{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$\\newcommand{\\vct}[1]{\\boldsymbol{#1}}\n",
    "\\newcommand{\\mtx}[1]{\\mathbf{#1}}\n",
    "\\newcommand{\\tr}{^\\mathrm{T}}\n",
    "\\newcommand{\\reals}{\\mathbb{R}}\n",
    "\\newcommand{\\lpa}{\\left(}\n",
    "\\newcommand{\\rpa}{\\right)}\n",
    "\\newcommand{\\lsb}{\\left[}\n",
    "\\newcommand{\\rsb}{\\right]}\n",
    "\\newcommand{\\lbr}{\\left\\lbrace}\n",
    "\\newcommand{\\rbr}{\\right\\rbrace}\n",
    "\\newcommand{\\fset}[1]{\\lbr #1 \\rbr}\n",
    "\\newcommand{\\pd}[2]{\\frac{\\partial #1}{\\partial #2}}$\n",
    "\n",
    "# Single layer models\n",
    "\n",
    "In this lab we will implement a single-layer network model consisting of solely of an affine transformation of the inputs. The relevant material for this was covered in [the slides of the first lecture](http://www.inf.ed.ac.uk/teaching/courses/mlp/2016/mlp01-intro.pdf). \n",
    "\n",
    "We will first implement the forward propagation of inputs to the network to produce predicted outputs. We will then move on to considering how to use gradients of a cost function evaluated on the outputs to compute the gradients with respect to the model parameters to allow us to perform an iterative gradient-descent training procedure.\n",
    "\n",
    "## A general note on random number generators\n",
    "\n",
    "It is generally a good practice (for machine learning applications **not** for cryptography!) to seed a pseudo-random number generator once at the beginning of each experiment. This makes it easier to reproduce results as the same random draws will produced each time the experiment is run (e.g. the same random initialisations used for parameters). \n",
    "\n",
    "Therefore generally when we need to generate random values during this course, we will create a seeded random number generator object as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "# Seed a random number generator to be used later\n",
    "seed = 27092016 \n",
    "rng = np.random.RandomState(seed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise 1: linear and affine transforms\n",
    "\n",
    "Any *linear transform* (also called a linear map) of a finite-dimensional vector space can be parametrised by a matrix. So for example if we consider $\\vct{x} \\in \\reals^{M}$ as the input space of a model with $M$ dimensional real-valued inputs, then a matrix $\\mtx{W} \\in \\reals^{N\\times M}$ can be used to define a prediction model consisting solely of a linear transform of the inputs\n",
    "\n",
    "\\begin{equation}\n",
    "    \\vct{y} = \\mtx{W} \\vct{x}\n",
    "    \\qquad\n",
    "    \\Leftrightarrow\n",
    "    \\qquad\n",
    "    y_i = \\sum_{j=1}^M \\lpa W_{ij} x_j \\rpa \\qquad \\forall i \\in \\fset{1 \\dots N}\n",
    "\\end{equation}\n",
    "\n",
    "with here $\\vct{y} \\in \\reals^N$ the $N$-dimensional real-valued output of the model. Geometrically we can think of a linear transform doing some combination of rotation, scaling, reflection and shearing of the input.\n",
    "\n",
    "An *affine transform* consists of a linear transform plus an additional translation parameterised by a vector $\\vct{b} \\in \\reals^N$. A model consisting of an affine transformation of the inputs can then be defined as\n",
    "\n",
    "\\begin{equation}\n",
    "    \\vct{y} = \\mtx{W}\\vct{x} + \\vct{b}\n",
    "    \\qquad\n",
    "    \\Leftrightarrow\n",
    "    \\qquad\n",
    "    y_i = \\sum_{j=1}^M \\lpa W_{ij} x_j \\rpa + b_i \\qquad \\forall i \\in \\fset{1 \\dots N}\n",
    "\\end{equation}\n",
    "\n",
    "In machine learning we will usually refer to the matrix $\\mtx{W}$ as a *weight matrix* and the vector $\\vct{b}$ as a *bias vector*. \n",
    "\n",
    "Implement *forward propagation* for a single-layer model consisting of an affine transformation of the inputs. This should work for a batch of inputs of shape `(batch_size, input_dim)` producing a batch of outputs of shape `(batch_size, output_dim)`. \n",
    "\n",
    "You may find the NumPy [`dot`](http://docs.scipy.org/doc/numpy/reference/generated/numpy.dot.html) function and [broadcasting features](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) useful."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def fprop(inputs, weights, biases):\n",
