multivariate-calculus/Backpropagation.ipynb

{
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  {
   "cell_type": "markdown",
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   "source": [
    "# Backpropagation\n",
    "## Instructions\n",
    "\n",
    "In this assignment, you will train a neural network to draw a curve.\n",
    "The curve takes one input variable, the amount travelled along the curve from 0 to 1, and returns 2 outputs, the 2D coordinates of the position of points on the curve.\n",
    "\n",
    "To help capture the complexity of the curve, we shall use two hidden layers in our network with 6 and 7 neurons respectively.\n",
    "\n",
    "![Neural network with 2 hidden layers. There is 1 nodes in the zeroth layer, 6 in the first, 7 in the second, and 2 in the third.](readonly/bigNet.png \"The structure of the network we will consider in this assignment.\")\n",
    "\n",
    "You will be asked to complete functions that calculate the Jacobian of the cost function, with respect to the weights and biases of the network. Your code will form part of a stochastic steepest descent algorithm that will train your network.\n",
    "\n",
    "### Matrices in Python\n",
    "Recall from assignments in the previous course in this specialisation that matrices can be multiplied together in two ways.\n",
    "\n",
    "Element wise: when two matrices have the same dimensions, matrix elements in the same position in each matrix are multiplied together\n",
    "In python this uses the '$*$' operator.\n",
    "```python\n",
    "A = B * C\n",
    "```\n",
    "\n",
    "Matrix multiplication: when the number of columns in the first matrix is the same as the number of rows in the second.\n",
    "In python this uses the '$@$' operator\n",
    "```python\n",
    "A = B @ C\n",
    "```\n",
    "\n",
    "This assignment will not test which ones to use where, but it will use both in the starter code presented to you.\n",
    "There is no need to change these or worry about their specifics.\n",
    "\n",
    "### How to submit\n",
    "To complete the assignment, edit the code in the cells below where you are told to do so.\n",
    "Once you are finished and happy with it, press the **Submit Assignment** button at the top of this worksheet.\n",
    "Test your code using the cells at the bottom of the notebook before you submit.\n",
    "\n",
    "Please don't change any of the function names, as these will be checked by the grading script."
   ]
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   "cell_type": "markdown",
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   "source": [
    "## Feed forward\n",
    "\n",
    "In the following cell, we will define functions to set up our neural network.\n",
    "Namely an activation function, $\\sigma(z)$, it's derivative, $\\sigma'(z)$, a function to initialise weights and biases, and a function that calculates each activation of the network using feed-forward.\n",
    "\n",
    "Recall the feed-forward equations,\n",
    "$$ \\mathbf{a}^{(n)} = \\sigma(\\mathbf{z}^{(n)}) $$\n",
    "$$ \\mathbf{z}^{(n)} = \\mathbf{W}^{(n)}\\mathbf{a}^{(n-1)} + \\mathbf{b}^{(n)} $$\n",
    "\n",
    "In this worksheet we will use the *logistic function* as our activation function, rather than the more familiar $\\tanh$.\n",
    "$$ \\sigma(\\mathbf{z}) = \\frac{1}{1 + \\exp(-\\mathbf{z})} $$\n",
    "\n",
    "There is no need to edit the following cells.\n",
    "They do not form part of the assessment.\n",
    "You may wish to study how it works though.\n",
    "\n",
