# loss_function = nn.L1Loss()
import random
sequence = 10
def sample(data, targets, s):
n = random.randint(0, len(data) - (s + 5))
# return data[n: n + s + 1], targets[n: n + s + 1]
for _ in range(1, epochs + 1):
epoch_loss = 0
l.reset_hidden()
# sq, t = sample(data, targets, sequence)
outputs = l.forward(data[:-1])
loss = loss_function(outputs.unsqueeze(-1), targets)
epoch_loss += loss
loss.backward()
optimizer.step()
if _ % 5 == 0:
print(f"Epoch Number {_} Loss ---------------> {epoch_loss}")
epoch_outputs = []
l.reset_hidden()
for xi in range(len(data) - 1):
# if xi % sequence == 0:
# l.reset_hidden()
# l.reset_hidden()
outputs = l.forward(data[xi].unsqueeze(0))
loss = loss_function(outputs, targets[xi])
### BOOTSTRAPPING
# get seed sequence
numpy_seed_sequence = data[args.seed_index][:, 130:]
# convert to tensor + add batch dimension
seed_sequence = torch.FloatTensor(numpy_seed_sequence).unsqueeze(0)
print('-> INFERENCE')
### SAMPLING LOOP
for n in range(args.n_samples):
# reset RNN hidden states
model.hidden = model.init_hidden()
# feed sequence through RNN and get last output
o = torch.exp(
model.forward(seed_sequence, None, None,
temperature=args.temperature)[0, -1, :])
# sample rhythm and chord
rhythm = torch.multinomial(o[:13], 1)[0]
chord = torch.multinomial(o[13:], 1)[0]
if chord == 48 or rhythm == 12: #enforce consistent barlines
rhythm = 12
chord = 48
# generate one-hot vector
one_hot = torch.zeros([1, 1, len(o)], dtype=torch.float32)
one_hot[0, 0, rhythm] = 1.0
one_hot[0, 0, 13 + chord] = 1.0
seed_sequence = torch.cat((seed_sequence, one_hot), 1)
