def learn(dataset,
dim=2,
hyp=1,
edim=1,
euc=0,
sdim=1,
sph=0,
scale=1.,
riemann=False,
learning_rate=1e-1,
decay_length=1000,
decay_step=1.0,
momentum=0.0,
tol=1e-8,
epochs=100,
burn_in=0,
use_yellowfin=False,
use_adagrad=False,
resample_freq=1000,
print_freq=1,
model_save_file=None,
model_load_file=None,
batch_size=16,
num_workers=None,
lazy_generation=False,
log_name=None,
log=False,
warm_start=None,
learn_scale=False,
checkpoint_freq=100,
sample=1.,
subsample=None,
logloss=False,
distloss=False,
squareloss=False,
symloss=False,
exponential_rescale=None,
extra_steps=1,
use_svrg=False,
T=10,
use_hmds=False,
visualize=False):
# Log configuration
formatter = logging.Formatter('%(asctime)s %(message)s')
logging.basicConfig(
level=logging.DEBUG,
format='%(asctime)s %(message)s',
datefmt='%FT%T',
)
if log_name is None and log:
log_name = f"{os.path.splitext(dataset)[0]}.H{dim}-{hyp}.E{edim}-{euc}.S{sdim}-{sph}.lr{learning_rate}.log"
if log_name is not None:
logging.info(f"Logging to {log_name}")
log = logging.getLogger()
fh = logging.FileHandler(log_name)
fh.setFormatter(formatter)
log.addHandler(fh)
#############
loss_list = []
#############
logging.info(f"Commandline {sys.argv}")
if model_save_file is None: logging.warning("No Model Save selected!")
G = load_graph.load_graph(dataset)
GM = nx.to_scipy_sparse_matrix(G, nodelist=list(range(G.order())))
# grab scale if warm starting:
if warm_start:
scale = pandas.read_csv(warm_start, index_col=0).as_matrix()[0, -1]
n = G.order()
logging.info(f"Loaded Graph {dataset} with {n} nodes scale={scale}")
if exponential_rescale is not None:
# torch.exp(exponential_rescale * -d)
def weight_fn(d):
if d <= 2.0: return 5.0
elif d > 4.0: return 0.01
else: return 1.0
else:
def weight_fn(d):
return 1.0
Z, z = build_dataset(G, lazy_generation, sample, subsample, scale,
batch_size, weight_fn, num_workers)
if model_load_file is not None:
logging.info(f"Loading {model_load_file}...")
m = torch.load(model_load_file).to(device)
logging.info(
f"Loaded scale {unwrap(m.scale())} {torch.sum(m.embedding().data)} {m.epoch}"
)
else:
logging.info(f"Creating a fresh model warm_start?={warm_start}")
m_init = None
if warm_start:
# load from DataFrame; assume that the julia combinatorial embedding has been saved
ws_data = pandas.read_csv(warm_start, index_col=0).as_matrix()
scale = ws_data[0, ws_data.shape[1] - 1]
m_init = torch.DoubleTensor(ws_data[:,
range(ws_data.shape[1] - 1)])
elif use_hmds:
# m_init = torch.DoubleTensor(mds_warmstart.get_normalized_hyperbolic(mds_warmstart.get_model(dataset,dim,scale)[1]))
m_init = torch.DoubleTensor(
mds_warmstart.get_model(dataset, dim, scale)[1])
logging.info(
f"\t Warmstarting? {warm_start} {m_init.size() if warm_start else None} {G.order()}"
)
# initial_scale = z.dataset.max_dist / 3.0
# print("MAX DISTANCE", z.dataset.max_dist)
# print("AVG DISTANCE", torch.mean(z.dataset.val_cache))
initial_scale = 0.0
m = ProductEmbedding(G.order(),
dim,
hyp,
edim,
euc,
sdim,
sph,
initialize=m_init,
learn_scale=learn_scale,
initial_scale=initial_scale,
logrel_loss=logloss,
dist_loss=distloss,
square_loss=squareloss,
sym_loss=symloss,
exponential_rescale=exponential_rescale,
riemann=riemann).to(device)
m.normalize()
m.epoch = 0
logging.info(
f"Constructed model with dim={dim} and epochs={m.epoch} isnan={np.any(np.isnan(m.embedding().cpu().data.numpy()))}"
)
if visualize:
name = 'animations/' + f"{os.path.split(os.path.splitext(dataset)[0])[1]}.H{dim}-{hyp}.E{edim}-{euc}.S{sdim}-{sph}.lr{learning_rate}.ep{epochs}.seed{seed}"
fig, ax, writer = vis.setup_plot(m=m, name=name, draw_circle=True)
else:
