DIGIT_MLN_DROPOUT.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "matplotlib.use('nbagg')\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_digits\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "digits = load_digits()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1797, 64) (1797,)\n"
     ]
    }
   ],
   "source": [
    "print digits.data.shape, digits.target.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/miaofan/anaconda/lib/python2.7/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cross_validation import train_test_split\n",
    "from sklearn.preprocessing import LabelBinarizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X, y = digits.data, digits.target\n",
    "\n",
    "X = StandardScaler().fit_transform(X)\n",
    "y = LabelBinarizer().fit_transform(y)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 33)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1257, 64) (1257, 10)\n",
      "[ 0.         -0.33501649 -1.09493684  0.50949529 -1.13109185 -1.02065685\n",
      " -0.40972392 -0.12502292 -0.05907756 -0.62400926 -1.91557297  0.75962245\n",
      "  0.98730398 -1.18584066 -0.51499146 -0.13043338 -0.04462507 -0.72764628\n",
      " -1.38915755  1.55269115  1.27989088 -1.09857754 -0.54880546 -0.11422184\n",
      " -0.03337973 -0.78510524 -0.49937093  1.22058589  0.8248106  -1.28625035\n",
      " -0.62889588 -0.04723238  0.         -0.67237227  0.21078478  1.10556952\n",
      "  0.45489876 -1.48986148 -0.82269451  0.         -0.06134367 -0.5312841\n",
      "  0.17113031  1.36217503  1.17098727 -1.27091315 -0.79827225 -0.08874162\n",
      " -0.03543326 -0.40357499 -0.62147837  1.23639392  1.24208282 -0.45750018\n",
      " -0.75743581 -0.20978513 -0.02359646 -0.29908135 -1.08938309  0.20829258\n",
      "  0.03869656 -1.14664746 -0.5056698  -0.19600752]\n"
     ]
    }
   ],
   "source": [
    "print X_train.shape, y_train.shape\n",
    "print X_train[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "INPUT_UNITS = 64\n",
    "HIDDEN_UNITS = [50]\n",
    "N_CLASSES = 10\n",
    "KEEP_PROB = 0.5\n",
    "LEARNING_RATE = 0.1\n",
    "TRAINING_ITER = 2000\n",
    "\n",
    "\n",
    "def init_weights(shape):\n",
    "    return tf.Variable(tf.random_normal(shape))\n",
    "\n",
    "def init_biases(shape):\n",
    "    return tf.Variable(tf.zeros(shape) + 0.1)\n",
    "\n",
    "def add_layer(x, weights, biases, keep_prob, activation_function = None):\n",
    "    score = tf.matmul(x, weights) + biases\n",
    "    \n",
    "    if activation_function:\n",
    "        return tf.nn.dropout(activation_function(score), keep_prob)\n",
    "    else:\n",
    "        return tf.nn.dropout(score, keep_prob)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "x = tf.placeholder(tf.float32, [None, INPUT_UNITS])\n",
    "y = tf.placeholder(tf.float32, [None, N_CLASSES])\n",
    "keep_prob = tf.placeholder(tf.float32)\n",
    "\n",
    "weights = {'l1': init_weights([INPUT_UNITS, HIDDEN_UNITS[0]]), 'l2': init_weights([HIDDEN_UNITS[0], N_CLASSES])}\n",
