From 03b7f1c4625826559745970060223db11e9dccb2 Mon Sep 17 00:00:00 2001
From: Qun Huang <qh2169@columbia.edu>
Date: Mon, 26 Sep 2016 23:26:13 -0400
Subject: [PATCH 1/6] first commit

this is my first commit if the course
---
 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..1e99858 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: qh2169\n"
+     ]
+    }
+   ],
    "source": [
-    "print \"uni:\", \"fill in your uni here\""
+    "print \"uni:\", \"qh2169\""
    ]
   }
  ],

From ce8d236a04ee7b66b407a5d2bca08a2c5d62ea58 Mon Sep 17 00:00:00 2001
From: Qun Huang <qh2169@columbia.edu>
Date: Fri, 30 Sep 2016 00:13:19 -0400
Subject: [PATCH 2/6] Assignment 2 completed

---
 notebooks/week-2/04 - Lab 2 Assignment.ipynb | 232 ++++++++++---------
 1 file changed, 128 insertions(+), 104 deletions(-)

diff --git a/notebooks/week-2/04 - Lab 2 Assignment.ipynb b/notebooks/week-2/04 - Lab 2 Assignment.ipynb
index 5d51991..e7a6f38 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": 41,
    "metadata": {
     "collapsed": true
    },
@@ -31,7 +31,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 42,
    "metadata": {
     "collapsed": true
    },
@@ -45,26 +45,30 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Next, let's define a new class to represent each player in the game. I have provided a rough framework of the class definition along with comments along the way to help you complete it. Places where you should write code are denoted by comments inside [] brackets and CAPITAL TEXT."
+    "Next, let's define a new class to represent each player in the game."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 43,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "scrolled": true
    },
    "outputs": [],
    "source": [
     "class Player:\n",
     "    \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",
+    "    ID = 0\n",
+    "    pot = 0\n",
     "    \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",
+    "        self.ID = inputID\n",
+    "        self.Pot = startingPot\n",
     "        \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",
@@ -79,17 +83,20 @@
     "        # the 'pot' variable tracks the player's money\n",
     "        \n",
     "        if playerCard < dealerCard:\n",
-    "            # [INCREMENT THE PLAYER'S POT, AND RETURN A MESSAGE]\n",
+    "            self.Pot -= gameStake\n",
+    "            return 'player ' + str(self.ID) + ' lose, ' + str(playerCard) + ' vs ' + str(dealerCard)\n",
     "        else:\n",
-    "            # [INCREMENT THE PLAYER'S POT, AND RETURN A MESSAGE]\n",
+    "            self.Pot += gameStake\n",
+    "            return 'player ' + str(self.ID) + ' win, ' + str(playerCard) + ' vs ' + str(dealerCard)\n",
     "        \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.ID"
    ]
   },
   {
@@ -101,7 +108,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 44,
    "metadata": {
     "collapsed": true
    },
@@ -111,7 +118,8 @@
     "    \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",
+    "        print player.play(dealerCard)"
    ]
   },
   {
@@ -123,7 +131,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 45,
    "metadata": {
     "collapsed": false
    },
@@ -132,7 +140,8 @@
     "def checkBalances(players):\n",
     "    \n",
     "    for player in players:\n",
-    "        #[PRINT OUT EACH PLAYER'S BALANCE BY USING EACH PLAYER'S ACCESSOR FUNCTIONS]"
+    "        #[PRINT OUT EACH PLAYER'S BALANCE BY USING EACH PLAYER'S ACCESSOR FUNCTIONS]\n",
+    "        print 'Player ' + str(player.returnID()) + ' has $' + str(player.returnPot()) + ' left.'"
    ]
   },
   {
@@ -144,7 +153,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 46,
    "metadata": {
     "collapsed": true
    },
@@ -162,7 +171,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 47,
    "metadata": {
     "collapsed": false
    },
@@ -181,11 +190,89 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 48,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "scrolled": true
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "start game 0\n",
+      "player 0 lose, 2 vs 5\n",
+      "player 1 win, 7 vs 5\n",
+      "player 2 win, 8 vs 8\n",
+      "player 3 lose, 1 vs 5\n",
+      "player 4 lose, 0 vs 4\n",
+      "\n",
+      "start game 1\n",
+      "player 0 win, 4 vs 3\n",
+      "player 1 lose, 5 vs 9\n",
+      "player 2 win, 8 vs 8\n",
+      "player 3 lose, 1 vs 2\n",
+      "player 4 lose, 0 vs 5\n",
+      "\n",
+      "start game 2\n",
+      "player 0 lose, 0 vs 6\n",
