def _loss(self):
self.loss += softmax_cross_entropy(self.outputs, self.labels,
self.class_weights, self.use_class_weights)
# alpha = 0.001
# for var in self.vars:
# self.loss += alpha * tf.nn.l2_loss(self.vars[var])
tf.summary.scalar('loss', self.loss)
def forward(self, u, v, r_matrix):
u_z, v_z = self.gcl1(self.u_features, self.v_features,
range(self.num_users), range(self.num_items), r_matrix)
u_z, v_z = self.gcl2(u_z, v_z, u, v, r_matrix)
u_f = torch.relu(self.denseu1(self.u_features_side[u]))
v_f = torch.relu(self.densev1(self.v_features_side[v]))
u_h = self.denseu2(F.dropout(torch.cat((u_z, u_f), 1), self.dropout))
v_h = self.densev2(F.dropout(torch.cat((v_z, v_f), 1), self.dropout))
output, m_hat = self.bilin_dec(u_h, v_h, u, v)
r_mx = r_matrix.index_select(1, u).index_select(2, v)
loss = softmax_cross_entropy(output, r_mx.float())
rmse_loss = rmse(m_hat, r_mx.float())
return output, loss, rmse_loss
def _loss(self):
self.loss += softmax_cross_entropy(self.outputs, self.labels,
self.class_weights, self.use_class_weights)
tf.summary.scalar('loss', self.loss)
def _loss(self):
self.loss += softmax_cross_entropy(self.outputs, self.labels)
tf.summary.scalar('loss', self.loss)