    "biases = {'l1': init_biases([HIDDEN_UNITS[0]]), 'l2': init_biases([N_CLASSES])}\n",
    "\n",
    "h1 = add_layer(x, weights['l1'], biases['l1'], keep_prob, tf.nn.relu)\n",
    "\n",
    "#h2 = add_layer(h1, weights['l2'], biases['l2'], keep_prob, tf.nn.tanh)\n",
    "\n",
    "predictions = add_layer(h1, weights['l2'], biases['l2'], 1.0, None)\n",
    "\n",
    "loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y, logits=predictions))\n",
    "\n",
    "train_step = tf.train.GradientDescentOptimizer(LEARNING_RATE).minimize(loss)\n",
    "\n",
    "accuracy = tf.reduce_mean(tf.cast(tf.equal(tf.arg_max(y, 1), tf.arg_max(predictions, 1)), tf.float32))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN_LOSS@0: 38.4874\n",
      "TEST_LOSS@0: 38.9495\n",
      "TRAIN_LOSS@50: 1.25937\n",
      "TEST_LOSS@50: 3.07837\n",
      "TRAIN_LOSS@100: 0.468905\n",
      "TEST_LOSS@100: 2.32762\n",
      "TRAIN_LOSS@150: 0.239498\n",
      "TEST_LOSS@150: 2.12294\n",
      "TRAIN_LOSS@200: 0.133417\n",
      "TEST_LOSS@200: 2.01681\n",
      "TRAIN_LOSS@250: 0.0845236\n",
      "TEST_LOSS@250: 1.94062\n",
      "TRAIN_LOSS@300: 0.0641747\n",
      "TEST_LOSS@300: 1.88991\n",
      "TRAIN_LOSS@350: 0.052511\n",
      "TEST_LOSS@350: 1.85718\n",
      "TRAIN_LOSS@400: 0.0448192\n",
      "TEST_LOSS@400: 1.83358\n",
      "TRAIN_LOSS@450: 0.0385924\n",
      "TEST_LOSS@450: 1.81548\n",
      "TRAIN_LOSS@500: 0.0330258\n",
      "TEST_LOSS@500: 1.80099\n",
      "TRAIN_LOSS@550: 0.0280479\n",
      "TEST_LOSS@550: 1.7904\n",
      "TRAIN_LOSS@600: 0.0236367\n",
      "TEST_LOSS@600: 1.7843\n",
      "TRAIN_LOSS@650: 0.0195731\n",
      "TEST_LOSS@650: 1.78248\n",
      "TRAIN_LOSS@700: 0.0156954\n",
      "TEST_LOSS@700: 1.78375\n",
      "TRAIN_LOSS@750: 0.0119886\n",
      "TEST_LOSS@750: 1.78601\n",
      "TRAIN_LOSS@800: 0.00839939\n",
      "TEST_LOSS@800: 1.78943\n",
      "TRAIN_LOSS@850: 0.00536926\n",
      "TEST_LOSS@850: 1.79131\n",
      "TRAIN_LOSS@900: 0.00396934\n",
      "TEST_LOSS@900: 1.78568\n",
      "TRAIN_LOSS@950: 0.00327716\n",
      "TEST_LOSS@950: 1.77842\n",
      "TRAIN_LOSS@1000: 0.00283275\n",
      "TEST_LOSS@1000: 1.77182\n",
      "TRAIN_LOSS@1050: 0.00251358\n",
      "TEST_LOSS@1050: 1.76597\n",
      "TRAIN_LOSS@1100: 0.00226923\n",
      "TEST_LOSS@1100: 1.76067\n",
      "TRAIN_LOSS@1150: 0.00207452\n",
      "TEST_LOSS@1150: 1.75583\n",
      "TRAIN_LOSS@1200: 0.00191436\n",
      "TEST_LOSS@1200: 1.75137\n",
      "TRAIN_LOSS@1250: 0.00177964\n",
      "TEST_LOSS@1250: 1.74724\n",
      "TRAIN_LOSS@1300: 0.0016644\n",
      "TEST_LOSS@1300: 1.74339\n",
      "TRAIN_LOSS@1350: 0.00156444\n",
      "TEST_LOSS@1350: 1.73979\n",
      "TRAIN_LOSS@1400: 0.00147675\n",
      "TEST_LOSS@1400: 1.7364\n",
      "TRAIN_LOSS@1450: 0.00139908\n",
      "TEST_LOSS@1450: 1.7332\n",
      "TRAIN_LOSS@1500: 0.00132975\n",
      "TEST_LOSS@1500: 1.73017\n",
      "TRAIN_LOSS@1550: 0.00126738\n",
      "TEST_LOSS@1550: 1.72728\n",
      "TRAIN_LOSS@1600: 0.00121098\n",
      "TEST_LOSS@1600: 1.72453\n",
      "TRAIN_LOSS@1650: 0.00115975\n",
