12/12/2023 0 Comments Tensorflow datageneratorIs tf.data more efficient for building data pipelines?įigure 2: The “tf.data” module is significantly faster than the “ImageDataGenerator” class due to an optimized producer/consumer relationship ( image source). Working with data is now significantly easier using tf.data - and as we’ll see, it’s also worlds faster and more efficient than relying on the old ImageDataGenerator class. The tf.data API makes it possible to handle large amounts of data, read from different data formats, and perform complex transformations. The pipeline for a text model might involve extracting symbols from raw text data, converting them to embedding identifiers with a lookup table, and batching together sequences of different lengths. For example, the pipeline for an image model might aggregate data from files in a distributed file system, apply random perturbations to each image, and merge randomly selected images into a batch for training. The tf.data API enables you to build complex input pipelines from simple, reusable pieces. The TensorFlow v2 API has gone through a number of changes, and arguably one of the biggest/most important changes is the introduction of the tf.data module. The ImageDataGenerator function, while a perfectly fine option, wasn’t the fastest method either. Manually implementing your own data loading functions is hard work and can result in bugs. Utilize Keras’ ImageDataGenerator function for working with image datasets too large to fit into memory and/or when data augmentation needed to be applied.Manually define their own data loading functions. Up until TensorFlow v2, Keras and TensorFlow users would have to either: Users of the PyTorch library are likely familiar with the Dataset and DatasetLoader classes - they make loading and preprocessing data incredibly easy, efficient, and fast. Figure 1: The “tf.data” module can be used to build fast, efficient data pipelines using Keras and TensorFlow ( image source).
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