def top_K_loss(self,sentence,image,K=50,margin=0.4):
sim_matrix = tf.matmul(sentence, image, transpose_b=True)
s_square = tf.reduce_sum(tf.square(sentence), axis=1)
im_square = tf.reduce_sum(tf.square(image), axis=1)
d = tf.reshape(s_square,[-1,1]) - 2 * sim_matrix + tf.reshape(im_square, [1, -1])
positive = tf.stack([tf.matrix_diag_part(d)] * K, axis=1)
length = tf.shape(d)[-1]
d = tf.matrix_set_diag(d, 8 * tf.ones([length]))
sen_loss_K ,_ = tf.nn.top_k(-1.0 * d, K, sorted=False) # note: this is negative value
im_loss_K,_ = tf.nn.top_k(tf.transpose(-1.0 * d), K, sorted=False) # note: this is negative value
sentence_center_loss = tf.nn.relu(positive + sen_loss_K + margin)
image_center_loss = tf.nn.relu(positive + im_loss_K + margin)
self.d_neg = tf.reduce_mean((sen_loss_K + im_loss_K)/-2.0)
self.d_pos =tf.reduce_mean(positive)
self.endpoint['debug/im_loss_topK'] = -1.0 * im_loss_K
self.endpoint['debug/sen_loss_topK'] = -1.0 * sen_loss_K
self.endpoint['debug/d_Matrix'] = d
self.endpoint['debug/positive'] = positive
self.endpoint['debug/s_center_loss'] = sentence_center_loss
self.endpoint['debug/i_center_loss'] = image_center_loss
self.endpoint['debug/S'] = sim_matrix
self.endpoint['debug/sentence_square'] = s_square
self.endpoint['debug/image_square'] = im_square
return tf.reduce_sum(sentence_center_loss), tf.reduce_sum(image_center_loss)
python类matrix_diag_part()的实例源码
Bidirectionnet_GMM_softmaxloss.py 文件源码
项目：image-text-matching
作者: llltttppp
项目源码
文件源码
阅读 29
收藏 0
点赞 0
评论 0
def top_K_loss(self,sentence,image,K=50,margin=0.3):
sim_matrix = tf.matmul(sentence, image, transpose_b=True)
s_square = tf.reduce_sum(tf.square(sentence), axis=1)
im_square = tf.reduce_sum(tf.square(image), axis=1)
d = tf.reshape(s_square,[-1,1]) - 2 * sim_matrix + tf.reshape(im_square, [1, -1])
positive = tf.stack([tf.matrix_diag_part(d)] * K, axis=1)
length = tf.shape(d)[-1]
d = tf.matrix_set_diag(d, 8 * tf.ones([length]))
sen_loss_K ,_ = tf.nn.top_k(-1.0 * d, K, sorted=False) # note: this is negative value
im_loss_K,_ = tf.nn.top_k(tf.transpose(-1.0 * d), K, sorted=False) # note: this is negative value
sentence_center_loss = tf.nn.relu(positive + sen_loss_K + margin)
image_center_loss = tf.nn.relu(positive + im_loss_K + margin)
self.d_neg = tf.reduce_mean((sen_loss_K + im_loss_K)/-2.0)
self.d_pos =tf.reduce_mean(positive)
self.endpoint['debug/im_loss_topK'] = -1.0 * im_loss_K
self.endpoint['debug/sen_loss_topK'] = -1.0 * sen_loss_K
self.endpoint['debug/d_Matrix'] = d
self.endpoint['debug/positive'] = positive
self.endpoint['debug/s_center_loss'] = sentence_center_loss
self.endpoint['debug/i_center_loss'] = image_center_loss
self.endpoint['debug/S'] = sim_matrix
self.endpoint['debug/sentence_square'] = s_square
self.endpoint['debug/image_square'] = im_square
return tf.reduce_sum(sentence_center_loss), tf.reduce_sum(image_center_loss)
Bidirectionnet_GMM_softmaxloss.py 文件源码
项目：image-text-matching
作者: llltttppp
项目源码
文件源码
阅读 23
收藏 0
点赞 0
评论 0
def top_K_loss_margin(self,sentence,image,K=50,margin=0.3):
sim_matrix = tf.matmul(sentence, image, transpose_b=True)
s_square = tf.reduce_sum(tf.square(sentence), axis=1)
im_square = tf.reduce_sum(tf.square(image), axis=1)
d = tf.reshape(s_square,[-1,1]) - 2 * sim_matrix + tf.reshape(im_square, [1, -1])
positive = tf.stack([tf.matrix_diag_part(d)] * K, axis=1)
length = tf.shape(d)[-1]
d = tf.matrix_set_diag(d, 8 * tf.ones([length]))
flag =8-7*tf.sign(tf.nn.relu(self.sen_margin-self.sen_similarity))
sen_loss_K ,_ = tf.nn.top_k(-1.0 * d *flag, K, sorted=False) # note: this is negative value
im_loss_K,_ = tf.nn.top_k(tf.transpose(-1.0 * d*flag)*tf.sign(tf.nn.relu(self.image_margin-self.im_similarity)), K, sorted=False) # note: this is negative value
sentence_center_loss = tf.nn.relu(positive + sen_loss_K + margin)
image_center_loss = tf.nn.relu(positive + im_loss_K + margin)
self.d_neg = tf.reduce_mean((sen_loss_K + im_loss_K)/-2.0)
self.d_pos =tf.reduce_mean(positive)
self.endpoint['debug/im_loss_topK'] = -1.0 * im_loss_K
self.endpoint['debug/sen_loss_topK'] = -1.0 * sen_loss_K
self.endpoint['debug/d_Matrix'] = d
self.endpoint['debug/positive'] = positive
self.endpoint['debug/s_center_loss'] = sentence_center_loss
self.endpoint['debug/i_center_loss'] = image_center_loss
self.endpoint['debug/S'] = sim_matrix
self.endpoint['debug/sentence_square'] = s_square
self.endpoint['debug/image_square'] = im_square
return tf.reduce_sum(sentence_center_loss), tf.reduce_sum(image_center_loss)
def top_K_loss(self,sentence,image,K=30,margin=0.3):
sim_matrix = tf.matmul(sentence, image,transpose_b=True)