fig = None
ax = None
writer = None
#
# Build the Optimizer
#
# TODO: Redo this in a sensible way!!
# per-parameter learning rates
exp_params = [p for p in m.embed_params if p.use_exp]
learn_params = [p for p in m.embed_params if not p.use_exp]
hyp_params = [p for p in m.hyp_params if not p.use_exp]
euc_params = [p for p in m.euc_params if not p.use_exp]
sph_params = [p for p in m.sph_params if not p.use_exp]
scale_params = m.scale_params
# model_params = [{'params': m.embed_params}, {'params': m.scale_params, 'lr': 1e-4*learning_rate}]
# model_params = [{'params': learn_params}, {'params': m.scale_params, 'lr': 1e-4*learning_rate}]
model_params = [{
'params': hyp_params
}, {
'params': euc_params
}, {
'params': sph_params,
'lr': 0.1 * learning_rate
}, {
'params': m.scale_params,
'lr': 1e-4 * learning_rate
}]
# opt = None
if len(model_params) > 0:
opt = torch.optim.SGD(model_params,
lr=learning_rate / 10,
momentum=momentum)
# opt = torch.optim.SGD(learn_params, lr=learning_rate/10, momentum=momentum)
# opt = torch.optim.SGD(model_params, lr=learning_rate/10, momentum=momentum)
# exp = None
# if len(exp_params) > 0:
# exp = torch.optim.SGD(exp_params, lr=1.0) # dummy for zeroing
if len(scale_params) > 0:
scale_opt = torch.optim.SGD(scale_params, lr=1e-3 * learning_rate)
scale_decay = torch.optim.lr_scheduler.StepLR(scale_opt,
step_size=1,
gamma=.99)
else:
scale_opt = None
scale_decay = None
lr_burn_in = torch.optim.lr_scheduler.MultiStepLR(opt,
milestones=[burn_in],
gamma=10)
# lr_decay = torch.optim.lr_scheduler.StepLR(opt, decay_length, decay_step) #TODO reconcile multiple LR schedulers
if use_yellowfin:
from yellowfin import YFOptimizer
opt = YFOptimizer(model_params)
if use_adagrad:
opt = torch.optim.Adagrad(model_params)
if use_svrg:
from svrg import SVRG
base_opt = torch.optim.Adagrad if use_adagrad else torch.optim.SGD
opt = SVRG(m.parameters(),
lr=learning_rate,
T=T,
data_loader=z,
opt=base_opt)
# TODO add ability for SVRG to take parameter groups
logging.info(opt)
# Log stats from import: when warmstarting, check that it matches Julia's stats
logging.info(f"*** Initial Checkpoint. Computing Stats")
major_stats(GM, n, m, lazy_generation, Z, z, fig, ax, writer, visualize,
subsample)
logging.info("*** End Initial Checkpoint\n")
# track best stats
best_loss = 1.0e10
best_dist = 1.0e10
best_wcdist = 1.0e10
best_map = 0.0
for i in range(m.epoch + 1, m.epoch + epochs + 1):
lr_burn_in.step()
# lr_decay.step()
# scale_decay.step()
# print(scale_opt.param_groups[0]['lr'])
# for param_group in opt.param_groups:
# print(param_group['lr'])
# print(type(opt.param_groups), opt.param_groups)
l, n_edges = 0.0, 0.0 # track average loss per edge
m.train(True)
if use_svrg:
for data in z:
def closure(data=data, target=None):
_data = data if target is None else (data, target)
c = m.loss(_data.to(device))
c.backward()
return c.data[0]
l += opt.step(closure)
# Projection
m.normalize()
else:
# scale_opt.zero_grad()
for the_step in range(extra_steps):
# Accumulate the gradient
for u in z:
