src/demo.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib notebook\n",
    "import torch\n",
    "import matplotlib.pyplot as plt\n",
    "from data import BirdsDataset\n",
    "from argument import Args\n",
    "from model import VGG16Transfer, ClassificationStatsManager, Resnet18Transfer\n",
    "import nntools as nt\n",
    "from utils import imshow, plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "args = Args()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'root_dir': '../dataset/',\n",
       " 'output_dir': '../checkpoints/',\n",
       " 'num_epochs': 20,\n",
       " 'plot': False,\n",
       " 'model': 'vgg',\n",
       " 'lr': 0.001,\n",
       " 'image_size': (224, 224),\n",
       " 'batch_size': 16}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vars(args)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "args.plot=True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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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     "output_type": "display_data"
    },
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\" 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      "text/plain": [
       "<IPython.core.display.HTML object>"
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start/Continue training from epoch 0\n",
      "Epoch 1 | Time: 15.31s | Training Loss: 1.579827 | Evaluation Loss: 0.735421\n",
      "Epoch 2 | Time: 16.88s | Training Loss: 0.484397 | Evaluation Loss: 0.575804\n",
      "Epoch 3 | Time: 16.69s | Training Loss: 0.344628 | Evaluation Loss: 0.546770\n",
      "Epoch 4 | Time: 16.49s | Training Loss: 0.255804 | Evaluation Loss: 0.533436\n",
      "Epoch 5 | Time: 16.61s | Training Loss: 0.203335 | Evaluation Loss: 0.531494\n",
      "Epoch 6 | Time: 16.51s | Training Loss: 0.196841 | Evaluation Loss: 0.495609\n",
      "Epoch 7 | Time: 16.27s | Training Loss: 0.171930 | Evaluation Loss: 0.497623\n",
      "Epoch 8 | Time: 16.21s | Training Loss: 0.130213 | Evaluation Loss: 0.511590\n",
      "Epoch 9 | Time: 16.64s | Training Loss: 0.118600 | Evaluation Loss: 0.506606\n",
      "Epoch 10 | Time: 15.69s | Training Loss: 0.116316 | Evaluation Loss: 0.497882\n",
      "Epoch 11 | Time: 15.65s | Training Loss: 0.106292 | Evaluation Loss: 0.531919\n",
      "Epoch 12 | Time: 17.42s | Training Loss: 0.133141 | Evaluation Loss: 0.542378\n",
      "Epoch 13 | Time: 16.73s | Training Loss: 0.093042 | Evaluation Loss: 0.521776\n",
      "Epoch 14 | Time: 16.60s | Training Loss: 0.085757 | Evaluation Loss: 0.529821\n",
      "Epoch 15 | Time: 16.18s | Training Loss: 0.078770 | Evaluation Loss: 0.560282\n",
      "Epoch 16 | Time: 15.78s | Training Loss: 0.058873 | Evaluation Loss: 0.516175\n",
      "Epoch 17 | Time: 16.74s | Training Loss: 0.076792 | Evaluation Loss: 0.506742\n",
      "Epoch 18 | Time: 15.83s | Training Loss: 0.077938 | Evaluation Loss: 0.557134\n",
      "Epoch 19 | Time: 16.25s | Training Loss: 0.061053 | Evaluation Loss: 0.504936\n",
      "Epoch 20 | Time: 16.85s | Training Loss: 0.058077 | Evaluation Loss: 0.551696\n",
      "Finish training for 20 epochs\n"
     ]
    }
   ],
   "source": [
    "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
    "train_set = BirdsDataset(args.root_dir, image_size=args.image_size)\n",
    "val_set = BirdsDataset(args.root_dir, mode='val',\n",
    "                       image_size=args.image_size)\n",
    "num_classes = train_set.number_of_classes()\n",
    "\n",
    "if args.model == 'vgg':\n",
    "    net = VGG16Transfer(num_classes)\n",
    "else:\n",
    "    net = Resnet18Transfer(num_classes)\n",
    "net = net.to(device)\n",
    "adam = torch.optim.Adam(net.parameters(), lr=args.lr)\n",
    "stats_manager = ClassificationStatsManager()\n",
    "exp = nt.Experiment(net, train_set, val_set, adam, stats_manager, batch_size=args.batch_size,\n",
    "                    output_dir=args.output_dir, perform_validation_during_training=True)\n",
    "\n",
    "if args.plot:\n",
    "    fig, axes = plt.subplots(ncols=2, figsize=(7, 3))\n",
    "    exp.run(num_epochs=args.num_epochs,\n",
    "            plot=lambda exp: plot(exp, fig=fig, axes=axes))\n",
    "else:\n",
    "    exp.run(num_epochs=args.num_epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}