FYS-STK4155/doc/Programs/ProjectsData/project_solution.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Image classification of simulated AT-TPC events\n",
    "\n",
    "Welcome to this project in applied machine learning. In this project we will tackle a simple classification problem of two different classes. The classes are  simulated reaction types for the Ar(p, p') experiment conducted at MSU, in this task we'll focus on the classification task and simply treat the experiment as a black box. \n",
    "\n",
    "### This is a completed notebook with solution examples, for your implementation we suggest you implement your own solution in the `project.ipynb` notebook\n",
    "\n",
    "This project has three tasks with a recommendation for the time to spend on each task: \n",
    "\n",
    "- Preparation, Data exploration and standardization: 0.5hr\n",
    "- Model construction: 1hr\n",
    "- Hyperparameter tuning and performance validation: 1hr\n",
    "\n",
    "There is a notebook `project_solution.ipynb` included with suggestions to solutions for each task included, for reference or to easily move on to a part of the project more appealing to your interests. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preparation: \n",
    "\n",
    "This project uses python and the machine learning library `keras`. As well as some functionality from `numpy` and `scikit-learn`. We recommend a `Python` verson of `>3.4`. These libraries should be installed to your specific system by using the command `pip3 install --user LIBRARY_NAME`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 1: Data exploration and standardization \n",
    "\n",
    "In machine learning, as in many other fields, the task of preparing data for analysis is as vital as it can be troublesome and tedious. In data-analysis the researcher can expect to spend the majority of their time merely processing data to prepare for analysis. In this projcet we will focus more on the entire pipeline of analysis, and so the data at hand has already been shaped to an image format suitable for our analysis. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 1a: Loading the data\n",
    "\n",
    "The data is stored in the `.npy` format using vecotrized code to speed up the read process. The files pointed to in this task are downsampled images with dimensions $64 x 64$ (if the images are to big for your laptop to handle, the script included in `../scripts/downsample_images.py` can further reduce the dimension). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data shape:  (8000, 64, 64, 1)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np # we'll be using this shorthand for the NumPy library throughout\n",
    "\n",
    "dataset = np.load(\"../data/images/project_data.npy\")\n",
    "n_samples = dataset.shape[0]\n",
    "\n",
    "print(\"Data shape: \", dataset.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 1b: Inspecting the data\n",
    "\n",
    "The data is stored as xy projections of the real events, who take place in a 3d volume. This allows a simple exploratiuon of the data as images. In this task you should plot a few different events in a grid using `matplotlib.pyplot` "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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",
       "        fig.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 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": [
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\" width=\"1000\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "rows = 2\n",
    "cols = 2\n",
    "n_plots = rows*cols\n",
    "fig, axs = plt.subplots(nrows=rows, ncols=cols, figsize=(10, 10 ))\n",
    "\n",
    "\n",
    "for row in axs: \n",
    "    for ax in row:\n",
    "        \"\"\"\n",
    "        one of pythons most wonderful attributes is that if an object is iterable it can be\n",
    "        directly iterated over, like above. \n",
    "        ax is an axis object from the 2d array of axis objects\n",
    "        \"\"\"\n",
    "\n",
    "        which = np.random.randint(0, n_samples)\n",
    "        ax.imshow(dataset[which].reshape(64, 64))\n",