# Zero out the gradients
# if opt is not None: opt.zero_grad() # This is handled by the SVRG.
# if exp is not None: exp.zero_grad()
opt.zero_grad()
for p in exp_params:
if p.grad is not None:
p.grad.detach_()
p.grad.zero_()
# Compute loss
_loss = m.loss(cu_var(u))
_loss.backward()
l += _loss.item() * u[0].size(0)
# print(weight)
n_edges += u[0].size(0)
# modify gradients if necessary
RParameter.correct_metric(m.parameters())
# step
opt.step()
for p in exp_params:
lr = opt.param_groups[0]['lr']
p.exp(lr)
# Projection
m.normalize()
# scale_opt.step()
l /= n_edges
# m.epoch refers to num of training epochs finished
m.epoch += 1
# Logging code
# if l < tol:
# logging.info("Found a {l} solution. Done at iteration {i}!")
# break
if i % print_freq == 0:
logging.info(f"{i} loss={l}")
############
if log_name is not None:
loss_list.append(l)
#############
if (i <= burn_in and i %
(checkpoint_freq / 5) == 0) or i % checkpoint_freq == 0:
logging.info(f"\n*** Major Checkpoint. Computing Stats and Saving")
avg_dist, wc_dist, me, mc, mapscore = major_stats(
GM, n, m, True, Z, z, fig, ax, writer, visualize, subsample)
best_loss = min(best_loss, l)
best_dist = min(best_dist, avg_dist)
best_wcdist = min(best_wcdist, wc_dist)
best_map = max(best_map, mapscore)
if model_save_file is not None:
fname = f"{model_save_file}.{m.epoch}"
logging.info(
f"Saving model into {fname} {torch.sum(m.embedding().data)} "
)
torch.save(m, fname)
logging.info("*** End Major Checkpoint\n")
if i % resample_freq == 0:
if sample < 1. or subsample is not None:
Z, z = build_dataset(G, lazy_generation, sample, subsample,
scale, batch_size, weight_fn, num_workers)
logging.info(f"final loss={l}")
logging.info(
f"best loss={best_loss}, distortion={best_dist}, map={best_map}, wc_dist={best_wcdist}"
)
final_dist, final_wc, final_me, final_mc, final_map = major_stats(
GM, n, m, lazy_generation, Z, z, fig, ax, writer, False, subsample)
if log_name is not None:
###
with open(log_name + "_loss.stat", "w") as f:
for loss in loss_list:
f.write("%f\n" % loss)
###
with open(log_name + '_final.stat', "w") as f:
f.write("Best-loss MAP dist wc Final-loss MAP dist wc me mc\n")
f.write(
f"{best_loss:10.6f} {best_map:8.4f} {best_dist:8.4f} {best_wcdist:8.4f} {l:10.6f} {final_map:8.4f} {final_dist:8.4f} {final_wc:8.4f} {final_me:8.4f} {final_mc:8.4f}"
)
if visualize:
writer.finish()
if model_save_file is not None:
fname = f"{model_save_file}.final"
logging.info(
f"Saving model into {fname}-final {torch.sum(m.embedding().data)} {unwrap(m.scale())}"
)
torch.save(m, fname)
def test_measurement(zero_debias=True):
dtype = torch.FloatTensor
w = Variable(torch.ones(n_dim, 1).type(dtype), requires_grad=True)
