def debug_z_by_group_matrix(t):
fig, ax = plt.subplots()
W_col_norms = torch.sqrt(
torch.sum(torch.pow(generative_net.Ws[t].data, 2), dim=2))
ax.imshow(W_col_norms, aspect='equal')
ax.set_xlabel('z')
ax.set_ylabel('group')
ax.xaxis.tick_top()
ax.xaxis.set_label_position('top')
lr = 1e-3
optimizer = torch.optim.Adam([
{
'params': inference_net.parameters(),
'lr': lr
},
# {'params': [inference_net_log_stddev], 'lr': lr},
{
'params': generative_net.group_generators_parameters(),
'lr': lr
},
{
'params': [
gen.sigma_net.extra_args[0]
for gen in generative_net.group_generators
],
'lr':
lr
}
lr = 1e-4
betas = (0.9,0.999)
lr_inferencenet = 1e-4
betas_inferencenet = (0.9,0.999)
lr_generativenet = 1e-4
betas_generativenet = (0.9,0.999)
optimizer = torch.optim.Adam([
{'params': inference_net.parameters(), 'lr': lr_inferencenet, 'betas':betas_inferencenet},
# {'params': [inference_net_log_stddev], 'lr': lr},
{'params': generative_net.group_generators_parameters(), 'lr': lr_generativenet,'betas': betas_generativenet},
{'params': [gen.sigma_net.extra_args[0] for gen in generative_net.group_generators], 'lr': lr, 'betas':betas}
])
Ws_lr = 1e-4
optimizer_Ws = torch.optim.SGD([
{'params': [generative_net.Ws], 'lr': Ws_lr, 'momentum': 0}
])
vae = OIVAE(
inference_model=inference_net,
generative_model=generative_net,
#prior_z=prior_z,