sgd-update-visualization.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SGD Update Visualization for Single Parameter\n",
    "\n",
    "Interactive plot of SGD updates for a single parameter. The gradients for the parameter are random values with a standard normal distribution. The following arguments to SGD can be interactively modified:\n",
    "- learning rate    \n",
    "- learning rate linear decay step size\n",
    "- momentum\n",
    "- gradient clip value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib notebook\n",
    "\n",
    "import matplotlib.patches as mpatches\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "from matplotlib.widgets import Button, Slider"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "class SGD(object):\n",
    "    \"\"\"Calculate single paramter SGD updates with optional momentum.\"\"\"\n",
    "    \n",
    "    def __init__(self, momentum=0):\n",
    "        self.momentum = momentum\n",
    "        self.prev_update = 0\n",
    "    \n",
    "    def update(self, gradient, lr):\n",
    "        \"\"\"Returns SGD update for single parameter.\n",
    "        \n",
    "        # Arguments\n",
    "            gradient: float\n",
    "            lr: float, learning rate\n",
    "        \"\"\"\n",
    "        \n",
    "        update = self.momentum * self.prev_update - lr * gradient\n",
    "        self.prev_update = update\n",
    "        return update   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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       "/* 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",
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       "        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",
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       "\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",
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       "    var fig = this;\n",
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       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
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       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
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       "            fig.send_message(\"refresh\", {});\n",
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       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
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       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    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",
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       "\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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     "output_type": "display_data"
    },
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width=\"800\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# initial conditions\n",
    "lr = 0.1 # used for vanilla SGD too\n",
    "decay_step = 2e-03\n",
    "momentum = 0.95\n",
    "clip_value = 5.0\n",
    "view_epoch = 1\n",
    "\n",
    "# static\n",
    "epochs = np.arange(1, 41, 1)\n",
    "steps_per_epoch = np.arange(1, 592, 1)\n",
    "iterations = len(epochs) * len(steps_per_epoch) # same as 40 epochs of MNIST with batch size 64\n",
    "\n",
    "plt.rcParams['figure.figsize'] = (8.0, 8.0)\n",
    "fig, ax = plt.subplots(nrows=2)\n",
    "plt.subplots_adjust(left=0.1, right=0.99, bottom=0.48, hspace=0)\n",
    "\n",
    "ax[0].axis([steps_per_epoch[0] - 1, steps_per_epoch[-1] + 1, -1.5, 1.5])\n",
    "ax[0].set_facecolor('lavender')\n",
    "ax[0].grid(axis='y', color='0.99', linestyle='--')\n",
    "ax[0].hlines(0, 0, len(steps_per_epoch) + 1, color='0.3', linewidth=1)\n",
    "ax[0].spines['bottom'].set_visible(False)\n",
    "ax[0].set_yticks([-1, -0.3, 0, 0.3, 1]) # place ticks at 3 standard deviations\n",
    "ax[0].set_ylabel('Step Update', fontsize='small')\n",
    "ax[0].tick_params(bottom=False, labelbottom=False, labelsize='small')\n",
    "\n",
    "ax[1].axis([epochs[0] - 1, epochs[-1] + 1, -1.5, 1.5])\n",
    "ax[1].grid(axis='y', color='0.9', linestyle='--')\n",
    "ax[1].hlines(0, 0, len(epochs) + 1, color='0.3', linewidth=1)\n",
    "ax[1].spines['top'].set_visible(False)\n",
    "ax[1].set_xlabel('Epoch', fontsize='small')\n",
    "ax[1].set_xticks(epochs, minor=True)\n",
    "ax[1].set_yticks([-1, -0.3, 0, 0.3, 1], ) # place ticks at 3 standard deviations\n",
    "ax[1].set_ylabel('End of Epoch Update', fontsize='small')\n",
    "ax[1].tick_params(axis='y', labelsize='small')\n",
    "\n",
    "# updatable\n",
    "view_rect = ax[1].axvspan(view_epoch - 1, view_epoch, facecolor='lavender')\n",
    "gradients = np.random.normal(size=iterations)\n",
    "lrs = [np.maximum(lr - decay_step * (i // 591), 0.001) for i in range(iterations)] # learning rate linear decay\n",
    "\n",
    "sgd = SGD()\n",
    "updates = [sgd.update(g, lr) for g in gradients]\n",
    "vs_steps, = ax[0].plot(\n",
    "    steps_per_epoch, updates[591*(view_epoch-1):591*view_epoch], \n",
    "    color='0.6', label='SGD (lr=' + str(lr) + ')')\n",