    "        ax.axis(\"off\")\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 1c: Standardizing the data\n",
    "An important part of the preprocessing of data is the standardization of the input. The intuition here is simply that the model should expect similar values in the input to mean the same. \n",
    "\n",
    "You should implement a standardization of the input. Perhaps the most common standardization is the centering of the mean of the distribution, and scaling by the standard deviation: \n",
    "\n",
    "$X_s = \\frac{X - \\mu}{\\sigma}$\n",
    "\n",
    "Note that for our data we only want to standardize the signal part of our image, we know the rest is zero and we don't want the standardization to be unduly effected. This also means we don't necessarily want a zero mean for our signal distribution. So for this example we stick with the scaling:\n",
    "\n",
    "$X_s = \\frac{X}{\\sigma}$\n",
    "\n",
    "Another important fact is that at already at this point is it recommended to separate the data in train and test sets. The partion of the data to test on should be roughly 10-20%. And to remember to compute the standardization variables only from the training set.\n",
    "\n",
    "\n",
    "### MORE OPEN ENDED !!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Mean:  1.1315160335486445\n",
      "Train Std.:  4.3519420757141924\n",
      "-------------\n",
      "Test Mean:  1.3043909829464193\n",
      "Test Std.:  5.012595631160625\n",
      "############\n",
      "Train Mean:  0.26000254917523286\n",
      "Train Std.:  1.0\n",
      "-------------\n",
      "Test Mean:  0.29972618207064644\n",
      "Test Std.:  1.1518066058675684\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "targets = np.load(\"../data/targets/project_targets.npy\")\n",
    "train_X, test_X, train_y, test_y = train_test_split(dataset, targets, test_size=0.15)\n",
    "\n",
    "nonzero_indices = np.nonzero(train_X)\n",
    "nonzero_elements = train_X[nonzero_indices]\n",
    "\n",
    "print(\"Train Mean: \", nonzero_elements.mean())\n",
    "print(\"Train Std.: \", nonzero_elements.std())\n",
    "print(\"-------------\")\n",
    "print(\"Test Mean: \", test_X[np.nonzero(test_X)].mean())\n",
    "print(\"Test Std.: \", test_X[np.nonzero(test_X)].std())\n",
    "print(\"############\")\n",
    "\n",
    "nonzero_scaled = nonzero_elements/nonzero_elements.std()\n",
    "train_X[nonzero_indices] = nonzero_scaled\n",
    "test_X[np.nonzero(test_X)] /= nonzero_elements.std()\n",
    "\n",
    "print(\"Train Mean: \", nonzero_scaled.mean())\n",
    "print(\"Train Std.: \", nonzero_scaled.std())\n",
    "print(\"-------------\")\n",
    "print(\"Test Mean: \", test_X[np.nonzero(test_X)].mean())\n",
    "print(\"Test Std.: \", test_X[np.nonzero(test_X)].std())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We also want to plot up the data again to confirm that our scaling is sensible, you should reuse your code from above for this. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "rows = 2\n",
    "cols = 2\n",
    "n_plots = rows*cols\n",
    "fig, axs = plt.subplots(nrows=rows, ncols=cols, figsize=(10, 10 ))\n",
    "\n",
    "\n",
    "for row in axs: \n",
    "    for ax in row:\n",
    "        \"\"\"\n",
    "        one of pythons most wonderful attributes is that if an object is iterable it can be\n",
    "        directly iterated over, like above. \n",
    "        ax is an axis object from the 2d array of axis objects\n",
    "        \"\"\"\n",
    "\n",
    "        which = np.random.randint(0, train_X.shape[0])\n",
    "        ax.imshow(train_X[which].reshape(64, 64))\n",
    "        ax.text(5, 5, \"{}\".format(int(train_y[which])), bbox={'facecolor': 'white', 'pad': 10})\n",
    "        ax.axis(\"off\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1d: Encoding the targets: \n",
    "\n",
    "For classification one ordinarily encodes the target as a n-element zero vector with one element valued at 1 indicating the target class. This is simply called one-hot encoding.\n",
    "\n",
    "You should inspect the values of the target vectors and use the imported `OneHotEncoder` to convert the targets. \n",
    "\n",
