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Analysis of the Application Efficiency of TensorFlow and PyTorch in Convolutional Neural Network
Ovidiu-Constantin Novac1, Mihai Cristian Chirodea1, Cornelia Mihaela Novac2
1Department of Computers and Information Technology, Electrical Engineering and Information Technology Faculty, University of Oradea, 410087 Oradea, Romania.
Sensors (Basel, Switzerland)
|November 26, 2022
Summary
The choice of neural network library, such as PyTorch or TensorFlow, significantly impacts application performance during training and design. This analysis provides criteria for selecting the optimal library for specific tasks.
Area of Science:
- Computer Science
- Artificial Intelligence
- Machine Learning
Background:
- Developing neural network applications involves critical choices in software libraries.
- Popular libraries like PyTorch and TensorFlow offer different functionalities and performance characteristics.
Purpose of the Study:
- To analyze the impact of neural network library selection on application performance.
- To establish criteria for evaluating the advantages and disadvantages of PyTorch and TensorFlow.
- To guide the optimal choice of library for specific machine learning tasks.
Main Methods:
- Comparative analysis of PyTorch and TensorFlow.
- Extraction of key development and training aspects.
- Performance evaluation across different tasks.
Main Results:
- Identified specific performance differences between PyTorch and TensorFlow during training and design.
- Extracted criteria for library selection based on task requirements.
- Demonstrated that library choice influences overall system performance.
Conclusions:
- The selection of a neural network library (PyTorch vs. TensorFlow) is a crucial factor affecting application development and performance.
- Task-specific suitability exists for each library, guiding developers towards optimal choices.
- This study provides a framework for evaluating and selecting appropriate neural network libraries.
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