+      "player 1 win, 1 vs 0\n",
+      "player 2 lose, 6 vs 9\n",
+      "player 3 lose, 2 vs 7\n",
+      "player 4 lose, 0 vs 9\n",
+      "\n",
+      "start game 3\n",
+      "player 0 win, 3 vs 1\n",
+      "player 1 win, 9 vs 1\n",
+      "player 2 lose, 5 vs 8\n",
+      "player 3 win, 7 vs 0\n",
+      "player 4 win, 4 vs 3\n",
+      "\n",
+      "start game 4\n",
+      "player 0 lose, 2 vs 7\n",
+      "player 1 lose, 1 vs 4\n",
+      "player 2 lose, 2 vs 7\n",
+      "player 3 lose, 5 vs 7\n",
+      "player 4 win, 7 vs 7\n",
+      "\n",
+      "start game 5\n",
+      "player 0 lose, 2 vs 5\n",
+      "player 1 win, 8 vs 5\n",
+      "player 2 lose, 7 vs 9\n",
+      "player 3 win, 3 vs 2\n",
+      "player 4 win, 9 vs 8\n",
+      "\n",
+      "start game 6\n",
+      "player 0 lose, 1 vs 3\n",
+      "player 1 lose, 2 vs 8\n",
+      "player 2 lose, 1 vs 5\n",
+      "player 3 lose, 0 vs 4\n",
+      "player 4 lose, 6 vs 9\n",
+      "\n",
+      "start game 7\n",
+      "player 0 win, 7 vs 7\n",
+      "player 1 lose, 1 vs 5\n",
+      "player 2 win, 8 vs 8\n",
+      "player 3 win, 2 vs 1\n",
+      "player 4 win, 5 vs 3\n",
+      "\n",
+      "start game 8\n",
+      "player 0 win, 9 vs 5\n",
+      "player 1 lose, 8 vs 9\n",
+      "player 2 win, 7 vs 6\n",
+      "player 3 win, 8 vs 0\n",
+      "player 4 lose, 6 vs 8\n",
+      "\n",
+      "start game 9\n",
+      "player 0 win, 5 vs 2\n",
+      "player 1 win, 5 vs 3\n",
+      "player 2 win, 3 vs 1\n",
+      "player 3 win, 3 vs 0\n",
+      "player 4 lose, 2 vs 3\n"
+     ]
+    }
+   ],
    "source": [
     "for i in range(10):\n",
     "    print ''\n",
@@ -202,11 +289,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 49,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "scrolled": true
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "game results:\n",
+      "Player 0 has $500 left.\n",
+      "Player 1 has $500 left.\n",
+      "Player 2 has $500 left.\n",
+      "Player 3 has $500 left.\n",
+      "Player 4 has $400 left.\n"
+     ]
+    }
+   ],
    "source": [
     "print ''\n",
     "print 'game results:'\n",
@@ -223,91 +325,13 @@
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": [
-    "```\n",
-    "start game 0\n",
-    "player 0 Lose, 4 vs 7\n",
-    "player 1 Win, 2 vs 0\n",
-    "player 2 Lose, 0 vs 4\n",
-    "player 3 Win, 7 vs 2\n",
-    "player 4 Win, 5 vs 0\n",
-    "\n",
-    "start game 1\n",
-    "player 0 Win, 1 vs 0\n",
-    "player 1 Lose, 1 vs 5\n",
-    "player 2 Lose, 6 vs 9\n",
-    "player 3 Lose, 1 vs 8\n",
-    "player 4 Lose, 0 vs 9\n",
-    "\n",
-    "start game 2\n",
-    "player 0 Win, 3 vs 3\n",
-    "player 1 Lose, 0 vs 2\n",
-    "player 2 Win, 9 vs 6\n",
-    "player 3 Win, 8 vs 7\n",
-    "player 4 Win, 8 vs 6\n",
-    "\n",
-    "start game 3\n",
-    "player 0 Win, 9 vs 7\n",
-    "player 1 Lose, 7 vs 8\n",
-    "player 2 Lose, 2 vs 3\n",
-    "player 3 Lose, 0 vs 8\n",
-    "player 4 Lose, 0 vs 6\n",
-    "\n",
-    "start game 4\n",
-    "player 0 Win, 7 vs 4\n",
-    "player 1 Win, 3 vs 0\n",
-    "player 2 Win, 8 vs 5\n",
-    "player 3 Win, 2 vs 1\n",
-    "player 4 Lose, 4 vs 7\n",
-    "\n",
-    "start game 5\n",
-    "player 0 Lose, 2 vs 8\n",
-    "player 1 Lose, 4 vs 6\n",
-    "player 2 Win, 2 vs 0\n",
-    "player 3 Lose, 4 vs 5\n",
-    "player 4 Lose, 3 vs 8\n",
-    "\n",
-    "start game 6\n",
-    "player 0 Lose, 3 vs 6\n",
-    "player 1 Win, 8 vs 0\n",
-    "player 2 Win, 5 vs 5\n",
-    "player 3 Lose, 2 vs 6\n",
-    "player 4 Win, 8 vs 7\n",
-    "\n",
-    "start game 7\n",
-    "player 0 Lose, 0 vs 9\n",
-    "player 1 Lose, 6 vs 8\n",
-    "player 2 Lose, 1 vs 9\n",
-    "player 3 Lose, 4 vs 8\n",
-    "player 4 Win, 9 vs 8\n",
-    "\n",
-    "start game 8\n",
-    "player 0 Lose, 1 vs 8\n",
-    "player 1 Lose, 3 vs 9\n",
-    "player 2 Win, 5 vs 4\n",
-    "player 3 Win, 6 vs 2\n",
-    "player 4 Win, 3 vs 0\n",
-    "\n",
-    "start game 9\n",
-    "player 0 Lose, 5 vs 6\n",
-    "player 1 Win, 6 vs 1\n",
-    "player 2 Lose, 8 vs 9\n",
-    "player 3 Lose, 3 vs 9\n",
-    "player 4 Win, 7 vs 5\n",
-    "\n",
-    "game results:\n",
-    "player 0 has $400 left.\n",
-    "player 1 has $400 left.\n",
-    "player 2 has $500 left.\n",
-    "player 3 has $400 left.\n",
-    "player 4 has $600 left.\n",
-    "```"
-   ]
+   "source": []
   }
  ],
  "metadata": {
+  "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python 2",
+   "display_name": "Python [default]",
    "language": "python",
    "name": "python2"
   },