    "Note that this is not necessary for the two class case, but we do it to demonstrate a general approach. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Onehot train targets: (6800, 2)\n",
      "Onehot test targets: (1200, 2)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "onehot_train_y = OneHotEncoder(sparse=False, categories=\"auto\").fit_transform(train_y.reshape(-1, 1))\n",
    "onehot_test_y = OneHotEncoder(sparse=False, categories=\"auto\").fit_transform(test_y.reshape(-1, 1))\n",
    "\n",
    "print(\"Onehot train targets:\", onehot_train_y.shape)\n",
    "print(\"Onehot test targets:\",onehot_test_y.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prelude to task 2: \n",
    "\n",
    "In this task we'll be constructing a CNN model for the two class data. Before that it is useful to characterize the performance of a less complex model, for example a logistic regression model. For this task then you should construct a logistic regression model to classify the two class problem. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 5780 samples, validate on 1020 samples\n",
      "Epoch 1/200\n",
      " - 0s - loss: 0.7375 - acc: 0.4744 - val_loss: 0.7357 - val_acc: 0.4951\n",
      "Epoch 2/200\n",
      " - 0s - loss: 0.7325 - acc: 0.4777 - val_loss: 0.7311 - val_acc: 0.5039\n",
      "Epoch 3/200\n",
      " - 0s - loss: 0.7283 - acc: 0.4829 - val_loss: 0.7272 - val_acc: 0.5118\n",
      "Epoch 4/200\n",
      " - 0s - loss: 0.7247 - acc: 0.4901 - val_loss: 0.7238 - val_acc: 0.5137\n",
      "Epoch 5/200\n",
      " - 0s - loss: 0.7215 - acc: 0.4939 - val_loss: 0.7210 - val_acc: 0.5196\n",
      "Epoch 6/200\n",
      " - 0s - loss: 0.7188 - acc: 0.4960 - val_loss: 0.7185 - val_acc: 0.5265\n",
      "Epoch 7/200\n",
      " - 0s - loss: 0.7164 - acc: 0.4972 - val_loss: 0.7163 - val_acc: 0.5176\n",
      "Epoch 8/200\n",
      " - 0s - loss: 0.7143 - acc: 0.4981 - val_loss: 0.7145 - val_acc: 0.5147\n",
      "Epoch 9/200\n",
      " - 0s - loss: 0.7124 - acc: 0.5010 - val_loss: 0.7128 - val_acc: 0.5127\n",
      "Epoch 10/200\n",
      " - 0s - loss: 0.7107 - acc: 0.5022 - val_loss: 0.7113 - val_acc: 0.5284\n",
      "Epoch 11/200\n",
      " - 0s - loss: 0.7091 - acc: 0.5073 - val_loss: 0.7099 - val_acc: 0.5265\n",
      "Epoch 12/200\n",
      " - 0s - loss: 0.7077 - acc: 0.5052 - val_loss: 0.7087 - val_acc: 0.5216\n",
      "Epoch 13/200\n",
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      "Epoch 87/200\n",
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      "Epoch 93/200\n",
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      "Epoch 94/200\n",
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      "Epoch 95/200\n",
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      "Epoch 96/200\n",
      " - 0s - loss: 0.6698 - acc: 0.6033 - val_loss: 0.6756 - val_acc: 0.6059\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 97/200\n",
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      "Epoch 98/200\n",
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      "Epoch 100/200\n",
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      "Epoch 101/200\n",
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      "Epoch 102/200\n",
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      " - 0s - loss: 0.6574 - acc: 0.6144 - val_loss: 0.6653 - val_acc: 0.6147\n",
      "Epoch 163/200\n",
      " - 0s - loss: 0.6573 - acc: 0.6144 - val_loss: 0.6652 - val_acc: 0.6147\n",
      "Epoch 164/200\n",
      " - 0s - loss: 0.6571 - acc: 0.6144 - val_loss: 0.6651 - val_acc: 0.6147\n",
      "Epoch 165/200\n",
      " - 0s - loss: 0.6570 - acc: 0.6145 - val_loss: 0.6649 - val_acc: 0.6147\n",
      "Epoch 166/200\n",
      " - 0s - loss: 0.6568 - acc: 0.6149 - val_loss: 0.6648 - val_acc: 0.6147\n",
      "Epoch 167/200\n",
      " - 0s - loss: 0.6567 - acc: 0.6152 - val_loss: 0.6647 - val_acc: 0.6147\n",
      "Epoch 168/200\n",
      " - 0s - loss: 0.6565 - acc: 0.6152 - val_loss: 0.6646 - val_acc: 0.6147\n",
      "Epoch 169/200\n",
      " - 0s - loss: 0.6564 - acc: 0.6152 - val_loss: 0.6645 - val_acc: 0.6147\n",
      "Epoch 170/200\n",
      " - 0s - loss: 0.6562 - acc: 0.6154 - val_loss: 0.6643 - val_acc: 0.6147\n",
      "Epoch 171/200\n",
