def __init__(self):
nb_seq, len_prompt, len_result = 100, 5, 5
self.seq = torch.randint(10, (nb_seq, len_prompt + 1 + len_result))
def __init__(self):
nb_seq, len_prompt, len_result = 100, 5, 5
self.seq = torch.randint(10, (nb_seq, len_prompt + 1 + len_result))
def generate_sequences(self, nb):
return self.seq[torch.randint(self.seq.size(0), (nb,))]
def generate_sequences(self, nb):
return self.seq[torch.randint(self.seq.size(0), (nb,))]
test_seq = generate_sequences(nb_test_samples)
for strain, stest in zip(train_seq, test_seq):
test_seq = generate_sequences(nb_test_samples)
for strain, stest in zip(train_seq, test_seq):