bs = tf.shape(sim_matrix)[0]
s_square = tf.reduce_sum(tf.square(sentence),axis=1)
im_square =tf.reduce_sum(tf.square(image),axis=1)
d = tf.reshape(s_square,[-1,1])-2*sim_matrix+tf.reshape(im_square,[1,-1])
positive = tf.stack([tf.matrix_diag_part(d)]*K,1)
length = tf.shape(d)[-1]
d = tf.matrix_set_diag(d, 100*tf.ones([length]))
sen_loss_K ,_= tf.nn.top_k(-d,K,sorted=False)
im_loss_K,_=tf.nn.top_k(tf.transpose(-d),K,sorted=False)
sentence_center_loss = tf.nn.relu(sen_loss_K + positive +margin)
image_center_loss = tf.nn.relu(im_loss_K + positive +margin)
self.d_neg =tf.reduce_mean(-sen_loss_K-im_loss_K)/2
self.d_pos = tf.reduce_mean(positive)
self.endpoint['debug/sentence_center_loss']=sentence_center_loss
self.endpoint['debug/image_center_loss']=image_center_loss
self.endpoint['debug/sim_matrix']=sim_matrix
self.endpoint['debug/sen_loss_K']=-sen_loss_K
self.endpoint['debug/image_loss_K']=-im_loss_K
self.endpoint['debug/distance']=d
self.endpoint['debug/positive']=positive
return tf.reduce_sum(sentence_center_loss),tf.reduce_sum(image_center_loss)
def top_K_loss(self,sentence,image,K=50,margin=0.4):
sim_matrix = tf.matmul(sentence, image, transpose_b=True)
s_square = tf.reduce_sum(tf.square(sentence), axis=1)
im_square = tf.reduce_sum(tf.square(image), axis=1)
d = tf.reshape(s_square,[-1,1]) - 2 * sim_matrix + tf.reshape(im_square, [1, -1])
positive = tf.stack([tf.matrix_diag_part(d)] * K, axis=1)
length = tf.shape(d)[-1]
d = tf.matrix_set_diag(d, 8 * tf.ones([length]))
sen_loss_K ,_ = tf.nn.top_k(-1.0 * d, K, sorted=False) # note: this is negative value
im_loss_K,_ = tf.nn.top_k(tf.transpose(-1.0 * d), K, sorted=False) # note: this is negative value
sentence_center_loss = tf.nn.relu(positive + sen_loss_K + margin)
image_center_loss = tf.nn.relu(positive + im_loss_K + margin)
self.d_neg = tf.reduce_mean((sen_loss_K + im_loss_K)/-2.0)
self.d_pos =tf.reduce_mean(positive)
self.endpoint['debug/im_loss_topK'] = -1.0 * im_loss_K
self.endpoint['debug/sen_loss_topK'] = -1.0 * sen_loss_K
self.endpoint['debug/d_Matrix'] = d
self.endpoint['debug/positive'] = positive
self.endpoint['debug/s_center_loss'] = sentence_center_loss
self.endpoint['debug/i_center_loss'] = image_center_loss
self.endpoint['debug/S'] = sim_matrix
self.endpoint['debug/sentence_square'] = s_square
self.endpoint['debug/image_square'] = im_square
return tf.reduce_sum(sentence_center_loss), tf.reduce_sum(image_center_loss)
def top_K_loss(self,sentence,image,K=50,margin=0.4):
sim_matrix = tf.matmul(sentence, image, transpose_b=True)
s_square = tf.reduce_sum(tf.square(sentence), axis=1)
im_square = tf.reduce_sum(tf.square(image), axis=1)
d = tf.reshape(s_square,[-1,1]) - 2 * sim_matrix + tf.reshape(im_square, [1, -1])
positive = tf.stack([tf.matrix_diag_part(d)] * K, axis=1)
length = tf.shape(d)[-1]
d = tf.matrix_set_diag(d, 8 * tf.ones([length]))
sen_loss_K ,_ = tf.nn.top_k(-1.0 * d, K, sorted=False) # note: this is negative value
im_loss_K,_ = tf.nn.top_k(tf.transpose(-1.0 * d), K, sorted=False) # note: this is negative value
sentence_center_loss = tf.nn.relu(positive + sen_loss_K + margin)
image_center_loss = tf.nn.relu(positive + im_loss_K + margin)
self.d_neg = tf.reduce_mean((sen_loss_K + im_loss_K)/-2.0)
self.d_pos =tf.reduce_mean(positive)
self.endpoint['debug/im_loss_topK'] = -1.0 * im_loss_K
self.endpoint['debug/sen_loss_topK'] = -1.0 * sen_loss_K
self.endpoint['debug/d_Matrix'] = d
self.endpoint['debug/positive'] = positive
self.endpoint['debug/s_center_loss'] = sentence_center_loss
self.endpoint['debug/i_center_loss'] = image_center_loss
self.endpoint['debug/S'] = sim_matrix
self.endpoint['debug/sentence_square'] = s_square
self.endpoint['debug/image_square'] = im_square
return tf.reduce_sum(sentence_center_loss), tf.reduce_sum(image_center_loss)
Bidirectionnet_GMM_better_topK_9000feat.py 文件源码
项目：image-text-matching
作者: llltttppp
项目源码
文件源码
阅读 28
收藏 0
点赞 0
评论 0
def top_K_loss(self,sentence,image,K=50,margin=0.3):
sim_matrix = tf.matmul(sentence, image, transpose_b=True)
s_square = tf.reduce_sum(tf.square(sentence), axis=1)
im_square = tf.reduce_sum(tf.square(image), axis=1)
d = tf.reshape(s_square,[-1,1]) - 2 * sim_matrix + tf.reshape(im_square, [1, -1])
positive = tf.stack([tf.matrix_diag_part(d)] * K, axis=1)
length = tf.shape(d)[-1]
d = tf.matrix_set_diag(d, 8 * tf.ones([length]))
sen_loss_K ,_ = tf.nn.top_k(-1.0 * d, K, sorted=False) # note: this is negative value
im_loss_K,_ = tf.nn.top_k(tf.transpose(-1.0 * d), K, sorted=False) # note: this is negative value
sentence_center_loss = tf.nn.relu(positive + sen_loss_K + margin)
