def multi_dimensional_attention(rep_tensor,
rep_mask,
scope=None,
keep_prob=1.,
is_train=None,
wd=0.,
activation='elu',
tensor_dict=None,
name=None):
bs, sl, vec = tf.shape(rep_tensor)[0], tf.shape(rep_tensor)[1], tf.shape(
rep_tensor)[2]
ivec = rep_tensor.get_shape()[2]
with tf.variable_scope(scope or 'multi_dimensional_attention'):
map1 = bn_dense_layer(rep_tensor, ivec, True, 0., 'bn_dense_map1',
activation, False, wd, keep_prob, is_train)
map2 = bn_dense_layer(map1, ivec, True, 0., 'bn_dense_map2', 'linear',
False, wd, keep_prob, is_train)
map2_masked = exp_mask_for_high_rank(map2, rep_mask)
soft = tf.nn.softmax(map2_masked, 1) # bs,sl,vec
attn_output = tf.reduce_sum(soft * rep_tensor, 1) # bs, vec
# save attn
if tensor_dict is not None and name is not None:
tensor_dict[name] = soft
return attn_output
def bi_sru_recurrent_network(rep_tensor,
rep_mask,
is_train=None,
keep_prob=1.,
wd=0.,
scope=None,
hn=None,
reuse=None):
"""
:param rep_tensor: [Tensor/tf.float32] rank is 3 with shape [batch_size/bs, max_sent_len/sl, vec]
:param rep_mask: [Tensor/tf.bool]rank is 2 with shape [bs,sl]
:param is_train: [Scalar Tensor/tf.bool]scalar tensor to indicate whether the mode is training or not
:param keep_prob: [float] dropout keep probability in the range of (0,1)
:param wd: [float]for L2 regularization, if !=0, add tensors to tf collection "reg_vars"
:param scope: [str]variable scope name
:param hn:
:param
:return: [Tensor/tf.float32] with shape [bs, sl, 2vec] for forward and backward
"""
bs, sl, vec = tf.shape(rep_tensor)[0], tf.shape(rep_tensor)[1], tf.shape(
rep_tensor)[2]
ivec = rep_tensor.get_shape().as_list()[2]
ivec = hn or ivec
with tf.variable_scope(scope or 'bi_sru_recurrent_network'):
# U_d = bn_dense_layer([rep_tensor], 6 * ivec, False, 0., 'get_frc', 'linear',
# False, wd, keep_prob, is_train) # bs, sl, 6vec
# U_d_fw, U_d_bw = tf.split(U_d, 2, 2)
with tf.variable_scope('forward'):
U_d_fw = bn_dense_layer([rep_tensor], 3 * ivec, False, 0.,
'get_frc_fw', 'linear', False, wd,
keep_prob, is_train) # bs, sl, 6vec
U_fw = tf.concat([rep_tensor, U_d_fw], -1)
fw_SRUCell = SwitchableDropoutWrapper(
SRUCell(ivec, tf.nn.tanh, reuse), is_train, keep_prob)
fw_output, _ = dynamic_rnn(fw_SRUCell,
U_fw,
tf.reduce_sum(
tf.cast(rep_mask, tf.int32), -1),
dtype=tf.float32,
scope='forward_sru') # bs, sl, vec
with tf.variable_scope('backward'):
U_d_bw = bn_dense_layer([rep_tensor], 3 * ivec, False, 0.,
'get_frc_bw', 'linear', False, wd,
keep_prob, is_train) # bs, sl, 6vec
U_bw = tf.concat([rep_tensor, U_d_bw], -1)
bw_SRUCell = SwitchableDropoutWrapper(
SRUCell(ivec, tf.nn.tanh, reuse), is_train, keep_prob)
bw_output, _ = bw_dynamic_rnn(bw_SRUCell,
U_bw,
tf.reduce_sum(
tf.cast(rep_mask, tf.int32), -1),
dtype=tf.float32,
scope='backward_sru') # bs, sl, vec
all_output = tf.concat([fw_output, bw_output], -1) # bs, sl, 2vec
return all_output
def traditional_attention(rep_tensor,
rep_mask,
scope=None,
keep_prob=1.,
is_train=None,
wd=0.,
activation='elu',
tensor_dict=None,
name=None):
bs, sl, vec = tf.shape(rep_tensor)[0], tf.shape(rep_tensor)[1], tf.shape(
rep_tensor)[2]
ivec = rep_tensor.get_shape()[2]
with tf.variable_scope(scope or 'traditional_attention'):
rep_tensor_map = bn_dense_layer(rep_tensor, ivec, True, 0.,
'bn_dense_map', activation, False, wd,
keep_prob, is_train)
rep_tensor_logits = get_logits([rep_tensor_map],
None,
False,
scope='self_attn_logits',
mask=rep_mask,
input_keep_prob=keep_prob,
is_train=is_train) # bs,sl
attn_res = softsel(rep_tensor, rep_tensor_logits, rep_mask) # bs,vec
# save attn
if tensor_dict is not None and name is not None:
tensor_dict[name] = tf.nn.softmax(rep_tensor_logits)
return attn_res
def visit_multi_dimensional_attention(rep_tensor,
keep_prob=1.,
is_train=None,
wd=0.,
activation='relu'):
# bs, sl, vec = tf.shape(rep_tensor)[0], tf.shape(rep_tensor)[1], tf.shape(rep_tensor)[2]
ivec = rep_tensor.get_shape()[2]
with tf.variable_scope('multi_dimensional_attention'):
map1 = bn_dense_layer(rep_tensor, ivec, True, 0., 'bn_dense_map1',
activation, False, wd, keep_prob, is_train)
map2 = bn_dense_layer(map1, ivec, True, 0., 'bn_dense_map2', 'linear',
False, wd, keep_prob, is_train)
soft = tf.nn.softmax(map2, 1) # bs,sl,vec
attn_output = tf.reduce_sum(soft * rep_tensor, 1) # bs, vec
return attn_output
def first_level_sa(rep_tensor,
rep_mask,
keep_prob=1.,
is_train=None,
wd=0.,
activation='relu'):
# bs, sw, cl, vec = tf.shape(rep_tensor)[0], tf.shape(rep_tensor)[1], tf.shape(rep_tensor)[2], tf.shape(rep_tensor)[3]
ivec = rep_tensor.get_shape()[3]
with tf.variable_scope('first_level_sa'):
print('original: ', rep_tensor.get_shape())
map1 = bn_dense_layer(rep_tensor, ivec, True, 0., 'bn_dense_map1',
activation, False, wd, keep_prob, is_train)
print('map1: ', map1.get_shape())
map2 = bn_dense_layer(map1, ivec, True, 0., 'bn_dense_map2', 'linear',
False, wd, keep_prob, is_train)
print('map2: ', map2.get_shape())
map2_masked = exp_mask_for_high_rank(map2, rep_mask)
soft = tf.nn.softmax(map2_masked, 2) # bs,sk,code_len,vec
attn_output = tf.reduce_sum(soft * rep_tensor, 2) # bs, sk, vec
return attn_output
def normal_attention(rep_tensor,
rep_mask,
scope=None,
keep_prob=1.,
is_train=None,
wd=0.,
activation='elu',
tensor_dict=None,
name=None):
batch_size, code_len, vec_size = tf.shape(rep_tensor)[0], tf.shape(
rep_tensor)[1], tf.shape(rep_tensor)[2]
ivec = rep_tensor.get_shape()[2]
with tf.variable_scope(scope or 'normal_attention'):
rep_tensor_map = bn_dense_layer(rep_tensor, ivec, True, 0.,
'bn_dense_map', activation, False, wd,
keep_prob, is_train)
rep_tensor_logits = get_logits([rep_tensor_map],
None,
False,
scope='self_attn_logits',
mask=rep_mask,
input_keep_prob=keep_prob,
is_train=is_train) # bs,sl
attn_result = softsel(rep_tensor, rep_tensor_logits,
rep_mask) # bs,vec
# save attn
if tensor_dict is not None and name is not None:
tensor_dict[name] = tf.nn.softmax(rep_tensor_logits)
with tf.variable_scope('output'):
o_bias = tf.get_variable('o_bias', [ivec], tf.float32,
tf.constant_initializer(0.))
