tf.data: Build Efficient TensorFlow Input Pipelines for Image Datasets
This tutorial will focus on how to Build Efficient TensorFlow Input Pipelines for Image Datasets in Deep Learning with Tensorflow & Keras.
First, we will review the tf.data library. Then, we will download a sample image and label files. After gathering all the image file paths in the directories, we will merge file names with labels to create the train and test datasets. Using tf.data.Dataset methods, we will learn how to map, prefetch, cache, and batch the datasets correctly so that the data input pipeline will be efficient in terms of time and performance. We will discuss how map, prefetch, cache, and batch functions affect the performance of the tf.data.Dataset input pipeline performance.
Moreover, we will see how to use the TensorBoard add-on “TF Profiler” for monitoring the performance and bottlenecks of the tf.data input pipeline.
You can access this Colab Notebook using the link. You can access all the tf.data: Tensorflow Data Pipeline tutorials at muratkarakaya.net.
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If you are ready, let’s get started!
Photo by Mourizal Zativa on Unsplash