image_center_loss = tf.nn.relu(positive + im_loss_K + margin)
self.d_neg = tf.reduce_mean((sen_loss_K + im_loss_K)/-2.0)
self.d_pos =tf.reduce_mean(positive)
self.endpoint['debug/im_loss_topK'] = -1.0 * im_loss_K
self.endpoint['debug/sen_loss_topK'] = -1.0 * sen_loss_K
self.endpoint['debug/d_Matrix'] = d
self.endpoint['debug/positive'] = positive
self.endpoint['debug/s_center_loss'] = sentence_center_loss
self.endpoint['debug/i_center_loss'] = image_center_loss
self.endpoint['debug/S'] = sim_matrix
self.endpoint['debug/sentence_square'] = s_square
self.endpoint['debug/image_square'] = im_square
return tf.reduce_sum(sentence_center_loss), tf.reduce_sum(image_center_loss)
Bidirectionnet_GMM9000feat_softmaxloss.py 文件源码
项目：image-text-matching
作者: llltttppp
项目源码
文件源码
阅读 27
收藏 0
点赞 0
评论 0
def top_K_loss(self,sentence,image,K=50,margin=0.3):
sim_matrix = tf.matmul(sentence, image, transpose_b=True)
s_square = tf.reduce_sum(tf.square(sentence), axis=1)
im_square = tf.reduce_sum(tf.square(image), axis=1)
d = tf.reshape(s_square,[-1,1]) - 2 * sim_matrix + tf.reshape(im_square, [1, -1])
positive = tf.stack([tf.matrix_diag_part(d)] * K, axis=1)
length = tf.shape(d)[-1]
d = tf.matrix_set_diag(d, 8 * tf.ones([length]))
sen_loss_K ,_ = tf.nn.top_k(-1.0 * d, K, sorted=False) # note: this is negative value
im_loss_K,_ = tf.nn.top_k(tf.transpose(-1.0 * d), K, sorted=False) # note: this is negative value
sentence_center_loss = tf.nn.relu(positive + sen_loss_K + margin)
image_center_loss = tf.nn.relu(positive + im_loss_K + margin)
self.d_neg = tf.reduce_mean((sen_loss_K + im_loss_K)/-2.0)
self.d_pos =tf.reduce_mean(positive)
self.endpoint['debug/im_loss_topK'] = -1.0 * im_loss_K
self.endpoint['debug/sen_loss_topK'] = -1.0 * sen_loss_K
self.endpoint['debug/d_Matrix'] = d
self.endpoint['debug/positive'] = positive
self.endpoint['debug/s_center_loss'] = sentence_center_loss
self.endpoint['debug/i_center_loss'] = image_center_loss
self.endpoint['debug/S'] = sim_matrix
self.endpoint['debug/sentence_square'] = s_square
self.endpoint['debug/image_square'] = im_square
return tf.reduce_sum(sentence_center_loss), tf.reduce_sum(image_center_loss)
Bidirectionnet_GMM9000feat_softmaxloss.py 文件源码
项目：image-text-matching
作者: llltttppp
项目源码
文件源码
阅读 29
收藏 0
点赞 0
评论 0
def top_K_loss_margin(self,sentence,image,K=50,margin=0.3):
sim_matrix = tf.matmul(sentence, image, transpose_b=True)
s_square = tf.reduce_sum(tf.square(sentence), axis=1)
im_square = tf.reduce_sum(tf.square(image), axis=1)
d = tf.reshape(s_square,[-1,1]) - 2 * sim_matrix + tf.reshape(im_square, [1, -1])
positive = tf.stack([tf.matrix_diag_part(d)] * K, axis=1)
length = tf.shape(d)[-1]
d = tf.matrix_set_diag(d, 8 * tf.ones([length]))
flag =8-7*tf.sign(tf.nn.relu(self.sen_margin-self.sen_similarity))
sen_loss_K ,_ = tf.nn.top_k(-1.0 * d *flag, K, sorted=False) # note: this is negative value
im_loss_K,_ = tf.nn.top_k(tf.transpose(-1.0 * d*flag), K, sorted=False) # note: this is negative value
sentence_center_loss = tf.nn.relu(positive + sen_loss_K + margin)
image_center_loss = tf.nn.relu(positive + im_loss_K + margin)
self.d_neg = tf.reduce_mean((sen_loss_K + im_loss_K)/-2.0)
self.d_pos =tf.reduce_mean(positive)
self.endpoint['debug/im_loss_topK'] = -1.0 * im_loss_K
self.endpoint['debug/sen_loss_topK'] = -1.0 * sen_loss_K
self.endpoint['debug/d_Matrix'] = d
self.endpoint['debug/positive'] = positive
self.endpoint['debug/s_center_loss'] = sentence_center_loss
self.endpoint['debug/i_center_loss'] = image_center_loss
self.endpoint['debug/S'] = sim_matrix
self.endpoint['debug/sentence_square'] = s_square
self.endpoint['debug/image_square'] = im_square
return tf.reduce_sum(sentence_center_loss), tf.reduce_sum(image_center_loss)
def top_K_loss(self,sentence,image,K=30,margin=0.3):
sim_matrix = tf.matmul(sentence, image,transpose_b=True)
bs = tf.shape(sim_matrix)[0]
s_square = tf.reduce_sum(tf.square(sentence),axis=1)
im_square =tf.reduce_sum(tf.square(image),axis=1)
d = tf.reshape(s_square,[-1,1])-2*sim_matrix+tf.reshape(im_square,[1,-1])
positive = tf.stack([tf.matrix_diag_part(d)]*K,1)
length = tf.shape(d)[-1]
d = tf.matrix_set_diag(d, 100*tf.ones([length]))
sen_loss_K ,_= tf.nn.top_k(-d,K,sorted=False)
im_loss_K,_=tf.nn.top_k(tf.transpose(-d),K,sorted=False)
sentence_center_loss = tf.nn.relu(sen_loss_K + positive +margin)
image_center_loss = tf.nn.relu(im_loss_K + positive +margin)
self.d_neg =tf.reduce_mean(-sen_loss_K-im_loss_K)/2
self.d_pos = tf.reduce_mean(positive)
self.endpoint['debug/sentence_center_loss']=sentence_center_loss
self.endpoint['debug/image_center_loss']=image_center_loss
