# Assignment #6: Neural Networks (with Tensorflow) CSCI 374 Fall 2017 Oberlin College Due: Tuesday November 21 at 11:59 PM

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1 Background Assignment #6: Neural Networks (with Tensorflow) CSCI 374 Fall 2017 Oberlin College Due: Tuesday November 21 at 11:59 PM Our final assignment this semester has three main goals: 1. Implement neural networks as a powerful approach to supervised machine learning, 2. Practice using state-of-the-art software tools and programming paradigms for machine learning, 3. Investigate the impact of parameters to learning on neural network performance as evaluated on empirical data sets. Gitting Started To begin this assignment, please follow this link: Data Sets For this assignment, we will learn from the same pre-defined data sets that we began the semester with: 1. monks1.csv: A data set describing two classes of robots using all nominal attributes and a binary label. This data set has a simple rule set for determining the label: if head_shape = body_shape jacket_color = red, then yes, else no. Each of the attributes in the monks1 data set are nominal. Monks1 was one of the first machine learning challenge problems ( This data set comes from the UCI Machine Learning Repository: 2. iris.csv: A data set describing observed measurements of different flowers belonging to three species of Iris. The four attributes are each continuous measurements, and the label is the species of flower. The Iris data set has a long history in machine learning research, dating back to the statistical (and biological) research of Ronald Fisher from the 1930s (for more info, see This data set comes from Weka 3.8: and is also on the UCI Machine Learning Repository: 3. mnist_100.csv: A data set of optical character recognition of numeric digits from images. Each instance represents a different grayscale 28x28 pixel image of a handwritten numeric digit (from 0 through 9). The attributes are the intensity values of the 784 pixels. Each attribute is ordinal (treat them as continuous for the purpose of this assignment) and a nominal label. This version of MNIST contains 100 instances of each handwritten numeric digit, randomly sampled from the original training data for MNIST. The overall MNIST data set is one of the main benchmarks in machine learning:

2 It was converted to CSV file using the python code provided at: Digits-Dataset-into-CSV-with-Sorting-and-Extracting-Labels-and-Features-into- Different-CSV-using-Python.html The file format for each of these data sets is as follows: The first row contains a comma-separated list of the names of the label and attributes Each successive row represents a single instance The first entry (before the first comma) of each instance is the label to be learned, and all other entries (following the commas) are attribute values. Some attributes are strings (representing nominal values), some are integers, and others are real numbers. Each label is a string. Program Your assignment is to write a program called nn that behaves as follows: 1) It should take as input six parameters: a. The path to a file containing a data set (e.g., monks1.csv) b. The number of neurons to use in the hidden layer c. The learning rate η to use during backpropagation d. The number of iterations to use during training e. The percentage of instances to use for a training set f. A random seed as an integer For example, if I wrote my program in Python 3, I might run python3 nn.py mnist_1000.csv which will create a neural network with 20 neurons in the hidden layer, train the network using a learning rate of η = and 1000 iterations through monks1.csv with a random seed of 12345, where 75% of the data will be used for training (and the remaining 25% will be used for testing) 2) Next, the program should read in the data set as a set of instances, which should be split into training and test sets (using the random seed input to the program) a. Unlike our previous learning, instances are now represented by a pair of lists: i. A list of all attribute values ii. A list of label values b. For the attribute values in monks1.csv, you will need to convert each of the attributes into m-1 indicator variables using one hot coding (where the attribute originally took m values) since each attribute is nominal. For example, the body_shape attribute takes m = 3 values (round, square, octagon), so we can create m-1 = 2 indicator variables:

3 body_shape = 1 if body_shape = round, else 0 body_shape 67389: = 1 if body_shape = square, else 0. Note: We will treat all attributes as continuous in iris.csv and mnist_100.csv, so you don t have to do any pre-processing for these data sets. c. There is now a list of label values since labels are discrete. i. For binary classification tasks (monks1.csv), the list will contain a single label value: 0 if No, 1 if Yes. ii. For multinomial classification tasks (iris.csv and mnist_100.csv), there is one value per possible label value. For example, in the MNIST data sets, the 0 th -entry in the list will be a 1 if the label is zero, else it will be 0, the 1 st -entry in the list will be a 1 if the label is one, else it will be 0, etc. To illustrate, a seven label will be represented by [0, 0, 0 0, 0, 0, 0, 1, 0, 0]. For iris.csv, you can pick any ordering of the label values as long as it is consistent for every attribute. Note that this process is slightly different from one-hot coding as we don t throw away one of the labels when there are three or more. 3) You should create a neural network in Tensorflow that will be learned from the training data. The key parameters to the architecture of the neural network are based on your inputted parameters and the size of your data set: a. The number of attributes in the input layer is the length of each instance s attribute list (which is the same for all instances) b. The number of neurons in a hidden layer will be inputted to the program as a parameter. Each hidden neuron should use tf.sigmoid as its activation function. c. The number of output neurons is the length of each instance s label list i. For monks1.csv, there will be 1 output neuron that should use tf.sigmoid as its activation function ii. For iris.csv, there should be 3 output neurons that should use tf.nn.softmax as their activation function iii. For mnist_100.csv, there should be 10 output neurons that should use tf.nn.softmax as their activation function 4) You should use different cost/loss functions that the network tries to minimize depending on the number of labels: a. For binary classification in monks1.csv, use the sum of squared error SSE X = 4 y? y? A?BC The function tf.reduce_sum will allow you to sum across all instances.

5 b. How did the accuracy change as the number of hidden neurons change? Why do you think this result occurred? c. Calculate a 95% confidence interval for the best accuracy on each data set. How does this accuracy compare to the confidence intervals you calculated in HW1 for k-nearest Neighbor? Did the neural network learn to outperform knn? 2) Pick five different training set percentages. Use the number of neurons that gave the highest accuracy in Q1 and the same learning rate and same random seed. With 1000 for the number of iterations: a. Plot a line chart (using the tool of your choice: Excel, R, matplotlib in Python, etc.) of the accuracy on each data set as the training set size increased. b. Compare the accuracies within each data set how did they change as the training percentage increased? Do we see the same trends across all three data sets? Why do you think this result occurred? 3) For the mnist_100.csv data set, use the three learning rates η = 0.001, 0.01, Use the number of neurons that gave the highest accuracy in Q1, the training set percentage that gave the highest accuracy in Q2, and the same random seed used in both. Using 1000 for the number of iterations, track the accuracy on the training set and accuracy on the test set of the network trained with each learning rate every 50 iterations. a. For each learning rate, plot the training and test accuracy of the network as the number of iterations increased on a line chart (again using your favorite tool). b. Compare the training accuracy across the three learning rates. What trends do you observe in your line charts? What do you think this implies about choosing a learning rate? c. Compare the testing accuracy across the three learning rates. What trends do you observe in your line charts? What do you think this implies about choosing a learning rate? Bonus Question (5 points) Modify your program to be able to have multiple hidden layers, all with the same number of neurons. The number of hidden layers to create should be taken in as a seventh parameter on the command line (after the random seed). Pick three different numbers of hidden layers. Repeat Question 1, except also vary the number of hidden layers based on the set of three that you picked (so that you have 15 different combinations of hidden layers and neurons per layer). How does changing the number of layers further impact the accuracy of the neural network as you also vary the number of hidden neurons per layer?

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