    "    \"\"\"Forward propagates activations through the layer transformation.\n",
    "\n",
    "    For inputs `x`, outputs `y`, weights `W` and biases `b` the layer\n",
    "    corresponds to `y = W x + b`.\n",
    "\n",
    "    Args:\n",
    "        inputs: Array of layer inputs of shape (batch_size, input_dim).\n",
    "        weights: Array of weight parameters of shape \n",
    "            (output_dim, input_dim).\n",
    "        biases: Array of bias parameters of shape (output_dim, ).\n",
    "\n",
    "    Returns:\n",
    "        outputs: Array of layer outputs of shape (batch_size, output_dim).\n",
    "    \"\"\"\n",
    "    return weights.dot(inputs.T).T + biases"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check your implementation by running the test cell below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All outputs correct!\n"
     ]
    }
   ],
   "source": [
    "inputs = np.array([[0., -1., 2.], [-6., 3., 1.]])\n",
    "weights = np.array([[2., -3., -1.], [-5., 7., 2.]])\n",
    "biases = np.array([5., -3.])\n",
    "true_outputs = np.array([[6., -6.], [-17., 50.]])\n",
    "\n",
    "if not np.allclose(fprop(inputs, weights, biases), true_outputs):\n",
    "    print('Wrong outputs computed.')\n",
    "else:\n",
    "    print('All outputs correct!')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise 2: visualising random models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this exercise you will use your `fprop` implementation to visualise the outputs of a single-layer affine transform model with two-dimensional inputs and a one-dimensional output. In this simple case we can visualise the joint input-output space on a 3D axis.\n",
    "\n",
    "For this task and the learning experiments later in the notebook we will use a regression dataset from the [UCI machine learning repository](http://archive.ics.uci.edu/ml/index.html). In particular we will use a version of the [Combined Cycle Power Plant dataset](http://archive.ics.uci.edu/ml/datasets/Combined+Cycle+Power+Plant), where the task is to predict the energy output of a power plant given observations of the local ambient conditions (e.g. temperature, pressure and humidity).\n",
    "\n",
    "The original dataset has four input dimensions and a single target output dimension. We have preprocessed the dataset by [whitening](https://en.wikipedia.org/wiki/Whitening_transformation) it, a common preprocessing step. We will only use the first two dimensions of the whitened inputs (corresponding to the first two principal components of the inputs) so we can easily visualise the joint input-output space.\n",
    "\n",
    "The dataset has been wrapped in the `CCPPDataProvider` class in the `mlp.data_providers` module. Running the cell below will initialise an instance of this class, get a single batch of inputs and outputs and import the necessary `matplotlib` objects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "from mlp.data_providers import CCPPDataProvider\n",
    "%matplotlib notebook\n",
    "\n",
    "data_provider = CCPPDataProvider(\n",
    "    which_set='train',\n",
    "    input_dims=[0, 1],\n",
    "    batch_size=5000, \n",
    "    max_num_batches=1, \n",
    "    shuffle_order=False\n",
    ")\n",
    "\n",
    "input_dim, output_dim = 2, 1\n",
    "\n",
    "inputs, targets = data_provider.next()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now run the cell below to plot the predicted outputs of a randomly initialised model across the two dimensional input space as well as the true target outputs. This sort of visualisation can be a useful method (in low dimensions) to assess how well the model is likely to be able to fit the data and to judge appropriate initialisation scales for the parameters. Each time you re-run the cell a new set of random parameters will be sampled\n",
    "\n",
    "Some questions to consider:\n",
    "\n",
    "  * How do the weights and bias initialisation scale affect the sort of predicted input-output relationships?\n",
    "  * Does the linear form of the model seem a good fit for the data here?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        this.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width);\n",