From ba73881f5ff54165bb12e7f4559159ecbff2986f Mon Sep 17 00:00:00 2001
From: Qun Huang <qh2169@columbia.edu>
Date: Sun, 9 Oct 2016 14:41:16 -0400
Subject: [PATCH 3/6] Week 3 assignment completed

Sorry but still confused about the mechanism... All I know is that after
I adjusted some certain parameters it suddenly worked... Really looking
forward to learning more about it.
---
 notebooks/week-3/01-basic ann.ipynb | 372 +++++++++++++++++++++-------
 1 file changed, 279 insertions(+), 93 deletions(-)

diff --git a/notebooks/week-3/01-basic ann.ipynb b/notebooks/week-3/01-basic ann.ipynb
index 15da5f8..834f358 100755
--- a/notebooks/week-3/01-basic ann.ipynb	
+++ b/notebooks/week-3/01-basic ann.ipynb	
@@ -20,9 +20,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 40,
    "metadata": {
-    "collapsed": true
+    "collapsed": false
    },
    "outputs": [],
    "source": [
@@ -44,7 +44,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 41,
    "metadata": {
     "collapsed": false
    },
@@ -219,7 +219,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 42,
    "metadata": {
     "collapsed": true
    },
@@ -246,22 +246,273 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 43,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 0 : 14 / 36 - 38.89 % acc\n",
+      "Epoch 1 : 14 / 36 - 38.89 % acc\n",
+      "Epoch 2 : 11 / 36 - 30.56 % acc\n",
+      "Epoch 3 : 15 / 36 - 41.67 % acc\n",
+      "Epoch 4 : 18 / 36 - 50.00 % acc\n",
+      "Epoch 5 : 15 / 36 - 41.67 % acc\n",
+      "Epoch 6 : 19 / 36 - 52.78 % acc\n",
+      "Epoch 7 : 20 / 36 - 55.56 % acc\n",
+      "Epoch 8 : 22 / 36 - 61.11 % acc\n",
+      "Epoch 9 : 20 / 36 - 55.56 % acc\n",
+      "Epoch 10 : 22 / 36 - 61.11 % acc\n",
+      "Epoch 11 : 23 / 36 - 63.89 % acc\n",
+      "Epoch 12 : 24 / 36 - 66.67 % acc\n",
+      "Epoch 13 : 27 / 36 - 75.00 % acc\n",
+      "Epoch 14 : 25 / 36 - 69.44 % acc\n",
+      "Epoch 15 : 25 / 36 - 69.44 % acc\n",
+      "Epoch 16 : 27 / 36 - 75.00 % acc\n",
+      "Epoch 17 : 25 / 36 - 69.44 % acc\n",
+      "Epoch 18 : 29 / 36 - 80.56 % acc\n",
+      "Epoch 19 : 29 / 36 - 80.56 % acc\n",
+      "Epoch 20 : 30 / 36 - 83.33 % acc\n",
+      "Epoch 21 : 26 / 36 - 72.22 % acc\n",
+      "Epoch 22 : 30 / 36 - 83.33 % acc\n",
+      "Epoch 23 : 29 / 36 - 80.56 % acc\n",
+      "Epoch 24 : 27 / 36 - 75.00 % acc\n",
+      "Epoch 25 : 29 / 36 - 80.56 % acc\n",
+      "Epoch 26 : 30 / 36 - 83.33 % acc\n",
+      "Epoch 27 : 30 / 36 - 83.33 % acc\n",
+      "Epoch 28 : 32 / 36 - 88.89 % acc\n",
+      "Epoch 29 : 33 / 36 - 91.67 % acc\n",
+      "Epoch 30 : 31 / 36 - 86.11 % acc\n",
+      "Epoch 31 : 31 / 36 - 86.11 % acc\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAssAAAHlCAYAAAADRrEAAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSBCWAQAgQVgGAIAEYRkAABL+P/WyXfOhNaAuAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f8ea001ce10>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "iris_data = sns.load_dataset(\"iris\")\n",
+    "%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",
+    "        \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 for 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) 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. 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",
+    "            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",
+    "        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",
+    "        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",
-    "# randomly shuffle data\n",
-    "iris_data = shuffle(iris_data)\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",
+    "\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",
+    "X = X / X.max(axis=0)\n",
+    "\n",
+    "_, y = np.unique(y, return_inverse=True)\n",
+    "\n",
+    "y = y.reshape(-1,1)\n",
+    "\n",
+    "data = []\n",
+    "for i in range(X.shape[0]):\n",
+    "    data.append(tuple([X[i].reshape(-1,1), y[i][0]]))\n",
+    "\n",
+    "trainingSplit = int(.8 * len(data))\n",
+    "training_data = data[:trainingSplit]\n",
+    "test_data = data[trainingSplit:]\n",
+    "\n",
+    "enc = OneHotEncoder()\n",
+    "\n",
+    "enc.fit(y)\n",
     "\n",
-    "# print first 5 data points\n",
-    "print iris_data[:5]\n",
+    "training_data = [[_x, enc.transform(_y.reshape(-1,1)).toarray().reshape(-1,1)] for _x, _y in training_data]\n",
     "\n",
-    "# create pairplot of iris data\n",
-    "g = sns.pairplot(iris_data, hue=\"species\")"
+    "\n",
+    "net = Network([13, 32, 3])\n",
+    "\n",
+    "results = net.SGD(training_data, 32, 11, 0.2, test_data=test_data)\n",
+    "\n",
+    "\n",
+    "plt.plot(results)\n",
+    "plt.ylabel('accuracy (%)')\n",
+    "plt.ylim([0,100.0])\n",
+    "plt.show()"
    ]
   },
   {
@@ -286,43 +537,16 @@
     "collapsed": false
    },
    "outputs": [],
-   "source": [
-    "# convert iris data to numpy format\n",
-    "iris_array = iris_data.as_matrix()\n",
-    "\n",
-    "# split data into feature and target sets\n",
-    "X = iris_array[:, :4].astype(float)\n",
-    "y = iris_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",
-    "# 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(.7 * 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]"
-   ]
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
   },
   {
    "cell_type": "code",
@@ -331,19 +555,7 @@
     "collapsed": false
    },
    "outputs": [],
-   "source": [
-    "# define the network\n",
-    "net = Network([4, 32, 3])\n",
-    "\n",
-    "# train the network using SGD, and output the results\n",
-    "results = net.SGD(training_data, 30, 10, 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()"
-   ]
+   "source": []
   },
   {
    "cell_type": "markdown",
@@ -365,11 +577,7 @@
     "collapsed": false
    },
    "outputs": [],
-   "source": [
-    "import mnist_loader\n",
-    "\n",
-    "training_data, validation_data, test_data = mnist_loader.load_data_wrapper()"
-   ]
+   "source": []
   },
   {
    "cell_type": "markdown",
@@ -385,14 +593,7 @@
     "collapsed": false
    },
    "outputs": [],
-   "source": [
-    "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()"
-   ]
+   "source": []
   },
   {
    "cell_type": "code",
@@ -401,15 +602,7 @@
     "collapsed": false
    },
    "outputs": [],
-   "source": [
-    "net = Network([784, 30, 10])\n",
-    "results = 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()"
-   ]
+   "source": []
   },
   {
    "cell_type": "markdown",
@@ -439,14 +632,7 @@
     "collapsed": true
    },
    "outputs": [],
-   "source": [
-    "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]"
-   ]
+   "source": []
   }
  ],
  "metadata": {