# input gate
fusion_gate = tf.nn.sigmoid(
linear(rep_tensor_map, ivec, True, 0., 'linear_fusion_i',
False, wd, keep_prob, is_train) +
linear(attn_result, ivec, True, 0., 'linear_fusion_a', False,
wd, keep_prob, is_train) + o_bias)
output = fusion_gate * rep_tensor_map + (1 -
fusion_gate) * attn_result
output = mask_for_high_rank(output, rep_mask) # bs,sl,vec
return output
def __call__(self, inputs, state, scope=None):
"""
:param inputs: [bs, vec]
:param state:
:param scope:
:return:
"""
with tf.variable_scope(scope or "SRU_cell"):
b_f = tf.get_variable('b_f', [self._num_units], dtype=tf.float32,
initializer=tf.constant_initializer(0))
b_r = tf.get_variable('b_r', [self._num_units], dtype=tf.float32,
initializer=tf.constant_initializer(0))
U_d = bn_dense_layer(inputs, 3 * self._num_units, False, 0., 'get_frc', 'linear') # bs, 3vec
x_t = tf.identity(inputs, 'x_t')
x_dt, f_t, r_t = tf.split(U_d, 3, 1)
f_t = tf.nn.sigmoid(f_t + b_f)
r_t = tf.nn.sigmoid(r_t + b_r)
c_t = f_t * state + (1 - f_t) * x_dt
h_t = r_t * self._activation(c_t) + (1 - r_t) * x_t
return h_t, c_t
def sentence_encoding_models(
rep_tensor, rep_mask, method, activation_function,
scope=None, wd=0., is_train=None, keep_prob=1., **kwargs):
method_name_list = [
'cnn_kim',
'no_ct',
'lstm', 'gru', 'sru', 'sru_normal', # rnn
'multi_cnn', 'hrchy_cnn',
'multi_head', 'multi_head_git', 'disa',
'block'
]
if 'hn' in kwargs.keys():
hn = kwargs['hn']
else:
hn = None
ivec = hn or rep_tensor.get_shape().as_list()[2]
with tf.variable_scope(scope or 'sentence_encoding_models'):
if method == 'cnn_kim':
assert 2 * ivec % 3 == 0
sub_hn = 2 * ivec // 3
sent_encoding = cnn_for_sentence_encoding(
rep_tensor, rep_mask, (3,4,5), sub_hn, 'sent_encoding_cnn_kim', is_train, keep_prob, wd)
else:
ct_rep = None
if method == 'no_ct':
ct_rep = bn_dense_layer(
rep_tensor, 2*ivec, True, 0., 'no_ct', activation_function, False, wd, keep_prob, is_train)
else:
ct_rep = context_fusion_layers(
rep_tensor, rep_mask, method, activation_function,
None, wd, is_train, keep_prob, **kwargs)
sent_encoding = multi_dimensional_attention(
ct_rep, rep_mask, 'multi_dim_attn_for_%s' % method,
keep_prob, is_train, wd, activation_function)
return sent_encoding
def bi_sru_recurrent_network(rep_tensor,
rep_mask,
is_train=None,
keep_prob=1.,
wd=0.,
scope=None):
bs, sl, vec = tf.shape(rep_tensor)[0], tf.shape(rep_tensor)[1], tf.shape(
rep_tensor)[2]
ivec = rep_tensor.get_shape().as_list()[2]
with tf.variable_scope(scope or 'bi_sru_recurrent_network'):
U_d = bn_dense_layer([rep_tensor], 6 * ivec, True, 0., 'get_frc',
'linear', False, wd, keep_prob,
is_train) # bs, sl, 6vec
U_d_fw, U_d_bw = tf.split(U_d, 2, 2)
with tf.variable_scope('forward'):
U_fw = tf.concat([rep_tensor, U_d_fw], -1)
fw_SRUCell = SwitchableDropoutWrapper(SRUCell(ivec, tf.nn.tanh),
is_train, keep_prob)
fw_output, _ = dynamic_rnn(fw_SRUCell,
U_fw,
tf.reduce_sum(
tf.cast(rep_mask, tf.int32), -1),
dtype=tf.float32,
scope='forward_sru') # bs, sl, vec
with tf.variable_scope('backward'):
U_bw = tf.concat([rep_tensor, U_d_bw], -1)
bw_SRUCell = SwitchableDropoutWrapper(SRUCell(ivec, tf.nn.tanh),
is_train, keep_prob)
bw_output, _ = bw_dynamic_rnn(bw_SRUCell,
U_bw,
tf.reduce_sum(
tf.cast(rep_mask, tf.int32), -1),
dtype=tf.float32,
scope='backward_sru') # bs, sl, vec
all_output = tf.concat([fw_output, bw_output], -1) # bs, sl, 2vec
return all_output
def build_loss_optimizer(self):
with tf.name_scope('loss_optimization'):
logits = bn_dense_layer(self.output, 1, True, 0.,
'bn_dense_map', 'sigmoid',
False, wd=0., keep_prob=1.,
is_train=True)
losses = tf.nn.sigmoid_cross_entropy_with_logits(logits=logits, labels=tf.cast(self.labels, tf.float32))
tf.add_to_collection('losses', tf.reduce_mean(losses, name='loss_mean'))
loss = tf.add_n(tf.get_collection('losses', self.scope), name='loss')
tf.summary.scalar(loss.op.name, loss)
tf.add_to_collection('ema/scalar', loss)
optimizer = tf.train.AdamOptimizer(learning_rate=0.0001).minimize(loss)
with tf.name_scope('correct_prediction'):
correct_prediction = tf.equal(tf.round(logits), tf.cast(self.labels, tf.float32))
with tf.name_scope('accuracy'):
# Mean accuracy over all labels:
# http://stackoverflow.com/questions/37746670/tensorflow-multi-label-accuracy-calculation
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
return loss, optimizer, accuracy, logits, tf.round(logits)
def build_network(self):
tds, tel, hn = self.tds, self.tel, self.hn
bs, sn, sl, ql = self.bs, self.sn, self.sl, self.ql
with tf.variable_scope('emb'):
token_emb_mat = generate_embedding_mat(
tds,
tel,
init_mat=self.token_emb_mat,
extra_mat=self.glove_emb_mat,
scope='gene_token_emb_mat')
c_emb = tf.nn.embedding_lookup(token_emb_mat,
self.context_token) # bs,sn,sl,tel
q_emb = tf.nn.embedding_lookup(token_emb_mat,
self.question_token) # s,ql,tel
with tf.variable_scope('prepro'):
q_rep = multi_dimensional_attention(q_emb,
self.question_token_mask,
'q2coding', cfg.dropout,
self.is_train, cfg.wd,
'relu') # bs, hn
q_rep_map = bn_dense_layer(q_rep, hn, True, 0., 'q_rep_map',
'relu', False, cfg.wd, cfg.dropout,
self.is_train) # bs, hn
with tf.variable_scope('sent_emb'):
c_emb_rshp = tf.reshape(c_emb, [bs * sn, sl, tel],
'c_emb_rshp') # bs*sn,sl,tel
c_mask_rshp = tf.reshape(self.context_token_mask, [bs * sn, sl],
'c_mask_rshp') # bs*sn,sl,tel
sent_enc_rshp = sentence_encoding_models(