       "        canvas.attr('height', height);\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'];\n",
       "    var y0 = fig.canvas.height - msg['y0'];\n",
       "    var x1 = msg['x1'];\n",
       "    var y1 = fig.canvas.height - msg['y1'];\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x;\n",
       "    var y = canvas_pos.y;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\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": [
    "weights_init_range = 0.5\n",
    "biases_init_range = 0.1\n",
    "\n",
    "# Randomly initialise weights matrix\n",
    "weights = rng.uniform(\n",
    "    low=-weights_init_range, \n",
    "    high=weights_init_range, \n",
    "    size=(output_dim, input_dim)\n",
    ")\n",
    "\n",
    "#  Randomly initialise biases vector\n",
    "biases = rng.uniform(\n",
    "    low=-biases_init_range, \n",
    "    high=biases_init_range, \n",
    "    size=output_dim\n",
    ")\n",
    "# Calculate predicted model outputs\n",
    "outputs = fprop(inputs, weights, biases)\n",
    "\n",
    "# Plot target and predicted outputs against inputs on same axis\n",
    "fig = plt.figure(figsize=(8, 8))\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "ax.plot(inputs[:, 0], inputs[:, 1], targets[:, 0], 'r.', ms=2)\n",
    "ax.plot(inputs[:, 0], inputs[:, 1], outputs[:, 0], 'b.', ms=2)\n",
    "ax.set_xlabel('Input dim 1')\n",
    "ax.set_ylabel('Input dim 2')\n",
    "ax.set_zlabel('Output')\n",
    "ax.legend(['Targets', 'Predictions'], frameon=False)\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise 3: computing the SSE cost function and its gradient"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\\begin{equation}\n",
    "    C = \\frac{1}{2} \\sum_{i=1}^N \\lbr (y_i - t_i)^2 \\rbr\n",
    "\\end{equation}\n",
    "\n",
    "\\begin{equation}\n",
    "    \\pd{C}{y_k} = y_k - t_k\n",
    "\\end{equation}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   * Implement sum squared error cost function and gradient with respect to outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def cost(outputs, targets):\n",
    "    \"\"\"Calculates cost function given a batch of outputs and targets.\n",
    "\n",
    "    Args:\n",
    "        outputs: Array of model outputs of shape (batch_size, output_dim).\n",
    "        targets: Array of target outputs of shape (batch_size, output_dim).\n",
    "\n",
    "    Returns:\n",
    "        Scalar cost function value.\n",
    "    \"\"\"\n",
    "    return 0.5 * np.mean(np.sum((outputs - targets)**2, axis=1))\n",
    "    \n",
    "def cost_grad(outputs, targets):\n",
    "    \"\"\"Calculates gradient of cost function with respect to outputs.\n",
    "\n",
    "    Args:\n",
    "        outputs: Array of model outputs of shape (batch_size, output_dim).\n",
    "        targets: Array of target outputs of shape (batch_size, output_dim).\n",
    "\n",
    "    Returns:\n",
    "        Gradient of cost function with respect to outputs.\n",
    "    \"\"\"\n",
    "    return outputs - targets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check your implementation by running the test cell below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cost function and gradient computed correctly!\n"
     ]
    }
   ],
   "source": [
    "outputs = np.array([[1., 2.], [-1., 0.], [6., -5.], [-1., 1.]])\n",
    "targets = np.array([[0., 1.], [3., -2.], [7., -3.], [1., -2.]])\n",
    "true_cost = 5.\n",
    "true_cost_grad = np.array([[1., 1.], [-4., 2.], [-1., -2.], [-2., 3.]])\n",
    "\n",
    "if not cost(outputs, targets) == true_cost:\n",
    "    print('Cost calculated incorrectly.')\n",
    "elif not np.allclose(cost_grad(outputs, targets), true_cost_grad):\n",
    "    print('Cost gradient calculated incorrectly.')\n",
    "else:\n",
    "    print('Cost function and gradient computed correctly!')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Exercise 4: computing gradients with respect to the parameters\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\\begin{equation}\n",
    "    \\pd{C}{W_{ij}} = \\sum_{k=1}^N \\lbr \\pd{C}{y_k} \\pd{y_k}{W_{ij}} \\rbr\n",
    "    \\qquad\n",
    "    \\pd{y_k}{W_{ij}} = \\delta_{ik} x_j\n",
    "\\end{equation}\n",
    "\n",
    "\\begin{equation}\n",
    "    \\pd{C}{b_{i}} = \\sum_{k=1}^N \\lbr \\pd{C}{y_k} \\pd{y_k}{b_{i}} \\rbr\n",
