def Segment_Sim(Q, K, A_matrix):
'''calculate segment-by-segment similarity u*A*r'''
assert Q.shape == K.shape and Q.shape[-1] == A_matrix.shape[0] and K.shape[
-1] == A_matrix.shape[1]
matrix2 = tf.einsum('aij,jk->aik', Q, A_matrix)
matrix2 = op.dot_sim(matrix2, K)
return matrix2
def attention(Q,
K,
V,
Q_lengths,
K_lengths,
attention_type='dot',
is_mask=True,
mask_value=-2**32 + 1,
drop_prob=None):
'''Add attention layer.
Args:
Q: a tensor with shape [batch, Q_time, Q_dimension]
K: a tensor with shape [batch, time, K_dimension]
V: a tensor with shape [batch, time, V_dimension]
Q_length: a tensor with shape [batch]
K_length: a tensor with shape [batch]
Returns:
a tensor with shape [batch, Q_time, V_dimension]
Raises:
AssertionError: if
Q_dimension not equal to K_dimension when attention type is dot.
'''
assert attention_type in ('dot', 'bilinear')
if attention_type == 'dot':
assert Q.shape[-1] == K.shape[-1]
Q_time = Q.shape[1]
K_time = K.shape[1]
if attention_type == 'dot':
logits = op.dot_sim(Q, K) #[batch, Q_time, time]
if attention_type == 'bilinear':
logits = op.bilinear_sim(Q, K)
if is_mask:
mask = op.mask(Q_lengths, K_lengths, Q_time,
K_time) #return [batch, Q_time, K_time]
# mask is a tensor with the same shape with logits
# where the real word location is labeled by 1
# where the padded/masked word location is labeled by 0
# mask * logits is element-wise product
# + (1 - mask) is to add very small negative value on
# masked positions (0). after softmax, this position becomes 0
# similar tricks also used in BERT
logits = mask * logits + (1 - mask) * mask_value
attention = tf.nn.softmax(logits)
if drop_prob is not None:
print('use attention drop')
attention = tf.nn.dropout(attention, drop_prob)
return op.weighted_sum(attention, V)
def attention(Q,
K,
V,
Q_lengths,
K_lengths,
attention_type='dot',
is_mask=True,
mask_value=-2**32 + 1,
drop_prob=None):
'''Add attention layer.
Args:
Q: a tensor with shape [batch, Q_time, Q_dimension]
K: a tensor with shape [batch, time, K_dimension]
V: a tensor with shape [batch, time, V_dimension]
Q_length: a tensor with shape [batch]
K_length: a tensor with shape [batch]
Returns:
a tensor with shape [batch, Q_time, V_dimension]
Raises:
AssertionError: if
Q_dimension not equal to K_dimension when attention type is dot.
'''
assert attention_type in ('dot', 'bilinear')
if attention_type == 'dot':
assert Q.shape[-1] == K.shape[-1]
Q_time = Q.shape[1]
K_time = K.shape[1]
if attention_type == 'dot':
logits = op.dot_sim(Q, K) #[batch, Q_time, time]
if attention_type == 'bilinear':
logits = op.bilinear_sim(Q, K)
if is_mask:
mask = op.mask(Q_lengths, K_lengths, Q_time,
K_time) #[batch, Q_time, K_time]
logits = mask * logits + (1 - mask) * mask_value
attention = tf.nn.softmax(logits)
if drop_prob is not None:
print('use attention drop')
attention = tf.nn.dropout(attention, drop_prob)
return op.weighted_sum(attention, V)
def Word_Sim(Q,
K,
attention_type='dot',
is_mask=True,
mask_value=-2**32 + 1,
drop_prob=None):
'''calculate word-by-word similarity u*r '''
assert attention_type in ('dot', 'bilinear')
if attention_type == 'dot':
assert Q.shape[-1] == K.shape[-1]
if attention_type == 'dot':
logits = op.dot_sim(Q, K)
if attention_type == 'bilinear':
logits = op.bilinear_sim(Q, K)
return logits
def attention(Q, K, V, attention_type='dot', drop_prob=None):
'''Add attention layer.'''
assert attention_type in ('dot', 'bilinear')
if attention_type == 'dot':
assert Q.shape[-1] == K.shape[-1]
Q_time = Q.shape[1]
K_time = K.shape[1]
if attention_type == 'dot':
logits = op.dot_sim(Q, K) #[batch, Q_time, K_time]
if attention_type == 'bilinear':
logits = op.bilinear_sim(Q, K)
attention = tf.nn.softmax(logits)
if drop_prob is not None:
attention = tf.nn.dropout(attention, drop_prob)
return op.weighted_sum(attention, V)