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
   },
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   "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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   "cell_type": "code",
   "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": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the 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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\" 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      "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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