深度前馈网络/mlp_func_approx.ipynb

{
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    {
     "name": "stderr",
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     "text": [
      "Using Theano backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Activation\n",
    "from keras.optimizers import Adam\n",
    "import numpy\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "from matplotlib import cm\n",
    "%matplotlib notebook "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(Dense(output_dim=64, input_dim=2))\n",
    "model.add(Activation(\"tanh\"))\n",
    "model.add(Dense(output_dim=1))\n",
    "model.compile(loss='mean_squared_error', optimizer=Adam())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
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   "source": [
    "# generate data based on f\n",
    "def gen_train_data(f, data_range):\n",
    "    X, Y = numpy.meshgrid(numpy.arange(*data_range), numpy.arange(*data_range))\n",
    "    Z = f((X, Y))\n",
    "    return X, Y, Z\n",
    "\n",
    "def plot_surf(X, Y, Z, ax):\n",
    "    surf = ax.plot_surface(X, Y, Z, rstride=1, cstride=1, linewidth=0, cmap=cm.Reds_r, antialiased=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
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   },
   "outputs": [
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       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
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       "\n",
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       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
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       "\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",
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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",
       "    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'];\n",
       "    var y0 = fig.canvas.height - msg['y0'];\n",
       "    var x1 = msg['x1'];\n",
       "    var y1 = fig.canvas.height - msg['y1'];\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
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       "    y1 = Math.floor(y1) + 0.5;\n",
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       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
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       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
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       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
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       "    var cursor = msg['cursor'];\n",
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       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
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       "        break;\n",
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       "        break;\n",
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       "    fig.rubberband_canvas.style.cursor = cursor;\n",
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       "\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",
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       "    // Called whenever the canvas gets updated.\n",
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       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
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       "    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",
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       "\n",
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       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
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       "\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;\n",
       "    var y = canvas_pos.y;\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 overriden (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",
       "    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 + '\">');\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 dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
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     "output_type": "display_data"
    },
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\">"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/200\n",
      "90000/90000 [==============================] - 1s - loss: 31.4996     \n",
      "Epoch 2/200\n",
      "90000/90000 [==============================] - 1s - loss: 23.0188     \n",
      "Epoch 3/200\n",
      "90000/90000 [==============================] - 1s - loss: 19.9382     \n",
      "Epoch 4/200\n",
      "90000/90000 [==============================] - 1s - loss: 19.7047     \n",
      "Epoch 5/200\n",
      "90000/90000 [==============================] - 1s - loss: 17.1799     \n",
      "Epoch 6/200\n",
      "90000/90000 [==============================] - 1s - loss: 4.5474     \n",
      "Epoch 7/200\n",
      "90000/90000 [==============================] - 1s - loss: 2.1169     \n",
      "Epoch 8/200\n",
      "90000/90000 [==============================] - 1s - loss: 1.3929     \n",
      "Epoch 9/200\n",
      "90000/90000 [==============================] - 1s - loss: 1.0536     \n",
      "Epoch 10/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.8533     \n",
      "Epoch 11/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.7380     \n",
      "Epoch 12/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.6601     \n",
      "Epoch 13/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.6058     \n",
      "Epoch 14/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.5647     \n",
      "Epoch 15/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.5375     \n",
      "Epoch 16/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.5092     \n",
      "Epoch 17/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.4600     \n",
      "Epoch 18/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.3766     \n",
      "Epoch 19/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.2413     \n",
      "Epoch 20/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.1290     \n",
      "Epoch 21/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0882     \n",
      "Epoch 22/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0710     \n",
      "Epoch 23/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0479     \n",
      "Epoch 24/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0373     \n",
      "Epoch 25/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0320     \n",
      "Epoch 26/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0280     \n",
      "Epoch 27/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0245     \n",
      "Epoch 28/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0226     \n",
      "Epoch 29/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0215     \n",
      "Epoch 30/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0210     \n",
      "Epoch 31/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0203     \n",
      "Epoch 32/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0197     \n",
      "Epoch 33/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0195     \n",
      "Epoch 34/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0191     \n",
      "Epoch 35/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0185     \n",
      "Epoch 36/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0182     \n",
      "Epoch 37/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0179     \n",
      "Epoch 38/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0172     \n",
      "Epoch 39/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0170     \n",
      "Epoch 40/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0164     \n",
      "Epoch 41/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0158     \n",
