TensorFlow provides us with two methods we can use to apply data augmentation to our tf.data pipelines: ![]() By applying data augmentation we can increase the ability of our model to generalize and make better, more accurate predictions on data it was not trained on. How can we apply data augmentation inside a tf.data pipeline?ĭata augmentation is a critical aspect of training neural networks that are to be deployed in real-world scenarios. However, one question we haven’t discussed is: Once built, these pipelines can train your neural networks significantly faster than using standard methods. Throughout this series we’ve discovered how fast and efficient the tf.data module is for building data processing pipelines. Data augmentation with tf.data (today’s tutorial).Data pipelines with tf.data and TensorFlow.This tutorial is part in our three part series on the tf.data module:
0 Comments
Leave a Reply. |
Details
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |