def __init__(self, input_dim, hidden_size):
super(SelfAttention, self).__init__()
self.input_dim = input_dim
self.hidden_size = hidden_size
self.linear_query = CustomLinear(input_dim, hidden_size, bias=True, bn=True, act=t.nn.ReLU())
self.linear_passage = CustomLinear(input_dim, hidden_size, bias=True, bn=True, act=t.nn.ReLU())
self.gate = CustomLinear(input_dim, input_dim, bias=False, bn=True, act=t.nn.Sigmoid())
def __init__(self, input_dim, hidden_size, num_head, output_dim):
super(MultiHeadSelfAttention, self).__init__()
self.num_head = num_head
self.hidden_size = hidden_size
self.passage_linear = CustomLinear(input_dim, hidden_size)
self.query_linear = CustomLinear(input_dim, hidden_size)
self.output_linear = CustomLinear(num_head*hidden_size, output_dim)
def __init__(self, input_dim, hidden_size):
super(AttentionPooling, self).__init__()
self.linear1 = CustomLinear(input_dim, hidden_size, act=t.nn.Tanh())
self.linear2 = CustomLinear(hidden_size,
1,
bias=False,
bn=False,
act=None)
def __init__(self, query_dim, passage_dim, hidden_size):
super(DotAttention, self).__init__()
self.query_dim = query_dim
self.passage_dim = passage_dim
self.hidden_size = hidden_size
self.linear_query = CustomLinear(query_dim, hidden_size, True, True,
t.nn.ReLU())
self.linear_passage = CustomLinear(passage_dim, hidden_size, True,
True, t.nn.ReLU())
def __init__(self, embedding_dim, hidden_size, num_head, output_dim):
super(EncoderFact, self).__init__()
self.multiheadselfattention = MultiHeadSelfAttention(
input_dim=embedding_dim,
hidden_size=hidden_size,
num_head=num_head,
output_dim=output_dim)
self.layernorm = LayerNorm(output_dim)
self.feedforward1 = CustomLinear(output_dim, output_dim)
self.feedforward2 = CustomLinear(output_dim, output_dim)
def __init__(self, input_dim):
super(Gate, self).__init__()
self.nonlinear_input = CustomLinear(input_dim,
input_dim,
bias=True,
bn=True,
act=t.nn.ReLU())
self.gate = CustomLinear(input_dim,
input_dim,
bias=False,
bn=True,
act=t.nn.Sigmoid())
def __init__(self, fact_dim, accusation_dim, hidden_size):
super(Fusion, self).__init__()
self.fact_accusation_attention = DotAttention(accusation_dim, fact_dim,
hidden_size)
self.fusion_nonlinear = CustomLinear(fact_dim,
fact_dim,
bias=True,
bn=True,
act=t.nn.ReLU())
self.gate = CustomLinear(fact_dim,
fact_dim,
bias=False,
bn=True,
act=t.nn.Sigmoid())
def __init__(self, query_dim, passage_dim, hidden_size):
super(DotAttentionGated, self).__init__()
self.query_dim = query_dim
self.passage_dim = passage_dim
self.hidden_size = hidden_size
self.linear_query = CustomLinear(query_dim, hidden_size, True, True,
t.nn.ReLU())
self.linear_passage = CustomLinear(passage_dim, hidden_size, True,
True, t.nn.ReLU())
self.gate = CustomLinear(hidden_size,
hidden_size,
bias=False,
bn=True,
act=t.nn.Sigmoid())