      " - 0s - loss: 0.6561 - acc: 0.6154 - val_loss: 0.6642 - val_acc: 0.6147\n",
      "Epoch 172/200\n",
      " - 0s - loss: 0.6559 - acc: 0.6152 - val_loss: 0.6641 - val_acc: 0.6147\n",
      "Epoch 173/200\n",
      " - 0s - loss: 0.6558 - acc: 0.6156 - val_loss: 0.6640 - val_acc: 0.6147\n",
      "Epoch 174/200\n",
      " - 0s - loss: 0.6556 - acc: 0.6156 - val_loss: 0.6639 - val_acc: 0.6147\n",
      "Epoch 175/200\n",
      " - 0s - loss: 0.6555 - acc: 0.6156 - val_loss: 0.6638 - val_acc: 0.6147\n",
      "Epoch 176/200\n",
      " - 0s - loss: 0.6553 - acc: 0.6157 - val_loss: 0.6636 - val_acc: 0.6147\n",
      "Epoch 177/200\n",
      " - 0s - loss: 0.6552 - acc: 0.6157 - val_loss: 0.6635 - val_acc: 0.6147\n",
      "Epoch 178/200\n",
      " - 0s - loss: 0.6550 - acc: 0.6159 - val_loss: 0.6634 - val_acc: 0.6147\n",
      "Epoch 179/200\n",
      " - 0s - loss: 0.6549 - acc: 0.6164 - val_loss: 0.6633 - val_acc: 0.6147\n",
      "Epoch 180/200\n",
      " - 0s - loss: 0.6548 - acc: 0.6166 - val_loss: 0.6632 - val_acc: 0.6147\n",
      "Epoch 181/200\n",
      " - 0s - loss: 0.6546 - acc: 0.6166 - val_loss: 0.6631 - val_acc: 0.6147\n",
      "Epoch 182/200\n",
      " - 0s - loss: 0.6545 - acc: 0.6164 - val_loss: 0.6630 - val_acc: 0.6147\n",
      "Epoch 183/200\n",
      " - 0s - loss: 0.6543 - acc: 0.6166 - val_loss: 0.6629 - val_acc: 0.6157\n",
      "Epoch 184/200\n",
      " - 0s - loss: 0.6542 - acc: 0.6166 - val_loss: 0.6628 - val_acc: 0.6157\n",
      "Epoch 185/200\n",
      " - 0s - loss: 0.6541 - acc: 0.6166 - val_loss: 0.6626 - val_acc: 0.6157\n",
      "Epoch 186/200\n",
      " - 0s - loss: 0.6539 - acc: 0.6171 - val_loss: 0.6625 - val_acc: 0.6157\n",
      "Epoch 187/200\n",
      " - 0s - loss: 0.6538 - acc: 0.6171 - val_loss: 0.6624 - val_acc: 0.6157\n",
      "Epoch 188/200\n",
      " - 0s - loss: 0.6536 - acc: 0.6171 - val_loss: 0.6623 - val_acc: 0.6167\n",
      "Epoch 189/200\n",
      " - 0s - loss: 0.6535 - acc: 0.6173 - val_loss: 0.6622 - val_acc: 0.6167\n",
      "Epoch 190/200\n",
      " - 0s - loss: 0.6534 - acc: 0.6173 - val_loss: 0.6621 - val_acc: 0.6167\n",
      "Epoch 191/200\n",
      " - 0s - loss: 0.6532 - acc: 0.6175 - val_loss: 0.6620 - val_acc: 0.6167\n",
      "Epoch 192/200\n",
      " - 0s - loss: 0.6531 - acc: 0.6175 - val_loss: 0.6619 - val_acc: 0.6167\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 193/200\n",
      " - 0s - loss: 0.6530 - acc: 0.6178 - val_loss: 0.6618 - val_acc: 0.6167\n",
      "Epoch 194/200\n",
      " - 0s - loss: 0.6528 - acc: 0.6178 - val_loss: 0.6617 - val_acc: 0.6167\n",
      "Epoch 195/200\n",
      " - 0s - loss: 0.6527 - acc: 0.6178 - val_loss: 0.6616 - val_acc: 0.6167\n",
      "Epoch 196/200\n",
      " - 0s - loss: 0.6526 - acc: 0.6178 - val_loss: 0.6615 - val_acc: 0.6167\n",
      "Epoch 197/200\n",
      " - 0s - loss: 0.6524 - acc: 0.6178 - val_loss: 0.6614 - val_acc: 0.6167\n",
      "Epoch 198/200\n",
      " - 0s - loss: 0.6523 - acc: 0.6178 - val_loss: 0.6613 - val_acc: 0.6176\n",
      "Epoch 199/200\n",
      " - 0s - loss: 0.6522 - acc: 0.6178 - val_loss: 0.6612 - val_acc: 0.6176\n",
      "Epoch 200/200\n",
      " - 0s - loss: 0.6520 - acc: 0.6178 - val_loss: 0.6611 - val_acc: 0.6176\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential, Model\n",
    "from keras.layers import Dense\n",
    "from keras.regularizers import l2\n",
    "from keras.optimizers import SGD, adam\n",
    "\n",
    "\n",
    "flat_train_X = np.reshape(train_X, (train_X.shape[0], train_X.shape[1]*train_X.shape[2]*train_X.shape[3]))\n",
    "flat_test_X = np.reshape(test_X, (test_X.shape[0], train_X.shape[1]*train_X.shape[2]*train_X.shape[3]))\n",
    "\n",
    "logreg = Sequential()\n",
    "logreg.add(Dense(2, kernel_regularizer=l2(0.01), activation=\"softmax\"))\n",
    "\n",
    "eta = 0.001\n",
    "optimizer = SGD(eta)\n",
    "logreg.compile(optimizer, loss=\"binary_crossentropy\", metrics=[\"accuracy\",])\n",
    "\n",
    "history = logreg.fit(\n",
    "    x=flat_train_X,\n",
    "    y=onehot_train_y,\n",
    "    batch_size=100,\n",
    "    epochs=200,\n",
    "    validation_split=0.15,\n",