b = Variable(torch.ones(1).type(dtype), requires_grad=True)
x = Variable(torch.ones(1, n_dim).type(dtype), requires_grad=False)
opt = YFOptimizer([w, b], lr=1.0, mu=0.0, zero_debias=zero_debias)
target_h_max = 0.0
target_h_min = 0.0
g_norm_squared_avg = 0.0
g_norm_avg = 0.0
g_avg = 0.0
target_dist = 0.0
for i in range(n_iter):
opt.zero_grad()
loss = (x.mm(w) + b).sum()
loss.backward()
w.grad.data = (i + 1) * torch.ones([
n_dim,
]).type(dtype)
b.grad.data = (i + 1) * torch.ones([
1,
]).type(dtype)
opt.step()
res = [opt._h_max, opt._h_min, opt._grad_var, opt._dist_to_opt]
g_norm_squared_avg = 0.999 * g_norm_squared_avg \
+ 0.001 * np.sum(((i + 1) * np.ones([n_dim + 1, ]))**2)
g_norm_avg = 0.999 * g_norm_avg \
+ 0.001 * np.linalg.norm((i + 1) * np.ones([n_dim + 1, ]))
g_avg = 0.999 * g_avg + 0.001 * (i + 1)
target_h_max = 0.999 * target_h_max + 0.001 * (i + 1)**2 * (n_dim + 1)
target_h_min = 0.999 * target_h_min + 0.001 * \
max(1, i + 2 - 20)**2 * (n_dim + 1)
if zero_debias:
target_var = g_norm_squared_avg / (1 - 0.999**(i + 1)) \
- g_avg**2 * (n_dim + 1) / (1 - 0.999**(i + 1))**2
else:
target_var = g_norm_squared_avg - g_avg**2 * (n_dim + 1)
target_dist = 0.999 * target_dist + 0.001 * g_norm_avg / g_norm_squared_avg
if i == 0:
continue
if zero_debias:
# print "iter ", i, " h max ", res[0], target_h_max/(1-0.999**(i + 1) ), \
# " h min ", res[1], target_h_min/(1-0.999**(i + 1) ), \
# " var ", res[2], target_var, \
# " dist ", res[3], target_dist/(1-0.999**(i + 1) )
assert np.abs(target_h_max / (1 - 0.999**(i + 1)) -
res[0]) < np.abs(res[0]) * 1e-3
assert np.abs(target_h_min / (1 - 0.999**(i + 1)) -
res[1]) < np.abs(res[1]) * 1e-3
assert np.abs(target_var - res[2]) < np.abs(target_var) * 1e-3
assert np.abs(target_dist / (1 - 0.999**(i + 1)) -
res[3]) < np.abs(res[3]) * 1e-3
else:
# print "iter ", i, " h max ", res[0], target_h_max, " h min ", res[1], target_h_min, \
# " var ", res[2], target_var, " dist ", res[3], target_dist
assert np.abs(target_h_max - res[0]) < np.abs(target_h_max) * 1e-3
assert np.abs(target_h_min - res[1]) < np.abs(target_h_min) * 1e-3
assert np.abs(target_var - res[2]) < np.abs(res[2]) * 1e-3
assert np.abs(target_dist - res[3]) < np.abs(res[3]) * 1e-3
print "sync measurement test passed!"
def test_lr_mu(zero_debias=False):
dtype = torch.FloatTensor
w = Variable(torch.ones(n_dim, 1).type(dtype), requires_grad=True)
b = Variable(torch.ones(1).type(dtype), requires_grad=True)
x = Variable(torch.ones(1, n_dim).type(dtype), requires_grad=False)
opt = YFOptimizer([w, b], lr=1.0, mu=0.0, zero_debias=zero_debias)
target_h_max = 0.0
target_h_min = 0.0
g_norm_squared_avg = 0.0