      "TEST_LOSS@1650: 1.7219\n",
      "TRAIN_LOSS@1700: 0.0011129\n",
      "TEST_LOSS@1700: 1.71937\n",
      "TRAIN_LOSS@1750: 0.00106989\n",
      "TEST_LOSS@1750: 1.71695\n",
      "TRAIN_LOSS@1800: 0.00103025\n",
      "TEST_LOSS@1800: 1.71461\n",
      "TRAIN_LOSS@1850: 0.000993595\n",
      "TEST_LOSS@1850: 1.71237\n",
      "TRAIN_LOSS@1900: 0.000959626\n",
      "TEST_LOSS@1900: 1.7102\n",
      "TRAIN_LOSS@1950: 0.00092801\n",
      "TEST_LOSS@1950: 1.7081\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x111fa6a50>"
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     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "init = tf.global_variables_initializer()\n",
    "\n",
    "epoch = []\n",
    "\n",
    "train_acc = []\n",
    "test_acc = []\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    sess.run(init)\n",
    "    \n",
    "    for i in range(TRAINING_ITER):\n",
    "        sess.run(train_step, feed_dict = {x: X_train, y: y_train, keep_prob: 1.0})\n",
    "        \n",
    "        if i % 50 == 0:\n",
    "            epoch.append(i)\n",
    "            \n",
    "            print 'TRAIN_LOSS@%d:' %i, sess.run(loss, feed_dict = {x: X_train, y: y_train, keep_prob: 1.0})\n",
    "            train_acc.append(sess.run(accuracy, feed_dict = {x: X_train, y: y_train, keep_prob: 1.0}))\n",
    "            print 'TEST_LOSS@%d:' %i, sess.run(loss, feed_dict = {x: X_test, y: y_test, keep_prob: 1.0})\n",
    "            test_acc.append(sess.run(accuracy, feed_dict = {x: X_test, y: y_test, keep_prob: 1.0}))\n",
    "\n",
    "plt.title('DIGIT_MLN_DROPOUT')\n",
    "train_line, = plt.plot(epoch, train_acc, 'r-', label='Train-keep-prob: 1.0')\n",
    "\n",
    "test_line, = plt.plot(epoch, test_acc, 'b-', label='Test-keep-prob: 1.0')\n",
    "plt.xlabel('EPOCH')\n",
    "plt.ylabel('ACCURACY')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN_LOSS@0: 45.9471\n",
      "TEST_LOSS@0: 48.5646\n",
      "TRAIN_LOSS@50: 1.45504\n",
      "TEST_LOSS@50: 2.20316\n",
      "TRAIN_LOSS@100: 0.636273\n",
      "TEST_LOSS@100: 1.32716\n",
      "TRAIN_LOSS@150: 0.374345\n",
      "TEST_LOSS@150: 1.01594\n",
      "TRAIN_LOSS@200: 0.252821\n",
      "TEST_LOSS@200: 0.870478\n",
      "TRAIN_LOSS@250: 0.184118\n",
      "TEST_LOSS@250: 0.732364\n",
      "TRAIN_LOSS@300: 0.152072\n",
      "TEST_LOSS@300: 0.688636\n",
      "TRAIN_LOSS@350: 0.126143\n",
      "TEST_LOSS@350: 0.634294\n",
      "TRAIN_LOSS@400: 0.108949\n",
      "TEST_LOSS@400: 0.596334\n",
      "TRAIN_LOSS@450: 0.0944738\n",
      "TEST_LOSS@450: 0.56161\n",
      "TRAIN_LOSS@500: 0.081578\n",
      "TEST_LOSS@500: 0.505243\n",
      "TRAIN_LOSS@550: 0.0723223\n",
      "TEST_LOSS@550: 0.488462\n",
      "TRAIN_LOSS@600: 0.0674514\n",
      "TEST_LOSS@600: 0.461601\n",
      "TRAIN_LOSS@650: 0.0618676\n",
      "TEST_LOSS@650: 0.457365\n",
      "TRAIN_LOSS@700: 0.0597484\n",
      "TEST_LOSS@700: 0.453548\n",
      "TRAIN_LOSS@750: 0.0602785\n",
      "TEST_LOSS@750: 0.437523\n",
      "TRAIN_LOSS@800: 0.0573785\n",
      "TEST_LOSS@800: 0.416215\n",
