more details about various popular machine learning libraries in [this comparison](http://deeplearning4j.org/compare-dl4j-torch7-pylearn.html).\n",
+    "\n",
+    "To test our basic network, we will use the [Boston Housing Dataset](https://archive.ics.uci.edu/ml/datasets/Housing), which represents data on 506 houses in Boston across 14 different features. One of the features is the median value of the house in $1000’s. This is a common data set for testing regression performance of machine learning algorithms. All 14 features are continuous values, making them easy to plug directly into a neural network (after normalizing ofcourse!). The common goal is to predict the median house value using the other columns as features.\n",
+    "\n",
+    "This lab will conclude with two assignments:\n",
+    "\n",
+    "- Assignment 1 (at bottom of this notebook) asks you to experiment with various regularization parameters to reduce overfitting and improve the results of the model.\n",
+    "- Assignment 2 (in the next notebook) asks you to take our regression problem and convert it to a classification problem."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's start by importing some of the libraries we will use for this tutorial:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 391,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import math\n",
+    "import random\n",
+    "\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "from sklearn.datasets import load_boston\n",
+    "\n",
+    "import numpy as np\n",
+    "import tensorflow as tf\n",
+    "\n",
+    "sns.set(style=\"ticks\", color_codes=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next, let's import the Boston housing prices dataset. This is included with the scikit-learn library, so we can import it directly from there. The data will come in as two numpy arrays, one with all the features, and one with the target (price). We will use pandas to convert this data to a DataFrame so we can visualize it. We will then print the first 5 entries of the dataset to see the kind of data we will be working with."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 392,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "      CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX  \\\n",
+      "0  0.00632  18.0   2.31   0.0  0.538  6.575  65.2  4.0900  1.0  296.0   \n",
+      "1  0.02731   0.0   7.07   0.0  0.469  6.421  78.9  4.9671  2.0  242.0   \n",
+      "2  0.02729   0.0   7.07   0.0  0.469  7.185  61.1  4.9671  2.0  242.0   \n",
+      "3  0.03237   0.0   2.18   0.0  0.458  6.998  45.8  6.0622  3.0  222.0   \n",
+      "4  0.06905   0.0   2.18   0.0  0.458  7.147  54.2  6.0622  3.0  222.0   \n",
+      "\n",
+      "   PTRATIO       B  LSTAT  target  \n",
+      "0     15.3  396.90   4.98    24.0  \n",
+      "1     17.8  396.90   9.14    21.6  \n",
+      "2     17.8  392.83   4.03    34.7  \n",
+      "3     18.7  394.63   2.94    33.4  \n",
+      "4     18.7  396.90   5.33    36.2  \n"
+     ]
+    }
+   ],
+   "source": [
+    "#load data from scikit-learn library\n",
+    "dataset = load_boston()\n",
+    "\n",
+    "#load data as DataFrame\n",
+    "houses = pd.DataFrame(dataset.data, columns=dataset.feature_names)\n",
+    "#add target data to DataFrame\n",
+    "houses['target'] = dataset.target\n",
+    "\n",
+    "#print first 5 entries of data\n",
+    "print houses.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can see that the dataset contains only continuous features, which we can feed directly into the neural network for training. The target is also a continuous variable, so we can use regression to try to predict the exact value of the target. You can see more information about this dataset by printing the 'DESCR' object stored in the data set."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 393,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Boston House Prices dataset\n",
+      "\n",
+      "Notes\n",
+      "------\n",
+      "Data Set Characteristics:  \n",
+      "\n",
+      "    :Number of Instances: 506 \n",
+      "\n",
+      "    :Number of Attributes: 13 numeric/categorical predictive\n",
+      "    \n",
+      "    :Median Value (attribute 14) is usually the target\n",
+      "\n",
+      "    :Attribute Information (in order):\n",
+      "        - CRIM     per capita crime rate by town\n",
+      "        - ZN       proportion of residential land zoned for lots over 25,000 sq.ft.\n",
+      "        - INDUS    proportion of non-retail business acres per town\n",
+      "        - CHAS     Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)\n",
+      "        - NOX      nitric oxides concentration (parts per 10 million)\n",
+      "        - RM       average number of rooms per dwelling\n",
+      "        - AGE      proportion of owner-occupied units built prior to 1940\n",
+      "        - DIS      weighted distances to five Boston employment centres\n",
+      "        - RAD      index of accessibility to radial highways\n",
+      "        - TAX      full-value property-tax rate per $10,000\n",
+      "        - PTRATIO  pupil-teacher ratio by town\n",
+      "        - B        1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town\n",
+      "        - LSTAT    % lower status of the population\n",
+      "        - MEDV     Median value of owner-occupied homes in $1000's\n",
+      "\n",
+      "    :Missing Attribute Values: None\n",
+      "\n",
+      "    :Creator: Harrison, D. and Rubinfeld, D.L.\n",
+      "\n",
+      "This is a copy of UCI ML housing dataset.\n",
+      "http://archive.ics.uci.edu/ml/datasets/Housing\n",
+      "\n",
+      "\n",
+      "This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.\n",
+      "\n",
+      "The Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic\n",
+      "prices and the demand for clean air', J. Environ. Economics & Management,\n",
+      "vol.5, 81-102, 1978.   Used in Belsley, Kuh & Welsch, 'Regression diagnostics\n",
+      "...', Wiley, 1980.   N.B. Various transformations are used in the table on\n",
+      "pages 244-261 of the latter.\n",
+      "\n",
+      "The Boston house-price data has been used in many machine learning papers that address regression\n",
+      "problems.   \n",
+      "     \n",
+      "**References**\n",
+      "\n",
+      "   - Belsley, Kuh & Welsch, 'Regression diagnostics: Identifying Influential Data and Sources of Collinearity', Wiley, 1980. 244-261.\n",
+      "   - Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning. In Proceedings on the Tenth International Conference of Machine Learning, 236-243, University of Massachusetts, Amherst. Morgan Kaufmann.\n",
+      "   - many more! (see http://archive.ics.uci.edu/ml/datasets/Housing)\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print dataset['DESCR']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next, we will do some exploratory data visualization to get a general sense of the data and how the different features are related to each other and to the target we will try to predict. First, let's plot the correlations between each feature. Larger positive or negative correlation values indicate that the two features are related (large positive or negative correlation), while values closer to zero indicate that the features are not related (no correlation)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 394,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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bw8FfvidPpURuY0eVxq1o0btwvm2f6nVp2KUPG2d9QK4yG3tXD7q9/qFRbeSj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OmEwmfvjhB8xmM59++ikJCQmEh4c7t09PT+f48eMMGjTIZb8/rFzJokWLeKt5BFOahCGVwOzm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\nJ0iFaXexdPUyDrawQNGsK9k7fsGmaskqqJW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K+svYb7WoyY87TtBrzmYKCnVU83Fl/MvN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8f777+PUqm01Mu1bduWxo0bM27cOA4cOEBcXBzHjx9n7ty5nD171hLP9evXuXDh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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f68d8c8b5d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create a datset of correlations between house features\n",
+    "corrmat = houses.corr()\n",
+    "\n",
+    "# Set up the matplotlib figure\n",
+    "f, ax = plt.subplots(figsize=(9, 6))\n",
+    "\n",
+    "# Draw the heatmap using seaborn\n",
+    "sns.set_context(\"notebook\", font_scale=0.7, rc={\"lines.linewidth\": 1.5})\n",
+    "sns.heatmap(corrmat, annot=True, square=True)\n",
+    "f.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can get a more detailed picture of the relationship between any two variables in the dataset by using seaborn's `jointplot` function and passing it two features of our data. This will show a single-dimension histogram distribution for each feature, as well as a two-dimension density scatter plot for how the two features are related. From the correlation matrix above, we can see that the `RM` feature has a strong positive correlation to the target, while the `LSTAT` feature has a strong negative correlation to the target. Let's create `jointplots` for both sets of features to see how they relate in more detail:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 395,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.JointGrid at 0x7f68d72b9750>"
+      ]
+     },
+     "execution_count": 395,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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0gYtfMoqXHhjGo7+YwskZf81acEXXwNiwg6JrtbV92h4MRkREp1k+3LRZ74NS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\nCAAAIEUwAgAASP0/SIcBFuu07esAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f68d7c53d90>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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HtS04lgk/kKiGcSpjiuF/DJVD+GGM5S0ecp6dytgAYAiBrGvBsRTKfoyFcjyP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\nhiYiIiIiE/4/uaZc8TIBg+gAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f68d72b95d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.jointplot(houses['target'], houses['RM'], kind='hex')\n",
+    "sns.jointplot(houses['target'], houses['LSTAT'], kind='hex')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As expected, the plots show a positive relationship between the `RM` feature and the target, and a negative relationship between the `LSTAT` feature and the target.\n",
+    "\n",
+    "This type of exploratory visualization is not strictly necessary for using machine learning, but it does help to formulate your solution, and to troubleshoot your implementation incase you are not getting the results you want. For example, if you find that two features have a strong correlation with each other, you might want to include only one of them to speed up the training process. Similarly, you may want to exclude features that show little correlation to the target, since they have little influence over its value.\n",
+    "\n",
+    "Now that we know a little bit about the data, let's prepare it for training with our neural network. We will follow a process similar to the previous lab:\n",
+    "\n",
+    "- We will first re-split the data into a feature set (X) and a target set (y)\n",
+    "- Then we will normalize the feature set so that the values range from 0 to 1\n",
+    "- Finally, we will split both data sets into a training and test set."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 396,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "('Training set', (354, 13), (354,))\n",
+      "('Test set', (152, 13), (152,))\n"
+     ]
+    }
+   ],
+   "source": [
+    "# convert housing data to numpy format\n",
+    "houses_array = houses.as_matrix().astype(float)\n",
+    "\n",
+    "# split data into feature and target sets\n",
+    "X = houses_array[:, :-1]\n",
+    "y = houses_array[:, -1]\n",
+    "\n",
+    "# normalize the data per feature by dividing by the maximum value in each column\n",
+    "X = X / X.max(axis=0)\n",
+    "\n",
+    "# split data into training and test sets\n",
+    "trainingSplit = int(.7 * houses_array.shape[0])\n",
+    "\n",
+    "X_train = X[:trainingSplit]\n",
+    "y_train = y[:trainingSplit]\n",
+    "X_test = X[trainingSplit:]\n",
+    "y_test = y[trainingSplit:]\n",
+    "\n",
+    "print('Training set', X_train.shape, y_train.shape)\n",
+    "print('Test set', X_test.shape, y_test.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next, we set up some variables that we will use to define our model. The first group are helper variables taken from the dataset which specify the number of samples in our training set, the number of features, and the number of outputs. The second group are the actual hyper-parameters which define how the model is structured and how it performs. In this case we will be building a neural network with two hidden layers, and the size of each hidden layer is controlled by a hyper-parameter. The other hyper-parameters include:\n",
+    "\n",
+    "- `batch size`, which sets how many training samples are used at a time\n",
+    "- `learning rate` which controls how quickly the gradient descent algorithm works\n",
+    "- `training epochs` which sets how many rounds of training occurs\n",
+    "- `dropout keep probability`, a regularization technique which controls how many neurons are 'dropped' randomly during each training step (note in Tensorflow this is specified as the 'keep probability' from 0 to 1, with 0 representing all neurons dropped, and 1 representing all neurons kept). You can read more about dropout [here](http://neuralnetworksanddeeplearning.com/chap3.html#other_techniques_for_regularization)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 397,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# helper variables\n",
+    "num_samples = X_train.shape[0]\n",
+    "num_features = X_train.shape[1]\n",
+    "num_outputs = 1\n",
+    "\n",
+    "# Hyper-parameters\n",
+    "batch_size = 45\n",
+    "num_hidden_1 = 10\n",
+    "num_hidden_2 = 8\n",
+    "learning_rate = 0.0001\n",
+    "training_epochs = 500\n",
+    "dropout_keep_prob = .22 # set to no dropout by default\n",
+    "\n",
+    "# variable to control the resolution at which the training results are stored\n",
+    "display_step = 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next, we define a few helper functions which will dictate how error will be measured for our model, and how the weights and biases should be defined.\n",
+    "\n",
+    "The `accuracy()` function defines how we want to measure error in a regression problem. The function will take in two lists of values - `predictions` which represent predicted values, and `targets` which represent actual target values. In this case we simply compute the absolute difference between the two (the error) and return the average error using numpy's `mean()` fucntion.\n",
+    "\n",
+    "The `weight_variable()` and `bias_variable()` functions help create parameter variables for our neural network model, formatted in the proper type for Tensorflow. Both functions take in a shape parameter and return a variable of that shape using the specified initialization. In this case we are using a 'truncated normal' distribution for the weights, and a constant value for the bias. For more information about various ways to initialize parameters in Tensorflow you can consult the [documentation](https://www.tensorflow.org/versions/r0.11/api_docs/python/constant_op.html)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 398,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def accuracy(predictions, targets):\n",
+    "    error = np.absolute(predictions.reshape(-1) - targets)\n",
+    "    return np.mean(error)\n",
+    "\n",
+    "def weight_variable(shape):\n",
+    "    initial = tf.truncated_normal(shape, stddev=0.1)\n",
+    "    return tf.Variable(initial)\n",
+    "\n",
+    "def bias_variable(shape):\n",
+    "    initial = tf.constant(0.1, shape=shape)\n",
+    "    return tf.Variable(initial)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we are ready to build our neural network model in Tensorflow.\n",
+    "\n",
+    "Tensorflow operates in a slightly different way than the procedural logic we have been using in Python so far. Instead of telling Tensorflow the exact operations to run line by line, we build the entire neural network within a structure called a `Graph`. The Graph does several things:\n",
+    "\n",
+    "- describes the architecture of the network, including how many layers it has and how many neurons are in each layer\n",
+    "- initializes all the parameters of the network\n",
+    "- describes the 'forward' calculation of the network, or how input data is passed through the network layer by layer until it reaches the result\n",
+    "- defines the loss function which describes how well the model is performing\n",
+    "- specifies the optimization function which dictates how the parameters are tuned in order to minimize the loss\n",
+    "\n",
+    "Once this graph is defined, we can work with it by 'executing' it on sets of training data and 'calling' different parts of the graph to get back results. Every time the graph is executed, Tensorflow will only do the minimum calculations necessary to generate the requested results. This makes Tensorflow very efficient, and allows us to structure very complex models while only testing and using certain portions at a time. In programming language theory, this type of programming is called ['lazy evaluation'](https://en.wikipedia.org/wiki/Lazy_evaluation)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 399,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "'''First we create a variable to store our graph'''\n",
+    "graph = tf.Graph()\n",
+    "\n",
+    "'''Next we build our neural network within this graph variable'''\n",
+    "with graph.as_default():\n",
+    "    \n",
+    "    '''Our training data will come in as x feature data and \n",
+    "    y target data. We need to create tensorflow placeholders \n",
+    "    to capture this data as it comes in'''\n",
+    "    \n",
+    "    x = tf.placeholder(tf.float32, shape=(None, num_features))\n",
+    "    _y = tf.placeholder(tf.float32, shape=(None))\n",
+    "    \n",
+    "    '''Another placeholder stores the hyperparameter \n",
+    "    that controls dropout'''\n",
+    "    \n",
+    "    keep_prob = tf.placeholder(tf.float32)\n",
+    "    \n",
+    "    '''Finally, we convert the test and train feature data sets \n",
+    "    to tensorflow constants so we can use them to generate \n",
+    "    predictions on both data sets'''\n",
+    "    \n",
+    "    tf_X_test = tf.constant(X_test, dtype=tf.float32)\n",