c_emb_rshp,
c_mask_rshp,
cfg.context_fusion_method,
'relu',
'sent2enc',
cfg.wd,
self.is_train,
cfg.dropout,
hn,
block_len=cfg.block_len) # bs*sn, 2*hn
sent_enc = tf.reshape(sent_enc_rshp,
[bs, sn, 2 * hn]) # bs,sn, 2*hn
sent_enc_map = bn_dense_layer(sent_enc, hn, True, 0.,
'sent_enc_map', 'relu', False,
cfg.wd, cfg.dropout, self.is_train)
with tf.variable_scope('fusion'):
q_rep_map_ex = tf.tile(tf.expand_dims(q_rep_map, 1),
[1, sn, 1]) # bs, sn, hn
fusion_rep = tf.concat([
sent_enc_map, q_rep_map_ex, sent_enc_map - q_rep_map_ex,
sent_enc_map * q_rep_map_ex
], -1) # bs,sn,4hn
with tf.variable_scope('output'):
out_cf = context_fusion_layers(fusion_rep,
self.context_sent_mask,
cfg.context_fusion_method,
'relu',
'out_cf',
cfg.wd,
self.is_train,
cfg.dropout,
hn,
block_len=4)
pre_output = bn_dense_layer(out_cf, hn, True, 0., 'pre_output',
'relu', False, cfg.wd, cfg.dropout,
self.is_train)
logits = get_logits( # exp masked
pre_output, None, True, 0., 'logits', self.context_sent_mask,
cfg.wd, cfg.dropout, self.is_train, 'linear')
return logits
def directional_attention_with_dense(rep_tensor,
rep_mask,
direction=None,
scope=None,
keep_prob=1.,
is_train=None,
wd=0.,
activation='elu',
tensor_dict=None,
name=None,
hn=None):
def scaled_tanh(x, scale=5.):
return scale * tf.nn.tanh(1. / scale * x)
bs, sl, vec = tf.shape(rep_tensor)[0], tf.shape(rep_tensor)[1], tf.shape(
rep_tensor)[2]
ivec = hn or rep_tensor.get_shape()[2]
with tf.variable_scope(scope or 'directional_attention_%s' % direction
or 'diag'):
# mask generation
sl_indices = tf.range(sl, dtype=tf.int32)
sl_col, sl_row = tf.meshgrid(sl_indices, sl_indices)
if direction is None:
direct_mask = tf.cast(
tf.diag(-tf.ones([sl], tf.int32)) + 1, tf.bool)
else:
if direction == 'forward':
direct_mask = tf.greater(sl_row, sl_col)
else:
direct_mask = tf.greater(sl_col, sl_row)
direct_mask_tile = tf.tile(tf.expand_dims(direct_mask, 0),
[bs, 1, 1]) # bs,sl,sl
rep_mask_tile = tf.tile(tf.expand_dims(rep_mask, 1),
[1, sl, 1]) # bs,sl,sl
attn_mask = tf.logical_and(direct_mask_tile, rep_mask_tile) # bs,sl,sl
# non-linear
rep_map = bn_dense_layer(rep_tensor, ivec, True, 0., 'bn_dense_map',
activation, False, wd, keep_prob, is_train)
rep_map_tile = tf.tile(tf.expand_dims(rep_map, 1),
[1, sl, 1, 1]) # bs,sl,sl,vec
rep_map_dp = dropout(rep_map, keep_prob, is_train)
# attention
with tf.variable_scope('attention'): # bs,sl,sl,vec
f_bias = tf.get_variable('f_bias', [ivec], tf.float32,
tf.constant_initializer(0.))
dependent = linear(rep_map_dp,
ivec,
False,
scope='linear_dependent') # bs,sl,vec
dependent_etd = tf.expand_dims(dependent, 1) # bs,1,sl,vec
head = linear(rep_map_dp, ivec, False,
scope='linear_head') # bs,sl,vec
head_etd = tf.expand_dims(head, 2) # bs,sl,1,vec
logits = scaled_tanh(dependent_etd + head_etd + f_bias,
5.0) # bs,sl,sl,vec
logits_masked = exp_mask_for_high_rank(logits, attn_mask)
attn_score = tf.nn.softmax(logits_masked, 2) # bs,sl,sl,vec
attn_score = mask_for_high_rank(attn_score, attn_mask)
attn_result = tf.reduce_sum(attn_score * rep_map_tile,
2) # bs,sl,vec
with tf.variable_scope('output'):
o_bias = tf.get_variable('o_bias', [ivec], tf.float32,
tf.constant_initializer(0.))
# input gate
fusion_gate = tf.nn.sigmoid(
linear(rep_map, ivec, True, 0., 'linear_fusion_i', False, wd,
keep_prob, is_train) +
linear(attn_result, ivec, True, 0., 'linear_fusion_a', False,
wd, keep_prob, is_train) + o_bias)
output = fusion_gate * rep_map + (1 - fusion_gate) * attn_result
output = mask_for_high_rank(output, rep_mask)
# save attn
if tensor_dict is not None and name is not None:
tensor_dict[name + '_dependent'] = dependent
tensor_dict[name + '_head'] = head
tensor_dict[name] = attn_score
tensor_dict[name + '_gate'] = fusion_gate
return output
def masked_positional_self_attention(sigma, rep_tensor, rep_mask, direction=None, scope=None,
keep_prob=1., is_train=None, wd=0., activation='elu',
tensor_dict=None, name=None):
def scaled_tanh(x, scale=5.):
return scale * tf.nn.tanh(1./scale * x)
bs, sl, vec = tf.shape(rep_tensor)[0], tf.shape(rep_tensor)[1], tf.shape(rep_tensor)[2]
ivec = rep_tensor.get_shape()[2]
with tf.variable_scope(scope or 'directional_attention_%s' % direction or 'diag'):
# mask generation
sl_indices = tf.range(sl, dtype=tf.int32)
sl_col, sl_row = tf.meshgrid(sl_indices, sl_indices)
if direction is None:
direct_mask0 = tf.greater(sl_row + sigma, sl_col)
direct_mask1 = tf.greater(sl_col + sigma, sl_row)
direct_mask2 = tf.cast(1 - tf.diag(tf.ones([sl], tf.int32)), tf.bool)
direct_mask = tf.logical_and(tf.logical_and(direct_mask0, direct_mask1), direct_mask2)
else:
if direction == 'forward':
direct_mask = tf.greater(sl_row, sl_col)
else:
direct_mask = tf.greater(sl_col, sl_row)
direct_mask_tile = tf.tile(tf.expand_dims(direct_mask, 0), [bs, 1, 1]) # bs,sl,sl
rep_mask_tile = tf.tile(tf.expand_dims(rep_mask, 1), [1, sl, 1]) # bs,sl,sl
attn_mask = tf.logical_and(direct_mask_tile, rep_mask_tile) # bs,sl,sl
# non-linear
rep_map = bn_dense_layer(rep_tensor, ivec, True, 0., 'bn_dense_map', activation,
False, wd, keep_prob, is_train)
rep_map_tile = tf.tile(tf.expand_dims(rep_map, 1), [1, sl, 1, 1]) # bs,sl,sl,vec
rep_map_dp = dropout(rep_map, keep_prob, is_train)
# attention
with tf.variable_scope('attention'): # bs,sl,sl,1
f_bias = tf.get_variable('f_bias', [1], tf.float32, tf.constant_initializer(0.))
