modify feed_dict

.gitignore

@@ -7,4 +7,5 @@ __pycache__/
 model/
 
 # 通用
-envi/
+envi/
+Summaries/

config.py

@@ -19,14 +19,14 @@ def __init__(self):
     unk = '[UNK]'
     pad = '[PAD]'
     vocab_path = './data/vocab.txt'
-    file_train = './data/oppo_round1_train_20180929_mini.txt'
-    # file_train = './data/oppo_round1_train_20180929.txt'
-    file_vali = './data/oppo_round1_vali_20180929_mini.txt'
-    # file_vali = './data/oppo_round1_vali_20180929.txt'
+    file_train = './data/oppo_round1_train_20180929.mini'
+    file_train = './data/oppo_round1_train_20180929.txt'
+    file_vali = './data/oppo_round1_vali_20180929.mini'
+    file_vali = './data/oppo_round1_vali_20180929.txt'
     max_seq_len = 10
     hidden_size_rnn = 100
     use_stack_rnn = False
-    learning_rate = 0.01
+    learning_rate = 0.001
     # max_steps = 8000
     num_epoch = 100
     summaries_dir = './Summaries/'

dssm_rnn.py

@@ -4,9 +4,7 @@
 TensorFlow=1.2.1
 """
 
-import pandas as pd
-from scipy import sparse
-import collections
+
 import random
 import time
 import numpy as np
@@ -275,8 +273,8 @@ def pull_batch(data_map, batch_id):
     return query_in, doc_positive_in, doc_negative_in, query_len, doc_positive_len, doc_negative_len
 
 
-def feed_dict(on_training, batch_id, drop_prob):
-    query_in, doc_positive_in, doc_negative_in, query_seq_len, pos_seq_len, neg_seq_len = pull_batch(data_vali,
+def feed_dict(on_training, data_set, batch_id, drop_prob):
+    query_in, doc_positive_in, doc_negative_in, query_seq_len, pos_seq_len, neg_seq_len = pull_batch(data_set,
                                                                                                      batch_id)
     query_len = len(query_in)
     query_seq_len = [conf.max_seq_len] * query_len
@@ -305,12 +303,12 @@ def feed_dict(on_training, batch_id, drop_prob):
         random.shuffle(batch_ids)
         for batch_id in batch_ids:
             # print(batch_id)
-            sess.run(train_step, feed_dict=feed_dict(True, batch_id, 0.5))
+            sess.run(train_step, feed_dict=feed_dict(True, data_train, batch_id, 0.5))
         end = time.time()
         # train loss
         epoch_loss = 0
         for i in range(train_epoch_steps):
-            loss_v = sess.run(loss, feed_dict=feed_dict(False, i, 1))
+            loss_v = sess.run(loss, feed_dict=feed_dict(False, data_train, i, 1))
             epoch_loss += loss_v
 
         epoch_loss /= (train_epoch_steps)
@@ -323,7 +321,7 @@ def feed_dict(on_training, batch_id, drop_prob):
         start = time.time()
         epoch_loss = 0
         for i in range(vali_epoch_steps):
-            loss_v = sess.run(loss, feed_dict=feed_dict(False, i, 1))
+            loss_v = sess.run(loss, feed_dict=feed_dict(False, data_vali, i, 1))
             epoch_loss += loss_v
         epoch_loss /= (vali_epoch_steps)
         test_loss = sess.run(loss_summary, feed_dict={average_loss: epoch_loss})