Generating handwritten digits using DCGAN with keras.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating fake handwritten digits with Keras\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing Libraries and Helper Functions\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "eBoStgCOlD6V",
    "outputId": "b35d9172-40ac-494b-9c38-6b1c892f7ec7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TensorFlow version: 2.2.0\n"
     ]
    }
   ],
   "source": [
    "\n",
    "%matplotlib notebook\n",
    "\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import os\n",
    "import tfutils\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "from tensorflow.keras.layers import Dense, Flatten, Conv2D, BatchNormalization\n",
    "from tensorflow.keras.layers import Conv2DTranspose, Reshape, LeakyReLU\n",
    "from tensorflow.keras.models import Model, Sequential\n",
    "from PIL import Image\n",
    "\n",
    "print('TensorFlow version:', tf.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing and Plotting the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "(x_train, y_train), (x_test, y_test) = tfutils.datasets.mnist.load_data(one_hot=False)\n",
    "\n",
    "x_train = tfutils.datasets.mnist.load_subset([0], x_train, y_train)\n",
    "x_test = tfutils.datasets.mnist.load_subset([0], x_test, y_test)\n",
    "\n",
    "x = np.concatenate([x_train, x_test], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 252
    },
    "colab_type": "code",
    "id": "vJrX749GlD6k",
    "outputId": "3cdecd15-5704-4840-a984-649fb104ca42"
   },
   "outputs": [
    {
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       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
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       "\n",
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       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
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       "    this.context = canvas[0].getContext(\"2d\");\n",
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       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
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       "        canvas_div.css('width', width)\n",
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       "            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",
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       "        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",
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       "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",
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       "\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",
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       "        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 / mpl.ratio, fig.canvas.height / mpl.ratio);\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",
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       "        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",
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       "\n",
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       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
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       "    // 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",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
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     "output_type": "display_data"
    },
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\" width=\"1000\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tfutils.datasets.mnist.plot_ten_random_examples(plt, x, np.zeros((x.shape[0], 1))).show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Discriminator Network\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 722
    },
    "colab_type": "code",
    "id": "JHga1nihlD6q",
    "outputId": "2270db26-8e3b-4ec1-b89d-005a886b8f68"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d (Conv2D)              (None, 13, 13, 64)        640       \n",
      "_________________________________________________________________\n",
      "leaky_re_lu (LeakyReLU)      (None, 13, 13, 64)        0         \n",
      "_________________________________________________________________\n",
      "batch_normalization (BatchNo (None, 13, 13, 64)        256       \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 5, 5, 128)         204928    \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_1 (LeakyReLU)    (None, 5, 5, 128)         0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_1 (Batch (None, 5, 5, 128)         512       \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 1, 1, 256)         819456    \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_2 (LeakyReLU)    (None, 1, 1, 256)         0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_2 (Batch (None, 1, 1, 256)         1024      \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 1)                 257       \n",
      "=================================================================\n",
      "Total params: 1,027,073\n",
      "Trainable params: 1,026,177\n",
      "Non-trainable params: 896\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "size = 28\n",
    "noise_dim = 1\n",
    "\n",
    "discriminator = Sequential([\n",
    "    Conv2D(64, 3, strides=2, input_shape=(28, 28, 1)),\n",
    "    LeakyReLU(),\n",
    "    BatchNormalization(),\n",
    "    \n",
    "    Conv2D(128, 5, strides=2),\n",
    "    LeakyReLU(),\n",
    "    BatchNormalization(),\n",
    "    \n",
    "    Conv2D(256, 5, strides=2),\n",
    "    LeakyReLU(),\n",
    "    BatchNormalization(),\n",
    "    \n",
    "    Flatten(),\n",
    "    Dense(1, activation='sigmoid')\n",
    "])\n",
    "\n",
    "opt = tf.keras.optimizers.Adam(lr=2e-4, beta_1=0.5)\n",
    "\n",
    "discriminator.compile(loss='binary_crossentropy', optimizer=opt, metrics=['accuracy'])\n",
    "discriminator.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generator Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 655
    },
    "colab_type": "code",
    "id": "-8mG_PlhlD6s",
    "outputId": "76a03388-e6ac-401b-94e2-3651136ddf0c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_1 (Dense)              (None, 256)               512       \n",
      "_________________________________________________________________\n",
      "reshape (Reshape)            (None, 1, 1, 256)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_transpose (Conv2DTran (None, 5, 5, 256)         1638656   \n",
      "_________________________________________________________________\n",
