mimic_keras_predict.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Mimicking Keras' predict   \n",
    "\n",
    "The goal is that given a trained Keras model and an input event I can produce the same prediction as Keras."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "#In this example I will use Keras to navigate the model, may need to do this manually if I fully port the code to C or C++\n",
    "\n",
    "#System imports\n",
    "from __future__ import print_function\n",
    "\n",
    "#External imports \n",
    "from keras import layers, models\n",
    "from keras import backend as K\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "#Local imports \n",
    "from naive_prediction.data_utils import read_keras_model_from_file\n",
    "from naive_prediction.forward_pass import predict_with_keras_model\n",
    "from data import generate_straight_tracks\n",
    "from drawing import draw_2d_event, draw_2d_input_and_pred\n",
    "import naive_prediction.tests\n",
    "\n",
    "#For consistent printing\n",
    "import numpy as np\n",
    "np.set_printoptions(suppress=True)\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So I've trained an example model (the training is worthless, but it's a model created by Keras nonetheless). \n",
    "\n",
    "I'm going to load it into this notebook, have a look at it, and try to complete a forward pass using my modules."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "____________________________________________________________________________________________________\n",
      "Layer (type)                     Output Shape          Param #     Connected to                     \n",
      "====================================================================================================\n",
      "input_3 (InputLayer)             (None, 64, 64)        0                                            \n",
      "____________________________________________________________________________________________________\n",
      "reshape_5 (Reshape)              (None, 64, 64, 1)     0           input_3[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "convolution2d_27 (Convolution2D) (None, 64, 64, 8)     80          reshape_5[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "convolution2d_28 (Convolution2D) (None, 64, 64, 8)     584         convolution2d_27[0][0]           \n",
      "____________________________________________________________________________________________________\n",
      "maxpooling2d_7 (MaxPooling2D)    (None, 32, 32, 8)     0           convolution2d_28[0][0]           \n",
      "____________________________________________________________________________________________________\n",
      "convolution2d_29 (Convolution2D) (None, 32, 32, 8)     584         maxpooling2d_7[0][0]             \n",
      "____________________________________________________________________________________________________\n",
      "maxpooling2d_8 (MaxPooling2D)    (None, 16, 16, 8)     0           convolution2d_29[0][0]           \n",
      "____________________________________________________________________________________________________\n",
      "convolution2d_30 (Convolution2D) (None, 16, 16, 16)    1168        maxpooling2d_8[0][0]             \n",
      "____________________________________________________________________________________________________\n",
      "maxpooling2d_9 (MaxPooling2D)    (None, 8, 8, 16)      0           convolution2d_30[0][0]           \n",
      "____________________________________________________________________________________________________\n",
      "convolution2d_31 (Convolution2D) (None, 8, 8, 16)      2320        maxpooling2d_9[0][0]             \n",
      "____________________________________________________________________________________________________\n",
      "convolution2d_32 (Convolution2D) (None, 8, 8, 16)      2320        convolution2d_31[0][0]           \n",
      "____________________________________________________________________________________________________\n",
      "convolution2d_33 (Convolution2D) (None, 8, 8, 16)      2320        convolution2d_32[0][0]           \n",
      "____________________________________________________________________________________________________\n",
      "convolution2d_34 (Convolution2D) (None, 8, 8, 16)      2320        convolution2d_33[0][0]           \n",
      "____________________________________________________________________________________________________\n",
      "upsampling2d_7 (UpSampling2D)    (None, 16, 16, 16)    0           convolution2d_34[0][0]           \n",
      "____________________________________________________________________________________________________\n",
      "convolution2d_35 (Convolution2D) (None, 16, 16, 16)    2320        upsampling2d_7[0][0]             \n",
      "____________________________________________________________________________________________________\n",
      "upsampling2d_8 (UpSampling2D)    (None, 32, 32, 16)    0           convolution2d_35[0][0]           \n",
      "____________________________________________________________________________________________________\n",
      "convolution2d_36 (Convolution2D) (None, 32, 32, 8)     1160        upsampling2d_8[0][0]             \n",
      "____________________________________________________________________________________________________\n",
      "upsampling2d_9 (UpSampling2D)    (None, 64, 64, 8)     0           convolution2d_36[0][0]           \n",
      "____________________________________________________________________________________________________\n",
