INNER CODE UNIT · Python

full_seq

lucidrains/x-transformers · train_belief_state.py:89

        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

for i in tqdm.tqdm(range(NUM_BATCHES), mininterval = 10., desc = 'training'):

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