self.endpoint['debug/sim_matrix']=sim_matrix
self.endpoint['debug/sen_loss_K']=-sen_loss_K
self.endpoint['debug/image_loss_K']=-im_loss_K
self.endpoint['debug/distance']=d
self.endpoint['debug/positive']=positive
return tf.reduce_sum(sentence_center_loss),tf.reduce_sum(image_center_loss)
Bidirectionnet_GMM_dataflow.py 文件源码
项目：image-text-matching
作者: llltttppp
项目源码
文件源码
阅读 20
收藏 0
点赞 0
评论 0
def top_K_loss(self,sentence,image,K=50,margin=0.4):
sim_matrix = tf.matmul(sentence, image, transpose_b=True)
s_square = tf.reduce_sum(tf.square(sentence), axis=1)
im_square = tf.reduce_sum(tf.square(image), axis=1)
d = tf.reshape(s_square,[-1,1]) - 2 * sim_matrix + tf.reshape(im_square, [1, -1])
positive = tf.stack([tf.matrix_diag_part(d)] * K, axis=1)
length = tf.shape(d)[-1]
d = tf.matrix_set_diag(d, 8 * tf.ones([length]))
sen_loss_K ,_ = tf.nn.top_k(-1.0 * d, K, sorted=False) # note: this is negative value
im_loss_K,_ = tf.nn.top_k(tf.transpose(-1.0 * d), K, sorted=False) # note: this is negative value
sentence_center_loss = tf.nn.relu(positive + sen_loss_K + margin)
image_center_loss = tf.nn.relu(positive + im_loss_K + margin)
self.d_neg = tf.reduce_mean((sen_loss_K + im_loss_K)/-2.0)
self.d_pos =tf.reduce_mean(positive)
self.endpoint['debug/im_loss_topK'] = -1.0 * im_loss_K
self.endpoint['debug/sen_loss_topK'] = -1.0 * sen_loss_K
self.endpoint['debug/d_Matrix'] = d
self.endpoint['debug/positive'] = positive
self.endpoint['debug/s_center_loss'] = sentence_center_loss
self.endpoint['debug/i_center_loss'] = image_center_loss
self.endpoint['debug/S'] = sim_matrix
self.endpoint['debug/sentence_square'] = s_square
self.endpoint['debug/image_square'] = im_square
return tf.reduce_sum(sentence_center_loss), tf.reduce_sum(image_center_loss)
def top_K_loss(self, sentence, image, K=30, margin=0.5):
sim_matrix = tf.matmul(sentence, image, transpose_b=True)
s_square = tf.reduce_sum(tf.square(sentence), axis=1)
im_square = tf.reduce_sum(tf.square(image), axis=1)
d = tf.reshape(s_square,[-1,1]) - 2 * sim_matrix + tf.reshape(im_square, [1, -1])
positive = tf.stack([tf.matrix_diag_part(d)] * K, axis=1)
length = tf.shape(d)[-1]
d = tf.matrix_set_diag(d, 8 * tf.ones([length]))
sen_loss_K ,_ = tf.nn.top_k(-1.0 * d, K, sorted=False) # note: this is negative value
im_loss_K,_ = tf.nn.top_k(tf.transpose(-1.0 * d), K, sorted=False) # note: this is negative value
sentence_center_loss = tf.nn.relu(positive + sen_loss_K + margin)
image_center_loss = tf.nn.relu(positive + im_loss_K + margin)
self.d_neg = (sen_loss_K + im_loss_K)/-2.0
self.d_pos = positive
self.endpoint['debug/im_loss_topK'] = -1.0 * im_loss_K
self.endpoint['debug/sen_loss_topK'] = -1.0 * sen_loss_K
self.endpoint['debug/d_Matrix'] = d
self.endpoint['debug/positive'] = positive
self.endpoint['debug/s_center_loss'] = sentence_center_loss
self.endpoint['debug/i_center_loss'] = image_center_loss
self.endpoint['debug/S'] = sim_matrix
self.endpoint['debug/sentence_square'] = s_square
self.endpoint['debug/image_square'] = im_square
return tf.reduce_sum(sentence_center_loss), tf.reduce_sum(image_center_loss)
def top_K_loss(self,sentence,image,K=50,margin=0.1):
sim_matrix = tf.matmul(sentence, image, transpose_b=True)
s_square = tf.reduce_sum(tf.square(sentence), axis=1)
im_square = tf.reduce_sum(tf.square(image), axis=1)
d = tf.reshape(s_square,[-1,1]) - 2 * sim_matrix + tf.reshape(im_square, [1, -1])
positive = tf.stack([tf.matrix_diag_part(d)] * K, axis=1)
length = tf.shape(d)[-1]
d = tf.matrix_set_diag(d, 8 * tf.ones([length]))
sen_loss_K ,_ = tf.nn.top_k(-1.0 * d, K, sorted=False) # note: this is negative value
im_loss_K,_ = tf.nn.top_k(tf.transpose(-1.0 * d), K, sorted=False) # note: this is negative value
sentence_center_loss = tf.nn.relu(positive + sen_loss_K + margin)
image_center_loss = tf.nn.relu(positive + im_loss_K + margin)
self.d_neg = tf.reduce_mean((sen_loss_K + im_loss_K)/-2.0)
self.d_pos =tf.reduce_mean(positive)
self.endpoint['debug/im_loss_topK'] = -1.0 * im_loss_K
self.endpoint['debug/sen_loss_topK'] = -1.0 * sen_loss_K
self.endpoint['debug/d_Matrix'] = d
self.endpoint['debug/positive'] = positive
self.endpoint['debug/s_center_loss'] = sentence_center_loss
self.endpoint['debug/i_center_loss'] = image_center_loss
self.endpoint['debug/S'] = sim_matrix
self.endpoint['debug/sentence_square'] = s_square
self.endpoint['debug/image_square'] = im_square
return tf.reduce_sum(sentence_center_loss), tf.reduce_sum(image_center_loss)
def top_K_loss(self, sentence, image, K=30, margin=0.5):
sim_matrix = tf.matmul(sentence, image, transpose_b=True)
