Week 5. Cycle GAN.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Code example for talk, not meant to be read. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
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     "output_type": "display_data"
    },
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\" width=\"500\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/fhv/anaconda3/envs/ml18/lib/python3.6/site-packages/keras/engine/training.py:490: UserWarning: Discrepancy between trainable weights and collected trainable weights, did you set `model.trainable` without calling `model.compile` after ?\n",
      "  'Discrepancy between trainable weights and collected trainable'\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-2-2acfd15b2818>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     64\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     65\u001b[0m     \u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 66\u001b[0;31m     \u001b[0;32mfor\u001b[0m \u001b[0mj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrepeat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mdiscriminator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_on_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdisc_X\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdisc_y\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     67\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrepeat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mgan\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_on_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnoise\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/ml18/lib/python3.6/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mtrain_on_batch\u001b[0;34m(self, x, y, sample_weight, class_weight)\u001b[0m\n\u001b[1;32m   1215\u001b[0m             \u001b[0mins\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0msample_weights\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1216\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_train_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1217\u001b[0;31m         \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mins\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1218\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0munpack_singleton\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1219\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/ml18/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   2713\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_legacy_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2714\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2715\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2716\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2717\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mpy_any\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mis_tensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/ml18/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   2673\u001b[0m             \u001b[0mfetched\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_callable_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0marray_vals\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_metadata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2674\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2675\u001b[0;31m             \u001b[0mfetched\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_callable_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0marray_vals\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2676\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mfetched\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2677\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/ml18/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1380\u001b[0m           ret = tf_session.TF_SessionRunCallable(\n\u001b[1;32m   1381\u001b[0m               \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_handle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatus\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1382\u001b[0;31m               run_metadata_ptr)\n\u001b[0m\u001b[1;32m   1383\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1384\u001b[0m           \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "%matplotlib notebook \n",
    "import keras\n",
    "import numpy as np\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Activation\n",
    "from keras.optimizers import Adam\n",
    "import matplotlib.pyplot as plt # plot how normal distribution changes. \n",
    "\n",
    "\"\"\"\n",
    "    Construct a very simple generator and a somehow larger discriminator. \n",
    "    Train the generator on N(0,1) with real data N(4,2) and plot each iteration. \n",
    "\"\"\"\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(5, 3))\n",
    "\n",
    "def plot(): \n",
    "    ax.cla()\n",
    "    ax.hist(generator.predict_on_batch(noise) , alpha=0.3, label=\"Fake p_g\")\n",
    "    ax.hist(real_data , alpha=0.3, label=\"Real p_d\")\n",
    "    xs = np.arange(-8, 8, 0.1)\n",
    "    pred = discriminator.predict_on_batch(xs)\n",
    "    ax.plot(xs, pred*250, label=\"D(x)\")\n",
    "    ax.legend(loc=3)\n",
    "    ax.set_ylim([0, 280])\n",
    "    ax.set_xlim([-9, 9])\n",
    "    \n",
    "    fig.suptitle(\"Iteration: [%i / %i]\"%(i, iterations))\n",
    "    plt.tight_layout(rect=[0, 0.03, 1, 0.95])\n",
    "    fig.canvas.draw()\n",
    "    plt.pause(.01)\n",
    "\n",
    "# number of samples. \n",
    "n = 1000\n",
    "iterations = 100000\n",
    "repeat = 10\n",
    "\n",
    "# define generator\n",
    "generator = Sequential()\n",