dependent = linear(rep_map_dp, 1, False, scope='linear_dependent') # bs,sl,1
dependent_etd = tf.expand_dims(dependent, 1) # bs,1,sl,1
head = linear(rep_map_dp, 1, False, scope='linear_head') # bs,sl,1
head_etd = tf.expand_dims(head, 2) # bs,sl,1,1
logits = scaled_tanh(dependent_etd + head_etd + f_bias, 5.0) # bs,sl,sl,1
logits_masked = exp_mask_for_high_rank(logits, attn_mask)
if direction is not None:
dis_mask = -tf.log(tf.cast(tf.abs(sl_col - sl_row) +
tf.diag(tf.ones([sl], tf.int32)), tf.float32))
logits_masked = dis_mask_for_high_rank(logits_masked, dis_mask)
attn_score = tf.nn.softmax(logits_masked, 2) # bs,sl,sl,vec
attn_score = mask_for_high_rank(attn_score, attn_mask)
attn_score = tf.tile(tf.expand_dims(tf.reshape(attn_score, [bs, sl, sl]), 3), [1, 1, 1, ivec])
attn_result = tf.reduce_sum(attn_score * rep_map_tile, 2) # bs,sl,vec
with tf.variable_scope('output'):
output = attn_result
# save attn
if tensor_dict is not None and name is not None:
tensor_dict[name + '_dependent'] = dependent
tensor_dict[name + '_head'] = head
tensor_dict[name] = attn_score
return output
def simple_block_attention(rep_tensor,
rep_mask,
block_len=5,
scope=None,
direction=None,
keep_prob=1.,
is_train=None,
wd=0.,
activation='elu',
hn=None):
assert direction is not None
def scaled_tanh(x, scale=5.):
return scale * tf.nn.tanh(1. / scale * x)
bs, sl, vec = tf.shape(rep_tensor)[0], tf.shape(rep_tensor)[1], tf.shape(
rep_tensor)[2]
org_ivec = rep_tensor.get_shape().as_list()[2]
ivec = hn or org_ivec
with tf.variable_scope(scope or 'block_simple'):
# @1. split sequence
with tf.variable_scope('split_seq'):
block_num = tf.cast(
tf.ceil(
tf.divide(tf.cast(sl, tf.float32),
tf.cast(block_len, tf.float32))), tf.int32)
comp_len = block_num * block_len - sl
rep_tensor_comp = tf.concat(
[rep_tensor,
tf.zeros([bs, comp_len, org_ivec], tf.float32)], 1)
rep_mask_comp = tf.concat([
rep_mask,
tf.cast(tf.zeros([bs, comp_len], tf.int32), tf.bool)
], 1)
rep_tensor_split = tf.reshape(
rep_tensor_comp,
[bs, block_num, block_len, org_ivec]) # bs,bn,bl,d
rep_mask_split = tf.reshape(rep_mask_comp,
[bs, block_num, block_len]) # bs,bn,bl
# non-linear
rep_map = bn_dense_layer(rep_tensor_split, ivec, True, 0.,
'bn_dense_map', activation, False, wd,
keep_prob, is_train) # bs,bn,bl,vec
rep_map_tile = tf.tile(tf.expand_dims(rep_map, 2),
[1, 1, block_len, 1, 1]) # bs,bn,bl,bl,vec
# rep_map_dp = dropout(rep_map, keep_prob, is_train)
bn = block_num
bl = block_len
with tf.variable_scope('self_attention'):
# @2.self-attention in block
# mask generation
sl_indices = tf.range(block_len, dtype=tf.int32)
sl_col, sl_row = tf.meshgrid(sl_indices, sl_indices)
if direction == 'forward':
direct_mask = tf.greater(sl_row, sl_col) # bl,bl
else:
direct_mask = tf.greater(sl_col, sl_row) # bl,bl
direct_mask_tile = tf.tile(
tf.expand_dims(tf.expand_dims(direct_mask, 0), 0),
[bs, bn, 1, 1]) # bs,bn,bl,bl
rep_mask_tile_1 = tf.tile(tf.expand_dims(rep_mask_split, 2),
[1, 1, bl, 1]) # bs,bn,bl,bl
rep_mask_tile_2 = tf.tile(tf.expand_dims(rep_mask_split, 3),
[1, 1, 1, bl]) # bs,bn,bl,bl
rep_mask_tile = tf.logical_and(rep_mask_tile_1, rep_mask_tile_2)
attn_mask = tf.logical_and(direct_mask_tile,
rep_mask_tile,
name='attn_mask') # bs,bn,bl,bl
# attention
f_bias = tf.get_variable('f_bias', [ivec], tf.float32,
tf.constant_initializer(0.))
dependent_head = linear(rep_map, 2 * ivec, False, 0.,
'linear_dependent_head', False, wd,
keep_prob, is_train) # bs,bn,bl,2vec
dependent, head = tf.split(dependent_head, 2, 3)
dependent_etd = tf.expand_dims(dependent, 2) # bs,bn,1,bl,vec
head_etd = tf.expand_dims(head, 3) # bs,bn,bl,1,vec
logits = scaled_tanh(dependent_etd + head_etd + f_bias,
5.0) # bs,bn,bl,bl,vec
logits_masked = exp_mask_for_high_rank(logits, attn_mask)
attn_score = tf.nn.softmax(logits_masked, 3) # bs,bn,bl,bl,vec
attn_score = mask_for_high_rank(attn_score,
attn_mask) # bs,bn,bl,bl,vec
self_attn_result = tf.reduce_sum(attn_score * rep_map_tile,
3) # bs,bn,bl,vec
with tf.variable_scope('source2token_self_attn'):
inter_block_logits = bn_dense_layer(self_attn_result, ivec, True,
0., 'bn_dense_map', 'linear',
False, wd, keep_prob,
is_train) # bs,bn,bl,vec
inter_block_logits_masked = exp_mask_for_high_rank(
inter_block_logits, rep_mask_split) # bs,bn,bl,vec
inter_block_soft = tf.nn.softmax(inter_block_logits_masked,
2) # bs,bn,bl,vec
inter_block_attn_output = tf.reduce_sum(
self_attn_result * inter_block_soft, 2) # bs,bn,vec
with tf.variable_scope('self_attn_inter_block'):
inter_block_attn_output_mask = tf.cast(tf.ones([bs, bn], tf.int32),
tf.bool)
block_ct_res = directional_attention_with_dense(
inter_block_attn_output, inter_block_attn_output_mask,
direction, 'disa', keep_prob, is_train, wd,
activation) # [bs,bn,vec]
block_ct_res_tile = tf.tile(tf.expand_dims(
block_ct_res, 2), [1, 1, bl, 1]) #[bs,bn,vec]->[bs,bn,bl,vec]
with tf.variable_scope('combination'):
# input:1.rep_map[bs,bn,bl,vec]; 2.self_attn_result[bs,bn,bl,vec]; 3.rnn_res_tile[bs,bn,bl,vec]
rep_tensor_with_ct = tf.concat(
[rep_map, self_attn_result, block_ct_res_tile],
-1) # [bs,bn,bl,3vec]
new_context_and_gate = linear(rep_tensor_with_ct, 2 * ivec, True,
0., 'linear_new_context_and_gate',
False, wd, keep_prob,
is_train) # [bs,bn,bl,2vec]
new_context, gate = tf.split(new_context_and_gate, 2,
3) # bs,bn,bl,vec
if activation == "relu":