    "**Run the following cells before continuing.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%run \"readonly/BackpropModule.ipynb\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# PACKAGE\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# PACKAGE\n",
    "# First load the worksheet dependencies.\n",
    "# Here is the activation function and its derivative.\n",
    "sigma = lambda z : 1 / (1 + np.exp(-z))\n",
    "d_sigma = lambda z : np.cosh(z/2)**(-2) / 4\n",
    "\n",
    "# This function initialises the network with it's structure, it also resets any training already done.\n",
    "def reset_network (n1 = 6, n2 = 7, random=np.random) :\n",
    "    global W1, W2, W3, b1, b2, b3\n",
    "    W1 = random.randn(n1, 1) / 2\n",
    "    W2 = random.randn(n2, n1) / 2\n",
    "    W3 = random.randn(2, n2) / 2\n",
    "    b1 = random.randn(n1, 1) / 2\n",
    "    b2 = random.randn(n2, 1) / 2\n",
    "    b3 = random.randn(2, 1) / 2\n",
    "\n",
    "# This function feeds forward each activation to the next layer. It returns all weighted sums and activations.\n",
    "def network_function(a0) :\n",
    "    z1 = W1 @ a0 + b1\n",
    "    a1 = sigma(z1)\n",
    "    z2 = W2 @ a1 + b2\n",
    "    a2 = sigma(z2)\n",
    "    z3 = W3 @ a2 + b3\n",
    "    a3 = sigma(z3)\n",
    "    return a0, z1, a1, z2, a2, z3, a3\n",
    "\n",
    "# This is the cost function of a neural network with respect to a training set.\n",
    "def cost(x, y) :\n",
    "    return np.linalg.norm(network_function(x)[-1] - y)**2 / x.size"
   ]
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   "cell_type": "markdown",
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   "source": [
    "## Backpropagation\n",
    "\n",
    "In the next cells, you will be asked to complete functions for the Jacobian of the cost function with respect to the weights and biases.\n",
    "We will start with layer 3, which is the easiest, and work backwards through the layers.\n",
    "\n",
    "We'll define our Jacobians as,\n",
    "$$ \\mathbf{J}_{\\mathbf{W}^{(3)}} = \\frac{\\partial C}{\\partial \\mathbf{W}^{(3)}} $$\n",
    "$$ \\mathbf{J}_{\\mathbf{b}^{(3)}} = \\frac{\\partial C}{\\partial \\mathbf{b}^{(3)}} $$\n",
    "etc., where $C$ is the average cost function over the training set. i.e.,\n",
    "$$ C = \\frac{1}{N}\\sum_k C_k $$\n",
    "You calculated the following in the practice quizzes,\n",
    "$$ \\frac{\\partial C}{\\partial \\mathbf{W}^{(3)}} =\n",
    "   \\frac{\\partial C}{\\partial \\mathbf{a}^{(3)}}\n",
    "   \\frac{\\partial \\mathbf{a}^{(3)}}{\\partial \\mathbf{z}^{(3)}}\n",
    "   \\frac{\\partial \\mathbf{z}^{(3)}}{\\partial \\mathbf{W}^{(3)}}\n",
    "   ,$$\n",
    "for the weight, and similarly for the bias,\n",
    "$$ \\frac{\\partial C}{\\partial \\mathbf{b}^{(3)}} =\n",
    "   \\frac{\\partial C}{\\partial \\mathbf{a}^{(3)}}\n",
    "   \\frac{\\partial \\mathbf{a}^{(3)}}{\\partial \\mathbf{z}^{(3)}}\n",
    "   \\frac{\\partial \\mathbf{z}^{(3)}}{\\partial \\mathbf{b}^{(3)}}\n",
    "   .$$\n",
    "With the partial derivatives taking the form,\n",
    "$$ \\frac{\\partial C}{\\partial \\mathbf{a}^{(3)}} = 2(\\mathbf{a}^{(3)} - \\mathbf{y}) $$\n",
    "$$ \\frac{\\partial \\mathbf{a}^{(3)}}{\\partial \\mathbf{z}^{(3)}} = \\sigma'({z}^{(3)})$$\n",
    "$$ \\frac{\\partial \\mathbf{z}^{(3)}}{\\partial \\mathbf{W}^{(3)}} = \\mathbf{a}^{(2)}$$\n",
    "$$ \\frac{\\partial \\mathbf{z}^{(3)}}{\\partial \\mathbf{b}^{(3)}} = 1$$\n",
    "\n",
    "We'll do the J_W3 ($\\mathbf{J}_{\\mathbf{W}^{(3)}}$) function for you, so you can see how it works.\n",
    "You should then be able to adapt the J_b3 function, with help, yourself."
