def playInteraction3(self):
'''
输入：两个
过程：
1.执行playInteraction1获取每个的新表达
2.执行attention-over-attention获取其中一个的权重
:return: new_x,new_y,y_weight
'''
y_len = self.data[1].get_shape().as_list()[1]
x_2_y, y_2_x = comp.genericAttention(self.data[0], self.data[1])
# 获取x和y的各自总权重
x_weight = tf.reduce_sum(y_2_x, axis=-1)
y_weight = tf.reduce_sum(x_2_y, axis=1)
# 计算对齐后的向量矩阵
new_x = tf.einsum('abc,ab->abc', self.data[0], x_weight)
new_y = tf.einsum('abc,ab->abc', self.data[1], y_weight)
#计算Y的权重
x_weight_ = tf.expand_dims(x_weight,axis=1)
y_weight_ = tf.matmul(x_weight_,x_2_y) #[Batch, 1, len2]
y_weight_output = tf.reshape(y_weight_,shape=[-1,y_len])
return new_x, new_y,y_weight_output
def playInteraction7(self):
assert len(self.data) == 2, ValueError(
'the number of input data is wrong, it should be 2,but{0}'.format(len(self.data)))
# 首先计算attention
x_2_y, y_2_x = comp.genericAttention(self.data[0], self.data[1])
feature_b = tf.matmul(y_2_x, self.data[0], transpose_a=True)
feature_a = tf.matmul(x_2_y, self.data[1])
return feature_a, feature_b
def playInteraction4(self):
inputX1 = self.data[0]
inputX2 = self.data[1]
len1 = inputX1.get_shape().as_list()[1]
len2 = inputX2.get_shape().as_list()[1]
x1_2_x2,x2_2_x1 = comp.genericAttention(self.data[0], self.data[1])
# 计算x1每个词获取的总weight
x1_weight = tf.reduce_mean(x2_2_x1, axis=2) # [Batch, len1]
# 计算x2最后获取的每个词的总的weight
x1_weight_ = tf.expand_dims(x1_weight, axis=1) # [Batch, 1, len1]
x2_weight = tf.matmul(x1_weight_, x1_2_x2) # [Batch, 1, len2]
x2_weight = tf.reshape(x2_weight, shape=[-1, len2])
return x2_weight
def playInteraction2(self):
'''
该interaction过程如下
输入：两个
过程：
1. 计算普通的attention矩阵
2. 然后分别对行求softmax和列softmax
3. 分别对行和列做加和，获取x和y的权重
:return: x_weight, y_weight
'''
x_2_y, y_2_x = comp.genericAttention(self.data[0], self.data[1])
# 获取x和y的各自总权重
x_weight = tf.reduce_sum(y_2_x, axis=-1)
y_weight = tf.reduce_sum(x_2_y, axis=1)
return x_weight, y_weight
def playInteraction11(self):
'''
输入：两个
过程：
1.执行playInteraction1获取每个的新表达
:return: new_x,new_y
'''
y_len = self.data[1].get_shape().as_list()[1]
x_2_y, y_2_x = comp.genericAttention(self.data[0], self.data[1])
# 获取x和y的各自总权重
x_weight = tf.reduce_sum(y_2_x, axis=-1)
y_weight = tf.reduce_sum(x_2_y, axis=1)
# 计算对齐后的向量矩阵
new_x = tf.einsum('abc,ab->abc', self.data[0], x_weight)
new_y = tf.einsum('abc,ab->abc', self.data[1], y_weight)
return new_x, new_y
def playInteraction1(self):
'''
该interaction包含两个输入
过程如下：
1.计算普通的attention的矩阵
2.然后分别对行求softmax和列softmax
3.计算两个输入各自被对齐后的向量矩阵
'''
assert len(self.data) == 2, ValueError(
'the number of input data is wrong, it should be 2,but{0}'.format(len(self.data)))
# 首先计算attention
x_2_y, y_2_x = comp.genericAttention(self.data[0], self.data[1])
# 获取x和y的各自总权重
x_weight = tf.reduce_sum(y_2_x, axis=-1)
y_weight = tf.reduce_sum(x_2_y, axis=1)
# 计算对齐后的向量矩阵
new_x = tf.einsum('abc,ab->abc', self.data[0], x_weight)
new_y = tf.einsum('abc,ab->abc', self.data[1], y_weight)
return new_x, new_y