vector-visualization.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualizations of the examples from train set nearest to a given vector from the last capsule layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "\n",
    "from keras import backend as K\n",
    "from keras.datasets import mnist\n",
    "from keras.models import load_model\n",
    "\n",
    "from keras.layers import Input, Conv2D, Activation, Dense, Dropout\n",
    "from keras.layers import BatchNormalization, MaxPooling2D, Flatten\n",
    "from convcaps.capslayers import ConvertToCaps, Conv2DCaps, FlattenCaps\n",
    "from convcaps.capslayers import DenseCaps, CapsToScalars\n",
    "\n",
    "from keras.utils import to_categorical\n",
    "import numpy as np\n",
    "import matplotlib.cm as cm\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from ipywidgets import interact, interactive\n",
    "import ipywidgets as widgets\n",
    "\n",
    "from naive import do_routing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "img_rows, img_cols = 28, 28\n",
    "num_classes = 10\n",
    "\n",
    "# the data, shuffled and split between train and test sets\n",
    "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
    "\n",
    "# since we use only tf the channel is last\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",
    "x_train = x_train.astype(np.float32)\n",
    "x_test = x_test.astype(np.float32)\n",
    "x_train /= 255\n",
    "x_test /= 255\n",
    "\n",
    "# convert class vectors to binary class matrices\n",
    "y_train = to_categorical(y_train, num_classes)\n",
    "y_test = to_categorical(y_test, num_classes)\n",
    "\n",
    "batch_size = 32"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load pretrained model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = load_model('cc_weights.0.08-0.0003-0.0003-0.9924-0.0005-0.9875.hdf5',\n",
    "                   custom_objects={'ConvertToCaps': ConvertToCaps,\n",
    "                                   'Conv2DCaps': Conv2DCaps,\n",
    "                                   'FlattenCaps': FlattenCaps,\n",
    "                                   'DenseCaps': DenseCaps,\n",
    "                                   'CapsToScalars': CapsToScalars},\n",
    "                   compile=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Take apart the model and get weights of next to last layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "inp = model.input\n",
    "layers = [layer for layer in model.layers]\n",
    "outputs = [layer.output for layer in layers]\n",
    "functors = [K.function([inp] + [K.learning_phase()], [out]) for out in outputs]\n",
    "\n",
    "x = x_test[0:batch_size]\n",
    "res = [functors[i]([x, False])[0] for i in range(len(functors))]\n",
    "\n",
    "w = layers[7].get_weights()[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate pv - the output vectors "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sz = x_train.shape[0] // batch_size * batch_size\n",
    "p = np.zeros((sz, 10, 8))\n",
    "\n",
    "for i in range(0, sz, batch_size):\n",
    "    x = x_train[i:i+batch_size]\n",
    "    y = x_train[i:i+batch_size]\n",
    "    xi = functors[6]([x, False])[0]\n",
    "    p[i:i+batch_size, :, :] = do_routing(xi, w, 3)\n",
    "\n",
    "pl = np.sum(np.square(p), axis=-1)\n",
    "pn = np.argmax(pl, axis=-1)\n",
    "pv = np.array([p[n, pn[n]] for n in range(pn.shape[0])])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Show digits that have vectors nearest to a given vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        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",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        this.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\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",
       "    var titletext = $(\n",
       "        '<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",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\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",
       "\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"
    },
    {
     "data": {
      "text/html": [
       "<img src=\"data:image/png;base64,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\" width=\"638.888905813665\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3aa1b9fcb8594098afe27e10cb85c7f2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f = plt.figure()\n",
    "pics = []\n",
    "vecs = []\n",
    "\n",
    "for r in range(4):\n",
    "    for c in range(8):\n",
    "        pics.append(f.add_subplot(8, 8, r*2*8 + c + 1))\n",
    "        vecs.append(f.add_subplot(8, 8, r*2*8 + 8 + c + 1))\n",
    "\n",
    "def clear_pics():\n",
    "    for i in range(len(pics)):\n",
    "        pics[i].cla()\n",
    "        pics[i].axis('off')\n",
    "        vecs[i].cla()\n",
    "        vecs[i].axis('off')\n",
    "        \n",
    "def update(**kwargs):\n",
    "    vvals = np.array(list(kwargs.values()))\n",
    "    clear_pics()\n",
    "\n",
    "    # idx = np.logical_and((pv[:, 1] > 0.9), (pv[:, 1] < 0.91))\n",
    "    ds = np.sum(np.sqrt(np.square((pv - vvals))), axis=-1)\n",
    "    idx = np.argsort(ds)[:32]\n",
    "    #print(ds[idx])\n",
    "\n",
    "    lx = x_train[idx]\n",
    "    lpv = pv[idx]\n",
    "\n",
    "    for i in range(32):\n",
    "        pics[i].imshow(lx[i, :, :, 0], cmap=cm.gray)\n",
    "        vecs[i].imshow(lpv[i].reshape((1, -1)), cmap=cm.coolwarm,\n",
    "                       vmin=-1, vmax=1)\n",
    "\n",
    "    f.canvas.draw()\n",
    "    \n",
    "tmp = pv[1313]  # choose a vector of en example from train set\n",
    "sls = {'n%d' % (i,): widgets.FloatSlider(min=-1.0, max=1.0, step=0.05, value=tmp[i])\n",
    "       for i in range(8)}\n",
    "\n",
    "plt.show()\n",
    "\n",
    "interact(update, **sls);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}