g_norm_avg = 0.0
g_avg = 0.0
target_dist = 0.0
target_lr = 1.0
target_mu = 0.0
for i in range(n_iter):
opt.zero_grad()
loss = (x.mm(w) + b).sum()
loss.backward()
w.grad.data = (i + 1) * torch.ones([
n_dim,
]).type(dtype)
b.grad.data = (i + 1) * torch.ones([
1,
]).type(dtype)
opt.step()
res = [
opt._h_max, opt._h_min, opt._grad_var, opt._dist_to_opt, opt._lr,
opt._mu
]
g_norm_squared_avg = 0.999 * g_norm_squared_avg \
+ 0.001 * np.sum(((i + 1) * np.ones([n_dim + 1, ]))**2)
g_norm_avg = 0.999 * g_norm_avg \
+ 0.001 * np.linalg.norm((i + 1) * np.ones([n_dim + 1, ]))
g_avg = 0.999 * g_avg + 0.001 * (i + 1)
target_h_max = 0.999 * target_h_max + 0.001 * (i + 1)**2 * (n_dim + 1)
target_h_min = 0.999 * target_h_min + 0.001 * \
max(1, i + 2 - 20)**2 * (n_dim + 1)
if zero_debias:
target_var = g_norm_squared_avg / (1 - 0.999**(i + 1)) \
- g_avg**2 * (n_dim + 1) / (1 - 0.999**(i + 1))**2
else:
target_var = g_norm_squared_avg - g_avg**2 * (n_dim + 1)
target_dist = 0.999 * target_dist + 0.001 * g_norm_avg / g_norm_squared_avg
if i == 0:
continue
if zero_debias:
# print "iter ", i, " h max ", res[0], target_h_max/(1-0.999**(i + 1) ), \
# " h min ", res[1], target_h_min/(1-0.999**(i + 1) ), \
# " var ", res[2], target_var, \
# " dist ", res[3], target_dist/(1-0.999**(i + 1) )
assert np.abs(target_h_max / (1 - 0.999**(i + 1)) -
res[0]) < np.abs(res[0]) * 1e-3
assert np.abs(target_h_min / (1 - 0.999**(i + 1)) -
res[1]) < np.abs(res[1]) * 1e-3
assert np.abs(target_var - res[2]) < np.abs(target_var) * 1e-3
assert np.abs(target_dist / (1 - 0.999**(i + 1)) -
res[3]) < np.abs(res[3]) * 1e-3
else:
# print "iter ", i, " h max ", res[0], target_h_max, " h min ", res[1], target_h_min, \
# " var ", res[2], target_var, " dist ", res[3], target_dist
assert np.abs(target_h_max - res[0]) < np.abs(target_h_max) * 1e-3
assert np.abs(target_h_min - res[1]) < np.abs(target_h_min) * 1e-3
assert np.abs(target_var - res[2]) < np.abs(res[2]) * 1e-3
assert np.abs(target_dist - res[3]) < np.abs(res[3]) * 1e-3
if i > 0:
if zero_debias:
lr, mu = tune_everything(
(target_dist / (1 - 0.999**(i + 1)))**2, target_var, 1,
target_h_min / (1 - 0.999**(i + 1)),
target_h_max / (1 - 0.999**(i + 1)))
else:
lr, mu = tune_everything(target_dist**2, target_var, 1,
target_h_min, target_h_max)
lr = np.real(lr)
mu = np.real(mu)
target_lr = 0.999 * target_lr + 0.001 * lr
target_mu = 0.999 * target_mu + 0.001 * mu
# print "lr ", target_lr, res[4], " mu ", target_mu, res[5]
assert target_lr == 0.0 or np.abs(target_lr -
res[4]) < np.abs(res[4]) * 1e-3
assert target_mu == 0.0 or np.abs(target_mu -
res[5]) < np.abs(res[5]) * 5e-3
print "lr and mu computing test passed!"
