fix ISSUE=1534

fluid/PaddleNLP/sequence_tagging_for_ner/infer.py

@@ -38,12 +38,10 @@ def infer(model_path, batch_size, test_data_file, vocab_file, target_file,
         for data in test_data():
             word = to_lodtensor([x[0] for x in data], place)
             mark = to_lodtensor([x[1] for x in data], place)
-            target = to_lodtensor([x[2] for x in data], place)
             crf_decode = exe.run(
                 inference_program,
                 feed={"word": word,
-                      "mark": mark,
-                      "target": target},
+                      "mark": mark},
                 fetch_list=fetch_targets,
                 return_numpy=False)
             lod_info = (crf_decode[0].lod())[0]
@@ -66,7 +64,7 @@ def infer(model_path, batch_size, test_data_file, vocab_file, target_file,
 
 if __name__ == "__main__":
     infer(
-        model_path="models/params_pass_0",
+        model_path="models/params_pass_16",
         batch_size=6,
         test_data_file="data/test",
         vocab_file="data/vocab.txt",

fluid/PaddleNLP/sequence_tagging_for_ner/train.py

@@ -61,22 +61,26 @@ def main(train_data_file,
     avg_cost, feature_out, word, mark, target = ner_net(
         word_dict_len, label_dict_len, parallel)
 
-    sgd_optimizer = fluid.optimizer.SGD(learning_rate=1e-3)
-    sgd_optimizer.minimize(avg_cost)
-
     crf_decode = fluid.layers.crf_decoding(
         input=feature_out, param_attr=fluid.ParamAttr(name='crfw'))
 
     (precision, recall, f1_score, num_infer_chunks, num_label_chunks,
      num_correct_chunks) = fluid.layers.chunk_eval(
-         input=crf_decode,
-         label=target,
-         chunk_scheme="IOB",
-         num_chunk_types=int(math.ceil((label_dict_len - 1) / 2.0)))
+        input=crf_decode,
+        label=target,
+        chunk_scheme="IOB",
+        num_chunk_types=int(math.ceil((label_dict_len - 1) / 2.0)))
     chunk_evaluator = fluid.metrics.ChunkEvaluator()
 
     inference_program = fluid.default_main_program().clone(for_test=True)
     test_fetch_list = [num_infer_chunks, num_label_chunks, num_correct_chunks]
+    sgd_optimizer = fluid.optimizer.SGD(learning_rate=1e-2)
+    sgd_optimizer.minimize(avg_cost)
+
+
+    #print(inference_program)
+    #exit(0)
+    #test_fetch_list = [num_infer_chunks, num_label_chunks, num_correct_chunks]
 
     if "CE_MODE_X" not in os.environ:
         train_reader = paddle.batch(
@@ -111,6 +115,7 @@ def main(train_data_file,
     for pass_id in six.moves.xrange(num_passes):
         chunk_evaluator.reset()
         for batch_id, data in enumerate(train_reader()):
+            #print(data)
             cost_var, nums_infer, nums_label, nums_correct = exe.run(
                 fluid.default_main_program(),
                 feed=feeder.feed(data),
@@ -135,7 +140,7 @@ def main(train_data_file,
               " pass_f1_score:" + str(test_pass_f1_score))
 
         save_dirname = os.path.join(model_save_dir, "params_pass_%d" % pass_id)
-        fluid.io.save_inference_model(save_dirname, ['word', 'mark', 'target'],
+        fluid.io.save_inference_model(save_dirname, ['word', 'mark'],
                                       crf_decode, exe)
 
     if "CE_MODE_X" in os.environ:
@@ -146,8 +151,8 @@ def main(train_data_file,
 
 if __name__ == "__main__":
     main(
-        train_data_file="data/train",
-        test_data_file="data/test",
+        train_data_file="data/train.txt",
+        test_data_file="data/test.txt",
         vocab_file="data/vocab.txt",
         target_file="data/target.txt",
         emb_file="data/wordVectors.txt",