new_context_act = tf.nn.relu(new_context)
elif activation == "elu":
new_context_act = tf.nn.elu(new_context)
elif activation == "linear":
new_context_act = tf.identity(new_context)
else:
raise RuntimeError
gate_sig = tf.nn.sigmoid(gate)
combination_res = gate_sig * new_context_act + (
1 - gate_sig) * rep_map # bs,bn,bl,vec
with tf.variable_scope('restore_original_length'):
combination_res_reshape = tf.reshape(
combination_res, [bs, bn * bl, ivec]) # bs,bn*bl,vec
output = combination_res_reshape[:, :sl, :]
return output
def gated_self_attention(rep_tensor,
rep_mask,
scope=None,
keep_prob=1.,
is_train=None,
wd=0.,
activation='elu',
hn=None,
position_mask_type=None):
bs, sl, vec = tf.shape(rep_tensor)[0], tf.shape(rep_tensor)[1], tf.shape(
rep_tensor)[2]
ivec = rep_tensor.get_shape().as_list()[2]
ivec = hn or ivec
with tf.variable_scope(scope or 'gated_self_attention_%s' %
(position_mask_type or 'None')):
rep_map = bn_dense_layer(rep_tensor, ivec, True, 0., 'bn_dense_map',
activation, False, wd, keep_prob, is_train)
# mask generation
rep_mask_epd1 = tf.expand_dims(rep_mask, 1) # bs,1,sl
rep_mask_epd2 = tf.expand_dims(rep_mask, 2) # bs,sl,1
rep_mask_mat = tf.logical_and(rep_mask_epd1, rep_mask_epd2) # bs,sl,sl
if position_mask_type in ['forward', 'backward']:
sl_indices = tf.range(sl, dtype=tf.int32)
sl_col, sl_row = tf.meshgrid(sl_indices, sl_indices)
if position_mask_type == 'forward':
position_mask = tf.greater(sl_row, sl_col)
else:
position_mask = tf.greater(sl_col, sl_row)
position_mask = tf.tile(tf.expand_dims(position_mask, 0),
[bs, 1, 1])
position_mask = tf.logical_and(rep_mask_mat, position_mask)
else:
position_mask = rep_mask_mat
position_mask_ft = tf.cast(position_mask, tf.float32)
# attention
with tf.variable_scope('intra_sent_attn'): # bs,sl,hn
# rep_tensor_mean = pooling_with_mask(rep_tensor, rep_mask, 'mean') # bs, hn
rep_tensor_for_attn = rep_map
pre_align_score = bn_dense_layer( # bs,sl,hn
rep_tensor_for_attn, ivec, True, 0., 'intra_sent_map1',
activation, False, wd, keep_prob, is_train)
align_score = bn_dense_layer( # bs,sl,hn
pre_align_score, ivec, True, 0., 'intra_sent_map2', 'linear',
False, wd, keep_prob, is_train)
align_score_w_mask = exp_mask_for_high_rank(align_score,
rep_mask) # bs,sl,hn
exp_align_score = tf.exp(align_score_w_mask) # bs,sl,hn
accum_z_deno = tf.matmul(position_mask_ft, exp_align_score)
accum_z_deno = tf.where(
tf.greater(accum_z_deno, tf.zeros_like(accum_z_deno)),
accum_z_deno, tf.ones_like(accum_z_deno))
rep_mul_score = rep_map * exp_align_score
accum_rep_mul_score = tf.matmul(position_mask_ft, rep_mul_score)
attn_res = accum_rep_mul_score / accum_z_deno
with tf.variable_scope('context_fusion_gate'):
fusion_gate = tf.nn.sigmoid(
bn_dense_layer([rep_map, attn_res], hn, True, 0.,
'linear_fusion_gate', activation, False, wd,
keep_prob, is_train))
output = fusion_gate * rep_map + (1 - fusion_gate) * attn_res
output = mask_for_high_rank(output, rep_mask)
return output
def build_network(self):
_logger.add()
_logger.add('building %s neural network structure...' % cfg.network_type)
tds, cds = self.tds, self.cds
tl = self.tl
tel, cel, cos, ocd, fh = self.tel, self.cel, self.cos, self.ocd, self.fh
hn = self.hn
bs, sl1, sl2 = self.bs, self.sl1, self.sl2
with tf.variable_scope('emb'):
token_emb_mat = generate_embedding_mat(tds, tel, init_mat=self.token_emb_mat,
extra_mat=self.glove_emb_mat, extra_trainable=self.finetune_emb,
scope='gene_token_emb_mat')
s1_emb = tf.nn.embedding_lookup(token_emb_mat, self.sent1_token) # bs,sl1,tel
s2_emb = tf.nn.embedding_lookup(token_emb_mat, self.sent2_token) # bs,sl2,tel
self.tensor_dict['s1_emb'] = s1_emb
self.tensor_dict['s2_emb'] = s2_emb
with tf.variable_scope('hard_network'):
# s1_act, s1_logpa, s2_act, s2_logpa, choose_percentage
s1_act = self.sent1_token_mask
s1_logpa = tf.cast(s1_act, tf.float32)
s2_act = self.sent2_token_mask
s2_logpa = tf.cast(s2_act, tf.float32)
s1_percentage = tf.ones([bs], tf.float32)
s2_percentage = tf.ones([bs], tf.float32)
with tf.variable_scope('ct_attn'):
s1_fw = directional_attention_with_dense(
s1_emb, self.sent1_token_mask, 'forward', 'dir_attn_fw',
cfg.dropout, self.is_train, cfg.wd,
tensor_dict=self.tensor_dict, name='s1_fw_attn')
s1_bw = directional_attention_with_dense(
s1_emb, self.sent1_token_mask, 'backward', 'dir_attn_bw',
cfg.dropout, self.is_train, cfg.wd,
tensor_dict=self.tensor_dict, name='s1_bw_attn')
s1_seq_rep = tf.concat([s1_fw, s1_bw], -1)
tf.get_variable_scope().reuse_variables()
s2_fw = directional_attention_with_dense(
s2_emb, self.sent2_token_mask, 'forward', 'dir_attn_fw',
cfg.dropout, self.is_train, cfg.wd,
tensor_dict=self.tensor_dict, name='s2_fw_attn')
s2_bw = directional_attention_with_dense(
s2_emb, self.sent2_token_mask, 'backward', 'dir_attn_bw',
cfg.dropout, self.is_train, cfg.wd,
tensor_dict=self.tensor_dict, name='s2_bw_attn')
s2_seq_rep = tf.concat([s2_fw, s2_bw], -1)
with tf.variable_scope('sentence_enc'):
s1_rep = multi_dimensional_attention(
s1_seq_rep, self.sent1_token_mask, 'multi_dimensional_attention',
cfg.dropout, self.is_train, cfg.wd,
tensor_dict=self.tensor_dict, name='s1_attn')
tf.get_variable_scope().reuse_variables()
s2_rep = multi_dimensional_attention(
s2_seq_rep, self.sent2_token_mask, 'multi_dimensional_attention',
cfg.dropout, self.is_train, cfg.wd,