      "Epoch 42/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0153     \n",
      "Epoch 43/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0149     \n",
      "Epoch 44/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0143     \n",
      "Epoch 45/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0140     \n",
      "Epoch 46/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0135     \n",
      "Epoch 47/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0132     \n",
      "Epoch 48/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0128     \n",
      "Epoch 49/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0123     \n",
      "Epoch 50/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0121     \n",
      "Epoch 51/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0117     \n",
      "Epoch 52/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0116     \n",
      "Epoch 53/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0112     \n",
      "Epoch 54/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0109     \n",
      "Epoch 55/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0106     \n",
      "Epoch 56/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0100     \n",
      "Epoch 57/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0099     \n",
      "Epoch 58/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0096     \n",
      "Epoch 59/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0090     \n",
      "Epoch 60/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0088     \n",
      "Epoch 61/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0083     \n",
      "Epoch 62/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0080     \n",
      "Epoch 63/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0076     \n",
      "Epoch 64/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0071     \n",
      "Epoch 65/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0069     \n",
      "Epoch 66/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0064     \n",
      "Epoch 67/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0061     \n",
      "Epoch 68/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0060     \n",
      "Epoch 69/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0058     \n",
      "Epoch 70/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0054     \n",
      "Epoch 71/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0054     \n",
      "Epoch 72/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0052     \n",
      "Epoch 73/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0049     \n",
      "Epoch 74/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0049     \n",
      "Epoch 75/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0050     \n",
      "Epoch 76/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0047     \n",
      "Epoch 77/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0047     \n",
      "Epoch 78/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0045     \n",
      "Epoch 79/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0044     \n",
      "Epoch 80/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0044     \n",
      "Epoch 81/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0043     \n",
      "Epoch 82/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0043     \n",
      "Epoch 83/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0041     \n",
      "Epoch 84/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0042     \n",
      "Epoch 85/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0040     \n",
      "Epoch 86/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0040     \n",
      "Epoch 87/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0038     \n",
      "Epoch 88/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0039     \n",
      "Epoch 89/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0037     \n",
      "Epoch 90/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0039     \n",
      "Epoch 91/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0038     \n",
      "Epoch 92/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0038     \n",
      "Epoch 93/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0037     \n",
      "Epoch 94/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0037     \n",
      "Epoch 95/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0037     \n",
      "Epoch 96/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0036     \n",
      "Epoch 97/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0035     \n",
      "Epoch 98/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0035     \n",
      "Epoch 99/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0034     \n",
      "Epoch 100/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0034     \n",
      "Epoch 101/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0035     \n",
      "Epoch 102/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0034     \n",
      "Epoch 103/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0034     \n",
      "Epoch 104/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0035     \n",
      "Epoch 105/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0034     \n",
      "Epoch 106/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0033     \n",
      "Epoch 107/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0033     \n",
      "Epoch 108/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0032     \n",
      "Epoch 109/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0032     \n",
      "Epoch 110/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0033     \n",
      "Epoch 111/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0033     \n",
      "Epoch 112/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0032     \n",
      "Epoch 113/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0033     \n",
      "Epoch 114/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0031     \n",
      "Epoch 115/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0033     \n",
      "Epoch 116/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0031     \n",
      "Epoch 117/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0031     \n",
      "Epoch 118/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0032     \n",
      "Epoch 119/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0031     \n",
      "Epoch 120/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0030     \n",
      "Epoch 121/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0031     \n",
      "Epoch 122/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0030     \n",
      "Epoch 123/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0030     \n",
      "Epoch 124/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0031     \n",
      "Epoch 125/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0031     \n",
      "Epoch 126/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0030     \n",
      "Epoch 127/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0030     \n",
      "Epoch 128/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0030     \n",
      "Epoch 129/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0030     \n",
      "Epoch 130/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0028     \n",
      "Epoch 131/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0029     \n",
      "Epoch 132/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0031     \n",
      "Epoch 133/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0028     \n",
      "Epoch 134/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0029     \n",
      "Epoch 135/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0029     \n",
      "Epoch 136/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0028     \n",
