diff --git a/fastNLP/core/dataset.py b/fastNLP/core/dataset.py
index ee0e5590..e2a990ca 100644
--- a/fastNLP/core/dataset.py
+++ b/fastNLP/core/dataset.py
@@ -1,4 +1,5 @@
 import numpy as np
+from copy import copy
 
 from fastNLP.core.fieldarray import FieldArray
 from fastNLP.core.instance import Instance
@@ -37,7 +38,7 @@ class DataSet(object):
             self.idx += 1
             if self.idx >= len(self.dataset):
                 raise StopIteration
-            return self
+            return copy(self)
 
         def add_field(self, field_name, field):
             """Add a new field to the instance.
@@ -270,6 +271,12 @@ class DataSet(object):
         else:
             return results
 
+    def drop(self, func):
+        results = [ins for ins in self if not func(ins)]
+        for name, old_field in self.field_arrays.items():
+            self.field_arrays[name].content = [ins[name] for ins in results]
+            # print(self.field_arrays[name])
+
     def split(self, dev_ratio):
         """Split the dataset into training and development(validation) set.
 
diff --git a/fastNLP/core/trainer.py b/fastNLP/core/trainer.py
index 10d8cfab..baff2c53 100644
--- a/fastNLP/core/trainer.py
+++ b/fastNLP/core/trainer.py
@@ -27,7 +27,7 @@ class Trainer(object):
     """
     def __init__(self, train_data, model, n_epochs=3, batch_size=32, print_every=-1, validate_every=-1,
                  dev_data=None, use_cuda=False, save_path="./save",
-                 optimizer=Optimizer("Adam", lr=0.001, weight_decay=0), need_check_code=True,
+                 optimizer=Optimizer("Adam", lr=0.01, weight_decay=0), need_check_code=True,
                  **kwargs):
         super(Trainer, self).__init__()
 
@@ -84,7 +84,14 @@ class Trainer(object):
             start = time.time()
             self.start_time = str(datetime.now().strftime('%Y-%m-%d-%H-%M-%S'))
             print("training epochs started " + self.start_time)
-            if self.save_path is not None:
+            if self.save_path is None:
+                class psudoSW:
+                    def __getattr__(self, item):
+                        def pass_func(*args, **kwargs):
+                            pass
+                        return pass_func
+                self._summary_writer = psudoSW()
+            else:
                 path = os.path.join(self.save_path, 'tensorboard_logs_{}'.format(self.start_time))
                 self._summary_writer = SummaryWriter(path)
 
@@ -98,7 +105,6 @@ class Trainer(object):
                 # validate_every override validation at end of epochs
                 if self.dev_data and self.validate_every <= 0:
                     self.do_validation()
-                self.save_model(self.model, 'training_model_' + self.start_time)
                 epoch += 1
         finally:
             self._summary_writer.close()
diff --git a/fastNLP/modules/encoder/conv_maxpool.py b/fastNLP/modules/encoder/conv_maxpool.py
index 7aa897cf..42994bcd 100644
--- a/fastNLP/modules/encoder/conv_maxpool.py
+++ b/fastNLP/modules/encoder/conv_maxpool.py
@@ -34,8 +34,6 @@ class ConvMaxpool(nn.Module):
                 bias=bias)
                 for oc, ks in zip(out_channels, kernel_sizes)])
 
-            for conv in self.convs:
-                xavier_uniform_(conv.weight)  # weight initialization
         else:
             raise Exception(
                 'Incorrect kernel sizes: should be list, tuple or int')