def get_hyper_model(model_str, placeholders, H_dim): discriminator = Discriminator() d_real = discriminator.construct(placeholders['real_distribution']) model = None if model_str == 'hyper_arga_ae': model = HCAE(placeholders, H_dim) return d_real, discriminator, model
def get_model(model_str, placeholders, num_features, num_nodes, features_nonzero): discriminator = Discriminator() d_real = discriminator.construct(placeholders['real_distribution']) model = None if model_str == 'arga_ae': model = ARGA(placeholders, num_features, features_nonzero) elif model_str == 'arga_vae': model = ARVGA(placeholders, num_features, num_nodes, features_nonzero) return d_real, discriminator, model
def get_model(model_str, placeholders, num_features, num_nodes, features_nonzero, num_classes=1, cat=True): discriminator = Discriminator() d_real = discriminator.construct(placeholders['real_distribution']) model = None if model_str == 'vgc' or model_str == 'vgcg': model = VGC(placeholders, num_features, num_nodes, features_nonzero, num_classes, cat) return d_real, discriminator, model
def get_model(model_str, placeholders, num_features, num_nodes, features_nonzero): # 计算图构建 discriminator = Discriminator() D_Graph = D_graph(num_features) d_real = discriminator.construct(placeholders['real_distribution']) GD_real = D_Graph.construct(placeholders['features_dense']) model = None if model_str == 'arga_ae': model = GCN(placeholders, num_features, features_nonzero) elif model_str == 'DBGAN': model = GCN(placeholders, num_features, features_nonzero) model_z2g = Generator_z2g(placeholders, num_features,features_nonzero) return d_real, discriminator, model, model_z2g, D_Graph, GD_real
def get_model(placeholders, num_features, num_nodes, features_nonzero,pri_attr,dim_attr): discriminator = Discriminator() d_real = discriminator.construct(placeholders['real_distribution']) model = None model = APGE(placeholders, num_features, features_nonzero,pri_attr,dim_attr) return d_real, discriminator, model