s_square = tf.reduce_sum(tf.square(sentence), axis=1)
im_square = tf.reduce_sum(tf.square(image), axis=1)
d = tf.reshape(s_square,[-1,1]) - 2 * sim_matrix + tf.reshape(im_square, [1, -1])
positive = tf.stack([tf.matrix_diag_part(d)] * K, axis=1)
length = tf.shape(d)[-1]
d = tf.matrix_set_diag(d, 8 * tf.ones([length]))
sen_loss_K ,_ = tf.nn.top_k(-1.0 * d, K, sorted=False) # note: this is negative value
im_loss_K,_ = tf.nn.top_k(tf.transpose(-1.0 * d), K, sorted=False) # note: this is negative value
sentence_center_loss = tf.nn.relu(positive + sen_loss_K + margin)
image_center_loss = tf.nn.relu(positive + im_loss_K + margin)
self.d_neg = (sen_loss_K + im_loss_K)/-2.0
self.d_pos = positive
self.endpoint['debug/im_loss_topK'] = -1.0 * im_loss_K
self.endpoint['debug/sen_loss_topK'] = -1.0 * sen_loss_K
self.endpoint['debug/d_Matrix'] = d
self.endpoint['debug/positive'] = positive
self.endpoint['debug/s_center_loss'] = sentence_center_loss
self.endpoint['debug/i_center_loss'] = image_center_loss
self.endpoint['debug/S'] = sim_matrix
self.endpoint['debug/sentence_square'] = s_square
self.endpoint['debug/image_square'] = im_square
return tf.reduce_sum(sentence_center_loss), tf.reduce_sum(image_center_loss)
def top_K_loss(self,sentence,image,K=50,margin=0.4):
sim_matrix = tf.matmul(sentence, image, transpose_b=True)
s_square = tf.reduce_sum(tf.square(sentence), axis=1)
im_square = tf.reduce_sum(tf.square(image), axis=1)
d = tf.reshape(s_square,[-1,1]) - 2 * sim_matrix + tf.reshape(im_square, [1, -1])
positive = tf.stack([tf.matrix_diag_part(d)] * K, axis=1)
length = tf.shape(d)[-1]
d = tf.matrix_set_diag(d, 8 * tf.ones([length]))
sen_loss_K ,_ = tf.nn.top_k(-1.0 * d, K, sorted=False) # note: this is negative value
im_loss_K,_ = tf.nn.top_k(tf.transpose(-1.0 * d), K, sorted=False) # note: this is negative value
sentence_center_loss = tf.nn.relu(positive + sen_loss_K + margin)
image_center_loss = tf.nn.relu(positive + im_loss_K + margin)
self.d_neg = tf.reduce_mean((sen_loss_K + im_loss_K)/-2.0)
self.d_pos =tf.reduce_mean(positive)
self.endpoint['debug/im_loss_topK'] = -1.0 * im_loss_K
self.endpoint['debug/sen_loss_topK'] = -1.0 * sen_loss_K
self.endpoint['debug/d_Matrix'] = d
self.endpoint['debug/positive'] = positive
self.endpoint['debug/s_center_loss'] = sentence_center_loss
self.endpoint['debug/i_center_loss'] = image_center_loss
self.endpoint['debug/S'] = sim_matrix
self.endpoint['debug/sentence_square'] = s_square
self.endpoint['debug/image_square'] = im_square
return tf.reduce_sum(sentence_center_loss), tf.reduce_sum(image_center_loss)
Bidirectionnet_GMM_clustertopK_9000feat.py 文件源码
项目：image-text-matching
作者: llltttppp
项目源码
文件源码
阅读 25
收藏 0
点赞 0
评论 0
def top_K_loss(self,sentence,image,K=50,margin=0.3):
sim_matrix = tf.matmul(sentence, image, transpose_b=True)
s_square = tf.reduce_sum(tf.square(sentence), axis=1)
im_square = tf.reduce_sum(tf.square(image), axis=1)
d = tf.reshape(s_square,[-1,1]) - 2 * sim_matrix + tf.reshape(im_square, [1, -1])
positive = tf.stack([tf.matrix_diag_part(d)] * K, axis=1)
length = tf.shape(d)[-1]
d = tf.matrix_set_diag(d, 8 * tf.ones([length]))
sen_loss_K ,_ = tf.nn.top_k(-1.0 * d, K, sorted=False) # note: this is negative value
im_loss_K,_ = tf.nn.top_k(tf.transpose(-1.0 * d), K, sorted=False) # note: this is negative value
sentence_center_loss = tf.nn.relu(positive + sen_loss_K + margin)
image_center_loss = tf.nn.relu(positive + im_loss_K + margin)
self.d_neg = tf.reduce_mean((sen_loss_K + im_loss_K)/-2.0)
self.d_pos =tf.reduce_mean(positive)
self.endpoint['debug/im_loss_topK'] = -1.0 * im_loss_K
self.endpoint['debug/sen_loss_topK'] = -1.0 * sen_loss_K
self.endpoint['debug/d_Matrix'] = d
self.endpoint['debug/positive'] = positive
self.endpoint['debug/s_center_loss'] = sentence_center_loss
self.endpoint['debug/i_center_loss'] = image_center_loss
self.endpoint['debug/S'] = sim_matrix
self.endpoint['debug/sentence_square'] = s_square
self.endpoint['debug/image_square'] = im_square
return tf.reduce_sum(sentence_center_loss), tf.reduce_sum(image_center_loss)
Bidirectionnet_GMM_sigmod9000feat.py 文件源码
项目：image-text-matching
作者: llltttppp
项目源码
文件源码
阅读 28
收藏 0
点赞 0
评论 0
def top_K_loss(self,sentence,image,K=50,margin=0.3):
sim_matrix = tf.matmul(sentence, image, transpose_b=True)
s_square = tf.reduce_sum(tf.square(sentence), axis=1)
im_square = tf.reduce_sum(tf.square(image), axis=1)
d = tf.reshape(s_square,[-1,1]) - 2 * sim_matrix + tf.reshape(im_square, [1, -1])
positive = tf.stack([tf.matrix_diag_part(d)] * K, axis=1)
length = tf.shape(d)[-1]
d = tf.matrix_set_diag(d, 8 * tf.ones([length]))
