Academic Journal
Convolutional neural network optimization using genetic algorithms
| Τίτλος: | Convolutional neural network optimization using genetic algorithms |
|---|---|
| Συγγραφείς: | Reiling, Anthony Joseph |
| Πηγή: | Graduate Theses and Dissertations |
| Στοιχεία εκδότη: | eCommons |
| Έτος έκδοσης: | 2017 |
| Συλλογή: | University of Dayton: eCommons |
| Θεματικοί όροι: | Genetic algorithms Data processing, Neural networks (Computer science), System design Data processing, Artificial Intelligence, Computer Engineering, Computer Science, deep learning hyper parameter genetic algorithm evolutionary computation convolutional neural network optimization CNN DL GA CIFAR10 |
| Περιγραφή: | This thesis proposes the use of a genetic algorithm (GA) to optimize the accuracy of a convolutional neural network (CNN). The GA modifies the structure of the CNN such as the number of convolutional filters, strides, kernel size, nodes, learning parameters, etc. Each modification of the network is trained and evaluated. Mutation of evolved networks create more successful networks over multiple generations. The final evolved network is 4.77% more accurate than a network proposed in the previous literature. Additionally, the evolved network is 13.4% less computationally complex. |
| Τύπος εγγράφου: | text |
| Γλώσσα: | unknown |
| Relation: | https://ecommons.udayton.edu/graduate_theses/1337; http://rave.ohiolink.edu/etdc/view?acc_num=dayton1512662981172387 |
| Διαθεσιμότητα: | https://ecommons.udayton.edu/graduate_theses/1337 http://rave.ohiolink.edu/etdc/view?acc_num=dayton1512662981172387 |
| Rights: | Copyright © 2017, author |
| Αριθμός Καταχώρησης: | edsbas.DEA2F439 |
| Βάση Δεδομένων: | BASE |
| Η περιγραφή δεν είναι διαθέσιμη |