def __call__(self, annotion_list, back_word_list, p):
"""
Calculate the annotion and back word value
:param annotion_list:
:param back_word_list:
:param p: hidden value
:return:
"""
batch_size = p.data.shape[0]
exponential_list = []
sum_exponential = XP.fzeros((batch_size, 1))
# Calculate the total value list and total value
# Prepare the Convoluation
for annotion, back_word in zip(annotion_list, back_word_list):
weight = functions.tanh(
self.annotion_weight(annotion) + self.back_weight(back_word) +
self.pw(p))
exponential = functions.exp(self.weight_exponential(weight))
exponential_list.append(exponential)
sum_exponential += exponential
ZEROS = XP.fzeros((batch_size, self.hidden_size))
annotion_value = ZEROS
back_word_value = ZEROS
# Calculate the Convolution Value each annotion and back word
for annotion, back_word, exponential in zip(annotion_list,
back_word_list,
exponential_list):
exponential /= sum_exponential
annotion_value += functions.reshape(
functions.batch_matmul(annotion, exponential),
(batch_size, self.hidden_size))
back_word_value += functions.reshape(
functions.batch_matmul(back_word, exponential),
(batch_size, self.hidden_size))
return annotion_value, back_word_value
def __call__(self, annotion_list, back_word_list, p):
"""
Calculate the annotion and back word value
:param annotion_list:
:param back_word_list:
:param p: hidden value
:return:
"""
batch_size = p.data.shape[0]
exponential_list = []
sum_exponential = XP.fzeros((batch_size, 1))
# Calculate the total value list and total value
# Prepare the Convoluation
for annotion, back_word in zip(annotion_list, back_word_list):
weight = functions.tanh(self.annotion_weight(annotion) + self.back_weight(back_word) + self.pw(p))
exponential = functions.exp(self.weight_exponential(weight))
exponential_list.append(exponential)
sum_exponential += exponential
ZEROS = XP.fzeros((batch_size, self.hidden_size))
annotion_value = ZEROS
back_word_value = ZEROS
# Calculate the Convolution Value each annotion and back word
for annotion, back_word, exponential in zip(annotion_list, back_word_list, exponential_list):
exponential /= sum_exponential
annotion_value += functions.reshape(functions.batch_matmul(annotion, exponential), (batch_size, self.hidden_size))
back_word_value += functions.reshape(functions.batch_matmul(back_word, exponential), (batch_size, self.hidden_size))
return annotion_value, back_word_value