sen_loss_K ,_ = tf.nn.top_k(-1.0 * d, K, sorted=False) # note: this is negative value
im_loss_K,_ = tf.nn.top_k(tf.transpose(-1.0 * d), K, sorted=False) # note: this is negative value
sentence_center_loss = tf.nn.relu(positive + sen_loss_K + margin)
image_center_loss = tf.nn.relu(positive + im_loss_K + margin)
self.d_neg = tf.reduce_mean((sen_loss_K + im_loss_K)/-2.0)
self.d_pos =tf.reduce_mean(positive)
self.endpoint['debug/im_loss_topK'] = -1.0 * im_loss_K
self.endpoint['debug/sen_loss_topK'] = -1.0 * sen_loss_K
self.endpoint['debug/d_Matrix'] = d
self.endpoint['debug/positive'] = positive
self.endpoint['debug/s_center_loss'] = sentence_center_loss
self.endpoint['debug/i_center_loss'] = image_center_loss
self.endpoint['debug/S'] = sim_matrix
self.endpoint['debug/sentence_square'] = s_square
self.endpoint['debug/image_square'] = im_square
return tf.reduce_sum(sentence_center_loss), tf.reduce_sum(image_center_loss)
Bidirectionnet_GMM_sigmod9000feat.py 文件源码
项目：image-text-matching
作者: llltttppp
项目源码
文件源码
阅读 26
收藏 0
点赞 0
评论 0
def top_K_loss_margin(self,sentence,image,K=50,margin=0.2):
sim_matrix = tf.matmul(sentence, image, transpose_b=True)
s_square = tf.reduce_sum(tf.square(sentence), axis=1)
im_square = tf.reduce_sum(tf.square(image), axis=1)
d = 1-tf.sigmoid(sim_matrix)
positive = tf.stack([tf.matrix_diag_part(d)] * K, axis=1)
length = tf.shape(d)[-1]
dd = tf.matrix_set_diag(d, 8 * tf.ones([length]))
flag =8-7*tf.sign(tf.nn.relu(self.sen_margin-self.sen_similarity))
sen_loss_K ,_ = tf.nn.top_k(-1.0 * dd *flag, K, sorted=False) # note: this is negative value
im_loss_K,_ = tf.nn.top_k(-tf.transpose(1.0 * dd*flag), K, sorted=False) # note: this is negative value
sentence_center_loss = -tf.log(1-positive+1e-12)-tf.log(-sen_loss_K+1e-12)
image_center_loss = -tf.log(1-positive+1e-12)-tf.log(-im_loss_K+1e-12)
self.d_neg = tf.reduce_mean((sen_loss_K + im_loss_K)/-2.0)
self.d_pos =tf.reduce_mean(positive)
self.endpoint['debug/im_loss_topK'] = -1.0 * im_loss_K
self.endpoint['debug/sen_loss_topK'] = -1.0 * sen_loss_K
self.endpoint['debug/d_Matrix'] = d
self.endpoint['debug/positive'] = positive
self.endpoint['debug/s_center_loss'] = sentence_center_loss
self.endpoint['debug/i_center_loss'] = image_center_loss
self.endpoint['debug/S'] = sim_matrix
self.endpoint['debug/sentence_square'] = s_square
self.endpoint['debug/image_square'] = im_square
return tf.reduce_sum(sentence_center_loss), tf.reduce_sum(image_center_loss)
def multivariate_normal(x, mu, L):
"""
L is the Cholesky decomposition of the covariance.
x and mu are either vectors (ndim=1) or matrices. In the matrix case, we
assume independence over the *columns*: the number of rows must match the
size of L.
"""
d = x - mu
alpha = tf.matrix_triangular_solve(L, d, lower=True)
num_col = 1 if tf.rank(x) == 1 else tf.shape(x)[1]
num_col = tf.cast(num_col, settings.float_type)
num_dims = tf.cast(tf.shape(x)[0], settings.float_type)
ret = - 0.5 * num_dims * num_col * np.log(2 * np.pi)
ret += - num_col * tf.reduce_sum(tf.log(tf.matrix_diag_part(L)))
ret += - 0.5 * tf.reduce_sum(tf.square(alpha))
return ret
def _define_full_covariance_probs(self, shard_id, shard):
"""Defines the full covariance probabilties per example in a class.
Updates a matrix with dimension num_examples X num_classes.
Args:
shard_id: id of the current shard.
shard: current data shard, 1 X num_examples X dimensions.
"""
diff = shard - self._means
cholesky = tf.cholesky(self._covs + self._min_var)
log_det_covs = 2.0 * tf.reduce_sum(tf.log(tf.matrix_diag_part(cholesky)), 1)
x_mu_cov = tf.square(
tf.matrix_triangular_solve(
cholesky, tf.transpose(
diff, perm=[0, 2, 1]), lower=True))
diag_m = tf.transpose(tf.reduce_sum(x_mu_cov, 1))
self._probs[shard_id] = -0.5 * (
diag_m + tf.to_float(self._dimensions) * tf.log(2 * np.pi) +
log_det_covs)
def _define_full_covariance_probs(self, shard_id, shard):
"""Defines the full covariance probabilties per example in a class.
Updates a matrix with dimension num_examples X num_classes.
Args:
shard_id: id of the current shard.
shard: current data shard, 1 X num_examples X dimensions.
"""
diff = shard - self._means
cholesky = tf.cholesky(self._covs + self._min_var)
log_det_covs = 2.0 * tf.reduce_sum(tf.log(tf.matrix_diag_part(cholesky)), 1)
x_mu_cov = tf.square(
tf.matrix_triangular_solve(
cholesky, tf.transpose(
diff, perm=[0, 2, 1]), lower=True))
diag_m = tf.transpose(tf.reduce_sum(x_mu_cov, 1))
self._probs[shard_id] = -0.5 * (
diag_m + tf.to_float(self._dimensions) * tf.log(2 * np.pi) +
log_det_covs)
def _chollogdet(L):
"""Log det of a cholesky, where L is (..., D, D)."""
