def get_default_config() -> ConfigDict:
c = ConfigDict(
num_components=1,
train_epochs=1000,
# Component
components_learning_rate=1e-3,
components_batch_size=1000,
components_num_epochs=10,
components_net_reg_loss_fact=0.,
components_net_drop_prob=0.0,
components_net_hidden_layers=[50, 50],
# Gating
gating_learning_rate=1e-3,
gating_batch_size=1000,
gating_num_epochs=10,
gating_net_reg_loss_fact=0.,
gating_net_drop_prob=0.0,
gating_net_hidden_layers=[50, 50],
# Density Ratio Estimation
dre_reg_loss_fact=
0.0, # Scaling Factor for L2 regularization of density ratio estimator
dre_early_stopping=
True, # Use early stopping for density ratio estimator training
dre_drop_prob=
0.0, # If smaller than 1 dropout with keep prob = 'keep_prob' is used
dre_num_iters=
1000, # Number of density ratio estimator steps each iteration (i.e. max number if early stopping)
dre_batch_size=
1000, # Batch size for density ratio estimator training
dre_hidden_layers=[
30, 30
] # width of density ratio estimator hidden layers
)
c.finalize_adding()
return c
def get_default_config():
c = ConfigDict(
num_components=1,
samples_per_component=500,
train_epochs=1000,
initialization="random",
# Component Updates
component_kl_bound=0.01,
# Mixture Updates
weight_kl_bound=0.01,
# Density Ratio Estimation
dre_reg_loss_fact=
0.0, # Scaling Factor for L2 regularization of density ratio estimator
dre_early_stopping=
True, # Use early stopping for density ratio estimator training
dre_drop_prob=
0.0, # If smaller than 1 dropout with keep prob = 'keep_prob' is used
dre_num_iters=
1000, # Number of density ratio estimator steps each iteration (i.e. max number if early stopping)
dre_batch_size=
1000, # Batch size for density ratio estimator training
dre_hidden_layers=[
30, 30
] # width of density ratio estimator hidden layers
)
c.finalize_adding()
return c
def get_default_config() -> ConfigDict:
config = ConfigDict(num_basis=15,
bandwidth=3,
trans_net_hidden_units=[64, 64],
trans_net_hidden_activation="Tanh",
learn_trans_covar=True,
trans_covar=0.1,
learn_initial_state_covar=False,
initial_state_covar=10,
learning_rate=1e-3,
enc_out_norm='pre',
clip_gradients=True,
never_invalid=True)
config.finalize_adding()
return config
def get_default_config() -> ConfigDict:
config = ConfigDict(num_basis=15,
bandwidth=3,
trans_net_hidden_units=[],
control_net_hidden_units=[60],
trans_net_hidden_activation="Tanh",
control_net_hidden_activation='ReLU',
learn_trans_covar=True,
trans_covar=1,
learn_initial_state_covar=True,
initial_state_covar=1,
learning_rate=7e-3,
enc_out_norm='post',
clip_gradients=True,
never_invalid=True)
config.finalize_adding()
return config