def contextual_bi_rnn(tensor_rep,
mask_rep,
hn,
cell_type,
only_final=False,
wd=0.,
keep_prob=1.,
is_train=None,
scope=None):
"""
fusing contextual information using bi-direction rnn
:param tensor_rep: [..., sl, vec]
:param mask_rep: [..., sl]
:param hn:
:param cell_type: 'gru', 'lstm', basic_lstm' and 'basic_rnn'
:param only_final: True or False
:param wd:
:param keep_prob:
:param is_train:
:param scope:
:return:
"""
with tf.variable_scope(scope or 'contextual_bi_rnn'): # correct
reuse = None if not tf.get_variable_scope().reuse else True
#print(reuse)
if cell_type == 'gru':
cell_fw = tf.contrib.rnn.GRUCell(hn, reuse=reuse)
cell_bw = tf.contrib.rnn.GRUCell(hn, reuse=reuse)
elif cell_type == 'lstm':
cell_fw = tf.contrib.rnn.LSTMCell(hn, reuse=reuse)
cell_bw = tf.contrib.rnn.LSTMCell(hn, reuse=reuse)
elif cell_type == 'basic_lstm':
cell_fw = tf.contrib.rnn.BasicLSTMCell(hn, reuse=reuse)
cell_bw = tf.contrib.rnn.BasicLSTMCell(hn, reuse=reuse)
elif cell_type == 'basic_rnn':
cell_fw = tf.contrib.rnn.BasicRNNCell(hn, reuse=reuse)
cell_bw = tf.contrib.rnn.BasicRNNCell(hn, reuse=reuse)
else:
raise AttributeError('no cell type \'%s\'' % cell_type)
cell_dp_fw = SwitchableDropoutWrapper(cell_fw, is_train, keep_prob)
cell_dp_bw = SwitchableDropoutWrapper(cell_bw, is_train, keep_prob)
tensor_len = tf.reduce_sum(tf.cast(mask_rep, tf.int32), -1) # [bs]
(outputs_fw,
output_bw), _ = bidirectional_dynamic_rnn(cell_dp_fw,
cell_dp_bw,
tensor_rep,
tensor_len,
dtype=tf.float32)
rnn_outputs = tf.concat([outputs_fw, output_bw], -1) # [...,sl,2hn]
if wd > 0:
add_reg_without_bias()
if not only_final:
return rnn_outputs # [....,sl, 2hn]
else:
return get_last_state(rnn_outputs, mask_rep) # [...., 2hn]
def do_reduce(self, data_for_reduce, mask_for_reduce):
with tf.variable_scope('sr_%s' % self.method_type):
seq_len = tf.reduce_sum(tf.cast(mask_for_reduce, tf.int32), -1)
(fw, bw), _ = tf.nn.bidirectional_dynamic_rnn(
self.cell_dp_fw,
self.cell_dp_bw,
data_for_reduce,
seq_len,
dtype=tf.float32,
scope='shift_reduce_bilstm_loop')
value = tf.concat([fw, bw], -1)
processed_reduced_data = get_last_state(value, mask_for_reduce)
return processed_reduced_data
def one_direction_rnn(tensor_rep,
mask_rep,
hn,
cell_type,
only_final=False,
wd=0.,
keep_prob=1.,
is_train=None,
is_forward=True,
scope=None):
assert not is_forward # todo: waiting to be implemented
with tf.variable_scope(scope or '%s_rnn' %
'forward' if is_forward else 'backward'):
reuse = None if not tf.get_variable_scope().reuse else True
# print(reuse)
if cell_type == 'gru':
cell = tf.contrib.rnn.GRUCell(hn, reuse=reuse)
elif cell_type == 'lstm':
cell = tf.contrib.rnn.LSTMCell(hn, reuse=reuse)
elif cell_type == 'basic_lstm':
cell = tf.contrib.rnn.BasicLSTMCell(hn, reuse=reuse)
elif cell_type == 'basic_rnn':
cell = tf.contrib.rnn.BasicRNNCell(hn, reuse=reuse)
else:
raise AttributeError('no cell type \'%s\'' % cell_type)
cell_dp = SwitchableDropoutWrapper(cell, is_train, keep_prob)
tensor_len = tf.reduce_sum(tf.cast(mask_rep, tf.int32), -1) # [bs]
rnn_outputs, _ = dynamic_rnn(cell_dp,
tensor_rep,
tensor_len,
dtype=tf.float32)
if wd > 0:
add_reg_without_bias()
if not only_final:
return rnn_outputs # [....,sl, 2hn]
else:
return get_last_state(rnn_outputs, mask_rep) # [...., 2hn]