tensor_dict=self.tensor_dict, name='s2_attn')
with tf.variable_scope('output'):
out_rep = tf.concat([s1_rep, s2_rep, s1_rep - s2_rep, s1_rep * s2_rep], -1)
out_rep_map = bn_dense_layer(
out_rep, hn, True, 0., 'out_rep_map', 'elu', False, cfg.wd, cfg.dropout, self.is_train)
pre_output1 = highway_network(
out_rep_map, hn, True, 0., 'pre_output1', 'elu', False, cfg.wd, cfg.dropout, self.is_train)
logits = linear([pre_output1], self.output_class, True, 0., scope='logits', squeeze=False,
wd=cfg.wd, input_keep_prob=cfg.dropout, is_train=self.is_train)
return logits, (s1_act, s1_logpa), (s2_act, s2_logpa), (s1_percentage, s2_percentage) # logits
def directional_attention_with_selections(
rep_tensor, rep_mask, dep_selection, head_selection, direction=None, hn=None, keep_unselected=True,
scope=None, keep_prob=1., is_train=None, wd=0., activation='elu'):
bs, sl, vec = tf.shape(rep_tensor)[0], tf.shape(rep_tensor)[1], tf.shape(rep_tensor)[2]
org_ivec = rep_tensor.get_shape().as_list()[2]
ivec = hn or org_ivec
with tf.variable_scope(scope or 'directional_attention_%s' % direction or 'diag'):
# non-linear
rep_map = bn_dense_layer(rep_tensor, ivec, True, 0., 'bn_dense_map', activation,
False, wd, keep_prob, is_train)
# ensure the seletion is right
dep_selection = tf.logical_and(rep_mask, dep_selection)
head_selection = tf.logical_and(rep_mask, head_selection)
rep_dep_tensor, rep_dep_mask, dep_org_idx = reduce_data_rep_max_len(rep_map, dep_selection)
rep_head_tensor,rep_head_mask, head_org_idx = reduce_data_rep_max_len(rep_map, head_selection)
sl_dep, sl_head = tf.shape(rep_dep_tensor)[1], tf.shape(rep_head_tensor)[1]
if keep_unselected:
unhead_selection = tf.logical_and(rep_mask, tf.logical_not(head_selection))
rep_unhead_tensor, rep_unhead_mask, unhead_org_idx = reduce_data_rep_max_len(rep_map, unhead_selection)
sl_unhead = tf.shape(rep_unhead_tensor)[1]
attn_result = tf.cond(
tf.equal(sl_head, 0),
lambda: tf.zeros([bs, 0, hn], tf.float32),
lambda: self_attention_for_selected_head(
head_selection, head_org_idx, sl_head, rep_head_mask,
dep_selection, dep_org_idx, sl_dep, rep_dep_mask,
rep_map, rep_dep_tensor, keep_prob, is_train, direction, ivec
)
)
if keep_unselected:
input_idx = tf.tile(tf.expand_dims(tf.range(sl), 0), [bs, 1])
pooling_result = tf.cond(
tf.equal(sl_unhead, 0),
lambda: tf.zeros([bs, 0, hn], tf.float32),
lambda: mean_pooling_for_unselected_head(
unhead_org_idx, sl_unhead, rep_unhead_mask,
input_idx, sl, rep_mask, rep_map, None) # todo: point !
)
with tf.variable_scope('output'):
if keep_unselected:
range_head = tf.tile(tf.expand_dims(tf.range(bs), -1), [1, sl_head])
scatter_attn = tf.cond(
tf.equal(sl_head, 0),
lambda: tf.zeros([bs, sl+1, hn], tf.float32),
lambda: tf.scatter_nd(
tf.stack([range_head, head_org_idx], -1), attn_result, [bs, sl+1, hn])
)
range_unhead = tf.tile(tf.expand_dims(tf.range(bs), -1), [1, sl_unhead])
scatter_pooling = tf.cond(
tf.equal(sl_unhead, 0),
lambda: tf.zeros([bs, sl+1, hn], tf.float32),
lambda: tf.scatter_nd(
tf.stack([range_unhead, unhead_org_idx], -1), pooling_result, [bs, sl+1, hn])
)
self_attn_input = rep_map
context_features = tf.add(scatter_attn[:, :-1], scatter_pooling[:, :-1], 'context_features')
output_mask = rep_mask
else:
self_attn_input = rep_head_tensor
context_features = attn_result
output_mask = rep_head_mask
# context fusion gate
o_bias = tf.get_variable('o_bias', [ivec], tf.float32, tf.constant_initializer(0.))
fusion_gate = tf.nn.sigmoid(
linear(self_attn_input, ivec, True, 0., 'linear_fusion_i', False, wd, keep_prob, is_train) +
linear(context_features, ivec, True, 0., 'linear_fusion_a', False, wd, keep_prob, is_train) +
o_bias)
output = fusion_gate * self_attn_input + (1 - fusion_gate) * context_features
return output, output_mask
def generate_mask_with_rl_real(rep_tensor, rep_mask, is_mat=False,scope=None,
keep_prob=1., is_train=None, wd=0., activation='elu', hn=None):
"""
:param rep_tensor: 3d tensor
:param rep_mask: 2d tensor
:param is_mat: [True|False]
:param start_rl:
:param end_rl_gain:
:param scope:
:param keep_prob:
:param is_train:
:param wd:
:param activation:
:param global_step:
:return:
"""
bs, sl, vec = tf.shape(rep_tensor)[0], tf.shape(rep_tensor)[1], tf.shape(rep_tensor)[2]
ivec = hn or rep_tensor.get_shape()[2]
with tf.variable_scope(scope or 'generate_mask_with_rl_real'):
if is_mat:
rep_row = tf.tile(tf.expand_dims(rep_tensor, 1), [1, sl, 1, 1])
rep_col = tf.tile(tf.expand_dims(rep_tensor, 2), [1, 1, sl, 1])
rep_h0 = tf.concat([rep_row, rep_col], -1)
else:
rep_h0 = rep_tensor
rep_h1 = bn_dense_layer([rep_h0], ivec, True, 0., 'dense_rep_mat_h1',
activation, False, wd, keep_prob, is_train)
rep_h2 = bn_dense_layer([rep_h1], 1, True, 0., 'dense_rep_mat_h2',
'linear', False, wd, keep_prob, is_train)
rep_h2 = tf.squeeze(rep_h2, 3 if is_mat else 2) # bs,sl,sl / bs,sl
rep_prob = tf.nn.sigmoid(rep_h2)
# sampling
# Here, need a dynamic policy to add the random
# todo:text
# mode_is_train = tf.constant(mode == 'train', tf.bool, [], 'mode_is_train')
# random_values = tf.cond(
# tf.logical_and(mode_is_train, is_train),
# lambda: tf.random_uniform([bs, sl, sl] if is_mat else [bs, sl]),
# lambda: tf.ones([bs, sl, sl] if is_mat else [bs, sl], tf.float32) * 0.5
# )
random_values = tf.random_uniform([bs, sl, sl] if is_mat else [bs, sl])