      "TRAIN_LOSS@850: 0.0556188\n",
      "TEST_LOSS@850: 0.417351\n",
      "TRAIN_LOSS@900: 0.0542399\n",
      "TEST_LOSS@900: 0.40181\n",
      "TRAIN_LOSS@950: 0.0506803\n",
      "TEST_LOSS@950: 0.406672\n",
      "TRAIN_LOSS@1000: 0.0492533\n",
      "TEST_LOSS@1000: 0.390263\n",
      "TRAIN_LOSS@1050: 0.0476348\n",
      "TEST_LOSS@1050: 0.390406\n",
      "TRAIN_LOSS@1100: 0.0479433\n",
      "TEST_LOSS@1100: 0.396488\n",
      "TRAIN_LOSS@1150: 0.0459996\n",
      "TEST_LOSS@1150: 0.387674\n",
      "TRAIN_LOSS@1200: 0.0432124\n",
      "TEST_LOSS@1200: 0.37742\n",
      "TRAIN_LOSS@1250: 0.0442692\n",
      "TEST_LOSS@1250: 0.385473\n",
      "TRAIN_LOSS@1300: 0.0424802\n",
      "TEST_LOSS@1300: 0.376811\n",
      "TRAIN_LOSS@1350: 0.0428573\n",
      "TEST_LOSS@1350: 0.382016\n",
      "TRAIN_LOSS@1400: 0.0420158\n",
      "TEST_LOSS@1400: 0.37821\n",
      "TRAIN_LOSS@1450: 0.0414828\n",
      "TEST_LOSS@1450: 0.377299\n",
      "TRAIN_LOSS@1500: 0.0397885\n",
      "TEST_LOSS@1500: 0.361396\n",
      "TRAIN_LOSS@1550: 0.0391168\n",
      "TEST_LOSS@1550: 0.375973\n",
      "TRAIN_LOSS@1600: 0.0393923\n",
      "TEST_LOSS@1600: 0.366566\n",
      "TRAIN_LOSS@1650: 0.0374867\n",
      "TEST_LOSS@1650: 0.359957\n",
      "TRAIN_LOSS@1700: 0.0382015\n",
      "TEST_LOSS@1700: 0.362788\n",
      "TRAIN_LOSS@1750: 0.0359129\n",
      "TEST_LOSS@1750: 0.35347\n",
      "TRAIN_LOSS@1800: 0.0373544\n",
      "TEST_LOSS@1800: 0.363296\n",
      "TRAIN_LOSS@1850: 0.0349661\n",
      "TEST_LOSS@1850: 0.347569\n",
      "TRAIN_LOSS@1900: 0.0350106\n",
      "TEST_LOSS@1900: 0.353715\n",
      "TRAIN_LOSS@1950: 0.0345636\n",
      "TEST_LOSS@1950: 0.35703\n"
     ]
    },
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       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_acc = []\n",
    "test_acc = []\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    sess.run(init)\n",
    "    \n",
    "    for i in range(TRAINING_ITER):\n",
    "        sess.run(train_step, feed_dict = {x: X_train, y: y_train, keep_prob: KEEP_PROB})\n",
    "        \n",
    "        if i % 50 == 0:\n",
    "            print 'TRAIN_LOSS@%d:' %i, sess.run(loss, feed_dict = {x: X_train, y: y_train, keep_prob: 1.0})\n",
    "            train_acc.append(sess.run(accuracy, feed_dict = {x: X_train, y: y_train, keep_prob: 1.0}))\n",
    "            \n",
    "            print 'TEST_LOSS@%d:' %i, sess.run(loss, feed_dict = {x: X_test, y: y_test, keep_prob: 1.0})\n",
    "            test_acc.append(sess.run(accuracy, feed_dict = {x: X_test, y: y_test, keep_prob: 1.0}))\n",
    "            \n",
    "            \n",
    "train_dropout_line, = plt.plot(epoch, train_acc, 'g-', label='Train-keep-prob:%f' %KEEP_PROB)\n",
    "\n",
    "test_dropout_line, = plt.plot(epoch, test_acc, 'y-', label='Test_keep-prob:%f' %KEEP_PROB)\n",
    "\n",
    "plt.legend(handles = [train_line, test_line, train_dropout_line, test_dropout_line])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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