def __init__(self, word_features_vocab_sizes: List[int],
vec_features_size: int, hidden_size: int,
num_layers: int) -> None:
super().__init__()
self._word_features_encoder = maybe_cuda(
WordFeaturesEncoder(word_features_vocab_sizes, hidden_size, 1,
hidden_size))
self._features_classifier = maybe_cuda(
DNNScorer(hidden_size + vec_features_size, hidden_size,
num_layers))
def __init__(self, vec_features_size: int, word_feature_sizes: List[int],
hidden_size: int, num_layers: int) -> None:
super().__init__()
# Consider making the word embedding the same for all
# token-type inputs, also for tactic-type inputs
self._word_features_encoder = maybe_cuda(
WordFeaturesEncoder(word_feature_sizes, hidden_size, 1,
hidden_size))
self._features_classifier = maybe_cuda(
DNNScorer(hidden_size + vec_features_size, hidden_size,
num_layers))
def __init__(self,
input_vocab_size : int,
hidden_size : int,
num_layers : int) -> None:
super().__init__()
self._token_embedding = maybe_cuda(nn.Embedding(input_vocab_size, hidden_size))
self._scorer = maybe_cuda(DNNScorer(hidden_size, hidden_size, num_layers))
self._hidden_size = hidden_size
self._lstm = maybe_cuda(nn.LSTM(input_size=hidden_size,
hidden_size=hidden_size,
num_layers=1,
batch_first=True))
def __init__(self, num_tactics: int, hidden_size: int,
num_layers: int) -> None:
super().__init__()
self.num_tactics = num_tactics
# self.embedding = maybe_cuda(nn.Embedding(num_tactics, hidden_size))
self.dnn = maybe_cuda(DNNScorer(num_tactics, hidden_size, num_layers))