      "Epoch 137/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0028     \n",
      "Epoch 138/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0028     \n",
      "Epoch 139/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0028     \n",
      "Epoch 140/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0027     \n",
      "Epoch 141/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0027     \n",
      "Epoch 142/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0027     \n",
      "Epoch 143/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0028     \n",
      "Epoch 144/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0027     \n",
      "Epoch 145/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0026     \n",
      "Epoch 146/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0025     \n",
      "Epoch 147/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0026     \n",
      "Epoch 148/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0025     \n",
      "Epoch 149/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0025     \n",
      "Epoch 150/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0025     \n",
      "Epoch 151/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0024     \n",
      "Epoch 152/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0025     \n",
      "Epoch 153/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0025     \n",
      "Epoch 154/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0025     \n",
      "Epoch 155/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0024     \n",
      "Epoch 156/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0024     \n",
      "Epoch 157/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0024     \n",
      "Epoch 158/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0024     \n",
      "Epoch 159/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0024     \n",
      "Epoch 160/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0024     \n",
      "Epoch 161/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0025     \n",
      "Epoch 162/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0023     \n",
      "Epoch 163/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0024     \n",
      "Epoch 164/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0023     \n",
      "Epoch 165/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0023     \n",
      "Epoch 166/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0023     \n",
      "Epoch 167/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0023     \n",
      "Epoch 168/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0023     \n",
      "Epoch 169/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0023     \n",
      "Epoch 170/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0021     \n",
      "Epoch 171/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0022     \n",
      "Epoch 172/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0022     \n",
      "Epoch 173/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0022     \n",
      "Epoch 174/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0022     \n",
      "Epoch 175/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0022     \n",
      "Epoch 176/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0022     \n",
      "Epoch 177/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0021     \n",
      "Epoch 178/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0022     \n",
      "Epoch 179/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0021     \n",
      "Epoch 180/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0022     \n",
      "Epoch 181/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0021     \n",
      "Epoch 182/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0021     \n",
      "Epoch 183/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0021     \n",
      "Epoch 184/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0022     \n",
      "Epoch 185/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0020     \n",
      "Epoch 186/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0021     \n",
      "Epoch 187/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0020     \n",
      "Epoch 188/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0020     \n",
      "Epoch 189/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0022     \n",
      "Epoch 190/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0021     \n",
      "Epoch 191/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0020     \n",
      "Epoch 192/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0021     \n",
      "Epoch 193/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0021     \n",
      "Epoch 194/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0021     \n",
      "Epoch 195/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0020     \n",
      "Epoch 196/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0020     \n",
      "Epoch 197/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0021     \n",
      "Epoch 198/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0020     \n",
      "Epoch 199/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0020     \n",
      "Epoch 200/200\n",
      "90000/90000 [==============================] - 1s - loss: 0.0020     \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7fd47f992518>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# training\n",
    "# simple function\n",
    "# f = lambda x: 2 * x[0] ** 2 - 3 * x[1] ** 2 + 1\n",
    "# love function\n",
    "f = lambda x: x[0] ** 2 + (x[1] - (x[0] ** 2.0)**(1.0/3))**2\n",
    "\n",
    "data_range = (-3, 3, 0.02)\n",
    "X_train, Y_train, Z_train = gen_train_data(f, data_range)\n",
    "XY_train = numpy.vstack((X_train.flatten(),Y_train.flatten())).T\n",
    "fig = plt.figure()\n",
    "ax = fig.gca(projection='3d')\n",
    "plot_surf(X_train, Y_train, Z_train, ax)\n",
    "ax.view_init(90, 270)\n",
    "plt.show()\n",
    "\n",
    "model.fit(XY_train, Z_train.flatten(), nb_epoch=200, batch_size=32)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\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",
       "            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",
       "        this.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 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);\n",
       "        canvas.attr('height', height);\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'];\n",
       "    var y0 = fig.canvas.height - msg['y0'];\n",
       "    var x1 = msg['x1'];\n",
       "    var y1 = fig.canvas.height - msg['y1'];\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;\n",
       "    var y = canvas_pos.y;\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 overriden (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",
       "    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 + '\">');\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 dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\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": {
      "text/html": [
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\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0513524832079\n"
     ]
    }
   ],
   "source": [
    "# test\n",
    "data_range = (-3.1, 2.9, 0.02)\n",
    "X_test, Y_test, Z_test = gen_train_data(f, data_range)\n",
    "XY_test = numpy.vstack((X_test.flatten(),Y_test.flatten())).T\n",
    "Z_predict = model.predict(XY_test, batch_size=32)\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.gca(projection='3d')\n",
    "plot_surf(X_test, Y_test, Z_predict.reshape(Z_test.shape), ax)\n",
    "ax.view_init(90, 270)\n",
    "plt.show()\n",
    "print(numpy.sqrt(numpy.mean((Z_test.flatten()-Z_predict.flatten())**2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}