Learning Wizard
This section describes the intended audience, development process of a TensorFlow 1.15-based model, and precautions for using this document.
Intended Audience
This document is intended for AI algorithm engineers. It describes how to port network scripts developed with TensorFlow 1.15 Python APIs to the Ascend AI Processor for optimal accuracy and performance. The Ascend AI Processor supports porting of network scripts developed with three TensorFlow 1.15 APIs: Estimator, Session, and Keras.
To better understand this document, you should:
- Be familiar with the basic CANN architecture and features.
- Have familiarity with TensorFlow 1.15 APIs.
- Possess knowledge on machine learning and deep learning, especially network training basics.
- Be proficient in Python programming.
Supported Products
Precautions
- Before model porting to the Ascend AI Processor, prepare a training model developed on TensorFlow 1.15 and a matched dataset, and run the model on the GPU or CPU to test if the accuracy is converged as expected. Also, record the accuracy and performance results for later comparison with those on the Ascend AI Processor.
- The code snippets in this document are only examples. Manual tweaking is needed.
Model Development Learning Map
The model development aims to port the original TensorFlow-based model to the Ascend AI Processor and start training. The process is as follows:
