{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "##

Nicolas Dronchi

" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Day 23 Pre-Class assignment: Back propagation\n", "This pre-class assignment finishes out the videos from \"Neural Networks Demystified\" module. Please watch the videos. Again, you do not have to understand the equations but the math is included for completeness.\n", "\n", "If you are lost, I highly recommend reviewing the entire \"Neural Networks Demystified\" module which can be downloaded from github:\n", "\n", " git clone https://github.com/stephencwelch/Neural-Networks-Demystified\n", "\n", "\n", "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Goals for this pre-class assignment:\n", "

\n", "1. Reviewing gradient descent\n", "1. Performing Back Propagation\n", "1. Training at network\n", "\n", "## Assignment instructions\n", "\n", "**This assignment is due by 11:59 p.m. the day before class** and should be uploaded into the appropriate \"Pre-class assignments\" dropbox folder in the Desire2Learn website.\n", "\n", "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Gradient Decent\n", "\n", "✅ Do This - watch the following video:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/jpeg": 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"text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import YouTubeVideo\n", "YouTubeVideo('5u0jaA3qAGk',width=640,height=360)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Question 1**: In simple terms, explain the \"Curse of Dimensionality\"?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In order to grid search/brute force solve for the best variables for all dimensions it would take N^D operations where D is the dimension. This can quickly reach amounts of time that aren't reasonably computable. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "\n", "## 2. Back Propagation:\n", "\n", "Now watch the following video:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "image/jpeg": 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"text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "YouTubeVideo('GlcnxUlrtek',width=640,height=360)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Question 2**: The gradient decent algorithm in Neural Networks is often called \"back propagation.\" What is being passed back though the algorithm and causing the weights to be updated? " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The weights of how much they contribute to the error. The goal is to find out where the most error came from and then change that." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here is a link to the entire code so far:\n", "\n", "https://raw.githubusercontent.com/stephencwelch/Neural-Networks-Demystified/master/partSix.py" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "✅ Do This - Download and inspect the partSix.py file and run the following command:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[0.3 1. ]\n", " [0.5 0.2]\n", " [1. 0.4]]\n", "[[0.75]\n", " [0.82]\n", " [0.93]]\n" ] } ], "source": [ "from partSix import *\n", "print(X)\n", "print(y)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "✅ Do This - Create an instance of the Neural Network and apply forward function to estimate $\\hat{y}$:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[0.52155376],\n", " [0.41215014],\n", " [0.41576012]])" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# put your code here\n", "NN = Neural_Network()\n", "NN.forward(X)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Question 3**: How good is this initial estimation?" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([0.24148593])" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "NN.costFunction(X, y )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "pretty bad. The cost function gives an error of 0.241. It only guesses variables around 0.5." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "\n", "## 3. Training:\n", "\n", "Please watch the following video:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "image/jpeg": 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Fc2ttGqpgDvOHHH28859gFKzljwUJJZ+ORo6ro17JcSzwX5tw6QRqnDMqoIlu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\nVBOydJYo9pPNT1hoOY8Wak65zV7LU7mLhnqCYkglVg1wSr2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dR+0Kd2d0o9QaB8F1H7Qo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\nSgFKUoBSlKAUpSgFKUoD/9k=\n", "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import YouTubeVideo\n", "YouTubeVideo('9KM9Td6RVgQ',width=640,height=360)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "✅ Do This - Now, create an instance of the ```trainer``` class from the partSix.py file. Call the objects ```train``` function by passing it the original ```X``` and ```y``` data:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Optimization terminated successfully.\n", " Current function value: 0.000000\n", " Iterations: 36\n", " Function evaluations: 39\n", " Gradient evaluations: 39\n" ] } ], "source": [ "#Put your code here\n", "T = trainer(NN)\n", "T.train(X,y)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[0.74998259]\n", " [0.82002105]\n", " [0.93000224]]\n", "[3.75622733e-10]\n", "[[0.75]\n", " [0.82]\n", " [0.93]]\n" ] } ], "source": [ "print(NN.forward(X))\n", "print(NN.costFunction(X, y ))\n", "print(y)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "✅ Do This - If done correctly, the ```NN``` object should now be trained. Apply the forward function again to see the new estimation of $\\hat{y}$." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Question 4**: Hopefully this worked and the estimation is better than the previous one. How close are these values to the original grades? What shortcomings are there to testing using this approach?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "These are very close values. I think some short comings is when there are local minimums in the data as well as when you have a low amount of data. I think it takes a large amount of data to train to be something useful and not just spit back out the same answers you trained it on." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "----\n", "# Assignment wrap-up\n", "\n", "Please fill out the form that appears when you run the code below. **You must completely fill this out in order to receive credit for the assignment!**" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "\n", "\n" ], "text/plain": [ "" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import HTML\n", "HTML(\n", "\"\"\"\n", "\n", "\"\"\"\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---------\n", "### Congratulations, you're done with your pre-class assignment!\n", "\n", "Now, you just need to submit this assignment by uploading it to the course Desire2Learn web page for today's dropbox (Don't forget to add your name in the first cell)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "© Copyright 2017, Michigan State University Board of Trustees" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.0" } }, "nbformat": 4, "nbformat_minor": 2 }