    "    \\qquad\n",
    "    \\pd{y_k}{b_i} = \\delta_{ik}\n",
    "\\end{equation}\n",
    "\n",
    "   * Implement function to calculate gradient with respect to weight and bias parameters given gradient with respect to outputs "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def grads_wrt_params(inputs, grads_wrt_outputs):\n",
    "    \"\"\"Calculates gradients with respect to model parameters.\n",
    "\n",
    "    Args:\n",
    "        inputs: array of inputs to model of shape (batch_size, input_dim)\n",
    "        grads_wrt_to_outputs: array of gradients with respect to the model\n",
    "            outputs of shape (batch_size, output_dim)\n",
    "\n",
    "    Returns:\n",
    "        list of arrays of gradients with respect to the model parameters\n",
    "        `[grads_wrt_weights, grads_wrt_biases]`.\n",
    "    \"\"\"\n",
    "    grads_wrt_weights = np.dot(grads_wrt_outputs.T, inputs)\n",
    "    grads_wrt_biases = np.sum(grads_wrt_outputs, axis=0)\n",
    "    return [grads_wrt_weights, grads_wrt_biases]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check your implementation by running the test cell below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All parameter gradients calculated correctly!\n"
     ]
    }
   ],
   "source": [
    "inputs = np.array([[1., 2., 3.], [-1., 4., -9.]])\n",
    "grads_wrt_outputs = np.array([[-1., 1.], [2., -3.]])\n",
    "true_grads_wrt_weights = np.array([[-3., 6., -21.], [4., -10., 30.]])\n",
    "true_grads_wrt_biases = np.array([1., -2.])\n",
    "\n",
    "grads_wrt_weights, grads_wrt_biases = grads_wrt_params(\n",
    "    inputs, grads_wrt_outputs)\n",
    "\n",
    "if not np.allclose(true_grads_wrt_weights, grads_wrt_weights):\n",
    "    print('Gradients with respect to weights incorrect.')\n",
    "elif not np.allclose(true_grads_wrt_biases, grads_wrt_biases):\n",
    "    print('Gradients with respect to biases incorrect.')\n",
    "else:\n",
    "    print('All parameter gradients calculated correctly!')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise 5: wrapping the functions into reusable components\n",
    "\n",
    "In the previous exercises you implemented methods to compute the predicted outputs of our model, evaluate the cost function and its gradient on the outputs and finally to calculate the gradients of the cost with respect to the model parameters. Together they constitute all the basic ingredients we need to implement a gradient-descent based iterative learning procedure for the model.\n",
    "\n",
    "Although you could implement training code which directly uses the functions you defined, this would only be usable for this particular model architecture. In subsequent labs we will want to use the affine transform functions as the basis for more interesting multi-layer models. We will therefore wrap the implementations you just wrote in to reusable components that we can build more complex models with later in the course.\n",
    "\n",
    "  * In the `mlp.layers` module, use your implementations of `fprop` and `grad_wrt_params` above to implement the corresponding methods in the skeleton `AffineLayer` class provided.\n",
    "  * In the `mlp.costs` module use your implementation of `cost` and `cost_grad` to implement the `__call__` and `grad` methods respectively of the skeleton `MeanSquaredErrorCost` class provided. Note `__call__` is a special Python method that allows an object to be used with a function call syntax."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the cell below to use your completed `AffineLayer` and `MeanSquaredErrorCost` implementations to train a single-layer model using gradient descent on the CCCP dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 1: 0.0s to complete\n",
      "    cost(train)=7.66e-02, cost(valid)=7.66e-02, cost(param)=0.00e+00\n",
      "Epoch 2: 0.0s to complete\n",
      "    cost(train)=7.67e-02, cost(valid)=7.66e-02, cost(param)=0.00e+00\n",
      "Epoch 3: 0.0s to complete\n",
      "    cost(train)=7.66e-02, cost(valid)=7.66e-02, cost(param)=0.00e+00\n",
      "Epoch 4: 0.0s to complete\n",
      "    cost(train)=7.65e-02, cost(valid)=7.67e-02, cost(param)=0.00e+00\n",
      "Epoch 5: 0.0s to complete\n",
