char_segmentation_CNN.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training: CNN - Characeter Segmentation\n",
    "Training of convolutional nerual network for character segmentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "import cv2\n",
    "import glob\n",
    "from math import ceil\n",
    "\n",
    "sys.path.append('src')\n",
    "from ocr.helpers import implt\n",
    "from ocr.mlhelpers import TrainingPlot, DataSet\n",
    "from ocr.datahelpers import load_gap_data\n",
    "\n",
    "%matplotlib notebook\n",
    "plt.rcParams['figure.figsize'] = (9.0, 5.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "slider = (60, 60)                        # Height is set to 60 by data and\n",
    "                                         # width should be less than 120\n",
    "learning_rate = 5e-5\n",
    "dropout = 0.5                            # Percentage of dopped out data\n",
    "train_set = 0.8                          # Percentage of training data\n",
    "\n",
    "TRAIN_STEPS = 500000\n",
    "TEST_ITER = 150\n",
    "COST_ITER = 50\n",
    "SAVE_ITER = 2000\n",
    "BATCH_SIZE = 64\n",
    "\n",
    "save_loc = 'models/gap-clas/large/CNN-CG'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Load Images and Lables in CSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading gap data...\n",
      "-> Number of gaps and letters: 13591\n"
     ]
    }
   ],
   "source": [
    "images, labels = load_gap_data('data/gapdet/large/', slider=slider)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of images: 13591\n",
      "Training images: 10872\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of images: \" + str(len(images)))\n",
    "\n",
    "# Splitting on train and test data\n",
    "div = int(train_set * len(images))\n",
    "\n",
    "trainData = images[0:div]\n",
    "trainLabels = labels[0:div]\n",
    "\n",
    "evalData = images[div:]\n",
    "evalLabels = labels[div:]\n",
    "\n",
    "print(\"Training images: %g\" % div)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prepare training dataset\n",
    "trainSet = DataSet(trainData, trainLabels)\n",
    "evalSet = DataSet(evalData, evalLabels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Convulation Neural Network"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "sess = tf.InteractiveSession()\n",
    "\n",
    "def weights(name, shape):\n",
    "    return tf.get_variable(name, shape=shape,\n",
    "                           initializer=tf.contrib.layers.xavier_initializer(),\n",
    "                           regularizer=tf.contrib.layers.l2_regularizer(scale=SCALE))\n",
    "\n",
    "def bias(const, shape, name=None):\n",
    "    return tf.Variable(tf.constant(const, shape=shape), name=name)\n",
    "\n",
    "# Help functions for standard layers\n",
    "def conv2d(x, W, name=None):\n",
    "    return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME', name=name)\n",
    "\n",
    "def max_pool_2x2(x, name=None):\n",
    "    return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME', name=name)\n",
    "\n",
    "# Regularization scale - FOR TWEAKING\n",
    "SCALE = 0.001\n",
    "# Weighting cross entropy\n",
    "POS_WEIGHT = (len(labels) - sum(labels)) / sum(labels)\n",
    "\n",
    "# Place holders for data (x) and labels (y_)\n",
    "x = tf.placeholder(tf.float32, [None, slider[0]*slider[1]], name='x')\n",
    "targets = tf.placeholder(tf.int64, [None])\n",
    "\n",
    "# Reshape input data\n",
    "reshape_images = tf.reshape(x, [-1, slider[0], slider[1], 1])\n",
    "\n",
    "# Image standardization\n",
    "x_images = tf.map_fn(\n",
    "    lambda img: tf.image.per_image_standardization(img), reshape_images)\n",
    "\n",
    "# 1. Layer - Convulation + Subsampling\n",
    "W_conv1 = weights('W_conv1', shape=[8, 8, 1, 10])\n",
    "b_conv1 = bias(0.1, shape=[10], name='b_conv1')\n",
    "\n",
    "h_conv1 = tf.nn.relu(conv2d(x_images, W_conv1) + b_conv1, name='h_conv1')\n",
    "\n",
    "# 2. Layer - Max Pool\n",
    "h_pool1 = max_pool_2x2(h_conv1, name='h_pool1')\n",
    "\n",
    "# 3. Layer - Convulation + Subsampling\n",
    "W_conv2 = weights('W_conv2', shape=[5, 5, 10, 20])\n",
    "b_conv2 = bias(0.1, shape=[20], name='b_conv2')\n",
    "\n",
    "h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2, name='h_conv2')\n",
    "\n",
    "# 4. Layer - Max Pool\n",
    "h_pool2 = max_pool_2x2(h_conv2, name='h_pool2')\n",
    "\n",
    "# 5. Fully Connected layer\n",
    "W_fc1 = weights('W_fc1', shape=[ceil(slider[0]/4)*ceil(slider[1]/4)*20, 1000])\n",
    "b_fc1 = bias(0.1, shape=[1000], name='b_fc1')\n",
    "\n",
    "h_conv2_flat = tf.reshape(\n",
    "    h_pool2, [-1, ceil(slider[0]/4)*ceil(slider[1]/4)*20], name='h_conv2_flat')\n",
    "h_fc1 = tf.nn.relu(tf.matmul(h_conv2_flat, W_fc1) + b_fc1, name='h_fc1')\n",
    "\n",
    "# 6. Dropout\n",
    "keep_prob = tf.placeholder(tf.float32, name='keep_prob')\n",
    "h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob, name='h_fc1_drop')\n",
    "\n",
    "# 7. Output layer\n",
    "W_fc2 = weights('W_fc2', shape=[1000, 2])\n",
    "b_fc2 = bias(0.1, shape=[2], name='b_fc2')\n",
    "\n",
    "y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2\n",
    "\n",
    "# Activation function for real use in application\n",
    "activation = tf.argmax(tf.matmul(h_fc1, W_fc2) + b_fc2, 1, name='activation')\n",
    "\n",
    "\n",
    "# Cost: cross entropy + regularization\n",
    "# Regularization with L2 Regularization with decaying learning rate\n",
    "# cross_entropy = tf.nn.weighted_cross_entropy_with_logits(logits=y_conv, targets=y_)\n",
