AAS 390 Chapter Notes - Chapter 1: Deep Learning, Hyperparameter Optimization, Bayesian Optimization

9 views5 pages

yingmingcy1

29 Jun 2022

School

A&M Texarkana, TAMUT

Department

Applied Arts & Sciences

Course

AAS 390

Professor

For unlimited access to Textbook Notes, a Class+ subscription is required.

Document Summary

In practical bayesian optimization, we must often search over structures with dif- fering numbers of parameters. For instance, we may wish to search over neural network architectures with an unknown number of layers. To relate performance data gathered for different architectures, we de ne a new kernel for conditional parameter spaces that explicitly includes information about which parameters are relevant in a given structure. We show that this kernel improves model quality and. Bayesian optimization (bo) is an ef cient approach for solving blackbox optimization problems of the form arg minx x f (x) (see [1] for a detailed overview), where f is expensive to evaluate. It employs a prior distribution p(f ) over functions that is updated as new information on f be- comes available. Gps, and the ef ciency and effectiveness of bayesian optimization suffers as a result.

Related Questions

A university issues badges through a homegrown identity management system to all staff and students. Each week during the summer, temporary summer school students arrive and need to be issued a badge to access minimal campus resources. The security team received a report from an outside auditor indicating the homegrown system is not consistent with best practices in the security field and leaves the institution vulnerable.

Which of the following should the security team recommend FIRST?

Options:
A. Investigating a potential threat identified in logs related to the identity management system
B. Updating the identity management system to use discretionary access control
C. Beginning research on two-factor authentication to later introduce into the identity management system
D. Working with procurement and creating a requirements document to select a new IAM system/vendor

aminul2005

In this assignment, we build a simple ANN model for a dataset and we explore some methods to optimize model parameters.

Dependencies

Python > 2.7
Scikit Learn package
Pandas toolkit (If it is needed) Numpy toolkit (If it is needed)

Dataset

This dataset is originally from the National Institute of Diabetes and Digestive and Kidney Diseases. The objective of the dataset is to diagnostically predict whether a patient has diabetes, based on certain diagnostic measurements included in the dataset. Several constraints were placed on the selection of these instances from a larger database. In particular, all patients here are females at least 21 years old of Pima Indian heritage.

Note: You do not need to download the data. Two subsets of data for training and test is created and posted for downloading. The original data set also is included for your further testing and experimenting.

The list of the fields in order of columns in the data file is:
PregnanciesNumber of times pregnant
GlucosePlasma glucose concentration 2 hours in an oral glucose tolerance test BloodPressureDiastolic blood pressure (mm Hg)
SkinThicknessTriceps skin fold thickness (mm)
Insulin2-Hour serum insulin (mu U/ml)
BMIBody mass index (weight in kg/(height in m)^2)
DiabetesPedigreeFunctionDiabetes pedigree function
Age (years)
OutcomeClass variable (0 No Diabetes or 1 Diabetes) 268 of 768 are 1, the others are 0.

ANNprojectdescription
– Loading the required libraries and modules
To finish this assignment, you may use any module that you think is necessary. These modules includes:
1- Pandas
2- Numpy
3- Matplotlib
4- Sklearn

– Reading the data and performing basic data checks

1- Use describe() method of the dataframe (e.g df.describe()) to output the data dimensions and fields basic stats.

– Creating the train and test datasets

1- Splityourdataintotrain,testand,validation.
2- Splityourdatabasedontheratiooftrain=0.75,validation=0.15and,test=0.10
3- Printoutthetotalnumberofrowsandthenumberofrowsallocatedforeach

category above.
Note1: There are many ways you can achieve splitting dataset.
You can use any method as long as you have a balanced ratio of classes in each category.
4- Useyourtestdatasetonlyforfinaltesting.Youshouldnotusetestdatasetfor adjusting parameters.

