diff --git a/constructor.py b/constructor.py
index a4f3159..f16397f 100644
--- a/constructor.py
+++ b/constructor.py
@@ -1,5 +1,7 @@
 import tensorflow as tf
 import numpy as np
+
+from load_data import load_data_1
 from model import GCN, Generator_z2g, Discriminator, D_graph
 from optimizer import OptimizerAE, OptimizerCycle
 import scipy.sparse as sp
@@ -51,7 +53,8 @@ def get_model(model_str, placeholders, num_features, num_nodes, features_nonzero
 def format_data(data_name):
     # Load data
 
-    adj, features, y_test, tx, ty, test_maks, true_labels = load_data(data_name)
+    # adj, features, y_test, tx, ty, test_maks, true_labels = load_data(data_name)
+    adj, features, y_test, tx, ty, test_maks, true_labels = load_data_1("e")
 
     # Store original adjacency matrix (without diagonal entries) for later
     adj_orig = adj
diff --git a/link_prediction.py b/link_prediction.py
index 06112af..c90197b 100644
--- a/link_prediction.py
+++ b/link_prediction.py
@@ -91,8 +91,7 @@ class Link_pred_Runner():
             roc_curr, ap_curr, _ = lm_train.get_roc_score(emb, feas)
             val_roc_score.append(roc_curr)
             print("Epoch:", '%04d' % (epoch + 1),
-                  "train_loss= {:.5f}, d_loss= {:.5f}, g_loss= {:.5f}, GD_loss= {:.5f}, GG_loss= {:.5f}".format(avg_cost[0], avg_cost[1], avg_cost[2], avg_cost[3],
-                                                                                                                avg_cost[4]), "val_roc=",
+                  "train_loss= {:.5f}, d_loss= {:.5f}, g_loss= {:.5f}, GD_loss= {:.5f}, GG_loss= {:.5f}".format(avg_cost[0], avg_cost[1], avg_cost[2], avg_cost[3], avg_cost[4]), "val_roc=",
                   "{:.5f}".format(val_roc_score[-1]), "val_ap=", "{:.5f}".format(ap_curr))
 
             if (epoch + 1) % 10 == 0:
@@ -106,6 +105,5 @@ class Link_pred_Runner():
         index = rec[:, 0].tolist().index(max(rec[:, 0].tolist()))
         emb = record_emb[index]
         ana = record[index]
-        scio.savemat('result/{}_link_64_64_new.mat'.format(self.data_name), {'embedded': emb,
-                                                                             'labels': feas['true_labels']})
+        # scio.savemat('result/{}_link_64_64_new.mat'.format(self.data_name), {'embedded': emb,'labels': feas['true_labels']})
         print('The peak val_roc=%f, ap = %f' % (ana[0], ana[1]))
diff --git a/load_data.py b/load_data.py
new file mode 100644
index 0000000..1b96e05
--- /dev/null
+++ b/load_data.py
@@ -0,0 +1,27 @@
+import pickle
+
+import numpy as np
+import scipy.sparse as sp
+
+
+def load_data_1(dataset):
+    adj = np.loadtxt('./data/partitioned_data/{}/orig/e_adj_orig.txt'.format(dataset), dtype=int)
+    adj = sp.csr_matrix(adj)
+
+    features = pickle.load(open("data/partitioned_data/e/feature/e_feature.pkl",'rb'))
+
+    y_test = 0
+
+    tx = 0
+
+    ty = 0
+
+    test_mask = 0
+
+    labels = 0
+
+    return adj, features, y_test, tx, ty, test_mask, labels
+
+
+if __name__ == "__main__":
+    load_data_1('e')