    "weights = tf.multiply(targets, POS_WEIGHT) + 1\n",
    "cross_entropy = tf.losses.sparse_softmax_cross_entropy(\n",
    "    logits=y_conv,\n",
    "    labels=targets,\n",
    "    weights=weights)\n",
    "\n",
    "# Using cross entropy for sigmoid as loss\n",
    "regularization = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)\n",
    "cost = tf.reduce_mean(cross_entropy) + sum(regularization)\n",
    "\n",
    "\n",
    "# Optimizer\n",
    "train_step = tf.train.AdamOptimizer(learning_rate).minimize(cost, name='train_step')\n",
    "\n",
    "\n",
    "# Evaluating\n",
    "correct_prediction = tf.equal(tf.argmax(y_conv,1), targets)\n",
    "accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32), name='accuracy')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "window.mpl = {};\n",
       "\n",
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       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
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       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
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       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
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       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
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       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
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       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
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       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\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",
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       "            pass_mouse_events = false;\n",
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       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
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       "        stop: function(event, ui) {\n",
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       "            return fig.mouse_event(event, event['data']);\n",
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       "\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",
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       "\n",
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       "    canvas_div.append(rubberband);\n",
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       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
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       "        canvas_div.css('width', width)\n",
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       "\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",
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       "\n",
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       "        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",
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       "\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 overridden (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",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\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"
    },
    {
     "data": {
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\" width=\"900\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy 0.874\n"
     ]
    }
   ],
   "source": [
    "sess.run(tf.global_variables_initializer())\n",
    "saver = tf.train.Saver()\n",
    "\n",
    "# Graph for live ploting\n",
    "trainPlot = TrainingPlot(TRAIN_STEPS, TEST_ITER, COST_ITER)\n",
    "\n",
    "try:\n",
    "    for i in range(TRAIN_STEPS):\n",
    "        trainBatch, labelBatch = trainSet.next_batch(BATCH_SIZE)\n",
    "\n",
    "        if i%COST_ITER == 0:\n",
    "            # Plotting cost\n",
    "            tmpCost = cost.eval(feed_dict={x: trainBatch,\n",
    "                                           targets: labelBatch,\n",
    "                                           keep_prob: 1.0})\n",
    "            trainPlot.updateCost(tmpCost, i // COST_ITER)\n",
    "\n",
    "        if i%TEST_ITER == 0:\n",
    "            # Plotting accuracy\n",
    "            evalD, evalL = evalSet.next_batch(500)\n",
    "            accEval = accuracy.eval(feed_dict={x: evalD,\n",
    "                                               targets: evalL,\n",
    "                                               keep_prob: 1.0})\n",
    "            accTrain = accuracy.eval(feed_dict={x: trainBatch,\n",
    "                                                targets: labelBatch,\n",
    "                                                keep_prob: 1.0})\n",
    "            trainPlot.updateAcc(accEval, accTrain, i // TEST_ITER)\n",
    "\n",
    "        if i%SAVE_ITER == 0:\n",
    "            # Saving model\n",
    "            saver.save(sess, save_loc)\n",
    "\n",
    "        train_step.run(feed_dict={x: trainBatch,\n",
    "                                  targets: labelBatch,\n",
    "                                  keep_prob: 1 - dropout})\n",
    "except KeyboardInterrupt:\n",
    "    pass\n",
    "\n",
    "saver.save(sess, save_loc)\n",
    "evalD, evalL = evalSet.next_batch(500)\n",
    "print(\"Accuracy %g\" % accuracy.eval(feed_dict={x: evalD,\n",
    "                                               targets: evalL,\n",
    "                                               keep_prob: 1.0}))\n",
    "sess.close()"
   ]
  }
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