      "    cost(train)=7.67e-02, cost(valid)=7.68e-02, cost(param)=0.00e+00\n"
     ]
    }
   ],
   "source": [
    "from mlp.layers import AffineLayer\n",
    "from mlp.costs import MeanSquaredErrorCost\n",
    "from mlp.models import SingleLayerModel\n",
    "from mlp.initialisers import UniformInit, ConstantInit\n",
    "from mlp.learning_rules import GradientDescentLearningRule\n",
    "from mlp.optimisers import Optimiser\n",
    "import logging\n",
    "\n",
    "logger = logging.getLogger()\n",
    "logger.setLevel(logging.INFO)\n",
    "logger.addHandler(logging.StreamHandler())\n",
    "\n",
    "train_data = CCPPDataProvider('train', [0, 1], batch_size=100)\n",
    "valid_data = CCPPDataProvider('train', [0, 1], batch_size=100)\n",
    "input_dim, output_dim = 2, 1\n",
    "\n",
    "layer = AffineLayer(input_dim, output_dim, UniformInit(-0.1, 0.1, rng=rng), ConstantInit(0))\n",
    "model = SingleLayerModel(layer)\n",
    "cost = MeanSquaredErrorCost()\n",
    "learning_rule = GradientDescentLearningRule(learning_rate=1e-3)\n",
    "optimiser = Optimiser(model, cost, learning_rule, train_data, valid_data)\n",
    "stats = optimiser.train(5, 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using similar code to previously we can now visualise the joint input-output space for the trained model. If you implemented the required methods correctly you should now see a much improved fit between predicted and target outputs when running the cell below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        this.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width);\n",
       "        canvas.attr('height', height);\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'];\n",
       "    var y0 = fig.canvas.height - msg['y0'];\n",
       "    var x1 = msg['x1'];\n",
       "    var y1 = fig.canvas.height - msg['y1'];\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x;\n",
       "    var y = canvas_pos.y;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\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": [
    "data_provider = CCPPDataProvider(\n",
    "    which_set='train',\n",
    "    input_dims=[0, 1],\n",
    "    batch_size=5000, \n",
    "    max_num_batches=1, \n",
    "    shuffle_order=False\n",
    ")\n",
    "\n",
    "inputs, targets = data_provider.next()\n",
    "\n",
    "# Calculate predicted model outputs\n",
    "outputs = model.fprop(inputs)[-1]\n",
    "\n",
    "# Plot target and predicted outputs against inputs on same axis\n",
    "fig = plt.figure(figsize=(8, 8))\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "ax.plot(inputs[:, 0], inputs[:, 1], targets[:, 0], 'r.', ms=2)\n",
    "ax.plot(inputs[:, 0], inputs[:, 1], outputs[:, 0], 'b.', ms=2)\n",
    "ax.set_xlabel('Input dim 1')\n",
    "ax.set_ylabel('Input dim 2')\n",
    "ax.set_zlabel('Output')\n",
    "ax.legend(['Targets', 'Predictions'], frameon=False)\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Optional exercise: visualising training trajectories in parameter space"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Running the cell below will display an interactive widget which plots the trajectories of gradient-based training of the single-layer affine model on the CCPP dataset in the three dimensional parameter space (two weights plus bias) from random initialisations. Also shown on the right is a plot of the evolution of the cost function (evaluated on the current batch) over training. By moving the sliders you can alter the training hyperparameters to investigate the effect they have on how training procedes.\n",
    "\n",
    "Some questions to explore:\n",
    "\n",
    "  * Are there multiple local minima in parameter space here? Why?\n",
    "  * What happens to learning for very small learning rates? And very large learning rates?\n",
    "  * How does the batch size affect learning?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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qzuHfr5LUTdIn4TW3bjLG7Ju06h/MCT/GM8aYr4y3blsq4VlfSeustVvj1Ba5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\nIiIiIiIiIlH+P5h+3QRkF5tVAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f85000cc6d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from ipywidgets import interact\n",
    "%matplotlib inline\n",
    "\n",
    "def setup_figure():\n",
