def _init_model(self):
self.model = BNN(input_size=self.data.num_features,
hidden_sizes=self.model_params['hidden_sizes'],
output_size=self.data.num_classes,
act_func=self.model_params['act_func'],
prior_prec=self.model_params['prior_prec'],
prec_init=self.optim_params['prec_init'])
if self.use_cuda:
self.model = self.model.cuda()
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=[])