# if global_step is not None:
# policy_rep_prob = tf.cond(tf.logical_and(mode_is_train,
# tf.less(global_step,
# tf.constant(int(x2), tf.int32))),
# lambda: rep_prob + prob_gain,
# lambda: rep_prob)
#
# else:
# policy_rep_prob = rep_prob
policy_rep_prob = rep_prob
actions = tf.less_equal(random_values, policy_rep_prob)
actions = tf.stop_gradient(actions)
if is_mat:
rep_mask_new = tf.logical_and(
tf.expand_dims(rep_mask, 1),
tf.expand_dims(rep_mask, 2)
)
else:
rep_mask_new = rep_mask
actions = tf.logical_and(actions, rep_mask_new)
# log p(a)
logpa = - binary_entropy(rep_prob, actions) * tf.cast(rep_mask_new, tf.float32)
if is_mat:
logpa = - tf.reshape(logpa, [bs, sl * sl])
# percentage
actions_flat = tf.reshape(actions, [bs, -1])
rep_mask_new_flat = tf.reshape(rep_mask_new, [bs, -1])
percentage = tf.reduce_sum(tf.cast(actions_flat, tf.float32), -1) / \
tf.reduce_sum(tf.cast(rep_mask_new_flat, tf.float32), -1)
return logpa, actions, percentage
def build_network(self):
_logger.add()
_logger.add('building %s neural network structure...' %
cfg.network_type)
tds, cds = self.tds, self.cds
tl = self.tl
tel, cel, cos, ocd, fh = self.tel, self.cel, self.cos, self.ocd, self.fh
hn = self.hn
bs, sl1, sl2 = self.bs, self.sl1, self.sl2
with tf.variable_scope('emb'):
token_emb_mat = generate_embedding_mat(
tds,
tel,
init_mat=self.token_emb_mat,
extra_mat=self.glove_emb_mat,
extra_trainable=self.finetune_emb,
scope='gene_token_emb_mat')
s1_emb = tf.nn.embedding_lookup(token_emb_mat,
self.sent1_token) # bs,sl1,tel
s2_emb = tf.nn.embedding_lookup(token_emb_mat,
self.sent2_token) # bs,sl2,tel
self.tensor_dict['s1_emb'] = s1_emb
self.tensor_dict['s2_emb'] = s2_emb
with tf.variable_scope('hard_network'):
# for sentence 1
s1_emb_new = sequence_conditional_feature(s1_emb,
self.sent1_token_mask)
s1_logpa_dep, s1_act_dep, s1_percentage_dep = generate_mask_with_rl(
s1_emb_new, self.sent1_token_mask, False,
'generate_mask_with_rl_dep', cfg.dropout, self.is_train,
cfg.wd, 'relu', self.disable_rl, self.global_step, cfg.mode,
cfg.start_only_rl, hn) # [bs, sl] & [bs, sl]
s1_logpa_head, s1_act_head, s1_percentage_head = generate_mask_with_rl(
s1_emb_new, self.sent1_token_mask, False,
'generate_mask_with_rl_head', cfg.dropout, self.is_train,
cfg.wd, 'relu', self.disable_rl, self.global_step, cfg.mode,
cfg.start_only_rl, hn) # [bs, sl] & [bs, sl]
s1_logpa = tf.concat([s1_logpa_dep, s1_logpa_head], -1)
s1_act = tf.logical_and(tf.expand_dims(s1_act_dep, 1),
tf.expand_dims(s1_act_head, 2))
s1_percentage = s1_percentage_dep * s1_percentage_head
tf.get_variable_scope().reuse_variables()
# for sentence 2
s2_emb_new = sequence_conditional_feature(s2_emb,
self.sent2_token_mask)
s2_logpa_dep, s2_act_dep, s2_percentage_dep = generate_mask_with_rl(
s2_emb_new, self.sent2_token_mask, False,
'generate_mask_with_rl_dep', cfg.dropout, self.is_train,
cfg.wd, 'relu', self.disable_rl, self.global_step, cfg.mode,
cfg.start_only_rl, hn) # [bs, sl] & [bs, sl]
s2_logpa_head, s2_act_head, s2_percentage_head = generate_mask_with_rl(
s2_emb_new, self.sent2_token_mask, False,
'generate_mask_with_rl_head', cfg.dropout, self.is_train,
cfg.wd, 'relu', self.disable_rl, self.global_step, cfg.mode,
cfg.start_only_rl, hn) # [bs, sl] & [bs, sl]
s2_logpa = tf.concat([s2_logpa_dep, s2_logpa_head], -1)
s2_act = tf.logical_and(tf.expand_dims(s2_act_dep, 1),
tf.expand_dims(s2_act_head, 2))
s2_percentage = s2_percentage_dep * s2_percentage_head
keep_unselected = True
with tf.variable_scope('ct_attn'):
s1_fw, s1_token_mask_new = directional_attention_with_selections(
s1_emb, self.sent1_token_mask, s1_act_dep, s1_act_head,
'forward', hn, keep_unselected, 'dir_attn_fw', cfg.dropout,
self.is_train, cfg.wd, 'relu')
s1_bw, _ = directional_attention_with_selections(
s1_emb, self.sent1_token_mask, s1_act_dep, s1_act_head,
'backward', hn, keep_unselected, 'dir_attn_bw', cfg.dropout,
self.is_train, cfg.wd, 'relu')
s1_seq_rep = tf.concat([s1_fw, s1_bw], -1)
tf.get_variable_scope().reuse_variables()
s2_fw, s2_token_mask_new = directional_attention_with_selections(
s2_emb, self.sent2_token_mask, s2_act_dep, s2_act_head,
'forward', hn, keep_unselected, 'dir_attn_fw', cfg.dropout,
self.is_train, cfg.wd, 'relu')
s2_bw, _ = directional_attention_with_selections(
s2_emb, self.sent2_token_mask, s2_act_dep, s2_act_head,
'backward', hn, keep_unselected, 'dir_attn_bw', cfg.dropout,
self.is_train, cfg.wd, 'relu')
s2_seq_rep = tf.concat([s2_fw, s2_bw], -1)
with tf.variable_scope('sentence_enc'):
s1_rep = multi_dimensional_attention(s1_seq_rep,
s1_token_mask_new,
'multi_dimensional_attention',
cfg.dropout,
self.is_train,
cfg.wd,
'relu',
tensor_dict=self.tensor_dict,
name='s1_attn')
tf.get_variable_scope().reuse_variables()
s2_rep = multi_dimensional_attention(s2_seq_rep,
s2_token_mask_new,
'multi_dimensional_attention',
cfg.dropout,
self.is_train,
cfg.wd,
'relu',
tensor_dict=self.tensor_dict,
name='s2_attn')
with tf.variable_scope('output'):
out_rep = tf.concat([s1_rep * s2_rep, tf.abs(s1_rep - s2_rep)], -1)
out_rep_map = bn_dense_layer(out_rep, hn, True, 0., 'out_rep_map',
'relu', False, cfg.wd, cfg.dropout,
self.is_train)
if cfg.use_mse and cfg.mse_logits:
logits = tf.nn.sigmoid(
linear(out_rep_map,
1,
True,
0.,
scope='logits',
squeeze=True,
wd=cfg.wd,
input_keep_prob=cfg.dropout,
is_train=self.is_train)) * 2. + 3.