    "    verbose=2\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The logistic regression model doesn't work, clearly. What about the data prohobits it from doing so? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2a: Creating a model\n",
    "\n",
    "In this task we will create a CNN with fully connected bottom-layers for classification. You should base your code on Morten's code for a model. We suggest you complete one of the following for this task: \n",
    "\n",
    "1. Implement a class or function `cnn` that returns a compiled Keras model with an arbitrary number of convolutional and fully connected layers with optional configuration of regularization terms or layers.\n",
    "2. Implement a simple hard-coded function `cnn` that returns a Keras model object. The architecture should be specified in the function. \n",
    "\n",
    "Both implementations should include multiple convolutional layers and ending with a couple fully connected layers. The output of the network should be a softmax or log-softmax layer of logits.  \n",
    "\n",
    "You should experiment with where in the network you place the non-linearities and whether to use striding or pooling to reduce the input. As well as the use of padding, would you need one for the first layer?  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_config = {\n",
    "    \"n_conv\":2,\n",
    "    \"receptive_fields\":[3, 3],\n",
    "    \"strides\":[1, 1,],\n",
    "    \"n_filters\":[2, 2],\n",
    "    \"conv_activation\":[1, 1],\n",
    "    \"max_pool\":[1, 1],\n",
    "    \n",
    "    \"n_dense\":1,\n",
    "    \"neurons\":[10,],\n",
    "    \"dense_activation\":[1,]\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n",
      "/usr/local/Cellar/python/3.7.3/Frameworks/Python.framework/Versions/3.7/lib/python3.7/importlib/_bootstrap.py:219: RuntimeWarning: compiletime version 3.6 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.7\n",
      "  return f(*args, **kwds)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_1 (Conv2D)            (None, 64, 64, 2)         20        \n",
      "_________________________________________________________________\n",
      "re_lu_1 (ReLU)               (None, 64, 64, 2)         0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 32, 32, 2)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 32, 32, 2)         38        \n",
      "_________________________________________________________________\n",
      "re_lu_2 (ReLU)               (None, 32, 32, 2)         0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 16, 16, 2)         0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 10)                5130      \n",
      "_________________________________________________________________\n",
      "re_lu_3 (ReLU)               (None, 10)                0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 2)                 22        \n",
      "=================================================================\n",
      "Total params: 5,210\n",
      "Trainable params: 5,210\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential, Model\n",
    "from keras.layers import Dense, Conv2D, Flatten, MaxPooling2D, ReLU, Input, Softmax\n",
    "from keras.regularizers import l2\n",
    "\n",
    "def create_convolutional_neural_network_keras(input_shape, config, n_classes=2):\n",
    "    \"\"\"\n",
    "    Modified from MH Jensen's course on machine learning in physics: \n",
    "    https://github.com/CompPhysics/MachineLearningMSU/blob/master/doc/pub/CNN/ipynb/CNN.ipynb\n",
    "    \"\"\"\n",
    "    \n",
    "    model=Sequential()\n",
    "    \n",
    "    for i in range(config[\"n_conv\"]):\n",
    "        receptive_field = config[\"receptive_fields\"][i]\n",
    "        strides = config[\"strides\"][i]\n",
    "        n_filters = config[\"n_filters\"][i]\n",
    "        pad = \"same\" if i == 0 else \"same\" \n",
    "        input_shape = input_shape if i==0 else None\n",
    "        \n",
    "        if i == 0:\n",
    "            conv = Conv2D( \n",
    "                n_filters,\n",
    "                (receptive_field, receptive_field),\n",
    "                input_shape=input_shape,\n",
    "                padding=pad,\n",
    "                strides=strides,\n",