    "generator.add(Dense(1, input_dim=1)) # one neuron except bias, don't have the relu activation!\n",
    "\n",
    "# define discrimiantor\n",
    "discriminator = Sequential()\n",
    "discriminator.add(Dense(10, input_dim=1, activation=\"relu\")) # non linearity has some use here. \n",
    "discriminator.add(Dense(1,  activation=\"sigmoid\"))\n",
    "discriminator.compile(loss='binary_crossentropy', optimizer=Adam())\n",
    "\n",
    "# Combine models\n",
    "gan = Sequential()\n",
    "gan.add(generator)\n",
    "discriminator.trainable = False # from the gan model we freeze discriminator to use it as loss function\n",
    "gan.add(discriminator)\n",
    "gan.compile(loss='binary_crossentropy', optimizer=Adam())\n",
    "\n",
    "# Do several updates, plot some interval of iterations fake / real data. \n",
    "\n",
    "for i in range(iterations):\n",
    "    \n",
    "    noise     = np.random.normal(0, 1, size=(n, 1))\n",
    "    fake_data = generator.predict_on_batch(noise)\n",
    "    real_data = np.random.normal(4, 2, size=(n, 1))\n",
    "    \n",
    "    disc_X = np.concatenate((real_data, fake_data), axis=0)\n",
    "    disc_y = np.concatenate((np.zeros(n), np.ones(n)), axis=0) # flip labels since we min instead of max. \n",
    "    \n",
    "    plot()\n",
    "    for j in range(repeat): discriminator.train_on_batch(x=disc_X, y=disc_y)\n",
    "    for j in range(repeat): gan.train_on_batch(x=noise, y=np.zeros(n))\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Translate between normal distributions, e.g. \n",
    "\n",
    "$p_x = N(\\mu_x, \\sigma^2_x)$ and $p_y = N(\\mu_y, \\sigma^2_y)$ and we don't know the specific values of $\\mu_x,\\mu_x$ and $\\sigma_x, \\sigma_y$\n",
    "\n",
    "extend afterwards to more interesting case, e.g. translate between cifar dogs and cats. \n",
    "\n",
    "\n",
    "Inspired by https://github.com/eriklindernoren/Keras-GAN/blob/master/cyclegan/cyclegan.py "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "        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",
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       "\n",
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       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
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       "\n",
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       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
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       "\n",
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       "\n",
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       "\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",
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       "    canvas_div.resizable({\n",
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       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"1000\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/fhv/anaconda3/envs/ml18/lib/python3.6/site-packages/keras/engine/training.py:490: UserWarning: Discrepancy between trainable weights and collected trainable weights, did you set `model.trainable` without calling `model.compile` after ?\n",
      "  'Discrepancy between trainable weights and collected trainable'\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-2-40e2afa92ee8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m    112\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    113\u001b[0m         \u001b[0mDY\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_on_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mY\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalid\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 114\u001b[0;31m         \u001b[0mDY\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_on_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfake_Y\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfake\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    115\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    116\u001b[0m         \u001b[0mDX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_on_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalid\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/ml18/lib/python3.6/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mtrain_on_batch\u001b[0;34m(self, x, y, sample_weight, class_weight)\u001b[0m\n\u001b[1;32m   1215\u001b[0m             \u001b[0mins\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0msample_weights\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1216\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_train_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1217\u001b[0;31m         \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mins\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1218\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0munpack_singleton\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1219\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/ml18/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   2713\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_legacy_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2714\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2715\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2716\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2717\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mpy_any\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mis_tensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/ml18/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   2673\u001b[0m             \u001b[0mfetched\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_callable_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0marray_vals\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_metadata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2674\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2675\u001b[0;31m             \u001b[0mfetched\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_callable_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0marray_vals\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2676\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mfetched\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2677\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/ml18/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1380\u001b[0m           ret = tf_session.TF_SessionRunCallable(\n\u001b[1;32m   1381\u001b[0m               \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_handle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatus\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1382\u001b[0;31m               run_metadata_ptr)\n\u001b[0m\u001b[1;32m   1383\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1384\u001b[0m           \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "%matplotlib notebook \n",
    "import keras\n",
    "import numpy as np\n",
    "from keras.models import Sequential, Model\n",
    "from keras.layers import Dense, Activation, Input\n",
    "from keras.optimizers import Adam\n",
    "import matplotlib.pyplot as plt # plot how normal distribution changes. \n",
    "\n",
    "\"\"\"\n",
    "    Translate between different normal distributions. \n",
    "    p_x = N(2, 1) and p_y = N(-2, 1)\n",
    "\"\"\"\n",
    "\n",
    "fig, ax = plt.subplots(1, 2, figsize=(10, 3)) \n",
    "\n",
    "def plot(display): \n",
    "    ax[0].cla()\n",
    "    ax[0].hist(X, alpha=0.3, label=\"X\")\n",
    "    ax[0].hist(Y, alpha=0.3, label=\"Y\")\n",
    "    \n",
    "    xs = np.arange(-5, 15, 0.1)\n",
    "    \n",
    "    if display == 1 or display == 2: \n",
    "        ax[0].hist(fake_X, alpha=0.3, label=\"Fake X\")\n",
    "        ax[0].hist(fake_Y, alpha=0.3, label=\"Fake Y\") \n",
    "        \n",
    "        ax[0].plot(xs, DX.predict(xs)*250, label=\"DX\")\n",
    "        ax[0].plot(xs, DY.predict(xs)*250, label=\"DY\")\n",
    "    \n",
    "    if display == 0 or display == 2:\n",
    "        ax[0].hist(rec_X, alpha=0.3, label=\"Rec X\")\n",
    "        ax[0].hist(rec_Y, alpha=0.3, label=\"Rec Y\") \n",
    "    \n",
    "    ax[0].legend(loc=1)\n",
    "    ax[0].set_ylim([0, 280])\n",
    "    ax[0].set_xlim([-7, 12])\n",
    "    \n",
    "    fig.suptitle(\"Iteration: [%i / %i]\"%(i, iterations))\n",
    "    plt.tight_layout(rect=[0, 0.03, 1, 0.95])\n",
    "    fig.canvas.draw()\n",
    "    #plt.savefig(\"gifs/cycle_gan_normals/%i.png\"%i)\n",
    "    plt.pause(.01)\n",
    "\n",
    "# number of samples. \n",
    "n          = 1000\n",
    "iterations = 1000\n",
    "repeat     = 10\n",
    "\n",
    "# For both F and G we know a single neuron with bias is sufficient. \n",
    "\n",
    "\n",
    "# G: X -> Y\n",
    "G = Sequential()\n",
    "G.add(Dense(1, input_dim=1))\n",
    "\n",
    "# DY: discriminate {y} and {G(x)}\n",
    "DY = Sequential()\n",
    "DY.add(Dense(10, input_dim=1, activation=\"relu\")) \n",
    "DY.add(Dense(1,  activation=\"sigmoid\"))\n",
    "DY.compile(loss='binary_crossentropy', optimizer=Adam(0.005), metrics=['acc'])\n",
    "\n",
    "# F: Y -> X\n",
    "F = Sequential()\n",
    "F.add(Dense(1, input_dim=1))\n",
    "\n",
    "# DX: discriminate {x} and {F(y)}\n",
    "DX = Sequential()\n",
    "DX.add(Dense(10, input_dim=1, activation=\"relu\")) \n",
    "DX.add(Dense(1,  activation=\"sigmoid\"))\n",
    "DX.compile(loss='binary_crossentropy', optimizer=Adam(0.005), metrics=['acc'])\n",
    "\n",
    "input_X = Input(shape=(1, ))\n",
    "input_Y = Input(shape=(1, ))\n",
    "\n",
    "fake_X = F(input_Y)\n",
    "fake_Y = G(input_X)\n",
    "\n",
    "rec_X = F(fake_Y)\n",
    "rec_Y = G(fake_X)\n",
    "\n",
    "DX.trainable = False \n",
    "DY.trainable = False # freeze discriminator to use it as loss function\n",
    "\n",
    "adv_X = DX(fake_X)\n",
    "adv_Y = DY(fake_Y)\n",
    "\n",
    "combined = Model(inputs =[input_X, input_Y], \n",
    "                 outputs=[adv_X,   adv_Y,\n",
    "                          rec_X,   rec_Y])\n",
    "\n",
    "combined.compile(loss         = ['binary_crossentropy', 'binary_crossentropy', 'mae', 'mae'],\n",
    "                 loss_weights = [1, 1, 5, 5],\n",
    "                 optimizer    = Adam(0.01) )\n",
    "\n",
    "# Adversarial loss ground truths\n",
    "valid = np.ones(n)\n",
    "fake  = np.zeros(n)\n",
    "\n",
    "for i in range(iterations):\n",
    "    #print(\"\\r--- [%i / %i] ---\"%(i, iterations), end=\"\")\n",
    "    \n",
    "    X = np.random.normal(-4, 1, size=(n, 1))\n",
    "    Y = np.random.uniform(6, 8, size=(n, 1))\n",
    "    \n",
    "    fake_X = F.predict(Y)\n",
    "    fake_Y = G.predict(X)\n",
    "    \n",
    "    rec_X = F.predict(fake_Y)\n",
    "    rec_Y = G.predict(fake_X)\n",
    "\n",
    "    # Update discriminator\n",
    "    for j in range(10):\n",
    "        DY.train_on_batch(Y, valid)\n",
    "        DY.train_on_batch(fake_Y, fake) \n",
    "\n",
    "        DX.train_on_batch(X, valid)\n",
    "        DX.train_on_batch(fake_X, fake)\n",
    "    \n",
    "    # Update generator\n",
    "    combined.train_on_batch([X, Y], [valid, valid, X, Y])\n",
    "    \n",
    "    if i % 10 == 0: \n",
    "        plot(2)\n",
    "    \n",
    "        "
   ]
  }
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