def dembed_loss(real_one_hot, gen_one_hot, show_partial=False):
deemb_loss = MSELoss()(real_one_hot, gen_one_hot)
if show_partial:
print('\tDEEMB deemb_loss ' + str(deemb_loss.item()))
return deemb_loss
def ae_main_loss(input, output, lat_mu, lat_var, show_partial=False) -> Variable:
mse_loss = MSELoss()(input, output)
kld_loss = torch.mean(-0.5 * torch.sum(1 + lat_var - lat_mu ** 2 - lat_var.exp(), dim=1), dim=0)
if show_partial:
print('\tMAIN mse_loss ' + str(mse_loss.item()))
print('\tMAIN kld_loss ' + str(kld_loss.item()))
return mse_loss + kld_loss*0.00
def one_batch_train(self, Xs, Y):
Ws = Xs['word_vectors']
self.optimizer.zero_grad()
outputs = self.model(Ws)
mse_loss = MSELoss(outputs, Y)
mse_loss.backward()
self.optimizer.step()
self.save_checkpoint('../../../data/nn/lstm.pth.tar')
return self.model.data.cpu().numpy(), mse_loss.item()
def ae_prtcl_loss(input_x, output_x, show_partial=False) -> Variable:
mse_loss = MSELoss()(input_x, output_x)
''' # VAE
kld_loss = torch.mean(-0.5 * torch.sum(1 + lat_var - lat_mu ** 2 - lat_var.exp(), dim=1), dim=0)
'''
if show_partial:
print('\tPRTCL mse_loss ' + str(mse_loss.item()))
# print('\tPRTCL kld_loss ' + str(kld_loss.item()))
return mse_loss # + kld_loss * 0.001
def train_model():
model.train()
total_loss = 0
pbar = tqdm(train_loader)
for data in pbar:
if not args.multi_gpu: data = data.to(device)
optimizer.zero_grad()
#print(data)
#print(args)
if args.use_orig_graph:
x = data.x if args.use_feature else None
edge_weight = data.edge_weight if args.use_edge_weight else None
node_id = data.node_id if emb else None
out = model(data.z, data.edge_index, data.batch, x, edge_weight,
node_id).view(-1)
else:
out = model(data).view(-1)
if args.multi_gpu:
y = torch.cat([d.y.to(torch.float) for d in data]).to(out.device)
else:
y = data.y.to(torch.float)
if args.neg_edge_percent != 100:
y_neg = y[y == 0]
out_neg = out[y == 0]
y_pos = y[y != 0]
out_pos = out[y != 0]
num_neg = int(args.neg_edge_percent / 100 * len(out_neg))
out_neg, y_neg = out_neg[:num_neg], y_neg[:num_neg]
y = torch.cat([y_pos, y_neg])
out = torch.cat([out_pos, out_neg])
loss = MSELoss()(out, y)
loss = torch.sqrt(loss)
loss.backward()
optimizer.step()
total_loss += loss.item() * len(data)
return total_loss / len(train_dataset)
def main(screen):
forces = [hor_impulse]
ground_truth_mass = torch.tensor([TOTAL_MASS], dtype=DTYPE)
world, chain = make_world(forces, ground_truth_mass, num_links=NUM_LINKS)
rec = None
# rec = Recorder(DT, screen)
ground_truth_pos = positions_run_world(world, run_time=10, screen=screen, recorder=rec)
ground_truth_pos = [p.data for p in ground_truth_pos]
ground_truth_pos = torch.cat(ground_truth_pos)
learning_rate = 0.5
max_iter = 100
next_mass = torch.rand_like(ground_truth_mass, requires_grad=True)
print('\rInitial mass:', next_mass.item())
print('-----')
optim = torch.optim.RMSprop([next_mass], lr=learning_rate)
loss_hist = []
mass_hist = [next_mass.item()]
last_loss = 1e10
for i in range(max_iter):
if i % 1 == 0:
world, chain = make_world(forces, next_mass.clone().detach(), num_links=NUM_LINKS)
run_world(world, run_time=10, print_time=False, screen=None, recorder=None)
world, chain = make_world(forces, next_mass, num_links=NUM_LINKS)
positions = positions_run_world(world, run_time=10, screen=None)
positions = torch.cat(positions)
positions = positions[:len(ground_truth_pos)]
clipped_ground_truth_pos = ground_truth_pos[:len(positions)]
optim.zero_grad()
loss = MSELoss()(positions, clipped_ground_truth_pos)
loss.backward()
optim.step()
print('Iteration: {} / {}'.format(i+1, max_iter))
print('Loss:', loss.item())
print('Gradient:', next_mass.grad.item())
print('Next mass:', next_mass.item())
print('-----')
if abs((last_loss - loss).item()) < STOP_DIFF:
print('Loss changed by less than {} between iterations, stopping training.'
