def __init__(self, args):
super(DialogueClassifierNetwork, self).__init__()
self.args = args
self.dialogue_embedder = DialogueEmbedder(args)
## Define class networkict_
dict_ = {
"input_size": args.output_input_size,
"hidden_size": args.output_hidden_size,
"output_size": 1,
"num_layers": args.output_num_layers[0],
}
self.current_dl_trasnformer1 = model_factory.get_model_by_name(
args.output_layer[0], args, kwargs=dict_)
self.current_dl_trasnformer2 = model_factory.get_model_by_name(
args.output_layer[0], args, kwargs=dict_)
dict_ = {
"input_size": args.output_input_size,
"hidden_size": args.output_hidden_size,
"output_size": 1,
"num_layers": args.output_num_layers[0],
}
self.next_dl_trasnformer = model_factory.get_model_by_name(
args.output_layer[0], args, kwargs=dict_)
self.prev_dl_trasnformer = model_factory.get_model_by_name(
args.output_layer[0], args, kwargs=dict_)
def __init__(self, args):
super(DialogueBowNetwork, self).__init__()
self.dialogue_embedder = DialogueEmbedder(args)
self.args = args
## Define class network
dict_ = {"input_size": args.output_input_size, "hidden_size": args.output_hidden_size[0], "num_layers" : args.output_num_layers[0],
"output_size": args.output_size}
self.next_bow_scorer = model_factory.get_model_by_name(args.output_layer[0], args, kwargs = dict_)
self.prev_bow_scorer = model_factory.get_model_by_name(args.output_layer[0], args, kwargs = dict_)
def __init__(self, args, **kwargs):
super(DialogueEmbedder, self).__init__()
self.args = args
if not args.fixed_token_encoder:
self.token_encoder = model_factory.get_model_by_name(
args.token_encoder, args)
if not args.fixed_utterance_encoder:
self.utterance_encoder = model_factory.get_model_by_name(
args.utterance_encoder, args)
conversation_dict = {
"input_size": args.embed_size,
"hidden_size": args.hidden_size,
"num_layers": args.num_layers
}
self.conversation_encoder = model_factory.get_model_by_name(
args.conversation_encoder, args, kwargs=conversation_dict)
def __init__(self, args): ## Initialize environment self.args = args self.updates = 0 ## If token encodings are not computed on the fly using character CNN based models but are obtained from a pretrained model if args.fixed_token_encoder: self.token_encoder = model_factory.get_embeddings(args.token_encoder, args) self.network = model_factory.get_model_by_name(args.network, args)
def __init__(self, args):
super(DialogueActClassifierNetwork, self).__init__()
self.dialogue_embedder = DialogueEmbedder(args)
## Define class network
## output labels size
dict_ = {
"input_size": args.output_input_size,
"hidden_size": args.output_hidden_size[0],
"num_layers": args.output_num_layers[0],
"output_size": args.output_size
}
self.classifier = model_factory.get_model_by_name(args.output_layer[0],
args,
kwargs=dict_)
