--

link_prediction.py

@@ -3,7 +3,7 @@ from __future__ import print_function
 import os
 
 # Train on CPU (hide GPU) due to memory constraints
-os.environ['CUDA_VISIBLE_DEVICES'] = " 0,4,2,3"
+os.environ['CUDA_VISIBLE_DEVICES'] = "0"
 
 import tensorflow as tf
 import settings
@@ -66,8 +66,7 @@ class Link_pred_Runner():
         kde = KernelDensity(bandwidth=0.7).fit(featuresCompress)
 
         # construct model
-        d_real, discriminator, ae_model, model_z2g, D_Graph, GD_real = get_model(model_str, placeholders, feas['num_features'], feas['num_nodes'],
-                                                                                 feas['features_nonzero'])
+        d_real, discriminator, ae_model, model_z2g, D_Graph, GD_real = get_model(model_str, placeholders, feas['num_features'], feas['num_nodes'], feas['features_nonzero'])
 
         # Optimizer
         opt = get_optimizer(model_str, ae_model, model_z2g, D_Graph, discriminator, placeholders, feas['pos_weight'], feas['norm'], d_real, feas['num_nodes'], GD_real)

preprocessing.py

@@ -24,7 +24,6 @@ def preprocess_graph(adj):
     adj_normalized = adj_.dot(degree_mat_inv_sqrt).transpose().dot(degree_mat_inv_sqrt).tocoo()  # 这一步的实质是做归一化，即A* × d转置 × d
     return sparse_to_tuple(adj_normalized)
 
-
 def construct_feed_dict(adj_normalized, adj, features, placeholders):
     # construct feed dictionary
     # .update()用法就是将()内的字段增加到dict当中