      "batch_normalization_3 (Batch (None, 5, 5, 256)         1024      \n",
      "_________________________________________________________________\n",
      "conv2d_transpose_1 (Conv2DTr (None, 9, 9, 128)         819328    \n",
      "_________________________________________________________________\n",
      "batch_normalization_4 (Batch (None, 9, 9, 128)         512       \n",
      "_________________________________________________________________\n",
      "conv2d_transpose_2 (Conv2DTr (None, 21, 21, 64)        204864    \n",
      "_________________________________________________________________\n",
      "batch_normalization_5 (Batch (None, 21, 21, 64)        256       \n",
      "_________________________________________________________________\n",
      "conv2d_transpose_3 (Conv2DTr (None, 25, 25, 32)        51232     \n",
      "_________________________________________________________________\n",
      "batch_normalization_6 (Batch (None, 25, 25, 32)        128       \n",
      "_________________________________________________________________\n",
      "conv2d_transpose_4 (Conv2DTr (None, 28, 28, 1)         513       \n",
      "=================================================================\n",
      "Total params: 2,717,025\n",
      "Trainable params: 2,716,065\n",
      "Non-trainable params: 960\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "generator = Sequential([\n",
    "    Dense(256, activation='relu', input_shape=(noise_dim,)),\n",
    "    Reshape((1, 1, 256)),\n",
    "    \n",
    "    Conv2DTranspose(256, 5, activation='relu'),\n",
    "    BatchNormalization(),\n",
    "    Conv2DTranspose(128, 5, activation='relu'),\n",
    "    BatchNormalization(),\n",
    "\n",
    "    Conv2DTranspose(64, 5, strides=2, activation='relu'),\n",
    "    BatchNormalization(),\n",
    "    Conv2DTranspose(32, 5, activation='relu'),\n",
    "    BatchNormalization(),\n",
    "\n",
    "    Conv2DTranspose(1, 4, activation='sigmoid')\n",
    "\n",
    "])\n",
    "\n",
    "generator.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 265
    },
    "colab_type": "code",
    "id": "pSb3O5MDlD6u",
    "outputId": "d0e44e62-1da4-47de-909c-c12267950c91"
   },
   "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 / mpl.ratio, fig.canvas.height / mpl.ratio);\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",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "noise = np.random.randn(1, noise_dim)\n",
    "gen_image = generator.predict(noise)[0]\n",
    "\n",
    "plt.figure()\n",
    "plt.imshow(np.reshape(gen_image, (28, 28)), cmap='binary');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generative Adversarial Network (GAN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 252
    },
    "colab_type": "code",
    "id": "6YdMQyzclD69",
    "outputId": "159ad710-ce47-4c1a-9459-b14e348ad80d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         [(None, 1)]               0         \n",
      "_________________________________________________________________\n",
      "sequential_1 (Sequential)    (None, 28, 28, 1)         2717025   \n",
      "_________________________________________________________________\n",
      "sequential (Sequential)      (None, 1)                 1027073   \n",
      "=================================================================\n",
      "Total params: 3,744,098\n",
      "Trainable params: 2,716,065\n",
      "Non-trainable params: 1,028,033\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "input_layer = tf.keras.layers.Input(shape=(noise_dim,))\n",
    "gen_out = generator(input_layer)\n",
    "disc_out = discriminator(gen_out)\n",
    "\n",
    "gan = Model(\n",
    "    input_layer,\n",
    "    disc_out\n",
    ")\n",
    "\n",
    "discriminator.trainable = False\n",
    "gan.compile(loss='binary_crossentropy', optimizer=opt, metrics=['accuracy'])\n",
    "gan.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training the GAN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "HEb3xQ0LlD6_",
    "outputId": "7045c7f6-8cc4-46e0-b411-fbdfc1d7fbbc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Steps per epoch= 107\n"
     ]
    },
    {
     "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 / mpl.ratio, fig.canvas.height / mpl.ratio);\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",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
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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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 1h 1min 51s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "epochs = 25\n",
    "batch_size = 128\n",
    "steps_per_epoch = int(2 * x.shape[0]/batch_size)\n",
    "\n",
    "print('Steps per epoch=', steps_per_epoch)\n",
    "\n",
    "dp = tfutils.plotting.DynamicPlot(plt, 5, 5, (8, 8))\n",
    "\n",
    "for e in range(0, epochs):\n",
    "    \n",
    "    dp.start_of_epoch(e)\n",
    "    \n",
    "    for step in range(0, steps_per_epoch):\n",
    "        true_examples = x[int(batch_size/2)*step: int(batch_size/2)*(step + 1)]\n",
    "        true_examples = np.reshape(true_examples, (true_examples.shape[0], 28, 28, 1))\n",
    "\n",
    "        noise = np.random.randn(int(batch_size/2), noise_dim)\n",
    "        generated_examples = generator.predict(noise)\n",
    "\n",
    "        x_batch = np.concatenate([generated_examples, true_examples], axis=0)\n",
    "        y_batch = np.array([0] * int(batch_size/2) + [1] * int(batch_size/2))\n",
    "\n",
    "        indices = np.random.choice(range(batch_size), batch_size, replace=False)\n",
    "        x_batch = x_batch[indices]\n",
    "        y_batch = y_batch[indices]\n",
    "\n",
    "        # train the discriminator\n",
    "        discriminator.trainable = True\n",
    "        discriminator.train_on_batch(x_batch, y_batch)\n",
    "        discriminator.trainable = False\n",
    "\n",
    "        # train the generator\n",
    "        loss, _ = gan.train_on_batch(noise, np.ones((int(batch_size/2), 1)))\n",
    "\n",
    "        _, acc = discriminator.evaluate(x_batch, y_batch, verbose=False)\n",
    "\n",
    "    noise = np.random.randn(1, noise_dim)\n",
    "    generated_example = generator.predict(noise)[0]\n",
    "    \n",
    "    dp.end_of_epoch(np.reshape(generated_example, (28, 28)), 'binary',\n",
    "                   'DiscAcc:{:.2f}'.format(acc), 'GANLoss:{:.2f}'.format(loss))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "name": "Generative Adversarial Network in Keras.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}