I have a list of LongTensors, and another list of labels. I’m new to PyTorch and RNN’s so I’m quite confused as to how to implement minibatch training for the data I have. There is much more to this data, but I want to keep it simple, so I can understand only how to implement the minibatch training part. I’m doing multiclass classification based on the final hidden state of an LSTM/GRU trained on variable length inputs. I managed to get it working with batch size 1(basically SGD) but I’m struggling with implementing minibatches.
Do I have to pad the sequences to the maximum size and create a new tensor matrix of larger size which holds all the elements? I mean like this:
inputs = pad(sequences)
train = DataLoader(train, batch_size=batch_size, shuffle=True)
for i, data in train:
   #do stuff using LSTM and/or GRU models

Is this the accepted way of doing minibatch training on custom data? I couldn’t find any tutorials on loading custom data using DataLoader(but I assume that’s the way to create batches using pyTorch?)
Another doubt I have is with regards to padding. The reason I’m using LSTM/GRU is because of the variable length of the input. Doesn’t padding defeat the purpose? My question is basically is padding necessary for minibatch training?