+    "    tf_X_train = tf.constant(X_train, dtype=tf.float32)\n",
+    "    \n",
+    "    '''Next we create the parameter variables for the model.\n",
+    "    Each layer of the neural network needs it's own weight \n",
+    "    and bias variables which will be tuned during training.\n",
+    "    The sizes of the parameter variables are determined by \n",
+    "    the number of neurons in each layer.'''\n",
+    "    \n",
+    "    W_fc1 = weight_variable([num_features, num_hidden_1])\n",
+    "    b_fc1 = bias_variable([num_hidden_1])\n",
+    "    \n",
+    "    W_fc2 = weight_variable([num_hidden_1, num_hidden_2])\n",
+    "    b_fc2 = bias_variable([num_hidden_2])\n",
+    "    \n",
+    "    W_fc3 = weight_variable([num_hidden_2, num_outputs])\n",
+    "    b_fc3 = bias_variable([num_outputs])\n",
+    "    \n",
+    "    '''Next, we define the forward computation of the model.\n",
+    "    We do this by defining a function model() which takes in \n",
+    "    a set of input data, and performs computations through \n",
+    "    the network until it generates the output.'''\n",
+    "    \n",
+    "    def model(data, keep):\n",
+    "        \n",
+    "        # computing first hidden layer from input, using relu activation function\n",
+    "        fc1 = tf.nn.sigmoid(tf.matmul(data, W_fc1) + b_fc1)\n",
+    "        # adding dropout to first hidden layer\n",
+    "        fc1_drop = tf.nn.dropout(fc1, keep)\n",
+    "        \n",
+    "        # computing second hidden layer from first hidden layer, using relu activation function\n",
+    "        fc2 = tf.nn.sigmoid(tf.matmul(fc1_drop, W_fc2) + b_fc2)\n",
+    "        # adding dropout to second hidden layer\n",
+    "        fc2_drop = tf.nn.dropout(fc2, keep)\n",
+    "        \n",
+    "        # computing output layer from second hidden layer\n",
+    "        # the output is a single neuron which is directly interpreted as the prediction of the target value\n",
+    "        fc3 = tf.matmul(fc2_drop, W_fc3) + b_fc3\n",
+    "        \n",
+    "        # the output is returned from the function\n",
+    "        return fc3\n",
+    "    \n",
+    "    '''Next we define a few calls to the model() function which \n",
+    "    will return predictions for the current batch input data (x),\n",
+    "    as well as the entire test and train feature set'''\n",
+    "    \n",
+    "    prediction = model(x, keep_prob)\n",
+    "    test_prediction = model(tf_X_test, 1.0)\n",
+    "    train_prediction = model(tf_X_train, 1.0)\n",
+    "    \n",
+    "    '''Finally, we define the loss and optimization functions \n",
+    "    which control how the model is trained.\n",
+    "    \n",
+    "    For the loss we will use the basic mean square error (MSE) function,\n",
+    "    which tries to minimize the MSE between the predicted values and the \n",
+    "    real values (_y) of the input dataset.\n",
+    "    \n",
+    "    For the optimization function we will use basic Gradient Descent (SGD)\n",
+    "    which will minimize the loss using the specified learning rate.'''\n",
+    "    \n",
+    "    loss = tf.reduce_mean(tf.square(tf.sub(prediction, _y)))\n",
+    "    optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss)\n",
+    "    \n",
+    "    '''We also create a saver variable which will allow us to \n",
+    "    save our trained model for later use'''\n",
+    "    \n",
+    "    saver = tf.train.Saver()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now that we have specified our model, we are ready to train it. We do this by iteratively calling the model, with each call representing one training step. At each step, we:\n",
+    "\n",
+    "- Feed in a new set of training data. Remember that with SGD we only have to feed in a small set of data at a time. The size of each batch of training data is determined by the 'batch_size' hyper-parameter specified above.\n",
+    "\n",
+    "- Call the optimizer function by asking tensorflow to return the model's 'optimizer' variable. This starts a chain reaction in Tensorflow that executes all the computation necessary to train the model. The optimizer function itself will compute the gradients in the model and modify the weight and bias parameters in a way that minimizes the overall loss. Because it needs this loss to compute the gradients, it will also trigger the loss function, which will in turn trigger the model to compute predictions based on the input data. This sort of chain reaction is at the root of the 'lazy evaluation' model used by Tensorflow."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 400,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Initialized\n",
+      "Model saved in file: model_houses.ckpt\n"
+     ]
+    }
+   ],
+   "source": [
+    "# create an array to store the results of the optimization at each epoch\n",
+    "results = []\n",
+    "\n",
+    "'''First we open a session of Tensorflow using our graph as the base. \n",
+    "While this session is active all the parameter values will be stored, \n",
+    "and each step of training will be using the same model.'''\n",
+    "with tf.Session(graph=graph) as session:\n",
+    "    \n",
+    "    '''After we start a new session we first need to\n",
+    "    initialize the values of all the variables.'''\n",
+    "    tf.initialize_all_variables().run()\n",
+    "    print('Initialized')\n",
+    "\n",
+    "    '''Now we iterate through each training epoch based on the hyper-parameter set above.\n",
+    "    Each epoch represents a single pass through all the training data.\n",
+    "    The total number of training steps is determined by the number of epochs and \n",
+    "    the size of mini-batches relative to the size of the entire training set.'''\n",
+    "    for epoch in range(training_epochs):\n",
+    "        \n",
+    "        '''At the beginning of each epoch, we create a set of shuffled indexes \n",
+    "        so that we are using the training data in a different order each time'''\n",
+    "        indexes = range(num_samples)\n",
+    "        random.shuffle(indexes)\n",
+    "        \n",
+    "        '''Next we step through each mini-batch in the training set'''\n",
+    "        for step in range(int(math.floor(num_samples/float(batch_size)))):\n",
+    "            offset = step * batch_size\n",
+    "            \n",
+    "            '''We subset the feature and target training sets to create each mini-batch'''\n",
+    "            batch_data = X_train[indexes[offset:(offset + batch_size)]]\n",
+    "            batch_labels = y_train[indexes[offset:(offset + batch_size)]]\n",
+    "\n",
+    "            '''Then, we create a 'feed dictionary' that will feed this data, \n",
+    "            along with any other hyper-parameters such as the dropout probability,\n",
+    "            to the model'''\n",
+    "            feed_dict = {x : batch_data, _y : batch_labels, keep_prob: dropout_keep_prob}\n",
+    "            \n",
+    "            '''Finally, we call the session's run() function, which will feed in \n",
+    "            the current training data, and execute portions of the graph as necessary \n",
+    "            to return the data we ask for.\n",
+    "            \n",
+    "            The first argument of the run() function is a list specifying the \n",
+    "            model variables we want it to compute and return from the function. \n",
+    "            The most important is 'optimizer' which triggers all calculations necessary \n",
+    "            to perform one training step. We also include 'loss' and 'prediction' \n",
+    "            because we want these as ouputs from the function so we can keep \n",
+    "            track of the training process.\n",
+    "            \n",
+    "            The second argument specifies the feed dictionary that contains \n",
+    "            all the data we want to pass into the model at each training step.'''\n",
+    "            _, l, p = session.run([optimizer, loss, prediction], feed_dict=feed_dict)\n",
+    "\n",
+    "        '''At the end of each epoch, we will calcule the error of predictions \n",
+    "        on the full training and test data set. We will then store the epoch number, \n",
+    "        along with the mini-batch, training, and test accuracies to the 'results' array \n",
+    "        so we can visualize the training process later. How often we save the data to \n",
+    "        this array is specified by the display_step variable created above''' \n",
+    "        if (epoch % display_step == 0):\n",
+    "            batch_acc = accuracy(p, batch_labels)\n",
+    "            train_acc =  accuracy(train_prediction.eval(session=session), y_train)\n",
+    "            test_acc =  accuracy(test_prediction.eval(session=session), y_test)\n",
+    "            results.append([epoch, batch_acc, train_acc, test_acc])\n",
+    "\n",
+    "    '''Once training is complete, we will save the trained model so that we can use it later'''\n",
+    "    save_path = saver.save(session, \"model_houses.ckpt\")\n",
+    "    print(\"Model saved in file: %s\" % save_path)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "Now that the model is trained, let's visualize the training process by plotting the error we achieved in the small training batch, the full training set, and the test set at each epoch. We will also print out the minimum loss we were able to achieve in the test set over all the training steps."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 401,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Minimum test loss: 5.52597245543\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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lqqQ8uOTP4w0c95yp0DK/QMCM2Z5dRERERERaXrsKU2kpNR32Ijv6Oe11K1Mh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\nX3I15lRNBwPDttadePjt3+Lr/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VpUd96F0N592HLzLZi57ja46kbOLBwxemCMoaFkHBpK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\nQRAEQRAEQRDEIPj/nNT6sdLFrv8AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f68d7d8cb10>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df = pd.DataFrame(data=results, columns = [\"epoch\", \"batch_acc\", \"train_acc\", \"test_acc\"])\n",
+    "df.set_index(\"epoch\", drop=True, inplace=True)\n",
+    "\n",
+    "fig, ax = plt.subplots(1, 1, figsize=(10, 4))\n",
+    " \n",
+    "ax.plot(df)\n",
+    "ax.set(xlabel='Epoch',\n",
+    "       ylabel='Error',\n",
+    "       title='Training result')\n",
+    " \n",
+    "ax.legend(df.columns, loc=1)\n",
+    "\n",
+    "print \"Minimum test loss:\", np.min(df[\"test_acc\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "From the plot you can see several things:\n",
+    "\n",
+    "- the error on the training data smoothly improves throughout the training, which is to be expected from the gradient descent algorithm\n",
+    "- the error of each mini-batch is more noisy than the entire training set (which is also to be expected since we are only using a portion of the data each time) but in general follows the same trajectory\n",
+    "- the error over the training set bottoms out around the 120th epoch, which might represent the best model fit for this dataset\n",
+    "\n",
+    "All of this is to be expected, however the most important thing to notice with this plot is the error over the test set (which is actually measuring the generalized performance of the model). You can see that for the first 35 or so epochs the error over the test set improves in pace with the error over the training set. This means that the tuning of the model is fitting the underlying structures in both datasets. However, after the 70th epoch, the error over the test starts to move back up. This is a very common indication that overfitting of the training set has occured. After this point, further tuning of the model is representing particular features of the training set itself (such as it's particular error or noise), which do not generalize well to other data not seen by the training process. This shows why it is so important to use a separate testing set to evaluate a model, since otherwise it would be impossible to see exactly where this point of overfitting occurs.\n",
+    "\n",
+    "### Assignment - part 1\n",
+    "\n",
+    "There are several common strategies for addressing overfitting which we will cover in class and are also covered [here](http://neuralnetworksanddeeplearning.com/chap3.html). Go back to the neural network and experiment with different settings for some of the hyper-parameters to see if you can fix this 'double-dip' in the test set error. One approach might be to reduce the number of layers in the network or the number of neurons in each layer, since a simpler model is less likely to overfit. Another approach might be to increase the amount of dropout, since this will artificially limit the complexity of the model.\n",
+    "\n",
+    "_Bonus:_ there is one fundamental issue with how I'm using the data from the very beginning which is also contributing to the overfitting problem in this particular case. Can you think of something we can do to the data before training which would ensure that the training and test sets are more similar?\n",
+    "\n",
+    "Once you fix the overfitting problem and achieve a minimum test loss of less than 6.0, submit your work as a pull request back to the main project."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python [default]",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}

From f22fdd62ed29e021f8cd9d1db1c06bc60572b674 Mon Sep 17 00:00:00 2001
From: yiiinghuang <mac@dyn-129-236-233-70.dyn.columbia.edu>
Date: Fri, 21 Oct 2016 18:01:39 -0400
Subject: [PATCH 5/8] yh2792_Lab4-part2

02-tensorflow ANN for classification
---
 notebooks/week-4/Lab4-Part2.ipynb | 383 ++++++++++++++++++++++++++++++
 1 file changed, 383 insertions(+)
 create mode 100644 notebooks/week-4/Lab4-Part2.ipynb

diff --git a/notebooks/week-4/Lab4-Part2.ipynb b/notebooks/week-4/Lab4-Part2.ipynb
new file mode 100644
index 0000000..4491fc6
--- /dev/null
+++ b/notebooks/week-4/Lab4-Part2.ipynb
@@ -0,0 +1,383 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import math\n",
+    "import random\n",
+    "\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "from sklearn.datasets import load_boston\n",
+    "\n",
+    "from sklearn.preprocessing import OneHotEncoder\n",
+    "\n",
+    "import numpy as np\n",
+    "import tensorflow as tf\n",
+    "\n",
+    "sns.set(style=\"ticks\", color_codes=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "dataset = load_boston()\n",
+    "houses = pd.DataFrame(dataset.data, columns=dataset.feature_names)\n",
+    "houses['target'] = dataset.target"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# WRITE CODE TO CONVERT 'TARGET' COLUMN FROM CONTINUOUS TO CATEGORICAL\n",
+    "mean = np.mean(dataset.target)\n",
+    "lengthOfTarget = len(dataset.target)\n",
+    "i = 0\n",
+    "while i < lengthOfTarget:\n",
+    "    if dataset.target[i] > mean:\n",
+    "        houses.target.set_value(i,1)\n",
+    "    else:\n",
+    "        houses.target.set_value(i,0)\n",
+    "    i = i+1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.0 <-- should be 1\n",
+      "0.0 <-- should be 0\n"
+     ]
+    }
+   ],
+   "source": [
+    "'''check your work'''\n",
+    "print np.max(houses['target']), \"<-- should be 1\"\n",
+    "print np.min(houses['target']), \"<-- should be 0\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "('Training set', (455, 13), (455, 2))\n",
+      "('Test set', (51, 13), (51, 2))\n"
+     ]
+    }
+   ],
+   "source": [
+    "houses_array = houses.as_matrix().astype(float)\n",
+    "np.random.shuffle(houses_array)\n",
+    "\n",
+    "X = houses_array[:, :-1]\n",
+    "y = houses_array[:, -1]\n",
+    "\n",
+    "#one hot encoding\n",
+    "y=y.reshape(-1,1)\n",
+    "enc=OneHotEncoder()\n",
+    "enc.fit(y)\n",
+    "\n",
+    "y=enc.transform(y).toarray()\n",
+    "\n",
+    "X = X / X.max(axis=0)\n",
+    "\n",
+    "trainingSplit = int(.9 * houses_array.shape[0])\n",
+    "X_train = X[:trainingSplit]\n",
+    "y_train = y[:trainingSplit]\n",
+    "X_test = X[trainingSplit:]\n",
+    "y_test = y[trainingSplit:]\n",
+    "\n",
+    "print('Training set', X_train.shape, y_train.shape)\n",
+    "print('Test set', X_test.shape, y_test.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2 <-- should be 2\n",
+      "2 <-- should be 2\n",
+      "[ 0.  1.] <-- should be either [0. 1.] or [1. 0.]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print y_train.shape[1], \"<-- should be 2\"\n",
+    "print y_test.shape[1], \"<-- should be 2\"\n",
+    "print y_train[0], \"<-- should be either [0. 1.] or [1. 0.]\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# helper variables\n",
+    "num_samples = X_train.shape[0]\n",
+    "num_features = X_train.shape[1]\n",
+    "num_outputs = y_train.shape[1]\n",
+    "\n",
+    "# Hyper-parameters\n",
+    "batch_size = 18\n",
+    "num_hidden_1 = 32\n",
+    "num_hidden_2 =  30\n",
+    "learning_rate =  0.1\n",
+    "training_epochs =  500\n",
+    "dropout_keep_prob = 0.9 # 0.5 # set to no dropout by default\n",
+    "\n",
+    "# variable to control the resolution at which the training results are stored\n",
+    "display_step = 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def accuracy(predictions, targets):\n",
+    "    \n",
+    "    # IMPLEMENT THE NEW ACCURACY MEASURE HERE\n",
+    "    max_pred = np.argmax(predictions,1)\n",
+    "    max_target = np.argmax(targets,1)\n",
+    "    accuracy = (np.sum((max_pred == max_target))/float(max_pred.shape[0]))*100.00\n",
+    "    return accuracy\n",
+    "\n",
+    "def weight_variable(shape):\n",
+    "    initial = tf.truncated_normal(shape, stddev=0.1)\n",
+    "    return tf.Variable(initial)\n",
+    "\n",
+    "def bias_variable(shape):\n",
+    "    initial = tf.constant(0.1, shape=shape)\n",
+    "    return tf.Variable(initial)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "graph = tf.Graph()\n",
+    "\n",
+    "with graph.as_default():\n",
+    "    \n",
+    "    x = tf.placeholder(tf.float32, shape=(None, num_features))\n",
+    "    _y = tf.placeholder(tf.float32, shape=(None))\n",
+    "    \n",
+    "    keep_prob = tf.placeholder(tf.float32)\n",
+    "    \n",
+    "    tf_X_test = tf.constant(X_test, dtype=tf.float32)\n",
+    "    tf_X_train = tf.constant(X_train, dtype=tf.float32)\n",
+    "    \n",
+    "    \n",
+    "    W_fc1 = weight_variable([num_features, num_hidden_1])\n",
+    "    b_fc1 = bias_variable([num_hidden_1])\n",
+    "    \n",