    "    # create figure and axes\n",
    "    fig = plt.figure(figsize=(12, 6))\n",
    "    ax1 = fig.add_axes([0., 0., 0.5, 1.], projection='3d')\n",
    "    ax2 = fig.add_axes([0.6, 0.1, 0.4, 0.8])\n",
    "    # set axes properties\n",
    "    ax2.spines['right'].set_visible(False)\n",
    "    ax2.spines['top'].set_visible(False)\n",
    "    ax2.yaxis.set_ticks_position('left')\n",
    "    ax2.xaxis.set_ticks_position('bottom')\n",
    "    ax2.set_yscale('log')\n",
    "    ax1.set_xlim((-2, 2))\n",
    "    ax1.set_ylim((-2, 2))\n",
    "    ax1.set_zlim((-2, 2))\n",
    "    #set axes labels and title\n",
    "    ax1.set_title('Parameter trajectories over training')\n",
    "    ax1.set_xlabel('Weight 1')\n",
    "    ax1.set_ylabel('Weight 2')\n",
    "    ax1.set_zlabel('Bias')\n",
    "    ax2.set_title('Batch costs over training')\n",
    "    ax2.set_xlabel('Batch update number')\n",
    "    ax2.set_ylabel('Batch cost')\n",
    "    return fig, ax1, ax2\n",
    "\n",
    "def visualise_training(n_epochs=1, batch_size=200, log_lr=-3.5, n_inits=5,\n",
    "                       w_scale=1., b_scale=1., elev=30., azim=0.):\n",
    "    fig, ax1, ax2 = setup_figure()\n",
    "    # create seeded random number generator\n",
    "    rng = np.random.RandomState(1234)\n",
    "    # create data provider\n",
    "    data_provider = CCPPDataProvider(\n",
    "        input_dims=[0, 1],\n",
    "        batch_size=batch_size, \n",
    "        shuffle_order=False,\n",
    "    )\n",
    "    learning_rate = 10 ** log_lr\n",
    "    n_batches = data_provider.num_batches\n",
    "    weights_traj = np.empty((n_inits, n_epochs * n_batches + 1, 1, 2))\n",
    "    biases_traj = np.empty((n_inits, n_epochs * n_batches + 1, 1))\n",
    "    costs_traj = np.empty((n_inits, n_epochs * n_batches))\n",
    "    # randomly initialise parameters\n",
    "    weights = rng.uniform(-w_scale, w_scale, (n_inits, 1, 2))\n",
    "    biases = rng.uniform(-b_scale, b_scale, (n_inits, 1))\n",
    "    # store initial parameters\n",
    "    weights_traj[:, 0] = weights\n",
    "    biases_traj[:, 0] = biases\n",
    "    # iterate across different initialisations\n",
    "    for i in range(n_inits):\n",
    "        # iterate across epochs\n",
    "        for e in range(n_epochs):\n",
    "            # iterate across batches\n",
    "            for b, (inputs, targets) in enumerate(data_provider):\n",
    "                outputs = fprop(inputs, weights[i], biases[i])\n",
    "                costs_traj[i, e * n_batches + b] = cost(outputs, targets)\n",
    "                grad_wrt_outputs = cost_grad(outputs, targets)\n",
    "                weights_grad, biases_grad = grads_wrt_params(inputs, grad_wrt_outputs)\n",
    "                weights[i] -= learning_rate * weights_grad\n",
    "                biases[i] -= learning_rate * biases_grad\n",
    "                weights_traj[i, e * n_batches + b + 1] = weights[i]\n",
    "                biases_traj[i, e * n_batches + b + 1] = biases[i]\n",
    "    # choose a different color for each trajectory\n",
    "    colors = plt.cm.jet(np.linspace(0, 1, n_inits))\n",
    "    # plot all trajectories\n",
    "    for i in range(n_inits):\n",
    "        lines_1 = ax1.plot(\n",
    "            weights_traj[i, :, 0, 0], \n",
    "            weights_traj[i, :, 0, 1], \n",
    "            biases_traj[i, :, 0], \n",
    "            '-', c=colors[i], lw=2)\n",
    "        lines_2 = ax2.plot(\n",
    "            np.arange(n_batches * n_epochs),\n",
    "            costs_traj[i],\n",
    "            c=colors[i]\n",
    "        )\n",
    "    ax1.view_init(elev, azim)\n",
    "    plt.show()\n",
    "\n",
    "w = interact(\n",
    "    visualise_training,\n",
    "    elev=(-90, 90, 2),\n",
    "    azim=(-180, 180, 2), \n",
    "    n_epochs=(1, 5), \n",
    "    batch_size=(100, 1000, 100),\n",
    "    log_lr=(-5., -2.),\n",
    "    w_scale=(0., 2.),\n",
    "    b_scale=(0., 2.),\n",
    "    n_inits=(1, 10)\n",
    ")\n",
    "\n",
    "for child in w.widget.children:\n",
    "    child.layout.width = '100%'"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda env:mlp]",
   "language": "python",
   "name": "conda-env-mlp-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}