def __init__(self, param):
"""
:param param: embedding, hidden_size, dropout_p, encoder_dropout_p, encoder_direction_num, encoder_layer_num
"""
super(Model, self).__init__()
self.mode = param['mode']
self.hidden_size = param['hidden_size']
self.dropout_p = param['dropout_p']
self.encoder_dropout_p = param['encoder_dropout_p']
self.encoder_layer_num = param['encoder_layer_num']
self.is_bn = param['is_bn']
self.embedding = embedding.ExtendEmbedding(param['embedding'])
# encoder
input_size = self.embedding.embedding_dim - 9
self.encoder_a = encoder.Rnn(mode=self.mode,
input_size=input_size,
hidden_size=self.hidden_size,
dropout_p=self.encoder_dropout_p,
bidirectional=True,
layer_num=self.encoder_layer_num,
is_bn=False)
input_size = self.embedding.embedding_dim
self.encoder_p_q = encoder.Rnn(mode=self.mode,
input_size=input_size,
hidden_size=self.hidden_size,
dropout_p=self.encoder_dropout_p,
bidirectional=True,
layer_num=self.encoder_layer_num,
is_bn=True)
# match rnn
input_size = self.hidden_size * 2
self.match_rnn = match_rnn.MatchRNN(mode=self.mode,
input_size=input_size,
hidden_size=self.hidden_size,
dropout_p=self.dropout_p,
gated_attention=True,
is_bn=self.is_bn)
# addition_rnn
input_size = self.hidden_size * 2
self.addition_rnn = encoder.Rnn(mode=self.mode,
input_size=input_size,
hidden_size=self.hidden_size,
bidirectional=True,
dropout_p=self.dropout_p,
layer_num=1,
is_bn=self.is_bn)
# mean passage based on attn
self.mean_p = pointer.AttentionPooling(input_size=self.hidden_size * 2,
output_size=self.hidden_size *
2)
# outputs
self.choose = choose.Choose(self.hidden_size * 2, self.hidden_size * 2)
def __init__(self, param):
"""
:param param: embedding, hidden_size, dropout_p, encoder_dropout_p, encoder_direction_num, encoder_layer_num
"""
super(Model, self).__init__()
self.w2v_size = param['embedding'].shape[1]
self.vocab_size = param['embedding'].shape[0]
self.embedding_type = param['embedding_type']
self.embedding_is_training = param['embedding_is_training']
self.mode = param['mode']
self.hidden_size = param['hidden_size']
self.dropout_p = param['dropout_p']
self.encoder_dropout_p = param['encoder_dropout_p']
self.encoder_bidirectional = param['encoder_bidirectional']
self.encoder_layer_num = param['encoder_layer_num']
self.is_bn = param['is_bn']
if self.embedding_type == 'standard':
self.embedding = embedding.Embedding(param['embedding'])
is_bn = False
else:
self.embedding = embedding.ExtendEmbedding(param['embedding'])
is_bn = True
# encoder
input_size = self.embedding.embedding_dim
self.encoder = encoder.Rnn(
mode=self.mode,
input_size=input_size,
hidden_size=self.hidden_size,
dropout_p=self.encoder_dropout_p,
bidirectional=self.encoder_bidirectional,
layer_num=self.encoder_layer_num,
is_bn=is_bn
)
# match rnn
input_size = self.hidden_size * 2 if self.encoder_bidirectional else self.hidden_size
self.match_rnn = match_rnn.MatchRNN(
mode=self.mode,
input_size=input_size,
hidden_size=self.hidden_size,
dropout_p=self.dropout_p,
gated_attention=False,
is_bn=self.is_bn
)
# pointer
self.pointer_net = pointer.BoundaryPointer(
mode=self.mode,
input_size=self.hidden_size*2,
hidden_size=self.hidden_size,
dropout_p=self.dropout_p,
bidirectional=True,
is_bn=self.is_bn
)
def __init__(self, param):
"""
:param param: embedding, hidden_size, dropout_p, encoder_dropout_p, encoder_direction_num, encoder_layer_num
"""
super(Model, self).__init__()
self.mode = param['mode']
self.hidden_size = param['hidden_size']
self.dropout_p = param['dropout_p']
self.encoder_dropout_p = param['encoder_dropout_p']
self.encoder_layer_num = param['encoder_layer_num']
self.is_bn = param['is_bn']
self.embedding = embedding.ExtendEmbedding(param['embedding'])
# encoder
input_size = self.embedding.embedding_dim
self.encoder = encoder.Rnn(mode=self.mode,
input_size=input_size,
hidden_size=self.hidden_size,
dropout_p=self.encoder_dropout_p,
bidirectional=True,
layer_num=self.encoder_layer_num,
is_bn=True)
# match rnn
input_size = self.hidden_size * 2
self.match_rnn = match_rnn.MatchRNN(mode=self.mode,
input_size=input_size,
hidden_size=self.hidden_size,
dropout_p=self.dropout_p,
gated_attention=True,
is_bn=self.is_bn)
# addition_rnn
input_size = self.hidden_size * 2
self.addition_rnn = encoder.Rnn(mode=self.mode,
input_size=input_size,
hidden_size=self.hidden_size,
bidirectional=True,
dropout_p=self.dropout_p,
layer_num=1,
is_bn=self.is_bn)
# init state of pointer
self.init_state = pointer.AttentionPooling(
input_size=self.hidden_size * 2, output_size=self.hidden_size)
# pointer
self.pointer_net = pointer.BoundaryPointer(
mode=self.mode,
input_size=self.hidden_size * 2,
hidden_size=self.hidden_size,
dropout_p=self.dropout_p,
bidirectional=True,
is_bn=self.is_bn)