+    "    W_fc2 = weight_variable([num_hidden_1, num_hidden_2])\n",
+    "    b_fc2 = bias_variable([num_hidden_2])\n",
+    "    \n",
+    "    W_fc3 = weight_variable([num_hidden_2, num_outputs])\n",
+    "    b_fc3 = bias_variable([num_outputs])\n",
+    "    \n",
+    "    \n",
+    "    def model(data, keep):\n",
+    "        \n",
+    "        fc1 = tf.nn.sigmoid(tf.matmul(data, W_fc1) + b_fc1)\n",
+    "        fc1_drop = tf.nn.dropout(fc1, keep)\n",
+    "        \n",
+    "        fc2 = tf.nn.sigmoid(tf.matmul(fc1_drop, W_fc2) + b_fc2)\n",
+    "        fc2_drop = tf.nn.dropout(fc2, keep)\n",
+    "        \n",
+    "        fc3 = tf.matmul(fc2_drop, W_fc3) + b_fc3\n",
+    "        \n",
+    "        return fc3\n",
+    "    \n",
+    "    '''for our loss function we still want to get the raw outputs \n",
+    "    of the model, but since it no longer represents the actual prediction \n",
+    "    we rename the variable to ‘output’'''\n",
+    "    output = model(x, keep_prob)\n",
+    "    \n",
+    "    # WHEN WE CALCULATE THE PREDICTIONS, WE NEED TO WRAP EACH OUTPUT IN A\n",
+    "    # tf.nn.softmax() FUNCTION. THE FIRST ONE HAS BEEN DONE FOR YOU:\n",
+    "    prediction = tf.nn.softmax(output)\n",
+    "    test_prediction = model(tf_X_test, 1.0)\n",
+    "    train_prediction = model(tf_X_train, 1.0)\n",
+    "    \n",
+    "    '''finally, we replace our previous MSE cost function with the\n",
+    "    cross-entropy function included in Tensorflow. This function takes in the\n",
+    "    raw output of the network and calculates the average loss with the target'''\n",
+    "    loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(output, _y))\n",
+    "    optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss)\n",
+    "    \n",
+    "    saver = tf.train.Saver()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Initialized\n",
+      "Model saved in file: model_houses_classification.ckpt\n"
+     ]
+    }
+   ],
+   "source": [
+    "results = []\n",
+    "\n",
+    "with tf.Session(graph=graph) as session:\n",
+    "    \n",
+    "    tf.initialize_all_variables().run()\n",
+    "    print('Initialized')\n",
+    "\n",
+    "    for epoch in range(training_epochs):\n",
+    "        \n",
+    "        indexes = range(num_samples)\n",
+    "        random.shuffle(indexes)\n",
+    "        \n",
+    "        for step in range(int(math.floor(num_samples/float(batch_size)))):\n",
+    "            offset = step * batch_size\n",
+    "            \n",
+    "            batch_data = X_train[indexes[offset:(offset + batch_size)]]\n",
+    "            batch_labels = y_train[indexes[offset:(offset + batch_size)]]\n",
+    "\n",
+    "            feed_dict = {x : batch_data, _y : batch_labels, keep_prob: dropout_keep_prob}\n",
+    "            \n",
+    "            _, l, p = session.run([optimizer, loss, prediction], feed_dict=feed_dict)\n",
+    "\n",
+    "        if (epoch % display_step == 0):\n",
+    "            batch_acc = accuracy(p, batch_labels)\n",
+    "            train_acc =  accuracy(train_prediction.eval(session=session), y_train)\n",
+    "            test_acc =  accuracy(test_prediction.eval(session=session), y_test)\n",
+    "            results.append([epoch, batch_acc, train_acc, test_acc])\n",
+    "\n",
+    "    save_path = saver.save(session, \"model_houses_classification.ckpt\")\n",
+    "    print(\"Model saved in file: %s\" % save_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Maximum test accuracy: 98.04%\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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LiEjtJhSfMLrTfaxSaJNCjRQTu2tR40UJfEdm9za17URjo0HAmUWY1b6A8tad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WDNpHTa5RI2EicVYvqjQJipp/rPUWtd2dyYqa3L8sDVRhImmImi4RNU70NeZFrNd7D9DYQo6S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\nzUSMN9OePxp/Ro3rBgDAaMPf/gUI24sqtXYn4Vuj5g0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2LPT56667TmvXrs35uBUTqC1Y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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fcd68491a50>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df = pd.DataFrame(data=results, columns = [\"epoch\", \"batch_acc\", \"train_acc\", \"test_acc\"])\n",
+    "df.set_index(\"epoch\", drop=True, inplace=True)\n",
+    "\n",
+    "fig, ax = plt.subplots(1, 1, figsize=(10, 4))\n",
+    " \n",
+    "ax.plot(df)\n",
+    "ax.set(xlabel='Epoch',\n",
+    "       ylabel='Error',\n",
+    "       title='Training result')\n",
+    " \n",
+    "ax.legend(df.columns, loc=1)\n",
+    "\n",
+    "print \"Maximum test accuracy: %.2f%%\" % np.max(df[\"test_acc\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python [default]",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}

From b3c589b05876e0c4fa25bd041b29f921bf865cbb Mon Sep 17 00:00:00 2001
From: yiiinghuang <mac@dyn-129-236-232-40.dyn.columbia.edu>
Date: Mon, 24 Oct 2016 13:22:06 -0400
Subject: [PATCH 6/8] Lab5

01
02
02
---
 .../week-5/01-CNN in keras for mnist.ipynb    | 491 ++++++++++++++++++
 .../week-5/02-using your own images.ipynb     | 332 ++++++++++++
 .../03-CNN in keras for dogs and cats.ipynb   | 445 ++++++++++++++++
 3 files changed, 1268 insertions(+)
 create mode 100644 notebooks/week-5/01-CNN in keras for mnist.ipynb
 create mode 100644 notebooks/week-5/02-using your own images.ipynb
 create mode 100644 notebooks/week-5/03-CNN in keras for dogs and cats.ipynb

diff --git a/notebooks/week-5/01-CNN in keras for mnist.ipynb b/notebooks/week-5/01-CNN in keras for mnist.ipynb
new file mode 100644
index 0000000..4b8e88b
--- /dev/null
+++ b/notebooks/week-5/01-CNN in keras for mnist.ipynb	
@@ -0,0 +1,491 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Lab 5.1 - Keras for CNN\n",
+    "\n",
+    "In this lab we will use the [Keras deep learning library](https://keras.io/) to construct a simple convolutional neural network (CNN) to classify the digits from the MNIST dataset. Keras is a very high-level machine learning library that wraps many of the functions and data formats we developed from scratch in earlier labs into simple, easy to use functions. Infact, the library does not actually implement any computation itself, but borrows the necessary functions from either the TensorFlow or Theano libraries. Thus, the goal of Keras is not to compete with these state-of-the-art machine learning libraries, but simply to make them easier to use, especially for non-expert users.\n",
+    "\n",
+    "As a result, you can see that this week's code, despite representing a more complex network, is actually much shorter and easier to understand. This ease of use has made Keras quite popular for neural network development in recent years. The tradeoff to this ease of use is that, while Keras implements many typical machine learning processes and methods, much of its funcitonality is hidden from the user. Thus, it is harder to access the model's parameters, investigate what is happening during model training, and to implement custom features such as your own activation and cost functions. In short, higher level libraries such as Keras may be easier to use for typical tasks, but they are harder to hack and use in non-typical ways.\n",
+    "\n",
+    "We will be using Keras to develop more complex neural networks for the rest of the course, but as you develop your own applications you may want or need to go back to more low-level implementations such as in basic numpy or a medium-level library such as Tensorflow.\n",
+    "\n",
+    "Let's begin this week's lab by importing the libraries we will be using:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "using ordering: tf\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "np.random.seed(1337)  # for reproducibility\n",
+    "\n",
+    "from keras.datasets import mnist\n",
+    "from keras.models import Sequential\n",
+    "from keras.layers import Dense, Dropout, Activation, Flatten\n",
+    "from keras.layers import Convolution2D, MaxPooling2D\n",
+    "from keras.utils import np_utils\n",
+    "from keras import backend as K\n",
+    "\n",
+    "from keras.datasets import mnist\n",
+    "\n",
+    "import pickle\n",
+    "\n",
+    "print \"using ordering:\", K.image_dim_ordering()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "After you import the Keras library you should see a message telling you whether it is using the Theano or Tensorflow library as its backend. Because it relies on one of these libraries to perform its calculations, you need to have at least one of them installed to use Keras. If you are using the virtual machine from the first tutorial you should already have both installed, and for our purposes it does not matter which Keras is configured to use.\n",
+    "\n",
+    "Next, let's import the MNIST dataset from Keras' data library:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "X_train shape: (60000, 28, 28)\n",
+      "60000 train samples\n",
+      "10000 test samples\n"
+     ]
+    }
+   ],
+   "source": [
+    "(X_train, y_train), (X_test, y_test) = mnist.load_data()\n",
+    "\n",
+    "print 'X_train shape:', X_train.shape\n",
+    "print X_train.shape[0], 'train samples'\n",
+    "print X_test.shape[0], 'test samples'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The data comes in as separate variables for feature (X) and target (y) data, and is already split between training and test sets.\n",
+    "\n",
+    "The format for the X data is a three-dimensional numpy array of integers. The first dimension represents the individual images, and the second and third dimensions represent the grayscale pixels values of each 28x28 image. The pixel values are represented as integers in the range 0-255. The y data contains a list of single integers representing the type of digit 0-9.\n",
+    "\n",
+    "Before we can use this data for training, we need to convert the integer (whole number) numbers in the range 0-255 to float (decimal) numbers in the range 0-1. We do this by casting the numpy arrays to 'float' type, and dividing each value by 255."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "X_train = X_train.astype('float32')\n",
+    "X_test = X_test.astype('float32')\n",
+    "X_train /= 255.0\n",
+    "X_test /= 255.0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To use this data with Keras we also need to reformat it to match the specific needs of the library. Because Keras' convolutional operations are made specifically for processing images, they expect each piece of image data to be represented by three dimensions: two for the dimensions of the image, and an extra dimension to represent the RGB channels of a full-color image. \n",
+    "\n",
+    "Since in this case we are working with black and white images, we will simply add an extra dimension to the end of both X datasets so it matches the expected format. We will also use the `to_categorical()` utility included with the Keras library to convert both of the y datasets to the categorical format expected by Keras."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(60000, 28, 28, 1)\n",
+      "(60000,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# number of classes\n",
+    "num_classes = 10\n",
+    "\n",
+    "# image dimensions\n",
+    "img_rows, img_cols = X_train.shape[1],  X_train.shape[2]\n",
+    "\n",
+    "if K.image_dim_ordering() == 'th':\n",
+    "    X_train = X_train.reshape(X_train.shape[0], 1, img_rows, img_cols)\n",
+    "    X_test = X_test.reshape(X_test.shape[0], 1, img_rows, img_cols)\n",
+    "    input_shape = (1, img_rows, img_cols)\n",
+    "else:\n",
+    "    X_train = X_train.reshape(X_train.shape[0], img_rows, img_cols, 1)\n",
+    "    X_test = X_test.reshape(X_test.shape[0], img_rows, img_cols, 1)\n",
+    "    input_shape = (img_rows, img_cols, 1)\n",
+    "\n",
+    "Y_train = np_utils.to_categorical(y_train, num_classes)\n",
+    "Y_test = np_utils.to_categorical(y_test, num_classes)\n",
+    "\n",
+    "print X_train.shape\n",
+    "print y_train.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Before we begin training the network, we can use the matplotlib library to visualize one of the digits to make sure everything has been loaded correctly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(28, 28)\n",
+      "(60000,)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7f482d2d2a10>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWEAAAFfCAYAAACfj30KAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f482b677690>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "from matplotlib.pyplot import imshow\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "img_num = 0\n",
+    "\n",
+    "if K.image_dim_ordering() == 'th':\n",
+    "    img = X_train[img_num][0,:,:]\n",
+    "else:\n",
+    "    img = X_train[img_num][:,:,0]\n",
+    "\n",
+    "print img.shape\n",
+    "print y_train.shape\n",
+    "imshow(img, cmap = plt.get_cmap('gray'), vmin = 0, vmax = 1,  interpolation='nearest')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we define the hyper-parameters of our model and the architecture of the network. In this case we will base our network on the one described in chapter 6 of the textbook:\n",
+    "\n",
+    "http://neuralnetworksanddeeplearning.com/chap6.html\n",
+    "\n",
+    "This is a fairly shallow CNN model utilizing two convolutional layers with 2x2 max pooling for data reduction, and two fully connected layers with 50% dropout for regularization. All layers except for the final classification layer utilize the ReLu activation function. The final layer uses the softmax activation function to convert the raw outputs to a probability distribution. The layers have the following dimensions:\n",
+    "\n",
+    "- The first convolutional layer will use a patch (filter) size of 5 x 5 pixels, and a depth of 20 feature layers\n",
+    "- The second convolutional layer will use a patch (filter) size of 5 x 5 pixels, and a depth of 40 feature layers\n",
+    "- The two fully connected hidden layers will each contain 1000 neurons\n",
+    "\n",
+    "We will train the model for 30 epochs, using mini-batches of 128 samples at a time with the SGD algorithm."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# model hyperparameters\n",
+    "batch_size = 128\n",
+    "nb_epoch = 30\n",
+    "\n",
+    "# network architecture\n",
+    "patch_size_1 = 5\n",
+    "patch_size_2 = 5\n",
+    "\n",
+    "depth_1 = 20\n",
+    "depth_2 = 40\n",
+    "\n",
+    "pool_size = 2\n",
+    "\n",
+    "num_hidden_1 = 1000\n",
+    "num_hidden_2 = 1000\n",
+    "\n",
+    "dropout = 0.5"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now it is time to build the actual model in Keras. To specify the architecture of the neural network, Keras uses an abstracted sequential or 'stack' model. Instead of specifying each step of computation as we did with numpy and Tensorflow, we simply create a new Sequential model, and then use its `.add()` function to add layers to the network according to the parameters specified above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# create new Keras Sequential model\n",
+    "model = Sequential()\n",
+    "\n",
+    "# add first convolutional layer to model and specify it's depth and filter size\n",
+    "# for the first layer we also have to specify the size of each input image\n",
+    "# which we calculated above\n",
+    "model.add(Convolution2D(depth_1, patch_size_1, patch_size_1,\n",
+    "                        border_mode='valid',\n",
+    "                        input_shape=input_shape))\n",
+    "# apply 'relu' activation function for first layer\n",
+    "model.add(Activation('relu'))\n",
+    "# apply max pooling to reduce the size of the image by a factor of 2\n",
+    "model.add(MaxPooling2D(pool_size=(pool_size, pool_size)))\n",
+    "\n",
+    "# repeat these operations for the second convolutional layer\n",
+    "# this time Keras can figure out the input size \n",
+    "# from the previous layer on it's own\n",
+    "model.add(Convolution2D(depth_2, patch_size_2, patch_size_2,\n",
+    "                        border_mode='valid'))\n",
+    "model.add(Activation('relu'))\n",
+    "model.add(MaxPooling2D(pool_size=(pool_size, pool_size)))\n",
+    "\n",
+    "# flatten the three-dimensional convolutional layer to a single layer of neurons\n",
+    "model.add(Flatten())\n",
+    "\n",
+    "# add the first fully connected layer, applying 'relu' activation and dropout\n",
+    "model.add(Dense(num_hidden_1))\n",
+    "model.add(Activation('relu'))\n",
+    "model.add(Dropout(dropout))\n",
+    "\n",
+    "# add the second fully connected layer\n",
+    "model.add(Dense(num_hidden_2))\n",
+    "model.add(Activation('relu'))\n",
+    "model.add(Dropout(dropout))\n",
+    "\n",
+    "# add the final classification layer with the number of neurons \n",
+    "# matching the number of classes we are trying to learn\n",
+    "model.add(Dense(num_classes))\n",
+    "\n",
+    "# apply the 'softmax' activation to the final layer to convert the output to \n",
+    "# a probability distribution\n",
+    "model.add(Activation('softmax'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next, we ask Keras to compile our model, and specify the loss function we want to use, along with the optimization algorithm and the performance metrics we want the model to output after training. You can see that this is much easier and more intuitive than our previous examples where we had to actually write out the forumulas for the loss function and explicitly develop the optimization algorithm. However, if you wanted to use a different loss function or optimization strategy than those already included with Keras you would again have to specify it explicitly using additional functions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "model.compile(loss='categorical_crossentropy',\n",
+    "              optimizer='adadelta',\n",
+    "              metrics=['accuracy'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Once we have compiled the model, we can begin training. The entire training operation  is now wrapped into a single function called `.fit()`. Instead of creating our own structure to loop over every epoch and mini-batch, we simply have to call the `.fit()` function of our Sequential model, and pass in our training and test sets, along with the hyper-parameters that specify the number of epochs and mini-batch size we want to use. If you set the optional `verbose` variable to `1`, Keras will output information about loss and accuracy on both training and test sets as it is training so you can monitor the process."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train on 60000 samples, validate on 10000 samples\n",