for param in optimizer._optimizer.param_groups[0]['params']:
param.grad = target_norm * param.grad / grad_norm
# You can enable this to see some slow reaction from our estimators
# when the zero gradients start
if True and (t > 6490 or t < 20):
print(t, loss.data.item())
print('Curvatures', optimizer._h_max, optimizer._h_min)
print('mu_t, lr_t', optimizer._mu_t, optimizer._lr_t)
print
else:
print(t, loss.data.item())
# Calling the step function on an Optimizer makes an update to its parameters
optimizer.step()
# grad_norm = torch_list_grad_norm(optimizer._optimizer.param_groups[0]['params'])
# 4/(np.sqrt(optimizer._h_max)+np.sqrt(optimizer._h_min))**2
# 4/(2*np.sqrt(grad_norm**2))**2
# optimizer._lr_t
# # In[14]:
# optimizer._lr
# # In[ ]:
def learn(dataset,
rank=2,
scale=1.,
learning_rate=1e-1,
tol=1e-8,
epochs=100,
use_yellowfin=False,
use_adagrad=False,
print_freq=1,
model_save_file=None,
model_load_file=None,
batch_size=16,
num_workers=None,
lazy_generation=False,
log_name=None,
warm_start=None,
learn_scale=False,
checkpoint_freq=1000,
sample=1.,
subsample=None,
exponential_rescale=None,
extra_steps=1,
use_svrg=False,
T=10,
use_hmds=False):
# Log configuration
formatter = logging.Formatter('%(asctime)s %(message)s')
logging.basicConfig(
level=logging.DEBUG,
format='%(asctime)s %(message)s',
datefmt='%FT%T',
)
if log_name is not None:
logging.info(f"Logging to {log_name}")
log = logging.getLogger()
fh = logging.FileHandler(log_name)
fh.setFormatter(formatter)
log.addHandler(fh)
logging.info(f"Commandline {sys.argv}")
if model_save_file is None: logging.warn("No Model Save selected!")
G = load_graph.load_graph(dataset)
GM = nx.to_scipy_sparse_matrix(G)
# grab scale if warm starting:
if warm_start:
scale = pandas.read_csv(warm_start, index_col=0).as_matrix()[0, -1]
n = G.order()
logging.info(f"Loaded Graph {dataset} with {n} nodes scale={scale}")
Z = None
def collate(ls):
x, y = zip(*ls)
return torch.cat(x), torch.cat(y)
if lazy_generation:
if subsample is not None:
z = DataLoader(GraphRowSubSampler(G, scale, subsample),
batch_size,
shuffle=True,
collate_fn=collate)
else:
z = DataLoader(GraphRowSampler(G, scale),
batch_size,
shuffle=True,
collate_fn=collate)
logging.info("Built Data Sampler")
else:
Z = gh.build_distance(G,
scale,
num_workers=int(num_workers) if num_workers
is not None else 16) # load the whole matrix
logging.info(f"Built distance matrix with {scale} factor")
if subsample is not None:
z = DataLoader(GraphRowSubSampler(G, scale, subsample, Z=Z),
batch_size,
shuffle=True,
collate_fn=collate)
else:
idx = torch.LongTensor([(i, j) for i in range(n)
for j in range(i + 1, n)])
Z_sampled = gh.dist_sample_rebuild_pos_neg(
Z, sample) if sample < 1 else Z
vals = torch.DoubleTensor(
[Z_sampled[i, j] for i in range(n) for j in range(i + 1, n)])
z = DataLoader(TensorDataset(idx, vals),
batch_size=batch_size,
shuffle=True,
pin_memory=torch.cuda.is_available())
logging.info("Built data loader")
if model_load_file is not None:
logging.info(f"Loading {model_load_file}...")
m = cudaify(torch.load(model_load_file))
logging.info(
f"Loaded scale {m.scale.data[0]} {torch.sum(m.w.data)} {m.epoch}")
else:
logging.info(f"Creating a fresh model warm_start?={warm_start}")
m_init = None
if warm_start:
# load from DataFrame; assume that the julia combinatorial embedding has been saved
ws_data = pandas.read_csv(warm_start, index_col=0).as_matrix()
scale = ws_data[0, ws_data.shape[1] - 1]
m_init = torch.DoubleTensor(ws_data[:,
range(ws_data.shape[1] - 1)])
elif use_hmds:
# m_init = torch.DoubleTensor(mds_warmstart.get_normalized_hyperbolic(mds_warmstart.get_model(dataset,rank,scale)[1]))
m_init = torch.DoubleTensor(
mds_warmstart.get_model(dataset, rank, scale)[1])
logging.info(
f"\t Warmstarting? {warm_start} {m_init.size() if warm_start else None} {G.order()}"
)
m = cudaify(
Hyperbolic_Emb(G.order(),
rank,
initialize=m_init,
learn_scale=learn_scale,
exponential_rescale=exponential_rescale))
m.normalize()
m.epoch = 0
logging.info(
f"Constructed model with rank={rank} and epochs={m.epoch} isnan={np.any(np.isnan(m.w.cpu().data.numpy()))}"
)
