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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"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."
]
},
{
"cell_type": "markdown",
"metadata": {},
"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"
]
},
{
"cell_type": "markdown",
"metadata": {},
"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."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# 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"
]
},
{
"cell_type": "markdown",
"metadata": {},
"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": {
"collapsed": true
},
"outputs": [],
"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": 9,
"metadata": {},
"outputs": [
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" 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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]
},
{
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"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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