class ExperimentBBBMLPReg(Experiment):
def __init__(self, data_set, model_params, train_params, optim_params, evals_per_epoch=1, normalize_x=False, normalize_y=False, results_folder="./results", data_folder=DEFAULT_DATA_FOLDER, use_cuda=torch.cuda.is_available()):
super(type(self), self).__init__(data_set, model_params, train_params, optim_params, evals_per_epoch, normalize_x, normalize_y, results_folder, data_folder, use_cuda)
# Define name for experiment class
experiment_name = "bbb_mlp_reg"
# Define folder name for results
self.folder_name = folder_name(experiment_name, data_set, model_params, train_params, optim_params, results_folder)
# Initialize model
self.model = BNN(input_size = self.data.num_features,
hidden_sizes = model_params['hidden_sizes'],
output_size = self.data.num_classes,
act_func = model_params['act_func'],
prior_prec = model_params['prior_prec'],
prec_init = optim_params['prec_init'])
if use_cuda:
self.model = self.model.cuda()
# Define prediction function
def prediction(x):
mu_list = [self.model(x) for _ in range(self.train_params['train_mc_samples'])]
return mu_list
self.prediction = prediction
# Define objective
def objective(mu_list, y):
return metrics.avneg_elbo_gaussian(mu_list, y, tau = self.model_params['noise_prec'], train_set_size = self.data.get_train_size(), kl = self.model.kl_divergence())
self.objective = objective
# Initialize optimizer
self.optimizer = Adam(self.model.parameters(),
lr = optim_params['learning_rate'],
betas = optim_params['betas'],
eps = 1e-8)
# Initialize metric history
self.metric_history = dict(elbo_neg_ave = [],
train_pred_logloss=[], train_pred_rmse=[],
test_pred_logloss=[], test_pred_rmse=[])
# Initialize final metric
self.final_metric = dict(elbo_neg_ave = [],
train_pred_logloss=[], train_pred_rmse=[],
test_pred_logloss=[], test_pred_rmse=[])
def _evaluate_model(self, metric_dict, x_train, y_train, x_test, y_test):
# Unnormalize noise precision
if self.normalize_y:
tau = self.model_params['noise_prec'] / (self.y_std**2)
else:
tau = self.model_params['noise_prec']
# Normalize train x
if self.normalize_x:
x_train = (x_train-self.x_means)/self.x_stds
# Get train predictions
mu_list = [self.model(x_train) for _ in range(self.train_params['eval_mc_samples'])]
# Unnormalize train predictions
if self.normalize_y:
mu_list = [self.y_mean + self.y_std * mu for mu in mu_list]
# Store train metrics
metric_dict['train_pred_logloss'].append(metrics.predictive_avneg_loglik_gaussian(mu_list, y_train, tau = tau).detach().cpu().item())
metric_dict['train_pred_rmse'].append(metrics.predictive_rmse(mu_list, y_train).detach().cpu().item())
metric_dict['elbo_neg_ave'].append(metrics.avneg_elbo_gaussian(mu_list, y_train, tau = tau, train_set_size = self.data.get_train_size(), kl = self.model.kl_divergence()).detach().cpu().item())
# Normalize test x
if self.normalize_x:
x_test = (x_test-self.x_means)/self.x_stds
# Get test predictions
mu_list = [self.model(x_test) for _ in range(self.train_params['eval_mc_samples'])]
# Unnormalize test predictions
if self.normalize_y:
mu_list = [self.y_mean + self.y_std * mu for mu in mu_list]
# Store test metrics
metric_dict['test_pred_logloss'].append(metrics.predictive_avneg_loglik_gaussian(mu_list, y_test, tau = tau).detach().cpu().item())
metric_dict['test_pred_rmse'].append(metrics.predictive_rmse(mu_list, y_test).detach().cpu().item())
def _print_progress(self, epoch):
# Print progress
print('Epoch [{}/{}], test_pred_rmse: {:.4f}, Logloss: {:.4f}, Test Logloss: {:.4f}'.format(
epoch+1,
self.train_params['num_epochs'],
