def __init__(self, memory_size: int, answer_size: int, num_labels: int):
super(OutputModule, self).__init__()
self.memory_size = memory_size
self.answer_size = answer_size
self.combination = torch.nn.Linear(answer_size, memory_size)
initalise_weights(xavier_normal_, self.combination)
def __init__(self,
word_embeddings: TextFieldEmbedder,
encoder: Seq2VecEncoder,
embedding_dropout: float = 0.5,
encoder_dropout: float = 0.5):
super(AnswerModule, self).__init__()
self.word_embeddings = word_embeddings
self.encoder = encoder
self.embedding_dropout = torch.nn.Dropout(embedding_dropout)
self.encoder_dropout = torch.nn.Dropout(encoder_dropout)
initalise_weights(xavier_normal_, self.encoder)
def __init__(self,
word_embeddings: TextFieldEmbedder,
sentence_encoder: Seq2VecEncoder,
document_encoder: Seq2VecEncoder,
embedding_dropout: float = 0.5,
encoder_dropout: float = 0.5):
super(InputModule, self).__init__()
self.word_embeddings = word_embeddings
self.sentence_encoder = sentence_encoder
self.document_encoder = document_encoder
self.embedding_dropout = torch.nn.Dropout(embedding_dropout)
self.encoder_dropout = torch.nn.Dropout(encoder_dropout)
initalise_weights(xavier_normal_, self.sentence_encoder)
initalise_weights(xavier_normal_, self.document_encoder)
def __init__(self, hidden_dim: int, num_hops: int, dropout: float = 0.5):
super(MemoryModule, self).__init__()
self.hidden_dim = hidden_dim
self.attention_gru = AttentionGRU(hidden_dim, hidden_dim)
self.gate_nn = torch.nn.Sequential(
torch.nn.Linear(8 * hidden_dim, hidden_dim),
torch.nn.Tanh(),
torch.nn.Linear(hidden_dim, 1, bias=False),
)
memory = torch.nn.Sequential(
torch.nn.Linear(4 * hidden_dim, hidden_dim),
torch.nn.ReLU(inplace=True),
)
self.memories = clone_module(memory, num_hops)
self.dropout = torch.nn.Dropout(dropout)
initalise_weights(xavier_normal_, self.attention_gru)
initalise_weights(xavier_normal_, self.gate_nn)
for memory in self.memories:
initalise_weights(xavier_normal_, memory)