   ]
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   "execution_count": 4,
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    "# GRADED FUNCTION\n",
    "\n",
    "# Jacobian for the third layer weights. There is no need to edit this function.\n",
    "def J_W3 (x, y) :\n",
    "    # First get all the activations and weighted sums at each layer of the network.\n",
    "    a0, z1, a1, z2, a2, z3, a3 = network_function(x)\n",
    "    # We'll use the variable J to store parts of our result as we go along, updating it in each line.\n",
    "    # Firstly, we calculate dC/da3, using the expressions above.\n",
    "    J = 2 * (a3 - y)\n",
    "    # Next multiply the result we've calculated by the derivative of sigma, evaluated at z3.\n",
    "    J = J * d_sigma(z3)\n",
    "    # Then we take the dot product (along the axis that holds the training examples) with the final partial derivative,\n",
    "    # i.e. dz3/dW3 = a2\n",
    "    # and divide by the number of training examples, for the average over all training examples.\n",
    "    J = J @ a2.T / x.size\n",
    "    # Finally return the result out of the function.\n",
    "    return J\n",
    "\n",
    "# In this function, you will implement the jacobian for the bias.\n",
    "# As you will see from the partial derivatives, only the last partial derivative is different.\n",
    "# The first two partial derivatives are the same as previously.\n",
    "# ===YOU SHOULD EDIT THIS FUNCTION===\n",
    "def J_b3 (x, y) :\n",
    "    # As last time, we'll first set up the activations.\n",
    "    a0, z1, a1, z2, a2, z3, a3 = network_function(x)\n",
    "    # Next you should implement the first two partial derivatives of the Jacobian.\n",
    "    # ===COPY TWO LINES FROM THE PREVIOUS FUNCTION TO SET UP THE FIRST TWO JACOBIAN TERMS===\n",
    "    J = 2 * (a3 - y)\n",
    "    J = J * d_sigma(z3)\n",
    "    # For the final line, we don't need to multiply by dz3/db3, because that is multiplying by 1.\n",
    "    # We still need to sum over all training examples however.\n",
    "    # There is no need to edit this line.\n",
    "    J = np.sum(J, axis=1, keepdims=True) / x.size\n",
    "    return J"
   ]
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   "source": [
    "We'll next do the Jacobian for the Layer 2. The partial derivatives for this are,\n",
    "$$ \\frac{\\partial C}{\\partial \\mathbf{W}^{(2)}} =\n",
    "   \\frac{\\partial C}{\\partial \\mathbf{a}^{(3)}}\n",
    "   \\left(\n",
    "   \\frac{\\partial \\mathbf{a}^{(3)}}{\\partial \\mathbf{a}^{(2)}}\n",
    "   \\right)\n",
    "   \\frac{\\partial \\mathbf{a}^{(2)}}{\\partial \\mathbf{z}^{(2)}}\n",
    "   \\frac{\\partial \\mathbf{z}^{(2)}}{\\partial \\mathbf{W}^{(2)}}\n",
    "   ,$$\n",
    "$$ \\frac{\\partial C}{\\partial \\mathbf{b}^{(2)}} =\n",
    "   \\frac{\\partial C}{\\partial \\mathbf{a}^{(3)}}\n",
    "   \\left(\n",
    "   \\frac{\\partial \\mathbf{a}^{(3)}}{\\partial \\mathbf{a}^{(2)}}\n",
    "   \\right)\n",
    "   \\frac{\\partial \\mathbf{a}^{(2)}}{\\partial \\mathbf{z}^{(2)}}\n",
    "   \\frac{\\partial \\mathbf{z}^{(2)}}{\\partial \\mathbf{b}^{(2)}}\n",
    "   .$$\n",
    "This is very similar to the previous layer, with two exceptions:\n",
    "* There is a new partial derivative, in parentheses, $\\frac{\\partial \\mathbf{a}^{(3)}}{\\partial \\mathbf{a}^{(2)}}$\n",
    "* The terms after the parentheses are now one layer lower.\n",
    "\n",
    "Recall the new partial derivative takes the following form,\n",
    "$$ \\frac{\\partial \\mathbf{a}^{(3)}}{\\partial \\mathbf{a}^{(2)}} =\n",
    "   \\frac{\\partial \\mathbf{a}^{(3)}}{\\partial \\mathbf{z}^{(3)}}\n",
    "   \\frac{\\partial \\mathbf{z}^{(3)}}{\\partial \\mathbf{a}^{(2)}} =\n",
    "   \\sigma'(\\mathbf{z}^{(3)})\n",
    "   \\mathbf{W}^{(3)}\n",
    "$$\n",
    "\n",
    "To show how this changes things, we will implement the Jacobian for the weight again and ask you to implement it for the bias."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
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   "source": [
    "# GRADED FUNCTION\n",
    "\n",
    "# Compare this function to J_W3 to see how it changes.\n",
    "# There is no need to edit this function.\n",
    "def J_W2 (x, y) :\n",
    "    #The first two lines are identical to in J_W3.\n",
    "    a0, z1, a1, z2, a2, z3, a3 = network_function(x)    \n",
    "    J = 2 * (a3 - y)\n",
    "    # the next two lines implement da3/da2, first σ' and then W3.\n",
    "    J = J * d_sigma(z3)\n",
    "    J = (J.T @ W3).T\n",
    "    # then the final lines are the same as in J_W3 but with the layer number bumped down.\n",
    "    J = J * d_sigma(z2)\n",
    "    J = J @ a1.T / x.size\n",
