The code above loads the entire dataset at once.In fact, this task can cause problems because all training samples may not be loaded into memory at the same time. X, y = np.load('some_training_set_with_labels.npy') Course Previous practicesīefore reading this article, your Keras-based code might look like this: import numpy as np Beginners can quickly build deep networks. The framework used in this tutorial, Keras, is an advanced API for TensorFlow, heano, etc. So this blog will showcase real-time dataset generation and data enhancements (which support multiple threads) and deliver them immediately to the in-depth learning model.A simple and easy-to-understand idea is that when each batch is generated, we read the data from disk through a path instead of loading it all at the beginning. If data enhancements are made again, it is likely that memory will not be able to read so much data at once.We need a flexible way to read and enhance data. On small datasets, we tend to read data into memory at once.However, as more and more data is available, dataset loading itself already takes up a lot of memory. The main purpose is to share its common framework and custom data enhancements.īy Afshine Amidi and Shervine Amidi Motivations for data enhancement Text translation: A detailed example of how to use data generators with Keras.
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