ds.pytorch(). If your model training is highly sensitive to the randomization of the input data, please pre-shuffle the data, or explore our writeup onShuffling in ds.pytorch().
ds.pytorch()is a dictionary where the
keyis the tensor name and the
valueis the transformation function for that tensor. If a tensor's data does not need to be returned, the tensor should be omitted from the keys. If a tensor's data does not need to be modified during preprocessing, the transformation function for the tensor is set as
dsobject to the PyTorch Dataset's constructor and pulling data in the
ds.tensorflow(). Downstream, functions from the
tf.DataAPI such as map, shuffle, etc. can be applied to process the data before training.