      "convolution2d_37 (Convolution2D) (None, 64, 64, 8)     584         upsampling2d_9[0][0]             \n",
      "____________________________________________________________________________________________________\n",
      "convolution2d_38 (Convolution2D) (None, 64, 64, 8)     584         convolution2d_37[0][0]           \n",
      "____________________________________________________________________________________________________\n",
      "convolution2d_39 (Convolution2D) (None, 64, 64, 1)     73          convolution2d_38[0][0]           \n",
      "____________________________________________________________________________________________________\n",
      "reshape_6 (Reshape)              (None, 64, 64)        0           convolution2d_39[0][0]           \n",
      "====================================================================================================\n",
      "Total params: 16,417\n",
      "Trainable params: 16,417\n",
      "Non-trainable params: 0\n",
      "____________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "#load model, summary\n",
    "model = read_keras_model_from_file('/Users/Thomas/Desktop/opencl-cnn/model1.h5')\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This looks like what we'd expect. Now let us create an input example, predict using Keras, and see if we can reproduce the prediction with out own code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Batch generator function, from heptrkx\n",
    "def batch_generator_convae(num_event, det_shape, num_tracks, binary=True):\n",
    "    \"\"\"\n",
    "    Generator function for batches of toy data for training.\n",
    "    This function generates a fixed number of tracks in a batch.\n",
    "    \"\"\"\n",
    "    while True:\n",
    "        events = sum(generate_straight_tracks(num_event, det_shape)\n",
    "                     for i in range(num_tracks))\n",
    "        if binary:\n",
    "            events[events > 1] = 1\n",
    "        # Input and target are the same here\n",
    "        yield events, events"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
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       "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",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_test = 1\n",
    "det_shape = (64, 64)\n",
    "num_tracks = 8\n",
    "test_events, test_target = batch_generator_convae(num_test, det_shape, num_tracks).next()\n",
    "test_pred = model.predict(test_events)\n",
    "draw_2d_input_and_pred(test_events[0], test_pred[0], cmap='gray_r');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So we were able to make our prediction with Keras. Now let's try to do the same using our methods.\n",
    "\n",
    "Let's make a prediction using our own methods:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "our_prediction = predict_with_keras_model(model, test_events)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Comparing Keras' result to ours\n",
    "\n",
    "In the plots first we have my result and then Keras':"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test_events vs our prediction\n"
     ]
    },
    {
     "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",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test_events vs keras prediction\n"
     ]
    },
    {
     "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",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
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\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pred_output = np.reshape(our_prediction, (64, 64))\n",
    "print(\"test_events vs our prediction\")\n",
    "draw_2d_input_and_pred(test_events[0], pred_output, cmap='gray_r');\n",
    "print(\"test_events vs keras prediction\")\n",
    "draw_2d_input_and_pred(test_events[0], test_pred[0], cmap='gray_r');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's very clear that our prediction is the exact same as Keras' visually. I've made a little testing script that tests each layer individually and compares Keras' output on that layer to mine.\n",
    "\n",
    "For the most part you can see my script produces the same output as Keras, however values being within '0.00001' (arbitrary small number, could probably be smaller) may be a good metric for equality. I think operation precision causes some outputs not to be equal which makes my output different from Keras' after some convolution operations even though the outputs are all extremely close to eachother. \n",
    "\n",
    "Long story short, every layer produces the same output as Keras, which you can see below where every layer is compared to Keras. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "===========================================================================\n",
      "Forward pass on a reshape_5 layer.\n",
      "\u001b[32mPASS: This layer successfully produces the same results as Keras.\u001b[0m\n",
      "Using self output as input 4096  of 4096  correctly predicted\n",
      "Using keras output as input 4096  of 4096  correctly predicted\n",
      "\u001b[32mSUCCESS: All output values within 0.00001 of eachother.\u001b[0m\n",
      "===========================================================================\n",
      "\n",
      "===========================================================================\n",
      "Forward pass on a convolution2d_27 layer.\n",
      "\u001b[32mPASS: This layer successfully produces the same results as Keras.\u001b[0m\n",
