test=develop (#2249)

PaddleNLP/models/dialogue_model_toolkit/auto_dialogue_evaluation/net.py

@@ -11,22 +11,24 @@ import math
 import paddle.fluid as fluid
 import paddle
 
+
 class Network(object):
     """
     Network
     """
-    def __init__(self, 
-        vocab_size, 
-        emb_size, 
-        hidden_size, 
-        clip_value=10.0,
-        word_emb_name="shared_word_emb",
-        lstm_W_name="shared_lstm_W",
-        lstm_bias_name="shared_lstm_bias"):
+
+    def __init__(self,
+                 vocab_size,
+                 emb_size,
+                 hidden_size,
+                 clip_value=10.0,
+                 word_emb_name="shared_word_emb",
+                 lstm_W_name="shared_lstm_W",
+                 lstm_bias_name="shared_lstm_bias"):
         """
         Init function
         """
-        
+
         self.vocab_size = vocab_size
         self.emb_size = emb_size
         self.hidden_size = hidden_size
@@ -44,8 +46,7 @@ class Network(object):
             name="context_wordseq", shape=[1], dtype="int64", lod_level=1)
         response_wordseq = fluid.layers.data(
             name="response_wordseq", shape=[1], dtype="int64", lod_level=1)
-        label = fluid.layers.data(
-            name="label", shape=[1], dtype="float32") 
+        label = fluid.layers.data(name="label", shape=[1], dtype="float32")
 
         self._feed_name = ["context_wordseq", "response_wordseq", "label"]
         self._feed_infer_name = ["context_wordseq", "response_wordseq"]
@@ -58,7 +59,7 @@ class Network(object):
             param_attr=fluid.ParamAttr(
                 name=self.word_emb_name,
                 initializer=fluid.initializer.Normal(scale=0.1)))
-    
+
         response_emb = fluid.layers.embedding(
             input=response_wordseq,
             size=[self.vocab_size, self.emb_size],
@@ -66,7 +67,7 @@ class Network(object):
             param_attr=fluid.ParamAttr(
                 name=self.word_emb_name,
                 initializer=fluid.initializer.Normal(scale=0.1)))
-    
+
         #fc to fit dynamic LSTM
         context_fc = fluid.layers.fc(
             input=context_emb,
@@ -96,10 +97,10 @@ class Network(object):
             bias_attr=fluid.ParamAttr(name=self.lstm_bias_name))
         response_rep = fluid.layers.sequence_last_step(input=response_rep)
         print('response_rep shape: %s' % str(response_rep.shape))
-    
+
         logits = fluid.layers.bilinear_tensor_product(
             context_rep, response_rep, size=1)
-        print('logits shape: %s' % str(logits.shape)) #[batch,1]
+        print('logits shape: %s' % str(logits.shape))  #[batch,1]
 
         if loss_type == 'CLS':
             loss = fluid.layers.sigmoid_cross_entropy_with_logits(logits, label)
@@ -111,6 +112,7 @@ class Network(object):
         elif loss_type == 'L2':
             norm_score = 2 * fluid.layers.sigmoid(logits)
             loss = fluid.layers.square_error_cost(norm_score, label) / 4
+            loss = fluid.layers.reduce_mean(loss)
         else:
             raise ValueError
 
@@ -129,10 +131,9 @@ class Network(object):
         Return feed names
         """
         return self._feed_name
-        
+
     def get_feed_inference_names(self):
         """
         Return inference names
         """
         return self._feed_infer_name
-