From 94536d92259a7d6a26ca566a2c088345b994ad02 Mon Sep 17 00:00:00 2001
From: Qun Huang <qh2169@columbia.edu>
Date: Sun, 23 Oct 2016 17:28:46 -0400
Subject: [PATCH 4/6] Assignment4 completed by qh2169

---
 .../01-tensorflow ANN for regression.ipynb    | 210 ++++++++++++++++--
 ...02-tensorflow ANN for classification.ipynb | 123 +++++++---
 2 files changed, 284 insertions(+), 49 deletions(-)

diff --git a/notebooks/week-4/01-tensorflow ANN for regression.ipynb b/notebooks/week-4/01-tensorflow ANN for regression.ipynb
index 33456aa..3d8b4bb 100644
--- a/notebooks/week-4/01-tensorflow ANN for regression.ipynb	
+++ b/notebooks/week-4/01-tensorflow ANN for regression.ipynb	
@@ -34,7 +34,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 101,
    "metadata": {
     "collapsed": false
    },
@@ -64,11 +64,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 102,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "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",
@@ -91,11 +111,72 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 103,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Boston House Prices dataset\n",
+      "===========================\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']"
    ]
@@ -109,11 +190,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 104,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsEAAAJTCAYAAADpH8s8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fcbc03691d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Create a datset of correlations between house features\n",
     "corrmat = houses.corr()\n",
@@ -136,11 +228,42 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 105,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.JointGrid at 0x7fcb939bf490>"
+      ]
+     },
+     "execution_count": 105,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkYAAAJRCAYAAACgDnhPAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nCAAAIEUwAgAASP0/SIcBFuu07esAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fcbc66e4e90>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAk0AAAJRCAYAAABY7oO4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nhiYiIiIiE/4/uaZc8TIBg+gAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fcb939bf450>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "sns.jointplot(houses['target'], houses['RM'], kind='hex')\n",
     "sns.jointplot(houses['target'], houses['LSTAT'], kind='hex')"
@@ -163,11 +286,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 106,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "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",
@@ -205,7 +337,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 107,
    "metadata": {
     "collapsed": false
    },
@@ -218,11 +350,11 @@
     "\n",
     "# Hyper-parameters\n",
     "batch_size = 50\n",
-    "num_hidden_1 = 16\n",
-    "num_hidden_2 = 16\n",
+    "num_hidden_1 = 4\n",
+    "num_hidden_2 = 3\n",
     "learning_rate = 0.0001\n",
     "training_epochs = 200\n",
-    "dropout_keep_prob = 1.0 # set to no dropout by default\n",
+    "dropout_keep_prob = 0.4 # set to no dropout by default\n",
     "\n",
     "# variable to control the resolution at which the training results are stored\n",
     "display_step = 1"
@@ -241,7 +373,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 108,
    "metadata": {
     "collapsed": true
    },
@@ -279,7 +411,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 109,
    "metadata": {
     "collapsed": false
    },
@@ -389,11 +521,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 110,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "scrolled": true
    },
-   "outputs": [],
+   "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",
@@ -474,11 +616,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 111,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Minimum test loss: 5.64475702988\n",
+      "Maximum test loss: 16.4258384952\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1MAAAGHCAYAAABViAiMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fcbc00f2950>"
+      ]
+     },
+     "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",
@@ -492,7 +653,8 @@
     " \n",
     "ax.legend(df.columns, loc=1)\n",
     "\n",
-    "print \"Minimum test loss:\", np.min(df[\"test_acc\"])"
+    "print \"Minimum test loss:\", np.min(df[\"test_acc\"])\n",
+    "print \"Maximum test loss:\", np.max(df[\"test_acc\"])"
    ]
   },
   {
@@ -531,7 +693,7 @@
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python 2",
+   "display_name": "Python [default]",
    "language": "python",
    "name": "python2"
   },
diff --git a/notebooks/week-4/02-tensorflow ANN for classification.ipynb b/notebooks/week-4/02-tensorflow ANN for classification.ipynb
index a9e5f5d..0cc435b 100644
--- a/notebooks/week-4/02-tensorflow ANN for classification.ipynb	
+++ b/notebooks/week-4/02-tensorflow ANN for classification.ipynb	
@@ -11,7 +11,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 31,
    "metadata": {
     "collapsed": true
    },
@@ -51,26 +51,46 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 32,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "22.5328063241\n"
+     ]
+    }
+   ],
    "source": [
     "dataset = load_boston()\n",
     "houses = pd.DataFrame(dataset.data, columns=dataset.feature_names)\n",
     "houses['target'] = dataset.target\n",
     "\n",
-    "# WRITE CODE TO CONVERT 'TARGET' COLUMN FROM CONTINUOUS TO CATEGORICAL"
+    "# WRITE CODE TO CONVERT 'TARGET' COLUMN FROM CONTINUOUS TO CATEGORICAL\n",
+    "houses_mean = np.mean(houses['target'])\n",
+    "print np.mean(houses['target'])\n",
+    "houses['target'] = (dataset.target > houses_mean).astype(int)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 33,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1 <-- should be 1\n",
+      "0 <-- should be 0\n"
+     ]
+    }
+   ],
    "source": [
     "'''check your work'''\n",
     "print np.max(houses['target']), \"<-- should be 1\"\n",
@@ -90,11 +110,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 34,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "('Training set', (354, 13), (354, 2))\n",
+      "('Test set', (152, 13), (152, 2))\n"
+     ]
+    }
+   ],
    "source": [
     "houses_array = houses.as_matrix().astype(float)\n",
     "np.random.shuffle(houses_array)\n",
@@ -104,6 +133,10 @@
     "\n",
     "# USE SCIKIT-LEARN'S ONE-HOT ENCODING MODULE TO \n",
     "# CONVERT THE y ARRAY OF TARGETS TO ONE-HOT ENCODING.\n",
+    "y = y.reshape(-1,1)\n",
+    "enc = OneHotEncoder()\n",
+    "enc.fit(y)\n",
+    "y = enc.transform(y).toarray()\n",
     "\n",
     "X = X / X.max(axis=0)\n",
     "\n",
@@ -119,11 +152,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 35,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2 <-- should be 2\n",
+      "2 <-- should be 2\n",
+      "[ 1.  0.] <-- should be either [0. 1.] or [1. 0.]\n"
+     ]
+    }
+   ],
    "source": [
     "'''check your work'''\n",
     "print y_train.shape[1], \"<-- should be 2\"\n",
@@ -133,7 +176,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 36,
    "metadata": {
     "collapsed": true
    },
@@ -145,12 +188,12 @@
     "num_outputs = y_train.shape[1]\n",
     "\n",
     "# Hyper-parameters\n",
-    "batch_size = 50\n",
-    "num_hidden_1 = 32\n",
-    "num_hidden_2 =  32\n",
-    "learning_rate =  0.03\n",
-    "training_epochs =  400\n",
-    "dropout_keep_prob = 0.25 # 0.5 # set to no dropout by default\n",
+    "batch_size = 12\n",
+    "num_hidden_1 = 30\n",
+    "num_hidden_2 =  25\n",
+    "learning_rate =  0.3\n",
+    "training_epochs =  500\n",
+    "dropout_keep_prob = 0.5 # 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"
@@ -183,7 +226,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 37,
    "metadata": {
     "collapsed": true
    },
@@ -192,7 +235,9 @@
     "def accuracy(predictions, targets):\n",
     "    \n",
     "    # IMPLEMENT THE NEW ACCURACY MEASURE HERE\n",
-    "    accuracy = \n",
+    "    max_predicted = np.argmax(predictions, 1)\n",
+    "    max_target = np.argmax(targets,1)   \n",
+    "    accuracy = (np.sum((max_predicted == max_target))/float(max_predicted.shape[0]))*100.0\n",
     "    \n",
     "    return accuracy\n",
     "\n",
@@ -220,7 +265,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 38,
    "metadata": {
     "collapsed": false
    },
@@ -290,11 +335,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 39,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Initialized\n",
+      "Model saved in file: model_houses_classification.ckpt\n"
+     ]
+    }
+   ],
    "source": [
     "results = []\n",
     "\n",
@@ -330,11 +384,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 40,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Maximum test accuracy: 92.11%\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA2oAAAGUCAYAAABELNFLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f1ce2ccbdd0>"
+      ]
+     },
+     "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",
@@ -368,8 +440,9 @@
   }
  ],
  "metadata": {
+  "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python 2",
+   "display_name": "Python [default]",
    "language": "python",
    "name": "python2"
   },

From 691249024c15d225131b8e8832daab3309cab102 Mon Sep 17 00:00:00 2001
From: Qun Huang <qh2169@columbia.edu>
Date: Fri, 16 Dec 2016 16:06:57 -0500
Subject: [PATCH 5/6] Assignment 6 Error

It seems like that when I just opened and ran the first and second file
in the notebook and there are error reports. I asked some other guy in
this class and we cannot identify what is wrong. And in the third file
after the improvement code, the output seems like that the code
functioned well but afterwards another error report came up. Would you
might help me identify where the problem is? Or is it that something is
wrong with my computer? Apologize and thanks!
---
 .../01-training a RNN model in Keras.ipynb    | 100 ++++++++++++++---
 ...using a pre-trained model with Keras.ipynb |  28 ++++-
 notebooks/week-6/03-model improvement.ipynb   | 105 +++++++++++++++---
 3 files changed, 201 insertions(+), 32 deletions(-)

