INNER CODE UNIT · Python
rand_start
lucidrains/x-transformers · train_belief_state.py:88
rand_start = torch.randint(0, self.data.size(0) - self.seq_len - 1, (1,))
full_seq = self.data[rand_start: rand_start + self.seq_len + 1].long()
return full_seq.to(DEVICE)
def __len__(self):
return self.data.size(0) // self.seq_len
train_dataset = TextSamplerDataset(data_train, SEQ_LEN)
val_dataset = TextSamplerDataset(data_val, SEQ_LEN)
train_loader = cycle(DataLoader(train_dataset, batch_size = BATCH_SIZE, drop_last = True))
val_loader = cycle(DataLoader(val_dataset, batch_size = BATCH_SIZE, drop_last = True))
# optimizer
optim = torch.optim.Adam(model.parameters(), lr=LEARNING_RATE)
# training