self.metric_history['test_pred_rmse'][-1],
self.metric_history['train_pred_logloss'][-1],
self.metric_history['test_pred_logloss'][-1]))
class ExperimentBBBMLPClass(Experiment):
def __init__(self,
data_set,
model_params,
train_params,
optim_params,
evals_per_epoch=1,
normalize_x=False,
results_folder="./results",
data_folder=DEFAULT_DATA_FOLDER,
use_cuda=torch.cuda.is_available()):
super(type(self),
self).__init__(data_set, model_params, train_params,
optim_params, evals_per_epoch, normalize_x,
results_folder, data_folder, use_cuda)
# Define name for experiment class
experiment_name = "bbb_mlp_class"
# Define folder name for results
self.folder_name = folder_name(experiment_name, data_set, model_params,
train_params, optim_params,
results_folder)
# Initialize model
self.model = BNN(input_size=self.data.num_features,
hidden_sizes=model_params['hidden_sizes'],
output_size=self.data.num_classes,
act_func=model_params['act_func'],
prior_prec=model_params['prior_prec'],
prec_init=optim_params['prec_init'])
if use_cuda:
self.model = self.model.cuda()
# Define prediction function
def prediction(x):
logits_list = [
self.model(x)
for _ in range(self.train_params['train_mc_samples'])
]
return logits_list
self.prediction = prediction
# Define objective
def objective(logits_list, y):
return metrics.avneg_elbo_categorical(
logits_list,
y,
train_set_size=self.data.get_train_size(),
kl=self.model.kl_divergence())
self.objective = objective
# Initialize optimizer
self.optimizer = Adam(self.model.parameters(),
lr=optim_params['learning_rate'],
betas=optim_params['betas'],
eps=1e-8)
# Initialize metric history
self.metric_history = dict(elbo_neg_ave=[],
train_pred_logloss=[],
train_pred_accuracy=[],
test_pred_logloss=[],
test_pred_accuracy=[])
# Initialize final metric
self.final_metric = dict(elbo_neg_ave=[],
train_pred_logloss=[],
train_pred_accuracy=[],
test_pred_logloss=[],
test_pred_accuracy=[])
def _evaluate_model(self, metric_dict, x_train, y_train, x_test, y_test):
# Normalize train x
if self.normalize_x:
x_train = (x_train - self.x_means) / self.x_stds
# Get train predictions
logits_list = [
self.model(x_train)
for _ in range(self.train_params['eval_mc_samples'])
]
# Store train metrics
metric_dict['train_pred_logloss'].append(
metrics.predictive_avneg_loglik_categorical(
logits_list, y_train).detach().cpu().item())
metric_dict['train_pred_accuracy'].append(
metrics.softmax_predictive_accuracy(logits_list,
y_train).detach().cpu().item())
metric_dict['elbo_neg_ave'].append(
metrics.avneg_elbo_categorical(
logits_list,
y_train,
train_set_size=self.data.get_train_size(),
kl=self.model.kl_divergence()).detach().cpu().item())
# Normalize test x
if self.normalize_x:
x_test = (x_test - self.x_means) / self.x_stds
# Get test predictions
logits_list = [
self.model(x_test)
for _ in range(self.train_params['eval_mc_samples'])
]
# Store test metrics
metric_dict['test_pred_logloss'].append(
metrics.predictive_avneg_loglik_categorical(
logits_list, y_test).detach().cpu().item())
metric_dict['test_pred_accuracy'].append(
metrics.softmax_predictive_accuracy(logits_list,
y_test).detach().cpu().item())
def _print_progress(self, epoch):
# Print progress
print(
'Epoch [{}/{}], Neg. Ave. ELBO: {:.4f}, Logloss: {:.4f}, Test Logloss: {:.4f}'
.format(epoch + 1, self.train_params['num_epochs'],
self.metric_history['elbo_neg_ave'][-1],
self.metric_history['train_pred_logloss'][-1],
self.metric_history['test_pred_logloss'][-1]))
def _closure(self, x, y):
self.optimizer.zero_grad()
logits = self.prediction(x)
loss = self.objective(logits, y)
loss.backward()
return loss