    "                kernel_regularizer=l2(0.01)\n",
    "                )\n",
    "        else:\n",
    "            conv = Conv2D( \n",
    "                    n_filters,\n",
    "                    (receptive_field, receptive_field),\n",
    "                    padding=pad,\n",
    "                    strides=strides,\n",
    "                    kernel_regularizer=l2(0.01)\n",
    "                    )\n",
    "        \n",
    "        model.add(conv)\n",
    "        \n",
    "        pool = config[\"max_pool\"][i]\n",
    "        activation = config[\"conv_activation\"][i]\n",
    "        \n",
    "        if activation:\n",
    "            model.add(ReLU())\n",
    "            \n",
    "        if pool:\n",
    "            model.add(MaxPooling2D(2))\n",
    "    \n",
    "    model.add(Flatten())\n",
    "    \n",
    "    for i in range(config[\"n_dense\"]):\n",
    "        n_neurons = config[\"neurons\"][i]\n",
    "        model.add(\n",
    "            Dense(\n",
    "                n_neurons,\n",
    "                kernel_regularizer=l2(0.01)\n",
    "            ))\n",
    "        \n",
    "        activation = config[\"dense_activation\"][i]\n",
    "        if activation:\n",
    "            model.add(ReLU())\n",
    "    \n",
    "    model.add(\n",
    "        Dense(\n",
    "            n_classes,\n",
    "            activation='softmax',\n",
    "            kernel_regularizer=l2(0.01))\n",
    "            )\n",
    "    return model\n",
    "\n",
    "model_o = create_convolutional_neural_network_keras(train_X.shape[1:], model_config, n_classes=2)\n",
    "#model_o = mhj(train_X.shape[1:], 3, 2, 10, 2, 0.01)\n",
    "print(model_o.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2b: Plot your model\n",
    "\n",
    "`Keras` provides a convenient class for plotting your model architecture. You should both do this and inspect the model summary to see how many trainable parameters you have as well as to confirm that your model is reasonably put together with no dangling edges in the graph etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.utils import plot_model\n",
    "\n",
    "plot_model(model_o, to_file=\"convnet.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![a plot of the model graph](./convnet.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2b: Compiling your model\n",
    "\n",
    "With the constructed model ready it can now be compiled. Compiling entails unrolling the computational graph underlying the model and attaching losses at the layers you specify. For more complex models one can attach loss functions at arbitrary layers or one could define a specific loss for your particular problem.\n",
    "\n",
    "For our case we will simply use a categorical cross-entropy, which means our network parametrizes an output of logits which we softmax to produce probabilities. In this task you should simply compile the above model with an optimizer of your choice and a categorical cross-entropy loss."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "eta = 0.01\n",
    "sgd = SGD(lr=eta, )\n",
    "adam = adam(lr=eta, beta_1=0.5, )\n",
    "model_o.compile(loss='binary_crossentropy', optimizer=adam, metrics=['accuracy'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2c: Running your model\n",
    "\n",
    "Here you should simply use the `.fit` method of the model to train on the training set. Select a suitable subset of \n",
    "train to use as validation, this should not be the test-set. Take care to note how many trainable parameters your model has. A model with $10^5$ parameters takes about a minute per epoch to run on a 7th gen i9 intel processor. If your laptop has a nvidia GPU training should be considerably faster. \n",
    "\n",
    "Hint: this model is quite easy to over-fit, you should build your network with a relatively low complexity ($10^3$ parameters).\n",
    "\n",
    "The `.fit` method returns a `history` object that you can use to plot the progress of your training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 5780 samples, validate on 1020 samples\n",
      "Epoch 1/40\n",
      " - 2s - loss: 0.6566 - acc: 0.6301 - val_loss: 0.6303 - val_acc: 0.6441\n",