+      "Epoch 1/30\n",
+      "60000/60000 [==============================] - 89s - loss: 0.2910 - acc: 0.9081 - val_loss: 0.0639 - val_acc: 0.9796\n",
+      "Epoch 2/30\n",
+      "60000/60000 [==============================] - 95s - loss: 0.0789 - acc: 0.9755 - val_loss: 0.0416 - val_acc: 0.9857\n",
+      "Epoch 3/30\n",
+      "60000/60000 [==============================] - 95s - loss: 0.0565 - acc: 0.9827 - val_loss: 0.0306 - val_acc: 0.9892\n",
+      "Epoch 4/30\n",
+      "60000/60000 [==============================] - 106s - loss: 0.0457 - acc: 0.9859 - val_loss: 0.0263 - val_acc: 0.9909\n",
+      "Epoch 5/30\n",
+      "60000/60000 [==============================] - 105s - loss: 0.0376 - acc: 0.9886 - val_loss: 0.0262 - val_acc: 0.9900\n",
+      "Epoch 6/30\n",
+      "60000/60000 [==============================] - 104s - loss: 0.0316 - acc: 0.9902 - val_loss: 0.0243 - val_acc: 0.9921\n",
+      "Epoch 7/30\n",
+      "60000/60000 [==============================] - 94s - loss: 0.0272 - acc: 0.9915 - val_loss: 0.0270 - val_acc: 0.9908\n",
+      "Epoch 8/30\n",
+      "60000/60000 [==============================] - 94s - loss: 0.0247 - acc: 0.9920 - val_loss: 0.0241 - val_acc: 0.9917\n",
+      "Epoch 9/30\n",
+      "60000/60000 [==============================] - 110s - loss: 0.0209 - acc: 0.9932 - val_loss: 0.0230 - val_acc: 0.9916\n",
+      "Epoch 10/30\n",
+      "60000/60000 [==============================] - 108s - loss: 0.0188 - acc: 0.9940 - val_loss: 0.0206 - val_acc: 0.9924\n",
+      "Epoch 11/30\n",
+      "60000/60000 [==============================] - 104s - loss: 0.0171 - acc: 0.9946 - val_loss: 0.0222 - val_acc: 0.9928\n",
+      "Epoch 12/30\n",
+      "60000/60000 [==============================] - 97s - loss: 0.0159 - acc: 0.9950 - val_loss: 0.0258 - val_acc: 0.9923\n",
+      "Epoch 13/30\n",
+      "60000/60000 [==============================] - 102s - loss: 0.0133 - acc: 0.9958 - val_loss: 0.0223 - val_acc: 0.9932\n",
+      "Epoch 14/30\n",
+      "60000/60000 [==============================] - 99s - loss: 0.0130 - acc: 0.9959 - val_loss: 0.0211 - val_acc: 0.9935\n",
+      "Epoch 15/30\n",
+      "60000/60000 [==============================] - 106s - loss: 0.0114 - acc: 0.9965 - val_loss: 0.0202 - val_acc: 0.9937\n",
+      "Epoch 16/30\n",
+      "60000/60000 [==============================] - 96s - loss: 0.0105 - acc: 0.9968 - val_loss: 0.0228 - val_acc: 0.9927\n",
+      "Epoch 17/30\n",
+      "60000/60000 [==============================] - 95s - loss: 0.0096 - acc: 0.9971 - val_loss: 0.0202 - val_acc: 0.9937\n",
+      "Epoch 18/30\n",
+      "60000/60000 [==============================] - 104s - loss: 0.0084 - acc: 0.9974 - val_loss: 0.0237 - val_acc: 0.9939\n",
+      "Epoch 19/30\n",
+      "60000/60000 [==============================] - 100s - loss: 0.0082 - acc: 0.9973 - val_loss: 0.0222 - val_acc: 0.9938\n",
+      "Epoch 20/30\n",
+      "60000/60000 [==============================] - 99s - loss: 0.0075 - acc: 0.9975 - val_loss: 0.0217 - val_acc: 0.9937\n",
+      "Epoch 21/30\n",
+      "60000/60000 [==============================] - 107s - loss: 0.0072 - acc: 0.9977 - val_loss: 0.0239 - val_acc: 0.9935\n",
+      "Epoch 22/30\n",
+      "60000/60000 [==============================] - 109s - loss: 0.0068 - acc: 0.9980 - val_loss: 0.0236 - val_acc: 0.9932\n",
+      "Epoch 23/30\n",
+      "60000/60000 [==============================] - 111s - loss: 0.0064 - acc: 0.9982 - val_loss: 0.0241 - val_acc: 0.9928\n",
+      "Epoch 24/30\n",
+      "60000/60000 [==============================] - 114s - loss: 0.0055 - acc: 0.9981 - val_loss: 0.0229 - val_acc: 0.9934\n",
+      "Epoch 25/30\n",
+      "60000/60000 [==============================] - 111s - loss: 0.0053 - acc: 0.9985 - val_loss: 0.0235 - val_acc: 0.9926\n",
+      "Epoch 26/30\n",
+      "60000/60000 [==============================] - 103s - loss: 0.0047 - acc: 0.9987 - val_loss: 0.0262 - val_acc: 0.9930\n",
+      "Epoch 27/30\n",
+      "60000/60000 [==============================] - 99s - loss: 0.0042 - acc: 0.9987 - val_loss: 0.0246 - val_acc: 0.9931\n",
+      "Epoch 28/30\n",
+      "60000/60000 [==============================] - 98s - loss: 0.0042 - acc: 0.9987 - val_loss: 0.0249 - val_acc: 0.9940\n",
+      "Epoch 29/30\n",
+      "60000/60000 [==============================] - 99s - loss: 0.0038 - acc: 0.9989 - val_loss: 0.0224 - val_acc: 0.9944\n",
+      "Epoch 30/30\n",
+      "60000/60000 [==============================] - 105s - loss: 0.0034 - acc: 0.9991 - val_loss: 0.0246 - val_acc: 0.9937\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<keras.callbacks.History at 0x7f47ffdc0c10>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.fit(X_train, Y_train, batch_size=batch_size, nb_epoch=nb_epoch,\n",
+    "          verbose=1, validation_data=(X_test, Y_test))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Depending on the computer you're using, training this network through 30 epochs will take some time. Using the virtual machine from Lab 1, each epoch takes approximately 40-50 seconds. Once the training is complete, we can use the model's `.evaluate()` function to apply the model to the entire test set, and get back the model's performance in terms of total loss and prediction accuracy."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test score: 0.0246312292547\n",
+      "Test accuracy: 99.37%\n"
+     ]
+    }
+   ],
+   "source": [
+    "score = model.evaluate(X_test, Y_test, verbose=0)\n",
+    "\n",
+    "print 'Test score:', score[0]\n",
+    "print 'Test accuracy: {:.2%}'.format(score[1])"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python [default]",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks/week-5/02-using your own images.ipynb b/notebooks/week-5/02-using your own images.ipynb
new file mode 100644
index 0000000..a96261b
--- /dev/null
+++ b/notebooks/week-5/02-using your own images.ipynb	
@@ -0,0 +1,332 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Lab 5.2 - Using your own images\n",
+    "\n",
+    "In the next part of the lab we will download another set of images from the web and format them for use with a Convolutional Neural Network (CNN). In this example we will use cat and dog images from a [recent competition on Kaggle](https://www.kaggle.com/c/dogs-vs-cats) but you will be able to follow the same process to import and format your own sets of images and use them to solve your own image classification problems if you wish.\n",
+    "\n",
+    "Let's begin by importing some of the libraries we will be using in this lab:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "\n",
+    "from matplotlib.pyplot import imshow\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from scipy import misc\n",
+    "\n",
+    "import os\n",
+    "import random\n",
+    "import pickle"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Go to https://www.kaggle.com/c/dogs-vs-cats/data and download the **\"train\" dataset only** to your computer. You will have to register for a Kaggle account before you can download the data. [Kaggle](https://www.kaggle.com) is an online repository for Machine Learning (ML) and Artificial Intelligence (AI) competitions and is a great resource for getting data to test your learning algorithms, and to keep up with the state-of-the-art in the ML and AI fields.\n",
+    "\n",
+    "Once the `train.zip` file has been downloaded, uncompress it to a folder on your computer. The folder contains 25,000 images named according to whether they are a 'cat' or 'dog'. To make sure that these images work with the code below, create a new folder in the `week-5` folder in your local repository (the same folder that contains this notebook file) called `-catsdogs`. Notice the dash (`-`) before the name, this is important so that Github does not sync the images to your account (which is not necessary and would take a really long time). Within this folder create two new folders called `0` and `1`. Your folder structure should look like this:\n",
+    "\n",
+    "    .\n",
+    "    ├── dmc\n",
+    "    |   ├── notebooks\n",
+    "    |   |   └── week-5\n",
+    "    |   |   |   └── -catsdogs\n",
+    "    |   |   |   |   └── 0\n",
+    "    |   |   |   |   └── 1\n",
+    "\n",
+    "\n",
+    "Finally, move all the cat images into the `0` folder, and all dog images into the `1` folder. From now on, we will consider the category `0` to represent `cat` and the category `1` to represent `dog`.\n",
+    "\n",
+    "Next, we will use the `os` library to find the folders inside the main `-catsdogs` folder. This will make the code extensible to image recognition problems with any number of categories. In this case we only have two categories (cats and dogs) but you can extend it to more categories simply by adding more folders with images and labeling the folders sequentially starting with `0`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['0', '1']\n"
+     ]
+    }
+   ],
+   "source": [
+    "imageFolder = \"-catsdogs\"\n",
+    "\n",
+    "folders = os.listdir(imageFolder)\n",
+    "num_categories = len(folders)\n",
+    "\n",
+    "print folders"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we will look through each folder and generate a data set of properly formatted image data matched with the proper category label."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Load data complete\n"
+     ]
+    }
+   ],
+   "source": [
+    "# specify desired image properties\n",
+    "# in this case we want black and white square images 64x64 pixels in size\n",
+    "image_dim = 1 # black and white\n",
+    "image_size = 64\n",
+    "\n",
+    "# create an empty array to store the image data\n",
+    "data = []\n",
+    "\n",
+    "# look inside each folder which represents the categories of our data\n",
+    "for folder in folders:\n",
+    "    \n",
+    "    # find the files within each folder\n",
+    "    fileNames = os.listdir(\"/\".join([imageFolder, folder]))\n",
+    "    \n",
+    "    # for each file, load and process each image\n",
+    "    # in this case we limit the number of images used per cateogry to 10,000\n",
+    "    # to prevent overloading our RAM memory\n",
+    "    for fileName in fileNames[:10000]:\n",
+    "        \n",
+    "        # read in the image data into a numpy array\n",
+    "        img = misc.imread(\"/\".join([imageFolder, folder, fileName]))\n",
+    "        \n",
+    "        # if the image contains more than one color channel,\n",
+    "        # take only the first channel (in effect, convert it to black and white)\n",
+    "        if image_dim == 1 and len(img.shape) > 2: \n",
+    "            img = img[:,:,0] # convert to black and white\n",
+    "\n",
+    "        # resize to target resolution if necessary\n",
+    "        if img.shape[0] != image_size or img.shape[1] != image_size:\n",
+    "            img = misc.imresize(img, (image_size, image_size), interp='nearest')\n",
+    "\n",
+    "        # normalize data to have mean 0 and standard deviation 1\n",
+    "        # then rescale it to roughly the range 0-1\n",
+    "        img = (img - img.mean()) / img.std() / 4 + 0.5\n",
+    "        \n",
+    "        # add the image data and the associated category \n",
+    "        # (which is stored in the folder variable) to the data set\n",
+    "        # for this to work you need to make sure your folders \n",
+    "        # are named sequentially starting with 0\n",
+    "        data.append([img, folder])\n",
+    "\n",
+    "print \"Load data complete\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The process of loading all the image data and putting them into the `data` array will take some time so be patient and wait for the cell to finish running before continuing with the rest of the notebook. \n",
+    "\n",
+    "If you get an error saying the kernel has crashed, you are probably running out of RAM memory. The entire data array with all image information needs to be stored dynamically in your RAM while the process is running, so depending on your computer's available RAM, using too many images or too high of a resolution can cause the RAM to fill up completely before the process has finished running, which will unfortunately cause Python to crash. If you run into this issue try setting a lower target resolution for the images or loading less images from the folder.\n",
+    "\n",
+    "Once the data is loaded, we will shuffle the whole dataset to ensure random distribution of both categories."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "random.shuffle(data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next we will make two new blank numpy arrays for both the feature (X) and target (y) data, and fill them with data from the `data` array. It might seem redundant to first load the data into a Python array and then transfer them to the numpy arrays we actually want. However, Python arrays have a more flexible data structure which allows us to fill the data set without first knowing how many images we have, and lets us keep the feature and target data together for each sample. This makes it easier to shuffle the entire data set in one move, and makes the process more flexible for other sets of images."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "X = np.ndarray((len(data), image_size, image_size), dtype=np.float32)\n",
+    "y = np.ndarray((len(data), 1), dtype=np.int32)\n",
+    "\n",
+    "for i, d in enumerate(data):\n",
+    "    X[i] = d[0]\n",
+    "    y[i] = d[1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's make sure the data set has been properly imported and formatted by visualizing one of the images in the X feature dataset and printing the corresponding category from the y target dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "image dimensions: (64, 64)\n",
+      "target category: dog\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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Vr5xGJlnHjh2d1rx588w2LQlF94DSYAUFBU7bvXu308hQWr58udPsskpkwtGz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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fe0f0fdf190>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "img_index = 2\n",
+    "\n",
+    "img = X[img_index]\n",
+    "print \"image dimensions:\", img.shape\n",
+    "print \"target category:\", (['cat', 'dog'][y[img_index][0]])\n",
+    "\n",
+    "imshow(img, cmap = plt.get_cmap('gray'), vmin = 0, vmax = 1,  interpolation='nearest')\n",
+    "plt.axis('off')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we will split both the X and y data sets by an arbitrary factor to create separate training and test sets. As before we will use the first 70% of the data for training, and the remaining 30% of the data for testing."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "trainingSplit = int(.7 * X.shape[0])\n",
+    "    \n",
+    "X_train = X[:trainingSplit]\n",
+    "y_train = y[:trainingSplit]\n",
+    "X_test = X[trainingSplit:]\n",
+    "y_test = y[trainingSplit:]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally, we will use the `pickle` library to save these datasets out to a local file. \n",
+    "\n",
+    "The `pickle` library is extremely useful for saving the state of variables in your Python programs for later reuse. The library is able to take variables of any data type and output them to efficiently compressed local binary files. When you need the data again you can use the `pickle` library to reload the variables  from the generated file. This is especially useful for storing sets of data that you may want to reuse several times, but take a long time to produce. This way you won't need to run the process in this notebook each time you want to use the images to train a model.\n",
+    "\n",
+    "*Warning:* the saved dataset with 10,000 images per category will be over 300mb, so make sure you have enough space on your hard drive before running the following cell:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved data to -catsdogs.pickle\n",
+      "Compressed pickle size: 327760375\n"
+     ]
+    }
+   ],
+   "source": [
+    "pickle_file = imageFolder + '.pickle'\n",
+    "\n",
+    "try:\n",
+    "    f = open(pickle_file, 'wb')\n",
+    "    save = {\n",
+    "        'X_train': X_train,\n",
+    "        'y_train': y_train,\n",
+    "        'X_test': X_test,\n",
+    "        'y_test': y_test,\n",
+    "    }\n",
+    "    pickle.dump(save, f, pickle.HIGHEST_PROTOCOL)\n",
+    "    f.close()\n",
+    "except Exception as e:\n",
+    "    print 'Unable to save data to', pickle_file, ':', e\n",
+    "    raise\n",
+    "    \n",
+    "statinfo = os.stat(pickle_file)\n",
+    "print 'Saved data to', pickle_file\n",
+    "print 'Compressed pickle size:', statinfo.st_size"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python [default]",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks/week-5/03-CNN in keras for dogs and cats.ipynb b/notebooks/week-5/03-CNN in keras for dogs and cats.ipynb
new file mode 100644
index 0000000..7802a38
--- /dev/null
+++ b/notebooks/week-5/03-CNN in keras for dogs and cats.ipynb	
@@ -0,0 +1,445 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Lab 5.3 - CNN for cats and dogs\n",
+    "\n",
+    "Now that we have imported our custom image data, formatted them as proper feature and target numpy arrays, and split them between individual training and test data sets, we can use Keras to create another Convolutional Neural Network (CNN) and train it to classify images of cats and dogs (the holy grail of Arificial Intelligence!)\n",
+    "\n",
+    "First, let's use the pickle library to bring in the data sets we generated in the previous part of the lab:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "('Training set', (14000, 64, 64), (14000, 1))\n",
+      "('Test set', (6000, 64, 64), (6000, 1))\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pickle\n",
+    "\n",
+    "pickle_file = '-catsdogs.pickle'\n",
+    "\n",
+    "with open(pickle_file, 'rb') as f:\n",
+    "    save = pickle.load(f)\n",
+    "    X_train = save['X_train']\n",
+    "    y_train = save['y_train']\n",
+    "    X_test = save['X_test']\n",
+    "    y_test = save['y_test']\n",
+    "    del save  # hint to help gc free up memory\n",
+    "    print('Training set', X_train.shape, y_train.shape)\n",
+    "    print('Test set', X_test.shape, y_test.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now that the data is imported, go through and implement the CNN from scratch based on the one developed in Lab 5.1. \n",
+    "\n",
