딥러닝

Neural Network Implementation Flow in Tensorflow

집빈지노 2021. 5. 28. 18:09

TensorFlow is an open source software library for numerical computation using data flow graphs.

내용은 모두를 위한 딥러닝 시즌2 강의영상을 참고하였습니다.

https://www.youtube.com/watch?v=OR_NwgouflE&list=PLQ28Nx3M4Jrguyuwg4xe9d9t2XE639e5C&index=36 

 

* Process Flow

1. Set hyperparameters - learning rate, training epochs, batch size, etc.

2. Make a data-pipeline - use tf.data

3. Build a neural network model - use tf.keras sequential API

4. Define a loss function - cross entropy

5. Calculate gradient - use tf.GradientTape

6. Select an optimizer - Adam optimizer

7. Define a metric for model's performance - accuracy

8. (optional) Make a checkpoint for saving

9. Train and Validate a NN model.

 

 

'딥러닝' 카테고리의 다른 글

Transfer Learning (전이학습)  (0) 2021.08.17
CNN 기초  (0) 2021.05.25
Introduction to Deep Learning : 딥러닝의 시작  (0) 2021.01.02