Exam Professional Machine Learning Engineer topic 1 question 135 discussion

Option A: By embedding augmentation in the tf.data pipeline, data augmentation is applied on-the-fly during training, reducing the need to store pre-augmented data. Option B could be a choice, but since Keras generators are built on top of tf.data, they are less flexible and have a lower level of optimization compared to tf.data.

A is best because, 1. It allows you to apply the augmentations on-the-fly during training, which eliminates the need for pre-processing and storing a large number of augmented images. This saves both storage space and compute resources. 2. The tf.Data pipeline is highly optimized for efficient data loading and processing, ensuring that your model training process is not bottlenecked by data preprocessing. 3. By applying augmentations randomly to each training batch, you increase the diversity of your training data, which can help your model generalize better to unseen data. Keras generators can be used for data augmentation, but tf.Data pipelines are generally more efficient and flexible for creating complex data processing pipelines.

B (but also A) B is a common and efficient approach for applying data augmentation during training. This allows you to apply data augmentation on-the-fly without the need to pre-generate or store augmented images separately, which saves storage space and reduces the preprocessing time. Keras provides various tools and functions for data augmentation, and you can easily incorporate them into your training data pipeline. A can also be a good choice, especially if you are using TensorFlow's tf.data API for data loading and preprocessing. It can provide similar benefits by applying augmentations on-the-fly, but it may require more custom code to implement compared to Keras data generators.

by abylead: B) Keras generators embedded augmentation functions offers at least Translation, Crop, and Contrast preprocessing. You can either permanently integrate the functions or you randomly use a dataset with non CPU blocking async training batches & optimized GPU processing overlapping. By applying Keras embedded augmentation functions, the tf.data pipeline can still be performance optimized. With tf.image pipelines you lack pipeline performance optimization & the deprecated translation function. In addition, the complex application hinders random operation flexibilty.

B - TensorFlow's Keras API provides built-in support for data augmentation using various image preprocessing layers, such as RandomTranslation, RandomCrop, and RandomContrast, among others. You can create custom image augmentation functions and include them as part of your Keras generators, tailoring them to your specific use case and needs. In summary, Option B, embedding the augmentation functions dynamically as part of Keras generators, offers efficient on-the-fly data augmentation, reduced storage overhead, optimized resource utilization, and greater flexibility, making it the best choice for thisscenario.

Went with A

https://www.tensorflow.org/tutorials/load_data/images?hl=ja#tfdata_%E3%82%92%E4%BD%BF%E7%94%A8%E3%81%97%E3%81%A6%E3%82%88%E3%82%8A%E7%B2%BE%E5%AF%86%E3%81%AB%E5%88%B6%E5%BE%A1%E3%81%99%E3%82%8B

https://towardsdatascience.com/time-to-choose-tensorflow-data-over-imagedatagenerator-215e594f2435

A. Embed the augmentation functions dynamically in the tf.Data pipeline is the best approach to optimize the data processing pipeline for runtime and compute resource utilization. Using the tf.data pipeline, you can apply data augmentation functions dynamically to each batch during training. This approach avoids the overhead of creating preprocessed TFRecords or Keras generators, which can consume additional disk space, memory, and CPU. Additionally, using the tf.data pipeline, you can parallelize data preprocessing, input pipeline operations, and model training

Embedding the augmentation functions dynamically in the tf.Data pipeline allows the data pipeline to apply the augmentations on the fly as the data is being loaded into the model during training. This means that the model can utilize the compute resources effectively by loading and processing the data as needed, rather than pre-generating all possible augmentations ahead of time (as in options C and D), which could be computationally expensive and time-consuming. Option B is also a viable choice, but it may not be as efficient as option A since the data augmentation functions would be applied during training using Keras generators, which could cause some overhead.

will go for B too https://www.analyticsvidhya.com/blog/2020/08/image-augmentation-on-the-fly-using-keras-imagedatagenerator/

Either of A or B : I am not convinced of what the right answer is. but it is on https://www.tensorflow.org/tutorials/images/data_augmentation#apply_augmentation_to_a_dataset certainly

A (not sure)

will go for B https://stanford.edu/~shervine/blog/keras-how-to-generate-data-on-the-fly

incorporating the augmentation functions into the pipeline, you can apply them dynamically to each training batch, without the need to generate all possible augmentations in advance or stage them as TFRecords.