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
index 7a44731..a66556e 100644
--- a/notebooks/week-6/01-training a RNN model in Keras.ipynb	
+++ b/notebooks/week-6/01-training a RNN model in Keras.ipynb	
@@ -25,11 +25,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using TensorFlow backend.\n"
+     ]
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "from keras.models import Sequential\n",
@@ -56,11 +64,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "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",
@@ -89,11 +106,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "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",
@@ -130,11 +157,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "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",
@@ -159,7 +194,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {
     "collapsed": false
    },
@@ -181,11 +216,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "only seemed to increase the isolation of corporations and elites, who often seem to live by a differ --> e\n"
+     ]
+    }
+   ],
    "source": [
     "print inputs[0], \"-->\", outputs[0]"
    ]
@@ -204,11 +247,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "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",
@@ -238,11 +290,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "'module' object has no attribute 'control_flow_ops'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-8-89f3b7ecfef0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mSequential\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mLSTM\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m128\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreturn_sequences\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput_shape\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mDropout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0.50\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mDense\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mactivation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'softmax'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'categorical_crossentropy'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptimizer\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'adam'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/home/vagrant/anaconda2/lib/python2.7/site-packages/keras/models.pyc\u001b[0m in \u001b[0;36madd\u001b[0;34m(self, layer)\u001b[0m\n\u001b[1;32m    306\u001b[0m                  output_shapes=[self.outputs[0]._keras_shape])\n\u001b[1;32m    307\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 308\u001b[0;31m             \u001b[0moutput_tensor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlayer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    309\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput_tensor\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    310\u001b[0m                 raise Exception('All layers in a Sequential model '\n",
+      "\u001b[0;32m/home/vagrant/anaconda2/lib/python2.7/site-packages/keras/engine/topology.pyc\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, x, mask)\u001b[0m\n\u001b[1;32m    512\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0minbound_layers\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    513\u001b[0m             \u001b[0;31m# this will call layer.build() if necessary\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 514\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_inbound_node\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minbound_layers\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnode_indices\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtensor_indices\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    515\u001b[0m             \u001b[0minput_added\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    516\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/home/vagrant/anaconda2/lib/python2.7/site-packages/keras/engine/topology.pyc\u001b[0m in \u001b[0;36madd_inbound_node\u001b[0;34m(self, inbound_layers, node_indices, tensor_indices)\u001b[0m\n\u001b[1;32m    570\u001b[0m         \u001b[0;31m# creating the node automatically updates self.inbound_nodes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    571\u001b[0m         \u001b[0;31m# as well as outbound_nodes on inbound layers.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 572\u001b[0;31m         \u001b[0mNode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate_node\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minbound_layers\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnode_indices\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtensor_indices\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    573\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    574\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_output_shape_for\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput_shape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/home/vagrant/anaconda2/lib/python2.7/site-packages/keras/engine/topology.pyc\u001b[0m in \u001b[0;36mcreate_node\u001b[0;34m(cls, outbound_layer, inbound_layers, node_indices, tensor_indices)\u001b[0m\n\u001b[1;32m    147\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    148\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_tensors\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 149\u001b[0;31m             \u001b[0moutput_tensors\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mto_list\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutbound_layer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_tensors\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minput_masks\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    150\u001b[0m             \u001b[0moutput_masks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mto_list\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutbound_layer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompute_mask\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_tensors\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput_masks\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    151\u001b[0m             \u001b[0;31m# TODO: try to auto-infer shape if exception is raised by get_output_shape_for\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/home/vagrant/anaconda2/lib/python2.7/site-packages/keras/layers/core.pyc\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, x, mask)\u001b[0m\n\u001b[1;32m     88\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;36m0.\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mp\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m1.\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     89\u001b[0m             \u001b[0mnoise_shape\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_noise_shape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 90\u001b[0;31m             \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mK\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0min_train_phase\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mK\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdropout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnoise_shape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     91\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     92\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/home/vagrant/anaconda2/lib/python2.7/site-packages/keras/backend/tensorflow_backend.pyc\u001b[0m in \u001b[0;36min_train_phase\u001b[0;34m(x, alt)\u001b[0m\n\u001b[1;32m   1302\u001b[0m     \u001b[0;31m# else: assume learning phase is a placeholder.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1303\u001b[0m     \u001b[0mx_shape\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_shape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1304\u001b[0;31m     x = tf.python.control_flow_ops.cond(tf.cast(_LEARNING_PHASE, 'bool'),\n\u001b[0m\u001b[1;32m   1305\u001b[0m                                         \u001b[0;32mlambda\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1306\u001b[0m                                         lambda: alt)\n",
+      "\u001b[0;31mAttributeError\u001b[0m: 'module' object has no attribute 'control_flow_ops'"
+     ]
+    }
+   ],
    "source": [
     "# define the LSTM model\n",
     "model = Sequential()\n",
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
index b8e161c..c142449 100644
--- 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	
@@ -13,11 +13,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using TensorFlow backend.\n"
+     ]
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "from keras.models import Sequential\n",
@@ -41,11 +49,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "IOError",
+     "evalue": "[Errno 2] No such file or directory: '-basic_data.pickle'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mIOError\u001b[0m                                   Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-2-de90786b6aa8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mpickle_file\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'-basic_data.pickle'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpickle_file\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'rb'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m     \u001b[0msave\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpickle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msave\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'X'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mIOError\u001b[0m: [Errno 2] No such file or directory: '-basic_data.pickle'"
+     ]
+    }
+   ],
    "source": [
     "pickle_file = '-basic_data.pickle'\n",
     "\n",
diff --git a/notebooks/week-6/03-model improvement.ipynb b/notebooks/week-6/03-model improvement.ipynb
index 422529c..d43dafb 100644
--- a/notebooks/week-6/03-model improvement.ipynb	
+++ b/notebooks/week-6/03-model improvement.ipynb	
@@ -32,11 +32,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using TensorFlow backend.\n"
+     ]
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "from keras.models import Sequential\n",
@@ -56,11 +64,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "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",
@@ -75,13 +110,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {
-    "collapsed": true
+    "collapsed": false
    },
-   "outputs": [],
+   "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"
+    "# write your code here\n",
+    "# extract all unique characters in the text\n",
+    "#The method list() takes sequence types and converts them to lists. This is used to convert a given tuple into list\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",
+    "#create dictionary of characters and numbers\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]"
    ]
   },
   {
@@ -93,7 +152,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {
     "collapsed": true
    },
@@ -110,7 +169,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {
     "collapsed": true
    },
@@ -139,11 +198,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {
-    "collapsed": true
+    "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "this time alice waited patiently until it chose to speak again. in a minute or two the caterpillar t"
+     ]
+    },
+    {
+     "ename": "NameError",
+     "evalue": "global name 'X' 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-6-e52c1907bff9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mseed\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"this time alice waited patiently until it chose to speak again. in a minute or two the caterpillar t\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mgenerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mseed\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprediction_length\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m.50\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-5-64254dd50b2f>\u001b[0m in \u001b[0;36mgenerate\u001b[0;34m(sentence, sample_length, diversity)\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msample_length\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mchar\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msentence\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m             \u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mchar_to_int\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mchar\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mNameError\u001b[0m: global name 'X' 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",

