# %%
plt.scatter(X0[:, 0], X0[:, 1], s=5, c=colors)
# %%
blocks = sum(
[[StructuredAffineFlow(2), PReLUFlow(2)] for _ in range(5)] + [[StructuredAffineFlow(2)]],
[])
# %%
flow = NormalizingFlow(
*blocks,
base_dist=base_dist,
)
opt = optim.Adam(flow.parameters(), lr=5e-4)
# %%
count_parameters(flow)
# %%
writer = SummaryWriter(f"/workspace/sandbox/tensorboard_logs/{now_str()}")
best_loss = torch.Tensor([float("+inf")])
attempts = 0
for it in trange(int(1e5)):
opt.zero_grad()
loss = -flow.final_density.log_prob(x_samples).mean()
#blocks = sum(
# [[AffineLUFlow(2), PReLUFlow(2), BatchNormFlow(2)] for _ in range(5)] +
# [[AffineLUFlow(2)]],
#[])
blocks = sum(
[[StructuredAffineFlow(2),
PReLUFlow(2), BatchNormFlow(2)]
for _ in range(5)] + [[StructuredAffineFlow(2)]], [])
flow = NormalizingFlow(
*blocks,
base_dist=base_dist,
)
opt = optim.Adam(flow.parameters(), lr=1e-2)
# %%
count_parameters(flow)
# %%
n_epochs = 2000
bs = 512
clip_grad = 1e6
# %%
writer = SummaryWriter(f"../tensorboard_logs/{now_str()}")
best_loss = torch.Tensor([float("+inf")])
attempts = 0
elif args.POTENTIAL == 'POT_3':
target_density = pot_3
elif args.POTENTIAL == 'POT_4':
target_density = pot_4
else:
raise ValueError("Invalid potential function option passed")
plot_pot_func(target_density)
plt.savefig(pjoin(OUT_DIR, 'target_density.png'))
plt.close()
model = NormalizingFlow(2, args.N_FLOWS)
# RMSprop is what they used in renzende et al
opt = torch.optim.RMSprop(params=model.parameters(),
lr=args.LR,
momentum=args.MOMENTUM)
scheduler = ReduceLROnPlateau(opt, 'min', patience=1000)
losses = []
for iter_ in range(args.N_ITERS):
if iter_ % 100 == 0:
print("Iteration {}".format(iter_))
samples = Variable(random_normal_samples(args.BATCH_SIZE))
z_k, sum_log_det = model(samples)
log_p_x = target_density(z_k)
colors[idx_3] = 3
# %%
plt.scatter(X0[:, 0], X0[:, 1], s=5, c=colors)
# %%
blocks = sum(
[[StructuredAffineFlow(2), PReLUFlow(2)] for _ in range(5)] + [[StructuredAffineFlow(2)]],
[])
flow = NormalizingFlow(
*blocks,
base_dist=base_dist,
)
opt = optim.Adam(flow.parameters(), lr=2e-3)
# %%
count_parameters(flow)
# %%
n_epochs = 50000
bs = 512
# %%
writer = SummaryWriter(f"./tensorboard_logs/{now_str()}")
best_loss = torch.Tensor([float("+inf")])
attempts = 0
# %%
plt.scatter(X0[:, 0], X0[:, 1], s=5, c=colors)
# %%
blocks = sum(
[[StructuredAffineFlow(2), PReLUFlow(2), BatchNormFlow(2)] for _ in range(20)] + [[StructuredAffineFlow(2)]],
[])
# %%
flow = NormalizingFlow(
*blocks,
base_dist=base_dist,
)
opt = optim.Adam(flow.parameters(), lr=1e-2)
# %%
count_parameters(flow)
# %%
n_epochs = 150
bs = 64
# %%
writer = SummaryWriter(f"../tensorboard_logs/{now_str()}")
best_loss = torch.Tensor([float("+inf")])
attempts = 0