– Building your model

1- Useneural_networkclassifiertobuildyournetwork.
2- Thereareafewparametersneedstobeadjustedinthismodel:

a. Hidden_layer_sizes b. Activation
c. Solver
d. Batch_size

e. Learning_rate
f. Learning_rate_init
3- Mostofparametersabovehavedefaultvalues.Youmayrunyournetworkon default values to get an idea about your network
4- Youmayadjustparametersbasedwhatwetalkedintheclassetc

– Network metric and plots

1- To evaluate your network, calculate confusion matrix. For that you may use confusion_matrix function from sklearn.metrics library.
How do you interpret precision and recall?
2- Plot error plot for each iteration (epoch) for both validate and train.
Based on your parameter setting you may see an overfitting problem. Try to spot it if that’s happened.
3- Once you finish your parameter tunning, run your model on your test data set and create a plot for your test the same as previous step

Whatareyourdeliverables?

a. A Jupyter document containing your model and experiment results.

dha2021

8. Write a class called WordSet representing a set of String objects which can contain an arbitrary number of strings, limited only by the memory available to the Java Virtual Machine. A set is a collection of elements, however, unlike a list, a set cannot contain duplicate elements. Therefore, as its name implies, a WordSet object cannot contain duplicate Strings that is, if one attempts to da String to a Wordset, and the wordset already contains an element which is equal to this string, the attempt will fail and the state of the Wordset will not change. All String comparisons performed in the methods defined in the Wordset class are done in a Case SENSITIVE manner. Note that in the method descriptions below, the phrase "this Wordset" means "the Wordset on which the method in question is called." Your Wordset class MUST provide the following INSTANCE methods • A constructor, which takes no parameters and initializes the new Wordset ohject so that it contains no elements • A method called add(), which takes as its only parameter a string and returns a value of type boolean. If this Wordset already contains a string which is equal to the parameter string, or the parameter String is null, then the state of this Wordset does not change and the method return false; otherwise, the parameter String is added to this Wordset. and the method returns true. • A method called remove().which takes as its only parameter a string and returns a value of type boolean. If this wordSet contains a string which is equal to the parameter String this string is removed from the WordSet and the method returns true. On the other hand, if the string parameter is null or this Wordset does not contain a string which is equal to the parameter string, then the state of this wordset does not change, and the method return false. • A method called get.size(), which takes no parameters and returns the number of strings currently stored in this Wordset. This method MUST NOT change the state of this Wordset. • A method called isMember(). which takes as its only parameter a string and retumsa value of type boolean which is true if this wordset contains the parameter String false otherwise. If the parameter String is null, this method also return false. This method MUST NOT change the state of this wordset. • A method called computebirrerence().which takes as its only parameter a Wordset and returns a new Wordset representing the difference between this wordset and the parameter Wordset: in other words, the WordSet returned by this method contains all the strings that are in this wordset, but not in the parameter Wordset. If the parameter Wordset contains all the elements of this WordSet or this WordSet contains no elements, then this method returns a new Wordset containing no elements. Finally, if the parameter Wordset is null or contains ne strings, then this method returns a new wordset which contains all the elements of this wordset. This method MUST NOT change the state of this wordset or the parameter Wordset. • A method called tostring , which takes the parameters and turns a string which is the textual representation of this wordset. This textual representation consists of the concatena tion of the following clements - The String - The text representation of each clement in the WordSet; cach pair of adjacent clements is separated by the String", but this String MUST NOT appear before the first clement or after the last element - The String") The order in which the elements of this Wordset appear in the textual representation generated by the tostring() method does not matter. If the WordSet contains to Brings, then the textual representation returned by this method is ". Note that you MUST respect proper encapsulation practices that is, the attributes and methods of your class MUST be declared using the proper visibility modifier NINT Um an array list (of string) to keep track of the string stored in a wordt ob joot Is there a method defined in the Array loist class which determines whether in clementis already wored in an array ist, and which will do a case-sensitive comparison when liked on an Arraylint of strings? See the pages 24-26 of this examination for information about potentially useful methods defined by the ArrayList class WRITE YOUR Wordset CLASS IN THE SPACE BELOW:

AAS 390 Chapter Notes - Chapter 1: Deep Learning, Hyperparameter Optimization, Bayesian Optimization

Document Summary

Get access

Related Documents

AAS 390 Chapter Notes - Chapter 1: Université Libre De Bruxelles, Answer Set Programming, Ibanez

AAS 390 Chapter Notes - Chapter 1: Automated Theorem Proving, Boolean Satisfiability Problem, Memory Access Pattern

AAS 390 Chapter Notes - Chapter 1: Network Architecture, Incorporated Council Of Law Reporting, Reinforcement Learning

Related Questions