From f958931fb9f557fefd7f84801d939c780d3b8d60 Mon Sep 17 00:00:00 2001
From: Qun Huang <qh2169@columbia.edu>
Date: Fri, 16 Dec 2016 16:25:41 -0500
Subject: [PATCH 6/6] Assignment 5

Same problem with the 6. It seems that my computer cannot correctly run
the code provided in the notebooks. Could you identify how may I improve
this?
---
 .../week-5/01-CNN in keras for mnist.ipynb    | 106 ++++++++-
 .../week-5/02-using your own images.ipynb     |  18 +-
 .../03-CNN in keras for dogs and cats.ipynb   | 209 +++++++++++++++++-
 3 files changed, 315 insertions(+), 18 deletions(-)

diff --git a/notebooks/week-5/01-CNN in keras for mnist.ipynb b/notebooks/week-5/01-CNN in keras for mnist.ipynb
index d84b7ad..97946b0 100644
--- a/notebooks/week-5/01-CNN in keras for mnist.ipynb	
+++ b/notebooks/week-5/01-CNN in keras for mnist.ipynb	
@@ -17,11 +17,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using TensorFlow backend.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "using ordering: tf\n"
+     ]
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "np.random.seed(1337)  # for reproducibility\n",
@@ -51,11 +66,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Downloading data from https://s3.amazonaws.com/img-datasets/mnist.pkl.gz\n",
+      "15253504/15296311 [============================>.] - ETA: 0sX_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",
@@ -77,7 +103,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {
     "collapsed": false
    },
@@ -100,11 +126,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(60000, 28, 28, 1)\n",
+      "(60000,)\n"
+     ]
+    }
+   ],
    "source": [
     "# number of classes\n",
     "num_classes = 10\n",
@@ -137,11 +172,40 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(28, 28)\n",
+      "(60000,)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7fd945d38e10>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWEAAAFfCAYAAACfj30KAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fd980048690>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "%matplotlib inline\n",
     "from matplotlib.pyplot import imshow\n",
@@ -178,7 +242,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {
     "collapsed": true
    },
@@ -212,11 +276,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "'module' object has no attribute 'control_flow_ops'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-7-1f122fb1162f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     27\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mDense\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum_hidden_1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     28\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mActivation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'relu'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 29\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mDropout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdropout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     30\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     31\u001b[0m \u001b[0;31m# add the second fully connected layer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/home/vagrant/anaconda2/lib/python2.7/site-packages/keras/models.pyc\u001b[0m in \u001b[0;36madd\u001b[0;34m(self, layer)\u001b[0m\n\u001b[1;32m    306\u001b[0m                  output_shapes=[self.outputs[0]._keras_shape])\n\u001b[1;32m    307\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 308\u001b[0;31m             \u001b[0moutput_tensor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlayer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    309\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput_tensor\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    310\u001b[0m                 raise Exception('All layers in a Sequential model '\n",
+      "\u001b[0;32m/home/vagrant/anaconda2/lib/python2.7/site-packages/keras/engine/topology.pyc\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, x, mask)\u001b[0m\n\u001b[1;32m    512\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0minbound_layers\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    513\u001b[0m             \u001b[0;31m# this will call layer.build() if necessary\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 514\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_inbound_node\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minbound_layers\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnode_indices\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtensor_indices\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    515\u001b[0m             \u001b[0minput_added\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    516\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/home/vagrant/anaconda2/lib/python2.7/site-packages/keras/engine/topology.pyc\u001b[0m in \u001b[0;36madd_inbound_node\u001b[0;34m(self, inbound_layers, node_indices, tensor_indices)\u001b[0m\n\u001b[1;32m    570\u001b[0m         \u001b[0;31m# creating the node automatically updates self.inbound_nodes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    571\u001b[0m         \u001b[0;31m# as well as outbound_nodes on inbound layers.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 572\u001b[0;31m         \u001b[0mNode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate_node\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minbound_layers\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnode_indices\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtensor_indices\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    573\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    574\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_output_shape_for\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput_shape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/home/vagrant/anaconda2/lib/python2.7/site-packages/keras/engine/topology.pyc\u001b[0m in \u001b[0;36mcreate_node\u001b[0;34m(cls, outbound_layer, inbound_layers, node_indices, tensor_indices)\u001b[0m\n\u001b[1;32m    147\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    148\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_tensors\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 149\u001b[0;31m             \u001b[0moutput_tensors\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mto_list\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutbound_layer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_tensors\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minput_masks\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    150\u001b[0m             \u001b[0moutput_masks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mto_list\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutbound_layer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompute_mask\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_tensors\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput_masks\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    151\u001b[0m             \u001b[0;31m# TODO: try to auto-infer shape if exception is raised by get_output_shape_for\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/home/vagrant/anaconda2/lib/python2.7/site-packages/keras/layers/core.pyc\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, x, mask)\u001b[0m\n\u001b[1;32m     88\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;36m0.