def __call__(self, iteration, loss, elbo, generative_model,
inference_network, control_variate):
if iteration % self.logging_interval == 0:
util.print_with_time(
'Iteration {} loss = {:.3f}, elbo = {:.3f}'.format(
iteration, loss, elbo))
self.loss_history.append(loss)
self.elbo_history.append(elbo)
if iteration % self.checkpoint_interval == 0:
stats_filename = util.get_stats_filename(self.model_folder)
util.save_object(self, stats_filename)
util.save_models(generative_model, inference_network,
self.pcfg_path, self.model_folder)
util.save_control_variate(control_variate, self.model_folder)
if iteration % self.eval_interval == 0:
self.p_error_history.append(
util.get_p_error(self.true_generative_model, generative_model))
self.q_error_to_true_history.append(
util.get_q_error(self.true_generative_model,
inference_network))
self.q_error_to_model_history.append(
util.get_q_error(generative_model, inference_network))
util.print_with_time(
'Iteration {} p_error = {:.3f}, q_error_to_true = {:.3f}, '
'q_error_to_model = {:.3f}'.format(
iteration, self.p_error_history[-1],
self.q_error_to_true_history[-1],
self.q_error_to_model_history[-1]))
def __call__(self, iteration, wake_theta_loss, wake_phi_loss, elbo,
generative_model, inference_network, optimizer_theta,
optimizer_phi):
if iteration % self.logging_interval == 0:
util.print_with_time(
'Iteration {} losses: theta = {:.3f}, phi = {:.3f}, elbo = '
'{:.3f}'.format(iteration, wake_theta_loss, wake_phi_loss,
elbo))
self.wake_theta_loss_history.append(wake_theta_loss)
self.wake_phi_loss_history.append(wake_phi_loss)
self.elbo_history.append(elbo)
if iteration % self.checkpoint_interval == 0:
stats_filename = util.get_stats_filename(self.model_folder)
util.save_object(self, stats_filename)
util.save_models(generative_model, inference_network,
self.pcfg_path, self.model_folder)
if iteration % self.eval_interval == 0:
self.p_error_history.append(
util.get_p_error(self.true_generative_model, generative_model))
self.q_error_to_true_history.append(
util.get_q_error(self.true_generative_model,
inference_network))
self.q_error_to_model_history.append(
util.get_q_error(generative_model, inference_network))
util.print_with_time(
'Iteration {} p_error = {:.3f}, q_error_to_true = {:.3f}, '
'q_error_to_model = {:.3f}'.format(
iteration, self.p_error_history[-1],
self.q_error_to_true_history[-1],
self.q_error_to_model_history[-1]))
def __call__(self, iteration, theta_loss, phi_loss, generative_model,
inference_network, memory, optimizer):
if iteration % self.logging_interval == 0:
util.print_with_time(
'Iteration {} losses: theta = {:.3f}, phi = {:.3f}'.format(
iteration, theta_loss, phi_loss))
self.theta_loss_history.append(theta_loss)
self.phi_loss_history.append(phi_loss)
if iteration % self.checkpoint_interval == 0:
stats_filename = util.get_stats_path(self.model_folder)
util.save_object(self, stats_filename)
util.save_models(generative_model, inference_network,
self.model_folder, iteration, memory)
if iteration % self.eval_interval == 0:
self.p_error_history.append(
util.get_p_error(self.true_generative_model, generative_model))
self.q_error_history.append(
util.get_q_error(self.true_generative_model, inference_network,
self.test_obss))
# TODO
# self.memory_error_history.append(util.get_memory_error(
# self.true_generative_model, memory, generative_model,
# self.test_obss))
util.print_with_time(
'Iteration {} p_error = {:.3f}, q_error_to_true = '
'{:.3f}'.format(iteration, self.p_error_history[-1],
self.q_error_history[-1]))
def __call__(self, iteration, loss, elbo, generative_model,
inference_network, optimizer):
if iteration % self.logging_interval == 0:
util.print_with_time(
'Iteration {} loss = {:.3f}, elbo = {:.3f}'.format(
iteration, loss, elbo))
self.loss_history.append(loss)
self.elbo_history.append(elbo)
if iteration % self.checkpoint_interval == 0:
stats_filename = util.get_stats_path(self.model_folder)
util.save_object(self, stats_filename)
util.save_models(generative_model, inference_network,
self.model_folder, iteration)
if iteration % self.eval_interval == 0:
self.p_error_history.append(
util.get_p_error(self.true_generative_model, generative_model))
self.q_error_history.append(
util.get_q_error(self.true_generative_model, inference_network,
self.test_obss))
stats = util.OnlineMeanStd()
for _ in range(10):
inference_network.zero_grad()
if self.train_mode == 'vimco':
loss, elbo = losses.get_vimco_loss(generative_model,
inference_network,
self.test_obss,
self.num_particles)
elif self.train_mode == 'reinforce':
loss, elbo = losses.get_reinforce_loss(
generative_model, inference_network, self.test_obss,
self.num_particles)
loss.backward()
stats.update([p.grad for p in inference_network.parameters()])
self.grad_std_history.append(stats.avg_of_means_stds()[1])
util.print_with_time(
'Iteration {} p_error = {:.3f}, q_error_to_true = '
'{:.3f}'.format(iteration, self.p_error_history[-1],
self.q_error_history[-1]))
