Since its launch in November 2015, there has been a steady growth in interest in Tensorflow. Tensorflow’s own binary storage format, the TFRecord file format, is a lesser-known component.

  • The TFRecord format is a Tensorflow format for storing a list of binary data. TFRecord may be used to store photos and 1D vectors in addition to sequential data.

Advantages of TFRecord

When dealing with big datasets, selecting a binary file format for data storage can have a considerable influence on the speed of your import pipeline and, as a result, on your model’s training time. Binary data takes up less disk space, takes less time to transfer, and can be read from the disk considerably faster. This is especially true if your data is stored on spinning drives, which have far poorer read/write speeds than SSDs. Another significant benefit of TFRecords is the ability to store sequence data — such as a time series or word encodings — in a fashion that allows for highly efficient and (from a coding standpoint) comfortable import. To learn more about reading TFRecord files, see the Reading Data tutorial.

As a result, there are several benefits to utilizing TFRecords.

Disadvantages of TFRecord

However, where there is light, there must also be shade, and in the case of TFRecords, the disadvantage is that you must first convert your data to this format, with very few instructions available on how to do so.

Your data is stored in a TFRecord file as a series of binary strings. This means that you must first define the structure of your data before writing it to a file. For this, Tensorflow provides two components: tf.train.example and tf.train.sequenceexample. You must save each sample of your data in one of these structures, serialize it, then save it to disk using Python.

If you’re a software developer, the primary issue I encountered at first was that many of the components in the Tensorflow API lack a description of the class’s characteristics or methods. For example, just a “.proto” file with cryptic structures named “message” is given, along with pseudocode examples. The reason for this is because the tf.train.example is a protocol buffer, not a standard Python class. A protocol buffer is a Google-developed mechanism for efficiently serializing structured data. Now I’ll go through the two major ways to organize Tensorflow TFRecords, offer a developer’s overview of the components, and show you how to utilize examples and tf.train.sequenceexample in detail.

Save data and extract

We must first construct TensorFlow examples before we can put any data in tfrecords. The tf.train.examples routines may be used to construct these TensorFlow examples. This function will construct an example object that has several characteristics.

Our photographs, data, and the filename of that data are all stored in these characteristics. The matching labels of an image will also be included in features if someone is using a supervised algorithm.

After we’ve created an image example, we’ll need to save it as a tfrecord file. These may be done with tfrecord writer, where tfrecord file name is the name of the tfrecord file in which the photos should be saved.

If you know how to write tfrecord files correctly, reading them is much easier. The procedure is the same. First, we’ll need to make a feature dictionary, which will be utilized to produce the tfrecord file. Then we’ll use tf.train to generate a dataset object. The TFRecordDataset method is used to create a record set.

After we’ve generated the dataset object, we’ll map it to the dataset we want to use.

We’ll create an iterator on the dataset object to use data collected from tfrecord for training a model.

We’ll loop through this iterator once we’ve created it so that we can train the model on every picture pulled from it. Using iterator, the function extract image does this for each image in tfrecord. get next().

Today, TensorFlow is one of the most widely used Deep Learning frameworks. A TensorFlow repository should be familiar to every Machine Learning (ML) researcher or developer. In this discipline, there is a lot of innovation, and a lot of it is conveyed using TensorFlow.