修复bug

应该没有bug了，可以拿来调参

.gitignore

@@ -143,4 +143,4 @@ ENV/
 # User defined files
 *.score
 word2vec/zhwiki_2017_03.sg_50d.word2vec
-*.model
+/model/

execute.py

@@ -13,7 +13,7 @@
 tf.flags.DEFINE_string("test_file", "data/testing.data", "test corpus file")
 tf.flags.DEFINE_string("valid_file", "data/develop.data", "test corpus file")
 tf.flags.DEFINE_string("result_file", "predictRst.score", "result file")
-saveFile = "savedModel"
+saveFile = "model/savedModel"
 tf.flags.DEFINE_string("embedding_file", "word2vec\zhwiki_2017_03.sg_50d.word2vec", "embedding file")
 tf.flags.DEFINE_integer("embedding_size", 50, "embedding size")
 tf.flags.DEFINE_float("dropout", 1, "the proportion of dropout")
@@ -103,14 +103,14 @@ def valid_model(sess, lstm, valid_questions, valid_answers, valid_file, result_f
             tvars = tf.trainable_variables()
             grads, _ = tf.clip_by_global_norm(tf.gradients(lstm.loss, tvars),
                                               FLAGS.max_grad_norm)
+            saver = tf.train.Saver()
             sess.run(tf.global_variables_initializer())
 
             tqs, tta, tfa = [], [], []
             for ori_train, cand_train, neg_train in batch_iter(train_questions, train_answers,
                                                                train_labels, train_questionId, FLAGS.batch_size):
                 tqs.append(ori_train), tta.append(cand_train), tfa.append(neg_train)
-            saver = tf.train.Saver()
-            for i in range(1,4):
+            for i in range(3):
                 train_op = tf.train.GradientDescentOptimizer(learningRate).apply_gradients(zip(grads, tvars),
                                                                                            global_step=global_step)
                 for epoch in range(FLAGS.epochs):
@@ -119,4 +119,5 @@ def valid_model(sess, lstm, valid_questions, valid_answers, valid_file, result_f
                     valid_model(sess, lstm, valid_questions, valid_answers, FLAGS.valid_file, FLAGS.result_file)
                     saver.save(sess, saveFile + str(i*FLAGS.epochs+epoch) + '.model')
                 learningRate /= 2
-            valid_model(sess, lstm, test_questions, test_answers, FLAGS.test_file, FLAGS.result_file, False)
+            #saver.restore(sess,saveFile+"20.model")
+            valid_model(sess, lstm, test_questions, test_answers, FLAGS.test_file, FLAGS.result_file,False)