def _match(self):
"""
The core of RC model, get the question-aware passage encoding with either BIDAF or MLSTM
"""
with tf.variable_scope('match'):
if self.algo == 'MLSTM':
match_layer = MatchLSTMLayer(self.hidden_size)
elif self.algo == 'BIDAF':
match_layer = AttentionFlowMatchLayer(self.hidden_size)
else:
raise NotImplementedError(
'The algorithm {} is not implemented.'.format(self.algo))
self.match_p_encodes, _ = match_layer.match(
self.sep_p_encodes, self.sep_q_encodes, self.p_t_length,
self.q_t_length)
self.match_p_encodes, _ = rnn('bi-lstm',
self.match_p_encodes,
self.p_t_length,
self.hidden_size,
layer_num=1)
if self.use_dropout:
self.match_p_encodes = tf.nn.dropout(self.match_p_encodes,
self.dropout_keep_prob)
def _match(self):
if self.algo == 'MLSTM':
match_layer = MatchLSTMLayer(self.hidden_size)
elif self.algo == 'BIDAF':
match_layer = AttentionFlowMatchLayer5(self.hidden_size)
else:
raise NotImplementedError('The algorithm {} is not implemented.'.format(self.algo))
self.match_p_encodes, _ = match_layer.match(self.sep_p_encodes, self.sep_q_encodes,self.c_mask,self.q_mask,1-self.dropout,self.p_length, self.q_length)
def _match(self):
"""
The core of QDR model, get the query-aware passage encoding with either BIDAF or MLSTM
"""
if self.algo == 'MLSTM':
match_layer = MatchLSTMLayer(self.hidden_size)
elif self.algo == 'BIDAF':
match_layer = AttentionFlowMatchLayer(self.hidden_size)
else:
raise NotImplementedError('The algorithm {} is not implemented.'.format(self.algo))
self.match_p_encodes, _ = match_layer.match(self.sep_p_encodes, self.sep_q_encodes,
self.p_length, self.q_length)
if self.use_dropout:
self.match_p_encodes = tf.nn.dropout(self.match_p_encodes, self.dropout_keep_prob)
def _match(self):
"""
The core of RC model, get the question-aware passage encoding with either BIDAF or MLSTM
"""
if self.algo == 'MLSTM':
match_layer = MatchLSTMLayer(self.hidden_size)
elif self.algo == 'BIDAF':
match_layer = AttentionFlowMatchLayer(self.hidden_size)
else:
raise NotImplementedError('The algorithm {} is not implemented.'.format(self.algo))
self.match_p_encodes, _ = match_layer.match(self.sep_p_encodes, self.sep_q_encodes,
self.p_length, self.q_length)
if self.use_dropout:
self.match_p_encodes = tf.nn.dropout(self.match_p_encodes, self.dropout_keep_prob)
def _match(self):
"""
阅读理解模型的核心.使用BiDAF或MLSTM来获取文章对问题的感知情况
"""
if self.algo == 'MLSTM':
match_layer = MatchLSTMLayer(self.hidden_size)
elif self.algo == 'BIDAF':
match_layer = AttentionFlowMatchLayer(self.hidden_size)
self.match_p_encodes, self.context2question_attn, self.question2context_attn, _ = match_layer.match(
self.sep_p_encodes, self.sep_q_encodes, self.p_length,
self.q_length)
if self.use_dropout:
self.match_p_encodes = tf.nn.dropout(self.match_p_encodes,
self.dropout_keep_prob)
def _self_att(self):
"""
Self attention layer
"""
with tf.variable_scope('self_att'):
if self.algo == 'MLSTM':
self_att_layer = MatchLSTMLayer(self.hidden_size)
elif self.algo == 'BIDAF':
self_att_layer = AttentionFlowMatchLayer(self.hidden_size)
else:
raise NotImplementedError(
'The algorithm {} is not implemented.'.format(self.algo))
self.self_att_p_encodes, _ = self_att_layer.match(
self.match_p_encodes, self.match_p_encodes, self.p_t_length,
self.p_t_length)
if self.use_dropout:
self.self_att_p_encodes = tf.nn.dropout(
self.self_att_p_encodes, self.dropout_keep_prob)
def _match(self):
"""
The core of RC model, get the question-aware passage encoding with either BIDAF or MLSTM
"""
if self.simple_net in [0]:
return
if self.algo == 'MLSTM':
match_layer = MatchLSTMLayer(self.hidden_size)
elif self.algo == 'BIDAF':
match_layer = AttentionFlowMatchLayer(self.hidden_size)
else:
raise NotImplementedError(
'The algorithm {} is not implemented.'.format(self.algo))
self.match_p_encodes, self.sim_matrix, self.context2question_attn, self.b, self.question2context_attn = match_layer.match(
self.sep_p_encodes, self.sep_q_encodes, self.p_length,
self.q_length)
if self.use_dropout:
self.match_p_encodes = tf.nn.dropout(self.match_p_encodes,
self.dropout_keep_prob)
def _match(self):
if self.algo == 'MLSTM':
match_layer = MatchLSTMLayer(self.hidden_size)
elif self.algo == 'BIDAF':
match_layer = AttentionFlowMatchLayer(self.hidden_size)
else:
raise NotImplementedError(
'The algorithm {} is not implemented.'.format(self.algo))
self.match_p_encodes, _ = match_layer.match(self.sep_p_encodes,
self.sep_q_encodes,
self.p_length,
self.q_length)
self.match_p_encodes = tf.layers.dense(self.match_p_encodes,
self.hidden_size * 2,
activation=tf.nn.relu)
if self.use_dropout:
self.match_p_encodes = tf.nn.dropout(self.match_p_encodes,
1 - self.dropout)
#!/usr/bin/python3