ldiag = pos(tf.matrix_diag_part(L)) # keep > 0, and no vanashing gradient
logdet = 2. * tf.reduce_sum(tf.log(ldiag))
return logdet
def get_L_sym(self, L_vec_var):
L = tf.reshape(L_vec_var, (-1, self._action_dim, self._action_dim))
return tf.matrix_band_part(L, -1, 0) - \
tf.matrix_diag(tf.matrix_diag_part(L)) + \
tf.matrix_diag(tf.exp(tf.matrix_diag_part(L)))
def get_e_A_sym(self, P_var, mu_var, policy_mu_var, policy_sigma_var):
e_A_var1 = self.get_A_sym(P_var, mu_var, policy_mu_var)
e_A_var2 = - 0.5 * tf.reduce_sum(tf.matrix_diag_part(
tf.matmul(P_var, policy_sigma_var)), 1)
#e_A_var2 = - 0.5 * tf.trace(tf.matmul(P_var, policy_sigma_var))
return e_A_var1 + e_A_var2
Bidirectionnet_GMM_softmaxloss.py 文件源码
项目：image-text-matching
作者: llltttppp
项目源码
文件源码
阅读 26
收藏 0
点赞 0
评论 0
def softmax_topK_loss(self,sentence,image,K=50,margin=0.2):
sim_matrix = []
self.sparse_loss = tf.reduce_sum(2-(tf.reduce_sum(tf.nn.top_k(sentence, k=20,
sorted=False)[0],axis=1)+tf.reduce_sum(tf.nn.top_k(image, k=20,
sorted=False)[0],axis=1)))
with tf.device('cpu:0'):
for i in range(self.batch_size):
sim_matrix.append(tf.reduce_sum(tf.abs(sentence-image[i,:]),axis=1))
d=tf.stack(sim_matrix,axis=1)
positive = tf.stack([tf.matrix_diag_part(d)] * K, axis=1)
length = tf.shape(d)[-1]
d = tf.matrix_set_diag(d, 8 * tf.ones([length]))
sen_loss_K ,_ = tf.nn.top_k(-1.0 * d, K, sorted=False) # note: this is negative value
im_loss_K,_ = tf.nn.top_k(tf.transpose(-1.0 * d), K, sorted=False) # note: this is negative value
sentence_center_loss = tf.nn.relu(positive + sen_loss_K + margin)
image_center_loss = tf.nn.relu(positive + im_loss_K + margin)
self.d_neg = tf.reduce_mean((sen_loss_K + im_loss_K)/-2.0)
self.d_pos =tf.reduce_mean(positive)
self.endpoint['debug/im_loss_topK'] = -1.0 * im_loss_K
self.endpoint['debug/sen_loss_topK'] = -1.0 * sen_loss_K
self.endpoint['debug/d_Matrix'] = d
self.endpoint['debug/positive'] = positive
self.endpoint['debug/s_center_loss'] = sentence_center_loss
self.endpoint['debug/i_center_loss'] = image_center_loss
self.endpoint['debug/S'] = sim_matrix
return tf.reduce_sum(sentence_center_loss), tf.reduce_sum(image_center_loss),self.sparse_loss
Bidirectionnet_GMM_better_topK.py 文件源码
项目：image-text-matching
作者: llltttppp
项目源码
文件源码
阅读 28
收藏 0
点赞 0
评论 0
def top_K_loss(self,sentence,image,K=50,margin=0.3):
sim_matrix = tf.matmul(sentence, image, transpose_b=True)
s_square = tf.reduce_sum(tf.square(sentence), axis=1)
im_square = tf.reduce_sum(tf.square(image), axis=1)
d = tf.reshape(s_square,[-1,1]) - 2 * sim_matrix + tf.reshape(im_square, [1, -1])
positive = tf.stack([tf.matrix_diag_part(d)] * K, axis=1)
length = tf.shape(d)[-1]
d = tf.matrix_set_diag(d, 8 * tf.ones([length]))
sen_loss_K ,_ = tf.nn.top_k(-1.0 * d, K, sorted=False) # note: this is negative value
im_loss_K,_ = tf.nn.top_k(tf.transpose(-1.0 * d), K, sorted=False) # note: this is negative value
sentence_center_loss = tf.nn.relu(positive + sen_loss_K + margin)
image_center_loss = tf.nn.relu(positive + im_loss_K + margin)
self.d_neg = tf.reduce_mean((sen_loss_K + im_loss_K)/-2.0)
self.d_pos =tf.reduce_mean(positive)
self.endpoint['debug/im_loss_topK'] = -1.0 * im_loss_K
self.endpoint['debug/sen_loss_topK'] = -1.0 * sen_loss_K
self.endpoint['debug/d_Matrix'] = d
self.endpoint['debug/positive'] = positive
self.endpoint['debug/s_center_loss'] = sentence_center_loss
self.endpoint['debug/i_center_loss'] = image_center_loss
self.endpoint['debug/S'] = sim_matrix
self.endpoint['debug/sentence_square'] = s_square
self.endpoint['debug/image_square'] = im_square
return tf.reduce_sum(sentence_center_loss), tf.reduce_sum(image_center_loss)
def top_K_loss(self, sentence, image, K=50, margin=0.3):
# change: K=300, but i choose index 25 to 75 for training.
# so, the real 'K' is 50
sim_matrix = tf.matmul(sentence, image, transpose_b=True)
s_square = tf.reduce_sum(tf.square(sentence), axis=1)
im_square = tf.reduce_sum(tf.square(image), axis=1)
d = tf.reshape(s_square,[-1,1]) - 2 * sim_matrix + tf.reshape(im_square, [1, -1])
positive = tf.stack([tf.matrix_diag_part(d)] * K, axis=1)
length = tf.shape(d)[-1]
d = tf.matrix_set_diag(d, 8 * tf.ones([length]))
sen_loss_K ,_ = tf.nn.top_k(-1.0 * d, K, sorted=False) # note: this is negative value
im_loss_K,_ = tf.nn.top_k(tf.transpose(-1.0 * d), K, sorted=False) # note: this is negative value
#sen_loss_K = sen_loss_K[:, 25:75]
#im_loss_K = im_loss_K[:, 25:75]
sentence_center_loss = tf.nn.relu(positive + sen_loss_K + margin)
image_center_loss = tf.nn.relu(positive + im_loss_K + margin)
self.d_neg = (sen_loss_K + im_loss_K)/-2.0
self.d_pos = positive
self.endpoint['debug/im_distance_topK'] = -1.0 * im_loss_K
self.endpoint['debug/sen_distance_topK'] = -1.0 * sen_loss_K
self.endpoint['debug/d_Matrix'] = d
self.endpoint['debug/positive'] = positive
self.endpoint['debug/s_center_loss'] = sentence_center_loss
self.endpoint['debug/i_center_loss'] = image_center_loss
self.endpoint['debug/S'] = sim_matrix
self.endpoint['debug/sentence_square'] = s_square
self.endpoint['debug/image_square'] = im_square
return tf.reduce_sum(sentence_center_loss),tf.reduce_sum(image_center_loss)
def top_K_loss(self, sentence, image, K=50, margin=0.3):