+    "Experiment with different hyper-parameters as well as different architectures for your network. If you're not getting the results you want try a deeper network by adding more convolutional or fully connected layers. Remember that with CNN's, all convolutional layers should go in the beginning, and the fully connected layers should go at the end. You can also try to make the network 'wider' by adding more depth to each convolutional layer or more neurons to the fully connected layers. If you are noticing problems with over-fitting you can experiment with larger dropout rates or other regularlization strategies.\n",
+    "\n",
+    "You can also experiment with filters of a larger size in the convolutional layers. Larger filters will capture more information in the image at the expense of longer training times. For more information about the tradeoffs between depth and width in a CNN, you can read this paper: \n",
+    "\n",
+    "https://arxiv.org/pdf/1409.1556.pdf\n",
+    "\n",
+    "Known as the 'VGG paper', this research is currently one of the state-of-the-art benchmarks for image recognition using CNN's. The authors' hypothesis for the paper was that depth in a CNN (total number of layers) is much more important than the size of the filters or the depth within each convolutional layer. Thus they used very small filter sizes (only 3x3) but focused on making the networks as deep as possible. If you are still getting poor results and want to develop a deeper network, a good place to start would be to try to implement one of the networks from the 'VGG paper'. The deepest ones will probably take too long to train without having a dedicated graphics card, but you should be able to train one of the medium ones (for example network 'B') using just the virtual machine developed in the first lab.\n",
+    "\n",
+    "Just like when we initially loaded the data, with large networks you again run the risk of overloading your RAM memory, which will either throw an error during model compilation or training, or cause your Python kernel to crash. If you run into these issues, try reducing the complexity of your network (either using less layers, or reducing the depth of each layer) or using a smaller mini-batch size. If you are using the virtual machine, your RAM will be quite limited so you will not be able to train very deep or complex networks. This is ok for the demonstration purposes of the class, but for your own work you may want to use a native installation of Python and the related libraries so that you can use the full potential of your computer.\n",
+    "\n",
+    "Ofcourse classifying dogs and cats is a much more difficult problem than digit classification, so you should not expect to reach the same level of performance we did before. With an average sized network training over night on the virtual machine, you should be able to get at least 80% accuracy on the test dataset. Once you get a result you like, submit your work on this file as a pull request back to the main project."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using TensorFlow backend.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "using ordering: tf\n"
+     ]
+    }
+   ],
+   "source": [
+    "## implement your CNN starting here.\n",
+    "import pickle\n",
+    "from keras.models import Sequential\n",
+    "from keras.layers import Dense, Dropout, Activation, Flatten\n",
+    "from keras.layers import Convolution2D, MaxPooling2D, ZeroPadding2D\n",
+    "from keras.utils import np_utils\n",
+    "from keras.optimizers import SGD\n",
+    "from keras import backend as K\n",
+    "\n",
+    "K.set_image_dim_ordering('tf')\n",
+    "\n",
+    "print \"using ordering:\", K.image_dim_ordering()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(14000, 64, 64)\n",
+      "64 64\n",
+      "(14000, 64, 64, 1)\n",
+      "(14000, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "num_classes = 2\n",
+    "img_rows, img_cols = X_train.shape[1],  X_train.shape[2]\n",
+    "\n",
+    "print X_train.shape\n",
+    "print img_rows, img_cols\n",
+    "\n",
+    "if K.image_dim_ordering()== 'th':\n",
+    "    X_train = X_train.reshape(X_train.shape[0],1, img_rows, img_cols)\n",
+    "    X_test = X_test.reshape(X_test.shape[0], 1, img_rows, img_cols)\n",
+    "    input_shape = (1, img_rows, img_cols)\n",
+    "else:\n",
+    "    X_train = X_train.reshape(X_train.shape[0], img_rows, img_cols, 1)\n",
+    "    X_test = X_test.reshape(X_test.shape[0], img_rows, img_cols, 1)\n",
+    "    input_shape = (img_rows, img_cols, 1)\n",
+    "\n",
+    "Y_train = np_utils.to_categorical(y_train, num_classes)\n",
+    "Y_test = np_utils.to_categorical(y_test, num_classes)\n",
+    "\n",
+    "print X_train.shape\n",
+    "print y_train.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(64, 64)\n",
+      "(14000, 1)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7fdcc05ab090>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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ylf6+bE52VtQ6VokVPWMo2gmb2SwAKwCcCOAAgD92zm0ws/MAOAB+zmM9cs5Z\nCCGER2fehNcDmAtgMIDrATxmZgu6tVdCCHGcULQTds4dAVCV31xlZvOQiwU/AMAAVCD7NlwBICyR\n7/HKK69k/vzt06cPZs6cidNPP73YLgohxFGjrq4OdXV1mZBNMd9Vd0eyRh8AA5xzW8ysDsClANYA\ngJmVATgHwEMdHeTiiy/OxMhYvFMIIUqNyspKVFZWZmLCjY2NeP3116P2L8oJm9k/AfgtgO0ATgHw\nZwAuAnB5vsmDAO42s00AtgK4H8AOAM92dOwBAwZkhInYDDE/GB5b6o6VX2SZTuyc7Lfc2Wefndne\nvn170IaJPSzzK1bsYdmBfllGlrnH9osVDZl4xERIvx9M1GpoaAhsbM22adOmBTZWQvInP/lJYPPL\nNE6cODFo45eUBLjIwspx3nfffYGNiVgrVqwIbP4Y2JpqrL9sfrByn6zd7t27A1sx66EVwgTCmPKq\nbfWtt1P4DMVk/7VQ7JvwqQB+BmAkgAbk3ngvd869nD/xA2Y2EMAjAMoBLAVwpXMu9KhCCCGKc8LO\nuTABPmxzL4B7O9kfIYQ4rlDtCCGESIicsBBCJKRkSln27ds3E8BnAhBbK86HBcSZOMVEhViBgp3D\n7y/LEKuurg5sLDuO9ZcJRUy89L8qYfuxNdUYsSUNWWadL+qxsc+fPz+wMUWZXUt/bUGAZwf6AuyH\nH34YtFm4cGFgu/rqqwMb+2KHXSMGmzO+iMxE2tGjRwc29mwwITi2b/6878yaai30tsw61l9/rLHX\no/B+FiPM6U1YCCESIicshBAJkRMWQoiElExM+NChQ5lqWexjfBab8RMsWCyGxfJYYkZXlgLyK32x\neFxZWVlgY1WtWDIF+7g9JjbN+s+OH7u8ETseq/Tlt2P9Z/FlFg9nMWcWv2fLD9XU1GS2Fy1aFLRh\n8+OSSy4JbCwWy2LM5eXlga2zc5c9B88991xgY/HkWD3Avzds7rLEndglhLozMSMmhlsMMfvGVtkr\n7JuWNxJCiF6CnLAQQiRETlgIIRIiJyyEEAkpGWHOzDLBbJaIwCqJ+QIQE9xiP7aOrQEaszQSC8yP\nGTMmsNXV1QU2VumKiVNMUPLFxdiSoOwasevBhDkmWPn3j12PlStXBjbW37179wY2djyW2PDTn/40\nsz1p0qSgDRvnkiVLAttVV10V2FiSBBNgmXjpj4GJXywphYm5bF8m1rHqgb5wxpKiYpcIY89tT8Pm\nH1sSq1QcfXD0AAAcSklEQVTRm7AQQiRETlgIIRIiJyyEEAmRExZCiISUjDA3YMAAKhoUwjJvfCGH\nCUfMxoQdJk7F4h8vNsuGiTj79u0LbEyUjKnMFdOmLVgGYWwmkC92MRGHCW6sv7W1tVH9mDFjRmDz\nl0bylzsCgK997WuBjWXfMZgQtWzZssDGBEdfAGPLJ73zzjuBLXbJKibMMdHNv1exmaOxc7w7YceP\nFeFi565/jp4ek96EhRAiIXLCQgiREDlhIYRIiJywEEIkpGSEuSNHjnSYscbEKT9TiAkgLJuIiRtM\nGIwtzecLT0yIig3ws2V6mPgwfPjwwOZnNrFrGpv9xMbOhJ0YwYMt48QyvxoaGqJs7BrFlCxlAtPj\njz8e2P7+7/8+sDFGjBgR2FgWJJuDvph28cUXB23Y0kuxy3CxTDJ2zX0hjs0P9rwcq/jzOVbI7uzS\nTnoTFkKIhMgJCyFEQuSEhRAiIXLCQgiRkJIR5vxSlqx0Y2cFMSaKxApnLCjP9o0J3sdm5DFBJaZc\nJBCKi0yYY9eDCTZsrTR2D9jYv/Wtb2W2hw4dGrRhLF68OLCNHz8+sG3ZsiWwsezDm2++ObP96KOP\nBm2mTp0a2FgGGoMJjizDb/78+YHNX5+OZfOxNebY/IsVzmLmaWw5ytg15kqF2HKtMWVpu3OtO70J\nCyFEQuSEhRAiIXLCQgiREDlhIYRISMkIc83NzRnRhwkITFDyBSsWfGciC1uLLrZcX4yoxwSsWFGL\nZQay9dNY6UOW0ebD+s+EP3Ysti/r729+85vMti+QtQUbJxOd2Fpxq1atCmwHDx7MbLNyl4y33347\nsFVWVgY2dg9OP/30wMbWitu8eXNme+fOnUEblgXI5nhX5pt/PHbO3rRmW1uw69FZYsvjxqA3YSGE\nSIicsBBCJEROWAghEiInLIQQCSkZYe6EE07IZHvFrl/lZ7x0ZW03Jj4wMTAmKB+bHcfaMSFq5syZ\ngW3MmDGBbcWKFZntU089NWjDhDQmXrK1xpiNiU5r167NbMeWDmUCIWtXVVUV2Hbt2tVh36699tqg\nDRNUWCbVhg0bAhu7lrHrBvpZhKNGjQravPfee4GNZZPGCnjM5s/njkrKHkvEiOw9nRmoN2EhhEhI\nl5ywmf2DmTWb2Q88+31mVmNmH5nZS2Y2pWvdFEKIY5NOO2Ez+xyA2wCs9ux3Abg9/7N5AJoALDSz\n8O8lIYQ4zulUTNjMTgbwcwC3ArjH+/EdAO53zj2fb3sLgHoA1wJ4qq1j9unTJzqOWkjM8kYshsvi\nXizWxmAffftxI7ZEDEsQiYXFC4cMGRLY/Lg2i/Wy/Visl42BxchZXLSpqSmzXV1dHbRh1ctYrI31\njY1h7Nixgc2vDvfII48EbVgcmvXjoosuCmzTpk0LbO+++25gmzBhQmA7cOBAZpv1n1WLY7FvVvGO\nxeo7m2RQytXRukKshtSTdPZN+CEAzznnXi40mtlEAJUAWusROucaAawEcF5nOymEEMcqRb8Jm9mN\nAP4AwNnkx5UAHHJvvoXU538mhBCigKKcsJmNAfAggMucc+HfpUIIIYqi2DfhswCMAPC2/U+QqC+A\nBWZ2O4AZAAxABbJvwxUAwuoqBbzwwgvB96Bz5szBnDlziuyiEEIcPWpra1FXV5exFfOttRUThDaz\nQQD8tWYeBbAOwD8759aZWQ2A7znnfpjfpww5h3yLc+6X5JhnAvj9V7/6VYwcObLVzgSEGPEodukh\nJgKy47Pr09DQENhilsNhN4YtJXPrrbdG9Y0JNL4gxhIH2DViIhyzxS4L5Vcvq6ioCNowG7u2sUs0\n7d+/P7D5QiW73sOGDQts/nUEeOILW8ooFv94bOwjRowIbCyB47HHHgts7P5t3bq1w3bs2ehs0tLx\nSmNjI1auXAkAZznnwpJ8BRT1JuycawLwfqHNzJoA7HXOrcubHgRwt5ltArAVwP0AdgB4tphzCSHE\n8UB3pC1nXoOccw+Y2UAAjwAoB7AUwJXOubjVA4UQ4jiiy07YOXcJsd0L4N6uHlsIIY51VDtCCCES\nUjJV1AYOHJjJvGJL68QsecSEAT8zCeCZdSxzKrZilS9YMSGD9W3WrFlRx2djZ8vh+Jl1sYIbs7HM\nQHY9WDtf3GH3gAlzrJIdywZjx2PCmX/NWSU01g82F5gAybLjxo/3tWu+rJI/VnYP2DnZ8SdOnBjY\nWKU5JvD6MJGZiYaie9CbsBBCJEROWAghEiInLIQQCZETFkKIhJSMMHfkyJGMmMUys5hg4At4bD9W\n0o9lTjGBibWLWQ6HCWnMdu655wY2Jp6w5YFiyj6y7Ccm9rCxM2LTMf2xsnvgZ9UBfBmgysqw9hO7\nRqxUqC92sZKSbOysb6x85tlnh3Ws6uv9+lVATU1NYPP7O27cuKDNunXrAtvcuXMD21e+8pXA9s1v\nfjOwxZQnZXM+ZlmkYwX/uerp0pZ6ExZCiITICQshRELkhIUQIiFywkIIkZCSEeb69OlDRbVCmBDg\nCw1MsGHCC2vHsrWGDh3abp9a8IUzJpoxcePpp58ObNddd11gixUq/evBrllsKUuW9cdsMWvzFZYp\nbY+ZM2dGtWPiESvb6Zf7jO1HLKwMJptvLJsvBibCMbZv3x7VD5aJ6hO7FuKxWrayt6wxJ4QQohuQ\nExZCiITICQshRELkhIUQIiElI8wdPny4Q9GA/dwXY5hYwIQjlvnFhDPWjp3DF7ZiBBCAl6NkwgAT\nS5jA5p+XlcWMzZhjx48phQgAe/bsyWyz6xh7LEbMmn5A5wWxrpDinCzbLmaeAlzkFEcPvQkLIURC\n5ISFECIhcsJCCJEQOWEhhEhIyQhzAwcOxCmnnNK63dTUFLRhgpKfqcbWBmOCG2vHMuZYCUkmdvmC\nB9uPZa+xvrGxM/GE9TemlGVsdhwbJxPw2L6+kNgVEe5Y4Oc//3lgu/rqqzPbTGyMyUYEgO9///uB\nLWZNRiAUYGPLmoruQW/CQgiREDlhIYRIiJywEEIkpGQCdQMGDMCJJ57YbhsWq/KTAFiSBItHxtr8\npAMAqKioCGx+rI3F41j/Wbvdu3cHttiP7P3xxyaqsOPHJrSw2HRsJS6fRYsWBbbLLrusU8cCgG3b\ntmW2x48f3+ljMdg92LFjR2CbPHlyYPPHunz58qDNtGnTAlt1dXVgq62tDWzz5s0LbMuWLQtsigGn\nRW/CQgiREDlhIYRIiJywEEIkRE5YCCESUjLC3IEDB/Dhhx+2bjORjH3M7gsjsRXImAjIkhOYjR1v\n0KBBme3Y6mtMFNm/f39gYxXNDh48GNj888ZW0oqt+sZobGwMbLHLQvnEinBMxDr//PMDW3cLcT5s\nfkyaNCnK5sOEtFWrVgU2Nv+YWPfqq68GNiasirTojgghRELkhIUQIiFywkIIkRA5YSGESEjJCHPO\nuYyoxoQtRkz1sliY+MWELZYlFSPMMVGEZcyxLD3WN3YOX6xj52SiJxOY2LVkY9+1a1dgYyJZd9LT\nx+8K7J6ya+kLbFu3bg3anHXWWYHtnXfeCWxMHGXEzkFx9NCbsBBCJKQoJ2xm3zWzZu/f+16b+8ys\nxsw+MrOXzGxK93ZZCCGOHTrzJrwWQAWAyvy/+S0/MLO7ANwO4DYA8wA0AVhoZuHfukIIIToVEz7i\nnAvLfOW4A8D9zrnnAcDMbgFQD+BaAE91rotCCHHs0hknPNXMdgI4BGAFgG8756rNbCJyb8aLWxo6\n5xrNbCWA89CBE+7Xr19GZGMCUEzGGROwWBZdYXZeC0y0YMdj7Nu3L7Mdm5XGRDImvJSXlwc2lkHo\nw8pM+iJiWzDhj4mG27dvD2znnntu1Dl6E6xEJStryjLaqqqqAtvMmTMz2/Pnzw/avPfee4GNCXhs\n7rJ5r7KVpUex4YjXAfwFgC8A+DqAiQBeNbNByDlgh9ybbyH1+Z8JIYTwKOpN2Dm3sGBzrZm9AWAb\ngC8DWN+dHRNCiOOBLn0n7JxrMLONAKYAWALAkBPtCt+GKwCEVUg8nn/++cwKyM3NzZg9ezZmz57d\nlS4KIUSPUltbi7q6uowtNs8B6KITNrOTkXPAP3PObTGzOgCXAliT/3kZgHMAPNTRsb74xS9i9OjR\nrdssJiyEEKXGyJEjMXLkyIytsbERK1eujNq/KCdsZt8D8BxyIYjRAP4PgMMAfpFv8iCAu81sE4Ct\nAO4HsAPAsx0d+7PPPsv89mBCA7P5a7sx8Y4JFKyUJRPOmPjFMsT8c7BSjrG/WNi6cKy8JRMX/eyn\n2HOya8uy6JhQycTFjtYLLHWYoDlmzJjAxq7v4MGDA9sZZ5zR4Tl9cRcAfvGLXwQ2Nsebmpo6PH5v\nI/ZZ7u0U+yY8BsATAIYB2A1gGYBznXN7AcA594CZDQTwCIByAEsBXOmc02utEEIQihXmbopocy+A\nezvZHyGEOK5Q7QghhEiInLAQQiSkZEpZ9u3bNyPwxGYA+cIIC+YzGzt+7LpwrN0pp5yS2WYZc50t\nR9lWP9gY/PPGZNW1BesbE+HYWGfNmtXp85YChZ9LtgcTaZmA9+677wY2/55u2rQpaLNx48bAduqp\npwY2dl980Rrgc8bPootdl5CVwGT9iP1cy3++2fPO+saIFfWYzR8XG3tHpWqLWctPb8JCCJEQOWEh\nhEiInLAQQiRETlgIIRJSMsJcDCwLyxfmWKCdBdFjy/yxzCkWqPfLF7K+MiEjVgyMLdHpHy9WoIgV\nEtkYjsX6Hqxkp18fAOBZi5s3bw5sa9asCWxbtmzJbLOsN5Z5ye4Bu1dMmOss7FhsHsWKgbEiWQr8\nZyZ2Db7CZ4gJlG2hN2EhhEiInLAQQiRETlgIIRJSMjFh51wmJhT7obZvY3FdFi9j1a9iYz/seAcO\nHMhs+8kbAI//MhvrG0u6YLE2PxbNjs/6H3vdTj755MB2ww03BLZShVXK2707XDKRLdnEkiTYPGX3\nhd3TDz74ILM9YcKEoE1sslB3LlvEzsmWcWLPC4ubM71hzpw5gW3dunWZbTYmFmudN29eYFu+fHlg\n62x5XNaPjpZaK+Z+6E1YCCESIicshBAJkRMWQoiEyAkLIURCSkaYa25u7jCwzWy+MNJRwLyt/QDg\n0KFDgS12GaSYClAxySZA/BiYSMHG4BNb4YmN4Rvf+EZgY2JdCvyEGSC8V3v37g3asKWjmLDKxJ4L\nL7wwsK1YsSKwMVFv7Nix7fYV4NeWCWKDBg0KbA0NDYEtJjmI3feYpbQAXn3ummuuCWz/9V//Fdj8\n+cwSPx5++OHAdueddwa2X//614HtjjvuCGz+2nBAbuHOQmpqaoI27NoWXo9iEk/0JiyEEAmRExZC\niITICQshRELkhIUQIiElI8z5GXOxy5j4AXAmFjBRiwkUscu1MAHohBNOyGyz6mtdqSbFRL0YAS9W\n5Bs1alRgu+mmcHHtESNGBLYUMGGE3RdfdGPV0QYMGBDYFi9eHNiYcLZ06dLAxkS4qqqqwObPN3aP\nGawfbJ6y+RaTFcrmDOsbq/rGhOGFCxcGtvLy8sDW2NiY2S4rKwva3H333YHtl7/8ZWBjsL6xTMBV\nq1ZlttmzzCi8trHZt4DehIUQIilywkIIkRA5YSGESIicsBBCJKRkhLnm5uZMMLuzogJrw4QM1o4J\nYrElL32xKzbjL5bY0p6+QMgYMmRIYLv55puj2sUKpt3JwYMHAxsrP8mEIj/7iWVhVVdXB7arr746\nqm9MtGEZeEz880s8xoq0sXMrdhmkmHnJnkf2XDGYiMrG5WcH+uVhAX69r7jiiqh+sJKaTzzxRIft\n2JzpzudAb8JCCJEQOWEhhEiInLAQQiRETlgIIRJSMsLc4cOHM4F+tqYaC5D7ggETEJiNZcwxWJYN\ny07yRQoWuI/tGxNPWLsYQYVluF1//fWBjWXMMSGjp2HCCxPmWLutW7cGtn379mW2d+7cGbRh14OV\nkGT7MiGUXTcmzPlzJHa9wdiSqLFirg8T9FgJ0Fhxij0v7Bx+xmPs+nqsHTs+8x/sXvn79rQYrTdh\nIYRIiJywEEIkRE5YCCESIicshBAJKRlh7pNPPsmIYCxgHiNsMbGKBeRj152LLUnn95dlEzERJzbo\nz8Q6do4ZM2Zkti+77LKgzZgxY6KO1dPCHCs9yQQ3tr4ZE+FYWckbbrghs+1fH4CPPVaQZXOLtWNi\nmj8ffBGxLdj6d+xapshuZMSK4P4cZ/2PXbuN7Vsq18NHb8JCCJGQop2wmY0ys8fNbI+ZfWRmq83s\nTK/NfWZWk//5S2Y2pfu6LIQQxw5FOWEzKwfwGoBPAHwBwGkA/g7A/oI2dwG4HcBtAOYBaAKw0Mw6\nriwjhBDHGcXGhP8BwHbn3K0Ftm1emzsA3O+cex4AzOwWAPUArgXwVGc7KoQQxyLFOuEvAXjRzJ4C\ncBGAnQAeds79OwCY2UQAlQBaF+hyzjWa2UoA56EdJ+ycy4hgrCzh0KFDA5ufKRRb+i9WUIkVAnyB\nkGUwdbaMIMDLI5577rmB7cILL8xsDx8+POr4LEOsO4nJkAL49WBjP+mkkwLbdddd12E7JmqxsbPy\nlrHCDuvboEGDAps/Z1jfmGjIxEv2vJSqEBVL7LPB5lasMFcK16jYmPAkAH8FYAOAywH8BMCPzKyl\nGG0lAIfcm28h9fmfCSGEKKDYN+E+AN5wzt2T315tZrMAfB3A413pyKuvvpr5ZMfMMGPGDJx22mld\nOawQQvQotbW1qKury9hiP8sDinfCtQDWebZ1AP5X/v91AAxABbJvwxUAVqEdFixYkFkqnIUGhBCi\n1Bg5ciRGjhyZsTU2NmLlypVR+xfr6V4DMN2zTUdenHPObTGzOgCXAlgDAGZWBuAcAA+1d2B/eSMW\nC9uzZ09g8+N5sRWVWHyW/fZiCSKxSx75sAQAVl2L9ePyyy8PbOPGjQts/vVgcUZ2zu7Gj6WzSmgs\nJtzY2BjY3nzzzcB2zjnnBLaYanlsyRx2vdk1YvePxWLZ3GXn9e9Nfb0fxeP9YDpF7HJgXVliq6fx\n+xu73FNsXDf2eEebYp3wDwG8ZmbfRk5kOwfArQD+d0GbBwHcbWabAGwFcD+AHQCe7XJvhRDiGKMo\nJ+yce8vM/hjAPwO4B8AWAHc4535R0OYBMxsI4BEA5QCWArjSORe+PgohxHFO0YFX59wLAF7ooM29\nAO7tXJeEEOL4QbUjhBAiISXzCULfvn0zX0SwZAomjPiCRFlZWYdt2jo+I1as821MFIn9qJwtt8Mq\nnzFhrry8PLOd6iuThoaGzDZbHoeJVZs2bQpso0ePDmxMxBo8eHBg8xMn2P1kIi1LEGFzho2BzUFW\nRc0XyViVPQYbQ+wnUWzu+uIUm5NsHrEqe+wZjRW/YtqVQnJFd6M3YSGESIicsBBCJEROWAghElJy\nTnj9+vWpu9AlNm/enLoLXebJJ59M3YUusXjx4o4blTDHwhyqra1N3YUucTT7XzLC3JEjR3D48GGs\nW7cOkydPpkF/Jj742UksC4uJCizTiQkjbN/2suOqqqowefJkKjIwwebSSy8NbDGCW1v9iBEImThT\nyJNPPombbrqp3TaFsGvuC3Msy3DHjh2BbdmyZYFtwYIFgY1d3xYx7cUXX8SZZ+bWGfDnDBN2WN/Y\n/IgVOVlmJ8sY9Jczaun/+vXrWysGsvvH+hsrWMWKwzG0J27X1dUFqbzF0pXljbpKd/Q/lpJ7ExZC\niOMJOWEhhEiInLAQQiSkFGLCJwL/Ew/79NNPsWvXLhp/Y9Wp/A/t2aoG7ANyFqNjcejYqmwtfPrp\np9izZw9tw1ZEYCIMi7GyamgsAYDFjn06igk3NDTg7bff7vA4LcQsU8/uAVu9YteuXYGtqqoqsLWX\ndNDU1NR6XdmKFj6sUhkbE0vqYO2YdsHuqT/Wlmfg8OHDrf9nMVAWi41NPupsFTI2dkbLvD9y5Ehr\nRbzOxnFTxoQL+98ZCjSF8CH1sNSl3MzsTwH8R9JOCCFEz/Bnzrkn2mtQCk54GHIrN28FEL4uCSFE\n7+NEABMALHTOhTn7BSR3wkIIcTwjYU4IIRIiJyyEEAmRExZCiITICQshREJKxgmb2d+Y2RYz+9jM\nXjezz6XuU1uY2YVm9msz22lmzWZ2DWlzn5nVmNlHZvaSmU1J0VeGmX3bzN4ws0Yzqzezp81sGmlX\nkmMws6+b2Woza8j/W25mV3htSrLvDDP7h/w8+oFnL9kxmNl3830u/Pe+16Zk+w8AZjbKzB43sz35\nPq42szO9Nj0+hpJwwmZ2A4DvA/gugDMArAaw0MyGJ+1Y2wwC8A6AvwYQfF5iZncBuB3AbQDmAWhC\nbjxxSyf0PBcC+FfkVsu+DEB/AL8zs9ZMlxIfQzWAuwCcCeAsAC8DeNbMTgNKvu8Z8i8btyE35wvt\nvWEMawFUAKjM/5vf8oNS77+ZlQN4DcAnyH0iexqAvwOwv6DN0RmDcy75PwCvA/h/BdsGYAeAb6Xu\nW0TfmwFc49lqANxZsF0G4GMAX07d3zbGMDw/jvm9eAx7AXylN/UdwMkANgC4BMArAH7QW64/ci9M\nb7fz81Lv/z8D+O8O2hyVMSR/Ezaz/si9zbQWgXW5ES8CcF6qfnUWM5uI3FtB4XgaAaxE6Y6nHLk3\n+n1A7xqDmfUxsxsBDASwvDf1HcBDAJ5zzr1caOxFY5iaD8ltNrOfm9lYoNf0/0sA3jKzp/IhubfN\n7NaWHx7NMSR3wsi9hfUF4K/cWI/cRehtVCLn0HrFeCyXoP8ggGXOuZaYXsmPwcxmmdkB5P6cfBjA\nHzvnNqAX9B0A8r84/gDAt8mPe8MYXgfwF8j9Kf91ABMBvGpmg9A7+j8JwF8h95fI5QB+AuBHZnZz\n/udHbQylUMBHpOVhADMBXJC6I0WyHsBcAIMBXA/gMTMLq7+XIGY2BrlffJc55+Iq75QYzrmFBZtr\nzewNANsAfBm5e1Pq9AHwhnPunvz2ajObhdwvlMePdkdSswfAZ8gF+AupAFB39LvTZeqQi2mX/HjM\n7McArgLweedc4XouJT8G59wR51yVc26Vc+47yAlbd6AX9B258NsIAG+b2WEzOwzgIgB3mNmnyL1t\nlfoYMjjnGgBsBDAFveMe1AJY59nWAWhZ1uaojSG5E86/CfweQOs6P/k/kS8FsDxVvzqLc24Lcjep\ncDxlyH2JUDLjyTvgPwJwsXNue+HPessYPPoAGNBL+r4IwGzkwhFz8//eAvBzAHOdc1Uo/TFkMLOT\nkXPANb3kHrwGYLpnm47c2/zRfQZSq5R51fHLAD4CcAuAGQAeQU7tHpG6b230dxByD84fIPdVwTfz\n22PzP/9Wvv9fQu5hewbABwBOSN33fP8eRu5TnAuR+83e8u/EgjYlOwYA/5Tv+3gAswD8XwBHAFxS\n6n1vZ0z+1xElPQYA3wOwIH8PzgfwEnJv8MN6Sf/PRk5P+DaAyQD+FMABADce7XuQ/GIUDPivkStn\n+TGAFQDOTt2ndvp6Ud75fub9+2lBm3uR+8TlIwALAUxJ3e+CvrG+fwbgFq9dSY4BwL8DqMrPlToA\nv2txwKXe93bG9HKhEy71MQB4ErnPSD8GsB3AEwAm9pb+5/t3FYA1+f69B+CrpE2Pj0GlLIUQIiHJ\nY8JCCHE8IycshBAJkRMWQoiEyAkLIURC5ISFECIhcsJCCJEQOWEhhEiInLAQQiRETlgIIRIiJyyE\nEAmRExZCiITICQshREL+P/hLxBMEddKZAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fdcc18c8d10>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "from matplotlib.pyplot import imshow\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "img_num = 0\n",
+    "\n",
+    "if K.image_dim_ordering() == 'th':\n",
+    "    img = X_train[img_num][0,:,:]\n",
+    "else:\n",
+    "    img = X_train[img_num][:,:,0]\n",
+    "\n",
+    "print img.shape\n",
+    "print y_train.shape\n",
+    "imshow(img, cmap = plt.get_cmap('gray'), vmin = 0, vmax = 1,  interpolation='nearest')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# model hyperparameters\n",
+    "batch_size = 128\n",
+    "nb_epoch = 30\n",
+    "\n",
+    "# network architecture\n",
+    "patch_size_1 = 3\n",
+    "patch_size_2 = 3\n",
+    "patch_size_3 = 3\n",
+    "\n",
+    "depth_1 = 10\n",
+    "depth_2 = 15\n",
+    "depth_3 = 20\n",
+    "\n",
+    "pool_size = 2\n",
+    "\n",
+    "num_hidden_1 = 850\n",
+    "num_hidden_2 = 850\n",
+    "num_hidden_3 = 850\n",
+    "\n",
+    "dropout = 0.5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# create new Keras Sequential model\n",
+    "model = Sequential()\n",
+    "\n",
+    "# add first convolutional layer to model and specify it's depth and filter size\n",
+    "# for the first layer we also have to specify the size of each input image\n",
+    "# which we calculated above\n",
+    "model.add(Convolution2D(depth_1, patch_size_1, patch_size_1,\n",
+    "                        border_mode='valid',\n",
+    "                        input_shape=input_shape))\n",
+    "# apply 'relu' activation function for first layer\n",
+    "model.add(Activation('relu'))\n",
+    "# apply max pooling to reduce the size of the image by a factor of 2\n",
+    "model.add(MaxPooling2D(pool_size=(pool_size, pool_size)))\n",
+    "\n",
+    "# repeat these operations for the second convolutional layer\n",
+    "# this time Keras can figure out the input size \n",
+    "# from the previous layer on it's own\n",
+    "model.add(Convolution2D(depth_2, patch_size_2, patch_size_2,\n",
+    "                        border_mode='valid'))\n",
+    "model.add(Activation('relu'))\n",
+    "model.add(MaxPooling2D(pool_size=(pool_size, pool_size)))\n",
+    "\n",
+    "model.add(Convolution2D(depth_3, patch_size_3, patch_size_3,border_mode='valid'))\n",
+    "model.add(Activation('relu'))\n",
+    "model.add(MaxPooling2D(pool_size=(pool_size, pool_size)))\n",
+    "\n",
+    "# flatten the three-dimensional convolutional layer to a single layer of neurons\n",