    "vs_epochs, = ax[1].plot(\n",
    "    epochs, updates[590::591], \n",
    "    color='0.6', linestyle='None', marker='o', markersize=4, \n",
    "    label='SGD (lr=' + str(lr) + ')')\n",
    "\n",
    "sgd = SGD(momentum=momentum)\n",
    "updates = [sgd.update(g, lr) for g, lr in zip(np.clip(gradients, -clip_value, clip_value), lrs)]\n",
    "sm_steps, = ax[0].plot(\n",
    "    steps_per_epoch, updates[591*(view_epoch-1):591*view_epoch], \n",
    "    label='SGD + momentum')\n",
    "sm_epochs, = ax[1].plot(\n",
    "    epochs, updates[590::591], \n",
    "    linestyle='--', marker='o', markersize=4, \n",
    "    label='SGD + momentum')\n",
    "\n",
    "# legends\n",
    "lr_patch = mpatches.Patch(color=sm_epochs.get_color(), label=\"lr = \" + str(lrs[view_epoch]))\n",
    "ax[0].legend(handles=[lr_patch], loc='upper left', fontsize='small')\n",
    "ax[0].set_title(\"Epoch \" + str(view_epoch), fontsize='small')\n",
    "vs_patch = mpatches.Patch(color=vs_epochs.get_color(), label='SGD (lr=' + str(lr) + ')')\n",
    "sm_patch = mpatches.Patch(color=sm_epochs.get_color(), label='SGD + momentum')\n",
    "ax[1].legend(handles=[vs_patch, sm_patch], loc='lower right', fontsize='small')\n",
    "\n",
    "# controls\n",
    "axcolor = 'lightgoldenrodyellow'\n",
    "top = 0.42\n",
    "slider_width = 0.71\n",
    "\n",
    "ax_lr = plt.axes([0.2, top - 0.05, slider_width, 0.03], facecolor=axcolor)\n",
    "ax_decay_step = plt.axes([0.2, top - 2*0.05, slider_width, 0.03], facecolor=axcolor)\n",
    "ax_momentum = plt.axes([0.2, top - 3*0.05, slider_width, 0.03], facecolor=axcolor)\n",
    "ax_clip = plt.axes([0.2, top - 4*0.05, slider_width, 0.03], facecolor=axcolor)\n",
    "ax_epoch = plt.axes([0.2, top - 5*0.05, slider_width, 0.03], facecolor=axcolor)\n",
    "ax_grad = plt.axes([0.2 + 2 * slider_width / 3, top - 6*0.05 - 0.03, slider_width / 3, 0.05], facecolor=axcolor)\n",
    "\n",
    "s_lr = Slider(ax_lr, 'Initial Learning Rate', 0.0, 1.0, valstep=0.01, valinit=lr)\n",
    "s_decay_step = Slider(ax_decay_step, 'lr Decay Step Size', 0.0, 5e-3, valstep=1e-4, valinit=decay_step, valfmt='%1.4f')\n",
    "s_momentum = Slider(ax_momentum, 'Momentum', 0.0, 0.99, valstep=0.01, valinit=momentum)\n",
    "s_clip = Slider(ax_clip, 'Clip Value', 0.5, 5, valstep=0.5, valinit=clip_value)\n",
    "s_epoch = Slider(ax_epoch, 'View Epoch', 1, len(epochs), valstep=1, valinit=view_epoch, valfmt='%d')\n",
    "b_grad = Button(ax_grad, 'New Gradients', color='0.9', hovercolor='0.8')\n",
    "\n",
    "def new_gradients(val):\n",
    "    global gradients \n",
    "    gradients = np.random.normal(size=iterations)\n",
    "    update_view(val)    \n",
    "    \n",
    "def update_view(val):\n",
    "    l = s_lr.val\n",
    "    m = s_momentum.val\n",
    "    d = s_decay_step.val\n",
    "    c = s_clip.val\n",
    "    e = int(s_epoch.val)\n",
    "    \n",
    "    view_rect.get_xy()[:,0] = [e-1, e-1, e, e, e-1]\n",
    "    \n",
    "    sgd = SGD()\n",
    "    updates = [sgd.update(g, lr) for g in gradients]\n",
    "    vs_epochs.set_ydata(updates[590::591])\n",
    "    vs_steps.set_ydata(updates[591*(e-1):591*e])\n",
    "    \n",
    "    sgd = SGD(momentum=m)\n",
    "    lrs = [np.maximum(l - d * (i // 591), 0.001) for i in range(len(gradients))]\n",
    "    lr_patch.set_label(\"lr = \" + str(round(lrs[591*(e-1)], 4)))\n",
    "    ax[0].legend(handles=[lr_patch], loc='upper left', fontsize='small')\n",
    "    ax[0].set_title(\"Epoch \" + str(e), fontsize='small')\n",
    "    updates = [sgd.update(g, lr) for g, lr in zip(np.clip(gradients, -c, c), lrs)]\n",
    "    sm_epochs.set_ydata(updates[590::591])\n",
    "    sm_steps.set_ydata(updates[591*(e-1):591*e])\n",
    "    \n",
    "    fig.canvas.draw_idle()        \n",
    "  \n",
    "s_lr.on_changed(update_view)\n",
    "s_decay_step.on_changed(update_view)\n",
    "s_momentum.on_changed(update_view)\n",
    "s_clip.on_changed(update_view)\n",
    "s_epoch.on_changed(update_view)\n",
    "b_grad.on_clicked(new_gradients)\n",
    "\n",
    "plt.suptitle('SGD Single Parameter Updates', x=0.1, y=0.95, ha='left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Notes\n",
    "- The graphs show value of the update that will be applied to the parameter. They don't show the actual value of the parameter. \n",
    "- Gradients for the parameter are random values with a standard normal distribution.\n",
    "- There are 591 steps per epoch.\n",
    "- Decay step size is subtracted from the learning rate at the end of each epoch.\n",
    "- Clip value of 5 is basically the same as no clip value, since gradients have a standard normal distribution.\n",
    "- 99.7% of SGD updates with a learning rate of 0.1 are between &plusmn;0.3 (3 standard deviations away from the mean SGD update with a learning rate of 0.1). The gridlines at &plusmn;0.3 give a sense of how momentum can cause SGD to explore more of the solution space.\n",
    "- End of epoch updates give a sense of the effects of decay but are probably not useful otherwise."
   ]
  }
 ],
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