# change: K=300, but i choose index 25 to 75 for training.
# so, the real 'K' is 50
sim_matrix = tf.matmul(sentence, image, transpose_b=True)
s_square = tf.reduce_sum(tf.square(sentence), axis=1)
im_square = tf.reduce_sum(tf.square(image), axis=1)
d = tf.reshape(s_square,[-1,1]) - 2 * sim_matrix + tf.reshape(im_square, [1, -1])
positive = tf.stack([tf.matrix_diag_part(d)] * K, axis=1)
length = tf.shape(d)[-1]
d = tf.matrix_set_diag(d, 8 * tf.ones([length]))
sen_loss_K ,_ = tf.nn.top_k(-1.0 * d, K, sorted=False) # note: this is negative value
im_loss_K,_ = tf.nn.top_k(tf.transpose(-1.0 * d), K, sorted=False) # note: this is negative value
#sen_loss_K = sen_loss_K[:, 25:75]
#im_loss_K = im_loss_K[:, 25:75]
sentence_center_loss = tf.nn.relu(positive + sen_loss_K + margin)
image_center_loss = tf.nn.relu(positive + im_loss_K + margin)
self.d_neg = (sen_loss_K + im_loss_K)/-2.0
self.d_pos = positive
self.endpoint['debug/im_distance_topK'] = -1.0 * im_loss_K
self.endpoint['debug/sen_distance_topK'] = -1.0 * sen_loss_K
self.endpoint['debug/d_Matrix'] = d
self.endpoint['debug/positive'] = positive
self.endpoint['debug/s_center_loss'] = sentence_center_loss
self.endpoint['debug/i_center_loss'] = image_center_loss
self.endpoint['debug/S'] = sim_matrix
self.endpoint['debug/sentence_square'] = s_square
self.endpoint['debug/image_square'] = im_square
return tf.reduce_sum(sentence_center_loss),tf.reduce_sum(image_center_loss)
BidirectionNet_lstm.py 文件源码
项目：Sohu-LuckData-Image-Text-Matching-Competition
作者: WeitaoVan
项目源码
文件源码
阅读 27
收藏 0
点赞 0
评论 0
def top_K_loss(self, sentence, image, K=50, margin=0.3):
# change: K=300, but i choose index 25 to 75 for training.
# so, the real 'K' is 50
sim_matrix = tf.matmul(sentence, image, transpose_b=True)
s_square = tf.reduce_sum(tf.square(sentence), axis=1)
im_square = tf.reduce_sum(tf.square(image), axis=1)
d = tf.reshape(s_square,[-1,1]) - 2 * sim_matrix + tf.reshape(im_square, [1, -1])
positive = tf.stack([tf.matrix_diag_part(d)] * K, axis=1)
length = tf.shape(d)[-1]
d = tf.matrix_set_diag(d, 8 * tf.ones([length]))
sen_loss_K ,_ = tf.nn.top_k(-1.0 * d, K, sorted=False) # note: this is negative value
im_loss_K,_ = tf.nn.top_k(tf.transpose(-1.0 * d), K, sorted=False) # note: this is negative value
#sen_loss_K = sen_loss_K[:, 25:75]
#im_loss_K = im_loss_K[:, 25:75]
sentence_center_loss = tf.nn.relu(positive + sen_loss_K + margin)
image_center_loss = tf.nn.relu(positive + im_loss_K + margin)
self.d_neg = (sen_loss_K + im_loss_K)/-2.0
self.d_pos = positive
self.endpoint['debug/im_distance_topK'] = -1.0 * im_loss_K
self.endpoint['debug/sen_distance_topK'] = -1.0 * sen_loss_K
self.endpoint['debug/d_Matrix'] = d
self.endpoint['debug/positive'] = positive
self.endpoint['debug/s_center_loss'] = sentence_center_loss
self.endpoint['debug/i_center_loss'] = image_center_loss
self.endpoint['debug/S'] = sim_matrix
self.endpoint['debug/sentence_square'] = s_square
self.endpoint['debug/image_square'] = im_square
return tf.reduce_sum(sentence_center_loss),tf.reduce_sum(image_center_loss)
BidirectionNet_tfidf.py 文件源码
项目：Sohu-LuckData-Image-Text-Matching-Competition
作者: WeitaoVan
项目源码
文件源码
阅读 26
收藏 0
点赞 0
评论 0
def top_K_loss(self, sentence, image, K=50, margin=0.5, img_input_feat=None, text_input_feat=None):
sim_matrix = tf.matmul(sentence, image, transpose_b=True)
s_square = tf.reduce_sum(tf.square(sentence), axis=1)
im_square = tf.reduce_sum(tf.square(image), axis=1)
d = tf.reshape(s_square,[-1,1]) - 2 * sim_matrix + tf.reshape(im_square, [1, -1])
positive = tf.stack([tf.matrix_diag_part(d)] * K, axis=1)
length = tf.shape(d)[-1]
d = tf.matrix_set_diag(d, 8 * tf.ones([length]))
if img_input_feat is not None:
img_input_norm1 = img_input_feat / tf.norm(img_input_feat, axis=-1, keep_dims=True)
S_input_img = tf.matmul(img_input_norm1, img_input_norm1, transpose_b=True)
img_coeff = 8 - 7 * tf.sign(tf.nn.relu(0.99 - S_input_img))
sen_loss_K ,_ = tf.nn.top_k(-1.0 * d * img_coeff, K, sorted=False) # note: this is negative value
self.endpoint['debug/S_input_img'] = S_input_img
self.endpoint['debug/img_coeff'] = img_coeff
else:
sen_loss_K ,_ = tf.nn.top_k(-1.0 * d, K, sorted=False) # note: this is negative value
if text_input_feat is not None:
text_input_norm1 = text_input_feat / (tf.norm(text_input_feat, axis=-1, keep_dims=True) + 1e-10)
S_input_text = tf.matmul(text_input_norm1, text_input_norm1, transpose_b=True)
text_coeff = 8 - 7 * tf.sign(tf.nn.relu(0.98 - S_input_text))
im_loss_K,_ = tf.nn.top_k(tf.transpose(-1.0 * d * text_coeff), K, sorted=False)
self.endpoint['debug/S_input_text'] = S_input_text
self.endpoint['debug/text_coeff'] = text_coeff
else:
im_loss_K,_ = tf.nn.top_k(tf.transpose(-1.0 * d), K, sorted=False) # note: this is negative value
sentence_center_loss = tf.nn.relu(positive + sen_loss_K + margin)
image_center_loss = tf.nn.relu(positive + im_loss_K + margin)
self.d_neg = (sen_loss_K + im_loss_K)/-2.0
self.d_pos = positive
self.endpoint['debug/im_loss_topK'] = -1.0 * im_loss_K
self.endpoint['debug/sen_loss_topK'] = -1.0 * sen_loss_K
self.endpoint['debug/d_Matrix'] = d
self.endpoint['debug/positive'] = positive
self.endpoint['debug/s_center_loss'] = sentence_center_loss
self.endpoint['debug/i_center_loss'] = image_center_loss
self.endpoint['debug/S'] = sim_matrix
self.endpoint['debug/sentence_square'] = s_square
self.endpoint['debug/image_square'] = im_square
return tf.reduce_sum(sentence_center_loss), tf.reduce_sum(image_center_loss)