      "Using self output as input 31739  of 32768  correctly predicted\n",
      "Using keras output as input 32768  of 32768  correctly predicted\n",
      "\u001b[32mSUCCESS: All output values within 0.00001 of eachother.\u001b[0m\n",
      "===========================================================================\n",
      "\n",
      "===========================================================================\n",
      "Forward pass on a convolution2d_28 layer.\n",
      "\u001b[31mFAIL: This layer does no perform in the same way as Keras.\u001b[0m\n",
      "Using self output as input 27628  of 32768  correctly predicted\n",
      "Using keras output as input 27569  of 32768  correctly predicted\n",
      "\u001b[32mSUCCESS: All output values within 0.00001 of eachother.\u001b[0m\n",
      "===========================================================================\n",
      "\n",
      "===========================================================================\n",
      "Forward pass on a maxpooling2d_7 layer.\n",
      "\u001b[32mPASS: This layer successfully produces the same results as Keras.\u001b[0m\n",
      "Using self output as input 6238  of 8192  correctly predicted\n",
      "Using keras output as input 8192  of 8192  correctly predicted\n",
      "\u001b[32mSUCCESS: All output values within 0.00001 of eachother.\u001b[0m\n",
      "===========================================================================\n",
      "\n",
      "===========================================================================\n",
      "Forward pass on a convolution2d_29 layer.\n",
      "\u001b[31mFAIL: This layer does no perform in the same way as Keras.\u001b[0m\n",
      "Using self output as input 4480  of 8192  correctly predicted\n",
      "Using keras output as input 4817  of 8192  correctly predicted\n",
      "\u001b[32mSUCCESS: All output values within 0.00001 of eachother.\u001b[0m\n",
      "===========================================================================\n",
      "\n",
      "===========================================================================\n",
      "Forward pass on a maxpooling2d_8 layer.\n",
      "\u001b[32mPASS: This layer successfully produces the same results as Keras.\u001b[0m\n",
      "Using self output as input 943  of 2048  correctly predicted\n",
      "Using keras output as input 2048  of 2048  correctly predicted\n",
      "\u001b[32mSUCCESS: All output values within 0.00001 of eachother.\u001b[0m\n",
      "===========================================================================\n",
      "\n",
      "===========================================================================\n",
      "Forward pass on a convolution2d_30 layer.\n",
      "\u001b[31mFAIL: This layer does no perform in the same way as Keras.\u001b[0m\n",
      "Using self output as input 2122  of 4096  correctly predicted\n",
      "Using keras output as input 2244  of 4096  correctly predicted\n",
      "\u001b[32mSUCCESS: All output values within 0.00001 of eachother.\u001b[0m\n",
      "===========================================================================\n",
      "\n",
      "===========================================================================\n",
      "Forward pass on a maxpooling2d_9 layer.\n",
      "\u001b[32mPASS: This layer successfully produces the same results as Keras.\u001b[0m\n",
      "Using self output as input 407  of 1024  correctly predicted\n",
      "Using keras output as input 1024  of 1024  correctly predicted\n",
      "\u001b[32mSUCCESS: All output values within 0.00001 of eachother.\u001b[0m\n",
      "===========================================================================\n",
      "\n",
      "===========================================================================\n",
      "Forward pass on a convolution2d_31 layer.\n",
      "\u001b[31mFAIL: This layer does no perform in the same way as Keras.\u001b[0m\n",
      "Using self output as input 311  of 1024  correctly predicted\n",
      "Using keras output as input 379  of 1024  correctly predicted\n",
      "\u001b[32mSUCCESS: All output values within 0.00001 of eachother.\u001b[0m\n",
      "===========================================================================\n",
      "\n",
      "===========================================================================\n",
      "Forward pass on a convolution2d_32 layer.\n",
      "\u001b[31mFAIL: This layer does no perform in the same way as Keras.\u001b[0m\n",
      "Using self output as input 432  of 1024  correctly predicted\n",
      "Using keras output as input 506  of 1024  correctly predicted\n",
      "\u001b[32mSUCCESS: All output values within 0.00001 of eachother.\u001b[0m\n",
      "===========================================================================\n",
      "\n",
      "===========================================================================\n",
      "Forward pass on a convolution2d_33 layer.\n",
      "\u001b[31mFAIL: This layer does no perform in the same way as Keras.\u001b[0m\n",
      "Using self output as input 274  of 1024  correctly predicted\n",
      "Using keras output as input 389  of 1024  correctly predicted\n",
      "\u001b[32mSUCCESS: All output values within 0.00001 of eachother.\u001b[0m\n",
      "===========================================================================\n",
      "\n",
      "===========================================================================\n",
      "Forward pass on a convolution2d_34 layer.\n",
      "\u001b[31mFAIL: This layer does no perform in the same way as Keras.\u001b[0m\n",