\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mp\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m1.\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     89\u001b[0m             \u001b[0mnoise_shape\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_noise_shape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 90\u001b[0;31m             \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mK\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0min_train_phase\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mK\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdropout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnoise_shape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     91\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     92\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/home/vagrant/anaconda2/lib/python2.7/site-packages/keras/backend/tensorflow_backend.pyc\u001b[0m in \u001b[0;36min_train_phase\u001b[0;34m(x, alt)\u001b[0m\n\u001b[1;32m   1302\u001b[0m     \u001b[0;31m# else: assume learning phase is a placeholder.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1303\u001b[0m     \u001b[0mx_shape\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_shape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1304\u001b[0;31m     x = tf.python.control_flow_ops.cond(tf.cast(_LEARNING_PHASE, 'bool'),\n\u001b[0m\u001b[1;32m   1305\u001b[0m                                         \u001b[0;32mlambda\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1306\u001b[0m                                         lambda: alt)\n",
+      "\u001b[0;31mAttributeError\u001b[0m: 'module' object has no attribute 'control_flow_ops'"
+     ]
+    }
+   ],
    "source": [
     "# create new Keras Sequential model\n",
     "model = Sequential()\n",
diff --git a/notebooks/week-5/02-using your own images.ipynb b/notebooks/week-5/02-using your own images.ipynb
index 938f8af..2901e30 100644
--- a/notebooks/week-5/02-using your own images.ipynb	
+++ b/notebooks/week-5/02-using your own images.ipynb	
@@ -13,7 +13,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {
     "collapsed": true
    },
@@ -55,11 +55,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "OSError",
+     "evalue": "[Errno 2] No such file or directory: '-catsdogs'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-2-586f0fcfb267>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mimageFolder\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"-catsdogs\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mfolders\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlistdir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimageFolder\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0mnum_categories\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfolders\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mOSError\u001b[0m: [Errno 2] No such file or directory: '-catsdogs'"
+     ]
+    }
+   ],
    "source": [
     "imageFolder = \"-catsdogs\"\n",
     "\n",
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 347b179..7c5f784 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	
@@ -13,11 +13,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "IOError",
+     "evalue": "[Errno 2] No such file or directory: '-catsdogs.pickle'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mIOError\u001b[0m                                   Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-6-e38780beb1cf>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mpickle_file\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'-catsdogs.pickle'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpickle_file\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'rb'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m     \u001b[0msave\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpickle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0mX_train\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msave\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'X_train'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mIOError\u001b[0m: [Errno 2] No such file or directory: '-catsdogs.pickle'"
+     ]
+    }
+   ],
    "source": [
     "import pickle\n",
     "\n",
@@ -53,6 +65,196 @@
     "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": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "## implement your CNN starting here.\n",
+    "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",
+    "K.set_image_dim_ordering('tf')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'X_train' 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-8-3bcbe0783b10>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;31m# image dimensions\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mimg_rows\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg_cols\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX_train\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m  \u001b[0mX_train\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mX_train\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX_train\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg_rows\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg_cols\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'X_train' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "# number of classes\n",
+    "num_classes = 2\n",
+    "\n",
+    "# image dimensions\n",
+    "img_rows, img_cols = X_train.shape[1],  X_train.shape[2]\n",
+    "\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": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "from matplotlib.pyplot import imshow\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "img_num = 0\n",
+    "\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": null,
+   "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 = 20\n",
+    "depth_2 = 40\n",
+    "depth_3 = 60\n",
+    "\n",
+    "pool_size = 2\n",
+    "\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": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "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",
+    "#extra below:\n",
+    "\n",
+    "model.add(Convolution2D(depth_3, patch_size_3, patch_size_3,\n",
+    "                        border_mode='valid'))\n",
+    "model.add(Activation('relu'))\n",
+    "model.add(MaxPooling2D(pool_size=(pool_size, pool_size)))\n",
+    "\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",
+    "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": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "model.compile(loss='categorical_crossentropy',\n",
+    "              optimizer='adadelta',\n",
+    "              metrics=['accuracy'])"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -61,7 +263,8 @@
    },
    "outputs": [],
    "source": [
-    "## implement your CNN starting here."
+    "model.fit(X_train, Y_train, batch_size=batch_size, nb_epoch=nb_epoch,\n",
+    "          verbose=1, validation_data=(X_test, Y_test))"
    ]
   }
  ],