def train(data_hparams, model_hparams, training_hparams):
"""Executes the training pipeline for SNPs."""
all_context_action_pairs, all_rewards = procure_dataset(data_hparams,
num_wheels=100,
seed=0)
train_dataset = (all_context_action_pairs, all_rewards)
all_context_action_pairs, all_rewards = procure_dataset(data_hparams,
num_wheels=10,
seed=42)
valid_dataset = (all_context_action_pairs, all_rewards)
model = regressor.Regressor(
input_dim=data_hparams.context_dim + data_hparams.num_actions,
output_dim=1,
x_encoder_sizes=model_hparams.x_encoder_sizes,
x_y_encoder_sizes=model_hparams.x_y_encoder_sizes,
global_latent_net_sizes=model_hparams.global_latent_net_sizes,
local_latent_net_sizes=model_hparams.local_latent_net_sizes,
heteroskedastic_net_sizes=model_hparams.heteroskedastic_net_sizes,
att_type=model_hparams.att_type,
att_heads=model_hparams.att_heads,
uncertainty_type=model_hparams.uncertainty_type,
mean_att_type=model_hparams.mean_att_type,
scale_att_type_1=model_hparams.scale_att_type_1,
scale_att_type_2=model_hparams.scale_att_type_2,
activation=model_hparams.activation,
output_activation=model_hparams.output_activation,
data_uncertainty=model_hparams.data_uncertainty,
local_variational=model_hparams.local_variational)
training_loop(train_dataset, valid_dataset, model, training_hparams)
def __init__(self, name, hparams, optimizer='RMS'):
self.name = name
self.hparams = hparams
self.verbose = getattr(hparams, 'verbose', True)
self.update_freq_lr = hparams.training_freq
self.update_freq_nn = hparams.training_freq_network
self.t = 0
self.num_epochs = hparams.training_epochs
self.data_h = contextual_dataset.ContextualDataset(hparams.context_dim,
hparams.num_actions,
intercept=False)
self.gradient_updates = tf.Variable(0, trainable=False)
if self.hparams.activate_decay:
self.lr = tf.train.inverse_time_decay(self.hparams.initial_lr,
self.gradient_updates, 1,
self.hparams.lr_decay_rate)
else:
self.lr = tf.Variable(self.hparams.initial_lr, trainable=False)
optimizer = tf.train.RMSPropOptimizer(self.lr)
self._optimizer_config = {
'optimizer': optimizer,
'max_grad_norm': hparams.max_grad_norm
}
if self.verbose:
print('Initializing model {}.'.format(self.name))
self.snp = regressor.Regressor(
input_dim=hparams.context_dim + hparams.num_actions,
output_dim=1,
x_encoder_sizes=hparams.x_encoder_sizes,
x_y_encoder_sizes=hparams.x_y_encoder_sizes,
global_latent_net_sizes=hparams.global_latent_net_sizes,
local_latent_net_sizes=hparams.local_latent_net_sizes,
heteroskedastic_net_sizes=hparams.heteroskedastic_net_sizes,
att_type=hparams.att_type,
att_heads=hparams.att_heads,
uncertainty_type=hparams.uncertainty_type,
mean_att_type=hparams.mean_att_type,
scale_att_type_1=hparams.scale_att_type_1,
scale_att_type_2=hparams.scale_att_type_2,
activation=hparams.activation,
output_activation=hparams.output_activation,
data_uncertainty=hparams.data_uncertainty,
local_variational=hparams.local_variational,
model_path=hparams.model_path)
self._step = tf.function(utils.mse_step.python_function) # pytype: disable=module-attr
self._one_hot_vectors = tf.one_hot(indices=np.arange(
hparams.num_actions),
depth=hparams.num_actions)
def __init__(self, name, hparams):
self.name = name
self.hparams = hparams
self.verbose = getattr(hparams, 'verbose', True)
self._is_anp = getattr(hparams, 'is_anp', False)
if self._is_anp:
input_dim = hparams.context_dim
output_dim = hparams.num_actions
else:
input_dim = hparams.context_dim + hparams.num_actions
output_dim = 1
self.t = 0
self.data_h = contextual_dataset.ContextualDataset(hparams.context_dim,
hparams.num_actions,
intercept=False)
if self.verbose:
print('Initializing model {}.'.format(self.name))
self.snp = regressor.Regressor(
input_dim=input_dim,
output_dim=output_dim,
x_encoder_sizes=hparams.x_encoder_sizes,
x_y_encoder_sizes=hparams.x_y_encoder_sizes,
global_latent_net_sizes=hparams.global_latent_net_sizes,
local_latent_net_sizes=hparams.local_latent_net_sizes,
heteroskedastic_net_sizes=hparams.heteroskedastic_net_sizes,
att_type=hparams.att_type,
att_heads=hparams.att_heads,
uncertainty_type=hparams.uncertainty_type,
mean_att_type=hparams.mean_att_type,
scale_att_type_1=hparams.scale_att_type_1,
scale_att_type_2=hparams.scale_att_type_2,
activation=hparams.activation,
output_activation=hparams.output_activation,
data_uncertainty=hparams.data_uncertainty,
local_variational=hparams.local_variational,
model_path=hparams.model_path)
self._one_hot_vectors = tf.one_hot(indices=np.arange(
hparams.num_actions),
depth=hparams.num_actions)