else:
logits = linear([out_rep_map],
self.output_class,
True,
0.,
scope='logits',
squeeze=False,
wd=cfg.wd,
input_keep_prob=cfg.dropout,
is_train=self.is_train)
return logits, (s1_act, s1_logpa), (s2_act, s2_logpa), (s1_percentage,
s2_percentage
) # logits
def visit_sa_with_dense(rep_tensor,
keep_prob=1.,
is_train=None,
wd=0.,
activation='relu',
hn=None,
is_scale=True,
is_plus_sa=True):
batch_size, sw_len, vec_size = tf.shape(rep_tensor)[0], tf.shape(
rep_tensor)[1], tf.shape(rep_tensor)[2]
ivec = rep_tensor.get_shape().as_list()[2]
ivec = hn or ivec
with tf.variable_scope('temporal_attention'):
# mask generation
attn_mask = tf.cast(
tf.diag(-tf.ones([sw_len], tf.int32)) + 1,
tf.bool) # batch_size, code_len, code_len
# non-linear for context
rep_map = bn_dense_layer(rep_tensor, ivec, True, 0., 'bn_dense_map',
activation, False, wd, keep_prob, is_train)
rep_map_tile = tf.tile(tf.expand_dims(rep_map, 1),
[1, sw_len, 1, 1]) # bs,sl,sl,vec
rep_map_dp = dropout(rep_map, keep_prob, is_train)
# attention
with tf.variable_scope('attention'): # bs,sl,sl,vec
f_bias = tf.get_variable('f_bias', [ivec], tf.float32,
tf.constant_initializer(0.))
dependent = linear(
rep_map_dp, ivec, False,
scope='linear_dependent') # batch_size, code_len, vec_size
dependent_etd = tf.expand_dims(
dependent, 1) # batch_size, code_len,code_len, vec_size
head = linear(
rep_map_dp, ivec, False,
scope='linear_head') # batch_size, code_len, vec_size
head_etd = tf.expand_dims(
head, 2) # batch_size, code_len,code_len, vec_size
if is_plus_sa:
attention_fact = dependent_etd + head_etd + f_bias
else:
return rep_map
if is_scale:
logits = scaled_tanh(attention_fact, 5.0) # bs,sl,sl,vec
else:
logits = linear(tf.nn.tanh(attention_fact),
ivec,
True,
scope='linear_attn_fact')
logits_masked = exp_mask_for_high_rank(logits, attn_mask)
attn_score = tf.nn.softmax(logits_masked, 2) # bs,sl,sl,vec
attn_score = mask_for_high_rank(attn_score, attn_mask)
attn_result = tf.reduce_sum(attn_score * rep_map_tile,
2) # bs,sl,vec
with tf.variable_scope('output'):
o_bias = tf.get_variable('o_bias', [ivec], tf.float32,
tf.constant_initializer(0.))
# input gate
fusion_gate = tf.nn.sigmoid(
linear(rep_map, ivec, True, 0., 'linear_fusion_i', False, wd,
keep_prob, is_train) +
linear(attn_result, ivec, True, 0., 'linear_fusion_a', False,
wd, keep_prob, is_train) + o_bias)
output = fusion_gate * rep_map + (1 - fusion_gate) * attn_result
return output
def build_network(self):
with tf.name_scope('code_embeddings'):
if self.model_type == 'raw':
# init_code_embed = tf.random_uniform([self.vocabulary_size, self.embedding_size], -1.0, 1.0)
# code_embeddings = tf.Variable(init_code_embed)
init_code_embed = tf.one_hot(self.inputs, self.vocabulary_size,on_value=1.0, off_value=0.0,axis=-1)
inputs_embed = bn_dense_layer(init_code_embed, self.embedding_size, True, 0.,
'bn_dense_map_linear', 'linear',
False, wd=0., keep_prob=1.,
is_train=True)
elif self.model_type == 'tesa':
init_code_embed = tesan_trans(self.model_type)
# code_embeddings = tf.Variable(init_code_embed, dtype=tf.float32)
code_embeddings = tf.Variable(init_code_embed, dtype=tf.float32)
inputs_embed = tf.nn.embedding_lookup(code_embeddings, self.inputs)
elif self.model_type == 'delta':
init_code_embed = tesan_trans(self.model_type)
# code_embeddings = tf.Variable(init_code_embed, dtype=tf.float32)
code_embeddings = tf.Variable(init_code_embed, dtype=tf.float32)
inputs_embed = tf.nn.embedding_lookup(code_embeddings, self.inputs)
elif self.model_type == 'sa':
init_code_embed = tesan_trans(self.model_type)
# code_embeddings = tf.Variable(init_code_embed, dtype=tf.float32)
code_embeddings = tf.Variable(init_code_embed, dtype=tf.float32)
inputs_embed = tf.nn.embedding_lookup(code_embeddings, self.inputs)
elif self.model_type == 'normal':
init_code_embed = tesan_trans(self.model_type)
# code_embeddings = tf.Variable(init_code_embed, dtype=tf.float32)
code_embeddings = tf.Variable(init_code_embed, dtype=tf.float32)
inputs_embed = tf.nn.embedding_lookup(code_embeddings, self.inputs)
elif self.model_type == 'cbow':
init_code_embed = tesan_trans(self.model_type)
# code_embeddings = tf.Variable(init_code_embed, dtype=tf.float32)
code_embeddings = tf.Variable(init_code_embed, dtype=tf.float32)
inputs_embed = tf.nn.embedding_lookup(code_embeddings, self.inputs)
elif self.model_type == 'sg':
init_code_embed = tesan_trans(self.model_type)
# code_embeddings = tf.Variable(init_code_embed, dtype=tf.float32)
code_embeddings = tf.Variable(init_code_embed, dtype=tf.float32)
inputs_embed = tf.nn.embedding_lookup(code_embeddings, self.inputs)
elif self.model_type == 'mce':
init_code_embed = mce_trans()
# code_embeddings = tf.Variable(init_code_embed, dtype=tf.float32)
code_embeddings = tf.Variable(init_code_embed, dtype=tf.float32)
inputs_embed = tf.nn.embedding_lookup(code_embeddings, self.inputs)
elif self.model_type == 'glove':
init_code_embed = glove_trans()
# code_embeddings = tf.Variable(init_code_embed, dtype=tf.float32)
code_embeddings = tf.Variable(init_code_embed, dtype=tf.float32)
inputs_embed = tf.nn.embedding_lookup(code_embeddings, self.inputs)
else:
init_code_embed = med2vec_trans()
# code_embeddings = tf.constant(init_code_embed, dtype=tf.float32)
code_embeddings = tf.Variable(init_code_embed, dtype=tf.float32)
inputs_embed = tf.nn.embedding_lookup(code_embeddings, self.inputs)
with tf.name_scope('visit_embedding'):
# bs, max_visits, max_len_visit, embed_size
inputs_masked = mask_for_high_rank(inputs_embed, self.inputs_mask)
inputs_reduced = tf.reduce_mean(inputs_masked, 2) # batch_size, max_visits, embed_size
with tf.name_scope('visit_masking'):
visit_mask = tf.reduce_sum(tf.cast(self.inputs_mask, tf.int32), -1) # [bs,max_visits]
visit_mask = tf.cast(visit_mask, tf.bool)
tensor_len = tf.reduce_sum(tf.cast(visit_mask, tf.int32), -1) # [bs]
with tf.name_scope('RNN_computaion'):
reuse = None if not tf.get_variable_scope().reuse else True
if cfg.cell_type == 'gru':
cell = tf.contrib.rnn.GRUCell(cfg.hn, reuse=reuse)
elif cfg.cell_type == 'lstm':
cell = tf.contrib.rnn.LSTMCell(cfg.hn, reuse=reuse)
elif cfg.cell_type == 'basic_lstm':
cell = tf.contrib.rnn.BasicLSTMCell(cfg.hn, reuse=reuse)
elif cfg.cell_type == 'basic_rnn':
cell = tf.contrib.rnn.BasicRNNCell(cfg.hn, reuse=reuse)
outputs, final_state = dynamic_rnn(cell, inputs_reduced, tensor_len, dtype=tf.float32)
return outputs, final_state, tensor_len