+    "model.add(Flatten())\n",
+    "\n",
+    "# add the first fully connected layer, applying 'relu' activation and dropout\n",
+    "model.add(Dense(num_hidden_1))\n",
+    "model.add(Activation('relu'))\n",
+    "model.add(Dropout(dropout))\n",
+    "\n",
+    "# add the second fully connected layer\n",
+    "model.add(Dense(num_hidden_2))\n",
+    "model.add(Activation('relu'))\n",
+    "model.add(Dropout(dropout))\n",
+    "\n",
+    "# add the third fully connected layer\n",
+    "model.add(Dense(num_hidden_3))\n",
+    "model.add(Activation('relu'))\n",
+    "model.add(Dropout(dropout))\n",
+    "\n",
+    "# add the final classification layer with the number of neurons \n",
+    "# matching the number of classes we are trying to learn\n",
+    "model.add(Dense(num_classes))\n",
+    "\n",
+    "# apply the 'softmax' activation to the final layer to convert the output to \n",
+    "# a probability distribution\n",
+    "model.add(Activation('softmax'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "model.compile(loss='categorical_crossentropy',\n",
+    "              optimizer='adadelta',\n",
+    "              metrics=['accuracy'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train on 14000 samples, validate on 6000 samples\n",
+      "Epoch 1/30\n",
+      "14000/14000 [==============================] - 52s - loss: 0.6920 - acc: 0.5166 - val_loss: 0.6926 - val_acc: 0.5083\n",
+      "Epoch 2/30\n",
+      "14000/14000 [==============================] - 53s - loss: 0.6833 - acc: 0.5588 - val_loss: 0.6591 - val_acc: 0.6258\n",
+      "Epoch 3/30\n",
+      "14000/14000 [==============================] - 50s - loss: 0.6681 - acc: 0.5976 - val_loss: 0.6545 - val_acc: 0.6048\n",
+      "Epoch 4/30\n",
+      "14000/14000 [==============================] - 51s - loss: 0.6506 - acc: 0.6224 - val_loss: 0.7089 - val_acc: 0.5557\n",
+      "Epoch 5/30\n",
+      "14000/14000 [==============================] - 52s - loss: 0.6291 - acc: 0.6469 - val_loss: 0.6113 - val_acc: 0.6780\n",
+      "Epoch 6/30\n",
+      "14000/14000 [==============================] - 51s - loss: 0.5870 - acc: 0.6949 - val_loss: 0.5827 - val_acc: 0.6830\n",
+      "Epoch 7/30\n",
+      "14000/14000 [==============================] - 51s - loss: 0.5594 - acc: 0.7146 - val_loss: 0.5512 - val_acc: 0.7208\n",
+      "Epoch 8/30\n",
+      "14000/14000 [==============================] - 52s - loss: 0.5393 - acc: 0.7334 - val_loss: 0.5407 - val_acc: 0.7317\n",
+      "Epoch 9/30\n",
+      "14000/14000 [==============================] - 52s - loss: 0.5195 - acc: 0.7404 - val_loss: 0.5373 - val_acc: 0.7295\n",
+      "Epoch 10/30\n",
+      "14000/14000 [==============================] - 52s - loss: 0.4968 - acc: 0.7613 - val_loss: 0.5208 - val_acc: 0.7390\n",
+      "Epoch 11/30\n",
+      "14000/14000 [==============================] - 52s - loss: 0.4818 - acc: 0.7689 - val_loss: 0.5051 - val_acc: 0.7525\n",
+      "Epoch 12/30\n",
+      "14000/14000 [==============================] - 52s - loss: 0.4623 - acc: 0.7804 - val_loss: 0.5124 - val_acc: 0.7433\n",
+      "Epoch 13/30\n",
+      "14000/14000 [==============================] - 52s - loss: 0.4483 - acc: 0.7896 - val_loss: 0.5085 - val_acc: 0.7627\n",
+      "Epoch 14/30\n",
+      "14000/14000 [==============================] - 52s - loss: 0.4281 - acc: 0.8053 - val_loss: 0.4842 - val_acc: 0.7702\n",
+      "Epoch 15/30\n",
+      "14000/14000 [==============================] - 55s - loss: 0.4083 - acc: 0.8139 - val_loss: 0.5083 - val_acc: 0.7662\n",
+      "Epoch 16/30\n",
+      "14000/14000 [==============================] - 58s - loss: 0.3907 - acc: 0.8253 - val_loss: 0.4977 - val_acc: 0.7685\n",
+      "Epoch 17/30\n",
+      "14000/14000 [==============================] - 59s - loss: 0.3721 - acc: 0.8345 - val_loss: 0.6386 - val_acc: 0.7158\n",
+      "Epoch 18/30\n",
+      "14000/14000 [==============================] - 55s - loss: 0.3479 - acc: 0.8521 - val_loss: 0.5130 - val_acc: 0.7717\n",
+      "Epoch 19/30\n",
+      "14000/14000 [==============================] - 52s - loss: 0.3188 - acc: 0.8647 - val_loss: 0.5715 - val_acc: 0.7552\n",
+      "Epoch 20/30\n",
+      "14000/14000 [==============================] - 53s - loss: 0.2975 - acc: 0.8750 - val_loss: 0.5671 - val_acc: 0.7593\n",
+      "Epoch 21/30\n",
+      "14000/14000 [==============================] - 62s - loss: 0.2667 - acc: 0.8934 - val_loss: 0.5494 - val_acc: 0.7698\n",
+      "Epoch 22/30\n",
+      "14000/14000 [==============================] - 58s - loss: 0.2403 - acc: 0.9047 - val_loss: 0.5742 - val_acc: 0.7802\n",
+      "Epoch 23/30\n",
+      "14000/14000 [==============================] - 57s - loss: 0.2281 - acc: 0.9096 - val_loss: 0.6391 - val_acc: 0.7500\n",
+      "Epoch 24/30\n",
+      "14000/14000 [==============================] - 55s - loss: 0.1824 - acc: 0.9310 - val_loss: 0.6509 - val_acc: 0.7635\n",
+      "Epoch 25/30\n",
+      "14000/14000 [==============================] - 61s - loss: 0.1725 - acc: 0.9336 - val_loss: 0.6648 - val_acc: 0.7687\n",
+      "Epoch 26/30\n",
+      "14000/14000 [==============================] - 57s - loss: 0.1451 - acc: 0.9442 - val_loss: 0.8377 - val_acc: 0.7450\n",
+      "Epoch 27/30\n",
+      "14000/14000 [==============================] - 55s - loss: 0.1314 - acc: 0.9489 - val_loss: 0.7340 - val_acc: 0.7672\n",
+      "Epoch 28/30\n",
+      "14000/14000 [==============================] - 54s - loss: 0.1169 - acc: 0.9567 - val_loss: 0.7735 - val_acc: 0.7703\n",
+      "Epoch 29/30\n",
+      "14000/14000 [==============================] - 59s - loss: 0.1023 - acc: 0.9619 - val_loss: 0.8346 - val_acc: 0.7663\n",
+      "Epoch 30/30\n",
+      "14000/14000 [==============================] - 54s - loss: 0.0900 - acc: 0.9671 - val_loss: 0.8794 - val_acc: 0.7667\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<keras.callbacks.History at 0x7fdcb826b7d0>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.fit(X_train, Y_train, batch_size=batch_size, nb_epoch=nb_epoch,\n",
+    "          verbose=1, validation_data=(X_test, Y_test))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test score: 0.879394139975\n",
+      "Test accuracy: 76.67%\n"
+     ]
+    }
+   ],
+   "source": [
+    "score = model.evaluate(X_test, Y_test, verbose=0)\n",
+    "\n",
+    "print 'Test score:', score[0]\n",
+    "print 'Test accuracy: {:.2%}'.format(score[1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python [default]",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}

From f17faa8dc70347e841c35406aef93b2ccfb1874d Mon Sep 17 00:00:00 2001
From: yiiinghuang <mac@dyn-129-236-232-178.dyn.columbia.edu>
Date: Mon, 24 Oct 2016 20:13:40 -0400
Subject: [PATCH 7/8] Assignment 5_part3_new

dogs and cats
---
 .../03-CNN in keras for dogs and cats.ipynb   | 111 ++++++++----------
 1 file changed, 52 insertions(+), 59 deletions(-)

diff --git a/notebooks/week-5/03-CNN in keras for dogs and cats.ipynb b/notebooks/week-5/03-CNN in keras for dogs and cats.ipynb
index 7802a38..7a05069 100644
--- a/notebooks/week-5/03-CNN in keras for dogs and cats.ipynb	
+++ b/notebooks/week-5/03-CNN in keras for dogs and cats.ipynb	
@@ -13,7 +13,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 10,
    "metadata": {
     "collapsed": false
    },
@@ -64,18 +64,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 11,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Using TensorFlow backend.\n"
-     ]
-    },
     {
      "name": "stdout",
      "output_type": "stream",
@@ -101,7 +94,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 12,
    "metadata": {
     "collapsed": false
    },
@@ -142,7 +135,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 13,
    "metadata": {
     "collapsed": false
    },
@@ -158,10 +151,10 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7fdcc05ab090>"
+       "<matplotlib.image.AxesImage at 0x7f2f542e14d0>"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -169,7 +162,7 @@
      "data": {
       "image/png": 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ylf6+bE52VtQ6VokVPWMo2gmb2SwAKwCcCOAAgD92zm0ws/MAOAB+zmM9cs5Z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\nEAmRExZCiITICQshREL+P/hLxBMEddKZAAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fdcc18c8d10>"
+       "<matplotlib.figure.Figure at 0x7f2f543865d0>"
       ]
      },
      "metadata": {},
@@ -195,7 +188,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 14,
    "metadata": {
     "collapsed": true
    },
@@ -210,22 +203,22 @@
     "patch_size_2 = 3\n",
     "patch_size_3 = 3\n",
     "\n",
-    "depth_1 = 10\n",
-    "depth_2 = 15\n",
-    "depth_3 = 20\n",
+    "depth_1 = 20\n",
+    "depth_2 = 40\n",
+    "depth_3 = 80\n",
     "\n",
     "pool_size = 2\n",
     "\n",
-    "num_hidden_1 = 850\n",
-    "num_hidden_2 = 850\n",
-    "num_hidden_3 = 850\n",
+    "num_hidden_1 = 1000\n",
+    "num_hidden_2 = 1000\n",
+    "num_hidden_3 = 1000\n",
     "\n",
     "dropout = 0.5"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 15,
    "metadata": {
     "collapsed": false
    },
@@ -286,7 +279,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 16,
    "metadata": {
     "collapsed": false
    },
@@ -299,7 +292,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 17,
    "metadata": {
     "collapsed": false
    },
@@ -310,74 +303,74 @@
      "text": [
       "Train on 14000 samples, validate on 6000 samples\n",
       "Epoch 1/30\n",
-      "14000/14000 [==============================] - 52s - loss: 0.6920 - acc: 0.5166 - val_loss: 0.6926 - val_acc: 0.5083\n",
+      "14000/14000 [==============================] - 97s - loss: 0.6926 - acc: 0.5186 - val_loss: 0.6974 - val_acc: 0.4917\n",
       "Epoch 2/30\n",
-      "14000/14000 [==============================] - 53s - loss: 0.6833 - acc: 0.5588 - val_loss: 0.6591 - val_acc: 0.6258\n",
+      "14000/14000 [==============================] - 107s - loss: 0.6808 - acc: 0.5723 - val_loss: 0.6959 - val_acc: 0.5513\n",
       "Epoch 3/30\n",
-      "14000/14000 [==============================] - 50s - loss: 0.6681 - acc: 0.5976 - val_loss: 0.6545 - val_acc: 0.6048\n",
+      "14000/14000 [==============================] - 112s - loss: 0.6606 - acc: 0.6098 - val_loss: 0.6733 - val_acc: 0.5818\n",
       "Epoch 4/30\n",
-      "14000/14000 [==============================] - 51s - loss: 0.6506 - acc: 0.6224 - val_loss: 0.7089 - val_acc: 0.5557\n",
+      "14000/14000 [==============================] - 108s - loss: 0.6482 - acc: 0.6220 - val_loss: 0.6382 - val_acc: 0.6403\n",
       "Epoch 5/30\n",
-      "14000/14000 [==============================] - 52s - loss: 0.6291 - acc: 0.6469 - val_loss: 0.6113 - val_acc: 0.6780\n",
+      "14000/14000 [==============================] - 114s - loss: 0.6310 - acc: 0.6446 - val_loss: 0.6093 - val_acc: 0.6628\n",
       "Epoch 6/30\n",
-      "14000/14000 [==============================] - 51s - loss: 0.5870 - acc: 0.6949 - val_loss: 0.5827 - val_acc: 0.6830\n",
+      "14000/14000 [==============================] - 100s - loss: 0.5959 - acc: 0.6836 - val_loss: 0.5705 - val_acc: 0.7025\n",
       "Epoch 7/30\n",
-      "14000/14000 [==============================] - 51s - loss: 0.5594 - acc: 0.7146 - val_loss: 0.5512 - val_acc: 0.7208\n",
+      "14000/14000 [==============================] - 98s - loss: 0.5570 - acc: 0.7135 - val_loss: 0.5663 - val_acc: 0.7155\n",
       "Epoch 8/30\n",
-      "14000/14000 [==============================] - 52s - loss: 0.5393 - acc: 0.7334 - val_loss: 0.5407 - val_acc: 0.7317\n",
+      "14000/14000 [==============================] - 98s - loss: 0.5218 - acc: 0.7394 - val_loss: 0.4898 - val_acc: 0.7652\n",
       "Epoch 9/30\n",
-      "14000/14000 [==============================] - 52s - loss: 0.5195 - acc: 0.7404 - val_loss: 0.5373 - val_acc: 0.7295\n",
+      "14000/14000 [==============================] - 100s - loss: 0.4901 - acc: 0.7631 - val_loss: 0.4733 - val_acc: 0.7743\n",
       "Epoch 10/30\n",
-      "14000/14000 [==============================] - 52s - loss: 0.4968 - acc: 0.7613 - val_loss: 0.5208 - val_acc: 0.7390\n",
+      "14000/14000 [==============================] - 98s - loss: 0.4659 - acc: 0.7786 - val_loss: 0.5907 - val_acc: 0.7202\n",
       "Epoch 11/30\n",
-      "14000/14000 [==============================] - 52s - loss: 0.4818 - acc: 0.7689 - val_loss: 0.5051 - val_acc: 0.7525\n",
+      "14000/14000 [==============================] - 98s - loss: 0.4478 - acc: 0.7883 - val_loss: 0.4546 - val_acc: 0.7878\n",
       "Epoch 12/30\n",
-      "14000/14000 [==============================] - 52s - loss: 0.4623 - acc: 0.7804 - val_loss: 0.5124 - val_acc: 0.7433\n",
+      "14000/14000 [==============================] - 100s - loss: 0.4230 - acc: 0.8044 - val_loss: 0.4636 - val_acc: 0.7753\n",
       "Epoch 13/30\n",
-      "14000/14000 [==============================] - 52s - loss: 0.4483 - acc: 0.7896 - val_loss: 0.5085 - val_acc: 0.7627\n",
+      "14000/14000 [==============================] - 99s - loss: 0.3980 - acc: 0.8215 - val_loss: 0.4305 - val_acc: 0.8007\n",
       "Epoch 14/30\n",
-      "14000/14000 [==============================] - 52s - loss: 0.4281 - acc: 0.8053 - val_loss: 0.4842 - val_acc: 0.7702\n",
+      "14000/14000 [==============================] - 98s - loss: 0.3783 - acc: 0.8296 - val_loss: 0.4313 - val_acc: 0.8077\n",
       "Epoch 15/30\n",
-      "14000/14000 [==============================] - 55s - loss: 0.4083 - acc: 0.8139 - val_loss: 0.5083 - val_acc: 0.7662\n",
+      "14000/14000 [==============================] - 98s - loss: 0.3495 - acc: 0.8476 - val_loss: 0.4245 - val_acc: 0.8107\n",
       "Epoch 16/30\n",
-      "14000/14000 [==============================] - 58s - loss: 0.3907 - acc: 0.8253 - val_loss: 0.4977 - val_acc: 0.7685\n",
+      "14000/14000 [==============================] - 99s - loss: 0.3293 - acc: 0.8569 - val_loss: 0.4702 - val_acc: 0.7855\n",
       "Epoch 17/30\n",
-      "14000/14000 [==============================] - 59s - loss: 0.3721 - acc: 0.8345 - val_loss: 0.6386 - val_acc: 0.7158\n",
+      "14000/14000 [==============================] - 99s - loss: 0.2921 - acc: 0.8776 - val_loss: 0.4461 - val_acc: 0.8097\n",
       "Epoch 18/30\n",
-      "14000/14000 [==============================] - 55s - loss: 0.3479 - acc: 0.8521 - val_loss: 0.5130 - val_acc: 0.7717\n",
+      "14000/14000 [==============================] - 99s - loss: 0.2577 - acc: 0.8938 - val_loss: 0.4578 - val_acc: 0.8117\n",
       "Epoch 19/30\n",
-      "14000/14000 [==============================] - 52s - loss: 0.3188 - acc: 0.8647 - val_loss: 0.5715 - val_acc: 0.7552\n",
+      "14000/14000 [==============================] - 99s - loss: 0.2256 - acc: 0.9094 - val_loss: 0.4712 - val_acc: 0.8123\n",
       "Epoch 20/30\n",
-      "14000/14000 [==============================] - 53s - loss: 0.2975 - acc: 0.8750 - val_loss: 0.5671 - val_acc: 0.7593\n",
+      "14000/14000 [==============================] - 99s - loss: 0.1920 - acc: 0.9241 - val_loss: 0.5232 - val_acc: 0.8100\n",
       "Epoch 21/30\n",
-      "14000/14000 [==============================] - 62s - loss: 0.2667 - acc: 0.8934 - val_loss: 0.5494 - val_acc: 0.7698\n",
+      "14000/14000 [==============================] - 99s - loss: 0.1468 - acc: 0.9435 - val_loss: 0.5478 - val_acc: 0.8177\n",
       "Epoch 22/30\n",
-      "14000/14000 [==============================] - 58s - loss: 0.2403 - acc: 0.9047 - val_loss: 0.5742 - val_acc: 0.7802\n",
+      "14000/14000 [==============================] - 99s - loss: 0.1208 - acc: 0.9564 - val_loss: 0.6147 - val_acc: 0.8107\n",
       "Epoch 23/30\n",
-      "14000/14000 [==============================] - 57s - loss: 0.2281 - acc: 0.9096 - val_loss: 0.6391 - val_acc: 0.7500\n",
+      "14000/14000 [==============================] - 99s - loss: 0.0870 - acc: 0.9686 - val_loss: 0.7668 - val_acc: 0.7943\n",
       "Epoch 24/30\n",
-      "14000/14000 [==============================] - 55s - loss: 0.1824 - acc: 0.9310 - val_loss: 0.6509 - val_acc: 0.7635\n",
+      "14000/14000 [==============================] - 99s - loss: 0.0708 - acc: 0.9766 - val_loss: 0.7088 - val_acc: 0.8133\n",
       "Epoch 25/30\n",
-      "14000/14000 [==============================] - 61s - loss: 0.1725 - acc: 0.9336 - val_loss: 0.6648 - val_acc: 0.7687\n",
+      "14000/14000 [==============================] - 100s - loss: 0.0584 - acc: 0.9798 - val_loss: 0.7654 - val_acc: 0.8045\n",
       "Epoch 26/30\n",
-      "14000/14000 [==============================] - 57s - loss: 0.1451 - acc: 0.9442 - val_loss: 0.8377 - val_acc: 0.7450\n",
+      "14000/14000 [==============================] - 99s - loss: 0.0402 - acc: 0.9864 - val_loss: 0.8468 - val_acc: 0.8132\n",
       "Epoch 27/30\n",
-      "14000/14000 [==============================] - 55s - loss: 0.1314 - acc: 0.9489 - val_loss: 0.7340 - val_acc: 0.7672\n",
+      "14000/14000 [==============================] - 98s - loss: 0.0359 - acc: 0.9873 - val_loss: 0.8582 - val_acc: 0.8168\n",
       "Epoch 28/30\n",
-      "14000/14000 [==============================] - 54s - loss: 0.1169 - acc: 0.9567 - val_loss: 0.7735 - val_acc: 0.7703\n",
+      "14000/14000 [==============================] - 99s - loss: 0.0297 - acc: 0.9902 - val_loss: 0.9454 - val_acc: 0.8120\n",
       "Epoch 29/30\n",
-      "14000/14000 [==============================] - 59s - loss: 0.1023 - acc: 0.9619 - val_loss: 0.8346 - val_acc: 0.7663\n",
+      "14000/14000 [==============================] - 98s - loss: 0.0249 - acc: 0.9913 - val_loss: 0.9665 - val_acc: 0.8062\n",
       "Epoch 30/30\n",
-      "14000/14000 [==============================] - 54s - loss: 0.0900 - acc: 0.9671 - val_loss: 0.8794 - val_acc: 0.7667\n"
+      "14000/14000 [==============================] - 99s - loss: 0.0208 - acc: 0.9922 - val_loss: 0.9869 - val_acc: 0.8132\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "<keras.callbacks.History at 0x7fdcb826b7d0>"
+       "<keras.callbacks.History at 0x7f2f1c1fdb10>"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -389,7 +382,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 18,
    "metadata": {
     "collapsed": false
    },
@@ -398,8 +391,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Test score: 0.879394139975\n",
-      "Test accuracy: 76.67%\n"
+      "Test score: 0.986947076691\n",
+      "Test accuracy: 81.32%\n"
      ]
     }
    ],

From 4a879cb61ad7e8e59f35dc3ece8052abd954d6ff Mon Sep 17 00:00:00 2001
From: yiiinghuang <mac@dyn-129-236-233-145.dyn.columbia.edu>
Date: Sun, 18 Dec 2016 15:52:16 -0500
Subject: [PATCH 8/8] Assignment6
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

The model is too complex for my old computer. It can’t run through it.