      "Epoch 2/40\n",
      " - 2s - loss: 0.6337 - acc: 0.6382 - val_loss: 0.6174 - val_acc: 0.6461\n",
      "Epoch 3/40\n",
      " - 2s - loss: 0.6386 - acc: 0.6374 - val_loss: 0.6275 - val_acc: 0.6471\n",
      "Epoch 4/40\n",
      " - 2s - loss: 0.6245 - acc: 0.6462 - val_loss: 0.6318 - val_acc: 0.6422\n",
      "Epoch 5/40\n",
      " - 2s - loss: 0.6153 - acc: 0.6813 - val_loss: 0.5978 - val_acc: 0.8020\n",
      "Epoch 6/40\n",
      " - 2s - loss: 0.6424 - acc: 0.6618 - val_loss: 0.6292 - val_acc: 0.6422\n",
      "Epoch 7/40\n",
      " - 2s - loss: 0.6329 - acc: 0.6931 - val_loss: 0.6347 - val_acc: 0.8206\n",
      "Epoch 8/40\n",
      " - 2s - loss: 0.6165 - acc: 0.7820 - val_loss: 0.6447 - val_acc: 0.7510\n",
      "Epoch 9/40\n",
      " - 2s - loss: 0.5525 - acc: 0.8578 - val_loss: 0.5272 - val_acc: 0.8961\n",
      "Epoch 10/40\n",
      " - 2s - loss: 0.5092 - acc: 0.8822 - val_loss: 0.5536 - val_acc: 0.8971\n",
      "Epoch 11/40\n",
      " - 2s - loss: 0.5103 - acc: 0.8913 - val_loss: 0.5681 - val_acc: 0.8422\n",
      "Epoch 12/40\n",
      " - 2s - loss: 0.5074 - acc: 0.8955 - val_loss: 0.5371 - val_acc: 0.8892\n",
      "Epoch 13/40\n",
      " - 2s - loss: 0.4682 - acc: 0.9078 - val_loss: 0.4817 - val_acc: 0.8863\n",
      "Epoch 14/40\n",
      " - 2s - loss: 0.4531 - acc: 0.9173 - val_loss: 0.4635 - val_acc: 0.9118\n",
      "Epoch 15/40\n",
      " - 2s - loss: 0.4594 - acc: 0.9196 - val_loss: 0.5155 - val_acc: 0.9137\n",
      "Epoch 16/40\n",
      " - 2s - loss: 0.4648 - acc: 0.9092 - val_loss: 0.5196 - val_acc: 0.8451\n",
      "Epoch 17/40\n",
      " - 2s - loss: 0.4379 - acc: 0.9258 - val_loss: 0.4699 - val_acc: 0.9275\n",
      "Epoch 18/40\n",
      " - 2s - loss: 0.5216 - acc: 0.8849 - val_loss: 0.6609 - val_acc: 0.7637\n",
      "Epoch 19/40\n",
      " - 2s - loss: 0.4965 - acc: 0.9007 - val_loss: 0.4744 - val_acc: 0.9127\n",
      "Epoch 20/40\n",
      " - 2s - loss: 0.4587 - acc: 0.9253 - val_loss: 0.4474 - val_acc: 0.9402\n",
      "Epoch 21/40\n",
      " - 2s - loss: 0.4417 - acc: 0.9358 - val_loss: 0.4751 - val_acc: 0.9353\n",
      "Epoch 22/40\n",
      " - 2s - loss: 0.4421 - acc: 0.9344 - val_loss: 0.4660 - val_acc: 0.9137\n",
      "Epoch 23/40\n",
      " - 2s - loss: 0.4361 - acc: 0.9396 - val_loss: 0.4468 - val_acc: 0.9255\n",
      "Epoch 24/40\n",
      " - 2s - loss: 0.4313 - acc: 0.9431 - val_loss: 0.5309 - val_acc: 0.8510\n",
      "Epoch 25/40\n",
      " - 2s - loss: 0.4382 - acc: 0.9386 - val_loss: 0.4500 - val_acc: 0.9382\n",
      "Epoch 26/40\n",
      " - 2s - loss: 0.4831 - acc: 0.9211 - val_loss: 0.7402 - val_acc: 0.7716\n",
      "Epoch 27/40\n",
      " - 2s - loss: 0.5047 - acc: 0.9157 - val_loss: 0.4675 - val_acc: 0.9196\n",
      "Epoch 28/40\n",
      " - 2s - loss: 0.4341 - acc: 0.9374 - val_loss: 0.4826 - val_acc: 0.9363\n",
      "Epoch 29/40\n",
      " - 2s - loss: 0.4315 - acc: 0.9474 - val_loss: 0.4518 - val_acc: 0.9412\n",
      "Epoch 30/40\n",
      " - 2s - loss: 0.4263 - acc: 0.9483 - val_loss: 0.4349 - val_acc: 0.9431\n",
      "Epoch 31/40\n",
      " - 2s - loss: 0.4254 - acc: 0.9497 - val_loss: 0.4586 - val_acc: 0.9441\n",
      "Epoch 32/40\n",
      " - 2s - loss: 0.4196 - acc: 0.9528 - val_loss: 0.4572 - val_acc: 0.9324\n",
      "Epoch 33/40\n",
      " - 2s - loss: 0.5379 - acc: 0.8640 - val_loss: 0.4335 - val_acc: 0.9382\n",
      "Epoch 34/40\n",
      " - 2s - loss: 0.4148 - acc: 0.9519 - val_loss: 0.4528 - val_acc: 0.9392\n",
      "Epoch 35/40\n",
      " - 2s - loss: 0.4133 - acc: 0.9533 - val_loss: 0.4366 - val_acc: 0.9490\n",
      "Epoch 36/40\n",
      " - 2s - loss: 0.4138 - acc: 0.9578 - val_loss: 0.4613 - val_acc: 0.9245\n",
      "Epoch 37/40\n",
      " - 2s - loss: 0.4219 - acc: 0.9512 - val_loss: 0.4355 - val_acc: 0.9480\n",
      "Epoch 38/40\n",
      " - 2s - loss: 0.4230 - acc: 0.9540 - val_loss: 0.4309 - val_acc: 0.9461\n",
      "Epoch 39/40\n",
      " - 2s - loss: 0.4329 - acc: 0.9486 - val_loss: 0.4381 - val_acc: 0.9451\n",
      "Epoch 40/40\n",
      " - 2s - loss: 0.4217 - acc: 0.9524 - val_loss: 0.4234 - val_acc: 0.9441\n"
     ]
    }
   ],
   "source": [
    "%matplotlib notebook \n",
    "import matplotlib.pyplot as plt\n",