      "Using self output as input 306  of 1024  correctly predicted\n",
      "Using keras output as input 396  of 1024  correctly predicted\n",
      "\u001b[32mSUCCESS: All output values within 0.00001 of eachother.\u001b[0m\n",
      "===========================================================================\n",
      "\n",
      "===========================================================================\n",
      "Forward pass on a upsampling2d_7 layer.\n",
      "\u001b[32mPASS: This layer successfully produces the same results as Keras.\u001b[0m\n",
      "Using self output as input 1224  of 4096  correctly predicted\n",
      "Using keras output as input 4096  of 4096  correctly predicted\n",
      "\u001b[32mSUCCESS: All output values within 0.00001 of eachother.\u001b[0m\n",
      "===========================================================================\n",
      "\n",
      "===========================================================================\n",
      "Forward pass on a convolution2d_35 layer.\n",
      "\u001b[31mFAIL: This layer does no perform in the same way as Keras.\u001b[0m\n",
      "Using self output as input 2162  of 4096  correctly predicted\n",
      "Using keras output as input 2426  of 4096  correctly predicted\n",
      "\u001b[32mSUCCESS: All output values within 0.00001 of eachother.\u001b[0m\n",
      "===========================================================================\n",
      "\n",
      "===========================================================================\n",
      "Forward pass on a upsampling2d_8 layer.\n",
      "\u001b[32mPASS: This layer successfully produces the same results as Keras.\u001b[0m\n",
      "Using self output as input 8648  of 16384  correctly predicted\n",
      "Using keras output as input 16384  of 16384  correctly predicted\n",
      "\u001b[32mSUCCESS: All output values within 0.00001 of eachother.\u001b[0m\n",
      "===========================================================================\n",
      "\n",
      "===========================================================================\n",
      "Forward pass on a convolution2d_36 layer.\n",
      "\u001b[31mFAIL: This layer does no perform in the same way as Keras.\u001b[0m\n",
      "Using self output as input 2922  of 8192  correctly predicted\n",
      "Using keras output as input 3742  of 8192  correctly predicted\n",
      "\u001b[32mSUCCESS: All output values within 0.00001 of eachother.\u001b[0m\n",
      "===========================================================================\n",
      "\n",
      "===========================================================================\n",
      "Forward pass on a upsampling2d_9 layer.\n",
      "\u001b[32mPASS: This layer successfully produces the same results as Keras.\u001b[0m\n",
      "Using self output as input 11688  of 32768  correctly predicted\n",
      "Using keras output as input 32768  of 32768  correctly predicted\n",
      "\u001b[32mSUCCESS: All output values within 0.00001 of eachother.\u001b[0m\n",
      "===========================================================================\n",
      "\n",
      "===========================================================================\n",
      "Forward pass on a convolution2d_37 layer.\n",
      "\u001b[31mFAIL: This layer does no perform in the same way as Keras.\u001b[0m\n",
      "Using self output as input 15564  of 32768  correctly predicted\n",
      "Using keras output as input 19155  of 32768  correctly predicted\n",
      "\u001b[31mFAIL: All output values NOT within 0.00001 of eachother.\u001b[0m\n",
      "===========================================================================\n",
      "\n",
      "===========================================================================\n",
      "Forward pass on a convolution2d_38 layer.\n",
      "\u001b[31mFAIL: This layer does no perform in the same way as Keras.\u001b[0m\n",
      "Using self output as input 18892  of 32768  correctly predicted\n",
      "Using keras output as input 21944  of 32768  correctly predicted\n",
      "\u001b[31mFAIL: All output values NOT within 0.00001 of eachother.\u001b[0m\n",
      "===========================================================================\n",
      "\n",
      "===========================================================================\n",
      "Forward pass on a convolution2d_39 layer.\n",
      "\u001b[31mFAIL: This layer does no perform in the same way as Keras.\u001b[0m\n",
      "Using self output as input 472  of 4096  correctly predicted\n",
      "Using keras output as input 1967  of 4096  correctly predicted\n",
      "\u001b[32mSUCCESS: All output values within 0.00001 of eachother.\u001b[0m\n",
      "===========================================================================\n",
      "\n",
      "===========================================================================\n",
      "Forward pass on a reshape_6 layer.\n",
      "\u001b[32mPASS: This layer successfully produces the same results as Keras.\u001b[0m\n",
      "Using self output as input 472  of 4096  correctly predicted\n",
      "Using keras output as input 4096  of 4096  correctly predicted\n",
      "\u001b[32mSUCCESS: All output values within 0.00001 of eachother.\u001b[0m\n",
      "===========================================================================\n"
     ]
    }
   ],
   "source": [
    "naive_prediction.tests.full_run_comparison(model, test_events)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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