fix: bug fix for the example of hyperparameter optimization.

clinfo · Oct 31, 2019 · 49aba31 · 49aba31
1 parent e710820
commit 49aba31
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Showing 2 changed files with 7 additions and 6 deletions.
diff --git a/example_model/opt_param.py b/example_model/opt_param.py
@@ -1,7 +1,7 @@
 import tensorflow as tf
 import numpy as np
 import joblib
-import layers
+import kgcn.layers
 import tensorflow.contrib.keras as K
 
 def build_placeholders(info,config,batch_size=4):
@@ -39,14 +39,14 @@ def build_model(placeholders,info,config,batch_size=4):
     input_dim=info.feature_dim
     print(info.param["num_gcn_layer"])
     for i in range(int(info.param["num_gcn_layer"])):
-        layer=layers.GraphConv(internal_dim,adj_channel_num)(layer,adj=in_adjs)
-        layer=layers.GraphBatchNormalization()(layer,
+        layer=kgcn.layers.GraphConv(internal_dim,adj_channel_num)(layer,adj=in_adjs)
+        layer=kgcn.layers.GraphBatchNormalization()(layer,
             max_node_num=info.graph_node_num,enabled_node_nums=enabled_node_nums)
         layer=tf.sigmoid(layer)
         layer=K.layers.Dropout(dropout_rate)(layer)
-    layer=layers.GraphDense(internal_dim)(layer)
+    layer=kgcn.layers.GraphDense(internal_dim)(layer)
     layer=tf.sigmoid(layer)
-    layer=layers.GraphGather()(layer)
+    layer=kgcn.layers.GraphGather()(layer)
     output_dim=2
     layer=K.layers.Dense(output_dim)(layer)
     prediction=tf.nn.softmax(layer)

diff --git a/example_param/domain.json b/example_param/domain.json
@@ -2,7 +2,8 @@
 	{
 		"name":"num_gcn_layer",
 		"type": "discrete",
-		"domain": [0,1,2]
+		"domain": [0,1,2],
+		"data_type": "int"
 	}
 ]