    "history = model_o.fit(\n",
    "    x=train_X,\n",
    "    y=onehot_train_y,\n",
    "    batch_size=50,\n",
    "    epochs=40,\n",
    "    validation_split=0.15,\n",
    "    verbose=2\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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",
       "        fig.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 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>"
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width=\"1000\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x12d84b6a0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# copied from https://keras.io/visualization/\n",
    "# Plot training & validation accuracy values\n",
    "fig, axs= plt.subplots(figsize=(10, 8), nrows=2)\n",
    "fig.suptitle('Model performance')\n",
    "axs[0].plot(history.history['acc'], \"x-\",alpha=0.8)\n",
    "axs[0].plot(history.history['val_acc'], \"x-\", alpha=0.8)\n",
    "\n",
    "axs[0].set_ylabel('Accuracy')\n",
    "axs[0].set_xlabel('Epoch')\n",
    "axs[0].legend(['Train', 'Test'], loc='upper left')\n",
    "\n",
    "\n",
    "# Plot training & validation loss values\n",
    "\n",
    "axs[1].plot(history.history['loss'], \"o-\",alpha=0.8)\n",
    "axs[1].plot(history.history['val_loss'], \"o-\", alpha=0.8)\n",
    "\n",
    "axs[1].set_ylabel('Loss')\n",
    "axs[1].legend(['Train', 'Test'], loc='upper left')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3a: Performance validation\n",
    "\n",
    "As mentioned in the lectures machine learning models suffer from the problem of overfitting as scaling to an almost arbitrary complexity is simple with todays hardware. The challenge then is often that of finding the correct architecture and type of model suitable for your problem. In this task you will be familiarized with some tools to monitor and estimate the degree of overfitting.\n",
    "\n",
    "In task 1c you separated your data in training and test sets. The test set is used to estimate generalization performance. Typically we use this to fine-tune the hyperparameters. Another trick is to use a validation set during training, which we implemented in the last task for the training. \n",
    "\n",
    "In this task we will start with attaching callbacks to the fitting process. They are listed in the documentation for `Keras`here: https://keras.io/callbacks/\n",
    "Pick ones you think are suitable for our problem and re-run the training from above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 5780 samples, validate on 1020 samples\n",
      "Epoch 1/150\n",
      " - 2s - loss: 0.4109 - acc: 0.9566 - val_loss: 0.4189 - val_acc: 0.9510\n",
      "Epoch 2/150\n",
      " - 2s - loss: 0.4241 - acc: 0.9502 - val_loss: 0.4276 - val_acc: 0.9569\n",
      "Epoch 3/150\n",
      " - 2s - loss: 0.4174 - acc: 0.9559 - val_loss: 0.4488 - val_acc: 0.9343\n",
      "Epoch 4/150\n",
      " - 2s - loss: 0.4388 - acc: 0.9514 - val_loss: 0.4379 - val_acc: 0.9451\n",
      "Epoch 5/150\n",
      " - 2s - loss: 0.4200 - acc: 0.9543 - val_loss: 0.4451 - val_acc: 0.9392\n"
     ]
    }
   ],
   "source": [
    "from keras.callbacks import EarlyStopping, ModelCheckpoint\n",
    "\n",
    "callbacks = [EarlyStopping(min_delta=0.0001, patience=4), ModelCheckpoint(\"../checkpoints/ckpt\")\n",
    "\n",
    "history = model_o.fit(\n",
    "    x=train_X,\n",
    "    y=onehot_train_y,\n",
    "    batch_size=50,\n",
    "    epochs=150,\n",
    "    validation_split=0.15,\n",
    "    verbose=2,\n",
    "    callbacks=callbacks\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3b: Hyperparameter tuning\n",
    "\n",
    "Hyperparameters, like the number of layers, learning rate or others can have a very big impact on the model quality. The model performance should also be statistically quantified using cross validation, bootstrapped confidence intervals for your performance metrics or other tools depending on model. \n",
    "In this task you should then implement a function or for loop doing either random search or a grid search over parameters and finally you should plot those results in a suitable way."
   ]
  }
 ],
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