#
# Build the Optimizer
#
# TODO: Redo this in a sensible way!!
#
opt = torch.optim.SGD(m.parameters(), lr=learning_rate)
if use_yellowfin:
from yellowfin import YFOptimizer
opt = YFOptimizer(m.parameters())
if use_adagrad:
opt = torch.optim.Adagrad(m.parameters())
if use_svrg:
from svrg import SVRG
base_opt = torch.optim.Adagrad if use_adagrad else torch.optim.SGD
opt = SVRG(m.parameters(),
lr=learning_rate,
T=T,
data_loader=z,
opt=base_opt)
logging.info(opt)
# Log stats from import: when warmstarting, check that it matches Julia's stats
logging.info(f"*** Initial Checkpoint. Computing Stats")
major_stats(GM, 1 + m.scale.data[0], n, m, lazy_generation, Z, z)
logging.info("*** End Initial Checkpoint\n")
for i in range(m.epoch, m.epoch + epochs):
l = 0.0
m.train(True)
if use_svrg:
for data in z:
def closure(data=data, target=None):
_data = data if target is None else (data, target)
c = m.loss(cu_var(_data))
c.backward()
return c.data[0]
l += opt.step(closure)
# Projection
m.normalize()
else:
opt.zero_grad() # This is handled by the SVRG.
for the_step in range(extra_steps):
# Accumulate the gradient
for u in z:
_loss = m.loss(cu_var(u, requires_grad=False))
_loss.backward()
l += _loss.data[0]
Hyperbolic_Parameter.correct_metric(
m.parameters()) # NB: THIS IS THE NEW CALL
# print("Scale before step: ", m.scale.data)
opt.step()
# print("Scale after step: ", m.scale.data)
# Projection
m.normalize()
#l += step(m, opt, u).data[0]
# Logging code
if l < tol:
logging.info("Found a {l} solution. Done at iteration {i}!")
break
if i % print_freq == 0:
logging.info(f"{i} loss={l}")
if i % checkpoint_freq == 0:
logging.info(f"\n*** Major Checkpoint. Computing Stats and Saving")
major_stats(GM, 1 + m.scale.data[0], n, m, True, Z, z)
if model_save_file is not None:
fname = f"{model_save_file}.{m.epoch}"
logging.info(
f"Saving model into {fname} {torch.sum(m.w.data)} ")
torch.save(m, fname)
logging.info("*** End Major Checkpoint\n")
m.epoch += 1
logging.info(f"final loss={l}")
if model_save_file is not None:
fname = f"{model_save_file}.final"
logging.info(
f"Saving model into {fname}-final {torch.sum(m.w.data)} {m.scale.data[0]}"
)
torch.save(m, fname)
major_stats(GM, 1 + m.scale.data[0], n, m, lazy_generation, Z, z)
x = numpy.asarray(x, dtype=numpy.float32)
x = torch.from_numpy(x)
x = x.view(x.size()[0], x.size()[1], input_size)
y = torch.cat((x[:, 1:, :], torch.zeros([x.size()[0], 1, input_size])),
1)
images = Variable(x).cuda()
labels = Variable(y).long().cuda()
opt.zero_grad()
outputs = rnn(images)
shp = outputs.size()
outputs_reshp = outputs.view([shp[0] * shp[1], num_classes])
labels_reshp = labels.view(shp[0] * shp[1])
loss = criterion(outputs_reshp, labels_reshp)
loss.backward()
opt.step()
# For plotting
#unclip_g_norm_list.append(opt._unclip_grad_norm)
t += time.time()
loss_list.append(loss.data[0])
local_curv_list.append(opt._global_state['grad_norm_squared'])
max_curv_list.append(opt._h_max)
min_curv_list.append(opt._h_min)
lr_list.append(opt._lr)
mu_list.append(opt._mu)
dr_list.append((opt._h_max + 1e-6) / (opt._h_min + 1e-6))
dist_list.append(opt._dist_to_opt)