---
 .../01-training a RNN model in Keras.ipynb    | 533 ++++++++++++++++++
 ...using a pre-trained model with Keras.ipynb | 215 +++++++
 notebooks/week-6/03-model improvement.ipynb   | 429 ++++++++++++++
 3 files changed, 1177 insertions(+)
 create mode 100644 notebooks/week-6/01-training a RNN model in Keras.ipynb
 create mode 100644 notebooks/week-6/02-using a pre-trained model with Keras.ipynb
 create mode 100644 notebooks/week-6/03-model improvement.ipynb

diff --git a/notebooks/week-6/01-training a RNN model in Keras.ipynb b/notebooks/week-6/01-training a RNN model in Keras.ipynb
new file mode 100644
index 0000000..591321c
--- /dev/null
+++ b/notebooks/week-6/01-training a RNN model in Keras.ipynb	
@@ -0,0 +1,533 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Lab 6.1 - Keras for RNN\n",
+    "\n",
+    "In this lab we will use the [Keras deep learning library](https://keras.io/) to construct a simple recurrent neural network (RNN) that can *learn* linguistic structure from a piece of text, and use that knowledge to *generate* new text passages. To review general RNN architecture, specific types of RNN networks such as the LSTM networks we'll be using here, and other concepts behind this type of machine learning, you should consult the following resources:\n",
+    "\n",
+    "- http://www.wildml.com/2015/09/recurrent-neural-networks-tutorial-part-1-introduction-to-rnns/\n",
+    "- http://ml4a.github.io/guides/recurrent_neural_networks/\n",
+    "- http://colah.github.io/posts/2015-08-Understanding-LSTMs/\n",
+    "- http://karpathy.github.io/2015/05/21/rnn-effectiveness/\n",
+    "\n",
+    "This code is an adaptation of these two examples:\n",
+    "\n",
+    "- http://machinelearningmastery.com/text-generation-lstm-recurrent-neural-networks-python-keras/\n",
+    "- https://github.com/fchollet/keras/blob/master/examples/lstm_text_generation.py\n",
+    "\n",
+    "You can consult the original sites for more information and documentation.\n",
+    "\n",
+    "Let's start by importing some of the libraries we'll be using in this lab:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using TensorFlow backend.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "from keras.models import Sequential\n",
+    "from keras.layers import Dense\n",
+    "from keras.layers import Dropout\n",
+    "from keras.layers import LSTM\n",
+    "from keras.callbacks import ModelCheckpoint\n",
+    "from keras.utils import np_utils\n",
+    "\n",
+    "from time import gmtime, strftime\n",
+    "import os\n",
+    "import re\n",
+    "import pickle\n",
+    "import random\n",
+    "import sys"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The first thing we need to do is generate our training data set. In this case we will use a recent article written by Barack Obama for The Economist newspaper. Make sure you have the `obama.txt` file in the `/data` folder within the `/week-6` folder in your repository."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "length of text: 18312\n",
+      "text preview: wherever i go these days, at home or abroad, people ask me the same question: what is happening in the american political system? how has a country that has benefitedperhaps more than any otherfrom immigration, trade and technological innovation suddenly developed a strain of anti-immigrant, anti-innovation protectionism? why have some on the far left and even more on the far right embraced a crude populism that promises a return to a past that is not possible to restoreand that, for most americ\n"
+     ]
+    }
+   ],
+   "source": [
+    "# load ascii text from file\n",
+    "filename = \"data/obama.txt\"\n",
+    "raw_text = open(filename).read()\n",
+    "\n",
+    "# get rid of any characters other than letters, numbers, \n",
+    "# and a few special characters\n",
+    "raw_text = re.sub('[^\\nA-Za-z0-9 ,.:;?!-]+', '', raw_text)\n",
+    "\n",
+    "# convert all text to lowercase\n",
+    "raw_text = raw_text.lower()\n",
+    "\n",
+    "n_chars = len(raw_text)\n",
+    "print \"length of text:\", n_chars\n",
+    "print \"text preview:\", raw_text[:500]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next, we use python's `set()` function to generate a list of all unique characters in the text. This will form our 'vocabulary' of characters, which is similar to the categories found in typical ML classification problems. \n",
+    "\n",
+    "Since neural networks work with numerical data, we also need to create a mapping between each character and a unique integer value. To do this we create two dictionaries: one which has characters as keys and the associated integers as the value, and one which has integers as keys and the associated characters as the value. These dictionaries will allow us to do translation both ways."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "number of unique characters found: 44\n",
+      "a - maps to -> 18\n",
+      "25 - maps to -> h\n"
+     ]
+    }
+   ],
+   "source": [
+    "# extract all unique characters in the text\n",
+    "chars = sorted(list(set(raw_text)))\n",
+    "n_vocab = len(chars)\n",
+    "print \"number of unique characters found:\", n_vocab\n",
+    "\n",
+    "# create mapping of characters to integers and back\n",
+    "char_to_int = dict((c, i) for i, c in enumerate(chars))\n",
+    "int_to_char = dict((i, c) for i, c in enumerate(chars))\n",
+    "\n",
+    "# test our mapping\n",
+    "print 'a', \"- maps to ->\", char_to_int[\"a\"]\n",
+    "print 25, \"- maps to ->\", int_to_char[25]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "Now we need to define the training data for our network. With RNN's, the training data usually takes the shape of a three-dimensional matrix, with the size of each dimension representing:\n",
+    "\n",
+    "[# of training sequences, # of training samples per sequence, # of features per sample]\n",
+    "\n",
+    "- The training sequences are the sets of data subjected to the RNN at each training step. As with all neural networks, these training sequences are presented to the network in small batches during training.\n",
+    "- Each training sequence is composed of some number of training samples. The number of samples in each sequence dictates how far back in the data stream the algorithm will learn, and sets the depth of the RNN layer.\n",
+    "- Each training sample within a sequence is composed of some number of features. This is the data that the RNN layer is learning from at each time step. In our example, the training samples and targets will use one-hot encoding, so will have a feature for each possible character, with the actual character represented by `1`, and all others by `0`.\n",
+    "\n",
+    "To prepare the data, we first set the length of training sequences we want to use. In this case we will set the sequence length to 100, meaning the RNN layer will be able to predict future characters based on the 100 characters that came before.\n",
+    "\n",
+    "We will then slide this 100 character 'window' over the entire text to create `input` and `output` arrays. Each entry in the `input` array contains 100 characters from the text, and each entry in the `output` array contains the single character that came after."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total sequences:  18212\n"
+     ]
+    }
+   ],
+   "source": [
+    "# prepare the dataset of input to output pairs encoded as integers\n",
+    "seq_length = 100\n",
+    "\n",
+    "inputs = []\n",
+    "outputs = []\n",
+    "\n",
+    "for i in range(0, n_chars - seq_length, 1):\n",
+    "    inputs.append(raw_text[i:i + seq_length])\n",
+    "    outputs.append(raw_text[i + seq_length])\n",
+    "    \n",
+    "n_sequences = len(inputs)\n",
+    "print \"Total sequences: \", n_sequences"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now let's shuffle both the input and output data so that we can later have Keras split it automatically into a training and test set. To make sure the two lists are shuffled the same way (maintaining correspondance between inputs and outputs), we create a separate shuffled list of indeces, and use these indeces to reorder both lists."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "indeces = range(len(inputs))\n",
+    "random.shuffle(indeces)\n",
+    "\n",
+    "inputs = [inputs[x] for x in indeces]\n",
+    "outputs = [outputs[x] for x in indeces]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's visualize one of these sequences to make sure we are getting what we expect:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "lead to broadly shared prosperity and growth. economists have long recognised that markets, left to  --> t\n"
+     ]
+    }
+   ],
+   "source": [
+    "print inputs[0], \"-->\", outputs[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next we will prepare the actual numpy datasets which will be used to train our network. We first initialize two empty numpy arrays in the proper formatting:\n",
+    "\n",
+    "- X --> [# of training sequences, # of training samples, # of features]\n",
+    "- y --> [# of training sequences, # of features]\n",
+    "\n",
+    "We then iterate over the arrays we generated in the previous step and fill the numpy arrays with the proper data. Since all character data is formatted using one-hot encoding, we initialize both data sets with zeros. As we iterate over the data, we use the `char_to_int` dictionary to map each character to its related position integer, and use that position to change the related value in the data set to `1`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "X dims --> (18212, 100, 44)\n",
+      "y dims --> (18212, 44)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# create two empty numpy array with the proper dimensions\n",
+    "X = np.zeros((n_sequences, seq_length, n_vocab), dtype=np.bool)\n",
+    "y = np.zeros((n_sequences, n_vocab), dtype=np.bool)\n",
+    "\n",
+    "# iterate over the data and build up the X and y data sets\n",
+    "# by setting the appropriate indices to 1 in each one-hot vector\n",
+    "for i, example in enumerate(inputs):\n",
+    "    for t, char in enumerate(example):\n",
+    "        X[i, t, char_to_int[char]] = 1\n",
+    "    y[i, char_to_int[outputs[i]]] = 1\n",
+    "    \n",
+    "print 'X dims -->', X.shape\n",
+    "print 'y dims -->', y.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next, we define our RNN model in Keras. This is very similar to how we defined the CNN model, except now we use the `LSTM()` function to create an LSTM layer with an internal memory of 128 neurons. LSTM is a special type of RNN layer which solves the unstable gradients issue seen in basic RNN. Along with LSTM layers, Keras also supports basic RNN layers and GRU layers, which are similar to LSTM. You can find full documentation for recurrent layers in [Keras' documentation](https://keras.io/layers/recurrent/)\n",
+    "\n",
+    "As before, we need to explicitly define the input shape for the first layer. Also, we need to tell Keras whether the LSTM layer should pass its sequence of predictions or its internal memory as the output to the next layer. If you are connecting the LSTM layer to a fully connected layer as we do in this case, you should set the `return_sequences` parameter to `False` to have the layer pass the value of its hidden neurons. If you are connecting multiple LSTM layers, you should set the parameter to `True` in all but the last layer, so that subsequent layers can learn from the sequence of predictions of previous layers.\n",
+    "\n",
+    "We will use dropout with a probability of 50% to regularize the network and prevent overfitting on our training data. The output of the network will be a fully connected layer with one neuron for each character in the vocabulary. The softmax function will convert this output to a probability distribution across all characters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# define the LSTM model\n",
+    "model = Sequential()\n",
+    "model.add(LSTM(128, return_sequences=False, input_shape=(X.shape[1], X.shape[2])))\n",
+    "model.add(Dropout(0.50))\n",
+    "model.add(Dense(y.shape[1], activation='softmax'))\n",
+    "model.compile(loss='categorical_crossentropy', optimizer='adam')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next, we define two helper functions: one to select a character based on a probability distribution, and one to generate a sequence of predicted characters based on an input (or 'seed') list of characters.\n",
+    "\n",
+    "The `sample()` function will take in a probability distribution generated by the `softmax()` function, and select a character based on the 'temperature' input. The temperature (also often called the 'diversity') effects how strictly the probability distribution is sampled. \n",
+    "\n",
+    "- Lower values (closer to zero) output more confident predictions, but are also more conservative. In our case, if the model has overfit the training data, lower values are likely to give back exactly what is found in the text\n",
+    "- Higher values (1 and above) introduce more diversity and randomness into the results. This can lead the model to generate novel information not found in the training data. However, you are also likely to see more errors such as grammatical or spelling mistakes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def sample(preds, temperature=1.0):\n",
+    "    # helper function to sample an index from a probability array\n",
+    "    preds = np.asarray(preds).astype('float64')\n",
+    "    preds = np.log(preds) / temperature\n",
+    "    exp_preds = np.exp(preds)\n",
+    "    preds = exp_preds / np.sum(exp_preds)\n",
+    "    probas = np.random.multinomial(1, preds, 1)\n",
+    "    return np.argmax(probas)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `generate()` function will take in:\n",
+    "\n",
+    "- input sentance ('seed')\n",
+    "- number of characters to generate\n",
+    "- and target diversity or temperature\n",
+    "\n",
+    "and print the resulting sequence of characters to the screen."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def generate(sentence, prediction_length=50, diversity=0.35):\n",
+    "    print '----- diversity:', diversity \n",
+    "\n",
+    "    generated = sentence\n",
+    "    sys.stdout.write(generated)\n",
+    "\n",
+    "    # iterate over number of characters requested\n",
+    "    for i in range(prediction_length):\n",
+    "        \n",
+    "        # build up sequence data from current sentence\n",
+    "        x = np.zeros((1, X.shape[1], X.shape[2]))\n",
+    "        for t, char in enumerate(sentence):\n",
+    "            x[0, t, char_to_int[char]] = 1.\n",
+    "\n",
+    "        # use trained model to return probability distribution\n",
+    "        # for next character based on input sequence\n",
+    "        preds = model.predict(x, verbose=0)[0]\n",
+    "        \n",
+    "        # use sample() function to sample next character \n",
+    "        # based on probability distribution and desired diversity\n",
+    "        next_index = sample(preds, diversity)\n",
+    "        \n",
+    "        # convert integer to character\n",
+    "        next_char = int_to_char[next_index]\n",
+    "\n",
+    "        # add new character to generated text\n",
+    "        generated += next_char\n",
+    "        \n",
+    "        # delete the first character from beginning of sentance, \n",
+    "        # and add new caracter to the end. This will form the \n",
+    "        # input sequence for the next predicted character.\n",
+    "        sentence = sentence[1:] + next_char\n",
+    "\n",
+    "        # print results to screen\n",
+    "        sys.stdout.write(next_char)\n",
+    "        sys.stdout.flush()\n",
+    "    print"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next, we define a system for Keras to save our model's parameters to a local file after each epoch where it achieves an improvement in the overall loss. This will allow us to reuse the trained model at a later time without having to retrain it from scratch. This is useful for recovering models incase your computer crashes, or you want to stop the training early."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "filepath=\"-basic_LSTM.hdf5\"\n",
+    "checkpoint = ModelCheckpoint(filepath, monitor='loss', verbose=0, save_best_only=True, mode='min')\n",
+    "callbacks_list = [checkpoint]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we are finally ready to train the model. We want to train the model over 50 epochs, but we also want to output some generated text after each epoch to see how our model is doing. \n",
+    "\n",
+    "To do this we create our own loop to iterate over each epoch. Within the loop we first train the model for one epoch. Since all parameters are stored within the model, training one epoch at a time has the same exact effect as training over a longer series of epochs. We also use the model's `validation_split` parameter to tell Keras to automatically split the data into 80% training data and 20% test data for validation. Remember to always shuffle your data if you will be using validation!\n",
+    "\n",
+    "After each epoch is trained, we use the `raw_text` data to extract a new sequence of 100 characters as the 'seed' for our generated text. Finally, we use our `generate()` helper function to generate text using two different diversity settings.\n",
+    "\n",
+    "*Warning:* because of their large depth (remember that an RNN trained on a 100 long sequence effectively has 100 layers!), these networks typically take a much longer time to train than traditional multi-layer ANN's and CNN's. You shoud expect these models to train overnight on the virtual machine, but you should be able to see enough progress after the first few epochs to know if it is worth it to train a model to the end. For more complex RNN models with larger data sets in your own work, you should consider a native installation, along with a dedicated GPU if possible."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch: 1 / 50\n",
+      "Train on 14569 samples, validate on 3643 samples\n",
+      "Epoch 1/1\n",
+      " 8960/14569 [=================>............] - ETA: 42s - loss: 3.2841"
+     ]
+    }
+   ],
+   "source": [
+    "epochs = 50\n",
+    "prediction_length = 100\n",
+    "\n",
+    "for iteration in range(epochs):\n",
+    "    \n",
+    "    print 'epoch:', iteration + 1, '/', epochs\n",
+    "    model.fit(X, y, validation_split=0.2, batch_size=256, nb_epoch=1, callbacks=callbacks_list)\n",
+    "    \n",
+    "    # get random starting point for seed\n",
+    "    start_index = random.randint(0, len(raw_text) - seq_length - 1)\n",
+    "    # extract seed sequence from raw text\n",
+    "    seed = raw_text[start_index: start_index + seq_length]\n",
+    "    \n",
+    "    print '----- generating with seed:', seed\n",
+    "    \n",
+    "    for diversity in [0.5, 1.2]:\n",
+    "        generate(seed, prediction_length, diversity)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "That looks pretty good! You can see that the RNN has learned alot of the linguistic structure of the original writing, including typical length for words, where to put spaces, and basic punctuation with commas and periods. Many words are still misspelled but seem almost reasonable, and it is pretty amazing that it is able to learn this much in only 50 epochs of training. \n",
+    "\n",
+    "You can see that the loss is still going down after 50 epochs, so the model can definitely benefit from longer training. If you're curious you can try to train for more epochs, but as the error decreases be careful to monitor the output to make sure that the model is not overfitting. As with other neural network models, you can monitor the difference between training and validation loss to see if overfitting might be occuring. In this case, since we're using the model to generate new information, we can also get a sense of overfitting from the material it generates.\n",
