Writing Tfrecord Files the Right Way

Writing Tfrecord Files the Right Way

How to properly generate TFRecord files from your datasets. The TFRecord format is a simple format for storing a sequence of binary records. In this post you will learn why and when you should use the TFRecords format, and the code necessary to use it

The TFRecord format is a simple format for storing a sequence of binary records. In this post you will learn why and when you should use the TFRecords format, and the code necessary to use it. This post is a more detailed version of the tutorial that I wrote for the official Keras.io code example page. Converting your data into TFRecord has many advantages, such as: More efficient storage: the TFRecord data can take up less space than the original data; it can also be partitioned into multiple files. Fast I/O: the TFRecord format can be read with parallel I/O operations, which is useful for TPUs or multiple hosts. Self-contained files: the TFRecord data can be read from a single source — for example, the COCO2017 dataset originally stores data in two folders (“images” and “annotations”).

machine-learning data keras tensorflow tfrecord

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