def __init__(self, config):
super(Model, self).__init__(config=config)
self.convolutional_encoder = get_component("convolutional_encoder",
config)
self.variational_encoder = get_component("variational_encoder", config)
self.state_transition_model = get_component("state_transition_model",
config)
self.weights = self.get_weights_dict()
def __init__(self, config):
super(Model, self).__init__(config=config)
self.convolutional_encoder = get_component("convolutional_encoder",
config)
self.state_transition_model = get_component(
"stochastic_state_transition_model", config)
self.convolutional_decoder = get_component(
"stochastic_convolutional_decoder", config)
self.prior_model = get_component("prior_model", config)
self.posterior_model = get_component("posterior_model", config)
self.use_consistency_model = False
if (self.config.model.imagination_model.consistency_model.alpha !=
0.0):
self.use_consistency_model = True
_consistency_model_name = self.config.model.imagination_model.consistency_model.name
self.is_consistency_model_euclidean = False
if _consistency_model_name == "euclidean":
self.is_consistency_model_euclidean = True
self.consistency_model = get_component(
"consistency_model.{}".format(_consistency_model_name), config)
self.use_imitation_learning_model = False
if self.config.model.imagination_model.imitation_learning_model.should_train:
self.use_imitation_learning_model = True
if (self.use_imitation_learning_model):
self.imitation_learning_model = get_component(
"imitation_learning_model.{}".format(
self.config.model.imagination_model.
imitation_learning_model.name), config)