.format(STOP_DIFF))
break
last_loss = loss
loss_hist.append(loss.item())
mass_hist.append(next_mass.item())
world = make_world(forces, next_mass, num_links=NUM_LINKS)[0]
rec = None
positions = positions_run_world(world, run_time=10, screen=screen, recorder=rec)
positions = torch.cat(positions)
positions = positions[:len(ground_truth_pos)]
clipped_ground_truth_pos = ground_truth_pos[:len(positions)]
loss = MSELoss()(positions, clipped_ground_truth_pos)
print('Final loss:', loss.item())
print('Final mass:', next_mass.item())
plot(loss_hist)
plot(mass_hist)
def main(screen):
dateTimeObj = datetime.now()
timestampStr = dateTimeObj.strftime("%d-%b-%Y(%H:%M)")
print('Current Timestamp : ', timestampStr)
if not os.path.isdir(timestampStr):
os.mkdir(timestampStr)
#if torch.cuda.is_available():
# dev = "cuda:0"
#else:
# dev = "cpu"
forces = []
rec = None
#rec = Recorder(DT, screen)
#plot(zhist)
learning_rate = 0.5
max_iter = 100
plt.subplots(21)
plt.subplot(212)
plt.gca().invert_yaxis()
optim = torch.optim.RMSprop([utT], lr=learning_rate)
last_loss = 1e10
lossHist = []
for i in range(1, 20000):
world, chain = make_world(forces, ctT, utT)
hist = positions_run_world(world,
run_time=runtime,
screen=screen,
recorder=rec)
xy = hist[0]
vel = hist[1]
control = hist[2]
xy = torch.cat(xy).to(device=Defaults.DEVICE)
x1 = xy[0::2]
y1 = xy[1::2]
control = torch.cat(control).to(device=Defaults.DEVICE)
optim.zero_grad()
targetxy = []
targetControl = []
j = 0
while (j < xy.size()[0]):
targetxy.append(500)
targetxy.append(240)
targetControl.append(0)
targetControl.append(0)
j = j + 2
tt = torch.tensor(targetxy,
requires_grad=True,
dtype=DTYPE,
device=Defaults.DEVICE).t()
tc = torch.tensor(targetControl,
requires_grad=True,
dtype=DTYPE,
device=Defaults.DEVICE).t()
#loss = MSELoss()(zhist, 150*torch.tensor(np.ones(zhist.size()),requires_grad=True, dtype=DTYPE,device=Defaults.DEVICE))/100
#loss = MSELoss()(xy, tt) / 10 + 0*MSELoss()(control, tc) / 1000 + abs(vel[-1][0]) + abs(vel[-1][1]) + abs(vel[-1][2])
loss = MSELoss()(xy, tt) / 10 + 0 * MSELoss()(
control, tc) / 1000 + abs(vel[-1][0]) + abs(vel[-1][1]) + abs(
vel[-1][2])
#loss = zhist[-1]
loss.backward()
lossHist.append(loss.item())
optim.step()
print('Loss:', loss.item())
print('Gradient:', utT.grad)
print('Next u:', utT)
#plt.axis([-1, 1, -1, 1])
plt.ion()
plt.show()
plt.subplot(211)
plt.plot(lossHist)
plt.draw()
pl1 = xy.cpu()
plt.subplot(212)
plt.plot(pl1.clone().detach().numpy()[::4],
pl1.clone().detach().numpy()[1::4])
#plt.plot(zhist)
plt.draw()
plt.pause(0.001)
plt.savefig('2step.png')
if (i % 20) == 0:
plt.subplot(211)
plt.cla()
plt.subplot(212)
plt.cla()
plt.gca().invert_yaxis()
lossHist.clear()
torch.save(utT, timestampStr + '/file' + str(i) + '.pt')
utTCurrent = utT.clone()
world, chain = make_world(forces, ctT, utTCurrent)
positions_run_world(world, run_time=runtime, screen=None, recorder=rec)