+    "\n",
+    "A good indication of overfitting is if the model outputs exactly what is in the original text given a seed from the text, but jibberish if given a seed that is not in the original text. Remember we don't want the model to learn how to reproduce exactly the original text, but to learn its style to be able to generate new text. As with other models, regularization methods such as dropout and limiting model complexity can be used to avoid the problem of overfitting.\n",
+    "\n",
+    "Finally, let's save our training data and character to integer mapping dictionaries to an external file so we can reuse it with the model at a later time."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "pickle_file = '-basic_data.pickle'\n",
+    "\n",
+    "try:\n",
+    "    f = open(pickle_file, 'wb')\n",
+    "    save = {\n",
+    "        'X': X,\n",
+    "        'y': y,\n",
+    "        'int_to_char': int_to_char,\n",
+    "        'char_to_int': char_to_int,\n",
+    "    }\n",
+    "    pickle.dump(save, f, pickle.HIGHEST_PROTOCOL)\n",
+    "    f.close()\n",
+    "except Exception as e:\n",
+    "    print 'Unable to save data to', pickle_file, ':', e\n",
+    "    raise\n",
+    "    \n",
+    "statinfo = os.stat(pickle_file)\n",
+    "print 'Saved data to', pickle_file\n",
+    "print 'Compressed pickle size:', statinfo.st_size"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python [default]",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/notebooks/week-6/02-using a pre-trained model with Keras.ipynb b/notebooks/week-6/02-using a pre-trained model with Keras.ipynb
new file mode 100644
index 0000000..b8e161c
--- /dev/null
+++ b/notebooks/week-6/02-using a pre-trained model with Keras.ipynb	
@@ -0,0 +1,215 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Lab 6.2 - Using a pre-trained model with Keras\n",
+    "\n",
+    "In this section of the lab, we will load the model we trained in the previous section, along with the training data and mapping dictionaries, and use it to generate longer sequences of text.\n",
+    "\n",
+    "Let's start by importing the libraries we will be using:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "from keras.models import Sequential\n",
+    "from keras.layers import Dense\n",
+    "from keras.layers import Dropout\n",
+    "from keras.layers import LSTM\n",
+    "from keras.callbacks import ModelCheckpoint\n",
+    "from keras.utils import np_utils\n",
+    "\n",
+    "import sys\n",
+    "import re\n",
+    "import pickle"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next, we will import the data we saved previously using the `pickle` library."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "pickle_file = '-basic_data.pickle'\n",
+    "\n",
+    "with open(pickle_file, 'rb') as f:\n",
+    "    save = pickle.load(f)\n",
+    "    X = save['X']\n",
+    "    y = save['y']\n",
+    "    char_to_int = save['char_to_int']  \n",
+    "    int_to_char = save['int_to_char']    \n",
+    "    del save  # hint to help gc free up memory\n",
+    "    print('Training set', X.shape, y.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we need to define the Keras model. Since we will be loading parameters from a pre-trained model, this needs to match exactly the definition from the previous lab section. The only difference is that we will comment out the dropout layer so that the model uses all the hidden neurons when doing the predictions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# define the LSTM model\n",
+    "model = Sequential()\n",
+    "model.add(LSTM(128, return_sequences=False, input_shape=(X.shape[1], X.shape[2])))\n",
+    "# model.add(Dropout(0.50))\n",
+    "model.add(Dense(y.shape[1], activation='softmax'))\n",
+    "model.compile(loss='categorical_crossentropy', optimizer='adam')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next we will load the parameters from the model we trained previously, and compile it with the same loss and optimizer function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# load the parameters from the pretrained model\n",
+    "filename = \"-basic_LSTM.hdf5\"\n",
+    "model.load_weights(filename)\n",
+    "model.compile(loss='categorical_crossentropy', optimizer='adam')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We also need to rewrite the `sample()` and `generate()` helper functions so that we can use them in our code:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def sample(preds, temperature=1.0):\n",
+    "    preds = np.asarray(preds).astype('float64')\n",
+    "    preds = np.log(preds) / temperature\n",
+    "    exp_preds = np.exp(preds)\n",
+    "    preds = exp_preds / np.sum(exp_preds)\n",
+    "    probas = np.random.multinomial(1, preds, 1)\n",
+    "    return np.argmax(probas)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def generate(sentence, sample_length=50, diversity=0.35):\n",
+    "    generated = sentence\n",
+    "    sys.stdout.write(generated)\n",
+    "\n",
+    "    for i in range(sample_length):\n",
+    "        x = np.zeros((1, X.shape[1], X.shape[2]))\n",
+    "        for t, char in enumerate(sentence):\n",
+    "            x[0, t, char_to_int[char]] = 1.\n",
+    "\n",
+    "        preds = model.predict(x, verbose=0)[0]\n",
+    "        next_index = sample(preds, diversity)\n",
+    "        next_char = int_to_char[next_index]\n",
+    "\n",
+    "        generated += next_char\n",
+    "        sentence = sentence[1:] + next_char\n",
+    "\n",
+    "        sys.stdout.write(next_char)\n",
+    "        sys.stdout.flush()\n",
+    "    print"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can use the `generate()` function to generate text of any length based on our imported pre-trained model and a seed text of our choice. For best result, the length of the seed text should be the same as the length of training sequences (100 in the previous lab section). \n",
+    "\n",
+    "In this case, we will test the overfitting of the model by supplying it two seeds:\n",
+    "\n",
+    "- one which comes verbatim from the training text, and\n",
+    "- one which comes from another earlier speech by Obama\n",
+    "\n",
+    "If the model has not overfit our training data, we should expect it to produce reasonable results for both seeds. If it has overfit, it might produce pretty good results for something coming directly from the training set, but perform poorly on a new seed. This means that it has learned to replicate our training text, but cannot generalize to produce text based on other inputs. Since the original article was very short, however, the entire vocabulary of the model might be very limited, which is why as input we use a part of another speech given by Obama, instead of completely random text.\n",
+    "\n",
+    "Since we have not trained the model for that long, we will also use a lower temperature to get the model to generate more accurate if less diverse results. Try running the code a few times with different temperature settings to generate different results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "prediction_length = 500\n",
+    "seed_from_text = \"america has shown that progress is possible. last year, income gains were larger for households at t\"\n",
+    "seed_original = \"and as people around the world began to hear the tale of the lowly colonists who overthrew an empire\"\n",
+    "\n",
+    "for seed in [seed_from_text, seed_original]:\n",
+    "    generate(seed, prediction_length, .50)\n",
+    "    print \"-\" * 20"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python [default]",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/notebooks/week-6/03-model improvement.ipynb b/notebooks/week-6/03-model improvement.ipynb
new file mode 100644
index 0000000..02ca8ee
--- /dev/null
+++ b/notebooks/week-6/03-model improvement.ipynb	
@@ -0,0 +1,429 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Lab 6.3 - Improving the model\n",
+    "\n",
+    "In this section of the lab, you will be asked to apply what you have learned to create a RNN model that can generate new sequences of text based on what it has learned from a large set of existing text. In this case we will be using the full text of Lewis Carroll's *Alice in Wonderland*. Your task for the assignment is to:\n",
+    "\n",
+    "- format the book text into a set of training data\n",
+    "- define a RNN model in Keras based on one or more LSTM or GRU layers\n",
+    "- train the model with the training data\n",
+    "- use the trained model to generate new text\n",
+    "\n",
+    "Our previous model based on Obama's essay was prone to overfitting since there was not that much data to learn from. Thus, the generated text was either unintelligeable (not enough learning) or exactly replicated the training data (over-fitting). In this case, we are working with a much bigger data set, which should provide enough data to avoid over-fitting, but will also take more time to train. To improve your model, you can experiment with tuning the following hyper-parameters:\n",
+    "\n",
+    "- Use more than one recurrent layer and/or add more memory units (hidden neurons) to each layer. This will allow you to learn more complex structures in the data.\n",
+    "- Use sequences longer than 100 characters, which will allow you to learn from patterns further back in time.\n",
+    "- Change the way the sequences are generated. For example you could try to break up the text into real sentances using the periods, and then either cut or pad each sentance to make it 100 characters long.\n",
+    "- Increase the number of training epochs, which will give the model more time to learn. Monitor the validation loss at each epoch to make sure the model is still improving at each epoch and is not overfitting the training data.\n",
+    "- Add more dropout to the recurrent layers to minimize over-fitting.\n",
+    "- Tune the batch size - try a batch size of 1 as a (very slow) baseline and larger sizes from there.\n",
+    "- Experiment with scale factors (temperature) when interpreting the prediction probabilities.\n",
+    "\n",
+    "If you get an error such as `alloc error` or `out of memory error` during training it means that  your computer does not have enough RAM memory to store the model parameters or the batch of training data needed during a training step. If you run into this issue, try reducing the complexity of your model (both number and depth of layers) or the mini-batch size.\n",
+    "\n",
+    "The last three code blocks will use your trained model to generate a sequence of text based on a predefined seed. Do not change any of the code, but run it before submitting your assignment. Your work will be evaluated based on the quality of the generated text. A good result should be legible with decent grammar and spelling (this indicates a high level of learning), but the exact text should not be found anywhere in the actual text (this indicates over-fitting).\n",
+    "\n",
+    "Let's start by importing the libraries we will be using, and importing the full text from Alice in Wonderland:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using TensorFlow backend.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "from keras.models import Sequential\n",
+    "from keras.layers import Dense\n",
+    "from keras.layers import Dropout\n",
+    "from keras.layers import LSTM\n",
+    "from keras.callbacks import ModelCheckpoint\n",
+    "from keras.utils import np_utils\n",
+    "\n",
+    "from time import gmtime, strftime\n",
+    "import os\n",
+    "import re\n",
+    "import pickle\n",
+    "import random\n",
+    "import sys"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "length of text: 141266\n",
+      "text preview: alices adventures in wonderland\n",
+      "\n",
+      "lewis carroll\n",
+      "\n",
+      "the millennium fulcrum edition 3.0\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "chapter i. down the rabbit-hole\n",
+      "\n",
+      "alice was beginning to get very tired of sitting by her sister on the\n",
+      "bank, and of having nothing to do: once or twice she had peeped into the\n",
+      "book her sister was reading, but it had no pictures or conversations in\n",
+      "it, and what is the use of a book, thought alice without pictures or\n",
+      "conversations?\n",
+      "\n",
+      "so she was considering in her own mind as well as she could, for the\n",
+      "hot day mad\n"
+     ]
+    }
+   ],
+   "source": [
+    "filename = \"data/wonderland.txt\"\n",
+    "raw_text = open(filename).read()\n",
+    "\n",
+    "raw_text = re.sub('[^\\nA-Za-z0-9 ,.:;?!-]+', '', raw_text)\n",
+    "raw_text = raw_text.lower()\n",
+    "\n",
+    "n_chars = len(raw_text)\n",
+    "print \"length of text:\", n_chars\n",
+    "print \"text preview:\", raw_text[:500]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "number of unique characters found: 37\n",
+      "a - maps to -> 11\n",
+      "25 - maps to -> o\n"
+     ]
+    }
+   ],
+   "source": [
+    "# write your code here\n",
+    "# extract all unique characters in the text\n",
+    "chars = sorted(list(set(raw_text)))\n",
+    "n_vocab = len(chars)\n",
+    "print \"number of unique characters found:\", n_vocab\n",
+    "\n",
+    "# create mapping of characters to integers and back\n",
+    "char_to_int = dict((c, i) for i, c in enumerate(chars))\n",
+    "int_to_char = dict((i, c) for i, c in enumerate(chars))\n",
+    "\n",
+    "# test our mapping\n",
+    "print 'a', \"- maps to ->\", char_to_int[\"a\"]\n",
+    "print 25, \"- maps to ->\", int_to_char[25]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total sequences:  141166\n"
+     ]
+    }
+   ],
+   "source": [
+    "# prepare the dataset of input to output pairs encoded as integers\n",
+    "seq_length = 100\n",
+    "\n",
+    "inputs = []\n",
+    "outputs = []\n",
+    "\n",
+    "for i in range(0, n_chars - seq_length, 1):\n",
+    "    inputs.append(raw_text[i:i + seq_length])\n",
+    "    outputs.append(raw_text[i + seq_length])\n",
+    "    \n",
+    "n_sequences = len(inputs)\n",
+    "print \"Total sequences: \", n_sequences"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "indeces = range(len(inputs))\n",
+    "random.shuffle(indeces)\n",
+    "\n",
+    "inputs = [inputs[x] for x in indeces]\n",
+    "outputs = [outputs[x] for x in indeces]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dnt be so proud\n",
+      "as all that.\n",
+      "\n",
+      "with extras? asked the mock turtle a little anxiously.\n",
+      "\n",
+      "yes, said alic --> e\n"
+     ]
+    }
+   ],
+   "source": [
+    "print inputs[0], \"-->\", outputs[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "X dims --> (141166, 100, 37)\n",
+      "y dims --> (141166, 37)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# create two empty numpy array with the proper dimensions\n",
+    "X = np.zeros((n_sequences, seq_length, n_vocab), dtype=np.bool)\n",
+    "y = np.zeros((n_sequences, n_vocab), dtype=np.bool)\n",
+    "\n",
+    "# iterate over the data and build up the X and y data sets\n",
+    "# by setting the appropriate indices to 1 in each one-hot vector\n",
+    "for i, example in enumerate(inputs):\n",
+    "    for t, char in enumerate(example):\n",
+    "        X[i, t, char_to_int[char]] = 1\n",
+    "    y[i, char_to_int[outputs[i]]] = 1\n",
+    "    \n",
+    "print 'X dims -->', X.shape\n",
+    "print 'y dims -->', y.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# define the LSTM model\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def sample(preds, temperature=1.0):\n",
+    "    # helper function to sample an index from a probability array\n",
+    "    preds = np.asarray(preds).astype('float64')\n",
+    "    preds = np.log(preds) / temperature\n",
+    "    exp_preds = np.exp(preds)\n",
+    "    preds = exp_preds / np.sum(exp_preds)\n",
+    "    probas = np.random.multinomial(1, preds, 1)\n",
+    "    return np.argmax(probas)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def generate(sentence, prediction_length=50, diversity=0.35):\n",
+    "    print '----- diversity:', diversity \n",
+    "\n",
+    "    generated = sentence\n",
+    "    sys.stdout.write(generated)\n",
+    "\n",
+    "    # iterate over number of characters requested\n",
+    "    for i in range(prediction_length):\n",
+    "        \n",
+    "        # build up sequence data from current sentence\n",
+    "        x = np.zeros((1, X.shape[1], X.shape[2]))\n",
+    "        for t, char in enumerate(sentence):\n",
+    "            x[0, t, char_to_int[char]] = 1.\n",
+    "\n",
+    "        # use trained model to return probability distribution\n",
+    "        # for next character based on input sequence\n",
+    "        preds = model.predict(x, verbose=0)[0]\n",
+    "        \n",
+    "        # use sample() function to sample next character \n",
+    "        # based on probability distribution and desired diversity\n",
+    "        next_index = sample(preds, diversity)\n",
+    "        \n",
+    "        # convert integer to character\n",
+    "        next_char = int_to_char[next_index]\n",
+    "\n",
+    "        # add new character to generated text\n",
+    "        generated += next_char\n",
+    "        \n",
+    "        # delete the first character from beginning of sentance, \n",
+    "        # and add new caracter to the end. This will form the \n",
+    "        # input sequence for the next predicted character.\n",
+    "        sentence = sentence[1:] + next_char\n",
+    "\n",
+    "        # print results to screen\n",
+    "        sys.stdout.write(next_char)\n",
+    "        sys.stdout.flush()\n",
+    "    print"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "filepath=\"-basic_LSTM.hdf5\"\n",
+    "checkpoint = ModelCheckpoint(filepath, monitor='loss', verbose=0, save_best_only=True, mode='min')\n",
+    "callbacks_list = [checkpoint]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Do not change this code, but run it before submitting your assignment to generate the results**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch: 1 / 60\n"
+     ]
+    },
+    {
+     "ename": "NameError",
+     "evalue": "name 'model' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-12-760cacf0d850>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0;32mprint\u001b[0m \u001b[0;34m'epoch:'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0miteration\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'/'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m     \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalidation_split\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m256\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnb_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcallbacks_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m     \u001b[0;31m# get random starting point for seed\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'model' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "prediction_length = 500\n",
+    "seed = \"this time alice waited patiently until it chose to speak again. in a minute or two the caterpillar t\"\n",
+    "epochs = 60\n",
+    "\n",
+    "for iteration in range(epochs):\n",
+    "    \n",
+    "    print 'epoch:', iteration + 1, '/', epochs\n",
+    "    model.fit(X, y, validation_split=0.2, batch_size=256, nb_epoch=1, callbacks=callbacks_list)\n",
+    "    \n",
+    "    # get random starting point for seed\n",
+    "    start_index = random.randint(0, len(raw_text) - seq_length - 1)\n",
+    "    # extract seed sequence from raw text\n",
+    "    seed = raw_text[start_index: start_index + seq_length]\n",
+    "    \n",
+    "    print '----- generating with seed:', seed\n",
+    "    \n",
+    "    for diversity in [0.5, 1.2]:\n",
+    "        generate(seed, prediction_length, diversity)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python [default]",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}