def _create_loss(self):
with tf.name_scope("loss"):
self.loss = learner.pointwise_loss(self.loss_function, self.input_R, self.output)
self.reg_loss = self.reg*(tf.nn.l2_loss(self.weights['encoder'])+tf.nn.l2_loss(self.weights['decoder'])+
tf.nn.l2_loss(self.biases['encoder'])+tf.nn.l2_loss(self.biases['decoder']))
self.loss = self.loss + self.reg_loss
def _create_loss(self):
with tf.name_scope("loss"):
p1, q1, self.output = self._create_inference(self.item_input)
if self.ispairwise == True:
_, q2, self.output_neg = self._create_inference(
self.item_input_neg)
result = self.output - self.output_neg
self.loss = learner.pairwise_loss(self.loss_function,result) + self.reg_mf * ( tf.reduce_sum(tf.square(p1)) \
+ tf.reduce_sum(tf.square(q2)) + tf.reduce_sum(tf.square(q1)))
else:
self.loss = learner.pointwise_loss(self.loss_function, self.lables,self.output)+self.reg_mlp * (tf.reduce_sum(tf.square(p1)) \
+ tf.reduce_sum(tf.square(q1)))
def _create_loss(self):
with tf.name_scope("loss"):
p1, q1, self.output = self._create_inference(
self.user_input, self.item_input, self.num_idx)
if self.ispairwise == True:
_, q2, output_neg = self._create_inference(
self.user_input_neg, self.item_input_neg, self.num_idx_neg)
self.result = self.output - output_neg
self.loss = learner.pairwise_loss(self.loss_function,self.result) + self.lambda_bilinear * ( tf.reduce_sum(tf.square(p1))) \
+self.gamma_bilinear*(tf.reduce_sum(tf.square(q2)) + tf.reduce_sum(tf.square(q1)))
else:
self.loss = learner.pointwise_loss(self.loss_function, \
self.lables,self.output) + self.lambda_bilinear *\
(tf.reduce_sum(tf.square(p1)))+self.gamma_bilinear *(tf.reduce_sum(tf.square(q1)))
def _create_loss(self):
with tf.name_scope("loss"):
# loss for L(Theta)
UI_u, IU_i, IL_i, LI_l, self.output = self._create_inference(
self.item_input)
if self.ispairwise == True:
_, IU_j, IL_j, _, output_neg = self._create_inference(
self.item_input_neg)
self.result = self.output - output_neg
self.loss = learner.pairwise_loss(self.loss_function,self.result) + self.reg_mf * ( tf.reduce_sum(tf.square(UI_u)) \
+ tf.reduce_sum(tf.square(IU_i)) + tf.reduce_sum(tf.square(IL_i)) + tf.reduce_sum(tf.square(LI_l))+ \
tf.reduce_sum(tf.square(LI_l))+tf.reduce_sum(tf.square(IU_j))+tf.reduce_sum(tf.square(IL_j)))
else:
self.loss = learner.pointwise_loss(self.loss_function,self.lables,self.output) + self.reg_mf * (tf.reduce_sum(tf.square(UI_u)) \
+tf.reduce_sum(tf.square(IU_i))+ tf.reduce_sum(tf.square(IL_i))+tf.reduce_sum(tf.square(LI_l)))
def _create_loss(self):
with tf.name_scope("loss"):
UI_u, IU_i, LI_l, self.output = self._create_inference()
self.loss = learner.pointwise_loss(self.loss_function,self.lables,self.output) + self.reg* (tf.reduce_sum(tf.square(UI_u)) \
+tf.reduce_sum(tf.square(IU_i))+tf.reduce_sum(tf.square(LI_l)))
def _create_loss(self):
with tf.name_scope("loss"):
self.loss = learner.pointwise_loss(self.loss_function, self.lables,
self.output)
def _create_loss(self):
with tf.name_scope("loss"):
# loss for L(Theta)
p1,q1,r1,self.output = self._create_inference()
self.loss = learner.pointwise_loss(self.loss_function,self.lables,self.output) + self.reg_mf * (tf.reduce_sum(tf.square(p1)) \
+tf.reduce_sum(tf.square(r1))+ tf.reduce_sum(tf.square(q1)))