    "    return J\n",
    "\n",
    "# As previously, fill in all the incomplete lines.\n",
    "# ===YOU SHOULD EDIT THIS FUNCTION===\n",
    "def J_b2 (x, y) :\n",
    "    a0, z1, a1, z2, a2, z3, a3 = network_function(x)\n",
    "    J = 2 * (a3 - y)\n",
    "    J = J * d_sigma(z3)\n",
    "    J = (J.T @ W3).T\n",
    "    J = J * d_sigma(z2)\n",
    "    J = np.sum(J, axis=1, keepdims=True) / x.size\n",
    "    return J"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Layer 1 is very similar to Layer 2, but with an addition partial derivative term.\n",
    "$$ \\frac{\\partial C}{\\partial \\mathbf{W}^{(1)}} =\n",
    "   \\frac{\\partial C}{\\partial \\mathbf{a}^{(3)}}\n",
    "   \\left(\n",
    "   \\frac{\\partial \\mathbf{a}^{(3)}}{\\partial \\mathbf{a}^{(2)}}\n",
    "   \\frac{\\partial \\mathbf{a}^{(2)}}{\\partial \\mathbf{a}^{(1)}}\n",
    "   \\right)\n",
    "   \\frac{\\partial \\mathbf{a}^{(1)}}{\\partial \\mathbf{z}^{(1)}}\n",
    "   \\frac{\\partial \\mathbf{z}^{(1)}}{\\partial \\mathbf{W}^{(1)}}\n",
    "   ,$$\n",
    "$$ \\frac{\\partial C}{\\partial \\mathbf{b}^{(1)}} =\n",
    "   \\frac{\\partial C}{\\partial \\mathbf{a}^{(3)}}\n",
    "   \\left(\n",
    "   \\frac{\\partial \\mathbf{a}^{(3)}}{\\partial \\mathbf{a}^{(2)}}\n",
    "   \\frac{\\partial \\mathbf{a}^{(2)}}{\\partial \\mathbf{a}^{(1)}}\n",
    "   \\right)\n",
    "   \\frac{\\partial \\mathbf{a}^{(1)}}{\\partial \\mathbf{z}^{(1)}}\n",
    "   \\frac{\\partial \\mathbf{z}^{(1)}}{\\partial \\mathbf{b}^{(1)}}\n",
    "   .$$\n",
    "You should be able to adapt lines from the previous cells to complete **both** the weight and bias Jacobian."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# GRADED FUNCTION\n",
    "\n",
    "# Fill in all incomplete lines.\n",
    "# ===YOU SHOULD EDIT THIS FUNCTION===\n",
    "def J_W1 (x, y) :\n",
    "    a0, z1, a1, z2, a2, z3, a3 = network_function(x)\n",
    "    J = 2 * (a3 - y)\n",
    "    J = J * d_sigma(z3)\n",
    "    J = (J.T @ W3).T\n",
    "    J = J * d_sigma(z2)\n",
    "    J = (J.T @ W2).T\n",
    "    J = J * d_sigma(z1)\n",
    "    J = J @ a0.T / x.size\n",
    "    return J\n",
    "\n",
    "# Fill in all incomplete lines.\n",
    "# ===YOU SHOULD EDIT THIS FUNCTION===\n",
    "def J_b1 (x, y) :\n",
    "    a0, z1, a1, z2, a2, z3, a3 = network_function(x)\n",
    "    J = 2 * (a3 - y)\n",
    "    J = J * d_sigma(z3)\n",
    "    J = (J.T @ W3).T\n",
    "    J = J * d_sigma(z2)\n",
    "    J = (J.T @ W2).T\n",
    "    J = J * d_sigma(z1)\n",
    "    J = np.sum(J, axis=1, keepdims=True) / x.size\n",
    "    return J"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test your code before submission\n",
    "To test the code you've written above, run all previous cells (select each cell, then press the play button [ ▶| ] or press shift-enter).\n",
    "You can then use the code below to test out your function.\n",
    "You don't need to submit these cells; you can edit and run them as much as you like."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we generate training data, and generate a network with randomly assigned weights and biases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x, y = training_data()\n",
    "reset_network()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, if you've implemented the assignment correctly, the following code will iterate through a steepest descent algorithm using the Jacobians you have calculated.\n",
    "The function will plot the training data (in green), and your neural network solutions in pink for each iteration, and orange for the last output.\n",
    "\n",
    "It takes about 50,000 iterations to train this network.\n",
    "We can split this up though - **10,000 iterations should take about a minute to run**.\n",
    "Run the line below as many times as you like."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the 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",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_training(x, y, iterations=10000, aggression=7, noise=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you wish, you can change parameters of the steepest descent algorithm (We'll go into more details in future exercises), but you can change how many iterations are plotted, how agressive the step down the Jacobian is, and how much noise to add.\n",
    "\n",
    "You can also edit the parameters of the neural network, i.e. to give it different amounts of neurons in the hidden layers by calling,\n",
    "```python\n",
    "reset_network(n1, n2)\n",
    "```\n",
    "\n",
    "Play around with the parameters, and save your favourite result for the discussion prompt - *I ❤️ backpropagation*."
   ]
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