def nsc_document_layer(x,
max_doc_len,
doc_len,
identities,
hidden_size,
doc_hop_cnt=1,
bidirectional_lstm=True,
lstm_cells=None,
auged=False,
attention_type='additive'):
outputs = []
with tf.variable_scope('document_layer'):
# lstm_outputs, _state = lstm(outputs, doc_len, hidden_size, 'lstm')
lstm_bkg, _state = lstm(x,
doc_len,
hidden_size,
'lstm_bkg',
bidirectional=bidirectional_lstm,
lstm_cells=lstm_cells)
lstm_outputs = lstm_bkg
doc_bkg = [i for i in identities]
for ihop in range(doc_hop_cnt):
if doc_bkg:
doc_bkg[0] = hop('hop',
lstm_outputs,
lstm_bkg,
doc_bkg[0],
doc_bkg[1:],
doc_len,
max_doc_len,
'',
auged=auged,
attention_type=attention_type)
output = doc_bkg[0]
else:
output = hop('hop',
lstm_outputs,
lstm_bkg,
None,
None,
doc_len,
max_doc_len,
'',
auged=auged,
attention_type=attention_type)
outputs.append(output)
outputs = tf.concat(outputs, axis=-1)
return outputs
def dnsc(self, x, max_sen_len, max_doc_len, sen_len, doc_len, identities):
x = tf.reshape(x, [-1, max_sen_len, self.emb_dim])
sen_len = tf.reshape(sen_len, [-1])
def lstm(inputs, sequence_length, hidden_size, scope):
cell_fw = tf.nn.rnn_cell.LSTMCell(hidden_size // 2,
forget_bias=0.,
initializer=xavier())
cell_bw = tf.nn.rnn_cell.LSTMCell(hidden_size // 2,
forget_bias=0.,
initializer=xavier())
outputs, state = tf.nn.bidirectional_dynamic_rnn(
cell_fw=cell_fw,
cell_bw=cell_bw,
inputs=inputs,
sequence_length=sequence_length,
dtype=tf.float32,
scope=scope)
outputs = tf.concat(outputs, axis=2)
return outputs, state
with tf.variable_scope('sentence_layer'):
# lstm_outputs, _state = lstm(x, sen_len, self.hidden_size, 'lstm')
# lstm_outputs = tf.reshape(lstm_outputs, [-1, max_sen_len, self.hidden_size])
lstm_bkg, _state = lstm(x, sen_len, self.hidden_size, 'lstm_bkg')
lstm_bkg = tf.reshape(lstm_bkg,
[-1, max_sen_len, self.hidden_size])
lstm_outputs = lstm_bkg
sen_bkg = [
tf.reshape(tf.tile(bkg[:, None, :], (1, max_doc_len, 1)),
(-1, self.hidden_size)) for bkg in identities
]
sen_bkg = hop('attention', lstm_outputs, lstm_bkg, sen_bkg[0],
sen_bkg[1:], sen_len, max_sen_len, '')
outputs = tf.reshape(sen_bkg, [-1, max_doc_len, self.hidden_size])
with tf.variable_scope('document_layer'):
# lstm_outputs, _state = lstm(outputs, doc_len, self.hidden_size, 'lstm')
lstm_bkg, _state = lstm(outputs, doc_len, self.hidden_size,
'lstm_bkg')
lstm_outputs = lstm_bkg
doc_bkg = [i for i in identities]
doc_bkg = hop('attention', lstm_outputs, lstm_bkg, doc_bkg[0],
doc_bkg[1:], doc_len, max_doc_len, '')
outputs = doc_bkg
return outputs
def nsc_sentence_layer(x,
max_sen_len,
max_doc_len,
sen_len,
identities,
hidden_size,
emb_dim,
sen_hop_cnt=1,
bidirectional_lstm=True,
lstm_cells=None,
auged=False,
attention_type='additive'):
x = tf.reshape(x, [-1, max_sen_len, x.shape[-1]])
sen_len = tf.reshape(sen_len, [-1])
outputs = []
with tf.variable_scope('sentence_layer'):
# lstm_outputs, _state = lstm(x, sen_len, hidden_size, 'lstm')
# lstm_outputs = tf.reshape(lstm_outputs, [-1, max_sen_len, hidden_size])
lstm_bkg, _state = lstm(x,
sen_len,
hidden_size,
'lstm_bkg',
bidirectional=bidirectional_lstm,
lstm_cells=lstm_cells)
lstm_bkg = tf.reshape(lstm_bkg, [-1, max_sen_len, hidden_size])
lstm_outputs = lstm_bkg
sen_bkg = [
tf.reshape(tf.tile(bkg[:, None, :], (1, max_doc_len, 1)),
(-1, emb_dim)) for bkg in identities
]
for ihop in range(sen_hop_cnt):
if sen_bkg:
sen_bkg[0] = hop('hop',
lstm_outputs,
lstm_bkg,
sen_bkg[0],
sen_bkg[1:],
sen_len,
max_sen_len,
'',
auged=auged,
attention_type=attention_type)
output = sen_bkg[0]
else:
output = hop('hop',
lstm_outputs,
lstm_bkg,
None,
None,
sen_len,
max_sen_len,
'',
auged=auged,
attention_type=attention_type)
outputs.append(output)
# outputs = tf.concat(outputs, axis=-1)
# outputs = tf.layers.dense(outputs, hidden_size, use_bias=False)
outputs = tf.reshape(output, [-1, max_doc_len, hidden_size])
return outputs