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If I use
dataset.batch(n).prefetch(m),
m batches or m samples will be prefetched?
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Tensorflow.js is an open-source library developed by Google for running machine learning models and deep learning neural networks in the browser or node environment. The tf.data.Dataset class .prefetch () function is used to produce a dataset that prefetches the specified elements from this given dataset. Hey geek!
The Dataset.prefetch (m) transformation prefetches m elements of its direct input. In this case, since its direct input is dataset.batch (n) and each element of that dataset is a batch (of n elements), it will prefetch m batches.
With tf.data, you can do this with a simple call to dataset.prefetch (1) at the end of the pipeline (after batching). This will always prefetch one batch of data and make sure that there is always one ready. In some cases, it can be useful to prefetch more than one batch.
The simplest form of batching stacks n consecutive elements of a dataset into a single element. The Dataset.batch () transformation does exactly this, with the same constraints as the tf.stack () operator, applied to each component of the elements: i.e. for each component i, all elements must have a tensor of the exact same shape.
The Dataset.prefetch(m) transformation prefetches m elements of its direct input. In this case, since its direct input is dataset.batch(n) and each element of that dataset is a batch (of n elements), it will prefetch m batches.
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